Explore the vast range of titles available.

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 statistics and counts were used to produce a narrative summary. Results In total, 292 studies were included, of which 126 studies came from environmental, climate change or disaster planning fields of study and 156 studies were from the fields of health or medicine. The mean number of items per index was 19 (SD 10.5) and the most common source of data was from censuses. There were 122 distinct items in the composition of these indices, categorized into 29 domains. The top three domains included in the SVIs were: at risk populations (e.g., % older adults, children or dependents), education, and socioeconomic status. SVIs were used to predict outcomes in 47.9% of studies, and rate of Covid-19 infection or mortality was the most common outcome measured. Conclusions We provide an overview of SVIs in the literature up to December 2021, providing a novel summary of commonly used variables for social vulnerability indices. We also demonstrate that SVIs are commonly used in several fields of research, especially since 2010. Whether in the field of disaster planning, environmental science or health sciences, the SVIs are composed of similar items and domains. SVIs can be used to predict diverse outcomes, with implications for future use as tools in interdisciplinary collaborations.

To explore associations between histoplasmosis and race and ethnicity, socioeconomic status, and rurality, we conducted an in-depth analysis of social determinants of health and histoplasmosis in 8 US states. Using the Minority Health Social Vulnerability Index (MH SVI), we analyzed county-level histoplasmosis incidence (cases/100,000 population) from the 8 states by applying generalized linear mixed hurdle models. We found that histoplasmosis incidence was higher in counties with limited healthcare infrastructure and access as measured by the MH SVI and in more rural counties. Other social determinants of health measured by the MH SVI tool either were not significantly or were inconsistently associated with histoplasmosis incidence. Increased awareness of histoplasmosis, more accessible diagnostic tests, and investment in rural health services could address histoplasmosis-related health disparities.

Vulnerability to hazards not only relies on the extent of natural hazards but also depends on the social and economic conditions of the communities. Therefore, this study intends to construct a household-level social vulnerability at the microscale in the nine wards of Chalna Municipality (CM), Dacope upazila, in southwest coastal Bangladesh by employing the social vulnerability index (SoVI). We surveyed 30 households from each ward to collect data on 33 vulnerability indicators. Of these, seven indicators were extracted by principal component analysis (PCA), which explained 96.34% of the total variance. The PCA results indicate that high population density, poor economic condition, the presence of vulnerable groups, unstable income generating sources, unplanned urban and poor infrastructure, lack of services, and lack of adequate sewage systems are the key drivers of social vulnerability of the CM. The SoVI score was generated using seven PCA outcomes for the respective wards. Results revealed that 44.45% of the areas are medium–high to high (wards 2, 4, 5, and 6) vulnerable. The highest level of social vulnerability was distributed in ward 5, while ward 8 was identified as the least vulnerable. About 33.33% of the CM was found as medium vulnerable areas (wards 1, 7, and 9). Ward 3 was categorized as a low–medium vulnerable area. The findings of the study will provide useful information for decision-makers and disaster managers to develop sustainable disaster management plans for coastal Bangladesh to reduce social vulnerability as well as to decrease the impacts of natural disasters.

BackgroundOhio ranks 43rd in the nation in infant mortality rates (IMR); with IMR among non-Hispanic black infants is three times higher than white infants.ObjectiveTo identify the social factors determining the vulnerability of Ohio counties to IMR and visualize the spatial association between relative social vulnerability and IMR at county and census tract levels.MethodsThe social vulnerability index (SVICDC) is a measure of the relative social vulnerability of a geographic unit. Five out of 15 social variables in the SVICDC were utilized to create a customized index for IMR (SVIIMR) in Ohio. The bivariate descriptive maps and spatial lag model were applied to visualize the quantitative relationship between SVIIMR and IMR, accounting for the spatial autocorrelation in the data.ResultsSoutheastern counties in Ohio displayed highest IMRs and highest overall SVIIMR; specifically, highest vulnerability to poverty, no high school diploma, and mobile housing. In contrast, extreme northwestern counties exhibited high IMRs but lower overall SVIIMR. Spatial regression showed five clusters where vulnerability to low per capita income in one county significantly impacted IMR (p = 0.001) in the neighboring counties within each cluster. At the census tract-level within Lucas county, the Toledo city area (compared to the remaining county) had higher overlap between high IMR and SVIIMR.ConclusionThe application of SVI using geospatial techniques could identify priority areas, where social factors are increasing the vulnerability to infant mortality rates, for potential interventions that could reduce disparities through strategic and equitable policies.SignificancePrior studies have recognized the impact of social determinants on infant mortality and explored the spatial distribution in the state of Ohio. This study introduces a novel integration of the Social Vulnerability Index with geospatial analysis to pinpoint where social vulnerabilities overlap with high infant mortality rates. By mapping these intersections at the county and census tract levels, our research identifies specific areas in Ohio that are priority targets for intervention. This contribution not only advances the understanding of spatial patterns but also strategizes a prioritized response to addressing social determinants to reduce disparities in infant mortality.

Although social vulnerability has recently gained attention in academic studies, Brazil lacks frameworks and indicators to assess it for the entire country.Social vulnerability highlights differences in the human capacity to prepare for, respond to, and recover from disasters. It varies over space and time, and among and between social groups, largely due to differences in socioeconomic and demographic characteristics. This article provides a social vulnerability index(SoVI~) replication study for Brazil and shows how SoVI~concepts and indicators were adapted to the country. SoVI~Brazil follows the place-based framework adopted in the Social Vulnerability Index initially developed for the United States. Using a principal component analysis(PCA), 45city-level indicators were reduced to 10 factors that explain about 67 % of the variance in the data. Clearly identified spatial patterns showed a concentration of the most socially vulnerable cities in the North and Northeast regions of Brazil, as well as the social vulnerability of metropolitan areas and state capitals in the South and Southeast regions.The least vulnerable cities are mainly concentrated in the inland regions of the Southeast. Although different factors contribute to the social vulnerability in each city, the overall results confirm the social and economic disparities among Brazilian’s regions and reflect a differential vulnerability to natural hazards at local to regional scales.

The presence of anthropogenic activities in the coastal areas of the South Central Timor (SCT) Regency has weakened coastal resilience, which may exacerbate the impact of rising sea levels. One important factor that needs to be analyzed is the vulnerability assessment. This study, conducted from July to September 2024, aimed to determine the spatial distribution and variables that can influence the vulnerability in the coastal areas. The methods used were the Coastal Vulnerability Index (CVI) and the Social Vulnerability Index (SoVI), which then used Multi Criteria Analysis (MCA) to perform the standardization value. The integrated index values were then integrated into the Geographic Information System (GIS) for comprehensive spatial information. The results showed that, in general, the coastal areas of the SCT Regency were in the low (35%), medium (48%), and high (66%) risk categories. Areas of high physical vulnerability were alluvial lowland areas and those near hills. The karst hills that are characteristic of the coastal areas of the SCT regency have become a threat to the lives of coastal communities. Communities living in coastal hill areas, including the Kolbano and Oetuke coasts, and in the alluvial lowlands like the Tuafanu, Kualin, and Oni coasts, need to be the focus and priority areas for recovery efforts. This is due to the high level of vulnerability, both physically and socio-economically. Geomorphology is the primary contributor to physical vulnerability because these coastal hills and lowlands are prone to erosion and land degradation caused by waves, tides, and human activities. On the socio-economic side, land use, particularly mining activities, increases vulnerability by degrading the environment and threatening the livelihood of coastal communities. Key recovery efforts should focus on revegetation, which can help stabilize the soil, reduce erosion, and restore ecological balance while offering sustainable economic benefits to the local population.

Fatalities caused by natural hazards are driven not only by population exposure, but also by their vulnerability to these events, determined by intersecting characteristics such as education, age and income. Empirical evidence of the drivers of social vulnerability, however, is limited due to a lack of relevant data, in particular on a global scale. Consequently, existing global‐scale risk assessments rarely account for social vulnerability. To address this gap, we estimate regression models that predict fatalities caused by past flooding events (n = 913) based on potential social vulnerability drivers. Analyzing 47 variables calculated from publicly available spatial data sets, we establish five statistically significant vulnerability variables: mean years of schooling; share of elderly; gender income gap; rural settlements; and walking time to nearest healthcare facility. We use the regression coefficients as weights to calculate the “Global‐Empirical Social Vulnerability Index (GlobE‐SoVI)” at a spatial resolution of ∼1 km. We find distinct spatial patterns of vulnerability within and across countries, with low GlobE‐SoVI scores (i.e., 1–2) in for example, Northern America, northern Europe, and Australia; and high scores (i.e., 9–10) in for example, northern Africa, the Middle East, and southern Asia. Globally, education has the highest relative contribution to vulnerability (roughly 58%), acting as a driver that reduces vulnerability; all other drivers increase vulnerability, with the gender income gap contributing ∼24% and the elderly another 11%. Due to its empirical foundation, the GlobE‐SoVI advances our understanding of social vulnerability drivers at global scale and can be used for global (flood) risk assessments. Plain Language Summary Social vulnerability is rarely accounted for in global‐scale risk assessments. We develop an empirical social vulnerability map (“GlobE‐SoVI”) based on five key drivers of social vulnerability to flooding, that is, education, elderly, income inequality, rural settlements and travel time to healthcare, which we establish based on flood fatalities caused by past flooding events. Globally, we find education to have a high and reducing effect on social vulnerability, while all other drivers increase vulnerability. Integrating social vulnerability in global‐scale (flood) risk assessments can help inform global policy frameworks that aim to reduce risks posed by natural hazards and climate change as well as to foster more equitable development globally. Key Points We develop a global map of social vulnerability at ∼1 km spatial resolution based on five key vulnerability drivers (“GlobE‐SoVI”) We establish vulnerability drivers empirically based on their contribution to predicting fatalities caused by past flooding events Accounting for social vulnerability in global‐scale (flood) risk assessments can inform global policy frameworks that aim to reduce risk

Social vulnerability plays a critical role in shaping the impacts of flooding, yet the ways in which it is measured remain inconsistent and often disconnected from local realities. As climate-related flood events intensify globally, there is an urgent need to understand how social vulnerability is conceptualized and assessed to inform more equitable and effective risk reduction strategies. This paper reports the results of a systematic literature review on indicators and indexes used to assess social vulnerability to floods. Utilizing the PRISMA methodology, we identified and screened 1621 studies published between 2013 and 2023, selecting 36 peer-reviewed publications for examining how social vulnerability indices have been conceptualized, constructed, and applied, as well as the range of indicators, and methodological approaches used. We identified 78 indicator sets across 22 thematic domains. Results indicate that most attention has been given to a small set of indicators focusing on at-risk populations, socioeconomic factors, housing, and employment, while other factors such as health conditions, disaster preparedness, social connectedness, gender minorities, and sexuality are consistently underrepresented. Many studies rely on established frameworks without adapting them to local socio-cultural contexts and use census and secondary data sources, providing limited household-level and qualitative insights. Lack of methodological transparency, particularly regarding normalization and weighting, is common as is insufficient validation and ground-truthing. Greater attention to these issues, along with empirical case studies, is needed to provide in-depth insights into the root causes of social vulnerability and inform policies better tailored to local realities. This review highlights significant conceptual and methodological gaps, calling for more context-sensitive, mixed-method, and empirically validated approaches to improve the robustness and local relevance of social vulnerability assessments in flood-prone areas.

Global projections of climate change are generally linked to an increase in extreme weather events like extreme precipitation. Thus, improving risk management strategies depends on knowing what the most vulnerable to natural disasters areas and populations are. In this study, the Social Vulnerability Index (SoVI), created by Cutter et al. (Soc Sci Q 84(2):242–261, 2003 ) is used to identify those areas and populations in the city of Sao Paulo, Brazil. This flood-prone area has high levels of social inequality and has not been evaluated from the perspective of vulnerability to natural disasters, but from the economic one. The study is focused on examining the basin-level social vulnerability in the city of Sao Paulo in the year 2010. Based on the principal component analysis, a SoVI was computed for the city. Results were displayed on maps and then analyzed to monitor trends in spacial distribution. Five main components were found in the analysis: ‘Urbanization level and vulnerable populations’ that explains 21 % of all social vulnerability, ‘Favorable environmental and social conditions’ explaining 18 %, ‘Alternative basic sanitation solutions’ (14 %), ‘Unfavorable social conditions’ (10 %) and ‘Development indicators’ that explain 8 %. Vulnerability increases in the center–periphery direction. Sixty-nine point seven percent of the basins were classified within the medium vulnerability score, 23.2 % within medium–high or medium–low vulnerability and 7 % within very low, low, high or very high vulnerability. These results show that the components contributing to social vulnerability are different for each basin and represent how social fragmentation of the city hinders efforts of risk management strategies.