Development and validation of a type 2 diabetes model to estimate the cost-effectiveness of diabetes interventions across the care continuum
Bibliographic record
Abstract
OBJECTIVES: The aim of this study is to develop a patient-level model for type 2 diabetes mellitus (T2DM) progression that can estimate the cost-effectiveness of T2DM interventions from prevention to management. METHODS: We developed an individual-level microsimulation model, the Institute of Health Economics Diabetes Model (IHE-DM), that simulates: (i) T2DM progression from normal glucose tolerance (NGT) to T2DM, (ii) the occurrence and timing of eight comorbidities and death, and (iii) the correlated progression of risk factors over time. We report model validation and use a case study to investigate the cost-effectiveness of a hypothetical T2DM prevention program. RESULTS: The internal validation indicated excellent performance with mean absolute differences between the predicted and observed values for all endpoints of less than 1 percent. External validation results were mixed. The model under-predicted cumulative T2DM incidence in the first 8 years, predicted well from years eight through eleven, and over-predicted from years twelve through fifteen. Our case study estimated an incremental net monetary benefit of CAD 2,701 (USD 2,289) (95% Uncertainty Interval: CAD 1,316 to 4,000 [USD 1,115 to 3,390]) over the 15-year time horizon. CONCLUSIONS: Prominent T2DM models focus on patients with diagnosed T2DM whereas our model simulates progression from NGT to T2DM and incorporates important correlations in the progression of risk factors. These adaptations allow us to evaluate preventative interventions and better capture the long-term impacts, filling an important gap in the evidence base. Our model can be used to inform future funding decisions for T2DM interventions across the care continuum.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".