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Algorithm development and the clinical and economic burden of Cushing's disease in a large US health plan database

Tanya Burton1, Elisabeth Le Nestour2, Maureen Neary3

  • 1Optum, 950 Winter Street, Waltham, MA, 02451, USA. tanya.burton@optum.com.

Pituitary
|December 16, 2015
PubMed

Insights

An algorithm identified patients with Cushing's disease (CD), revealing significantly higher comorbidity and healthcare costs compared to controls. Early diagnosis and improved treatments are crucial for managing CD's burden.

Area of Science:

  • Endocrinology
  • Health Services Research

Background:

  • Cushing's disease (CD) lacks a unique diagnostic code in many health databases.
  • Identifying CD patients is crucial for understanding their clinical and economic impact.

Purpose of the Study:

  • Develop an algorithm to identify CD patients using claims data.
  • Quantify the clinical and economic burden of CD compared to CD-free individuals.

Main Methods:

  • Retrospective cohort study (2007-2011) in a US commercial health plan database.
  • Developed an algorithm based on eight pituitary conditions/procedures.
  • Matched 877 CD patients with 2631 CD-free controls (1:3 ratio).
  • Compared comorbidity rates and healthcare costs between groups.

Main Results:

  • Algorithm identified 877 CD patients.
  • CD patients exhibited 2-5 times higher comorbidity rates.
  • CD patients incurred 4-7 times higher healthcare costs than controls.
  • Age and sex distribution aligned with known CD epidemiology.

Conclusions:

  • An algorithm using specific pituitary conditions can identify CD patients in claims databases.
  • CD patients experience substantial comorbidity and healthcare cost burdens.
  • Earlier diagnosis and improved treatments are recommended to mitigate CD's impact.
Abstract

Keywords:
Case-finding algorithmComorbiditiesCushing’s diseaseHealth care costs

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