Policy‐induced selection bias in pharmacoepidemiology: The example of coverage for Alzheimer's medications in British Columbia
Bibliographic record
Abstract
PURPOSES: To assess the impact of a government-sponsored reimbursement policy for cholinesterase inhibitors (ChEIs) on trends in physician visits with a diagnosis of Alzheimer's disease (AD). METHODS: Longitudinal population-based study using interrupted time series methods. British Columbia outpatient claims data for individuals aged 65 and older were used to compute monthly AD visit rates and examine the impact of the ChEI reimbursement policy on the coding of AD. We examined trends in the number of patients with AD visits, the number of AD visits per patient, and visits with "competing" diagnoses (mental, neurological, and cerebrovascular disorders and accidental falls). Finally, we described demographic and clinical features of diagnosed patients. RESULTS: We analyzed 1.9 million AD visits. Faster growth in recorded AD visits was observed after the policy was implemented, from monthly growth of 7.5 visits per 100 000 person-months before the policy (95% confidence interval [CI], 6.1-8.9) to monthly growth of 16.5 per 100 000 person-months after the policy (95% CI, 14.8-18.3). After the implementation of the policy, we observed increased growth in the number of patients with recorded AD visits and the number of AD visits per patient, as well as a shift in diagnoses away from mental diseases and accidental falls to AD (diagnosis substitution). CONCLUSIONS: British Columbia's reimbursement policy for ChEIs was associated with a significant acceleration in Alzheimer's visits. Evaluations of health services utilization and clinical outcomes following drug policy changes need to consider policy-induced influences on the reliability of the data used in the analysis.
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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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".