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Filling in the Gaps: State-specific childhood obesity prevalence corrected for self-report bias
Methods: We developed a non-parametric statistical matching approach to combine data from the 2010 U.S. Census, 2010 American Community Survey (ACS) (5-year), 2003-2004 and 2007-2008 National Survey of Children’s Health (NSCH) (n=133,214), and 2005-2010 National Health and Nutrition Examination Surveys (NHANES) (n=9,422 ages 2-17). Measured height and weight data from statistically matched NHANES individuals were assigned to a random sample of the Census population by state, age, sex, race/ethnicity and household income, accounting for state-level variation in parent-reported height and weight from NSCH. The resulting obesity prevalence estimates were validated against surveillance data from five states (AR, FL, MA, PA, TN) conducting a census of all children across a range of grades.
Results: Estimated state-specific childhood obesity prevalence ranged from 11.7% to 21.5%. For the five validation states, our model-derived obesity prevalence estimates aligned closely with measured data, with a mean difference of 0.68 percentage points (range: 0.31-1.38) and a high correlation coefficient (r=0.90, p=0.037).
Conclusion: Non-parametric statistical matching provides a novel method for estimating state-specific childhood obesity prevalence corrected for reporting bias. In the absence of statewide surveillance systems, this approach provides data needed to target limited obesity prevention resources.
Learning Areas:
Biostatistics, economicsEpidemiology
Public health or related research
Learning Objectives:
Demonstrate importance of non-parametric statistical matching methods for synthesizing new datasets from existing publicly available administrative and public health sources.
Evaluate validity of estimated childhood obesity prevalence using non-parametric statistical matching against data from published statewide BMI surveillance systems.
Keyword(s): Surveillance, Obesity
Qualified on the content I am responsible for because: I have earned a masters and doctoral degree in social and behavioral sciences and obesity epidemiology and prevention. I have presented at APHA and published on a range of obesity epidemiology and quantitative obesity policy evaluation topics. For the past four years, I have been working with a transdisciplinary team to build a simulation model of the U.S. population and ongoing obesity epidemic to evaluate costs and benefits of obesity prevention policies.
Any relevant financial relationships? No
I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.