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JAIDS Journal of Acquired Immune Deficiency Syndromes:
doi: 10.1097/01.qai.0000243090.32866.4e
Epidemiology and Social Science

The Economic Burden of HIV in the United States in the Era of Highly Active Antiretroviral Therapy: Evidence of Continuing Racial and Ethnic Differences

Hutchinson, Angela B PhD, MPH*; Farnham, Paul G PhD†; Dean, Hazel D ScD, MPH‡; Ekwueme, Donatus U PhD§; del Rio, Carlos MD; Kamimoto, Laurie MD, MPH*; Kellerman, Scott E MD, MPH*¶

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Author Information

From the *Division of HIV/AIDS Prevention, National Center for HIV, STD, and TB Prevention (NCHSTP), Centers for Disease Control and Prevention (CDC), Atlanta, GA; †Andrew Young School of Policy Studies, Georgia State University, Atlanta, GA; ‡NCHSTP, CDC, Atlanta, GA; §Division of Cancer Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, CDC, Atlanta, GA; Emory University Center for AIDS Research, Atlanta, GA; and ¶S. E. Kellerman's current affiliation is The City of New York Department of Health and Mental Hygiene, Bureau of HIV/AIDS Prevention & Control, New York, NY.

The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the CDC.

Received for publication December 3, 2005; accepted July 13, 2006.

Reprints: Angela B. Hutchinson, PhD, MPH, Division of HIV/AIDS Prevention, National Center for HIV, STD, and TB Prevention, Centers for Disease Control and Prevention, 1600 Clifton Road NE, Mail Stop E-46, Atlanta GA 30333 (e-mail: ahutchinson@cdc.gov).

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Abstract

Background: Assessing the economic burden of HIV/AIDS can help to quantify the effect of the epidemic on a population and assist policy makers in allocating public health resources.

Objective: To estimate the economic burden of HIV/AIDS in the United States and provide race/ethnicity-specific estimates.

Methods: We conducted an incidence-based cost-of-illness analysis to estimate the lifetime cost of HIV/AIDS resulting from new infections diagnosed in 2002. Data from the HIV/AIDS Reporting System of the Centers for Disease Control and Prevention were used to determine stage of disease at diagnosis and proportion of cases by race/ethnicity. Lifetime direct medical costs and mortality-related productivity losses were estimated using data on cost, life expectancy, and antiretroviral therapy (ART) use from the literature.

Results: The cost of new HIV infections in the United States in 2002 is estimated at $36.4 billion, including $6.7 billion in direct medical costs and $29.7 billion in productivity losses. Direct medical costs per case were highest for whites ($180,900) and lowest for blacks ($160,400). Productivity losses per case were lowest for whites ($661,100) and highest for Hispanics ($838,000). In a sensitivity analysis, universal use of ART and more effective ART regimens decreased the overall cost of illness.

Conclusion: Direct medical costs and productivity losses of HIV/AIDS resulting from infections diagnosed in 2002 are substantial. Productivity losses far surpass direct medical costs and are disproportionately borne by minority races/ethnicities. Our analysis underscores economic benefits of more effective ART regimens and universal access to ART.

Although the economic impact of HIV/AIDS has been a concern since the beginning of the epidemic, most cost-of-illness estimates comprise only the direct costs associated with medical treatment of HIV/AIDS.1,2 Indirect costs, the value of lost productivity because of illness, disability, and premature death, should be included in cost-of-illness studies because they are part of the economic burden of disease on society; yet, we were only able to identify 2 published studies conducted early in the epidemic that included indirect costs of HIV/AIDS in the United States.3,4 Additionally, we were not able to identify a published study since the advent of ART that assessed the economic burden of HIV/AIDS for the US population. In this article, the term HIV/AIDS includes persons with a diagnosis of HIV infection, regardless of their AIDS status at diagnosis.

A number of studies have documented racial and ethnic differences in disease morbidity and mortality, health status, and health services use.5-7 These differences are particularly evident in the HIV/AIDS epidemic. In 2002, minority races and ethnicities accounted for nearly 70% of all new HIV/AIDS diagnoses, even though they composed only approximately 23% of the US population.8,9 In addition, persons of minority races/ethnicities are more likely to be diagnosed later in the course of disease and are less likely to receive antiretroviral therapy (ART).10-13 Assessing the economic burden of HIV/AIDS on minority populations can help to quantify the effect of the epidemic on these populations, aid policy makers in allocating limited health resources to treatment and prevention programs, and assist in estimating the economic effect of reducing health disparities.

Incidence-based cost-of-illness estimates include the total lifetime costs resulting from new cases of disease that occurred within a set time period and are preferable for predicting the economic effects of an epidemic.14 An estimated 40,000 new HIV infections occur in the United States each year.15 In this article, (1) we provide an incidence-based national estimate of the cost of HIV/AIDS that includes direct and indirect costs for new infections diagnosed in 2002, (2) we provide race/ethnicity-specific estimates of the economic burden of disease, and (3) we estimate the lifetime costs of HIV/AIDS in the era of highly active ART.

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METHODS

The study was conducted from the societal perspective, which includes all relevant costs regardless of who pays.16 We used data from the HIV/AIDS Reporting System of the Centers for Disease Control and Prevention (CDC) to estimate stage of disease at diagnosis and proportion of cases by race/ethnicity. We used data from the literature for direct medical costs, progression of HIV/AIDS, and use of ART. All costs were adjusted for inflation to year 2002 US dollars using the medical care component of the consumer price index, and future costs were discounted at a 3% rate.16,17 For indirect costs, we used published estimates of the current value of earnings and household production.18 We did not include morbidity-related costs because of the lack of current disability estimates for persons with HIV infection.

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Epidemiologic Data

We analyzed data on adults and adolescents (≥13 years of age) whose diagnoses were made in 2002 and reported to the CDC through June 2003 from 30 areas (29 states and the US Virgin Islands) with confidential name-based HIV infection reporting since 1998.8 The 30 areas have had confidential HIV reporting for sufficient time to allow for stabilization of data collection and to account for delays in reporting.

To obtain the proportion of newly diagnosed infections by race/ethnicity and CD4 count, we examined cases of HIV/AIDS for the presence of a CD4 cell count test result within 12 months after diagnosis. We excluded cases without CD4 counts (range: 28%-46%). We stratified by race/ethnicity and initial CD4 count using the following 3 categories, ≥500 cells/μL, 200 to 499 cells/μL, and 0 to 199 cells/μL, to obtain the proportionate contribution of each race/ethnicity by stage of disease at diagnosis. To obtain the national estimate, we applied the proportionate contribution of the categories of race/ethnicity and stage of disease to the national incidence estimate of 40,000 new HIV infections per year.15 This gave us an estimate of the stage of disease at diagnosis of the 40,000 new HIV infections in 2002 stratified by race/ethnicity.

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Lifetime Direct Medical Costs

Similar to previous studies, we used stage of disease at diagnosis and access to care to calculate lifetime direct medical costs.19,20 In our analysis, we account for different initial CD4 counts, recent life expectancy data, and use of ART. First, we classified HIV infection into 4 stages reflecting the severity of disease based on CD4 count: <50 cells/μL, 51 to 199 cells/μL, 200 to 499 cells/μL (for persons on ART, we divided the category into 200-350 cells/μL and 351-499 cells/μL), and >500 cells/μL. Although our total cost-of-illness estimates are based on 3 stages of disease at diagnosis, to calculate direct medical costs, we used 4 stages of disease (by adding CD4 count <50 cells/μL) to incorporate progression through the disease stages. Second, we estimated the amount of time spent in each stage. Third, we estimated a mean cost per stage and developed estimates of lifetime direct medical costs for persons who received ART and those who did not. Finally, we summed the costs to obtain average lifetime direct medical costs.

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Life Expectancy

We used published estimates for the long-term survival of patients receiving and not receiving ART.21,22 To calculate the life expectancy (12.4 years) for persons diagnosed with CD4 counts of ≥500 cells/μL not on ART, we assumed that the life expectancy for persons in that disease stage who did and did not receive ART was proportionately similar to that for persons with CD4 counts from 200 to 499 cells/μL.21 To estimate the time spent in each stage or CD4 cell count category, we used data from the literature and the expert opinion of the authors.19,23 For persons on ART, we assumed that ART would increase their CD4 cell counts to the next highest category.24

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Drug and Nondrug Costs

We developed separate estimates of direct medical costs for persons in each disease stage who received ART when eligible and those who did not. We assume that ART was started when the CD4 count was at or below 350 cells/μL and continued throughout the course of illness.25 Medical costs for persons on ART were derived from the literature and included the cost of HIV infection or AIDS (ie, inpatient and outpatient care, emergency room visits, non-ART drug costs) by disease stage, the cost of ART and CD4 count and viral load testing, and the cost of ART-related side effects and genotypic resistance testing.24,26 Annual costs for CD4 counts ≥500 cells/μL included the cost of HIV infection for persons with CD4 counts ≥500 cells/μL ($2743) and the cost of CD4 cell count and viral load testing ($85 and $112, respectively) twice per year.24 For CD4 counts from 200 to 499 cells/μL, annual costs included the cost of HIV infection ($4693) and the costs of CD4 cell count and viral load testing (3 times yearly for persons not on ART and 4 times yearly for persons on ART). For persons on ART, annual costs included triple-drug ART ($12,665), ART-related side effects ($136), 2 episodes, and yearly genotypic resistance testing ($368).24,26 For CD4 counts from 51 to 199 cells/μL, annual costs included the annual cost of AIDS ($10,129), CD4 cell count and viral load monitoring, 3 episodes of ART-related side effects, triple-drug therapy, the incremental cost of 4-drug therapy ($2281), and genotypic resistance testing twice yearly.24 For CD4 counts ≤50 cells/μL, annual costs included the annual costs of AIDS, salvage therapy ($14,948), incremental cost of 4-drug therapy, 4 episodes of ART-related side effects, and genotypic resistance testing twice yearly.

For persons not receiving ART, we used treatment cost estimates in Holtgrave and Pinkerton's low-cost scenario, which assumes no use of combination therapy.19 For all stages with a CD4 count <500 cells/μL, annual drug costs were $4190. Annual nondrug costs were $5597 for a CD count from 200 to 499 cells/μL, $15,837 for a CD4 count from 51 to 199 cells/μL, and $44,974 for a CD4 count ≤50 cells/μL.19 For the category with a CD4 count ≥500 cells/μL, we assumed that nondrug costs for persons not on ART were equal to the nondrug costs for persons on ART.

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Antiretroviral Therapy Use

We used data from the HIV Cost and Services Utilization Study (HCSUS) on the proportion of persons who had ever used ART, reported by race/ethnicity (white, 78%; black, 59%; hispanic, 73%; and other, 77%) to allocate cases by use of ART.11

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Indirect Costs

Indirect costs of HIV/AIDS were calculated using the human capital approach, which measures income foregone because of morbidity and mortality.14 We included mortality-related productivity losses (the value of productivity lost for those who died prematurely as a result of HIV/AIDS) but not morbidity-related productivity losses (eg, disability) or other indirect costs. Productivity losses are measured as the present value of a person's lifetime stream of wage income and other productive activity. To calculate productivity losses, we estimated average life expectancy for each racial/ethnic group and CD4 cell count category by adding life expectancy to the median age at diagnosis (range: 31-39 years) for each race and gender from our cohort of HIV/AIDS cases. To estimate the value of future earnings, we used estimates of the present value of earnings and household production for the year 2000, which were based on a life expectancy of 75 years.18

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Total Cost of Illness

To calculate the total cost of illness, we added mortality-related productivity losses to direct medical costs to obtain the total US cost-of-illness estimate and race/ethnicity-specific cost-of-illness estimates. We also calculated the cost per case for each of these categories.

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Sensitivity Analysis

To assess uncertainty in our analysis, we conducted 1-way sensitivity analyses on the following: (1) receipt of ART, (2) life expectancy, (3) effect of missing CD4 cell count data, and (4) race/ethnicity-specific earning estimates. To investigate the impact of universal access to ART in the sensitivity analysis, we assumed that 100% of persons for whom ART was indicated actually received ART. Given uncertainty in our estimate of life expectancy for persons receiving ART and recent advances in treatment, we investigated the effect of longer life expectancy. We increased life expectancy to 26.1 years from 24.4 years for persons with an initial CD4 cell count of ≥500 cells/μL and increased the other CD4 categories proportionately.27 Because a large proportion of the cases missing CD4 count data were cases of HIV infection without AIDS, we conducted sensitivity analysis in which we classified those cases as HIV infection and assigned CD4 values of ≥500 cells/μL or 200 to 499 cells/μL proportionate to the cases with CD4 counts that fell into these categories. To assess the impact of race/ethnicity-specific earnings, we adjusted the productivity loss estimates by the proportionate differences in household income by race/ethnicity.28 Because no estimates were reported for American Indian/Alaska natives, we used an average of the estimates for blacks and hispanics.

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RESULTS

Of the estimated 40,000 new HIV infections that occurred in 2002, blacks accounted for 21,829 (55%), followed by whites (12,655 or 32%) and hispanics (5035 or 13%) (Table 1). Overall, 56% of the cases had CD4 counts of <200 cells/μL at diagnosis, which indicated simultaneous diagnoses of HIV infection and AIDS; only 18% had CD4 counts of ≥500 cells/μL at diagnosis. Of the 22,358 cases of AIDS, 12,754 (57%) were in blacks, 6289 (28%) were in whites, and 3079 (14%) were in hispanics. Among whites, 23% were diagnosed with a CD4 count ≥500 cells/μL; the percentage of such cases in other races/ethnicities ranged from 11% in Asians/Pacific islanders to 16% in blacks. Among hispanics, 61% had a diagnosis with a CD4 count of <200 cells/μL; the comparable percentages were 58% for blacks and 50% for whites.

Table 1
Table 1
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In Table 2, we present our estimates of lifetime direct medical costs. For each initial CD4 cell category, we present the cost per stage based on the stages of declining CD4 cell counts and time spent in each stage. Thus, for an initial CD4 cell count of ≥500 cells/μL for persons receiving ART, we estimate total lifetime direct medical costs to be $361,944 ($230,044 discounted) with a life expectancy of 24.4 years. For persons not receiving ART, we estimate total lifetime direct medical costs to be $145,218 ($114,938 discounted) with a life expectancy of 12.4 years.

Table 2
Table 2
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For the estimated 40,000 new HIV infections in 2002, we calculated the discounted total lifetime cost of illness at $36.4 billion, including $29.7 billion in mortality-related productivity losses and $6.7 billion in lifetime direct medical costs (Table 3). These estimates undiscounted are: total cost of illness, $52.8 billion; productivity losses, $44.8 billion; and direct medical costs, $8.0 billion. The estimated discounted lifetime cost of new diagnoses for blacks ($20.2 billion) was almost twice that for whites ($10.7 billion); the cost was $5.1 billion for hispanics, $204 million for Asians/Pacific islanders, and $242 million for American Indians/Alaska natives.

Table 3
Table 3
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Direct medical costs per case were the highest for whites ($180,900) and the lowest for blacks ($160,400) (Table 3). Productivity losses per case were the lowest for whites ($661,100) and the highest for hispanics ($838,000). Productivity losses per case for blacks ($766,800) were substantially higher than for whites but lower than those for hispanics and American Indian/Alaska natives.

The distribution of costs for persons receiving ART also differed by race/ethnicity. The largest percentages of total illness costs attributed to persons receiving ART, calculated from Table 3, were for whites (75%) and American Indians/Alaska natives and Asians/Pacific islanders (74%), followed by those for hispanics (71%) and blacks (56%).

In sensitivity analyses, when we assumed that all persons were treated with ART, direct medical costs increased by approximately $1.2 billion (to $8.0 billion), but productivity losses decreased by $3 billion, resulting in a net reduction in total cost of illness of $1.7 billion (Table 4). When we investigated the impact of more effective ART regimens by increasing life expectancy for persons receiving ART, the per person lifetime direct medical costs increased. Total direct medical costs increased by approximately $98 million, but productivity losses decreased by $2.7 billion, resulting in a net reduction in the total cost of illness of $2.6 billion. When we classified missing CD4 cell count data as cases of HIV infection only (≥200 cells/μL), there was a net reduction in the total cost of illness of $3.2 billion. Using race/ethnicity-specific earnings estimates, productivity losses per case were lower for blacks, hispanics, and American Indian/Alaska natives compared with whites and higher for Asian/Pacific islanders reducing the total cost of illness by $5.6 billion.

Table 4
Table 4
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DISCUSSION

The economic burden of HIV/AIDS resulting from an estimated 40,000 new HIV infections diagnosed in 2002 is substantial, with 81% of the $36.4 billion in total cost consisting of mortality-related productivity losses. This result is important considering that our total cost underestimates true indirect costs of HIV/AIDS, because morbidity-related productivity losses and caregiver costs were not included. Our findings are consistent with those of one of the few studies that evaluated indirect costs of HIV/AIDS in the United States, in which 75% of total costs were mortality-related productivity losses.4 As those authors found, the reason why indirect costs are so much greater than direct medical costs is that most persons with HIV/AIDS are young and in their most productive years. The $6.7 billion in direct medical costs is also substantial, particularly considering that we adjusted our estimates for the actual use of ART. These estimates are conservative, because medical costs such as long-term care and hospice care were not included.

To the best of our knowledge, this is the first study to assess the economic burden of HIV/AIDS among minority races/ethnicities in the United States systematically and to include productivity losses in the era of highly active ART. When the costs per case are compared, racial/ethnic disparities become evident. For blacks and hispanics, mortality-related productivity losses per case were 16% and 26%, respectively, higher than for whites, and direct medical costs per case were 13% and 5%, respectively, lower. In fact, whites had the highest per case direct medical costs and lowest productivity losses of all race/ethnicity groups. These results are attributable to diagnosis in the later stages of disease, delays in getting into care, and less access to ART for these minority groups.10-12,29

Our sensitivity analyses highlight the critical issue of access to ART. Although we did not assess the cost-effectiveness of ART, achieving 100% coverage of ART was cost-saving because it decreased years of life lost, and thus lowered productivity losses. We also found that increases in life expectancy based on more effective ART resulted in cost savings, providing additional evidence of the value of investing in interventions that increase the effectiveness of and access to ART.30,31

This study is subject to several limitations. We did not consider morbidity costs because we could not identify current (post-ART) measures of morbidity-related productivity losses (eg, disability days) in the US population. Additionally, we did not consider productivity losses for caregivers or intangible costs such as pain and suffering. Thus, our results underestimate the true economic burden of HIV/AIDS. Scitovsky and Rice4 found that morbidity-related productivity losses accounted for 5% of the total cost of illness in 1991. Because of increases in life expectancy since then, current morbidity costs are likely more than 5% of total costs, which, if included, could increase our total cost-of-illness estimate by more than $2.0 billion.

In our analysis, we used race/ethnicity-specific data for the number of HIV/AIDS cases, CD4 cell count at diagnosis, and estimates of ART use. There may be other racial/ethnic differences that we did not consider, however, such as level of adherence to ART and life expectancy, that may affect outcomes of care.29,32 In our sensitivity analysis, use of race/ethnicity-specific earning estimates resulted in lower productivity losses per case for blacks, hispanics, and American Indians/Alaska natives compared with whites. In our base-case analysis, however, we used average wages rather than race/ethnicity- or gender-specific wages to value productivity losses because the latter reflect inequities in the labor market.16 Therefore, the use of average wage rates enabled us to highlight loss attributable to excess disease burden alone.

In 2 situations, we may have overestimated the economic burden. In accordance with previous studies, our analysis assumes that persons with HIV/AIDS had similar earnings and rates of labor force participation as did the general population.4 If the proportion of HIV-infected persons in the labor market was substantially smaller than that of the general public, however, we may have overestimated mortality-related productivity losses. If the missing data on initial CD4 count predominantly reflected persons with HIV infection without AIDS, we may have overestimated the total cost of illness by approximately $3.2 billion. Additionally, there is some uncertainty in our annual incidence estimate of 40,000 new infections per year that could increase or decrease our cost-of-illness estimate.

There is also uncertainty in our estimates of lifetime direct medical costs. Our cost-of-illness estimates incorporate some assumptions and expert opinion regarding disease progression. Nevertheless, our discounted estimate of lifetime costs ($230,044 for persons diagnosed with a CD4 count ≥500 cells/μL) is close to Holtgrave and Pinkerton's estimate ($193,239)19 adjusted to 2002 US dollars. Although one might expect a larger increase in costs attributable to ART, current ART guidelines have resulted in initiation of ART later in the course of disease than was the case for pre-ART combination therapy. There is also some uncertainty in our estimate of ART, which may not reflect current ART use. In addition, because HCSUS data only included persons receiving care, persons with limited or no access may be underrepresented, which could mean that we overestimated ART use and direct medical costs and underestimated productivity losses.

Because of limited data (eg, ART use for some races/ethnic groups) and cases of HIV/AIDS for Asians/Pacific islanders and American Indians/Alaska natives, estimates for these 2 groups should be interpreted cautiously. Additionally, we used HIV surveillance data for persons aged ≥13 years, which is defined by the CDC HIV/AIDS surveillance system as the “adult population,” whereas HCSUS data included persons aged ≥18 years. Therefore, caution should be used when applying these results to persons aged 13 to 18 years.

An additional limitation is that we based our analysis on surveillance data from 30 areas that had conducted confidential name-based reporting of HIV infection; thus, some high-morbidity areas with large populations of minority races/ethnicities, such as California and New York, were not included in the analysis. Based on comparisons made with 2002 HIV/AIDS surveillance data reported by California and New York, we believe our estimates slightly underestimate the proportion of cases among hispanics and, to a lesser degree, slightly overestimate the cases among blacks33 (California Department of Health Services, unpublished data, 2004). Additionally, these data may be subject to variability in states' CD4 count reporting practices.

Despite these limitations, our national estimates of the economic burden of HIV/AIDS in 2002 demonstrate an economic burden far greater than previous estimates that considered only direct medical costs. Because of premature mortality, productivity losses far surpass direct medical costs and are disproportionately borne by minority races and ethnicities, particularly the black and hispanic populations. Differences in disease stage at diagnosis and lack of access to ART among minorities seem to have contributed to racial/ethnic differences in the cost of illness. Finally, our analysis underscores the savings that could be achieved with more effective ART regimens and universal access to ART.

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Acknowledgment

The authors thank Dr. Jianmin Li, CDC, for providing HIV/AIDS surveillance data. They are also grateful to Dr. David Holtgrave, Johns Hopkins Bloomberg School of Public Health, Ms. Marie Morgan, CDC, and 2 anonymous reviewers for their thoughful comments on earlier drafts.

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Back to Top | Article Outline
Keywords:

HIV/AIDS; cost of illness; race-ethnicity; productivity loss; direct medical costs; economic burden

© 2006 Lippincott Williams & Wilkins, Inc.

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