Unequal health risk alarms: How thresholds drive false-positive burdens across sex and race

Unequal health risk alarms: How thresholds drive false-positive burdens across sex and race | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 8 January 2026 V1 Latest version Share on Unequal health risk alarms: How thresholds drive false-positive burdens across sex and race Author : Ela Adhikari 0009-0004-6673-6050 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.176790714.41177516/v1 105 views 52 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Health risk stratification algorithms are increasingly deployed in clinical and public health settings. Despite their potential to predict future outcomes, improve efficiency and consistency, these tools can perpetuate disparities when error patterns fall disproportionately across demographic groups. This study evaluated a simple cardiovascular risk index using NHANES 2017-2018 to examine how a threshold-based classification distributes predictive errors among sex and race subgroups. At a cutoff labeling the top 70% of individuals as 'high risk,' sensitivity was perfect across all subgroups. Yet false positives were not evenly distributed: males were misclassified more often than females (59% vs. 42%), and differences across racial groups reached more than 10 percentage points. These results highlight how technical choices, particularly thresholds, translate into unequal burdens in real-world clinical practice. We discuss the implications for equity, operational trust, and accountability, and recommend fairness auditing, threshold calibration, and transparent reporting as essential components of responsible model deployment. Supplementary Material File (unequal health risk alarms.pdf) Download 370.05 KB Information & Authors Information Version history V1 Version 1 08 January 2026 Copyright This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License Keywords algorithm artificial intelligence false positive health risk threshold Authors Affiliations Ela Adhikari 0009-0004-6673-6050 [email protected] Basis Oro Valley High School, Oro Valley View all articles by this author Metrics & Citations Metrics Article Usage 105 views 52 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Ela Adhikari. Unequal health risk alarms: How thresholds drive false-positive burdens across sex and race. Authorea . 08 January 2026. DOI: https://doi.org/10.22541/au.176790714.41177516/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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