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Accurate estimates of subnational populations are important for policyformulation and monitoring population health indicators. For example, estimatesof the number of women of reproductive age are important to understand thepopulation at risk to maternal mortality and unmet need for contraception.However, in many low-income countries, data on population counts and componentsof population change are limited, and so levels and trends subnationally areunclear. We present a Bayesian constrained cohort component model for theestimation and projection of subnational populations. The model builds on acohort component projection framework, incorporates census data and estimatesfrom the United Nation's World Population Prospects, and uses characteristicmortality schedules to obtain estimates of population counts and the componentsof population change, including internal migration. The data required as inputsto the model are minimal and available across a wide range of countries,including most low-income countries. The model is applied to estimate andproject populations by county in Kenya for 1979-2019, and validated against the2019 Kenyan census.

A Bayesian cohort component projection model to estimate adult populations at the subnational level in data-sparse settings