trutz haase & jonathan pratschke the pobal hp deprivation index an inter-temporal and...

Trutz Haase & Jonathan Pratschke

THE POBAL HP DEPRIVATION INDEX

An Inter-temporal and Multi-jurisdictional Analysis

Stormont, 13th November 2013

THE 2011 POBAL HP DEPRIVATION INDEX

The purpose of the presentation is

• to provide an overview of the conceptual components which underlie the HP Deprivation Index

• to discuss the treatment of rural deprivation

• to provide a practical demonstration of the HP Index

• to draw out the Index’ advantages when modelling the social gradient of health outcomes and developing resource allocation models

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


Lone Parents 20069

Male Unemployment Rate 200614

Female Unemployment Rate 200615


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


Lone Parents 2011 19

Male Unemployment Rate 2011 24

Female Unemployment Rate 2011 25


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

There is only a small correlation between the urban and rural components of the index.

This confirms the theoretical underpinning of the model which stipulates that urban and rural disadvantage are conceptually different and that the unemployment rate, for example, is not a useful indicator of rural deprivation.

R2= .92

R2= .90

R2= .83

R2= .94

R2= .95

R2= .80

DemographicGrowth 02

Social ClassComposition 02

Labour MarketSituation 02







47

58

69

0.80 1.19

0.66 0.81

0.97 1.11

0.15 -0.51

0.86

0.56

0.28

-0.33-0.02

0.20

0.23

0.08

0.12

R2= .94

R2= .89

R2= .85




7

8

9

0.93

0.83

1.02

-0.03

-0.06

-0.16

0.27

-0.16

0.04

0.31

0.30

0.01

-0.04

Initial Growth Rapid Growth Slow-Down

A LONGITUDINAL ED-LEVEL SEM MODEL, 1991-2006

A MULTIPLE GROUP MODEL SPANNING FIVE CENSUS WAVES, 1991-2011

1991

1996

2002

2006

2011

Multiple Group Model fitted simultaneously across five census waves

•imposing identical structure matrix

•and identical path coefficients

91 96 02 06 11

THE POBAL HP DEPRIVATION INDEX SPANNING 5 CENSUS WAVES, BASED AN ON ED-LEVEL ANALYSIS

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., 1996

Haase, 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 disadvantagedmost disadvantaged most affluentmost 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)



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)



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)



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)


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)



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)



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)



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)



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. Deviation

HP 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

OVERLAY OF PAIRED RELATIVE INDEX SCORES, 1991-2006

The Pobal HP Deprivation Measures

Small Area (SA) Level Analysis, 2006 - 2011

THE POBAL HP DEPRIVATION INDEX - DUBLIN INNER CITY (ED LEVEL), 2006

THE POBAL HP DEPRIVATION INDEX - DUBLIN INNER CITY (SA LEVEL), 2006

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)

Towards a Deprivation Index

Covering Multiple Jurisdictions

Comparability of Spatial Units (COA, SA)

Comparability of Indicator Variables

Temporal Synchronicity (2011 Census)

Common Dimensionality of Deprivation

Common Statistical Model

Standardisation of Index Scores across Multiple Jurisdictions

METHODOLOGICAL CHALLENGES (OVERVIEW)

Comparable Indicator Variables Population Change

Age Dependency

Lone Parents Ratio

Male Unemployment Rate

Female Unemployment Rate

Average Number of Persons per Room

Significantly Varying Indicator Variables Proportion of Adult Population with Primary Education Only

Proportion of Adult Population with Third Level Education

Proportion of Population in Higher and Lower Professional Classes

Proportion of Population in Semi- and Unskilled Manual Classes

COMPARABILITY OF INDICATOR VARIABLES

Demographic Decline

Social Class Disadvantage

Labour Market Deprivation

v3 Age Dependency Rate

3

v2 Population Change

2

v5 Primary Education Only 5

v6 Third Level Education6

v11 Persons per Room 11

v7 Professional Classes

7

v8 Semi/Unskilled Classes8

v4 Lone Parents

4

v9 Male Unemployment

9

v10 Female Unemployment

10

COMMON DIMENSIONALITY OF DEPRIVATION

Joint standardisation of all factor scores

Simple additive approach to combining factors scores

Resulting in comparable deprivation scores North and South, based on an identical factor structure.

STANDARDISATION OF INDEX SCORES ACROSS MULTIPLE JURISDICTIONS

HP DEPRIVATION INDEX FOR NORTHERN IRELAND AND REPUBLIC OF IRELAND2001/2006

All-Island Deprivation Index, 2001, 2006Haase & Pratschke, 2011

30 to 50 (75)20 to 30 (468)10 to 20 (2291)0 to 10 (7183)

-10 to 0 (6879)-20 to -10 (2588)-30 to -20 (465)-60 to -30 (12)

HP DEPRIVATION INDEX 2001 / 2006

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 and 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

can be developed for multiple jurisdictions

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 RISKS AND RELATIVE AFFLUENCE / DEPRIVATION

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 Deprivation Medium Deprivation High Deprivation Current Dis tribution Target Dis tribution

Data Sources

Reference Models

Model Choices

OPTIMISING SAMPLING METHODOLOGIES FOR CSO HOUSEHOLD SURVEYS

ModelSample 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

Analysis

Use Pobal HP Deprivation Index

trutz haase & jonathan pratschke the pobal hp deprivation index an inter-temporal and...

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