The evolution of anthropometric data quality methods and outcomes from 1993 to 2021 in the Comprehensive National Nutrition Survey and five rounds of the National Family Health Survey

Le résumé fourni par la source

Abstract Introduction High-quality anthropometric data is essential to creating effective nutrition policy, but there’s limited evidence on the quality of data from population-based household surveys in India, particularly at the sub-national level. Methods We compared survey procedures implemented by the National Family Health Survey (NFHS), rounds 1, 2, 3, 4, and 5, and the Comprehensive National Nutrition Survey (CNNS). We conducted a disaggregated analysis at the state level to observe quality performance for height-for-age (HAZ), weight-for-age (WAZ), and weight-for-height (WHZ) z-scores for children under five years. Nine data quality parameters, guided by WHO-UNICEF guidelines, were assessed: completeness of birth date and anthropometric measurement, sex ratio, age-heaping, position mismatch, digit preferences, implausible values, z-score distribution and growth faltering graphs. Results Survey methods improved over time, including enhanced training, increased use of computer-assisted devices, and real-time supervision. Consequently, from 1992 to 2020, there were improvements in anthropometric data quality, including more balanced age and sex distribution, increased completeness of birth date (from 53% to 95%) and anthropometric measures (from 60% to 91%), and decreased rounding of measurements. Steady albeit small improvements were observed in the standard deviation of HAZ (from 1.9 to 1.8) and WHZ (from 1.5 to 1.3). Implausible HAZ values were reduced from 5% to 2%. Data quality varied across states but generally showed a positive trend. Conclusions Anthropometric data quality in Indian population-level surveys shows satisfactory progress, reflecting global advancement. However, gaps persist, particularly in errors in birth date and height and weight measurement. Continued efforts are needed to further enhance data quality through improved survey planning, training, data collection, real-time supervision, analysis, and reporting, together with the strengthening of administrative data and civil records. High implausible values, especially in surveys with otherwise strong data quality, likely reflect, at least in part, underlying population inequities.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.