A Bayesian Approach to Modeling Risk of Hospital Admissions Associated With Schizophrenia Accounting for Underdiagnosis of the Disorder in Administrative Records

Eileen M Stock; James D Stamey; John E Zeber; Alexander W Thompson; Laurel A Copeland

doi:10.1162/CPSY_a_00010

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A Bayesian Approach to Modeling Risk of Hospital Admissions Associated With Schizophrenia Accounting for Underdiagnosis of the Disorder in Administrative Records

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A Bayesian Approach to Modeling Risk of Hospital Admissions Associated With Schizophrenia Accounting for Underdiagnosis of the Disorder in Administrative Records

Eileen M Stock, James D Stamey, John E Zeber, Alexander W Thompson and Laurel A Copeland

Computational psychiatry, Vol.2, pp.1-10

02/01/2018

DOI: 10.1162/CPSY_a_00010

PMCID: PMC6067824

PMID: 30090859

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Abstract

Schizophrenia is a debilitating serious mental illness characterized by a complex array of symptoms with varying severity and duration. Patients may seek treatment only intermittently, contributing to challenges diagnosing the disorder. A misdiagnosis may potentially bias and reduce study validity. Thus we developed a statistical model to assess the risk of 1-year hospitalization for patients diagnosed with schizophrenia, accounting for when schizophrenia is underreported in administrative databases. A retrospective study design identified patients seeking care during 2010 within an integrated health care system from the Health Maintenance Organization Research Network located in the southwestern United States. Bayesian analysis addressed the problem of underdiagnosed schizophrenia with a statistical measurement error model assuming varying rates of underreporting. Results were then compared to classical multivariable logistic regression. Assuming no underreporting, there was an 87% greater relative odds of hospitalization associated with schizophrenia, OR = 1.87, CI [1.08, 3.23]. Effect sizes and interval estimates representing the association between hospitalization and schizophrenia were reduced with the Bayesian approach accounting for underdiagnosis, suggesting that less severe patients may be underrepresented in studies of schizophrenia. The analytical approach has useful applications in other contexts where the identification of patients with a given condition may be underreported in administrative records.

Schizophrenia

measurement error

Research

Bayesian analysis

underdiagnosis

Details

Title: Subtitle: A Bayesian Approach to Modeling Risk of Hospital Admissions Associated With Schizophrenia Accounting for Underdiagnosis of the Disorder in Administrative Records
Creators: Eileen M Stock - Texas A&M Health Science Center, Bryan, Texas, USA
James D Stamey - Department of Statistical Science, Baylor University, Waco, Texas, USA
John E Zeber - Texas A&M Health Science Center, Bryan, Texas, USA
Alexander W Thompson - Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, Iowa, USA
Laurel A Copeland - Texas A&M Health Science Center, Bryan, Texas, USA
Resource Type: Journal article
Publication Details: Computational psychiatry, Vol.2, pp.1-10
DOI: 10.1162/CPSY_a_00010
PMID: 30090859
PMCID: PMC6067824
NLM abbreviation: Comput Psychiatr
ISSN: 2379-6227
eISSN: 2379-6227
Publisher: MIT Press
Language: English
Date published: 02/01/2018
Academic Unit: Psychiatry
Record Identifier: 9984003471602771

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