Dataset Information

Disparities in access to trauma care in the United States: A population-based analysis.

ABSTRACT: Injury is a major contributor to morbidity and mortality in the United States. Accordingly, expanding access to trauma care is a Healthy People priority. The extent to which disparities in access to trauma care exist in the US is unknown. Our objective was to describe geographic, demographic, and socioeconomic disparities in access to trauma care in the United States.Cross-sectional study of the US population in 2010 using small units of geographic analysis and validated estimates of population access to a Level I or II trauma center within 60minutes via ambulance or helicopter. We examined the association between geographic, demographic, and socioeconomic factors and trauma center access, with subgroup analyses of urban-rural disparities.Of the 309 million people in the US in 2010, 29.7 million lacked access to trauma care. Across the country, areas with higher income were significantly more likely to have access (OR 1.30, 95% CI 1.12-1.50), as were major cities (OR 2.13, 95% CI 1.25-3.62) and suburbs (OR 1.27, 95% CI 1.02-1.57). Areas with higher rates of uninsured (OR 0.09, 95% CI 0.07-0.11) and Medicaid or Medicare eligible patients (OR 0.69, 95% CI 0.59-0.82) were less likely to have access. Areas with higher proportions of blacks and non-whites were more likely to have access (OR 1.37, 95% CI 1.19-1.58), as were areas with higher proportions of Hispanics and foreign-born persons (OR 1.51, 95% CI 1.13-2.01). Overall, rurality was associated with significantly lower access to trauma care (OR 0.20, 95% CI 0.18-0.23).While the majority of the United States has access to trauma care within an hour, almost 30 million US residents do not. Significant disparities in access were evident for vulnerable populations defined by insurance status, income, and rurality.

SUBMITTER: Carr BG

PROVIDER: S-EPMC5292279 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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Disparities in access to trauma care in the United States: A population-based analysis.

Carr Brendan G BG Bowman Ariel J AJ Wolff Catherine S CS Mullen Michael T MT Holena Daniel N DN Branas Charles C CC Wiebe Douglas J DJ

Injury 20170103 2

<h4>Background</h4>Injury is a major contributor to morbidity and mortality in the United States. Accordingly, expanding access to trauma care is a Healthy People priority. The extent to which disparities in access to trauma care exist in the US is unknown. Our objective was to describe geographic, demographic, and socioeconomic disparities in access to trauma care in the United States.<h4>Methods</h4>Cross-sectional study of the US population in 2010 using small units of geographic analysis and ...[more]

PMID: 28069138

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Project description:Purpose of reviewSub-Saharan Africa is a diverse context with a large burden of injury and trauma-related deaths. Relative to high-income contexts, most of the region is less mature in prehospital and facility-based trauma care, education and training, and trauma care quality assurance. The 2030 Agenda for Sustainable Development recognizes rising inequalities, both within and between countries as a deterrent to growth and development. While disparities in access to trauma care between the region and HICs are more commonly described, internal disparities are equally concerning. We performed a narrative review of internal disparities in trauma care access using a previously described conceptual model.Recent findingsA broad PubMed and EMBASE search from 2010 to 2021 restricted to 48 sub-Saharan African countries was performed. Records focused on disparities in access to trauma care were identified and mapped to de Jager's four component framework. Search findings, input from contextual experts, comparisons based on other related research, and disaggregation of data helped inform the narrative. Only 21 studies were identified by formal search, with most focused on urban versus rural disparities in geographical access to trauma care. An additional 6 records were identified through citation searches and experts. Disparity in access to trauma care providers, detection of indications for trauma surgery, progression to trauma surgery, and quality care provision were thematically analyzed. No specific data on disparities in access to injury care for all four domains was available for more than half of the countries. From available data, socioeconomic status, geographical location, insurance, gender, and age were recognized disparity domains. South Africa has the most mature trauma systems. Across the region, high quality trauma care access is skewed towards the urban, insured, higher socioeconomic class adult. District hospitals are more poorly equipped and manned, and dedicated trauma centers, blood banks, and intensive care facilities are largely located within cities and in southern Africa. The largest geographical gaps in trauma care are presumably in central Africa, francophone West Africa, and conflict regions of East Africa. Disparities in trauma training opportunities, public-private disparities in provider availability, injury care provider migration, and several other factors contribute to this inequity. National trauma registries will play a role in internal inequity monitoring, and deliberate development implementation of National Surgical, Obstetrics, and Anesthesia plans will help address disparities. Human, systemic, and historical factors supporting these disparities including implicit and explicit bias must be clearly identified and addressed. Systems approaches, strategic trauma policy frameworks, and global and regional coalitions, as modelled by the Global Alliance for Care of the Injured and the Bellagio group, are key. Inequity in access can be reduced by prehospital initiatives, as used in Ghana, and community-based insurance, as modelled by Rwanda.SummarySub-Saharan African countries have underdeveloped trauma systems. Consistent in the narrative is the rural-urban disparity in trauma care access and the disadvantage of the poor. Further research is needed in view of data disparity. Recognition of these disparities should drive creative equitable solutions and focused interventions, partnerships, accompaniment, and action.Supplementary informationThe online version contains supplementary material available at 10.1007/s40719-022-00229-1.

Project description:ImportanceThe use of artificial intelligence (AI) in clinical medicine risks perpetuating existing bias in care, such as disparities in access to postinjury rehabilitation services.ObjectiveTo leverage a novel, interpretable AI-based technology to uncover racial disparities in access to postinjury rehabilitation care and create an AI-based prescriptive tool to address these disparities.Design, setting, and participantsThis cohort study used data from the 2010-2016 American College of Surgeons Trauma Quality Improvement Program database for Black and White patients with a penetrating mechanism of injury. An interpretable AI methodology called optimal classification trees (OCTs) was applied in an 80:20 derivation/validation split to predict discharge disposition (home vs postacute care [PAC]). The interpretable nature of OCTs allowed for examination of the AI logic to identify racial disparities. A prescriptive mixed-integer optimization model using age, injury, and gender data was allowed to "fairness-flip" the recommended discharge destination for a subset of patients while minimizing the ratio of imbalance between Black and White patients. Three OCTs were developed to predict discharge disposition: the first 2 trees used unadjusted data (one without and one with the race variable), and the third tree used fairness-adjusted data.Main outcomes and measuresDisparities and the discriminative performance (C statistic) were compared among fairness-adjusted and unadjusted OCTs.ResultsA total of 52 468 patients were included; the median (IQR) age was 29 (22-40) years, 46 189 patients (88.0%) were male, 31 470 (60.0%) were Black, and 20 998 (40.0%) were White. A total of 3800 Black patients (12.1%) were discharged to PAC, compared with 4504 White patients (21.5%; P < .001). Examining the AI logic uncovered significant disparities in PAC discharge destination access, with race playing the second most important role. The prescriptive fairness adjustment recommended flipping the discharge destination of 4.5% of the patients, with the performance of the adjusted model increasing from a C statistic of 0.79 to 0.87. After fairness adjustment, disparities disappeared, and a similar percentage of Black and White patients (15.8% vs 15.8%; P = .87) had a recommended discharge to PAC.Conclusions and relevanceIn this study, we developed an accurate, machine learning-based, fairness-adjusted model that can identify barriers to discharge to postacute care. Instead of accidentally encoding bias, interpretable AI methodologies are powerful tools to diagnose and remedy system-related bias in care, such as disparities in access to postinjury rehabilitation care.

Dataset Information

Disparities in access to trauma care in the United States: A population-based analysis.

Publications

Disparities in access to trauma care in the United States: A population-based analysis.

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