Department of Epidemiology, Human Genetics & Environmental Sciences, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America.
Department of Management, Policy and Community Health, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America.
PLoS One. 2023 Mar 14;18(3):e0280620. doi: 10.1371/journal.pone.0280620. eCollection 2023.
Food insecurity is prevalent and associated with poor health outcomes, but little is known about its geographical nature. The aim of this study is to utilize geospatial modeling of individual-level food insecurity screening data ascertained in health care settings to test for neighborhood hot and cold spots of food insecurity in a large metropolitan area, and then compare these hot spot neighborhoods to cold spot neighborhoods in terms of the CDC's Social Vulnerability Index. In this cross-sectional secondary data analysis, we geocoded the home addresses of 6,749 unique participants screened for food insecurity at health care locations participating in CMS's Accountable Health Communities (AHC) Model, as implemented in Houston, TX. Next, we created census-tract level incidence profiles of positive food insecurity screens per 1,000 people. We used Anselin's Local Moran's I statistic to test for statistically significant census tract-level hot/cold spots of food insecurity. Finally, we utilized a Mann-Whitney-U test to compare hot spot tracts to cold spot tracts in relation to the CDC's Social Vulnerability Index. We found that hot spot tracts had higher overall social vulnerability index scores (P <0.001), higher subdomain scores, and higher percentages of individual variables like poverty (P <0.001), unemployment (P <0.001), limited English proficiency (P <0.001), and more. The combination of robust food insecurity screening data, geospatial modeling, and the CDC's Social Vulnerability Index offers a solid method to understand neighborhood food insecurity.
食物不安全普遍存在，并与不良健康结果相关，但对其地理性质知之甚少。本研究旨在利用医疗保健环境中个体水平食物不安全筛查数据的地理空间建模，来检验一个大城市地区食物不安全的邻里热点和冷点，并根据疾病预防控制中心的社会脆弱性指数比较这些热点邻里和冷点邻里。在这项横断面二次数据分析中，我们对在德克萨斯州休斯顿参与 CMS 的负责任社区（AHC）模式的医疗保健地点接受食物不安全筛查的 6749 名独特参与者的家庭住址进行了地理编码。接下来，我们创建了每 1000 人中有阳性食物不安全筛查的普查区层面发生率概况。我们使用 Anselin 的局部 Moran's I 统计量来检验普查区层面食物不安全的统计学显著热点/冷点。最后，我们利用 Mann-Whitney-U 检验比较了热点普查区与冷点普查区与疾病预防控制中心社会脆弱性指数的关系。我们发现热点普查区的总体社会脆弱性指数得分更高（P<0.001），子域得分更高，且个体变量的百分比更高，如贫困（P<0.001）、失业（P<0.001）、英语水平有限（P<0.001）等。强大的食物不安全筛查数据、地理空间建模和疾病预防控制中心社会脆弱性指数的结合为了解邻里食物不安全提供了一种可靠的方法。
