A population-based study of prescribing trends in a potentially vulnerable paediatric population from 1999 to 2012
Bibliographic record
Abstract
OBJECTIVES: There is a limited understanding of paediatric medication prescribing trends and patterns, thus poorly positioning decision-makers to identify quality and safety concerns related to medication use. The objective of this study was to determine overall medication prescribing trends and patterns among children receiving Ontario Drug Benefits over a thirteen-year period in the province of Ontario, Canada. METHODS: Administrative health databases housed within the Institute for Clinical Evaluative Sciences (ICES), Ontario, Canada, were used to identify outpatient prescriptions dispensed from 1999 to 2012 through a publicly funded programme to children ≤18 years of age. Medications were classified according to the American Hospital Formulary Service Pharmacologic-Therapeutic Classification system. Descriptive statistics were used to summarize prescribing patterns. KEY FINDINGS: This study identified 457 037 children who were dispensed a new prescription between 1999 and 2012. About 56% received their first prescription before 6.5 years of age, and 85% of the children in this study were from families who received social assistance. The most commonly prescribed drugs were antiinfectives (56.1%). Prescriptions for several central nervous system agents, including antipsychotics and agents for attention-deficit/hyperactivity disorder, increased across the study period. Changes in prescribing patterns within opioids, hormones and autonomic agents were noted. The results suggest that historically, prescribing trends have shifted with public policy, pharmaceutical marketing and diagnostic patterns, thus identifying them as a possible tool to measure the impact of policydriven practice changes. Anti-infective prescribing increased markedly with the global H1N1 pandemic. Pharmaceutical marketing, formulary decisions and diagnostic trends may affect the prescribing of ADHD medications globally. The prescribing of codeine-containing products and medroxyprogesterone appeared to fluctuate in response to important publications in the medical literature, and the use of epinephrine syringes increased after public policy changes in the province of Ontario. The steady rise in the use of medications whose long-term effects in children are unknown, such as antipsychotics and proton pump inhibitors, identifies areas in need of future research. CONCLUSIONS: This study presents the first overview of Canadian prescribing trends for children, the majority of which are of low socioeconomic status and represent a potentially vulnerable population. Our analysis suggests that future research is required to determine whether prescribing trends could be used as indicators of policy effectiveness, pharmacovigilance and diagnostic trends.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".