CDC Social Vulnerability Index (SVI): Nevada Counties

International Journal of Applied Geospatial Research (IJAGR) , 2020

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.

Natural Hazards, 2022

Social vulnerability index (SoVI) has been widely used to measure the extent to which people or places are socially vulnerable. The SoVI is an aggregate composite index that linearly combines a few principal components resulted from the principal components analysis on a number of selected social vulnerability indicator variables, and it can quantify the relative level of overall social vulnerability but cannot inform the specific local social indicators that contribute to the vulnerability in various degrees. The specific social indicators that either attenuate or amplify local social vulnerability are of much need in policy making to reduce social vulnerability. This study explores the differential contributions of the constituent components of SoVI and investigates how the local indicator variables have evolved over time and across the Greater Houston metropolitan area in the USA using the geographically weighted principal components analysis. It found that the overall social vulnerability as measured by SoVI has exhibited persistent spatial patterns in the Greater Houston area since 1970; however, the spatial patterns of the SoVI are not equally constituted by the components of the SoVI. In particular, the high social vulnerability of suburban areas is mainly the result of one principal component that highly correlates with the percentage of mobile homes. It also found that the indicator variables of social vulnerability have exhibited great spatial heterogeneity and dependence at local scale, and they vary over time but persist on disadvantages in economic condition, mobility, and family structure.

Risk Analysis, 2008

In 2003, the Social Vulnerability Index (SOVI), which utilizes Principal Component Analysis (PCA), was created by Cutter, Boruff, and Shirley to examine spatial patterns of social vulnerability at the county level in the United States. This paper seeks to identify the sensitivity of this approach to changes in its construction, the scale at which it is applied, and to various geographic contexts. To determine the impact of scalar changes, the SOVI was calculated for multiple aggregation levels in the state of South Carolina. To examine the sensitivity of the algorithm to changes in construction, and determine if that sensitivity was constant in various geographic contexts, census data was collected at a sub-metropolitan level for portions of three U.S. cities. Fifty-four unique variations of the SOVI were calculated for each area. Each set of indexes was then evaluated using factorial analysis to see if substantial changes in assigned values occurred. These results were then compared across study areas to evaluate the impact of changing geographic context. While decreases in the scale of aggregation were found to result in decreases in the variability explained by the PCA, and increases in the variance of the resulting index values, the subjective interpretations yielded from the SOVI remained fairly stable. The algorithm was found to be sensitive to certain changes in index construction, which differed somewhat between study areas. Understanding the impacts of changes in index construction and scale are crucial in increasing confidence in attempts to represent the extremely complex phenomenon of social vulnerability.

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.

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.

2005

This dissertation is dedicated to everyone who believed, trusted, supported, amused, comforted, and generally had the patience to put up with me from the start of my educational expedition until the completion of this document. I would not be where I am today without the love and energy of these many people. Specifically, I dedicated this manuscript to my wife and best friend, Laurie, without whose unconditional support I would not have made it through this process, and to my family whose love and understanding has helped me to endure this research and not loose my positive outlook. To all those who find themselves in the same predicament that I was in not too long ago-to far from the beginning to drop it all, and too far from the end to notice that you have made progress along the journey-keep the faith, you will reach the goal lineafter all, if I can finish anyone can. I would especially like to dedicate this piece of labor and love to my departed Uncle LJ, Aunt Heidi, Grandma and Grandpa Emrich, and Noreen Flynn. Not a day went by during the composition of this tome that their love and spiritual support could not be felt by me-especially on the days when the end seemed no where in sight. Their departure from this world has taught me to take hold of every moment and cherish every day that we have here on earth. Through their lives, I have learned about passion and compassion, tolerance and empathy, pride and humility. I have grown into the person that I am today because of the hand that they have played in my life. Additionally, I would like to acknowledge all of the faculty and staff of the USC Geography department, especially Capers-who has been there for moral support since the first day I met him-when he offered to take me fishing, and Elizabeth, whose jovial demeanor makes me smile every morning whether I am happy or not. Special thanks is given to all of my fellow graduate students who have taken the good with the bad and are still there for support, especially all of the HRLers, past and present, who have put up with my moodiness, mindlessness, insanity and occasional normalcy through the years. Thanks for being there to bounce ideas off of and to set me straight when I had no idea that I was lost. To all those unnamed persons who played a part in the completion of this dissertation-I thank you.

BMC Public Health

Background Social vulnerability occurs when the disadvantage conveyed by poor social conditions determines the degree to which one’s life and livelihood are at risk from a particular and identifiable event in health, nature, or society. A common way to estimate social vulnerability is through an index aggregating social factors. This scoping review broadly aimed to map the literature on social vulnerability indices. Our main objectives were to characterize social vulnerability indices, understand the composition of social vulnerability indices, and describe how these indices are utilized in the literature. Methods A scoping review was conducted in six electronic databases to identify original research, published in English, French, Dutch, Spanish or Portuguese, and which addressed the development or use of a social vulnerability index (SVI). Titles, abstracts, and full texts were screened and assessed for eligibility. Data were extracted on the indices and simple descriptive statist...

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.

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.