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
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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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 | 0.018 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".