1. The Pobal HP Deprivation Index An Inter-temporal Analysis 1991 - 2011
Geary Institute, Dublin, February 2013

2. The 2011 Pobal HP Deprivation Index
The purpose of the presentation is
to provide an overview of the changes in absolute and relative deprivation between 1991 and 2011
to provide an overview of the conceptual components which underlie the Index, and
to draw out the Index’ features which are of relevance when modelling the social gradient of health and other well-being outcomes.

3. The Pobal HP Deprivation Measures
Electoral Division (ED) Level Analysis,
Important Note:
The analysis spanning 5 census waves is based on ED-level deprivation scores. These are different to the scores derived from the SA-level analysis and should note be confused with the ED-level scores derived from the SA-level analysis as shown in Pobal Maps!

4. The Pobal HP Deprivation Index spanning 5 census Waves, based an on ED-Level Analysis
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

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

6. ED-Level absolute Index Scores 1991

7. ED-Level absolute Index Scores 1996

8. ED-Level absolute Index Scores 2002

9. ED-Level absolute Index Scores 2006

10. ED-Level absolute Index Scores 2011

11. ED-Level Relative Index Scores 1991

12. ED-Level Relative Index Scores 1996

13. ED-Level Relative Index Scores 2002

14. ED-Level Relative Index Scores 2006

15. ED-Level Relative Index Scores 2011

16. HP Deprivation Scores in Comparison, 1991-2011
 HP Deprivation Index N
Minimum
Maximum
Mean
Std. Deviation
HP 1991 ED absolute
3,409
-28.0
73.3
0.0
10.0
HP 1996 ED absolute
-27.4
45.7
4.3
9.2
HP 2002 ED absolute
-30.6
42.1
8.4
9.9
HP 2006 ED absolute
-35.0
39.9
9.3
HP 2011 ED absolute
-43.7
41.6
-1.4
10.1
HP 1991 ED relative
HP 1996 ED relative
-34.4
45.1
HP 2002 ED relative
-39.4
34.0
HP 2006 ED relative
-47.4
32.9
HP 2011 ED relative
-41.9
42.7

17. The Pobal HP Deprivation Measures Small Area (SA) Level Analysis, 2006 - 2011

18. The Pobal HP deprivation Index - Dublin Inner City (ED level)
Look at North Dock C and Mansion House A, which are defined as “marginally below average deprivation” in an ED-level deprivation analysis

19. The Pobal HP deprivation Index - Dublin Inner City (SA level)
The SA-level analysis shows the detail of the distribution of affluence and deprivation within North Dock C and Mansion House A.

20. The Pobal HP Deprivation Index for 2006-2011, based an on SA-Level Analysis
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

21. SA-Level Absolute Index Scores 2006

22. SA-Level Absolute Index Scores 2011

23. SA-Level Relative Index Scores 2006

24. SA-Level Relative Index Scores 2011

25. Conceptual Underpinnings of the Pobal HP Deprivation Index

26. 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)

27. Traditional Approach: Exploratory Factor Analysis (EFA)
Ordinary Factor Analysis (EFA) reduces variables to a smaller number of underlying Dimensions or Factors V1 F1 V2 V3 V4 V5 F2 V6
EFA is essentially an exploratory technique; .i.e. 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

28. New Approach: 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
the researcher decides which of the observed variables are to be associated with which of the latent constructs
variables are conceptualised as the imperfect manifestations of the latent concepts
CFA model allows the comparison of outcomes over time
CFA facilitates the objective evaluation of the quality of the model through fit statistics

29. 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

30. The Basic Model of the SA-Level 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

31. A Longitudinal SA-Level SEM Model, 2006-2011

32. The Basic Model of the ED-Level 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

33. A Multiple Group Model Spanning five Census Waves
Multiple Group Model fitted simultaneously across five census waves
imposing identical structure matrix
and identical path coefficients
1991
1996
2002
2006
2011

34. Strengths of CFA-based 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

35. Applications of the Pobal HP Deprivation Index

36. Applications of the Pobal HP Deprivation Index
Local development
Local Community Development Programme (LCDP), RAPID
Childcare Initiatives, Family Resource Centres, County Development Plans
Health
Mortality Studies, Epidemiological Studies, Primary Health Care, Health Inequality
Education
Educational Disadvantage, Higher Education Access Route
Environment
National Transport Planning, National Spatial Strategy
Statistical Methods and Research Design
Optimising the Sampling Strategy for CSO Household Surveys
Social Equality / Inequality (EU-SILC, QNHS, GUI, TILDA, SLAN, NDS)

37. Health Risk and Relative Affluence / Deprivation
Deprived
Affluent
Health
Risks SD
0.1% % % % % % % %
High
Moderate
Low

38. SEM Model To Assess Equality of Access to Cancer Care
Measurement Model
for Possessions
Measurement Model
for Attributes
Risk and Protective Factors
Risk and Protective Factors
Attributes
Possessions
Access to Cancer Care
Measurement Model for Access to Cancer Care

39. Modelling Population Shares according to relative Deprivation T – Total Population L – low (48.3%) M – Medium (22.4%) H – High ( 7.4%)
L: 0 STD
48.3% Population
M: -1 STD
22.4%
T : >5 STD
(Total Population)
H: -2 STD
7.4%

40. The HSE Resource Analyser
2011 Census of Population
Reference Database for 18,488 Small Areas
Administrative data on current allocations
Data Sources
2011 Pobal HP Deprivation Index
Reference Models
Total Population 100%
Low Deprivation 48.2%
Medium Deprivation 22.4%
High Deprivation 7.4%
Combined Target Allocation
Model Choices
60% 5%
15%
20%
Data aggregation to spatial area of interest (Region, ISA, PCT etc.)

41. Relative Standard Error
Optimising Sampling Methodologies for CSO Household Surveys
Comparison of Sampling Designs in the Estimation of Employment (E), Unemployment (UE), Long-term Limiting Illness (LLI) and Education (ED)
Model
Sample Design
Relative Standard Error
Mean Square Error
95% Confidence Interval E UE
LLI ED
EU - SILC
2SCS
1,300x4 3
2SSCS NUTS 4 x Area 8 2
2SSCS NUTS 3 x Area 5 x HP Ind 5 1
2SSCS NUTS 3 x HP Index 10
QNHS
1,300x20
Haase, T. and Pratschke, J. Optimising the Sampling Methodology for CSO Household Surveys, CSO, 2012

42. Imperial College London
Small Area Estimation
The BIAS project
Imperial College London
Small area estimation
Nicky Best, Sylvia Richardson, Virgilio Gómez Rubio
This work is being carried out in collaboration with ONS. The basic methodological problem is to estimate the value of a given indicator (e,g. income, crime rate, unemployment) for every small area, using data on the indicator from individual-level surveys in a partial sample of areas, plus relevant area-level covariates available for all areas from e.g. census and administrative sources.

43. Evaluating the resource Distribution for Elderly care: Small Area Estimation (SAE)
Use CFA to create Multidimensional Needs Index
HSE Administrative data on current Resource Distribution
Survey data:
TILDA
(n = 8,000)
Combine data using spatial covariates for Small Area Estimation (SAE)
Combine to Area Level (Region, ISA, PCT)
SAPS (SA):
2011 Census
(n = 18,499)
Undertake Gap and Equality Analysis
Use Pobal HP Deprivation Index