Health system delay in the treatment of tuberculosis patients in Ethiopia: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background: Delay in diagnosis and initiation of effective treatment associated with increase in morbidity, mortality and on-going person-to-person transmission in the community at large. In Ethiopia, several studies have been conducted regarding health system delay among tuberculosis patients. However, studies assessing the health system delay in treatment of tuberculosis patients in Ethiopia had inconsistent and inconclusive findings. Therefore, this systematic review and meta-analysis aimed to determine the pooled median time of the health system delay in the treatment of tuberculosis and its determinants in Ethiopia. Methods: We systematically searched from different databases: Google Scholar, Science Direct, PubMed, Embase, Scopus and Springer link databases for studies published from June 6, 1997 up to December 20, 2020. The quality of the studies was assessed using the Newcastle-Ottawa scale adapted for observational studies. Heterogeneity was evaluated using I squared statistic. We conducted a meta-analysis for the pooled median time of health system delay and its determinants using random-effects model in R version 4.0.3 software(for median estimation) and Stata version 14 (for metan). The pooled estimates with 95% confidence intervals (CI) were presented using forest plots. Results: A total of 14 studies which comprising 6161 patients satisfying a priori set criteria were included. Our meta-analysis showed that, the estimated pooled median time of the health system delay was 15.29(95%CI: 9.94–20.64) days. In the subgroup analysis, studies conducted from 1997 to 2015 the pooled median health system delay was 21.63(95% CI: 14.38-28.88) days, whereas studies conducted after 2015 the pooled median time of 9.33(95% CI: 3.95-14.70) days. Living in rural area (pooled OR: 2.42, 95%CI: 1.16-5.02) was significantly associated with health system delay. Conclusions: In Ethiopia, patients are delayed more-than two weeks in the treatment of tuberculosis. Being from rural residence was more likely to lead prolonged health system delay. Implementing efforts by targeting rural residence may help to shorten the health system delay and important implications for the success of tuberculosis control.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.046 |
| Bibliometrics | 0.007 | 0.007 |
| 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) | 0.002 | 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".