Prescribing patterns and adherence to medication among South‐Asian, Chinese and white people with Type 2 diabetes mellitus: a population‐based cohort study
Bibliographic record
Abstract
AIM: To determine the prescribing of and adherence to oral hypoglycaemic agents, insulin, angiotensin-converting enzyme inhibitors, angiotensin receptor blockers and statin therapy among South-Asian, Chinese and white people with newly diagnosed diabetes. METHODS: The present study was a population-based cohort study using administrative and pharmacy databases to include all South-Asian, Chinese and white people aged ≥ 35 years with diabetes living in British Columbia, Canada (1997-2006). Adherence to each class of medication was measured using proportion of days covered over 1 year with optimum adherence defined as ≥ 80%. RESULTS: The study population included 9529 South-Asian, 14 084 Chinese and 143 630 white people with diabetes. The proportion of people who were prescribed angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, statin or oral hypoglycaemic agents was ≤ 50% for all groups. South-Asian and Chinese people had significantly lower adherence for all medications than white people, with the lowest adherence to angiotensin-converting enzyme inhibitor treatment (South-Asian people: adjusted odds ratio 0.37, 95% CI 0.34-0.39; P<0.0001; Chinese people: adjusted odds ratio 0.50, 95% CI 0.47-0.54; P<0.0001) and statin therapy (South-Asian people: adjusted odds ratio 0.47, 95% CI 0.41 - 0.53, P < 0.0001; Chinese people: adjusted odds ratio 0.72, 95% CI 0.67 - 0.77; P<0.0001) compared with white people. CONCLUSION: Adherence to evidence-based pharmacotherapy was substantially worse among the South-Asian and Chinese populations. Care providers need to be alerted to the high levels of non-adherence in these groups and the underlying causes need to be investigated.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".