smoking and deprivation: are there neighbourhood effects?

Smoking and deprivation: are there neighbourhood e�ects?

Craig Duncan *, Kelvyn Jones, Graham Moon

Institute for the Geography of Health, Department of Geography, University of Portsmouth, Buckingham Building, Lion Terrace,

Portsmouth PO1 3HE, UK

Abstract

Debate has centred on whether the character of places plays an independent role in shaping individual smokingbehaviour. At the small-area scale, particular attention has focused on whether measures of neighbourhood

deprivation predict an individual's smoking status independent of their own personal characteristics. This studyapplies multilevel modelling techniques to data from the British Health and Lifestyle Survey and ward (localneighbourhood) level deprivation scores based on four variables from the national Census. Results suggest that after

taking account of a large range of individual characteristics, both as main e�ects and interactions, together withcomplex structures of between-individual variation, measures of neighbourhood deprivation continue to have anindependent e�ect on individual smoking status. In addition, signi®cant between-ward di�erences in smoking

behaviour remain which cannot be explained either by population composition or ward-level deprivation. The studysuggests that the character of the local neighbourhood plays a role in shaping smoking behaviour. # 1998 ElsevierScience Ltd. All rights reserved.

Keywords: Smoking; Deprivation; Neighbourhood e�ects; Multilevel modelling

1. Introduction

Over the last two decades a series of empirical stu-

dies have identi®ed signi®cant geographical variations

in health-related behavioural practices in Britain

(Cummins et al., 1981; Balarajan and Yuen, 1986;

Dunbar and Morgan, 1987; Braddon et al., 1988;

Richards, 1989; Blaxter, 1990; Whichelow et al., 1991).

In most of this work, attention has focused on di�er-

ences at the regional level with only a few studies

examining more local in¯uences (Cummins et al., 1981;

Richards, 1989; Blaxter, 1990). Similar research has

been conducted in America with signi®cant area-based

di�erences in health-related behavioral practices being

identi®ed (Marks et al., 1985; MMWR, 1987; Hilton,

1988; Klein and Pittman, 1993; Colby et al., 1994). As

is now well-recognised, geographical variations in out-

come measures do not necessarily mean that places

have signi®cance in their own right. Variations may

arise due to particular types of people, whose personal

characteristics are closely linked to the outcome under

study, being found more commonly in certain places.

Thus, geographical variations may not re¯ect indepen-

dent area e�ects; they may simply be the result of

spatially di�erentiated population composition.

Given the aetiological signi®cance now attached to it

(Doll and Peto, 1981; Royal College of Physicians,

1983; Department of Health, 1992), it is not surprising

that nearly all studies of geographical variations in

health-related behaviour in Britain have considered

smoking. The majority of this work has suggested,

either implicitly or explicitly, that variations in

Social Science & Medicine 48 (1999) 497±505

0277-9536/98/$ - see front matter # 1998 Elsevier Science Ltd. All rights reserved.

PII: S0277-9536(98 )00360-8

PERGAMON

* Corresponding author. Tel.: +44-1705-842-507; fax: +44-

1705-842-512; e-mail: [email protected].

smoking behaviour across Britain are not simply an

artefact of varying population compositions but are,additionally, the result of independent `area' or `con-textual' e�ects. This position is well exempli®ed by M.

Blaxter's major piece of analytical, academic research,Health and Lifestyles, in which she concludes that``class is related to smoking in di�erent ways in di�er-

ent types of areas'' (Blaxter, 1990, p. 117). Blaxter'sanalysis does, however, only involve the calculation of

area-based rates standardised for a limited number ofpersonal characteristics. Whilst other studies haveadopted a more sophisticated regression modelling

strategy, they have tended to rely on traditional single-level techniques. Such techniques can be a�ected by

serious problems of mis-estimation associated withauto-correlation since people living in the same placecan be expected to be more alike than a random

sample (Scott and Holt, 1982; Aitken and Longford,1986; Skinner et al., 1989). In addition, they are alsoprone to technical problems associated with small

numbers and sampling ¯uctuations (Jones, 1991).Multilevel regression modelling techniques

(Goldstein, 1995) have been shown to o�er a moresuitable way of conducting analyses of area e�ects(Jones and Duncan, 1996). In the United States, such

an approach has found signi®cant area e�ects forsmoking behaviour with people in the highest preva-

lence communities being ``about 7 percentage pointsmore likely to smoke'' after taking account of person-level variables (Diehr et al., 1993, p. 1147). In Britain,

initial work based on similar techniques has focusedmore on the regional level rather than smaller geo-graphical scales (Duncan et al., 1993). Results

suggested that, in comparison with individual-levelcompositional e�ects, neither wards nor regions play a

signi®cant independent role. The models used in thisanalysis were, however, rather limited. More recentwork in Scotland by Hart et al. (1997) has obtained

similar results at the local scale. In this study, smokingwas found to be the coronary heart disease risk factormost closely related to individual-level compositional

e�ects. Indeed, after taking account of these, thedegree of contextual variation in smoking behaviour

was minimal. While this analysis did usefully considergender di�erences in detail (though, signi®cantly, nonewere found for smoking) the models used were, once

again, relatively simple. Furthermore, the study wasbased on only 22 local government districts.

In a slightly earlier piece of work, more complexmultilevel models have been applied in a study focus-ing on the North West Thames Regional Health

Authority in England (Kleinschmidt et al., 1995).Interestingly, this work did not originate from thedebate concerning the relevance of `place' for smoking

behaviour but from a desire to control for the possibleconfounding e�ect that smoking may have in ecologi-

cal studies examining the health e�ects of point sourcesof environmental pollution. Since smoking data are

usually unavailable in such studies, researchers havebeen interested in examining whether small area depri-vation indicators o�er a suitable surrogate for predict-

ing smoking behaviour. Through models including ahigher-level variable (Jones and Duncan, 1995), thisstudy found that measures of deprivation at the elec-

toral ward level were valid predictors of individualsmoking behaviour. Most importantly, results showedthat the level of deprivation of the immediate area of

residence remained a signi®cant predictor of smokingstatus after taking into account the age, gender andsocio-economic group of individuals.The present study seeks to contribute further to

work in this area by using a multilevel approach toexamine ward-level e�ects for smoking and deprivationusing data from a nationally representative survey

rather than just one regional health authority. LikeKleinschmidt et al. (1995), such e�ects are measuredthrough the inclusion of a higher-level composite vari-

able based on Census data. In contrast, however, thepresent study considers a broader range of individualsocio-structural characteristics (e.g.: housing tenure,

employment status, educational status as well as socialclass) rather than just a single detailed socio-economicgroup classi®cation. In addition, a wide range of indi-vidual-level interactions are also explored. This is im-

portant since, unless this is done, it is always possibleto argue, like Hauser (1970), that apparent contextuale�ects are simply the result of the mis-speci®cation of

individual e�ects. The present study also extends recentwork by applying other, more complex models whichtake into account di�erences between di�erent types of

people in their degree of variability. Again, this hasbeen shown to have an important confounding in¯u-ence on estimates of area e�ects (Bullen et al., 1997).Finally, unlike some earlier work, the results reported

here are based on recently improved estimation pro-cedures.

2. Methods

The data used in this analysis were obtained fromthe Health and Lifestyle Survey (Cox et al., 1987). Thesurvey focuses on the ``lifestyles, behaviours and cir-

cumstances relating to the physical and mental healthof the population'' (p. 1) and is recognised as beingone of the most comprehensive studies of factors per-

taining to the health of the adult British population todate. Although not conceived as a longitudinal panelstudy, additional funding meant that respondents to

the original survey conducted in 1984/1985 (HALS1)could be reinterviewed in 1991/1992 (HALS2). As sub-stantially fewer people were included in the repeat

C. Duncan et al. / Social Science & Medicine 48 (1999) 497±505498

exercise (5352 compared to 9003) we have used data

from the original survey, HALS1, in order to ensure

that the present study comes as close as possible in

meeting the guidelines for sample sizes in multilevel

analysis of Paterson and Goldstein (1991)1. While

other more recent surveys include data on smoking

behaviour (e.g. The Health Survey for England, (White

et al., 1993) and the General Household Survey

(Smyth and Brown, 1992)), none at present, unlike

HALS1, allow the speci®c residential location of

respondents to be identi®ed making it impossible to

include measures of ward deprivation as is required

here. Two ®nal justi®cations for using HALS1 is that

such data formed the basis of Blaxter's important

work discussed earlier and results here will provide a

useful comparative baseline should other, more suit-

able large-scale surveys be undertaken in the future.

Since the survey was based on a three stage

sampling design Ð individuals (9003) within wards

(396) within constituencies (198) Ð it is ideally suited

to a multilevel approach. Previous research examining

ward-level e�ects on health outcomes using data from

HALS1 has been restricted to working with a subset of

wards as not all wards could be matched with those

used in the 1981 Census (Humphreys and Carr-Hill,

1991). As part of the present research, however, com-

plete matching was achieved by using the individual

postcodes of each respondent which were obtained

from the survey's principal investigator2.Having completed the identi®cation procedure, an

index of deprivation was derived for each ward using

data from the 1981 Census. Four variables were used:percentage of economically active population who are

seeking work; percentage of people in householdswhere the home is rented from the Local Authority;percentage of people in households where the head of

household is in social class IV/V; percentage of peoplein households without access to a car. Each of thesevariables was ranked and substituted with an equiva-

lently ranked normal score and the ®nal index wasformed from the sum of these ranked normal values.

Such a procedure ensures that the index is not undulya�ected by a highly non-normal distribution for anyparticular census variable. The ®nal index can be taken

to represent the broad socio-economic ecology of thearea in which respondents lived and scores rangedfrom ÿ9.2 for the least deprived area (Northaw at

Welwyn and Hat®eld in Hertfordshire) to 10.3 for themost deprived (Hulme in Manchester).

As stated earlier, it is essential that individual e�ectsare well-speci®ed if contextual e�ects are to be esti-mated accurately. In order to try and achieve this, an

ordinary single-level analysis was ®rst carried out usingthe S-Plus software package. As the response consistedof a binary variable classifying individuals as either

being regular smokers or not, models based on a logitlink function were used. Regular smokers were de®ned

as those who responded to a question concerning theirdaily cigarette consumption by declaring a rate of onecigarette or more. Variation in this response was re-

lated to a series of explanatory variables re¯ecting arange of individual characteristics. Besides age, genderand social class, as used by Kleinschmidt et al. (1995),

housing tenure, employment status, educational statusand marital status were also taken into account as pre-

vious research has shown that these are important pre-dictors of smoking status (Hunt et al., 1988; Pierce,1989; Pugh et al., 1991; Oakley, 1994). As well as

including the main e�ects, all two-way interactionswere explored between the following variables: gender,social class, tenure and education.

The fundamentals of multilevel modelling as itapplies to health research have been covered compre-

hensively elsewhere (Rice and Leyland, 1996). The pre-sent study focused on a three level analysis of theHALS1 data with individuals at level 1 nested within

electoral wards at level 2 and regions, based on TheEconomist classi®cation, (Johnston et al., 1988) at

level 3. Regions were included to re¯ect the possibilityof higher-level contextual e�ects although it is at theward level that results are explored in detail. For the

reason outlined above, logistic multilevel models basedon a logit link function were used (Goldstein, 1991).

1 In HALS1, the average number of individuals per ward is

22.375 (S.D. = 3.35). Obviously, this is much higher than in

HALS2 and is much closer to meeting Paterson and

Goldstein's recommended suggestion of 25. Since we are

using such a large number of places (see later), it is believed

that the present analysis does provide realistic and valuable

results.2 These postcodes were associated with grid references

through geographical information systems technology avail-

able at the University of Manchester Computing Centre.

Research has shown that this procedure, which uses the

Central Postcode Directory, can be prone to error and an

improvement process has been suggested involving the simple

addition of 50 ms to the x and y coordinates returned

(Gatrell et al., 1991). This procedure was followed. The `point

in polygon' technique available within GIS technology was

then used to associate the postcodes with a set of digitised

ward boundaries from the 1981 Census (ESRI, 1993). Due to

the spatial coarseness of the grid-referencing, generalisation in

the ward boundary ®les, and the lack of coincidence between

the 1981 and the 1984/1985 boundaries, it was found that

mis-allocation could occur. Consequently, the results were

examined in detail and respondents were allocated to the 1981

ward in which the majority of them with the same HALS1

identi®er were found to be located. An indicator variable was

included in the models to identify wards that proved di�cult

to identify (22 of the 396). Analyses were undertaken with

and without this indicator variable and no substantial nor sig-

ni®cant di�erences were found.

C. Duncan et al. / Social Science & Medicine 48 (1999) 497±505 499

Models were ®tted which included the individual-level

main e�ects on their own and then together with any

signi®cant two-way interactions found in the single-

level analysis. Apart from age, which was represented

as a continuous variable centred about its mean, the

main e�ects were represented by a set of dummy, indi-

cator variables which were contrasted with the base

category of a 46-year old employed woman who left

school before the age of 16, is married and lives in an

owner occupied household, the head of which is in

social class III-manual. This base category represents

the set of individual characteristics that occur most fre-

quently amongst the 9003 respondents to the question-

naire Ð that is, the stereotypical individual. In

addition to including the individual-level e�ects, the

deprivation scores for each ward were also included as

a higher-level continuous variable (Jones and Duncan,

1995)3.

Any variation remaining after taking account of the

individual and ward level characteristics is apportioned

to the appropriate level through the inclusion of ran-

dom terms at each level of the model. At the individual

level, extra-binomial variation was allowed for

(Collett, 1991). Research has shown that in multilevel

logistic models the results for the higher level random

e�ects can be seriously underestimated (Breslow and

Clayton, 1993; Rodriguez and Goldman, 1995). In the

present analysis, the software package MLn was uti-

lised which allows users to select estimation procedures

based on the second-order Taylor expansion and par-

tial quasi-likelihood approximations (Rasbash and

Woodhouse, 1995). As these have been shown to be

least a�ected by problems of underestimating higher-

level random terms they were used in all of the models

reported here (Goldstein, 1994). Importantly, since

such procedures were not available at the time of the

original multilevel analysis (Duncan et al., 1993) the

present analysis will con®rm whether the conclusionsof this earlier British work are reasonable4.

3. Results

3.1. Individual interaction e�ects

As outlined, a single-level analysis was ®rst per-

formed to establish whether there were any signi®cantinteraction e�ects. Of all the interaction terms ®tted,only those between male and missing tenure and localauthority renter and `other' social class were found to

be signi®cant. Multilevel analyses were conducted bothwith and without these terms. Signi®cantly, no import-ant di�erences were found in terms of the substantive

interpretation of neighbourhood e�ects. This ®ndingcasts doubt on suggestions that neighbourhood e�ectsare simply the result of mis-speci®ed individual level

models neglecting interaction e�ects. Consequently, theresults presented in the following subsections are forthe simpler main e�ects only multilevel models5.

3.2. Individual main e�ects

Table 1 shows results for models in which predictor

variables were included and any remaining variationwas apportioned to a single intercept term at eachlevel. The ®rst four columns are for a model in which

only the individual-level predictors are included; thelatter four for when the ward-level deprivation par-ameter is also included. In both cases, the estimates, as

well as their standard errors and corresponding Zscores are given. In addition, odds ratios derived asthe exponential of the estimated coe�cients are givenfor the categorical predictors.

If we consider the model with only individual-levelpredictors, we can see a number of important featuresconcerning the patterning of smoking behaviour on the

basis of individual characteristics as indicated by thelevel-1 ®xed e�ects. To interpret these results it mustbe remembered that they represent contrasts on each

variable from the base categories that characterise thestereotypical individual. For an estimate to be signi®-cantly di�erent from zero at the 0.05 level, the Z rationeeds to be more than22. In accordance with previous

research, men are more likely to be smokers thanwomen and the probability of smoking decreases asage increases. Besides age and gender di�erences, we

see strong patterning on the basis of the other individ-ual characteristics with the chances of an individualbeing a smoker being signi®cantly higher should they

be in the lower social class categories, live in rented ac-commodation, have fewer years of schooling, be out ofwork or divorced or separated.

3 It is important to note that including place characteristics

as higher-level variables in multilevel models provides a more

accurate estimation of their explanatory power than if they

are included within traditional single-level models (Aitken and

Longford, 1986). A full technical account of this, together

with several empirical illustrations, can be found in Bryk and

Raudenbush (1992).4 It should be noted that these improved estimation pro-

cedures were applied in the later work of Hart et al. (1997) in

Scotland (pers. comm).5 It should be noted that analyses were also undertaken

which included ethnicity and household income as main

e�ects whilst nonlinear relationships with age and age±gender,

age±tenure, age±education and gender±unemployment inter-

actions were also considered. Once again, the ®ndings with

regards to neighbourhood e�ects were neither substantially

nor signi®cantly di�erent.


3.3. Neighbourhood (ward) e�ects

Turning our attention to the model that also

includes the level-2 ®xed e�ect, deprivation, we can see

that the parameter estimate is statistically signi®cant.

Furthermore, including this variable leads to a sub-

stantial reduction in the likelihood (w 2=22, p < 0.01).

Given the inclusion of the other variables in the

model, this shows that ward-level deprivation does

seem to have an independent e�ect on individual

smoking status. Interestingly, the size of the e�ect for

the index used here is virtually identical to that

Table 1

Results for main e�ects models excluding and including ward-level deprivation

Excluding ward-level deprivation Including ward-level deprivation

estimate standard Z odds estimate standard Z odds

(logits) error ratio ratio (logits) error ratio ratio

Fixed e�ects

Level 1 (individual)

Intercept ÿ0.74 0.06 ÿ12.33 ÿ0.74 0.06 ÿ12.33Age ÿ0.02 0.002 ÿ10.00 ÿ0.02 0.001 ÿ20.00

Gender

Male 0.14 0.05 2.80 1.15 0.14 0.05 2.80 1.15

Social class

I&II ÿ0.32 0.07 ÿ4.57 0.73 ÿ0.29 0.07 ÿ4.14 0.75

III nonmanual ÿ0.30 0.08 ÿ3.75 0.74 ÿ0.28 0.08 ÿ3.50 0.76

IV&V 0.04 0.06 0.67 1.04 0.04 0.06 0.67 1.04

Missing/other ÿ0.32 0.18 ÿ1.78 0.73 ÿ0.29 0.18 ÿ1.61 0.75

Employment status

Unemployed 0.51 0.11 4.64 1.67 0.49 0.11 4.45 1.63

Housing status

Local authority renter 0.68 0.06 11.33 1.97 0.61 0.06 10.17 1.84

Other renter 0.49 0.09 5.44 1.63 0.47 0.09 5.22 1.60

Missing 0.89 0.40 2.23 2.44 0.85 0.41 2.07 2.34

Marital status

Single ÿ0.19 0.07 ÿ2.71 0.83 ÿ0.19 0.07 ÿ2.71 0.83

Widowed ÿ0.18 0.10 ÿ1.80 0.84 ÿ0.19 0.10 ÿ1.90 0.83

Divorced/separated 0.49 0.10 4.90 1.63 0.49 0.10 4.90 1.63

Age leaving school

16 ÿ0.41 0.07 ÿ5.86 0.66 ÿ0.40 0.07 ÿ5.71 0.67

Post-16 ÿ0.58 0.07 ÿ8.29 0.56 ÿ0.56 0.08 ÿ7.00 0.57

Missing ÿ1.59 0.63 ÿ2.52 0.20 ÿ1.59 0.63 ÿ2.52 0.20

Fixed e�ects

Level 2 (ward)

Deprivation 0.04 0.01 4.00

Random e�ects variance

Level 3 (region)

Intercept 0.011 0.008 0.002 0.004

Level 2 (ward)

Intercept 0.051 0.02 0.045 0.02

Level 1 (individual)

Intercept 0.992 0.02 0.993 0.02


obtained in the North West Thames study for theCarstairs index (Carstairs and Morris, 1989). In terms

of the present analysis, the odds ratio of the stereotypi-cal individual described earlier being a smoker for the95% percentiles on the area deprivation index

(ÿ6.5401; 6.1946) is 1.66 (95% C.I. 1.29, 2.15).Inspecting the odds ratios given in Table 1 shows thatthis is comparable to most of the e�ects for important

individual level explanatory variables.Further work was conducted to investigate the

nature of this e�ect in more detail. First, a series of

random coe�cient models were estimated to assesswhether the coe�cients for any of the individualcharacteristics varied between wards. Such modelsallow for the possibility that people with di�erent indi-

vidual characteristics may be di�erentially likely to besmokers depending on where they live. The resultsshowed no signi®cant di�erences for any of the charac-

teristics. In terms of social class characteristics thiscon®rms the ®ndings of the North West Thames studyand is in contrast to Blaxter's conclusions: people of

di�erent social classes did not appear to display di�er-ent degrees of between-neighbourhood variability inthe probability of being a smoker. Models were also

estimated to assess whether the e�ect of neighbour-hood deprivation was di�erent for di�erent types ofpeople. This was done through the inclusion of cross-level interaction variables as described in detail by

Jones and Duncan (1995). While there was some sug-gestion that the e�ect of neighbourhood deprivationwas reduced for local authority renters and those who

left school at 16, no other di�erences were found.Given these results, another model was estimated toexamine if there was any suggestion that a general

deprivation e�ect could be nonlinear. No evidence wasfound to support this. Lastly, a model was estimatedin which a general deprivation coe�cient was allowedto be random at the region level. Again, no signi®cant

variation was found, suggesting that the e�ect ofneighbourhood deprivation on individual smoking sta-tus is the same across all regions of the country. By

way of summary, it would appear from all of theseresults that neighbourhood deprivation does have agenuinely independent e�ect upon smoking behaviour,

but it is an e�ect which is not, for the large part,socially or geographically variable.

3.4. Between-individual variation

As multilevel research in other areas has shown,

complex forms of between-individual variation can beimportant, both in their own right and as a confound-ing factor (Goldstein, 1995; Bullen et al., 1997). As sta-

ted above, extra-binomial variation was allowed for atthe individual level. All of the previous models have,however, only allowed for under- and overdispersion

in the sample as a whole, yet it is possible that suche�ects are speci®c to particular types of people. Failing

to take such complex structures of between-individualvariation into account has been shown to be respon-sible for arti®cially exaggerating the size of area e�ects

in the case of continuous response data (Bullen et al.,1997). To consider whether this may also apply here inthe case of discrete response data, a series of ®nal

models were estimated in which indicator variables forage, gender and social class were included in the level-1 random part. No signi®cant degree of over- or

underdispersion was found in any of these models.This suggests that the results in Table 1, column (b)can be thought of as representing the ®nal model.

3.5. Between-neighbourhood variation

Contrary to previous work (Diehr et al., 1993;

Duncan et al., 1993), it is not possible to decomposethe remaining variation into level 1 and level 2 pro-portions as the estimates are based on di�erent

measurement scales (Rasbash, pers comm). Table 1shows, however, that after including both individualand ward predictors the ward-level random term

remains fairly large and is, in fact, still signi®cant(w 2=5.40, p = 0.02). Thus, neither the level of neigh-bourhood deprivation nor di�erences in populationcomposition explain all of the between-ward variability

in individual smoking status. Obtaining the posteriorestimates for each particular neighbourhood showsthat this `unexplained' variation amounts to a gap of 8

percentage points in the probability of individualsbeing regular smokers between the 95% percentiles forthese random e�ects. Whilst this is reasonably large,

the con®dence intervals associated with each of theseestimates show that there is a high degree of overlapdue to the large degree of uncertainty associated withthem. Thus, while in the population at large there are

signi®cant di�erences remaining between wards, identi-fying particular neighbourhoods as being either highor low smoking places is problematic and cannot prop-

erly be attempted.

4. Discussion and conclusions

The results presented here, based on data from a

national survey, support the recent suggestion thatthere are area e�ects on smoking behaviour whichrelate to the level of deprivation of the immediate area

of residence of individuals. Importantly, the presentanalysis recognises a broader range of personal charac-teristics than previous work both as main e�ects and

as interactions. At the same time, complex structuresof between-individual variation have also been takeninto account. Hence, the present work can be thought


of as giving added weight to the suggestion that neigh-

bourhood deprivation has a genuinely independente�ect and that it is not simply an artefact of popu-lation composition.

In substantive terms, these ®ndings con®rm the con-tention of Kleinschmidt et al. (1995), that small area

deprivation measures may be used to correct for thepossible confounding e�ect of smoking in ecologicalstudies examining the relationship between point

sources of environmental pollution and health. AsKleinschmidt et al. point out, however, census-basedsmall area deprivation measures are rather crude and

do not summarise variations in smoking prevalence aswell as corresponding individual measures. Thus, while

using deprivation indicators as surrogate variables inpollution studies does make sense, they are not a par-ticularly well-re®ned measure. To improve on this, one

alternative would be to combine estimates of both areaand individual e�ects derived from modelling nationalsurvey data with appropriate small area data from the

population Census. Procedures for producing such syn-thetic estimates of smoking prevalence rates is cur-

rently being developed in other work. Full details canbe found in Twigg et al. (1998).The ®ndings here also have relevance for the wider

debate concerning the importance of independent areae�ects on health-related behaviour, or, more speci®-cally, smoking status. This study has demonstrated

that places, at the neighbourhood level, are related tosmoking behaviour independent of the type of people

that they contain. Although it is not possible in astudy such as this to be precise about the mechanismsand processes by which neighbourhood characteristics

in¯uence individual smoking behaviour, the resultsobtained do provide some useful pointers. First, it isapparent that the contextual e�ects operate in the

same `direction' as the individual e�ects. That is, lowindividual status is characterised by a greater chance

of being a smoker as is living in a low status place. Inthe political science literature, this is known as a `con-sensual environmental e�ect' (Jones and Duncan,

1995, p. 32). Such a description connects with therecent typology of spatial e�ects in work on healthoutcomes of Macintyre (1996). As she points out, one

way in which place can matter is through a `socialmiasma' e�ect; that is, `collective' group properties

exert some in¯uence over and above individual proper-ties. Hence, in the present case, as you go into lowerstatus areas the behavioural characteristics of lower

status people are reinforced and dominate those ofothers.

What is also apparent from the present research,however, is that neighbourhood characteristics tend tohave a general rather than a socially speci®c e�ect.

With the exception of local authority renters and thosewho left school at 16, the e�ect of area deprivation

upon smoking behaviour is uniform across di�erent

types of people. It may be the case, therefore, that ad-ditionally (or, perhaps, alternatively) place may matterthrough what Macintyre describes as a `contextual'

e�ect. Thus, living in deprived areas has an impact onbehaviour not (simply) because of the presence ofmore smokers but (also) due to living in unpleasant,

undesirable, unsafe environments in which there arefewer opportunities for making `healthy choices'

(Sooman and Macintyre, 1995; Macintyre, 1996).Obviously, untangling these two possibilities is a

complex task. Indeed, it might be argued that the latter

should be thought of as producing, at least in part, theformer. Notwithstanding this, it may be possible that

in similar future work the inclusion of a range ofhigher-level predictors, some re¯ecting social collectiv-ities, others re¯ecting environmental quality, may go

some way to disentangling the di�erent types of e�ects.For the moment, what is clear is that the social en-vironment in which people live and lead their lives is

of signi®cance. As argued elsewhere, therefore, improv-ing health should not simply involve targeting individ-

uals (McKinlay, 1994; Syme, 1996; Marmot, 1998).Besides these substantive ®ndings, we would also

like to emphasise one important methodological point.

In studies such as this, it is always possible to arguethat independent place e�ects are simply an artefactcaused by the mis-speci®cation of individual-level

e�ects. In the present case, we would contend thatsuch an argument is invalid given the wide range of in-

dividual characteristics taken into account togetherwith the fact that interaction e�ects were also con-sidered. More importantly, we would further contend

that such an argument needs to be routinely appliedthe other way round. By this, we mean to suggest thatin the same way that place e�ects may be exaggerated

if important individual characteristics are not included,the signi®cance of individual e�ects may be overesti-

mated if place characteristics are excluded. This can beexpected, at least in the British context, since asMacintyre et al. (1993) emphasise, and as we have

already suggested, certain types of people are morelikely to live in certain types of area. Thus, if import-ant higher-level variables are excluded which are corre-

lated with individual level variables that are included,models can be expected to overstate the signi®cance of

individual e�ects.Some evidence in support of this can be seen in the

present case by comparing the results in Table 1 for

housing tenure and social class before and after includ-ing the ward-level predictor. What is most important

to realise, however, and which actually does not applyhere, is that if higher-level variance terms are estimatedas being nonsigni®cant after including individual-level

variables, it does not necessarily follow that higher-level variables should be ignored. Omitted variable


problems may, therefore, apply just as much at thecontextual level as at the individual one. Given this, it

is possible that had higher-level predictors beenincluded in the earlier work of Hart et al. (1997) sig-ni®cant (®xed) place e�ects for smoking may have

been found.The present study has also shown that (random)

place e�ects exist which cannot be `explained' either by

their population composition or their level of depri-vation. As discussed, the type of analysis conductedhere does, in theory, provide a means of identifying

particular neighbourhoods which exhibit either high orlow rates of smoking occurrence having taken accountof these factors. Since this would o�er guidance forcommunity health promotion e�orts, such an exercise

would undoubtedly be of interest to those working inpublic health. As with recent work on health servicesperformance (Goldstein and Spiegelhalter, 1996) it has,

however, been suggested that, in practice, very realproblems exist with such an identi®cation proceduredue to the degree of uncertainty associated with the

estimates.In conclusion, the present study has shown that the

level of deprivation in the immediate neighbourhood

of residence does have a signi®cant independent e�ecton individuals' smoking status. At the same time,other unidenti®ed factors associated with the characterof the local neighbourhood also play some role.

Acknowledgements

We would like to thank the ESRC Data Archive at

the University of Essex for providing the Health andLifestyle survey data (Cox, 1988), Professor Brian Coxfor supplying the individual postcodes, Steve

Frampton for much hard work on the geo-referencingand three anonymous referees for very helpful com-ments.

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