Estimating the health impacts of tobacco harm reduction policies: a simulation modeling approach.
With adult smoking prevalence rates declining too slowly to reach national objectives, opinion leaders are considering policies to improve tobacco-related outcomes by regulating the composition of cigarettes to be (1) less harmful and/or (2) less addictive. Because harm reduction efforts may actually encourage higher cigarette consumption by promoting a safer image, and addictiveness reduction may increase the harmfulness of cigarettes by encouraging compensatory smoking behaviors, policymakers must consider the tradeoffs between these two approaches when proposing legislation to control cigarette content. To estimate health impacts, we developed a dynamic computer model simulating changes in the age- and gender-specific smoking behaviors of the U.S. population over time. Secondary data for model parameters were obtained from publicly available sources. Population health impacts were measured as change in smoking prevalence and the change in cumulative quality-adjusted life-years (QALYs) in the U.S. population over 75 years. According to the risk-use threshold matrix generated by the simulation, modifying cigarettes to reduce their harmfulness and/or addictiveness could result in important gains to the nation's health. Addictiveness reduction efforts producing a 60% improvement in smoking behavior change probabilities would produce a net gain in population health at every plausible level of increase of smoking-related harm that was modeled. A 40% reduction in smoking-related harm would produce a net QALY gain at every level of behavior change considered. This research should prove useful to policymakers as they contemplate giving the FDA the authority to regulate the composition of cigarettes.
Ahmad
Sajjad
S
Department of Civil, Architectural and Environmental Engineering, University of Miami, Coral Gables, FL 33146-0630, USA. sajjad@miami.edu
Billimek
John
J
D000328:Adult; D006801:Humans; D015993:Life Tables; D015233:Models, Statistical; D012309:Risk-Taking; D012907:Smoking / Q000009:adverse effects / Q000331:legislation & jurisprudence / Q000401:mortality; D016540:Smoking Cessation / Q000379:methods / Q000706:statistics & numerical data; D000074606:Smoking Prevention; D014481:United States / Q000453:epidemiology; D014026:Nicotiana