Predictors of Discharge Against Medical Advice in a Tertiary Paediatric Hospital

Louise Sealy, Karen Zwi, Gordon McDonald, Aldo Saavedra, Lisa Crawford and Hasantha Gunasekera
Additional contact information
Louise Sealy: Community Child Health, Sydney Children’s Hospital, Randwick, NSW 2031, Australia
Karen Zwi: Community Child Health, Sydney Children’s Hospital, Randwick, NSW 2031, Australia
Gordon McDonald: Sydney Informatics Hub, The University of Sydney, Sydney, NSW 2008, Australia
Aldo Saavedra: Centre for Translational Data Science; The University of Sydney, Sydney, NSW 2006, Australia
Lisa Crawford: The Children’s Hospital at Westmead, Sydney, NSW 2145, Australia
Hasantha Gunasekera: The Children’s Hospital at Westmead, Sydney, NSW 2145, Australia

IJERPH, 2019, vol. 16, issue 8, 1-11

Abstract: Background: Patients who discharge against medical advice (DAMA) from hospital carry a significant risk of readmission and have increased rates of morbidity and mortality. We sought to identify the demographic and clinical characteristics of DAMA patients from a tertiary paediatric hospital. Methods: Data were extracted retrospectively from electronic medical records for all inpatient admissions over a 5-year period. Demographic characteristics (age, sex, Aboriginality, socioeconomic status and remoteness of residence) and clinical characteristics (admitting hospital site, level of urgency on admission, diagnosis and previous DAMA) were extracted and logistic regression models were used to identify predictors of DAMA with 95% confidence intervals. Results: There were 246,359 admissions for 124,757 patients, of which 1871 (0.8%) admissions and 1730 patients (1.4%) DAMA. Predictors of DAMA in a given admission were hospital site (OR 4.8, CI 4.2–5.7, p < 0.01), a mental health/behavioural diagnosis (OR 3.3, CI 2.2–4.8, p < 0.01), Aboriginality (OR 1.6, CI 1.3–2.1, p < 0.01), emergency rather than elective admissions (OR 0.7ha, CI 0.6–0.8, p < 0.01), a gastrointestinal diagnosis (OR 1.5, CI 1.1–2.0, p = 0.04) and a history of previous DAMA (OR 2.0, CI 1.2–3.2, p = 0.05). Conclusions: There are clear predictors of DAMA in this tertiary hospital admission cohort and identification of these provides opportunities for intervention at a practice and policy level in order to prevent adverse outcomes.

Keywords: discharge; self-discharge; treatment refusal; discharge against medical advice; discharge at own risk (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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