Screening & diagnosing errors in longitudinal measures of body size

Abstract

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.

A nationwide school fruit and vegetable policy and childhood and adolescent overweight: A quasi-natural experimental study

“…There is fluctuation around these target ages, and some appointments are missed (see S2 Fig ). All height and weight values were cleaned using a longitudinal algorithm that checked for logical errors and internally inconsistent values [ 23 ]. Full details of these quality assurance processes are described elsewhere [ 21 , 22 ].…”

“…inconsistent values [23]. Full details of these quality assurance processes are described elsewhere [21,22].…”

“…The use of AU : Pleasecheckthattheeditstothesentence}Theuseof :::}capt longitudinal data in the current study allowed the assessment of pre-intervention weight trajectories and the construction of a more plausible counterfactual to estimate the policy effect compared to difference-in-difference or cross-sectional designs used in similar previous evaluations [8,10]. The high-quality objective data, which were standardized and cleaned using a systematic approach [23], and the use of models that made use of all available outcome measures and handled the relatively small amount of missingness in a principled way, are also strengths. Further, we were also able to look at WC as an outcome, acknowledging that BMI has limitations as a marker of excess adiposity among children [40].…”

Overweight, obesity, and thinness among a nationally representative sample of Norwegian adolescents and changes from childhood: Associations with sex, region, and population density

“…Our results are from a nationally representative study of objectively measured height and weight. Data were checked and cleaned using a rigorous and systematic screening algorithm [ 16 ]. For analyzing age-related trends, the longitudinal design is a particular strength, and the use of multilevel models make fewer assumptions about the missing data at each age.…”

“…The previous measurement recorded by the School Health Service was also conducted in accordance with the same national guidelines. All measurements were entered by nurses onto an electronic questionnaire and cleaned using a longitudinal algorithm that checked for logical errors and internally inconsistent values (see details in S1 Text ) [ 16 ].…”