Global and regional prevalence and outcomes of COVID-19 in people living with HIV: A cutting-edge systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background The relationship between HIV infection and COVID-19 clinical outcome is uncertain, with conflicting data and hypotheses. We aimed to assess the prevalence and risk of severe COVID-19 and death in people living with HIV (PLWH) on the global and continental level. Methods Electronic databases were systematically searched in July 2021. Studies were screened and then extracted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Narratives were synthesised and data pooled for global and continental prevalence and relative risk of severity and mortality in HIV-infected COVID-19 patients using random-effect model. Risk of bias was assessed using the Newcastle-Ottawa score, Egger’s test and presented as funnel plots. Results A total of 46 studies were included involving 18,034,947 COVID-19 cases of which 31,269 were PLWH. The global prevalence of PLWH with SARS-CoV-2 infection was 1% (95% CI = 0.9% -1.1%) with the highest prevalence observed in sub-Saharan Africa. The relative risk (RR) of COVID-19 severity was significant only in Africa (RR, 95% CI = 1.14, 1.08 – 1.24) while risk of COVID-19 mortality was 1.53% (95% CI = 1.45 – 2.03) globally. The prevalence of PLWH in COVID-19 cases was significantly low, and the calculated global risk ratio show that HIV infection may be linked with increased COVID-19 death. The between-studies heterogeneity was significantly high while risk of publication bias was not significant. Conclusion There is low prevalence of HIV-SARS-CoV-2 co-infection. HIV infection was linked with severe COVID-19 in Africa and increased risk of death globally.
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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 | 0.018 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.037 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".