Title: Health measurement in population surveys: Combining information from self reported and observer meas
1Health 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
3Self 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) -
5Observer 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
6But 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
7Why 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
8Why 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
10Descriptive statistics of the 6 health indicators
11A unidimensional measurement model
Grip strength
e
Chair rise
e
Lung function
Somatic Health
e
Functional limitations
e
Self rated health
e
Long standing illness
e
12A 2nd order model
Grip strength
e
Observer measured
Chair rise
e
Lung function
Somatic Health
e
Functional limitations
e
Self reported
Self rated health
e
Long standing illness
e
13A bifactor multimethod model
Grip strength
e
Observer measured
Chair rise
e
Lung function
e
Somatic health
Functional limitations
e
Self reported
Self rated health
e
Long standing illness
e
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
16Standardised factor loadings and proportions of
the health indicators explained by the model
- Self report of Functional Limitations is the most
reliable health indicator!!!!
17The distribution of somatic health in the
population
High values indicate good somatic health
18MIMIC model standardised parameters
19External criterion validity I
20External criterion validity II
21External 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
23Conclusions 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
24Future 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.