Factors Associated With Attainment of Glycemic Targets Among Adults With Type 1 and Type 2 Diabetes in Canada: A Cross-sectional Study Using Primary and Specialty Care Electronic Medical Record Data
Bibliographic record
Abstract
OBJECTIVE: Using a new database combining primary and specialty care electronic medical record (EMR) data in Canada, we determined attainment of glycemic targets and associated predictors among adults with diabetes. METHODS: We conducted a cross-sectional observational study combining primary and specialty care EMR data in Canada. Adults with diabetes whose primary care provider contributed to the National Diabetes Repository or who were assessed at a diabetes specialty clinic (LMC Diabetes and Endocrinology) between July 3, 2015, and June 30, 2019, were included. Diabetes type was categorized as type 2 diabetes (T2D) not prescribed insulin, T2D prescribed insulin, and type 1 diabetes (T1D). Covariates were age, sex, income quintile, province, rural/urban location, estimated glomerular filtration rate, medications, and insulin pump use. Associations between predictors and the outcome (glycated hemoglobin [A1C] of ≤7.0%) were assessed by multivariable logistic regressions. RESULTS: Among 122,106 adults, consisting of 91,366 with T2D not prescribed insulin, 25,131 with T2D prescribed insulin, and 5,609 with T1D, attainment of an A1C of ≤7.0% was 60%, 25%, and 23%, respectively. Proportions with an A1C of ≤7.5% and ≤8.0% were 75% and 84% for those with T2D not prescribed insulin, 41% and 57% for those with T2D prescribed insulin, and 37% and 53% for those with T1D. Highest vs lowest income quintile was associated with greater odds of meeting the A1C target (adjusted odds ratio [95% confidence interval] for each diabetes category: 1.15 [1.10 to 1.21], 1.21 [1.10 to 1.33], and 1.29 [1.04 to 1.60], respectively). Individuals in Alberta and Manitoba had less antihyperglycemic medication use and attainment of A1C target than other provinces. CONCLUSIONS: Attainment of glycemic targets among adults with diabetes was poor and differed by income and geographic location, which must be addressed in national diabetes strategies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".