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在一个没有烟草零售点许可证的州,用于清点零售烟草店的二手数据源的实地验证。

Field validation of secondary data sources for enumerating retail tobacco outlets in a state without tobacco outlet licensing.

作者信息

D'Angelo Heather, Fleischhacker Sheila, Rose Shyanika W, Ribisl Kurt M

机构信息

Department of Health Behavior, University of North Carolina (UNC), Gillings School of Global Public Health, CB 7440, Chapel Hill, NC 27599-7440, USA.

National Institutes of Health, Division of Nutrition Research Coordination, Bethesda, MD, USA.

出版信息

Health Place. 2014 Jul;28:38-44. doi: 10.1016/j.healthplace.2014.03.006. Epub 2014 Apr 17.

Abstract

Identifying tobacco retail outlets for U.S. FDA compliance checks or calculating tobacco outlet density 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 examine the evidence of validity for two 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. More than 90% of the outlets identified by ReferenceUSA were geocoded 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.

摘要

在美国,对于13个没有烟草零售许可或无法获取用于研究的许可清单的州来说,确定用于美国食品药品监督管理局合规检查的烟草零售点或计算烟草零售点密度是困难的。本研究采用原始数据收集方法,在一个无许可州的三个县识别烟草零售点,并验证两个商业二手数据源。我们计算了敏感性和阳性预测值(PPV),以检验两个二手数据源的有效性证据,并进行了地理空间分析,以确定普查区的正确分配。ReferenceUSA在识别烟草零售点方面具有近乎完美的敏感性(0.82),而邓白氏(D&B)的敏感性较高(0.69);综合起来,敏感性提高到了0.89。与ReferenceUSA的0.71相比,D&B识别出的“假阳性”较少,PPV为0.82。ReferenceUSA识别出的超过90%的零售点被地理编码到正确的普查区。结合两个商业数据源,在一个三县地区统计出了近90%的烟草零售点。商业数据库似乎为执法行动和密度估计提供了一种合理准确的识别烟草零售点的方法。

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