District-level HIV estimates using the spectrum model in five states of India, 2017 - Kumar, Pradeep (Medicine (Baltimore), 2021)
Decentralized response has been the hallmark of the National AIDS Control Programme in India. District-level HIV burden estimates quantifying the distribution of the epidemics are needed to enhance this decentralized response further to monitor the progress on prevention, testing, and treatment interventions. In this paper, we describe the methodology and results of district-level estimates using the Spectrum model piloted in 5 states of India under National AIDS Control Programme. Using state spectrum model for HIV estimations 2017, we disaggregated state results by the district in pilot states. Each district was considered a subepidemic and HIV epidemic configuration was carried out in its general population as well as in key population. We used HIV surveillance data from antenatal clinics and routine pregnant women testing to model the general population's epidemic curve. We used HIV prevalence data available from HIV sentinel surveillance and integrated biological and behavioral surveys to inform the epidemic curve for key population. Estimation and projection packgage classic platform was used for the curve fitting. District-wide estimates extracted from subpopulation summary in Spectrum results section were used to calculate relative burden for each district and applied to approved State HIV Estimations 2017 estimates. No district in Tamil Nadu had an adult HIV prevalence of higher than 0.5% except for one, and the epidemic seems to be declining. In Maharashtra, the epidemic has shown a decline, with all except 5 districts showing an adult prevalence of less than 0.50%. In Gujarat and Uttar Pradesh, few districts showed rising HIV prevalence. However, none had an adult prevalence of higher than 0.50%. In Mizoram, 6 of 8 districts showed a rising HIV trend with an adult prevalence of 1% or more in 5 districts. Disaggregation of state-level estimates by districts provided insights on epidemic diversity within the analyzed states. It also provided baseline evidence to measure the progress toward the goal of end of AIDS by 2030.