Introduction:
Epidemiological research in recent years has identified multiple factors primarily associated with a wide range of health conditions like cardiovascular diseases, poor birth outcomes, infectious diseases, undernutrition, depression, inactivity, and all-causes of mortality. (1-5) Public health practitioners identified these factors as important determinants of health like housing arrangements, sanitation, access to safe drinking water, food habits, hygiene practice, level of education, earning opportunities, income, etc. (3)
Prolonged deprivation of different nutrients is the main cause of major health problems among males and females of all ages for most of the developing countries. (6-7) A recent report of the World Health Organization (WHO) stated around 45% children of below 5 years of age died every year due to undernutrition (8) as it increases susceptibility to infectious diseases. (9-11) Generally, nutrition-related studies on adults specifically from low-income countries are more often been unaddressed (12) and particularly among males as the researchers have studied mostly the undernourishment among women, infants, and children. (13) In India, tribal people are from the vulnerable section of the population and develop various forms of morbidities due to their primitive agricultural practices, lack of formal education, socio-cultural taboos, poverty, poor infrastructural facilities, different health-seeking behaviour, poor living condition in geographical isolation, etc. (14-15) Consequently, the prevalence of undernutrition (BMI<18.5 kg/m2) was quite high (ranging 40% to 50%) among the tribal males in India as reported by different studies on tribal health and nutrition. (14-16)
Sabars, the 10th largest tribal group in West Bengal, are mostly scattered in jungle areas of the districts of Purulia, Medinipur, Jhargram, and Bankura. (17) Traditionally, they were forest dwellers and found in the states of Odissa, Jharkhand and West Bengal but after the enactment of forest protection acts, they were forced to leave the forests and most of them became settled in the proximity of forest areas. Their lack of the skill of agriculture in addition to their low literacy has left them with no other options of earning their livelihood except as wage labour. This problem is compounded by a stigma of criminality on them as British rulers declared them since Criminal Tribe under Criminal Tribes Act, 1871 which was removed after the independence as the criminal label was replaced and they declared as Denotified Tribes (DNTS). (13) But they have to bear the tag of criminality till today which also contributes to their chronic poverty and makes them more marginalized compare to the other tribal groups of West Bengal. In this study, the main objective was to understand the relationship between socio-demographic factors and health conditions of Sabar adult males living in West Bengal.
Materials and Methods
This cross-sectional study was conducted among 350 Sabar adult males, ages 18-60 years of Purulia district in the Indian province of West Bengal during September-December 2018 and October 2019-January 2020. Participants were selected at random based on their availability during fieldwork from 15 villages under 4 administrative blocks of Purulia. These villages are Akarbad of Purulia-I block; Kuda, Punru, Bamni, Jonarah, Jabla, Kasidih, and Makarkendi of Manbazar-I block; Damodarpur, Babuijor, Balakdih, Bankanali and Bengthupi of Puncha; and Amjharna and Popo of Bandwan block. Necessary permissions were obtained from the local administration and community leaders prior to the commencement of the study. Participants were explained about the objectives of this study and consent was obtained before data collection.
Socio-demographic particulars, such as age, types of family, family size, marital status, occupation, education, income, house type, dwellings, sanitation, etc. were collected from all the participants by using a pre-tested questionnaire to access the socio-economic status. The participants were categorised into three income groups based on their per capita income in rupees (Rs.) using tartiles: Low (Rs. ≤593.04), medium (Rs. 593.05-675.53), and high-income group (Rs. ≥ 675.54). The size of the families based on the number of family members have been divided into small (1-4), medium (5-7), large (10-14), and very large (15 and above) groups. Types of houses were categorized into kutcha (made up of mud, straw, bamboo sticks, etc), semi-pucca (made up of brick build walls and floor and the roof was thatched), and pucca (built of materials such as brick, cement, or concrete). Besides, for a better understanding of their livelihood pattern, semi-structured in-depth interviews were also conducted.
Anthropometric measurements of height and weight were taken following standard techniques. (18) Height was recorded to the nearest 0.1 cm and weight was 0.5 kg by using Martin’s Anthropometer rod and digital weighing machine Omron HN 289. Technical error of measurements was found within acceptable limits. (19) Body Mass Index (BMI) was calculated by using the following standard formula: BMI= Weight (kg)/Height (m2). The participants were divided into three BMI based categories following the classification of WHO (20):
- Undernutrition (BMI <18.5 kg/m2)
- Normal (BMI 18.5-24.9 kg/m2)
- Overweight (BMI ≥ 25.0 kg/m2)
For all statistical analyses, we used the SPSS Statistical Package (Version 16.0). Descriptive statistics were done to get mean, standard deviation, minimum and maximum values for all anthropometric variables. The Chi-square test was used to see the association between different socio-demographic variables and nutritional categories. Furthermore, one way ANOVA and independent-sample t-test were performed to find out significant differences among different socio-demographic categories in mean height, weight, and BMI. Statistical significance was set at p<0.05.
Results
| Table 1: Distribution of Socio-demographic characteristics among the studied population |
| Variables |
Frequency |
Percentage (%) |
Family Size |
Small (1-4) |
93 |
26.6 |
Medium (5-9) |
227 |
64.9 |
Large (10-14) |
27 |
7.7 |
Very Large (15 and above) |
3 |
0.9 |
Family Type |
Nuclear |
162 |
46.3 |
Joint |
101 |
28.9 |
Extended |
87 |
24.9 |
House Type |
Kutcha |
110 |
31.4 |
Pucca |
3 |
0.9 |
Semi-pucca |
237 |
67.7 |
Number of Rooms |
Single |
83 |
23.7 |
Double |
112 |
32.0 |
Triple |
155 |
44.3 |
Separate Kitchen |
Present |
92 |
26.3 |
Absent |
258 |
73.7 |
Source of Drinking water |
Well |
11 |
3.1 |
Tube well |
285 |
81.4 |
Pond |
27 |
7.7 |
Stream |
27 |
7.7 |
Electrification |
Present |
246 |
70.3 |
Absent |
104 |
29.7 |
Sanitary Latrine |
Absent |
255 |
72.9 |
Present |
03 |
0.9 |
Present but not in use |
92 |
26.3 |
Educational Status |
Non-literate |
172 |
49.1 |
Can sign only |
68 |
19.4 |
Primary |
23 |
6.6 |
Upper-primary |
67 |
19.1 |
Secondary |
15 |
4.3 |
Higher secondary |
4 |
1.3 |
Graduation |
1 |
0.4 |
Marital Status |
Married |
313 |
89.4 |
Unmarried |
23 |
6.6 |
Widower |
13 |
3.7 |
Separated |
1 |
0.3 |
Primary source of earnings |
Daily wage labour |
298 |
85.1 |
Agricultural labour |
17 |
4.9 |
Business |
1 |
0.3 |
Service |
17 |
4.9 |
Dependent |
17 |
4.9 |
Per capita income (Rs.) |
Low (≤ 593.04) |
124 |
35.4 |
Medium (593.05-675.53) |
131 |
37.4 |
High (≥ 675.54) |
95 |
27.1 |
The distribution of socio-economic characteristics of the studied population is presented in Table 1. The highest number of participants were from the medium-sized families (64.9%) followed by the small (26.6%), large (7.7%), and very large (0.9%) families. Almost half of them were from nuclear families (46.3%). Data revealed very poor educational standard of the participants where non-literate were 49.1% followed by 19.4% of those who can write their names only and a meagre 0.4% had the education up to undergraduate level. Most participants were married (89.4%). Most of them were working as daily wage labour (85.1%), whereas, agricultural labour, service, and business were also found. The maximum number of participants (37.4%) reported their per capita income as ranging from Rs. 593.05-675.53 which was categorized as medium income group followed by 35.4% of low (Rs. ≤ 593.04) and 27.1% of the high-income group (Rs. ≥ 675.54). The majority of them were living in semi-pucca houses (67.7%) where 70.3% of households have electricity connections. Facilities like the separate kitchen (26.3%) or sanitary latrine (0.9%) were found scarcely. The drinking water source for the majority of the sample group was tubewell (81.4%) while the other used ponds (7.7%) and rivulets (3.1%).
| Table 2: Nutritional status of the studied people based on BMI |
| Nutritional Status |
Frequency |
Percentage (%) |
Undernutrition |
159 |
45.4 |
Normal |
177 |
50.5 |
Overweight |
14 |
4.0 |
Table 2 represented the nutritional status of the studied people based on BMI, where the prevalence of undernutrition, normal and overweight were 45.4%, 50.5% and 4.0% respectively.
| Table 3: Descriptive statistics of the anthropometric variables among the adult Sabar males of Purulia |
| Variables |
Mean (±SD) |
Minimum |
Maximum |
Age (years) |
38.08±13.69 |
18.00 |
60.00 |
Height (cm) |
162.22±5.71 |
140.50 |
176.00 |
Weight (kg) |
50.25±7.47 |
35.00 |
77.10 |
BMI (kg/m2) |
19.08±2.58 |
13.3 |
30.7 |
In Table 3, the descriptive statistics of the anthropometric variables of the Sabar males have been demonstrated. The mean with standard deviation (SD) values of age, height, weight and BMI were 38.08±13.69 years, 162.22±5.71 cm, 50.25±7.47 kg, and 19.08±2.58 kg/m2 respectively.
| Table 4: Association between the socio-demographic variables and the nutritional status of the studied population |
| Variables |
Nutritional Categories |
Total |
Chi-Square |
Df |
Sig. |
Undernutrition |
Normal |
Overweight |
Family Size |
Small |
40
(25.2) |
49 (27.7) |
4
(28.6) |
93 (26.6) |
3.528 |
6 |
0.740 |
Medium |
108
(67.9) |
109 (61.6) |
10
(71.4) |
227 (64.9) |
Large |
10
(6.3) |
17
(9.6) |
0
(0.00) |
27 (7.7) |
Very Large |
1
(0.6) |
2
(1.1) |
0
(0.00) |
3
(0.9) |
Family Type |
Nuclear |
69
(43.4) |
88 (49.7) |
5
(35.7) |
162 (46.3) |
15.193 |
4 |
0.004 |
Joint |
52
(32.7) |
49 (27.7) |
0
(0.00) |
101 (28.9) |
Extended |
38
(23.9) |
40 (22.6) |
9
(64.3) |
87 (24.9) |
House Type |
Kutcha |
43
(27.0) |
59 (33.3) |
8
(57.1) |
110 (31.4) |
13.533 |
4 |
0.009 |
Pucca |
1
(0.6) |
1
(0.6) |
1
(7.1) |
3
(0.9) |
Semi-pucca |
115
(72.3) |
117 (66.1) |
5
(35.7) |
237 (67.7) |
Number of Rooms |
Single |
33
(20.8) |
41 (23.2) |
9
(64.3) |
83 (23.7) |
21.177 |
4 |
0.000 |
Double |
42
(26.4) |
68 (38.4) |
2
(14.3) |
112 (32.0) |
Triple |
84
(52.8) |
68 (38.4) |
3
(21.4) |
155 (44.3) |
Separate Kitchen |
Present |
59
(37.1) |
33 (18.6) |
0
(0.00) |
92 (26.3) |
19.935 |
2 |
0.000 |
Absent |
100
(62.9) |
144 (81.4) |
14
(100.0) |
258 (73.7) |
Source of Drinking water |
Well |
7
(4.4) |
4
(2.3) |
0
(0.00) |
11 (3.1) |
8.062 |
6 |
0.234 |
Tube well |
124
(78.0) |
147 (83.1) |
14
(100.0) |
285 (81.4) |
Pond |
17
(10.7) |
10
(5.6) |
0
(0.00) |
27 (7.7) |
Stream |
11
(6.9) |
16
(9.0) |
0
(0.00) |
27 (7.7) |
Electrification |
Present |
117
(73.6) |
115 (65.0) |
14
(100.0) |
246 (70.3) |
9.141 |
2 |
0.010 |
Absent |
42
(26.4) |
62 (35.0) |
0
(0.00) |
104 (29.7) |
Sanitary Latrine |
Absent |
136
(85.5) |
107 (60.5) |
12
(85.7) |
255 (72.9) |
28.724 |
4 |
0.000 |
Present |
0
(0.00) |
3
(1.7) |
0
(0.00) |
3
(0.9) |
Present but not in use |
23
(14.5) |
67 (37.9) |
2
(14.3) |
92 (26.2) |
Educational Status |
Non-literate |
79
(49.7) |
87 (49.2) |
6
(42.9) |
172 (49.1) |
7.527 |
12 |
0.821 |
Can sign only |
27
(17.0) |
37 (20.9) |
4
(28.6) |
68 (19.4) |
Primary |
8
(5.0) |
13
(7.3) |
2
(14.3) |
23 (6.6) |
Upper-primary |
34
(21.4) |
31 (17.5) |
2
(14.3) |
67 (19.1) |
Secondary |
7
(4.4) |
8
(4.5) |
0
(0.00) |
15 (4.3) |
Higher secondary |
3
(1.9) |
1
(0.6) |
0
(0.00) |
4
(1.3) |
Graduation |
1
(0.6) |
0
(0.00) |
0
(0.00) |
1
(0.4) |
Marital Status |
Married |
147
(92.5) |
155 (87.6) |
11
(78.6) |
313 (89.4) |
13.969 |
6 |
0.030 |
Unmarried |
11
(6.9) |
10
(5.6) |
2
(14.3) |
23 (6.6) |
Widower |
0
(0.00) |
12
(6.8) |
1
(7.1) |
13 (3.7) |
Separated |
1
(0.6) |
0
(0.00) |
0
(0.00) |
1
(0.3) |
Primary source of earnings |
Daily wage labour |
134
(84.3) |
152 (85.9) |
12
(85.7) |
298 (85.1) |
6.523 |
8 |
0.589 |
Agricultural labour |
10
(6.3) |
7
(4.0) |
0
(0.00) |
17 (4.9) |
Business |
1
(0.6) |
0
(0.00) |
0
(0.00) |
1
(0.3) |
Service |
7
(4.4) |
8
(4.5) |
2
(14.3) |
17 (4.9) |
Dependent |
7
(4.4) |
10
(5.6) |
0
(0.00) |
17 (4.9) |
Per capita income (Rs.) |
Low |
115
(66.66) |
9
(6.21) |
0
(0.00) |
124 (35.42) |
136.829 |
4 |
0.000 |
Medium |
42
(26.41) |
89 (41.80) |
0
(0.00) |
131 (37.42) |
High |
2
(6.91) |
79 (51.97) |
14
(100.00) |
95 (27.14) |
| Percentages are presented in parentheses |
Table 4 depicted the association between the socio-demographic variables and the nutritional categories of the studied population. Nuclear (43.4%) families have higher undernourished participants than joint (32.7%) and extended (23.9%) families, whereas, overweight males were found in highest frequency among extended families (64.3%). Undernutrition was highest among participants living in semi-pucca houses (72.3%). The frequency of undernutrition was increased from single room houses (20.8%) to three-room houses (52.8%). The highest proportion of undernutrition was found in those participants who lack the facilities of a separate kitchen (62.9%) or sanitary latrine (85.5%). While 49.7% of undernourished, as well as 49.2% of normal-weight people, were found as non-literate. Maximum males were married (89.4%) irrespective of all BMI categories. Total 85.1% of participants were daily wage labour in which the highest frequency was reported from the normal category (85.9%) of BMI, where undernutrition was 84.3%. Undernourishment was found 6.3% among agricultural labour and 4.4% among the category of service. The highest frequency of undernutrition was reported from the low-income group (66.6%) and all overweight males belong to the high-income group. Statistically significant association were observed in the socio-demographic variables viz. family type (chi square= 15.193, df= 4, p= 0.004), house type (chi square= 13.533, df= 4, p= 0.009), number of rooms (chi square= 21.177, df= 4, p= 0.000), presence of separate kitchen (chi square= 19.935, df= 2, p= 0.000), electrification (chi square= 9.141, df= 2, p= 0.010), sanitary latrine (chi square= 28.724, df= 4, p= 0.000) and per capita income (chi square= 136.829, df= 4, p= 0.000) with nutritional categories, and whereas rest of the variables could not show any significant association.
Table 5: Mean (±SD) values for anthropometric variables among different socio-demographic characteristics of studied population. |
(a) Family Type |
Variables |
Nuclear |
Joint |
Extended |
F |
Sig. |
Height |
162.49±6.28 |
161.90±5.61 |
162.22±5.71 |
.230 |
.794 |
Weight |
50.28±7.73 |
50.80±7.27 |
49.55±7.30 |
.416 |
.660 |
BMI |
19.04±2.78 |
19.34±2.34 |
18.48±2.48 |
.583 |
.559 |
(b) House Type |
Variables |
Kutcha |
Pucca |
Semi-pucca |
F |
Sig. |
Height |
162.35±6.33 |
168.30±6.92 |
162.07±5.38 |
1.201 |
.303 |
Weight |
49.90±7.18 |
52.85±6.01 |
50.38±7.65 |
.218 |
.804 |
BMI |
18.92±2.47 |
18.61±0.58 |
19.16±2.65 |
.245 |
.783 |
(c) Number of Rooms |
Variables |
Single |
Double |
Triple |
F |
Sig. |
Height |
160.97±6.35 |
162.72±5.39 |
162.51±5.53 |
1.689 |
.187 |
Weight |
49.44±8.01 |
50.00±6.94 |
50.86±7.57 |
.685 |
.505 |
BMI |
19.04±2.71 |
18.87±2.41 |
19.25±2.64 |
.454 |
.636 |
(d) Sanitary Latrine |
Variables |
Absent |
Present |
Present but not in use |
F |
Sig. |
Height |
162.29±5.73 |
161.15±3.74 |
162.05±5.77 |
.071 |
.931 |
Weight |
50.40±7.66 |
69.20±11.17 |
49.19±5.94 |
7.452 |
.001 |
BMI |
19.11±2.60 |
26.74±5.54 |
18.72±2.01 |
10.166 |
.000 |
(e) Marital Status |
Variables |
Married |
Unmarried |
Widower |
Separated |
F |
Sig. |
Height |
162.27±5.79 |
162.20±5.64 |
160.72±4.33 |
162.80 |
.191 |
.902 |
Weight |
50.39±7.44 |
51.40±7.33 |
45.31±7.87 |
44.20 |
1.535 |
.206 |
BMI |
19.12±2.60 |
19.48±2.06 |
17.50±2.69 |
16.67 |
1.430 |
.235 |
(f) Separate Kitchen |
Variables |
Absent |
Present |
t |
Sig. |
Height |
162.42±5.91 |
161.64±5.10 |
.907 |
.371 |
Weight |
50.37±7.63 |
49.91±7.06 |
.410 |
.833 |
BMI |
19.07±2.60 |
19.10±2.56 |
-.064 |
.883 |
(g) Electrification |
Variables |
Absent |
Present |
t |
Sig. |
Height |
162.22±6.78 |
162.21±5.21 |
.011 |
.014 |
Weight |
48.44±6.99 |
51.01±7.56 |
-2.383 |
.401 |
BMI |
18.39±2.37 |
19.37±2.62 |
-2.620 |
.164 |
(h) Family Size |
Variables |
Small |
Medium |
Large |
Very Large |
F |
Sig. |
Height |
162.63±6.31 |
162.14±5.59 |
161.26±4.58 |
163.20±6.78 |
.289 |
.833 |
Weight |
49.56±7.53 |
50.57±7.64 |
50.00±6.28 |
49.45±5.16 |
.271 |
.846 |
BMI |
18.75±2.84 |
19.21±2.56 |
19.16±1.68 |
18.69±3.49 |
.455 |
.714 |
(i) Per capita income (Rs.) |
Variables |
Low |
Medium |
High |
F |
Sig. |
Height |
162.11±5.99 |
162.23±5.45 |
162.34±5.77 |
.029 |
.971 |
Weight |
44.87±4.50 |
49.91±4.35 |
57.98±7.58 |
99.895 |
.000 |
BMI |
17.06±1.29 |
18.95±1.20 |
21.99±2.62 |
140.70 |
.000 |
(j) Source of Drinking water |
Variables |
Well |
Tube well |
Pond |
Stream |
F |
Sig. |
Height |
164.90±6.20 |
162.04±5.57 |
162.62±8.48 |
162.57±4.32 |
.615 |
.606 |
Weight |
48.40±6.67 |
50.04±7.16 |
48.14±7.24 |
55.35±9.58 |
3.378 |
.019 |
BMI |
17.78±2.17 |
19.04±2.50 |
18.16±1.93 |
20.91±3.34 |
4.320 |
.006 |
(k) Primary source of earnings |
Variables |
Daily wage labour |
Agricultural labour |
Business |
Services |
Dependent |
F |
Sig. |
Height |
162.13±5.97 |
161.58±3.52 |
169.70 |
163.23±3.72 |
162.61±4.47 |
.569 |
.685 |
Weight |
49.87±7.12 |
54.23±10.89 |
44.90 |
55.01±8.86 |
48.60±6.25 |
2.337 |
.056 |
BMI |
18.95±4.202.35 |
20.77±4.20 |
15.59 |
20.72±3.79 |
18.34±1.96 |
3.219 |
.014 |
(l) Educational Status |
Variables |
Non-literate |
Can sign only |
Primary |
Upper-primary |
Secondary |
Higher secondary |
Graduation |
F |
Sig. |
Height |
162.03±6.13 |
162.52±5.35 |
159.98±6.51 |
162.28±4.97 |
164.09±
3.77 |
166.26±
2.80 |
169.50 |
1.130 |
.346 |
Weight |
48.36±6.46 |
51.36±7.81 |
51.29±
8.14 |
52.33±
8.29 |
54.65±
8.79 |
52.70±
3.15 |
53.90 |
2.716 |
.015 |
BMI |
18.41±2.15 |
19.39±2.44 |
19.99±
2.68 |
19.88±
3.22 |
20.29±
3.20 |
19.02±
1.62 |
18.76 |
2.840 |
.011 |
Table 5 has shown the results of the one-way ANOVA test as well as the independent sample t-test for anthropometric variables among different socio-demographic characteristics of the studied population. Results revealed that weight and BMI have statistically significant (p<0.05) differences between different categories of several socio-demographic factors whereas height has no significant effect. In the case of facilities like the use of sanitary latrine, higher mean values were observed in the category of users compared to non-users for the weight (69.20±11.17) and BMI (26.74±5.54). Both weight (F= 7.452, p= .001) and BMI (F= 10.166, p= .000) have shown statistically significant differences. Similarly, high statistically significant differences were observed for both weight (F= 99.859. p= .000) and BMI (F= 140.70, p= .000) in case of per capita income. Both these variables were increased with increasing income. An interesting finding has been seen in case of the source of drinking water, where mean BMI was found highest (20.91±3.34) among those using the water of the stream and lowest among the users of well (17.78±2.17). It also showed statistically significant impact on nutritional status (F= 4.320, p= .006). Unemployed participants had lowest mean values for both weight (48.60±6.25) and BMI (18.34±1.96) and highest for weight among service holder (55.01±8.86) and for BMI among the agricultural labour (20.77±4.20) followed by service holders (20.72±3.79) where BMI showed statistically significant F value (F= 3.219, p= .014). In case of the level of education, the lowest mean value for the weight (48.36±6.46) and BMI (18.41±2.15) was found among the non-literate males and increasing with the level of education and both weight (F= 2.716, p= .015) and BMI (F= 2.840, p= .011) showed the statistically significant impact. On the other hand, there were non-significant results found for socio-demographic characteristics like family type, family size, house type, number of rooms, and marital status. Independent sample t-test was performed for the factors like the presence of separate kitchen and access to electricity, but in both cases, anthropometric variables were found to be non-significant.
Discussion
This cross-sectional study was conducted primarily to determine the effect of socio-demographic factors on health conditions among 350 Sabar adult males, ages 18-60 years of Purulia district of West Bengal, India. It has been found that the overall prevalence of undernutrition (BMI<18.5 kg/m2) was very high (45.4%). To find out the possible effect of different socio-demographic factors on their health condition, a Chi-square test was performed which revealed statistically significant association for variables like family type, house type, number of rooms, presence of separate kitchen, electrification, sanitary latrine, and per capita income with nutritional categories. On the other hand, variables like the source of drinking water, sanitary latrine, levels of education, the primary source of earnings, and per capita income depicted a statistically significant (p<0.05) impact on weight and BMI.
Family as the basic unit of the society with its various forms, structures, and functions act as a resource to meet the needs of and provide care for the physical and emotional wellbeing of its members in particular and the community at large. (21-23). In the present study, nuclear families were found in the highest frequency (46.3%) followed by joint families (28.9%). This finding was in contradiction with an earlier work conducted by Dhargupta et al. (24) stated that 70% of Sabars were preferred to live in joint families because of remote locality in Purulia and in good agreement with the National Nutritional Monitoring Bureau (NNMB) report of 2009, (16) where the frequency of nuclear family was highest among tribals in West Bengal (60%). Besides, in agreement with the study of Dhargupta et al. (2009), (24) it has been found that the families comprised of 5-9 members were highest in frequency (64.9%). Now Table 4 has exhibited a significant association between family type and health status but a non-significant association between family size and health. But NNMB report (16) had demonstrated a significant association for both the family type and family size with health status among the tribal males in India. It was evident from this study that the family size became an important factor to provide support in a positive direction as per capita demand for services declined with the increasing family size and with larger family income, the living standard specifically housing and the sanitary condition got better and influenced the domain of health as suggested by different studies. (25-26)
Due to their association to the forest area, forest-dependent economic practices were also found among the participants in large scale; such as preparing plates from sal leaves (Shorea robusta), making mats, baskets, and brooms from bamboo as well as date leaves, firewood collection for selling as well as their own cooking fuel. And a large portion of their daily diet comprises roots, tubers, leafy vegetables, rats, ants etc. which are collected from the forest. It was clear from Table 1 that maximum Sabar participants were dependent on wage labour (90%) as most of them were landless and therefore, in search of earning opportunities, they had to work as migratory labour in different districts of West Bengal for at least 3 to 4 months in a calendar year. They frequently worked as agricultural labour in the neighbouring communities’ land and received cash or paddy as a mode of payment. Furthermore, a maximum number of participants felled under the medium income group (37.4%) based on per capita income ranging from Rs. 593.05 to Rs. 675.53 and a considerable number of participants (35.4%) were from the low-income group (Rs. ≤ 593.04). Both per capita income and occupation have revealed a positive impact on health which has in good agreement with the NNMB report. (16)
This study also revealed the importance of education towards better health. About 31.5% of adult men in this study were found to be literate—much lower than male tribal literacy rate of national (India: 68.5%) as well as state-level (West Bangal: 68.2%). (27) This study showed a statistically significant impact of educational status on health revealed by one-way ANOVA test (p<0.05) which corroborated with the earlier studies conducted in India. (16, 28-29)
The effect of Limited access to safe drinking water and poor sanitation has cascading effects on every aspect of life – health, nutrition, development, economy, dignity, and empowerment. (30) Contaminated drinking water is estimated to cause 4,85,000 diarrheal deaths each year in the world. (31) The practice of open defecation contributes significantly to the spread of communicable diseases and may increase the likelihood of contracting worm infection from soil contaminated by feces. (32) Tubewell was found as a major source of drinking water (81.4%) among the participants corroborating the findings in the NNMB report. (16) Nevertheless, poor sanitation and hygiene standards exacerbate the severity and impact of undernutrition. The findings of this study also reflected the same phenomenon in terms of the high prevalence of undernutrition among Sabar adult males of Purulia, West Bengal. Only 0.9% of participants have confirmed the use of latrines even a considerable number of participants (26.2%) still prefer to go to the field though they have the government-provided latrines in their houses. They stated that the lack of water supply, poor construction, as well as irregular cleaning and sanitization services for the toilets made it more difficult to use them. The narratives from the local government officials often attribute the failure to implement the government scheme on sanitary toilets to these people. Even the NNMB report (16) also showed the vulnerable condition of the tribals where only 14% of the households had a sanitary latrine. Therefore, the whole scenario indicated the failure of public policies to reconcile the imbalance in social and other power relations that thrust upon the studied population formed a major reason for the continuation of the practice of open defecation.
Evidence showed that living standards may considerably impact the health of the residents. (2, 33-34) Poor livening standards add susceptivity of chemical and physical hazards associated with the range of health conditions. (3) Features of the standard of housing including access to safe drinking water, mode of ineffective waste disposal, poor sanitation, inadequate ventilation, pest infection provides a linkage between living standards and chronic illness. (35-37). Semi pucca houses (67.7%) were found in highest frequency followed by kutcha houses (31.4%) among Sabar households studied here. In the absence of a separate kitchen, most of the households (73.7%) use to manage their cooking in an open space in the courtyard which indicated poor food hygiene conditions. This was also a high-risk practice where food could get contaminated by insects or other vectors. The rest of the households (26.3%) were having either clay tile-roofed or thatched kitchen. Most of the households manage in three rooms (44.3%) followed by double rooms (32.0%). Panchayat members of villages under study confirmed that facilities of construction of houses under government developmental programme, namely Indira Aawas Yojana and Geetanjali from Central Government and State Government respectively were extended to the Sabar community but due to the unproductive habit of excessive alcohol consumption and other financial urgencies, many of them had sold the corrugated roof sheet, tin roof sheet, doors, and windows of their houses. Other reasons behind their choices of living in kutcha or semi-pucca houses were their unwillingness to get a house under such schemes or due to their failure to prove eligibility to become beneficiaries for the schemes or to misuse of the grant as stated by one panchayat pradhan during an interview.
Conclusion
The authors argue that overall poor demographic and socio-economic conditions have a substantial negative effect on the health condition. The studied people were relatively disadvantaged in terms of access to safe drinking water and sanitary latrine. The findings of this study confirmed that the difference in overall health status was associated strongly with financial well-being, educational attainment, and poor sanitation. Hence, the authors suggested that socio-demographic determinants were very important factors to be taken into consideration while evaluating the health standard of any community. Since a considerable number of participants were identified as undernourished in this study, they required a variety of interventions to improve their nutritional status. It becomes imperative from the public health perspectives that immediate appropriate nutritional intervention programmes including a) assessment of nutritional status irrespective of age and gender, b) identification of serious nutritional deficiencies and imbalances, and c) formulation of diet plans specific to their nutritional needs to be implemented. On a larger context, at the time of implementation of intervention programmes among the tribal areas, the policymakers and the implement authorities should mandatorily take into account the socio-economic determinants of health for the betterment in the health standard, wellbeing, and quality of life of the poor and vulnerable tribal people in India.
Acknowledgments
All the study participants were thankfully acknowledged for their help and cooperation during fieldwork. Special mention to the organization Paschim Banga Kheriya Sabar Kalyan Samity for providing necessary facilities for the smooth running of the fieldwork.
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