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Optimizing diabetes screening frequencies for at-risk groups.

Chou-Chun Wu1, Sze-Chuan Suen2

  • 1Daniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, CA, USA. chouchuw@usc.edu.

Health Care Management Science
|August 6, 2021
PubMed
Summary
This summary is machine-generated.

New diabetes screening guidelines, informed by body mass index (BMI) and age, can improve early diagnosis and reduce healthcare costs. Personalized recommendations aim to identify at-risk individuals more effectively than current methods.

Keywords:
ObesityOperations researchPOMDPPersonalized disease screeningThreshold policyType 2 diabetes

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Area of Science:

  • Health Informatics
  • Preventive Medicine
  • Decision Science

Background:

  • Diabetes remains significantly underdiagnosed in the US, with an estimated 25% of individuals unaware of their condition.
  • Current screening guidelines may not be optimally tailored to individual risk profiles, potentially delaying diagnosis.

Purpose of the Study:

  • To develop personalized diabetes screening guidelines using a decision-analytic framework.
  • To optimize screening frequency based on individual risk factors like body mass index (BMI) and age.

Main Methods:

  • A Partially Observed Markov Decision Process (POMDP) framework was employed to model screening decisions.
  • Nationally representative empirical data were used to identify an optimal screening policy from age 45 to 90.

Main Results:

  • The study identified structural results supporting threshold solutions for screening policies.
  • Optimal policy recommendations suggest stratification by age and finer BMI thresholds than current guidelines.
  • The proposed policy is estimated to be more cost-effective than the status quo.

Conclusions:

  • Personalized diabetes screening strategies, informed by BMI and age, can enhance diagnostic timeliness.
  • Optimized screening policies offer potential for significant cost savings and improved health outcomes.
  • The findings advocate for a shift towards more individualized approaches in diabetes screening protocols.