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 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.

scholarly journals Pre-existing conditions in Latin America and factors associated with adverse outcomes of COVID-19: A review

Medwave ◽  
2021 ◽  
Vol 21 (04) ◽  
pp. e8180-8180
Author(s):  
Teresa Balboa-Castillo ◽  
Omar Andrade-Mayorga ◽  
Gabriel Nasri Marzuca-Nassr ◽  
Gladys Morales Illanes ◽  
Manuel Ortiz ◽  
...  
Keyword(s):  
Health Status ◽  
Social Determinants Of Health ◽  
Latin American ◽  
Social Determinants ◽  
Adverse Outcomes ◽  
Determinants Of Health ◽  
Factors Associated ◽  
The Caribbean ◽  
The Impact ◽  
Latin American And Caribbean

The coronavirus disease 2019 (COVID-19) pandemic, produced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread rapidly throughout the world. Latin American and the Caribbean countries have been harshly affected by the pandemic mainly due to less prepared healthcare systems and fragmented social safety nets. In the region, health status population-based indicators are worse than compared to the Organization for Economic Cooperation and Development. Recent evidence suggests that the progression and severity of COVID-19 are associated with the prior health status of individuals, and studies have shown that the case fatality rate is highly stratified among different populations. This narrative review aims to describe factors associated with adverse outcomes of COVID-19 in the context of social determinants of health in Latin American and Caribbean countries. In this review, we state that genetic and biological factors interact in a sophisticated way with social determinants of health, impacting the rapid spread of COVID-19 in Latin American and Caribbean countries. Behavioral factors, such as physical inactivity, smoking, and unhealthy diets, are related to chronic systemic inflammation. Also, air pollution can prolong inflammation and the hyper-activation of the immune system. Air pollutants could facilitate the spread of the virus. Finally, frailty and comorbidities can be associated with COVID-19 severity through increasing vulnerability to stressors and leading to more severe symptoms of COVID-19 disease, including a higher mortality risk. All these factors contribute to increasing the impact of COVID-19 in Latin American and Caribbean countries. We highlight the relevance of considering social determinants of health in Latin American and the Caribbean countries, not only in controlling the likelihood of getting the disease but also its progression and severity. All these social determinants can guide the design and implementation of tailored interventions promoting healthy lifestyle behaviors, which should lower the spread of the disease, its severity, and lethality.

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 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.

scholarly journals Identification of factors related to food insecurity and the implications for social determinants of health screenings

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ashley R. Banks ◽  
Bethany A. Bell ◽  
David Ngendahimana ◽  
Milen Embaye ◽  
Darcy A. Freedman ◽  
...  
Keyword(s):  
Food Insecurity ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Supplemental Nutrition Assistance Program ◽  
Determinants Of Health ◽  
Healthy Foods ◽  
Supplemental Nutrition ◽  
Assistance Program ◽  
And Behaviors ◽  
Nutrition Assistance

Abstract Background Food insecurity and other social determinants of health are increasingly being measured at routine health care visits. Understanding the needs and behaviors of individuals or families who screen positive for food insecurity may inform the types of resources they need. The goal of this research was to identify modifiable characteristics related to endorsement of two food insecurity screener questions to better understand the resources necessary to improve outcomes. Methods Analysis was conducted focusing on cross-sectional survey data collected in 2015–2016 from participants (N = 442) living in urban neighborhoods in Ohio with limited access to grocery stores. Food insecurity was assessed by the endorsement of at least one of two items. These were used to categorize participants into two groups: food insecure(N = 252) or food secure (N = 190). Using logistic regression, we estimated the association between several variables and the food insecure classification. Results Those that used their own car when shopping for food had lower odds of reporting food insecurity, as did those with affirmative attitudes related to the convenience of shopping for and ease of eating healthy foods. As shopping frequency increased, the odds of food insecurity increased. Food insecurity also increased with experience of a significant life event within the past 12 months. There was an 81% increase in the odds of reporting food insecurity among participants who received Supplemental Nutrition Assistance Program benefits compared to those not receiving Supplemental Nutrition Assistance Program benefits. Conclusions Along with referrals to SNAP, clinicians can further address screening-identified food insecurity through provision of transportation supports and linkages to other social services while collaborating on community initiatives to promote convenient and easy access to healthy foods. The needs and behaviors associated with screens indicating food insecurity also have implications for impacting other SDH, and thus, health outcomes.

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 Identifying Long-Term Effects of SARS-CoV-2 and Their Association with Social Determinants of Health in a Cohort of Over One Million COVID-19 Survivors

2021 ◽  
Author(s):  
Sumit Mukherjee ◽  
Meghana Kshirsagar ◽  
Nicholas Becker ◽  
Yixi Xu ◽  
William B Weeks ◽  
...  
Keyword(s):  
Social Determinants Of Health ◽  
Social Determinants ◽  
Determinants Of Health ◽  
P Value ◽  
Long Term Effects ◽  
Medical Claims ◽  
Icd 10 ◽  
And Gender ◽  
Medical Claims Database

Abstract Background: Despite an abundance of information on the risk factors of SARS-CoV-2, large scale studies of long-term effects are lacking. In this paper we analyzed a large medical claims database of US based individuals to identify common long-term effects as well as their associations with various social and medical risk factors.Methods: The medical claims database was obtained from a prominent US based claims data processing company, namely Change Healthcare. In addition to the claims data, the dataset also consisted of various social determinants of health such as race, income, education level and veteran status of the individuals. A self-controlled cohort design (SCCD) observational study was performed to identify ICD-10 codes whose proportion was significantly increased in the outcome period compared to the control period to identify significant long-term effects. A logistic regression-based association analysis was then performed between identified long-term effects and social determinants of health.Results: Among the over 1.37 million COVID patients in our datasets we found 36 out of 1,724 3-digit ICD-10 codes to be statistically significantly increased in the post-COVID period (p-value <0.05). We also found one combination of ICD-10 codes, corresponding to ‘other anemias’ and ‘hypertension’, that was statistically significantly increased in the post-COVID period (p-value <0.05). Our logistic regression-based association analysis with social determinants of health variables, after adjusting for comorbidities and prior conditions, showed that age and gender were significantly associated with the multiple long-term effects. Race was only associated with ‘other sepsis’, income was only associated with ‘Alopecia areata’, while education level was only associated with ‘Maternal infectious and parasitic diseases’ (p-value <0.05). Conclusion: We identified several long-term effects of SARS-CoV-2 through a self-controlled study on a cohort of over one million patients. Furthermore, we found that while age and gender are commonly associated with the long-term effects, other social determinants of health such as race, income and education levels have rare or no significant associations.

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.

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