Risk factors of MDR-TB and impacts of COVID-19 pandemic on escalating of MDR-TB incidence in lower-middle-income countries: A scoping review
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) pandemic is affecting tuberculosis (TB) treatment in many ways that might lead to increasing the prevalence of multi-drugs-resistance tuberculosis (MDR-TB), especially in lower-middle-income-countries (LMICs). This scoping review aimed to identify the risk factors of MDR-TB and to determine the impacts of the COVID-19 pandemic on MDR-TB prevalence in LMICs. This study was reported according to the preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews (PRISMA-ScR) guideline. The relevant keywords were used to search studies in three databases (PubMed, ScienceDirect and SpringerLink) to identify the related articles. The English-written articles published from January 2012 to December 2022 that explored risk factors or causes of MDR-TB in LMICs were included. Out of 1,542 identified articles, 17 retrospective, prospective, case-control and cross-sectional studies from ten LMICs met were included in this scoping review. Twenty-one risk factors were discovered, with prior TB treatment (relapsed cases), diabetes, living area, living condition, smoking and low socioeconomic status were the main factors in developing MDR-TB during COVID-19 pandemic. The pandemic increased the MDR-TB prevalence through drug resistance transmission inside households, the distance between home and healthcare facilities and low socioeconomic status. This scoping review demonstrates how the COVID-19 pandemic has affected the rising incidence of MDR-TB in LMICs.
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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 | 0.006 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".