The Impact Calculation Tool (ICT) is a user-friendly modelling tool for quantification of health impacts from environmental exposures. It applies dynamic life table modelling for calculation of population specific loss of disability adjusted life years (DALY) from an exposure of interest.

ICT allows analysis of health impacts from one environmental exposure in one given population at a time. The follow-up period (time period for which impacts are determined), specific mortality and morbidity impacts analysed, and the target population can be defined according to the needs of the assessment at hand. In one model run, impacts are modelled for a reference, business-as-usual (BAU), and one alternative exposure scenario. Impact indicator outputs include age-specific mortality/disease cases, life expectancy (age-specific and birth cohort) and age-specific loss of disability adjusted life years (DALY, YLL (years of life lost due to mortality), and YLD (years of life lost due to disease)). Both time discounting and age-weighting can be applied. All key input data have to be provided by the user.

ICT runs in Analytica, which is a modelling software with a user-friendly graphical interface. The software enables probabilistic modelling using Monte Carlosimulation and, therefore, advanced uncertainty analysis. Full use of the Analytica programme requires a software licence. However, ICT can also be run with a free Analytica player, which can be downloaded from www.lumina.com. The player allows the user to view to the model contents and calculation specifics, to input data for key parameters, and to calculate results and run probabilistic uncertainty analysis. ICT contains a simple user interface, which enables these functions without advanced knowledge of Analytica or the model technicalities.

Health impact modelling in ICT

Mortality impacts are modelled using dynamic life table approach. First, total mortality risk is modelled for the reference and alternative exposure scenarios using the population and baseline mortality data, exposure levels and exposure-response functions. Based on the total mortality risk and population input data, the future population structure is then projected for each scenario using life table methodology. These population projections are applied in determining the age-conditional life expectancies in the different scenarios. YLL (years of life lost due to mortality) can subsequently be calculated directly from the life tables as the difference in the life years lived by the projected populations in different scenarios, or indirectly based on the age-specific attributable deaths and age-specific life expectancies in a given scenario.

In the case of morbidity impacts, the morbidity risk attributable to the exposure is first modelled for each scenario based the population and baseline morbidity data, exposure level and exposure-response functions. Number of attributable morbidity cases is then calculated using the attributable morbidity risk and the modelled population projections for each scenario, and YLD (years of life lost due to disease) subsequently based on the attributable cases, severity weight and duration.

Benefits of life table modelling

The advantage of dynamic life table modelling in health impact assessment is that it enables to predict impacts in a real life population over time as the population structure and risk level changes. Life table models take into account effect of competing causes of death. The concept of competing causes of deaths refer to the fact, that changing the risk due to a certain cause of death will affect the mortality from other causes if their risk level stays the same. Thus, a life table model gives the net change in the life years saved or lost over time. By taking competing causes of death into account it is also possible to avoid the risk of over-estimating the overall mortality impact when evaluating impacts from multiple mortality endpoints for a single exposure or combined mortality effects from multiple exposures.

Life table modelling provides most benefits in terms of estimating the net total impacts in a real life population when used in a direct way, i.e. comparing the life tables and life years predicted for different scenarios. However, the indirect use, i.e. when life table modelling is used to determine age-conditional life expectancy in a real life population, which can be further multiplied with attributable deaths to derive YLL, can be more preferable in some situations. This is, for example, in cases where impacts are modelled for a short follow-up period (one or few years), but the aim is to estimate total loss of life years due to the attributable deaths. A simplified solution would be to use age-conditional life expectancy data for the current population. However, if the aim is to model impacts due to an existing risk factor, this approximation would lead to underestimation of YLL because it ignores that in the (theoretical) absence of the risk life expectancy would, in fact, be a fraction higher. In many cases this difference would be negligible, but could in some cases be of importance. This source of bias is avoided when applying life table modelling in the impact assessment, because the model also predicts the impact of the risk factor to the current life-expectancy in the target population.

The model and extensive user guidance is available from: http://en.opasnet.org/w/Impact_calculation_tool