Income-Based Disparities in Opioid Prescription Dispensing Among Public Drug Plan Beneficiaries in Canada from 2010 to 2018: A Population-Based and Sex-Stratified Retrospective Study
Bibliographic record
Abstract
BACKGROUND: Previous research in Canada has examined opioids prescription dispensing at the population level but did not examine the potential relationship with area-level income and rates of opioid dispensing. OBJECTIVE: The aim was to estimate average and annual opioid dispensing rate ratios (RRs) between lowest and highest income quintile geographic areas in Canada. METHODS: We performed a population-based retrospective study using the National Prescription Drug Utilization Information System (NPDUIS) between 2010 and 2018 that contains prescription records for all public drug plan beneficiaries (65+) in all Canadian provinces, excluding Quebec, Nova Scotia, and New Brunswick. We used census median household income, calculated at the Forward Sortation Area (FSA-the first three letters of the postal code) to assign income quintiles. Morphine milligram equivalent (MME) was calculated for all opioid dispensing and was divided by population of the FSA quintile. Population census year 2016 was used for population and income estimations. We calculated the average and annual RR between lowest and highest quintiles and stratified them by patients' sex. The significance of the trend of annual RR was tested by linear regression. RESULTS: The average MME per capita for the 65+ population ranged from 2321.8 in quintile 1 to 5831.9 in quintile 5. The RR between highest and lowest quintile was 2.5 (95% confidence interval [CI] 1.3-3.7), and was more profound for males (3.2, 95% CI 1.4-4.9) than females (2.2, 95% CI 1.2-3.3). Over the study period, the RR reduced slightly from 2.7 to 2.3 (p < 0.01). However, this trend was only significant for females. CONCLUSION: Inequity in opioid prescriptions dispensing was persistent over time. Patients in the lowest income quintiles received higher amounts of opioids per capita, with some sex variation. Dispensing policies must take these equity issues into account.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".