Delays in diagnosis and treatment of pulmonary tuberculosis, and patient care-seeking pathways in China: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Early diagnosis and treatment is a cornerstone of the effective control of tuberculosis (TB), both in China and elsewhere. OBJECTIVES: To undertake a systematic review of the delay between the onset of TB symptoms and initiation of appropriate treatment, based on existing studies, and to summarize available information on patients' care-seeking behavior and the pathways to treatment in China. METHODS: A systematic review and meta-analysis was performed. PubMed, EMBASE, the Web of Science and the China National Knowledge Infrastructure (CNKI) databases were searched to identify relevant studies. Study selection, data extraction and quality assessment were undertaken by two independent reviewers. The median length and interquartile range (IQR) of the median delays reported by the primary studies were summarized. In addition, further estimates were made of the mean delay and variation of delay at an individual study level, based on known medians, sample sizes, and the IQR/range for each case. These results were used to pool the delay duration using a random effects mode. Finally, to meta-analyze care-seeking behaviors, a random effects model was deployed, using exact binomial likelihood. The review was reported according to the PRISMA standards. Subgroup analyses were conducted of data from different regions of China, urbanization level, and type of TB.RESULTS: A total of 94 studies were included in the final analysis. The median of length of the reported patient delay was 18 days (IQR = 10 to 23 days) and the pooled mean was 18.4 days (95% CI = 11.8 to 25 days, I2=0). The median length of diagnostic delay was 11 days (IQR = 5 to 24 days) with a pooled mean of 8.8 days (95% CI = 3.6 to 13.9 days, I2=19.8%). The figures for the median and pooled mean of total delay were 55.5 days (IQR = 43.8 to 64.3 days) and 52.5 days respectively (95% CI = -6.5 to 111.4 days, I2=0). There was significant variation identified in treatment patterns across China. The total patient delay in western China (85.2 days, 95% CI=23.8 to 146.7 days) was substantially longer than that in eastern China (17.4 days, 95% CI=10.4 to 24.4 days). Of the 64 studies that reported care-seeking behavior, 23 indicated that village clinics were used for initial health consultations and 36 indicated that general hospitals (county level and above) were the initial contact points. Importantly, 82.7% of patients, who initially sought care in general hospitals, were referred directly to TB dispensaries (95% CI = 51.2 to 95.6%), whereas the percentage was only 14.3% among patients who initially sought care in village clinics (95% CI = 6.1 to 63.2%). Overall, people who initially sought care in village clinics had a more complex route to take, before ultimate referral to TB dispensaries, visiting more healthcare providers. CONCLUSIONS: The findings highlight the significant patient delay experienced by patients in western China and the complex pathways to care that confront TB patients in rural areas. Additionally, this is one of the first meta-analyses of median outcomes in this area that also considers estimations of mean and variation. As such, the pooled results using estimated mean and variation provide a useful indication of the strengths and limitations of this methodological approach. This methodological approach will be useful in identifying areas for research in future.
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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 | 0.016 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.033 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".