Adults Hospitalized With Coronavirus Disease 2019 (COVID-19)—United States, March–June and October–December 2020: Implications for the Potential Effects of COVID-19 Tier-1 Vaccination on Future Hospitalizations and Outcomes
Bibliographic record
Abstract
BACKGROUND: Because of the increased risk for severe coronavirus disease 2019 (COVID-19), the Advisory Committee on Immunization Practices (ACIP) initially prioritized COVID-19 vaccination for persons in long-term care facilities (LTCF), persons aged ≥65 years, and persons aged 16-64 years with high-risk medical conditions when there is limited vaccine supply. We compared characteristics and severe outcomes of hospitalized patients with COVID-19 in the United States between early and later in the pandemic categorized by groups at higher risk of severe COVID-19. METHODS: Observational study of sampled patients aged ≥18 years who tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and admitted to one of 14 academic hospitals in the United States during March-June and October-December 2020. Demographic and clinical information were gathered from electronic health record data. RESULTS: Among 647 patients, 91% met ≥1 of the following risk factors for severe COVID-19 [91% March-June (n = 434); 90% October-December (n = 213)]; 19% were LTCF residents, 45% were aged ≥65-years, and 84% had ≥1 high-risk condition. The proportion of patients who resided in a LTCF declined significantly (25% vs 6%) from early to later pandemic periods. Compared with patients at lower risk for severe COVID-19, in-hospital mortality was higher among patients at high risk for severe COVID-19 (20% vs 7%); these differences were consistently observed between March-June and October-December. CONCLUSIONS: Most adults hospitalized with COVID-19 were those recommended to be prioritized for vaccination based on risk for developing severe COVID-19. These findings highlight the continued urgency to vaccinate patients at high risk for severe COVID-19 and monitor vaccination impact on hospitalizations and outcomes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".