Cost-of-illness analysis reveals potential healthcare savings with reductions in type 2 diabetes and cardiovascular disease following recommended intakes of dietary fiber in Canada
Bibliographic record
Abstract
BACKGROUND: Type 2 diabetes (T2D) and cardiovascular disease (CVD) are leading causes of mortality and two of the most costly diet-related ailments worldwide. Consumption of fiber-rich diets has been repeatedly associated with favorable impacts on these co-epidemics, however, the healthcare cost-related economic value of altered dietary fiber intakes remains poorly understood. In this study, we estimated the annual cost savings accruing to the Canadian healthcare system in association with reductions in T2D and CVD rates, separately, following increased intakes of dietary fiber by adults. METHODS: A three-step cost-of-illness analysis was conducted to identify the percentage of individuals expected to consume fiber-rich diets in Canada, estimate increased fiber intakes in relation to T2D and CVD reduction rates, and independently assess the potential annual savings in healthcare costs associated with the reductions in rates of these two epidemics. The economic model employed a sensitivity analysis of four scenarios (universal, optimistic, pessimistic, and very pessimistic) to cover a range of assumptions within each step. RESULTS: Non-trivial healthcare and related savings of CAD$35.9-$718.8 million in T2D costs and CAD$64.8 million-$1.3 billion in CVD costs were calculated under a scenario where cereal fiber was used to increase current intakes of dietary fiber to the recommended levels of 38 g per day for men and 25 g per day for women. Each 1 g per day increase in fiber consumption resulted in annual CAD$2.6 to $51.1 million savings for T2D and $4.6 to $92.1 million savings for CVD. CONCLUSION: Findings of this analysis shed light on the economic value of optimal dietary fiber intakes. Strategies to increase consumers' general knowledge of the recommended intakes of dietary fiber, as part of healthy diet, and to facilitate stakeholder synergy are warranted to enable better management of healthcare and related costs associated with T2D and CVD 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".