Transfers to acute care hospitals at the end of life: do rural/remote regions differ from urban regions?
Bibliographic record
Abstract
INTRODUCTION: In population-based studies, transfers into hospitals and hospital deaths are typically considered to be indicators of potentially inappropriate care settings at the end of life. Despite a plethora of research into where people die, few studies have examined whether hospital transfers at the end of life differ in rural versus urban areas. In the present study hospitalizations in the last month before death in one mid-Western Canadian province were examined. The study had three main objectives, to: (1) compare hospitalizations in rural/remote with urban regions; (2) examine the role of healthcare resources in hospitalizations; and (3) explore more specifically whether day-to-day patterns of hospitalization shortly before death differ between rural/remote and urban areas. METHODS: The source of data was administrative healthcare records, with the study including all adults (aged over 19 years; excluding nursing home residents) who died in the province of Manitoba in 2003-2004 (n = 6523). Whether the decedents were hospitalized in the 30 days before death was determined from hospital files. The number of hospital days incurred was counted. Region of residence was defined along regional health authority boundaries, with 7 regions identified as rural/remote and 2 as urban. Healthcare resources were measured in terms of the number of: physicians, hospital beds, nursing home beds, and home care services per 1000 population. Age, sex and trajectory groups, which categorized decedents according to their cause of death, were included in all analyses. RESULTS: Residents of 4 of the 7 rural/remote regions had increased odds of being hospitalized relative to the comparison, the larger urban region (adjusted odds ratios [AOR] ranged from 1.25 to 1.70). Hospital days did not differ across regions. Further analyses showed that having more physicians (AOR = .75) and more hospital beds per 1000 population (AOR = .95) both significantly reduced the odds of being hospitalized. Nursing home beds and home care services were not related to hospitalizations. Growth curve models indicated that daily patterns of hospitalizations generally did not differ across rural/remote versus urban regions. CONCLUSION: The findings suggest that residents of some rural/remote regions were at a disadvantage in terms of access to an appropriate care setting at the end of life. The regional variation in hospitalization can, at least in part, be attributed to the availability of healthcare resources, specifically the number of physicians and hospital beds (per 1000 population). However, the variation that emerged across regions also suggests that conclusions should not be over-generalized to all rural/remote regions; rather, local differences in healthcare resources should be considered when examining healthcare usage at the end of life.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".