Accurate estimates of subnational populations are important for policy formulation and monitoring population health indicators. For example, estimates of the number of women of reproductive age are important to understand the population at risk to maternal mortality and unmet need for contraception. However, in many low-income countries, data on population counts and components of population change are limited, and so levels and trends subnationally are unclear. We present a Bayesian constrained cohort component model for the estimation and projection of subnational populations. 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, including internal migration. 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 1979-2019, and validated against the 2019 Kenyan census.

翻译：准确估计国家以下各级人口数对政策制定和监测人口健康指标十分重要,例如,估计育龄妇女人数对于了解面临孕产妇死亡风险的人口和未满足的避孕需求十分重要,然而,在许多低收入国家,关于人口数和人口变化组成部分的数据有限,因此国家以下各级的水平和趋势不明确。我们为估算和预测国家以下各级人口提出了贝叶西亚受限制组群组成部分模型。该模型以组群组成部分预测框架为基础,纳入了人口普查数据和《联合国世界人口展望》的估计数,并利用典型死亡率表获得人口数和人口变化组成部分(包括国内移徙)的估计数。该模型所需的数据极少,可供包括大多数低收入国家在内的广大国家使用。该模型用于肯尼亚各州1979-2019年的估计数和预测人口项目,并与2019年肯尼亚人口普查相比得到验证。