Skip to main content
Download PDF

Prevalence and correlates of tobacco use in Botswana: evidence from the 2014 Botswana STEPwise survey

BMC Public Health volume 23, Article number: 40 (2023) Cite this article

Abstract

Background

Tobacco use is one of the notable risk factors for non-communicable diseases globally. The objective of this study was to assess the prevalence of tobacco use and identify its correlates in the general population of Botswana aged 15 to 69 years.

Methods

This study used a nationally representative WHO STEPwise Approach to Non-Communicable Disease Risk Factor Surveillance (STEPS) risk factors conducted in 2014 to explore the prevalence of tobacco use and its correlates in Botswana. Using IBM SPSS version 27, data on 4062 people aged 15 to 69 years who had been selected using multistage cluster sampling and had successfully completed the individual questionnaire were analysed. The prevalence of current tobacco smoking and smokeless tobacco use was determined using descriptive statistics while multivariable logistic regression was employed to assess correlates of current tobacco smoking and smokeless tobacco use. All comparisons were statistically significant at 5% significance level.

Results

From a total sample of 4062 participants the prevalence of current tobacco smoking was estimated to be 12.9% while smokeless tobacco use was 3.2%. Adjusted results indicate that the odds of current tobacco smoking were eight times (AOR = 8.57, C.I = 6.28-11.7) higher among males compared to their female counterparts; six(AOR = 6.52, C.I 3.64-11.6) and three (AOR = 3.27, C.I. =2.07-5.15) times higher among respondents with no education and primary level education respectively, compared to their counterparts with tertiary or higher education; while for alcohol users the odds of current tobacco smoking were four times (AOR = 4.28, C.I = 2.93-6.24) higher than among non-alcohol users. The odds of smokeless tobacco use were significantly higher among women compared to men (AOR = 7.34, C.I = 4.01-13.4); individuals aged 50-59 (AOR = 1.15, C.I = 1.06-3.37) and 60-69 years (AOR = 1.23, C.I. =1.08-3.63) compared to 15-29 years; individuals with no education (AOR = 2.07, C.I = 1.03-4.02) and primary education (AOR = 1.05, C.I = 1.01-2.23) compared to individuals with tertiary education. However, the odds of smokeless tobacco use were significantly lower among individuals who consume alcohol (AOR = 0.48, C.I. = 0.29-0.80) compared to non-alcohol consumers.

Conclusion

Findings of this study indicate the need to strengthen existing national policies to reduce harmful use of tobacco among men, women, older adults, no or primary education level individuals and alcohol users.

Peer Review reports

Background

Globally, the use of tobacco has been noted as one of the leading risk factors for non-communicable diseases (NCDs) [1]. As a result, tobacco use is a major public health concern and a modifiable cause of morbidity and mortality. According to the World Health Organization (WHO) report over 1.1 billion people smoked tobacco globally in 2015, which represents around 15% of the global population [2]. On the other hand, tobacco use is predicted to kill around 6 to 7 million people annually [1, 3, 4]. Most (80%) of these individuals who have been killed by tobacco live in low- and middle-income countries (LMICs) [3, 4]. Moreover, tobacco smoking has been attributed to 150 million disability-adjusted life years and 11.5% of total global deaths [3].

It is estimated that in Sub Saharan Africa (SSA), around one in five adults smoke tobacco products [5]. This makes SSA to have low prevalence of tobacco use compared to other regions of the world [6, 7]. Most of the countries in SSA are in the first stage of the tobacco epidemic continuum which is a notion that nicotine and tobacco products fall along a spectrum from most to least risk, which denotes the impact of tobacco use on human health at the individual and population level [7]. In this stage there is higher prevalence of smoking among men compared to women [7]. A recent systematic review by Brathwaite et al. [7] conducted using studies published between 2007 and 2014 found that men smoked tobacco products more than women in all the SSA countries represented. More recently a study using the latest Global Adult Tobacco Survey (GATS) data by Chandrashekhar et al. [8] found that current tobacco smoking varied widely between countries among men and women. For instance, the prevalence of current tobacco use among male participants ranged from 4.5% in Ghana to 46.0% in Lesotho during the most recent surveys. Except for Lesotho, which also had the highest rate of tobacco use, in all other SSA countries except Zambia, tobacco use rates had significantly decreased in terms of prevalence rates ranging from 1.5% (Ghana) to 9.6% (Sierra Leone) [8]. The highest percentage decrease of tobacco smoking (43.7%) was in Cameroon (from 14.5 to 8.1%) between 2011 and 2018 [8].

Botswana has fully adopted the WHO best buys strategies for tobacco use. However, it is among the countries with high smoking prevalence in the Southern Africa region, especially among men [9]. The 2008 WHO STEPwise survey results showed that prevalence of current smoking was 19.7% among people aged 25-64 years and the average age of initiation of daily smoking for the general population was 23.6 years [9]. The latest GATS data indicate that current tobacco use among people aged 15+ years in Botswana was estimated to be 17.6% for both men and women in 2017 [10]. Despite the high prevalence of tobacco use, there is paucity of information about factors influencing tobacco use in Botswana. Consequently, little is known about correlates of tobacco use among people aged 15-69 years. The main objective of this study was to assess prevalence and correlates of tobacco use in the general population of Botswana. Since this study is the first comprehensive national analysis of the correlates of tobacco use in the general population, it provides evidence for a more targeted programmatic response to tobacco use in the country.

Materials and methods

Study design

The current analysis uses data from a cross-sectional, population-based survey called the Botswana WHO STEPwise Survey II which was conducted in 2014 based on people aged 15-69 years. STEPS Survey II used a multi-stage cluster sampling methodology to arrive at nationally representative sample. A detailed report on the methodology of the Botswana STEPS Survey has been presented elsewhere [11]. This study is based on secondary data analysis of the population-based 2014 Botswana STEPS survey.

Sampling procedure

A multi-stage stratified sampling procedure was used in the 2014 Botswana STEPS Survey; therefore, the use of standard statistical methods for analyzing the data would produce unreliable estimates of the desired parameters [5]. As a result, during analysis of data, the complex sample module in SPSS was used to cater for the multiple stages of sampling. The target sample size for the 2014 Botswana STEPS Survey was 6410 but 4074 individuals participated, yielding an overall response rate of 64% [11]. From a total sample of 4074 individuals who completed the STEPS survey questionnaire, only 4062 successfully completed and responded to the tobacco use section of the questionnaire.

Measures

Dependent variables

The main outcomes of interest for the study are current tobacco smoking and current smokeless tobacco use. Current tobacco smoking was derived from the question; ‘Do you currently smoke any tobacco products, such as cigarettes, cigars or pipes?’. Current tobacco smoking involves the combustion of the tobacco product and inhalation of tobacco smoke through the mouth [8]. Current smokeless tobacco use was derived from the question asking respondents; ‘Do you currently use any smokeless tobacco products such as snuff, chewing tobacco, betel?’ Smokeless tobacco products are those whose use involves chewing the tobacco product or sniffing it through the mouth or nose [8]. Current tobacco smoking and current smokeless tobacco use were coded into binary variables, with codes ‘yes = 1’ indicating tobacco use and ‘no = 0’ indicating non-use of tobacco for both current tobacco smoking and smokeless tobacco use.

Independent variables

The selection of the independent variables used for analysis was based on literature review [12] and on the availability of the limited socio-demographic variables collected during the 2014 Botswana STEPS survey. The following sociodemographic variables were used as covariates of tobacco use: sex, age, marital status, education level, and employment status. Categories for age were: 15-29, 30-39, 40-49, 50-59 and 60-69 years. Marital status was coded as currently married, formerly married (widowed, divorced and separated) and never married (includes cohabiting). Education was categorized into, no education, primary, secondary and tertiary or higher education level. Employment status was created based on the participants’ main work status over the past year; public sector (government employee, parastatal), private sector (non-governmental employee), self-employed, unpaid/students (non-paid/unpaid family helper, student), homemaker/house worker, and unemployed (able to work and unable to work, retired). Alcohol consumption was also used as an independent variable consistent with available literature that has shown that tobacco use is associated with alcohol consumption [13]. Alcohol consumption was derived from a question asking respondents whether they have consumed alcohol in the past 12 months before the survey. Information about the dangers of smoking was also used as an explanatory variable consistent with literature [14]. This variable was derived from a question which asked respondents whether they received any information about the dangers of smoking in the past 30 days.

Statistical analyses

In the first level of analysis descriptive statistics, frequency and proportions are presented. A chi-square (χ2) statistic was used in bivariate analyses to assess the association between current tobacco smoking and smokeless tobacco use and sociodemographic and behavioral variables. Weights were created within the dataset and the weights command was activated during analysis to produce weighted estimates for both bivariate and multivariate analyses.

In multivariable analyses we included all independent variables that were conceptually associated with tobacco use [15]. All independent variables were used as covariates of current tobacco smoking and current smokeless tobacco use. The variables, alcohol consumption and information about the dangers of tobacco use were hypothesized to be important factors given their known association with sociodemographic factors and tobacco use [16]. In the tables for results we present unadjusted (OR) and adjusted odds ratios (AOR) for variables included in the models. Both unadjusted and adjusted odds ratios are presented with associated 95% confidence intervals (CIs). All the statistical analyses were done using IBM SPSS version 27.

Ethical considerations

This study uses secondary data provided by the Ministry of Health and Wellness hence no ethical protocols were needed for this study. However, during the survey, the survey’s protocol was reviewed and approved by the Ministry of Health and Wellness Institutional Review Board. Informed Consent was obtained from participants and parents/LAR (legally authorized representative) in case of minors and no individually identifiable information was collected [9].

Results

Characteristics of the study population

There were 4062 participants sampled for analysis in this study and 12.9% of them were currently smoking tobacco while 3.2% were currently using smokeless tobacco (Table 1). Using a chi-square (χ2) statistic and weighted percentages, a significantly higher proportion among males (28.6%), individuals with no-education (43.5%) and people in ages 50-69 years (13.9%), self-employed individuals (20.3%), never married (12.0%), and alcohol consumers (31.3%) reported current tobacco smoking. Current smokeless tobacco use was significantly higher among; females (4.0%), people aged 60-69 years (10.2%), individuals with no education (20.8%), formerly married individuals (8.7%), and home makers (4.9%).

Table 1 Breakdown of tobacco use variables by sociodemographic characteristics of the population, Botswana WHO STEPwise Survey II-2014
Full size table

Correlates of current tobacco smoking

The adjusted logistic regression model results indicates that the odds of current tobacco smoking were more than eight times (AOR = 8.57, C.I. = 6.28-11.70) higher among males compared to their female counterparts (Table 2). On the other hand, the odds of current tobacco smoking were significantly low among individuals in ages 60-69 (AOR = 0.32, C.I. = 0.16-0.64) compared to people in ages 15-29 years, while for other age groups there were no variations in current tobacco smoking compared to individual aged 15-29 years. On the other hand, the odds of current tobacco smoking were six (AOR = 6.52, C.I 3.64-11.6) and three (AOR = 3.27, C.I. =2.07-5.15) times higher among individuals with no education and primary education level, respectively, compared to their counterparts with tertiary or higher education. The odds of current tobacco smoking were four times (AOR = 4.28, C.I. = 2.93-6.24) higher among people who consumed alcohol in the past 12 months compared to those who did not consume alcohol in the past 12 months. There was no significant variation between current tobacco smoking based on the marital status, employment status and access to information about the dangers of smoking in the past 30 days.

Table 2 Covariates associated with current tobacco smoking in Botswana, Botswana WHO STEPwise Survey II-2014
Full size table

Correlates of current smokeless tobacco use

In the adjusted analyses, the odds of smokeless tobacco use were observed to be seven times (AOR = 7.34, C.I. = 4.01-13.4) higher among females compared to their male counterparts (Table 3). For age, the odds of smokeless tobacco use were significantly high among individuals aged 50-59 (AOR = 1.15, C.I. = 1.06-3.37) and 60-69 years (AOR = 1.23, C.I. =1.08-3.63) than among 15–29-year-olds. For education, the odds of smokeless tobacco use were significantly higher among individuals with no education (AOR = 2.07, C.I. = 1.03-4.02) and primary education (AOR = 1.05, C.I. = 1.01-2.23) compared to tertiary education. It was also observed that the odds of reporting smokeless tobacco use were significantly lower (AOR = 0.48, C.I. = 0.29-0.80) among individuals who consumed alcohol compared to non-alcohol consumers. There was no significant statistical association between marital status, employment status and having received information about the dangers of smoking in the past 30 days and reporting to have used smokeless tobacco. However, in the crude logistic regression model, these variables were significant but non-significant after introduction of control variables suggesting that their association with smokeless tobacco use was spurious.

Table 3 Covariates associated with current smokeless tobacco use in Botswana, Botswana WHO STEPwise survey-2014
Full size table

Discussion

This study provides the nationally representative estimates on the prevalence of tobacco use in Botswana. Overall, 12.9% of the respondents reported that they were currently smoking tobacco while 3.2% reported that they were using smokeless tobacco. The prevalence of both current tobacco smoking and smokeless tobacco use is comparatively low compared than in most sub-Saharan Africa countries. For example, countries such as South Africa (15%), Algeria (21.8%), Burkina Faso (19.8%) and Zambia (15.8%) have higher current tobacco use [8, 10]. The plausible explanation for the comparatively low prevalence rate of tobacco smoking in Botswana is the rigorous campaigns, including tobacco advertising ban together with other educational programmes aimed at different sectors of the population and the general public. The efforts may have contributed to sensitizing the general public about tobacco products’ harmful effects on human health and fostered a positive political climate [17].

Sex differences were observed in current tobacco smoking, with men eight times more likely to report current tobacco smoking compared to women. This finding is consistent with findings from other studies in SSA, which found that smoking prevalence rates tend to be consistently higher among men than women [18,19,20]. While biomedical literature suggests that the lower consumption of tobacco among women may be related to sex differences in motivations for smoking [20,21,22], psychological literature on the other hand posits that sex differences in tobacco smoking are mainly due to different behaviours, having its roots in traditional sex roles [23, 24]. The latter seems to be relevant to Botswana context where traditional sex roles lead to social pressure against female smoking [25]. Moreover, traditional sex norms cause differences in personal characteristics leading to acceptance of smoking among men than women [26].

Quite conversely after adjusting for cofounders the odds of smokeless tobacco use were found to be seven times higher among women than men. This finding contradicts most findings in LMICs [20,21,22,23]. The plausible explanation for the high prevalence of smokeless tobacco use among women can be attributed to the common anecdotal beliefs in Botswana that smokeless tobacco especially snuff is good for relieving stress, depression and hypertension. Smokeless tobacco use was significantly higher among individuals aged 50-69 years than among 15–29-year-olds and this could also be attributed to the anecdotal beliefs that it relieves stress, depression and hypertension which are more common among the older adults.

The odds of current tobacco use were found to be lower in ages 60-69 years while for other age groups there were no variations in current tobacco use, compared to 15-29 years. This finding is not consistent with other previous studies from countries such as Italy [27], Germany [28], Brazil [29], South Africa [30] and Zambia [31] which found that the odds of smoking were highest among older adults, than adolescents and middle-aged adults. Tobacco use among young adults in Botswana could be attributed to the fact that smoking is socially acceptable, and it is perceived to be glamorous to smoke even though much is known about the health risks of smoking. Cumulative effects of smoking during adolescence may predispose the young adult population to tobacco-caused diseases in later life [32].

For education, we found that people with no education or primary education were more likely to report current tobacco smoking compared to their counterparts with tertiary or higher education. Similarly, smokeless tobacco use was found to be significantly higher among individuals with no education or primary education. Pampel [26] using population-based data from 16 Demographic Health Surveys (DHS) found that tobacco use in SSA was more prevalent among low education populations. This suggests that higher education level seems to have a protective effect against tobacco smoking. This is because individuals with higher education are often exposed to disease prevention services and sufficient access to health promotion information compared to the less educated [33].

We found that people who reported alcohol use were four times more likely to smoke tobacco. Consistent with this study, many studies have found that alcohol consumption and tobacco smoking are closely related behaviours [17, 18, 34]. Thus, not only are the people who drink alcohol more likely to smoke (and vice versa) but also people who drink larger amounts of alcohol tend to smoke more tobacco products [35]. Contrarily the odds of smokeless tobacco use were significantly low among alcohol users. This connection has been observed in few cross-sectional studies that were also not precisely intended to reveal the likely sequence of substance use behaviours [36,37,38,39,40]. Drinkers have been found to be more likely to use smoked tobacco products than smokeless tobacco products [41]. Given the direction of the relationship between alcohol consumption and tobacco use, future studies should focus on the influence of alcohol use on other substances in order to come up with effective holistic interventions.

Strengths and limitations

To the best of our knowledge, this is one of the few national cross-sectional studies on the prevalence, patterns and correlates of tobacco use in Botswana. Patterns of tobacco use show age, gender, educational differences in the sampled population. Furthermore, smoking was more prevalent among alcohol users than non-alcohol users. This cross-sectional study was not designed to infer causality and there is no data to assess the temporal sequence of tobacco use. There is also a possibility of under-reporting of tobacco use due to the cultural implications and potential stigmatisation. Moreover, although this is the latest survey with tobacco use among adults in Botswana tobacco use patterns have likely evolved since 2014 when the data was collected.

Conclusion

This study provides vital insights into understanding prevalence of tobacco use (current smoking and smokeless tobacco use) and its correlates in Botswana, using a nationally representative survey data. Findings indicate that men, people with no education and primary level education and alcohol users were more likely to report current tobacco smoking. On the other hand, the odds of smokeless tobacco use were significantly higher among women; individuals aged 50-59 and 60-69 years, and individuals with no education and primary education. Findings of this study indicate the need to strengthen existing national policies to reduce harmful use of tobacco among men, women, older adults, no or primary education level individuals and alcohol users.

Availability of data and materials

The data used for this study is not publicly available due to ethical restrictions but interested parties may contact the authors for more information.

Abbreviations

AOR:

Adjusted Odd Ratios

CI:

Confidence Interval

DHS:

Demographic Health Survey

LMICs:

Low and Middle Income Countries

NCDs:

Non Communicable Diseases

SSA:

Sub-Saharan Africa

WHO:

World Health Organization

References

  1. 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. Lancet. 2016;388:1659–724.

    Article  Google Scholar 

  2. WHO. WHO report on the global tobacco epidemic, 2017: monitoring tobacco use and prevention policies. Geneva: World Health Organization. p. 2017.

  3. GBD 2015 Tobacco Collaborators. Smoking prevalence and attributable disease burden in 195 countries and territories, 1990–2015: a systematic analysis from the global burden of disease study 2015. Lancet. 2017;389(10082):1885–906.

    Article  Google Scholar 

  4. Owili PO, Muga MA, Pan WC, Kuo HW. Indoor secondhand tobacco smoke and risk of under-five mortality in 23 sub-Saharan Africa countries: a population based study and meta-analysis. PLoS One. 2017;12(5):e0177271.

    Article  Google Scholar 

  5. Ngaruiya C, Abubakar H, Kiptui D, Kendagor A, Ntakuka MW, Nyakundi P, et al. Tobacco use and its determinants in the2015 Kenya WHO STEPS survey. BMC Public Health. 2018;18(Suppl 3):1223.

    Article  Google Scholar 

  6. Ng M, Freeman MK, Fleming TD, Robinson M, Dwyer-Lindgren L, Thomson B, et al. Smoking prevalence and cigarette consumption in 187 countries, 1980-2012. JAMA. 2014;311(2):183–92.

    Article  CAS  Google Scholar 

  7. Brathwaite R, Addo J, Smeeth L, Lock K. A Systematic Review of Tobacco Smoking Prevalence and Description of Tobacco Control Strategies in Sub-Saharan African Countries; 2007 to 2014 [published correction appears in PLoS One. 2016; 11(5):e0155572]. PLoS One. 2015;10(7):e0132401. https://doi.org/10.1371/journal.pone.0132401.

    Article  CAS  Google Scholar 

  8. Sreeramareddy CT, Acharya K. Trends in prevalence of tobacco use by sex and socioeconomic status in 22 sub-Saharan African countries, 2003-2019. JAMA Netw Open. 2021;4(12):e2137820. https://doi.org/10.1001/jamanetworkopen.2021.37820https://www.who.int/publications/i/item/9789241516204. WHO report on the global tobacco epidemic 2019: offer help to quit tobacco use.

    Article  Google Scholar 

  9. Republic of Botswana. Botswana STEPS survey report on non-communicable disease risk Factors-2007: Ministry of Health & World Health Organization; 2015. p. 1–242.

    Google Scholar 

  10. WHO report on the global tobacco epidemic 2019: offer help to quit tobacco use. Geneva: World Health Organization; 2019. https://www.who.int/publications/i/item/9789241516204

  11. Republic of Botswana. Botswana STEPS survey report on non-communicable disease risk Factors-2014. Gaborone: Ministry of Health & World Health Organization; 2015. p. 1–132.

  12. Huang WC, Pham NY, Nguyen TA, et al. Smoking behaviour among adult patients presenting to health facilities in four provinces of Vietnam. BMC Public Health. 2021;21:845. https://doi.org/10.1186/s12889-021-10880-z.

    Article  CAS  Google Scholar 

  13. Bobo JK, Husten C. Sociocultural influences on smoking and drinking. Alcohol Res Health. 2000;24:225–32.

    CAS  Google Scholar 

  14. Abdel-Salam A-SG, Ramzan M, Siddiqui Z. Statistical analysis for the impact of smoking on the behavior and health of Qatari adolescents. Int J Adolesc Med Health. 2021;33(1):20180045. https://doi.org/10.1515/ijamh-2018-0045.

    Article  Google Scholar 

  15. Kabwama SN, Ndyanabangi S, Mutungi G, Wesonga R, Bahendeka SK, Guwatudde D. Tobacco use and associated factors among adults in Uganda: findings from a nationwide survey. Tob Induc Dis. 2016;14:27. https://doi.org/10.1186/s12971-016-0093-8.

    Article  Google Scholar 

  16. Ngaruiya C, Abubakar H, Kiptui D, et al. Tobacco use and its determinants in the 2015 Kenya WHO STEPS survey. BMC Public Health. 2018;18:1223. https://doi.org/10.1186/s12889-018-6058-5.

    Article  Google Scholar 

  17. Brathwaite R, Addo J, Smeeth L, Lock K. A systematic review of tobacco smoking prevalence and description of tobacco control strategies in sub-Saharan African countries; 2007 to 2014. PLoS One. 2015;10(7):e0132401. https://doi.org/10.1371/journal.pone.0132401.

    Article  CAS  Google Scholar 

  18. Owolabi EO, Goon DT, Adeniyi OV, Seekoe E, Adedokun AO. Prevalence and factors associated with tobacco use among adults attending selected healthcare facilities in Buffalo City metropolitan municipality, South Africa. S Afr Fam Pract. 2017;59(6):201–7.

    Article  Google Scholar 

  19. Owusu-Dabo E, Lewis S, McNeill A, Gilmore A, Britton J. Smoking uptake and prevalence in Ghana. Tob Control. 2009;18(5):365–70.

    Article  CAS  Google Scholar 

  20. Msyamboza KP, Ngwira B, Dzowela T, Mvula C, Kathyola D, Harries AD, et al. The burden of selected chronic non-communicable diseases and their risk factors in Malawi: nationwide STEPS Survey. PLoS One. 2011;6(5):e20316.

    Article  CAS  Google Scholar 

  21. John RM, Mamudu HM, Liber AC. Socioeconomic implications of tobacco use in Ghana. Nicotine Tob Res. 2012;14(10):1205–12.

    Article  Google Scholar 

  22. Nuwaha F, Musinguzi G. Pre-hypertension in Uganda: a cross-sectional study. BMC Cardiovasc Disord. 2013;13:101.

    Article  Google Scholar 

  23. Allen L, Williams J, Townsend N. Socioeconomic status and non-communicable disease behavioural risk factors in low-income and lower-middle-income countries: a systematic review. Lancet Glob Health. 2017;5:e277–89.

    Article  Google Scholar 

  24. Bensley LS, Spieker SJ, Van Eenwyk J, Schoder J. Self-reported abuse history and adolescent problem behaviors. II. J Adolesc Health. 1999;24:173–80.

    Article  CAS  Google Scholar 

  25. Keetile M.2019 Socioeconomic inequalities in health: chronic disease prevalence, health care utilization, Health Expenditure and Life course Perspective in Botswana: https://ubrisa.ub.bw/bitstream/handle/10311/2025/Keetile_Unpublished%20%28PhD%29_2019.pdf?sequence=1&isAllowed=y.

    Google Scholar 

  26. Pampel FC. Global patterns and determinants of sex differences in smoking. Int J Comp Sociol. 2006;47(6):466–87. https://doi.org/10.1177/0020715206070267.

    Article  Google Scholar 

  27. Yeomans MR. Alcohol, appetite and energy balance: is alcohol intake a risk factor for obesity? Physiol Behav. 2010;100:82–9.

    Article  CAS  Google Scholar 

  28. Gallus S, La Vecchia C. A population-based estimate of tobacco dependence. Eur J Pub Health. 2004;14:93–4.

    Article  Google Scholar 

  29. Gallus S, Pacifici R, Colombo P, La Vecchia C, Garattini S, Apolone G, et al. Tobacco dependence in the general population in Italy. Ann Oncol. 2005;16:703–6.

    Article  CAS  Google Scholar 

  30. John U, Meyer C, Hapke U, Rumpf HJ, Schumann A, Adam C, et al. Ludemann J, the Fagerstrom test for nicotine dependence in two adult population samples-potential influence of lifetime amount of tobacco smoked on the degree of dependence. Drug Alcohol Depend. 2003;71:1–6.

    Article  CAS  Google Scholar 

  31. Jeremias E, Chatkin JM, Chatkin G, Seibert J, Martins M, Wagner M. Smoking cessation in older adults. Int J Tuberc Lung Dis. 2012;16:273–8.

    Article  CAS  Google Scholar 

  32. National Center for Chronic Disease Prevention and Health Promotion (US) Office on Smoking and Health. Preventing tobacco use among youth and young adults: a report of the surgeon general. Atlanta: Centers for Disease Control and Prevention (US); 2012. p. 2 Available from: https://www.ncbi.nlm.nih.gov/books/NBK99242. The Health Consequences of Tobacco Use Among Young People.

    Google Scholar 

  33. Raghupathi V, Raghupathi W. The influence of education on health: an empirical assessment of OECD countries for the period 1995–2015. Arch Public Health. 2020;78:20. https://doi.org/10.1186/s13690-020-00402-5.

    Article  Google Scholar 

  34. Pampel F. Tobacco use in sub-Sahara Africa: estimates from the demographic health surveys. Soc Sci Med. 2008;66(8):1772–83.

    Article  Google Scholar 

  35. Jiang N, Lee YQ, Ling PM. Young adult social smokers: their co-use of tobacco and alcohol, tobacco related attitudes, and quitting efforts. Prev Med. 2014;69:166–71. https://doi.org/10.1016/j.ypmed.2014.09.013.

    Article  Google Scholar 

  36. Lee YH, Wang Z, Chiang TC, Liu CT. Beverage intake, smoking behavior, and alcohol consumption in contemporary China-a cross sectional analysis from the 2011 China health and nutrition survey. Int J Environ Res Public Health. 2017;14(5):e493. https://doi.org/10.3390/ijerph14050493.

    Article  Google Scholar 

  37. Méndez D, Alshanqeety O, Warner KE. The potential impact of smoking control policies on future global smoking trends. Tob Control. 2013;22(1):46–51. https://doi.org/10.1136/tobaccocontrol-2011-050147.

    Article  Google Scholar 

  38. Dawson DA, Goldstein RB, Grant BF. Prospective correlates of drinking cessation: variation across the life course. Addition. 2013;108(4):712–22. https://doi.org/10.1111/add.12079.

    Article  Google Scholar 

  39. Takahashi R, Wilunda C, Magutah K, Mwaura-Tenambergen W, Wilunda B, Perngparn U. Correlates of alcohol consumption in rural western Kenya: a cross-sectional study. BMC Psychiatry. 2017;17(1):175.

    Article  Google Scholar 

  40. Peltzer K, Chao LW, Ramlagan S, Szrek H. Daily tobacco use and problem drinking among urban adults in South Africa: a longitudinal study. Pan Afr Med J. 2019;1:32.

    Google Scholar 

  41. Kuteesa MO, Seeley J, Cook S, Webb EL. Multi-level experiences and determinants of alcohol misuse and illicit drug use among occupational groups at high-risk of HIV in sub-Saharan Africa: a thematic synthesis of qualitative findings. Glob Public Health. 2020;15(5):715–33. https://doi.org/10.1080/17441692.2019.1679216.

    Article  Google Scholar 

Download references

Acknowledgements

The authors wish to thank Statistics Botswana for providing the data that has been used for analyses in this study. Furthermore the authors wish to thank the Department of Population Studies, University of Botswana, for providing resources to conduct the analysis of data for the study.

Funding

This study did not receive any financial assistance or funding.

Author information

Authors and Affiliations

  1. Department of Population Studies, University of Botswana, Gaborone, Botswana

    Mpho Keetile

  2. Department of Computer Science, University of Botswana, Gaborone, Botswana

    Mpho Keetile & Kagiso Ndlovu

  3. Department of Economics, University of Botswana, Gaborone, Botswana

    Naomi Setshegetso

  4. School of International Development and Global Studies, University of Ottawa, Ottawa, Canada

    Sanni Yaya

  5. Public Health Department, Institute of Health Sciences, Lobatse, Botswana

    Fattimah Serojane

Authors
  1. Mpho Keetile
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kagiso Ndlovu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Naomi Setshegetso
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sanni Yaya
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Fattimah Serojane
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The study was conceived by MK; MK did the analysis and wrote the first draft of the manuscript. KN, NS, SY and FS reviewed the manuscript and provided critical comments on its improvement. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Mpho Keetile.

Ethics declarations

Ethics approval and consent to participate

This study uses secondary data provided by the Ministry of Health and Wellness hence no ethical approval was needed for this study. However, during the STEPS survey, the Ministry of Health and Wellness Institutional Review Board deemed the survey to be of a minimal risk to participants, and approved the survey. Informed consent was obtained from participants and parents or guardians in case of minors and no individually identifiable information was collected during the STEPS survey. All relevant protocols, guidelines and regulations for undertaking research among humans were followed in conducting the 2014 STEPs survey.

Consent for publication

N/A.

Competing interests

The authors declare no competing interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Keetile, M., Ndlovu, K., Setshegetso, N. et al. Prevalence and correlates of tobacco use in Botswana: evidence from the 2014 Botswana STEPwise survey. BMC Public Health 23, 40 (2023). https://doi.org/10.1186/s12889-022-14879-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12889-022-14879-y

Keywords

BMC Public Health

ISSN: 1471-2458

Contact us