Geographic Disparities and Determinants of COVID-19 Incidence Risk in the Greater St. Louis Area, Missouri (original) (raw)

Geographic disparities and predictors of COVID-19 hospitalization risks in the St. Louis Area, Missouri (USA)

BMC Public Health

Background There is evidence of geographic disparities in COVID-19 hospitalization risks that, if identified, could guide control efforts. Therefore, the objective of this study was to investigate Zip Code Tabulation Area (ZCTA)-level geographic disparities and identify predictors of COVID-19 hospitalization risks in the St. Louis area. Methods Hospitalization data for COVID-19 and several chronic diseases were obtained from the Missouri Hospital Association. ZCTA-level data on socioeconomic and demographic factors were obtained from the American Community Survey. Geographic disparities in distribution of COVID-19 age-adjusted hospitalization risks, socioeconomic and demographic factors as well as chronic disease risks were investigated using choropleth maps. Predictors of ZCTA-level COVID-19 hospitalization risks were investigated using global negative binomial and local geographically weighted negative binomial models. Results COVID-19 hospitalization risks were significantly high...

Geographic disparities and determinants of COVID-19 incidence risk in the greater St. Louis Area, Missouri (United States)

PLOS ONE, 2022

Background Evidence seems to suggest that the risk of Coronavirus Disease 2019 (COVID-19) might vary across communities due to differences in population characteristics and movement patterns. However, little is known about these differences in the greater St Louis Area of Missouri and yet this information is useful for targeting control efforts. Therefore, the objectives of this study were to investigate (a) geographic disparities of COVID-19 risk and (b) associations between COVID-19 risk and socioeconomic, demographic, movement and chronic disease factors in the Greater St. Louis Area of Missouri, USA. Methods Data on COVID-19 incidence and chronic disease hospitalizations were obtained from the Department of Health and Missouri Hospital Association, respectively. Socioeconomic and demographic data were obtained from the 2018 American Community Survey while population mobility data were obtained from the SafeGraph website. Choropleth maps were used to identify geographic disparities of COVID-19 risk and several sociodemographic and chronic disease factors at the ZIP Code Tabulation Area (ZCTA) spatial scale. Global negative binomial and local geographically weighted negative binomial models were used to investigate associations between ZCTA-level COVID-19 risk and socioeconomic, demographic and chronic disease factors. Results There were geographic disparities found in COVID-19 risk. Risks tended to be higher in ZCTAs with high percentages of the population with a bachelor's degree (p<0.0001) and obesity hospitalizations (p<0.0001). Conversely, risks tended to be lower in ZCTAs with high percentages of the population working in agriculture (p<0.0001). However, the

Geographic Disparities and Predictors of COVID-19 Hospitalization Risk in the St. Louis Area, Missouri (USA)

2021

BackgroundCOVID-19 has overwhelmed the US healthcare system, with over 44 million cases and over 700,000 deaths as of October 6, 2021. There is evidence that some communities are disproportionately affected. This may result in geographic disparities in COVID-19 hospitalization risk that, if identified, could guide control efforts. Therefore, the objective of this study is to investigate Zip Code Tabulation Area (ZCTA)-level geographic disparities and identify predictors of COVID-19 hospitalization risk in the St. Louis area.MethodsHospitalization data for COVID-19 and several chronic diseases were obtained from the Missouri Hospital Association. ZCTA-level data on socioeconomic and demographic factors were obtained from the US Census Bureau American Community Survey. Age-adjusted COVID-19 and several chronic disease hospitalization risks were calculated. Geographic disparities in distribution of COVID-19 age-adjusted hospitalization risk, socioeconomic and demographic factors as wel...

Determinants of COVID-19 Incidence and Mortality in the US: Spatial Analysis

2020

ABSTRACTOBJECTIVESThe US continues to account for the highest proportion of the global Coronavirus Disease-2019 (COVID-19) cases and deaths. Currently, it is important to contextualize the spread and success of mitigation efforts. The objective of this study was to assess the ecological determinants (policy, health behaviors, socio-economic, physical environment, and clinical care) of COVID-19 incidence and mortality in the US.METHODSData from the New York Times’ COVID-19 repository (01/21/2020-10/27/2020), 2020 County Health Rankings, 2016 County Presidential Election Returns, and 2018-2019 Area Health Resource File were used. County-level logged incidence and mortality rate/million were modeled using the Spatial Autoregressive Combined model and spatial lag model.RESULTSCounties with higher proportions of racial minorities (African American β= 0.007, Native Americans β= 0.008, Hispanics β= 0.015), non-English speakers (β= 0.010), population density ([logged] β= 0.028), and air pol...

Spatial Disparities in Coronavirus Incidence and Mortality in the United States: An Ecological Analysis as of May 2020

Journal of Rural Health, 2020

This ecological analysis investigates the spatial patterns of the COVID‐19 epidemic in the United States in relation to socioeconomic variables that characterize US counties. Data on confirmed cases and deaths from COVID‐19 for 2,814 US counties were obtained from Johns Hopkins University. We used Geographic Information Systems (GIS) to map the spatial aspects of this pandemic and investigate the disparities between metropolitan and nonmetropolitan communities. Multiple regression models were used to explore the contextual risk factors of infections and death across US counties. We included population density, percent of population aged 65+, percent population in poverty, percent minority population, and percent of the uninsured as independent variables. A state‐level measure of the percent of the population that has been tested for COVID‐19 was used to control for the impact of testing. The impact of COVID‐19 in the United States has been extremely uneven. Although densely populated large cities and their surrounding metropolitan areas are hotspots of the pandemic, it is counterintuitive that incidence and mortality rates in some small cities and nonmetropolitan counties approximate those in epicenters such as New York City. Regression analyses support the hypotheses of positive correlations between COVID‐19 incidence and mortality rates and socioeconomic factors including population density, proportions of elderly residents, poverty, and percent population tested.

County-level Predictors of Coronavirus Disease 2019 (COVID-19) Cases and Deaths in the United States: What Happened, and Where Do We Go from Here?

Clinical Infectious Diseases

Background The United States has been heavily impacted by the coronavirus disease 2019 (COVID-19) pandemic. Understanding microlevel patterns in US rates of COVID-19 can inform specific prevention strategies. Methods Using a negative binomial mixed-effects regression model, we evaluated the associations between a broad set of US county-level sociodemographic, economic, and health status–related characteristics and cumulative rates of laboratory-confirmed COVID-19 cases and deaths between 22 January 2020 and 31 August 2020. Results Rates of COVID-19 cases and deaths were higher in US counties that were more urban or densely populated or that had more crowded housing, air pollution, women, persons aged 20–49 years, racial/ethnic minorities, residential housing segregation, income inequality, uninsured persons, diabetics, or mobility outside the home during the pandemic. Conclusions To our knowledge, this study provides results from the most comprehensive multivariable analysis of coun...

Determinants of COVID-19 Case Fatality Rate in the US: Spatial Analysis Over One Year of the Pandemic

Journal of Health Economics and Outcomes Research

Background: The United States continues to account for the highest proportion of the global Coronavirus Disease-2019 (COVID-19) cases and deaths. Currently, it is important to contextualize COVID-19 fatality to guide mitigation efforts. Objectives: The objective of this study was to assess the ecological factors (policy, health behaviors, socio-economic, physical environment, and clinical care) associated with COVID-19 case fatality rate (CFR) in the United States. Methods: Data from the New York Times’ COVID-19 repository and the Centers for Disease Control and Prevention Data (01/21/2020 - 02/27/2021) were used. County-level CFR was modeled using the Spatial Durbin model (SDM). The SDM estimates were decomposed into direct and indirect impacts. Results: The study found percent positive for COVID-19 (0.057% point), stringency index (0.014% point), percent diabetic (0.011% point), long-term care beds (log) (0.010% point), premature age-adjusted mortality (log) (0.702 % point), incom...

Racial/Ethnic Heterogeneity and Rural-Urban Disparity of COVID-19 Case Fatality Ratio in the USA: a Negative Binomial and GIS-Based Analysis

Journal of Racial and Ethnic Health Disparities

The 2019 coronavirus disease (COVID-19) has exacerbated inequality in the United States of America (USA). Black, indigenous, and people of color (BIPOC) are disproportionately affected by the pandemic. This study examines determinants of COVID-19 case fatality ratio (CFR) based on publicly sourced data from January 1 to December 18, 2020, and sociodemographic and rural-urban continuum data from the US Census Bureau. Nonspatial negative binomial Poisson regression and geographically weighted Poisson regression were applied to estimate the global and local relationships between the CFR and predictors-rural-urban continuum, political inclination, and race/ethnicity in 2407 rural counties. The mean COVID-19 CFR among rural counties was 1.79 (standard deviation (SD) = 1.07; 95% CI 1.73-1.84) higher than the total US counties (M = 1.69, SD = 1.18; 95% CI: 1.65-1.73). Based on the global NB model, CFR was positively associated with counties classified as "completely rural" (incidence rate ratio (IRR) = 1.24; 95% CI: 1.12-1.39) and "mostly rural" (IRR = 1.26; 95% CI: 1.15-1.38) relative to "mostly urban" counties. Nonspatial regression indicates that COVID-19 CFR increases by a factor of 8.62, 5.87, 2.61, and 1.36 for one unit increase in county-level percent Blacks, Hispanics, American Indians, and Asian/Pacific Islanders, respectively. Local spatial regression shows CFR was significantly higher in rural counties with a higher share of BIPOC in the Northeast and Midwest regions, and political inclination predicted COVID-19 CFR in rural counties in the Midwest region. In conclusion, spatial and racial/ethnic disparities exist for COVID-19 CFR across the US rural counties, and findings from this study have implications for public health.

Assessment of Structural Barriers and Racial Group Disparities of COVID-19 Mortality With Spatial Analysis

JAMA Network Open

IMPORTANCE Although social determinants of health (SDOH) are important factors in health inequities, they have not been explicitly associated with COVID-19 mortality rates across racial and ethnic groups and rural, suburban, and urban contexts. OBJECTIVES To explore the spatial and racial disparities in county-level COVID-19 mortality rates during the first year of the pandemic. DESIGN, SETTING, AND PARTICIPANTS This cross-sectional study analyzed data for all US counties in 50 states and the District of Columbia for the first full year of the COVID-19 pandemic (January 22, 2020, to February 28, 2021). Counties with a high concentration of a single racial and ethnic population and a high level of COVID-19 mortality rate were identified as concentrated longitudinalimpact counties. The SDOH that may be associated with mortality rate across these counties and in urban, suburban, and rural contexts were examined. The 3 largest racial and ethnic groups in the US were selected: Black or African American, Hispanic or Latinx, and non-Hispanic White populations. EXPOSURES County-level characteristics and community health factors (eg, income inequality, uninsured rate, primary care physicians, preventable hospital stays, severe housing problems rate, and access to broadband internet) associated with COVID-19 mortality. MAIN OUTCOMES AND MEASURES Data on county-level COVID-19 mortality rates (deaths per 100 000 population) reported by the US Centers for Disease Control and Prevention were analyzed. Four indexes were used to measure multiple dimensions of SDOH: socioeconomic advantage index, limited mobility index, urban core opportunity index, and mixed immigrant cohesion and accessibility index. Spatial regression models were used to examine the associations between SDOH and countylevel COVID-19 mortality rate. RESULTS Of the 3142 counties included in the study, 531 were identified as concentrated longitudinal-impact counties. Of these counties, 347 (11.0%) had a large Black or African American population compared with other counties, 198 (6.3%) had a large Hispanic or Latinx population compared with other counties, and 33 (1.1%) had a large non-Hispanic White population compared with other counties. A total of 489 254 COVID-19-related deaths were reported. Most concentrated longitudinal-impact counties with a large Black or African American population compared with other counties were spread across urban, suburban, and rural areas and experienced numerous disadvantages, including higher income inequality (297 of 347 [85.6%]) and more preventable hospital stays (281 of 347 [81.0%]). Most concentrated longitudinal-impact counties with a large Hispanic or Latinx population compared with other counties were located in urban areas (114 of 198 [57.6%]), and 130 (65.7%) of these counties had a high percentage of people who lacked health insurance. Most concentrated longitudinal-impact counties with a large non-Hispanic White population compared with other counties were in rural areas (23 of 33 [69.7%]), included a large (continued) Key Points Question How do the associations between structural factors and COVID-19 mortality help explain the disproportionate outcomes experienced by different racial and ethnic groups? Findings In this cross-sectional study of 3142 counties in 50 US states and the District of Columbia, the associations between different measures of social determinants of health and COVID-19 mortality varied across racial and ethnic groups (Black or African American, Hispanic or Latinx, and non-Hispanic White populations) and different community types (rural, suburban, and urban areas). Meaning Findings from this study suggest the need for future research that addresses health inequity and guides policies and programs by further exploring the different dimensions and regional patterns of social determinants of health.

Geographic disparities and temporal changes of COVID-19 hospitalization risks in North Dakota

Frontiers in Public Health

BackgroundAlthough the burden of the coronavirus disease 2019 (COVID-19) has been different across communities in the US, little is known about the disparities in COVID-19 burden in North Dakota (ND) and yet this information is important for guiding planning and provision of health services. Therefore, the objective of this study was to identify geographic disparities of COVID-19 hospitalization risks in ND.MethodsData on COVID-19 hospitalizations from March 2020 to September 2021 were obtained from the ND Department of Health. Monthly hospitalization risks were computed and temporal changes in hospitalization risks were assessed graphically. County-level age-adjusted and spatial empirical Bayes (SEB) smoothed hospitalization risks were computed. Geographic distributions of both unsmoothed and smoothed hospitalization risks were visualized using choropleth maps. Clusters of counties with high hospitalization risks were identified using Kulldorff's circular and Tango's flexib...