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Factors associated with tobacco use among Nepalese men aged 15–49 years: Data from Nepal demographic and Health Survey 2016

  • Author Footnotes
    1 These authors contributed equally to this work.
    Rajat Das Gupta
    Correspondence
    Corresponding author. Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA.
    Footnotes
    1 These authors contributed equally to this work.
    Affiliations
    Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA

    Centre for Non-communicable Disease and Nutrition, BRAC James P Grant School of Public Health, BRAC University, Bangladesh

    Centre for Science of Implementation and Scale-up, BRAC James P Grant School of Public Health, BRAC University, Bangladesh
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  • Author Footnotes
    1 These authors contributed equally to this work.
    Mahmuda Jahan
    Footnotes
    1 These authors contributed equally to this work.
    Affiliations
    Refugee and Migratory Movements Research Unit, University of Dhaka, Dhaka, Bangladesh
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  • Author Footnotes
    1 These authors contributed equally to this work.
    Mehedi Hasan
    Footnotes
    1 These authors contributed equally to this work.
    Affiliations
    Centre for Non-communicable Disease and Nutrition, BRAC James P Grant School of Public Health, BRAC University, Bangladesh

    Centre for Science of Implementation and Scale-up, BRAC James P Grant School of Public Health, BRAC University, Bangladesh
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    1 These authors contributed equally to this work.
    Ipsita Sutradhar
    Footnotes
    1 These authors contributed equally to this work.
    Affiliations
    Centre for Non-communicable Disease and Nutrition, BRAC James P Grant School of Public Health, BRAC University, Bangladesh

    Centre for Science of Implementation and Scale-up, BRAC James P Grant School of Public Health, BRAC University, Bangladesh
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  • Ibrahim Hossain Sajal
    Affiliations
    Centre for Science of Implementation and Scale-up, BRAC James P Grant School of Public Health, BRAC University, Bangladesh

    Department of Mathematical Sciences, School of Natural Sciences & Mathematics, The University of Texas at Dallas, Dallas, TX, USA
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  • Shams Shabab Haider
    Affiliations
    Centre for Science of Implementation and Scale-up, BRAC James P Grant School of Public Health, BRAC University, Bangladesh
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  • Hemraj Joshi
    Affiliations
    Modern Technical College, Sanepa, Lalitpur, Nepal
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  • Mohammad Rifat Haider
    Affiliations
    Department of Social and Public Health, College of Health Sciences and Professions, Ohio University, Athens, OH, USA
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  • Malabika Sarker
    Affiliations
    Centre for Non-communicable Disease and Nutrition, BRAC James P Grant School of Public Health, BRAC University, Bangladesh

    Centre for Science of Implementation and Scale-up, BRAC James P Grant School of Public Health, BRAC University, Bangladesh

    Institute of Public Health, University of Heidelberg, Heidelberg, 69120, Germany
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  • Author Footnotes
    1 These authors contributed equally to this work.
Published:January 31, 2020DOI:https://doi.org/10.1016/j.cegh.2020.01.014

      Abtsract

      Background

      Tobacco is one of the leading causes of premature death around the world. In Nepal, tobacco kills 15,000 people every year. Men are also the primary users of tobacco. This study aimed to discern the prevalence and associated factors of tobacco use among Nepalese men aged 15–49 years.

      Methods

      This was a cross-sectional study. This study used data from the Nepal Demographic and Health Survey 2016. A total of 4059 study participants aged 15–49 years were included in the final analysis. The primary outcome of interest in this study was ‘tobacco use’, which was further categorized into smoked and smokeless tobacco use. Multivariable logistic regression was performed to identify determinants of tobacco use, smoking, and smokeless tobacco use.

      Results

      The prevalence of overall tobacco, smoked tobacco, and smokeless tobacco use were 52.3% (95% CI: 50.0–54.6), 27.3% (95% CI: 24.5–30.3), and 40.2% (95% CI: 38.0–42.4), respectively. The prevalence of tobacco use was significantly higher among the elderly, manual workers, those of lower educational status, those of lower economic status, and residents of Province No. 2. In multivariable logistic regression analysis, older age, poor education, poor economic status, and residence in the Terai region were each found to be significantly associated with tobacco use.

      Conclusion

      As one out of every two Nepalese men is a tobacco consumer, pertinent public health programs need to increase advocacy against tobacco use among the mass population. Tobacco control programs should also target high-risk groups when designing interventions.

      Keywords

      1. Background

      In 2016, around 72% of all deaths globally were attributable to non-communicable diseases (NCDs).
      • GBD 2015 Mortality and Causes of Death Collaborators
      Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980-2015: a systematic analysis for the Global Burden of Disease Study 2015.
      At the same time, around 61% of Disability-Adjusted Life Years (DALYs) lost were caused by NCDs.
      • GBD 2016 DALYs and HALE Collaborators
      Global, regional, and national disability-adjusted life-years (DALYs) for 333 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016.
      Tobacco use including both smoked and smokeless tobacco (SLT) is a well-known NCD risk factor.
      • Thakur J.S.
      • Garg R.
      • Narain J.P.
      • Menabde N.
      Tobacco use: a major risk factor for non communicable diseases in South-East Asia region.
      Several studies have shown that the risk of coronary heart disease (CHD), stroke, chronic obstructive pulmonary disease (COPD), and lung cancer increases several fold due to tobacco use.
      • Thakur J.S.
      • Garg R.
      • Narain J.P.
      • Menabde N.
      Tobacco use: a major risk factor for non communicable diseases in South-East Asia region.
      Smoking is the second largest contributor to global DALYs.
      GBD 2015 Risk Factors Collaborators.Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015.
      Smoking accounts for 5 million deaths annually, since the 1990s, and this burden is increasing day by day, particularly in low- and middle-income countries.
      GBD 2015 Risk Factors Collaborators.Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015.
      Similarly, SLT use results in 1.7 million DALYs lost annually and is a leading preventable cause of oral, esophageal, and pharyngeal cancers worldwide.
      • Siddiqi K.
      • Shah S.
      • Abbas S.M.
      • et al.
      Global burden of disease due to smokeless tobacco consumption in adults: analysis of data from 113 countries.
      The rate of tobacco usage is high in the South and Southeast Asian region, causing 1.3 million deaths per year.
      • World Health Organization
      WHO Report on the Global Tobacco Epidemic, 2017: Monitoring Tobacco Use and Prevention Policies.
      The prevalence of smoking ranges from 31% to 72% in this region.
      • Sreeramareddy C.T.
      • Pradhan P.M.S.
      • Mir I.A.
      • Sin S.
      Smoking and smokeless tobacco use in nine South and Southeast Asian countries: prevalence estimates and social determinants from Demographic and Health Surveys.
      Countries like Bangladesh, India, Nepal, and Pakistan have a high prevalence of SLT use, ranging from 16 to 37%.
      • Sreeramareddy C.T.
      • Pradhan P.M.S.
      • Mir I.A.
      • Sin S.
      Smoking and smokeless tobacco use in nine South and Southeast Asian countries: prevalence estimates and social determinants from Demographic and Health Surveys.
      Almost half of all deaths in the region are attributable to NCDs, including CHD, stroke, COPD, and cancer.
      • Ghaffar A.
      • Reddy K.S.
      • Singhi M.
      Burden of non-communicable diseases in South Asia.
      Furthermore, tobacco is a major risk factor for each of those NCDs.
      GBD 2015 Risk Factors Collaborators.Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015.
      It has been estimated that around 10% of all deaths in the South and Southeast Asian region are attributable to tobacco use.
      • World Health Organization
      WHO Global Report: Mortality Attributable to Tobacco.
      Nepal is experiencing an epidemiological transition due to the increasing burden of NCDs.
      • Mishra S.R.
      • Neupane D.
      • Bhandari P.M.
      • Khanal V.
      • Kallestrup P.
      Burgeoning burden of non-communicable diseases in Nepal: a scoping review.
      Tobacco use is responsible for the death of around 15,000 people per year in Nepal.
      • Ministry of Health and Population
      Tobacco Control Reference Book.
      According to the Nepal Demographic and Health Survey (NDHS) 2006, the estimated prevalence of tobacco use was 30.3% among the population. The survey reported that the proportion of tobacco consumption was higher among men than women (56.5% versus 19.6%).
      • Sreeramareddy C.T.
      • Ramakrishnareddy N.
      • Harsha Kumar H.
      • Sathian B.
      • Arokiasamy J.T.
      Prevalence, distribution and correlates of tobacco smoking and chewing in Nepal: a secondary data analysis of Nepal Demographic and Health Survey-2006.
      However, according to the NDHS 2011, the estimated prevalence of tobacco use was 51.9% among Nepalese men, which was almost four times higher than women (13%).
      • Ministry of Health and Population
      Tobacco Control Reference Book.
      ,
      • Khanal V.
      • Adhikari M.
      • Karki S.
      Social determinants of tobacco consumption among Nepalese men: findings from Nepal Demographic and Health Survey 2011.
      Therefore, men are the primary tobacco consumers in Nepal and are more at risk of developing NCDs.
      In the ‘Global NCD Action Plan 2013–2020’, the World Health Organization (WHO) has recommended the reduction of tobacco usage by at least 30% among the population (aged 15 years or above) in order to reduce the global burden of NCDs.
      • World Health Organization
      Global Action Plan for the Prevention and Control of Noncommunicable Diseases 2013-2020.
      Updated epidemiological data is necessary to monitor the country's progress towards achieving that goal. In addition, up-to-date information on social determinants is warranted to plan and design the tobacco control program for effective prevention and control of tobacco-related NCDs. This study aimed to ascertain the prevalence and associated social factors of tobacco use (both smoked and SLT products) among Nepalese men aged 15–49 years using NDHS 2016 data.

      2. Methods

      2.1 Study design

      A secondary data analysis was carried out using data from the Nepal Demographic and Health Survey (NDHS) 2016; collected using the men's questionnaire.
      • Ministry of Health and Population (MoHP)
      Nepal New ERA and ICF International Inc. Nepal Demographic and Health Survey 2016.
      The NDHS 2016 adopted a cross-sectional survey design and was conducted by NEW ERA under the direct supervision of the Ministry of Health (MOH), Government of Nepal. The objective of the survey was to collect up-to-date information on selected health and demographic indicators of Nepalese population.
      • Ministry of Health and Population (MoHP)
      Nepal New ERA and ICF International Inc. Nepal Demographic and Health Survey 2016.
      Multistage stratified cluster sampling design was used by the NDHS 2016. Stratification was done based on place of residence (urban or rural). In the rural areas two-stage stratified clustered sampling was followed. At first, primary sampling units (PSUs) were selected based on probability proportional to size (PPS). Then, households were selected from the PSUs. In the urban areas, household selection followed three-stage stratified clustered sampling. At first, PSUs were selected according to PPS method. Next, enumeration areas (EAs; which is a ward/part of a ward) were selected. At the third stage, households were systematically selected. In total, 383 PSUs (urban: 184 and rural: 199) and 11,490 households (urban: 5520 and rural: 5970) were selected for the survey. The sampling strategy is detailed in the NDHS 2016 report.
      • Ministry of Health and Population (MoHP)
      Nepal New ERA and ICF International Inc. Nepal Demographic and Health Survey 2016.

      2.2 Study participants

      The participants of this study were 4059 Nepalese men aged between 15 and 49 years. The selection process of the participants is shown in Fig. 1.

      2.3 Outcome of interest

      The outcome of interest in this study was ‘tobacco use’, which was further categorized into ‘smoked’ and ‘smokeless tobacco use’. Participants fell under the ‘smoked’ category when they reported smoking manufactured cigarettes, hand-rolled cigarettes, tobacco pipes, cigars, cheroots, cigarillos, or water pipes during the survey. Similarly, if any participant reported the use of snuff (by mouth or by nose), chewing tobacco products, or consumption of betel quid with tobacco during the survey, they were categorized under the ‘smokeless tobacco use’ category.

      2.4 Associated factors

      In this study, four types of associated factors were considered, as identified through literature review: demographic, sociocultural, spatial, and access to information.
      • Sreeramareddy C.T.
      • Ramakrishnareddy N.
      • Harsha Kumar H.
      • Sathian B.
      • Arokiasamy J.T.
      Prevalence, distribution and correlates of tobacco smoking and chewing in Nepal: a secondary data analysis of Nepal Demographic and Health Survey-2006.
      ,
      • Khanal V.
      • Adhikari M.
      • Karki S.
      Social determinants of tobacco consumption among Nepalese men: findings from Nepal Demographic and Health Survey 2011.
      ,
      • Dahal D.R.
      • Gurung Y.B.
      • Acharya B.
      • Hemchuri K.
      • Swarnakar D.
      National Dalit Strategy Report: Part 1, Situational Analysis of Dalits in Nepal.
      • Guo S.E.
      • Huang T.J.
      • Huang J.C.
      • et al.
      Alcohol, betel-nut and cigarette consumption are negatively associated with health promoting behaviors in Taiwan: a cross-sectional study.
      • Pandey J.P.
      • Dhakal M.R.
      • Karki S.
      • Poudel P.
      • Pradhan M.S.
      Maternal and Child Health in Nepal: The Effects of Caste, Ethnicity, and Regional Identity: Further Analysis of the 2011 Nepal Demographic and Health Survey.
      Demographic variables were the age and marital status of the respondents. Sociocultural factors were their educational status, occupation, ethnicity, religion, and wealth index. Place of residence (urban vs. rural), province of residence, and ecological region were considered spatial factors. Lastly, access to information included frequency of reading newspapers/magazines, watching television, and listening to the radio.

      2.5 Statistical analysis

      At first, univariate analysis was performed in order to examine the distribution of the variables. Then, bivariate analysis was done between dependent variables (tobacco use, smoking, and smokeless tobacco) and selected independent variables. Multivariable logistic regression was performed to identify associated factors of tobacco use, smoking, and smokeless tobacco use. Both crude odds ratio (COR) and adjusted odds ratio (AOR) were reported along with a 95% confidence interval (CI). A p-value of less than 0.05 was considered statistically significant. The variance inflation factor (VIF) was derived to find out whether any multicollinearity existed among the covariates; however, no significant multicollinearity was found. The sample weight calculated for the NDHS 2016 was used and the cluster effect was adjusted in the final analysis. All the statistical analyses were performed using Stata 13.0.

      2.6 Ethical consideration

      The ethical review boards of the Nepal Research Council and ICF International reviewed and approved the protocol of the NDHS 2016. Informed consent was taken from the respondents before data collection.

      3. Findings

      3.1 Prevalence of different forms of tobacco use

      The overall prevalence of tobacco use was 52.3% (95% CI: 50.0–54.6). The prevalence of smoking was 27.3% (95% CI: 24.5–30.3) and SLT use was 40.2% (95% CI: 38.0–42.4). The weighted prevalence of different forms of tobacco use among Nepalese men is presented in Table 1.
      Table 1Prevalence of different forms of tobacco use among Nepalese men, Nepal Demographic and Health Survey 2016 (N = 4059).
      OutcomesFrequencyPercentage (95% CI)
      Smokes any form of tobacco110827.3 (24.5, 30.3)
      Uses smokeless tobacco163040.2 (38.0, 42.4)
      Consumes tobacco in any form212252.3 (50.0, 54.6)
      Among the respondents, 22.9% belonged to the 15–19 years age group followed by 15.9% in the 20–24 years age group. Around two-thirds (65.9%) of the respondents were married. Almost half of them (49.0%) were educated up to the secondary level and roughly one-fifth (22.0%) received higher education. Professional/clerical/service was the main occupation category for one-third (33.4%) of the respondents. In terms of spatial factors, a majority of the study participants resided in the urban area (65.2%), Province No. 3 (24.8%), and in the Terai region (49.7%). More than half of the respondents (51%) watched television at least once a week. Around 40% of them listened to the radio at least once a week. But, comparatively few of them (21.7%) read newspapers or magazines (Table 2).
      Table 2Prevalence of tobacco use among Nepalese men by socio-demographic characteristics, Nepal Demographic and Health Survey 2016 (N = 4059).
      FactorsTotalAny Tobacco UseSmokingSmokeless Tobacco
      N%n%p-valuen%p-valuen%p-value
      Demographic factors
      Age of the respondents
      15–1993122.924826.6<0.00114715.8<0.00115416.5<0.001
      20–2464715.931248.220131.119830.6
      25–2952312.928754.916331.222242.4
      30–3453513.234664.816530.828052.5
      35–3954413.433862.114626.829554.2
      40–4446311.430565.913328.826156.4
      45–4941510.228668.915336.922153.1
      Marital status
      Single135333.341030.3<0.00127020.0<0.00123917.7<0.001
      Married267365.9168363.081530.5136451.0
      Separated/Divorced/Widow330.82990.02270.22783.9
      Sociocultural factors
      Educational Status
      No education391.19.6302.777.41<0.001149.138.11<0.001253.964.92<0.001
      Primary786.719.4556.270.7287.636.56466.759.33
      Secondary198949.0968.648.69503.425.31730.336.71
      Higher891.522.0294.933.09168.318.88179.520.14
      Occupation
      Agriculture114428.263055.1<0.00131927.9<0.00150544.1<0.001
      Professional/clerical/service135733.466449.033424.750337.0
      Manual (skilled/unskilled)952.423.565768.934336.052655.2
      Not working/not specified605.114.917128.311218.69816.1
      Ethnicity
      Advantaged224355.3111749.80.000450122.30.000491040.60.0082
      Disadvantaged (Dalit)476.711.830363.615833.223148.5
      Disadvantaged (Janjati)133933.070252.545033.648936.5
      Religion
      Hindu346685.4183653.00.154698528.40.0042140840.60.3767
      Others59314.628648.312320.822337.6
      Wealth quintile
      Poorest62315.335857.4<0.00121334.20.012426442.3<0.001
      Poor70617.440457.219928.233046.8
      Middle75618.643657.720527.2362.47.9
      Rich98124.253054.027828.343544.3
      Richest99224.439539.821321.523924.1
      Spatial factors
      Place of Residence
      Urban264565.2136251.50.298975528.50.1703100738.10.0093
      Rural141434.876153.835425.062344.1
      Province of Residence
      Province 168917.036953.6<0.00118426.70.008528541.3<0.001
      Province 279519.649462.217421.846558.5
      Province 3100724.845044.732232.024224.1
      Province 43769.315842.09224.610728.4
      Province 565816.236455.214522.032349.2
      Province 62045.09747.95929.07235.4
      Province 73308.119057.613240.213641.2
      Ecological zone
      Mountain2526.211947.2<0.0018131.90.48277228.4<0.001
      Hill178944.181045.350028.053429.9
      The Terai201749.7119359.252826.2102550.8
      Access to information
      Reading newspaper or magazine
      Not at all181744.8106158.4<0.00151228.20.668987648.2<0.001
      Less than once a week136033.569250.937227.452238.4
      At least once a week88121.737042.022525.523326.4
      Frequency of watching television
      Not at all80619.948059.60.001123829.50.228739348.8<0.001
      Less than once a week118229.162753.034228.949541.8
      At least once a week207051101649.152925.574335.9
      Frequency of listening radio
      Not at all115028.364155.80.018731627.50.946848742.30.2523
      Less than once a week144035.576453.139727.658740.8
      At least once a week146836.271748.839627.055737.9

      3.2 Prevalence of tobacco use among Nepalese men by socio-demographic characteristics

      The prevalence of tobacco use, including both smoking and SLT use, was significantly higher among the older age group (≥20 years) compared to the younger age group (<20 years), but was lower among those with a higher education level compared to those with lower educational attainment. The prevalence was significantly greater among respondents who were separated/divorced/widowed, manual workers, within the poorest wealth index, or were residents of Province No. 2 (Table 2). The prevalence of tobacco use and smoking was also significantly higher among the disadvantaged ethnic population. Expectedly, residents of the Terai region and those who did not consume print/visual media at all had a higher prevalence of smokeless tobacco use (Table 2).

      3.3 Factors associated with tobacco use

      Table 3 demonstrates the results from the logistic regression analysis, including both COR and AOR with 95% CI, of the factors associated with tobacco use among Nepalese men. In the final model, respondents' age, marital status, educational status, wealth index, religion, ecological region of residence, and frequency of listening to radio, were all individually associated with tobacco use. The odds of being a smoker increased with age; respondents of 40–44 and 45–49 years of age both had almost four times (40–44 age group: AOR = 3.598, 95% CI: 2.371–5.460, p < 0.001; 45–49 age group: AOR = 3.903, 95% CI: 2.646–5.758, p < 0.001) higher odds of consuming tobacco compared to those in the 15–19 years age group. Educational status was negatively associated with tobacco use. Those with a higher than secondary level education had 69% lower odds (AOR = 0.311, 95% CI: 0.211–0.458, p < 0.001) of being a tobacco consumer. Being a manual worker (AOR = 1.639, 95% CI: 1.264–2.124, p < 0.001) increased the odds of being a tobacco consumer when compared to agricultural workers. Like education, tobacco use had an inverse association with one's wealth quintile. Men belonging to the richest quintile had around 50% lower odds (AOR = 0.516, 95% CI: 0.360–0.739, p < 0.001) of consuming tobacco products compared to those belonging to the poorest quintile. Respondents following other religions were 26% less likely to be tobacco consumers (AOR = 0.742, 95% CI: 0.558–0.986, p = 0.040) when compared to Hindus, the dominant religious group.
      Table 3Bivariate and multivariable logistic regression to identify factors associated with tobacco use among Nepalese men, Nepal Demographic and Health Survey 2016.
      FactorsUnadjusted OR95% CIp-valueAdjusted OR95% CIp-value
      Lower LimitUpper LimitLower LimitUpper Limit
      Demographic factors
      Age of the respondents
      15–19RefRef
      20–242.5652.0223.252<0.0012.4741.8823.251<0.001
      25–293.3532.5294.446<0.0012.8081.8874.179<0.001
      30–345.0733.8506.683<0.0013.5892.3965.376<0.001
      35–394.5103.3776.023<0.0012.8221.8534.295<0.001
      40–445.3253.9697.144<0.0013.5982.3715.460<0.001
      45–496.0964.6537.988<0.0013.9032.6465.758<0.001
      Marital status
      SingleRefRef
      Married3.9083.2424.712<0.0011.5001.0942.0580.012
      Separated/Divorced/Widow20.5905.77073.468<0.0017.8031.90731.9280.004
      Sociocultural factors
      Educational Status
      No educationRefRef
      Primary0.7040.5120.9690.0310.9760.6801.4000.895
      Secondary0.2770.2090.367<0.0010.5890.4170.8300.003
      Higher0.1440.1020.203<0.0010.3110.2110.458<0.001
      Occupation
      AgricultureRefRef
      Professional/clerical/service0.7830.6460.9490.0131.0480.8101.3560.719
      Manual (skilled/unskilled)1.8111.4502.260<0.0011.6391.2642.124<0.001
      Not working/not specified0.3220.2520.412<0.0010.9340.7001.2460.642
      Ethnicity
      AdvantagedRefRef
      Disadvantaged (Dalit)1.7601.3272.335<0.0011.2900.8102.0540.282
      Disadvantaged (Janjati)1.1120.9261.3350.2561.0110.8271.2360.914
      Religion
      HinduRefRef
      Others0.8280.6381.0740.1550.7420.5580.9860.040
      Wealth index
      PoorestRefRef
      Poor0.9940.7851.2590.9590.8290.6501.0570.129
      Middle1.0120.7901.2970.9240.7270.5340.9900.043
      Rich0.8710.6761.1230.2870.7450.5371.0350.079
      Richest0.4910.3850.625<0.0010.5160.3600.739<0.001
      Spatial factors
      Place of Residence
      UrbanRefRef
      Rural1.0980.9201.3100.2990.7970.6690.9510.012
      Province of Residence
      Province 1RefRef
      Province 21.4261.1061.8380.0061.0770.7981.4520.628
      Province 30.7020.4881.0090.0560.8830.6491.2000.424
      Province 40.6270.4510.8730.0060.7200.5011.0330.075
      Province 51.0710.8061.4230.6360.8420.6371.1140.228
      Province 60.7990.5861.0880.1530.8580.6011.2250.398
      Province 71.1810.8961.5560.2381.2350.9321.6360.142
      Ecological zone
      MountainRefRef
      Hill0.9260.7121.2040.5651.1550.8991.4850.259
      The Terai1.6221.2962.031<0.0011.6741.2092.3190.002
      Access to information
      Reading newspaper or magazine
      Not at allRefRef
      Less than once a week0.5960.6150.8870.0011.0160.8031.2860.892
      At least once a week0.5220.3780.705<0.0010.9750.7061.3470.879
      Frequency of watching television
      Not at allRefRef
      Less than once a week0.7650.5960.9830.0361.0410.7691.4090.795
      At least once a week0.6530.5220.818<0.0011.2660.9331.7170.129
      Frequency of listening radio
      Not at allRefRef
      Less than once a week0.8960.7461.0770.2420.8430.6751.0530.131
      At least once a week0.7570.6150.9310.0090.7360.6000.9020.003
      In the case of spatial factors, residents of the Terai region were more likely to consume tobacco compared to residents of the Mountain region (AOR = 1.674, 95% CI: 1.209–2.319, p = 0.002). None of the variables categorized under ‘access to information’ had any significant association with tobacco use, except those who listened to the radio at least once a week, who were 26% less likely to consume tobacco (AOR = 0.736, 95% CI: 0.600–0.902, p = 0.003).

      3.4 Factors associated with smoking and smokeless tobacco use

      Most of the factors associated with tobacco use were also associated with smoking and SLT use. Higher age, lower educational status, and being separated/divorced/widowed were associated with both smoking and SLT use (Table 4, Table 5). Belonging to the disadvantaged Janjati ethnic group increased one's odds of being a smoker (AOR = 1.601, 95% CI: 1.320–1.941, p < 0.001) but decreased one's odds of consuming SLT by almost 25% (AOR = 0.757, 95% CI: 0.594–0.966, p = 0.025).
      Table 4Bivariate and multivariable logistic regression to identify factors associated with smoking among Nepalese men, Nepal Demographic and Health Survey 2016.
      FactorsUnadjusted OR95% CIp-valueAdjusted OR95% CIp-value
      Lower LimitUpper LimitLower LimitUpper Limit
      Demographic factors
      Age of the respondents
      15–19RefRef
      20–242.3951.8453.109<0.0012.4411.7973.314<0.001
      25–292.4061.7563.296<0.0012.4681.5633.896<0.001
      30–342.3651.7093.274<0.0012.1821.3733.4670.001
      35–391.9481.4192.674<0.0011.6320.9942.6790.053
      40–442.1441.5053.056<0.0012.0001.2473.2060.004
      45–493.1072.2564.280<0.0012.8241.7804.480<0.001
      Marital status
      SingleRefRef
      Married1.7571.4442.137<0.0011.0230.7311.4300.896
      Separated/Divorced/Widow9.4323.97422.382<0.0014.4911.86110.8410.001
      Sociocultural factors
      Educational Status
      No educationRefRef
      Primary0.9360.7341.1940.5930.9170.6811.2350.566
      Secondary0.5500.4050.748<0.0010.6340.4590.8760.006
      Higher0.3780.2590.552<0.0010.3870.2590.579<0.001
      Occupation
      AgricultureRefRef
      Professional/clerical/service0.8460.6661.0750.1711.0120.7291.4060.941
      Manual (skilled/unskilled)1.4541.1691.8090.0011.4401.1331.8300.003
      Not working/not specified0.5900.4410.788<0.0011.1200.8141.5410.487
      Ethnicity
      AdvantagedRefRef
      Disadvantaged (Dalit)1.7291.1072.7000.0161.2550.7672.0520.365
      Disadvantaged (Janjati)1.7571.4362.151<0.0011.6011.3201.941<0.001
      Religion
      HinduRefRef
      Others0.6600.4960.8780.0040.5220.3920.696<0.001
      Wealth index
      PoorestRefRef
      Poor0.7580.5711.0050.0540.8010.6071.0560.115
      Middle0.7190.5330.9690.0310.8450.5901.2120.359
      Rich0.7610.5081.1410.1860.9030.6131.3290.604
      Richest0.5260.3730.743<0.0010.6020.4040.8970.013
      Spatial factors
      Place of Residence
      UrbanRefRef
      Rural0.8370.6481.0800.1710.7470.6010.9270.008
      Province of Residence
      Province 1RefRef
      Province 20.7690.5641.0480.0960.7250.5221.0090.057
      Province 31.2940.7992.0950.2941.4530.9802.1560.063
      Province 40.8970.6591.2210.4890.9940.6791.4560.975
      Province 50.7780.5711.0600.1110.6690.4940.9060.009
      Province 61.1220.8011.5730.5031.1660.7781.7470.456
      Province 71.8491.3682.499<0.0011.8241.3372.487<0.001
      Ecological zone
      MountainRefRef
      Hill0.8280.5381.2740.3890.8270.5751.1920.308
      The Terai0.7560.5271.0830.1270.9460.6191.4460.798
      Access to information
      Reading newspaper or magazine
      Not at allRefRef
      Less than once a week0.9620.7861.1760.7031.1140.8871.4000.353
      At least once a week0.8750.5491.3940.5721.2140.8391.7570.303
      Frequency of watching television
      Not at allRefRef
      Less than once a week0.9710.7351.2820.8341.0370.7721.3940.807
      At least once a week0.8180.6531.0240.0791.0640.8221.3770.636
      Frequency of listening radio
      Not at allRefRef
      Less than once a week1.0060.8111.2480.9570.8580.6831.0770.185
      At least once a week0.9750.7681.2380.8360.8340.6761.0270.088
      Table 5Bivariate and multivariable logistic regression to identify factors associated with smokeless tobacco among Nepalese men, Nepal Demographic and Health Survey 2016.
      FactorsUnadjusted OR95% CIp-valueAdjusted OR95% CIp-value
      Lower LimitUpper LimitLower LimitUpper Limit
      Demographic factors
      Age of the respondents
      15–19RefRef
      20–242.2231.6582.981<0.0011.8871.3322.674<0.001
      25–293.7102.6715.153<0.0012.8481.9344.193<0.001
      30–345.5714.0207.720<0.0013.5922.3145.574<0.001
      35–395.9614.4917.911<0.0013.6722.4645.471<0.001
      40–446.5254.8398.800<0.0014.1392.7566.215<0.001
      45–495.7184.1087.958<0.0013.3642.1355.299<0.001
      Marital status
      SingleRef
      Married4.8393.8736.047<0.0011.7011.2222.3660.002
      Separated/Divorced/Widow24.2048.13172.051<0.00110.8712.67844.1260.001
      Sociocultural factors
      Educational Status
      No educationRef
      Primary0.7880.5801.0710.1281.2020.8561.6860.287
      Secondary0.3130.2290.429<0.0010.7410.5231.0520.093
      Higher0.1360.0950.196<0.0010.3620.2400.545<0.001
      Occupation
      AgricultureRef
      Professional/clerical/service0.7450.6110.9080.0041.1380.8941.4490.294
      Manual (skilled/unskilled)1.5591.2161.998<0.0011.4151.0761.8620.013
      Not working/not specified0.2440.1750.340<0.0010.7390.5301.0310.074
      Ethnicity
      AdvantagedRef
      Disadvantaged (Dalit)1.3771.0861.7450.0080.9940.7171.3790.972
      Disadvantaged (Janjati)0.8420.6691.0600.1420.7570.5940.9660.025
      Religion
      HinduRef
      Others0.8790.6611.1700.3770.9050.6711.2190.510
      Wealth index
      PoorestRef
      Poor1.1990.9601.4970.110.9350.6721.3000.688
      Middle1.2540.9691.6230.0850.7580.5081.1310.174
      Rich1.0870.8481.3930.5090.8710.5571.3620.543
      Richest0.4340.3280.574<0.0010.4700.2910.7580.002
      Spatial factors
      Place of Residence
      UrbanRef
      Rural1.2821.0641.5460.0090.8220.6741.0020.052
      Province of Residence
      Province 1Ref
      Province 21.9981.5262.616<0.0011.5441.0812.2060.017
      Province 30.4500.3170.640<0.0010.5490.3470.8700.011
      Province 40.5640.3940.8070.0020.6370.4410.9210.016
      Province 51.3741.0101.8710.0431.1510.8391.5800.383
      Province 60.7790.5561.0920.1470.8570.5771.2720.442
      Province 70.9970.7331.3560.9841.0290.7231.4640.876
      Ecological zone
      MountainRef
      Hill1.0720.8251.3940.61.6121.2212.1290.001
      The Terai2.6012.0433.313<0.0012.3951.5193.778<0.001
      Access to information
      Reading newspaper or magazin
      Not at allRef
      Less than once a week0.6690.5580.802<0.0010.9150.7321.1430.431
      At least once a week0.3850.2980.497<0.0010.7810.5801.0520.104
      Frequency of watching television
      Not at allRef
      Less than once a week0.7550.6000.9510.0171.0630.7911.4290.686
      At least once a week0.5870.4680.737<0.0011.2390.9161.6740.164
      Frequency of listening radio
      Not at allRef
      Less than once a week0.9390.7601.1590.5570.9870.7961.2250.905
      At least once a week0.8330.6401.0820.1710.9750.7581.2550.845
      Men belonging to the richest wealth quintile had a 40% lower likelihood of being a SLT consumer (AOR = 0.602, 95% CI: 0.404–0.897, p = 0.013). Regional differences were also noticed in smoking and SLT usage. Residents of Province No. 5 were 34% less likely to be smokers (AOR = 0.669, 95% CI: 0.494–0.906, p = 0.009) than residents of Province No. 1. On the other hand, the residents of the Hills (AOR = 1.612, 95% CI: 1.221–2.129, p = 0.001) and the Terai (AOR = 2.395, 95% CI: 1.519–3.778, p=<0.001) regions had higher odds of consuming SLT than the residents of the Mountain region (Table 4, Table 5).

      4. Discussion

      4.1 Prevalence of tobacco use

      This study aimed to determine the prevalence and associated social determinants of tobacco use among Nepalese men using the NDHS 2016. The study found that more than half of the respondents were tobacco consumers (52.3%). Around two-fifth (40.2%) of Nepalese men were SLT consumers, while more than one-fourth (27.3%) were smokers. Prevalence of tobacco use was higher among those who were elderly, separated/divorced/widowed, had a lower educational status, in the poorest quintile, part of a disadvantaged ethnic group, residents of Province No. 2 and those who did not read newspapers or watch television.
      The prevalence of tobacco use in 2016 was 52.3%, which was slightly higher than the NDHS 2011 estimate (51.9%),
      • Khanal V.
      • Adhikari M.
      • Karki S.
      Social determinants of tobacco consumption among Nepalese men: findings from Nepal Demographic and Health Survey 2011.
      but lower than 2006 estimate (56.5%).
      • Sreeramareddy C.T.
      • Ramakrishnareddy N.
      • Harsha Kumar H.
      • Sathian B.
      • Arokiasamy J.T.
      Prevalence, distribution and correlates of tobacco smoking and chewing in Nepal: a secondary data analysis of Nepal Demographic and Health Survey-2006.
      The prevalence is higher than in neighboring India, where 44.8% of males (15–49 years) were tobacco consumers in 2015–16.
      Ministry of Health and Family Welfare, Government of India
      The National Family Health Survey (NFHS 4).
      The prevalence of smoking, 27.3%,was reduced from 32.8% in 2006
      • Sreeramareddy C.T.
      • Ramakrishnareddy N.
      • Harsha Kumar H.
      • Sathian B.
      • Arokiasamy J.T.
      Prevalence, distribution and correlates of tobacco smoking and chewing in Nepal: a secondary data analysis of Nepal Demographic and Health Survey-2006.
      and 33.6% in 2011.
      • Khanal V.
      • Adhikari M.
      • Karki S.
      Social determinants of tobacco consumption among Nepalese men: findings from Nepal Demographic and Health Survey 2011.
      In 2016, around 40.2% of Nepalese men consumed SLT, which is higher than both the 2011 estimates, 34.82%, as well as the rate reported in India, 36.72%.
      • Sreeramareddy C.T.
      • Pradhan P.M.S.
      • Mir I.A.
      • Sin S.
      Smoking and smokeless tobacco use in nine South and Southeast Asian countries: prevalence estimates and social determinants from Demographic and Health Surveys.
      In our study, the prevalence of SLT use was higher than the prevalence of smoking. An almost similar finding was reported in India.
      Ministry of Health and Family Welfare, Government of India
      Global Adult Tobacco Survey-[Fact Sheet].
      There is a similarity between the residents of South Nepal (the Terai region) and North India in terms of sociocultural and demographic characteristics. Moreover, an enormous number of Nepalese men earn their livelihood in India, which may influence the tobacco usage in Nepal.
      • Khanal V.
      • Adhikari M.
      • Karki S.
      Social determinants of tobacco consumption among Nepalese men: findings from Nepal Demographic and Health Survey 2011.
      ,
      • Arora M.
      • Reddy K.S.
      • Stigler M.H.
      • Perry C.L.
      Associations between tobacco marketing and use among urban youth in India.
      Conversely, this finding is not in accordance with findings from Bangladesh, another country in South Asia, where smoking (58.68%) had a much higher prevalence than tobacco chewing (21.63%) among Bangladeshi men.
      • Rahman M.S.
      • Mondal M.N.
      • Islam M.R.
      • Rahman M.M.
      • Hoque M.N.
      • Alam M.S.
      Determinant factors of tobacco use among ever-married men in Bangladesh.
      It should be mentioned that although Bangladesh also shares its border with India, the sociocultural and demographic profiles of the people on either side are different. This may explain, the difference between Bangladesh and Nepal.

      4.2 Factors associated with tobacco use

      In our study, increased age was associated with tobacco use, which is in accordance with the findings from previous studies conducted in Nepal,
      • Sreeramareddy C.T.
      • Ramakrishnareddy N.
      • Harsha Kumar H.
      • Sathian B.
      • Arokiasamy J.T.
      Prevalence, distribution and correlates of tobacco smoking and chewing in Nepal: a secondary data analysis of Nepal Demographic and Health Survey-2006.
      ,
      • Khanal V.
      • Adhikari M.
      • Karki S.
      Social determinants of tobacco consumption among Nepalese men: findings from Nepal Demographic and Health Survey 2011.
      Bangladesh,
      • Rahman M.S.
      • Mondal M.N.
      • Islam M.R.
      • Rahman M.M.
      • Hoque M.N.
      • Alam M.S.
      Determinant factors of tobacco use among ever-married men in Bangladesh.
      India,
      • Manimunda S.P.
      • Benegal V.
      • Sugunan A.P.
      • et al.
      Tobacco use and nicotine dependency in a cross-sectional representative sample of 18,018 individuals in Andaman and Nicobar Islands, India.
      Ethiopia,
      • Rudatsikira E.
      • Abdo A.
      • Muula A.S.
      Prevalence and determinants of adolescent tobacco smoking in Addis Ababa, Ethiopia.
      and a multi-country study conducted in South and Southeast Asia.
      • Sreeramareddy C.T.
      • Pradhan P.M.S.
      • Mir I.A.
      • Sin S.
      Smoking and smokeless tobacco use in nine South and Southeast Asian countries: prevalence estimates and social determinants from Demographic and Health Surveys.
      This is reflected in the lower incidence of tobacco initiation within the last few years prior to this survey as well as the lower prevalence among the younger respondents.
      • Sreeramareddy C.T.
      • Ramakrishnareddy N.
      • Harsha Kumar H.
      • Sathian B.
      • Arokiasamy J.T.
      Prevalence, distribution and correlates of tobacco smoking and chewing in Nepal: a secondary data analysis of Nepal Demographic and Health Survey-2006.
      Our study found that educational attainment was negatively associated with tobacco use, which is also supported by previous studies.
      • Sreeramareddy C.T.
      • Ramakrishnareddy N.
      • Harsha Kumar H.
      • Sathian B.
      • Arokiasamy J.T.
      Prevalence, distribution and correlates of tobacco smoking and chewing in Nepal: a secondary data analysis of Nepal Demographic and Health Survey-2006.
      ,
      • Khanal V.
      • Adhikari M.
      • Karki S.
      Social determinants of tobacco consumption among Nepalese men: findings from Nepal Demographic and Health Survey 2011.
      ,
      • Rahman M.S.
      • Mondal M.N.
      • Islam M.R.
      • Rahman M.M.
      • Hoque M.N.
      • Alam M.S.
      Determinant factors of tobacco use among ever-married men in Bangladesh.
      ,
      • Manimunda S.P.
      • Benegal V.
      • Sugunan A.P.
      • et al.
      Tobacco use and nicotine dependency in a cross-sectional representative sample of 18,018 individuals in Andaman and Nicobar Islands, India.
      Similarly, a lower wealth index is associated with increased tobacco use, which is also coherent with the literature in developing countries.
      • Sreeramareddy C.T.
      • Ramakrishnareddy N.
      • Harsha Kumar H.
      • Sathian B.
      • Arokiasamy J.T.
      Prevalence, distribution and correlates of tobacco smoking and chewing in Nepal: a secondary data analysis of Nepal Demographic and Health Survey-2006.
      ,
      • Rahman M.S.
      • Mondal M.N.
      • Islam M.R.
      • Rahman M.M.
      • Hoque M.N.
      • Alam M.S.
      Determinant factors of tobacco use among ever-married men in Bangladesh.
      ,
      • Jarvis M.J.
      • Wardle J.
      Social patterning of individual health behaviours: the case of cigarette smoking.
      It has been found that education and economic status are two important social determinants. Individuals with lower educational achievement and poor economic status are prone to risky behavior, like tobacco use and alcohol intake, especially in developing countries.
      • Sreeramareddy C.T.
      • Pradhan P.M.S.
      • Mir I.A.
      • Sin S.
      Smoking and smokeless tobacco use in nine South and Southeast Asian countries: prevalence estimates and social determinants from Demographic and Health Surveys.
      ,
      • Sreeramareddy C.T.
      • Ramakrishnareddy N.
      • Harsha Kumar H.
      • Sathian B.
      • Arokiasamy J.T.
      Prevalence, distribution and correlates of tobacco smoking and chewing in Nepal: a secondary data analysis of Nepal Demographic and Health Survey-2006.
      This also explains the higher probability of tobacco use among manual workers. Manual workers are less aware of the health impact of tobacco use. This may be due to any combination of having less education, having poor awareness due to lack of information about the harmful effects of tobacco use, and being in a poor economic condition.
      • Manimunda S.P.
      • Benegal V.
      • Sugunan A.P.
      • et al.
      Tobacco use and nicotine dependency in a cross-sectional representative sample of 18,018 individuals in Andaman and Nicobar Islands, India.
      ,
      • Jarvis M.J.
      • Wardle J.
      Social patterning of individual health behaviours: the case of cigarette smoking.
      Ethnicity was also an important predictor of tobacco use. The disadvantaged Janjati group had higher odds of being smokers. This is similar to the findings of Pradhan et al. (2013)
      • Pradhan P.M.
      • Niraula S.R.
      • Ghimire A.
      • Singh S.B.
      • Pokharel P.K.
      Tobacco use and associated factors among adolescent students in Dharan, Eastern Nepal: a cross-sectional questionnaire survey.
      and the Nepal Adolescent and Youth Survey,
      Population Division, Ministry of Health and Population
      Nepal Adolescent and Youth Survey 2010/11.
      where they found that tobacco use is highly prevalent among Janjatis.
      The residents of the Terai region had a higher propensity to consume tobacco. These findings are supported by that of Khanal et al. (2013).
      • Khanal V.
      • Adhikari M.
      • Karki S.
      Social determinants of tobacco consumption among Nepalese men: findings from Nepal Demographic and Health Survey 2011.
      Province No. 2 had a higher prevalence of tobacco use than the other provinces, though it was not statistically significant in the final model. The Terai region and Province No. 2 of Nepal shares a border with Northern India. This region has population movement with India due to the porous and free border. Tobacco and its associated products are available in the bordering Indian cities, which can be easily accessed by anyone.
      • Khanal V.
      • Adhikari M.
      • Karki S.
      Social determinants of tobacco consumption among Nepalese men: findings from Nepal Demographic and Health Survey 2011.
      ,
      • Manimunda S.P.
      • Benegal V.
      • Sugunan A.P.
      • et al.
      Tobacco use and nicotine dependency in a cross-sectional representative sample of 18,018 individuals in Andaman and Nicobar Islands, India.
      Also, betel quid and betel are delicacies in the Terai region. The accessibility to tobacco, tobacco products, and betel quid along with its cultural acceptance are an important contributing factor to the high prevalence of tobacco use in the Terai region.
      • Khanal V.
      • Adhikari M.
      • Karki S.
      Social determinants of tobacco consumption among Nepalese men: findings from Nepal Demographic and Health Survey 2011.

      4.3 Policy and programmatic perspectives

      The Government of Nepal has introduced several laws and policies in order to control tobacco. In 2010, the Tobacco Product (Control and Regulation) Act 2010 was introduced, which prohibited smoking in public places and set limits to the advertisement and promotion of tobacco.
      Population Division, Ministry of Health and Population
      Tobacco Control Act 2010.
      In the following year, a directive was introduced by the Nepalese government to further control the advertisement and promotion of tobacco. In particular, it instructs manufacturers to add warning labels on every individual tobacco packet (Population Division, Ministry of Health and Population a, 2011). The requirements were implemented in April 2014, following an unsuccessful challenge by the tobacco industry initiated in 2011. The Tobacco Product Control and Regulatory Directive 2014 provided implementing details under the Tobacco Product (Control and Regulation) Act 2010. It further included definitions of several key terms, which helped to clarify the scope of the restrictions on smoking in public places, workplaces, and public transport. In addition, it strengthened the ban on tobacco advertising, promotion, and sponsorship and contained provisions to protect against industry interference.
      Population Division, Ministry of Health and Population
      Tobacco Product Control and Regulatory Directive - 2014.
      In that same year, another directive was introduced, which increased the size of mandatory health warnings to 90% of the front and back of all tobacco product packaging. The Directive also instituted new text pictorial warnings.
      Population Division, Ministry of Health and Population
      The Directive on Printing Warning Messages and Pictures on Tobacco Product Boxes, Packets, Cartons, Parcels and Packaging Materials.
      The reduction in the prevalence of smoking may be attributable to the new rules and regulations; however, the overall prevalence of tobacco usage has not decreased. In fact, the increase in SLT prevalence has mostly negated the progress made by reductions in smoking tobacco usage. In South Asian countries, SLT is considered less harmful than smoking tobacco.
      • Mutti S.
      • Reid J.L.
      • Gupta P.C.
      • et al.
      Patterns of use and perceptions of harm of smokeless tobacco in Navi Mumbai, India and Dhaka, Bangladesh.
      Therefore, policymakers in Nepal should come forward to develop a novel and cost-effective model in order to prevent the burden of SLT. Programs should target the highest at-risk population, such as the less educated, those of lower economic status, manual workers, and the populations of the Terai region and Province No. 2, in order to curb tobacco use. Social Behavior Change Communication (SBCC) campaigns should be launched in order to spread health messaging regarding the harmful use of tobacco, especially SLT. Finally, the implementation of rules and legislation to control tobacco use should regularly be monitored and its effectiveness should be evaluated.

      4.4 Strengths and limitations

      This study utilized a nationally representative sample to estimate the prevalence of tobacco use among men aged 15–49 years in Nepal. As such, the findings of the study are generalizable to the target population. Moreover, the probability of the existence of measurement error in the study is lower, compared to any other cross-sectional study of the Nepalese population, because of the utilization of standard and validated measurement tools by the DHS program. However, the study is not free from limitations. The temporal relationship between the exposure and the outcome variable cannot be established in a cross-sectional study. There may be some under-reporting of the prevalence rate due to the presence of social desirability bias among the study participants.

      5. Conclusions

      The current study has shown that more than half of Nepalese men aged 15–49 years are tobacco consumers. The prevalence of SLT use is higher than that of smoking. Older age, lower educational attainment, lower economic status, manual work as an occupation, and residence in the Terai region are some important predictors of tobacco use in Nepal. Implementation of tobacco control acts should be enforced and efforts should be taken to create cultural unacceptability of tobacco use, with an equal emphasis on both smoking and SLT use.

      Ethical consideration

      NDHS 2016 protocol was reviewed and approved by the ethical review board of the Nepal Research Council and ICF International. Written informed consent was taken from the head of the households and the respondents before data collection. The DHS program provided permission and access to the dataset for this study in February 2019.

      Funding statement

      This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

      Declaration of competing interest

      The authors declare that there is no conflict of interest.

      Acknowledgements

      This study was carried out using the datasets of Nepal Demographic Health Survey (NDHS) 2016. Hence, the authors of this study are thankful to DHS programme for providing access to the dataset.

      Appendix A. Supplementary data

      The following are the supplementary data related to this article:

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