Social determinants of tobacco use among tribal communities in India: Evidence from the first wave of Longitudinal Ageing Study in India

Jogesh Murmu; Ritik Agrawal; Sayantani Manna; Sweta Pattnaik; Shishirendu Ghosal; Abhinav Sinha; Ardhendu Sekhar Acharya; Srikanta Kanungo; Sanghamitra Pati

doi:10.1371/journal.pone.0282487

. 2023 Mar 2;18(3):e0282487. doi: 10.1371/journal.pone.0282487

Social determinants of tobacco use among tribal communities in India: Evidence from the first wave of Longitudinal Ageing Study in India

Jogesh Murmu ^1,^#, Ritik Agrawal ^1,^#, Sayantani Manna ^1,^#, Sweta Pattnaik ^1,^#, Shishirendu Ghosal ^1,^#, Abhinav Sinha ^2,^#, Ardhendu Sekhar Acharya ¹, Srikanta Kanungo ^1,^*, Sanghamitra Pati ^1,^*

Editor: Chandan Kumar³

PMCID: PMC9980830 PMID: 36862703

Abstract

Background

Evidence on tobacco use among indigenous communities is scarce with available literature based either on a specific region or a particular tribe. Considering the large tribal population in India, it is pertinent to generate evidence on tobacco use among this community. Using nationally representative data, we aimed to estimate the prevalence of tobacco use and assess its determinants and regional variations among older tribal adults in India.

Methods

We analysed data from Longitudinal Ageing Study in India (LASI), wave-1 conducted in 2017–18. A sample of 11,365 tribal individuals aged ≥ 45 years was included in this study. Descriptive statistics was used to assess the prevalence of smokeless tobacco (SLT), smoking, and any form of tobacco use. Separate multivariable regression models were executed to assess the association of various socio-demographic variables with different forms of tobacco use, reported as adjusted odds ratio (AOR) with 95% confidence interval.

Results

The overall prevalence of tobacco use was around 46%, with 19% of smokers and nearly 32% smokeless tobacco (SLT) users. Participants from the lowest MPCE quintile group had a significantly higher risk of consuming (SLT) [AOR: 1.41 (95% CI: 1.04–1.92)]. Alcohol was found to be associated with both smoking [AOR: 2.09 (95% CI: 1.69–2.58)] and (SLT) [AOR: 3.05 (95% CI: 2.54–3.66)]. Relatively higher odds of consuming (SLT) were associated with the eastern region [AOR: 6.21 (95% CI: 3.91–9.88)].

Conclusion

This study highlights the high burden of tobacco use and its social determinants among the tribal population in India, which can help tailor anti-tobacco messages for this vulnerable population to make tobacco control programs more effective.

Introduction

Globally, tobacco use is one of the greatest public health threats. The rampant use of various tobacco products is a matter of concern in low-and middle-income countries (LMICs). Evidence suggests that almost 1.3 billion people use tobacco globally, out of which 80% live in LMICs, where tobacco-related morbidities and mortality are highest [1]. Among LMICs, India is the second-largest consumer and still a large-scale producer of tobacco products [2,3]. The tobacco ecosystem in India is complex [4]. According to Global Adult Tobacco Survey-2 (GATS-2, 2016–17), there were nearly 267 million tobacco users aged ≥15 years in India, and among them, about 42.4% were men, and 14.2% were women [5].

Tobacco is commonly used in two ways, i.e., smokeless and smoking form. Due to socio-cultural acceptability, smokeless tobacco (SLT) use is highly prevalent in India, which includes chewing tobacco such as khaini, gutkha, betel quid with tobacco, mishri, gul, and gudakhu [6]. Other forms include smoking (cigarette, hookah, and bidi) and the use of any form of tobacco, which simultaneously predisposes a risk of oral submucous fibrosis (OSMF), a premalignant disorder with potency to transform into oral cancer [7]. Evidence supports that smoking is responsible for health ailments of the cardiovascular and respiratory systems [6]. Tobacco not only results in loss of lives but also levies associated social and economic costs. In India, the total financial cost of tobacco uses for all illnesses during 2017–18 among people aged ≥35 years is approximately INR 177 billion (US $ 27.5 billion) [8].

India is a diversified land of many cultures and ethnic groups with one of the largest tribal populations in the world. According to the Census of India-2011, the scheduled tribes (STs) comprised around 104 million people, 8.6% of the national population [9]. There are 550 tribes in India, including particularly vulnerable tribal groups (PVTGs). Approximately 90% of the country’s tribal population lives in rural areas, while the remaining 10% resides in urban areas [10]. Still, a gap exists between the indigenous and non-tribal people as the former prefer to live in their geographical habitats, which are often secluded in forests and hard-to-reach areas. Their culture and traditions broadly vary, which disintegrates them from mainstream socio-economic activities; a life often marred with subsistence-based existence, invariably leading to their lower education (41% with no formal education) and socio-economic attainment (41% below the poverty line) [11]. This social exclusion often compels these marginalized communities to have disparities in accessing public health services.

Moreover, deleterious habits such as tobacco use are deeply rooted in their social beliefs and cultural practices. It often remains entrenched in their practices which is difficult to mould. As per the social affirmation and traditional practices, the pervasiveness of tobacco consumption is quite apparent. For instance, SLT is wrongly perceived as safer than smoking, resulting in higher consumption, early initiation, and persistence as a norm [12,13]. Among the indigenous communities, mortality, morbidity, and malnutrition rates are still higher than the average Indian population due to various barriers such as language, education, and infrastructural advancements [14].

Since ancient times, the habitual consumption of tobacco has been an eminent practice among tribal communities [15]. Tobacco uses among the general population is widely studied based on the Global Youth Tobacco Survey (GYTS) and Global Adult Tobacco Survey (GATS). However, it is scarce in the context of the tribal population [16,17]. Most of the evidence on tobacco use among tribes is either from a specific region or based on a survey from a particular tribe with no nationally representative study. Even within tribals, the available evidence largely focuses on adolescents or young adults, with limited literature on the aging population. Considering the large tribal population in India and their unique health-related behaviours, it is pertinent to generate evidence on tobacco use among this community to pave the way for future tobacco control programs and policies targeting this marginalized group. Hence, we aimed to estimate the prevalence of tobacco use, and assess its determinants and regional variations among older tribal adults in India using nationally representative data.

Materials and methods

Overview of data

This study is based on the Longitudinal Ageing Study in India (LASI), wave-1, which was full-scale national survey to scientifically investigate health, economics, social determinants, and consequences of population aging in India; conducted from April 2017 to December 2018 by Harvard TH Chan School of Public Health, the University of Southern California, in partnership with the International Institute for Population Sciences (IIPS), Mumbai. Following our primary objective to estimate the prevalence of tobacco use among older adults we selected LASI data for our analysis. Although the Global Adult Tobacco Survey-2 (GATS-2) also provides information on tobacco consumption, however, LASI primarily focuses on older adults, thus providing a large representative sample of participants aged 45 years or older aligned with our objective. LASI captured detailed socio-economic factors, which is another strength over GATS-2. LASI used three instruments for data collection: household survey schedule, individual survey schedule, and community survey schedule. LASI interviewed a sample of 72,250 individuals aged ≥45 years (and their spouses irrespective of age) who provided written consent before the interviews. A multistage stratified area probability cluster sampling design was employed to reach the final observation [18]. The precise method for LASI, wave-1, has been mentioned on the website of IIPS, Mumbai [19]. We followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline to report this study (S1 Table) [20].

Study participants and sample size

An individual survey schedule was employed among 72,250 participants aged ≥45 years. Participants who referred to their caste as "Scheduled tribes" (STs) were included in this study. Following this, the conclusive sample size of 11,365 tribal individuals aged ≥45 years was achieved as per the objective of this study (Fig 1).

Outcome variable

The outcome variable for this analysis was tobacco use, i.e., smoking, SLT, and any form of tobacco use among the tribal population in India. The tobacco use status was classified based on the combined responses to the following two questions: "Have you ever smoked or used smokeless tobacco?". Those who responded ‘yes’ were further enquired: “what type of tobacco product have you used or consumed?” with options such as smoking, SLT, and both smoking and SLT. So, when we created the binary variable "smokers" as an outcome variable, both only smokers and dual users were merged; the sub-groups under smokers were smokers and non-smokers (includes SLT users but don’t use smoking tobacco and those who don’t use any form of tobacco products). Similarly, when we created another binary variable as " SLT users", the subgroups under this variable were SLT users (only SLT users + dual users, as they used SLT products also) and non-SLT users (included those who didn’t use tobacco of any kind & those who were consumer of smoking tobacco only). Any tobacco group included all forms of tobacco users (only smokers+ only SLT users + dual users) irrespective of the type of products they were using. We conducted three separate logistic regression models for smokers, SLT users and any tobacco users to avoid collinearity.

Covariates

We employed various socio-demographic variables such as age, gender (male/female), residence (rural/urban), education, occupation, partner status, regions of the country, and wealth index. Additionally, we included two attributes of personal behaviour, i.e., physical activity and alcohol consumption, in the analysis. Age, a continuous variable provided in the LASI dataset, was divided into three categories according to the LASI report, i.e., 45 to 59 years, 60 to 74 years, and ≥75 years. Previous literature has also categorized age groups in this manner [21]. The education was classified based on the responses to two questions "Have you ever attended school?" Those who answered "no" were categorized as having no formal education, whereas those who answered "yes" were then asked about their "Highest level of education”. Respondents who had completed primary school and less than primary were grouped as "up to standard VII”. Respondents who completed middle school and secondary school were clubbed as ’’standard VIII-X’’. Additionally, individuals who completed secondary school, a diploma, a graduate degree, a postgraduate degree, or a professional degree were classified as "upper secondary and above”. Respondents who did not work for more than three months in their lifetime and were not employed were grouped as "currently not working" while the rest were "currently working". We classified living situation into three categories: “living alone”, "living with spouse" (which includes people who live with their spouse, others and with children), and "living without spouse" (which includes people who live with children and others; or other people only). MPCE quintiles (lowest, lower, middle, higher, highest) were based on the monthly per capita expenditure of the general population. 29 States and 6 Union Territories (except Sikkim) of India were arranged into six regions (north, central, east, north-east, west, and south) based on their geographical location. Participants who were hardly or never involved in sports or vigorous activities were grouped as "physically inactive." In contrast, others (daily, once a week, more than once a week, as frequent as one to three times a month) were merged into the "physically active" group.

Statistical analysis

Before analyzing, the data were filtered for all flagged, missing, and no information cases from LASI. Data were analyzed using STATA, v16·0 (STATA Corp., Texas) for Windows. Descriptive analysis was presented for continuous variables, such as age in mean with standard deviation (SD). The frequency and proportions (n, %) of tobacco use for each sub-group were described for categorical variables. The distribution of tobacco consumption in all three forms was tabulated with socio-demographic and behavioral covariates. 95% confidence interval (CI) for all weighted proportions was reported as a measure of uncertainty. We considered a p-value of < 0.05 to be significant. Univariate logistic regression was applied to determine the crude odds ratio between distinct outcomes and attributes. All socio-demographic characteristics of the study participants were adjusted in separate multivariable logistic regression models to obtain an adjusted association between outcome variables and various attributes, presented as an adjusted odds ratio (AOR) with 95% CI.

Segregating the LASI data at the state level will lead to very few samples of tribal population in smaller states. Therefore, we presented the regional differences in the prevalence of tobacco use, for the four most populated tribal states (Madhya Pradesh, Odisha, Maharashtra, Rajasthan) based on the census, 2011 data. Sampling weights were considered in the analysis to adjust for the multistage sampling design.

Ethical consideration

Indian Council of Medical Research (ICMR), New Delhi, and IIPS, Mumbai granted ethical approval for the first wave of the LASI survey. However, we used anonymous secondary data, which is available in the public domain. Hence, there is no participant risk. Appropriate permission to use the dataset was received, and data is being acknowledged wherever entailed.

Inclusivity in global research

Additional information regarding the ethical, cultural, and scientific considerations specific to inclusivity in global research is included in the (S2 Table)”.

Results

For this analysis, 11,365 scheduled tribe participants aged 45 years and above were included. Their average age was 59.3 (±10·7) years. Most of them belonged to the age group of 45–59 years (54.5%). A nearly equal number of male and female interviewees were included, with a little female predilection (53.7%). Most of the tribal population never went to school (54.6%) and lived in rural settings (77.4%). Most of the participants were physically inactive (52.6%) and worked (56.2%) while interviewed (Table 1).

Table 1. Sociodemographic characteristics of study population (n = 11,365).

Socio-demographic Characteristics		n (%)
Age (n = 11,365)	45 to 59 years	6192 (54.5%)
	60 to 74 years	4058 (35.7%)
	≥75 years	1115 (9.8%)
Gender (n = 11,365)	Male	5256 (46.3%)
Gender (n = 11,365)	Female	6109 (53.7%)
Residence (n = 11,365)	Rural	8801 (77.4%)
Residence (n = 11,365)	Urban	2564 (22.6%)
Education (n = 11,364)	No formal education	6205 (54.6%)
	Up to standard VII	2942 (25.9%)
	Standard VIII-X	1633 (14.4%)
	Higher secondary & above	584 (5.1%)
Occupation (n = 11,360)	Currently working	6383 (56.2%)
Occupation (n = 11,360)	Currently not working	4977 (43.8%)
Living arrangement (n = 11,365)	Living alone	418 (3.68%)
	Living with spouse	8,170 (71.89%)
	Living without spouse	2,777 (24.43%)
Region (n = 11,365)	North	367 (3.2%)
	Central	1440 (12.7%)
	East	1257 (11.1%)
	Northeast	4951 (43.6%)
	West	1663 (14.6%)
	South	1687 (14.8%)
Physical Activity (n = 11,287)	Active	5346 (47.4%)
Physical Activity (n = 11,287)	Inactive	5941 (52.6%)
Alcohol use (n = 11,288)	Yes	3267 (28.9%)
Alcohol use (n = 11,288)	No	8021 (71.1%)
MPCE Quintile (n = 11,365)	Lowest	3274 (28.8%)
	Lower	2438 (21.5%)
	Middle	2066 (18.2%)
	Higher	1776 (15.6%)
	Highest	1811 (15.9%)

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The overall prevalence of tobacco use was 46.1%, while smoking and SLT use was estimated 18.6% and 31.7%, respectively; tobacco in both forms (dual-use) was consumed by 4.2% of the participants. Smoking and SLT use were more prevalent among males (smoking: 34.8% and SLT: 40.1%) (Fig 2).

The prevalence of smoking and SLT use was estimated the highest (23.7% and 38.4%, respectively) among the population aged 75 years and above. Smoking (24.1%) was most prevalent in tribals from southern India, while the eastern tribal population predominantly used SLT (48.6%). The prevalence of any form of tobacco use among the four most tribal populated states Madhya Pradesh, Maharashtra, Odisha, and Rajasthan were 39%, 43%, 61%, and 46%, respectively (Fig 3). The detailed state-wise prevalence of various forms of tobacco is presented in S3 Table.

Tobacco use of any variant was relatively higher among alcohol consumers (smokers: 32.9%, SLT use: 52.5%, and any form of tobacco use: 75.8%). The physically active participants used tobacco of any form more (smokers: 20.0%, SLT use: 36.8%) than those who were not into sports or regular physical work (smokers: 17.3% and SLT use: 26.3%). The detailed distribution of tobacco use is provided in Table 2.

Table 2. Prevalence of various forms of tobacco and its univariate association with socio-demographic characteristics.

Socio-demographic Characteristics	Smoking		SLT use			Any Tobacco use
Socio-demographic Characteristics	n, % (95% CI)	OR (95% CI)	n, % (95% CI)		OR (95% CI)	n, % (95% CI)	OR (95% CI)
Age
45 to 59 years	1014, 17.0% (16.0–18.0)	Reference	1861, 31.2% (30.0–32.4)	Reference		2640, 44.2% (42.9–45.5)	Reference
60 to 74 years	858, 19.6% (18.5–20.9)	1.19 (0.97–1.47)	1342, 30.7% (29.4–32.1)	0.97 (0.82–1.16)		2039, 46.7% (45.2–48.2)	1.10 (0.93–1.31)
≥75 years	243, 23.7% (21.1–26.4)	1.52 (1.06–2.19)	393, 38.4% (35.4–41.4)	1.37 (1.00–1.88)		557, 54.4% (51.3–57.5)	1.50 (1.13–2.01)
Gender
Male	1790, 34.8% (33.5–36.1)	9.67 (7.84–11.92)	2063, 40.1% (38.7–41.4)	2.04 (1.73–2.40)		3417, 66.4% (65.1–67.7)	4.77 (4.06–5.61)
Female	325 (5.2%) (4.7–5.8)	Reference	1533, 24.7% (23.6–25.8)	Reference		1819, 29.3% (28.1–30.4)	Reference
Residence
Rural	1952, 19.5% (18.7–20.3)	1.77 (1.21–2.59)	3240, 32.4% (31.4–33.3)	1.34 (1.00–1.79)		4734, 47.3% (46.3–48.3)	1.52 (1.14–2.04)
Urban	163, 12.0% (10.4–13.9)	Reference	356, 26.3% (24.0–28.7)	Reference		502, 37.1% (34.5–39.7)	Reference
Education
No formal education	1313, 17.2% (16.4–18.1)	1.02 (0.65–1.60)	2306, 30.3% (29.2–31.3)	1.56 (1.02–2.38)		3389, 44.5% (43.3–45.6)	1.56 (1.05–2.32)
Up to standard VII	556, 24.8% (23.0–26.7)	1.63 (1.01–2.62)	840, 37.5% (35.5–39.5)	2.15 (1.37–3.38)		1227, 54.8% (52.7–56.9)	2.36 (1.54–3.62)
Standard VIII-X	172, 16.2% (14.1–18.6)	0.95 (0.56–1.60)	354, 33.4% (30.6–36.3)	1.80 (1.11–2.92)		470, 44.3% (41.3–47.4)	1.55 (0.98–2.45)
Higher secondary & above	74, 16.8% (13.4–20.6)	Reference	96, 21.8% (18.0–25.9)	Reference		150, 33.9% (29.5–38.6)	Reference
Occupation
Currently working	1412, 20.6% (19.7–21.6)	1.41 (1.13–1.75)	2391, 34.9% (33.8–36.1)	1.47 (1.23–1.76)		3503, 51.2% (50.0–52.4)	1.67 (1.41–1.99)
Currently not working	704, 15.6% (14.6–16.7)	Reference	1208, 26.8% (25.5–28.1)	Reference		1736, 38.5% (37.0–39.9)	Reference
Living arrangement
Living alone	40, 10.6% (7.7–14.1)	0.96 (0.52–1.75)	142, 37.7% (32.7–42.7)	1.37 (0.89–2.13)		179, 47.3% (42.2–52.5)	1.41 (0.93–2.12)
Living with spouse	1764, 21.7% (20.8–22.6)	2.26 (1.79–2.85)	2587, 31.8% (30.7–32.8)	1.06 (0.87–1.29)		3951, 48.6% (47.4–49.6)	1.48 (1.22–1.80)
Living without spouse	311, 11% (9.8–12.1)	Reference	867, 30.4% (28.7–32.2)	Reference		1107, 38.9% (37.0–40.7)	Reference
Region
Central	799, 23.7% (22.3–25.2)	1.30 (0.87–1.95)	803, 23.8% (22.4–25.3)	3.35 (2.13–5.26)		1513, 44.9% (43.2–46.6)	2.28 (1.61–3.23)
East	433, 14.9% (13.7–16.3)	0.73 (0.49–1.10)	1407, 48.6% (46.8–50.4)	10.12 (6.55–15.64)		1611, 55.6% (53.8–57.5)	3.51 (2.51–4.92)
Northeast	307, 22.0% (19.8–24.2)	1.18 (0.82–1.71)	420, 30.1% (27.7–32.5)	4.60 (3.01–7.04)		634, 45.4% (42.7–48.0)	2.33 (1.69–3.21)
West	281, 11.5% (10.3–12.8)	0.54 (0.34–0.85)	802, 32.8% (30.1–34.7)	5.24 (3.33–8.23)		1037, 42.5% (40.5–44.5)	2.07 (1.45–2.94)
South	257, 24.1% (21.6–26.8)	1.33 (0.84–2.11)	147, 13.8% (11.8–16.0)	1.72 (1.01–2.93)		391, 36.6% (33.8–39.7)	1.62 (1.06–2.48)
North	37, 19.1% (13.8–25.3)	Reference	17, 8.5% (5.2–13.7)	Reference		51, 26.3% (20.2–33.1)	Reference
Physical Activity
Active	1206, 20.0% (19.0–21.0)	1.19 (0.98–1.46)	2222, 36.8% (35.6–38.1)	1.63 (1.37–1.94)		3145, 52.2% (50.9–53.4)	1.64 (1.39–1.93)
Inactive	909, 17.3% (16.3–18.3)	Reference	1383, 26.3% (25.1–27.5)	Reference		2099, 39.9% (38.6–41.3)	Reference
Alcohol use
Yes	1198, 32.9% (31.5–34.5)	3.60 (2.96–4.37)	1909, 52.5% (50.9–54.2)	3.90 (3.29–4.61)		2753, 75.8% (74.4–77.2)	6.48 (5.46–7.71)
No	921, 12.0% (11.3–12.8)	Reference	1695, 22.1% (21.2–23.1)	Reference		2494, 32.6% (31.5–33.6)	Reference
MPCE Quintile
Lowest	619, 16.2% (15.1–17.5)	0.70 (0.50–0.98)	1454, 38.2% (36.6–39.7)	2.01 (1.47–2.74)		1888, 49.5% (47.9–51.1)	1.46 (1.07–1.98)
Lower	455, 16.0% (14.7–17.4)	0.69 (0.49–0.97)	899, 31.6% (29.9–33.4)	1.51 (1.08–2.09)		1276, 44.9% (43.0–46.7)	1.21 (0.88–1.67)
Middle	421, 21.8% (20.0–25.2)	1.01 (0.70–1.44)	570, 29.5% (27.5–31.6)	1.36 (0.97–1.91)		917, 47.5% (45.2–49.7)	1.34 (0.97–1.86)
Higher	351, 23.0% (20.9–23.7)	1.08 (0.70–1.66)	379, 24.8% (22.7–27.1)	1.08 (0.72–1.62)		651, 42.6% (40.1–45.2)	1.10 (0.75–1.61)
Highest	270, 21.6% (19.3–24.0)	Reference	294, 23.5% (21.2–25.9)	Reference		504, 40.3% (37.5–43.0)	Reference

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Univariate logistic regression models identified participants aged 75 years and above, males, rural residents with lesser years of schooling, currently working, living with a spouse, and alcohol use were associated with tobacco use (Table 2). Table 3 shows the association of socio-demographic and behavioral factors with different forms of tobacco use among the study population after adjusting for all the covariates. There was a 76% greater likelihood of tobacco use among the 75+ age group (AOR: 1.76 (1.25–2.46). Males were highly associated with using all forms of tobacco (smoking, SLT, or any form) compared to females. Relatively higher odds of smoking were evident from the central [AOR: 1.45 (0.91–2.31)] and southern [AOR: 1.20 (0.73–1.97)] tribal population; however, SLT was highly associated with the eastern [AOR: 6.17 (3.88–9.80)] and western region [AOR: 4.01 (2.49–6.44)]. Alcohol was found to be an attributing factor for any form of tobacco, i.e., smoking [AOR: 2.09 (1.69–2.58)] or SLT [AOR: 3.05 (2.54–3.66)] or any form of tobacco [AOR: 4.06 (3.39–4.87)]. Physical activity was associated with SLT [AOR: 1.33(1.09–1.63)] and any tobacco use [AOR: 1.27 (1.05–1.52)]. The odds of SLT use were 41% higher among the poorest group [AOR: 1.41 (1.04–1.92)].

Table 3. Multivariable association between various forms of tobacco with socio-demographic characteristics.

Socio-demographic Characteristics	Smoking	SLT use	Any Tobacco use
Socio-demographic Characteristics	AOR (95% CI)	AOR (95% CI)	AOR (95% CI)
Age
45 to 59 years	Reference	Reference	Reference
60 to 74 years	1.10 (0.88–1.38)	0.99 (0.82–1.20)	1.09 (0.91–1.32)
≥75 years	1.47 (0.99–2.18)	1.65 (1.15–2.35)	1.76 (1.25–2.46)
Gender
Male	9.96 (7.76–12.77)	1.41 (1.17–1.73)	3.99 (3.28–4.85)
Female	Reference	Reference	Reference
Residence
Rural	1.42 (0.98–2.07)	0.98 (0.73–1.32)	1.01 (0.76–1.34)
Urban	Reference	Reference	Reference
Education
No formal education	2.92 (1.75–4.85)	1.45 (0.93–2.26)	3.09 (1.96–4.88)
Up to standard VII	2.44 (1.48–4.02)	1.78 (1.14–2.79)	2.98 (1.88–4.72)
Standard VIII-X	1.23 (0.72–2.11)	1.57 (0.98–2.53)	1.72 (1.07–2.76)
Higher secondary & above	Reference	Reference	Reference
Occupation
Currently working	0.84 (0.65–1.10)	1.07 (0.86–1.33)	1.04 (0.86–1.28)
Currently not working	Reference	Reference	Reference
Living arrangement
Living alone	0.61 (0.32–1.17)	1.48 (0.94–2.31)	1.31 (0.84–2.04)
Living with spouse	1.18 (0.90–1.54)	0.86 (0.70–1.07)	0.95 (0.77–1.17)
Living without spouse	Reference	Reference	Reference
Region
Central	1.45 (0.91–2.31)	2.38 (1.47–3.85)	2.02 (1.37–2.99)
East	0.61 (0.37–0.99)	6.17 (3.88–9.80)	2.52 (1.70–3.74)
Northeast	1.13 (0.73–1.74)	3.23 (2.08–5.03)	1.94 (1.36–2.79)
West	0.52 (0.31–0.85)	4.01 (2.49–6.44)	1.86 (1.25–2.77)
South	1.20 (0.73–1.97)	1.40 (0.81–2.41)	1.36 (0.87–2.09)
North	Reference	Reference	Reference
Physical Activity
Active	0.97(0.76–1.23)	1.33(1.09–1.63)	1.27 (1.05–1.52)
Inactive	Reference	Reference	Reference
Alcohol use
Yes	2.09 (1.69–2.58)	3.05 (2.54–3.66)	4.06 (3.39–4.87)
No	Reference	Reference	Reference
MPCE Quintile
Lowest	0.51 (0.35–0.73)	1.41 (1.04–1.92)	0.97 (0.72–1.31)
Lower	0.53 (0.36–0.77)	1.13 (0.82–1.55)	0.87 (0.64–1.18)
Middle	0.93 (0.64–1.35)	1.09 (0.79–1.52)	1.11 (0.81–1.52)
Higher	0.93 (0.62–1.37)	0.99 (0.67–1.46)	0.96 (0.67–1.38)
Highest	Reference	Reference	Reference

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Discussion

This study reported the prevalence and determinants of tobacco use among older adults from indigenous communities of India using nationally representative data. Overall, 46 percent of tribal population was estimated using any form of tobacco, with 19 percent smokers and approximately 32 percent SLT users. Those who were male had no formal education, and consumed alcohol were found to be highly associated with smoking. On the other hand, individuals over the age of 75, male, residing in the eastern region, and reported to consume alcohol were linked with the use of SLT. The male tribal population, those having no formal education, those who consumed alcohol, and those belonging to the eastern region were more likely to use any form of tobacco.

The overall prevalence of tobacco observed in our study is consistent with a study from Madhya Pradesh, which found that tobacco was used by 48.1% of urban tribal people aged 40–65 years [22]. Here, it is worth noting that the prevalence of tobacco use among tribes is much higher than that of the general population (28.6%) in India, as reported by GATS-2 [5].

In comparing the prevalence of tobacco use from LASI (2017–18) and GATS-2 (2016–17), we found that in GATS-2, the prevalence of smoking was 28.6%, and SLT was 23.5% among the 45–59 years, age group. However, in our study, the prevalence of smoking was 17%, and SLT use was 31.2% among the same age group, which indicates a reduction in the use of smoking tobacco. Still, an increase in the use of SLT was observed [23].

Another study conducted among the Gond tribe in central India observed the prevalence of smoking to be around 22% which is in harmony with the findings of our study [24]. In contrast to the prevalence of SLT use estimated in this study, a study conducted among indigenous communities of Kerala observed the prevalence of SLT use to be around 92% [14]. A probable reason for higher SLT use among tribes could be the socio-cultural acceptance as a part of custom and perceived health benefits of SLT use compared to smoking. Nonetheless, almost half of the sample tribal population consumed nicotine, which may lead to deleterious effects on the oral cavity, respiratory, and cardiovascular systems [25].

We identified older age to be an essential predictor of tobacco use among tribal communities, which is consistent with findings of a study among a nationally representative population using GATS and GYTS that reported tobacco dependency was highest among individuals aged ≥45 years [26]. In our analysis, male participants were more likely to use tobacco than their female counterparts, similar to a study conducted among various tribes of Kerala [27]. A study conducted among the general population indicated that smoking was more prevalent among males. Conversely, smokeless tobacco usage was observed to be more common among females, indicating differences in the factors that determine tobacco usage being different among different groups [23]. Additionally, the study found a correlation between tobacco usage and lower levels of educational attainment. Smoking tobacco was more common among those who did not have formal education, whereas participants who completed primary education were more frequent users of the SLT. These findings are similar to a study conducted among urban tribal people in Madhya Pradesh, which reported maximum tobacco consumption among participants with lesser years of schooling [22].

Our study revealed that individuals who engaged in physical activity were more prone to consume SLT and any tobacco product in comparison to those who didn’t. However, Jeon et al. (2021) discovered that there were marked differences in physical fitness between smokers and non-smokers, with the latter having a higher level of physical capability [28]. We found smoking was insignificantly associated with physical activity which was inconsistent with the results of a study conducted by Cram et al. (2014) found that individuals who smoked and were less physically active tended to have poor health outcomes in the population [29].

Tribal individuals belonging to higher MPCE quintiles were found to have a greater risk of smoking. However, a review suggested smoking to be higher in the medium MPCE quintiles group of the general population [13]. It also reported that SLT use in India was more among the lowest MPCE quintile class [13]. We observed tobacco use was associated with the simultaneous use of alcohol among our study participants [30]. A recent meta-analysis reported that the synergistic consumption of any form of tobacco with alcohol is significantly associated with a higher risk of oral squamous cell carcinoma [31]. NFHS-5 (2019–2021) a nationally representative survey, suggests that tobacco prevalence was higher in Northeastern states. Our present study also observed a higher odds of SLT and any form of tobacco use in the eastern region. NFHS-5 showed a decline in tobacco use prevalence among most of the states except Bihar, Himachal Pradesh, and Mizoram, where still an uptrend can be seen [32]. We also estimated a higher tobacco burden in states such as Bihar, West Bengal, Jharkhand, and Odisha in the present study. A study conducted by Yadav et al. (2020) to evaluate the burden of SLT use indicates that the prevalence in eastern states, Bihar (23.5%), Jharkhand (35.4%), and Odisha (42.9%) was higher than the national average (21.4%), whereas in West Bengal (20.1%) it is less than the national average [33]. This could be due to various factors including family environment (as the child learns from immediate family members), and social traditions, both containing the elements that promoted tobacco use [34]. The peer pressure, easy access to tobacco and gutkha at shops nearby, and the absence of opposition from the parents, or other family members likely contributed to tobacco use [34–36]. Additionally, tobacco is used as a mean to concentrate on work [37]. A study conducted by Nanda et al. among Donghria Kondhs tribes in Odisha showed that tobacco is used to reduce abdominal pain during menstruation [38]. These are some of the driving factors that influence tobacco use among tribals. The regional differences emphasize further strengthening of tobacco control programs and policies with a focus on the eastern tribal populations.

Even though tobacco is banned in most states, the prevalence of tobacco consumption is still high in Bihar and Odisha, as evidenced by our findings. The Jharkhand Government, in a recent order, has banned the consumption of any form of tobacco products for all State Government employees from 01 April 2021. Such initiatives should be taken in all other states for tobacco control.

Several existing tobacco control programs and policies, such as the National Tobacco Control Programme (NTCP) and The Cigarettes and Other Tobacco Products (Prohibition of Advertisement and Regulation of Trade and Commerce, Production, Supply, and Distribution) Act, COTPA, 2003, have been effective in reducing the burden of tobacco in India [39]. SLT taxation as a policy imperative remains an under-researched area and needs further attention from researchers and policymakers for effective outcomes [40]. Still, a wide gap exists between the burden of tobacco consumption among tribes and the general population. Our study reflects the prevalence of tribal tobacco use is higher than the average national use, which calls for urgent action in the tribal-dominated regions [5]. The existing programs should be tailored so that they are culturally acceptable and linguistically understandable among these groups, which can help create a positive effect.

Implications

Our study suggests that the prevalence of tobacco use was high among those who had no formal education. So, culturally targeted messages are a more effective way to testify positive effects on individual health status and control tobacco among indigenous populations [41]. Culturally appropriate and acceptable behavioural change communication (BCC) is required for this group. Folk dances, puppet shows, and other such traditions can be employed for better acceptability and interest. Socio-cultural and religious events of particular tribal groups can be used to communicate anti-tobacco messages. Anti-tobacco campaigns should use messages in the local language about the specific tribal group for their better understanding and arouse interest in the subject matter. In addition, the tribal youths who are literate, local, and share a standard dialect can actively participate in the propagation of tobacco-related hazards and associated health risks. Widespread sensitization attempts to resolve the gap regarding tribal focused plans and leveraging government programs in the context of tribal health may settle the tobacco problem. Although tobacco consumption is high irrespective of gender, males are still at a higher risk of tobacco use, making them the priority target group. The Eastern region needs a strengthened program owing to the higher burden.

Strength and limitations

LASI provides an extensive and nationally representative dataset and an adequate sample size. It provides valuable data on the consumption of various forms of tobacco such as smoking, SLT, and any tobacco use. It establishes a strong association between the prevalence of tobacco and various socio-demographic factors. However, we considered the self-reported cases of different forms of tobacco use, which may lead to underestimation (misclassification bias, recall bias) of the actual burden of tobacco. The cross-sectional design hampered our abilities to draw causal conclusions and investigate longitudinal relationships over time. Additionally, the sampling weights utilized in this study are for the general population.

Conclusion

This study highlights the high burden of tobacco use along with its social determinants, which can help in tailoring the tobacco control program among tribes in India. Tobacco control policies should target males, rural residents, individuals without formal education, and alcoholics. Regional variations should be managed by adopting good practices of regions with higher tobacco control. Tobacco control measures should be prioritized in high-burden states such as Bihar, Jharkhand, Odisha, and West Bengal. Future studies should explore the behavioural and social linkages to tobacco use among tribals.

Supporting information

S1 Table. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline to report this study.

(DOC)

Click here for additional data file.^{(81.5KB, doc)}

S2 Table. Additional information regarding the ethical, cultural, and scientific considerations specific to inclusivity in global research is included in the Supporting Information.

(DOCX)

Click here for additional data file.^{(60.9KB, docx)}

S3 Table. State wise prevalence of different forms of tobacco in India among tribes.

(DOCX)

Click here for additional data file.^{(15.7KB, docx)}

Acknowledgments

The authors are grateful to the Longitudinal Ageing Study in India (LASI) for assembling and publishing accurate, nationally representative data on a range of health, biomarkers, and healthcare utilization indicators for the population in the age range of 45 years and older. The authors are also grateful to LASI’s project partners, the International Institute for Population Sciences (IIPS), Harvard T. H. Chan School of Public Health (HSPH), and the University of Southern California (USC).

Data Availability

The dataset analysed during the current study is available in the LASI data repository held at ICT, IIPS [https://g2aging.org/?section=overviews&study=lasi].

Funding Statement

The author(s) received no specific funding for this work.

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PLoS One. doi: 10.1371/journal.pone.0282487.r001

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PONE-D-22-13582Social determinants of tobacco use among tribal communities in India: evidence on ethnicity and tobacco use from LASI, wave-1PLOS ONE

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3. Mention why you have categorised age as 14 years difference, the answer my be supported with previous literature.

4. If the authors has taken "living arrangent variable" for 'living with partner' and 'living without partner', then how the authors manage 'living alone category'. Also in Independent variable/covariates section, mention the name of "variable taken from LASI", and how you have recoded the variables for your study.

5. Go through some already published plosone paper's and make tables properly. Also, use Inactive instead of not active, its sound better in research article.

6. In table 2, authors have make hapazard table. Please try to take the same category in reference among all parameters. Its better to re-do the table 2, specially for variables like- age, education, life-partner, religion, and asset quintile. If not agreed for re-doing then give valid reason for keeping such vigorous category referencing in methodilogy section.

7. Asset quintile category should be named in a similar fashion, keep same type of naming for all five categories. Don't use 2,3,4 suddenly in between most deprived and most affluent. Authors may use (extremely, less, medium, high, very high) type of categorization.

8. Do not use 'our participants'. Use correct english. Authors may use 'study participants'.

There are few things that are required to do for this paper,

1. Most of the literature cited were taken from less prevalent states. (Example- Madhya Pradesh, & Kerala). But north-eastern are highly tobacco prevalent states. Please go through some papers on north-eastern states, there are plenty of works available for north eastern states.

2. In conclusion writing a line like (Regional variations should be managed by adopting good practices of regions with higher tobacco control) is not justified. Authors must name the states where government intervention is required.

3. Authors have perfecly done a regional analysis. But naming the states within that region is necessary. For example there is high prevalence of tobacco in north eastern states, and Assam government banned tobacco products on 22 November 2019. Then Tripura government can take similar steps, is a conclusion. Authors may write conclusion section in such a way.

4. Last but not least, authors should concentrate more on high tribal population states like Madhya Pradesh, Orissa, Maharashtra, Rajasthan, & Chattisgarh. Alongwith higher percentage of Tribal population states like Arunachal Pradesh, Nagaland, Mizoram, Meghalaya, & Manipur.

Reviewer #2: In this manuscript authors have made an attempt to estimate the prevalence of tobacco use among tribal population aged 45 years or above in India based on secondary data-LASI. Efforts shown by the authors are good but there some major concerns in this current version of the manuscript listed below:

1. The literature search appears less robust and authors have missed on many recent publications. The high prevalence of tobacco use among this socially vulnerable group has been already established in the last literature and information provided here is not adding anything new to the existing knowledge. Therefore, rationale to conduct this study on tobacco use among aged tribal population needs a stronger rationale.

2. The citations need to be updated and should follow a specific format as per the journal guideline. There are also some repetitions or in a format difficult to understand say for example 18. 19

3. In methodology section, the analysis part seems incomplete. Even for regression analysis, what covariates were adjusted were not clear. The association between alcohol consumption and tobacco use might be misleading unless controlling covariates are clearly mentioned. It is also not clear how did they adjust for sample weights. The presentations in the the table appear much cluttered and hard to comprehend.

4. Apart from misclassification error, there are possibilities of other potential sources of biases which authors didn't mention and also how did authors try to adjust for such biases is also not mentioned

5. The overall writing style and presentations needs to be more comprehensive

Reviewer #3: The paper is trying to address an important question on tobacco use behaviour of the tribal population in India and understand their determinants within this group. However, I do have some major comments to improve this paper.

1. This paper is based on LASI data which is nationally and state representative. However, since this study is conducted on a specific set of population, any estimation for ST population at the state level may not be appropriate, as the total sample of ST population in this data is 11,365 tribal individuals aged ≥ 45 years. Segregating this data at the state level will lead to very few cases in smaller states. Therefore, I would recommend Authors to remove Figure 3 completely from this paper. If they want they can provide a table of prevalence of tobacco use in bigger states only in the form of a table in place of Figure 3.

2. I also fail to understand the reason for taking difference reference group each time for wealth quintile for tobacco, Smokeless and smoking outcome. Similarly, west region was used as reference for smoking and North for Smokeless and tobacco use. Can the Authors provide their reason for changing references in these models for the same socio demographic characteristics, or they should make these reference consistent and re do the logistic regression analysis, otherwise it may be difficult to compare the results for different form of tobacco use and their association with these characteristics.

3. Are there any evidence which suggest that physical activity leads to higher/lower tobacco use. I did find literature the other way around. Authors should provide some appropriate references to substantiate their choice of physical activity as one of the variable.

Jeon, H. G., Kim, G., Jeong, H. S., & So, W. Y. (2021, February). Association between Cigarette Smoking and Physical Fitness Level of Korean Adults and the Elderly. In Healthcare (Vol. 9, No. 2, p. 185). MDPI.

Conway, T. L., & Cronan, T. A. (1992). Smoking, exercise, and physical fitness. Preventive medicine, 21(6), 723-734.

4. GATS-2 has collected information about the tobacco consumption and provide caste wise data as well. Why the authors have chosen LASI data for this specific study need to be mentioned clearly. For example, GATS collected selective socioeconomic characteristics data while LASI provides a detail information on this front.

5. Authors can also provide some comparison of GATS-2 and LASI tobacco use data for ST population in the discussion.

**********

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Reviewer #1: Yes: Tushar Dakua

Reviewer #2: No

Reviewer #3: Yes: Akansha Singh

**********

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PLoS One. 2023 Mar 2;18(3):e0282487. doi: 10.1371/journal.pone.0282487.r002

Author response to Decision Letter 0

22 Jul 2022

Point-by-Point Response Reviewers’ Comments

We would like to thank the Editor and reviewers for their thoughtful comments and efforts towards improving our manuscript. The suggestions offered have been extremely helpful in revising the manuscript. We have incorporated all the changes suggested. Please find the response to all the comments highlighted in a point-by-point basis. We hope the revised manuscript is appropriate for publication in the journal. But we are open to further revisions if required.

Reviewer #1: There are few things to be corrected within the already written paper, which were also highlighted in supporting reviewed file.

The authors must go through following points:

1. Go through some already published plos one paper's and make proper citation.

Authors’ reply- Thank you for your suggestions we have gone through the literature and changed our citations accordingly.

2. For some words use synonyms or different words.

Authors’ reply- We have tried to improve the entire manuscript by editing it and we have added synonyms also as suggested by the reviewer.

3. Mention why you have categorized age as 14 years difference, the answer may be supported by previous literature.

Authors’ reply- We have categorized age in line with the previous literatures which have now been cited in the manuscript.

4. If the authors have taken "living arrangement variable" for 'living with partner' and 'living without partner', then how the authors manage 'living alone category'. Also, in independent variable/covariates section, mention the name of "variable taken from LASI", and how you have recoded the variables for your study.

Authors’ reply- Initially we had taken dm005 (current marital status) as our partner status variable. But as suggested by the reviewer, we have taken the new variable from the LASI dataset which is the “living arrangement variable” to include the category of living alone.

5. Go through some already published plos one paper's and make tables properly. Also, use Inactive instead of not active, its sound better in research article.

Authors’ reply- As per the suggestions of the reviewer we have changed the tables according to plos-one guidelines and included standard fonts and standard size. Additionally, for physical activity we have changed not active to inactive.

6. In table 2, authors have make a haphazard table. Please try to take the same category in reference among all parameters. Its better to re-do table 2, specially for variables like- age, education, life-partner, religion, and asset quintile. If not agreed for re-doing then give valid reason for keeping such vigorous category referencing in the methodology section.

Authors’ reply- Previously we had taken different reference groups as mentioned by the reviewer. However, as per the suggestions we have redone the table-2 (univariate regression analysis) and we have kept uniform reference groups of all the models.

Authors’ reply- Thank you for the suggestion. We have changed the names of five categories of asset quintile to poorest, poorer, middle, richer, and richest.

8. Do not use 'our participants'. Use correct English. Authors may use 'study participants'.

Authors’ reply- Thank you for pointing this out. We have now revised it with study participants in our manuscript.

There are few things that are required to do for this paper,

Authors’ reply- We agree with the reviewer’s suggestions. However, most of the literature from north eastern region mainly focussed either on general population or on tribal adolescents with almost no study in context to older tribal adults which makes our study incomparable with them. This has forced us to not cite those studies in our manuscript and also this is the novelty of our study that it focuses on an age group in which less attention has been paid till now as most studies focus on young adults.

Authors’ reply- Thank you for your suggestions. This is an important point that we have missed and it is worth noting the name of the states where the burden of tobacco use is high and we have addressed this point in the conclusion under the regional variations paragraph.

3. Authors have perfectly done a regional analysis. But naming the states within that region is necessary. For example, there is high prevalence of tobacco in north eastern states, and Assam government banned tobacco products on 22 November 2019. Then Tripura government can take similar steps, is a conclusion. Authors may write conclusion section in such a way.

Authors’ reply- The reviewer has raised a very valid point and we have now mentioned the names of the states. Additionally, in the discussion section we have discussed on the similar lines as suggested by the reviewer.

4. Last but not least, authors should concentrate more on high tribal population states like Madhya Pradesh, Orissa, Maharashtra, Rajasthan, & Chhattisgarh. Along with a higher percentage of Tribal population states like Arunachal Pradesh, Nagaland, Mizoram, Meghalaya, & Manipur.

Authors’ reply- Thank you for the suggestion. We have now focussed on high tribal populated states and mentioned it in the discussion section.

We again thank the reviewer for their valuable time and inputs.

Reviewer #2: In this manuscript, authors have made an attempt to estimate the prevalence of tobacco use among tribal population aged 45 years or above in India based on secondary data-LASI. Efforts shown by the authors are good but there some major concerns in this current version of the manuscript listed below:

Authors’ reply- We appreciate the observations of reviewer. However, we affirm that the available literature among the tribal focuses mainly on younger age groups with a scarcity of data among older adult. We generated evidence using a nationally representative data of older tribal adults which is novel and a strength of this study.

2. The citations need to be updated and should follow a specific format as per the journal guideline. There are also some repetitions or in a format difficult to understand say for example 18. 19

Authors’ reply- We have gone through the guidelines of plos one for citation of articles and have modified the references accordingly.

3. In the methodology section, the analysis part seems incomplete. Even for regression analysis, what covariates were adjusted were not clear. The association between alcohol consumption and tobacco use might be misleading unless controlling covariates are clearly mentioned. It is also not clear how did they adjust for sample weights. The presentations on the table appear much cluttered and hard to comprehend.

Authors’ reply- For multivariable regression analysis we have adjusted for all the socio-demographic variables. WE have now mentioned this in the statistical analysis portion of the methodology. Sampling weights have been utilized in entire analysis to compensate for the complex survey design which has now been mentioned in the manuscript for better understanding of the readers.

4. Apart from misclassification error, there are possibilities of other potential sources of biases which the authors didn't mention and also how did authors try to adjust for such biases is also not mentioned

Authors’ reply- Thank you for your suggestion. We have now addressed other potential biases also.

5. The overall writing style and presentations need to be more comprehensive

Authors’ reply- As per your suggestion we have now modified the entire manuscript to check for grammar, punctuations in order to enhance writing and presentations style.

We again thank the reviewer for their valuable time and inputs.

1. This paper is based on LASI data which is nationally and state representative. However, since this study is conducted on a specific set of population, any estimation for ST population at the state level may not be appropriate, as the total sample of ST population in this data is 11,365 tribal individuals aged ≥ 45 years. Segregating this data at the state level will lead to very few cases in smaller states. Therefore, I would recommend Authors to remove Figure 3 completely from this paper. If they want, they can provide a table of prevalence of tobacco use in bigger states only in the form of a table in place of Figure 3.

Authors’ reply- As per your suggestions, we have removed the figure-3 (State-wise prevalence of various forms of tobacco among tribals in India). Instead of that, we have added the state-wise prevalence of various forms of tobacco in the supplementary file and also added the figure of top 4 tribal populated states according to census-2011 population.

Authors’ reply- Thank you for this valuable suggestion. We have changed the reference groups of the variables where the reference seems to be inconsistent like age, asset quintile, education age, region, living arrangement variables. And we have redone table-2 and kept the reference group same as suggested by the reviewer.

Conway, T. L., & Cronan, T. A. (1992). Smoking, exercise, and physical fitness. Preventive medicine, 21(6), 723-734.

Authors’ reply- The choice of physical activity as one of the variables as most of the tribals are involved in vigorous activities and it was mentioned in detail in the methodology section. However, the suggested literature have been used to compare our findings in the discussion section.

4. GATS-2 has collected information about tobacco consumption and provides caste-wise data as well. Why the authors have chosen LASI data for this specific study needs to be mentioned clearly. For example, GATS collected selective socioeconomic characteristics data while LASI provides a detailed information on this front.

Authors’ reply- We have given justification for selection of LASI data for our analysis.

5. Authors can also provide some comparison of GATS-2 and LASI tobacco use data for ST population in the discussion.

Authors’ reply- We have addressed the comparison in the discussion section and compared the GATS-2 and LASI tobacco use data for the scheduled tribes population.

We again thank the reviewer for their valuable time and inputs.

________________________________________

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(22.2KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0282487.r003

Decision Letter 1

Mohsen Abbasi-Kangevari

28 Nov 2022

PONE-D-22-13582R1Social determinants of tobacco use among tribal communities in India: evidence on ethnicity and tobacco use from LASI, wave-1PLOS ONE

Dear Dr. Pati,

ACADEMIC EDITOR:

Dear Authors,

Please address the following comments:

1) In the title, “Social determinants of tobacco use among tribal communities in India: evidence on ethnicity and tobacco use from LASI, wave-1”, the term like ‘tobacco use’ is repetitive. Again, the term tribal has already been mentioned in the title, the term ‘ethnicity’ may not be required. The title may be simplified to ‘Social determinants of tobacco use among tribal communities in India: evidence from the first wave of LASI (full form??)’.

2) Why were smokers and users of tobacco in dual for clubbed into smokers? They are one more dangerous category. The study might have a greater weightage if the severity of the tobacco consumption is considered like only smokeless/other form users, only smokers and both smokers and non-smokers.

4) The sampling weights given in LASI are meant for the general population and may not be applicable to the tribal population.

5) P10 L212: ‘For this analysis, 11,365 scheduled tribes aged 45 years and above were included.’ I think you mean scheduled tribe participants here?

6) It should be clearly mentioned in the methodology that the wealth quintiles have been generated from the general population, not from the tribal population.

7) Table 2 headings are given as n (%) but data is reflected in n, % (CI). The variable names may be aligned to left (Age, Gender etc). Why are one reference category at the top and the other at the bottom?

8) Why do tribal groups from eastern regions have the highest tobacco consumption? This may be reflected from the existing literature. Does it reflect socio-cultural upbringing of any major tribal group/s?

9) Proper referencing should be done for some of the references like “National_Fact_Sheet_of_fourth_round_of_Global_Youth_Tobacco_Survey_GYTS- 4.pdf.”

10) Please correct the spelling of state names like Maharashtra and Rajasthan in Figure 3.

11) Manuscripts may be carefully edited for scientific language and grammatical errors. Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Mohsen Abbasi-Kangevari

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #4: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #4: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #4: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #4: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #4: Yes

**********

6. Review Comments to the Author

Reviewer #1: I have reviewed the paper comprehensively. I found that authors have addressed the issues, which I have raised earlier.

Reviewer #4: This present study deals with an important aspect of social determinants of tobacco use among tribals in India. The paper is revised by the authors after the first round of review. However, I have the following comments on the paper:

4) The sampling weights given in LASI are meant for the general population and may not be applicable to the tribal population.

5) P10 L212: ‘For this analysis, 11,365 scheduled tribes aged 45 years and above were included.’ I think you mean scheduled tribe participants here?

6) It should be clearly mentioned in the methodology that the wealth quintiles have been generated from the general population, not from the tribal population.

9) Proper referencing should be done for some of the references like “National_Fact_Sheet_of_fourth_round_of_Global_Youth_Tobacco_Survey_GYTS- 4.pdf.”

10) Please correct the spelling of state names like Maharashtra and Rajasthan in Figure 3.

11) Manuscripts may be carefully edited for scientific language and grammatical errors.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Tushar Dakua

Reviewer #4: No

**********

PLoS One. 2023 Mar 2;18(3):e0282487. doi: 10.1371/journal.pone.0282487.r004

Author response to Decision Letter 1

6 Dec 2022

Point-by-Point Response Reviewers’ Comments

1. In the title, “Social determinants of tobacco use among tribal communities in India: evidence on ethnicity and tobacco use from LASI, wave-1”, the term like ‘tobacco use’ is repetitive. Again, the term tribal has already been mentioned in the title, the term ‘ethnicity’ may not be required. The title may be simplified to ‘Social determinants of tobacco use among tribal communities in India: evidence from the first wave of LASI (full form??)’.

Authors’ reply- Thank you for your suggestion. We have changed the title of the study as suggested.

2. Why were smokers and users of tobacco in dual for clubbed into smokers? They are one more dangerous category. The study might have a greater weightage if the severity of the tobacco consumption is considered like only smokeless/other form users, only smokers and both smokers and non-smokers.

Authors’ reply- when we created the binary variable "smokers" as an outcome variable, both only smokers and dual users were merged; the sub-groups under smokers were smokers and non-smokers (includes smokeless tobacco users but don't use smoking tobacco and those who don't use any kind of tobacco products). Similarly, when we created another binary variable as "smokeless tobacco users", the subgroups under this variable were SLT users (only SLT users + dual users, as they used SLT products also) and non-SLT users (included those who didn't use tobacco of any kind & those who were consumer of smoking tobacco only). Any tobacco group included all kind of tobacco users (only smokers+ only SLT users + dual users) irrespective of type of products they were using. We conducted three separate logistic regression model for smokers, smokeless tobacco users and any tobacco users to avoid collinearity.

4. The sampling weights given in LASI are meant for the general population and may not be applicable to the tribal population.

Authors’ reply- We agree with the reviewer. However, studies such as “Puri P, Pati S. Exploring the Linkages Between Non-Communicable Disease Multimorbidity, Health Care Utilization and Expenditure Among Aboriginal Older Adult Population in India. International Journal of Public Health. 2022:5.” have utilized survey weights in analysing LASI data for tribal population. We feel weighted analysis can better present the exact scenario; hence we have used weighted analysis. Nonetheless, following reviewers’ suggestion in methods section we have mentioned this as one of the limitations of the study.

5. P10 L212: ‘For this analysis, 11,365 scheduled tribes aged 45 years and above were included.’ I think you mean scheduled tribe participants here?

Authors’ reply- We have added the word “participants” as suggested by the reviewer.

6. It should be clearly mentioned in the methodology that the wealth quintiles have been generated from the general population, not from the tribal population.

Authors’ reply- As suggested by the reviewer, we have mentioned that the wealth quintiles are based on monthly per capita expenditure of the household from the general population, not from the tribal population.

7. Table 2 headings are given as n (%) but data is reflected in n, % (CI). The variable names may be aligned to left (Age, Gender etc). Why are one reference category at the top and the other at the bottom?

Authors’ reply- As per the suggestions by the reviewer we have changed the headings and subheadings in table-2 and wherever it is required in our study. We have placed all the reference groups in bottom but in age category the 1st category (i.e., 45-59 years) was taken as reference, hence it remained at the top of the table.

8.Why do tribal groups from eastern regions have the highest tobacco consumption? This may be reflected from the existing literature. Does it reflect socio-cultural upbringing of any major tribal group/s?

Authors’ reply- We have now added this in the discussion section.

9. Proper referencing should be done for some of the references like “National_Fact_Sheet_of_fourth_round_of_Global_Youth_Tobacco_Survey_GYTS- 4.pdf.”

Authors’ reply- Thank you for pointing this out. All references have been revised and re-checked.

10. Please correct the spelling of state names like Maharashtra and Rajasthan in Figure-3

Authors’ reply- As per the reviewer’s suggestion all the spelling has been corrected in the figure-3

11. Manuscripts may be carefully edited for scientific language and grammatical errors.

Authors’ reply- Manuscript has been checked for grammatical error and corrected accordingly.

We again thank the reviewer for their valuable time and inputs. ________________________________________

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PLoS One. doi: 10.1371/journal.pone.0282487.r005

Decision Letter 2

Chandan Kumar

1 Feb 2023

PONE-D-22-13582R2Social determinants of tobacco use among tribal communities in India: evidence from the first wave of Longitudinal Aging study of IndiaPLOS ONE

Dear Dr. Pati,

1. The revised manuscript still needs language editing in some of its sections. Please follow some of the editing done in the attached copy of the manuscript (with track change).

2. In line 185-187: Authors have used the sentence “Wealth quintiles (poorest, poorer, middle, richer, richest) were based on the monthly per capita expenditure of the general population.” LASI data does not provide wealth quintile; it is MPCE quintile and they can’t be used interchangeably. So, replace the wealth quintile with the MPCE quintile, wherever used. Authors can avoid the limitation of using the indicator calculated based on the general population by calculating the MPCE quintile for their sample (tribal population) as well. This can be easily done using the expenditure details on food and non-food items including spending on health, education, utilities etc. given in the household dataset.

3. Follow the other comments and suggestions mentioned in the attached edited copy of the manuscript.

Please submit your revised manuscript by Mar 18 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Chandan Kumar, Ph.D.

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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PLoS One. 2023 Mar 2;18(3):e0282487. doi: 10.1371/journal.pone.0282487.r006

Author response to Decision Letter 2

3 Feb 2023

Response to Reviewers

We would like to thank the Editor and reviewers for their thoughtful comments and efforts toward improving our manuscript. The suggestions offered have been extremely helpful in revising the manuscript. We have incorporated all the changes suggested. Please find the response to all the comments highlighted on a point-by-point basis. We hope the revised manuscript is appropriate for publication in the journal. But we are open to further revisions if required.

Academic Editor's Comments:

1. The revised manuscript still needs language editing in some of its sections. Please follow some of the editing done in the attached copy of the manuscript (with track change).

Author’s Response: Thank you for your suggestion. We have changed the revised manuscript with language editing as suggested by the editors. In lines 259-260: as suggested by the editor, we have replaced smokeless with SLT and followed with throughout the manuscript.

Authors can avoid the limitation of using the indicator calculated based on the general population by calculating the MPCE quintile for their sample (tribal population) as well. This can be easily done using the expenditure details on food and non-food items including spending on health, education, utilities etc. given in the household dataset.

Author’s Response: Thank you for this important insight. We understand the concern raised by the editor, in this comment. The analysis was based on the individual dataset and there were several households, from where more than one individual was interviewed.

Using the household dataset for calculating MPCE might repeat (if: both husband and wife were included) or not repeat (if two or more siblings from the same household were included) those variables required for principal component analysis, which we wanted to avoid here.

3. Follow the other comments and suggestions mentioned in the attached edited copy of the manuscript.

Author’s Response: Thank you for your valuable suggestion. We have addressed suggestions made by the editor, particularly in regard to adding references, reframing the sentences, and highlighting changes done.

Attachment

Submitted filename: Response to Reviewers.docx

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PLoS One. doi: 10.1371/journal.pone.0282487.r007

Decision Letter 3

Chandan Kumar

16 Feb 2023

Social determinants of tobacco use among tribal communities in India: evidence from the first wave of Longitudinal Aging study of India

PONE-D-22-13582R3

Dear Dr. Pati,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Chandan Kumar, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0282487.r008

Acceptance letter

Chandan Kumar

20 Feb 2023

PONE-D-22-13582R3

Social determinants of tobacco use among tribal communities in India: evidence from the first wave of Longitudinal Ageing study in India

Dear Dr. Pati:

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Table. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline to report this study.

(DOC)

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S2 Table. Additional information regarding the ethical, cultural, and scientific considerations specific to inclusivity in global research is included in the Supporting Information.

(DOCX)

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S3 Table. State wise prevalence of different forms of tobacco in India among tribes.

(DOCX)

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Submitted filename: PONE-D-22-13582.pdf

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Submitted filename: Response to Reviewers.docx

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Submitted filename: Manuscript_main_edited_Editors Comments.docx

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Data Availability Statement

The dataset analysed during the current study is available in the LASI data repository held at ICT, IIPS [https://g2aging.org/?section=overviews&study=lasi].

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PERMALINK

Social determinants of tobacco use among tribal communities in India: Evidence from the first wave of Longitudinal Ageing Study in India

Jogesh Murmu

Ritik Agrawal

Sayantani Manna

Sweta Pattnaik

Shishirendu Ghosal

Abhinav Sinha

Ardhendu Sekhar Acharya

Srikanta Kanungo

Sanghamitra Pati

Roles

Abstract

Background

Methods

Results

Conclusion

Introduction

Materials and methods

Overview of data

Study participants and sample size

Fig 1. Selection of study population.

Outcome variable

Covariates

Statistical analysis

Ethical consideration

Inclusivity in global research

Results

Table 1. Sociodemographic characteristics of study population (n = 11,365).

Fig 2. Type of tobacco consumption across gender among the tribal populations.

Fig 3. Prevalence of different forms of tobacco use across major tribal populated states.

Table 2. Prevalence of various forms of tobacco and its univariate association with socio-demographic characteristics.

Table 3. Multivariable association between various forms of tobacco with socio-demographic characteristics.

Discussion

Implications

Strength and limitations

Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Jayanta Kumar Bora

Roles

Author response to Decision Letter 0

Decision Letter 1

Mohsen Abbasi-Kangevari

Roles

Author response to Decision Letter 1

Decision Letter 2

Chandan Kumar

Roles

Author response to Decision Letter 2

Decision Letter 3

Chandan Kumar

Roles

Acceptance letter

Chandan Kumar

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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