Projecting prevalence, costs and evaluating simulated interventions for diabetic end stage renal disease in a Canadian population of aboriginal and non-aboriginal people: an agent based approach
Bibliographic record
Abstract
BACKGROUND: Diabetes-related end stage renal disease (DM-ESRD) is a devastating consequence of the type 2 diabetes epidemic, both of which disproportionately affect Indigenous peoples. Projecting case numbers and costs into future decades would help to predict resource requirements, and simulating hypothetical interventions could guide the choice of best practices to mitigate current trends. METHODS: An agent based model (ABM) was built to forecast First Nations and non-First Nations cases of DM-ESRD in Saskatchewan from 1980 to 2025 and to simulate two hypothetical interventions. The model was parameterized with data from the Canadian Institute for Health Information, Saskatchewan Health Administrative Databases, the Canadian Organ Replacement Register, published studies and expert judgement. Input parameters without data sources were estimated through model calibration. The model incorporated key patient characteristics, stages of diabetes and chronic kidney disease, renal replacement therapies, the kidney transplant assessment and waiting list processes, costs associated with treatment options, and death. We used this model to simulate two interventions: 1) No new cases of diabetes after 2005 and 2) Pre-emptive renal transplants carried out on all diabetic persons with new ESRD. RESULTS: There was a close match between empirical data and model output. Going forward, both incidence and prevalence cases of DM-ESRD approximately doubled from 2010 to 2025, with 250-300 new cases per year and almost 1300 people requiring RRT by 2025. Prevalent cases of First Nations people with DM-ESRD increased from 19% to 27% of total DM-ESRD numbers from 1990 to 2025. The trend in yearly costs paralleled the prevalent DM-ESRD case count. For Scenario 1, despite eliminating diabetes incident cases after 2005, prevalent cases of DM-ESRD continued to rise until 2019 before slowly declining. When all DM-ESRD incident cases received a pre-emptive renal transplant (scenario 2), a substantial increase in DM-ESRD prevalence occurred reflecting higher survival, but total costs decreased reflecting the economic advantage of renal transplantation. CONCLUSIONS: This ABM can forecast numbers and costs of DM-ESRD in Saskatchewan and be modified for application in other jurisdictions. This can aid in resource planning and be used by policy makers to evaluate different interventions in a safe and economical manner.
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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 | 0.000 | 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".