Global prevalence, treatment and outcome of tuberculosis and COVID-19 coinfection: a systematic review and meta-analysis (from November 2019 to March 2021)
Bibliographic record
Abstract
INTRODUCTION: and SARS-CoV-2 infection. Currently, the prevalence status, treatment and outcomes of the coinfection are poorly characterised. We aimed to systematically review the evidence on this topic and provide comprehensive information to guide the control and treatment of TB-COVID coinfection. METHODS: An extensive screening was conducted using six electronic databases to search eligible studies from 1 November 2019 to 19 March 2021. Prevalence rate, treatment and outcomes of TB-COVID coinfection were extracted. Random-effects models were used to calculate mean fatality rates of coinfection with 95% CIs. The risks of bias were assessed with the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Study Reporting Prevalence Data and JBI Critical Appraisal Checklist for Case Report. A meta-analysis was conducted for subgroups on in-hospital fatality rate. RESULTS: Forty-two studies were included into the analysis (35 case reports and 7 retrospective cohort studies). Nineteen countries reported coinfected patients, including high and low TB prevalence countries. The only study revealing prevalence rate came from West Cape Province, South Africa (people aged above 20 years, 0.04% until 1 June 2020 and 0.06% until 9 June 2020). The treatment regimens for coinfected patients were highly heterogeneous. The mean overall and in-hospital fatality rates of coinfection were 13.9% (95% CI: 1.6% to 26.2%) and 17.5% (95% CI: 8.9% to 26.0%). The mean in-hospital fatality rates for high-income countries (Italy and Argentina) and low/middle-income countries (LMICs) (India, Philippines, South Africa) were 6.5% (95% CI: -0.8% to ~13.9%) and 22.5% (95% CI: 19.0% to ~26.0%). CONCLUSION: TB-COVID coinfection is common globally, and the coinfected patients suffer from higher fatality risk than patients with normal COVID-19. Outcomes shared significant differences between high-income countries and LMICs. PROSPERO REGISTRATION NUMBER: CRD42021253660.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.030 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".