Resistance Pattern of Tuberculosis Before and During COVID-19 Era in Nigeria: A Systematic Review
Bibliographic record
Abstract
Background: Tuberculosis (TB), one of the leading infectious diseases of global health concern has drawn the attention of researchers towards investigating the trends of the infection, its resistance and associated risk factors in Nigeria in the pre-COVID-19 era and during the COVID-19 era to understand how the pandemic may have possibly influenced the prevalence trends of TB infection. Objective: This review was aimed at examining the trend of TB infection, the resistant patterns and associated risk factors before COVID-19 era and then during COVID-19 era in Nigeria Methodology: Following the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) protocol, a systematic review and meta-analysis was conducted. A total of 4 electronic databases (African journal online library (AJOL), PubMed, ScienceDirect and Semantic scholar) were searched using Boolean functions and search filters to streamline the search results to the research focus. A total of 563 studies were gathered from the databases and imported to EndNote, from where they were exported to Covidence via XML file. The exported studies underwent two levels of screenings; title and abstract screening, and full article screening based on the inclusion criteria. A total of 14 studies passed the screening and were eligible for data extraction, quality assessment and risk of bias using Newcastle Ottawa Scale (NOS) for cross-sectional studies. Results: The mean prevalence of TB before and during COVID-19 era were 15.8% and 28.8% respectively. The pooled prevalence rifampicin (RIF) resistance was 20.8% in both pre-COVID-19 and COVID-19 eras among TB patients. The pooled prevalence of multi-drug resistant TB (MDR-TB) before the COVID-19 era was 1.01% while in the COVID-19 era it was 10.1%. The prevalent assocated risk factors before COVID-19 era were age and settlement while in COVID-19 era were age and sex. Conclusion: This review has shown an upward trend in TB and MDR-TB rates during the pandemic in Nigeria with age appearing as the leading risk factors in both eras.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".