Gender Disparities in Tobacco, Alcohol Consumption and Dietary Diversity Among Indian Adults (15-49): Insights from the National Family Health Survey (NFHS-5) 2019-21

Gender Disparities in Tobacco, Alcohol Consumption and Dietary Diversity Among Indian Adults (15-49): Insights from the National Family Health Survey (NFHS-5) 2019-21 | 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 Gender Disparities in Tobacco, Alcohol Consumption and Dietary Diversity Among Indian Adults (15-49): Insights from the National Family Health Survey (NFHS-5) 2019-21 Priyanka Yadav-Jagtap, Nandita Saikia This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5278220/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 Understanding gender-specific health behaviors is crucial for assessing mortality risk factors. In India, the paucity of data has hindered research in this area. This study fills this gap by investigating gender differences in smoking, alcohol consumption, and diversified dietary habits among Indian adults. This study further explored the differences in gender-specific health behaviors among rural urban areas. Methods Using data from the National Family Health Surveys (NFHS-5) 2019-21, we analyzed individuals aged 15–49 years via bivariate and multivariate statistical techniques. We carried out binary logistic regression to assess the likelihood of engaging in these behaviors on the basis of gender and other socioeconomic factors. Results According to the NFHS-5 data, tobacco use is substantially greater among men (42.19%) than among women (6.5%), and alcohol consumption is also more prevalent among men (25.43%) than among women (1.87%). Men demonstrate greater dietary diversity, with 28.25% consuming diverse foods, whereas 23.6% of women do so. Gender dynamics remain consistent when differences by rural and urban residence are analyzed. However, rural men consume more alcohol and tobacco substances than their urban counterparts do (34.3% vs. 31.2%), whereas rural women have lower dietary diversity than urban women do (21.3% vs. 30.7%). Notably, the use of smokeless tobacco among women (5.53%) exceeds that of smokers (0.52%), with this difference being more pronounced in rural areas. Conclusion This study highlights significant gender disparities in health behaviors among Indian adults. Men are more likely to consume substances such as alcohol and tobacco (25.6 times higher, 95% CI: 24.29 26.12) but have better dietary diversity (1.34 times higher, 95% CI: 1.31 1.37) than women. Rural areas are disadvantaged in terms of a higher prevalence of consuming these substances, especially in men, and a lower percentage of people eat diverse diets, especially women, whereas urban areas present relative advantages. Targeted, gender-specific health interventions are needed, particularly in rural settings, to address these disparities and promote healthier lifestyles. Gender tobacco alcohol selected substance use diversified diet India NFHS Figures Figure 1 Background Public health research underscores the profound impact of unhealthy behaviors on morbidity and mortality (WHO, 2013; WHO, 2014). Most diseases are linked to health behaviors (WHO, 2008; Lloyd-Jones et al., 2010 ). Therefore, defining and measuring health behaviors are critical for effective policy development (Conner & Norman, 2017 ). Disparities in health behaviors and dietary patterns significantly contribute to social variations in health outcomes (Stringhini et al., 2010 ), affecting premature mortality and recovery from illness. Despite their importance, understanding these behaviors and their clustering remains limited, complicating intervention strategies (Spring et al., 2012 ). Interestingly, health behaviors vary across social classes, socioeconomic status, gender, race, and geography, influencing key risk factors for chronic diseases such as harmful alcohol use, smoking, sedentary lifestyles, and poor dietary practices (Borrell et al., 2000 ; Braveman et al., 2010 ; Pampel et al., 2010 ; Ding et al., 2015 ; Malta et al., 2015 ). Lynch et al. ( 1997 ) established that socioeconomic status inversely affects behavioral and psychosocial characteristics, which are crucial in determining health risks. Gender differences in smoking, diet, and access to health care contribute to divergent mortality patterns between men and women (Gjonça, 1999). Given the dynamic nature of health and mortality trends, understanding these gender disparities is crucial (Charmaz, 1995 ). Gender identities influence illness experiences and health outcomes, underscoring the importance of examining gender in epidemiological studies (Vlassoff, 2007 ). Gender differences in health behaviors are essential for addressing health disparities and informing policy, planning, and health services. A more systematic approach to gender-focused research is needed. In India, gender disparities in mortality have been documented extensively (Saikia, 2019 ; Bora & Saikia, 2015 ; Canudas-Romo, Saikia, & Diamond-Smith, 2015 , Dhakad & Saikia, 2023 ). In the Indian context, gender differences in health behaviors reflect sociocultural norms shaped by patriarchal structures (Sen, 1992; Dasgupta, 2003). Compared with men, women's restricted access to alcohol and tobacco is influenced by sociocultural factors, limiting their usage (Wilsnak et al., 2005; Pathania, 2011 ; Hitchman & Fong, 2011 ). Despite Indian women having greater life expectancies, they report poorer health and lower healthcare expenditures than men do (Saikia et al., 2016; Bora & Saikia, 2015 ; Canudas-Romo et al., 2015 ). However, studies on gender disparities in health behaviors among Indian adults are rare (Patel & Chauhan, 2020 ; Singh & Chattopadhyay, 2023 ; Marla & Padmaja, 2023 ). A limited number of studies have assessed gender differences in health behaviors, which may explain the gender gap in life expectancy and overall health outcomes. Given the above context, to fill the research gaps, we analyzed National Family Health Survey (NFHS 5- 2019-2021) data to explore gender differentials in smoking, alcohol consumption, and dietary habits. Understanding these behaviors is crucial, as they are significant risk factors for chronic diseases and mortality (WHO, 2009). The analysis uncovered how gender influences these health behaviors among Indian adults, hypothesizing variations in both positive and negative health practices by gender. Conceptual Framework Gender relations in society are dynamic and evolving and are influenced by societal progress and changing norms. As women increasingly occupy roles historically dominated by men, there is a growing need for a nuanced examination of gender dynamics. However, traditional gender roles still intersect with social, economic, and cultural factors, creating diverse patterns of exposure to health risks and differential access to health information, services, and care. These disparities significantly impact health outcomes (WHO, 2002). Despite sporadic recognition, gender has not been fully integrated as a critical determinant in health promotion efforts (Gelb et al., 2012 ). Gender interacts with socioeconomic and contextual variables to shape health and behavioral outcomes differently for men and women (Vlassoff, 2007 ). To comprehensively understand these dynamics, it is essential to consider the complexity and intersectionality of social, cultural, and economic contexts alongside demographic and epidemiological indicators. This study adopts a gendered perspective to explore how inherent and contextual factors influence individual behaviors, particularly in the Indian context, where gender-specific behaviors are pronounced. The framework posits that the interaction of gender with various background factors contributes to distinct health behaviors across genders. By examining these interactions, the study aims to elucidate the nuanced pathways through which gender influences health behaviors, thereby informing targeted interventions and policies aimed at promoting health equity. From the understanding developed from the available literature, this study conceptualizes the framework below in Fig. 1 . Data and methods Data Source The NFHS-5 (2019–21) data were used to examine health behaviors by gender. Data were analyzed via Stata software, with a focus on individuals aged 15–49 years, categorized by urban and rural residence. The NFHS, initiated in the early 1990s, is a comprehensive survey that provides critical data on population, health, and nutrition across India and its states. NFHS-5 (2019-21) (IIPS, 2022) is the fifth round of this survey, followed by NFHS-1 (1992-93), NFHS-2 (1998-99), NFHS-3 (2005-06) and NFHS-4 (2015-16) (IIPS, 2018). These surveys offer essential indicators of family welfare, maternal and child health, nutrition, and health behaviors. The NFHS-5 adhered to the DHS (Demographic and Health Survey) via standardized questionnaires, sample strategies, and field methodology. A two-stage sampling design was adopted in the NFHS-5 (IIPS, 2022). The NFHS-5 (2019-21) includes 724,115 women and 93,144 men in the 15–49 years age group. The focus was on individual-level socioeconomic information relevant to health behaviors such as tobacco use, alcohol consumption, and diet, ensuring a comprehensive gender comparison. Ethics statement The present study is based on publicly available NFHS 5 datasets with no identifiable information on the survey participants; hence, there is no requirement of ethics statement. Methods We performed a chi-square test for significance assessment. Behavioral factors were examined through bivariate and multivariate statistical analyses. Binary logistic regression was employed to establish associations between outcome variables (use of selected substances and a diversified diet) and other explanatory factors. The use of selected substances was coded as 1 and 0 for no use of selected substances, whereas diet diversity was coded as 1 for a diversified diet and 0 for a lack of diversity in a diet. The binary logistic regression model is typically presented in the following compact form: Logit [ P (Y = 1)] = β 0 + β _ X + ϵ The parameter β₀ estimates the log odds of the use of selected substances or a diversified diet for the reference group, whereas β estimates the maximum likelihood of the differential log odds associated with predictor X compared with the reference group. ε represents the residual in the model. Indicators In this study, categories of selected substances are defined on the basis of literature sources (Saunders et al., 1993 ; Wilson et al., 1999 ; Johnson et al., 2007 ; Girish et al., 2010 ; Pulvers et al., 2014 ). Variables related to alcohol and tobacco use are defined on the basis of the current use of alcohol/tobacco. Categories were defined on the basis of questions such as “How often do you consume alcohol/tobacco? and What type of alcohol/tobacco do you usually consume?”. The frequency of alcohol is recoded according to the availability of categories of variables in the NFHS data. Table 1 Description of important variables used in the present study Indicators Categories Alcohol consumption Light alcohol consumption (low alcoholic content approximately 15% or less) Hard alcohol consumption (Higher Alcoholic content ranged from 40–50%) beer; wine, tadi madi hard liquor and country liquor Tobacco Smoke Smokeless cigarette, pipe, cigars, bidis, hookah chewing tobacco, snuff, gutkha/paan masala, paan, khaini Frequency of Alcohol Consumption Moderate High frequency less than once a week and about once a week consumes alcohol daily Frequency of Smoking Moderate Heavy smokes 1–10 Cigarettes/Bidis daily smokes more than 10 Cigarettes/Bidis daily Consumption of any of selected substances Use of substance Not Using includes smoking bidis, cigarettes, and drinking alcohol not consuming any of these Table 1 gives the description of important variables used in the present study, besides that analysis also combined both the categories of alcohol (consuming both light and hard alcohol) and tobacco (consuming both smoking and smokeless together) to obtain an all-inclusive picture of selected substance consumption. The Diet Diversity Index Diet, comprising a complex mix of foods and nutrients (Kant et al., 1993 ), is crucial for adults because of their high energy needs from work and reproduction. A varied diet including vegetables, fruits, dairy, and meat is essential for optimal health (Lichtenstein et al., 2006 ). While NFHS data lack detailed nutrient intake data, dietary diversity is inferred from food frequency. The diet diversity index in this study integrates dietary diversity and food intake frequencies. Adapted from Kennedy et al. (1995), the index incorporates consumption frequencies of vegetables, fruits, dairy, eggs, fish, and meat. In this study, we developed a diet diversity index on the basis of the frequency of consumption of various food items. We considered healthy foods, such as vegetable proteins from pulses or beans; green leafy vegetables; fruits; and animal proteins, such as milk/curd, eggs, fish, chicken, or meat, as well as unhealthy foods, including fried food and aerated drinks. The consumption frequencies were categorized as daily, weekly, occasionally, or never. Responses regarding the frequency of consumption were assigned values as follows: 3 for daily consumption, 2 for weekly consumption, 1 for occasional consumption, and 0 for never consumed. For unhealthy foods, the scoring was reversed, with 3 indicating never consumed and 0 indicating daily consumption. The diet diversity score was computed by aggregating these values, resulting in a range from zero (indicating no dietary diversity) to twenty-one (indicating high dietary diversity). On the basis of this score, diets were classified into three categories: “less diversified diet/unhealthy”, “moderately diversified diet/moderate”, and “diversified diet/healthy”. A 'Diversified Diet' is characterized by the daily consumption of vegetable and animal proteins and minimal or occasional consumption of unhealthy foods. Conversely, a 'less diversified diet' is characterized by occasional consumption of healthy foods combined with regular consumption of unhealthy foods. The composite variable for the Diet Diversity Index was calculated separately for women and men. This index enables comprehensive assessment and cross-regional comparison of dietary diversity in simple ways. Results Results from bivariate analysis Table 2 presents gender-wise tobacco and alcohol consumption among Indian adults (15–49). Males have a significantly greater rate of tobacco use than females do. The difference was statistically significant. Tobacco use is lower in urban areas for both genders, but males still have a much higher rate than females do. Such consumption is higher in rural areas for both genders, with males still significantly outpacing females. In the case of alcohol, males also have a much greater prevalence of alcohol use than females do. Alcohol consumption is lower in urban areas than in rural areas for both genders, but the male‒female disparity persists in both rural and urban areas. Males are far more likely to use any selected substance than females are in both rural and urban areas. Table 2 Percentages of tobacco and alcohol consumption by gender and type of residence (rural/urban) among adults (15–49), India, and the NFHS-5 (2019-21) Indicator NFHS 5 (2019–2021) Female Male Total Overall Tobacco 6.5 42.19 10.57 Pearson chi2 = 110000 Pr = 0.000 Alcohol 1.87 25.43 4.56 Pearson chi2 = 110000 Pr = 0.000 Urban Tobacco 4.58 35.23 8.22 Pearson chi2 = 27000 Pr = 0.000 Alcohol 0.92 23.72 3.63 Pearson chi2 = 32000 Pr = 0.000 Rural Tobacco 7.13 44.63 11.35 Pearson chi2 = 86000 Pr = 0.000 Alcohol 2.18 26.03 4.86 Pearson chi2 = 75000 Pr = 0.000 Overall Use of any selected Substance 2.14 33.56 5.73 Pearson chi2(2) = 150000, Pr = 0.000 Urban Use of any selected Substance 1.13 31.2 4.71 Pearson chi2(2) = 43000, Pr = 0.000 Rural Use of any selected Substance 2.47 34.38 6.06 Pearson chi2(2) = 110000, Pr = 0.000 Table 3 presents the distribution of tobacco and alcohol consumption among adults aged 15–49 years in India on the basis of data from the NFHS-5 (2019–2021). The data are broken down by sex (female or male) and include categories for different forms of tobacco and alcohol consumption. A Pearson chi-square test was also used to assess the statistical significance of the gender differences. A significantly greater percentage of females (93.5%) reported not using tobacco than did males (57.81%). The majority of both genders do not consume tobacco, but males are more likely to consume it. It seems that tobacco smoking is predominantly a male behavior, with 18.76% of males reporting smoking, whereas only 0.52% of females. Smokeless tobacco is more common among males (20.83%) than females (5.53%). However, this form of tobacco is the most prevalent among females than other forms are. A small percentage of both genders use both forms of tobacco, with males (2.6%) being more likely to do so than females (0.43%). The chi-square test revealed that the differences in tobacco consumption by gender were statistically significant, with a p value of 0.000. A vast majority of females (98.13%) do not consume alcohol, whereas 74.57% of males do not consume it. This suggests that alcohol consumption is much more common among males. Light alcohol consumption is more common among males (10.4%) than females (1.06%). Hard alcohol consumption is predominantly a male behavior, with 8.18% of males consuming hard alcohol compared with only 0.37% of females. The chi-square test indicates that the differences in alcohol consumption by gender are statistically significant, with a p value of 0.000. In summary, across all categories (tobacco, alcohol, and any substance use), males consistently had higher consumption rates than females did. The urban‒rural divide shows that substance use is generally greater in rural areas, but the male‒female disparity remains significant in both settings. Table 3 Distribution of the consumption of different forms of tobacco and alcohol by gender among adults (15–49), India, NFHS-5 (2019-21) INDICATORS NFHS 5 (2019-21) Female Male Total TOBACCO None 93.5 57.81 89.43 With Smoke Only 0.52 18.76 2.6 With Smokeless Only 5.53 20.83 7.27 Both Smoke and Smokeless 0.43 2.6 0.7 Pearson chi2(3) = 150000 Pr = 0.000 ALCOHOL None 98.13 74.57 95.44 Light Alcohol 1.06 10.4 2.13 Hard Alcohol 0.37 8.18 1.26 Both Light and Hard Alcohol 0.44 6.84 1.17 Pearson chi2(3) = 110000 Pr = 0.000 Table 4 details the frequency of both healthy and unhealthy behaviors by gender, offering a deeper analysis of behavioral patterns by gender. A vast majority of females (99.77%) do not smoke bidis, compared to 90.74% of males. A very small percentage of females (0.20%) are moderate bidi smokers, while this percentage was significantly greater among males (6.87%). Overall, 0.96% of the population are moderate smokers. Heavy bidi smoking is almost nonexistent among females (0.03%), but 2.39% of males fall into this category. Overall, 0.3% of the total population are heavy smokers. In the case of cigarette smoking, almost all females (99.83%) did not smoke cigarettes, compared to 87.97% of males. Only 0.15% of females are moderate cigarette smokers, whereas 11.28% of males fell into this category. Heavy cigarette smoking is rare among females (0.02%) and slightly more common among males (0.74%). Overall, 0.1% of the population are heavy smokers. In terms of alcohol consumption, 1.62% of females and 21.4% of males drink alcohol. High frequency drinking is rare among females (0.25%) but more prevalent among males (4.02%). Overall, 0.68% of the population drinks alcohol daily. In the terms of healthy eating habits, 31.28% of the population eats healthy food regularly. Approximately 30.36% of females and 38.37% of males regularly consumed healthy food. A total of 34.32% of females and 33.93% of males moderately consumed healthy food. Overall, 34.27% of the population falls into this category; approximately 35.32% of females and 27.7% of males occasionally eat healthy food. In terms of unhealthy eating habits, 56.97% of the population reported unhealthy habits, approximately 57.31% of females and 54.33% of males occasionally consumed unhealthy food. Approximately 24.61% of females and 21.76% of males moderately consumed unhealthy food. Overall, 24.28% of the population moderately eats unhealthy food. Approximately 18.08% of females and 23.91% of males regularly consumed unhealthy food. Overall, 18.74% of the population regularly eats unhealthy food. These results show that males are significantly more likely to smoke both bidis and cigarettes and alcohol than females are. The disparity is evident in both the moderate and heavy smoking categories. Males are much more likely to consume alcohol than females are, both moderately and daily. The gender gap is stark, with nearly a quarter of males drinking alcohol compared with a very small percentage of females. Interestingly, males are slightly more likely than females to regularly consume healthy food. Females are more likely to occasionally consume unhealthy food, whereas males are more likely to regularly consume unhealthy food. The significant p values (Pr = 0.000) across all categories suggest that these differences are statistically significant. Table 4 Gender distribution (in percentages) of the frequency of healthy and unhealthy behavior among adults (15–49) in India, NFHS 5 (2019-21) NFHS 5 (2019-21) INDICATORS Female Male Total TOBACCO Bidi None 99.77 90.74 98.74 Moderate Smoker 0.20 6.87 0.96 Heavy Smoker 0.03 2.39 0.3 Pearson chi2(2) = 55000 Pr = 0.000 Cigarette None 99.83 87.97 98.48 Moderate Smoker 0.15 11.28 1.42 Heavy Smoker 0.02 0.74 0.1 Pearson chi2(2) = 91000, Pr = 0.000 ALCOHOL None 98.13 74.57 95.44 Moderate frequency Drinker 1.62 21.4 3.88 High frequency (Daily) Drinker 0.25 4.02 0.68 Pearson chi2(2) = 110000, Pr = 0.000 Frequency of eating healthy food Regular Eater (Better) 30.36 38.37 31.28 Moderate Eater 34.32 33.93 34.27 Occasional Eater (Worse) 35.32 27.7 34.45 Pearson chi2(2) = 3100, Pr = 0.000 Frequency of eating unhealthy food Occasional Eater (Better) 57.31 54.33 56.97 Moderate Eater 24.61 21.76 24.28 Regular Eater (Worse) 18.08 23.91 18.74 Pearson chi2(2) = 1900 Pr = 0.000 Table 5 Diversified Diet Index by gender and place of residence for adults (15–49 years) India, NFHS − 5 (2019-21). NFHS 2019-21 DIET INDEX Female Male Total OVERALL Less diversified diet (Unhealthy) 40.49 32.73 39.6 Moderately diversified diet (Moderate) 35.85 39.03 36.22 Diversified diet (Healthy) 23.66 28.25 24.18 Pearson chi2(2) = 2200, Pr = 0.000 URBAN Less diversified diet (Unhealthy) 33.58 27.87 32.9 Moderately diversified diet (Moderate) 35.68 36.5 35.78 Diversified diet (Healthy) 30.74 35.63 31.32 Pearson chi2(2) = 378.7148 Pr = 0.000 RURAL Less diversified diet (Unhealthy) 42.76 34.43 41.83 Moderately diversified diet (Moderate) 35.91 39.91 36.36 Diversified diet (Healthy) 21.32 25.66 21.81 Pearson chi2(2) = 1800 Pr = 0.000 Table 5 presents the Diversified Diet Index by gender and place of residence. A greater percentage of females followed a less diversified (unhealthy) diet than did males (40.49% of females and 32.73% of males). While 35.85% of females had a moderately diversified diet, 39.03% of males were in this category. Approximately 28.25% of the males were on a diverse diet (healthy), whereas only 23.66% of the females were on a healthy, diversified diet. In urban areas, females are more likely to have unhealthy diets than males are (33.58% of urban females vs 27.87% of urban males). The pattern of having a diversified diet by gender is the same in both rural and urban areas. Overall, females, both in urban and rural areas, are more likely to have a less diversified, unhealthy diet than males. Conversely, males are more likely to have a moderately diversified or diversified (healthy) diet. The gender gap in diet diversification is consistent across urban and rural areas, with females generally having lower diet diversity. Urban residents tend to have better diet diversification than rural residents do, but within each area, males consistently have diversified healthier diets than females do. Overall, a large proportion of men consume a moderate diet, whereas a large proportion of women consume a less diversified diet; however, notably, rural women have a disadvantage in consuming a diversified diet. Results from regression analysis Table 6 displays the likelihood of engaging in the consumption of any selected substance-based or diversified diet based on the NFHS-5 data. Men are approximately 25 times more likely than women to engage in alcohol and tobacco consumption after other background variables, such as age, place of residence, education, marital status, working status, religion, caste, and wealth index, are adjusted for. Similarly, men are more likely to have a diversified diet (OR: 1.34; CI: 1.31 1.37) than women are after controlling for background variables. Table 6 Odds ratios of binary logistic regression for the use of any selected substance and having a diversified diet by background characteristics in Indian adults (15–49), NFHS 5 (2019–21) Background Characteristics NFHS 5 (2019-21) NFHS 5 (2019-21) Use of any selected substance Diversified Diet Odds Ratio Std. Err. 95% CI Odds Ratio Std. Err. 95% CI Sex Female R Male 25.19*** 0.466 24.29 26.12 1.34*** 0.013 1.31 1.37 The dependent variable was the use of any of the above-mentioned substances, including smoking bidi or cigarettes and drinking alcohol. Dependent variable: Diversified diet includes daily consumption of vegetable and animal proteins and minimal or occasional consumption of unhealthy foods. R : Reference category; ***p < 0.01; **p < 0.05; *p < 0.1. This model is adjusted for age, place of residence, education, marital status, working status, religion, caste, and wealth index. The full table can be found in Appendix 8. Discussion This study underscores significant gender disparities in substance use and dietary diversity among Indian adults (15–49), as evidenced by the National Family Health Survey (NFHS-5) data. The findings revealed that males are consistently more likely than females to engage in higher consumption of substances such as tobacco and alcohol, irrespective of the urban or rural setting. Conversely, males also tend to consume a more diversified and healthier diet than females do, although this does not offset the health risks posed by their higher substance use. However, rural areas are disadvantaged in terms of a higher prevalence of consuming these substances, especially men (34.38%), and a lower percentage of people, especially women (42.76%), eat diverse diets. The persistence of these disparities aligns with the literature that highlights men's greater propensity for substance use and the consequent negative impacts on their health. Earlier studies revealed that men are more likely to engage in high-substance consumption, whereas women are more inclined toward low-moderate consumption, contributing to poorer survival rates among men (Pampel, 2002 ; Saikia & Bhat, 2008 , Dhakad & Saikia,2023). The study's results reaffirm that substance use among men, particularly in rural areas, contributes significantly to the gender gap in mortality rates. This gap is further exacerbated by cultural norms that permit or even encourage substance use among men while restricting women's access to the same substances​. In rural areas, a greater proportion of women engage in moderate-risk behaviors, possibly due to cultural acceptance of bidi smoking and smokeless tobacco such as 'Misri' (A Pratinidhi et al., 2010). Meta-analyses confirm that substance abuse is more prevalent among men and in rural areas (Reddy & Chandrasekhar, 1998; Mohan et al., 1978 ; Varma et al., 1980 ; Chakravarthy, 1990 ; Mohan et al., 2001 ), driving premature male mortality—a trend observed in Europe as well (Waldron, 1995 ; McCartney, 2011). The excessive consumption of alcohol and tobacco plays a significant role in widening the survival gap between male-female particularly in adulthood. Dhakad and Saikia ( 2023 ) reported that Indian men experience a greater probability of death than Indian women do during adulthood. This disparity in substance use may be a more pronounced factor influencing survival differences across both genders. Interestingly, the study also highlights the paradox where despite their higher substance use, men in India generally report better dietary diversity than women do. This could be attributed to socioeconomic factors that favor men's access to a wider variety of food, as well as the cultural practices that prioritize men's nutritional needs over those of women. The gendered nature of dietary patterns, as indicated by this study, reflects deep-rooted societal norms that continue to influence health outcomes across different regions of India​. The study revealed that men are more likely to consume a diversified diet than women are (OR: 1.34; CI: 1.31 1.37). In low-income countries, women's nutritional status is often compromised by their subordinate decision-making roles (Hindin, 2000 ; Miller, 1997 ). Cultural norms and patriarchy influence dietary choices, leading to undernourishment among women (Jensen & Holm, 1999 ; Sen, 1998 ; Natrajan & Jacob, 2018 ). Women often consume less nutritious food, reinforcing gender dynamics within households (Hathi et al., 2021 ). Interestingly, Indian women from higher socioeconomic backgrounds also prefer vegetarian food because of their religious beliefs. While this study explored the relationship between the consumption of selected substances and diverse dietary patterns, correlation analysis revealed a moderate association (0.492) between them, among women (0.534) and men (0.362). This reflects the complex interplay of factors influencing both diversified diets and selected substance consumption beyond simple correlation. Gender-based and urban‒rural differences follow relatively similar patterns in both the NFHS-4 and the NFHS-5, although there is an overall decline in alcohol and tobacco consumption from the NFHS-4 (2015-16) to the NFHS-5 (2019-21) (Tables of NFHS-4 (2015-16) are attached in appendix 2 to appendix 7 ) . Possible reasons for the declining trends in alcohol, tobacco, and dietary diversity from NFHS-4 to NFHS-5 can be attributed primarily to the impact of the COVID-19 pandemic because the resulting lockdowns limited the availability of tobacco, alcohol, and a variety of food items. The pandemic's socioeconomic effects, including emotional stress, not only affected the data collected from the NFHS-5 but also affected health, behavioral, and dietary outcomes. However, it is crucial to acknowledge the limitations of this study. The reliance on self-reported data for dietary diversity and selected substance use introduces the potential for recall bias, which may affect the accuracy of the findings. Additionally, the NFHS data lack detailed information on portion sizes, which limits the ability to assess the nutritional adequacy of the reported diets. In India, the predominant population's adherence to religious and cultural beliefs often leads to a preference for vegetarianism, resulting in a reduced diversity of food options. This may also affect our diet diversity indicator analyzed in the present study. Despite these limitations, the study provides valuable insights into the gender-specific health behaviors that contribute to the disparities in health outcomes observed in India. Conclusion This study highlights significant gender disparities in selected substance consumption and dietary diversity. Despite their limited access to nutritious food, women exhibit lower rates of behaviors such as smoking and alcohol use than men do; women are more prone to this selected substance consumption but generally maintain a healthier diversified diet. Both genders face risks of premature mortality, underscoring the need for gender-sensitive health policies. Public health strategies must include stricter tobacco and alcohol regulations, improved preventive infrastructure, and accessible support for behavior modification. Targeted interventions are essential to address alcohol and tobacco use, particularly among men. Article 47 of the Indian Constitution advocates alcohol prohibition, yet only Gujarat and Bihar have implemented it. States without prohibition show significantly greater alcohol consumption, with men being 18 times more likely to consume alcohol than women (appendix 1), suggesting that prohibition could be effective despite economic challenges. A higher GST for tobacco and alcohol has had a limited impact, underscoring the need for more robust policies (Bapat et al., 2020 ). Educational campaigns should also address alcohol and tobacco use among men. Gender-specific policies are crucial, particularly for smokeless tobacco use among women. Despite recent efforts, more initiatives are needed to increase dietary diversity among women. Tobacco control policies must address the rising oral cancer rates among women (IHME, 2017) and consider potential underreporting of selected substance use due to cultural stigma. Government programs should expand their focus beyond early childhood and pregnancy to improve adult nutrition and reduce selected substance consumption, with particular emphasis on rural areas. This study underscores the need for focused public health interventions that address the gender disparities in substance use and dietary practices in India. Efforts should rely on promoting healthier lifestyles, particularly among men, who are more likely to engage in high-risk behaviors such as tobacco and alcohol use. Moreover, strategies to improve women's access to diverse and nutritious foods are essential to mitigate the long-term health consequences of gender-based nutritional inequities. Future research should continue to explore these disparities, with a focus on developing effective interventions that can bridge the gender gap in health outcomes across the country​. Declarations Ethics approval and consent to participate Not Applicable Consent for publication Not Applicable Availability of data and materials The National Family Health Survey (NFHS) is the Demographic Health Survey of India. The NFHS data is available in the public domain, can be accessed at https://dhsprogram.com/data/available-datasets.cfm Competing interests The authors have declared that no competing interests exist. Funding The authors received no specific funding for this work. Authors' contributions PJ and NS conceived the study. All authors designed the study. PJ gathered data and did the analysis. All authors interpreted the results. PJ drafted the manuscript. NS provided critical comments on the manuscript. PJ revised the manuscript. 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Journal of studies on alcohol, 64(6), 790-801. https://www.jsad.com/doi/abs/10.15288/jsa.2003.64.790 Additional Declarations No competing interests reported. Supplementary Files Appendix.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5278220","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":367525389,"identity":"f1fa07f9-5ab8-403e-ba45-3e3c1ce2cbdf","order_by":0,"name":"Priyanka Yadav-Jagtap","email":"data:image/png;base64,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","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Priyanka","middleName":"","lastName":"Yadav-Jagtap","suffix":""},{"id":367525390,"identity":"a679bee5-f7c9-4ef0-b9b0-3defb3f921fd","order_by":1,"name":"Nandita Saikia","email":"","orcid":"","institution":"International Institute for Population Sciences","correspondingAuthor":false,"prefix":"","firstName":"Nandita","middleName":"","lastName":"Saikia","suffix":""}],"badges":[],"createdAt":"2024-10-16 20:08:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5278220/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5278220/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67719268,"identity":"eafa6898-34cf-485e-8825-097e34a6ad40","added_by":"auto","created_at":"2024-10-29 04:49:26","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":272950,"visible":true,"origin":"","legend":"\u003cp\u003eFramework conceptualizing gender disparities in behavior\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5278220/v1/9133ce8fcba2f9c40c14a861.jpeg"},{"id":70622624,"identity":"b5e5dd3d-297c-47ab-9ee8-d6c746c6ac9c","added_by":"auto","created_at":"2024-12-05 03:47:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1287110,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5278220/v1/8c1ee562-2131-4027-9c61-6b071dbc8cc6.pdf"},{"id":67719269,"identity":"61192141-e6c1-486d-ac60-445bea65148b","added_by":"auto","created_at":"2024-10-29 04:49:26","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":52179,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-5278220/v1/f29c1fe2f7f568602f6178e0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Gender Disparities in Tobacco, Alcohol Consumption and Dietary Diversity Among Indian Adults (15-49): Insights from the National Family Health Survey (NFHS-5) 2019-21","fulltext":[{"header":"Background","content":"\u003cp\u003ePublic health research underscores the profound impact of unhealthy behaviors on morbidity and mortality (WHO, 2013; WHO, 2014). Most diseases are linked to health behaviors (WHO, 2008; Lloyd-Jones et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Therefore, defining and measuring health behaviors are critical for effective policy development (Conner \u0026amp; Norman, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Disparities in health behaviors and dietary patterns significantly contribute to social variations in health outcomes (Stringhini et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), affecting premature mortality and recovery from illness. Despite their importance, understanding these behaviors and their clustering remains limited, complicating intervention strategies (Spring et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInterestingly, health behaviors vary across social classes, socioeconomic status, gender, race, and geography, influencing key risk factors for chronic diseases such as harmful alcohol use, smoking, sedentary lifestyles, and poor dietary practices (Borrell et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Braveman et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Pampel et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Ding et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Malta et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Lynch et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) established that socioeconomic status inversely affects behavioral and psychosocial characteristics, which are crucial in determining health risks. Gender differences in smoking, diet, and access to health care contribute to divergent mortality patterns between men and women (Gjon\u0026ccedil;a, 1999). Given the dynamic nature of health and mortality trends, understanding these gender disparities is crucial (Charmaz, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Gender identities influence illness experiences and health outcomes, underscoring the importance of examining gender in epidemiological studies (Vlassoff, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Gender differences in health behaviors are essential for addressing health disparities and informing policy, planning, and health services. A more systematic approach to gender-focused research is needed.\u003c/p\u003e \u003cp\u003eIn India, gender disparities in mortality have been documented extensively (Saikia, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Bora \u0026amp; Saikia, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Canudas-Romo, Saikia, \u0026amp; Diamond-Smith, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Dhakad \u0026amp; Saikia, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In the Indian context, gender differences in health behaviors reflect sociocultural norms shaped by patriarchal structures (Sen, 1992; Dasgupta, 2003). Compared with men, women's restricted access to alcohol and tobacco is influenced by sociocultural factors, limiting their usage (Wilsnak et al., 2005; Pathania, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Hitchman \u0026amp; Fong, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Despite Indian women having greater life expectancies, they report poorer health and lower healthcare expenditures than men do (Saikia et al., 2016; Bora \u0026amp; Saikia, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Canudas-Romo et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). However, studies on gender disparities in health behaviors among Indian adults are rare (Patel \u0026amp; Chauhan, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Singh \u0026amp; Chattopadhyay, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Marla \u0026amp; Padmaja, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). A limited number of studies have assessed gender differences in health behaviors, which may explain the gender gap in life expectancy and overall health outcomes.\u003c/p\u003e \u003cp\u003eGiven the above context, to fill the research gaps, we analyzed National Family Health Survey (NFHS 5- 2019-2021) data to explore gender differentials in smoking, alcohol consumption, and dietary habits. Understanding these behaviors is crucial, as they are significant risk factors for chronic diseases and mortality (WHO, 2009). The analysis uncovered how gender influences these health behaviors among Indian adults, hypothesizing variations in both positive and negative health practices by gender.\u003c/p\u003e\n\u003ch3\u003eConceptual Framework\u003c/h3\u003e\n\u003cp\u003eGender relations in society are dynamic and evolving and are influenced by societal progress and changing norms. As women increasingly occupy roles historically dominated by men, there is a growing need for a nuanced examination of gender dynamics. However, traditional gender roles still intersect with social, economic, and cultural factors, creating diverse patterns of exposure to health risks and differential access to health information, services, and care. These disparities significantly impact health outcomes (WHO, 2002). Despite sporadic recognition, gender has not been fully integrated as a critical determinant in health promotion efforts (Gelb et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Gender interacts with socioeconomic and contextual variables to shape health and behavioral outcomes differently for men and women (Vlassoff, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). To comprehensively understand these dynamics, it is essential to consider the complexity and intersectionality of social, cultural, and economic contexts alongside demographic and epidemiological indicators.\u003c/p\u003e \u003cp\u003eThis study adopts a gendered perspective to explore how inherent and contextual factors influence individual behaviors, particularly in the Indian context, where gender-specific behaviors are pronounced. The framework posits that the interaction of gender with various background factors contributes to distinct health behaviors across genders. By examining these interactions, the study aims to elucidate the nuanced pathways through which gender influences health behaviors, thereby informing targeted interventions and policies aimed at promoting health equity.\u003c/p\u003e \u003cp\u003eFrom the understanding developed from the available literature, this study conceptualizes the framework below in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData and methods\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eData Source\u003c/h2\u003e \u003cp\u003eThe NFHS-5 (2019\u0026ndash;21) data were used to examine health behaviors by gender. Data were analyzed via Stata software, with a focus on individuals aged 15\u0026ndash;49 years, categorized by urban and rural residence.\u003c/p\u003e \u003cp\u003eThe NFHS, initiated in the early 1990s, is a comprehensive survey that provides critical data on population, health, and nutrition across India and its states. NFHS-5 (2019-21) (IIPS, 2022) is the fifth round of this survey, followed by NFHS-1 (1992-93), NFHS-2 (1998-99), NFHS-3 (2005-06) and NFHS-4 (2015-16) (IIPS, 2018). These surveys offer essential indicators of family welfare, maternal and child health, nutrition, and health behaviors.\u003c/p\u003e \u003cp\u003eThe NFHS-5 adhered to the DHS (Demographic and Health Survey) via standardized questionnaires, sample strategies, and field methodology. A two-stage sampling design was adopted in the NFHS-5 (IIPS, 2022). The NFHS-5 (2019-21) includes 724,115 women and 93,144 men in the 15\u0026ndash;49 years age group. The focus was on individual-level socioeconomic information relevant to health behaviors such as tobacco use, alcohol consumption, and diet, ensuring a comprehensive gender comparison.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eEthics statement\u003c/h3\u003e\n\u003cp\u003eThe present study is based on publicly available NFHS 5 datasets with no identifiable information on the survey participants; hence, there is no requirement of ethics statement.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eWe performed a chi-square test for significance assessment. Behavioral factors were examined through bivariate and multivariate statistical analyses. Binary logistic regression was employed to establish associations between outcome variables (use of selected substances and a diversified diet) and other explanatory factors. The use of selected substances was coded as 1 and 0 for no use of selected substances, whereas diet diversity was coded as 1 for a diversified diet and 0 for a lack of diversity in a diet.\u003c/p\u003e \u003cp\u003eThe binary logistic regression model is typically presented in the following compact form:\u003c/p\u003e \u003cp\u003eLogit [ P (Y\u0026thinsp;=\u0026thinsp;1)] = \u003cem\u003eβ\u003c/em\u003e0\u0026thinsp;+\u0026thinsp;\u003cem\u003eβ _ X\u003c/em\u003e + \u003cem\u003eϵ\u003c/em\u003e\u003c/p\u003e \u003cp\u003eThe parameter β₀ estimates the log odds of the use of selected substances or a diversified diet for the reference group, whereas β estimates the maximum likelihood of the differential log odds associated with predictor X compared with the reference group. ε represents the residual in the model.\u003c/p\u003e\n\u003ch3\u003eIndicators\u003c/h3\u003e\n\u003cp\u003eIn this study, categories of selected substances are defined on the basis of literature sources (Saunders et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Wilson et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Johnson et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Girish et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Pulvers et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eVariables related to alcohol and tobacco use are defined on the basis of the current use of alcohol/tobacco. Categories were defined on the basis of questions such as \u0026ldquo;How often do you consume alcohol/tobacco? and What type of alcohol/tobacco do you usually consume?\u0026rdquo;. The frequency of alcohol is recoded according to the availability of categories of variables in the NFHS data.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of important variables used in the present study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCategories\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\u003eAlcohol\u0026nbsp;consumption\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLight alcohol consumption\u003c/b\u003e \u003cem\u003e(low alcoholic content approximately 15% or less)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eHard alcohol consumption\u003c/b\u003e \u003cem\u003e(Higher Alcoholic content ranged from 40\u0026ndash;50%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ebeer; wine, \u003cem\u003etadi madi\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ehard liquor and country liquor\u003c/p\u003e \u003c/td\u003e \u003c/tr\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 \u003cp\u003e\u003cb\u003eSmoke\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSmokeless\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ecigarette, pipe, cigars, bidis, hookah\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003echewing tobacco, snuff, gutkha/paan masala, paan, khaini\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFrequency of Alcohol Consumption\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eModerate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eHigh frequency\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eless than once a week and about once a week\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003econsumes alcohol daily\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFrequency of Smoking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eModerate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eHeavy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003esmokes 1\u0026ndash;10 Cigarettes/Bidis daily\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003esmokes more than 10 Cigarettes/Bidis daily\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConsumption of any of selected substances\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUse of substance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNot Using\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eincludes smoking bidis, cigarettes, and drinking alcohol\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003enot consuming any of these\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e gives the description of important variables used in the present study, besides that analysis also combined both the categories of alcohol (consuming both light and hard alcohol) and tobacco (consuming both smoking and smokeless together) to obtain an all-inclusive picture of selected substance consumption.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eThe Diet Diversity Index\u003c/h2\u003e \u003cp\u003eDiet, comprising a complex mix of foods and nutrients (Kant et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1993\u003c/span\u003e), is crucial for adults because of their high energy needs from work and reproduction. A varied diet including vegetables, fruits, dairy, and meat is essential for optimal health (Lichtenstein et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile NFHS data lack detailed nutrient intake data, dietary diversity is inferred from food frequency. The diet diversity index in this study integrates dietary diversity and food intake frequencies. Adapted from Kennedy et al. (1995), the index incorporates consumption frequencies of vegetables, fruits, dairy, eggs, fish, and meat.\u003c/p\u003e \u003cp\u003eIn this study, we developed a diet diversity index on the basis of the frequency of consumption of various food items. We considered healthy foods, such as vegetable proteins from pulses or beans; green leafy vegetables; fruits; and animal proteins, such as milk/curd, eggs, fish, chicken, or meat, as well as unhealthy foods, including fried food and aerated drinks. The consumption frequencies were categorized as daily, weekly, occasionally, or never. Responses regarding the frequency of consumption were assigned values as follows: 3 for daily consumption, 2 for weekly consumption, 1 for occasional consumption, and 0 for never consumed. For unhealthy foods, the scoring was reversed, with 3 indicating never consumed and 0 indicating daily consumption. The diet diversity score was computed by aggregating these values, resulting in a range from zero (indicating no dietary diversity) to twenty-one (indicating high dietary diversity).\u003c/p\u003e \u003cp\u003eOn the basis of this score, diets were classified into three categories: \u0026ldquo;less diversified diet/unhealthy\u0026rdquo;, \u0026ldquo;moderately diversified diet/moderate\u0026rdquo;, and \u0026ldquo;diversified diet/healthy\u0026rdquo;. A 'Diversified Diet' is characterized by the daily consumption of vegetable and animal proteins and minimal or occasional consumption of unhealthy foods. Conversely, a 'less diversified diet' is characterized by occasional consumption of healthy foods combined with regular consumption of unhealthy foods. The composite variable for the Diet Diversity Index was calculated separately for women and men. This index enables comprehensive assessment and cross-regional comparison of dietary diversity in simple ways.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eResults from bivariate analysis\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents gender-wise tobacco and alcohol consumption among Indian adults (15\u0026ndash;49).\u003c/p\u003e \u003cp\u003eMales have a significantly greater rate of tobacco use than females do. The difference was statistically significant. Tobacco use is lower in urban areas for both genders, but males still have a much higher rate than females do. Such consumption is higher in rural areas for both genders, with males still significantly outpacing females. In the case of alcohol, males also have a much greater prevalence of alcohol use than females do. Alcohol consumption is lower in urban areas than in rural areas for both genders, but the male‒female disparity persists in both rural and urban areas. Males are far more likely to use any selected substance than females are in both rural and urban areas.\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\u003ePercentages of tobacco and alcohol consumption by gender and type of residence (rural/urban) among adults (15\u0026ndash;49), India, and the NFHS-5 (2019-21)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eNFHS 5\u0026nbsp;(2019\u0026ndash;2021)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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 \u003cp\u003e\u003cb\u003e6.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e42.19\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e10.57\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePearson chi2\u0026thinsp;=\u0026thinsp;110000 \u0026nbsp; Pr\u0026thinsp;=\u0026thinsp;0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.87\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e25.43\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e4.56\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePearson chi2\u0026thinsp;=\u0026thinsp;110000 \u0026nbsp; Pr\u0026thinsp;=\u0026thinsp;0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUrban\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 \u003c/tr\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 \u003cp\u003e\u003cb\u003e4.58\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e35.23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e8.22\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePearson chi2\u0026thinsp;=\u0026thinsp;27000 Pr\u0026thinsp;=\u0026thinsp;0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.92\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e23.72\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3.63\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePearson chi2\u0026thinsp;=\u0026thinsp;32000 \u0026nbsp; Pr\u0026thinsp;=\u0026thinsp;0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRural\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 \u003c/tr\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 \u003cp\u003e\u003cb\u003e7.13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e44.63\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e11.35\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePearson chi2\u0026thinsp;=\u0026thinsp;86000 \u0026nbsp; Pr\u0026thinsp;=\u0026thinsp;0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e26.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e4.86\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePearson chi2\u0026thinsp;=\u0026thinsp;75000 \u0026nbsp; Pr\u0026thinsp;=\u0026thinsp;0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOverall\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUse of any selected Substance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e33.56\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e5.73\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePearson chi2(2)\u0026thinsp;=\u0026thinsp;150000, Pr\u0026thinsp;=\u0026thinsp;0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUrban\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUse of any selected Substance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e31.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e4.71\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePearson chi2(2)\u0026thinsp;=\u0026thinsp;43000, Pr\u0026thinsp;=\u0026thinsp;0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRural\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUse of any selected Substance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.47\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e34.38\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e6.06\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePearson chi2(2)\u0026thinsp;=\u0026thinsp;110000, Pr\u0026thinsp;=\u0026thinsp;0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the distribution of tobacco and alcohol consumption among adults aged 15\u0026ndash;49 years in India on the basis of data from the NFHS-5 (2019\u0026ndash;2021). The data are broken down by sex (female or male) and include categories for different forms of tobacco and alcohol consumption. A Pearson chi-square test was also used to assess the statistical significance of the gender differences. A significantly greater percentage of females (93.5%) reported not using tobacco than did males (57.81%). The majority of both genders do not consume tobacco, but males are more likely to consume it. It seems that tobacco smoking is predominantly a male behavior, with 18.76% of males reporting smoking, whereas only 0.52% of females. Smokeless tobacco is more common among males (20.83%) than females (5.53%). However, this form of tobacco is the most prevalent among females than other forms are. A small percentage of both genders use both forms of tobacco, with males (2.6%) being more likely to do so than females (0.43%). The chi-square test revealed that the differences in tobacco consumption by gender were statistically significant, with a p value of 0.000.\u003c/p\u003e \u003cp\u003eA vast majority of females (98.13%) do not consume alcohol, whereas 74.57% of males do not consume it. This suggests that alcohol consumption is much more common among males. Light alcohol consumption is more common among males (10.4%) than females (1.06%). Hard alcohol consumption is predominantly a male behavior, with 8.18% of males consuming hard alcohol compared with only 0.37% of females. The chi-square test indicates that the differences in alcohol consumption by gender are statistically significant, with a p value of 0.000. In summary, across all categories (tobacco, alcohol, and any substance use), males consistently had higher consumption rates than females did. The urban‒rural divide shows that substance use is generally greater in rural areas, but the male‒female disparity remains significant in both settings.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of the consumption of different forms of tobacco and alcohol by gender among adults (15\u0026ndash;49), India, NFHS-5 (2019-21)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eINDICATORS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eNFHS 5 (2019-21)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTOBACCO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith Smoke Only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith Smokeless Only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoth Smoke and Smokeless\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePearson chi2(3)\u0026thinsp;=\u0026thinsp;150000\u0026nbsp; \u0026nbsp; Pr\u0026thinsp;=\u0026thinsp;0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eALCOHOL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight Alcohol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHard Alcohol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoth Light and Hard Alcohol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePearson chi2(3)\u0026thinsp;=\u0026thinsp;110000 Pr\u0026thinsp;=\u0026thinsp;0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e details the frequency of both healthy and unhealthy behaviors by gender, offering a deeper analysis of behavioral patterns by gender.\u003c/p\u003e \u003cp\u003eA vast majority of females (99.77%) do not smoke bidis, compared to 90.74% of males. A very small percentage of females (0.20%) are moderate bidi smokers, while this percentage was significantly greater among males (6.87%). Overall, 0.96% of the population are moderate smokers. Heavy bidi smoking is almost nonexistent among females (0.03%), but 2.39% of males fall into this category. Overall, 0.3% of the total population are heavy smokers. In the case of cigarette smoking, almost all females (99.83%) did not smoke cigarettes, compared to 87.97% of males. Only 0.15% of females are moderate cigarette smokers, whereas 11.28% of males fell into this category. Heavy cigarette smoking is rare among females (0.02%) and slightly more common among males (0.74%). Overall, 0.1% of the population are heavy smokers.\u003c/p\u003e \u003cp\u003eIn terms of alcohol consumption, 1.62% of females and 21.4% of males drink alcohol. High frequency drinking is rare among females (0.25%) but more prevalent among males (4.02%). Overall, 0.68% of the population drinks alcohol daily.\u003c/p\u003e \u003cp\u003eIn the terms of healthy eating habits, 31.28% of the population eats healthy food regularly. Approximately 30.36% of females and 38.37% of males regularly consumed healthy food. A total of 34.32% of females and 33.93% of males moderately consumed healthy food. Overall, 34.27% of the population falls into this category; approximately 35.32% of females and 27.7% of males occasionally eat healthy food. In terms of unhealthy eating habits, 56.97% of the population reported unhealthy habits, approximately 57.31% of females and 54.33% of males occasionally consumed unhealthy food. Approximately 24.61% of females and 21.76% of males moderately consumed unhealthy food. Overall, 24.28% of the population moderately eats unhealthy food. Approximately 18.08% of females and 23.91% of males regularly consumed unhealthy food. Overall, 18.74% of the population regularly eats unhealthy food.\u003c/p\u003e \u003cp\u003eThese results show that males are significantly more likely to smoke both bidis and cigarettes and alcohol than females are. The disparity is evident in both the moderate and heavy smoking categories. Males are much more likely to consume alcohol than females are, both moderately and daily. The gender gap is stark, with nearly a quarter of males drinking alcohol compared with a very small percentage of females. Interestingly, males are slightly more likely than females to regularly consume healthy food. Females are more likely to occasionally consume unhealthy food, whereas males are more likely to regularly consume unhealthy food. The significant p values (Pr\u0026thinsp;=\u0026thinsp;0.000) across all categories suggest that these differences are statistically significant.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGender distribution (in percentages) of the frequency of healthy and unhealthy behavior among adults (15\u0026ndash;49) in India, NFHS 5 (2019-21)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eNFHS 5 (2019-21)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINDICATORS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTOBACCO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBidi\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate Smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeavy Smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePearson chi2(2)\u0026thinsp;=\u0026thinsp;55000\u0026nbsp; Pr\u0026thinsp;=\u0026thinsp;0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCigarette\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate Smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeavy Smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePearson chi2(2)\u0026thinsp;=\u0026thinsp;91000, Pr\u0026thinsp;=\u0026thinsp;0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eALCOHOL\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate frequency Drinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh frequency (Daily) Drinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePearson chi2(2)\u0026thinsp;=\u0026thinsp;110000, Pr\u0026thinsp;=\u0026thinsp;0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFrequency of eating healthy food\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegular Eater (Better)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate Eater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccasional Eater (Worse)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePearson chi2(2)\u0026thinsp;=\u0026thinsp;3100, Pr\u0026thinsp;=\u0026thinsp;0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFrequency of eating unhealthy food\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccasional Eater (Better)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate Eater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegular Eater (Worse)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePearson chi2(2)\u0026thinsp;=\u0026thinsp;1900 Pr\u0026thinsp;=\u0026thinsp;0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiversified Diet Index by gender and place of residence for adults (15\u0026ndash;49 years) India, NFHS \u0026minus;\u0026thinsp;5 (2019-21).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eNFHS 2019-21\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDIET INDEX\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eOVERALL\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLess diversified diet (Unhealthy)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e40.49\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e32.73\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e39.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerately diversified diet (Moderate)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiversified diet (Healthy)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e23.66\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e28.25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e24.18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePearson chi2(2)\u0026thinsp;=\u0026thinsp;2200, Pr\u0026thinsp;=\u0026thinsp;0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eURBAN\u003c/span\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLess diversified diet (Unhealthy)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e33.58\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e27.87\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e32.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerately diversified diet (Moderate)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiversified diet (Healthy)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e30.74\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e35.63\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e31.32\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePearson chi2(2)\u0026thinsp;=\u0026thinsp;378.7148 Pr\u0026thinsp;=\u0026thinsp;0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eRURAL\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLess diversified diet (Unhealthy)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e42.76\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e34.43\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e41.83\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerately diversified diet (Moderate)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiversified diet (Healthy)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e21.32\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e25.66\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e21.81\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePearson chi2(2)\u0026thinsp;=\u0026thinsp;1800\u0026nbsp; Pr\u0026thinsp;=\u0026thinsp;0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the Diversified Diet Index by gender and place of residence. A greater percentage of females followed a less diversified (unhealthy) diet than did males (40.49% of females and 32.73% of males). While 35.85% of females had a moderately diversified diet, 39.03% of males were in this category. Approximately 28.25% of the males were on a diverse diet (healthy), whereas only 23.66% of the females were on a healthy, diversified diet. In urban areas, females are more likely to have unhealthy diets than males are (33.58% of urban females vs 27.87% of urban males). The pattern of having a diversified diet by gender is the same in both rural and urban areas. Overall, females, both in urban and rural areas, are more likely to have a less diversified, unhealthy diet than males. Conversely, males are more likely to have a moderately diversified or diversified (healthy) diet. The gender gap in diet diversification is consistent across urban and rural areas, with females generally having lower diet diversity. Urban residents tend to have better diet diversification than rural residents do, but within each area, males consistently have diversified healthier diets than females do.\u003c/p\u003e \u003cp\u003eOverall, a large proportion of men consume a moderate diet, whereas a large proportion of women consume a less diversified diet; however, notably, rural women have a disadvantage in consuming a diversified diet.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eResults from regression analysis\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e displays the likelihood of engaging in the consumption of any selected substance-based or diversified diet based on the NFHS-5 data. Men are approximately 25 times more likely than women to engage in alcohol and tobacco consumption after other background variables, such as age, place of residence, education, marital status, working status, religion, caste, and wealth index, are adjusted for. Similarly, men are more likely to have a diversified diet (OR: 1.34; CI: 1.31 1.37) than women are after controlling for background variables.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOdds ratios of binary logistic regression for the use of any selected substance and having a diversified diet by background characteristics in Indian adults (15\u0026ndash;49), 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=\"left\" 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\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eNFHS 5 (2019-21)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eNFHS 5 (2019-21)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eUse of any selected substance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eDiversified Diet\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Err.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStd. Err.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e95% 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\u003eSex\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003csup\u003eR\u003c/sup\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.19***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.34***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe dependent variable was the use of any of the above-mentioned substances, including smoking bidi or cigarettes and drinking alcohol.\u003c/p\u003e \u003cp\u003eDependent variable: Diversified diet includes daily consumption of vegetable and animal proteins and minimal or occasional consumption of unhealthy foods.\u003c/p\u003e \u003cp\u003e \u003csup\u003eR\u003c/sup\u003e: Reference category; ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; **p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; *p\u0026thinsp;\u0026lt;\u0026thinsp;0.1. This model is adjusted for age, place of residence, education, marital status, working status, religion, caste, and wealth index. The full table can be found in Appendix 8.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study underscores significant gender disparities in substance use and dietary diversity among Indian adults (15\u0026ndash;49), as evidenced by the National Family Health Survey (NFHS-5) data.\u003c/p\u003e \u003cp\u003eThe findings revealed that males are consistently more likely than females to engage in higher consumption of substances such as tobacco and alcohol, irrespective of the urban or rural setting. Conversely, males also tend to consume a more diversified and healthier diet than females do, although this does not offset the health risks posed by their higher substance use. However, rural areas are disadvantaged in terms of a higher prevalence of consuming these substances, especially men (34.38%), and a lower percentage of people, especially women (42.76%), eat diverse diets.\u003c/p\u003e \u003cp\u003eThe persistence of these disparities aligns with the literature that highlights men's greater propensity for substance use and the consequent negative impacts on their health. Earlier studies revealed that men are more likely to engage in high-substance consumption, whereas women are more inclined toward low-moderate consumption, contributing to poorer survival rates among men (Pampel, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Saikia \u0026amp; Bhat, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Dhakad \u0026amp; Saikia,2023). The study's results reaffirm that substance use among men, particularly in rural areas, contributes significantly to the gender gap in mortality rates. This gap is further exacerbated by cultural norms that permit or even encourage substance use among men while restricting women's access to the same substances​. In rural areas, a greater proportion of women engage in moderate-risk behaviors, possibly due to cultural acceptance of bidi smoking and smokeless tobacco such as 'Misri' (A Pratinidhi et al., 2010). Meta-analyses confirm that substance abuse is more prevalent among men and in rural areas (Reddy \u0026amp; Chandrasekhar, 1998; Mohan et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1978\u003c/span\u003e; Varma et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e1980\u003c/span\u003e; Chakravarthy, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Mohan et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), driving premature male mortality\u0026mdash;a trend observed in Europe as well (Waldron, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; McCartney, 2011).\u003c/p\u003e \u003cp\u003eThe excessive consumption of alcohol and tobacco plays a significant role in widening the survival gap between male-female particularly in adulthood. Dhakad and Saikia (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) reported that Indian men experience a greater probability of death than Indian women do during adulthood. This disparity in substance use may be a more pronounced factor influencing survival differences across both genders.\u003c/p\u003e \u003cp\u003eInterestingly, the study also highlights the paradox where despite their higher substance use, men in India generally report better dietary diversity than women do. This could be attributed to socioeconomic factors that favor men's access to a wider variety of food, as well as the cultural practices that prioritize men's nutritional needs over those of women. The gendered nature of dietary patterns, as indicated by this study, reflects deep-rooted societal norms that continue to influence health outcomes across different regions of India​. The study revealed that men are more likely to consume a diversified diet than women are (OR: 1.34; CI: 1.31 1.37). In low-income countries, women's nutritional status is often compromised by their subordinate decision-making roles (Hindin, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Miller, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Cultural norms and patriarchy influence dietary choices, leading to undernourishment among women (Jensen \u0026amp; Holm, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Sen, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Natrajan \u0026amp; Jacob, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Women often consume less nutritious food, reinforcing gender dynamics within households (Hathi et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Interestingly, Indian women from higher socioeconomic backgrounds also prefer vegetarian food because of their religious beliefs.\u003c/p\u003e \u003cp\u003eWhile this study explored the relationship between the consumption of selected substances and diverse dietary patterns, correlation analysis revealed a moderate association (0.492) between them, among women (0.534) and men (0.362). This reflects the complex interplay of factors influencing both diversified diets and selected substance consumption beyond simple correlation.\u003c/p\u003e \u003cp\u003eGender-based and urban‒rural differences follow relatively similar patterns in both the NFHS-4 and the NFHS-5, although there is an overall decline in alcohol and tobacco consumption from the NFHS-4 (2015-16) to the NFHS-5 (2019-21) (Tables of NFHS-4 (2015-16) are attached in appendix 2 to appendix 7\u003cb\u003e)\u003c/b\u003e. Possible reasons for the declining trends in alcohol, tobacco, and dietary diversity from NFHS-4 to NFHS-5 can be attributed primarily to the impact of the COVID-19 pandemic because the resulting lockdowns limited the availability of tobacco, alcohol, and a variety of food items. The pandemic's socioeconomic effects, including emotional stress, not only affected the data collected from the NFHS-5 but also affected health, behavioral, and dietary outcomes.\u003c/p\u003e \u003cp\u003eHowever, it is crucial to acknowledge the limitations of this study. The reliance on self-reported data for dietary diversity and selected substance use introduces the potential for recall bias, which may affect the accuracy of the findings. Additionally, the NFHS data lack detailed information on portion sizes, which limits the ability to assess the nutritional adequacy of the reported diets. In India, the predominant population's adherence to religious and cultural beliefs often leads to a preference for vegetarianism, resulting in a reduced diversity of food options. This may also affect our diet diversity indicator analyzed in the present study. Despite these limitations, the study provides valuable insights into the gender-specific health behaviors that contribute to the disparities in health outcomes observed in India.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study highlights significant gender disparities in selected substance consumption and dietary diversity. Despite their limited access to nutritious food, women exhibit lower rates of behaviors such as smoking and alcohol use than men do; women are more prone to this selected substance consumption but generally maintain a healthier diversified diet. Both genders face risks of premature mortality, underscoring the need for gender-sensitive health policies.\u003c/p\u003e \u003cp\u003ePublic health strategies must include stricter tobacco and alcohol regulations, improved preventive infrastructure, and accessible support for behavior modification. Targeted interventions are essential to address alcohol and tobacco use, particularly among men. Article 47 of the Indian Constitution advocates alcohol prohibition, yet only Gujarat and Bihar have implemented it. States without prohibition show significantly greater alcohol consumption, with men being 18 times more likely to consume alcohol than women (appendix 1), suggesting that prohibition could be effective despite economic challenges. A higher GST for tobacco and alcohol has had a limited impact, underscoring the need for more robust policies (Bapat et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEducational campaigns should also address alcohol and tobacco use among men. Gender-specific policies are crucial, particularly for smokeless tobacco use among women. Despite recent efforts, more initiatives are needed to increase dietary diversity among women. Tobacco control policies must address the rising oral cancer rates among women (IHME, 2017) and consider potential underreporting of selected substance use due to cultural stigma. Government programs should expand their focus beyond early childhood and pregnancy to improve adult nutrition and reduce selected substance consumption, with particular emphasis on rural areas.\u003c/p\u003e \u003cp\u003eThis study underscores the need for focused public health interventions that address the gender disparities in substance use and dietary practices in India. Efforts should rely on promoting healthier lifestyles, particularly among men, who are more likely to engage in high-risk behaviors such as tobacco and alcohol use. Moreover, strategies to improve women's access to diverse and nutritious foods are essential to mitigate the long-term health consequences of gender-based nutritional inequities. Future research should continue to explore these disparities, with a focus on developing effective interventions that can bridge the gender gap in health outcomes across the country​.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe National Family Health Survey (NFHS) is the Demographic Health Survey of India. The NFHS data is available in the public domain, can be accessed at https://dhsprogram.com/data/available-datasets.cfm\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have declared that no competing interests exist.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no specific funding for this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePJ and NS conceived the study. All authors designed the study. PJ gathered data and did the analysis. All authors interpreted the results. PJ drafted the manuscript. NS provided critical comments on the manuscript. PJ revised the manuscript. Both authors approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information (optional)\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAsha Pratinidhi, A. P., Sudesh Gandham, S. G., Aparna Shrotri, A. S., Archana Patil, A. P., \u0026amp; Shrikar Pardeshi, S. P. (2010). Use of \u0026apos;Mishri\u0026apos; a smokeless form of tobacco during pregnancy and its perinatal outcome. \u0026nbsp;\u003cu\u003ehttps://www.cabidigitallibrary.org/doi/full/10.5555/20103163455\u003c/u\u003e\u003c/li\u003e\n \u003cli\u003eBapat, S. V., Gaikwad, R., Bramhankar, M., \u0026amp; Mishra, N. L. (2020). 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Gender comparison of alcohol exposure on drinking occasions. Journal of studies on alcohol, 64(6), 790-801.\u0026nbsp;\u003cu\u003ehttps://www.jsad.com/doi/abs/10.15288/jsa.2003.64.790\u003c/u\u003e\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":"Gender, tobacco, alcohol, selected substance use, diversified diet, India, NFHS","lastPublishedDoi":"10.21203/rs.3.rs-5278220/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5278220/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eUnderstanding gender-specific health behaviors is crucial for assessing mortality risk factors. In India, the paucity of data has hindered research in this area. This study fills this gap by investigating gender differences in smoking, alcohol consumption, and diversified dietary habits among Indian adults. This study further explored the differences in gender-specific health behaviors among rural urban areas.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUsing data from the National Family Health Surveys (NFHS-5) 2019-21, we analyzed individuals aged 15\u0026ndash;49 years via bivariate and multivariate statistical techniques. We carried out binary logistic regression to assess the likelihood of engaging in these behaviors on the basis of gender and other socioeconomic factors.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAccording to the NFHS-5 data, tobacco use is substantially greater among men (42.19%) than among women (6.5%), and alcohol consumption is also more prevalent among men (25.43%) than among women (1.87%). Men demonstrate greater dietary diversity, with 28.25% consuming diverse foods, whereas 23.6% of women do so. Gender dynamics remain consistent when differences by rural and urban residence are analyzed. However, rural men consume more alcohol and tobacco substances than their urban counterparts do (34.3% vs. 31.2%), whereas rural women have lower dietary diversity than urban women do (21.3% vs. 30.7%). Notably, the use of smokeless tobacco among women (5.53%) exceeds that of smokers (0.52%), with this difference being more pronounced in rural areas.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study highlights significant gender disparities in health behaviors among Indian adults. Men are more likely to consume substances such as alcohol and tobacco (25.6 times higher, 95% CI: 24.29 26.12) but have better dietary diversity (1.34 times higher, 95% CI: 1.31 1.37) than women. Rural areas are disadvantaged in terms of a higher prevalence of consuming these substances, especially in men, and a lower percentage of people eat diverse diets, especially women, whereas urban areas present relative advantages. Targeted, gender-specific health interventions are needed, particularly in rural settings, to address these disparities and promote healthier lifestyles.\u003c/p\u003e","manuscriptTitle":"Gender Disparities in Tobacco, Alcohol Consumption and Dietary Diversity Among Indian Adults (15-49): Insights from the National Family Health Survey (NFHS-5) 2019-21","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-29 04:49:21","doi":"10.21203/rs.3.rs-5278220/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":"dd705d19-3b6d-4cfe-aeae-6deaeffaa95f","owner":[],"postedDate":"October 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-05T03:38:49+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-29 04:49:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5278220","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5278220","identity":"rs-5278220","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}