Adults with diabetes mellitus in Newfoundland and Labrador: a population-based, cross-sectional analysis
Bibliographic record
Abstract
Background: Although the province of Newfoundland and Labrador has the highest rates of chronic disease in Canada, the current state of many chronic diseases in the province, including diabetes mellitus, has not been well explored. We profiled the demographic characteristics associated with, and the management of, diabetes in Newfoundland and Labrador, including any rural–urban differences. Methods: We performed a population-based, cross-sectional analysis using data from the provincial Chronic Disease Registry for fiscal year 2015/16. Patients in the study sample were 20 years of age or older, with documented identifiers for age, sex and geographic location. We examined demographic characteristics, results of screening and diabetes clinical tests (glycated hemoglobin [HbA1c], low-density lipoprotein [LDL] cholesterol and urine albumin-to-creatinine ratio) and hospitalization rates. We described and compared demographic, clinical and hospitalization variables across urban and rural residents of the province. Results: The study sample consisted of 66 325 individuals with diabetes in Newfoundland and Labrador (mean age 64.1 yr; 56.3% rural residents). Larger proportions of rural than urban residents with diabetes were aged 65 to 79 years (41.2% v. 37.5%), were female (50.2% v. 48.7%) and were identified as having the disease by laboratory tests only (19.6% v. 13.1%). Rural residents had worse clinical test outcomes than their urban counterparts, specifically with respect to HbA1c (mean and standard deviation [SD], 7.41% [SD 1.49] v. 7.26% [SD 1.50]) and LDL cholesterol (mean 2.46 [SD 0.95] v. mean 2.36 [SD 0.94] mmol/L). A total of 13.7% of individuals were admitted to hospital during the cohort year, with slightly more rural residents admitted for renal disease (standardized difference 0.021, 95% confidence interval 0.005 to 0.036). Interpretation: For many individuals with diabetes in Newfoundland and Labrador, recommended targets for diabetes management are not being met, and residents in rural areas have poorer clinical outcomes. To inform the development and implementation of targeted provincial strategies for chronic disease management, further research is needed to determine how outcomes relate to the availability of primary health care services.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".