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In cross-population comparisons of disease occurrence (prevalence, incidence), a common public health question is the extent to which variations in the distribution of risk factors for the disease explain observed differences. Limited work has been done on formalizing this problem, which is conceptually tantamount to quantifying the degree of confounding for the 'population effect' induced by different factors. A common approach is to compare 'unadjusted' and 'adjusted' regression-based estimates of that parameter, but the interpretation of the resulting 'contribution' measures may be hindered by other confounding sources and non-collapsibility issues. Interactions also raise interpretational challenges.

Measuring the impact of differences in risk factor distributions on cross-population differences in disease occurrence: a causal approach