Achieving effective universal health coverage with equity: evidence from Chile
Bibliographic record
Abstract
Chile's 'health guarantees' approach to providing universal and equitable coverage for quality healthcare in a dual public-private health system has generated global interest. The programme, called AUGE, defines legally enforceable rights to explicit healthcare benefits for priority health conditions, which incrementally covered 56 problems representing 75% of the disease burden between 2005 and 2009. It was accompanied by other health reform measures to increase public financing and public sector planning to secure the guarantees nationwide, as well as the state's stewardship role. We analysed data from household surveys conducted before and after the AUGE reform to estimate changes in levels of unmet health need, defined as the lack of a healthcare visit for a health problem occurring in the last 30 days, by age, sex, income, education, health insurance, residence and ethnicity; fitting logistic regression models and using predictive margins. The overall prevalence of unmet health need was much lower in 2009 (17.6%, 95% CI: 16.5%, 18.6%) than in 2000 (30.0%, 95% CI: 28.3%, 31.7%). Differences by income and education extremes and rural-urban residence disappeared. In 2009, people who had been in treatment for a condition covered by AUGE in the past year had a lower adjusted prevalence of unmet need for their recent problem (11.7%, 95% CI: 10.5%, 13.2%) than who had not (21.0%, 95% CI: 19.6%, 22.4%). Despite limitations including cross-sectional and self-reported data, our findings suggest that the Chilean health system has become more equitable and responsive to need. While these changes cannot be directly attributed to AUGE, they were coincident with the AUGE reforms. However, healthcare equity concerns are still present, relating to quality of care, health system barriers and differential access for health conditions that are not covered by AUGE.
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".