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Sampling plan of the Brazilian National Survey on Child Nutrition (ENANI-2019): a population-based household survey

Plano amostral do Estudo Nacional de Alimentação e Nutrição Infantil (ENANI-2019): inquérito domiciliar de base populacional

Plan de muestreo del Estudio Nacional de Alimentación y Nutrición Infantil (ENANI-2019): encuesta domiciliaria de base poblacional

The article describes methodological aspects in defining the study population, sampling plan, and sample weigthing and calibration of effective sample of the Brazilian National Survey on Child Nutrition (ENANI-2019). This population-based household survey assessed breastfeeding and dietary intake, anthropometric assessment of nutritional status, and micronutrient deficiencies by blood biomarkers in children under five years of age. The data were obtained with a probability sample, with stratification by the five geographic regions in the country and clustering by census enumeration areas (CEAs). The sample was calculated at 15,000 households distributed in 1,500 CEAs, with 300 allocated in each of Brazil’s five major geographic regions and 10 eligible households per CEA, sampled using inverse sampling. The required population parameters were thus estimated to reach the study’s objectives. The basic sampling design weights were calculated as the inverse probabilities of the households’ inclusion in the study. Imputation was used to compensate for non-response to items in the target variables, except for data on the blood biomarkers. Finally, calibration used population totals of children in 60 post-strata, defined by cross-classification of the following variables: major geographic region, sex, and age. The final sample included 14,558 children residing in 12,524 households, distributed in 1,382 CEAs in the 26 states of Brazil and the Federal District. The data from the ENANI-2019 survey will support strategies for the promotion and implementation of public policies for children under five years of age.

Keywords:
Infant; Preschool Child; Statistical Models; Sampling Studies; Methods


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