The use of anthropometric and clinical parameters for early identification and categorisation of nutritional risk in pre-school children in Benin City, Nigeria.

This study was conducted in Benin City, Nigeria between June and August 1996 to assess nutritional status and health risks of three to five-year-old children, with the view to suggesting practical approaches to their early detection and intervention. A total of 165 children comprising 90 males and 75 females was studied. Mid-upper arm circumference (MUAC), weight-for-age (WFA), weight-for-height (WFH) and height-for-age (HFA) z-scores were determined and used to calculate percentage prevalence of malnutrition. Clinical features of macro- and micro-nutrient deficiency were used to develop a clinical scoring system which was subsequently matched with anthropometric z-scores. The results showed that MUAC z-scores (-1.91 SD +/- 0.74) gave the highest percentage prevalence of malnutrition of 45.2% in this population, followed by the WFA (-1.22 SD +/- 1.07) and HFA (-0.84 SD +/- 1.42) z-scores with a percentage prevalence of 23.3% and 20.6% respectively. The WFH z-score (-0.89 SD +/- 1.06) was the least sensitive in detecting malnutrition (14.7% prevalence). The percentage prevalence calculated from MUAC z-scores matched FAO figures (43%) for the sub-Saharan African region in 1996. MUAC z-scores also correlated more closely with the clinical features of malnutrition (R2 = 0.7087). Progressively worsening clinical features were also seen with decreasing z-scores for all variables. Even though moderate differences in clinical and anthropometric variables were detected between the sexes with females fairing better than male subjects, these differences were not statistically significant. Comparisons between anthropometric variables showed only weak correlation, except for WFA vs. HFA z-scores (R2 = 0.5233) and WFH vs. WFA z-scores (R2 = 0.4559) which showed moderately positive correlation. We conclude that whereas MUAC z-scores were most sensitive in detecting the extent of malnutrition in this population, merely using anthropometric variables alone may lead to significant under-reporting of the prevalence of malnutrition in a community. A combination of various anthropometric z-scores with clinical features will however help in the early identification and categorisation of subjects in terms of degree of nutritional risk. The training of field health and nutrition workers should therefore emphasise the routine use and combination of anthropometric and clinical variables in the determination of prevalence of malnutrition and in the formulation of intervention strategies for nutrition rehabilitation.