scholarly journals Differences in COVID-19 Risk by Race and County-Level Social Determinants of Health among Veterans

2021 ◽  
Vol 18 (24) ◽  
pp. 13140
Author(s):  
Hoda S. Abdel Magid ◽  
Jacqueline M. Ferguson ◽  
Raymond Van Cleve ◽  
Amanda L. Purnell ◽  
Thomas F. Osborne
Keyword(s):  
Social Determinants Of Health ◽  
Social Determinants ◽  
Linear Models ◽  
Determinants Of Health ◽  
Health Concern ◽  
County Level ◽  
Department Of Veterans Affairs ◽  
Optimal Care ◽  
Level Data ◽  
Significant Public Health

COVID-19 disparities by area-level social determinants of health (SDH) have been a significant public health concern and may also be impacting U.S. Veterans. This retrospective analysis was designed to inform optimal care and prevention strategies at the U.S. Department of Veterans Affairs (VA) and utilized COVID-19 data from the VAs EHR and geographically linked county-level data from 18 area-based socioeconomic measures. The risk of testing positive with Veterans’ county-level SDHs, adjusting for demographics, comorbidities, and facility characteristics, was calculated using generalized linear models. We found an exposure–response relationship whereby individual COVID-19 infection risk increased with each increasing quartile of adverse county-level SDH, such as the percentage of residents in a county without a college degree, eligible for Medicaid, and living in crowded housing.

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 Enhancing understandings of social determinants of health in China: Linkage and analysis of a national multilevel population health surveillance with routinely collected mortality records for 98 058 people in 31 provinces of mainland China

2019 ◽  
Author(s):  
Yunning Liu ◽  
Thomas Astell-Burt ◽  
Xiaoqi Feng ◽  
Fan Mao ◽  
Ruiming Liang ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Social Determinants Of Health ◽  
Population Health ◽  
Social Determinants ◽  
Communicable Disease ◽  
Mainland China ◽  
Health Surveillance ◽  
Determinants Of Health ◽  
County Level ◽  
Injury Mortality

Abstract Background: The aim of this study was to enhance capability in research on social determinants of health in China by linking and analyzing routinely-collected death records over 5 years with national population health surveillance.Methods: Linkage of 98 058 participants in the 2010 China Chronic Disease and Risk Factor Surveillance (CCDRFS) to records in the national death surveillance data from 2011 to 2015 was conducted through a matching program involving identification numbers, name, gender and residential address, followed by a structured checking process. Multilevel regressions were used to investigate five-year odds of all-cause, non-communicable disease (NCD), infectious disease and injury mortality in relation to person- and county-level factors.Results: A total of 3,365 deaths were observed in the linked mortality and population health surveillance. Cross-checks and comparisons with national mortality distributions provided assurance that the linkage was reasonable. Geographic variation in mortality was observed via age and gender adjusted median odds ratios for all-cause mortality (>1.30), infectious disease (>2.01), NCD (>1.24) and injury (>1.12). Increased odds of all-cause and all three cause-specific mortality outcomes were higher with age and among men. Low educational attainment was a predictor of all-cause, NCD and injury mortality. Longer mean years of education at the county-level was only associated with lower injury mortality. Divorcees had a higher odd of all-cause and NCD mortality than singletons. Rurality was a predictor of all-cause and NCD mortality.Conclusion: The results of this study provide utility for future investigations of social determinants of health and mortality using linked data in China.

Association of area social determinants of health with prostate cancer mortality.

2020 ◽  
Vol 38 (6_suppl) ◽  
pp. 49-49
Author(s):  
John Clark Henegan
Keyword(s):  
Prostate Cancer ◽  
Linear Regression ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Cancer Mortality ◽  
County Level ◽  
Clinical Factors ◽  
Independent Variables ◽  
Prostate Cancer Mortality ◽  
Level Data

49 Background: Validated clinical nomograms prognosticate survival for men with metastatic castrate resistant prostate cancer. In addition to clinical factors, significant disparities in survival in advanced prostate cancer are associated with social determinants of health (SDH). We performed a study of county-level data to evaluate SDH associated with prostate cancer mortality. Methods: County-level data regarding prostate cancer crude mortality rate (dependent variable) and crude incidence rate was extracted from the ten states with information at www.cancer-rates.info for the years 2006-2015. The 2014 Robert Wood Johnson County Health Rankings dataset was used for information regarding county demographics, clinical information, and available SDH(independent variables). A correlation analysis was performed between independent variables and only one variable was analyzed if a criterion of a correlation of ≥ 0.65 was present. All counties with ≤ 30 death in the specified time period or with incomplete independent variable information were excluded from analysis. A univariate linear regression analysis was performed. All individual independent variables with a statistically significant (p ≤ 0.05) association with the dependent variable were included in a multivariate linear regression model. Results: 275 counties were available for analysis. On multivariate analysis, independent variables statistically significantly associated with the crude rate of prostate cancer mortality included household income and the percentage of: a population ≥ 65 years of age, the population < 18 years of age, African Americans, the population classified as rural, women reporting a screening mammography, smokers, and uninsured patients. The multivariate model was associated with a R-squared of 0.62. Conclusions: In addition to differences in baseline clinical factors, prostate cancer quality metrics that evaluate survival should account SDH. Information from this study, if validated in subsequent work, could be used to develop a stepwise model to adjust for both clinical factors and SDH when measuring survival as a quality metric.

Abstract P178: Social Determinants Of Health And Preventable Hospitalization For Hypertension

Hypertension ◽  
2021 ◽  
Vol 78 (Suppl_1) ◽  
Author(s):  
Priyadarshini Pattath ◽  
Rexford Anson-Dwamena
Keyword(s):  
Air Quality ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Principal Component ◽  
Healthcare Research ◽  
Hospitalization Rate ◽  
Material Deprivation ◽  
Determinants Of Health ◽  
County Level ◽  
Preventable Hospitalization

Introduction: Social determinants of health (SDH) can have a significant impact on the risk of hypertension related hospitalization. Understanding the drivers of potentially preventable hospitalizations for hypertension improves health outcomes and reduce associated costs. Identifying place and associated SDH has implications for tailored interventions to address disparities for high burden population for preventable hypertension hospitalization as hypertension and associated cardiovascular disease. Methods: County level preventable hospitalization rates for hypertension for Virginia was obtained using prevention quality indicators published by the Agency for Healthcare Research and Quality. The Health Opportunity Index (HOI), developed by the Virginia Department of Health to identify vulnerable populations is a multivariate tool that uses complex SDH indicators of a community and comprises of 13 indices - affordability, income inequality, Townsend/Material deprivation, job participation, employment access, education, air quality, segregation, food accessibility, population density, population churning, walkability, and access to care. Principal component analysis was used to develop the composite HOI and further aggregated into simple quintiles at the county level. Step-wise multiple regression analysis was performed to explore SDH and preventable hospitalization for hypertension. Results: Material deprivation index ( r = -.44), affordability index ( r = -.42) and air quality index ( r = -.23) were found to be significantly associated with preventable hospitalization rate for hypertension. Together the model accounted for 23% of the variability in the preventable hypertension hospitalization rate ( p > 0.05). Conclusions: Findings indicate that neighborhoods that spend most on housing and transportation and that are materially deprived of goods, services, amenities and resources and physical environment have higher rates of preventable hospitalization for hypertension. Areas with higher air pollution may result in hypertension. Addressing these disparities by targeted approach is one possible approach to reducing the burden of preventable hospitalization for hypertension in Virginia.

scholarly journals Screening for poverty and intervening in a primary care setting: an acceptability and feasibility study

2019 ◽  
Vol 36 (5) ◽  
pp. 634-638
Author(s):  
Andrew D Pinto ◽  
Madeleine Bondy ◽  
Anne Rucchetto ◽  
John Ihnat ◽  
Adam Kaufman
Keyword(s):  
Primary Care ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Low Income ◽  
Determinants Of Health ◽  
Health Concern ◽  
Evidence Based ◽  
Health Providers ◽  
Financial Benefits ◽  
The Social

Abstract Background A movement is emerging to encourage health providers and health organizations to take action on the social determinants of health. However, few evidence-based interventions exist. Digital tools have not been examined in depth. Objective To assess the acceptability and feasibility of integrating, within routine primary care, screening for poverty and an online tool that helps identify financial benefits. Methods The setting was a Community Health Centre serving a large number of low-income individuals in Toronto, Canada. Physicians were encouraged to use the tool at every possible encounter during a 1-month period. A link to the tool was easily accessible, and reminder emails were circulated regularly. This mixed-methods study used a combination of pre-intervention and post-intervention surveys, focus groups and interviews. Results Thirteen physicians participated (81.25% of all) and represented a range of genders and years in practice. Physicians reported a strong awareness of the importance of identifying poverty as a health concern, but low confidence in their ability to address poverty. The tool was used with 63 patients over a 1-month period. Although screening and intervening on poverty is logistically challenging in regular workflows, online tools could assist patients and health providers identify financial benefits quickly. Future interventions should include more robust follow-up. Conclusions Our study contributes to the evidence based on addressing the social determinants of health in clinical settings. Future approaches could involve routine screening, engaging other members of the team in intervening and following up, and better integration with the electronic health record.

scholarly journals A County-Level Examination of the Relationship Between HIV and Social Determinants of Health: 40 States, 2006-2008

2012 ◽  
Vol 6 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Gant Z ◽  
Lomotey M ◽  
Hall H.I ◽  
Hu X ◽  
Guo X ◽  
...  
Keyword(s):  
Income Inequality ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
The United States ◽  
Determinants Of Health ◽  
Physical Factors ◽  
County Level ◽  
Hiv Diagnosis ◽  
Hiv Surveillance ◽  
The Relationship

Background: Social determinants of health (SDH) are the social and physical factors that can influence unhealthy or risky behavior. Social determinants of health can affect the chances of acquiring an infectious disease – such as HIV – through behavioral influences and limited preventative and healthcare access. We analyzed the relationship between social determinants of health and HIV diagnosis rates to better understand the disparity in rates between different populations in the United States. Methods: Using National HIV Surveillance data and American Community Survey data at the county level, we examined the relationships between social determinants of health variables (e.g., proportion of whites, income inequality) and HIV diagnosis rates (averaged for 2006-2008) among adults and adolescents from 40 states with mature name-based HIV surveillance. Results: Analysis of data from 1,560 counties showed a significant, positive correlation between HIV diagnosis rates and income inequality (Pearson correlation coefficient ρ = 0.40) and proportion unmarried – ages >15 (ρ = 0.52). There was a significant, negative correlation between proportion of whites and rates (ρ = -0.67). Correlations were low between racespecific social determinants of health indicators and rates. Conclusions/Implications: Overall, HIV diagnosis rates increased as income inequality and the proportion unmarried increased, and rates decreased as proportion of whites increased. The data reflect the higher HIV prevalence among non-whites. Although statistical correlations were moderate, identifying and understanding these social determinants of health variables can help target prevention efforts to aid in reducing HIV diagnosis rates. Future analyses need to determine whether the higher proportion of singles reflects higher populations of gay and bisexual men.

scholarly journals The Use of Penalized Regression Analysis to Identify County-Level Demographic and Socioeconomic Variables Predictive of Increased COVID-19 Cumulative Case Rates in the State of Georgia

2020 ◽  
Vol 17 (21) ◽  
pp. 8036
Author(s):  
Holly L. Richmond ◽  
Joana Tome ◽  
Haresh Rochani ◽  
Isaac Chun-Hai Fung ◽  
Gulzar H. Shah ◽  
...  
Keyword(s):  
Social Determinants Of Health ◽  
Social Determinants ◽  
Penalized Regression ◽  
Determinants Of Health ◽  
County Level ◽  
Lasso Regression ◽  
Cross Sectional ◽  
Individual Level ◽  
The Social ◽  
The Individual

Systemic inequity concerning the social determinants of health has been known to affect morbidity and mortality for decades. Significant attention has focused on the individual-level demographic and co-morbid factors associated with rates and mortality of COVID-19. However, less attention has been given to the county-level social determinants of health that are the main drivers of health inequities. To identify the degree to which social determinants of health predict COVID-19 cumulative case rates at the county-level in Georgia, we performed a sequential, cross-sectional ecologic analysis using a diverse set of socioeconomic and demographic variables. Lasso regression was used to identify variables from collinear groups. Twelve variables correlated to cumulative case rates (for cases reported by 1 August 2020) with an adjusted r squared of 0.4525. As time progressed in the pandemic, correlation of demographic and socioeconomic factors to cumulative case rates increased, as did number of variables selected. Findings indicate the social determinants of health and demographic factors continue to predict case rates of COVID-19 at the county-level as the pandemic evolves. This research contributes to the growing body of evidence that health disparities continue to widen, disproportionality affecting vulnerable populations.

scholarly journals Structural and social determinants of health factors accounted for county-level variation in non-adherence to antihypertensive medication treatment

2020 ◽  
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 ◽  
Structural Equation Models ◽  
Social Supports ◽  
Determinants Of Health ◽  
P Value ◽  
County Level ◽  
Medication Treatment

AbstractBackgroundNon-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 have 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.MethodsTwo 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 both 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.ResultsAmong 3,000 counties in the US, the weighted mean prevalence of AHM non-adherence (PDC<80%) in 2015 was 25.0%, Standard Deviation (SD), 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 a third (R2=30.0%) of the variation in county-level AHM non-adherence.ConclusionAHM non-adherence varies by geographical location, a third of which is explained by contextual SDH factors including poverty/food insecurity, weak social supports and healthy built environments.

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