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Predicting the long-term effects of electronic cigarette use on population health: a systematic review of modelling studies
  1. Giang T Vu1,2,
  2. Daniel Stjepanović1,
  3. Tianze Sun1,2,
  4. Janni Leung1,2,
  5. Jack Chung1,2,
  6. Jason Connor1,2,3,
  7. Phong K Thai4,
  8. Coral E Gartner5,
  9. Bach Xuan Tran6,7,
  10. Wayne D Hall1,4,
  11. Gary Chan1
  1. 1National Centre for Youth Substance Use Research, The University of Queensland, Brisbane, Queensland, Australia
  2. 2School of Psychology, The University of Queensland, Brisbane, Queensland, Australia
  3. 3Discipline of Psychiatry, The University of Queensland, Brisbane, Queensland, Australia
  4. 4Queensland Alliance for Environmental Health Sciences, The University of Queensland, Brisbane, Queensland, Australia
  5. 5NHMRC Centre of Research Excellence on Achieving the Tobacco Endgame, School of Public Health, The University of Queensland, Brisbane, Queensland, Australia
  6. 6Institute for Preventive Medicine and Public Health, Hanoi Medical University, Hanoi, Viet Nam
  7. 7Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
  1. Correspondence to Giang T Vu, National Centre for Youth Substance Use Research (NSYCUR), The University of Queensland, Brisbane, Queensland, Australia;{at}


Objective To systematically review and synthesise the findings of modelling studies on the population impacts of e-cigarette use and to identify potential gaps requiring future investigation.

Data source and study selection Four databases were searched for modelling studies of e-cigarette use on population health published between 2010 and 2023. A total of 32 studies were included.

Data extraction Data on study characteristics, model attributes and estimates of population impacts including health outcomes and smoking prevalence were extracted from each article. The findings were synthesised narratively.

Data synthesis The introduction of e-cigarettes was predicted to lead to decreased smoking-related mortality, increased quality-adjusted life-years and reduced health system costs in 29 studies. Seventeen studies predicted a lower prevalence of cigarette smoking. Models that predicted negative population impacts assumed very high e-cigarette initiation rates among non-smokers and that e-cigarette use would discourage smoking cessation by a large margin. The majority of the studies were based on US population data and few studies included factors other than smoking status, such as jurisdictional tobacco control policies or social influence.

Conclusions A population increase in e-cigarette use may result in lower smoking prevalence and reduced burden of disease in the long run, especially if their use can be restricted to assisting smoking cessation. Given the assumption-dependent nature of modelling outcomes, future modelling studies should consider incorporating different policy options in their projection exercises, using shorter time horizons and expanding their modelling to low-income and middle-income countries where smoking rates remain relatively high.

  • Electronic nicotine delivery devices
  • Cessation
  • Harm Reduction
  • Nicotine
  • Public policy

Data availability statement

All data relevant to the study are included in the article or uploaded as supplementary information.

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Data availability statement

All data relevant to the study are included in the article or uploaded as supplementary information.

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  • Contributors GTV: Investigation; data extraction; writing initial draft; review and editing. DS: Conceptualisation; study registration and data quality assessment. TS and JL: Review and editing. J Chung: Investigation. J Connor, PKT, CEG and BT: Review and editing; data quality assessment. WDH: Conceptualisation; review and editing. GC: Conceptualisation; review and editing, guarantor. All authors contributed to the data interpretation, writing and revisions of the report and have full access to all data in the study.

  • Funding GV and TS are funded by Higher Degree by Research scholarships provided by The University of Queensland. GC and JL are funded by a NHMRC Investigator Grants. CG is funded by NHMRC Centre of Research Excellence Grant. NCYSUR is supported by Commonwealth funding from the Australian Government provided under the Drug and Alcohol Program. The funding bodies had no role in the study design, collection, analysis or interpretation of the data, writing the manuscript, or the decision to submit the paper for publication.

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.