A quantitative comparison between the essential medicines for rheumatic diseases in children and young people in Africa and the WHO model list
Bibliographic record
Abstract
BACKGROUND: The World Health Organisation Essential Medicines List (WHO EML) guides National Essential Medicines Lists and Standard Treatment Guidelines for clearly identified disease priorities especially in low- and middle-income countries. This study compares the degree to which the basket of medicines recommended for rheumatic diseases in children and young people in National Essential Medicines Lists of countries in the WHO Africa region, corresponds to the 2021 WHO EML and WHO EML for children, as a proxy of availability. METHODS: An online search of the WHO medicines and health technology portal, the Health Ministry websites of the 54 African countries, PUBMED and Google Scholar, with search terms for 'National Essential Medicines List', AND/OR 'standard treatment guidelines' AND/OR 'Lista Nacional de Medicamentos Essenciais' AND/ OR 'Liste Nationale de Medicaments Essentiels' AND Africa AND/OR < Name of African country > was conducted. The number of medicines on the national lists were compared according to a predefined template of medicines; and the percentage similarity calculated. Descriptive statistics were derived using STATA. RESULTS: Forty-seven countries in the WHO Africa region have developed a National Essential Medicines List. Eleven countries do not have any medicines listed for rheumatic diseases. The majority of countries had less than or equal to 50% similarity with the WHO EML for rheumatic disease in children and young people, median 3 medicines (IQR 1- 4). The most common medicines on the national lists from Africa were methotrexate, sulfasalazine and azathioprine, with etanercept available in 6 countries. Seven countries had only one medicine, acetylsalicylic acid listed in the section 'Juvenile Joint diseases'. A multiple linear regression model for the predictors of the number of medicines on the national lists established that 20% of the variability was predicted by health expenditure per capita, socio-demographic index and the availability of rheumatology services (adult and/or paediatric) p = 0.006, with socio-demographic index (p = 0.035, 95% CI 0.64-16.16) and the availability of rheumatology services (p = 0.033, 95% CI 0.13 - 2.90) significant. CONCLUSION: Four countries (8.5%) in Africa have updated their National Essential Medicines Lists to reflect adequate care for children and young people with rheumatic diseases. Moving forward, efforts should focus on aligning available medicines with the WHO EML, and strengthening healthcare policy for rheumatology and pharmaceutical services, for affordable access to care and medicines.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".