No UHC without medicines: out-of-pocket payments for non-communicable diseases in 18 countries
Bibliographic record
Abstract
Abstract Background In 2014 the United Nations agreed on a goal to reduce premature mortality from NCDs by improving financial risk protection. We are far from achieving this: households with NCDs are at an increased risk of catastrophic health spending and impoverishment, particularly in lower middle- and low-income countries. There is a need to better understand the drivers of health spending among households with NCDs, to inform interventions aimed at achieving universal health coverage. Methods Using data from the Prospective Urban and Rural Epidemiology Study, we analyse out-of-pocket expenditure (OOP) among households with NCDs (cancer, cardiovascular disease, hypertension, diabetes, respiratory disease or kidney disease) in 18 countries: Canada, Sweden, Brazil, Chile, Malaysia, Poland, South Africa, Turkey, China, the Philippines, Colombia, Iran, the Occupied Palestinian Territory (OPT), Bangladesh, India, Pakistan, Zimbabwe and Tanzania. Results The leading driver of OOP on health care in almost all countries included is medicine. For example, the monthly OOP on medicines among NCD households in Iran, where roughly 18% of NCD households experience catastrophic spending, is USD 13.50, representing 36% of OOP on health. In Brazil this figure is USD 25.85, representing 46% of OOP on health. A large proportion of OOP is also made up by consultation fees, particularly in Sub-Saharan African countries. In Poland, 63% of OOP on health is spent on alternative medicine consultation fees. Conclusions Our findings echo the message shared by the Director General of the World Health Organization in 2018, that there is “no Universal Health Coverage without access to quality medicines”. Medicine costs impose a significant economic burden on NCD households in countries at all levels of development, highlighting the need to include essential medicines for NCDs in universal health coverage benefit packages. Key messages To achieve the goal of improved financial risk protection for NCDs we need to understand drivers of out-of-pocket spending among households with NCDs. Medicines are by far the largest driver of OOP in countries at all levels of development and require urgent attention to ensure universal health coverage.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".