Outlier Detection in Growth Data: Beyond Biologically Implausible Values

Abstract

Abstract Objectives Eliminating anthropometry measurement error and employing outlier and biological implausible values (BIV) detection methods adapted to longitudinal measurements is important for the study of growth. This work aimed to review and assess the accuracy of the available BIV and outlier detection methods and propose a growth trajectory outlier detection method. Methods We included 2354 infants from the Applied Research Group for Kids (TARGet Kids! ) cohort-based in Toronto (ON, Canada) that recruits healthy children from birth to 5 years of age. We considered infants with at least 8 length and weight measurements available between the 1st and the 24th month of age. Weight-for-length z-scores (wflz) were calculated using the WHO growth standards. Outlier measurements were randomly introduced in 5% of the wflz measurements using a normal distribution (μ = 0, σ = 1). We employed 4 outlier detection methods; an empirical detection method for BIV using the cut-offs derived from the WHO Child Growth Standards, a clustering method, a method based on cluster prototypes for individual outlier measurements and a method based on cluster prototypes for entire growth trajectories. Each method was applied individually and evaluated using the sensitivity and specificity indexes based on the manually introduced outliers. We also calculated the Kappa statistic to evaluate the agreement of each method against the manual outliers. Results After excluding premature (<37 weeks), low birth weight (<1500 g) neonates and children with missing length and weight measurements, we analyzed 393 children with a total of 3144 measurements. Sensitivity and specificity for the four methods ranged between 4.4%–55.0% and 83.7% −99.7%, respectively, with kappa being non-significant (P > 0.05) only for the empirical. The clustering detection method reported a higher finding rate, while the empirical method found most of the BIV, but few of the rest of the outliers. Conclusions BIV account for a small portion of the possible outliers in growth datasets. We show that additional statistical or model-based methods are required for a more comprehensive outlier detection process, which has implications for growth analysis and nutritional assessment. Funding Sources Joannah and Brian Lawson Center for Child Nutrition, Connaught Fund, Onassis Foundation.