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Mullachery PH, Bilal U, Li R, McClure LA. Area-Level Social Vulnerability and Severe COVID-19: A Case-Control Study Using Electronic Health Records from Multiple Health Systems in the Southeastern Pennsylvania Region. J Urban Health 2024; 101:845-855. [PMID: 38740710 PMCID: PMC11329477 DOI: 10.1007/s11524-024-00876-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/16/2024] [Indexed: 05/16/2024]
Abstract
Knowledge about neighborhood characteristics that predict disease burden can be used to guide equity-based public health interventions or targeted social services. We used a case-control design to examine the association between area-level social vulnerability and severe COVID-19 using electronic health records (EHR) from a regional health information hub in the greater Philadelphia region. Severe COVID-19 cases (n = 15,464 unique patients) were defined as those with an inpatient admission and a diagnosis of COVID-19 in 2020. Controls (n = 78,600; 5:1 control-case ratio) were a random sample of individuals who did not have a COVID-19 diagnosis from the same geographic area. Retrospective data on comorbidities and demographic variables were extracted from EHR and linked to area-level social vulnerability index (SVI) data using ZIP codes. Models adjusted for different sets of covariates showed incidence rate ratios (IRR) ranging from 1.15 (95% CI, 1.13-1.17) in the model adjusted for individual-level age, sex, and marital status to 1.09 (95% CI, 1.08-1.11) in the fully adjusted model, which included individual-level comorbidities and race/ethnicity. The fully adjusted model indicates that a 10% higher area-level SVI was associated with a 9% higher risk of severe COVID-19. Individuals in neighborhoods with high social vulnerability were more likely to have severe COVID-19 after accounting for comorbidities and demographic characteristics. Our findings support initiatives incorporating neighborhood-level social determinants of health when planning interventions and allocating resources to mitigate epidemic respiratory diseases, including other coronavirus or influenza viruses.
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Affiliation(s)
- Pricila H Mullachery
- Department of Health Services Administration and Policy, College of Public Health, Temple University, 1301 Cecil B. Moore Ave., Philadelphia, PA, 19122, USA.
| | - Usama Bilal
- Urban Health Collaborative, Dornsife School of Public Health, Drexel University, 3600 Market St, Philadelphia, PA, 19104, USA
- Department of Epidemiology and Biostatistics, Dornsife School of Public Health, Drexel University, 3215 Market St, Philadelphia, PA, 19104, USA
| | - Ran Li
- Urban Health Collaborative, Dornsife School of Public Health, Drexel University, 3600 Market St, Philadelphia, PA, 19104, USA
| | - Leslie A McClure
- Department of Epidemiology and Biostatistics, Dornsife School of Public Health, Drexel University, 3215 Market St, Philadelphia, PA, 19104, USA
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Pangan G, Woodard V. A Study Examining the Impact of County-Level Demographic, Socioeconomic, and Political Affiliation Characteristics on COVID-19 Vaccination Patterns in Indiana. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2024; 21:892. [PMID: 39063468 PMCID: PMC11276591 DOI: 10.3390/ijerph21070892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Revised: 06/27/2024] [Accepted: 07/05/2024] [Indexed: 07/28/2024]
Abstract
The COVID-19 vaccination campaign resulted in uneven vaccine uptake throughout the United States, particularly in rural areas, areas with socially and economically disadvantaged groups, and populations that exhibited vaccine hesitancy behaviors. This study examines how county-level sociodemographic and political affiliation characteristics differentially affected patterns of COVID-19 vaccinations in the state of Indiana every month in 2021. We linked county-level demographics from the 2016-2020 American Community Survey Five-Year Estimates and the Indiana Elections Results Database with county-level COVID-19 vaccination counts from the Indiana State Department of Health. We then created twelve monthly linear regression models to assess which variables were consistently being selected, based on the Akaike Information Criterion (AIC) and adjusted R-squared values. The vaccination models showed a positive association with proportions of Bachelor's degree-holding residents, of 40-59 year-old residents, proportions of Democratic-voting residents, and a negative association with uninsured and unemployed residents, persons living below the poverty line, residents without access to the Internet, and persons of Other Race. Overall, after April, the variables selected were consistent, with the model's high adjusted R2 values for COVID-19 cumulative vaccinations demonstrating that the county sociodemographic and political affiliation characteristics can explain most of the variation in vaccinations. Linking county-level sociodemographic and political affiliation characteristics with Indiana's COVID-19 vaccinations revealed inherent inequalities in vaccine coverage among different sociodemographic groups. Increased vaccine uptake could be improved in the future through targeted messaging, which provides culturally relevant advertising campaigns for groups less likely to receive a vaccine, and increasing access to vaccines for rural, under-resourced, and underserved populations.
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Affiliation(s)
- Giuseppe Pangan
- Department of Applied & Computational Mathematics & Statistics, University of Notre Dame, Notre Dame, IN 46556, USA;
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Noppert GA, Clarke P, Hoover A, Kubale J, Melendez R, Duchowny K, Hegde ST. State variation in neighborhood COVID-19 burden across the United States. COMMUNICATIONS MEDICINE 2024; 4:36. [PMID: 38429552 PMCID: PMC10907669 DOI: 10.1038/s43856-024-00459-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 02/12/2024] [Indexed: 03/03/2024] Open
Abstract
BACKGROUND A lack of fine, spatially-resolute case data for the U.S. has prevented the examination of how COVID-19 infection burden has been distributed across neighborhoods, a key determinant of both risk and resilience. Without more spatially resolute data, efforts to identify and mitigate the long-term fallout from COVID-19 in vulnerable communities will remain difficult to quantify and intervene on. METHODS We leveraged spatially-referenced data from 21 states collated through the COVID Neighborhood Project to examine the distribution of COVID-19 cases across neighborhoods and states in the U.S. We also linked the COVID-19 case data with data on the neighborhood social environment from the National Neighborhood Data Archive. We then estimated correlations between neighborhood COVID-19 burden and features of the neighborhood social environment. RESULTS We find that the distribution of COVID-19 at the neighborhood-level varies within and between states. The median case count per neighborhood (coefficient of variation (CV)) in Wisconsin is 3078.52 (0.17) per 10,000 population, indicating a more homogenous distribution of COVID-19 burden, whereas in Vermont the median case count per neighborhood (CV) is 810.98 (0.84) per 10,000 population. We also find that correlations between features of the neighborhood social environment and burden vary in magnitude and direction by state. CONCLUSIONS Our findings underscore the importance that local contexts may play when addressing the long-term social and economic fallout communities will face from COVID-19.
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Affiliation(s)
- Grace A Noppert
- Institute for Social Research, University of Michigan, Ann Arbor, USA.
| | - Philippa Clarke
- Institute for Social Research, University of Michigan, Ann Arbor, USA
| | - Andrew Hoover
- Institute for Social Research, University of Michigan, Ann Arbor, USA
| | - John Kubale
- Institute for Social Research, University of Michigan, Ann Arbor, USA
| | - Robert Melendez
- Institute for Social Research, University of Michigan, Ann Arbor, USA
| | - Kate Duchowny
- Institute for Social Research, University of Michigan, Ann Arbor, USA
| | - Sonia T Hegde
- Department of Epidemiology, Johns Hopkins University, Baltimore, USA
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Forsyth J, Wang L, Thomas-Bachli A. COVID-19 case rates, spatial mobility, and neighbourhood socioeconomic characteristics in Toronto: a spatial-temporal analysis. CANADIAN JOURNAL OF PUBLIC HEALTH = REVUE CANADIENNE DE SANTE PUBLIQUE 2023; 114:806-822. [PMID: 37526916 PMCID: PMC10486339 DOI: 10.17269/s41997-023-00791-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 05/29/2023] [Indexed: 08/02/2023]
Abstract
OBJECTIVES This study has two primary research objectives: (1) to investigate the spatial clustering pattern of mobility reductions and COVID-19 cases in Toronto and their relationships with marginalized populations, and (2) to identify the most relevant socioeconomic characteristics that relate to human mobility and COVID-19 case rates in Toronto's neighbourhoods during five distinct time periods of the pandemic. METHODS Using a spatial-quantitative approach, we combined hot spot analyses, Pearson correlation analyses, and Wilcoxon two-sample tests to analyze datasets including COVID-19 cases, a mobile device-derived indicator measuring neighbourhood-level time away from home (i.e., mobility), and socioeconomic data from 2016 census and Ontario Marginalization Index. Temporal variations among pandemic phases were examined as well. RESULTS The paper identified important spatial clustering patterns of mobility reductions and COVID-19 cases in Toronto, as well as their relationships with marginalized populations. COVID-19 hot spots were in more materially deprived neighbourhood clusters that had more essential workers and people who spent more time away from home. While the spatial pattern of clusters of COVID-19 cases and mobility shifted slightly over time, the group socioeconomic characteristics that clusters shared remained similar in all but the first time period. A series of maps and visualizations were created to highlight the dynamic spatiotemporal patterns. CONCLUSION Toronto's neighbourhoods have experienced the COVID-19 pandemic in significantly different ways, with hot spots of COVID-19 cases occurring in more materially and racially marginalized communities that are less likely to reduce their mobility. The study provides solid evidence in a Canadian context to enhance policy making and provide a deeper understanding of the social determinants of health in Toronto during the COVID-19 pandemic.
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Affiliation(s)
- Jack Forsyth
- Toronto Metropolitan University, Toronto, ON, Canada
- BlueDot, Toronto, ON, Canada
| | - Lu Wang
- Toronto Metropolitan University, Toronto, ON, Canada.
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Al Juboori R, Subramaniam DS, Hinyard L, Sandoval JSO. Unveiling Spatial Associations between COVID-19 Severe Health Index, Racial/Ethnic Composition, and Community Factors in the United States. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:6643. [PMID: 37681783 PMCID: PMC10487993 DOI: 10.3390/ijerph20176643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 07/20/2023] [Accepted: 08/16/2023] [Indexed: 09/09/2023]
Abstract
There are limited efforts to incorporate different predisposing factors into prediction models that account for population racial/ethnic composition in exploring the burden of high COVID-19 Severe Health Risk Index (COVID-19 SHRI) scores. This index quantifies the risk of severe COVID-19 symptoms among a county's population depending on the presence of some chronic conditions. These conditions, as identified by the Centers for Disease Control and Prevention (CDC), include Chronic Obstructive Pulmonary Disease (COPD), heart disease, high blood pressure, diabetes, and obesity. Therefore, the objectives of this study were (1) to investigate potential population risk factors preceding the COVID-19 pandemic that are associated with the COVID-19 SHRI utilizing non-spatial regression models and (2) to evaluate the performance of spatial regression models in comparison to non-spatial regression models. The study used county-level data for 3107 United States counties, utilizing publicly available datasets. Analyses were carried out by constructing spatial and non-spatial regression models. Majority White and majority Hispanic counties showed lower COVID-19 SHRI scores when compared to majority Black counties. Counties with an older population, low income, high smoking, high reported insufficient sleep, and a high percentage of preventable hospitalizations had higher COVID-19 SHRI scores. Counties with better health access and internet coverage had lower COVID-19 SHRI scores. This study helped to identify the county-level characteristics of risk populations to help guide resource allocation efforts. Also, the study showed that the spatial regression models outperformed the non-spatial regression models. Racial/ethnic inequalities were associated with disparities in the burden of high COVID-19 SHRI scores. Therefore, addressing these factors is essential to decrease inequalities in health outcomes. This work provides the baseline typology to further explore many social, health, economic, and political factors that contribute to different health outcomes.
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Affiliation(s)
- Ruaa Al Juboori
- School of Applied Sciences, The University of Mississippi, Oxford, MS 38677, USA
| | - Divya S. Subramaniam
- Department of Health and Clinical Outcomes Research, Advanced HEAlth Data (AHEAD) Institute, Saint Louis University, St. Louis, MO 63103, USA; (D.S.S.); (L.H.)
| | - Leslie Hinyard
- Department of Health and Clinical Outcomes Research, Advanced HEAlth Data (AHEAD) Institute, Saint Louis University, St. Louis, MO 63103, USA; (D.S.S.); (L.H.)
| | - J. S. Onésimo Sandoval
- Department of Sociology and Anthropology, Saint Louis University, St. Louis, MO 63103, USA;
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Noppert GA, Clarke P, Hoover A, Kubale J, Melendez R, Duchowny K, Hegde ST. State Variation in Neighborhood COVID-19 Burden: Findings from the COVID Neighborhood Project. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.19.23290222. [PMID: 37293100 PMCID: PMC10246150 DOI: 10.1101/2023.05.19.23290222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
A lack of fine, spatially-resolute case data for the U.S. has prevented the examination of how COVID-19 burden has been distributed across neighborhoods, a known geographic unit of both risk and resilience, and is hampering efforts to identify and mitigate the long-term fallout from COVID-19 in vulnerable communities. Using spatially-referenced data from 21 states at the ZIP code or census tract level, we documented how the distribution of COVID-19 at the neighborhood-level varies significantly within and between states. The median case count per neighborhood (IQR) in Oregon was 3,608 (2,487) per 100,000 population, indicating a more homogenous distribution of COVID-19 burden, whereas in Vermont the median case count per neighborhood (IQR) was 8,142 (11,031) per 100,000. We also found that the association between features of the neighborhood social environment and burden varied in magnitude and direction by state. Our findings underscore the importance of local contexts when addressing the long-term social and economic fallout communities will face from COVID-19.
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Affiliation(s)
| | | | - Andrew Hoover
- Institute for Social Research, University of Michigan
| | - John Kubale
- Institute for Social Research, University of Michigan
| | | | - Kate Duchowny
- Institute for Social Research, University of Michigan
| | - Sonia T Hegde
- Department of Epidemiology, Johns Hopkins University
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Sebastian T, Carlson JJ, Gaensbauer J, Podewils LJ. Epidemiology and Transmission Dynamics of COVID-19 in an Urban Pediatric US Population. Public Health Rep 2022; 137:1013-1022. [PMID: 35786113 PMCID: PMC9357825 DOI: 10.1177/00333549221105232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
OBJECTIVE This analysis summarizes observational epidemiologic data and transmission dynamics of SARS-CoV-2 among people aged <18 years to better characterize the pediatric COVID-19 pandemic. METHODS We conducted a retrospective study of public health surveillance data among children in Denver, Colorado, who were reported to have COVID-19 from March 1, 2020, through September 30, 2021. We used descriptive statistics and bivariate rate ratios (RRs) to describe demographic and clinical characteristics, transmission dynamics, case trends, and ecological associations. RESULTS A total of 9815 children and adolescents who had COVID-19 were reported during the study period. Adolescents aged 14-17 years had the highest incidence rate (IR) per 1000 people (IR = 107.5; 3021 of 28 108). Hispanic/Latino children had a 1.6 times higher rate of infection than non-Hispanic White children (RR = 1.57; 95% CI, 1.50-1.65; P < .001). Few hospitalizations (n = 138, 1.4%) and deaths (n = 3, 0%) occurred. Most children were symptomatic (4487 of 5499, 81.6%). Within household clusters, a large proportion of pediatric cases (n = 6136) were a secondary case (n = 3959, 64.5%), followed by index case (n = 1170, 19.1%) and co-index case (n = 1007, 16.4%). Non-Hispanic White children had an increased risk of being an index or co-index case (RR = 1.14; 95% CI, 1.06-1.23; P < .001), while Hispanic/Latino children had an increased risk of being a secondary case (RR = 1.07; 95% CI, 1.03-1.11; P < .001). From 2020 to 2021, the association between pediatric case rates and neighborhoods with higher poverty and households with ≥3 people decreased. CONCLUSIONS Older children and those identifying as Hispanic/Latino had a disproportionate incidence of disease. A sizable proportion of children were considered index cases or co-index cases. Pediatric prevention strategies, especially vaccinations, are vital for pandemic control.
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Affiliation(s)
- Thresia Sebastian
- Department of Pediatrics, Denver Health
and Hospital Authority, Denver, CO, USA
- Department of Pediatrics, University of
Colorado School of Medicine, Aurora, CO, USA
| | - Jesse J. Carlson
- Public Health Institute at Denver
Health, Denver Health and Hospital Authority, Denver, CO, USA
| | - James Gaensbauer
- Department of Pediatrics, Denver Health
and Hospital Authority, Denver, CO, USA
- Department of Pediatrics, University of
Colorado School of Medicine, Aurora, CO, USA
- Public Health Institute at Denver
Health, Denver Health and Hospital Authority, Denver, CO, USA
- School of Public Health, University of
Colorado Anschutz Medical Campus, Denver, CO, USA
- Department of Pediatrics and Adolescent
Medicine, Division of Infectious Diseases, Mayo Clinic, Rochester, MN, USA
| | - Laura Jean Podewils
- Public Health Institute at Denver
Health, Denver Health and Hospital Authority, Denver, CO, USA
- School of Public Health, University of
Colorado Anschutz Medical Campus, Denver, CO, USA
- Office of Research, Denver Health and
Hospital Authority, Denver, CO, USA
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da Silva CFA, Silva MC, dos Santos AM, Rudke AP, do Bonfim CV, Portis GT, de Almeida Junior PM, Coutinho MBDS. Spatial analysis of socio-economic factors and their relationship with the cases of COVID-19 in Pernambuco, Brazil. Trop Med Int Health 2022; 27:397-407. [PMID: 35128767 PMCID: PMC9115538 DOI: 10.1111/tmi.13731] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
OBJECTIVES To analyse the spatial distribution of rates of COVID-19 cases and its association with socio-economic conditions in the state of Pernambuco, Brazil. METHODS Autocorrelation (Moran index) and spatial association (Geographically weighted regression) models were used to explain the interrelationships between municipalities and the possible effects of socio-economic factors on rates. RESULTS Two isolated clusters were revealed in the inner part of the state in sparsely inhabited municipalities. The spatial model (Geographically Weighted Regression) was able to explain 50% of the variations in COVID-19 cases. The variables proportion of people with low income, percentage of rented homes, percentage of families in social programs, Gini index and running water had the greatest explanatory power for the increase in infection by COVID-19. CONCLUSIONS Our results provide important information on socio-economic factors related to the spread of COVID-19 and can serve as a basis for decision-making in similar circumstances.
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Affiliation(s)
| | - Mayara Costa Silva
- Department of Cartographic and Survey EngineeringFederal University of PernambucoRecifeBrazil
| | - Alex Mota dos Santos
- Center of Agroforestry Sciences and TechnologiesFederal University of Southern BahiaItabunaBrazil
| | - Anderson Paulo Rudke
- Department of Sanitary and Environmental EngineeringFederal University of Minas GeraisBelo HorizonteBrazil
- Federal University of Technology ‐ ParanáLondrinaBrazil
| | - Cristine Vieira do Bonfim
- Social Research DepartmentJoaquim Nabuco FoundationRecifeBrazil
- Postgraduate Program in Collective HealthFederal University of PernambucoRecifeBrazil
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Hong K, Yum S, Kim J, Yoo D, Chun BC. Epidemiology and Regional Predictors of COVID-19 Clusters: A Bayesian Spatial Analysis Through a Nationwide Contact Tracing Data. Front Med (Lausanne) 2021; 8:753428. [PMID: 34746188 PMCID: PMC8563697 DOI: 10.3389/fmed.2021.753428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 09/21/2021] [Indexed: 11/13/2022] Open
Abstract
Purpose: Revealing the clustering risks of COVID-19 and prediction is essential for effective quarantine policies, since clusters can lead to rapid transmission and high mortality in a short period. This study aimed to present which regional and social characteristics make COVID-19 cluster with high risk. Methods: By analyzing the data of all confirmed cases (14,423) in Korea between January 10 and August 3, 2020, provided by the Korea Disease Control and Prevention Agency, we manually linked each case and discovered clusters. After classifying the cases into clusters as nine types, we compared the duration and size of clusters by types to reveal high-risk cluster types. Also, we estimated odds for the risk factors for COVID-19 clustering by a spatial autoregressive model using the Bayesian approach. Results: Regarding the classified clusters (n = 539), the mean size was 19.21, and the mean duration was 9.24 days. The number of clusters was high in medical facilities, workplaces, and nursing homes. However, multilevel marketing, religious facilities, and restaurants/business-related clusters tended to be larger and longer when an outbreak occurred. According to the spatial analysis in COVID-19 clusters of more than 20 cases, the global Moran's I statistics value was 0.14 (p < 0.01). After adjusting for population size, the risks of COVID-19 clusters were related to male gender (OR = 1.29) and low influenza vaccination rate (OR = 0.87). After the spatial modeling, the predicted probability of forming clusters was visualized and compared with the actual incidence and local Moran's I statistics 2 months after the study period. Conclusions: COVID-19 makes different sizes of clusters in various contact settings; thus, precise epidemic control measures are needed. Also, when detecting and screening for COVID-19 clusters, regional risks such as vaccination rate should be considered for predicting risk to control the pandemic cost-effectively.
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Affiliation(s)
- Kwan Hong
- Department of Preventive Medicine, Korea University College of Medicine, Seoul, South Korea
| | - Sujin Yum
- Graduate School of Public Health, Korea University, Seoul, South Korea
| | - Jeehyun Kim
- Department of Preventive Medicine, Korea University College of Medicine, Seoul, South Korea.,Transdisciplinary Major in Learning Health Systems, Department of Healthcare Sciences, Graduate School, Korea University, Seoul, South Korea
| | - Daesung Yoo
- Transdisciplinary Major in Learning Health Systems, Department of Healthcare Sciences, Graduate School, Korea University, Seoul, South Korea.,Veterinary Epidemiology Division, Animal and Plant Quarantine Agency, Gimcheon, South Korea
| | - Byung Chul Chun
- Department of Preventive Medicine, Korea University College of Medicine, Seoul, South Korea.,Graduate School of Public Health, Korea University, Seoul, South Korea.,Transdisciplinary Major in Learning Health Systems, Department of Healthcare Sciences, Graduate School, Korea University, Seoul, South Korea
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Kathe NJ, Wani RJ. Determinants of COVID-19 Case Fatality Rate in the United States: Spatial Analysis Over One Year of the Pandemic. JOURNAL OF HEALTH ECONOMICS AND OUTCOMES RESEARCH 2021; 8:51-62. [PMID: 34017883 PMCID: PMC8112906 DOI: 10.36469/jheor.2021.22978] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 04/15/2021] [Indexed: 05/07/2023]
Abstract
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), income inequality ratio (0.078% point), social association rate (log) (0.014% point), percent 65 years old and over (0.055% point), and percent African Americans (0.016% point) in a given county were positively associated with its COVID-19 CFR. The study also found food insecurity, long-term beds (log), mental health-care provider (log), workforce in construction, social association rate (log), and percent diabetic of a given county as well as neighboring county were associated with given county's COVID-19 CFR, indicating significant externalities. Conclusion: The spatial models identified percent positive for COVID-19, stringency index, elderly, college education, race/ethnicity, residential segregation, premature mortality, income inequality, workforce composition, and rurality as important ecological determinants of the geographic disparities in COVID-19 CFR.
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Affiliation(s)
| | - Rajvi J Wani
- Real World Evidence Manager, Amgen Canada Inc, Mississauga, ON, Canada
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