Janne Boone-Heinonen1, Carrie J Tillotson2, Jean P O'Malley3, Miguel Marino4, Sarah B Andrea5, Andrew Brickman6, Jennifer DeVoe3, Jon Puro2. 1. OHSU-PSU School of Public Health, Portland, OR. Electronic address: boonej@ohsu.edu. 2. OCHIN, Portland, OR. 3. OHSU Department of Family Medicine, Portland, OR. 4. OHSU-PSU School of Public Health, Portland, OR; OHSU Department of Family Medicine, Portland, OR. 5. OHSU-PSU School of Public Health, Portland, OR. 6. Health Choice Network, Miami, FL.
Abstract
PURPOSE: Implausible anthropometric measures are typically identified using population outlier definitions, conflating implausible and extreme measures. We determined the impact of a longitudinal outlier approach on prevalence of body mass index (BMI) categories and mean change in anthropometric measures in pediatric electronic health record data. METHODS: We examined 996,131 observations from 147,375 children (10-18 years) in the ADVANCE Clinical Data Research Network, a national network of community health centers. Sex-stratified, mixed effects, linear spline regression modeled weight, height, and BMI as a function of age. Longitudinal outliers were defined as observations with studentized residual greater than |6|; population outliers were defined by Centers for Disease Control-defined z-score thresholds. RESULTS: At least 99.7% of anthropometric measures were not extreme by longitudinal or population definitions (agreement ≥ 0.995). BMI category prevalence after excluding longitudinal or population outliers differed by less than 0.1%. Among children greater than 85th percentile at baseline, annual mean changes in anthropometric measures were larger in data that excluded longitudinal (girls: 1.24 inches, 12.39 pounds, 1.53 kg/m2; boys: 2.34, 14.08, 1.07) versus population outliers (girls: 0.61 inches, 8.22 pounds, 0.75 kg/m2; boys: 1.53, 11.61, 0.48). CONCLUSIONS: Longitudinal outlier methods may reduce underestimation of anthropometric change in children with elevated baseline values.
PURPOSE: Implausible anthropometric measures are typically identified using population outlier definitions, conflating implausible and extreme measures. We determined the impact of a longitudinal outlier approach on prevalence of body mass index (BMI) categories and mean change in anthropometric measures in pediatric electronic health record data. METHODS: We examined 996,131 observations from 147,375 children (10-18 years) in the ADVANCE Clinical Data Research Network, a national network of community health centers. Sex-stratified, mixed effects, linear spline regression modeled weight, height, and BMI as a function of age. Longitudinal outliers were defined as observations with studentized residual greater than |6|; population outliers were defined by Centers for Disease Control-defined z-score thresholds. RESULTS: At least 99.7% of anthropometric measures were not extreme by longitudinal or population definitions (agreement ≥ 0.995). BMI category prevalence after excluding longitudinal or population outliers differed by less than 0.1%. Among children greater than 85th percentile at baseline, annual mean changes in anthropometric measures were larger in data that excluded longitudinal (girls: 1.24 inches, 12.39 pounds, 1.53 kg/m2; boys: 2.34, 14.08, 1.07) versus population outliers (girls: 0.61 inches, 8.22 pounds, 0.75 kg/m2; boys: 1.53, 11.61, 0.48). CONCLUSIONS: Longitudinal outlier methods may reduce underestimation of anthropometric change in children with elevated baseline values.
Authors: Callie L Brown; Asheley C Skinner; Michael J Steiner; Tracy Truong; Cynthia L Green; Charles T Wood Journal: Acad Pediatr Date: 2022-03-24 Impact factor: 2.993
Authors: Hamed Javidi; Arshiya Mariam; Gholamreza Khademi; Emily C Zabor; Ran Zhao; Tomas Radivoyevitch; Daniel M Rotroff Journal: NPJ Digit Med Date: 2022-07-27
Authors: Charlotte S C Woolley; Ian G Handel; B Mark Bronsvoort; Jeffrey J Schoenebeck; Dylan N Clements Journal: PLoS One Date: 2020-01-24 Impact factor: 3.240