A Dynamic Population Model for Estimating All-Cause Mortality Due to Lifetime Exposure History

• Activity Number: 672
• Type: Topic Contributed
• Date/Time: Thursday, August 8, 2013 : 10:30 AM to 12:20 PM
• Sponsor: Social Statistics Section
• Author(s): Annette M. Bachand*+ and Sandra Sulsky
• Companies: Environ; Colorado State University and Environ
• Keywords: Population simulation ; harm reduction ; unintended consequences ; policy

Abstract

Under the Family Smoking Prevention and Tobacco Control Act, a tobacco product may be designated as a modified risk product if it benefits the population "taking into account both users of tobacco products and persons who do not currently use tobacco products". We developed a comprehensive dynamic model that allows the estimation of all-cause mortality for a hypothetical cohort. All model input is user-specified. In the base case, members of the cohort may be exposed to a high risk product. The counterfactual scenario includes exposure to a high risk and/or a modified risk product. The model sorts the population into age and exposure categories, and applies appropriate mortality rates to each category. The model tracks individual exposure histories, and estimates, at the end of each modeled age category, the number of survivors in the two exposure scenarios, and the difference between them. Markov Chain Monte Carlo techniques are used to estimate the variability of the results. Model output was compared against US and Swedish life tables using tobacco exposure transition probabilities derived from the literature, and it produced similar survival estimates.