Vulnerability Index Research Articles (Page 1)

To examine the association between neighborhood-level social determinants of health and stillbirth. We performed a retrospective cohort study of deliveries that occurred at a gestational age of at least 20 weeks within a five-hospital system (2012-2022). Electronic health record data mapped to the Observational Medical Outcomes Partnership Common Data Model were geocoded using addresses at the time of delivery and linked to neighborhood health indices, which included the Area Deprivation Index (ADI), Maternal Vulnerability Index (MVI), and Social Vulnerability Index (SVI) at the Census tract level. Stillbirths were adjudicated by medical record review. Modified Poisson regression generated relative risks (RRs) and 95% CIs, controlling for maternal age, body mass index (BMI), parity, marital status, chronic hypertension, and pregestational diabetes. Among 61,008 pregnancies, 288 (0.5%, 95% CI, 0.4-0.5%) resulted in stillbirths. The ADI quartiles (relative to Census tracts within the United States as a whole) 2, 3, and 4 were associated with an increased risk of stillbirth (RR [95% CI] 2.32 [1.34-4.03], 3.08 [1.74-5.44], and 2.07 [1.03-4.14], respectively) compared with quartile 1. The ADI relative to Census tracts within the states showed comparable gradients. MVI quartiles 2 and 3 were associated with an increased risk of stillbirth (RR [95% CI] 1.44 [1.01-2.05] and 1.49 [1.02-2.19], respectively) compared with quartile 1. Similarly, SVI quartiles 2 and 3 were associated with an increased risk of stillbirth (RR [95% CI] 1.46 [1.03-2.07] and 1.86 [1.32-2.63], respectively) compared with quartile 1. Neither MVI nor SVI quartile 4 showed a statistically significant association with stillbirth. Among MVI subthemes, the mental health domain demonstrated a strong association with stillbirth (quartiles 2-4 RR range 1.64-2.07). Neighborhood deprivation, quantified by ADI score, was a robust independent predictor of stillbirth, whereas the associations between the MVI or SVI and stillbirth were modest. Integrating the ADI into obstetric risk assessment and directing resources, especially perinatal mental health services, to highly deprived areas may help reduce persistent stillbirth disparities.

Frailty in older adults has become a major concern. It is influenced by biological, social, psychological, and environmental factors, with social frailty playing a particularly significant role. The relationship between social frailty and health outcomes can accelerate frailty transitions, offering new insights into strategies to improve health in older adults. We obtained the data from the Fourth Sample Survey of the Aged Population in Urban and Rural China, with 2017 as the baseline and 2019 as the follow-up. We used the frailty index (FI) to assess physiological frailty and the social vulnerability index (SVI) to assess social frailty. We used logistic regression to analyse the impact of SVI on frailty transitions. The analysis included 9093 older individuals, with an average age of 71.2 (standard deviation = 7.0) years, comprising 4495 women and 4598 men. Of these, 39.3% were robust, 45.1% were prefrail, and 15.6% were frail. Both the FI and SVI increased with age, and women showed higher frailty levels. Over the two years, 56.2% maintained stable frailty status, 14.2% improved, and 29.6% deteriorated. Correlation analysis revealed a moderate relationship between FI and SVI (r = 0.337; P < 0.001). Logistic regression analysis indicated that higher social frailty was associated with an increased risk of worsening frailty in non-frail (odds ratio (OR) = 1.017; P < 0.05) and prefrail individuals (OR = 1.021; P < 0.05), but had no effect on those who were already frail. Frailty deterioration with age is more common than improvement. Increased social frailty is a significant risk factor for the worsening of frailty, particularly in non-frail and prefrail individuals. Early identification and prevention of social frailty may help delay its progression.

A genetic indicator of the parasites' vulnerability to anti-malarial medications is the Plasmodium falciparum multidrug resistance gene 1 (pfmdr1). In this study, malaria patients aged 0–14 who were treated at Murtala Muhammad Specialist Hospital in Kano, Nigeria, were evaluated for multidrug-resistant resistance gene 1 (MDR1) mutations. After confirming the malaria parasite density in 100 children's samples, the samples were genotyped using BigDye (v3.1) terminator cycle sequencing to look for two SNPs in pfmdr1 on samples with high and moderate parasite densities. Fisher's exact (FE) tests and Pearson Chi-square were used to evaluate the data. Of the 100 samples, 57% of the patients had low (+) malaria parasite densities, 28% had moderate (++) densities, and 15% had high (+++) densities. Only seven samples were successfully amplified for the pfmdr1 gene located at codon 1246, whereas 31 were successfully amplified and processed for the pfmdr1 gene located at codon 86 with an amplicon size of 534 bp. A Pfmdr1-N86Y mutation was found in one sample (3.2%). Additionally, the results indicated no correlation (P = 0.4237) between sex and the pfmdr1SNP mutation. Nonetheless, there was a significant correlation (P = 0.0043) between the pfmdr1 mutation and the age groups. According to the current study, Kano state in northern Nigeria may have strains of P. falciparum that are less sensitive to the artemisinin component of artemisinin-based combination therapy (ACT). The Plasmodium falciparum parasites' development of this resistance gene puts malaria chemotherapy at serious risk because the parasite will be immune to the widely prescribed anti-malarial medications.

Multi-decadal village-scale assessment of riverbank erosion-accretion and restoration priorities in the Lower Ganga.

Global recreational consumption of non-native inland fish: higher economic benefits, but lower nutritional value and climate resilience.

This study investigates how climate variability affects rural and urban electricity access differently and how these disparities influence agricultural transformation in the East African Community. Based on a panel dataset covering Kenya, Tanzania, Uganda, Rwanda, and Burundi from 2000 to 2024, we apply climate anomaly analysis, correlation assessment, and performance indexing to explore the links between climate shocks, infrastructure vulnerability, and development outcomes. The results show that rural electricity systems are consistently more sensitive to climate fluctuations than urban ones, with vulnerability indices ranging from 0.234 in Kenya to 1.234 in Burundi. Rather than narrowing, rural–urban electricity gaps widened in most countries, most notably by 23.5 percent in Tanzania and 14 percent in Burundi, while Kenya made substantial progress, reducing its gap by 15.2 percent. Countries with larger electricity access gaps also showed stronger negative correlations between climate variability and agricultural output, including −0.678 in Burundi, highlighting the importance of reliable electricity in reducing climate impacts. In contrast, Kenya’s near-zero correlation suggests that improved electricity access can help buffer agricultural systems from climate stress. These findings highlight a growing adaptation gap, where rural communities, despite facing greater exposure to climate risks, are often the least equipped to respond due to limited access to reliable electricity and enabling infrastructure. The study underscores the need to treat electricity access not only as a development priority but as a vital form of climate adaptation, and calls for spatially targeted, climate-resilient infrastructure strategies to promote equitable and sustainable rural transformation.

Background: Emergency department utilization is common for patients with congenital heart disease (CHD) after congenital heart surgery. Children with CHD who present to the ED are more likely to require hospitalization, incur higher healthcare costs, and experience worse outcomes compared to children without CHD. The impact of social determinants of health (SDOH) on ED utilization following CHD surgery is not well described. Our objective was to evaluate the relationship between SDOH and ED utilization in children within 12 months following CHD surgery. Methods: This is a retrospective cohort study of all patients, aged 0-21 years, who underwent cardiac surgery at Primary Children’s Hospital between 2014 and 2023. Patients were categorized by the number of ED visits following surgery (0, 1, 2-3, or 4+) and by disposition from ED. Associations between SDOH and ED utilization and disposition were evaluated using ordinal logistic regression with generalized estimating equations to account for within-subject correlation due to multiple surgeries per patient and multiple ED visit per surgery. Results: Among 3,858 surgeries analyzed (43% female, mean age 3.3 years) a higher ED utilization was associated with Black/African American and Native Hawaiian or other Pacific Islander (p=0.007 and &lt;0.001, respectively), Hispanic/Latino ethnicity (P=0.01), and languages other than English or Spanish (p=0.007). Patients with more ED visits were more likely to be younger at time of surgery (p=0.015), publicly insured (p&lt;0.001) and reside in areas with lower child opportunity index (COI) (p=0.008). Number of ED visits was not associated with either distance to the main hospital ED or to the presenting ED. Higher STAT mortality category and single ventricle status were not associated with more ED visits but were associated with admission or transfer when presenting to ED (p&lt;0.001). Patients who were Hispanic/Latino, lived in areas with lower COI or had public insurance were more likely to be discharged from the ED, while patients from rural areas were more likely to be admitted. Results are summarized in Tables I and II. Conclusion: ED utilization following CHD surgery is associated with markers of socioeconomic vulnerability, including race, ethnicity, language, and childhood opportunity index as well as proximity to familiar emergency departments. These findings highlight the need for targeted discharge interventions and planning for these groups at higher risk of ED utilization.

Background: Prevention quality indicators in inpatient settings, such as hypertension-related hospitalizations (HRH), are population-based admissions that can be prevented through timely and effective outpatient care. However, the impact of county-level social disadvantage on HRH has not been extensively studied. Objectives: To examine the association between county-level social disadvantage indices [social vulnerability index (SVI) and social deprivation index (SDI)] and HRH in all US counties from 2020-2022. We hypothesized that counties with higher SVI and SDI would have higher HRH. Methods: In this retrospective study, county-level risk-adjusted HRH rate (RA-HRHR) data for individuals aged 18 years and older were obtained from the Agency for Healthcare Research and Quality and linked to their corresponding 2022 SVI data from the CDC. The SVI measures social drivers of health in US counties based on 16 social attributes related to socioeconomic factors, household characteristics, minority status, and housing. transportation. SVI values were assigned percentile scores, with higher values indicating greater social disadvantage. The linked dataset was divided into 4 quartiles (1st quartile [Q1] and least vulnerable, and 4th quartile [Q4] and most vulnerable). The mean RA-HRHR per 100,000 population, along with 95% confidence intervals (CI), was calculated for each quartile and subsequently stratified by age groups (18-44, 45-64, ≥65 years) and gender. A negative impact of SVI was characterized by a higher RA-HRHR in Q4 than in Q1, with non-overlapping CIs. The analysis was repeated with SDI, which is computed from 7 sociodemographic variables. Results: A total of 1793 counties from 33 states (&gt;70% of the US population) were included. For SVI, the RA-HRHR increased in a stepwise manner from 27.7 (CI: 25.9-29.6) in Q1 to 67.6 (CI: 63.3-73.2) in Q4, with an excess of 39.9 in Q4 (Figure 1). The trend was similar in both genders and all age groups. Similarly, for SDI, the RA-HRHR also increased in a stepwise manner from 29.3 (CI: 27.5-31.1) in Q1 to 69.6 (CI: 64.6-76.1) in Q4, with an excess of 40.3 in Q4 (Figure 2). Conclusion: Higher county-level SVI and SDI are linked to increased HRH in the US, indicating that these counties may have unmet outpatient care needs. Both indices could be used as proxies for identifying persons with hypertension at high risk for HRHs.

Background: Social determinants of health (SDOH) are tied to cardiovascular disease (CVD) prevalence, disease progression, and outcomes. New federal and Oregon requirements mandate SDOH screening in inpatient and primary care settings. Objective: Examine rates and predictors of SDOH screening, positive screens, and clinical outcomes for CVD patients. Methods: Retrospective cohort study of all CVD patients who received care in the 7 hospitals and 63 outpatient cardiology clinics in the Providence Oregon network between 10/01/2023 – 4/24/25. In-hospital admissions with a CVD primary diagnosis, outpatient cardiology encounters, and outpatient cardiology referrals were included. SDOH screening included the following six domains: food, housing, transportation, utilities, financial strain, and intimate partner violence. SDOH screenings, patient characteristics, comorbidities, and outcomes were obtained from the electronic medical record. Rural-urban commuting area (RUCA) and the Center for Disease Control&amp;Prevention social vulnerability index (SVI) were linked to patient home address. The primary outcome was a positive screen in ≥1 SDOH domain. Secondary outcomes were new CVD or related comorbidities after the positive screen and 1-year mortality. Logistic regression models accounting for age, sex, race, ethnicity, insurance, RUCA, and SVI were run. Results: There were 71,852 CVD patients included, the majority of whom were seen at an outpatient cardiology clinic (70%) (Table 1). Across all settings, 65% of patients were screened for SDOH and 4.8% had a positive screen. Screening rate and positive screens were highest in the inpatient setting (94% and 6.9%, respectively). Patients with positive screens were significantly younger, more often Black, American Indian, Native Hawaiian, or of other or unknown races, Hispanic ethnicity, and had higher rates of Medicaid insurance (27%). Of patients screened, 7.5% screened positive, with the highest rates in the food and housing domains (Figure 1). Predictors of positive screens are shown in Figure 2. Positive screens were not associated with greater odds of new CVD or 1-year mortality. Conclusions: Following new regulatory requirements, there is a high rate of screening for SDOH within our health system. Positive screens were more common in populations with known health disparities, yet preliminary results suggest positive screens may not be associated with short term negative cardiovascular outcomes.

Background: Cardiac rhythm monitoring is a mainstay of secondary stroke prevention. Despite this, prior studies have highlighted gaps in real-world monitoring after stroke. Objective: To assess whether failure to complete cardiac monitoring is associated with worse clinical outcomes in patients (pts) who have had an acute ischemic stroke or TIA. Methods: We evaluated 891 stroke pts who were prescribed ambulatory cardiac rhythm monitoring between 2019-2023. Electronic health records identified those who completed (≥ 3 days) and did not complete (&lt; 3 days) wear. We also obtained census tract-level CDC/ATSDR Social Vulnerability Index (SVI) data for each patient. Residential addresses were geocoded in ArcGIS Pro (ESRI) and linked to overall SVI scores, which represent national percentile rankings (range 0-1). Higher values indicate greater social vulnerability. Cox proportional hazards models evaluated the associations between monitor completion and the risk for recurrent strokes, cardiovascular events, and deaths. Results: Of 891 post-stroke pts, 229 (26%) did not complete cardiac monitoring. Compared to the monitored group, pts who did not complete monitoring were more likely to be Black and have hypertension, diabetes, and Medicaid or no insurance. Median SVI rank was significantly higher in non-monitored pts (0.83 vs. 0.58, p &lt;0.001), indicating higher social vulnerability. After a median follow-up of 970 days [IQR 413, 1265], there were 325 events. The non-monitored group had a higher unadjusted risk of clinical events than the monitored group (Figure 1). After multivariable adjustment for age, sex, hypertension, diabetes, hyperlipidemia, smoking, prior stroke, heart failure, and coronary heart disease, non-monitored pts had a higher risk of recurrent stroke (HR 1.52, 95% CI [1.02, 2.25]), cardiovascular events (HR 1.50, 95% CI [1.00, 2.25]), and all-cause mortality (HR 2.79, 95% CI [1.81, 4.31]). In stratified analysis, failure to complete monitoring was independently associated with an increased risk of the combined endpoint in both the most socially vulnerable (SVI Quartile 4) (adjusted HR 1.54, 95% CI [1.08, 2.19]) and less vulnerable (SVI Quartiles 1-3) (HR 2.14, 95% CI [1.39, 3.27]) pts. Conclusions: Failure to complete cardiac monitoring is associated with worse post-stroke outcomes. While more socially vulnerable pts are less likely to complete monitoring, monitoring is associated with better outcomes across highly and less vulnerable populations.

Background: Sudden cardiac death (SCD) is a leading cause of mortality in the U.S., with emerging evidence pointing to racial disparities and the impact of social vulnerability. Yet, geographic patterns integrating race and county-level vulnerability remain underexplored. Objective: To identify U.S. counties where socially vulnerable communities, stratified by race/ethnicity, experience disproportionate burdens of SCD-related mortality. Methods: We analyzed county-level data (1999-2020), integrating age-adjusted mortality rates (AAMR) for SCD with the 2020 CDC Social Vulnerability Index (SVI). Counties were categorized into SVI quartiles (Q1: Low to Q4: High Social Vulnerability). Analyses were stratified by race/ethnicity: Black or African American, Hispanic/Latino, White, American Indian/Alaska Native (AI/AN), and Asian/Pacific Islander (API). We used RStudio (v2023.06.1) for data cleaning, ANOVA testing, and descriptive analysis. “Unreliable” mortality values were handled as missing or set to zero, with only valid AAMR &gt; 0 included. Hotspot counties were identified by selecting the top decile of AAMR SCD within Q4 SVI counties. Results: Significant disparities emerged across SVI quartiles for Black (p = 0.0003), Hispanic (p &lt; 0.0001), and White (p &lt; 0.0001) groups. Mean AAMR more than doubled from low to high SVI counties in Black communities (17.0 to 39.4 per 100,000). White and Hispanic groups showed smaller but consistent increases. API (p = 0.60) or AI/AN (p = 0.63) groups showed no significant trends. Madison Parish, LA appeared in the top-10 highest-burden counties across all groups. Black populations faced extreme rates in some SVI counties (e.g., Madison Parish, LA: 351.4 per 100,000), while API groups had low AAMR even in vulnerable areas. Data misclassification limited interpretation in the Hispanic group. Conclusions: Black, Hispanic, and White populations showed significant differences in SCD mortality tied to social vulnerability. Black populations in the most vulnerable areas experienced more than twice the rate of mortality compared to those in less vulnerable areas, suggesting a strong correlation between social vulnerability and SCD mortality in Black communities. API and AI/AN groups showed no significant associations, likely due to small sample sizes. Madison Parish, LA reflects the compounded impact of structural disadvantage. Findings support targeted interventions in socially and racially vulnerable counties to reduce preventable cardiac deaths.

Efferocytosis is impaired in atherosclerosis and recognized as a hallmark of plaque vulnerability. Defective efferocytosis can be reactivated by ‘macrophage checkpoint inhibitors’. Only the key ‘don’t-eat-me’ molecule CD47, and its myeloid receptor, SIRPα, have entered prospective clinical trials. These anti-efferocytic molecules activate the phosphatase SHP-1. To target SIRPα, we developed a macrophage-specific nanotherapy loaded with a SHP-1 inhibitor. We demonstrated that this pro-efferocytic treatment reduces plaque inflammation both in mice and large animals. However, its efficacy in humans remains uncertain. Το address this question, we developed an in silico random forest classifier method to predict the effects on the transcriptomic profile of human atheromas. We leveraged transcriptomic information from 654 human carotid endarterectomy samples to model the clinical impact of SHP-1 inhibition (SHP-1i, Figure A ). We found that SHP-1 protein expression was upregulated in both lipo-necrotic and fibro-inflammatory lesions, as well as positively associated with histologically-assessed macrophage content and higher vulnerability index scores ( Figure B ). We mapped the gene expression changes that occur in human macrophages following SHP-1i onto genes linked to high plaque vulnerability scores. SHP-1i led to the downregulation of several genes associated with MACE. Pathway analyses indicated induced gene clusters that drive phagocytic signaling, leukocyte migration, and antigen presentation ( Figure C ). We then performed computational modeling to estimate how SHP-1i might influence the landscape of plaque subtypes. First, we trained a random forest classification model and then simulated the effects of SHP-1i by altering the gene expression profiles of these plaques based on the expression changes observed from in vitro studies. Finally, we used the trained model to reclassify the plaque subtypes. Notably, SHP-1i is predicted to manifest with a shift from a lipo-necrotic to a fibro-collagenous composition, indicating a reduction of lesion vulnerability ( Figure D ). To validate the method, we performed a neutral control and positive controls using two well-established treatments. Our method correctly predicted the impact of each drug on lesion vulnerability ( Figure E ).

Soil organic carbon, clay content and cation exchange capacity play a key role in the productivity of agricultural soils, and are therefore fundamental parameters for environmental monitoring and modelling. However, studying these properties using traditional laboratory methods is labour-intensive and costly. An equally important factor is the steepness of slopes, which affects erosion processes and nutrient distribution in the soil. Geospatial analysis is a powerful tool for examining spatial patterns and the distribution of various indicators. When assessing soil quality indicators, GIS technologies enable the accurate and detailed monitoring of soil conditions in various areas, the assessment of their characteristics, and the identification of potential problem areas. This study presents observation and analysis of the impact of soil quality indicators, including soil organic carbon (SOC), physical clay, and cation exchange capacity (CEC), on the development of soil quality degradation using SoilGrids 250 m 2.0 data. To estimate the level of erosion, a slope steepness map was generated using the SRTM digital elevation model, which was downloaded through Google Earth Engine at a 30-m resolution. The results showed that high organic carbon content and optimal CEC values reduce soil vulnerability to the development of erosion, while steep slopes and low organic carbon content increase the risk of degradation. The vulnerability index developed based on these data allows us to effectively identify areas at high risk of soil degradation and develop protection strategies.

Background: Heart failure (HF) is a major cause of morbidity and hospitalization in the United States. Place-based social determinants of health, including community-level vulnerability and deprivation, influence cardiovascular disease prevalence, risk factors, and outcomes, but their association with heart failure hospitalizations (HFH) has not been extensively studied. We examined the relationship between county-level social disadvantage indices [Social Vulnerability Index (SVI) and the Social Deprivation Index (SDI)] and risk-adjusted HFH rates (RA-HFHR) in the US from 2020 to 2022. Methods: In this retrospective study, county-level RA-HFHR data for persons aged ≥18 years obtained from the Agency for Healthcare Research and Quality were linked to their corresponding 2022 SVI data (obtained from the CDC) and the 2019 SDI data (https://www.graham-center.org/maps-data-tools/social-deprivation-index.html). These indices are computed from sociodemographic factors from the American Community Survey. SVI and SDI scores were assigned percentile scores, with higher values indicating greater social disadvantage. The linked dataset was divided into four quartiles (1st quartile [Q1] and least vulnerable or deprived, and 4 th quartile [Q4] and most vulnerable/deprived). RA-HFHR per 100,000 with 95% confidence intervals (CI) were computed for each quartile and then stratified by age (18-44, 45-64, ≥65 years) and gender. A negative impact by SVI or SDI was characterized by a higher RA-HFHR in Q4 than in Q1, with non-overlapping CI. Rate differences (excess or fewer hospitalizations per 100,000) were derived by obtaining the differences between Q4 and Q1. Results: A total of 1905 counties from 33 states (&gt;70% of the US population) were included. For SVI, the RA-HFHR increased in a stepwise manner from Q1 221.6 (CI: 212.1-231.3) in Q1 to 390.9 (CI: 77.9-404.3) in Q4, with an excess of 169.3 in Q4. The trend was similar in both genders and across all age groups (Table 1). Similarly, for SDI, the RA-HFHR significantly and progressively from 220.7 (CI: 212.2-228.9) in Q1 to 408.9 (CI: 393.9-423.4) in Q4, yielding an excess of 188.2 in Q4. Again, the trend was similar in both genders and across all age groups (Table 2). Conclusion: Counties with higher social vulnerability and deprivation are associated with higher RA-HFHRs. The inclusion of these indices in risk prediction models could help identify patients with HF at high risk of getting hospitalized.

Background: The CDC Social Vulnerability Index (SVI) quantifies community-level social vulnerability across four domains: socioeconomic (SES), household characteristics (e.g., single-parent), minority status, and housing/transportation. We examined differences in Life’s Essential 8 (LE8) cardiovascular health (CVH) scores across SVI quartiles, explored which SVI domains and sex-race subgroups accounted for the largest disparities in LE8 scores, and evaluated associations between LE8 sub-components and SVI. Methods: We analyzed data from CARDIA participants at Year 15 (Y15; 2000–01; n = 3,175; mean age 40 y) and Year 30 (Y30; 2015–16; n = 2,370; mean age 55 y). SVI from years 2000 and 2016, corresponding with Y15 and Y30 of CARDIA, was assigned via geocoded residential address and grouped into quartiles (Q1 = lowest vulnerability, Q4 = highest). Overall LE8 and eight component metric scores were calculated per AHA definitions (range 0-100; higher is better CVH). Using linear regression adjusted for age, sex, race, and maximum education, we compared mean LE8 scores (with 95% CIs) across SVI quartiles. Results: At both Y15 and Y30, there was an inverse association between SVI and LE8 score ( Figure ). The mean adjusted overall LE8 score difference for Q4 vs. Q1 of SVI was –4.95 (95% CI: –6.36, –3.54) at Y15 and –5.37 points (–7.24, –3.49) at Y30. For Q3 vs. Q1, adjusted LE8 difference was –2.21 (–3.47, –0.94) at Y15 and –3.56 (–5.27, –1.84) at Y30. Among the four SVI domains, SES and household characteristics drove most of the disparity in LE8 scores (Y30 Q4 vs. Q1: SES Δ = –6.59; household Δ = –6.45 points). Across the extremes of SVI quartiles, LE8 metric-level differences were largest for smoking (Y15 Q4 vs. Q1: Δ = –12.37; Y30 Δ = –8.91 points) and physical activity (Y15 Q4 vs. Q1: Δ = –7.57; Y30 Δ = –13.78 points). In the SES domain of SVI, Black participants had mean SVI scores of 0.61 vs. 0.30 for White participants at Y15, with a similar difference at Y30, whereas sex differences within each race were minimal, indicating that racial disparities in community SES vulnerability far exceeded those by sex. Conclusions: Higher social vulnerability is associated with significantly lower CVH, and these disparities endure into midlife, with racial disparities far exceeding those by sex. SES and household factors, along with behavioral gaps in smoking and physical activity, may be key targets for community-level interventions to improve cardiovascular health equity.

Social determinants of health are known to influence cardiovascular disease (CVD) risk. Communities experiencing greater social vulnerability often face structural disadvantages that contribute to higher morbidity and mortality from chronic diseases, including CVD. However, their relationship to sudden death (SD) has not been thoroughly researched yet. The present study aims to explore the relationship between CVD risk factors, CVD outcomes, and social vulnerability in victims of sudden death. As part of the SUDDEN Project at the University of North Carolina, we screened and adjudicated 399 sudden death victims from Wake County, NC. Each victim’s address was geocoded and linked to census tract level data, including the CDC PLACES: Local Data for Better Health, and the CDC Social Vulnerability Index (SVI) datasets. We compared our sample of SD victims against census tract data in terms of CVD, other comorbidities (e.g., DM2), and healthcare utilization. We assessed the distribution of CVD risk factors (e.g., smoking), and healthcare utilization in our sample across SVI quartiles. Chi-square tests were used to evaluate differences in demographics, risk factors, and outcomes. Overall, the 399 sudden death victims were uniformly distributed across all four SVI quartiles. However, African Americans who died suddenly were more likely to reside in high-SVI areas compared to Whites (p &lt; 0.001). Contrary to established population-level patterns from prior studies, CVD risk factors were also uniformly distributed across SVI quartiles in our sample of SD victims. No significant differences in comorbidities were found across the SVI categories except for chronic kidney disease, which was more common in higher SVI areas, p &lt; 0.05). However, living in an area with a higher SVI was associated with lower healthcare utilization prior to death. While overall CVD risk factors and comorbidities were largely similar across the different levels of social vulnerability, certain disparities could be found (e.g., regarding healthcare utilization). The findings suggest that structural and access-related factors may influence healthcare engagement prior to sudden death, even when traditional CVD risk profiles appear similar. Further research is needed to clarify the relationship between social vulnerability, healthcare access, and sudden death to determine how tailored interventions can reduce these disparities.

Background: Community-level social disadvantage is a recognized determinant of adverse cardiovascular outcomes, yet its relationship with hypertension-related hospitalizations (HTNH) among older adults remains unclear. Composite indices such as the Social Vulnerability Index (SVI) and Social Deprivation Index (SDI) have emerged as tools to quantify area-level social disadvantage. This study assessed the association of county-level SVI and SDI scores with HTNH rates among Medicare beneficiaries aged ≥65 years and compared the predictive utility of each index. Methods: We conducted a retrospective cross-sectional analysis using publicly available county-level data. The SDI, developed by the Robert Graham Center, is a measure of deprivation based on seven demographic characteristics. The SVI, developed by the CDC, is based on 16 social determinants of health. We linked 2019 SDI and 2018 SVI data to HTNH data from the CDC’s Interactive Atlas of Heart Disease and Stroke for adults aged 65 and older in the United States from 2019 to 2021 for each county. The linked dataset was divided into quartiles (Q1 = least disadvantaged; Q4 = most disadvantaged) based on SDI scores (0–100) and SVI scores (0–1). Mean HTNH rates per 1,000 person-years were calculated with 95% confidence intervals (CIs) and stratified by quartile, gender, and race/ethnicity. A higher rate in Q4 vs Q1, with nonoverlapping CIs, indicated a negative impact of SDI or SVI. Rate differences were calculated as excess or fewer hospitalizations per 1,000 person-years by subtracting Q1 from Q4. Results: The overall HTNH rate was 13.6 (95% CI: 13.4–13.8). For SDI, rates rose from 10.9 (10.6–11.2) in Q1 to 16.8 (16.4–17.2) in Q4, representing a 5.9 excess hospitalizations. Among men, rates increased from 12.6 to 18.2; among women, from 9.9 to 15.7. For SVI, rates rose from 10.6 (10.3–11.0) in Q1 to 16.5 (16.1–16.9) in Q4, again reflecting 5.9 excess hospitalizations. Disparities were also evident by race/ethnicity, with Q4 counties showing significantly higher HTNH rates than Q1 across Black, White, and Hispanic groups. Conclusion: Older adults with hypertension living in counties with high social disadvantage experience markedly higher HTNH rates. Both SDI and SVI effectively identify counties with elevated hospitalization risk and may support targeted public health interventions to reduce HTNH and healthcare resource utilization among Medicare beneficiaries.

Background: Statin medication can effectively manage some types of cardiovascular disease (CVD), but not everyone who could benefit from statins is taking them. While adherence to a statin regimen is complex, patients’ experience with the healthcare system can be disparate based on race/ethnicity, which may contribute to barriers to adherence. Research Question: We examined factors potentially associated with statin receipt and adherence, including characteristics of patients, their neighborhoods, and the patient-physician relationship. Methods: The study sample included patients with commercial or Medicare Advantage health plan coverage through a large national insurer. All patients were eligible for the HEDIS measure “Statin Therapy for Patients with CVD” in 2020-2023 and had non-missing demographic data, at least one visit to a primary care physician (PCP) or cardiologist in the year before the measurement year, and their attributed physician had complete demographic, educational, and professional data. We estimated the cross-sectional association between each factor and statin receipt and adherence using multivariate linear regression and physician fixed effect models. Patient factors included age, gender, race/ethnicity, and baseline engagement in care and comorbidities. Patient neighborhood factors included state, urbanicity, Social Vulnerability Index, and PCP and pharmacy shortage area. Physician factors included gender, race/ethnicity, years in practice, and graduation from an international medical school. We also examined the role of patient-physician racial/ethnic concordance. Results: In the sample (N=201,964), the average rate of statin receipt was 80.3% and adherence was 80.7%. The following patient characteristics, shown in Table 1, were most positively associated with statin receipt and adherence in physician fixed effects models: male gender, 3+ comorbidities, age &gt; 65 years, and higher engagement in primary care. Differences in statin receipt and adherence between racial/ethnic groups remained after adjusting for other factors, including patient-physician racial/ethnic concordance, which was not statistically significant. Conclusions: The pattern of results suggests that factors associated with clinical risk and engagement in care are most strongly associated with statin adherence among patients with CVD. Efforts to improve statin adherence should prioritize consistent engagement with and equitable access to primary and preventive care.

Background: Angina and myocardial infarction (MI) are major contributors to cardiovascular morbidity and mortality. We assessed recent trends in angina and MI prevalence by sex, region, and Social Vulnerability Index (SVI). Methods: Serial cross-sectional analysis using 2019–2023 National Health Interview Survey (NHIS) data. Adults aged 18–64 were included for angina; ≥18 years for MI. Outcomes were self-reported, physician-diagnosed angina or MI. Analyses were stratified by sex, region (Northeast, Midwest, South, West), and SVI level (Little/None, Low, Medium, High). Linear regression tested annual trends and interaction effects. Survey-weighted analyses were conducted using R v4.4.2. Results: National angina prevalence remained stable (1.3%–1.7%), while MI prevalence increased (+0.58%/year, p &lt; 0.05). Sex: Males consistently had higher prevalence for both conditions. In 2023, angina was 1.9% in males vs. 1.4% in females; MI was 3.8% vs. 2.3%. Only MI showed a significant upward trend in males (p &lt; 0.05); trend differences by sex were non-significant (p = 0.17 for angina; p = 0.975 for MI). Region: Highest prevalence for both conditions was seen in the South and Northeast (angina up to 1.9%, MI up to 3.4%), lowest in the West (angina ~1.5%, MI 2.1%). No significant regional trend differences were observed (p &gt; 0.60). SVI: Medium vulnerability areas had the highest prevalence for both angina (~1.7%) and MI (3.5%). MI prevalence declined (−0.20%/year, p &lt; 0.05), with significant trend differences in Low and Medium SVI groups (both p &lt; 0.05). Angina trends by SVI were stable (interaction p = 0.17), but disparities persisted. Conclusion: From 2019–2023, angina remained stable while MI prevalence increased. Males, residents of the South, and individuals in medium SVI areas had consistently higher cardiovascular burden. Persistent disparities underscore the need for targeted public health interventions.

Background: Animal models facilitate study of coronary atherosclerotic disease. Currently, there is no formalclassification of coronary plaques in the animal models. Pigs are the FDA-preferred species for testing cardiovascular devices and the primary choice for preclinical toxicological testing of anti-atherosclerotic drugs.Pigs with familial hypercholesterolemia (FH pigs) fed with high-fat diet develop early atherosclerotic lesions and complex atheromas in coronaries mimicking human disease and FH pigs are a gold standard model for translational atherosclerosis research. Methods: We isolated 104 coronary fragments from FH pigs. Intima-media thickness ratio, vessel size, necrotic core (NC) area and fibrous cap (FC) thickness were used as morphological criteria to classify plaques by K means clustering. Levels of cell type markers (α-smooth muscle actin, smooth muscle cells, SMC; scavenger receptor type A, SRA, macrophages, MF; CD31, endothelial cells, EC) were quantified by IHC. Intraplaque neovascularization, collagen levels (Trichrome staining), calcification (Von Kossa stain) and intraplaque hemorrhage (Carstairs method) were used as additional criteria and to establish the similarity of porcine plaque to human lesions. Results: We identified 4 clearly distinguishable plaque groups (A-D) in porcine coronary arteries. Plaque group A has no FC and NC, high level of SMC (&gt;18%, as normalized per plaque area) and low MF level (&lt;10%) consistent with the definition of intermediate (type III) human lesions in accordance with the AHA-developed histological classification of human coronary plaques. Group B has small NC area (&lt;15%), thick FC, and large amount of intracellular lipid droplets in intima like type IV atheroma. Group C has increased vessel size (1.6-fold increase vs. group A and B, P&lt;0.05), marked thinning of the tunica media, and a high level of collagen (&gt;43%) mimicking type V fibroatheroma. Group D has a low level of SMC (&lt;7%), high MF content (&gt;15%), large NC area (&gt;30%), multiple breaks in the endothelial layer, severe calcium deposition and neovascularization, and high vulnerability index consistent with type VI complicated lesion. Conclusions: Our results establish the suitability of hypercholesteremic pigs as a pre-clinical model closely mimicking human coronary atherosclerosis and provide researchers with an instrument to study mechanism of atherosclerosis and to assess changes in specific lesion phenotype induced by interventions.