Fisher-Rao distance and sex differences in disease prevalence trajectories
Bibliographic record
Abstract
Abstract Objectives We introduce a new application of the Fisher–Rao geodesic distance to quantify sex differences in age-stratified chronic-disease prevalence trajectories, modelling those trajectories as dynami-cal systems on the hyperbolic plane and using GBD 2021 data. Methods We analysed prevalence for 10 major chronic conditions across three regions—US states (50 states + DC), 24 Western European countries, and 47 Japanese prefectures—over 1990–2019. We logit-transformed prevalence and summarised each age-sex cohort by normal-approximation parameters ( µ, σ ), which were then embedded in the hyperbolic plane. Sex differences were quantified as the difference between the total Fisher–Rao trajectory lengths for males and females. We assessed cross-regional consistency using parametric (mean differences) and nonparametric (Cohen’s g) summaries, and compared Fisher–Rao results to KL divergence, absolute mean differences, and absolute SD differences. Study design Cross-sectional analysis of GBD 2021 prevalence data modelled as trajectories in the hyperbolic plane. Results The Fisher–Rao distance showed greater cross-regional consistency than the alternative metrics. Males showed greater trajectory shifts in neoplasms, cardiovascular diseases, chronic respiratory diseases, diabetes/kidney diseases, skin/subcutaneous diseases, and sense organ diseases. Females showed greater shifts in neurological disorders, mental disorders, and substance use disorders. Digestive diseases exhibited mixed patterns. Conclusions This geometry-informed metric outperforms alternatives in assessing sex disparities in disease burdens, enhancing public health surveillance and equity in chronic disease management. Future extensions should incorporate gender dimensions.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".