Generating Older Adult Multimorbidity Trajectories Using Various Comorbidity Indices and Calculation Methods

  • Michael G. Newman
  • , Christina A. Porucznik
  • , Ankita P. Date
  • , Samir Abdelrahman
  • , Karen C. Schliep
  • , James A. Vanderslice
  • , Ken R. Smith
  • , Heidi A. Hanson

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Background and Objectives: Older adult multimorbidity trajectories are helpful for understanding the current and future health patterns of aging populations. The construction of multimorbidity trajectories from comorbidity index scores will help inform public health and clinical interventions targeting those individuals that are on unhealthy trajectories. Investigators have used many different techniques when creating multimorbidity trajectories in prior literature, and no standard way has emerged. This study compares and contrasts multimorbidity trajectories constructed from various methods. Research Design and Methods: We describe the difference between aging trajectories constructed with the Charlson Comorbidity Index (CCI) and Elixhauser Comorbidity Index (ECI). We also explore the differences between acute (single-year) and chronic (cumulative) derivations of CCI and ECI scores. Social determinants of health can affect disease burden over time; thus, our models include income, race/ethnicity, and sex differences. Results: We use group-based trajectory modeling (GBTM) to estimate multimorbidity trajectories for 86,909 individuals aged 66-75 in 1992 using Medicare claims data collected over the following 21 years. We identify low-chronic disease and high-chronic disease trajectories in all 8 generated trajectory models. Additionally, all 8 models satisfied prior established statistical diagnostic criteria for well-performing GBTM models. Discussion and Implications: Clinicians may use these trajectories to identify patients on an unhealthy path and prompt a possible intervention that may shift the patient to a healthier trajectory.

Original languageEnglish
Article numberigad023
JournalInnovation in Aging
Volume7
Issue number3
DOIs
StatePublished - 2023

Funding

This work was supported by the National Institutes of Health (grant number AG022095) to K. C. Schliep; the National Institutes of Health (grant numbers 5K07CA230150-03, 1K07CA23015-04) to H. A. Hanson; the National Institute on Aging of the National Institutes of Health (grant number K01AG058781) to K. C. Schliep; the National Cancer Institute (grant number P30 CA2014). This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The publisher, by accepting the article for publication, acknowledges that the U.S. government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for U.S. government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ).

Keywords

  • Charlson Comorbidity Index
  • Comorbidity index
  • Elixhauser Comorbidity Index
  • Group-based trajectory modeling

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