Expanded HIV Screening in the United States: Effect on Clinical Outcomes, HIV Transmission, and Costs

Context Two unsolved problems in HIV screening policy are the maximum cost-effective screening frequency and the minimum HIV prevalence for cost-effective screening. Contribution The authors used a decision model to estimate the cost-effectiveness of same-day rapid test HIV screening, considering outcomes experienced by the infected person and his or her sexual contacts. One-time screening was cost-effective when the prevalence of HIV was as low as 0.20%. Repeated screening every 5 years was cost-effective with an annual incidence of 0.0075% and an HIV prevalence of 0.45%. Cautions The authors did not count HIV transmission from infected contacts. Implications Screening for HIV is cost-effective when HIV prevalence is similar to that of average-risk populations. The Editors Early detection and timely access to medical care can substantially improve the course of HIV disease among infected persons (1, 2). Whether they also reduce the risk for transmitting the virus to others (37) is not clear because survival gains from antiretroviral therapy prolong infectious lifetimes and may lead to complacency toward HIV risk behavior (8). Recent studies report increases in HIV infections, other sexually transmitted diseases, and sexual risk behaviors in vulnerable populations (911); access to effective antiretroviral therapy may also be associated with sexual risk-taking (1214). As with any new method of screening for chronic disease (for example, hypercholesterolemia and breast, cervical, prostate, and colon cancer [1519]), the challenge facing both physicians and public health experts is to determine whom to test for HIV infection and how frequently. We address the particular difficulties posed by HIV infection, an infectious disease whose detection and treatment have implications for both the individual being tested and the broader population. Methods Study Design We used a simulation model (20) to project the performance of increasingly frequent HIV screening of all adults using a rapid testing protocol (2123) in communi ties with independently varying levels of prevalence of undetected HIV infection (0.05% to 1.0%) and annual HIV incidence (0.0084% to 0.12%). We considered medical outcomes at the level of the individual HIV-infected patient and transmission at the population level (Appendix). To evaluate transmission-related effects, we used published data on secondary HIV transmission and model-based estimates of lifetime costs and health-related quality-of-life losses attributable to new HIV infections. Following the recommendations of the U.S. Panel on Cost-Effectiveness in Health and Medicine (24), we evaluated outcomes from the societal perspective using a 3% annual discount rate. We expressed comparative value in 2004 U.S. dollars per quality-adjusted life-year (QALY) gained and used multiway sensitivity analysis to examine the effects of uncertainty about the data in the model. Individual-Level Simulation We used a widely published computer simulation, the Cost-Effectiveness of Preventing AIDS Complications (CEPAC) Model, to characterize the progress of HIV disease in an infected individual (20, 2527). The health states summarize the essential elements of patient status (CD4 cell count and HIV RNA level, history of opportunistic infections, quality of life, and resource use) (28). Effective antiretroviral therapy increases the probability of viral suppression and concomitant CD4 cell count increases, according to clinical trial results. Treated patients receive a sequence of up to 4 therapeutic regimens in which efficacy progressively diminishes. The model tracks each patient's clinical course from entry until death. It then aggregates the simulated clinical courses of individuals to estimate the average quality-adjusted survival and costs for screening and treatment alternatives. Table 1. Summary of Key Model Input Parameters and Sources for Efficacy of Antiretroviral Therapy and Rapid Test Protocol* The model's screening simulation accounts for whether and when detection, follow-up, and linkage to HIV care occur (2931). Detection of HIV takes place through 1 of 3 discrete mechanisms: a specific HIV screening program; nonroutine, background testing (for example, testing in medical settings, sexually transmitted disease clinics, or correctional institutions or for employment or immigration purposes); and clinical presentation with an AIDS-defining illness. While the model simulates the course of HIV illness for all infected individuals, only patients with detected HIV infection who are successfully linked to care are eligible for antiretroviral therapy and opportunistic infection prophylaxis. Key input data are (2123, 3246) provided in Tables 1 and 2. We considered rapid testing because of its current policy relevance (47, 48). Rapid testing elicits higher levels of test acceptance, follow-up, and linkage to care (baseline overall likelihood of test acceptance, follow-up, and linkage is 77.6% vs. 32.6% for standard antibody testing [29, 30]). However, rapid testing may exacerbate the distress associated with false-positive results, since the patients learn the preliminary findings before Western blot confirmation. We conducted extensive sensitivity analysis on the morbidity penalty (base value, 14 quality-adjusted life-days) attributable to false-positive results, ranging from no penalty to 30 quality-adjusted life-days. Table 2. Summary of Key Model Input Parameters and Sources for Target Population Characteristics and Effect of Patient Care on HIV Transmission* Target Populations We considered all adults (mean age, 33 years) with unknown HIV status in U.S. health care settings. The baseline analysis uses a population (1.0% undetected HIV prevalence, 0.12% annual incidence, and 6.1% lifetime HIV infection risk) that reflects preSeptember 2006 guidelines for HIV screening (39). We also simulated the effects of screening the U.S. general population (0.1% prevalence, 0.014% annual incidence, and 0.7% lifetime HIV infection risk), using the widely cited estimate of 252000 to 312000 undetected, prevalent HIV infections and 40000 annual infections in a population of 290 million (42). In sensitivity analysis, we considered additional target populations, varying both the prevalence (0.05% to 1.0%) and the annual incidence (0.0084% to 0.12%) as estimated by interpolating and extrapolating the specific values reported here. Effect of Patient Care on HIV Transmission To describe secondary HIV transmission, we used the basic reproductive number, R0, a central concept in infectious disease epidemiology. R0 can be interpreted as the lifetime number of subsequent infections, regardless of method of transmission, attributable to a single infected individual in a susceptible population. R0 captures the interaction of 3 factorsHIV transmission efficiency; number of risky contacts; and duration of infectiousnessin producing a summary measure of the power of an infection to emerge and to persist (49). When R0 is greater than 1, the average infected person generates at least 1 subsequent case and an epidemic can ensue; when R0 is less than or equal to 1, the epidemic cannot persist. We applied a baseline R0 of 1.44 to all cases of undetected HIV infection and to situations where we assume no effect of screening and treatment (Table 2) (43). We also applied this value to cases of HIV infection detected through presentation with an opportunistic infection, an assumption that adopts the conservative view that individuals who decline HIV testing will respond less favorably to behavioral counseling (50). The favorable transmission impact scenario reflects the potential virologic benefit of antiretroviral therapy to reduce (R0< 1.44) HIV transmission. The value R0 of 1.27 applies to all individuals identified through HIV screening (43). The adverse transmission impact scenario assumes that patients with screening-detected HIV infection are at higher risk for transmitting HIV, presumably because of treatment-related behavioral disinhibition (45, 46). Lacking scientific evidence, we arbitrarily chose an R0 value of 1.61, which is as far above the baseline value (R0= 1.44) as the favorable impact value (R0= 1.27) is below it. Recognizing the critical role played by the transmission impact assumption, we conducted extensive sensitivity analyses by considering values ranging from 1.00 to 1.00 for R0, the difference between R0 in the presence and absence of care. R0 can be interpreted as the lifetime number of secondary HIV infections averted when an HIV-infected person in a susceptible population is identified by screening, counseled, and linked to treatment. By using R0 to represent the effect of screening on transmission, we could estimate the incremental cost-effectiveness ratios without specifying a base value for R0. Population-Level Analysis The expected number of secondary infections under a given HIV screening program is a key outcome measure. To estimate it, we first obtained the proportions of HIV infections identified by each detection mechanism from the simulation model (Table 3) (29). We used these proportions to compute a weighted average of the reproductive numbers for each mechanism for HIV detection (Table 2). This weighted average represents our estimate of the number of secondary transmissions per infected individual. We calculated total transmissions by multiplying this value by the lifetime risk for HIV infection (Table 2) in the target population. Table 3. Mechanisms of Detection through Alternative Screening Practices, Baseline Population Scenario* We assigned each secondary infection a survival loss and an economic cost (51). We obtained a survival loss of 30.49 discounted quality-adjusted life-months (QALMs) by comparing a model-based estimate of life expectancy for a secondary infectionassuming current standards of care (52) and HIV-specific quality-of-life weights (53)with survival without HIV infection. We obtai