Microsimulation models are powerful tools for assessing the value of health promotion and simulating counterfactual scenarios. We illustrate with a microsimulation model of reduced male smoking in South Korea, which has one of the highest male smoking rates in the world despite a recent decline.

 Drawing from the nationally-representative Korean Longitudinal Study on Aging, we developed the South Korean Future Elderly Model, a dynamic micro-simulation model. We simulated three types of interventions that reduce the cumulative number of packs of cigarettes that a man smoked before age 50: a 30% overall reduction, eliminating smoking among light smokers (bottom 30th percentile of smoking intensity), or among heavy smokers (top 70th percentile).

 All three interventions led to significant improvements in health, with that targeting the heaviest smokers yielding gains of 2.36 life years, 1.4 disability-free life years and 1.63 disease-free life years, as well as significant reductions in prevalence of cancer, diabetes, heart disease, hypertension, lung disease and stroke. An across-the-board 30% reduction in smoking intensity resulted in an increase in life expectancy of 1.16 years, an increase of 0.83 disability-free life years, an increase of 0.28 disease-free life years, and a reduction in cancer prevalence. Eliminating smoking among light smokers yielded few health gains. For tobacco control to significantly raise life expectancy and reduce the chronic disease burden among the future elderly in South Korea, interventions should target heavy smokers.  

Abstract ················································································································1

Ch 1. Introduction ···························································································5

  A. Research Background ······························································································7

  B. Research Purpose ····························································································10

Ch 2. Modeling ··················································································13

  A. Overview ·······································································································15

  B. Transition Model Estimation ·····································21

  C. Decision-making Using Monte Carlo Simulation ···············································································22

  D. Data and Estimation Results ················································································23

Ch 3. Policy Simulation ······································································29

  A. Prevalence of Diseases in Ageing Population ·······························································31

  B. The Impact of the Declining Smoking Rate on the Burden of Disease ··································································35

Ch 4. Simulation Results and Conclusion ···························································37

  A. Simulation Results ·····················································································39

  B. Conclusion ·······································································································45

References ····························································································49