Impact of the COVID-19 pandemic on non-COVID-19 community-acquired pneumonia: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic could impact frequency and mortality of non-COVID-19 community-acquired pneumonia (CAP). Changes in frequency, patient mix, treatment and organ dysfunction could cascade together to increase mortality of CAP during compared with pre-COVID-19. METHODS: Hospitalised CAP patients at St. Paul's Hospital, Vancouver, Canada pre-COVID-19 (fiscal years 2018/2019 and 2019/2020) and during COVID-19 pandemic (2020/2021 and 2021/2022) were evaluated. RESULTS: In 5219 CAP patients, there was no significant difference prepandemic versus during pandemic in mean age, gender and Charlson Comorbidity Score. However, hospital mortality increased significantly from pre-COVID-19 versus during COVID-19 (7.5% vs 12.1% respectively, (95% CI for difference: 3.0% to 6.3%), p<0.001), a 61% relative increase, coincident with increases in ICU admission (18.3% vs 25.5%, respectively, (95% CI for difference: 5.0% to 9.5%) p<0.001, 39% relative increase) and ventilation (12.7% vs 17.5%, respectively, (95% CI for difference: 2.8% to 6.7%) p<0.001, 38% relative increase). Results remained the same after regression adjustment for age, sex and Charlson score. CAP hospital admissions decreased 27% from pre-COVID-19 (n=1349 and 1433, 2018/2019 and 2019/2020, respectively) versus the first COVID-19 pandemic year (n=1047 in 2020/2021) then rose to prepandemic number (n=1390 in 2021/2022). During prepandemic years, CAP admissions peaked in winter; during COVID-19, the CAP admissions peaked every 6 months. CONCLUSIONS AND RELEVANCE: This is the first study to show that the COVID-19 pandemic was associated with increases in hospital mortality, ICU admission and invasive mechanical ventilation rates of non-COVID-19 CAP and a transient, 1-year frequency decrease. There was no winter seasonality of CAP during the COVID-19 pandemic era. These novel findings could be used to guide future pandemic planning for CAP hospital care.
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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 | 0.067 | 0.345 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.000 | 0.004 |
| 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; both teacher heads agree on what is shown here.
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".