A brief conceptual tutorial of multilevel analysis in social epidemiology: linking the statistical concept of clustering to the idea of contextual phenomenon.
OBJECTIVE
This didactical essay is directed to readers disposed to approach multilevel regression analysis (MLRA) in a more conceptual than mathematical way. However, it specifically develops an epidemiological vision on multilevel analysis with particular emphasis on measures of health variation (for example, intraclass correlation). Such measures have been underused in the literature as compared with more traditional measures of association (for example, regression coefficients) in the investigation of contextual determinants of health. A link is provided, which will be comprehensible to epidemiologists, between MLRA and social epidemiological concepts, particularly between the statistical idea of clustering and the concept of contextual phenomenon.


METHODS
The study uses an example based on hypothetical data on systolic blood pressure (SBP) from 25,000 people living in 39 neighbourhoods. As the focus is on the empty MLRA model, the study does not use any independent variable but focuses mainly on SBP variance between people and between neighbourhoods.


RESULTS
The intraclass correlation (ICC = 0.08) informed of an appreciable clustering of individual SBP within the neighbourhoods, showing that 8% of the total individual differences in SBP occurred at the neighbourhood level and might be attributable to contextual neighbourhood factors or to the different composition of neighbourhoods.


CONCLUSIONS
The statistical idea of clustering emerges as appropriate for quantifying "contextual phenomena" that is of central relevance in social epidemiology. Both concepts convey that people from the same neighbourhood are more similar to each other than to people from different neighbourhoods with respect to the health outcome variable.
Merlo
Juan
J
Department of Clinical Sciences (Community Medicine), Malmö University Hospital, Faculty of Medicine (Campus Malmö), Lund University, S-205 02 Malmö, Sweden. juan.merlo@med.lu.se
Chaix
Basile
B
Yang
Min
M
Lynch
John
J
Råstam
Lennart
L
D000328:Adult; D016000:Cluster Analysis; D004812:Epidemiologic Methods; D006304:Health Status; D006801:Humans; D008875:Middle Aged; D015233:Models, Statistical; D012044:Regression Analysis; D012111:Residence Characteristics; D012962:Sociology, Medical / Q000379:methods*