title: Complexity in mathematical models of public health policies: a guide for consumers of models.
abstract: Sanjay Basu and colleagues explain how models are increasingly used to inform public health policy yet readers may struggle to evaluate the quality of models. All models require simplifying assumptions, and there are tradeoffs between creating models that are more "realistic" versus those that are grounded in more solid data. Indeed, complex models are not necessarily more accurate or reliable simply because they can more easily fit real-world data than simpler models can. Please see later in the article for the Editors' Summary.
authors.affiliation: Prevention Research Center, Stanford University, Stanford, California, United States of America ; Centers for Health Policy, Primary Care and Outcomes Research, Stanford University, Stanford, California, United States of America ; Center for Poverty and Inequality, Stanford University, Stanford, California, United States of America ; Department of Public Health and Policy, London School of Hygiene & Tropical Medicine, London, United Kingdom.
authors.forename: Sanjay
authors.initials: S
authors.lastname: Basu
authors.forename: Jason
authors.initials: J
authors.lastname: Andrews
mesh_terms: D006291:Health Policy; D006801:Humans; D008962:Models, Theoretical; D011634:Public Health / Q000331:legislation & jurisprudence