A comparative analysis estimating aspergillosis and histoplasmosis encounters among inpatients in the United States using multi-regional electronic health record and National Discharge Data, 2014-2020.

Abstract

Fungal diseases are an emerging public health threat, yet their epidemiology remains poorly understood due to limited surveillance. Electronic health record (EHR) datasets offer opportunities for studying fungal diseases, but their national representativeness is unclear. We compared inpatient encounters for aspergillosis and histoplasmosis between Oracle EHR Real World Data (OERWD) and the Healthcare Cost and Utilization Project National Inpatient Sample (HCUP NIS), a nationally representative discharge-derived database. We analyzed inpatient encounters from both databases and applied calibration weights to OERWD to improve alignment with national benchmarks. Relative differences (RDs) in encounter counts were compared using Wald tests. Survey-weighted quasibinomial models estimated prevalence and 95% CIs; temporal trends were evaluated with Mann-Kendall tests. Dataset differences were assessed using interaction terms between a dataset indicator and demographic or geographic subgroups. OERWD had fewer total and disease-specific encounters than HCUP NIS. Weighting improved precision, and prevalence estimates did not differ significantly for most strata (31 of 42), indicating broad agreement in demographic and geographic distributions. Both datasets showed higher aspergillosis prevalence in the Pacific division and higher histoplasmosis prevalence in the East South Central division, with males and older adults consistently exhibiting higher prevalence. These findings indicate that, while HCUP NIS is optimal for nationally representative prevalence estimates, weighting EHR-derived data can reproduce key demographic and geographic prevalence patterns observed in national benchmarks. Benchmarking EHR-derived estimates against national standards represents an important step toward establishing their representativeness and supporting future studies leveraging EHR data to investigate fungal disease epidemiology in greater clinical detail.