Patient delay in the diagnosis of tuberculosis in Ethiopia: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Delay in the diagnosis of Tuberculosis (TB) remains a major challenge against achieving effective TB prevention and control. Though a number of studies with inconsistent findings were conducted in Ethiopia; unavailability of a nationwide study determining the median time of patient delays to TB diagnosis is an important research gap. Therefore, this study aimed to determine the pooled median time of the patient delay to TB diagnosis and its determinants in Ethiopia. METHODS: We followed PRISMA checklist to present this study. We searched from Google Scholar, PubMed, Science Direct, Web of Science, CINAHL, and Cochrane Library databases for studies. The comprehensive search for relevant studies was done by two of the authors (MA and LY) up to the 10th of October 2019. Risk of bias was assessed using the Newcastle-Ottawa scale adapted for observational studies. Data were pooled and a random effect meta-analysis model was fitted to provide the overall median time of patient delay and its determinants in Ethiopia. Furthermore, subgroup analyses were conducted to investigate how the median time of patient delay varies across different groups of studies. RESULTS: Twenty-four studies that satisfied the eligibility criteria were included. Our meta-analysis showed that the median time of the patient delay was 24.6 (95%CI: 20.8-28.4) days. Living in rural area (OR: 2.19, 95%CI: 1.51-3.18), and poor knowledge about TB (OR: 2.85, 95%CI: 1.49-5.47) were more likely to lead to prolonged delay. Patients who consult non-formal health providers (OR: 5.08, 95%CI: 1.56-16.59) had a prolonged delay in the diagnosis of TB. Moreover, the narrative review of this study showed that age, educational level, financial burden and distance travel to reach the nearest health facility were significantly associated with a patient delay in the diagnosis of TB. CONCLUSIONS: In conclusion, patients are delayed more-than three weeks in the diagnosis of TB. Lack of awareness about TB, consulting non-formal health provider, and being in the rural area had increased patient delay to TB diagnosis. Increasing public awareness about TB, particularly in rural and disadvantaged areas could help to early diagnosis of TB.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.012 | 0.004 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".