Estimating the Population Prevalence of Diagnosed and Undiagnosed Diabetes
Bibliographic record
Abstract
OBJECTIVE: Health administrative data are frequently used for diabetes surveillance, but validation studies are limited, and undiagnosed diabetes has not been considered in previous studies. We compared the test properties of an administrative definition with self-reported diabetes and estimated prevalence of undiagnosed diabetes by measuring glucose levels in mailed-in capillary blood samples. RESEARCH DESIGN AND METHODS: A stratified random sample of 6,247 individuals (Quebec province) was surveyed by telephone and asked to mail in fasting blood samples on filter paper to a central laboratory. An administrative definition was applied (two physician claims or one hospitalization for diabetes within a 2-year period) and compared with self-reported diabetes alone and with self-reported diabetes or elevated blood glucose level (≥7 mmol/L). Population-level prevalence was estimated with the use of the administrative definition corrected for its sensitivity and specificity. RESULTS: Compared with self-reported diabetes, sensitivity and specificity were 84.3% (95% CI 79.3-88.5%) and 97.9% (97.4-98.4%), respectively. Compared with diabetes by self-report and/or glucose testing, sensitivity was lower at 58.2% (52.2-64.6%), whereas specificity was similar at 98.7% (98.0-99.3%). Adjusted for sampling weights, population-level prevalence of physician-diagnosed diabetes was 7.2% (6.3-8.0%). Prevalence of total diabetes (physician-diagnosed and undiagnosed) was 13.4% (11.7-15.0%), indicating that ∼40% of diabetes cases are undiagnosed. CONCLUSIONS: A substantial proportion of diabetes cases are missed by surveillance methods that use health administrative databases. This finding is concerning because individuals with undiagnosed diabetes are likely to have a delay in treatment and, thus, a higher risk for diabetes-related complications.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".