Kevin A. Henry

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: Ethnic enclaves are culturally or ethnically distinct neighborhoods with high concentrations of individuals with shared ethnic origins, immigrants, and/or ethnic-specific businesses. We examined whether residence in Asian American enclaves was associated with the incidence of three screening-amenable cancers, using a composite enclave index across five US states. Methods: Using cancer registry data, we identified 74,485 breast, 4,134 cervical, and 35,736 colorectal cancer tumors diagnosed in California, Florida, New Jersey, New York, and Texas from January 1, 2006, to December 31, 2017, in Asian American adults. Age-adjusted incidence rates and incidence rate ratios (IRR) were calculated for 2006 to 2011 and 2012 to 2017 for census tract (CT)–level enclave and poverty measures, separately and jointly. Results: Breast and colorectal cancer incidence rates were lower among Asian American adults residing in the least culturally distinct neighborhoods compared with the most distinct [2012–2017: IRR 0.71; 95% confidence interval (CI), 0.65–0.76 in quintile 1 (Q1; low) compared with Q5 (high) for breast; IRR 0.62; 95% CI, 0.52–0.74 in Q1 vs. Q5 for colorectal cancer in males], whereas no association was found with enclave for cervical cancer. Higher cervical cancer rates were associated with high-poverty areas. For breast cancer, the highest incidence rates were observed in low-poverty enclaves, whereas for colorectal cancer, the highest rates were observed in high-poverty enclaves. Conclusions: Breast and colorectal cancer incidence rates were associated with residence in Asian American enclaves and CT poverty, though patterns differed by cancer site. Impact: Cancer prevention outreach may be beneficial within Asian American enclaves.

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.

Poverty is a known risk factor for burn injury and is associated with residency in food deserts and food swamps. Our aim was to determine the prevalence of residency in food deserts and food swamps and to investigate the relationship between food environment, comorbidities, and wound healing in patients with burns. We performed a retrospective chart review of all patients with burns aged ≥ 18 seen in the emergency department or admitted to the burn service at an American Burn Association-verified urban academic center between January 2016 and January 2022. Patient GeoIDs were used to classify residency in food deserts and food swamps, and comorbidities and demographics were recorded. A subset of patients with <20% total body surface area burns who underwent single-operation split-thickness skin grafting was identified for wound healing analysis. A total of 3063 patients were included, with 206 in the heal time analysis. In total, 2490 (81.3%) lived in food swamps and 96 (3.1%) lived in food deserts. Diabetes, hypertension, and tobacco smoking were more prevalent in food swamps than in food deserts or good access areas. While there was no significant effect of the food environment on wound healing, diabetes was associated with longer healing times. Most patients with burns reside in food swamps, which are associated with a higher prevalence of hypertension, diabetes, and smoking. The food environment was not significantly associated with wound healing. Not having diabetes was associated with a shorter time for wound healing.

Cutaneous T-cell lymphoma (CTCL) is a rare type of extranodal non-Hodgkin lymphoma (NHL). This study uses population-based data from the New Jersey (NJ) State Cancer Registry to examine geographic variation in CTCL incidence and evaluates whether CTCL risk varies by race/ethnicity and census tract socioeconomic status (SES). The study included 1163 cases diagnosed in NJ between 2006 and 2014. Geographic variation and possible clustering of high CTCL rates were assessed using Bayesian geo-additive models. The associations between CTCL risk and race/ethnicity and census tract SES, measured as median household income, were examined using Poisson regression. CTCL incidence varied across NJ, but there were no statistically significant geographic clusters. After adjustment for age, sex, and race/ethnicity, the relative risk (RR) of CTCL was significantly higher (RR = 1.47, 95% confidence interval: 1.22–1.78) in the highest income quartile than in the lowest. The interactions between race/ethnicity and SES indicated that the income gradients by RR were evident in all groups. Compared to non-Hispanic White individuals in low-income tracts, CTCL risk was higher among non-Hispanic White individuals in high-income tracts and among non-Hispanic Black individuals in tracts of all income levels. Our findings suggest racial disparities and a strong socioeconomic gradient with higher CTCL risk among cases living in census tracts with higher income compared to those living in lower-income tracts.

Purpose Cutaneous T-cell lymphoma (CTCL) is a rare type of non-Hodgkin lymphoma. Previous studies have reported geographic clustering of CTCL based on the residence at the time of diagnosis. We explore geographic clustering of CTCL using both the residence at the time of diagnosis and past residences using data from the New Jersey State Cancer Registry. Methods CTCL cases (n = 1,163) diagnosed between 2006–2014 were matched to colon cancer controls (n = 17,049) on sex, age, race/ethnicity, and birth year. Jacquez's Q-Statistic was used to identify temporal clustering of cases compared to controls. Geographic clustering was assessed using the Bernoulli-based scan-statistic to compare cases to controls, and the Poisson-based scan-statisic to compare the observed number of cases to the number expected based on the general population. Significant clusters (p < 0.05) were mapped, and standard incidence ratios (SIR) reported. We adjusted for diagnosis year, sex, and age. Results The Q-statistic identified significant temporal clustering of cases based on past residences in the study area from 1992 to 2002. A cluster was detected in 1992 in Bergen County in northern New Jersey based on the Bernoulli (1992 SIR 1.84) and Poisson (1992 SIR 1.86) scan-statistics. Using the Poisson scan-statistic with the diagnosis location, we found evidence of an elevated risk in this same area, but the results were not statistically significant. Conclusion There is evidence of geographic clustering of CTCL cases in New Jersey based on past residences. Additional studies are necessary to understand the possible reasons for the excess of CTCL cases living in this specific area some 8–14 years prior to diagnosis.

Landscape characteristics have been shown to influence health outcomes, but few studies have examined their relationship with cancer survival. We used data from the National Land Cover Database to examine associations between regional-stage colon cancer survival and 27 different landscape metrics. The study population included all adult New Jersey residents diagnosed between 2006 and 2011. Cases were followed until 31 December 2016 (N = 3949). Patient data were derived from the New Jersey State Cancer Registry and were linked to LexisNexis to obtain residential histories. Cox proportional hazard regression was used to estimate hazard ratios (HR) and 95% confidence intervals (CI95) for the different landscape metrics. An increasing proportion of high-intensity developed lands with 80–100% impervious surfaces per cell/pixel was significantly associated with the risk of colon cancer death (HR = 1.006; CI95 = 1.002–1.01) after controlling for neighborhood poverty and other individual-level factors. In contrast, an increase in the aggregation and connectivity of vegetation-dominated low-intensity developed lands with 20–<40% impervious surfaces per cell/pixel was significantly associated with the decrease in risk of death from colon cancer (HR = 0.996; CI95 = 0.992–0.999). Reducing impervious surfaces in residential areas may increase the aesthetic value and provide conditions more advantageous to a healthy lifestyle, such as walking. Further research is needed to understand how these landscape characteristics impact survival.

Many neighborhood socioeconomic index measures (nSES) that capture neighborhood deprivation exist but the impact of measure selection on liver cancer (LC) geographic disparities remains unclear. We introduce a Bayesian geoadditive modeling approach to identify clusters in Pennsylvania (PA) with higher than expected LC incidence rates, adjusted for individual-level factors (age, sex, race, diagnosis year) and compared them to models with 7 different nSES index measures to elucidate the impact of nSES and measure selection on LC geospatial variation. LC cases diagnosed from 2007–2014 were obtained from the PA Cancer Registry and linked to nSES measures from U.S. census at the Census Tract (CT) level. Relative Risks (RR) were estimated for each CT, adjusted for individual-level factors (baseline model). Each nSES measure was added to the baseline model and changes in model fit, geographic disparity and state-wide RR ranges were compared. All 7 nSES measures were strongly associated with high risk clusters. Tract-level RR ranges and geographic disparity from the baseline model were attenuated after adjustment for nSES measures. Depending on the nSES measure selected, up to 60% of the LC burden could be explained, suggesting methodologic evaluations of multiple nSES measures may be warranted in future studies to inform LC prevention efforts.