Impact and Economic Evaluation of the Patient-Provider Support Agency Model Under India’s National Tuberculosis Elimination Program: Protocol for a Cohort Study
Bibliographic record
Abstract
Background: India is set to eliminate tuberculosis (TB) from the country by 2025, 5 years ahead of the global target. Optimizing TB care pathways is challenged by the predominance of private sector care-seeking among symptomatic patients (60%-70%), a setting associated with significant under-notification. To improve the notification of TB among private providers and improve patient management in the private sector, the National Tuberculosis Elimination Program (NTEP), India, has implemented the Patient-Provider Support Agency (PPSA). The PPSA model enables state and district TB units to engage third-party agencies to involve private providers in TB care, providing comprehensive services including diagnosis, notification, and treatment support. Objective: The objective of this study is to assess the cost-effectiveness and feasibility of introducing the PPSA in the National TB elimination program to improve patient management by improving TB case notification, drug-resistant TB case notification, and treatment outcome and reducing loss to follow-up compared to baseline ongoing programs. Methods: A cohort study will be conducted in 2 parallel groups in the private sector to assess the impact of the PPSA intervention on the primary and secondary outcomes of the NTEP. Eligible patients with TB from the intervention and control district, notified within a defined time frame, will be included. The intervention group will receive all the facilities and benefits under the PPSA model under the NTEP. The control group will receive care under usual conditions. The study will consist of three parts: (1) impact assessment study, (2) cost-effectiveness study, and (3) feasibility and accessibility study. The primary outcomes shall be TB case notification, drug-resistant TB case notification, universal drug susceptibility testing coverage, and treatment outcome. Secondary outcomes shall include the time between the onset of symptoms and treatment initiation, treatment adherence, adverse drug reaction management, improvement in quality of life, and societal costs. Outcome assessments will be done using questionnaires at baseline, after 3 months, at the completion of treatment, and 6 months after the completion of treatment. Results: The study received Institutional ethical approval from Jodhpur School of Public Health Institutional Review Board, India, in accordance with the compliance of Section 4, National Ethical Guidelines for Biomedical and Health Research Involving Human Participants, ICMR (2017), with IRB Number: 10032/IRB/20-21. Data collection is underway. Conclusions: Our work will add to other implementation studies capturing the role of the private sector in TB care in India. A key strength of this work is its use of detailed patient pathway data from the study site, suggesting that private sector engagement can support TB elimination while improving the quality and coordination of TB services across India's fragmented health care system.
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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 | 0.058 | 0.059 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.047 | 0.004 |
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".