Place-based Climate Vulnerability Index (CVI) may shape metabolic risk through environmental and socioeconomic stressors, but its association with incident type 2 diabetes (T2D) is not well characterized. To determine whether residence in communities with higher CVI is associated with risk of incident T2D. This retrospective cohort study used data from the Houston Methodist Learning Health System Registry, a large integrated health system that primarily serves Greater Houston, Texas. Participants included adults aged 18 years or older without T2D at baseline and with at least 1 outpatient encounter and at least 1 subsequent health care encounter from June 2016 to August 2023. Data analysis was conducted September 2025. US census tract-level CVI categorized into quartiles (Q1-Q4). The primary outcome was incident T2D identified by International Statistical Classification of Diseases, Tenth Revision, Clinical Modification codes, antihyperglycemic prescriptions, or hemoglobin A1c 6.5% or higher. Primary measures were incidence rates (cases per 100 person-years) and adjusted hazard ratios (HRs) with 95% CIs comparing Q4 vs Q1 from Cox models adjusted for demographics, insurance, cardiometabolic risk factors, and baseline hemoglobin A1c. Among 1 003 526 participants (mean [SD] age, 50.9 [18.4] years; 605 829 women [60.4%]; 132 451 African American or Black [13.2%]; 71 408 Asian [7.1%]; 156 989 Hispanic or Latinx [15.6%]; 566 632 White [56.5%]; 35 565 other [3.5%]; 42 942 unknown or not reported [4.3%]), 40 152 developed T2D over 2.1 million person-years (overall incidence, 1.88 cases per 100 person-years). Diabetes incidence was higher among participants residing in Q4 vs Q1 CVI areas (2.66 vs 1.48 cases per 100 person-years), and the 7-year risk was 14.1% for Q4 participants vs 8.6% for Q1 participants. Residence in Q4 vs Q1 CVI was associated with higher T2D risk (HR, 1.23; 95% CI, 1.11-1.36), a statistically significant difference. In this cohort study of 1 003 526 adults, higher community CVI was associated with greater risk of incident T2D independently of traditional risk factors. Linking geocoded CVI to electronic health records may support targeted prevention, risk stratification, and population health planning.

Urban parks provide critical benefits for public health, mental well-being, and social connection. However, inequities in park access and use persist, particularly among socially and economically vulnerable populations. While previous studies have established that segregation and social vulnerability each contribute to uneven park access, little is known about how these two forces interact to shape real visitation patterns. This study addresses this research gap and answers the research question: How does highway segregation relate to differences in the different aspects of social vulnerability in influencing park access across Austin’s east–west divide? SafeGraph mobility data from 2019 and the Social Vulnerability Index (SVI), which included four themes (i.e., socioeconomic status, household composition, minority status and language, and housing and transportation characteristics), were analyzed through fixed-effects regression models for Austin, Texas. Results show that household composition and minority vulnerabilities have negative associations with park visitation, indicating that areas with more elderly, single-parent, or minority residents visit parks less frequently. Interaction terms reveal that highway segregation functions as a structural barrier that conditions the influence of social vulnerability on park use. Those associated with socioeconomic resources diminish, while the disadvantages linked to household composition and minority status intensify on the east side of I-35, reflecting the cumulative effects of segregation and infrastructural division. These findings confirm that inequities in park access are more pronounced on the east side of the I-35, consistent with the highway’s role in reinforcing segregation. Efforts to strengthen connectivity represent key strategies for advancing equitable park visitation across Austin.

Metabolic stress-response in female rats: β-oxidation-induced vulnerability vs adaptive resilience.

Caregiver Neighborhood Vulnerability and Visitation Responses to an Environmental Shock in Nursing Homes.

Evaluating the association of social determinants of health and patient-reported outcome measures in patients with lisfranc injuries.

Association of social vulnerability with a risk of readmission following cardiac surgery in a statewide collaborative.

Livelihood vulnerability index assessment of the informal coal mining workers using IPCC frameworks

The role of socioeconomic and structural vulnerability risk factors in employment outcomes for individuals with Schizophrenia and other psychotic disorders.

Groundwater depletion causes a critical risk for the sustainability of urban and agricultural resilience in semi-arid regions such as Tangshan city. This study deployed an integrated framework that combines understandable machine learning (Shapley Additive exPlanations (SHAP), Geographically Weighted Regression (GWR), spatial autocorrelation (Local Indicators of Spatial Association or LISA), and scenario-based recharge forecasting to evaluate the spatial drivers and patterns of groundwater stress and project potential future risks. Using spatial groundwater table data from 2022 and key environmental and anthropogenic variables such as evapotranspiration (ET), population, temperature, precipitation, and land use and land cover changes, an XGBoost (Extreme Gradient Boosting) regression model was trained to capture nonlinear spatial patterns. SHAP analysis found that ET and population density are prominent contributors to groundwater depletion in agricultural and urban zones. To capture spatial heterogeneity, GWR was utilized to estimate localized coefficients and construct a Vulnerability and Resilience Index (VRI) from normalized coefficients and residuals. LISA validated vulnerability zones and revealed transitional stress regions. Future risks are also projected using Coupled Model Intercomparison Project Phase 6 (CMIP6) climate data and land-use data to run recharge modeling from 2023 to 2049 for both representative concentration pathway (RCP) 4.5 and RCP 8.5. Results show that RCP 8.5 demonstrates highly unstable recharge with frequent negative episodes (ET > P), while RCP 4.5 shows relatively stable patterns of groundwater table. Furthermore, coupled with urban and agricultural expansion, RCP 8.5 intensifies depletion risks. This combined framework provides analytical understandings of spatial driver patterns and scenario-based risk assessments under climate and land use change. The findings of the study recommend priority zones for intervention and underline the importance of adaptive, scenario-sensitive groundwater governance in semi-arid, urbanizing regions.

The coastal communities of southern Kerala, though rich in cultural and ecological heritage, face mounting socio-economic challenges due to both natural and human-induced pressures. This study offers a comprehensive assessment of the socio-economic vulnerability of 253 villages spread across the coastal plains from Ernakulam to Thiruvananthapuram. Using data from the 2011 Census and household records, the research analyzes key indicators such as population and household density, child and female population ratios, literacy rates, primary workers, and socially weaker groups to evaluate levels of vulnerability. These indicators were spatially mapped and ranked to create a composite vulnerability index, providing a clear picture of where and why vulnerability is most pronounced. Geographic Information System (GIS) tools played a central role in visualizing and interpreting these spatial patterns. To deepen the analysis, hotspot mapping was carried out using the Getis-Ord Gi* statistic in ArcGIS, identifying statistically significant clusters of high vulnerability, particularly in villages located in Thiruvananthapuram, Kollam, and Alappuzha. Villages like Ochira, Arattupuzha, Attingal, and Vakkam emerged as major hotspots requiring immediate attention. The findings highlight the spatial disparities in vulnerability across districts and underscore the importance of localized, data-driven strategies for disaster risk reduction and sustainable planning. This study emphasizes the value of integrating socio-economic data with spatial analysis to better target interventions and build resilience among Kerala’s most at-risk coastal populations. Keywords: Disaster, Vulnerability, Kerala, GIS, Coastal plains

Health vulnerability reflects the adverse state of below-normal health of middle-aged and elderly adults in the process of population aging under the influence of internal and external risk perturbations, and is a comprehensive assessment of individual health status. However, there is a lack of clarity about whether and how the community environment influences the health vulnerability of middle-aged and older adults. This study aims to elaborate the complex pathways of sleep quality, social values, and anti-fragile capacity between community environment and health vulnerability, and to provide a scientific basis for promoting healthy aging in China. This study was screened to finally include 8,032 middle-aged and older adults who participated in the China Family Panel Studies Database 2020 survey. While controlling for confounders we used structural equation modeling (SEM) to analyze the parallel mediating roles of sleep quality, social values, and anti-fragile capacity in community environment and health vulnerability, and subgroup analyses to explore the heterogeneity of the effects across subgroups of the population. The composite index of health vulnerability of Chinese middle-aged and elderly adults was 0.227 (SD = 0.129). Community environment was positively correlated with sleep quality, social values, and anti-fragile capacity (p < 0.05). Community environment, sleep quality, and anti-fragile capacity were negatively correlated with health vulnerability. In addition, after adjusting for confounders, sleep quality, social values, and anti-fragile capacity were able to partially mediate the association between community environment and health vulnerability among middle-aged and olderadults, with mediating effects of 19.50%, 3.06%, and 2.20%, respectively(p < 0.05). Subgroup analyses further showed that the mediating effects of sleep quality, social values, and anti-fragile capacity on community environment and health vulnerability differed across age, gender, and place of residence subgroups. This study found a complex relationship between community environment, sleep quality, social values, anti-fragile capacity, and health vulnerability in middle-aged and older adults. Various intervention paths should be taken to improve the aging-friendly level of community environments and build home-friendly retirement communities to alleviate the health vulnerability of the middle-aged and elderly groups and promote healthy aging in China. Not applicable.

Climate change poses growing risks to ecologically fragile and socio-economically vulnerable regions such as Meghalaya in the Indian Himalayan Region. This study aims to assess and compare district-level climate vulnerability in Meghalaya using an indicator-based framework aligned with the IPCC’s AR5 conceptualization. The primary objective is to identify key drivers of vulnerability and provide spatially explicit insights to inform adaptation planning. The analysis uses eight indicators representing both biophysical and socio-economic dimensions, slope gradient, forest cover, crop yield variability, population density, female literacy, infant mortality, Multidimensional Poverty Index (MPI), and NREGS man-days, derived from official government sources for the period 2020–2024. Data were normalized according to their functional relationships with vulnerability, and unequal indicator weights were assigned through expert consultation. Composite Vulnerability Indices (CVI) were generated to rank districts, supported by GIS-based spatial analysis and attribution of indicator contributions. Results reveal that West Khasi Hills and East Garo Hills are the most vulnerable districts, primarily due to steep slopes, forest degradation, and high poverty levels. Conversely, East Jaintia Hills and South West Khasi Hills exhibit lower vulnerability owing to stronger adaptive capacities. The study concludes that enhancing forest conservation, livelihood diversification, and social protection mechanisms is critical for reducing vulnerability in high-risk districts. This framework offers a replicable approach for rapid vulnerability assessment in data-scarce mountainous regions.

Dizziness is common in later life and may signal early cognitive vulnerability, but its long-term implications are not well-understood. Data from six waves of the Health and Retirement Study (2000-2020) were used to examine the association of persistent dizziness, defined as dizziness reported in ≥2 waves on cognitive outcomes including longitudinal cognitive score trajectories, dementia risk, and incidence, with age, sex, education, multimorbidity, and polypharmacy controlled. Individuals with persistent dizziness had lower baseline cognitive scores and significantly faster decline over time (β = -0.24, p < .001). Persistent dizziness was associated with a faster rise in dementia risk over time (OR = 1.23, p < .001) and a 36% higher hazard of incident dementia (HR = 1.36, p = 0.004). Persistent dizziness is linked to steeper cognitive decline and elevated dementia risk. These findings support dizziness as a potential early indicator of cognitive vulnerability and highlight its value for screening and preventive care in aging populations.

To evaluate whether there is an association between the Centers for Disease Control and Prevention (CDC) Social Vulnerability Index (SVI) score and postpartum care attendance. This is a retrospective cohort study of patients who delivered at 20 0/7 weeks of gestation or later at any of three hospitals within a New York City health care system from January 1, 2022, to February 1, 2024, and had at least one prenatal outpatient visit at an affiliated site before delivery. The primary outcome was at least one outpatient in-person obstetric visit within 12 weeks of delivery. The exposure was CDC-defined SVI score, with a validated technique for geocoding patient addresses used to assign overall SVI score and SVI theme scores. The SVI score was categorized into quartiles representing low to high social vulnerability. Demographic, medical, and obstetric factors were examined with univariable and multivariable logistic regression. Of 21,539 eligible patients, 14,026 (65.1%) attended an in-person postpartum visit within 12 weeks of delivery. In-person postpartum visit attendance was documented in 2,147 (50.3%) of those with high social vulnerability, 3,020 (64.4%) of those with medium-to-high SVI score, 4,568 (70.6%) of those with low-to-medium SVI score, and 4,191 (70.6%) of those with low SVI score. In univariable analysis, both medium-to-high SVI score and high SVI score were associated with lower odds of attending a postpartum visit (odds ratio [OR] 0.75 [95% CI, 0.70-0.82] and 0.42 [95% CI, 0.39-0.46], respectively) compared with those with low SVI score. These associations were also detected among the SVI socioeconomic, household composition, and racial and ethnic minority themes but not housing type and transportation theme. However, only high SVI score was associated with decreased odds of attending an in-person postpartum visit in multivariable models (adjusted OR 0.85 [95% CI, 0.78-0.94]). A high SVI score was associated with lower likelihood of in-person postpartum visit attendance within 12 weeks of delivery.

IntroductionMonitoring and addressing health inequalities is important. However, socioeconomic variables are usually unavailable within health datasets. Area deprivation measures provide access to open-source reliable socioeconomic data within low/middle-income countries and can contribute to the monitoring of the Sustainable Development Goals and assessing the growing burden of health inequalities.ObjectiveTo create a small-area deprivation measure for the whole of Brazil - the Brazilian Deprivation Index (Índice Brasileiro de Privação - IBP),MethodsUsing Census Sector data (mean population size=615) from the most recently available Brazilian Demographic Census (2010), variables measuring literacy, household income and housing conditions were standardised using z-scores and summed into a single measure. The IBP was validated using regional small-area measures of vulnerability: Belo Horizonte’s Health Vulnerability Index (IVS) and São Paulo’s Social Vulnerability Index (IPVS). Mortality data from Minas Gerais were used to estimate age-standardised mortality rates (ASMR) by ill-defined causes across IBP deprivation quintiles.ResultsThe IBP was created for 303,218 (97.8%) census sectors (99.7% population). Substantial regional variation in deprivation was found using the IBP measure, with higher deprivation in rural than urban areas. The IBP was correlated with the other indicators used for validation: the IVS (r = 0.96) and the IPVS (r = 0.68). We found gradients across the ill-defined causes ASMR, in Minas Gerais mortality was 2.6 higher in the most deprived quintile of IBP, compared with the least deprived. Main challenges in creating a deprivation measure for LMICs and possible solutions are demonstrated.ConclusionA small area deprivation index was created for Brazil, a large and highly diverse middle-income country. The IBP improves our understanding and monitoring of inequalities, serving as a valuable tool for informing targeted public policies. Although the index is based on Brazil’s specific context, the challenges faced, and the strategies implemented to tackle them are relevant for other low- and middle-income countries aiming to develop similar tools.

To evaluate the impact of a comprehensive set of social determinants of health (SDOH) on treatments, timing, and key biomarker testing for early-stage non-small cell lung cancer (NSCLC). Patients with the first diagnosis of stage I-III NSCLC from 1 January 2015 to 15 October 2023 and treated at community health systems in the United States were eligible for this retrospective database study. We summarized initial primary treatment and time-to-treatment initiation (TTI) by Social Vulnerability Index (SVI), primary care provider (PCP) shortage areas, household income, and insurance type. Data cutoff was 15 October 2024. Of 8501 patients with stage I-III NSCLC, 32% underwent surgery-only and 14% also received neoadjuvant and/or adjuvant therapy. Greater percentages underwent surgery (with/without neoadjuvant/adjuvant therapy) in counties with lowest SVI/vulnerability, highest median income, and no PCP shortage, and among those with private healthcare insurance (vs. Medicare/Medicaid). Median (range) TTI for any NSCLC-related treatment after diagnosis was 41 days (0-1846); TTI increased across treatment strategies by increasing SVI/vulnerability and decreasing household income. Annual rates of programmed death-ligand 1/EGFR mutation testing rose from 60%/51% in 2020 to 84%/82% in 2023, with greatest rates in counties with no PCP shortage. Disparities in early-stage NSCLC treatment by SDOH factors call for efforts to improve access to timely care for NSCLC.

Pediatric inflammatory bowel disease (IBD) has a rising incidence. While the underlying etiology is not fully understood, genetics and environmental factors are known to play a role. Social determinants of health (SDoH) also play a role in worsening morbidity and mortality for chronic diseases. This study evaluated the association between SDoH and pediatric IBD, hypothesizing that individuals residing in more socially vulnerable areas will have worse outcomes during the first year of diagnosis. This retrospective cohort study included 222 patients diagnosed with IBD at Children's of Alabama from 2018 to 2022. The social vulnerability index (SVI) was used to quantify neighborhood-level socioeconomic vulnerability. Primary outcomes were hospitalization at diagnosis, hospitalization during the first year, disease activity at 6 months, and persistent disease activity at 1 year. Patients from neighborhoods in the most socioeconomically vulnerable quartile (top 25% of SVI) were more likely to be hospitalized at diagnosis (adjusted odds ratio [aOR] 2.51, p = 0.028), more likely to be hospitalized during the first year (aOR 3.44, p = 0.031), and more likely to have active disease at 6 months (unadjusted OR 2.47, p = 0.016) compared to those from neighborhoods in the least vulnerable quartile. However, there were no significant differences by neighborhood socioeconomic vulnerability in active disease at 1 year. Social vulnerability contributes to increased risk of hospitalization and delays in achieving remission during the first year after new diagnosis of IBD. This finding highlights the importance of SDoH screening to identify at-risk patients at the time of diagnosis to guide targeted interventions and prevent worse disease outcomes.

To evaluate frailty in severe progressive multiple sclerosis (PMS) and to investigate the underlying mechanisms. This prospective, cross-sectional, multicenter study enrolled a late severe PMS group requiring skilled nursing (n = 53) and an age, sex, and disease duration-matched control PMS group (n = 53). Participants received neurological and MRI assessments and provided blood samples. Frailty was measured on the Edmonton Frail Scale. Disability was measured on the Expanded Disability Status Scale (EDSS), and fatigue was assessed on the Fatigue Severity Scale. The inflammatory vulnerability index (IVX) and metabolic vulnerability index (MVX) were computed from nuclear magnetic resonance spectroscopy-derived metabolomic profiling. Serum neurofilament (sNfL), glial fibrillary acidic protein (GFAP), and growth differentiation factor 15 (GDF15) levels were obtained. The late severe PMS group had a higher median EDSS (8.0 vs. 6.0, p < 0.001) than the matched control PMS group. The late severe PMS group had a higher prevalence of frailty (73.1% vs. 23.1%, p < 0.001) and higher frailty scores (8.87 vs. 5.52, p < 0.001) than the control PMS group. EFS was associated with EDSS in both PMS groups. Positive frailty status was associated with a 1.19-point greater EDSS (p = 0.012) in the control PMS group and a 0.436-point greater EDSS in the late severe PMS group (p = 0.002). In PMS controls, the EFS and frailty status were associated with IVX (p = 0.044 for EFS) and MVX (p = 0.036 for EFS). Frailty is positively associated with MS disability. Inflammatory and metabolic vulnerability are associated with frailty in PMS.

Abstract Moving beyond income-based poverty measures, this paper provides an exploratory analysis of the relationship between climate change vulnerability and multidimensional poverty, aiming to identify the key non-monetary deprivations faced by the poor in the most vulnerable areas and to inform policy priorities. Using harmonized data from the Global Multidimensional Poverty Index and the ND-GAIN Vulnerability Index for a sample of 85 countries, we describe patterns of co-occurrence between poverty and vulnerability, showing that a disproportionate share of the poor live in highly vulnerable countries. The most prominent deprivations in these contexts relate to living conditions, particularly access to cooking fuel, sanitation, electricity, and housing. These findings suggest that targeted interventions in these sectors could be prioritized in highly vulnerable contexts to address multidimensional poverty more effectively. We also document that the combination of adverse climate events and high vulnerability is associated with higher poverty levels, with the nature of the event influencing the pattern: for example, droughts, as slow-onset events, are more strongly associated with elevated poverty indicators. Moreover, higher institutional quality, measured through government effectiveness, appears to moderate these patterns, with countries exhibiting better governance showing relatively lower associations between vulnerability and multidimensional poverty. Overall, the results indicate that multidimensional poverty and climate vulnerability are closely interconnected, highlighting the importance of considering both dimensions jointly in the design of adaptation and poverty reduction strategies.

Abstract The Indian Himalayan Region is increasingly affected by flash floods and landslides, posing severe risks to mountain communities. In Gangtok city of Sikkim, evaluating these risks is critical for developing adaptive strategies and informing policy decisions. This study employs the Inherent Vulnerability and Risk Index (IVRI) framework, integrating 25 indicators across four dimensions exposure, hazard, sensitivity, and adaptive capacity using both qualitative and quantitative datasets. Principal Component Analysis (PCA) was applied to derive the Inherent Vulnerability Index (IVI) and Risk Index (RI) at the ward level, enabling spatial assessment of vulnerability and risk distribution. Results indicate that 41.17% of the area falls under very low vulnerability, nearly one-third (29.71%) under high vulnerability, and 1.22% under very high vulnerability. For the risk index (RI), 12.50% of the area remains in the very low category, while 34.48% is classified as very high risk, indicating high susceptibility to landslides and flash floods. In addition, certain hazard-exposed wards demonstrate reduced vulnerability due to stronger adaptive capacity. These findings underscore the importance of targeted mitigation strategies and provide actionable guidance for prioritizing interventions, allocating resources, and enhancing resilience. By adopting a multi-hazard perspective, this study contributes a robust framework for disaster risk reduction in rapidly urbanizing Himalayan cities.