BACKGROUND: A goal of the US Department of Health and Human Services' Ending the HIV Epidemic (EHE) in the United States initiative is to reduce the annual number of incident HIV infections in the United States by 75% within 5 years and by 90% within 10 years. We developed a resource allocation analysis to understand how these goals might be met. METHODS: We estimated the current annual societal funding [$2.8 billion (B)/yr] for 14 interventions to prevent HIV and facilitate treatment of infected persons. These interventions included HIV testing for different transmission groups, HIV care continuum interventions, pre-exposure prophylaxis, and syringe services programs. We developed scenarios optimizing or reallocating this funding to minimize new infections, and we analyzed the impact of additional EHE funding over the period 2021-2030. RESULTS: With constant current annual societal funding of $2.8 B/yr for 10 years starting in 2021, we estimated the annual incidence of 36,000 new cases in 2030. When we added annual EHE funding of $500 million (M)/yr for 2021-2022, $1.5 B/yr for 2023-2025, and $2.5 B/yr for 2026-2030, the annual incidence of infections decreased to 7600 cases (no optimization), 2900 cases (optimization beginning in 2026), and 2200 cases (optimization beginning in 2023) in 2030. CONCLUSIONS: Even without optimization, significant increases in resources could lead to an 80% decrease in the annual HIV incidence in 10 years. However, to reach both EHE targets, optimization of prevention funding early in the EHE period is necessary. Implementing these efficient allocations would require flexibility of funding across agencies, which might be difficult to achieve.

In 2018, the U.S. Public Health Service (USPHS) published updated clinical guidelines for the use of preexposure prophylaxis (PrEP) to reduce the risk of HIV infection among men who have sex with men (MSM), heterosexual women and men, and persons who inject drugs.1 PrEP is one of the main tools being used to achieve the Ending the HIV Epidemic in the U.S. incidence-reduction goals.2 Thus, policy makers need accurate estimates of the number of U.S. adults having indications for PrEP.

Objectives. To optimize combined public and private spending on HIV prevention to achieve maximum reductions in incidence.Methods. We used a national HIV model to estimate new infections from 2018 to 2027 in the United States. We estimated current spending on HIV screening, interventions that move persons with diagnosed HIV along the HIV care continuum, pre-exposure prophylaxis, and syringe services programs. We compared the current funding allocation with 2 optimal scenarios: (1) a limited-reach scenario with expanded efforts to serve eligible persons and (2) an ideal, unlimited-reach scenario in which all eligible persons could be served.Results. A continuation of the current allocation projects 331 000 new HIV cases over the next 10 years. The limited-reach scenario reduces that number by 69%, and the unlimited reach scenario by 94%. The most efficient funding allocations resulted in prompt diagnosis and sustained viral suppression through improved screening of high-risk persons and treatment adherence support for those infected.Conclusions. Optimal allocations of public and private funds for HIV prevention can achieve substantial reductions in new infections. Achieving reductions of more than 90% under current funding will require that virtually all infected receive sustained treatment. (Am J Public Health. Published online ahead of print November 19, 2020: e1-e8. https://doi.org/10.2105/AJPH.2020.305965).

OBJECTIVE: Build a dynamic model system to assess the effects of HIV intervention and prevention strategies on future annual numbers of new HIV infections, newly diagnosed cases of HIV infection, and deaths among persons infected with HIV. DESIGN AND SETTING: Model parameters are defined to quantify the putative effects of HIV prevention strategies that would increase HIV testing, thereby diagnosing infection earlier; increase linkage to care and viral suppression, thereby reducing infectiousness; and increase the use of preexposure prophylaxis, thereby protecting persons at risk of infection. Surveillance data are used to determine the initial values of the model system's variables and parameters, and the impact on the future course of various outcome measures of achieving either specified target values or specified rates of change for the model parameters is examined. PARTICIPANTS: A hypothetical population of persons with HIV infection and persons at risk of acquiring HIV infection. MAIN OUTCOME MEASURES: HIV incidence, HIV prevalence, proportion of persons infected with HIV whose infection is diagnosed, and proportion of persons with diagnosed HIV infection who are virally suppressed. RESULTS: A model system based on the basic year-to-year algebraic relationships among the model variables and relying almost exclusively on HIV surveillance data was developed to project the course of HIV disease over a specified time period. Based on the most recent HIV surveillance data in the United States-which show a relatively high proportion of infections having been diagnosed but a relatively low proportion of diagnosed persons being virally suppressed-increasing the proportion of diagnosed persons who are virally suppressed and increasing preexposure prophylaxis use appear to be the most effective ways of reducing new HIV infections and accomplishing national HIV prevention and care goals. CONCLUSIONS: Both having current and accurate information regarding the epidemiologic dynamics of HIV infection and knowing the potential impact of prevention strategies are critical in order for limited HIV prevention resources to be optimally allocated.

Accurate interpretations and comparisons of linkage results across jurisdictions require valid and reliable matching methods. We compared existing matching methods used by 6 US state and local health departments (Houston, Texas; Louisiana; Michigan; New York City, New York; North Dakota; and Wisconsin) to link human immunodeficiency virus and viral hepatitis surveillance data with a 14-key automated, hierarchical deterministic matching method. Applicable years varied by disease and jurisdiction, ranging from 1979 to 2016. We calculated percentage agreement and Cohen's kappa coefficient to compare the matching methods used within each jurisdiction. We calculated sensitivity, specificity, and positive predictive value of each matching method, compared with a new standard that included manual review of discrepant cases. Agreement between the existing matching method and the deterministic matching method was 99.6% or higher in all jurisdictions; Cohen's kappa values ranged from 0.87 to 0.98. Sensitivity of the deterministic matching method ranged from 97.4% to 100% in the 6 jurisdictions; specificity ranged from 99.7% to 100%; and positive predictive value ranged from 97.4% to 100%. Although no gold standard exists, prior assessments of existing methods and review of discrepant classifications suggest good accuracy and reliability of our deterministic matching method, with the advantage that our method reduces the need for manual review and allows for standard comparisons across jurisdictions when linking human immunodeficiency virus and viral hepatitis data.

INTRODUCTION: The incidence and prevalence of HIV infection are important measures of HIV trends; however, they are difficult to estimate because of the long incubation period between infection and symptom development and the relative infrequency of HIV screening. A new method is introduced to estimate HIV incidence, prevalence and the number of undiagnosed infections in the United States using data from the HIV case surveillance system and CD4 test results. METHODS: Persons with HIV diagnosed during 2006-2013 and their CD4 test results were used to estimate the distribution of diagnosis delay from HIV infection to diagnosis based on a well characterized CD4 depletion model. This distribution was then used to estimate HIV incidence, prevalence, and the number of undiagnosed infections. RESULTS: Applying this method, we estimated that the annual number of new HIV infections decreased after 2007, from 48,300 (95% confidence interval [CI] 47,300, 49,400) to 39,000 (95% CI 36,600, 41,400) in 2013. Prevalence increased from 923,200 (95% CI 914,500, 931,800) in 2006 to 1,104,600 (95% CI 1,084,300, 1,124,900) in 2013, while the proportion of undiagnosed infections decreased from 21.0% in 2006 (95% CI 20.2%, 21.7%) to 16.4% (95% CI 15.7%, 17.2%) in 2013. CONCLUSIONS: HIV incidence, prevalence, and undiagnosed infections can be estimated using HIV case surveillance data and information on first CD4 test result after diagnosis. Similar to earlier findings, the decreases in incidence and undiagnosed infections are encouraging but intensified efforts for HIV testing and treatment are needed to meet the goals of the National HIV/AIDS Strategy.

HIV incidence estimates are used to monitor HIV-1 infection in the United States. Use of laboratory biomarkers that distinguish recent from longstanding infection to quantify HIV incidence rely on having accurate knowledge of the average time that individuals spend in a transient state of recent infection between seroconversion and reaching a specified biomarker cutoff value. This paper describes five estimation procedures from two general statistical approaches, a survival time approach and an approach that fits binomial models of the probability of being classified as recently infected, as a function of time since seroconversion. We compare these procedures for estimating the mean duration of recent infection (MDRI) for two biomarkers used by the U.S. National HIV Surveillance System for determination of HIV incidence, the Aware BED EIA HIV-1 incidence test (BED) and the avidity-based, modified Bio-Rad HIV-1/HIV-2 plus O ELISA (BRAI) assay. Collectively, 953 specimens from 220 HIV-1 subtype B seroconverters, taken from 5 cohorts, were tested with a biomarker assay. Estimates of MDRI using the non-parametric survival approach were 198.4 days (SD 13.0) for BED and 239.6 days (SD 13.9) for BRAI using cutoff values of 0.8 normalized optical density and 30%, respectively. The probability of remaining in the recent state as a function of time since seroconversion, based upon this revised statistical approach, can be applied in the calculation of annual incidence in the United States.

OBJECTIVE: To develop a resource allocation model to optimize health departments' Centers for Disease Control and Prevention (CDC)-funded HIV prevention budgets to prevent the most new cases of HIV infection and to evaluate the model's implementation in 4 health departments. DESIGN, SETTINGS, AND PARTICIPANTS: We developed a linear programming model combined with a Bernoulli process model that allocated a fixed budget among HIV prevention interventions and risk subpopulations to maximize the number of new infections prevented. The model, which required epidemiologic, behavioral, budgetary, and programmatic data, was implemented in health departments in Philadelphia, Chicago, Alabama, and Nebraska. MAIN OUTCOME MEASURES: The optimal allocation of funds, the site-specific cost per case of HIV infection prevented rankings by intervention, and the expected number of HIV cases prevented. RESULTS: The model suggested allocating funds to HIV testing and continuum-of-care interventions in all 4 health departments. The most cost-effective intervention for all sites was HIV testing in nonclinical settings for men who have sex with men, and the least cost-effective interventions were behavioral interventions for HIV-negative persons. The pilot sites required 3 to 4 months of technical assistance to develop data inputs and generate and interpret the results. Although the sites found the model easy to use in providing quantitative evidence for allocating HIV prevention resources, they criticized the exclusion of structural interventions and the use of the model to allocate only CDC funds. CONCLUSIONS: Resource allocation models have the potential to improve the allocation of limited HIV prevention resources and can be used as a decision-making guide for state and local health departments. Using such models may require substantial staff time and technical assistance. These model results emphasize the allocation of CDC funds toward testing and continuum-of-care interventions and populations at highest risk of HIV transmission.

BACKGROUND: The number of strategies to prevent HIV transmission has increased following trials evaluating antiretroviral therapy (ART), preexposure prophylaxis (PrEP) and male circumcision. Serodiscordant couples need guidance on the effects of these strategies alone, and in combination with each other, on HIV transmission. METHODS: We estimated the sexual risk of HIV transmission over 1-year and 10-year periods among male-male and male-female serodiscordant couples. We assumed the following reductions in transmission: 80% from consistent condom use; 54% from circumcision in the negative male partner of a heterosexual couple; 73% from circumcision in the negative partner of a male-male couple; 71% from PrEP in heterosexual couples; 44% from PrEP in male-male couples; and 96% from ART use by the HIV-infected partner. FINDINGS: For couples using any single prevention strategy, a substantial cumulative risk of HIV transmission remained. For a male-female couple using only condoms, estimated risk over 10 years was 11%; for a male-male couple using only condoms, estimated risk was 76%. ART use by the HIV-infected partner was the most effective single strategy in reducing risk; among male-male couples, adding consistent condom use was necessary to keep the 10-year risk below 10%. CONCLUSION: Focusing on 1-year and longer term transmission probabilities gives couples a better understanding of risk than those illustrated by data for a single sexual act. Long-term transmission probabilities to the negative partner in serodiscordant couples can be high, though these can be substantially reduced with the strategic use of preventive methods, especially those that include ART.

BACKGROUND: Performance of the BED assay in estimating HIV-1 incidence has previously been evaluated by using longitudinal specimens from persons with incident HIV infections, but questions remain about its accuracy. We sought to assess its performance in three longitudinal cohorts from Thailand where HIV-1 CRF01_AE and subtype B' dominate the epidemic. DESIGN: BED testing was conducted in two longitudinal cohorts with only incident infections (a military conscript cohort and an injection drug user cohort) and in one longitudinal cohort (an HIV-1 vaccine efficacy trial cohort) that also included long-term infections. METHODS: Incidence estimates were generated conventionally (based on the number of annual serocoversions) and by using BED test results in the three cohorts. Adjusted incidence was calculated where appropriate. RESULTS: For each longitudinal cohort the BED incidence estimates and the conventional incidence estimates were similar when only newly infected persons were tested, whether infected with CRF01_AE or subtype B'. When the analysis included persons with long-term infections (to mimic a true cross-sectional cohort), BED incidence estimates were higher, although not significantly, than the conventional incidence estimates. After adjustment, the BED incidence estimates were closer to the conventional incidence estimates. When the conventional incidence varied over time, as in the early phase of the injection drug user cohort, the difference between the two estimates increased, but not significantly. CONCLUSIONS: Evaluation of the performance of incidence assays requires the inclusion of a substantial number of cohort-derived specimens from individuals with long-term HIV infection and, ideally, the use of cohorts in which incidence remained stable. Appropriate adjustments of the BED incidence estimates generate estimates similar to those generated conventionally.

OBJECTIVES: To estimate the potential future burden of HIV in the United States under different intervention scenarios. METHODS: We modeled future HIV incidence, prevalence, and infections averted using 2006 estimates of HIV incidence (55,400 new infections per year), prevalence (1,107,000 persons living with HIV), and transmission rate (5.0 per 100 persons living with HIV). We modeled 10-year trends for 3 base-case scenarios (steady incidence, steady transmission rate, declining transmission rate based on the 2000-2006 trend) and 2 intensified HIV intervention scenarios (50% reduction in transmission rate within 10 and 5 years). RESULTS: Base-case scenarios predicted HIV prevalence increases of 24%-38% in 10 years. Reducing the transmission rate by 50% within 10 years reduces incidence by 40%; prevalence increases 20% to an estimated 1,329,000 persons living with HIV. Halving the transmission rate within 5 years reduces incidence by 46%; prevalence increases 13%, to 1,247,000. Although in year 10 incidence is similar regardless of the intervention time frame, more infections are averted when halving the transmission rate within 5 years. CONCLUSIONS: HIV prevalence will likely increase creating additional demands for health care services. These analyses are instructive for setting HIV prevention goals for the nation and assessing potential cost savings of intensified HIV prevention efforts.

OBJECTIVES: To describe adults/adolescents (age 13 years and older) living with undiagnosed HIV infection in the United States at the end of 2006. METHODS: HIV prevalence and percentage undiagnosed were estimated from cumulative HIV incidence using an extended back-calculation model (using both HIV and AIDS data, the time of first diagnosis with HIV, and disease severity at diagnosis) and estimated cumulative deaths. RESULTS: An estimated 1,106,400 adults/adolescents (95% confidence interval = 1,056,400-1,156,400) were living with HIV in the United States at the end of 2006; overall, 21.0% (232,700; 95% confidence interval = 221,200-244,200) were undiagnosed. Whites had the lowest percentage undiagnosed (18.8%) compared with Hispanics/Latinos (21.6%), blacks/African Americans (22.2%), American Indians/Alaska Natives (25.8%), and Asians/Pacific Islanders (29.5%; all P < 0.001). Persons with a behavioral risk of injection drug use (IDU) had the lowest percentage undiagnosed (female IDU: 13.7% and male IDU: 14.5%); men exposed through heterosexual contact had the highest (26.7%) followed by men who have sex with men (23.5%). CONCLUSIONS: Differences in undiagnosed HIV were evident across demographic and behavior groups. Effective testing programs and early access to treatment and prevention services are necessary to reduce undiagnosed HIV infections and HIV prevalence.