Effect of a pharmacy comprehensive chronic diseases care plan on use of lipid-lowering drugs among patients with hypertension
Bibliographic record
Abstract
BACKGROUND: The management of chronic diseases is a continuing challenge for health care systems and patients. OBJECTIVE: To assess the effect of a pharmacist-specific chronic diseases management incentive plan (the Comprehensive Annual Care Plan [CACP]) implemented by the government of Alberta (Canada) on adherence to lipid-lowering drugs (LLD) among patients with hypertension. METHODS: We conducted a cohort study of patients with hypertension who received the CACP between 2012 and 2015, using administrative health data. Patients who qualified to receive the CACP but did not receive it were selected as controls. Adherence was assessed 1 year before and after the CACP as the proportion of days covered (PDC) by any LLD. We conducted 2 distinct logistic regressions to assess the likelihood of an increase of the post-CACP PDC by 0.20 among patients with poor pre-CACP adherence (i.e., pre-CACP PDC < 0.80), and the post-CACP PDC decrease by 0.20 among those with previous good adherence. RESULTS: Data for 12,763 CACP patients and 14,555 controls were analysed. CACP patients who had a pre-CACP PDC < 0.80 were more likely to increase their PDC compared with controls (44.7% vs. 37.8%; adjusted odds ratio [aOR] = 1.34; 95% CI = 1.22-1.46). Conversely, CACP and control patients with a pre-CACP PDC ≥ 0.80 had similar likelihood to decrease their PDC (13.4% vs. 14.1%; aOR = 0.96; 95% CI = 0.88-1.04). CONCLUSIONS: The pharmacy CACP was associated with a modest improvement of adherence to LLD. The incentive system for improved care seemed more effective among patients who had low baseline adherence rates with minimal effect in those with previous good adherence. DISCLOSURES: This work was supported by a grant from the Institute of Health Economics, with funding from Alberta Innovates and Eli Lilly Canada. The sponsor had no role in the study design, data acquisition, analysis, interpretation of the results, and the decision to publish. The authors have no conflicts of interest to disclose. This study is based on data provided by Alberta Health. The interpretation and conclusions contained herein are those of the researchers and do not necessarily represent the views of the government of Alberta nor the funder (Institute of Health Economics). Neither the government nor Alberta Health nor the Institute of Health Economics express any opinion in relation to this study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".