Characterizing Fungal Infections in the All of Us Research Program

TL;DR

This study leverages the All of Us Research Program to characterize fungal infections, analyzing epidemiological patterns and data quality issues in clinical biomarkers to improve future research and surveillance efforts.

Contribution

It provides a comprehensive analysis of fungal infections within a large cohort, highlighting epidemiological patterns and assessing data quality of clinical biomarkers.

Findings

Confirmed geographic distribution patterns of fungal infections.

Identified demographic groups disproportionately affected.

Revealed data quality issues in biomarker measurement and conformance.

Abstract

Fungal infections, such as Coccidioidomycosis, Aspergillosis, and Histoplasmosis, represent a growing public health concern in the United States. The rising incidence of these mycoses is linked to climate shifts, demographic changes, and social determinants of health. However, the actual burden of these infections is often underestimated by traditional surveillance methods. Therefore, this study aims to characterize these infections within the All of Us Research Program and evaluate the quality of clinical and health data related to fungal infections. We constructed three fungi cohorts of Coccidioidomycosis (n=1,173), Aspergillosis (n=687), and Histoplasmosis (n=345) among over 400,000 participants using electronic health record data. We analyzed geographic and sociodemographic distributions and performed a data quality assessment on ten key laboratory biomarkers to evaluate data completeness, unit conformance, and measurement concordance within a 90-day window of diagnosis. Our analysis confirmed known epidemiological patterns, including the geographic distributions of Coccidioidomycosis in the Southwest and Histoplasmosis in the Midwest. Fungal infections disproportionately affected older adults, males, and White non-Hispanic individuals. The data quality assessment revealed high completeness for general hematology markers (e.g., Hemoglobin > 70%) but limited availability for biomarkers, such as Beta 1,3 Glucan (< 15%). While measurement concordance was strong (e.g., hemoglobin-hematocrit correlation, r = 0.94), unit conformance was poor for key inflammatory markers, such as erythrocyte sedimentation rate. In conclusion, the All of Us dataset is a valuable resource for characterizing fungal infections. However, significant data quality issues related to completeness and conformance for specialized biomarkers must be addressed to enhance their applicability for robust clinical research.