Expanded HIV Screening in the United States: What Will It Cost Government Discretionary and Entitlement Programs? A Budget Impact Analysis

Introduction

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 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 Variable Base-case (Range) Sources General  Discount rate 0% 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  Female 30% 24, 28 and T. Westmoreland, pers. comm.  CD4 at simulation entry (mean, std) 390 (260) cells/mm3 28  Viral load Published natural history data 29, see appendix  Prior OI experience Published 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  Female 30% 28  CD4 at simulation entry (mean, std) Published natural history data 29, 31, see appendix  Viral load Published natural history data 29, see appendix  Prior OI experience None Assumption Characteristics of incident cases  N* 45,800 per year (upper 48,100) See appendix  Age (mean) 33 years 32, see appendix  Female 30% 46  CD4 at simulation entry (mean, std) 534 (164) cells/mm3 31  Viral load >100,000 copies/mL Assumption, see appendix  Prior OI experience None Assumption, 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 preseroconversion 0.1% Calculated  Sensitivity postseroconversion 99.6% [63]  Specificity postseroconversion 99.9% [64]  HIV-positive test return rate 97% (90–100%) 52  HIV-negative test return rate 97% (90–100%) 52 ELISA test characteristics  Sensitivity preseroconversion 0.1% Calculated  Sensitivity postseroconversion 99.6% [64]  Specificity postseroconversion 99.9% [64]  HIV-positive test return rate 75% (50–100%) 52  HIV-negative test return rate 67% (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 $9.02 Assumption (0.5 h of administrative staff time and $1.00 for mail and phone reminders) * All calculations exclude individuals eligible for testing and care through the Veterans Administration or private insurance. † Assumption; technical appendix at: http://www.ispor.org/Publications/value/ViHsupplementary/ViH13i8_Martin.asp. ‡ 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.

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

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 practice Expanded screening Number identified over a 5-year period  Prevalent cases in year 1 (N) 54,343 63,747  Prevalent cases in year 2 (N) 18,362 24,062  Prevalent cases in year 3 (N) 17,276 19,755  Prevalent cases in year 4 (N) 14,759 15,106  Prevalent cases in year 5 (N) 11,366 10,651  Total prevalent cases in period (N) 116,107 133,321  Incident cases in year 1 (N) 4,099 6,701  Incident cases in year 2 (N) 8,379 13,258  Incident cases in year 3 (N) 12,340 18,764  Incident cases in year 4 (N) 16,086 23,417  Incident cases in year 5 (N) 19,618 27,361  Total incident cases in period (N) 60,523 89,501 Mechanism of detection, prevalent cases  Screening (%) 19.7 33.1  Opportunistic infection (%) 68.3 57.8  Never detected (%) 12.0 9.1 Mechanism of detection, incident cases  Screening (%) 39.3

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