{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T21:51:51Z","timestamp":1791409911378,"version":"4.3.3"},"reference-count":37,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Med Internet Res"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n National surveillance of commercial retail environments remains limited by data sources that are updated infrequently and capture narrow dimensions of food access. The Google Places API provides continuously updated and programmatically accessible information on business locations across the United States, but its use as a population-level built-environment exposure measure has not been systematically evaluated.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aims to develop and evaluate a Google Places\u2013derived US Built Environment Retail (UBER) Index as a scalable measure of county-level commercial retail infrastructure in the United States and to estimate its spatial association with age-adjusted diabetes prevalence.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n We conducted a cross-sectional ecological study of contiguous US counties in accordance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) Statement. Counts of alcohol outlets, fast-food and convenience stores, grocery stores, and fitness and recreation facilities were extracted from the Google Places API in February 2026. Principal component analysis of 4 standardized indicators produced a composite index. Construct validity was assessed against benchmarks from the United States Department of Agriculture Food Access Research Atlas and County Health Rankings, with adequate convergence prespecified as |\n r<\/jats:italic>\n |\u22650.40. We estimated associations with age-adjusted diabetes prevalence from the Centers for Disease Control and Prevention PLACES 2025 dataset, using a spatial error model adjusted for the Area Deprivation Index, urbanicity, and census division. Quantile regression, county-versus-tract comparisons, and outcome-specificity analyses assessed robustness, outcome-resolution sensitivity, and specificity.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n The analytic sample comprised 1701 of 2957 (57.5%) US counties. The first principal component explained 92.9% of variance, with near-equal loadings (0.492\u20100.506). Convergent validity was weak (strongest Pearson\n r<\/jats:italic>\n =\u20130.20; no comparison reached the prespecified threshold of absolute |\n r<\/jats:italic>\n |\u22650.40). Each 1-SD increase in the UBER Index was associated with 0.24 percentage points higher diabetes prevalence (95% CI 0.16\u20100.32;\n P<\/jats:italic>\n <.001), whereas area deprivation was the strongest predictor (0.086 per percentile; 95% CI 0.081\u20100.091). Associations were stable across diabetes quantiles. The index was not associated with obesity and was inversely associated with coronary heart disease. The county-level association reversed at the tract level, indicating scale-dependent ecological confounding.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n Using programmatically accessible Google Places data, we developed the UBER Index as a scalable measure of county-level commercial retail infrastructure. This study is innovative because it shows how updated digital platform data can extend built-environment surveillance beyond static food-access measures. The index captures the broader commercial establishment volume and reveals spatial, distributional, and scale-dependent patterns relevant to diabetes research. It brings a new digital surveillance approach to built-environment epidemiology. In practice, it may help public health agencies monitor changing retail environments, while requiring longitudinal and individual-level validation before policy or practice use.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/95977","type":"journal-article","created":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T07:15:07Z","timestamp":1788333307000},"page":"e95977-e95977","source":"Crossref","is-referenced-by-count":0,"title":["Google Places API\u2013Based US Built Environment Retail (UBER) Index and Its Spatial Association With Diabetes Prevalence Across US Counties: Cross-Sectional Ecological Study"],"prefix":"10.2196","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0581-7808","authenticated-orcid":false,"given":"Akshaya Srikanth","family":"Bhagavathula","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5807-5281","authenticated-orcid":false,"given":"Michelle A","family":"Williams","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,10,7]]},"reference":[{"key":"R1","unstructured":"National Diabetes Statistics Report. 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