Login / Signup

Initiation versus Cessation Control Policies: Deriving Optimal Resource Allocation Strategies to Decrease Smoking Prevalence Under a Fixed Budget.

Ruoyan SunDavid Mendez
Published in: MDM policy & practice (2019)
Background. Over several decades the tobacco control community has recommended and implemented smoking initiation and cessation interventions to reduce the smoking toll. It is necessary to study the combined effect of these interventions to allocate resources optimally. However, there is a paucity of studies that address the right combination of initiation and cessation policies over time to reduce smoking prevalence. Objective. To derive optimal trajectories of initiation and cessation interventions that minimize overall smoking prevalence over a specified period while satisfying a budget constraint. Methods. Using an established dynamic model of smoking prevalence, we employ an optimal control formulation to minimize overall smoking prevalence within a specified time period. The budget constraint is handled through an iterative application of a penalty function on above-budget expenditures. We further derive the optimal cost ratio of initiation versus cessation programs over time. To parameterize our model, we use results from two empirical interventions. The demographic data are from the National Health Interview Survey in the United States. Results. For our example, our results show that the optimal cost ratio (initiation over cessation) starts around 2.02 and gradually increases to 5.28 in 30 years. Smoking prevalence decreases significantly compared with the status quo, 8.54% in 30 years with no interventions versus the estimated 6.43% with interventions. In addition, the optimal units of initiation and cessation interventions increase over time. Conclusions. Our model provides a general framework to incorporate policy details in determining the optimal mix of smoking interventions.
Keyphrases
  • smoking cessation
  • physical activity
  • risk factors
  • public health
  • healthcare
  • mental health
  • cross sectional
  • machine learning
  • electronic health record
  • big data
  • contrast enhanced
  • data analysis