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Cited by 42Smoking Behavior and CessationGlobal Cancer Incidence and ScreeningUrban and Rural Development Challenges

Prevalence and correlates of smoking among urban adult men in Bangladesh: slum versus non-slum comparison

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Khan, Md Mobarak Hossain, Khan, Aklimunnessa, Kraemer, Alexander, Mori, Mitsuru·2009·BMC Public Health
DOI 10.1186/1471-2458-9-149PMC PMC2705350
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Abstract

BACKGROUND: Smoking is one of the leading causes of premature death particularly in developing countries. The prevalence of smoking is high among the general male population in Bangladesh. Unfortunately smoking information including correlates of smoking in the cities especially in the urban slums is very scarce, although urbanization is rapid in Bangladesh and slums are growing quickly in its major cities. Therefore this study reported prevalences of cigarette and bidi smoking and their correlates separately by urban slums and non-slums in Bangladesh. METHODS: We used secondary data which was collected by the 2006 Urban Health Survey. The data were representative for the urban areas in Bangladesh. Both slums and non-slums located in the six City Corporations were considered. Slums in the cities were identified by two steps, first by using the satellite images and secondly by ground truthing. At the next stage, several clusters of households were selected by using proportional sampling. Then from each of the selected clusters, about 25 households were randomly selected. Information of a total of 12,155 adult men, aged 15-59 years, was analyzed by stratifying them into slum (= 6,488) and non-slum (= 5,667) groups. Simple frequency, bivariable and multivariable logistic regression analyses were performed using SPSS. RESULTS: Overall smoking prevalence for the total sample was 53.6% with significantly higher prevalences among men in slums (59.8%) than non-slums (46.4%). Respondents living in slums reported a significantly (P < 0.001) higher prevalence of smoking cigarettes (53.3%) as compared to those living in non-slums (44.6%). A similar pattern was found for bidis (slums = 11.4% and non-slums = 3.2%, P < 0.001). Multivariable logistic regression revealed significantly higher odds ratio (OR) of smoking cigarettes (OR = 1.12, 95% CI = 1.03-1.22), bidis (OR = 1.90, 95% CI = 1.58-2.29) and any of the two (OR = 1.23, 95% CI = 1.13-1.34) among men living in slums as compared to those living in non-slums when controlled for age, division, education, marital status, religion, birth place and types of work. Division, education and types of work were the common significant correlates for both cigarette and bidi smoking in slums and non-slums by multivariable logistic regressions. Other significant correlates of smoking cigarettes were marital status (both areas), birth place (slums), and religion (non-slums). Similarly significant factors for smoking bidis were age (both areas), marital status (slums), religion (non-slums), and birth place (both areas). CONCLUSION: The men living in the urban slums reported higher rates of smoking cigarettes and bidis as compared to men living in the urban non-slums. Some of the significant correlates of smoking e.g. education and division should be considered for prevention activities. Our findings clearly underscore the necessity of interventions and preventions by policy makers, public health experts and other stakeholders in slums because smoking was more prevalent in the slum communities with detrimental health sequelae.

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  • The association between tobacco use and rural dwelling is in line with low-income countries but dissimilar to middle- and high-income countries where tobacco use correlates with dwellers in large cities.

    “our findings as regards the association between tobacco use and rural dwelling is in line with low-income countries (43) but dissimilar to findings in middle- and high-income countries where tobacco use correlates with dwellers in large cities (44).”

    From Community-Based Screening, Brief Intervention, and Referral for Treatment for Unhealthy Tobacco Use: Single Arm Study Experience and Implementation Success in Rural and Semi-Rural Settings, South-West Nigeria (2016) · cites this paper as [43]

    The citation is attached to the claim about tobacco use among large-city dwellers in middle- and high-income countries. The Discussion reports higher smoking among men in urban slums than urban non-slums in Bangladesh, but neither it nor the searched passages address the claimed pattern in middle- or high-income countries (searched: large cities, high-income countries, urban and rural smoking). (LLM, conf=0.76)

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Authors
Khan, M. M. H., Khan, A., Kraemer, A., & Mori, M.
Affiliations
  • Bielefeld University (DE)
  • Sapporo Medical University (JP)
Corresponding author
Khan, Md Mobarak Hossain
ORCID
Venue
BMC Public Health
Publisher
Springer Science and Business Media LLC
Published
2009-05-22
Volume
9
Issue
1
Pages
149-149
ISSN
1471-2458 (ISSN-L)
DOI
10.1186/1471-2458-9-149
PMC
PMC2705350
OpenAlex
W2012472045
Cited by
42
References
45
Citations by year
2025: 2 · 2024: 2 · 2023: 2 · 2022: 4 · 2021: 4 · 2020: 5 · 2019: 3 · 2018: 1 · 2017: 2 · 2016: 2 · 2015: 5 · 2014: 5 · 2013: 1 · 2012: 2
FWCI
0.89
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Public Health (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • PUB – Publications at Bielefeld University (Bielefeld University) (submittedVersion, other-oa)
  • Publikationen an der Universität Bielefeld (Universität Bielefeld) (submittedVersion, other-oa)
Topics
  • Health Sciences › Medicine › Physiology › Smoking Behavior and Cessation
  • Health Sciences › Medicine › Oncology › Global Cancer Incidence and Screening
  • Social Sciences › Urban Studies › Urban and Rural Development Challenges
Keywords
cigarette smoking, bidis, urban slums, urbanization, urban men, smoking risk factors, Bangladesh, urban health, urbanization and health
MeSH terms
Adolescent, Adult, Bangladesh, Health Surveys, Humans, Male, Middle Aged, Poverty Areas, Regression Analysis, Smoking, Urban Population, Prevalence, Cluster Analysis, Young Adult

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