Enhancing tuberculosis case notifications through mapping sales of medicine in the private sector: a quasi-experimental study in Punjab province, Pakistan
Bibliographic record
Abstract
In Pakistan, almost one-third of people who develop tuberculosis (TB) are missed by the National TB Program (NTP). A considerable number of people with TB receive treatment in the private sector but remain unnotified. This study documents the outcomes of an intervention to identify people with TB through private pharmacy engagement, building on mapping TB medicine sales in Punjab Province. A comprehensive intervention was carried out in four districts of Punjab with high anti-TB drug sales, comprising a policy change requiring mandatory notification of TB medication sales, mapping and engaging pharmacies selling anti-TB drugs, reporting through a novel ‘eTB’ mobile application and providing support via a call center. We collected both historical and prospective TB notification data from the intervention and control districts. The primary outcome was the change in TB notifications during the intervention period compared with historical and control notifications. Over the 12-month intervention period, 15,669 people with TB were reported from 2,943 pharmacies in four districts. Among the people identified, 88% were male (n = 13,673), 95% had pulmonary disease (n = 14,969), and 4,256 (27%) were bacteriologically confirmed. Chain pharmacies (n = 14) contributed to 39% of the yield. TB notifications increased by 17,462 (+ 34%) over the baseline period compared with an 8% increase in the control districts. The number of bacteriologically confirmed notifications increased by 32% compared with 16% in the control districts. The proportion of bacteriological confirmation was similar before and during the intervention. The results of the largest TB intervention with pharmacies globally showed incredible potential to link people with TB who are receiving care in the private sector. Mapping TB medicine sales in the private sector with tailored interventions can contribute to closing the gap in notifications where anti-TB drug sales in the private sector are prevalent. What is already known on this topic? A significant number of people with TB receive care in the private sector as evidenced by the significant volumes of anti-TB medicines sold, particularly in countries with substantive private TB drug markets like Pakistan. However, there is still a gap in notifications, particularly from small scale private pharmacies. Most pharmacy-based TB interventions have been done at a small scale and have engaged pharmacies to refer people with TB-related symptoms for testing thus creating a potentially perverse economic incentive for participation. What this study adds • Using TB drug sales data can help target interventions engaging private pharmacies at a large scale. Enormous numbers of people with TB can be captured in the public notification system to close the existing notification gap. • Private sector engagement approaches that support existing business models are more likely to create buy-in and more successful in increasing collaboration. • In many countries, men with TB are missed more often than women, and this type of private sector intervention has the potential to bring the gap in TB notifications among men. How this study might affect research, practice or policy • A combination of regulatory approaches and enablers applied by NTPs could be useful in engaging with private sector, particularly for-profit private pharmacies in identifying people with TB who are currently missed. • There is potential to improve case identification and management through enhanced data sharing between private pharmacies and public health authorities supported by intermediary agencies.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".