Bridging ethics and epidemiology: Modelling ethical standards of health equity
Bibliographic record
Abstract
Health inequities are differences in health that are 'unjust'. Yet, despite competing ethical views about what counts as an 'unjust difference in health', theoretical insights from ethics have not been systematically integrated into epidemiological research. Using diabetes as an example, we explore the impact of adopting different ethical standards of health equity on population health outcomes. Specifically, we explore how the implementation of population-level weight-loss interventions using different ethical standards of equity impacts the intervention's implementation and resultant population health outcomes. We conducted a risk prediction modelling study using the nationally representative 2015-16 Canadian Community Health Survey (n = 75,044, 54% women). We used the Diabetes Population Risk Tool (DPoRT) to calculate individual-level 10-year diabetes risk. Hypothetical weight-loss interventions were modelled in individuals with overweight or obesity based on each ethical standard: 1) health sufficiency (reduce DPoRT risk below a high-risk threshold (16.5%); 2) health equality (equalize DPoRT risk to the low risk group (5%)); 3) social-health sufficiency (reduce DPoRT risk <16.5 in individuals with lower education); 4) social-health equality (equalize DPoRT risk to the level of individuals with high education). For each scenario, we calculated intervention impacts, diabetes cases prevented or delayed, and relative and absolute educational inequities in diabetes. Overall, we estimated that achieving health sufficiency (i.e., all individuals below the diabetes risk threshold) was more feasible than achieving health equality (i.e., diabetes risk equalized for all individuals), requiring smaller initial investments and fewer interventions; however, fewer diabetes cases were prevented or delayed. Further, targeting only diabetes inequalities related to education reduced the target population size and number of interventions required, but consequently resulted in even fewer diabetes cases prevented or delayed. Using diabetes as an example, we found that an explicit, ethically-informed definition of health equity is essential to guide population-level interventions that aim to reduce health inequities.
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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 | 0.089 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.006 |
| 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".