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Anthropometric data quality assessment in multisurvey studies of child growth
Authors: Nandita Perumal, Sorrel Namaste, Huma Qamar, Ashley Aimone, Diego G. Bassani, and Daniel E. Roth
Source: American Journal of Clinical Nutrition, 112(Suppl 2):806S-815S; DOI: 10.1093/ajcn/nqaa162
Topic(s): Biomarkers
Child health
Data quality
Country: More than one region
  Multiple Regions
Published: SEP 2020
Abstract: Background: Population-based surveys collect crucial data on anthropometric measures to track trends in stunting [height-for-age z score (HAZ) < -2SD] and wasting [weight-for-height z score (WHZ) < -2SD] prevalence among young children globally. However, the quality of the anthropometric data varies between surveys, which may affect population-based estimates of malnutrition. Objectives: We aimed to develop composite indices of anthropometric data quality for use in multisurvey analysis of child health and nutritional status. Methods: We used anthropometric data for children 0-59 mo of age from all publicly available Demographic and Health Surveys (DHS) from 2000 onwards. We derived 6 indicators of anthropometric data quality at the survey level, including 1) date of birth completeness, 2) anthropometric measure completeness, 3) digit preference for height and age, 4) difference in mean HAZ by month of birth, 5) proportion of biologically implausible values, and 6) dispersion of HAZ and WHZ distribution. Principal component factor analysis was used to generate a composite index of anthropometric data quality for HAZ and WHZ separately. Surveys were ranked from the highest (best) to the lowest (worst) index values in anthropometric quality across countries and over time. Results: Of the 145 DHS included, the majority (83 of 145; 57%) were conducted in Sub-Saharan Africa. Surveys were ranked from highest to lowest anthropometric data quality relative to other surveys using the composite index for HAZ. Although slightly higher values in recent DHS suggest potential improvements in anthropometric data quality over time, there continues to be substantial heterogeneity in the quality of anthropometric data across surveys. Results were similar for the WHZ data quality index. Conclusions: A composite index of anthropometric data quality using a parsimonious set of individual indicators can effectively discriminate among surveys with excellent and poor data quality. Such indices can be used to account for variations in anthropometric data quality in multisurvey epidemiologic analyses of child health.