Revisiting out-of-pocket requirements: trends in spending, financial access barriers, and policy in ten high-income countries
Bibliographic record
Abstract
Background Countries rely on out-of-pocket (OOP) spending to different degrees and employ varying techniques. The article examines trends in OOP spending in ten high-income countries since 2000, and analyzes their relationship to self-assessed barriers to accessing health care services. The countries are Australia, Canada, France, Germany, the Netherlands, New Zealand, Norway, Sweden, Switzerland, the United Kingdom, and the United States. Methods Data from three sources are employed: OECD statistics, the Commonwealth Fund survey of individuals in each of ten countries, and country-specific documents on health care policies. Based on trends in OOP spending, we divide the ten countries into three groups and analyze both trends and access barriers accordingly. As part of this effort, we propose a conceptual model for understanding the key components of OOP spending. Results There is a great deal of variation in aggregate OOP spending per capita spending but there has been convergence over time, with the lowest-spending countries continuing to show growth and the highest spending countries showing stability. Both the level of aggregate OOP spending and changes in spending affect perceived access barriers, although there is not a perfect correspondence between the two. Conclusions There is a need for better understanding the root causes of OOP spending. This will require data collection that is broken down into OOP resulting from cost sharing and OOP resulting from direct payments (due to underinsurance and lacking benefits). Moreover, data should be disaggregated by consumer groups (e.g. income-level or health status). Only then can we better link the data to specific policies and suggest effective solutions to policy makers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".