Delays in tuberculosis diagnosis and treatment in India: A patient journey analysis from Mumbai and Patna
Bibliographic record
Abstract
ABSTRACT Background Tuberculosis (TB) patient services in India are often fragmented, undermining timely access to timely diagnosis and treatment. Understanding patient journeys is critical to strengthening TB care delivery and achieving elimination goals. Methods We conducted a cross-sectional study of 400 TB patients diagnosed between 2020– 2022 in two major Indian cities: Mumbai (n=200) and Patna (n=200). Using structured interviews, we examined health-seeking behavior, delays to diagnosis and treatment, the number and type of healthcare encounters, and out-of-pocket costs. Results Patients predominantly initiated care in the private sector (91% in Mumbai; 85% in Patna), often with pharmacies or private clinics. Care pathways were fragmented, requiring multiple provider visits before diagnosis. The median total delay from symptom onset to treatment initiation was 35 days (IQR: 13–81) in Patna and 26 days (IQR: 12–59) in Mumbai. Provider delays accounted for nearly 19 days in both settings. Patients made a median of 3 healthcare visits pre-diagnosis, with 23% experiencing ≥6 encounters. The financial burden of TB care was substantial, particularly in Mumbai, where consultation and diagnostic costs were markedly higher than in Patna. Longer delays and higher numbers of encounters were associated with being male, unemployed, having larger household size, and hesitation to seek care during the study period. Conclusion TB patient pathways in urban India pandemic were prolonged, costly, and fragmented — especially within the private sector during the COVID-19. Strengthening public-private integration, improving early diagnosis strategies, and protecting patients from financial hardship are essential priorities to accelerate TB elimination and strengthen health system resilience against future disruptions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".