Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.
ADVANCED SEARCH
Log in
Top
BMC Public Health
Published in: BMC Public Health 1/2023

Open Access 01-12-2023 | Research

Factors associated with smoking intensity among adult smokers: findings from the longitudinal cohort of the Tehran lipid and glucose study

Authors: Marjan Abbasi-Dokht-Rafsanjani, Samaneh Hosseinzadeh, Enayatollah Bakhshi, Fereidoun Azizi, Davood Khalili

Published in: BMC Public Health | Issue 1/2023

Login to get access

Abstract

Background

Smoking is a significant public health problem, and there is a scarcity of documents regarding its severity, particularly in developing countries. This study aimed to determine factors related to the number of cigarettes consumed daily by adult smokers in Tehran.

Methods

This study was conducted within the framework of the longitudinal study of Tehran Lipid and Glucose Study (TLGS). The study included 786 adult smokers living during four consecutive follow-ups from 2005 to 2016. The intensity of smoking was measured by the number of cigarettes consumed daily by adult smokers. Data analysis was done longitudinally and based on the mixed effects zero-inflated discrete Weibull (ZIDW) regression model.

Results

The mean age of the individuals was 40.35 ± 12.68 years, and 643 (81.8%) of them were men. Also, 52.7% of individuals were daily smokers, 15.6% were occasional smokers, and 31.7% were non-smokers who became smokers during the study. Variables of age 1.005 (95%CI: 1.001–1.008), gender of male 1.196 (95%CI: 1.051–1.39), and marital status (divorced/widowed vs. single) 1.168 (95%CI: 1.015–1.39) were positively associated with smoking intensity. Education level (master and higher vs. illiterate) 0.675 (95%CI: 0.492–0.926)), employment status (student vs. unemployed) 0.683 (95%CI: 0.522–0.917), (housewife vs. unemployed) 0.742 (95%CI: 0.606–0.895), (Unemployed with income vs. unemployed) 0.804 (95%CI: 0.697, 0.923), implementation of smoking prohibition regulations (yes vs. no) 0.88 (95%CI: 0.843–0.932), and history of cardiovascular disease in male relatives (yes vs. no) 0.85 (95%CI: 0.771–0.951) were associated with lower smoking intensity.

Conclusion

We showed that demographic factors are associated with the intensity of smoking among adults and should be considered in policymakers’ intervention programs to reduce smoking and quit smoking.
Literature
1.
Organization WH. WHO report on the global Tobacco epidemic, 2021: addressing new and emerging products. World Health Organization; 2021.
2.
Parizadeh D, Momenan AA, Amouzegar A, Azizi F, Hadaegh F. Tobacco Smoking: findings from 20 years of the Tehran lipid and glucose study. Int J Endocrinol Metabolism. 2018;16(4 Suppl):e84738.
3.
Organization WH. WHO report on the global Tobacco epidemic, 2017: monitoring Tobacco use and prevention policies. World Health Organization; 2017.
4.
Roble AK, Osman MO, Lathwal OP, Aden AA. Prevalence of cigarette Smoking and associated factors among adolescents in eastern Ethiopia, 2020. Subst Abuse Rehabilitation. 2021;12:73–80.
5.
6.
Bagheri M, Roozbahani N, Shamsi M. Predictive factors of Smoking among adults based on Transtheoretical Model. Iran J Health Educ Health Promotion. 2015;3(3):211–8.
7.
Mahdi Ebadi M, Vahdaninia A, Azin A, Aeenparast S, Omidvari K, Jahangiri, et al. Prevalence of Tobacco consumption: Iranian health perception survey. Payesh (Health Monitor) Journal. 2011;10(3):365–72.
8.
Hackshaw A, Morris JK, Boniface S, Tang J-L, Milenković D. Low cigarette consumption and risk of coronary Heart Disease and Stroke: meta-analysis of 141 cohort studies in 55 study reports. BMJ. 2018;360:j5855.
9.
10.
Azizi F, Rahmani M, Emami H, Mirmiran P, Hajipour R, Madjid M, et al. Cardiovascular risk factors in an Iranian urban population: Tehran lipid and glucose study (phase 1). Sozial-und präventivmedizin. 2002;47:408–26.CrossRefPubMed
11.
Khalili D, Azizi F, Asgari S, Zadeh-Vakili A, Momenan AA, Ghanbarian A et al. Outcomes of a longitudinal population-based cohort study and pragmatic community trial: findings from 20 years of the Tehran lipid and glucose study. Int J Endocrinol Metabolism. 2018;16(4 Suppl):e84748.
12.
Fonseca V, Jawa A. Endothelial and erectile dysfunction, Diabetes Mellitus, and the metabolic syndrome: common pathways and treatments? Am J Cardiol. 2005;96(12):13–8.CrossRef
13.
Burger DA, Schall R, Ferreira JT, Chen DG. A robust bayesian mixed effects approach for zero inflated and highly skewed longitudinal count data emanating from the zero inflated discrete Weibull distribution. Stat Med. 2020;39(9):1275–91.CrossRefPubMed
14.
Fallahzadeh H, Asadi F. Generalized Linear Mixed Models: Introduction, Estimation Methods and Their Application in Medical Studies. Paramedical Sciences and Military Health. 2019;14(1):33–8.
15.
Perumean-Chaney SE, Morgan C, McDowall D, Aban I. Zero-inflated and overdispersed: what’s one to do? J Stat Comput Simul. 2013;83(9):1671–83.CrossRef
16.
Kalktawi HS. Discrete Weibull regression model for count data. Brunel University London; 2017.
17.
Schuurman N, Grasman R, Hamaker E. A comparison of inverse-wishart prior specifications for covariance matrices in multilevel autoregressive models. Multivar Behav Res. 2016;51(2–3):185–206.CrossRef
18.
Huang A, Wand MP. Simple Marginally Noninformative Prior Distributions for Covariance Matrices. Bayesian Analysis. 2013;8:439–52.
19.
Gelfand AE, Smith AF. Sampling-based approaches to calculating marginal densities. J Am Stat Assoc. 1990;85(410):398–409.CrossRef
20.
Nemati S, Rafei A, Freedman ND, Fotouhi A, Asgary F, Zendehdel K. Cigarette and water-pipe use in Iran: geographical distribution and time trends among the adult population; a pooled analysis of national STEPS surveys, 2006–2009. Arch Iran Med. 2017;20(5):295–301.PubMed
21.
Fahimfar N, Kohansal K, Asgari S, Ostovar A, Hadaegh F, Khalili D. The Trend of Risk for Cardiovascular Diseases during the past Decade in Iran, applying No-Lab and Lab-based prediction models. Global Heart. 2023;18(1):3.
22.
Alimohammadi M, Jafari-Mansoorian H, Hashemi SY, Momenabadi V, Ghasemi SM, Karimyan K. Review on the implementation of the Islamic Republic of Iran about Tobacco control, based on MPOWER, in the framework convention on Tobacco control by the World Health Organization. Addict Health. 2017;9(3):183.PubMedPubMedCentral
23.
Raei B, Emamgholipour S, Takian A, Yaseri M, Abdoli G, Alizadeh A. Distributional health and financial consequences of increased cigarette tax in Iran: extended cost-effectiveness analysis. Health Econ Rev. 2021;11(1):1–10.CrossRef
24.
The health, poverty, and financial consequences of a cigarette price increase among 500 million male smokers in 13 middle income countries: compartmental model study. BMJ (Clinical research ed). 2018;361:k1162.
25.
Organization WH. WHO report on the global Tobacco epidemic, 2019: offer help to quit Tobacco use. World Health Organization; 2019.
26.
Wiecha JM, Lee V, Hodgkins J. Patterns of Smoking, risk factors for Smoking, and smoking cessation among Vietnamese men in Massachusetts (United States). Tob Control. 1998;7(1):27–34.CrossRefPubMedPubMedCentral
27.
Mohammadian M, Sarrafzadegan N, Sadeghi M, Salehiniya H, Roohafza HR, Hosseini S, et al. Factors associated with Smoking initiation and continuation in Isfahan. J Mazandaran Univ Med Sci. 2015;25(123):99–110.
28.
Abdollahifard G, Vakili V, Danaei M, Askarian M, Romito L, Palenik CJ. Are the predictors of hookah Smoking differ from those of cigarette Smoking? Report of a population-based study in Shiraz, Iran, 2010. Int J Prev Med. 2013;4(4):459.PubMedPubMedCentral
29.
Lee Y, Lee K-S. Factors related to Smoking status among young adults: an analysis of younger and older young adults in Korea. J Prev Med Public Health. 2019;52(2):92.CrossRefPubMedPubMedCentral
30.
Shuja M, Sarrafzadegan N, Roohafza HR, Sadeghi M, Ghafari M, Mohammadian M, et al. Factors associated with cigarette Smoking in central parts of Iran. Asian Pac J Cancer Prev. 2017;18(3):647–53.PubMedPubMedCentral
31.
Handebo S, Birara S, Kassie A, Nigusie A, Aleminew W. Smoking Intensity and Associated Factors among Male Smokers in Ethiopia: Further Analysis of 2016 Ethiopian Demographic and Health Survey. Biomed Res Int. 2020;2020:4141370.
32.
Workie MS, Azene AG. Bayesian negative binomial logit hurdle and zero-inflated model for characterizing Smoking intensity. J Big Data. 2021;8:1–20.CrossRef
33.
Parami S, Tapak L, Poorolajal J, Moghimbeigi A, Ghaleiha A. Identifying factors associated with the hospital readmission rate among patients with major depressive disorder. BMC Psychiatry. 2021;21(1):1–9.CrossRef
34.
Moghimbeigi A, Eshraghian M, Mohammad K, Nourijelyani K, Husseini M. Determinants number of cigarette smoked with Iranian adolescents: a multilevel zero inflated poisson regression model. Iran J Public Health. 2009;38(4):91–6.
35.
Tüzen MF, Erbaş S. A comparison of count data models with an application to daily cigarette consumption of young persons. Commun Statistics-Theory Methods. 2018;47(23):5825–44.CrossRef
36.
Farshidi H, Aghamolaei T, Soleimani Ahmadi M, Madani AH. Epidemiological study of cigarette Smoking among over 15 years old population of Hormozgan in 2014. J Prev Med. 2016;3(2):29–35.
37.
Sarraf-Zadegan N, Boshtam M, Shahrokhi S, Naderi GA, Asgary S, Shahparian M, et al. Tobacco use among Iranian men, women and adolescents. Eur J Public Health. 2004;14(1):76–8.CrossRefPubMed
38.
Nketiah-Amponsah E, Afful-Mensah G, Ampaw S. Determinants of cigarette Smoking and Smoking intensity among adult males in Ghana. BMC Public Health. 2018;18(1):1–10.CrossRef
39.
Ngaruiya C, Abubakar H, Kiptui D, Kendagor A, Ntakuka MW, Nyakundi P, et al. Tobacco use and its determinants in the 2015 Kenya WHO STEPS survey. BMC Public Health. 2018;18:1–13.CrossRef
40.
Khanal V, Adhikari M, Karki S. Social determinants of Tobacco consumption among Nepalese men: findings from Nepal Demographic and Health Survey 2011. Harm Reduct J. 2013;10:1–10.CrossRef
41.
Mohamadnejad N, Masoumi M, Javan-Noughabi J. Analysis of factors affecting the Tobacco Use in Iran: a Household Budget Survey. Sadra Med Sci J. 2018;6(3):215–26.
42.
Zhang G, Zhan J, Fu H. Trends in Smoking Prevalence and Intensity between 2010 and 2018: implications for Tobacco control in China. Int J Environ Res Public Health. 2022;19(2):670.CrossRefPubMedPubMedCentral
43.
Mehrabi S, Delavari A, Moradi G, Esmailnasab EN, Pooladi A, Alikhani S, et al. Smoking among 15-to 64-year-old Iranian people in 2005. Iran J Epidemiol. 2007;3(1):1–9.
44.
Rad EH, Hajizadeh M, Rezaei S, Reihanian A. Cigarette Smoking and its financial burden among Iranian households: evidence from household income and expenditures survey. J Res Health Sci. 2020;20(4):e00494.CrossRef
45.
Ghorbani R, Malek M, Eskandarian R, Rashidy-Pour A. Epidemiology of smoking in an Iranian population (Semnan province): a population-based study. Koomesh. 2012;13:247–53.
46.
Jha P, Peto R. Global effects of Smoking, of quitting, and of taxing Tobacco. N Engl J Med. 2014;370(1):60–8.CrossRefPubMed
47.
Callinan JE, Clarke A, Doherty K, Kelleher C. Legislative Smoking bans for reducing secondhand smoke exposure, smoking prevalence and Tobacco consumption. Cochrane Database of Systematic Reviews. 2010(4):Cd005992.
48.
Van Loon AJM, Tijhuis M, Surtees PG, Ormel J. Determinants of Smoking status: cross-sectional data on Smoking initiation and cessation. Eur J Public Health. 2005;15(3):256–61.CrossRefPubMed
49.
Emami H, Habibian S, Salehi P, Azizi F. Pattern and Smoking habit in an urban area in Tehran, 2001. Res Med. 2003;27(1):47–52.
Metadata
Title
Factors associated with smoking intensity among adult smokers: findings from the longitudinal cohort of the Tehran lipid and glucose study
Authors
Marjan Abbasi-Dokht-Rafsanjani
Samaneh Hosseinzadeh
Enayatollah Bakhshi
Fereidoun Azizi
Davood Khalili
Publication date
01-12-2023
Publisher
BioMed Central
Published in
BMC Public Health / Issue 1/2023
Electronic ISSN: 1471-2458
DOI
https://doi.org/10.1186/s12889-023-17232-z

Other articles of this Issue 1/2023

BMC Public Health 1/2023 Go to the issue

Research

Needs and unmet needs for support services for recently pregnant intimate partner violence survivors in Ethiopia during the COVID-19 pandemic

Research

The association between fatty liver index and onset of diabetes: secondary analysis of a population-based cohort study

Research

Facilitators and barriers to engaging communities in health service research on dengue control in Indo-Pacific region: a systematic review

Research

Integrating intimate partner violence prevention content into a digital parenting chatbot intervention during COVID-19: Intervention development and remote data collection

Research

A tool to assess fitness among adults in public health studies – Predictive validity of the FFB-Mot questionnaire

Research

Information needs of informal caregivers in caring and rehabilitation for dioxin victims in Vietnam