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Asymmetric Paternalism to Improve Health Behaviors

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Asymmetric Paternalism to Improve Health Behaviors
COMMENTARY

Asymmetric Paternalism to Improve Health Behaviors
George Loewenstein, PhD Troyen Brennan, MD, JD, MPH Kevin G. Volpp, MD, PhD

ease burden faced by society. Many major health problems in the United States and other developed nations, such as lung cancer, hypertension, and diabetes, are exacerbated by unhealthy behaviors. Modifiable behaviors such as tobacco use, overeating, and alcohol abuse account for nearly one-third of all deaths in the United States.1,2 Moreover, realizing the potential benefit of some of the most promising advances in medicine, such as medications to control blood pressure, lower cholesterol levels, and prevent stroke, has been stymied by poor adherence rates among patients.3 For example, by 1 year after having a myocardial infarction, nearly half of patients prescribed cholesterol-lowering medications have stopped taking them.4 Reducing morbidity and mortality may depend as much on motivating changes in behavior as on developing new treatments.5 Economics, as the social science discipline traditionally most closely tied to public policy, could be a key discipline in addressing behaviors that are potentially harmful to health. Yet conventional economics does not provide satisfactory policy solutions to problems caused by self-harmful behavior. Economics is premised on a rational choice perspective which, by assuming that individuals make optimal decisions given their information, resources, and preferences, in effect assumes away these problems. The main policy tools suggested by conventional economics, providing information or changing prices, only partially address these problems because they fail to exploit what is known about human motivation and behavior change. Responding in part to these limitations of conventional economics, the new field of behavioral economics has, over the last few decades, begun to import concepts from psychology.6 Behavioral economists have identified a number of decision biases and pitfalls in



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