Diabetes Prevalence in North America and Caribbean Region in 2017 and 2045
Bibliographic record
Abstract
Aims and Objectives: Diabetes is a serious and increasing global epidemic, and accurate estimates are essential for more efficient allocation of resources. The International Diabetes Federation (IDF) North America and Caribbean Region (NAC) consist of the U.S.A, Mexico and Canada, as well as 25 Caribbean countries and territories. The new edition of the International Diabetes Federation (IDF) Atlas (IDF, 2017) provides estimates of the numbers of people (18-99 years) living with diabetes in the IDF North America and Caribbean (NAC) Region. Methodology: Estimates for diabetes in adults were taken from 24 data sources in the IDF NAC region, representing 14 out of 28 countries. Selected characteristics of these data sources are as follows: Barbados, Mexico, Suriname, Trinidad and Tobago and the U.S. had studies conducted within the last five years; Belize, Haiti, Mexico and the U.S. Virgin Islands had studies that performed oral glucose tolerance tests. Prevalence rates for other countries may be underestimated due to limited data sources on oral glucose tolerance test. The details of the methodology were described in IDF Diabetes Atlas 8th Ed. Results and Conclusion: Approximately 50.1 (41.8-56.1) million people or 13.1% (10.9-14.6%) of adults aged 18-99, are living with diabetes in the IDF NAC region in 2017. The age-adjusted prevalence is 10.8%, which is highest among all seven IDF regions. If the trend continues, the number of people with diabetes is projected to reach 73.4 (58.8-83.4) million in 2045. About 37.6% of those people living with diabetes in this region are estimated to be undiagnosed in 2017. The number of deaths attributed to diabetes from age 20 to 99 years is 378,720 in 2017. The total healthcare expenditure related to diabetes in NAC region is USD 439,858 million in 2017 and will reach USD 508,315 million by 2045. Diabetes exerts a heavy burden in this region. Therefore effective diabetes prevention and management programs should be implemented in order to control diabetes prevalence. Disclosure Y. Huang: None. S. Karuranga: None. E.J. Boyko: None. B. Malanda: None.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| 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) | 0.003 | 0.001 |
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".