Measuring Deprivation: Problems Of Spatial Scale

1. Measuring Deprivation: Problems Of Spatial Scale Dennis Pringle & Ronan Foley, Dept. of Geography, National Institute For Regional And Spatial Analysis, and National Centre For Geocomputation, NUI Maynooth All Ireland Social Medicine Meeting, Belfast, 1st April, 2006.

2. Deprivation And Health • Deprivation is now widely regarded as an important cause of social and spatial inequalities in health. • Wilkinson reported a 2 to 4 fold variation in death rates between social classes within societies. • Wilkinson also reported a positive association between 80 per cent of the major causes of death and low social class. • Deprivation scores are used in the funding model for GPs in the UK.

3. Concept Of Deprivation • Despite the centrality of deprivation in much research in social medicine, the concept of deprivation remains fuzzy. • Most people have an intuitive idea of what is meant by ‘deprivation’. • Generally regarded as ‘multivariate’. • However, there is little agreement in practice as to what exactly should or should not be regarded as evidence of deprivation.

4. Measurement Of Deprivation • There are many different deprivation indices, but they tend to be calculated in a similar manner. Three steps: • Identification of several domains (e.g. education, housing, unemployment). • Identification of suitable indicators for each domain. • Combination of the individual indicators into a single composite index. • There is clearly scope for diversity at each stage.

5. 1. Domains • Most deprivation indices contains measures of housing overcrowding, unemployment, education and low social class. • However there is considerable disagreement over other domains, such as car ownership, single parents, elderly population. • Apart from the absence of a strict guiding theory, the choice of domains is usually restricted by the availability of data, especially census data. • Thus, for example, income, despite being an obviously suitable domain, is rarely included.

6. 2. Indicators • The second step entails the calculation of one or more indicators for each domain for each of a number of areas. • Different deprivation indices vary in the way in which the selected domains are measured, e.g. with regard to the choice of numerator and denominator when calculating indicators. • Given the volatility of indicators calculated for areas with small populations, the raw indicators are sometimes ‘shrunk’ to the mean for a larger area.

7. 3. Composite Indices • Deprivation indices vary in the way in which the individual indicators are combined into a composite index. • This generally enatils the calculation of a weighted average of the individual indicator scores. • The weights are sometimes assigned subjectively, but usually they are determined using some empirical method, such as PCA. • PCA assigns weights to each indicator to reflect the extent to which it is correlated with each of the other indicators. This, in turn, will be influenced by the choice of domains and method of calculating indicators. • PCA determined weights create complications when making comparisons over time.

8. The Spatial Dimension • The above mentioned problems are all fairly obvious and are well-known. • We would therefore like to focus on the problematic aspects of the areas for which the indicators and indices are calculated. • We will focus on the situation in the Republic, but the main points apply in other countries.

9. Electoral Divisions (EDs) • Deprivation indices in the Republic are normally calculated for Electoral Divisions (EDs) – formerly DEDs. • These are the smallest areas for which census data (SAPS) are routinely available for the whole country. • EDs are very problematic for analytical purposes: • For so called ‘small areas’, they are very large. • They vary considerably in both population size and area. • Most are socially heterogeneous (thereby reducing the likelihood of pockets of deprivation being correctly identified).

10. Ongoing Developments • There is a growing recognition that EDs are inadequate for small area analysis. • There are consequently two ongoing developments: • Discussions about the introduction of postal codes. These may be small area based. • The CSO, in conjunction with the NCG, is exploring the possibilty of smaller more homogeneous output areas for SAPS data. • It will be at least a year or two before we see the benefits of these developments.

11. Enumerator Areas (EAs) • The CSO currently provides data for areas smaller than EDS, namely Eas, but only for the cities. • We decided to explore the implications of using smaller areas for the identification of areas of deprivation.

12. Methodology • Our methodology is very straightforward. • We calculated SAHRU-like deprivation indices using EDs, calculated similar indices using EAs and then compared the resulting maps. • The SAHRU index shrinks the indicator values towards the county means. To retain consistency between the two scale levels we shrank the indicators for both EDs and EAs to the national means. • Comparison of the indices for EDs shows that this change in the method of shrinkage makes very little difference to the index values (r=0.99).

36. Discussion - Areas • The urban EDs examined here are much more socially homogeneous than those in non-urban areas. The problems discussed here would be much more acute in non-urban areas. • The EAs are not specifically designed to maximise social homogeneity, therefore this study probably understates the possible advantages of using well-designed smaller areas. • Despite these limitations, it is argued that this study highlights the need for smaller and more socially homogeneous statistical areas in the Republic if one is to identify areas of deprivation with a reasonable degree of accuracy.

37. Discussion – Resource Allocation • Given the points made earlier about the subjective nature of how deprivation indices are defined, plus a serendipidy effect introduced by the choice of areas used for their measurement, one must also ask questions about the usefulness of deprivation indices for allocating resources.

38. Discussion - Epidemiology • One must also ask questions about their usefulness for epidemiological studies. • For example, if one finds a correlation between a disease and a deprivation index, what does that tell us about the causes of the disease? We would suggest that it tells you very little unless you decompose the index to find out which of the indicators have the high correlations. • By correlating a disease with a composite deprivation index, there is also the danger that a causally significant factor may be overlooked because it was allocated a small weight in the construction of the composite index.