Misattribution bias of COVID-19 hospitalizations in Alberta using an admission algorithm
Bibliographic record
Abstract
Background: With initial waves of COVID-19, many public health systems assumed each COVID-19 positive hospitalization was a direct cause from COVID-19 infection. Since January 2022, Alberta Health Services Communicable Disease Control Hospitalization Team (CDC-HT) implemented an admission criteria algorithm to adjudicate COVID-19 as a direct, contributing, or unrelated cause for all COVID-19 admissions in Alberta. Methods: This quality improvement initiative sought to improve the admission algorithm's precision in reporting COVID-19 admissions. Patient hospitalization records from January-February 2022 with a positive COVID-19 test in the last 30 days were proportionally sampled in a geographically stratified manner across Alberta health zones. 261 patient records were sampled and determination of COVID-19 attribution by CDC-HT algorithm was compared to adjudication by a panel of infectious diseases physicians with extensive COVID-19 clinical experience. Results: Of 261 sampled COVID-19 admissions, blinded physician adjudication determined 39.9% were direct-cause, 17.2% contributing-cause, and 37.6% unrelated-cause. Within the same cohort the CDC-HT admission algorithm adjudicated 42.9% direct-cause, 24.5% contributing-cause, and 30.3% unrelated-cause. Cohen's kappa was 0.475, providing only moderate agreement. The majority of discrepancy was from over-attribution of unrelated hospitalizations as contributing cause. Implementation of this algorithm in Alberta throughout 2022 showed a fluctuating proportion of direct plus contributing COVID-19 hospitalizations as low as 40%. Conclusion: There was misattribution bias in COVID-19 hospitalization determination using the admission algorithm. The findings from this analysis led to improvements in the algorithm to improve precision. Public health jurisdictions should review their COVID-19 hospitalization reporting approaches to ensure validity and consideration of incidental cases.
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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 | 0.063 | 0.198 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".