Assessing the Impact of COVID-19 on HIV Outcomes in the United States: A Modeling Study.

BACKGROUND: The COVID-19 pandemic impacted sexual behaviors and the HIV continuum of care in the United States, reducing HIV testing and diagnosis, and use of preexposure prophylaxis and antiretroviral therapy. We aimed to understand the future implications of these effects through a modeling study. METHODS: We first ran our compartmental model of HIV transmission in the United States accounting for pandemic-related short-term changes in transmission behavior and HIV prevention and care provision in 2020 to 2021 only. We then ran a comparison scenario that did not apply pandemic effects but assumed a continuation of past HIV prevention and care trends. We compared results from the 2 scenarios through 2024. RESULTS: HIV incidence was 4·4% lower in 2020 to 2021 for the pandemic scenario compared with the no-pandemic scenario because of reduced levels of transmission behavior, despite reductions in HIV prevention and care caused by the pandemic. However, reduced care led to less viral load suppression among people with HIV in 2020, and in turn, our model resulted in a slightly greater incidence of 2·0% from 2022 to 2024 in the COVID-19 scenario, as compared with the non-COVID scenario. DISCUSSION: Disruptions in HIV prevention and care services during COVID-19 may lead to somewhat higher postpandemic HIV incidence than assuming prepandemic trends in HIV care and prevention continued. These results underscore the importance of continuing to increase HIV prevention and care efforts in the coming years.

Assessing the individual benefits of reducing HIV diagnosis delay and increasing adherence to HIV care and treatment.

We used an agent-based simulation model (Progression and Transmission of HIV) to follow for 20 years a cohort of persons in the United States infected with HIV in 2015. We assessed the benefits of reducing the delay between HIV infection and diagnosis and increasing adherence to HIV care and treatment on the percent of persons surviving 20 years after infection, average annual HIV transmission rates, and time spent virally suppressed. We examined average diagnosis delays of 1.0-7.0 years, monthly care drop-out rates of 5% to 0.1%, and combinations of these strategies. The percent of the cohort surviving the first 20 years of infection varied from 70.8% to 77.5%, and the annual transmission risk, from 1.5 to 5.2 HIV transmissions per 100 person-years. Thus, individuals can enhance their survival and reduce their risk of transmission to partners by frequent testing for HIV and adhering to care and treatment.

Estimating the HIV Effective Reproduction Number in the United States and Evaluating HIV Elimination Strategies.

CONTEXT: The reproduction number is a fundamental epidemiologic concept used to assess the potential spread of infectious diseases and whether they can be eliminated. OBJECTIVE: We estimated the 2017 United States HIV effective reproduction number, Re, the average number of secondary infections from an infected person in a partially infected population. We analyzed the potential effects on Re of interventions aimed at improving patient flow rates along different stages of the HIV care continuum. We also examined these effects by individual transmission groups. DESIGN: We used the HIV Optimization and Prevention Economics (HOPE) model, a compartmental model of disease progression and transmission, and the next-generation matrix method to estimate Re. We then projected the impact of changes in HIV continuum-of-care interventions on the continuum-of-care flow rates and the estimated Re in 2020. SETTING: United States. PARTICIPANTS: The HOPE model simulated the sexually active US population and persons who inject drugs, aged 13 to 64 years, which was stratified into 195 subpopulations by transmission group, sex, race/ethnicity, age, male circumcision status, and HIV risk level. MAIN OUTCOME MEASURES: The estimated value of Re in 2017 and changes in Re in 2020 from interventions affecting the continuum-of-care flow rates. RESULTS: Our estimated HIV Re in 2017 was 0.92 [0.82, 0.94] (base case [min, max across calibration sets]). Among the interventions considered, the most effective way to reduce Re substantially below 1.0 in 2020 was to maintain viral suppression among those receiving HIV treatment. The greatest impact on Re resulted from changing the flow rates for men who have sex with men (MSM). CONCLUSIONS: Our results suggest that current prevention and treatment efforts may not be sufficient to move the country toward HIV elimination. Reducing Re to substantially below 1.0 may be achieved by an ongoing focus on early diagnosis, linkage to care, and sustained viral suppression especially for MSM.

The Estimated Number and Lifetime Medical Cost of HIV Infections Attributable to Sexually Transmitted Infections Acquired in the United States in 2018: A Compilation of Published Modeling Results.

BACKGROUND: The purpose of this study was to estimate the number and lifetime medical cost of HIV infections attributable to incident sexually transmitted infections (STIs) in the United States in 2018. METHODS: We combined data from published models regarding the number or percentage of HIV infections attributable to STIs with updated estimates of the lifetime medical cost per HIV infection. We used 2 distinct calculation methods. Our first calculation used recent estimates of the percentage of HIV infections in men who have sex with men (MSM) attributable to gonorrhea and chlamydia. Our second calculation, based on older studies, used estimates of the expected number of STI-attributable HIV infections per new STI infection, for gonorrhea, chlamydia, syphilis, and trichomoniasis. RESULTS: Our first calculation method suggested that 2489 (25th-75th percentiles, 1895-3000) HIV infections in 2018 among MSM could be attributed to gonorrhea and chlamydia, at an estimated lifetime medical cost of $1.05 billion (25th-75th percentiles, $0.79-$1.26 billion). Our second calculation method suggested that 2349 (25th-75th percentiles, 1948-2744) HIV infections in the general population (including MSM) could be attributed to chlamydia, gonorrhea, syphilis, and trichomoniasis acquired in 2018, at an estimated lifetime medical cost of $0.99 billion (25th-75th percentiles, $0.80-$1.16 billion). CONCLUSIONS: Despite ambiguity regarding the degree to which STIs affect HIV transmission, our combination of data from published STI/HIV transmission models and an HIV lifetime medical cost model can help to quantify the estimated burden of STI-attributable HIV infections in the United States.

Estimated Lifetime HIV-Related Medical Costs in the United States.

BACKGROUND: Lifetime cost estimates are a useful tool in measuring the economic burden of HIV in the United States. Previous estimation methods need to be updated, given improving antiretroviral therapy regimens and updated costs. METHODS: We used an updated version of the agent-based model progression and transmission of HIV (PATH) 3.0 to reflect current regimens and costs. We simulated a cohort of those infected in 2015 until the last person had died to track the lifetime costs for treatment of HIV, including HIV health care utilization costs (inpatient, outpatient, opportunistic infection prophylaxis, non-HIV medication, and emergency department), opportunistic infection treatment costs, and testing costs. We assumed a median per-person diagnosis delay of 3 years and a 3% base monthly probability of dropout from care for a base-case scenario. Additionally, we modeled a most favorable scenario (median diagnosis delay of 1 year and 1% base dropout rate) and a least favorable scenario (median diagnosis delay of 5 years and 5% base dropout rate). RESULTS: We estimated an average lifetime HIV-related medical cost for a person with HIV of $420,285 (2019 US$) discounted (3%) and $1,079,999 undiscounted for a median 3-year diagnosis delay and 3% base dropout rate. Our discounted cost estimate was $490,045 in our most favorable scenario and $326,411 in our least favorable scenario. CONCLUSIONS: Lifetime per-person HIV-related medical costs depend on the time from infection to diagnosis and the likelihood of dropping out of care. Our results, which are similar to previous studies, reflect updated antiretroviral therapy regimens and costs for HIV treatment.

The Estimated Direct Lifetime Medical Costs of Sexually Transmitted Infections Acquired in the United States in 2018.

BACKGROUND: We estimated the lifetime medical costs attributable to sexually transmitted infections (STIs) acquired in 2018, including sexually acquired human immunodeficiency virus (HIV). METHODS: We estimated the lifetime medical costs of infections acquired in 2018 in the United States for 8 STIs: chlamydia, gonorrhea, trichomoniasis, syphilis, genital herpes, human papillomavirus (HPV), hepatitis B, and HIV. We limited our analysis to lifetime medical costs incurred for treatment of STIs and for treatment of related sequelae; we did not include other costs, such as STI prevention. For each STI, except HPV, we calculated the lifetime medical cost by multiplying the estimated number of incident infections in 2018 by the estimated lifetime cost per infection. For HPV, we calculated the lifetime cost based on the projected lifetime incidence of health outcomes attributed to HPV infections acquired in 2018. Future costs were discounted at 3% annually. RESULTS: Incident STIs in 2018 imposed an estimated $15.9 billion (25th-75th percentile: $14.9-16.9 billion) in discounted, lifetime direct medical costs (2019 US dollars). Most of this cost was due to sexually acquired HIV ($13.7 billion) and HPV ($0.8 billion). STIs in women accounted for about one fourth of the cost of incident STIs when including HIV, but about three fourths when excluding HIV. STIs among 15- to 24-year-olds accounted for $4.2 billion (26%) of the cost of incident STIs. CONCLUSIONS: Incident STIs continue to impose a considerable lifetime medical cost burden in the United States. These results can inform health economic analyses to promote the use of cost-effective STI prevention interventions to reduce this burden.

Effects of Reaching National Goals on HIV Incidence, by Race and Ethnicity, in the United States.

CONTEXT: Human immunodeficiency virus (HIV) incidence and prevalence in the United States are characterized by significant disparities by race/ethnicity. National HIV care goals, such as boosting to 90% the proportion of persons whose HIV is diagnosed and increasing to 80% the proportion of persons living with diagnosed HIV who are virally suppressed, will likely reduce HIV incidence, but their effects on HIV-related disparities are uncertain. OBJECTIVE: We sought to understand by race/ethnicity how current HIV care varies, the level of effort required to achieve national HIV care goals, and the effects of reaching those goals on HIV incidence and disparities. DESIGN: Using a dynamic model of HIV transmission, we identified 2016 progress along the HIV care continuum among blacks, Hispanics, and whites/others compared with national 2020 goals. We examined disparities over time. SETTING: United States. PARTICIPANTS: Beginning in 2006, our dynamic compartmental model simulated the sexually active US population 13 to 64 years of age, which was stratified into 195 subpopulations by transmission group, sex, race/ethnicity, age, male circumcision status, and HIV risk level. MAIN OUTCOME MEASURE: We compared HIV cumulative incidence from 2016 to 2020 when goals were reached compared with base case assumptions about progression along the HIV care continuum. RESULTS: The 2016 proportion of persons with diagnosed HIV who were on treatment and virally suppressed was 50% among blacks, 56% among Hispanics, and 61% among whites/others, compared with a national goal of 80%. When diagnosis, linkage, and viral suppression goals were reached in 2020, cumulative HIV incidence fell by 32% (uncertainty range: 18%-37%) for blacks, 25% (22%-31%) for Hispanics, and 25% (21%-28%) for whites/others. Disparity measures changed little. CONCLUSIONS: Achieving national HIV care goals will require different levels of effort by race/ethnicity but likely will result in substantial declines in cumulative HIV incidence. HIV-related disparities in incidence and prevalence may be difficult to resolve.

From Theory to Practice: Implementation of a Resource Allocation Model in Health Departments.

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.

Mortality Among Persons With HIV in the United States During the COVID-19 Pandemic: A Population-Level Analysis.

BACKGROUND: Whether the COVID-19 pandemic has had a disproportionate impact on mortality among persons with diagnosed HIV (PWDH) in the United States is unclear. Through our macroscale analysis, we seek to better understand how the COVID-19 pandemic affected mortality among PWDH. METHODS: We obtained mortality and population data for the years 2018-2020 from the National HIV Surveillance System for the US PWDH population and from publicly available data for the general population. We computed mortality rates and excess mortality for both the general and PWDH populations. Stratifications by age, race/ethnicity, and sex were considered. For each group, we determined whether the 2020 mortality rates and mortality risk ratio showed a statistically significant change from 2018 to 2019. RESULTS: Approximately 1550 excess deaths occurred among PWDH in 2020, with Black, Hispanic/Latino, and PWDH aged 55 years and older comprising the majority of excess deaths. Mortality rates increased in 2020 from 2018-2019 across the general population in all groups. Among PWDH, mortality rates either increased or showed no statistically significant change. These increases were similar to, or smaller than, those observed in the general population, resulting in a 7.7% decrease in the mortality risk ratio between PWDH and the general population. CONCLUSIONS: While mortality rates among PWDH increased in 2020 relative to 2018-2019, the increases were smaller, or of similar magnitude, to those observed in the general population. We thus do not find evidence of elevated mortality risk from the COVID-19 pandemic among PWDH. These findings held across subpopulations stratified by age, sex, and racial/ethnic group.

Estimating the Cost-Effectiveness of HIV Self-Testing in the United States Using Net Benefit Regression.

BACKGROUND: Cost-effectiveness analysis of HIV self-testing using patient-level data from a randomized clinical trial can inform HIV prevention funding decisions. Cost-effectiveness analysis using net-benefit regression addresses the sampling uncertainty in the trial data and the variability of policymakers' willingness to pay (WTP). METHODS: We used published data from a 12-month longitudinal randomized clinical trial that enrolled 2665 men who had sex with men randomly assigned to the self-testing arm (participants receiving self-test kits) and control arm (participants receiving standard-of-care), and the self-testing arm identified 48 additional new HIV cases. We used net-benefit regression to investigate the cost-effectiveness of an HIV self-testing intervention, which compared the incremental cost per new HIV diagnosis with policymakers' WTP thresholds. We addressed the uncertainties in estimating the incremental cost and the policymakers' WTP per new diagnosis through the incremental net-benefit (INB) regression and cost-effectiveness acceptability curve (CEAC) analyses. RESULTS: From the health care provider's perspective, the INB analysis showed a positive net benefit of HIV self-testing compared with standard-of-care when policymakers' WTP per new HIV diagnosis was $9365 (95% confidence interval: $5700 to $25,500) or higher. The CEAC showed that the probability of HIV self-testing being cost-effective compared with standard-of-care was 58% and >99% at a WTP of $10 000 and $50 000 per new HIV diagnosis, respectively. CONCLUSION: The INB and CEAC analyses suggest that HIV self-testing has the potential to be cost-effective for relatively low values of policymakers' WTP.

Simulation of Full HIV Cluster Networks in a Nationally Representative Model Indicates Intervention Opportunities.

BACKGROUND: Clusters of rapid HIV transmission in the United States are increasingly recognized through analysis of HIV molecular sequence data reported to the National HIV Surveillance System. Understanding the full extent of cluster networks is important to assess intervention opportunities. However, full cluster networks include undiagnosed and other infections that cannot be systematically observed in real life. METHODS: We replicated HIV molecular cluster networks during 2015-2017 in the United States using a stochastic dynamic network simulation model of sexual transmission of HIV. Clusters were defined at the 0.5% genetic distance threshold. Ongoing priority clusters had growth of ≥3 diagnoses/year in multiple years; new priority clusters first had ≥3 diagnoses/year in 2017. We assessed the full extent, composition, and transmission rates of new and ongoing priority clusters. RESULTS: Full clusters were 3-9 times larger than detected clusters, with median detected cluster sizes in new and ongoing priority clusters of 4 (range 3-9) and 11 (range 3-33), respectively, corresponding to full cluster sizes with a median of 14 (3-74) and 94 (7-318), respectively. A median of 36.3% (range 11.1%-72.6%) of infections in the full new priority clusters were undiagnosed. HIV transmission rates in these clusters were >4 times the overall rate observed in the entire simulation. CONCLUSIONS: Priority clusters reflect networks with rapid HIV transmission. The substantially larger full extent of these clusters, high proportion of undiagnosed infections, and high transmission rates indicate opportunities for public health intervention and impact.

COVID-19-related excess missed HIV diagnoses in the United States in 2021.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) and related disruptions led to a significant decline in HIV diagnoses in the United States in 2020. A previous analysis estimated 18% fewer diagnoses than expected among persons with HIV (PWH) acquiring infection in 2019 or earlier, suggesting that the decline in overall diagnoses cannot be attributed solely to decreased transmission. This analysis evaluates the progress made towards closing the 2020 diagnosis deficit in 2021. METHODS: We apply previously developed methods analyzing 2021 diagnosis data from the National HIV Surveillance System to determine whether 2021 diagnosis levels of PWH infected pre-2020 are above or below the expected pre-COVID trends. Results are stratified by assigned sex at birth, transmission group, geographic region, and race/ethnicity. RESULTS: In 2021, HIV diagnoses returned to pre-COVID levels among all PWH acquiring infection 2011-2019. Among Hispanic/Latino PWH and male individuals, diagnoses returned to pre-COVID levels. White PWH, MSM, and PWH living in the south and northeast showed higher-than-expected levels of diagnosis in 2021. For the remaining populations, there were fewer HIV diagnoses in 2021 than expected. CONCLUSION: Although overall diagnoses among persons acquiring HIV pre-2020 returned to pre-COVID levels, the diagnosis gap observed in 2020 remained unclosed at the end of 2021. Fewer than expected diagnoses among certain populations indicate that COVID-19-related disruptions to HIV diagnosis trends remained in 2021. Although some groups showed higher-than-expected levels of diagnoses, such increases were smaller than corresponding 2020 decreases. Expanded testing programs designed to close these gaps are essential.

Isolating the Effect of COVID-19-Related Disruptions on HIV Diagnoses in the United States in 2020.

BACKGROUND: Diagnoses of HIV in the United States decreased by 17% in 2020 due to COVID-related disruptions. The extent to which this decrease is attributable to changes in HIV testing versus HIV transmission is unclear. We seek to better understand this issue by analyzing the discrepancy in expected versus observed HIV diagnoses in 2020 among persons who acquired HIV between 2010 and 2019 because changes in diagnosis patterns in this cohort cannot be attributed to changes in transmission. METHODS: We developed 3 methods based on the CD4-depletion model to estimate excess missed diagnoses in 2020 among persons with HIV (PWH) infected from 2010 to 2019. We stratified the results by transmission group, sex assigned at birth, race/ethnicity, and region to examine differences by group and confirm the reliability of our estimates. We performed similar analyses projecting diagnoses in 2019 among PWH infected from 2010 to 2018 to evaluate the accuracy of our methods against surveillance data. RESULTS: There were approximately 3100-3300 (approximately 18%) fewer diagnoses than expected in 2020 among PWH infected from 2010 to 2019. Females (at birth), heterosexuals, persons who inject drugs, and Hispanic/Latino PWH missed diagnoses at higher levels than the overall population. Validation and stratification analyses confirmed the accuracy and reliability of our estimates. CONCLUSIONS: The substantial drop in number of previously infected PWH diagnosed in 2020 suggests that changes in testing played a substantial role in the observed decrease. Levels of missed diagnoses differed substantially across population subgroups. Increasing testing efforts and innovative strategies to reach undiagnosed PWH are needed to offset this diagnosis gap. These analyses may be used to inform future estimates of HIV transmission during the COVID-19 pandemic.

Closing the gaps in the continuum of depression care for persons with HIV: modeling the impact on viral suppression in the United States.

OBJECTIVE: Depression is prevalent among persons with HIV (PWH) and is associated with poorer adherence and lack of viral load suppression (VLS). When treated for depression, PWH are more likely to stay in HIV care and adhere to medications; however, for many PWH, depression is not adequately diagnosed or treated. We adapted Progression and Transmission of HIV (PATH 3.0), a U.S. agent-based dynamic stochastic simulation model, by incorporating a continuum of depression care and estimating the impact on VLS of an enhanced depression diagnosis and care scenario (EDC). METHODS: We compared EDC - whereby every PWH is assessed for depression, gets treatment if diagnosed, and of those, half achieve remission - to a status quo scenario (SQ) on VLS. Based on published findings, assumptions for SQ were: 34.7% depressed, 45% diagnosed, 55.3% treated and 33% of treated achieving remission. Compared to PWH without depression, we assumed the probability of being non-virally suppressed increased by 1.57 times for PWH with depression (PWH-D), and by 0.95 times for PWH with remitted depression. RESULTS: There was an average increase of 14.6% (11.5-18.5) in the proportion of PWH-D who achieved VLS in EDC compared to SQ. Among all PWH, there was a 4.7% (3.4-6.0) increase in the proportion who achieved VLS in EDC compared to SQ. CONCLUSIONS: Fully diagnosing and adequately treating depression would improve health and quality of life for a substantial proportion of PWH-D and result in a nearly 5% increase in expected rates of VLS in the United States, supporting national prevention goals.

Comparison of methods for estimating the cost of human immunodeficiency virus-testing interventions.

CONTEXT: The Centers for Disease Control and Prevention (CDC), Division of HIV/AIDS Prevention, spends approximately 50% of its $325 million annual human immunodeficiency virus (HIV) prevention funds for HIV-testing services. An accurate estimate of the costs of HIV testing in various settings is essential for efficient allocation of HIV prevention resources. OBJECTIVES: To assess the costs of HIV-testing interventions using different costing methods. DESIGN, SETTINGS, AND PARTICIPANTS: We used the microcosting-direct measurement method to assess the costs of HIV-testing interventions in nonclinical settings, and we compared these results with those from 3 other costing methods: microcosting-staff allocation, where the labor cost was derived from the proportion of each staff person's time allocated to HIV testing interventions; gross costing, where the New York State Medicaid payment for HIV testing was used to estimate program costs, and program budget, where the program cost was assumed to be the total funding provided by Centers for Disease Control and Prevention. MAIN OUTCOME MEASURES: Total program cost, cost per person tested, and cost per person notified of new HIV diagnosis. RESULTS: The median costs per person notified of a new HIV diagnosis were $12 475, $15 018, $2697, and $20 144 based on microcosting-direct measurement, microcosting-staff allocation, gross costing, and program budget methods, respectively. Compared with the microcosting-direct measurement method, the cost was 78% lower with gross costing, and 20% and 61% higher using the microcosting-staff allocation and program budget methods, respectively. CONCLUSIONS: Our analysis showed that HIV-testing program cost estimates vary widely by costing methods. However, the choice of a particular costing method may depend on the research question being addressed. Although program budget and gross-costing methods may be attractive because of their simplicity, only the microcosting-direct measurement method can identify important determinants of the program costs and provide guidance to improve efficiency.

Optimizing HIV Prevention Efforts to Achieve EHE Incidence Targets.

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.

Progression and transmission of HIV (PATH 4.0)-A new agent-based evolving network simulation for modeling HIV transmission clusters.

We present the Progression and Transmission of HIV (PATH 4.0), a simulation tool for analyses of cluster detection and intervention strategies. Molecular clusters are groups of HIV infections that are genetically similar, indicating rapid HIV transmission where HIV prevention resources are needed to improve health outcomes and prevent new infections. PATH 4.0 was constructed using a newly developed agent-based evolving network modeling (ABENM) technique and evolving contact network algorithm (ECNA) for generating scale-free networks. ABENM and ECNA were developed to facilitate simulation of transmission networks for low-prevalence diseases, such as HIV, which creates computational challenges for current network simulation techniques. Simulating transmission networks is essential for studying network dynamics, including clusters. We validated PATH 4.0 by comparing simulated projections of HIV diagnoses with estimates from the National HIV Surveillance System (NHSS) for 2010-2017. We also applied a cluster generation algorithm to PATH 4.0 to estimate cluster features, including the distribution of persons with diagnosed HIV infection by cluster status and size and the size distribution of clusters. Simulated features matched well with NHSS estimates, which used molecular methods to detect clusters among HIV nucleotide sequences of persons with HIV diagnosed during 2015-2017. Cluster detection and response is a component of the U.S. Ending the HIV Epidemic strategy. While surveillance is critical for detecting clusters, a model in conjunction with surveillance can allow us to refine cluster detection methods, understand factors associated with cluster growth, and assess interventions to inform effective response strategies. As surveillance data are only available for cases that are diagnosed and reported, a model is a critical tool to understand the true size of clusters and assess key questions, such as the relative contributions of clusters to onward transmissions. We believe PATH 4.0 is the first modeling tool available to assess cluster detection and response at the national-level and could help inform the national strategic plan.

Cost-effectiveness of pooled nucleic acid amplification testing for acute HIV infection after third-generation HIV antibody screening and rapid testing in the United States: a comparison of three public health settings.

BACKGROUND: Detection of acute HIV infection (AHI) with pooled nucleic acid amplification testing (NAAT) following HIV testing is feasible. However, cost-effectiveness analyses to guide policy around AHI screening are lacking; particularly after more sensitive third-generation antibody screening and rapid testing. METHODS AND FINDINGS: We conducted a cost-effectiveness analysis of pooled NAAT screening that assessed the prevention benefits of identification and notification of persons with AHI and cases averted compared with repeat antibody testing at different intervals. Effectiveness data were derived from a Centers for Disease Control and Prevention AHI study conducted in three settings: municipal sexually transmitted disease (STD) clinics, a community clinic serving a population of men who have sex with men, and HIV counseling and testing sites. Our analysis included a micro-costing study of NAAT and a mathematical model of HIV transmission. Cost-effectiveness ratios are reported as costs per quality-adjusted life year (QALY) gained in US dollars from the societal perspective. Sensitivity analyses were conducted on key variables, including AHI positivity rates, antibody testing frequency, symptomatic detection of AHI, and costs. Pooled NAAT for AHI screening following annual antibody testing had cost-effectiveness ratios exceeding US$200,000 per QALY gained for the municipal STD clinics and HIV counseling and testing sites and was cost saving for the community clinic. Cost-effectiveness ratios increased substantially if the antibody testing interval decreased to every 6 months and decreased to cost-saving if the testing interval increased to every 5 years. NAAT was cost saving in the community clinic in all situations. Results were particularly sensitive to AHI screening yield. CONCLUSIONS: Pooled NAAT screening for AHI following negative third-generation antibody or rapid tests is not cost-effective at recommended antibody testing intervals for high-risk persons except in very high-incidence settings.

Comparing the costs of HIV screening strategies and technologies in health-care settings.

OBJECTIVES: In 2006, the Centers for Disease Control and Prevention (CDC) recommended routine human immunodeficiency virus (HIV) screening for people aged 13 to 64 years in all U.S. health-care settings. Earlier recommendations focused on those at high risk for HIV and included more extensive pretest counseling. HIV screening may also involve either rapid or conventional testing. The purpose of this research was to estimate the costs of these different testing procedures and the cost per HIV-infected patient correctly receiving test results in three health-care scenarios that illustrated these policy differences. METHODS: The study estimated the costs of rapid and conventional HIV testing in the following scenarios: (1) sexually transmitted disease (STD) clinic counseling and testing (CT), (2) STD clinic screening, and (3) emergency department (ED) screening. Costs were estimated from the provider perspective in 2006 dollars. A decision analytic model was developed to estimate the cost per HIV-infected patient notified of test results using the two testing procedures in the three scenarios. RESULTS: Although the complete rapid testing procedure was more expensive than conventional testing, the cost per HIV-infected patient receiving test results was lower for the rapid test compared with conventional testing in all scenarios. Per-patient costs of receiving results were lowest in the ED screening scenario and highest in the STD CT scenario. These costs were sensitive to changes in test costs, HIV prevalence, and return rates following conventional tests. CONCLUSION: HIV screening in general health-care settings is economically feasible, particularly with rapid tests that lower the cost of HIV-infected patients receiving their test results.

Optimal allocation of HIV prevention funds for state health departments.

OBJECTIVE: To estimate the optimal allocation of Centers for Disease Control and Prevention (CDC) HIV prevention funds for health departments in 52 jurisdictions, incorporating Health Resources and Services Administration (HRSA) Ryan White HIV/AIDS Program funds, to improve outcomes along the HIV care continuum and prevent infections. METHODS: Using surveillance data from 2010 to 2012 and budgetary data from 2012, we divided the 52 health departments into 5 groups varying by number of persons living with diagnosed HIV (PLWDH), median annual CDC HIV prevention budget, and median annual HRSA expenditures supporting linkage to care, retention in care, and adherence to antiretroviral therapy. Using an optimization and a Bernoulli process model, we solved for the optimal CDC prevention budget allocation for each health department group. The optimal allocation distributed the funds across prevention interventions and populations at risk for HIV to prevent the greatest number of new HIV cases annually. RESULTS: Both the HIV prevention interventions funded by the optimal allocation of CDC HIV prevention funds and the proportions of the budget allocated were similar across health department groups, particularly those representing the large majority of PLWDH. Consistently funded interventions included testing, partner services and linkage to care and interventions for men who have sex with men (MSM). Sensitivity analyses showed that the optimal allocation shifted when there were differences in transmission category proportions and progress along the HIV care continuum. CONCLUSION: The robustness of the results suggests that most health departments can use these analyses to guide the investment of CDC HIV prevention funds into strategies to prevent the most new cases of HIV.