Estimating underdiagnosis of patients in chronically Ill populations

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      <subfield code="a">Estimating underdiagnosis of patients in chronically Ill populations</subfield>
      <subfield code="c">Andrew Stocking, Ian Duncan and Nhan Huynh</subfield>
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      <subfield code="a">In this article, we propose a method to estimate more precisely the prevalence of patients with a chronic condition in a claims-based dataset. We introduce a group of latent members who have not yet been diagnosed with the condition through claims but are likely to have the condition, as supported by the presence of other comorbidities that are highly correlated with the condition. The proposed method aims to address the underreporting of a range of chronic conditions that are associated with social stigma, detection and treatment challenges, or inconsistent engagement of treatment (e.g., mental disorders, obesity, hypertension, sleep disorders, etc.). We demonstrate the method with an application using incontinence as our chronic condition of interest</subfield>
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