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Identification of Obesity Intervention Candidate Schools Using Diverse Datasets and Discriminant Function Analysis in the Absence of Student Anthropometric Data

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By: Wasantha P. Jayawardene, Stephanie L. Dickinson & Published: 2020 | Product: SAGE Research Methods Cases: Medicine and Health
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Abstract

Schools are important settings for multicomponent preventive programs that improve weight status of children and adolescents by addressing knowledge, attitudes, and behaviors related to physical activity, nutrition, and mental health as well as creating healthy environments such as better playgrounds and cafeteria policies. To prioritize dissemination of resources and implementation of evidence-based prevention policies and programs, we need to identify schools with high obesity levels. However, a vast majority of schools do not conduct student body mass index screening, while the risks versus benefits in body mass index screening is an ongoing debate. Therefore, we used statistical modeling with discriminant function analysis to develop three simple equations that can be used to estimate the obesity burden in schools, in the absence of student body mass index screenings. To develop these equations with discriminant function analysis, we utilized student body mass index data from schools in Pennsylvania that did have routine body mass index screenings, as well as publicly available school-level and community-level aggregate variables, to identify the combination of variables which best discriminated between the schools with high, average, or low obesity burden. These resultant equations can then be used with publicly available data to estimate obesity burden in other schools without student body mass index data available. School- and county-level variables for poverty, number of students, percent of minority students, adult education level, and adult obesity all contributed to the model’s prediction. In our study, resultant models almost doubled the percentage of correctly classified schools (67.86%) from publicly available data compared to chance alone (34.23%) for obesity classification (low/average/high).

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Discriminant analysis

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