scholarly journals Assessment of Social Vulnerability to Wildfire in Missouri, United States of America

2019 ◽  
Vol 12 (4) ◽  
pp. 76
Author(s):  
Omolola Victoria Akinola ◽  
Jimmy Adegoke ◽  
Temi Emmanuel Ologunorisa
Keyword(s):  
United States ◽  
Low Income ◽  
Social Vulnerability ◽  
Rating Scale ◽  
Biodiversity Loss ◽  
Vulnerability Index ◽  
The United States ◽  
Social Vulnerability Index ◽  
Wildfire Risk ◽  
Property Loss

Wildfire is a major environmental hazard causing property damage and destruction including biodiversity loss in the United States. In order to reduce property loss and destruction arising from wildfire, this study assessed and identified social vulnerability to wildfire in Missouri using the American Community Survey data on social and demographic variables for the state of Missouri and social vulnerability index (S0VI). The study divided Missouri into five geopolitical zones from which ten counties were randomly selected for this study. The selected counties formed the basis on which fourteen social and demographic indicators were identified and assessed using Bogardi, Birkmann and Cadona conceptual framework. The result of the analysis shows that S0VI estimated for the five geopolitical zones of Missouri is moderate with a rating scale of 1.42 – 1.71. Education, income and marital status have a rating scale of 2.0 - 3.0 attributed for the high value of Social Vulnerability to wildfire. Race / ethnicity, language spoken, employment and percentage of house units that are mobile homes had a low S0VI value of 1.0 thereby contributing positively to resilience to wildfire risk. The study observes that government involvement in wildfire risk reduction is quite impressive and should still be intensified. The policy implication of this study is that education and income are key variables that contribute to high wildfire risk in Missouri. The need for government to formulate a policy on environmental education of the populace especially for people of low income and education become imperative. This will go a long way in reducing damage and property loss arising from wildfire.

Abstract 16562: Association of County-Level Social Vulnerability Index and Cardiovascular Disease in the United States, 1999-2018

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Quentin R Youmans ◽  
Megan E McCabe ◽  
Clyde W Yancy ◽  
Lucia Petito ◽  
Kiarri N Kershaw ◽  
...  
Keyword(s):  
United States ◽  
Cardiovascular Disease ◽  
Social Vulnerability ◽  
Cardiovascular Health ◽  
Vulnerability Index ◽  
The United States ◽  
County Level ◽  
Social Vulnerability Index ◽  
Cross Sectional ◽  
Cvd Mortality

Introduction: Social determinants of health are multi-dimensional and span various interrelated domains. In order to inform community-engaged clinical and policy efforts, we sought to examine the association between a national social vulnerability index (SVI) and age-adjusted mortality rate (AAMR) of CVD. Hypothesis: Higher county-level SVI or greater vulnerability will be associated with higher AAMR of CVD between 1999-2018 in the United States. Methods: In this serial, cross-sectional analysis, we queried CDC WONDER for age-adjusted mortality rates (AAMRs) per 100,000 population for cardiovascular disease (I00-78) at the county-level between 1999-2018. We quantified the association of county-level SVI and CVD AAMR using Spearman correlation coefficients and examined trends in CVD AAMR stratified by median SVI at the county-level. Finally, we performed geospatial county-level analysis stratified by combined median SVI and CVD AAMR (high/high, high/low, low/high, and low/low). Results: We included data from 2766 counties (representing 95% of counties in the US) with median SVI 0.53 (IQR 0.28, 0.76). Overall SVI and the household and socioeconomic subcomponents were strongly correlated with 2018 CVD AAMR (0.47, 0.50, and 0.56, respectively with p<0.001 for all). CVD mortality declined between 1999-2011 and was stagnant between 2011-2018 with similar patterns in high and low SVI counties (FIGURE). Counties with high SVI and CVD AAMR were clustered in the South and Midwest (n=977, 35%). Conclusion: County-level social vulnerability is associated with higher CVD mortality. High SVI and CVD AAMR coexist in more than 1 in 3 US counties and have persisted over the past 2 decades. Identifying counties that are disproportionately vulnerable may inform targeted and community-based strategies to equitably improve cardiovascular health across the country.

scholarly journals Revising Ncei’s Climate Extremes Index And The Cdc’s Social Vulnerability Index To Analyze Climate Extremes Vulnerability Across The United States

2020 ◽  
pp. 1-42
Author(s):  
Emily L. Pauline ◽  
John A. Knox ◽  
Lynne Seymour ◽  
Andrew J. Grundstein
Keyword(s):  
United States ◽  
Social Vulnerability ◽  
Demographic Data ◽  
Climate Extremes ◽  
Vulnerability Index ◽  
The United States ◽  
Social Vulnerability Index ◽  
Social Demographic

CapsuleWhere are climate extremes happening? This information is urgently needed. We combine this information with social demographic data to create an index identifying U.S. locations vulnerable to climate extremes.

scholarly journals Spatial Modeling of COVID-19 Vaccine Hesitancy in the United States

2021 ◽  
Vol 18 (18) ◽  
pp. 9488
Author(s):  
Abolfazl Mollalo ◽  
Moosa Tatar
Keyword(s):  
United States ◽  
Healthcare Providers ◽  
Vulnerability Index ◽  
The United States ◽  
Vaccine Hesitancy ◽  
Social Vulnerability Index ◽  
Vaccination Programs ◽  
Public Health Decision ◽  
Vaccination Rates ◽  
The Impact

Vaccine hesitancy refers to delay in acceptance or refusal of vaccines despite the availability of vaccine services. Despite the efforts of United States healthcare providers to vaccinate the bulk of its population, vaccine hesitancy is still a severe challenge that has led to the resurgence of COVID-19 cases to over 100,000 people during early August 2021. To our knowledge, there are limited nationwide studies that examined the spatial distribution of vaccination rates, mainly based on the social vulnerability index (SVI). In this study, we compiled a database of the percentage of fully vaccinated people at the county scale across the continental United States as of 29 July 2021, along with SVI data as potential significant covariates. We further employed multiscale geographically weighted regression to model spatial nonstationarity of vaccination rates. Our findings indicated that the model could explain over 79% of the variance of vaccination rate based on Per capita income and Minority (%) (with positive impacts), and Age 17 and younger (%), Mobile homes (%), and Uninsured people (%) (with negative effects). However, the impact of each covariate varied for different counties due to using separate optimal bandwidths. This timely study can serve as a geospatial reference to support public health decision-makers in forming region-specific policies in monitoring vaccination programs from a geographic perspective.

scholarly journals Geographic Associations Between Social Factors and SARS-CoV-2 Testing Early in the COVID-19 Pandemic, February–June 2020, Massachusetts

2021 ◽  
pp. 003335492110367
Author(s):  
Scott Troppy ◽  
Grete E. Wilt ◽  
Ari Whiteman ◽  
Elaine Hallisey ◽  
Molly Crockett ◽  
...  
Keyword(s):  
Spatial Heterogeneity ◽  
Social Vulnerability ◽  
Limited English Proficiency ◽  
Vulnerability Index ◽  
The United States ◽  
Social Vulnerability Index ◽  
Public Health Action ◽  
Infected People ◽  
Molecular Tests ◽  
Negative Results

Objectives Widespread SARS-CoV-2 testing is critical to identify infected people and implement public health action to interrupt transmission. With SARS-CoV-2 testing supplies and laboratory capacity now widely available in the United States, understanding the spatial heterogeneity of associations between social determinants and the use of SARS-CoV-2 testing is essential to improve testing availability in populations disproportionately affected by SARS-CoV-2. Methods We assessed positive and negative results of SARS-CoV-2 molecular tests conducted from February 1 through June 17, 2020, from the Massachusetts Virtual Epidemiologic Network, an integrated web-based surveillance and case management system in Massachusetts. Using geographically weighted regression and Moran’s I spatial autocorrelation tests, we quantified the associations between SARS-CoV-2 testing rates and 11 metrics of the Social Vulnerability Index in all 351 towns in Massachusetts. Results Median SARS-CoV-2 testing rates decreased with increasing percentages of residents with limited English proficiency (median relative risk [interquartile range] = 0.96 [0.95-0.99]), residents aged ≥65 (0.97 [0.87-0.98]), residents without health insurance (0.96 [0.95-1.04], and people residing in crowded housing conditions (0.89 [0.80-0.94]). These associations differed spatially across Massachusetts, and localized models improved the explainable variation in SARS-CoV-2 testing rates by 8% to 12%. Conclusion Indicators of social vulnerability are associated with variations in SARS-CoV-2 testing rates. Accounting for the spatial heterogeneity in these associations may improve the ability to explain and address the SARS-CoV-2 pandemic at substate levels.

scholarly journals Exploratory Bivariate and Multivariate Geovisualizations of a Social Vulnerability Index

2020 ◽  
Author(s):  
Georgianna Strode ◽  
Victor Mesev ◽  
Susanne Bleisch ◽  
Kathryn Ziewitz ◽  
Fennis Reed ◽  
...  
Keyword(s):  
Social Vulnerability ◽  
Visual Analytics ◽  
Vulnerability Index ◽  
The United States ◽  
Public Health Research ◽  
Household Composition ◽  
Vulnerable Groups ◽  
Social Vulnerability Index ◽  
National Agency ◽  
Emergency Responders

In the United States, the Centers for Disease Control and Prevention (CDC) is the national agency that conducts and supports public health research and practice. Among the CDC’s many achievements is the development of a social vulnerability index (SVI) to aid planners and emergency responders when identifying vulnerable segments of the population, especially during natural hazard events. The index includes an overall social vulnerability ranking as well as four individual themes: socioeconomic, household composition & disability, ethnicity & language, and housing & transportation. This makes the SVI dataset multivariate, but it is typically viewed via maps that show one theme at a time. This paper explores a suite of cartographic techniques that can represent the SVI beyond the univariate view. Specifically, we recommend three techniques: (1) bivariate mapping to illustrate overall vulnerability and population density, (2) multivariate mapping using cartographic glyphs to disaggregate levels of the four vulnerability themes, and (3) visual analytics using Euler diagrams to depict overlap between the vulnerability themes. The CDC’s SVI, and by extension, vulnerability indices in other countries, can be viewed in a variety of cartographic forms that illustrate the location of vulnerable groups of society. Viewing data from various perspectives can facilitate the understanding and analysis of the growing amount and complexity of data.

scholarly journals Application of Social Vulnerability Index to Identify High- risk Population of Contracting COVID-19 Infection: a state-level study.

2020 ◽  
Author(s):  
Odalys Estefania Lara Garcia ◽  
Violeta Alvarez Retamales ◽  
Oswaldo A Madrid Suarez ◽  
Priyanka Parajuli ◽  
Susan Hingle ◽  
...  
Keyword(s):  
Low Income ◽  
Social Factors ◽  
Social Vulnerability ◽  
Minority Group ◽  
Vulnerability Index ◽  
High Risk Population ◽  
Age Group ◽  
Risk Of Infection ◽  
Social Vulnerability Index ◽  
The Social

Social factors that determine the health of a population are known as the social determinants of health. During the past few weeks, as COVID-19 cases grew exponentially, the discrepancy among the number of cases distribution was evident.By applying the social vulnerability index and analyzing data from a total of 102 counties across the state of Illinois, we investigated which factors enhanced the risk of contracting the infection and which were related to a lower risk of infection. Our results showed that social factors such as belonging to a minority group, speaking English less than well, living in a multi-unit structure, and households with individuals of age group of 17 or younger were associated with a higher risk of infection. On the other hand, we found that factors such as living in a mobile home, individuals of age group 65 or older, low income per capita and, older than age 5 with disability were protective. We propose that communities with disproportionate health burdens can be identified by the application of these factors. Future efforts need to focus on decreasing the gap of disparity by modifying these social factors.

Spatial and Temporal Dynamics of Social Vulnerability in the United States from 1970 to 2010

2020 ◽  
Vol 11 (1) ◽  
pp. 36-54
Author(s):  
Gainbi Park ◽  
Zengwang Xu
Keyword(s):  
United States ◽  
Social Vulnerability ◽  
Temporal Dynamics ◽  
The United States ◽  
Human Society ◽  
Social Vulnerability Index ◽  
Frequent Change ◽  
Spatial And Temporal Dynamics ◽  
The Social ◽  
Over Time

Social vulnerability has been an important concept to characterize the extent to which human society is vulnerable to hazards. Although it is well known that social vulnerability varies across space and over time, there is only a paucity of studies to examine the basic patterns of the spatial and temporal dynamics of the social vulnerability in the United States. This study examines the spatial and temporal dynamics of social vulnerability of the U.S. counties from 1970 to 2010. For each decade, social vulnerability of counties is quantified by the social vulnerability index (SoVI) using county-level social, economic, demographic, and built environment characteristics. The SoVI is mainly designed to quantify the cross-sectional variation of social vulnerability and is not conducive to direct comparison over time. This study implements a methodology that integrates quantile standardization, sequence alignment analysis, and cluster analysis to investigate how social vulnerability of U.S. counties has changed over time. The authors find that U.S. counties exhibit distinctive spatial and longitudinal patterns, and there are counties/areas which have persistent high or low social vulnerability as well as frequent change in their social vulnerability over time. The results can be useful for policymakers, disaster managers, planning officials, and social scientists in general.

scholarly journals Social Vulnerability across the Great Lakes Basin: A County-Level Comparative and Spatial Analysis

2021 ◽  
Vol 13 (13) ◽  
pp. 7274
Author(s):  
Joshua T. Fergen ◽  
Ryan D. Bergstrom
Keyword(s):  
Great Lakes ◽  
Spatial Patterns ◽  
Social Vulnerability ◽  
Vulnerability Index ◽  
Social Vulnerability Index ◽  
Great Lakes Basin ◽  
Empirical Examination ◽  
Regional Patterns ◽  
Urban Rural ◽  
Social Vulnerabilities

Social vulnerability refers to how social positions affect the ability to access resources during a disaster or disturbance, but there is limited empirical examination of its spatial patterns in the Great Lakes Basin (GLB) region of North America. In this study, we map four themes of social vulnerability for the GLB by using the Center for Disease Control’s Social Vulnerability Index (CDC SVI) for every county in the basin and compare mean scores for each sub-basin to assess inter-basin differences. Additionally, we map LISA results to identify clusters of high and low social vulnerability along with the outliers across the region. Results show the spatial patterns depend on the social vulnerability theme selected, with some overlapping clusters of high vulnerability existing in Northern and Central Michigan, and clusters of low vulnerability in Eastern Wisconsin along with outliers across the basins. Differences in these patterns also indicate the existence of an urban–rural dimension to the variance in social vulnerabilities measured in this study. Understanding regional patterns of social vulnerability help identify the most vulnerable people, and this paper presents a framework for policymakers and researchers to address the unique social vulnerabilities across heterogeneous regions.

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