Citation
Relationships among Pain, Pain Medication, and Pain Outcomes in Older Adults with Arthritis

Material Information

Title:
Relationships among Pain, Pain Medication, and Pain Outcomes in Older Adults with Arthritis
Creator:
Onubogu, Uloma
Place of Publication:
[Gainesville, Fla.]
Publisher:
University of Florida
Publication Date:
Language:
english
Physical Description:
1 online resource (143 p.)

Thesis/Dissertation Information

Degree:
Doctorate ( Ph.D.)
Degree Grantor:
University of Florida
Degree Disciplines:
Nursing Sciences
Nursing
Committee Chair:
Horgas, Ann L.
Committee Members:
Rowe, Meredeth A.
Yoon, Saun-Joo L.
Marsiske, Michael
McCrae, Christina S.
Graduation Date:
8/9/2008

Subjects

Subjects / Keywords:
Analgesics ( jstor )
Arthritis ( jstor )
Chronic pain ( jstor )
Medications ( jstor )
Modeling ( jstor )
Older adults ( jstor )
Pain ( jstor )
Pain management ( jstor )
Suffering ( jstor )
Symptomatology ( jstor )
Nursing -- Dissertations, Academic -- UF
adults, analysis, arthritis, chronic, elderly, equation, functional, interference, limitation, longitudinal, management, medication, modeling, older, outcomes, pain, persistent, pmi, secondary, structural
Genre:
Electronic Thesis or Dissertation
born-digital ( sobekcm )
Nursing Sciences thesis, Ph.D.

Notes

Abstract:
Persistent pain is a common experience among older adults and is largely due to chronic diseases, such as osteoarthritis. Pain is associated with many negative consequences, including functional limitations. Despite research and clinical attention to this problem, evidence suggests that pain management remains inadequate. Thus, the purpose of this longitudinal study was to examine the extent to which the adequacy of prescribed pain medication influences the relationship between the pain and pain outcomes (physical, social, and emotional functioning) among older adults with arthritis over a 5-year time span. The study was conducted in a sample of 1,409 community-dwelling older adults (male = 18.4%, female = 81.6%), with a mean age of 74 years. The study was a secondary analysis of data collected in the ACTIVE Study, a multi-site, randomized clinical trial of cognitive training interventions. Data were analyzed from 3 time points: baseline, 3 years later (Time 2) and 5 years later (Time 3). Measures included the OARS checklist for demographic, health, and pain location variables; Medical Outcomes StudySF-36 surveys for bodily pain and pain outcomes (e.g., physical, social, and emotional functioning); the Center for Epidemiological Studies-Depression (CES-D) scale; and the Pain Management Index (PMI), a computed indicator for the adequacy of prescribed pain medication. Data were analyzed using descriptive and bivariate statistics, as well as mixed effects modeling and longitudinal structural equation modeling. Results indicated the majority of participants experienced pain that interfered with their activities, were prescribed inadequate pain medications, had moderate functional limitations at each measurement and across time, and that increased pain interference was related to worse functioning. In longitudinal models, limitations in social, physical, and emotional functioning were predicted by pain experience and this relationship was partially mediated by lower adequacy of prescribed pain medication at each time of measurement. However, relationships cross time showed that individuals improved in their functioning as the adequacy of their prescribed pain medications improved. These results highlight prevalence of pain interference as a significant problem in older adults with arthritis, and the importance of adequate pain medication in the management of adverse pain related outcomes. ( en )
General Note:
In the series University of Florida Digital Collections.
General Note:
Includes vita.
Bibliography:
Includes bibliographical references.
Source of Description:
Description based on online resource; title from PDF title page.
Source of Description:
This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Thesis:
Thesis (Ph.D.)--University of Florida, 2008.
Local:
Adviser: Horgas, Ann L.
Statement of Responsibility:
by Uloma Onubogu.

Record Information

Source Institution:
University of Florida
Holding Location:
University of Florida
Rights Management:
Copyright Onubogu, Uloma. Permission granted to the University of Florida to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Classification:
LD1780 2008 ( lcc )

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Full Text





and emotional vitality, at each study period, which can be attributed to poorly managed pain

(Dunlap et al., 2002). Incongruence with above results, sex differences (with females reporting

more pain) have been robust findings in the pain literature (Dunlop et al.).

Race and age differences are frequently documented in the literature, with blacks

typically reporting more pain (Rheulman, Karoly, & Newton, 2005). In contrast, race and age in

this study were not associated with the pain construct at any time of measurement. Similarly,

Horgas, Yoon, Lee, & Marsiske (2008) found no association between race, age, and pain among

older adults with chronic pain. However, race had significant effects on pain outcomes. Overall,

functional scores showed a moderate increase in limitation for all races over time, however,

Caucasian subjects showed less limitation in physical functioning, emotional role, and

depression scores than other races. These findings are consistent with reports that non-white

study participants reported more pain disability than their white counterparts (Horgas et al.). This

study effect, however, may have been biased by a higher ratio of white to non-white participants

by more than 50%.

Older age was related to poorer overall functioning in all dimensions measured. Contrary

to findings by Hybels and colleagues (2001) and Thomas and colleagues (2004), and despite

common assumptions that pain interference increases as individuals get older, age was not

correlated with pain interference or pain locations in this study. However, number of diagnosed

diseases was associated with more pain interference and pain sites. These findings suggest that

increased pain interference is a function of disease rather than the age of the individual

experiencing the pain (Dunlop et al., 2002).

Relationships among Pain Interference, Pain Sites, Pain Management, and Pain Outcomes

This study was designed to examine whether pain was associated with functional

consequences among older adults, and whether pain management was adequate. Relationships









using descriptive and bivariate statistics, as well as mixed effects modeling and longitudinal

structural equation modeling. Results indicated the majority of participants experienced pain that

interfered with their activities, were prescribed inadequate pain medications, had moderate

functional limitations at each measurement and across time, and that increased pain interference

was related to worse functioning.

In longitudinal models, limitations in social, physical, and emotional functioning were

predicted by pain experience and this relationship was partially mediated by lower adequacy of

prescribed pain medication at each time of measurement. However, relationships cross time

showed that individuals improved in their functioning as the adequacy of their prescribed pain

medications improved. These results highlight prevalence of pain interference as a significant

problem in older adults with arthritis, and the importance of adequate pain medication in the

management of adverse pain related outcomes.









CHAPTER 2
REVIEW OF THE LITERATURE

This review focuses on the following study concepts: pain, pain medication, and pain

outcomes among older adults with persistent pain related to chronic skeletal/joint conditions,

especially arthritis. Prevalence of persistent pain and pain interference among older adults in

general populations, specifically among those with arthritis is examined. Further, the relationship

between pain, pain medications, and pain outcomes (e.g., functional status physical and social

functioning; and emotional status) is discussed.

Persistent Pain in Older Adults

Within the last two decades, literature on chronic pain among older adults has focused

mostly on establishing the high prevalence of pain and the pattern of the pain experience in terms

of severity (intensity), frequency, and duration (Ferrell & Ferrell, 1990; Hutt et al., 2006;

Rutledge, Donaldson, & Pravikoff, 2002; Nour & Laforest, 2003; Won et al., 2004). Recently,

researchers have begun to examine other descriptors of the pain experience, such as pain-related

interference with activity (Jinks et al., 2007; Mossey & Gallagher, 2004; Zarit et al., 2004).

These reports have been documented mostly in relation to older adults in nursing home settings

where persistent pain is most prevalent (Allcock, McGarry, & Elkan, 2002; Brockopp, Brockopp

et al., 1998; Fox, Raina, & Jadad, 1999; Hall-Lord, 2003; Hutt et al., 2006; Tse, Pun, & Benzie,

2003). Thus, this review will encompass reports on persistent pain experience in the general

population of older adults, and specifically in those with a diagnosis of arthritis.

Pain, is defined by the International Association for the Study of pain (IASP, n.d.) as an

unpleasant sensory and emotional experience associated with actual or potential tissue damage.

Pain is further viewed as a complex phenomenon associated with interplay of psychological and

pathophysiological processes that cause pain perception or expression (Hawthorn & Redmond,









the experience of individuals with persistent pain, preventing severe consequences and

disabilities, and improving their quality of life. Measured against these standards, numerous

studies have examined the profile of pharmacologic pain regimens (quality and quantity) of older

adults with chronic pain, to determine congruence between pain experience and (1) amount of

prescribed pain medications or medication use (Fisher et al., 2002), (2) pain severity and

relationships to pain outcomes (Hutt et al., 2006; Mantyselka et al., 2003; Mzorek & Werner,

2001; Won et al., 2004) or pain severity and interference, and (3) relationships to functional

limitations (Donald & Foy, 2004; Duong et al., 2005). Results of these studies consistently

support cross-sectional evidence of the inadequacy of pain management among the elderly, and

prevalence of emotional distress (e.g., depression) and functional limitations (physical and

social) among older adults with chronic pain.

In light of the above reviews, this study seeks empirical evidence to document the

consequences of poor pain management over time. Hence, a longitudinal analysis was considered

appropriate to examine the relationships between pain, pain management, and pain outcomes

over time. Findings indicating that poor management of persistent pain worsened the impact of

pain on severity of pain outcomes or vice versa, will validate ample cross-sectional evidence and

expand limited longitudinal evidence of these relationships in the pain literature. Clinically, such

findings will contribute to the basis for decision-making regarding prescription of analgesic

medications for older adults with persistent pain. Health care providers will be able to utilize

evidence-based support to formulate and implement treatment goals for the purpose of

management and/or prevention of functional and emotional decline in older adults with persistent

joint pain.















Age Sex Race DZ


VITAL



DEP


(


PAIN-INT


PHYS -PF




PHYS-PR
4---


PAIN
PAIN-INT = Pain interference
PAIN-LOC Pain locations
EMOT-F= Emotional Functioning
VITAL= Vitality
DEP= Depression
EMOT-R= Emotional role
PHYS-F= Physical Functioning
PHYS-PF= Physical functioning
PHYS-PR= Physical role
SOC-SF= Social Functioning
PMI= Pain Management Index
DZ= Number of diseases diagnosed

Fig 1-3. Conceptual model of the study


LEGEND:
































dzl


-.06 LL I

/.8
.5
S .74 -.68


erl pi


Pain= Pain
Emot= Emotional functioning
er Emotional role
vt Emotional vitality
pmi = pain management index


pn = Pain interference
pnl Pain locations
dz = Diseases diagnosed
dp Depression


Figure 4-14. Final reduced model of pain, pain medications, and emotional functioning


./ .75









CHAPTER 4
RESULTS

The main purpose of this study was to examine (a) the relationships among pain (e.g.,

pain interference and pain location) and pain outcomes (e.g. physical functioning, social

functioning, and emotional functioning) in older adults with arthritic pain, across three time

points over a 5 year period, and (b) the mediating role of the adequacy of prescribed pain

medications (e.g., pain management index [PMI]) in the relationships between pain and pain

outcomes at each time of measurement and across the 5 year time span. The results of these

analyses are presented in this chapter. First, descriptive characteristics of the sample and study

variables are presented, followed by description of the bivariate relationships among the study

variables. Finally, the relationships pain, adequacy of pain medication, and pain outcomes are

examined cross-sectionally at each time point and longitudinally across the 5 years time span.

Sample Selection, Characteristics, and Attrition

Sample Selection

The analyzed sample was derived from a sample of 2,802 community-dwelling older

adults who were enrolled in a study to examine the effects of cognitive training interventions on

cognitive and functional performance. A subsample of 1,409 older adults (hereafter referred to as

the current study sample or simply as the study sample) was selected who were diagnosed with

arthritis and who reported the presence of bodily pain at the baseline period (Time 1). A

flowchart of study inclusion and available sample size on key study variables is presented in

Figure 4-1.

Sample attrition across the 5-year study period is shown in Figure 4-1. Of the original

1,409 participants in the sample at baseline, 1, 032 (73%) remained in the study at Time 2, which

was 3 years later. At Time 5, 910 participants (65% of the baseline sample) remained in the










Table 4-21. Goodness of fit indices for simple mediationall) one-occasion structural model estimation at time 1
Physical function Social function Emotional function
~2 761.80 488.75 517.42
Df 20 14 28
p < .00 <.00 < .00
RMSEA .16 (p =.00) .16 (p =.00) .11 (p =.00)
NFI .55 .46 .69


RFI
IFI
TLI
CFI
AIC
Test of 2 difference a
X2 diff
df diff


-.01


-.01
.55
829.80

698.35
9


-.38
.47
-.39
.46
548.76

480.42
9
.00


.41
.71
.42
.71
591.41

348.36
9
.00


Note: df= degree of freedom; NFI = normed fit index, RFI = relative fit index, IFI = incremental fit index, TLI = Turker-Lewis index,
CFI = comparative fit index, AIC = Akaike; RMSEA = Root mean square error of approximation. a Test of chi-square
difference relative to Time 1 measurement model.









older adults (AGS, 1998, 2002; Creamer, Lethbridge-Cejku, & Hochberg, 2000; Williams et al.,

2006). Although the safest and most effective pharmacologic strategies for pain management are

recommended (AGS, 2002), evidence of suboptimal prescription and treatment of persistent pain

among older adults with persistent pain continues to be supported. Pain management

recommendations for older adults address concerns about unrelieved pain, inadequate

prescription of pain medications, and high prevalence of adverse pain related outcomes.

However, these reviews do not completely explain the role of inadequate prescription of pain

medications in pain-related emotional and functional outcomes. Therefore, this study proposes to

extend existing cross-sectional research by investigating longitudinal relationships between pain

and related outcomes, and the role of prescribed pain medications in determining these

outcomes. This review underpins the study's primary hypothesis that the relationship between

pain (e.g., pain interference and number of pain locations) and pain outcomes (e.g., emotional,

physical, and social functioning) over time is mediated by the adequacy of prescribed pain

medications (e.g., pain management index [PMI]).









4-21 Goodness of fit indices for simple mediationall) one-occasion structural model
estim ation at tim e 1 ................................................ ......... ............ 103

4-22 Goodness of fit indices for simple mediational structural model (Step 2 models)
estimation at time 1 .... ............... .................. .......... .. 104

4-23 Goodness of fit indices for simple three occasion mediation structural model
e stim a tio n ...................................................................................... 1 0 5

4-24 Goodness of fit indices for the final reduced 3-occasion pain-mediated models .......... 106

4 -25 Final reduced form structural models mediated by pain management index: Model
param eters and estim ates at Tim e 1 ........................................... .................... ..... 107

4-26 Final reduced form structural models mediated by pain management index: Model
parameters and estimates at Time 2 ................................................ ................ 107

4-27 Final reduced form structural models mediated by pain management index: Model
param eters and estim ates at Tim e 3 ........................................ ..................... ..... 108

4-28 Final reduced form structural models mediated by pain management index: Model
parameters and estimates of mediating Time 1 endogenous predictors of Time 1
outcome es. ................................................................ ........... ..... 109

4-29 Final reduced form structural models mediated by pain management index: Model
parameters and estimates of mediating Time 2 endogenous predictors of Time 2
outcome es. ................................................................ ........... ..... 110

4-30 Final reduced form structural models mediated by pain management index: Model
parameters and estimates of mediating Time 3 endogenous predictors of Time 3
outcome es ...................... ................................... ....... .......... ..... 111

4-31 Final reduced form structural models mediated by pain management index: Model
parameters and estimates of mediating Time 1 endogenous predictors of Time 2
outcome es. ................................................................ ........... ..... 112

4-32 Final reduced form structural models mediated by pain management index: Model
parameters and estimates of mediating Time 1 endogenous predictors of Time 3
outcome es. ................................................................ ........... ..... 113

4-33 Final reduced form structural models mediated by pain management index: Model
parameters and estimates of ...................................... 114

4-34 Pain outcomes predicted at Time 1, 2, and 3 in the final reduced model..................... 115









Table 4-12. Correlations between exogenous and endogenous variables at Time 3
Exogenous variables
Endogenous variables Age (yrs) # Diagnoses Sexa Race
Pain
Pain interference r=-.04 n.s. (n=770) r=.09* (n=770) r=.12** (n=770) r=-.05 n.s. (n=770)
Pain locations r=-.02 n.s. (n=779) r=.09* (n=779) r=.05 n.s. (n=779) r=-.01 n.s. (n=779)
PMI r=-.06 n.s. (n=691) r=-.01 n.s. (n=691) r=-.14** (n=691) r=.05 n.s. (n=691)
Physical functioning
Physical functioning r=-.22* (n=910) r=-.25** (n=910) r=-.17** (n=910) r=-.04** (n=910)
Physical role r=-.19** (n=910) r=-.17** (n=910) r=-.ll** (n=910) r=.03** (n=910)
Social functioning r=-.12** (n=910) r=-.08* (n=910) r=-.05 n.s. (n=910) r=.01 n.s. (n=910)
Emotional functioning
Emotional role r=-.09** (n=910) r=-.09** (n=910) r=-.06 n.s. (n=910) r=-.08* (n=910)
Emotional vitality r=-.10** (n=909) r=-.17** (n=909) r=-.10** (n=909) r=.09* (n=909)
Depression r=.10** (n=769) r=.11** (n=769) r=.04 n.s. (n=769) r=-.03 n.s. (n=769)
Note: p < .05, ** p < .01, *** p < .001, n.s. = not significant. a Sex was coded as 0 = males and 1 = females; b Race was coded as
(0=White; 1 = Non-white)









Effective chronic pain management should result in reduction of adverse consequences

across multiple dimensions, including physical, emotional, psychological, social, and spiritual

(AGS, 2002; Kurtze & Svebak, 2001). Thus, pain management should include the use of both

pharmacologic and nonpharmacologic strategies (AGS, 2002; Ferrell, 1995). Pharmacologic pain

management, however, is the most commonly used approach among elderly adults (AGS, 2002).

Often, the scope of pharmacologic pain management is expanded to include nonpain drugs such

as anti-anxiety, antidepressants, muscle relaxants, anti-inflammatory agents, sedative, and anti-

epileptics, to treat a variety of chronic symptoms or conditions associated with persistent pain

(AGS, 2002; Harden et al., 2005; Leininger, 2002). However, analgesic medications (opioid and

nonopioid) are prescribed most often to treat a variety of persistent joint pain among older adults

(AGS, 2002; Clark, 2002; Jakobsson, Hallberg, & Westergren, 2004).

It is expected that when analgesic medications are used appropriately and in adequate

amounts to treat persistent pain, individuals achieve better pain control (Furlan, Sandoval,

Mailis-Gagnon, & Tunks, 2006; Hutt et al., 2006), inactivity is reduced (Allen et al., 2003), and

there are better functional outcomes (Furlan et al.). On the other hand, inadequate pain treatment

is associated with more adverse clinical outcomes such as unrelieved pain, depression, poor

perception of health (Herrick et al., 2004; Won et al., 1999), and poor functional outcomes due to

activity limitation (Herrick et al.; Mossey & Gallagher, 2004). Studies examining

prescription/use of pain medication and pain experience among elderly with chronic pain have

found a prevalence of inadequate prescription of pain medications (Hutt et al., 2006; Horgas &

Tsai, 1998; Won et al., 2004). These studies accentuate the fact that under-treatment of pain is

widespread among older adults (Berry & Dahl, 2000; Grant & Haverkamp, 1995; Horgas & Tsai;









Horgas & Tsai, 1998; Hutt et al., 2006; Mzorek & Werner, 2001; Rodriguez, 2001; Won et al.,

2004). Horgas and Tsai surveyed analgesic drug prescriptions and use among 339 elderly nursing

home residents. They found that while the majority of residents were prescribed at least one

analgesic medication, very few were actually administered any doses of that medication during

the one-month study period. In addition, they reported that cognitively impaired residents were

prescribed and administered significantly less equianalgesic medications than those who were

cognitively intact. In a more recent study, Won and colleagues investigated persistent

nonmalignant pain and analgesic prescribing patterns in over 21,000 elderly nursing home

residents. Their results showed that one quarter of the residents received no analgesics and less

than 50% of all analgesics were given as standing doses.

Hutt and colleagues (2006) recently provided further evidence of inadequate pain

management among elderly adults in their study. These authors screened the quality of nursing

home pain medication prescribing practice using a pre-post intervention design aimed at nurses

and physicians. They found that fewer than 50% of residents with predictable recurrent pain

were prescribed scheduled pain medications pre-intervention. The mean score on the pain

medication appropriateness scale (PMAS) was 64% optimal pre-intervention, but was better

(69%) in nursing homes in which nurses' knowledge of pain assessment and management

improved post-intervention. Overall, PMAS scores for residents in pain improved from baseline

(60%) to intervention (65%). In a primary care population of 83,000 adult patients aged 60 and

above, only a small sample of 209 (.25% of patients) was identified as taking opioids for relief of

their chronic pain (Adams, Plane, Fleming, Mundt, & Saunders, 2001). These finding indicate

that chronic pain is generally undertreated in primary health care setting and lend support to









S.D. = .85). These scores indicate less adequate pain treatment at each measurement occasion,

and were statistically significant for each study period (t = 9.726, p < .001, Figure 4-5).

Pain outcomes. Pain outcomes investigated were indicators of physical, social, and

emotional functioning obtained from the Short-Form 36 health surveys, as well as the CES-D

depression scale. Scores on these pain outcomes were analyzed using descriptive statistics as

well as repeated effects mixed models to analyze change over the three study occasions. Physical

functioning subscale scores ranged from 0-100 (where 0 = severe impairment and 100 = no

impairment). Social functioning and emotional functioning subscales also were evaluated on a 0

to 100 scale, with lower scores indicating worse functioning. Depression scores (based on the

CES-D) were evaluated on a 4-point scale, with higher scores indicating more depression.

At baseline, participants' subscale scores for physical functioning and physical role were

60.9 (S.D. = 24.3) and 60.0 (S.D. = 39.6) respectively. These scores indicated moderate

limitations in the physical functioning. Participants had high social functioning scores

(Mean = 83.1, S.D. =21.6), but had moderate limitations in emotional role and emotional vitality

(Mean = 70.1, S.D. =37.9 and 56.9, S.D. = 20.0, respectively). In addition, participants reported

mild depressive symptoms at baseline (Mean = 5.8, S.D. =5.4, Table 4-6).

In examining longitudinal change in our study predictors, we conducted repeated

measures analyses using mixed effects modeling. This model has the advantage of using all

available data (without listwise deletion) at each occasion of measurement. Fixed effects of time

on average of each predictor variable was tested. The results were as follows. Significant time

effects were found for the following variable: PMI [F(2245.757) = 635.778, p <.001]; pain

interference [F(1940.645) = 17.595, p<.001], pain locations [F(2034.222) =5.467, p =.004];

physical functioning [F(2048.342) = 113.361, p< .001]; physical role [F(2144.971) = 30.659, p









Won, A., Lapane, K., Gambassi, G., Bernabei, R., Mor, V., & Lipsitz, L. (1999). Correlates and
management of nonmalignant pain in the nursing home. Journal ofAmerican Geriatrics
Society, 47(8), 936-942.

Won, A., Lapane, K., Vallow, S., Schein, J., Morris, J., & Lipsitz, L. (2004). Persistent
nonmalignant pain and analgesic prescribing patterns in elderly nursing home residents.
Journal ofAmerican Geriatrics Society, 52, 867-874.

Wolinsky, F., Unverzagt, F., Smith, D., Jones, R., Stoddard, A., & Tennstedt, S (2006). The
ACTIVE cognitive training trial and health-related quality of life: Protection that lasts for 5
years. Journal of Gerontology: Medical Sciences, 61(12), 1324-1329.

World Health Organization (1986). Cancer pain relief Geneva: World Health Organization.

Zarit, S., Griffiths, P., & Berg, S. (2004). Pain perceptions of the oldest old: A longitudinal
study. The Gerontologist, 44(4), 459-468.

Zimmerman, M. (2004). Basic physiology of pain perception. In S. Lautenbacher & R. Fillingim
(Eds.), Pathophysiology ofpain perception (pp. 1-24). New York: Kluwer
Academic/Plenum.









The gold standard for the study of symptoms is based on the perception of the individual
experiencing the symptom and his/her self-report.

An individual does not have to experience any symptoms in order to apply the model; an
intervention may be initiated if a risk factor is determined.

All uncomfortable symptoms must be treated. Individuals' experiences of chronic pain
are multidimensional and usually associated with multiple symptoms (AGS, 2002; Ferrell
& Ferrell, 1990; Harden et al., 2005; Patel, 2003). Thus, pain medications (analgesics and
adjuvant analgesics) are prescribed for the treatment/management of pain symptoms
experienced by individuals (Dodd et al., 2001; Harden et al.).

Management strategies may also be multidimensional and targeted not only individuals,
but their families, groups, or work environments.

Symptom management is dynamic process, modified by individual outcomes and the
influences of the nursing domain of person, health/illness, or environment. Interference
caused by pain experience is influenced by prescribed pain medications and ultimately
affects severity of pain outcomes over time. Therefore, persistent or worsening pain
experience and pain outcomes may implicate inadequate modification of pain
medications.

A middle range theoretical model to guide the present study was derived from the Model

of Symptom Management (Figure 1-2). A middle-range theory focuses on aspects of a complex

human experience or reality (Polit & Hungler, 1995), considers a limited number of variables,

and is more susceptible to empirical testing (Meleis, 1997). Thus, deriving a middle range theory

for this study permitted selection of only the concepts, variables, and relationships specific to the

study (Meleis; Polit & Hungler).

In the derived model, the three main constructs of the MSM and three corresponding

concepts are included (Figure 1-3). The constructs are symptom experience, symptom

management strategy, and outcomes. The corresponding concepts are pain experience,

prescribed pharmacologic pain management, and clinical pain outcomes, respectively. Pain

experience was indicated by report of pain that limits usual activity (severity of pain interference

and number of pain locations). Prescribed pharmacologic management was indicated by

participants' prescribed pain medication regimens (analgesic and adjuvant analgesic






























NSAIDS Opiates


Muscle
Relaxants


Salicylates Anti- Acetanrinophen Anti-
depressants convulsants
tricyclicc) (GABA)


Medication Classes



Figure 4-4. Distribution of pain medications classes at 3 occasions of measurement. Percent prescribed analgesics: Time 1 =44%;
Time 2 =37%; Time 3 = 34%; (F=27.129, p=.00)


U Time 1
E Time 2
* Time 3













o Hypothesis 4a: The adequacy of prescribed pain medications (PMI) will significantly
mediate the relationships between pain (pain interference and number of pain sites)
and pain outcomes (physical functioning, social functioning, and emotional status) at
each measurement occasion, and across occasions over a 5-year time span, after
controlling for age, sex, race, and number of diseases diagnosed.









indicated a good model fit. A Chi-square comparison for model fit showed that this model was

significantly similar in fit as the full 3-occasion invariant measurement model (Measurement -2).

All variances were positive and all standardized regression coefficients were below the value of

1.00. Fit indices are shown in Table 4-20.

Test for pain mediation of the fully recursive three-occasion model. To test the fitness

of a pain-mediated three-occasion fully recursive model, all direct paths from pain to social,

physical, and emotional functioning were dropped so that pain was not mediated in the models;

and models were re-estimated. Goodness of fit indices indicated overall poor fit for all estimated

models. A test of chi-square difference between the no-pain mediated models and the fully

recursive pain-mediated models showed that the fit of the no-pain mediated models were

significantly worse. These results suggest that pain relationship to pain outcomes was mediated

by PMI in the tested models. Thus, these fully recursive pain-mediated models were accepted as

the best fitting mode for all three pain outcomes. All subsequent structural modeling analyses

were conducted using the full three-occasion recursive pain-mediated longitudinal models.

Finally, modification of the fully recursive model via model trimming (Kline, 2005) was

performed using a nested model approach. Model trimming was systematically implemented by

dropping non-significant regression paths from the models and re-estimating models until all

remaining regression paths became statistically significant, deriving a reduced three-occasion

recursive model. All three reduced models estimated with good model fit indices-incremental

indices >.9 and absolute indices <.05. A comparison of Chi-square difference between the fully

recursive three-occasion model and the reduced recursive three-occasion model showed that the

differences in the chi-square values between each pair of nested models were not statistically

significant. The three reduced models were therefore accepted as the final structural equation









estimates. All variances estimated with positive values and all correlations estimated below 1.00.

See Time 1 measurement model diagram in Figure 4-7 and fit indices in Table 4-19.

Estimation of longitudinal (three-occasion) measurement models. Following

estimation of the initial single occasion measurement models, full 3-occasion measurement

models (with loading paths as previously described) were specified for each pain outcome

construct: physical, emotional, and social functioning. All exogenous (control) variables were

specified to correlate with all endogenous constructs and variables. All endogenous constructs

were specified to correlate within each time of measurement and longitudinally across the three

time periods. In addition, all uniquenesses of the endogenous indicators were correlated

longitudinally. The goal of these model specifications is to achieve an identified model, the

greatest reduction in the Chi-square value, and a good model fit.

A three-step variance-invariance test involving a series of nested models was performed

for each of the three specified full models in order to identify the strongest and most

parsimonious model for analysis. Both standardized and un-standardized estimates were

requested. In Step 1, three invariance measurement models (Measurement-1) (physical,

emotional, and social) were scaled such that both the factor loadings and factor variances were

constrained to be equal across the three occasions of measurement. All models estimated and the

results indicated that all three models had good fit, as evidenced by incremental fit indices >.9

and absolute fit indexes <.05. In step 2, invariance models (Measurement-2) were specified such

that only the factor loadings were constrained to be equal across all occasions, while the

constraints on the factor variances were free. All three models estimated with good incremental

and absolute fit indices. In Step 3, the invariance model (Measurement-3) analyses were

specified such that no constraints were placed on either the factor loadings or the factor variances









functioning subscale score, physical role subscale score, social functioning subscale score,

emotional functioning subscale score, vitality, and depression), and the exogenous (control)

variables (e.g., age, sex, race, and number of diagnoses). Each model was specified to include an

endogenous latent construct of pain (with two measured endogenous factor indicators, pain

interference and number of pain locations), one singly-indicated endogenous pain management

construct (indicated by the calculated PMI variable), and four exogenous control variables (age,

sex, race, and number of diagnosed diseases). The same measurement model was constructed for

each outcome construct, yielding three separate measurement models. For the first model, the

latent construct of physical functioning was used as the outcome (indicated by two indicators:

physical functioning and physical role). For the second measurement model, the latent construct

of emotional functioning was considered as the outcome, with the construct indicated by three

endogenous indicators (emotional role, vitality, and depression). Finally, the social functioning

model was constructed, with a singly-indicated endogenous social functioning construct as the

outcome.

To derive an identified model for each estimation, each endogenous latent construct was

specified such that one factor loading was fixed to a constant value of 1.00 (as an arbitrary

constant to facilitate parameter estimation) while the factor variances were allowed to be freely

estimated. The purpose of this technique was to prevent under-identification of the model,

meaning that the number of free parameters to be estimated exceeds the number of distinct

observations in the model (Kline, 2005; Schumacker & Lomax, 2004). Under-identification of a

model usually results in model failure (Schumacker & Lomax). All exogenous variables, all

endogenous variables, and all endogenous constructs were allowed to correlate freely without

constraint. Each model converged into an admissible solution with reasonable to good fit









Table 4-4. Comparison between longitudinal sample and study drop-outs at Time 3
Time 3 continuing sample Dropouts after Time 2
n=910 n=122
Mean (S.D.) Mean (S.D.) t (df) p
Pain


Pain interference
Pain locations
Pain medications: PMI
Pain outcomes


2.31(1.15)
.92 (1.56)
-.87 (.94)


Physical functioning 57.74 (25.02)
Physical role 46.72(39.90)
Social functioning 80.33 (23.15)
Emotional role 68.97 (38.17)
Emotional vitality 55.64 (20.32)
Depression 5.47 (5.48)
Note: *p<.05; **p<.01; ***p<.001, n.s. = not significant


.00


52.81 (29.74)
45.32 (41.16)
76.42 (27.50)
85.22 (30.44)
55.19 (21.95)


.59 (901)


2.00 (1030)
.36(1028)
1.71(1027)
-4.55(1028)
.23(1027)


Table 4-5. Description of pain variables and PMI at Time 1, 2, and 3
Time 1 Time 2 Time 3
mean (S.D.) mean (S.D.) mean (S.D.) F p
Pain
Pain interference [range = 1-5] 2.2(1.1) 2.3 (1.2) 2.4 (1.2) 10.021 ***
# Pain locations [range =0-11] 1.0(1.6) .91 (1.6) 1.1 (1.7) 3.059 *
PMIa [range =3 to -3] -1.0 (.91) -.9 (.94) -1.1 (.85) 9.726 ***
a PMI = Pain management Index; *p<.05; **p<.01; ***p<.001, n.s. = not significant









contributing factors. Future research should intensify efforts to identify a multi-factorial
mediator of negative or positive pain outcomes through which effective strategies for
treatment of pain, prevention or reduction of functional disability, and maintenance of
physical, social, and emotional functioning among older adults with arthritis could be
instituted.

Investigate the role of other correlates of chronic pain such as socioeconomic factors or
social support to provide a useful perspective for better understanding of the relationship
between pain and functional disability.

Focus on unique groups of older adults with chronic pain (e.g., chronic back pain) or
selected demographic groups (e.g., female subjects) to provide more information about
subgroups of older adults with pain. A relationship between pain and pain limitation
mediated by pain medication may have a stronger fit among more homogeneous groups.

Replicate this study with primary data and control for the use of CAM and
noncompliance with therapy to provide additional information about the relationships in
question. This may further enhance the role of PMI in the analyzed model.

A clinical trial utilizing adequate standards for pain will help determine the most
effective therapy to manage pain interference and maintain functioning in older adults
with arthritis.

Conclusion

Pain is prevalent among older adults with arthritis and prescribed pain medications are

typically inadequate to manage pain effectively for this population. Trends throughout the study

periods indicate that pain (pain interference and number of pain locations) and functional pain

limitations (physical, social, and emotional) were prevalent and persistent. At each occasion of

the study, we found that worse pain-related limitations occurred among participants and that

these limitations were partially mitigated by less adequate prescribed analgesics. This finding

suggests that factors other than adequacy of prescribed medications might influence the negative

relationship between worse interfering pain and lower pain outcomes. However, across time,

relationships show that better functional outcomes were mediated by more adequate pain

medication as the pain experience worsens.









outcomes (e.g. physical, social, emotional functioning) within each occasion and across
the five year time span [e.g., Time 1 (baseline), Time 2 (3 years later), and Time 3 (5
years later)], after controlling for background and health variables (e.g., age, sex, race,
and number of diseases diagnosed)?

It was hypothesized that the adequacy of prescribed pain medication would significantly

mediate the relationship between pain and pain outcomes at each occasion and across the

measurement occasions. It was also expected that at subsequent occasions, residual change in

pain would have an effect on residual change in pain outcomes, and this effect would be

mediated by change in pain medication adequacy. Analysis of the mediating relationships

between pain, pain medication adequacy, and pain outcomes across the three study periods was

conducted using a structural equation modeling approach. The analysis plan required several

steps (a) model specification, (b) model estimation, (c) model testing, and (d) model

modification where necessary (Kline, 2005). Confirmatory measurement models were estimated

to establish the latent constructs, followed by structural equation models to analyze the

relationships between the latent constructs within and across measurement occasions. AMOS

16.0 software (Arbuckle, 2007) was used to conduct the modeling procedures.

Modeling Procedures

The modeling procedures were conducted in the following order. First, measurement models

were estimated. This involved baseline (Time 1) measurement models, estimated separately for

the three pain outcome constructs. Then, three-occasion measurement models were conducted

separately for the three pain outcome constructs. These models were followed by tests of nested

variance-invariance measurement models to determine the best fit to the data. Second, the

structural models were estimated. These were conducted in the following order: (a) Time 1

simple mediating structural model (without pain to outcomes path), estimated separately for the

three outcomes constructs, (b) Time 1 simple mediating structural models (with direct pain to









higher levels of depression. The results (Tables 4-13, 4-14, and 4-15) support the hypotheses.

More pain interference and more painful locations were significantly associated with lower

functioning in all of the domains assessed and more depression at Time 1 (Table 4-13). The same

pattern of relationships was noted at Time 2 and Time 3. Outcome variables intercorrelated

significantly and positively within each occasion of study except for depression which correlated

negatively with other pain outcomes. These correlations were comparable across the three

measurement occasions (Tables 4-14 to 4-15). Autocorrelations within time points (e.g.,

correlations between the same variables at time) are shown on the diagonals, and all are

statistically significant.

Additional cross-time correlations among the endogenous variables were conducted to

determine the nature and strength of the longitudinal relationships among the endogenous

variables prior to the proposed structural model analyses. Correlations between Time 1 and

Time 2 variables are shown in Table 4-16; correlations between Time 1 and Time 3 variables are

in Table 4-17; and correlations between Time 2 and Time variables are shown in Table 4-18.

The results show that all endogenous variables with the exception of PMI, intercorrelated

significantly with each other in the direction expected (positively or negatively) and magnitude

comparable to previous results. PMI intercorrelated with few variables across time. From Time

1, lower PMI correlated with more pain locations Time 1, and more vitality and lower social

functioning at Time 3. From Time 2, lower PMI correlated only with more severe pain

interference at Time 3. These results indicate that over time, adequacy of prescribed pain

medications weakened in its influence on pain interference and pain outcomes.

Mediating Relationships between Pain, Pain Management, and Pain Outcomes within
Measurement Occasions and Longitudinally Across the 5-year Period

Question 4: Does the adequacy of prescribed pain medication (e.g. PMI) mediate the
relationships between pain (e.g., pain interference and number of pain locations) and pain

62









Four subscales applicable to the proposed study are bodily pain, physical functioning, social

functioning, and mental health functioning. The bodily pain subscale was used to assess pain

intensity and pain interference. Pain intensity was evaluated as self-report of how much bodily

pain an individual has experienced in the past four weeks. A 6-point Likert-type verbal

descriptor scale was used to score pain intensity as follows: 1= none, 2 = very mild, 3 = mild, 4 =

moderate, 5 = severe, and 6 = very severe. Pain interference referred to the degree to which pain

interfered with normal activities (housework and outside work) during the past four weeks. A 5-

point verbal descriptor scale was used to score pain interference as follows: 1= not at all, 2 = a

little bit, 3 = moderately, 4 = quite a bit, 5 = extremely.

The SF-36 physical functioning subscales were used to evaluate physical activity and

physical role limitations. Physical functioning limitation was measured by the extent to which

participants reported thatl0 typical activities were limited by their health. For example, "Does

your health now limit you in lifting or carrying groceries?" Responses were scored on a 3-point

scale scored as 1 = yes, limited a lot; 2 = yes, limited a little; 3 = no, not limited at all.

The social functioning subscale was used to evaluate social activity limitations. Social

functioning limitation was evaluated with 2 items measuring the extent and amount of the time

physical health interfered with social activities (e.g. visiting friends or relatives) on a 5-point

scale. The extent of social functioning limitation scores ranged from 1 = not at all, to 5 =

extremely. The amount of time health interfered with social function was scored from 1 = all of

the time to 5 = none of the time. The mental health subscale was used to evaluate emotional

limitations. Scores from emotional role and emotional vitality subscales were used to indicate

level of emotional functioning. Three items measuring interference with participants' daily

activities in the past week as a result of emotional problems (e.g. depression or anxiety) were









Jakobsson, U., Hallberg, I., & Westergren, A. (2004). Pain management in elderly persons who
require assistance with activities of daily living: A comparison of those living at home with
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Jensen, M., Mendoza, T., Hanna, D., Chen, C., & Cleeland, C. (2004). The analgesic effects that
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Table 4-22. Goodness of fit indices for simple mediational structural model (Step 2 models) estimation at time 1
Physical function Social function Emotional function
~2 106.96 17.31 190.56
Df 19 13 27
p < .00 < .00 < .00
RMSEA .06 (p = .12) .02 (p = 1.0) .07 (p = .002)
NFI .93 .98 .89
RFI .85 .95 .77
IFI .95 .99 .90
TLI .87 .99 .79
CFI .95 .99 .90
AIC 176.96 79.31 266.56
Test of f2 difference a
X2 diff 43051 8.98 21.50
df diff 8 8 8
p .00 .34 .00
Note: df= degree of freedom; NFI = normed fit index, RFI = relative fit index, IFI= incremental fit index, TLI = Turker-Lewis index,
CFI = comparative fit index, AIC = Akaike; RMSEA = Root mean square error of approximation. a Test of chi-square
difference relative to initial Time 1 measurement model









models for physical, social, and emotional functioning as pain outcomes. Table 4-25 shows

model fit results. Diagrams of the reduced final models are shown in Figures 4-12 to 4-14.

Three final pain models, one for each outcome of social functioning, physical

functioning, and emotional functioning were derived. Results include loading values on

statistically significant regression paths, exogenous demographic predictors of endogenous

variables at Time 1, 2, and 3; and, Time 1, 2, and 3 and cross time mediating endogenous

predictors of social, physical, and emotional functioning outcomes.

Final Model: Path Coefficients

For the three final reduced models, standardized path coefficients are reported as

indicators of actual estimated value of the relationships tested. Path coefficients on direct single

time or cross-time paths from Pain to PMI, physical functioning, social functioning, and

emotional functioning were at least >.3 for all models. Path coefficients on mediation paths from

Pain to PMI to physical, social, and emotional functioning range from good (P >.72) to poor

(P =.06). On the other hand, the path estimates for the pain to physical, social, and emotional

functioning paths were strong (p > .6), while path estimates between the exogenous predictors-

age, sex, race, and number of diseases and the pain outcomes in these models-were generally

low to poor (P < .2). Although regression paths from pain to PMI to the three pain outcomes

were statistically significant, they generally had lower coefficient values compared to direct

loading of pain on these outcomes. Detailed results on all parameter estimates are presented in

Tables 4 25 to 4 -30.

Demographic and Health Variables as Exogenous Predictors of Pain Medication,
and Pain Outcomes

Age, sex, race, and number of diagnosed diseases were analyzed as control exogenous

variables in the three structural models. At the baseline period, advanced age was significantly









1998; McCaffery & Pasero, 1999; Zimmermann, 2004). Often, pain is described or measured by

its intensity (level of severity), duration (length of time it has occurred), frequency (how often it

occurs), or interference (extent to which pain interferes with usual activity or function) (Arthritis

Foundation, 2008; Ferrell & Ferrell, 1990; McCaffery & Pasero, 1999; Donald & Fay, 2004;

Zarit et al., 2004).

Duration of pain is classified clinically as acute and persistent (e.g., chronic), or

pathophysiologically by type of neuronal activity such as nociceptive and neuropathic (Hawthorn

& Redmond, 1998; McCaffery & Pasero, 1999). The terms chronic pain and persistent pain are

used interchangeably to denote pain that has exceeded three months in duration and is associated

with chronic pathological process (AGS, 2002; Hawthorn & Redmond, 1998). The AGS (2002)

panel further defines persistent pain as "a painful experience that continues for a prolonged

period of time that may or may not be associated with a recognizable disease process" (p. S205).

Pain chronicity, according to Zimmermann (2004), "is the result of prolonged nociceptive

activity which leads to nervous system sensitization and progressive intensification of pain"

(p. 2). Thus, individuals with chronic pain perceive complex stimuli that may be facilitated and

sustained by a multitude of physiological and psychological reactions. As such, chronic pain may

progress from being a disease symptom to being an independent chronic disease in itself.

Therefore, it is important to recognize that persistent pain could be either the manifestation of a

biophysiological process or the outcome of ineffectively treated pain.

Persistent pain can result from a poorly healed or poorly treated primary injury, or

abnormal functioning of the nervous system (McCaffery & Pasero, 1999). Sources of chronic

pain can be found in many body systems. Commonly, pain occurs in the musculoskeletal,

cardiovascular, and neurological systems, and manifests as chronic joint pain, headaches, angina,
































2008 Uloma Onubogu









published literature, study reports indicate that when analgesic medications are used

appropriately and in adequate amounts to treat persistent pain, individuals achieve better pain

control (Furlan et al., 2006; Hutt et al., 2006), reduce inactivity (Allen et al., 2003), and have

better functional outcomes (Furlan et al.).

The consistent pattern of results indicating that worsening of previous pain experience

predicted increased adequacy of prescribed medications and improvement in functional pain

outcomes over time provides strong evidence that individuals improved in their social, physical,

and emotional functioning over time because of improved pain management. This result is

congruent with the goals of chronic pain management to prevent disability and/or maintain

function for as long as possible (AGS, 2002) and calls for ongoing review and improvement of

prescribed pain medication.

Finally, overall variance in participants' level of functioning was predicted by worse pain

in the three structural models. Change in physical, social, and emotional functioning limitations

were predicted by at least 45% at the baseline period and at least 60% at the 3- and 5-Year

periods. Increased prediction of pain effects on measured pain outcomes over the 3 study periods

suggests that persistent pain has a continuous deleterious effect on functioning over time in this

age group. However, findings also support a projection that interfering pain can be managed

effectively with more adequate pain medication to increase functioning.

An important conclusion of this study is that the analyzed data and the underpinning

Model of Symptom Management are a good fit. Although the primary purpose of this study was

not to test the MSM, Meleis (1997) recognized that theoretical propositions derived from nursing

theories are important in theory testing. In this study, a mid-level recursive conceptual model

derived from the MSM was used to test the proposition that individuals' pain experience, pain









and neuralgias respectively. Musculoskeletal joint disorders such as arthritis are the most

common cause of chronic pain among the elderly (AGS, 2002; Brooks, 2005).

The prevalence of persistent, unrelieved pain is high among the elderly population.

Generally, about 49% to 84% of elders experience persistent pain (Nour & Laforest, 2003;

Rutledge, Donaldson, & Pravikoff, 2002; Won et al., 2004). Over 60% of elders (Jakobsson,

Hallberg, & Westergren, 2004; Thomas, Peat, Harris, Wilkie, & Croft, 2004) and more than 80%

of nursing home residents have significant problems with persistent pain (Allcock, McGarry, &

Elkan, 2002; Brockopp et al., 1998; Fox et al., 1999; Hall-Lord et al., 2003; Tse, Pun, & Benzie,

2005). Recently, Hutt and colleagues (2006) reported an even higher prevalence of pain (95%)

among elderly nursing home residents. High prevalence of persistent pain in older adults is

attributed to equally high prevalence of chronic painful musculoskeletal conditions such as

arthritis (AGS, 2002). Over 46 million U.S. adults (>21%) reported a diagnosis of arthritis

according to 2003-2005 CDC surveys (CDC, 2006). Within the stated adult population, about

50% of persons over age 65 years reported a current diagnosis of arthritis. It is projected that the

prevalence of persistent joint pain and related interference with activity will increase due to the

aging of the U.S. population, as more older adults are diagnosed with arthritis (CDC). Together,

these reports support persistent pain as a common problem for a large portion of the population,

and they provide evidence for the clinical problem that is the basis for this proposed study.

Interference with activities due to pain is common among older adults (Donald & Foy,

2004; Mossey, et al., 200; Mavandadi et al., 2007; Ross et al., 1998; Thomas et al., 2004),

particularly for those diagnosed with arthritis (Arthritis Foundation, 2008; CDC, 2006). The

CDC summary report for 2003-2005 indicated that 46 million U.S. adults reported a diagnosis of

arthritis and more than 38% reported activity limitation attributed to arthritis. Among older









Table 4-24. Goodness of fit indices for the final reduced 3-occasion pain-mediated models
Physical function Social function
72 83.54 211.66
Df 71 111
p .15 .00
RMSEA .01 (p =1.0) .03 (p =1.0)
NFI .97 .97
RFI .95 .94
IFI .99 .98
TLI .99 .97
CFI .99 .96
AIC 245.54 407.66
Test of f2 difference a
2I


Emotional function
438.30
168
.00
.04 (p =1.0)
.93
.90
.96
.90
.91
652.30


X diff 36.77 30.24 31.08
df diff 39 38 39
p .57 .81 .81
Note: df= degree of freedom; NFI = normed fit index, RFI = relative fit index, IFI = incremental fit index, TLI = Turker-Lewis index,
0 CFI = comparative fit index, AIC = Akaike; RMSEA = Root mean square error of approximation. a Test of chi-square
difference relative to the three-occasion fully recursive mediational model.









major or clinical depression in adolescents and adults (Radloff, 1977). Four components or

factors of the CES-D are depressive affect, somatic symptoms, positive affect, and interpersonal

relations. Items in the CES-D refer to how an individual has felt and behaved during the last

week e.g., "During the past week, I felt depressed." Scores range from 0-3, indicating that the

feeling or behavior occurred as follows: for negative items, 0 points = rarely or none of the time

(< 1 day); 1 point = some or a little of the time (1-2 days); 2 points = occasionally or a moderate

amount of the time (3-4 days); and 3 points = most or all of the time (5-7 days). An example of a

feeling in the 3-point is "I did not feel like eating; my appetite was poor." Scoring is reversed for

positive items, whereby "most or all of the time" is scored 0 points and "rarely or none of the

time" is scored as 3 points. An example of a positive response is "I felt that I was just as good as

other people." CES-D scores range from 0-60; higher scores indicate greater depressive

symptoms. The cut off score for depression is usually 16 (Radloff; Reid, Williams, Concato,

Tinetti, & Thomas, 2003), but a cut off score of 23 has been used to indicate depression (Hybels,

Blazer, & Pieper, 2001). Studies using the CES-D indicate that it has high internal consistency

(alpha=0.79-0.90, Kothe et al., 2006; Reid et al.), good test-retest stability (k=0.84, Reid et al.,),

and adequate construct validity based on positive predictive values reported in several studies

(Hybels et al.).

In a study by Geisser and colleagues (1997), the CES-D demonstrated good predictive

value in discriminating between types of depression among patients with chronic pain

(sensitivity >80%). Hybels and colleagues (2001) also demonstrated that the CES-D could

reliably identify clinically significant depression in community dwelling elders. The CES-D

short version yielded a Cronbach's alpha of .87 at baseline and .82 at follow-up for a sample of

older adults (Zarit et al., 2004). In the ACTIVE study, the CES-D was administered by trained









phenomenon include health care professionals' lack of knowledge and accurate clinical decision

making ability about pain management and pain relief.

Pain and Related Pain Outcomes

Persistent pain in older adults negatively impacts the general health and the emotional

and functional status of individuals affected (Mantyselka et al., 2003; Oster et al., 2005; Silkey et

al., 2005). There is consistent evidence that supports a significant relationship between persistent

pain and outcome measures such as depression and functional limitations (Mantyselka et al.;

Zarit et al., 2004). Pain variables that have been implicated in the relationships with depression

and functional outcomes include pain intensity, pain interference with activity, duration of pain

experience, frequency of the pain experience, as well as number of pain sites or locations present

(Mavandadi et al., 2007; Scudd & Robertson, 2004; Thomas et al, 2004; Zarit et al.). Also,

various components of functional limitations such as physical and social functions are affected

by pain (Bookwala, Harralson, & Parmalee, 2003; Williams, Tinetti, Kasl, & Peduzzi, 2006).

This section explores evidence of the relationships between pain and functional limitations

(social, physical, and emotional) among older adults with persistent pain. Specific attention is

paid to emerging significance of pain interference with regard to depression and other functional

limitations.

Pain and Emotional Distress

Depression is well documented in the literature as a correlate of persistent pain

(Bookwala et al., 2003; Sharp, Sensky, & Allard 2001; Zarit et al., 2004) as well as a major

indicator of emotional distress among patients with chronic pain (Greenberg & Burns, 2003).

Depression is known to initiate, exacerbate, and maintain the pain experience (McCracken,

Spertus, Janeck, Sinclair, & Wetzel, 1999). Among a primary care sample (N= 209) of

individuals 60 years and older with reported persistent joint pain, 36% were diagnosed with



















30


25


20
o
0.

15
C.

" 0 10
a.

5


0


Time 1 Time2 Time 3
Occasion


O None

Little Bit

Moderate

] Quite a lot

Extremely





F=10.021, p <.000


Figure 4-2. Intensity of pain interference across measurement occasions










Table 4-14. Intercorrelations among endogenous variables at Time 2


PAIN-INT PAIN-LOC PMI


PHYS-PF PHYS-PR SOC-SF


EMOT-R VITAL


Pain
Pain interference
(PAIN-INT)
Pain locations
(PAIN-LOC)
PMI


Physical functioning
Physical function
(PHYS-PF)
Physical role
(PHYS-PR)

Social functioning
^ (SOC-SF)

Emotional functioning
Emotional role
(EMOT-R)
Emotional vitality
(VITAL)
Depression
(DEP)


r=.44* 1
(n=902)
r=-.34** r=-15**
(n=864) (n=858)


r=-.57**
(n=908)
r=-.56**
(n=908)

r=-.58**
(n=907)



r=-.34**
(n=907)
r=-.54**
(n=907)
r=.35**
(n=909)


r=-35**
(n=903)
r=-30**
(n=902)

r=-35**
(n=901)



r=-23**
(n=901)
r=-33**
(n=901)
r=.29**
(n=901)


r=.16**
(n=863)
r=.20**
(n=862)


r=.10** r=.52** r=.53**
(n=862) (n=1029) (n=1028)


r=.02 n.s.
(n=861)
r=.15**
(n=862)
r=-09**
(n=863)


Note: p < .05, ** p < .01, *** p < .001, ns = not significant


DEP


r=.60**
(n=1030)


r=.25**
(n=1030)
r=.51**
(n=1029)
r=-3**
(n=907)


r=.39**
(n=1030)
r=.54**
(n=1028)
r=-30**
(n=907)


r=.42**
(n=1028)
r=.52**
(n=1029)
r=-52**
(n=906)


r=.37**
(n=1028)
r=-.48**
(n=906)


r=-.54**
(n=906)











90 -

80
ns
70
***
60
S***
8 50 Time 1
00 50
[C EOTime 2
C
c40- Time 3

30

20

10 n..s

0
Physical Physical Role Social Emotional Vitality Depression
Functioning Functioning Role
Pain Outcome

Note: *p<.05, **p <.01, ***p<.001, n.s. = not significant

Figure 4-6. Pain outcomes scores across measurement occasions









These drugs also exert effects on the CNS and have been found to be effective with neuropathic

pain such as that caused by stroke and neuralgia. Other pain modulating drugs identified in this

group are steroids, anti-arrhythmics, and local anesthetics (AGS, 2002). Harden and colleagues

(2005) profile 22 pain medication classes, which include all the above as well as anxiolytics,

muscle relaxants, antispasmodics, sedatives, antipsychotics, and anti-hypertensives. Clearly,

there is a large array of potential medications available to treat pain.

The AGS (2002) panel examined the role of pharmacologic agents in the treatment of

chronic pain and posited that analgesics and pain modulating drugs can be used safely and

effectively in the older adult population. Safe and effective use of pain drugs may be achieved

through adherence to evidence-based guidelines, which include simplifying drug regimens as

much as possible. Contrary to these recommendations, however, medication regimens of older

adults continue to show higher complexity due to overall increase in the rate of drugs prescribed

to treat multiple symptoms and conditions prevalent among this population (Chin et al., 1999;

Patel, 2003; Sloane, Zimmerman, Brown, Ives, & Walsh, 2002).

Consequently, many older adults typically consume a large variety of medications,

despite the fact that studies on pain management report prevalence of under-treatment of

persistent pain in this population (Mzorek & Werner, 2001; Rodriguez, 2001; Sofaer, 1984).

Recently, Oster and colleagues (2005) reported that 31% of elderly adults with post-herpetic

neuralgia stated they were a little or not at all satisfied with their pain medication. This finding is

supported by a more recent report that prescribing of pain medication was adequate for only 40%

of nursing home residents with neuropathic pain (Hutt et al., 2006).

Problems of inadequate relief of chronic pain have been related to inadequate and

ineffective prescription of pain medications (Berry & Dahl, 2000; Grant & Haverkamp, 1995;









CHAPTER 5
DISCUSSION

The purpose of this study was to examine the role of the adequacy of prescribed pain

medications in the relationship between pain (pain interference and number of pain sites) and

pain outcomes (physical, social, and emotional functioning). In this section, study results,

implications of the findings for nursing, limitations of the study, and directions for future

research are presented. A brief discussion on the sample attrition effects is presented first.

Pain, Pain Medication, and Pain Outcomes among Community Dwelling Older Adults
with Arthritis

The results of this study suggest that pain is a common symptom among older adults. The

characteristics of pain, pain medication, and pain outcomes among older adults with arthritis

were analyzed at three time periods, over a five-year period.

Pain among Older Adults with Arthritis

At the beginning of the study, almost 50% of participants reported that pain and painful

sites interfered with their daily activities. About 65% of those reporting pain rated their pain

interference severity at least "a little bit." This rate increased to approximately 72% by year five

for returning participants (n=780). These figures far exceed projected prevalence predicted by

Centers for Disease Control (CDC, 2006) of approximately 37%, but they are consistent with

higher prevalence (69%) reported by Scudd and Robertson (2000). These findings are also

consistent with current pain literature that documents high prevalence of pain interference and its

association with arthritis among older adults (Arthritis Foundation, 2008; CDC). It is important

to note that findings of high prevalence may have been influenced by a bias toward individuals

with higher levels of pain at baseline. As expected, proportion of the sample that reported that

pain interfered with their daily activities increased over time. At time 1, about 12% of the sample

reported that pain interfered "quite a bit," but this proportion increased to more than 20% of the









Nour, K., & Laforest, S. (2003). Behavioural changes in seniors: A definite possibility. Retrieved
January 17, 2004, from http://www.geronto.org/vitalaging/february2003/behaviour.htm

Osborne, T., Jensen, M., Ehde, D., Hanley, M., & Kraft, G. (2007). Psychosocial factors
associated with pain intensity, pain-related interference, and psychological functioning in
persons with multiple sclerosis and pain. Pain, 127, 52-62.

Oster, G., Harding, G., Dukes, E., Edelsberg, J., and Cleary, P. (2005). Pain, medication use,
health-related quality of life in older persons with postherpetic neuralgia: Results from a
population-based survey. The Journal ofPain, 6(6), 356-363.

Patel, R. (2003). Polypharmacy and the elderly. Journal ofInfusion Nursing, 26(3), 166-169.

Philips, C., & Rachman, S. (1996). The psychological management of chronic pain: A treatment
manual (2nd ed.). New York: Springer.

Polit, D. & Hungler, B. (1995). Nursing research. Principles and methods (6th ed.). Philadelphia:
Lippincott.

Portney, L. & Watkins, M. (2000). Foundations of clinical research. Applications to practice
(2nd ed.). Upper Saddle River: Prentice Hall Health.

Puntillo, K., Neighbor, M., O'Neil, N., & Nixon, R. (2003). Accuracy of emergency nurses in
assessment of patients' pain. Pain Management Nursing, 4(4), 171-175.

Radloff, L. (1997). The CES-D scale: A self-report depression scale for research in the general
population. Applied Psychological Measurement, 1, 385-401.

Reid, C. (2003). Persistent pain in older adults. Journal ofAmerican Geriatrics Society, 51,
1668.

Reid, C., Williams, C., Concato, J., Tinetti, M., & Thomas, G. (2003). Depressive symptoms as a
risk factor for disabling back pain in community-dwelling older persons. Journal of
American Geriatrics Society, 51, 1720-1717.

Ruehlman, L., Karoly, P., Newton, C., & Aiken, L. (2005). The development and preliminary
validation of a brief measure of chronic pain impact for use in the general population.
Pain, 113, 82-90.

Roberto, K., & Gold, D. (2001). Chronic pain in later life: A selectively annotated bibliography.
Westport: Greenwood Press.

Rodriguez, C. (2001). Pain measurement in the elderly: A review. Pain Management Nursing,
2(2), 38-46.

Rosenthal, N., Silverfield, J., Wu, S., Jordan, D., & Kamin, M. (2004).
Tramadol/Acetaminophen combination tablets for the treatment of pain associated with









worsening of the model fit indices was attributed to the effects of eliminating the paths mediated

by adequacy of prescribed pain medication. Thus, evidence that adequacy of prescribed pain

medications had a significant role in how much pain outcomes were predicted by pain was

supported.

The hypothesis that adequacy of prescribed pain medications mediates the effects of pain

on pain outcomes was partially supported in this study. In the first instance, worse pain directly

and indirectly (mediated by less adequate PMI) predicted worse physical, social, and emotional

functioning, but not at all time points. Second, longitudinal relationships in the model indicate

that worse pain predicted increased adequacy of pain medication between time points over the

five-years period. In addition, worse pain experience predicted improved pain outcomes

mediated by more adequate pain medication. This pattern was seen in the social and emotional

outcome models although mediation of these relationships beyond 3 years was not sustained.

These results suggest that total mediation of the relationship between pain and pain outcomes

was not achieved. However, the importance of lower adequacy of pain management in mitigating

negative consequences of functional pain outcome was supported. The relationships trends

among pain, pain medication, and pain outcomes reflect the complex nature of chronic pain as a

phenomenon encompassing multifactorial and multidimensional experiences, which include

physical, psychological, emotional, social, and spiritual (AGS, 2000). Thus, a singly-indicated

mediator (e.g. PMI) may be inadequate to explain the complexity within the relationships

analyzed between pain and pain outcomes within and across time points.

We derived a final reduced structural equation model for the three pain outcomes: social

functioning, physical functioning, and emotional functioning. At baseline, results showed that

pain had strong direct negative effects (3 = -.69 to -.89) on each pain outcomes measured









RELATIONSHIPS AMONG PAIN, PAIN MEDICATION, AND PAIN OUTCOMES IN
OLDER ADULTS WITH ARTHRITIS





















BY

ULOMA ONUBOGU


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILMENT
OF THE REQUIREMENT FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2008









Table 4 -25. Final reduced form structural models mediated by pain management index: Model parameters and estimates at Time 1
Exogenous predictors of Time 1
Age Sex Race Diagnoses
Models unstandardized unstandardized unstandardized Unstandardized (standardized)
(standardized) (standardized) (standardized)
Social function Pain -- -- .11*** (.22)
(SOC) PMI -- -.15* (.06)
SOC-F -- -
Physical function Pain -- .12* (.07) -- .11*** (.21)
(PHYS) PMI -- -
PHYS-F -.48*** (-.16) -- -2.0** (-.05) -1.00*** (-.08)
Emotional Pain -- .11* (.06) -- .11*** (.21)
(EMOT) PMI -- -.13*(-.06)
EMOT-F -.36*** (-.10) -- -- -.96* (-.06)
Note: p < .05, ** p < .01, *** p < .001, -- = nonsignificant; these values were dropped from final reduced form models.

Table 4-26. Final reduced form structural models mediated by pain management index: Model parameters and estimates at Time 2
Exogenous Predictors of Time 2
Age Sex Race Diagnoses
unstandardized unstandardized unstandardized unstandardized
Models (standardized) (standardized) (standardized) (standardized)
Social function Pain -- .21*** (.11) -- .04*(.06)
(SOC) PMI
SOC-F -.28** (-.07) 4.21* (.07)
Physical function Pain -- .19**(.10) -- .04*(.07)
(PHYS) PMI
PHYS-F -- -2.68* (-.05) --

Emotional Pain -- .04* (.07)
(EMOT) PMI --
EMOT-F -.21* (-.05) -- -- -
Note: p < .05, ** p < .01, *** p < .001; -- = not significant; these values were dropped from the final reduced form models









medications). Clinical pain outcomes associated with the experience of persistent pain were

indicators of the concept of functional status (physical functioning, social functioning, and

emotional functioning). See Table 1-1 for definition of concepts. The study examined

relationships between pain and pain outcomes, and the role of prescribed pharmacologic pain

management in this relationship.

Purpose of Study

The purpose of this study was to (a) examine the relationship between pain (e.g., pain

interference and number of pain locations) and pain outcomes (e.g. physical functioning, social

functioning, and emotional functioning) in older adults with arthritic pain and (b) determine

whether the adequacy of prescribed pain medications (e.g., pain management index-PMI)

mediates the relationship between pain and pain outcomes. These relationships are examined

cross-sectionally at three different time points and longitudinally over a 5-year time period. A

model of the proposed study is shown in Figure 3. The following research questions and

hypotheses were investigated:

Research Questions

What are the characteristics of pain (e.g., severity of pain interference, number of pain
locations), the adequacy of prescribed pain medication (e.g., PMI), and pain outcomes
(e.g., physical functioning, social functioning, and emotional functioning) among older
adults with arthritis at Time 1 (baseline), Time 2 (3 years later), and Time 3 (5 years
later)?

o Hypothesis la: The majority of the sample will report pain that interfered with
activities at each measurement point, and this rate will increase significantly across
the 5-year study period.

o Hypothesis Ib: The majority of the sample will have inadequate analgesic
prescription relative to their reported level of pain at each measurement point, and
this rate will increase/remain stable over the 5-year study period.

o Hypothesis Ic: More than 50% of the sample will have moderate levels of physical,
social, and emotional functioning at each time of measurement, and these levels of
functional impairment will increase over the 5-year time frame.









medication. In the case of the current study, low rates of opioid prescription and poor congruence

with pain levels may be attributed to poor pain assessment, lack of knowledge or reluctance

among the health care provider about use of opioid therapy (AGS, 2002; Green et al., 2002;

Gloth, 2000; Tarzan & Hoffman, 2004).

It is possible that the older adults in this study did not have the level of pain that

warranted opioid medication. Thus, the adequacy of pain medications prescribed was evaluated

using the pain management index (PMI). The evaluation of this criteria revealed that only about

22% of the study sample were prescribed adequate pain medication (PMI> 0) at Time 1

(baseline). Three years later, 10% more participants (32%, n= 865) were prescribed adequate

analgesic for pain, but this number declined by 5 % (27%, n=693) 5 years later. Thus, across the

5-year study period, an average of 27% of the study participants received adequate pain

medication. In contrast, over 68% of participants at Time 1, 67% at Time 2, and 72% at Time 3

received inadequate pain medication (PMI < 0) for their reported level of pain. Thus, across the 5

years period, an average of 69% of the sample was classified as having less adequate pain

management. These figures exceed previously reported rates of slightly over 50% of less

adequate prescription of pain medication for patients with persistent pain (Hutt et al., 2006).

Findings suggest that, for the majority of older adults with arthritis, pain is inadequately

managed.

One documented consequence of inadequate analgesia is persistent pain that interferes

with function or activity (Cleeland et al., 2001). From the study results, we can see inadequate

pain management was associated with more self-reported pain interference with activities among

this study sample. With only an average of about 38% of the study sample prescribed analgesics

across the three study periods, among whom only about 27% were prescribed adequate









=.92) 3 years later at Time 2. These relationships were mediated by higher PMI scores at

baseline for social (P =. 11) and emotional ( =. 10) functioning at Time 2 (Table 4-31 to 4-33).

Overall, some variances in the pain outcome variables were predicted by the mediated

models analyzed in this study. At baseline, the mediated effects of pain on emotional functioning

was predicted by 55% (R2 =.55); physical functioning was predicted by 84% (R2=.84); and social

functioning was predicted by 45% (R2=.45). Predicted variances in pain outcomes increased for

physical functioning 92% (R2 = .92) and 93% (R2 = .93) at Time 2 and 3 respectively (Table

4-34).

In summary, results have been presented on the characteristics of pain, pain medications,

and pain outcomes across three occasions of measurement at baseline, 3 years and 5 years later.

Correlations analysis results covered all bivariate relationships among pain, pain medication, and

pain outcomes, and between control demographic and health variables. Results of the structural

model analyses explained the longitudinal direct and mediated relationships among pain, pain

medications, and pain outcomes while controlling for age, sex, race, and number of diseases. In

the study models tested, variances in social, physical, and emotional outcomes are partly

predicted by the effects of interfering pain and adequacy of mediating prescribed pain

medications.
























age



sex i ini 1



race | \ \



m 1




.42


Pain = Pain Pn = Pain interference
Soc = Social functioning P1 = Pain locations

Figure 4-13. Final reduced model of pain, pain medications, and social functioning









cognitive decline (score < 22 on the MMSE), > 2 ADL disabilities (score on the self-rated

ADL/IADL checklist), or verbally reported medical conditions associated with imminent

functional decline or death (such as stroke or cancer); and/or exhibited severe loss of vision,

hearing, or poor verbal communication.

Selection Criteria

For the purpose of this study, a subsample was selected from the ACTIVE sample based

on a reported diagnosis of arthritis and presence of bodily pain rated at least mild on the SF-36

bodily pain scale, at the baseline period. Presence of bodily pain was required at baseline to more

accurately reflect the course of pain management for those individuals who manifested

symptoms of pain experience. Participants who did not report a diagnosis of arthritis at baseline

and did not have self-reported pain were excluded from the study.

Ethical Considerations

The ACTIVE study has approval from the University of Florida Institutional Review

Board (IRB), as well as the IRB of each participating university. Approval of the proposed study

was obtained from the University of Florida Institutional Review Board prior to accessing

existing data. All sample data and related information were handled using approved standards for

confidentiality. All identifying information was stripped from the data. Participants were

identified using only assigned subject numbers. All study documents and equipment containing

participants' information were secured by the Principal Investigator (PI) in locked cabinets and

locked offices, and were accessible only to the PI and the designated dissertation committee.

Electronic equipment or media used for data storage, processing, or transmission were password

secured, and accessed only by the PI.









functioning (t = 2.972, p <.01), lower emotional role functioning (t = 2.985, p <.01), but, higher

emotional vitality (t = -2.609, p = <.01) than white participants (Table 4-9).

Pearson's correlation analysis was used to analyze the relationship between age, number

of diagnoses, and the pain variables of interest (e.g., pain, PMI, and pain outcomes. These

correlations are summarized in Tables 4-10, 4-11, and 4-12 (separately for each time of

measurement). As a precursor to the structural equation models, bivariate correlations for sex

and race (both coded as dummy variables) are included in the tables, although it was expected

that the results of the correlation analysis would be the same as the results of the t-test previously

reported.

The results show that age was not significantly correlated with pain interference, number

of pain locations, or the adequacy of pain management at any of the three measurement

occasions. Age was significantly correlated with all of the pain outcome variables. Specifically,

at baseline, advanced age was significantly correlated with lower physical functioning (r -.15,

p<.01), lower physical role functioning (r=-.14, p<.01), lower social functioning (r = -.051,

p<.01), lower emotional role functioning (r = -.13, p<.01), lower vitality (r = -.06, p<.05), and

more depression (r = .10, p<.01). The same pattern of relationships held for Time 2 and Time 3

(Tables 4-11 and 4-12).

The number of medical diagnoses was also significantly correlated with the pain

indicators. More medical diagnoses was correlated with significantly more pain interference

(r = .18, p < .01) and more pain locations (r = .12, p < .01). This finding held at Time 2 and Time

3 as well. In addition, more medical diagnoses were significantly correlated with lower physical

functioning, lower physical role functioning, lower social functioning, lower emotional









60


50


40


30 03


20 E- O 5+


10


0
Time 1 Time 2 Time 3
Occasion
ccasio F=3.059, p <.05

Figure 4-3. Number of pain locations reported across measurement occasions. Time 1 (N=1409), Time 2 (n=914), Time 3 (n=780)































00

Outcome 1





Figure 4-8. Step Simple mediating one-occasion (Time 1) structural model diagram

Figure 4-8. Step 1: Simple mediating one-occasion (Time 1) structural model diagram









LIST OF TABLES


Table page

1-1 Constructs, variables, operational definitions, and measures ....................................... 27

3-1 Summary of variables and measurement points ......................... ............................. 53

4-1 Description of sample characteristics between selected and excluded samples
at Time 1 .................................... ......................... 87

4-2 Comparison of pain, pain medication, and pain outcomes between selected and
excluded sam ples at Tim e 1 ......... .................................... .................... .............. 87

4-3 Comparison between longitudinal sample and study drop-outs at Time 2................... 88

4-4 Comparison between longitudinal sample and study drop-outs at Time 3...................... 89

4-5 Description of pain variables and PMI at Time 1, 2, and 3 ............................................ 89

4-6 Description of pain outcome variables at Time 1, 2, and 3 ............................................ 90

4-7 Time effects on predictor and outcome variables........................................................ 90

4-8 Sex differences in pain, PMI, and pain outcomes at Time 1, 2, and 3............................ 91

4-9 Race differences in pain, PMI, and pain outcomes at Time 1, 2, and 3........................ 91

4-10 Correlations between exogenous and endogenous variables at Time 1........................... 92

4-11 Correlations between exogenous and endogenous variables at Time 2........................ 93

4-12 Correlations between exogenous and endogenous variables at Time 3........................... 94

4-13 Intercorrelations among endogenous variables at Time 1 ........................................... 95

4-14 Intercorrelations among endogenous variables at Time 2 ........................................... 96

4-15 Intercorrelations among endogenous variables at Time 3 ........................................... 97

4-16 Cross-time endogenous intercorrelations, Time 1 and Time 2.................................... 98

4-17 Cross-time endogenous intercorrelations, Time 1 and Time 3 .................................... 99

4-18 Cross-time endogenous intercorrelations, Time 2 and Time 3 ................................... 100

4-19 Goodness of fit indices for initial baseline measurement models estimation................. 101

4-20 Goodness of fit indices for best-fitting full 3-occasion measurement models............ 102









ACKNOWLEDGMENTS

I would like to thank God for keeping the breath of life in me. My heartfelt thanks go to

my supervisory committee chair, Dr. Ann Horgas, whose tireless and dedicated effort ensured

that this dissertation was completed. I would like to thank my committee members (Dr. Rowe,

Dr. Yoon, and Dr. McCrae), whose experience and knowledge helped to shape and mold not

only this dissertation but me as well. I have immense gratitude for Dr. Marsiske for providing

access to the ACTIVE data and most of all for being available at all times and for his wisdom. I

would also like to thank the ACTIVE committee for granting access to the ACTIVE data and

giving me the opportunity for a wonderful learning experience. I thank my loving husband and

children who studied along with me every inch of the way and sacrificed all that was needed to

ensure that my study was completed successfully. I thank my elderly mother who made several

long and dangerous trips across the Atlantic, each time praying for life so that she can witness

the completion of my study.










N=2,802
Parent Study Sample


Figure 4-1. Flowchart of sample selection and sample size for key study variables in longitudinal
sample across the three study periods.









adults with arthritis, Hybels, Blazer, & Pieper (2001) reported pain interference ranging from

21.9% for those 65 to 74 years to 32.3% for those 85 years and above. Thomas and colleagues

(2004) reported a high prevalence (58.7%) of pain interference among a general population of

elderly persons who reported pain. These findings provide evidence that the prevalence of pain

interference rises with increasing age (Hybels et al., 2001; Thomas et al., 2004).

Pain with its interference in daily activities and functioning of individuals exerts adverse

effects on depressive symptoms, thereby delaying improvement of symptoms (Mavandadi et al.,

2007). As more and more people join the ranks of 65 years and older, more pain interference will

likely be associated with increased prevalence of depressive symptoms among this population.

Thus, empirical evidence is needed to direct effective clinical pain management and decrease

pain interference as individuals age.

Pharmacologic Pain Management

Pharmacologic pain management is the most commonly used approach to treat persistent

pain (AGS, 2002), and effective treatment is typically multidimensional. Based on symptom

assessment, treatment may incorporate specific or various categories and classes of

pharmacologic agents (AGS; Roberto & Gold, 2001), including analgesics, nonsteroidal anti-

inflammatory agents (NSAIDs), antidepressants, anxiolytics, muscle relaxants, anti-spasmodics,

and others (AGS; Harden et al., 2005).

Analgesics are the most commonly used pain relieving drugs (AGS, 2002; Ferrell, 2004).

Analgesics include nonopioids (e.g., non-narcotics such as acetaminophen and NSAIDs), opioids

(e.g. narcotics such as opioid receptor agonists and antagonists), and adjuvant analgesics (e.g.,

antidepressants) (AGS; McCaffery & Pasero, 1999). NSAIDs have both a central nervous system

(CNS) effect and a strong peripheral action that inhibit prostalglandins at the site of injury.

However, the common side effects of gastric irritation may be problematic in terms of gastric-











CHAPTER 1
INTRODUCTION

Chronic or persistent pain (pain lasting more than 3 months) is a common experience

among older adults (American Geriatric Society (AGS), 2002; Hall-Lord, Johansson, Schmidt, &

Larsson, 2003; Higgins, Madjar, & Walton, 2004; Hutt, Pepper, Vojir, Fink, & Jones, 2006;

Koltyn, 2002; Reid, 2003). Over 50% of older adults have a significant problem with persistent

pain (Hall-Lord et al., 2003; Hutt et al., 2006; Won et al., 2004). The high prevalence of

persistent pain in elderly adults is linked to equally high prevalence of chronic and co-morbid

diseases in this population (AGS, 2002; Ferrell & Ferrell, 1990; Harden et al., 2005; Patel,

2003). Studies have shown that over 860% of adults over the age of 65 years have at least one

chronic illness (CDC, 2003), and overall, more than 80% of older adults surveyed reported one

to more than three chronic illnesses (Chan, Chong, Basilikas, Mathie, & Hung, 2002).

Joint disorders such as arthritis, are recognized as the most common cause of chronic or

persistent joint pain among the elderly (AGS, 2002; Lawrence et al., 1998). Currently, 46 million

Americans are diagnosed with Arthritis (CDC, 2006). Projection studies estimate that as many as

18.2% or 59.4 million will be diagnosed with arthritis by the year 2020 (Lawrence et al., 1998).

By 2030, as many as 67 million persons (25% of American adults population) are expected to be

diagnosed with arthritis, over 50% of them will be older than 65 years of age, and more than

37% (25 million) are projected to have activity limitation (CDC, 2006; Hootman & Helmick,

2006). These projections are consistent with Lawrence and colleagues' projection of 49%

prevalence of arthritis among persons 65 years and older (1998). As the prevalence of arthritis

increase, persistent pain as a public health problem is also expected to increase.









Second, the mediational models was further developed by adding direct path from pain to

the outcome constructs (physical functioning, social functioning, and emotional functioning) in

each model. This was done to assess the direct effects of pain on pain outcomes (e.g., physical,

social, and emotional functioning) and to determine if this relationship was indirectly mediated

through PMI. All three models re-estimated with reasonable to good fit indices. Based on their

chi-square test of difference comparing with the Time 1 measurement model fit, the social

functioning model was statistically as good as the measurement model; the physical and

emotional models estimated slightly worse than the measurement mode (Table 4-22). Diagram of

one occasion mediational structural model in is shown in Figure 4-9.

Estimation of longitudinal (three-occasion) structural models. The next step in the

model building procedures was to specify a simple three-occasion model. In addition to the

within-occasion paths as previously specified, auto-regression and paths were added between

Time 1 and 2, Time 1 and 3, and Time 2 and 3 for pain, PMI, and each outcome construct. Three

structural models were estimated, one for each outcome construct and each estimated with good

fit. However, Chi-square comparisons with the full invariant three-occasion measurement models

showed that all three models were significantly worse, and further model building was required.

Thus, the support needed to estimate a fully recursive three-occasion structural model. See Table

4-23 for goodness of fit estimates and Figure 4-10 for the model diagram.

In the next phase, a fully recursive mediated longitudinal three-occasion model for each

outcome construct was specified. All cross-time regression paths between the main study

constructs (e.g., pain. PMI, and pain outcomes) were specified for each model. Figure 4-11 is a

diagram showing addition of cross-time regression paths for major relationships tested in the

models. All uniqueness were allowed to correlate for each endogenous construct. The results









Theoretical Framework

The Model of Symptom Management (MSM; Dodd et al., 2001) provided the theoretical

underpinning for this study (Figure 1-1). This theoretical model is comprised of three interrelated

constructs: symptom experience, symptom management strategy, and outcomes. Symptom

experience describes an individual's perception of symptom, evaluation of the meaning of a

symptom, and response to a symptom. Symptoms are perceived when an individual notices a

change from the usual pattern of feeling or behavior. Perceived symptoms are evaluated by

making judgments about severity, cause, prognosis, and effects on their lives. An individual's

response to symptoms perceived or evaluated may have physiological, psychological,

sociocultural, and behavioral components, which are involved in a bi-directional relationship.

Symptom experience is usually obtained through self-report.

Symptom management is a dynamic process, which involves the assessment of the

symptom experience from an individual's perspective and the identification of intervention

strategies. Symptom management strategies includes the specification of what (nature of

strategy), when, where, why, how much (intervention dose), to whom (recipient of intervention),

and how (route of delivery). The goal of symptom management is to relieve symptoms and avert

or delay negative outcome through biomedical, professional, and self-care strategies.

Outcomes are expected results and consequences that emerge from symptom

management strategies as well as from the symptom experience. The outcomes dimension

focuses on eight interrelated factors, which also relate to the symptom status of the individual.

These factors include functional status, emotional status, self-care, quality of life, morbidity and

co-morbidity, mortality, and cost.

The Model of Symptom Management (MSM) has several important assumptions:










Table 4-16. Cross-time endogenous intercorrelations, Time 1 and Time 2
Time 1 variables


Time 2 variables
Pain
Pain interference
(PAIN-INT)
Pain locations
(PAIN-LOC)
PMI


Physical functioning
Physical function
(PHYS-PF)
Physical role
(PHYS-PR)

0 Social functioning
(SOC-SF)

Emotional functioning
Emotional role
(EMOT-R)
Emotional vitality
(VITAL)
Depression
(DEP)


AIN-INT PAIN-LOC


r=.39**
(n=910)
r=.26**
(n=823)
r=-.11**
(n=864)



r=-.34**
(n=1032)
r=-.28**
(n=1031)

r=-.28**
(n=1030)



r=-.16**
(n=1031)
r=-.29**
(n=1030)
r=.22**
(n=909)


r=.25**
(n=910)
r=.40**
(n=913)
r=-.07*
(n=)864



r=-.22**
(n=1032)
r=-.18**
(n=1031)

r=-.14**
(n=1030)



r=-.02 n.s.
(n=1031)
r=-.19**
(n=1030)
r=.09**
(n=909)


PMI

r=-.06 n.s.
(n=823)
r=-.08*
(n=828)
r=.22**
(n=803)



r=.05 n.s.
(n=935)
r=.00 n.s.
(n=934)

r=.00 n.s.
(n=934)



r=-.03 n.s.
(n=935)
r=.06 n.s.
(n=934)
r=.03 n.s.
(n=822)


PHYS-PF PHYS-PR SOC-SF EMOT-R VITAL DEP


r=-.42**
(n=910)
r=-.28**
(n=913)
r=.06**
(n=864)



r=.69**
(n=1032)
r=.40**
(n=1031)

r=.34**
(n=1030)



r=.20**
(n=1031)
r-.41**
(n=1030)
r=-.24**
(n=909)


r=-.36**
(n=901)
r=-.25**
(n=913)
r=.06**
(n=864)



r=.41**
(n=1032)
r=.46**
(n=1031)

r=.31**
(n=1030)



r=.26**
(n=1031)
r=.38**
(n=1030)
r=-.25**
(n=909)


r=-.32**
(n=910)
r=-.22**
(n=913)
r=.03**
(n=864)



r=.30**
(n=1032)
r=.28**
(n=1032)

r=.40**
(n=1030)



r=.21**
(n=1031)
r=.31**
(n=1034)
r=-.34**
(n=909)


r=.21**
(n=910)
r=-.16**
(n=913)
r=.05**
(n=864)



r=.24**
(n=1032)
r=.27**
(n=1031)

r=.29**
(n=1030)



r=.36**
(n=1031)
r=.27**
(n=1030)
r=-.34**
(n=909)


r=.39**
(n=908)
r=-.29**
(n=911)
r=.ll**
(n=863)



r=.37**
(n=1028)
r=.35**
(n=1027)

r=.32**
(n=1026)



r=.24**
(n=1027)
r=.64**
(n=1026)
r=-.39**
(n=907)


r=.25**
(n=907)
r=.16**
(n=911)
r=-.05**
(n=863)



r=-.22**
(n=1027)
r=-.21**
(n=1026)

r=-.29**
(n=1025)



r=-.32**
(n=1026)
r=-.37**
(n=1025)
r=.51**
(n=906)


Note: p <.05, ** p <.01, *** p <.001, n.s.


not significant









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bothered by pain in the two weeks before they were surveyed, and up to 50% of them were

limited in the performance of their usual activities (Mossey & Gallagher). They also found that

individuals who reported pain interference were more than four times as likely to have impaired

physical performance. These findings highlight the clinical importance of evaluating pain

interference as a strategy for effective pain management.

Social functioning usually refers to performing roles, responsibilities, and activities such

as visiting with friends or relatives, attending social engagements, and recreation (Ross & Crook,

1998; Ware, 1993). Persistent pain has been found to impact the social functioning of individuals

(AGS, 1998, 2002; Ware). In a study investigating the relationship between pain and indices of

disability and functional competence, researchers found that pain frequently interfered with

aspects of social responsibilities such as recreational and family activities in 36% of the

respondents (Ross & Crook). For 22% of the respondents, interference was experienced most of

the time. In a study examining the effects of pain on functioning and well-being in older adults

with osteoarthritis of the knee, Bookwala and colleagues (2003) measured both physical and

social functioning. They found that more intense pain was correlated with poorer physical

functioning and lower social functioning. To support this finding, Elliot and colleagues (2002)

reported that limitation with social functioning significantly predicted an individual's ability to

recover from chronic pain over time. This finding extends the clinical role of social functioning

as an outcome measure in pain management. These reviews highlight the need to recognize the

role of social functioning limitations when developing pain interventions and to maintain pain

relief in order to sustain recovery (Williams et al., 2006).

Summary

This review has highlighted important research findings that document the prevalence of

persistent pain and its deleterious impact on the emotional, physical, and social functioning of











Time2


Outcome 1




1 2


Figure 4-11. Three-occasion recursive longitudinal model showing addition of cross-time paths for major relationships tested


Time


Time3









In this study sample, inadequate prescription of pain medications negatively affected
physical, social, and emotional outcomes, and more adequate pain regimens positively
affected the same outcomes. This evidence highlights the need for health care
professionals to conduct effective ongoing strategies to evaluate adequacy of prescribed
pain medications. Strategies to evaluate prescribed pain medication relative to the
severity of pain may be enhanced by using standardized tools such as the PMI. In
addition, individuals experiencing pain interference with activity should be involved in
their own care plans to establish goals for better pain outcomes. These approaches would
benefit vulnerable groups, e.g., women, who were found to receive less than adequate
pain medication compared to men (although they had more limiting pain).

Evidence of a mediating role in the relationships between pain and pain outcomes
through the adequacy of prescribed pain medications (even partially) is a unique
contribution of this study. There are very few empirical longitudinal studies of pain in the
literature. This finding provides empirical support for the long-term benefit of effective
pain management among older adults. Rather than consider pain a normal part of aging
that cannot be eliminated, health care providers should develop and refine strategies to
manage pain in older adults with arthritis. Thus, making a commitment to provide pain
medication commensurate with pain experience would reduce the consequence of
persistent pain over time.

Limitations of the Study

As with any study, there are several limitations that must be acknowledged. These include

possible threats to the study's internal or external validity. Some of these limitations are

discussed below:

This study was conducted using existing data analysis. Thus, certain constraints such as
lack of control over the size or characteristics of the sample, missing data, initial
measurements, and handling/manipulation of the medication data may have affected the
results.

The primary study was a randomized, multi-site clinical trial to test effects of three
cognitive training interventions on cognitive, health, and functional outcomes. In the
present study, the effects of the cognitive training intervention group was not statistically
controlled in the analyses. Failure to control the effects of training could have potentially
enhanced functional status and diminished study effects. However, the results of a recent
study indicated that although cognitive training of the primary sample had a significantly
protective effect on functioning over time, there was no consistent pattern to indicate that
either group (treatment or control) was advantaged or disadvantaged over the other
(Wolinsky et al., 2006). Future analyses of these data should control for possible training
effects as these may vary based on unique characteristics of study participants.

The results of this study are generalizable only to a population of community-dwelling,
cognitively-intact older adults with arthritis. Because of very small percentage of races









BIOGRAPHICAL SKETCH

Uloma Dike Onubogu was born in Nigeria, West Africa, where she began her career in

nursing with a diploma in nursing, midwifery, and a Bachelor of Science degree in nursing in

1987. Uloma immigrated to the United States in 1991 and pursued further education, obtaining a

Master of Science in Educational Leadership in 1997 from Florida A&M University (FAMU).

Uloma's experience working as a registered nurse in the United States brought her into contact

with many older adults and revealed the critical need for care in this vulnerable group. Inspired

to pursue further education, she obtained a Master of Science in Nursing in 2002 from FAMU

and was certified as an Adult/Gerontological Nurse Practitioner. With a firm commitment to

contribute to education and research in the area of gerontological nursing and practice, Uloma

then accepted a faculty position at FAMU School of Nursing in 2003 and thereafter pursued

doctoral studies in nursing. Uloma's doctoral work has focused on the issue of chronic pain and

pain management among older adults. Her future goal is to improve pain management and

decrease pain disability among older adults through research and education of nurses and health

care professionals.



























00
PMI 1








SOutcome 1)
1 2 S



Figure 4-9. Step 2: Simple mediational one-occasion (Time 1) structural model diagram









intestinal bleeding. Examples of NSAIDs include such drugs as ibuprofen, ketoprofen, naproxen,

and acetylsalycilic acid (aspirin). Unlike the NSAIDs, the mechanism of action by which

acetaminophen relieves pain is not well known (AGS; McCaffery & Pasero, 1999). However,

acetaminophen is believed to produce analgesia via mechanisms in the central nervous system

(CNS). Overall, adverse effects associated with acetaminophen are considerably fewer compared

to other analgesics. Thus, it is one of the first lines of treatment for elderly with chronic mild to

moderate nociceptive pain. Opioid agonist analgesics are useful in managing a wide variety of

moderate to severe acute and chronic pain, particularly pain of nociceptive origin. Analgesia is

achieved through a CNS mechanism that modulates descending pain thereby diminishing its

discrimination and perception (American Academy of Pain Management [AAPM], 2005).

Opioid agonists are safe if used within existing guidelines (AGS, 1999, 2002; McCaffery &

Pasero).

Recommendations for the treatment of arthritic pain must consider pain control, reduction

of joint inflammation, and preservation of function with minimal side effects (Arthritis

Foundation, 2008; Stone, Wyman, & Salisbury, 1999). Thus, pharmacologic management of

arthritic pain usually requires regular use of acetaminophen to provide analgesia. However,

opioid medication such as codeine may be used alone or in combination with acetaminophen to

control more severe symptoms (Arthritis Foundation; Stone et al., 1999). Short-acting NSAIDs

such as naproxen and ibuprofen are considered safer to use with the elderly due to altered

absorption, distribution, elimination, and higher risk for gastro-intestinal damage in this

population (Stone et al.).

Adjuvant analgesics serve a multi-purpose in the treatment of pain (McCaffery & Pasero,

1999). This category of drugs includes antidepressants, anticonvulsants, and antispasmodics.










Table 4-18. Cross-time endogenous intercorrelations, Time 2


Time 3 Variables


PAIN-INT PAIN-LOC


PMI


and Time 3
Time 2 Variables
PHYS-PF PHYS-PR SOC-SF EMOT-R VITAL


Pain
Pain Interference
(PAIN-INT)
Pain Locations
(PAIN-LOC)
PMI



Physical Functioning
Physical Function
(PHYS-PF)
Physical Role
(PHYS-PR)

Social Functioning
(SOC-SF)

Emotional Functioning
Emotional Role
(EMOT-R)
Emotional Vitality
(VITAL)
Depression
(DEP)


Note: p <.05, ** p <.01, *** p <.001, n.s.


DEP


r=.48**
(n=722)
r=.32**
(n=728)
r=-.22**
(n=648)



r=-.44**
(n=792)
r=-.38**
(n=792)

r=-.35**
(n=792)



r=-.22**
(n=792)
r=-.42**
(n=791)
r=.26**
(n=722)


r=.36**
(n=722)
r=.38**
(n=729)
r=.10**
(n=647)



r=-.27**
(n=791)
r=-.25**
(n=791)

r=-.27**
(n=791)



r=-.16**
(n=791)
r=-.26**
(n=790)
r=.24**
(n=722)


r=-.17**
(n=687)
r=-.06 n.s.
(n=693)
r=.28**
(n=630)


r=.06 n.s.
(n=753)
r=-.06 n.s.
(n=752)

r=-.03 n.s.
(n=753)



r=-.03 n.s.
(n=753)
r=.09*
(n=752)
r=.00 n.s.
(n=687)


r=-.45**
(n=750)
r=-.32**
(n=757)
r-.19**
(n=674)



r=.76**
(n=873)
r=.47**
(n=873)

r=.37**
(n=873)



r=.23**
(n=873)
r=.43**
(n=872)
r=-.28**
(n=750)


r=-.40**
(n=749)
r=-.26**
(n=756)
r-.19**
(n=673)



r=.49**
(n=872)
r=.51**
(n=872)

r=.36**
(n=872)



r=.27**
(n=872)
r=.42**
(n=871)
r=-.26**
(n=749)


r=-.38**
(n=748)
r=-.24**
(n=755)
r=.28**
(n=672)



r=.39**
(n=870)
r=.37**
(n=870)

r=.45**
(n=870)



r=.30**
(n=870)
r=.38**
(n=869)
r=-.35**
(n=748)


r=-.25**
(n=748)
r=-.16**
(n=755)
r=.ll**
(n=672)



r=.23**
(n=871)
r=.31**
(n=871)

r=.31**
(n=871)



r=.42**
(n=871)
r=.30**
(n=870)
r=-.38**
(n=748)


r=-.44**
(n=748)
r=-.30**
(n=755)
r=.17**
(n=672)


r=.44**
(n=870)
r=.43**
(n=870)

r=.41**
(n=870)



r=.30**
(n=870)
r=.67**
(n=869)
r=-.41*
(n=748)


r=.34**
(n=721)
r=23**
(n=728)
r=-.15 **
(n=647)


r=-.28**
(n=791)
r=-.26**
(n=791)

r=-.40**
(n=791)



r=-.36**
(n=791)
r=-.42**
(n=790)
r=-.62**
(n=721)


not significant.













Symptom Experience Symptom
Management ,"I.._I_


OARS
Checklist


SF-36 -VDS Activity
Limiting Pain Scale
-Not at all
-A little bit
-Moderately
-Quite a bit
-Extremely
Number of pain sites


Figure 1-2. Derived model showing constructs, concepts, variables, relationships, and measures


Outcomes


Measures









4 R E S U L T S ................................................................ ................ 5 4

Sample Selection, Characteristics, and Attrition ............ ............................................. 54
Sam ple Selection ............................................................. ... .. .. ........ 54
Sample Characteristics of Selected and Excluded Samples................. ............. .....55
Description of Key Study Variables for Selected and Excluded Samples ...................55
Differences between Longitudinal Sample and Study Drop-outs .................................55
Descriptive Characteristics of Pain, Pain Medication, and Pain Outcomes .........................56
Relationships among Demographic Variables and Pain, Pain Medication, and
Pain Outcom es ............................................... ... .... ....................... 59
Relationships among Pain, Pain Medication, and Pain Outcomes ......................................61
Mediating Relationships between Pain, Pain Management, and Pain Outcomes within
Measurement Occasions and Longitudinally Across the 5-year Period.................62
M modeling Procedures .......... ...................... .... ....................... ... 63
M easurem ent M models ........................ ........ ..... ......... .. .............. 64
Structural Equation M odels.................................................. ............................... 67
Final Model: Path Coefficients................ .............. .......................70
Demographic and Health Variables as Exogenous Predictors of Pain Medication,
and Pain O utcom es ............. ...... .... .. ...................... ...... ............. .............. 70
Pain and Pain Medication as Mediating Endogenous Predictors of Pain Outcomes .............71

5 D ISC U SSIO N .............. ............ ... ...................... .... ......... ................. 116

Pain, Pain Medication, and Pain Outcomes among Community Dwelling Older Adults
w ith A rth ritis ........... ............................ ................................................................. 1 16
Pain among Older Adults with Arthritis...................................................116
Pain Management among Older Adults with Arthritis...............................117
Pain Outcomes among Older Adults with Arthritis .................................................119
Pain, Pain Medications, Pain Outcomes, and Demographic Variables ..............................119
Relationships among Pain Interference, Pain Sites, Pain Management, and
Pain O utcom es ........................................................................ ....... .......... 120
Adequacy of Pain Medication Partially Mediates the Relationship between Pain and
Pain Outcomes ................................................................... .... ......... 121
Sam ple A attrition E effects ............................................................................. .................... 12 5
Implication for Nursing ................................... .. .. ......... .. ............125
Lim stations of the Study ........................... ........................... .... ......... ......... 126
Suggestions for Future R research ................................................ .............................. 129
C o n clu sio n ................... ...................1...................3.........0

L IST O F R E F E R E N C E S ............................................................................. ..........................132

B IO G R A PH IC A L SK E T C H ......................................................................... ... ..................... 143









(physical, social, and emotional functioning) at each occasion. In contrast, pain showed very

weak indirect effects (P <. 1) through adequacy of prescribed pain medication. These results

indicate that worse pain predicted lower physical, social, and emotional functioning, mediated

through lower adequacy of prescribed medications (PMI). These patterns were repeated at Time

periods 2 and 3, and concurrent increase in magnitude of negative direct and indirect effects of

pain on these outcomes were noted (in all/most regression paths analyzed). These increases

suggest a mismatch between pain experience and adequacy of pain medication prescribed to

manage individuals' pain. Thus, the hypothesis that pain severely interfering with activities

among older adults with arthritis is related to less adequate pain medication and worse social,

physical, and emotional functioning was supported.

To the extent seen, less adequate prescribed analgesic medications contributed to

worsening impact of pain on pain outcomes measured in this sample of older adults. First,

evidence shows that worse pain predicted worse physical, social, and emotional functioning at

each study period. Second, these relationships were mediated by less adequate pain medication

which increased progressively from weakly negative (P -.06) at baseline to a stronger negative

relationship (3 =-.13) 5 years later. This pattern is consistent with findings of less adequate

prescribed pain medications, which persisted through the study time span.

On the longitudinal regression paths, worse pain and more adequacy of prescribed pain

medications (PMI) at earlier occasion was related to how much individuals' functioning changed

at a later occasion. Specifically, we found that worse pain experience at baseline predicted better

change in social and emotional functioning at Time 2, and this relationship was mediated by

more adequate pain medication at Time 1. These findings suggest that improvement in social and

emotional functioning can be projected with better pain management. In congruence with









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analgesics, it was not surprising that over 65% of the participants continued to experience pain

that interfered with performing their activity a 'little bit' to 'extremely' across the 3 study

periods.

Pain Outcomes among Older Adults with Arthritis

Seven pain outcome indicators were examined in the study sample. Moderate decline in

functional outcome occurred across the study time span in more than 50% of the study

participant, except in emotional role and depression, which remained stable. These results

support the literature regarding high prevalence of adverse physical and social consequences of

pain interference among older adults with arthritis (Arthritis Foundation, 2008; CDC, 2006).

More notable decline occurred in the physical functioning and physical role performance

compared to emotional and social outcome indicators. This decline could be explained as a direct

consequence of arthritis as a degenerative disease (Arthritis Foundation; CDC).

Pain, Pain Medications, Pain Outcomes, and Demographic Variables

Consistent with the chronic pain literature, results support evidence of significant pain,

sex, age, and race related limitations among older adults with persistent pain. Pain was

associated with several demographic variables investigated. For instance, there were significant

sex differences as well as correlation with pain experience and pain medication adequacy, with

females reporting more pain interference and painful locations and less adequate pain

medications than males at each measurement time point. Although correlations among these

variables appeared weak (r < .2), there was a consistent increase in their magnitude from a least

of .05 to a high of .14 by 5 years. This result suggests that while overall severity of pain

interference and pain outcomes limitation for individuals (especially females) significantly

worsened across the study periods, there was no improvement in the level of pain management

provided. Thus, females in this study had significantly lower physical functioning, physical role,









Persistent joint pain contributes to an array of adverse consequences that include adverse

psychological symptoms such as depression and anxiety (AGS, 2002; Kurtze & Svebak, 2001;

Leveille, Cohen-Mansfield, & Guranik, 2003); interference with physical and role functioning

such as walking, gripping, and general and instrumental activities of daily living (ADLs and

IADLs); and difficulty with usual tasks (Arthritis Foundation, 2006; Donald & Foy, 2004;

Duong, Kerns, Towle, & Reid, 2005, 2005; Zarit, Griffiths, & Berg, 2004). Persistent joint pain

interferes with performance of usual activities (activity limitation) in majority of older adults

with arthritis (Arthritis Foundation, 2008; CDC, 2006). About 11.6 million Americans aged 65

years and older who are diagnosed with arthritis are projected to have activity limitations by the

year 2020 (Lawrence et al., 1998). This an increase of 60% from Lawrence and colleagues' 1998

projected prevalence.

Presence or diagnosis of arthritis was also found to be associated with depressed moods

in people with persistent pain. This association is attributable to the role of arthritis (attributable

risk =18.1%) in creating functional limitations among the individuals affected (Donald & Foy,

2004; Dunlop, Lyons, Manheim, Song, & Chang, 2004; Duong, Kerns, Towle, & Reid, 2005).

According to several sources reviewed, persistent joint pain is associated with limitation in

physical and social functioning (AGS, 2002; Donald & Foy, 2004; Duong et al., 2005; Jinks,

Jordan, & Croft, 2007). These relationships were validated in studies where greater arthritis-

related pain was correlated with poorer physical and lower social functioning (Bookwala,

Harralson, & Parmalee, 2003), and occurrence of new joint pain at 1 year (Donald & Foy, 2004)

and 3 years (Jinks et al., 2007) follow-up periods produced more severe limitation in physical

functioning.










Table 4-6. Description of pain outcome variables at Time 1, 2, and 3
Time 1
Mean (S.D.) Me


Pain outcomes
Physical functioning
Physical role
Social functioning
Emotional role
Emotional vitality
Depression


Table 4-7. Time effects


60.9 (24.3)
51.0(39.6)
83.1 (21.6)
70.1 (37.9)
56.9 (20.1)
5.7 (5.4)


on predictor and outcome variables
Numerator


Variables df
PMI 2
o Pain interference 2
Pain locations 2
Physical functioning 2
Physical role 2
Social function 2
Emotional role 2
Emotional vitality 2
Depression 2
Note: *p<.05, **p <.01, ***p<.001, n.s. = not significant


Time 2
;an (S.D.)


57.2(25.7)
46.6 (40.0)
79.8 (23.8)
71.0(37.7)
55.6 (20.6)
5.5 (5.5)


Denominator
df
1890.16
1940.65
2034.22
2048.34
2144.97
2145.45
2142.11
2050.93
1873.65


Time 3
Mean (S.D.)

53.7 (26.5)
42.3 (40.4)
78.8 (24.7)
71.4(38.1)
54.1 (20.8)
5.8 (5.6)


F
12.00
17.60
5.47
113.36
30.66
25.28
.07
22.42
2.50


24.201
13.343
11.564
.326
5.398
1.004


***



n.s.
**
n.s.


p
***






n.s.
**s.
***
n.s.

n.s.






































Note: PMI1 = pain management index at Time 1; Pnintl = pain interference at Time 1; Painlocl = pain locations at Time 1

Figure 4-7. Diagram of a single occasion measurement model estimated at time









Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

RELATIONSHIPS AMONG PAIN, PAIN MEDICATION,
AND PAIN OUTCOMES IN OLDER ADULTS WITH ARTHRITIS

By

Uloma Onubogu

August 2008

Chair: Ann Horgas
Major: Nursing

Persistent pain is a common experience among older adults and is largely due to chronic

diseases, such as osteoarthritis. Pain is associated with many negative consequences, including

functional limitations. Despite research and clinical attention to this problem, evidence suggests

that pain management remains inadequate. Thus, the purpose of this longitudinal study was to

examine the extent to which the adequacy of prescribed pain medication influences the

relationship between the pain and pain outcomes (physical, social, and emotional functioning)

among older adults with arthritis over a 5-year time span. The study was conducted in a sample

of 1,409 community-dwelling older adults (male = 18.4%, female = 81.6%), with a mean age of

74 years. The study was a secondary analysis of data collected in the ACTIVE Study, a multi-

site, randomized clinical trial of cognitive training interventions. Data were analyzed from 3 time

points: baseline, 3 years later (time 2) and 5 years later (time 3). Measures included the OARS

checklist for demographic, health, and pain location variables; Medical Outcomes StudySF-36

surveys for bodily pain and pain outcomes (e.g., physical, social, and emotional functioning); the

Center for Epidemiological Studies-Depression (CES-D) scale; and the Pain Management Index

(PMI), a computed indicator for the adequacy of prescribed pain medication. Data were analyzed









Table 4-32. Final reduced form structural models mediated by pain management index: Model parameters and estimates of mediating
Time 1 endogenous predictors of Time 3 outcomes.
Mediating endogenous Dependent variables
unstandardized (standardized) unstandardized (standardized)
Models Predictors Pain PMI SOC-F PHYS-F EMOT-F
Social Functioning Pain .15* (.12) n.s. n.s. -- --
(SOC) PMI .12*** (.13) n.s. n.s. -- --
Physical functioning Pain n.s. n.s. -- n.s.
(PHYS) PMI n.s. n.s. -- n.s.

Emotional Functioning Pain n.s. n.s. -- -- n.s.
(EMOT) PMI n.s. n.s. -- -- n.s.
SOC-F
PHYS-F
EMOT-F -- -- .26*** (.24)
S Note: p < .05, ** p < .01, *** p < .001, -- indicates a path which was not estimated in the fully recursive model. The three
dependent variables were estimated in three separate models and were not allowed to interrelate, ns = nonsignificant; these values
were dropped from the final reduced form models.









testers in an interview format. CES-D scores from baseline, 3 years, and 5 years' follow-up were

used in this study.

Medications

A medication audit sheet (MedAudit) was used to assess participants' current drug use,

both over-the-counter (OTC) and prescription. Participants were asked to bring all currently used

medications ordered by their health care provider to the in-person interview. On a standardized

form (MedAudit), trained testers recorded drug names, doses, routes, frequency of

administration, pro re nata (prn) or routine use status, whether the drugs were prescribed or

purchased OTC, and any comments related to medication administration.

Data were cleaned to standardize the drug names and then categorized according to

American Hospital Formulary System codes (AHFS) into therapeutic drug categories and

subclasses. The AHFS is a functional classification widely used in the health care professions

(McEvoy, 1996). Drug categories and identifying codes used in the proposed study are

analgesics (AHFS codes: 280802-280892) and adjuvant analgesics (antidepressants, AHFS code:

281604; anti-convulsants, AHFS codes: 281292; and muscle relaxants, AHFS codes: 122004-

122008). Only specific drugs known for pain management in the antidepressant (e.g., tricyclic

antidepressant) and anticonvulsant (e.g., gabapentin) categories were included. Medication data

collected at all study occasions (baseline, 3 years, and 5 years) were used in the proposed study.

Additional data manipulation was required to compute the Pain Management Index Score

(PMI). The purpose of the PMI is to assess adequacy of prescribed analgesic medications as an

indicator of a prescriber's response to a patient's pain by comparing prescribed analgesics with a

patient's reported level of pain. A PMI score was calculated for each study participant based on

pain intensity score on a verbal descriptor scale and potency of prescribed analgesic medications

on an analgesic ladder. The computed PMI is a numerical rating scale whereby scores can range










Table 4-27. Final reduced form structural models mediated by pain management index: Model parameters and estimates at Time 3
Exogenous predictors of Time 3


Age
unstandardized
Models (standardized)


Sex
unstandardized
(standardized)


Race
unstandardized
(standardized)


Diagnoses
unstandardized
(standardized)


Social function
(SOC)


Physical function
(PHYS)


Emotional function
(EMOT)


Pain
PMI
SOC-F

Pain
PMI
PHYS-F

Pain
PMI
EMOT-F


-.28** (-.07)



-.24* (-.07)


-.16* (-.07)
n.s.


n.s.


n.s.


-.14** (-.08)


-.84* (-.06)


-.10* (-.06)


-.21* (-.05)


Note: p < .05, ** p < .01, *** p < .001, -- = not significant; these values were dropped from the final reduced form models









from -3 (e.g., a patient with severe pain receiving no analgesic drugs) to +3 (e.g., a patient

receiving a strong analgesic and reporting no pain). Negative scores indicate inadequate potency

of analgesic drugs to treat pain, and scores of zero or higher are indicative of sufficient or

acceptable treatment of pain. Pain management was considered adequate if there is congruence

between the patient's reported pain level and the potency of the prescribed analgesic drug.

Therefore, inadequate pain management was indicated for those participants with severe pain

who received no analgesics or only nonopioids/adjuvant analgesics, or for those with moderate

pain who received no analgesics (Breitbart et al., 1996; Cleeland et al., 1994).

Reliability and validity characteristics of the PMI are not documented in the pain

literature. However, as a numerical rating scale (NRS) computed from two verbal descriptor

scales (rank-ordered verbal descriptor pain scale and analgesic potency ladder), an examination

of the reliability and validity status of NRS and VDS in the pain literature was warranted. The

NRS and VDS have been independently applied in numerous studies involving pain and

analgesic drug therapy and have been found reliable (Gagliese, Weizblit, Wendy, Ellis, & Chan,

2005; Lara-Munoz, de Leon, Feinstein, Puentee, & Well, 2004; Shega et al, 2006). Both the NRS

and VDS scales have demonstrated good convergent and divergent validity when used to assess

pain in older adults (Gagliese et al.). Thus, as an index of two core components of pain

management (assessment and drug therapy), the PMI has the potential to be of clinical value for

researchers who evaluate effectiveness of pain management (Cleeland et al., 1994; Shega et al.).

Procedure

In order to access the ACTIVE data, a proposal was submitted to the ACTIVE Steering

Committee, consisting of all Principal Investigators and the Coordinating Center that functions

as the data repository for all of the ACTIVE data from all of the study sites, the New England

Research Institute (NERI). Approval was obtained in February 2007. NERI was responsible for









related to lower physical (P = -.16) and emotional functioning (P = -.10). Number of diseases was

significantly related to more pain on all three models (P = .21 to .22) as well as related to poorer

physical and emotional functioning (3 =-.08 and 0 =-.06, Table 4-25 to 4-27).

Pain and Pain Medication as Mediating Endogenous Predictors of Pain Outcomes

Pain and prescribed pain medications were analyzed as mediating predictor variables of

pain outcomes at baseline, three years, and 5 years, as well across measurement occasions, for

each separate pain outcome construct: social, physical, and emotional functioning. Results of the

cross-sectional paths show that at baseline, increased pain was directly related to lower social

functioning (P =-.69), lower physical functioning (P =-.89), and lower emotional functioning

(P =-.89). Increased pain was also significantly related lower PMI score in all three final

models-social functioning model (P =-.30), physical functioning model (P =-.29), and

emotional functioning model (P =-.31). These negative relationships were stronger at Time 2 (P

> .5) and Time 3 (P > .6). Cross-sectionally, pain to pain outcomes relationship was mediated by

lower PMI scores in all three models at baseline. This mediational pattern was repeated at Time

2 for social and emotional functioning and at Time 3 for physical functioning. The strength of

the mediational relationships was also stronger at Time 2 and Time 3 compared to baseline

(Table 4-28 to 4-30).

Cross-time structural regression paths for the three pain outcome models (social,

physical, and emotional) show a different results from those described above. Analysis of cross-

time relationships between endogenous predictors and outcomes showed overall, increased pain

and more adequate PMI scores at earlier time was significantly related to improvement in

functional outcome later in all three models. For example, increased pain at baseline was related

to better social functioning (P = .59), emotional functioning (P =.78), and physical functioning (P










Table 4-8. Sex differences in pain, PMI, and pain outcomes at Time 1, 2, and 3
Time 1 Time 2 Time 3
Mean (S.D.) Mean (S.D.) Mean (S.D.)
Males Females p Males Females p Males Females p


Pain
Pain Interference
# Pain Locations


2.0 (1.1)
0.8 (1.4)


2.2(1.2)
1.1 (1.6)


PMI -0.9 (0.9) -1.1 (0.9) **
Physical Functioning
Physical Functioning 66.6 (23.8) 59.6 (24.2) ***
Physical Role 53.9 (39.7) 50.3 (39.6) n.s.
Social Functioning
Social Functioning 84.3 (20.5) 82.9(21.8) n.s.
Emotional functioning
Emotional Role 73.8 (36.4) 69.3 (38.2) n.s.
Emotional Vitality 57.8 (21.6) 56.8 (10.6) n.s.
Depression 5.4 (5.2) 5.9 (5.5) n.s.
*p<.05; **p<.01; ***p<.001; p>.05; n.s. = not significant


2.0 (1.4)
.59 (1.1)
-0.7 (0.8)

68.1(24.7)
55.5 (39.4)

81.6 (25.0)

74.8 (36.2)
58.1 (21.6)
4.9 (5.4)


2.4 (1.1)
.98 (1.6)
-0.9 (1.0)

55.0 (25.3)
44.7 (40.0)


79.5 (23.5) n.s.


70.2 (37.9)
55.0 (20.3)
5.6 (5.5)


*** 2.0(1.1)
** 0.9(1.4)
* -0.8 (0.8)


63.9 (28.2)
52.7 (40.5)


81.7(24.4) 78.2 (24.8) n.s.


76.8 (35.9)
58.9 (22.0)
5.3 (5.7)


Table 4-9. Race differences in pain, PMI, and pain outcomes at Time 1, 2, and 3
Time 1 Time 2 Time 3
Mean (S.D.) Mean (S.D.) Mean (S.D.)
White Non-white p White Non-white p White Non-white p


Pain
Pain Interference 2.4(1.1) 2.3(1.4) n.s.
# Pain Locations 2.2(1.2) 2.2(1.1) n.s.
PMI -1.1 (0.9) -1.0 (0.9) n.s.
Physical Functioning
Physical Functioning 62.1(24.3) 58.0 (24.1) **
Physical Role 50.8 (39.2) 51.2(40.5) n.s.
Social Functioning 83.3 (21.9) 82.7 (20.9) n.s.
Emotional Functioning
Emotional Role 72.1(37.1) 65.6(39.5) **
Emotional Vitality 56.0 (20.9) 59.0 (17.8) *
Depression 5.9 (5.6) 5.5 (4.8) n.s.
*p<.05; **p<.01; ***p<.001; p>.05; ns = not significant


2.3 (1.2)
0.9 (1.5)
-0.9 (0.9)

58.9 (25.7)
47.0 (39.3)
80.3 (23.8)

72.3 (37.0)
55.1 (21.0)
5.6 (5.7)


2.4(1.1)
0.8 (1.6)
-0.9 (1.0)

52.7 (25.1)
45.4 (42.0)
78.4 23.8)

67.5 (39.3)
56.7 (19.3)
5.2 (4.9)


n.s. 2.3 (1.2)
n.s. 1.1 (1.7)
n.s. -1.1 (0.9)

*** 54.0 (26.7)
n.s. 41.5 (40.0)
n.s. 78.5 (25.0)

n.s. 73.2(37.1)
n.s. 53.0 (21.5)
n.s. 5.9(5.8)


2.4(1.4)
1.1 (1.7)
-1.0 (0.8)

51.9(25.8)
44.2 (41.6)
79.4 (23.9)

66.3 (40.5)
57.0 (18.7)
5.6 (5.0)


2.5 (1.2)
1.1 (1.7)
-1.1 (0.9)

51.6(25.7)
40.3 (40.1)


70.4 (38.4)
53.2 (20.5)
5.9 (5.5)









Pain medication. Prescribed pain medications were assessed by reviewing all

medications currently prescribed or ordered for study participants. Medications were classified

according codes indicating their primary therapeutic category. For the purpose of these analyses,

medications in the following classifications were considered pain medications: NSAIDs, opioids,

tricyclic antidepressants, muscle relaxants, salicylates (e.g., aspirin), acetaminophen, and anti-

convulsants.

At baseline, 43.9% (n=1272) of the study participants were prescribed at least one pain

medication. This number decreased to 37.2% (n=861) at Time 2 and 34.1% (n=691) at Time 3.

These proportions were statistically different across the 5-year study period (F =3.862, p=.021).

The distribution of pain medication classes is summarized in Figure 4-4. Of the seven

different analgesic classes identified at baseline, the majority of participants (about 19%) were

prescribed NSAIDs. Only about 6% of participants reported opioid analgesics in their pain

regimen at baseline. These distribution of types of prescribed pain medications were statistically

significant different across the 3 measurement occasions (F =27.129, p =.00).

Pain medications were evaluated for the adequacy of prescribed analgesics by computing

a pain management index score (PMI). The PMI scores is used as an indicator of the adequacy of

analgesic treatment and is computed based on the strongest pain medication prescribed for each

participant (scored on a 3 point analgesic ladder; 1= mild, 3 = strong analgesic) Computed PMI

scores can range from -3 to 3. Negative scores (scores < 0) indicate inadequate pain

management and positive scores (zero and higher) indicate adequate pain management relative to

the self-reported level of pain intensity.

In this sample, the mean PMI score was below zero across all points of measurement;

Time 1 (Mean = -1.0, S.D. = .87), Time 2 (Mean = -.9, S.D. = .94), and Time 3 (Mean = -1.0,










Table 4-17. Cross-time endogenous intercorrelations, Time 1 and Time 3
Time 1 variables


Time 3 Variables

Pain
Pain Interference
(PAIN-INT)
Pain Locations
(PAIN-LOC)
PMI


Physical Functioning
Physical Function
(PHYS-PF)
Physical Role
(PHYS-PR)


Social Functioning
(SOC-SF)

Emotional Functioning
Emotional Role
(EMOT-R)
Emotional Vitality
(VITAL)
Depression
(DEP)


PAIN-INT PAIN-LOC


r=.35**
(n=770)
r=.25**
(n=779)
r=-.14**
(n=691)


r=-.30**
(n=910)
r=-.32**
(n=910)


r=.19**
(n=770)
r=.37**
(n=779)
r=-.05 n.s.
(n=691)


r=-.18**
(n=910)
r=-.ll**
(n=910)


PMI


r=-.06 n.s.
(n=694)
r=-.06 n.s.
(n=701)
r=.21**
(n=639)


r=.03 n.s.
(n=825)
r=-.01 n.s.
(n=825)


PHYS-PF PHYS-PR SOC-SF EMOT-R VITAL DEP


r=-.327**
(n=770)
r=-.27**
(n=701)
r=.13**
(n=691)


r=.66**
(n=910)
r=.37**
(n=910)


r=-.33**
(n=770)
r=-.23**
(n=779)
r=.19**
(n=691)


r=.42**
(n=910)
r=.44**
(n=910)


r=-.27**
(n=770)
r=-.13**
(n=779)
r=.13**
(n=691)


r=.27**
(n=910)
r=.26**
(n=910)


r=-.16**
(n=770)
r=-.06 n.s.
(n=779)
r=.15**
(n=691)


r=.20**
(n=910)
r=.25**
(n=910)


r=-.37**
(n=769)
r=-.30**
(n=776)
r=.14**
(n=690)


r=.36**
(n=907)
r=.33**
(n=907)


r=.25**
(n=769)
r=.12**
(n=776)
r=-.14**
(n=690)


r=-.21**
(n=909)
r=-.21**
(n=909)


r=-.28** r=-.14** r=-.07* r=.33** r=.32** r=.39** r=.22** r=.36** r=-.32**
(n=910) (n=910) (n=825) (n=910) (n=910) (n=910) (n=910) (n=910) (n=909)


r=-.21**
(n=910)
r=-.30**
(n=909)
r=21**
(n=769)


r=-.09**
(n=910)
r=-.20**
(n=909)
r=13**
(n=769)


r=.02 n.s.
(n=825)
r=.078*
(n=825)
r=.01 n.s.
(n=693)


r=.21**
(n=910)
r=.36**
(n=909)
r=-.23**
(n=769)


r=.26**
(n=910)
r=.36**
(n=909)
r=-.23**
(n=769)


r=.30**
(n=910)
r=.29**
(n=909)
r=-.34**
(n=769)


r=.35**
(n=910)
r=.25**
(n=909)
r=-.30**
(n=769)


r=.30**
(n=907)
r=.60**
(n=906)
r=-.38**
(n=768)


r=-.33**
(n=909)
r==.36**
(n=908)
r=.53**
(n=768)


Note: p < .05, ** p < .01, *** p < .001, ns = not significant










Person
Dremographio. psychological, sociological,
physiological, developmefltal
,,M 'E ->. 7fn *-** 8 ..
/ .~al S( C-^' -g ^

Symptom components of
4 -1111110 Cmpa nents of


IL
symptol

II



fnvron e n
5*



Physical
\ CIlL
N 1%


experience *. symptom management \ Y
ion 0 Evaluation I strategies
of | Who? (Delivers) %
ma symptoms What? How?
Respoe Wen? To Whom?r
4 Response ;
to \ Where? How r"cnh?
symptorns OutcomesS Why?

Functional *
.sttus +-Adherenc"
E-moiornal Sef-cre
status Symptom /Health & Illness.
.u status Costs / Risk factors
\, aMortality y .o / Heallh staus
Quality of iy / Disease & jury*
Morbidity 5 .
co-morbldily -
b i_. .. .......--m"


Figure 1-1. Model of symptom management (Dodd et al., 2001)









Table 4-19. Goodness of fit indices for initial baseline measurement models estimation
Physical function Social function
22 63.45 8.33


RMSEA
NFI
RFI
IFI


<.001
.06 (p=.15)
.96
.85
.97


.14
.02 (p=.97)
.99
.93


Emotional function
169.06
19
<.001
.08 (p=.00)
.90
.72


TLI .87 .97 .72
CFI .97 .97 .91
AIC 149.45 86.33 261.06
Note: df= degree of freedom; NFI = normed fit index, RFI = relative fit index, IFI = incremental fit index, TLI = Turker-Lewis index,
CFI = comparative fit index, AIC = Akaike; RMSEA = Root mean square error of approximation









Table 4-31. Final reduced form structural models mediated by pain management index: Model parameters and estimates of mediating
Time 1 endogenous predictors of Time 2 outcomes.
Mediating endogenous Dependent variables
unstandardized (standardized) unstandardized (standardized)
Models Predictors: Pain PMI SOC-F PHYS-F EMOT-F
Social Function Pain .92*** (.80) .51*** (.35) 22.18*** (.59) --
(SOC) PMI .14*** (.17) .31*** (.29) 2.86*** (.11)
Physical Function Pain .73*** (.63) n.s. -- 26.69*** (.92)
(PHYS) PMI .11*** (.13) .25*** (.13) -- n.s.
Emotional Function Pain .79*** (.70) .48*** (.34) -- 25.68*** (.78)
(EMOT) PMI .12*** (.14) .29*** (.28) -- 2.37* (.10)
SOC-F .48*** (.42) --
PHYS-F -- 1.23*** (1.12)
EMOT-F -- -- .93*** (.87)
Note: p < .05, ** p < .01, *** p < .001, -- indicates a path which was not estimated in the fully recursive model. The three
dependent variables were estimated in three separate models and were not allowed to interrelate, ns = nonsignificant; these values
t were dropped from the final reduced form models
Note: -- indicates a path which was not estimated in the fully recursive model. The three dependent variables were estimated in three
separate models and were not allowed to interrelate.









among pain (pain interference and number of pain locations), adequacy of pain medications, and

pain outcomes (physical, social, and emotional functioning) at each of the three study periods

were examined via correlations analyses to determine if relationships existed among these

variables. For all study periods, the results indicated that a worse pain experience (more pain

interference and more pain locations) was related to less adequate prescribed analgesics, and

both were related to poorer functional pain outcomes (physical, social, and emotional) for

individuals with arthritis. In contrast, more adequate pain management, as indicated by a higher

index of prescribed analgesics, was related to higher functional performance among study

participants. In congruence with published pain reports, more arthritis-related pain was

correlated with poorer physical and social functioning (Bookwala et al., 2003). Another study

found that new joint pain occurring 3 years (Jinks et al., 2007) or as short as 1 year (Donald &

Foy, 2004) following initial measurement produced more severe limitation in physical

functioning. In addition, the degree of emotional or depressive symptom associated with chronic

pain was usually the result of concurrent pain intensity and pain interference (Mavandadi et al.,

2007). Thus, the tested hypothesis (see below) was supported, and was congruent with the

current published research findings.

Adequacy of Pain Medication Partially Mediates the Relationship between Pain and Pain
Outcomes

The tested hypothesis was that the adequacy of pain medication would mediate the

relationship between pain and pain outcomes; adequate pain medication would mitigate the

relationship between pain and poorer functional outcomes. Using structural equation modeling,

PMI-mediated physical, social, and emotional pain outcomes models were tested to establish

whether adequacy of prescribed pain medications (PMI) impacts the above relationships after

controlling for age, sex, race, and number of diseases. A Chi-square test of difference showing









Table 4-10. Correlations between exogenous and endogenous variables at Time 1


Endogenous variables
Pain
Pain interference
Pain locations

PMI
Physical functioning
Physical functioning
Physical role
Social functioning
Emotional functioning
Emotional role
Emotional vitality
Depression


Age (yrs)

r=.05 n.s. (n=1407)
r=-.03 n.s. (n=1407)

r=-.04 n.s. (n=1271)

r=-.15** (n=1407)
r=-.14** (n=1407)
r=-.05** (n=1407)

r=-. 13** (n=1406)
r=-.06* (n=1400)
r=.10** (n=1401)


Exogenous variables
# Diagnoses


r=.18**(n=1408)
r=.12** (n=1408)

r=-.07 ns (n=1273)

r=-.27**(n=1408)
r=-.16** (n=1408)
r=-.16** (n=1408)

r=-. 13** (n=1407)
r=-.18** (n=1401)
r=.12** (n=1402)


Sex a


r=.05* (n=1408)
r=.06* (n=1408)

r=-.08** (n=1272)

r=-.11** (n=1408)
r=-.04** (n=1408)
r=-.03 n.s. (n=1408)

r=-.05 n.s. (n=1407)
r=-.02 n.s. (n=1401)
r=.04 n.s. (n=1402)


Race b


r=.00 n.s. (n=1408)
r=.02 n.s. (n=1408)

r=.02 n.s (n=1271)

r=-.08** (n=1408)
r=.01 n.s. (n=1408)
r=-.01 n.s. (n=1408)

r=-.08** (n=1407)
r=.07 (n=1401)
r=-.01 n.s. (n=1402)


Note: p < .05, ** p < .01, *** p < .001, ns =
(0=White; 1 = Non-white)


not significant. a Sex was coded as 0 = males and 1 = females; b Race was coded as









Table 4-29. Final reduced form structural models mediated by pain management index: Model parameters and estimates of mediating
Time 2 endogenous predictors of Time 2 outcomes.
Mediating endogenous
unstandardized Dependent variables
(standardized) unstandardized (standardized)
Models Predictors Pain PMI SOC-F PHYS-F EMOT-F


-- -.76*** (-.60)


-- -.64*** (-.52)


-32.20*** (-.99)
-5.26*** (-.21)


-18.74*** (.-74)
n.s.


Emotional Pain -- -.76*** (-.60) -- -25.04*** (-.86)
(EMOT) PMI -- -- 4.33* (-.19)
SOC-F
PHYS-F
EMOT-F ..
Note: p < .05, ** p < .01, *** p < .001, -- indicates a path which was not estimated in the fully recursive model. The three
dependent variables were estimated in three separate models and were not allowed to interrelate, n.s. = nonsignificant; these
values were dropped from the final reduced form models


Social
(SOC)

Physical
(PHYS)


Pain
PMI

Pain
PMI










Table 4-13. Intercorrelations among endogenous variables at Time 1


PAIN-LOC


PMI PHYS-PF PHYS-PR SOC-SF EMOT-R


VITAL DEP


Pain
Pain interference
(PAIN-INT)
Pain Locations
(PAIN-LOC)
PMI


Physical functioning
Physical function
(PHYS-PF)
Physical role
(PHYS-PR)

Social functioning
S (SOC-SF)


Emotional functioning
Emotional role
(EMOT-R)
Emotional vitality
(VITAL)
Depression
(DEP)


r=.35** 1
(n=1408)
r=-25** r=-15**
(n=1272) (n=1272)


r=-48**
(n=1408)
r=-56**
(n=1408)


r=-54**
(n=1408)


r=-35**
(n=1407)
r=-48**
(n=1401)
r=.31**
(n=1402)


r=-31**
(n=1408)
r=-27**
(n=1408)


r=-27**
(n=1408)


r=-14**
(n=1407)
r=-29**
(n=1401)
r=.12**
(n=1402)


Note: p < .05, ** p < .01, *** p < .001, ns = not significant


PAIN-INT


r=.14**
(n=1272)
r=.17**
(n=1272)


r=.15**
(n=1272)


r=.10**
(n=1271)
r=.14**
(n=1265)
r=--10**
(n=1267)


r=.55**
(n=1408)


r=.45**
(n=1408)


r=.31**
(n=1407)
r=.50**
(n=1401)
r=-29**
(n=1402)


r=.53**
(n=1408)


r=.44**
(n=1407)
r=.51**
(n=1401)
r=-.33**
(n=1402)


r=.46**
(n=1407)
r=.52**
(n=1401)
r=-50**
(n=1402)


r=.35**
(n=1400)
r=-.48**
(n=1401)


r=-.49**
(n=1396)









Data Analysis


Data analysis was conducted using descriptive, correlational, and multivariate statistics.

Appropriate statistical programs were used to analyze relevant data as follows:

Descriptive statistics: SPSS (version 16.0, SPSS Inc., 2007) was used to analyze and
describe characteristics of background and health variables at baseline; and pain
interference, pain locations, prescribed pain medication regimens, and pain outcomes
(e.g., physical, social, and emotional functioning) at all 3 study periods.

Univariate and multivariate statistics: Chi-square statistics, t-tests, and mixed effect
modeling in the SPSS statistical program (version 16.0, SPSS Inc., 2007) were utilized
where appropriate to compare differences in sample means between study occasions, and
between returning and attritted samples, or to analyze longitudinal distribution of mean
sample characteristics over time.

Bivariate statistics: Pearson's product-moment correlations in the SPSS statistical
program (version 16.0, SPSS Inc., 2007) were applied to analyze (a) correlations of
demographic and health variables with pain (e.g., pain interference, number of pain
locations), adequacy of prescribed analgesics (e.g., PMI) and pain outcomes (e.g.,
physical, social, and emotional functioning) at all study occasions; (b) correlations and
intercorrelations of pain (e.g., pain interference and number of pain locations), adequacy
of prescribed analgesics (e.g., PMI), and pain outcomes (e.g., physical, social, and
emotional functioning) at all study occasions.

Structural equation modeling: AMOS program version 16.0 (Arbuckle, 2007, by SPSS
Inc., 2007) was used to analyze all steps in the proposed structural model to examine the
relationships between pain experience (e.g., pain interference and number of pain
locations) and functional status (e.g., physical, social, and emotional functioning); and the
mediating role of adequacy of prescribed pain medications (PMI), at each study occasion
and across time from baseline to five years later. For the purpose of structural equation
model analyses, the seven-level race variable was recorded into a two-level dichotomous
(dummy) variable (0) white and (1) non-white.

Initial data screening for each time of measurement indicated longitudinal loss of data

due to attrition (Table 3-1), as well as data missing at random. This result suggested possible

violation of multivariate normality criteria, which would normally require listwise or pairwise

deletion of missing data or data imputation/transformation procedures to permit model

estimation (Kline, 2005). To avoid listwise deletion of data, which can introduce selective

attrition biases, direct full-information maximum likelihood estimation (FIMLE) of the









Table 4-1. Description of sample characteristics between selected and excluded samples at Time 1
Time 1 selected study sample Time 1 excluded sample
n= 1,409 n= 1,393


% Min Max Mean (S.D.)


Male
Female
Race
White
Non-white


18.4
81.6

68.9
31.1


% Min Max Mean (S.D.) X2(df)
51.08(1)
29.8
70.1
17.00(1)
75.9


Age 64.9 94.2 74.3 (5.9) 64.9 93.9 73.9(6.0) 1.67(279
Number of 0 10 3.1(1.3) 0 8 2.1(1.4) 26.78(281
Disease
MMSE 23 30 27.3 (2.0) 23 30 27.3 (2.0) .132(28
Note: *p<.05; **p<.01; ***p<.001; ns = not significant; MMSE = Mini Mental State Examination. Race was recorded into a
dichotomous variable-White and Non-White for analysis.

Table 4-2. Comparison of pain, pain medication, and pain outcomes between selected and excluded samples at Time 1.
Time 2 study sample Time 1 unselected
n= 1,409 n= 1,393
Mean (S.D.) Mean (S.D.) t (df)


Pain
Pain interference
Pain locations
Pain medications: PMI
Pain outcomes


2.1(1.1)
1.0(1.5)
-1.0 (.9)


Physical functioning 60.89 (24.28)
Physical role 50.95 (39.61)
Social functioning 83.14 (21.58)
Emotional role 70.12 (37.93)
Emotional vitality 56.94 (20.21)
Depression 5.77 (5.40)
*p<.05; **p<.01; ***p<.001; ns p>.05; ns = not significant


1.4 (1.15)
0.3 (.71)
-.95 (.75)


77.01
69.73
89.84
79.33
66.23
4.65


(20.95)
(36.42)
(17.81)
(32.82)
(19.28)
(4.78)


17.34
15.71
-1.61


-18.61
-12.95
-8.87
-6.810
-12.403
5.740


t(df)


)9)
00)


00) n.s.


n.s.









sample over the ensuing 5 years. Furthermore, older adults often experience pain in multiple

body locations simultaneously. The number of older adults in the sample reporting three or more

pain sites associated with activity interference increased by almost 3% by 5 years. An increase in

the number of painful joints is consistent with the degenerative nature and progression of

arthritis, whereby more joint inflammation is expected to occur over time. Supporting studies

found that older adults with arthritis developed new pain sites and more pain interference over a

period of 1 year (Donald & Foy, 2004) and 3 years (Jinks et al., 2007). These findings suggest

that pain interference and painful body sites are a significant problem among older adults with

arthritis.

Pain Management among Older Adults with Arthritis

The primary means of managing chronic pain associated with arthritis is through pain

medications. The rate of analgesic use among the study sample was evaluated as the number of

participants prescribed pain medication from the seven pain drug classes recorded. To evaluate

the adequacy of prescribed pain medications in the study sample, the level of analgesic

medications ordered for participants was evaluated relative to self-reported level of pain via the

pain management index (PMI). At baseline, only about 40% of the sample was prescribed at

least one pain medication. The overall rate of pain medication prescription decreased to about

37% at three years, and 34% at 5 years, as did the rate of specific medications (e.g., opiates). For

example, only 6.2% of the study sample were prescribed opioid analgesics at the baseline period,

and this rate decreased to 5.5% at 3 years, and 4.8% at 5 years, yielding a longitudinal rate of

only 5.5% of participant prescribed opioid medications over a period of 5 years. Compared to a

related cross-sectional study of chronic pain patients (attending a pain clinic) in which less than

1% were prescribed opioid analgesics (Adams et al., 2001), the current study corroborates

findings that few older adults in various community settings were prescribed strong pain









Table 4-11. Correlations be

Endogenous Variables
Pain
Pain interference
Pain locations
PMI
Physical functioning
Physical functioning
Physical role
Social functioning
Emotional functioning
Emotional role
Emotional vitality
Depression
Note: p < .05, ** p < .01,
(0=White; 1 = Non-white)


tween exogenous and endogenous variables at Time 2
Exogenous variables
Age (yrs) # Diagnoses


r=.05 n.s. (n=910)
r=-.01 n.s. (n=913)
r=.01 n.s. (n=864)

r=-.18** (n=1032)
r=-.17** (n=1031)
r=-.11** (n=1030)


r=.164** (n=910)
r=.15** (n=902)
r=-.08 n.s. (n=864)

r=-.24** (n=1032)
r=-.22** (n=1031)
r=-.14** (n=1030)


r=-.11** (n=1031) r=-.12** (n=1031)
r=-.09** (n=1030) r=-.19** (n=1030)
r=.07* (n=909) r=.10** (n=909)
*** p < .001, ns = not significant. a Sex was coded as 0


Sex a


r=.12** (n=910)
r=.09** (n=913)
r=-.07** (n=864)

r=-.20** (n=1032)
r=-.10** (n=1031)
r=-.04 n.s. (n=1030)

r=-.05 n.s. (n=1031)
r=-.06 n.s. (n=1030)
r=.05 n.s. (n=909)
= males and 1 = females;


Race b

r=.04 n.s. (n=910)
r=-.03 n.s. (n=913)
r=.01 n.s. (n=864)

r=-.11** (n=1032)
r=-.02 n.s. (n=1031)
r=-.04 n.s. (n=1030)

r=-.06 n.s. (n=1031)
r=.04 n.s. (n=1030)
r=-.03 n.s. (n=909)
b Race was coded as




Full Text

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RELATIONSHIPS AMONG PAIN, PAIN ME DICATION, AND PAIN OUTCOMES IN OLDER ADULTS WITH ARTHRITIS BY ULOMA ONUBOGU A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILMENT OF THE REQUIREMENT FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2008 1

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2008 Uloma Onubogu 2

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To hope, love, and faith. 3

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ACKNOWLEDGMENTS I would like to thank God for k eeping the breath of life in me. My heartfelt thanks go to my supervisory committee chair, Dr. Ann Horgas whose tireless and dedicated effort ensured that this dissertation was completed. I would like to thank my committee members (Dr. Rowe, Dr. Yoon, and Dr. McCrae), whose experience a nd knowledge helped to shape and mold not only this dissertation but me as well. I have immense gratitude for Dr. Marsiske for providing access to the ACTIVE data and most of all for be ing available at all times and for his wisdom. I would also like to thank the ACTIVE committe e for granting access to the ACTIVE data and giving me the opportunity for a wonderful learni ng experience. I thank my loving husband and children who studied along with me every inch of the way and sacr ificed all that was needed to ensure that my study was completed successfully. I thank my elderly mother who made several long and dangerous trips across th e Atlantic, each time praying for life so that she can witness the completion of my study. 4

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TABLE OF CONTENTS page ACKNOWLEDGMENTS ...............................................................................................................4LIST OF TABLES ...........................................................................................................................7LIST OF FIGURES .........................................................................................................................9ABSTRACT ...................................................................................................................................10 CHAPTER 1 INTRODUCTION ..................................................................................................................12Background and Significance of the Problem ........................................................................15Rationale and Need for this Study ..........................................................................................17Theoretical Framework ...........................................................................................................19Purpose of Study .....................................................................................................................21Research Questions .................................................................................................................212 REVIEW OF THE LITERATURE ........................................................................................28Persistent Pain in Older Adults ...............................................................................................28Pharmacologic Pain Management ..........................................................................................31Pain and Related Pain Outcomes ............................................................................................37Pain and Emotional Distress ...................................................................................................37Pain and Functional Limitations .............................................................................................38Summary .................................................................................................................................403 METHODS .............................................................................................................................42Design ....................................................................................................................................42The ACTIVE Study ................................................................................................................42Measures .................................................................................................................................43Demographic and Health Data ........................................................................................43The SF-36 Health Survey ................................................................................................43Center for Epidemiological Studies-Depression Scale (CES-D) ....................................45Medications .....................................................................................................................47Procedure ................................................................................................................................48Sample, Setting, and Recruitment ...................................................................................49Selection Criteria .............................................................................................................50Ethical Considerations .....................................................................................................50Data Analysis ..........................................................................................................................51 5

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4 RESULTS ...............................................................................................................................54Sample Selection, Characte ristics, and Attrition ....................................................................54Sample Selection .............................................................................................................54Sample Characteristics of Selected and Excluded Samples ............................................55Description of Key Stud y Variables for Selected and Excluded Samples ......................55Differences between Longitudinal Sample and Study Drop-outs ...................................55Descriptive Characteristics of Pai n, Pain Medication, and Pain Outcomes ...........................56Relationships among Demographic Vari ables and Pain, Pain Medication, and Pain Outcomes ............................................................................................................59Relationships among Pain, Pain Medication, and Pain Outcomes .........................................61Mediating Relationships between Pain, Pain Management, and Pain Outcomes within Measurement Occasions and Longitudinally Across the 5-year Period .....................62Modeling Procedures ..............................................................................................................63Measurement Models ......................................................................................................64Structural Equation Models .............................................................................................67Final Model: Path Coefficients ........................................................................................70Demographic and Health Variables as Exogenous Predictors of Pain Medication, and Pain Outcomes .....................................................................................................70Pain and Pain Medication as Mediating Endogenous Predictors of Pain Outcomes ..............715 DISCUSSION .......................................................................................................................116Pain, Pain Medication, and Pain Outcomes among Community Dwe lling Older Adults with Arthritis .............................................................................................................116Pain among Older Adults with Arthritis ........................................................................116Pain Management among Older Adults with Arthritis ..................................................117Pain Outcomes among Older Adults with Arthritis ......................................................119Pain, Pain Medications, Pain Outc omes, and Demographic Variables ................................119Relationships among Pain Interference, Pain Sites, Pain Management, and Pain Outcomes ..........................................................................................................120Adequacy of Pain Medication Partially Medi ates the Relationship between Pain and Pain Outcomes ..........................................................................................................121Sample Attrition Effects .......................................................................................................125Implication for Nursing ........................................................................................................125Limitations of the Study .......................................................................................................126Suggestions for Future Research ..........................................................................................129Conclusion ............................................................................................................................130 LIST OF REFERENCES .............................................................................................................132BIOGRAPHICAL SKETCH .......................................................................................................143 6

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LIST OF TABLES Table page 1-1 Constructs, variables, oper ational definitions, and measures ........................................... 273-1 Summary of variable s and measurement points ............................................................... 534-1 Description of sample characteristic s between selected and excluded samples at Time 1 ........................................................................................................................... 874-2 Comparison of pain, pain medication, a nd pain outcomes between selected and excluded samples at Time 1. ............................................................................................. 874-3 Comparison between longitudinal sa mple and study drop-outs at Time 2 ....................... 884-4 Comparison between longitudinal sa mple and study drop-outs at Time 3 ....................... 894-5 Description of pain variab les and PMI at Time 1, 2, and 3 .............................................. 894-6 Description of pain outcome variables at Time 1, 2, and 3 .............................................. 904-7 Time effects on predictor and outcome variables ............................................................. 904-8 Sex differences in pain, PMI, a nd pain outcomes at Time 1, 2, and 3 .............................. 914-9 Race differences in pain, PMI, and pain outcomes at Time 1, 2, and 3 ............................ 914-10 Correlations between exogenous an d endogenous variables at Time 1 ............................ 924-11 Correlations between exogenous an d endogenous variables at Time 2 ............................ 934-12 Correlations between exogenous an d endogenous variables at Time 3 ............................ 944-13 Intercorrelations among e ndogenous variables at Time 1 ................................................ 954-14 Intercorrelations among e ndogenous variables at Time 2 ................................................ 964-15 Intercorrelations among e ndogenous variables at Time 3 ................................................ 974-16 Cross-time endogenous intercorrelations, Time 1 and Time 2 ......................................... 984-17 Cross-time endogenous intercorrelations, Time 1 and Time 3 ......................................... 994-18 Cross-time endogenous intercorrelations, Time 2 and Time 3 ....................................... 1004-19 Goodness of fit indices for initial baseline measurement models estimation ................. 1014-20 Goodness of fit indices for best-fitt ing full 3-occasion measurement models ................ 102 7

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4-21 Goodness of fit indices for simple (med iational) one-occasion structural model estimation at time 1 ......................................................................................................... 1034-22 Goodness of fit indices for simple medi ational structural m odel (Step 2 models) estimation at time 1 ......................................................................................................... 1044-23 Goodness of fit indices for simple thr ee occasion mediation structural model estimation ........................................................................................................................ 1054-24 Goodness of fit indices for the final reduced 3-occasion pain-mediated models ........... 1064 -25 Final reduced form structural models mediated by pain management index: Model parameters and estimates at Time 1 ................................................................................ 1074-26 Final reduced form structural models me diated by pain management index: Model parameters and estimates at Time 2 ................................................................................ 1074-27 Final reduced form structural models me diated by pain management index: Model parameters and estimates at Time 3 ................................................................................ 1084-28 Final reduced form structural models me diated by pain management index: Model parameters and estimates of mediating Time 1 endogenous predictors of Time 1 outcomes. ........................................................................................................................ 1094-29 Final reduced form structural models me diated by pain management index: Model parameters and estimates of mediating Time 2 endogenous predictors of Time 2 outcomes. ........................................................................................................................ 1104-30 Final reduced form structural models me diated by pain management index: Model parameters and estimates of mediating Time 3 endogenous predictors of Time 3 outcomes ......................................................................................................................... 1114-31 Final reduced form structural models me diated by pain management index: Model parameters and estimates of mediating Time 1 endogenous predictors of Time 2 outcomes. ........................................................................................................................ 1124-32 Final reduced form structural models me diated by pain management index: Model parameters and estimates of mediating Time 1 endogenous predictors of Time 3 outcomes. ........................................................................................................................ 1134-33 Final reduced form structural models me diated by pain management index: Model parameters and estimates of ............................................................................................ 1144-34 Pain outcomes predicted at Time 1, 2, and 3 in the final reduced model ....................... 115 8

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LIST OF FIGURES Figure page 1-1 Model of symptom management ...................................................................................... 241-2 Derived model showing constructs, concep ts, variables, relatio nships, and measures .... 251-3 Conceptual model of the study ......................................................................................... 264-1 Flowchart of sample selection and sample size for key study vari ables in longitudinal sample across the three study periods. .............................................................................. 734-2 Intensity of pain interfer ence across measurement occasions .......................................... 744-3 Number of pain locations reported across measurement occasions .................................. 754-4 Distribution of pain medications classes at 3 occasions of measurement ........................ 764-5 Frequency distribution of PMI scores across measurement occasions ............................. 774-6 Pain outcomes scores across measurement occasions ...................................................... 784-7 Diagram of a single occasion measurement model estimated at time .............................. 794-8 Step 1: Simple mediating one-occa sion (Time 1) structural model diagram ................... 804-9 Step 2: Simple mediational one-occa sion (Time 1) structural model diagram ................. 814-10 Step 3: Simple mediational 3occasion structural model diagram .................................... 824-11 Three-occasion recursive l ongitudinal model showing addition of cross-time paths for major relationships tested ................................................................................................. 834-12 Final reduced model of pain, pain medications, and physical functioning ....................... 844-13 Final reduced model of pain, pain medications, and social functioning ........................... 854-14 Final reduced model of pain, pain medications, and emotional functioning .................... 86 9

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Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy RELATIONSHIPS AMONG PAIN, PAIN MEDICATION, AND PAIN OUTCOMES IN OLDER ADULTS WITH ARTHRITIS By Uloma Onubogu August 2008 Chair: Ann Horgas Major: Nursing Persistent pain is a common experience among ol der adults and is la rgely due to chronic diseases, such as osteoarthritis. Pain is asso ciated with many negativ e consequences, including functional limitations. Despite res earch and clinical attention to this problem, evidence suggests that pain management remains inadequate. Thus, the purpose of this longitudinal study was to examine the extent to which the adequacy of prescribed pain medication influences the relationship between the pain and pain outcome s (physical, social, and emotional functioning) among older adults with arthritis over a 5-year time span. The study was conducted in a sample of 1,409 community-dwelling older adults (male = 18.4%, female = 81.6%), with a mean age of 74 years. The study was a secondary analysis of data collected in the ACTIVE Study, a multisite, randomized clinical tr ial of cognitive training interventions. Data were analyzed from 3 time points: baseline, 3 years later (time 2) and 5 years later (time 3). Measures included the OARS checklist for demographic, health, and pain location variables; Medical Outcomes StudySF-36 surveys for bodily pain and pain outcomes (e.g., physical, social, and emo tional functioning); the Center for Epidemiological Stud ies-Depression (CES-D) scale; a nd the Pain Management Index (PMI), a computed indicator for the adequacy of prescribed pain medication. Data were analyzed 10

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using descriptive and bivariate statistics, as well as mixed ef fects modeling and longitudinal structural equation modeling. Result s indicated the majority of par ticipants experienced pain that interfered with their activities were prescribed inadequate pain medications, had moderate functional limitations at each measurement and acro ss time, and that increased pain interference was related to worse functioning. In longitudinal models, limitations in social physical, and emotional functioning were predicted by pain experience and this relationship was partially mediated by lower adequacy of prescribed pain medication at each time of measurement. Howe ver, relationships cross time showed that individuals improved in their functioning as the ade quacy of their prescribed pain medications improved. These results highlight prev alence of pain interf erence as a significant problem in older adults with ar thritis, and the importance of adequate pain medication in the management of adverse pain related outcomes. 11

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CHAPTER 1 INTRODUCTION Chronic or persistent pain (pain lasting mo re than 3 months) is a common experience among older adults (American Geri atric Society (AGS), 2002; Hall -Lord, Johansson, Schmidt, & Larsson, 2003; Higgins, Madjar & Walton, 2004; Hutt, Pepper Vojir, Fink, & Jones, 2006; Koltyn, 2002; Reid, 2003). Over 50% of older adults have a significant problem with persistent pain (Hall-Lord et al., 2003; Hutt et al., 2006; Won et al., 2004). The high prevalence of persistent pain in elderly adu lts is linked to equally high pr evalence of chronic and co-morbid diseases in this population ( AGS, 2002; Ferrell & Ferrell, 1990; Harden et al., 2005; Patel, 2003). Studies have shown that over 860% of adults over the age of 65 years have at least one chronic illness (CDC, 2003), and ove rall, more than 80% of older adults surveyed reported one to more than three chronic illnesses (C han, Chong, Basilikas, Mathie, & Hung, 2002). Joint disorders such as arthritis, are recogni zed as the most common cause of chronic or persistent joint pain among the elderly (AGS, 2002; Lawrence et al., 1998). Currently, 46 million Americans are diagnosed with Arthritis (CDC, 2006) Projection studies estimate that as many as 18.2% or 59.4 million will be diagnosed with arthritis by the year 2020 (Lawrence et al., 1998). By 2030, as many as 67 million persons (25% of Amer ican adults population) are expected to be diagnosed with arthritis, over 50% of them will be older than 65 years of age, and more than 37% (25 million) are projected to have activity limitation (CDC, 2006; Hootman & Helmick, 2006). These projections are consistent with Lawrence and colleagues projection of 49% prevalence of arthritis among pers ons 65 years and older (1998). As the prevalence of arthritis increase, persistent pain as a public health problem is al so expected to increase. 12

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Persistent joint pain contributes to an array of adverse consequences that include adverse psychological symptoms such as depression and anxiety (AGS, 2002; Kurtze & Svebak, 2001; Leveille, Cohen-Mansfield, & Guranik, 2003); inte rference with physical and role functioning such as walking, gripping, and general and inst rumental activities of daily living (ADLs and IADLs); and difficulty with usual tasks (A rthritis Foundation, 2006; Donald & Foy, 2004; Duong, Kerns, Towle, & Reid, 2005, 2005; Zarit, Gri ffiths, & Berg, 2004). Persistent joint pain interferes with performance of usual activities (a ctivity limitation) in majority of older adults with arthritis (Arthr itis Foundation, 2008; CDC, 2006). A bout 11.6 million Americans aged 65 years and older who are diagnosed with arthritis are projected to have ac tivity limitations by the year 2020 (Lawrence et al., 1998). This an increas e of 60% from Lawrence and colleagues 1998 projected prevalence. Presence or diagnosis of arthri tis was also found to be associated with depressed moods in people with persistent pain. This association is attribut able to the role of arthritis (attributable risk =18.1%) in creating functi onal limitations among the indivi duals affected (Donald & Foy, 2004; Dunlop, Lyons, Manheim, Song, & Chang, 2004; Duong, Kerns, Towle, & Reid, 2005). According to several sources reviewed, persistent joint pain is associ ated with limitation in physical and social functioni ng (AGS, 2002; Donald & Foy, 2004; Duong et al., 2005; Jinks, Jordan, & Croft, 2007). These relationships were validated in studies wh ere greater arthritisrelated pain was correlated w ith poorer physical and lower social functioning (Bookwala, Harralson, & Parmalee, 2003), and occurrence of ne w joint pain at 1 year (Donald & Foy, 2004) and 3 years (Jinks et al., 2007) follow-up periods produced more severe limitation in physical functioning. 13

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Effective chronic pain management should re sult in reduction of a dverse consequences across multiple dimensions, including physical, em otional, psychological, social, and spiritual (AGS, 2002; Kurtze & Svebak, 2001). Thus, pain management should include the use of both pharmacologic and nonpharmacologic strategies ( AGS, 2002; Ferrell, 1995). Pharmacologic pain management, however, is the most commonly us ed approach among elderly adults (AGS, 2002). Often, the scope of pharmacologic pain management is expanded to include nonpain drugs such as anti-anxiety, antidepressants, muscle relaxant s, anti-inflammatory agents, sedative, and antiepileptics, to treat a variety of chronic symptoms or conditions associated with persistent pain (AGS, 2002; Harden et al., 2005; Leininger, 2002). However, analgesic me dications (opioid and nonopioid) are prescribed most often to treat a variety of persiste nt joint pain among older adults (AGS, 2002; Clark, 2002; Jakobsson, Hallberg, & Westergren, 2004). It is expected that when analgesic medicati ons are used appropriately and in adequate amounts to treat persistent pai n, individuals achieve better pa in control (Furlan, Sandoval, Mailis-Gagnon, & Tunks, 2006; Hutt et al., 2006), inactivity is redu ced (Allen et al., 2003), and there are better functional outcomes (Furlan et al .). On the other hand, inadequate pain treatment is associated with more adverse clinical outcomes such as unrelieved pain, depression, poor perception of health (Herrick et al., 2004; W on et al., 1999), and poor functional outcomes due to activity limitation (Herrick et al.; Mo ssey & Gallagher, 2004). Studies examining prescription/use of pain medication and pain ex perience among elderly with chronic pain have found a prevalence of inadequate prescription of pain medications (Hutt et al., 2006; Horgas & Tsai, 1998; Won et al., 2004). These studies accentuate the fact that under-treatment of pain is widespread among older adults (Berry & Dahl, 2000; Grant & Haverkamp, 1995; Horgas & Tsai; 14

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Mzorek & Werner, 2001; Won et al., 2004), and highlights the need for more study on this important issue. Background and Significance of the Problem Persistent pain may be considered one of the most pervasive and expensive health care problems in the twenty-first century due to the high prevalen ce of adverse responses among individuals affected (Arnstein, Vidal, Wells-Federman, Morgan, & Caudill, 2002). Therefore, the goal of effective pain therapy is to prevent, cont rol, or relieve adverse symptoms associated with persistent pain. However, evidence of high prev alence of persistent pain among older adults, poor treatment of pain, and prevalence of advers e outcomes, suggests that these goals have not been achieved. Clinical trials and longitudinal pain studies have thus focused on isolating specific contributing factors associated with non-achievement of pain management goals. Studies have shown that pain management in older adults is inade quate (Berry & Dahl, 2000; Grant & Haverkamp, 1995; Horgas & Tsai, 1998; Mzorek & Werner, 2001; Won et al., 2004). Although older adults typi cally consume a large amount of medication, studies on pain management have shown that under-treatment of persistent pain is preval ent in this population (Mzorek & Werner; Rodriguez, 200 1; Sofaer, 1984). Among the elderly, suboptimal prescription is a major contributing factor to inadequate use of pain medica tion (Hutt et al., 2006). This may occur if the healthcare provider fa ils to assess pain adequately (Gloth, 2000; Wagner, Goodwin, Campbell, French, & Shepherd, 1997), has diffi culty choosing the right pain drug, fears the consequences of opioids or of polypharmacy complications such as drug side effects, interactions, and addiction ( AGS, 2002; Green, Wheeler, LaPort e, Marchant, & Guerrero, 2002; Tarzan & Hoffman, 2004). Suboptimal prescrip tion of analgesics may also results if inappropriate medication scheduling is prescribed for the type a nd nature of pain the patient presents (Hutt et al.), or if the prescriber lack s knowledge of standardized approaches to treating 15

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pain (AGS, 2002; Tarzan & Hoffman, 2004; Worl d Health Organization [WHO], 1986). The clinical implication of suboptim al prescription of pain medica tions for older adults with persistent pain is poor management and nega tive functional and psyc hosocial outcomes. The extent to which suboptimal prescr iption of analgesic medications impacts pain outcomes in older adults over time warrants further investigation. Several studies have reported on outcomes of pain experience among older adults (Ferrell & Ferrell, 1990; Gonzalez, Martel li, & Baker, 2000; Harden et al., 2005; Zarit et al., 2004). For example, depression is well documented as a correlate of persistent pain (Zar it et al.). Persistent pain negatively also impacts clin ical outcomes such as self-per ceived health, emotional status, and functional status (Mantyselka, Turune n, Ahonen, & Kumpusalo, 2003; Oster, Harding, Dukes, Edelsberg, & Cleary, 2005; Silkey et al ., 2005). These studies sup port clinical evidence that pain related outcomes such as depressi on and functional limitati ons are prevalent among older adults with chroni c pain (Won et al., 2004). To date, most of the studies of the rela tionship between the inadequacy of pain management/medication and adverse pain outcomes have used cross-sectio nal research designs. These studies fail to explain the direct effect s of analgesic medication on pain outcomes over time, because cross-sectional analysis limits the an alytical ability of such studies to adequately inform and predict relationships between pres cribed analgesic therapy and pain outcomes. According to Portney & Watkins (2000), validity of cross-sectional studies is threatened by the difficulty of estimating the effects of age or the passage of time on study results. Hence, longitudinal studies are needed to analyze documen ted growth or change over time, as empirical evidence of developmental cha nge (Portney & Watkins). 16

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Longitudinal studies of the relationships be tween pain, analgesic medications, and pain outcomes are few, the time intervals are different and the concepts and variables investigated have varied. For example, in one cohort study of exercise training tr ials after hip surgery (Herrick et al., 2004), correlations of pain se verity with pain medication use, depression, difficulty with activities of da ily living, and measures of qual ity of life, among others, were measured at baseline as well as 1-year posttraining intervention. In another study conducted over a period of 6 months, Mossey and Gallagher (2004) measured pain intensity, comorbid depression, and presence of activity limiting pain, as predictors of poor physical functioning. In other outcome studies, improvement in pain expe rience with analgesic treatment was assessed at various follow-up evaluation peri ods ranging from 7 days to 4 years (Doleys, Brown, & Ness, 2006; Jensen, Mendoza, Hanna, Chen, & Cleeland, 2004; Rosenthal, Silverfield, Wu, Jordan, & Kamin, 2004). Findings of these studies validate evidence of bivariate relationships between pain, treatment with analgesic medi cations, and stated pain outcomes. However, the paucity of published studies highlights the need for more syst ematic evaluation of the impact of pain and pain management on an individuals outcomes over time. Therefore, the proposed study will examine longitudinal relationships between pa in and pain outcomes (such as functional limitations and emotional distress) among elderly with persistent ar thritic pain, and the mediating role of prescribed analgesic me dications in this relationship. Rationale and Need for this Study According to these reviews, pain experience, under-treatment of pain, and adverse pain outcomes are prevalent among older adults. Nu merous empirical studies and scholarly publications have described the chronic pain experience of the elderly and associated outcomes as multidimensional and multi-factorial phenomen a (AGS, 2002; Harden et al, 2005; Patel, 2003). Hence, effective management goals should be directed to understanding the complexity of 17

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the experience of individuals with persistent pain, preven ting severe consequences and disabilities, and improving their quality of life. Measured agai nst these standards, numerous studies have examined the profile of pharmacologi c pain regimens (quality and quantity) of older adults with chronic pain, to determine congrue nce between pain experience and (1) amount of prescribed pain medications or medication use (Fisher et al., 2002), (2) pain severity and relationships to pain outcomes (Hutt et al., 2006; Mantyselka et al., 2003; Mzorek & Werner, 2001; Won et al., 2004) or pain se verity and interference, and (3 ) relationships to functional limitations (Donald & Foy, 2004; Duong et al., 2005) Results of these studies consistently support cross-sectional evidence of the inadequa cy of pain management among the elderly, and prevalence of emotional distress (e.g., depressi on) and functional lim itations (physical and social) among older adults with chronic pain. In light of the above reviews, this stu dy seeks empirical evidence to document the consequences of poor pain management over time. Hence, a longitudinal analysis was considered appropriate to examine the relationships betwee n pain, pain management, and pain outcomes over time. Findings indicating that poor management of persistent pain worsened the impact of pain on severity of pain outcomes or vice versa, will validate ample cross-sectional evidence and expand limited longitudinal evidence of these relationships in the pa in literature. C linically, such findings will contribute to the basis for deci sion-making regarding prescription of analgesic medications for older adults with persistent pai n. Health care providers will be able to utilize evidence-based support to formulate and im plement treatment goals for the purpose of management and/or prevention of functional and emo tional decline in older adults with persistent joint pain. 18

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Theoretical Framework The Model of Symptom Management (MSM; D odd et al., 2001) provided the theoretical underpinning for this study (Figure 1-1). This theo retical model is comprised of three interrelated constructs: symptom experien ce, symptom management strategy, and outcomes. Symptom experience describes an individua ls perception of symptom, ev aluation of the meaning of a symptom, and response to a symptom. Symptoms are perceived when an individual notices a change from the usual pattern of feeling or behavior. Percei ved symptoms are evaluated by making judgments about severity, cause, prognosis, and effects on their lives. An individuals response to symptoms perceived or evalua ted may have physiological, psychological, sociocultural, and behavioral components, whic h are involved in a bi-directional relationship. Symptom experience is usually obtained through self-report. Symptom management is a dynamic process, which involves the assessment of the symptom experience from an individuals perspe ctive and the identification of intervention strategies. Symptom management strategies in cludes the specification of what (nature of strategy), when, where, why, how much (interven tion dose), to whom (recipient of intervention), and how (route of delivery). The goal of symptom management is to relieve symptoms and avert or delay negative outcome through biomedical, professional, and self-care strategies. Outcomes are expected results and consequences that emerge from symptom management strategies as well as from th e symptom experience. The outcomes dimension focuses on eight interrelated factors, which also relate to the symptom status of the individual. These factors include functional st atus, emotional status, self-care, quality of life, morbidity and co-morbidity, mortality, and cost. The Model of Symptom Management (MSM ) has several impor tant assumptions: 19

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The gold standard for the study of symptoms is based on the perception of the individual experiencing the symptom and his/her self-report. An individual does not have to experience a ny symptoms in order to apply the model; an intervention may be initiated if a risk factor is determined. All uncomfortable symptoms must be treate d. Individuals experiences of chronic pain are multidimensional and usually associated with multiple symptoms (AGS, 2002; Ferrell & Ferrell, 1990; Harden et al., 2005; Patel, 2003 ). Thus, pain medications (analgesics and adjuvant analgesics) are prescribed for th e treatment/management of pain symptoms experienced by individuals (Dodd et al., 2001; Harden et al.). Management strategies may also be multidim ensional and targeted not only individuals, but their families, groups, or work environments. Symptom management is dynamic process, modified by individual outcomes and the influences of the nursing domain of person, health/illness, or envi ronment. Interference caused by pain experience is influenced by prescribed pain medications and ultimately affects severity of pain outcomes over time. Therefore, persistent or worsening pain experience and pain outcomes may impli cate inadequate modification of pain medications. A middle range theoretical model to guide th e present study was derived from the Model of Symptom Management (Figure 1-2). A middle-range theory focuses on aspects of a complex human experience or reality (Polit & Hungler, 1995), considers a limited number of variables, and is more susceptible to empirical testing (Meleis, 1997). Thus, deriving a middle range theory for this study permitted selection of only the concep ts, variables, and relationships specific to the study (Meleis; Polit & Hungler). In the derived model, the three main c onstructs of the MSM and three corresponding concepts are included (Figure 1-3). The c onstructs are symptom experience, symptom management strategy, and outcomes. The co rresponding concepts ar e pain experience, prescribed pharmacologic pain management, and clinical pain outcomes, respectively. Pain experience was indicated by report of pain that limits usual activity (severity of pain interference and number of pain locations). Prescribed pharmacologic management was indicated by participants prescribed pain medication regimens (analgesic a nd adjuvant analgesic 20

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medications). Clinical pain outcomes associated with the experience of persistent pain were indicators of the concept of functional status (physical f unctioning, social functioning, and emotional functioning). See Table 1-1 for de finition of concepts. The study examined relationships between pain and pain outcomes, a nd the role of prescribed pharmacologic pain management in this relationship. Purpose of Study The purpose of this study was to (a) examin e the relationship between pain (e.g., pain interference and number of pain locations) and pain outcomes (e.g. physical functioning, social functioning, and emotional functioning) in older a dults with arthritic pain and (b) determine whether the adequacy of prescribed pain medications (e.g., pain management indexPMI) mediates the relationship between pain and pain outcomes. These relationships are examined cross-sectionally at three different time point s and longitudinally over a 5-year time period. A model of the proposed study is shown in Figure 3. The following research questions and hypotheses were investigated: Research Questions What are the characteristics of pain (e.g., seve rity of pain interference, number of pain locations), the adequacy of prescribed pa in medication (e.g., PMI), and pain outcomes (e.g., physical functioning, social functioni ng, and emotional functioning) among older adults with arthritis at Time 1 (baseline), Time 2 (3 years later), and Time 3 (5 years later)? o Hypothesis 1a: The majority of the sample will report pain that interfered with activities at each measurement point, and th is rate will increase significantly across the 5-year study period. o Hypothesis 1b : The majority of the sample will have inadequate analgesic prescription relative to their reported leve l of pain at each measurement point, and this rate will increase/remain st able over the 5-year study period. o Hypothesis 1c : More than 50% of the sample will have moderate levels of physical, social, and emotional functioning at each time of measurement, and these levels of functional impairment will increase over the 5-year time frame. 21

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What demographic (e.g., age, sex, race) and health variables (e.g., number of diseases diagnosed) are related to pain (e.g., severity of pain interference, number of pain locations), adequacy of prescribed pain medications (e.g., PMI), and pain outcomes (e.g., physical functioning, social functioning, and emotional functioning) at each measurement point [e.g., Time 1 (baseline), Time 2 (3 years later), and Time 3 (5 years later)]? o Hypothesis 2a: Age will demonstrate no significant relationship with pain, but older age will be significantly associated with less adequate pain management and poorer functional outcomes at each time point. o Hypothesis 2b : Females will report higher levels of pain, less adequate pain management, and poorer pain outcomes (phys ical, social, and emotional functioning) at each time point. o Hypothesis 2c : Race will demonstrate significant relationships with the study variables. Non-white participan ts will report higher levels of pain, less adequate pain management, and poorer pain outcomes (phys ical, social, and emotional functioning) at each time point. o Hypothesis 2d : Number of diseases diagnosed will be significantly related to the study variables such that more diagnosed me dical conditions will be associated with more pain, less adequate pain manageme nt, and poorer pain outcomes (physical, social, and emotional functioning) at each time point. Is pain (e.g., pain interferen ce and number of pain locations ) related to the adequacy of prescribed pain medications (e.g., PMI) a nd pain outcomes (e.g., physical functioning, social functioning, and emotional functioning) among older adults with arthritis at each time of measurement [e.g., Time 1 (baseline), Ti me 2 (3 years later), and Time 3 (5 years later)]? o Hypothesis 3a: Pain (e.g., interference and num ber of pain locations) will be significantly and negatively related to the adequacy of prescribed pain medications at each time point. o Hypothesis 3b : Pain (e.g., interference and number of pain locations) will be significantly and negatively related to pa in outcomes at each time point such that more pain will be associated with poorer physical, social, and emotional functioning.. o Hypothesis 3c : Pain (e.g., interference and number of pain locations), adequacy of prescribed medications, and pain outc omes (physical, social, and emotional functioning) will be significantly intercorrelated positively and negatively at each time point and across time points. Does the adequacy of prescr ibed pain medications (e.g., PM I) mediate the relationships between pain (e.g., pain interference and num ber of pain locations) and pain outcomes (e.g. physical, social, and emotional functioni ng) at each occasion and across the 5-year 22

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o Hypothesis 4a: The adequacy of prescribed pain medications (PMI) will significantly mediate the relationships between pain (pai n interference and number of pain sites) and pain outcomes (physical functioning, so cial functioning, and emotional status) at each measurement occasion, and across occasions over a 5-year time span, after controlling for age, sex, race, and number of diseases diagnosed. 23

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24 Figure 1-1. Model of symptom management (Dodd et al., 2001)

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Symptom Experience Symptom Management Strategy Outcomes CES-D score Sf-36 MH score Adequacy of Prescribed Analgesics Social Physical Prescribed Pain Medication Regimen SF-36 Social Functioning Score SF-36 Physical Functioning Score SF-36 VDS Activity Limiting Pain Scale -Not at all -A little bit -Moderately -Quite a bit -Extremely Number of pain sites Demographics: Age, Sex, Race Health: Number of Diseases Diagnosed Measures Background Conditions Person and Health Pain Management Index Score Emotional Functional Limitations Pain Experience Pain Interference Number of Pain Locations OARS Checklist 25 Figure 1-2. Derived model showing constructs, concepts, variables, relationships, and measures

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PHYS -PF DEP PMI PHYS-F EMOT-F PAIN-LOC PAIN PHYS-PR VITAL SOC-SF EMOT-R PAIN-INT DZ Age Sex Race LEGEND: PAIN PAIN-INT = Pain interference PAIN-LOC = Pain locations EMOT-F = Emotional Functioning VITAL= Vitality DEP= Depression EMOT-R = Emotional role PHYS-F = Physical Functioning PHYS-PF = Physical functioning PHYS-PR = Physical role SOC-SF = Social Functioning PMI = Pain Management Index DZ = Nu m be r o f d i se a s e s d i ag n os e d 26 Fig 1-3. Conceptual model of the study

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27 Table 1-1. Constructs, variables, oper ational definitions, and measures Constructs Variables Operational Definition Instrument (Level of Measurement) Background characteristics Demographic characteristics Health conditions Self-reported age, sex, race Self-reported number of diagnosed diseases (DZ) Demographic survey: Age (continuous), sex (categorical), race (categorical) OARS checklist: Number of diseases (Continuous) Pain experience Pain interference Number of pain locations Self-report of the extent to which bodily pain interferes with activities (on a 6point Verbal Descriptor Scale) Self-report of the number of painful body locations that interfere with activities SF-36: Bodily pain subscale (ordinal) OARS Checklist: Summary of the number of painful locations (continuous) Prescribed pain medication Adequacy of prescribed analgesics Calculated score of the relationship between ordered analgesic medications (AHFS scores of opioids, nonopioids, and adjuvant analgesic medications) and self-reported pain. Pain management index (PMI) (ordinal) Pain outcomes Physical functioning Social functioning Emotional functioning Physical functioning Physical role Social functioning Emotional role Emotional vitality Depression Self-report of the numbe r/the severity of functional limitations in the physical, social, and emotional domains Self-reported level of disturbances in mood and affect SF-36: Physical Functioning Subscale SF-36: Social Functioning Subscale SF-36: Emotional Functioning Subscale CES-D (ordinal)

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CHAPTER 2 REVIEW OF THE LITERATURE This review focuses on the following study concepts: pain, pain medication, and pain outcomes among older adults with persistent pain related to chronic sk eletal/joint conditions, especially arthritis. Prevalence of persistent pain and pain inte rference among older adults in general populations, specifically among those with arthritis is exam ined. Further, the relationship between pain, pain medications, and pain outcomes (e.g., functional status physical and social functioning; and emotional status) is discussed. Persistent Pain in Older Adults Within the last two decades, literature on ch ronic pain among older adults has focused mostly on establishing the high prev alence of pain and the pattern of the pain experience in terms of severity (intensity), frequency, and duration (Ferrell & Ferrell, 1990; Hutt et al., 2006; Rutledge, Donaldson, & Pravikoff, 2002; Nour & Laforest, 2003; Won et al., 2004). Recently, researchers have begun to examine other descriptor s of the pain experience, such as pain-related interference with activity (Jinks et al., 2007; Mossey & Gallag her, 2004; Zarit et al., 2004). These reports have been documented mostly in re lation to older adults in nursing home settings where persistent pain is mo st prevalent (Allcock, McGarr y, & Elkan, 2002; Brockopp, Brockopp et al., 1998; Fox, Raina, & Jada d, 1999; Hall-Lord, 2003; Hutt et al., 2006; Tse, Pun, & Benzie, 2003). Thus, this review will encompass reports on persistent pain experience in the general population of older adults, and specifically in those with a diagnos is of arthritis. Pain, is defined by the Internati onal Association for the Study of pain (IASP, n.d.) as an unpleasant sensory and emotional experience associated with actual or po tential tissue damage. Pain is further viewed as a complex phenomenon associated with interp lay of psychological and pathophysiological processes that cause pain perception or expression (Hawthorn & Redmond, 28

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1998; McCaffery & Pasero, 1999; Zimmermann, 2004). Often, pain is described or measured by its intensity (level of severity ), duration (length of time it has occurred), frequency (how often it occurs), or interference (extent to which pain inte rferes with usual activity or function) (Arthritis Foundation, 2008; Ferrell & Ferr ell, 1990; McCaffery & Pase ro, 1999; Donald & Fay, 2004; Zarit et al., 2004). Duration of pain is classified clinically as acute and persistent (e.g., chronic), or pathophysiologically by type of ne uronal activity such as nocicepti ve and neuropathic (Hawthorn & Redmond, 1998; McCaffery & Pasero, 1999). The terms chronic pain and persistent pain are used interchangeably to denote pain that has exc eeded three months in duration and is associated with chronic pathological process (AGS, 2002; Hawthorn & Redmond, 1998). The AGS (2002) panel further defines persistent pain as a pa inful experience that continues for a prolonged period of time that may or may not be associated with a recognizable disease process (p. S205). Pain chronicity according to Zimmermann (2004), is the result of prolonged nociceptive activity which leads to nervous system sensitiza tion and progressive inte nsification of pain (p. 2). Thus, individuals with ch ronic pain perceive complex stim uli that may be facilitated and sustained by a multitude of physiological and psyc hological reactions. As such, chronic pain may progress from being a disease symptom to bei ng an independent chronic disease in itself. Therefore, it is important to rec ognize that persistent pain could be either the manifestation of a biophysiological process or the outcome of ineff ectively treated pain. Persistent pain can result from a poorly h ealed or poorly treated primary injury, or abnormal functioning of the nervous system (M cCaffery & Pasero, 1999). Sources of chronic pain can be found in many body systems. Commonl y, pain occurs in the musculoskeletal, cardiovascular, and neurological systems, and mani fests as chronic joint pain, headaches, angina, 29

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and neuralgias respectively. Musculoskeletal jo int disorders such as arthritis are the most common cause of chronic pain among the elderly (AGS, 2002; Brooks, 2005). The prevalence of persistent, unrelieved pain is high among the elderly population. Generally, about 49% to 84% of elders experi ence persistent pain (Nour & Laforest, 2003; Rutledge, Donaldson, & Pravikoff, 2002; Won et al., 2004). Over 60% of elders (Jakobsson, Hallberg, & Westergren, 2004; Thom as, Peat, Harris, Wilkie, & Croft, 2004) and more than 80% of nursing home residents have significant problems with persistent pain (Allcock, McGarry, & Elkan, 2002; Brockopp et al., 1998; Fox et al., 1999; Hall-Lord et al., 2003; Tse, Pun, & Benzie, 2005). Recently, Hutt and colleague s (2006) reported an even hi gher prevalence of pain (95%) among elderly nursing home resident s. High prevalence of persistent pain in older adults is attributed to equally high pr evalence of chronic painful musculoskeletal conditions such as arthritis (AGS, 2002). Over 46 million U.S. adults (>21%) reported a di agnosis of arthritis according to 2003-2005 CDC surveys (CDC, 2006). Within the stated adult population, about 50% of persons over age 65 years repor ted a current diagnosis of arthritis. It is projected that the prevalence of persistent joint pain and related in terference with activity will increase due to the aging of the U.S. population, as more older adults are diagnosed with arth ritis (CDC). Together, these reports support persistent pain as a comm on problem for a large portion of the population, and they provide evidence for the clinical probl em that is the basis for this proposed study. Interference with activities due to pain is common among older adults (Donald & Foy, 2004; Mossey, et al., 200; Mavandadi et al., 2007; Ross et al., 1998; Thomas et al., 2004), particularly for those diagnosed with arthri tis (Arthritis Founda tion, 2008; CDC, 2006). The CDC summary report for 2003-2005 indicated that 46 million U.S. adults reported a diagnosis of arthritis and more than 38% reported activity limitation attributed to arthritis. Among older 30

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adults with arthritis, Hybels, Blazer, & Piep er (2001) reported pain interference ranging from 21.9% for those 65 to 74 years to 32.3% for t hose 85 years and above. Thomas and colleagues (2004) reported a high prevalence (58.7%) of pain interference among a general population of elderly persons who reported pai n. These findings provide evidence that the prevalence of pain interference rises wi th increasing age (Hybels et al., 2001; Thomas et al., 2004). Pain with its interference in daily activities and functioning of indi viduals exerts adverse effects on depressive symptoms, thereby delaying improvement of symptoms (Mavandadi et al., 2007). As more and more people join the ranks of 65 years and older, more pain interference will likely be associated with increased prevalence of depressive symptoms among this population. Thus, empirical evidence is needed to direct e ffective clinical pain management and decrease pain interference as individuals age. Pharmacologic Pain Management Pharmacologic pain management is the most commonly used approach to treat persistent pain (AGS, 2002), and effective treatment is typically multidimensional. Based on symptom assessment, treatment may incorporate specif ic or various categories and classes of pharmacologic agents (AGS; Roberto & Gold, 2 001), including analgesics, nonsteroidal antiinflammatory agents (NSAIDs), antidepressants, anxiolytics, muscle rela xants, anti-spasmodics, and others (AGS; Harden et al., 2005). Analgesics are the most commonly used pa in relieving drugs (AGS 2002; Ferrell, 2004). Analgesics include nonopioids (e.g., non-narcotics such as acet aminophen and NSAIDs), opioids (e.g. narcotics such as opioid receptor agonists and antagonists), and adjuvant analgesics (e.g., antidepressants) (AGS; McCaffery & Pasero, 1999). NSAIDs have both a central nervous system (CNS) effect and a strong peripheral action that inhibit prostalglandins at the site of injury. However, the common side effects of gastric irr itation may be problematic in terms of gastric31

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intestinal bleeding. Examples of NSAIDs include such drugs as ibuprofe n, ketoprofen, naproxen, and acetylsalycilic acid (aspirin). Unlike the NSAIDs, the mechanism of action by which acetaminophen relieves pain is not well known (AGS; McCaffery & Pasero, 1999). However, acetaminophen is believed to produce analgesia via mechanisms in the central nervous system (CNS). Overall, adverse effects associated with acetaminophen are considerably fewer compared to other analgesics. Thus, it is on e of the first lines of treatment for elderly with chronic mild to moderate nociceptive pain. Opioid agonist analgesics are usef ul in managing a wide variety of moderate to severe acute and ch ronic pain, particularly pain of nociceptive origin. Analgesia is achieved through a CNS mechanism that modulates descending pain thereby diminishing its discrimination and perception (American Acad emy of Pain Management [AAPM], 2005). Opioid agonists are safe if used within existing guidelines (AGS, 1999, 2002; McCaffery & Pasero). Recommendations for the treatment of arthritic pain must consider pain control, reduction of joint inflammation, and preservation of func tion with minimal side effects (Arthritis Foundation, 2008; Stone, Wyman, & Salisbury, 1999). Thus, pharmacologic management of arthritic pain usually requires regular use of acetaminophen to provide analgesia. However, opioid medication such as codein e may be used alone or in co mbination with acetaminophen to control more severe symptoms (Arthritis Foun dation; Stone et al., 199 9). Short-acting NSAIDs such as naproxen and ibuprofen are considered safer to use with the elderly due to altered absorption, distribution, elimination, and higher ri sk for gastro-intestinal damage in this population (Stone et al.). Adjuvant analgesics serve a multi-purpose in the treatment of pain (McCaffery & Pasero, 1999). This category of drugs incl udes antidepressants, antic onvulsants, and antispasmodics. 32

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These drugs also exert effects on the CNS and have been found to be effective with neuropathic pain such as that caused by str oke and neuralgia. Othe r pain modulating drugs identified in this group are steroids, anti-arrhythmics, and local anes thetics (AGS, 2002). Harden and colleagues (2005) profile 22 pain medication classes, which include all the above as well as anxiolytics, muscle relaxants, antispasmodics, sedatives, an tipsychotics, and anti-hypertensives. Clearly, there is a large array of potential me dications available to treat pain. The AGS (2002) panel examined the role of pharmacologic agents in the treatment of chronic pain and posited that analgesics and pain modulating drugs can be used safely and effectively in the older adult population. Safe and effective use of pain drugs may be achieved through adherence to evidence-based guidelines, which include simplifying drug regimens as much as possible. Contrary to these recomme ndations, however, medication regimens of older adults continue to show higher complexity due to overall increase in the rate of drugs prescribed to treat multiple symptoms and conditions pr evalent among this population (Chin et al., 1999; Patel, 2003; Sloane, Zimmerman, Brown, Ives, & Walsh, 2002). Consequently, many older adults typically consume a large variety of medications, despite the fact that studies on pain management report prevalence of under-treatment of persistent pain in this po pulation (Mzorek & Werner, 2001; Rodriguez, 2001; Sofaer, 1984). Recently, Oster and colleagues (2005) reported that 31% of elderly adults with post-herpetic neuralgia stated they were a little or not at all satisfied with their pain medication. This finding is supported by a more recent report that prescribin g of pain medication was adequate for only 40% of nursing home residents with neur opathic pain (Hutt et al., 2006). Problems of inadequate relief of chronic pain have been related to inadequate and ineffective prescription of pain medications (Berry & Dahl, 2000; Grant & Haverkamp, 1995; 33

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Horgas & Tsai, 1998; Hutt et al., 2006; Mzorek & Werner, 2001; Rodriguez, 2001; Won et al., 2004). Horgas and Tsai surveyed analgesic drug prescriptions and use among 339 elderly nursing home residents. They found that while the majori ty of residents were prescribed at least one analgesic medication, very few were actually admi nistered any doses of that medication during the one-month study period. In addition, they report ed that cognitively impaired residents were prescribed and administered significantly less equianalgesic medications than those who were cognitively intact. In a more recent study, W on and colleagues investigated persistent nonmalignant pain and analgesic prescribi ng patterns in over 21,000 elderly nursing home residents. Their results showed that one quarter of the resident s received no analgesics and less than 50% of all analgesics we re given as standing doses. Hutt and colleagues (2006) recently provide d further evidence of inadequate pain management among elderly adults in their study. These authors screened the quality of nursing home pain medication prescribing practice usin g a pre-post intervention design aimed at nurses and physicians. They found that fewer than 50% of residents with predictable recurrent pain were prescribed scheduled pain medications pre-intervention. The m ean score on the pain medication appropriateness scale (PMAS) was 64% optimal pre-interven tion, but was better (69%) in nursing homes in which nurses know ledge of pain assessment and management improved post-intervention. Overall, PMAS scores for residents in pain improved from baseline (60%) to intervention (65%). In a primary care population of 83,000 adult patients aged 60 and above, only a small sample of 209 (.25% of patients) was identified as taking opioids for relief of their chronic pain (Adams, Plane, Fleming, Mundt, & Saunders, 2001). These finding indicate that chronic pain is generally undertreated in primary health ca re setting and lend support to 34

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other research findings of inadequa te pain treatment in a wide ra nge of patient gr oups, including those living with a variety of painful chr onic conditions and acro ss various settings. Evidence suggests that the under-tr eatment of pain is often re lated to knowledge deficits among physicians and other healthcare practitio ners regarding pain assessment and pain management (Green et al., 2002; Rodriguez, 2001 ; Mzorek & Werner, 200 1; Puntillo, Neighbor, ONeil, & Nixon, 2003; Simpson, Kautzman, & Dodd, 2002). Specifically, knowledge deficits about pain medication (Allcock et al., 2002; Mzorek & Werner; P untillo et al.; Rodriguez; Simmonds & Scudds, 2001) and decision making about pain treatment have been cited (Erkes, Parker, Carr, & Mayo, 2001; Puntillo et al.; Simpson et al.; Tarz ian & Hoffman, 2005). A prospective cohort study of pain physicians f ound that physicians person al experiences with chronic pain, frequency of pres cribing pain medication, knowle dge of treatment choices, and opinions or goals for pain management are vari ables that impact effectiveness in managing persistent pain (Green et al .). These authors found that fewe r than 10% of respondents had received any pain management education; howev er, the majority reported they do treat chronic pain. A large number exhibited poor knowledge of pain treatment, e.g., many physicians chose a poor or worse treatment option for rheumatoid arthri tis. A majority also agreed that chronic pain was under-treated but expressed concern that prescription of strong opioids would attract regulatory scrutiny or medical sa nction. Thus, most physicians usua lly chose to refer the patient to a specialist rather than provide treatment for pain. Overall, physicians goals for pain relief were poor, averaging only 2.2 on a scale of 1-5, 1 being absolute and complete pain relief and 5 being no pain relief. An important finding of th is study is that physicians with a goal of more pain relief were more likely to provide high quality care. 35

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In a survey report of nursing home direct ors, Tarzian and Hoffman (2005) also documented that lack of knowledge about pa in management among nurses and physicians, including fear of addiction, over dose, and difficulty choosing the ri ght analgesics, are barriers in providing adequate pain management. Sometimes poor assessment of pain by the practitioner directly results in poor treatment choice for pain management (Herr, 2002). The result is suboptimal pain management (Tarzan & Hoffman; Won et al., 2004). These factors and associated outcomes will also be true for olde r adults with arthritic pain whose recommended regimens include the use of both opioid and/or nonopioid analgesics (Arthritis Foundation, 2008). There are also patient-related factors implicat ed in the problem of poor pain management. Among some community dwelling cancer patients re ceiving treatment for pain, age (more than 70 years), female gender, black ethnicity, and pe rception of being less ill have been listed as factors that contribute to under-t reatment of pain (Cleeland et al., 1994). Other community based studies of older adults with non-cancer related pain corroborate some of the above findings (Breitbart et al., 1996; Shega, Hougham, Stocking, Cox-Hayle y, & Sachs, 2006) and implicate dementia as a factor in suboptimal pain mana gement (Shega et al.). Thus, without exception, persistent pain among older adults is poorly treated. In summary, patients with less adequate analgesia will report less pain relief and greater pain-related impairment of function (Cleeland et al., 1994). Studies have shown that both health care provider and patient related factors imp act pain management. Prescription of pain medication among the elderly remains inadequate due to inadequate scheduling, inadequate amount, and/or inappropriatene ss of prescribed drug(s). Fact ors that contribute to this 36

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phenomenon include health care professionals l ack of knowledge and accurate clinical decision making ability about pain mana gement and pain relief. Pain and Related Pain Outcomes Persistent pain in older adults negatively impacts the general health and the emotional and functional status of individuals affected (Mantyselka et al., 2003; Oster et al., 2005; Silkey et al., 2005). There is consistent evid ence that supports a significant relationship between persistent pain and outcome measures such as depression and functional limitations (Mantyselka et al.; Zarit et al., 2004). Pain variables that have been implicated in th e relationships with depression and functional outcomes include pain intensity, pain interference with acti vity, duration of pain experience, frequency of the pain experience, as well as number of pain sites or locations present (Mavandadi et al., 2007; Scudd & Robertson, 2004; Thomas et al, 2004; Zarit et al.). Also, various components of functional limitations such as physical and social functions are affected by pain (Bookwala, Harralson, & Parmalee, 2003; Williams, Tinetti, Kasl, & Peduzzi, 2006). This section explores evidence of the relati onships between pain and functional limitations (social, physical, and emotional) among older adults with persistent pain. Specific attention is paid to emerging significance of pa in interference with regard to depression and other functional limitations. Pain and Emotional Distress Depression is well documented in the literatu re as a correlate of persistent pain (Bookwala et al., 2003; Sharp, Sensky, & Allard 2001; Zarit et al., 2004) as well as a major indicator of emotional distress among patients with chronic pain (Greenberg & Burns, 2003). Depression is known to initiate, exacerbate, a nd maintain the pain experience (McCracken, Spertus, Janeck, Sinclair, & Wetzel, 1999). Among a primary care sample (N= 209) of individuals 60 years and older with reported persistent joint pa in, 36% were diagnosed with 37

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depression (Adams et al., 2001). A higher prevalence of self-reported depression was reported by over 50% of a general population of older adults with persistent pain (Mossey & Gallagher, 2004). More depressive symptoms in persons with persistent pain may be attributed to their response to multiple distressing symptoms from increased sensory and emotional stimuli (AGS, 2002; Ferrell & Ferrell, 1990; Gonzalez, Martel li, & Baker, 2000; Harden et al., 2005). For example, Ryan and Frederick (1997) found that v itality (indicator of affective well-being or disposition) was lower among older adults with chronic pain, especi ally if they perceive their pain to be disabling or frightening. This furthe r contributes to their emotional distress. When assessed longitudinally at six points over a period of 21 mont hs, Sharp and colleagues (2001) found that pain intensity was one of two variables that consistently predicted level of depression in a population of older adults. Following another longitudinal study of the relationships between pain severity, pain interference, and change in depressive symptoms in older adults at baseline and 3, 6, and 12 months, Mavandadi and colleague s (2007) found that at higher pain intensity levels, improvements in depressive symptoms of persons surveyed were blunted. However, when pain interference was considered alongside pain intensity, these au thors found that the extent to which pain interfered with functioning had grea ter negative impact on indi viduals recovery from depression. Further study is needed to determ ine which factors mediate this relationship. Pain and Functional Limitations Persistent pain is an important predictor of functional limitation or disability in older adults (Arthritis Foundation, 2006; Mossey & Ga llagher, 2004; Rudy, Weiner, Lieber, Sloboda, & Boston, 2007). The relationships between persiste nt pain and functional limitations have been examined for both physical functioning and social functioning (Bookwala et al., 2003; Elliot, Ranier, & Palcher, 2003; Rudy et al.; Williams et al., 2006). Researchers have found that persistent pain was correlated with poorer physical functioning, lowe r social functioning, and 38

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greater depressive symptomatology (Bookwala et al.; Elliot et al.). A wider scope of investigation of functional limitations in th ese studies is supported by the multidimensional nature of persistent pain and its related outcomes which involve not only the physical but also social, emotional, and other dimensions (Os borne, Jensen, Ehde, Hanley, & Kraft, 2007). The following paragraphs concentrate on the impact of persistent pain on physical and social functioning among older adults, a focus of specific interest in this study. In the literature, physical functioning generally refers to performance of general/instrumental activities of daily and performance of physic al activities such as walking, gripping, reaching, gait, speed, et c. (Kothe, Kohlmann, Klink, Ru ther, & Klinger, 2007; Mossey & Gallaher, 2004; Rudy et al., 2007 ; Weiner et al., 2006). Research ers have found that physical activity limitations are prevalent in older adults with persistent pain. Scudd & Robertson (2000) examined the association between the presence of physical activity limitations and specific characteristics of musculoskele tal pain. Approximately, 73% of the respondents (N = 885) reported presence of musculoskeletal pain during the two weeks prior to the study, while about 69% were identified as having physical limitatio ns. The majority reported some difficulty performing numerous physical task s, including heavy household or yard work chores, standing up from an armless chair, reaching above the head to get a 5-pound object, and climbing stairs. These researchers found that the odds of having a physical disability from the effect of moderate pain intensity was almost twice as likely (OR=1.54, CI=0.97, 2.44, p=.00) and more than four times as likely (OR=4.32, CI=2.01, 9.31) if severe, extr eme, or worst pain intensity was present. Further analysis revealed that i ndividuals whose pain experience occurred all or nearly all of the time were at least twice as likely (OR=2.00, CI=1.07, 3.72, p= .03) to have physical disability. Consistent with above findings, other investigators re ported that 79% of their sample was 39

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bothered by pain in the two weeks before they were surveyed, and up to 50% of them were limited in the performance of th eir usual activities (Mossey & Gallagher). They also found that individuals who reported pa in interference were more than four times as likely to have impaired physical performance. These findings highlight the clinical importance of evaluating pain interference as a strategy for effective pain management. Social functioning usually refers to performing roles, responsibilities, a nd activities such as visiting with friends or relatives, attendi ng social engagements, and recreation (Ross & Crook, 1998; Ware, 1993). Persistent pain has been found to impact the social f unctioning of individuals (AGS, 1998, 2002; Ware). In a study investigating the relatio nship between pain and indices of disability and functional compet ence, researchers found that pain frequently interfered with aspects of social responsibilitie s such as recreational and fam ily activities in 36% of the respondents (Ross & Crook). For 22 % of the respondents, interfer ence was experienced most of the time. In a study examining the effects of pain on functioning and well-being in older adults with osteoarthritis of the knee, Bookwala a nd colleagues (2003) measured both physical and social functioning. They found that more inte nse pain was correlated with poorer physical functioning and lower social functioning. To support this finding, Elliot and colleagues (2002) reported that limitation with social functioning si gnificantly predicted an individuals ability to recover from chronic pain over time. This finding extends the clinical role of social functioning as an outcome measure in pain management. These reviews highlight the need to recognize the role of social functioning limitations when deve loping pain interventions and to maintain pain relief in order to sustain r ecovery (Williams et al., 2006). Summary This review has highlighted important resear ch findings that document the prevalence of persistent pain and its deleteri ous impact on the emotional, phys ical, and social functioning of 40

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older adults (AGS, 1998, 2002; Creamer, Lethbr idge-Cejku, & Hochberg, 2000; Williams et al., 2006). Although the safest and most effective pharmacologic strategies for pain management are recommended (AGS, 2002), evidence of suboptimal pr escription and treatment of persistent pain among older adults with persistent pain c ontinues to be supported. Pain management recommendations for older adults address c oncerns about unrelieved pain, inadequate prescription of pain medications, and high pr evalence of adverse pain related outcomes. However, these reviews do not completely explain the role of inadequate prescription of pain medications in pain-related emotional and functio nal outcomes. Therefore, this study proposes to extend existing cross-sectional re search by investigating longitudi nal relationships between pain and related outcomes, and the role of prescr ibed pain medications in determining these outcomes. This review underpins the studys pr imary hypothesis that th e relationship between pain (e.g., pain interference and number of pain locations) and pain outcomes (e.g., emotional, physical, and social functioning) over time is mediated by the adequacy of prescribed pain medications (e.g., pain management index [PMI]). 41

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CHAPTER 3 METHODS Design This study investigated relati onships between pain, pain medications, and pain outcomes in older adults with arthritic pain, and examined whether ad equacy of prescribed pain medications mediates any of these relationshi ps. The study was conducted using existing data from a large-scale, NIH-funded, parent study: Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE). A description of the ACTIVE study, specific measures used in the study, and procedures used to extract and manipu late the data are presented below. Finally, statistical procedures used for data analysis are presented. The ACTIVE Study The ACTIVE study is a randomized, controlled, single-masked clinical trial designed to test specific interventions select ed to determine the effects of cognitive training on memory and functioning. The ACTIVE data was collected at five time points: baseline and annual follow-up points at Years One, Two, Three, and Five Baseline data included measurements of demographics, health conditions (medical diagnose s), cognitive status, health outcomes surveys (e.g., bodily pain, emotional, and functional stat us), and prescribed medications. Each annual follow-up measurement included all of the above data except demographics. To be eligible for the study, prospective participants had to be age 65 years or older, cognitiv ely intact, and without a diagnosis of terminal illness (e.g., cancers) or conditions causing functional decline (e.g., stroke or dementia). An initial telephone interview was used as the first-level screening for eligibility. Subjects who pa ssed this screening were invited fo r a subsequent in-person interview to obtain additional data for elig ibility (cognitive testing). Eligible subjects were then given a 42

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take-home packet of measures to be complete d and brought on their ne xt interview one week later. Measures For the purpose of this secondary analysis study, data collected from several ACTIVE study measures at baseline screenings (telephone and follow-up in-person interviews), 3-year, and 5-year follow-up data collect ion points were used. A descript ion of selected measures and application to the current study is provided below. Table 3-1 summarizes selected variables. Demographic and Health Data A demographic and health stat us screening to determine ba sic eligibility was conducted at baseline via a telephone screening interview. Se lf-reported date of birth (to compute age), sex, race, medical conditions (diagnosed diseases asse ssed via OARS checklist), and cognitive status assessed via the telephone intervie w for cognition (TIC) were colle cted. A subsequent in-person interview was conducted to obtain additional data including a mental status screening, to determine eligibility. Variables selected for this study were age, sex, race, and medical conditions (diagnosed diseases). The SF-36 Health Survey The Medical Outcomes Short Form-36 (SF-36) was used to assess participants selfreported pain and functioning at baseline (Time 1), year 3 (Time 2), and year 5 (Time 3). Participants were asked to complete the SF-36 at home as part of a take-home packet of measures and return the packet at their next interview session, typically one week later. If participants failed to bring the completed measur e with them to their next appointment, the SF36 was administered in-person during the interview in order to minimize missing data. The SF-36 is a generic, multi-purpose, Likert-type 36-item health survey, which yields 8 scales and psychometric physical and mental health summary measures (Ware et al., 1993). 43

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Four subscales applicable to the proposed study are bodily pain, physic al functioning, social functioning, and mental health functioning. The b odily pain subscale was used to assess pain intensity and pain interference. Pain intensity was evaluated as self-re port of how much bodily pain an individual has experienced in the past four weeks. A 6-point Likert-type verbal descriptor scale was used to score pain intensity as follows: 1= none, 2 = very mild, 3 = mild, 4 = moderate, 5 = severe, and 6 = very severe. Pain interference referre d to the degree to which pain interfered with normal activities (housework and outside work) during the past four weeks. A 5point verbal descriptor sc ale was used to score pain interferen ce as follows: 1= not at all, 2 = a little bit, 3 = moderately, 4 = quite a bit, 5 = extremely. The SF-36 physical functioning subscales were used to evaluate physical activity and physical role limitations. Physical functioning li mitation was measured by the extent to which participants reported that10 typical activities we re limited by their health. For example, Does your health now limit you in lifting or carrying gr oceries? Responses were scored on a 3-point scale scored as 1 = yes, limited a lot; 2 = yes, limited a little; 3 = no, not limited at all. The social functioning subscale was used to ev aluate social activity limitations. Social functioning limitation was evaluated with 2 items measuring the extent and amount of the time physical health interfered with social activities (e.g. visiting friends or relatives) on a 5-point scale. The extent of social functioning limitation scores ranged from 1 = not at all, to 5 = extremely. The amount of time health interfered w ith social function was scored from 1 = all of the time to 5 = none of the time. The mental he alth subscale was used to evaluate emotional limitations. Scores from emotional role and emoti onal vitality subscales were used to indicate level of emotional functioning. Three items meas uring interference with participants daily activities in the past week as a result of emotional problems (e.g. depression or anxiety) were 44

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scored as 1 = yes, had problem; or 2 = no, did no t have problem. Scores on vitality assessed how participants felt in the past four weeks, e.g., During the pa st 4 weeks, have you felt downhearted and blue? Responses to nine items in this meas ure were scored on a 6-point verbal descriptor scale. Scores ranged from 1 = feeling occurred a ll the time, to 6 = feelin g occurred none of the time. As a general health measure, the SF-36 is beneficial in comparing th e relative burden of diseases and symptoms (e.g., pain), and in di fferentiating the health benefits of various treatments (Ware et al., 1993). The pain and f unctioning subscales measured for the proposed study are summated scores that have been transf ormed to range from zero (lowest score) to 100 (highest possible score) for each subscale (Ware et al.). Scores between these values represent the percentage of the total possible score achieved; lower scores represent worse health outcomes. A cutoff score of 42 has been appl ied to the mental component summary scale yielding a sensitivity of 74% and a specificity of 81% in detecting patients diagnosed with depressive disorder (Ware, Kosinski, & Keller 1994). Overall reliability of the SF-36 scales using both internal consistency and test-retest methods has exceeded the minimum standard of 0.70 in many studies (McHorney, Ware, Rachel & Sherbourne, 1994; Tsai, Bayliss, & Ware, 1997; Ware et al., 1994; Ware et al., 1993), and content, conc urrent, criterion, construct, predictive evidence of validity, a nd the meaning of high and low SF-36 scores are well supported in validity studies (Ware et al., 1993, 1994). For exam ple, empirical validity of all 8 scales of the SF-36 scales has been achieved in studies invo lving physical and mental health (McHorney et al.). Center for Epidemiological Studi es-Depression Scale (CES-D) The CES-D tool was used to assess depression as a third variab le contributing to emotional limitations in this study. This 20-item Likert-type instrument was developed to detect 45

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major or clinical depression in adolescents and adults (Radloff, 1977). Four components or factors of the CES-D are depressive affect, soma tic symptoms, positive affect, and interpersonal relations. Items in the CES-D refer to how an individual has felt and be haved during the last week e.g., During the past week, I felt depresse d. Scores range from 0-3, indicating that the feeling or behavior occurred as follows: for ne gative items, 0 points = rarely or none of the time (< 1 day); 1 point = some or a little of the time (1-2 days); 2 points = occasionally or a moderate amount of the time (3-4 days); and 3 points = most or all of the time (5-7 days). An example of a feeling in the 3-point is I did not feel like eating; my appetite was poor. Scoring is reversed for positive items, whereby most or all of the time is scored 0 points and rarely or none of the time is scored as 3 points. An example of a positiv e response is I felt that I was just as good as other people. CES-D scores range from 0-60; higher scores indicate greater depressive symptoms. The cut off score for depression is usually 16 (Radloff; Reid, Williams, Concato, Tinetti, & Thomas, 2003), but a cut off score of 23 has been used to indicate depression (Hybels, Blazer, & Pieper, 2001). Studies using the CES-D i ndicate that it has high internal consistency (alpha=0.79-0.90, Kothe et al., 2006; Reid et al.), good test-retest stability ( k= 0.84, Reid et al.,), and adequate construct validity based on positive predictive values reported in several studies (Hybels et al.). In a study by Geisser and colleagues (1997) the CES-D demonstr ated good predictive value in discriminating between types of depression among patients with chronic pain (sensitivity >80%). Hybels a nd colleagues (2001) also demons trated that the CES-D could reliably identify clinically significant depression in community dwelling elders. The CES-D short version yielded a Cronbachs alpha of .87 at baseline and .82 at follow-up for a sample of older adults (Zarit et al., 2004) In the ACTIVE study, the CES-D was administered by trained 46

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testers in an interview format. CES-D scores from baseline, 3 years, and 5 years follow-up were used in this study. Medications A medication audit sheet (MedAudit) was used to assess participants current drug use, both over-the-counter (OTC) and pres cription. Participants were aske d to bring all currently used medications ordered by their health care provider to the in-person interview. On a standardized form (MedAudit), trained testers recorded drug names, doses, routes, frequency of administration, pro re nata (prn) or routine use status, whether the drugs were prescribed or purchased OTC, and any comments related to medication administration. Data were cleaned to standardize the dr ug names and then categorized according to American Hospital Formulary System codes ( AHFS) into therapeutic drug categories and subclasses. The AHFS is a functiona l classification widely used in the health care professions (McEvoy, 1996). Drug categories and identifying codes used in the proposed study are analgesics (AHFS codes: 280802-280892) and adjuvant analgesics (antidepressants, AHFS code: 281604; anti-convulsants, AHFS co des: 281292; and muscle relaxants, AHFS codes: 122004122008). Only specific drugs known for pain management in the antidepressant (e.g., tricyclic antidepressant) and anticonvulsant (e.g., gabapentin) ca tegories were included. Medication data collected at all study occasions (b aseline, 3 years, and 5 years) were used in the proposed study. Additional data manipulation was required to compute the Pain Management Index Score (PMI). The purpose of the PMI is to assess adequacy of prescribed analgesic medications as an indicator of a prescribers response to a patient s pain by comparing prescr ibed analgesics with a patients reported level of pain. A PMI score was calculated for each study participant based on pain intensity score on a verbal descriptor scale and potency of prescribed analgesic medications on an analgesic ladder. The computed PMI is a numerical rating scale whereby scores can range 47

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from -3 (e.g., a patient with severe pain rece iving no analgesic drugs) to +3 (e.g., a patient receiving a strong analgesic and reporting no pain). Negative scores indicate inadequate potency of analgesic drugs to treat pai n, and scores of zero or higher ar e indicative of sufficient or acceptable treatment of pain. Pain management wa s considered adequate if there is congruence between the patients reported pain level and the potency of the prescribed analgesic drug. Therefore, inadequate pain management was indicated for those participants with severe pain who received no analgesics or only nonopioids/adj uvant analgesics, or for those with moderate pain who received no analgesics (Breitbart et al., 1996; Cleeland et al., 1994). Reliability and validity characteristics of the PMI are not documented in the pain literature. However, as a nume rical rating scale (NRS) computed from two verbal descriptor scales (rank-ordered verbal desc riptor pain scale and analgesic potency ladder), an examination of the reliability and validity status of NRS and VDS in the pain literature was warranted. The NRS and VDS have been independently applie d in numerous studies involving pain and analgesic drug therapy and have been found relia ble (Gagliese, Weizblit, Wendy, Ellis, & Chan, 2005; Lara-Munoz, de Leon, Feinstein, Puentee, & Well, 2004; Shega et al, 2006). Both the NRS and VDS scales have demonstrated good convergent and divergent validity when used to assess pain in older adults (Gagliese et al.). Thus, as an index of two core components of pain management (assessment and drug therapy), the PMI has the potential to be of clinical value for researchers who evaluate effectiveness of pain management (Cleeland et al., 1994; Shega et al.). Procedure In order to access the ACTIVE data, a pr oposal was submitted to the ACTIVE Steering Committee, consisting of all Principal Investigator s and the Coordinating Center that functions as the data repository for all of the ACTIVE da ta from all of the study sites, the New England Research Institute (NERI). Approval was obtaine d in February 2007. NERI was responsible for 48

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cleaning the data and on-site sta tisticians were responsible for coding the data and creating the study constructs for established measures (e.g., the SF-36, MMSE). Thus, the ACTIVE data were considered clean and ready for statistical analysis. The PMI score was computed as an indicator of the adequacy of pain management. First, level of pain intensity was determined from participants self reports of bodily pain on the SF-36 VDS. Pain severity (1 = none to 6 = very severe ) was collapsed into a 4-point VDS to match the World Health Organization (WHO) 4-point analgesic ladder (Shega et al., 2006): none = 1, very mild and mild = 2, moderate = 3, and severe a nd very severe = 4. A WH O-score was assigned to each prescribed analgesic according to its rank on the analgesic ladder: 0 = no analgesic drug prescribed; 1 = nonopioid analgesi c (e.g. NSAIDs or acetaminophen); 2 = weak opioid analgesic (e.g., codeine or tramadol); and 3 = strong opioid analgesic (e.g., mo rphine). Adjuvant analgesics (e.g., anti-depressants and anticonvulsants) and low dose aspirin (81mg or 325mg) were included on the analgesic ladder as nonopioids and assigned a score of 1 (B reitbart et al., 1996). Each participant was assigned a score th at corresponded to the highest pot ency of analgesic prescribed. For example, a participant receiving a weak opioid (e.g., codeinelevel 2) and an adjuvant analgesic (e.g., antidepressantLevel 1) received a score of 2 (Breitbart et al.). The PMI score was computed by subtracting the pain intensity score from the analgesic score and computed scores level are ordinal. Sample, Setting, and Recruitment The ACTIVE study involved 2,802 elderly s ubjects living independently in six metropolitan areas in the United States: Birm ingham, Alabama; Detroit Michigan; Boston, Massachusetts; Baltimore, Maryland; Indianapolis, Indiana; and State College, Pennsylvania. Participants were recruited from senior housing, community centers and hospital settings. Persons were excluded from the ACTIVE study if they were younger than age 65 years, had 49

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cognitive decline (score 22 on the MMSE), 2 ADL disabilities (score on the self-rated ADL/IADL checklist), or verbally reported me dical conditions associated with imminent functional decline or death (such as stroke or cancer); and/or exhibited severe loss of vision, hearing, or poor verbal communication. Selection Criteria For the purpose of this study, a subsample wa s selected from the ACTIVE sample based on a reported diagnosis of arthritis and presence of bodily pain rated at least mild on the SF-36 bodily pain scale, at the baseline period. Presence of bodily pain was required at baseline to more accurately reflect the course of pain manage ment for those individuals who manifested symptoms of pain experience. Participants who di d not report a diagnosis of arthritis at baseline and did not have self-reported pain were excluded from the study. Ethical Considerations The ACTIVE study has approval from the Univ ersity of Florida Institutional Review Board (IRB), as well as the IRB of each partic ipating university. Approva l of the proposed study was obtained from the University of Florida Institutional Review Board prior to accessing existing data. All sample data and related inform ation were handled using approved standards for confidentiality. All identifying information was stripped from the data. Participants were identified using only assigned subject numbers. All study documents and equipments containing participants information were secured by the Prin cipal Investigator (PI) in locked cabinets and locked offices, and were accessible only to the PI and the designated dissertation committee. Electronic equipment or media used for data storage, processing, or transmission were password secured, and accessed only by the PI. 50

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Data Analysis Data analysis was conducted using descriptive, correlational, and multivariate statistics. Appropriate statistical programs were used to analyze relevant data as follows: Descriptive statistics : SPSS (version 16.0, SPSS Inc., 2007) was used to analyze and describe characteristics of background and health variables at baseline; and pain interference, pain locations, prescribed pain medication regimens, and pain outcomes (e.g., physical, social, and emotional f unctioning) at all 3 study periods. Univariate and multivariate statistics : Chi-square statistics, ttests, and mixed effect modeling in the SPSS statisti cal program (version 16.0, SPSS Inc., 2007) were utilized where appropriate to compare differences in sample means between study occasions, and between returning and attritted samples, or to analyze longitudinal distribution of mean sample characteristics over time. Bivariate statistics : Pearsons product-moment correlations in the SPSS statistical program (version 16.0, SPSS Inc., 2007) were ap plied to analyze (a) correlations of demographic and health variables with pain (e.g., pain interfer ence, number of pain locations), adequacy of prescribed anal gesics (e.g., PMI) and pain outcomes (e.g., physical, social, and emotional functioning) at all study occas ions; (b) correlations and intercorrelations of pain (e.g., pain interference and number of pain locations), adequacy of prescribed analgesics (e.g., PMI), a nd pain outcomes (e.g., physical, social, and emotional functioning) at all study occasions. Structural equation modeling : AMOS program version 16.0 (Arbuckle, 2007, by SPSS Inc., 2007) was used to analyze all steps in the proposed structural model to examine the relationships between pain experience (e .g., pain interference and number of pain locations) and functional status (e.g., physical, social, and em otional functioning); and the mediating role of adequacy of prescribed pa in medications (PMI), at each study occasion and across time from baseline to five years la ter. For the purpose of structural equation model analyses, the seven-leve l race variable was recoded in to a two-level dichotomous (dummy) variable (0) white and (1) non-white. Initial data screening for each time of meas urement indicated longitudinal loss of data due to attrition (Table 3-1), as well as data missing at random. This result suggested possible violation of multivariate normality criteria, whic h would normally require listwise or pairwise deletion of missing data or data imputation/ transformation procedures to permit model estimation (Kline, 2005). To avoid listwise de letion of data, which can introduce selective attrition biases, direct full-information maximum likelihood estimation (FIMLE) of the 51

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52 covariance matrix was utilized for the structural analyses. As a robust statistic, FIMLE handles missing at random cases such that all sample s ubjects are retained (i.e., there is no listwise deletion and participants data is used at each occasion in which they contributed).

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Table 3-1. Summary of variables and measurement points Variables (measure) Time 1 (baseline) Time 2 (3 years) Time 3 (5 years) Background characteristics: Age, sex race X Number of diagnoses (OARS checklist) X Pain experience: Pain interference (SF-36: bodily pain subscale) X X X Number of pain locations (OARS Checklist) X X X Adequacy of pain management Pain Management Index (PMI) calculated from pain intensity score (SP-36) and MedAudit data X X X Pain outcomes: Physical functioning (SF-36: Physi cal role and physical functioning Subscales) X X X Social functioning (SF-36: Soci al functioning subscale) X X X Emotional functioning (SF-36: Emoti onal role and emotional vitality subscales) X X X Depression (CES-D) X X X 5 3 53

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CHAPTER 4 RESULTS The main purpose of this study was to exam ine (a) the relationships among pain (e.g., pain interference and pain lo cation) and pain outcomes (e .g. physical functioning, social functioning, and emotional functioning) in older adults with arthritic pain, across three time points over a 5 year period, and (b) the mediating role of the adequacy of prescribed pain medications (e.g., pain management index [PMI]) in the relationships between pain and pain outcomes at each time of measurement and across the 5 year time span. The results of these analyses are presented in this ch apter. First, descriptive charac teristics of the sample and study variables are presented, followe d by description of the bivari ate relationships among the study variables. Finally, the relationships pain, ade quacy of pain medication, and pain outcomes are examined cross-sectionally at each time point and longitudinally across the 5 years time span. Sample Selection, Characteristics, and Attrition Sample Selection The analyzed sample was derived from a sample of 2,802 community-dwelling older adults who were enrolled in a st udy to examine the effects of c ognitive training interventions on cognitive and functional performance. A subsampl e of 1,409 older adults (h ereafter referred to as the current study sample or simply as the study sample) was selected who were diagnosed with arthritis and who reported the presence of bodily pain at the baseline period (Time 1). A flowchart of study inclusion and available samp le size on key study variables is presented in Figure 4-1. Sample attrition across the 5-year study peri od is shown in Figure 4-1. Of the original 1,409 participants in the sample at baseline, 1, 03 2 (73%) remained in the study at Time 2, which was 3 years later. At Time 5, 910 participants (65% of the base line sample) remained in the 54

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study. A total of 499 participants were lost to follow-up, yielding a total sa mple attrition rate of 35%. Comparison of the selected and excluded as well as returning and attritted samples are shown in Tables 4-1 to 4-4. Sample Characteristics of Selected and Excluded Samples The study sample consisted of 1,150 females (81.6%) and 259 males (18.4%), with mean age of 74.3 years (S.D.=5.9 years), mean number of diseases of 3.1 (S.D.=1.3), and mean MMSE score of 27.3 (S.D.=2.0). The majority was Caucas ian (68.9%). Comparison of the included and excluded samples indicated no sign ificant differences between the samples with regard to age and cognitive status. Signifi cant group differences were found for sex, race, and number of diseases diagnosed. The study sample had significantly more females, a higher proportion of non-white participants, and significantly more diagnosed medical conditions than the excluded sample (Table 4-1). Description of Key Study Variables for Selected and Excluded Samples A description of the pain, pain medications and pain outcome variables for the study sample and excluded sample at baseline are pres ented in Tables 4-2. Descriptive data on key study variables differed significan tly between the two samples. Results showed that the study sample (with diagnosed arthriti s) had significantly more severe pain interference, and more painful body locations than the excluded sample In addition, the study sample had significantly more physical, social, and emotional limitations. Differences between Longitudina l Sample and Study Drop-outs Between Time 1 and Time 2, a total of 377 participants dropped out of the study. Differences between the continuing and non-retu rning participants ar e shown in Table 4-3. Participants who dropped out of the study after Time 1 have significantly poorer physical functioning and emotional role functioning, lower vitality, and had depression scores. Table 4-4 55

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shows the differences between continuing, longit udinal participants and study participants who dropped out of the study after Time 2 (n = 122) Time 2 drop-outs had significantly lower physical and emotional functioning than the longitudinal sample. Descriptive Characteristics of Pain, Pain Medication, and Pain Outcomes Question 1 : What are the characteristics of pain (e.g., severity of pain interference, number of pain locations), the adequacy of prescribed pain medication adequacy (e.g., PMI), and pain outcomes (e.g. physic al functioning, social functioning, and emotional functioning) among older adults with arthritis at Time 1 (baseline), Time 2 (3 years later), and Time 3 (5 years later)? The characteristics of pain, pain medicati ons, and pain outcomes were analyzed using descriptive statistics (means, sta ndard deviations, and percentages) Analysis of mean differences or associations (t-test, chi-s quare, and mixed models for repeat ed measures) were conducted to examine whether descriptive characteristics differed across the 3 measurement occasions. Pain At baseline, almost 50% (48.8%) of the sample reported the pres ence of pain that interfered with their activity. On average, particip ants described the intensity of pain interference at baseline as 2.2 (S.D.=1.14) on a scale of 1-5 (1= no pain interference to 5 = extreme pain interference). Participants reported an average of 1.0 (S.D. = 1.6) pain lo cations at baseline. At Time 2, 3 years later, 42% (n=914) of the sample reported pain that interfered with their activities, and this proporti on increased slightly to 48% (n= 780) by Time 3 (5 years later), and were statistically significantly (2 = 15.698, p = .047). At Time 2, participants reported the severity of pain interference as a mean of 2.3, which was relatively stable at Time 3 (mean = 2.4). The severity of pain interference was significan tly different (t= 10.021, p =.00) across the 5 year period. On average, particip ants reported pain in a mean of .91 and 1.1 locations at Time 2 and Time 3 respectively; the number of painful locations was significantly different across the 3 measurement occasions (t = 3.059, p =.045) (Figures 4-2 and 4-3). 56

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Pain medication Prescribed pain medications were assessed by reviewing all medications currently prescribed or ordered for study participants Medications were classified according codes indicating their primary therapeutic category. For the purpose of these analyses, medications in the following cla ssifications were considered pa in medications: NSAIDs, opioids, tricyclic antidepressants, musc le relaxants, salicylates (e.g., aspirin), acetaminophen, and anticonvulsants. At baseline, 43.9% (n=1272) of the study participants were pr escribed at least one pain medication. This number decreased to 37.2% (n= 861) at Time 2 and 34.1% (n=691) at Time 3. These proportions were statistically different across the 5-year study period (F =3.862, p=.021). The distribution of pain medi cation classes is summarized in Figure 4-4. Of the seven different analgesic classes identi fied at baseline, the majority of participants (about 19%) were prescribed NSAIDs. Only about 6% of participan ts reported opioid analgesics in their pain regimen at baseline. These distribution of types of prescribed pain medications were statistically significant different across the 3 measurement occasions (F =27.129, p =.00). Pain medications were evaluated for the adequacy of prescribed analgesics by computing a pain management index score (PMI). The PMI scores is used as an indicator of the adequacy of analgesic treatment and is computed based on th e strongest pain medication prescribed for each participant (scored on a 3 point analgesic ladder; 1= mild, 3 = strong analgesic) Computed PMI scores can range from to 3. Negative scor es (scores < 0) indica te inadequate pain management and positive scores (zero and higher) indicate adequate pain management relative to the self-reported level of pain intensity. In this sample, the mean PMI score was below zero across all points of measurement; Time 1 (Mean = -1.0, S.D. = .87), Time 2 (Mean = -.9, S.D. = .94), and Time 3 (Mean = -1.0, 57

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S.D. = .85). These scores indicate less adequate pain treatment at each measurement occasion, and were statistically sign ificant for each study period (t = 9.726, p < .001, Figure 4-5). Pain outcomes. Pain outcomes investigated were indicators of physical, social, and emotional functioning obtained from the Short-Fo rm 36 health surveys, as well as the CES-D depression scale. Scores on these pain outcomes were analyzed using descriptive statistics as well as repeated effects mixed models to analyz e change over the three study occasions. Physical functioning subscale scores ranged from 0-100 (where 0 = severe impairment and 100 = no impairment). Social functioning and emotional f unctioning subscales also were evaluated on a 0 to 100 scale, with lower scores indicating worse functioning. De pression scores (based on the CES-D) were evaluated on a 4-poi nt scale, with higher scores indicating more depression. At baseline, participants subs cale scores for physical functio ning and physical role were 60.9 (S.D. = 24.3) and 60.0 (S.D. = 39.6) respec tively. These scores indicated moderate limitations in the physical functioning. Partic ipants had high social functioning scores (Mean = 83.1, S.D. =21.6), but had moderate limitati ons in emotional role and emotional vitality (Mean = 70.1, S.D. =37.9 and 56.9, S.D. = 20.0, respec tively). In addition, pa rticipants reported mild depressive symptoms at baseli ne (Mean = 5.8, S.D. =5.4, Table 4-6). In examining longitudinal change in our study predictors, we conducted repeated measures analyses using mixed effects modeling. This model has the advantage of using all available data (without listwise de letion) at each occasion of meas urement. Fixed effects of time on average of each predictor variable was tested. The results were as follows. Significant time effects were found for the following variable: PMI [F(2245.757) = 635.778, p <.001]; pain interference [F(1940.645) = 17.595, p<.001], pain locations [F(2034.222) =5.467, p =.004]; physical functioning [F(2048.342) = 113.361, p< .001]; physical role [F(2144.971) = 30.659, p 58

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<.001); social functioning [F(2145.450) =25.282, p =<.001]; and emotional vitality [F(2050.929) =22.422, p <.001] (Table 4 7). Relationships among Demographic Variab les and Pain, Pain Medication, and Pain Outcomes Question 2 : What demographic (e.g., age, sex, race) and health variables (e.g., number of diseases diagnosed) are related to pain (e.g., seve rity of pain interfer ence, number of pain locations), adequacy of prescribed pain medications (e.g., PMI), and pain outcomes (e.g., physical functioning, social functioning, and em otional functioning) at each measurement point [e.g., Time 1 (baseline), Time 2 (3 years), and Time 3 (5 years)]? Sex and race differences in mean pain, pa in medications, and pain outcome variables were analyzed using t-tests. With regard to se x differences in pain experience, females reported significantly more pain interfer ence (t = -2.023, p=.04), more numb er of pain sites (t = -2.083, p=.04), and had significantly less adequate pain medications pr escribed (t=-2.688, p <.01) than their male counterparts. This pa ttern of sex differences was cons istent across all three times of measurement (Table 4-8). With regard to sex differences in pain out comes, females had significantly lower physical functioning than males at Time 1, but there were no other significant differences in the other pain outcomes investigated. This finding held across all three times of measurement. At Time 2 and Time 3, however, females also showed significan tly lower physical role functioning scores. At Time 3, females also had significantly lower emotional vitality scores. Thus, females showed significantly worse functional status scores over time th an did men (Table 4-8). For the purpose of examining race differences race was categorized as either White or Non-white. Race differences in pain, PMI, and pain outcomes are summarized in Table 4-9. There were no significant ra ce differences in pain interference, number of pain sites, or adequacy of pain management at any time of measurement. There were, however, significant race differences in pain outcomes. At Time 1, non-whit e participants had significantly lower physical 59

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functioning (t = 2.972, p <.01), lower emotional ro le functioning (t = 2.985, p <.01), but, higher emotional vitality (t = -2.609, p = <.01) th an white participants (Table 4-9). Pearsons correlation analysis was used to analyze the relationship between age, number of diagnoses, and the pain variables of intere st (e.g., pain, PMI, and pain outcomes. These correlations are summarized in Tables 4-10, 4-11, and 4-12 (separately for each time of measurement). As a precursor to the structural equation models, bivariate correlations for sex and race (both coded as dummy variables) are included in the tables, although it was expected that the results of the correlation analysis would be the same as the results of the t-test previously reported. The results show that age was not significan tly correlated with pain interference, number of pain locations, or the ade quacy of pain management at any of the three measurement occasions. Age was significantly correlated with a ll of the pain outcome variables. Specifically, at baseline, advanced age was significantly correlated with lower physical functioning (r -.15, p<.01), lower physical role functioning (r=.14, p<.01), lower social functioning (r = -.051, p<.01), lower emotional role functioning (r = -. 13, p<.01), lower vitali ty (r = -.06, p<.05), and more depression (r = .10, p<.01). The same pattern of relationships held for Time 2 and Time 3 (Tables 4-11 and 4-12). The number of medical diagnoses was also significantly correlat ed with the pain indicators. More medical diagnoses was correlate d with significantly more pain interference ( r = .18, p < .01) and more pain locations ( r = .12, p < .01). This finding held at Time 2 and Time 3 as well. In addition, more medical diagnoses were significantly correla ted with lower physical functioning, lower physical role functioning, lower social functioning, lower emotional 60

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functioning, lower vitality, and more depression at Time 1 (Table 4-10), Time 2 (Table 4-11) and Time 3 (Table 4-12). Relationships among Pain, Pain Medication, and Pain Outcomes Question 3 : Is pain (e.g., pain inte rference and number of pain locations) related to the adequacy of prescribed pain medications (e.g., PMI) and pain outcomes (e.g. physical functioning, social functioning, and emotiona l functioning) among older adults with arthritis at each time of measurement [Time 1 (baseline), Time 2 (3 years later), and Time 3 (5 years later)]? Two hypotheses were tested. Firs t, it was hypothesized that pa in will be significantly and negatively related to the adequacy of pres cribed pain medications at each time point. Specifically, it was expected that pain interference and number of pain locations will correlate negatively with PMI such that more pain w ould be associated with less adequate pain management. The results of bivariate Pearsons correlation analyses for each measurement point are presented in Tables 4-13, 4-14, and 4-15. Corre lations between all endogenous variables are summarized. The results indicate that the pain indicators were associated with PMI as hypothesized. At Time 1, mo re pain interference ( r = -.25, p < .01) and more painful locations ( r = -.15, p < .01) were significantly correlated with less adequate pain medication. The same pattern of relationships was noted at Time 2 and Time 3, although the strength of the correlation increased for pain interferen ce and PMI over time [Time 2: ( r = -.34, p < .01), Time 3: ( r = -.43, p <.01). The second hypothesis was that pain would be significantly co rrelated with pain outcomes at each time point. More pain interf erence and pain locations were expected to negatively correlate with physical functioning, physical role, soci al functioning, emotional role, vitality such that more pain would be associated with lower functioning in each of the domains assessed. Further, pain (interference and number of pain locations) was expected to positively correlate with depression at each time period m easured; more pain would be associated with 61

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higher levels of depression. The results (Tab les 4-13, 4-14, and 4-15) support the hypotheses. More pain interference and more painful locatio ns were significantly associated with lower functioning in all of the domains assessed and more depression at Time 1 (Table 4-13). The same pattern of relationships was noted at Time 2 and Time 3. Outcome variables intercorrelated significantly and positively within each occasion of study except for depression which correlated negatively with other pain outcomes. These correlations were comparable across the three measurement occasions (Tables 4-14 to 4-15). Autocorrelations within time points (e.g., correlations between the same variables at tim e) are shown on the diagonals, and all are statistically significant. Additional cross-time correlations among th e endogenous variables were conducted to determine the nature and strength of the longitudinal relationshi ps among the endogenous variables prior to the proposed structural model analyses. Co rrelations between Time 1 and Time 2 variables are shown in Table 4-16; correl ations between Time 1 and Time 3 variables are in Table 4-17; and correlations between Time 2 and Time variables are shown in Table 4-18. The results show that all e ndogenous variables with the excep tion of PMI, intercorrelated significantly with each other in the direction expe cted (positively or negatively) and magnitude comparable to previous results. PMI intercorrelated with few variables across time. From Time 1, lower PMI correlated with more pain locatio ns Time 1, and more vitality and lower social functioning at Time 3. From Time 2, lower PM I correlated only with more severe pain interference at Time 3. These results indicate that over time, adequacy of prescribed pain medications weakened in its influence on pain interference an d pain outcomes. Mediating Relationships between Pain, Pain Management, and Pain Outcomes within Measurement Occasions and Longitudinally Across the 5-year Period Question 4 : Does the adequacy of prescribed pain medicatio n (e.g. PMI) mediate the relationships between pain (e .g., pain interference and number of pain locations) and pain 62

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outcomes (e.g. physical, social, emotional f unctioning) within each occasion and across the five year time span [e.g., Time 1 (baselin e), Time 2 (3 years later), and Time 3 (5 years later)], after controlli ng for background and health variables (e.g., age, sex, race, and number of diseases diagnosed)? It was hypothesized that the adequacy of pr escribed pain medication would significantly mediate the relationship between pain and pa in outcomes at each occasion and across the measurement occasions. It was also expected that at subsequent occasions, residual change in pain would have an effect on residual change in pain outcomes, and this effect would be mediated by change in pain medication adequacy. Analysis of the me diating relationships between pain, pain medication adequacy, and pa in outcomes across the three study periods was conducted using a structural equation modeling a pproach. The analysis plan required several steps (a) model specification, (b) model es timation, (c) model testing, and (d) model modification where necessary (Kline, 2005). Confir matory measurement models were estimated to establish the latent constructs, followed by structural equation m odels to analyze the relationships between the latent constructs within and across measurement occasions. AMOS 16.0 software (Arbuckle, 2007) was used to conduct the modeling procedures. Modeling Procedures The modeling procedures were conducted in th e following order. First, measurement models were estimated. This involved baseline (Time 1) measurement models, estimated separately for the three pain outcome constructs. Then, three-occasion measurement models were conducted separately for the three pain outcome constructs. These models were followed by tests of nested variance-invariance measurement models to determine the best fit to the data. Second, the structural models were estimated. These were conducted in the following order: (a) Time 1 simple mediating structural model (without pain to outcomes path), estimated separately for the three outcomes constructs, (b) Time 1 simple mediating structural models (with direct pain to 63

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outcome paths), estimated for each of the three outcome constructs, (c) a three-occasion simple model with within-time mediation paths and auto-re gression paths, conducted separately for the three pain outcomes, and (d) a fully recursive three-occasion model with all possible cross-time paths estimated separately for the three pain outcome constructs. This final model was followed by tests of nested reduced models to determine the best fit to the data. Measurement Models Estimation of baseline (Tim e 1) measurement models. At the Time 1, three separate measurement models were estimated to analyze the relationships among pain, pain medications, and each pain outcome construct. Separate m easurement models were conducted for each pain outcome: physical functioning, emotional functioning, and social functioning. The measurement phase was necessary to evaluate the strength and fit of the proposed models. Model fit was determined by evaluating several fit indices. A good model fit has a Chi-square value < 1 times the degree of freedom and a non-significant p value. The literature indicates that the Chi-square value is often inflated in a large sample analysis, resulting in p -values < .05. Thus, in a study such as this in which there was a large sample size, model fit was not determined by the Chisquare value alone. Additional incremental and ab solute fit indices evaluated included the root mean square error of approximation (RMSEA), norm ed fit index (NFI), rela tive fit index (RFI), incremental fit index (IFI), Turker-Lewis i ndex (TLI), and compara tive fit index (CFI). Incremental index values (e.g. NF I, RFI, IFI, TLI, & CFI) for a good fit were expected to be above .9 on a scale of 0-1.0 (1.0 being a perfect fit) and absolute fit index values were expected to be close to 0.00 (e.g., RMSEA less than .05). Each Time 1 (baseline) measurement model was specified to indicate the proposed relationship paths between th e endogenous latent construc ts (e.g., physical functioning, emotional functioning, and social functioning), th e endogenous indicator va riables (e.g., physical 64

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functioning subscale score, physical role subscale score, social functioning subscale score, emotional functioning subscale score, vitality, and depression), and the exogenous (control) variables (e.g., age, sex, race, and number of dia gnoses). Each model was specified to include an endogenous latent construct of pain (with two measured endogenous factor indicators, pain interference and number of pain locations), one singly-indicated endogenous pain management construct (indicated by the calculated PMI variab le), and four exogenous control variables (age, sex, race, and number of diagnosed diseases). The same measurement mo del was constructed for each outcome construct, yielding three separate measurement models. For the first model, the latent construct of physical functioning was used as the outcome (indicated by two indicators: physical functioning and physical role). For the s econd measurement model, the latent construct of emotional functioning was considered as the ou tcome, with the construct indicated by three endogenous indicators (emotional ro le, vitality, and depression). Finally, the social functioning model was constructed, with a singly-indicated endogenous social functioning construct as the outcome. To derive an identified model for each estim ation, each endogenous latent construct was specified such that one factor loading was fixed to a constant value of 1.00 (as an arbitrary constant to facilitate parameter estimation) while the factor variances we re allowed to be freely estimated. The purpose of this technique was to prevent under-identific ation of the model, meaning that the number of free parameters to be estimated exceeds the number of distinct observations in the model (Kline, 2005; Schum acker & Lomax, 2004). Under-identification of a model usually results in model failure (Schum acker & Lomax). All exog enous variables, all endogenous variables, and all endog enous constructs were allowed to correlate freely without constraint. Each model converged into an ad missible solution with reasonable to good fit 65

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estimates. All variances estimated with positive values and all correlations estimated below 1.00. See Time 1 measurement model diagram in Figure 4-7 and fit indices in Table 4-19. Estimation of longitudinal (three -occasion) measurement models. Following estimation of the initial single occasion meas urement models, full 3-occasion measurement models (with loading paths as previously described) were specified for each pain outcome construct: physical, emotional, and social func tioning. All exogenous (control) variables were specified to correlate with all endogenous constructs and variab les. All endogenous constructs were specified to correlate within each time of measurement and longitudinally across the three time periods. In addition, all uniquenesses of the endogenous indicato rs were correlated longitudinally. The goal of these model specifica tions is to achieve an identified model, the greatest reduction in the Chi-square value, and a good model fit. A three-step variance-invarian ce test involving a series of nested models was performed for each of the three specified full models in order to identify the strongest and most parsimonious model for analysis. Both standa rdized and un-standardized estimates were requested. In Step 1, three invariance meas urement models (Measurement-1) (physical, emotional, and social) were scaled such that both the factor loadings and factor variances were constrained to be equal across the three occasions of measurement. All m odels estimated and the results indicated that all three models had good fit, as evidenced by incremental fit indices >.9 and absolute fit indexes <.05. In step 2, invariance models (Measurement-2) were specified such that only the factor loadings were constrai ned to be equal across all occasions, while the constraints on the factor variances were free. All three models estimat ed with good incremental and absolute fit indices. In Step 3, the invariance model (Measurement-3) analyses were specified such that no constraints were placed on either the factor loadings or the factor variances 66

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across all 3 measurement periods. Again, all three models estimated with good fit. Subsequently, a model fit comparison test was performed to determine the model with the best fit for the structural analysis. A chi-square difference test was used to co mpare the most constrained invariance models (Measurement-1) with each subsequent le ss constrained models (Measurement-2 and Measurement-3) for each physical, social, a nd emotional measurement models. Measurement models with invariant factor loadings only (Measurement-2) demonstrated a chi-square difference that was significantly better than the most constrai ned models (Measurement-1) but not significantly different from the no invariance models (Meas urement-3). Thus, the invariant factor loadings only measurement models (Measurement-2) were selected as the best fitting measurement models for the subsequent stru ctural equation model estimation. Model goodness of fit statistics are presented in Tables 4 20. Structural Equation Models Estimation of baseline (Time 1) structural equation model The structural analysis was performed using the following procedures. Firs t, a simple mediating, one occasion structural model (Step 1), was specified for eac h pain outcome construct. The pur pose of this initial, simple (one-occasion) model was to determine whet her the derived measurement model fits the structural equation model. Each model was st ructured with direct paths from the exogenous variables (age, sex, ra ce, and number of diagnosed diseases) to pain, a dir ect path from pain to PMI, and a direct path from PMI to each of the pain outcome latent constructs, conducted separately for physical functioning, social f unctioning, and emotional functioning. The fit of these models were poor, and using the chi-squa re comparison test, these models estimated significantly worse than the single occasion Time 1 measurement model. Further model building was required. See model fit indices in Table 4-21 and diagram in Figure 4-8. 67

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Second, the mediational models was further deve loped by adding direct path from pain to the outcome constructs (physical functioning, social functioning, and emotional functioning) in each model. This was done to assess the direct effects of pain on pain outcomes (e.g., physical, social, and emotional functioning) and to determin e if this relationship wa s indirectly mediated through PMI. All three m odels re-estimated with reasonabl e to good fit indices. Based on their chi-square test of difference comparing with the Time 1 measurement model fit, the social functioning model was statistically as good as the measurement model; the physical and emotional models estimated slightly worse than the measurement mode (Table 4-22). Diagram of one occasion mediational structural model in is shown in Figure 4-9. Estimation of longitudinal (three-occasion) structural models The next step in the model building procedures was to specify a simp le three-occasion model. In addition to the within-occasion paths as previously specified, auto-regression and paths were added between Time 1 and 2, Time 1 and 3, and Time 2 and 3 for pain, PMI, and each outcome construct. Three structural models were estimated, one for each outcome construct and each estimated with good fit. However, Chi-square comparisons with the full invariant three-occasion measurement models showed that all three models were significantl y worse, and further model building was required. Thus, the support needed to estimate a fully recursive three-occasion structural model. See Table 4-23 for goodness of fit estimates and Figure 4-10 for the model diagram. In the next phase, a fully recursive mediat ed longitudinal three-o ccasion model for each outcome construct was specified. All cross-ti me regression paths be tween the main study constructs (e.g., pain. PMI, and pain outcomes) were specified for each model. Figure 4-11 is a diagram showing addition of cro ss-time regression paths for major relationships tested in the models. All uniqueness were allo wed to correlate for each endogenous construct. The results 68

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indicated a good model fit. A Chi-square compar ison for model fit showed that this model was significantly similar in fit as the full 3-occasion invariant measurement model (Measurement -2). All variances were positive and all standardized regression coefficients were below the value of 1.00. Fit indices are shown in Table 4-20. Test for pain mediation of the fully recursive three-occasion model. To test the fitness of a pain-mediated three-occasion fully recursive model, all direct paths from pain to social, physical, and emotional functioning were dropped so that pain was not mediated in the models; and models were re-estimated. Goodness of fit indices indicated overall poor fit for all estimated models. A test of chi-square difference between the no-pain mediated models and the fully recursive pain-mediated models showed that th e fit of the no-pain mediated models were significantly worse. These results suggest that pa in relationship to pain outcomes was mediated by PMI in the tested models. Thus, these fully recursive pain-mediated m odels were accepted as the best fitting mode for all three pain outcome s. All subsequent structural modeling analyses were conducted using the full three-occasion r ecursive pain-mediated longitudinal models. Finally, modification of the fully recursive model via model trimming (Kline, 2005) was performed using a nested model approach. Mode l trimming was systematically implemented by dropping non-significant regression paths from the models and re-estimating models until all remaining regression paths became statistically significant, deriving a reduced three-occasion recursive model. All three reduc ed models estimated with good model fit indicesincremental indices > .9 and absolute indices <.05. A comparison of Chi-square difference between the fully recursive three-occasion model and the reduced r ecursive three-occasion model showed that the differences in the chi-square values between each pair of nested models were not statistically significant. The three reduced models were ther efore accepted as the final structural equation 69

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models for physical, social, and emotional func tioning as pain outcomes. Table 4-25 shows model fit results. Diagrams of the reduced final models ar e shown in Figures 4 to 4. Three final pain models, one for each outcome of social functioning, physical functioning, and emotional functioning were de rived. Results include loading values on statistically significant regression paths, exogenous demographic predictors of endogenous variables at Time 1, 2, and 3; and, Time 1, 2, and 3 and cross time mediating endogenous predictors of social, physical, and emotional functioning outcomes. Final Model: Path Coefficients For the three final reduced models, standard ized path coefficients are reported as indicators of actual estimated value of the relations hips tested. Path coefficients on direct single time or cross-time paths from Pain to PMI, physical functioning, social functioning, and emotional functioning were at least .3 for all models. Path coeffici ents on mediation paths from Pain to PMI to physical, social, and emotional functioning range from good ( >.72) to poor ( =.06). On the other hand, the path estimates fo r the pain to physical, social, and emotional functioning paths were strong ( .6), while path estimates be tween the exogenous predictors age, sex, race, and number of diseases and the pain outcomes in these modelswere generally low to poor ( .2). Although regression paths from pain to PMI to the three pain outcomes were statistically significant, th ey generally had lower coefficien t values compared to direct loading of pain on these outcomes. Detailed resu lts on all parameter estimates are presented in Tables 4 25 to 4 -30. Demographic and Health Variables as Exoge nous Predictors of Pain Medication, and Pain Outcomes Age, sex, race, and number of diagnosed dise ases were analyzed as control exogenous variables in the three structural models. At the baseline period, advanced age was significantly 70

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related to lower physical ( = -.16) and emotional functioning ( = -.10). Number of diseases was significantly related to more pain on all three models ( = .21 to .22) as well as related to poorer physical and emotional functioning ( =-.08 and =-.06, Table 4 to 4). Pain and Pain Medication as Mediating Endogenous Predictors of Pain Outcomes Pain and prescribed pain medications were an alyzed as mediating predictor variables of pain outcomes at baseline, three years, and 5 years, as well across measurement occasions, for each separate pain outcome construct: social, physical, and emotional functioning. Results of the cross-sectional paths show that at baseline, increased pain was directly related to lower social functioning ( =-.69), lower phys ical functioning ( =-.89), and lower emotional functioning ( =-.89). Increased pain was also significantl y related lower PMI score in all three final modelssocial functioning model ( =-.30), physical functioning model ( =-.29), and emotional functioning model ( =-.31). These negative relationshi ps were stronger at Time 2 ( > .5) and Time 3 ( > .6). Cross-sectionally, pain to pain outcomes relationship was mediated by lower PMI scores in all three models at baseline. This mediational patter n was repeated at Time 2 for social and emotional functioning and at Time 3 for physical functioning. The strength of the mediational relationships was also stronger at Time 2 and Time 3 compared to baseline (Table 4 to 4-30). Cross-time structural regression paths for the three pain outcome models (social, physical, and emotional) show a di fferent results from those descri bed above. Analysis of crosstime relationships between endogenous predictors and outcomes showed overall, increased pain and more adequate PMI scores at earlier time was significantly related to improvement in functional outcome later in all three models. For example, increased pain at baseline was related to better social functioning ( = .59), emotional functioning ( =.78), and physical functioning ( 71

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=.92) 3 years later at Time 2. These relations hips were mediated by higher PMI scores at baseline for social ( =.11) and emotional ( =.10) functioning at Time 2 (Table 4 to 4-33). Overall, some variances in the pain outcome variables were predicted by the mediated models analyzed in this study. At baseline, the mediated effects of pain on emotional functioning was predicted by 55% (R2 =.55); physical functioni ng was predicted by 84% (R2=.84); and social functioning was predicted by 45% (R2=.45). Predicted variances in pain outcomes increased for physical functioning 92% (R2 = .92) and 93% (R2 = .93) at Time 2 and 3 respectively (Table 4-34). In summary, results have been presented on th e characteristics of pa in, pain medications, and pain outcomes across three occasions of measur ement at baseline, 3 years and 5 years later. Correlations analysis results covered all bivari ate relationships among pain, pain medication, and pain outcomes, and between control demographic a nd health variables. Resu lts of the structural model analyses explained the longitudinal direct and mediated relationships among pain, pain medications, and pain outcomes while controlling for age, sex, race, and number of diseases. In the study models tested, variances in social physical, and emotiona l outcomes are partly predicted by the effects of in terfering pain and adequacy of mediating prescribed pain medications. 72

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73 N=2,802 Parent Stud y Sam p le n=1,393 excluded based on exclusion criteria Time 1 (Baseline) N=1,409 Met Critee Current Study ria for th Time 2 (Year 3) N=1,032 n=377 cases lost to follow-up n=122 cases lost to follow-up Time 1 (Baseline) N=1,409 Study Sample Time 3 (Year 5) N=911 Figure 4-1. Flowchart of sample se lection and sample size for key study variables in longitudinal sample across the three study periods.

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0 5 10 15 20 25 30 35 Time 1 Time 2 Time 3 OccasionPercent Reporting None Little Bit Moderate Quite a lot Extremely 74 F=10.021, p <.000 Figure 4-2. Intensity of pain inte rference across meas urement occasions

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0 10 20 30 40 50 60 Time 1 Time 2 Time 3 OccasionPercent 1 2 3 4 5+ 75 F=3.059, p <.05 Figure 4-3. Number of pain locations reported across measuremen t occasions. Time 1 (N=1409), Time 2 (n=914), Time 3 (n=780)

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0 5 10 15 20 25 30 35NSAIDSOpiatesMuscle Relaxants SalicylatesAntidepressants (tricyclic) AcetaminophenAnticonvulsants (GABA)Medication ClassesPercent Time 1 Time 2 Time 3 76 Figure 4-4. Distribution of pa in medications classes at 3 occasions of measurement Percent prescribed analgesics: Time 1 =44%; Time 2 =37%; Time 3 = 34%; (F=27.129, p=.00)

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0 5 10 15 20 25 30 35 40 45 50 Time 1 Time 2 Time 3 OccasionPercent -3 -2 -1 0 1 2 3 77 F=9.726, p <.000 Note : PMI score of to indicate inad equate analgesic medications; scores 0 3 indicate adequate medications Figure 4-5. Frequency distribution of PMI scores across measurement occasions.

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0 10 20 30 40 50 60 70 80 90 Physical Functioning Physical RoleSocial Functioning Emotional Role VitalityDepressionPain Outcome Mean Score Time 1 Time 2 Time 3 *** n.s. *** ** *** n..s 78 Note : *p<.05, **p <.01, ***p<.001, n.s. = not significant Figure 4-6. Pain outcomes scores across measurement occasions

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Age Sex Race Disease pmi_10, Pain-1 pnint1 painloc1 1 2 1 0, e31 0, e41 0, e51 0, e61 0, Outcome_11 79 Note : PMI1 = pain management index at Time 1; Pnint1 = pain in terference at Time 1; Painloc1 = pain locations at Time 1 Figure 4-7. Diagram of a single occasion m easurement model estimated at time

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A g e Sex Race Disease PMI_1 Outcome_1 1 2 1 2 Pain_1 80 Figure 4-8. Step 1: Simple mediating one-o ccasion (Time 1) struct ural model diagram

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A g e Sex Race Disease 1 2 PMI_1 Outcome_1 1 2 Pain_1 81 Figure 4-9. Step 2: Simple mediational one-o ccasion (Time 1) struct ural model diagram

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Time1 Time2 Time3 A g e Sex Race Disease 1 2 PMI_1 1 2 PMI_2 1 2 1 2 Pain 1 1 2 PMI_3 1 2 Pain_2 Pain_1 Outcome_2 Outcome_1 Outcome_3 82 Figure 4-10. Step 3: Simple mediational 3-occasion structural model diagram

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Time1 Time2 Time3 A g e Sex Race Disease 1 2 PMI_1 1 2 Outcome_1 Pain_1 PMI_2 1 2 Outcome_3 Pain_2 Outcome_1 1 2 1 2 PMI_3 1 2 Pain_3 Outcome_2 83 Figure 4-11. Three-occasion recursive longitudinal model showing addition of cross-ti me paths for major relationships tested

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Pain = Pain Pn = Pain interference Phys = Physical functioning Pnl = Pain locations pf = Physical functioning Dz = Diseases diagnosed pr = Physical role p n1 p l1 p n2 p l2 p n3 p l3 84 pmi1 pmi2 pmi3 a g e sex race dz1 p r1 p f1 p f2 p r2 p f3 p r3 p ain2 p ain1 p p h y s1 p h y s2 p h y s3 ain31.12 .78 .47 .86 .79 .43 .72 .73 .77 .77 .77 .80 .50 .89 -.08 -.06 -.07 .60 -.20 -.26 -.26 -.08 .07 .07 .10 .21 .92 .63 .61 .13 .24 .23 .33 -.16 -.05 .13 -.74 -.82 -.89 -.06 -.13 -.69 -.52 -.29 -.06 Figure 4-12. Final reduced model of pain, pain medications, and physical functioning

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Pain = Pain Pn = Pain interference Soc = Social functioning Pl = Pain locations pmi1 pmi2 pmi3 p l1 p n1 a g e sex race dz1 p n2 p l2 p n3 p l3 soc1 soc2 soc3 p ain1 p ain2 p ain3.42 .32 .47 .91 .80 .42 .48 .90 -.08 -.21 -.99 -.70 -.69 -.06 -.62 -.60 -.30 .36 -.15 .11 .06 .35 .22 .59 .80 .64 .13 .29 .25 .29 .17 .08 -.07 .07 .11 -.07 -.06 .12 -.07 85 Figure 4-13. Final reduced model of pain, pain medications, and social functioning

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Pain = Pain pn = Pain interference Emot = Emotional functioning pnl = Pain locations er = Emotional role dz = Diseases diagnosed vt = Emotional vitality dp = Depression p mi = pain management index p n1 p l1 p n2 p l2 p n3 p l3 86 pmi1 pmi2 pmi3 a g e sex race dz1 d p 1 vt1 vt2 d p 2 er3 vt3 p ain2 p ain1 p Emot1 Emot2 Emots3 ain3.87 .55 .48 .88 .78 .44 .55 -.68 .76 .56 .75 .57 .50 .87 -.06 -.05 -.86 -.54 -.73 -.07 .34 -.72 -.60 -.31 .36 -.13 .10 06 -.06 .06 .07 .07 .21 .70 .73 .14 .28 .28 .38 -.10 .14 .78 -.19 -.13 er1 d p .24 .74 -.71 -.71 er2 Figure 4-14. Final reduced model of pain, pa in medications, and emotional functioning

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Table 4-1. Description of sample characteristics between selected and excluded samples at Time 1 Time 1 selected study sample n = 1,409 Time 1 excluded sample n = 1,393 % Min Max Mean (S.D.) % Min Max Mean (S.D.) 2(df) t (df) p Sex 51.08(1) Male 18.4 29.8 *** Female 81.6 70.1 *** Race 17.00(1) *** White 68.9 75.9 Non-white 31.1 24.1 Age 64.9 94.2 74.3 (5.9) 64.9 93.9 73.9 (6.0) 1.67(2799) n.s. Number of Disease 0 10 3.1 (1.3) 0 8 2.1 (1.4) 26.78(2800) *** MMSE 23 30 27.3 (2.0) 23 30 27.3 (2.0) .132(2800) n.s. Note: *p<.05; **p<.01; ***p<.001; ns = not si gnificant; MMSE = Mini Mental Stat e Examination. Race was recoded into a dichotomous variableWhite and Non-White for analysis. 87 Table 4-2. Comparison of pain, pain medication, and pain out comes between selected and excluded samples at Time 1. Time 2 study sample n = 1,409 Time 1 unselected n = 1,393 Mean (S.D.) Mean (S.D.) t (df) p Pain Pain interference 2.1 (1.1) 1.4 (1.15) 17.34 *** Pain locations 1.0 (1.5) 0.3 (.71) 15.71 *** Pain medications: PMI -1.0 (.9) -.95 (.75) -1.61 n.s. Pain outcomes Physical functioning 60.89 (24.28) 77.01 (20.95) -18.61 *** Physical role 50.95 (39.61) 69.73 (36.42) -12.95 *** Social functioning 83.14 (21.58) 89.84 (17.81) -8.87 *** Emotional role 70.12 (37.93) 79.33 (32.82) -6.810 *** Emotional vitality 56.94 (20.21) 66.23 (19.28) -12.403 *** Depression 5.77 (5.40) 4.65 (4.78) 5.740 *** *p<.05; **p<.01; ***p<.001; ns p> .05; ns = not significant

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Table 4-3. Comparison between longitudinal sample and study drop-outs at Time 2 Time 2 continuing sample n = 1,032 Dropouts after Time 1 n = 377 Mean (S.D.) Mean (S.D.) t (df) p Pain Pain Interference 2.16 (1.10) 2.20 (1.25) -.71 (1406) n.s. Pain Locations 1.00 (1.55) 1.03 (1.55) .22 (1406) n.s. Pain medications: PMI -1.02 (.89) -1.02 (.95) .05 (1270) n.s. Pain Outcomes Physical Functioning 62.38 (23.72) 56.67 (25.26) 2.93 (1406) *** Physical Role 52.01 (39.39) 47.94 (40.09) 1.71 (1406) n.s. Social Functioning 84.06 (20.71) 80.58 (23.67) 2.68 (1406) ** Emotional Role 71.19 (37.15) 67.11 (39.94) 1.78 (1405) n.s. Emotional Vitality 57.62 (19.39) 54.97 (21.50) 2.19 (1399) Depression 5.55 (5.30) 6.4 (5.46) -2.63 (1400) ** Note: *p<.05; **p<.01; ***p<.001, n.s. = not significant 88

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Table 4-4. Comparison between longitudinal sample and study drop-outs at Time 3 Time 3 continuing sample n = 910 Dropouts after Time 2 n = 122 Mean (S.D.) Mean (S.D.) t (df) p Pain Pain interference 2.31(1.15) Pain locations .92 (1.56) .00 .59 (901) n.s. Pain medications: PMI -.87 (.94) Pain outcomes Physical functioning 57.74 (25.02) 52.81 (29.74) 2.00 (1030) Physical role 46.72(39.90) 45.32 (41.16) .36(1028) n.s. Social functioning 80.33 (23.15) 76.42 (27.50) 1.71(1027) n.s. Emotional role 68.97 (38.17) 85.22 (30.44) -4.55(1028) *** Emotional vitality 55.64 (20.32) 55.19 (21.95) .23(1027) n.s. Depression 5.47 (5.48) Note: *p<.05; **p<.01; ***p<.001, n.s. = not significant 89 Table 4-5. Description of pain va riables and PMI at Time 1, 2, and 3 Time 1 Time 2 Time 3 mean (S.D.) mean (S.D.) mean (S.D.) F p Pain Pain interference [range = 1-5] 2.2 (1.1) 2.3 (1.2) 2.4 (1.2) 10.021 *** # Pain locations [range = 0-11] 1.0 (1.6) .91 (1.6) 1.1 (1.7) 3.059 PMIa [range = 3 to ] -1.0 (.91) -.9 (.94) -1.1 (.85) 9.726 *** a PMI = Pain management Index; *p<.05; **p<.01; ***p<.001, n.s. = not significant

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Table 4-6. Description of pain outco me variables at Time 1, 2, and 3 Time 1 Time 2 Time 3 Mean (S.D.) Mean (S.D.) Mean (S.D.) F p Pain outcomes Physical functioning 60.9 (24.3) 57.2 (25.7) 53.7 (26.5) 24.201 *** Physical role 51.0 (39.6) 46.6 (40.0) 42.3 (40.4) 13.343 *** Social functioning 83.1 (21.6) 79.8 (23.8) 78.8 (24.7) 11.564 *** Emotional role 70.1 (37.9) 71.0 (37.7) 71.4 (38.1) .326 n.s. Emotional vitality 56.9 (20.1) 55.6 (20.6) 54.1 (20.8) 5.398 ** Depression 5.7 (5.4) 5.5 (5.5) 5.8 (5.6)1.004 n.s. Table 4-7. Time effects on pred ictor and outcome variables Variables Numerator df Denominator df F p PMI 2 1890.16 12.00 *** Pain interference 2 1940.65 17.60 *** Pain locations 2 2034.22 5.47 ** Physical functioning 2 2048.34 113.36 *** Physical role 2 2144.97 30.66 *** Social function 2 2145.45 25.28 *** Emotional role 2 2142.11 .07 n.s. Emotional vitality 2 2050.93 22.42 *** Depression 2 1873.65 2.50 n.s. 90 Note : *p<.05, **p <.01, ***p<.001, n.s. = not significant

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Table 4-8. Sex differences in pain, PMI, and pain outcomes at Time 1, 2, and 3 Time 1 Time 2 Time 3 Mean (S.D.) Mean (S.D.) Mean (S.D.) Males Females p Males Females p Males Females p Pain Pain Interference 2.0 (1.1) 2.2 (1.2) 2.0 (1.4) 2.4 (1.1) *** 2.0 (1.1) 2.5 (1.2) *** # Pain Locations 0.8 (1.4) 1.1 (1.6) .59 (1.1) .98 (1.6) ** 0.9 (1.4) 1.1 (1.7) n.s. PMI -0.9 (0.9) -1.1 (0.9) ** -0.7 (0.8) -0.9 (1.0) -0.8 (0.8) -1.1 (0.9) *** Physical Functioning Physical Functioning 66.6 (23.8) 59.6 (24.2) *** 68.1 (24.7) 55.0 (25.3) *** 63.9 (28.2) 51.6 (25.7) *** Physical Role 53.9 (39.7) 50.3 (39.6) n.s. 55.5 (39.4) 44.7 (40.0) ** 52.7 (40.5) 40.3 (40.1) ** Social Functioning Social Functioning 84.3 (20.5) 82.9 (21.8) n.s. 81.6 (25.0) 79.5 (23.5) n.s. 81.7 (24.4) 78.2 (24.8) n.s. Emotional functioning Emotional Role 73.8 (36.4) 69.3 (38.2) n.s. 74.8 (36.2) 70.2 (37.9) n.s. 76.8 (35.9) 70.4 (38.4) n.s. Emotional Vitality 57.8 (21.6) 56.8 (10.6) n.s. 58.1 (21.6) 55.0 (20.3) n.s. 58.9 (22.0) 53.2 (20.5) Depression 5.4 (5.2) 5.9 (5.5) n.s. 4.9 (5 .4) 5.6 (5.5) n.s. 5.3 (5.7) 5.9 (5.5) n.s. *p<.05; **p<.01; ***p<.001; p>.05; n.s. = not significant 91 Table 4-9. Race differences in pain, PMI, and pain outcomes at Time 1, 2, and 3 Time 1 Time 2 Time 3 Mean (S.D.) Mean (S.D.) Mean (S.D.) White Non-white p White Non-white p White Non-white p Pain Pain Interference 2.4 (1.1) 2.3 (1.4) n.s. 2.3 (1.2) 2.4 (1.1) n.s. 2.3 (1.2) 2.4 (1.4) n.s. # Pain Locations 2.2 (1.2) 2.2 (1.1) n.s. 0.9 (1.5) 0.8 (1.6 ) n.s. 1.1 (1.7) 1.1 (1.7 ) n.s. PMI -1.1 (0.9) -1.0 (0.9) n.s. -0.9 (0.9) -0.9 (1.0) n.s. -1.1 (0.9) -1.0 (0.8) n.s. Physical Functioning Physical Functioning 62.1 (24.3) 58.0 (24.1) ** 58.9 (25.7) 52.7 (25.1) *** 54.0 (26.7) 51.9 (25.8) n.s. Physical Role 50.8 (39.2) 51.2 (40.5) n.s. 47.0 (39.3) 45.4 (42.0) n.s. 41.5 (40.0) 44.2 (41.6) n.s. Social Functioning 83.3 (21.9) 82.7 (20.9) n.s. 80.3 (23.8) 78.4 23.8) n.s. 78.5 (25.0) 79.4 (23.9) n.s. Emotional Functioning Emotional Role 72.1 (37.1) 65.6 (39.5) ** 72.3 (37.0) 67.5 (39.3) n.s. 73.2 (37.1) 66.3 (40.5) Emotional Vitality 56.0 (20.9) 59.0 (17.8) 55.1 (21.0) 56.7 (19.3) n.s. 53.0 (21.5) 57.0 (18.7) Depression 5.9 (5.6) 5.5 (4.8) n.s. 5.6 (5 .7) 5.2 (4.9) n.s. 5.9 (5.8) 5.6 (5.0) n.s. *p<.05; **p<.01; ***p<.001; p>.05; ns = not significant

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Table 4-10. Correlations be tween exogenous and endogenous variables at Time 1 Exogenous variables Endogenous variables Age (yrs) # Diagnoses Sex a Race b Pain Pain interference r=.05 n.s. (n=1407) r=.18**(n=1408) r=.05* (n=1408) r=.00 n.s. (n=1408) Pain locations r=-.03 n.s. (n=1407) r=.12** (n=1408) r=.06* (n=1408) r=.02 n.s. (n=1408) PMI r=-.04 n.s. (n=1271) r=-.07 ns (n= 1273) r=-.08** (n=1272) r=.02 n.s (n=1271) Physical functioning Physical functioning r=-.15** (n=1407) r=-.27**(n=1408) r=-.11** (n=1408) r=-.08** (n=1408) Physical role r=-.14** (n=1407) r=.16** (n=1408) r=-.04** (n=1408) r=.01 n.s. (n=1408) Social functioning r=-.05** (n=1407) r=-.16** (n= 1408) r=-.03 n.s. (n=1408) r=-.01 n.s. (n=1408) Emotional functioning Emotional role r=-.13** (n=1406) r=-.13** (n=1407) r=-.05 n.s. (n =1407) r=-.08** (n=1407) Emotional vitality r=-.06* (n=1400) r=-.18** (n=1401) r=-.02 n.s. (n=1401) r=.07 (n=1401) Depression r=.10** (n=1401) r=.12** (n= 1402) r=.04 n.s. (n=1402) r=-.01 n.s. (n=1402) 92 Note: p < .05, ** p < .01, *** p < .001, ns = not significant. a Sex was coded as 0 = males and 1 = females; b Race was coded as (0=White; 1 = Non-white)

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Table 4-11. Correlations betw een exogenous and endogenous variables at Time 2 Exogenous variables Endogenous Variables Age (yrs) # Diagnoses Sex a Race b Pain Pain interference r=.05 n.s. (n=910) r=.164** (n=910) r=.12** (n=910) r=.04 n.s. (n=910) Pain locations r=-.01 n.s. (n=913) r=.15** (n=902) r=.09** (n=913) r=-.03 n.s. (n=913) PMI r=.01 n.s. (n=864) r=-.08 n.s. (n= 864) r=-.07** (n=864) r=.01 n.s. (n=864) Physical functioning Physical functioning r=-.18** (n=1032) r=-.24** (n=1032) r=-.20** (n =1032) r=-.11** (n=1032) Physical role r=-.17** (n=1031) r=.22** (n=1031) r=-.10** (n=1031) r=-.02 n.s. (n=1031) Social functioning r=-.11** (n=1030) r=-.14** (n =1030) r=-.04 n.s. (n=1030) r=-.04 n.s. (n=1030) Emotional functioning Emotional role r=-.11** (n=1031) r=-.12** (n=1031) r=-.05 n.s. (n=1031) r=-.06 n.s. (n=1031) Emotional vitality r=-.09** (n=1030) r=-.19** (n=1030) r=-.06 n.s. (n=1030) r=.04 n.s. (n=1030) Depression r=.07* (n=909) r=.10** (n= 909) r=.05 n.s. (n=909) r=-.03 n.s. (n=909) Note: p < .05, ** p < .01, *** p < .001, ns = not significant. a Sex was coded as 0 = males and 1 = females; b Race was coded as (0=White; 1 = Non-white) 93

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Table 4-12. Correlations betw een exogenous and endogenous variables at Time 3 Exogenous variables Endogenous variables Age (yrs) # Diagnoses Sex a Race b Pain Pain interference r=-.04 n.s. (n=770) r=.09* (n=770) r=.12** (n=770) r=-.05 n.s. (n=770) Pain locations r=-.02 n.s. (n=779) r=.09* (n=779) r=.05 n.s. (n=779) r=-.01 n.s. (n=779) PMI r=-.06 n.s. (n=691) r=-.01 n.s. (n=691) r=-.14** (n=691) r=.05 n.s. (n=691) Physical functioning Physical functioning r=-.22* (n=910) r=-.25** (n=910) r=-.17** (n =910) r=-.04** (n=910) Physical role r=-.19** (n=910) r= -.17** (n=910) r=-.11** (n=910) r=.03** (n=910) Social functioning r=-.12** (n=910) r=-.08* (n =910) r=-.05 n.s. (n=910) r=.01 n.s. (n=910) Emotional functioning Emotional role r=-.09** (n=910) r=-.09** (n=910) r=-.06 n.s. (n =910) r=-.08* (n=910) Emotional vitality r=-.10** (n=909) r=-.17** (n=909) r=-.10** (n=909) r=.09* (n=909) Depression r=.10** (n=769) r=.11** (n= 769) r=.04 n.s. (n=769) r=-.03 n.s. (n=769) Note: p < .05, ** p < .01, *** p < .001, n.s. = not significant. a Sex was coded as 0 = males and 1 = females; b Race was coded as (0=White; 1 = Non-white) 94

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Table 4-13. Intercorrelations among endogenous variables at Time 1 PAIN-INT PAIN-LOC PMI PHYS-PF P HYS-PR SOC-SF EMOT-R VITAL DEP Pain Pain interference (PAIN-INT) 1 Pain Locations (PAIN-LOC) r=.35** (n=1408) 1 PMI r=-25** (n=1272) r=-15** (n=1272) 1 Physical functioning Physical function (PHYS-PF) r=-48** (n=1408) r=-31** (n=1408) r=.14** (n=1272) 1 Physical role (PHYS-PR) r=-56** (n=1408) r=-27** (n=1408) r=.17** (n=1272) r=.55** (n=1408) 1 Social functioning (SOC-SF) r=-54** (n=1408) r=-27** (n=1408) r=.15** (n=1272) r=.45** (n=1408) r=.53** (n=1408) 1 Emotional functioning Emotional role (EMOT-R) r=-35** (n=1407) r=-14** (n=1407) r=.10** (n=1271) r=.31** (n=1407) r=.44** (n=1407) r=.46** (n=1407) 1 Emotional vitality (VITAL) r=-48** (n=1401) r=-29** (n=1401) r=.14** (n=1265) r=.50** (n=1401) r=.51** (n=1401) r=.52** (n=1401) r=.35** (n=1400) 1 Depression (DEP) r=.31** (n=1402) r=.12** (n=1402) r=-10** (n=1267) r=-29** (n=1402) r=-.33** (n=1402) r=-50** (n=1402) r=-.48** (n=1401) r=-.49** (n=1396) 1 95 Note: p < .05, ** p < .01, *** p < .001, ns = not significant

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Table 4-14. Intercorrelations among endogenous variables at Time 2 PAIN-INT PAIN-LOC PMI PHYS-PF PHYS-PR SOC-SF EMOT-R VITAL DEP Pain Pain interference (PAIN-INT) 1 Pain locations (PAIN-LOC) r=.44* (n=902) 1 1 PMI r=-.34** (n=864) r=-15** (n=858) Physical functioning Physical function (PHYS-PF) r=-.57** (n=908) r=-35** (n=903) r=.16** (n=863) 1 Physical role (PHYS-PR) r=-.56** (n=908) r=-30** (n=902) r=.20** (n=862) r=.60** (n=1030) 1 Social functioning (SOC-SF) r=-.58** (n=907) r=-35** (n=901) r=.10** (n=862) r=.52** (n=1029) r=.53** (n=1028) 1 Emotional functioning Emotional role (EMOT-R) r=-.34** (n=907) r=-23** (n=901) r=.02 n.s. (n=861) r=.25** (n=1030) r=.39** (n=1030) r=.42** (n=1028) 1 Emotional vitality (VITAL) r=-.54** (n=907) r=-33** (n=901) r=.15** (n=862) r=.51** (n=1029) r=.54** (n=1028) r=.52** (n=1029) r=.37** (n=1028) 1 Depression (DEP) r=.35** (n=909) r=.29** (n=901) r=-09** (n=863) r=-3** (n=907) r=-30** (n=907) r=-52** (n=906) r=-.48** (n=906) r=-.54** (n=906) 1 96 Note: p < .05, ** p < .01, *** p < .001, ns = not significant

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Table 4-15. Intercorrelations among endogenous variables at Time 3 PAIN-INT PAIN-LOC PMI PHYS-PF PHYS-PR SOC-SF EMOT-R VITAL DEP Pain Pain interference (PAIN-INT) 1 Pain locations (PAIN-LOC) r=.41** (n=776) 1 1 PMI r=-.43** (n=691) r=-17** (n=689) Physical functioning Physical function (PHYS-PF) r=-.52** (n=769) r=-33** (n=766) r=.24** (n=691) 1 Physical role (PHYS-PR) r=-.58** (n=769) r=-36** (n=766) r=.26** (n=691) r=.60** (n=910) 1 Social functioning (SOC-SF) r=-.57** (n=769) r=-33** (n=766) r=.27**(n =691) r=.47** (n=910) r=.55** (n=910) 1 Emotional functioning Emotional role (EMOT-R) r=-.31** (n=) r=-14** (n=766) r=.13** (n=691) r=.29** (n=910) r=.41** (n=910) r=.41** (n=910) 1 Emotional vitality (VITAL) r=-.51** (n=769) r=-36** (n=765) r=.26** (n=690) r=.54** (n=909) r=.55** (n=909) r=.56** (n=909) r=.36** (n=909) 1 Depression (DEP) r=.40** (n=769) r=.26** (n=765) r=-20** (n=691) r=-34** (n=769) r=.34** (n=769) r=-.56** (n=769) r=-.49** (n=769) r=-.53** (n=769) 1 97 Note: p < .05, ** p < .01, *** p < .001, ns = not significant

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Table 4-16. Cross-time endogenous interc orrelations, Time 1 and Time 2 Time 1 variables Time 2 variables A IN-INT PAIN-LOC PMI PHYS-PF PHYS-PR SOC-SF EMOT-R VITAL DEP Pain Pain interference (PAIN-INT) r=.39** (n=910) r=.25** (n=910) r=-.06 n.s. (n=823) r=-.42** (n=910) r=-.36** (n=901) r=-.32** (n=910) r=.21** (n=910) r=.39** (n=908) r=.25** (n=907) Pain locations (PAIN-LOC) r=.26** (n=823) r=.40** (n=913) r=-.08* (n=828) r=-.28** (n=913) r=-.25** (n=913) r=-.22** (n=913) r=-.16** (n=913) r=-.29** (n=911) r=.16** (n=911) PMI r=-.11** (n=864) r=-.07* (n=)864 r=.22** (n=803) r=.06** (n=864) r=.06** (n=864) r=.03** (n=864) r=.05** (n=864) r=.11** (n=863) r=-.05** (n=863) Physical functioning Physical function (PHYS-PF) r=-.34** (n=1032) r=-.22** (n=1032) r=.05 n.s. (n=935) r=.69** (n=1032) r=.41** (n=1032) r=.30** (n=1032) r=.24** (n=1032) r=.37** (n=1028) r=-.22** (n=1027) Physical role (PHYS-PR) r=-.28** (n=1031) r=-.18** (n=1031) r=.00 n.s. (n=934) r=.40** (n=1031) r=.46** (n=1031) r=.28** (n=1032) r=.27** (n=1031) r=.35** (n=1027) r=-.21** (n=1026) Social functioning (SOC-SF) r=-.28** (n=1030) r=-.14** (n=1030) r=.00 n.s. (n=934) r=.34** (n=1030) r=.31** (n=1030) r=.40** (n=1030) r=.29** (n=1030) r=.32** (n=1026) r=-.29** (n=1025) Emotional functioning Emotional role (EMOT-R) r=-.16** (n=1031) r=-.02 n.s. (n=1031) r=-.03 n.s. (n=935) r=.20** (n=1031) r=.26** (n=1031) r=.21** (n=1031) r=.36** (n=1031) r=.24** (n=1027) r=-.32** (n=1026) Emotional vitality (VITAL) r=-.29** (n=1030) r=-.19** (n=1030) r=.06 n.s. (n=934) r=.41** (n=1030) r=.38** (n=1030) r=.31** (n=1034) r=.27** (n=1030) r=.64** (n=1026) r=-.37** (n=1025) Depression (DEP) r=.22** (n=909) r=.09** (n=909) r=.03 n.s. (n=822) r=-.24** (n=909) r=-.25** (n=909) r=-.34** (n=909) r=-.34** (n=909) r=-.39** (n=907) r=.51** (n=906) 98 Note: p < .05, ** p < .01, *** p < .001, n.s. = not significant

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Table 4-17. Cross-time endogenous inte rcorrelations, Time 1 and Time 3 Time 1 variables Time 3 Variables PAIN-INT PAIN-LOC PMI PHYS-PF PHYS-PR SOC-SF EMOT-R VITAL DEP Pain Pain Interference (PAIN-INT) r=.35** (n=770) r=.19** (n=770) r=-.06 n.s. (n=694) r=-.327** (n=770) r=-.33** (n=770) r=-.27** (n=770) r=-.16** (n=770) r=-.37** (n=769) r=.25** (n=769) Pain Locations (PAIN-LOC) r=.25** (n=779) r=.37** (n=779) r=-.06 n.s. (n=701) r=-.27** (n=701) r=-.23** (n=779) r=-.13** (n=779) r=-.06 n.s. (n=779) r=-.30** (n=776) r=.12** (n=776) PMI r=-.14** (n=691) r=-.05 n.s. (n=691) r=.21** (n=639) r=.13** (n=691) r=.19** (n=691) r=.13** (n=691) r=.15** (n=691) r=.14** (n=690) r=-.14** (n=690) Physical Functioning Physical Function (PHYS-PF) r=-.30** (n=910) r=-.18** (n=910) r=.03 n.s. (n=825) r=.66** (n=910) r=.42** (n=910) r=.27** (n=910) r=.20** (n=910) r=.36** (n=907) r=-.21** (n=909) Physical Role (PHYS-PR) r=-.32** (n=910) r=-.11** (n=910) r=-.01 n.s. (n=825) r=.37** (n=910) r=.44** (n=910) r=.26** (n=910) r=.25** (n=910) r=.33** (n=907) r=-.21** (n=909) Social Functioning (SOC-SF) r=-.28** (n=910) r=-.14** (n=910) r=-.07* (n=825) r=.33** (n=910) r=.32** (n=910) r=.39** (n=910) r=.22** (n=910) r=.36** (n=910) r=-.32** (n=909) Emotional Functioning Emotional Role (EMOT-R) r=-.21** (n=910) r=-.09** (n=910) r=.02 n.s. (n=825) r=.21** (n=910) r=.26** (n=910) r=.30** (n=910) r=.35** (n=910) r=.30** (n=907) r=-.33** (n=909) Emotional Vitality (VITAL) r=-.30** (n=909) r=-.20** (n=909) r=.078* (n=825) r=.36** (n=909) r=.36** (n=909) r=.29** (n=909) r=.25** (n=909) r=.60** (n=906) r==.36** (n=908) Depression (DEP) r=.21** (n=769) r=.13** (n=769) r=.01 n.s. (n=693) r=-.23** (n=769) r=-.23** (n=769) r=-.34** (n=769) r=-.30** (n=769) r=-.38** (n=768) r=.53** (n=768) 99 Note: p < .05, ** p < .01, *** p < .001, ns = not significant

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Table 4-18. Cross-time endogenous interc orrelations, Time 2 and Time 3 Time 2 Variables Time 3 Variables PAIN-INT PAIN-LOC PMI PHYS-PF PHYS-PR SOC-SF EMOT-R VITAL DEP Pain Pain Interference (PAIN-INT) r=.48** (n=722) r=.36** (n=722) r=-.17** (n=687) r=-.45** (n=750) r=-.40** (n=749) r=-.38** (n=748) r=-.25** (n=748) r=-.44** (n=748) r=.34** (n=721) Pain Locations (PAIN-LOC) r=.32** (n=728) r=.38** (n=729) r=-.06 n.s. (n=693) r=-.32** (n=757) r=-.26** (n=756) r=-.24** (n=755) r=-.16** (n=755) r=-.30** (n=755) r=23** (n=728) PMI r=-.22** (n=648) r=.10** (n=647) r=.28** (n=630) r=.19** (n=674) r=.19** (n=673) r=.28** (n=672) r=.11** (n=672) r=.17** (n=672) r=-.15 ** (n=647) Physical Functioning Physical Function (PHYS-PF) r=-.44** (n=792) r=-.27** (n=791) r=.06 n.s. (n=753) r=.76** (n=873) r=.49** (n=872) r=.39** (n=870) r=.23** (n=871) r=.44** (n=870) r=-.28** (n=791) Physical Role (PHYS-PR) r=-.38** (n=792) r=-.25** (n=791) r=-.06 n.s. (n=752) r=.47** (n=873) r=.51** (n=872) r=.37** (n=870) r=.31** (n=871) r=.43** (n=870) r=-.26** (n=791) Social Functioning (SOC-SF) r=-.35** (n=792) r=-.27** (n=791) r=-.03 n.s. (n=753) r=.37** (n=873) r=.36** (n=872) r=.45** (n=870) r=.31** (n=871) r=.41** (n=870) r=-.40** (n=791) Emotional Functioning Emotional Role (EMOT-R) r=-.22** (n=792) r=-.16** (n=791) r=-.03 n.s. (n=753) r=.23** (n=873) r=.27** (n=872) r=.30** (n=870) r=.42** (n=871) r=.30** (n=870) r=-.36** (n=791) Emotional Vitality (VITAL) r=-.42** (n=791) r=-.26** (n=790) r=.09* (n=752) r=.43** (n=872) r=.42** (n=871) r=.38** (n=869) r=.30** (n=870) r=.67** (n=869) r=-.42** (n=790) Depression (DEP) r=.26** (n=722) r=.24** (n=722) r=.00 n.s. (n=687) r=-.28** (n=750) r=-.26** (n=749) r=-.35** (n=748) r=-.38** (n=748) r=-.41** (n=748) r=-.62** (n=721) 100 Note: p < .05, ** p < .01, *** p < .001, n.s. = not significant.

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Table 4-19. Goodness of fit indices for initia l baseline measurement models estimation Physical function Social function Emotional function 2 63.45 8.33 169.06 df 11 5 19 p <.001 .14 <.001 RMSEA .06 (p=.15) .02 (p=.97) .08 (p=.00) NFI .96 .99 .90 RFI .85 .93 .72 IFI .97 .97 .91 TLI .87 .97 .72 CFI .97 .97 .91 AIC 149.45 86.33 261.06 Note : df = degree of freedom; NFI = normed f it index, RFI = relative fit index, IFI = incremental fit index, TLI = Turker-Lewis ind ex, CFI = comparative fit index, AIC = Akaike; RMSE A = Root mean square error of approximation 101

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Table 4-20. Goodness of fit indices for best -fitting full 3-occasi on measurement models Physical function Social function Emotional function 2 181.42 46.77 407.22 Df 73 32 129 p <.00 .04 <.00 RMSEA .03 (p =1.0) .02 ( p=1.0) .04 ( p = 1.0) NFI .97 .99 .94 RFI .92 .94 .88 IFI .98 .99 .96 TLI .96 .98 .91 CFI .98 .99 .96 AIC 453.42 286.77 699.22 Test of 2 difference a 2 diff 35.90 46.61 22.18 df diff 6 6 6 p .00 .00 .00 Note: df = degree of freedom; NFI = normed f it index, RFI = relative fit index, IFI = incremental fit index, TLI = Turker-Lewis inde x, CFI = comparative fit index, AIC = Akaike; RMSEA = Root mean square error of approximation. a Test of chi-square difference relative to initial most constrained full occasion invariant measurement model (Measurement-1). 102

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Table 4-21. Goodness of fit indices for si mple (mediational) one-occasion struct ural model estimation at time 1 Physical function Social function Emotional function 2 761.80 488.75 517.42 Df 20 14 28 p < .00 < .00 < .00 RMSEA .16 (p = .00) .16 ( p = .00) .11 ( p = .00) NFI .55 .46 .69 RFI -.01 -.38 .41 IFI .56 .47 .71 TLI -.01 -.39 .42 CFI .55 .46 .71 AIC 829.80 548.76 591.41 Test of 2 difference a 2 diff 698.35 480.42 348.36 df diff 9 9 9 p .00 .00 .00 103 Note: df = degree of freedom; NFI = normed fit index, RFI = relative fit index, IFI = incremental fit index, TLI = Turker-Lewis ind ex, CFI = comparative fit index, AIC = Akaike; RMSEA = Root mean square error of approximation. a Test of chi-square difference relative to Time 1 measurement model.

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Table 4-22. Goodness of fit indices for simple mediational structural model (Step 2 m odels) estimation at time 1 Physical function Social function Emotional function 2 106.96 17.31 190.56 Df 19 13 27 p < .00 < .00 < .00 RMSEA .06 (p = .12) .02 ( p = 1.0) .07 ( p = .002) NFI .93 .98 .89 RFI .85 .95 .77 IFI .95 .99 .90 TLI .87 .99 .79 CFI .95 .99 .90 AIC 176.96 79.31 266.56 Test of 2 difference a 2 diff 43051 8.98 21.50 df diff 8 8 8 p .00 .34 .00 Note : df = degree of freedom; NFI = normed fit index, RFI = relative fit index, IFI = incremental fit index, TLI = Turker-Lewis in dex, CFI = comparative fit index, AIC = Akaike; RMSEA = Root mean square error of approximation. a Test of chi-square difference relative to initial Time 1 measurement model 104

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105 Table 4-23. Goodness of fit indices for simple thr ee occasion mediation stru ctural model estimation Physical function Social function Emotional function 2 461.79 219.80 614.94 Df 123 82 179 p <. 00 <. 00 <. 00 RMSEA .04 (p =.98) .04 ( p =1.0) .04 ( p =1.0) NFI .93 .93 .91 RFI .89 .89 .87 IFI .95 .96 .93 TLI .92 .93 .90 CFI .95 .96 .93 AIC 633.79 359.80 806.94 Test of 2 difference a 2 diff 280.36 173.03 207.72 df diff 50 50 50 p .00 .00 .00 Note : df = degree of freedom; NFI = normed fit index, RFI = relative fit index, IFI = incremental fit index, TLI = Turker-Lewis in dex, CFI = comparative fit index, AIC = Akaike; RMSEA = Root mean square error of approximation. a Test of chi-square difference relative to Full 3-occasion invariant measurement model (Measurement-2).

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Table 4-24. Goodness of fit indices for the final reduced 3-occasion pain-mediated models Physical function Social function Emotional function 2 83.54 211.66 438.30 Df 71 111 168 p .15 .00 .00 RMSEA .01 (p =1.0) .03 ( p =1.0) .04 ( p =1.0) NFI .97 .97 .93 RFI .95 .94 .90 IFI .99 .98 .96 TLI .99 .97 .90 CFI .99 .96 .91 AIC 245.54 407.66 652.30 Test of 2 difference a 2 diff 36.77 30.24 31.08 df diff 39 38 39 p .57 .81 .81 Note : df = degree of freedom; NFI = normed fit index, RFI = relative fit index, IFI = incremental fit index, TLI = Turker-Lewis in dex, CFI = comparative fit index, AIC = Akaike; RMSEA = Root mean square error of approximation. a Test of chi-square difference relative to the three-occasi on fully recursive mediational model. 106

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Table 4 -25. Final reduced form structural m odels mediated by pain management index: Model parameters and estimates at Time 1 Exogenous predictors of Time 1 Models Age unstandardized (standardized) Sex unstandardized (standardized) Race unstandardized (standardized) Diagnoses Unstandardized (standardized) Social function (SOC) Pain ---.11*** (.22) PMI --.15* (.06) --SOC-F ----Physical function (PHYS) Pain -.12* (.07) -.11*** (.21) PMI ----PHYS-F -.48*** (-.16) --2.0** (-.05) -1.00*** (-.08 ) Emotional (EMOT) Pain -.11* (.06) -.11 *** (.21) PMI --.13*(-.06) --EMOT-F -.36*** (-.10) ---.96* (-.06) Note: p < .05, ** p < .01, *** p < .001, -= nonsignificant; these va lues were dropped from final reduced form models. 107 Table 4-26. Final reduced form struct ural models mediated by pain management index: Model parameters and estimates at Time 2 Exogenous Predictors of Time 2 Models Age unstandardized (standardized) Sex unstandardized (standardized) Race unstandardized (standardized) Diagnoses unstandardized (standardized) Social function (SOC) Pain -.21*** (.11) -.04*(.06) PMI ----SOC-F -.28** (-.07) 4.21* (.07) --Physical function (PHYS) Pain -.19**(.10) -.04*(.07) PMI ----PHYS-F --2.68* (-.05 ) --Emotional (EMOT) Pain --.04* (.07) PMI ----EMOT-F -.21* (-.05) ---Note : p < .05, ** p < .01, *** p < .001; -= not significant; these values were dropped fr om the final reduced form models

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Table 4-27. Final reduced form struct ural models mediated by pain management index: Model parameters and estimates at Time 3 Exogenous predictors of Time 3 Models Age unstandardized (standardized) Sex unstandardized (standardized) Race unstandardized (standardized) Diagnoses unstandardized (standardized) Social function (SOC) Pain ----PMI --.16* (-.07) --SOC-F -.28** (-.07) n.s. n.s. n.s. Physical function (PHYS) Pain ---.14** (-.08) -PMI ----PHYS-F -.24* (-.07) n.s. n.s. -.84* (-.06) Emotional function (EMOT) Pain ---.10* (-.06) -PMI ----EMOT-F -.21* (-.05) n.s. n.s. n.s. Note: p < .05, ** p < .01, *** p < .001, -= not signi ficant; these values were dropped from the final reduced form models 108

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Table 4-28. Final reduced form st ructural models mediated by pain management i ndex: Model parameters and estimates of mediatin g Time 1 endogenous predictors of Time 1 outcomes. Mediating endogenous unstandardized (standardized) Dependent variables unstandardized (standardized) Models Predictors Pain PMI SOC-F PHYS-F EMOT-F Social (SOC) Pain --.51*** (-.30) -23.05*** (-.69) --PMI ---1.47* (-.06) --Physical (PHYS) Pain --.40*** (-.29) --23.65*** (-.89) -PMI ----1.21** (-.06) -Emotional (EMOT) Pain --.42*** (-.31) ---22.49*** (-.89) PMI -----1.56* (-.07) SOC-F ---PHYS-F ---EMOT-F ---Note : p < .05, ** p < .01, *** p < .001, -indicates a path which was not estimated in the fully recursive model. The three dependent variables were estimated in three separate models a nd were not allowed to interrelat e, n.s. = nonsignificant; these values were dropped from the final reduced form models 109

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Table 4-29. Final reduced form stru ctural models mediated by pain management inde x: Model parameters and estimates of mediating Time 2 endogenous predictors of Time 2 outcomes. Mediating endogenous unstandardized (standardized) Dependent variables unstandardized (standardized) Models Predictors Pain PMI SOC-F PHYS-F EMOT-F Social (SOC) Pain --.76*** (-.60) -32.20*** (-.99) --PMI ---5.26*** (-.21) --Physical (PHYS) Pain --.64*** (-.52) --18.74*** (.-74) -PMI ---n.s. -Emotional (EMOT) Pain --.76*** (-.60) ---25.04*** (-.86) PMI -----4.33* (-.19) SOC-F ---PHYS-F ---EMOT-F ---110 Note : p < .05, ** p < .01, *** p < .001, -indicates a path which was not estimated in the fully recursive model. The three dependent variables were estimated in three separate models a nd were not allowed to interrelat e, n.s. = nonsignificant; these values were dropped from the final reduced form models

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Table 4-30. Final reduced form stru ctural models mediated by pain management inde x: Model parameters and estimates of mediating Time 3 endogenous predicto rs of Time 3 outcomes Mediating endogenous unstandardized (standardized) Dependent variables unstandardized (standardized) Models Predictors Pain PMI SOC-F PHYS-F EMOT-F Social function (SOC) Pain --.68*** (-.62) -22.342*** (-.70) --PMI --ns --Physical function (PHYS) Pain --.75*** (-.69) --20.95*** (-.82) -PMI ----2.94** (-.13) -Emotional function (EMOT) Pain --.76*** (-.72) ---15.03*** (-.54) PMI ----n.s. SOC ---PHYS-F ---EMOT-F ---111 Note : p < .05, ** p < .01, *** p < .001, -indicates a path which was not estimated in the fully recursive model. The three dependent variables were estimated in three separate models and were not allowed to interrelate, n.s. = nonsignificant; these v alues were dropped from the final reduced form models.

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Table 4-31. Final reduced form stru ctural models mediated by pain management inde x: Model parameters and estimates of mediating Time 1 endogenous predictors of Time 2 outcomes. Mediating endogenous unstandardized (standardized) Dependent variables unstandardized (standardized) Models Predictors: Pain PMI SOC-F PHYS-F EMOT-F Social Function (SOC) Pain .92*** (.80) .51*** (.35) 22.18*** (.59) --PMI .14*** (.17) .31*** (.29) 2.86*** (.11) --Physical Function (PHYS) Pain .73*** (.63) n.s. -26.69*** (.92) PMI .11*** (.13) .25*** (.13) -n.s. -Emotional Function (EMOT) Pain .79*** (.70) .48*** (.34) --25.68*** (.78) PMI .12*** (.14) .29*** (.28) --2.37* (.10) SOC-F .48*** (.42) --PHYS-F -1.23*** (1.12) -EMOT-F --.93*** (.87) Note : p < .05, ** p < .01, *** p < .001, -indicates a path which was not estimated in the fully recursive model. The three dependent variables were estimated in three separate models a nd were not allowed to interrelat e, ns = nonsignificant; these val ues were dropped from the final reduced form models 112 Note: -indicates a path which was not estimated in the fully recursive model. The three dependent variables were estimated in three separate models and were not allowed to interrelate.

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Table 4-32. Final reduced form stru ctural models mediated by pain management inde x: Model parameters and estimates of mediating Time 1 endogenous predictors of Time 3 outcomes. Mediating endogenous unstandardized (standardized) Dependent variables unstandardized (standardized) Models Predictors Pain PMI SOC-F PHYS-F EMOT-F Social Functioning (SOC) Pain .15* (.12) n.s. n.s. --PMI .12*** (.13) n.s. n.s. --Physical functioning (PHYS) Pain n.s. n.s. -n.s. -PMI n.s. n.s. -n.s. -Emotional Functioning (EMOT) Pain n.s. n.s. --n.s. PMI n.s. n.s. --n.s. SOC-F ---PHYS-F ---EMOT-F --.26*** (.24) Note : p < .05, ** p < .01, *** p < .001, -indicates a path which was not estimated in the fully recursive model. The three dependent variables were estimated in three separate models a nd were not allowed to interrelat e, ns = nonsignificant; these val ues were dropped from the final reduced form models. 113

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114 Table 4-33. Final reduced form stru ctural models mediated by pain management inde x: Model parameters and estimates of mediating Time 2 endogenous predictors of Time 3 outcomes. Mediating endogenous unstandardized (standardized) Dependent variables unstandardized (standardized) Models Predictors Pain PMI SOC-F PHYS-F EMOT-F Social Functioning (SOC) Pain .68*** (.64) .34*** (.29) 12.14*** (.36) --PMI .07*** (.08) .23*** (.25) n.s. --Physical functioning (PHYS) Pain .63*** (.61) .38*** (.33) -15.94*** (.60) -PMI n.s. .21*** (.23) -n.s. -Emotional Functioning (EMOT) Pain .78*** (.73) .43*** (.38) --10.68*** (.36) PMI .11* (.13) .25*** (.28) --n.s. SOC-F ---PHYS-F ---EMOT-F ---Note : p < .05, ** p < .01, *** p < .001, -indicates a path which was not estimated in the fully recursive model. The three dependent variables were estimated in three separate models a nd were not allowed to interrelat e, ns = nonsignificant; these val ues were dropped from the final reduced form models

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Table 4-34. Pain outcomes predicted at Time 1, 2, and 3 in the final reduced model Time 1 Time 2 Time 3 Variables predicted R2 R2 R2 Physical functioning .84 .92 .93 Social functioning .45 .60 .55 Emotional functional .55 .88 .81 115

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CHAPTER 5 DISCUSSION The purpose of this study was to examine the role of the adequacy of prescribed pain medications in the relationship be tween pain (pain interference and number of pain sites) and pain outcomes (physical, social, and emotiona l functioning). In this section, study results, implications of the findings for nursing, limitatio ns of the study, and directions for future research are presented. A brief discussion on the sample attriti on effects is presented first. Pain, Pain Medication, and Pain Outcomes among Community Dwelling Older Adults with Arthritis The results of this study s uggest that pain is a common symptom among older adults. The characteristics of pain, pain medication, and pain outcomes among older adults with arthritis were analyzed at three time periods, over a five-year period. Pain among Older Adults with Arthritis At the beginning of the study, almost 50% of participants reported that pain and painful sites interfered with their daily activities. About 65% of those reporting pain rated their pain interference severity at least a little bit. This rate increase d to approximately 72% by year five for returning participants (n=780). These figures far exceed projected prevalence predicted by Centers for Disease Control (CDC 2006) of approximately 37%, but they are consistent with higher prevalence (69%) reported by Scudd and R obertson (2000). These findings are also consistent with current pain lite rature that documents high prevalen ce of pain interference and its association with arthri tis among older adults (Arthritis F oundation, 2008; CDC). It is important to note that findings of high prev alence may have been influenced by a bias toward individuals with higher levels of pain at baseline. As expected, proportion of the sample that reported that pain interfered with their daily activities increased ove r time. At time 1, about 12% of the sample reported that pain interfered qui te a bit, but this proportion incr eased to more than 20% of the 116

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sample over the ensuing 5 years. Furthermore, older adults often experience pain in multiple body locations simultaneously. The number of older adults in the sample reporting three or more pain sites associated with activity interference in creased by almost 3% by 5 years. An increase in the number of painful joints is consistent wi th the degenerative nature and progression of arthritis, whereby more joint inflammation is expected to occur over time. Supporting studies found that older adults with arthritis developed new pain sites and more pain interference over a period of 1 year (Donald & Foy, 2004) and 3 year s (Jinks et al., 2007). These findings suggest that pain interference and painful body sites are a significa nt problem among older adults with arthritis. Pain Management among Old er Adults with Arthritis The primary means of managing chronic pain associated with arth ritis is through pain medications. The rate of analgesic use among the study sample was evaluated as the number of participants prescribed pain medication from the seven pain drug classes recorded. To evaluate the adequacy of prescribed pain medications in the study sample, the level of analgesic medications ordered for participan ts was evaluated relative to self -reported level of pain via the pain management index (PMI). At baseline, only about 40% of the sample was prescribed at least one pain medication. The ove rall rate of pain medication pr escription decreased to about 37% at three years, and 34% at 5 years, as did the rate of specific medi cations (e.g., opiates). For example, only 6.2% of the study sample were prescr ibed opioid analgesics at the baseline period, and this rate decreased to 5.5% at 3 years, and 4.8% at 5 years, yieldi ng a longitudinal rate of only 5.5% of participant prescribed opioid medica tions over a period of 5 years. Compared to a related cross-sectional study of chronic pain patien ts (attending a pain clin ic) in which less than 1% were prescribed opioid analgesics (Adams et al., 2001), the curren t study corroborates findings that few older adults in various commu nity settings were prescribed strong pain 117

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medication. In the case of the current study, low rates of opioid prescription and poor congruence with pain levels may be attributed to poor pa in assessment, lack of knowledge or reluctance among the health care provider about use of opi oid therapy (AGS, 2002; Green et al., 2002; Gloth, 2000; Tarzan & Hoffman, 2004). It is possible that the older adults in this study did not ha ve the level of pain that warranted opioid medication. Thus, the adequacy of pain medications pr escribed was evaluated using the pain management index (PMI). The eval uation of this criteria revealed that only about 22% of the study sample were prescr ibed adequate pain medication (PMI 0) at Time 1 (baseline). Three years later, 10% more particip ants (32%, n= 865) were prescribed adequate analgesic for pain, but this number declined by 5 % (27%, n=693) 5 years later. Thus, across the 5-year study period, an average of 27% of the study participan ts received adequate pain medication. In contrast, over 68% of participants at Time 1, 67% at Time 2, and 72% at Time 3 received inadequate pain medica tion (PMI < 0) for their reported level of pain. Thus, across the 5 years period, an average of 69% of the sample was classifi ed as having less adequate pain management. These figures exceed previously re ported rates of slightly over 50% of less adequate prescription of pain medication for pa tients with persistent pain (Hutt et al., 2006). Findings suggest that, for the majority of older adults with arthritis, pain is inadequately managed. One documented consequence of inadequate anal gesia is persistent pain that interferes with function or activit y (Cleeland et al., 2001). From the st udy results, we can see inadequate pain management was associated with more sel f-reported pain interferen ce with activities among this study sample. With only an average of about 38% of the study sample prescribed analgesics across the three study periods, among whom only about 27% were prescribed adequate 118

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analgesics, it was not surprising that over 65% of the participants continued to experience pain that interfered with performi ng their activity a little bit to extremely across the 3 study periods. Pain Outcomes among Older Adults with Arthritis Seven pain outcome indicators were examined in the study sample. Moderate decline in functional outcome occurred across the study time span in more than 50% of the study participant, except in emotional role and depression, which remained stable. These results support the literature regarding high prevalence of adverse physical and social consequences of pain interference among older ad ults with arthriti s (Arthritis Foundation, 2008; CDC, 2006). More notable decline occurred in the physical functioning and physical role performance compared to emotional and social outcome indicators. This decline could be explained as a direct consequence of arthritis as a degenerativ e disease (Arthritis Foundation; CDC). Pain, Pain Medications, Pain Outcomes, and Demographic Variables Consistent with the chronic pa in literature, results support evidence of significant pain, sex, age, and race related limitations among olde r adults with persistent pain. Pain was associated with several demographic variables in vestigated. For instance, there were significant sex differences as well as correlation with pain experience and pain me dication adequacy, with females reporting more pain interference a nd painful locations and less adequate pain medications than males at each measuremen t time point. Although correlations among these variables appeared weak (r < .2), there was a consis tent increase in their magnitude from a least of .05 to a high of .14 by 5 years. This result suggests that while overall severity of pain interference and pain outcomes limitation for individuals (especially females) significantly worsened across the study periods there was no improvement in th e level of pain management provided. Thus, females in this study had signifi cantly lower physical func tioning, physical role, 119

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and emotional vitality, at each study period, which can be attributed to poorly managed pain (Dunlap et al., 2002). Incongruen ce with above results, sex differences (with females reporting more pain) have been robust findings in the pain literature (Dunlop et al.). Race and age differences are frequently doc umented in the literature, with blacks typically reporting more pain (Rheulman, Karoly, & Newton, 2005). In contrast, race and age in this study were not associated with the pain c onstruct at any time of measurement. Similarly, Horgas, Yoon, Lee, & Marsiske (2008) found no a ssociation between race, age, and pain among older adults with chronic pain. However, race ha d significant effects on pain outcomes. Overall, functional scores showed a moderate increase in limitation for all races over time, however, Caucasian subjects showed less limitation in physical functioning, emotional role, and depression scores than other races. These finding s are consistent with reports that non-white study participants reported more pa in disability than their white c ounterparts (Horgas et al.). This study effect, however, may have been biased by a higher ratio of white to non-white participants by more than 50%. Older age was related to poorer overall functioni ng in all dimensions measured. Contrary to findings by Hybels and colleagues (2001) and Thomas and colleagues (2004), and despite common assumptions that pain in terference increases as indivi duals get older, age was not correlated with pain interference or pain locations in this study. However, number of diagnosed diseases was associated with more pain interference and pain sites. These findings suggest that increased pain interference is a function of disease rather th an the age of the individual experiencing the pain (Dunlop et al., 2002). Relationships among Pain Interference, Pain Sites, Pain Management, and Pain Outcomes This study was designed to examine whether pain was associated with functional consequences among older adults, and whether pa in management was adequate. Relationships 120

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among pain (pain interference and nu mber of pain locations), adequacy of pain medications, and pain outcomes (physical, social, and emotional functioning) at each of the three study periods were examined via correlations analyses to determine if relationships existed among these variables. For all study periods, the results indi cated that a worse pain experience (more pain interference and more pain locations) was relate d to less adequate pres cribed analgesics, and both were related to poorer functional pain outcomes (physical, social, and emotional) for individuals with arthriti s. In contrast, more adequate pain management, as indicated by a higher index of prescribed analgesi cs, was related to higher functional performance among study participants. In congruence w ith published pain reports, more arthritis-related pain was correlated with poorer physical and social functioning (Bookwala et al., 2003). Another study found that new joint pain occurring 3 years (Jinks et al., 2007) or as short as 1 year (Donald & Foy, 2004) following initial measurement produ ced more severe limitation in physical functioning. In addition, the degree of emotional or depressive sy mptom associated with chronic pain was usually the result of c oncurrent pain intensity and pain interference (Mavandadi et al., 2007). Thus, the tested hypothesi s (see below) was supported, and was congruent with the current published research findings. Adequacy of Pain Medication Partially Mediates the Relationship between Pain and Pain Outcomes The tested hypothesis was that the adequ acy of pain medication would mediate the relationship between pain and pain outcomes; adequate pain medication would mitigate the relationship between pain and poorer functional outcomes. Using structural equation modeling, PMI-mediated physical, social, and emotional pain outcomes models were tested to establish whether adequacy of prescribed pain medicati ons (PMI) impacts the a bove relationships after controlling for age, sex, race, and number of dis eases. A Chi-square test of difference showing 121

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worsening of the model fit indices was attributed to the effects of eliminating the paths mediated by adequacy of prescribed pain medication. Thus evidence that adequacy of prescribed pain medications had a significant ro le in how much pain outcome s were predicted by pain was supported. The hypothesis that adequacy of prescribed pain medications mediates the effects of pain on pain outcomes was partially suppo rted in this study. In the first instance, worse pain directly and indirectly (mediated by less adequate PMI) predicted worse physical, social, and emotional functioning, but not at all time poi nts. Second, longitudinal relationships in the model indicate that worse pain predicted increased adequacy of pain medication between time points over the five-years period. In addition, worse pain experience predicted improved pain outcomes mediated by more adequate pain medication. This pattern was seen in th e social and emotional outcome models although mediation of these relationships beyond 3 years was not sustained. These results suggest that total mediation of the relationship between pain and pain outcomes was not achieved. However, the importance of lower adequacy of pain management in mitigating negative consequences of functional pain out come was supported. The relationships trends among pain, pain medication, and pain outcomes refl ect the complex nature of chronic pain as a phenomenon encompassing multifactorial and multidimensional experiences, which include physical, psychological, emotional, social, and spiritual (AGS, 2000). T hus, a singly-indicated mediator (e.g. PMI) may be inadequate to explain the complexity within the relationships analyzed between pain and pain outco mes within and across time points. We derived a final reduced structural equati on model for the three pain outcomes: social functioning, physical functioning, and emotional f unctioning. At baseline, results showed that pain had strong direct negative effects ( = -.69 to -.89) on each pain outcomes measured 122

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(physical, social, and emotional functioning) at each occasion. In contrast, pain showed very weak indirect effects ( <.1) through adequacy of prescribed pain medication. These results indicate that worse pain pred icted lower physical, social, and emotional functioning, mediated through lower adequacy of prescrib ed medications (PMI). These patterns were repeated at Time periods 2 and 3, and concurrent increase in magnit ude of negative direct a nd indirect effects of pain on these outcomes were noted (in all/most regression paths analyzed). These increases suggest a mismatch between pain experience and adequacy of pain medication prescribed to manage individuals pain. Thus, the hypothesis that pain severely interf ering with activities among older adults with arthritis is related to less adequate pain medication and worse social, physical, and emotional functioning was supported. To the extent seen, less adequate prescrib ed analgesic medications contributed to worsening impact of pain on pain outcomes measur ed in this sample of older adults. First, evidence shows that worse pain predicted wors e physical, social, and em otional functioning at each study period. Second, these relationships were mediated by less adequate pain medication which increased progressively from weakly negative ( -.06) at baseline to a stronger negative relationship ( =-.13) 5 years later. This pattern is consistent with findings of less adequate prescribed pain medications, which pe rsisted through the study time span. On the longitudinal regression pa ths, worse pain and more adequacy of prescribed pain medications (PMI) at earlier occas ion was related to how much i ndividuals functioning changed at a later occasion. Specifically, we found that wo rse pain experience at ba seline predicted better change in social and emotiona l functioning at Time 2, and this relationship was mediated by more adequate pain medication at Time 1. These findings suggest that improvement in social and emotional functioning can be projected with better pain management. In congruence with 123

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published literature, study reports indicate th at when analgesic medications are used appropriately and in adequate amounts to treat pe rsistent pain, individuals achieve better pain control (Furlan et al., 2006; Hutt et al., 2006), reduce inactivity (Allen et al., 2003), and have better functional outcome s (Furlan et al.). The consistent pattern of results indicating th at worsening of previous pain experience predicted increased adequacy of prescribed me dications and improvement in functional pain outcomes over time provides strong evidence that individuals improved in their social, physical, and emotional functioning over time because of improved pain management. This result is congruent with the goals of chronic pain manage ment to prevent disability and/or maintain function for as long as possible (AGS, 2002) a nd calls for ongoing review and improvement of prescribed pain medication. Finally, overall variance in participants leve l of functioning was predicted by worse pain in the three structural models. Change in phys ical, social, and emotional functioning limitations were predicted by at least 45% at the baseline period and at least 60% at the 3and 5-Year periods. Increased prediction of pain effects on measured pain outcomes over the 3 study periods suggests that persistent pain ha s a continuous deleterious effect on functioning over time in this age group. However, findings also support a projection that inte rfering pain can be managed effectively with more adequate pain medication to increase functioning. An important conclusion of this study is th at the analyzed data and the underpinning Model of Symptom Management are a good fit. Although the primary purpose of this study was not to test the MSM, Meleis ( 1997) recognized that theoretical propositions derived from nursing theories are important in theory testing. In this study, a mid-level recursive conceptual model derived from the MSM was used to test the prop osition that individuals pain experience, pain 124

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management strategies, and pain outcomes were related nursing concepts. Thus, as a major aim, testing the hypothesis that pain experience was related to adequacy of prescribed pain medication and limitations in social, physical, and emo tional functioning was supported by the derived conceptual model. Sample Attrition Effects Several analyses were conducted to determ ine the effects of attrition on the study outcomes. Twenty-seven percent of the study sample was lost to attrition after one year and an additional 8% was lost after three years. Analyses comparing returning and lost participants showed an overall sample loss of 35% by Year 5 of the study. There was no difference in the pain characteristics, and in th e physical and emotional role li mitations between continuing and attritted participants after Time1. After Time 2, limitation in majority of the pain outcomes were not different between participants who continued and who were attritted from the sample. These findings suggest that the effect of attrition on study va riables was moderate, indicating a stability in sample characteristics and lending credence to overall quality of data analyzed for this study. Implication for Nursing The role of health care providers in pain management has been the focus in many pain studies. Empirical findings have co nsistently associated inadequa te pain management with poor outcomes and have implicated health care provide rs lack of understanding of effective pain treatment. Thus, findings of this study have seve ral important implications for the health care providers and clinical pain management of community dwelling older ad ults with arthritis. The high prevalence of pain that interferes with activities and th e high number of pain sites that impact functional pe rformance among older adults with arthritis over time calls for more routine and ongoing individual pain ev aluation. This will be particularly true for women, who were found to be at higher risk fo r having more severe pain interference and receiving less than adequate pain medicati on. Thus, treatment plans targeting individual needs would help ensure that adequate pain management is provided to all. 125

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In this study sample, inadequate prescripti on of pain medications negatively affected physical, social, and emotional outcomes, and more adequate pain regimens positively affected the same outcomes. This evidence highlights the need for health care professionals to conduct effectiv e ongoing strategies to evalua te adequacy of prescribed pain medications. Strategies to evaluate prescribed pain medication relative to the severity of pain may be enhanced by using standardized tools such as the PMI. In addition, individuals experiencing pain interf erence with activity s hould be involved in their own care plans to establ ish goals for better pain outcomes. These approaches would benefit vulnerable groups, e.g., women, who we re found to receive less than adequate pain medication compared to men (although they had more limiting pain). Evidence of a mediating role in the rela tionships between pain and pain outcomes through the adequacy of prescribed pain medications (even partially) is a unique contribution of this study. There are very few empirical longitudinal st udies of pain in the literature. This finding provides empirical suppo rt for the long-term benefit of effective pain management among older adults. Rather than consider pain a normal part of aging that cannot be eliminated, health care provide rs should develop and refine strategies to manage pain in older adults with arthritis. Thus, making a commitment to provide pain medication commensurate with pain expe rience would reduce th e consequence of persistent pain over time. Limitations of the Study As with any study, there are several limitati ons that must be acknowledged. These include possible threats to the studys internal or external validity. Some of these limitations are discussed below: This study was conducted using existing data an alysis. Thus, certain constraints such as lack of control over the size or characteris tics of the sample, missing data, initial measurements, and handling/manipulation of th e medication data may have affected the results. The primary study was a randomized, multi-site clinical trial to test effects of three cognitive training interventions on cognitive, health, and functional outcomes. In the present study, the effects of the cognitive tr aining intervention group was not statistically controlled in the analyses. Failu re to control the effects of training could have potentially enhanced functional status and diminished study effects. However, the results of a recent study indicated that although cognitive training of the primary sample had a significantly protective effect on functioning ove r time, there was no consistent pattern to indicate that either group (treatment or control) was advantaged or disadvantaged over the other (Wolinsky et al., 2006). Future analyses of th ese data should contro l for possible training effects as these may vary based on unique characteristics of study participants. The results of this study are generalizable only to a population of community-dwelling, cognitively-intact older adults with arthritis. Because of very small percentage of races 126

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represented, other than Caucasians and Blacks, findings could not be generalized to other racial groups. In addition, cau tion should be exercise when applying findings to the men because a disproportionately larger number of women who participat ed in this study. The selected sample was based on a baseline di agnosis of arthritis, and pain that was at least mild in intensity. This selection term eliminated about 9% (n=183) of participants who reported no pain at baseline, but thes e people may have had adequate pain management. A baseline comparison of the selected and unselected study sample showed that they differed significantly in pain outco mes; the selected sample had more diagnosed diseased and had more interfering pain a nd worse pain outcomes. This finding was consistent with the progressive, degenerative nature of arthritis and its adverse consequences on the sufferer. However, eliminating individuals with no report of bodily pain may have biased the results toward fi ndings of more pain interference among the participants. The medication audit assessed all medications ordered by a health care provider. Overthe-counter (OTC) medications (e.g., aspirin, acetaminophen, na proxen) were assessed if the participant indicated th at a health care professiona l (most commonly a physician) ordered them. It is likely, however, that some OTC pain medications were overlooked by this medication survey method. Thus, the analge sic pain regimen evaluated in this study (and the corresponding PMI index of adequ acy of pain medications) may have underestimated the extent of pain medicati ons used by study participants. This would have the potential effect of biasing findings towards inadequate pain management, since not all pain drugs were included. Future studies should carefully analyze all pain medications used by individuals, in cluding prescription and OTC drugs. Use of the PMI as a quantifying measure of pain medication adequacy is an important study limitation in that the PMI does not c onsider the actual dose of medication prescribed or individuals compliance with this regimen. No system was in place to verify completeness or accuracy of medication audits Thus, poor account of analgesic use could result in estimation of lower PMI due to a negative match between pain report and prescribed pain medication. The match between pain report and pain me dication use within the period of data collection was assumed. This introduced the possi bility of timing inc ongruence that could lead to over-estimation of the adequacy of prescribed pain medication. Future studies should more carefully assess th ese medication timing issues. Report of compliance with administration of pr escribed pain medications or participants preferences for mild/no analgesic treatmen t were not documented. This is relevant because one study found that some older adul ts may prefer nondrug pain management due to concerns about drug side effects (Lansbury, 2000). When it became necessary to use pain drugs, their preference was for m ild analgesics. Thus, poor compliance with prescribed pain regimen, or choice of in effective pain drug as the case may be, would likely inflate reported pain levels in the samp le, and subsequently result in a negative match between reported pain leve l and prescribed analgesics. 127

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Medication data was not collect ed specifically for this study. Hence, certain classes of medications such as tricyclic antidepre ssants or GABA anticonvulsive drugs were included in the pain regimen based on genera l support for their anal gesic effects in the pharmacologic literature. It is not known whether or not they were prescribed specifically to treat pain. In addition, low dose aspirin therapy (81mg and 325 mg ) was included in the pain regimen. Although these doses are usua lly prescribed for chronic anticoagulation therapy, long term analgesic effects of medica tions cannot be ruled ou t. The inclusion of these drugs may have overestimated both the analgesic pain medications prescribed, and overestimated the adequacy of pain manageme nt. Even considering these medications in analyses, pain management in older adu lts is poor; findings would be even more pronounced if a more conservative approach was used (e.g., not considering adjunctive pain medications or low dose analgesic drugs). Another potential limitation of this study is that it focused only on pharmacologic pain management, which did not take into cons ideration any complementary alternative medications (CAM) or treatments (e.g. massage, heat/cold) that may have been used for pain management. Use of CAM for relief of ch ronic joint pain associated with arthritis has been documented (Horgas & Dunn, 2000). It is likely that partic ipants use of pain therapies unaccounted for in th is study may have resulted in decreased severity of reported pain and falsely enhanced the computed match between reported pain and prescribed analgesic if other prescribed pain medications were being used. On the other hand, mismatch between reported pain and pain medication may result in a negative PMI if no prescribed pain medications are being used or prescribed. Overall, descriptive analysis of differen ce in adequacy of prescribed analgesics demonstrated statistical significance between occasions or groups although the actual mean differences were small. These findings coul d be attributed to effects associated with the large study sample. These significant find ings may be indicative of a possible Type 1 error, in which a null hypothesis of no relationshi p is rejected when it is in fact true (Polit & Hungler, 1995). Thus, study results showing statistically significant differences in adequacy of prescribed pain medications may not reflect clinical significance, as these results may be exaggerated. Analysis of longitudinal models such as in those in this st udy, result in rapidly increasing complexity of the panel models as more variables are added in the form of repeated measurement for each occasion in the study (Kline, 2005). Complex models are difficult to interpret as clarity in st ated relationships may be obscured. Thus, parsimony of the analyzed models structure were ensured through appropriate model specification and testing in both measurement and structural phases, as well as modification of each structural model to derive the be st reduced model fit (Kline). The nature of longitudinal studies with rega rd to sample loss and changes in sample characteristics (e.g., development of new illnesse s, increasing age, attrition) has been known to impact the accuracy of measurement ove r time with threats to both internal and external validity (Stratford, Mulligan, Down ie, & Voss, 1998). First, in the current study, sample loss at 5 years reached more than 50% of the baseline size of 1,409 for some of the variables analyzed. This loss posed a seriou s threat to both internal validity (higher 128

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risk of statistical error) and external validity (generalizability) of findings. However, this threat was resolved by using a robus t estimation method (maximum likelihood estimation) to correct for missing data in th e structural equation model analyses where estimation of a longitudinal model was necessary. Second, individuals in the study developed more chronic illnesses and reported more pain over the 5-year study period. It has been documented that prevalence of persiste nt pain in older adults is associated with high rates of comorbidities in this po pulation (AGS, 2002; Fe rrell & Ferrell, 1990; Harden et al, 2005; Patel, 2003), and these rate s increase with increasing age (Chan et al., 2002). Thus, increased pain experiences and poorer pain outcomes reported over 5 years could be co-influenced by new diseases and sources other than muscul oskeletal pain (e.g. neuropathic or cardiac pain). These influences could also result in overestimation of the pain experience and underestimation of adequacy of prescribed pain medication. In this study, these influences were resolved by controlling for their effect s on the pain outcomes at each study period. In this study, both multiple indicated constr ucts and singly indicated variables were utilized in the structural equation model anal ysis. Thus, some constructs proposed in the conceptual model, e.g., social functioning (i ndicated by social f unctioning scores) and pain medication (indicated by PMI scores) were analyzed as singly indicated variables. This situation limits knowledge of how much the variable truly characterizes the construct it purports to represent. Having mu ltiple indicators for each factor would permit more accurate characterization of the factor represented, based on correlations among the indicators. Suggestions for Future Research There are very few longitudinal studies of pain in general and even fewer of pain in older adults. Thus, this study provides important eviden ce of longitudinal relati onships between pain and pain outcomes and the mediating role of ade quate pain management. Future research efforts should consider findings that pain interferi ng with activity has severe ongoing negative consequences on functioning in late life and th at pain management among older adults with arthritis is inadequate. Therefore, the follo wing recommendations for future research are suggested: Identify long-term strategies to optimize c linical pain management among older adults with arthritis. Strategies should include tool s for providing effective assessment of pain interference with activity and adequate individualized pain management. A relationship between pain and pain outco mes partially mediated by adequacy of prescribed pain medications suggests that a singly indicated mediator may not have adequately represented this relationship. More research is needed to identify other 129

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contributing factors. Future research should in tensify efforts to identify a multi-factorial mediator of negative or positive pain outco mes through which effective strategies for treatment of pain, pr evention or reduction of functional disability, and maintenance of physical, social, and emotiona l functioning among older adults with arthritis could be instituted. Investigate the role of other correlates of ch ronic pain such as socioeconomic factors or social support to provide a useful perspect ive for better understanding of the relationship between pain and functional disability. Focus on unique groups of older adults with chronic pain (e.g., chronic back pain) or selected demographic groups (e.g., female subj ects) to provide more information about subgroups of older adults with pain. A re lationship between pain and pain limitation mediated by pain medication may have a str onger fit among more homogeneous groups. Replicate this study with primary data and control for the use of CAM and noncompliance with therapy to provide additio nal information about the relationships in question. This may further enhance the role of PMI in the analyzed model. A clinical trial utilizing adequate standards for pain will help determine the most effective therapy to manage pain interferen ce and maintain functioning in older adults with arthritis. Conclusion Pain is prevalent among older adults with arthritis and pres cribed pain medications are typically inadequate to manage pain effectiv ely for this population. Trends throughout the study periods indicate that pain (pain interference and number of pain locations) and functional pain limitations (physical, social, and emotional) were prevalent and persistent. At each occasion of the study, we found that worse pain-related limita tions occurred among participants and that these limitations were partially mitigated by less adequate prescribed analgesics. This finding suggests that factors other than adequacy of prescribed medicatio ns might influence the negative relationship between worse interf ering pain and lower pain outcomes. However, across time, relationships show that bette r functional outcomes were mediated by more adequate pain medication as the pain experience worsens. 130

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Findings of this study contribute to the em pirical base of evid ence in chronic pain management as well as provide empirical longitudi nal support that pain is a strong predictor of functional limitations in older adults with arth ritis over time. Prescribed pain medication was also shown to have an important role in the level of functioning achieved over time by older adults with interfering pain. This study bega n by seeking to determine the extent to which suboptimal prescription of analgesic medications affects pain outcomes in adults over time. Findings suggested the need to further explore the extent to which optimal prescription of analgesic medications will improve functional pain outcomes in older adults with activityinterfering pain. Recommendations for provision of effective clinical pain management include appropriate ongoing, individualized pain evaluations and adequate pain medication for each persons changing needs. 131

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143 BIOGRAPHICAL SKETCH Uloma Dike Onubogu was born in Nigeria, West Africa, where she be gan her career in nursing with a diploma in nursing, midwifery, and a Bachelor of Science degree in nursing in 1987. Uloma immigrated to the United States in 1991 and pursued further education, obtaining a Master of Science in Educational Leadership in 1997 from Florida A&M University (FAMU). Ulomas experience working as a registered nurse in the United States brought her into contact with many older adults and reveal ed the critical need for care in this vulnerable group. Inspired to pursue further education, she obtained a Mast er of Science in Nursing in 2002 from FAMU and was certified as an Adult/Gerontological Nurse Practitioner. With a firm commitment to contribute to education and research in the ar ea of gerontological nursi ng and practice, Uloma then accepted a faculty positi on at FAMU School of Nursing in 2003 and thereafter pursued doctoral studies in nursing. Ulom as doctoral work has focused on the issue of chronic pain and pain management among older adults. Her futu re goal is to improve pain management and decrease pain disability among older adults thro ugh research and educatio n of nurses and health care professionals.