Tobacco smoking and biomarker profile among beverage industrial workers and their spouses in Rwanda
A cross-sectional study
Bibliographic Data
| ID | 19591304 |
|---|---|
| Authors | Charles Nsanzabera (0000-0003-2926-5558, Jomo Kenyatta University of Agriculture and Technology), Jean claude Rukundo (Mount Kenya University), Mustafe Yusuf Said (0009-0002-7138-7858, University of Hargeisa), Leonard Ndayisenga (Mount Kenya University) |
| Editors | Julia Robinson (0000-0003-0719-3995) |
| Year | 2025 |
| Volume | 5 |
| Issue | 7 |
| Pages | e0003946 |
| Publication date | 2025-07-03 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | PLOS Global Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2767-3375 • E-ISSN: 2767-3375 |
| Publisher | Public Library of Science (PLoS) (PUBLISHER) |
| DOI | 10.1371/journal.pgph.0003946 |
| PMID | 40608695 |
| OpenAlex | W4411997622 |
| Language | EN |
| References cited | 16 |
Globally, smoking leads to over 7 million preventable deaths annually, with higher rates in men (16%) than women (7%). Although smoking rates have declined in high-income countries, tobacco use in Rwanda remains relatively low, with 14% of men and 3% of women affected. The focus on beverage industry workers and their spouses is justified by their higher income levels and potential exposure to stressful occupational factors that are commonly associated with smoking. The study aimed to assess tobacco smoking prevalence and the biomarker profiles of beverage industrial workers and their spouses in Rwanda. This research employed a cross-sectional study design conducted in a beverage manufacturing industry in Rwanda. The study targeted 822 individuals, including beverage industry employees and their spouses, aged 30–75 years. An initial sample size of 384 was calculated using the Cochrane formula, which was adjusted to 440 to account for non-responses. Data collection was conducted from May to December 2018. Data analysis was conducted using SPSS version 22, with chi-square and logistic regression tests to assess tobacco use and other associated factors with a significance cutoff of p
Biostatistics · Confounding · Cross-sectional study · Environmental health · Logistic regression · Multicollinearity · Odds · Odds ratio · Regression analysis · Sample size determination · Statistics · Stratified sampling · Alcohol Consumption and Health Effects · Demography · Influenza Virus Research Studies · Medicine · Smoking Behavior and Cessation · Epidemiology · Internal Medicine
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| Highly cited | No |
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