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Record W2965162711 · doi:10.1111/ijpp.12565

A population-based study of prescribing trends in a potentially vulnerable paediatric population from 1999 to 2012

2019· article· en· W2965162711 on OpenAlex
Kim Sears, Sherri Elms, Marlo Whitehead, Joan Tranmer, Dana Edge, Elizabeth G. VanDenKerkhof

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueInternational Journal of Pharmacy Practice · 2019
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical studies and practices
Canadian institutionsInstitute for Clinical Evaluative SciencesQueen's University
FundersQueen's UniversityOntario Ministry of Health and Long-Term CareInstitute for Clinical Evaluative SciencesHealth Research Board
KeywordsMedicineFormularyMedical prescriptionFamily medicinePopulationPharmacyPublic healthDescriptive statisticsPediatricsEnvironmental healthNursing

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.057
GPT teacher head0.422
Teacher spread0.366 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it