Prescription Drug Effectiveness: A Re-Examination of the Aggregate Data
Bibliographic record
Abstract
BACKGROUND Analyses conducted by Cremieux et al (Health Econ, 2004) suggest that pharmaceutical expenditure has dramatically enhanced life expectancy (LE) and reduced infant mortality rates (IMRs) in Canada. Their methods, however, are questionable. We re-examine the evidence on the issue using updated data and arguably more robust estimators. These estimators correct for potential endogeneity of health care spending, and omitted variables bias. METHODS We model province and year specific outcomes (LE and IMRs) over the period 1975-2002 as a function of lagged outcomes, lagged real per capita drug, hospital and physician spending, and provincial and year fixed effects. RESULTS We find no evidence of any effect of higher drug spending on IMRs. We do, however, find some evidence that drug spending improves LE, although the effects are much smaller than those reported by Cremieux. CONCLUSIONS A re-examination of the aggregate data suggests that drug spending is effective, but perhaps not as effective as previous studies suggest.
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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 | 0.018 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".