SARS‐CoV‐2 infection and effects of age, sex, comorbidity, and vaccination among older individuals: A national cohort study
Bibliographic record
Abstract
BACKGROUND: We investigated the contribution of age, coexisting medical conditions, sex, and vaccination to incidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and of severe, critical, or fatal COVID-19 in older adults since pandemic onset. METHODS: A national retrospective cohort study was conducted in the population of Qatar aged ≥50 years between February 5, 2020 and June 15, 2023. Adjusted hazard ratios (AHRs) for infection and for severe coronavirus disease 2019 (COVID-19) outcomes were estimated through Cox regression models. RESULTS: Cumulative incidence was 25.01% (95% confidence interval [CI]: 24.86-25.15%) for infection and 1.59% (95% CI: 1.55-1.64%) for severe, critical, or fatal COVID-19 after a follow-up duration of 40.9 months. Risk of infection varied minimally by age and sex but increased significantly with coexisting conditions. Risk of infection was reduced with primary-series vaccination (AHR: 0.91, 95% CI: 0.90-0.93) and further with first booster vaccination (AHR: 0.75, 95% CI: 0.74-0.77). Risk of severe, critical, or fatal COVID-19 increased exponentially with age and linearly with coexisting conditions. AHRs for severe, critical, or fatal COVID-19 were 0.86 (95% CI: 0.7-0.97) for one dose, 0.15 (95% CI: 0.13-0.17) for primary-series vaccination, and 0.11 (95% CI: 0.08-0.14) for first booster vaccination. Sensitivity analysis restricted to only Qataris yielded similar results. CONCLUSION: Incidence of severe COVID-19 in older adults followed a dynamic pattern shaped by infection incidence, variant severity, and population immunity. Age, sex, and coexisting conditions were strong determinants of infection severity. Vaccine protection against severe outcomes showed a dose-response relationship, highlighting the importance of booster vaccination for older adults.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.000 | 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".