Despite Increased Use And Sales Of Statins In India, Per Capita Prescription Rates Remain Far Below High-Income Countries
Bibliographic record
Abstract
Statin use has increased substantially in North America and Europe, with resultant reductions in cardiovascular mortality. However, little is known about statin use in lower-income countries. India is of interest because of its burden of cardiovascular disease, the unique nature of its prescription drug market, and the growing globalization of drug sales. We conducted an observational study using IMS Health data for the period February 2006-January 2010. During the period, monthly statin prescriptions increased from 45.8 to 84.1 per 1,000 patients with coronary heart disease-an increase of 0.80 prescriptions per month. The proportion of the Indian population receiving a defined daily statin dose increased from 3.35 percent to 7.78 percent. Nevertheless, only a fraction of those eligible for a statin appeared to receive the therapy, even though there were 259 distinct statin products available to Indian consumers in January 2010. Low rates of statin use in India may reflect problems with access to health care, affordability, underdiagnosis, and cultural beliefs. Because of the growing burden of cardiovascular disease in lower-income countries such as India, there is an urgent need to increase statin use and ensure access to safe products whose use is based on evidence. Policies are needed to expand insurance, increase medications' affordability, educate physicians and patients, and improve regulatory oversight.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".