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Field Validation of Secondary Data Sources for Enumerating Retail Tobacco Outlets in a State without Tobacco Outlet Licensing

机译:在没有烟草出口许可证的州用于枚举零售烟草出口的二级数据源的现场验证

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摘要

Identifying tobacco retail outlets for U.S. FDA compliance checks or calculating tobacco outlet is difficult in the 13 states without tobacco retail licensing or where licensing lists are unavailable for research. This study uses primary data collection to identify tobacco outlets in three counties in a non-licensing state and validate two commercial secondary data sources. We calculated sensitivity and positive predictive values (PPV) to validate the secondary data sources, and conducted a geospatial analysis to determine correct allocation to census tract. ReferenceUSA had almost perfect sensitivity (0.82) while Dun & Bradstreet (D&B) had substantial sensitivity (0.69) for identifying tobacco outlets; combined, sensitivity improved to 0.89. D&B identified fewer “false positives” with a PPV of 0.82 compared to 0.71 for ReferenceUSA. ReferenceUSA geocoded over 90% of outlets to the correct census tract. Combining two commercial data sources resulted in enumeration of nearly 90% of tobacco outlets in a three county area. Commercial databases appear to provide a reasonably accurate way to identify tobacco outlets for enforcement operations and density estimation.
机译:在没有烟草零售许可或没有研究许可清单的13个州中,很难确定烟草零售店以进行美国FDA符合性检查或计算烟草店。这项研究使用主要数据收集来识别处于非许可状态的三个县的烟草出口,并验证两个商业辅助数据源。我们计算了敏感性和阳性预测值(PPV)以验证辅助数据源,并进行了地理空间分析以确定对普查区域的正确分配。 ReferenceUSA具有近乎完美的敏感性(0.82),而Dun&Bradstreet(D&B)具有较高的敏感性(0.69)来识别烟草出口。综合起来,灵敏度提高到0.89。邓白氏识别出的“假阳性” PPV为0.82,而ReferenceUSA为0.71。 ReferenceUSA已将90%以上的网点地理编码到正确的人口普查区。结合两个商业数据源,得出了三个县地区近90%的烟草经销点。商业数据库似乎提供了一种合理准确的方法来识别烟草出口,以进行执法行动和密度估算。

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