ORIGINAL ARTICLE Year : 2007  Volume : 32  Issue : 1  Page : 3539 Measuring malnutrition The role of Z scores and the composite index of anthropometric failure (CIAF) N Seetharaman, TV Chacko, SLR Shankar, AC Mathew Department of Community Medicine, PSG Institute of Medical Sciences and Research Peelamedu, Coimbatore. Tamilnadu, India Correspondence Address: Background : The current WHO recommendation is to use the ZScore or SD system to grade undernutrition which allows us to measure all the three indices and express the results in terms of Z scores or standard deviation units from the median of the international reference population. Objectives : To estimate the prevalence of undernutrition among underfive children in Coimbatore slums, using the ZScore system of classification and the recently constructed Composite Index of Anthropometric Failure (CIAF). 2. To compare the ZScore system with the Indian Academy of Pediatrics (IAP) classification of undernutrition. Methods : Nutritional assessment was done using anthropometry and clinical examination. Children were weighed and measured as per the WHO guidelines on Anthropometry. EpiInfo 2002 software package was used to calculate the Z scores and for statistical analysis. Results : Only 31.4% of the children studied were normal; 68.6% were in a state of «DQ»Anthropometric Failure«DQ». As per the Z score system, 49.6% were underweight (21.7% severely); 48.4% were stunted (20.3% severely) and 20.2% were wasted (6.9% severely). Whereas, as per IAP criteria, 51.4% were undernourished and 3.2% were severely undernourished. Using Underweight (low weightforage) as the only criterion for identifying undernourished children (as done in the Integrated Child Development Services currently) may underestimate the true prevalence of undernutrition, by as much as 21.9%. Conclusions : More widespread use of the ZScore system is recommended for identifying all the facets of undernutrition. Estimates of the true prevalence of undernutrition must incorporate a composite index of anthropometric failure.
Material and Methods Ten slums coming under the fieldpractice area of the Urban Health Centre, PSG Institute of Medical Sciences & Research, Coimbatore formed the study area. The Study population comprised of Children less than five years of age residing in the abovementioned slums. The total number of underfive children in these 10 slums was 625. Sample size for the crosssectional prevalence study was calculated using the formula Sample size (n) =4PQ/d 2 . With an expected prevalence of undernutrition (P) of 50% and a relative precision (d) of 10% of P, the required sample size was calculated as 400. To arrive at the required sample size of 400, six out of the ten slums were randomly selected and all the underfive children in the six selected slums were included in the study. The actual number of children in these six slums was 405 and this was taken as the study population (n = 405). All the children up to 59 months of age living in the selected slums were included for the study. Children who were not resident of the slum, but visiting and children of families who had moved into the slum within the past 1 month were excluded from the study. The exact age of the child was computed from the child's date of birth. When data on the exact date of birth was not available, the age as told by the mother was used, corrected to the nearest month. A regional localevents calendar was used to assist the mothers for better recall. Nutritional assessment was done using anthropometry and clinical examination. Children were weighed and measured as per the WHO guidelines on Anthropometry [4] . For children less than two years, the recumbent length was measured with the children lying down. Data collection was done over a period of two months. Statistical analysis was done using EPIINFO 2002 software package, from CDC. The Zscores for the different nutritional indices  weightforage, heightforage and weightforheight were calculated in reference to NCHS International reference population by using the EPINUT component of the software. The prevalence of underweight (low weightforage), stunting (low heightforage) and wasting (low weight for height) were calculated at the cutoff level of [9] and Punjab [10] report comparable prevalence levels. The relatively high prevalence of wasting observed among the children in the current study is indicative of a state of acute undernutrition, indicative of recent food deprivation and/or illness. NFHS 2 uses the Z score system of classification to grade undernutrition among Indian children. At the national level, the prevalence of underweight, stunting and wasting were 47%, 45.5 % and 15.5% respectively and the corresponding values for Tamilnadu were 36.7%, 29.4% and 19.9%. Currently the IAP classification based on weightforage, is followed in the 'anganwadi' centers throughout the country to grade undernutrition at the grassroot level for the Government of India's project on the Integrated Child Development Services (ICDS). As revealed by [Table 5] the IAP system identifies 4.8% more children as undernourished, whereas the Z score system identifies significantly more children as severely undernourished. These "Severely undernourished" children are the ones who get additional nutritional supplementation under the ICDS. In our study, 64 out of the 77 children graded as "Severely undernourished" by Z score system fall under the Grade II "Moderately undernourished" category as per the IAP system. This has high practical significance, in light of the fact that priorities of nutritional supplementation through ICDS are inclined towards the "severely undernourished" children  Grades III & IV of the IAP system. A similar study [11] in West Bengal comparing the IAP and the Z score systems, found comparable results  61% of the children were undernourished (3.9% severely) as per IAP criteria, whereas 46.6% were undernourished (6.9% severely) as per Z score system. As seen from [Table 4], underweight children form only one subgroup of the total number of undernourished children i.e. children who show evidence of "anthropometric failure". Nandy et al have improved on the CIAF (originally proposed by Svedberg) which they have applied to the entire NFHS 2 dataset. In their study, "Children with no failure" (Group A) account for 40.2% while children with "Wasting and Stunting and Underweight" (Group D) account for 7.2% and children with "Stunting only" (Group F) account for 10.1%. In the current study, considerably fewer children only 31.4% were normal or had 'no anthropometric failure' and 5.7% of children had "Wasting and Stunting and Underweight". The prevalence of "Stunting only" is relatively high  19.3% among the slum children studied. As evidenced by the current study, the use of underweight (low weightforage) as the sole criterion for identifying undernourished children may be underestimating the true load of undernutrition. Use of the CIAF helps us to visualize the extent of underestimation. Nearly 22% of the present study population  89 undernourished children  would be missed if low weightforage is considered as the only indicator of undernutrition. CIAF provides an overall estimate on the number of undernourished children in a population, which none of the conventional indices provide. Attempts at estimating the overall prevalence of undernutrition in the population must integrate such an aggregate index of undernutrition. This could be a tool of considerable interest to health planners and policy makers  especially considering the fact that to compute the CIAF, the only additional data that needs to be collected is the height of the child. Measurement of the child's height as part of the routine ICDS growth monitoring is worth considering. The limitations of this study include the approximation of children's weight to the nearest 500 grams, which might have had an influence on the prevalence estimates. The date of birth as told by the mother has been used; crosschecking with records could not be done for all of the children. There have been concerns about the appropriateness of using the NCHS data as the reference population for Indian children [12] . To address this concern, WHO is in the process of developing a more appropriate reference population, which would be available soon [13] . Conclusions Overall, only 31.4% of the underfive children studied were anthropometrically normal. In other words, more than two thirds of the children were undernourished. This is a very serious problem, by any scale. Under such conditions, our intervention efforts need to be broader than providing supplementary nutrition alone. More widespread use of the ZScore system of classification, especially in communitybased studies, is recommended. This system enables us to estimate/express the prevalence of undernutrition using all the three indices  underweight, stunting and wasting. This also allows meaningful comparisons with the nationally representative NFHS 2 database. The process of calculating the Z scores has been made very simple by the use of Epiinfo software package developed and distributed freely by the CDC. Current measures of undernutrition are, on their own, unable to give a reliable estimate of the overall number of undernourished children in a population. This issue has been addressed with the construct of the new indicator, CIAF. Findings from the current study suggest that conventional measures of undernutrition may be missing out a considerable proportion of undernourished children present in the population. The proportion of children identified as "severely undernourished" receive additional nutritional supplementation under the ICDS. Hence, underestimating this proportion might prevent undernourished children from receiving the benefit of the extra supplementation they deserve. The dissagregation of undernourished children in to different subgroups (as done in CIAF) allows the researcher to further examine the relationship between particular combinations of undernutrition and poverty or morbidity/ mortality data (when available). Studies have shown that children with double or triple failures are more likely to be from poorer families and also have a higher morbidity risk than children with single failures [8] . Identification of these children with multiple failures has obvious implications in antipoverty policies. A comprehensive measure of the total load of undernutrition  such as the Use of the Composite Index of Anthropometric Failure discussed in this paper  must be incorporated in our attempts at quantifying undernutrition. Acknowledgement The authors would like to thank Mr.Nanjappan for his help in data collection, Miss.Narmada for her help in data analysis and Dr.YSS.Sivan for his continual efforts in improving the paper. References


