Use of standardised patients to assess antibiotic dispensing for tuberculosis by pharmacies in urban India: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: India's total antibiotic use is the highest of any country. Patients often receive prescription-only drugs directly from pharmacies. Here we aimed to assess the medical advice and drug dispensing practices of pharmacies for standardised patients with presumed and confirmed tuberculosis in India. METHODS: In this cross-sectional study in the three Indian cities Delhi, Mumbai, and Patna, we developed two standardised patient cases: first, a patient presenting with 2-3 weeks of pulmonary tuberculosis symptoms (Case 1); and second, a patient with microbiologically confirmed pulmonary tuberculosis (Case 2). Standardised patients were scheduled to present each case once to sampled pharmacies. We defined ideal management for both cases a priori as referral to a health-care provider without dispensing antibiotics or steroids or both. FINDINGS: Between April 1, 2014, and Nov 29, 2015, we sampled 622 pharmacies in Delhi, Mumbai, and Patna. Standardised patients completed 1200 (96%) of 1244 interactions. We recorded ideal management (defined as referrals without the use of antibiotics or steroids) in 80 (13%) of 599 Case 1 interactions (95% CI 11-16) and 372 (62%) of 601 Case 2 interactions (95% CI 58-66). Antibiotic use was significantly lower in Case 2 interactions (98 [16%] of 601, 95% CI 13-19) than in Case 1 (221 [37%] of 599, 95% CI 33-41). First-line anti-tuberculosis drugs were not dispensed in any city. The differences in antibiotic or steroid use and number of medicines dispensed between Case 1 and Case 2 were almost entirely attributable to the difference in referral behaviour. INTERPRETATION: Only some urban Indian pharmacies correctly managed patients with presumed tuberculosis, but most correctly managed a case of confirmed tuberculosis. No pharmacy dispensed anti-tuberculosis drugs for either case. Absence of a confirmed diagnosis is a key driver of antibiotic misuse and could inform antimicrobial stewardship interventions. FUNDING: Grand Challenges Canada, Bill & Melinda Gates Foundation, Knowledge for Change Program, and World Bank Development Research Group.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".