Trutz Haase & Jonathan Pratschke THE POBAL HP DEPRIVATION INDEX An Inter-temporal and Multi-jurisdictional Analysis Stormont, 13 th 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 Rate  1 Population Change  2 Primary Education only  3 Third Level Education  4 Professional Classes  5 Persons per Room  6 Lone Parents  7 Semi- and Unskilled Classes  8 Male Unemployment Rate  9 Female Unemployment Rate  10 Demographic Growth Social Class Composition Labour Market Situation THE BASIC MODEL OF THE SA-LEVEL POBAL HP DEPRIVATION INDEX

A LONGITUDINAL SA-LEVEL SEM MODEL,

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. Initial GrowthRapid GrowthSlow-Down A LONGITUDINAL ED-LEVEL SEM MODEL,

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

THE POBAL HP DEPRIVATION INDEX SPANNING 5 CENSUS WAVES, BASED AN ON ED-LEVEL ANALYSIS 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, 2004 Haase & Pratschke, 2005 Haase & Pratschke, 2008 Haase & Pratschke, 2010 Haase & Pratschke, Pratschke & Haase, NI Haase & Pratschke, 2011 Level at which model is estimated Level to which data is aggregated 0611

most disadvantaged most affluent marginally below the averagemarginally above the average disadvantagedaffluent very disadvantagedvery affluent extremely disadvantagedextremely affluent MAPPING DEPRIVATION

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, HP Deprivation IndexNMinimumMaximumMeanStd. Deviation HP 1991 ED absolute3, HP 1996 ED absolute3, HP 2002 ED absolute3, HP 2006 ED absolute3, HP 2011 ED absolute3, HP 1991 ED relative3, HP 1996 ED relative3, HP 2002 ED relative3, HP 2006 ED relative3, HP 2011 ED relative3,

OVERLAY OF PAIRED RELATIVE INDEX SCORES,

The Pobal HP Deprivation Measures Small Area (SA) Level Analysis,

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 SCORES 2006

SA-LEVEL ABSOLUTE INDEX SCORES 2011

SA-LEVEL RELATIVE INDEX SCORES 2006

SA-LEVEL RELATIVE INDEX SCORES 2011

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 Education  6 v11 Persons per Room  11 v7 Professional Classes  7 v8 Semi/Unskilled Classes  8 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 IRELAND 2001/2006

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 AffluentDeprived SD % 2.1% 13.6% 34.1% 34.1% 13.6% 2.1% 0.1% Health Risks HEALTH RISKS AND RELATIVE AFFLUENCE / DEPRIVATION

MODELLING POPULATION SHARES ACCORDING TO RELATIVE DEPRIVATION T – TOTAL POPULATION L – 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 Data Sources Reference Models Model Choices

OPTIMISING SAMPLING METHODOLOGIES FOR CSO HOUSEHOLD SURVEYS Model Sample Design Relative Standard ErrorMean Square Error95% Confidence Interval EUELLIEDEUELLIEDEUELLIED EU - SILC 2SCS1,300x SSCS NUTS 4 x Area 81,300x SSCS NUTS 3 x Area 5 x HP Ind 51,300x SSCS NUTS 3 x HP Index 101,300x QNHS 2SCS1,300x SSCS NUTS 4 x Area 81,300x SSCS NUTS 3 x Area 5 x HP Ind 51,300x SSCS NUTS 3 x HP Index 101,300x 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 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.

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