Nutrimetry: BMI assessment as a function of development

• Published in: Endocrinología, diabetes y nutrición. Vol. 65; no. 2; pp. 84 - 91
• Main Authors: Selem-Solís, Jorge Enrique, Alcocer-Gamboa, Alberto, Hattori-Hara, Mónica, Esteve-Lanao, Jonathan, Larumbe-Zabala, Eneko
• Format: Journal Article
• Language: English
• Published: Elsevier España, S.L.U 01.02.2018
• Subjects: BMI; Endocrinology and Metabolism; Epidemiology; Epidemiología; Estado nutricional; Height; IMC; Nutrimetry; Nutrimetría; Nutritional status; Public health; Salud pública; Talla; Epidemiología; Estado nutricional; Salud pública; Nutrimetría; Talla; Height; Epidemiology; Nutrimetry; Nutritional status; Public health; IMC; BMI
• ISSN: 2530-0180; 2530-0180
• DOI: 10.1016/j.endien.2018.03.004

Abstract Background and objective Adequate nutritional assessment is required to fight malnutrition (undernutrition and overfeeding) in children and adolescents. For this, joint interpretation of certain indicators (body mass index [BMI], height, weight, etc.) is recommended. This is done clinically, but not epidemiologically. The aim of this paper is to present “nutrimetry”, a simple method that crosses anthropometric information allowing for bivariate interpretation at both levels (clinical and epidemiological). Materials and methods Data from 41,001 children and adolescents aged 0–19 years, taken from Mexico's National Health and Nutrition Survey 2012, were analyzed. Data crossed were BMI-for-age Z -scores (BAZ) with height-for-age Z -scores (HAZ) according to the World Health Organization (WHO) standards. Conditional prevalences were calculated in a 3 × 3 grid and were compared with expected values. Results This method identified subgroups in each BAZ category showing heterogeneity of the sample with regard to WHO standards for HAZ and nutritional status. According to the method, nutritional status patterns differed among Mexican states and age and sex groups. Conclusion Nutrimetry is a helpful and accessible tool to be used in epidemiology. It allows for detecting unexpected distributions of conditional prevalences, its graphical representation facilitates communication of results by geographic areas, and enriched interpretation of BAZ helps guide intervention actions according to their codes.