Department of Biostatistics, University of Nebraska Medical Center, Omaha, Nebraska, USA
Stanford Prevention Research Center, Stanford UniversitySchool of Medicine, Stanford, California, USA.
Tob Control. 2022 Dec;31(e2):e148-e155. doi: 10.1136/tobaccocontrol-2021-056742. Epub 2021 Oct 25.
Conducting routine inspections for compliance with age-of-sale laws is essential to reducing underage access to tobacco. We seek to develop a multilevel propensity score model (PSM) to predict retail violation of sales to minors (RVSM).
The Food and Drug Administration compliance check of tobacco retailers with minor-involved inspections from 2015 to 2019 (n=683 741) was linked with multilevel data for demographics and policies. Generalised estimating equation was used to develop the PSM using 2015-2016 data to predict the 2017 RVSM. The prediction accuracy of the PSM was validated by contrasting PSM deciles against 2018-2019 actual violation data.
In 2017, 44.3% of 26 150 zip codes with ≥1 tobacco retailer had 0 FDA underage sales inspections, 11.0% had 1 inspection, 13.5% had 2-3, 15.3% had 4-9, and 15.9% had 10 or more. The likelihood of having an RVSM in 2017 was higher in zip codes with a lower number of inspections (adjusted OR (aOR)=0.988, 95% CI (0.987 to 0.990)) and penalties (aOR=0.97, 95% CI (0.95 to 0.99)) and a higher number of violations (aOR=1.07, 95% CI (1.06 to 1.08)) in the previous 2 years. Urbanicity, socioeconomic status, smoking prevalence and tobacco control policies at multilevels also predicted retail violations. Prediction accuracy was validated with zip codes with the highest 10% of the PSM 3.4 times more likely to have retail violations in 2019 than zip codes in the bottom decile.
The multilevel PSM predicts the RVSM with a good rank order of retail violations. The model-based approach can be used to identify hot spots of retail violations and improve the sampling plan for future inspections.
开展销售年龄合规常规检查对于减少未成年人接触烟草至关重要。我们旨在开发一个多层次倾向评分模型（PSM）来预测零售向未成年人销售违规行为（RVSM）。
将食品和药物管理局（FDA）对 2015 年至 2019 年涉及未成年检查的烟草零售商的合规检查（n=683741）与多层次人口统计学和政策数据相关联。使用广义估计方程（GEE），基于 2015-2016 年的数据来开发 PSM，以预测 2017 年的 RVSM。通过将 PSM 十分位数与 2018-2019 年实际违规数据进行对比，验证 PSM 的预测准确性。
在 2017 年，有 44.3%的有≥1 家烟草零售商的邮政编码中，有 0 家 FDA 对未成年人销售进行了检查；11.0%有 1 次检查；13.5%有 2-3 次；15.3%有 4-9 次；15.9%有 10 次或更多次。在 2017 年，有较低数量检查（调整后的比值比（aOR）=0.988，95%可信区间（CI）（0.987 至 0.990））和处罚（aOR=0.97，95%CI（0.95 至 0.99））以及在前 2 年有更高违规数量（aOR=1.07，95%CI（1.06 至 1.08））的邮政编码中，RVSM 的发生可能性更高。多层次的城市人口统计学、社会经济地位、吸烟率和烟草控制政策也预测了零售违规行为。用 PSM 最高的 10%的邮政编码进行验证，发现这些邮政编码在 2019 年发生零售违规的可能性是最低十分位数邮政编码的 3.4 倍。
多层次 PSM 可以很好地预测 RVSM，对零售违规行为进行排序。基于模型的方法可以用于识别零售违规的热点地区，并改进未来检查的抽样计划。
