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Methods for identifying 30 chronic conditions: application to administrative data.

Marcello Tonelli1, Natasha Wiebe2, Martin Fortin3

  • 1Department of Medicine, University of Calgary, Calgary, Canada. tonelli.admin@ucalgary.ca.

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Summary
This summary is machine-generated.

A new scheme using administrative data successfully identified 30 chronic conditions and multimorbidity in over half a million people. This approach aids in studying and monitoring multimorbidity prevalence across populations.

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Area of Science:

  • Health Services Research
  • Epidemiology
  • Data Science

Background:

  • Multimorbidity is prevalent, leading to adverse health outcomes and increased healthcare expenses.
  • Administrative data offers a valuable resource for investigating multimorbidity patterns.
  • Current methods require refinement for accurate multimorbidity assessment using administrative claims.

Purpose of the Study:

  • To develop and implement a novel framework for identifying chronic conditions and multimorbidity from administrative data.
  • To validate algorithms for chronic condition ascertainment using ICD-9 CM/ICD-10 codes.
  • To demonstrate the feasibility of this scheme in a large population cohort.

Main Methods:

  • Validated algorithms for 40 morbidities were identified and assessed for positive predictive value and sensitivity.
  • Algorithms with high (≥70% PPV, ≥70% sensitivity) or moderate (≥70% PPV, <70% sensitivity) validity were selected.
  • The selected algorithms were applied to administrative data from 574,409 individuals in Edmonton, Canada (2008/2009).

Main Results:

  • Thirty out of 40 morbidities could be identified with high or moderate validity.
  • Approximately 25% of the study population exhibited multimorbidity (≥2 conditions).
  • Another 25% had a single identified morbidity, while the remaining had none of the 30 identified conditions.

Conclusions:

  • A validated panel of 30 chronic conditions identifiable from administrative data was established.
  • This scheme enables robust study and surveillance of multimorbidity.
  • Widespread adoption of this approach will facilitate comparative research across diverse settings and jurisdictions.