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.

Does Decentralization Improve Health System Performance and Outcomes in Low- and Middle-Income Countries? A Systematic Review of Evidence From Quantitative Studies.

Policy Points: For more than 3 decades, international development agencies have advocated health system decentralization to improve health system performance in low- and middle-income countries. We found little rigorous evidence documenting the impact of decentralization processes on health system performance or outcomes in part due to challenges in measuring such far-reaching and multifaceted system-level changes. We propose a renewed research agenda that focuses on discrete definitions of decentralization and how institutional factors and mechanisms affect health system performance and outcomes within the general context of decentralized governance structures. CONTEXT: Despite the widespread adoption of decentralization reforms as a means to improve public service delivery in developing countries since the 1980s, empirical evidence of the role of decentralization on health system improvement is still limited and inconclusive. This study reviewed studies published from 2000 to 2016 with adequate research designs to identify evidence on whether and how decentralization processes have impacted health systems. METHODS: We conducted a systematic review of peer-reviewed journal articles from the public health and social science literature. We searched for articles within 9 databases using predefined search terms reflecting decentralization and health system constructs. Inclusion criteria were original research articles, low- and middle-income country settings, quantifiable outcome measures, and study designs that use comparisons or statistical adjustments. We excluded studies in high-income country settings and/or published in a non-English language. FINDINGS: Sixteen studies met our prespecified inclusion and exclusion criteria and were grouped based on outcomes measured: health system inputs (n = 3), performance (n = 7), and health outcomes (n = 7). Numerous studies addressing conceptual issues related to decentralization but without any attempt at empirical estimation were excluded. Overall, we found mixed results regarding the effects of decentralization on health system indicators with seemingly beneficial effects on health system performance and health outcomes. Only 10 studies were considered to have relatively low risks of bias. CONCLUSIONS: This study reveals the limited empirical knowledge of the impact of decentralization on health system performance. Mixed empirical findings on the role of decentralization on health system performance and outcomes highlight the complexity of decentralization processes and their systemwide effects. Thus, we propose a renewed research agenda that focuses on discrete definitions of decentralization and how institutional factors and mechanisms affect health system performance and outcomes within the general context of decentralized governance structures.

Evaluating the Contribution of the Built Environment on Obesity Among New York State Students.

OBJECTIVES: One third of school-aged children in New York State (NYS) are overweight or obese, with large geographic disparities across local regions. We used NYS student obesity surveillance data to assess whether these geographical variations are attributable to the built environment. METHOD: We combined NYS Student Weight Status Category Reporting System 2010-2012 data with other government publicly available data. Ordinary least squares regression models identified key determinants of school district-level student obesity rates for elementary and middle/high schools. Geographical weighted regression models explored spatial variations in local coefficients of the built environment predictors. RESULTS: From ordinary least squares models, higher farmers' market density was only significantly associated with lower obesity rates among elementary school students (b = -0.116; p < .01). Higher fast-food restaurant density was significantly associated with higher obesity rates (b = 0.014; p < .05), and higher land use mix was only significantly associated with lower obesity rates (b = -0.054; p < .01) among middle/high school students. In geographical weighted regression analyses, the inverse association between market density and obesity rates among elementary school students was more pronounced in the eastern portion of the state. The relationship between higher fast-food restaurant density and higher obesity rates among middle/high school students was found in the southeastern portion of the state. CONCLUSIONS: Different patterns of food consumption may explain varying determinants of obesity between younger and older students. Regional variations in local associations between the built environment variables and obesity may suggest differences in how healthy food sources are accessed locally.