One of the most important elements of an assessment report is invariably a summary of the main findings, as indicated by the various measures of health impact used to describe the outcomes.   In most assessments, a wide range of health impacts need to be considered.  Typically, moreover, these impacts relate to several different scenarios, so users need to be able to compare not only between health effects but also between the scenarios, in order to draw conclusions about how best to respond.  Often (though not always), trades-off need to be made in this process, between different sets of interests or objectives, or between different types of outcome.  For example, different scenarios may impact differently on morbidity and mortality, on short-term and long-term health, or in different areas or on different population sub-groups. 

One way of dealing with these multiple outcomes is by aggregating the impacts into a single, summary measure (e.g. DALYs or monetary value) to represent the overall burden of disease.  This is certainly useful in most cases, and in so far as these measures can be considered consistent they provide a means of comparing directly between the different scenarios (e.g. in terms of lives or money saved).  Compound measures such ad these, however, hide as much as they reveal, and are inevitablky based on a series of weights (to reflect the severity etc of different diseases), which imply some form of value judgements.  To interpret the results, and understand how they have arisen, users will often need additional information.  In addition, policy makers and other stakeholders often expect and demand more freedom of choice than these summary measures imply, and they may wish to challenge or change the weights that are inherent (and often buried) in these measures - for example, to reflect different value-systems (see comparing and ranking outcomes).  Even where aggregate measures of impact are used, therefore, it is helpful to provide information on the components that make up these overall impacts (e.g. on the main disease or population sub-grou ps).  

A useful technique in this context is to construct some form of indicator scorecard.   Scorecards can be designed in various ways, depending on the purpose.  They are most useful in showing the changes in impact under different scenarios.  To compare a small number of different scenarios or policy options, graphs can be used (as in Figure 1 below). Where the number of comparisons is larger (e.g. in terms of impacts between different population groups or areas), it may be better to develop a series of graphs or matrices - one for each scenario.  In this case, scores can be presented in different ways - from smiley/sad faces, to colour coding or actual indicator values.  Figure 2 shows an example from a study of the health impacts of a road project in Sweden.    However they are designed, however, the goal of scorecards must be to summarise the results in a way that both provides insight into the findings of the assessment and forms a basis for further discussion.  In this way, the scorecards can feed into the process of comparing and ranking  outcomes, and thence into evaluation of the different policy options.

Figure 1.  An example of an indicator scorecard showing health impacts of two road transport scenarios.  Impact scores represent the attributable burden of disease for each measure, relative to the business-as-usual scenario.

Figure 2.  An example of an indicator scorecard, showing the health and environmental impacts of a road project in Sweden (Swedish National Institute of Public Health 2005).