Implications of rapid population growth on survey design and HIV estimates in the Rakai Community Cohort Study (RCCS), Uganda

Background Longitudinal population-based cohorts are critical in HIV surveillance programs in Africa but continued rapid population growth poses serious challenges to maintaining cohort representativeness with limited resources. In one such cohort, we evaluated if systematic exclusion of some residents due to growing population size biases key HIV metrics like prevalence and viremia. Methods Data were obtained from the Rakai Community Cohort study (RCCS) in south central Uganda, an open population-based cohort which began excluding some residents of newly constructed household structures within its surveillance boundaries in 2008. We evaluated the extent to which changing inclusion criteria may bias recent population HIV seroprevalence and viremia estimates from the RCCS using ensemble machine learning models fit to 2019-2020 RCCS census and survey data. Results Of the 24,729 census-eligible residents, 2,920 (12%) were living within new household structures and excluded. Predicted seroprevalence for excluded residents was 11.4% (95% Confidence Interval: 10.2, 12.3) compared to 11.8% in the observed sample. However, predicted seroprevalence for younger excluded residents 15-24 years was 5.1% (3.6, 6.1), which was significantly higher than that in the observed sample for the same age group (2.6%). Over all ages, predicted prevalence of viremia in excluded residents (2.8% [2.2, 3.3]) was higher than that in the observed sample (1.7%), resulting in a somewhat higher overall population viremia estimate of 1.9% [1.8, 2.0]). Conclusions Exclusion of residents in new households may modestly bias HIV viremia estimates and some age-specific seroprevalence estimates in the RCCS. Overall HIV seroprevalence estimates were not significantly affected. Key messages (3-5) In-migrants in the observed sample in the RCCS surveillance area differ from currently excluded in-migrants on various demographic characteristics. Machine learning methods may be useful tools in estimating biases introduced by the systematic exclusion of populations for which we have some data. In the context of rapid population growth, population-based open cohorts in sub-Saharan Africa must prioritize limited resources while ensuring HIV estimates are representative of the population. Funding Funding for this project was supported by the National Institute of Allergy and Infectious Diseases (R01AI143333 and R01AI155080) and the National Institute of Mental Health (R01MH115799). The findings and conclusions in this article are those of the authors and do not necessarily represent the official position of the funding agencies. Research by Aleya Khalifa reported in this publication was supported by the National Institute of Allergy And Infectious Diseases (T32AI114398). Larry Chang was supported by the National Heart, Lung, and Blood Institute (R01HL152813), Fogarty International Center (D43TW010557) and the Johns Hopkins University Center for AIDS Research (P30AI094189). Susie Hoffman and John Santelli were supported by the U.S. National Institute of Child Health and Human Development (NICHD) (R01HD091003; Santelli, PI). Susie Hoffman was also supported by the National Institute of Mental Health (P30-MH43520; Remien, PI). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Ethics approval This study was approved by the Uganda National Council for Science and Technology (approval number HS 540), the Uganda Virus Research Institution Research and Ethics Committee (approval number GC/127/08/12/137), Johns Hopkins Institutional Review Board (approval number IRB-00217467), and the Columbia University Institutional Review Board (approval number IRB-AAAR5428).