Trutz HaaseJonathan Pratschke

Kieran McKeown

HEALTHY IRELAND

A Conceptual Approach towards a Research and Data Plan

Presentation at Department of Health, 12th February 2014

KEY CONCEPTS IN HEALTHY IRELAND

Current adverse health trends in Ireland are similar to those causing concern in other developed countries. They include projected significant increases in levels of

chronic disease,

exposure to health risks,

growing health inequalities, and

difficulty in accessing care when it is needed.

Healthy Ireland, page 6

KEY CONCEPTS IN HEALTHY IRELAND cont. …

Healthy Ireland is designed to bring about real, measurable change and is based on an understanding of the determinants of health. Health and well-being are affected by all aspects of a person’s life;

economic status, education, housing, the physical environment in which people live and work.

Health and well-being are also affected by policy decisions taken by Government, the individual choices people make about how they live, and the participation of people in their communities.

Healthy Ireland, page 6

KEY CONCEPTS IN HEALTHY IRELAND cont. …

This means addressing risk factors and promoting protective factors at every stage of life

from pre-natal, through early childhood, adolescence, adulthood and into old age, to support lifelong health and well-being.

Healthy Ireland, page 6

HIGHLIGHTED RISK FACTORS AFFECTING POSITIVE HEALTH OUTCOMES

The major risk factors identified in Healthy Ireland are:

Overweight and Obesity

Mental Health

Smoking

Alcohol and Drugs

But note the conceptual confusion in the above: smoking, alcohol and drugs are pure behaviours and thus potential risk factors. Obesity is a health outcome, but could still be seen as a proxy for behaviour in a similar way. By contrast, mental health is first and foremost a health outcome!

Healthy Ireland, page 10

IN SUMMARY

Healthy Ireland provides an integrated approach to looking at higher level Health and Well-being outcomes,

HI attempts to identify the key Risk and Protective Factors that affect these outcomes,

HI attempts to understand the differentials in health outcomes for different groups within Irish society (i.e. the Social Gradient) and

HI aims at mapping out the movement of key indicators over time, both in terms of health and well-being outcomes and the key factors which affect these.

A CONCEPTUAL APPROACH TO DEVELOPING A RESEARCH AND DATA PLAN

When formulating a conceptual approach to a Research and Data Plan aimed at supporting evidence-based policy making, it is our contention that this can best be achieved on the basis of understanding the structural relationships between key health outcomes and their principal determinants.

This can best be achieved by analysing major Irish datasets (GUI, TILDA, SLAN) using appropriate statistical techniques.

Key latent concepts can be operationalised as composite indices, to facilitate ongoing monitoring of risk and protection factors and health outcomes.

Data collection should be based on a need-to-know approach and guided by the a priori identification of key concepts.

OVERVIEW OVER REST OF PRESENTATION

The remainder of this presentation is organised in four sections:

1.The first section will demonstrate the usefulness of a Structural Equation Model in understanding Child Well-being using the Growing UP in Ireland data.

2.This is followed by a demonstration of the determinants of Health and Well-being of older people using TILDA.

3.The third Section discusses the underlying principles informing Composite Index Construction.

4.The final Section outlines how the approach(es) may be applied in the context of Healthy Ireland, and particularly the development of a Research and Data Plan.

UNDERSTANDING CHILD WELL-BEINGA STUDY BASED ON THE 9-YEAR COHORT OF GUI

The central question guiding our research is:

What influences the well-being of children and their families?

Drawing on Ryff and Keyes (1995), we define well-being as a multi-dimensional construct

situated between the individual and the social whole, comprising:

Emotional well-being (absence of depression, internalising behaviours)

Subjective well-being (e.g. life satisfaction)

Relational well-being (including family and intimate relationships)

Positive self-concept (self-esteem, self-efficacy)

Positive work and/or study role

Absence of symptoms or externalising behaviours

OUR APPROACH

We situate the well-being of children within the context of the “family system”.

We develop an integrated theoretical model of well-being and the family system, based on previous research.

We seek robust latent multi-item measures of key concepts in this model.

We distinguish between

the measurement model (items and scales used to measure key concepts),

the structural model (relationships between the key concepts), and

the risk and protective factors that constitute the context of child development.

We use Structural Equation Modelling techniques to estimate parameters in our model.

AN ECOLOGICAL MODEL OF CHILD WELL-BEING(BRONFENBRENNER)

Child Well-being

PCG Well-being

SCG Well-being

Measurement Model for PCG Well-being

Measurement Model for SCG Well-being

Measurement Model for Child Well-being

BROAD OUTLINE OF A SECOND ORDER LATENT VARIABLE MODEL

Child Well-being

PCG Well-being

SCG Well-being

Dyadic RelationshipParenting Depression Parenting Depression

Scholastic Achievement

Child Difficulties

Self-Concept

Dyadic Relationship

MEASURING CHILD WELL-BEING

Child Well-being

Scholastic Achievement

Self-Concept

Em

otional - PC

G

Conduct - P

CG

Hyp

eractivity - PC

G

Pee

r Relations -

PC

G

Happine

ss - PC

G

App

earance - PC

G

Pop

ularity - PC

G

Intellectual - PC

G

Reading

- PC

G

Maths - P

CG

Teacher E

valuation

Strengths & Difficulties Questionnaire

Piers – Harris II Drumcondra

Child Difficulties

MEASURING PARENTAL WELL-BEING

PCG Well-being

Positive A

spect – P1

Positive A

spect – P2

Positive A

spect – P3

Consensus

Cohesion

Satisfaction

PIANTA - Child Parent Relationship Scale

Dyadic Adjustment Scale

Parenting DepressionDyadic

Relationship

PCG Well-being

RISK AND PROTECTIVE FACTORS

Financial Difficulties

Child Well-being

SCG Well-being

Non-Irish Ethnicity

Low Social Class

Equivalised Household Income Decile

Low Education (PCG)

Health Status (PCG)

Age (PCG)

Local Problem Scale

Local Services Scale

Haase-Pratschke Deprivation Score

ESRI Basic Deprivation

Health Status (Child)

Life Events (Child)

Gender (Child)

A STRUCTURAL EQUATION MODEL OF CHILD AND FAMILY WELL-BEING

Note 1: covariances between disturbance terms for Child Well-being and Parenting (PCG and SCG) not included in figure.

Note 2: all covariances between independent variables omitted from figure

Child well-being

PCG well-being

SCG well-being

ChildDifficulties

EM

O_

P

CO

N_

P

HY

P_

P

RE

L_

P

Self-Concept

PH

_H

AP

PH

_A

PP

PH

_P

OP

PH

_I

NT

ScholasticAchievement

D_R

EA

D

D_M

AT

H

T_

EV

AL

DepressionSCG

ParentingPCG

P1_

PC

G

P2_

PC

G

P3_

PC

G

DepressionPCG

d

d d d d d

DyadicSCG

DC

ON

_S

DC

OH

_S

DS

AT

_S

d d d

d

DyadicPCG

DC

ON

_P

DC

OH

_P

DS

AT

_P

d

ParentingSCG

P1_

SC

G

P2_

SC

G

P3_

SC

G

d d d

d d

Financialdifficulties

Non-Irishethnicity

Low socialclass

Localservices

Haase-Pratschke

Health ofChild

Localproblems

dd d

Life Eventsof child

Genderof child

Age ofPCG

Low educ.of PCG

Healthof PCG

d

dd

dd

HHincome

ESRIDeprivation

INFLUENCE OF RISK AND PROTECTIVE FACTORSON FAMILY WELL-BEING

Explanatory variableChild

well-beingPrimary Caregiver

well-beingSecondary Caregiver

well-being

Neighbourhood variables

Local problems * -0.06 * -0.15 * -0.10

Local services 0.01 0.02 -0.01

Haase-Pratschke Deprivation Score * 0.09 -0.04 -0.00

Child variables

Health of Child * -0.10 * -0.10 -0.02

Life Events of Child * -0.04

Gender of Child male * 0.07

Family variables

Financial Difficulties -0.03 * -0.10 * -0.08

Non-Irish Ethnicity * -0.06 0.01 0.01

Low Social Class -0.03 -0.01 0.04

Equivalised Household Income Decile 0.01 0.00 0.01

ESRI Basic Deprivation Scale -0.04 * -0.11 -.01

Primary caregiver variables

Low Education PCG * -0.07 -0.02

Health of PCG * -0.04 * -0.28 * -0.11

Age of PCG * 0.12 * 0.08

Latent constructs

SCG Well-being 0.04

PCG Well-being * 0.41

R2 0.31 0.17 0.04

Standardised coefficients

PCG Well-being

SIGNIFICANT INFLUENCES ON CHILD WELL-BEING BASED ON THE 9-YEAR COHORT OF GUI

Financial Difficulties

Child Well-being

SCG Well-beingNon-Irish Ethnicity

Low Social Class

Equivalised Household Income Decile

Low Education (PCG)

Health Status (PCG)

Age (PCG)

Local Problem Scale

Local Services Scale

Haase-Pratschke Deprivation Score

ESRI Basic Deprivation

Health Status (Child)

Life Events (Child)

Gender (Child)R²=.31

R²=.17

R²=.04

. 41 . 04

- . 10

- . 15

- . 06

. 09

- . 10

- . 10

- . 04

. 07

- . 08

- . 10

- . 06

- . 11

- . 11

- . 28

. 08

- . 07

- . 04

. 12

All effects significant at p < .05

Goodness of Fit:

N: 4,881CFI: .95RMSEA: .02

KEY FINDINGS

1. The analysis confirms the importance of the mother’s well-being as a mediating factor on the child. A one unit improvement in the mother’s well-being is associated with a 0.4 unit direct improvement in child well-being.

2. In stark contrast, the direct effect of the father’s well-being on the child (.04) is almost negligible once we control for other factors.

3. A striking result is the strongly mediated effect of many contextual influences, in harmony with the ecological model of child well-being.

4. With the exception of the mother’s health and the Haase-Pratschke Deprivation Index, which have a significant direct effect on child well-being, all other socio-economic factors, including financial variables and local area problems, have a distal effect on child well-being that is mediated by the mother’s well-being.

DISCUSSION

1. The conceptualisation of well-being as a higher-order latent concept reveals itself to be a powerful and well-supported hypothesis.

2. The assumption that the well-being of children cannot be understood without simultaneously analysing the well-being of their parents is reinforced.

3. All of the key influences identified in this analysis are in line with our previous research on child and family well-being using independent data – including the finding that a unit change in maternal well-being is associated with almost half a unit change in child well-being.

4. Parents act as a buffer between economic risk factors and child well-being.

5. Socio-economic risks do influence parental well-being, and thus have a mediated effect on children.

6. The model presented here reflects the situation of two-parent families only. As we elected to study the dyadic relationships between caregivers and between caregivers and children, single parents were excluded. The next step would therefore be to focus on the primary caregiver and child, thus including single parent families.

Parental Well-being

SIGNIFICANT INFLUENCES ON CHILD WELL-BEINGBASED ON A META-STUDY OF 6 WELL-BEING STUDIES

At Work

Child Well-being

Problem Solving

Medical C.

Financial D.

Positive Affect (Parent)

Gender (Parent)

Age (Parent)

Local Problem Scale

Local Services Scale

Support Networks

Negative Affect (Parent)Neighbourliness

Age (Child)

Gender (Child)R²=.36

R²=.79

. 46

- . 12

- . 26

- . 04

. 43

- . 08

. 11

Goodness of Fit:

N: 1,600CFI: .95RMSEA: .02 All effects significant at p < .05

Socio-economic Well-being

Age Educ.

Depression Life SatisfactionParent Child Relationship

Partner Relationship

R²=.58 R²=.22 R²=.50 R²=.43 R²=.67

Conduct P. Emotional P Hyperactivity. Peer Prob.

. 76 . 47 . 71 - . 59 . 82

. 15

. 09

. 36. 05

. 06

REFLECTING ON THE DESIGN OF THE GUI DATASET

The GUI 9 year-old cohort data has a number of strengths…

a large sample, panel design, multiple outcome measures, independent assessments, and a clustered sampling design consistent with an “ecological” approach.

…but, given the overwhelming influence of parental well-being on child well-being, the data has also some weaknesses:

It does not provide sufficient information on relationships (reciprocity, support, intimacy, conflict) within the neighbourhood, family or friendship group.

It lacks a range of important measures, such as conflict between intimate partners, subjective well-being, physical symptoms, positive/negative affect, adult self-concept.

In developing a Research and Data Plan for Healthy Ireland it is important that the key structural components that determine overall health and well-being are well understood before signing off on what data should be collected for the future.

LEARNING FROM TILDA

There are four questions which guide our analysis of the TILDA data:

What is the relationship between Overall Health and Overall Well-being?

What is the influence of mediating factors such as an Active Lifestyle and the level of

Health Care utilised?

How are these concepts influenced by a wide range of contextual factors?

How do we develop an understanding of Ageing, other than being simply synonymous

with age?

Note the similarity of questions raised in developing a Research and Data Plan for

Healthy Ireland!

KEY CONCEPTS TO BE CONSIDERED

Well-being

Again, we define well-being as a multi-dimensional construct situated between the

individual and the social whole, comprising:

Emotional well-being (absence of depression)

Loneliness

Subjective well-being (e.g. life satisfaction)

Positive self-concept (self-esteem, self-efficacy)

KEY CONCEPTS TO BE CONSIDERED

Overall Health

Rather than looking at the effect of contextual factors on each specific dimension of

Health, we construct a measure of Overall Health Status as a higher-level latent concept

comprising five components:

Blood Measures

Movement Measures

Neuropsychological Measures

Eyesight, and

Sensory Functioning

THE DATASET

Our analysis is based on the first wave of TILDA data, which has many strengths…

Large sample, panel design, multiple measures, independent assessments, clustered sampling design, “ecological” approach.

Unlike the GUI dataset, TILDA also provides information on self-concept , subjective well-being, physical symptoms, reciprocity and support within the neighbourhood, family or friendship group.

…but the TILDA data still has some weaknesses:

It notably lacks a measure of personality traits (positive/negative affect), which has been shown to be of importance and conceptually and empirically different from depression.

Nevertheless, almost all of the constituents to well-being thought to be of importance can be implemented within the TILDA analysis.

AN ECOLOGICAL MODEL OF THE WELL-BEING OF OLDER PEOPLE

PersonalWell-being

Overall Health

Active Lifestyle

Measurement Model for Overall Health Status

Measurement Model for Active Lifestyle

Measurement Model for Personal Well-being

Social Class

Measurement Model for Social Class

AN IMPORTANT NOTE ON MISSING DATA

The dataset used to estimate the TILDA Well-being Model comprises of all subjects where there exists a complete set of data for all constructs included. It is divided into two samples, a male sample with 1,828 cases, and a female sample with 1,833 cases.

Missing data resulted in the following loss of records:

Failure to return the essential self-completion questionnaire leads to a loss of roughly 15% of the sample, or about 1,300 cases.

Failure to participate in the health assessment leads to a further reduction of almost 2,000 cases.

Failure to provide data on incomes and assets imposes the loss of a further 1,300 cases approximately.

The need to exclude young partners from the sample leads to a further loss of roughly 20 men and 150 women.

Although the complete TILDA sample contains 8,504 cases, the cumulative impact of missing data implies a severe amount of data lost.

AN SEM MODEL OF THE WELL-BEING OF OLDER PERSONS

Age

Lives Alone

Relationship Quality

Close Friends/Relatives

Alcoholic Parent

Abused Childhood

Helping Neighbours

Drinks Regularly

Alcohol Problem

Unemployed

ADL/IADL Impairments

Care Received

PersonalWell-being

Overall Health

Active Lifestyle

Social Class

Smokes

Medical Screening

Transport Problems

ReligiosityNote: Paths between independent variables omitted from figure

Neuropsychol. Health

MODEL COMPLEXITY

The final Model is estimated separately for the male and female samples and comprises:

36 variables per sample

A total of 1,332 observed variances and covariances

Requiring the estimation of 363 parameters (107 regression coefficients for each sample, 16 constrained to be equal across samples

41 variances for each sample, 21 constrained to be equal across samples

52 covariances for each sample

Overall more than 10 cases per parameter estimated.

Despite the significant amount of data lost, this represents a very powerful and well-supported model.

MEASURING SOCIAL CLASS

Social Class

Third-levelEducation

Assets Income Occupation

.60 .29 .40 .51

MEASURING OVERALL HEALTH STATUS

Neuropsycho-logical Health

MMSE Montreal Test Memory Test Exec. Function

Overall Health

Cholesterol Movement L/R EyesightSensory

Functioning

.63 .74 .73 .53

.20 -.62 -.35 .33.57

Social Class

.41

.32

MEASURING ACTIVE LIFESTYLE

Active Lifestyle

Active Social Participation

Involved in Club/Group

Works, Studies or volunteers

Physical Exercise

.60 .43 .27 .25

Age

-.46

-.14

MEASURING PERSONAL WELL-BEING

Personal Well-being

Depression Loneliness Life Satisfaction

Self-Concept

-.60 -.62 .51 .85

.24

SIGNIFICANT INFLUENCES ON PERSONAL WELL-BEING (MALES)

Goodness of Fit:

N: 1,851CFI: .95RMSEA: .028

All effects significant at p < .05

Age

Lives Alone

Relationship Quality

Close Friends/Relatives

Abused Childhood

Helping Neighbours

Drinks Regularly

Alcohol Problem

UnemployedADL/IADL Impairments

Care Received

PersonalWell-being

Overall Health

Active Lifestyle

Social Class

Smokes

Medical Screening

Transport Problems

Religiosity

-.75

.77

.32

.28

.48

.23

.42

-.39

-.26

KEY FINDINGS

1. The analysis confirms the pivotal importance of age for the health and well-being of older persons. A one unit (STD) increase in a person’s age is associated with a 0.75 unit deterioration in the person’s overall health.

2. This, however, does not automatically translate into a similar deterioration of personal well-being. Whilst a person’s health is a strong influence on his/her well-being, the relationship (.24) is not as strong as one might expect and indeed, the moderate negative influence of age via the deterioration in health is partially offset by the positive direct effect of age on well-being (.23) .

3. The other major factor which decouples a person’s well-being from his/her overall health alone, is the strong positive effect (.42) associated with a high quality relationship. It should, however, be noted that one could equally postulate a model in which the quality of relationship is taken as a measurement of a person’s well-being.

KEY FINDINGS

4. As is well-supported by other research, social class has a strong effect (.32) on the health of an older person, but surprisingly, no statistically significant direct effect on personal well-being.

5. Instead, the effect of social class on well-being is entirely mediated; firstly via the already established path from health to well-being and secondly, by the complex way in which it affects a person’s active lifestyle.

6. There exists a strong (.28) direct effect of social class on living an active life style. Poorer health has a strong negative effect (-.72) and an active life style has a positive effect (.14) on a person’s well-being.

OTHER IMPORTANT FINDINGS

1. Medical Screening is positively associated with High Social Class (.18), but negatively associated with Age (-.26) and being Unemployed (-.09).

2. The amount of Care Received from relatives reflects the degree of Impairments (.17), Close Relatives/Friends (.07) and Car Access Problems (.08). Lesser Care Received is associated with better Overall Health (-.08).

3. Living Alone has a negative effect on Health and Personal Well-being, as well as an indirect negative effect on well-being via Alcohol Problems.

4. A good Relationship Quality and having Close Friends and Relatives have positive effects on Active Lifestyle, Overall Health and Personal Well-being.

5. Experiencing Transport Problems negatively affects both Overall Health and Personal Well-being.

6. Religiosity does improve a person’s Well-being, both directly and indirectly via improved drinking and smoking behaviour.

7. In contrast, being unemployed has a negative effect on well-being.

IMPLICATIONS FOR THE DEVELOPMENT OF THE RESEARCH AND DATA PLAN

Our analysis of TILDA provides strong support for a conceptual approach which builds on the use of latent variables in the context of a structural equation modelling environment.

The approach is able to distinguish between the health and well-being of a person, as well as quantifying the influence that key concepts such as social class and lifestyle have on these outcomes.

Of the four underlying tenets of Healthy Ireland, Mental Health, Smoking and Alcohol and Drugs are already successfully identified with their respective effects on Health and well-being.

It may be possible to specify the model in such way as to align it further with the assumptions underlying Healthy Ireland.

FROM LATENT VARIABLE TO COMPOSITE INDICATOR CONSTRUCTION

The conceptualisation of key aspects of health and well-being and their determinants as latent concepts is in line with the rising interest internationally in the construction of Composite Indicators.

It facilitates an understanding of the key dimensions of these concepts a definition as to how these can be measured the ability to model their structural relationships the identification and measurement of key influences on the latent concepts The monitoring of changes in both outcomes and key determinants over time.

Composite Indicators are tools for policy-making

Rapid development of economic indicators after WWII, but slow catch-up in “social” arena.

Indicators are a proxy for unobservable phenomena (broad picture)

Practical, intervention-oriented tools for decision-making

Without theories, statistical data merely “describe the symptoms”

The following slides are excerpts from a recent 2-day workshop delivered at the European Monitoring Centre for Drugs and Drug Addiction (EMCDDA).

COMPOSITE INDEX CONSTRUCTION IN CONTEXT

PRINCIPLES IN THE CONSTRUCTION OF COMPOSITE INDICATORS

What is badly defined is likely to be badly measured …

A sound theoretical framework is the starting point in constructing composite indicators. The framework should clearly define the phenomenon to be measured and its sub-components and select individual indicators and weights that reflect their relative importance and the dimensions of the overall composite. Ideally, this process would be based on what is desirable to measure and not which indicators are available.

PRINCIPLES IN THE CONSTRUCTIONOF COMPOSITE INDICATORS

Overview of Composite Index Construction:

PRINCIPLES IN THE CONSTRUCTIONOF COMPOSITE INDICATORS

PRINCIPLES IN THE CONSTRUCTIONOF COMPOSITE INDICATORS

PRINCIPLES IN THE CONSTRUCTIONOF COMPOSITE INDICATORS

Evaluation Criteria for Selection of Variables: Relevance: are the data what the user expects? Accuracy: are the figures reliable? Comparability: are the data in all necessary respects comparable

across countries (units of analysis)? Completeness: are domains for which statistics are available

reflecting the needs expressed by users? Coherence: are the data coherent with other data? Timeliness and punctuality: does the user receive the data in time

and according to pre-established dates? Accessibility and clarity: is the figure accessible and

understandable?

PRINCIPLES IN THE CONSTRUCTIONOF COMPOSITE INDICATORS

Preliminary Data-Analysis in Variable Selection:

Uni-dimensional Construct; i.e. construct covering a single dimension

Undertake Reliability Analysis Cronbach’s Alpha, which measures internal consistency of a set of items

Cronbach’s alpha (α) Internal Consistency

greater than 0.9 excellent

0.8 – 0.9 good

0.7 – 0.8 acceptable

0.6 – 0.7 questionable

0.5 – 0.6 poor

less than 0.5 unacceptable

PRINCIPLES IN THE CONSTRUCTIONOF COMPOSITE INDICATORS

Preliminary Data-Analysis in Variable Selection:

Multi-dimensional Construct; i.e. construct covering multiple dimensions

Undertake Factor Analysis to identify sufficiently supported dimensions

(subject to sufficient n)

Ask whether the dimensions have substantive meaning

Avoid constructs which are “indicator rich but information poor”

Undertake Reliability Analysis (Cronbach’s Alpha) to assess internal

consistency of each dimension.

FROM OBJECTIVES TO DESIGN

Dimensionality

Composite indicators seek to measure multi-dimensional concepts which cannot be captured by single indicators

Other terms: domain, facet, sub-component, factor, pillar

Relates to the pattern of correlations between variables

Multi-level, hierarchical phenomenon

Has consequences for weighting and aggregation

Analysis may be theory-driven or data-driven

No empirical answers to questions about dimensionality

FROM OBJECTIVES TO DESIGN

Timeliness and Temporal Dynamics

Timeliness refers to the period that elapses between the availability of

an indicator and the phenomenon described

Temporal dynamics are important for benchmarking, programme

assessment and causal analysis

Uses of composite indicators should be anticipated to ensure that

design allows for required temporal dynamics:

“For time-dependent studies, in order to assess country performance across years, the average across countries … and the standard deviation across countries … are calculated for a reference year, usually the initial time point...” (Nardo et al., 2005, pp. 60-1).

FROM OBJECTIVES TO DESIGN

Sensitivity Analysis (OECD Indicator Handbook)

1. inclusion and exclusion of sub-indicators.

2. modelling data error based on the available information on variance estimation.

3. using alternative editing schemes, e.g. single or multiple imputation.

4. using alternative data normalisation schemes, such as re-scaling, standardisation, use of rankings.

5. using different weighting schemes, e.g., methods from the participatory family (budget allocation, analytic hierarchy process) and endogenous weighting (benefit of the doubt).

6. using different aggregation systems, e.g., linear, geometric mean of un-scaled variable, and multi-criteria ordering.

7. using different plausible values for the weights.

EXAMPLES OF COMPOSITE INDICATORS

Examples of Composite Indicators:

UN Human Development Index (HDI)

OECD Technology Achievement Index (TDI)

UK Index of Multiple Deprivation (IMD)

Pobal HP Deprivation Index in Ireland (HP Index)

KEY LESSONS FROM THE HISTORY OF INDICATOR CONSTRUCTION (COBB & RIXFORD, 1998)

1. Having a number does not necessarily mean that you have a good indicator.

2. Effective indicators require a clear conceptual basis.

3. There's no such thing as a value-free indicator.

4. Comprehensiveness may be the enemy of effectiveness.

5. The symbolic value of an indicator may outweigh its value as a literal measure.

6. Don't conflate indicators with reality.

7. A democratic indicators program requires more than good public participation processes.

KEY LESSONS FROM THE HISTORY OF INDICATOR CONSTRUCTION (COBB & RIXFORD, 1998) CONT.

8. Measurement does not necessarily induce appropriate action.

9. Better information may lead to better decisions and improved outcomes, but not as easily as it might seem.

10. Challenging prevailing wisdom about what causes a problem is often the first step to fixing it.

11. To take action, look for indicators that reveal causes, not symptoms

12. You are more likely to move from indicators to outcomes if you have control over resources.

HEALTHY IRELAND: MONITORING AND EVALUATION

Healthy Ireland states that it will be subject to rigorous monitoring and evaluation.

An Outcomes Framework will be developed that will specify key indicators to underpin each of the four high-level goals.

Targets for quantifiable improvements will be set, where appropriate.

Regular measurement of these indicators will allow progress to be assessed over time.

HEALTHY IRELAND’S GOALS

Healthy Ireland states four central goals:

Increase the proportion of people who are healthy at all stages of life

Reduce health inequalities

Protect the public from threats to health and wellbeing

Create an environment where every individual and sector of society can play their part in achieving a healthy Ireland

PATHWAY TOWARDS A RESEARCH AND DATA PLAN FOR HEALTHY IRELAND

1. Identify and agree on key concepts (not individual data!) to be measured. Delphi method may be appropriate for this step.

2. Identify possible data sources from which key concepts can by approximated.

3. Test the coherence of these measurement models using Reliability and Confirmatory Factor Analysis.

4. Determine the structural relationships between key concepts using Structural Equation Modelling.

5. Develop a plan how these measurements can be repeated over time to assist monitoring and evaluation.