Validation of multilevel regression and poststratification methodology for small area estimation of health indicators from the behavioral risk factor surveillance system.
Validation of multilevel regression and poststratification methodology for small area estimation of health indicators from the behavioral risk factor surveillance system.
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DOI：
10.1093/aje/kwv002
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发表时间：
2015-07-15
影响因子：
5
通讯作者：
Croft JB
中科院分区：
文献类型：
--
作者：
Zhang X;Holt JB;Yun S;Lu H;Greenlund KJ;Croft JB
小区域估计是一种统计技术，用于为比原始调查设计的地理区域更小的地理区域产生可靠的估计。这种小面积估计（SAE）往往缺乏严格的外部验证。在本研究中，我们使用来自2011年密苏里州县级研究和美国社区调查数据的直接估计值，验证了来自2011年行为风险因素监测系统数据的多水平回归和后分层SAE。密苏里州115个县的基于模型的严重不良事件与密苏里州县级研究直接估计值之间的相关系数均为显著正相关（肥胖和无医疗保险为0.28，当前吸烟为0.40，糖尿病为0.51，慢性阻塞性肺病为0.69）。基于模型的严重不良事件和美国社区调查直接估计的无医疗保险覆盖率之间的相关系数在县一级（811个县）为0.85，在州一级为0.95。将未加权和加权的基于模型的SAE与直接估计值进行比较;未加权模型表现更好。外部验证结果表明，使用单年行为风险因素监测系统数据的多水平回归和基于后分层模型的SAE是有效的，并且可以用于在没有高质量的当地调查数据时表征当地水平（如县）健康指标的地理差异。
Small area estimation is a statistical technique used to produce reliable estimates for smaller geographic areas than those for which the original surveys were designed. Such small area estimates (SAEs) often lack rigorous external validation. In this study, we validated our multilevel regression and poststratification SAEs from 2011 Behavioral Risk Factor Surveillance System data using direct estimates from 2011 Missouri County-Level Study and American Community Survey data at both the state and county levels. Coefficients for correlation between model-based SAEs and Missouri County-Level Study direct estimates for 115 counties in Missouri were all significantly positive (0.28 for obesity and no health-care coverage, 0.40 for current smoking, 0.51 for diabetes, and 0.69 for chronic obstructive pulmonary disease). Coefficients for correlation between model-based SAEs and American Community Survey direct estimates of no health-care coverage were 0.85 at the county level (811 counties) and 0.95 at the state level. Unweighted and weighted model-based SAEs were compared with direct estimates; unweighted models performed better. External validation results suggest that multilevel regression and poststratification model-based SAEs using single-year Behavioral Risk Factor Surveillance System data are valid and could be used to characterize geographic variations in health indictors at local levels (such as counties) when high-quality local survey data are not available.