SEARCH

SEARCH BY CITATION

Keywords:

  • AIDS;
  • budget impact analysis;
  • computer simulation;
  • cost analysis

ABSTRACT

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusions
  8. Acknowledgments
  9. References

Objective:  The US Centers for Disease Control and Prevention (CDC) recently revised their HIV screening guidelines to promote testing and earlier entry to care. Prior analyses have examined the policy's cost-effectiveness but have not evaluated its impact on government budgets.

Methods:  We used a simulation model of HIV screening, disease, and treatment to determine the budget impact of expanded HIV screening to US government discretionary, entitlement, and testing programs. We estimated total and incremental testing and treatment costs over a 5-year time horizon under current and expanded screening scenarios. We used CDC estimates of HIV prevalence and annual incidence, and considered variations in screening frequency, test return rates, linkage to care, test characteristics, and eligibility for government screening and treatment programs.

Results:  Under current practice, 177,000 new HIV cases will be identified over 5 years. Expanded screening will identify an additional 46,000 cases at an incremental 5-year cost of $2.7 billion. The financial burden of expanded HIV screening will fall disproportionately on discretionary programs that fund care for newly identified patients and will not be offset by entitlement program savings. Testing will represent a small proportion (18%) of the total budget increase. Costs are sensitive to the frequency of screening and the proportion linked to care.

Conclusions:  The expanded HIV screening program will have a large downstream impact on government programs that fund HIV care. Expanded HIV screening will not meet early treatment goals unless government programs have sufficient budgets to expand testing and provide care for newly identified cases.


Introduction

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusions
  8. Acknowledgments
  9. References

In September 2006, the US Centers for Disease Control and Prevention (CDC) revised their HIV screening guidelines to increase rates of detection and facilitate early entry into care. Benefits of earlier detection and entry into care include better outcomes for patients themselves and lowered rates of HIV transmission to others because of biological (decreased infectivity as a result of treatment) and behavioral (reductions in risk behavior due to knowledge and counseling) mechanisms [1–4]. The revised CDC guidelines encourage routine testing in all health-care settings for the general population and annual testing for high-risk populations [5]. Nevertheless, financing concerns are a major barrier to implementing the recommendations [6]. If government testing and care programs are underfunded, expanded screening may result in large numbers of newly identified cases who are unable to receive care.

The CDC considered cost-effectiveness evaluations of expanded screening policies in its recent decision [7]. Previous analyses have indicated that expanded HIV screening is cost-effective in many settings [8–10] and alternative targeted screening strategies have also been advocated on efficiency grounds [11]. Nevertheless, efficient policies may not be affordable from a payer's perspective if the stream of financial costs is larger than budget allocations [12]. The budget requirements for government programs to provide HIV testing and appropriate medical care to the entire eligible US population are unknown [11]. It is unclear how expanded screening in the US may differentially affect discretionary government programs funded by fixed annual appropriations versus entitlement programs with budgets that automatically expand as demand increases. This question is particularly timely given the recent reauthorization legislation in the US Congress for the Ryan White HIV/AIDS Program (RW), and passage of health-care reform legislation.

We conducted a budget impact analysis of expanded HIV screening using a published simulation model of HIV screening, disease, and treatment and national data on HIV epidemiology, enrollment in public health-care programs, and program eligibility. We forecasted the impact on government budgets for testing programs, discretionary treatment programs (such as RW), and entitlement programs (Medicaid and Medicare) under current practice and expanded screening scenarios over a 5-year period. We excluded patients covered by the Veterans Administration (VA) because the VA has a distinct single-payer system. We also excluded patients with private insurance to focus on government budgets.

Overview of HIV Finance in the United States

The predominant sources of government funding for HIV care in the United States are the federal-funded Medicare and federal- and state-funded Medicaid entitlement programs, and the discretionary RW [13–16]. Although specific eligibility criteria vary across states, HIV-infected individuals generally qualify for Medicaid after they meet low-income and “disability” criteria. Individuals with sufficient work experience may qualify for Medicare by reaching age 65 or meeting income and “permanent disability” standards [15]. RW is mandated to be a “payer of last resort” and targets uninsured and underinsured HIV-infected individuals, particularly those who have not yet progressed to AIDS. Its federal budget is set annually by Congress, and some state legislatures provide supplementary annual appropriations. The largest components of RW spending are state-administered AIDS Drug Assistance Programs (ADAPs), which finance HIV medications [14,17]. We distinguish between discretionary and entitlement programs in our budget impact analysis because entitlement program budgets automatically expand in response to increased case load. Because of their discretionary structure, RW budgets are more vulnerable to unexpected increases in the number eligible for care.

HIV patient eligibility for public insurance programs fluctuates over time. Patients detected early in their disease may not initially meet disability criteria to qualify for Medicare or Medicaid; however, they may later transition from a discretionary to an entitlement program with age or development of symptomatic illness. In contrast, patients identified late in their disease may be immediately eligible for an entitlement program.

Methods

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusions
  8. Acknowledgments
  9. References

Analytic Overview

Wherever possible, we adhered to the ISPOR guidelines on budget impact analysis [12]. We used a published Monte Carlo state transition simulation model of HIV testing, disease, and treatment to estimate incremental testing and treatment costs for prevalent and incident cohorts of HIV-infected individuals over a 5-year time horizon. The testing cost calculations also include the cost of screening noninfected individuals. In conformity with the ISPOR principles [12], we selected a planning horizon that reflects the time frames used for budget planning in publicly financed HIV care [18]. Our baseline horizon of 5 years matches the 3 to 5-year interval typical of RW reauthorizations. Recognizing that some readers may be interested in the budget impact over a longer time horizon, we also consider a 10-year horizon in a sensitivity analysis. We examined two testing strategies: current practice (defined by completing a test, on average, every 10 years) and expanded screening (defined by completing a test, on average, every 5 years), and evaluated alternative testing frequencies in sensitivity analyses. The derivation of the testing time frames is described next. We also examined alternatives for the type of test, rate of return for test results and linkage to care, and likelihood of qualifying for a government testing and care program.

We report the following outcomes: the number identified over 5 years, the fraction of cases identified through presentation to care with testing versus clinical AIDS, mean CD4 count at diagnosis (a measure of immune function), the incremental quality-adjusted life-years, and costs to government programs. Costs were forecasted separately for programs that pay for HIV testing, discretionary treatment programs (including federal RW funds, state matching funds, and uncompensated care pools), and entitlement treatment programs (Medicare and Medicaid).

National data on health insurance coverage and HIV epidemiology were used to estimate the proportion of cases eligible for discretionary and entitlement programs at the time of HIV detection and over the subsequent 5 years. We report undiscounted dollar outlays in each budget period in 2009 dollars, which is consistent with ISPOR's recommendation to report undiscounted costs [12,19].

Populations and Program Eligibility

We focused on adults (>18 years) because of our objective to project costs to RW, Medicaid, Medicare, and uncompensated care pools. Although RW does fund some services for children and youth, these funds represent a small percentage of overall RW grants [20]. The federal and state-funded State Children's Health Insurance Program would likely incur most of the costs of treating newly identified HIV cases among children and adolescents, and is not included in our budget analysis. Additionally, the clinical parameters in our model are derived from adult populations.

We tracked HIV-related treatment costs for all adults, including the elderly. We excluded testing costs for elderly adults (>64 years) because neither past screening efforts nor the revised guidelines target this group [5]. All cost calculations also excluded patients for whom testing and treatment costs were likely to be financed through the VA or private insurance.

We used national estimates of insurance status to estimate the total number of nonelderly adults (aged 19–64 years) without private or VA insurance who would be eligible to receive HIV tests through a government testing program [21,22]. For modeling purposes, we assumed that only those eligible for public sector-financed care (excluding VA coverage) would receive a public sector-financed test. Sensitivity analyses include scenarios where additional costs are incurred by government programs to test or eventually treat individuals who are currently insured through private insurance or the VA. We used 2008 national HIV incidence data (approximately 56,000 new cases annually) [23] to project the total number of HIV-infected individuals potentially eligible for testing and linkage to care upon diagnosis. We used national HIV prevalence data (approximately 1.1 million prevalent cases, of whom 21% do not know their infection status) [24] to calculate the number of prevalent cases currently aware of their infection and eligible for care. These data were also used to estimate the number of undetected prevalent cases potentially eligible for testing and linkage to care upon diagnosis. We assumed a constant incidence of new infections over the 5-year period [23]. We estimated the fraction of all cases (detected and undetected) likely to qualify for government discretionary and entitlement programs using data from the HIV Research Network (HIVRN) [25]. We used data on the incidence and prevalence of HIV among veterans to subtract those likely to receive care through the VA from our population estimates [26]. Our calculations yield the following populations eligible for government-financed testing and treatment in 2009: 50,100,000 HIV-negative individuals; 711,000 prevalent cases aware of their infection; 189,000 undetected prevalent cases; and 46,000 incident cases. Further details of these calculations are provided in the technical appendix at: http://www.ispor.org/Publications/value/ViHsupplementary/ViH13i8_Martin.asp.

We assumed that treatment costs for newly diagnosed individuals without private or VA insurance are financed through discretionary programs until they qualify for Medicare by attaining age 65 or they experience an AIDS-defining opportunistic infection (OI), thereby qualifying them for Medicaid or Medicare. Newly diagnosed individuals may be immediately eligible for entitlement programs before presentation with an OI due to pregnancy, prior disability, or state-specific poverty-level expansions [14,27]. Because national estimates of this population are unavailable and the number is likely to be small, we did not account for such eligibility.

Table 1 displays the data sources used to generate demographic and clinical characteristics of cases eligible for government care upon diagnosis. We used a different set of characteristics for HIV-infected individuals in the three cohorts (prevalent aware, prevalent unaware, and incident cases).

Table 1.  Inputs and source data for simulation model to project budget impact of expanded HIV screening to public payers
VariableBase-case (Range)Sources
  • *

    All calculations exclude individuals eligible for testing and care through the Veterans Administration or private insurance.

  • Aware is defined as aware of their infection and currently in care through discretionary or entitlement programs, at the start of the simulation.

  • §

    Unaware is defined as being unaware of their infection at the start of the simulation, and only eligible for government-financed care upon detection.

  • Incident cases are uninfected at the start of the simulation, and are only eligible for government-financed care upon detection.

  • **

    See technical appendix at: http://www.ispor.org/Publications/value/ViHsupplementary/ViH13i8_Martin.asp for a more detailed description of the linkage to care probability and the test return rate.

  • ELISA, enzyme-linked immunosorbent assay; OI, opportunistic infection; std, standard.

General  
 Discount rate0%[12]
Number of HIV-negative individuals who will be screened, at start of simulation  
 N*50,100,000 (upper 55,100,000)See appendix
Characteristics of prevalent cases aware of their infection  
 N*711,000 (upper 747,000)See appendix
 Age (mean)41 years[28]
 Female30%[24,28] and T. Westmoreland, pers. comm.
 CD4 at simulation entry (mean, std)390 (260) cells/mm3[28]
 Viral loadPublished natural history data[29], see appendix
 Prior OI experiencePublished natural history data[29], see appendix
Characteristics of prevalent cases unaware of their infection§  
 N*189,000 (upper 198,000)See appendix
 Age (mean)41 years[28]
 Female30%[28]
 CD4 at simulation entry (mean, std)Published natural history data[29,31], see appendix
 Viral loadPublished natural history data[29], see appendix
 Prior OI experienceNoneAssumption
Characteristics of incident cases  
 N*45,800 per year (upper 48,100)See appendix
 Age (mean)33 years[32], see appendix
 Female30%[46]
 CD4 at simulation entry (mean, std)534 (164) cells/mm3[31]
 Viral load>100,000 copies/mLAssumption, see appendix
 Prior OI experienceNoneAssumption, see appendix
Test characteristics  
 Probability of monthly test receipt  
  Current practice (%)0.83% (0–1.67%)[49]
  Expanded (%)1.67% (0.83–8.33%)See text
 Probability detected case linked to care**80% (50–100%)[40,53,54]
Rapid test characteristics  
 Sensitivity preseroconversion0.1%Calculated
 Sensitivity postseroconversion99.6%[63]
 Specificity postseroconversion99.9%[64]
 HIV-positive test return rate97% (90–100%)[52]
 HIV-negative test return rate97% (90–100%)[52]
ELISA test characteristics  
 Sensitivity preseroconversion0.1%Calculated
 Sensitivity postseroconversion99.6%[64]
 Specificity postseroconversion99.9%[64]
 HIV-positive test return rate75% (50–100%)[52]
 HIV-negative test return rate67% (50–100%)[52]
Testing costs  
 HIV test kit, administration, and laboratory analysis  
  Rapid test$12.23[37]
  ELISA$7.05[37]
 Confirmatory testing for positive results  
  Rapid test$44.28[37]
  ELISA$52.72[37]
 Pretest counseling$0 ($7.76)[37]
 Post-test counseling for negative test result$7.53[37]
 Post-test linkage and counseling for positive test result$14.61[37]
 Administrative cost for nonreturn for results$9.02Assumption (0.5 h of administrative staff time and $1.00 for mail and phone reminders)

The mean age, gender, and CD4 distribution for the prevalent aware cohort were obtained from summary data of all patients in the HIVRN study [28]. We assumed that before ART, the distribution of viral loads after acute HIV infection for each CD4 stratum was similar to that of the Multicenter AIDS Cohort Study (MACS) cohort [29]. We determined the distribution of prior OIs for each CD4 stratum through an initialization cohort, in which we simulated healthy cohorts with the same mean age and gender proportions until they reached each CD4 stratum [30]. We assigned these patients to discretionary and entitlement programs based on insurance status reported in HIVRN.

By definition, demographic characteristics of prevalent unaware cases are unknown. We assumed that the age and gender of “prevalent unaware” cases were similar to that of “prevalent aware” cases. Untreated disease lasts on average 10 years (120 months), of which the first 2 months are in the acute state, the last 2 years (24 months) are in the symptomatic chronic state, and the remainder of time (94 months) is spent in the asymptomatic chronic state. An undetected patient thereby had a 1.7% chance of being in the acute state (in 2 of 120), 78.3% chance of being in the asymptomatic chronic state (in 96 of 120), and 20% chance of being in the symptomatic chronic state (in 24 of 120) [8,10]. The mean CD4 distribution of acute cases was estimated from published studies of individuals with primary infection [31], and the mean CD4 distribution of chronic cases was estimated from the MACS cohort [29]. We assumed that no patients had a history of OIs, as they otherwise would have been identified and linked to care upon presentation.

It is difficult to derive population characteristics of incident cases because these cases are not immediately detected. We derived the mean age of new cases through back calculation. Past research has estimated that on average, there is a duration of 8 years between infection and presentation to care [32]. We subtracted this value from data on prevalent cases to calculate the mean age of 33 years for the incident cohort [10]. All individuals in this cohort entered the model at the time of infection, during an acute state of illness. Clinical characteristics included no OI history and a very high viral load (>100,000 copies/mL). The mean CD4 distribution of this cohort was estimated from published studies of individuals with primary infection [31]. After individuals progressed past the acute state (approximately 2 months), their viral load decreased and patients were moved to a lower viral load stratum. The viral load distribution was derived from the MACS cohort [29].

Model Description

The Cost-Effectiveness of Preventing AIDS Complications (CEPAC) Model is a widely published computer simulation of HIV disease and treatment [8,33–35]. It contains two components. The Screening Module simulates an HIV screening program and determines when each simulated HIV-infected patient will become detected through testing or presentation to care with an AIDS-defining OI. For those detected through testing, the Screening Module additionally determines whether they were effectively linked to care. The Disease Module tracks the clinical progress of all patients, irrespective of whether they have been detected; however, only patients that have been successfully detected and linked to care through testing or AIDS-defining presentation are eligible for treatment.

The Screening Module allows user-defined inputs on test characteristics, testing frequency, linkage to care, and costs. We simulated cohorts of currently unidentified incident and prevalent cases likely to qualify for government care upon detection, as described previously.

In the base-case, patients were screened using the rapid HIV test. Many health departments have implemented rapid tests, and there is evidence of a shift from conventional (enzyme-linked immunosorbent assay [ELISA]) to rapid technologies [36] because patients can receive preliminary results at the time of testing.

The Disease Module uses a Monte Carlo state-transition framework to track the natural history of illness in simulated patients with user-specified care. Data sources and details are described in the technical appendix at: http://www.ispor.org/Publications/value/ViHsupplementary/ViH13i8_Martin.asp. Simulated patients undergo monthly transitions among health states, categorized by chronic illness, acute illness, and death. Monthly probabilities of events include changes in CD4 counts and HIV viral load, the development of an OI, adverse drug reactions, and death related to OIs, chronic AIDS, or non-AIDS-related causes. Summary statistics are collected for each simulated patient on age, mean projected survival, cause of death, OIs, the length of time spent in each health state, and cost. Simulated patients may receive antiretroviral therapy, medications for treatment and prevention of OIs, and ongoing routine care.

Cost Inputs

Table 1 summarizes the sources used to tabulate costs. We adapted testing costs from a recent analysis conducted by CDC researchers [37], which updates earlier studies [38,39]. Cost estimates are consistent with recent literature that reports economic outcomes of HIV testing in emergency departments [40–43]. All screened individuals were assigned the cost of the test, irrespective of their HIV status. Those with preliminary positive tests (true and false positives) additionally incurred the cost of a confirmatory test. All individuals were assigned the cost of post-test counseling, which differed by test result. Post-test counseling costs for HIV-infected persons included costs to facilitate linkage to care. Not all individuals received their test results; those who did not receive them did not incur costs for confirmatory testing and post-test counseling.

Prior to the CDC's revised screening guidelines, pretest counseling was encouraged. The current guidelines promote opt-out testing, without separate written consent and pretest counseling [5,7]. In practice, legal requirements for pretest counseling and written consent vary by state [44]. Furthermore, some testing sites may continue to offer pretest counseling in the absence of a requirement. To simulate the CDC guidelines as closely as possible, we excluded pretest counseling costs from the base-case analysis of both screening scenarios. We included these costs in a sensitivity analysis, and we assumed that providers' decisions to offer pretest counseling were independent of the testing frequency. The CDC's revised recommendations for opt-out testing are controversial [6,11]. We assumed that if individuals who did not receive pretest counseling changed their risk behavior, the cost impact to government programs would be minimal in our 5-year time frame, although they may become significant in future years.

Pharmaceutical costs were calculated using published average wholesale prices [45], which were adjusted for the average state Medicaid reimbursement rate by weighting state-specific Medicaid discounts by AIDS prevalence [46]. Costs of laboratory tests were derived from the Medicare fee schedule [47]. Medical care utilization of patients at different stages of HIV disease was obtained from data collected by the HIVRN [46]. Costs of inpatient services were derived from the University HealthSystem Consortium database [46,48].

Health-Care Cost Projections

We conducted separate simulations for three groups of patients and aggregated the results. The first group is the 711,000 prevalent cases eligible for entitlement and discretionary programs and receiving care in 2009. The second group is the 189,000 prevalent cases whose infection is undetected in 2009 and who will not incur costs to the government programs until they are detected and linked to care. The third group is the 46,000 incident cases eligible for government care each year, who will also only incur treatment costs upon detection and linkage to care.

Screening Strategies

To model current practice, we estimated that on average, individuals receive a test every 10 years, equivalent to a 0.83% chance of being tested each month and 5.0 million tests annually. This estimate is derived from a CDC analysis of national health data surveys [49], although other surveys suggest that current screening rates may be higher [50]. We validated our estimate by comparing our model results to CDC estimates that 39% of HIV-infected individuals received an AIDS diagnosis within a year of their first HIV test [51]. We found that our current practice of once every 10 years reasonably approximated these data on late presentation to care.

To model expanded screening, we assumed that on average, individuals are offered and accept a test every 5 years, equivalent to a 1.67% monthly chance of testing and 10.0 million tests annually. This represents a twofold increase in testing from current practice. Although this test rate does not match the CDC's recommendation for routine screening in the general population and repeat annual testing for high-risk populations [5], we believe that it best represents the population effect of reasonable efforts to implement the policy. Prior analyses of expanded HIV testing have also used a 5-year testing frame [8,9].

In the base-case for current practice and expanded screening, we assumed that 97% of patients received their rapid test result [52], and that 80% of all identified cases were successfully linked to care [53,54].

Sensitivity Analyses

To examine if our results were robust to parameter uncertainty, we conducted extensive sensitivity analyses, listed in Table 1. One set of analyses used the ELISA test, which differs from the rapid test with respect to costs and the rate of receipt of test results. We varied the testing frequency, from a minimum of “no testing” to a maximum of “annual testing.” We varied the rate of receipt of rapid test results from 90% to 100%, and receipt of ELISA results from 50% to 100%. We estimated a range of linkage to care probabilities (for identified cases who had received their results), from perfect linkage (100%) to 50% linkage. We assessed the cost impact of including pretest counseling. We calculated the impact of a 10% increase in the population eligible for government-financed testing and care if additional costs are incurred by government programs to test and treat individuals who are currently privately insured but have incomplete coverage or lose coverage in the future. Finally, we considered a 10-year time horizon.

Results

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusions
  8. Acknowledgments
  9. References

Table 2 displays clinical characteristics of newly identified HIV-infected adults eligible for government-financed testing and care (excluding the VA), for the base-case of each screening scenario. If testing continues at an average frequency of once every 10 years, 177,000 cases (116,000 prevalent; 61,000 incident) will be identified from 2009 to 2013. Over the course of their lifespan, 68% of currently unidentified prevalent cases and 49% of incident cases will receive a diagnosis after presenting to care with an AIDS-defining OI. If expanded testing increases testing to once every 5 years, an additional 46,000 cases (17,000 prevalent; 29,000 incident) will be identified from 2009 to 2013. The fraction of cases receiving a diagnosis as a result of an AIDS-defining OI will drop to 58% and 32% for prevalent and incident cases, respectively. The mean CD4 count at detection (a measure of HIV disease progression) will be higher under expanded screening for all cases, reflecting earlier detection.

Table 2.  Clinical characteristics of newly detected HIV-infected individuals eligible for care through discretionary and entitlement programs*
 Current practiceExpanded screening
  • *

    Clinical characteristics are different from Table 1 input parameters. Table 1 input parameters refer to the actual but unobserved characteristics at the start of the simulation. Table 2 input parameters refer to observed clinical characteristics upon detection. CD4 counts are lower in Table 2 because it takes time for HIV-infected cases to become detected, during which time CD4 counts generally fall. “Prevalent aware” cases are not included in this table because they have already been detected.

  • These numbers refer to the quality-adjusted survival over the newly detected cases' lifetime, and not just the 5-year time horizon of the budget impact analysis.

  • QALYs, quality-adjusted life-years.

Number identified over a 5-year period  
 Prevalent cases in year 1 (N)54,34363,747
 Prevalent cases in year 2 (N)18,36224,062
 Prevalent cases in year 3 (N)17,27619,755
 Prevalent cases in year 4 (N)14,75915,106
 Prevalent cases in year 5 (N)11,36610,651
 Total prevalent cases in period (N)116,107133,321
 Incident cases in year 1 (N)4,0996,701
 Incident cases in year 2 (N)8,37913,258
 Incident cases in year 3 (N)12,34018,764
 Incident cases in year 4 (N)16,08623,417
 Incident cases in year 5 (N)19,61827,361
 Total incident cases in period (N)60,52389,501
Mechanism of detection, prevalent cases  
 Screening (%)19.733.1
 Opportunistic infection (%)68.357.8
 Never detected (%)12.09.1
Mechanism of detection, incident cases  
 Screening (%)39.360.2
 Opportunistic infection (%)49.032.3
 Never detected (%)11.77.5
CD4 count at detection  
 Prevalent (mean cells/mm3)122140
 Incident (mean cells/mm3)251312
Incremental quality-adjusted survival per person  
 Prevalent cases (ΔQALYs)2.0
 Incident cases (ΔQALYs)3.2

Table 3 lists the projected total testing and care costs to public payers, under each screening scenario. Costs are separated by program type (testing, discretionary, and entitlement) and then summed at the bottom of the table. In the base-case, continued testing at the current rate will incur a total cost of $83.7 billion over 5 years. Expanded screening will incur an additional cost of $2.7 billion, for a total of $86.4 billion. Five-year testing costs will increase from $504 million to $1.0 billion. Budget projections are dominated by treatment costs. Testing costs represent a small fraction of total costs (0.6 and 1.2% for the current practice and expanded screening scenarios, respectively) and 18.3% of additional costs for expanded screening. Five-year projected costs to discretionary programs will increase by $2.9 billion (from $26.0 billion to $28.9 billion), which will be partially offset by a savings of $624 million in the entitlement program budget. In both scenarios, most costs will be incurred by entitlement programs.

Table 3.  Projected 5-year costs (in millions) to US government testing, discretionary, and entitlement programs for HIV screening and care, 2009 to 2013
 Total 5-year government budget impact (millions)
Current practice ($)Expanded screening ($)Incremental cost ($)
  • *

    80% test return rate; 50% linkage to care probability.

  • Rapid test with pretest counseling and perfect rates of test return and linkage to care.

  • ELISA test with no pretest counseling and low rates of test return and linkage to care.

  • ELISA, enzyme-linked immunosorbent assay.

Government-financed testing programs   
 Base-case (rapid test)504.21,006.8502.6
 ELISA test383.6766.4382.7
 Inclusion of pretest counseling699.11,396.5697.4
Discretionary programs   
 Base-case26,030.028,899.12,869.1
 Perfect rates of test return and linkage to care26,897.930,319.73,421.9
 Low rates of test return and linkage to care*24,611.126,518.51,907.4
Entitlement programs   
 Base-case57,128.456,504.8−623.6
 Perfect rates of test return and linkage to care56,938.556,196.2−742.3
 Low rates of test return and linkage to care*57,451.957,030.9−421.0
Total costs   
 Base-case83,662.686,410.72,748.1
 Ten percent increase in eligible population92,028.995,051.73,022.9
 High-cost scenario84,535.587,912.43,376.9
 Low-cost scenario82,446.684,315.81,869.2

Under a 10-year time horizon, 158,000 cases will be identified under current practice (19,000 prevalent; 139,000 incident) in the second 5-year period from 2014 to 2018. Over time, an increasing number of incident cases are identified via current practice as they progress through HIV disease and develop OIs. Nevertheless, continuing expanded screening for the additional 5 years yields an additional 31,000 cases identified between 2014 and 2018. Compared to current practice, expanded screening will yield incremental costs of $1.1 billion to testing programs and $10.9 billion to discretionary programs, and incremental savings of $2.8 billion to entitlement programs.

Figure 1 shows annual incremental costs for people identified through expanded screening. In each budget year, expanded screening would incur an additional screening program cost of approximately $101 million. This incremental cost remains constant. The projected annual incremental cost of care to discretionary budgets would rise throughout the period, from $133 million in 2009 to $983 million in 2013. Entitlement programs will experience cost savings, which will increase over time. We project annual savings of $2 million in 2009 and $280 million in 2013. Incremental costs to discretionary programs are not fully offset by cost savings to entitlement programs.

image

Figure 1. Incremental costs (in millions) to US government testing, discretionary, and entitlement programs, comparing current practice and expanded screening, 2009 to 2013.

Download figure to PowerPoint

Figure 2 displays the projected pharmaceutical costs to discretionary programs under current and expanded screening in comparison to the 2007 RW ADAP budget (inflated to $2009), including federal and state contributions [17]. In the first year, pharmaceutical costs to discretionary programs would be about $3.2 billion under both scenarios, in comparison to the 2007 ADAP budget of $1.5 billion. By 2013, the annual pharmaceutical cost would increase by $0.2 billion under current screening and $0.8 billion under expanded screening.

image

Figure 2. Projected pharmaceutical costs (in millions) to US discretionary programs under current practice and expanded screening, 2009 to 2013, compared to current AIDS Drug Assistance Program (ADAP) budget.

Download figure to PowerPoint

Results of sensitivity analyses are shown in Table 3. Using ELISA tests will decrease testing costs to $384 million and $766 million for current practice and expanded screening, because of the lower test cost and return rate. Inclusion of pretest counseling will increase testing costs to $699 million and $1.4 billion for current practice and expanded screening. Perfect rates of test return and linkage to care would increase total care costs to $83.8 billion for current practice ($26.9 billion discretionary; $56.9 billion entitlement) and $86.5 billion for expanded screening ($30.3 billion discretionary; $56.2 billion entitlement). Low rates of test return and linkage to care would decrease total care costs to $82.1 billion for current practice ($24.6 billion discretionary; $57.5 billion entitlement) and $83.5 billion for expanded screening ($26.5 billion discretionary; $57.0 billion entitlement).

Combining these sensitivity analyses, we estimate that the high-cost scenario (rapid test with pretest counseling and perfect rates of test return and linkage to care) will incur total testing and care costs of $84.5 billion and $87.9 billion for current practice and expanded screening, for an incremental cost of $3.4 billion. The low-cost scenario (ELISA test with no pretest counseling and low rates of test return and linkage to care) will incur total costs of $82.4 billion and $84.3 billion for current practice and expanded screening, for an incremental cost of $1.9 billion.

If there is a 10% increase in the population eligible for government-financed testing and care, total costs will increase to $92.0 billion under current practice and $95.1 billion under expanded screening, for an incremental cost of $3.0 billion.

Discussion

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusions
  8. Acknowledgments
  9. References

Both cost-effectiveness and budget impact analysis can inform policy decisions [12,55,56]. Because of the fragmented US health-care delivery system, the societal perspective used in cost effectiveness analysis (CEA) may not apply to public payers. Health-care dollars are often earmarked for specific purposes, and decision-makers have limited capacity to determine how and when to spend budgets.

The CDC considered economic evidence in formulating its recent recommendation to increase HIV testing in routine care [7] but did not estimate total costs [11]. Peer-reviewed literature has described the potential of the CDC's screening guidelines to be cost-effective [8,9], as well as the cost-effectiveness of other testing policies [11]. Nevertheless, government budget outlays for the CDC's policy are unknown. We projected the budget impact of doubling testing rates from once every 10 years to once every 5 years. Although testing every 5 years does not perfectly match the CDC recommendation, our aim was to project the budget impact of the policy as it is likely to be implemented.

Expanded screening will increase the costs of HIV testing and care to government programs by $2.7 billion over 5 years. This increase is large in comparison to the annual RW budget ($2.1 billion per year) [20]. It is smaller relative to the total entitlement budget for HIV care (annual federal budgets of $6.3 billion for Medicaid and $3.2 billion for Medicare) [16,27]. In both current practice and expanded screening strategies, HIV testing represents a small fraction of total costs. The downstream pathway of costs triggered by expanded screening and the cost-shifting that will occur between entitlement and discretionary programs are important budget concerns.

Discretionary programs such as RW will be most affected by expanded screening. Pharmaceutical costs will be the main driver of budget increases, as antiretroviral medications represent nearly three-fourths of lifetime HIV care costs [46]. An expanded screening program will impose an additional pharmaceutical cost to discretionary programs of $1.9 billion over the 5-year period, which is equivalent to 25% of the current ADAP budget if it remained constant during the time period. RW has been flat-funded since 2000 despite higher HIV case counts [57]. Congress earmarks a portion of RW funds for ADAP and many states supplement their federal allocations with local contributions [20]. Nevertheless, budget constraints have already forced many states to implement ADAP cost-containment strategies such as wait lists, copayments, reduced drug formularies, and restricted eligibility [17].

As the expanded screening program increases HIV care costs to discretionary programs, costs to entitlement programs will decrease slightly. Cost savings to entitlement programs will be very small in the first year but will increase over time. Some cost savings are because of the earlier detection of cases, with averted OIs and hospitalizations. Additionally, earlier detection will cause cost-shifting from entitlement to discretionary programs. On average, newly identified cases will incur fewer treatment costs than previously diagnosed cases, owing to the fact that they will tend to be younger and healthier, less likely to initiate antiretroviral therapy, and more likely to be initially assigned to discretionary programs.

We project that an increase of $503 million in the budget for government-financed testing programs would be required to implement expanded screening, which is larger than the $53 million budget increase proposed for all CDC HIV prevention and surveillance work in the next fiscal year [58]. If expanded screenings were implemented consistent with the CDC guidelines, the policy may not be feasible without additional funds from state and local governments.

RW is scheduled for reauthorization later this year. Activists argue that RW, particularly ADAP, is chronically underfunded [59]; this is confirmed by our results which show that even under current rates of testing, projected discretionary pharmaceutical costs surpass the ADAP budget. If expanded HIV screening increases demand for discretionary programs as we project, RW will be further underfunded and unable to cover the costs of those eligible for services. Expanding Medicaid services to low-income individuals who have not yet progressed to AIDS may alleviate some demand for RW [16]. Congress has intermittently considered—but not enacted—the Early Treatment for HIV Act, which would give states the option to extend Medicaid benefits (T. Westmoreland, pers. comm.). Although entitlement programs generally respond more readily to increased demand, it may be politically challenging to enact legislation to shift costs from discretionary to entitlement programs. Policymakers may want to consider the downstream care costs of expanded HIV screening as they deliberate RW's reauthorization.

Limitations

One set of assumptions may have underestimated costs. We did not account for new drugs or technologies, or future changes in treatment guidelines toward earlier initiation of antiretroviral therapy, all of which may increase care costs. We assumed that privately insured individuals retain their coverage, are not underinsured, and are ineligible for government-funded care. We further assumed a constant percentage of newly identified individuals with private or VA coverage. Nevertheless, as the epidemic continues to move disproportionately into underserved populations, fewer new cases will have existing coverage. Individuals with private insurance may transition to public programs as they exhaust their benefits or age. Additionally, the weak economy has increased the number of individuals eligible for government programs, as individuals lose employer-based coverage [60]. It may be infeasible for government-funded HIV testing centers to exclude individuals with existing coverage. We addressed these limitations through a sensitivity analysis in which we increased the population eligible for government-financed testing and care. Our analysis did not include the budget impact of testing and care costs for youth, even though the CDC guidelines included this group. We also did not explicitly budget the costs of scaling up outreach programs such as the National HIV Testing and Mobilization Campaign [61], because the focus of the CDC recommendations are routine screening in health-care settings.

Other assumptions may have overestimated costs. We did not model patients dropping out of care or discontinuing antiretroviral therapy. Results did not incorporate financial benefits of preventing HIV transmission to potential partners. Although any reduction in the number of secondary infections will likely reduce long-term treatment costs, the magnitude of these cost reductions over a 5-year horizon may not be perceptible [10]. We did not account for potential economies of scale with higher testing rates.

Our data sources have several limitations. First, it is difficult to obtain precise population estimates of HIV-infected individuals in the VA. Second, although HIVRN includes sites from multiple regions, its data may not be nationally representative. Third, existing observational data do not allow us to assess whether the mix of unaware and aware cases differs by insurance status.

Another limitation is our failure to report year-by-year changes in the demographic composition (e.g., age and sex) of new HIV cases identified. These would provide a fuller description of the impact of alternative screening strategies.

Our analysis ignores the fact that individuals self-select into HIV testing. There is reason to believe that there exists a population of individuals who chronically refuse HIV testing. This group might be large if the CDC guidelines were attempted in earnest. In the emergency department context, for example, it is hard to test more than 10% to 20% of potentially eligible patients [40,53]. Differences in risk characteristics between refusers and accepters are currently unknown. Furthermore, there are limited data from these settings on test acceptance conditional on prior refusal. The budget impact of hard-to-test populations warrants further analysis.

All budgets were estimated on an aggregate national basis, rather than to specific states. Because Medicare is a federal program, administrative decisions are often made at the national level. In contrast, RW and Medicaid are primarily administered by states. Consequently, there is substantial interstate variation in program management and service delivery. Variation in eligibility for RW and Medicaid may result in cost-shifting across these programs at the state level. Additionally, the actual unit cost of services and medications to government programs differs nationally due to the variation in pharmaceutical prices obtained by state ADAPs, the extent to which state programs pay insurance premiums to fund individuals' care rather than incurring direct costs, client cost-sharing, and reimbursement rates to providers [17,62]. We ignored interstate variation because our goal was to predict aggregate US costs, rather than the budget impact for specific states. Assessing the budget impact of interstate differences in program administration is an important area of future research.

Finally, the budget impact of expanded HIV screening may be influenced by the recent health reform legislation. Nearly all US residents living with HIV will be insured after 2014. Those below 133% of the federal poverty level will be eligible for Medicaid, and the remainder will most likely be enrolled in the insurance exchange, have employer-based coverage, be enrolled in Medicare, or have access through federally qualified health centers. We anticipate that RW budgets will be affected. Although our analysis is relevant to current decision-making, this major policy change warrants future research.

Conclusions

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusions
  8. Acknowledgments
  9. References

Expanded screening can identify new HIV cases and facilitate early entry to care; treating these individuals will also increase budget requirements for government programs by $2.7 billion. The burden will fall disproportionately on discretionary programs, because persons identified with HIV early are less likely to be immediately eligible for entitlement programs. Expanded HIV screening will not meet early treatment goals unless government programs have sufficient budgets to conduct testing and provide care for newly identified cases.

Acknowledgments

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusions
  8. Acknowledgments
  9. References

We are grateful to Kenneth Freedberg for making available the CEPAC Model and its associated resources; Caroline Sloan for her technical assistance; and Mark Schlesinger, Patricia Keenan, and Haiqun Lin for helpful comments on an earlier draft.

Source of financial support: This work was supported by the National Institute on Drug Abuse (R01DA015612), National Institute of Allergy and Infectious Disease (R37AI042006), the Agency for Health Research and Quality (T32HS017589), the National Institute of Mental Health (R01MH65869 and R01MH073445), and the Doris Duke Charitable Foundation (Clinical Scientist Development Award).

References

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusions
  8. Acknowledgments
  9. References