Standard Tool for Quantification in Health Impact Assessment A Review Stefan K. Lhachimi, MPP, MSc, Wilma J. Nusselder, PhD, Hendriek C. Boshuizen, PhD, Johan P. Mackenbach, MD, PhD Background: The health impact assessment (HIA) of policy proposals is becoming common practice. HIA represents a broad approach with quantifıcation of the impact of policy options at its core. However, no standard tool is available and it remains unclear whether any current model can serve as a standard for the fıeld. Purpose: The aim of this study is to assess whether already existing models can be used as a standard tool for the quantifıcation step in an HIA. Methods: A search in 2008 identifıed 20 models for HIA, of which six are suffıciently generic to allow for various and multiple diseases and different risk factors: Age-Related Morbidity and Death Analysis, Global Burden of Disease, Population Health Modeling, PREVENT, Proportional Life Table Method, and the National Institute for Public Health and the Environment (the Netherlands) Chronic Disease Model. These were evaluated along three proposed model structure criteria (real-life population, dynamic pro- jection, explicit risk-factor states) and three usability criteria (modest data requirements, rich model output, generally accessible) developed to address the needs and requirements of the HIA framework. Results: Of the six generic models investigated, none fulfılls all the proposed criteria as a standard HIA tool. The models are either technically advanced with no or limited accessibility, or they are accessible but oversimplifıed. Conclusions: Further work on models for HIA with equal emphasis on technical appropriateness, availability of data, and end-user–friendly implementation is warranted if the fıeld is to move forward. (Am J Prev Med 2010;38(1):78 – 84) © 2010 American Journal of Preventive Medicine Introduction H ealth impact assessment (HIA) assesses the ef- fect of a program, project, or policy on overall population health and the distributional effects within a population. 1 The rationale for HIA is that many risk factors for chronic diseases are affected by policy mea- sures outside the realm of health policy (e.g., transportation, food, or urban planning). Assessments have been carried out at all governmental levels (e.g., local, 2 regional, 3 national, 4 and supranational 5 ), and the number of HIAs is likely to rise due to increased institutional adoption and political will, in par- ticular in the European Union. 6,7 An HIA can take many forms, ranging from a rapid assessment to establish if health is affected at all to a comprehensive HIA that appraises all health aspects. 8 Generally, an HIA can be divided into fıve steps (Figure 1). According to a recent defınition, 9 an HIA consists of two core tasks: (1) supporting decision makers in choos- ing among options and (2) predicting the future conse- quences of implementing different options. To predict future developments of complex systems, such as a population, models are indispensable. 10 Despite the increasing role of guidelines in HIA and the existence of models that allow to quantify the effect of a policy on popu- lation health, there is no commonly accepted practice in the prediction of the impact of policy on health for HIA purposes. 11 Quantifıcation is seldom attempted in HIA, 12 and standard tools to conduct this are lacking. 11–14 However, rational decision making requires that costs and benefıts in terms of health effects are estimated. 15,16 From the Department of Public Health, Erasmus MC, University Medical Center Rotterdam (Lhachimi, Nusselder, Mackenbach), Rotterdam; and the National Institute of Public Health and the Environment (Boshuizen, Lhachimi), Bilthoven, the Netherlands Address correspondence and reprint requests to: Stefan K. Lhachimi, MPP, MSc, Department of Public Health, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, 3000 CA, Rotterdam, the Netherlands. E-mail: s.lhachimi@erasmusmc.nl. 0749-3797/00/$17.00 doi: 10.1016/j.amepre.2009.08.030 78 Am J Prev Med 2010;38(1):78 – 84 © 2010 American Journal of Preventive Medicine • Published by Elsevier Inc.