Anti-Hyperglycemic Medication Adherence and Health Services Utilization in People with Diabetes: A Longitudinal Study of Alberta’s Tomorrow Project
Bibliographic record
Abstract
Ming Ye,1 Jennifer E Vena,2 Jeffrey A Johnson,1 Grace Shen-Tu,2 Dean T Eurich1 1School of Public Health, University of Alberta, Edmonton, Alberta, Canada; 2Alberta’s Tomorrow Project, CancerCare Alberta, Alberta Health Services, Calgary, Alberta, CanadaCorrespondence: Dean T Eurich, School of Public Health, University of Alberta, Canada, Email deurich@ualberta.caBackground: Little is known about the long-term (> 2 years) relationship between the time-varying drug adherence and healthcare utilization for patients with diabetes.Objective: To characterize the relationship between time-varying anti-hyperglycemic medication adherence and healthcare utilization in patients with diabetes, using data from Alberta’s Tomorrow Project, a population-based cohort study in Alberta, Canada.Methods: Incident cases of diabetes with at least 24 months of follow-up were included in the study. Anti-hyperglycemic drug adherence was measured by proportion of days covered (PDC) in the past 12 months for each year after diagnosis. The rate of healthcare utilization was assessed for the subsequent 12 months, 36 months and 60 months. A time-varying, negative binomial generalized estimating equation model was used to examine the association between medication adherence and healthcare utilization.Results: Among 2155 incident cases of diabetes, average age at diagnosis was 59.6± 9.3, 51.0% were female and average duration of follow-up was 7.3± 3.7 (range, 2.0– 16.2) years. The proportion of patients taking anti-hyperglycemic medications was 47.6% during the first year of diagnosis, which increased to 77.3% by the end of follow-up. Compared to adherent patients (PDC≥ 0.8), non-adherent patients (PDC< 0.8) had substantially higher rate of all-cause hospitalization [incident rate ratio, IRR=1.48 (1.22– 1.79), ED visits [1.30 (1.15– 1.47)] and GP visits [1.17 (1.08– 1.27)] in the subsequent 12 months. However, these associations became weaker with longer follow-up [eg, IRR=1.18 (0.98– 1.39) and 1.05 (0.94– 1.18) for all-cause hospitalization in the subsequent 36 and 60 months, respectively].Conclusion: Poor adherence among diabetic patients was associated with substantially higher rate of healthcare utilization in the short term (eg, 12 months); however, this association weakened over a longer period (eg, 36– 60 months).Keywords: diabetes, anti-hyperglycemia, adherence, time-varying, healthcare, longitudinal
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".