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Since its development, Singh's 2003 Area Deprivation Index (ADI) has been routinely used by researchers to measure a global construct of neighborhood socioeconomic deprivation and to investigate how living in neighborhoods of different levels of socioeconomic deprivation affects individuals' health. We empirically tested the ADI's dimensionality, using 2013–2017 American Community Survey tract-level estimates (N = 73,056), and the stability of its performance across time and place. Factor analysis findings illuminated three distinct dimensions, the ADI-3, consisting of neighborhood financial strength, economic hardship and inequality, and educational attainment. The prior-assumed unidimensional ADI measure fails standard tests of construct validity. Findings from multigroup structural equation modeling across 2009 and 2017 and between New York and Minnesota suggest that the ADI performs with only partial stability across time and place. In order to most precisely understand the complex role of neighborhood socioeconomic position in health, public health researchers must integrate construct-valid and regionally and temporally relevant measures. [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]