Measures Of Social Vulnerability Research Articles

Disparities in gynecologic oncology clinical trial availability in the United States: A state-level analysis.

e23128 Background: Disparities in minority participation in gynecologic cancer clinical trials is well documented. FDA guidance stipulates geographic informed analyses to reduce barriers to trial participation. We performed a geographical analyses of gynecologic cancer clinical trials in the United States relative to racial distribution and social vulnerability indices to identify areas of increased need. Methods: We performed a cross-sectional analysis of NIH ClinicalTrials.gov by retrieving all trials first posted 1/1/2013 through to 1/10/2024. We searched for ovarian, uterine, cervical, endometrial, vaginal/vulvar and gynecological cancer. We reviewed enrollment criteria to exclude non-gynecological cancers (1643 trials) or non-invasive gynecological conditions (224 trials). We aggregated census data, state-level total population size, percent Non-Hispanic White (NHW), and the FEMA Expected Annual Loss per state as a measure of social vulnerability. Using R statistical software (v 4.3.1), we measured the association between these variables and the number of gynecological trials per 100,000 persons using Pearson’s correlation. Results: We identified 3,428 trials, filtered down to 1,561 invasive cancer trials. The most common trials were ovarian (911, 58.3%) and cervical (438, 28.0%), followed by endometrial (385, 24.6%), uterine (158, 10.1%), vulva-vaginal (78, 4.99%), and 7.62% genetic-based. Texas had the highest number of trials (501) followed by California (454) and New York (427). The state with the highest population-adjusted number of trials was South Dakota (8.57 trials per 100,000 persons), followed by Rhode Island at 8.38 per 100,000 persons. California, Mississippi, and Puerto Rico had the lowest number of trials per 100,000 persons with 1.148, 0.979, 0.466 respectively. States/territories with greater than 4 trials per 100,000 were comprised of populations that were > 50% NHW. The correlation between the state-level percentage of NHW in 2020 and the number of trials per 100,000 persons was p = 0.057 indicating that there was a correlation between Whiteness at the state-level and trial availability. We found that states with higher FEMA expected annual loss had lower numbers of gynecological trials per 100,000 persons (p < 0.001). Conclusions: We found a disparity in the geographical distribution of trials available for gynecological cancers that was correlated with racial disparities and social vulnerability. Efforts to increase decentralization of trials and improve access and availability of gynecologic cancer clinical trials are necessary to increase equity for minority representation in clinical trials at the national level.

Community Culture Survey – Revised: Measuring neighborhood culture and exploring geographic, socioeconomic, and cultural determinants of health in samples across the United States and in Thailand

ObjectivesResearch on links between social, geographic, and cultural determinants of health has been thwarted by inadequate measures of culture. The purpose of this study was to improve the measurement of community culture, defined as shared patterns of attitudes and behaviors among people within a neighborhood that distinguish it from others, and to examine dimensions of culture, independent of socioeconomic and demographic factors, and their relationships with health. Study designA survey research design with correlational analyses was used. MethodsA survey packet including the Community Culture Survey – Revised (CCS-R), demographic, health, and other individual-level measures was administered through convenience sampling across the United States (US) and to a sample in Thailand from 2016 to 2018. US county-level variables were obtained from zip codes. Results1930 participants from 49 US states (n = 1592) and Thailand (n = 338) completed all CCS-R items, from which 12 subscales were derived: Social Support & Connectedness, Responsibility for Self & Others, Family Ties & Duties, Social Distress, Urban Diversity, Discontinuity, Church-Engaged, External Resource-Seeking, Locally Owned Business-Active, Power Deference, Next Generation Focus, and Self-Reliance. Neighborhood culture subscale scores varied more by geography than by participant's demographics. All subscales predicted one or more health indicator, and some of these relationships were significant after adjusting for participant age and county-level socioeconomic variables. Most of the significant differences on subscales by race/ethnicity were no longer significant after adjusting for participant's age and county-level socioeconomic variables. Most rural/urban and regional differences in culture within the US persisted after these adjustments. Based on correlational analyses, Social Support & Connectedness and Responsibility for Self & Others were the best predictors of participants' overall health and quality of life, and Responsibility for Self & Others was the best predictor (inversely) of the CDC's measures of social vulnerability. ConclusionsNeighborhood culture is measurable, multi-dimensional, distinct from race/ethnicity, and related to health even after controlling for age and socioeconomic factors. The CCS-R is useful for advancing research and practice addressing the complex interactions between individuals, their neighborhood communities, and health outcomes.

Assessing socioeconomic vulnerability and COVID-19 infection risk among NCD patients in rural Sierra Leone: a cross-sectional study

BackgroundIn Sierra Leone, non-communicable diseases (NCDs) are an increasingly important source of mortality and morbidity. However, Sierra Leonean NCD patients’ experience of direct exposure to COVID-19-related risks and indirect effects of the COVID-19 pandemic on socioeconomic determinants of health has not been described.MethodsWe conducted a cross-sectional telephone survey among adult (≥ 18 years) hypertensive, diabetic, and heart failure patients receiving treatment at the NCD clinic at Koidu Government Hospital (KGH) in rural Sierra Leone. We described patient demographics, COVID-19 related knowledge, and practice of infection prevention measures. Patients were categorized into nationally representative wealth quintiles using an asset-based wealth index and measures of social vulnerability were reported by clinical program and wealth category.ResultOf the 400 respondents, 80.5% were between 40 and 69 years old and 46.1% were male. The majority of patients (> 90%) knew utilizing masks, social distancing, isolation from positive cases, and avoiding hand shaking were effective COVID-19 prevention measures. However, only 27.3% of the population had access to adequate handwashing facilities, 25.5% had attended crowded events in the past two weeks, and only 5.8% always used face masks. Compared with the national distribution of wealth, 33.0% of our population belonged in the richest quintile, 34.8% in the second-richest quintile, and 32.2% in the bottom 3 poorest-middle quintiles. Socioeconomic vulnerability was high overall with significant disparities between wealth categories. In the 30 days before the interview, almost 60% of the poorest-middle categories experienced one barrier to essential health services, 87.4% used at least one emergency coping mechanism to cover food, housing, or health care, and 98.4% were worried about having food. In the richest category, the proportion of patients experiencing these challenges was 32.3%, 39.5% and 81.6%, respectively.ConclusionOur patients had good knowledge of COVID-19 prevention measures; however, we found substantial discrepancies between patients’ self-reported knowledge and practices. Although our population was wealthier than the national average, the NCD patients were still exposed to unacceptable levels of socioeconomic vulnerability, reflecting a high absolute poverty in Sierra Leone. Furthermore, wealth-based disparities in access to essential resources persist among NCD patients.

Agreement in extreme precipitation exposure assessment is modified by race and social vulnerability.

Epidemiologic investigations of extreme precipitation events (EPEs) often rely on observations from the nearest weather station to represent individuals' exposures, and due to structural factors that determine the siting of weather stations, levels of measurement error and misclassification bias may differ by race, class, and other measures of social vulnerability. Gridded climate datasets provide higher spatial resolution that may improve measurement error and misclassification bias. However, similarities in the ability to identify EPEs among these types of datasets have not been explored. In this study, we characterize the overall and temporal patterns of agreement among three commonly used meteorological data sources in their identification of EPEs in all census tracts and counties in the conterminous United States over the 1991-2020 U.S. Climate Normals period and evaluate the association between sociodemographic characteristics with agreement in EPE identification. Daily precipitation measurements from weather stations in the Global Historical Climatology Network (GHCN) and gridded precipitation estimates from the Parameter-elevation Relationships on Independent Slopes Model (PRISM) and the North American Land Data Assimilation System (NLDAS) were compared in their ability to identify EPEs defined as the top 1% of precipitation events or daily precipitation >1 inch. Agreement among these datasets is fair to moderate from 1991 to 2020. There are spatial and temporal differences in the levels of agreement between ground stations and gridded climate datasets in their detection of EPEs in the United States from 1991 to 2020. Spatial variation in agreement is most strongly related to a location's proximity to the nearest ground station, with areas furthest from a ground station demonstrating the lowest levels of agreement. These areas have lower socioeconomic status, a higher proportion of Native American population, and higher social vulnerability index scores. The addition of ground stations in these areas may increase agreement, and future studies intending to use these or similar data sources should be aware of the limitations, biases, and potential for differential misclassification of exposure to EPEs. Most importantly, vulnerable populations should be engaged to determine their priorities for enhanced surveillance of climate-based threats so that community-identified needs are met by any future improvements in data quality.

Substance use disorders and social determinants of health from electronic medical records obtained during Kentucky's “triple wave”

Social determinants of health (SDOH) play a critical role in the risk of harmful drug use. Examining SDOH as a means of differentiating populations with multiple co-occurring substance use disorders (SUDs) is particularly salient in the era of prevalent opioid and stimulant use known as the “Third Wave”. This study uses electronic medical records (EMRs) from a safety net hospital system from 14,032 patients in Kentucky from 2017 to 2019 in order to 1) define three types of SUD cohorts with shared/unique risk factors, 2) identify patients with unstable housing using novel methods for EMRs and 3) link patients to their residential neighborhood to obtain quantitative perspective on social vulnerability. We identified patients in three cohorts with statistically significant unique risk factors that included race, biological sex, insurance type, smoking status, and urban/rural residential location. Adjusting for these variables, we found a statistically significant, increasing risk gradient for patients experiencing unstable housing by cohort type: opioid-only (n = 7385, reference), stimulant-only (n = 4794, odds ratio (aOR) 1.86 95 % confidence interval (CI): 1.66–2.09), and co-diagnosed (n = 1853, aOR = 2.75, 95 % CI: 2.39 to 3.16). At the neighborhood-level, we used 8 different measures of social vulnerability and found that, for the most part, increasing proportions of patients with stimulant use living in a census tract was associated with more social vulnerability. Our study identifies potentially modifiable factors that can be tailored by substance type and demonstrates robust use of EMRs to meet national goals of enhancing research on social determinants of health.

Abstract 12208: Ecological Analysis of the Temporal Trends in the Association of Social Vulnerability and Race/Ethnicity With County-Level COVID-19 Incidence and Outcomes in the United States

Background: The COVID-19 pandemic adversely affected socially vulnerable and minority communities in the US initially, but the temporal trends during the year-long pandemic remain unknown. Objective: We examined temporal association between county-level Social Vulnerability Index (SVI), a percentile-based measure of social vulnerability to disasters, its subcomponents and race/ethnic composition with COVID-19 incidence and mortality in US in the year starting in March 2020. Methods: Counties (n=3091) with > 50 COVID-19 cases by March 6th, 2021 were included. Associations between SVI (and its subcomponents) and county level racial composition with incidence and death per capita were assessed using a negative-binomial mixed-effects model. This model was used to examine potential time varying associations between weekly number of cases/deaths and SVI or racial composition. Data was adjusted for percentage of population aged ≥65 years, state level testing rate, county-level comorbidities, and environmental factors. Results: Higher SVI, indicative of greater social vulnerability, was independently associated with higher COVID-19 incidence (adjusted incidence rate ratio [IRR] per-10 percentile increase:1.02, (95% CI 1.02, 1.03, p<0.001), and death per capita (1.04, (95% CI 1.04, 1.05, p<0.001). SVI became an independent predictor of incidence starting from March 2020, but this association became weak or insignificant by winter, a period that coincided with sharp increase in infection rates and mortality, and when counties with higher proportion of White residents were disproportionately represented (“third wave”). By Spring 2021, SVI was again a predictor of COVID-19 outcomes. Counties with greater proportion of Blacks also observed similar temporal trends in COVID-19-related adverse outcomes. Counties with greater proportion of Hispanics had worse outcomes throughout the duration of the analysis. Conclusion: Except for winter “third wave” when majority White communities had highest incidence of cases, counties with greater social vulnerability and higher minority populations, experienced worse COVID-19 outcomes.

Disproportionate Impact of COVID-19 Pandemic on Racial and Ethnic Minorities.

BackgroundHealth disparities are prevalent in many areas of medicine. We aimed to investigate the impact of the COVID-19 pandemic on racial/ethnic groups in the United States (US) and to assess the effects of social distancing, social vulnerability metrics, and medical disparities.MethodsA cross-sectional study was conducted utilizing data from the COVID-19 Tracking Project and the Centers for Disease Control and Prevention (CDC). Demographic data were obtained from the US Census Bureau, social vulnerability data were obtained from the CDC, social distancing data were obtained from Unacast, and medical disparities data from the Center for Medicare and Medicaid Services. A comparison of proportions by Fisher’s exact test was used to evaluate differences between death rates stratified by age. Negative binomial regression analysis was used to predict COVID-19 deaths based on social distancing scores, social vulnerability metrics, and medical disparities.ResultsCOVID-19 cumulative infection and death rates were higher among minority racial/ethnic groups than whites across many states. Older age was also associated with increased cumulative death rates across all racial/ethnic groups on a national level, and many minority racial/ethnic groups experienced significantly greater cumulative death rates than whites within age groups ≥ 35 years. All studied racial/ethnic groups experienced higher hospitalization rates than whites. Older persons (≥ 65 years) also experienced more COVID-19 deaths associated with comorbidities than younger individuals. Social distancing factors, several measures of social vulnerability, and select medical disparities were identified as being predictive of county-level COVID-19 deaths.ConclusionCOVID-19 has disproportionately impacted many racial/ethnic minority communities across the country, warranting further research and intervention.

Disastrous Inferences? The Ecological Fallacy in Disaster and Emergency Management Research

In research on disaster and emergency management, the limited available data is often summarized at higher levels of aggregation, particularly summaries for geographic areas – sometimes referred to as “ecological data.” Measures of social vulnerability and disaster post-mortems based on such data purporting to measure or explain individual-level phenomena have been criticized as committing an “ecological fallacy” (Beccari 2016; Duneier 2006). Stated briefly, one commits an ecological fallacy if one assumes that relationships observed at an aggregate level imply that the same relationships exist at the individual level. In fact, estimates of causal effects from aggregate data can be wrong both in magnitude and direction (Robinson 1950). This article examines the factors that contribute to incorrect inferences that can arise from the analysis of aggregate data and provides guidance to disaster and emergency management researchers to help them avoid committing ecological fallacies.

Twelve-year mortality in HIV-infected patients receiving antiretroviral therapy: the role of social vulnerability. The ANRS CO8 APROCO-COPILOTE cohort.

Although the role of clinical/biological factors associated with mortality has already been explored in HIV-infected patients on antiretroviral therapy (ART), to date little attention has been given to the potential role of social vulnerability. This study aimed to construct an appropriate measure of social vulnerability and to evaluate whether this measure is predictive of increased mortality risk in ART-treated patients followed up in the ANRS CO8 APROCO-COPILOTE cohort. The cohort enrolled 1,281 patients initiating a protease inhibitor-based regimen in 1997-1999. Clinical/laboratory data were collected every 4 months. Self-administered questionnaires collected psycho-social/behavioural characteristics at enrolment (month [M] 0), M4 and every 8-12 months thereafter. A multiple correspondence analysis using education, employment and housing indicators helped construct a composite indicator measuring social vulnerability. The outcome studied was all-cause deaths occurring after M4. The relationship between social vulnerability and mortality, after adjustment for other predictors, was studied using a shared-frailty Cox model, taking into account informative study dropout. Over a median (IQR) follow-up of 7.9 (3.0-11.2) years, 121 deaths occurred among 1,057 eligible patients, corresponding to a mortality rate (95% CI) of 1.64 (1.37, 1.96)/100 person-years. Leading causes of death were non-AIDS defining cancers (n=26), AIDS (n=23) and cardiovascular diseases (n=12). Social vulnerability (HR [95% CI] =1.2 [1.0, 1.5]) was associated with increased mortality risk, after adjustment for other known behavioural and bio-medical predictors. Social vulnerability remains a major mortality predictor in ART-treated patients. A real need exists for innovative interventions targeting individuals cumulating several sources of social vulnerability, to ensure that social inequalities do not continue to lead to higher mortality.

Physical and political boundaries as barriers to the continuity of social vulnerability

The dynamics of social vulnerability are of key interest to many government agencies and departments. Identifying the geographic distribution of vulnerability within regions, and analysing how localised areas of social need change over time, is a key information requirement for decision-making, and the resultant allocation of resources. Typically, the delineation of areas for the determination of social vulnerability occurs using a combination of political and census boundaries. In many instances, the boundaries of these areas align to natural geographic features such as rivers or lakes. In other cases, a boundary is aligned to a man-made structure such as a road. The boundary may also be arbitrarily positioned based on some measure of distance and not align to any physical feature. In this research, we identify the various boundary types present in a political region. Using two measures of social vulnerability, we assess these boundaries as barriers to the continuity of social vulnerability. From our results, we identify motorways/highways and watercourses as potential barriers. We find no significant effects with lesser road structures suggesting there is no “wrong side of the street”. These results have implications for decision-makers and emphasise the need to recognise the “softness” of boundaries, and consider the relationships between areas, when allocating resources.

Integrating social vulnerability into federal flood risk management planning

AbstractWhile flood risk management planning in the United States has focused on flood control structures designed to protect the economic value of property, it has consistently undervalued other social impacts associated with flooding. The US Army Corps of Engineers (USACE) recently initiated research aimed at understanding how to incorporate social characteristics into the measures currently utilised in flood control project evaluation and consideration. This paper proposes a methodology for incorporating a known measure of social vulnerability, the Social Vulnerability Index (SoVI), into USACE civil works planning. Using the USACE South Atlantic Division as the study area, this paper evaluates eight different variations of the social vulnerability metric and their potential deployment in USACE projects. Each formulation is compared with the original‐computed SoVI as a means to test its spatial and statistical sensitivity, including an assessment of each variant's robustness, reducibility, scalability, and transferability. Results indicate that while it is possible to create simplified, yet robust, versions of SoVI for individual places, such ‘lite’ metrics tend to fall short in areas of scalability and transferability in relation to the original SoVI formulation.

Spatio-temporal patterns of tuberculosis incidence in Ribeirão Preto, State of São Paulo, southeast Brazil, and their relationship with social vulnerability: a Bayesian analysis

The purpose of this ecological study was to evaluate the urban spatial and temporal distribution of tuberculosis (TB) in Ribeirão Preto, State of São Paulo, southeast Brazil, between 2006 and 2009 and to evaluate its relationship with factors of social vulnerability such as income and education level. We evaluated data from TBWeb, an electronic notification system for TB cases. Measures of social vulnerability were obtained from the SEADE Foundation, and information about the number of inhabitants, education and income of the households were obtained from Brazilian Institute of Geography and Statistics. Statistical analyses were conducted by a Bayesian regression model assuming a Poisson distribution for the observed new cases of TB in each area. A conditional autoregressive structure was used for the spatial covariance structure. The Bayesian model confirmed the spatial heterogeneity of TB distribution in Ribeirão Preto, identifying areas with elevated risk and the effects of social vulnerability on the disease. We demonstrated that the rate of TB was correlated with the measures of income, education and social vulnerability. However, we observed areas with low vulnerability and high education and income, but with high estimated TB rates. The study identified areas with different risks for TB, given that the public health system deals with the characteristics of each region individually and prioritizes those that present a higher propensity to risk of TB. Complex relationships may exist between TB incidence and a wide range of environmental and intrinsic factors, which need to be studied in future research.

Experiences of Interpersonal Violence and Criminal Legal Control

Incarcerated women are substantially more likely to have experienced interpersonal violence than are women in the general population. Some scholars argue that increased likelihoods of committing crime among survivors of violence explain this association. However, previous research fails to control for measures of social vulnerability. Thus, the relationship between experiencing interpersonal violence and experiencing imprisonment may not be a causal one. To examine the links between social vulnerability, experiences of interpersonal violence, and experiences of incarceration, the authors analyze both quantitative and qualitative data. The authors’ findings suggest that social vulnerability—especially being Black, having a parent who has been incarcerated, and being unemployed at the time of the arrest—does mediate the relationship between experiencing violence, using drugs, and believing that interpersonal violence contributed to one’s imprisonment. However, even when controlling for social vulnerability, real effects of experiences of violence on both women’s drug use and their understandings of the causes of their imprisonment remain.

Population Evacuation: Assessing Spatial Variability in Geophysical Risk and Social Vulnerability to Natural Hazards

Developing an effective evacuation strategy for hurricane zones presents challenges to emergency planners because of spatial differences in geophysical risk and social vulnerability. This study examines spatial variability in evacuation assistance needs as related to the hurricane hazard. Two quantitative indicators are developed: a geophysical risk index, based on National Hurricane Center and National Flood Insurance Program data, and a social vulnerability index, based on census information. These indices are combined to determine spatial patterns of evacuation assistance needs in Hillsborough County, Florida. Four evacuation dimensions are analyzed: population traits and building structures, differential access to resources, special evacuation needs, and a combination of variables. Results indicate that geophysical risk and social vulnerability can produce different spatial patterns that complicate emergency management. Different measures of social vulnerability also confound evacuation strategies and can result in ineffective practices. It is argued that careful consideration be given to the characteristics of local populations.