Screening & diagnosing errors in longitudinal measures of body size

This paper presents a novel multi-step automated algorithm to screen for errors in longitudinal height and weight data and describes the frequency and characteristics of errors in three datasets. It also offers a taxonomy of published cleaning routines from a scoping review. Illustrative data are from three Norwegian retrospective cohorts containing 87,792 assessments (birth to 14y) from 8,428 children. Each has different data pipelines, quality control and data structure. The algorithm contains 43 steps split into 3 sections; (a) dates, (b) Identifiable data entry errors, (c) biologically impossible/ implausible change, and uses logic checks, and cross-sectional and longitudinal routines. The WHO cross-sectional approach was also applied as a comparison. Published cleaning routines were taxonomized by their design, the marker used to screen errors, the reference threshold and how threshold was selected. Fully automated error detection was not possible without false positives or reduced sensitivity. Error frequencies in the cohorts were 0.4%, 2.1% and 2.4% of all assessments, and the percentage of children with [≥]1 error was 4.1%, 13.4% and 15.3%. In two of the datasets, >2/3s of errors could be classified as inliers (within {+/-}3SD scores). Children with errors had a similar distribution of HT and WT to those without error. The WHO cross-sectional approach lacked sensitivity (range 0-55%), flagged many false positives (range: 7-100%) and biased estimates of overweight and thinness. Elements of this algorithm may have utility for built-in data entry rules, data harmonisation and sensitivity analyses. The reported error frequencies and structure may also help design more realistic simulation studies to test routines. Multi-step distribution-wide algorithmic approaches are recommended to systematically screen and document the wide range of ways in which errors can occur and to maximise sensitivity for detecting errors, naive cross-sectional trimming as a stand-alone method may do more harm than good.