A population-based retrospective cohort study of end-of-life emergency department visits by people with dementia: multilevel modelling of individual- and service-level factors using linked data
Bibliographic record
Abstract
BACKGROUND: emergency department (ED) visits have inherent risks for people with dementia yet increase towards the end-of-life. Although some individual-level determinants of ED visits have been identified, little is known about service-level determinants. OBJECTIVE: to examine individual- and service-level factors associated with ED visits by people with dementia in the last year of life. METHODS: retrospective cohort study using hospital administrative and mortality data at the individual-level, linked to health and social care service data at the area-level across England. The primary outcome was number of ED visits in the last year of life. Subjects were decedents with dementia recorded on the death certificate, with at least one hospital contact in the last 3 years of life. RESULTS: of 74,486 decedents (60.5% women; mean age 87.1 years (standard deviation: 7.1)), 82.6% had at least one ED visit in their last year of life. Factors associated with more ED visits included: South Asian ethnicity (incidence rate ratio (IRR) 1.07, 95% confidence interval (CI) 1.02-1.13), chronic respiratory disease as the underlying cause of death (IRR 1.17, 95% CI 1.14-1.20) and urban residence (IRR 1.06, 95% CI 1.04-1.08). Higher socioeconomic position (IRR 0.92, 95% CI 0.90-0.94) and areas with higher numbers of nursing home beds (IRR 0.85, 95% CI 0.78-0.93)-but not residential home beds-were associated with fewer ED visits at the end-of-life. CONCLUSIONS: the value of nursing home care in supporting people dying with dementia to stay in their preferred place of care must be recognised, and investment in nursing home bed capacity prioritised.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".