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Learn more: PMC Disclaimer | PMC Copyright Notice BMJ Glob Health . 2025 Dec 23;10(12):e017392. doi: 10.1136/bmjgh-2024-017392 Search in PMC Search in PubMed View in NLM Catalog Add to search Association of alcohol and different types of alcoholic beverages on the risk of buccal mucosa cancer in Indian men: a multicentre case-control study Grace Sarah George Grace Sarah George 1 Division of Molecular Epidemiology and Population Genomics, Center for Cancer Epidemiology, Navi Mumbai, Maharashtra, India 2 Homi Bhabha National Institute, Mumbai, Maharashtra, India Find articles by Grace Sarah George 1, 2 , Aniket Patil Aniket Patil 1 Division of Molecular Epidemiology and Population Genomics, Center for Cancer Epidemiology, Navi Mumbai, Maharashtra, India 2 Homi Bhabha National Institute, Mumbai, Maharashtra, India Find articles by Aniket Patil 1, 2 , Romi Moirangthem Romi Moirangthem 1 Division of Molecular Epidemiology and Population Genomics, Center for Cancer Epidemiology, Navi Mumbai, Maharashtra, India 2 Homi Bhabha National Institute, Mumbai, Maharashtra, India Find articles by Romi Moirangthem 1, 2 , Pravin Narayanrao Doibale Pravin Narayanrao Doibale 1 Division of Molecular Epidemiology and Population Genomics, Center for Cancer Epidemiology, Navi Mumbai, Maharashtra, India 2 Homi Bhabha National Institute, Mumbai, Maharashtra, India Find articles by Pravin Narayanrao Doibale 1, 2 , Ankita Manjrekar Ankita Manjrekar 1 Division of Molecular Epidemiology and Population Genomics, Center for Cancer Epidemiology, Navi Mumbai, Maharashtra, India 2 Homi Bhabha National Institute, Mumbai, Maharashtra, India Find articles by Ankita Manjrekar 1, 2 , Shruti Vishwas Golapkar Shruti Vishwas Golapkar 1 Division of Molecular Epidemiology and Population Genomics, Center for Cancer Epidemiology, Navi Mumbai, Maharashtra, India 2 Homi Bhabha National Institute, Mumbai, Maharashtra, India Find articles by Shruti Vishwas Golapkar 1, 2 , Nandkumar Panse Nandkumar Panse 3 Nargis Dutt Memorial Cancer Hospital, Barshi, Maharashtra, India Find articles by Nandkumar Panse 3 , Manigreeva Krishnatreya Manigreeva Krishnatreya 4 Dr Bhubaneswar Borooah Cancer Institute, Guwahati, Assam, India Find articles by Manigreeva Krishnatreya 4 , Aseem Mishra Aseem Mishra 5 Mahamana Pandit Madan Mohan Malaviya Cancer Centre, Varanasi, Uttar Pradesh, India Find articles by Aseem Mishra 5 , Arjun Singh Arjun Singh 2 Homi Bhabha National Institute, Mumbai, Maharashtra, India 6 Department of Head and Neck Oncology, Tata Memorial Hospital, Mumbai, Maharashtra, India Find articles by Arjun Singh 2, 6 , Harriet Rumgay Harriet Rumgay 7 Cancer Surveillance Branch, International Agency for Research on Cancer, Lyon, France Find articles by Harriet Rumgay 7 , Bayan Hosseini Bayan Hosseini 8 Environment and Lifestyle Epidemiology Branch, International Agency for Research on Cancer, Lyon, France Find articles by Bayan Hosseini 8 , Anil Chaturvedi Anil Chaturvedi 9 Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA Find articles by Anil Chaturvedi 9 , Preetha Rajaraman Preetha Rajaraman 10 Radiation Effects Research Foundation, Hiroshima, Japan Find articles by Preetha Rajaraman 10 , Ann Olsson Ann Olsson 8 Environment and Lifestyle Epidemiology Branch, International Agency for Research on Cancer, Lyon, France Find articles by Ann Olsson 8 , Isabelle Soerjomataram Isabelle Soerjomataram 7 Cancer Surveillance Branch, International Agency for Research on Cancer, Lyon, France Find articles by Isabelle Soerjomataram 7 , Pankaj Chaturvedi Pankaj Chaturvedi 6 Department of Head and Neck Oncology, Tata Memorial Hospital, Mumbai, Maharashtra, India 11 Advanced Centre for Treatment Research and Education in Cancer, Navi Mumbai, Maharashtra, India Find articles by Pankaj Chaturvedi 6, 11 , Rajesh Dikshit Rajesh Dikshit 1 Division of Molecular Epidemiology and Population Genomics, Center for Cancer Epidemiology, Navi Mumbai, Maharashtra, India 2 Homi Bhabha National Institute, Mumbai, Maharashtra, India Find articles by Rajesh Dikshit 1, 2 , Sharayu Mhatre Sharayu Mhatre 1 Division of Molecular Epidemiology and Population Genomics, Center for Cancer Epidemiology, Navi Mumbai, Maharashtra, India 2 Homi Bhabha National Institute, Mumbai, Maharashtra, India Find articles by Sharayu Mhatre 1, 2, ✉ Author information Article notes Copyright and License information 1 Division of Molecular Epidemiology and Population Genomics, Center for Cancer Epidemiology, Navi Mumbai, Maharashtra, India 2 Homi Bhabha National Institute, Mumbai, Maharashtra, India 3 Nargis Dutt Memorial Cancer Hospital, Barshi, Maharashtra, India 4 Dr Bhubaneswar Borooah Cancer Institute, Guwahati, Assam, India 5 Mahamana Pandit Madan Mohan Malaviya Cancer Centre, Varanasi, Uttar Pradesh, India 6 Department of Head and Neck Oncology, Tata Memorial Hospital, Mumbai, Maharashtra, India 7 Cancer Surveillance Branch, International Agency for Research on Cancer, Lyon, France 8 Environment and Lifestyle Epidemiology Branch, International Agency for Research on Cancer, Lyon, France 9 Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA 10 Radiation Effects Research Foundation, Hiroshima, Japan 11 Advanced Centre for Treatment Research and Education in Cancer, Navi Mumbai, Maharashtra, India Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise. Supplement: Additional supplemental material is published online only. To view, please visit the journal online ( https://doi.org/10.1136/bmjgh-2024-017392 ). None declared. ✉ Dr Sharayu Mhatre; [email protected] Received 2024 Aug 30; Accepted 2025 Nov 12; Collection date 2025. Copyright © Author(s) (or their employer(s)) 2025. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ Group. This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: https://creativecommons.org/licenses/by-nc/4.0/ . PMC Copyright notice PMCID: PMC13059843 PMID: 41436185 Abstract Introduction While a large proportion of buccal mucosa cancer (BMC) is attributed to tobacco use, the contribution of alcohol is little-known. In India, alcohols include internationally-recognised (IRL) and locally-brewed liquor (LBL) types, which might contribute differently to the risk of BMC. We conducted an observational study to evaluate the association of local and foreign alcoholic beverage use on the risk of developing BMC. Methods Data from 1803 BMC cases and 1903 visitor controls from a multicentric case-control study was analysed for 11 IRLs and 30 LBLs. Healthy visitor controls were randomly sampled from the source population of the study centres which enrolled the cases. Quantitative data on the amount, the number of times consumed per day or week, and the lifetime duration of consumption for each of the alcoholic beverages were collected using an interviewer administered standardised questionnaire, which was then used to estimate the grams per day consumption of alcohol. Odds ratios (OR) and 95% CI were estimated after adjustment for potential confounders, including tobacco use. The joint effect of tobacco and alcohol on BMC risk, the attributable fraction (AF) of cases and state-wise population attributable fraction (PAF) were estimated. Results An increased risk of 1.68 (95% CI=1.44–1.97), 1.72 (95% CI=1.46–2.04), and 1.87 (95% CI=1.46–2.39) was observed for ever-users of any alcohol, IRLs and LBLs, respectively for BMC. The findings show 9 grams/day of alcohol increased the risk of BMC by approximately 50%, and 62% of cases could be attributed to alcohol drinking and chewing tobacco, with an overall PAF of 11.3% for India. Conclusion This study shows that alcohol, even in low quantities, increases the risk for BMC. Prevention of consumption of tobacco and alcohol together could substantially reduce the incidence of BMC. Keywords: Epidemiology, Cancer, Public Health, Global Health, Alcohols WHAT IS ALREADY KNOWN ON THIS TOPIC Studies have found alcohol to be a risk factor for oral cancers, mostly focusing on internationally recognised types like beer, whisky, etc., but not those consumed locally/regionally. Only a few studies estimated interaction between alcohol and tobacco with sufficient power, and no literature exists on alcohol’s association with buccal mucosa cancers (BMC). WHAT THIS STUDY ADDS This is the first large-scale study to analyse the association of alcohol with BMC in India, focusing on locally-brewed liquors, which are regularly consumed among the dominant rural segment of the population. We also studied the interaction between alcohol and chewing tobacco. HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY This study identified that low alcohol consumption can increase the risk of BMC, with 62% of the cancers linked to alcohol and tobacco use. It also highlighted the need for prevention programmes targeting these factors and policy actions to curb the consumption of locally-brewed liquors. Introduction As of 2022, there were an estimated 400 000 new oral cancer cases globally. It is the second most common cancer in India, with an estimated 143 759 new cases and 79 979 deaths occurring each year in India alone. 1 The rates of oral cancer have risen steadily over the years, 2 with current rates of 14.7 per 100 000 among Indian males. 1 As per the National Cancer Registry Programme (NCRP) report, most regional Indian cancer registries reported an increase in the rates of mouth cancers over the last two decades, up until 2016. 2 Buccal mucosa cancers (BMC) are the predominant form of oral cancer in the Indian sub-continent, 3 and despite its poor survival rate (43% net 5 year survival), 4 very few large-scale epidemiological studies of BMC have been conducted either in India or globally. An estimated 75 000 cases of oral cavity cancer in 2020 were attributable to alcohol consumption globally. However, this estimate was derived mostly from cancer risk in European and North American studies and population-level data on alcohol drinking from the Global Information System on Alcohol and Health, rather than from individual-level data. 5 As per estimates from the International Agency for Research on Cancer, approximately 17.7% of new cases of oral cancers in 2020 (21.7 % among males) could be attributed to alcohol in India. 6 While previous studies have reported a positive interaction between tobacco and alcohol on risk of oral cancers, with an increased joint risk compared with the individual effects, 7 8 most of the evidence is derived from European and East Asian populations. 9 Furthermore, as alcohol consumption is almost always concurrent with tobacco use, there is a lack of data addressing the direct and independent role of alcohol in oral cancers, especially in a population like India, with a high prevalence of smokeless tobacco use. Finally, there is little data on the effect of locally-brewed liquor (LBL). Prevalence of alcohol in India shows a large geographical variation in terms of quantity and the types of alcoholic beverage intake. Indian states like Arunachal Pradesh have reported the highest prevalence of alcohol consumption (26%), while states like Gujarat have reported very low alcohol consumption (4%). 10 The most common type of LBL consumed in the country are of the Desi Daru group, and Mahua, while there are some types that are specific to certain states like Sekmai Yu in Manipur and Bangla in West Bengal. In this study we investigated the impact of alcohol consumption overall and of various specific types, including 11 internationally-recognised liquors (IRLs) (eg, beer, rum, whisky), and 30 LBLs from different regions of India (eg, Tharra, Desi, Mahua, Apong) on the risk of BMC among Indian men. The combined effect of alcohol and tobacco on BMC incidence was also examined. Methods Study design This was a case-control study to evaluate mainly the effect of tobacco smoking, tobacco chewing and alcohol drinking on the risk of development of BMC. Assuming prevalence of exposure to be 10% in controls and with 5% alpha and 80% power, we estimated that around 2000 cases and an equal number of controls are required to estimate an OR of 1.3. ( online supplemental figure 1 ). 11 A total of 1803 histopathological confirmed BMC cases and 1903 frequency matched controls were enrolled for this multicentre hospital-based case-control study during the period of 2010–2021 ( online supplemental figure 2 ). Participants were recruited from five different study centres of the Tata Memorial Centre (TMC) by the Centre for Cancer Epidemiology (CCE), TMC, namely – the Tata Memorial Hospital (TMH), Mumbai; Advanced Centre for Treatment, Research and Education in Cancer (ACTREC), Navi Mumbai; Dr Bhubaneshwar Borooah Cancer Institute (BBCI), Guwahati; Homi Bhabha Cancer Hospital (HBCH) and Mahamana Pandit Madan Mohan Malaviya Cancer Centre (MPMMCC), Varanasi – and also the Nargis Dutt Memorial Cancer Hospital (NDMCH), Barshi. These centres, being cancer referral centres, receive cases from different regions of India, covering almost all the states of India ( online supplemental table 1 ). Eligible cases were individuals residing in the country for at least a year, aged 19–75 years, and with a date of diagnosis of BMC not exceeding 6 months before the interview. Primary cancer sites of the buccal or cheek mucosa (C06.0), retromolar trigones (C06.2), bucco-alveolar sulci or gingivo-buccal sulci, upper and lower vestibule of mouth (C06.1), and upper and lower alveolus (C03.0 and C03.1), coded as per the International Classification of Diseases for Oncology (ICD-O3), were included. All the patients fulfilling the eligibility criteria, who underwent diagnosis and treatment at the study centres were approached. Around 3% either refused to participate or could not complete the questionnaire. The healthy visitor controls, with no prior cancer diagnoses, were randomly sampled from the source population of different centres from where the cases were recruited. The centre consists of Disease Management Groups (DMGs) for each cancer site. The interviewer visited various DMGs to enrol the controls. The participants who gave their consent were included in the study. It was ensured that the proportion of controls enrolled did not exceed 20% of each DMG of the patients they accompanied. Controls were selected independently of exposure status and had to be Indian residents for at least a year. They were drawn from a uniform pool of friends, family, relatives and neighbours accompanying cancer patients from all centres. Individuals whose first-degree relatives were diagnosed with BMC or any other head and neck cancer were excluded as controls. Controls were frequency-matched to cases based on age (±10 years) at the time of diagnosis, and current zone of residence (North, North-east, West, South and Central India). Exposure assessment Exposure data were collected verbally, in-person, by trained investigators using a pre-designed questionnaire. The questionnaire was developed by group meetings and discussions with public health researchers. In order to assess the logistics of administration and responses to the questionnaire, we pilot tested it by conducting mock interviews, and then interviewing a small number of cases and controls to assess their responses. To ensure high data quality, interviewers were trained monthly using a standardised in-house manual. Information on alcohol, smoking and potential confounders were collected from study participants using a pre-tested questionnaire. 12 13 Interviews were conducted by 11 interviewers in the different centres. There were five interviewers in TMH and ACTREC since maximum enrolment occurred in these two centres, while the other centres had 1–3 interviewers based on enrolment load in the respective hospitals. They were calibrated against each other during the mock interviews. Moreover, 4% of the participants were later re-interviewed by a different set of interviewers to validate their responses by checking reproducibility. We collected detailed information on alcohol consumption. An ‘ever-user’ of alcohol was defined as an individual who consumed any amount of an alcoholic drink at least once a month, for at least 6 months in a year. Any duration less than the aforementioned criteria was classified as a ‘never-user’ of alcohol, which was used as the reference category for the entire analysis. Additionally, participants were asked about the type of alcohol they consumed, including use of IRLs such as beer, whisky, vodka, rum and breezer, and specific LBLs like apong, bangla, chulli, desi daru, mahua, sekmai yu, sulai, toddy, tharra and xaj which are prepared in either one or multiple regions of India. Study participants were questioned about each specific liquor type by name, to avoid missing any information on consumption of any given type. The age of start and end of consumption, number and times of drinks per day and per month, and consumption unit per day in glasses were also collected. The variable ‘total alcohol grams per day’ was generated by multiplying the glasses of alcoholic beverage consumed per day, the approximate volume in a glass (~188 mL), the density of pure ethanol and the alcohol percentage in the specific alcohol type ( online supplemental table 2 ; online supplemental figure 3 ). If the same product was consumed at different time periods of the individual’s life, then the time period with the maximum grams per day was chosen. The summation of the quantities calculated for all alcohol types consumed by the individual was taken as the overall grams of alcohol consumed per day (‘total alcohol grams per day’). To manage outliers, the outer limit for amount was restricted to 250 grams per day using the 99th percentile value of controls as a reference. All quantities above that value were replaced to 250 grams. Information was collected on potential confounders including age (in years) at time of enrolment, urban-rural status of residence in the 3 years before enrolment, maximum duration of tobacco use (for chewing and smoking), and self-reported education level (<5 years of education; ≥5–8 years; completed high school; college graduate or more). Detailed methodology regarding the aforementioned confounders is available in Gholap et al. 13 Statistical analysis All analysis was performed on STATA 15.0. 14 Since very few females (0.82 % cases, and 0% controls) reported alcohol drinking habits, the analysis was restricted to males. Never-users of alcohol were used as the reference group for all sets of analyses. The risks of BMC among ever-users of alcohol, inclusive ever-users of IRL, ever-users of LBL and specific IRL and LBL types were estimated using a multivariate unconditional logistic regression model, with calculation of the OR and 95% CI. All regression models were adjusted for the potential confounders of age at enrolment, last 3 years urban-rural residential status, participant education levels (<5, and ≥5 years of schooling), and duration of tobacco use (including both smoking and chewing). To ensure stable estimates, analysis for an alcohol type was performed only if there were at least five cases and five controls present ( online supplemental tables 3 and 4 ). Continuous variables like total alcohol (grams consumed per day), and the grams per day for each of the IRLs and LBLs were categorised into three groups: never-users, ≤median, and >median grams per day, with median values obtained as per the values reported by the controls. The ≤median was considered as low consumption and >median as high consumption. Per gram increase was estimated using the underlying continuous variable for grams of alcohol per day for all liquor types. The joint effect between tobacco use and alcohol was also formally assessed. Interaction was tested for between: grams of alcohol consumed per day and duration of tobacco smoking; grams of alcohol and duration of use of chewing/smokeless tobacco; and grams of alcohol and overall tobacco use duration. Grams per day of alcohol were categorised into three groups as mentioned above, duration of use of the tobacco products was treated as a continuous term with never-users of tobacco as reference. The interaction risk after 10 years of tobacco consumption for 9 grams or more of alcohol consumed per day was also estimated. A margins plot was prepared for interaction of tobacco and alcohol, showing the increase in risk of BMC with each level of alcohol and tobacco. Attributable fraction (AF), or aetiological fraction, was estimated from our study for BMC using the risk for interaction of alcohol and tobacco, and the combined prevalence of alcohol and tobacco use in our cases, calculated using Miettinen’s formula 15 for the exposures reported in our interaction analysis. State-wise population attributable fraction (PAF) was estimated using Levin’s PAF formula, 15 with prevalence of alcohol use in each state obtained from the National Family Health Survey (NFHS-5), 2019–2021, using the exposure reported for ever-versus-never analysis. 10 Results were presented using an online map generating tool - iipmaps. 16 Patient and public involvement The patients and public involved in this study had no role in the study design, conduct, reporting, or dissemination plans of our research. Results Our study population consisted of 3706 males, with 1803 confirmed primary BMC cases and 1903 frequency matched controls. The mean age was 46 (±9.9) and 44 (±10.5) years-old in cases and controls, respectively. In both cases and controls, most participants belonged to the 35–54 year-old age group. The mean duration of tobacco use was higher for cases (~21 years) than controls (~18 years). Among both cases and controls, the majority had received more than 5 years of schooling, but the proportion was greater among controls. Cases were more likely to reside in rural sectors of the country. Alcohol consumption also significantly differed, with cases consuming 36.6 (±54.2), and controls 28.8 (±51.1) grams per day ( table 1 ). Table 1. Descriptive characteristics of male cases and controls within the multicentric buccal mucosa cancer study in India. Cases (n=1803) Controls (n=1903) P value Age (years) <25 10 (0.55%) 53 (2.79%) ≤0.001 25–34 223 (12.37%) 365 (19.18%) 35–44 606 (33.61%) 620 (32.58%) 45–54 570 (31.61%) 561 (29.48%) 55–64 337 (18.69%) 250 (13.14%) ≥65 57 (3.16%) 54 (2.84%) Missing 0 (0.00%) 0 (0.00%) Mean (± SD) 45.98 (±9.90) 43.45 (±10.50) Education <5 years of schooling 410 (22.74%) 167 (8.78%) ≤0.001 ≥5 years of schooling 1390 (77.09%) 1733 (91.07%) Missing 3 (0.17%) 3 (0.16%) Study centres TMH 1428 (79.20%) 1468 (77.14%) ≤0.001 NDMCH 58 (3.22%) 0 (0%) MPMMCC 180 (9.98%) 226 (11.88%) BBCI 101 (5.60%) 129 (6.78%) ACTREC 36 (2.00%) 80 (4.20%) Missing 0 (0%) 0 (0%) Urban-rural residential status Urban 865 (47.98%) 1057 (55.54%) ≤0.001 Rural 909 (50.42%) 804 (42.25%) Missing 29 (1.61%) 42 (2.21%) Duration of tobacco use (years) 20.56 (±10.74) 17.93 (±11.27) ≤0.001 Alcohol consumption (grams per day) 36.58 (±54.16) 28.79 (±51.09) 0.012 Open in a new tab Data are n (%) unless otherwise stated. ACTREC, Advanced Centre for Treatment, Research and Education in Cancer, Navi Mumbai, Maharashtra; BBCI, Dr Bhubaneshwar Borooah Cancer Institute, Guwahati, Assam; MPMMCC, Mahamana Pandit Madan Mohan Malviya Cancer Centre, Varanasi, Uttar Pradesh; NDMCH, Nargis Dutt Memorial Cancer Hospital, Barshi, Maharashtra; TMH, Tata Memorial Hospital, Mumbai, Maharashtra. Estimates of the association between alcohol consumption and the risk of developing BMC are summarised in table 2 . We observed an increased risk for overall alcohol consumption for ever-users vs never-users, with an OR of 1.68 (95% CI=1.44–1.97). Compared with never-use of alcohol, even consumption of less than 9 grams of alcohol per day was associated with a significantly higher risk of developing BMC (OR ≤9grams/day =1.56, 95% CI=1.27–1.91). BMC risk was higher with increased alcohol consumption of more than 9 grams/day (OR >9grams/day =1.81, 95% CI=1.49–2.21, table 2 ). The per gram increased risk for overall alcohol use was found to be small but statistically significant. Overall consumption of ever-users of IRL and LBL vs never-users of alcohol also indicated increased risk for BMC, shown in tables3 4 . Table 2. Association of alcohol consumption with buccal mucosa cancer risk among Indian males. Case/Control OR * (95% CI) P value * OR † (95% CI) P value † Minimal adjustment Maximal adjustment Alcohol consumption Never-users 1019/1420 1 (ref) 1 (ref) Ever-users 781/481 2.35 (2.03 to 2.72) <0.0001 1.68 (1.44 to 1.97) <0.0001 Alcohol intake (grams per day) ‡ Never-users 1019/1420 1 (ref) 1 (ref) ≤ 9 319/240 1.96 (1.62 to 2.38) <0.0001 1.56 (1.27 to 1.91) <0.0001 > 9 443/231 2.78 (2.31 to 3.35) <0.0001 1.81 (1.49 to 2.21) <0.0001 Per gram increase 1.01 (1.01 to 1.01) <0.0001 1.00 (1.00 to 1.01) 0.0036 Open in a new tab * Adjusted for age (continuous), urban-rural status of residence in last 3 years (dichotomous), and education (<5 years, ≥5 years of education). † Adjusted for age (continuous), urban-rural status of residence in last 3 years (dichotomous), education (<5 years, ≥5 years of education), and maximum duration of tobacco consumption in years (continuous). ‡ Categorised as per median values of controls. Table 3. Association of the inclusive use of internationally-recognised liquors with buccal mucosa risk among Indian males. Case/Control OR * (95% CI) P value * OR † (95% CI) P value † Minimal adjustment Maximal adjustment International liquor consumption Never-users 1019/1420 1 (ref) 1 (ref) Ever-users 604/400 2.37 (2.03 to 2.78) <0.0001 1.72 (1.46 to 2.04) <0.0001 Intake of international liquor (gram per day) ‡ Never-users 1019/1420 1 (ref) 1 (ref) ≤ 8 291/222 2.03 (1.66 to 2.48) <0.0001 1.61 (1.30 to 1.98) <0.0001 > 8 313/178 2.83 (2.29 to 3.49) <0.0001 1.86 (1.49 to 2.33) <0.0001 Per gram increase 1.00 (1.00 to 1.01) 0.11 1.00 (1.00 to 1.01) 0.72 Beer use Never-users 1019/1420 1 (ref) 1 (ref) Ever-users 238/162 2.57 (2.04 to 3.22) <0.0001 1.91 (1.50 to 2.43) <0.0001 Grams of beer consumed per day ‡ Never-users 1019/1420 1 (ref) 1 (ref) ≤ 2 115/94 2.11 (1.57 to 2.84) <0.0001 1.59 (1.16 to 2.18) 0.0035 > 2 118/66 3.16 (2.28 to 4.39) <0.0001 2.34 (1.66 to 3.29) <0.0001 Per gram increase 1.01 (0.99 to 1.04) 0.34 1.01 (0.98 to 1.04) 0.46 Whisky use Never-users 1019/1420 1 (ref) 1 (ref) Ever-users 453/283 2.55 (2.14 to 3.04) <0.0001 1.78 (1.48 to 2.15) <0.0001 Grams of whisky consumed per day ‡ Never-users 1019/1420 1 (ref) 1 (ref) ≤ 8 200/156 2.00 (1.59 to 2.52) <0.0001 1.48 (1.16 to 1.88) 0.0017 > 8 245/123 3.27 (2.56 to 4.16) <0.0001 2.15 (1.66 to 2.78) <0.0001 Per gram increase 1.00 (1.00 to 1.01) 0.2 1.00 (1.00 to 1.01) 0.62 Vodka use Never-users 1019/1420 1 (ref) 1 (ref) Ever-users 29/23 2.34 (1.32 to 4.14) 0.0036 1.35 (0.74 to 2.46) 0.32 Grams of vodka consumed per day ‡ Never-users 1019/1420 1 (ref) 1 (ref) ≤ 3 10/11 1.58 (0.65 to 3.84) 0.31 0.84 (0.34 to 2.11) 0.71 > 3 17/9 3.60 (1.56 to 8.31) 0.0026 2.23 (0.94 to 5.33) 0.070 Per gram increase 1.15 (1.01 to 1.32) 0.039 1.15 (1.01 to 1.31) 0.038 Rum use Never-users 1019/1420 1 (ref) 1 (ref) Ever-users 97/75 2.02 (1.46 to 2.80) <0.0001 1.31 (0.93 to 1.85) 0.12 Grams of rum consumed per day ‡ Never-users 1019/1420 1 (ref) 1 (ref) ≤ 4 30/42 1.22 (0.75 to 1.99) 0.42 0.88 (0.53 to 1.45) 0.61 > 4 65/31 3.11 (1.97 to 4.89) <0.0001 1.91 (1.19 to 3.07) 0.0076 Per gram increase 1.00 (0.98 to 1.01) 0.51 1.00 (0.99 to 1.01) 0.28 Wine users Never-users 1019/1420 1 (ref) 1 (ref) Ever-users 35/24 2.19 (1.28 to 3.75) 0.0043 1.42 (0.81 to 2.48) 0.22 Grams of wine consumed per day ‡ Never-users 1019/1420 1 (ref) 1 (ref) ≤ 2 18/13 2.37 (1.13 to 4.93) 0.022 1.59 (0.75 to 3.41) 0.23 > 2 16/11 1.84 (0.84 to 4.05) 0.13 1.15 (0.50 to 2.61) 0.74 Per gram increase 1.02 (0.95 to 1.10) 0.56 1.02 (0.94 to 1.10) 0.63 Open in a new tab * Adjusted for age (continuous), urban-rural status of residence in last 3 years (dichotomous), and education (<5 years, ≥5 years of education). † Adjusted for age (continuous), urban-rural status of residence in last 3 years (dichotomous), education (<5 years, ≥5 years of education), and maximum duration of tobacco consumption (continuous). ‡ Categorised as per median values of controls. Table 4. Association of the inclusive use of locally-brewed liquors with buccal mucosa cancer risk among Indian males. Case/control OR * (95% CI) P value * OR † (95% CI) P value † Minimal adjustment Maximal adjustment Locally produced country liquor Never-users 1019/1420 1 (ref) 1 (ref) Ever-users 305/125 2.93 (2.32 to 3.70) <0.0001 1.87 (1.46 to 2.39) <0.0001 Intake of locally produced country liquor (grams per day) ‡ Never-users 1019/1420 1 (ref) 1 (ref) ≤ 15 147/65 2.73 (1.99 to 3.74) <0.0001 1.87 (1.34 to 2.60) 0.00019 > 15 158/60 3.15 (2.29 to 4.34) <0.0001 1.87 (1.33 to 2.61) 0.00027 Per gram increase 1.00 (1.00 to 1.00) 0.93 1.00 (1.00 to 1.00) 0.88 Mahua use Never-users 1019/1420 1 (ref) 1 (ref) Ever-users 52/26 2.51 (1.52 to 4.15) 0.00031 1.59 (0.95 to 2.66) 0.076 Grams of mahua consumed per day ‡ Never-users 1019/1420 1 (ref) 1 (ref) ≤ 2 23/14 2.01 (0.99 to 4.09) 0.054 1.11 (0.54 to 2.30) 0.77 > 2 25/10 3.24 (1.51 to 6.93) 0.0024 2.15 (0.99 to 4.65) 0.052 Per gram increase 0.97 (0.93 to 1.01) 0.092 0.96 (0.93 to 1.00) 0.061 Toddy use Never-users 1019/1420 1 (ref) 1 (ref) Ever-users 29/13 2.61 (1.32 to 5.14) 0.0056 1.73 (0.84 to 3.58) 0.14 Grams of toddy consumed per day ‡ Never-users 1019/1420 1 (ref) 1 (ref) ≤ 16 21/7 3.16 (1.31 to 7.60) 0.01 2.30 (0.90 to 5.85) 0.080 > 16 5/6 1.02 (0.29 to 3.57) 0.98 0.53 (0.14 to 2.04) 0.35 Per gram increase 0.98 (0.94 to 1.01) 0.24 0.98 (0.94 to 1.02) 0.24 Tharra use Never-users 1019/1420 1 (ref) 1 (ref) Ever-users 48/11 5.86 (2.91 to 11.81) <0.0001 3.09 (1.51 to 6.33) 0.0020 Grams of tharra consumed per day ‡ Never-users 1019/1420 1 (ref) 1 (ref) ≤ 15 8/6 2.09 (0.66 to 6.63) 0.21 1.14 (0.34 to 3.75) 0.83 > 15 39/5 9.31 (3.60 to 24.06) <0.0001 4.89 (1.87 to 12.80) 0.0012 Per gram increase 1.00 (0.99 to 1.01) 0.72 1.00 (0.99 to 1.01) 0.76 Desi Daru use Never-users 1019/1420 1 (ref) 1 (ref) Ever-users 175/71 2.86 (2.12 to 3.86) <0.0001 1.84 (1.34 to 2.52) 0.00014 Grams of desi daru consumed per day ‡ Never-users 1019/1420 1 (ref) 1 (ref) ≤ 31 80/36 2.51 (1.66 to 3.81) <0.0001 1.76 (1.14 to 2.71) 0.01 > 31 95/35 3.24 (2.14 to 4.92) <0.0001 1.92 (1.24 to 2.97) 0.0032 Per gram increase 1.00 (1.00 to 1.00) 0.79 1.00 (1.00 to 1.00) 0.067 Open in a new tab * Adjusted for age (continuous), urban-rural status of residence in last 3 years (dichotomous), and education (<5 years, ≥5 years of education). † Adjusted for age (continuous), urban-rural status of residence in last 3 years (dichotomous), education (<5 years, ≥5 years of education), and maximum duration of tobacco consumption (continuous). ‡ Categorised as per median values of controls. Analysis of the individual liquor types showed an increased risk for the following alcohol types as compared with never-alcohol users, after controlling for use of tobacco: beer (OR beer =1.91, 95% CI=1.50–2.43), whisky (OR whisky =1.78, 95% CI=1.48–2.15) among IRLs, and Desi daru (OR Desi daru =1.84, 95% CI=1.34–2.52) and Tharra (OR Tharra =3.09, 95% CI=1.51–6.33) among LBLs. Interestingly, a significant per gram increased risk was observed only for vodka with an OR of 1.15 (95% CI=1.01–1.31). An increased risk was seen for lower than median categories of consumption ≤2 grams alcohol/day of beer (OR ≤2 grams/day =1.59, 95% CI=1.16–2.18), ≤8 grams alcohol/day of whisky (OR ≤8 grams/day =1.48, 95% CI=1.16–1.88), and ≤31 grams alcohol/day of desi daru (OR ≤31 grams/day =1.76, 95% CI=1.14–2.71) categories, as well as for the >median categories ( tables3 4 ). A statistically significant increased risk for rum was observed at >4 grams alcohol/day consumption (OR >4 grams/day =1.91, 95% CI=1.19–3.06), and a borderline significant increase risk was seen for >3 grams alcohol/day of vodka (OR >3 grams/day =2.23, 95% CI=0.94–5.33). Ever-use of Mahua showed a borderline statistically significant increased risk (OR Mahua =1.59, 95% CI=0.95–2.66). Other liquor types were excluded from the analysis due to lack of sufficient numbers among the cases and controls ( online supplemental table 4 ). We observed a statistically significant, greater than additive interaction between alcohol consumption and tobacco use. compared with the individual effects of tobacco use (OR=2.11, 95% CI=1.94–2.29) and alcohol (OR ≤9g/day =1.76, 95% CI=1.24–2.50; OR >9g/day =3.46, 95% CI=2.40–4.99), the joint effect after 10 years of consuming tobacco was found to be about 1.5 times higher ( table 5 ). The joint effect for chewing tobacco only and >9 grams per day of alcohol intake was also found to be nearly twice the independent effects (OR=4.86, 95% CI=3.87–6.11, table 5 ). Notably, alcohol played a role in increasing risk of BMC, even after adjustment for duration of tobacco use. The probability of BMC risk increased gradually with both grams of alcohol and duration of tobacco as shown in the margins plot ( online supplemental figure 4 ). Table 5. Joint effects of alcohol consumption and tobacco use on buccal mucosa cancer risk among Indian males. Exposure Alcohol grams per day consumption Never drinker ≤9 >9 Duration of overall tobacco * Never-tobacco user 1.0 1.76 (1.24–2.50) 3.46 (2.40–4.99) Tobacco user 2.11 (1.94–2.29) 3.40 (2.67–4.32) 5.05 (3.91–6.54) p-value interaction 0.0002 Duration of smoking tobacco * Never-tobacco smoker 1.0 1.82 (1.46–2.28) 3.22 (2.56–4.06) Tobacco smoker 1.09 (0.99–1.19) 2.18 (1.77–2.70) 2.83 (2.34–3.42) p-value interaction 0.0127 Duration of chewing tobacco * Never-tobacco chewer 1.0 1.68 (1.22–2.32) 2.66 (1.94–3.65) Tobacco chewer 2.44 (2.24–2.67) 3.63 (2.90–4.57) 4.86 (3.87–6.11) p-value interaction 0.0053 Open in a new tab Minimal adjustment model used. * Continuous variable with never-users as reference. Using risk estimates from our study, the estimated AF, or aetiological fraction, for BMC cases due to both alcohol and chewing tobacco in our study was 62.3% as per Miettinen’s formula ( online supplemental tables 5–7 ). The PAF for BMC due to alcohol in India was estimated to be 11.3% ( online supplemental table 8 ). State-wise PAFs indicated a range of estimated PAFs across India, with the smallest PAF of 0.3% for the union territory of Lakshadweep in the south-west of India, and the highest of 26.3% for Arunachal Pradesh in the north-east of India ( online supplemental figure 5 & online supplemental table 8 ). Discussion In this large multicentric study from India, we observed an increased risk of BMC for locally-brewed liquors, as well as internationally recognised liquors. The observed increased risk for LBLs is consistent with observations that locally brewed types may have been contaminated and might contain toxins such as methanol and acetaldehyde. 17 Furthermore, since informal sectors largely dominate the production of these local liquors, tailored regulations are key to ensure effective alcohol control programmes, since they do not fall under any existing tax laws. More awareness and vigilance at the societal level is needed. Our study found elevated risks for specific types of IRLs including beer and whisky. Beer and other hard liquors (like tharra) have similarly been shown to increase risk in other studies as well, 8 18 especially among heavy drinkers. 8 19 20 We also found a greater than additive joint risk for tobacco chewing and alcohol use (p multiplicative interaction =0.0053), consistent with observations that ethanol might alter the lipid content of the inner epithelium of the mouth, increasing permeability and making it more susceptible to other potential carcinogens present in chewing tobacco products. 21 The increased risk was observed even at low amounts of consumption of alcohol (<9 grams of alcohol per day – i.e. ~one glass/day, and also for <2 grams per day for beer), which is even lower than the standard alcohol consumption units of the USA and Europe (10–14 grams). 22 23 The average population attributable fraction for BMC in India was observed to be 11.3%. We estimate that 62.3% of the BMC cases in our study were attributed to alcohol drinking and chewing tobacco. The AF for alcohol drinking only was estimated to be 17% among the cases. In India, the PAF due to alcohol drinking, for BMC, was observed to be 11.3%. Some of the states with high burden for BMC like Meghalaya, Assam, Madhya Pradesh have over 14% (on average) of their BMC cases that were caused by alcohol use ( online supplemental figure 5 ). The increased risk for alcohol consumption in developing BMC seems to be biologically plausible as ethanol in alcoholic beverages has been classified by the International Agency for Research on Cancer (IARC) as a Group 1 carcinogen, and acetaldehyde, a by-product of alcohol metabolism, as a possible carcinogen to humans (Group 2B; when associated with alcohol consumption, it is a Group 1 carcinogen). 24 25 Additionally, it is also known to alter oncogenes thus affecting the initiation and progress of oral carcinoma. 18 Studies have also demonstrated that alcohol cessation has benefit in reducing risk of oral cancer, indicating that observed association in our study might be causal. 26 The reported 5 year survival rate for locally advanced BMC is about 43%, 4 making it one of the most urgent targets for prevention and control. Rates of tobacco use in India seem to have dropped by 6% from 2010 to 2017, with a 3.3% drop in use of smoking tobacco products and a 4.5% drop in smokeless tobacco products as per the Global Adult Tobacco Survey (GATS-2), 27 but other risk factors such as alcohol continue to be prevalent (18.8%), 10 and need to be explored in more depth. The impact of both tobacco and alcohol consumption remains a key issue in prevention of BMC. In addition to BMC, alcohol consumption increases the risk of seven other cancer types. 5 The WHO reports that around 200 diseases and injuries are also linked to alcohol use, making it a cause for concern as a risk factor for poor health outcomes. The causal association between multiple mental health-related issues have also been established. 28 It is thus imperative to have strict alcohol consumption policies. The current legal framework for alcohol control in India is complex and involves both central and state laws. Central legislation provides protection of citizens where alcohol is included in the State List under the Seventh Schedule of the Indian Constitution, giving states the power to regulate and control alcohol production, distribution and sale. However, the locally-brewed liquor market is unregulated with some forms used by participants containing up to 90% alcohol content. The results in this paper are useful for policymakers when regulating the manufacture and sale of this type of alcohol. Other laws such as The Prohibition Act, 1949, enforced in certain states, The Narcotic Drugs and Psychotropic Substances Act, 1985, and The Juvenile Justice (Care and Protection of Children) Act, 2015, have helped regulate the manufacture, sale and consumption of alcohol and its preparations, although for commercial brands. With stronger measures around alcohol control being taken by state governments such as in Maharashtra, Gujarat, Kerala and Delhi, there seems to be a correlation with reduction in prevalence of alcohol consumption. While strong measures such as licensing, age restriction, dry days, regulated advertising, taxes and duties, are in place, enforcement is still lacking. According to the National Centre for Disease Informatics and Research, the reported mean age of initiation of alcohol consumption in India is 22 years, with a higher consumption in the rural parts of the country. 29 This young age of initiation subsequently results in the early onset of many non-communicable diseases such as oral cancer, the management of which can often be debilitating in both financial and societal impacts. Our study also reflects this unfortunate reality with ~46% of the cancer cases being in the 25 year-old to 45 year-old age group. This productivity loss among society can be exponential when all tobacco and alcohol related cancers are combined, and it comes nowhere close to equalling the revenue generated by the sale of these substances. With an increased risk of developing BMC from ≤2 grams alcohol/day (OR ≤2grams/day for beer =1.59, 95% CI=1.16-2.18, Table 3), our results underscore the fact that any form of alcohol use is unsafe. This information needs to be highlighted in regulatory policies pertaining especially to the sale of alcohol, as surrogate advertisements often label products as ‘safe’ forms in an effort to target health-conscious youth. Since only two females consumed alcohol in our study, we restricted this analysis to males. Analysis on stratification on gender did not change our results on alcohol risk for BMC ( online supplemental table 9 ). While rates of oral cancers are higher among men, 10 exploring associations in women would be of interest in future studies with greater power of detection. Although exposure information on alcohol and tobacco chewing was found to be reproducible for 84% of alcohol users (with 74% correlated for alcohol grams per day) and 87% of tobacco chewers (after agreement was determined between the first interview and re-interview data), it is possible that misclassification due to recall bias in alcohol consumption may have occurred as alcohol drinking is still a social taboo and people may not be willing to report their true consumption, which would have attenuated the estimated OR. The interviewers were trained to show the images of different types of glasses and types of alcohol consumed to minimise the recall bias. Further, we also particularly enquired about types of alcohol consumed by their regional names which is known to be prevalent in the area of residence of the participants (like Tharra, Madi, Toddy Mahua, Feni in the western part of India) so as to improve the participant’s recall about their alcohol use ( online supplemental tables 3 and 4 ). In summary, our study demonstrates that there is no safe limit of alcohol consumption for BMC risk, and that there is a joint effect of smoking or chewing tobacco with alcohol, on the risk of BMC. Our findings suggest that public health action towards prevention of alcohol and tobacco use could largely eliminate BMC from India. Policies related to informal sectors need to be framed and implemented as locally manufactured alcohols from these sectors are shown to increase the risk of BMC. Supplementary material online supplemental file 1 bmjgh-10-12-s001.docx (514.8KB, docx) DOI: 10.1136/bmjgh-2024-017392 Acknowledgements We would like to sincerely acknowledge all the participants, especially the patients, who actively participated in providing their information. The staff at the Division of Molecular Epidemiology and Population Genomics, CCE have relentlessly contributed to laying the foundations of this research work. Especially we would like to thank the enrolment team, from the data entry and quality check team - Seema Shedge, Varsha Kamble, Mridula Ram, Kamlesh Kadam and the rest of the data entry team, and Shubham Chavan for preparing the map for population attributable fractions. Where authors are identified as personnel of the International Agency for Research on Cancer and World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy, or views of the International Agency for Research on Cancer and World Health Organization. Where authors are identified as personnel of the Radiation Effects Research Foundation, the views of the authors do not necessarily reflect those of the Radiation Effects Research Foundation or its funding agencies the Ministry of Health, Labour and Welfare of Japan, the United States Department of Energy, or the US National Academies of Sciences, Engineering and Medicine. Footnotes Funding: Department of Health Research, Indian Council of Medical Research, New Delhi – Grant No. ICMR/EU/13/2012/NCD-III. Provenance and peer review: Not commissioned; externally peer reviewed. 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[ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials online supplemental file 1 bmjgh-10-12-s001.docx (514.8KB, docx) DOI: 10.1136/bmjgh-2024-017392 Data Availability Statement Data are available upon reasonable request. 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