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Performance of ICD10 Code-Based Dementia Case Definition in the Health and Retirement Study.

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  • 1NORC at the University of Chicago, Washington DC 20036, USA.

Medrxiv : the Preprint Server for Health Sciences
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Summary

Medicare claims data can identify dementia treatment recipients but often miss cases, especially in rural areas or among younger, healthier individuals. This variation can be leveraged to improve dementia surveillance accuracy.

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Area of Science:

  • Gerontology
  • Public Health
  • Health Informatics

Background:

  • The Dementia DataHub (DDH) utilizes Medicare data to report U.S. dementia prevalence and incidence.
  • Interpretation challenges arise due to variations in diagnostic accuracy influenced by geography and participant characteristics.

Purpose of the Study:

  • To evaluate the performance of Dementia DataHub (DDH)-defined Medicare claims diagnoses against Health and Retirement Study (HRS) survey-based dementia classifications.
  • To identify patterns in diagnostic accuracy across different geographic and demographic factors.

Main Methods:

  • Linked Medicare claims data and HRS survey data were used for comparison.
  • Performance metrics including sensitivity, specificity, and accuracy were calculated for DDH dementia definitions against HRS classifications.

Main Results:

  • The DDH's likely-or-higher dementia definition showed 50% sensitivity, 97% specificity, and 91% accuracy compared to HRS.
  • Diagnostic sensitivity was higher in urban settings, with variations observed across census divisions.
  • Dementia cases missed by Medicare claims predominantly included younger and healthier individuals.

Conclusions:

  • Medicare claims provide valuable insights into dementia treatment patterns but exhibit differential case ascertainment.
  • Geographic and patient characteristic variations in Medicare dementia diagnoses can be utilized to enhance surveillance utility.
  • Understanding these discrepancies is crucial for accurate public health monitoring of dementia.