Access to linked administrative healthcare utilization data for pharmacoepidemiology and pharmacoeconomics research in Canada: anti‐viral drugs as an example
Bibliographic record
Abstract
PURPOSE: Administrative healthcare utilization data from Canadian provinces have been used for pharmacoepidemiology and pharmacoeconomics research, but limited transparency exists about opportunities for data access, who can access them, and processes to obtain data. An attempt was made to obtain data from all 10 provinces to evaluate access and its complexity. METHODS: An initial enquiry about the process and requirements to obtain data on individual, anonymized patients dispensed any of four anti-viral drugs in the ambulatory setting, linked with data from hospital and physician service claims, was sent to each province. Where a response was encouraging, a technical description of the data of interest was submitted. RESULTS: Data were unavailable from the provinces of New Brunswick, Newfoundland and Labrador, and Prince Edward Island, and inaccessible from British Columbia, Manitoba and Ontario due to policies that prohibit collaborative work with pharmaceutical industry researchers. In Nova Scotia, patient-level data were available but only on site. Data were accessible in Alberta, Quebec and Saskatchewan, although variation exists in the currency of the data, time to obtain data, approval requirements and insurance coverage eligibility. CONCLUSIONS: As Canada moves towards a life-cycle management approach to drug regulation, more post-marketing studies will be required, potentially using administrative data. Linked patient-level drug and healthcare data are presently accessible to pharmaceutical industry researchers in four provinces, although only logistically realistic in three and limited to seniors and low-income individuals in two. Collaborative endeavours to improve access to provincial data and to create other data resources should be encouraged.
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.
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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.023 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".