Strengthening Health Systems of Developing Countries: Inclusion of Surgery in Universal Health Coverage
Bibliographic record
Abstract
INTRODUCTION: Universal health coverage (UHC) has its roots in the Universal Declaration of Human Rights and has recently gained momentum. Out-of-pocket payments (OPP) remain a significant barrier to care. There is an increasing global prevalence of non-communicable diseases, many of which are surgically treatable. We sought to provide a comparative analysis of the inclusion of surgical care in operating plans for UHC in low- and middle-income countries (LMIC). METHODS: We systematically searched PubMed and Google Scholar using pre-defined criteria for articles published in English, Spanish, or French between January 1991 and November 2013. Keywords included "insurance," "OPP," "surgery," "trauma," "cancer," and "congenital anomalies." World Health Organization (WHO), World Bank, and Joint Learning Network for UHC websites were searched for supporting documents. Ministries of Health were contacted to provide further information on the inclusion of surgery. RESULTS: We found 696 articles and selected 265 for full-text review based on our criteria. Some countries enumerated surgical conditions in detail (India, 947 conditions). Other countries mentioned surgery broadly. Obstetric care was most commonly covered (19 countries). Solid organ transplantation was least covered. Cancer care was mentioned broadly, often without specifying the therapeutic modality. No countries were identified where hospitals are required to provide emergency care regardless of insurance coverage. OPP varied greatly between countries. Eighty percent of countries had OPP of 60% or more, making these services, even if partially covered, largely inaccessible. CONCLUSION: While OPP, delivery, and utilization continue to represent challenges to health care access in many LMICs, the inclusion of surgery in many UHC policies sets an important precedent in addressing a growing global prevalence of surgically treatable conditions. Barriers to access, including inequalities in financial protection in the form of high OPP, remain a fundamental challenge to providing surgical care in LMICs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".