1. The 2016 Pobal HP Deprivation Index for Small Areas (SA) Conceptual Basis
Dublin, August 2017

2. A New Interest in Health and Education inequalities
There is a growing interest in the effects of social class on key outcomes in relation to health and education
Social gradients in these arenas are important from an equality perspective and must be taken into account when planning services
Measuring the effects of social class on health and education permits the development of evidence-based resource allocation models and facilitates the monitoring of social gradients
The precise measurement of social gradients plays an important part in the design of performance measurement frameworks and in the evaluation of interventions to improve social equality

3. (Average Health Risks)
Health Outcomes With normally Distributed Measure of Relative Affluence / Deprivation
Deprived
Affluent
Above Average Risks
Health
Risks
Below Average Risks
(Average Health Risks)
This model assumes a normally distributed deprivation index (as e.g. the HP Deprivation Indices) and assesses the relative risk to and protection from health risks across the full affluence to disadvantage spectrum. Relative health risks should be age and gender standardised and expressed relative to the national average. SD
0.1% % % % % % % %
High
Moderate
Low
Source: Haase, Pratschke (2017)

4. Some Considerations when thinking about Health inequalities
Health Outcomes: Mortality, Life Expectancy, Self-rated Health, Long-term Limiting Conditions, Cancer, Respiratory Diseases, Coronary Heart and Cardio-vascular Diseases
Socio-economic Contrasts: Education, Social Class, Deprivation Indices
Geographic Scale: specific spatial contrasts - e.g. individual towns, counties or regions, Electoral Divisions (ED), Small Areas (SA)
Display: Emphasis on Relative Risks, using Bar and Line Charts, Boxplots, and Maps
Category Scales: Quintiles, Deciles, Standard Deviations
Y-Scale: raw percentages, percentage deviation (Mean = 0), standardised mean deviation (e.g. Mean = 0, STD = 10)

5. Why do we use deprivation Indices?
The Census and many sample surveys (TILDA, GUI, Healthy Ireland and the Irish Health Survey) include variables which relate to social class.
Social class is often measured by educational attainments, occupation, employment status or income.
Whilst each of these indicators allow us to identify social gradients, it is arguably more effective to measure social class using a multivariate measurement model based on clearly-defined and theoretically-supported dimensions.
The resulting composite index is likely to perform better than any individual indicator when predicting health-related and educational outcomes. It also has the advantage that it may be used even where individual-level measures of social class are not available (e.g. in administrative datasets such as POD, P-POD, HIPE, NDTRS, NDRDI, CTL, etc.).
Primary and Post-Primary Online Pupil Database (Department of Education and Skills), Hospital Inpatient Enquiry (Department of Health), National Cancer Registry Ireland (NCRI), National Drug Treatment Reporting System (HRB), National Drug-related Death Index (HRB), Central Treatment List (DTCB), etc.

6. Advantages of Composite Deprivation Indices
It is difficult to simultaneously comprehend the spatial distribution of multiple socio-economic indicators at a high level of spatial disaggregation at multiple points in time.
For practical purposes, therefore, it is helpful to have a composite measure of social class which draws together a variety of related observations. This is a more reliable predictor of health and educational outcomes than any individual indicator.
Deprivation indices such as the Pobal HP Deprivation Index provide a reliable means of assessing the effect of social class on health and education, facilitating monitoring and evaluation.
The Pobal HP Deprivation Index also provides a means to support the targeting of disadvantaged areas and communities through the application of evidence-based Resource Allocation Models.

7. The Starting Point for designing a deprivation iNdex: A Comprehensive Definition of Poverty
Relative Poverty
“People are living in poverty if their income and resources (material, cultural and social) are so inadequate as to preclude them from having a standard of living which is regarded as acceptable by Irish society generally.”
(Government of Ireland, NAPS, 1997)
Relative Deprivation
“The fundamental implication of the term deprivation is of an absence – of essential or desirable attributes, possessions and opportunities which are considered no more than the minimum by that society.”
(Coombes et al., DoE – UK, 1995)

8. Traditional Approach to combining multiple Indicators: Exploratory Factor Analysis (EFA)
Exploratory Factor Analysis (EFA) reduces variables to a smaller number of underlying Dimensions or Factors V1 F1 V2 V3 V4 V5 F2 V6
EFA is an exploratory technique; .i.e. essentially data-driven
all variables load on all factors
the structure matrix is the (accidental) outcome of the variables available
EFA cannot be used to compare outcomes over time

9. Approach underlying the HP Deprivation Indices: Confirmatory Factor Analysis (CFA)
Confirmatory Factor Analysis also reduces observations to the underlying Factors, however d V1 1 L1 d V2 2 d V3 3 d V4 4 d V5 5 L2 d V6 6
CFA requires a strong theoretical justification before the model is specified
Researchers determine which observed variables are associated with which latent construct
variables are treated as imperfect manifestations of the latent concepts
CFA models can allow the comparison of outcomes over time
CFA facilitates the objective evaluation of the quality of the model through fit statistics

10. Overview of Successive Deprivation Indices, Haase & Pratschke 1996 - 2012
SA n=18,488 06 06 11 06 11
01NI
ED n = 3,409 91 96 86 91 96 91 96 02 91 96 02 06 06 91 96 02 06 11 06 11
01NI
NUTS 4 n = 34 91 96 86 91 96 91 96 02 91 96 02 06 06 91 96 02 06 11 06 11
01NI
NUTS 3 n = 8 91 96 86 91 96 91 96 02 91 96 02 06 06 91 96 02 06 11 06 11
01NI
NUTS 2 n = 2 91 96 86 91 96 91 96 02 91 96 02 06 06 91 96 02 06 11 06 11
01NI
NUTS 1 n = 1 91 96 86 91 96 91 96 02 91 96 02 06 06 91 96 02 06 11 06 11
01NI
Haase et al., 1996
Haase, 1999
Pratschke & Haase, 2001
Pratschke & Haase, 2004
Haase & Pratschke, 2005
Haase & Pratschke, 2008
Level at which model is estimated
Level to which data is aggregated
Haase & Pratschke, 2010
Haase & Pratschke, 2011
Haase & Pratschke, 2012

11. HP Deprivation Indices 2011-2016
2011 Pobal HP Deprivation Index
All-Island HP Deprivation Index
2016 Pobal HP Deprivation Index SA 06 11
11 RI
11 NI 06 11 16
Longitudinal
HP Deprivation Index
Longitudinal
HP Deprivation Index ED 06 11
11 RI
11 NI 91 96 02 06 11 06 11 16 91 96 02 06 11 16
NUTS 4 06 11
11 RI
11 NI 91 96 02 06 11 06 11 16 91 96 02 06 11 16
NUTS 3 06 11
11 RI
11 NI 91 96 02 06 11 06 11 16 91 96 02 06 11 16
NUTS 2 06 11
11 RI
11 NI 91 96 02 06 11 06 11 16 91 96 02 06 11 16
NUTS 1 06 11
11 RI
11 NI 91 96 02 06 11 06 11 16 91 96 02 06 11 16
Haase & Pratschke, 2012
Haase & Pratschke, August 2017
Pratschke & Haase, 2014
Haase & Pratschke, October 2017
Haase, Pratschke & Gleeson, 2014
Level at which model is estimated
Level to which data is aggregated

12. The Underlying Dimensions of Social Disadvantage
Demographic Decline (predominantly rural)
population loss and the social and demographic effects of emigration (age dependency, low education of adult population)
Social Class Deprivation (applying in rural and urban areas)
social class composition, education, housing quality
Labour Market Deprivation (predominantly urban)
unemployment, lone parents, low skills base

13. The Measurement Model of the Pobal HP Deprivation Index
Age Dependency Rate 1
Demographic d
Population Change
Growth 2 d
Primary Education only 3 d
Third Level Education 4 d
Persons per Room 5
Social Class
Composition d
Professional Classes 6 d
Semi- and Unskilled Classes 7 d
Lone Parents 8
Labour Market d
Male Unemployment Rate
Situation 9 d
Female Unemployment Rate 10
The Pobal HP Deprivation Index is estimated using a multiple group means and covariance structure model
This makes it possible to measure the change that occurred in the mean of the latent variables between 2006, 2011 and 2016

14. Strengths of the HP Deprivation Indices
true multidimensionality, based on theoretical considerations
provides for an appropriate treatment of both urban and rural deprivation
no double-counting
rational approach to indicator selection
uses variety of alternative fit indices to test model adequacy
identical structure matrix across multiple waves
identical measurement scale across multiple waves
true distances to means are maintained (i.e. measurement, not ranking)
distinguishes between measurement of absolute and relative deprivation
allows for true inter-temporal comparisons

15. Mapping Deprivation most disadvantaged most affluent
marginally below the average
marginally above the average
disadvantaged
affluent
very disadvantaged
very affluent
extremely disadvantaged
extremely affluent

16. Distribution of Absolute and Relative HP Index Scores, 2006, 2011 and 2016
HP Deprivation Index Score
Min
Max
Mean
Standard Deviation
2006 absolute HP Deprivation Score
-41.52
38.34
0.00
10.00
2011 absolute HP Deprivation Score
-37.77
32.96
-6.87
9.60
2016 absolute HP Deprivation Score
-43.47
36.10
-4.24
10.08
2006 relative HP Deprivation Score
2011 relative HP Deprivation Score
-30.76
39.97
2016 relative HP Deprivation Score
-39.25
40.31

17. Distribution of Absolute HP Index Scores, 2006, 2011 and 2016
most disadvantaged
most affluent
The Figure shows the decline by 7.0 points in the mean of the Absolute HP Index Scores between 2006 and 2011 (or 0.7 of a standard deviation) and the subsequent recovery by 2.8 Index Scores (or 0.28 of a standard deviation) between 2011 and 2016.

18. Absolute Index Scores 2006

19. Absolute Index Scores 2011

20. Absolute Index Scores 2016

21. Relative Index Scores 2006

22. Relative Index Scores 2011

23. Relative Index Scores 2016

24. Comparison of Relative Deprivation Scores between 2006 and 2016
The most basic pattern of affluence and disadvantage has remained broadly intact over this 15-year period: affluence is highest in the urban peripheries and gradually declines as one moves towards more rural locations.
There is some indication that the reach of affluent commuter belts has diminished, particularly in the years following the recession.
Within the Greater Dublin Area, there has been a shift in the location of the most affluent areas. Whereas in 2006 the Western part of this area scored highly, in 2011 and 2016 affluence is once again concentrated in its traditional heartlands (e.g. Dun Laoghaire/Rathdown.

25. Change in Relative Index Scores 2006-2011