Peterson Emily N, Nethery Rachel C, Padellini Tullia, Chen Jarvis T, Coull Brent A, Piel Frédéric B, Wakefield Jon, Blangiardo Marta, Waller Lance A
Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University.
Department of Biostatistics, Harvard T.H. Chan School of Public Health.
Ann Appl Stat. 2024 Jun;18(2):1565-1595. doi: 10.1214/23-aoas1849. Epub 2024 Apr 5.
Small area population counts are necessary for many epidemiological studies, yet their quality and accuracy are often not assessed. In the United States, small area population counts are published by the United States Census Bureau (USCB) in the form of the decennial census counts, intercensal population projections (PEP), and American Community Survey (ACS) estimates. Although there are significant relationships between these three data sources, there are important contrasts in data collection, data availability, and processing methodologies such that each set of reported population counts may be subject to different sources and magnitudes of error. Additionally, these data sources do not report identical small area population counts due to post-survey adjustments specific to each data source. Consequently, in public health studies, small area disease/mortality rates may differ depending on which data source is used for denominator data. To accurately estimate annual small area population counts associated uncertainties, we present a Bayesian population (BPop) model, which fuses information from all three USCB sources, accounting for data source specific methodologies and associated errors. We produce comprehensive small area race-stratified estimates of the true population, and associated uncertainties, given the observed trends in all three USCB population estimates. The main features of our framework are: (1) a single model integrating multiple data sources, (2) accounting for data source specific data generating mechanisms and specifically accounting for data source specific errors, and (3) prediction of population counts for years without USCB reported data. We focus our study on the Black and White only populations for 159 counties of Georgia and produce estimates for years 2006-2023. We compare BPop population estimates to decennial census counts, PEP annual counts, and ACS multi-year estimates. Additionally, we illustrate and explain the different types of data source specific errors. Lastly, we compare model performance using simulations and validation exercises. Our Bayesian population model can be extended to other applications at smaller spatial granularity and for demographic subpopulations defined further by race, age, and sex, and/or for other geographical regions.
对于许多流行病学研究而言，小区域人口计数是必要的，但它们的质量和准确性往往未得到评估。在美国，美国人口普查局（USCB）以十年一次的人口普查计数、两次普查间的人口预测（PEP）以及美国社区调查（ACS）估计数的形式发布小区域人口计数。尽管这三个数据源之间存在显著关系，但在数据收集、数据可用性和处理方法方面存在重要差异，以至于每组报告的人口计数可能受到不同来源和程度的误差影响。此外，由于每个数据源特定的调查后调整，这些数据源报告的小区域人口计数并不相同。因此，在公共卫生研究中，小区域疾病/死亡率可能因用于分母数据的数据源不同而有所差异。为了准确估计年度小区域人口计数及其相关不确定性，我们提出了一种贝叶斯人口（BPop）模型，该模型融合了来自USCB所有三个数据源的信息，考虑了数据源特定的方法和相关误差。鉴于USCB所有三个人口估计数的观测趋势，我们生成了按种族分层的真实人口的全面小区域估计数以及相关不确定性。我们框架的主要特点是：（1）一个整合多个数据源的单一模型；（2）考虑数据源特定的数据生成机制，并特别考虑数据源特定的误差；（3）对没有USCB报告数据年份的人口计数进行预测。我们将研究重点放在佐治亚州159个县仅按黑人和白人划分的人口上，并生成2006 - 2023年的估计数。我们将BPop人口估计数与十年一次的人口普查计数、PEP年度计数以及ACS多年估计数进行比较。此外，我们阐述并解释了不同类型的数据源特定误差。最后，我们通过模拟和验证练习比较模型性能。我们的贝叶斯人口模型可以扩展到其他空间粒度更小的应用，以及针对按种族、年龄和性别进一步定义的人口亚群体，和/或其他地理区域。
