Abstract
<jats:p>Background: Food swamps, which have a high density of outlets selling unhealthy food relative to healthier options, are a major driver of diet-related disparities in urban communities. However, no validated food swamp measurement tools exist at the neighborhood level. Methods: We developed the Food Swamp Environment Audit Tool (FS-EAT) using community-based participatory research. FS-EAT includes 19 items as-sessing street-level food outlet types, accessibility features, food marketing, pricing, social features, and store-level information and food availability. Reliability and validity testing were performed using stakeholder surveys and feedback from the community advisory board (CAB). We compared secondary National Establishment Time Series (NETS) 2020 with FS-EAT food store data using positive predictive values and sensitivity scores. We computed Spearman&#039;s rank correlation coefficients to measure the alignment between block group-level food swamp and non-food swamp classifications, comparing FS-EAT data with NETS 2020 data. Results: Interrater reliability was strong (κ = 0.82), and 80% of stakeholders rated FS-EAT items as relevant/extremely relevant. CAB members con-firmed that FS-EAT maps aligned better with lived experiences than the secondary NETS data. GIS analyses showed that FS-EAT maps captured more accurate and timely food swamp exposure compared to NETS. When validated against the NETS 2020 business list, the FS-EAT demonstrated an overall sensitivity of 45.3% and a PPV of 68.2%. We con-firmed fewer than half of NETS-listed outlets through FS-EAT ground observation, yet more than two-thirds of audit-identified stores were also present in the NETS database. Sensitivity was highest for convenience stores (74.4%) and lowest for limited-service restaurants (29.2%). Resident perceptions were moderately correlated with FS-EAT scores (r = 0.5). While both sources agreed on the food swamp classification in 18 block groups, 15 block groups (38.5%) were classified as food swamps by the FS-EAT audit data but as non-food swamps by the NETS data. Discussion: The FS-EAT tool captures nuanced features needed to assess the level of neighborhood food swamp exposure and contributes a systematic, cost-effective way to identify neighborhood-level food swamps.</jats:p>