Nested multilevel modelling study of smoking and smokeless tobacco consumption among middle aged and elderly Indian adults: distribution, determinants and socioeconomic disparities.

Abstract

Introduction: The Global Adult Tobacco Survey (GATS) shows a drop in tobacco use worldwide. Despite the drop, there still continues to be a significant number of tobacco users in India. Research on tobacco use among young persons is commonly prioritised in India, while studies on tobacco use among middle-aged (45-59 years) and elderly (≥ 60 years) adults are noticeably lacking. We have conducted this study with objective to estimate the distribution, determinants and socioeconomic inequalities of smoking (SM) and smokeless tobacco (SLT) consumption across Indian states and union territories.

Methods: This study was based on 66,606 participants aged ≥ 45 years using Longitudinal Aging Study in India (LASI)-1 (2017-2018) data. Distribution of tobacco consumption (any form, smoking (SM), smokeless (SLT) and both) was documented as per Indian states and union territories with spatial distribution by Indian map. Demographic, socioeconomic, health related and behavioural determinants were established using nested multilevel regression modelling. Socioeconomic disparities were documented using concentration curve. P-value < 0.05 was considered as statistically significant.

Results: Overall, 36.78% participants documented using any form of tobacco; with higher consumption of SLT (19.88%) than smoking/SM (13.92%). Only 2.98% consumed both. Mizoram had highest consumption of tobacco in any form (78.21%) and smoking (35.18%). Elderly participants had higher odds of consuming tobacco (any 1.23 (1.18-1.28), SM 1.99 (1.14-1.27), SLT 1.08 (1.03-1.14) and both 1.27 (1.14-1.40 times) than middle aged participants. Females, OBC (other backward castes), urban residence had lower odds in all the categories, while being widow/ separated/ divorced, belonging to Muslim community, having clerical and skilled occupation, poor self-rated health, comorbidity and multimorbidity had higher odds. With decrease in the wealth index, educational status and frequency of physical activity the odds of tobacco consumption increased. The odds of higher tobacco consumption were documented from northeast region (2.56 (2.37-2.76) higher than north). Alcohol consumption had the highest odds (4.94 (4.69-5.21)). Participants exposed to media had lower odds (11% lower) of consuming tobacco. The socioeconomic inequalities in tobacco consumption were significantly distributed more among the poorest (any -0.064 (-0.072 to -0.056) and SLT -0.069 (-0.072 to -0.056)).

Conclusion: Prioritising tobacco prevention and increasing availability and accessibility of cessation programmes that are suited with unique requirements and circumstances, even for elderly population, are essential focusing on the higher determinants across poorest section in the country.