All‐cause and cardiorenal mortality in 6 million adults with and without type 2 diabetes: A comparative, trend analysis in Canada, Spain, and the <scp>UK</scp>
Bibliographic record
Abstract
AIMS: To understand geographical and temporal patterns in the diabetes gap, the excess mortality risk associated with type 2 diabetes (T2D), in three high-income countries. METHODS: Using databases from Canada (Ontario), Spain (Catalonia) and the UK (England), we harmonized the study design and the analytical strategy to extract information on subjects aged over 35 years with incident T2D between 1998 and 2018 matched to up to five subjects without diabetes. We used Poisson models to estimate age-specific mortality trends by diabetes status and rate ratios and rate differences associated with T2D. RESULTS: In more than 6 million people, 694 454 deaths occurred during a follow-up of 52 million person-years. Trends in all-cause mortality rates differed between Ontario and England; yet, the diabetes gaps were very similar in recent years: in 2018, we estimated 1.3 (95% confidence interval: 0.8, 1.8) and 0.8 (0.2, 1.5) more deaths per 1000 person-years in 50-year-old men with diabetes in Ontario and England, respectively, and 8.9 (6.1, 11.7) and 12.1 (9.1, 15.1) in 80-year-old men; between-country differences were small also in women. In Catalonia, rate ratios comparing T2D with no diabetes in men in 2018 were 1.53 (1.11, 2.11) at 50 years old, 0.88 (0.72, 1.06) at 60 years old, 0.74 (0.60, 0.90) at 70 years old and 0.81 (0.66, 1.00) at 80 years old, indicating lower mortality rates in men with T2D from the age of 60 years; rates were similar in women with and without diabetes at all ages. The diabetes gaps in cardiorenal mortality mirrored those of all-cause mortality: we observed consistent reductions in the proportions of cardiorenal deaths in subjects aged 80 years but variations in subjects aged ≤70 years, regardless of the presence of diabetes. CONCLUSIONS: By reducing the confounding impact of epidemiological and analytical differences, this study showed geographical similarities and differences in the diabetes gap: an excess risk of all-cause and cardiorenal mortality in subjects with T2D is still present in Ontario and England in recent years, particularly in elderly subjects. Conversely, there were very small gaps in young men with T2D or even lower mortality rates in older subjects with T2D in Catalonia.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".