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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.

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.

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

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.

Social vulnerability approaches seek to identify social, economic, and political drivers that exacerbate environmental risks, and inform adaptation strategies that redress uneven vulnerabilities. Social Vulnerability Indices (SVIs), one such approach, have exponentially increased in use since their inception in 2003. This paper contributes the most comprehensive and rigorous systematic assessment of SVIs to date, as applied in hazard and disaster contexts. We evaluate how 246 peer-reviewed articles, published online between 2003 and 2021, conceptualized, constructed, and applied SVIs across 20 distinct hazard and disaster contexts in 91 countries. Our review extends previous assessments, not only by analyzing a larger diversity and volume of burgeoning scholarship, but by linking the content, method, and objectives of SVIs to synthesize their strengths and limitations for addressing social vulnerability. Three overarching results are reported. First, we find indicators used to assess social vulnerability across hazards, spatial scales, and geographical contexts, were relatively homogenous. Most articles (81%) drew indicators and theories from already existing SVIs. While such replication is not inherently problematic, and to some extent, reflects established risk factors associated with hazards globally, the epistemological and methodological processes through which SVIs are readily reproduced and replicated warrant serious deliberation. Second, and relatedly, articles commonly used deductive, a priori approaches to identify indicators from secondary datasets, often at the expense of inductively derived representations of vulnerability. Most articles exclusively relied upon quantitative and/or spatial methods (94%) and used secondary or tertiary data alone (80%), without validation and ground-truthing processes (76%). Together, the replication of previous SVIs through deductive research approaches, and their wide application across diverse hazards and geographies, undermines the ability to capture vulnerability as a place-based and context-specific phenomena. Third, and compounding potential ineffectiveness, SVIs appear most often as reactive and post-hazard risk mitigation instruments, with data and findings rarely applied for policy change to address socioeconomic and political causes of vulnerability. Overall, SVIs are an increasingly used instrument; however, their replication without evolving epistemic and methodological approaches, combined with their constrained focus on reactive policy measures, hamper novel and necessary research for countering social vulnerability to increasingly severe socio-environmental and public health risks.

Objective: Vulnerability indices use quantitative indicators and geospatial data to examine the level of vulnerability to morbidity in a community. The Centers for Disease Control and Prevention (CDC) uses 3 indices for the COVID-19 response: the CDC Social Vulnerability Index (CDC-SVI), the US COVID-19 Community Vulnerability Index (CCVI), and the Pandemic Vulnerability Index (PVI). The objective of this review was to describe these tools and explain the similarities and differences between them. Methods: We described the 3 indices, outlined the underlying data sources and metrics for each, and discussed their use by CDC for the COVID-19 response. We compared the percentile score for each county for each index by calculating Spearman correlation coefficients (Spearman ρ). Results: These indices have some, but not all, component metrics in common. The CDC-SVI is a validated metric that estimates social vulnerability, which comprises the underlying population-level characteristics that influence differences in health risk among communities. To address risk specific to the COVID-19 pandemic, the CCVI and PVI build on the CDC-SVI and include additional variables. The 3 indices were highly correlated. Spearman ρ for comparisons between the CDC-SVI score and the CCVI and between the CCVI and the PVI score was 0.83. Spearman ρ for the comparison between the CDC-SVI score and PVI score was 0.73. Conclusion: The indices can empower local and state public health officials with additional information to focus resources and interventions on disproportionately affected populations to combat the ongoing pandemic and plan for future pandemics.

Neighborhood measures of social vulnerability encompassing multiple sociodemographic factors can be used to quantify disparities in outcomes. We hypothesize patients with high Social Vulnerability Index (SVI) are at increased risk of morbidity following colectomy. We used local 2012–2017 National Surgical Quality Improvement Program (NSQIP) data to study colectomy patients, examining associations between SVI and postoperative outcomes. We included 976 patients from five hospitals. High SVI (>75th percentile) was associated with increased postoperative morbidity on unadjusted analysis (OR 1.84, 95% CI 1.35–2.52, p < 0.001); this association persisted after adjusting for demographics and comorbidities (OR 1.63, 95% CI 1.15–2.31, p = 0.005). The association with SVI was not significant after adjusting for perioperative risk modifiers such as emergent presentation (OR 1.37, 95% CI 0.95–1.98, p = 0.10). High social vulnerability is associated with increased postoperative complications. This effect appears mediated by perioperative risk factors, suggesting potential to improve outcomes by facilitating timely surgical intervention. •The Social Vulnerability Index (SVI) is associated with surgical outcomes.•Patients undergoing colectomy at five hospitals were examined.•Postoperative morbidity was higher for patients with high SVI.•This effect appears to be mediated by perioperative/intraoperative risk factors.•This suggests potential to improve outcomes by facilitating timely surgical intervention.

Social vulnerability influences the ability to prepare for, respond to, and recover from disasters. The identification of vulnerable populations and factors that contribute to their vulnerability are crucial for effective disaster risk reduction. Nepal exhibits multihazard risk and has experienced socioeconomic and political upheaval in recent decades, further increasing susceptibility to hazards. However, we still know little regarding social vulnerability in Nepal. Here, we investigate social vulnerability in Nepal by adapting Social Vulnerability Index (SoVI) methods to the Nepali context. Variables such as caste, and populations who cannot speak/understand Nepali were added to reflect the essence of the Nepali context. Using principal component analysis, 39 variables were reduced to seven factors that explained 63.02% of variance in the data. Factor scores were summarized to calculate final SoVI scores. The highest levels of social vulnerability are concentrated in the central and western Mountain, western Hill, and central and eastern Tarai regions of Nepal, while the least vulnerable areas are in the central and eastern Hill regions. These findings, supplemented with smaller-scale analyses, have the potential to assist village officers, policymakers, and emergency managers in the development of more effective and geographically targeted disaster management programs.

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.

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.