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Estimating subnational adult populations in data-sparse contexts
Accurate estimates of subnational populations are important for policy formulation and monitoring population health indicators. However, in many countries, data on population counts are limited and are of poor quality, and so levels and trends subnationally are unclear. We present a Bayesian hierarchical model to estimate adult populations at the subnational level. The model builds on a cohort component projection framework, incorporates census data and estimates from the United Nation's World Population Prospects, and uses characteristic mortality schedules to obtain estimates of population counts and the components of population change. The data required as inputs to the model are minimal and available across a wide range of countries, including most low-income countries. The model is applied to estimate and project populations by county in Kenya for 1979-2020.
Monica Alexander is an Assistant Professor in Statistical Sciences and Sociology at the University of Toronto. Her research focuses on developing statistical methods to help measure disparities in demographic and health outcomes. She received a PhD in Demography and Masters in Statistics from the University of California, Berkeley. Prior to that she received a Masters of Social Research from the ANU and a Bachelor of Science at the University of Tasmania. She has worked on research projects with organizations such as UNICEF, UNHCR, and the WHO. Her work has appeared in journals such as Demography, Epidemiology, The Lancet Global Health, and JAMA.