Equity in Health Financing: Review of Health Care financing in Four organizations for economic cooperation development (OECD) countries, Canada, The republic of Korea, Mexico and the United Kingdom
Bibliographic record
Abstract
Background: The World Health Assembly Resolution in 2005 urges Member States to introduce and/or strengthen universal coverage policy in order to offer financial risk protection (FRP) to households in order to avoid catastrophic health expenditures and impoverishment from seeking care. The other goal of universal coverage is to ensure equitable access to healthcare based on relative need, irrespective of ability to make health care payments, social status or geographical location. The two prepaid financing mechanisms that guarantee universal coverage are social health insurance and general tax revenue. Aim: To undertake a comparative analysis of selected OECD countries with universal coverage to derive lessons that could inform the development of universal coverage policy in low-to-middle income (LMICs) countries. Methods: Empirical evidence from the OECD was sourced through an extensive review of published literature from print and electronic sources. Selection sought to include a range of countries in different continents and health systems with a long history as universal systems. Most universal systems are in OECD countries. OECD countries were selected because of availability of quality and credible data. The data for the analysis is drawn from the OECD Health Data 2008 dataset. Kutzin's conceptual framework is the analytical tool for the critical analysis of evidence, including OECD data, to evaluate the functionality of each health system based on the concepts of equity, sustainability, efficiency and feasibility. Results: Findings from the analysis show that publicly funded (primarily tax-funded) systems have lower out-of-pocket expenditures and offer greater financial risk protection. Systems with a single risk pool and a single payer tend to be more administratively efficient than multiple pools and payers. Allocating health resources based on a needs-based allocation formula is more equitable than historical budgeting. Capitation provider payment promotes greater efficiency than fee-for-service. A purchaser-provider split can improve efficiency.
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.020 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".