County-Level Factors Associated With Cardiovascular Mortality by Race/Ethnicity.
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ABSTRACT:
SUBMITTER: Zuma BZ
PROVIDER: S-EPMC8174200 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ImportanceCardiovascular (CV) mortality has declined for more than 3 decades in the US. However, differences in declines among residents at a US county level are not well characterized.ObjectiveTo identify unique county-level trajectories of CV mortality in the US during a 35-year study period and explore county-level factors that are associated with CV mortality trajectories.Design, setting, and participantsThis longitudinal cross-sectional analysis of CV mortality trends used data from 3133 US counties from 1980 to 2014. County-level demographic, socioeconomic, environmental, and health-related risk factors were compiled. Data were analyzed from December 2019 to September 2021.ExposuresCounty-level characteristics, collected from 5 county-level data sets.Main outcomes and measuresCardiovascular mortality data were obtained for 3133 US counties from 1980 to 2014 using age-standardized county-level mortality rates from the Global Burden of Disease study. The longitudinal K-means approach was used to identify 3 distinct clusters based on underlying mortality trajectory. Multinomial logistic regression models were constructed to evaluate associations between county characteristics and cluster membership.ResultsAmong 3133 US counties (median, 49.5% [IQR, 48.9%-50.5%] men; 30.7% [IQR, 27.1%-34.4%] older than 55 years; 9.9% [IQR, 4.5%-22.7%] racial minority group [individuals self-identifying as Black or African American, American Indian or Alaska Native, Asian, Native Hawaiian, Pacific Islander, other, or multiple races/ethnicities]), CV mortality declined by 45.5% overall and by 38.4% in high-mortality strata (694 counties), by 45.0% in intermediate-mortality strata (1382 counties), and by 48.3% in low-mortality strata (1057 counties). Counties with the highest mortality in 1980 continued to demonstrate the highest mortality in 2014. Trajectory groups were regionally distributed, with high-mortality trajectory counties focused in the South and in portions of Appalachia. Low- vs high-mortality groups varied significantly in demographic (racial minority group proportion, 7.6% [IQR, 4.1%-14.5%]) vs 23.9% [IQR, 6.5%-40.8%]) and socioeconomic characteristics such as high-school education (9.4% [IQR, 7.3%-12.6%] vs 20.1% [IQR, 16.1%-23.2%]), poverty rates (11.4% [IQR, 8.8%-14.6%] vs 20.6% [IQR, 17.1%-24.4%]), and violent crime rates (161.5 [IQR, 89.0-262.4] vs 272.8 [IQR, 155.3-431.3] per 100 000 population). In multinomial logistic regression, a model incorporating demographic, socioeconomic, environmental, and health characteristics accounted for 60% of the variance in the CV mortality trajectory (R2 = 0.60). Sociodemographic factors such as racial minority group proportion (odds ratio [OR], 1.70 [95% CI, 1.35-2.14]) and educational attainment (OR, 6.17 [95% CI, 4.55-8.36]) and health behaviors such as smoking (OR for high vs low, 2.04 [95% CI, 1.58-2.64]) and physical inactivity (OR, 3.74 [95% CI, 2.83-4.93]) were associated with the high-mortality trajectory.Conclusions and relevanceCardiovascular mortality declined in all subgroups during the 35-year study period; however, disparities remained unchanged during that time. Disparate trajectories were associated with social and behavioral risks. Health policy efforts across multiple domains, including structural and public health targets, may be needed to reduce existing county-level cardiovascular mortality disparities.
| S-EPMC8634057 | biostudies-literature
Project description:BackgroundCommunities with more Black or Hispanic residents have higher coronavirus rates than communities with more White residents, but relevant community characteristics are underexplored. The purpose of this study was to investigate poverty-, race- and ethnic-based disparities and associated economic, housing, transit, population health and health care characteristics.MethodsSix-month cumulative coronavirus incidence and mortality were examined using adjusted negative binomial models among all U.S. counties (n = 3142). County-level independent variables included percentages in poverty and within racial/ethnic groups (Black, Hispanic, Native American, Asian), and rates of unemployment, lacking a high school diploma, housing cost burden, single parent households, limited English proficiency, diabetes, obesity, smoking, uninsured, preventable hospitalizations, primary care physicians, hospitals, ICU beds and households that were crowded, in multi-unit buildings or without a vehicle.ResultsCounties with higher percentages of Black (IRR = 1.03, 95% CI: 1.02-1.03) or Hispanic (IRR = 1.02, 95% CI: 1.01-1.03) residents had more coronavirus cases. Counties with higher percentages of Black (IRR = 1.02, 95% CI: 1.02-1.03) or Native American (IRR = 1.02, 95% CI: 1.01-1.04) residents had more deaths. Higher rates of lacking a high school diploma was associated with higher counts of cases (IRR = 1.03, 95% CI: 1.01-1.05) and deaths (IRR = 1.04, 95% CI: 1.01-1.07). Higher percentages of multi-unit households were associated with higher (IRR = 1.02, 95% CI: 1.01-1.04) and unemployment with lower (IRR = 0.96, 95% CI: 0.94-0.98) incidence. Higher percentages of individuals with limited English proficiency (IRR = 1.09, 95% CI: 1.04-1.14) and households without a vehicle (IRR = 1.04, 95% CI: 1.01-1.07) were associated with more deaths.ConclusionsThese results document differential pandemic impact in counties with more residents who are Black, Hispanic or Native American, highlighting the roles of residential racial segregation and other forms of discrimination. Factors including economic opportunities, occupational risk, public transit and housing conditions should be addressed in pandemic-related public health strategies to mitigate disparities across counties for the current pandemic and future population health events.
| S-EPMC8240081 | biostudies-literature
Project description:Background Hispanics are the fastest growing ethnic group in the United States, and little is known about how Hispanic ethnic population density impacts cardiovascular disease ( CVD ) mortality. Methods and Results We examined county-level deaths for Hispanics and non-Hispanic whites from 2003 to 2012 using data from the National Center for Health Statistics' Multiple Cause of Death mortality files. Counties with more than 20 Hispanic deaths (n=715) were included in the analyses. CVD deaths were identified using International Classification of Diseases, Tenth Revision (ICD-10), I00 to I78, and population estimates were calculated using linear interpolation from 2000 and 2010 census data. Multivariate linear regression was used to examine the association of Hispanic ethnic density with Hispanic and non-Hispanic white age-adjusted CVD mortality rates. County-level age-adjusted CVD mortality rates were adjusted for county-level demographic, socioeconomic, and healthcare factors. There were a total of 4 769 040 deaths among Hispanics (n=382 416) and non-Hispanic whites (n=4 386 624). Overall, cardiovascular age-adjusted mortality rates were higher among non-Hispanic whites compared with Hispanics (244.8 versus 189.0 per 100 000). Hispanic density ranged from 1% to 96% in each county. Counties in the highest compared with lowest category of Hispanic density had 60% higher Hispanic mortality (215.3 versus 134.2 per 100 000 population). In linear regression models, after adjusting for county-level demographic, socioeconomic, and healthcare factors, increasing Hispanic ethnic density remained strongly associated with mortality for Hispanics but not for non-Hispanic whites. Conclusions CVD mortality is higher in counties with higher Hispanic ethnic density. County-level characteristics do not fully explain the higher CVD mortality among Hispanics in ethnically concentrated counties.
| S-EPMC6404884 | biostudies-literature
Project description:BackgroundData on the characteristics of COVID-19 patients disaggregated by race/ethnicity remain limited. We evaluated the sociodemographic and clinical characteristics of patients across racial/ethnic groups and assessed their associations with COVID-19 outcomes.MethodsThis retrospective cohort study examined 629,953 patients tested for SARS-CoV-2 in a large health system spanning California, Oregon, and Washington between March 1 and December 31, 2020. Sociodemographic and clinical characteristics were obtained from electronic health records. Odds of SARS-CoV-2 infection, COVID-19 hospitalization, and in-hospital death were assessed with multivariate logistic regression.Results570,298 patients with known race/ethnicity were tested for SARS-CoV-2, of whom 27.8% were non-White minorities. 54,645 individuals tested positive, with minorities representing 50.1%. Hispanics represented 34.3% of infections but only 13.4% of tests. While generally younger than White patients, Hispanics had higher rates of diabetes but fewer other comorbidities. 8,536 patients were hospitalized and 1,246 died, of whom 56.1% and 54.4% were non-White, respectively. Racial/ethnic distributions of outcomes across the health system tracked with state-level statistics. Increased odds of testing positive and hospitalization were associated with all minority races/ethnicities. Hispanic patients also exhibited increased morbidity, and Hispanic race/ethnicity was associated with in-hospital mortality (OR: 1.39 [95% CI: 1.14-1.70]).ConclusionMajor healthcare disparities were evident, especially among Hispanics who tested positive at a higher rate, required excess hospitalization and mechanical ventilation, and had higher odds of in-hospital mortality despite younger age. Targeted, culturally-responsive interventions and equitable vaccine development and distribution are needed to address the increased risk of poorer COVID-19 outcomes among minority populations.
| S-EPMC7885938 | biostudies-literature
Project description:BackgroundData on the characteristics of coronavirus disease 2019 (COVID-19) patients disaggregated by race/ethnicity remains limited. We evaluated the sociodemographic and clinical characteristics of patients across racial/ethnic groups and assessed their associations with COVID-19 outcomes.MethodsThis retrospective cohort study examined 629 953 patients tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in a large health system spanning California, Oregon, and Washington between March 1 and December 31, 2020. Sociodemographic and clinical characteristics were obtained from electronic health records. Odds of SARS-CoV-2 infection, COVID-19 hospitalization, and in-hospital death were assessed with multivariate logistic regression.ResultsA total of 570 298 patients with known race/ethnicity were tested for SARS-CoV-2, of whom 27.8% were non-White minorities: 54 645 individuals tested positive, with minorities representing 50.1%. Hispanics represented 34.3% of infections but only 13.4% of tests. Although generally younger than White patients, Hispanics had higher rates of diabetes but fewer other comorbidities. A total of 8536 patients were hospitalized and 1246 died, of whom 56.1% and 54.4% were non-White, respectively. Racial/ethnic distributions of outcomes across the health system tracked with state-level statistics. Increased odds of testing positive and hospitalization were associated with all minority races/ethnicities. Hispanic patients also exhibited increased morbidity, and Hispanic race/ethnicity was associated with in-hospital mortality (odds ratio [OR], 1.39; 95% confidence interval [CI], 1.14-1.70).ConclusionMajor healthcare disparities were evident, especially among Hispanics who tested positive at a higher rate, required excess hospitalization and mechanical ventilation, and had higher odds of in-hospital mortality despite younger age. Targeted, culturally responsive interventions and equitable vaccine development and distribution are needed to address the increased risk of poorer COVID-19 outcomes among minority populations.
| S-EPMC7929051 | biostudies-literature
Project description:The Centers for Medicare and Medicaid Services recently released a five star rating system as part of 'Dialysis Facility Compare' to help patients identify and choose high performing clinics in the US. Eight dialysis-related measures determine ratings. Little is known about the association between surrounding community sociodemographic characteristics and star ratings. Using data from the U.S. Census and over 6000 dialysis clinics across the country, we examined the association between dialysis clinic star ratings and characteristics of the local population: 1) proportion of population below the federal poverty level (FPL); 2) proportion of black individuals; and 3) proportion of Hispanic individuals, by correlation and regression analyses. Secondary analyses with Quality Incentive Program (QIP) scores and population characteristics were also performed. We observed a negligible correlation between star ratings and the proportion of local individuals below FPL; Spearman coefficient, R = -0.09 (p<0.0001), and a stronger correlation between star ratings and the proportion of black individuals; R = -0.21 (p<0.0001). Ordered logistic regression analyses yielded adjusted odds ratio of 0.91 (95% confidence interval [0.80-1.30], p = 0.12) and 0.55 ([0.48-0.63], p<0.0001) for high vs. low level of proportion below FPL and proportion of black individuals, respectively. In contrast, a near-zero correlation was observed between star ratings and the proportion of Hispanic individuals. Correlations varied substantially by country region, clinic profit status and clinic size. Analyses using clinic QIP scores provided similar results. Sociodemographic characteristics of the surrounding community, factors typically outside of providers' direct control, have varying levels of association with clinic dialysis star ratings.
| S-EPMC5645143 | biostudies-literature
Project description:County-level ASD prevalence was estimated using an age-resolved snapshot from the California Department of Developmental Services (DDS) for birth years 1993-2013. ASD prevalence increased among all children across birth years 1993-2000 but plateaued or declined thereafter among whites from wealthy counties. In contrast, ASD rates increased continuously across 1993-2013 among whites from lower income counties and Hispanics from all counties. Both white ASD prevalence and rate of change in prevalence were inversely correlated to county income from birth year 2000-2013 but not 1993-2000. These disparate trends within the dataset suggest that wealthy white parents, starting around 2000, may have begun opting out of DDS in favor of private care and/or making changes that effectively lowered their children's risk of ASD.
| S-EPMC7557477 | biostudies-literature
Project description:Background Amid stagnating declines in national cardiovascular disease (CVD) mortality, documenting trends in county-level hypertension-related CVD death rates can help activate local efforts prioritizing hypertension prevention, detection, and control. Methods and Results Using death certificate data from the National Vital Statistics System, Bayesian spatiotemporal models were used to estimate county-level hypertension-related CVD death rates and corresponding trends during 2000 to 2010 and 2010 to 2019 for adults aged ≥35 years overall and by age group, race or ethnicity, and sex. Among adults aged 35 to 64 years, county-level hypertension-related CVD death rates increased from a median of 23.2 per 100 000 in 2000 to 43.4 per 100 000 in 2019. Among adults aged ≥65 years, county-level hypertension-related CVD death rates increased from a median of 362.1 per 100 000 in 2000 to 430.1 per 100 000 in 2019. Increases were larger and more prevalent among adults aged 35 to 64 years than those aged ≥65 years. More than 75% of counties experienced increasing hypertension-related CVD death rates among patients aged 35 to 64 years during 2000 to 2010 and 2010 to 2019 (76.2% [95% credible interval, 74.7-78.4] and 86.2% [95% credible interval, 84.6-87.6], respectively), compared with 48.2% (95% credible interval, 47.0-49.7) during 2000 to 2010 and 66.1% (95% credible interval, 64.9-67.1) for patients aged ≥65 years. The highest rates for both age groups were among men and Black populations. All racial and ethnic categories in both age groups experienced widespread county-level increases. Conclusions Large, widespread county-level increases in hypertension-related CVD mortality sound an alarm for intensified clinical and public health actions to improve hypertension prevention, detection, and control and prevent subsequent CVD deaths in counties across the nation.
| S-EPMC9075476 | biostudies-literature
Project description:The effect of socio-economic factors, ethnicity, and other factors, on the morbidity and mortality of COVID-19 at the sub-population-level, rather than at the individual level, and their temporal dynamics, is only partially understood. Fifty-three county-level features were collected between 4/2020 and 11/2020 from 3,071 US counties from publicly available data of various American government and news websites: ethnicity, socio-economic factors, educational attainment, mask usage, population density, age distribution, COVID-19 morbidity and mortality, presidential election results, and ICU beds. We trained machine learning models that predict COVID-19 mortality and morbidity using county-level features and then performed a SHAP value game theoretic importance analysis of the predictive features for each model. The classifiers produced an AUROC of 0.863 for morbidity prediction and an AUROC of 0.812 for mortality prediction. A SHAP value-based analysis indicated that poverty rate, obesity rate, mean commute time, and mask usage statistics significantly affected morbidity rates, while ethnicity, median income, poverty rate, and education levels heavily influenced mortality rates. Surprisingly, the correlation between several of these factors and COVID-19 morbidity and mortality gradually shifted and even reversed during the study period; our analysis suggests that this phenomenon was probably due to COVID-19 being initially associated with more urbanized areas and, then, from 9/2020, with less urbanized ones. Thus, socio-economic features such as ethnicity, education, and economic disparity are the major factors for predicting county-level COVID-19 mortality rates. Between counties, low variance factors (e.g., age) are not meaningful predictors. The inversion of some correlations over time can be explained by COVID-19 spreading from urban to rural areas.
| S-EPMC8979577 | biostudies-literature
Project description:The surgical work force distribution at the county level varies widely across the United States, and the impact of differential access on cancer outcomes is unclear. We used urologists as a test case because they are the first care providers for urologic cancers, can easily be identified from available data sources, and are unevenly distributed throughout the country. The goal of this study was to determine the effect of increasing urologist density on local prostate, bladder, and kidney cancer mortality.Using county-level data from the Area Resource File, US Census, National Cancer Institute, and Centers for Disease Control, regression models were built for prostate, bladder, and kidney cancer mortality, controlling for categorized urologist density, county demographics, socioeconomic factors, and preexisting health care infrastructure.For each of the three cancers, there was a statistically significant cancer-specific mortality reduction associated with counties that had more than zero urologists (16% to 22% reduction for prostate cancer, 17% to 20% reduction for bladder cancer, and 8% to 14% reduction for kidney cancer with increasing urologist density) relative to zero urologists. However, increasing density greater than two urologists per 100,000 people had no statistically significant impact on mortality for any of the tumors studied.The presence of a urologist is associated with lower mortality for urologic cancers in that county, but increasing urologist density does not yield further improvements. Therefore, a nuanced and geographically aware policy toward the size and distribution of the future work force is most likely to provide the greatest population-level improvement in cancer mortality outcomes.
| S-EPMC2881728 | biostudies-other