Social vulnerability to heat in Greater Atlanta, USA: spatial pattern of heat, NDVI, socioeconomics and household composition

2017 ◽  
Author(s):  
Sunhui Sim
Keyword(s):  
Spatial Pattern ◽  
Social Vulnerability ◽  
Household Composition

scholarly journals Applying the Social Vulnerability Index as a Leading Indicator to Protect Fire-Based Emergency Medical Service Responders’ Health

2021 ◽  
Vol 18 (15) ◽  
pp. 8049
Author(s):  
Emily J. Haas ◽  
Alexa Furek ◽  
Megan Casey ◽  
Katherine N. Yoon ◽  
Susan M. Moore
Keyword(s):  
Emergency Response ◽  
Social Vulnerability ◽  
Vulnerability Index ◽  
Fire Service ◽  
Household Composition ◽  
Fire Departments ◽  
Social Vulnerability Index ◽  
Response Planning ◽  
Leading Indicator ◽  
Increased Risk

During emergencies, areas with higher social vulnerability experience an increased risk for negative health outcomes. However, research has not extrapolated this concept to understand how the workers who respond to these areas may be affected. Researchers from the National Institute for Occupational Safety and Health (NIOSH) merged approximately 160,000 emergency response calls received from three fire departments during the COVID-19 pandemic with the CDC’s publicly available Social Vulnerability Index (SVI) to examine the utility of SVI as a leading indicator of occupational health and safety risks. Multiple regressions, binomial logit models, and relative weights analyses were used to answer the research questions. Researchers found that higher social vulnerability on household composition, minority/language, and housing/transportation increase the risk of first responders’ exposure to SARS-CoV-2. Higher socioeconomic, household, and minority vulnerability were significantly associated with response calls that required emergency treatment and transport in comparison to fire-related or other calls that are also managed by fire departments. These results have implications for more strategic emergency response planning during the COVID-19 pandemic, as well as improving Total Worker Health® and future of work initiatives at the worker and workplace levels within the fire service industry.

Social vulnerability and clinical trial access for older adults with myeloma in North Carolina.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e18540-e18540
Author(s):  
Shakira Jeanene Grant ◽  
Matthew Jansen ◽  
Sascha Tuchman ◽  
Samuel M. Rubinstein ◽  
Eben I. Lichtman ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Socioeconomic Status ◽  
North Carolina ◽  
Social Vulnerability ◽  
Household Composition ◽  
Comprehensive Cancer Center ◽  
County Level ◽  
Phase 2 ◽  
Financial Barriers ◽  
Trial Site

e18540 Background: Multiple myeloma (MM) is a disease of aging, associated with one of the greatest black-white disparities in incidence and mortality among all US cancer types. Clinical trials provide the critical evidence-base to inform clinical management in all cancers, including MM. However, clinical trial participants are often younger (age < 65 years) and white, limiting the generalizability of published data to real-world MM care. Although geographical and financial barriers to clinical trial participation are well recognized, less is known about the association of county-level social vulnerability with MM trial availability. We examined county-level variation in the number of registered myeloma trials per 10,000 North Carolina (NC) residents age ≥ 65 years as a function of social vulnerability and the presence of a National Cancer Institute Comprehensive Cancer Center (CCC). Methods: We conducted a cross-sectional study using data from ClinicalTrials.gov to identify all registered interventional myeloma trials involving adults age ≥ 65 years with sites in NC. Records were downloaded on January 24th, 2021. This strategy yielded 456 non-unique NC sites for 223 trials. We obtained county locations for all trial sites by matching city, zip code, or institution name. We obtained NC population data for residents age ≥ 65 years (in 2019) from the American Community Survey. The four themes (socioeconomic status, household composition, ethnic and racial minority status/language, housing/transportation) within the Centers for Disease Control Social Vulnerability Index (CDC SVI) (composite score: 0-1, with a higher number indicating more vulnerability) were used to characterize county-level social vulnerability. We performed negative binomial regression and tabulations using R, version 3.6.1. A p-value < 0.05 was considered statistically significant. Results: Across 100 counties in NC, trial site counts by county per 10,000 residents age ≥ 65 years ranged from 0 to 23.2 (mean: 1.5, median: 0; IQR, 0-0.7). Controlling for the 4 SVI themes, counties with CCCs (Durham, Forsyth, Orange) had 77% more trials than those without CCCs [Incidence Rate Ratio (IRR): 7.74; p = 0.05]. We observed a 3.3% reduction in trial counts with each percentile increase in socioeconomic vulnerability (IRR: 0.97; p = 0.008). Counties with higher representation by racial and ethnic minorities had similar trial site counts to counties with lower minority populations (IRR: 1.01; p = 0.08). Sub-group analyses of early-stage studies (phase 1/2 and phase 2; n = 268) and late-stage studies (phase 2/3 and phase 3; n = 168) were similar. Conclusions: Our preliminary results suggest county-level socioeconomic status is associated with the distribution of MM clinical trial sites across NC. Further work is planned to explore whether additional variances in trial distribution could be explained by site- and study-specific characteristics.

scholarly journals A GIS-Based Approach for Flood Risk Zoning by Combining Social Vulnerability and Flood Susceptibility: A Case Study of Nanjing, China

2021 ◽  
Vol 18 (21) ◽  
pp. 11597
Author(s):  
Yi Chen ◽  
Zhicong Ye ◽  
Hui Liu ◽  
Ruishan Chen ◽  
Zhenhuan Liu ◽  
...  
Keyword(s):  
Spatial Pattern ◽  
Flood Risk ◽  
Social Vulnerability ◽  
Flood Hazard ◽  
Central City ◽  
Flood Disaster ◽  
Social Vulnerability Index ◽  
Urban Centre ◽  
Flood Susceptibility

The identification of vulnerable people and places to flood is crucial for effective disaster risk management. Here, we combine flood hazard and social vulnerability index to capture the potential risk of flood. In this paper, Nanjing was taken as the case study to explore the spatial pattern of social vulnerability towards flood at the community scale by developing an index system. Based on the flood risk results of ArcSWAT, the risk of flood disaster in Nanjing was evaluated. The results show the following. (1) Social vulnerability exhibits a central–peripheral pattern in general, which means that the social vulnerability degree is high in the central city and decreases gradually to the suburbs. (2) The susceptibility to flood disaster has a similar circle-layer pattern that is the highest in the urban centre, lower in the exurban areas, and the lowest in the suburb areas. (3) By using the GIS-based zoning approach, communities are classified into four types by comprehensively considering their flood susceptibility and social vulnerability. The spatial pattern is explained, and policy recommendation for reducing flood risk is provided for each type of community. The research has important reference significance for identifying the spatial pattern of social vulnerability to flood and then formulating targeted adaptation countermeasures.

Can the Social Vulnerability Index Be Used for More Than Emergency Preparedness? An Examination Using Youth Physical Fitness Data

2016 ◽  
Vol 13 (2) ◽  
pp. 121-130 ◽  
Author(s):  
Jennifer L. Gay ◽  
Sara W. Robb ◽  
Kelsey M. Benson ◽  
Alice White
Keyword(s):  
Body Mass Index ◽  
Physical Fitness ◽  
Body Mass ◽  
Emergency Preparedness ◽  
Social Vulnerability ◽  
Vulnerability Index ◽  
Household Composition ◽  
Minority Status ◽  
Social Vulnerability Index ◽  
The Social

Background:The Social Vulnerability Index (SVI), a publicly available dataset, is used in emergency preparedness to identify communities in greatest need of resources. The SVI includes multiple socioeconomic, demographic, and geographic indicators that also are associated with physical fitness and physical activity. This study examined the utility of using the SVI to explain variation in youth fitness, including aerobic capacity and body mass index.Methods:FITNESSGRAM data from 2,126 Georgia schools were matched at the census tract level with SVI themes of socioeconomic, household composition, minority status and language, and housing and transportation. Multivariate multiple regression models were used to test whether SVI factors explained fitness outcomes, controlling for grade level (ie, elementary, middle, high school) and stratified by gender.Results:SVI themes explained the most variation in aerobic fitness and body mass index for both boys and girls (R2 values 11.5% to 26.6%). Socioeconomic, Minority Status and Language, and Housing and Transportation themes were salient predictors of fitness outcomes.Conclusions:Youth fitness in Georgia was related to socioeconomic, demographic, and geographic themes. The SVI may be a useful needs assessment tool for health officials and researchers examining multilevel influences on health behaviors or identifying communities for prevention efforts.

scholarly journals INDICADORES SOCIAIS A PARTIR DA CARTOGRAFIA DE VULNERABILIDADE: exemplos de Bragança Paulista e Campos do Jordão (São Paulo – Brasil)

2020 ◽  
Vol 5 (19) ◽  
pp. 202015
Author(s):  
Franciele Caroline Guerra ◽  
Bruno Zucherato ◽  
Roger Dias Gonçalves
Keyword(s):  
Social Vulnerability ◽  
Social Indicators ◽  
Sao Paulo ◽  
Living Condition ◽  
Household Composition ◽  
São Paulo ◽  
Toma De Decisiones ◽  
Local Decision ◽  
Basic Services ◽  
Peripheral Areas

SOCIAL INDICATORS FROM THE VULNERABILITY CARTOGRAPHY: examples of Bragança Paulista and Campos do Jordão (São Paulo – Brazil)INDICADORES SOCIALES DE LA CARTOGRAFÍA DE VULNERABILIDAD: ejemplos de Bragança Paulista y Campos do Jordão (São Paulo – Brasil)RESUMOEste artigo aborda uma pesquisa que conduziu um estudo comparativo sobre a espacialização da vulnerabilidade social na área de setores censitários urbanos de dois municípios brasileiros: Bragança Paulista/SP e Campos do Jordão/SP, por meio da utilização de um indicador simplificado de vulnerabilidade social. Após a revisão de literatura em estudo, foram selecionados 12 temas de variáveis para a seleção de dados quantitativos para a composição de um indicador simplificado de vulnerabilidade social. Assim, foram verificadas, no rol de informações disponibilizadas pelo Censo 2010, dez variáveis que se relacionavam aos seguintes eixos temáticos: Educação, Etnias, Gênero, Idade, Renda, Condição de Moradia, Características dos Moradores, Infraestrutura e Serviços básicos. Após a recolha e a tabulação desses dados, foi procedida a criação de um índice de vulnerabilidade social, sendo este espacializado para que fosse possível estabelecer a representação da vulnerabilidade nas duas áreas de estudo. Ao final, foi possível observar as áreas que são mais vulneráveis e as áreas com menor vulnerabilidade, o que permitiu estabelecer os padrões de urbanização que impulsionaram e frearam esses processos. Tal espacialização da vulnerabilidade social expôs áreas centrais das cidades com baixa vulnerabilidade e áreas periféricas com alta vulnerabilidade, obedecendo ao padrão de oposição centro-periferia. Os indicadores sociais podem ser empregados como suporte para os tomadores de decisões locais identificarem os padrões de vulnerabilidade, melhorar a avaliação dos impactos sociais de suas decisões e preparar instrumentos multisetoriais no planejamento de redução de riscos de desastres que abordem os fatores de vulnerabilidade social.Palavras-chave: Vulnerabilidade Socioambiental; Indicador Social; Análise de Risco; Cartografia.ABSTRACTThis research conducts a comparative analysis of the spatial variability of social vulnerability in the area of urban census tracts encompassing the Brazilian municipalities of Bragança Paulista/SP and Campos do Jordão/SP, employing a simplified indicator of social vulnerability. After reviewing the literature, 12 themes of variables compose the quantitative dataset from which the simplified indicator of social vulnerability was calculated. Accordingly, the list of information provided by Censo 2010 allowed us to determine ten variables related to the following thematic axes: Education, Ethnicity, Gender, Age, Income, Living Condition, Household composition, Infrastructure and Basic Services. After the collection and tabulation of these data, an index was created and spatialized in order to establish the representation of vulnerability in the two study areas. Finally, it was possible to properly observe the areas that are most vulnerable and the areas with the lowest vulnerability, which allowed to better understand the patterns of urbanization that impelled and stopped these processes. The spatial patterns of social vulnerability show low vulnerability in central areas and high vulnerability in peripheral areas, obeying the opposing-pattern center-periphery. These social indicators may be used to help local decision makers to understand patterns of vulnerability, to improve the assessment of social impacts of their decisions and to prepare multi-sector disaster risk reduction planning instruments that address the drivers of social vulnerability.Keywords: Socio-environmental Vulnerability; Social Indicator; Risk Analysis; Cartography.RESUMENEl objetivo de esta investigación fue realizar un estudio comparativo sobre la espacialización de la vulnerabilidad social en el área de las secciones censales urbanas en dos municipios brasileños: Bragança Paulista/SP y Campos do Jordão/SP, utilizando un indicador simplificado de vulnerabilidad social. Después de revisar la literatura de estudio, se seleccionaron 12 temas variables para la selección de datos cuantitativos para la composición de un indicador simplificado de vulnerabilidad social. Así, en la lista de información proporcionada por el Censo 2010, 9 variables se relacionaron con los siguientes ejes temáticos: Educación, Etnia, Género, Edad, Ingresos, Condición de la vivienda, Características residenciales, Infraestructura y Servicios básicos. Después de recopilar y tabular estos datos, se creó un índice y este se espacializó para que fuera posible establecer la representación de la vulnerabilidad en ambas áreas de estudio. Al final, fue posible observar las áreas más vulnerables y las áreas con menos vulnerabilidad, lo que permitió el establecimiento de patrones de urbanización que impulsaron y detuvieron estos procesos. La espacialización de la vulnerabilidad mostró áreas centrales de ciudades con baja vulnerabilidad y áreas periféricas con alta vulnerabilidad que obedecen al patrón de oposición centro-periferia. Estos indicadores sociales pueden usarse para ayudar a los responsables locales de la toma de decisiones a comprender los patrones de vulnerabilidad, mejorar la evaluación de los impactos sociales de sus decisiones y preparar instrumentos de planificación multisectoriales para reducción de riesgos de desastres y que aborden los impulsores de la vulnerabilidad social.Palabras clave: Vulnerabilidad Socioambiental; Indicador Social; Análisis de Riesgo; Cartografía.

scholarly journals Examining Social Vulnerability and the Association With COVID-19 Incidence in Harris County, Texas

2022 ◽  
Vol 9 ◽  
Author(s):  
Guillermo A. Tortolero ◽  
Marcia de Oliveira Otto ◽  
Ryan Ramphul ◽  
Jose-Miguel Yamal ◽  
Alison Rector ◽  
...  
Keyword(s):  
Social Vulnerability ◽  
Household Composition ◽  
Community Level ◽  
Minority Status ◽  
Harris County ◽  
Census Tracts ◽  
Incidence Data ◽  
The Relationship ◽  
Staying At Home ◽  
At Home

Studies have investigated the association between social vulnerability and SARS-CoV-2 incidence. However, few studies have examined small geographic units such as census tracts, examined geographic regions with large numbers of Hispanic and Black populations, controlled for testing rates, and incorporated stay-at-home measures into their analyses. Understanding the relationship between social vulnerability and SARS-CoV-2 incidence is critical to understanding the interplay between social determinants and implementing risk mitigation guidelines to curtail the spread of infectious diseases. The objective of this study was to examine the relationship between CDC's Social Vulnerability Index (SVI) and SARS-CoV-2 incidence while controlling for testing rates and the proportion of those who stayed completely at home among 783 Harris County, Texas census tracts. SARS-CoV-2 incidence data were collected between May 15 and October 1, 2020. The SVI and its themes were the primary exposures. Median percent time at home was used as a covariate to measure the effect of staying at home on the association between social vulnerability and SARS-CoV-2 incidence. Data were analyzed using Kruskal Wallis and negative binomial regressions (NBR) controlling for testing rates and staying at home. Results showed that a unit increase in the SVI score and the SVI themes were associated with significant increases in SARS-CoV-2 incidence. The incidence risk ratio (IRR) was 1.090 (95% CI, 1.082, 1.098) for the overall SVI; 1.107 (95% CI, 1.098, 1.115) for minority status/language; 1.090 (95% CI, 1.083, 1.098) for socioeconomic; 1.060 (95% CI, 1.050, 1.071) for household composition/disability, and 1.057 (95% CI, 1.047, 1.066) for housing type/transportation. When controlling for stay-at-home, the association between SVI themes and SARS-CoV-2 incidence remained significant. In the NBR model that included all four SVI themes, only the socioeconomic and minority status/language themes remained significantly associated with SARS-CoV-2 incidence. Community-level infections were not explained by a communities' inability to stay at home. These findings suggest that community-level social vulnerability, such as socioeconomic status, language barriers, use of public transportation, and housing density may play a role in the risk of SARS-CoV-2 infection regardless of the ability of some communities to stay at home because of the need to work or other reasons.

Abstract P133: Geospatial Mapping of Prehospital Delay in Acute Ischemic Stroke and Association With Social Vulnerability

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Amar Dhand ◽  
Amber Nieves ◽  
Molly Jarman ◽  
Regan Bergmark ◽  
Robert Semco ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Social Vulnerability ◽  
Arrival Time ◽  
Census Data ◽  
The United States ◽  
Household Composition ◽  
Social Vulnerability Index ◽  
Major Barrier ◽  
Prehospital Delay ◽  
Geospatial Mapping

Introduction: Prehospital delay, defined as the delay between symptom discovery and hospital arrival, remains a major barrier to timely acute stroke treatments. Delay is worse in socially vulnerable populations. A geospatial map of prehospital delay may identify high-risk areas and highlight the role of community social vulnerability in delay. We hypothesized that a community’s social vulnerability would be associated with delay. Methods: We analyzed national Get With The Guidelines ischemic stroke data between 2015 and 2017. We calculated the median arrival time (symptom discovery-to-door times) for each Zip Code Tabulation Area (ZCTA), and created geospatial map using ArcGIS. The primary exposure variable was the Center for Disease Control’s Social Vulnerability Index (SVI), and its 4 subcomponents. The SVI is a composite metric of community vulnerability using U.S. Census data (0, least vulnerable to 1, most vulnerable). To account for clustering within ZCTAs, we performed a multilevel linear regression of community-level SVI and patient-level prehospital delay. Results: During the study period, 149,774 patients had an ischemic stroke in 16,949 ZCTAs. Across patients, the median time of arrival was 140 mins, IQR was 60-459 mins, and range was 1-1439 mins. Arrival by 2h occurred in 46% of patients. Multilevel regression showed a strong positive association between the SVI and prehospital delay, evident in the maps (Figure). For every 10% increase in the SVI, the arrival time increased by 38 minutes [CI, 30 - 47] (p<0.001). Considering the 4 SVI subcomponents, delay was most strongly associated with socioeconomic status, household composition, and housing/transportation, but not minority status/language. Conclusion: Using geospatial mapping of prehospital delay across the United States, we show that community SVI is strongly associated with delayed ischemic stroke arrival. These maps help identify communities to target for stroke preparedness campaigns.

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 Biclustering Method to Capture the Spatial Pattern and to Identify the Causes of Social Vulnerability in Indonesia: A New Recommendation for Disaster Mitigation Policy

2019 ◽  
Vol 157 ◽  
pp. 31-37 ◽  
Author(s):  
Puspita Anggraini Kaban ◽  
Robert Kurniawan ◽  
Rezzy Eko Caraka ◽  
Bens Pardamean ◽  
Budi Yuniarto ◽  
...  
Keyword(s):  
Spatial Pattern ◽  
Social Vulnerability ◽  
Disaster Mitigation ◽  
Mitigation Policy

Social Vulnerability and Access of Local Medical Care During Hurricane Harvey: A Spatial Analysis

2021 ◽  
pp. 1-9
Author(s):  
David S. Rickless ◽  
Grete E. Wilt ◽  
J. Danielle Sharpe ◽  
Noelle Molinari ◽  
William Stephens ◽  
...  
Keyword(s):  
Medical Care ◽  
Social Vulnerability ◽  
Vulnerability Index ◽  
Emergency Department Visits ◽  
Household Composition ◽  
Social Vulnerability Index ◽  
Socioeconomic Vulnerability ◽  
Control And Prevention ◽  
Impact Area ◽  
Education And Employment

Abstract Objectives: When Hurricane Harvey struck the coastline of Texas in 2017, it caused 88 fatalities and over US $125 billion in damage, along with increased emergency department visits in Houston and in cities receiving hurricane evacuees, such as the Dallas-Fort Worth metroplex (DFW). This study explored demographic indicators of vulnerability for patients from the Hurricane Harvey impact area who sought medical care in Houston and in DFW. The objectives were to characterize the vulnerability of affected populations presenting locally, as well as those presenting away from home, and to determine whether more vulnerable communities were more likely to seek medical care locally or elsewhere. Methods: We used syndromic surveillance data alongside the Centers for Disease Control and Prevention Social Vulnerability Index to calculate the percentage of patients seeking care locally by zip code tabulation area. We used this variable to fit a spatial lag regression model, controlling for population density and flood extent. Results: Communities with more patients presenting for medical care locally were significantly clustered and tended to have greater socioeconomic vulnerability, lower household composition vulnerability, and more extensive flooding. Conclusions: These findings suggest that populations remaining in place during a natural disaster event may have needs related to income, education, and employment, while evacuees may have more needs related to age, disability, and single-parent household status.

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