Annals of Epidemiology

Community-led urban greening may contribute to neighborhood safety, but its relationship with crime remains uncertain. We examined whether Keep Indianapolis Beautiful Project GreenSpaces in Indianapolis were associated with changes in violence and total crime. We used a matched-pair difference-in-differences design comparing funded sites with matched unfunded applicant sites. The primary analysis included 29 matched pairs with two years of pre- and post-grant follow-up. Monthly Part I violent offenses and total crime within 600-m buffers were modeled using Poisson pseudo-maximum likelihood. In the first post-grant year, the violent-crime estimate was lower but imprecise (IRR, 0.903; 95% CI, 0.805-1.014). In the second post-grant year, the relative rate was lower (IRR, 0.830; 95% CI, 0.718-0.961). Total crime estimates showed no clear post-grant change. Additional analyses indicated some sensitivity to spatial scale, but no sustained divergence in pre-grant crime patterns. Analyses of short-term crime dependence showed clear month-to-month persistence in violent crime within sites but limited evidence of stable spatial dependence across nearby sites. Therefore, within the two-year post-grant period examined, Project GreenSpaces was associated most consistently with lower relative violent-crime rates in the second year, while no corresponding decrease was observed for total recorded crime. Longer follow-up is needed to determine whether these patterns persist or change as projects mature.

Purpose: Metastatic pancreatic ductal adenocarcinoma (PDAC) portends a poor prognosis. Prior studies have assessed the association of socioeconomic deprivation (SED) in PDAC often with large geographic areas. This study employed a causal framework to characterize neighborhood SED on treatment receipt and survival in metastatic PDAC. Methods: Using the incidence-based Florida Cancer Data System, metastatic PDAC patients diagnosed from 2007-2015 were identified. The Area Deprivation Index, a composite measure of SED that ranks neighborhoods from 1-100 (higher scores = higher deprivation), was used to assess receipt of systemic therapy and overall survival (OS). Exposures and covariates were assessed using descriptive statistics and a causal inference framework. Results: Overall, 9,574 patients met inclusion criteria. 46.6% of patients received systemic therapy, ranging 39.4% to 54% in the highest and lowest SED quartiles, respectively. After adjustment, the lowest quartile had increased odds of systemic therapy relative to the highest (OR 1.93; 95% CI 1.70-2.18). Median OS was 3.8 months for the lowest quartile and 2.4 months for the highest (p = 0.01). Patients in the highest quartile had an estimated 32% higher hazard of death than the lowest (HR 1.32, 95% bootstrap CI 1.20-1.40). Conclusion: In an incidence-based statewide cohort, most patients did not receive treatment for metastatic PDAC and median OS was poor-2.9 months. Using a causal inference framework, higher SED led to lower rates of systemic therapy receipt and worse overall survival in metastatic PDAC. Future research should focus on the mechanisms that shape these findings.

Background: Neighborhood disadvantage indices used in public health research and policy include multiple economic, social, and housing items. However, research has failed to question whether it is necessary to include a multitude of economic, social, and housing variables in a single index. The purpose of this work was to examine three different neighborhood indices: a multidimensional disadvantage index, a unidimensional disadvantage index, and a unidimensional affluence index, and examine their performance with respect to distinguishing between healthy and unhealthy census tract neighborhoods in the United States. Methods: The 2022 disadvantage and affluence indices came from the National Neighborhood Data Archive, which are derived from census tract data from the American Community Survey 5-year estimates (2018-2022). The multidimensional disadvantage index included seven economic, social (e.g., single parent households), and housing items; the unidimensional disadvantage index included three poverty and income items; the unidimensional affluence index included 3 items capturing greater social and economic resources. Data on neighborhood health status (census tract prevalence of obesity, diabetes, and coronary heart disease) was obtained from the Population Level Analysis and Community EStimates database for 2022 and linked to the disadvantage and affluence indices for 83,522 census tracts. Contingency tables examined the degree of correspondence in quintiles across the three different indices and the corresponding disease prevalence in each cell. Generalized linear mixed models regressed the disease prevalence variables on index quintiles to determine the predicted prevalence of disease across the disadvantage gradient for each index. Results: Compared to the unidimensional disadvantage and affluence indices, the multidimensional disadvantage index underestimated disease burden in the most disadvantaged census tracts, and overestimated disease burden in the least disadvantaged tracts. Conclusions: Using a disadvantage or affluence index with a more parsimonious set of items would have greater precision in identifying communities at risk for poor health.

Introduction Smoking prevalence remains higher in rural than in urban populations in the United States. To examine recent trends, we assessed state-level differences in cigarette smoking between urban and rural areas from 2018 to 2024. Methods Using repeated cross-sectional data from the Behavioral Risk Factor Surveillance System, we estimated state-specific logistic regression models to examine the relationship between urban-rural county residence and cigarette smoking. Unadjusted models (model 1) included urban-rural county status and year. Subsequent models (model 2) added age, sex, and race/ethnicity. A final model (model 3) included education and an interaction term between urban-rural county status and year to examine whether gaps in urban-rural smoking changed over time. In states with significant interactions, simple effects tests compared trends for urban-rural groups separately. Results Compared to urban adults, rural adults had higher unadjusted odds of cigarette smoking (odds ratio [OR] range:1.07-1.88) in 88.4% (38/43) of states. Adjusting for demographic covariates (model 2) increased the proportion of states with significant marginal effects of rurality to 90.7% (ORs:1.09-1.87). A final model that also controlled for education (model 3) decreased the proportion of states with significant marginal effects of rurality to 60.5% (ORs:1.10-1.54). Among the 14 states with significant interaction terms, the odds of smoking declined faster among urban than rural residents. Conclusion Urban-rural differences in smoking persist across most states. No state showed a reduction in urban-rural disparities over time, and the urban-rural gap widened in 14 states. Demographic variation accounted for some, but not the majority, of observed urban-rural differences.

Documenting and measuring mortality in correctional facilities is essential for understanding the health consequences of incarceration, identifying preventable deaths, and informing policy interventions. We undertook a compilation of publicly available mortality records in state-operated correctional facilities, encompassing data gathered from administrative, nonprofit, and public records. The Harmonized Carceral Mortality Database (HCMD) standardizes decedent characteristics, facility identifiers, locations, dates of death, and causes of death to enable epidemiological analyses of mortality in U.S. prisons, outside of federal reporting. At the time of writing, the HCMD includes 49,682 deaths from at least 1,089 prisons in 50 states, spanning 1996-2024, with regular updates annually. The HCMD supports carceral health research across a range of disciplines from environmental health to sociology to criminology and supports use beyond the scientific community, including journalists, lawyers, and community-based organizers.

The U.S. House's approval of the Sunshine Protection Act has revived the debate over permanent daylight saving time (DST) versus permanent standard time (ST). Health and sleep organizations favor permanent ST because it benefits health, especially for children with rigid school schedules. Further, permanent DST would push school start times to before sunrise in many regions, leading to dark-morning commutes. However, the safety consequences of this shift remain unquantified. Using real school start times for 14 states that have enacted permanent DST legislation, together with local sunrise time, we counted the school days on which students must leave home before sunrise under permanent ST, the current system, and permanent DST. In Washington State, where schools start on average at 08:27, neither permanent ST nor the current system requires any pre-sunrise departure, whereas permanent DST would for most of the winter. Using real school start-time data, permanent DST would add about 35 million child-days of pre-sunrise travel in Washington alone relative to the current system, with similar patterns across the other 13 states. Extrapolated to all U.S. public schools and assuming an 8:00 departure, permanent DST would generate more than 2 billion additional dark-morning commutes each year relative to the current system. Finally, analyzing Seattle traffic collisions, we found that the odds that a crash involved a pedestrian were 143% higher on dark mornings (adjusted odds ratio 2.4). Permanent DST would therefore expose many more children, on many more days, to elevated pedestrian-crash risk, evidence that deserves consideration as the United States chooses a time standard.

Introduction: Breast, cervical, and colorectal cancer screening guidelines changed substantially between 2010 and 2019. We examined trends in the annual utilization of these screenings among commercially insured enrollees in the United States from 2010 to 2019 by age group, geographic region, and screening modality. Methods: We conducted a retrospective, serial cross-sectional analysis of the MarketScan Commercial Claims Database from 2010 through 2019, comprising approximately 141.2 million privately insured enrollees. Annual screening rates, defined as the proportion of eligible enrollees receiving a given test within each calendar year, were estimated for cervical, breast, and colorectal cancer using procedure codes, stratified by age group, screening modality, and geographic residence. These reflect annual utilization rather than up-to-date (guideline-concordant) screening. Temporal trends were evaluated using two-sided Poisson regression, and urban-rural disparities in 2019 were assessed using multivariate generalized estimating equations. Results: Cancer screening utilization remained stagnant or declined across all three cancer types over the study period. Among women aged 30-64 years, cervical cytology alone declined substantially from 28.2% in 2010 to 8.8% in 2019, while co-testing increased from 11.4% to 20.3%. Screening mammography among women aged 50-64 showed minimal change, remaining stable at 45.7% in 2010 and 45.8% in 2019. Colorectal cancer screening across enrollees aged <64 decreased modestly from 7.7% in 2010 to 6.5% in 2019, with a more pronounced decline among adults aged 45-49 years. Across all three cancer types, screening utilization was higher among urban residents than rural residents, with incidence rate ratios ranging from 1.02 to 1.05 in 2019. Conclusions: Utilization of cervical, breast, and colorectal cancer screening among commercially insured adults did not improve between 2010 and 2019. Persistent urban-rural disparities highlight ongoing gaps in preventive care delivery. Targeted interventions may help improve screening utilization, particularly in rural and underserved populations.

Background Alcohol-associated injury is a leading cause of emergency department (ED) utilization in the United States and a clinically important driver of preventable morbidity across the adult lifespan. Prior surveillance research has characterized how the rate and severity of alcohol-associated injury vary by patient age, but whether the seasonal timing of injury risk is equally predictable across age groups (a question directly relevant to the timing of clinical screening intensification and public health intervention) has not been formally tested. Methods We conducted a retrospective surveillance analysis of 45,876 alcohol-associated ED visits among adults aged 18 years and older, identified from the National Electronic Injury Surveillance System (NEISS), 2019-2025 (weighted national estimate: 2,092,319 visits), using the structured Alcohol_Involved indicator introduced into NEISS case abstraction in 2019. Patients were stratified by sex and five age groups (18-24, 25-34, 35-49, 50-64, and [&ge;]65 years). Single-harmonic cosinor (Poisson) regression was used to estimate the seasonal peak day of injury risk (acrophase) for each stratum. To assess reliability, we performed leave-one-year-out jackknife resampling (seven iterations per group), case-resampling bootstrap confidence intervals (1,000 iterations), and likelihood-ratio tests of seasonal-phase interactions. Results Peak injury timing differed significantly across age groups (X^2 [8] = 2356.2, p < .0001). Adults aged 25-64 years showed a highly reproducible early-to-mid-July peak, with jackknife estimates shifting [&le;]14 days when any single study year was excluded. Adults aged [&ge;]65 years showed significant seasonal variation annually (all p < .0001, amplitude comparable to younger groups) but a pooled peak estimate that shifted by up to 100 days across jackknife iterations. Sex-stratified analyses revealed that this instability was driven entirely by females aged [&ge;]65 years (jackknife range: 332 days, peak consistently in late October through early January) rather than males aged [&ge;]65 (jackknife range: 31 days, peak consistently in early August). Hospital admission rates increased monotonically with age from 9.0% (18-24 years) to 31.8% ([&ge;]65 years). Conclusions Alcohol-associated injury follows a reproducible, calendar-stable summer seasonal pattern in adults aged 25-64 years. Among adults [&ge;]65 years, the previously reported temporal instability is concentrated in the female subgroup, whose seasonal injury risk does not converge on a fixed calendar window. These findings suggest that fixed-calendar prevention and screening strategies are well suited to working-age adults and older men, but older women may require a year-round, individually tailored approach. Keywords: Alcohol-related injury; Emergency department; Seasonality; Age factors; Sex differences; Injury surveillance; Cosinor analysis; Older adults

Indiana still faces significant health challenges, ranking among the least healthy U.S. states due to high obesity rates, mental health issues, and other chronic conditions. These disparities are closely linked to inequities in healthcare access, which are largely shaped by social determinants of health. Using data from the Social Vulnerability Index and County Health Rankings and Roadmaps, this study analyzes trends in obesity, mental health, and premature death across Indiana counties before, during, and after the COVID-19 pandemic. Descriptive statistics, correlation analyses, and Negative Binomial regression models were used to evaluate county-level disparities. In 2018, higher rates of uninsured, obese, and physically inactive populations were associated with increased premature death. In 2020, diabetes, smoking, and alcohol consumption were significant factors. By 2022, unemployment, education, obesity, insurance, exercise access, and mental health provider availability were associated with premature death. Findings indicate that socially vulnerable counties experienced amplified health impacts, with obesity rising most sharply where exercise infrastructure was limited and poor mental health days increasing across all counties. These results highlight persistent service gaps and the critical need for targeted investments in recreational infrastructure and mental healthcare. Future research should examine policy influences and causal relationships to inform equity-focused interventions.

Introduction. Perceived neighborhood problems (PNP), such as safety concerns and limited resources, have been linked to elevated hypertension risk through chronic stress pathways. The Identity Vitality Pathology (IVP) framework captures the extent to which individuals experience threats to identity, belonging, and psychosocial vitality in the face of structural disadvantage, and may modify these pathways. Methods. We conducted logistic regression analyses to estimate the association between PNP and hypertension. Models were stratified by race and gender groups (Black women, Black men, White women, White men). Both main effects models and adjusted models included an interaction term between PNP and Identity Vitality Pathology score (IVPS) to assess effect modification. Adjusted models controlled for age, education, perceived support, financial strain, physical activity, diabetes, smoking status, and self-rated health. Interaction terms were used to evaluate whether the association between PNP and hypertension varied by levels of IVPS within each race/gender group. Results. Higher PNP was associated with increased odds of hypertension among White women and men, but not consistently among Black women and men. Identity state moderated this association only among Black women, such that each 1-SD increase in IVPS score was associated with 59% lower odds of hypertension at the highest level of PNP (aOR-0.41; 95% CI: 0.19, 0.88), with no meaningful moderation observed in other groups. Conclusion. Identity state may moderate the association between subjective neighborhood disadvantage and hypertension, particularly among Black women, representing a potentially novel, modifiable protective factor among multiply marginalized persons living in communities with increased risk for hypertension.

This study examines the impact of various maternal factors and continuous labor support on birth outcomes among 124 birthing people in the Baltimore region of Maryland who sought and/or received doula care. The cohort primarily consists of Black birthing people and includes detailed data on maternal age, race, education level, employment status, health insurance status, income level, preexisting health challenges, and pregnancy or delivery-related health challenges. The study also explores whether the pregnancy was the birthing person's first, as well as neonatal outcomes such as NICU admission, low birth weight, and other newborn health concerns. Due dates and delivery dates were compared and analyzed, where available, to provide a comprehensive understanding of the timing of births among the cohort. The findings highlight known correlations between maternal socioeconomic factors, health factors, and adverse birth outcomes, emphasizing the critical role of doula support during the prenatal period and labor and delivery in improving maternal and neonatal health, as well as in providing culturally competent care and positive birth experiences. This research contributes to the growing body of evidence on the benefits of doula care, particularly for marginalized communities, and underscores the need for targeted interventions to address health disparities among Black Birthing persons.

Purpose: This study assesses the association between colorectal cancer (CRC) screening and a validated, housing-based measure of individual-level socioeconomic status (SES, called HOUSES hereafter) within rural communities and determines whether HOUSES-integrated geospatial analysis can be used to tailor interventions. Methods: We used CRC screening data from a subset of Mayo Clinic Midwest patients living in cities without ready access to routine care in the Mayo Clinic Health System in 2019 to represent rural communities. At the individual level, we assessed the association between CRC screening rates and the HOUSES index, adjusting for age, sex, race/ethnicity, comorbidity, distance from home address to clinic, and area deprivation index, using a multilevel mixed-effects logistic regression model. Additionally, we conducted geospatial analysis to examine the correlation between hotspots of 1) lower CRC screening rates and 2) lower SES of the subject population (HOUSES quartile 1). Findings: Among 34,489 individuals (median age 64.0 years, 52.4% female), those with the lowest SES (HOUSES Q1) had 37% lower odds of being CRC screening adherent than those with the highest SES (HOUSES Q4) (adj. OR [95% CI]: 0.63 [0.58-0.69]). In the 14 identified HOUSES Q1 hotspots, there was a significant correlation in counts of HOUSES Q1 and low CRC screening (correlation coefficient=0.81). Conclusion: Lower SES was significantly associated with lower CRC screening among rural populations. HOUSES-enabled geospatial analysis identified geographic hotspots with lower CRC screening rates for targeted interventions to address disparities in CRC screening in rural communities. HOUSES may be a useful digital tool for cancer preventive care and research.

Objectives: Healthcare data can reveal actionable opportunities to prevent asthma hospitalizations. Limited national-level data exist regarding social determinants of health (SDOH) and asthma hospitalizations. We examined SDOH-related International Classification of Diseases, Tenth Revision (ICD-10) Z-codes in national administrative data on asthma hospitalizations and described patient- and hospital-level characteristics associated with documented SDOH Z-codes. Methods: Pooled cross-sectional analysis of 2016-2022 Nationwide Inpatient Sample for 200,452 U.S. hospitalizations (all ages) with a primary diagnosis of asthma. Presence of SDOH Z-codes (codes Z55-Z65) assessed by descriptive statistics and multivariable logistic regression to calculate odds ratios (ORs) and 95% confidence intervals (95% CIs) for associations between SDOH Z-codes and patient- and hospital-level characteristics. Results: In unweighted analyses, 3,149 asthma hospitalizations had SDOH Z-codes (1.57%). The most common SDOH Z-codes were homelessness (Z59.0; n=942) and unemployment (Z56.0; n=349). Weighted chi-square analyses found all selected variables were associated with asthma hospitalization SDOH Z-code documentation. Logistic regression results varied; adjusted odds for SDOH Z-code documentation were higher for asthma hospitalizations involving male patients (aOR=1.51; 95% CI, 1.39-1.63; P < .001) compared to female patients. Asthma hospitalizations involving rural hospitals had lower odds of SDOH Z-codes documentation (aOR=0.57; 95% CI, 0.47-0.70; P < .001) compared to urban teaching hospitals. Conclusions: National 2016-2022 data indicate housing- and employment-related Z-codes were the most commonly documented SDOH within asthma hospitalizations. Future analyses could consider establishing causality and exploring how relationships between these SDOH may be used by public health practitioners and others to improve program interventions.

Importance: Chronic stress may contribute to adverse health outcomes through cumulative physiologic dysregulation. Allostatic load (AL), a composite measure of multisystem physiologic burden, may capture biologic effects of structural, social, and psychosocial stress not reflected by self-reported measures. Objective: To evaluate racial and ethnic differences in AL among women with familial cancer risk and examine how socioeconomic status, psychosocial factors, clinical characteristics, and health behaviors contribute to variations in AL. Design: Cross-sectional study of underrepresented minority participants enrolled in the HERSTORY cohort from October 2023 through September 2025, with comparison participants from the UCLA ATLAS biobank. Setting: UCLA academic health system. Participants: The study included 303 racially and ethnically diverse female HERSTORY participants aged [&ge;]35 years with a family history of cancer and matched non-Hispanic White female ATLAS participants (n=709). Exposures: Race and ethnicity, age, neighborhood deprivation, cancer history and stage, depression, perceived stress, cancer worry, and physical activity. Main Outcomes and Measures: The primary outcome was AL, calculated from cardiometabolic and organ-function measures. A secondary index incorporated race- and ethnicity-specific neutrophil-to-lymphocyte ratio (NLR) derived from 326,826 women in the UCLA Health population. Multivariable regression models evaluated factors associated with elevated AL. Results: Compared with matched non-Hispanic White participants, Black and Asian/Pacific Islander HERSTORY participants had significantly higher AL after adjustment. Hispanic/Latina participants did not have significantly elevated AL. Older age, greater area-level socioeconomic deprivation, and depression were independently associated with higher AL. Prior cancer diagnosis, cancer worry and perceived stress were not significantly associated with AL, whereas regular physical activity was associated with lower AL. Among cancer patients, advanced stage was associated with greater AL. Conclusions and Relevance: This study demonstrates elevated AL among understudied racial/ethnic minority groups with familial cancer risk and identifies associations with neighborhood deprivation, depression, and physical activity. The association between cancer stage and AL suggests that physiologic stress may reflect variation in cancer burden. The lack of association with perceived stress and cancer worry further indicates that physiologic and self-reported psychosocial measures capture distinct dimensions of stress. The development of race/ethnicity-specific NLR thresholds derived from large population samples provide a benchmark for future studies.

Objective: To evaluate the rate, timing, and predictors of additional therapy among women with idiopathic overactive bladder (OAB) following initial minimally invasive treatments (MIT). Study Design: Retrospective single center cohort study of women with idiopathic OAB treated between 2012 and 2021. Using ICD and procedural codes, we identified women who underwent posterior tibial nerve stimulation (PTNS), sacral neuromodulation (SNM), or intradetrusor onabotulinumtoxinA (BTX). The primary outcome was receipt of additional OAB treatments,OAB medication initiation or a different MIT. Kaplan-Meier analysis estimated time to additional therapies; Cox proportional hazards and random survival forest models identified predictors. Results: 1,007 women were included (PTNS: 459; SNM: 192; BTX: 356). At three years, 75% of PTNS patients, 58% of BTX patients, and 40% of SNM women required additional therapies with most patients choosing additional pharmacotherapy rather than crossover to a different MIT. Median time to additional treatments was 10 months (PTNS), 19 months (BTX), and 53 months (SNM). Higher BMI was associated with increased risk of further treatment after SNM (HR 2.1, 95% CI: 1.1-4.2), while recurrent urinary tract infections were associated with needing additional therapies in the BTX cohort (HR 1.8, 95% CI: 1.1-3.2). Random survival forest models resulted in poor model performance. Conclusions: Following initial MIT for idiopathic OAB, many women required additional treatment within three years, many choosing pharmacotherapy rather than transition to another MIT.

Cancer prevalence is distributed unevenly across regions and caused by the interaction of multiple risk factors. Previous studies focused on the use of global modeling techniques to predict cancer at the county level that overlooks important spatial differences. This study aims to develop geographically weighted machine learning models to predict cancer prevalence at the census tract level in the United States and identify local determinants of cancer burden. First, a scoping review was conducted to find a list of measurable drivers of cancer in the United States. Using this list, the data of these variables for 84415 census tracts were obtained from the Center for Disease Control and Prevention PLACES dataset and other publicly accessible resources. Then, several predictive models, including Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR), as well as Random Forest, XGBoost, and Deep Neural Network and their geographically weighted counterparts, were developed and compared using the Coefficient of Determination, Root Mean Square Error, and Absolute Error. Results presented that geographically weighted models outperformed other methods, and geographically weighted XGBoost achieved the strongest and most consistent overall performance with pseudo-R2 ranging between 0.89 and 0.98. Feature importance analysis of this model illustrated that most important cancer drivers changed location by location. Aged people, racial composition, preventative behaviors, and metabolic conditions such as diabetes, hypertension, and high cholesterol were determined as influential predictors, although their relative importance varied across regions. These findings revealed the value of localized models at a small geographic scale to identify regional cancer risk patterns and help the allocation of proper resources to hotspot areas. Keywords: Cancer prevalence, Census tracts, geographically weighted machine learning models, Deep neural network, XGBoost, Random Forest, Ordinary Least Squares, risk factor, determinant

Objective: Over the past decades, US policies intended to reduce air pollution emissions from electric generating units (EGUs), mobile sources (e.g., cars and trucks), and port activities have been implemented to improve air quality. This study aimed to estimate and compare counterfactual air pollution concentrations (i.e., concentrations that would have occurred without these policies) to observed concentrations, and then evaluate the health impacts of such policies in New York City, Los Angeles, and Atlanta from 2005 to 2019. Materials and Methods: We obtained data on respiratory emergency department (ED) visits and cardiovascular disease ED visits that result in hospitalizations for the three cities from 2005-2019. Daily concentrations of fine particulate matter (PM2.5), criteria gases [carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3)], and 1-in-3-day measured concentrations of PM2.5 components and PM sources estimated using positive matrix factorization were acquired from six monitoring sites in the three cities. To estimate health impacts of selected EGU, mobile, and port policies we estimated: 1) counterfactual daily pollutant concentrations at each of the 6 city-sites; 2) associations between daily pollutant concentrations and rates of cardiorespiratory visits using city-site specific multi-pollutant Poisson models; and 3) the percent of cardiorespiratory visits prevented by the implementation of the selected policies, through applying observed and counterfactual concentrations to the fitted health models. Results: Air quality policies were estimated to reduce ambient pollutant concentrations across the three cities, with median PM2.5 reductions of 27%-62% due to all policies combined during 2005-2019. Changes in criteria-pollutant concentrations associated with the selected policies were estimated to avert 7.1% (95% UI: 5.4%, 8.9%) of respiratory visits in New York City, 2.4% (95% UI: 1.4%, 3.4%) in Los Angeles, and 4.5% (95% UI: 0.8%, 8.2%) in Atlanta. In addition, 2.6% (95% UI: 0.9%, 4.2%) and 1.2% (95% UI: 0.3%, 2.1%) of cardiovascular visits were averted in New York City and Los Angeles, while the estimate in Atlanta did not indicate cardiovascular visits averted. Conclusion: The selected EGU, mobile-source, and port policies evaluated during 2005-2019 were estimated to reduce ambient pollutant concentrations and avert respiratory visits in all three cities and cardiovascular visits in New York City and Los Angeles.

Background: The incidence of early-onset colorectal cancer (EOCRC), defined as CRC diagnosed age <50, is rising in the United States and is the leading cause of cancer death in among those with early-onset cancer. Epigenetic exposure signatures based on methylation risk scores (MRS) offer a novel approach to identify long-term exposures that may contribute to EOCRC risk. We sought to replicate findings from a recent study in a racially and geographically diverse CRC cohort. Methods: We computed weighted MRS across lifestyle, air pollution, and pesticide exposome traits in 146 participants from the Disparities and Cancer Epidemiology (DANCE) cohort, a diverse population-based CRC study, applying the Maas et al. (2026) framework to tumor tissue. Associations between MRS and age at CRC onset (EOCRC, n=29; intermediate-onset (IOCRC) 50-64 years, n=69; late-onset, [&ge;]65 years, n=48) were evaluated using linear and polytomous regression, with adjustment and stratification for race, geography, Area Deprivation Index (ADI), and Multi-Environmental Exposure Index (MEEI). Spearman correlations were calculated between MRSes and observed exposures. Results: EOCRC cases were predominately Black (65.5% vs. 31.3% among LOCRC) and resided in higher-deprivation neighborhoods. We found higher odds of EOCRC associated with epigenetic exposure signatures for 2,4-D herbicide, heptachlor, coarse particulate matter (PM), Mediterranean Diet Score, and smoking, and borderline associations for fine PM and nitrogen dioxide. IOCRC was associated with signatures for 2,4-D, lindane, and obesity. Heterogeneity was observed by race and area-level factors, though associations were largely consistent across strata. ADI and MEEI were correlated with multiple pesticide and pollutant epigenetic signatures. Conclusions: In a diverse US cohort, epigenetic exposure signatures distinguish CRC cohorts, particularly EOCRC from later-onset disease, with associations consistent with but not identical to previous research. EOCRC in DANCE was characterized by higher epigenetic exposure signatures for air pollution, smoking, and distinct pesticide exposure profiles. IOCRC had overlapping characteristics with early- and late-onset and were the only group with a significant obesity signature. These findings support the hypothesis that exposome-driven epigenetic changes contribute to EOCRC risk and highlight the importance of studying diverse populations to characterize the full spectrum of environmental risk factors.

Purpose: We compared the association of concurrent TP53 pathogenic variants (PV) with overall survival (OS) by driver status in non-small cell lung cancer (NSCLC). Patients and Methods: The discovery cohort included 3,295 patients with stage IV NSCLC treated at Kaiser Permanente Northern California. The validation cohort included 12,982 NSCLC from 37 public cBioPortal studies. Driver-positive disease was defined as presence of EGFR, ALK, or ROS1 alteration. We used Cox regression modeling to estimate hazard ratios (HRs) for mortality, adjusting for demographics and other factors. Results: Among the discovery cohort, TP53 PV was associated with worse OS among patients with an EGFR, ALK, or ROS1 alteration (HR = 1.80, [95% confidence interval (CI), 1.50-2.15]) but not among those without a driver mutation (HR = 1.08, [95% CI, 0.97-1.21]). Among the validation cohort, TP53 PV was associated with worse OS with HR of 2.27 [1.92-2.69], 2.42 [1.42-4.10], 1.30 [1.20-1.42], and 3.09 [95% CI, 1.47-6.47], for EGFR-activating, ALK-fusion, EGFR/ALK wild-type disease and RET-fusion respectively. The differential association was largest in EGFR exon 20 insertion tumors, in which TP53 PV carried HR = 4.68 [95% CI, 2.34-9.37], 3.9-fold that in driver-negative disease; the corresponding ratio for ERBB2 exon 20 insertions were 1.23 [95% CI, 0.75-2.02]. TP53 PV was not associated with OS in squamous carcinoma. Conclusions: Association of TP53 PV with OS in advanced NSCLC appears to depend on co-occurrence of other driver mutations. This driver-dependent effect modification informs interpretation of TP53 status and may be useful for risk stratification in clinical practice.

Aims: To assess whether recent declines in U.S. rolling 12-month-ending drug overdose death counts are projected to continue, compare the retrospective performance of short-term forecasting models, and estimate national and regional 12-month changes. Design: Comparative time-series forecasting study with a retrospective March 2025-February 2026 forecast evaluation and subsequent 12-month-ahead projections through February 2027. Setting: United States and four U.S. Census regions: Northeast, Midwest, South, and West. Cases: Aggregate drug overdose deaths reported in the National Center for Health Statistics Vital Statistics Rapid Release system (VSRR) and identified using ICD-10 underlying cause-of-death codes X40-X44, X60-X64, X85, and Y10-Y14. Measurements: The primary outcome was the monthly series of rolling 12-month-ending overdose deaths. Candidate models included ARIMA, generalized additive models, Prophet, and AICc-ranked n-sub-epidemic models. Models were calibrated using January 2020-February 2025 data and evaluated against March 2025-February 2026 observations using mean absolute error, mean squared error, empirical 95% prediction interval coverage, and weighted interval score (WIS). Individual models were ranked by retrospective WIS, and normalized inverse-WIS weights were used to construct ensembles from the top-ranked models. Final forecasts were generated for March 2026-February 2027 after recalibrating models using January 2020-February 2026 data. Results: Retrospective WIS performance differed geographically: GAM performed best nationally and in the Midwest and West, the leading n-sub-epidemic model in the Northeast, and ARIMA in the South. Median forecasts from all individual models and ensembles projected declines from February 2026 to February 2027 nationally and in each region, although prediction intervals varied substantially. Individual national median projections ranged from declines of 12.8% to 25.8%, while weighted-ensemble median projections indicated declines of 20.5% to 21.7%. Projected weighted-ensemble declines were larger in the Northeast (25.4%-30.0%) and Midwest (24.3%-26.3%) than in the South (16.6%-19.5%) and West (18.0%-21.8%). Summary: Ensemble forecasts of the reported provisional VSRR series were consistent with continued declines in rolling annual overdose death counts nationally and across U.S. Census regions. The projected magnitude of decline differed by region and model specification. Because the forecasts used provisional rolling 12-month-ending counts, they should be interpreted as surveillance projections rather than exact monthly mortality predictions.