Berkowitz Zahava, Zhang Xingyou, Richards Thomas B, Peipins Lucy, Henley S Jane, Holt James
National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention (CDC), Chamblee, Georgia.
Cancer Epidemiol Biomarkers Prev. 2016 Oct;25(10):1402-1410. doi: 10.1158/1055-9965.EPI-16-0244.
Smoking is the leading preventable cause of death; however, small-area estimates for detailed smoking status are limited. We developed multilevel small-area estimate mixed models to generate county-level estimates for six smoking status categories: current, some days, every day, former, ever, and never.
Using 2012 Behavioral Risk Factor Surveillance System (BRFSS) data (our sample size = 405,233 persons), we constructed and fitted a series of multilevel logistic regression models and applied them to the U.S. Census population to generate county-level prevalence estimates. We mapped the estimates by sex and aggregated them into state and national estimates. We conducted comparisons for internal consistency with BRFSS states' estimates using Pearson correlation coefficients, and external validation with the 2012 National Health Interview Survey current smoking prevalence.
Correlation coefficients ranged from 0.908 to 0.982, indicating high internal consistency. External validation indicated complete agreement (prevalence = 18.06%). We found large variations in current and former smoking status between and within states and by sex. County prevalence of former smokers was highest among men in the Northeast, North, and West. Utah consistently had the lowest smoking prevalence.
Our models, which include demographic and geographic characteristics, provide reliable estimates that can be applied to multiple category outcomes and any demographic group. County and state estimates may help understand the variation in smoking prevalence in the United States and provide information for control and prevention.
Detailed county and state smoking category estimates can help identify areas in need of tobacco control and prevention and potentially allow planning for health care. Cancer Epidemiol Biomarkers Prev; 25(10); 1402-10. ©2016 AACR.
吸烟是可预防的主要死因；然而，关于详细吸烟状况的小区域估计数据有限。我们开发了多水平小区域估计混合模型，以生成县级层面六种吸烟状况类别的估计值：当前吸烟者、有时吸烟者、每日吸烟者、既往吸烟者、曾经吸烟者和从不吸烟者。
利用2012年行为危险因素监测系统（BRFSS）数据（我们的样本量为405,233人），我们构建并拟合了一系列多水平逻辑回归模型，并将其应用于美国人口普查数据，以生成县级层面的患病率估计值。我们按性别绘制了估计值，并汇总为州级和国家级估计值。我们使用Pearson相关系数对与BRFSS各州估计值的内部一致性进行了比较，并通过2012年国家健康访谈调查的当前吸烟患病率进行了外部验证。
相关系数范围为0.908至0.982，表明内部一致性较高。外部验证显示完全一致（患病率 = 18.06%）。我们发现各州之间、州内以及不同性别之间当前和既往吸烟状况存在很大差异。东北部、北部和西部男性中的既往吸烟者县级患病率最高。犹他州的吸烟患病率一直最低。
我们的模型纳入了人口统计学和地理特征，提供了可靠的估计值，可应用于多种类别结果和任何人口群体。县级和州级估计值可能有助于了解美国吸烟患病率的差异，并为控制和预防提供信息。
详细的县级和州级吸烟类别估计值有助于确定需要烟草控制和预防的地区，并可能有助于医疗保健规划。《癌症流行病学、生物标志物与预防》；25(10)；1402 - 1410。©2016美国癌症研究协会。
