The Influence of Insurance, Race, and Gender on Emergency Department Disposition
Abstract
Abstract Objectives: To examine the influence of insurance, race, and gender on the likelihood of hospitalization among trauma patients. Methods: Statewide hospital discharge and emergency department (ED) visit data collected between 1996 and 2000 were merged to examine factors that influence hospitalization among patients who sustained an injury. Multivariate logistic regression was used to model the likelihood of hospitalization as a function of patient, injury, and hospital characteristics. Results: Of 1,512,611 patients who presented to an ED in South Carolina for treatment of a traumatic injury during the five‐year study period, 8% were hospitalized and 92% were treated and released. One fourth (26%) of the study population was uninsured. Insurance, race, and gender were significant predictors of hospitalization despite controlling for injury severity, comorbidities, age, trauma center level, place of residence, and year of injury. Regardless of injury severity, uninsured patients were significantly less likely to be hospitalized compared with privately insured patients (odds ratio [OR] 0.63, 99% CI = 0.62 to 0.65). Among those mildly to moderately injured, patients covered by Medicare or other government insurance policies were significantly more likely to be admitted compared with those with private coverage (OR 1.46, 99% CI = 1.41 to 1.52; OR 1.56, 99% CI = 1.36 to 1.78). Finally, among those mildly injured, African American females were significantly less likely to be admitted compared with white females (OR 0.63, 99% CI = 0.61 to 0.65). Conclusions: These results suggest that the disposition of trauma patients from the ED may be influenced by insurance and demographic characteristics in addition to the patient's clinical condition.
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Variables correlated with age and markers of socioeconomic status such as insurance type have been previously linked to hospital admission.
“that have been previously linked to hospital admission [46–48].”
From Predicting hospital admission at emergency department triage using machine learning (2018) · cites this paper as [46]
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- Authors
- Selassie, A. W., McCarthy, M. L., & Pickelsimer, E. E.
- Affiliations
- Medical University of South Carolina (US)
- Johns Hopkins University (US)
- Venue
- Academic Emergency Medicine
- Publisher
- Wiley
- Published
- 2003-11
- Volume
- 10
- Issue
- 11
- Pages
- 1260-1270
- ISSN
- 1069-6563 (ISSN-L)
- 1553-2712
- OpenAlex
- W2139580402
- Cited by
- 63
- References
- 60
- Citations by year
- 2025: 2 · 2024: 4 · 2021: 1 · 2020: 3 · 2019: 4 · 2018: 2 · 2017: 3 · 2016: 3 · 2014: 1 · 2013: 1 · 2012: 3
- FWCI
- 4.36
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Academic Emergency Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Emergency Medicine › Trauma and Emergency Care Studies
- Health Sciences › Medicine › Emergency Medicine › Emergency and Acute Care Studies
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Healthcare Policy and Management
- Keywords
- emergency department disposition, hospitalization, trauma patients, uninsured patients, health insurance, racial disparities, gender disparities, African American women, Medicare, private insurance, injury severity, health care access
- MeSH terms
- Adolescent, Adult, Aged, Black or African American, Child, Child, Preschool, Emergency Service, Hospital, Female, Hospitalization, Humans, Infant, Infant, Newborn, Insurance, Health, Male, Middle Aged, Sex Factors, South Carolina, Wounds and Injuries, Injury Severity Score
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