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利用基于地点的特征为 FDA 烟草销售检查提供信息:来自多层次倾向评分模型的结果。

Using place-based characteristics to inform FDA tobacco sales inspections: results from a multilevel propensity score model.

机构信息

Department of Biostatistics, University of Nebraska Medical Center, Omaha, Nebraska, USA

Stanford Prevention Research Center, Stanford UniversitySchool of Medicine, Stanford, California, USA.

出版信息

Tob Control. 2022 Dec;31(e2):e148-e155. doi: 10.1136/tobaccocontrol-2021-056742. Epub 2021 Oct 25.

Abstract

BACKGROUND

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).

METHODS

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.

RESULTS

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.

CONCLUSION

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.

摘要

背景

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)。

方法

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。

结果

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。

结论

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。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2410/9726945/93c15e459348/tobaccocontrol-2021-056742f01.jpg

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