Health System Capacity and Access Barriers to Diagnosis and Treatment of CVD and Diabetes in Nepal
Bibliographic record
Abstract
Background: Universal access to essential medicines and routine diagnostics is required to combat the growing burden of cardiovascular disease (CVD) and diabetes. Evaluating health systems and various access dimensions availability, affordability, accessibility, acceptability, and quality is crucial yet rarely performed, especially in low- and middle-income countries. Objective: To evaluate health system capacity and barriers in accessing diagnostics and essential medicines for CVD and diabetes in Nepal. Methods: We conducted a WHO/HAI nationally-representative survey in 45 health-facilities (public-sector: 11; private-sector: 34) in Nepal to collect availability and price data for 21 essential medicines for treating CVD and diabetes, during MayJuly 2017. Data for 13 routine diagnostics was obtained in 12 health facilities. Medicines were considered unaffordable if the lowest paid worker spends >1 days wage to purchase a monthly supply. To evaluate accessibility, we conducted facility exit interviews among 636 CVD patients. Accessibility (e.g., private-public health facility mix, travel to hospital/pharmacy) and acceptability (i.e. Nepals adoption of WHO Essential Medicine List, and patient medication adherence) were summarized using descriptive statistics, and we conducted a systematic review of relevant literature. We did not evaluate medicine quality. Results: We found that mean availability of generic medicines is low (<50%) in both public and private sectors, and less than one-third medicines met WHOs availability target (80%). Mean (SD) availability of diagnostics was 73.1% (26.8%). Essential medicines appear locally unaffordable. On average, the lowest-paid worker would spend 1.03 (public-sector) and 1.26 (private-sector) days wages to purchase a monthly supply. For a person undergoing CVD secondary preventive-interventions in the private sector, the associated expenditure would be 7.511.2% of monthly household income. Exit-interviews suggest that a long/expensive commute to health-facilities and poor medicine affordability constrain access. Conclusions: This study highlights critical gaps in Nepals health system capacity to offer basic health services to CVD and diabetes patients, owing to low availability, poor affordability and accessibility of essential medicines and diagnostics. Research and policy initiatives are needed to ensure uninterrupted supply of affordable essential medicines and diagnostics.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".