Risk of COVID-19 hospitalization in people living with HIV and HIV-negative individuals and the role of COVID-19 vaccination: A retrospective cohort study
Bibliographic record
Abstract
OBJECTIVE: To examine the risk of hospitalization within 14 days of COVID-19 diagnosis among people living with HIV (PLWH) and HIV-negative individuals who had laboratory-confirmed SARS-CoV-2 infection. METHODS: We used Cox proportional hazard models to compare the relative risk of hospitalization in PLWH and HIV-negative individuals. Then, we used propensity score weighting to examine the influence of sociodemographic factors and comorbid conditions on risk of hospitalization. These models were further stratified by vaccination status and pandemic period (pre-Omicron: December 15, 2020, to November 21, 2021; Omicron: November 22, 2021, to October 31, 2022). RESULTS: The crude hazard ratio (HR) for risk of hospitalization in PLWH was 2.44 (95% confidence interval [CI]: 2.04-2.94). In propensity score-weighted models that included all covariates, the relative risk of hospitalization was substantially attenuated in the overall analyses (adjusted HR [aHR]: 1.03; 95% CI: 0.85-1.25), in vaccinated (aHR 1.00; 95% CI: 0.69-1.45), inadequately vaccinated (aHR: 1.04; 95% CI: 0.76-1.41) and unvaccinated individuals (aHR: 1.15; 95% CI: 0.84-1.56). CONCLUSION: PLWH had about two times the risk of COVID-19 hospitalization than HIV-negative individuals in crude analyses which attenuated in propensity score-weighted models. This suggests that the risk differential can be explained by sociodemographic factors and history of comorbidity, underscoring the need to address social and comorbid vulnerabilities (e.g., injecting drugs) that were more prominent among PLWH.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".