An innovative application of Bayesian disease mapping methods to patient safety research: a Canadian adverse medical event study
Bibliographic record
Abstract
Recently developed disease mapping and ecological regression methods have become important techniques in studies of disease epidemiology and in health services research. This increase in importance is partially a result of the development of Bayesian statistical methodologies that make it possible to study associations between health problems and risk factors at an aggregate (i.e. areal) level while taking into account such matters as unmeasured confounding and spatial relationships. In this paper we present a demonstration of the joint use of empirical Bayes (EB) and full Bayesian inferential techniques in a small area study of adverse medical events (also known as 'iatrogenic injury') in British Columbia, Canada. In particular, we illustrate a unified Bayesian hierarchical spatial modelling framework that enables simultaneous examinations of potential associations between adverse medical event occurrence and regional characteristics, age effects, residual variation and spatial autocorrelation. We propose an analytic strategy for complementary use of EB and FB inferential techniques for risk assessment and model selection, presenting an EB-FB combined approach that draws on the strengths of each method while minimizing inherent weaknesses. The work was motivated by the need to explore relatively efficient ways to analyse regional variations of health services outcomes and resource utilization when a considerable amount of statistical modelling and inference are required.
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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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".