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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]

The article presents a study on the obstacles to self-management of serious mental illness (SMI) and diabetes mellitus (DM). It discusses findings on the domains of barriers to such self-management including personal level barriers such as stress, isolation and stigma, family and community level barriers such as lack of support from family and friends and provider and health care system level barriers such as poor relationships and communication with providers as well as fragmentation of care.

Objective While previous work has demonstrated elevation of both comorbid anxiety disorders and diabetes mellitus type II in individuals with serious mental illness, little is known regarding the impact of comorbid anxiety on diabetes mellitus type II outcomes in serious mental illness populations. We analyzed baseline data from patients with serious mental illness and diabetes mellitus type II to examine relationships between comorbid anxiety, glucose control as measured by hemoglobin A1c score, and overall illness burden. Methods Using baseline data from an ongoing prospective treatment study involving 157 individuals with serious mental illness and diabetes mellitus type II, we compared individuals with and without a comorbid anxiety disorder and compared hemoglobin A1c levels between these groups to assess the relationship between anxiety and management of diabetes mellitus type II. We conducted a similar analysis using cumulative number of anxiety diagnoses as a proxy for anxiety load. Finally, we searched for associations between anxiety and overall medical illness burden as measured by Charlson score. Results Anxiety disorders were seen in 33.1% (N = 52) of individuals with serious mental illness and diabetes mellitus type II and were associated with increased severity of depressive symptoms and decreased function. Hemoglobin A1c levels were not significantly different in those with or without anxiety, and having multiple anxiety disorders was not associated with differences in diabetes mellitus type II control. However, depressive symptoms were significantly associated with higher hemoglobin A1c levels. Neither comorbid anxiety nor anxiety load was significantly associated with overall medical burden. Conclusion One in three people with serious mental illness and diabetes mellitus type II had anxiety. Depressive symptoms were significantly associated with Hb1Ac levels while anxiety symptoms had no relation to hemoglobin A1c; this is consistent with previously published work. More studies are needed to better understand the relationship between depression, anxiety, and health management in people with serious mental illness and diabetes mellitus type II. [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]