Evidence map›Paper›PMID 35650253›Full record

ArticleInternational journal of obesity (2005)2022

Learning from missing data: examining nonreporting patterns of height, weight, and BMI among Canadian youth.

Amanda Doggett, Ashok Chaurasia, Jean-Philippe Chaput, Scott T Leatherdale

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Article in International journal of obesity (2005), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Independent and joint associations of cardiorespiratory fitness and weight status with health-related quality of life among Brazilian adolescents.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2023
    Article
  5. Using classification and regression trees to model missingness in youth BMI, height and body mass data.Health promotion and chronic disease prevention in Canada : research, policy and practice · 2023
    Article
4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Amanda DoggettSchool of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada. adoggett@uwaterloo.ca.ORCID http://orcid.org/0000-0001-8538-432X
Ashok ChaurasiaSchool of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada.
Jean-Philippe ChaputDepartment of Pediatrics, University of Ottawa, Ottawa, ON, Canada.ORCID http://orcid.org/0000-0002-5607-5736
Scott T LeatherdaleSchool of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundYouth body mass index (BMI), derived from self-reported height and weight, is commonly prone to nonreporting. A considerable proportion of overweight and obesity (OWOB) research relies on such self-report data, however little literature to date has examined this nonreporting and the potential impact on research conclusions. The objective of this study was to examine the characteristics and predictors of missing data in youth BMI, height, and weight.

methodsUsing a sample of 74,501 Canadian secondary school students who participated in the COMPASS study in 2018/19, sex-stratified generalized linear mixed models were run to examine predictors of missing data while controlling for school-level clustering.

resultsIn this sample, 31% of BMI data were missing. A variety of diet, exercise, mental health, and substance use variables were associated with BMI, height, and weight missingness. Perceptions of being overweight (females: 95% CI (1.42,1.62), males: 95% CI (1.71,2.00)) as well as intentions to lose weight (females: 95% CI (1.17,1.33), males: 95% CI (1.13,1.32)) were positively associated with BMI missingness.

conclusionsFindings from this study suggest that nonreporting in youth height and weight is likely somewhat related to the values themselves, and hint that social desirability may play a substantial role in nonreporting. The predictors of missingness identified in this study can be used to inform future studies on the potential bias stemming from missing data and identify auxiliary variables that may be used for multiple imputation approaches.

Indexed as

Body HeightOverweightAdolescentBody Mass IndexBody WeightCanadaFemaleHumansMale

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.