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[Martin, Brigham, Roderick, Barnett and Diamond, 2000] The definitive, peer-reviewed and edited version of this article is published in Environment and Planning A, 32(4) 735 – 751, 2000, [doi:10.1068/a32130]
The (mis)representation of rural deprivation
David Martin Department of Geography, University of Southampton, Southampton, SO17 1BJ
Philip Brigham, Paul Roderick Health Care Research Unit, University of Southampton, Southampton, SO16 6YD
Sarah Barnett, Ian Diamond Department of Social Statistics, University of Southampton, Southampton, SO17 1BJ
Abstract This paper concerns the definition and measurement of deprivation and of rurality in the context of health care research. Parallels are drawn between the methodological issues involved in the measurement of deprivation and rurality. An empirical study of the South West of England reveals the extent of disagreement between standard rurality measures. In particular, we suggest that rural deprivation will be poorly represented by the conventional approaches. The paper argues for the development of new approaches to the measurement of deprivation in rural areas, taking advantage of contemporary data sources.
2 1 Introduction
In health care resource allocation and health services research, the concept of material deprivation has become widely used as a proxy for health care need. The application of the concept in resource allocation and research relies on indirect indicators, of which multivariate census-based deprivation indicators are the most commonly used. Not only is the precise association between the various conceptualisations and measures of deprivation unclear, but the calculation of such indicators is also problematic, generally involving the standardisation and combination of a number of variables considered to be associated with aspects of deprivation (Martin et al, 1994; Senior, 1991). A feature of national standardisation in a primarily urban country is that the resulting indicators are standardised around typical urban values. Although this makes for ease of use, it is only valid if the interpretation of the index is equally meaningful across all neighbourhood types. Numerous studies, most recently those by Higgs et al. (1998) and Senior et al. (1998) have demonstrated associations between indicators such as the Townsend index (discussed further below) and health outcomes such as mortality rates. This paper is concerned to review and demonstrate problems of definition and measurement, not only of deprivation but also of rurality, and particularly with their intersection in the issue of rural deprivation. We argue that rural deprivation is a special issue, which has been insufficiently acknowledged in substantive studies to date.
There are good grounds for considering that the nature of material deprivation in rural areas is rather unique and that it will therefore be to some extent misrepresented in nationally-based indicators (Cullingford and Openshaw, 1982; Payne et al., 1996a). Indeed, most rural areas do not score highly on standard deprivation indicators, and this may be exacerbated by the aggregation of small areas of rural disadvantage into large, internally heterogeneous zones. An additional obstacle to assessing the extent of this impact is that the definition of rurality is itself far from straightforward, and a number of alternative approaches exist. Many of the issues surrounding the definition and measurement of rurality parallel the discussion of material deprivation.
3 The rest of this paper is divided into seven sections. In the next three sections, we review the major aspects of deprivation and rurality measurement, and then address the identification of deprived rural areas. Section 5 presents data from our own research on rural health in South West England, and section 6 discusses the issues raised. Summary conclusions are drawn in section 7.
2 Measuring deprivation Deprivation and the related concept of poverty have provoked much terminological debate and confusion. Townsend (1979) suggests that poverty is the result of a lack of resources that prevents participation in the everyday lifestyles of the majority. Townsend (1987) defines deprivation as ‘a state of observable and demonstrable disadvantage relative to the local community or the wider society or nation to which an individual, family or group belongs.’ Payne (1995) also stresses the relative nature of the deprivation concept, noting that it may apply to individuals, groups or physical localities. In practice, the aggregate nature of published data sources means that use of standard deprivation indicators involves data for small areas.
Measures of deprivation are frequently used as a basis for resource allocation, for example in the allocation of funds from central to local government and to agencies within the health care system (eg. Flowerdew et al., 1994; Martin et al., 1994), treating it as a proxy for need for health and social care. In order to implement such resource allocation systems, a range of different statistical measures of deprivation have been constructed, each of which can be calculated from nationally available census data. The most popular measures were originally calculated at ward level (typically populations of 5-6000), although in many applications, the enumeration district (ED) level, typically with populations of 2-400, may be preferable. It is important to note, however, that such indicators may become unstable when based on very small populations. In each case, a selection of variables from the census are taken as proxies for aspects of deprivation. The variables used in the four most widely used indicators are shown in Table 1. Values of these variables are normalised (in some cases), standardised to national means, and combined according to some weighting
4 scheme. The precise techniques differ between the indicators but this general approach is common to each.
[ Table 1 about here ]
Use of data from the decennial census is unsatisfactory, as important variables such as unemployment change very rapidly, while there is generally a long time lag between the measurement of deprivation by a census and the implementation of policies based on the resulting indicators. The aggregate nature of census data also leads to ecological fallacy problems, in that there is an implicit assumption that all persons living in a deprived area are in some way deprived. This confusion is a feature of all aggregate data sources, and can only truly be overcome by the use of individual-level data. However, it is important to note that there may also be neighbourhood characteristics, for example associated with local community and environmental factors, which contribute to deprivation but which cannot be captured by the direct aggregation of individuals. Congdon (1995) illustrates this issue in his discussion of the need for multilevel approaches to the analysis of area effects on health outcomes.
The choice of census variables is contested because the target concept is so poorly defined and the original purposes of the indicators diverse. In the case of the Townsend index (Townsend et al., 1988), variables were selected on the basis of the authors’ expertise and experience, whereas variables in the Jarman underprivileged area score (Jarman, 1984) were derived from a survey of general practitioner workloads. Similar variables appearing in the main deprivation indicators include unemployment, overcrowding, lack of amenities, low social class, housing tenure and car ownership, although these may be interpreted differently in different neighbourhood types. Dolk (1995) illustrates how indicators may be undesirably sensitive to local variation in age structure. For example, in a predominately elderly population access to a car may take on a different meaning in relation to deprivation than in an area of predominately young families. These types of consideration are of particular importance to our analysis of rural areas.
5 Reading et al. (1994) note that deprivation indicators are unidimensional scores, having the advantage of allowing areas to be ranked for statistical comparison. However, deprivation is not a unidimensional concept, and it is therefore unreasonable to expect it to be fully captured by any of the standard indicators. A particular difficulty of unidimensional approaches is that of determining the threshold values defining deprivation. This is a highly contentious issue for resource allocation, as described by Senior (1991) for example, in the context of deprivation payments made to general practitioners. To reflect the social diversity of areas, the multidimensional neighbourhood classifications used in the geodemographic industry (Birkin, 1995) have considerable potential. Openshaw and Wymer (1995) review procedures for the construction of such multivariate indicators, which commonly include non-census data sources. This approach results in a descriptive classification in which areas sharing similar combinations of socioeconomic, demographic, housing and environmental characteristics are grouped together. Geodemographic classification is most commonly used for commercial purposes, and has seen little use in the identification of deprivation as a measure of need for health care, although there has been some use in health applications of geographical information systems (GIS), such as that described by Hirschfield et al. (1995).
3 Defining rural areas In this paper, we are concerned with the ways in which the nature and measurement of deprivation interacts with the degree of rurality of the area in question. The identification of rural areas is itself used in a various contexts for the allocation of additional resources to overcome service delivery problems or lack of amenities. As with deprivation, there is no unambiguous definition of rurality, nor is there any generally accepted approach to measurement. Most researchers and policymakers choose approaches that are best suited to their own application, taking into account pragmatic issues of data availability, quality, consistency and ease of collection. The methodological problems are remarkably similar to those already reviewed in the context of measuring material deprivation. A range of measures used to define rural areas in the UK is given in Table 2, and in the rest of this section, the major approaches are reviewed.
6
[ Table 2 about here ]
The first group of measures identified in Table 2 are concerned with population size. Various studies, for example Shucksmith (1990) and Phillimore and Reading (1992), treat settlements below a certain population size as ‘rural’, but there are no universally applicable threshold values and this, combined with the difficulty of defining a settlement, make it an unattractive measure for general use.
Population density is probably the most widely used measure of rurality, and should intuitively discriminate rural areas well. It has the advantages of being transparent and easy to calculate. As a continuous variable the results can be ranked, making comparison with other areas straightforward. The major difficulty with any area-based density measures is that the values interact in complex ways with the size and position of the areal units (Coombes and Openshaw, 1991). Craig (1987) illustrates how the varying settlement patterns of Cornwall and Somerset result in different levels of rurality at the ward level, while the counties appear similar if measured at the smaller ED level. Population density as a definition of rurality also suffers the same problems of threshold setting found in other contexts. Shucksmith (1990), for example, uses one person per hectare (pph), while Payne et al. (1996b) use ten pph.
The Department of Environment, Transport and the Regions (DETR) has chosen to reexpress population density as ‘sparsity’. The calculation of sparsity in their Standard Spending Assessment defines wards with a population density of 0.5 to 4 pph as ‘sparse’, and those with a population density of less than 0.5 pph ‘super-sparse’. The latter are double weighted in an attempt to allow for the extra cost of service provision. There are various forms of weighting that can be applied to population density, but it cannot discriminate well between levels of population clustering or proximity to built up areas.
An interesting alternative measure of rurality is that of nearest neighbour distance, a concept widely discussed in the spatial analysis literature (see, for example, Bailey and Gatrell, 1995). The mean distance between residential locations is a continuous
7 variable but, unlike population density, it takes some account of the degree of clustering within the spatial unit of interest. Two administrative areas with the same geographical area and population will have very different values if the population of one is widely dispersed and the other is concentrated in a single settlement. This measure may be complementary to measures of density (above) and accessibility (below) as it describes geographical isolation at the local level.
Measures of accessibility are rather different to the other methods of identifying rural areas discussed so far. Accessibility concerns separation from or availability of facilities or services. In empirical work this is frequently reduced to crow-fly distance or road travel time due to difficulties in obtaining appropriate data. The generalized measurement of travel time by public transport is problematic, due to the need to include waiting times and the availability of the service. Such measures are commonly computed using GIS. White et al. (1997) suggest more advanced uses of GIS technology in this area, but Higgs and White (1997) note that actual applications to date have been limited, both in geographical extent, and by data constraints. Bentham and Haynes (1986) have used remoteness from major population centres as a proxy for rurality, although there may be small towns that are further away from metropolitan facilities than farming communities on the edge of cities. Peripherality is an important concept in the overall definition of rurality and is used when distance to the facilities and markets of large urban centres gives a level of disadvantage to the population of the area.
As with deprivation, no univariate measure of rurality can hope adequately to capture such a complex multidimensional concept. One way to avoid many of these problems is to use multivariate area classification schemes, and to attempt to identify those area types, which together define rural areas. Many of the standard geodemographic classifiers (Birkin, 1995) include rural area types, but these do not constitute a standard definition. ONS have produced a national ward classification based on 1991 census data comprising 43 clusters which are aggregated into 14 groups, two of which are ‘rural areas’ and ‘rural fringe’, used in the empirical work below (Wallace et al., 1995). The theoretical advantages of these classifications are that they provide a multidimensional view of social circumstances allowing areas with a similar balance
8 of characteristics to be grouped. Corresponding disadvantages are that there is no rank order to the classification, and the labelling of certain groups as ‘rural’ is problematic. In a second national classification, ONS have also defined every ED in the country as urban or rural on an urban land use basis (OPCS, 1992). In this scheme, every ED that is not urban is by default rural. As EDs are the building blocks of wards, this allows wards to be classified as urban or rural on the basis of their constituent EDs.
So far we have considered the identification of deprivation using standard indicators. We have also looked at some of the problems of identifying and measuring rurality. In both cases, the demand for nationally applicable measures has led to a reliance on widely available data, particularly from the census. This restricts the measures to use of the standard geographical areas for which census data are published. Wide variations in area sizes and populations are themselves obstacles to the interpretation of indicators. Larger administrative areas such as local authority districts and health authorities tend to include a great variety of urban and rural environments, making the application of a single descriptor to the entire area problematic. Despite the numerous difficulties described, use of univariate deprivation and rurality indicators is widespread, frequently involving arbitrarily defined threshold values in both policy and research contexts. In the following section we draw the two major strands of our review together by exploring the particular difficulties of identifying deprivation in rural areas.
4 Identifying rural deprivation It is argued here that in combination, the two issues discussed above misrepresent rural areas in a number of ways, which will have implications for resource allocation and health services research. Recognising the weaknesses of current deprivation and rurality measures should seriously challenge our understanding of rural deprivation and its relationships with health status, as measured, for example, by morbidity, limiting long term illness (LLTI) and mortality. Senior et al. (1998) examine the relationships between mortality and deprivation in Wales. They note that some of the largest standardised residuals in their models relate to rural wards. Improved
9 specification of the urban/rural contextual effects may go some way towards resolving these types of difficulty.
One of principal conclusions of our review is that there is a tendency for both rurality and deprivation to be treated as unidimensional phenomena. In reality there is no unidimensional transition from urban to rural, nor from deprived to not deprived. The standard deprivation indicators are predominately urban-based measures, which do not reflect the rather different types of deprivation that is experienced in rural areas. Multivariate measures, by contrast, do not clearly identify any areas as ‘rural’ or ‘urban’. The identification of rural deprivation was not a specific consideration in the development of any of the standard indicators presented in Table 1. Indeed, Talbot (1991) criticizes the Jarman score for its London bias, while McLaren and Bain (1998) argue that the Carstairs index fails to discriminate deprived rural communities as well as urban ones. Action with Communities in Rural England, the rural interest group, believes that currently there is no appropriate measure of rural deprivation (ACRE, 1998).
Deprivation experienced by people in rural areas is at least nominally similar to that experienced by urban populations, including such factors as unemployment and vulnerability due to age. Indeed, some problems such as low pay and a high incidence of extremely low incomes, low availability of affordable homes, declining services coupled with poor transport infrastructure and difficulties accessing childcare may be experienced to an even greater extent in rural populations (Woodward, 1996). Nevertheless, interpretation of some of the variables included in the standard deprivation indicators varies significantly between rural and urban areas. For example, ownership of one or even two cars is so basic a necessity for households in the most remote areas that it cannot reasonably be interpreted as an indicator of wealth. Likewise, high concentrations of ethnic minorities simply do not occur in rural areas in the UK.
Rural areas incorporate many different types of community. Some rural areas are affluent while others are severely deprived; some are stable while others are changing rapidly. Rural employment structures have been affected by the decline of traditional
10 agricultural and extractive industries and in some areas by the rise of tourism, with a particular concentration of part-time and low-paid jobs. Payne et al. (1996a) note that the flow of high-income households into accessible rural areas helps conceal the widespread disadvantage in more remote areas. Dormitory settlements continue to emerge which are largely dependent on urban areas, and associated with a continued decline in local services. Thus deprivation and rurality intersect in many different ways, and the assumption that rural deprivation will be captured by the intersection of two univariate indicators, themselves inadequately specified, is naive.
5 Deprivation and rurality in the South West The empirical analysis presented here has arisen from our ongoing work on inequalities in health in the rural South and West of England, but the issues raised are applicable beyond the health field. The area comprises the counties of Avon, Cornwall, Devon, Dorset, Gloucestershire, Hampshire, the Isle of Wight, Somerset and Wiltshire with a combined population of over six million. Regardless of the definition chosen, many of these counties have a high proportion of rural areas. In this section we present empirical evidence to support the contentions (a) that use of different rurality measures produce different results, and (b) that the standard measures are all in some respects inadequate in the identification of rural deprivation.
In this study we use three different measures of rurality. Population density, because it is widely used and easily understood; a nearest neighbour measure because it overcomes one of the key weaknesses of density measures, as discussed above, and the ONS multivariate ward classification, as it represents a completely different ‘official’ approach to the identification of rural areas. In each of the following tables and figures, the small number of wards for which one or more of the measures cannot be computed (due to differences between the ward lists available from different sources) have been excluded. For population density and nearest neighbour measures, a threshold value needs to be determined, and we have taken the least dense and most dispersed 40% of wards respectively as our rural definitions. All such decisions are subjective, but the rationale for this approach is revealed by Table 3, which shows the relationship between the ONS ‘rural areas’ and ‘rural fringes’ groups, their constituent
11 clusters as noted above, and population density divided into deciles. These deciles cannot be mapped directly onto other definitions, but we note that the most rural 40% in density terms include all but one of the wards identified by ONS as ‘rural areas’, and deciles 1 and 2 approximate to the ‘super sparse’ DETR category discussed above. It should be noted that the ONS group ‘prosperous areas’ contains a cluster labelled ‘affluent villages’. 89% of the affluent villages group falls within the lowest 40% by density, but the cluster labelling means that none of these are included within the ONS rural areas or rural fringes groups. The affluent villages cluster is concentrated heavily in the north and east of the study region, particularly Hampshire and Gloucestershire, but is almost completely absent from Devon and Cornwall in the extreme south west. It can also be seen that 19% of wards in the remaining groups (‘the rest’ in the table) fall in the lowest 40% by density. The lowest population densities in these groups are classed as ‘transient populations’ by ONS. Most wards in the ‘rural fringe’ clusters fall in density deciles 4 and 5, but the range is from deciles 2 to 10.
[ Table 3 about here ]
The relationship between the ONS classification and the nearest neighbour variable is broadly similar to that for population density. Table 4 compares the deciles of population density with those of the nearest neighbour measure. We have measured nearest neighbour distances between unit postcodes, averaged across wards. The entire analysis is based on the locations and pseudo-ED codes found in the 1991 directory of enumeration districts and postcodes. As discussed in section three, it may be argued that for many applications in which access to, or delivery of, services is important, isolation is more relevant than density. Nearest neighbour highlights this aspect. The measure contains no explicit social information, but embodies aspects of population distribution which are not captured by density measurement. It is important to note that although there is broad correspondence between the two variables, there is a wide spread about the diagonal, indicating that many areas change ranking depending on the measure used. This is discussed further in section six.
12 [ Table 4 about here ]
The three ways of defining rurality (density and nearest neighbour lowest 40% and the ONS ‘rural areas’ group) each embody different aspects of the rurality concept. Despite the similarities discussed above, the level of disagreement between them is indicative of the extent to which analyses and policies might produce different results if different rurality measures were used. This is particularly important in the context of resource allocation, in which the starting definition may determine those areas which are eligible for some specific benefit. Figure 1 shows the geographical distribution of agreement between the three approaches, and Figure 2 shows their overlaps by counts of wards and total population. As might be expected, the map shows a general tendency for greatest agreement at the extremes of the urban-rural spectrum, with confusion in the remaining areas. Remarkably, only 245 wards (16%) fall into the rural category according to all three definitions, and due to their small population sizes these account for only 7% of the region’s population, although covering a much greater proportion of the land area. By contrast, 805 (54%) of wards are excluded from the rural definition by all three measures, accounting for 75% of the population. Using population density and nearest neighbour measures the rural 40% (four deciles) of wards account for 19-22% of the population, while the 17% of wards classed as rural by ONS account for only 7% of the population. Unsurprisingly, the ONS classification reveals the greatest differences from the other two approaches, but the scale of disagreement between the different definitions should be a matter of concern, as it indicates the extreme sensitivity of any rurality discussion to the measurement method and thresholds adopted.
[ Figure 1 about here ]
[Figure 2 about here ]
Table 5 shows the high correlations between the four deprivation indicators (discussed above) in the South West. Each of the standard indicators is generally higher in urban areas, and each produces above average values in some of the ex-mining settlements in the far west. There are minor differences in the geographical distribution of each
13 index, reflecting differences in the detailed distributions of the census variables on which they are based. The Townsend index has been used in a number of other studies where it has been found to perform well in explaining health variations (Morris and Carstairs, 1991; Higgs et al., 1998; Senior et al., 1998). In the context of our interest in health, the Townsend index will be used to further explore the relationship between deprivation and rurality definition. Table 5 also shows the correlations between each of the deprivation indicators and the 1991 census LLTI variable.
[ Table 5 about here ]
The mean Townsend index for the South West region is -1.09, with a standard deviation of 2. The index is standardised to England and Wales, and the least deprived wards are those with the highest negative numbers, thus the South West region as a whole is less deprived than the national mean. There is very little difference between the urban (-0.45, -0.46) and rural means (-2.05, -2.06) as defined by population density and nearest neighbour respectively. All measures show lower deprivation values in the rural areas, with the greatest rural deprivation suggested by the ONS definition with an urban mean of -0.85 and a rural mean of -1.70. This is largely due to the exclusion of the affluent villages cluster from the rural groups in the ONS classification, while these will generally be included in the most rural 40% of the other two definitions, due to their dispersed settlement patterns.
While most of this evidence points to rural areas being, in general, less deprived than urban areas it should not be assumed that the relationship is linear. The boxplots in Figures 3, 4 and 5 show the Townsend index by each of the three measures of rurality. The ONS groups in Figure 3 display little variation, although there are generally lower deprivation scores in rural areas and the urban areas display the greatest range of values. In Figures 4 and 5 the leftmost four boxes represent the four deciles which we are considering as rural. While the urban deciles have higher median scores and a greater range, the fall in deprivation scores begins to level out and rise slightly again in the most sparsely populated areas. There is a slight U-shape in the relationship, such that the deprivation scores are lowest in the rural fringes, but rise markedly in
14 urban areas and more gently in remoter areas. The nearest neighbour measure, which is most sensitive to settlement pattern, reveals this most strongly. Detailed analysis of the other deprivation indicators reveals very similar patterns, although only relationships with the Townsend score are presented here for clarity. These figures are also affected by the exclusion of the affluent villages from the ONS rural groups and their inclusion in the density and nearest neighbour definitions. Effectively, the relationship observed in Figures 4 and 5 is present even after the damping effect, which might be expected from the inclusion of the wealthiest rural areas in the lowest density deciles. Without these, the U-shape would be more pronounced.
[ Figure 3 about here ]
[ Figure 4 about here ]
[ Figure 5 about here ]
In our ongoing work we are examining the relationship between deprivation and LLTI in the South West in more detail than can be presented here. We have chosen to look at standardised LLTI under the age of 65 (premature LLTI) in order to overcome some of the more severe effects of retirement migration on LLTI patterns. Regional-level correlations between the deprivation measures and LLTI were given in Table 5. Figure 6 shows LLTI against nearest neighbour deciles and is thus comparable with Figure 5. The U-shape of the relationship is even more marked in this case, and it is interesting to note that in the most rural decile the seven wards with the highest LLTI rates are all in Cornwall, the most peripheral county. In the second decile the five highest LLTI rates are in Cornwall. Although the Townsend index does highlight some wards in Cornwall, the relationship is weaker, and it would be reasonable to conclude that other factors, inadequately captured by Townsend are associated with observed LLTI. In general, the relationships between LLTI and Townsend are stronger in the urban wards than the rural ones, and of the various measures of rurality, nearest neighbour appears to indicate the strongest relationship with socioeconomic conditions. Taking only the rural areas ONS group, the correlation between population density and LLTI is virtually non-existent with r = 0.058, whereas for the
15 nearest neighbour measure the figure is 0.371 (significant at the 0.01 level). The internal heterogeneity of the rural areas are further revealed by Figure 7 which shows the relationship between LLTI and the six clusters of the ONS rural areas and rural fringes groups. There is a clear gradient in premature LLTI levels from the accessible countryside to remoter coast and country. Interestingly, in these results it is the multivariate ONS classification and nearest neighbour measure, sensitive to settlement pattern, which provide a clearer indication of the U-shape of the relationship than the more conventional deprivation and population density measures.
[ Figure 6 about here ]
[ Figure 7 about here ] 6 Discussion We have reviewed the principal methods for the measurement of rurality and deprivation and their weaknesses. While researchers and policy-makers continue to use area rather than individual measures of deprivation, two aspects of the current situation might be improved. The first is to provide more meaningful spatial units, and the second is to provide more meaningful descriptors of rurality and socioeconomic conditions within those units. The redefinition of spatial units is beyond the scope of this paper, but current plans to implement a 2001 census output geography using more explicit design criteria including greater social homogeneity of output areas should offer some improvement (Martin, 1998).
The second and more important aspect, the provision of more useful descriptors of the existing spatial units, has been addressed in our review and study. Population density, nearest neighbour distance and the ONS ward classification have been applied to our study of the South West of England. Not only is there major disagreement between the areas included by population density and ONS methods, but even the population density and nearest neighbour measures display important differences in the ranking of areas. Population density and its many derivatives have an attractive simplicity, but there are many circumstances in which this can be misleading, particularly at the urban fringe and in small settlements, where administrative boundary placement may
16 have as great an impact on density values as the underlying settlement pattern. The ONS classification is based on a broader range of area characteristics, but the labelling of groups, such as the exclusion of affluent villages from the rural groups, can dramatically affect interpretation.
While the general pattern of population density and nearest neighbour are related, nearest neighbour statistics tell us more about where people live in relation to each other, and unlike most other measures, they become more sensitive at the rural end of the scale. Mean distance between postcodes has an element of accessibility (unrelated to the provision of any specific service), which is not included in the other two measures. Further, the association between standard deprivation measures and rurality indicates an increase in measured deprivation with increasing isolation. LLTI displays a stronger U-shaped relationship, with the highest rates occurring in urban and remote areas, and the lowest in suburban and rural fringe areas, despite the inclusion of lowdensity affluent villages in the most rural deciles. The previous section showed how the nearest neighbour definition suggested the greatest levels of deprivation in rural areas.
One further aspect, which has not been examined empirically here, is that of peripherality. The concept of peripherality is most commonly used to refer to isolation from major centres of population, employment, services and policy-making. The extended geographical shape of the South West region means that places in Cornwall and the extreme west are further away from all major metropolitan services, and they appear to display higher deprivation than more accessible rural areas. It is beyond the scope of this paper to attempt to quantify the peripherality concept further, but we conclude that it may have an important role to play in further understanding those aspects of rural deprivation which are related to access to services.
Shucksmith (1990) believes that the requirement for nationally available data at the small area level has led to an over-reliance on indicator variables from the census which are related in essentially unknown ways to deprivation. These are seldom direct measures or based on adequate causal explanations. In the geodemographic industry, with which some comparisons may be drawn, there is increasing use of non-census
17 variables and individual lifestyle data in order to overcome the shortcomings of census-based indicators. For the future, the proposed inclusion of an income question in the 2001 census would provide a useful benchmark for the existing deprivation measures. Postcoded administrative data such as income support, housing benefit and council tax benefit data are now being assembled by local government at ward level (Noble and Wright, forthcoming), and these have the potential to provide continually updated measures of poverty for small areal units. Homogeneity of the spatial units is one of the key issues: in towns with higher concentrations of population there is a tendency for disadvantaged areas to be large enough to be reflected in the census areas. In rural environments, census areas tend to be much larger and heterogeneous, and may currently hide genuine areas of social disadvantage. Rural deprivation is thus frequently invisible because of small community size, methods of measurement and inappropriate indicators (RDC, 1998). These rural areas have potentially the most to gain from the use of higher resolution administrative datasets in a policy context. Our analysis has used ward-level data primarily due to our desire to evaluate the ONS classification and the standard ward-level deprivation indicators, and for comparison with ward-level health outcome data, although density and nearest neighbour measures are easily computed at ED level.
As noted above, there is already a tendency for researchers and policy makers to choose the standard deprivation and rurality measures that appear to be best suited to their particular application. Indeed, the performance of deprivation-based models can be improved by a custom combination of variables for the health outcome in question (Martin et al., 1998; Diamond et al., 1999). If general-purpose measures are required, then there are strong arguments in favour of splitting these into the different dimensions of disadvantage, thus explicitly recognising its multivariate nature. Dunn et al. (1998) explore the grouping of linked indicators into eight separate ‘bundles’. Each bundle is concerned with a separate aspect of deprivation: access to employment; quality of employment; vulnerability of employment in the local economy; low incomes; housing access and affordability; access to services and physical isolation, and other researchers are experimenting with a similar decomposition of rural deprivation into separate factors. The objective of the indicators in each bundle is to provide an estimate of the number of people
18 experiencing that particular aspect of rural disadvantage. The bundles focus on different policy areas, so the counts or proportions from each indicator in one bundle would not be combined with those from another bundle. It would be a logical step from the assembly of such indicators to use only those which are relevant to a specific application. Interestingly, their proposed bundles include separate measures of physical isolation and peripherality, which have figured largely in our consideration of the weaknesses of the existing measurement approaches. Although the data series required for the creation of such bundles are not presently part of a national data infrastructure, each of them relies on a range of administrative information that is more or less routinely collated by various authorities. It is not unreasonable to anticipate an environment in which their use could become widespread.
7 Conclusion In this paper we have considered the definition of deprivation and rurality, and the range of standard measures which may be used to implement these concepts in research and policy contexts. We identify the assumed unidimensionality of both phenomena as one of the greatest difficulties, as it is clear that unidimensional indicators can adequately capture neither. On the basis of our review, we conclude that the nature of deprivation, in rural areas particularly, is poorly represented in the standard approaches, and suggest that this is likely to work to the disadvantage of deprived rural populations in resource allocation and service planning contexts. In particular, the urban bias inherent in national standardisation of deprivation measures overlooks aspects of deprivation which are peculiar to rural environments.
In our study of the South West of England, the large degree of disagreement between three alternative measures of rurality should be a matter of concern in the interpretation of all work which purports to address the special nature of rural areas. The role of low-density affluent rural settlement in such definitions needs to be addressed. The most commonly used deprivation indicators are standardised nationally, but even these indicators display some increase at the extreme rural end of the settlement spectrum. If nationally computed measures are to be used, those that separately measure the physical isolation and peripheral nature of rural communities
19 would be more appropriate than existing indicators. Certainly, there are conceptual justifications for such an approach, and evidence of stronger relationships with health measures such as LLTI. Most promising is perhaps the development of approaches using new and alternative data sources to create deprivation bundles more closely associated with specific application needs, and there is an urgent need for further work in this area, particularly on the relationships between such indicators and policy objectives.
Acknowledgements The work reported in this paper was supported by a South and West Regional Health Authority research and development grant C/MV/20/04.97/Roderick. S Barnett is supported by Medical Research Council studentship G610/47.
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24 Figure captions
Figure 1. Map South West Region showing level of agreement between three definitions of rurality.
Figure 2. Levels of agreement between rural definitions in the South West region (numbers of wards and populations in brackets)
Figure 3. Boxplot of ONS rural, rural fringe and other groups by Townsend index.
Figure 4. Population density in deciles by Townsend index.
Figure 5. Nearest neighbour in deciles by Townsend index
Figure 6. LLTI by Nearest Neighbour deciles.
Figure 7. Boxplot of LLTI by cluster within ONS rural groups.
25
Table 1. Census variables in four major deprivation indicators
No car Overcrowding Unemployed Low social class Elderly alone Under 5’s One parent Moved Ethnic Children in low earning h/h Lack of amenities Educ. participation (17yrs) Not owner occupied Children in unsuitable acco
DOE (level) X (ED) X (ED) X (ED)
Jarman
X X X X X X X X
Carstairs
Townsend
X X X (male) X
X X X
X (ED) X (ED) X (ward) X X (ED
26
Table 2 Measures used to describe urban/rural areas. Measure Settlement size Population density
Nearest neighbour Accessibility
ONS land use definition of urban. Multivariate area classification
Definition An urban area, town or village is defined on the basis of maximum and/or minimum population thresholds. Number of people in the spatial unit divided by area of the unit. May be weighted by population, or reexpressed as sparsity. Algorithm to find mean and standard deviation of distances between postcodes in a given area. Ease with which a facility can be accessed by a defined population. Generalized inaccessibility may be described as peripherality Built up areas Selections of variables combined into factors that describe the nature of discreet areas. Super Profiles, Mosaic, Acorn and ONS families are all such products.
27 Table 3. ONS groups and clusters by population density deciles (numbers of wards) Rural areas group Population density deciles (pph)
Accessible countryside
Agricultural heartland
< 0.32 0.32 – 0 .53 0.53 – 0.88 0.88 – 1.51 1.51 – 3.96 3.96 – 8.37 8.37 – 16.22 16.22 – 27.20 27.20 – 38.42 38.44 +
39 55 36 10
50 20 1
Rural fringe group Remoter coast and country 10 16 15 2 1
Edge of town
1 1 8 8 4 4 5 1
Industrial margins
Town and country
3 7 19 18 9 2
2 8 13 17 9 7 3 4 1
Prosperous areas group Affluent villages
All other groups
12 34 32 18 11 1
31 18 47 81 93 121 136 142 139 146
28
Table 4. Population density by nearest neighbour deciles (numbers of wards) Nearest neighbour deciles Pop density 1 2 3 4 5 6 7 8 9 10 deciles (pph) 1 8 1 2 12 7 2 57 41 18 2 43 43 44 14 4 1 3 39 17 48 35 6 3 4 11 28 23 42 31 8 1 1 5 16 13 30 41 31 9 3 4 6 1 4 14 39 38 25 14 6 7 7 6 16 31 33 30 18 15 8 5 23 45 27 28 21 9 2 12 13 47 39 35 10 3 20 31 36 58
29
Table 5. Correlation of four deprivation indices and LLTI.
Carstairs DoE Jarman Townsend LLTI
Carstairs DoE 1.00 0.81 1.00
Jarman 0.82 0.71 1.00
Townsend 0.90 0.83 0.87 1.00
LLTI 0.734 0.610 0.633 0.692 1.00
