Evidence map›Paper›PMID 42459930›Full record

ArticleAJE advances : research in epidemiology2026

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.

Brittany L Morgan Bustamante, Juliana G E Bartels, Aidan Lee, Natalie J Kane, Rose M Reynolds, Theodore C White, Mark Hoffman, Jennifer Head, Justin V Remais

Abstract read
In one paragraph

Article in AJE advances : research in epidemiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

9 authors.

Brittany L Morgan BustamanteDivision of Environmental Health Sciences, University of California, Berkeley, Berkeley, CA 94720, United States.ORCID 0000-0002-0280-0904
Juliana G E BartelsDivision of Environmental Health Sciences, University of California, Berkeley, Berkeley, CA 94720, United States.
Aidan LeeSchool of Letters and Sciences, University of California, Berkeley, Berkeley, CA 94720, United States.
Natalie J KaneChildren's Mercy Research Institute, UMKC School of Medicine, Kansas City, MO 64108, United States.
Rose M ReynoldsChildren's Mercy Research Institute, UMKC School of Medicine, Kansas City, MO 64108, United States.
Theodore C WhiteSchool of Science and Engineering, University of Missouri-Kansas City, Kansas City, MO 64110, United States.
Mark HoffmanChildren's Mercy Research Institute, UMKC School of Medicine, Kansas City, MO 64108, United States.ORCID 0000-0003-3140-6524
Jennifer HeadDepartment of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, United States.
Justin V RemaisDivision of Environmental Health Sciences, University of California, Berkeley, Berkeley, CA 94720, United States.ORCID 0000-0002-0223-4615

Funding

Using massive, multi-regional EHR data to estimate the impacts of environmental and other risk factors on fungal disease epidemiology in the U.S.R01AI176770 · NIAID · UNIVERSITY OF MISSOURI KANSAS CITY · PI Mark Hoffman, Justin V Remais · 2023 to 2026
$3.2M
Advancing Fungal Disease Epidemiology through Multilevel Modeling and Data IntegrationK01AI190048 · NIAID · UNIVERSITY OF CALIFORNIA-IRVINE · PI Brittany Lauren Morgan Bustamante · 2025 to 2026
$396k
NIAID NIH HHS K01 AI190048NIAID NIH HHS R01 AI176770
6 · The paper itself

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.

Indexed as

data validationelectronic health record datafungal diseasesreal world data

Identifiers

PMID42459930
PMCPMC13372256

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LicenceCC BY-NC
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.