Profiles of frequent emergency department users with chronic conditions: a latent class analysis

Abstract

Objectives: Frequent emergency department users represent a small proportion of users while cumulating many visits. Previously identified factors of frequent use include high physical comorbidity, mental health disorders, poor socioeconomic status and substance abuse. However, frequent users do not necessarily exhibit all these characteristics and they constitute a heterogeneous population. This study aims to establish profiles of frequent emergency department users in an adult population with chronic conditions.

Design: This is a retrospective cohort study using administrative databases.

Setting: All adults who visited the emergency department between 2012 and 2013 (index date) in the province of Quebec (Canada), diagnosed with at least one chronic condition, and without dementia were included. Patients living in remote areas and who died in the year following their index date were excluded. We used latent class analysis, a probability-based model to establish profiles of frequent emergency department users. Frequent use was defined as having five visits or more during 1 year. Patient characteristics included sociodemographic characteristics, physical and mental comorbidities and prior healthcare utilisation.

Results: Out of 4 51 775 patients who visited emergency departments at least once in 2012-2013, 13 676 (3.03%) were frequent users. Four groups were identified: (1) 'low morbidity' (n=5501, 40.2%), (2) 'high physical comorbidity' (n=3202, 23.4%), (3) 'injury or chronic non-cancer pain' (n=2313, 19.5%) and (4) 'mental health or alcohol/substance abuse' (n=2660, 16.9%).

Conclusions: The four profiles have distinct medical and socioeconomic characteristics. These profiles provide useful information for developing tailored interventions that would address the specific needs of each type of frequent emergency department users.

Keywords: ACCIDENT & EMERGENCY MEDICINE; HEALTH SERVICES ADMINISTRATION & MANAGEMENT; PUBLIC HEALTH; STATISTICS & RESEARCH METHODS.

Figure 1

Flowchart of the study cohort selection. ED, emergency department.

Figure 2

Fit criteria depending on the number of classes. AIC, Akaike Information Criterion, BIC, Bayes Information Criterion, Chi², Pearson Chi-square statistic; ENT, entropy; G2, likelihood–ratio statistic, LL, log likelihood.

Figure 3

Item-response probabilities for each class. CNCP, chronic non-cancer pain; CMD, common mental disorders; GIS, guaranteed income supplement; LRFA, last-resort financial assistance; PPDIP, public prescription drug insurance plan; SMD, serious mental disorders. Reg, GIS, nPPDIP and LRFA refer to PPDIP status while 0, 1 2, 3 4, and 5+ refer to the comorbidity index.