Dataset Information

Can social vulnerability indices predict county trauma fatality rates?

ABSTRACT:

Background

Social vulnerability indices were created to measure resiliency to environmental disasters based on socioeconomic and population characteristics of discrete geographic regions. They are composed of multiple validated constructs that can also potentially identify geographically vulnerable populations after injury. Our objective was to determine if these indices correlate with injury fatality rates in the US.

Methods

We evaluated three social vulnerability indices: The Hazards & Vulnerability Research Institute's Social Vulnerability Index (SoVI), the Center for Disease Control's Social Vulnerability Index (SVI), and the Economic Innovation Group's Distressed Community Index (DCI). We analyzed SVI subindices and common individual census variables as indicators of socioeconomic status. Outcomes included age-adjusted county-level overall, firearm, and motor vehicle collision deaths per 100,000 population. Linear regression determined the association of injury fatality rates with the SoVI, SVI, and DCI. Bivariate choropleth mapping identified geographic variation and spatial autocorrelation of overall fatality, SoVI, and DCI.

Results

A total of 3,137 US counties were included. Only 24.6% of counties fell into the same vulnerability quintile for all three indices. Despite this, all indices were associated with increasing fatality rates for overall, firearm, and motor vehicle collision fatality. The DCI performed best by model fit, explanation of variance, and diagnostic performance on overall injury fatality. There is significant geographic variation in SoVI, DCI, and injury fatality rates at the county level across the United States, with moderate spatial autocorrelation of SoVI (Moran's I, 0.35; p < 0.01) and high autocorrelation of injury fatality rates (Moran's I, 0.77; p < 0.01) and DCI (Moran's I, 0.53; p < 0.01).

Conclusion

While the indices contribute unique information, higher social vulnerability is associated with higher injury fatality across all indices. These indices may be useful in the epidemiologic and geographic assessment of injury-related fatality rates. Further study is warranted to determine if these indices outperform traditional measures of socioeconomic status and related constructs used in trauma research.

Level of evidence

Epidemiological, level IV.

SUBMITTER: Phelos HM

PROVIDER: S-EPMC8375410 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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Publications

Can social vulnerability indices predict county trauma fatality rates?

Phelos Heather M HM Deeb Andrew-Paul AP Brown Joshua B JB

The journal of trauma and acute care surgery 20210801 2

<h4>Background</h4>Social vulnerability indices were created to measure resiliency to environmental disasters based on socioeconomic and population characteristics of discrete geographic regions. They are composed of multiple validated constructs that can also potentially identify geographically vulnerable populations after injury. Our objective was to determine if these indices correlate with injury fatality rates in the US.<h4>Methods</h4>We evaluated three social vulnerability indices: The Ha ...[more]

PMID: 33852559

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Project description:BackgroundThe incidence of sudden unexpected infant death (SUID) in the United States has persisted at roughly the same level since the mid-2000s, despite intensive prevention efforts around safe sleep. Disparities in outcomes across racial and socioeconomic lines also persist. These disparities are reflected in the spatial distribution of cases across neighborhoods. Strategies for prevention should be targeted precisely in space and time to further reduce SUID and correct disparities.ObjectiveWe sought to aid neighborhood-level prevention efforts by characterizing communities where SUID occurred in Cook County, IL, from 2015 to 2019 and predicting where it would occur in 2021-2025 using a semiautomated, reproducible workflow based on open-source software and data.MethodsThis cross-sectional retrospective study queried geocoded medical examiner data from 2015-2019 to identify SUID cases in Cook County, IL, and aggregated them to "communities" as the unit of analysis. We compared demographic factors in communities affected by SUID versus those unaffected using Wilcoxon rank sum statistical testing. We used social vulnerability indicators from 2014 to train a negative binomial prediction model for SUID case counts in each given community for 2015-2019. We applied indicators from 2020 to the trained model to make predictions for 2021-2025.ResultsValidation of our query of medical examiner data produced 325 finalized cases with a sensitivity of 95% (95% CI 93%-97%) and a specificity of 98% (95% CI 94%-100%). Case counts at the community level ranged from a minimum of 0 to a maximum of 17. A map of SUID case counts showed clusters of communities in the south and west regions of the county. All communities with the highest case counts were located within Chicago city limits. Communities affected by SUID exhibited lower median proportions of non-Hispanic White residents at 17% versus 60% (P<.001) and higher median proportions of non-Hispanic Black residents at 32% versus 3% (P<.001). Our predictive model showed moderate accuracy when assessed on the training data (Nagelkerke R2=70.2% and RMSE=17.49). It predicted Austin (17 cases), Englewood (14 cases), Auburn Gresham (12 cases), Chicago Lawn (12 cases), and South Shore (11 cases) would have the largest case counts between 2021 and 2025.ConclusionsSharp racial and socioeconomic disparities in SUID incidence persisted within Cook County from 2015 to 2019. Our predictive model and maps identify precise regions within the county for local health departments to target for intervention. Other jurisdictions can adapt our coding workflows and data sources to predict which of their own communities will be most affected by SUID.

Project description:BackgroundSocial vulnerability occurs when the disadvantage conveyed by poor social conditions determines the degree to which one's life and livelihood are at risk from a particular and identifiable event in health, nature, or society. A common way to estimate social vulnerability is through an index aggregating social factors. This scoping review broadly aimed to map the literature on social vulnerability indices. Our main objectives were to characterize social vulnerability indices, understand the composition of social vulnerability indices, and describe how these indices are utilized in the literature.MethodsA scoping review was conducted in six electronic databases to identify original research, published in English, French, Dutch, Spanish or Portuguese, and which addressed the development or use of a social vulnerability index (SVI). Titles, abstracts, and full texts were screened and assessed for eligibility. Data were extracted on the indices and simple descriptive statistics and counts were used to produce a narrative summary.ResultsIn total, 292 studies were included, of which 126 studies came from environmental, climate change or disaster planning fields of study and 156 studies were from the fields of health or medicine. The mean number of items per index was 19 (SD 10.5) and the most common source of data was from censuses. There were 122 distinct items in the composition of these indices, categorized into 29 domains. The top three domains included in the SVIs were: at risk populations (e.g., % older adults, children or dependents), education, and socioeconomic status. SVIs were used to predict outcomes in 47.9% of studies, and rate of Covid-19 infection or mortality was the most common outcome measured.ConclusionsWe provide an overview of SVIs in the literature up to December 2021, providing a novel summary of commonly used variables for social vulnerability indices. We also demonstrate that SVIs are commonly used in several fields of research, especially since 2010. Whether in the field of disaster planning, environmental science or health sciences, the SVIs are composed of similar items and domains. SVIs can be used to predict diverse outcomes, with implications for future use as tools in interdisciplinary collaborations.

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Project description:BackgroundDespite being the second leading cause of death in the United States, cancer disproportionately affects underserved communities due to multiple social factors like economic instability and limited healthcare access, leading to worse survival outcomes. This cross-sectional database study involves real-world data to explore the relationship between the Social Vulnerability Index (SVI), a measure of community resilience to disasters, and disparities in screening, incidence, and mortality rates of breast, colorectal, and lung cancer. The SVI encompasses four themes: socioeconomic status, household composition & disability, minority status & language, and housing type & transportation.Materials and methodsUsing county-level data, this study compared cancer metrics in U.S. counties and the impact of high and low SVI. Two-sided statistical analysis was performed to compare SVI tertiles and cancer screening, incidence, and mortality rates. The outcomes were analyzed with logistic regression to determine the odds ratio of SVI counties having cancer metrics at or above the median.ResultsOur study encompassed 3,132 United States counties. From publicly available SVI data, we demonstrated that high SVI scores correlate with low breast and colorectal cancer screening rates, along with high incidence and mortality rates for all three types of cancers. County level SVI has impact on incidence rates of cancers; breast cancer rates were lowest in high SVI counties, while colorectal and lung cancer rates were highest in the same counties. Age-adjusted mortality rates for all three cancers increased across SVI tertiles. After risk adjustment, a 10-point SVI increase correlated with lower screening and higher mortality rates.ConclusionIn conclusion, our study establishes a significant correlation between SVI and cancer metrics, highlighting the potential to identify marginalized communities with health disparities for targeted healthcare initiatives. It underscores the need for further longitudinal studies on bridging the gap in overall cancer care in the United States.

Dataset Information

Can social vulnerability indices predict county trauma fatality rates?

Background

Methods

Results

Conclusion

Level of evidence

Publications

Can social vulnerability indices predict county trauma fatality rates?

Similar Datasets

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