Uncovering the Health Risks: The Association Between Tobacco Use and Multimorbidity Among Indian Men—Insights from NFHS

Uncovering the Health Risks: The Association Between Tobacco Use and Multimorbidity Among Indian Men—Insights from NFHS | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Uncovering the Health Risks: The Association Between Tobacco Use and Multimorbidity Among Indian Men—Insights from NFHS Moslem Hossain, Sanjit Sarkar, Pritam Ghosh, Mriganka Dolui, Harekrishna Manna This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6236595/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background & Objective: Tobacco use is a significant risk factor for multimorbidity among men and a major public health concern in India, with high rates of tobacco consumption and associated health problems. The study aims to outline the pattern of tobacco consumption and morbidity among Indian men and to examine the link between this tobacco consumption and multimorbidity. Method: This study utilized data from the National Family Health Survey (NFHS-5) conducted between 2019 and 2021 to examine the prevalence of tobacco use and its association with multimorbidity among men in India. The study included a sample of 101,839 men aged 15-54 years. Descriptive statistics, bivariate analysis, and multinomial logistic regression were employed to analyse the data and examine the relationship between tobacco use and multimorbidity. Results: The study identified Indian men belonging to older age groups (45–54 years), less educated, alcohol users, poor socioeconomic status, marginalized social groups, rural areas, and central and eastern India as having a higher level of smoking, smokeless, or both types of tobacco consumption. Morbidity was also higher among the men belonging to older age groups, lower education levels, working groups, alcohol users, richer sections, other social groups, and the eastern and southern parts of India. The study demonstrated a significant association between tobacco use and both single morbidity and multimorbidity among men. Men who engaged in smoking had 1.18 times more probability (p < 0.001) of single morbidity, while smokeless tobacco users and both users significantly had 1.11 and 1.14 times more chance single morbidity. Moreover, men who smoked or were dual tobacco users exhibited a higher prevalence of multimorbidity. Conclusion: Policymakers must frame adequate policies considering the tobacco consumption pattern to reduce tobacco consumption among Indian men (SDG 3a), so that the associated chronic disease burden can be reduced. Implementing comprehensive tobacco control policies and promoting healthy behaviours are essential in reducing tobacco use and its associated risks. Tobacco use Multimorbidity Smoking Smokeless India Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction 1.1. Background of the study Tobacco use is exceptionally high in low- and middle-income countries, which are home to more than 80% of the world's 1.3 billion tobacco users (Theilmann et 2022). According to the Global Adult Tobacco Survey India (2016-17), approximately 267 million adults (aged 15 years and above) in India use tobacco, accounting for about 29% of all adults (MoHFW, 2017). On the other hand, tobacco use has been more prevalent among men, with a significant gender disparity in consumption. Men have higher tobacco usage rates than women, particularly in both high-income countries (HICs) and low- and middle-income countries (LMICs), including India (Wallace et al., 2003; Thun et al., 2012; Aryal et al., 2013; Lipsky et al., 2021; Menon et al., 2021). In India, about 42.4% of adult men used tobacco, whereas the prevalence was nearly 14.2% among adult women in 2016-17 (TISS & MoHFW, 2017). Beneath this national proportion of men, there are variations across different socio-demographic groups, places of residence, and geographic regions in India. In this context, identifying the key determinants of the high prevalence of tobacco use among Indian adult men is essential. On the other hand, alongside changes in demographic dynamics, food habits, access to healthcare, life expectancy, and quality of life, the co-occurrence of two or more chronic diseases among adults and elderly individuals is increasing globally, particularly in lower-middle-income countries. India is no exception to this trend. According to the latest National Family Health Survey data (IIPS & ICF, 2021) life expectancy has increased over the last decade, the total fertility rate has dropped to 1.9 (below the replacement level), and the urban population is growing. Additionally, various non-communicable diseases (NCDs) such as hypertension, diabetes, and respiratory, heart, and kidney diseases are on the rise. Furthermore, the prevalence of these NCDs is responsible for 60% of deaths in India (John et al., 2014). Moreover, this situation is becoming more vulnerable due to the coexistence of multiple NCDs. In this context, while the entire world is focused on reducing mortality (SDG 03), studying multimorbidity is also highly relevant to the Indian population Tobacco use is one of the most significant and preventable health-risk behaviors, strongly associated with a wide range of severe non-communicable diseases (Thakur et al., 2011; Zahra et al., 2015; Uddin et al., 2020; Giacaman et al., 2022). Previous research has identified that tobacco use is responsible for causing significant harm, with up to half of its users eventually dying from tobacco-related chronic diseases, including cancer, lung disease, cardiovascular disease, and stroke(Fagerström et al., 2002; Beaglehole et al., 2003; Thankappan,& Thresia, 2007; Athyros et al., 2013; Hatsukami & Carroll.,2020; van der Rijst & Garfield, 2023). The high consumption of tobacco products has severe health consequences, as evidenced by the annual death toll of 1.35 million due to tobacco-related chronic diseases in India (WHO, 2021). However, the higher prevalence of tobacco uses among Indian men compared to women indicates a relatively greater probability of NCD prevalence. While the United Nations has set the goal to 'strengthen the implementation of the WHO Framework Convention on Tobacco Control' (target 3a of SDG 3), quantifying the morbidity pattern influenced by tobacco use and estimating the link between tobacco use and morbidity patterns among Indian men is essential. Evidence in the previous literatures and research gaps The association between tobacco use and multimorbidity has been widely explored globally, yet significant gaps remain in the Indian context. Recent studies have mapped the geospatial distribution of multimorbidity across India (Prenissi et al., 2022; Khan et al., 2022; Dolui et al., 2023). Other studies have highlighted regional variations in tobacco consumption(Abdulkader et al., 2019; Singh et al., 2022; Shaikh et al., 2022). While previous research has highlighted geographical disparities in tobacco use, there is a lack of updated and comprehensive insights into the geospatial patterns of tobacco consumption among Indian men. Additionally, past studies have specifically examined the socio-demographic and economic determinants influencing tobacco use in this population (Singh & Ladusingh, 2014; Thakur & Paika, 2018; Ruhil, 2019;Islam et al., 2020; Murmu et al., 2023). Moreover, the current prevalence of specific types of tobacco use (i.e., smoking and smokeless) among Indian adult men, along with the factors determining this prevalence, remains unidentified in India. Although global studies have established a connection between tobacco use and specific non-communicable diseases (Athyros et al., 2013; van der Rijst eta al., 2023; Zhou et al., 2014; Raghuveer et al., 2016; Alsharairi et al., 2019; Hatsukami, et al., 2020), Indian studies have primarily focused on individual diseases, rather than exploring the broader spectrum of multimorbidity. A meta-analysis-based study reported a positive association between smoking and multimorbidity among older adults in India(Goel et al., 20230. A recent study, based on NFHS-4 data, highlighted a higher likelihood of multimorbidity among Indian women who consume tobacco(Mishra et al., 2021). The lack of systematic research estimating the relationship between different types of tobacco use and morbidity patterns among vulnerable Indian adult men is a significant evidence gap. Therefore, two key questions arise: What is the current pattern of smoking and smokeless tobacco consumption among Indian men? And, is there a significant influence of tobacco consumption on the prevalence of multimorbidity among Indian men? This study aims to answer these two specific research questions. While previous studies have explored geospatial disparities and overall determinants of tobacco consumption, as well as its influence on the prevalence of NCDs in India, this study uniquely focuses on the Indian male population. It significantly examines the geospatial patterns and determinants of specific types of tobacco consumption, such as smoking and smokeless tobacco, in India Objectives The study aims to investigate the prevalence of different types of tobacco consumption across various socio-demographic, economic, and regional backgrounds, and to identify the key factors influencing smoking behaviour among Indian men using the latest nationally representative data. Additionally, the study seeks to assess the strength of the relationship between tobacco use and single or multimorbidity patterns among Indian men. 2. Methodology 2.1. Database and sample design The data for this study was used from the recent release of the National Family Health Survey (NFHS-5), conducted from 2019 to 2021. The survey is a cross-sectional national representative survey conducted by the International Institute for Population Sciences (IIPS) in Mumbai, under the supervision of the Ministry of Health and Family Welfare (MoHFW) of the Indian Government. It aims to gather comprehensive information on households' population, health, nutrition, and demographic aspects, focusing on men, women, and young children at various levels such as national, state, and district. In this study, the NFHS-5 data from 2019-2021 was used as a benchmark to examine the progress made by India in various health indicators over time. The survey interviewed a large sample size, including 636,699 households, 101,839 men aged 15–54, and 724,115 eligible women aged 15–49. The response rates reported for households, men, and women were 98%, 97%, and 92%, respectively. The findings of this study are based on a total of 101,839 men aged 15-54 from NFHS-5. Detailed information regarding the methodology, including the sampling frame, survey design, and data collection process, was published elsewhere (IIPS & ICF, 2021). 2.2. Outcome variable This analysis focuses on chronic morbidity as the outcome variable, covering seven chronic health conditions: heart disease, hypertension, diabetes, goitre or any other thyroid disorder, chronic kidney disease, cancer and chronic respiratory diseases, including asthma. The outcome variable was constructed based on the following questions asked to the respondents: (a) Do you currently have heart disease? (b) Do you currently have hypertension? (c) Do you currently have diabetes? (d) Do you currently have goitre or any other thyroid disorder? (e) Do you currently have chronic kidney disease? (f) Do you currently have cancer? (g) Do you currently have chronic respiratory diseases, including asthma? If any respondent (aged 15-54) reported any of these morbidities, they were assigned a value of 'yes'; otherwise, it was recorded as 'no'. To determine chronic morbidity status, the binary variables for the seven chronic diseases were combined. If an individual reported having at least one of the chronic diseases (any one out of the seven), they were assigned a “single morbidity.” If two or more chronic diseases were reported, they were categorized as "multimorbidity." If an individual reported none of the chronic diseases, they were assigned "no morbidity" in the overall chronic morbidity variable. 2.3. Explanatory variable The key explanatory variable was tobacco use, consisting of four categories – smoking, smokeless, dual-use, and non-tobacco user. Smoking includes individuals who use tobacco while smoking, such as cigarettes, pipes, cigars, hookah, bidi, or other smoking substances. Further, Smokeless includes individuals who use smokeless tobacco in various forms, such as Gutkha, pan masala with tobacco, khaini, pan with tobacco, snuff, or other chewing tobacco. Dual-use describes respondents who engage in both smoking and smokeless tobacco use. Non-tobacco user includes individuals who neither smoke nor use smokeless tobacco substances. All categories were coded as '1' for individuals who used at least one substance in the respective category and '0' for individuals who did not use any substance. Finally, a group variable was created as overall 'tobacco user'. Alongside, based on the previous literatures on the determinants of multimorbidity in India, and considering the Indian current situation twelve covariates have been selected in this study. Detail information about the covariates has been represented in Table 1. Table 1 Description of explanatory variables. Explanatory Variable Description Coding Age Categorized into three groups. 15–24 (0), 25–44 (1), 45–54 (2) Marital Status Categorized into five groups: never married, currently married, widowed, divorced, and separated. Never married (0), Currently married (1), Widowed (2), Divorced (3), Separated (4) Educational Status Classified into no education, primary, secondary, and higher education. No education (0), Primary (1), Secondary (2), Higher (3) Wealth Status Categorized into five wealth groups. Poorest (0), Poorer (1), Middle (2), Richer (3), Richest (4) Working Status Indicates whether the respondent was working during the survey. No (0), Yes (1) Occupation Classified into no occupation, agricultural, service, skilled/unskilled manual, and others. No occupation (0), Agricultural (1), Service (2), Skilled/Unskilled manual (3), Others (4) Alcohol Consumption Indicates whether the respondent consumes alcohol. No (0), Yes (1) Mass Media Exposure Indicates whether the respondent is exposed to mass media. No (0), Yes (1) Place of Residence Categorized as rural or urban. Urban (0), Rural (1) Religion Classified into seven religious groups. Hindu (0), Muslim (1), Christian (2), Sikh (3), Buddhist (4), Jain (5), Others (6) Social Group Categorized as Scheduled Caste, Scheduled Tribe, Other Backward Class, and Others. Scheduled Caste (0), Scheduled Tribe (1), Other Backward Class (2), Others (3) Geographical Region Categorized into six major regions of India. Northern (0), Southern (1), Eastern (2), Western (3), North-Eastern (4), Central (5) 2.4. Statistical analysis In this study, several statistical analyses were conducted to assess the prevalence of tobacco use and multimorbidity, as well as the association between multimorbidity and other explanatory variables. Descriptive statistics were used to summarize and present the prevalence of tobacco use and multimorbidity, along with other key explanatory variables. This helps provide an overview of the data. The bivariate analysis examined the relationship between multimorbidity and tobacco use prevalence among men, considering their background characteristics. The Chi- square (χ2) test was used to assess the significance level of this relationship. Multinomial logistic regression analysis was conducted to understand the relationship between pattern of tobacco consumption and the outcome variable (single and multimorbidity). Outcome of two regression models have been generated in this study. We only considered pattern of tobacco consumption as explanatory variable and pattern of morbidity as dependent variable in the first models. In second model, we considered personal level, community level and regional level backgrounds of Indian men as covariate along with the pattern of tobacco consumption. The regression analysis results were reported using predicted probability percentages with a 95% confidence interval (CI). The statistical analyses were carried out using Stata version 14.2. Individual weights were applied to the estimates to ensure the results were nationally representative. 3. Results 3.1. Respondent characteristics Almost 50% of men belonged to the 25–44 age group, and about 63% were married. A higher proportion of men achieved secondary education (56.88%), followed by higher education (19.28%). The proportion of men was more or less the same in all five categories of wealth status, with the poorest having a slightly lower proportion (16.69%). Moreover, about three-fourths of men (76.18%) were working, and nearly one-third were engaged in agricultural occupations. Regarding religious affiliation, most of the men were Hindu (79.27%), followed by 15.44% of Muslims. However, about one-third belonged to Other Backward Classes, and one-fifth of men belonged to the Scheduled Caste. A greater proportion of respondents were exposed to mass media (87.66%). The table also shows the six major regions-wise distribution of respondents; nearly half of the respondents lived in the southern and western regions of the country. A higher proportion of men (22.85%) consumed alcohol (Table 2 ) . Table 2 Prevalence of tobacco use and morbidity among men by their background characteristics in India, NFHS-5, 2019–21. Background Characteristics Sample number Sample % Prevalence of Tobacco use Prevalence of Morbidity Smoking Smokeless Dual user Single Morbidity Multimorbidity % % % % % Age Group *** *** *** *** *** 15–24 31070 30.21 7.28 10.96 4.27 2.25 0.35 25–44 51364 50.25 14.9 23.73 8.13 5.96 1.26 45–54 19405 19.53 21.04 25 7.67 12.9 4.43 Marital Status *** *** *** *** *** Never married 36892 35.99 8.41 10.23 4.4 2.8 0.47 Currently married 63377 62.59 16.69 25.61 8.12 8.09 2.24 Widowed 739 0.64 24.07 30.22 15.37 9.21 3.73 Divorced 364 0.35 16.3 29.58 14.05 6.82 0.93 Separated 467 0.43 27.09 25.31 14.13 9.29 1.01 Educational Status *** *** *** *** *** No education 12269 11.82 21.72 28.4 12.79 7.37 2.4 Primary 11710 12.02 20.45 30.39 12.08 7.54 1.95 Secondary 60018 56.88 12.23 20.06 5.87 5.84 1.38 Higher 17842 19.28 9.43 8.8 2.95 5.68 1.54 Working Status *** *** *** *** *** No 25985 23.82 8.07 9.93 3.44 3.55 1.14 Yes 75854 76.18 15.59 23.3 7.95 7.02 1.75 Occupation *** *** *** *** *** No occupation 19241 18.04 6.11 5.47 1.86 2.75 0.84 Agricultural 32864 27.51 15.97 28.12 8.38 6.86 1.77 Services 6341 7.05 16.87 21.33 6.69 6.49 2.26 Skilled and unskilled manual 23930 25.79 15.71 25.59 9.71 6.76 1.35 Others 19463 21.61 14.18 15.24 5.82 7.46 2.1 Alcohol use *** *** *** *** *** No 75391 77.15 9.73 18.91 3.61 5.29 1.33 Yes 26448 22.85 27.53 24.18 17.89 9.25 2.52 Mass media Exposer *** *** *** *** *** No 15253 12.34 16.11 30.62 10.68 5.44 1.25 Yes 86586 87.66 13.47 18.64 6.34 6.3 1.65 Wealth Index *** *** *** *** *** Poorest 19796 16.69 16.6 31.17 13.24 5.3 1.3 Poorer 22599 19.69 15.48 25.86 9.42 5.95 1.4 Middle 21715 21.3 13.51 20.56 6.21 6.39 1.53 Richer 20209 22.29 12.8 15.88 4.44 6.74 1.59 Richest 17520 20.04 11.23 9.52 2.49 6.36 2.14 Religion *** *** *** *** *** Hindu 77211 79.27 13.17 20.84 6.86 6.06 1.68 Muslim 12112 15.44 16.45 18.95 7.21 6.46 1.34 Christian 7267 2.68 22.54 9.2 6.17 7.2 1.25 Sikh 2386 0.94 7.07 3.5 2.64 8.23 1.03 Buddhist 1405 1.09 8.83 25.2 7.01 6.94 0.98 Jain 143 0.31 6.55 18.41 6.89 3.53 0.64 Others 1315 0.28 11.76 23.06 13.37 13.13 1.19 Social Group *** *** *** *** *** SC 19240 20.18 16.1 20.42 9.13 6.03 1.34 ST 19354 8.95 15.35 28.48 9.6 5.33 1.41 OBC 39326 41.82 10.97 20.36 5.71 5.64 1.47 Others 23919 29.06 15.79 16.99 6.15 7.37 2.02 Resident *** *** *** *** *** Urban 26420 35.19 13.38 14.67 5.26 6.3 1.78 Rural 75419 64.81 14.03 23.08 7.75 6.14 1.51 Regional Zone *** *** *** *** *** North 21134 8.53 19.51 9.53 5.46 5.22 1.18 Central 23965 20.88 6.36 31.26 10.65 4.21 0.95 East 14474 16.06 27.36 18.13 11.72 7.86 2.44 Northeast 14860 5.46 17.91 24.43 14.96 7.01 1.05 West 11275 23.83 6.98 28.44 3.78 5.58 1.19 South 16131 25.23 14.94 6.95 2.31 7.52 2.25 Total 101839 13.8 20.12 6.87 6.2 1.6 Note: Heterogeneity in the prevalence of smoking, smokeless, both types of tobacco consumption, single and multi-morbidity across various categories of the background variables are examined by Pearson’s Chi-square test. Level of significance *** p < .01, ** p < .05, * p < .1 3.2. Pattern of tobacco use and morbidity About 13.8%, and 20.12% of Indian men used smoking and smokeless tobacco products, respectively ( Fig. 2 ). Approximately 6.87% of the population were dual users. On the other hand, the prevalence of single morbidity in India was 6.20%, and multimorbidity, which refers to the presence of multiple health conditions or diseases at the same time, was observed in 1.60% of the population. Chi square results indicate a significant heterogeneity in the prevalence of smoking, smokeless, and both types of tobacco consumption among Indian men belonging to different categories of sociodemographic, economic, and regional backgrounds (Table 2 ). Smoking was significantly higher among the men who were less educated, working in service sectors and agricultural activities, living separately from their wives, belonging to higher age groups (45–54 years), poor families, Christian religious groups, marginalized communities (SC/ST), and living in the eastern, northern, and northeastern parts of India. On the other side, smokeless tobacco consumption is relatively higher among men who are aged 45–54 years), less educated, widowed or divorced, poor, involved in agricultural activities, use alcohol, belong to other religions, are members of the ST community, and live in rural areas in the central and western parts of India. Smoking and smokeless dual-type tobacco consumption are comparatively higher among men who are in the middle age group (20–44 years), widowed and separated, less educated, addicted to alcohol, belonging to poor families, other religious groups, scheduled tribes, living in rural areas, and in the northeast or eastern part of India. Other side, single morbidity or any type of chronic disease are observed higher among the men who are aged, widowed or separated, agricultural workers, consuming alcohol, belonging to rich families, other religion and other social groups, in the central, eastern and southern part of India. Prevalence of multimorbidity was higher among the men who belong to a higher age group (45–54 years), widows, non-educated, alcohol users, rich families, Hindu religion, and other social groups, in the southern and eastern parts of India. Besides, Fig. 3 a indicates a higher prevalence of smoking in West Bengal, Assam, Meghalaya, Tripura, and Manipur. Further, smokeless tobacco use is comparatively higher in central India, particularly in Uttar Pradesh, Bihar, and Madhya Pradesh, exhibiting higher prevalence (Fig. 3 b). The districts in north-eastern, eastern, and central India are more likely to have a higher prevalence of dual tobacco users (Fig. 3 c). Other side prevalence of single and multimorbidity is comparatively higher in south Indian districts in 2019-21 (Fig. 4 a and b ). 3.3. Impact of tobacco use on the prevalence of single and multimorbidity The unadjusted regression model (Fig. 5 ) revealed a relatively higher risk of any type of chronic disease among men smokers, smokeless tobacco users and dual tobacco users in India. However, after considering relevant men’s personal level, community level and residential and regional level explanatory variables, adjusted model also explained the significant effect of different types of tobacco use on the prevalence of single or multimorbidity among Indian men ( Table 3 ). The odds of single morbidity declined from 1.85 to 1.18 (p < 0.001) among the smokers when we incorporated other control variables in the regression model. In the case of smokeless tobacco users and dual types of tobacco consumers, the probability of single morbidity is also reduced after considering selected control variables. On the other hand, the probability of multimorbidity was also higher among different types of tobacco consumers than non-consumers. However, after considering all selected control variables the risk of multimorbidity slightly declined among the smokers and dual type of tobacco consumers. Moreover, we have not found a significant prevalence of multimorbidity among smokeless tobacco consumers after incorporating control variables in the regression model. Several control variables have significant effects on the prevalence of single and multimorbidity among Indian men. The risk of single and multimorbidity was significantly higher among the men who were older, currently married or widow, higher education level, and other social groups, in south and eastern parts of India. Table-3 Adjusted Odds of morbidity among the men in India − 2019-21 (NFHS-5). Background Characteristics Single Morbidity Multimorbidity AOR ± 95% CI AOR ± 95% CI Tobacco Non-user ref ref Smoking 1.18*** [1.10–1.28] 1.34*** [1.17–1.57] Smokeless 1.11*** [1.03–1.20] 0.91 [0.78–1.06] Both or dual user 1.14** [1.02–1.26] 1.76*** [1.46–2.13] Age Group 15–24 ref ref 25–44 1.87*** [1.67–2.09] 2.64 [2.01–3.47] 45–54 4.37*** [3.87–4.94] 9.17 [6.91–12.18] Marital Status Never married ref ref Currently married 1.46*** [1.32–1.60] 2.05*** [1.64–2.55] Widowed 1.43** [1.07–1.90] 2.43*** [1.52–3.88] Divorced 1.32 [0.86–2.02] 0.99 [0.33–2.97] Separated 1.70*** [1.21–2.38] 0.87 [0.33–2.99] Educational Status No education ref ref Primary 1.10* [1.00-1.22] 0.92 [0.77–1.10] Secondary 1.12** [1.03–1.22] 0.85** [0.73-1.00] Higher 1.12** [1.00-1.25] 0.79** [0.65–0.97] Working Status No ref ref Yes 0.94 [0.83–1.05] 0.62*** [0.51–0.75] Occupation No occupation ref ref Agricultural 1.03 [0.88–1.20] 0.77* [0.58–1.01] Services 0.99 [0.83–1.18] 1.02 [0.76–1.38] Skilled and unskilled manual 1.08 [0.92–1.26] 0.67*** [0.50–0.88] Others 1.21 [1.04–1.42] 1.03 [0.78–1.35] Alcohol use No ref ref Yes 1.49*** [1.40–1.59] 1.43*** [1.27–1.61] Mass media Exposer No ref ref Yes 1.15*** [1.05–1.26] 1.36*** [1.13–1.64] Wealth Index Poorest ref ref Poorer 1.08 [0.98–1.19] 1.04 [0.87–1.26] Middle 1.13** [1.02–1.25] 1.1 [0.90–1.33] Richer 1.17*** [1.06–1.30] 1.13 [0.92–1.40] Richest 1.12* [1.00-1.27] 1.59*** [1.26-2.00] Religion Hindu ref ref Muslim 1.14*** [1.05–1.23] 0.82** [0.70–0.96] Christian 0.97 [0.83–1.14] 0.56*** [0.40–0.80] Sikh 1.70*** [1.32–2.19] 0.85 [0.44–1.65] Buddhist 1.25 [0.98–1.60] 0.93 [0.50–1.72] Jain 0.43*** [0.23–0.79] 0.25* [0.06–1.02] Others 2.15*** [1.50–3.07] 0.69 [0.23–2.05] Social Group SC ref ref ST 0.88** [0.79–0.99] 1.2 [0.96–1.49] OBC 0.95 [0.88–1.02] 1.10*** [0.94–1.28] Others 1.26*** [1.16–1.36] 1.58 [1.35–1.85] Resident Urban ref ref Rural 1.09*** [1.02–1.17] 1.09 [0.96–1.24] Zone North ref ref Central 0.97 [0.85–1.10] 1.02 [0.79–1.32] East 1.52*** [1.35–1.72] 2.13*** [1.68–2.69] Northeast 1.33*** [1.14–1.55] 0.97 [0.69–1.36] West 1.19*** [1.05–1.34] 1.18 [0.93–1.50] South 1.55*** [1.38–1.74] 2.09*** [1.66–2.62] Constant 0.01 [0.01–0.01] 0.002 [0.01–0.02] Pseudo r-squared 0.076 0.035 Level of significance*** p < .01, ** p < .05 *p < .1, ref- reference category. 4. Discussion 4.1. Key Findings The study significantly identified the prevalence of various types of tobacco use among Indian men from different socio-demographic, economic, and regional backgrounds. Additionally, the study established a significant relationship between the type of tobacco use and the prevalence of single and multimorbidity among Indian men, aligning with previous studies conducted in other countries and India (Pednekar, M. S. et al., 2006; Banjare & Pradhan, 2014 ) this study found a comparatively higher prevalence of single and multimorbidity among Indian men who smoke tobacco, consume smokeless tobacco, or use both forms of tobacco. In line with prior findings (Pednekar, M. S. et al., 2006; Banjare & Pradhan, 2014 ), The study notably reveals that men who use both smoking and smokeless tobacco are particularly at risk of experiencing multiple health conditions simultaneously. The study highlighted the significant impact of smoking on the prevalence of chronic diseases such as chronic respiratory disease, hypertension, diabetes, cancer, heart disease, thyroid disease, and chronic kidney disease. This finding is also consistent with previously available literature from various settings (Athyros et al., 2013 ; van der Rijst eta al., 2023; Zhou et al., 2014 ; Raghuveer et al., 2016; Alsharairi et al., 2019; Hatsukami, et al., 2020; Mishra et al., 2021 ; Khowaja et al., 2017). These findings corroborate previous studies conducted in other countries, such as Canada, China, and others (Pengpid & Peltzer, 2017 ; Wikström et al., 2025; Geda et al., 2021 ; Han et al., 2021; Zou et al., 2023) and Indian women (Mishra et al., 2021 ), Our study also revealed that smoking habits and smokeless tobacco consumption significantly affect the prevalence of multimorbidity among Indian men. Corroborating previous findings (Abdulkader et al., 2019 ; Singh et al., 2022; Shaikh et al., 2022 ), this study highlighted the higher prevalence of tobacco use in central, eastern, and northeastern India, which may be attributed to lower levels of human development (HDI), socio-economic backwardness, and a higher concentration of marginalized populations (SC/ST) in these regions( Ghosh et al., 2023 ; Suryanarayana, et al, 2011; Kundu et al., 2013 ; Jose, 2020). In this context, corroborating previous findings on the Indian population (Singh & Ladusingh,2014; Thakur & Paika, 2018 ; Ruhil, 2019 ; Islam et al., 2020 ; Murmu et al.,2023) the present study also highlighted a higher prevalence of tobacco use among less educated men, the poorer sections of society, and the SC/ST community (Table 2 ). In line with previous studies on India (Abdulkader et al., 2019 ; Singh et al., 2022; Shaikh et al., 2022 ; Singh & Ladusingh,2014; Thakur & Paika, 2018 ; Ruhil, 2019 ; Islam et al., 2020 ; Murmu et al.,2023) this study also outlined the significant effect of age group, marital status, education level, alcohol consumption, social groups, and geographic regions on the prevalence of single or multimorbidity among Indian men. In support of previous findings (Khan et al., 2022 ; Dolui et al., 2023 ; Talukdar, 2017 ), the current study also highlighted a higher prevalence of multimorbidity in southern and eastern part of India. It may be linked to their life style, food habits, prevalence of obesity and other socioeconomic determinants. This study reveals that age is vital in understanding tobacco use and multimorbidity. While the 25–44 age group has the highest prevalence of tobacco use, the prevalence of multimorbidity increases with age, peaking in the 45–54 age group (Boutayeb et al., 2013 ; Griffith et al., 2017; Ma et al., 2020 ; He et al., 2021 ). Conversely, younger men aged 15–24 have a lower likelihood of experiencing multiple health conditions concurrently, which is consistent with earlier studies (Carreras et al., 2013 ; Danawala et al., 2014 ). As evident in previous studies on India and elsewhere, the present study also shows that unmarried men had a lower prevalence of smoking and smokeless tobacco use, while widowed men had a higher prevalence of both and a higher prevalence of multimorbidity, indicating a heightened susceptibility to multiple health conditions(Islam et al., 2020 ;Banjare & Pradha, 2014; Sreeramareddy et al., 2014 ; Wang et al., 2022 ). The study also found that men with lower education levels are more likely to smoke and have a higher prevalence of multimorbidity (Narayana et al., 1996; Lindström & Janzon, 2007 ; Prazeres & Santiago, 2015 ). The study found that religion and caste differences contribute to variations in tobacco use (Rani et al., 2003 ). Hindus and Muslims exhibit higher tobacco use rates, while Muslims and scheduled tribes have a higher prevalence of smokeless tobacco use. In consistent with previous findings present study found that scheduled castes and scheduled tribes in India have higher rates of smoking and smokeless tobacco use habits compared to forward castes (Singh & Ladusingh, 2014 ; Kashyap et al., 2020 ; Anushree, K. N. & Mishra, 2022). Consistent with earlier findings, this study also confirms that socioeconomic factors, such as wealth quintiles, occupational status, and residential area, are associated with both tobacco use and multimorbidity. Men in higher wealth quintiles and working individuals, particularly those engaged in agricultural work and living in rural areas, have a higher tobacco use (Islam et al, 2020 ) and multimorbidity prevalence (Carreras,2013; Danawala, 2014). The study suggests exposure to mass media and alcohol consumption are associated with a higher prevalence of both tobacco use and multimorbidity, underscoring the need for comprehensive health promotion strategies that consider these factors (Mishra, S. et al., 2021 ; Keetile et al, 2020 ; Yao et al., 2020). The Government of India has launched significant tobacco control measures under the Cigarettes and Other Tobacco Products Act (COTPA), 2003, and the National Tobacco Control Programme (NTCP). However, findings of the current study suggest the need for further strengthening of policy implementation, including: (a) Strengthening COTPA, 2003, particularly in rural areas and disadvantaged regions, (b) educating poor, marginalized people about the long-term risks of tobacco use and multimorbidity through different awareness programmes under National Health Mission (NHM) and NTCP, (c) Improving access to early screening, diagnosis, and treatment of chronic diseases associated with tobacco use through Ayushman Bharat – Pradhan Mantri Jan Arogya Yojana (PMJAY), (d) Increasing tobacco taxation under GST to fund cessation programs. (e) Enhancing grassroots engagement through ASHAs and Panchayati Raj for last-mile health services. 4.2. Value added to the global literature The study will advance the knowledge in this field in two ways. Firstly, by disaggregating tobacco use into three categories such as smoking tobacco, smokeless tobacco and both or dual type of tobacco consumption the study has highlighted the pattern of tobacco use among men belongs to different demographic, social, economic, religious groups, residence and geographic regions or units based on the most recent nationally representative database. Along with that, it also has helped to outline the pattern of single and multimorbidity among Indian men at subnational level. This can inform policymakers and help refine strategies for tobacco control and multimorbidity prevention and management in India. Secondly, the study also outlined the relationship between the pattern of tobacco consumption and morbidity among Indian men using nationally representative most recent data. 4.3. Limitations and strength of the study The study is limited in a few aspects. Firstly, self-reported personal information may have some recall bias that may overestimate or underestimate the relationship. Secondly, the prevalence of multimorbidity is recorded based on self-reported information. The surveyor had not clinically examined the existence of morbidities among the target population. There may be some error in the estimation of the level of multimorbidity among males belonging to different backgrounds. Finally, the study has not considered the behavioural, and biological aspects (lifestyle habits, food intake, psychological factors, heredity of disease, etc.), utilization of medical services, etc due to the lack of information in the dataset. We have not incorporated the mentioned factors in the adjusted regression models that may overestimate or underestimate the relationship between tobacco consumption and the prevalence of multimorbidity in this study. However, trained professionals collected the information during the time of the cross-sectional survey, and the database was nationally representative, most recent, and reliable in the context of India. In addition to that, this is the first comprehensive endeavour to outline the subnational pattern of different types of tobacco consumption and morbidity and to establish the relationship between these two patterns among Indian men. 5. Conclusion The study identified Indian men belonging to older age groups (45–54 years), less educated, alcohol users, poor socioeconomic status, marginalized social groups (ST/ST), rural areas, and central and eastern India as having a higher level of smoking, smokeless, or both types of tobacco consumption. Morbidity was also higher among the men belonging to older age groups, lower education levels, working groups, alcohol users, richer sections, other social groups, and the eastern and southern parts of India. The study also confirms the strong influence of tobacco consumption on the prevalence of single morbidity (chronic diseases) and multimorbidity among Indian men based on the NFHS-5 (2019–21) database. The treatments for this tobacco induced morbidity pattern among Indian men have also compounded the economic burden on households as well as the nation. In this context, the study suggests an urgent need for comprehensive, multi-sectoral policies to control tobacco consumption. In this context, the study suggests strengthening the COTPA Act (2003), emphasizing awareness programs on the risk of tobacco use and multimorbidity, improving early screening and treatment for the disease caused by tobacco use, and increasing tobacco taxation to control tobacco consumption in India. Considering the tobacco consumption pattern, the Government and local administrations should take adequate measures to reduce tobacco consumption among Indian men (SDG 3a), so that the chronic disease burden can be reduced. Implementing and enforcing comprehensive tobacco control measures, such as increased taxation, strict regulations on tobacco advertising, and smoke-free laws, can help reduce tobacco use and lower the prevalence of multiple chronic diseases. Tailored interventions, including targeted awareness campaigns, cessation programs, and healthcare services, should be developed and implemented to address the needs of these high-risk groups. Declarations Authors Contribution: M.H.: Conceptualization, Original Draft Writing, Visualization, Validation, Methodology, Investigation, Formal Analysis, Data Curation. S.S.: Review & Editing, Visualization, Validation, Methodology. P.G.: Review & Editing, Investigation, Methodology, Conceptualization, Visualization. M.D.: Review & Editing, Formal Analysis. H.M: Review & Editing, Formal Analysis. Funding: The authors received no funding for this research. Competing Interests: The authors confirm that they have no competing interests. Acknowledgement: The authors acknowledge the School of Earth Sciences, Department of Geography, Central University of Karnataka, and express their gratitude to DHS for providing freely available data. Human Ethics and Consent to Participate declarations: not applicable Clinical trial number: not applicable. Consent to Publish declaration: not applicable Data Availability: The data material is available on request from the corresponding author. As we have used data from the National Family Health Survey (NFHS-5), which is publicly accessible and available at no cost through the Demographic and Health Surveys (DHS) program. The dataset can be accessed via the DHS repository at https://dhsprogram.com. 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Hossain","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYHACxgNgir0BSBhYEKcHooUHRBlIkKJFIgFMElau297+4MDHnFo5fsnnVzf8KJBg4G/vTsCrxezMGYODM7cdN5acnVN2swfoMIkzZzfg13Ijh+Ew77ZjiRtu56Td4AFqMZDIJaQl/cHhv9uO1W+4eSbt5h/itCQYHGbcVpNgcIP92G3ibAH5pXfbAcOZPTlst2UMJHgI++V4+8MHP7fVyfOzH392880fGzn+9l78WqDgMBDzGIBYPMQoB4E6IGZ/QKzqUTAKRsEoGGEAAOcEUJB9QJoEAAAAAElFTkSuQmCC","orcid":"","institution":"Central University of Karnataka","correspondingAuthor":true,"prefix":"","firstName":"Moslem","middleName":"","lastName":"Hossain","suffix":""},{"id":454729346,"identity":"98553a70-9aac-4889-8888-b61742c87fd6","order_by":1,"name":"Sanjit Sarkar","email":"","orcid":"","institution":"Central University of Karnataka","correspondingAuthor":false,"prefix":"","firstName":"Sanjit","middleName":"","lastName":"Sarkar","suffix":""},{"id":454729348,"identity":"715661d7-67de-4fda-b6c4-edd2dfbc8ee9","order_by":2,"name":"Pritam Ghosh","email":"","orcid":"","institution":"Hijli College","correspondingAuthor":false,"prefix":"","firstName":"Pritam","middleName":"","lastName":"Ghosh","suffix":""},{"id":454729350,"identity":"397d8eb9-eaf5-48fc-a7ea-6586b1f4e0fb","order_by":3,"name":"Mriganka Dolui","email":"","orcid":"","institution":"Central University of Karnataka","correspondingAuthor":false,"prefix":"","firstName":"Mriganka","middleName":"","lastName":"Dolui","suffix":""},{"id":454729351,"identity":"e3abb746-8c7b-4524-bed5-b5519fd78a50","order_by":4,"name":"Harekrishna Manna","email":"","orcid":"","institution":"Central University of Karnataka","correspondingAuthor":false,"prefix":"","firstName":"Harekrishna","middleName":"","lastName":"Manna","suffix":""}],"badges":[],"createdAt":"2025-03-16 08:53:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6236595/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6236595/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82487850,"identity":"cab4963e-2a25-4839-bb9d-532a5474b4df","added_by":"auto","created_at":"2025-05-12 06:03:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":82712,"visible":true,"origin":"","legend":"\u003cp\u003ePresent the flow chart of outcome variable\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6236595/v1/e598887a70bbc06b612fcb93.png"},{"id":82487848,"identity":"3b3df91e-9e73-4ee1-8003-aa66c0299438","added_by":"auto","created_at":"2025-05-12 06:03:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":132457,"visible":true,"origin":"","legend":"\u003cp\u003ePresent the prevalnace of tobacco use (1a) and morbidity (1b) in India , NFHS-5, 2019-21.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6236595/v1/511688f99331c1cbf6e7c3b6.png"},{"id":82487852,"identity":"c8f56b96-1322-488c-8933-bf5c1d56af20","added_by":"auto","created_at":"2025-05-12 06:03:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":538839,"visible":true,"origin":"","legend":"\u003cp\u003eDistrict-level pattern of prevalence of tobacco use and morbidity among men in India, NFHS-5, 2019-2021.(a) prevalence of smoking, (b) prevalence of smokeless tobacco user, (c) prevalence of dual users,\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6236595/v1/f54905ac63c9acba16f7dccb.png"},{"id":82487854,"identity":"4661c7d6-ee9e-4201-8976-0c4320ac53d1","added_by":"auto","created_at":"2025-05-12 06:03:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":316186,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence of (a) single morbidity and (b) multimorbidity among Indian men (15-54 years)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6236595/v1/e49bf3b1e06c795ec81e7de5.png"},{"id":82488583,"identity":"f8255cee-9f43-4461-819f-76428280547f","added_by":"auto","created_at":"2025-05-12 06:11:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":56737,"visible":true,"origin":"","legend":"\u003cp\u003ePresent the unadjusted predicted probability percentage of single and multimorbidity across different types of tobacco consumer among Indian men, in 2019-21 (NFHS-5).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6236595/v1/044d4215d30d027bb76cfc81.png"},{"id":97326441,"identity":"d5213740-9f63-4658-9653-5513b22c0b4f","added_by":"auto","created_at":"2025-12-03 08:40:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2707485,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6236595/v1/d169463b-5b51-4861-86c8-7443b89bd7f4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Uncovering the Health Risks: The Association Between Tobacco Use and Multimorbidity Among Indian Men—Insights from NFHS","fulltext":[{"header":"1.\tIntroduction","content":"\u003cp\u003e\u003cem\u003e1.1. Background of the study\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTobacco use is exceptionally high in low- and middle-income countries, which are home to more than 80% of the world\u0026apos;s 1.3 billion tobacco users (Theilmann et 2022). According to the Global Adult Tobacco Survey India (2016-17), approximately 267 million adults (aged 15 years and above) in India use tobacco, accounting for about 29% of all adults (MoHFW, 2017). On the other hand, tobacco use has been more prevalent among men, with a significant gender disparity in consumption. Men have higher tobacco usage rates than women, particularly in both high-income countries (HICs) and low- and middle-income countries (LMICs), including India (Wallace et al., 2003; Thun et al., 2012; Aryal et al., 2013; Lipsky et al., 2021; Menon et al., 2021). In India, about 42.4% of adult men used tobacco, whereas the prevalence was nearly 14.2% among adult women in 2016-17 (TISS \u0026amp; MoHFW, 2017). Beneath this national proportion of men, there are variations across different socio-demographic groups, places of residence, and geographic regions in India. In this context, identifying the key determinants of the high prevalence of tobacco use among Indian adult men is essential. On the other hand, alongside changes in demographic dynamics, food habits, access to healthcare, life expectancy, and quality of life, the co-occurrence of two or more chronic diseases among adults and elderly individuals is increasing globally, particularly in lower-middle-income countries. India is no exception to this trend. According to the latest National Family Health Survey data (IIPS \u0026amp; ICF, 2021) life expectancy has increased over the last decade, the total fertility rate has dropped to 1.9 (below the replacement level), and the urban population is growing. Additionally, various non-communicable diseases (NCDs) such as hypertension, diabetes, and respiratory, heart, and kidney diseases are on the rise. Furthermore, the prevalence of these NCDs is responsible for 60% of deaths in India (John et al., 2014). Moreover, this situation is becoming more vulnerable due to the coexistence of multiple NCDs. In this context, while the entire world is focused on reducing mortality (SDG 03), studying multimorbidity is also highly relevant to the Indian population\u003c/p\u003e\n\u003cp\u003eTobacco use is one of the most significant and preventable health-risk behaviors, strongly associated with a wide range of severe non-communicable diseases (Thakur et al., 2011; Zahra et al., 2015; Uddin et al., 2020; Giacaman et al., 2022). Previous research has identified that tobacco use is responsible for causing significant harm, with up to half of its users eventually dying from tobacco-related chronic diseases, including cancer, lung disease, cardiovascular disease, and stroke(Fagerstr\u0026ouml;m et al., 2002; Beaglehole et al., 2003; Thankappan,\u0026amp; Thresia, 2007;\u0026nbsp;Athyros et al., 2013; Hatsukami \u0026amp; Carroll.,2020; van der Rijst \u0026amp; Garfield, 2023).\u0026nbsp;The high consumption of tobacco products has severe health consequences, as evidenced by the annual death toll of 1.35 million due to tobacco-related chronic diseases in India (WHO, 2021). However, the higher prevalence of tobacco uses among Indian men compared to women indicates a relatively greater probability of NCD prevalence. While the United Nations has set the goal to \u0026apos;strengthen the implementation of the WHO Framework Convention on Tobacco Control\u0026apos; (target 3a of SDG 3), quantifying the morbidity pattern influenced by tobacco use and estimating the link between tobacco use and morbidity patterns among Indian men is essential.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEvidence in the previous literatures and research gaps\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe association between tobacco use and multimorbidity has been widely explored globally, yet significant gaps remain in the Indian context. Recent studies have mapped the geospatial distribution of multimorbidity across India (Prenissi et al., 2022; Khan et al., 2022; Dolui et al., 2023). Other studies have highlighted regional variations in tobacco consumption(Abdulkader et al., 2019; Singh et al., 2022; Shaikh et al., 2022). While previous research has highlighted geographical disparities in tobacco use, there is a lack of updated and comprehensive insights into the geospatial patterns of tobacco consumption among Indian men. Additionally, past studies have specifically examined the socio-demographic and economic determinants influencing tobacco use in this population (Singh \u0026amp; Ladusingh, 2014; Thakur \u0026amp; Paika, 2018; Ruhil, 2019;Islam et al., 2020; Murmu et al., 2023). Moreover, the current prevalence of specific types of tobacco use (i.e., smoking and smokeless) among Indian adult men, along with the factors determining this prevalence, remains unidentified in India.\u003c/p\u003e\n\u003cp\u003eAlthough global studies have established a connection between tobacco use and specific non-communicable diseases (Athyros et al., 2013; van der Rijst eta al., 2023; Zhou et al., 2014; Raghuveer et al., 2016; Alsharairi et al., 2019; Hatsukami, et al., 2020),\u0026nbsp;Indian studies have primarily focused on individual diseases, rather than exploring the broader spectrum of multimorbidity. A meta-analysis-based study reported a positive association between smoking and multimorbidity among older adults in India(Goel \u0026nbsp;et al., 20230. A recent study, based on NFHS-4 data, highlighted a higher likelihood of multimorbidity among Indian women who consume tobacco(Mishra et al., 2021). The lack of systematic research estimating the relationship between different types of tobacco use and morbidity patterns among vulnerable Indian adult men is a significant evidence gap. Therefore, two key questions arise: What is the current pattern of smoking and smokeless tobacco consumption among Indian men? And, is there a significant influence of tobacco consumption on the prevalence of multimorbidity among Indian men? This study aims to answer these two specific research questions. While previous studies have explored geospatial disparities and overall determinants of tobacco consumption, as well as its influence on the prevalence of NCDs in India, this study uniquely focuses on the Indian male population. It significantly examines the geospatial patterns and determinants of specific types of tobacco consumption, such as smoking and smokeless tobacco, in India\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eObjectives\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe study aims to investigate the prevalence of different types of tobacco consumption across various socio-demographic, economic, and regional backgrounds, and to identify the key factors influencing smoking behaviour among Indian men using the latest nationally representative data. Additionally, the study seeks to assess the strength of the relationship between tobacco use and single or multimorbidity patterns among Indian men. \u0026nbsp; \u0026nbsp;\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.1.\u0026nbsp;Database and sample design\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data for this study was used from the recent release of the National Family Health Survey (NFHS-5), conducted from 2019 to 2021. The survey is a cross-sectional national representative survey conducted by the International Institute for Population Sciences (IIPS) in Mumbai, under the supervision of the Ministry of Health and Family Welfare (MoHFW) of the Indian Government. It aims to gather comprehensive information on households\u0026apos; population, health, nutrition, and demographic aspects, focusing on men, women, and young children at various levels such as national, state, and district. In this study, the NFHS-5 data from 2019-2021 was used as a benchmark to examine the progress made by India in various health indicators over time. The survey interviewed a large sample size, including 636,699 households, 101,839 men aged 15\u0026ndash;54, and 724,115 eligible women aged 15\u0026ndash;49. The response rates reported for households, men, and women were 98%, 97%, and 92%, respectively. The findings of this study are based on a total of 101,839 men aged 15-54 from NFHS-5. Detailed information regarding the methodology, including the sampling frame, survey design, and data collection process, was published elsewhere (IIPS \u0026amp; ICF, 2021).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.2.\u0026nbsp;Outcome variable\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis analysis focuses on chronic morbidity as the outcome variable, covering seven chronic health conditions: heart disease, hypertension, diabetes, goitre or any other thyroid disorder, chronic kidney disease, cancer and chronic respiratory diseases, including asthma. The outcome variable was constructed based on the following questions asked to the respondents: (a) Do you currently have heart disease? (b) Do you currently have hypertension? (c) Do you currently have diabetes? (d) Do you currently have goitre or any other thyroid disorder? (e) Do you currently have chronic kidney disease? (f) Do you currently have cancer? (g) Do you currently have chronic respiratory diseases, including asthma? If any respondent (aged 15-54) reported any of these morbidities, they were assigned a value of \u0026apos;yes\u0026apos;; otherwise, it was recorded as \u0026apos;no\u0026apos;. To determine chronic morbidity status, the binary variables for the seven chronic diseases were combined. If an individual reported having at least one of the chronic diseases (any one out of the seven), they were assigned a \u0026ldquo;single morbidity.\u0026rdquo; If two or more chronic diseases were reported, they were categorized as \u0026quot;multimorbidity.\u0026quot; If an individual reported none of the chronic diseases, they were assigned \u0026quot;no morbidity\u0026quot; in the overall chronic morbidity variable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.3.\u0026nbsp;Explanatory variable\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe key explanatory variable was tobacco use, consisting of four categories \u0026ndash; smoking, smokeless, dual-use, and non-tobacco user. Smoking includes individuals who use tobacco while smoking, such as cigarettes, pipes, cigars, hookah, bidi, or other smoking substances. Further, Smokeless includes individuals who use smokeless tobacco in various forms, such as Gutkha, pan masala with tobacco, khaini, pan with tobacco, snuff, or other chewing tobacco. Dual-use describes respondents who engage in both smoking and smokeless tobacco use. Non-tobacco user includes individuals who neither smoke nor use smokeless tobacco substances. All categories were coded as \u0026apos;1\u0026apos; for individuals who used at least one substance in the respective category and \u0026apos;0\u0026apos; for individuals who did not use any substance. Finally, a group variable was created as overall \u0026apos;tobacco user\u0026apos;. Alongside, based on the previous literatures on the determinants of multimorbidity in India, and considering the Indian current situation twelve covariates have been selected in this study. Detail information about the covariates has been represented in \u003cstrong\u003eTable 1.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Description of explanatory variables.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"641\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExplanatory Variable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 277px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 261px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoding\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 277px;\"\u003e\n \u003cp\u003eCategorized into three groups.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 261px;\"\u003e\n \u003cp\u003e15\u0026ndash;24 (0), 25\u0026ndash;44 (1), 45\u0026ndash;54 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 277px;\"\u003e\n \u003cp\u003eCategorized into five groups: never married, currently married, widowed, divorced, and separated.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 261px;\"\u003e\n \u003cp\u003eNever married (0), Currently married (1), Widowed (2), Divorced (3), Separated (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducational Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 277px;\"\u003e\n \u003cp\u003eClassified into no education, primary, secondary, and higher education.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 261px;\"\u003e\n \u003cp\u003eNo education (0), Primary (1), Secondary (2), Higher (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWealth Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 277px;\"\u003e\n \u003cp\u003eCategorized into five wealth groups.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 261px;\"\u003e\n \u003cp\u003ePoorest (0), Poorer (1), Middle (2), Richer (3), Richest (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWorking Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 277px;\"\u003e\n \u003cp\u003eIndicates whether the respondent was working during the survey.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 261px;\"\u003e\n \u003cp\u003eNo (0), Yes (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 277px;\"\u003e\n \u003cp\u003eClassified into no occupation, agricultural, service, skilled/unskilled manual, and others.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 261px;\"\u003e\n \u003cp\u003eNo occupation (0), Agricultural (1), Service (2), Skilled/Unskilled manual (3), Others (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol Consumption\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 277px;\"\u003e\n \u003cp\u003eIndicates whether the respondent consumes alcohol.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 261px;\"\u003e\n \u003cp\u003eNo (0), Yes (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMass Media Exposure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 277px;\"\u003e\n \u003cp\u003eIndicates whether the respondent is exposed to mass media.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 261px;\"\u003e\n \u003cp\u003eNo (0), Yes (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlace of Residence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 277px;\"\u003e\n \u003cp\u003eCategorized as rural or urban.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 261px;\"\u003e\n \u003cp\u003eUrban (0), Rural (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReligion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 277px;\"\u003e\n \u003cp\u003eClassified into seven religious groups.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 261px;\"\u003e\n \u003cp\u003eHindu (0), Muslim (1), Christian (2), Sikh (3), Buddhist (4), Jain (5), Others (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSocial Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 277px;\"\u003e\n \u003cp\u003eCategorized as Scheduled Caste, Scheduled Tribe, Other Backward Class, and Others.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 261px;\"\u003e\n \u003cp\u003eScheduled Caste (0), Scheduled Tribe (1), Other Backward Class (2), Others (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeographical Region\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 277px;\"\u003e\n \u003cp\u003eCategorized into six major regions of India.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 261px;\"\u003e\n \u003cp\u003eNorthern (0), Southern (1), Eastern (2), Western (3), North-Eastern (4), Central (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.4.\u0026nbsp;Statistical analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, several statistical analyses were conducted to assess the prevalence of tobacco use and multimorbidity, as well as the association between multimorbidity and other explanatory variables. Descriptive statistics were used to summarize and present the prevalence of tobacco use and multimorbidity, along with other key explanatory variables. This helps provide an overview of the data. The bivariate analysis examined the relationship between multimorbidity and tobacco use prevalence among men, considering their background characteristics. The Chi- square (\u0026chi;2) test was used to assess the significance level of this relationship. Multinomial logistic regression analysis was conducted to understand the relationship between pattern of tobacco consumption and the outcome variable (single and multimorbidity). Outcome of two regression models have been generated in this study. We only considered pattern of tobacco consumption as explanatory variable and pattern of morbidity as dependent variable in the first models. In second model, we considered personal level, community level and regional level backgrounds of Indian men as covariate along with the pattern of tobacco consumption. The regression analysis results were reported using predicted probability percentages with a 95% confidence interval (CI). The statistical analyses were carried out using Stata version 14.2. Individual weights were applied to the estimates to ensure the results were nationally representative.\u0026nbsp;\u003c/p\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Respondent characteristics\u003c/h2\u003e \u003cp\u003eAlmost 50% of men belonged to the 25\u0026ndash;44 age group, and about 63% were married. A higher proportion of men achieved secondary education (56.88%), followed by higher education (19.28%). The proportion of men was more or less the same in all five categories of wealth status, with the poorest having a slightly lower proportion (16.69%). Moreover, about three-fourths of men (76.18%) were working, and nearly one-third were engaged in agricultural occupations. Regarding religious affiliation, most of the men were Hindu (79.27%), followed by 15.44% of Muslims. However, about one-third belonged to Other Backward Classes, and one-fifth of men belonged to the Scheduled Caste. A greater proportion of respondents were exposed to mass media (87.66%). The table also shows the six major regions-wise distribution of respondents; nearly half of the respondents lived in the southern and western regions of the country. A higher proportion of men (22.85%) consumed alcohol (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrevalence of tobacco use and morbidity among men by their background characteristics in India, NFHS-5, 2019\u0026ndash;21.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBackground Characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSample number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSample %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003ePrevalence of Tobacco use\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003ePrevalence of Morbidity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmokeless\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDual user\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSingle Morbidity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMultimorbidity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge Group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrently married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeparated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWorking Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOccupation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo occupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgricultural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eServices\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkilled and unskilled manual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol use\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMass media Exposer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e86586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWealth Index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReligion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHindu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuslim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChristian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSikh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuddhist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocial Group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResident\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegional Zone\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNortheast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e101839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: \u003cem\u003eHeterogeneity in the prevalence of smoking, smokeless, both types of tobacco consumption, single and multi-morbidity across various categories of the background variables are examined by Pearson\u0026rsquo;s Chi-square test. Level of significance *** p\u0026thinsp;\u0026lt;\u0026thinsp;.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;.1\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Pattern of tobacco use and morbidity\u003c/h2\u003e \u003cp\u003eAbout 13.8%, and 20.12% of Indian men used smoking and smokeless tobacco products, respectively \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Approximately 6.87% of the population were dual users. On the other hand, the prevalence of single morbidity in India was 6.20%, and multimorbidity, which refers to the presence of multiple health conditions or diseases at the same time, was observed in 1.60% of the population.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eChi square results indicate a significant heterogeneity in the prevalence of smoking, smokeless, and both types of tobacco consumption among Indian men belonging to different categories of sociodemographic, economic, and regional backgrounds (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Smoking was significantly higher among the men who were less educated, working in service sectors and agricultural activities, living separately from their wives, belonging to higher age groups (45\u0026ndash;54 years), poor families, Christian religious groups, marginalized communities (SC/ST), and living in the eastern, northern, and northeastern parts of India. On the other side, smokeless tobacco consumption is relatively higher among men who are aged 45\u0026ndash;54 years), less educated, widowed or divorced, poor, involved in agricultural activities, use alcohol, belong to other religions, are members of the ST community, and live in rural areas in the central and western parts of India. Smoking and smokeless dual-type tobacco consumption are comparatively higher among men who are in the middle age group (20\u0026ndash;44 years), widowed and separated, less educated, addicted to alcohol, belonging to poor families, other religious groups, scheduled tribes, living in rural areas, and in the northeast or eastern part of India.\u003c/p\u003e \u003cp\u003eOther side, single morbidity or any type of chronic disease are observed higher among the men who are aged, widowed or separated, agricultural workers, consuming alcohol, belonging to rich families, other religion and other social groups, in the central, eastern and southern part of India. Prevalence of multimorbidity was higher among the men who belong to a higher age group (45\u0026ndash;54 years), widows, non-educated, alcohol users, rich families, Hindu religion, and other social groups, in the southern and eastern parts of India.\u003c/p\u003e \u003cp\u003eBesides, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003ea indicates a higher prevalence of smoking in West Bengal, Assam, Meghalaya, Tripura, and Manipur. Further, smokeless tobacco use is comparatively higher in central India, particularly in Uttar Pradesh, Bihar, and Madhya Pradesh, exhibiting higher prevalence (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). The districts in north-eastern, eastern, and central India are more likely to have a higher prevalence of dual tobacco users (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Other side prevalence of single and multimorbidity is comparatively higher in south Indian districts in 2019-21 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003ea \u003cb\u003eand b\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Impact of tobacco use on the prevalence of single and multimorbidity\u003c/h2\u003e \u003cp\u003eThe unadjusted regression model (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003e) revealed a relatively higher risk of any type of chronic disease among men smokers, smokeless tobacco users and dual tobacco users in India. However, after considering relevant men\u0026rsquo;s personal level, community level and residential and regional level explanatory variables, adjusted model also explained the significant effect of different types of tobacco use on the prevalence of single or multimorbidity among Indian men (\u003cb\u003eTable\u0026nbsp;3\u003c/b\u003e). The odds of single morbidity declined from 1.85 to 1.18 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) among the smokers when we incorporated other control variables in the regression model. In the case of smokeless tobacco users and dual types of tobacco consumers, the probability of single morbidity is also reduced after considering selected control variables. On the other hand, the probability of multimorbidity was also higher among different types of tobacco consumers than non-consumers. However, after considering all selected control variables the risk of multimorbidity slightly declined among the smokers and dual type of tobacco consumers. Moreover, we have not found a significant prevalence of multimorbidity among smokeless tobacco consumers after incorporating control variables in the regression model. Several control variables have significant effects on the prevalence of single and multimorbidity among Indian men. The risk of single and multimorbidity was significantly higher among the men who were older, currently married or widow, higher education level, and other social groups, in south and eastern parts of India.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eTable-3\u003c/b\u003e Adjusted Odds of morbidity among the men in India \u0026minus;\u0026thinsp;2019-21 (NFHS-5).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBackground Characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eSingle Morbidity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMultimorbidity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTobacco\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-user\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.18***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.10\u0026ndash;1.28]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.34***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[1.17\u0026ndash;1.57]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmokeless\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.03\u0026ndash;1.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[0.78\u0026ndash;1.06]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoth or dual user\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.14**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.02\u0026ndash;1.26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.76***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[1.46\u0026ndash;2.13]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge Group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.87***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.67\u0026ndash;2.09]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[2.01\u0026ndash;3.47]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.37***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[3.87\u0026ndash;4.94]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[6.91\u0026ndash;12.18]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrently married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.46***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.32\u0026ndash;1.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.05***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[1.64\u0026ndash;2.55]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.43**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.07\u0026ndash;1.90]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.43***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[1.52\u0026ndash;3.88]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.86\u0026ndash;2.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[0.33\u0026ndash;2.97]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeparated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.70***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.21\u0026ndash;2.38]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[0.33\u0026ndash;2.99]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.10*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.00-1.22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[0.77\u0026ndash;1.10]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.12**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.03\u0026ndash;1.22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.85**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[0.73-1.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.12**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.00-1.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.79**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[0.65\u0026ndash;0.97]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWorking Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.83\u0026ndash;1.05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.62***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[0.51\u0026ndash;0.75]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOccupation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo occupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgricultural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.88\u0026ndash;1.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.77*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[0.58\u0026ndash;1.01]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eServices\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.83\u0026ndash;1.18]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[0.76\u0026ndash;1.38]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkilled and unskilled manual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.92\u0026ndash;1.26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.67***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[0.50\u0026ndash;0.88]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.04\u0026ndash;1.42]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[0.78\u0026ndash;1.35]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol use\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.49***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.40\u0026ndash;1.59]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.43***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[1.27\u0026ndash;1.61]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMass media Exposer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.15***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.05\u0026ndash;1.26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.36***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[1.13\u0026ndash;1.64]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWealth Index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.98\u0026ndash;1.19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[0.87\u0026ndash;1.26]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.13**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.02\u0026ndash;1.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[0.90\u0026ndash;1.33]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.17***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.06\u0026ndash;1.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[0.92\u0026ndash;1.40]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.12*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.00-1.27]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.59***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[1.26-2.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReligion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHindu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuslim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.14***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.05\u0026ndash;1.23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.82**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[0.70\u0026ndash;0.96]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChristian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.83\u0026ndash;1.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.56***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[0.40\u0026ndash;0.80]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSikh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.70***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.32\u0026ndash;2.19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[0.44\u0026ndash;1.65]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuddhist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.98\u0026ndash;1.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[0.50\u0026ndash;1.72]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.43***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.23\u0026ndash;0.79]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.25*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[0.06\u0026ndash;1.02]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.15***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.50\u0026ndash;3.07]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[0.23\u0026ndash;2.05]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocial Group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.88**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.79\u0026ndash;0.99]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[0.96\u0026ndash;1.49]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.88\u0026ndash;1.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.10***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[0.94\u0026ndash;1.28]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.26***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.16\u0026ndash;1.36]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[1.35\u0026ndash;1.85]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResident\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.09***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.02\u0026ndash;1.17]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[0.96\u0026ndash;1.24]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eZone\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.85\u0026ndash;1.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[0.79\u0026ndash;1.32]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.52***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.35\u0026ndash;1.72]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.13***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[1.68\u0026ndash;2.69]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNortheast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.33***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.14\u0026ndash;1.55]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[0.69\u0026ndash;1.36]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.19***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.05\u0026ndash;1.34]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[0.93\u0026ndash;1.50]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.55***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.38\u0026ndash;1.74]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.09***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[1.66\u0026ndash;2.62]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.01\u0026ndash;0.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[0.01\u0026ndash;0.02]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePseudo r-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eLevel of significance*** p\u0026thinsp;\u0026lt;\u0026thinsp;.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;.05 *p\u0026thinsp;\u0026lt;\u0026thinsp;.1, ref- reference category.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Key Findings\u003c/h2\u003e \u003cp\u003eThe study significantly identified the prevalence of various types of tobacco use among Indian men from different socio-demographic, economic, and regional backgrounds. Additionally, the study established a significant relationship between the type of tobacco use and the prevalence of single and multimorbidity among Indian men, aligning with previous studies conducted in other countries and India (Pednekar, M. S. et al., 2006; Banjare \u0026amp; Pradhan, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) this study found a comparatively higher prevalence of single and multimorbidity among Indian men who smoke tobacco, consume smokeless tobacco, or use both forms of tobacco. In line with prior findings (Pednekar, M. S. et al., 2006; Banjare \u0026amp; Pradhan, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), The study notably reveals that men who use both smoking and smokeless tobacco are particularly at risk of experiencing multiple health conditions simultaneously. The study highlighted the significant impact of smoking on the prevalence of chronic diseases such as chronic respiratory disease, hypertension, diabetes, cancer, heart disease, thyroid disease, and chronic kidney disease. This finding is also consistent with previously available literature from various settings (Athyros et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; van der Rijst eta al., 2023; Zhou et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Raghuveer et al., 2016; Alsharairi et al., 2019; Hatsukami, et al., 2020; Mishra et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Khowaja et al., 2017). These findings corroborate previous studies conducted in other countries, such as Canada, China, and others (Pengpid \u0026amp; Peltzer, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wikstr\u0026ouml;m \u003cem\u003eet al., 2025;\u003c/em\u003e Geda et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Han et al., 2021; Zou et al., 2023) and Indian women (Mishra et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), Our study also revealed that smoking habits and smokeless tobacco consumption significantly affect the prevalence of multimorbidity among Indian men.\u003c/p\u003e \u003cp\u003eCorroborating previous findings (Abdulkader et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Singh et al., 2022; Shaikh et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), this study highlighted the higher prevalence of tobacco use in central, eastern, and northeastern India, which may be attributed to lower levels of human development (HDI), socio-economic backwardness, and a higher concentration of marginalized populations (SC/ST) in these regions( Ghosh et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Suryanarayana, et al, 2011; Kundu et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Jose, 2020). In this context, corroborating previous findings on the Indian population (Singh \u0026amp; Ladusingh,2014; Thakur \u0026amp; Paika, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ruhil, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Islam et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Murmu et al.,2023) the present study also highlighted a higher prevalence of tobacco use among less educated men, the poorer sections of society, and the SC/ST community (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn line with previous studies on India (Abdulkader et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Singh et al., 2022; Shaikh et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Singh \u0026amp; Ladusingh,2014; Thakur \u0026amp; Paika, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ruhil, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Islam et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Murmu et al.,2023) this study also outlined the significant effect of age group, marital status, education level, alcohol consumption, social groups, and geographic regions on the prevalence of single or multimorbidity among Indian men. In support of previous findings (Khan et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Dolui et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Talukdar, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), the current study also highlighted a higher prevalence of multimorbidity in southern and eastern part of India. It may be linked to their life style, food habits, prevalence of obesity and other socioeconomic determinants.\u003c/p\u003e \u003cp\u003eThis study reveals that age is vital in understanding tobacco use and multimorbidity. While the 25\u0026ndash;44 age group has the highest prevalence of tobacco use, the prevalence of multimorbidity increases with age, peaking in the 45\u0026ndash;54 age group (Boutayeb et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Griffith et al., 2017; Ma et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; He et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Conversely, younger men aged 15\u0026ndash;24 have a lower likelihood of experiencing multiple health conditions concurrently, which is consistent with earlier studies (Carreras et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Danawala et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). As evident in previous studies on India and elsewhere, the present study also shows that unmarried men had a lower prevalence of smoking and smokeless tobacco use, while widowed men had a higher prevalence of both and a higher prevalence of multimorbidity, indicating a heightened susceptibility to multiple health conditions(Islam et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e;Banjare \u0026amp; Pradha, 2014; Sreeramareddy et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The study also found that men with lower education levels are more likely to smoke and have a higher prevalence of multimorbidity (Narayana et al., 1996; Lindstr\u0026ouml;m \u0026amp; Janzon, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Prazeres \u0026amp; Santiago, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The study found that religion and caste differences contribute to variations in tobacco use (Rani et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Hindus and Muslims exhibit higher tobacco use rates, while Muslims and scheduled tribes have a higher prevalence of smokeless tobacco use. In consistent with previous findings present study found that scheduled castes and scheduled tribes in India have higher rates of smoking and smokeless tobacco use habits compared to forward castes (Singh \u0026amp; Ladusingh, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kashyap et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Anushree, K. N. \u0026amp; Mishra, 2022).\u003c/p\u003e \u003cp\u003eConsistent with earlier findings, this study also confirms that socioeconomic factors, such as wealth quintiles, occupational status, and residential area, are associated with both tobacco use and multimorbidity. Men in higher wealth quintiles and working individuals, particularly those engaged in agricultural work and living in rural areas, have a higher tobacco use (Islam et al, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and multimorbidity prevalence (Carreras,2013; Danawala, 2014). The study suggests exposure to mass media and alcohol consumption are associated with a higher prevalence of both tobacco use and multimorbidity, underscoring the need for comprehensive health promotion strategies that consider these factors (Mishra, S. et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Keetile et al, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yao et al., 2020).\u003c/p\u003e \u003cp\u003eThe Government of India has launched significant tobacco control measures under the Cigarettes and Other Tobacco Products Act (COTPA), 2003, and the National Tobacco Control Programme (NTCP). However, findings of the current study suggest the need for further strengthening of policy implementation, including: (a) Strengthening COTPA, 2003, particularly in rural areas and disadvantaged regions, (b) educating poor, marginalized people about the long-term risks of tobacco use and multimorbidity through different awareness programmes under National Health Mission (NHM) and NTCP, (c) Improving access to early screening, diagnosis, and treatment of chronic diseases associated with tobacco use through Ayushman Bharat \u0026ndash; Pradhan Mantri Jan Arogya Yojana (PMJAY), (d) Increasing tobacco taxation under GST to fund cessation programs. (e) Enhancing grassroots engagement through ASHAs and Panchayati Raj for last-mile health services.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Value added to the global literature\u003c/h2\u003e \u003cp\u003eThe study will advance the knowledge in this field in two ways. Firstly, by disaggregating tobacco use into three categories such as smoking tobacco, smokeless tobacco and both or dual type of tobacco consumption the study has highlighted the pattern of tobacco use among men belongs to different demographic, social, economic, religious groups, residence and geographic regions or units based on the most recent nationally representative database. Along with that, it also has helped to outline the pattern of single and multimorbidity among Indian men at subnational level. This can inform policymakers and help refine strategies for tobacco control and multimorbidity prevention and management in India. Secondly, the study also outlined the relationship between the pattern of tobacco consumption and morbidity among Indian men using nationally representative most recent data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Limitations and strength of the study\u003c/h2\u003e \u003cp\u003eThe study is limited in a few aspects. Firstly, self-reported personal information may have some recall bias that may overestimate or underestimate the relationship. Secondly, the prevalence of multimorbidity is recorded based on self-reported information. The surveyor had not clinically examined the existence of morbidities among the target population. There may be some error in the estimation of the level of multimorbidity among males belonging to different backgrounds. Finally, the study has not considered the behavioural, and biological aspects (lifestyle habits, food intake, psychological factors, heredity of disease, etc.), utilization of medical services, etc due to the lack of information in the dataset. We have not incorporated the mentioned factors in the adjusted regression models that may overestimate or underestimate the relationship between tobacco consumption and the prevalence of multimorbidity in this study.\u003c/p\u003e \u003cp\u003eHowever, trained professionals collected the information during the time of the cross-sectional survey, and the database was nationally representative, most recent, and reliable in the context of India. In addition to that, this is the first comprehensive endeavour to outline the subnational pattern of different types of tobacco consumption and morbidity and to establish the relationship between these two patterns among Indian men.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe study identified Indian men belonging to older age groups (45\u0026ndash;54 years), less educated, alcohol users, poor socioeconomic status, marginalized social groups (ST/ST), rural areas, and central and eastern India as having a higher level of smoking, smokeless, or both types of tobacco consumption. Morbidity was also higher among the men belonging to older age groups, lower education levels, working groups, alcohol users, richer sections, other social groups, and the eastern and southern parts of India. The study also confirms the strong influence of tobacco consumption on the prevalence of single morbidity (chronic diseases) and multimorbidity among Indian men based on the NFHS-5 (2019\u0026ndash;21) database. The treatments for this tobacco induced morbidity pattern among Indian men have also compounded the economic burden on households as well as the nation. In this context, the study suggests an urgent need for comprehensive, multi-sectoral policies to control tobacco consumption. In this context, the study suggests strengthening the COTPA Act (2003), emphasizing awareness programs on the risk of tobacco use and multimorbidity, improving early screening and treatment for the disease caused by tobacco use, and increasing tobacco taxation to control tobacco consumption in India. Considering the tobacco consumption pattern, the Government and local administrations should take adequate measures to reduce tobacco consumption among Indian men (SDG 3a), so that the chronic disease burden can be reduced. Implementing and enforcing comprehensive tobacco control measures, such as increased taxation, strict regulations on tobacco advertising, and smoke-free laws, can help reduce tobacco use and lower the prevalence of multiple chronic diseases. Tailored interventions, including targeted awareness campaigns, cessation programs, and healthcare services, should be developed and implemented to address the needs of these high-risk groups.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAuthors Contribution: M.H.: Conceptualization, Original Draft Writing, Visualization, Validation, Methodology, Investigation, Formal Analysis, Data Curation. S.S.: Review \u0026amp; Editing, Visualization, Validation, Methodology. P.G.: Review \u0026amp; Editing, Investigation, Methodology, Conceptualization, Visualization. M.D.: Review \u0026amp; Editing, Formal Analysis. H.M: Review \u0026amp; Editing, Formal Analysis.\u003c/p\u003e\n\u003cp\u003eFunding:\u0026nbsp;The authors received no funding for this research.\u003c/p\u003e\n\u003cp\u003eCompeting Interests: The authors confirm that they have no competing interests.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAcknowledgement:\u0026nbsp;The authors acknowledge the School of Earth Sciences, Department of Geography, Central University of Karnataka, and express their gratitude to DHS for providing freely available data.\u003c/p\u003e\n\u003cp\u003eHuman Ethics and Consent to Participate declarations: not applicable\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003eConsent to Publish declaration: not applicable\u003c/p\u003e\n\u003cp\u003eData Availability: The data material is available on request from the corresponding author. As we have used data from the National Family Health Survey (NFHS-5), which is publicly accessible and available at no cost through the Demographic and Health Surveys (DHS) program. The dataset can be accessed via the DHS repository at https://dhsprogram.com. Interested researchers can gain access to the data by registering on the portal and submitting an online request, which is typically approved for significant research purposes\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbdulkader, SR., Sinha, D.N., Jeyashree, K. \u003cem\u003eet al.\u003c/em\u003e Trends in tobacco consumption in India 1987\u0026ndash;2016: impact of the World Health Organization Framework Convention on Tobacco Control. \u003cem\u003eInt J Public Health\u003c/em\u003e64, 841\u0026ndash;851 (2019). https://doi.org/10.1007/s00038-019-01252-x\u003c/li\u003e\n \u003cli\u003eAlsharairi, N. A. (2019). 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Association of smoking status and nicotine dependence with multi-morbidity in China: A nationally representative cross-sectional study. \u003cem\u003eTobacco Induced Diseases\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e, 81.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Tobacco use, Multimorbidity, Smoking, Smokeless, India","lastPublishedDoi":"10.21203/rs.3.rs-6236595/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6236595/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u0026amp; Objective:\u003c/strong\u003e Tobacco use is a significant risk factor for multimorbidity among men and a major public health concern in India, with high rates of tobacco consumption and associated health problems. The study aims to outline the pattern of tobacco consumption and morbidity among Indian men and to examine the link between this tobacco consumption and multimorbidity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod:\u003c/strong\u003e This study utilized data from the National Family Health Survey (NFHS-5) conducted between 2019 and 2021 to examine the prevalence of tobacco use and its association with multimorbidity among men in India. The study included a sample of 101,839 men aged 15-54 years. Descriptive statistics, bivariate analysis, and multinomial logistic regression were employed to analyse the data and examine the relationship between tobacco use and multimorbidity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The study identified Indian men belonging to older age groups (45–54 years), less educated, alcohol users, poor socioeconomic status, marginalized social groups, rural areas, and central and eastern India as having a higher level of smoking, smokeless, or both types of tobacco consumption. Morbidity was also higher among the men belonging to older age groups, lower education levels, working groups, alcohol users, richer sections, other social groups, and the eastern and southern parts of India. The study demonstrated a significant association between tobacco use and both single morbidity and multimorbidity among men. Men who engaged in smoking had 1.18 times more probability (p \u0026lt; 0.001) of single morbidity, while smokeless tobacco users and both users significantly had 1.11 and 1.14 times more chance single morbidity. Moreover, men who smoked or were dual tobacco users exhibited a higher prevalence of multimorbidity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003ePolicymakers must frame adequate policies considering the tobacco consumption pattern to reduce tobacco consumption among Indian men (SDG 3a), so that the associated chronic disease burden can be reduced.\u003cstrong\u003e \u0026nbsp;\u003c/strong\u003eImplementing comprehensive tobacco control policies and promoting healthy behaviours are essential in reducing tobacco use and its associated risks.\u003c/p\u003e","manuscriptTitle":"Uncovering the Health Risks: The Association Between Tobacco Use and Multimorbidity Among Indian Men—Insights from NFHS","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-12 06:03:42","doi":"10.21203/rs.3.rs-6236595/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3f3fc3cb-7bd9-47dc-8f7f-d45b6f707533","owner":[],"postedDate":"May 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-03T08:39:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-12 06:03:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6236595","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6236595","identity":"rs-6236595","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}