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This summary is machine-generated.

This review identifies best practices and pitfalls in tobacco control policy simulation models. It aims to develop recommendations for assessing model quality, improving tobacco policy decision-making.

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Area of Science:

  • Public Health
  • Health Policy Research
  • Mathematical Modeling

Background:

  • Tobacco control models are mathematical tools for predicting tobacco-related outcomes.
  • Policy simulation models are a subcategory used to assess tobacco control policies.
  • No existing tools specifically assess the quality of these tobacco control models.

Purpose of the Study:

  • Identify best practices in tobacco control modeling.
  • Highlight common pitfalls in tobacco control models.
  • Develop recommendations for assessing the quality of tobacco control policy simulation models.

Main Methods:

  • Systematic methodology review of studies published July 2013-August 2019.
  • Searched five databases (Embase, EconLit, PsycINFO, PubMed, CINAHL Plus).
  • Included papers projecting tobacco-related outcomes focusing on tobacco control policies.

Main Results:

  • Data collection is ongoing; results are expected by April 2021.
  • This review will summarize trends, approaches, and data quality in tobacco control models.
  • Findings will inform recommendations for model quality assessment.

Conclusions:

  • This review will provide a list of recommendations for high-quality tobacco control simulation models.
  • Standardized and quality-assured models benefit modelers and policymakers.
  • Improved models can lead to better public health decision-making.