The Impact of Age and Sex Concordance Between Patients and Physicians on Medication Adherence: A Population-Based Study
Bibliographic record
Abstract
PURPOSE: Age or sex concordance (same sex or same age range) may also be associated with medication adherence but was not fully investigated. We aim to quantify the impact of age and sex concordance on optimal adherence to statin medications. PATIENTS AND METHODS: A retrospective cohort study was conducted using population-based health administrative data from Saskatchewan, Canada. Participants were individuals newly initiated on statin medications between January 1, 2012, and December 31, 2017. The outcome was optimal adherence (proportion of days covered ≥ 80%) measured at one year after the first statin claim. The independent variables were sex and age concordance (age within five years) between patients and prescribers. The association between adherence outcome and sex/age concordance was analyzed by multivariable logistic regression models using generalized estimating equations controlled by a package of potential confounding factors. RESULTS: Among 51,874 new statin users, 20.6% (n = 10,710) were age concordant with prescriber. The vast majority of age concordance occurred in patients younger than 66 years (88.6%, 9,486/10,710). Sex concordance was observed in 62.8% (n = 32,551) of patients and age-sex combined concordance in 13.2% (n = 6,856). Among patients younger than 66 years (n = 36,641/51,874, 70.6%), age concordance did not have a significant impact on optimal adherence [adjusted OR (aOR) = 1.02, 95% CI 0.97 to 1.07]. Weak association between sex concordance (aOR = 1.05, 95% CI 1.00 to 1.11), and age-sex combined concordance (aOR = 1.05, 95% CI 0.99 to 1.12) was observed. CONCLUSION: Age and sex concordance were not statistically significant predictors of optimal statin adherence. However, a weak association was detected for sex concordance. Future studies should examine this factor in different health care settings.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".