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 Spatial and temporal trends in social vulnerability and COVID-19 incidence and death rates in the United States

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248702
Author(s):  
Brian Neelon ◽  
Fedelis Mutiso ◽  
Noel T. Mueller ◽  
John L. Pearce ◽  
Sara E. Benjamin-Neelon
Keyword(s):  
Social Vulnerability ◽  
Primary Care Physicians ◽  
Negative Binomial ◽  
Temporal Trends ◽  
The United States ◽  
Social Vulnerability Index ◽  
Crossover Effect ◽  
Death Rates ◽  
The Impact ◽  
Over Time

Background Socially vulnerable communities may be at higher risk for COVID-19 outbreaks in the US. However, no prior studies examined temporal trends and differential effects of social vulnerability on COVID-19 incidence and death rates. Therefore, we examined temporal trends among counties with high and low social vulnerability to quantify disparities in trends over time. Methods We conducted a longitudinal analysis examining COVID-19 incidence and death rates from March 15 to December 31, 2020, for each US county using data from USAFacts. We classified counties using the Social Vulnerability Index (SVI), a percentile-based measure from the Centers for Disease Control and Prevention, with higher values indicating more vulnerability. Using a Bayesian hierarchical negative binomial model, we estimated daily risk ratios (RRs) comparing counties in the first (lower) and fourth (upper) SVI quartiles, adjusting for rurality, percentage in poor or fair health, percentage female, percentage of smokers, county average daily fine particulate matter (PM2.5), percentage of primary care physicians per 100,000 residents, daily temperature and precipitation, and proportion tested for COVID-19. Results At the outset of the pandemic, the most vulnerable counties had, on average, fewer cases per 100,000 than least vulnerable SVI quartile. However, on March 28, we observed a crossover effect in which the most vulnerable counties experienced higher COVID-19 incidence rates compared to the least vulnerable counties (RR = 1.05, 95% PI: 0.98, 1.12). Vulnerable counties had higher death rates starting on May 21 (RR = 1.08, 95% PI: 1.00,1.16). However, by October, this trend reversed and the most vulnerable counties had lower death rates compared to least vulnerable counties. Conclusions The impact of COVID-19 is not static but can migrate from less vulnerable counties to more vulnerable counties and back again over time.

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.

The Creationist Movement in the United States

2020 ◽  
pp. 63-88
Author(s):  
Kathleen C. Oberlin
Keyword(s):  
United States ◽  
Social Movement ◽  
The United States ◽  
Political Environment ◽  
Effective Leaders ◽  
Political Opportunities ◽  
The Social ◽  
Tactical Decision ◽  
Historical Examination ◽  
Over Time

Through a close historical examination of archived newsletters (1963-2007) from four different creationist organizations, this chapter traces potential sites Answers in Genesis might have built instead to reach and influence a broader public such as a college or a research center among other strategies. In light of these available alternatives, it shows how the museum emerged over time when Young Earth Creationists shifted the focus of the social movement away from Old Earth Creationism, advanced effective leaders who reassessed previous movement actions, and adapted to the sociocultural as well as political environment of the 1970s and 1980s. It argues the rise of Answers in Genesis as an organization and its tactical decision to build a museum only came as a surprise because scholars were previously limited to examining political opportunities and legislation advanced by the movement.

The Transformation of the Republican and Democratic Party Coalitions in the U.S.

2008 ◽  
Vol 6 (3) ◽  
pp. 433-450 ◽  
Author(s):  
Gary Miller ◽  
Norman Schofield
Keyword(s):  
United States ◽  
Republican Party ◽  
Democratic Party ◽  
The United States ◽  
Two Dimensional ◽  
The Social ◽  
William Jennings Bryan ◽  
The U.S ◽  
Over Time ◽  
Social Conservatives

Because the space of policies is two-dimensional, parties in the United States are coalitions of opposed interests. The Republican Party contains both socially conservative and socially liberal groups, though both tend to be pro-business. The increasing dominance of the social conservatives has angered some prominent Republicans, even causing a number of them to change party allegiance. Over time, the decreasing significance of the economic axis may cause the Republican Party to adopt policies that are analogous to those proposed by William Jennings Bryan in 1896: populist and anti-business. In parallel, the Democratic Party will increasingly appeal to pro-business, social liberals, so the party takes on the mantel of Lincoln.

scholarly journals Family Policy in the United States: State-Level Variation in Policy and Poverty Outcomes from 1980 to 2015

2020 ◽  
pp. 459-483
Author(s):  
Zachary Parolin ◽  
Rosa Daiger von Gleichen
Keyword(s):  
United States ◽  
Family Policy ◽  
State Level ◽  
The United States ◽  
Social Consequences ◽  
Level Variation ◽  
Transfer Programs ◽  
Medical Spending ◽  
The Social ◽  
Over Time

AbstractThis chapter investigates the diversity and divergence of three sets of family policy indicators across the 50 United States: money, services, and time. Our findings show that the 50 United States vary considerably in their family policy packages. States have become more dissimilar over time with respect to social assistance transfers and statutory minimum wages, but have become more similar in their subsidization of low-pay employment. Moreover, states vary greatly in their levels of support for early childhood education and healthcare. State-level variation in out-of-pocket medical spending has more than doubled from 1980 to 2015, in large part due to some states deciding to expand Medicaid access from 2009 onward. Despite large diversity and some divergence in states’ family policy packages, post-tax/transfer poverty rates have remained relatively stable over time. This is partially due to an increase in federally funded transfer programs mitigating the social consequences of state-level diversity.

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 &ndash; 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.

scholarly journals Spatial and temporal trends in social vulnerability and COVID-19 incidence and death rates in the United States

2020 ◽  
Author(s):  
Brian Neelon ◽  
Fedelis Mutiso ◽  
Noel T Mueller ◽  
John L Pearce ◽  
Sara E Benjamin-Neelon
Keyword(s):  
United States ◽  
Social Vulnerability ◽  
Negative Binomial ◽  
Temporal Trends ◽  
The United States ◽  
Systems Science ◽  
Crossover Effect ◽  
Death Rates ◽  
The Impact ◽  
Over Time

Background: Emerging evidence suggests that socially vulnerable communities are at higher risk for coronavirus disease 2019 (COVID-19) outbreaks in the United States. However, no prior studies have examined temporal trends and differential effects of social vulnerability on COVID-19 incidence and death rates. The purpose of this study was to examine temporal trends among counties with high and low social vulnerability and to quantify disparities in these trends over time. We hypothesized that highly vulnerable counties would have higher incidence and death rates compared to less vulnerable counties and that this disparity would widen as the pandemic progressed. Methods: We conducted a retrospective longitudinal analysis examining COVID-19 incidence and death rates from March 1 to August 31, 2020 for each county in the US. We obtained daily COVID-19 incident case and death data from USAFacts and the Johns Hopkins Center for Systems Science and Engineering. We classified counties using the Social Vulnerability Index (SVI), a percentile-based measure from the Centers for Disease Control and Prevention in which higher scores represent more vulnerability. Using a Bayesian hierarchical negative binomial model, we estimated daily risk ratios (RRs) comparing counties in the first (lower) and fourth (upper) SVI quartiles. We adjusted for percentage of the county designated as rural, percentage in poor or fair health, percentage of adult smokers, county average daily fine particulate matter (PM2.5), percentage of primary care physicians per 100,000 residents, and the proportion tested for COVID-19 in the state. Results: In unadjusted analyses, we found that for most of March 2020, counties in the upper SVI quartile had significantly fewer cases per 100,000 than lower SVI quartile counties. However, on March 30, we observed a crossover effect in which the RR became significantly greater than 1.00 (RR = 1.10, 95% PI: 1.03, 1.18), indicating that the most vulnerable counties had, on average, higher COVID-19 incidence rates compared to least vulnerable counties. Upper SVI quartile counties had higher death rates on average starting on March 30 (RR = 1.17, 95% PI: 1.01,1.36). The death rate RR achieved a maximum value on July 29 (RR = 3.22, 95% PI: 2.91, 3.58), indicating that most vulnerable counties had, on average, 3.22 times more deaths per million than the least vulnerable counties. However, by late August, the lower quartile started to catch up to the upper quartile. In adjusted models, the RRs were attenuated for both incidence cases and deaths, indicating that the adjustment variables partially explained the associations. We also found positive associations between COVID-19 cases and deaths and percentage of the county designated as rural, percentage of resident in fair or poor health, and average daily PM2.5. Conclusions: Results indicate that the impact of COVID-19 is not static but can migrate from less vulnerable counties to more vulnerable counties over time. This highlights the importance of protecting vulnerable populations as the pandemic unfolds.

scholarly journals As Disaster Costs Rise, So Does Inequality

2018 ◽  
Vol 4 ◽  
pp. 237802311881679 ◽  
Author(s):  
Junia Howell ◽  
James R. Elliott
Keyword(s):  
United States ◽  
Natural Hazards ◽  
Random Sample ◽  
Natural Hazard ◽  
Social Inequalities ◽  
The United States ◽  
County Level ◽  
The Social ◽  
Damage Data ◽  
Over Time

Across the United States, communities are experiencing increases in the frequency and severity of natural hazards. The pervasiveness and upward trajectory of these damages are worrisome enough, but equally disconcerting are the social inequalities they can leave in their wake. To examine these inequalities, the authors linked county-level damage data to a random sample of American households. The authors visualize the pervasiveness of natural hazards as well as their influence on racial wealth gaps over time. The results show that natural hazard damages and how relief is provided afterward exacerbate the growing gap between white and black wealth.

scholarly journals Word embeddings quantify 100 years of gender and ethnic stereotypes

2018 ◽  
Vol 115 (16) ◽  
pp. E3635-E3644 ◽  
Author(s):  
Nikhil Garg ◽  
Londa Schiebinger ◽  
Dan Jurafsky ◽  
James Zou
Keyword(s):  
United States ◽  
Machine Learning ◽  
Temporal Dynamics ◽  
Temporal Analysis ◽  
The United States ◽  
English Word ◽  
Attitudes Toward Women ◽  
Word Embeddings ◽  
Asian Immigration ◽  
Over Time

Word embeddings are a powerful machine-learning framework that represents each English word by a vector. The geometric relationship between these vectors captures meaningful semantic relationships between the corresponding words. In this paper, we develop a framework to demonstrate how the temporal dynamics of the embedding helps to quantify changes in stereotypes and attitudes toward women and ethnic minorities in the 20th and 21st centuries in the United States. We integrate word embeddings trained on 100 y of text data with the US Census to show that changes in the embedding track closely with demographic and occupation shifts over time. The embedding captures societal shifts—e.g., the women’s movement in the 1960s and Asian immigration into the United States—and also illuminates how specific adjectives and occupations became more closely associated with certain populations over time. Our framework for temporal analysis of word embedding opens up a fruitful intersection between machine learning and quantitative social science.

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