THEORY AND METHODS

A brief conceptual tutorial on multilevel analysis in social epidemiology: interpreting neighbourhood differences and the effect of neighbourhood characteristics on individual health

Juan Merlo¹, Basile Chaix^1,2, Min Yang³, John Lynch⁴ and Lennart Råstam¹

¹ Department of Community Medicine (Preventive Medicine), Malmö University Hospital, Lund University, Malmö, Sweden
² Research Team on the Social Determinants of Health and Healthcare, National Institute of Health and Medical Research, Paris, France
³ Institute of Community Health Sciences, Queen Mary University of London, London, UK
⁴ Department of Epidemiology, Center for Social Epidemiology and Population Health, University of Michigan, Ann Arbor, USA

Correspondence to:
Correspondence to:
Professor J Merlo
Department of Community Medicine (Section of Preventive Medicine), Malmö University Hospital, Faculty of Medicine (Campus Malmö), Lund University, S-205 02 Malmö, Sweden; juan.merlo{at}med.lu.se

Study objective: Using a conceptual rather than a mathematical approach, this article proposed a link between multilevel regression analysis (MLRA) and social epidemiological concepts. It has been previously explained that the concept of clustering of individual health status within neighbourhoods is useful for operationalising contextual phenomena in social epidemiology. It has been shown that MLRA permits investigating neighbourhood disparities in health without considering any particular neighbourhood characteristic but only information on the neighbourhood to which each person belongs. This article illustrates how to analyse cross level (neighbourhood–individual) interactions, how to investigate associations between neighbourhood characteristics and individual health, and how to use the concept of clustering when interpreting those associations and geographical differences in health.

Design and participants: A MLRA was performed using hypothetical data pertaining to systolic blood pressure (SBP) from 25 000 subjects living in the 39 neighbourhoods of an imaginary city. Associations between individual characteristics (age, body mass index (BMI), use of antihypertensive drug, income) or neighbourhood characteristic (neighbourhood income) and SBP were analysed.

Results: About 8% of the individual differences in SBP were located at the neighbourhood level. SBP disparities and clustering of individual SBP within neighbourhoods increased along individual BMI. Neighbourhood low income was associated with increased SBP over and above the effect of individual characteristics, and explained 22% of the neighbourhood differences in SBP among people of normal BMI. This neighbourhood income effect was more intense in overweight people.

Conclusions: Measures of variance are relevant to understanding geographical and individual disparities in health, and complement the information conveyed by measures of association between neighbourhood characteristics and health.

Abbreviations: MLRA, multilevel regression analysis; SBP, systolic blood pressure; BMI, body mass index; AHD, antihypertensive drug; MLRA, multilevel regression analysis; VPC, variance partition coefficient; PCV, proportional change in variance

Keywords: multilevel analysis; social epidemiology; health inequalities; tutorial

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