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de Mello E Silva JF, de Jesus Silva N, Carrilho TRB, Jesus Pinto ED, Rocha AS, Pedroso J, Silva SA, Spaniol AM, da Costa Santin de Andrade R, Bortolini GA, Paixão E, Kac G, de Cássia Ribeiro-Silva R, Barreto ML. Identifying biologically implausible values in big longitudinal data: an example applied to child growth data from the Brazilian food and nutrition surveillance system. BMC Med Res Methodol 2024; 24:38. [PMID: 38360575 PMCID: PMC10868032 DOI: 10.1186/s12874-024-02161-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 01/24/2024] [Indexed: 02/17/2024] Open
Abstract
BACKGROUND Several strategies for identifying biologically implausible values in longitudinal anthropometric data have recently been proposed, but the suitability of these strategies for large population datasets needs to be better understood. This study evaluated the impact of removing population outliers and the additional value of identifying and removing longitudinal outliers on the trajectories of length/height and weight and on the prevalence of child growth indicators in a large longitudinal dataset of child growth data. METHODS Length/height and weight measurements of children aged 0 to 59 months from the Brazilian Food and Nutrition Surveillance System were analyzed. Population outliers were identified using z-scores from the World Health Organization (WHO) growth charts. After identifying and removing population outliers, residuals from linear mixed-effects models were used to flag longitudinal outliers. The following cutoffs for residuals were tested to flag those: -3/+3, -4/+4, -5/+5, -6/+6. The selected child growth indicators included length/height-for-age z-scores and weight-for-age z-scores, classified according to the WHO charts. RESULTS The dataset included 50,154,738 records from 10,775,496 children. Boys and girls had 5.74% and 5.31% of length/height and 5.19% and 4.74% of weight values flagged as population outliers, respectively. After removing those, the percentage of longitudinal outliers varied from 0.02% (<-6/>+6) to 1.47% (<-3/>+3) for length/height and from 0.07 to 1.44% for weight in boys. In girls, the percentage of longitudinal outliers varied from 0.01 to 1.50% for length/height and from 0.08 to 1.45% for weight. The initial removal of population outliers played the most substantial role in the growth trajectories as it was the first step in the cleaning process, while the additional removal of longitudinal outliers had lower influence on those, regardless of the cutoff adopted. The prevalence of the selected indicators were also affected by both population and longitudinal (to a lesser extent) outliers. CONCLUSIONS Although both population and longitudinal outliers can detect biologically implausible values in child growth data, removing population outliers seemed more relevant in this large administrative dataset, especially in calculating summary statistics. However, both types of outliers need to be identified and removed for the proper evaluation of trajectories.
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Affiliation(s)
| | - Natanael de Jesus Silva
- Centre for Data and Knowledge Integration for Health, Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, BA, Brazil
- ISGlobal, Hospital Clínic. Universitat de Barcelona, Barcelona, Spain
| | - Thaís Rangel Bousquet Carrilho
- Nutritional Epidemiology Observatory, Josué de Castro Nutrition Institute, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil
- Department of Obstetrics and Gynaecology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Elizabete de Jesus Pinto
- Centre for Data and Knowledge Integration for Health, Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, BA, Brazil
- Federal University of Recôncavo da Bahia, Santo Antônio de Jesus, BA, Brazil
| | - Aline Santos Rocha
- Centre for Data and Knowledge Integration for Health, Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, BA, Brazil
- Food and Nutrition Coordinating Unit, Ministry of Health, Brasília, DF, Brazil
| | - Jéssica Pedroso
- Food and Nutrition Coordinating Unit, Ministry of Health, Brasília, DF, Brazil
| | - Sara Araújo Silva
- Food and Nutrition Coordinating Unit, Ministry of Health, Brasília, DF, Brazil
| | - Ana Maria Spaniol
- Food and Nutrition Coordinating Unit, Ministry of Health, Brasília, DF, Brazil
| | | | | | - Enny Paixão
- London School of Hygiene & Tropical Medicine, London, UK
| | - Gilberto Kac
- Nutritional Epidemiology Observatory, Josué de Castro Nutrition Institute, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil
| | - Rita de Cássia Ribeiro-Silva
- Centre for Data and Knowledge Integration for Health, Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, BA, Brazil.
- School of Nutrition, Federal University of Bahia, Av. Araújo Pinho, nº 32, Canela, Salvador, Bahia, CEP: 40.110-150, BA, Brazil.
| | - Maurício L Barreto
- Centre for Data and Knowledge Integration for Health, Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, BA, Brazil
- Institute of Collective Health, Federal University of Bahia, Salvador, BA, Brazil
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