Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia; South Carolina Rural Health Research Center, Arnold School of Public Health, University of South Carolina, Columbia; Cancer Prevention and Control Program, Arnold School of Public Health, University of South Carolina, Columbia.
Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia.
Ann Epidemiol. 2018 Jul;28(7):481-488.e4. doi: 10.1016/j.annepidem.2018.03.015. Epub 2018 Mar 30.
Local health statistics are increasingly requested for policy-making and programmatic purposes; however, population-based surveys are often inadequate to support direct estimation for small areas. Model-based estimation techniques can be used to create local estimates for public health outcomes. Using the 2014-2015 South Carolina (SC) Adult Tobacco Survey, we examined tobacco-related outcomes at the county level using a spatial multilevel, poststratification approach.
To create county-level tobacco estimates, we used a two-level model with a spatially intrinsic conditional autoregressive random intercept. Stratum-specific (race, age, and sex) estimates for each county were then created and averaged based on population data obtained from the U.S. Census.
The estimated prevalence of current smoking in SC counties among adults ranged from 7.4% to 35.1%, and the percentage reporting ever trying an e-cigarette ranged from 4.2% to 30.2%. Model validation showed considerable agreement between direct and indirect estimates (Pearson and Spearman correlations all >0.75) that varied by the sample size of the outcome, as hypothesized.
Data from the SC Adult Tobacco Survey were used to develop county-level estimates of multiple tobacco-related outcomes using a spatial multilevel, poststratification approach. The results showed heterogeneity in smoking behaviors across the state along with marked spatial correlation.
地方卫生统计数据越来越多地被用于决策和规划目的;然而,基于人群的调查通常不足以支持对小区域的直接估计。基于模型的估计技术可用于为公共卫生结果创建地方估计值。本研究使用 2014-2015 年南卡罗来纳州(SC)成人烟草调查,采用空间多水平后分层方法,在县一级检查与烟草相关的结果。
为了创建县一级的烟草估计值,我们使用了具有空间固有条件自回归随机截距的两水平模型。然后,根据从美国人口普查中获得的人口数据,为每个县创建并平均特定层(种族、年龄和性别)的估计值。
SC 县成年人当前吸烟的估计流行率范围为 7.4%至 35.1%,报告曾经尝试过电子烟的百分比范围为 4.2%至 30.2%。模型验证表明,直接和间接估计之间存在相当大的一致性(皮尔逊和斯皮尔曼相关系数均>0.75),这与预期的结果一致,即与结果的样本量有关。
本研究使用南卡罗来纳州成人烟草调查的数据,采用空间多水平后分层方法,开发了多个与烟草相关结果的县一级估计值。结果显示,该州的吸烟行为存在异质性,同时存在明显的空间相关性。