Territorial justice and primary health care: An example from London

02'7-3536 S6 S3.00 f0.00

Sot. SCI. .Med. Vol. 23, So. IO. pp. 11)93-1103. 1984 Pnnted in Great Bnrain. All nghrs resened

Copyr!$

c

1956

Psr_eamon Journals

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TERRITORIAL JUSTICE AND PRIMARY HEALTH CARE: AN EXAMPLE FROM LONDON A.

MARTIS

Department

of Geography Queen Mary

POWELL

and Earth Science and Health and Health College. Mile End Road, London El 4X.

Care Research England

Centre,

Abstract-This paper is part of a larger piece of research which examines the spatial relationship between need for and provision of primary health care in London. The research reported here is concerned with empirically testing the ‘inverse care law’ at the DHA level. One concept that may be used to guide this analysis is ‘territorial justice’. Several conceptual problems associated with the use of territorial justice are outlined. These include inadequate conceptualisation of the form of social justice assumed, of the problem of deriving need indices and of the nature of resources. A final problem is concerned with the spatial scale of the analysis. The concept of territorial justice is made operational so as to identify relatively under and overprovided DHAs. The result is that the often held assumption of a simple dichotomy of relatively underprovided inner DHAs and overprovided outer DHAs is shown not to be tenable. However, this research concentrates on the quantity of care, and does not focus on the important aspect of its quality. This preliminary analysis reveals the need for further research on this important topic. Ke,v words-territorial

justice,

primary

health

care.

London,

INTRODUmION

For many years, it has been observed that large spatial variations exist in both the need for health care and its provision. It has been suggested that the relationship between need and provision can best be expressed by an ‘inverse care law’: “the availability of good medical care tends to vary inversely with the need of the population served” [I]. As Joseph and Phillips comment, this statement is notable for a number of reasons. First, it does not specify a particular geographical scale. Second, it introduces the notion of need into the consideration of unequal availability of health care. Third, it draws attention to the quality in addition to the quantity of care [2, p. 811. If the inverse care law is valid, this would be very much contrary to the ideals expressed at the inception of the National Health Service (NHS) in 1948: that need was to be the only factor governing access to the best medical care available. It seems pertinent, therefore, to test empirically the inverse care law at a variety of spatial scales. The research reported here forms part of a larger project examining the spatial relationship between need for and provision of primary health care in London [3]. One of the most significant levels of analysis in Britain has involved examining the relationship between needs and provision for Health Authorities, namely regions (RHAs), the now defunct areas (AHAs) and districts (DHAs) [4-61. One concept for examining these relationships at an area level has been termed ‘territorial justice’: “In the services for which the most apparent appropriate distribution is ‘to each according to his needs,’ the most appropriate distribution between areas must be ‘to each area according to the needs of the population of that a&‘. Since the former criterion is synonymous with social justice, we can call the latter ‘territorial justice’ ” [7, p. 161.

relative

provision

CONCEPTUAL PROBLEMS OF TERRITORIAL JUSTICE ANALYSIS

The concept of territorial justice has been used a number of times to examine particular aspects of the spatial balance between needs and resources in the NHS, perhaps due to its perceived simplicity, since the statistical definition of territorial justice is a high correlation between indices of resource use, or standards of provision and an index measuring the relative needs of an area’s population for the services [7, P. 161. There are, however, a number of problems in the application for the ‘Daviesian’ form of territorial justice, many of which have been side-stepped in the use of the concept, thus making the results of the analysis open to serious reservations. These problems may be outlined as follows:

The form

of social justice assumed

Social justice is not necessarily synonymous with “to each according to his need” as Davies [-/1implies. Social justice is essentially to be thought of as a principle (or set of principles) for resolving conflicting claims on resources. Unfortunately there is no one generally accepted principle of social justice to which we can appeal. The range of competing principles include need, merit and contribution to the common good [S-lo]. The moral basis of welfare provision is unlikely to remain constant over time and space: for example, the principle of ‘need’ is less universal in the U.S. than in the U.K. [I 11. Furthermore, there have been calls in the U.K. to remove health care from the sphere of welfare policy [12, 131. So, Davies’ [7] concept of social justice is merely one possible alternative, and the resulting form of territorial justice becomes an analysis of justice from a certain point of view. For the remainder of this paper, territorial justice refers to the form as defined by Davies [7].

1093

Even accepting this caveat, the criterion of‘to each area according to the needs of the population of that area’ still requires a Specific conceptualisation of equity to become operational. Mooney [14] lists seven possible definitions of equity that could be used to allocate health service resourceS between areas. These range from simi;le criteria such as ‘equality of expenditure per capita’ through ‘equality of inputs for equal need’ and ‘equality of access for equal need’ to the highest order function of ‘equality of outcomes’ (health), which would require a very skewed distribution of health service resources, Unfortunately. the ‘Daviesian’ form of territorial justice can apply only to equality of inputs and outputs. Inadequate

conceptualisation

of need

Recent work has emphasised the essential contestability of need, and the philosophical gulf between those who believe that need can be ascribed to a person irrespective of their own views, and those who believe that need can be ascribed to persons only on the basis of their own ‘felt needs’ [S. 151. The choice of needs indices at the empirical level implicitly assumes a certain conceptualisation of need at the theoretical level. For example, the use of census data implicitly embraces the view that need can be ascribed by a third party. Even if the conceptual problems are assumed away, many problems Still exist at the empirical level [16]. Defining need or deprivation is problematic. Many possible indices exist, and the choice of indices may be crucial. Thunhurst [16, p. 1011 defines three setS of indices: (i) Direct indicators, which consist of deprivations in themselves, e.g. overcrowding. (ii) Indirect indicators, which do not necessarily constitute deprivations in themselves, but enable the existence of deprivation to be inferred. These variables can crudely be viewed as either proxy measures of possible lack of income, factors which might make poor households poorer, or measures of people particularly likely to be discriminated against (e.g. New Commonwealth households). These indicators may also be termed ‘aggravating’ since they represent deprivation only in combination with other factors ]l71. (iii) Interpretative indicators, which are not measures of deprivation, but which aid the geographical analysis of the distribution of direct and indirect indicators, e.g. the number of in-migrants during the previous year. Defining need for health care is more complex than defining deprivation or, for example, need for housing improvement grants. If a household lacks all basic amenities, then the need for an improvement grant seems fairly clear. iviorbidity is generally said to represent need for health care. As the Resource Allocation Working Party [lS] found, reliable data on morbidity is very scarce. Therefore, we are often forced to use social indicators as a proxy for morbidity on the basis of past evidence, or -Statistical associations, e.g. morbidity tends to be associated with poor housing or unemployment. At best, then. only a proxy for need is being measured. Further, “Not enough is known about the determinants of

health needs. Even u-hen parricul?; factors can be seen to pia) a part in causing hczi:h need. it is often difficult to quantify the relationship” [IS. p. I I]. Both Single vanables and composite indices may be used as need indices. It is doubtful v+hether a single variable can capture the multi-dimensional nature of need. However. composite indicts LX plagued by the problem of vveighting. or the ‘arithmetic of woe’ [l7]. Both endogenous and exogenous iislghting systems have been used. Pinch [ 191 derived a need index by using principal components analysis. in which the index of need, the component score. depends on the structure of the correlation matrrr between variables. Edwards [20, p. ZSI] criticises this type of approach in which variables are “thrown into the statistical melting pot and those which emerged glued together by high correlation coefficients ha\-e been used as composite indices of urban deprivation.” Jarman [>I] has used general practitioner (GP) perceptions of workload as exogenous weightings in his index to measure need for primary health care This may build in the deficiencies of the existing system, as GP perceptions of workload may bear little relationship to ‘real needs‘, for these perceptions may be filtered by other provision in the area (e.g. Accident and Emergency Departments), the patients’ problems of access and previous experience lvith the GP and. perhaps, appointment systems and receptionists. Another problem is that severe multi-collinearity invariably exists in data sets which are utilised to produce indices. However, the straight index approach ignores the presence of and the effect of multi-collinearity. If an index includes variables with high inter-correlations, then an element of ‘doublecounting’ may occur. Care should be taken to distinguish direct and indirect indicators; partial correlation analysis may help to identify the ‘direct’ influence of each variable. while controlling for the effect of other variables. As a result of this problem, composite indices may be scale-specific, since the correlation matrix between variables will change for another level of analysis. e.g. if both “‘,b of population over 65’ and ‘% of pensioners living alone’ are used in an index, the correlation between them may be high at an enumeration-distinct (ED) level and low at the DHA level. Multi-collinearity may thus be severe at one level of analysis, but less marked at another. A final, apparently insoluble, problem of composite indices is the ecological fallacy. Areas which appear to be very similar on available census data may be quite different in reality. If an area has 50% of its households lacking amenities and 50% being overcrowded a cross-tabulation might reveal complete overlap (i.e. all overcrowded households lack amenities) or no overlap at all (i.e. the overcrowded households and the households lacking amenities are two separate groups). It is likely that these two situations would result in different degrees of total area need for health care. Inadequate

conceptualisation

of the naiure ofresources

The question of the nature of resources subsumes two problems. First, what type of resource is considered, and second, should the input or output Stage of the resource by analysed? In specifying provisions indices. the question of

Terrirsnai

~ustm

and primary healrh care

what type of resource is appropriate should be asked. Often the answer will be constrained by, existing systems of provision, e.g. GPs. health visitors and district nurses are the main ‘resources‘ in the case of primary health care. These ‘resources’ are not homogeneous, and particular types of resources, e.g. women GPs, nurses able to speak other languages, may be most appropriate in some contexts. If possible, the overall ‘resource mix’ should be considered rather than the individual resources in isolation. Sometimes there is an element of substitutability: the current policy in the U.K. is to replace hospital beds by ‘the community’ as the main resource for the elderly presently in institutional care [Xl. The question of whether the input or output stages of the resources should be considered relates to the conceptualisation of equity employed. which was discussed earlier [14]. It is possible to consider expenditure or staffing levels (input) or the number of visits made to clients in the case of health visiting or district nursing (output). Expenditure of staffing levels in two areas of identical need may be the same, yet staff in one area may manage more visits than in the other. If the objective is to equalise access from the point of view of the client, as is stated by RAWP [ 181, then the number of visits should be the stage of the resource considered. However, a low number of visits may result from a more difficult and time-consuming workload in this area, a policy objective (e.g. first visits could be traded off against revisits; a high priority could be given to certain age groups) or from inefficiency, all other things being equal, one person makes fewer visits than another. It is impossible to identify the main reason without a more detailed study of each area. The spatial scale of the analysis

Even if perfect territorial justice exists at a certain spatial scale, it is impossible to make any statement about social justice at a finer spatial scale. Analyses at the regional scale can say little about the balance between needs and provision at the district level, since regional averages mask the diversity between districts. Studies that use provision indices such as GPs/million population for each region are too crude to yield meaningful results. A related problem is that at such a large spatial scale the units of analysis are few. With standard regions, for example. the number of observations is equal to IO. and the validity of correlation techniques are subject to serious doubt. Probably the best spatial scale is the one at which the service is provided, e.g. the DHA for most aspects of health care.

that in parts of the inner cities, especially inner London, the NHS is dismally failing to provide an adequate primary care service [23. p. 891. Two vectors of forces are said to produce this situation. First, GP workload is high, even with an average list size, due to severe social deprivation and the presence of groups with a high level of need, e.g. elderly living alone, ethnic minorities. Second, many elements of what is commonly seen as poor general practice exist: many elderly, single handed practitioners, problems of both large and small lists, poor development of health centres and group practice, low levels of health visitor and district nurse attachment and high use of deputising services [24]. However, it is acknowledged that inner London is not homogeneous with respect to either its needs or its provision. In particular, ‘East End’ and ‘West End’ zones have been distinguished, each with a different pattern of needs and provision [Xl. It should be noted that many of the problems of inner London general practice are problems of quality rather than quantity. In fact, much of inner London is officially ‘over-doctored,’ since London has low average list sizes, which is the criterion used by the Medical Practices Committee to define ‘overdoctored’ areas [X]. METHOD

Obtaining reliable morbidity data has always caused problems in medical geography research. Consequently, the main source of needs indices at an area level is inevitably the National Census, which biases the perspective of need and the choice of indices. Seventeen needs indices (variables l-17) including a wide range of housing, demographic and socio-economic variables thought to be associated with need, were extracted from the 1981 census (Table I). These variables included direct, indirect and interpretative indicators [16]. A wide range of indices is necessary to represent different dimensions of need, since, if for example, housing variables are’ the only ones included, then the final index will represent only this dimension of need. The more conventional census data was supplemented with indices from other sources. An attempt was made to build in a ‘felt need’ perspective using self reported morbidity from the 1981 General Household Survey, an annual national survey containing self reported morbidity data for some 32,000 individuals. ‘Expected morbidity’ indices (variables 18-21) were calculated for acute and chronic sickness using both age-sex and socio-economic groups. Each index is calculated as follows:

TERRITORIAL JUSTICE AND PRIZIARY HEALTH CARE IN LOSDOS

An example of territorial justice analysis is now described taking the case of primary health care for the 31 DHAS within the Greater London Council area (Fig. 1). The system of primary health care delivery in London is a particularly interesting area of study which has come under increasing scrutiny in recent years. Most studies have contrasted an a& quate system of delivery in outer London with special problems that exist for primary health centre in inner London. The Royal Commission on the NHS noted

1095

% reporting

morbidity

= T 7

where p, = % reporting morbidity in group q, = population in group i; N = total population of DHA.

i;

This equation is similar to the ‘expected mortality’ component of the indirect method of mortality standardisation (SMR). Clearly, this assumes that the percentage of a particular group reporting morbidity is constant in every DHA, and may consequently

MARTIS

1096

under-estimate morbidity in areas of severe deprivation. Mortality data was extracted from the 1982 Area Mortality Tables, since 1982 was the first year for which this data was available for the new DHAs created in the 1982 NHS reorganisation. Four mortality indices (variables 22-25) were calculated. Finally, two composite ‘underprivileged area’ scores, based on GPs’ perceptions of workload, were chosen (variables 26-27) [26]. If Table 1 is studied in conjunction with the map of the London DHAs, it may be seen that, in general, the inner London DHAs, e.g. Tower Hamlets, Paddington are the authorities with the highest needs indices, while the outer London DHAs, e.g. Harrow, Kingston have the lowest scores. Tower Hamlets has the highest score (or joint score) on 13 occasions, and has the least owner-occupation, while Paddington

A. Powerr

heads the list five times. Inner London DHAs account for several of the top ten places of Jarman’s underpivileged area scores of the 192 English DH.4s [26]. However, two inner DHAs have the lowest scores on two criteria: Victoria has the fewest large families (variable 8). while Paddington has the fewest pensioners (variable 10). Similarly. while the outer DHAs tend to have the lowest scores, Brent heads the list for householders with New Commonwealth born heads (variable 12). The provision data were collected from three sources. The staffing levels were extracted from the four Thames RHAs’ ‘Regional Manpower Information Systems’: a database which shows the number of staff in post differentiated by category in each DHA. The number of GPs was calculated from Family Practitioner Committee (FPC) medical lists. The

North

East Thames

Thames

South West

Thames

0 London DHA’s

Inner

-

Regional Health Authority

-

District

North 1 2 3 4 5 6 7 8 9

1 2 3 4 5

Health Authority

South and Fioehampton -

Fig. 1. District

East Thames

1 Barking, Havering and Brentwood 2 Bloomsbury 3 City and Hackney 4 Enfield 5 Hampstead 6 Haringey 7 lsllngton 8 Newham 9 Redbridge 10 Tower Hamlets 1 1 Waltham Forest

West Thamas

Croydon Kingston and Esher Merton and Sutton Richmond,Twrckenham Wandsworth

Boundary Boundan/

North

West Thames

Bamet Brent Eating Hammersmith and Fulham Harrow HillIngdon Hounslow and Spelthome Paddington and North Kensington Vlctona

Sotih

15km

health

1 2 3 4 5 6

East Thames

Bexley Bromley Camberwell Greenwich Lewisham and North Southward West Lambeth

authorities

in London

1097

Territorial justice and primary health care Table 1. Descriptive Variable number

Variable

tiean

Standard deviation

45.6 31.7 16.7 7.7

20.4 15.9 9.2 4.2

3.6

2.2

2. 3 4.

OWIiOCC COUN PRIV LAM

5.

OVER

6. 7. 9 IO. II. 12. 13 14. 15.

SEVOVER NOCAR 3DEP ONEPAR PEN LONEPEN NEWCOiMM BORNOUT UMEMP PERM

16.

TEMP

17. 19. 20. 21.

SCV CHSEG CHAGE ACSEG ACAGE

22.

CDR

Il.4

1.0

23.

IMR

10.6

2.5

24.

SMR

101.8

8.6

25. 26. 21.

SMR64 JIO

101.4 la.7 13.5

16.2 29.3 22.8

I.

a.

ta.

See Appendix

Ja for definition

1.5 46.5 6.2 6.3 34.2 32. I 10.7 19.8 a.9 I.5

13.1 I.4 1.9 2.3 5.3 5.8 9.3 3.1 0.3

0.6

0.2

5.6 29.1 29.8 II.6 II.6

2.6 0.9 0.8 0.2 0.1

I.1

staustxs

District

Harrow Tower Hamlets Victoria Hammersnuth Tower Hamlets Paddington > Paddington Tower Hamlets Newham City and Hackney Bloomsbury Victoria Brent Paddington Tower Hamlets Tower Hamlets Tower Hamlets City and Hackney > Tower Hamlets Tower Hamlets Bloomsbury Tower Hamlets Bloomsbury Tower Hamlets Wandsworth > West Lambeth Tower Hamlets Paddington > Paddington Tower Hamlets Tower Hamlets

District 74.7 82.0 38.6 15.2

with rnmimun value

Tower Hamlets Harrow Barking and Havermg Hillingdon

2.4

10.0

Bromley

4.4 67.4 9. I 10.3 39.4 45.5 24.4 38.5 15.2 2.3

Bexley Hillingdon Victona Harrow Paddington Harrow Barking and Hawing Barking and Havermg Kingston Kingston

1.0 12.2 31.2 32.2 12.2 II.9

46 13.0 3.7 1.3

Bexley

0.3 28.8 2.9 3.8 28.3 25.5 2.2 4.6 5.0 1.0 0.3 2.3 27.7 28.6 I I.2 Il.5

Harrow Kingston Hillingdon Kingston Croydon

9.3

13.3 16.3

Kingston

5. I

II9

Harrow

aa

I34 60 55

Harrow Bexley Bromley

77 -24 -21

of variables.

number of visits by community nursing staff were calculated from ‘LHS 27/3’ returns. The provision data should be treated with some caution: the definition of a ‘health visitor’ or ‘district nurse’ may differ between regions. For examples, students, tutors and senior nurses may or may not be included. The LHS 27/3 returns are collated from individual field workers’ returns, and are suspected to be not entirely accurate. However, this data is the only data available for the study of primary health care and, despite its limitations, it is considered accurate enough to be useable for this project. Table 2 shows that the best (i.e. minimum) staffing ratios (variables 28-31, 4W1) are concentrated in the inner London DHAs of Paddington and Victoria. The greatest number of visits also tend to be concenTable 2. Descriutive Variable Variable 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41.

of need indices

with maximum value

POPjGP PCP/GPhr POP/HV POP;DN %HV %HV 6 5 HVvis HV < 5vis ?/.DN %DN 3 65 % DNhome %DN > 65home $5jHV ?z 65/DN

Mean 1798 136 4292 3120 9.3 89.1 0.3 3.0 5.0 I a.4 57. I 75.4 250 454

Standard deviation 305 25 ii82 933 2.5 17.1 0.1 I.0 2.9 10.2 23.8 16.0 ai 142

trated in inner London, although they tend to occur in DHAs other than Paddington and Victoria. Conversely, the highest staffing ratios and the lowest number of visits generally occur in outer London, although the inner DHA of Newham has the least favourable provision on two criteria (variables 35 and 37). There is a considerable difference between the best and worst provided DHAs: the former having approximately twice as many GPs per capita as the latter, but this difference is sometimes nearer a factor of four for other criteria and reaches a striking difference for district nurse visiting (variables 36 and 37). The correlation matrix between provision indices (not presented; see [3]) shows that staffing ratios tend to be positively and statistically significantly cor-

statistics District

of orovision

indices District

with maximum value

Barking and Havering Merton and Sutton Hounslow Bromley Bloomsbury Hammersmith Bloomsbury Paddington Croydon Hampstead Haringey Haringey How&w Bromlev

2263 I75 6625 5412 16.7 144.5. 0.75 5.9 13.4 56.3 97. I 99.3 420 788

Victoria Victoria Paddington Paddington Bromley Barnet Redbridge Ntwham Haringey Newham Croydon Croydon Paddington Paddinnton

with minimum value II37 90 1554 1419 5.7 64.8 0.15 I.5 1.6 4.9 21.3 46.6 79 185

See Appendix for list of variables. *A percentage of over 100% may occur because the base populations in the census and the LHS 2713 returns are different: the former category represents ‘those under 4 at last birthday’ and the latter represents ‘visits to those aged 5 and under in the year ending 31st December’.

related. suggesting that areas which are well provided with GPs are also well provided with health visitors and district nurses. Correlations between visits of health visitors and district nurses tend, however, to be low. .-\reas well provided with health visitors receive in general many health visitor visits, but the relationship is far from linear (Pearsons r = -0.6). There is, however. little relationship between the number of district nurses and their visits. This may partly be explained by the different pattern of home visits: the correlation between % DN and % DN home is -0.57, although there seems to be little relationship between %DN 3 65 and %DN 3 65 home. There seems to be little trade-off between ‘breadth’ (the number of persons visited) and ‘depth’ (the number of visits) for health visitors. Areas with many health visitors tend to be those in which a large number of the population is visited: the correlation between %HV and HV vis is 0.84, while it is 0.74 between %HV < 5 and HV < 5 vis. The correlation between the number of GPs and GP hours is 0.99 and GP hours was therefore discarded as a variable. The variables on home treatments by district nurses (variables 38 and 39) were also discarded since there were deemed of minor importance compared to the number of visits. RESULTS

justice. 12-1 reached statistical gesting territorial significance at the 5”$ level. ~.~th 80 reaching statistical significance at the lo’0 level. Of the correlations suggesting the inverse care Iau., only six reached statistical significance at the 5’+, level, uirh four reaching statistical significance at the 1“G 1s~cl. These were all correlated with the variable ?DEP, and an inspection of the correlation matrix between needs variables (not presented; see [3]) suggests that this ‘indirect’ variable can be discounted in this case as an index of need. It is difficult to come to any tirm conclusions concerning the degree of territorial justice in the system as the strength of the correlations betvveen needs and resources depend on which need and provision indices are examined. For most of the needs indices, the correlations for the staffing ratios (input) are higher than those for the number of visits (output). This suggests that areas of high need tend to have more staff per capita, but do not necessarily have a higher number of visits for their residents. Many of the correlations for %DN $ 65 are negative, indicating that high need areas receir,e fewer district nurse first visits. However, no information is available for the number of re-visits made by district nurses, and there may be more re-visits in high need areas. The fact that high need areas tend to have more staff, yet do not necessarily have more visits, is difficult to explain. It seems unlikely that staff in all these areas make fewer visits because they are ‘inefficient’. The most probable reason might be that visits in these areas are more difficult and timeconsuming. Some needs indices have consistently high correlations with provision indices, e.g. PRIV. SEVOVER, LONEPEN and BORSOL’T (variables 3, 6, 11 and 13). These are mainly ‘indirect’ or ‘interpretive’ indicators, often associated with privately rented accommodation. The variables considered on conceptual criteria to be the best indices of need (see

AND DISCUSSION

The degree of territorial justice in the system may now be assessed by correlating the needs and provision variables. The results are shown in Table 3. Of the 297 correlations between needs and provision (Table 3). 228 indicate some degree of territorial justice (i.e. a negative association between needs and staffing ratios and a positive association between needs and visits), while the remaining 69 suggest a tendency towards the inverse care law. Many of these correlations are small, however, and of those sug-

Table 3. Correlation POP:GP

POP;HV

owsocc cous PRI\ L.411 OVER SEVO\‘ER SOC.AR 3DEP ONEP.AR PEN LOSEPEN &EWCOII1>1 BORSOUT USEMP PERhI TE>fP SCV CHTEG CHAGE .ACSEG .ACAGE CDR IXIR SSlR

All coethcnts

POP,‘DN jl

-2’3

-jI

-j3 37 -09 05

04

-6J -32 -7-1 -35 -19 -34 -3-l -II

-js

-js

-28 -60 -5 -39 -4J

-07 -13 -3-l -26 -39

-4J

-s

-17

-19 -23


by 100, I e. 0.53 becomes

between

>65/DN

sz I4 -66 -js -36 -60 -js js -26 IO -66 -09 -js -9 -23 -9 -24 O‘i -16

-I2 -17 -06 -03 30

multiphed

matrix

59 -40 -4J

-

42

-g

-60 -5 15 -27 29

-5J -26 -g -5c/ -38 -3 -47 -Q -07 -@ 28 I5 -25

-jz

needs and rescurces %HV

HVws

-37 I9 31 13 27

-35 IO

32 _78

-30 15 04

39 ?I

5! ?I

14 23 26 I7 24 24 17 00 18 22

%HV < 5

HV < 5~1s ?ODN 46DN 3 45

02

-32 -04

39

4!

:‘i

33 -jo -01 I2

J-l -?j 22 -05

32 -;o 01 -05

-01 42 lo 03 IO IS -03

OJ 42 23 I2 22 19 0.l

-16 42 06 -06 06 08 -19

-01 27 5!? 11 36

-19 I6 n 26 21

16

-02

I8 J2 zs 26

-3J

s

._

33

53. Two tailed test: P $ 0.05 = 0 36, P 3 0.01 = 0.44 wtii

29 d::

13 -18 _ II _ 17 -?I -?I .t 05 01

-II 03 18 14 - 0I 04 II -I4 7,

00 -22 -06 -I5 -?I -26 -29 -25 -24 -04 -23 -05 -16 -34 -28 -23 _ 23

-I5 12 -02 06 Ii 03 00 -05 -I3 05 -05 - IO -II -26 -0’ 06 III

Territorial

justice and primary health care

below), e.g. the social class based indices tend to have the lowest correlations with the provision indices. .4s the level of territorial justice depends upon which needs indices are selected, the choice of indices is extremely important. This preliminary analysis suggests the same degree of territorial justice exists in the primary health care system among the DHAs in London, i.e. the system tends towards territorial justice rather than the inverse care law. This conclusion is contrary to many of the other studies that have examined the relationship between needs and provision at an area level: West and Lowe (41 for child health services, Vetter et al. [5] on services for the elderly and Jones and Bourne [6] for general health care. Pinch [I91 found a relatively high positive association between needs of and provision of the elderly in London, although there tended to be a negative association for health visitors and home nurses. It is not clear why this present study has reported different conclusions to many previous studies. Possible reasons include the reliance of some previous studies on a limited number of needs indices. The use of other indices may have changed their conclusions. This study examined only the London DHAs. Resource allocation is channelled through the four Thames RHAs, some of which incorporate a ‘deprivation weighting’ in their allocation, and provision in one DHA may be influenced by that district comparing itself to its neighbours. A study of the whole of the U.K. may well suggest that territorial injustice exists. A final related reason is the larger spatial scales used by some of the previous studies which range from counties to regions. A high degree of multi-collinearity exists between the needs indices, which suggests that some indices may be discarded. However, variables with low intercorrelations cannot be discarded out of hand since they may represent a different dimension of need. The choice of indices should be made with reference to both conceptual and empirical criteria and the evidence of previous studies, for example Fox and Goldblatt [27], Carstairs [28,29] and Mersey RHA 1301. First, the conceptual criteria and the evidence of previous studies are considered. Taking the studies as a whole, the best need indices appear to be households lacking a car, unemployment, social class and tenure measures. However, not all of these studies included an extended range of variables: only one study, for example, included the census measures of morbidity [30]. On the basis of these previous studies, the best single indicator was considered to be households without a car. This wa; the only variable showing a consistent trend for both chronic and acute sickness in Carstairs’ analysis of GHS data at an individual level [29], i.e. households lacking a car reported more sickness than households with a car, which, in turn, reported more sickness than households with two or more cars. In the Mersey RHA study (301, the number of households lacking a-ear was positively and statistically significantly correlated with permanent and temporary sickness, and the SMR. Fox and Goldblatt [27] considered that access to cars, after tenure, was the most powerful socioeconomic discriminant with respect to variations in

I099

the SMR. Carstairs [28] derived an ‘urban deprivation factor’ and compared this with ‘health indicators’ such as deaths, hospital discharges and bed-days. However. it does not appear that the composite factor has much to offer over and above the explanatory power of the individual variables. The individual variables with the greatest ‘explanatory power’ were unemployment, households headed by a social class V person and households lacking a car. The variable ‘households without a car’ also seem a suitable candidate for a needs indicator because it is generally considered as a proxy for lack of affluence and because it covers a large section of the population, which gives a better profile of an area than an index that gives information on only a small proportion of the population and says nothing about the vast majority of the area’s population. Turning to the empirical criteria, a factor analysis was employed to help to illuminate the underlying dimensions of need. A factor analysis may help to determine structure in the data, and the pattern of association between factors and original variables might help in the selection of an index or indices of need. The data consisted of 27 needs variables for 31 areas. A principal-factoring method with iterations was chosen. Four factors emerged with eigenvalues greater than one in the initial solution. These were rotated, with the closest approximation to simple structure being achieved by using an oblique rotation, with delta equal -2.0, suggesting a reasonably orthogonal solution, i.e. one in which the factors have low inter-correlations [31] (Table 4). The first, and most important, factor is clearly a socio-economic factor which loads-very highly on the variables COUN, PERIM, SCV, CHSEG and ACSEG (variables 2, IS, 17, 18 andTO). The remaining factors are of approximately equal importance, as indicated by the sum of squares of the loadings. The second factor defies simple naming, but may perhaps best be described as a lone pensioner and small families private renting factor. The third factor is an age factor, with high loadings for PEN, CHAGE, ACAGE and CDR (variables IO, 19, 21 and 22). The last factor is an ethnic private renting factor, which loads highly on BORNOUT, NEWCOMM and PRIV (variables 13, I2 and 3). Most of the variables load highly on only one factor. These include most of the socio-economic and age variables. No variable loads highly on both factor 1 and 3, and these factors are almost orthogonal: the correlation between these factors is -0.08. This implies that the socioeconomic and age dimensions are virtually uncorrelated. There are many problems associated with deriving a needs index. The factor analysis suggests that no one variable or factor score can satisfactorily capture the multi-dimensional nature of need (i.e. statistically explain all of the variance in the data). It was finally decided to use single variable indices. (A further discussion is presented in [3].) The chosen variables were PERM (a direct indicator, representing factor l), NOCAR (a variable often used as a surrogate for lack of affluence; this variable has the highest communality) and J8 (a composite indicator that has been accepted by the Underprivileged Area Committee of the British Medical Association as representing need

1100

MARTIN A. POWELL Table 4. Factor Factor OWNOCC COUN PRIV LAM OVER SEOVER NOCAR 3DEP ONEPAR PEN LONEPEN

-87

All coefficients omitted.

=

34 59 36 77

=

I

a

1

Factor

3

Factor

4

-30 66 61 18 51 31

31 -97 94

-19

70 -53 84

80 98 82 98 95 S7

70

98 48 59 87 82 73 80 12.2

loadmgs multiplied

87 69

35

3.0

by 100, i.e. 0.87 becomes

YLx 100% YL YE- y, x 100% y,

2

-31

11

-

-

P

matrix

Factor

56

for primary health care [Xl). Finally, the DHA population was used as an index of need under an assumption of arithmetic equality. IMany analyses of territorial justice have relied solely on the use of the correlation coefficient. However, this gives information only on the degree of territorial justice in the system as a whole, and cannot give any information on the relative level of provision in any given DHA. The relative level of provision in each DHA can be calculated in two ways, depending on the level of equity assumed. First, if equity is defined as equality of provision per capita, the deviation from the Greater London mean level of provision may be calculated. Second, if equity is defined as equality of inputs or outputs for equal need, then deviation from a normative level of provision predicted by using a needs index in a regression equation may be utilised as an attempt to allow for the need of the DHA. The two indices of relative over or under provision are given as follows: I

I

102

NEWCOMM BORNOUT UNEMP PERM TEMP SC CHSEH CHAGE ACSEG ACAGE CDR IMR SMR S&f R64 JIO J8 Sum of squared

pattern

Y,

44 34 3.3

2.8 87. Loadings

below 30 are

a given level of provision relative to need. As an example of the methodology, the health visiting service will be examined using both indices for the total population of the DHA and the main client groupthe under fives. The regression equations used to calculate the needs based index, I,, are given in Table 5. NOCAR is used as the independent variable. Table 6 shows that while some inner city authorities (such as Paddington and Bloomsbury) seem over provided on most criteria, others (such as Newham and Wandsworth) are under provided. Similarly, while a suburban DHA like Merton and Sutton seems over provided, Barking and Havering appears to be under provided. Tower Hamlets, on the other hand, seems over provided on its staffing levels, yet less well provided considering the number of visits performed. This situation of output appearing worse than input occurs quite frequently with the other high need areas, e.g. City and Hackney and West Lambeth. This suggests that visits may be more difficult and time consuming in these high need areas. However, this situation also appears to occur in the more affluent DHAs of Redbridge and Bamet. All these DHAs-high and low need areas-have comparatively few visits made by each health visitor. On the

Where Ip = I,, = Y, = Yt = Y, =

population based index; ‘needs’ based index; level of provision in DHA i; London mean level of provision; predicted level of provision in DHKi by regression equation.

Table 5. Summary

Y

given

It should be noted that these indices can be used only to define reiaticely under and over-provided DHAs, and can say nothing about the adequacy of

POP/HV <5,JHV %HV HVvn %HV < 5 HVvis < 5

of regression equations calculate I”

used to

X = NOCAR equation

R’ WI

Y= Y= Y= Y= Y= Y=

6091- 55.8X 409 - 3.4x 5.97 +0.07x 1.52 +0.03x 61.7 +0.%X 1.86 + 0.02X

38.3 30.1 14.5 10.8 20.2 9.9

1101

Territorial justice and primary health care Table 6. Populatmn

based index of relative

provision

DHA

POP, HV

< 5IHV

% HV

Barking and Hawing Bloomsbury City and Hackney Enfield Hampsread Haringey lslington Newham Redbridge Tower Hamlets Waltham Forest Barnet Brent Ealing Hammersmith and Fulham Harrow Hillingdon Hounslow Paddington and North Kensington Victoria Bexley Bromley Camberwell Greenwich Lewisham and North Southwark West Lambeth Croydon Kingston Merton and Sutton Richmond. Twickenham and Roehampton Wandsworth

+41 (27) -46 (2) -16(6) f47 (30) -12(11) +8 (18) -14(E) f2I (25) SlZ(2I) - 37 (4) + 25 (26) -21 (5) -4(l4) + IO (20) -38 (3) -l2(9) fl(l5) + 58 (31) -63 (I) +3(16) +45 (29) + I9 (22) -6(13) f41(28) -12(10) - l6(7) + 20 (24) -lO(l2) +7(17) + 20 (23) +lo(l9)

i 40 (26) -57 (2) -3(13) + 45 (28) - 39 (4) fl(20) -17(9) + 47 (29) +7(19) - 27 (6) + 34 (25) -22 (7) +2(15) + 22 (23) -45 (3) -I I(l2) +6(18) +72(31) -68(l) -28 (5) +44 (27) +9(2I) -2(14) + 57 (30) -14(10) -lS(8) + 24 (24) -13(ll) +4(16) +5(17) + IO (22)

- 20 (25) +86(l) O(l6) +5 (12) - 27 (28) -7 (19) f4l (4) - 20 (24) - 30 (29) +5(1-I) -4(l8) - 22 (27) +47 (2) -9 (20) + 27 (6) + 27 (7) +31(j) - IO (22) +41 (3) O(l7) -21 (26) -37(31) + 20 (8) +ll(lo) f5 (13) +5 (15) - I8 (23) +l9(9) +9(11) -9 (21) - 33 (30)

other hand, Islington, often thought of as a high need DHA, has health Visitors who make on average as many visits as many more affluent DHAs. Consequently this area transforms an average level of input to quite high levels of output. Clearly, many

(I,) HVvis

%HV < 5

HV 6 5~1s

38 (28) tl66(1) - 26 (‘4) - I5 (22) -I I(l9) - 23 (23) + 28 (7) -44 (29) -45(31) +28 (6) - 36 (26) +3(16) t40 (4) +5(15) +16(ll) f25 (8) +ls(lo) +7(14) + IO1 (2) +1X(9) -30 (25) - I3 (20) +14(I2) f3(17) f32 (5) - 36 (27) - I4 (21) +42 (3) +13(13) -9 (18) -45 (30)

-l2(27) +42 (2) +5(14) +3(16) + 17(4) - IO (25) Of17) - 24 (30) - 5 (20) -5(19) +7(13) -24(31) +15(j) - I I (26) +69(l) +8(ll) +8(lo) -?I (29) +41 (3) +l4(6) -6 (21) - 20 (28) + 14(9) 0(1X) +8(12) +5(15) - 7 (22) + I4 (7) + I4 (8) -8 (23) -9 (24)

-31 (29) -71(3) - 29 (28) -?I (23) - 38 (7) - 29 (27) t?(l5) -47 (31) - 28 (25) -16(E) - 28 (26) f7(13) Sl5(9) - 5 (20) 775 (2) +12(12) -4(l9) +I (17) +112(l) ~46 (5) - 20 (22) +lJ(ll) +7(14) -5(21) +-II (6) - 34 (30) ~2(16) t-16 (4) f15(10) -I (18) - 23 (24)

-

other factors, such as DHA policy on ‘breadth’ (the number of persons visited) versus ‘depth’ (the number of re-visits); the priority given to the under-fives as opposed to the rest of the population; the question of whether health visitors are responsible for patients on

Table 7. Needs based index of relative

provision

(1”)

DHA

POP/HV

c5/HV

%HV

HVvis

%HV $ 5

HV < 5vis

Barking and Havering Bloomsbury City and Hackney Enfield Hampstead Haringey Islington Newham Redbridge Tower Hamlets Waltham Forest Barnet Brent Ealing Hammersmith and Fulham Harrow Hillingdon Hounslow Paddington and North Kensington Victoria Bexlcy Bromlcy Camberwell Greenwich Lewisham and North Southwark West Lambeth Croydon Kingston Merton and Sutton Richmond, Twickenham and Roehampton Wandsworth

+2l (26) - 29 (4) + 7 (20) + 25 (28) -I (16) +lz(sz) + IO (21) + 30 (29) -7(ll) -l7(8) +21 (27) - 34 (2) -7(lO) -I (IS) -26 (6) -30 (3) -20 (7) f30 (31) -52 (I) + I4 (23) +l7(24) -6(12) f7(19) +36(30) -4(13) -l(l4) + I (17) -26 (5) -9 (9) +6 (18) - I9 (25)

+ 20 (24) -43 (2) + 26 (27) + 23 (26) -31 (5) + I2 (21) +8(19) +60(31) -II (12) -2(16) f30 (28) -35 (3) - I(l7) + IO (20) - 34 (4) - 29 (7) -l7(9) f48 (29) -57(l) -19(E) + I6 (23) -14(10) +l2(22) + 52 (30) -6(l4) -2(15) +4(18) -29 (6) -12(ll) ---E(l3) + 20 (25)

-l7(26) +57(l) -15(24) +l2(9) - 34 (30) - I3 (22) + I9 (6) -27 (28) -25 (27) -l3(21) -7(16) -l6(25) f43 (3) -8(17) +ll(lo) + 40 (4) f47 (2) -6(15) +l8(7) -lO(l9) -l3(23) - 29 (29) +6(12) +8(ll) -4(13) -9(18) -l3(20) + 30 (5) +l5(8) -6 (14) -39 (31)

-35 (25) +110(l) -41 (28) -9 (21) -24 (23) -31 (24) f2(I2) -51(30) -40 (27) O(l5) - 39 (26) +13(10) f33 (6) +6(ll) -4(18) +44 (4) +37(j) +13 (9) +52 (3) +2(13) -20 (22) + I (14) -3 (17) -2(16) +15(g) -46 (29) -6 (20) + 58 (2) + 22 (7) -6(19) -52(31)

- IO (22) f2I (3) -10(21) +7(9) +5 (IO) -16(26) - I4 (25) -31 (31) O(l5) - 20 (29) f4(ll) -21(30) + I I (8) - I I (23) +49(l) +l7(71 +l8(6) - IX (28) +21 (4) +3 (12) + l(l4) -l3(24) +2(13) -4(18) -2(l6) -7 (20) -2(17) f22 (2) +19(j) -6(19) - I6 (27)

-30 (25) + 38 (4) -42 (29) - I8 (23) i 20 (9) - 36 (28) - I6 (22) -53(31) - 25 (24) -7(18) -33 (27) +l2(ll) +8(12) -6(17) t-18 (3) +22 (8) +6(13) f3 (IS) +70(l) +26 (5) -14(21) f25 (6) -8 (19) -1 I (20) + 24 (7) -44 (30) +5 (14) + 55 (2) +l9(lo) -I (16) - 32 (26)

Tables 6 and 7 show the index of relative provision (I,/&) followed by in parentheses the rank of DHA for each criteria: I = the best provided DHA. 31 = the worst provided DHA. For the staffing ratios-POP/HV and <5/HV, a negative index implies overprovision while for the visiting indices, a negative index implies underprovision. This is due to the staffing ratios representing persons per health wsiror, and consequently a high number indicates poor provision.

1102

h’ftiTIN

a particular GP’s list (‘attachment’) or for a particular geographical area, all impinge on the ef?iciency of transforming inputs into outputs. When needs are taken into account (Table 7) high need areas, e.g. City and Hackney, Tower Hamlets appear less well provided than was suggested if provision per capita alone was considered. Conversely, low need areas, e.g. Harrow, Kingston appear to be more favourably provided with services. It is probably better to treat the results on an ordinal (ranked) scale rather than on interval scale. This is due to possible inaccuracies in the provision data, and the problems of deriving normative piovision levels (what should be provided) from empirical data (what is provided) via the regression equation, especially considering the low levels of statistical explanation in some cases. Such value-judgements invariably perpetuate the status quo. On the ordinal scale, the rankings produced by regressing a given provision index on the three needs indices (NOCAR, PERM and 58) are remarkably similar (the lowest Spearman rank correlation coefficient, rr, between each provision index and the needs indices is over 0.9) [3].

The tentative and exploratory nature of the analysis should be stressed: the analysis can reveal only relarive over or under provision; the needs-provision relationship was assumed to be of a linear (Y = a + /IX) form; normative provision levels were derived from empirical data, and the analysis is valid for London only at the scale of analysis employed. However, it has been shown that a simple dichotomy of under provided inner DHAs and over provided outer DHAs is not tenable. Some DHAs appear to be over provided at the staffing level, yet under provided considering the number of visits. This casts serious doubts on the often-held assumption of a linear relationship between inputs and outputs. This was a key assumption in the RAWP report 1181. Other DHAs appear over provided with district nurses and over provided with health visitors, and vice versa [3]. The reasons for this complex situation remains unclear. Perhaps the most complex situation is the one of the GPs. Many inner DHAs with many GPs, e.g. Bloomsbury. Paddington and Victoria are the same ones that have a high proportion of elderly, singlehanded GPs with low average lists sizes. This is possible because this type of analysis can deal only with quantity and not with quality. A similar emphasis on quantity rather than quality has led to these areas being termed .overdoctered’ under the designated area policy, although there is evidence of difficulties of registration, availability and accessibility. These areas also have high ‘inflation factors’, i.e. more people are registered with GPs than nominally reside in the area, due to population changes and administrative delays in adjusting recordr(inner London general practice is fully discussed in the Acheson report [24]). This analysis suggests that in strictly numerical terms, the inner DHAs have generally adequate provision. However, this assumes that the structure of general practice is similar in different

A.

POWELL

areas. Since this is not the case. the solution to the problems of inner London general practice may be a change towards younger GPs in group practice associated with the development of more links between GPs and other primary health care workers [Xl. However, at present, the primary tem in London is characterised

health

care

sys-

by large spatial variations in both staffing levels and visiting patterns. The presence of these large spatial variations, many of which cannot be statistically explained by considering needs indices, and the presence of some consistently under provided areas should be a cause for concern and a resulting policy response. Acknor~~ledgemenrs-I would like to thank John Eyles, David Smith and Kevin Woods for their helpful comments on an earlier version of this paper.

REFERENCES I. Hart J. T. The inverse care law. Lancer 1, 405-412. 1971. 2. Joseph A. E. and Phillips D. R. Accesstbility and Utiliration : Geographical Perspectives on Health Care Delivery. Harper & Row, New York, 1984. 3. Powell M. A. PhD thesis in preparation. Deoartment of Geography and Earth Scieke; Queen Mary College. University of London. 4. West R. R. and Lowe C. R. Regional variations in need for and provision and use of child health services in England and Wales. Br. med. J. 272, 843-846. 1976. 5. Vetter N. J., Jones D. A. and Victor C. R. Variations in care for the elderly in Wales. J. Epidem. Commun. Hlth 35, 128-132, 1981. 6. Jones D. and Bourne A. Monitoring the distribution of resources in the National Health Service. Sot. econ. Admin. 10, 92-105, 1976. 7. Davies B. Social Needs and Resources in Local Services. Joseph, London, 1968. P. Political 8. Plant R., Lesser H. and Taylor-Gooby Philosophy and Social Welfare. Routledge & Kegan Paul, London, 1980. 9. Smith D. M. Human Geography: A Welfare Approach. Arnold, London, 1977. 10. Harvey D. Social Justice and The City. Arnold, London, 1973. II. Higgins J. States of Welfare: Comparatice Analysis in Social Policy. Blackwell & Robertson, Oxford, 1981. 12. Seldon A. (Ed.) The Litmus Papers: A National Health Disservice. Centre for Policy Studies, London, 1980. The Omega File: Health and 13. Adam Smith Institute. Social Service Policy. London, 1984. Paper 14. Mooney G. Equity in Health Care. Discussion I l/82, Health Economics Research Unit, University of Aberdeen, 1982. P. and Dale J. Social Theory and Social 15. Taylor-Gooby We&are. Arnold, London, 1981. C. The analysis of small area statistics and 16 Thunhurst planning for health. The Statistn 34, 93-106, 1985. and com17. Craig J. and Driver A. The identification parison of small areas of adverse social conditions. Appl. Statist. 21, 25-35, 1972. of Health and Social Security. Sharing 18. Department Resourcesfor Health in England. Report of the Resource Allocation Working Party (RAWP). HMSO, London, 1976. 19. Pinch S. Territorial justice and the city: a case study of the Social Services for the elderly in Greater London. In Social Problems and the City (Edited by Herbert D. T. and Smith D. M.). Oxford University Press, Oxford, 1979.

Territorial

justice

and primary

20. Edwards

J. Social indicators. urban deprivation and positive discrimination. J. sec. Policy 4, 275-287, 1975. 21. Jarman B. Identification of underprivileged areas. Br. med. J. 236, 1705-1708, 1983. 22. Department of Health and Social Security. PriorItiesfor He&lrh and Consul[arire

Personal Social Services in England:. A Documeni. HMSO. London. 1976. Britain. Royal Commission on the NH.7 Report.

23. Great Cmnd 7615. HIMSO, London, 1979. of Health and Social Security. 24. Department Health

Care

in Inner

London

(The

Acheson

Primary Report).

HMSO, London, 198 I. 2s. Jarman B. .d Surte~ of Primary Care in London. Royal College of General Practitioners Occasional Paper 16, London. 198 I. areas. In The Medical An26. Jarman B. Underprivileged nual 1985. Wright, Bristol, 1985. 27. Fox J. and Goldblatt P. Longitudinal Study: SocioDemographic .Morraliry Differentials. Series L5 Number I. OPCS, HMSO, London, 1982. V. Multiple deprivation and health state. 28. Carstairs Commun. ,Med. 3, 4-l 3. 198 I. V. .Measures of Long-Standing and Acute 29. Carstairs Sickness from [he General Household Survey: arions wi[h Social and Economic Characteristics.

Associ-

Scottish Health Service Common Services Agency, Information Services Division, unpublished paper, 1981. 30. Mersey Regional Health Authority. S.M.R.s, Morbidity and Depricarion. Document ORS 8312, 1983. 31. Nie N., Bent D. H. and Hull F. H. Sfaristicaf Package for the Social Sciences. McGraw-Hill, New York, 1970.

12.

13.

14.

15.

16.

17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27.

APPENDIX Dejnirion

Variable number I.

2.

3.

4.

5.

6.

7.

8.

9.

IO.

1 I.

of Need and Precision

Indices

28.

29 OWNOCC:Owner-occupied households as percentage of permanent private households. COUN:Local authority rented households as of permanent private percentage households. PRIV:Furnished and unfurnished privately rented households as percentage of permanent private households. LAM:Households lacking exclusive use of any basic amenities (inside W.C. or bath/shower) as percentage of permanent private households. 0VER:Households with more than 1.0 person/room as percentage of permanent private households. SEVOVER:Households with more than 1.5 persons/room as percentage of permanent private households. NOCAR:Households lacking a car as percentage of permanent private households 3DEP:Households with three or more dependent children as percentage of permanent private households. 0NEPAR:Households containing at least one one-parent family as percentage of permanent private households. PEN:Households containing at least one pensioner as percentage of permanent private households. LONEPEN:Pensioners living alone as percentage of all pensioners.

30.

31.

32. 33.

34. 35.

36. 37. 38. 39.

40.

41.

health

care

1103

NEWCOMM:Households headed by a person born in the New Commonwealth and Pakistan as percentage of permanent private households. BORNOUT:Residents born outside U.K. as percentage of all residents. UNEMP:Persons seeking work and temporarily sick as percentage of all economically active persons. PER,M:Persons permanently sick as percentage of all residents aged 16 or over in private households. TEMP:Persons temporarily sick as percentage of all residents aged 16 or over in _ private households. SCV:Households headed by a social class v - person as percentage of all householdc CHSEG:Expected chronic sickness predicted by socio-economic group. CHAGE:Expected chronic sickness predicted by age. ACSEG:Expected acute sickness predicted by socio-economic group. ACAGE:Expected acute sickness predicted by age. CDR:Crude death rate (s/w). 1MR:Infant mortality rate (o/m). SMR:Standardised mortality ratio. SMR64:Standardised mortality ratio calculated by omitting pensioners. J 10:Underprivileged area score calculated using 10 variables. J8:Underprivileged area score calculated using 8 variables. POP/GP:District resident population divided by the number of GPs whose main surgery is located in the district. POP/GPhr:District resident population divided by the number of GP hours available in the district. POP/HV:District resident population divided by the number of whole time equivalent (W.T.E.) health visitors in the district, POP/DN:District resident population divided by the number of W.T.E. district nurses in the District. %HV:Percentage of district population visited by a health visitor. %HV < 5:Percentage of district population aged five and under visited by a health visitor. HVvis:Number of health visitor visits divided by district population. HV < jvis:Number of health visitor visits to fives and under divided by the district population aged five and under. %DN:Percentage of district population visited by a district nurse. % DN > 65:Percentage of district population aged 65 and over visited by a district nurse. %DNhome:Percentage of all district nurse treatments in patients’ homes. %DN > 65home:Percentage of all district nurse treatments to persons aged 65 or over in patients’ homes. < 5/HV:District population aged five and under divided by the number of W.T.E. health visitors in the district. 3 65iDN:District population aged 65 and over divided by the number of W.T.E. district nurses in the district.