Evidence map›Paper›PMID 40266617›Full record

ArticleJAMA network open2025

Detection Bias in EHR-Based Research on Clinical Exposures and Dementia.

Jingxuan Wang, Minhyuk Choi, Peter Buto, J Daniel Kelly, Renaud La Joie, John Kornak, Scott C Zimmerman, Ruijia Chen, Eva Raphael, Catherine A Schaefer and 2 more

Abstract read
In one paragraph

Article in JAMA network open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Comment on: "Association between modifiable lifestyle factors and the incidences of sudden sensorineural hearing loss and Meniere's disease: a population-based nested case-control study".European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Jingxuan WangDepartment of Epidemiology and Biostatistics, University of California, San Francisco.
Minhyuk ChoiDepartment of Epidemiology and Biostatistics, University of California, San Francisco.
Peter ButoDepartment of Epidemiology and Biostatistics, University of California, San Francisco.
J Daniel KellyDepartment of Epidemiology and Biostatistics, University of California, San Francisco.
Renaud La JoieMemory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco.
John KornakDepartment of Epidemiology and Biostatistics, University of California, San Francisco.
Scott C ZimmermanDepartment of Epidemiology and Biostatistics, University of California, San Francisco.
Ruijia ChenDepartment of Epidemiology, Boston University, Boston, Massachusetts.
Eva RaphaelDepartment of Epidemiology and Biostatistics, University of California, San Francisco.
Catherine A SchaeferKaiser Permanente Division of Research, Pleasanton, California.
Deborah BlackerDepartment of Psychiatry, Massachusetts General Hospital, Charlestown, Massachusetts.
M Maria GlymourDepartment of Epidemiology, Boston University, Boston, Massachusetts.

Funding

Tradeoffs in the Design of Public Long-Term Care BenefitsP01AG032952 · NIA · HARVARD MEDICAL SCHOOL · PI Leila S. Agha · 2009 to 2026
$39.0M
Research Education ComponentP30AG062422 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Serggio Carlo Lanata · 2019 to 2026
$36.9M
Research Education ComponentP30AG062421 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI Christine S Ritchie · 2019 to 2026
$36.5M
A Resource for Genetic Epidemiology Research in Adult Health and AgingRC2AG036607 · NIA · KAISER FOUNDATION RESEARCH INSTITUTE · PI RISCH, NEIL J., SCHAEFER, CATHERINE ANN · 2009 to 2010
$24.8M
Harnessing Diverse Bioinformatic Approaches To Repurpose Drugs For Alzheimers Disease And Related DementiasR01AG058063 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI MARK W ALBERS, PETER Karl SORGER · 2018 to 2026
$7.8M
Impact of Medicare Polices on Beneficiaries with ADRDRF1AG083033 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI JOHN HSU, SHARON-LISE Teresa NORMAND · 2023 to 2026
$4.1M
Building an unbiased pooled cohort for the study of lifecourse social and vascular determinants of Alzheimer's Disease and Related DisordersR01AG072681 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI GLYMOUR, MEDELLENA MARIA, ZEKI AL HAZZOURI, ADINA · 2021 to 2025
$4.0M
Closing the gap between observational research and randomized trials for prevention of Alzheimer's Disease and dementiaR01AG057869 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GRAFF, REBECCA ELIZABETH, POWER, MELINDA C · 2018 to 2022
$3.9M
Statin Treatment and Incident Alzheimer's Disease and Related Dementias in a Large, Multi-ethnic Health PlanRF1AG069259 · NIA · KAISER FOUNDATION RESEARCH INSTITUTE · PI GLYMOUR, MEDELLENA MARIA, KRAUSS, RONALD M · 2020 to 2020
$3.0M
Long-term Neuropsychiatric Sequelae of SARS-CoV-2 Infection in Late LifeR01AG076819 · NIA · NORTHERN CALIFORNIA INSTITUTE/RES/EDU · PI BYERS, AMY LYNN, KEYHANI, SALOMEH · 2023 to 2025
$2.2M
A deep learning algorithm to detect signs of cognitive impairment in electronic health recordsR01AG082698 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI Sudeshna Das · 2024 to 2026
$2.1M
The inverse association between cancer and Alzheimers disease: comparing spurious and causal explanations to illuminate the causes of Alzheimers diseaseRF1AG059872 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GLYMOUR, MEDELLENA MARIA · 2018 to 2021
$1.9M
NIAID NIH HHS K23 AI166030NIA NIH HHS F99 AG083306NIA NIH HHS K00 AG068431NIA NIH HHS K99 AG065501NIA NIH HHS P01 AG032952NIA NIH HHS P30 AG062421NIA NIH HHS P30 AG062422NIA NIH HHS R01 AG057869NIA NIH HHS R01 AG058063NIA NIH HHS R01 AG072681NIA NIH HHS R01 AG076819NIA NIH HHS R01 AG082698NIA NIH HHS RC2 AG036607NIA NIH HHS RF1 AG059872NIA NIH HHS RF1 AG069259NIA NIH HHS RF1 AG083033
6 · The paper itself

Abstract

Importance: Detection bias occurs when an exposure is associated with a systematic difference in outcome ascertainment or diagnosis. For dementia research, diagnosed health conditions that bring patients into frequent interaction with health care may increase the chance that an individual receives a dementia diagnosis. Objective: To evaluate potential detection bias or misdiagnosis bias in evaluation of clinical factors associated with dementia using electronic health record (EHR) data. Design, Setting, and Participants: This prospective cohort study used data from 2 population-based volunteer cohorts: UK Biobank (UKB) and All of Us (AOU). Participants were aged 55 years or older, were dementia-free at baseline, and had linked EHRs. Participants in UKB were followed up from baseline (2006-2010) until December 2022, and in AOU, from baseline (2017-2022) until July 2022. Data were analyzed from November 2023 through February 2025. Exposures: Diagnoses of type 2 diabetes, depression, hypertension, urinary tract infection, kidney stones, forearm fracture, and gastrointestinal (GI) bleeding. Main Outcomes and Measures: Rate of incident all-cause dementia diagnosis from EHRs and associations between clinical exposures and incident dementia diagnosis, assessed using Cox proportional hazards regression models. Results: Among 228 392 participants from UKB (n = 137 374; mean [SD] age at baseline, 62.5 [4.1] years; 53.8% female) and AOU (n = 91 018; mean [SD] age at baseline, 66.9 [7.8] years; 57.1% female), those with a history of a clinical exposure at baseline had higher dementia incidence rates compared with those without such history. For example, among participants with a history of GI bleeding, the dementia incidence rates were 3.0 (UKB) and 7.7 (AOU) per 1000 person-years compared with 2.2 (UKB) and 2.4 (AOU) per 1000 person-years among those without a history of GI bleeding. All exposures were significantly associated with incident dementia, with hazard ratios (HRs) ranging from 1.18 (95% CI, 1.00-1.40) to 3.51 (95% CI, 3.08-4.01). Risk of incident dementia was typically highest in the first year following exposure diagnosis and attenuated thereafter. For example, in the first year after GI bleeding, there were larger elevations in risk of incident dementia (HR, 2.17 [95% CI, 1.46-3.22] in UKB; HR, 2.56 [95% CI, 1.62-4.04] in AOU) compared with 1 to 5 years after bleeding (HR, 1.46 [95% CI, 1.15-1.86] in UKB; HR, 2.14 [95% CI, 1.63-2.81] in AOU). Conclusions and Relevance: In this cohort study of 2 large datasets, diagnoses of several conditions associated with varying risks of dementia were associated with a higher short-term likelihood of dementia diagnosis. This finding suggests that diagnostic bias or misdiagnoses may lead to spurious associations between conditions requiring clinical care and subsequent dementia diagnoses.

Indexed as

DementiaDiagnostic ErrorsElectronic Health RecordsAgedAged, 80 and overBiasFemaleHumansMaleMiddle AgedProspective StudiesUnited KingdomUnited States

Identifiers

PMID40266617
PMCPMC12019524

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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.