The need for better data about counterfeit drugs in developing countries: a proposed standard research methodology tested in Chennai, India
Bibliographic record
Abstract
WHAT IS KNOWN AND OBJECTIVE: There is still surprisingly little basic research data to support widely repeated claims about the prevalence of drug counterfeiting. To meet the need for more reliable drug quality data, we designed a study framework that includes clear definitions of measured end points, sampling methods and assay technique. Our objective was to test this research design in Chennai (formerly Madras), India, using a joint Indian and Canadian team. METHODS: The city was divided into ten areas along municipal lines. From each area, ten stores and pharmacies selling drugs were selected. At each of these 100 outlets, three study drugs (artesunate, ciprofloxacin and rifampicin) were purchased. The 300 samples were tested by Liquid Chromatography-Mass Spectrometry. Assay content was expressed as a percentage of stated tablet content. Based on assay results and their distribution, we developed drug quality definitions for normal manufacturing standards, counterfeiting, decomposition, poor quality control and adulteration. RESULTS: The group mean for ciprofloxacin was close to normal manufacturing limits (99·2 ± 7·1%) but rifampicin (91·6 ± 5·7%), and artesunate (80·1 ± 9·1%), were both below normal pharmaceutical standards. Overall, 43% of all samples fell below the widely accepted manufacturing range of 90-110% of stated content. No tablet from any sample contained less than 50% of the stated dose. WHAT IS NEW AND CONCLUSION: The quality of at least some anti-infective drugs in Chennai is below commonly accepted standards but we found no evidence of criminal counterfeiting. Poor drug quality was most likely due to decomposition during storage or poor manufacturing standards. Our research methodology worked well under practical conditions and should hopefully be of value to others working in this area.
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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 | 0.116 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.005 | 0.004 |
| 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".