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Updated: Jan 19, 2026

Cigarette Smoke Exposure in Mice using a Whole-Body Inhalation System
Published on: October 22, 2020
Burden of disease attributable to second-hand smoke exposure: A systematic review
Giulia Carreras1, Alessandra Lugo2, Silvano Gallus2
1Oncologic Network, Prevention and Research Institute (ISPRO), via Cosimo il Vecchio 2, 50139 Florence, Italy.
Abstract:
Our aim was to provide a systematic review of studies on the burden of disease due to second-hand smoke (SHS) exposure, reviewing methods, exposure assessment, diseases causally linked to SHS, health outcomes, and estimates available to date. A literature review of studies on the burden of disease from SHS exposure, available in PubMed and SCOPUS, published 2007-2018 in English language, was carried out following the PRISMA recommendations. Overall, 588 studies were first identified, and 94 were eligible. Seventy-two studies were included in the systematic review. Most of them were based on the comparative risk assessment approach, assessing SHS exposure using mainly surveys on exposure at home/workplaces. Diseases more frequently studied were: lung cancer, ischemic heart disease, stroke, chronic obstructive pulmonary disease, asthma and breast cancer in adults; lower respiratory tract infection, otitis media, asthma, sudden infant death syndrome and low birth weight in children. The SHS exposure assessment and the reported population attributable fractions (PAF) were largely heterogeneous. As an example, the PAF from lung cancer varied between 0.6% and 20.5%. Moreover, PAF were estimated applying relative risks and SHS exposures with no consistent definitions or with different age classes. The research gap on the SHS exposure burden is shrinking. However, estimates are not yet available for a number of countries, particularly the Middle Eastern and African countries, and not all diseases with the strongest evidence of causation, such as sudden infant death syndrome, have been explored. Moreover, in some cases the applied methodology revealed relatively low quality of data.
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