Assessment of Body Size by Structural Equation Model Using Anthropometric Traits of Fishermen Community: A Methodological Approach

Objectives Aim of the study was to develop a ‘composite body size score’ (CBSS) using anthropometric traits to estimate body size and to assess the nutritional status of each study individual on the basis of CBSS. Materials and Methods Data on seventeen anthropometric traits were collected from 710 individuals (Male, Female) from fishermen community inhabiting coastal villages of West Bengal, India. For estimating body sizes, Structural Equation Model (SEM) was constructed with Path Analysis (PA). Later, second order Confirmatory Factor Analysis (CFA) was applied on SEM to determine CBSS. It was hypothesized in the models that CBSS is composed with three sets of latent variables viz., linear, circular and skinfold, constructed from anthropometric traits. Applying new derived optimal cut off points of CBSS was used to determine lean, normal and robust body sizes. Individuals with negative values of CBSS were categorised as lean body size,. Positive values of CBSS were categorised into two categoriesnormal and robust body size.


Introduction
The assessment of body size in adult populations has been established by several methods using different parameters [1,2,3]. For example, somatotype of an individual, which provides information of body shape and size, is derived from selected anthropometric traits. The use of methods like, body mass index (BMI), waist hip ratio (WHR), fat mass index (FMI) and conicity index (CI), derived from anthropometric variables are mainly used to assess the nutritional status of a population. However, these techniques are perhaps neither comprehensive to assess the body size nor an appropriate measure of assessing nutritional status of individuals [4,5,6]. Another study demonstrated using three techniques namely, mid upper arm circumference (MUAC), BMI (construct only two variables) and confirmatory factor analysis (CFA) (construct with more than two variables) in assessing the under nutrition and found that the statistical model, namely, CFA as the best measure compared to the other two [7,8].
The use of statistical modelling in determining the best model fit of the data, association and distribution pattern among the parameters has come to use during the end of the 20 th century [9][10][11][12][13][14] highlighted statistical model for predicting the human head shape and [15] developed a statistical model for estimating the relationship between different anthropometric measures and standing height.
In India, attempts were made towards statistical modelling, concerned with the human body. For example, [16,17] proposed a measure of group divergence and generalized distance (D 2 ); [18,19,20] developed theories of statistics using anthropometric traits; [21] used univariate statistical analysis to describe the body composition and distribution of body fat between high and low altitude people of Himalayas.
Moreover, [22] showed the relationship among the anthropometric variables at various high altitude populations. Besides these studies, a number of researchers [23,24,25] found the association of anthropometric traits with various parameters of biological and demographic aspects. However, none of the aforementioned studies attempted to provide an accurate statistical model to estimate different types of body size among individuals using anthropometric traits.
Thus, it becomes imperative to develop a relatively improved model to capture the maximum variance among the parameters so that the model can be applied to every individual in a community or a population. Such a model needs to be developed with two important purposes (1) models should help in interpreting observed data using the measurement variables in the fitted model to the data and (2) models should be proposed to study the interaction of underlying variables (latent variables) in the future events.
To the best of my knowledge, in the Indian subcontinent, there is hardly any research which used anthropometric traits in developing a composite body size score to determine the body size.
In view of the above, the aim of the present study was to develop a composite size score using anthropometric traits to estimate body sizes and to assess the contribution of each of these anthropometric traits in determining body size.

Subjects and Methods
The study was conducted on a group of adult    In the next step, I assessed the maximum standardized predicted weight (say, X) among the individuals and find its corresponding CBSS (say, Y).
Hence Y is a first cut off point between robust and normal body size and was designated as the upper limit of normal body size or lower limit of robust body size.
The lowest positive CBSS (say, Z) was designated as the lower limit of normal body size. Therefore, individuals having CBSS between a lower limit (Z) and upper limit Lean body size: Body size score < 0 [negative value]            components are high to robust body size, followed by normal and lean body sizes.

Discussion
Since CBSS is independent of age and sex (shown in figure 3) it can be considered as a unique method to classify the distinct body size in any population. The accuracy of the classification of body size using this statistical model may be considered better and satisfactory due to the consideration of a number of anthropometric traits in the analysis [8]. As a result, the higher order three-factor model was

Conclusion
It may be concluded that the proposed models may be used as a precise estimator to assess the body size of a population and in determining the nutritional status. The advantages of this method lie in terms of assessment of body size are: reliability and feasibility and cost effectiveness.

Limitations
The model would have been more précised with the increase in sample size. The method is generalized concerning the cut-off points of different categories of body size but may remain inconsistence across populations. An adequate number of community-based studies can give better insights into population variation in body size cut-off values.

Acknowledgment
The author is indebted to the study participants

Availability of Data and Material
The dataset used and/or analysed during the current study are available from the author on reasonable request.

Author's Contributions
The Author solely contributed to the set design, data analysis and interpretation of the results as well as preparing the drafts of the manuscript.