Factors associated with chronic frequent emergency department utilization in a population with diabetes living in metropolitan areas: a population-based retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: A small proportion of patients utilizes a disproportionately large amount of emergency department (ED) resources. Being able to properly identify chronic frequent ED users, i.e. frequent ED users over a multiple-year period, would allow healthcare professionals to intervene before it occurs and, if possible, redirect these patients to more appropriate health services. The objective of this study was to explore the factors associated with chronic frequent ED utilization in a population with diabetes. METHODS: A population-based retrospective cohort study using administrative data was conducted on 62,316 patients with diabetes living in metropolitan areas of Quebec (Canada), having visited an ED during 2006, and still alive in 31 December 2009. The dependant variable was being a chronic frequent ED user, defined as having at least 3 ED visits per year during three consecutive years (2007-2009). Independent variables, measured during 2006, included age, sex, neighbourhood deprivation, affiliation to a general practitioner, and number of physical and mental health comorbidities. Logistic regression and tree-based method were used to identify factors associated with chronic frequent ED use. RESULTS: A total of 2.6% of the cohort (patients with diabetes and at least one ED visit in 2006) was identified as chronic frequent ED users. These patients accounted for 16% of all ED visits made by the cohort during follow-up. The cumulative effect of a high illness burden combined with mental health disorders was associated with an increased risk of chronic frequent ED use. CONCLUSIONS: Interventions must target the population at higher risk of becoming chronic frequent ED users and should be designed to manage the complex interaction between high illness burden and mental health.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".