Simulating the End of AIDS in New York: Using Participatory Dynamic Modeling to Improve Implementation of the Ending the Epidemic Initiative.

OBJECTIVES: In 2014, the governor of New York announced the Ending the Epidemic (ETE) plan to reduce annual new HIV infections from 3000 to 750, achieve a first-ever decrease in HIV prevalence, and reduce AIDS progression by the end of 2020. The state health department undertook participatory simulation modeling to develop a baseline for comparing epidemic trends and feedback on ETE strategies. METHODS: A dynamic compartmental model projected the individual and combined effects of 3 ETE initiatives: enhanced linkage to and retention in HIV treatment, increased preexposure prophylaxis (PrEP) among men who have sex with men, and expanded housing assistance. Data inputs for model calibration and low-, medium-, and high-implementation scenarios (stakeholders' rollout predictions, and lower and upper bounds) came from surveillance and program data through 2014, the literature, and expert judgment. RESULTS: Without ETE (baseline scenario), new HIV infections would decline but remain >750, and HIV prevalence would continue to increase by 2020. Concurrently implementing the 3 programs would lower annual new HIV infections by 16.0%, 28.1%, and 45.7% compared with baseline in the low-, medium-, and high-implementation scenarios, respectively. In all concurrent implementation scenarios, although annual new HIV infections would remain >750, there would be fewer new HIV infections than deaths, yielding the first-ever decrease in HIV prevalence. PrEP and enhanced linkage and retention would confer the largest population-level changes. CONCLUSIONS: New York State will achieve 1 ETE benchmark under the most realistic (medium) implementation scenario. Findings facilitated framing of ETE goals and underscored the need to prioritize men who have sex with men and maintain ETE's multipronged approach, including other programs not modeled here.

Policy modeling to support administrative decisionmaking on the New York state HIV testing law.

A recent New York law requires medical providers to offer HIV tests as part of routine care. We developed a system dynamics simulation model of the HIV testing and care system to help administrators understand the law's potential epidemic impact, resource needs, strategies to improve implementation, and appropriate outcome indicators for future policy evaluations once postlaw data become available. Policy modeling allowed us to synthesize information from numerous sources including quantitative administrative data sets and practitioners' content expertise, structure the information to be viewed both numerically and visually, and organize consensus for decisionmaking purposes. This case illustrates how policy modeling can provide an integrated framework for administrators to examine policy problems in complex systems, particularly when data time lags limit pre--post comparisons and key outcomes cannot be measured directly.

Modeling the Declining Positivity Rates for Human Immunodeficiency Virus Testing in New York State.

OBJECTIVE: New York health care providers have experienced declining percentages of positive human immunodeficiency virus (HIV) tests among patients. Furthermore, observed positivity rates are lower than expected on the basis of the national estimate that one-fifth of HIV-infected residents are unaware of their infection. We used mathematical modeling to evaluate whether this decline could be a result of declining numbers of HIV-infected persons who are unaware of their infection, a measure that is impossible to measure directly. DESIGN AND SETTING: A stock-and-flow mathematical model of HIV incidence, testing, and diagnosis was developed. The model includes stocks for uninfected, infected and unaware (in 4 disease stages), and diagnosed individuals. Inputs came from published literature and time series (2006-2009) for estimated new infections, newly diagnosed HIV cases, living diagnosed cases, mortality, and diagnosis rates in New York. MAIN OUTCOME MEASURES: Primary model outcomes were the percentage of HIV-infected persons unaware of their infection and the percentage of HIV tests with a positive result (HIV positivity rate). RESULTS: In the base case, the estimated percentage of unaware HIV-infected persons declined from 14.2% in 2006 (range, 11.9%-16.5%) to 11.8% in 2010 (range, 9.9%-13.1%). The HIV positivity rate, assuming testing occurred independent of risk, was 0.12% in 2006 (range, 0.11%-0.15%) and 0.11% in 2010 (range, 0.10%-0.13%). The observed HIV positivity rate was more than 4 times the expected positivity rate based on the model. CONCLUSIONS: HIV test positivity is a readily available indicator, but it cannot distinguish causes of underlying changes. Findings suggest that the percentage of unaware HIV-infected New Yorkers is lower than the national estimate and that the observed HIV test positivity rate is greater than expected if infected and uninfected individuals tested at the same rate, indicating that testing efforts are appropriately targeting undiagnosed cases.

Mandating the offer of HIV testing in New York: simulating the epidemic impact and resource needs.

BACKGROUND: A 2010 New York law requires that patients aged 13-64 years be offered HIV testing in routine medical care settings. Past studies report the clinical outcomes, cost-effectiveness, and budget impact of expanded HIV testing nationally and within clinics but have not examined how state policies affect resource needs and epidemic outcomes. METHODS: A system dynamics model of HIV testing and care was developed, where disease progression and transmission differ by awareness of HIV status, engagement in care, and disease stage. Data sources include HIV surveillance, Medicaid claims, and literature. The model projected how alternate implementation scenarios would change new infections, diagnoses, linkage to care, and living HIV cases over 10 years. RESULTS: Without the law, the model projects declining new infections, newly diagnosed cases, individuals newly linked to care, and fraction of undiagnosed cases (reductions of 62.8%, 59.7%, 54.1%, and 57.8%) and a slight increase in living diagnosed cases and individuals in care (2.2% and 6.1%). The law will further reduce new infections, diagnosed AIDS cases, and the fraction undiagnosed and initially increase and then decrease newly diagnosed cases. Outcomes were consistent across scenarios with different testing offer frequencies and implementation times but differed according to the level of implementation. CONCLUSIONS: A mandatory offer of HIV testing may increase diagnoses and avert infections but will not eliminate the epidemic. Despite declines in new infections, previously diagnosed cases will continue to need access to antiretroviral therapy, highlighting the importance of continued funding for HIV care.