Cyclical vs structural effects on health care expenditure trends in OECD countries
Bibliographic record
Abstract
Health care expenditure per person, after accounting for changes in overall price levels, began to slow in many OECD countries in the early-to-mid 2000s, well before the economic and fiscal crisis. Using available estimates from the OECD’s System of Health Accounts (SHA) database, we explore common trends in health care expenditure since 1996 in a set of 22 OECD countries. We assess the extent to which the trends observed are the results of cyclical economic influences, and the respective contributions of changes in relative prices, health care volumes and coverage to the slowdown in health care expenditure growth. Our analysis suggests that cyclical factors may account for a little less than one half of the estimated slowdown in health care spending since the crisis, suggesting that structural changes have contributed to the trends. Before the crisis the slowdown in health care expenditure growth was accounted for by health care prices growing less than general prices and a reduction in care volumes, whereas the latter accounts for most of the steeper deceleration after the crisis. Although both privately and publically financed health care expenditure grew at a reduced pace during the study period, the sharp post-crisis deceleration happened mostly in the public component. When examined by function, the slowdown in publicly-financed expenditure has been largest in curative and rehabilitative care (particularly after the crisis) and in medical goods (especially pharmaceuticals), whereas the deceleration in the privately financed component is largely in medical goods (including pharmaceuticals). We conclude that structural changes in publicly financed health care have constrained the growth of care volumes (especially) and prices leading to a marked reduction in health care expenditure growth rates, beyond what could be expected based on cyclical economic fluctuations. We examine a range of government policies enacted in a selection of OECD countries that likely contributed to the structural changes observed in our analysis.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".