A cross-sectional community based study was carried out amongst Toddlers in slums of Raipur city, Chhattisgarh (C.G). List of slums was obtained from Muncipal Corporation, Raipur. Slums were sampled by multistage simple random sampling. Two wards were selected randomly from each zone and two slums were picked from each ward randomly. With 8 zones available 32 slums were selected. Households were selected by convenient sampling. Considering that distribution of population within slum is homogenous house to house survey was carried out starting from a random point and moving with right thumb until the desired number of subjects was obtained. Sample size was estimated using Emergency Nutritional assessment (ENA) software by SMART [11]. Sample size was 400 with confidence interval of 95%. It was made 600 considering design effect (DE) of 1.5. Size was divided amongst 32 slums according to PPS as mentioned below.

For example, Adarsha nagar has a population of 1,268 and total cumulative population of 32 slums is 51,187. Number of subjects from Adarsh nagar = 600*1,268/51,187 = 15(Rounded off). Similarly size for each slum is calculated and subjects were selected accordingly from respective slums. After rounding off for all the slums 602 was obtained. Household survey was carried out to locate toddlers and were measured for height, weight with other basic information like age and sex. Weight was measured with Salter,s weighing scale and height with a marking on the wall with measuring tape. Strict guidelines were followed for the measurements (Trained with manual of UNICEF for anthropometric measurements). Informed consent was obtained by the informat before hand.

Data was checked for its completeness and correctness before it was compiled and analysed using Microsoft Excel.

In current study, out of 602 Toddlers 50% were male, (50.7%) were of 12-23 months and rest were of 24-35 months [Table/Fig-1].

As shown in [Table/Fig-2], CIAF is 62.1% and group wise distribution is as follows, No failure (A)- 37.9%, wasting only (B)- 1%, underweight + wasted (C)- 7.6%, underweight + stunted + wasted (D)- 9.1%, underweight + stunted (E)- 21.8%, stunting only (F)- 16%, underweight only (Y)- 6.7%.

[Table/Fig-3] depicts that measurement of underweight (45.2%), stunting (46.8%), wasting (17.8%) under-estimates the burden of malnourishment. Composite index of Anthropometric failure (CIAF) (62.1%) better estimate the burden of Under-nutrition in the community compared to underweight or Stunted or wasted alone.

CIAF has certain advantages and disadvantages as well. In the current study Underweight which is known to be proxy for Stunting and Wasting was found to be 45.2% while CIAF was 62.1%. S Nandy et al., showed prevalence of Underweight of India to be 47.1% In the year 2005which is higher compared to current study while CIAF was only 59.9% for under three children [8]. CIAF was found to be 65.25% for Toddlers in a study by N Seetharaman et al., and underweight was found to be 46.6% [12]. A study by GC mandal et al., showed 71.7% CIAF which is higher compared to current study while prevalence of Underweight was found to be 60.9% [13]. A cross sectional study conducted in Bankura, West Bengal, S Shit et al., showed 78.1% CIAF among Toddlers higher compared to current study while Underweight were only 38.5% which is lesser compared to that of current study [14]. Even in study by S das et al., Underweight (41.25%) was found to be lesser than CIAF (48.3%) and the margin was found to be lesser compared to other studies and the current one as well [15]. Dasgupta et al., showed CIAF of 32.7% which is far low compared to current study while Underweight weight was few (17.7%) as well for under 5 children [16]. (Comparision has been shown in [Table/Fig-4]).

In addition to height for age (HA) and weight for height (WH), the CIAF classification uses weight for age (WA) — a measure that does not differentiate acute, chronic, and past (recent or remote) under nutrition. The CIAF classification introduces two new groups of children (Group B and Group C) [17]. Group B has normal HA and WA but low WH, an improbable anthropometric combination; group C, (with higher HA but low WH and WA, is of little immediate concern and can be considered “healthy”, presumably growing up to become thin tall adults [18]. Other groups, A, D, E, F and Y in the CIAF classification are covered by the Waterlow classification [19]. The CIAF classification does not address the limitations of the Waterlow classification [18]. Firstly, it does not satisfy the long felt need for a combined clinical and anthropometric classification that would be useful for clinical as well as community health work [17].

Lastly, although in the Waterlow classification wasting means low WH, as a clinical sign it means visible loss of subcutaneous fat and skeletal muscles. Low WH is observed with clinical wasting in cases of acute under nutrition and in chronic under nutrition of marasmic, but not mild to moderate or severe (florid kwashiorkor) types where fat masks muscle wasting, if present. Hence, low WH may or may not be associated with clinical wasting, and wasting in the waterlow classification should be differentiated as anthropometric wasting. In their paper, Nandy et al., do not seem to have appreciated this difference and have incorrectly stated that “wasting is an indicator of acute under nutrition” [17]. The CIAF classification introduces two new groups (B and C), A, D, E, F and Y were covered in Waterlow classification. It does not address the limitation of the Waterlow classification. Failed to satisfy the long felt need for a combined clinical and anthropometric classification that would be useful for clinical as well as community health work [20].

From the perspective of policy makers it is must to know over all burdens so that sufficient attention is paid through resource allocation. Conventional indicators do provide information on different aspects of under nutrition. This is important to clinicians and fields workers who need to respond differently in different situations. The CIAF takes the differences between the three conventional indicators into account, and so is more able to provide an indication of changes in under nutrition. It might also be noted that underweight may be exaggerating the magnitude of change. While underweight in India and Peru suggests falls in relative terms of around 11%, the CIAF shows progress may be more limited, with relative falls of only 6 and 4%, respectively. This raises serious questions about the use of underweight as an indicator for monitoring progress towards the first MDG [8].

Cannot be commented on trend as it is a cross-sectional study CIAF does not mention the severity of Under nutrition. It does not determine micro nutrient deficiencies and Clinical correlation is not possible as well.

CIAF better estimate the burden of Under nutrition in the community compared to conventional indicator, Under weight. It should be used as a tool for screening systems of Under nutrition and monitoring of Nutritional programmes and Results of CIAF should be based for policy making on Nutrition CIAF should be used as a indicator for tracking achievement of MDG.