The Effect of Socioeconomic Status on the Survival of People Receiving Care for HIV Infection in the United States

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1 ORIGINAL Cunningham, PAPERS Hays, Duan, Andersen, Nakazono, Bozzette, and Shapiro 655 The Effect of Socioeconomic Status on the Survival of People Receiving Care for HIV Infection in the United States William E. Cunningham, MD, MPH Ron D. Hays, PhD Naihua Duan, PhD Ronald M. Andersen, PhD Terry T. Nakazono, MA Samuel A. Bozzette, MD, PhD Martin F. Shapiro, MD, PhD Abstract: HIV-infected people with low socioeconomic status (SES) and people who are members of a racial or ethnic minority have been found to receive fewer services, including treatment with Highly Active Antiretroviral Therapy (HAART), than others. We examined whether these groups also have worse survival than others and the degree to which service use and antiretroviral medications explain these disparities in a prospective cohort study of a national probability sample of 2,864 adults receiving HIV care. The independent variables were wealth (net accumulated financial assets), annual income, educational attainment, employment status (currently working or not working), race/ethnicity, insurance status, use of services, and use of medications at baseline. The main outcome variable was death between January 1996 and December The analysis was descriptive and multivariate adjusted Cox proportional hazards regression analysis of survival. By December 2000, 20% (13% from HIV, 7% non-hiv causes) of the sample had died. Those with no accumulated financial assets had an 89% greater risk of death (RR=1.89, 95% CI= ) and those with less than a high school education had a 53% greater risk of death (RR=1.53, 95% CI= ) than their counterparts, after adjusting for sociodemographic and clinical variables only. Further adjusting for use of services and antiretroviral treatment WILLIAM CUNNINGHAM is a Professor in the Division of General Internal Medicine and Health Services Research, Department of Medicine and in the Department of Health Services at the School of Public Health at UCLA. RON HAYS and RONALD ANDERSON are also both Professors in the Department of Health Services Research at UCLA. In addition, Hays is a Professor of Medicine at UCLA and is affiliated with the RAND Corporation s Health Science s Program. NAIHUA DUAN is a Professor in Residence at UCLA s Dept. of Psychiatry and Biobehavioral Sciences and is affiliated with RAND. TERRY NAKAZONO is a Programmer in the Department of Health Services at the School of Public Health at UCLA. SAMUEL BOZZETTE is a Professor at the VA Medical Center at the University of California at San Diego and is affiliated with RAND. MARTIN SHAPIRO is a Professor in UCLA s Dept. of Medicine. Address correspondence and reprint requests to the first author, William Cunningham, MD, MPH, at: UCLA School of Public Health, Le Conte Ave., Rm A, Center for Health Sciences, Department of Health Services, Los Angeles, CA Phone: (310) , fax: (310) ; wcunningham@mednet.ucla.edu. Journal of Health Care for the Poor and Underserved 16 (2005):

2 656 Socioeconomic status and survival with HIV diminished, but did not eliminate, the elevated relative risk of death for those with low SES by three of the four measures. The finding of markedly elevated relative risks of death for those with HIV infection and low SES is of particular concern given the disproportionate rates of HIV infection in these groups. Effective interventions are needed to improve outcomes for low SES groups with HIV infection. Key words: HIV, AIDS, socioeconomic status, health services, outcomes, survival. D isparities in treatment and outcomes according to socioeconomic status (SES) and race/ethnicity are of particular concern for providers and policymakers. HIV is one of the conditions for which there is alarm about disparities in treatment because the consequences for outcomes are potentially great. The mortality rate for HIV is substantial if infected persons do not receive highly active anti-retroviral therapy (HAART), but is markedly reduced by treatment. 1 The finding of disparities in medical care naturally raises the question of whether such disparities result in worse health outcomes for groups less likely to receive care. A series of papers from the HIV Costs and Service Utilization Study (HCSUS) have demonstrated that those with low SES (low education and low income) and racial and ethnic minority groups often have sub-optimal patterns of medical care use. One previous analysis of HCSUS data showed that low-education groups, lowincome groups, blacks, and Hispanics received fewer ambulatory service visits, fewer antiretroviral medications, more emergency room visits, and more hospitalizations than comparison groups. 2 Another analysis of HCSUS revealed that low-education groups, low-income groups, blacks, and Hispanics were less likely than comparison groups to receive early access to HAART. 3 In follow-up analyses two years later, the gap in HAART use between low and high SES groups and between minorities and whites was reduced, but a significant gap still remained. 4 Similar disparities by SES and race/ethnicity were observed in health insurance coverage, which, in turn, helped to explain the disparities in care. 5 Given these SES-related and racial disparities in service use and treatment for HIV, the question naturally arises whether there are corresponding SES-related and racial disparities in survival. 6 Studies in San Francisco, 7,8 Canada 9,10 and Italy 11 showed there was relatively high HIV mortality among low SES groups. HIV is an important condition for examining these issues in the U.S. as a whole because of the high mortality rate, 12 disparities in care by SES and race, 4 and the disproportionate prevalence of HIV in minority and low SES groups To address the question of whether there are disparities in survival by SES and/or race/ethnicity among people receiving care in the United States, we analyzed data from HCSUS, a study of a nationally representative cohort of persons with HIV infection who were receiving care in the United States starting in The purpose of this study was to examine whether groups having low income, limited financial wealth, low educational attainment, lack of employment, or minority race/ethnicity had worse survival than their counterparts, after adjusting for covariates. In addition, we examined whether or not existing differences in the use of antiretroviral therapy, insurance status or the delivery of services explain disparities in survival.

3 Cunningham, Hays, Duan, Andersen, Nakazono, Bozzette, and Shapiro 657 Methods Study sample. Full details of the HCSUS sampling design are presented elsewhere. 16 In brief, the reference population consisted of people at least 18 years old with known HIV infection who made at least one visit, in the context of regular or ongoing care, to a non-military, non-prison medical provider (other than an emergency department) in the contiguous U.S., during the period January 5 to February 29, The HCSUS used a three stage sampling design, in which geographical areas, medical providers, and patients were sampled. In the first stage, we sampled 28 metropolitan areas and 25 clusters of rural counties within the U.S. In the second stage, we sampled a total of 148 urban and 51 rural providers. In the third stage, we sampled patients from de-identified lists of all eligible patients who visited participating providers during January and February of We constructed several weights to adjust for selection probabilities, for nonresponse, and for the probability of entering the sample more than once. Applying the weights permits inference to the population represented by the baseline sample. We sampled 4,042 eligible subjects, of whom 2,864 (71%) completed full baseline interviews. After obtaining informed consent, all interviews were conducted using computer-assisted personal interviewing (CAPI) programs designed for this study. Baseline interviews were conducted between January of 1996 and April of Patients were followed prospectively through December 2000 for survival. The median duration of follow-up was 53 months. Conceptual model and hypotheses. Our conceptual model was that low socioeconomic groups and racial/ethnic minorities with HIV-infection are less likely to receive optimal treatment and experience high mortality rates as a result. Based on this model, we hypothesized that these groups would have worse survival than their counterparts and that differences in survival would persist after adjusting for potential confounders. Furthermore, we hypothesized that differences between these groups would be explained by mediating variables representing service use, insurance coverage, and treatment with antiretroviral medications. All independent variables were measured at the baseline interview. The independent variables are described below. Mortality assessment. We used several methods to assess death. Field staff interviewed participants families, friends or other contacts and proxies documented from previous interviews and they examined newspaper obituaries. In addition, mortality searches were done under Equi-Fax Government and Special Systems, now known as Choice Point, a national death registry (ChoicePoint, Inc.; 1000 Alderman Drive, Alpharetta, Georgia 30005). Equi-Fax identifies mortality status through the use of a national database that compiles information from death certificates processed in the United States. Lastly, we conducted mortality searches through the National Death Index. Mortality status for patients was checked in these databases by matching each patient s social security number, age, and date of birth with those in the national database. As of December 31, 2000, 585 (20%) of the 2,864 patients enrolled at baseline were deceased. There were 19 (less than 4% of deceased, less than 1% of total) patients for whom the date of death was only known within a range (5.5 to 15.1 months); we imputed the date of death, randomly selecting a date in the interval during which death occurred. The results of the analysis were

4 658 Socioeconomic status and survival with HIV not sensitive to whether missing values were imputed at the high or the low end of the interval. We used ICD-9 codes to assign non-hiv or HIV causes of death based on the primary code. Socioeconomic status and race/ethnicity variables. The key independent variables characterizing SES were annual income ($0 5,000, $5,001 10,000, $10,001 25,000, or greater than $25,000), wealth (net accumulated assets of $0, $1 $1,000, $1001 $5,000, $5001 $50,000, or greater than $50,000), educational attainment (less than high school degree, high school degree, some college, college degree or more), and current work status (working full- or part-time vs. unemployed, disabled or not working for other reasons). 17 Wealth was measured using the following item: If you were to sell any real estate you own, any car or other vehicle you own, or farm or business or other assets you own, and pay off any existing debts on them, how much money would you be left with? We hypothesized that the following groups had worse survival than others: those with annual incomes less than $25,000, those with accumulated net wealth worth less than $50,000, those with less education than a college degree, and those not currently working (each group compared with its counterpart). We also hypothesized that blacks and Hispanics would have shorter survival than whites. Other sociodemographic and clinical variables. In multivariate analyses we adjusted for potential confounders, including age (18 34, 35 49, 50 years old and older), sex, geographic region of practice location (Northeast, South, Midwest, and West) and homelessness. We included lowest T-helper cell lymphocyte (known as CD4 count, greater than or equal to 500, , , 0 49 cells per mm 3 ), Center for Disease Control (CDC) stage of HIV infection (asymptomatic, symptomatic, AIDS), 21 time period of HIV infection (prior to 1992, from 1992 to 1993, and from 1994 to 1996), HIV exposure (defined hierarchically as follows: male-to-male sexual contact, injection drug use, heterosexual contact, unknown or other) drug dependence, alcohol use, and tobacco use. 22 Mediating variables. Several variables were included to represent service use, insurance coverage, and treatment because we thought they would help to partially explain why there were differences in survival by socioeconomic status and race/ ethnicity. These potentially mediating variables were selected based on previous HCSUS research that showed some of the same low socioeconomic and minority groups we selected received less care than comparison groups or had less insurance coverage. 2 These variables included use of less than two ambulatory medical visits in the prior six months, emergency department visits that did not lead to hospitalization, more than one hospitalization in the previous six months, and insurance status (private/fee-for-service, private/hmo, Medicare, Medicaid, and uninsured). To assess type of treatment received at each interview, we assessed the type and number of antiretroviral medications and whether or not antiretroviral medication combinations constituted HAART. Our working definition of HAART, which is based on the Department of Health and Human Services/Henry J. Kaiser Foundation s Guidelines for the use of antiretroviral agents in HIV infected adults and adolescents 23 was as follows: certain combinations of nucleoside reverse transcriptase inhibitors (NRTI, e.g., zidovudine and lamuvidine) plus certain protease inhibitors

5 Cunningham, Hays, Duan, Andersen, Nakazono, Bozzette, and Shapiro 659 (PI, e.g., indinavir), combinations of PIs (e.g., ritonavir and saquinavir) or the combination of a PI plus a non-nucleoside reverse transcriptase inhibitor (NNRTI, e.g., Delavirdine). 24 Each respondent was classified as taking HAART, if he/she reported taking any one of the HAART combinations at each interview. According to guidelines published close to the time of the baseline interview, 25 over 99% of the sample was eligible for HAART. Finally, we included a variable representing other types of medications, including 53 different medications to treat conditions such as tuberculosis or cancer (complete list of medications available from the authors upon request). We excluded medications for opportunistic infection prophylaxis because of collinearity with CD4 count and HIV stage. All independent variables were measured at the baseline interview. Analysis. We used Cox proportional hazards models to estimate the relative risks of death because hazard ratios approximate risk ratios and the model results are robust in the face of minor violations of the proportional hazards assumptions. 26,27 Relative hazard ratios (an estimate of relative risk) and 95% confidence intervals were computed to express the effect of each characteristic, compared with the reference group, on the relative risk of death. We performed a series of multivariate Cox proportional hazards regression models in three stages, first to adjust for potential confounders, and then to examine the mediating role of services use and treatment variables in explaining socioeconomic differences in the risk of death. Model 1 included only SES variables (income, wealth, education, employment) and CD4 count. Model 2 included all the variables from Model 1 plus, race/ethnicity, age, gender, drug use, alcohol use, homelessness, geographic region, CD4 count, CDC stage of HIV infection, time of HIV infection, and mode of HIV exposure. In Model 3, we inspected changes in the relative risks of death for the SES variables after adding the following service use, insurance status, and treatment variables: ambulatory utilization (fewer than three visits in the prior six months), emergency room visits that did not lead to hospitalization (one or more in the prior six months), hospitalization (more than one within the prior six months), insurance, use of combination antiretroviral medications (none, one medication, two non-haart medications, or HAART combinations), and use of non-antiretroviral medications (none, one, two or more); the latter were potentially mediating variables. Using the final model, we also examined separately the predictors of non-hiv (n=210) and HIV-related (n=375) causes of death, using a competing risk model which censors non-hiv deaths in modeling HIV-related deaths and vice versa. 28 An additional model using all the same independent variables examined the time-dependent effect of HAART, since 71% of patients were on HAART some time during the study. 4 Inter-correlations among independent variables and the tolerance coefficient revealed no problems with multicollinearity in these models. The Cox models were robust with respect to minor violations of the proportional hazards assumptions. Models were also explored for two-way interactions between socioeconomic status, race/ethnicity, age and sex variables, but no significant effects were found. Linearization methods were used in all models to account for clustering, stratification, and sampling weights, and to estimate hazard ratios, standard errors, and levels of significance. 29

6 660 Socioeconomic status and survival with HIV Results Selected sample characteristics. Annual incomes were greater than $25,000 for more than a quarter and less than $5,000 for 20%. Accumulated assets were greater than $50,000 for 10% and $0 for 62%. Educational attainment was less than high school for 25% and college degree or more for 19%. Over 60% were not currently working and, of these, 74% were disabled, 12% were unemployed, and 14% were not working for other reasons. About one-third were black and 15% were Hispanic. Nearly one-quarter were women. For more than 23%, CD4 counts were less than 50 (Table 1). Unadjusted mortality findings. By December 2000, 20% had died, 13% due to HIV-related causes, and 7 % due to non-hiv-related causes (such as cardiovascular disease, liver failure, or IV drug overdose). In unadjusted analysis, lower SES was significantly associated with higher mortality compared with the highest SES groups for all four measures: those with $10,000 or less in annual income, those with low wealth, those with less than a high school education, and those not currently working (Table 2). The relationship for income was not monotonic. In addition, older age was significantly associated with mortality, but race, sex, geographic region, and homelessness were not. Concerning clinical factors, mortality rose with decreasing CD4 counts and advancement to an AIDS diagnosis, but not with increased time of HIV infection. Compared with those exposed to HIV through male-to-male sexual contact, people with injection drug use and those with unknown or other modes of exposure had elevated mortality. Drug dependency and tobacco smoking, but not alcohol use, were associated with elevated mortality. Having an emergency room visit and having been hospitalized were associated with increased mortality, while ambulatory visits were unassociated. People with Medicaid or Medicare had higher mortality than those with private insurance. Taking HAART was unassociated with mortality, while taking two or more non-antiretrovirals was associated with elevated mortality. Multivariate predictors of the risk of death. In the first multivariate model including only SES and CD4, those with no wealth, those with low education, and the unemployed continued to be significant predictors of elevated relative risks of death, but those with low incomes did not (Table 2). In the subsequent model adjusting for all sociodemographic and clinical variables, higher wealth, education, and employment were all significantly associated with reduced relative risk of death. In contrast, low income became associated with a reduced relative risk of death, the direction opposite to that of its previous association. The reversal in the direction of effect of income on mortality was due to the moderately high correlation of income with wealth (0.48). Race/ethnicity became unassociated with the risk of mortality. The associations of age and clinical characteristics with mortality remained largely unchanged. In the final model we examined whether the elevated risk of death associated with low socioeconomic status in the previous model changed with addition of the potentially mediating variables of service use, insurance status, and antiretroviral medications. Adjusting for these variables diminished somewhat, but did not eliminate, the significantly elevated relative risk of death for those with low wealth, those without a high school degree and those not currently working

7 Cunningham, Hays, Duan, Andersen, Nakazono, Bozzette, and Shapiro 661 Table 1. SAMPLE CHARACTERISTICS AND UNADJUSTED MORTALITY IN THE HCSUS Characteristics % (n) Unadjusted Mortality (%) All Respondents 100 (2864) 19.6 Socioeconomic Status Annual Income More than $25, (779) 15.1 ** $10,001 $25, (736) 18.9 $5,001 $10, (740) 25.8 $0 $5, (609) 18.9 Net Wealth (accumulated assets) More than $50, (272) 13.7* $5,001 $50, (392) 16.0 $1,001 $5, (223) 17.0 $1 $1, (170) 22.6 Less than $ (1807) 21.5 Education College Degree 19.3 (526) 15.2* Some College 28.4 (810) 18.8 High School Degree 27.4 (805) 19.2 No High School Degree 24.9 (723) 24.4 Current Work Status Working 37.3 (1015) 9.6 **** Not Working 62.7 (1849) 25.6 Other Sociodemographics Race/Ethnicity White 49.2 (1399) 19.1 Black 32.8 (959) 21.5 Hispanic 14.8 (415) 18.5 Other 3.2 (91) 13.6 Age (in years) (987) 17.2 ** (1591) and up 11.4 (286) 25.2 Sex Male 77.4 (2017) 19.7 Female 22.6 (847) 19.4 (continued)

8 662 Socioeconomic status and survival with HIV Table 1. Continued. Characteristics % (n) Unadjusted Mortality (%) Geographic Region West 28.4 (909) 16.0 Northeast 24.7 (707) 20.2 Midwest 11.1 (332) 25.2 South 35.8 (916) 20.4 Homelessness No 92.3 (2625) 19.3 Yes 7.8 (239) 23.0 Clinical Variables Lowest CD4 Count over (253) 6.4 **** (1096) (854) 19.0 under (661) 42.6 CDC Stage of HIV Infection Asymptomatic 10.4 (243) 6.6 **** Symptomatic 51.2 (1495) 12.0 AIDS 38.4 (1126) 33.4 Time of HIV Infection Before (1451) (607) (806) 17.4 HIV Exposure Male to Male sexual contact 48.6 (1303) 17.3 *** Injecting Drug Use 24.1 (696) 24.2 Heterosexual Contact 18.4 (578) 14.4 Other or unknown Mode 8.9 (287) 30.6 Tobacco Smoking No 49.1 (1426) 16.8 ** Yes 50.9 (1438) 22.4 Alcohol Use No 93.6 (2688) 20.0 Yes 6.4 (176) 14.8 Drug Dependency No 88.9 (2539) 18.8 *** Yes 11.1 (325) 26.4 Utilization and Treatment Variables Ambulatory Visits last six months Fewer Than three 22.3 (657) 20.4* Three or more 77.7 (2207) 16.9

9 Cunningham, Hays, Duan, Andersen, Nakazono, Bozzette, and Shapiro 663 Emergency Room visits last six months None 23.3 (710) 17.7 **** One or more 76.7 (2154) 25.9 Hospitalizations last six months None 80.4 (687) 14.2 **** One or more 19.6 (2177) 41.7 Insurance Status Private/fee-for-service 16.5 (391) 14.6 **** Private HMO 15.3 (474) 15.4 Medicaid 29.2 (858) 22.5 Medicare 19.2 (544) 31.4 No Insurance 19.8 (597) 11.4 Antiretroviral Medications No antiretrovirals 18.5 (531) 16.5 One antiretroviral 21.7 (622) 24.6 Two or more antiretroviral 36.9 (1056) 18.3 HAART 22.9 (655) 19.4 Non-Antiretroviral Medications None 25.6 (625) 11.0 **** One or more medication 22.5 (593) 12.7 Two or more medications 52.0 (1141) 26.9 P-Values for differences in proportions across categories: *p<0.05; **p<0.01; ***p<0.001; ****p< compared with their counterparts. The associations of the other covariates remained similar to those in the preceding model. Hospitalization, but neither ambulatory visit nor emergency room visit, was associated with death. The relative risks of death were significantly reduced for those who were receiving HAART combination antiretroviral medications at baseline. In the time-dependent analysis, the reduced risk of mortality for HAART was very similar (HR = 0.56, 95% CI = , p < ; not shown). Other significant predictors of death included old age, low CD4 count, AIDS stage of HIV infection, drug dependency, and unknown or other mode of HIV exposure, while lack of insurance and Northeast geographic region were associated with a reduced relative risk of death. Comparing significant predictors of death due to non-hiv and HIV-related causes, the findings were similar despite lower power for detecting non-hiv predictors (Tables 3 and 4). Variables uniquely associated with higher mortality for HIV-related causes included low education, having AIDS, emergency room visits, and hospitalization; those uniquely associated with non-hiv related mortality included older age, and no antiretrovirals. Discussion HIV is a life-threatening, chronic infection. Currently available treatments offer the possibility of long-term survival. Sporadic or inadequate care increases the risk

10 664 Socioeconomic status and survival with HIV Table 2. MULTIVARIATE ANALYSIS OF RELATIVE RISKS OF DEATH FOR ALL CAUSES IN THE HCSUS (N=2,864) Characteristics (reference group) Adjusted for Socioeconomic Variables and CD4 only (Hazard Ratio [95% Confidence Interval]) Adjusted for all Sociodemographics and Clinical Variables (Hazard Ratio [95% Confidence Interval]) Adjusted for Sociodemographics, Clinical, and Treatment Variables (Hazard Ratio [95% Confidence Interval]) Socioeconomic Status Annual Income (more than $25,000) $10,001 $25, ( ) 0.85 ( ) 0.92 ( ) $5,001 $10, ( ) 0.91 ( ) 0.97 ( ) $0 $5, ( ) 0.71 ( )* 0.78 ( ) Net Wealth (accumulated assets; more than $50,000) $5,001 $50, ) 1.57 ( )* 1.58 ( ) $1,001 $5, ( ) 1.51 ( ) 1.53 ( ) $1 $1, ( ) 1.96 ( )* 1.83 ( )* less than $ ( )* 1.89 ( )* 1.81 ( )* Education (college degree) Some college 1.13 ( ) 1.14 ( ) 1.11 ( ) High school degree 1.08 ( ) 1.21 ( ) 1.17 ( ) No high school degree 1.38 ( )* 1.53 ( )** 1.52 ( )** Current Work Status (working) Not working 1.95 ( )**** 1.66 ( )*** 1.44 ( )*

11 Cunningham, Hays, Duan, Andersen, Nakazono, Bozzette, and Shapiro 665 Other Sociodemographics Race/Ethnicity (white) Black 1.15 ( ) 1.04 ( ) Hispanic 0.92 ( ) 0.92 ( ) Other 0.71 ( ) 0.66 ( ) Age (in years; 18 34) ( ) 1.11 ( ) 50 and up 1.58 ( )* 1.51 ( )* Sex (male) Female 0.97 ( ) 0.94 ( ) Geographic Region (West) Northeast 0.77 ( ) 0.70 ( )* Midwest 1.09 ( ) 0.95 ( ) South 0.96 ( ) 0.96 ( ) Homelessness (no) Yes 1.10 ( ) 0.99 ( ) Clinical Variables Lowest CD4 Count (over 500) ( ) 1.38 ( ) 1.48 ( ) ( )** 2.42 ( )* 2.72 ( )* under ( )**** 5.82 ( )**** 5.64 ( )**** CDC Stage of HIV Infection (asymptomatic) Symptomatic 1.51 ( ) 1.42 ( ) AIDS 2.45 ( )*** 2.05 ( )** (continued)

12 666 Socioeconomic status and survival with HIV Table 2. Continued. Characteristics (reference group) Adjusted for Socioeconomic Variables and CD4 only (Hazard Ratio [95% Confidence Interval]) Adjusted for all Sociodemographics and Clinical Variables (Hazard Ratio [95% Confidence Interval]) Adjusted for Sociodemographics, Clinical, and Treatment Variables (Hazard Ratio [95% Confidence Interval]) Time of HIV Infection (before 1992) ( ) 1.00 ( ) ( )* 0.88 ( ) HIV Exposure (male to male sexual contact) Injecting drug use 1.04 ( ) 1.02 ( ) Heterosexual contact 0.84 ( ) 0.85 ( ) Other or unknown mode 1.80 ( )*** 1.80 ( )** Tobacco Smoking (no) Yes 1.29 ( )* 1.24 ( )* Alcohol Use (no) Yes 0.91 ( ) 0.89 ( ) Drug Dependency (no) Yes 1.44 ( )** 1.34 ( )* Utilization and Treatment Variables Ambulatory Visits last six months (fewer than two) Two or more 1.02 ( ) Emergency Room visits last six months (none) One or more 1.22 ( )

13 Cunningham, Hays, Duan, Andersen, Nakazono, Bozzette, and Shapiro 667 Hospitalizations last six months (None) One or more 1.99 ( )**** Insurance Status (Private/fee-for-service) Private HMO 0.89 ( ) Medicaid 0.78 ( ) Medicare 1.00 ( ) No insurance 0.62 ( )** Antiretroviral Medications (No antiretrovirals) One antiretroviral 0.96 ( ) Two or more antiretroviral 0.74 ( )* HAART 0.58 ( )*** Non-Antiretroviral Medications (None) One or more medication 0.91 ( ) Two or more medications 1.10 ( ) Death due to all causes was ascertained through follow-up of all participants and through National Death Index search. * p<0.05; ** p<0.01; *** p<0.001; **** p<

14 668 Socioeconomic status and survival with HIV Table 3. RELATIVE RISKS OF DEATH FOR HIV RELATED DEATHS ONLY a Characteristics Relative Risk of Death for HIV Related Deaths (Hazard Ratio [95% Confidence Interval]) Socioeconomic Status Annual Income (more than $25,000) $10,001 $25, ( ) $5,001 $10, ( ) $0 $5, ( ) Net Wealth (accumulated assets; more than $50,000) $5,001 $50, ( ) $1,001 $5, ( ) $1 $1, ( )* less than $ ( )* Education (college degree) Some college 1.30 ( ) High school degree 1.30 ( ) No high school degree 1.82 ( )** Current Work Status (working) Not working 1.37 ( ) Other Sociodemographics Race/Ethnicity (white) Black 1.18 ( ) Hispanic 0.98 ( ) Other 0.58 ( ) Age (in years; 18 34) ( ) 50 and up 1.23 ( ) Sex (male) Female 0.86 ( ) Geographic Region (West) Northeast 0.73 ( ) Midwest 0.70 ( ) South 1.07 ( ) Homelessness (no) Yes 0.76 ( ) Clinical Variables Lowest CD4 Count (over 500) ( )

15 Cunningham, Hays, Duan, Andersen, Nakazono, Bozzette, and Shapiro ( )** under ( )*** CDC Stage of HIV Infection (asymptomatic) Symptomatic 1.67 ( ) AIDS 2.73 ( )* Time of HIV Infection (Before 1992) ( ) ( ) HIV Exposure (male to male sexual contact) Injecting drug use 0.85 ( ) Heterosexual contact 0.85 ( ) Other or unknown mode 1.72 ( )* Tobacco Smoking (no) Yes 1.19 ( ) Alcohol Use (no) Yes 0.96 ( ) Drug Dependency (no) Yes 1.32 ( ) Utilization and Treatment Variables Ambulatory Visits last six months (fewer than two) Two or more 1.09 ( ) Emergency Room visits last six months (none) One or more 1.34 ( )**** Hospitalizations last six months (none) One or more 2.24 ( )**** Insurance Status (private/fee-for-service) Private HMO 0.97 ( ) Medicaid 0.74 ( ) Medicare 0.81 ( ) No insurance 0.66 ( )* Antiretroviral Medications (no antiretrovirals) One antiretroviral 1.14 ( ) Two or more antiretrovirals 0.87 ( ) HAART 0.74 ( ) Non-Antiretroviral Medications (none) One or more medication 0.97 ( ) Two or more medications 1.24 ( ) P-Values for each category of each variable compared with the reference: *p<0.05; **p<0.01; ***p<0.001; **** p< a HIV-related deaths were determined by primary ICD-9 code, n=375

16 670 Socioeconomic status and survival with HIV Table 4. RELATIVE RISKS OF DEATH FOR NON-HIV RELATED DEATHS ONLY a Characteristics Relative Risk of Death for non-hiv Related Deaths (Hazard Ratio [95% Confidence Interval]) Socioeconomic Status Annual Income (more than $25,000) $10,001 $25, ( ) $5,001 $10, ( ) $0 $5, ( ) Net Wealth (accumulated assets; more than $50,000) $5,001 $50, ( ) $1,001 $5, ( ) $1 $1, ( ) less than $ ( )* Education (college degree) Some college 0.89 ( ) High school degree 1.18 ( ) No high school degree 1.37 ( ) Current Work Status (working) Not working 1.27 ( ) Other Sociodemographics Race/Ethnicity (white) Black 0.82 ( ) Hispanic 0.55 ( ) Other 1.03 ( ) Age (in years; 18 34) ( ) 50 and up 2.13 ( )* Sex (male) Female 0.96 ( ) Geographic Region (West) Northeast 0.87 ( ) Midwest 1.73 ( ) South 0.88 ( ) Homelessness (no) Yes 1.60 ( ) Clinical Variables Lowest CD4 Count (over 500) ( )

17 Cunningham, Hays, Duan, Andersen, Nakazono, Bozzette, and Shapiro ( ) under ( )* CDC Stage of HIV Infection (asymptomatic) Symptomatic 1.52 ( ) AIDS 1.83 ( ) Time of HIV Infection (before 1992) ( ) ( ) HIV Exposure (male to male sexual contact) Injecting drug use 1.39 ( ) Heterosexual contact 0.83 ( ) Other or unknown mode 2.48 ( )** Tobacco Smoking (no) Yes 1.29 ( ) Alcohol Use (no) Yes 0.94 ( ) Drug Dependency (no) Yes 1.24 ( ) Utilization and Treatment Variables Ambulatory Visits last six months (fewer than two) Two or more 0.83 ( ) Emergency Room visits last six months (none) One or more 1.00 ( ) Hospitalizations last six months (none) One or more 1.18 ( ) Insurance Status (private/fee-for-service) Private HMO 0.83 ( ) Medicaid 0.78 ( ) Medicare 1.27 ( ) No insurance 0.46 ( )** Antiretroviral Medications (no antiretrovirals) One antiretroviral 0.79 ( ) Two or more antiretrovirals 0.73 ( ) HAART 0.42 ( )** Non-Antiretroviral Medications (none) One or more medication 0.87 ( ) Two or more medications 0.90 ( ) P-Values for each category of each variable compared with the reference: *p<0.05; **p<0.01; ***p<0.001; ****p< a Non-HIV-related deaths were determined by primary ICD-9 code, n=210

18 672 Socioeconomic status and survival with HIV of serious health consequences, including death, over a relatively short period of time. Thus, identifying groups at greatest risk of mortality and the extent to which differences in utilization of care explain disparities between groups is essential for efforts aiming to improve outcomes of HIV infection. We hypothesized that low SES persons, blacks, and Hispanics would experience higher mortality than their counterparts. In the final model, the mortality rate for blacks was not significantly higher than for whites. The Hispanic death rate was also not higher than the rate for whites. Previous analyses of HCSUS have suggested that the gap in treatment between minorities and whites started closing after HAART was introduced. 4 Perhaps as a result, the difference in mortality between minorities and whites has diminished as well. It was also surprising that those with low income did not have elevated (and in fact had reduced) mortality in the multivariate analysis. This finding was because income was less strongly associated with mortality than wealth, education, end employment and all four of these socioeconomic variables were moderately intercorrelated. On the other hand, those with no wealth had an 81% higher risk of death from all causes than those in the highest category of wealth, after the full adjustment for all covariates. Similarly, those with less than a high school education had a 52% higher risk of death than their counterparts in the final model. The associations of SES variables with risk of death were similar for non-hiv and HIV related causes of death. The finding of a markedly elevated relative risk of death for those with HIV in low SES groups deserves attention, given the large proportions of persons with HIV in the U.S. who have low SES. 30 Our finding of elevated relative risks of death for those with low SES was strengthened by the consistency of the finding across most of the measures we used. Education is often regarded as an important measure of SES in the U.S., because it is stable and less confounded by changes in health status than is income. Low income was associated with mortality only in the unadjusted analysis, whereas low wealth remained strongly associated with mortality in the final model. Wealth (or financial assets) has been suggested as a better measure of SES than income, because it is less influenced by health status. 17 For this reason, our findings support the utility of using measures of wealth, in addition to income, in studies of health outcomes. Employment status is rarely suggested as a primary measure of SES because it too is often influenced by health status. However, in our study it added breadth to the other measures. Consistent with well-known predictors of survival, people with low CD4 counts and those with AIDS-defining illnesses had an elevated relative risk of death. Similarly, older age and taking non-antiretroviral medications predicted an elevated death risk, reflecting the greater burden of illness affecting older groups in the U.S. in general. 31,32 Another group that experienced excess mortality was actively drug dependent persons. In addition, we found that people exposed to HIV through injection drug use had a higher relative risk of death than those exposed through male-to-male sexual contact in unadjusted analysis, but not in subsequent models. Perhaps this difference derives from the fact that exposure to HIV through injection drug use more closely reflects past than it reflects recent drug use or dependency. 33,34 It is difficult to interpret the poor survival of those with unknown and other modes of HIV exposure. 35

19 Cunningham, Hays, Duan, Andersen, Nakazono, Bozzette, and Shapiro 673 Some of the variables we hypothesized to be mediators of the relationship between SES and mortality were significantly associated with the risk of death. Consistent with previous studies in HCSUS, insurance at baseline was associated with increased relative risk of death because obtaining health insurance increases as health status worsens. 36 Becoming hospitalized was predictive of an elevated death risk, possibly reflecting the burden of illness or lack of early treatment. As expected, use of HAART substantially lowered the risk of death. There were a number of limitations to our study. People who were not receiving any medical care could not be included. However, our findings complement those from population-based surveillance systems well. 37 Viral load testing was not in universal use at the start of our survey, so we did not have the data to adjust for this potential confounder. However, viral load is closely correlated with HAART use, 38,39 which we did measure, so it is unlikely that our results are biased by lack of viral load data. It is possible that, prior to the period of observation, illness from HIV/AIDS could have led to loss of employment, loss of income, and loss of wealth for some people, but this could not have accounted for the higher mortality of these groups in follow-up. We also were not able to examine whether or not detailed quality of care processes helped to explain the findings. Future studies should examine these processes in more detail to determine whether they help to explain the worse mortality for low SES groups, and to guide future interventions. Despite these limitations, the finding that low SES (by three of four measures) was associated with elevated mortality is of concern for clinicians and policymakers. On the other hand, it is encouraging that previously observed elevated mortality for blacks was not observed in this study. This was probably because the gap in HAART use between minorities and whites narrowed over time and all patients in HCSUS received some care. 4 It is unclear why disparities by SES and not race persisted, but the finding supports calls for investigation of the causes of SES disparities that go beyond utilization of care. 40 Clearly, health care systems must do a better job of addressing the special needs of low SES populations if their prospects for long-term survival with HIV infection are to be improved. Efforts to improve outcomes for low SES groups with HIV infection are particularly important given the disproportionate rates of HIV infection in these groups. Acknowledgments The HIV Cost and Services Utilization Study was conducted under cooperative agreement U-01HS08578 (M.F. Shapiro, PI; S.A. Bozzette, Co-PI) between RAND and the Agency for Health Research and Quality. Substantial additional funding for this cooperative agreement was provided by the Health Resources and Services Administration, the National Institute of Mental Health, the National Institute on Drug Abuse, and the National Institutes of Health Office of Research on Minority Health through the National Institute of Dental Research. Additional support was provided by Merck and Company, Inc., Glaxo-Wellcome, Incorporated, the National Institute on Aging, and the Office of the Assistant Secretary for Planning and Evaluation in the U.S. Department of Health and Human Services. Dr. Hays was supported by a grant from the National Institute of Health (AI 28697). Dr.

20 674 Socioeconomic status and survival with HIV Cunningham was a Doris Duke Charitable Foundation Clinical Scientist. Drs. Cunningham, Hays and Shapiro also received partial support from the UCLA-Drew Project Export, National Institutes of Health grant #1P20MD , from the National Center for Minority Health and Health Disparities. Dr. Cunningham also received partial support from the UCLA Center for Health Improvement in Minority Elders / Resource Centers for Minority Aging Research, National Institutes of Health, National Institute of Aging, (AG ). Notes 1. Palella FJ Jr, Delaney KM, Moorman AC, et al. Declining morbidity and mortality among patients with advanced human immunodeficiency virus infection. HIV Outpatient Study Investigators. N Engl J Med 1998 Mar 26;338(13): Shapiro MF, Morton SC, McCaffrey DF, et al. Variations in the care of HIV-infected adults in the United States: results from the HIV Cost and Services Utilization Study. JAMA 1999 Jun 23 30;281(24): Andersen R, Bozzette S, Shapiro M, et al. Access of vulnerable groups to antiretroviral therapy among persons in care for HIV disease in the U.S. HSCUS Consortium. HIV Cost and Utilization Study. Health Serv Res 2000 Jun;35(2): Cunningham WE, Markson LE, Andersen RM, et al. Prevalence and predictors of highly active antiretroviral therapy use in persons with HIV infection in the U.S. J Acquir Immune Defic Syndr 2000 Oct 1;25(2): AIDS Action. Connecting to Care: Addressing Unmet Need in HIV. Washington, DC: AIDS Action, 2004; Andrulis DP. Access to care is the centerpiece in the elimination of socioeconomic disparities in health. Ann Intern Med 1998 Sep 1;129(5): McFarland W, Chen S, Hsu L, et al. Low socioeconomic status is associated with a higher rate of death in the era of highly active antiretroviral therapy, San Francisco. J Acquir Immune Defic Syndr 2003 May 1;33(1): Schwarcz SK, Hsu LC, Vittinghoff E, et al. Impact of protease inhibitors and other antiretroviral treatments on acquired immunodeficiency syndrome survival in San Francisco, California, Am J Epidemiol 2000 Jul 15;152(2): Wood E, Montaner JS, Chan K, et al. Socioeconomic status, access to triple therapy, and survival from HIV- disease since AIDS Oct 18;16(15): Schechter MT, Hogg RS, Aylward B, et al. Higher socioeconomic status is associated with slower progression of HIV infection independent of access to health care. J Clin Epidemiol Jan;47(1): Rapiti E, Porta D, Forastiere F, et al. Socioeconomic status and survival of persons with AIDS before and after the introduction of highly active antiretroviral therapy. Lazio AIDS Surveillance Collaborative Group. Epidemiology 2000 Sep;11(5): Centers for Disease Control and Prevention (CDC). HIV/ AIDS Surveillance Supplemental Report, Vol. 8, No. 2. Deaths among persons with AIDS through December Atlanta: CDC, Centers for Disease Control and Prevention. HIV/AIDS Surveillance Report, December 2001 Year-end edition, Vol. 13, No. 2. U.S. HIV and AIDS cases reported through December Atlanta: CDC, Joyce GF, Goldman DP, Leibowitz AA, et al. A socioeconomic profile of older adults with HIV. J Health Care Poor Underserved 2005 Feb;16(1): Simon PA, Hu DJ, Diaz T, et al. Income and AIDS rates in Los Angeles County. AIDS 1995 Mar;9(3):281 4.

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