Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD, USA.
Center for Statistical Research and Methodology, US Census Bureau, Washington, DC, USA.
Nicotine Tob Res. 2021 Aug 4;23(8):1300-1307. doi: 10.1093/ntr/ntab015.
The workplace and home are sources of exposure to secondhand smoke, a serious health hazard for nonsmoking adults and children. Smoke-free workplace policies and home rules protect nonsmoking individuals from secondhand smoke and help individuals who smoke to quit smoking. However, estimated population coverages of smoke-free workplace policies and home rules are not typically available at small geographic levels such as counties. Model-based small-area estimation techniques are needed to produce such estimates.
Self-reported smoke-free workplace policies and home rules data came from the 2014-2015 Tobacco Use Supplement to the Current Population Survey. County-level design-based estimates of the two measures were computed and linked to county-level relevant covariates obtained from external sources. Hierarchical Bayesian models were then built and implemented through Markov Chain Monte Carlo methods.
Model-based estimates of smoke-free workplace policies and home rules were produced for 3134 (of 3143) US counties. In 2014-2015, nearly 80% of US adult workers were covered by smoke-free workplace policies, and more than 85% of US adults were covered by smoke-free home rules. We found large variations within and between states in the coverage of smoke-free workplace policies and home rules.
The small-area modeling approach efficiently reduced the variability that was attributable to small sample size in the direct estimates for counties with data and predicted estimates for counties without data by borrowing strength from covariates and other counties with similar profiles. The county-level modeled estimates can serve as a useful resource for tobacco control research and intervention.
Detailed county- and state-level estimates of smoke-free workplace policies and home rules can help identify coverage disparities and differential impact of smoke-free legislation and related social norms. Moreover, this estimation framework can be useful for modeling different tobacco control variables and applied elsewhere, for example, to other behavioral, policy, or health related topics.
工作场所和家庭是接触二手烟的场所，二手烟对不吸烟的成年人和儿童是严重的健康危害。无烟工作场所政策和家庭规定可以保护不吸烟的个人免受二手烟的侵害，并帮助吸烟的个人戒烟。然而，通常无法在小地理区域（如县）获得无烟工作场所政策和家庭规定的估计人群覆盖率。需要基于模型的小区域估计技术来生成此类估计值。
2014-2015 年烟草使用补充当前人口调查的数据来自自我报告的无烟工作场所政策和家庭规定。计算了这两个措施的县一级基于设计的估计值，并将其与从外部来源获得的县一级相关协变量进行了链接。然后建立并实施了分层贝叶斯模型，通过马尔可夫链蒙特卡罗方法实现。
为 3143 个（3134 个）美国县生成了无烟工作场所政策和家庭规定的基于模型的估计值。在 2014-2015 年，近 80%的美国成年工人受到无烟工作场所政策的覆盖，超过 85%的美国成年人受到无烟家庭规定的覆盖。我们发现，在州内和州之间，无烟工作场所政策和家庭规定的覆盖范围存在很大差异。
通过从协变量和其他具有相似特征的县借用力量来减少直接估计值的变异性，该小区域建模方法有效地减少了数据中县的变异性和数据中无县的预测估计值的变异性。基于模型的县一级估计值可以作为烟草控制研究和干预的有用资源。
详细的县和州一级无烟工作场所政策和家庭规定的估计值可以帮助识别覆盖差距和无烟立法及相关社会规范的不同影响。此外，该估计框架可用于建模不同的烟草控制变量，并在其他地方应用，例如，用于其他行为、政策或健康相关主题。
