Open Access: Measuring social equity in flood recovery funding

ABSTRACTDeconstructing causal linkages between place attributes and disaster outcomes at coarse scales like zip codes and counties is difficult because heterogeneous socio-economic characteristics operating at finer scales are masked. However, capturing detailed disaster outcomes about individuals and households for large areas can be equally complicated. This dichotomy highlights the need for a more nuanced and empirically-driven approach to understanding financial disaster recovery support. This study assessed how social characteristics influenced federal disaster recovery support following the 2015 South Carolina floods. Ordinary linear and spatial regression models provided a mechanism for pinpointing statistically significant links between individual/compound vulnerabilities and resource distribution from four federal disaster response and recovery programmes. The study makes two unique contributions. First, exploration of how social characteristics influence recovery support is a critical, yet understudied path toward fair and equitable disaster recovery. Second, finer scale inquiry across a large impact area is rare in quantitative case studies of US disasters. While we found flood recovery assistance to be strongly associated with physical damage overall the relationship was more tenuous in places with higher social vulnerability. Results indicate that future disaster recovery programs focusing on both physical damage and social vulnerable would lead to a more equitable disaster recoveries. Findings provide new understanding of equity at the intersection of social vulnerability, impacts, and disaster recovery and showcase both best-practices and areas for programme improvements for future disasters.

Long-term Recovery from Recent Disasters in Japan and the United States

Abstract. To what extent an individual or group will be affected by the damage of a hazard depends not just on their exposure to the event but on their social vulnerability – that is, how well they are able to anticipate, cope with, resist, and recover from the impact of a hazard. Therefore, for mitigating disaster risk effectively and building a disaster-resilient society to natural hazards, it is essential that policy makers develop an understanding of social vulnerability. This study aims to propose an optimal predictive model that allows decision makers to identify households with high social vulnerability by using a number of easily accessible household variables. In order to develop such a model, we rely on a large dataset comprising a household survey (n = 41 093) that was conducted to generate a social vulnerability index (SoVI) in Istanbul, Türkiye. In this study, we assessed the predictive ability of socio-economic, socio-demographic, and housing conditions on the household-level social vulnerability through machine learning models. We used classification and regression tree (CART), random forest (RF), support vector machine (SVM), naïve Bayes (NB), artificial neural network (ANN), k-nearest neighbours (KNNs), and logistic regression to classify households with respect to their social vulnerability level, which was used as the outcome of these models. Due to the disparity of class size outcome variables, subsampling strategies were applied for dealing with imbalanced data. Among these models, ANN was found to have the optimal predictive performance for discriminating households with low and high social vulnerability when random-majority under sampling was applied (area under the curve (AUC): 0.813). The results from the ANN method indicated that lack of social security, living in a squatter house, and job insecurity were among the most important predictors of social vulnerability to hazards. Additionally, the level of education, the ratio of elderly persons in the household, owning a property, household size, ratio of income earners, and savings of the household were found to be associated with social vulnerability. An open-access R Shiny web application was developed to visually display the performance of machine learning (ML) methods, important variables for the classification of households with high and low social vulnerability, and the spatial distribution of the variables across Istanbul neighbourhoods. The machine learning methodology and the findings that we present in this paper can guide decision makers in identifying social vulnerability effectively and hence let them prioritise actions towards vulnerable groups in terms of needs prior to an event of a hazard.

Background:Environmental hazards and heightened neighborhood social vulnerability coexist in the U.S. and disproportionately affect historically marginalized populations. Certain exposures like neighborhood poverty and air pollution contribute independently to rheumatic disease...

Lay Health Workers can play a pivotal role in improving disaster response and recovery because of their potential effectiveness in enhancing the overall health of their communities, increasing disaster preparedness, supplementing the efforts of disaster responders, and building relationships of trust among all interested parties. Such activities build social capital and significantly enhance community resiliency in anticipation of future disasters. Although there are a number of different types of lay health workers, the version with the greatest potential in this area is the Community Health Worker (CHW). Recent research findings confirm that CHWs serving in the communities where they live have been beneficial in emergency management planning and disaster recovery, following both natural and technological disasters. When properly trained, they constitute a proven strategy for timely interventions aimed at reducing long-term collective trauma and building social capital. In this paper, we elaborate the characteristics and roles of CHWs as a specific type of lay health worker; review research on the utility of CHWs in health care generally, as well as in the area of emergency management; describe their potential for building social capital and enhancing community resilience; and provide an overview of essential training needed to prepare them to participate in disaster preparedness, response, and recovery efforts. We conclude with some suggestions for future research.

Peatlands in Southeast Asia regularly experience fire due to clearance of forests and drainage for agriculture and plantation development. Fire represents a mainstay for rural communities managing tropical landscapes, but these can lead to uncontrolled ‘wild’ fires that pose a major threat to people and the ecosystem, leading to a cycle of increased susceptibility to fire and increased vulnerability of people and peat ecosystem to future fires. Using an exposure-sensitivity-adaptive capacity framework, we constructed indicators of exposure, sensitivity, and adaptive capacity of peatlands and communities to fires in Sumatra and Kalimantan (Indonesia) and used these indicators to calculate the social and ecological vulnerability of peatlands and communities to fires. We operationalized this framework and defined spatial indicators which we used to construct three indices of vulnerability (ecological vulnerability, social vulnerability of burning, and social vulnerability smoke-haze). Our assessment found peatlands with high ecological vulnerability on eastern Sumatra (Riau, Jambi, South Sumatra), southern Kalimantan (Central, South Kalimantan), and East Kalimantan. Majority of these provinces overlapped with sites of high social vulnerability for burning (North Sumatra, Riau, South Sumatra, Central Kalimantan, South Kalimantan) and high social vulnerability for smoke-haze (Riau, Jambi, South Sumatra, Central Kalimantan, South Kalimantan). As districts play an important role in land use decisions and fire mitigation efforts, we identified the top six districts that had high numbers of villages with high ecological and social vulnerability scores. Hotspot analyses showed that ecological vulnerability hotspots were co-located with social vulnerability hotspots but clusters of social vulnerability hotspots for burning did not completely overlap with social vulnerability hotspots for smoke-haze. Our vulnerability assessment of peatlands and villages is the foundation for an important tool for policymakers at multiple governance levels to identify high ecological and social vulnerability to peatland fires and channel aid and mitigation efforts where they are most needed.

Social vulnerability predicts cognitive decline in a prospective cohort of older Canadians

The relationship between social vulnerability and disaster recovery has been well examined; however, less attention paid to how differences in vulnerability affect recovery for healthcare and social service industry employees. The widespread impact of Hurricane Maria on Puerto Rico, including failure of the entire electrical grid, presents a unique case study for examination of the relationship between income and disaster recovery of frontline workers. Using data collected from healthcare and social service industry employees 10 months post-Maria, we found a significant relationship between household income and recovery of public services. Households with lower incomes experienced nearly a full month longer wait for electricity restoration as compared to higher-income households, after controlling for home damage and demographic characteristics. Additionally, lower income households were more likely to report higher levels of PTSS. Our findings suggest a need for targeted workplace interventions during disaster response and recovery that take into account economic inequities.

We trained local public health workers on disaster recovery roles and responsibilities by using a novel curriculum based on a threat and efficacy framework and a training-of-trainers approach. This study used qualitative data to assess changes in perceptions of efficacy toward Hurricane Sandy recovery and willingness to participate in future disaster recoveries. Purposive and snowball sampling were used to select trainers and trainees from participating local public health departments in jurisdictions impacted by Hurricane Sandy in October 2012. Two focus groups totaling 29 local public health workers were held in April and May of 2015. Focus group participants discussed the content and quality of the curriculum, training logistics, and their willingness to engage in future disaster recovery efforts. The training curriculum improved participants' understanding of and confidence in their disaster recovery work and related roles within their agencies (self-efficacy); increased their individual- and agency-level sense of role-importance in disaster recovery (response-efficacy); and enhanced their sense of their agencies' effective functioning in disaster recovery. Participants suggested further training customization and inclusion of other recovery agencies. Threat- and efficacy-based disaster recovery trainings show potential to increase public health workers' sense of efficacy and willingness to participate in recovery efforts. (Disaster Med Public Health Preparedness. 2016;10:615-622).

Social vulnerability and short-term disaster assistance in the United States

Assessing distributive inequities in FEMA's Disaster recovery assistance fund allocation

Social Vulnerability Is a Clinically Important Predictor of Outcomes after Allogeneic Hematopoietic Cell Transplantation

Introduction: Pandemics prior to the COVID-19 pandemic have been known to disproportionately affect counties and county-equivalents with high social vulnerability. Social vulnerability refers to the potential negative effects on communities caused by external stresses on human health. Such stresses include natural or human-caused disasters, or disease outbreaks. On the other hand, case fatality rate estimates the proportion of deaths among identified confirmed cases. Hypothesis: A higher social vulnerability is associated with a higher case fatality rate at the levels of counties and county-equivalents in the contiguous United States during the COVID-19 pandemic. Methods: Data from 2990 counties and county-equivalents such as independent cities, districts, and parishes that are considered county-equivalents for census purposes was analyzed. Counties and county-equivalents from the non-contiguous states of Alaska and Hawaii were excluded as well as 59 counties and county-equivalents without available SVI data and/or available CFR data. Available SVI public source data was collected from the Centers for Disease Control and Prevention, while available CFR public source data was collected from the Emory Clinical Cardiovascular Research Institute. Results: Median SVI was 0.5185 [Range: 0.0006-1] and median CFR was 1.56% [Range: 0%-8.77%]. A vaccination rate ratio (RR) and 95% CI for SVI was calculated using Wald’s unconditional maximum likelihood estimation to compare CFRs of counties and county-equivalents with low social vulnerability (SVI: 0%-33%), moderate social vulnerability (33%-66%), and high social vulnerability (66%-100%). A higher SVI was associated with a higher CFR such that the RR for relative differences in CFR between counties and county-equivalents with a low social vulnerability and counties and county-equivalents with a high social vulnerability was 0.83, while the RR for relative differences in CFR between counties and county-equivalents with a moderate social vulnerability and counties and county-equivalents with a high social vulnerability was 0.89. Conclusions: Public policy interventions need to target counties and county-equivalents with a higher social vulnerability to help the most vulnerable of people.

Acceptability of HPV self-collection: A qualitative study of Black women living with type II diabetes and social vulnerability

Special Report from the CDC: The association between social vulnerability and unintentional fatal drowning in the United States, 1999-2023.