The inclusion of diagnostics in national health insurance schemes in Cambodia, India, Indonesia, Nepal, Pakistan, Philippines and Viet Nam
Bibliographic record
Abstract
The Lancet Commission on Diagnostics highlighted a huge gap in access to diagnostic testing even for basic tests, particularly at the primary care level, and emphasised the need for countries to include diagnostics as part of their universal health coverage benefits packages. Despite the poor state of diagnostic-related services in low-income and middle-income countries (LMICs), little is known about the extent to which diagnostics are included in the health benefit packages. We conducted an analysis of seven Asian LMICs-Cambodia, India, Indonesia, Nepal, Pakistan, Philippines, Viet Nam-to understand this issue. We conducted a targeted review of relevant literature and applied a health financing framework to analyse the benefit packages available in each government-sponsored scheme. We found considerable heterogeneity in country approaches to diagnostics. Of the seven countries, only India has developed a national essential diagnostics list. No country presented a clear policy rationale on the inclusion of diagnostics in their scheme and the level of detail on the specific diagnostics which are covered under the schemes was also generally lacking. Government-sponsored insurance expansion in the eligible populations has reduced the out-of-pocket health payment burden in many of the countries but overall, there is a lack of access, availability and affordability for diagnostic-related services.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".