• Login
    View Item 
    •   UMB Digital Archive
    • School, Graduate
    • Theses and Dissertations All Schools
    • View Item
    •   UMB Digital Archive
    • School, Graduate
    • Theses and Dissertations All Schools
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of UMB Digital ArchiveCommunitiesPublication DateAuthorsTitlesSubjectsThis CollectionPublication DateAuthorsTitlesSubjects

    My Account

    LoginRegister

    Statistics

    Display statistics

    A Cluster Analytic Approach to Identify Insomnia Subtypes and Their Relationship with Economic Outcomes

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    Gandhi_umaryland_0373D_11296.pdf
    Size:
    1.868Mb
    Format:
    PDF
    Download
    Author
    Gandhi, Aakash Bipin
    Advisor
    Onukwugha, Eberechukwu
    Date
    2021
    Type
    dissertation
    
    Metadata
    Show full item record
    Abstract
    INTRODUCTION: Insomnia is a heterogenous condition with respect to underlying risk factors, presentation of symptoms, comorbidities, disease course, and outcomes. Consequently, individuals with insomnia may also have varying patterns of healthcare resource utilization and costs. However, the impact of insomnia heterogeneity on economic outcomes is not known. METHODS: We used an integrated claims-electronic health records dataset to identify individuals aged 18-64 with insomnia between 2009-2018. A k-modes clustering algorithm with a Jaccard coefficient similarity measure was used to identify clinically relevant insomnia subtypes based on sociodemographic, comorbidity, behavioral, life event, family history, medication use, vital sign, and insomnia symptom-related characteristics. An optimum cluster solution was chosen based on clinical interpretability and significance. Insomnia clusters were compared on baseline characteristics using Chi-square tests. Logistic regression models were used to identify the association between cluster membership and binary outcomes (inpatient hospitalization, emergency department [ED] visits). Generalized linear models were used to assess similar associations with count physician office visits, non-physician outpatient visits, prescription drug fills) and cost outcomes associated with all points of service. RESULTS: A total of 17,124 individuals with insomnia met the study inclusion criteria. The cluster analysis resulted in a five-cluster solution. The clusters were labelled as ‘Insomnia associated with obesity and hypertension’ (28.6%), ‘Insomnia associated with mental health conditions and chronic pain’ (25.4%), ‘Insomnia associated with older age, high comorbidity burden, and fatigue’ (24.6%), ‘Insomnia associated with substance use disorders’ (5.2%), and ‘Insomnia associated with overweight status, alcohol use, and low comorbidity burden’ (16.2%). Relative to the reference cluster ‘Insomnia associated with overweight status, alcohol use, and low comorbidity burden’, individuals in cluster labelled as ‘Insomnia associated with older age, high comorbidity burden, and fatigue’ displayed higher total healthcare costs (cost ratio [CR]: 1.46; 95% CI: 1.32, 1.62) primarily driven by higher inpatient (CR: 1.68; 95% CI: 1.48, 1.91) and prescription drug fill (CR: 1.49; 95% CI: 1.34, 1.65) costs. CONCLUSION: Findings from the present study can help improve our understanding about developmental trajectories for insomnia diagnosis and facilitate the design of tailored interventions that target those at the highest risk for adverse economic consequences.
    Description
    University of Maryland, Baltimore. Pharmaceutical Health Services Research, Ph.D. 2021.
    Keyword
    direct healthcare costs
    economic outcomes
    healthcare resource utilization
    insomnia
    Cluster Analysis
    Sleep Initiation and Maintenance Disorders
    Identifier to cite or link to this item
    http://hdl.handle.net/10713/18073
    Collections
    Theses and Dissertations School of Pharmacy
    Theses and Dissertations All Schools

    entitlement

     
    DSpace software (copyright © 2002 - 2023)  DuraSpace
    Quick Guide | Policies | Contact Us | UMB Health Sciences & Human Services Library
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.