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Accepted for/Published in: JMIR Public Health and Surveillance

Date Submitted: Jan 19, 2023
Open Peer Review Period: Jan 19, 2023 - Feb 2, 2023
Date Accepted: May 25, 2023
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

Contribution of Frailty to Multimorbidity Patterns and Trajectories: Longitudinal Dynamic Cohort Study of Aging People

Carrasco-Ribelles LA, Cabrera-Bean M, Danés-Castells M, Zabaleta-del-Olmo E, Roso-Llorach A, Violán C

Contribution of Frailty to Multimorbidity Patterns and Trajectories: Longitudinal Dynamic Cohort Study of Aging People

JMIR Public Health Surveill 2023;9:e45848

DOI: 10.2196/45848

PMID: 37368462

PMCID: 10365626

How frailty contributes to multimorbidity patterns and trajectories: a longitudinal dynamic cohort of ageing people

  • Lucía A. Carrasco-Ribelles; 
  • Margarita Cabrera-Bean; 
  • Marc Danés-Castells; 
  • Edurne Zabaleta-del-Olmo; 
  • Albert Roso-Llorach; 
  • Concepción Violán

ABSTRACT

Background:

Multimorbidity and frailty are characteristics of ageing that need individualized evaluation, and there is a two-way causal relationship between them. Thus, considering frailty in analyses of multimorbidity is important for tailoring social and health care to the specific needs of older people.

Objective:

This study aimed to assess how the inclusion of frailty contributes to identifying and characterising multimorbidity patterns in people aged 65 or more.

Methods:

Longitudinal data were drawn from electronic health records through the SIDIAP primary care database from the population aged 65 or older from 2010 to 2019 in Catalonia, Spain. Frailty and multimorbidity were measured annually using validated tools (eFRAGICAP, a cumulative deficit model; and SNAC-K, respectively). Two sets of 11 multimorbidity patterns were obtained using fuzzy c-means. Both considered the chronic conditions of the participants; in addition, one included age, and the other, frailty. Cox models were used to test their association with death, nursing home admission and home care need. Trajectories were defined as the evolution of the patterns over follow-up.

Results:

The study included 1,456,052 unique participants (mean follow-up of 7.0 years). Most patterns were similar in both sets in terms of the most prevalent conditions. However, the patterns that considered frailty were better for identifying the population whose main conditions imposed limitations on daily life, with a higher prevalence of frail individuals in patterns like \textit{chronic ulcers} \& \textit{peripheral vascular}. This set also included a dementia-specific pattern and showed a better fit with the risk of nursing home admission and home care need. On the other hand, the risk of death had a better fit with the set of patterns that did not include frailty. The change in patterns when considering frailty also led to a change in the trajectories. On average, participants were in 1.8 patterns during their follow-up, while 45\% remained in the same pattern.

Conclusions:

Our results suggest that frailty should be considered in addition to chronic diseases when studying multimorbidity patterns in older adults. Multimorbidity patterns and trajectories can help to identify patients with specific needs. The patterns that considered frailty were better for identifying the risk of certain age-related outcomes such as nursing home admission or homecare need, while those considering age were better for identifying the risk of death. Clinical and social intervention guidelines and resource planning can be tailored based on the prevalence of these patterns and trajectories.


 Citation

Please cite as:

Carrasco-Ribelles LA, Cabrera-Bean M, Danés-Castells M, Zabaleta-del-Olmo E, Roso-Llorach A, Violán C

Contribution of Frailty to Multimorbidity Patterns and Trajectories: Longitudinal Dynamic Cohort Study of Aging People

JMIR Public Health Surveill 2023;9:e45848

DOI: 10.2196/45848

PMID: 37368462

PMCID: 10365626

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