Prevalence and predictors of primary nonadherence to medications prescribed in primary care
Bibliographic record
Abstract
BACKGROUND: Most research on medication adherence has focused on secondary nonadherence and persistence to therapy. Medication prescriptions that are never filled by patients (primary nonadherence) remain understudied in the general population. METHODS: We linked prescribing data from primary care electronic medical records to comprehensive pharmacy dispensing claims between January 2013 and April 2019 in British Columbia (BC) to estimate primary nonadherence, defined as failure to dispense a new medication or its equivalent within 6 months of the prescription date. We used hierarchical multivariable logistic regression to determine prescriber, patient and medication factors associated with primary nonadherence among community-dwelling patients in primary care. RESULTS: Among 150 565 new prescriptions to 34 243 patients, 17% of prescriptions were never filled. Primary nonadherence was highest for drugs prescribed mostly on an as-needed basis, including topical corticosteroids (35.1%) and antihistamines (23.4%). In multivariable analysis, primary nonadherence was lower for prescriptions issued by male prescribers (odds ratio [OR] 0.66, 95% confidence interval [CI] 0.50-0.88). Primary nonadherence decreased with patient age (OR 0.91, 95% CI 0.90-0.92 for each additional 10 years) but increased with polypharmacy among patients aged 65 years or older. Patients filled more than 82% of their medication prescriptions within 2 weeks after their primary care provider visit. INTERPRETATION: The prevalence of primary nonadherence to new prescriptions was 17%. Interventions to address primary nonadherence could target older patients with multiple medication use and within the first 2 weeks of the prescription issue date.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".