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Geary Institute, Dublin, February 20 13. The Pobal HP Deprivation Index An Inter-temporal Analysis 1991 - 2011. 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 - PowerPoint PPT Presentation
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Trutz Haase & Jonathan Pratschke
THE POBAL HP DEPRIVATION INDEX
An Inter-temporal Analysis 1991 - 2011
Geary Institute, Dublin, February 2013
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.
The Pobal HP Deprivation Measures
Electoral Division (ED) Level Analysis, 1991- 2011
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!
THE POBAL HP DEPRIVATION INDEX SPANNING 5 CENSUS WAVES, BASED AN ON ED-LEVEL ANALYSIS
91 96 02 06 11
06 11
06 11
91 96 02 06 11 06 11
91 96 02 06 11 06 11
91 96 02 06 11 06 11
91 96 02 06 11 06 11
91 96 02 06
06
06
91 96 02 06 06
91 96 02 06 06
91 96 02 06 06
91 96 02 06 06
86 91 96
86 91 96
86 91 96
86 91 96
86 91 96
96
96
96
96
96
91
91
91
91
91
SA n=18,488
ED n = 3,409
NUTS 4 n = 34
NUTS 3 n = 8
NUTS 2 n = 2
NUTS 1 n = 1
Haase et al., 1996Haase, 1999
Pratschke & Haase, 2004Haase & Pratschke, 2005 Haase & Pratschke, 2008
Haase & Pratschke, 2010Haase & Pratschke, 2012
91 96 02
91 96 02
91 96 02
91 96 02
91 96 02
Pratschke & Haase, 2001
01NI
01NI
01NI
01NI
01NI
01NI
Haase & Pratschke, 2011Level at which model is estimated
Level to which data is aggregated
06 11
most disadvantaged most affluent
marginally below the average marginally above the average
disadvantaged affluent
very disadvantaged very affluent
extremely disadvantaged extremely affluent
MAPPING DEPRIVATION
ED-LEVELABSOLUTE INDEX SCORES1991
HP Deprivation Index ED 1991 absoluteHaase & Pratschke 2013
30 to 75 (53)20 to 30 (79)10 to 20 (252)0 to 10 (1184)
-10 to 0 (1431)-20 to -10 (360)-30 to -20 (49)
ED-LEVELABSOLUTE INDEX SCORES1996
HP Deprivation Index ED 1996 absoluteHaase & Pratschke 2013
30 to 75 (52)20 to 30 (133)10 to 20 (562)0 to 10 (1625)
-10 to 0 (875)-20 to -10 (151)-30 to -20 (10)
ED-LEVELABSOLUTE INDEX SCORES2002
HP Deprivation Index ED 2002 absoluteHaase & Pratschke 2013
30 to 75 (77)20 to 30 (314)10 to 20 (1021)
0 to 10 (1440)-10 to 0 (436)-20 to -10 (103)-30 to -20 (16)-50 to -30 (1)
ED-LEVELABSOLUTE INDEX SCORES2006
HP Deprivation Index ED 2006 absoluteHaase & Pratschke 2013
30 to 75 (55)20 to 30 (314)10 to 20 (1201)
0 to 10 (1385)-10 to 0 (341)-20 to -10 (93)-30 to -20 (18)-50 to -30 (1)
ED-LEVELABSOLUTE INDEX SCORES2011
HP Deprivation Index ED2011 absoluteHaase & Pratschke 2013
30 to 75 (14)20 to 30 (82)10 to 20 (296)0 to 10 (1026)
-10 to 0 (1414)-20 to -10 (460)-30 to -20 (98)-50 to -30 (18)
ED-LEVELRELATIVEINDEX SCORES1991
HP Deprivation Index ED 1991 relativeHaase & Pratschke 2013
30 to 75 (53)20 to 30 (79)10 to 20 (252)0 to 10 (1184)
-10 to 0 (1431)-20 to -10 (360)-30 to -20 (49)
ED-LEVELRELATIVEINDEX SCORES1996
HP Deprivation Index ED 1996 relativeHaase & Pratschke 2013
30 to 75 (37)20 to 30 (100)10 to 20 (325)0 to 10 (1112)
-10 to 0 (1390)-20 to -10 (375)-30 to -20 (66)-50 to -30 (3)
ED-LEVELRELATIVEINDEX SCORES2002
HP Deprivation Index ED 2002 relativeHaase & Pratschke 2013
30 to 75 (11)20 to 30 (86)10 to 20 (406)0 to 10 (1125)
-10 to 0 (1333)-20 to -10 (346)-30 to -20 (91)-50 to -30 (10)
ED-LEVELRELATIVEINDEX SCORES2006
HP Deprivation Index ED 2006 relativeHaase & Pratschke 2013
30 to 75 (2)20 to 30 (76)10 to 20 (420)0 to 10 (1204)
-10 to 0 (1267)-20 to -10 (317)-30 to -20 (98)-50 to -30 (24)
ED-LEVELRELATIVEINDEX SCORES2011
HP Deprivation Index ED 2011 relativeHaase & Pratschke 2013
30 to 75 (19)20 to 30 (91)10 to 20 (368)0 to 10 (1161)
-10 to 0 (1331)-20 to -10 (352)-30 to -20 (76)-50 to -30 (10)
HP DEPRIVATION SCORES IN COMPARISON, 1991-2011
HP Deprivation Index N Minimum Maximum Mean Std. DeviationHP 1991 ED absolute 3,409 -28.0 73.3 0.0 10.0
HP 1996 ED absolute 3,409 -27.4 45.7 4.3 9.2
HP 2002 ED absolute 3,409 -30.6 42.1 8.4 9.9
HP 2006 ED absolute 3,409 -35.0 39.9 9.2 9.3
HP 2011 ED absolute 3,409 -43.7 41.6 -1.4 10.1
HP 1991 ED relative 3,409 -28.0 73.3 0.0 10.0
HP 1996 ED relative 3,409 -34.4 45.1 0.0 10.0
HP 2002 ED relative 3,409 -39.4 34.0 0.0 10.0
HP 2006 ED relative 3,409 -47.4 32.9 0.0 10.0
HP 2011 ED relative 3,409 -41.9 42.7 0.0 10.0
The Pobal HP Deprivation Measures
Small Area (SA) Level Analysis, 2006 - 2011
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
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.
THE POBAL HP DEPRIVATION INDEX FOR 2006-2011, BASED AN ON SA-LEVEL ANALYSIS
91 96 02 06 11
06 11
06 11
91 96 02 06 11 06 11
91 96 02 06 11 06 11
91 96 02 06 11 06 11
91 96 02 06 11 06 11
91 96 02 06
06
06
91 96 02 06 06
91 96 02 06 06
91 96 02 06 06
91 96 02 06 06
86 91 96
86 91 96
86 91 96
86 91 96
86 91 96
96
96
96
96
96
91
91
91
91
91
SA n=18,488
ED n = 3,409
NUTS 4 n = 34
NUTS 3 n = 8
NUTS 2 n = 2
NUTS 1 n = 1
Haase et al., 1996Haase, 1999
Pratschke & Haase, 2004Haase & Pratschke, 2005 Haase & Pratschke, 2008
Haase & Pratschke, 2010Haase & Pratschke, 2012
91 96 02
91 96 02
91 96 02
91 96 02
91 96 02
Pratschke & Haase, 2001
01NI
01NI
01NI
01NI
01NI
01NI
Haase & Pratschke, 2011Level at which model is estimated
Level to which data is aggregated
06 11
SA-LEVEL ABSOLUTE INDEX SCORES2006
Absolute Index Score 2006Haase & Pratschke 2012
30 to 50 (22)20 to 30 (293)10 to 20 (2513)0 to 10 (6857)
-10 to 0 (5925)-20 to -10 (2294)-30 to -20 (564)-60 to -30 (20)
SA-LEVEL ABSOLUTE INDEX SCORES2011
Absolute Index Scores 2011Haase & Pratschke 2012
30 to 50 (2)20 to 30 (70)10 to 20 (838)0 to 10 (3397)
-10 to 0 (7181)-20 to -10 (5132)-30 to -20 (1719)-60 to -30 (149)
SA-LEVEL RELATIVE INDEX SCORES2006
Relative Index Score 2006Haase & Pratschke 2012
30 to 50 (22)20 to 30 (293)10 to 20 (2513)
0 to 10 (6857)-10 to 0 (5925)-20 to -10 (2294)-30 to -20 (564)-60 to -30 (20)
SA-LEVEL RELATIVE INDEX SCORES2011
Relative Index Score 2011Haase & Pratschke 2012
30 to 50 (30)20 to 30 (474)10 to 20 (2412)
0 to 10 (6232)-10 to 0 (6483)-20 to -10 (2408)-30 to -20 (447)-60 to -30 (2)
Conceptual Underpinnings of the
Pobal HP Deprivation Index
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)
A COMPREHENSIVE DEFINITION OF POVERTY
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
V1
V2
V3
V4
V5
V6
F1
F2
Ordinary Factor Analysis (EFA) reduces variables to a smaller number of underlying Dimensions or Factors
TRADITIONAL APPROACH: EXPLORATORY FACTOR ANALYSIS (EFA)
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
V1
V2
V3
V4
V5
V6
L1
L2
Confirmatory Factor Analysis also reduces observations to the underlying Factors, however
1
2
3
4
5
6
NEW APPROACH: CONFIRMATORY FACTOR ANALYSIS (CFA)
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
THE UNDERLYING DIMENSIONS OF SOCIAL DISADVANTAGE
Age Dependency Rate1
Population Change2
Primary Education only3
Third Level Education4
Professional Classes
5Persons per Room
6
Lone Parents
7 Semi- and Unskilled Classes
8
Male Unemployment Rate9
Female Unemployment Rate 10
DemographicGrowth
Social ClassComposition
Labour MarketSituation
THE BASIC MODEL OF THE SA-LEVELPOBAL HP DEPRIVATION INDEX
A LONGITUDINAL SA-LEVEL SEM MODEL, 2006-2011
Professional Classes 200612
Semi- and Unskilled Classes 200613
Social ClassComposition
Lone Parents 20069
Male Unemployment Rate 200614
Female Unemployment Rate 200615
Labour MarketSituation
Age Dependency Rate 20067
Population Change 2002-068
Primary Education only 200610
Third Level Education 200611
DemographicGrowth
Persons per Room 200616
Professional Classes 2011 22
Semi- and Unskilled Classes 2011 23
Social ClassComposition
Lone Parents 2011 19
Male Unemployment Rate 2011 24
Female Unemployment Rate 2011 25
Labour MarketSituation
Age Dependency Rate 2011 17
Population Change 2006-11 18
Primary Education only 2011 20
Third Level Education 2011 21
DemographicGrowth
Persons per Room 2011 26
2006
2006
2006
2011
2011
2011
1
3
2
-0.61
0.46
-0.63
-0.51
0.53
0.69
-0.57
0.24
0.95
-0.860.20
-0.65
-0.76
-0.68
0.17
0.820.14
-0.54
0.36
-0.59
0.46
0.49
-0.58
0.73
-0.51
0.97
-0.89
-0.64
-0.86
-0.74
0.89
0.92
0.61
-0.06
0.10
0.03
0.04
0.03
0.35
-0.17
0.63
0.01
0.18
2006 2011
Age Dependency Rate1
Population Change2
Primary Education only3
Third Level Education4
Professional Classes
5Persons per Room
6
Lone Parents
7 Semi- and Unskilled Classes
8
Male Unemployment Rate9
Female Unemployment Rate 10
DemographicGrowth
Social ClassComposition
Labour MarketSituation
THE BASIC MODEL OF THE ED-LEVELPOBAL HP DEPRIVATION INDEX
A MULTIPLE GROUP MODEL SPANNING FIVE CENSUS WAVES
1991
1996
2002
2006
2011
Multiple Group Model fitted simultaneously across five census waves
•imposing identical structure matrix
•and identical path coefficients
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
STRENGTHS OF CFA-BASED DEPRIVATION INDICES
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)
APPLICATIONS OF THE POBAL HP DEPRIVATION INDEX
LowModerateHigh
Affluent Deprived
SD -3 -2 -1 0 1 2 3
0.1% 2.1% 13.6% 34.1% 34.1% 13.6% 2.1% 0.1%
HealthRisks
HEALTH RISK AND RELATIVE AFFLUENCE / DEPRIVATION
SEM MODEL TO ASSESS EQUALITY OF ACCESS TO CANCER CARE
Access to Cancer Care
Possessions
Attributes
Measurement Model for Possessions
Measurement Model for Attributes
Measurement Model for Access to Cancer Care
MODELLING POPULATION SHARES ACCORDING TO RELATIVE DEPRIVATION
T – TOTAL POPULATIONL – LOW (48.3%)M – MEDIUM (22.4%)H – HIGH ( 7.4%)
T : >5 STD (Total
Population)
L: 0 STD 48.3% Population
M: -1 STD 22.4%
H: -2 STD 7.4%
THE HSE RESOURCE ANALYSER
2011 Census of Population
2011 Pobal HP Deprivation Index
Reference Database for 18,488 Small
Areas
Total Population
100%
Low Deprivation
48.2%
Medium Deprivation
22.4%
High Deprivation
7.4%
60% 15%5% 20%
Data aggregation to spatial area of interest (Region, ISA, PCT etc.)
Administrative data on current
allocations
Combined Target
Allocation
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
DML DNE SOUTH WEST
Population Low De privation Medi um Depriva tion High Deprivation Current Distribution Target Dis tributi on
Data Sources
Reference Models
Model Choices
OPTIMISING SAMPLING METHODOLOGIES FOR CSO HOUSEHOLD SURVEYS
Model Sample Design Relative Standard Error Mean Square Error 95% Confidence Interval
E UE LLI ED E UE LLI ED E UE LLI ED
EU - SILC
2SCS 1,300x4 3 3 3 3 3 3 3 3
2SSCS NUTS 4 x Area 8 1,300x4 3 2 3 2 3 3 2 3
2SSCS NUTS 3 x Area 5 x HP Ind 5 1,300x4 2 2 1 2 2 1 2 2 1
2SSCS NUTS 3 x HP Index 10 1,300x4 1 1 1 2 1 1 1 2 1 1 1 2
QNHS
2SCS 1,300x20 3 3 3
2SSCS NUTS 4 x Area 8 1,300x20 3 3 3 3 3 3 3 3 3
2SSCS NUTS 3 x Area 5 x HP Ind 5 1,300x20 2 1 1 2 2 1 1 2 2 1 1 2
2SSCS NUTS 3 x HP Index 10 1,300x20 1 1 2 1 1 2 2 1 1 2 2 1
Comparison of Sampling Designs in the Estimation of Employment (E), Unemployment (UE), Long-term Limiting Illness (LLI) and Education (ED)
Haase, T. and Pratschke, J. Optimising the Sampling Methodology for CSO Household Surveys, CSO, 2012
SMALL AREA ESTIMATION
The BIAS projectImperial 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. http://www.bias-project.org.uk/resdesc.htm#SAE
EVALUATING THE RESOURCE DISTRIBUTION FOR ELDERLY CARE: SMALL AREA ESTIMATION (SAE)
Survey data:TILDA
(n = 8,000)Combine
data using spatial
covariates for Small Area Estimation
(SAE)SAPS (SA):
2011 Census(n = 18,499)
Use CFA to create Multidimensional
Needs Index
HSE Administrative data on current
Resource Distribution
Combine to Area Level
(Region, ISA, PCT)
Undertake Gap and Equality
AnalysisUse Pobal HP
Deprivation Index