Craig Duncan

Context, composition and heterogeneity: Using multilevel models in health research

This paper considers the use of multilevel models in health research. Attention focuses on the structure and potential of such models and particular consideration is given to their use in elucidating the importance of contextual effects in relation to individual level social and demographic factors in understanding health outcomes, health-related behaviour and health service performance. Four graphical typologies are used to outline the questions that multilevel models can address and the paper illustrates their potential by drawing on published examples in a number of different research areas.

Individuals and their ecologies: analysing the geography of chronic illness within a multilevel modelling framework

Abstract This paper argues for the importance of place differences in understanding chronic illness. A conceptual distinction is drawn between individual and ecological effects and it is argued that aggregate analysis provides an inappropriate methodology for studying place differences. Multilevel modelling, in contrast, allows for the simultaneous analysis of individuals and their ecologies. This approach is applied to data derived from a nationally representative sample of over 9 000 United Kingdom individuals in nearly 400 places.

Health-related behaviour in context: a multilevel modelling approach

Recent attempts to place individual health-related behaviour in context have been judged largely unsuccessful. This paper examines how this situation might be improved and is especially concerned with the role of quantitative methodologies. It is argued that, whilst recent developments in social theory help provide important theoretical guidelines, they can only be implemented with difficulty in empirical health-related behaviour research if traditional quantitative methodologies are used. It is suggested that the best way to implement social theory within a quantitative framework is to apply the newly developed technique of multilevel modelling. This paper offers an overview of the multilevel approach and outlines its significance for health-related behaviour research. In addition, it details a number of ways in which the multilevel framework can be extended so as to achieve further improvements in the conceptualization of health-related behaviour. To illustrate the value of the technique, the paper finishes by considering one of these extensions in detail and applying it to data recording smoking behaviour in the United Kingdom.

Smoking and deprivation: are there neighbourhood effects?

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 individuals 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.

Do places matter? A multi-level analysis of regional variations in health-related behaviour in Britain

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.

Psychiatric morbidity: a multilevel approach to regional variations in the UK.

STUDY OBJECTIVE--To establish whether regional variations in psychiatric morbidity in Britain constitute a distinctive geography of mental health arising from factors that are context-specific at area level or whether these variations are an artifact generated by sampling fluctuations and differing population compositions in areas. DESIGN--Multilevel modelling techniques were applied to data from the 1984-85 health and lifestyle survey. The outcome was the prevalence of psychiatric morbidity as recorded by the application of the general health questionnaire in this survey. SETTING--The analysis was undertaken simultaneously at the individual level, electoral ward level, and regional level for England, Wales, and Scotland. PARTICIPANTS--A total of 6572 adults were selected from the electoral register. MAIN RESULTS--Regional variations were detected in crude aggregate general health questionnaire scores but these were found to be the result of sampling fluctuations and varying regional population compositions rather than higher level contextual effects. There was certainly no evidence of a clear north-south distinction in psychiatric morbidity as was suggested by earlier work. In addition, the local neighbourhood did not seem to have any importance beyond the type of people who lived there. A number of individual characteristics was shown to be associated with mental wellbeing but a large degree of individual variation remained unexplained. CONCLUSIONS--In terms of low level psychiatric disturbance it seems that the characteristics of individuals have greater importance than the characteristics of areas, although the latter may still operate as important mediating factors. Multilevel modelling represents a robust statistical method of examining area variations in health outcomes and further work needs to be conducted, particularly on more serious psychiatric conditions.

Placing geographies of public health

Following the move to a ‘post–medical’ geography, a large amount of research has come to focus on public health issues. This paper explores these current geographies of public health and argues for the development of a more critical perspective. In particular, it draws on commentary that has emerged out of debates that have taken place within a body of literature usually identified as the critical ‘new’ public health. The paper goes on to argue that such scholarship offers crucial insights for the production of a critical geography of public health.

Multilevel Perspectives on Modeling Census Data

Since most census data are released for spatial aggregates, the microscale of people and the macroscale of places are confounded in analyses. Although regrettable, this situation is usually tolerated owing to the other obvious attractions of census data. In this paper, we consider how multilevel statistical procedures offer a solution to this problem. Importantly, we show how they allow places to be considered in terms of several different scales simultaneously. As we demonstrate, this provides important connections with recent moves towards performance review in several areas of public policy. An analysis of data on illiteracy from the 1991 Indian Census provides an illustration of multilevel approach and its usefulness.

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

Geography is centrally concerned with difference and heterogeneity, yet much quantitative modelling has been concerned with finding average or general relationships thereby relegating variability to a single catchall ‘error’ term. Multilevel modelling, in contrast, anticipates complex between-individual and between-place heterogeneity. Previous accounts of the approach have stressed the modelling of higher level, between-place differences, but here the emphasis is placed on the simultaneous consideration of complex variation at all levels. A parade of models is presented each of which considers a particular facet of the model specification. Attention is drawn to the important contrasts between the modelling of categorical and continuous predictors. Illustrative results are provided for variations in British house prices, modelled with the MLn software. An appendix provides an example of the use of this software.

Geographies of health perception in Québec: a multilevel perspective.

Self perceived health is a widely used measure and, in Quebec, it has been shown to vary significantly between geographical areas. In the present study, these geographical variations are examined in a multilevel analysis in order to disentangle compositional (individual characteristics) and contextual (place) effects. The analysis recognizes four levels of variation: individual, household, local and regional. Similar analyses carried out in Britain, have considered only two levels: individual and local. Data come from the 1992-1993 Quebec Health and Social Survey, a general household survey using a stratified two-stage sampling design. Health perception (the response variable) is considered with a set of individual predictor characteristics reflecting gender, lifestyle, socio-economic conditions, marital status and social support. Results show the existence of significant local area variations in health perception after having allowed for individual characteristics and variations at the household level. At the regional level, however, no systematic and significant variations remain although some individual regions are found to have a significant impact on health perception.