Annals of Epidemiology

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.

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.

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.

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 To identify optimal initiation ages and screening intervals for low-dose computed tomography (LDCT) screening among never-smoking Asian women using an integrated polygenic risk score (PRS)-environmental tobacco smoke (ETS) risk model, and to evaluate the cost-effectiveness of alternative screening strategies at these optimized ages. Design Integrated PRS-ETS microsimulation modelling. Setting Japan. Participants Never-smoking women stratified into eight risk groups defined by combinations of PRS levels and ETS exposure. Interventions LDCT screening at intervals of 1 to 10 years, annual chest radiography (CXR), or no screening. Main outcome measures Costs, quality-adjusted life years (QALYs), incremental cost-effectiveness ratios (ICERs), net monetary benefits, lung adenocarcinoma incidence and mortality, and optimal LDCT initiation ages. Sensitivity analyses used a willingness-to-pay threshold of US$50,000 per QALY gained. Results Optimal initiation ages ranged from 40 to 55 years across the eight PRS-ETS risk groups, with higher PRS-ETS risk associated with younger optimal initiation ages. Annual LDCT was the most cost-effective strategy across all PRS-ETS risk strata, yielding an ICER of US$40,471 per QALY in the lowest risk stratum and becoming cost-saving in higher risk strata. Over a lifetime, annual LDCT averted 8,534 lung adenocarcinoma deaths compared with annual CXR and 14,940 deaths compared with no screening. Conclusions Tailoring LDCT initiation age across integrated PRS-ETS risk groups maximizes mortality reduction achievable with cost-effective annual LDCT screening among never-smoking Asian women. These findings highlight an urgent limitation of global lung cancer screening guidelines that rely exclusively on smoking history and provide policy-ready evidence supporting the integration of PRS and ETS into future recommendations for precision LDCT screening for never-smoking populations.

Unhealthy retail outlets, including liquor stores, bars, convenience stores, and fast food, are concentrated in lower-income neighbourhoods. This is a well-documented cross-sectional fact; the process that sustains it is not. A neighbourhood can hold more because more open there or because those already there survive longer, and these point to different responses. We assembled an establishment-level panel of every business in Rhode Island from 2016 to 2025 (480,923 geocoded establishment-years across nine annual cross-sections), following the same outlets year to year, and classified and counted unhealthy outlets by census tract. We estimated the tract income gradient three ways (negative binomial regression, a concentration index, and a Bayesian spatial model), tested its stability, and decomposed it into openings and closures. The gradient was strong, stable, and robust: about 30 percent fewer unhealthy outlets per resident per standard deviation of higher income, with racial composition and poverty no longer associated once income was included. It was reproduced through entry, not survival: closures were even-handed across income, while new unhealthy outlets opened about 2.2 times as often per resident in the lowest-income tracts as in the highest. This entry was not unhealthy-specific: new healthy food retail tilted toward lower-income tracts at least as strongly, and the unhealthy share of openings did not rise as income fell. The standing burden was nonetheless dominated by convenience stores and off-premise alcohol. Efforts to reshape the retail environment will have more leverage on new openings than on the existing stock, through instruments defined by outlet type.

Background: Chronic kidney disease of undetermined cause (CKDu) is a form of kidney disease not associated with traditional risk factors such as hypertension, diabetes or heavy proteinuria. 11.2% of men and 3.7% of women demonstrated low eGFR (a surrogate for CKDu) in the absence of these risk factors in a 2017 cross-sectional population-representative survey of adults in North Central Province, Sri Lanka. We therefore established a longitudinal cohort to track changes in kidney function over time and to identify risk factors for developing poor kidney health. Methods: This was a 6-year study of adults aged 20-60 years conducted in Puhudivula, Anuradhapura district. Exclusions included evidence of diabetes, hypertension or pre-existing CKD. We fitted hidden Markov models (HMMs) to estimate underlying state of kidney health and examine risk factors associated with departure from a healthy state. Results: We identified four kidney health trajectories in the population (n=425): always healthy (74%); unhealthy throughout (5%); transition from health to unhealthy (10%); and reversion from unhealthy to healthy (11%). Using smokeless tobacco, including betel quid, was associated with being in an unhealthy category (2.29 [1.17, 4.49]). Lagged exposure to smoking (2.26 [1.25, 4.10]), smokeless tobacco (1.98 [1.13, 3.48]) and weedkiller (1.72 [1.15, 2.59]) were associated with the point of transition to an unhealthy state. Conclusions: Almost a quarter of working age adults in this population demonstrated eGFR changes consistent with poor kidney health. Smokeless tobacco use was associated with both pre-existing evidence of poor kidney health and transitioning to the unhealthy category.

Background Stroke remains one of the leading causes of mortality, disability, and healthcare burden worldwide. Identifying demographic, socioeconomic, lifestyle, and clinical factors associated with stroke is essential for improving prevention strategies and reducing disease burden. This study aimed to identify independent predictors of stroke among U.S. adults using nationally representative Behavioral Risk Factor Surveillance System (BRFSS) data collected between 2021 and 2023. Methods A cross-sectional analysis was conducted using pooled BRFSS data from 2021 to 2023. Adults with complete information on stroke status and study variables were included in the multivariable analysis. Stroke status was determined from self-reported physician diagnosis. Survey-weighted multivariable logistic regression was performed to estimate adjusted odds ratios (aORs) and 95% confidence intervals (CIs) for demographic, socioeconomic, lifestyle, and clinical predictors while accounting for the complex BRFSS sampling design. Model discrimination was evaluated using receiver operating characteristic (ROC) curve analysis. Results Among 235,571 participants in the pooled dataset, stroke was more common among older adults and individuals with diabetes, poorer self-reported health, lower income, and smoking history. In the adjusted analysis, increasing age (aOR 1.04, 95% CI 1.04 to 1.04), diabetes (aOR 1.55, 95% CI 1.43 to 1.67), current smoking (aOR 1.44, 95% CI 1.31 to 1.58), multiracial ethnicity (aOR 1.44, 95% CI 1.12 to 1.82), Black race (aOR 1.31, 95% CI 1.15 to 1.50), and poorer general health (aOR 1.58, 95% CI 1.53 to 1.64) were independently associated with higher odds of stroke. Conversely, Asian race (aOR 0.65, 95% CI 0.43 to 0.94), Hispanic ethnicity (aOR 0.65, 95% CI 0.54 to 0.77), higher income (aOR 0.92, 95% CI 0.90 to 0.93), and regular physical activity (aOR 0.86, 95% CI 0.80 to 0.92) were associated with lower odds of stroke. The final model demonstrated good discrimination, with an area under the ROC curve of 0.781 (95% CI 0.774 to 0.788). Conclusions Stroke among U.S. adults is independently associated with a combination of demographic, socioeconomic, lifestyle, and clinical factors. Diabetes, smoking, poor general health, and socioeconomic disadvantage remain important potentially modifiable contributors to stroke risk, whereas regular physical activity appears protective. These findings support targeted public health interventions focused on improving cardiometabolic health, promoting smoking cessation and physical activity, and addressing socioeconomic disparities to reduce the burden of stroke in the United States.

Objective: To examine if food insecurity moderates the relationship between rurality and mental health outcomes (suicide mortality, poor mental health days, frequent mental distress) and to assess if these effects vary across U.S. Census divisions. Methods: This county-level (n=2,397) cross-sectional study used OLS and spatial error regression to analyze public data from sources including the County Health Rankings and USDA. We modeled suicide mortality, poor mental health days, and frequent mental distress as functions of the Index of Relative Rurality (IRR) and food insecurity, controlling for median income and provider rates. The suicide model was also tested across nine U.S. Census divisions. Results: Baseline models revealed a paradox: rurality was a direct risk factor for suicide (B=0.400) but protective for poor mental health days (B=-0.224). The national multivariable model revealed a significant, positive rurality-food insecurity interaction for suicide mortality (B=0.861), indicating a synergistic risk. This interaction was not significant for general mental distress, which was more strongly predicted by income and food insecurity. Regional analysis confirmed the suicide interaction was potent in five divisions, including the Pacific (B=3.048) and Mountain (B=1.712) , but absent in others (e.g., South Atlantic). Conclusions: The drivers of suicide are distinct from those of general mental distress and are geographically heterogeneous. The interaction of rurality and food insecurity creates a compounded risk for suicide. Suicide prevention must be regionally-tailored and address structural inequalities, such as food insecurity, alongside clinical care.

Objective: The purpose of this study is to determine how family history of cancer, genetic mutations, presenting symptoms, and comorbidity burden collectively influence cancer outcomes in patients with epithelial ovarian cancer. Methods: A retrospective analysis was conducted on all patients with epithelial ovarian cancer treated at the University of Missouri and Ellis Fischel Cancer Center between 2008 and 2024. Patient charts were reviewed for histological subtypes, stage of cancer, status of metastasis, CA-125 values, presenting symptoms, comorbidities, family history of cancer, genetic mutations, and survival outcome. Cox regression and association analyses were performed. Results: In this cohort of patients, comorbidities and genetic mutations did not influence ovarian cancer survival. While histological subtypes, CA-125 levels, and cancer stage remained strongly associated with survival. Significant associations were observed between certain presenting symptoms and cancer histological subtype, a family history of breast cancer, stage of cancer at diagnosis, the status of metastasis, and CA-125 levels. Conclusion: Comorbidities and genetic mutations were not significantly associated with ovarian cancer survival. Presenting symptoms were associated with several clinical and pathological variables linked to ovarian cancer diagnosis.

Long-term data describing cancer patterns in Iraq remain generally limited. This retrospective observational study was conducted to evaluate the distribution and longitudinal patterns of malignant solid tumors diagnosed over a period of 12 years (2014-2025) at a major tertiary oncology center in the Kurdistan Region of Iraq. Demographic characteristics, cancer types, and temporal trend changes in cancer distribution were analyzed. Comparisons were made between the first (2014-2019) and second (2020-2025) halves of the study period. Descriptive statistics, Chi-square tests, and linear regression analyses were used to evaluate the temporal trends. After excluding records with incomplete data, 11,704 patients were included. Breast cancer was the most frequently diagnosed malignancy, accounting for over one-quarter of all cases, followed by lung, colorectal, prostate, and bladder cancers, in descending order. Women represented the majority of patients, and the mean age at diagnosis increased significantly over time. In general, the relative distribution of colorectal, genitourinary, pancreatic, uterine, and thyroid cancers increased during the study period, whereas breast and lung cancers showed a modest but significant proportional decline despite remaining the most common malignancies. A marked reduction in case numbers was observed in 2020, followed by progressive recovery in subsequent years. To conclude, cancer patterns in the Kurdistan Region of Iraq changed substantially during the 12-year study period, with increasing proportions of colorectal and several other malignancies alongside an older age at diagnosis. These findings likely reflect a combination of demographic changes, evolving lifestyle-related risk factors, and improvements in cancer detection and referral. The results provide contemporary evidence to support regional cancer control strategies, screening programs, resource allocation, and future epidemiological research.

BackgroundObservational comparisons of former smokers who use non-combusted nicotine products with former smokers who quit without them require that two quantities be measured precisely: which product is being used, and how long ago cigarette smoking stopped. Neither quantity is recorded by the National Health Insurance Service (NHIS) screening instrument used in a recent Korean cohort study of post-cessation e-cigarette use and lung cancer risk. We characterized both quantities in a contemporaneous, nationally representative survey of the same population. MethodsWe analyzed the public-release microdata of the Korea National Health and Nutrition Examination Survey (KNHANES), 2018 to 2023, restricted to adults aged 19 years and older. Former smokers were identified by smoking status, and cessation duration was taken from the item recording months since the last cigarette. Former smokers currently using a heated tobacco product (HTP) or an e-cigarette (EC) were compared with former smokers using neither. KNHANES 2018 asked a generic e-cigarette question and, separately, a checklist naming HTP brands, allowing the two product classes to be separated. Distributions were compared with rank-based methods, the age-duration relationship with Theil-Sen regression, and residual imbalance by restricting the comparison group to respondents age-matched to within two years. ResultsThe 2018 analytic sample comprised 1,348 former smokers, of whom 43 currently used HTP or EC and 1,305 used neither. Among the product-using former smokers, 58% reported HTP use without e-cigarette use, 21% reported both, and 21% reported e-cigarette use without HTP use; 79% reported any HTP use. Median cessation duration was 0.7 years (IQR 0.25 to 1.5) among product users and 12.0 years (IQR 5.0 to 20.0) among those using neither (Kolmogorov- Smirnov D = 0.76, P < 0.001), with the product user having quit more recently in 92% of cross-group pairs. The separation persisted within the short-term (<5 year) stratum (D = 0.34, P < 0.001; 73% of pairs) and after age matching, where the residual gap was 9.3 years. Cessation duration rose with age among those using no product (Theil-Sen slope +0.30 years per year) but was flat among product users (-0.01). Restricting to the screening-eligible stratum used in the cohorts high-risk analysis did not attenuate the imbalance: among those aged 50 to 80, median cessation among no-product quitters rose to 15.5 years (n = 858), and adding a 20 pack-year criterion left 421 no-product quitters with a median of 11.0 years against three HTP/EC users who had quit 0.25, 1.0 and 2.0 years earlier, despite closely matched cumulative exposure (mean 37.6 vs 37.7 pack-years). The overall contrast reproduced in every wave from 2018 to 2023, with an age-matched residual of 9 to 11 years. ConclusionsIn a nationally representative survey of the same population and the same calendar year as the NHIS screening cohort analyzed by Kim et al., Korean former smokers using non-combusted nicotine products differed from other former smokers in two respects that bear directly on how such comparisons should be read. First, they were predominantly HTP users: 79% reported any HTP use, and only 21% reported e-cigarette use without HTP use. Second, they had stopped smoking approximately a decade more recently, a difference that survived stratification at five years and exact age matching. Neither quantity is recorded in the NHIS screening instrument. Cohort estimates comparing post-cessation product users with other quitters should therefore be interpreted with caution if they do not precisely characterize product composition and to time since cessation, and future studies should measure both directly.

Background: Population-based biomonitoring of contemporary-use pesticides remains limited in the United States, particularly in rural agricultural regions, and few studies have repeated measurements within the same individuals over time. Methods: We analyzed 28 urinary pesticide-related biomarkers among 600 adults from the population-based Survey of the Health of Wisconsin with archived urine collected during 2008-2016; 296 participants provided repeat urine and updated exposure information in 2025. Detection frequencies, co-detection, and within-person detection patterns were characterized. Generalized estimating equations were used for stacked, repeated-measures analyses of factors associated with detection of aminomethylphosphonic acid (AMPA), glyphosate, 2,4-dichlorophenoxyacetic acid (2,4-D), and any of these three. Prospective-only analyses evaluated more detailed agricultural and recent exposure measures. Results: Glyphosate, AMPA, and 2,4-D were detected in 7.7%, 6.2%, and 4.3% of retrospective specimens and 5.4%, 3.1%, and 4.1% of prospective specimens, respectively. Co-detection and persistent detection across the 9 to 17-year interval was rare. In repeated-measures models, greater fruit and vegetable intake, older age, and male sex were associated with higher 2,4-D detection. Lower household income was associated with lower AMPA detection, while afternoon/evening collection was associated with higher AMPA detection. In prospective analyses, working on field-crop agricultural land showed the strongest agricultural associations, particularly for 2,4-D and detection of any of the three pesticides. Associations were not seen with self-reported conventional versus organic produce consumption. Conclusions: Urinary pesticide detections were generally infrequent in this Wisconsin population. Diet and direct agricultural activities may be more informative exposure pathways than residing near cropland or private well drinking-water characteristics.