Elsevier

Social Science & Medicine

Volume 48, Issue 4, February 1999, Pages 497-505
Social Science & Medicine

Smoking and deprivation: are there neighbourhood effects?

https://doi.org/10.1016/S0277-9536(98)00360-8Get rights and content

Abstract

Debate has centred on whether the character of places plays an independent role in shaping individual smoking behaviour. 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 study applies multilevel modelling techniques to data from the British Health and Lifestyle Survey and ward (local neighbourhood) 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 effects and interactions, together with complex structures of between-individual variation, measures of neighbourhood deprivation continue to have an independent effect on individual smoking status. In addition, significant between-ward differences in smoking behaviour remain which cannot be explained either by population composition or ward-level deprivation. The study suggests that the character of the local neighbourhood plays a role in shaping smoking behaviour.

Introduction

Over the last two decades a series of empirical studies have identified significant 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 differences at the regional level with only a few studies examining more local influences (Cummins et al., 1981; Richards, 1989; Blaxter, 1990). Similar research has been conducted in America with significant area-based differences in health-related behavioral practices being identified (Marks et al., 1985; MMWR, 1987; Hilton, 1988; Klein and Pittman, 1993; Colby et al., 1994). As is now well-recognised, geographical variations in outcome measures do not necessarily mean that places have significance 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 reflect independent area effects; they may simply be the result of spatially differentiated population composition.

Given the aetiological significance 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 smoking behaviour across Britain are not simply an artefact of varying population compositions but are, additionally, the result of independent `area' or `contextual' effects. This position is well exemplified by M. Blaxter's major piece of analytical, academic research, Health and Lifestyles, in which she concludes that ``class is related to smoking in different ways in different types of areas'' (Blaxter, 1990, p. 117). Blaxter's analysis does, however, only involve the calculation of area-based rates standardised for a limited number of personal characteristics. Whilst other studies have adopted a more sophisticated regression modelling strategy, they have tended to rely on traditional single-level techniques. Such techniques can be affected by serious problems of mis-estimation associated with auto-correlation since people living in the same place can 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 also prone to technical problems associated with small numbers and sampling fluctuations (Jones, 1991).

Multilevel regression modelling techniques (Goldstein, 1995) have been shown to offer a more suitable way of conducting analyses of area effects (Jones and Duncan, 1996). In the United States, such an approach has found significant area effects for smoking behaviour with people in the highest prevalence communities being ``about 7 percentage points more 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 focused more on the regional level rather than smaller geographical scales (Duncan et al., 1993). Results suggested that, in comparison with individual-level compositional effects, neither wards nor regions play a significant independent role. The models used in this analysis were, however, rather limited. More recent work in Scotland by Hart et al. (1997)has obtained similar results at the local scale. In this study, smoking was found to be the coronary heart disease risk factor most closely related to individual-level compositional effects. Indeed, after taking account of these, the degree of contextual variation in smoking behaviour was minimal. While this analysis did usefully consider gender differences in detail (though, significantly, none were found for smoking) the models used were, once again, relatively simple. Furthermore, the study was based on only 22 local government districts.

In a slightly earlier piece of work, more complex multilevel models have been applied in a study focusing on the North West Thames Regional Health Authority in England (Kleinschmidt et al., 1995). Interestingly, this work did not originate from the debate concerning the relevance of `place' for smoking behaviour but from a desire to control for the possible confounding effect that smoking may have in ecological studies examining the health effects of point sources of environmental pollution. Since smoking data are usually unavailable in such studies, researchers have been interested in examining whether small area deprivation indicators offer a suitable surrogate for predicting smoking behaviour. Through models including a higher-level variable (Jones and Duncan, 1995), this study found that measures of deprivation at the electoral ward level were valid predictors of individual smoking behaviour. Most importantly, results showed that the level of deprivation of the immediate area of residence remained a significant predictor of smoking status after taking into account the age, gender and socio-economic group of individuals.

The present study seeks to contribute further to work in this area by using a multilevel approach to examine ward-level effects for smoking and deprivation using data from a nationally representative survey rather than just one regional health authority. Like Kleinschmidt et al. (1995), such effects are measured through the inclusion of a higher-level composite variable based on Census data. In contrast, however, the present study considers a broader range of individual socio-structural characteristics (e.g.: housing tenure, employment status, educational status as well as social class) rather than just a single detailed socio-economic group classification. In addition, a wide range of individual-level interactions are also explored. This is important since, unless this is done, it is always possible to argue, like Hauser (1970), that apparent contextual effects are simply the result of the mis-specification of individual effects. The present study also extends recent work by applying other, more complex models which take into account differences between different types of people in their degree of variability. Again, this has been shown to have an important confounding influence on estimates of area effects (Bullen et al., 1997). Finally, unlike some earlier work, the results reported here are based on recently improved estimation procedures.

Section snippets

Methods

The data used in this analysis were obtained from the Health and Lifestyle Survey (Cox et al., 1987). The survey focuses on the ``lifestyles, behaviours and circumstances relating to the physical and mental health of the population'' (p. 1) and is recognised as being one of the most comprehensive studies of factors pertaining to the health of the adult British population to date. Although not conceived as a longitudinal panel study, additional funding meant that respondents to the original

Individual interaction effects

As outlined, a single-level analysis was first performed to establish whether there were any significant interaction effects. Of all the interaction terms fitted, only those between male and missing tenure and local authority renter and `other' social class were found to be significant. Multilevel analyses were conducted both with and without these terms. Significantly, no important differences were found in terms of the substantive interpretation of neighbourhood effects. This finding casts

Discussion and conclusions

The results presented here, based on data from a national survey, support the recent suggestion that there are area effects on smoking behaviour which relate to the level of deprivation of the immediate area of residence of individuals. Importantly, the present analysis recognises a broader range of personal characteristics than previous work both as main effects and as interactions. At the same time, complex structures of between-individual variation have also been taken into account. Hence,

Acknowledgements

We would like to thank the ESRC Data Archive at the University of Essex for providing the Health and Lifestyle survey data (Cox, 1988), Professor Brian Cox for supplying the individual postcodes, Steve Frampton for much hard work on the geo-referencing and three anonymous referees for very helpful comments.

References (58)

  • R. Balarajan et al.

    British smoking and drinking habits: regional variations

    Community Medicine

    (1986)
  • Blaxter, M., 1990. Health and Lifestyles. Tavistock/Routledge,...
  • F.E.M. Braddon et al.

    Social and regional differences in food and alcohol consumption and their measurement in a national birth cohort

    Journal of Epidemiology and Community Health

    (1988)
  • N.E. Breslow et al.

    Approximate inference in generalised linear mixed models

    Journal of the American Statistical Association

    (1993)
  • Bryk, A.S., Raudenbush, S.W., 1992. Hierarchical Linear Models: Applications and Data Analysis Methods. Sage, Newbury...
  • N. Bullen et al.

    Modelling complexity: analyzing between-individual and between-place variation: a multilevel tutorial

    Environment and Planning A

    (1997)
  • V. Carstairs et al.

    Deprivation: explaining differences in mortality between Scotland and England and Wales

    British Medical Journal

    (1989)
  • Collett, D., 1991. Modelling Binary Data. Chapman and Hall,...
  • Cox, B.D., 1988. Health and Lifestyle Survey, 1984–1985 (computer file). ESRC Data Archive,...
  • Cox, B.D., Blaxter, M., Buckle, A.L.J., et al., 1987. The Health and Lifestyle Survey: a Preliminary Report. Health...
  • R.O. Cummins et al.

    Smoking and drinking by middle-aged British men: effects of social class and town of residence

    British Medical Journal

    (1981)
  • Department of Health, 1992. The Health of the Nation: a Strategy for Health in England. HMSO,...
  • Doll R., Peto R., 1981. The Causes of Cancer. Open University Press,...
  • G.C. Dunbar et al.

    The changing pattern of alcohol consumption in England and Wales 1978–1985

    British Medical Journal

    (1987)
  • ESRI, 1993. Arc/Info User Guide. Redlands,...
  • A.C. Gatrell et al.

    The relative utility of the Central Postcode Directory and Pinpoint Address Code in applications of geographical information systems

    Environment and Planning A

    (1991)
  • H. Goldstein

    Nonlinear multilevel models, with an application to discrete response data

    Biometrika

    (1991)
  • H. Goldstein

    Improved estimation for logit and loglinear multilevel models

    Multilevel Modelling Newsletter

    (1994)
  • Goldstein, H., 1995. Multilevel Statistical Models. Edward Arnold,...
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