Disclosure, transparency, and accountability: a qualitative survey of public sector pharmaceutical committee conflict of interest policies in the World Health Organization South-East Asia Region
Bibliographic record
Abstract
BACKGROUND: Weak governance over public sector pharmaceutical policy and practice limits access to essential medicines, inflates pharmaceutical prices, and wastes scarce health system resources. Pharmaceutical systems are technically complex and involve extensive interactions between the private and public sectors. For members of public sector pharmaceutical committees, relationships with the private sector can result in conflicts of interest, which may introduce commercial biases into decision-making, potentially compromising public health objectives and health system sustainability. We conducted a descriptive, qualitative study of conflict of interest policies and practices in the public pharmaceutical sector in ten countries in the World Health Organization (WHO) South-East Asia Region (SEAR) (Bangladesh, Bhutan, India, Indonesia, Maldives, Myanmar, Nepal, Sri Lanka, Thailand, and Timor-Leste) between September 2020 and March 2021. RESULTS: We identified 45 policy and regulatory documents and triangulated documentary data with 21 expert interviews. Key informants articulated very different governance priorities and conflict of interest concerns depending on the features of their country's pharmaceutical industry, market size, and national economic objectives related to the domestic pharmaceutical industry. Public sector pharmaceutical policies and regulations consistently contained provisions for pharmaceutical committee members to disclose relevant interests, but contained little detail about what should be declared, when, and how often, nor whether disclosures are evaluated and by whom. Processes for preventing or managing conflicts of interest were less well developed than those for disclosure except for a few key procurement processes. Where processes for managing conflicts of interest were specified, the dominant strategy was to recuse committee members with a conflict of interest from relevant work. Policies rarely specified that committee members should divest or otherwise be free from conflicts of interest. CONCLUSIONS: Robust processes for conflict of interest prevention and management could ensure the integrity of decision-making and build public trust in pharmaceutical processes to achieve public health objectives. Upstream approaches including supportive legislative frameworks, the creation of oversight bodies, and strengthening regulatory institutions can also contribute to building cultures of transparency, accountability, and trust.
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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 | 0.033 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".