Multiple-domain Versus Single-domain Measurements of Socioeconomic Status (SES) for Predicting Nonadherence to Statin Medications
Bibliographic record
Abstract
INTRODUCTION: Low socioeconomic status (SES) should be a robust predictor of medication nonadherence because it shares key features with the theoretical origins of this phenomenon. However, population-based studies have demonstrated weak associations overall, possibly because SES is inadequately represented. We compared the performance of multiple versus single-domain measures of SES as predictors of statin adherence. METHODS: This retrospective cohort study used population-based administrative data mapped to area-level census information of individuals who received a statin medication following a hospitalization for coronary heart disease. One-year adherence was calculated by dividing the sum of all tablets dispensed by the total number of days in the observation period (365 d following the first statin dispensation). Logistic regression models were constructed and the relative impact of each SES measure was assessed by its adjusted odds ratio (OR) and improvement over the predictive accuracy of a reference model that included non-SES factors only. RESULTS: More than two thirds (ie, 68.8%; 6517/9478) of eligible individuals exhibited optimal adherence (ie, ≥80%). The estimated impact of SES on optimal adherence differed depending on the SES measure tested. The highest performing single-domain measure, household income (OR=0.75; 95% confidence interval, 0.63-0.90; model c-statistic improvement 0.5%, P=0.04) generated a similar result to the multiple-domain measure (adjusted OR=0.74; 95% confidence interval, 0.62-0.88; model c-statistic improvement 0.7%, P=0.01). CONCLUSION: Multidomain measurements of SES using administrative databases mapped to census data are not associated with better performance in predicting statin medication adherence compared with single-domain measures such as household income.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".