---
title: "EHR evidence of collider stratification bias: obesity, diabetes, and rotator cuff tears"
id: "pubmed-42223248"
canonical_url: "https://medichelpline.com/clinical-feed/pubmed-42223248"
content_type: "clinical_feed_article"
specialty: "Pharmacology"
source_name: "PubMed / NCBI"
source_url: "https://pubmed.ncbi.nlm.nih.gov/42223248/"
doi: "10.1093/aje/kwag117"
published_at: "2026-10-07T00:00:00.000Z"
evidence_level: "Journal Article"
license: "CC-BY-NC-4.0 / Informational Use"
---
# EHR evidence of collider stratification bias: obesity, diabetes, and rotator cuff tears
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/pubmed-42223248
- **Specialty:** [Pharmacology](https://medichelpline.com/clinical-feed/pharmacology.md)
- **Primary Source:** PubMed / NCBI
- **Source URL:** [Original Journal Publication](https://pubmed.ncbi.nlm.nih.gov/42223248/)
- **DOI:** [10.1093/aje/kwag117](https://doi.org/10.1093%2Faje%2Fkwag117)
- **Published At:** 2026-10-07T00:00:00.000Z
- **Evidence Rating:** Journal Article
## Executive GIST (TL;DR)
- This study uses de-identified **electronic health record (EHR)** data to demonstrate how conditioning on imaging — a descendant of a collider via symptom-driven testing — can distort observed associations between metabolic exposures and **rotator cuff tears**. - Researchers constructed two control groups from the EHR: controls who had imaging and controls who did not, then compared each to validated cases of rotator cuff tear. - Cases were older and had higher prevalences of arthritis (57%), ligamentous disease (9%), and prior shoulder injury (99%) than controls. - Controls who required imaging resembled cases more closely than controls without imaging: arthritis 9% vs 1%, ligamentous disease 6% vs 2%, prior shoulder injury 54% vs 7%. - Type 1 diabetes (T1D) prevalence differed across groups: 3% in cases, 4% in controls with imaging, and 1% in controls without imaging; national prevalence is approximately 1%. - The association between T1D and rotator cuff tear reversed depending on control selection: using controls without imaging, T1D was positively associated with tears (adjusted OR = 1.78; 95% CI, 1.64–1.92); using controls with imaging, the association was inverse (adjusted OR = 0.75; 95% CI, 0.57–0.97). - The paper highlights how conditioning on a variable influenced by both exposure and outcome (here, imaging driven by symptomatology) can induce **selection bias** through collider stratification, altering conclusions in case-control analyses using EHR data. - Authors recommend careful consideration of control selection in EHR-based epidemiologic studies, especially when diagnostic procedures such as imaging are tied to both exposures and outcomes. - Keywords emphasized by the study include **electronic health records**, **rotator cuff**, **selection bias**, **shoulder**, and **sports injury**.
## Clinical Analysis & Structured Key Points
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more resources ](https://pubmed.ncbi.nlm.nih.gov/42223248/#linkout) Title & authors Abstract Similar articles MeSH terms Related information Grants and funding LinkOut - more resources Am J Epidemiol Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Am+J+Epidemiol%22%5Bjour%5D&sort=date&sort_order=desc) * [ Search in NLM Catalog ](https://www.ncbi.nlm.nih.gov/nlmcatalog?term=%22Am+J+Epidemiol%22%5BTitle+Abbreviation%5D) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42223248/) . 2026 Oct 7;195(10):2592-2601. doi: 10.1093/aje/kwag117. # Leveraging electronic health record data to evidence collider stratification bias and inform clinical epidemiology: obesity, diabetes, and rotator cuff tears [Simone D Ueland](https://pubmed.ncbi.nlm.nih.gov/?term=Ueland+SD&cauthor_id=42223248)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42223248/#full-view-affiliation-1 "Vanderbilt University School of Medicine, Nashville, TN, United States.")[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42223248/#full-view-affiliation-2 "Division of Epidemiology, Department of Medicine, Vanderbilt Health, Nashville, TN, United States.")[ 3 ](https://pubmed.ncbi.nlm.nih.gov/42223248/#full-view-affiliation-3 "Department of Orthopedic Surgery, Boston Medical Center, Boston, MA, United States."), [Til B Basnet](https://pubmed.ncbi.nlm.nih.gov/?term=Basnet+TB&cauthor_id=42223248)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42223248/#full-view-affiliation-2 "Division of Epidemiology, Department of Medicine, Vanderbilt Health, Nashville, TN, United States."), [Wenting Liu](https://pubmed.ncbi.nlm.nih.gov/?term=Liu+W&cauthor_id=42223248)[ 4 ](https://pubmed.ncbi.nlm.nih.gov/42223248/#full-view-affiliation-4 "Department of Biostatistics and Data Science, University of Texas Health Science Center at Houston, Houston, TX, United States."), [Henry H Ong](https://pubmed.ncbi.nlm.nih.gov/?term=Ong+HH&cauthor_id=42223248)[ 5 ](https://pubmed.ncbi.nlm.nih.gov/42223248/#full-view-affiliation-5 "Department of Biomedical Informatics, Vanderbilt Health, Nashville, TN, United States."), [Srushti Gangreddi](https://pubmed.ncbi.nlm.nih.gov/?term=Gangreddi+S&cauthor_id=42223248)[ 5 ](https://pubmed.ncbi.nlm.nih.gov/42223248/#full-view-affiliation-5 "Department of Biomedical Informatics, Vanderbilt Health, Nashville, TN, United States."), [Wei-Qi Wei](https://pubmed.ncbi.nlm.nih.gov/?term=Wei+WQ&cauthor_id=42223248)[ 5 ](https://pubmed.ncbi.nlm.nih.gov/42223248/#full-view-affiliation-5 "Department of Biomedical Informatics, Vanderbilt Health, Nashville, TN, United States."), [Wanqing Wen](https://pubmed.ncbi.nlm.nih.gov/?term=Wen+W&cauthor_id=42223248)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42223248/#full-view-affiliation-2 "Division of Epidemiology, Department of Medicine, Vanderbilt Health, Nashville, TN, United States."), [Katherine E Hartmann](https://pubmed.ncbi.nlm.nih.gov/?term=Hartmann+KE&cauthor_id=42223248)[ 6 ](https://pubmed.ncbi.nlm.nih.gov/42223248/#full-view-affiliation-6 "Center for Clinical & Translational Science, University of Kentucky, Lexington, KY, United States.")[ 7 ](https://pubmed.ncbi.nlm.nih.gov/42223248/#full-view-affiliation-7 "Department of Obstetrics and Gynecology, University of Kentucky, Lexington, KY, United States."), [Nitin B Jain](https://pubmed.ncbi.nlm.nih.gov/?term=Jain+NB&cauthor_id=42223248)[ 8 ](https://pubmed.ncbi.nlm.nih.gov/42223248/#full-view-affiliation-8 "Department of Physical Medicine and Rehabilitation, University of Michigan, Ann Arbor, MI, United States.")[ 9 ](https://pubmed.ncbi.nlm.nih.gov/42223248/#full-view-affiliation-9 "Department of Orthopaedic Surgery, University of Michigan, Ann Arbor, MI, United States."), [Ayush Giri](https://pubmed.ncbi.nlm.nih.gov/?term=Giri+A&cauthor_id=42223248)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42223248/#full-view-affiliation-2 "Division of Epidemiology, Department of Medicine, Vanderbilt Health, Nashville, TN, United States.")[ 10 ](https://pubmed.ncbi.nlm.nih.gov/42223248/#full-view-affiliation-10 "Division of Quantitative and Clinical Sciences, Department of Obstetrics and Gynecology, Vanderbilt Health, Nashville, TN, United States.") Affiliations Expand ### Affiliations * 1 Vanderbilt University School of Medicine, Nashville, TN, United States. * 2 Division of Epidemiology, Department of Medicine, Vanderbilt Health, Nashville, TN, United States. * 3 Department of Orthopedic Surgery, Boston Medical Center, Boston, MA, United States. * 4 Department of Biostatistics and Data Science, University of Texas Health Science Center at Houston, Houston, TX, United States. * 5 Department of Biomedical Informatics, Vanderbilt Health, Nashville, TN, United States. * 6 Center for Clinical & Translational Science, University of Kentucky, Lexington, KY, United States. * 7 Department of Obstetrics and Gynecology, University of Kentucky, Lexington, KY, United States. * 8 Department of Physical Medicine and Rehabilitation, University of Michigan, Ann Arbor, MI, United States. * 9 Department of Orthopaedic Surgery, University of Michigan, Ann Arbor, MI, United States. * 10 Division of Quantitative and Clinical Sciences, Department of Obstetrics and Gynecology, Vanderbilt Health, Nashville, TN, United States. * PMID: **42223248** * DOI: [ 10.1093/aje/kwag117 ](https://doi.org/10.1093/aje/kwag117) Free article Item in Clipboard # Leveraging electronic health record data to evidence collider stratification bias and inform clinical epidemiology: obesity, diabetes, and rotator cuff tears Simone D Ueland et al. Am J Epidemiol. 2026. Free article Show details Display options Display options Format Abstract PubMed PMID Am J Epidemiol Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Am+J+Epidemiol%22%5Bjour%5D&sort=date&sort_order=desc) * [ Search in NLM Catalog ](https://www.ncbi.nlm.nih.gov/nlmcatalog?term=%22Am+J+Epidemiol%22%5BTitle+Abbreviation%5D) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42223248/) . 2026 Oct 7;195(10):2592-2601. doi: 10.1093/aje/kwag117. ### Authors [Simone D Ueland](https://pubmed.ncbi.nlm.nih.gov/?term=Ueland+SD&cauthor_id=42223248)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42223248/#short-view-affiliation-1 "Vanderbilt University School of Medicine, Nashville, TN, United States.")[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42223248/#short-view-affiliation-2 "Division of Epidemiology, Department of Medicine, Vanderbilt Health, Nashville, TN, United States.")[ 3 ](https://pubmed.ncbi.nlm.nih.gov/42223248/#short-view-affiliation-3 "Department of Orthopedic Surgery, Boston Medical Center, Boston, MA, United States."), [Til B Basnet](https://pubmed.ncbi.nlm.nih.gov/?term=Basnet+TB&cauthor_id=42223248)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42223248/#short-view-affiliation-2 "Division of Epidemiology, Department of Medicine, Vanderbilt Health, Nashville, TN, United States."), [Wenting Liu](https://pubmed.ncbi.nlm.nih.gov/?term=Liu+W&cauthor_id=42223248)[ 4 ](https://pubmed.ncbi.nlm.nih.gov/42223248/#short-view-affiliation-4 "Department of Biostatistics and Data Science, University of Texas Health Science Center at Houston, Houston, TX, United States."), [Henry H Ong](https://pubmed.ncbi.nlm.nih.gov/?term=Ong+HH&cauthor_id=42223248)[ 5 ](https://pubmed.ncbi.nlm.nih.gov/42223248/#short-view-affiliation-5 "Department of Biomedical Informatics, Vanderbilt Health, Nashville, TN, United States."), [Srushti Gangreddi](https://pubmed.ncbi.nlm.nih.gov/?term=Gangreddi+S&cauthor_id=42223248)[ 5 ](https://pubmed.ncbi.nlm.nih.gov/42223248/#short-view-affiliation-5 "Department of Biomedical Informatics, Vanderbilt Health, Nashville, TN, United States."), [Wei-Qi Wei](https://pubmed.ncbi.nlm.nih.gov/?term=Wei+WQ&cauthor_id=42223248)[ 5 ](https://pubmed.ncbi.nlm.nih.gov/42223248/#short-view-affiliation-5 "Department of Biomedical Informatics, Vanderbilt Health, Nashville, TN, United States."), [Wanqing Wen](https://pubmed.ncbi.nlm.nih.gov/?term=Wen+W&cauthor_id=42223248)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42223248/#short-view-affiliation-2 "Division of Epidemiology, Department of Medicine, Vanderbilt Health, Nashville, TN, United States."), [Katherine E Hartmann](https://pubmed.ncbi.nlm.nih.gov/?term=Hartmann+KE&cauthor_id=42223248)[ 6 ](https://pubmed.ncbi.nlm.nih.gov/42223248/#short-view-affiliation-6 "Center for Clinical & Translational Science, University of Kentucky, Lexington, KY, United States.")[ 7 ](https://pubmed.ncbi.nlm.nih.gov/42223248/#short-view-affiliation-7 "Department of Obstetrics and Gynecology, University of Kentucky, Lexington, KY, United States."), [Nitin B Jain](https://pubmed.ncbi.nlm.nih.gov/?term=Jain+NB&cauthor_id=42223248)[ 8 ](https://pubmed.ncbi.nlm.nih.gov/42223248/#short-view-affiliation-8 "Department of Physical Medicine and Rehabilitation, University of Michigan, Ann Arbor, MI, United States.")[ 9 ](https://pubmed.ncbi.nlm.nih.gov/42223248/#short-view-affiliation-9 "Department of Orthopaedic Surgery, University of Michigan, Ann Arbor, MI, United States."), [Ayush Giri](https://pubmed.ncbi.nlm.nih.gov/?term=Giri+A&cauthor_id=42223248)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42223248/#short-view-affiliation-2 "Division of Epidemiology, Department of Medicine, Vanderbilt Health, Nashville, TN, United States.")[ 10 ](https://pubmed.ncbi.nlm.nih.gov/42223248/#short-view-affiliation-10 "Division of Quantitative and Clinical Sciences, Department of Obstetrics and Gynecology, Vanderbilt Health, Nashville, TN, United States.") ### Affiliations * 1 Vanderbilt University School of Medicine, Nashville, TN, United States. * 2 Division of Epidemiology, Department of Medicine, Vanderbilt Health, Nashville, TN, United States. * 3 Department of Orthopedic Surgery, Boston Medical Center, Boston, MA, United States. * 4 Department of Biostatistics and Data Science, University of Texas Health Science Center at Houston, Houston, TX, United States. * 5 Department of Biomedical Informatics, Vanderbilt Health, Nashville, TN, United States. * 6 Center for Clinical & Translational Science, University of Kentucky, Lexington, KY, United States. * 7 Department of Obstetrics and Gynecology, University of Kentucky, Lexington, KY, United States. * 8 Department of Physical Medicine and Rehabilitation, University of Michigan, Ann Arbor, MI, United States. * 9 Department of Orthopaedic Surgery, University of Michigan, Ann Arbor, MI, United States. * 10 Division of Quantitative and Clinical Sciences, Department of Obstetrics and Gynecology, Vanderbilt Health, Nashville, TN, United States. * PMID: **42223248** * DOI: [ 10.1093/aje/kwag117 ](https://doi.org/10.1093/aje/kwag117) Item in Clipboard Full text links Cite Display options Display options Format Abstract PubMed PMID Linked Discoveries ## Abstract Electronic health record (EHR) data are increasingly used for case-control investigations. Using multiple control groups in de-identified EHR data, we evidence how conditioning on imaging (descendant of a collider: symptomatology) can perturb exposure estimations enough to reverse conclusions. Because imaging is often required for rotator cuff tear diagnosis, some argue imaging should be required for control selection. We constructed 2 control groups (with vs without imaging) to evaluate selection bias through collider stratification involving metabolic exposures-body mass index (BMI), type 1 diabetes (T1D), and type 2 diabetes (T2D)-and rotator cuff tears. Cases and controls were identified using validated algorithms. We compared baseline characteristics and performed multivariable logistic regression across designs. Cases were older and more likely to have arthritis (57%), ligamentous disease (9%), and prior shoulder injury (99%) than controls. Controls requiring imaging more closely resembled cases than controls not requiring imaging, with more arthritis (9% vs 1%), ligamentous disease (6% vs 2%), and prior shoulder injury (54% vs 7%). T1D prevalence was 3% in cases, 4% in controls with imaging, and 1% in controls without, compared to ~1% nationally. T1D was positively associated with tears using controls without imaging (aOR = 1.78; 95% CI, 1.64-1.92), but inversely using controls with imaging (aOR = 0.75; 0.57-0.97). **Keywords:** electronic health records; rotator cuff; selection bias; shoulder; sports injury. © The Author(s) 2026. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. [PubMed Disclaimer](https://pubmed.ncbi.nlm.nih.gov/disclaimer/) ## Similar articles * [ Shoulder Arthrogram. ](https://pubmed.ncbi.nlm.nih.gov/35593869/) Roberts CC, Escobar E.Roberts CC, et al.2025 Sep 14. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2025 Jan–.2025 Sep 14. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2025 Jan–.PMID: 35593869Free Books & Documents. * [ Standardizing phenotypic algorithms for the classification of degenerative rotator cuff tear from electronic health record systems. ](https://pubmed.ncbi.nlm.nih.gov/40103752/) Herzberg SD, Garduno-Rapp NE, Ong HH, Gangireddy S, Chandrashekar AS, Wei WQ, LeClere LE, Wen W, Hartmann KE, Jain NB, Giri A.Herzberg SD, et al.JAMIA Open. 2025 Mar 18;8(2):ooaf014. doi: 10.1093/jamiaopen/ooaf014. eCollection 2025 Apr.JAMIA Open. 2025.PMID: 40103752Free PMC article. * [ Preoperative Shoulder Stiffness Was Accompanied by Distinct Supraspinatus Tendon Inflammatory Signatures but Not Inferior Long-term Outcomes After Rotator Cuff Repair. ](https://pubmed.ncbi.nlm.nih.gov/42248213/) Liu M, Cai H, Tang C, Luo J, Wang X, Liu H, Ruan D, Wang C, Chen X, Shen W.Liu M, et al.Clin Orthop Relat Res. 2026 Jun 5. doi: 10.1097/CORR.0000000000004010. Online ahead of print.Clin Orthop Relat Res. 2026.PMID: 42248213 * [ Magnetic resonance imaging, magnetic resonance arthrography and ultrasonography for assessing rotator cuff tears in people with shoulder pain for whom surgery is being considered
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