• Login
    View Item 
    •   Home
    • UA Graduate and Undergraduate Research
    • UA Theses and Dissertations
    • Dissertations
    • View Item
    •   Home
    • UA Graduate and Undergraduate Research
    • UA Theses and Dissertations
    • Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of UA Campus RepositoryCommunitiesTitleAuthorsIssue DateSubmit DateSubjectsPublisherJournalThis CollectionTitleAuthorsIssue DateSubmit DateSubjectsPublisherJournal

    My Account

    LoginRegister

    About

    AboutUA Faculty PublicationsUA DissertationsUA Master's ThesesUA Honors ThesesUA PressUA YearbooksUA CatalogsUA Libraries

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Doctors, Documents, and Diagnostic Disparities: Essays on the Economics of Mental Health

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    azu_etd_18700_sip1_m.pdf
    Size:
    1.597Mb
    Format:
    PDF
    Download
    Author
    Marquardt, Kelli
    Issue Date
    2021
    Advisor
    Gowrisankaran, Gautam
    
    Metadata
    Show full item record
    Publisher
    The University of Arizona.
    Rights
    Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction, presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
    Abstract
    Mental health conditions are costly, both to the impacted individual and society as a whole. However, due to the subjectivity of mental health medical guidelines, these conditions are associated with high potential for diagnostic errors. By combining clinical data with quantitative economic modeling, I explore how individuals are diagnosed with mental health conditions in the presence of such uncertainty. My dissertation seeks to understand mechanisms underlying the clinical diagnostic process and sheds light on sources of mental health diagnostic errors and disparities. In the first chapter, I develop and estimate a structural model of diagnosis for the most prevalent child mental health condition, Attention Deficit Hyperactivity Disorder (ADHD). The model incorporates both patient and physician influences, highlighting four key mechanisms of mental health diagnosis: true underlying prevalence, patient stigma, diagnostic uncertainty, and physician costs from type I and type II diagnostic errors. I estimate sex-specific structural model parameters using novel electronic health record data on doctors’ notes together with machine learning and natural language processing techniques (later detailed in Chapter 2). In raw comparisons, males are 2.3 times more likely to be diagnosed with ADHD than females. Counterfactual simulations using model estimates show that less than one-half of this disparity can be explained by true differences in underlying ADHD prevalence, very little explained by patient preferences, and about 50% attributed to differences in physician decision-making. I show that physicians view missed diagnosis to be costlier than misdiagnosis, especially for their male patients. Back of the envelope calculations estimate the national economic impact of ADHD diagnostic errors to be $60-$117 billion US dollars, suggesting a need for public and/or medical policy responses aimed at increasing diagnostic accuracy and reducing disparities in mental health care. The second chapter further details the natural language processing algorithm used in Chapter 1 and provides another application for its use– estimating physician practice style. I first propose the text-analysis algorithm applied to digitized clinical doctor notes as a way to measure how closely the patient interview matches mental health diagnostic guidelines. This measure can then be used as a control in a reduced-form model to identify two components of physician practice style: diagnostic intensity (the mean propensity to diagnose) and diagnostic compliance (the weight that physicians place on official medical guidelines). As an application, I estimate and analyze physician practice style for ADHD diagnosis. I find significant variation in physician practice style, with physician gender and experience being the strongest predictors of this variation. I also discuss how mental health practice style estimates can be used to guide potential health care policies, and I provide a list of extensions and suggestions of how these methods can be used in future mental health care research. In the final chapter, I switch the focus away from physicians and onto the family. In this study, I use 16 waves of the Medical Expenditure Panel Survey to empirically explore the relationship between family structure shocks and child mental health, focusing on clinical diagnoses. Comparing cross-sectional estimates to within-family models, I show that over half of the positive relationship between family stability and child mental health can be explained by unobserved family selection. Using a child fixed-effect estimation approach, I find that the probability a child is diagnosed with a mental health condition increases by 3.5 percentage points following a parental divorce or parental death. I explore potential mechanisms, noting an increase in both behavioral symptoms and mental health care utilization. However, there is no significant change in overall health care, suggesting that due to time constraints, parents may be substituting general/physical health appointments for mental health related services. While the first two chapters highlight how physicians influence the diagnosis decision, this final chapter demonstrates that family structure and utilization of care are also significant determinants of child mental health.
    Type
    text
    Electronic Dissertation
    Degree Name
    Ph.D.
    Degree Level
    doctoral
    Degree Program
    Graduate College
    Economics
    Degree Grantor
    University of Arizona
    Collections
    Dissertations

    entitlement

     
    The University of Arizona Libraries | 1510 E. University Blvd. | Tucson, AZ 85721-0055
    Tel 520-621-6442 | repository@u.library.arizona.edu
    DSpace software copyright © 2002-2017  DuraSpace
    Quick Guide | Contact Us | Send Feedback
    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.