Cross‐country comparisons in health price growth over time
Bibliographic record
Abstract
OBJECTIVE: To examine how the United States compares in terms of health price growth relative to four other countries - Australia, Canada, France, and the Netherlands. DATA SOURCES AND STUDY SETTING: Secondary data on health expenditure were extracted from international and national agencies spanning the years 2000-2020. STUDY DESIGN: International price indices specific to health were constructed using available international expenditure data and compared to existing health-specific national and general international price indices. DATA COLLECTION/EXTRACTION METHODS: Health expenditure data were extracted from the Organization for Economic Cooperation and Development (OECD) database. We obtained a time series of health price indices from the national agencies in each of the study countries. PRINCIPAL FINDINGS: We find meaningful variation across countries in the rate at which health prices grow relative to general prices. The United States had the highest cumulative health price growth compared to general price growth over the years 2000-2020 at 14%, followed by Canada and the Netherlands. Unlike the other study countries, health prices in France grew consistently in line with general prices. Price growth for health care paid for by public funds and households grew at different rates across countries, where price growth was higher for public payers. US households faced the greatest mean annual price growth. CONCLUSIONS: The choice of price index has major implications for comparative analysis. Despite their widespread use internationally, general price indices likely underestimate the contribution of price growth to overall health expenditure growth. We find that in addition to its reputation for having high health price levels compared to other high-income countries, the United States also faces health price growth for goods and services paid for by government and households in excess of general price growth. Furthermore, US households are exposed to greater health price growth than households in comparator countries.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".