Hierarchy of Associations Between BMI-for-Age z -Scores,Growth and Family Social Status Among Urban Bengali Girls of Siliguri Town,West Bengal: A St. Nicolas House Analysis

Abstract

The present study assesses the association of different socio-economic variables with children’s BMI-for-age z-scores (BAZ) and influence of children’s height on BAZ, a proxy index of nutritional status. The study was undertaken among 322 girls belonging to the Bengali Hindu Caste Population (BHCP) aged 5–13 years. These girls were the students of two schools located in Siliguri town of West Bengal, India. The prevalence of short stature, underweight and overweight was assessed using the World Health Organization’s (WHO, 2007, Growth Reference Data for 5–19 years) references. The statistical analyses used were St. Nicolas House Analysis (SNHA), analysis of variance (ANOVA), linear and logistic regression. The prevalence of underweight, overweight and obesity was 19.3%, 17.8% and 5.9%, respectively. The prevalence of short stature (−2.0 HAZ) was 8.7%. The range of BAZ was from −5.69 to 4.15. The variation of BAZ explained by height-for-age z-scores (HAZ) was 11.4%. The BAZ was observed to be associated with mothers’ occupation as revealed by 2% variation through SNHA analysis. The present study observed the usefulness of SNHA for non-parametric data with unequal sub-sample or categories. However, SNHA was not devised to assess the direction and magnitude of variables of interest. The finding of the present study supports the use of BAZ as a proxy adiposity measure among the overweight/obese populations and populations with normal growth in height. The study further supports the recommendations that mother empowerment can help improve nutritional status of a girl child.

Keywords

HAZ BAZ overweight underweight obesity St. Nicholas House Analysis

Introduction

According to the World Health Organization (WHO), the coexistence of undernutrition along with overweight and obesity, or diet-related non-communicable diseases (NCDs), within individuals, households and populations, and across the life course is called ‘double burden of malnutrition’ (DBM). The DBM may be defined as undernutrition (underweight, stunting, thinness, wasting), including micronutrient deficiencies, coexisting with overnutrition (overweight and obesity) (https://www.who.int/nutrition/double-burden-malnutrition/en). This is a public health challenge of recent decades, contributing to high mortality and morbidities, in populations worldwide (Doak et al., 2005). Even though Indian children experience an ongoing rise in overweight/obesity, the country shows highest prevalence of undernutrition in the global context (WHO, 2017; Zhou et al., 2020). The improved economic conditions, urbanisation, sedentary lifestyles and dietary changes have caused a steady increase in overweight/obesity (Agrawal et al., 2013), but with no increase in body height. The height-for-age z-scores ≤ −2.0 is regarded as undernutrition, as stunting at par with weight-for-height z-scores ≤ −2.0 (wasting/too thin-for-height) and weight-for-age z-scores ≤ −2.0 (underweight) by the definition. In fact, these are the widely used proxy measures of nutritional status recommended by the WHO (2007).

The body mass index (BMI) is a similar proxy anthropometric index that has been extensively used to assess thinness and overweight/obesity among children (Cole et al., 2007; WHO, 2007). Obesity is usually defined as BMI-for-age z-scores (BAZ) ≥ 2.0, which is unable to distinguish between fat mass and fat-free mass. Studies have emphasised cautions on interpretation of results observed using BMI (Bogin & Varela-Silva, 2012; Hoffman et al., 2006; Varela-Silva et al., 2012). Further, BAZ can be useful in a situation where direct measures of food consumption are not available as in the present study. The efficacy of proxy anthropometric measures of nutritional status remains valid in time of famine, war and feeding programmes for quick intervention.

Severe obesity was more common among children whose mother’s educational level was lower as reported from 21 European countries (Spinelli et al., 2019). Positive association of long working hours of a mother was observed by a Korean study (Kim et al., 2018). Mother’s occupation was observed to be associated with BMI of children in Nigeria (Akinsola et al., 2018). Obesity was reported high among high-income group schoolchildren of Delhi, India (Kaur et al., 2008). Increasing overweight and obesity among Indian children and adolescents were observed even in lower socio-economic groups of the country alongside undernutrition (Ranjani et al., 2016). India is in nutritional transition. However, both the forms of malnutrition will have an impact on the adult life of a child. This again makes the issue important for public health policies. Undernutrition is mainly associated with premature death, infection, lower adult height and cognitive problems (Pednekar et al., 2008; Poh et al., 2019; Sandjaja et al., 2013; Selvamani & Singh, 2018). These individuals in their adulthood will become more susceptible to NCDs (Agborsangaya et al., 2013; Gillespie & Haddad 2003; Hossain et al., 2007). The fatality due to a combination of NCDs with Coronavirus Disease 2019 (COVID-19) infection reminds us of the importance of its prevention more vividly. The role of nutrition on immunity against infectious diseases is another consideration. Health disparity across socio-economic gradients is parallel with human growth and nutrition.

Therefore, the present study assesses the associations of different socio-economic variables and children’s height with BAZ, a proxy index of nutritional status as well as prevalence of DBM among children. The present study hypothesised that the nutritional status defined by BAZ is independent of child growth in height (HAZ) and family social status.

Materials and Methods

The northern part of West Bengal, India, is popularly known as North Bengal and comprises seven districts. The region is inhabited by a number of indigenous tribal (e.g., Rabha, Meche, Toto, Oraon, Santal and Munda) and non-tribal (e.g., Rajbanshi, Bengali caste and Bengali Muslim) populations. One of the largest populations among them is the Bengali Hindu Caste Population (BHCP). Ethnically, the BHCP is a Bengali-speaking endogamous caste group of West Bengal and faithful to Hinduism (Das Chaudhuri et al., 1993).

The present cross-sectional study was carried out among urban school-going girls in the age group of 5.3–13.3 years and belonging to the BHCP. The children were the students of two schools located in the subdivisional town of Siliguri located in the district of Darjeeling. The girls covered in the course of this study resided in and around Siliguri town. The residents of Siliguri had access to all civic amenities and easy exposure to fast foods and globalised foods.

The schools were selected based on large students’ strength, dominance of students belonging to the BHCP and easy accessibility by road, using a purposive sampling method. Before commencement of the study, relevant authorities of the school were apprised about the study protocol. A total of 340 apparently healthy adolescent BHCP girls were approached to participate in the study. Of these 340 girls, 18 of them declined to participate in the same. The final sample size consisted of 322 girls in the age group of 5.3–13.3 years. The overall participation rate was 94.71%. All the children were observed to be free from any physical deformities, nutritional deficiency symptoms (e.g., hyperkeratosis, dermatosis, skin pigmentation, dull and dry hair, rusty reddish hair, goitre, spoon nail shape, brittle nails, ridged nails, swelling of mouth and lips, angular fissures and scars at the corner of the mouth, spongy gums, bleeding gums, and Bitot’s spot) and did not suffer from any disease at the time of data collection. Permission for the study was taken from the school authorities and the Department of Anthropology, North Bengal University, prior to data collection. Age of the children was taken from the school records, and ethnicity was ascertained from surnames. The work was conducted in accordance with the ethical guidelines for human experiments as laid down in the Helsinki Declaration of 2000 (Touitou et al., 2004). The study was conducted from February 2018 to May 2018.

The anthropometric measurements were recorded, following the standard techniques of Weiner and Lourie (1981). Height of the girls was recorded using an anthropometer rod to the nearest 0.10 cm. Weight of the girls wearing minimum clothing and with bare feet was taken using a portable weighing scale to the nearest 0.10 kg. Socio-economic and socio-demographic data were collected using a structured schedule. The variables recorded were number of siblings, birth order and family size, followed by drinking water facilities, father’s occupation and mother’s occupation with two ranked options each. The socio-economic and socio-demographic variables were recorded, following home visits and interviewing the parents.

The socio-economic variables (Table 1) like sibling size was categorised into two groups—one with 1–2 siblings and the other with more than three siblings (91.6% and 8.4%). The birth order of the children was categorised into two groups—first born (70.5%) and second born plus (29.5%). Family size for the present study was categorised into 1–3 members and greater than four members (26.4% and 73.6%). Availability of drinking water facilities were recorded as tap water (93.8%) and well/tube well (6.2%). Tap water is a government supply and well/tube well is a private arrangement. The occupations of the father were skill labour (89.7%) and other (10.3%) as recorded. Similarly, the mother’s occupations were housewife (7.5%) and working women (92.5%) as recorded.

Table 1.

Distribution of Categorical Variables in the Sample of Bengali Hindu Caste Population (BHCP) Girls of Siliguri.

Sibling	1–2 Siblings	91.6%	More than Three Siblings	8.4%
Border (birth order)	First born	70.5%	Second born and above	29.5%
FamSize (family size)	1–3 member	26.4%	Four member and more	73.6%
Water (drinking water)	Tap water	93.8%	Well/tube well	6.2%
father_occu (father’s occupation)	Skill labour	89.7%	Other	10.3%
mother_occu (mother’s occupation)	Working Woman	7.5%	Housewife	92.5%

Source: Primary data of the present study.

Assessment of Thinness, Overweight, Obesity and Height Growth

The nutritional status of the 322 schoolchildren belonging to the age group of 5–13 years was assessed using BAZ-based WHO (2007) reference. The prevalence of thinness, overweight and obesity were given based on <−2 SD, >+1 SD and >+2 SD, respectively. In fact, BAZ was a proxy index of nutritional status. The growth in height was assessed by height-for-age z-scores (HAZ).

Statistical Analysis Done

Statistical analyses were undertaken using R Gui (https://cran.r-project.org). To assess the association of socio-economic variables and HAZ with BAZ, the St. Nicolas House Analysis (SNHA) was applied.

The SNHA has been developed to analyse multiple linear correlations between growth parameters. This technique is suitable for handling multiple correlations usually encountered in anthropometric and various socio-economic and socio-demographic variables (Groth et al., 2019). This new technique translates correlation matrix into network graph, which is a useful visual aid. Correlations can visualise in the form of a network graph arranged in hierarchy of correlation coefficients. It has characteristics of an association chain formed based on correlation coefficients (R²) and ranked accordingly, for example, c[A × B], c[B × C], c[C × D], with the property c[A × B] > c[A × C] > c[A × D] and c[D × C] > c[D × B] > c[D × A]. So, SNHA is a network graph based on ranked-order correlations of parameters from strongest to weakest. Performance of SNHA was superior to other network graph techniques, which use partial correlations for analysing bands and hubs (Groth et al., 2019). The SNHA is now used by researchers to document the associations of various growth, socio-economic and socio-demographic variables in the form of a chain. Essentially, St. Nicolas’ house is a network, consisting of ‘node’ and ‘edge’ lines (Groth et al., 2019). Edges direct from one node to the next one. St. Nicolas’ house is a directed graph that has a topological ordering from earlier to later. The St. Nicolas house has been used as a model for analysing data that are linked by multiple linear correlations. Studies that have utilised the SNHA in nutritional studies include that of Dorjee et al. (2020) and Martin et al. (2020).

The associations were further analysed using multinomial logistic regression, analysis of variance (ANOVA) and linear regression. All the differences were considered to be statistically significant at the p < 0.05 level.

Results

The descriptive statistics of the present study is presented in Table 2. The age of the study individuals ranged from 5.3 years to 13.3 years with mean age of 9.2 ± 2.2 years. The mean of height, weight, HAZ and BAZ was 129.9 ± 13.7 cm, 28.9 ± 10.7 kg, −0.6 ± 1.0 and −0.4 ± 1.7, respectively. The range of HAZ was from −4.2 to 2.7. Similarly, range for BAZ was from −5.6 to 4.2. The results of spearman correlation is presented as Figure 1. The correlations observed between family size, sibling and birth orders were significant. All of these variables were related and so birth order was dropped from further analyses. Most of the other correlations were mild and non-significant. Mother’s occupation was observed to be mildly correlated with BAZ than HAZ. The ellipses become tighter with increasing coefficient. The colours blue and red indicate positive and negative correlations, respectively, also depicted by direction of inclination of ellipses.

Table 2.

Descriptive Statistics of Age, Height, Weight, HAZ and BAZ of the BHCP Girls (n = 321).

	n	Minimum	Maximum	Mean	Std. Deviation
Age	321	5.3 years	13.3 years	9.2	2.2
Height	321	101.0 cm	160.1 cm	129.9	13.7
Weight	321	10.50 kg	62.0 kg	28.9	10.7
HAZ	321	−4.2	2.7	−0.6	1.0
BAZ	321	−5.6	4.2	−0.4	1.7

Source: Primary data of the present study.

Figure 1.

Source: Primary data of the present study.

Figure 2.

Source:. Primary data of the present study.

The SNHA is based on correlations. It provides a hierarchy of correlations, from strongest to weakest, in a form of network chain, which is an ideal visual aid. In terms of network graph terminology, the SNHA consists of variables called nodes and the connecting lines called edges. In the present study, SNHA shows direct connecting edges between nodes of HAZ and BAZ and BAZ and mother’s occupation (mother_occu). This way, mother’s occupation is indirectly related to HAZ. The father’s occupation was indirectly linked to the node of BAZ through other nodes and edges (Figure 2). The data were further subjected to ANOVA (Table 3) and linear regression, which supported the association as shown by SNHA in case of mother’s occupation (Table 4). There was no group difference in HAZ between working and housewife mothers (Table 3). The linear regression suggests that a change in the mother’s occupation can bring about 2% variations on BAZ of girls in the present study. Similarly, HAZ can bring about 11.4% of variations on BAZ. Further, using logistic regression, the odd of girls with low (≤−2) HAZ for being overweight was observed non-significant (Table 5).

The prevalence of thinness, overweight and obesity was 19.3%, 17.8% and 5.9%, respectively. The distribution of thinness, overweight and obesity according to age among girls is presented in Figure 3.

Figure 3.

Source: Primary data of the present study.

Figure 4.

Source: Primary data of the present study.

Table 3.

The Mean (SD) of BMI-for-Age z-Scores (BAZ) and Height-for-Age z-Scores (HAZ) of Housewife and Working Mother is Presented with one-way analyses of variance (ANOVA) of the BHCP girls.

Mother Occupation	n	BAZ	HAZ
All mothers	320	−0.68 (1.02)	−0.39 (1.71)
Working mothers	24	−0.94 (1.34)	−1.25 (1.93)
Housewife mothers	296	−0.66 (0.99)	−0.32 (1.68)
F-value		6.568; p = 0.01	1.641; p = 0.201

Source: Primary data of the present study.

Table 4.

Stepwise Linear Regression of BMI-for-Age z-Scores (BAZ) a Dependent Variable with Independent Variables Height-for-Age z-Scores (HAZ) and Mother’s Occupation of the BHCP Girls.

Model		Unstandardised Coefficients		Standardised Coefficients	t	Sig.	R ²
Model		B	Std. Error	Beta	t	Sig.	R ²
1	(Constant)	−0.006	0.108		−0.057	0.954
1	HAZ	0.566	0.088	0.338	6.396	0.000	0.114
2	(Constant)	−1.499	0.670		−2.237	0.026
	HAZ	0.551	0.088	0.329	6.258	0.000	0.128
	mother_occu	0.770	0.341	0.119	2.257	0.025	0.128

Source: Primary data of the present study.

Note: Calculated R² for mother_occu is 0.02.

Table 5.

Multinomial Logistic Regression for Being Underweight Versus Normal Weight and Overweight Versus Normal Weight Against Mother’s Occupation and Low Height-for-Age z-Scores (HAZ) Among the BHCP Girls.

Nutritional Status		B	Std. Error	Wald	Sig.	Exp. (B)	95% Confidence Interval for Exp. (B)
Nutritional Status		B	Std. Error	Wald	Sig.	Exp. (B)	Lower Bound	Upper Bound
Thinness	Intercept	−1.236	0.165	56.106	0.000
	Housewife	0.539	0.487	1.226	0.268	1.714	0.660	4.449
	Working mother	0^b			.	.		.
	≤−2.0 HAZ	0.944	0.462	4.172	0.041	2.569	1.039	6.353
	≥−1.9 HAZ	0^b
Ow/Ob	Intercept	−0.877	0.145	36.381	0.000
	Housewife	−0.634	0.658	0.926	0.336	0.531	0.146	1.929
	Working mother	0^b			.	.
	≤−2.0 HAZ	0.263	0.523	0.252	0.615	1.301	0.466	3.626
	≥−1.9 HAZ	0^b

Source: Primary data of the present study.

Note: 0^b = Set as reference and Ow/Ob = overweight/obese.

Discussion

The short girls with thinness were less in number than normal growing thin girls represented by the plot of HAZ versus BAZ (Figure 4). In the present sample, 3.1% girls were simultaneously observed with thinness and short height (HAZ ≤ −2) when compared to 1.9% girls simultaneously overweight and short height (HAZ ≤ −2). The normal weight and overweight short girl decreases in number towards the right of the plot (Figure 4). It seems like partial support for the first component of the hypothesis, which is nutritional status defined by BAZ is independent of child growth in height (HAZ). However, all thin children are not short (HAZ ≤ −2). Further, there was modest influence of HAZ on BAZ given by regressions (11.4%). In contrast, studies have observed significantly low association of HAZ with BAZ and other anthropometric measures of nutritional status (Dorjee et al., 2020; Scheffler et al., 2020). The short height children with HAZ ≤ −2 were more likely to be thin defined by BAZ (OR: 2.57). There was no such effect of short-for-age (HAZ ≤ −2) on being overweight/obese with reference to normal weight (Table 5). This supports the notion that height category of being short and growing normal does not influence weight status given by BAZ entirely and can be used to assess thinness, overweight and obesity. Overall, there were 8.7% short-for-age children in the present study, which is lower when compared to 42.9% among the Limboo children of Sikkim (Dorjee et al., 2020). This could be an indication that BAZ is more suitable for a population having lower number of short-for-age children as a proxy measure of nutritional status.

So, growth in height among the children is not a deciding factor in nutritional status, defined using BAZ, except for thinness. The association of nutritional status, given by BAZ, is largely independent of HAZ. The second component of the hypothesis was the influence of family social status on overweight/obese children. The mother’s occupation has an influence on girls being overweight/obese. The 2% variation explained was significant (Table 4).

In the present study, the mother’s occupation as indicated by SNHA could be a form of women empowerment, which might determine the nutritional status of their children. This can be inferred from a direct edge connecting node of BAZ and mother_occu (mother’s occupation) in the network graph of SNHA (Figure 2). The group difference in the mean BAZ was observed using ANOVA for mother’s occupation (Table 3). The variation in the BAZ explained by mother’s occupation was observed to be significant using linear regression (Table 4). However, multinomial logistic regression could not explain these associations fully (Table 5). Association of education and income with nutritional status was shown in earlier studies (Meshram et al., 2019; Sen & Mondal, 2012; Soekatri et al., 2020; Tigga et al., 2015). In the present study, mother’s occupation seems to play a role even during adolescence; however, the variation explained was little. The earning opportunity and level of education usually coexist and are intricately related. Similarly, association of mother’s education was shown to have influence on growth in height directly and indirectly to BAZ using SNHA (Martin et al., 2020). The association of mother’s occupation with BAZ could be due to the absence of information on mother’s education in the present study.

The present study seems to support BAZ, which can be used to assess the overweight/obesity, and children’s height will not influence the assessment of nutritional status (Bogin & Varela-Silva, 2012; Hoffman et al., 2006; Varela-Silva et al., 2012). As the adiposity identified by BAZ could be fat around the trunk among children with low HAZ, this will be informative, if the population under study is obese (Hoffman et al., 2006). This is to say that nutritional status access using BAZ would be reliable if the population is overweight/obese. A study conducted among the preschool children of Thailand reported that BAZ may not be appropriate to assess the adiposity among children with low HAZ (Pongcharoen et al., 2017).

The prevalence of overweight and obesity in children and adolescents reported for developing countries had increased from 8.4% in the year 1980 to 13.4% in the year 2013 (Ng et al., 2014). In India, prevalence of overweight and obesity among children aged 8–18 years was 18.5% and 5.3%, respectively (Misra et al., 2011). Increase in the overweight category from 4.7% to 17.2% was reported from Kolkata among adolescents (Dasgupta et al., 2008). The other studies have also reported increasing trends among children (Marwaha et al., 2006; Raj et al., 2007). The present study observed the prevalence of underweight (19.3%), overweight (17.8%) and obesity (5.9%) in the schoolgirls of Siliguri, West Bengal. Within the underweight, normal weight and overweight categories, there was 3.1%, 3.7% and 1.9%, short girls with HAZ ≤ −2, respectively. In contrast, there was absence of short girls having HAZ ≤ −2 in the category identified as obese among the girls taking part in the present study.

The survey of nutritional status is important for documenting and managing such critical situations. In absence of dietary assessment, BAZ can be used as a proxy of adiposity in low-middle-income countries. Reporting growth status must be included alongside nutritional status, as there is increasing obesity and low growth in height. The number of thin and overweight children with low HAZ must be included while reporting undernutrition and overweight/obesity using BAZ. This is for two reasons—the first being for revealing a clear picture of overlapping if any, and the second is low HAZ could not be just due to nutritional deficiency, which may influence weight status defined using BAZ (Bogin, 2020).

Studies have suggested the poverty elimination, access to education and women empowerment important for the eradication of malnutrition in India (Bhutta, 2016; Narayan et al., 2019). The empowerment of mother through job/earning can influence the nutritional outcomes in children. Since this study lacks information on mother’s education, which can directly influence children’s growth, thereby leading to increase in BAZ (Martin et al., 2020). This study supports the notion that girls’ BAZ can be influenced by the status of their mother’s earnings. The empowerment of women with jobs improves the purchasing power of mothers, which will influence children’s health, nutrition and perception about their family’s social status. Further, mother’s empowerment could lead to education of children, especially girls in the Indian context. A significant study has suggested that an investment in the future mothers is important than supplementation during pregnancy (Thayer & Kuzawa, 2014). The children’s perception about family status and environment has shown to play a role in nutritional status and growth. The findings of the present study could be an indication of the children’s perception about their family’s social status rather than nutrition alone as reported by Rivenbark et al. (2019) and García et al. (2017). In this regard, the alternative views coming from the newer studies on human growth are important (Bogin et al., 2018; Hermanussen et al., 2018; Scheffler et al., 2020). The role of psychosocial wellness of children and their families is important in understanding human growth and weight status.

Utility of the Study

The present study has shown the use of a novel technique of SNHA to visualise children’s growth data. It can be used to spot multiple correlations in anthropometric and socio-economic data. This study further emphasises on the empowerment of mother, which can influence the psychosocial well-being of their daughter; this is equally important for the eradication of growth retardation and obesity among young girls.

Limitations of the Study

Such studies can be enhanced further with the inclusion of dietary information, which is lacking in the present study. Further, the present study lacks information on the mother’s education level and the family’s monthly income. The cross-sectional nature of the data is another limitation.

Conclusion

The modest dependency of BAZ on HAZ was not enough to influence the nutritional status such as thinness, overweight and obesity. The BAZ would be more suitable to be used in the population where there is high adiposity when compared to the undernourished populations. It also seems that use of only BAZ to define nutritional dual burden is not enough. Other measures should also be employed simultaneously. The association of mother’s occupation with BAZ of girl child, using SNHA, was also supported by ANOVA and regression. Furthermore, the SNHA, a new statistical approach suitable for non-parametric analyses of multi-correlated variables, seems fit for the challenges of managing data with unequal group distribution like the present one.

Footnotes

Acknowledgements

The children and adolescents, as well as their parents and teachers, are acknowledged for their participation in the study. The advice and help of Prof. Barry Bogin, Dr. Michael Hermanussen and PD Dr. Christiane Schefeller are also acknowledged.

Declaration of Conflicting Interests

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

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