RESEARCH ARTICLE Estimating the Effects of Obesity and Weight Change on Mortality Using a Dynamic Causal Model

Background A well-known challenge in estimating the mortality risks of obesity is reverse causality attrib-utable to illness-associated and smoking-associated weight loss. Given that the likelihood of chronic and acute illnesses rises with age, reverse causality is most threatening to esti-mates derived from elderly populations. Methods I analyzed data from 12,523 respondents over 50 years old from a nationally representative longitudinal dataset, the Health and Retirement Study (HRS). The effects of both baseline body weight and time-varying weight change on mortality are estimated, adjusting for demo-graphic and socio-economic variables, as well as time-varying confounders including illness and smoking. Body weight is measured by body mass index (BMI). In survival models for mortality, illness and smoking were lagged to minimize bias from reverse causality in esti-mates of the effect of weight change. Furthermore, because illness both causes and is caused by changes in BMI, I used a marginal structural model (MSM) rather than standard adjustment to control confounding by this and other time-dependent factors. Results Overall, relative to normal weight, underweight and Class II/III at baseline are associated with hazard ratios that are 2.07 (95 % confidence interval (CI): 1.28–3.37) and 1.82 (1.54– 2.16) respectively, whereas overweight and Class I obesity do not significantly lower or raise the mortality risks. Furthermore, relative to stable weight change, all types of weight change lead to significantly increased risk of mortality. Specifically, large weight loss results in a mortality risk that is nearly 3.86 (3.26–4.58) times of staying in the stable weight range and small weight loss is about 1.81 (1.55–2.11) times riskier. In contrast, large weight gain and small weight gain are associated with hazard ratios that are 1.98 (1.67–2.35) and 1.20 (1.02–1.41) respectively