Quantitative health impact assessment modelling

Health Impact Assessment (HIA) is spreading as a tool for policy and decision making. An important stage of HIA is to predict the future health consequences of implementing a potential decision in order to raise the level of knowledge for making the final decision. This journal published an editorial on HIA in November 2008 [1]. This editorial expanded the issue with the quantitative approach to HIA – in some parts of the international literature also called public health models. Long before the concept of HIA was established and the first practical guidance was published [2] and a state-of-the-art book printed [3], activities with the purpose of developing public health models were in progress for making scenarios of the future health effects of changing exposure to determinants of health and with a focus on health improvement interventions. Because of increasing computational ability simple causal models have been succeeded by models handling more complex causality webs. Some methods are based on dynamic micro-simulation techniques and model, for instance, individual life courses where shifts between states (e.g. unexposed, exposed, healthy, ill and dead) operate by transition probabilities. Generally, the use of these models is limited because of scarce data. Other models operate on aggregated data and are often restricted as to their ability to handle different topics. Typically, the models include input data such as prevalence of exposure to risk factors, incidence or prevalence of specific diseases, death rates and estimates from epidemiological studies on the association between risk factors and diseases or death. Because of the aim of evaluating the future consequences of health promotion interventions the models also include parameters of latency and lag time and data on the demographic evolution. Complementary to quantifying reduction in disease incidence or number of deaths or quantifying life years gained by health promotion interventions, summary measures of population health have been introduced in order to facilitate comparisons between various health states and across different population strata or to make comparisons between policies involving different fields easier. Thus, quality-adjusted life years (QALYs) is a composite measure of health-related quality of life developed by health economists and originally used with the purpose of comparing the health outcome of different medical therapies or interventions on patients suffering from the same specific disease, or to compare health care or therapeutic outcomes for patients suffering from different diseases. Summary measures of population health such as QALYs and health expectancy (i.e. average lifetime in a specific state of health) are increasingly important measures incorporating and summing up knowledge from different epidemiological evidence and with the characteristic feature to comprehend the possibility of comparisons between various population groups [4,5]. Disabilityadjusted life years (DALYs) is another popular indicator that combines mortality and disability. DALYs has been extensively used in the global burden of disease studies [6]. Diseases of the circulatory system are still highly frequent (despite many years of decline), and during the last three decades there has been a lot of focus on epidemiological research on these deceases and on