Geographic disparities in COVID-19 testing and outcomes in Florida

BMC Public Health, Journal Year: 2023, Volume and Issue: 23(1)

Published: Jan. 11, 2023

Abstract Background Understanding geographic disparities in Coronavirus Disease 2019 (COVID-19) testing and outcomes at the local level during early stages of pandemic can guide policies, inform allocation control prevention resources, provide valuable baseline data to evaluate effectiveness interventions for mitigating health, economic social impacts. Therefore, objective this study was identify COVID-19 testing, incidence, hospitalizations, deaths first five months Florida. Methods Florida county-level time period March-July 2020 were used compute various metrics including rates, positivity incidence risks, percent hospitalized cases, hospitalization case-fatality mortality risks. High or low risk clusters identified using either Kulldorff’s circular spatial scan statistics Tango’s flexible their locations visually displayed QGIS. Results Visual examination patterns showed high estimates all Southern Similar patterns, high-risk rates (i.e. hospitalizations deaths) concentrated The distributions these other parts more heterogeneous. For instance, Northwest well below state median (11,697 tests/100,000 persons) but they above North Central risks equal (878 cases/100,000 persons), converse true Consequently, a cluster Florida, while rate 1–3 case fatality had low-rate it cases. Conclusions Substantial distribution exist with counties generally having higher severe compared Northern These findings that is useful assessing preventive interventions, such as vaccinations, state. Future studies will need assess changes over lower geographical scales determinants any patterns.

Language: Английский

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Associations Between Built Environment Factors and SARS-CoV-2 Infections at the Neighbourhood Level in a Metropolitan Area in Germany

Journal of Urban Health, Journal Year: 2023, Volume and Issue: 100(1), P. 40 - 50

Published: Jan. 12, 2023

COVID-19-related health outcomes displayed distinct geographical patterns within countries. The transmission of SARS-CoV-2 requires close spatial proximity people, which can be influenced by the built environment. Only few studies have analysed infections related to environment urban areas at a high resolution. This study examined association between factors and in metropolitan area Germany. Polymerase chain reaction (PCR)-confirmed 7866 citizens Essen March 2020 May 2021 were analysed, aggregated neighbourhood level. We performed regression analyses investigate associations cumulative number per 1000 inhabitants (cum. infections) up 31.05.2021 factors. cum. neighbourhoods (median: 11.5, IQR: 8.1-16.9) followed marked socially determined north-south gradient. effect estimates adjusted models showed negative with greenness, i.e. normalized difference vegetation index (NDVI) (adjusted β = - 35.36, 95% CI: 57.68; 13.04), rooms person (- 10.40, 13.79; 7.01), living space 0.51, 0.66; 0.36), residential 0.07, 0.16; 0.01) commercial 0.15, 0.25; 0.05). Residential multi-storey buildings 0.03, 0.12; 0.06) green (0.03, 0.05; 0.11) did not show substantial association. Our results suggest that matters for spread infections, such as more spacious apartments or higher levels greenness are associated lower infection rates unequal intra-urban distribution these emphasizes prevailing environmental inequalities regarding COVID-19 pandemic.

Language: Английский

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County-Level Social Vulnerability is Associated With In-Hospital Death and Major Adverse Cardiovascular Events in Patients Hospitalized With COVID-19: An Analysis of the American Heart Association COVID-19 Cardiovascular Disease Registry

Circulation Cardiovascular Quality and Outcomes, Journal Year: 2022, Volume and Issue: 15(8)

Published: July 18, 2022

Background: The COVID-19 pandemic has disproportionately affected low-income and racial/ethnic minority populations in the United States. However, it is unknown whether hospitalized patients with from socially vulnerable communities experience higher rates of death and/or major adverse cardiovascular events (MACEs). Thus, we evaluated association between county-level social vulnerability in-hospital mortality MACE a national cohort patients. Methods: Our study population included American Heart Association Cardiovascular Disease Registry across 107 US hospitals January 14, 2020 to November 30, 2020. Social Vulnerability Index (SVI), composite measure community developed by Centers for Control Prevention, was used classify patients’ place residence. We fit hierarchical logistic regression model hospital-level random intercepts evaluate SVI MACE. Results: Among 16 939 registry, 5065 (29.9%) resided most (highest quartile SVI). Compared those lowest SVI, highest were younger (age 60.2 versus 62.3 years) more likely be Black adults (36.7% 12.2%) Medicaid-insured (31.1% 23.0%). After adjustment demographics (age, sex, race/ethnicity) insurance status, (compared lowest) associated likelihood (OR, 1.25 [1.03–1.53]; P =0.03) 1.26 [95% CI, 1.05–1.50]; =0.01). These findings not attenuated after accounting clinical comorbidities acuity illness on admission. Conclusions: Patients residing experienced MACE, independent race, ethnicity, several factors. Clinical health system strategies are needed improve outcomes

Language: Английский

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A Review of Bayesian Spatiotemporal Models in Spatial Epidemiology

ISPRS International Journal of Geo-Information, Journal Year: 2024, Volume and Issue: 13(3), P. 97 - 97

Published: March 18, 2024

Spatial epidemiology investigates the patterns and determinants of health outcomes over both space time. Within this field, Bayesian spatiotemporal models have gained popularity due to their capacity incorporate spatial temporal dependencies, uncertainties, intricate interactions. However, complexity modelling computations associated with vary across different diseases. Presently, there is a limited comprehensive overview applications in epidemiology. This article aims address gap through thorough review. The review commences by delving into historical development concerning disease mapping, prediction, regression analysis. Subsequently, compares these terms data distribution, general models, environmental covariates, parameter estimation methods, model fitting standards. Following this, essential preparatory processes are outlined, encompassing acquisition, preprocessing, available statistical software. further categorizes summarizes application Lastly, critical examination advantages disadvantages along considerations for application, provided. enhance comprehension dynamic distribution prediction epidemics. By facilitating effective scrutiny, especially context global COVID-19 pandemic, holds significant academic merit practical value. It also contribute improved ecological epidemiological prevention control strategies.

Language: Английский

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The Association Between Neighborhood Social Vulnerability and COVID-19 Testing, Positivity, and Incidence in Alabama and Louisiana

Journal of Community Health, Journal Year: 2021, Volume and Issue: 46(6), P. 1115 - 1123

Published: May 9, 2021

Language: Английский

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Spatiotemporal Associations Between Social Vulnerability, Environmental Measurements, and COVID‐19 in the Conterminous United States

GeoHealth, Journal Year: 2021, Volume and Issue: 5(8)

Published: July 21, 2021

This study summarizes the results from fitting a Bayesian hierarchical spatiotemporal model to coronavirus disease 2019 (COVID-19) cases and deaths at county level in United States for year 2020. Two models were created, one deaths, utilizing scaled Besag, York, Mollié with Type I spatial-temporal interaction. Each accounts 16 social vulnerability 7 environmental variables as fixed effects. The spatial pattern between COVID-19 is significantly different many ways. trend of pandemic illustrates shift out major metropolitan areas into Southeast Southwest during summer months upper Midwest beginning autumn. Analysis predictors infection death found that counties higher percentages those not having high school diploma, non-White status being Age 65 over be significant. Among variables, above ground temperature had strongest effect on relative risk both deaths. Hot cold spots, statistically significant low respectively, derived convolutional show probability average have Social Vulnerability Index composite scores. same analysis interaction term exemplifies more complex relationship vulnerability, measurements, cases,

Language: Английский

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Pre-COVID-19 Social Determinants of Health Among Mexican Migrants in Los Angeles and New York City and Their Increased Vulnerability to Unfavorable Health Outcomes During the COVID-19 Pandemic

Journal of Immigrant and Minority Health, Journal Year: 2021, Volume and Issue: 24(1), P. 65 - 77

Published: Oct. 1, 2021

Language: Английский

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The association of the four social vulnerability themes and COVID-19 mortality rates in U.S. Counties

International Journal of Environmental Health Research, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 10

Published: Jan. 20, 2025

The purpose of this study was to examine the relationship between social vulnerability and COVID-19 mortality rates during whole outbreak in U.S. counties. deaths were gleaned from USA Facts. Independent variables CDC's Social Vulnerability Index. Spatial autoregressive models used for data analysis. Results show that counties with more (socioeconomic) positively associated higher rates. Counties (household composition & disability) (minority status language) negatively (housing type transportation) In conclusion, county-level provides an useful framework identifying unequal distribution United States.

Language: Английский

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Increasing COVID-19 Testing and Vaccination Uptake in the Take Care Texas Community-Based Randomized Trial: Adaptive Geospatial Analysis

JMIR Formative Research, Journal Year: 2025, Volume and Issue: 9, P. e62802 - e62802

Published: Feb. 11, 2025

Geospatial data science can be a powerful tool to aid the design, reach, efficiency, and impact of community-based intervention trials. The project titled Take Care Texas aims develop test an adaptive, multilevel, increase COVID-19 testing vaccination uptake among vulnerable populations in 3 regions: Harris County, Cameron Northeast Texas. We aimed novel procedure for adaptive selections census block groups (CBGs) include randomized trial project. CBG selection was conducted across regions over 17-month period (May 2021 October 2022). developed persistent recent burden metrics, using real-time SARS-CoV-2 monitoring capture dynamic infection patterns. To identify populations, we also CBG-level community disparity index, 12 contextual social determinants health (SDOH) measures from US data. In each round, determined priority CBGs based on their ensuring geographic separation minimize "spillover." Community input feedback local partners workers further refined selection. selected were then into 2 arms-multilevel just-in-time intervention-and 1 control arm, covariate randomization, at 1:1:1 ratio. interactive dashboards, which included maps displaying locations community-level information, inform process guide delivery. Selection randomization occurred 10 rounds. A total 120 followed stepped planning interventions, with 60 30 counties. presented substantial temporal changes variations CBGs. exhibited some common geographical patterns but displayed distinct variations, particularly different time points throughout this study. This underscores importance incorporating both SDOH process. integrated geospatial enhance design delivery trial. Adaptive effectively prioritized most in-need communities allowed rigorous evaluation interventions multilevel methodology has broad applicability adapted other public prevention programs, providing improving population addressing disparities.

Language: Английский

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Social Vulnerability and Location of Death in Heart Failure in the United States

Current Problems in Cardiology, Journal Year: 2023, Volume and Issue: 48(7), P. 101689 - 101689

Published: March 9, 2023

Language: Английский

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