Drivers of expenditure on primary care prescription drugs in 10 high-income countries with universal health coverage
Bibliographic record
Abstract
BACKGROUND: Managing expenditures on pharmaceuticals is important for health systems to sustain universal access to necessary medicines. We sought to estimate the size and sources of differences in expenditures on primary care medications among high-income countries with universal health care systems. METHODS: We compared data on the 2015 volume and cost per day of primary care prescription drug therapies purchased in 10 high-income countries with various systems of universal health care coverage (7 from Europe, in addition to Australia, Canada and New Zealand). We measured total per capita expenditure on 6 categories of primary care prescription drugs: hypertension treatments, pain medications, lipid-lowering medicines, noninsulin diabetes treatments, gastrointestinal preparations and antidepressants. We quantified the contributions of 5 drivers of the observed differences in per capita expenditures. RESULTS: Across countries, the average annual per capita expenditure on the primary care medicines studied varied by more than 600%: from $23 in New Zealand to $171 in Switzerland. The volume of therapies purchased varied by 41%: from 198 days per capita in Norway to 279 days per capita in Germany. Most of the differences in average expenditures per capita were driven by a combination of differences in the average mix of drugs selected within therapeutic categories and differences in the prices paid for medicines prescribed. INTERPRETATION: Significant international differences in average expenditures on primary care medications are driven primarily by factors that contribute to the average daily cost of therapy, rather than differences in the volume of therapy used. Average expenditures were lower among single-payer financing systems that appeared to promote lower prices and the selection of lower-cost treatment options.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".