title: Redrawing the US Obesity Landscape: Bias-Corrected Estimates of State-Specific Adult Obesity Prevalence.
abstract: BACKGROUND
  State-level estimates from the Centers for Disease Control and Prevention (CDC) underestimate the obesity epidemic because they use self-reported height and weight. We describe a novel bias-correction method and produce corrected state-level estimates of obesity and severe obesity.
  
  
  METHODS
  Using non-parametric statistical matching, we adjusted self-reported data from the Behavioral Risk Factor Surveillance System (BRFSS) 2013 (n = 386,795) using measured data from the National Health and Nutrition Examination Survey (NHANES) (n = 16,924). We validated our national estimates against NHANES and estimated bias-corrected state-specific prevalence of obesity (BMI≥30) and severe obesity (BMI≥35). We compared these results with previous adjustment methods.
  
  
  RESULTS
  Compared to NHANES, self-reported BRFSS data underestimated national prevalence of obesity by 16% (28.67% vs 34.01%), and severe obesity by 23% (11.03% vs 14.26%). Our method was not significantly different from NHANES for obesity or severe obesity, while previous methods underestimated both. Only four states had a corrected obesity prevalence below 30%, with four exceeding 40%-in contrast, most states were below 30% in CDC maps.
  
  
  CONCLUSIONS
  Twelve million adults with obesity (including 6.7 million with severe obesity) were misclassified by CDC state-level estimates. Previous bias-correction methods also resulted in underestimates. Accurate state-level estimates are necessary to plan for resources to address the obesity epidemic.
authors.affiliation: Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.
authors.forename: Zachary J
authors.initials: ZJ
authors.lastname: Ward
authors.affiliation: Department of Prevention and Community Health, Milken Institute School of Public Health, the George Washington University, Washington, District of Columbia, United States of America.
authors.forename: Michael W
authors.initials: MW
authors.lastname: Long
authors.affiliation: Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.
authors.forename: Stephen C
authors.initials: SC
authors.lastname: Resch
authors.affiliation: Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.
authors.forename: Steven L
authors.initials: SL
authors.lastname: Gortmaker
authors.affiliation: Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.
authors.forename: Angie L
authors.initials: AL
authors.lastname: Cradock
authors.affiliation: Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.
authors.forename: Catherine
authors.initials: C
authors.lastname: Giles
authors.affiliation: Department of Health Policy and Management, Mailman School of Public Health, Columbia University, New York, New York, United States of America.
authors.forename: Amber
authors.initials: A
authors.lastname: Hsiao
authors.affiliation: Department of Health Policy and Management, Mailman School of Public Health, Columbia University, New York, New York, United States of America.
authors.forename: Y Claire
authors.initials: YC
authors.lastname: Wang
mesh_terms: D000328:Adult; D000367:Age Factors; D040343:Behavioral Risk Factor Surveillance System; D015992:Body Mass Index; D066264:Datasets as Topic; D005260:Female; D006801:Humans; D008297:Male; D009765:Obesity / Q000453:epidemiology*; D009767:Obesity, Morbid / Q000453:epidemiology; D015995:Prevalence; D062486:Public Health Surveillance; D057566:Self Report; D014481:United States / Q000453:epidemiology