Obesity Epidemiology

INTERPRETING EPIDEMIOLOGIC EVIDENCE AND CAUSAL INFERENCE 43Mediation and Effect ModificationIt is important to distinguish a mediator from a confounder. Unlike a confounder, amediator is the third variable that accounts, at least in part, for the relationship betweenexposure and outcome. In the study of obesity and mortality, for example, diabetes isconsidered a biological intermediate in the pathway. Controlling for diabetes, which isexpected to reduce estimated risks of obesity and mortality, can lead to overcontrol andunderestimation of obesity’s effects. Similarly, obesity can serve as a mediator whenstudying the relationship between lifestyle factors, such as physical activity and televisionwatching, and risk of type 2 diabetes. 24 The general test for mediation is a weakenedassociation between exposure and outcome after the mediator is included in the model.25 Theoretically, the proportion of the exposure-outcome association explained by themediator can be quantified by comparing the estimates with and without the mediator inthe models. 26 However such analyses require several assumptions, for example, continuousoutcomes, the absence of confounding in the relationship between the intermediatevariable and the outcome, and lack of interaction between the exposure and intermediatevariable. 27 Violation of these assumptions may lead to biased estimates of the proportionof exposure-outcome association explained by the mediator.An effect modifier or moderator is a variable that changes the direction and/or strengthof the relationship between an exposure and the outcome variable. 25 For example, studieshave found that the relationship between BMI and mortality varies by age group; 28 thatis, the RR of mortality with increasing BMI tends to decline with age. However, thisdoes not necessarily mean that obesity is less detrimental to older people than middleagedones. Assessing effect modification or the presence and extent of the interactionsdepends on the scale used. When a ratio measure (e.g., RR) is used, the interaction isassessed on the multiplicative scale; when an absolute risk measure (e.g., mortality rate)is used, the interaction is assessed on the additive scale. In some situations, measures ofinteractions on the additive and multiplicative scales may lead to divergent conclusions.For example, although the RR of death associated with obesity is lower in older peoplein than middle-aged individuals, the absolute increase in death rates associated withobesity is much greater in the elderly 29 (see Chapter 11). Thus, the decreased associationbetween obesity and mortality with age does not diminish the importance of obesity inolder individuals.Rothman and Greenland 30 argued that the additive model is the best model for interactionif the goal is to predict “disease load” or public health burden in a population.On the other hand, the multiplicative model is more appropriate if the goal is to unraveletiological factors of the disease. It is sometimes important to assess both multiplicativeand additive models when an interaction may have both public health and etiologicalimplications.Although the multiplicative interactions can be easily estimated using standard proceduressuch as the likelihood ratio test, methods for testing additive interactions are lesswell-developed. To assess interaction as a departure from joint additive effects, Rothmanand Greenland 30 suggested the computation of a synergy index (S) based on model coefficientsfrom a logistic regression. S compares the risk of disease or mortality in patientswith joint exposures to those with a single exposure.SR112 R00 RR11215 5R 2R 1R 2R RR 211RR 2110 00 01 00 10 01