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Structural Equation Models in Health Research: Analytical Techniques for Healthy Ireland Conference

This conference explores innovative analytical techniques, such as Structural Equation Models, in health research for achieving the goals of Healthy Ireland. It addresses the challenges in health research and the need for composite indicators, multi-dimensional concepts, and the analysis of risk and protective factors.

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Structural Equation Models in Health Research: Analytical Techniques for Healthy Ireland Conference

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  1. Knowledge 4 Health Conference, 25th May 2016, Royal Hospital Kilmainham Knowledge 4 HealthInnovative Analytical Techniques in Health Research: A showcase of Structural Equation Models

  2. 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

  3. 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

  4. 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 indicatorswith high reliability

  5. Issues that need to be addressed in Health research Definition of composite indicators for health, well-being and other key concepts Exploration of the multi-dimensional structure of health and well-being Identification of the risk and protective factors that influence health and well-being The importance of taking account of mediated effects when designing policy interventions 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.

  6. 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”

  7. Composite Indicators: Example: TILDA Wave 1 - Measuring SEP Third-level Education Assets Income Occupation .52 / .53 .46 / .47 .70 / .70 .55 / .57 Socio-Economic Position Parameters shown are the standardised coefficients for the male and female sub-samples

  8. 2. multi-dimensional CONCEPTS Many concepts in health research comprise distinct components(e.g. overall health comprises physical, cognitive, mental, socio-emotional 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

  9. Multi-dimensionalityExample: HP deprivation Index d Age Dependency Rate 1 Demographic d Population Change Growth 2 d Primary Education only 3 d Third Level Education 4 d Persons per Room HP Deprivation Social Class 5 Composition Index d Professional Classes 6 d Semi- and Unskilled Classes 7 d Lone Parents 8 Labour Market d Situation Male Unemployment Rate 9 d Female Unemployment Rate 10

  10. 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 operationalisedby 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 variablesand control for measurement error Example: Estimation of the effect of risk/protective factors and social context on health and well-being

  11. Risk and Protective FactorsExample: TILDA Wave 1 Socio-Economic Position Goodness of Fit (M/F): N: 3,740 / 4,423 CFI: .954 / .956 RMSEA: .024 / .026 Age Abused Childhood Smoker Lives Alone Intimate Relationship Regular Drinker Social Participation Problem Drinker Social Network Overall Health Physical Exercise Unemployed R² = .33 / .46 male or female effect only 0.03 to 0.10 0.11 to 0.20 0.20 to 0.50 Religiosity Personal Well-being All effects significant at p < .05 R² = .55 / .60

  12. 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

  13. MediatED EffectsExample: GuI 9-year olds – Wave 1 Goodness of Fit: N: 4,881 CFI: .951 RMSEA: .023 Financial Difficulties - . 08 Non-Irish Ethnicity SCG Well-being - . 10 Local Problem Scale - . 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 - . 10 Health Status (Child) - . 11 - . 06 - . 28 . 41 Low Education (PCG) . 04 - . 10 . 08 Life Events (Child) - . 07 - . 04 Health Status (PCG) - . 04 Child Well-being Gender (Child) . 07 . 12 Age (PCG) R²=.31 All effects significant at p < .05

  14. 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

  15. Latent Variables in Longitudinal RESEARCHExample: TILDA Waves 1 and 2 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) Stability Factor R2 = 0.72 Well-beingWave 1 Well-beingWave 2 0.75* -0.62* -0.70* 0.52* 0.84 0.81 -0.63* -0.67* 0.48* Depression1 Loneliness 1 Life Satis.1 Qualityof Life 1 Depression2 Loneliness2 Life Satis.2 Qualityof Life 2

  16. Risk factors in a COMPLEX Longitudinal MODEL EXAMPLE: TILDA Waves 1 and 2 Gender (M) Age SocialParticipation -0.04* Well-beingWave 1 Well-beingWave 2 -0.09* 0.07* 0.75* IntimateRelationships -0.09* 0.03* SocialNetwork 0.10* 0.02* Cog. FunctionWave 1 Cog. FunctionWave 2 0.89* -0.04* Smokes -0.03* 0.08* Drinkingproblem -0.02* Phys. HealthWave 1 Phys. HealthWave 2 -0.05* Lives Alone 0.79* -0.03* 0.04* Social Class PhysicalExercise 0.03* Comparative Fit Index (CFI): 0.96 Yuan-Bentler Corrected CFI: 0.96 Yuan-Bentler Corrected RMSEA: 0.036 (CI: 0.035, 0.037)

  17. For further information on our research: www.trutzhaase.eu

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