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Updated: Dec 17, 2025
Assessment of Child Anthropometry in a Large Epidemiologic Study
Published on: February 2, 2017
Automated data cleaning of paediatric anthropometric data from longitudinal electronic health records: protocol and
Hang T T Phan1,2, Florina Borca3,4, David Cable4
1NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, UK. hang.phan@soton.ac.uk.
Insights
A new protocol effectively cleans paediatric height and weight data, identifying errors missed by standard guidelines. This improves the quality of big data in healthcare for better clinical management and research.
Area of Science:
- Healthcare Informatics
- Biostatistics
- Paediatric Medicine
Background:
- Big data in healthcare requires quality control for clinical management and research.
- Height and weight are common anthropometric measurements, increasingly recorded electronically.
- Existing WHO guidelines are insufficient for cleaning longitudinal anthropometric data.
Purpose of the Study:
- To develop and optimize a protocol for cleaning paediatric height and weight data.
- To identify and address errors in longitudinal anthropometric measurements.
- To standardize data quality for clinical and research applications.
Main Methods:
- Developed a protocol using robust linear regression for outlier detection.
- Utilized a manually curated dataset of 6,279 patients' longitudinal measurements for optimization.
- Applied the protocol to a large cohort of 200,000 patient records from 60,000 paediatric patients.
Main Results:
- WHO guidelines detected <1% of biologically implausible data.
- The developed protocol identified additional errors: 3% for height and 0.2% for weight.
- Height errors were often due to implausible decreases; lowest errors occurred with routine staff recording.
Conclusions:
- The protocol automates the identification of poor-quality height and weight data from large longitudinal datasets.
- This standardization enhances data quality for both clinical decision-making and healthcare research.
- Optimized data cleaning is crucial for reliable big data analytics in paediatrics.
Abstract:
'Big data' in healthcare encompass measurements collated from multiple sources with various degrees of data quality. These data require quality control assessment to optimise quality for clinical management and for robust large-scale data analysis in healthcare research. Height and weight data represent one of the most abundantly recorded health statistics. The shift to electronic recording of anthropometric measurements in electronic healthcare records, has rapidly inflated the number of measurements. WHO guidelines inform removal of population-based extreme outliers but an absence of tools limits cleaning of longitudinal anthropometric measurements. We developed and optimised a protocol for cleaning paediatric height and weight data that incorporates outlier detection using robust linear regression methodology using a manually curated set of 6,279 patients' longitudinal measurements. The protocol was then applied to a cohort of 200,000 patient records collected from 60,000 paediatric patients attending a regional teaching hospital in South England. WHO guidelines detected biologically implausible data in <1% of records. Additional error rates of 3% and 0.2% for height and weight respectively were detected using the protocol. Inflated error rates for height measurements were largely due to small but physiologically implausible decreases in height. Lowest error rates were observed when data was measured and digitally recorded by staff routinely required to do so. The protocol successfully automates the parsing of implausible and poor quality height and weight data from a voluminous longitudinal dataset and standardises the quality assessment of data for clinical and research applications.
