The Irish Longitudinal Study on Ageing TILDA is supported by

1. Using Structural Equation Modeling and Network Analysis to Understand Frailty Bellinda King-Kallimanis, George Savva and Rose Anne Kenny The Irish Longitudinal Study on Ageing, Trinity College Dublin The Irish Longitudinal Study on Ageing TILDA is supported by the Department of Health and Children, Irish Life and the Atlantic Philanthropies The British & Irish Longitudinal Studies on Ageing Meeting; Dublin, Oct/Nov, 2013

2. Background - frailty • Several competing definitions of frailty • Fried’s phenotype is one definition and is characterised by five domains, each representing a different part of the frailty cascade • Weakness (low grip strength) • Slowness (low gait speed) • Low activity (activity questionnaire) • Exhaustion (depression scales..) • Weight loss (have you lost weight...) • Assumes frailty is a one-dimensionalconstruct

3. Background – frailty & complex relationships Fried’s phenotype of frailty is not capturing the complexity of the physiological cycle of decline, despite known relationships with: – disability – depression – cognitive impairment No study that we are aware of has attempted to look at the relationships between all four By understanding the complex interplay of physical and mental health symptoms we can learn where to intervene to decrease healthcare costs and improve quality of life.

4. Aims In general, we want to better understand how to measure frailty, for example: – less measurement error – appropriate models We would like to translate research on frailty into the clinic, but realize there may be a distinction between the two areas when measuring frailty Finally, we want to model the complex relationships between frailty and other overlapping domains

5. Conceptualization 3 conceptualizations of the relationship between symptoms and disorders • Constructive Perspective • Disorders are conveniently grouped as sets of symptoms • Diagnostic Perspective • Disorders are latent classes underlying symptoms • There is such a disorder and we can be right or wrong in the diagnosis • Dimensional Perspective • Symptoms measure latent continua Borsboom, D. (2008). Jr of Clin Psych. 64(9)

6. Latent variable analysis Latent variable analysis allows us to treat frailty as an un-observed variable To make statements about the frailty variable we use observed items, i.e., slowness, weight loss A number of assumptions are made: Key assumption -> the indicators of a latent variable are directly related only because of the single common cause of the latent variable, which is responsible for all correlations between the indicators. In other words, the relationships are spurious and not causal

7. Study 1 – Survey of Health, Ageing and Retirement in Europe

8. SHARE - frailty We used the Survey of Health, Ageing and Retirement in Europe to investigate whether frailty was assessed without measurement bias across 12 European countries. We used a uni-dimensional model for frailty to assess measurement bias, however, there were indications that the model was two-dimensional Raised the question of whether we should be considering frailty as a uni-dimensional construct.

9. SHARE - frailty Is this the frailty model uni-dimensional or multi-dimensional?

10. Study 2 - TILDA

11. TILDA - frailty We used nine items to capture frailty phenotype indicators that have been previously used in SHARE, Fried and Morley. These included: • Weakness (Grip) • Slowness (TUG) • Low activity (IPAQ) • Exhaustion (CES-D) • Weight loss (Self report) First model: uni-dimensional model – χ2(18) = 132.47, RMSEA 0.042 Second model: multidimensional – χ2(25) = 50.02, RMSEA 0.021 Third model: 2 year mortality included - χ2(50) = 205.83, RMSEA 0.038 – Illness (5+ out of 11) – Resistance (steps without rest) – Fatigue (feel tired) – Appetite loss (CES-D)

12. Background - frailty Sex Death .01NS Age NS -.01NS .03*** .11*** .68*** Frailty 2 Frailty 1 .32*** .76*** .66*** .73*** .54*** .70*** .48*** .37*** .49*** .41*** Slowness Low activity Resistance Weakness Fatigue Appetite loss Weight loss Exhaust Illness

13. Study 3 – TILDAL: Beyond Latent Variables

14. Complex Relationships Frailty Disability Depression Cognitive Impairment x10 x16 x11 x17 x12 x13 x18 x14 x19 x20 x15 x1 x5 x2 x9 x8 x7 x6 x3 x4 Using the latent variable approach to assess complex relationships

15. Beyond LVA There is a fourth conceptualization: 4. Disorders as Causal Systems • Disorders consist of sets of symptoms that are connected through a system of causal relations Borsboom, D. (2008). Jr of Clin Psych. 64(9)

16. Network Analysis • Examples: Six Degrees of Kevin Bacon (Hollywood) and the Erdős number (mathematicians and statisticians). • Describe complex network systems: • – If we replace people with the indicators of frailty, disability, depression and cognitive impairment and movies/papers with co-occurrence between indicators we can develop a network of these four conditions and learn how they interact. • Clinical scenario: patient whose reduced physical activity due to elements of cognitive decline, has led to slowing down, which may further lead to weakness and the inability to climb a flight of stairs and ultimately to a frail patient who is more vulnerable adverse health outcomes.

17. Hypothesized model - Network Analysis Bridging indicators x7 x2 Larger nodes indicate greater prevalence b1 x3 x8 x6 x1 b3 x4 x9 Disability b2 Frailty Heavier lines represent stronger associations – e.g. > relative risk x15 x12 Cognitive Impairment x16 x18 x11 x14 x17 x13 Node Depression Link

18. qgraph raw correlations - Network Analysis

19. Network Analysis – current model 5 clusters emerge 1. impairments + Frailty (physical activity) 2. IADLs + ADLs 3. Depression + frailty (exhaustion & weight loss) 4. Cognition – orientation/naming items 5. Cognition – executive function/memory Small world index 3.22

20. Network Analysis – current model The results at this moment aren’t very stable – I am experiencing convergence problems Regardless, the results do seem to suggest that disability, depression and cognition are distinct, and that the components of frailty do not cluster as a unique frailty cluster Exhaustion has a high centrality 914.45 suggesting it links other symptoms that would otherwise be poorly connected. The other frailty indicators have centrality less 1

21. Conclusion From both LVA and network analysis we can see that the exhaustion and weight loss indicators of frailty capture a different dimension of frailty From the preliminary network analysis results it appears that exhaustion may be an important symptom to watch in vulnerable older adults. Network analysis allows us to think about the relationships between symptoms in a way that is more fluid then attempting to make diagnoses or place rigid assumptions on our models

22. Questions Do we need a gold standard to measure frailty in our research studies? Should we consider different measures for frailty based on the setting? Something clinician appropriate and something research appropriate? Do people think there are really two dimensions of frailty?