Can community pharmacists improve tuberculosis case finding? A mixed methods intervention study in India
Bibliographic record
Abstract
INTRODUCTION: India has the world's highest burden of tuberculosis (TB). Private retail pharmacies are the preferred provider for 40% of patients with TB symptoms and up to 25% of diagnosed patients. Engaging pharmacies in TB screening services could improve case detection. METHODS: A novel TB screening and referral intervention was piloted over 18 months, under the pragmatic staggered recruitment of 105 pharmacies in Patna, India. The intervention was integrated into an ongoing public-private mix (PPM) programme, with five added components: pharmacy training in TB screening, referral of patients with TB symptoms for a chest radiograph (CXR) followed by a doctor consultation, incentives for referral completion and TB diagnosis, short message service (SMS) reminders and field support. The intervention was evaluated using mixed methods. RESULTS: 81% of pharmacies actively participated in the intervention. Over 132.49 pharmacy person-years of observation in the intervention group, 1674 referrals were made and 255 cases of TB were diagnosed. The rate of registration of symptomatic patients was 62 times higher in the intervention group compared with the control group (95% CI: 54 to 72). TB diagnosis was 25 times higher (95% CI: 20 to 32). Microbiological testing and test confirmation were also significantly higher among patients diagnosed in the intervention group (p<0.001). Perceived professional credibility, patient trust, symptom severity and providing access to a free screening test were seen to improve pharmacists' engagement in the intervention. Workload, patient demand for over-the-counter medicines, doctor consultation fees and programme documentation impeded engagement. An additional 240 cases of TB were attributed to the intervention, and the approximate cost incurred per case detected due to the intervention was US$100. CONCLUSIONS: It is feasible and impactful to engage pharmacies in TB screening and referral activities, especially if working within existing public-private mix (PPM) programmes, appealing to pharmacies' business mindset and among pharmacies with strong community ties.
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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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".