There is great interest in understanding the role of weight dynamics over the life cycle in predicting the incidence of disease and death. Beginning with a Medline search, we identify, classify, and evaluate the major approaches that have been used to study these dynamics. We identify four types of models: additive models, duration-of-obesity models, additive-weight-change models, and interactive models. We develop a framework that integrates the major approaches and shows that they are often nested in one another, a property that facilitates statistical comparisons. Our criteria for evaluating models are two-fold: the model’s interpretability and its ability to account for observed variation in health outcomes. We apply two sets of nested models to data on adults age 50–74 years at baseline in two national probability samples drawn from National Health and Nutrition Examination Survey. One set of models treats obesity as a dichotomous variable and the other treats it as a continuous variable. In three of four applications, a fully interactive model does not add significant explanatory power to the simple additive model. In all four applications, little explanatory power is lost by simplifying the additive model to a duration model in which the coefficients of weight at different ages are set equal to one another. Other versions of a duration-of-obesity model also perform well, underscoring the importance of obesity at early adult ages for mortality at older ages.
From the aPopulation Studies Center, University of Pennsylvania, Philadelphia, PA; and bDepartment of Global Health, Emory University, Atlanta, GA.
Submitted 12 March 2012; accepted 1 October 2012.
Supported by grant number R01AG040212 from the National Institute on Aging.
The authors report no conflicts of interest.
Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (www.epidem.com). This content is not peer-reviewed or copy-edited; it is the sole responsibility of the author.
Correspondence: Samuel H. Preston, Population Studies Center, University of Pennsylvania, 3718 Locust Walk, McNeil, Building, Room 239, Philadelphia, PA 19103. E-mail: email@example.com.