Frailty index transitions over eight years were frequent in The Irish Longitudinal Study on Ageing

Companion paper

This article is based on methodology first reported in Roman Romero-Ortuno, Peter Hartley, James Davis, Silvin P. Knight, Rossella Rizzo, Belinda Hernández, Rose Anne Kenny, Aisling M. O'Halloran, Transitions in frailty phenotype states and components over 8 years: Evidence from The Irish Longitudinal Study on Ageing, Archives of Gerontology and Geriatrics, Volume 95, 2021, https://doi.org/10.1016/j.archger.2021.104401¹.

What is new?

•    We described longitudinal transitions in frailty index states.

•    Transition probabilities were estimated with multi-state models.

•    Frail had a 21% probability of reversal to prefrail and 14% risk of death.

•    Frailty index transitions are dynamic and include improvement.

•    Opportunities exist for reducing the probability of adverse transitions.

Introduction

As populations get older, the association between chronological age and health status becomes increasingly variable, to the extent that for a large sector of the older population, chronological age is not a relevant marker for understanding the experience of ageing². To describe this heterogeneity in health status as we age, the concept of frailty has been proposed^3–5.

The frailty index (FI) methodology was introduced by Rockwood and colleagues^6,7 as a way to quantify the accumulation of people’s health ‘deficits’ (i.e. symptoms, clinical signs, medical conditions and disabilities) at a given chronological age. As per published standard procedure⁸, a FI can be constructed on any suitable health database by considering a minimum of 30 deficits that need to satisfy the following criteria: (a) be associated with health status, and not simply attributes (e.g. hair graying); (b) cover a range of systems; (c) not saturate too early (e.g. presbyopia is nearly universal by age 55); and (d) their prevalence must increase with age (excluding survivor effects); in addition, in repeated assessments the FI construction must be the same⁸.

Since FI deficits must increase with age, the FI has a statistically significant association with chronological age⁹. However, on account of the above-mentioned population heterogeneity, the effect size of this association has been found to be small^10,11. The sex-specific properties of the FI have also been studied. A systematic review and meta-analysis¹² consistently showed that women have higher FI scores than males at all ages. However, whilst women tend to accumulate more deficits than men of the same age, their risk of mortality tends to be lower⁶. Socioeconomic status, including education, has also been reported to explain variation in FI within individuals of the same chronological age¹³.

Frailty in older adults can be improved and even reversed with appropriate medical and non-medical interventions¹⁴. However, despite abundant research to the contrary, non-specialist clinicians and the general public often believe that frailty is a ‘fixed’ state with little potential to change over time¹⁵. Previous works have shown that the FI is longitudinally dynamic^16–21, but Irish data on FI transitions was lacking and few studies have employed long follow-up periods. Our aim was to describe the eight-year longitudinal transitions of FI states using data from The Irish Longitudinal Study on Ageing.

Methods

Results

TILDA wave 1 recruited a total of 8504 participants, of whom 330 (3.9%) were aged less than 50 years. The remaining 8174 had complete FI information (3744 men and 4430 women). The mean (SD; minimum, maximum) age of wave 1 participants (n=8174) was 63.8 (9.8; 50–105) years; for wave 2 (n=6994): 65.5 (9.5; 52–97); for wave 3 (n=6249): 67.5 (9.2; 54–98); for wave 4 (n=5571): 69.2 (8.9; 56–101); and for wave 5 (n=4874): 70.6 (8.5; 58–103). Overall, 6832 (83.6%) participants were aged <75 and 1342 (16.4%) 75 or more. The counts and proportions for FI states and deaths at each wave is presented in Table 1.

The alluvial plot for the FI states in the total sample is shown in Figure 1, and Appendices 2 and 3 (see Extended data) show the alluvial plots for age groups (<75 versus 75 and more), and each of the 31 FI items in the total sample, respectively. As expected, the cumulative proportions of deaths and missing data increased across waves. Numbers of FI state transitions in the total sample are detailed in Appendix 4 (see Extended data)²⁵.

Figure 1. Alluvial chart of the longitudinal transitions of frailty index states in The Irish Longitudinal Study on Ageing (n=8174).

Table 2 shows the probabilities of transition (with 95% confidence intervals) in frailty states from one wave to the next. Figure 2 visually shows the transition probabilities. In the age subanalyses presented in Appendix 5 (see Extended data)²⁵, in those age 75 or more, the risk of death from a frail state was 67%, and the probabilities of improvements from frail to prefrail and prefrail to non-frail were 12% and 6%, respectively.

Figure 2. Estimated transition probability for each frailty index state (from wave x to wave x + 1) in the total sample (n=8174).

Appendix 6 (Extended data)²⁵ shows the transition probabilities based on FI quartiles at baseline in the total sample. According to this FI categorization, severe frailty had a 25% risk of death, a 22% probability of transition to moderate frailty and a 6% probability of improvement to mild frailty. The probability of improvement from moderate to mild frailty was 26%, and the probability of improvement from mild frailty to fit state was 22%. Other transition probabilities according to the FI quartiles categorization are shown in Appendix 6 (see Extended data)²⁵.

Appendix 7 (Extended data)²⁵ shows a reanalysis modelling missing data as a fourth state. Table 3 shows the effects of sex, age and education in the multi-state models. Being older increased the risk of adverse state transitions from frail to death, from prefrail to frail, and from non-frail to prefrail. The opposite was suggested for favourable transitions from frail to prefrail, and prefrail to non-frail.

As regards sex, being female increased the risk of adverse transitions from non-frail to prefrail, and prefrail to frail; however, it reduced the risk of transition from frail to death. Being female reduced the risk of favourable transitions from pre-frail to non-frail and frail to prefrail. In terms of education, there were trends in the expected direction with higher levels of education being positively associated with the favourable transition from prefrail to non-frail and negatively associated to adverse transitions (Table 3).

Discussion

Using Irish data from a large population-based study of ageing spanning eight years, we corroborated that FI states are dynamic and many transitions are affected by age, sex, and education, in the expected directions. Indeed, frailty is not all steady state and progression, but reversion is also common³². Our study adds value to previous research by reporting a long follow-up period in an Irish sample and offers some new insights on the dynamics of the FI in relation to chronological age. Indeed, our age subanalyses suggested that the FI dynamics are not the same in older groups, with frailer people aged 75 or more having higher mortality and less reversibility than people aged less than 75. This agrees with previous research suggesting that chronological age and the FI may be complementary in predicting health outcomes^33,34. Specifically about sex, our results are in keeping with the known fact that whilst women tend to accumulate more deficits than men of the same age, their risk of mortality tends to be lower⁶. Our results also agree with previous observations that sociodemographic factors (e.g. education) are related to changes in FI status¹⁶. The age-sex-education effects are consistent with previous research and we did not model other time-varying covariates such as physical activity or polypharmacy¹⁷. However, in our FI operationalization, items related to physical activity difficulties and polypharmacy were included as defining FI deficits (Appendix 1, Extended data)²⁵. On the other hand, the efficient statistical handling of additional covariates would have probably required a larger sample size, judging by some of the wide CIs obtained in Table 3 for transitions with a relatively low number of events (Appendix 4, Extended data)²⁵. Even though we broke the FI into three categories utilizing a previously reported scheme and performed sensitivity analysis based on quartiles, the FI is continuous in nature and concern remains as to its optimal categorization²⁷.

Our study has further limitations. For the mortality outcome, specific causes of death were not studied, and addressing this in future studies could shed light into specific biological risks associated with FI states. Another limitation is that missing data was censored as missing completely at random. However, analyses in Appendix 7, Extended data²⁵, suggested that frailer individuals were not more likely to have missing data at future waves (11% for all frailty states).

Another limitation of the use of an FI that was based on self-report is measurement error or misclassification. As visually suggested by the individual-deficit alluvial plots in Appendix 3 (see Extended data)²⁵, some items showed implausible favourable transitions (i.e. from having history of a medical condition at one wave, to not reporting history of that same medical condition at the following wave). However, Appendix 8 (Extended data)²⁵ shows, for example, that implausible transitions from having to not having history of heart attack, diabetes, osteoporosis, cancer, and stroke/TIA, were less frequent (n = 155 to 657) than transitions from other deficits where improvement could be more plausible (e.g. self-rated health, daytime sleepiness, self-rated memory, and difficulties rising from a chair or carrying weights, n = 1946 – 4060). Research from other longitudinal studies has shown that self-reported health questions are prone to significant biases³⁵, and TILDA is not free of those.

As a limitation to the extrapolation of the study and its external validity, it is important to note that the operationalization of frailty does not have a universal consensus, and we here opted for the FI model. Hence, our results cannot be extrapolated to other frailty models such as the frailty phenotype⁵. In the latter case, polypharmacy is not included in the definition of frailty; hence, the frailty phenotype may be more suited for the study of that covariate than the FI. However, the frailty phenotype would be less suited for the study of physical activity because that item is included in the frailty definition.

In summary, given the importance of FI states transition information in planning public health interventions, there is a need to support data collection and projects that measure frailty trajectories and transitions between different levels of frailty severity³⁶, in a way that non-specialist clinicians and the general public can easily understand. We believe that it is important to create a body of international evidence that consistently supports the important public health message that frailty is dynamic over a long period of time, throughout which there is potential and opportunities for improvement. In future work, it would be possible to adapt more advanced methodologies^37,38 to explore the main clusters or groupings of factors that determine different trajectories to identify the best opportunities for reducing the probability of adverse frailty transitions.

Data availability