The identification of causal relations is fundamental to a science of intervention and prevention. Obesity is a major problem for which much progress in understanding, treatment, and prevention remains to be made. Behavior is a vital component contributing to variations in energy balance and body composition, the final common pathways of obesity. Social factors are key influences on behaviors, and perhaps even physiological factors, which affect energy balance. Understanding which social and behavioral factors cause variations in adiposity and which other factors (e.g., environmental) cause variations in behavioral and social factors is vital to producing, evaluating, and selecting among intervention and prevention strategies as well as to understanding obesity's root causes. Evidence for causation (or lack thereof) of hypothesized influential factors exists on a continuum from weakest to strongest. Yet, most dialogue and research in obesity does not consider the evidence continuum between ordinary association studies (observational non-intervention studies among unrelated individuals), which do not offer strong assessments of causal effects, and randomized controlled trials (RCTs), which do offer strong inferences, but cannot be done in all circumstances. In contrast to this polarized view, there are techniques that lie intermediar between ordinary association tests and RCTs, including but not limited to quasi-experimental studies and natural experiments. Such designs are increasingly used, especially in the disciplines of economics and genetics, but are rarely used in obesity research. Our ability to draw causal inferences in obesity research could be strengthened by increased judicious use of such approaches. In-depth understanding and appropriate use of the full continuum of these methods requires input from disciplines including statistics, economics, psychology, epidemiology, mathematics, philosophy, and in some cases behavioral or statistical genetics. The application of these techniques, however, does not involve routine well-known 'cookbook'approaches but requires understanding of underlying principles, so the investigator can tailor approaches to specific and varying situations. Yet, no ongoing resource exists to provide such training and role models of scientists who regularly can and do traverse these disciplines are in short supply. The proposed annual 5-day short course on methods for causal inference in obesity research features some of the world's finest scientists who will help to fill this unmet need. This course for established and up- and-coming obesity researchers will be held annually at the University of Alabama at Birmingham. The nine course modules are formatted to provide rigorous exposure to the key fundamental principles underlying a broad array of techniques and experience in applying those principles and techniques through guided discussion of real examples in obesity research. The NIH and the scientific community at large call for better assessment of causal effect in obesity research and more training on such methods. We request the opportunity to be part of the solution.

Public Health Relevance

Obesity affects over one-third of the US population generating a vital need for novel interdisciplinary strategies to identify factors causing (not merely correlating with) obesity and methods which cause reductions in obesity. A burgeoning array of research techniques exists to help scientists make more informed conclusions about causal effects, but many obesity researchers are unfamiliar with these techniques. The proposed course will train scientists in understanding and using such techniques, who in turn will be better positioned to help identify ways of reducing the burden of obesity.

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Education Projects (R25)
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Special Emphasis Panel (ZRG1-HDM-Q (52))
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Czajkowski, Susan
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University of Alabama Birmingham
Schools of Public Health
United States
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Dutton, G R; Fontaine, K R; Alcorn, A S et al. (2015) Randomized controlled trial examining expectancy effects on the accuracy of weight measurement. Clin Obes 5:38-41