Your browser doesn't support javascript.
loading
Generating Older Adult Multimorbidity Trajectories Using Various Comorbidity Indices and Calculation Methods.
Newman, Michael G; Porucznik, Christina A; Date, Ankita P; Abdelrahman, Samir; Schliep, Karen C; VanDerslice, James A; Smith, Ken R; Hanson, Heidi A.
Afiliación
  • Newman MG; Division of Public Health, Department of Family and Preventive Medicine, University of Utah School of Medicine, Salt Lake City, Utah, USA.
  • Porucznik CA; Utah Population Database, University of Utah, Salt Lake City, Utah, USA.
  • Date AP; Division of Public Health, Department of Family and Preventive Medicine, University of Utah School of Medicine, Salt Lake City, Utah, USA.
  • Abdelrahman S; Utah Population Database, University of Utah, Salt Lake City, Utah, USA.
  • Schliep KC; Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, Utah, USA.
  • VanDerslice JA; Computer Science Department, Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt.
  • Smith KR; Division of Public Health, Department of Family and Preventive Medicine, University of Utah School of Medicine, Salt Lake City, Utah, USA.
  • Hanson HA; Division of Public Health, Department of Family and Preventive Medicine, University of Utah School of Medicine, Salt Lake City, Utah, USA.
Innov Aging ; 7(3): igad023, 2023.
Article en En | MEDLINE | ID: mdl-37179657
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.
Palabras clave

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Aspecto: Determinantes_sociais_saude / Equity_inequality Idioma: En Revista: Innov Aging Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Aspecto: Determinantes_sociais_saude / Equity_inequality Idioma: En Revista: Innov Aging Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos