TY  - JOUR
AU  - Rountree, Lillian
AU  - Lin, Yi-Ting
AU  - Liu, Chuyu
AU  - Salvatore, Maxwell
AU  - Admon, Andrew
AU  - Nallamothu, Brahmajee
AU  - Singh, Karandeep
AU  - Basu, Anirban
AU  - Bu, Fan
AU  - Mukherjee, Bhramar
PY  - 2025
DA  - 2025/3/19
TI  - Reporting of Fairness Metrics in Clinical Risk Prediction Models Used for Precision Health: Scoping Review
JO  - Online J Public Health Inform
SP  - e66598
VL  - 17
KW  - bias
KW  - cardiovascular disease
KW  - COVID-19
KW  - risk stratification
KW  - sensitive features
KW  - clinical risk prediction
KW  - equity
AB  - Background: Clinical risk prediction models integrated into digitized health care informatics systems hold promise for personalized primary prevention and care, a core goal of precision health. Fairness metrics are important tools for evaluating potential disparities across sensitive features, such as sex and race or ethnicity, in the field of prediction modeling. However, fairness metric usage in clinical risk prediction models remains infrequent, sporadic, and rarely empirically evaluated. Objective: We seek to assess the uptake of fairness metrics in clinical risk prediction modeling through an empirical evaluation of popular prediction models for 2 diseases, 1 chronic and 1 infectious disease. Methods: We conducted a scoping literature review in November 2023 of recent high-impact publications on clinical risk prediction models for cardiovascular disease (CVD) and COVID-19 using Google Scholar. Results: Our review resulted in a shortlist of 23 CVD-focused articles and 22 COVID-19 pandemic–focused articles. No articles evaluated fairness metrics. Of the CVD-focused articles, 26% used a sex-stratified model, and of those with race or ethnicity data, 92% had study populations that were more than 50% from 1 race or ethnicity. Of the COVID-19 models, 9% used a sex-stratified model, and of those that included race or ethnicity data, 50% had study populations that were more than 50% from 1 race or ethnicity. No articles for either disease stratified their models by race or ethnicity. Conclusions: Our review shows that the use of fairness metrics for evaluating differences across sensitive features is rare, despite their ability to identify inequality and flag potential gaps in prevention and care. We also find that training data remain largely racially and ethnically homogeneous, demonstrating an urgent need for diversifying study cohorts and data collection. We propose an implementation framework to initiate change, calling for better connections between theory and practice when it comes to the adoption of fairness metrics for clinical risk prediction. We hypothesize that this integration will lead to a more equitable prediction world. 
SN  - 1947-2579
UR  - https://ojphi.jmir.org/2025/1/e66598
UR  - https://doi.org/10.2196/66598
UR  - http://www.ncbi.nlm.nih.gov/pubmed/39962044
DO  - 10.2196/66598
ID  - info:doi/10.2196/66598
ER  -