do places matter? a multi-level analysis of regional variations in health-related behaviour in...

Sot. Sci. Med. Vol. 31, No. 6, pp. 725-133, 1993 0277-9536/93 $6.00 + 0.00 Printed in Great Britain. All rights reserved Copyright 0 1993 Pergamon Press Ltd

DO PLACES MATTER? A MULTI-LEVEL ANALYSIS OF REGIONAL VARIATIONS IN HEALTH-RELATED

BEHAVIOUR IN BRITAIN

CRAIG DUNCAN,’ KELVYN JONES’ and GRAHAM MOON’

’ Department of Geography, University of Portsmouth, Buckingham Building, Lion Terrace, Portsmouth PO1 3HE, England and *School of Social and Historical Studies, University of Portsmouth, Milldam,

Burnaby Road, Portsmouth PO1 3AS, England

Abstract-A number of commentators have argued that there is a distinctive geography of health-related behaviour. Behaviour has to be understood not only in terms of individual characteristics, but also in relation to local cultures. Places matter, and the context in which behaviour takes place is crucial for understanding and policy. Previous empirical research has been unable to operationalize these ideas and take simultaneous account of both individual compositional and aggregate contextual factors. The present paper addresses this shortcoming through a multi-level analysis of smoking and drinking behaviours recorded in a large-scale national survey. It suggests that place, expressed as regional differences, may be less important than previously implied.

Key words-multi-level model, lifestyle behaviour, smoking, drinking, geography, place differences

INTRODUCTION

Most medical geographic research has used geographic space merely as a framework in which data can be ordered [l]. This extremely limited role for geography in the structuring of mortality, morbidity and health care activity rests on the simplistic use of space as “an organising framework for recognising regular associations” [2, p. 3991. These regular associations are usually assessed by statistical models which are assumed to apply uniformly across study areas and in which people are often reduced to ecological indicators. A conceptualization of people as agents and, what is more, of people as agents influenced by the different place situations in which they find themselves, is missing. This perspective is restrictive; as Jones and Moon argue:

Space and locality have a crucial role to play in explanation because the various processes concerned can be manifested in different ways at different places [2, p. 2661.

This restrictiveness is particularly evident in studies purporting to examine health-related behaviours such as smoking, drinking, diet, exercise, sexuality and drug misuse. These “lifestyles” might be expected to result from both individual predisposition and geo- graphically-based, cultural influences. Within recent years such studies have grown in number, being based, for the most part, on “lifestyle” questionnaires conducted throughout the United Kingdom at a variety of scales. Although such surveys constitute an important secondary data source, and have revealed much about aggregate trends in health-related behaviour, they have been little used in explorations of the difference that place makes in the structuration of lifestyle behaviour.

This paper aims to address this shortcoming. It presents an assessment of the role of place in structuring smoking and drinking behaviour in the United Kingdom and begins with a section on “lifestyle” surveys. Attention then turns to an outline of a quantitative approach to the establishment of the reality and nature of geographic variations in health- related behaviour. The following section summarizes the results of this investigation and places them within the context of existing knowledge about smoking and drinking behaviour. The implications of these results are outlined, and a short conclusion then considers the limitations of the findings.

HEALTH AND LIFESTYLE

Throughout the 1980s in the United Kingdom researchers consistently documented the existence of continuing, and in some cases widening, health inequalities between social classes [3]. Considerable academic and political debate raged over the various explanations proposed for these inequalities. The balance of medical and social scientific opinion argued that the key factor was material structural disadvantage leading either directly, or indirectly through constraining effects, to a greater probability of poorer health and earlier death amongst people in lower status occupations. This opinion was not however unanimous and alternative explanations focused on the roles of social selection [4] and health-related behaviours.

The behavioural or “lifestyle” explanation argued that independent and autonomous behaviour on the part of an individual generated morbidity or mortality [3, p. 1 lo]. Individuals, or groups of individuals

125

726 CRAIG DUNCAN et al.

behaving in a systematically similar fashion, were held to be individually responsible for the health

status). A three-stage sampling design was followed in which parliamentary constituencies within each

outcomes of inappropriate behaviour. In its raw standard region were randomly selected from three unconsidered form the explanation implied that in- bandings of population density with probabilities equalities in health were the fault of the sufferer’s proportional to the constituency populations. Two lifestyle or deviant culturally-guided behaviour. This electoral wards were selected from each of the 198 could be identified through lifestyle surveys. Targeted constituencies on a similar basis and individuals were health education messages of the “look after your- then selected randomly from the wards. In total the self’ variety would then place the onus on the survey recorded data on 9003 individuals in 396 individual to choose to correct “behavioural deficits”. different locations across mainland Britain. With its central thesis of individualism and freedom A number of workers have carried out secondary of choice, the explanation found considerable politi- analyses of the HALS data [lo] and it forms the basis cal favour with the Conservative Governments of the of the most important publication to date on British 1980s; its most trenchant manifestation was in the lifestyle behaviour-Mildred Blaxter’s Health and utterings of Edwina Currie, sometime junior minister Lifestyles [1 11. Accounting for geographical vari- of health, regarding the dietary habits of inhabitants ations in “lifestyle” was one of Blaxter’s primary of Northern England. objectives, her main concern being to:

Such a basic approach to health-related behaviour has been questioned by a number of commentators. The Health Divide questions the extent to which behaviour can be abstracted from its social context [3]. Others have noted that:

distinguish between the importance of those elements of lifestyle which are typical of socio-economic status and those elements which are truly regional or associated with the external living environment [l 1, p. 791.

it would run counter to the evidence to assume that people’s patterns of smoking, drinking, eating and sexual activity are determined by individual choices that are unaffected by social, economic or legislative factors [S, p. 751.

From a geographical perspective, it seems inherently likely that location could be added to these influences.

Away from the political and academic debates surrounding health inequality, health-related behaviours remained, throughout the 198Os, the oper- ational subject-matter of health education and, to a lesser extent, community medicine. Lifestyle surveys provided guidance for initiatives targeted on those sections of the community exhibiting risk behaviours; they also provided epidemiological assessments of the prevalence of such behaviour. More recently the rise of the “new public health” has questioned the effec- tiveness of targeted health education [6] but lifestyle surveys have gained the support of health service management as vehicles for the health needs assessments central to effective purchasing in the reformed National Health Service [7]. The attempt in 1991 to begin the development of a health policy for England saw further emphasis being placed on lifestyle and behaviourally-oriented health promotion strategies

PI.

This concern can be rephrased to indicate the need to establish the extent to which geographical variations in health-related behaviour are a consequence of the varying social compositions of areas, or of the more subjective cultural context: the spirit of place. Places with high levels of smoking, for example, may simply be composed of more people with individual characteristics indicating a predisposition to smoking. Alter- natively, all people in that place, regardless of their individual, personal characteristics may be affected by contextual, ecological factors (e.g. a regional culture that encourages smoking). Both sets of factors may operate-or only one.

Like other writers [12], both before and after HALS, Blaxter supports the idea of significant contextual effects. In relation to smoking she writes:

. class is related to smoking in different ways in different types of area [I 1, p. 1171.

and for alcohol consumption she concludes:

the relationship of social class to drinking habits depends very much on the environment [l 1, p. 1191.

Many individual health authorities have now un- dertaken health and lifestyle surveys and, in 1984/5, a national survey was carried out by the University of Cambridge School of Clinical Medicine. The “Health and Lifestyle Survey” (HALS) focused on: “lifestyles, behaviours and circumstances relating to the physical and mental health of the population” [9, p. 11. It collected detailed data on behaviours together with additional health information (e.g. attitudes and physiological measures) and individual background characteristics (e.g. income, occupation, age, marital

Blaxter thus emphasizes that people’s health-related behaviours are very much influenced by the places in which they live. The overriding message is that geography matters enormously in lifestyle behaviour; indeed Blaxter states that this is one of the more important conclusions of her analysis [1 1, p. 2361.

Unfortunately, this message is based upon a highly simplistic and problematic statistical analysis. Blax- ter’s preferred methodology is to calculate standard- ized ratios for different subgroups. In effect, the multilevel nature of both the problem and the data (people in areas) is lost as she is forced to work at a single aggregate level, grouping together qualitatively heterogenous individuals to ensure statistically re- liable rates based on large numbers of respondents.

Regional variations in health-related behaviour in Britain 72-l

This results in crude geographical and statistical assemblages of the form that Sayer disparagingly calls chaotic conceptions and taxonomic collectives [ 131. Thus, people become manual and non-manual classes, while place becomes North vs the South. This approach aggregates individuals and confounds the compositional with the contextual.

METHODOLOGICAL ISSUES

HALS was based on a multi-stage sampling design which generates an inherently hierarchical data set. Individuals, within wards, within constituencies, within regions were randomly selected. This data structure is ideally suited to a multi-level approach which distinguishes contextual and compositional differences, does not rely on crude geographical ag- gregations, and maintains the original complex nature of the data [14]. In this way, multi-level modelling differs from both Blaxter’s tabular risk analysis approach and other aggregate strategies in acknowledging that it is individuals, not groups or places that behave, thereby avoiding the ecological fallacy. Moreover, and unlike-individual-level logistic modelling [15], it recognizes that individuals behave in context, thus avoiding the atomistic fallacy [16].

The fundamentals of multi-level modelling as ap- plied to medical geographic problems have been comprehensively covered elsewhere [17] and will not be rehearsed here. This present paper focuses on a three-level analysis of the HALS data offering simultaneous consideration of i individuals, nested in j wards in k regions [18]. The wards and the regions provide levels at which the contextual impacts on compositional level-one effects can be assessed. While the wards are used in the analysis, it is results at the regional level that will be most fully explored.

Two multi-level models are central to the analysis:

Y,,,=Box,+(ek+~jk++Eilk) (1)

(Ok + pjk +&(/k) t2)

Equation (1) is a simple “null” three-level, random- intercepts model in which variation in the response variable Y is “explained” by the single fixed intercept term &, the national average, and three random terms associated with this intercept which reflect the remaining variation at the individual level ciikr the ward level pjk, and the region level Ok. This variation can be summarised in three variance terms; ai, a:, and u: which estimate respectively the between-individual, between-ward and between-region variation. This null model is expanded in equation (2) to include m fixed, national effects (the /Is) associated with m “predictor” variables (the Xs) for individuals. The actual equation (2) used in this paper employed eight level-one predictors: age, sex, social class, school- leaving age, employment status, housing tenure and

marital status which are specified as a set of 23 dummy, indicator variables. In this model, the two higher level random terms now assess the ward and regional differences after taking into account individual characteristics which may be influencing health- related behaviour. If the multi-level models reveal no significant nor substantial remaining variation at the second or third levels, this would imply, within the limitations of model specification, that there are no contextual influences on health-related behaviour at these broad geographical levels. If this were to be the case, there would be no distinctive geography of “lifestyle”, only a sociology. Conversely, if the higher-level variation increases following the inser- tion of level-one variables, then it would be possible to conclude that geographical difference is more marked than the apparent sociology would suggest

]191. This paper focuses on two health-related be-

haviours: drinking and smoking. The data on drinking consist of units of alcohol consumed in a week. This is a continuous variable and can therefore be modelled using the usual linear link function [20]. The smoking data, however, consist of a binary response variable. Individuals either are or are not current regular smokers. Such data could be modelled by treating the response as if it were the probability of smoking. However, this practice creates three problems: non-sensical values, inappropriate functional form, and variance heterogeneity. The appropriate solution is to use a non-linear form which can be linearized for estimation by taking a logit transform- ation and specifying a binomial error term [21]. All models were calibrated using the software package VARCL3 [22].

In all the fitted models the predictor, X, variables were coded to indicate departures from the most common variable characteristic [23]. The effect of this strategy is that the intercept term in the model (/IO) effectively represents the stereotypical individual survey respondent. This enables straightforward in- terpretation of the model output. The exact profile of the stereotypical individuals is described more fully in the relevant following section.

THE GEOGRAPHY OF HEALTH-RELATED BEHAVIOUR

This section details the results obtained from the multi-level modelling of the data recorded by HALS for smoking and drinking. It is separated into two sub-sections, one for each behaviour.

Analysis of smoking behaviour

Smoking contributes to deaths from coronary heart disease and many forms of cancer; it is the major cause of death from cancer of the lung [24]. Its health consequences are not only direct: “passive smoking” is thought to be responsible for some 20% of lung cancer cases [25]. These causes of death are particularly significant in the United Kingdom con-


text [26] where some 30% of women and 33% of men smoke [8]. Cigarette smoking behaviour first became common in the earlier half of this century; while women developed the habit more recently than men. Evidence from routine statistical sources such as the General Household Survey suggests that there has been a considerable decline in the number of smokers over the past quarter-century but that young people, particularly women, have been less affected by this trend; they also indicate a pronounced social class relationship. Smoking rates remain amenable to reduction and targets for age and sex-specific re- ductions have been proposed [8, p. 671.

A traditional geographical analysis of smoking behaviour would aggregate the number of individuals found smoking in each area, calculate the rate for each area and map or tabulate the results [27]. Table 1 shows that the Health and Lifestyle Survey recorded considerable variation in smoking behaviour around the country. The lowest level of smoking was recorded in Devon and Cornwall (76.2% of the sample did not smoke regularly), whilst the highest was found in Inner London (only 56.6% did not smoke regularly). This variation may relate to contextual, ecological factors or it may be an artefact created by different regional compositions of different types of individuals: Devon and Cornwall’s population could be composed of significantly more people who are unlikely to smoke due to age, class and other important consumption cleavages.

Table 2 (column A) gives the summary statistics for the null model of smoking behaviour [equation (l)]. The intercept, Do, gives a nation-wide, average estimate of an individual smoking. When transformed from its logit value, this represents a 66% chance that any individual is a non-smoker. This probability

Table I. Aggregate percentage rates of people NOT smoking by region based on the 1984/1985 Health &

Lifestyle Survey

Average percentage Region pop. NOT smoking

Strathcylde 58.5% (20) E. Central Scotland 62.5% (17) Rural Scotland 63.0% (16) Rural North 66.5% (1 I) Industrial North East 63.8% (13) Merseyside 57.4% (21) Gtr Manchester 58.7% (19) Rest of North West 65.0% (12) West Yorkshire 63.4% (15) South Yorkshire 70.1% (8) Rural Wales 67.3% (10) Industrial South Wales 59.0% (18) W. Mids Conurbation 63.5% (14) Rest of West Midlands 68.4% (9) E. Midlands 70.8% (7) E. Anglia 71.8% (4) Devon and Cornwall 76.2% (I) Wessex 71.5% (5) Inner London 56.6% (22) Outer London 71.0% (6) Outer Metropolitan 72.4% (3) Outer South East 73.8% (2)

Note: values in parentheses represent rank position, 1 = least smoke, 22 = most smoke.

Table 2. Multi-level estimates for models of smoking behaviour

(A) (B)

Fixed erects Level I Intercept sex

Male Social class

Other non-manual Employers/managers Semi-skilled Professional Unskilled

Age 16-24 yr 25-39 yr 6&54 yr 65-74 yr 75-97 yr

Age leave school <14yr 14yr 16yr 17yr 18yr 19+ yr

Employment status Unemployed

Housing status Local Authority Renter Other Renter

Marital status Single Widowed Divorced

Random effects variance Level I

Intercept, 0: Level 2

Intercept, 0: Lode13

-0.67 -0.65

0.14 (2.91)

-0.37 (-5.20) -0.14 (-1.98)

0.04 (0.55) -0.73 (-5.15)

0.18 (1.68)

0.14 (1.40) 0.09 (1.41)

-0.36 (-3.17) -0.83 (-6.36) -1.60 (-9.32)

-0.17 (-1.15) -0.12 (-1.56) -0.33 (-4.79) -0.31 (-3.16) -0.71 (-6.65) -0.47 (-2.12)

0.52 (4.89)

0.69 (11.8) 0.56 (6.46)

0.004 (0.06) 0.004 (0.04) 0.48 (4.97)

1 .oo I .oo

0.11 (9.89) 0.05 (5.27)

Intercept, 0: 0.04 (4.50) 0.01 (2.57)

Note: estimates represent logit values; figures in parentheses rep resent ratio of estimates to standard error.

value does not remain constant over the country and the values for the random effects-a:, 0: and &- show to which level the remaining variability can be apportioned. This reveals that, whilst the majority of variation in smoking (86.5%) occurs at level 1 (that is between individuals), ca 10% and 4% occurs at levels 2 and 3 respectively. This higher-level variation can be assessed for significance by calculating the ratio of the estimate to its standard error. If the ratio is in excess of plus or minus two, the estimate is considered significantly different from zero. As shown in Table 2, both values are significant and the differences at both higher levels are more than could be expected from sampling fluctuations.

It would seem, therefore, that there is some degree of significant ward and regional variation in smoking behaviour which does not originate in sampling fluctuations. The 22, level-three (region) differences from equation (1) are plotted in their logit form in Fig. 1. These differences measure the reduction or increase in comparison to the national logit of being a smoker in each region. When transformed, they indicate that the probability of an individual being a non-smoker ranges from 59% in Inner London to 71% in the Outer South-East.

Regional variations in health-related behaviour in Britain 129

The null model of equation (1) therefore suggests the tentative conclusion that there is a geography of smoking, but the higher level variation may be an artefact of compositional factor-ertain wards or regions may have a preponderance of individuals with characteristics associated with particular smoking behaviours. This hypothesis was tested using equation (2). The results are shown in Table 2, column B. The stereotypical individual is an employed woman, aged 4@59 yr, married and living in an owned house. She left school at 15 and is now in the skilled manual social class category. The fixed estimates in the model reveal that the chances of an individual not smoking decrease significantly should they be male, divorced, of a lower social class, unemployed, or a Local Authority tenant. At the same time those chances would increase significantly were they older and of the professional class.

The inclusion of these compositional fixed terms reduces the variance of the random intercepts at the two higher, ward and regional, levels. The variance that remains at these levels quantitatively measures contextual effects on smoking; geographical variations in composition have been “controlled” by the inclusion of the individual attributes. Only 4.5% of the variance can be attributed to the ward level and the regional variance has been reduced to c 1%. Although both these values retain statistical significance, it would appear that a large amount of the geographical variation in smoking behaviour can be attributed to compositional variations in the individual predisposition to smoke rather than to any higher-level contextual geographical effect.

The regional geography of smoking which remains after controlling for individual compositional factors is illustrated by the plot for equation (2) in Fig. 1. The plot reveals a generalized North-South gradient in

Least smoke

Strathclyde I-

smoking behaviour. Northern regions tend to have higher levels of smokers than their Southern counter- parts once social composition is controlled. It is possible to use these estimates of region effects in conjunction with the fixed level-one coefficients to generate estimates of the probability of individuals in particular localities being non-smokers: for example, a professional woman in the Outer South East has an 8 1% chance of not being a smoker whilst this reduces by 3% should the same woman be in Merseyside.

Taking the plots for equations (1) and (2) together, Fig. 1 also illustrates the changes in regional variation before and after composition is taken into account. It appears that regional variation collapses once compositional factors are included. This is strikingly conveyed by the convergence of most lines around the value 0 (which represents /IO, the overall national estimate). There are two exceptions: in Rural Wales people do not smoke who would be expected to and in the Rest of the North West the reverse is true. Overall Fig. 1 confirms the limited role that ecological factors play in regional smoking variations. There is some regional variation-but it is relatively small and to some extent originates in compositional effects. When transformed to probabilities, the regional differences after controlling for composition indicate a variation of only a +3% variation around the national average 65% chance that the stereotypical individual is a non-smoker. The regional differences in the probability of not smoking are clearly small in relation to individual socio-demographic differences.

Analysis of drinking behaviour

Alcohol misuse is associated with cirrhosis of the liver, digestive cancers, road traffic accidents, high blood pressure and psycho-social problems; it has

Most smoke

E. Cent. Scotlind - Rural Scotland -

Rural North -

Industrial N.E. - Merseyside -

Gtr. Manchester - Rest of North West -

West Yorks - South Yorks - Rural Wales -

Industrial S. Wales - West Midlands Con. -

Rest of West Mids - East Midlands -

East Anglia - Devon and Cornwall -

Wessex - Inner London - Outer London -

Outer Metropolitan r Outer South East L---l---.-..... I I ’ I I I

-0.3 -0.2 -0.1 0 0.1 0.2 0.3 0

Residual

Fig. 1. Model comparison-smoking behaviour.


Table 3. Aggregate rates of units of alcohol consumed per person per week by region based on the 1984/1985

Health & Lifestyle Survey

Average units consumed Region per person per wk

Strathcylde 12.87 (15) E. Central Scotland 12.69 (14) Rural Scotland 12.43 (13) Rural North 11.01 (6) Industrial North East 13.90 (18) Merseyside 14.40 (21) Gtr Manchester 13.30 (17) Rest of North West 10.39 (5) West Yorkshire 14.47 (22) South Yorkshire 14.00 (19) Rural Wales 9.63 (3) Industrial South Wales 13.16 (16) W. Mids Conurbation 14.30 (20) Rest of West Midlands 10.04 (4) E. Midlands 11.86 (11) E. Anglia 8.56 (I) Devon and Cornwall 1 I .06 (7) Wessex 11.71 (IO) Inner London 12.00 (12) Outer London 1 I .46 (9) Outer Metropolitan 11.22 (8) Outer South East 8.79 (2)

Note: values in parentheses represent rank position, 1 = least drink, 22 = most drink.

been estimated to be implicated in at least 25,000 premature deaths in Britain per year [28]. The research evidence suggests that alcohol is only damag- ing when used in quantity. This is commonly assessed by reference to “units’‘-a standard half-pint of beer, measure of spirits or glass of wine-with “safe” limits being set at 14 units for women and 21 for men [29]. The General Household Survey suggests that heavy drinking is commonest among manual workers and young single men and The Health of the Nation recommends a target of less than 1 in 6 men and 1 in 18 women drinking over sensible limits by the year 2000 [8, p. 701.

As with smoking, a traditional analysis of drinking would be based upon crude averages for geographical areas [27]. Regional level averages for the Health and Lifestyle Survey are given in Table 3 and display apparent variation. The average number of units of alcohol consumed in a week by an individual ranges from 8.56 in East Anglia to 14.47 in West Yorkshire.

A constant-only, “null” multi-level model [equation (l)] was fitted and the results are summar- ized in Table 4, column A. The estimated intercept, &, , represents the average number of units consumed over the entire country and is estimated at 11.87. This value does not remain constant across all regions and wards and the variation in consumption can be decomposed to each of these higher levels. When this is done the majority of the variation (98%) occurs at level 1, between individuals. However, ca 1% can be attributed to each of the higher levels and this variation is in fact significant when the ratio test is conducted. The region level residuals for drinking from equation (1) are plotted in Fig. 2. On the basis of these values, the average units consumed in a week

varies from 9.58 in the Outer South East to 13.33 in the West Midlands Conurbation.

The results for the random-intercepts, equation (2) model, taking account of compositional individual- level factors, are given in Table 4, column B. In this instance, the stereotypical individual changes as only a subset of the 9003 individuals surveyed responded to the questions concerning alcohol. The reduced dataset of 6211 persons produces a stereotype who is an employed, married, 25-39 yr old man, who left school at 15, owns his own house and is in a skilled manual occupation. With the addition of fixed estimates, the random variation at the ward and region level is further reduced, although both estimated variances (cr: and ai) remain significant [30].

Important differences between the individual effects on drinking and those for smoking can be noted. Men drink more as well as smoke more but the gender differential in relation to alcohol consumption is much more significant. With smoking there is an expected class gradient linking low social status to smoking. For drinking this is reversed: the professional and employer/manager social categories consumed more alcohol than any of the lower social classes (plus 0.95 and 1.12 units respectively). This

Table 4. Multi-level estimates for models of drinking behaviour

(A) (8)

Fixed effects Level 1 Intercept sex

Female Social class

Other non-manual Employers/managers Semi-skilled Professional Unskilled

Age 1624 yr 4&59 yr 60-64 yr 65-74 yr 75-97 yr

Age leave school <14yr 14yr 16yr 17yr 18yr 19+ yr

Employment mmo Unemployed

Housing status Local Authority Renter Other Renter

Marital status Single Widowed Divorced

Random effects variance Level I

Intercept, 0: Level 2

Intercept, ui Level 3 3

11.87 18.41

-12.08 (-31.5)

-1.32 (-2.37) 1.12 (1.99)

- 0.82 ( I .40) - 0.94 (1.08)

-0.01 (-0.01)

0.14 (0.20) -1.08 (-2.20) -3.30 (-3.38) -3.15 (-2.78) - 5.59 (-4.00)

-2.55 (-2.06) -0.75 (-1.19) -1.19 (-2.24) -0.67 (-0.91) -0.62 (-0.85) - 2.39 (- I .48)

1.75 (2.03)

1.42 (2.86) 0.50 (0.71)

3.67 (5.81) 1.85 (2.04) 3.45 (4.34)

253 210

2.53 (3.63) 1.71 (3.04)

Intercept, 0; 1.82 (4.05) 1.46 (4.04)

Note: estimates represent units of alcohol; figures in parentheses represent ratio of estimates to standard error

Regional variations in health-related behaviour in Britain

Strathclyde E. Cent. Scotland

Rural Scotland Rural North

Industrial N.E. Merseyside

Gtr. Manchester Rest of North West

West Yorks South Yorks Rural Wales

Industrial S. Wales West Midlands Con.

Rest of West Mids East Midlands

East Anglia Devon and Cornwall

Wessex

Inner London Outer London

Outer Metropolitan Outer South East

-3

Least drink Most drink

m Equation (1) 0 Equation (2)

-2 -1 0 1

Residual

Fig. 2. Model comparison-drinking behaviour.

731

finding is contrary to the aggregate research evidence (see above) but is marginal and not statistically significant when the standard error ratio is calculated. Overall, it would appear that whilst smoking behaviour is most closely related to social class and tenure, drinking is determined more by gender differences and marital status. Both behaviours diminish significantly with age.

The region residuals produced by the equation (2) alcohol model are also shown in Fig. 2. They represent units of alcohol above and below the overall national average, /I,,, for each individual region. A North-South gradient is again visible. As with the smoking analysis, the equation (1) and equation (2) values for drinking can also be compared in Fig. 2, giving a graphical representation of regional variation before and after regional composition is taken into account. The plot for alcohol differs substan- tially from that for smoking. The regional residuals for drinking do not collapse around /I0 once composition is included. Rather, there are many cases of absolute residual change (i.e. shifts away from the average &). There is heavier drinking in West York- shire, the Industrial North East, Merseyside, Greater Manchester and the East Midlands than would be expected on the basis of socio-demographic composition. In contrast, Inner London, East Central Scot- land and the Outer Metropolitan area all return unexpectedly low figures for alcohol consumption given their demographic and social composition. There is thus more support for greater ecological, contextual variation at the region level in drinking behaviour than smoking. However, this effect is still small when judged against individual-level differences. The regional alcohol consumption range, after controlling for composition, varies by only f 10% from the overall national average of 18.41 units for

the stereotypical individual. The variation within a region is much more substantial than between regions

[311.

Implications

On the basis of the multi-level analyses presented above, it would appear that the major determinants of geographical variation in two key lifestyle behaviours are individual-level factors. The direction and strength of these individual-level effects is sub- stantially in accordance with established research knowledge. Suggestions of an important role for contextual, geographical effect is, however, limited, especially in relation to smoking behaviour. Places- be they wards or regions-have little independent effect. In both cases there is much greater variations in behaviour between individuals than between wards and regions even after taking account of individual demographic and social characteristics. Most impor- tantly, geographical contextual variables specified at these higher levels cannot possibly account for this individual-level variation, for they will be effectively constant at this lower level.

It would appear that Blaxter’s conclusions about the importance of a contextual “area” effect are over-stated [ 111. There are geographical variations but only as a consequence of differing place composition. Similarly, contentions that people of similar age and occupation “tend to smoke and drink in a manner strongly influenced by where they live” [32] are undermined once compositional factors are prop- erly and completely taken into account. That is not to say, of course that other geographies or “settings” such as the work-place, are not of importance.

For health policy, the implication of the analyses presented in this paper is a questioning of the traditional belief that health care purchasers need to


conduct specific, local lifestyle surveys and respond to “unhealthy lifestyles” on an area-basis. The findings presented here strongly challenge the wisdom of such a dual approach. First, the limited role of contextual- ity allows the imputation of national survey results to local situations using a multi-level model in combi- nation with key local data. This strategy has been found to produce an accurate assessment of lifestyle behaviour and has considerable cost-saving potential [33]. Second, the importance of composition implies that health promotion in relation to smoking and drinking should not focus exclusively on area-based preventive strategies.

CONCLUSIONS

The analyses presented here suggest that assumed contextual geographical differences in lifestyle are not substantial. This finding should not, however, be taken as a denial of the geographical. First, it is based on relatively unsophisticated models. It is likely that individuals learn, experience and display health-related behaviours in particular social contexts; these social contexts are likely to be manifested in geographical settings. Second, the level-l variables used in this paper, whilst seemingly justified, could be challenged for their adequacy, although the range of available variables was obviously constrained by the questionnaire adopted by the Health and Lifestyle Survey research team. Third, the hierarchical defi- nition of the levels could be criticized as an inappro- priately formalistic and mechanistic attempt to capture the cultural geography of lifestyle. The levels reflect the data collection process and a convenient regional classification, it may be argued, not local or regional culture [34]. Fourth, given the novelty of multi-level modelling, certain technical problem areas remain imperfectly understood, notably the binomial assumption of the level-l variance in models with a binary response [17]. Finally, it should, of course, be emphasized that a multi-level approach retains many of the limitations of more traditional quantitative medical geography; in this research, lifestyle behaviour is modelled more powerfully than traditional techniques allow, yet it is still analysed in a rather crude and mechnical manner lacking in the ability to offer any insight into what causes people to smoke or drink. Moreover, while there may be little quantitative difference, the qualitative nature of the behaviour may be culturally specific. Nevertheless, within its limitations, multilevel analysis can contribute to the development of a place-sensitive medical geography and the debunking of crude regional stereotypes of health-related behaviour.

Acknowledgements-The authors would like to acknowl- edge the extremely useful comments of two anonymous referees and participants at the Ftfth International Sym- posium in Medical Geography, Charlotte, NC, U.S.A., August, 1992.

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It will be noted that the variances from the two models imply that the geography of drinking may, to

slope’ models in which, for example, the relationships

a small extent, be regional, while that for smoking may be local, i.e. ward-based. Further research is

between smoking and class are allowed to vary

currently addressing this issue and additionally consid- ering multivariate situations in which both behaviours

between areas. See [16] and [17] for examples of such

are examined simultaneously, and models which allow the effects of individual attributes to vary at

models.

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World Health Organisation. Controlling the Smoking 34, Further research is addressing this issue by developing Epidemic. WHO Technical Report 636, WHO, Geneva, models which specify contextual variables at levels 2 1979. and 3.

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