Hospitalization and hospital mortality rates during the first and second waves of the COVID-19 pandemic in Quebec: interrupted time series and decomposition analysis.
Bibliographic record
Abstract
OBJECTIVES: We investigated hospitalization and hospital mortality rates by cause during the first year of the COVID-19 pandemic in Quebec, Canada. STUDY DESIGN: Interrupted time series and decomposition analysis. METHODS: We analyzed hospital mortality during the first (February 25-August 22, 2020) and second waves (August 23, 2020-March 31, 2021), compared with 2019. We identified the cause of death and examined trends using: 1) interrupted time series analysis; 2) log-binomial regression; and 3) decomposition of cause-specific mortality. RESULTS: Hospitalization rates decreased; however, the proportion of deaths increased from 27.0 per 1000 in 2019 to 35.0 per 1000 in the first wave, for an excess of 8.0 deaths per 1000 admissions. COVID-19 was the cause of a third of excess deaths (2.6 per 1000). Other drivers of excess deaths included respiratory conditions (1.6 deaths per 1000), circulatory disorders (0.6 deaths per 1000), and cancer (0.9 deaths per 1000). COVID-19 was the cause of 58% of excess deaths in the second wave. Interrupted time series regression indicated that the proportion of deaths increased at the outset of the first wave but returned to prepandemic levels before increasing again in the second wave. Compared with 2019, the first wave was associated with 1.31 times (95% confidence interval [CI] 1.28-1.33) and the second wave with 1.17 times (95% CI 1.15-1.19) the risk of death during hospitalization. CONCLUSIONS: The pandemic was associated with a greater risk of hospital mortality. Excess deaths were driven by COVID-19 but also other causes, including respiratory conditions, circulatory disorders, and cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".