Fangyi Wang

Background: 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. Objective: 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. Methods: 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. Results: 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 _traditional =0.651 vs RMSPE _boundary =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. Conclusions: 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.

BACKGROUND Neighborhood disinvestment, characterized by built environment disrepair and deterioration, has been linked to health behaviors and outcomes, including cancer survival. While prior studies have assessed spatial variation, temporal dynamics of disinvestment remain underexplored, despite potential relevance for long-latency outcomes such as colon and rectum cancer (CRC). OBJECTIVE We describe and validate a spatio-temporal neighborhood audit protocol using Google Street View (GSV) imagery, develop and compare predictive spatio-temporal models of neighborhood disinvestment and examine time-lagged associations between disinvestment and CRC survival. METHODS We conducted 8,256 virtual audits across Franklin County, Ohio (2009–2022) using seven disinvestment indicators (e.g., garbage, graffiti, abandoned buildings). After excluding ineligible images, 5,751 audits of 2,214 unique locations were included. A neighborhood disinvestment score (NDS) was derived using item response theory and adjusted for rater bias. We fit mean trend and spatio-temporal residual processes using universal kriging with a simplified sum-metric covariance structure, comparing two candidate mean trend models via out-of-sample root mean square prediction error (RMSPE). One model accounted for discrete physical barriers to define neighborhood clusters, the other was traditional kriging where NDS was allowed to vary continuously across space and time. For exposure time validation, predictions were linked to 2,727 CRC cases (2012–2019) from the Ohio Cancer Incidence Surveillance System (OCISS). We use accelerated failure time (AFT) models to estimate associations between NDS averaged over 0, 3, 6, 12, 18, and 24 months prior to diagnosis and CRC survival, adjusting for age and stage at diagnosis and demographic covariates. We test stage-specific associations between NDS and survival. RESULTS Spatio-temporal modeling indicated substantial spatial but modest temporal variation in disinvestment, with small-scale spatial correlation measurable within 2.8 km and temporal correlation up to 270 days. The traditional spatio-temporal kriging model (RMSPE = 0.651) outperformed alternate models including neighborhood cluster fixed (RMSPE = 0.671) and random (RMSPE = 0.662) effect models. Seasonal effects were significant, with lower disinvestment scores in spring and summer compared to fall. The AFT model fit shows higher pre-diagnosis NDS was significantly associated with shorter survival among patients with stage 2 cancer (acceleration factor < 1 across all lag windows, P value < .05), but not among patients with stage 1 or stage 3. Associations were consistent across time lags, with slightly stronger effects for averages over 6–24 months prior to diagnosis. CONCLUSIONS Neighborhood disinvestment exhibits substantial spatial and moderate temporal variation. Traditional spatio-temporal universal kriging provides most accurate prediction compared to models accounting for discrete neighborhood boundaries. Neighborhood disinvestment is associated with reduced CRC survival time among cases diagnosed at regional stage, highlighting the utility of built environment spatio-temporal assessments within CRC research.