A Quantitative Review of Un-licensed and Off-label Medicines Use in Children Aged 0-2 Years in the Private Sector in South Africa: Extent, Challenges, and Implications
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Bibliographic record
Abstract
BACKGROUND: The global lack of suitable formulations for children leads to off-label and unlicensed medicine use, posing significant risks of adverse effects. Understanding this usage on a national level can help guide interventions for better formulations. This study aimed to measure the prevalence of off-label and unlicensed medicines among children in South Africa's private sector. METHODS: The study used a point prevalence methodology to review medicine use in children aged 0-2 years enrolled in a selected pharmaceutical benefit management company in South Africa from January to June 2022. A sample size of 1055 prescriptions was calculated using a 90% confidence interval, 50% prevalence rate, and 5% error margin. A systematic random sampling approach selected every 7th entry from 91,973 total entries, resulting in a final sample size of 13,139. Data included patient age, number and characteristics of medicines, quantity, and indications. Descriptive statistics analysed and reported the prevalence of unlicensed and off-label medicine use. RESULTS: Among the 13,139 prescribed medicines, 40% (5,246) were off-label or unlicensed, and 60% (7,893) were on-label. Of the off-label/unlicensed medicines, 16.85% (2,214) were unlicensed, and 23.08% (3,032) were off-label. Methylprednisolone was the top off-label medicine, probiotics were the top unlicensed, and the ICD10 code Z76.9 was the top diagnosis. CONCLUSION: The study found that 40% of children aged 0-2 years were prescribed unlicensed or off-label medicines in South Africa's private healthcare sector between January and June 2022. This suggests a widespread practice of off-label or unlicensed prescriptions in paediatric treatment in the South African private sector.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 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 it