Investigating variation in reported location of death: A comparison of administrative data sources
Bibliographic record
Abstract
BackgroundLocation of death is an important outcome in health research. Accordingly, the collection and assembly of these data is complex. In Canadian administrative healthcare data, the gold standard for mortality has been Vital Statistics (VS), which is based on death certificates. MethodsAll mortality records in the province of Alberta, Canada (population 4.6M) were extracted from VS between 2016 and 2021. These were deterministically linked on unique patient identifiers to databases including hospitalizations, emergency department (ED) visits, long-term care (LTC) and designated supportive living records. The primary outcome was location of death reported in administrative sources. ResultsThere were 162,835 mortality records identified in VS. Of deaths labeled as hospital, en route, or nursing in VS (113,554), 85.3% were linked to administrative data on inpatient, ED, LTC, and/or designated supporting living deaths. Of the 52.2% VS records reporting hospital deaths, 80.4% linked to a record of death to the inpatient database. Few records were linked and reported as occurring in the ED if not already classified through inpatient records (<0.5%). Of the 15.0% of VS deaths in nursing homes, 64.5% were linked to a record of death in LTC data, and a further 5.3% linked to a death record in designated supportive living records. ConclusionThere is considerable variation in the reported location of death across data sources in Alberta, with VS reporting lower than expected, confirmed numbers. These discrepancies require further validation in determining the ‘gold standard’ for location of death in subsequent research and quality improvement endeavors.
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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 | 0.033 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.004 |
| 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".