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KNOWLEDGE 4 HEALTH
Innovative Analytical Techniques in Health Research:
A showcase of Structural Equation Models
Trutz Haase
Jonathan Pratschke
Knowledge 4 Health Conference, 25th May 2016, Royal Hospital Kilmainham
HEALTHY IRELAND
“Accelerating the take-up of new knowledge and innovating through
advances in scientific knowledge is a key aspect of how we will achieve
the four high-level goals. The consistent application of evidence of what
works and what interventions positively impact on health behaviours, in a
cost-effective way, is critical to setting policy and investing in prevention
programmes. Excellent population health analysis capability is required
for understanding and predicting threats to public health.”
Department of Health, 2013, p. 28
A NEW DIRECTION
The Healthy Ireland strategy entails a paradigm shift involving:

a broader definition of health which embraces physical, mental and
social well-being

a “whole-of-society approach” to population health

a concern with the wider determinants of health

a stronger evidence-based approach to policy

a focus on the social determinants of health as direct and indirect
causes of illness

an emphasis on reliable indicators of health, well-being and risk
factors
CHALLENGES FOR HEALTH RESEARCH
This implies a number of difficult challenges for health researchers:

a move from specialist sectoral analyses to more systemic research

a shift away from random control trials and towards observational
data on the population

a drive towards more fully-specified explanatory models

a greater emphasis on causal inference

a concern with complex networks of direct and indirect influences

a demand for more comprehensive composite indicators with high
reliability
ISSUES THAT NEED TO BE ADDRESSED IN HEALTH
RESEARCH
1.
Definition of composite indicators for health, well-being and other
key concepts
2.
Exploration of the multi-dimensional structure of health and wellbeing
3.
Identification of the risk and protective factors that influence health
and well-being
4.
The importance of taking account of mediated effects when
designing policy interventions
5.
The monitoring of health outcomes and risk factors over time
This presentation aims to demonstrate the contribution that
Structural Equation Modelling can make to these tasks.
1. WHY DO WE NEED COMPOSITE INDICATORS?

Ease of interpretation – compared with results from many individual
variables, each with its own specificities

In harmony with the Common Risk Factor approach central to
Healthy Ireland

Can be used to assess progress over time and to facilitate
benchmarking and monitoring (powerful policy impacts)

Use of broadly-defined concepts facilitates communication of
research findings to the general public
Example: Socio-economic Position as a “latent variable”
COMPOSITE INDICATORS:
EXAMPLE: TILDA WAVE 1 - MEASURING SEP
Third-level
Education
Assets
.52 / .53
Income
.46 / .47
.70 / .70
Occupation
.55 / .57
SocioEconomic
Position
Parameters shown are the standardised coefficients for the male and female sub-samples
2. MULTI-DIMENSIONAL CONCEPTS

Many concepts in health research comprise distinct components
(e.g. overall health comprises physical, cognitive, mental, socioemotional dimensions)

The definition of these concepts should be driven by theory

This means that we should start by assessing/testing the
dimensionality of key concepts

Each dimension may be measured indirectly using a set of
criteria/variables, treated as “partial manifestations”
Example: Dimensionality of HP Deprivation Index based on
Social Class, Labour Market Deprivation, Demographic Decline
MULTI-DIMENSIONALITY
EXAMPLE: HP DEPRIVATION INDEX
d1
Age Dependency Rate
d2
Population Change
d3
Primary Education only
d4
Third Level Education
d5
Persons per Room
d6
Professional Classes
d7
Semi- and Unskilled Classes
d8
Lone Parents
d9
Male Unemployment Rate
d10
Female Unemployment Rate
Demographic
Growth
Social Class
Composition
Labour Market
Situation
HP Deprivation
Index
3. RISK AND PROTECTIVE FACTORS

Healthy Ireland emphasises health risk behaviours, which influence
many different outcomes

This Common Risk Factor approach needs to be built explicitly into
the design of research projects

This approach can be operationalised by analysing the effects of
risk/protective factors on health and well-being within a SEM model

If this is done using Structural Equation Modelling techniques, we can
use latent variables and control for measurement error
Example: Estimation of the effect of risk/protective factors
and social context on health and well-being
RISK AND PROTECTIVE FACTORS
EXAMPLE: TILDA WAVE 1
Goodness of Fit (M/F):
N:
CFI:
RMSEA:
3,740 / 4,423
.954 / .956
.024 / .026
SocioEconomic
Position
Age
Abused Childhood
Smoker
Lives Alone
Regular Drinker
Intimate Relationship
Social Participation
Problem Drinker
Physical Exercise
Social Network
Overall
Health
Unemployed
R² = .33 / .46
male or female effect only
0.03 to 0.10
0.11 to 0.20
0.20 to 0.50
All effects significant at p < .05
Religiosity
Personal
Well-being
R² = .55 / .60
4. MEDIATED EFFECTS

Classical regression models allow us to estimate the net direct
effect of a variable, without considering inter-relationships

To make research more relevant to policy-making, we have to
model how effects are generated (underlying mechanism)

It is essential to start with a theoretical model and to use path
diagrams to translate this into a statistical model
Example: Analysis of GUI 9-year-old cohort confirms importance
of mother’s well-being as mediator in relation to child well-being
It also reveals how many contextual influences are also
mediated, in line with the ecological model of child well-being
MEDIATED EFFECTS
EXAMPLE: GUI 9-YEAR OLDS – WAVE 1
Goodness of Fit:
N:
CFI:
RMSEA:
4,881
.951
.023
Financial Difficulties
- . 08
- . 10
Local Problem Scale
SCG Well-being
Non-Irish Ethnicity
- . 10
Low Social Class
R²=.04
- . 06
Local Services Scale
Equivalised Household
Income Decile
Haase-Pratschke
Deprivation Score
- . 15
ESRI Basic Deprivation
- . 11
PCG Well-being
. 09
R²=.17
Health Status (Child)
- . 10
- . 06
- . 11
. 41
. 04
- . 10
Life Events (Child)
Gender (Child)
. 08
- . 07
- . 04
. 07
Child Well-being
R²=.31
All effects significant at p < .05
- . 28
Low Education (PCG)
Health Status (PCG)
- . 04
. 12
Age (PCG)
5. MONITORING HEALTH OUTCOMES AND RISK
FACTORS OVER TIME

Monitoring key health policies should be carried out in relation to the
overall health and well-being of the population

Structural Equation Modelling allows for…

the specification of outcomes as latent concepts

the investigation of their inter-relationships

their change over time

measurement of the effects of risk behaviours and socio-economic
factors

SEM provides the most powerful analytical framework to respond
to the challenges posed by the Healthy Ireland “paradigm”
Example: Longitudinal study of the determinants of health and
well-being using data from Waves 1 and 2 of TILDA
LATENT VARIABLES IN LONGITUDINAL RESEARCH
EXAMPLE: TILDA WAVES 1 AND 2
Stability Factor
R2 = 0.72
Well-being
Wave 1
-0.62*
Depression
1
-0.70*
Loneliness
1
0.52*
Well-being
Wave 2
0.75*
0.84
Life Satis.
1
-0.63*
Quality
of Life 1
Depression: 20-item CESD score (Radloff, 1977)
Loneliness: 5-item UCLA Loneliness Scale (Russell, 1996)
Life Satisfaction: single item with a 7-point response scale
Quality of Life: 19-item CASP scale (Hyde et al., 2003)
Depression
2
-0.67*
Loneliness
2
0.48*
0.81
Life Satis.
2
Quality
of Life 2
RISK FACTORS IN A COMPLEX LONGITUDINAL MODEL
EXAMPLE: TILDA WAVES 1 AND 2
Gender (M)
Age
Social
Participation
-0.04*
Well-being
Wave 1
-0.09*
0.75*
0.03*
Well-being
Wave 2
Intimate
Relationships
-0.09*
0.10*
Cog. Function
Wave 1
0.07*
0.02*
Cog. Function
Wave 2
0.89*
Social
Network
-0.04*
-0.03*
Smokes
0.08*
-0.05*
Phys. Health
Wave 1
Phys. Health
Wave 2
0.79*
-0.02*
Drinking
problem
-0.03*
Lives Alone
0.03*
Physical
Exercise
0.04*
Social Class
Comparative Fit Index (CFI): 0.96
Yuan-Bentler Corrected CFI: 0.96
Yuan-Bentler Corrected RMSEA: 0.036 (CI: 0.035, 0.037)
For further information on our research:
www.trutzhaase.eu