Trends in Dispensed Opioid Analgesic Prescriptions to Children in South Carolina: 2010–2017
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Despite published declines in opioid prescribing and dispensing to children in the past decade, in few studies have researchers evaluated all children in 1 state or examined changes in mean daily opioid dispensed. In this study, we evaluated changes in the rate of dispensed opioid analgesics and the mean daily opioid dispensed to persons 0 to 18 years old in 1 state over an 8-year period. METHODS: We identified opioid analgesics dispensed to children 0 to 18 years old between 2010 and 2017 using South Carolina prescription drug monitoring program data. We used generalized linear regression analyses to examine changes over time in the following: (1) rate of dispensed opioid prescriptions and (2) mean daily morphine milligram equivalents (MMEs) per prescription. RESULTS: From the first quarter of 2010 to the end of the fourth quarter of 2017, the quarterly rate of opioids dispensed decreased from 18.68 prescriptions per 1000 state residents to 12.03 per 1000 residents (P < .0001). The largest declines were among the oldest individuals, such as the 41.2% decline among 18-year-olds. From 2010 through 2017, the mean daily MME dispensed declined by 7.6%, from 40.7 MMEs per day in 2010 to 37.6 MMEs per day in 2017 (P < .0001), but the decrease was limited to children 0 to 9 years old. CONCLUSIONS: The rate of opioid analgesic prescriptions dispensed to children 0 to 18 years old in South Carolina declined by 35.6% over the years 2010–2017; however, the MME dispensed per day declined minimally, suggesting that more can be done to improve opioid prescribing and dispensing.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".