Evaluating Hospital Admission Data as Indicators of COVID-19 Severity: A National Assessment in Qatar
Bibliographic record
Abstract
Background: Accurately assessing SARS-CoV-2 infection severity is essential for understanding the health impact of the infection and evaluating the effectiveness of interventions. This study investigated whether SARS-CoV-2-associated hospitalizations can reliably measure true COVID-19 severity. Methods: The diagnostic accuracy of SARS-CoV-2-associated acute care and ICU hospitalizations as indicators of infection severity was assessed in Qatar from 6 September 2021 to 13 May 2024. WHO criteria for severe, critical, and fatal COVID-19 served as the reference standard. Two indicators were assessed: (1) any SARS-CoV-2-associated hospitalization in acute care or ICU beds and (2) ICU-only hospitalizations. Results: A total of 644 176 SARS-CoV-2 infections were analyzed. The percent agreement between any SARS-CoV-2-associated hospitalization (acute care or ICU) and WHO criteria was 98.7% (95% confidence interval (CI), 98.6-98.7); however, Cohen's kappa was only 0.17 (95% CI, 0.16-0.18), indicating poor agreement. Sensitivity, specificity, PPV, and negative predictive value were 100% (95% CI, 99.6-100), 98.7% (95% CI, 98.6-98.7), 9.7% (95% CI, 9.1-10.3), and 100% (95% CI, 100-100), respectively. For SARS-CoV-2-associated ICU-only hospitalizations, the percent agreement was 99.8% (95% CI, 99.8-99.9), with a kappa of 0.47 (95% CI, 0.44-0.50), indicating fair-to-good agreement. Sensitivity, specificity, PPV, and negative predictive value were 46.6% (95% CI, 43.4-49.9), 99.9% (95% CI, 99.9-99.9), 47.9% (95% CI, 44.6-51.2), and 99.9% (95% CI, 99.9-99.9), respectively. Conclusions: Generic hospital admissions are unreliable indicators of COVID-19 severity, whereas ICU admissions are somewhat more accurate. The findings demonstrate the importance of applying specific, robust criteria-such as WHO criteria-to reduce bias in epidemiological and vaccine effectiveness studies.
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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 | 0.002 | 0.117 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| 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 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".