Andreea L Erciulescu, Jianzhu Li, Tom Krenzke, Machell Town
Westat, Maryland, United States.
Population Health Surveillance Branch, Division of Population Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Georgia, United States.
Stat Methods Appt. 2022 Dec;33:1171-1191. doi: 10.1007/s10260-022-00678-7.
The complexity of survey data and the availability of data from auxiliary sources motivate researchers to explore estimation methods that extend beyond traditional survey-based estimation. The U.S. Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System (BRFSS) collects a wide range of health information, including whether respondents have a personal doctor. While the BRFSS focuses on state-level estimation, there is demand for county-level estimation of health indicators using BRFSS data. A hierarchical Bayes small area estimation model is developed to combine county-level BRFSS survey data with county-level data from auxiliary sources, while accounting for various sources of error and nested geographical levels. To mitigate extreme proportions and unstable survey variances, a transformation is applied to the survey data. Model-based county-level predictions are constructed for prevalence of having a personal doctor for all the counties in the U.S., including those where BRFSS survey data were not available. An evaluation study using only the counties with large BRFSS sample sizes to fit the model versus using all the counties with BRFSS data to fit the model is also presented.
调查数据的复杂性以及辅助数据源数据的可得性促使研究人员探索超越传统基于调查的估计方法。美国疾病控制与预防中心的行为风险因素监测系统（BRFSS）收集了广泛的健康信息，包括受访者是否有私人医生。虽然BRFSS专注于州级估计，但人们也需要使用BRFSS数据进行县级健康指标估计。开发了一种分层贝叶斯小区域估计模型，将县级BRFSS调查数据与辅助数据源的县级数据相结合，同时考虑各种误差来源和嵌套的地理层次。为了减轻极端比例和不稳定的调查方差，对调查数据进行了变换。为美国所有县构建了基于模型的县级有私人医生患病率预测，包括那些没有BRFSS调查数据的县。还展示了一项评估研究，该研究比较了仅使用BRFSS样本量大的县来拟合模型与使用所有有BRFSS数据的县来拟合模型的情况。
