Social Determinants of Stroke Hospitalization and Mortality in United States' Counties.
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ABSTRACT:
SUBMITTER: Yadav RS
PROVIDER: S-EPMC9320068 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundInfluenza hospitalizations result in substantial morbidity and mortality each year. Little is known about the association between influenza hospitalization and census tract-based socioeconomic determinants beyond the effect of individual factors.ObjectiveTo evaluate whether census tract-based determinants such as poverty and household crowding would contribute significantly to the risk of influenza hospitalization above and beyond individual-level determinants.MethodsWe analyzed 33 515 laboratory-confirmed influenza-associated hospitalizations that occurred during the 2009-2010 through 2013-2014 influenza seasons using a population-based surveillance system at 14 sites across the United States.ResultsUsing a multilevel regression model, we found that individual factors were associated with influenza hospitalization with the highest adjusted odds ratio (AOR) of 9.20 (95% CI 8.72-9.70) for those ?65 vs 5-17 years old. African Americans had an AOR of 1.67 (95% CI 1.60-1.73) compared to Whites, and Hispanics had an AOR of 1.21 (95% CI 1.16-1.26) compared to non-Hispanics. Among census tract-based determinants, those living in a tract with ?20% vs <5% of persons living below poverty had an AOR of 1.31 (95% CI 1.16-1.47), those living in a tract with ?5% vs <5% of persons living in crowded conditions had an AOR of 1.17 (95% CI 1.11-1.23), and those living in a tract with ?40% vs <5% female heads of household had an AOR of 1.32 (95% CI 1.25-1.40).ConclusionCensus tract-based determinants account for 11% of the variability in influenza hospitalization.
| S-EPMC5720587 | biostudies-literature
Project description:BackgroundThe positive aspects of social interaction on health have been described often, with considerably less attention to their negative aspect. This study aimed to assess the impact of social associations on cardiovascular mortality in the United States.MethodsThe Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research (CDC WONDER) data sets from 2016 to 2020 were used to identify death records due to cardiovascular disease in the United States population aged 15 years and older. The social association rate defined as membership associations per 10,000 population, accessed from the 2020 County Health Rankings data was used as a surrogate for social participation. All United States counties were grouped into quartiles based on their social association rate; Q1 being the lowest quartile of social association, and Q4 the highest quartile. Age-adjusted mortality rate (AAMR) was calculated for each quartile. County health factor rankings for the state of Texas were used to adjust the AAMR for baseline comorbidities of county population, using Gaussian distribution linear regression.ResultsOverall, the AAMR was highest in the 4th social association rate quartile (306.73 [95% CI, 305.72-307.74]) and lowest in the 1st social association rate quartile (266.80 [95% CI, 266.41-267.20]). The mortality rates increased in a linear pattern from lowest to highest social association rate quartiles. After adjustment for the county health factor ranks of Texas, higher social association rate remained associated with a significantly higher AAMR (coefficient 15.84 [95% CI, 12.78-18.89]).ConclusionsOur study reported higher cardiovascular AAMR with higher social associations in the United States, with similar results after adjustment for County Health Rankings in the state of Texas.
| S-EPMC10898153 | biostudies-literature
Project description:BackgroundThe gap between the highest and lowest life expectancies for race-county combinations in the United States is over 35 y. We divided the race-county combinations of the US population into eight distinct groups, referred to as the "eight Americas," to explore the causes of the disparities that can inform specific public health intervention policies and programs.Methods and findingsThe eight Americas were defined based on race, location of the county of residence, population density, race-specific county-level per capita income, and cumulative homicide rate. Data sources for population and mortality figures were the Bureau of the Census and the National Center for Health Statistics. We estimated life expectancy, the risk of mortality from specific diseases, health insurance, and health-care utilization for the eight Americas. The life expectancy gap between the 3.4 million high-risk urban black males and the 5.6 million Asian females was 20.7 y in 2001. Within the sexes, the life expectancy gap between the best-off and the worst-off groups was 15.4 y for males (Asians versus high-risk urban blacks) and 12.8 y for females (Asians versus low-income southern rural blacks). Mortality disparities among the eight Americas were largest for young (15-44 y) and middle-aged (45-59 y) adults, especially for men. The disparities were caused primarily by a number of chronic diseases and injuries with well-established risk factors. Between 1982 and 2001, the ordering of life expectancy among the eight Americas and the absolute difference between the advantaged and disadvantaged groups remained largely unchanged. Self-reported health plan coverage was lowest for western Native Americans and low-income southern rural blacks. Crude self-reported health-care utilization, however, was slightly higher for the more disadvantaged populations.ConclusionsDisparities in mortality across the eight Americas, each consisting of millions or tens of millions of Americans, are enormous by all international standards. The observed disparities in life expectancy cannot be explained by race, income, or basic health-care access and utilization alone. Because policies aimed at reducing fundamental socioeconomic inequalities are currently practically absent in the US, health disparities will have to be at least partly addressed through public health strategies that reduce risk factors for chronic diseases and injuries.
| S-EPMC1564165 | biostudies-literature
Project description:ObjectiveTo synthesize the literature on associations between social determinants of health and pregnancy-related mortality and morbidity in the United States and to highlight opportunities for intervention and future research.Data sourcesWe performed a systematic search using Ovid MEDLINE, CINAHL, Popline, Scopus, and ClinicalTrials.gov (1990-2018) using MeSH terms related to maternal mortality, morbidity, and social determinants of health, and limited to the United States.Methods of study selectionSelection criteria included studies examining associations between social determinants and adverse maternal outcomes including pregnancy-related death, severe maternal morbidity, and emergency hospitalizations or readmissions. Using Covidence, three authors screened abstracts and two screened full articles for inclusion.Tabulation, integration, and resultsTwo authors extracted data from each article and the data were analyzed using a descriptive approach. A total of 83 studies met inclusion criteria and were analyzed. Seventy-eight of 83 studies examined socioeconomic position or individual factors as predictors, demonstrating evidence of associations between minority race and ethnicity (58/67 studies with positive findings), public or no insurance coverage (21/30), and lower education levels (8/12), and increased incidence of maternal death and severe maternal morbidity. Only 2 of 83 studies investigated associations between these outcomes and socioeconomic, political, and cultural context (eg, public policy), and 20 of 83 studies investigated material and physical circumstances (eg, neighborhood environment, segregation), limiting the diversity of social determinants of health studied as well as evaluation of such evidence.ConclusionEmpirical studies provide evidence for the role of race and ethnicity, insurance, and education in pregnancy-related mortality and severe maternal morbidity risk, although many other important social determinants, including mechanisms of effect, remain to be studied in greater depth.Systematic review registrationPROSPERO, CRD42018102415.
| S-EPMC7104722 | biostudies-literature
Project description:ImportanceBreast cancer mortality is complex and traditional approaches that seek to identify determinants of mortality assume that their effects on mortality are stationary across geographic space and scales.ObjectiveTo identify geographic variation in the associations of population demographics, environmental, lifestyle, and health care access with breast cancer mortality at the US county-level.Design, setting, and participantsThis geospatial cross-sectional study used data from the Surveillance, Epidemiology, and End Results (SEER) database on adult female patients with breast cancer. Statistical and spatial analysis was completed using adjusted mortality rates from 2015 to 2019 for 2176 counties in the US. Data were analyzed July 2022.ExposuresCounty-level population demographics, environmental, lifestyle, and health care access variables were obtained from open data sources.Main outcomes and measuresModel coefficients describing the association between 18 variables and age-adjusted breast cancer mortality rate. Compared with a multivariable linear regression (OLS), multiscale geographically weighted regression (MGWR) relaxed the assumption of spatial stationarity and allowed for the magnitude, direction, and significance of coefficients to change across geographic space.ResultsBoth OLS and MGWR models agreed that county-level age-adjusted breast cancer mortality rates were significantly positively associated with obesity (OLS: β, 1.21; 95% CI, 0.88 to 1.54; mean [SD] MGWR: β, 0.72 [0.02]) and negatively associated with proportion of adults screened via mammograms (OLS: β, -1.27; 95% CI, -1.70 to -0.84; mean [SD] MGWR: β, -1.07 [0.16]). Furthermore, the MGWR model revealed that these 2 determinants were associated with a stationary effect on mortality across the US. However, the MGWR model provided important insights on other county-level factors differentially associated with breast cancer mortality across the US. Both models agreed that smoking (OLS: β, -0.65; 95% CI, -0.98 to -0.32; mean [SD] MGWR: β, -0.75 [0.92]), food environment index (OLS: β, -1.35; 95% CI, -1.72 to -0.98; mean [SD] MGWR: β, -1.69 [0.70]), exercise opportunities (OLS: β, -0.56; 95% CI, -0.91 to -0.21; mean [SD] MGWR: β, -0.59 [0.81]), racial segregation (OLS: β, -0.60; 95% CI, -0.89 to -0.31; mean [SD] MGWR: β, -0.47 [0.41]), mental health care physician ratio (OLS: β, -0.93; 95% CI, -1.44 to -0.42; mean [SD] MGWR: β, -0.48 [0.92]), and primary care physician ratio (OLS: β, -1.46; 95% CI, -2.13 to -0.79; mean [SD] MGWR: β, -1.06 [0.57]) were negatively associated with breast cancer mortality, and that light pollution was positively associated (OLS: β, 0.48; 95% CI, 0.24 to 0.72; mean [SD] MGWR: β, 0.27 [0.04]). But in the MGWR model, the magnitude of effect sizes and significance varied across geographical regions. Inversely, the OLS model found that disability was not a significant variable for breast cancer mortality, yet the MGWR model found that it was significantly positively associated in some geographical locations.Conclusions and relevanceThis cross-sectional study found that not all social determinants associated with breast cancer mortality are spatially stationary and provides spatially explicit insights for public health practitioners to guide geographically targeted interventions.
| S-EPMC10502521 | biostudies-literature
Project description:Background:Educational attainment and income are two socioeconomic status indicators with strong protective effects against cigarette smoking. Marginalization-related Diminished Returns, however, refer to less than expected protective effects of socioeconomic status indicators for the members of the racial and ethnic minority groups, particularly Blacks and Hispanics, compared to non-Hispanic Whites. Aim:Borrowing data from a nationally representative study in the US, this study tested whether racial and ethnic differences exist in the effects of educational attainment and poverty status on cigarette smoking of American adults. Methods:This cross-sectional study entered 28,329 adult participants of the Population Assessment of Tobacco and Health (PATH; 2013). Both educational attainment and poverty status were the independent variables. The dependent variable was current hookah smoking. Age, gender, and region were the covariates. Race and ethnicity were the effect modifiers (moderators). Results:Overall, individuals with higher educational attainment were more likely to smoke a hookah. Individuals who lived out of poverty, however, had lower odds of current hookah smoking. Race and ethnicity both showed statistical interactions with both socioeconomic indicators suggesting that Blacks and Hispanics with high educational attainment and those who live out of poverty have disproportionately high odds of hookah smoking, compared to non-Hispanic Whites with high socioeconomic status. Conclusions:In the United States, middle-class racial and ethnic minority people remain at higher risk of smoking hookah. As a result, we should expect a high tobacco burden in middle-class Black and Hispanic adults. We suggest that policymakers should not take an over-simplistic way and reduce the problem of race/ethnic inequalities in tobacco use to gaps in socioeconomic status between groups. Marginalization-related diminished returns generate tobacco disparities in higher socioeconomic status levels. Middle-class racial and ethnic minority people need extra support to stay healthy.
| S-EPMC7153780 | biostudies-literature
Project description:IntroductionPrevious studies have shown that delayed detection of several cancers is related to socioeconomic deprivation as measured by the Wellbeing Index (WI) in Texas, the United States of America (USA). The current study investigates whether delayed cancer detection is related to lack of health insurance, physician shortage and higher percentages of Hispanics rather than WI per se since these factors are directly related to delayed cancer detection and may confound WI.MethodsCancer data and potential determinants of delayed cancer detection are derived from Texas Cancer Registry, Texas State Data Center, and Texas Department of State Health Services and U.S. Census Bureau. Texas cancer data from 1997 to 2003 are aggregated to calculate age-adjusted late- and early-stage cancer detection rates. The WI for each county is computed using data from the USA Census 2000. A weighted Tobit regression model is used to account for population size and censoring. The percentage of late-stage cancer cases is the dependent variable while independent variables include WI and the aforementioned potential confounders.ResultsDelayed detection of breast, lung, colorectal and female genital cancers is associated with higher percentage of uninsured residents (p < 0.05). Delayed detection is also associated with physician shortage and lower percentages of Hispanics for certain cancers ceteris paribus ( p < 0.05). The percentage of late-stage cases is positively correlated with WI for lung, and prostate cancers after adjusting for confounders ( p < 0.05).ConclusionsThe percentages of uninsured and Hispanic residents as well as physician supply are determinants of delayed detection for several cancers independently of WI, and vice versa. Identification of these determinants provides the evidence-base critical for decision makers to address specific issues for promoting early detection in effective cancer control.
| S-EPMC3517382 | biostudies-literature
Project description:BackgroundDeath from cardiovascular disease (CVD) has been a longstanding public health challenge in the US, whereas death from opioid use is a recent, growing public health crisis. While population-level approaches to reducing CVD risk are known to be effective in preventing CVD deaths, more targeted approaches in high-risk communities are known to work better for reducing risk of opioid overdose. For communities to plan effectively in addressing both public health challenges, they need information on significant community-level (vs individual-level) predictors of death from CVD or opioid use. This study addresses this need by examining the relationship between 1) county-level social determinants of health (SDoH) and CVD deaths and 2) county-level SDoH and opioid-use deaths in the US, over a ten-year period (2009-2018).MethodsA single national county-level ten-year 'SDoH Database' is analyzed, to address study objectives. Fixed-effects panel-data regression analysis, including county, year, and state-by-year fixed effects, is used to examine the relationship between 1) SDoH and CVD death-rate and 2) SDoH and opioid-use death-rate. Eighteen independent (SDoH) variables are included, spanning three contexts: socio-economic (e.g., race/ethnicity, income); healthcare (e.g., system-characteristics); and physical-infrastructure (e.g., housing).ResultsAfter adjusting for county, year, and state-by-year fixed effects, the significant county-level positive SDoH predictors for CVD death rate were, median age and percentage of civilian population in armed forces. The only significant negative predictor was percentage of population reporting White race. On the other hand, the four significant negative predictors of opioid use death rate were median age, median household income, percent of population reporting Hispanic ethnicity and percentage of civilian population consisting of veterans. Notably, a dollar increase in median household income, was estimated to decrease sample mean opioid death rate by 0.0015% based on coefficient value, and by 20.05% based on effect size.ConclusionsThe study provides several practice and policy implications for addressing SDoH barriers at the county level, including population-based approaches to reduce CVD mortality risk among people in military service, and policy-based interventions to increase household income (e.g., by raising county minimum wage), to reduce mortality risk from opioid overdoses.
| S-EPMC8817535 | biostudies-literature
Project description:BackgroundMost health effects studies of ozone and temperature have been performed in urban areas, due to the available monitoring data. We used observed and interpolated data to examine temperature, ozone, and mortality in 91 urban and non-urban counties.MethodsOzone measurements were extracted from the Environmental Protection Agency's Air Quality System. Meteorological data were supplied by the National Center for Atmospheric Research. Observed data were spatially interpolated to county centroids. Daily internal-cause mortality counts were obtained from the National Center for Health Statistics (1988-1999). A two-stage Bayesian hierarchical model was used to estimate each county's increase in mortality risk from temperature and ozone. We examined county-level associations according to population density and compared urban (?1,000 persons/mile(2)) to non-urban (<1,000 persons/mile(2)) counties. Finally, we examined county-level characteristics that could explain variation in associations by county.ResultsA 10 ppb increase in ozone was associated with a 0.45% increase in mortality (95% PI: 0.08, 0.83) in urban counties, while this same increase in ozone was associated with a 0.73% increase (95% PI: 0.19, 1.26) in non-urban counties. An increase in temperature from 70°F to 90°F (21.2°C 32.2°C) was associated with a 8.88% increase in mortality (95% PI: 7.38, 10.41) in urban counties and a 8.08% increase (95% PI: 6.16, 10.05) in non-urban counties. County characteristics, such as population density, percentage of families living in poverty, and percentage of elderly residents, partially explained the variation in county-level associations.ConclusionsWhile most prior studies of ozone and temperature have been performed in urban areas, the impacts in non-urban areas are significant, and, for ozone, potentially greater. The health risks of increasing temperature and air pollution brought on by climate change are not limited to urban areas.
| S-EPMC4417233 | biostudies-literature
Project description:For patients with chronic lower respiratory disease, hypobaric hypoxia at a high altitude is considered a risk factor for mortality. However, the effects of residing at moderately high altitudes remain unclear. We investigated the association between moderate altitude and chronic lower respiratory disease mortality. In particular, we examined the lower 48 United States counties for age-adjusted chronic lower respiratory disease mortality rates, altitude, and socioeconomic factors, including tobacco use, per capita income, population density, sex ratio, unemployment, poverty, and education between 1979 and 1998. The socioeconomic factors were incorporated into the correlation analysis as potential covariates. Considerable positive (R = 0.235; P <0.001) and partial (R = 0.260; P <0.001) correlations were observed between altitude and chronic lower respiratory disease mortality rate. In the subgroup with high COPD prevalence subgroup, even stronger positive (R = 0.346; P <0.001) and partial (R = 0.423, P <0.001) correlations were observed. Multivariate regression analysis of all available socioeconomic factors revealed that additional knowledge on altitude improved the adjusted R2 values from 0.128 to 0.186 for all counties and from 0.301 to 0.421 for counties with high COPD prevalence. We concluded that in the lower 48 United States counties, even a moderate altitude may pose considerable risks in patients with chronic lower respiratory disease.
| S-EPMC6040762 | biostudies-literature