Developing a Statewide Childhood Body Mass Index Surveillance Program

Abstract

Background Several states have implemented childhood obesity surveillance programs supported by legislation. Representatives from Idaho wished to develop a model for childhood obesity surveillance without the support of state legislation, and subsequently report predictors of overweight and obesity in the state. Methods A coalition comprised of the Idaho State Department of Education and 4 universities identified a randomized cluster sample of schools. After obtaining school administrator consent, measurement teams traveled to each school to measure height and weight of students. Sex and race/ethnicity data were also collected. Results The collaboration between the universities resulted in a sample of 6735 students from 48 schools and 36 communities. Overall, 29.2% of the youth in the sample were classified as overweight or obese, ranging from 24.0% for grade 1 to 33.8% for grade 5. The prevalence of overweight and obesity across schools was highly variable (31.2 ± 7.58%). Hierarchical logistic regression indicated that sex, age, race/ethnicity, socioeconomic status, and region were all significant predictors of overweight and obesity, whereas school was not. Conclusions This coalition enabled the state of Idaho to successfully estimate the prevalence of overweight and obesity on a representative sample of children from all regions of the state, and subsequently identify populations at greatest risk

Perceptions of a Child Who was Overweight or of Average Weight by African American Children

“…This study was cross-sectional in nature, and longitudinal studies to examine change in perceptions over time are needed. Data were collected in a Midwestern state in the United States, and children's frame of reference for judging weight status could have been influenced by their views of typical weights of children in the region (Paul et al 2014). This may be a threat to generalizability of child perception findings to other non-comparable populations.…”

A School‐Level Proxy Measure for Individual‐Level Poverty Using School‐Level Eligibility for Free and Reduced‐Price Meals

“…An income eligibility form must be completed for a student to be eligible for Free Level2 and reduced-price meals. Meal status is often used as a measure of individuallevel poverty because of its direct relationship with household income; 10,13,14,[31][32][33] in our analysis, we refer to meal status as ''Poverty MEAL '' when used as a 4-level measure of individual poverty.…”

“…[1][2][3][4][5][6] Previous research has shown that, in addition to behaviors, socioeconomic factors play an important role in health outcomes among adolescents. 4,[7][8][9][10][11][12][13][14] However, the YRBS does not directly measure poverty. Typical measures of individual socioeconomic status (SES) used for adults such as income, education-level, and occupation cannot be used for children because they usually are still in school, live with a parent or guardian, and often do not know the required information to determine household income.…”

“…Even these nonspecific approaches have shown that having some measure of poverty is important for understanding health behaviors and outcomes, emphasizing the need for a more specific measure of poverty that can be applied to local and state YRBS data. 4,13,14,18 Using the readily available school-level percent of students eligible for the National School Lunch Program (NSLP), we created and validated a schoollevel proxy measure of individual-level poverty for use in conjunction with YRBS data. 25,26 School-level NSLP eligibility has been shown to be highly associated with various poverty measures involving the household incomes of residents within a school's geographic neighborhood, indicating it is as good a measure of adolescent SES as widely used school area-based measures.…”

“…14,27 This measure could also be used by other school-based studies with student-level data that lack household income or other individual-level SES information but the students' school are known. 13 To validate this approach by means of comparison, we also evaluated 2 other approaches to create school-level proxy measures: (1) student home neighborhood-level poverty (HNP); and (2) school neighborhood-level poverty (SNP).…”