Introduction:
Rapid economic development (although not uniform) throughout the world during last few decades witnessed massive transformation in many sectors like technology, living standard and others including health. However, people living in rural areas particularly in the developing countries like India are still lagging behind the satisfactory level in health, nutrition and economic condition (1,2). Still today people earn their livelihood like food, shelter and clothing depending on their physical labour. Ability of physical labour is controlled by the constellation of factors like health, nutrition, socio-economic condition, etc. (3). Nutritional status is one of the major components that affect individuals' ability to perform hard physical labour. Many studies pointed out that under-nutrition undermines adult's capacity to perform greater physical activity (4,5). Therefore, it is essential to understand the nutritional status of the population engaged in physical labour for the sake of better productivity and improving overall health and well-being of the population and the nation at large.
Anthropometric measurements are generally used to assess human body composition objectively, which is universally applicable method for studying nutritional status and overall health of populations (6-9). Variation in anthropometric measurements was reported in many studies and also in different occupational pursuits. Agricultural workers of USA are generally shorter in height while, transportation workers are generally obese and taller in height (10). Over half of the driver populations in the developed countries were either over-weight or obese (11). Further, research pointed out that the trend of being obese was more prominent among the night-shift workers (12). In Japan, the abdominal measurements (WC, HC) were greater among the office workers than manual workers (13,14). The prevalence of poor nutritional condition was higher among the South African farmers and that was associated with household food insecurity, lack of financial resources and poor educational status (15). The garment workers of Bangladesh show higher prevalence of low BMI which was associated with their poor economic condition and poor dietary intake (16). In Vietnam, Nguyen et al. (17) also found positive relationship between nutritional status and economic condition, standard of living as well as education. In Ethiopia, one-third of the cobble stone project workers were under-nourished (low BMI) (18). However, most of the mine workers of Poland were over-weights (19). Tanaka et al. (20) pointed out that occupation specific difference in dietary intake and so also nutritional status among Japanese workers. The security workers consume more dairy products and calcium while, agricultural workers consume more pickles and salt. These findings indicate that each occupation has its unique characteristics including working condition and workplace environmental factors.
In India, studies reported the differences in anthropometric measurements between labourer groups of same ethnic origin practicing different occupations (21,22). Studies on blacksmiths (23) and carpenters (24) show higher values of hand and forearm measurements compared to general population and that was due to their nature of job. Dewangan et al. (25) found regional variation in anthropometric traits between/ among agricultural groups in northeast India. Many studies reported higher prevalence of poor nutritional condition among the labourer groups in India which was associated with their poor economic condition and poor dietary intake, e.g. tea garden labourers (26-28), factory workers (29), fishermen (30), coal miners (31) and agricultural labourer (32-36). The role of living standard on nutritional status was also documented in other study (37).
From the above it was understandable that nutritional status varies with occupation and with other concomitants. Importantly, the nature, duration and posture of work vary across occupations and thus, the concomitants including energy expenditure would also be different. In view of above, present study was conducted among the two Santal labourer population (i.e. stone mine and agriculture) of Birbhum district of West Bengal. The aim was to (a) compare the nutritional status of the study population and (b) to investigate the concomitants (age, socio-economic characteristics, intake and expenditure of calorie) on nutritional status of the workers.
Materials and Methods
Present data is a part of a larger bio-medical project. Cross-sectional data were collected from Santal labourer populations having two different occupations viz. stone mining work (male=63 and female=38) and agricultural work (male=67 and female=36) of Suri sub-division of Birbhum district, West Bengal. The study was restricted to a single ethnic group (i.e. Santal) in order to maintain ethnic/genetic homogeneity in the sample which was noted in elsewhere (38). Santals are the third largest marginal
(schedule tribe) community and distributed in most of the districts of West Bengal (39). They were classified as 'Pre-Dravidian' tribe. Their language,
Santali belongs to the Mundari branch of Austro-Asiatic language family (40) and now they have their own script i.e. 'Ol-Chiki'.
Data on anthropometric traits including height, sitting height, weight, mid upper arm circumference, waist and hip circumferences were collected following standard techniques and instruments (41). Further, some indices were computed using standard formula as follows:
Body Mass Index (BMI)= Weight (kg) / Height (in meter)2
Percent body fat (PBF)= (4.201/D-3.813) x 100, where D= 1.0890 - {0.0028 x Triceps skinfold thickness (mm)} (42)
Total body fat (kg)= PBF/100 x Weight (kg)
Fat free mass (kg) = Weight (kg) - Fat mass (kg)
WHR= Waist circumference (cm)/ Hip circumference (cm)
Data on concomitants: Socio-economic data including educational status, marital status, household size and economic condition in terms of per capita monthly expenditure (Rupees per month in Indian currency) were collected using standard well tested questionnaire/ schedule. In some cases, the written records of age were absent, in those cases the ages of the study participants were estimated with reference to important local events and cross-checked with elderly individuals, which were further compared with the ages of individuals for whom age records existed.
Data on calorie intake and expenditure were collected with the help of well-tested questionnaire/ schedule through 24 hours recall method. Intake of calorie (dietary) was calculated from cooked food items consumed by the individual, the previous day prior to the survey. Only the major sources of calorie dense food items like rice, chapati, potato, fish, meat, fruits, local beverage (handia/brewed rice) etc. were included in the present calculation. On the other, data on calorie expenditure/ habitual physical activity was collected throughout day and night (i.e. 24 hours). The unavoidable limitations of under/ over reporting of the data may not be ruled out.
No statistical sampling was attempted for the selection of study participants. Individuals who had been persuaded to participate and voluntarily agreed with written consent had been included in the present study without any bias. The research was conducted after prior approval from the Ethical Committee for the Protection of Research Risks to Humans, Indian Statistical Institute.
Classification of data: Nutritional status was assessed following cut-off values of BMI to classify under-weight (<18.5), normal (18.5-24.9) and over-weight (≥25.0). Educational status was categorized into 'non-literate', 'primary (class I-IV)' and 'above (class V and/ or above)'; marital status into 'never' and 'ever' married. Household size and economic status was classified into two categories each, by using median value. Household size was categorized as '‘≤6' and '7+' individuals; economic status was categorized as 'low' (Rs.
≤540.00/- per month) and 'high' (Rs. >540.00/- per month).
Data analysis: Descriptive statistics and cross-tabulation were computed for each variable. Student's 't' tests were performed to find out the mean differences between groups. To find out the concomitants of nutritional status, stepwise multivariate logistic regression analysis performed for each occupational group (i.e. stone mining work and agricultural work) and sex, considering nutritional status (i.e ‘normal’ and ‘under-nutrition’) as dependent variable while education, marital status, household size, economic group, age, daily calorie intake and daily calorie expenditure of the participants as independent variables. In stepwise method, the last model was considered for highest R2 (Negelkerke's R2) value. All the statistical analyses have been done using SPSS software 16.0 (SPSS Inc., Chicago, IL, USA).
Results
| Table 1: Descriptive statistics of socio-economic characteristics of two occupational groups in either sex |
Male (n=101) |
Socio-economic characteristics |
Stone mine (n=63) |
Agriculture (n=38) |
χ2 |
p |
n |
% |
n |
% |
Marital
status group |
Unmarried |
9 |
14.29 |
4 |
10.53 |
0.762 |
0.413 |
Ever married |
54 |
85.71 |
34 |
89.47 |
Educational
status |
Non-literate |
23 |
36.51 |
9 |
23.68 |
2.287 |
0.319 |
Primary |
24 |
38.10 |
15 |
39.47 |
Above |
16 |
25.40 |
14 |
36.84 |
Economic
condition |
Low (Rs. ≤540.00/-) |
42 |
66.67 |
15 |
39.47 |
7.129 |
0.008* |
High (Rs. >540.00/-) |
21 |
33.33 |
23 |
60.53 |
Household
size group |
≤6 |
37 |
58.73 |
25 |
65.79 |
0.498 |
0.480 |
7+ |
26 |
41.27 |
13 |
34.21 |
Female (n=103) |
Socio-economic characteristics |
Stone mine (n=67) |
Agriculture (n=36) |
χ2 |
p |
n |
% |
n |
% |
Marital
status group |
Unmarried |
2 |
2.99 |
3 |
8.33 |
0.340 |
0.230 |
Ever married |
65 |
97.01 |
33 |
91.67 |
Educational
status |
Non-literate |
42 |
62.69 |
16 |
44.44 |
3.172 |
0.205 |
Primary |
14 |
20.90 |
11 |
30.56 |
Above |
11 |
16.42 |
9 |
25.00 |
Economic
condition |
Low (Rs. ≤540.00/-) |
38 |
56.72 |
15 |
41.67 |
2.123 |
0.145 |
High (Rs. >540.00/-) |
29 |
43.28 |
21 |
58.33 |
Household
size group |
≤6 |
34 |
50.75 |
30 |
83.33 |
10.570 |
0.001* |
7+ |
33 |
49.25 |
6 |
16.67 |
| *p≤0.05 |
Table 1 shows socio-economic characteristics in terms of marital status, educational status, economic condition and household size of two occupational groups in either sex. In males, majority of the study participants irrespective of occupation were married and living in household size
≤6 individuals. Majority of non-literate and low economic group males were from stone mining group. Statistically significant association found between economic group and occupation. In females, majority of the study participants irrespective of occupation were married and living in household size
≤6 individuals. The percentages of non-literate females were more than 40% in both the occupational groups. Again, majority of the females of stone mining group were from low economic group. Statistically significant association found between household size group and occupation.
| Table 2: Descriptive statistics of anthropometric traits of two occupational groups in either sex |
Anthropometric traits |
Male (n=101) |
Female (n=103) |
Stone mine
(n=63) |
Agriculture
(n=38) |
t |
p |
Stone mine
(n=67) |
Agriculture
(n=36) |
t |
p |
Mean |
SD |
Mean |
SD |
Mean |
SD |
Mean |
SD |
Age (yrs) |
34.87 |
11.61 |
35.37 |
11.32 |
0.210 |
0.834 |
34.93 |
11.76 |
34.69 |
11.76 |
0.095 |
0.925 |
Height (cm) |
161.38 |
5.71 |
160.59 |
7.39 |
0.606 |
0.546 |
149.57 |
6.26 |
147.14 |
4.80 |
2.029 |
0.045* |
Sitting height (cm) |
81.37 |
3.01 |
80.94 |
4.29 |
0.580 |
0.563 |
74.56 |
3.14 |
74.52 |
2.48 |
0.057 |
0.955 |
Weight (kg) |
51.05 |
5.51 |
49.65 |
8.45 |
1.012 |
0.314 |
41.98 |
6.48 |
41.30 |
5.21 |
0.543 |
0.588 |
MUAC (cm) |
24.76 |
1.92 |
24.16 |
2.49 |
1.356 |
0.178 |
23.40 |
2.33 |
22.36 |
1.99 |
2.280 |
0.025* |
WC (cm) |
73.70 |
6.15 |
71.41 |
8.56 |
1.556 |
0.123 |
68.11 |
7.99 |
65.44 |
6.40 |
1.732 |
0.086 |
HC (cm) |
83.27 |
3.98 |
82.43 |
5.56 |
0.888 |
0.377 |
81.89 |
5.68 |
81.30 |
4.64 |
0.534 |
0.594 |
Total body fat (kg) |
5.16 |
1.03 |
5.42 |
2.50 |
0.721 |
0.473 |
5.58 |
1.86 |
5.56 |
1.59 |
0.048 |
0.962 |
Fat free mass (kg) |
45.90 |
4.71 |
44.23 |
6.33 |
1.505 |
0.135 |
36.41 |
4.87 |
35.74 |
3.76 |
0.711 |
0.478 |
Percent Body Fat (PBF) |
10.05 |
1.27 |
10.57 |
2.72 |
1.297 |
0.198 |
13.00 |
2.55 |
13.24 |
2.26 |
0.458 |
0.648 |
WHR |
0.88 |
0.06 |
0.87 |
0.08 |
1.437 |
0.154 |
0.83 |
0.06 |
0.80 |
0.06 |
2.067 |
0.041* |
BMI |
19.60 |
1.83 |
19.17 |
2.32 |
1.024 |
0.308 |
18.74 |
2.44 |
19.05 |
2.00 |
0.665 |
0.508 |
| *p≤0.05, MUAC= Mid-upper arm circumference, WC= Waist circumference, HC= Hip circumference, WHR= Waist Hip Ratio, BMI= Body Mass Index |
Table 2 shows descriptive statistics of anthropometric traits of two occupational groups in either sex. In males, the mean values of all of the anthropometric traits were more or less similar between groups. Results of t-test failed to show significant difference. In females, the mean values of most of the anthropometric traits were similar between the two groups. However, the mean values of height, MUAC and WHR significantly differed between the groups.
| Table 3: Descriptive statistics of calorie intake and calorie expenditure of two occupational groups in either sex |
| Male (n=101) |
Concomitants |
Stone mine |
Agriculture |
t |
p |
n |
Mean |
SD |
n |
Mean |
SD |
Calorie intake (Kcal/day) |
63 |
3392.34 |
1015.90 |
38 |
3309.54 |
904.56 |
0.413 |
0.680 |
Calorie expenditure (Kcal/day) |
63 |
3067.03 |
870.31 |
38 |
2482.71 |
719.80 |
3.481 |
0.001* |
Female (n=103) |
|
Stone mine |
Agriculture |
t |
p |
Concomitants |
n |
Mean |
SD |
n |
Mean |
SD |
Calorie intake (Kcal/day) |
67 |
2915.79 |
1018.24 |
36 |
2487.07 |
520.56 |
2.362 |
0.020* |
Calorie expenditure (Kcal/day) |
67 |
2151.00 |
665.83 |
36 |
2119.00 |
438.85 |
0.259 |
0.796 |
| *p≤0.05 |
Table 3 shows descriptive statistics of calorie intake and calorie expenditure of two occupational groups in both the sex. In males, both the occupational group shows more or less similar mean value in terms of calorie intake but differ in terms of calorie expenditure. In females, both the occupational group shows more or less similar mean value in terms of calorie expenditure but differ in terms of calorie intake.
Figure 1: Distribution of nutritional status by body mass index (BMI) across two occupational groups in either sex |
 |
 |
Males |
Females |
Figure 1 shows distribution of nutritional status by body mass index (BMI) across two occupational groups in either sex. In males, around 27% and 40% labourer were under-weight in stone mine and agricultural worker group respectively. In females, around 60% and 33% labourer were under-weight in stone mine and agricultural worker groups respectively. The association between nutritional status and occupational status was statistically significant.
| Table 4: Result of multivariate logistic regression of different concomitants on nutritional status in either sex |
Independent variables
(concomitants) |
Male |
Female |
Stone mine Worker |
Agricultural Worker |
Stone mine Worker |
Agricultural Worker |
Stepwise Model V |
Stepwise Model VII |
Stepwise Model V |
Stepwise Model VI |
OR (95% C.I.) |
p |
OR (95% C.I.) |
p |
OR (95% C.I.) |
p |
OR (95% C.I.) |
p |
Marital
status |
Never |
7.913 (0.953-65.736) |
0.056 |
- |
- |
- |
Ever |
Ref. |
Education |
Non-literate |
5.499 (0.763-39.625) |
0.091 |
- |
6.991 (1.128-43.344) |
0.037* |
- |
Primary |
0.334 (0.043-2.615) |
0.296 |
28.912 (2.844-293.889) |
0.004* |
Above |
Ref. |
Ref. |
Household
size |
≤6 |
- |
- |
- |
- |
7+ |
Economic |
Low |
- |
- |
- |
9.711 (1.308-72.081) |
0.026* |
High |
Ref. |
Age |
- |
- |
0.953 (0.899-1.010) |
0.104 |
- |
Calorie intake |
- |
- |
|
- |
Calorie expenditure |
0.998 (0.997-0.999) |
0.002* |
0.999 (0.998-1.000) |
0.073 |
0.999 (0.998-1.000) |
0.005* |
0.996 (0.993-0.999) |
0.008* |
Neglekerke's R2 |
0.530 |
0.143 |
0.335 |
0.449 |
Model correctly predicted (%) |
77.8 |
63.2 |
74.6 |
75.0 |
| *p≤0.05 |
Table 4 shows the result of multivariate logistic regression of different concomitants on nutritional status in either sex for both occupational groups. In males, among stone mine workers nutritional status was significantly associated with marital status, educational status and calorie expenditure. On the other, among agricultural workers nutritional status was significantly associated with calorie expenditure.
In females, among stone mine workers nutritional status was significantly associated with educational status, age and calorie expenditure. On the other, among agricultural workers nutritional status was significantly associated with economic condition and calorie expenditure.
Discussion
The aim of the study was to understand the nutritional status of two Santal occupational groups (viz. stone mine and agriculture) of Birbhum district, West Bengal and also to investigate the concomitants of their nutritional condition. The individuals of the present study were from same ethnic/ genetic stock, living in similar geographical set-up and socio-cultural background but engaged in two different occupations: stone mining and agriculture. The protocols for data collection for all the individuals were same and the data were collected by a single investigator.
The results of present study indicate similarities in most of the socio-economic characters that may be due to similarities in their ethnicity and cultural homogeneity. However, in terms of educational status, individuals from stone mine group were largely non-literate compared to the agricultural group. Such differences may be due to the stay of families of stone mine group in a remote area which was far away from urban center (nearest town Suri is 40 km away) where schooling facilities were very limited as compared to individuals of agricultural group (nearest town Suri is 10 km away).
The mean values of most of the anthropometric traits were similar between the two occupational groups. That may be due to similarities in genetic endowment, geo-environment and culture (43). This finding corroborates with the study of Chatterjee et al. (44), who reported that individuals sharing common genetic endowment would show similarities in anthropometric traits. However, some specific anthropometric measurements were different between two occupational groups. Mainly circumference measurements (like MUAC, WC and HC) were greater in stone mine worker than agricultural worker that may be due to differences in specific type of physical activity which requires specific muscles involvement. Present finding also corroborates with the study of Roy and Pal (21), who noted differences in diameter and circumference measurements between two occupational groups of north Bengal and explained such differences were due to differences in micro-adaptation factors (i.e. occupation). Stone mining work requires greater physical activity and the work continues throughout the year, whereas agricultural work is generally seasonal in nature. Similar findings were reported by Roy (27), who noted that higher circumference measurements (mid upper arm and chest) among the high productive agricultural workers, who were engaged in higher physical activity compared to low productive agricultural workers of Jalpaiguri district, West Bengal. Sengupta and Sahoo (45) reported that greater circumference measurements among fisherman of West Bengal were due to higher level of physical activity in their occupation. Study conducted among the blacksmith workers also shows developed upper extremity circumferences, which indicate developed muscle mass were associated with the heavy physical work (23).
The mean values of calorie expenditure were higher in stone mine worker group in both sex compared to agricultural worker group that may be due to the differences in the levels of daily physical activity. Stone mining work requires greater energy at work than agricultural work. On the other, the mean value of energy/ calorie intake was similar in males that may be due to similarities in cultural practice (tradition) of both the groups. Food habit of any group is generally determined by socio-economic condition (46), culture and availability of foods (47). Since, the study groups were living close to each other, economic condition and availability of foods were similar and therefore, they showed similarities in energy/ calorie intake. However, females show differences in calorie intake between groups. The intake of calorie was greater among female of stone mining work than agricultural work that may be explained as stone mining work requires greater energy in work compared to agricultural work. It is important to mention that present data on calorie intake and expenditure may have some unavoidable limitations like recall lapse, over and under reporting, seasonal variation of food, etc. (48), which cannot be ruled out.
Around 27% of males and 60% females of stone mine workers and around 40% of males and 34% females of agricultural workers showed low BMI (<18.5 under-weight), suffers from chronic energy deficiency (49), indicating worse nutritional status of both the occupational groups particularly in females. This finding corroborates with the studies conducted among Santals of Medinipur [50], Birbhum (34), Bankura (36) and Howrah (35) district of West Bengal. All the studies reported poor nutritional condition of the Santals engaged in different occupational pursuits and that was due to their poor economic condition as well as poor dietary intake of calorie. Several studies (51, 52) reported that among tribal population, intake of calories and protein were deficient than the Indian Recommended Dietary Allowance, which results high prevalence of under-weight among tribal population. Chakrabarty and Bharati (53, 54) pointed out that lower consumption of calorie in diet and poor economic condition contributes towards poor nutritional condition among Shabars of Odisha. Shah and Sah (55) reported that peoples living in rural areas with poverty had limited food resources, their diet primarily based on rice and lacks protein or fats. At the same time, they expend more energy by performing heavy manual work which may leads to nutritional deficiency. Besides, many studies (56, 57) reported that intestinal parasitic infection contributes greatly on nutritional status of the tribal population in India which may be also true in the present case.
The results of multivariate logistic regression analysis depicted that nutritional status was associated with educational status and calorie expenditure among individuals engaged in stone mining work. This finding corroborates with the study of Bose et al. (58), who reported that individual with formal schooling had lower chance of under-nutrition than individuals without any formal schooling. Similar finding also reported by Bhandari et al. (59), who found that higher prevalence of under-nutrition among non-literate than their literate Sabars of West Bengal. The results also depicted that among agricultural workers nutritional status was associated with economic condition and calorie expenditure. This finding corroborates with the study conducted by Sinha and Sharma (60), who suggested that poverty and faulty dietary habits including ignorance, superstitions, lack of knowledge regarding balanced food, unhygienic practice were also responsible for higher prevalence of poor nutrition among tribal population in India. Laxmaiah et al. (61) reported that around 51% of tribal population in southern part of India were suffers from chronic energy deficiency and that was mainly influenced by their poor economic condition.
Conclusion
Present study indicates that a large portion of males and females irrespective of occupation were under-weight and suffer from chronic energy deficiency. Education, economic condition and calorie expenditure were supposed to be the most important factors that have most significant influence on nutritional status on the population irrespective of sex. However, the result of the present study is based on a small sample size, data are cross-sectional in nature and the study concentrates on a particular ethnic group, therefore, it is hard to make a final conclusion. In view, future studies are necessary on other marginal populations, considering other concomitants also to get better insight into the problem.
Source of Funding: This work was supported by Indian Statistical Institute, Kolkata.
Conflict of Interest: The authors have no conflict of interests to declare.
Ethical Approval: Data of this study is a part of bio-medical research project approved by the Ethical Committee for the Protection of Research Risks to Humans, Indian Statistical Institute, Kolkata.
Acknowledgement: The authors are indebted to the participants of the present study and are also thankful to Indian Statistical Institute for providing financial and logistic support. Authors are also thankful to Prof. Subha Roy for his valuable suggestions during preparation of manuscript.
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