Evidence map›Paper›PMID 41816001›Full record

ArticleOpen forum infectious diseases2026

Epidemiology of Aspergillosis Diagnoses in US Adults Using a National EHR Database, 2013-2023.

Brittany L Morgan Bustamante, Elijah G Martinez, Aidan Lee, Natalie J Kane, Simon K Camponuri, Rose M Reynolds, Theo T Snow, Juliana G E Bartels, Mark Hoffman, Theodore C White and 1 more

Abstract read
In one paragraph

Article in Open forum infectious diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Brittany L Morgan BustamanteDivision of Environmental Health Sciences, University of California, Berkeley, California, USA.ORCID https://orcid.org/0000-0002-0280-0904
Elijah G MartinezDivision of Environmental Health Sciences, University of California, Berkeley, California, USA.
Aidan LeeCollege of Letters and Sciences, University of California, Berkeley, California, USA.ORCID https://orcid.org/0009-0005-1797-9766
Natalie J KaneResearch Informatics, Children's Mercy Research Institute, Kansas City, Missouri, USA.
Simon K CamponuriDivision of Environmental Health Sciences, University of California, Berkeley, California, USA.ORCID https://orcid.org/0000-0003-3787-170X
Rose M ReynoldsResearch Informatics, Children's Mercy Research Institute, Kansas City, Missouri, USA.ORCID https://orcid.org/0000-0002-3154-6802
Theo T SnowDivision of Environmental Health Sciences, University of California, Berkeley, California, USA.ORCID https://orcid.org/0000-0002-6862-0762
Juliana G E BartelsDivision of Environmental Health Sciences, University of California, Berkeley, California, USA.ORCID https://orcid.org/0009-0006-0734-0667
Mark HoffmanResearch Informatics, Children's Mercy Research Institute, Kansas City, Missouri, USA.ORCID https://orcid.org/0000-0003-3140-6524
Theodore C WhiteSchool of Science and Engineering, University of Missouri-Kansas City, Kansas City, Missouri, USA.ORCID https://orcid.org/0000-0003-3229-1378
Justin V RemaisDivision of Environmental Health Sciences, University of California, Berkeley, California, USA.ORCID https://orcid.org/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

Background: Aspergillosis is a fungal infection associated with rising hospitalizations and substantial morbidity and mortality. In the United States, data remain fragmented due to the absence of centralized surveillance. This study aimed to evaluate demographic, geographic, and temporal trends in aspergillosis diagnoses across the United States and evaluate changes in those patterns following the emergence of COVID-19. Methods: We conducted a retrospective cohort study using electronic health record data from 142 US healthcare systems (Oracle Health), including adults aged ≥18 years who received care between 2013 and 2023. The cohort included over 76 million patients and 127 million person-years. Aspergillosis prevalence was calculated using post-stratification weights. Adjusted prevalence ratios (aPRs) were estimated via quasi-Poisson and Bayesian spatiotemporal regression. COVID-19-related shifts were evaluated using estimated marginal means. Results: From 2013 to 2023, aspergillosis prevalence increased by 5% annually, peaking in 2022. Rhode Island had the highest state-level aPR; Utah the lowest. Diagnosis was higher among males (aPR 1.37), older adults (≥65 years vs 18-24 years: aPR 4.95), and urban residents (rural aPR 0.86). Following the emergence of COVID-19, prevalence increased disproportionately among Hispanic or Latino patients and several racial minority groups. A nonsignificant upward trend was also observed among rural residents. Conclusions: This study provides a comprehensive national assessment of aspergillosis diagnosis patterns in the United States, revealing rising prevalence and shifts in affected populations following the emergence of COVID-19. These findings may aid earlier clinical recognition, especially among groups not traditionally considered high-risk, and support efforts to expand diagnostic access and improve fungal disease surveillance.

Indexed as

aspergillosisCOVID-19 impactselectronic health records (EHR)fungal diseasereal-world data

Identifiers

PMID41816001
PMCPMC12973174

What OpenQuestion holds

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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.