title: DYNAMO-HIA--a Dynamic Modeling tool for generic Health Impact Assessments.
abstract: BACKGROUND
  Currently, no standard tool is publicly available that allows researchers or policy-makers to quantify the impact of policies using epidemiological evidence within the causal framework of Health Impact Assessment (HIA). A standard tool should comply with three technical criteria (real-life population, dynamic projection, explicit risk-factor states) and three usability criteria (modest data requirements, rich model output, generally accessible) to be useful in the applied setting of HIA. With DYNAMO-HIA (Dynamic Modeling for Health Impact Assessment), we introduce such a generic software tool specifically designed to facilitate quantification in the assessment of the health impacts of policies.
  
  
  RESULTS
  DYNAMO-HIA quantifies the impact of user-specified risk-factor changes on multiple diseases and in turn on overall population health, comparing one reference scenario with one or more intervention scenarios. The Markov-based modeling approach allows for explicit risk-factor states and simulation of a real-life population. A built-in parameter estimation module ensures that only standard population-level epidemiological evidence is required, i.e. data on incidence, prevalence, relative risks, and mortality. DYNAMO-HIA provides a rich output of summary measures--e.g. life expectancy and disease-free life expectancy--and detailed data--e.g. prevalences and mortality/survival rates--by age, sex, and risk-factor status over time. DYNAMO-HIA is controlled via a graphical user interface and is publicly available from the internet, ensuring general accessibility. We illustrate the use of DYNAMO-HIA with two example applications: a policy causing an overall increase in alcohol consumption and quantifying the disease-burden of smoking.
  
  
  CONCLUSIONS
  By combining modest data needs with general accessibility and user friendliness within the causal framework of HIA, DYNAMO-HIA is a potential standard tool for health impact assessment based on epidemiologic evidence.
authors.affiliation: Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands. S.Lhachimi@erasmusmc.nl
authors.forename: Stefan K
authors.initials: SK
authors.lastname: Lhachimi
authors.forename: Wilma J
authors.initials: WJ
authors.lastname: Nusselder
authors.forename: Henriette A
authors.initials: HA
authors.lastname: Smit
authors.forename: Pieter
authors.initials: P
authors.lastname: van Baal
authors.forename: Paolo
authors.initials: P
authors.lastname: Baili
authors.forename: Kathleen
authors.initials: K
authors.lastname: Bennett
authors.forename: Esteve
authors.initials: E
authors.lastname: Fernández
authors.forename: Margarete C
authors.initials: MC
authors.lastname: Kulik
authors.forename: Tim
authors.initials: T
authors.lastname: Lobstein
authors.forename: Joceline
authors.initials: J
authors.lastname: Pomerleau
authors.forename: Johan P
authors.initials: JP
authors.lastname: Mackenbach
authors.forename: Hendriek C
authors.initials: HC
authors.lastname: Boshuizen
mesh_terms: D004812:Epidemiologic Methods; D006801:Humans; D008962:Models, Theoretical; D012984:Software