https://orcid.org/0000-0001-7565-7515
Figures
Abstract
Introduction
Although it is known that maternal tobacco use during pregnancy substantially declined in higher-income countries, information on the magnitude and determinants of tobacco use among pregnant women in sub-Saharan Africa (SSA) remains limited. Establishing evidence on maternal tobacco during pregnancy is crucial for guiding targeted interventions in SSA. This study aimed to determine the overall prevalence of tobacco use and its determinants among pregnant women in SSA countries.
Methods
The study used data from Demographic and Health Surveys conducted in 33 countries across SSA from 2010 and 2021. Our analysis included a total weighted sample of 40,291 pregnant women. A multilevel logistic regression model was used to identify factors associated with maternal tobacco use during pregnancy. The measure of association between explanatory variables and the outcome was reported using adjusted odds ratios (AORs) with 95% confidence intervals (CIs).
Results
The pooled prevalence of tobacco use among pregnant women in SSA was 1.76% (95% CI: 1.41, 2.12). Our findings showed that pregnant women in the age groups of 25–34 years (AOR 1.44; 95% CI: 1.14, 1.82) and 35+ years (AOR 2.18; 95% CI: 1.68, 2.83) had higher odds of tobacco use during pregnancy. Pregnant women who attained primary education (AOR 0.57; 95% CI: 0.46, 0.70) and secondary or higher education (AOR 0.39; 95% CI: 0.30, 0.53) were associated with lower odds of tobacco use. Similarly, pregnant women who resided in households with a high wealth index (AOR 0.36; 95% CI: 0.55 0.90) and those with media exposure (AOR 0.81; 95% CI: 0.67, 0.99) were less likely to use tobacco during pregnancy.
Conclusion
This study revealed that the overall prevalence of maternal tobacco use during pregnancy was relatively low in SSA, but some countries exhibited higher estimates. To address this, it is crucial to implement targeted smoking prevention and cessation strategies, particularly for young pregnant women, those facing socioeconomic disadvantages, and those with lower educational status.
Citation: Aychiluhm SB, Mare KU, Dagnew B, Seid AA, Melaku MS, Sabo KG, et al. (2024) Determinants of tobacco use among pregnant women in sub-Saharan Africa. A multilevel mixed-effect logistic regression model. PLoS ONE 19(5): e0297021. https://doi.org/10.1371/journal.pone.0297021
Editor: Delfina Fernandes Hlashwayo, Eduardo Mondlane University: Universidade Eduardo Mondlane, MOZAMBIQUE
Received: June 15, 2023; Accepted: December 26, 2023; Published: May 21, 2024
Copyright: © 2024 Aychiluhm et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Data used in this study were obtained from the DHS Program and available online from the DHS website https://dhsprogram.com/data/available-datasets.cfm").
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Globally, tobacco smoking is a major public health concern, causing over seven million deaths annually, with six million attributed to active smoking and the remainder to passive exposure [1]. Given these statistics, several global initiatives have been implemented to decrease the use of tobacco products [2]. In 2003, the World Health Assembly adopted the World Health Organization (WHO) Framework Convention on Tobacco Control (FCTC) in response to the globalization of the tobacco epidemic [3]. As part of these efforts, the WHO has also set a 30% decrease in tobacco usage as a global objective by 2025 [4]. Emphasizing the importance of addressing tobacco use during pregnancy is crucial for ensuring the health and well-being of both mothers and their infants [5].
Maternal exposure to tobacco during pregnancy has both short-term and long-term consequences [6]. In the short term, exposure to tobacco products during pregnancy leads to complications such as placenta previa, placental abruption, and pre-eclampsia, along with adverse fetal outcomes including low birth weight, premature birth, and complete perinatal death [7,8]. Additionally, first-trimester maternal smoking is associated with congenital anomalies, including atrial septal anomalies, transposition of the main arteries, conotruncal malformations, and patent ductus arteriosus [9].
In the long term, maternal smoking during pregnancy is known to have adverse effects on children, including attention deficit disorders and learning disabilities, childhood cancer and obesity, elevated blood pressure in children, and a lack of breast milk due to insufficient lactation [10–12]. Smokeless tobacco usage among pregnant women has also consistently shown strong associations with important factors such as stress or depression, alcohol-drinking behaviors, and low educational attainment [13].
In 2016, the prevalence of smoking during pregnancy ranged from 1.7% to 4.5%, with variations from 0.8% in the African region to 8.1% in the European region [14]. While it is known that maternal tobacco use during pregnancy substantially declined in higher-income countries [15], information on the overall magnitude and determinants of tobacco use among pregnant women in sub-Saharan Africa (SSA) remains limited. The prevalence of tobacco use in SSA countries was expected to undergo the largest increase [16] as the SSA region is still in the early stage of the tobacco epidemic [17].
Previously published studies have shown that the partner’s smoking status, partner’s educational status, occupation, residence site, marital status, number of previous pregnancies, and adequate prenatal care are significant risk factors associated with tobacco use during pregnancy [18–22]. Understanding the overall prevalence and determinants of tobacco use among pregnant mothers is essential for guiding policy interventions in SSA. Therefore, this study aimed to determine the pooled prevalence of tobacco use and its determinants in 33 sub-Saharan African countries.
Methods
Data source and participants
This study used the latest available Demographic and Health Surveys (DHS) data sets from 33 SSA countries conducted between 2010 and 2021. Out of the 44 SSA countries with routine DHS, five countries (Central African Republic, Sudan, Nigeria Ondo State, Eswatini, and Sao Tome and Principe) had data older than 2010, four countries (Cape Verde, Equatorial Guinea, Botswana, and Eritrea) had data restrictions, and two countries (Chad and Senegal) had no observation on the outcome variable of tobacco use. In the end, our final analysis included 33 countries, including Angola, Benin, Burkina Faso, Burundi, Comoros, Ethiopia, Kenya, Democratic Republic of Congo, Congo, Cote D’Ivoire, Cameroon, Gabon, Ghana, Gambia, Guinea, Liberia, Lesotho, Madagascar, Mali, Mauritania, Malawi, Mozambique, Nigeria, Niger, Namibia, Rwanda, Sierra Leone, Togo, Tanzania, Uganda, South Africa, Zambia and Zimbabwe.
The DHS surveys across all countries employed standardized procedures to gather data on basic socio-demographic characteristics and different health indicators. In this analysis, since the study population was pregnant women, we extracted women’s records (IR) from each country and then appended them after managing missing observations. Finally, a total weighted sample of 40,206 pregnant women was included in the analysis. The data is available in the public domain and accessed at https://dhsprogram.com/data/available-datasets.cfm.
Study variables
Outcome variable.
The main outcome variable for this study was maternal tobacco use during pregnancy. This was measured using pregnant women’s self-reported use of various tobacco products, including cigarettes, pipes, chewing tobacco, snuffs by a nose, snuffs by mouth, smoking kreteks, smoking cigars/cheroots/cigarillos, smoking water pipes, and other country-specific tobacco products. Pregnant women were classified as “Tobacco users” if the response was ‘Yes’ for any of the above tobacco products; otherwise, they were classified as “Non-tobacco users”.
Independent variables.
We grouped independent variables into individual and community-level variables. Individual-level variables included age, marital status, women’s educational status, women’s current working status, partner’s education level, household wealth index, and media exposure. On the other hand, community-level variables included residence, region, community-level women’s literacy, community-level media exposure, and community-level poverty. Community-level variables, specifically community-level women’s literacy, community-level media exposure, and community-level poverty, were generated by aggregating the individual-level observations at the cluster level. Median values were then used to categorize the generated variables as “low” and “high”.
Statistical analysis and modeling
All analyses were conducted using STATA software (version 14.1, Stata Corp, College Station, TX, USA). In our analyses, we considered sampling weight to address potential imbalances or unequal probabilities in household selections and non-responses, as well as to consider clustering and stratification. Frequency and percentages were used to present descriptive statistics. To account for the hierarchal nature of DHS data, a multilevel logistic regression model was used to identify determinants of tobacco use among pregnant women.
Model building and comparison.
The DHS data is structured hierarchically, with women nested within clusters. Pregnant women in each cluster are anticipated to have greater similarities compared to the broader population. This suggests that advanced models need to account for the variability between clusters. To address this, the mixed-effect logistic regression analysis method was employed.
The multi-level models were specified in four steps. In the initial step, model I (null model) was fitted without independent variables to test random variability in the intercept. Subsequently, model II was then fitted with only community-level explanatory variables. In model III, a model with only individual-level explanatory variables was used. Finally, model IV (full model) was used based on both individual and community-level predictors simultaneously. The formula for the fitted model was as follows:
Where πij is the probability of pregnant women who use tobacco; 1—πij is the probability of pregnant women who do not use tobacco; β0 is log odds of the intercept β1… βn is the amount of effect by the individual and community-level variables; X1…Xn represent the independent variables at the individual and community level; uoj is the random error at community (cluster); and eij is the random error at the individual level.
Additionally, the intra-class correlation coefficient (ICC), Median odds ratio (MOR), and Proportional Change in Variance (PCV) were calculated to check the random variation across clusters. The ICC was calculated as the proportion of the between cluster variation in the total variation:
Where Var(uoj) is the community (cluster) level variance and Π2/3 is the standard logistic distribution which is Π2/3 = 3.29 [23]. The MOR is defined as the median value of the odds ratio between the cluster at the highest risk and the cluster at the lowest risk when randomly picking out two clusters. MOR was calculated using the formula
where Var(uoj) is a community (cluster) level variance [23,24]
The variability in the odds of tobacco use among pregnant women can be explained by successive models calculated by PCV using the following formula:
Where Ve is the variance in the null model and Vmi is the variance in the successive models.
The model selection was based on Akaike’s information criterion (AIC), favoring the one with the lowest AIC value. Model IV (full model), a model with both individual and community-level predictors, demonstrated the smallest AIC value. The measure of association between explanatory variables and the outcome was reported using adjusted odds ratios (AORs) with 95% confidence intervals (CIs).
Ethical statement
We requested authorization to access data from the Measure DHS program and obtained approval (AuthLetter_186779) to download and use the data for our study from the DHS website (http://www.measuredhs.com). The requirement for informed consent was waived as secondary data were used.
Results
Sociodemographic characteristics
The study included a weighted total sample of 40,206 pregnant women. Among these, 27,988 (69.61%) were married, and 12,632 (31.42%) had completed secondary education or higher. A total of 26,665 (66.32%) pregnant women resided in rural areas, and 17,323(42.99%) were from the Western African region. Our findings also found that 17,426 (43.34%) pregnant women resided in households with a low wealth index (Table 1).
Prevalence of tobacco product use among pregnant women in SSA
The pooled prevalence of tobacco product use among pregnant women in SSA was 1.76% (95% CI: 1.41, 2.12). Madagascar had the highest prevalence of tobacco use during pregnancy (Prevalence = 9.74%; 95% CI: 8.11, 11.38), followed by South Africa (Prevalence = 8.00%; 95% CI: 3.66, 12.33) and Lesotho (Prevalence = 4.71%; 95% CI: 2.25, 7.18). The lowest prevalence of tobacco use during pregnancy was found inGambia (Prevalence = 0.11%; 95% CI:-0.11, 0.32), followed by Cameroon (Prevalence = 0.13%; 95% CI:-0.07, 0.34) (Fig 1).
Further subgroup analyses were conducted considering the higher heterogeneity (higher I2 value) in the prevalence of tobacco use among pregnant women across SSA countries. The results from the subgroup analysis showed that the pooled prevalence of tobacco use among pregnant women ranged from 1.38% (95% CI: 0.91, 1.84) in the West African Region to 2.65% (95% CI: 1.13, 4.17) in the South African Region (Fig 2).
Random effect analysis
The ICC value in the null model showed a 14.5% variation in tobacco product use among pregnant women across clusters. The PCV value for the final model showed that the combined effect of individual and community-level variables explains 44.6% of the variance in tobacco use at the community level. Furthermore, the presence of heterogeneity in tobacco use levels across clusters was indicated by the MOR, with a value of 1.93 in the null model. This implies that the likelihood of tobacco use among pregnant women in clusters with higher tobacco use prevalence was nearly twofold compared to those in clusters with a lower tobacco use level. Model IV had the lowest AIC value (i.e. 6867), indicating the best-fitted model (Table 2).
Determinants of tobacco product use among pregnant women in SSA
In the multilevel multivariable logistic regression model, our findings showed that pregnant women aged 25–34 years and 35+ years were 1.44 times (AOR 1.44; 95% CI: 1.14, 1.82) and 2.18 times (AOR 2.18; 95% CI: 1.68, 2.83) more likely, respectively, to use tobacco products during pregnancy compared to women aged 24 years or younger, after adjusting for other covariates.
Pregnant women who had completed primary schooling were 43% less likely to use tobacco products (AOR 0.57; 95% CI: 0.46, 0.70) and those who had completed secondary education or higher were 61% less likely (AOR 0.39; 95% CI: 0.30, 0.53) compared to pregnant women who had no formal education. The odds of tobacco use during pregnancy among pregnant women with media exposure was lower by 19% compared to women who had no media exposure (AOR 0.81; 95% CI: 0.67, 0.99). Furthermore, pregnant women who resided in households with a high wealth index had 64% lower odds of tobacco product use compared to those pregnant women from households with a low wealth index (AOR 0.36; 95% CI: 0.55 0.90) (Table 3).
Discussion
This study has provided evidence on tobacco use among pregnant women using representative data from 33 SSA countries. Our findings are crucial for governments and policy-makers to assess and enhance Tobacco Control Directives, particularly for a special group of populations such as pregnant women. To the best of our knowledge, this is the first study to investigate the overall prevalence of tobacco product use and its determinants among pregnant women in 33 SSA countries using nationally representative data.
Our findings indicated a comparatively low overall prevalence of tobacco product use among pregnant women in 33 SSA countries, standing at 1.8%. This figure is slightly different from previously published studies conducted in low-income and middle-income countries, which reported a prevalence of 2.6%, as well as the SSA region, where the prevalence was noted at 2.0%. [25,26]. While the previous studies are important and commendable, it is essential to underscore that our study’s findings capture the current socioeconomic conditions, utilizing the most recent datasets available. Furthermore, the variation in the prevalence of tobacco product use across individual countries could also show the cultural influence and differences of tobacco product use across countries.
In this study, pregnant women who resided in high-wealth index households were less likely to use tobacco products compared to those who resided in low-wealth index households, consistent with previously published studies [27–29]. The reciprocal association between wealth and smoking could be explained by the possibility that pregnant women in poverty might resort to smoking as a form of self-medication to control their emotions, cope with stress, and ease the tensions associated with financial hardship [29,30]. Policy implications arising from our findings underscore the importance of targeted interventions and support for pregnant women in low-income households.
The results of this study revealed that pregnant women aged 25 years and above were associated with a higher risk of smoking, consistent with previous studies [31–33]. While smoking has been a long-standing cultural tradition in some communities, especially with smokeless tobacco products like snuff and snus, younger women may have more access to current health information sources, making them more aware of the risks associated with tobacco use [34,35]. Targeted interventions are imperative to address the unique challenges faced by the older age group, promoting healthier behaviors during pregnancy.
Pregnant women with primary, secondary, and higher education levels are less likely to use tobacco products than pregnant women without formal education, aligning with prior studies [36,37]. Our findings could be explained by three pathways. Firstly, education may increase awareness about the health risks of tobacco use, empowering women to make informed decisions for their health and that of their unborn child [38]. Secondly, educated women may have better access to resources, support networks, and healthcare services, creating an environment for healthier lifestyle choices [39]. Lastly, education can contribute to greater socioeconomic stability, reducing the chance of using smoking as a coping mechanism in times of stress [40]. Promoting universal primary education is crucial for fostering healthier behaviors among pregnant women and improving maternal and fetal health outcomes.
Furthermore, pregnant women exposed to media were less likely to use tobacco products, consistent with earlier studies [41]. Media plays a crucial role in influencing population smoking behaviors. Media campaigns that discourage tobacco use have the power to reshape people’s perceptions, prevent smoking initiation, and support adult quitting. Attitudes toward tobacco are significantly influenced by media communications, and evidence indicates that exposure to media messages about tobacco use impacts both its tobacco use and prevention [42,43]. Our findings suggest the importance of prioritizing the implementation of media campaigns to effectively convey anti-tobacco messages to pregnant women and the broader population.
Limitations and strengths of the study
This study has limitations. Firstly, the cross-sectional nature of the data makes it challenging to establish causality. Secondly, the measurement of tobacco use relies on self-reporting, introducing the possibility of underreporting due to social desirability bias. Thirdly, the surveys were conducted at different times in each respective country, caution must be exercised when comparing the prevalence of tobacco use between nations. Despite the limitations, the study used nationally representative DHS data sets from 33 SSA countries, enhancing the generalizability of our findings. Additionally, the DHS uses standardized methods and instruments to ensure consistency in survey design and data collection.
Conclusion and recommendation
This study provides timely evidence on the overall prevalence of tobacco use among pregnant women in SSA countries, using nationally representative data. While the overall prevalence is relatively low across 33 SSA countries, certain nations exhibit higher rates. Multilevel multivariable logistic regression results indicate that younger pregnant women, those with primary and secondary education, higher household wealth, and media exposure are less likely to use tobacco.
Educational interventions are crucial for women without formal education. Primary care settings are an essential source of prenatal care education for pregnant women from low-income households. Older women especially those who have no education and media exposure should be informed that smoking does not reduce stress levels. Enhancing awareness through education and offering smoke cessation programs through mass media and as part of antenatal care is crucial.
Despite a lower overall prevalence compared to high-income nations, specific SSA countries report high levels of tobacco use during pregnancy. This underscores the importance of targeted interventions in these countries to raise awareness about the health risks of tobacco use among women, especially during pregnancy.
Acknowledgments
The authors thank ICF International for granting access to the dataset used in this study.
References
- 1.
World Health Organization. WHO report on the global tobacco epidemic, 2017: monitoring tobacco use and prevention policies. World Health Organization; 2017.
- 2. Chugh A, Arora M, Jain N, Vidyasagaran A, Readshaw A, Sheikh A, et al. The global impact of tobacco control policies on smokeless tobacco use: a systematic review. The Lancet Global Health. 2023 Jun 1;11(6):e953–68. pmid:37202029
- 3.
World Health Organization (WHO). World Health Organization Framework Convention on Tobacco Control. 2003. Geneva: World Health Organization.
- 4.
World Health Organization. World report on ageing and health. World Health Organization; 2015 Oct 22.
- 5. Almeida R, Barbosa C, Pereira B, Diniz M, Baena A, Conde A. Tobacco smoking during pregnancy: Women’s perception about the usefulness of smoking cessation interventions. International Journal of Environmental Research and Public Health. 2022 May 28;19(11):6595. pmid:35682178
- 6. Banderali G, Martelli A, Landi M, Moretti F, Betti F, Radaelli G, et al. Short and long term health effects of parental tobacco smoking during pregnancy and lactation: a descriptive review. Journal of translational medicine. 2015 Dec;13(1):1–7. pmid:26472248
- 7. Pineles BL, Park E, Samet JM. Systematic review and meta-analysis of miscarriage and maternal exposure to tobacco smoke during pregnancy. American journal of epidemiology. 2014 Apr 1;179(7):807–23. pmid:24518810
- 8. Rogers JM. Tobacco and pregnancy. Reproductive Toxicology. 2009 Sep 1;28(2):152–60. pmid:19450949
- 9. Källén K. Maternal smoking and congenital heart defects. European journal of epidemiology. 1999 Sep;15:731–7. pmid:10555617
- 10.
Varvarigou AA, Fouzas S, Beratis NG. Effect of prenatal tobacco smoke exposure on fetal growth potential.
- 11. Wikström AK, Cnattingius S, Galanti MR, Kieler H, Stephansson O. Effect of Swedish snuff (snus) on preterm birth. BJOG: An International Journal of Obstetrics & Gynaecology. 2010 Jul;117(8):1005–10. pmid:20465559
- 12. Yadav A, Singh PK, Yadav N, Kaushik R, Chandan K, Chandra A, et al. Smokeless tobacco control in India: policy review and lessons for high-burden countries. BMJ global health. 2020 Jul 1;5(7):e002367. pmid:32665375
- 13. Singh JK, Acharya D, Kadel R, Adhikari S, Lombard D, Koirala S, et al. Factors associated with smokeless tobacco use among pregnant women in rural areas of the southern terai, nepal. Journal of Nepal Health Research Council. 2017 Jan 1;15(1):12–9. pmid:28714486
- 14. Lange S, Probst C, Rehm J, Popova S. National, regional, and global prevalence of smoking during pregnancy in the general population: a systematic review and meta-analysis. The Lancet Global Health. 2018 Jul 1;6(7):e769–76. pmid:29859815
- 15. Vellios N, Ross H, Perucic AM. Trends in cigarette demand and supply in Africa. PLoS One. 2018 Aug 17;13(8):e0202467. pmid:30118508
- 16. Méndez D, Alshanqeety O, Warner KE. The potential impact of smoking control policies on future global smoking trends. Tobacco control. 2013 Jan 1;22(1):46–51. pmid:22535364
- 17. Lopez AD, Collishaw NE, Piha T. A descriptive model of the cigarette epidemic in developed countries. Tobacco control. 1994 Sep;3(3):242.
- 18. Nair S, Schensul JJ, Begum S, Pednekar MS, Oncken C, Bilgi SM, et al. Use of smokeless tobacco by Indian women aged 18–40 years during pregnancy and reproductive years. PLoS One. 2015 Mar 18;10(3):e0119814. pmid:25786247
- 19. Verma P, Pandey P, Thakur A. Prevalence and determinants of tobacco consumption among pregnant women of three Central Indian Districts. Tropical Journal of Obstetrics and Gynaecology. 2017 Nov 3;34(2):99–106.
- 20. Mishra GA, Kulkarni SV, Gupta SD, Shastri SS. Smokeless tobacco use in Urban Indian women: Prevalence and predictors. Indian Journal of Medical and Paediatric Oncology. 2015 Jul;36(03):176–82. pmid:26681842
- 21. Palma S, Pérez-Iglesias R, Pardo-Crespo R, Llorca J, Mariscal M, Delgado-Rodríguez M. Smoking among pregnant women in Cantabria (Spain): trend and determinants of smoking cessation. BMC Public Health. 2007 Dec; 7:1–6.
- 22. Sontag LW, Wallace RF. The effect of cigaret smoking during pregnancy upon the fetal heart rate. American Journal of Obstetrics and Gynecology. 1935 Jan 1;29(1):77–83.
- 23. Merlo J, Yang M, Chaix B, Lynch J, Råstam L. A brief conceptual tutorial on multilevel analysis in social epidemiology: investigating contextual phenomena in different groups of people. Journal of Epidemiology & Community Health. 2005 Sep 1;59(9):729–36. pmid:16100308
- 24. Merlo J, Chaix B, Ohlsson H, Beckman A, Johnell K, Hjerpe P, et al. A brief conceptual tutorial of multilevel analysis in social epidemiology: using measures of clustering in multilevel logistic regression to investigate contextual phenomena. Journal of Epidemiology & Community Health. 2006 Apr 1;60(4):290–7. pmid:16537344
- 25. Caleyachetty R, Tait CA, Kengne AP, Corvalan C, Uauy R, Echouffo-Tcheugui JB. Tobacco use in pregnant women: analysis of data from Demographic and Health Surveys from 54 low-income and middle-income countries. The Lancet Global Health. 2014 Sep 1;2(9):e513–20. pmid:25304418
- 26. Yaya S, Uthman OA, Adjiwanou V, Bishwajit G. Exposure to tobacco use in pregnancy and its determinants among sub-Saharan Africa women: analysis of pooled cross-sectional surveys. The Journal of Maternal-Fetal & Neonatal Medicine. 2020 May 2;33(9):1517–25. pmid:30185088
- 27. Palipudi K, Rizwan SA, Sinha DN, Andes LJ, Amarchand R, Krishnan A, et al. Prevalence and sociodemographic determinants of tobacco use in four countries of the World Health Organization: South-East Asia region: findings from the Global Adult Tobacco Survey. Indian journal of cancer. 2014 Dec 1;51(Suppl 1):S24–32. pmid:25526244
- 28. Yaya S, Bishwajit G, Shah V, Ekholuenetale M. Socioeconomic disparities in smoking behavior and early smoking initiation among men in Malawi. Tobacco use insights. 2017 Aug 11; pmid:28839446
- 29. Abdeta T, Hunduma G. Tobacco Use Among Reproductive Age Women in Ethiopia: Evidence from the National Health Survey. Substance Abuse and Rehabilitation. 2021 Mar 18:1–0. pmid:33776506
- 30.
WHO Tobacco Free Initiative. Tobacco and poverty: a vicious circle. World Health Organization; 2004.
- 31. Khan MT, Hashmi S, Zaheer S, Aslam SK, Khan NA, Aziz H, et al. Burden of waterpipe smoking and chewing tobacco use among women of reproductive age group using data from the 2012–13 Pakistan demographic and health survey. BMC public health. 2015 Dec;15(1):1–8. pmid:26563874
- 32. Lakew Y, Haile D. Tobacco use and associated factors among adults in Ethiopia: further analysis of the 2011 Ethiopian Demographic and Health Survey. BMC public health. 2015 Dec;15(1):1–8. pmid:25966998
- 33. Sreeramareddy CT, Pradhan PM, Sin S. Prevalence, distribution, and social determinants of tobacco use in 30 sub-Saharan African countries. BMC medicine. 2014 Dec;12(1):1–3. pmid:25518855
- 34. Messer K, Trinidad DR, Al-Delaimy WK, Pierce JP. Smoking cessation rates in the United States: a comparison of young adult and older smokers. American journal of public health. 2008 Feb;98(2):317–22. pmid:18172143
- 35. Khuder SA, Dayal HH, Mutgi AB. Age at smoking onset and its effect on smoking cessation. Addictive behaviors. 1999 Sep 1;24(5):673–7. pmid:10574304
- 36. Kandel DB, Griesler PC, Schaffran C. Educational attainment and smoking among women: risk factors and consequences for offspring. Drug and alcohol dependence. 2009 Oct 1;104:S24–33. pmid:19179020
- 37. Higgins ST, Heil SH, Badger GJ, Skelly JM, Solomon LJ, Bernstein IM. Educational disadvantage and cigarette smoking during pregnancy. Drug and alcohol dependence. 2009 Oct 1;104:S100–5. pmid:19442460
- 38. Maralani V. Understanding the links between education and smoking. Social science research. 2014 Nov 1;48:20–34. pmid:25131272
- 39. Raghupathi V, Raghupathi W. The influence of education on health: an empirical assessment of OECD countries for the period 1995–2015. Archives of Public Health. 2020 Dec;78(1):1–8. pmid:32280462
- 40. Businelle MS, Kendzor DE, Reitzel LR, Costello TJ, Cofta-Woerpel L, Li Y, et al. Mechanisms linking socioeconomic status to smoking cessation: a structural equation modeling approach. Health Psychology. 2010 May;29(3):262. pmid:20496980
- 41. Pierce JP, Gilpin EA. News media coverage of smoking and health is associated with changes in population rates of smoking cessation but not initiation. Tobacco Control. 2001 Jun 1;10(2):145–53. pmid:11387535
- 42.
Davis RM, editor. The role of the media in promoting and reducing tobacco use. US Department of Health and Human Services, National Institutes of Health, National Cancer Institute; 2008.
- 43. Achia TN. Tobacco use and mass media utilization in sub-Saharan Africa. PloS one. 2015 Feb 23;10(2):e0117219. pmid:25706131