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Arch. med. res ; 27(2): 191-4, 1996. tab
Article in English | LILACS | ID: lil-200313

ABSTRACT

Prevalence of malnutrition in pre-school children may be used to determine the need for nutrition surveillance or nutritional care. That prevalence depends on the nutritional classification used. The authors consider that one of the most recommended classifications used for identifying Protein Energy Malnutrition (PEM) was developed by Waterlow. The criteria includes weight-for-height and height-for-age. Frisancho suggested that Arm Muscle Area (AMA), related to height, could provide useful measurements for assessing mass reserve, thus, in beginning stages of PEM it is worthwhile to determine those who could benefit from nutritional services. The aim of this study was to determine the percentages of pre-school children considered well nourished by Waterlow indicators, but wasted or below average using the AMA for heigh criteria. Children (881) of either sex, from 48 to 79 months of age, were studied from the Tijuana, Baja California, Mexico pre-school system. Standard anthropometric measurements were taken including wight, height, upper-arm circumference, and triceps skinfold. Arm muscle area was created from those measeurements. Using Waterlow indicators, results showed deficit of heigh-for-age alone was seen in 25.6 per cent; deficit of weight-for-height alone was observed in 1 per cent; deficit of AMC-for-height, but well nourished by Waterlow indicators was seen in 14.9 per cent. If one speculates that children with deficits of AMC-for-height could have different risks of acquiring a disease compared to those apparently normal, then 14.9 per cent of the studied sample would also need nutritional care. Thus, using these criteria, the need for nutritional surveillance and nutritional education is greater for PEM than that established exclusively using Waterlow criteria


Subject(s)
Child, Preschool , Child , Humans , Male , Female , Child, Preschool/statistics & numerical data , Health Surveys , Mexico , Infant Nutrition , Protein-Energy Malnutrition/physiopathology , Data Interpretation, Statistical
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