1615-P: A Multicountry Trend Analysis of Diabetes Incidence
Bibliographic record
Abstract
Several studies have suggested a fall or stabilisation of diabetes incidence rates in the last decade. We therefore conducted a multi-country analysis of trends in diabetes incidence over time. Data from 16 countries comprising 17 administrative sources and one set of annual surveys were analysed according to a standard protocol mainly from the period 1995-2015. We modelled incidence rates using age and calendar time as quantitative variables (scored as the midpoint of each interval), and using restricted cubic splines with 6 knots for age and one knot per 4 years of observation for calendar time. The estimated rates from the models were presented as age- and sex-standardized rates (standardized to the 2010 EU population) for each centre to provide an overview of general trends. To formally assess temporal changes in incidence rates, we also fitted joinpoint models with joinpoints at one of the dates 2009, 2010, 2011, and 2012, estimating the average trend before and after the joinpoint. Among the 18 populations we assembled, 4 of the studies were from predominantly non-Europid populations (Taiwan, Korea, Singapore, Hong Kong) and only 2 of the studies were from middle-income countries (Ukraine and Latvia). In general, from 2000 (or when data were available) until 2010 we saw increasing trends in incidence rates in populations from the U.S., Canada, Australia, Denmark, UK, Spain, Scotland and Latvia, while we saw decreasing incidence rates in Israel, Italy, UK, Hong Kong, Taiwan and Korea. In contrast, after 2010, incidence rates appeared stable or decreasing in 14/18 centres, with U.S. (Kaiser Permanente Healthcare), Ukraine, Singapore, and Taiwan showing increasing trend of diabetes incidence. In conclusion, incidence rates of diabetes have shown a tendency to stabilise or even fall across some high-income populations in Europe, Asia, Canada and Australia. Patterns in low to middle income countries may be different, but little information is currently available from these. Disclosure D.J. Magliano: None. R.M. Islam: None. L. Chen: None. B. Carstensen: Consultant; Self; Leo Pharma. Speaker's Bureau; Self; Novo Nordisk A/S. Stock/Shareholder; Self; Novo Nordisk Inc. M.E. Pavkov: None. E.W. Gregg: None. M. Tabesh: None. D. Koye: None. J.L. Harding: None. J.E. Shaw: Advisory Panel; Self; Abbott, Merck Sharp & Dohme Corp. Speaker's Bureau; Self; AstraZeneca, Mylan, Roche Diabetes Care, Sanofi-Aventis. Funding Centers for Disease Control and Prevention
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".