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ISSN 2473-4772

Prevalence of Undernutrition and Overweight or Obesity Among the Bengali Muslim Population of West Bengal, India

Pushpa Lata Tigga, Sampriti Debnath, Mousumi Das, Nitish Mondal and Jaydip Sen*

Jaydip Sen, PhD

Professor, Department of Anthropology, University of North Bengal, Raja Rammohanpur, Darjeeling 734013, West Bengal, India; E-mail:


The co-existence of undernutrition and overweight or obesity among populations is, known as the “double burden of malnutrition (DBM). The DBM is a major public health problem contributing to high mortality and morbidities among populations worldwide. India has achieved positive economic growth, but there persists substantial economic nutritional inequalities and insufficiencies (e.g., malnutrition).1,2,3,4,5,6,7,8 On one hand, a large proportion of the population of the country is suffering from severe to moderate undernutrition and on the other, there is the major problem of overweight or obesity. Recent studies have documented prevalence of a DBM which in turn, increases the burden of non-communicable diseases (NCDs) mainly in the developing countries.3,5,9,10,11,12,13 The simultaneous occurrence of both under- and overnutrition (e.g., overweight or obesity) within a population is likely to reflect the differential distribution of resources at the individual or household level, i.e., some people do not have sufficient resources to meet their caloric requirements, while others have the resources to purchase their calorie requirements and more.1,2,6,7,8 Several investigations demonstrated that a vast proportion of the population belongs to the underprivileged and undernourished segments in India.12,14,15,16 Recent studies have now observed that a considerable amount of overweight or obesity exist among Indian populations.2,5,13,17,18,19,20,21,22 Recent trends have indicated that the prevalence of overweight or obesity are also increasing among women belonging to lower socio-economic status in India.2,4,5,12,23,24 Studies also have documented the associations of several socio-economic, demographic and lifestyle variables on the prevalence of overweight or obesity.2,5,13,21,25 Therefore, it becomes necessary to understand the actual magnitude of malnutrition among the rural and marginalised populations of India. Several research studies have been conducted on various aspects of undernutrition among its populations.26,27,28,29,30

Overweight or obesity is a physical condition that contribute to the prevalence of numerous non-communicable diseases within populations. A steady increase in the prevalence of overweight or obesity has been reported from different Indian populations.3,17,18,19,20,21,25,31,32,33 Recent studies have also suggested that this ‘double burden’ is becoming increasingly apparent in addition to the burden of non-communicable diseases affecting developing countries such as India.2,10,22,34,35 However, undernutrition still remains a consistent public health problem among populations in the country.36

Anthropometry is a universally applicable, widely used, highly valid, inexpensive, and non-invasive technique available to researchers in the assessment of nutritional status.37,38,39,40 Body mass index (BMI) is widely used, non-invasive, inexpensive anthropometric indicator to determine the nutritional status (both under and overnutrition) across populations for large-scale epidemiological and clinical investigations.1,4,5,18,19,21,25,32,40,41,42,43,44 Extensive studies have been done using BMI as an indicator for assessing undernutrition5,42,45,46,47 and overweight or obesity.1,2,31,48,49

There is a scarcity of studies on nutritional status among the Bengali Muslim Population (BMP) of India. The objectives of the present investigation was to determine and compare the prevalence of under-and overnutrition and their associations with certain socio-economic and demographic determinants among the BMP in a rural area of West Bengal, India.


The present community-based cross-sectional investigation was carried out among 420 adult BMP individuals (males: 182; females: 238) aged 18-59 years residing in rural areas of Islampur subdivision of Uttar Dinajpur district, West Bengal, India. The Islampur subdivision (Latitude 26.27°N, Longitude 88.20°E) covers an area of 3142 km2. According to 2011 National Census, India, Islampur had a population of 308,518 (males: 51.54%; females: 48.48%). The region is predominantly inhabited by the BMP and Hindus. The BMP is the largest religious minority population in West Bengal and comprises a considerable percentage of the state’s population.50 According to the census of 2011, the Muslim population constituted 27.10% of West Bengal’s total population. Hindus and Muslims share more than 97% of the total population of the state.50 Ethnically, the BMP is a Bengali-speaking ethnic community and by religion faithful to the Islam.51 They contribute a large share to the social and economic well-being of the state and of the country as a whole. Therefore, it becomes important to assess the nutritional conditions of this population.

The individuals in the present investigation were selected using a stratified sampling method. Initially, the BMP individuals were identified in the present investigation based on their surnames and the data was verified from the block office records. Individuals belonging to the age group 18-59 years were then identified. Age of the subjects was recorded from their birth certificates and relevant official records issued by the local government officials. The minimum number of individuals required for reliably estimating the prevalence of overweight and obesity in the present investigation was calculated following a standard method of estimating sample size.52 In this method, the anticipated population proportion of 50%, absolute precision of 5% and confidence interval of 95% were taken into consideration. The investigation was conducted in accordance with the ethical guidelines for human experiments, as laid down in the Helsinki Declaration of 2000.53 Research permission to conduct the present investigation was obtained from the Department of Anthropology, University of North Bengal. Local leaders and authorities at community levels were informed prior to initiating the present investigation. The individuals were free from any physical deformities, previous histories of medical and surgical episodes and not suffering from any disease at the time of data collection. The data for this present investigation was recorded from January 2016 to April 2016.

Anthropometric Measurements

Anthropometric measurements were recorded using standard anthropometric procedures.54 The height of the individuals was recorded to the nearest 0.1 cm with help of an anthropometer rod with the head held in the Frankfort horizontal plane. The weight of the individuals wearing minimum clothing and was taken with bare feet using a portable weighing scale to the nearest 100 gm. The individuals were measured with ample precision for avoiding any possible systematic errors of anthropometric data collection (e.g., instrumental or landmarks).55

Intra-observer and inter-observer technical errors of the measurements (TEM) were calculated to determine the accuracy of the anthropometric measurement using the standard procedure.56 For calculating TEM, height and weight were recorded from different data set of 50 adults other than those selected for the investigation by Pushpalata Tigga (PT), Sampriti Debnath (SD) and Mousumi Das (MD). The coefficient of reliability (R) of the measurements were calculated for testing the reliability of the measurements. Very high values of R (>0.975) were obtained for height and weight and these values were observed to be within the recommended a cut-off limit of 0.95.56 Hence, the measurements recorded by PT, SD, and MD were considered to be reliable and reproducible. All the measurements in the present investigation were subsequently recorded by MD.

Socio-economic, Socio-demographic and Lifestyle Data

Data on age, sex, family size, toilet facility, house type, occupation and household income were collected using a structured schedule by visiting the households. A modified version of Kuppuswamy’s socio-economic scale was used to evaluate the socio-economic status (SES).57,58 The SES determination showed that all the individuals belonged to a lower-middle SES.

Assessment of Nutritional Status

Nutritional status has been assessed in terms of BMI. The BMI was calculated using the standard equation of the World Health Organization (WHO) (WHO 1995): BMI=Weight/Height² (kg/ m²)

The BMI cut-off points utilized for the assessment of undernourished and overweight or obesity were <18.50 kg/m2 and ≥25.00 kg/m2 respectively.37

Statistical Analysis

The data were statistically analyzed using the Statistical Package for Social Sciences (SPSS, Inc., Chicago, IL, USA; version 17.0). The descriptive statistical analysis of the data obtained was depicted in terms of mean and standard deviation (±SD). One way ANOVA has been performed to test the age-specific mean differences in anthropometric variables of the groups. Chi-square (χ2 ) analysis was used to assess sex-specific differences in the prevalence of undernutrition and overweight. Chi-square analysis was also applied to assess differences in nutritional status between different socio-economic and demographic variables. Binary logistic regression (BLR) analysis was undertaken to estimate the odds ratios (ORs), minimum of 95% confidence intervals (CIs) and to assess possible differences in risk factors associated with those individuals being undernourished BMI<188.50 kg/m2) and combined-overweight and obesity (BMI≥25.00 kg/m2), separately. In the BLR analysis, the different socio-economic, demographic and lifestyle predictor variables were used as univariate independent regression model analyses. To create the dichotomous dependent variables, individuals who were undernourished (BMI<18.50 kg/m2 ) were coded as ‘1’ in the respective BLR model. Similarly, individuals who happen to be combined overweight and obese (BMI≥25.00 kg/m2 ) were also coded as ‘1’ in the respective BLR model. Individuals who exhibited normal BMI were coded as ‘0’ in the BLR models. These different regression models were utilized to identify possible risk factor(s) associated with undernutrition and excess adiposity levels (overweight or obesity). The BLR analysis allows the creation of categorical depended variables and odds ratios were obtained by comparing with the reference categories. The predictor variables of sex, age, family size, toilet facility, house type, occupation and income were entered in the regression equation and the results were obtained by comparing them with the reference categories. The p values of <0.01 and <0.05 were considered to be statistically significant.


The sex-specific distribution of subjects, age groups, means, and standard deviations of height, weight, and BMI are shown in Table 1. The overall mean height and weight were observed to be higher among males than that of females. Age group-specific means of height of males ranged from 162.53 cm (in 40-49 years) to 165.46 cm (in 20-29 years). In females, the age group-specific means of height ranged from 150.49 cm (in 50-59 years) to 154.09 cm (in 39-39 years). Age group specific weight among males ranged from 55.03 kg (in 20-29 years) to 60.61 kg (in 30-39 years). Among females, the age group-specific means of weight ranged from 45.34 kg (in 50-59 years) to 53.13 kg (40-49 years). Age group-specific mean values of BMI among males ranged from 20.15 kg/m2 (in 20-29 years) to 22.60 kg/m2 (30-39 years). Among females, the age group specific mean values ranged from 19.95 kg/m2 (50-59 years) to 22.82 kg/m2 (40-49 years). Using ANOVA, the age-specific mean differences were observed to be statistically significant for males and females (in height, weight, and BMI). Age-sex specific mean differences in height, weight, and BMI were observed to be statistically insignificant (p>0.05). The age-specific differences were observed to be statistically significant in case of weight and BMI among males and females and only in case of mean height of females (Table 1).

Table 1. Age and Gender Differences in Height, Weight, and BMI among the BMP

Age (Years)

Sample size

Measurements and Index


F Height (cm) Weight (kg) BMI (kg/m2)







79 100 165.46±7.56 153.40±6.47 55.03±7.01 47.65±7.24 20.15±2.58



28 49 163.94±5.53 154.09±5.05 60.61±8.69 49.94±8.04 22.60±3.42 21.13±3.85
40-49 36 44 162.53±6.12 152.36±7.57 58.49±11.21 53.13±10.36 22.12±3.90



39 45 163.48±7.68 150.49±6.15 57.17±9.47 45.34±9.22 21.39±3.34 19.95±3.45
Total 182 238 164.22±7.09 152.65±6.44 57.03±8.93 48.70±8.76 21.18±3.30



1.67 2.71 3.26 7.28 5.80 7.01
p-value 0.18 0.05 0.02 0.00 0.00



Prevalence of Undernutrition and Overweight or Obesity

The overall prevalence of undernutrition and overweight or obesity is shown in Table 2. The overall prevalence of undernutrition was observed to be 22.86% (males: 18.68%; females: 26.05%). Age-specific prevalence of undernutrition was higher in the age groups of 20-29 years (29.11%) and 50-59 years (15.38%) among males. Among females, the prevalence of undernutrition was higher in the age groups of 50-59 years (37.78%) and 30-39 years (30.61%). The overall prevalence of overweight and obesity was observed to be 12.86% (males: 14.29%; females: 11.76%). Age-specific prevalence of overweight or obesity was higher among the age groups of 30-39 years (males: 25.00% and females: 16.33%) and 40-49 years (males: 22.22% and females: 20.45%) among males as well as among females. No age and sex-specific differences in the prevalence of undernutrition and overweight or obesity have been observed using Χ2 -analysis.

Table 2. Prevalence of Undernutrition and Overweight or Obesity among the BMP Individuals

Age (Years)

Sample size Undernutrition (BMI<18.5 kg/m2) Overweight or obesity (BMI ≥25 kg/m2)
Male Female Male Female Male


20-29 years

79 100 23 (29.11) 27 (27.00) 7 (8.86) 7 (7.00)
30-39 years 28 49 2 (7.14) 15 (30.61) 7 (25.00)

8 (16.33)

40-49 years

36 44 3 (8.33) 3 (6.82) 8 (22.22) 9 (20.45)
50-59 years 39 45 6 (15.38) 17 (37.78) 4 (10.26)

4 (8.89)


182 238 34 (18.68) 62 (26.05) 26 (14.29)

28 (11.76)

Values in parenthesis indicates percentage


Effect of Socio-economic, Demographic and Lifestyle Variables on Prevalence of Undernutrition and Overweight or Obesity

The differences in the prevalence of undernutrition and overweight with respect to the different socio-economic, demographic and lifestyle related variables are shown in Table 3. As noted earlier, a BLR model was fitted into the data to observe the odds ratio for the socio-economic, demographic and lifestyle-related variables to an individual being affected by undernutrition and overweight or obesity. In this BLR model, in case of every factor a reference category was selected and the result was determined by comparing the other factor with reference category. In case of undernutrition, the BLR analysis showed that individuals belonging to the lower age groups (18-39 years) have higher odds ratios (odds ratio: 1.65 times) with significantly higher risk of being undernourished (p<0.05). The occupation group of farmers and labourers were observed to be at a statistically significant (p<0.01) higher risk of undernutrition (odds ratio: 5.61) than the group of housewives and others. Other variables did not show any significant associations with the prevalence of undernutrition. The χ2 analysis showed statistically highly significant group differences in house type (nonbricked vs. bricked) (Χ2 -value=25.31) (p<0.01) and occupation (farmers and labours vs. housewives and others) (Χ2 -value=28.00). In case of overweight or obesity, the BLR results showed that the individuals with less number of family members were at a significantly higher risk of overweight or obesity (odds ratio: 2.09 times) (p<0.01) and significant associations have been documented between family size and prevalence of overweight or obesity. Other variables did show statistically significant association with prevalence of overweight or obesity. The Χ2 -analysis showed statistically significant group differences in family size (2-4 family members vs. 5 and above family members) (Χ2 -value= 3.95) (p<0.05) and house type (non-bricked vs. bricked) (Χ2 -value= 21.46) (p<0.01).


Table 3. Associations of Socio-economic, Demographic, and Lifestyle Related Variables with Undernutrition and Overweight or Obesity among the BMP
Variables Number of Individuals (N=420) Undernourished (BMI<18.5 kg/m2 ) (N=96) Undernutrition Overweight or obesity (BMI≥25.00 kg/m2) (N=54) Overweight or obesity
Wald Odds 95%CI Wald Odds 95%CI
Age (in years) 18-39 years 256 67 (69.79) 4.04 1.65* 1.01-2.69 29 (53.70) 1.36 1.41 0.79-2.50
40-59 years 164 29 (30.21) 25 (46.30)
Sex Male 182 34 (35.42) 26 (48.15) 0.58 1.25 0.71-2.22
Female 238 62 (64.58) 3.15 1.53 0.96-2.46 28 (51.85)
Family Size 2-4 83 22 (22.92) 0.78 1.28 0.74-2.23 17 (31.48) * 5.20 2.09* 1.11-3.93
5 and above 337 74 (77.08) 37 (68.52) *
Toilet Facility No 96 24 (25.00) 0.32 1.17 0.69-1.98 15 (27.78)
Yes 324 72 (75.00) 39 (72.22) 0.85 0.74 0.39-1.41
House Type Non-bricked 308 45 (46.88) ** 2.11 1.41 0.89-2.22 23 (42.59) **
Bricked 112 51 (53.13) ** 31 (57.41) ** 0.12 0.91 0.51-1.61
Occupation Farmers and Labourers 97 48 (50.00) ** 45.28 5.61** 3.40-9.28 10 (18.52)
Housewives and others 323 48 (50.00) ** 44 (81.48) 0.73 1.37 0.66-2.84
(in rupees)
≤4000 66 18 (18.75) 0.86 1.33 0.73-2.41 7 (12.96)
>4000 354 78 (81.25) 47 (87.04) 0.35 1.29 0.56-2.99
Values are parenthesis indicates percentage; **p<0.01;* p<0.05



Prevalence of overweight or obesity has reached epidemic proportions globally. The rising prevalence of overnutrition in developing countries is largely due to rapid urbanization and changes in energy expenditure. Recent nutritional trend has shown that individuals/populations belonging to the developing countries were observed to be particularly vulnerable to obesity-related diseases in addition to comorbidities.13,21,59,60,61 In India, there is an increasing trend of dual burden of malnutrition (both undernutrition and over-nutrition) which needs proper management to control the epidemic within populations. It also requires assessments and establishment of the socio-economic, educational, environmental and cultural factors involved in determining overweight or obesity within populations.5,13,21,62,63,64 Majority of the Indian states have shown the prevalence of undernutrition among adults to varying between 20% and 29%.65 Several studies have also reported high undernourishment among various Indian populations.1,2,4,36,40,44 Recent studies have documented populations of the country to exhibit a high prevalence of overweight or obesity.4,21,25,32 The prevalence of undernutrition is also a serious problem among the adult populations of East and Northeast India.5,25,38,66,67,68,69 Therefore, the co-existence of the DBM (both under and overnutrition) is presenting a unique difficulty for public health policymakers. Nutrition transition have taken place in developing countries which led to the transition from undernutrition to overweight or obesity in populations.33 Although, this type of transition may lead to the reduction of infectious diseases and mortality-morbidity associated with it but it may increase the burden to several preventable non-communicable and lifestyle diseases and incurred economic expenditures.

In the present investigation, utilizing the recent WHO37 BMI reference, the prevalence of undernutrition BMI<18.50 kg/ m2) was 22.86% and prevalence of combined overweight or obesity (BMI ≥25.00 kg/m2) was 12.86% among the BMP (Table 2). A comparative evaluation of undernutrition (Figure 1) of different ethnic groups present in India as reported by different studies showed that the prevalence observed to be in the present investigation was lower than those reported for the Ahom (52.00%)70, Koch (50.00%)70, Bhatudi (64.50%)45, Kora-Mudi (52.20%)43, Savar (43.50%)42, Oraon (49.60%)36, Rajbanshi (42.00%) (North-Eastern India)70, Dhimal (36.40%)72, Lalung (34.69%)70, Mishing/Miri (34.00%)70, Santal (29.60%) (Odisha)36, Rajbanshi (23.56%) (WB)71, Santal (37.60%) (WB)36 and Bhumij (28.20%) (WB and Odisha)36 populations. The prevalence of undernutrition in the present investigation was observed to be higher than those reported among adults of Boro Kachari (11.22%)70, Dibongiya (21.43%)66, Mech (13.20%)71 and Nyishi (10.50%)69 populations. The comparative evaluation of overweight and obesity (Figure 2) of different ethnic groups present in India as reported by different studies showed that the prevalence observed in the present investigation was lower than those reported for Bengali Kayastha (80.00%)29, Rengma Naga (43.34%)5, Tangkhul Nagas of North-east India (27.10%)25, Marwaris (25.91%)27, Bengali Hindus (19.50%)21, adults of Kashmir (16.30%)73, Nyishi (19.51%)69, and Karbi women (31.66%)13 populations. The prevalence of overweight or obesity in the present investigation was observed to be higher than the adults belonging to Bhumij (8.00%), Santal of Odisha (7.10%), Santal of West Bengal (6.10%), Bhatundi (3.30%) and Oraon (2.50%) populations as reported by Kshatriya and Acharya.36

Figure 1: Comparison of Undernutrition Prevalence Among Other Populations of India and the Present Investigation36,42,45,66,69,70,71,72


Figure 2
: Comparison of Overweight or Obesity Prevalence Among Other Populations of India and the Present Investigation5,13,19,20,21,25,36,69,73

Comparison of Overweight or Obesity Prevalence Among Other Populations of India and the Present Investigation

The present investigation has also observed that prevalence of undernutrition was higher among BMP females (26.05%) as compared to their male counterparts (18.68%). In case overweight or obesity the prevalence was observed to be slightly higher among the BMP males (14.28%) than the females (11.76%). Prevalence of undernutrition was observed to be higher in lower age groups (18-29 years) p<0.05). Results of the BLR analysis showed a statistically significant association of undernutrition with the socio-economic variables viz., age and occupation of the individuals. Higher odds ratios have been observed for lower age groups (18- 39 years) (odds ratio: 1.65; p<0.05) and in occupation group of Farmers and Labourers (odds ratio: 5.61; p<0.05) In case of overweight or obesity individuals with smaller family sizes (2-4 individuals) showed statistically significant association (odds ratio: 2.09; p<0.05). The prevalence of overweight or obesity was observed significantly associated with family size p<0.05). Family size has been used to indicate how much proportion of resources are available for each person as increased family size or the increased number of individuals in a family may be a cause for competition for scarce resources. Therefore, rich families will have less competition and more resources while poor families will have fewer resources and more competition.

Associations of several socio-economic, demographic variables with the DBM (undernutrition and overweight or obesity) were already reported in the literature.1,2,67,74,75 The present investigation has also observed associations of undernutrition and overweight or obesity with different socio-economic variables. However, these associations were not observed to be significant p>0.05) in most of the cases as reported by other studies.2,74,75 There were associations between some other socio-economic and demographic variables (e.g., sex, toilet facility and income) and the nutritional status of the studied population but these associations were not statistically significant (p>0.05). Presence or absence of toilets is an indicator of good hygiene and related to good health. However, the association of toilets was observed to be statistically not significant with prevalence of both undernutrition and overweight or obesity (Table 3).

Recent socio-economic and demographic transition which are taking place very rapidly leads to acceleration of several preventable, NCDs and higher prevalence of overweight or obesity and slightly lower prevalence of undernutrition among populations.3,15,23,34 The present investigation observed higher prevalence of undernutrition and lower prevalence of overweight or obesity. According to, National Nutrition Monitoring Bureau (NNMB) (2005-2006) data, the combined prevalence of overweight and obesity was 7.80 and 10.90% among adult men and women (aged 18- 59 years) in rural India, respectively.76 National Family and Health Survey (NFHS)-3 and NFHS-4 data have clearly reflected that the overall nutritional status of the adult population with respect to chronic energy deficiency (CED) (BMI<18.5kg/m2) has declined among both males (34.20-20.20%) and females (35.50-22.90%). On the contrary, there has been an increase in the proportion of overweight or obese (BMI ≥25.00 kg/m2 ) in males (9.30-18.60%) and females (12.60-20.70%).77 The rise in household income has shifted the burden of overweight or obesity progressively from the wealthy to the poorer groups.2 There are several factors such as socio-economic status, demographic conditions, diet, increasing sedentary lifestyle and decrease in physical activity) which have triggered the prevalence of overweight or obesity in populations.2,13,25 Moreover, middle-aged individuals are more prone to being overweight and obese who mainly belongs to the higher socio-economic status and living in urban-affluent societies.4,13,21 The results of the BLR analysis in the present investigation showed that only the family size of individuals is significantly associated with overweight or obesity. Other socio-economic and demographic variables did not show any significant association with overweight obesity. On the other hand, age and occupation of individuals have shown significant association with undernutrition as observed in other studies.2,21,25


The findings of the present investigation suggest that even though undernutrition is still a cause for concern among the BMP there is also an existence of overweight and obesity. This establishes the existence of the DBM and the greater magnitude of undernutrition and overweight or obesity among the BMP. This observation was also similar to the findings of recent studies of Indian adult population. This suggests necessary nutritional interventions in the population concerned. The limitation of the present investigation is that the present investigation did not observe significant associations of many socio-economic factors as observed in other similar studies. The results in the present investigation might be helpful to the Government agencies and policy makers to formulate appropriate strategies such as organising regular health checkup camps to identify the health problems among the individuals due to undernutrition and overweight or obesity and also to carry out proper dissemination of knowledge about healthy diet, healthy lifestyle, and food supplementation for the overall improvement of the health status of the BMP.


The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.

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