An Ecologic Study of Disparities in COVID-19 Incidence and Case Fatality in Oakland County, MI, USA, During a State-Mandated Shutdown.
Ontology highlight
ABSTRACT: Introduction
Data from the USA reveal disparities in hospitalization and mortality from coronavirus disease 2019 (COVID-19). Social determinants of health (SDoH) could account for disparities in disease incidence and outcomes. We investigated the association between zip code racial composition and COVID-19 incidence and case fatality in Oakland County, MI.Methods
We conducted an ecological study using publicly available data on COVID-19 in 70 zip codes in Oakland County, MI. We obtained demographic surrogate markers of SDoH by zip code from the US Census Bureau website. Using negative binomial regression models, we investigated the association between the percentage of Blacks in each zip code and COVID-19 incidence and case fatality, including markers of SDoH as potential confounders.Results
Reported COVID-19 cases ranged from 13.2 to 255.2 per 10,000 population. Each percentage increase in Blacks within a zip code was associated with a 3% increase in COVID-19 cases (95% CI: 1.02 to 1.04, p ≤ 0.0001), and this remained significant after adjusting for income or poverty level, number of persons per household, mode of transportation, age, and level of education (incidence rate ratio: 1.02, 95% CI: 1.01 to 1.03, p ≤ 0.0001). Zip codes with a higher percentage of Blacks also experienced a faster increase in COVID-19 rates from April 3 to May 16. However, the proportion of Blacks in a zip code was not associated with case fatality.Conclusion
Zip codes with larger Black populations were disproportionately affected by COVID-19.
SUBMITTER: Akanbi MO
PROVIDER: S-EPMC7595050 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundSARS-CoV-2 has affected every demography disproportionately, including even the native highland populations. Hypobaric-hypoxic settings at high-altitude (HA, >2,500 masl) present an extreme environment that impacts the survival of permanent residents, possibly including SARS-CoV-2. Conflicting hypotheses have been presented for COVID-19 incidence and fatality at HA.ObjectivesTo evaluate protection or risk against COVID-19 incidence and fatality in humans under hypobaric-hypoxic environment of high-altitude (>2,501 masl).MethodsGlobal COVID-19 data of March 2020-21, employed from official websites of the Indian Government, John Hopkins University, and Worldometer were clustered into 6 altitude categories. Clinical cofactors and comorbidities data were evaluated with COVID-19 incidence and fatality. Extensive comparisons and correlations using several statistical tools estimated the risk and protection.ResultsOf relevance, data analyses revealed four distinct responses, namely, partial risk, total risk, partial protection, and total protection from COVID-19 at high-altitude indicating a mixed baggage and complexity of the infection. Surprisingly, it included the countries within the same geographic region. Moreover, body mass index, hypertension, and diabetes correlated significantly with COVID-19 incidence and fatality rate (P ≤ 0.05).ConclusionsVaried patterns of protection and risk against COVID-19 incidence and fatality were observed among the high-altitude populations. It is though premature to generalize COVID-19 effects on any particular demography without further extensive studies.
| S-EPMC9922493 | biostudies-literature
Project description:BackgroundPreliminary studies have suggested a link between socio-economic characteristics and COVID-19 mortality. Such studies have been carried out on particular geographies within the USA or selective data that do not represent the complete experience for 2020.MethodsWe estimated COVID-19 mortality rates, number of years of life lost to SARS-CoV-2 and reduction in life expectancy during each of the three pandemic waves in 2020 for 3144 US counties grouped into five socio-economic status categories, using daily death data from the Johns Hopkins University of Medicine and weekly mortality age structure from the Centers for Disease Control.ResultsDuring March-May 2020, COVID-19 mortality was highest in the most socio-economically advantaged quintile of counties and lowest in the two most-disadvantaged quintiles. The pattern reversed during June-August and widened by September-December, such that COVID-19 mortality rates were 2.58 times higher in the bottom than in the top quintile of counties. Differences in the number of years of life lost followed a similar pattern, ultimately resulting in 1.002 (1.000, 1.004) million years in the middle quintile to 1.381 (1.378, 1.384) million years of life lost in the first (most-disadvantaged) quintile during the whole year.ConclusionsDiverging trajectories of COVID-19 mortality among the poor and affluent counties indicated a progressively higher rate of loss of life among socio-economically disadvantaged communities. Accounting for socio-economic disparities when allocating resources to control the spread of the infection and to reinforce local public health infrastructure would reduce inequities in the mortality burden of the disease.
| S-EPMC8755340 | biostudies-literature
Project description:Many countries, including the USA, publish predicted numbers of cancer incidence and death in current and future years for the whole country. These predictions provide important information on the cancer burden for cancer control planners, policymakers and the general public. Based on evidence from several empirical studies, the joinpoint (segmented-line linear regression) model (JPM) has been adopted by the American Cancer Society to estimate the number of new cancer cases in the USA and in individual states since 2007. Recently, cancer incidence in smaller geographic regions such as counties, and local policy makers are increasingly interested with Federal Information Processing Standard code regions. The natural extension is to directly apply the JPM to county-level cancer incidence data. The direct application has several drawbacks and its performance has not been evaluated. To address the concerns, we developed a spatial random-effects JPM for county-level cancer incidence data. The proposed model was used to predict both cancer incidence rates and counts at the county level. The standard JPM and the proposed method were compared through a validation study. The proposed method outperformed the standard JPM for almost all cancer sites, especially for moderate or rare cancer sites and for counties with small population sizes. As an application, we predicted county-level prostate cancer incidence rates and counts for the year 2011 in Connecticut.
| S-EPMC5933533 | biostudies-literature
Project description:Advertising exerts a powerful influence over consumer decision-making, and disproportionate marketing for unhealthy products may contribute to health inequities. The objective of this study was to examine socioeconomic and racial and ethnic disparities in outdoor branded advertising for products harmful to health in San Francisco and Oakland, CA. We collected cross-sectional data on outdoor advertising from 372 blocks with ≥ 1 residential or mixed-residential parcel in SF and Oakland in 2018-2019. Blocks were randomly sampled by city, land use, majority vs. non-majority Black and/or Hispanic composition, and upper and lower tertiles of household income. Advertisements were coded by product, healthfulness, and branding. Exposure variables were neighborhood household median income and percent of residents who were Hispanic of any race, non-Hispanic Asian, non-Hispanic Black, and non-Hispanic White. The primary outcome variable was block-level dichotomous presence of any unhealthy branded advertisement for food, beverage, alcohol, or tobacco. Analyses were unadjusted and adjusted for land use and number of total advertisements on each block. Each additional $10,000 in neighborhood household median income was associated with an 11% lower adjusted odds of having any unhealthy branded advertisements on the block (95%CI: 0.80-0.99; P = 0.03). There were no significant associations between neighborhood racial and ethnic composition and presence of unhealthy branded advertisements, but with each 10% higher neighborhood composition of Hispanic residents, there was a borderline significant higher presence of unhealthy branded advertisements (OR = 1.23; 95%CI: 1.00-1.51; P = 0.05). Results indicate that low-income neighborhoods were disproportionately exposed to outdoor branded advertisements for unhealthy products.
| S-EPMC9152783 | biostudies-literature
Project description:ObjectiveTo analyse the correlation between COVID-19 vaccination percentage and socioeconomic status (SES).MethodsA nationwide ecologic study based on open-sourced, anonymized, aggregated data provided by the Israel Ministry of Health. The correlations between municipal SES, vaccination percentage and active COVID-19 cases during the vaccination campaign were analysed by using weighted Pearson correlations. To assess the adequacy of first dose vaccination rollout relative to the municipality COVID-19 disease burden, a metric termed the vaccination need ratio was devised by dividing the total number of active cases (per 10 000 people) by the vaccination percentage of the population over 60 in each municipality, and its correlation with the SES was examined.Results23 days after initiation of the vaccination campaign, 760 916 (56.8%) individuals over the age of 60 were vaccinated in Israel with the first dose of the BNT162b2 COVID-19 vaccine. A negative correlation was found between the COVID-19 active case burden and the vaccination percentage of the study population in each municipality (r = -0.47, 95% CI -0.59 to -0.30). The vaccination percentage significantly correlated with the municipal SES (r = 0.83, 95% CI 0.79 to 0.87). This finding persisted but was attenuated over a 5-week period. A negative correlation between the vaccination need ratio and municipal SES (r = -0.80, 95% CI -0.88 to -0.66) was found.DiscussionLower COVID-19 vaccination percentage was associated with lower SES and high active disease burden. Vaccination efforts should focus on areas with lower SES and high disease burden to assure equality of vaccine allocation and potentially provide a more diligent disease mitigation.
| S-EPMC8183100 | biostudies-literature
Project description:BackgroundThere are large and persistent disparities in life expectancy among racial-ethnic groups in the USA, but the extent to which these patterns vary geographically on a local scale is not well understood. This analysis estimated life expectancy for five racial-ethnic groups, in 3110 US counties over 20 years, to describe spatial-temporal variations in life expectancy and disparities between racial-ethnic groups.MethodsWe applied novel small-area estimation models to death registration data from the US National Vital Statistics System and population data from the US National Center for Health Statistics to estimate annual sex-specific and age-specific mortality rates stratified by county and racial-ethnic group (non-Latino and non-Hispanic White [White], non-Latino and non-Hispanic Black [Black], non-Latino and non-Hispanic American Indian or Alaska Native [AIAN], non-Latino and non-Hispanic Asian or Pacific Islander [API], and Latino or Hispanic [Latino]) from 2000 to 2019. We adjusted these mortality rates to correct for misreporting of race and ethnicity on death certificates and then constructed abridged life tables to estimate life expectancy at birth.FindingsBetween 2000 and 2019, trends in life expectancy differed among racial-ethnic groups and among counties. Nationally, there was an increase in life expectancy for people who were Black (change 3·9 years [95% uncertainty interval 3·8 to 4·0]; life expectancy in 2019 75·3 years [75·2 to 75·4]), API (2·9 years [2·7 to 3·0]; 85·7 years [85·3 to 86·0]), Latino (2·7 years [2·6 to 2·8]; 82·2 years [82·0 to 82·5]), and White (1·7 years [1·6 to 1·7]; 78·9 years [78·9 to 79·0]), but remained the same for the AIAN population (0·0 years [-0·3 to 0·4]; 73·1 years [71·5 to 74·8]). At the national level, the negative difference in life expectancy for the Black population compared with the White population decreased during this period, whereas the negative difference for the AIAN population compared with the White population increased; in both cases, these patterns were widespread among counties. The positive difference in life expectancy for the API and Latino populations compared with the White population increased at the national level from 2000 to 2019; however, this difference declined in a sizeable minority of counties (615 [42·0%] of 1465 counties) for the Latino population and in most counties (401 [60·2%] of 666 counties) for the API population. For all racial-ethnic groups, improvements in life expectancy were more widespread across counties and larger from 2000 to 2010 than from 2010 to 2019.InterpretationDisparities in life expectancy among racial-ethnic groups are widespread and enduring. Local-level data are crucial to address the root causes of poor health and early death among disadvantaged groups in the USA, eliminate health disparities, and increase longevity for all.FundingNational Institute on Minority Health and Health Disparities; National Heart, Lung, and Blood Institute; National Cancer Institute; National Institute on Aging; National Institute of Arthritis and Musculoskeletal and Skin Diseases; Office of Disease Prevention; and Office of Behavioral and Social Science Research, US National Institutes of Health.
| S-EPMC9256789 | biostudies-literature
Project description:BackgroundSocial vulnerability indices were created to measure resiliency to environmental disasters based on socioeconomic and population characteristics of discrete geographic regions. They are composed of multiple validated constructs that can also potentially identify geographically vulnerable populations after injury. Our objective was to determine if these indices correlate with injury fatality rates in the US.MethodsWe evaluated three social vulnerability indices: The Hazards & Vulnerability Research Institute's Social Vulnerability Index (SoVI), the Center for Disease Control's Social Vulnerability Index (SVI), and the Economic Innovation Group's Distressed Community Index (DCI). We analyzed SVI subindices and common individual census variables as indicators of socioeconomic status. Outcomes included age-adjusted county-level overall, firearm, and motor vehicle collision deaths per 100,000 population. Linear regression determined the association of injury fatality rates with the SoVI, SVI, and DCI. Bivariate choropleth mapping identified geographic variation and spatial autocorrelation of overall fatality, SoVI, and DCI.ResultsA total of 3,137 US counties were included. Only 24.6% of counties fell into the same vulnerability quintile for all three indices. Despite this, all indices were associated with increasing fatality rates for overall, firearm, and motor vehicle collision fatality. The DCI performed best by model fit, explanation of variance, and diagnostic performance on overall injury fatality. There is significant geographic variation in SoVI, DCI, and injury fatality rates at the county level across the United States, with moderate spatial autocorrelation of SoVI (Moran's I, 0.35; p < 0.01) and high autocorrelation of injury fatality rates (Moran's I, 0.77; p < 0.01) and DCI (Moran's I, 0.53; p < 0.01).ConclusionWhile the indices contribute unique information, higher social vulnerability is associated with higher injury fatality across all indices. These indices may be useful in the epidemiologic and geographic assessment of injury-related fatality rates. Further study is warranted to determine if these indices outperform traditional measures of socioeconomic status and related constructs used in trauma research.Level of evidenceEpidemiological, level IV.
| S-EPMC8375410 | biostudies-literature
Project description:While studies indicate differences in incidence and case fatality risk of COVID-19, few efforts have shed light on regional variations in the intensity of initial community spread. We conducted a nationwide study using county-level data on COVID-19 from Center for Systems Science and Engineering at Johns Hopkins University. We characterized intensity of initial community COVID-19 attack by calculating the incidence and case fatality risk (CFR) for the first 4-week period of COVID-19 spread in each county. We used multivariate multilevel multinomial logistic regression to estimate the association of county-level characteristics with COVID-19 incidence and CFR. Of 3,143 counties, we included 1,052 with at least 100 reported cases on June 1st. Median incidence was 193.4 per 100,000 population (IQR: 94.2-397.5). Median case fatality risk was 3.6% (IQR: 1.4â€"7.3). Median age, rural population, population density, lower education, uninsured population, obesity, COPD prevalence were positively associated, while population, female sex, races (Asian, white), higher education, excessive drinking were negatively associated with initial COVID-19 incidence. Median age, female sex, Asian race, population density, higher education, excessive drinking, Intensive Care Unit beds, airborne infection isolation rooms were positively associated, while Hispanic ethnicity, lower education, obesity (paradox), uninsured population were negatively associated with initial COVID-19 CFR.
| S-EPMC7781326 | biostudies-literature
Project description:Marissa Renardy and Denise Kirschner University of Michigan Medical School The COVID-19 pandemic has highlighted the patchwork nature of disease epidemics, with infection spread dynamics varying wildly across countries and across states within the US. These heteroge- neous patterns are also observed within individual states, with patches of concentrated outbreaks. Data is being generated daily at all of these spatial scales, and answers to questions regarded re- opening strategies are desperately needed. Mathematical modeling is useful in exactly these cases, and using modeling at a county scale may be valuable to further predict disease dynamics for the purposes of public health interventions. To explore this issue, we study and predict the spread of COVID-19 in Washtenaw County, MI, the home to University of Michigan, Eastern Michigan University, and Google, as well as serving as a sister city to Detroit, MI where there has been a serious outbreak. Here, we apply a discrete and stochastic network-based modeling framework allowing us to track every individual in the county. In this framework, we construct contact net- works based on synthetic population datasets specific for Washtenaw County that are derived from US Census datasets. We assign individuals to households, workplaces, schools, and group quarters (such as prisons). In addition, we assign casual contacts to each individual at random. Using this framework, we explicitly simulate Michigan-specific government-mandated workplace and school closures as well as social distancing measures. We also perform sensitivity analyses to identify key model parameters and mechanisms contributing to the observed disease burden in the three months following the first observed cases on COVID-19 in Michigan. We then consider several scenarios for relaxing restrictions and reopening workplaces to predict what actions would be most prudent. In particular, we consider the effects of 1) different timings for reopening, and 2) different levels of workplace vs. casual contact re-engagement. Through simulations and sensitivity analyses, we explore mechanisms driving magnitude and timing of a second wave of infections upon re-opening. This model can be adapted to other US counties using synthetic population databases and data specific to those regions.
| S-EPMC7359538 | biostudies-literature
Project description:The COVID-19 pandemic has highlighted the patchwork nature of disease epidemics, with infection spread dynamics varying wildly across countries and across states within the US. To explore this issue, we study and predict the spread of COVID-19 in Washtenaw County, MI, which is home to University of Michigan and Eastern Michigan University, and in close proximity to Detroit, MI, a major epicenter of the epidemic in Michigan. We apply a discrete and stochastic network-based modeling framework allowing us to track every individual in the county. In this framework, we construct contact networks based on synthetic population datasets specific for Washtenaw County that are derived from US Census datasets. We assign individuals to households, workplaces, schools, and group quarters (such as prisons or long term care facilities). In addition, we assign casual contacts to each individual at random. Using this framework, we explicitly simulate Michigan-specific government-mandated workplace and school closures as well as social distancing measures. We perform sensitivity analyses to identify key model parameters and mechanisms contributing to the observed disease burden in the three months following the first observed cases of COVID-19 in Michigan. We then consider several scenarios for relaxing restrictions and reopening workplaces to predict what actions would be most prudent. In particular, we consider the effects of 1) different timings for reopening, and 2) different levels of workplace vs. casual contact re-engagement. We find that delaying reopening does not reduce the magnitude of the second peak of cases, but only delays it. Reducing levels of casual contact, on the other hand, both delays and lowers the second peak. Through simulations and sensitivity analyses, we explore mechanisms driving the magnitude and timing of a second wave of infections upon re-opening. We find that the most significant factors are workplace and casual contacts and protective measures taken by infected individuals who have sought care. This model can be adapted to other US counties using synthetic population databases and data specific to those regions.
| S-EPMC7455546 | biostudies-literature