Xiaoyue Maggie Niu1, Amrita Rao2, David Chen1, Ben Sheng1, Sharon Weir3, Eric Umar4, Gift Trapence5, Vincent Jumbe4, Dunker Kamba5, Katherine Rucinski2, Nikita Viswasam2, Stefan Baral2, Le Bao6. 1. Department of Statistics, Eberly College of Science, Pennsylvania State University, University Park. 2. Center for Public Health and Human Rights, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD. 3. Department of Epidemiology, University of North Carolina, Chapel Hill. 4. Department of Health Systems and Policy, School of Public Health and Family Medicine, University of Malawi College of Medicine, Blantyre, Malawi. 5. Center for Development of People, Blantyre, Malawi. 6. Department of Statistics, Eberly College of Science, Pennsylvania State University, University Park. Electronic address: lebao@psu.edu.
Abstract
PURPOSE: Human immunodeficiency virus (HIV) risks are heterogeneous in nature even in generalized epidemics. However, data are often missing for those at highest risk of HIV, including female sex workers. Statistical models may be used to address data gaps where direct, empiric estimates do not exist. METHODS: We proposed a new size estimation method that combines multiple data sources (the Malawi Biological and Behavioral Surveillance Survey, the Priorities for Local AIDS Control Efforts study, and the Malawi Demographic Household Survey). We used factor analysis to extract information from auxiliary variables and constructed a linear mixed effects model for predicting population size for all districts of Malawi. RESULTS: On average, the predicted proportion of female sex workers among women of reproductive age across all districts was about 0.58%. The estimated proportions seemed reasonable in comparing with a recent study Priorities for Local AIDS Control Efforts II (PLACE II). Compared with using a single data source, we observed increased precision and better geographic coverage. CONCLUSIONS: We illustrate how size estimates from different data sources may be combined for prediction. Applying this approach to other subpopulations in Malawi and to countries where size estimate data are lacking can ultimately inform national modeling processes and estimate the distribution of risks and priorities for HIV prevention and treatment programs.
PURPOSE: Human immunodeficiency virus (HIV) risks are heterogeneous in nature even in generalized epidemics. However, data are often missing for those at highest risk of HIV, including female sex workers. Statistical models may be used to address data gaps where direct, empiric estimates do not exist. METHODS: We proposed a new size estimation method that combines multiple data sources (the Malawi Biological and Behavioral Surveillance Survey, the Priorities for Local AIDS Control Efforts study, and the Malawi Demographic Household Survey). We used factor analysis to extract information from auxiliary variables and constructed a linear mixed effects model for predicting population size for all districts of Malawi. RESULTS: On average, the predicted proportion of female sex workers among women of reproductive age across all districts was about 0.58%. The estimated proportions seemed reasonable in comparing with a recent study Priorities for Local AIDS Control Efforts II (PLACE II). Compared with using a single data source, we observed increased precision and better geographic coverage. CONCLUSIONS: We illustrate how size estimates from different data sources may be combined for prediction. Applying this approach to other subpopulations in Malawi and to countries where size estimate data are lacking can ultimately inform national modeling processes and estimate the distribution of risks and priorities for HIV prevention and treatment programs.
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