County-Level Social Vulnerability Is Positively Associated with Cardiometabolic Disease in Colorado

Abstract

Cardiometabolic diseases are a group of interrelated diseases that pose greater burden among socially vulnerable communities. The social vulnerability index (SVI) identifies communities vulnerable to emergencies and may also help determine communities at risk of adverse chronic health outcomes. However, no studies have examined the relationship between the SVI and cardiometabolic health outcomes in Colorado or focused on rural settings. The aim of this ecological study was to determine whether the county-level SVI is associated with county-level cardiometabolic health indicators with a particular focus on rurality and racial/ethnic diversity. We obtained 2014 SVI scores from the Centers for Disease Control and Prevention (scored 0–1; higher = more vulnerable) and 2013–2015 cardiometabolic health estimates from the Colorado Department of Public Health and Environment. The distribution of social determinants of health was spatially evaluated. Bivariate relationships between the SVI and cardiometabolic indicators were estimated using simple linear regression models. The highest SVI scores were observed in rural areas, including the San Luis Valley (mean: 0.78, median: 0.91), Southeast (mean: 0.72, median: 0.73), and Northeast (mean: 0.66, median: 0.76) regions. Across Colorado, the SVI accounted for 41% of the variability in overweight and obesity prevalence (p < 0.001), 17% of the variability in diabetes prevalence (p = 0.001), and 58% of the age-adjusted myocardial infarction hospitalization rate (p < 0.001). SVI values may be useful in determining a community’s burden of cardiometabolic diseases.

Socio-ecological factors linked with changes in adults’ dietary intake in Los Angeles County during the peak of the coronavirus 2019 pandemic

“…At the neighbourhood level, after controlling for individual-level and social network predictors, neither food environments nor social vulnerability was associated with changes in dietary intake. Though we identified no studies that examined neighbourhood social vulnerability and diet during the pandemic, these findings contradict a few studies showing that higher neighbourhood social vulnerability is linked with increased risk for diet-related diseases (36,37) . With regard to the food environment, other studies have suggested that the mixed findings on the association between neighbourhood food environments and diet may potentially be due to confounding factors like neighbourhood socio-economic status (43) .…”

“…Neighbourhood social vulnerability captures a broader set of resources and social determinants of health available in one’s neighbourhood ( 36 , 37 ) . We use the Center for Disease Control and Prevention’s social vulnerability index, which captures communities’ vulnerability to the potential negative effects caused by external stresses on human health (e.g.…”

A Longitudinal Study of Exposure to Manganese and Incidence of Metabolic Syndrome

“…This longitudinal study differed methodologically from the three previous cross-sectional analyses of the associations between Mn and MetS in the US in that we focused on a rural population that experiences more population-level stress and health burden [ 7 , 16 , 21 , 48 ]. For example, the Mn concentrations were somewhat higher in our study sample than in the general US population during the same time period (SLVDS geometric mean (95% CI) = 0.67 (0.64, 0.70) µg/L; US National Health and Nutrition Examination Survey III (1988–1994) geometric mean (95% CI) Mn level = 0.53 (0.46, 0.61) µg/L) [ 49 ].…”

An Analysis of the Social Determinants of Health in South Carolina’s I-95 Corridor

“…In addition to disaster management, the SVI has been used to examine multilevel social influences on health outcomes at the local, state, and federal levels (B. Flanagan et al, 2021; see, for example, Gay et al, 2016; Wild et al, 2022; Yee et al, 2019). The SVI uses 15 U.S. census variables grouped into six SDOH (socioeconomic status, household composition and disability, minority status and language, housing type and transportation, health care infrastructure, and medical vulnerability), with percentile rankings for each SDOH and an overall ranking ranging between 0 ( least vulnerable ) and 1 ( most vulnerable ).…”