Trends in Diabetes Biomarkers and Treatment Statuses of Non-Institutionalized Canadians: Canadian Health Measures Survey 2007 to 2015
Bibliographic record
Abstract
Abstract Background Diabetes has been a major source of disease burden in Canada. Moreover, untreated diabetes can lead to complications and severe conditions. A few studies exist on the prevalence of diabetes and the adequacy of diabetes management for the Canadian population, and so this study aims to estimate the diabetes prevalence rates using biomarkers and the treatment statuses of non-institutionalized Canadian patients. Methods The Canadian Health Measures Survey (CHMS) cycles 1 to 4 were conducted between 2007 and 2015 as interviews with non-institutionalized Canadians. Four blood diabetic markers were measured: insulin, glycosylated hemoglobin percentages, random-spot glucose, and fasting glucose. Subjects with levels higher than normal ranges were considered to have pre-diabetes or diabetes. Treatment statuses were categorized into treated (using anti-diabetic agents or diagnosed with diabetes), probably treated (taking prescriptions or diagnosed with chronic conditions), potentially treated (taking any medications or diagnosed with chronic conditions), and untreated (not taking any medications and not diagnosed with chronic conditions). Weights were applied to generate nationally representative statistics. Results The blood insulin levels in cycle 4 were significantly higher than those in cycle 1 (ratio = 1.42, 95% CI = 1.04 to 1.79). The proportions of patients with pre-diabetes and diabetes were estimated differently at 0.75% using random-spot glucose and 42.17% using glycosylated hemoglobin percentages, respectively. The proportions of Canadians with uncontrolled pre-diabetes or diabetes varied from 0.59% using random-spot glucose levels to 4.63% using fasting glucose levels, respectively. Through cycles 1 to 4, the proportions of untreated Canadians with pre-diabetes or diabetes ranged from 3.86% to 3.73%. More than 93% of those with high fasting glucose levels were taking prescription medications or had been diagnosed with chronic conditions (probably treated). Less than 33% of those with high fasting glucose levels were diagnosed or actively being treated with anti-diabetic agents (treated). Conclusion Diabetes biomarkers might be useful for screening untreated and undertreated patients with pre-diabetes or diabetes. The treatment categories we used indicated different intensities of intervention that might be useful for determining levels of patient outreach and for planning targeted screening in Canada.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".