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A Computational Model for Assessing the Population Health Impact of Introducing a Modified Risk Claim on an Existing Smokeless Tobacco Product

Author

Listed:
  • Raheema S. Muhammad-Kah

    (Regulatory Affairs, Center for Research & Technology, Altria Client Services LLC, 601 East Jackson Street, Richmond, VA 23219, USA)

  • Yezdi B. Pithawalla

    (Regulatory Affairs, Center for Research & Technology, Altria Client Services LLC, 601 East Jackson Street, Richmond, VA 23219, USA)

  • Edward L. Boone

    (Department of Statistical Sciences & Operations Research, Virginia Commonwealth University, Richmond, VA 23284, USA)

  • Lai Wei

    (Regulatory Affairs, Center for Research & Technology, Altria Client Services LLC, 601 East Jackson Street, Richmond, VA 23219, USA)

  • Michael A. Jones

    (Regulatory Affairs, Center for Research & Technology, Altria Client Services LLC, 601 East Jackson Street, Richmond, VA 23219, USA)

  • Ryan A. Black

    (Regulatory Affairs, Center for Research & Technology, Altria Client Services LLC, 601 East Jackson Street, Richmond, VA 23219, USA)

  • Thomas M. Bryan

    (Regulatory Affairs, Center for Research & Technology, Altria Client Services LLC, 601 East Jackson Street, Richmond, VA 23219, USA)

  • Mohamadi A. Sarkar

    (Regulatory Affairs, Center for Research & Technology, Altria Client Services LLC, 601 East Jackson Street, Richmond, VA 23219, USA)

Abstract

Computational models are valuable tools for predicting the population effects prior to Food and Drug Administration (FDA) authorization of a modified risk claim on a tobacco product. We have developed and validated a population model using best modeling practices. Our model consists of a Markov compartmental model based on cohorts starting at a defined age and followed up to a specific age accounting for 29 tobacco-use states based on a cohort members transition pathway. The Markov model is coupled with statistical mortality models and excess relative risk ratio estimates to determine survival probabilities from use of smokeless tobacco. Our model estimates the difference in premature deaths prevented by comparing Base Case (“world-as-is”) and Modified Case (the most likely outcome given that a modified risk claim is authorized) scenarios. Nationally representative transition probabilities were used for the Base Case. Probabilities of key transitions for the Modified Case were estimated based on a behavioral intentions study in users and nonusers. Our model predicts an estimated 93,000 premature deaths would be avoided over a 60-year period upon authorization of a modified risk claim. Our sensitivity analyses using various reasonable ranges of input parameters do not indicate any scenario under which the net benefit could be offset entirely.

Suggested Citation

  • Raheema S. Muhammad-Kah & Yezdi B. Pithawalla & Edward L. Boone & Lai Wei & Michael A. Jones & Ryan A. Black & Thomas M. Bryan & Mohamadi A. Sarkar, 2019. "A Computational Model for Assessing the Population Health Impact of Introducing a Modified Risk Claim on an Existing Smokeless Tobacco Product," IJERPH, MDPI, vol. 16(7), pages 1-17, April.
  • Handle: RePEc:gam:jijerp:v:16:y:2019:i:7:p:1264-:d:221088
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    References listed on IDEAS

    as
    1. Annette M. Bachand & Sandra I. Sulsky & Geoffrey M. Curtin, 2018. "Assessing the Likelihood and Magnitude of a Population Health Benefit Following the Market Introduction of a Modified‐Risk Tobacco Product: Enhancements to the Dynamic Population Modeler, DPM(+1)," Risk Analysis, John Wiley & Sons, vol. 38(1), pages 151-162, January.
    2. Martin, Andrew D. & Quinn, Kevin M. & Park, Jong Hee, 2011. "MCMCpack: Markov Chain Monte Carlo in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i09).
    3. Pierce, J.P., 1989. "International comparisons of trends in cigarette smoking prevalence," American Journal of Public Health, American Public Health Association, vol. 79(2), pages 152-157.
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