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Simulating future public health benefits of tobacco control interventions: a systematic review of models
  1. Ankur Singh1,
  2. Nick Wilson2,
  3. Tony Blakely3,4
  1. 1Centre for Health Equity, Melbourne School of Population & Global Health, University of Melbourne, Melbourne, Victoria, Australia
  2. 2Public Health, University of Otago, Wellington, New Zealand
  3. 3Population Interventions Unit, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Victoria, Australia
  4. 4Burden of Disease Epidemiology, Equity and Cost-Effectiveness Program, University of Otago, Weliington, New Zealand
  1. Correspondence to Professor Tony Blakely, Population Interventions, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne VIC 3010, Victoria, Australia; antony.blakely{at}unimelb.edu.au

Abstract

Background To prioritise tobacco control interventions, simulating their health impacts is valuable. We undertook a systematic review of tobacco intervention simulation models to assess model structure and input variations that may render model outputs non-comparable.

Methods We applied a Medline search with keywords intersecting modelling and tobacco. Papers were limited to those modelling health outputs (eg, mortality, health-adjusted life years), and at least two of cancer, cardiovascular and respiratory diseases. Data were extracted for each simulation model with ≥3 arising papers, including: model type, untimed or with time steps and trends in business-as-usual (BAU) tobacco prevalence and epidemiology.

Results Of 1911 papers, 186 met the inclusion criteria, including 13 eligible simulation models. The SimSmoke model had the largest number of publications (n=46), followed by Benefits of Smoking Cessation on Outcomes (n=12) and Tobacco Policy Model (n=10). Two of 13 models only estimated deaths averted, 1 had no time steps, 5 had no future trends in BAU tobacco prevalence, 9 had no future trends in BAU disease epidemiology and 7 had no time lags from quitting tobacco to reversal of health harm.

Conclusions Considerable heterogeneity exists in simulation models, making outputs substantively non-comparable between models. Ranking of interventions by one model may be valid. However, this may not be true if, for example, interventions that differentially affect age groups (eg, a tobacco-free generation policy vs increased cessation among adults) do not account for plausible future trends. Greater standardisation of model structures and outputs will allow comparison across models and countries, and for comparisons of the impact of tobacco control interventions with other preventive interventions.

  • prevention
  • economics
  • litigation
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Footnotes

  • Twitter @drankursingh99

  • Contributors AS contributed to design, analysis, interpretation of results and prepared draft of the manuscript. NW contributed to interpretation of results and providing critical feedback on the manuscript. TB led the conception of the project and design, analysis, interpretation of data and oversaw the drafting of the manuscript.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests TB and NW have led the development of the BODE3 tobacco control model, and AS has applied BODE3 model.

  • Patient consent for publication Not required.

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

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