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Application of a system dynamics model to inform investment in smoking cessation services in New Zealand

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  • Tobias, M.I.
  • Cavana, R.Y.
  • Bloomfield, A.

Abstract

Objectives. We estimated the long-term effects of smoking cessation interventions to inform government decision-making regarding investment in tobacco control. Methods. We extracted data from the 2006 New Zealand Tobacco Use Survey and other sources and developed a system dynamics model with the iThink computer simulation package. The model derived estimates of population cessation rates from smoking behaviors and applied these over a 50-year period, from 2001 to 2051, under business-as-usual and enhanced cessation intervention scenarios. Results. The model predicted larger effects by 2051 with the enhanced cessation than with the business-as-usual scenario, including: an 11% greater decline in adult current smoking prevalence (9 versus 10 per 100 people), 16% greater decline in per capita tobacco consumption (370 versus 440 cigarette equivalents per year), and 11% greater reduction in tobacco-attributable mortality (3000 versus 3300 deaths per year). Conclusions. The model generated reliable estimates of the effects on health and on tobacco use of interventions designed to enhance smoking cessation. These results informed a decision announced in May 2007 to increase funding for smoking cessation by NZ$42 million over 4 years.

Suggested Citation

  • Tobias, M.I. & Cavana, R.Y. & Bloomfield, A., 2010. "Application of a system dynamics model to inform investment in smoking cessation services in New Zealand," American Journal of Public Health, American Public Health Association, vol. 100(7), pages 1274-1281.
    • Handle: RePEc:aph:ajpbhl:10.2105/ajph.2009.171165_2
    DOI: 10.2105/AJPH.2009.171165
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    Cited by:

    1. Negar Darabi & Niyousha Hosseinichimeh, 2020. "System dynamics modeling in health and medicine: a systematic literature review," System Dynamics Review, System Dynamics Society, vol. 36(1), pages 29-73, January.
    2. McGill, Elizabeth & Er, Vanessa & Penney, Tarra & Egan, Matt & White, Martin & Meier, Petra & Whitehead, Margaret & Lock, Karen & Anderson de Cuevas, Rachel & Smith, Richard & Savona, Natalie & Rutter, 2021. "Evaluation of public health interventions from a complex systems perspective: A research methods review," Social Science & Medicine, Elsevier, vol. 272(C).
    3. Haojia Kong & Lifan Shi & Dan Da & Zhijiang Li & Decai Tang & Wei Xing, 2022. "Simulation of China’s Carbon Emission based on Influencing Factors," Energies, MDPI, vol. 15(9), pages 1-15, April.
    4. Penny R. Breeze & Hazel Squires & Kate Ennis & Petra Meier & Kate Hayes & Nik Lomax & Alan Shiell & Frank Kee & Frank de Vocht & Martin O’Flaherty & Nigel Gilbert & Robin Purshouse & Stewart Robinson , 2023. "Guidance on the use of complex systems models for economic evaluations of public health interventions," Health Economics, John Wiley & Sons, Ltd., vol. 32(7), pages 1603-1625, July.
    5. Simon K Chiu & Louise Freebairn & Louise A Baur & L. Kurt Kreuger & Adam Skinner & Jo‐An Occhipinti, 2023. "Modeling distribution parameters in system dynamics: an application in childhood obesity," System Dynamics Review, System Dynamics Society, vol. 39(2), pages 103-124, April.
    6. Chang, Angela Y. & Ogbuoji, Osondu & Atun, Rifat & Verguet, Stéphane, 2017. "Dynamic modeling approaches to characterize the functioning of health systems: A systematic review of the literature," Social Science & Medicine, Elsevier, vol. 194(C), pages 160-167.

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