Patterns in the Incidence (1994–2018) and Prevalence (2004–2018) of Type 1 and Type 2 Diabetes Among Nova Scotian Youth Under 20 Years of Age
Bibliographic record
Abstract
OBJECTIVES: Rates of type 1 and type 2 diabetes (T1D and T2D, respectively) in youth may be increasing globally, but findings vary across populations. We aimed to determine changes in incidence and prevalence of T1D and T2D over a 25-year period (1994-2018) in youth <20 years of age in Nova Scotia (NS). METHODS: This population-based descriptive epidemiologic study used the Diabetes Care Program of the NS Registry that prospectively collects population-based records for all cases of diabetes in youth. Incidence (1994-2018) and prevalence (2004-2018) of T1D and T2D were calculated per 100,000 for 5-year periods using national census population estimates (0-19 years) and analyzed by sex, age group, and rural vs urban residence. RESULTS: Incidence (95% confidence interval [CI]) of T1D rose from 26.4 (23.0-29.7) in 1994-1998 to 37.9 (33.3-42.6) in 2014-2018 in 0-14-year-olds. The average annual increase was 0.7 (0.43-0.97) per 100,000. Incidence appeared to plateau after 2008, except in 10-14-year-olds, where it continued to rise. Prevalence (95% CI) of T1D for youth 0-19 years of age increased from 288.4 (278.2-298.6) per 100,000 in 2004-2008 to 333.4 (321.7-345.1) in 2014-2018. Incidence of T2D in 10-19-year-olds rose from 2.9 per 100,000 in 1994-1998 to 13.0 per 100,000 in 2014-2018 and was higher in females and youth living in rural areas. CONCLUSIONS: Incidence of both types of diabetes in NS is high and continuing to rise. Patterns in T1D incidence align with those reported in other high-incidence populations. Incidence and prevalence of T2D in NS youth were similar to or higher than most previous reports despite the lower ethnic diversity in NS compared with other high-incidence populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".