Physician influence on medication adherence, evidence from a population-based cohort
Bibliographic record
Abstract
BACKGROUND: The overall impact of physician prescribers on population-level adherence rates are unknown. We aimed to quantify the influence of general practitioner (GP) physician prescribers on the outcome of optimal statin medication adherence. METHODS: We conducted a retrospective cohort study using health administrative databases from Saskatchewan, Canada. Participants included physician prescribers and their patients beginning a new statin medication between January 1, 2012 and December 31, 2017. We grouped prescribers based on the prevalence of optimal adherence (i.e., proportion of days covered ≥ 80%) within their patient group. Also, we constructed multivariable logistic regression analyses on optimal statin adherence using two-level non-linear mixed-effects models containing patient and prescriber-level characteristics. An intraclass correlation coefficient was used to estimate the physician effect. RESULTS: We identified 1,562 GPs prescribing to 51,874 new statin users. The median percentage of optimal statin adherence across GPs was 52.4% (inter-quartile range: 35.7% to 65.5%). GP prescribers with the highest patient adherence (versus the lowest) had patients who were older (median age 61.0 vs 55.0, p<0.0001) and sicker (prior hospitalization 39.4% vs 16.4%, p<0.001). After accounting for patient-level factors, only 6.4% of the observed variance in optimal adherence between patients could be attributed to GP prescribers (p<0.001). The majority of GP prescriber influence (5.2% out of 6.4%) was attributed to the variance unexplained by patient and prescriber variables. INTERPRETATION: The overall impact of GP prescribers on statin adherence appears to be very limited. Even "high-performing" physicians face significant levels of sub-optimal adherence among their patients.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".