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Background: Evidence on the effects of neighborhood socioeconomic disadvantage on dementia risk in racially and ethically diverse populations is limited. Our objective was to evaluate the relative extent to which neighborhood disadvantage accounts for racial/ethnic variation in dementia incidence rates. Secondarily, we evaluated the spatial relationship between neighborhood disadvantage and dementia risk. Methods: In this retrospective study using electronic health records (EHR) at two regional health systems in Northeast Ohio, participants included 253,421 patients aged >60 years who had an outpatient primary care visit between January 1, 2005 and December 31, 2015. The date of the first qualifying visit served as the study baseline. Cumulative incidence of composite dementia outcome, defined as EHR‐documented dementia diagnosis or dementia‐related death, stratified by neighborhood socioeconomic deprivation (as measured by Area Deprivation Index) was determined by competing‐risk regression analysis, with non‐dementia‐related death as the competing risk. Fine‐Gray sub‐distribution hazard ratios were determined for neighborhood socioeconomic deprivation, race/ethnicity, and clinical risk factors. The degree to which neighborhood socioeconomic position accounted for racial/ethnic disparities in the incidence of composite dementia outcome was evaluated via mediation analysis with Poisson rate models. Results: Increasing neighborhood disadvantage was associated with increased risk of EHR‐documented dementia diagnosis or dementia‐related death (most vs. least disadvantaged ADI quintile HR = 1.76, 95% confidence interval = 1.69–1.84) after adjusting for age and sex. The effect of neighborhood disadvantage on this composite dementia outcome remained after accounting for known medical risk factors of dementia. Mediation analysis indicated that neighborhood disadvantage accounted for 34% and 29% of the elevated risk for composite dementia outcome in Hispanic and Black patients compared to White patients, respectively. Conclusion: Neighborhood disadvantage is related to the risk of EHR‐documented dementia diagnosis or dementia‐related death and accounts for a portion of racial/ethnic differences in dementia burden, even after adjustment for clinically important confounders. [ABSTRACT FROM AUTHOR]

Healthcare systems and providers have increasingly acknowledged the role and impact of social determinants in overall health. However, gender-diverse individuals face persistent health disparities due to their identities. There is limited research on the impact of clinical and sociodemographic characteristics on mood and quality of life (QoL) for transgender (TG) individuals. Our study aims to understand and better elucidate social and clinical characteristics of transmasculine (TM) and transfeminine (TF) individuals and their impact on quality of life and depressive symptoms. In this cross-sectional study, 298 TF and TM individuals on gender-affirming hormone therapy (GAHT) were surveyed about their demographic characteristics (age, gender identity, body mass index (BMI), and education), social needs, mood, and quality of life. Multivariable regression modelling was performed to assess the effect of each variable listed above on three domains of QoL (psychological, environmental, and physical) as well as depressive symptoms. We find that QoL scores are similar between TM and TF individuals, with scores in the psychological domain particularly low in both cohorts. TM individuals report higher rates of stress and restroom avoidance than TF individuals. In particular, psychological well-being (measured by the psychological domain of QoL and depressive symptoms) is significantly associated with increased BMI, financial instability, and stress in TM individuals while for TF individuals, psychological well-being is associated with stress and social integration. These data suggest that social circumstances are key drivers of QoL and psychological well-being among gender-diverse individuals receiving GAHT with specific differences between TF and TM individuals. This information may be utilized by healthcare providers and policymakers to address and improve clinical care and social policies to improve health equity for gender-diverse individuals. • Transmasculine individuals reported higher levels of stress than transfeminine individuals. • Higher levels of stress correlated with lower quality of life (QoL) scores in both groups. • Strong social support in transfeminine individuals correlated with higher QoL scores. • Financial stability in transmasculine individuals correlated with higher QoL scores. • Delayed urination due to inadequate facilities was more frequent in transmasculine individuals. [ABSTRACT FROM AUTHOR]

• Alzheimer's Disease (AD) is a major cause of dementia in the United States. • Non-traditional risk factors can help identify areas of higher AD Burden. • Machine learning techniques can help unravel this complex relationship. Geographical disparities in mortality among Alzheimer's disease (AD) patients have been reported and complex sociodemographic and environmental determinants of health (SEDH) may be contributing to this variation. Therefore, we aimed to explore high-risk SEDH factors possibly associated with all-cause mortality in AD across US counties using machine learning (ML) methods. We performed a cross-sectional analysis of individuals ≥65 years with any underlying cause of death but with AD in the multiple causes of death certificate (ICD-10,G30) between 2016 and 2020. Outcomes were defined as age-adjusted all-cause mortality rates (per 100,000 people). We analyzed 50 county-level SEDH and Classification and Regression Trees (CART) was used to identify specific county-level clusters. Random Forest, another ML technique, evaluated variable importance. CART's performance was validated using a "hold-out" set of counties. Overall, 714,568 individuals with AD died due to any cause across 2,409 counties during 2016–2020. CART identified 9 county clusters associated with an 80.1% relative increase of mortality across the spectrum. Furthermore, 7 SEDH variables were identified by CART to drive the categorization of clusters, including High School Completion (%), annual Particulate Matter 2.5 Level in Air, live births with Low Birthweight (%), Population under 18 years (%), annual Median Household Income in US dollars ($), population with Food Insecurity (%), and houses with Severe Housing Cost Burden (%). ML can aid in the assimilation of intricate SEDH exposures associated with mortality among older population with AD, providing opportunities for optimized interventions and resource allocation to reduce mortality among this population. [ABSTRACT FROM AUTHOR]