Competing risks analysis of end-stage-renal disease and mortality among adults with diabetes - a comparison of First Nations people and other Saskatchewan residents
Bibliographic record
Abstract
Background: End stage renal disease (ESRD) is a growing public health problem in Canada and it disproportionately affects Aboriginal people. Diabetes is the most common reported cause of ESRD. Objectives and methods: To determine whether there are significant disparities in the risk of ESRD and mortality without ESRD between diabetic First Nations (FN) and other Saskatchewan (OSK) people; to build and validate diabetic ESRD dynamic models. This is a population study of diabetes, utilizing data drawn from the Saskatchewan Ministry of Health administrative databases from 1980 to 2005. Competing risks survival analysis was used, including a Cox cause-specific model, Weibull proportional hazards (PH) model and piece-wise exponential PH hazards model. System Dynamics modeling (SDM) and agent-based modeling (ABM) methods were used to build dynamic models of diabetic patients’ progression to ESRD. Results: There were a total of 90,429 diabetic people in the study cohort, from 1980 to 2005. Among them, 8,254 (9%) of them were FN people. The average age at diabetes diagnosis for FN was 47.2 (SD=14) years old while for OSK, it was 61.6 (SD=15.3) years old (P-value<0.0001). After adjusting for sex and age at diabetes diagnosis, the risk of developing ESRD was 2.97 times higher for FN compared to OSK (95% CI: 2.51-3.54; P-value<0.0001). FN had lower risk of death than OSK before adjusting for age and sex difference. After adjusting for diabetes diagnosis age, sex, interaction between age and sex and interaction between age and ethnicity, FN had higher risk of death than OSK given the same sex and diabetes diagnosis age (younger than 81 years old). Using the same hazard rate estimations from competing risks survival analysis, the ABM model demonstrated a better match between historical data and model predicted data compared to the SD model. Conclusion: A much younger age of diabetes diagnosis among FN compared to OSK likely contributes to higher rates of ESRD because of a differential mortality effect – FN with diabetes are more likely to live long enough to develop ESRD.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".