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| ORIGINAL ARTICLE |
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| Year : 2019 | Volume
: 63
| Issue : 2 | Page : 119-127 |
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Behavioral and biological risk factors of noncommunicable diseases among tribal adults of rural siliguri in Darjeeling District, West Bengal: A cross-sectional study
Ditipriya Bhar1, Sharmistha Bhattacherjee2, Dilip Kumar Das3
1 Senior Resident, Department of Community and Family Medicine, All India Institute of Medical Sciences, Patna, Bihar, India
2 Assistant Professor, Department of Community Medicine, North Bengal Medical College, Darjeeling, India
3 Professor and Head, Department of Community Medicine, Burdwan Medical College, Burdwan, West Bengal, India
| Date of Web Publication | 18-Jun-2019 |
Correspondence Address:
Sharmistha Bhattacherjee
Department of Community Medicine, North Bengal Medical College, Sushrutanagar, Darjeeling - 734 012, West Bengal
India
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/ijph.IJPH_326_18
 Abstract | | |
Background: The increasing burden of noncommunicable diseases (NCDs) urges continuous survey of risk factors in different population groups. Objectives: The study was conducted to assess the prevalence and determinants of behavioral and biological risk factors of NCDs, in rural tribal population. Methods: A community-based cross-sectional study was conducted from June 2014 to May 2015, in rural Siliguri, among 172 tribal people aged 25–64 years selected by multistage cluster random sampling using WHO-STEPS instrument. Study participants were interviewed for sociodemographic and behavioral risk factors, and biological measurements were taken. Descriptive and logistic regression analyses were performed to explore the determinants of risk factors. Results: Among the study participants, the prevalence of current tobacco use and alcohol use were 69.8% and 40.7%, respectively; 96.5% consumed unhealthy diet and 2.9% were physically inactive. The prevalence of abdominal obesity and overweight were 26.2% and 12.2%, respectively. Odds of tobacco use were significantly raised among men (adjusted odds ratio [AOR]: 47.7 [95% confidence interval (CI) 11.1, 203.9]) and increased age of the participants. Men showed higher odds of alcohol consumption (AOR: 13.4 [95% CI 4.6, 38.9]). Odds of abdominal obesity were higher among older participants, whereas lower odds were found among men (AOR: 95% CI 0.11 [0.0, 0.5]) compared to women. Conclusions: Most of the behavioral and biological risk factors of NCDs were quite high among tribal population of rural Siliguri except physical inactivity. Increasing awareness about NCDs through locally accepted and culturally appropriate strategies need to be implemented in the study area.
Keywords: Noncommunicable diseases, risk factors, STEPS approach, tribal population
How to cite this article:
Bhar D, Bhattacherjee S, Das DK. Behavioral and biological risk factors of noncommunicable diseases among tribal adults of rural siliguri in Darjeeling District, West Bengal: A cross-sectional study. Indian J Public Health 2019;63:119-27 |
How to cite this URL:
Bhar D, Bhattacherjee S, Das DK. Behavioral and biological risk factors of noncommunicable diseases among tribal adults of rural siliguri in Darjeeling District, West Bengal: A cross-sectional study. Indian J Public Health [serial online] 2019 [cited 2023 Mar 21];63:119-27. Available from: https://www.ijph.in/text.asp?2019/63/2/119/260604 |
| Introduction | |  |
Noncommunicable diseases (NCDs) are of major concern in the 21st century. Modern lifestyle has made people vulnerable to many chronic NCDs. NCDs were attributed for 38 million deaths globally in 2012; 70% were premature.[1] Almost 80% of the NCD deaths were observed in low- and middle-income countries. In South-East Asian region, there has been an increase in NCD deaths since 2000. 60% of total deaths in India in 2012 were attributed to NCDs.[2] Mortality data from rural India depicted NCDs as the most common cause of death (32%).[3],[4]
Most of the NCDs are attributable to eight modifiable risk factors, and WHO has classified them into behavioral and biological risk factors.[5] The modifiable risk factors if timely controlled could prevent the emergence of future NCDs.[6] Thus, comprehensive population-wide surveillance is required to identify pattern and distribution of risk factors across population groups within and across countries. “WHO STEP-wise approach to NCD risk factor surveillance (STEPS)” is a standardized tool using standard protocols and questionnaire for collecting and analyzing the NCD risk factors.[7]
In India, NCD risk factor survey was conducted through integrated disease surveillance project in 2007–2008.[8] Some studies have also been conducted in this context,[4],[9],[10],[11],[12],[13],[14],[15],[16] but mostly are in general population.[4],[9],[10],[11],[12],[13],[14] India has the second largest tribal population in world (8.6% of the country's total population).[17] Tribal populations having distinctive characteristics, customs, and practices; living in isolated geographic areas; and remaining untouched with the modern society[18] are also vulnerable of developing lifestyle diseases. In West Bengal, Darjeeling district has the highest tribal population, living in variable geographic areas.[18] Only limited researches attempted to explore the NCD risk factors in West Bengal[10],[12],[13] and none among tribal population. Hence, the present study was conducted with the objectives to assess the prevalence of various behavioral and biological risk factors among rural tribal population of Siliguri subdivision and to explore the associated sociodemographic factors.
| Materials and Methods | | |
Study design, area, and subjects
The study was a descriptive, cross-sectional study conducted from June 2014 to May 2015 in all four community development blocks (Matigara, Phansidewa, Naxalbari, and Kharibari) of Siliguri subdivision of Darjeeling District, West Bengal. These four blocks comprised of 329 villages as per Census 2011with a total population of 512,800.[19]
Study participants were Scheduled Tribe adults aged 25–64 years residing in Siliguri subdivision for at least 1 year. Seriously ill, individuals with physical disability, and pregnant women were excluded.
Sample size and sampling technique
Sample size was estimated as per WHO STEPS guidelines;[7] using formula of n = 4p (1-p)/d2 and considering anticipated prevalence of risk factors (p) as 50% (as previous data on risk factors were unavailable in target population), 95% level of confidence, 10% absolute precision (d). A design effect of 1.5 and 20% nonresponse rate were also considered. The final sample size became 180.
Study participants were selected using two-stage cluster sampling technique. A list of all villages in the four blocks was prepared along with total households and individuals using 2011 census list.[19] Then, 30 clusters (villages) were selected according to probability proportional to size of the village population, and the cluster size was determined to be 6. Within each cluster, list of updated individual households was obtained from respective local authorities and a sampling frame of all Scheduled Tribe households of the village was prepared. From this sampling, six households were selected by simple random sampling using random number table in each cluster (village). From each household, one eligible individual was selected by the Kish method.[7] Thus, 180 individuals were selected from 30 clusters.
Data collection
Study participants were interviewed at their houses using a structured schedule based on WHO STEPS questionnaire.[7] Each participant was interviewed about sociodemographic variables such as age, gender, education, marital status, income, occupation, and behavioral risk factors (tobacco use, alcohol consumption, fruit and vegetables consumption, and physical activity). Anthropometric measurements of height, weight, and waist circumference were done using standardized methods.[7]
Body weight was recorded by portable calibrated weighing machine to the nearest 100 g. Height was measured using a tension-free standardized measuring tape to the nearest 0.1 cm. Waist circumferences was measured at the end of expiration by stretch-resistant tape to the nearest 0.1 cm.[7] Due to limited resources and feasibility, biochemical analysis was not conducted.
Operational definitions
Current tobacco use: Smoking of cigarettes, bidis, and/or using smokeless tobacco such as gutka, khaini or paan with zarda or pan masala containing at least betel nut or lime or chuna daily or occasionally in the past 30 days.[7]
Current alcohol use: Consumption of alcoholic drink within past 30 days; heavy drinking - consuming more than five standard drinks (males) or more than four standard drinks (females) on a single drinking occasion in the past 30 days.[7]
Unhealthy diet: Eating less than five servings of fruits and/or vegetables per day. In our study, we considered one standard serving of fruit/vegetable as 80–100 g of fruit or vegetable that approximately equals to one cup or bowl (200 g) of raw green leafy vegetables or half cup of cooked/chopped green leafy or other vegetables or one medium size piece of fruit or half cup of chopped fruit.[7]
Physical inactivity information was collected using Global Physical Activity Questionnaire; version 2, published by Department of Chronic Diseases and Health Promotion, WHO, Geneva, Switzerland.[7] Physical activity was assessed in three domains: at work, transport, and leisure time activities. Physical activity was classified according to the Metabolic Equivalents (MET)[7]-Minutes per week into three groups. Insufficient activity was defined as activities <600 MET-minutes per week, minimal activity as at least 600 MET-minutes/week but <3000 MET-minutes/week, and sufficient activity as at least 3000 MET-minutes/week.
Overweight[20] was defined by body mass index (BMI) ≥25 kg/m2 and obesity as BMI ≥ 30 kg/m2.
Abdominal obesity[21] was defined by waist circumference ≥90 cm in men and ≥80 cm in women.
Ethical considerations
The study was approved by the institutional ethics committee of North Bengal Medical College, Darjeeling. The study participants were briefed about the purpose and nature of the study, and written informed consent was obtained before data collection.
Statistical analysis
Data were analyzed using IBM SPSS Statistics for Windows Version 22.0 (Armonk, NY: IBM Corp). Categorical and continuous data were expressed as proportions with confidence interval (CI) and mean with standard deviation (SD), respectively. Pearson's Chi-square test was applied as test of significance for assessing distribution of risk factors. Bivariate analysis was performed to find the relation between sociodemographic variables and risk factors and expressed as crude odds ratio (OR) with CI. Multivariable logistic regression was done considering each risk factor as binary (0 = absence, 1 = presence)-dependent variable and sociodemographic factors (age, gender, education, occupation, socioeconomic status, and marital status) as independent predictor covariates. The final models were expressed in terms of adjusted OR (AOR) with 95% CI. P <0.05 was considered as statistically significant. The fitness of the models was assessed by Hosmer–Lemeshow goodness-of-fit test and Omnibus Chi-square statistics.
| Results | | |
Among 180 sample size, a total of 172 individuals could be studied with response rate of 95.6%. The mean age of study participants was 42.10 years (SD ± 11.3) (median 40, IQR: 16 [Q3-Q1:34,50)). The background characteristics of the participants are described in [Table 1]. Majority of participants were women (51.7%), illiterate (59.3%), and laborer (53.5%). The tribes found in the study were Oraon (43.0%), santhal (27.9%), Munda (12.2%), and Bhutia (5.2%). | Table 1: Sociodemographic characteristics of the study population (n=172)
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[Table 2] shows the prevalence of various risk factors. Among the behavioral risk factors, prevalence of current tobacco use was 69.8% (95% CI: 62.8, 76.7). The prevalence of smoking and smokeless tobacco use was 25.6% and 62.2%, respectively [Table 2]. Among the current smokers, 52.3% men and 9.1% women were daily smokers while 31.8% men and 6.8% women were occasional smokers. An average of seven and four cigarettes/bidis was smoked daily by men and women, respectively. Majority of smokers (74.1%) used hand-rolled cigarettes and others used manufactured cigarettes. Among the smokeless tobacco users, 59.8% men and 34.6% women were daily user whereas 3.7% men and 1.9% women were occasional user. Khaini was the major smokeless tobacco used by the participants (93.1%), while others used Gutkha (5.9%) and Jarda (1%). Men and women used smokeless tobacco on an average of eight and four times daily. | Table 2: Prevalence of behavioral and physical risk factors among tribal individuals (n=172)
Click here to view |
The study found that 40.7% of participants (95% CI: 33.1, 48.3) were current drinkers [Table 2], 15.1% (95% CI: 10.5, 21.2) were former drinkers, and 44.2% (95% CI: 37.0, 51.7) were lifetime abstainer. Men consumed alcohol an average of 20 days while women consumed 12 days per month; heavy drinking was reported by 44.3% of current drinkers (46.3% male vs. 37.5% female). Among participants who consumed alcohol in the past year, 28.4% had consumed daily, 3.4% consumed 5–6 times/week, 28.4% consumed 1–4 days/week, and 39.8% consumed less than once a week.
Among the participants, 96.5% (95% CI: 93.6, 98.8) consumed <5 servings fruit and/or vegetables per day [Table 2]. Further analysis found that 40.7% had not eaten any fruit in the previous week of survey whereas 45.3% consumed 1–2 times only. The consumption of one serving of fruit per day was observed among 48.8% of participants while 10.5% consumed two servings in a day. For vegetable consumption, 37.8% of participants had taken seven days a week, 19.8% consumed 1–3 times/week. About 56.4% and 9.9% had consumed two and one serving of vegetable per day, respectively.
The study showed that 72.1% (95% CI: 65.0, 78.3) of participants were sufficiently physically active, whereas 25.0% (95% CI: 19.1, 32.0) were minimally active. However, insufficient levels of physical activity were prevalent among 2.9% of participants [Table 2]. Men and women spent a median of 240 min (Q3-Q1:300, 120) and 120 min (Q3-Q1:240, 60) of work-related moderate/vigorous activity, respectively, whereas men spent 30 min (Q3-Q1:0,35) of transport-related activities each day. On average, men and women spent 360 min (Q3-Q1:240, 480) and 480 min (Q3-Q1:300, 600) in sedentary activities, respectively. The prevalence of overweight and abdominal obesity found in the study was 12.2% (95% CI: 7.6, 16.9) and 26.2% (95% CI: 19.2, 32.6), respectively. Moreover, 29.1% of participants were underweight; women (38.2%) were underweight twice compared to men (19.2%).
Results of regression analysis are shown in [Table 3] and [Table 4]. On bivariate analyses, increasing age and gender of the participants was found to be significantly associated with tobacco use. Furthermore, gender was significantly associated with alcohol consumption and abdominal obesity. No other factors were significantly associated with unhealthy diet, physical inactivity, overweight, and abdominal obesity. However, significant association of alcohol consumption was observed among unemployed/homemaker/retired participants. | Table 3: Association of sociodemographic variables with behavioral risk factors among the tribal individuals in rural Siliguri (n=172)
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 | Table 4: Association of sociodemographic variables with physical risk factors among the tribal individuals in rural Siliguri (n=172)
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On multivariable analysis for current tobacco use, increasing age of participants and male gender contributed significantly to the final model. For alcohol consumption, only male gender was shown as significant covariate in final model. Although occupation was significant covariate in unadjusted analysis for alcohol consumption, it did not contribute to the adjusted model [Table 3]. For the tobacco model, 36.2% (Cox and Snell R2) and 51.2% (Nagelkarke R2) of variance in tobacco use was associated with the predictor covariates. Whereas, 27.5% (Cox and Snell R2) and 37.1% (Nagelkarke R2) of variance in alcohol use was associated with the predictor covariates in the alcohol model. Overall, these models predicted 80.8% and 76.2% of tobacco and alcohol use in participants. Multivariable logistic regression analysis of unhealthy diet and physical inactivity were not shown as none of the sociodemographic factors were found to be significant on bivariate analysis. In the adjusted model for abdominal obesity, sequentially increasing odds were observed in older age groups: 35–44 years (AOR: 4.1 [95% CI: 1.0, 16.5]), 45–54 years (AOR: 6.2 [95% CI: 1.3, 30.1]), and 55–64 years (AOR: 22.2 [95% CI: 3.5, 140.5)). However, men (AOR: 0.1 [95% CI: 0.0, 0.5]) showed lower odds of abdominal obesity compared to women. Moreover, significant lower odds of abdominal obesity were observed among participants with monthly income of Rs. 5250–7000 in the adjusted model [Table 4]. For the overweight model, 8% (Cox and Snell R2) and 21.5% (Nagelkarke R2) of variance of being overweight was associated with the predictor covariates. Whereas, the predictor covariates in the abdominal obesity model were associated between 26.3% (Cox and Snell R2) and 38.5% (Nagelkarke R2) of variance of having abdominal obesity. Overall, these models predicted 87.8% and 83.1% of overweight and abdominal obesity in participants. The final regression models for tobacco and alcohol use and abdominal obesity were found to be adequately fitted as the Hosmer and Lemeshow test was not significant for these models.
| Discussion | | |
This study portrays the NCD risk factors profile of the tribal population in rural Siliguri of Darjeeling district, West Bengal. The significantly higher tobacco use in men in the present study was comparable with the tribes of Assam (93.8%).[9],[16] It was higher than the prevalence reported in urban Siliguri,[10] neighboring states,[8],[22] and neighboring countries.[23],[24],[25] Whereas Indonesia[26] had reported a higher prevalence of smoking (53.9%) in men compared to this study (44.6%). This might be because tobacco industry in Indonesia is one of the major sources of revenue collection in the country.[26] The prevalence of smoking in men in this study was considerably higher than that of the tribal men of Assam[16] (8.9%) and even more than the national average.[27] The increasing odds of tobacco use with higher age that was observed in this study represents contradictory situation to the findings in Assam tribes.[16] The prevalence of daily smoking in the study was similar to the prevalence reported in Nepal[25] (15.8%) and Indonesia[26] (15.7%). The prevalence of daily smoking among the general population of Delhi[11] (13.5%) and Bhutan[24] (4.3%) was lower than current study.
The prevalence of smokeless tobacco use in this study (62.2%) was higher compared to the Mishing tribes in Assam[16] (48.5%) and other tribal population in India.[27] The local farming of tobacco leaves (Ganja Patta) and illicit cross border trafficking of tobacco from neighboring countries might probably be responsible for higher prevalence in this region.[28] Chewing tobacco in form of gutkha, khaini, and pan with jarda is being a colonial custom in the study area. Although almost half of the women participants in this study were using smokeless tobacco, but overall the proportion of tobacco use (smoked and/or smokeless) was still higher in men; which relates to the global custom.[29] On contrary, in Mishing tribes of Assam, 70% women use smokeless tobacco compared to 30% in men.[16] The varied pattern of tobacco use in different tribes might signify regional and tribe specific variation of their indigenous characteristics. Whereas, studies conducted in general population of West Bengal[12],[13] reported a lower percentage of tobacco chewing compared to this study. Lack of awareness about the harmful effects and easy accessibility of tobacco could be responsible for the higher prevalence of tobacco use in present study.
The current alcohol use among men in the study (65.1%) was greater than the national average among tribal population.[27] On contrary, prevalence of alcohol use in men was even higher among the Mishing tribes of Assam[16] (81%). Studies conducted in urban Siliguri,[10] rural and slums in West Bengal,[12],[13] and urban localities of Delhi[11] and Kerala[14] reported lower prevalence of alcohol use in the general population. The aboriginal tradition of consuming alcohol among the tribal community may possibly be accountable for the higher prevalence of alcohol consumption.[30] Most of the people took the locally prepared rice-based fermented beverage (Haria) that is popular among tribal people of West Bengal and East-Central India.[30],[31] The tribal communities in Ethiopia[32] and Bhutan[24] reported a comparable percentage of alcohol use as in our study; whereas other South-East Asian countries[23],[25],[33] reported a lower proportion of drinking in general population. The sociocultural customs of tribal people, surrounding climatic conditions, and unawareness about the ill effects of alcoholic substances may be responsible for differing pattern. Moreover, the strenuous lifestyle of the participants could contribute to higher consumption of alcohol as it becomes a source of their recreation easing their physical exhaustion by providing a temporary sense of relief. Moreover, the simultaneous use of tobacco and alcohol use in current study (38.4%) was lower compared to the Mishing tribes of Assam[16](60%).
The higher prevalence of unhealthy diet observed in the present study (95.6%) was comparable to studies conducted in North[11] and South India[8] and neighboring countries.[23],[25] However, few studies among tribes of Assam[16] (68%) and general population of rural and urban India[10],[12],[13],[14] reported lesser prevalence of unhealthy diet. However, no association was found between unhealthy diet and age, gender, literacy and income, and occupation in our study. Encouraging people to grow leafy vegetables, seasonal fruits within the house premise or community, making people aware about locally available healthy food items, and in cases helping them by supplying good-quality seeds could change the present dietary pattern in the study population.
Both men and women participants in this study have reported vigorous physical activity which was similar to the characteristic pattern of physical activity of Mishing tribes in Assam[16] (86.4%), Kani tribes of Kerala[15] (77%) and people of Nepal.[25] Most of the participants in the study were tea garden laborer, agricultural laborer, or involved in stone cutting and masonry works. Moreover, the female homemakers have to travel long distance for carrying water, arranging fuel (woods) for cooking, and to fulfill other household requirements. On contrary, studies conducted in urban areas of Delhi[11] and South West Bengal[12],[13] found higher proportion of physical inactivity.
This study shows comparable prevalence of overweight among men and women with that of tribes of Assam.[16] However, most of the studies among the general population in India[10],[11],[13],[14] observed higher prevalence of overweight in women compared to this study. The comparative lower prevalence of overweight among the tribal participants compared to the general population could be due to their everyday heavy physical works and challenging lifestyle.
Higher prevalence of abdominal obesity among women compared to men in the current study was similar to the studies done in urban, rural, and slum areas of North[10],[11],[12],[13],[16] and South India[14],[27] and neighboring countries and other parts of the world.[23],[32],[33] The present study showed higher odds of abdominal obesity with increasing age group of the participants. Similar association was observed in tribal population of Assam[16] and general population of Kerala.[14] Lower odds of abdominal obesity were observed among men and illiterate participants. Female gender and sedentary lifestyle with increasing modern amenities among literate people could be a plausible explanation for the higher prevalence of central obesity.[34]
Strengths and limitations
Highlighting the NCD risk factors burden in a representative sample of tribal population of northern part of West Bengal is the greatest strength of the study, which often remains unaddressed. However, few limitations could not be avoided, particularly the recall bias in assessing self-reported behavioral risk factors. Moreover, we did not perform any biochemical analysis of locally prepared alcohol; the number of standard alcoholic drinks was also assessed as reported by the participants.
| Conclusions | | |
Most of the behavioral risk factors were observed to be alarmingly high among tribal adults, the only exception being physical inactivity. Although the use of tobacco was found to be comparable to other tribal communities, use of smokeless tobacco was substantially higher compared to other general as well as tribal communities of the country. The increasing use of tobacco in the older ages could further complicate the situation. In addition, the widespread use of locally prepared alcohol and unhealthy diet pattern would increase simultaneous exposure to multiple risk factors that could worsen the NCD burden in this region of the state. Despite being physically active, considerable proportion of participants was having abdominal obesity which is a scope of future exploration. Future research including the estimation of biochemical risk factors could be planned to explore the risk behaviors and observe the trend of the risk factors. Hence, awareness generation about the risk behaviors and exploration of appropriate, culturally accepted strategies to control the risk behaviors in the form of establishment of deaddiction centers in nearest possible health centers have become an urgent need in the study area.
Acknowledgment
The authors thank all research participants, government health authorities, and community health representatives in the respective study areas.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
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[Table 1], [Table 2], [Table 3], [Table 4]
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| Gokul Sarveswaran,Vaitheeswaran Kulothungan,Prashant Mathur | | Diabetes & Metabolic Syndrome: Clinical Research & Reviews. 2020; 14(5): 1005 | | [Pubmed] | [DOI] | | | 6 |
Burden and determinants of multimorbidity among women in reproductive age group: a cross-sectional study based in India |
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| Parul Puri,Ajinkya Kothavale,S.K. Singh,Sanghamitra Pati | | Wellcome Open Research. 2020; 5: 275 | | [Pubmed] | [DOI] | |
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