Abstract
Background
Discharge against medical advice (AMA) is an important, yet understudied, aspect of
health care—particularly in trauma populations. AMA discharges result in increased
mortality, increased readmission rates, and higher health care costs.
Objective
The goal of this analysis was to determine what factors impact a patient's odds of
leaving the hospital prior to treatment.
Methods
We performed a retrospective analysis of the National Trauma Data Bank on adult trauma
patients (older than 14 years) from 2013 to 2015. Of the 1,770,570 patients with known
disposition, excluding mortality, 24,191 patients (1.4%) left AMA. We ascertained
patient characteristics including age, sex, race, ethnicity, insurance status, ETOH,
drug use, geographic location, Injury Severity Score (ISS), injury mechanism, and
anatomic injury location. Multivariate logistic regression models were used to determine
which patient factors were associated with AMA status.
Results
Uninsured (odds ratio [OR] 2.72; 95% confidence interval [CI] 2.58–2.86) or Medicaid-insured
(OR 2.50; 95% CI 2.37–2.63) trauma patients were significantly more likely to leave
AMA than patients with private insurance. Compared to white patients, African-American
patients (OR 1.06; 95% CI 1.02–1.11) were more likely, and Native-American (OR 0.62;
95% CI 0.52–0.75), Asian (OR 0.59; 95% CI 0.49–0.69), and Hispanic (OR 0.80; 95% CI
0.75–0.85) patients were less likely, to leave AMA when controlling for age, sex,
ISS, and type of injury.
Conclusions
Insurance status, race, and ethnicity are associated with a patient's decision to
leave AMA. Uninsured and Medicaid patients have more than twice the odds of leaving
AMA. These findings demonstrate that racial and socioeconomic disparities are important
targets for future efforts to reduce AMA rates and improve outcomes from blunt and
penetrating trauma.
Keywords
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Article info
Publication history
Published online: March 12, 2020
Accepted:
December 16,
2019
Received in revised form:
December 10,
2019
Received:
July 11,
2019
Identification
Copyright
© 2020 Elsevier Inc. All rights reserved.
