"Food Insecurity, Social Vulnerability, and the Impact of COVID-19 on Population Dependent on Public Assistance / SNAP: A Case Study (original) (raw)
Related papers
2020
Apart from clinical and epidemiological factors, a multitude of demographic, social, and economic factors also influence the extent of the coronavirus disease prevalence within a population. Consequently, there is ongoing discourse regarding the socio-economic predictors of COVID-19. This study explores the influence of several demographic and socio-economic variables on COVID-19 cases in all 46 counties of South Carolina, USA as of October 18, 2020. To understand the level of association between the demographic and socio-economic variables with the coronavirus disease outcome, we employed a spatial mapping technique in a geographic information system (GIS) to assess social vulnerabilities of populations dependent on public assistance income and spatially compared the distribution with COVID-19 cases across the 46 counties in South Carolina, USA. We find that dependence on food stamps showed a positive but weak correlation to COVID-19. For individual variables, Age and poverty were ...
Journal of Food Security, 2020
Apart from clinical and epidemiological factors, a multitude of demographic, social, and economic factors also influence the extent of the coronavirus disease prevalence within a population. Consequently, there is ongoing discourse regarding the socioeconomic predictors of COVID-19. This study explores the influence of several demographic and socioeconomic variables on COVID-19 cases in all 46 counties of South Carolina, USA as of October 18, 2020. To understand the level of association between the demographic and socioeconomic variables with the coronavirus disease outcome, we employed a spatial mapping technique in a geographic information system (GIS) to assess social vulnerabilities of populations dependent on public assistance income and spatially compared the distribution with COVID-19 cases across the 46 counties in South Carolina, USA. We find that dependence on food stamps showed a positive but weak correlation to COVID-19. For individual variables, Age and poverty were strongly associated with dependence on public assistance and were determined to be major predictors of COVID-19. Social vulnerability assessment showed an interesting spatial pattern of counties with high prevalence of COVID-19 cases also having high social vulnerabilities. The results complement knowledge about the COVID-19 pandemic beyond clinical and biological risk factors by assessing socioeconomic perspectives and determinants. Findings from this study can inform policy decisions on poverty alleviation, public assistance, and food security programs.
International Journal of Environmental Health Research
This study aims to examine the spatially varying relationships between social vulnerability factors and COVID-19 cases and deaths in the contiguous United States. County-level COVID-19 data and the Centers for Disease Control and Prevention social vulnerability index (SVI) dataset were analyzed using local Spearman's rank correlation coefficient. Results suggested that SVI and four social vulnerability themes have spatially varying relationships with COVID-19 cases and deaths, which means spatial heterogeneity is an essential factor that influences the relationship, and the strength of association varies significantly across counties. County hot spots that were subject to all four social vulnerability themes during the pandemic were also identified. Local communities and health authorities should pay immediate attention to the most influential social vulnerability factors that are dominant in their region and incorporate measures tailored to the specific groups of people who are under the greatest risk of being affected during the COVID-19 pandemic.
Spatial Exploration of Social Vulnerability and COVID-19-Related Health Outcomes in Mississippi
Southeastern Geographer, 2022
The COVID-19 pandemic has caused more than 48 million cases and 800,000 deaths in the United States. Mississippi (MS) is one of the hardest-hit states with a high incidence and mortality compared to the US national average. This paper explores the relationship of MS county-level COVID19-related incidence and mortality (through December 2, 2021) with the Center for Disease Control’s Social Vulnerability Index (CDC SVI). The CDC SVI consists of four major subthemes: [1] socio-economic status, [2] household composition and disability, [3] minority status and language, and finally, [4] housing type and transportation. We found that the overall SVI ranking has a statistically significant association with reported COVID-19 cumulative mortality at the county level. Among the SVI subthemes, subtheme 1 (socio-economic status) and subtheme 2 (household composition and disability) showed a significant relationship with incidence and mortality (p < 0.05). The results of our analysis will assist in understanding the spatial relationship between CDC SVI themes and the health effects of COVID-19 in MS and the surrounding areas.
MMWR. Morbidity and Mortality Weekly Report, 2020
Poverty, crowded housing, and other community attributes associated with social vulnerability increase a community's risk for adverse health outcomes during and following a public health event (1). CDC uses standard criteria to identify U.S. counties with rapidly increasing coronavirus disease 2019 (COVID-19) incidence (hotspot counties) to support health departments in coordinating public health responses (2). County-level data on COVID-19 cases during June 1-July 25, 2020 and from the 2018 CDC social vulnerability index (SVI) were analyzed to examine associations between social vulnerability and hotspot detection and to describe incidence after hotspot detection. Areas with greater social vulnerabilities, particularly those related to higher representation of racial and ethnic minority residents (risk ratio [RR] = 5.3; 95% confidence interval [CI] = 4.4-6.4), density of housing units per structure (RR = 3.1; 95% CI = 2.7-3.6), and crowded housing units (i.e., more persons than rooms) (RR = 2.0; 95% CI = 1.8-2.3), were more likely to become hotspots, especially in less urban areas. Among hotspot counties, those with greater social vulnerability had higher COVID-19 incidence during the 14 days after detection (212-234 cases per 100,000 persons for highest SVI quartile versus 35-131 cases per 100,000 persons for other quartiles). Focused public health action at the federal, state, and local levels is needed not only to prevent communities with greater social vulnerability from becoming hotspots but also to decrease persistently high incidence among hotspot counties that are socially vulnerable. Daily county-level COVID-19 case counts were obtained through USAFacts (https://usafacts.org/), which compiles data reported by state and local health departments.* Beginning on March 8, 2020, hotspot counties were identified daily using standard criteria † (2). County-level social vulnerability data * https://usafacts.org/issues/coronavirus. † Areas defined as hotspot counties met all four of the following criteria, relative to the date assessed: 1) >100 new COVID-19 cases in the most recent 7 days, 2) higher COVID-19 incidence in the most recent 7 days incidence compared with the preceding 7 days, 3) a decrease of <60% or an increase in the most recent 3-day COVID-19 incidence over the preceding 3-day incidence, and 4) the ratio of 7-day incidence to 30-day incidence exceeds 0.31. In addition, hotspots must have met at least one of the following criteria: 1) >60% change in the most recent 3-day COVID-19 incidence or 2) >60% change in the most recent 7-day incidence. CDC and other federal agencies that are monitoring trends in COVID-19 are collaborating to refine approaches to define and monitor hotspots. As a result, terminology or definitions used in future reports might differ from the terminology used in this report.
2021
BackgroundThe COVID-19 pandemic has exacerbated health injustices in the U.S. driven by racism and other forms of structural violence. Research has shown the disproportionate impacts of COVID-19 morbidity and mortality in the most marginalized communities.ObjectivesWe examined the associations between COVID-19 cumulative incidence (CI) and case-fatality risk (CFR) and the CDC’s Social Vulnerability Index (SVI), a composite score assessing historical marginalization and thus vulnerability to disaster events.MethodsUsing county-level data from national databases, we used population density, Gini index, percent uninsured, and average annual temperature as covariates, and employed negative binomial regression to evaluate relationships between SVI and COVID-19 outcomes. Optimized hot spot analysis identified hot spots of COVID-19 CI and CFR, which were compared in terms of SVI using logistic regression.ResultsAs of 2/3/21, 26,452,031 cases of and 448,786 deaths from COVID-19 had been rep...
The Relationship Between Social Vulnerability and COVID-19 Incidence Among Louisiana Census Tracts
Frontiers in Public Health, 2021
Objective: To examine the association between the Centers for Disease Control and Prevention (CDC)'s Social Vulnerability Index (SVI) and COVID-19 incidence among Louisiana census tracts. Methods: An ecological study comparing the CDC SVI and census tract-level COVID-19 case counts was conducted. Choropleth maps were used to identify census tracts with high levels of both social vulnerability and COVID-19 incidence. Negative binomial regression with random intercepts was used to compare the relationship between overall CDC SVI percentile and its four sub-themes and COVID-19 incidence, adjusting for population density. Results: In a crude stratified analysis, all four CDC SVI sub-themes were significantly associated with COVID-19 incidence. Census tracts with higher levels of social vulnerability were associated with higher COVID-19 incidence after adjusting for population density (adjusted RR: 1.52, 95% CI: 1.41-1.65). Conclusions: The results of this study indicate that increased social vulnerability is linked with COVID-19 incidence. Additional resources should be allocated to areas of increased social disadvantage to reduce the incidence of COVID-19 in vulnerable populations.
Spatiotemporal Impacts of Ideology and Social Vulnerability on COVID-19 for the United States
medRxiv (Cold Spring Harbor Laboratory), 2023
In early 2020, the Coronavirus Disease 19 (COVID-19) rapidly spread across the United States, exhibiting significant geographic variability. While several studies have examined the predictive relationships of differing factors on COVID-19 deaths, few have looked at spatiotemporal variation at refined geographic scales. The objective of this analysis is to examine spatiotemporal variation of COVID-19 deaths in association with socioeconomic, health, demographic, and political factors, using regionalized multivariate regression as well as nationwide county-level geographically weighted random forest (GWRF) models. Analyses were performed on data from three sepearate timeframes: pandemic onset until May 2021, May 2021 through November 2021, and December 2021 until April 2022.Regionalized regression results across three time windows suggest that existing measures of social vulnerability for disaster preparedness (SVI) are associated with a higher degree of mortality from COVID-19. In comparison, GWRF models provide a more robust evaluation of feature importance and prediction, exposing the importance of local features, such as obesity, which is obscured by regional delineation. Overall, GWRF results indicate a more nuanced modeling strategy is useful for capturing the diverse spatial and temporal nature of the COVID-19 pandemic.
Journal of Agriculture, Food Systems, and Community Development, 2021
Food insecurity is a public health issue that has increased in the U.S. since the 2020 COVID-19 pandemic. Understanding how this increase occurs locally is crucial in informing appropriate food insecurity-related responses. Analyzing 2-1-1 call data is one way to examine food insecurity-related needs at a zip code level. The purpose of this work was to: (1) examine overall call trend data to 2-1-1 from March through July 2019 and March through July 2020, (2) examine changes in food need call volume to 2-1-1 during COVID-19 by zip code, and (3) identify areas with unmet food needs dur¬ing COVID-19 in central Texas. Data for 2-1-1 calls from Travis County zip codes for March through July 2020 were compared to calls for March through July 2019 and categorized by rea¬son for calling. Descriptive statistics and paired t-tests were used to analyze food need calls by zip code and mapped using ArcGIS. Communities with high food call volume and no emergency food assets located within the zip...
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.