Are Area-Based Deprivation Indices A Nonsense? Dennis Pringle Dept. of Geography, NUI Maynooth; National Institute For Regional And Spatial Analysis; and National Centre For Geocomputation. All Ireland Social Medicine Meeting, Druids Glen, 20 th October, 2007.

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 low social class and 80 per cent of the major causes of death. Area-based deprivation scores are used in the funding model for GPs in the UK. This paper does not contest the significance of deprivation. But, it does contest the usefulness of the ways in which it is measured.

Concept Of Deprivation Most people have an intuitive idea of what is meant by deprivation. However, despite the centrality of deprivation in much research in social medicine, the concept of deprivation remains fuzzy. Material (e.g. poverty) v social (e.g. social exclusion) interpretations. Generally regarded as multivariate, but there is little agreement in practice as to what exactly should or should not be regarded as evidence of deprivation.

Measurement Of Deprivation There are many different deprivation indices, but they tend to be calculated in a broadly similar manner. Three steps: 1.A number of domains (e.g. education, housing, unemployment) are identified. 2.Suitable indicators are identified for each domain. 3.The individual indicators are combined into a single composite index. There is clearly scope for diversity at each stage.

1. Domains Most deprivation indices contain 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. In 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, disposable income, despite being an obviously suitable domain, is rarely included.

2. Indicators The second step entails the calculation of one or more indicators for each domain for each of a number of small areas. Different deprivation indices vary in the choice of indicators and also the way in which indicators are measured, e.g. with regard to the choice of numerator and denominator when calculating indicators.

3. Composite Indices Deprivation indices vary in the way in which the individual indicators are combined into a composite index. This generally entails 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 principal compnents analysis (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.

Summary (1) The above mentioned problems are all fairly obvious and result in differences between the areas identified as most deprived by different indices. However, the extent of these differences is often not appreciated. Given that they all supposedly measure the same thing (i.e. deprivation), this raises concerns about their usefulness as a scientific instrument. However, the concerns go deeper. Even if one was to decide that one particular indicator was preferable to the others, other problems arise.

Possible Objectives There are several possible reasons for calculating a deprivation index: 1.To identify the most derpived areas (e.g. to designate areas for special assistance); 2.To monitor changes over time; 3.To explore the causal links between deprivation and ill-health. I will now look at the problems associated with each of these.

1. Spatial Comparisons (1) The likelihood of an area being identified as deprived depends upon its degree of social homogeneity. If the area is socially mixed, then pockets of deprivation will be counterbalanced by more affluent areas. Small areas are more likely to be socially homogeneous. They are therefore more likely to be identified as deprived (or affluent). The smallest areas census data in the Republic are EDs. The major cities are divided into numerous EDs, but medium sized towns are often treated as a single ED. Pockets of deprivation in medium sized towns therefore go undetected.

Spatial Comparisons (2) There are proposals to introduce smaller areas for reporting census data. This will help, but only up to a point. Rural areas often tend to be socially heterogenous by nature, so small pockets of rural deprivation will still tend to be overlooked. Small areas also introduce problems of their own. Smaller areas means smaller populations and therefore more statistical problems associated with small numbers – i.e. the indicators become less stable and hence so do the correlations between them (and also the weights in the indices). Shrinkage estimators provide only a partial solution.

2. Temporal Comparisons If one adopts policies to alleviate deprivation, then it would be useful to be able to monitor progress. Deprivation indices based on PCA are unsuitable because the weights used to combine the indicators will vary over time (e.g. from one census to the next). The indices provide no indication if deprivation in a given area are improving or disimproving between analyses. The indices will identify the worst and best areas at each time period, but they do not indicate whether the gap is widening or narowing.

3. Causal Analysis If one finds a correlation between a deprivation index and ill-health, then what does this tell us about the causes of the disease? I would suggest it tells us very little. Different types of deprivation cause different types of health risk, therefore deprivation indices tell 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.

Summary (2) We need data to be collected for smaller more homogeneous areas, but (depending on objectives) other non-area based strategies should be explored (e.g. kernel analysis). Need consistent theory-based weights for temporal comparisons of indices, but it may be preferable to work with the disaggregated indicators rather than composite indices. I am not suggesting that we should abandon deprivation indices. However, we need to treat them with much more caution than we would seem to at present.