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  • A brief conceptual tutorial on multilevel analysis in social epidemiology: investigating contextual phenomena in different groups of people

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Continuing professional education
A brief conceptual tutorial on multilevel analysis in social epidemiology: investigating contextual phenomena in different groups of people
  1. Juan Merlo1,
  2. Min Yang2,
  3. Basile Chaix3,
  4. John Lynch4,
  5. Lennart Råstam1
  1. 1Department of Community Medicine (Preventive Medicine), Malmö University Hospital, Lund University, Malmö, Sweden
  2. 2Institute of Community Health Sciences, Queen Mary University of London, London, UK
  3. 3Research Team on the Social Determinants of Health and Healthcare, National Institute of Health and Medical Research, Paris, France
  4. 4Department of Epidemiology, Center for Social Epidemiology and Population Health, University of Michigan, Ann Arbor, Michigan, USA
  1. Correspondence to:
 Professor J Merlo
 Department of Community Medicine, Lund University Hospital, S-205 02 Malmö, Sweden; juan.merlosmi.mas.lu.se

Abstract

Study objective: (1) To provide a didactic and conceptual (rather than mathematical) link between multilevel regression analysis (MLRA) and social epidemiological concepts. (2) To develop an epidemiological vision of MLRA focused on measures of health variation and clustering of individual health status within areas, which is useful to operationalise the notion of “contextual phenomenon”. The paper shows how to investigate (1) whether there is clustering within neighbourhoods, (2) to which extent neighbourhood level differences are explained by the individual composition of the neighbourhoods, (3) whether the contextual phenomenon differs in magnitude for different groups of people, and whether neighbourhood context modifies individual level associations, and (4) whether variations in health status are dependent on individual level characteristics.

Design and participants: Simulated data are used on systolic blood pressure (SBP), age, body mass index (BMI), and antihypertensive medication (AHM) ascribed to 25 000 subjects in 39 neighbourhoods of an imaginary city. Rather than assessing neighbourhood variables, the paper concentrated on SBP variance between individuals and neighbourhoods as a function of individual BMI.

Results: The variance partition coefficient (VPC) showed that clustering of SBP within neighbourhoods was greater for people with a higher BMI. The composition of the neighbourhoods with respect to age, AHM use, and BMI explained about one fourth of the neighbourhood differences in SBP. Neighbourhood context modified the individual level association between BMI and SBP. Individual level differences in SBP within neighbourhoods were larger for people with a higher BMI.

Conclusions: Statistical measures of multilevel variations can effectively quantify contextual effects in different groups of people, which is a relevant issue for understanding health inequalities.

  • MLRA, multilevel regression analysis
  • SBP, systolic blood pressure
  • AHM, antihypertensive medication
  • VPC, variance partition coefficient
  • BMI, body mass index
  • multilevel analysis
  • blood pressure
  • neighbourhoods
  • social epidemiology

https://doi.org/10.1136/jech.2004.023929

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    Footnotes

    • ↵* In MLRA, however, the mathematical interpretation of the regression coefficients is not exactly the same as in the standard non-multilevel model not adjusted for the neighbourhood residuals. Interested readers can obtain an extended explanation elsewhere.8

    • ↵† Note however that individual characteristics may be in the causal pathway between neighbourhood characteristics and individual differences in SBP, so including individual characteristics in the model may result in understating the contribution of contextual influences to SBP. The interpretation of the PCV therefore depends on the individual variables included in the model, and on their hypothesised role (that is, confounding role, mediating role).

    • ↵‡ The proportional change in variance is often referred to as “explained variance”. However, the addition of individual variables in the model may increase the second level variance. Indeed, in cases in which the neighbourhood differences are hidden by their individual composition, the total variance may decrease but the neighbourhood component of the variance increase. Therefore, “proportional change in the variance” is a more appropriate term than “explained variance”.

    • ↵§ Note however that individual variables like BMI may be in the causal pathway between neighbourhood characteristics and individual differences in SBP, so including BMI in the model may result in understating the contribution of contextual influences to SBP. The interpretation of the IPC therefore depends on the individual variables included in the model, and on their hypothesised role (that is, confounding role, mediating role).

    • ↵ Observe that the ICC was the same in the empty model and in the model with individual variables. The inclusion of individual level predictors reduced the individual and neighbourhood level variances by the same amount proportionally, what was reflected in the IPC.

    • Funding: this study is supported by grants (principal investigator Juan Merlo) from the Swedish Council for Working Life and Social Research) (number 2002-054 and number 2003-0580), and from the Swedish Research Council (number 2004-6155)

    • Conflicts of interest: none.