Variation in the prescription drugs covered by health systems across high-income countries: A review of and recommendations for the academic literature
Bibliographic record
Abstract
BACKGROUND: Because not all medicines are equally safe, effective, and affordable, health systems often use formularies to define explicitly which medicines will be included and excluded from coverage. OBJECTIVE: We sought to synthesize methods and findings from published studies of formulary variation across health systems in high-income countries. METHODS: We conducted a systematic review of peer-reviewed research papers published from 2000 to 2017, inclusively. Because of the heterogeneous nature of the literature, we used an inductive approach to summarize methods and findings. RESULTS: Nine studies met our study inclusion criteria. Included studies used a variety of methods for selecting medicines for analysis, for measuring coverage levels, and for measuring concordance between formularies. Studies assessing variations in coverage of all licensed medicines and found lower rates of cross-national coverage variation than studies of coverage for selected specialty drugs and indications. The one study that focused on coverage of high-volume medicines found the most complete and consistent levels of formulary listings across countries. CONCLUSION: Although published studies contain interesting findings that likely have prompted discussions about their policy implications, the literature can be improved with greater transparency concerning the overarching objective of work in this area and more rigor concerning the selection, analysis, and reporting of data.
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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 | 0.030 | 0.106 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.028 | 0.025 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".