scholarly journals Social Determinants of Health And The Onset of Dementia in Later Life

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 749-749
Author(s):  
Darren Liu ◽  
Takashi Yamashita ◽  
Betty Burston ◽  
Chetana Guthikonda
Keyword(s):  
Risk Factors ◽  
Systematic Review ◽  
Socioeconomic Status ◽  
Food Insecurity ◽  
Cognitive Decline ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Early Life ◽  
Later Life ◽  
Determinants Of Health

Abstract Dementia is a debilitating neurodegenerative syndrome characterized by deterioration in memory, cognitive, behavioral, and physical capacity. Recent research has indicated that some early-life social determinants of health (SDH), which vary by race/ethnicity may hold clues to the onset of dementia. Although early life clinical risk factors of dementia have been identified, early-life SDH such as education, sociodemographic and socioeconomic characteristics are yet to be collated. This review study focused on early-life (less than18 years of age) SDH in relation to cognitive decline in later life and differences across racial/ethnic groups in the U.S. A systematic review of articles in and after January of 1999 was conducted using Scoping Reviews - an approach for evidence synthesis to determine the coverage of a body of literature. Studies that report the impact of early-life social determinants on late-life cognitive decline were identified through the searches of CINAHL, Global Health, PsycINFO, PubMed and Scopus databases. Our initial database search resulted in 823 studies, and of those, 102 studies satisfied the inclusion criteria. The systematic review identified the following risk factors: lower education (34%), lower socioeconomic status (25%), Adverse Childhood Experiences (ACEs) (14%), exposure to environmental toxins (11%), food insecurity (6.8%), and rural residence (4%). Although education and socioeconomic status are well-known risk factors of cognitive decline in later life, other understudied factors such as food insecurity and residing in rural areas are yet to be explored. Implications in terms of understanding the link between early life SDH and dementia in later life are discussed.

Age-standardized prevalence of social determinants of health and associated race-ethnic disparities in stroke survivors

2021 ◽  
Vol 28 (Supplement_1) ◽  
Author(s):  
T Parekh
Keyword(s):  
Risk Factors ◽  
Food Insecurity ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Stroke Risk ◽  
Healthcare Access ◽  
Determinants Of Health ◽  
Stroke Survivors ◽  
Stroke Risk Factors ◽  
Marginal Effects

Abstract Funding Acknowledgements Type of funding sources: None. Background Stroke is the third leading cause of death in the United States, with evident differences in health outcomes by race and socioeconomic factors. We aim to focus on social determinants of health by race/ethnicity and education level that greatly influences the health-related quality of life in stroke survivors. Method Using the 2017 Behavior Risk Factor Surveillance System (BRFSS) survey data, the direct age-adjusted prevalence was standardized to the 2000 projected US population. Multivariable weighted logistic regression models were post-estimated to calculate marginal effects of age, gender, education, and race on social determinants of health (housing insecurity, food insecurity, healthcare access hardship) at mean values of other predictors for stroke survivors. Models were adjusted for demographics, socioeconomic position, and stroke risk factors. Marginal effects (ME) reported as predicted probabilities. Result Among stroke survivors, nearly 27% reported housing insecurity and healthcare access hardship, and 48% reported food insecurity. The prevalence of housing insecurity was significantly higher among female (31.69%) than male (21.98%) survivors, and of race, highest among Non-Hispanic-Black (37.49%), lower among Non-Hispanic-Whites (23.83%), and lowest among Hispanics (17.20%) stroke survivors. In contrast, food insecurity was highest among Hispanics (63.71%). Healthcare access hardship was similar across the group with a comparatively lower prevalence in Non-Hispanic-White stroke survivors (25.32%). The predicted probability of housing insecurity was significantly higher among young adults compared to older adults aged 65 or above [ME 26.8 (95CI: 14.5-39.1 vs. ME 1.4 (95CI: 0.9-2.0)]. Of race, Black, NH stroke survivors showed a higher probability of housing insecurity [ME 12.4 (95CI: 6.3-18.3)], while the probability of food insecurity [ME 39.3 (95CI: 11.1-67.6)] and healthcare access was higher among other Non-Hispanic groups. The probability of any insecurities was similar among male and female stroke survivors. Stroke survivors with less than high school education showed a significantly higher probability of housing and food insecurity, in addition to healthcare access. Conclusion Social inequalities along with racial disparities in stroke survivors necessitate tailored intervention to reduce the burden of stroke. It is crucial to address socioeconomic factors such as housing, food, and healthcare access that promote the development of stroke risk factors. Abstract Figure.

scholarly journals Influence of social determinants of health and county vaccination rates on machine learning models to predict COVID-19 case growth in Tennessee

2021 ◽  
Vol 28 (1) ◽  
pp. e100439
Author(s):  
Lukasz S Wylezinski ◽  
Coleman R Harris ◽  
Cody N Heiser ◽  
Jamieson D Gray ◽  
Charles F Spurlock
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Determinants Of Health ◽  
Learning Models ◽  
Vaccination Rates ◽  
Data Framework ◽  
The Impact ◽  
Machine Learning Models

IntroductionThe SARS-CoV-2 (COVID-19) pandemic has exposed health disparities throughout the USA, particularly among racial and ethnic minorities. As a result, there is a need for data-driven approaches to pinpoint the unique constellation of clinical and social determinants of health (SDOH) risk factors that give rise to poor patient outcomes following infection in US communities.MethodsWe combined county-level COVID-19 testing data, COVID-19 vaccination rates and SDOH information in Tennessee. Between February and May 2021, we trained machine learning models on a semimonthly basis using these datasets to predict COVID-19 incidence in Tennessee counties. We then analyzed SDOH data features at each time point to rank the impact of each feature on model performance.ResultsOur results indicate that COVID-19 vaccination rates play a crucial role in determining future COVID-19 disease risk. Beginning in mid-March 2021, higher vaccination rates significantly correlated with lower COVID-19 case growth predictions. Further, as the relative importance of COVID-19 vaccination data features grew, demographic SDOH features such as age, race and ethnicity decreased while the impact of socioeconomic and environmental factors, including access to healthcare and transportation, increased.ConclusionIncorporating a data framework to track the evolving patterns of community-level SDOH risk factors could provide policy-makers with additional data resources to improve health equity and resilience to future public health emergencies.

scholarly journals Structural and Social Determinants of Health Factors Associated with County-Level Variation in Non-Adherence to Antihypertensive Medication Treatment

2020 ◽  
Vol 17 (18) ◽  
pp. 6684
Author(s):  
Macarius M. Donneyong ◽  
Teng-Jen Chang ◽  
John W. Jackson ◽  
Michael A. Langston ◽  
Paul D. Juarez ◽  
...  
Keyword(s):  
Food Insecurity ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Antihypertensive Medication ◽  
Social Supports ◽  
Determinants Of Health ◽  
P Value ◽  
County Level ◽  
Medication Treatment ◽  
Factors Associated

Background: Non-adherence to antihypertensive medication treatment (AHM) is a complex health behavior with determinants that extend beyond the individual patient. The structural and social determinants of health (SDH) that predispose populations to ill health and unhealthy behaviors could be potential barriers to long-term adherence to AHM. However, the role of SDH in AHM non-adherence has been understudied. Therefore, we aimed to define and identify the SDH factors associated with non-adherence to AHM and to quantify the variation in county-level non-adherence to AHM explained by these factors. Methods: Two cross-sectional datasets, the Centers for Disease Control and Prevention (CDC) Atlas of Heart Disease and Stroke (2014–2016 cycle) and the 2016 County Health Rankings (CHR), were linked to create an analytic dataset. Contextual SDH variables were extracted from the CDC-CHR linked dataset. County-level prevalence of AHM non-adherence, based on Medicare fee-for-service beneficiaries’ claims data, was extracted from the CDC Atlas dataset. The CDC measured AHM non-adherence as the proportion of days covered (PDC) with AHM during a 365 day period for Medicare Part D beneficiaries and aggregated these measures at the county level. We applied confirmatory factor analysis (CFA) to identify the constructs of social determinants of AHM non-adherence. AHM non-adherence variation and its social determinants were measured with structural equation models. Results: Among 3000 counties in the U.S., the weighted mean prevalence of AHM non-adherence (PDC < 80%) in 2015 was 25.0%, with a standard deviation (SD) of 18.8%. AHM non-adherence was directly associated with poverty/food insecurity (β = 0.31, P-value < 0.001) and weak social supports (β = 0.27, P-value < 0.001), but inversely with healthy built environment (β = −0.10, P-value = 0.02). These three constructs explained one-third (R2 = 30.0%) of the variation in county-level AHM non-adherence. Conclusion: AHM non-adherence varies by geographical location, one-third of which is explained by contextual SDH factors including poverty/food insecurity, weak social supports and healthy built environments.

scholarly journals SOCIAL DETERMINANTS OF HEALTH DRIVE EMERGENCY ROOM AND HOSPITAL USE BY DUAL ELIGIBLE AND DISABLED PENNSYLVANIANS

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S964-S964
Author(s):  
Sih-Ting Cai ◽  
Howard Degenholtz ◽  
Hayley Germack
Keyword(s):  
Risk Factors ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Physical Dependence ◽  
Health Hazards ◽  
Determinants Of Health ◽  
Economic Insecurity ◽  
Potential Health ◽  
Icd 10 ◽  
Dual Eligible

Abstract The study examined correlates and consequences of social determinants of health risk factors (SDoH) among dual eligible aged and disabled individuals; Pennsylvania is transitioning this population into a managed care plan with responsibility for care coordination and incentives to prevent hospitalization and nursing home placement. Medicaid and Medicare claims were used to identify people with SDoH based on ICD-10 codes in 2016 in four domains: economic insecurity, life stressors, physical dependence, and potential health hazards. Of 281,918 people, 38.6% had one or more SDoH. Among people with severe mental illnesses (SMI; schizophrenia, psychosis, major depressive disorder, or bipolar disorder), the prevalence of SDoH was 57.9%. Of people with one or more SDoH, 42% visited the ED, compared to only 32% of people with no SDoH. Economic insecurity (OR 1.68; CI 1.59-1.78), life stressors (OR 1.39; CI 1.29-1.48), physical dependence, (OR 2.01; CI 1.97-2.06), and potential health hazards (OR 1.52; CI 1.47-1.56) were independently associated with risk of hospitalization, controlling for age, gender, race, SMI, chronic conditions and disability. The introduction of diagnosis codes for SDoH under ICD-10 has facilitated identifying individuals with deficits that might increase health care use above and beyond their underlying health status. Although the prevalence of these risk factors as captured in diagnosis data is likely an underestimate, the strong association with subsequent ED use and hospitalization lends credence to these indicators. Medicare and Medicaid claims data can be used to identify people with SDoH and target interventions to prevent downstream health services use.

Early-Life Risk Factors for Dementia and Cognitive Impairment in Later Life: A Systematic Review and Meta-Analysis

2019 ◽  
Vol 67 (1) ◽  
pp. 221-229 ◽  
Author(s):  
Xue-Jie Wang ◽  
Wei Xu ◽  
Jie-Qiong Li ◽  
Xi-Peng Cao ◽  
Lan Tan ◽  
...  
Keyword(s):  
Risk Factors ◽  
Systematic Review ◽  
Cognitive Impairment ◽  
Early Life ◽  
Meta Analysis ◽  
Later Life
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