在基于调查的面积比例估计的贝叶斯分解中调查设计的说明：对美国社区调查的应用
Accounting for survey design in Bayesian disaggregation of survey-based areal estimates of proportions: an application to the American Community Survey
摘要：
了解健康的社会决定因素对健康结果的影响，需要受试者居住的社区特征的数据。然而，对这些特征的估计往往在空间和时间上以一种减少其效用的方式进行汇总。以美国社区调查（ACS）的估计为例，ACS是美国人口普查局管理的一项多年期全国性调查：对小城市地区的估计是在5年期内汇总的，而1年期的估计仅适用于人口$>$ 65，000的城市地区。研究人员可能希望在人群健康研究中使用ACS估计值来表征社区水平的暴露。然而，5年的估计值可能无法正确表征时间变化或与研究中的其他数据在时间上对齐，而1年估计值的粗略空间分辨率降低了其在表征邻域暴露方面的效用。为了规避这个问题，在本文中，我们提出了一个建模框架来分解估计的比例来自抽样调查明确占调查设计效果。我们说明了我们的模型的效用，通过将其应用到ACS数据，生成估计贫困的密歇根州在精细的时空分辨率。
英文摘要：
Understanding the effects of social determinants of health on health outcomes requires data on characteristics of the neighborhoods in which subjects live. However, estimates of these characteristics are often aggregated over space and time in a fashion that diminishes their utility. Take, for example, estimates from the American Community Survey (ACS), a multi-year nationwide survey administered by the U.S. Census Bureau: estimates for small municipal areas are aggregated over 5-year periods, whereas 1-year estimates are only available for municipal areas with populations $>$65,000. Researchers may wish to use ACS estimates in studies of population health to characterize neighborhood-level exposures. However, 5-year estimates may not properly characterize temporal changes or align temporally with other data in the study, while the coarse spatial resolution of the 1-year estimates diminishes their utility in characterizing neighborhood exposure. To circumvent this issue, in this paper we propose a modeling framework to disaggregate estimates of proportions derived from sampling surveys which explicitly accounts for the survey design effect. We illustrate the utility of our model by applying it to the ACS data, generating estimates of poverty for the state of Michigan at fine spatio-temporal resolution.
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