SSM - Population Health

Objectives: To assess whether higher state-level community-based U.S. Immigration and Customs Enforcement (ICE) arrest rates are associated with adverse mental health outcomes among Hispanic and non-Hispanic adults in the United States. Design: Retrospective analysis using individual-level data from the 2023 and 2024 Behavioral Risk Factor Surveillance System (BRFSS) linked to monthly state-level ICE arrest records from the Deportation Data Project. Two-way fixed effects models assessed associations between mental health outcomes and ICE arrests, net of secular trends, state-specific time invariant factors, and individual covariates. Setting/participants: The sample included 534,099 US adults aged 18 years or older residing in all 50 states and the District of Columbia surveyed between September 2023 and December 2024. Analyses exploited within-state month-to-month variation in enforcement intensity with state and year-month fixed effects. Outcome measures: Number of poor mental health days in the past 30 days; any poor mental health days (binary); mental health status (3-level categorical); frequent mental distress ([&ge;]14 poor mental health days); and a composite indicator combining depressive disorder diagnosis with frequent mental distress. Results: Among 534,099 respondents (approximately 10% Hispanic), higher ICE arrest rates were significantly associated with worse mental health among Hispanic adults, including 0.19 additional poor mental health days per month (p < 0.05), a 2.2% higher likelihood of reporting any poor mental health days (p < 0.01), and a 2.4% increase in composite mental health problems (p < 0.01). Associations were concentrated among Hispanic women and those with a high school diploma or less. Among non-Hispanic adults, estimates were small and precisely centered around zero across outcomes. Similar findings obtained in difference-in-differences event study models, models including lagged exposures, and models with leave-one-out state exclusions. Conclusion: Higher community-based immigration enforcement was associated with worse mental health outcomes among Hispanic adults but not among non-Hispanic adults. Contemporary enforcement strategies may have broader psychological spillover effects within Hispanic communities, and mental health may be an underrecognized social cost of interior immigration enforcement.

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.

Homicide is a leading cause of preventable death in the United States and disproportionately affects Black and Indigenous communities. Structural racism and neighborhood disinvestment are central drivers of these disparities, yet national evidence on whether the association between neighborhood opportunity and homicide risk varies by race/ethnicity remains limited. Using 2020 data from the restricted-use National Violent Death Reporting System linked to Child Opportunity Index (COI 3.0) scores and Census population denominators across 30,077 ZIP codes in 48 states, we estimated age-adjusted log-linked generalized linear models to examine racial/ethnic disparities in homicide rates and their interaction with neighborhood opportunity. Black men experienced homicide rates nearly 20 times those of White men; Indigenous men experienced rates approximately 6 times higher. Higher neighborhood opportunity was independently associated with lower homicide risk across all groups and explained 43-59% of excess risk for Black and Indigenous individuals. Crucially, the association between neighborhood opportunity and homicide was nonlinear and significantly heterogeneous by race/ethnicity, with the steepest rate reductions occurring at the lowest opportunity levels for Black and Indigenous men. These findings suggest that place-based investments in severely deprived communities may yield the greatest reductions in homicide and racial/ethnic health inequities.

Rural-urban fertility differences may reflect unequal distributions of education, household resources, union timing, and regional characteristics rather than an independent effect of residence. This study examined whether the rural-urban gap in near-completed fertility among Nigerian women aged 40-49 persisted after accounting for these compositional factors. Data were drawn from 7,370 women in the 2024 Nigeria Demographic and Health Survey. Children ever born were analyzed using weighted descriptive statistics, residence-specific comparisons, and four survey-weighted Poisson generalized linear models with a log link and cluster-robust standard errors. The weighted mean number of children ever born was 5.53, ranging from 4.68 among urban women to 6.39 among rural women. In the age-adjusted model, rural women had 37% more children than urban women (IRR = 1.37, p < .001), but residence was no longer statistically significant after adjustment for education and household wealth. In the fully adjusted model, women aged 45-49 had more children than those aged 40-44 (IRR = 1.09, p < .001). Secondary and higher education were associated with lower fertility (IRR = 0.92 and 0.82, respectively; p < .001), as was residence in the richest households (IRR = 0.84, p < .001). First cohabitation at ages 20-24 and 25 or older was also associated with lower fertility (IRR = 0.82 and 0.64; p < .001). The rural-urban fertility gap largely reflected socioeconomic, marital-timing, and regional inequalities.

Objective We characterized social determinants of health (SDOH) in U.S. children who ever and never had Long COVID. Methods We used cross-sectional data from the 2022-2023 National Health Interview Survey (N=14,993 children 0-17 years). Parents reported child- and household-level information. Long COVID was defined as ever experiencing symptoms lasting [&ge;]3 months that were not present prior to having COVID-19. We produced weighted prevalence estimates by Long COVID status for 3 SDOH domains (Social and Community Context, Healthcare Access and Quality, and Economic Stability) and used Rao-Scott chi-squared tests to examine differences. Results In Social and Community Context, children who ever had Long COVID more often resided in single parent households (33.2% vs. 20.3%; p<0.0001), with someone with severe depression or mental illness (18.5% vs. 8.3%; p<0.0001) or substance abuse (16.7% vs. 8.1%; p<0.0001) or had a lifetime of being disparaged by adults in the home (9.2% vs. 3.9%; p=0.0009). In Healthcare Access and Quality, children who ever had Long COVID more often had public insurance (e.g., Medicaid) (51.3% vs. 41.6%; p=0.02), higher healthcare use, and difficulty paying medical bills (23.2% vs. 12.2%; p<0.0001). In Economic Stability, children who ever had Long COVID had lower parental education, lower food security, and higher participation in social safety net programs (p<0.01 for all comparisons). Conclusion Children who ever had Long COVID more frequently experienced adverse SDOH compared to their peers who never had Long COVID. These findings may help identify children who may benefit from additional resources related to their Long COVID care.

Community-level estimates of healthcare utilization are essential for identifying inequities, allocating resources, and evaluating place-based interventions. However, in the United States, no single data source adequately captures healthcare utilization within geographically defined populations. Population-based surveys often lack sufficient geographic resolution, insurance claims represent only covered populations, and electronic health records are limited to care delivered within participating health systems. Increasingly, researchers combine these fragmented data sources, yet limited guidance exists for conducting valid population-based descriptive analyses using incomplete and overlapping data. We review the strengths and limitations of major data sources used to characterize community healthcare utilization and propose an approach for conducting population-based descriptive analyses using fragmented data. Rather than focusing on the limitations of individual data sources, our approach begins by explicitly defining the target population and the ideal observational study that would answer the research question. Available data sources are then conceptualized as incomplete or imperfect realizations of that ideal, providing a structured approach to (a) identifying sources of selection bias, missingness, and measurement error, (b) articulating required assumptions, and (c) selecting appropriate analytic strategies. We illustrate our approach using colorectal cancer screening utilization among adults residing in Brooklyn, New York during 2022. By shifting attention from individual data sources to the target community and the assumptions required for valid inference, this approach provides a practical approach for strengthening descriptive analyses of community healthcare utilization and informing place-based public health research, policy, and practice.

Background: Cardiovascular disease (CVD) remains the leading cause of mortality among older U.S. adults, yet the contribution of neighborhood-based structural racism remains inadequately quantified. This study quantifies the association between the Structural Racism Effect Index (SREI) and CVD mortality among adults aged {greater than or equal to}65 years, evaluating how this relationship varies across U.S. geographic regions to identify key areas for intervention. Methods: This ecological study applied a hierarchical Bayesian spatiotemporal framework to 2017-2020 Centers for Disease Control and Prevention (CDC) Wide-Ranging Online Data for Epidemiologic Research (WONDER) data to estimate the association between SREI and CVD mortality across 3,007 U.S. counties. SREI was modeled continuously and categorically, adjusting for sociodemographic covariates. Population attributable fractions (PAF) and attributable deaths (AD) quantified the potentially preventable burden and its spatial disparities. Results: From 2017 to 2020, approximately 2.79 million CVD deaths were observed, with significant spatial clustering (Moran's I = 0.35, p < 0.001). Each standard-deviation increase in SREI was associated with 13% higher CVD mortality (IRR: 1.13, 95% CrI: 1.12-1.15). A positive dose-response gradient was observed across SREI quartiles, with mortality 24% higher in the highest quartile than in the lowest (IRR: 1.24, 95% CrI: 1.20-1.28). The PAF was 6.94% (95% CrI: 6.13-7.73), corresponding to 193,472 potentially preventable deaths. High exceedance probabilities (>0.95) were concentrated in the Southeast, Appalachia, and the Midwest. Conclusions: Structural racism is a spatially patterned, dose-dependent predictor of older adult CVD mortality, underscoring the need for public health monitoring and neighborhood-based upstream interventions where disease burden is concentrated. Keywords: Structural Racism Effect Index; Neighborhood disadvantage; Cardiovascular Disease Mortality; Bayesian Spatiotemporal Analysis; Population Attributable Fraction; Health Disparities; Health Equity.

Importance: The 2026 public charge rule could discourage immigrants from accessing health coverage programs, creating a chilling effect that potentially leads to negative health outcomes; However, its long-term health impact is poorly understood. Objective: To model potential impacts of the 2026 public charge rule on primary care and premature mortality among immigrants in New York City (NYC). Design, Setting, and Participants: The simulation used a deterministic compartmental model with Ordinary Differential Equations (ODEs) using 2023 NYC Vital statistics data and American Community Survey, and estimates obtained from 2 previous studies about effects of healthcare access on primary care and Medicaid expansion on premature mortality. Main Outcomes and Measures: Rates of primary care outcomes (access, doctor's visits) in 5 years, and premature mortality in 5 and 20 years, projected by the model under conservative, moderate, and aggressive scenarios of avoidance/disenrollment due to the public charge rule, known as the 'chilling effect'. Effects of the avoidance/disenrollment on primary care outcomes and premature mortality were obtained from 2 previous studies. Projected rates of the outcomes under each scenario were compared with counterfactuals to estimate the health impacts of the chilling effect. Results: Implementation of the public charge rule was projected to decrease the primary care access rate by 4.1% (conservative) to 9.9% (aggressive) over 5 years, relative to the counterfactual scenario without the rule. The rate of doctors' visits was projected to decrease over 5 years by 5.1% (conservative) to 12.2% (aggressive). Premature mortality was projected to increase by 4.4% (conservative) to 10.6% (aggressive) in 5 years and 7.4% (conservative) to 17.4% (aggressive) in 20 years. Legal noncitizens and Black immigrant New Yorkers were predicted to experience higher burdens of premature mortality attributed to the chilling effect, compared with other immigrant groups and racial/ethnic groups, respectively. Conclusions and Relevance: This study demonstrates adverse health consequences of federal public charge rule changes among immigrants in NYC. The model projected a decrease in primary care visits and increase in premature mortality across various scenarios. These findings suggest urgent reconsideration of a regulatory change that disproportionately increases risk of premature mortality among immigrants in NYC.

Importance: Loneliness is associated with premature mortality and poor mental health and was declared an epidemic by the US Surgeon General in 2023, but national surveillance has relied on state-based or experimental online surveys. In 2024, the National Health Interview Survey measured loneliness directly for the first time. Objective: To estimate the national prevalence of loneliness among US adults, identify the sociodemographic groups with the highest burden, and quantify associations with mental health, health status, and health care use. Design: Cross-sectional analysis of the 2024 National Health Interview Survey, a nationally representative household survey conducted continuously from January to December 2024. Setting: US households; face-to-face and telephone interviews. Participants: 32 629 sampled civilian noninstitutionalized adults aged 18 years or older (response rate, 47.9%); 31 470 (96.4%) had valid loneliness data. Exposures: Frequent loneliness, defined as feeling lonely always or usually on a 5-category item (always, usually, sometimes, rarely, never). Main Outcomes and Measures: Survey-weighted prevalence of loneliness overall and by sociodemographic characteristics, and associations of frequent loneliness with serious psychological distress (Kessler 6 scale score 13 or higher), frequent feelings of depression and anxiety, life dissatisfaction, fair or poor self-rated health, receipt of counseling or therapy, cost-related unmet mental health care need, and emergency department use. Results: In 2024, 4.9% (95% CI, 4.6%-5.2%) of US adults, an estimated 12.2 million people, felt lonely always or usually, and 23.7% (95% CI, 23.1%-24.3%), an estimated 59.3 million, felt lonely at least sometimes. Prevalence of frequent loneliness was highest among adults with family income below the federal poverty level (10.3%), adults with disability (13.6%), adults living alone (9.0%), and American Indian or Alaska Native adults (12.2%). Adults aged 65 years or older had the lowest prevalence of any age group (4.0%) and adults aged 18 to 29 years the highest (6.3%). After adjustment for sociodemographic characteristics, frequent loneliness was associated with serious psychological distress (adjusted odds ratio, 14.5; 95% CI, 12.1-17.3), life dissatisfaction (9.0; 95% CI, 7.6-10.8), cost-related unmet mental health care need (4.3; 95% CI, 3.5-5.2), and emergency department use (1.8; 95% CI, 1.5-2.0). Conclusions and Relevance: Loneliness among US adults was patterned by poverty, disability, and household structure rather than older age. These estimates from the nation's principal household health survey provide a benchmark for monitoring loneliness and suggest that strategies for social connection should address material hardship and access to mental health care.

Social relationships are an important social determinant of health. Loneliness, the perceived gap between one's actual and desired relationships, has emerged as an important mechanism through which social relationships impact health. Like other intrapersonal-level factors associated with health, loneliness is influenced by broader social and structural factors, including characteristics of one's neighborhood social environment. Although neighborhood-level protective and risk factors for loneliness and for mental health have been identified, prior studies have often focused solely on self-reported perceptions of the neighborhood environment. Further, few have considered aspects of the neighborhood social environments, such as neighborhood stability (i.e., stability of the community with long or short-term residents), independent of neighborhood socioeconomic conditions. In the current analysis, we explored longitudinal patterns of loneliness in conjunction with neighborhood stability among women with HIV (WWH) enrolled into the MACS/WIHS Combined Cohort Study (MWCCS) from 2014-2019 (N2019=1,394) to examine whether trajectories of loneliness and neighborhood stability were associated with depressive symptoms, non-prescription substance use, past-year cannabis use, number of alcoholic drinks per week, and several domains of quality of life. Loneliness at baseline (Betas = 0.24 - 0.54) and changes in loneliness over time (Betas = 0.11 - 0.26) were associated with each outcome, except for the association between changes in loneliness over time and drinks per week (Beta=0.13, p = 4.14x10-2), which did not persist after correcting for multiple comparisons. Neighborhood stability at baseline was associated with past year cannabis use (Beta=0.26, p = 1.00x10-2), depressive symptoms (Beta=-0.12, p = 1.54x10-3), and overall self-reported health (Beta=-0.08, p = 2.05x10-2). Changes in neighborhood stability across time were not associated with any outcome. Neighborhood stability moderated the association between changes in loneliness and general health perceptions. Our results demonstrate both overall loneliness and changes in loneliness over time have implications for current mental health in WWH, while changes in neighborhood stability did not.

Introduction: Research has documented that obesity and morbidity are associated. Black persons in the United States (U.S.) incur higher financial costs of obesity-related multimorbidity (ORM). However, lifetime healthcare costs (LHCs) remain underexamined for these populations. Objective: We quantified racial differences in 1) LHCs and 2) lifetime healthcare cost differential (LCD) associated with ORM for ages > 40 years. Methods: We used the 2008- 2012 Medical Expenditure Panel Survey Household Component to examine unique obesity-related diseases (ORDs): high blood sugar, hypertension, coronary heart disease, and stroke. We used a prior published Markov model to simulate a person's life history of ORDs and compute LHCs among ages > 40 years. We computed LCD-associated ORM as the difference in LHC for those with ORM and LHC for members without ORDs. We quantified differences in race as the difference between LHC or LCD among White and Black men and women. Results: Our analytic sample included 53,035 Black and White persons representing 97,229,611 (S.E., 2,104,365), 12.4% as Black and 87.6% as White persons. ORM was more prevalent in the Black (21.2%) than the White group (13.4%). LHCs by race (Black/White) for women/men with ORM and LCDs associated with ORM (2012$) were $3 1,035/43,595 and $11,350/26,948 for age 40-49, $2 1,567/25,6 115 and $3,846/9,808 for 50-59, $9,863/18,515 and -$2,566/7,426 for 60-69, -$8,220/16,285 and -$11,524/3,865 for 70-79. Conclusions: Racial Differences in LHCs and LCDs related to ORM persist and vary across subpopulations. Future interventions designed to prevent/manage ORM are crucial for prioritizing populations with high LHCs and advancing health equity.

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.

Background: Postpartum Medicaid coverage and support are central maternal health policy issues, but county-level tools for identifying where postpartum Medicaid populations may face overlapping administrative, clinical, and contextual access barriers remain limited. Methods: We developed and internally validated a county-level Postpartum Medicaid Access Barrier Index for all 3,144 counties and county equivalents in the 50 states and District of Columbia. Public data sources included geocoded Medicaid office locations from Shafer et al. (2024), U.S. Census county boundaries, American Community Survey 2024 5-year county indicators, the National Center for Health Statistics 2023 Urban-Rural Classification Scheme for Counties, and county-level hospital-based obstetric care status from the University of Minnesota Rural Health Research Center. Medicaid office locations were spatially assigned to counties, then merged with ACS indicators, rurality, and obstetric care status by county FIPS. The theoretical score range was 0-11; the index assigned higher weights to two core infrastructure measures and lower weights to contextual indicators. Internal validation assessed component structure, known-groups validity, geographic clustering, weighting sensitivity, added value over simpler infrastructure screens, and separation across concern levels. Results: Across 3,144 counties, observed scores ranged from 0 to 10 on the theoretical 0-11 score, with a mean of 3.65 and median of 3. High or highest concern counties accounted for 665 counties (21.2%), including 56 counties (1.8%) in the highest concern group. Component correlations were low-to-moderate, with an average absolute phi of 0.176 and no pairwise component correlation at or above 0.50. Known-groups validity was strong: dual administrative and clinical gap counties scored 4.43 points higher than counties with neither gap (Cohen's d = 3.28, p < 0.001). Scores were geographically clustered (Moran's I = 0.375, permutation p = 0.005). A dual-gap-only screen captured 386 of 665 high/highest concern counties (58.0%) but missed 279 high/highest counties; a parsimonious rule requiring one infrastructure gap plus at least four contextual flags recovered 265 of these 279 missed counties (95.0%) with 100.0% precision. Discussion: The Postpartum Medicaid Access Barrier Index provides a transparent county-level screening tool for identifying places where administrative, clinical, and contextual barriers may overlap for postpartum Medicaid populations and should be externally validated against Medicaid enrollment, renewal, churn, coverage continuity, and postpartum care outcomes.

Background: Multiple observational studies have reported associations between greater parity and increased CVD risk. Whether these associations reflect causal effects or are confounded by socioeconomic factors remains unclear. Methods: We investigated associations between number of children ever born (NEB) and 16 cardiometabolic traits in up to 172,122 females and 138,390 males in the UK Biobank, and an independent sample of 53,237 UK Biobank spousal pairs. We additionally conducted sex-stratified two-sample Mendelian randomization (MR) and applied a novel spousal MR framework, in which an individual's spouse's genotype was used as the instrumental variable to estimate the causal effect of NEB on cardiometabolic health outcomes, as an approach to minimize bias from horizontal pleiotropy. Results: NEB was associated with multiple cardiometabolic traits in the multivariable regression, even after adjustment for socioeconomic status, with differences in the strength of association observed between males and females. Traditional MR provided evidence that higher NEB causally increases type 2 diabetes risk in females, body mass index (BMI) in both sexes, female basal metabolic rate (BMR) and male body fat percentage but decreases female blood pressure. Spousal MR corroborated positive effects on female BMI and BMR and additionally suggested inverse causal effects on female HDL cholesterol and ApoA1 and male blood glucose. Conclusion: These findings indicate a possible causal relationship between NEB and long-term cardiometabolic health, although causal effects are likely to be small.

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.

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.

Objectives To assess the concurrent and lagged associations between quality of primary care and planned and unplanned secondary care use for patients with multimorbidity, examining the modifying role of frailty. Design A retrospective cohort study Setting This population-level analysis included 468,172 patients with multimorbidity in England from the Discover research platform (April 2022-March 2024). Participants Patients with multimorbidity Main outcome measures We used principal component analysis to combine a set of quality indicators (QIs) and assessed the impacts of QIs on both planned and unplanned care. Results Generally, patients with higher QI attainment also had higher likelihood of planned (outpatient visits) and unplanned care (emergency admissions and ED visits) utilisation. There was a lower lagged odds of elective hospital admissions in the following 12 months among those with higher attainment of multimorbidity-specific QIs (OR=0.94, 95%CI 0.93-0.95). In the complex multimorbidity cohort ([&ge;]3 conditions), multimorbidity-specific QIs were longitudinally associated with lower odds of elective admissions (OR=0.94, 95%CI 0.92-0.95) and outpatient visits (OR=0.96, 95%CI 0.95-0.98), while generic QIs were related to lower odds of outpatient non-attendance (OR=0.95, 95%CI 0.91-0.99). In non-frail patients with multimorbidity, multimorbidity-specific QIs were longitudinally associated with reduced odds of outpatient visits (OR=0.98, 95%CI 0.97-0.99), elective admissions (OR=0.92, 95%CI 0.90-0.94) and prolonged elective hospital stay (IRR=0.94, 95%CI 0.89-0.99). Conclusions Attainment of generic and multimorbidity QIs was generally associated with slightly increased planned and unplanned care. However, patients for whom we identified higher attainment of multimorbidity-specific QIs had lower odds of elective admissions and outpatient visits, especially for those with complex multimorbidity. Our research suggests that the quality of primary care may influence patients' use of secondary care, with the potential to improve care for people with multimorbidity and warrant further investigation into management strategies.

Incidence of type 2 diabetes is increasing at ages when education, work, family, and financial transitions are taking place, yet we lack robust evidence of whether earlier treatment changes life-course outcomes and over which time span this takes place. This paper uses the medical cutoff for diabetes diagnosis (HbA1c of 6.5 percent) as a natural experiment to study the effects of diabetes treatment using electronic health records (EHR) and panel data. This paper has three main findings. First, using EHR data, we find that there is a sharp increase in the probability of both diagnosis of diabetes and prescription when the HbA1c equals 6.5 percent. Second, we find that treating diabetes reduces HbA1c levels, weight, BMI, and blood pressure and increases the amount of care received, proxied by the number of HbA1c tests. Both the diagnosis and a prescription are independently able to produce positive changes in metabolic health, although a prescription is more effective in this regard. Third, we conclude that treating diabetes does not have a significant effect on life-course outcomes for a cohort of young Americans aged 24-32, although it does result in a reduction in HbA1c levels that are seen even eight years after the intervention. Taken together, these findings suggest that receiving a diagnosis and prescription are both effective treatments for diabetes, but they do not translate to significant alterations in the lives of young adults in the medium-term.

Background: Prenatal psychosocial distress is a risk factor for adverse birth outcomes such as preterm birth, but the biological pathways remain incompletely understood. Allopregnanolone (ALLO), a stress-responsive, progesterone-derived neuroactive steroid, may contribute to pregnancy maintenance by inhibiting uterine activation and modulating inflammatory and neuroendocrine pathways involved in parturition. Few human studies have examined ALLO during pregnancy in relation to birth timing and related outcomes, and no study to our knowledge has examined the entire pathway of prenatal psychosocial distress, ALLO, and birth outcomes. Here, we evaluated whether maternal ALLO concentrations and the ALLO-to-progesterone ratio mediate associations between prenatal psychosocial distress and birth outcomes. Methods: This study included 237 pregnant participants from the Healthy Start Cohort, enriched for psychosocial distress (n=57 high-distress; n=180 low-distress) assessed using the Edinburgh Perinatal Depression Scale (EPDS) and EPDS-3A anxiety subscale. We measured maternal serum ALLO and related steroid hormones at approximately 17 and 27 weeks gestation (range: 10-34 weeks) using a validated HPLC-MS/MS assay and evaluated natural log (ln)-transformed ALLO and the ALLO-to-progesterone ratio as potential mediators. The primary outcome was gestational age at birth; secondary outcomes included birthweight-for-gestational-age z-score (BW/GA), percent fat mass (%FM), and birth length-for-gestational-age z-score (BL/GA). We conducted regression-based mediation analyses by comparing the total and direct effects of prenatal psychosocial distress after adjustment for each proposed mediator. Results: Participants had a mean +- SD age of 29+-6 years; 38% were nulliparous, and 60% identified as non-Hispanic White. Of the birth outcomes assessed, prenatal psychosocial distress was only associated with BL/GA ({beta} = -0.49; 95% CI: -0.84, -0.14). Adjustment for ALLO at ~27 weeks as a mediator minimally attenuated the association between prenatal distress and BL/GA ({beta} = -0.46; 95% CI: -0.81, -0.10, 6.1% attenuation), whereas adjustment for the ALLO-to-progesterone ratio resulted in 20.8% attenuation ({beta} = -0.38; 95% CI: -0.74, -0.03). Conclusions: Maternal prenatal distress was associated with shorter birth length. The ALLO-to-progesterone ratio, but not ALLO itself, may partially mediate this relationship. These findings support neurosteroid metabolism as a biological pathway linking prenatal distress to fetal length accrual. Future mechanistic studies are needed to confirm these findings.

Abstract Background: Traditional occupational health models rely on simple binary classifications "burned out" versus "healthy" that mask the transitional phases of distress through which clinicians pass before reaching complete collapse. In resource-constrained acute care environments across sub-Saharan Africa, severe emotional exhaustion often represents a systemic baseline rather than an individual anomaly. This study moves beyond the burnout binary to identify multi-dimensional latent distress profiles and model the specific tipping points and biological pathways that drive overextended but empathy-preserved clinicians into complete clinical burnout. Methods: We conducted a secondary analysis of a cross-sectional dataset (N=135) capturing emergency medicine, anesthesia, and intensive care clinicians at Muhimbili National Hospital in Dar es Salaam, Tanzania. Using the Maslach Burnout Inventory-Human Services Survey (MBI-HSS), we categorized clinicians into five mutually exclusive latent profiles. Multivariable logistic regression identified independent tipping points for progression from isolated exhaustion to full burnout. Mediation analysis (Hayes PROCESS Macro, Model 4) examined the pathway through which extended shifts associate with burnout. Ethical clearance was obtained from the Muhimbili University of Health and Allied Sciences Research and Publication Committee (Ref. No. MUHAS-REC-6-2020-290), and all participants provided written informed consent. Results: Among 135 clinicians, 62.2% (n=84) met criteria for Fully Burned Out (high exhaustion, high cynicism, low personal accomplishment), 28.1% (n=38) were Overextended (isolated high exhaustion with preserved empathy and efficacy), and 9.6% (n=13) experienced Disengaged/Moderate Strain. Notably, 0.0% (n=0) met the criteria for the Resilient/Engaged profile. Within the exhausted cohort (n=122), multivariable modeling identified shift durations exceeding 12 hours (AOR 8.72, 95% CI [1.24, 61.15], p=0.012), poor coworker relationships (AOR 4.11, 95% CI [1.42, 11.90], p=0.009), sleep deprivation under 6 hours (AOR 4.25, 95% CI [1.74, 10.40], p=0.001), and lack of regular exercise (AOR 3.10, 95% CI [1.25, 7.68], p=0.015) as independent tipping points of collapse. Mediation analysis demonstrated that the link between extended shifts and burnout was fully mediated by sleep degradation (indirect effect ab=0.264, 95% CI [0.114, 0.458]). Conclusions: In low-resource acute care settings, emotional exhaustion is a universal baseline (90.4%) driven by severe systemic constraints rather than a failure of individual grit. The complete absence of a resilient cohort challenges the prevailing individual-level resilience paradigm. Drawing on the African philosophy of Ubuntu "I am because we are" this study demonstrates that collective team solidarity serves as the single strongest protective buffer against clinical collapse. Preventing clinical collapse requires structural policy shifts: capping shifts at 12 hours to protect biological rest, actively cultivating team solidarity as a workplace safety net, and investing in cadre-specific interventions that recognize the distinct vulnerabilities of nursing staff and trainees, who bear 80.9% of the burnout burden despite comprising 73.4% of the workforce.