Demographics of COVID-19 hospitalisations and related fatality risk patterns
Bibliographic record
Abstract
The assessment of hospitalisations and intensive care is crucial for planning health care resources needed over the course of the coronavirus disease 2019 (COVID-19) pandemic. Nonetheless, comparative empirical assessments of COVID-19 hospitalisations and related fatality risk patterns on a large scale are lacking. This paper exploits anonymised, individual-level data on SARS-CoV-2 confirmed infections collected and harmonized by the European Centre for Disease Prevention and Control to profile the demographics of COVID-19 hospitalised patients across nine European countries during the first pandemic wave (February - June 2020). We estimate the role of demographic factors for the risk of in-hospital mortality, and present a case study exploring individuals' comorbidities based on a subset of COVID-19 hospitalised patients available from the Dutch health system. We find that hospitalisation rates are highest among individuals with confirmed SARS-CoV-2 infection who are not only older than 70 years, but also 50-69 years. The latter group has a longer median time between COVID-19 symptoms' onset and hospitalisation than those aged 70+ years. Men have higher hospitalisation rates than women at all ages, and particularly above age 50. Consistently, men aged 50-59 years have a probability of hospitalisation almost double than women do. Although the gender imbalance in hospitalisation remains above age 70, the gap between men and women narrows at older ages. Comorbidities play a key role in explaining selection effects of COVID-19 confirmed positive cases requiring hospitalisation. Our study contributes to the evaluation of the COVID-19 burden on the demand of health-care during emergency phases. Assessing intensity and timing dimensions of hospital admissions, our findings allow for a better understanding of COVID-19 severe outcomes. Results point to the need of suitable calibrations of epidemiological projections and (re)planning of health services, enhancing preparedness to deal with infectious disease outbreaks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".