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Quantification of information gained by linking claims data to an electronic health record cohort of patients with metastatic breast cancer
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  • Jonah Geddes,
  • Julie Katz,
  • Alex Asiimwe,
  • M. Alan Brookhart,
  • Charlotte Carroll,
  • Lev Eldemir,
  • Vera Mucaj,
  • Kevin Nolan,
  • Ioanna Ntalla,
  • Carrie Nielson
Jonah Geddes
SimulStat Incorporated
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Julie Katz
Gilead Sciences Inc
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Alex Asiimwe
Gilead Sciences Inc
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M. Alan Brookhart
Duke University
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Charlotte Carroll
Gilead Sciences Inc
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Lev Eldemir
Datavant
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Vera Mucaj
Datavant
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Kevin Nolan
Gilead Sciences Ireland UC
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Ioanna Ntalla
Gilead Sciences Europe Ltd
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Carrie Nielson
Gilead Sciences Inc

Corresponding Author:[email protected]

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Abstract

Purpose: Linking claims data to electronic health record (EHR) data can improve completeness, often at a cost of decreased sample size. Quantifying information gained and differences in patient characteristics between EHR and EHR-claims linked cohorts may inform study design. Methods: Using ConcertAI Patient360 EHR linked to multiple closed insurance claims sources, we compared an EHR cohort of patients with incident metastatic breast cancer (mBC) to an EHR-claims subcohort (requiring ≥90 days claims coverage). We analyzed diagnosis coverage, patient-time during lookback and follow-up, baseline characteristics, and rates of 14 adverse events (AEs). Analyses were age-stratified due to insurance coverage changes at age 65. Results: For the EHR cohort (N = 6289), 1438 (23%) were in the EHR-claims subcohort. A greater proportion were aged ≥65 years in the EHR cohort (30%) than in the EHR-claims subcohort (17%). EHR-claims patients had longer observation periods and more unique diagnoses across both age groups. For most AEs, incidences were higher in both age groups in the EHR-claims subcohort than in EHR cohort. Conclusions: EHR-claims provided more diagnoses and observation time, at the cost of a reduction in sample size and underrepresentation of patients ≥65 years. Differing age proportions support age-stratified or standardized analyses for EHR-claims data. Results aid interpretation of differences between EHR and EHR-claims results due to shifts in age, completeness of diagnosis history, and duration of observation.
27 Nov 2024Submitted to Pharmacoepidemiology and Drug Safety
27 Nov 2024Submission Checks Completed
27 Nov 2024Assigned to Editor
27 Nov 2024Review(s) Completed, Editorial Evaluation Pending
10 Dec 2024Reviewer(s) Assigned