Multilevel analytical approaches in social epidemiology: measures of health variation compared with traditional measures of association: Figure 1

Considering both distribution and determinants of health In this issue of the journal Jennifer Ahern et al 1 present the results of a multilevel analysis showing the increased likelihood of preterm birth affecting both African American and white women living in a neighbourhood with deprived socioeconomic characteristics. This increased risk was independent of individual cigarette smoking and modified by socioeconomic characteristics of the women. The authors, taking a multilevel perspective, concluded that examining both neighbourhood and individual socioeconomic factors in combination with behavioural and biological factors is the most adequate way to study determinants of preterm delivery. The study of Ahern et al contributes to the growing stream of current multilevel analysis in modern health epidemiology. However, the analytical approach of Ahern’s multilevel analysis does not apply multilevel regression (synonymous with hierarchical regression )2–5 for statistical modelling. The authors describe the association between preterm birth and neighbourhood variables by population-average regression techniques that account for intra-neighbourhood correlation using a method called generalised estimating equations (GEE).6,7 In this way the authors simply aim to provide acceptable estimates for the standard errors around point estimates (that is, odds ratios, 95% CI), treating the intra-neighbourhood correlation as a “nuisance” that needs to be adjusted in the analysis but not explicitly investigated. Analogously to the study of Ahern, other social epidemiologists have adopted a similar analytical approach, applying SUDAAN statistical software (http://www.rti.org/) to perform multilevel analysis. As in Ahern’s study, SUDAAN analyses also consider the variance structure of the data as a necessary “nuisance”. These authors’ analytical approach and the estimation of the association between neighbourhood characteristics and health are, however, appropriate and formally correct. Is this the end of the story? Is the only reason for applying complicated statistical techniques so that correct confidence intervals may be obtained? Is …