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Medical Cost Offset Effects in Pulmonary and Cardiac Patients with Depression or Anxiety

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Title:
Medical Cost Offset Effects in Pulmonary and Cardiac Patients with Depression or Anxiety
Creator:
LEE, ANDREA M. ( Author, Primary )
Copyright Date:
2008

Subjects

Subjects / Keywords:
Anxiety ( jstor )
Comorbidity ( jstor )
Diseases ( jstor )
Health care costs ( jstor )
Health care finance ( jstor )
Health care industry ( jstor )
Hospitals ( jstor )
Insurance ( jstor )
Medical conditions ( jstor )
Mental health ( jstor )

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University of Florida
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University of Florida
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Copyright Andrea M. Lee. Permission granted to University of Florida to digitize and display 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.
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7/24/2006
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54846255 ( OCLC )

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MEDICAL COST OFFSET EFFECTS IN PULMONARY AND CARDIAC PATIENTS
WITH DEPRESSION OR ANXIETY















By

ANDREA M. LEE


A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE

UNIVERSITY OF FLORIDA


2006


































Copyright 2006

by

Andrea M. Lee

































This document is dedicated to my parents, Jack and Ellen Lee, and to my grandparents,
Harvey Lim, Lan Chan Lim, Sonny Lee, and Laura Lee.















ACKNOWLEDGMENTS

I would first like to thank my mentors, Robert G. Frank and Jeffrey S. Harman, for

their support and guidance on this masters thesis. They have been a tremendous help

throughout the process. I would also like to thank my parents, Jack and Ellen Lee, for

their unwavering support and firm belief in my abilities. Their support enables my

successes and gives me the strength to continue on this academic journey.
















TABLE OF CONTENTS

page

A C K N O W L E D G M E N T S ................................................................................................. iv

LIST OF TABLES .............. ..... ............................................... vii

A B S T R A C T .......................................... .................................................. v iii

CHAPTER

1 INTRODUCTION ............... .................................................... 1

2 D A TA A N D M ETH O D S ............................................................. ....................... 9

D ata S o u rc e ....................................................... ................ .. 9
V a ria b le s ....................................................................................................... 1 0
D ep en d ent V ariab les ............................................ ......................................... 10
Independent V ariables ............................................................. ............... 11
C control V ariables........... .............................................................. .. .... .. ... .. 12
Statistical A analyses ............................................................. ....... ...... 14

3 R E S U L T S ........................................................................................................1 5

Pulm onary C conditions ............................................. ....................................... 15
Com orbidity and Expenditures..................................................................... 15
Depression Treatment and Expenditures.........................................................16
Depression Treatment and Health Care Utilization......................................17
Anxiety Treatm ent and Expenditures ............................ ............................... 18
Anxiety Treatment and Health Care Utilization......................................19
C ardiac C on edition s ............... ......................................................... .. .....2 1
Com orbidity and Expenditures..................................................................... 21
Depression Treatment and Expenditures..........................................................22
Depression Treatment and Health Care Utilization............................................23
Anxiety Treatm ent and Expenditures............................................................... 24
Anxiety Treatment and Health Care Utilization......................................25

4 D ISCU SSION ................................................................ ...... .......... 38

L im stations ...................................................................................................... ....... 39
Im p licatio n s ................................................................4 0









L IST O F R E F E R E N C E S ...................................... .................................... ....................42

B IO G R A PH IC A L SK E T C H ...................................................................... ..................45
















LIST OF TABLES


Table p

3-1. Clinical Classification Codes and Diagnostic Categories. .......................................28

3-2. Antidepressant and Anti-anxiety Medication Names..............................................29

3-3. Descriptive Statistics of Pulmonary Respondents (Comorbidity)..............................30

3-4. Descriptive Statistics of Pulmonary Respondents (Treatment)................................31

3-5. Descriptive Statistics of Respondents with Cardiac Conditions (Comorbidity) ........32

3-6. Descriptive Statistics of Pulmonary Condition Respondents (Treatment) ...............33

3-7. Statistical Results of Pulmonary Condition Respondents (Total Expenditures) ........34

3-8. Statistical Results of Pulmonary Condition Respondents (Medical Expenditures) ...34

3-9. Statistical Results of Pulmonary Condition Respondents (Health Care Utilization) .35

3-10. Statistical Results of Cardiac Condition Respondents (Total Expenditures) ..........36

3-11. Statistical Results of Cardiac Condition Respondents (Medical Expenditures).......36

3-12. Statistical Results of Cardiac Recipients (Health Care Utilization)......................37















Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Science

MEDICAL COST OFFSET EFFECTS IN PULMONARY AND CARDIAC PATIENTS
WITH DEPRESSION OR ANXIETY

By

Andrea M. Lee

May 2006

Chair: Robert G. Frank
Major Department: Clinical and Health Psychology

An intervention that reduces or prevents usual costs to the health care system is

called a medical cost offset or the cost offset effect. This study examined a sample of

pulmonary patients and cardiac patients to determine if a cost offset effect was apparent.

Three research questions were examined in this study: (1) whether depression or anxiety

in pulmonary or heart patients increased health care expenditures, (2) whether depression

or anxiety treatment decreased health care expenditures, and (3) whether depression or

anxiety treatment decreased the number of emergency room visits, inpatient days,

outpatient visits, or office-based provider visits. Data were obtained from the Medical

Expenditure Panel Survey (MEPS), a nationally representative survey of the US non-

institutionalized, civilian population. The results of the study revealed that in pulmonary

patients, the presence of depression increased expenditures, whereas the presence of

anxiety decreased expenditures. Furthermore, depressed pulmonary patients showed a

decrease in expenditures and this effect was not explained by a decrease in the number of









outpatient hospital visits, inpatient hospital nights, office-based provider visits, or

emergency room visits. Anxious pulmonary patients who received mental health

treatment showed an increase in expenditures; however, there was a reduction in

outpatient hospital visits in this sample. The results suggest that the medical cost offset

effect is not a constant phenomenon and appears to vary across psychological and

medical conditions.














CHAPTER 1
INTRODUCTION

The health care system in the United States is in a state of fiscal crisis. Total health

care spending is on the rise each year and there is no evidence that this trend will subside.

Between 1987 and 2000, health care spending among the noninstitutionalized US

population increased by about $199 billion (about 3 percent per year) (Thorpe, Florence,

& Joski, 2004). Mental health treatment has been cited as a solution to reducing rising

costs in the health care system (Friedman et al., 1995). The cost offset effect occurs

when an intervention reduces or prevents usual costs to the health care system.

There are many reasons for the rise in health care expenditures, one of which is the

rise in the number of individuals with chronic diseases. With the aging of the population,

the rise in chronic diseases has seen a dramatic rise in recent years (World Health

Organization [WHO], 2006). The majority of this change was attributed to spending for

cardiac disease, psychological conditions, pulmonary disorders, cancer, and trauma. In a

report by the Agency for Healthcare Research and Quality (AHRQ), the most expensive

type of chronic condition in 1997 and 2002 was cardiac conditions and the greatest

increase in health care expenditures occurred for pulmonary conditions and psychological

conditions (Olin & Rhoades, 2005).

The utilization pattern of patients with chronic medical diseases is complicated

when patients have comorbid psychological conditions. Due to the ongoing nature of

chronic diseases, patients who have one or more chronic diseases tend to be high utilizers

of the health care system and thereby expensive to the system. Although cardiac









conditions, pulmonary conditions, and psychological conditions have been identified as

expensive chronic conditions in the US health care system, when cardiac conditions and

pulmonary conditions are comorbid with psychological conditions, expenditures tend to

be greater than the cost of each condition alone. Primary care patients with psychological

conditions tend to utilize the health care system more often than patients without

comorbid psychological conditions. In studies of primary care patients, medical costs of

patients with depressive symptoms or major depression were higher than patients without

depression (Katon, 2003). For example, patients with congestive heart failure who also

present with depression have medical costs 26 to 29 percent higher than those with

congestive heart failure only.

The prevalence of psychological conditions comorbid with cardiac conditions or

pulmonary conditions is high. In particular, depression and anxiety are more frequent in

these medical populations. A study determining the associations between anxiety

disorders and physical illness found that both males and females with an anxiety disorder

have higher rates of cardiac disorders and pulmonary illnesses compared to individuals

without anxiety disorders (Harter, Conway, & Merikangas, 2003). In a pilot study on

individuals with congenital heart disease, 27.3 percent met the Diagnostic and Statistical

Manual of Mental Disorders, Fourth Edition (DSM-IV) diagnostic criteria for depressive

episode and 9.1 percent met the DSM-IV criteria for generalized anxiety disorder (GAD)

(Bromberg et al., 2003). Depression comorbid with the respiratory condition, chronic

obstructive pulmonary disorder (COPD) is estimated to be up to four times more frequent

than in COPD alone (van Ede et al., 1999, as cited in Kunik et al., 2005). In a study









screening COPD patients for depression or anxiety, 80 percent screened positive for

depression, anxiety, or both (Kunik et al., 2005).

Despite the prevalence of depression and anxiety in medical populations, there is

evidence that the diagnosis and treatment of patients with depression or anxiety is

lacking. Epidemiological studies generally show that those with mental health problems

underutilize mental health care (Collins et al., 2004). Only 25 percent to 40 percent of

severely mentally ill patients access specialty mental health care and only 15.3 percent

receive adequate treatment (as cited in Collins et al., 2004). For those with anxiety and

mood disorders, the average delay for seeking professional treatment was 8 years.

Given the current trend toward inadequate detection and treatment of psychological

conditions, as well as the prevalence and high expenditures of patients with comorbid

psychological conditions and medical conditions, the question becomes whether treating

the mental health problems of medical patients would reduce expenditures in the health

care system. When an intervention reduces or prevents usual costs to the health care

system, this is called a medical cost offset or the cost offset effect (Carlson & Bultz,

2004).

Numerous studies have attempted to ascertain a medical cost-offset effect of mental

health care in primary care patients with psychological conditions. One of the first offset

studies was conducted by Follette and Cummings (1968). The medical records of 152

randomly selected adults who sought psychological services were examined. Data on

their health services utilization were collected one year prior to the beginning of

psychological treatment, as well as five years following treatment. Comparing the data to

a group matched for age, sex, socioeconomic status, and medical utilization rates who









had not received psychological treatment, it was found that this comparison group had

higher health care utilization rates over time, in addition to a reduction in health care

utilization for the group receiving psychological treatment.

Following the Follette and Cummings (1968) study, a series of cost-offset studies

were conducted. In 1984, two meta-analyses of the literature were published (Mumford

et al., 1984). One analysis was conducted on Blue Cross Blue Shield Federal Employee

Plan claims from 1974 to 1978, and the other analysis was conducted on 58 published

studies. The basic conclusion from these meta-analyses was that 85 percent of the studies

found a cost-offset effect, mainly observed in the reduction of inpatient days.

In another meta-analysis of 91 studies from 1967 to 1997, 90 percent of the studies

reported a reduction in medical utilization following mental health interventions (Chiles,

Lambert, & Hatch, 1999). Twenty-eight articles reported dollar savings and 31 percent

reported savings after taking into account the cost of mental health treatment. Overall, a

savings of about 20 to 30 percent was reported across the articles. The effect was most

evident for behavioral medicine and psychoeducational interventions.

Despite the evidence supporting the cost offset effect, several studies provide

evidence against the effect. The Medical Outcomes Study involved 22,000 outpatients

who were screened for several chronic medical conditions and these patients were

followed over time (Wells et al., 1996). One focus of the study was on the comparisons

between patients who received appropriate mental health treatment and those who had

not. The study produced no evidence of reduced inpatient or outpatient services. Instead,

cost-shifting occurred, in which the care received simply shifted from the patients'

general medical provider to a mental health provider.









The Fort Bragg Evaluation Project involved data collection of children and their

families over seven occasions to evaluate the effectiveness of comprehensive mental

health services to children and adolescents (Bickman, 1996). Compared to children

receiving care under traditional insurance, mental health expenditures were much higher

for children who received comprehensive care and this rise in cost was not offset by cost

savings elsewhere.

In two related studies looking at the medical utilization of patients with and without

psychological symptoms and the possible reduction in medical utilization in patients

referred to a psychiatry clinic, it was found that depressed and anxious patients who saw

a mental health provider had significantly more medical visits, emergency room visits,

and medical outpatient visits than patients with depression or anxiety who had not seen

mental health providers (Carbone et al., 2000). There were no significant differences in

medical costs between patients seeing mental health providers and those who had not.

However, both studies did not control for illness severity or comorbid medical or

psychiatric conditions. The patients in both studies had a relatively young median age

and one of the two studies had a small sample size. These factors may have made

medical cost-offset effects more difficult to demonstrate.

In a 2-year longitudinal study comparing adults who had major depression who had

remitted, improved but not remitted, or remained depressed, it was found that recovery

from depression was associated with increases in the probability of paid employment and

reductions in days missed from work due to illness (Simon et al., 2000). In terms of

health care costs, there were no significant differences in cost among groups in year one.

However, cost savings for patients with better outcomes in year two showed marginal









significance, suggesting the long-term nature of medical cost-offset. It should also be

noted that the study sample was derived from HMO clinics, which has implications for

cost offset effects. That is, to the extent that managed care restrictions reduce length of

treatment, dramatic cost offsets would also be reduced (Otto, 1999).

There are several limitations to cost offset studies and this study was an attempt to

address these limitations. First, when studies compare the costs of treated and untreated

patients, there may be a selection bias in which samples are not comparable (Sturm,

2001). That is, patients who received treatment may have different characteristics than

patients who did not receive treatment. If there is limited case-mix information in the

data, the selection bias is particularly pronounced. This is particularly problematic with

administrative datasets. In this study, the use of a large comprehensive dataset allowed

for greater control of potential confounding variables, such as illness severity and

comorbid medical conditions.

Second, cost offsets have traditionally been referred to as a general phenomenon

applying to all medical populations. Past cost offset research has not yet teased apart

which medical populations benefit from psychological interventions. This study is a

preliminary effort to identify specific cost offset effects in particular populations. The

populations of interest were pulmonary and cardiac patients who had comorbid

depression or anxiety. The dataset was a nationally representative sample, which allowed

for greater generalizability for the populations in question.

Rapid changes in healthcare financing and spending patterns necessitates frequent

review of offset effects refelcting current pricing in pharmacological and medical

treatments (Hunsley, 2003). It is difficult to generalize cost offset effects from one year









to another, due to pricing differences between years. This study used data from 2002,

which was the most recent data available at study onset. This study combined previous

years with 2002 data and prices of the previous years were inflated to reflect 2002 rates.

Research Questions

Research Question 1

After controlling for demographic characteristics, comorbidities, insurance status,

and perceived mental and physical health status, do pulmonary and cardiac patients with

comorbid depression or anxiety have higher health care expenditures than those with

pulmonary or cardiac conditions alone? The specific aim is to determine if, and to what

extent, depression or anxiety increases health care expenditures in pulmonary or cardiac

patients. The hypothesis is that the presence of depression or anxiety will correspond to

an increase in health care expenditures.

Research Question 2

After controlling for demographic characteristics, comorbidities, insurance status,

and perceived mental and physical health status, do pulmonary and cardiac patients with

comorbid depression or anxiety who received mental health treatment have lower health

care expenditures than those patients who did not receive mental health treatment? The

specific aim is to determine if, and to what extent, the treatment of depression or anxiety

affects health care expenditures in pulmonary or cardiac patients. The hypothesis is that

mental health treatment will correspond to a decrease in health care expenditures.

Research Question 3

After controlling for demographic characteristics, comorbidities, insurance status,

and perceived mental and physical health status, do pulmonary and cardiac patients with






8


comorbid depression or anxiety who received mental health treatment have decreased

health service utilization? The specific aim is to determine if, and to what extent,

treatment of depression or anxiety decreases health care service utilization in pulmonary

or cardiac patients. The hypothesis is that mental health treatment will correspond to a

decrease in health care service utilization.














CHAPTER 2
DATA AND METHODS

Data Source

Data were obtained from the Medical Expenditure Panel Survey (MEPS), a

nationally representative survey of the US non-institutionalized, civilian population,

sponsored by the Agency for Healthcare Research and Quality (AHRQ). The MEPS was

created in 1996 and consists of information on health services utilization, costs and

payments of health services, and health insurance information of respondents. The

MEPS Household Component (HC) obtains data from a sample of families and

individuals across the country. The MEPS HC has an overlapping panel design in which

each panel of households is interviewed five times during a two-year period. The HC

obtains detailed information on demographic characteristics, health conditions, health

status, use of medical care services, charges and payments, access to care, satisfaction

with care, health insurance coverage, income, and employment. During the second year

of the original panel, a new sample is drawn to create a new panel. Thus, two separate

panels are interviewed in the same year, which makes for an overlapping sampling

design. This thesis combined 1999, 2000, 2001, and 2002 MEPS data to assess the effect

of mental health treatment on health care expenditures and health services utilization.

As a nationally representative survey, each respondent in the MEPS data represents

a group of Americans that share similar characteristics used to sample from the

population. A sample weight for each case is developed to incorporate in the estimation

processes, in order to account for sample design, including unequal probability sampling









of the population (i.e., oversampling minority groups), as well as non-response rates and

partial responses from some survey participants. To maintain national representation, this

study used sample weights to test hypotheses.

Variables

Dependent Variables

Health care expenditures were divided into two variables: total health care

expenditures and medical expenditures. Expenditures in MEPS are defined as the sum of

direct payments for care provided during the year, including out-of-pocket payments and

payments by private insurance, Medicaid, Medicare, and other sources. Not included in

MEPS total expenditures are payments for over-the-counter drugs and for alternative care

services, as well as indirect payments not related to specific medical events, such as

Medicaid Disproportionate Share and Medicare Direct Medical Education subsidies.

Total expenditures are defined as total payments for all health care services included in

MEPS (outpatient department visits, office-based medical provider visits, prescribed

medicines, hospital inpatient visits, emergency room visits, home health, dental visits,

and other medical expenses). Medical expenditures are defined as total payments for all

health care services associated with medical conditions only. Put another way, any

medical expense associated with a psychological condition was excluded from the

calculation of the medical expenditure variable. When combining all four years of data

(1999 to 2002), both total expenditures and medical expenditures from 1999, 2000, and

2001 data were inflated to 2002 dollars using the consumer price index (BLS, 1999-

2002).









Health services utilization was defined using four separate variables: total number

of hospital outpatient visits, total hospital inpatient nights at discharge, total number of all

emergency room visits, and total number of office-based provider visits.

Independent Variables

The medical conditions of interest were identified using the MEPS HC medical

conditions file. The medical conditions file codes each self-reported medical condition

the individual experiences during the year. In order to preserve respondent

confidentiality, the condition codes provided on this file have been collapsed from fully-

specified codes to 3-digit code categories. Medical conditions were coded using the

International Classification of Diseases, Ninth Revision (ICD-9) codes and classification

codes (CC) as constructed using AHRQ's Clinical Classification Software (CCS). CCS

aggregates ICD-9 codes into clinically meaningful categories and these categories were

collapsed based on the clinical significance of categories, accurate reporting from

respondents, and the frequency of the reported condition.

From past research identifying spending and service use trends for various medical

conditions, pulmonary conditions were identified from the MEPS HC medical conditions

file using CC 127-134 and cardiac conditions were identified using CC 96, 97, 100-108

(Olin & Rhoades, 2005). For a breakdown of CC categories, see Table A-1.

Depression was identified using ICD-9 code 311. Although ICD-9 code 296

corresponds to depression, it also includes individuals with bipolar disorder. When

considering ICD-9 codes 296 and 311, 93 percent of respondents had a code of 311,

which corresponds to unspecified depression. The large number of patients with ICD-9

code 311 suggests that respondents are likely self-reporting depression (as opposed to

major depression), which then received a code of 311 instead of 296. Thus, ICD-9 code









311 was used to identify respondents with depression and ICD-9 code 296 was excluded.

Anxiety was identified using ICD-9 code 300.

Mental health treatment was defined in this study as psychotherapy or psychotropic

medications. Respondents who received psychotherapy were determined from the MEPS

HC office-based medical provider visit file and outpatient visit file. In the office-based

medical provider visit file, the best category for care that patient received was coded.

Respondents were considered to have undergone psychotherapy if the best category of

care was psychotherapy/mental health counseling. In the MEPS HC prescribed

medicines file, the presence of psychotropic medications were determined. If particular

anti-anxiety or anti-depressant drugs were coded under medication name (see Table A-2),

respondents were considered to be taking psychotropic medications for their mental

health condition.

Control Variables

Because some populations are at higher risk for poor health outcomes than others

and thus, higher health care expenditures, we adjusted for these differences to compare

health outcomes among different patient populations (lezzoni, 2003). Patient

demographic variables (age, sex, and race) and socioeconomic factors (education and

income), obtained directly from pre-existing MEPS variables, were used to control for

differences in mortality and morbidity. With regards to age, older persons generally have

worse clinical outcomes than younger persons (lezzoni, 2003). Sex is an important

control variable because men and women face different risks for certain diseases. Among

men and women 65 years of age and older, men have higher death rates than women for

cardiac disease and chronic lower respiratory disease (Anderson, 2002). Furthermore,

life spans for women tend to be longer on average than for men. Racial disparities in









health care outcomes were also taken into account in this study because differences in

disease prevalence and mortality exist among the races (lezzoni, 2003). Because of

socioeconomic disparities in health status and outcomes, we also controlled for income

and education factors (Braveman & Tarimo, 2002).

Proxy measures of illness severity were employed in the analysis to further control

for differences among patient populations. Self-perceived mental and physical health

status and number of comorbidities were used to control for illness severity. Self-

perceived mental health status and self-perceived physical health status were variables

defined in MEPS and these are considered risk factors in health care outcomes (lezzoni,

2003). Self-perceived mental and physical health status were reported by patients on a

likert scale of excellent, very good, good, fair, and poor. Comorbidites were a significant

consideration because patients with comorbidities tend to have higher risks of death,

complications, functional impairments, and higher health service use (lezzoni, 2003).

Comorbidities were determined from the MEPS HC medical conditions file in the

number of different ICD-9 codes in an individual's file were tallied.

Health insurance status was an additional variable that was created in order to

control for health service utilization. The MEPS HC full year consolidated file was used

to identify patients who were insured (i.e., insured all months of the year), intermittently

insured (i.e., at least one month of the year without health insurance), and uninsured (i.e.,

no health insurance for all months of the year). This was a control variable because it is

expected that individuals insured throughout the year would have higher expenditures

than those intermittently insured and uninsured throughout the year.









Statistical Analyses

In order to determine the relationship between comorbid depression or anxiety and

health care expenditures in pulmonary and cardiac patients, separate log-linear multiple

regressions were used for pulmonary patients and cardiac patients, with total health care

expenditures and medical expenditures as separate outcomes. Demographics,

socioeconomic factors, physical and mental health status, insurance status, and number of

comorbid conditions were control variables in each analysis. For significant results,

smearing estimation was used to determine differences between groups in dollars.

Next, the relationship between mental health treatment and health care expenditures

in pulmonary or cardiac patients with depression or anxiety were determined with

separate log-linear multiple regressions. Demographics, socioeconomic factors, physical

and mental health status, insurance status, and number of comorbid conditions were

control variables in each analysis. Smearing estimation was employed for significant

results to obtain group differences in dollars.

Finally, the relationship between mental health treatment and health care utilization

in pulmonary or cardiac patients with depression or anxiety were determined with

separate negative binomial regressions. The health care utilization variables were

number of office-based provider visits, number of outpatient hospital visits, number of

inpatient nights at discharge, and number of emergency room visits. Again,

demographics, socioeconomic factors, physical and mental health status, insurance status,

and number of comorbid conditions were control variables in each analysis.

For each of the above analyses, Stata statistical software was used (StataCorp,

2002). Sample weights were employed to take into account the MEPS sampling

procedures and to produce nationally representative estimates.














CHAPTER 3
RESULTS

Pulmonary Conditions

Comorbidity and Expenditures

Participant characteristics. The pulmonary sample used to determine the

relationship between comorbid depression or anxiety and health care expenditures

consisted of 7,866 respondents. In the sample, 649 respondents had depression and 358

respondents had anxiety (see Table 3-3 for descriptive statistics).

Results. To determine the relationship between comorbid depression or anxiety and

total health care expenditures after adjusting for demographics (age, sex, race/ethnicity),

socioeconomic status (years of education and income), insurance status, and illness

severity (perceived physical and mental health status, and comorbid conditions), a log-

linear multiple regression was conducted. A significant positive relationship between the

presence of depression in pulmonary patients and total health care expenditures was

found (t = 2.60, p = .01), but anxiety was not significantly related to total health care

expenditures (t = 1.29, p = 0.10). That is, total health care expenditures of the group with

comorbid depression was $8,338.52 more than the group without depression (see Table

3-7). Despite non-significance, the total health care expenditures for the group with

comorbid anxiety was $12,307 more than the group without anxiety.

To determine the relationship between comorbid depression or anxiety and medical

expenditures after adjusting for demographics (age, sex, race/ethnicity), socioeconomic

status (years of education and income), insurance status, and illness severity (perceived









physical and mental health status, and comorbid conditions), a log-linear multiple

regression was conducted. A significant negative relationship between the presence of

anxiety and medical expenditures only was found (t = -1.91, p = 0.03), whereas the

presence of depression yielded a non-significant relationship to medical expenditures

only (t = -.56, p = .29). That is, medical expenditures for the group with comorbid

anxiety was $3,331.77 less than the group without anxiety (see Table 3-8). Although not

statistically significant, the medical expenditures for the group with comorbid depression

was $3,123 less than the group without comorbid depression.

Depression Treatment and Expenditures

Participant characteristics. The sample used to determine the relationship

between treatment of depression and health care expenditures was 649 respondents with a

pulmonary condition and depression. In the sample, 100 respondents received mental

health treatment (see Table 3-4 for sample characteristics).

Results. To determine the relationship between depression treatment and total

health care expenditures after adjusting for demographics (age, sex, race/ethnicity),

socioeconomic status (years of education and income), insurance status, illness severity

(perceived physical and mental health status, and comorbid conditions), and anxiety

treatment, a log-linear multiple regression was conducted. The relationship between

depression treatment and total health care expenditures was non-significant (t = .54, p =

.30) (see Table 3-7). The group who received depression treatment cost $13,752,

whereas the group who had not received depression treatment cost $5,413.

To determine the relationship between depression treatment and medical

expenditures only after adjusting for demographics (age, sex, race/ethnicity),

socioeconomic status (years of education and income), insurance status, illness severity









(perceived physical and mental health status, and comorbid conditions), and anxiety

treatment, a log-linear multiple regression was conducted. A significant negative

relationship between depression treatment and medical expenditures only was found (t

3.31, p = .00). That is, with depression treatment, medical expenditures decreased by

$6,208.39 (see Table 3-8).

Depression Treatment and Health Care Utilization

Using the same sample of pulmonary condition respondents with comorbid

depression, the relationships between depression treatment and various measures of

health care utilization (number of office-based provider visits, outpatient hospital visits,

inpatient nights, and emergency room visits) were determined (see Table 3-9).

Office-based provider visits results. To determine the relationship between

depression treatment and number of office-based provider visits after adjusting for

demographics (age, sex, race/ethnicity), socioeconomic status (years of education and

income), insurance status, illness severity (perceived physical and mental health status,

and comorbid conditions), and anxiety treatment, a negative binomial regression was

conducted. The relationship between depression treatment and number of office-based

provider visits was non-significant (t = -.17, p = .43). The treatment group had 12.66

office-based provider visits, whereas the non-treatment group had 13.26 visits.

Outpatient hospital visits results. To determine the relationship between

depression treatment and number of outpatient hospital visits after adjusting for

demographics (age, sex, race/ethnicity), socioeconomic status (years of education and

income), insurance status, illness severity (perceived physical and mental health status,

and comorbid conditions), and anxiety treatment, a negative binomial regression was

conducted. The relationship between depression treatment and number of outpatient









hospital visits was non-significant (t = .55, p = .29). The treatment group had 1.67

outpatient hospital visits, whereas the non-treatment group had 2.19 visits.

Inpatient nights results. To determine the relationship between depression

treatment and number of inpatient nights after adjusting for demographics (age, sex,

race/ethnicity), socioeconomic status (years of education and income), insurance status,

illness severity (perceived physical and mental health status, and comorbid conditions),

and anxiety treatment, a negative binomial regression was conducted. The relationship

between depression treatment and number of inpatient nights was non-significant (t = -

.11, p = .45). The group that received depression treatment had an average of 1.18

inpatient nights, whereas the group who did not receive treatment had an average of 1.49

inpatient nights.

Emergency room results. To determine the relationship between depression

treatment and number of emergency room visits after adjusting for demographics (age,

sex, race/ethnicity), socioeconomic status (years of education and income), insurance

status, illness severity (perceived physical and mental health status, and comorbid

conditions), and anxiety treatment, a negative binomial regression was conducted. The

relationship between depression treatment and number of emergency room visits was

non-significant (t = -.13, p = .45). The group who had received depression treatment had

.37 emergency room visits, whereas the group who had not received depression treatment

had .47 emergency room visits.

Anxiety Treatment and Expenditures

Participant characteristics. The sample used to determine the relationship

between anxiety treatment and expenditures had 358 respondents with pulmonary









conditions and anxiety. In the sample, there were 60 respondents who received mental

health treatment (see Table 3-4 for sample characteristics).

Results. To determine the relationship between anxiety treatment and total health

care expenditures after adjusting for demographics (age, sex, race/ethnicity),

socioeconomic status (years of education and income), insurance status, illness severity

(perceived physical and mental health status, and comorbid conditions), and depression

treatment, a log-linear multiple regression was conducted. A significant positive

relationship between anxiety treatment and total health care expenditures was found (t =

1.83, p = .04). That is, the group who received treatment for anxiety had $4,442 more

total expenditures than the group who had not received treatment (see Table 3-7).

To determine the relationship between anxiety treatment and medical expenditures

only after adjusting for demographics (age, sex, race/ethnicity), socioeconomic status

(years of education and income), insurance status, illness severity (perceived physical and

mental health status, and comorbid conditions), and depression treatment, a log-linear

multiple regression was conducted. The relationship between anxiety treatment and

medical expenditures only was non-significant (t = -.92, p = .18) (see Table 3-8).

Although statistically non-significant, the group who received anxiety treatment had

$3,209 total health care expenditures less than the group who had not received anxiety

treatment.

Anxiety Treatment and Health Care Utilization

Using the same sample of pulmonary condition respondents with comorbid anxiety,

the relationships between anxiety treatment and various measures of health care

utilization (number of office-based provider visits, outpatient hospital visits, inpatient

days, and emergency room visits) were determined (see Table 3-9).









Office-based provider visits results. To determine the relationship between

anxiety treatment and number of office-based provider visits after adjusting for

demographics (age, sex, race/ethnicity), socioeconomic status (years of education and

income), insurance status, illness severity (perceived physical and mental health status,

and comorbid conditions), and depression treatment, a negative binomial regression was

conducted. The overall model in this analysis was non-significant (F = 1.23, p = .29).

Outpatient hospital visits results. To determine the relationship between anxiety

treatment and number of outpatient hospital visits after adjusting for demographics (age,

sex, race/ethnicity), socioeconomic status (years of education and income), insurance

status, illness severity (perceived physical and mental health status, and comorbid

conditions), and depression treatment, a negative binomial regression was conducted.

There was a significant negative relationship between anxiety treatment and the number

of outpatient hospital visits (t = -2.96, p = .00). The incidence rate of outpatient hospital

visits was .39 times lower with anxiety treatment.

Inpatient nights results. To determine the relationship between anxiety treatment

and number of inpatient nights after adjusting for demographics (age, sex, race/ethnicity),

socioeconomic status (years of education and income), insurance status, illness severity

(perceived physical and mental health status, and comorbid conditions), and anxiety

treatment, a negative binomial regression was conducted. The relationship between

anxiety treatment and number of inpatient nights was non-significant (t = 1.03, p = .15).

The number of inpatient nights was 1.49 for the group that received anxiety treatment,

whereas the group who had not received anxiety treatment had 1.16 visits.









Emergency room results. To determine the relationship between anxiety treatment

and number of emergency room visits after adjusting for demographics (age, sex,

race/ethnicity), socioeconomic status (years of education and income), insurance status,

illness severity (perceived physical and mental health status, and comorbid conditions),

and depression treatment, a negative binomial regression was conducted. The

relationship between anxiety treatment and the number of emergency room visits was

non-significant (t = -.21, p = .42). The group who had received anxiety treatment had .48

emergency room visits and the group who had not received anxiety treatment had .52

visits.

Cardiac Conditions

Comorbidity and Expenditures

Participant characteristics. The cardiac conditions sample used to determine the

relationship between comorbid depression or anxiety and health care expenditures

consisted of 2,403 respondents. In the sample, 293 respondents had depression (see

Table 3-5 for sample characteristics).

Results. To determine the relationship between comorbid depression or anxiety and

total health care expenditures after adjusting for demographics (age, sex, race/ethnicity),

socioeconomic status (years of education and income), insurance status, and illness

severity (perceived physical and mental health status, and comorbid conditions), a log-

linear multiple regression was conducted. The relationship between the presence of

depression and total health care expenditures was non-significant (t = 1.30, p = .10), as

was the relationship between anxiety and total health care expenditures (t = 1.30, p = .10)

(see Table 3-10). The depressed group cost $969 more than the non-depressed group,

and the anxiety group cost $5,186 more than the non-anxiety group.









To determine the relationship between comorbid depression or anxiety and

medical expenditures only after adjusting for demographics (age, sex, race/ethnicity),

socioeconomic status (years of education and income), insurance status, and illness

severity (perceived physical and mental health status, and comorbid conditions), a log-

linear multiple regression was conducted. The relationship between the presence of

depression and medical expenditures only was non-significant (t = -.87, p = .19), as was

the relationship between anxiety and total health care expenditures (t = .41, p = .34) (see

Table 3-11). The depressed group cost $8,339 more than the non-depressed group, and

the anxiety group cost $313 more than the non-anxiety group.

Depression Treatment and Expenditures

Participant characteristics. The sample used to determine the relationship

between treatment of depression and health care expenditures was 293 respondents with

cardiac conditions and depression. In the sample, 34 respondents had mental health

treatment for depression (see Table 3-6 for sample characteristics).

Results. To determine the relationship between depression treatment and total

health care expenditures after adjusting for demographics (age, sex, race/ethnicity),

socioeconomic status (years of education and income), insurance status, illness severity

(perceived physical and mental health status, and comorbid conditions), and anxiety

treatment, a log-linear multiple regression was conducted. The relationship between

depression treatment and total health care expenditures was non-significant (t = -.08, p =

.47) (see Table 3-10). The group that received depression treatment cost $7,466 less than

the group who had not received treatment.

To determine the relationship between depression treatment and medical

expenditures only after adjusting for demographics (age, sex, race/ethnicity),









socioeconomic status (years of education and income), insurance status, illness severity

(perceived physical and mental health status, and comorbid conditions), and anxiety

treatment, a log-linear multiple regression was conducted. The relationship between

depression treatment and medical expenditures only was non-significant (t = -1.06, p =

.15) (see Table 3-11). The group who had received depression treatment cost $8,900 less

than the group who had not received depression treatment.

Depression Treatment and Health Care Utilization

Using the same sample of cardiac condition respondents with comorbid depression,

the relationships between depression treatment and various measures of health care

utilization (number of office-based provider visits, outpatient hospital visits, hospital

inpatient nights, and emergency room visits) were determined (see Table 3-12).

Office-based provider visits results. To determine the relationship between

depression treatment and number of office-based provider visits after adjusting for

demographics (age, sex, race/ethnicity), socioeconomic status (years of education and

income), insurance status, illness severity (perceived physical and mental health status,

and comorbid conditions), and anxiety treatment, a negative binomial regression was

conducted. The relationship between depression treatment and number of office-based

provider visits was non-significant (t =.23, p = .41). The group that received depression

treatment had 14.81 office-based provider visits and the group that did not receive

depression treatment had 14.99 visits.

Outpatient hospital visits results. To determine the relationship between

depression treatment and number of outpatient hospital visits after adjusting for

demographics (age, sex, race/ethnicity), socioeconomic status (years of education and

income), insurance status, illness severity (perceived physical and mental health status,









and comorbid conditions), and anxiety treatment, a negative binomial regression was

conducted. The relationship between depression treatment and number of outpatient

hospital visits was non-significant (t = -1.29, p = .10). The group that received

depression treatment had 1.16 outpatient hospital visits and the group who had not

received treatment had 3.23 visits.

Inpatient nights results. To determine the relationship between depression

treatment and number of inpatient nights after adjusting for demographics (age, sex,

race/ethnicity), socioeconomic status (years of education and income), insurance status,

illness severity (perceived physical and mental health status, and comorbid conditions),

and anxiety treatment, a negative binomial regression was conducted. The relationship

between depression treatment and inpatient nights was non-significant (t = .16, p = .44).

The group who received depression treatment had 1.92 inpatient nights, whereas the

group who had not received depression treatment had 1.81 inpatient night stays.

Emergency room results. To determine the relationship between depression

treatment and number of emergency room visits after adjusting for demographics (age,

sex, race/ethnicity), socioeconomic status (years of education and income), insurance

status, illness severity (perceived physical and mental health status, and comorbid

conditions), and anxiety treatment, a negative binomial regression was conducted. The

overall model in this analysis was non-significant (F = 1.67, p = .10). The group who

received depression treatment had 1.05 emergency room visits and the group who had not

received depression treatment had .53 visits.

Anxiety Treatment and Expenditures

Participant characteristics. The sample used to determine the relationship

between anxiety treatment and expenditures included 175 respondents with cardiac









conditions and anxiety. There were 19 respondents who received mental health treatment

for anxiety (see Table 3-6 for sample characteristics).

Results. To determine the relationship between anxiety treatment and total health

care expenditures after adjusting for demographics (age, sex, race/ethnicity),

socioeconomic status (years of education and income), insurance status, illness severity

(perceived physical and mental health status, and comorbid conditions), and depression

treatment, a log-linear multiple regression was conducted. The relationship between

anxiety treatment and total health care expenditures was non-significant (t = .91, p = .19)

(see Table 3-10). The group who had received anxiety treatment cost $5,186 more than

the group who had not received anxiety treatment.

To determine the relationship between anxiety treatment and medical expenditures

only after adjusting for demographics (age, sex, race/ethnicity), socioeconomic status

(years of education and income), insurance status, illness severity (perceived physical and

mental health status, and comorbid conditions), and depression treatment, a log-linear

multiple regression was conducted. The relationship between anxiety treatment and

medical expenditures only was non-significant (t = .91, p = .19) (see Table 3-11). The

group who had received anxiety treatment cost $11,292 more than the group who had not

received treatment.

Anxiety Treatment and Health Care Utilization

Using the same sample of respondents with cardiac conditions and anxiety, the

relationships between anxiety treatment and various measures of health care utilization

(number of office-based provider visits, outpatient hospital visits, inpatient nights, and

emergency room visits) were determined (see Table 3-12).









Office-based provider visits results. To determine the relationship between

anxiety treatment and number of office-based provider visits after adjusting for

demographics (age, sex, race/ethnicity), socioeconomic status (years of education and

income), insurance status, illness severity (perceived physical and mental health status,

and comorbid conditions), and depression treatment, a negative binomial regression was

conducted. The relationship between anxiety treatment and number of office-based

provider visits was non-significant (t = -1.33, p = .10). The group who received anxiety

treatment had 9.98 office-based provider visits and the group who had not received

treatment had 13.34 visits.

Outpatient hospital visits results. To determine the relationship between anxiety

treatment and number of outpatient hospital visits after adjusting for demographics (age,

sex, race/ethnicity), socioeconomic status (years of education and income), insurance

status, illness severity (perceived physical and mental health status, and comorbid

conditions), and depression treatment, a negative binomial regression was conducted.

The relationship between anxiety treatment and number of outpatient hospital visits was

non-significant (t = 1.05, p = .15). The group who received anxiety treatment had 1.21

outpatient hospital visits and the group who had not received anxiety treatment had .89

visits.

Inpatient nights results. To determine the relationship between anxiety treatment

and number of inpatient nights after adjusting for demographics (age, sex, race/ethnicity),

socioeconomic status (years of education and income), insurance status, illness severity

(perceived physical and mental health status, and comorbid conditions), and anxiety

treatment, a negative binomial regression was conducted. The relationship between









anxiety treatment and inpatient nights was non-significant (t = -.65, p = .26). The group

that received anxiety treatment had 1.93 inpatient night stays, whereas the group who did

not receive treatment had 2.21 inpatient night stays.

Emergency room results. To determine the relationship between anxiety treatment

and number of emergency room visits after adjusting for demographics (age, sex,

race/ethnicity), socioeconomic status (years of education and income), insurance status,

illness severity (perceived physical and mental health status, and comorbid conditions),

and depression treatment, a negative binomial regression was conducted. The

relationship between anxiety treatment and number of emergency room visits was non-

significant (t = -.97, p = .17). The group who received anxiety treatment had .44

emergency room visits and the group who did not receive treatment had .74 visits.

















Table 3-1. Clinical Classification Codes and Diagnostic Categories.
Medical Condition Classification Code Clinical Classification
Software Diagnosis
Category
Pulmonary conditions 127 Chronic obstructive
pulmonary disease and
bronchiectasis
128 Asthma
129 Aspiration pneumonitis,
food/vomitus
130 Pleurisy, pneumothorax,
pulmonary collapse
131 Respiratory failure,
insufficiency, arrest (adult)
132 Lung disease due to
external agents
133 Other lower respiratory
disease
134 Other upper respiratory
disease
Cardiac conditions 96 Heart valve disorders
97 Peri-, endo-, and
myocarditis,
cardiomyopathy (except
that caused by tuberculosis)
100 Acute myocardial infarction
101 Coronary atherosclerosis
and other heart disease
102 Nonspecific chest pain
103 Pulmonary heart disease
104 Other and ill-defined heart
disease
105 Conduction disorders
106 Cardiac dysrhythmias
107 Cardiac arrest and
ventricular fibrillation
108 Congestive heart failure,
nonhypertensive









Table 3-2. Antidepressant and Anti-anxiety Medication Names.
Drug Class Generic Name Brand Name
Antidepressant Imipramine Tofanil
Desipramine Norpramin
Amitriptyline Elavil
Nortriptyline Aventyl, Pamelor
Protriptyline Vivacil
Trimipramine Surmontil
Doxepin Sinequan, Adapin
Maprotiline Ludiomil
Amoxapine Asendin
Trazodone Desyrel
Fluoxetine Prozac
Bupropion Wellbutrin
Sertraline Zoloft
Paroxetine Paxil
Venlafaxine Effexor
Nefazodone Serzone
Fluvoxamine Luvox
Phenelzine Nardil
Tranylcypromine Parnate
Anti-anxiety Diazepam Valium
Chlordiazepoxide Librium
Flurazepam Dalmane
Azepam Centrax
Clorazepate Tranxene
Temazepam Klonopin
Lorazepam Ativan
Alprazolam Xanax
Oxazepam Serax
Triazolam Halcyon
Estazolam ProSom
Quazepam Doral
Zolpidem Ambient
Buspirone BuSpar
Hydroxyzine Atarax, Vistaril
Diphenhydramine Benadryl
Propanolol Inderal
Atenolol Tenormin
Clonidine Catapres
Source: Handbook of Clinical Psychopharmacology for Therapists









Table 3-3. Descriptive Statistics of Pulmonary Respondents (Comorbidity)
No Depression Anxiety
Variables Depression Comorbidity No Anxiety Comorbidity
N 7217 649 7508 358
Mean Age (SD) 33.14 (23.36) 46.81 (17.03) 33.72 (23.28) 45.86 (18.1)
Mean Yrs 9.78(4.81) 11.82(3.61) 9.88(4.79) 11.47
Education (SD) (3.67)
Mean Income $ 16,889 19,385 17,025 18,556
% Male 45.4 25.6 44.7 24.3
% Female 54.6 74.4 55.3 75.7
% Caucasian 80.7 86.9 80.9 88.3
% African 14.1 9.0 14.0 7.3
American
% Asian 3.7 1.8 3.6 1.7
% Other 1.5 2.2 1.5 2.8
% Hispanic 22.6 19.0 22.7 14.0
% No 53.7 41.6 51.7 32.7
Comorbidity
% One 41.5 46.4 41.9 46.4
Comorbidity
% Two + 4.8 12.1 6.4 21.0
Comorbidity
% Uninsured 9.5 8.0 9.6 5.6
% Intermittent 14.1 13.7 14.1 14.2
Insured
% Insured 76.3 78.3 76.3 80.2
Modal Physical Very Good Good Very Good Good
Health
Modal Mental Excellent Good Excellent Good
Health









Table 3-4. Descriptive Statistics of Pulmonary Respondents (Treatment)
No
Variables Depression Depression No Anxiety Anxiety
Treatment Treatment Treatment Treatment
N 549 100 298 60
Mean Age (SD) 47.89 40.90 (16.37) 46.30 43.67
(16.94) (17.76) (19.74)
Mean Yrs 11.82 11.84 (3.85) 11.45 11.58
Education (SD) (3.57) (3.69) (3.60)
Mean Income $ 19,139 20,737 18,913 16,780
% Male 25.9 24.0 23.8 26.7
% Female 74.1 76.0 76.2 73.3
% Caucasian 87.6 83.0 88.6 86.7
% African 9.1 9.0 7.0 8.3
American
% Asian 1.5 4.0 1.7 1.7
% Other 1.8 4.0 2.7 3.3
% Hispanic 19.7 15.0 14.4 11.7
% No Comorbidity 40.1 50 30.9 41.7
% One 47.5 40 46.3 46.7
Comorbidity
% Two + 12.4 10 22.8 11.6
Comorbidity
% Uninsured 8.0 8.0 6.4 1.7
% Intermittent 12.9 18.0 15.1 10.0
Insured
% Insured 79.1 74.0 78.5 88.3
Modal Physical Good Very Good Good Good/
Health Very Good
Modal Mental Good Good Good Fair
Health










Table 3-5. Descriptive Statistics of Respondents with Cardiac Conditions (Comorbidity)
No Depression Anxiety
Variables Depression Comorbidity No Anxiety Comorbidity
N 2110 293 2228 175
Mean Age (SD) 60.05 58.64 60.01 (19.15) 58.12
(19.45) (16.95) (19.38)
Mean Yrs 11.15 11.50 11.19 (3.69) 11.29
Education (SD) (3.69) (3.50) (3.43)
Mean Income $ 20,500 16,602 20,377 15,542
% Male 48.3 31.4 47.4 30.9
% Female 51.7 68.6 52.6 69.1
% Caucasian 82.7 85.3 82.6 88.6
% African 14.0 10.9 14.1 8.0
American
% Asian 2.1 2.0 2.2 1.1
% Other 1.1 1.7 1.1 2.3
% Hispanic 13.4 17.1 13.7 16.0
% No 42.8 33.1 40.6 28.0
Comorbidity
% One 43.5 16.0 43.3 41.1
Comorbidity
% Two + 13.7 19.4 16.0 30.8
Comorbidity
% Uninsured 6.3 7.2 6.6 4.6
% Intermittent 11.9 5.1 12.3 12.6
Insured
% Insured 81.8 78.2 81.2 82.9
Modal Physical Good Fair Good Good
Health
Modal Mental Good Good Good Good
Health









Table 3-6. Descriptive Statistics of Pulmonary Condition Respondents (Treatment)
No Depression No Anxiety Anxiety
Variables Depression Treatment Treatment Treatment
Treatment
N 259 34 156 19
Mean Age (SD) 59.03 (16.58) 40.9 (19.74) 58.88 (18.69) 40.90 (16.37)
Mean Yrs 11.48 (3.57) 11.58 (3.60) 11.40 (3.35) 11.84 (3.85)
Education (SD)
Mean Income $ 16,610 16,780 15,280 20,737
% Male 30.9 35.3 31.4 26.3
% Female 69.1 64.7 68.6 73.7
% Caucasian 84.9 88.2 87.8 94.7
% African 11.6 5.9 9.0 0
American
% Asian 1.9 2.9 1.3 0
% Other 1.5 2.9 1.9 5.3
% Hispanic 17.0 17.6 17.3 5.3
% No Comorbidity 33.2 32.4 26.9 36.8
% One 46.7 52.9 41.0 42.1
Comorbidity
% Two + 20.1 14.7 32.1 21.0
Comorbidity
% Uninsured 6.9 8.8 5.1 0
% Intermittent 15.1 11.8 12.2 15.8
Insured
% Insured 78.0 79.4 82.7 84.2
Modal Physical Fair Fair Good Fair
Health
Modal Mental Good Fair Good Very Good
Health









Table 3-7. Statistical Results of Pulmonary Condition Respondents (Total Expenditures)


p-value


Predicted
Expenditures
($)


Depression .20 2.60 .01** 13,752
No Depression 5,413
Anxiety .10 1.29 .10 17,848
No Anxiety 5,541
Depression 13,752
Treatment .10 .54 .30
No Depression 5,413
Treatment
Anxiety 10,696
Treatment .33 1.83 .04**
No Anxiety 6,254
Treatment



Table 3-8. Statistical Results of Pulmonary Condition Respondents (Medical
Expenditures)
Predicted
P T p-value Expenditures
________~________________ ($)
Depression -.05 -.56 .29 7,089
No Depression 3,966
Anxiety -.17 -1.91 .03** 8,347
No Anxiety 5,015
Depression 2,722
Treatment -.66 -3.31 .00**
No Depression 8,931
Treatment
Anxiety 6,140
Treatment -.28 -.92 .18
No Anxiety 9,349
Treatment









Table 3-9. Statistical Results of Pulmonary Condition Respondents (Health Care
Utilization)
Office-Based Provider Visits


Incidence Rate
Ratio


p-value


Predicted Visit
Count


Depression 12.66
Treatment .98 -.17 .43
No Depression 13.26
Treatment
Anxiety 14.33
Treatment .10 1.29 .10
No Anxiety 11.87
Treatment
Outpatient Hospital Visits
Depression 1.67
Treatment 1.23 .55 .29
No Depression 2.19
Treatment
Anxiety .61
Treatment .39 -2.96 .00**
No Anxiety 1.28
Treatment
Inpatient Nights at Discharge
Depression 1.18
Treatment .96 -.11 .45
No Depression 1.49
Treatment
Anxiety 1.49
Treatment 1.48 1.03 .15
No Anxiety 1.16
Treatment
Emergency Room Visits
Depression .37
Treatment .97 -.13 .45
No Depression .47
Treatment
Anxiety .48
Treatment .95 -.21 .42
No Anxiety .52
Treatment












Condition Respondents (Total Expenditures)


p-value


Predicted
Expenditures
($)


Depression .13 1.30 .10 16,436
No Depression 15,467
Anxiety .18 1.30 .10 24,047
No Anxiety 14,921
Depression 9,475
Treatment -.03 -.08 .47
No Depression 16,941
Treatment
Anxiety 18,881
Treatment .35 .91 .19
No Anxiety 13,695
Treatment



Table 3-11. Statistical Results of Cardiac Condition Respondents (Medical Expenditures)
Predicted
P T p-value Expenditures
($)
Depression -.11 -.87 .19 13,752
No Depression 5,413
Anxiety .05 .41 .34 13,898
No Anxiety 13,585
Depression 5,181
Treatment -.40 -1.06 .15
No Depression 14,081
Treatment
Anxiety 22,077
Treatment .41 .91 .19
No Anxiety 10,785
Treatment


Table 3-10. Statistical Results of Cardiac











Table 3-12. Statistical Results of Cardiac Recipients (Health Care Utilization)
Office-Based Provider Visits


Incidence Rate
Ratio


p-value


Predicted Visit
Count


Depression 14.81
Treatment 1.04 .23 .41
No Depression 14.99
Treatment
Anxiety 9.98
Treatment .78 -1.33 .10
No Anxiety 13.34
Treatment
Outpatient Hospital Visits
Depression 1.16
Treatment .49 -1.29 .10
No Depression 3.23
Treatment
Anxiety 1.21
Treatment 1.77 1.05 .15
No Anxiety .89
Treatment
Inpatient Nights at Discharge
Depression 1.92
Treatment 1.05 .16 .44
No Depression 1.81
Treatment
Anxiety 1.93
Treatment .72 -.65 .26
No Anxiety 2.21
Treatment
Emergency Room Visits
Depression .04 (overall 1.05
Treatment 2.00 1.83 model not
No Depression significant) .53
Treatment
Anxiety .44
Treatment .66 -.97 .17
No Anxiety .74
Treatment














CHAPTER 4
DISCUSSION

The present study examined the relationship between comorbid depression or

anxiety and health care expenditures in pulmonary or heart patients. As expected, it was

found that depression increased total expenditures in pulmonary patients, but there was

no corresponding increase in medical expenditures only. Because medical expenditures

only excluded any medical event associated with a psychological diagnosis, it appears

that depressed patients may not use more medical services for their medical conditions,

but perhaps they do use more psychological services. Depressed patients may have more

diagnoses of other psychological conditions that prompt service-seeking.

Contrary to expectation, the presence of anxiety in pulmonary patients decreased

medical expenditures only, but there was no difference in total expenditures. Thus, it

appears that anxious pulmonary patients do not use more health care services overall and

in fact, they seek less health care services for their medical conditions. This could be

because their anxiety inhibits them from seeking needed care.

The main aim of the study was to examine the medical cost offset effect in

pulmonary or heart patients who sought treatment for depression or anxiety. This

analysis revealed that depressed pulmonary patients showed a cost offset effect, in that

depressed patients who received mental health treatment showed a decrease in medical

expenditures only. Further analysis revealed that this effect was not explained by a

decrease in the number of outpatient hospital visits, inpatient hospital nights, office-based

provider visits, or emergency room visits. Thus, this study suggests that the treatment of









pulmonary patients with comorbid depression would result in a cost offset effect not due

to cost shifting from medical treatment to psychological treatment.

Anxious pulmonary patients who received mental health treatment showed an

unexpected increase in total health care expenditures; however, there was a reduction in

outpatient hospital visits, supporting the idea that added psychological care would show a

reduction in health care utilization. The number of hospital inpatient nights, office-based

provider visits, and emergency room visits were not significantly different between the

treated and untreated groups. These results might suggest that anxiety patients are getting

the psychological services they need and added care costs more, but because needed care

is provided, utilization in the medical sector is reduced. Furthermore, treated patients

may also be more apt to recognize their anxiety symptoms as part of a psychological

disorder, as opposed to a medical problem.

Heart disease patients did not show any significant effects in any of the analyses.

However, it should be noted that the number of heart disease patients who received

psychological treatment was less than pulmonary patients, which limited the power of the

results from the heart disease group. Nevertheless, in this study, the variation in observed

cost-offset effects suggests that the issue of cost-offset may be complex and variable

across different psychological and medical conditions.

Limitations

Several limitations of the present study should be considered. First, the data

structure of MEPS seems to be unreliable. The present analysis included the years 1999

to 2002. A previous analysis using only the years 2000 to 2002 revealed different results.

When 1999 was added, the results changed. Previous results showed a cost offset effect

for both depression and anxiety treatment in pulmonary patients with comorbid









depression or anxiety, whereas the present results reveal a cost offset effect for only

depression treatment in pulmonary patients. The addition of data from 1999 appeared to

have changed the structure of the data set. Part of this instability could be due to cohort

effects, as well as a difference in power to detect statistical significance. Second, only a

relatively small number of patients received mental health treatment, particularly for the

heart disease groups. There were only 19 and 34 heart disease respondents who received

mental health treatment for anxiety and depression, respectively. Methodologically, this

poses a difficulty in terms of reliable estimates. Third, the validity of diagnostic coding

is somewhat questionable because data was obtained through self-report. Fourth,

aggregating multiple classification codes and psychotropic medication with

psychotherapy reduces the precision of the analysis. Fifth, treatment efficacy could not

be determined from the data. Finally, it is important to remember the cross-sectional and

correlational nature of the present analysis does not address causality.

Implications

The demonstration of cost offset effects has implications for the field of

psychology and its utility in reducing or containing rising health care costs in America.

Although psychologists would like to believe that a cost offset effect holds across

medical conditions and psychological conditions, the present data suggests that the

relationship between mental health treatment and cost offsets is not clear-cut. Using data

from the MEPS is a useful way to examine potential cost offset effects for specific

medical conditions because it provides large numbers of subjects, is nationally

representative, and allows for both cross-sectional and longitudinal analyses. Results

from further analyses on other medical conditions may help to further refine the nature of









cost offsets. Because the MEPS allows for longitudinal analyses, next steps would be to

determine cost offsets longitudinally.

An argument is that using cost offset as the only measure of the value of

psychological services is incomplete (Coyne and Thompson, 2003). Patients and families

who make treatment gains for depression or anxiety and employers who observe

increased productivity in their workers treated for depression or anxiety may feel that

these benefits are worth the additional costs of psychological services. Thus, the

effectiveness of treatment as measured by quality of life and work performance and

attendance would be important outcomes to consider in addition to cost issues. Although

treatment efficacy information is not available from the MEPS data, future research will

need to address the important issue of effective treatment and cost offsets. However, the

MEPS would allow for the analysis of employment variables relevant to the present

discussion.

In conclusion, the present study provided preliminary results on the cost offset

effects of specific medical and psychological populations. Results indicated that cost

offset issues are complex and the future direction of cost offset research will be focused

on teasing apart this complexity.















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44


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BIOGRAPHICAL SKETCH

Andrea Meredith Lee graduated with a Bachelor of Arts (first class honors) degree

in psychology in October 2004 from Simon Fraser University in Burnaby, British

Columbia, Canada. She plans to pursue a doctoral degree in clinical and health

psychology at the University of Florida. Her academic interests lie in health psychology

and health policy.




Full Text

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MEDICAL COST OFFSET EFFECTS IN PULMONARY AND CARDIAC PATIENTS WITH DEPRESSION OR ANXIETY By ANDREA M. LEE A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2006

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Copyright 2006 by Andrea M. Lee

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This document is dedicated to my parents, J ack and Ellen Lee, and to my grandparents, Harvey Lim, Lan Chan Lim, Sonny Lee, and Laura Lee.

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iv ACKNOWLEDGMENTS I would first like to thank my mentors, R obert G. Frank and Jeffrey S. Harman, for their support and guidance on this masters thes is. They have been a tremendous help throughout the process. I would also like to thank my parents, Jack and Ellen Lee, for their unwavering support and firm belief in my abilities. Their support enables my successes and gives me the strength to continue on this academic journey.

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v TABLE OF CONTENTS page ACKNOWLEDGMENTS.................................................................................................iv LIST OF TABLES............................................................................................................vii ABSTRACT.....................................................................................................................viii CHAPTER 1 INTRODUCTION........................................................................................................1 2 DATA AND METHODS.............................................................................................9 Data Source...................................................................................................................9 Variables.....................................................................................................................10 Dependent Variables...........................................................................................10 Independent Variables.........................................................................................11 Control Variables.................................................................................................12 Statistical Analyses.....................................................................................................14 3 RESULTS...................................................................................................................15 Pulmonary Conditions................................................................................................15 Comorbidity and Expenditures............................................................................15 Depression Treatment and Expenditures.............................................................16 Depression Treatment and H ealth Care Utilization.............................................17 Anxiety Treatment and Expenditures..................................................................18 Anxiety Treatment and Health Care Utilization..................................................19 Cardiac Conditions.....................................................................................................21 Comorbidity and Expenditures............................................................................21 Depression Treatment and Expenditures.............................................................22 Depression Treatment and H ealth Care Utilization.............................................23 Anxiety Treatment and Expenditures..................................................................24 Anxiety Treatment and Health Care Utilization..................................................25 4 DISCUSSION.............................................................................................................38 Limitations..................................................................................................................39 Implications................................................................................................................40

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vi LIST OF REFERENCES...................................................................................................42 BIOGRAPHICAL SKETCH.............................................................................................45

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vii LIST OF TABLES Table page 3-1. Clinical Classification C odes and Diagnostic Categories..........................................28 3-2. Antidepressant and Antianxiety Medication Names.................................................29 3-3. Descriptive Statistics of Pulm onary Respondents (Comorbidity)..............................30 3-4. Descriptive Statistics of Pu lmonary Respondents (Treatment)..................................31 3-5. Descriptive Statistics of Respondents with Cardiac Conditi ons (Comorbidity)........32 3-6. Descriptive Statistics of Pulmon ary Condition Respondents (Treatment).................33 3-7. Statistical Results of Pulmonary C ondition Respondents (Total Expenditures)........34 3-8. Statistical Results of Pulmonary C ondition Respondents (Medical Expenditures)...34 3-9. Statistical Results of Pulmonary Cond ition Respondents (Health Care Utilization).35 3-10. Statistical Results of Cardiac Condition Respondents (Total Expenditures)...........36 3-11. Statistical Results of Cardiac Cond ition Respondents (Medical Expenditures).......36 3-12. Statistical Results of Cardiac R ecipients (Health Care Utilization).........................37

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viii Abstract of Thesis Presen ted to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science MEDICAL COST OFFSET EFFECTS IN PULMONARY AND CARDIAC PATIENTS WITH DEPRESSION OR ANXIETY By Andrea M. Lee May 2006 Chair: Robert G. Frank Major Department: Clini cal and Health Psychology An intervention that reduces or prevents usual costs to the health care system is called a medical cost offset or the cost offset effect. This study examined a sample of pulmonary patients and cardiac patients to determ ine if a cost offset effect was apparent. Three research questions were examined in this study: (1) whether depression or anxiety in pulmonary or heart patients increased heal th care expenditures, (2) whether depression or anxiety treatment decreased health care expenditures, and (3) whether depression or anxiety treatment decreased the number of emergency room visits, inpatient days, outpatient visits, or office-base d provider visits. Data were obtained from the Medical Expenditure Panel Survey (MEPS), a nationa lly representative survey of the US noninstitutionalized, civilian popul ation. The results of the study revealed that in pulmonary patients, the presence of depression increas ed expenditures, whereas the presence of anxiety decreased expenditures. Furthermor e, depressed pulmonary patients showed a decrease in expenditures and this effect was not explained by a decrease in the number of

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ix outpatient hospital visits, inpatient hospita l nights, office-based provider visits, or emergency room visits. Anxious pulmonary patients who received mental health treatment showed an increase in expenditu res; however, there was a reduction in outpatient hospital visits in this sample. The results suggest that the medical cost offset effect is not a constant phenomenon and appears to vary across psychological and medical conditions.

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1 CHAPTER 1 INTRODUCTION The health care system in the United States is in a state of fiscal crisis. Total health care spending is on the rise each year and there is no evidence that this trend will subside. Between 1987 and 2000, health care spe nding among the noninstitutionalized US population increased by about $199 billion (about 3 percent pe r year) (Thorpe, Florence, & Joski, 2004). Mental health treatment ha s been cited as a solution to reducing rising costs in the health care system (Friedman et al., 1995). The cost offset effect occurs when an intervention reduces or prevents us ual costs to the health care system. There are many reasons for the rise in hea lth care expenditures, one of which is the rise in the number of individuals with chro nic diseases. With the aging of the population, the rise in chronic diseases has seen a dr amatic rise in recent years (World Health Organization [WHO], 2006). The majority of this change was attributed to spending for cardiac disease, psychological conditions, pulmona ry disorders, cancer, and trauma. In a report by the Agency for Healthcare Research and Quality (AHRQ), the most expensive type of chronic condition in 1997 and 2002 was cardiac conditions and the greatest increase in health care expenditures occurred for pulmonary conditions and psychological conditions (Olin & Rhoades, 2005). The utilization pattern of patients with chronic medical diseases is complicated when patients have comorbid psychological conditions. Due to th e ongoing nature of chronic diseases, patients who have one or mo re chronic diseases tend to be high utilizers of the health care system and thereby expensive to the system. Although cardiac

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2 conditions, pulmonary conditions, and psychologi cal conditions have been identified as expensive chronic conditions in the US hea lth care system, when cardiac conditions and pulmonary conditions are comorbid with ps ychological conditions, expenditures tend to be greater than the cost of each condition alone Primary care patients with psychological conditions tend to utilize the health care sy stem more often than patients without comorbid psychological conditions. In studies of primary care patien ts, medical costs of patients with depressive symptoms or major depression were higher than patients without depression (Katon, 2003). For example, patients with congestive heart failure who also present with depression have medical cost s 26 to 29 percent highe r than those with congestive heart failure only. The prevalence of psychological conditions comorbid with cardiac conditions or pulmonary conditions is high. In particular, depression and a nxiety are more frequent in these medical populations. A study determin ing the associations between anxiety disorders and physical illness found that both ma les and females with an anxiety disorder have higher rates of ca rdiac disorders and pulmonary illn esses compared to individuals without anxiety disorders (Har ter, Conway, & Merikangas, 2003). In a pilot study on individuals with conge nital heart disease, 27.3 percent met the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) diagnostic crit eria for depressive episode and 9.1 percent met the DSM-IV criteria for generali zed anxiety disorder (GAD) (Bromberg et al., 2003). Depression comorbid with the respiratory condition, chronic obstructive pulmonary disorder (COPD) is estima ted to be up to four times more frequent than in COPD alone (van Ede et al., 1999, as cited in Kunik et al., 2005). In a study

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3 screening COPD patients for depression or anxiety, 80 percent screened positive for depression, anxiety, or both (Kunik et al., 2005). Despite the prevalence of depression and anxiety in medical populations, there is evidence that the diagnosis and treatment of patients with depression or anxiety is lacking. Epidemiological studies generally s how that those with mental health problems underutilize mental health care (C ollins et al., 2004). Only 25 percent to 40 percent of severely mentally ill patients access specialty mental hea lth care and only 15.3 percent receive adequate treatment (as cited in Collin s et al., 2004). For those with anxiety and mood disorders, the average delay for seeki ng professional treatment was 8 years. Given the current trend toward inadequate detection and treatment of psychological conditions, as well as the prevalence and hi gh expenditures of patients with comorbid psychological conditions and medical conditio ns, the question becomes whether treating the mental health problems of medical patien ts would reduce expenditures in the health care system. When an intervention reduces or prevents usual cost s to the health care system, this is called a medical cost offset or the cost offset effect (Carlson & Bultz, 2004). Numerous studies have attempted to ascertain a medical cost-offset effect of mental health care in primary care patients with psyc hological conditions. On e of the first offset studies was conducted by Folle tte and Cummings (1968). The medical records of 152 randomly selected adults who sought psychologi cal services were examined. Data on their health services utili zation were collected one year prior to the beginning of psychological treatment, as well as five years following treatment. Comparing the data to a group matched for age, sex, socioeconomic status, and medical ut ilization rates who

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4 had not received psychological treatment, it was found that this comparison group had higher health care utilization rates over time, in addition to a reduction in health care utilization for the group receiv ing psychological treatment. Following the Follette and Cummings (1968) study, a series of cost-offset studies were conducted. In 1984, two me ta-analyses of the literature were published (Mumford et al., 1984). One analysis was conducted on Blue Cross Blue Shield Federal Employee Plan claims from 1974 to 1978, and the othe r analysis was conduc ted on 58 published studies. The basic conclusion from these meta -analyses was that 85 pe rcent of the studies found a cost-offset effect, mainly observed in the reduction of inpatient days. In another meta-analysis of 91 studies from 1967 to 1997, 90 percent of the studies reported a reduction in medical utilization follo wing mental health interventions (Chiles, Lambert, & Hatch, 1999). Twenty-eight arti cles reported dollar savings and 31 percent reported savings after taking into account the cost of mental health treatment. Overall, a savings of about 20 to 30 percent was reporte d across the articles. The effect was most evident for behavioral medicine a nd psychoeducational interventions. Despite the evidence supporting the cost o ffset effect, several studies provide evidence against the effect. The Medical Outcomes St udy involved 22,000 outpatients who were screened for several chronic me dical conditions and these patients were followed over time (Wells et al., 1996). On e focus of the study was on the comparisons between patients who received appropriate me ntal health treatment and those who had not. The study produced no evidence of reduced inpatient or outpatient services. Instead, cost-shifting occurred, in which the care received simply shifted from the patients general medical provider to a mental health provider.

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5 The Fort Bragg Evaluation Project involve d data collection of children and their families over seven occasions to evaluate the effectiveness of comprehensive mental health services to childre n and adolescents (Bickman, 1996). Compared to children receiving care under traditional insurance, ment al health expenditures were much higher for children who received comprehe nsive care and this rise in cost was not offset by cost savings elsewhere. In two related studies looking at the medical utilization of patients with and without psychological symptoms and the possible reduc tion in medical utilization in patients referred to a psychiatry clinic, it was found that depressed and anxious patients who saw a mental health provider had significantly more medical visits, emergency room visits, and medical outpatient visits than patients with depression or anxi ety who had not seen mental health providers (Carbone et al., 2000). There were no signi ficant differences in medical costs between patients seeing mental health providers and those who had not. However, both studies did not control for il lness severity or comorbid medical or psychiatric conditions. The patients in bot h studies had a relatively young median age and one of the two studies had a small sample size. These factors may have made medical cost-offset effects more difficult to demonstrate. In a 2-year longitudinal study comparing adults who had majo r depression who had remitted, improved but not remitted, or remain ed depressed, it was found that recovery from depression was associated with increases in the probability of paid employment and reductions in days missed from work due to illness (Simon et al., 2000). In terms of health care costs, there were no significant differences in cost among groups in year one. However, cost savings for patients with bett er outcomes in year two showed marginal

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6 significance, suggesting the longterm nature of medical cost-offset. It should also be noted that the study sample was derived from HMO clinics, which has implications for cost offset effects. That is, to the extent that managed care restrict ions reduce length of treatment, dramatic cost offsets would also be reduced (Otto, 1999). There are several limitations to cost offset studies and this study was an attempt to address these limitations. First, when studies compare the costs of treated and untreated patients, there may be a selection bias in which samples are not comparable (Sturm, 2001). That is, patients who received treatmen t may have different characteristics than patients who did not receive treatment. If there is limited case-mix information in the data, the selection bias is particularly pronounc ed. This is particularly problematic with administrative datasets. In this study, the use of a large comprehensive dataset allowed for greater control of potential confounding va riables, such as illness severity and comorbid medical conditions. Second, cost offsets have traditionally been referred to as a general phenomenon applying to all medical populations Past cost offset research has not yet teased apart which medical populations benefit from psyc hological interventions This study is a preliminary effort to identify specific cost o ffset effects in partic ular populations. The populations of interest were pulmonary and cardiac patients who had comorbid depression or anxiety. The dataset was a na tionally representative sample, which allowed for greater generalizability for the populations in question. Rapid changes in healthcare financing a nd spending patterns necessitates frequent review of offset effects refelcting curr ent pricing in pharmaco logical and medical treatments (Hunsley, 2003). It is difficult to generalize cost offset effects from one year

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7 to another, due to pricing differences betw een years. This study used data from 2002, which was the most recent data available at study onset. This study combined previous years with 2002 data and prices of the previous years were inflated to reflect 2002 rates. Research Questions Research Question 1 After controlling for demographic characteristics, comorbidities, insurance status, and perceived mental and physic al health status, do pulmonary and cardiac patients with comorbid depression or anxiety have higher health care expenditures than those with pulmonary or cardiac conditions alone? The spec ific aim is to determine if, and to what extent, depression or anxiety in creases health care expenditures in pulmonary or cardiac patients. The hypothesis is that the presence of depression or a nxiety will correspond to an increase in health care expenditures. Research Question 2 After controlling for demographic characteristics, comorbidities, insurance status, and perceived mental and physic al health status, do pulmonary and cardiac patients with comorbid depression or anxiety who received me ntal health treatment have lower health care expenditures than those patients who did not receive mental health treatment? The specific aim is to determine if, and to what extent, the treatment of depression or anxiety affects health care expenditures in pulmonary or cardiac patients. Th e hypothesis is that mental health treatment will correspond to a decrease in health care expenditures. Research Question 3 After controlling for demographic characteristics, comorbidities, insurance status, and perceived mental and physic al health status, do pulmonary and cardiac patients with

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8 comorbid depression or anxiety who received mental health treatment have decreased health service utilization? The specific aim is to determine if, and to what extent, treatment of depression or anxi ety decreases health care serv ice utilization in pulmonary or cardiac patients. The hypothesis is that mental health treatment will correspond to a decrease in health care service utilization.

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9 CHAPTER 2 DATA AND METHODS Data Source Data were obtained from the Medical Expenditure Panel Survey (MEPS), a nationally representative survey of th e US non-institutionalized, civilian population, sponsored by the Agency for Healthcare Re search and Quality (AHRQ). The MEPS was created in 1996 and consists of information on health services u tilization, costs and payments of health services, and health insurance information of respondents. The MEPS Household Component (H C) obtains data from a sample of families and individuals across the countr y. The MEPS HC has an overl apping panel design in which each panel of households is interviewed five times during a two-year period. The HC obtains detailed information on demographic characteristics, health conditions, health status, use of medical care services, char ges and payments, access to care, satisfaction with care, health insurance coverage, inco me, and employment. During the second year of the original panel, a new sample is draw n to create a new panel. Thus, two separate panels are interviewed in the same year which makes for an overlapping sampling design. This thesis combined 1999, 2000, 2001, and 2002 MEPS data to assess the effect of mental health treatment on health care expenditures and hea lth services utilization. As a nationally representative survey, each respondent in the MEPS data represents a group of Americans that share similar ch aracteristics used to sample from the population. A sample weight for each case is developed to incorporate in the estimation processes, in order to account for sample design, including unequal probability sampling

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10 of the population (i.e., oversampling minority groups), as well as non-response rates and partial responses from some survey participan ts. To maintain national representation, this study used sample weights to test hypotheses. Variables Dependent Variables Health care expenditures were divided into two variables: total health care expenditures and medical expenditures. Expendi tures in MEPS are defined as the sum of direct payments for care pr ovided during the year, includi ng out-of-pocket payments and payments by private insurance, Medicaid, Medi care, and other sources. Not included in MEPS total expenditures are payments for overthe-counter drugs and for alternative care services, as well as indirect payments not re lated to specific medical events, such as Medicaid Disproportionate Share and Medicare Direct Medical Education subsidies. Total expenditures are defined as total payments for all health care services included in MEPS (outpatient department vi sits, office-based medical pr ovider visits, prescribed medicines, hospital inpatient visits, emergenc y room visits, home health, dental visits, and other medical expenses). Medical expend itures are defined as total payments for all health care services associat ed with medical conditions only. Put another way, any medical expense associated with a psyc hological condition was excluded from the calculation of the medical expend iture variable. When combin ing all four years of data (1999 to 2002), both total expenditures and medical expenditures from 1999, 2000, and 2001 data were inflated to 2002 dollars us ing the consumer price index (BLS, 19992002).

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11 Health services utilization wa s defined using four separate variables: total number of hospital outpatient visits, total hospital inpa tient nights at discharg e, total number of all emergency room visits, and total numbe r of office-based provider visits. Independent Variables The medical conditions of interest were identified using th e MEPS HC medical conditions file. The medical conditions f ile codes each self-reported medical condition the individual experiences during the y ear. In order to preserve respondent confidentiality, the condition c odes provided on this file have been collapsed from fullyspecified codes to 3-digit code categories. Medical conditions were coded using the International Classification of Diseases, Ni nth Revision (ICD-9) codes and classification codes (CC) as constructed us ing AHRQs Clinical Classifica tion Software (CCS). CCS aggregates ICD-9 codes into clinically mean ingful categories and these categories were collapsed based on the clinical significan ce of categories, acc urate reporting from respondents, and the frequency of the reported condition. From past research identifying spending a nd service use trends for various medical conditions, pulmonary conditions were identif ied from the MEPS HC medical conditions file using CC 127-134 and cardiac conditions were identified using CC 96, 97, 100-108 (Olin & Rhoades, 2005). For a breakdow n of CC categories, see Table A-1. Depression was identified using IC D-9 code 311. Although ICD-9 code 296 corresponds to depression, it al so includes individuals with bipolar disorder. When considering ICD-9 codes 296 and 311, 93 per cent of respondents had a code of 311, which corresponds to unspecified depression. The large number of patients with ICD-9 code 311 suggests that responde nts are likely self-reporti ng depression (as opposed to major depression), which then received a code of 311 instead of 296. Thus, ICD-9 code

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12 311 was used to identify respondents with depression and ICD-9 code 296 was excluded. Anxiety was identified using ICD-9 code 300. Mental health treatment was defined in th is study as psychothe rapy or psychotropic medications. Respondents who received psychotherapy were determined from the MEPS HC office-based medical provider visit file and outpatient visit file. In the office-based medical provider visit file, the best category for care that patien t received was coded. Respondents were considered to have under gone psychotherapy if the best category of care was psychotherapy/mental health counseling. In the MEPS HC prescribed medicines file, the presence of psychotropic me dications were determined. If particular anti-anxiety or anti-depressant drugs were coded under medica tion name (see Table A-2), respondents were considered to be taking psychotropic medications for their mental health condition. Control Variables Because some populations are at higher risk for poor health outcomes than others and thus, higher health care expenditures, we adjusted for these differences to compare health outcomes among different pati ent populations (Iezzoni, 2003). Patient demographic variables (age, sex, and race) and socioeconomic factors (education and income), obtained directly from pre-existing MEPS variables, were used to control for differences in mortality and morbidity. With regards to age, older persons generally have worse clinical outcomes than younger persons (Iezzoni, 2003). Sex is an important control variable because men and women face different risks for certain diseases. Among men and women 65 years of age and older, me n have higher death ra tes than women for cardiac disease and chronic lo wer respiratory disease (Ande rson, 2002). Furthermore, life spans for women tend to be longer on aver age than for men. Racial disparities in

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13 health care outcomes were also taken into ac count in this study because differences in disease prevalence and mortality exist am ong the races (Iezzoni, 2003). Because of socioeconomic disparities in he alth status and outcomes, we also controlled for income and education factors (Braveman & Tarimo, 2002). Proxy measures of illness severity were em ployed in the analysis to further control for differences among patient populations. Se lf-perceived mental and physical health status and number of comorbid ities were used to control for illness severity. Selfperceived mental health status and self-perceived physical he alth status were variables defined in MEPS and these are considered risk factors in health care outcomes (Iezzoni, 2003). Self-perceived mental and physical health status were reported by patients on a likert scale of excellent, very good, good, fair, and poor. Comorbidites were a significant consideration because patients with comorbid ities tend to have higher risks of death, complications, functional impairments, and hi gher health service use (Iezzoni, 2003). Comorbidities were determined from the MEPS HC medical conditions file in the number of different ICD-9 codes in an individuals file were tallied. Health insurance status was an additional variable that was created in order to control for health service util ization. The MEPS HC full year consolidated file was used to identify patients who were insured (i.e., insu red all months of the year), intermittently insured (i.e., at least one mont h of the year without health insurance), and uninsured (i.e., no health insurance for all months of the year). This was a control variable because it is expected that individuals insured throughout the year would have higher expenditures than those intermittently insured and uninsured throughout the year.

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14 Statistical Analyses In order to determine the relationship be tween comorbid depression or anxiety and health care expenditures in pulmonary and cardi ac patients, separate log-linear multiple regressions were used for pulmonary patients and cardiac patients, with total health care expenditures and medical expenditures as separate outcomes. Demographics, socioeconomic factors, physical and mental he alth status, insurance status, and number of comorbid conditions were control variables in each analysis. For significant results, smearing estimation was used to determine differences between groups in dollars. Next, the relationship between mental health treatment and health care expenditures in pulmonary or cardiac patients with depres sion or anxiety were determined with separate log-linear multiple regressions. De mographics, socioeconomic factors, physical and mental health status, insurance status, and number of comorbid conditions were control variables in each analysis. Smear ing estimation was employed for significant results to obtain group differences in dollars. Finally, the relationship between mental hea lth treatment and health care utilization in pulmonary or cardiac patients with depres sion or anxiety were determined with separate negative binomial regressions. Th e health care utilization variables were number of office-based provide r visits, number of outpatient hospital visits, number of inpatient nights at discharge, and numb er of emergency room visits. Again, demographics, socioeconomic factors, physical and mental health stat us, insurance status, and number of comorbid conditions were c ontrol variables in each analysis. For each of the above analyses, Stata stat istical software was used (StataCorp, 2002). Sample weights were employed to take into account the MEPS sampling procedures and to produce nationa lly representative estimates.

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15 CHAPTER 3 RESULTS Pulmonary Conditions Comorbidity and Expenditures Participant characteristics The pulmonary sample used to determine the relationship between comorbid depression or anxiety and health care expenditures consisted of 7,866 respondents. In the sa mple, 649 respondents had depression and 358 respondents had anxiety (see Table 33 for descriptive statistics). Results To determine the relationship between comorbid depression or anxiety and total health care expenditures after adjusting fo r demographics (age, sex, race/ethnicity), socioeconomic status (years of education a nd income), insurance status, and illness severity (perceived physical a nd mental health status, and comorbid conditions), a loglinear multiple regression was conducted. A significant positive relationship between the presence of depression in pulmonary patie nts and total health care expenditures was found (t = 2.60, p = .01), but anxiety was not si gnificantly related to total health care expenditures (t = 1.29, p = 0.10). That is, total health care expenditures of the group with comorbid depression was $8,338.52 more than the group without de pression (see Table 3-7). Despite non-significance, the total health care expenditures for the group with comorbid anxiety was $12,307 more than the group without anxiety. To determine the relationship between como rbid depression or anxiety and medical expenditures after adjusting for demographics (age, sex, race/ethnicity), socioeconomic status (years of education a nd income), insurance status, a nd illness severity (perceived

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16 physical and mental health status, and co morbid conditions), a log-linear multiple regression was conducted. A significant nega tive relationship betw een the presence of anxiety and medical expenditures only was found (t = -1.91, p = 0.03), whereas the presence of depression yiel ded a non-significant relations hip to medical expenditures only (t = -.56, p = .29). That is, medical expenditures for the group with comorbid anxiety was $3,331.77 less than th e group without anxiety (see Table 3-8). Although not statistically significant, the medical expenditu res for the group with comorbid depression was $3,123 less than the group without comorbid depression. Depression Treatment and Expenditures Participant characteristics. The sample used to determine the relationship between treatment of depression and health care expenditures was 649 respondents with a pulmonary condition and depression. In th e sample, 100 respondents received mental health treatment (see Table 3-4 for sample characteristics). Results. To determine the relationship between depression treatment and total health care expenditures after adjusting for demographics (age, sex, race/ethnicity), socioeconomic status (years of education and income), insurance status, illness severity (perceived physical and mental health st atus, and comorbid conditions), and anxiety treatment, a log-linear multiple regression was conducted. The relationship between depression treatment and total health care expenditures was non-signi ficant (t = .54, p = .30) (see Table 3-7). The group who r eceived depression treatment cost $13,752, whereas the group who had not received depression treatment cost $5,413. To determine the relationship between depression treatment and medical expenditures only after adjusting for dem ographics (age, sex, race/ethnicity), socioeconomic status (years of education and income), insurance status, illness severity

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17 (perceived physical and mental health st atus, and comorbid conditions), and anxiety treatment, a log-linear multiple regression was conducted. A significant negative relationship between depression treatment a nd medical expenditures only was found (t = 3.31, p = .00). That is, with depression tr eatment, medical expenditures decreased by $6,208.39 (see Table 3-8). Depression Treatment and Health Care Utilization Using the same sample of pulmonary condition respondents with comorbid depression, the relationships between depres sion treatment and various measures of health care utilization (number of office-base d provider visits, outpa tient hospital visits, inpatient nights, and emergency room vi sits) were determined (see Table 3-9). Office-based provider visits results. To determine the relationship between depression treatment and number of office-b ased provider visits after adjusting for demographics (age, sex, race/ethnicity), so cioeconomic status (years of education and income), insurance status, illness severity (p erceived physical and mental health status, and comorbid conditions), and anxiety treatm ent, a negative binomial regression was conducted. The relationship between depression treatment and number of office-based provider visits was non-signi ficant (t = -.17, p = .43). The treatment group had 12.66 office-based provider visits whereas the non-treatment group had 13.26 visits. Outpatient hospital visits results. To determine the relationship between depression treatment and numb er of outpatient hospital vi sits after adjusting for demographics (age, sex, race/ethnicity), so cioeconomic status (years of education and income), insurance status, illness severity (p erceived physical and mental health status, and comorbid conditions), and anxiety treatm ent, a negative binomial regression was conducted. The relationship between depressi on treatment and number of outpatient

PAGE 27

18 hospital visits was non-significant (t = .55, p = .29). The treatment group had 1.67 outpatient hospital visits, whereas th e non-treatment group had 2.19 visits. Inpatient nights results. To determine the relationship between depression treatment and number of inpatient nights af ter adjusting for demographics (age, sex, race/ethnicity), socioeconomic status (years of education and income), insurance status, illness severity (perceived physical and mental health status, and comorbid conditions), and anxiety treatment, a negative binomial regression was conducted. The relationship between depression treatment a nd number of inpatient night s was non-significant (t = .11, p = .45). The group that received depr ession treatment ha d an average of 1.18 inpatient nights, whereas the group who did not receive treatment ha d an average of 1.49 inpatient nights. Emergency room results. To determine the relationship between depression treatment and number of emergency room vis its after adjusting for demographics (age, sex, race/ethnicity), socioeconomic status (y ears of education and income), insurance status, illness severity (per ceived physical and mental health status, and comorbid conditions), and anxiety treatment, a nega tive binomial regression was conducted. The relationship between depression treatment a nd number of emergency room visits was non-significant (t = -.13, p = .45). The group who had received depression treatment had .37 emergency room visits, whereas the group who had not received depression treatment had .47 emergency room visits. Anxiety Treatment and Expenditures Participant characteristics. The sample used to determine the relationship between anxiety treatment and expenditu res had 358 respondents with pulmonary

PAGE 28

19 conditions and anxiety. In the sample, ther e were 60 respondents who received mental health treatment (see Table 3-4 for sample characteristics). Results. To determine the relationship between anxiety treatment and total health care expenditures after adjusting for dem ographics (age, sex, race/ethnicity), socioeconomic status (years of education and income), insurance status, illness severity (perceived physical and mental health stat us, and comorbid conditions), and depression treatment, a log-linear multiple regression was conducted. A significant positive relationship between anxiety treatment and to tal health care expend itures was found (t = 1.83, p = .04). That is, the group who receiv ed treatment for anxiety had $4,442 more total expenditures than the group who had not received treatment (see Table 3-7). To determine the relationship between a nxiety treatment and medical expenditures only after adjusting for demographics (age, sex, race/ethnicity), so cioeconomic status (years of education and income), insurance st atus, illness severity (perceived physical and mental health status, and comorbid conditions ), and depression treatment, a log-linear multiple regression was conducted. The rela tionship between anxiety treatment and medical expenditures only was non-significan t (t = -.92, p = .18) (see Table 3-8). Although statistically non-signi ficant, the group who received anxiety treatment had $3,209 total health care expenditu res less than the group who had not received anxiety treatment. Anxiety Treatment and Health Care Utilization Using the same sample of pulmonary condition respondents with comorbid anxiety, the relationships between anxiety treatmen t and various measures of health care utilization (number of office-based provider visits, outpatient hospi tal visits, inpatient days, and emergency room visits) were determined (see Table 3-9).

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20 Office-based provider visits results. To determine the relationship between anxiety treatment and number of office-based provider visits after adjusting for demographics (age, sex, race/ethnicity), so cioeconomic status (years of education and income), insurance status, illness severity (p erceived physical and mental health status, and comorbid conditions), and depression trea tment, a negative binomial regression was conducted. The overall model in this anal ysis was non-significan t (F = 1.23, p = .29). Outpatient hospital visits results. To determine the relationship between anxiety treatment and number of outpatient hospital vi sits after adjusting fo r demographics (age, sex, race/ethnicity), socioeconomic status (y ears of education and income), insurance status, illness severity (per ceived physical and mental health status, and comorbid conditions), and depression treatment, a ne gative binomial regr ession was conducted. There was a significant negative relationship between anxiety treatment and the number of outpatient hospital visits (t = -2.96, p = .00). The incidenc e rate of outpatient hospital visits was .39 times lower with anxiety treatment. Inpatient nights results. To determine the relationship between anxiety treatment and number of inpatient nights af ter adjusting for demographics (age, sex, race/ethnicity), socioeconomic status (years of education and income), insurance status, illness severity (perceived physical and mental health st atus, and comorbid conditions), and anxiety treatment, a negative binomial regressi on was conducted. The relationship between anxiety treatment and number of inpatient nights was non-si gnificant (t = 1.03, p = .15). The number of inpatient nights was 1.49 for th e group that received anxiety treatment, whereas the group who had not received anxiety treatment had 1.16 visits.

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21 Emergency room results. To determine the relationship between anxiety treatment and number of emergency room visits afte r adjusting for demographics (age, sex, race/ethnicity), socioeconomic status (years of education and income), insurance status, illness severity (perceived physical and mental health status, and comorbid conditions), and depression treatment, a negative binomial regression was conducted. The relationship between anxiety treatment and th e number of emergenc y room visits was non-significant (t = -.21, p = .42). The group who had received anxiety treatment had .48 emergency room visits and the group who had not received anxiety treatment had .52 visits. Cardiac Conditions Comorbidity and Expenditures Participant characteristics The cardiac conditions sample used to determine the relationship between comorbid depression or anxiety and health care expenditures consisted of 2,403 respondents. In the sample, 293 respondents had depression (see Table 3-5 for sample characteristics). Results. To determine the relationship between comorbid depression or anxiety and total health care expenditures after adjusting fo r demographics (age, sex, race/ethnicity), socioeconomic status (years of education a nd income), insurance status, and illness severity (perceived physical a nd mental health status, and comorbid conditions), a loglinear multiple regression was conducted. The relationship between the presence of depression and total health care expenditure s was non-significant (t = 1.30, p = .10), as was the relationship between anxiety and tota l health care expenditu res (t = 1.30, p = .10) (see Table 3-10). The depressed group cost $969 more than the non-depressed group, and the anxiety group cost $5,186 mo re than the non-anxiety group.

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22 To determine the relationship between comorbid depression or anxiety and medical expenditures only afte r adjusting for demographics (age, sex, race/ethnicity), socioeconomic status (years of education a nd income), insurance status, and illness severity (perceived physical a nd mental health status, and comorbid conditions), a loglinear multiple regression was conducted. The relationship between the presence of depression and medical expenditures only wa s non-significant (t = -.87, p = .19), as was the relationship between anxiet y and total health care expendi tures (t = .41, p = .34) (see Table 3-11). The depressed group cost $8,339 more than the non-depressed group, and the anxiety group cost $313 more than the non-anxiety group. Depression Treatment and Expenditures Participant characteristics The sample used to determine the relationship between treatment of depression and health care expenditures wa s 293 respondents with cardiac conditions and depressi on. In the sample, 34 respondents had mental health treatment for depression (see Table 3-6 for sample characteristics). Results To determine the relationship between depression treatment and total health care expenditures after adjusting for demographics (age, sex, race/ethnicity), socioeconomic status (years of education and income), insurance status, illness severity (perceived physical and mental health st atus, and comorbid conditions), and anxiety treatment, a log-linear multiple regression was conducted. The relationship between depression treatment and total health care ex penditures was non-signifi cant (t = -.08, p = .47) (see Table 3-10). The group that receiv ed depression treatment cost $7,466 less than the group who had not received treatment. To determine the relationship between depression treatment and medical expenditures only after adjusting for dem ographics (age, sex, race/ethnicity),

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23 socioeconomic status (years of education and income), insurance status, illness severity (perceived physical and mental health st atus, and comorbid conditions), and anxiety treatment, a log-linear multiple regression was conducted. The relationship between depression treatment and medical expenditu res only was non-significant (t = -1.06, p = .15) (see Table 3-11). The group who had rece ived depression treatment cost $8,900 less than the group who had not received depression treatment. Depression Treatment and Health Care Utilization Using the same sample of cardiac conditi on respondents with comorbid depression, the relationships between depr ession treatment and various measures of health care utilization (number of office-based provider visits, outpatient hospital visits, hospital inpatient nights, and emergency room vi sits) were determined (see Table 3-12). Office-based provider visits results To determine the relationship between depression treatment and number of office-b ased provider visits after adjusting for demographics (age, sex, race/ethnicity), so cioeconomic status (years of education and income), insurance status, illness severity (p erceived physical and mental health status, and comorbid conditions), and anxiety treatm ent, a negative binomial regression was conducted. The relationship between depression treatment and number of office-based provider visits was non-significan t (t =.23, p = .41). The gro up that received depression treatment had 14.81 office-based provider visi ts and the group that did not receive depression treatment had 14.99 visits. Outpatient hospital visits results To determine the relationship between depression treatment and numb er of outpatient hospital vi sits after adjusting for demographics (age, sex, race/ethnicity), so cioeconomic status (years of education and income), insurance status, illness severity (p erceived physical and mental health status,

PAGE 33

24 and comorbid conditions), and anxiety treatm ent, a negative binomial regression was conducted. The relationship between depressi on treatment and number of outpatient hospital visits was non-significant (t = -1.29, p = .10). The group that received depression treatment had 1.16 outpatient hos pital visits and the group who had not received treatment had 3.23 visits. Inpatient nights results. To determine the relationship between depression treatment and number of inpatient nights af ter adjusting for demographics (age, sex, race/ethnicity), socioeconomic status (years of education and income), insurance status, illness severity (perceived physical and mental health status, and comorbid conditions), and anxiety treatment, a negative binomial regression was conducted. The relationship between depression treatment and inpatient nights was non-significan t (t = .16, p = .44). The group who received depression treatment had 1.92 inpatient nights, whereas the group who had not received depression trea tment had 1.81 inpatient night stays. Emergency room results. To determine the relationship between depression treatment and number of emergency room vis its after adjusting for demographics (age, sex, race/ethnicity), socioeconomic status (y ears of education and income), insurance status, illness severity (per ceived physical and mental health status, and comorbid conditions), and anxiety treatment, a nega tive binomial regression was conducted. The overall model in this analysis was nonsignificant (F = 1.67, p = .10). The group who received depression treatment had 1.05 emerge ncy room visits and the group who had not received depression trea tment had .53 visits. Anxiety Treatment and Expenditures Participant characteristics The sample used to determine the relationship between anxiety treatment and expenditu res included 175 respondents with cardiac

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25 conditions and anxiety. There were 19 respondents who received mental health treatment for anxiety (see Table 3-6 for sample characteristics). Results To determine the relationship between anxiety treatment and total health care expenditures after adjusting for dem ographics (age, sex, race/ethnicity), socioeconomic status (years of education and income), insurance status, illness severity (perceived physical and mental health stat us, and comorbid conditions), and depression treatment, a log-linear multiple regression was conducted. The relationship between anxiety treatment and total health care e xpenditures was non-significant (t = .91, p = .19) (see Table 3-10). The group who had received anxiety treatment cost $5,186 more than the group who had not received anxiety treatment. To determine the relationship between a nxiety treatment and medical expenditures only after adjusting for demographics (age, sex, race/ethnicity), so cioeconomic status (years of education and income), insurance st atus, illness severity (perceived physical and mental health status, and comorbid conditions ), and depression treatment, a log-linear multiple regression was conducted. The rela tionship between anxiety treatment and medical expenditures only was non-significan t (t = .91, p = .19) (see Table 3-11). The group who had received anxiety treatment co st $11,292 more than the group who had not received treatment. Anxiety Treatment and Health Care Utilization Using the same sample of respondents w ith cardiac conditions and anxiety, the relationships between an xiety treatment and various measur es of health care utilization (number of office-based provide r visits, outpatient hospital vi sits, inpatient nights, and emergency room visits) were determined (see Table 3-12).

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26 Office-based provider visits results To determine the relationship between anxiety treatment and number of office-based provider visits after adjusting for demographics (age, sex, race/ethnicity), so cioeconomic status (y ears of education and income), insurance status, illness severity (p erceived physical and mental health status, and comorbid conditions), and depression trea tment, a negative binomial regression was conducted. The relationship between anxiety treatment and number of office-based provider visits was non-significant (t = 1.33, p = .10). The group who received anxiety treatment had 9.98 office-based provider vi sits and the group w ho had not received treatment had 13.34 visits. Outpatient hospital visits results To determine the relationship between anxiety treatment and number of outpatient hospital vi sits after adjusting fo r demographics (age, sex, race/ethnicity), socioeconomic status (y ears of education and income), insurance status, illness severity (per ceived physical and mental health status, and comorbid conditions), and depression treatment, a ne gative binomial regr ession was conducted. The relationship between anxiety treatment and number of outpatient hospital visits was non-significant (t = 1.05, p = .15). The group who received anxiety treatment had 1.21 outpatient hospital visits and the group who had not receiv ed anxiety treatment had .89 visits. Inpatient nights results. To determine the relationship between anxiety treatment and number of inpatient nights af ter adjusting for demographics (age, sex, race/ethnicity), socioeconomic status (years of education and income), insurance status, illness severity (perceived physical and mental health st atus, and comorbid conditions), and anxiety treatment, a negative binomial regressi on was conducted. The relationship between

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27 anxiety treatment and inpatient nights was non-significant (t = -.65, p = .26). The group that received anxiety treatment had 1.93 inpa tient night stays, whereas the group who did not receive treatment had 2.21 inpatient night stays. Emergency room results. To determine the relationship between anxiety treatment and number of emergency room visits afte r adjusting for demographics (age, sex, race/ethnicity), socioeconomic status (years of education and income), insurance status, illness severity (perceived physical and mental health status, and comorbid conditions), and depression treatment, a negative binomial regression was conducted. The relationship between anxiety treatment and number of emergency room visits was nonsignificant (t = -.97, p = .17). The group who received anxiety treatment had .44 emergency room visits and the group who did not receive treatment had .74 visits.

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28 Table 3-1. Clinical Classification Codes and Diagnostic Categories. Medical Condition Classification Code Clinical Classification Software Diagnosis Category Pulmonary conditions 127 Chronic obstructive pulmonary disease and bronchiectasis 128 Asthma 129 Aspiration pneumonitis, food/vomitus 130 Pleurisy, pneumothorax, pulmonary collapse 131 Respiratory failure, insufficiency, arrest (adult) 132 Lung disease due to external agents 133 Other lower respiratory disease 134 Other upper respiratory disease Cardiac conditions 96 Heart valve disorders 97 Peri-, endo-, and myocarditis, cardiomyopathy (except that caused by tuberculosis) 100 Acute myocardial infarction 101 Coronary atherosclerosis and other heart disease 102 Nonspecific chest pain 103 Pulmonary heart disease 104 Other and ill-defined heart disease 105 Conduction disorders 106 Cardiac dysrhythmias 107 Cardiac arrest and ventricular fibrillation 108 Congestive heart failure, nonhypertensive

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29 Table 3-2. Antidepressant and Anti-anxiety Medication Names. Drug Class Generic Name Brand Name Antidepressant Imipramine Tofanil Desipramine Norpramin Amitriptyline Elavil Nortriptyline Aventyl, Pamelor Protriptyline Vivacil Trimipramine Surmontil Doxepin Sinequan, Adapin Maprotiline Ludiomil Amoxapine Asendin Trazodone Desyrel Fluoxetine Prozac Bupropion Wellbutrin Sertraline Zoloft Paroxetine Paxil Venlafaxine Effexor Nefazodone Serzone Fluvoxamine Luvox Phenelzine Nardil Tranylcypromine Parnate Anti-anxiety Diazepam Valium Chlordiazepoxide Librium Flurazepam Dalmane Azepam Centrax Clorazepate Tranxene Temazepam Klonopin Lorazepam Ativan Alprazolam Xanax Oxazepam Serax Triazolam Halcyon Estazolam ProSom Quazepam Doral Zolpidem Ambient Buspirone BuSpar Hydroxyzine Atarax, Vistaril Diphenhydramine Benadryl Propanolol Inderal Atenolol Tenormin Clonidine Catapres Source: Handbook of Clinical Psychopharmacology for Therapists

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30 Table 3-3. Descriptive St atistics of Pulmonary Re spondents (Comorbidity) Variables No Depression Depression Comorbidity No Anxiety Anxiety Comorbidity N 7217 649 7508 358 Mean Age (SD) 33.14 (23.36) 46.81 (17.03) 33.72 (23.28) 45.86 (18.1) Mean Yrs Education (SD) 9.78 (4.81) 11.82 (3.61) 9.88 (4.79) 11.47 (3.67) Mean Income $ 16,889 19,385 17,025 18,556 % Male 45.4 25.6 44.7 24.3 % Female 54.6 74.4 55.3 75.7 % Caucasian 80.7 86.9 80.9 88.3 % African American 14.1 9.0 14.0 7.3 % Asian 3.7 1.8 3.6 1.7 % Other 1.5 2.2 1.5 2.8 % Hispanic 22.6 19.0 22.7 14.0 % No Comorbidity 53.7 41.6 51.7 32.7 % One Comorbidity 41.5 46.4 41.9 46.4 % Two + Comorbidity 4.8 12.1 6.4 21.0 % Uninsured 9.5 8.0 9.6 5.6 % Intermittent Insured 14.1 13.7 14.1 14.2 % Insured 76.3 78.3 76.3 80.2 Modal Physical Health Very Good Good Very Good Good Modal Mental Health Excellent Good Excellent Good

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31 Table 3-4. Descriptive St atistics of Pulmonary Respondents (Treatment) Variables No Depression Treatment Depression Treatment No Anxiety Treatment Anxiety Treatment N 549 100 298 60 Mean Age (SD) 47.89 (16.94) 40.90 (16.37) 46.30 (17.76) 43.67 (19.74) Mean Yrs Education (SD) 11.82 (3.57) 11.84 (3.85) 11.45 (3.69) 11.58 (3.60) Mean Income $ 19,139 20,737 18,913 16,780 % Male 25.9 24.0 23.8 26.7 % Female 74.1 76.0 76.2 73.3 % Caucasian 87.6 83.0 88.6 86.7 % African American 9.1 9.0 7.0 8.3 % Asian 1.5 4.0 1.7 1.7 % Other 1.8 4.0 2.7 3.3 % Hispanic 19.7 15.0 14.4 11.7 % No Comorbidity 40.1 50 30.9 41.7 % One Comorbidity 47.5 40 46.3 46.7 % Two + Comorbidity 12.4 10 22.8 11.6 % Uninsured 8.0 8.0 6.4 1.7 % Intermittent Insured 12.9 18.0 15.1 10.0 % Insured 79.1 74.0 78.5 88.3 Modal Physical Health Good Very Good Good Good/ Very Good Modal Mental Health Good Good Good Fair

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32 Table 3-5. Descriptive Statistics of Responde nts with Cardiac Cond itions (Comorbidity) Variables No Depression Depression Comorbidity No Anxiety Anxiety Comorbidity N 2110 293 2228 175 Mean Age (SD) 60.05 (19.45) 58.64 (16.95) 60.01 (19.15) 58.12 (19.38) Mean Yrs Education (SD) 11.15 (3.69) 11.50 (3.50) 11.19 (3.69) 11.29 (3.43) Mean Income $ 20,500 16,602 20,377 15,542 % Male 48.3 31.4 47.4 30.9 % Female 51.7 68.6 52.6 69.1 % Caucasian 82.7 85.3 82.6 88.6 % African American 14.0 10.9 14.1 8.0 % Asian 2.1 2.0 2.2 1.1 % Other 1.1 1.7 1.1 2.3 % Hispanic 13.4 17.1 13.7 16.0 % No Comorbidity 42.8 33.1 40.6 28.0 % One Comorbidity 43.5 16.0 43.3 41.1 % Two + Comorbidity 13.7 19.4 16.0 30.8 % Uninsured 6.3 7.2 6.6 4.6 % Intermittent Insured 11.9 5.1 12.3 12.6 % Insured 81.8 78.2 81.2 82.9 Modal Physical Health Good Fair Good Good Modal Mental Health Good Good Good Good

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33 Table 3-6. Descriptive Sta tistics of Pulmonary Condition Respondents (Treatment) Variables No Depression Treatment Depression Treatment No Anxiety Treatment Anxiety Treatment N 259 34 156 19 Mean Age (SD) 59.03 (16.58) 40.9 (19.74) 58.88 (18.69) 40.90 (16.37) Mean Yrs Education (SD) 11.48 (3.57) 11.58 (3.60) 11.40 (3.35) 11.84 (3.85) Mean Income $ 16,610 16,780 15,280 20,737 % Male 30.9 35.3 31.4 26.3 % Female 69.1 64.7 68.6 73.7 % Caucasian 84.9 88.2 87.8 94.7 % African American 11.6 5.9 9.0 0 % Asian 1.9 2.9 1.3 0 % Other 1.5 2.9 1.9 5.3 % Hispanic 17.0 17.6 17.3 5.3 % No Comorbidity 33.2 32.4 26.9 36.8 % One Comorbidity 46.7 52.9 41.0 42.1 % Two + Comorbidity 20.1 14.7 32.1 21.0 % Uninsured 6.9 8.8 5.1 0 % Intermittent Insured 15.1 11.8 12.2 15.8 % Insured 78.0 79.4 82.7 84.2 Modal Physical Health Fair Fair Good Fair Modal Mental Health Good Fair Good Very Good

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34 Table 3-7. Statistical Results of Pulmonary Condition Respondents (Total Expenditures) T p-value Predicted Expenditures ($) Depression 13,752 No Depression .20 2.60 .01** 5,413 Anxiety 17,848 No Anxiety .10 1.29 .10 5,541 Depression Treatment 13,752 No Depression Treatment .10 .54 .30 5,413 Anxiety Treatment 10,696 No Anxiety Treatment .33 1.83 .04** 6,254 Table 3-8. Statistical Results of Pulm onary Condition Respondents (Medical Expenditures) T p-value Predicted Expenditures ($) Depression 7,089 No Depression -.05 -.56 .29 3,966 Anxiety 8,347 No Anxiety -.17 -1.91 .03** 5,015 Depression Treatment 2,722 No Depression Treatment -.66 -3.31 .00** 8,931 Anxiety Treatment 6,140 No Anxiety Treatment -.28 -.92 .18 9,349

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35 Table 3-9. Statistical Results of Pulmona ry Condition Respondents (Health Care Utilization) Office-Based Provider Visits Incidence Rate Ratio Z p-value Predicted Visit Count Depression Treatment 12.66 No Depression Treatment .98 -.17 .43 13.26 Anxiety Treatment 14.33 No Anxiety Treatment .10 1.29 .10 11.87 Outpatient Hospital Visits Depression Treatment 1.67 No Depression Treatment 1.23 .55 .29 2.19 Anxiety Treatment .61 No Anxiety Treatment .39 -2.96 .00** 1.28 Inpatient Nights at Discharge Depression Treatment 1.18 No Depression Treatment .96 -.11 .45 1.49 Anxiety Treatment 1.49 No Anxiety Treatment 1.48 1.03 .15 1.16 Emergency Room Visits Depression Treatment .37 No Depression Treatment .97 -.13 .45 .47 Anxiety Treatment .48 No Anxiety Treatment .95 -.21 .42 .52

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36 Table 3-10. Statistical Results of Cardiac Condition Respondents (Total Expenditures) T p-value Predicted Expenditures ($) Depression 16,436 No Depression .13 1.30 .10 15,467 Anxiety 24,047 No Anxiety .18 1.30 .10 14,921 Depression Treatment 9,475 No Depression Treatment -.03 -.08 .47 16,941 Anxiety Treatment 18,881 No Anxiety Treatment .35 .91 .19 13,695 Table 3-11. Statistical Results of Cardiac Condition Respondents (Med ical Expenditures) T p-value Predicted Expenditures ($) Depression 13,752 No Depression -.11 -.87 .19 5,413 Anxiety 13,898 No Anxiety .05 .41 .34 13,585 Depression Treatment 5,181 No Depression Treatment -.40 -1.06 .15 14,081 Anxiety Treatment 22,077 No Anxiety Treatment .41 .91 .19 10,785

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37 Table 3-12. Statistical Results of Cardiac Recipients (Health Care Utilization) Office-Based Provider Visits Incidence Rate Ratio Z p-value Predicted Visit Count Depression Treatment 14.81 No Depression Treatment 1.04 .23 .41 14.99 Anxiety Treatment 9.98 No Anxiety Treatment .78 -1.33 .10 13.34 Outpatient Hospital Visits Depression Treatment 1.16 No Depression Treatment .49 -1.29 .10 3.23 Anxiety Treatment 1.21 No Anxiety Treatment 1.77 1.05 .15 .89 Inpatient Nights at Discharge Depression Treatment 1.92 No Depression Treatment 1.05 .16 .44 1.81 Anxiety Treatment 1.93 No Anxiety Treatment .72 -.65 .26 2.21 Emergency Room Visits Depression Treatment 1.05 No Depression Treatment 2.00 1.83 .04 (overall model not significant) .53 Anxiety Treatment .44 No Anxiety Treatment .66 -.97 .17 .74

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38 CHAPTER 4 DISCUSSION The present study examined the relations hip between comorbid depression or anxiety and health care expenditures in pulmona ry or heart patients. As expected, it was found that depression increased total expenditu res in pulmonary patients, but there was no corresponding increase in medical expendi tures only. Because medical expenditures only excluded any medical event associated with a psychological diagnosis, it appears that depressed patients may not use more me dical services for their medical conditions, but perhaps they do use more psychological serv ices. Depressed patients may have more diagnoses of other psychological conditi ons that prompt service-seeking. Contrary to expectation, th e presence of anxiety in pulmonary patients decreased medical expenditures only, but there was no difference in to tal expenditures. Thus, it appears that anxious pulmonary patients do not use more health care services overall and in fact, they seek less health care services for their medical conditions. This could be because their anxiety inhibits them from seeking needed care. The main aim of the study was to examin e the medical cost offset effect in pulmonary or heart patients who sought tr eatment for depression or anxiety. This analysis revealed that depresse d pulmonary patients showed a cost offset effect, in that depressed patients who received mental health treatment showed a decrease in medical expenditures only. Further analysis revealed that this effect was not explained by a decrease in the number of outpatient hospital visits, inpatient hospita l nights, office-based provider visits, or emergency room visits. T hus, this study suggests th at the treatment of

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39 pulmonary patients with comorbid depression wo uld result in a cost o ffset effect not due to cost shifting from medical treatment to psychological treatment. Anxious pulmonary patients who received mental health treatment showed an unexpected increase in total health care e xpenditures; however, th ere was a reduction in outpatient hospital visits, supporting the idea that added psychologica l care would show a reduction in health care utiliz ation. The number of hospital inpatient nights, office-based provider visits, and emergency room visits were not significantly different between the treated and untreated groups. These results mi ght suggest that anxiet y patients are getting the psychological services they need and added care costs more, but because needed care is provided, utilization in the medical sector is reduced. Furthermore, treated patients may also be more apt to recognize their an xiety symptoms as pa rt of a psychological disorder, as opposed to a medical problem. Heart disease patients did not show any signi ficant effects in any of the analyses. However, it should be noted that the number of heart disease patients who received psychological treatment was less than pulmonary patients, which limited the power of the results from the heart disease group. Neverthele ss, in this study, the variation in observed cost-offset effects suggests that the issue of cost-offset may be complex and variable across different psychological and medical conditions. Limitations Several limitations of the pr esent study should be consid ered. First, the data structure of MEPS seems to be unreliable. The present analysis included the years 1999 to 2002. A previous analysis using only the years 2000 to 2002 revealed different results. When 1999 was added, the results changed. Previ ous results showed a cost offset effect for both depression and anxiety treatment in pulmonary patients with comorbid

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40 depression or anxiety, whereas the present results reveal a cost offset effect for only depression treatment in pulmonary patients. The addition of data fr om 1999 appeared to have changed the structure of the data set. Part of this instability could be due to cohort effects, as well as a difference in power to detect statistical signi ficance. Second, only a relatively small number of patients received ment al health treatment, particularly for the heart disease groups. There were only 19 a nd 34 heart disease respondents who received mental health treatment for anxiety and depr ession, respectively. Methodologically, this poses a difficulty in terms of reliable estimat es. Third, the validity of diagnostic coding is somewhat questionable because data was obtained through self-report. Fourth, aggregating multiple classification c odes and psychotropic medication with psychotherapy reduces the precision of the anal ysis. Fifth, treatment efficacy could not be determined from the data. Finally, it is important to remember the cross-sectional and correlational nature of the present anal ysis does not address causality. Implications The demonstration of cost offset eff ects has implications for the field of psychology and its utility in reducing or contai ning rising health care costs in America. Although psychologists would like to believe th at a cost offset effect holds across medical conditions and psychological conditio ns, the present data suggests that the relationship between mental hea lth treatment and cost offsets is not clear-cut. Using data from the MEPS is a useful way to examine potential cost offset effects for specific medical conditions because it provides larg e numbers of subjects, is nationally representative, and allows fo r both cross-sectional and long itudinal analyses. Results from further analyses on other medical conditions may help to further refine the nature of

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41 cost offsets. Because the MEPS allows for l ongitudinal analyses, next steps would be to determine cost offsets longitudinally. An argument is that using cost offset as the only measure of the value of psychological services is incomplete (Coyne and Thompson, 2003). Patients and families who make treatment gains for depression or anxiety and employers who observe increased productivity in their workers treated for depression or anxiety may feel that these benefits are worth the additional co sts of psychological se rvices. Thus, the effectiveness of treatment as measured by quality of life and work performance and attendance would be important outcomes to consider in addi tion to cost issues. Although treatment efficacy information is not available from the MEPS data, future research will need to address the important issue of effectiv e treatment and cost offsets. However, the MEPS would allow for the analysis of employ ment variables releva nt to the present discussion. In conclusion, the present study provided preliminary results on the cost offset effects of specific medical and psychological populations. Results indicated that cost offset issues are complex and the future direction of cost offset research will be focused on teasing apart this complexity.

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42 LIST OF REFERENCES Anderson, R. N. (2002). Deaths: Leading causes for 2000. Na tional vital statistics report, 50 (16). Hyattsville, MD: National Center for Health Statistics. Bickman, L. (1996). The evaluation of ch ildrens mental health managed care demonstration. Journal of the Mental He alth Administration, 23 : 7-15. Braveman, P., & Tarimo, E. (2002). Social in equalities in health within countries: Not only an issue for affluent nations. Social Science and Medicine, 54 (11): 1621-1635. Bromberg. J. I., Beasley, P. J., DAngelo, E. J., Landzberg, M., & DeMaso, D. R. (2003). Depression and anxiety in adults with congenital heart disease: A pilot study. Heart and Lung, 32 (2): 105-110. Bureau of Labor and Statistics. (1999-2002). Consumer price index for all urban consumers (CPI-U): U.S. city average, detailed expenditure categories (medical care). U.S. Department of Labor. Re trieved September, 2005, from http://www.bls.gov Carbone, L. A., Orav, E. J., Fricchione, G. L., & Borus, J. F. (2000). Psychiatric symptoms and medical utiliza tion in primary care patients. Psychosomatics, 41 (6): 512-518. Carlson, L. E., & Bultz, B. D. (2004). Efficacy and medical cost offset of psychological interventions in cancer care: Maki ng the case for economic analyses. PsychoOncology, 13 : 837-849. Chiles, J. A., Lambert, M. J., & Hatch, A. L. (1999). The impact of psychological interventions on medical cost o ffset: A meta-analytic review. Clinical Psychology: Science and Practice, 6 (2): 204-220. Collins, K. A., Westra, H. A., Dozois, D. J. A., & Burns, D. D. (2004). Gaps in accessing treatment for anxiety and depression: Challenges for the delivery of care. Clinical Psychology Review, 24 : 583-616. Coyne, J. C., & Thompson, R. (2003). Psychol ogists entering prim ary care: Manhattan cannot be bought for $24 worth of beads. Clinical Psychology: Science and Practice, 10 (1): 102-108. Follette, W. T., & Cummings, N. A. (1968). Ps ychiatric services a nd medical utilization in a prepaid health plan setting. Medical Care, 5 : 25-35.

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43 Friedman, R., Sobel, D., Myers, P., Ca udill, M., & Benson, H. (1995). Behavioral medicine, clinical health psychology, and cost offset. Health Psychology, 14 (6): 509-518. Harter, M. C., Conway, K. P., Merikangas, K. R. (2003). Associations between anxiety disorders and physical illness. European Archives of Psychiatry and Clinical Neuroscience, 253 : 313-320. Hunsley, J. (2003). Cost-effectiveness and medical cost-offset considerations in psychological service provision. Canadian Psychology, 44 (1): 61-73. Iezzoni, L. I. (2003). Risk adjustment for measuring health care outcomes, 3rd edition. Chicago, IL: Health Administration Press. Katon, W. J. (2003). Clinical and health services relationships between major depression, depressive symptoms, and general medical illness. Society of Biolog ical Psychiatry, 54 : 216-226. Kunik, M. E., Roundy, K., Veazey, C., Souche k, J., Richardson, P., Wray, N. P., & Stanley, M. A. (2005). Surp risingly high prevalence of anxiety and depression in chronic breathing disorders. Chest, 127 (4): 1205-1211. Mumford, E., Schlesinger, H. J., Glass, G. V., Patrick, C., & Cuerdon, T. (1984). A new look at evidence about reduced cost of medical utilizati on following mental health treatment. American Journal of Psychiatry, 141 : 1145-1158. Olin, G. L., & Rhoades, J. A. (2005). The five most costly medical conditions, 1997 and 2002: Estimates for the U.S. civilian noni nstitutionalized popul ation. Statistical brief #80. Agency for Healthcare Research and Quality. Rockville, MD: Retrieved August, 2005, from http://www.meps.ahrq.gov/papers/st80/stat80.pdf Otto, M. W. (1999). Psychol ogical interventions in the age of managed care: A commentary on medical cost offsets. Clinical Psychology: Science and Practice, 6 (2): 239-241. Simon, G. E., Revicki, D., Heiligenstein, J., Grothaus, L., VonKorff, M., Katon, W. J., & Hylan, T. R. (2000). Recovery from depr ession, work productivity, and health care costs among primary care patients. General Hospital Psychiatry, 22 : 153-162. StatCorp. Stata Statistical Software: Release 9.0 Special Edition. College Station, TX: Stata Corporation, 2002. Sturm, R. (2001). Economic grand rounds: The myth of medical cost offset. Psychiatric Services, 52 : 738-740. Thorpe, K. E., Florence, C. S., & Joski, P. (2004). Which medical conditions account for the rise in health care spending? Health Affairs web exclusive : Retrieved August, 2005, from http://content.healthaffairs.org/cgi/content/full/hlthaff.w4.437/DC1

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44 Van Ede, L., Yzermans, C. J., & Brouwer, H. J. (1999). Prevalence of depression in patients with chronic obstructive pulm onary disease: A systematic review. Thorax, 54 : 688-692. Wells, K. B., Sturm, R., Sherbourne, C. D., & Meredith, L. S. (1996). Caring for Depression. Cambridge, MA: Harvard University Press. World Health Organization. (2006). Chronic Conditions: The Economic Impact. Retrieved December, 2005, from http://www.who.int/chronic_condi tions/economics/en/index.html

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45 BIOGRAPHICAL SKETCH Andrea Meredith Lee graduated with a Bachel or of Arts (first class honors) degree in psychology in October 2004 from Simon Fraser University in Burnaby, British Columbia, Canada. She plans to pursue a doctoral degree in c linical and health psychology at the University of Florida. He r academic interests lie in health psychology and health policy.