{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T19:34:46Z","timestamp":1787168086239,"version":"build-2736575974"},"reference-count":43,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Form Res"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Neighborhood disinvestment, characterized by built environment disrepair and deterioration, has been linked to health behaviors and outcomes, including cancer survival. However, disinvestment temporal dynamics, including time-lagged exposure estimates among colorectal cancer (CRC) cases, remain underexplored.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aimed to describe and validate a spatiotemporal neighborhood audit protocol using Google Street View imagery, develop and compare predictive spatiotemporal models of neighborhood disinvestment, and examine time-lagged associations between disinvestment and CRC survival.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n We conducted 8256 virtual audits of Franklin County, Ohio, streetscapes sampled across locations and dates from 2009 to 2022 using 7 disinvestment indicators: garbage, graffiti, abandoned buildings, building conditions, yard conditions, road verge conditions, and large dumpsters. Of these, 5751 eligible location date audits were included. A neighborhood disinvestment score (NDS) was derived using item response theory. We fit spatiotemporal regression Kriging models with a simplified sum-metric covariance structure and compared two candidate models using out-of-sample root mean square prediction error (RMSPE): (1) a model incorporating major highways, waterways, and railways to define neighborhood boundaries, and (2) a traditional Kriging model allowing NDS to vary continuously across space and time. The best-fitting model was used to estimate NDS at the geocoded address and diagnosis date of 2727 CRC cases diagnosed from 2012 to 2019 and recorded within the Ohio Cancer Incidence Surveillance System. Covariates included age, sex, minoritized race-ethnicity, marital status, health insurance, and cancer stage at diagnosis (localized, regional, and distant). We built accelerated failure time models to estimate time ratios and 95% CIs for NDS averaged over 0-, 3-, 6-, 12-, 18-, and 24-month time lags. Models were adjusted for covariates. We tested stage by NDS interactions. Those alive through December 31, 2020, were right censored.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n NDS exhibited substantial spatial but modest temporal variation, with spatial correlation measurable within 2.8 kilometers and temporal correlation up to 270 days. Traditional spatiotemporal Kriging outperformed the boundary-based model (RMSPE\n traditional<\/jats:sub>\n =0.651 vs RMSPE\n boundary<\/jats:sub>\n =0.662). NDS varied by season, with lower disinvestment scores in spring and summer compared with fall. The accelerated failure time CRC survival model indicates an interaction between NDS and stage at diagnosis for all NDS time lags tested; higher prediagnosis NDS was significantly associated with shorter survival only among those with regional and not among those with localized or distant stage at diagnosis. For example, with a 6-month prediagnosis averaged time lag, each standard deviation increase in NDS was associated with a time ratio of 0.628 (95% CI 0.436-0.904). Associations were largely identical across time lags.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n The spatiotemporal neighborhood audit approach is feasible and efficient. NDS varies appreciably across season, was accurately estimated using regression Kriging, and time-lagged exposures from 0 to 24 months produced negligible change in the NDS-CRC survival association.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/86279","type":"journal-article","created":{"date-parts":[[2026,7,19]],"date-time":"2026-07-19T05:40:10Z","timestamp":1784439610000},"page":"e86279-e86279","source":"Crossref","is-referenced-by-count":0,"title":["Built Environment Audits Across Space and Time for Estimating Time-Varying Exposures: Cohort 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