Ma Xiaoguang, Battersby Sarah E, Bell Bethany A, Hibbert James D, Barnes Timothy L, Liese Angela D
Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA.
Department of Geography, University of South Carolina, Columbia, SC, USA.
Appl Geogr. 2013 Dec;45. doi: 10.1016/j.apgeog.2013.08.014.
Several spatial measures of community food access identifying so called "food deserts" have been developed based on geospatial information and commercially-available, secondary data listings of food retail outlets. It is not known how data inaccuracies influence the designation of Census tracts as areas of low access. This study replicated the U.S. Department of Agriculture Economic Research Service (USDA ERS) food desert measure and the Centers for Disease Control and Prevention (CDC) non-healthier food retail tract measure in two secondary data sources (InfoUSA and Dun & Bradstreet) and reference data from an eight-county field census covering169 Census tracts in South Carolina. For the USDA ERS food deserts measure accuracy statistics for secondary data sources were 94% concordance, 50-65% sensitivity, and 60-64% positive predictive value (PPV). Based on the CDC non-healthier food retail tracts both secondary data demonstrated 88-91% concordance, 80-86% sensitivity and 78-82% PPV. While inaccuracies in secondary data sources used to identify low food access areas may be acceptable for large-scale surveillance, verification with field work is advisable for local community efforts aimed at identifying and improving food access.
基于地理空间信息和食品零售网点的商业可用二手数据清单，已经开发出了几种用于识别所谓“食品荒漠”的社区食品获取空间测量方法。目前尚不清楚数据不准确如何影响将人口普查区指定为低获取区域。本研究在两个二手数据源（InfoUSA和邓白氏）以及来自南卡罗来纳州八个县的实地普查参考数据（涵盖169个人口普查区）中，复制了美国农业部经济研究局（USDA ERS）的食品荒漠测量方法和疾病控制与预防中心（CDC）的非健康食品零售区测量方法。对于USDA ERS的食品荒漠测量方法，二手数据源的准确性统计数据为一致性94%、灵敏度50 - 65%以及阳性预测值（PPV）60 - 64%。基于CDC的非健康食品零售区，两个二手数据的一致性均为88 - 91%、灵敏度为80 - 86%以及PPV为78 - 82%。虽然用于识别低食品获取区域的二手数据源中的不准确情况对于大规模监测而言可能是可以接受的，但对于旨在识别和改善食品获取的地方社区工作，建议通过实地调查进行核实。
