Bibliographic record
Abstract
BACKGROUND: Medication adherence is an important factor in maintaining and improving health, although adherence levels are often suboptimal. Previous studies have highlighted the importance of prior adherence behavior in understanding future adherence behaviors. OBJECTIVE: To improve understanding of adherence behavior and analyze the role of previous adherence in estimating the likelihood of future adherence for maintenance medications. METHODS: The adherence behaviors of 53,709 continuously enrolled individuals in employer-sponsored health plans were analyzed using a state-dependence framework (ie, adherence patterns in the past influence adherence in the future). This allowed for the estimation of the extent of carryover in adherence from one quarter to another while adjusting for observed and unobserved heterogeneity and enrollee characteristics. The role of the initial observation of adherence on the likelihood of future adherence was also analyzed. This study focuses on enrollee cohorts who filled prescriptions in 3 maintenance medication classes: lipid-lowering medications, antihypertensive medications, and oral antidiabetes medications. RESULTS: If an enrollee was adherent in the previous quarter, more than 80% of the time they remained adherent in the current quarter. Similarly, if they were nonadherent in the previous quarter, more than 75% of the time they remained nonadherent. Marginal effect estimates for prior adherence (previous quarter and initial quarter) showed increases in predicted adherence when adherent in the previous quarter (8.7 percentage points [pp] [95% CI = 8.0-9.3 pp] for lipid-lowering medications) and when adherent in the initial quarter (14.4 pp [13.8-15.1 pp] for lipid-lowering medications). Adherence in the initial and previous quarter increased predicted adherence considerably (22.7 pp [22.1-23.3 pp]). Similar patterns held for the antihypertensive medication cohort (antihypertensive medications) and the oral antidiabetes medication cohort (oral antidiabetes medications). The area under the curve (AUC) showed considerable improvement when moving from pooled probit models to dynamic random-effects probit models. AUC for the dynamic models exceeded 0.85 in the 3 medication cohorts, whereas the pooled probit models remained under 0.7. CONCLUSIONS: Adherence in the previous quarter is associated with adherence in the current quarter, after accounting for sources of observable and unobservable heterogeneity across enrollees. In addition, the initial value of adherence matters when explaining the likelihood of adherence.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".