RESUMO
CONTEXT: Poor anthropometric data quality affect the prevalence of malnutrition and could harm public policy planning. OBJECTIVE: This systematic review and meta-analysis was designed to identify different methods to evaluate and clean anthropometric data, and to calculate the frequency of implausible values for weight and height obtained from these methodologies. DATA SOURCES: Studies about anthropometric data quality and/or anthropometric data cleaning were searched for in the MEDLINE, LILACS, SciELO, Embase, Scopus, Web of Science, and Google Scholar databases in October 2020 and updated in January 2023. In addition, references of included studies were searched for the identification of potentially eligible studies. DATA EXTRACTION: Paired researchers selected studies, extracted data, and critically appraised the selected publications. DATA ANALYSIS: Meta-analysis of the frequency of implausible values and 95% confidence interval (CI) was estimated. Heterogeneity (I2) and publication bias were examined by meta-regression and funnel plot, respectively. RESULTS: In the qualitative synthesis, 123 reports from 104 studies were included, and in the quantitative synthesis, 23 studies of weight and 14 studies of height were included. The study reports were published between 1980 and 2022. The frequency of implausible values for weight was 0.55% (95%CI, 0.29-0.91) and for height was 1.20% (95%CI, 0.44-2.33). Heterogeneity was not affected by the methodological quality score of the studies and publication bias was discarded. CONCLUSIONS: Height had twice the frequency of implausible values compared with weight. Using a set of indicators of quality to evaluate anthropometric data is better than using indicators singly. SYSTEMATIC REVIEW REGISTRATION: PROSPERO registration no. CRD42020208977.