In the quest for “true” population health a plethora of health indicators have been used

1. Health measurement in population surveys: Combining information from self reported and observer measured health indicatorsGeorge B. Ploubidis

2. In the quest for “true” population health a plethora of health indicators have been used • Broadly they are classified into self reported and observer measured –performance based- indicators • Both types of health indicators are susceptible to measurement error

3. Self reported health indicators • Self rated health (the most widely used) • Self report of chronic illness • Self report of somatic symptoms • Activities of Daily Living/Instrumental activities of daily living

4. Self rated health is probably the most widely used indicator of “true” population health status • Easy to use and is the strongest predictor of all cause mortality • But!!! Problems with reporting styles (applies to all self reported indicators) • Measurement invariance between SEP groups also a problem • But, what does it really measure? (individual differences in health attribution as well as prior information)

5. Observer measured indicators Grip strength Spirometry (Forced Vital Capacity, Forced Expiratory Volume and others) Blood pressure Timed chair rise stands Blood analytes Missing data can be a problem, especially in the older population, where the most frail have a higher probability of not participating in the measurements

6. But what happens when true population health is the outcome of interest? Barsky paradox: Self reported and observer measured health indicators often in conflict - especially in estimation of health trends over time A more reliable estimate of “true” population health status is needed

7. Why Latent Variable Models? LVM provide an opportunity to combine self reported and observer measured indicators to a reliable estimate of population health According to psychometric theory responses to a health indicator can be decomposed to true health and measurement error Measurement error can be further decomposed to systematic and random Big (but testable) Assumption!!!!!!!!!!!!! There is a latent dimension common to all these indicators that represents health

8. Why Mplus? Only software that can combine binary, ordinal and continuous indicators in a latent variable within a single measurement model Measurement model can be used as outcome or predictor with various models, e.g Survival models, Growth Curve models, LCA models and more

9. Method • We used data from a nationally representative sample of the older population, the 2nd wave of the English Longitudinal Study of Ageing (ELSA) which was carried out in 2004 • Six health indicators were used, three self reported and three observer measured • Participants were included in the analysis if they had data on all six indicators, N= 5,964 (out of 8,780 total) – We also estimated models with missing data

10. Descriptive statistics of the 6 health indicators

11. A unidimensional measurement model e Grip strength Chair rise e Lung function e Somatic Health Functional limitations e Self rated health e Long standing illness e

12. A 2nd order model e Grip strength Observer measured Chair rise e Somatic Health Lung function e Functional limitations e Self reported Self rated health e Long standing illness e

13. A bifactor – multimethod model e Grip strength Observer measured Chair rise e e Lung function Somatic health Functional limitations e Self reported e Self rated health e Long standing illness

14. We carried out analyses with MLR and WLSMV – Very similar results • The selected model was regressed on covariates (MIMIC model) that represent SEP as well as external validation criteria

15. Results • The bifactor model was confirmed by the data and was superior compared to the two competing measurement models

16. Standardised factor loadings and proportions of the health indicators explained by the model • Self report of Functional Limitations is the most reliable health indicator!!!!

17. The distribution of somatic health in the population High values indicate good somatic health

18. MIMIC model standardised parameters

19. External criterion validity I

20. External criterion validity II

21. External criterion validity III

22. Conclusions I • Our results support a single latent dimension, common to both self reported and observer measured health indicators ,that represents somatic health • This latent dimension is a reliable quantitative estimate of “true” population (somatic) health • Measurement invariance of this population health metric over groups or time can me easily tested • Response bias and other forms of measurement error are largely controlled

23. Conclusions II • Self reported and observer measured health indicators appear to be equally reliable, thus both need to be considered in population health measurement • FL is the indicator of choice in the older population when time/budget constrains allow for one health indicator to be used • Responses of both genders to self rated health items are biased by exogenous to their somatic health status influences

24. Future plans Add mental health to the model as an indicator of health OR joint modelling of somatic and mental health outcomes (probably better) Apply the model to the younger population and use different health indicators Use the LISH in the calculation of Healthy life Expectancy – currently done with self rated health and functional limitations only

25. Limitations • The use of chair rise as an indicator of health excluded the most frail participants from the analysis, although models including missing data returned similar results • The indicators employed in this study are a sample of the population of possible health indicators.