Kelvyn Jones

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.

Specifying and estimating multilevel models for geographical research

It is argued that multi-level models based on shrinkage estimators represent a considerable improvement over single-level models estimated by ordinary-least squares. In substantive terms, the ML models allow relationships to vary in time and space according to context. Shrinkage estimators make very efficient use of the information contained in the hierprchical data sets that are estimated by ML models. A number of ML models for house-price variation are specified in terms of fixed and random, allowed-to-vary, effects. Empirical illustrations of some of these ML models are given for house-price variation in Southampton.

Contextual risk factors for the common mental disorders in Britain: a multilevel investigation of the effects of place

Study objective: To test hypotheses about associations between area level exposures and the prevalence of the most common mental disorders (CMD) in Britain. A statistically significant urban-rural gradient was predicted, but not a socioeconomic gradient, in the prevalence of CMD after adjusting for characteristics of individual respondents. The study tested the hypothesis that the effects of area level exposures would be greatest among those not in paid employment. Design: Cross sectional survey, analysed using multilevel logistic and linear regression. CMD were assessed using the General Health Questionnaire (GHQ). Electoral wards were characterised using the Carstairs index, the Office of National Statistics (ONS) Classification of Wards, and population density. Setting: England, Wales, and Scotland. Participants: Nearly 9000 adults aged 16–74 living in 4904 private households, nested in 642 electoral wards. Main results: Little evidence was found of statistically significant variance in the prevalence of CMD between wards, which ranged from 18.8% to 29.5% (variance 0.035, SE 0.026) (p=0.11). Associations between CMD and characteristics of wards, such as the Carstairs index, only reached statistical significance among those who were economically inactive (adjusted odds ratio for top v bottom Carstairs score quintile 1.58, 95% CI 1.08 to 2.31) (p<0.05). Conclusions: There may be multiple pathways linking socioeconomic inequalities and ill health. The effects of place of residence on mental health are greatest among those who are economically inactive and hence more likely to spend the time at home.

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.

Does organization matter? A multilevel analysis of the demand-control model applied to human services

The demand-control model (DC model) in occupational epidemiology suggests that health, an individual attribute, is partly determined by work organization, via the interplay of demand and control, job strain. The objective of this study was empirical assessment of the models tenet of an organizational determination of individual health. An emerging analytic method, multi-level modelling, permits such an assessment. The study encompasses two large Swedish human service organizations. It was based on a nationally representative sample of 291 local organizational units (level 2) with 8296 employees (level 1), a median of 18 employees per unit. 5730 persons (69.1%) completed the questionnaire. Listwise deletion of missing data left a net study base of 4756 individuals in 284 units. Missing data were largely random. Demand and control were measured by standard questions and combined into a job strain index. Two such indices were calculated, one for quantitative demands and one for emotional demands. Individual attributes included age, gender, marital status, having children, social anchorage, and education. There were two dependent variables, self-assessed psychovegetative symptoms (worry, anxiousness, sadness, sleep difficulties, restlessness, and tension) and exhaustion (fatigue, feelings of being used up and overworked), both measured as summative indices. For psychovegetative health, a null model yielded 2.2% level 2 variance, unchanging when individual attributes were included in a random intercepts model. Inclusion of the strain variables rendered level 2 variance non-significant, decreasing level 1 variance by 23% and level 2 variance by 62%. For exhaustion, level 2 variation was 8.3% in the null model and 1.6% in the final model, with strain variables. The strain variables utilized in the DC-model thus draw a substantial part of their variation from the organizational level. It is concluded that the claim of the DC model to rely on organizational factors receives support.

ANALYZING PERCEIVED LIMITING LONG-TERM ILLNESS USING U.K. CENSUS MICRODATA

The 1991 Census of England, Wales and Scotland is an improvement on previous censuses in providing fine-grained detail on the geography of limiting long-term illness. Another innovation of the 1991 Census is the release of a Sample of Anonymized Records (SARs). These provide a considerable sample of detailed data on individuals at the sub-regional level. This paper explores individual and geographical variations in morbidity through a multilevel analysis of the SARs. Geographical differences in morbidity are found even after allowing for age, sex, ethnicity, housing tenure, social class and car ownership.