Running head: HIV PAIN INTERFERENCE AND ASSOCIATED FACTORS 1 Severity of Pain Interference Among People with HIV and Factors Associated with the Pain Interference Lance Reccoppa University of Florida Author Note Lance Reccoppa, Department of Epidemiology, University of Florida. Correspondence concerning this article should be addressed to Lance Reccoppa, Department of Epidemiology, University of Florida, Gainesville, FL 32603 Contact: email@example.com
HIV PAIN INTERFERENCE AND ASSOCIATED FACTORS 2 Abstract People with the human immunodeficiency virus (HIV) often live wi th pain that interferes w ith their daily living P eople with HIV living with this pain interference often h ave difficulties with daily activities, chores around the house, and participation in social activities, which can lead them to seek pain managem ent Using data from the Florida Cohort Follow Up Survey, I looked at the distribution of the severity of pa in interference and possible factors associated with the severity of pain interference. The levels of pain interference were measured by the Pati ent Reported Outcome Measurement Information System ( PROMIS ) short form bank, and studies have shown its efficacy in measuring pain interference across differing pain groups and populations. Those PROMIS scores from the Florida Cohort Follow Up Survey were use d in bivariate analyses with age, gender, race, employment status, brief pain inventory, usage of alcohol for pain relief, and adherence to HIV appointments. The severity of pain interference amongst the study population showed a trend of normal distributi on. Age, gender, employment, and level of pain proved to have a significant relationship with pain interference.
HIV PAIN INTERFERENCE AND ASSOCIATED FACTORS 3 Severity of Pain Interference Among People with HIV and Factors Associated with the Pain Interference Introduction The human immunodeficiency virus (HIV) is a pathogen that attacks and replicates cells, greatly restraining the human immune system as defined by the World He alth Organization (WHO, 2016). 1.2 million people are living with the virus according to the Center for Disease Control and Prevention (2016). Studies have confirmed that people with HIV often live with pain which can cause disturbances in their daily lives as stated by Parker, R., Stein, D.J., & Jelsma, J. (2014). Pain interference can be de fined as the extent to which pain hinders the engagement of social, cognitive, emotional, physical, and recreational activities in ). Approxima tely 30% of people with HIV have been diagnosed with peripheral neu ropathy, according to the International Association of Providers of AIDS Care (2016), which can lead to pain interference. The p ain can also result from immune suppression and subsequent opportunistic infections according to Parker, R., Stein, D.J., & Jels ma, J. (2014). The possible associated factors for HIV pain interference that I am investigating can be sorted into two groups. One group is the factors that likel y influence pain interference, and the other group is factors that are likely influenced by pain interference. For the influences on the pain interference I am inves tigating age, gender, race, pain and alcohol use for pain relief. As for the factors likely influenced by pain interference I am in vestigatin g employment and adherence to H IV related appointments For factors likely influencing pain interference age and peripheral neuropathy could be correlated, but there is no evidence of a clear association between age and pain interference. I am investi gat ing age in hopes that health care providers can know whether to
HIV PAIN INTERFERENCE AND ASSOCIATED FACTORS 4 gender is also unclear, for multiple studies indicate a different gender reporting the most pain a s stated by Parker, R., Stein, D.J., & Jelsma, J. (2014) although numerous chronic pain conditions are more prevalent amongst women. Gender is an important factor to investigate because of HIV affects both men and women, so one needs to subsequently invest igate their respective severity of pain interference. As with any other disease, race is another important factor to consider when looking at severity of pain interfere nce amongst people with HIV Breitbarta et al. (1996) reported that females of a non Cau casian race reported the highest severity of pain However, t he relationship between pain severity and pain interference is also not fully understood, as Li, K.K ., Harris, K., Hadi, S., & Chow, E. (2007) reported a non linear relationship between the two v daily life to the same degree. For example, a study population of HIV people living in the U.S. and Denmark reported significant HIV pain functional impairment, while the African study population reported less functional impairment with the same degree of pain as the people living in the U.S. and Denmark according to Merlin et al. (2013). A l cohol use for pain relief is another entity that I am investigating. Goebel, et al. (2011) found that a higher severity of pain interference was associated with the usage of alcohol for pain relief amongst veterans. Alcohol is an interesting fac tor that merits consideration because it is an unorthodox way to relieve pain and could affect pain interference As for possible variables influenced by pain interference, I am investigating employment could be affected by pain interference. In a study of a group of South African women, unemployed participants were more likely to report pain and lower self efficacy than the employed participants, as reporte d by Parker, R., Jelsma, J., & Stein, D.J. (2017 ). Employment and a higher quality of life would seem
HIV PAIN INTERFERENCE AND ASSOCIATED FACTORS 5 to correlate with less pain interference. Additionally, I will be examining adherence to HIV appointments as it relates to pain interference. Berg, M.B, Safren, S.A., Mimiaga, M. J., Grasso, C., Boswell, S., & Mayer, K.H. (2007) found that a lack of attendance for HIV appointments correlated with lower CD4 immune cell counts, which in turn could affect pain interference. Aims and Hypotheses Through my Honors project, I aim to determ ine the distribution pain interference severity amongst people with HIV, and the possible factors associated with pain interference. I chose this topic because the li kelihood of people with HIV to able to complete household chores, social activities, and work around the house is unequivocally important. From my literature review, I hypothesize that unemployed, older, Caucasian women who do not adhere to their appointments and do not use alcohol for pain relief, will experience the highest level of pain interference compared t o the rest of the study po pulation. I have come to support this hypothesis because I think gender, age, and race can all affect pain. Subsequently, I believe pain affects pain interference, which in turn affects adherence to HIV appointme nts and employment Figure 1 is my concept map that le d me to my hypothesis. The width of the arrows represents the strength of the relationship I hypothesize between the given variables. Gender Race Age Pain BPI Alcohol Use for Pain Relief Pain Interference Adherence to Appointments Employment
HIV PAIN INTERFERENCE AND ASSOCIATED FACTORS 6 Method A follow up survey was given to t he people with HIV who completed the Florida Cohort Survey after 6 months, which includes questions regarding pain interferenc e. 243 participants answered the pain interference follow up survey question s and they constitute the study population for my Honors Project. Pain interfere nce was scored from 4 20 following the guidelines for adults on the Patient Reported Outcome Measurement Information System ( PROMIS ) Pain Interference Scoring Manual from the Assessment Center (2015). On the Florida Cohort Follow Up Survey, the pain interference questions read as follows: 1. How much did pain interfere with your day to day activities? 2. How much did pai n interfere with work around the home? 3. How much did pain interfere with your ability to participate in social activities? 4. How much did pain interfere with your household chores? = 1 point = 2 points = 3 poi nts = 4 points r choices was calculated. Since pain and pain interfere nce have a perceived relationship, we controlled for pain in the next part of my data analysis by stratifyi ng pain in multivariable models The Brief Pain Inventory respo nses were stratified using the following stratifications made by Li, K.K., Harris, K., Hadi, S., & Chow, E. (2007). Participants were asked to select a number between 0 10 to quantify the level of pain they had experienced in the last 24 hours. For a score between 0 and 7 average score greater than 7 s evere pain By stratifying the pain, I was able to determine if there were any statistically significant factors related to pain interference at each pain level.
HIV PAIN INTERFERENCE AND ASSOCIATED FACTORS 7 SAS was used to generate frequency tabl es. From the frequency tables, we looked at the trend s amongst each factor and whether it related to the intensity of pain interference. Pain interference was converted into a count variable and there was a means comparisons completed for the pain in terference scores so we could determine the means for age, gender, race, severity of pain, alcohol use for pain relief, employment, and adherence to appointments Subsequently we looked at the cross tables to deduce if any of the factors had a significant relationship with pain inte rference by using the Kruskal Wa llis T est. Kruskal Wallis is a non parametric method of testing whether there are statistically significant differences between two or more groups of an independent variable on the dependent variable. We used a generalized linear model with Poisson family and identity link function for the multivariable model. The multivariable models were stratified by pain levels, as pain interference was the outcome and age, gender, and employment were the predictors Results Pain Interference Scores Figure 2: Distribution of Pain Interference Scores Percent
HIV PAIN INTERFERENCE AND ASSOCIATED FACTORS 8 Characteristics Levels Frequency Means of Pain Interference Score Std.Dev Kruskal Wallis P value Age (1)18 34 15 4.87 5.58 0.0391 (2)35 44 38 7.39 4.23 (3)45 54 104 8.41 4.86 (4)>=55 86 7.07 4.33 Gender (1)Male 143 6.85 4.94 0.0038 (2)Female 100 8.57 4.30 Race (1)Hispanic 43 6.65 5.16 0.1736 (2)Not Hispanic, White 45 7.87 4.53 (3)Not Hispanic, Black 147 7.88 4.54 (4)Not Hispanic, Other 8 4.88 5.11 Employment (0)Unemployed 57 7.00 4.63 0.0430 (1)Unable to work/Disabled 137 8.20 4.76 (2)Employed 40 6.18 4.28 Brief Pain Inventory (0)No pain 24 1.96 2.76 <0.0001 (1)Mild pain 147 6.98 3.93 (2)Severe pain 56 11.59 3.96 Alcohol use for pain relief No 88 7.10 4.73 0.1246 Yes 51 8.37 4.74 Missed scheduled HIV health care appointments No 186 7.59 4.78 0.6051 Yes 41 7.20 4.15 Table 1: Possible Factors Associated with Pain Interference Analysis Of Maximum Likelihood Parameter Estimates No Pain Parameter DF Estimate Standard Error Wald 95% Confidence Limits Wald Chi Square Pr > ChiSq Intercept 1 4.2746 1.1169 2.0855 6.4637 14.65 0.0001 Age (2)35 44 1 0.6011 1.7521 2.8328 4.0351 0.12 0.7315 Age 45 54 1 1.3687 1.5558 1.6806 4.4180 0.77 0.3790 Age >=55 1 1.6745 1.7132 1.6833 5.0322 0.96 0.3284
HIV PAIN INTERFERENCE AND ASSOCIATED FACTORS 9 Analysis Of Maximum Likelihood Parameter Estimates No Pain Parameter DF Estimate Standard Error Wald 95% Confidence Limits Wald Chi Square Pr > ChiSq Gender Female 1 2.9224 1.4863 0.0093 5.8355 3.87 0.0493 Employment Unable to work/Disabled 1 1.1831 1.7480 4.6091 2.2429 0.46 0.4985 Employment Employed 1 0.1492 1.4895 3.0685 2.7701 0.01 0.9202 Table 2: Multivariable Model No Pain Analysis Of Maximum Likelihood Parameter Estimates Mild Pain Parameter DF Estimate Standard Error Wald 95% Confidence Limits Wald Chi Square Pr > ChiSq Intercept 1 9.4735 1.5932 6.3509 12.5960 35.36 <.0001 Age 35 44 1 0.9137 1.6586 2.3372 4.1645 0.30 0.5817 Age 45 54 1 1.7148 1.5323 1.2885 4.7181 1.25 0.2631 Age >=55 1 0.9244 1.5314 2.0772 3.9259 0.36 0.5461 Gender Female 1 1.3071 0.7223 0.1086 2.7229 3.27 0.0704 Employment Unable to work/Disabled 1 0.0486 0.8666 1.6500 1.7472 0.00 0.9553 Employment Employed 1 0.6126 1.0587 2.6876 1.4623 0.33 0.5628 Table 3: Multivariable Model Mild Pain Analysis Of Maximum Likelihood Parameter Estimates Severe Pain Parameter D F Estimat e Standar d Error Wald 95% Confidence Limits Wald Chi Squar e Pr > ChiS q Intercept 1 17.0468 3.5743 10.041 2 24.052 4 22.75 <.0001
HIV PAIN INTERFERENCE AND ASSOCIATED FACTORS 10 Analysis Of Maximum Likelihood Parameter Estimates Severe Pain Parameter D F Estimat e Standar d Error Wald 95% Confidence Limits Wald Chi Squar e Pr > ChiS q Age 35 44 1 1.1915 3.7541 8.5493 6.1664 0.10 0.7510 Age 45 54 1 0.3036 3.6065 6.7650 7.3722 0.01 0.9329 Age >=55 1 2.3501 3.6885 9.5794 4.8792 0.41 0.5240 Gender Female 1 0.0134 1.1985 2.3625 2.3357 0.00 0.9911 Employmen t Unable to work/Disable d 1 1.1397 1.4921 4.0641 1.7848 0.58 0.4450 Employmen t Employed 1 0.8674 2.7478 4.5181 6.2529 0.10 0.7522 Table 4: Multivariable Model Severe Pain The severity of pain in terference amongst the study population showed a trend of normal distribution, as s hown in Figure 2 Age (p=.03) gender (p=.003) employment (p=.04) and level of pain (p=<.0001) proved to have a significant relationship with pain interference. Respondents with ages between 45 54 reported a pain interference score of 12.41 out of 20, compared to 18 eported higher levels of pain interference than men, as women reported an average pain interference score of 12.57 12.20 out of 20, which was well above the employe d respondents average score of 10.18. The average score of someone with severe pain was 15.59, as compared to the average score o f 5.96 for the respondents experiencing no pain. As for variables with a statistically insignificant relationship with p ain in terference, white participants and black participants had a very simil ar mean for pain interference (11.87 vs. 11 .88). As for alcoho l use for pain relief, respondents who used alcohol only reported a
HIV PAIN INTERFERENCE AND ASSOCIATED FACTORS 11 marginally higher average score of 12.37 compared to the 11.10 average score of people who do not use alcohol. Also, for adherence to HIV appointments, people who missed their appointments recorded a mean of 11 .59 for pain interference which was very similar to the mean of the people who had not missed an appoi ntment (11 .20). As for the stratified pain multivariable model the female gender proved to be statistically significant (p=.04 9 ) in relation to pain interference compared to males who also reported no pain as seen in Table 2 At the severe pain level, none of the factors proved to have a statistically significant rela tionship with pain interference as shown in Table 4. Discussion D isabled/unemployed females, aged 45 54, with high levels of pain reported the highest levels of pain interference on average compared to the other participants in their respective categories The severity of pain interference amongst the study population showed a trend of normal distribution. When health care providers are presented with middle aged people or women with HIV they should be aware of proper medical treatment, vocational services, and pain management skills to aid those patients that are dealing with pain interference There were many studies that had similar results to my findings. Parker, R., Jelsma, J., & Stein, D.J. (2017) also found that unemployed people reported more pain compared to employed people. Additionally, Breitbarta et al. (1996) found that non Caucasian females report ed the highest pain severity, which was similar to my respondents. How ever, Li, K.K., Harris, K., Hadi, S., & Chow, E. (2007) found a non linear relationship with pain interference and pain. While there are some confounding variables that affect how one copes with pain, after pain proved to be statistically significant in my study, I believe that pain and pain interference should have a linear relationship. The nonexistent relationship between adherence to HIV appointments and pain interference was unexpected as I hypothesized that a higher intensity of pain interference
HIV PAIN INTERFERENCE AND ASSOCIATED FACTORS 12 w ould cause individuals to adher e more to their HIV appointments. I was also surprised by people between the ages of 45 54 having more severe pain interference than people over the age of 54 My Honors Project did have some limitati ons. 283 people who returned the Follow up Survey did not fill out the pain interference section so I had to exclude them from my project. Additionally, my project did not account for pain that could be unrelated to HIV, which could offset the factors associated with pain int erference. As for future studies, one could investigate as to why people age 45 54 report more severe pain interference than people over the age of 54. Additionally, one could research why people who use alcohol for pain relief report more severe pain inte rference than people who do not use alcohol.
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