The cost of diabetes in Canada over 10 years: applying attributable health care costs to a diabetes incidence prediction model
Bibliographic record
Abstract
INTRODUCTION: Our objective was to estimate the future direct health care costs due to diabetes for a 10-year period in Canada using national survey data, a validated diabetes risk prediction tool and individual-level attributable cost estimates. METHODS: We used the Diabetes Population Risk Tool to predict the number of new diabetes cases in those aged 20 years and above over a 10-year period (to 2022), using 2011 and 2012 Canadian Community Health Survey data. We derived attributable costs due to diabetes from a propensity-matched case control study using the Ontario Diabetes Database and other administrative data. We calculated total costs by applying the respective attributable costs to the incident cases, accounting for sex, year of diagnosis and annual disease-specific mortality rates. RESULTS: The predicted 10-year risk of developing diabetes for the Canadian population in 2011/12 was 9.98%, corresponding to 2.16 million new cases. Total health care costs attributable to diabetes during this period were $7.55 billion for females and $7.81 billion for males ($15.36 billion total). Acute hospitalizations accounted for the greatest proportion of costs (43.2%). A population intervention resulting in 5% body weight loss would save $2.03 billion in health care costs. A 30% risk-reduction intervention aimed at individuals with the highest diabetes risk (i.e. the top 10% of the highest-risk group) would save $1.48 billion. CONCLUSION: Diabetes represents a heavy health care cost burden in Canada through to the year 2022. Our future cost calculation method can provide decision makers and planners with an accessible and transparent tool to predict future expenditures attributable to the disease and the corresponding cost savings associated with interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".