Arch Public Health. 2026 May 8. doi: 10.1186/s13690-026-01928-w. Online ahead of print.
ABSTRACT
BACKGROUND: Group-based trajectory modeling (GBTM) can be applied using either time-based or age-based approaches to identify frailty trajectories; however, the comparative effectiveness of the methods for mortality prediction remains unclear. This study aimed to compare time-based and age-based trajectory modeling in identifying frailty trajectories and their predictive value for mortality among Japanese community-dwelling older adults.
METHODS: Of 1085 community-dwelling older adults aged ≥ 65 years, 512 participants with at least two frailty assessments who remained independent during 2011-2017 were included. Frailty was assessed using the Kihon Checklist. Both time-based and age-based GBTM used data from 2011, 2014, and 2017 to identify trajectories. Agreement between methods was assessed using Cohen's Kappa coefficient. Kaplan-Meier survival analysis and Cox proportional hazards models were used to evaluate mortality prediction during follow-up (May 2017-March 2021).
RESULTS: Among the 512 participants (mean age 72 ± 6 years; 54.7% female), both models identified two frailty trajectories: low increasing (88.3% vs. 83.8%) and high increasing (11.7% vs. 16.2%) groups for the time-based and age-based models, respectively. The methods showed substantial agreement (Kappa = 0.63, P < 0.001). During follow-up, 48 participants (9.4%) died, with a median survival time of 27 (IQR 18-33) months. The high increasing group showed a higher mortality risk than the low increasing group in the time-based (adjusted HR = 3.0, 95% CI = 1.4-6.5, P = 0.006) and age-based models (adjusted HR = 2.5, 95% CI = 1.2-5.1, P = 0.01). Both models showed comparable discriminative ability (C-index: 0.77 vs. 0.77) and model fit (AIC: 451 vs. 453).
CONCLUSIONS: In this three-wave Japanese community-based older adult study, both time-based and age-based GBTM identified similar two-group frailty trajectories with substantial agreement and effectively identified high-risk populations for mortality. These findings suggest that both modeling methods are suitable for frailty trajectory analysis and mortality risk prediction in this population; however, their generalizability to other populations requires further validation.
PMID:42104462 | DOI:10.1186/s13690-026-01928-w