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 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

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

ED-Level absolute Index Scores 1991

ED-Level absolute Index Scores 1996

ED-Level absolute Index Scores 2002

ED-Level absolute Index Scores 2006

ED-Level absolute Index Scores 2011

ED-Level Relative Index Scores 1991

ED-Level Relative Index Scores 1996

ED-Level Relative Index Scores 2002

ED-Level Relative Index Scores 2006

ED-Level Relative Index Scores 2011

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

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 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

SA-Level Absolute Index Scores 2006

SA-Level Absolute Index Scores 2011

SA-Level Relative Index Scores 2006

SA-Level Relative Index Scores 2011

Conceptual Underpinnings of the Pobal HP 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)

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

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

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

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

A Longitudinal SA-Level SEM Model, 2006-2011

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

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

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

Applications of the Pobal HP Deprivation Index

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)

Health Risk and Relative Affluence / Deprivation Deprived Affluent Health Risks SD -3 -2 -1 0 1 2 3 0.1% 2.1% 13.6% 34.1% 34.1% 13.6% 2.1% 0.1% High Moderate Low

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

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%

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.)

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

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. http://www.bias-project.org.uk/resdesc.htm#SAE

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