Childhood obesity is associated with high financial cost and increased morbidity and mortality in adulthood. Health care spending due to obesity is estimated to be as high as $210 billion annually, or 21 % of total national spending. A childhood obesity surveillance system will discern disparities, detect aberrant signals, design targeted interventions, and track change over time. In addition to national public health surveillance systems, local data are increasingly necessary to reflect regional trends. Current systems in Wisconsin are subject to (1) bias estimation of obesity rates, (2) limited data in school-aged children ages 5-10 years, (3) insufficient power to estimate disparities in local subgroups, and (4) limited ability to track local changes over time. Therefore, the impact of investments and collective efforts in Wisconsin childhood obesity prevention has been difficult to measure. This, in turn, demands approaches that overcome and transcend the posited limitations. Since large electronic health records (EHRs) systems have been developed to routinely collect information over time, there has been a paradigm shift and accelerated improvements in the use of EHR data for the purpose of population health promotion and tracking of progress. The University of Wisconsin Population Health Information Exchange (PHINEX) database contains de- identified EHR data from a multicenter healthcare system located primarily in south central Wisconsin. In partnership with The Wisconsin Obesity Prevention Initiative, we aim to create an EHR model for a childhood obesity prevention surveillance system. We propose to develop innovative statistical machine learning methods using the platform of PHINEX for childhood obesity prevention purposes. This enhance and extend surveillance activities of childhood obesity conditions, and increase the effectiveness of evidence-based interventions aimed at changing policies, systems, and environments associated with childhood obesity. We plan to address the following aims: (1) robust estimation of the spatiotemporal prevalence at area level, (2) hot spot detection on areas with aberrant obesity incidence, and (3) prediction for childhood weight gain phenotypes. The developed and implemented surveillance system can benefit future planning, legislation and implementation in other states and/or surveillance of other acute and chronic health conditions.
Childhood obesity has emerged as a leading health concern in the 21st century. Current data sources from which childhood obesity estimates are derived in most systems are primarily cross-sectional, are from small sample sizes, and/or have limited generalizability. Large electronic health records (EHRs) systems have been developed to routinely collect information over time, which provide unprecedented opportunities for individual and population health surveillance. We propose to develop innovative statistical machine learning methods to increase the use of large EHR data for childhood obesity prevention purposes, extending the scope of public health surveillance.