Overcrowding and COVID-19 mortality across U.S. counties: Are disparities growing over time?
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ABSTRACT:
SUBMITTER: Kamis C
PROVIDER: S-EPMC8219888 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:OBJECTIVE:To understand the role of county characteristics in the growing divide between rural and urban mortality from 1980 to 2010. DATA SOURCE:Age-adjusted mortality rates for all U.S. counties from 1980 to 2010 were obtained from the CDC Compressed Mortality File and combined with county characteristics from the U.S. Census Bureau, the Area Health Resources File, and the Inter-University Consortium for Political and Social research. STUDY DESIGN:We used Oaxaca-Blinder decomposition to assess the extent to which rural-urban mortality disparities are explained by observed county characteristics at each decade. PRINCIPAL FINDINGS:Decomposition shows that, at each decade, differences in rural/urban characteristics are sufficient to explain differences in mortality. Furthermore, starting in 1990, rural counties have significantly lower predicted mortality than urban counties when given identical county characteristics. We find changes in the effect of characteristics on mortality, not the characteristics themselves, drive the growing mortality divide. CONCLUSIONS:Differences in economic and demographic characteristics between rural and urban counties largely explain the differences in age-adjusted mortality in any given year. Over time, the role these characteristics play in improving mortality has increased differentially for urban counties. As characteristics continue changing in importance as determinants of health, this divide may continue to widen.
| S-EPMC6232410 | biostudies-literature
Project description:To investigate temporal trends in coronavirus disease 2019 (COVID-19)-related outcomes and to evaluate whether the impacts of potential risk factors and disparities changed over time, we conducted a retrospective cohort study with 249 075 patients tested or treated for COVID-19 at Michigan Medicine (MM), from 10 March 2020 to 3 May 2021. Among these patients, 26 289 were diagnosed with COVID-19. According to the calendar time in which they first tested positive, the COVID-19-positive cohort were stratified into three-time segments (T1: March–June, 2020; T2: July–December, 2020; T3: January–May, 2021). Potential risk factors that we examined included demographics, residential-level socioeconomic characteristics and preexisting comorbidities. The main outcomes included COVID-19-related hospitalisation and intensive care unit (ICU) admission. The hospitalisation rate for COVID-positive patients decreased from 36.2% in T1 to 14.2% in T3, and the ICU admission rate decreased from 16.9% to 2.9% from T1 to T3. These findings confirm that COVID-19-related hospitalisation and ICU admission rates were decreasing throughout the pandemic from March 2020 to May 2021. Black patients had significantly higher (compared to White patients) hospitalisation rates (19.6% vs. 11.0%) and ICU admission rates (6.3% vs. 2.8%) in the full COVID-19-positive cohort. A time-stratified analysis showed that racial disparities in hospitalisation rates persisted over time and the estimates of the odds ratios (ORs) stayed above unity in both unadjusted [full cohort: OR = 1.98, 95% confidence interval (CI) (1.79, 2.19); T1: OR = 1.70, 95% CI (1.36, 2.12); T2: OR = 1.40, 95% CI (1.17, 1.68); T3: OR = 1.55, 95% CI (1.29, 1.86)] and adjusted analysis, accounting for differences in demographics, socioeconomic status, and preexisting comorbid conditions (full cohort: OR = 1.45, 95% CI (1.25, 1.68); T1: OR = 1.26, 95% CI (0.90, 1.76); T2: OR = 1.29, 95% CI (1.01, 1.64); T3: OR = 1.29, 95% CI (1.00, 1.67)).
| S-EPMC8376857 | biostudies-literature
Project description:As overdose mortality is spiking during the COVID-19 pandemic, few race/ethnicity-stratified trends are available. This is of particular concern as overdose mortality was increasing most rapidly in Black and Latinx communities prior to the pandemic. We used quarterly, age-standardized overdose mortality rates from California to assess trends by race/ethnicity and drug involved over time. Rates from 2020 Q2-Q4 were compared to expected trends based on ARIMA forecasting models fit using data from 2006 to 2020 Q1. In 2020 Q2-Q4 overdose death rates rose by 49.8% from 2019, exceeding an expected increase of 11.5% (95%CI: 0.5%-22.5%). Rates significantly exceeded forecasted trends for all racial/ethnic groups. Black/African American individuals saw an increase of 52.4% from 2019, compared to 42.6% among their White counterparts. The absolute Black-White overdose mortality gap rose from 0.7 higher per 100,000 for Black individuals in 2018 to 4.8 in 2019, and further increased to 9.9 during the pandemic. Black overdose mortality in California was therefore 34.3% higher than that of White individuals in 2020 Q2-Q4. This reflects growing methamphetamine-, cocaine-, and fentanyl-involved deaths among Black communities. Growing racial disparities in overdose must be understood in the context of the unequal social and economic fallout from the COVID-19 pandemic, during which time Black communities have been subjected to the dual burden of disproportionate COVID-19 deaths and rising overdose mortality. Increased investments are required to ameliorate racial/ethnic disparities in substance use treatment, harm reduction, and the structural drivers of overdose, as part of the COVID-19 response and post-pandemic recovery efforts.
| S-EPMC8521065 | biostudies-literature
Project description:IntroductionThis study aimed to examine the heterogeneity of the associations between social determinants and COVID-19 fully vaccinated rate.MethodsThis study proposes 3 multiscale dimensions of spatial process, including level of influence (the percentage of population affected by a certain determinant across the entire area), scalability (the spatial process of a determinant into global, regional, and local process), and specificity (the determinant that has the strongest association with the fully vaccinated rate). The multiscale geographically weighted regression was applied to the COVID-19 fully vaccinated rates in U.S. counties (N=3,106) as of October 26, 2021, and the analyses were conducted in May 2022.ResultsThe results suggest the following: (1) Percentage of Republican votes in the 2020 presidential election is a primary influencer because 84% of the U.S. population lived in counties where this determinant is found the most dominant; (2) Demographic compositions (e.g., percentages of racial/ethnic minorities) play a larger role than socioeconomic conditions (e.g., unemployment) in shaping fully vaccinated rates; (3) The spatial process underlying fully vaccinated rates is largely local.ConclusionsThe findings challenge the 1-size-fits-all approach to designing interventions promoting COVID-19 vaccination and highlight the importance of a place-based perspective in ecological health research.
| S-EPMC9259504 | biostudies-literature
Project description:In this study, we introduce the 'racial opportunity gap' as a place-based measure of structural racism for use in population health research. We first detail constructing the opportunity gap using race-sex specific estimates of intergenerational economic mobility outcomes for a recent cohort. We then illustrate its utility in examining spatial variation in the racial mortality gap. First we demonstrate a correlation between the racial opportunity gap and the racial mortality gap across U.S. counties; where the gap in the adult earnings of black and white children born to families at the same income level is greater so, too, is the gap in mortality. Second, we show in a multivariable framework that the racial opportunity gap is associated with the racial mortality gap net of differences in the socioeconomic composition of the two groups. In so doing, we aim to provide population health researchers with a new empirical tool and analytic framework for examining the role of structural racism in generating racial health disparities.
| S-EPMC7076092 | biostudies-literature
Project description:The emergence of the novel coronavirus has necessitated immense research efforts to understand how several non-environmental and environmental factors affect transmission. With the United States leading the path in terms of case incidence, it is important to investigate how weather variables influence the spread of the disease in the country. This paper assembles a detailed and comprehensive dataset comprising COVID-19 cases and climatological variables for all counties in the continental U.S. and uses a developed econometric approach to estimate the causal effect of certain weather factors on the growth rate of infection. The results indicate a non-linear and significant negative relationship between the individual weather measures and the growth rate of COVID-19 in the U.S. Specifically, the paper finds that a 1 °C rise in daily temperature will reduce daily covid growth rate in the U.S. by approximately 6 percent in the following week, while a marginal increase in relative humidity reduces the same outcome by 1 percent over a similar period. In comparison, a 1 m/s increase in daily wind speed will bring about an 8 percent drop in daily growth rate of COVID-19 in the country. These results differ by location and are robust to several sensitivity checks, so large deviations are unexpected.
| S-EPMC8064870 | biostudies-literature
Project description:BackgroundThe Covid-19 pandemic in the United Kingdom has seen two waves; the first starting in March 2020 and the second in late October 2020. It is not known whether outcomes for those admitted with severe Covid were different in the first and second waves.MethodsThe study population comprised all patients admitted to a 1,500-bed London Hospital Trust between March 2020 and March 2021, who tested positive for Covid-19 by PCR within 3-days of admissions. Primary outcome was death within 28-days of admission. Socio-demographics (age, sex, ethnicity), hypertension, diabetes, obesity, baseline physiological observations, CRP, neutrophil, chest x-ray abnormality, remdesivir and dexamethasone were incorporated as co-variates. Proportional subhazards models compared mortality risk between wave 1 and wave 2. Cox-proportional hazard model with propensity score adjustment were used to compare mortality in patients prescribed remdesivir and dexamethasone.ResultsThere were 3,949 COVID-19 admissions, 3,195 hospital discharges and 733 deaths. There were notable differences in age, ethnicity, comorbidities, and admission disease severity between wave 1 and wave 2. Twenty-eight-day mortality was higher during wave 1 (26.1% versus 13.1%). Mortality risk adjusted for co-variates was significantly lower in wave 2 compared to wave 1 [adjSHR 0.49 (0.37, 0.65) p<0.001]. Analysis of treatment impact did not show statistically different effects of remdesivir [HR 0.84 (95%CI 0.65, 1.08), p = 0.17] or dexamethasone [HR 0.97 (95%CI 0.70, 1.35) p = 0.87].ConclusionThere has been substantial improvements in COVID-19 mortality in the second wave, even accounting for demographics, comorbidity, and disease severity. Neither dexamethasone nor remdesivir appeared to be key explanatory factors, although there may be unmeasured confounding present.
| S-EPMC8757902 | 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:PURPOSE:To evaluate associations between counties' COVID-19 cases and racial-ethnic and nativity composition, considering heterogeneity across Latin American-origin subgroups and regions of the U.S. METHODS:Using county-level data and multi-level negative binomial models, we evaluate associations between COVID-19 cases and percentages of residents that are foreign-born, Latinx, Black, or Asian, presenting estimates for all counties combined and stratifying across regions. Given varying risk factors among Latinx, we also evaluate associations for percentages of residents from specific Latin American-origin groups. RESULTS:Percentage of foreign-born residents is positively associated with COVID-19 case rate (IRR=1.106; 95%CI: 1.074-1.139). Adjusted associations for percentage Latinx are non-significant for all counties combined, but this obscures heterogeneity. Counties with more Central Americans have higher case rates (IRR=1.130; 95%CI: 1.067-1.197). And, in the Northeast and Midwest, counties with more Puerto Ricans have higher case rates. Associations with percentage Asians are non-significant after adjusting for percentage foreign-born. Confirming prior evidence, percentage of Black residents is positively and robustly associated with COVID-19 case rate (IRR=1.031; 95%CI: 1.025-1.036). CONCLUSIONS:Counties with more immigrants, as well as more Central American or Black residents have more COVID-19 cases. In the Northeast and Midwest, counties with more Puerto Rican residents also have more COVID-19 cases.
| S-EPMC7485497 | biostudies-literature
Project description:PurposeThe study was to examine county-level associations of physical activity with coronavirus disease 2019 (COVID-19) cases and deaths, per 100,000 county residents.MethodsData were collected from publicly available data sources for 3142 counties and equivalents, including the District of Columbia. Subjective health ratings, percentage uninsured, percentage unemployed, median household income, percentage female residents, percentage White residents, percentage of residents 65 years of age or older, and rural designation served as controls.ResultsThe two-level random intercept regression showed that physical activity rates at the county level were statistically and negatively associated with COVID-19 cases and deaths. Additional analyses showed that physical activity rates moderated the relationship between cases and deaths, such that the relationship was strongest when physical activity rates were low.ConclusionThe results presented here offer empirical evidence of the benefits of county-level physical activity during a pandemic. Implications for public health and physical activity provision are discussed.
| S-EPMC7997405 | biostudies-literature