Types of impact measures

A number of different metrics and methods have been devised to evaluate impacts.  These are based both on different measurement systems (or 'frameworks of commensuration') and, underlying these, different value systems - for example, the relative importance attached to issues such as social equity, economic effectiveness and individual freedoms.  

In the context of environmental health, understandably, the majority have used health-based metrics (see link to Health-based impact measures in panel to left).  These derive from measures of the incidence of morbidity and mortality, the simplest comprising counts of the total number of cases of mortality or of people with a specified disease.  More sophisticated measures weight these according to duration in some way: e.g. by counting the number of years with the disease, or the years of life lost (relative to a 'normal' lifespan).  The most elaborate measures, such as disease-adjusted life years or quality-adjusted life years also weight outcomes according to severity (see link to DALYS and QALYs, below).  

In recent years, however, monetary measures of impact have also been applied (see link to Monetisation methods, in panel to left).  These involve the estimation of the monetary value of health benefits and the costs of adverse effects, not only in terms of the more obvious and tangible costs of health care and lost productivity, but also relatively intangible costs such as reduced quality of life.  Although relatively complex in their formulation, they tend to be favoured both because they are, for many users, more intuitive and (apparently) readily understandable than health-based metrics, and because they are more directly relevant to policy decisions, in which cost-efficiency or cost-effectiveness are often crucial considerations.  Because they are expressed in terms of a more generally applicable measurement system, they also facilitate the comparison of health impacts with other potential considerations, such as environmental or economic impacts.   Monetary measures may also be explicitly linked to health-based metrics, for example by calculating the cost per DALY (or QALY) as a measure of policy effectiveness. 

A generic framework for impact analysis

Impact analysis can be seen as the attempt to combine different health outcomes, into a single, synoptic measure of health impact, on the basis of a consistent measurement scale.   As the summary above indicates, however, health outcomes vary not only in their form (i.e. type of disease), but also in their severity (from mild symptoms ultimately to death), duration and timing.  Health effects may also arise at different times, either more or less immediately, and their timing may affect the value placed upon them (since people would often prefer to defer illness for as long as possible).  In addition, the question often arises as to whether everyone is equal in terms of the implications of health effects: susceptible (under-priveleged) groups might be considered to suffer more, for example, because of their inability to pay for treatment or otherwise ameliorate the health effects.

These different factors are not always considered explicitly in impact analysis, and where they are considered may be dealt with somewhat differently.  Nevertheless, they are certainly implicit in every measure of impact.  Generically, therefore, impact analysis can be expressed as follows:

where: I_v is the overall health impact (in the context of value system v);

i specifies the individual (or sub-group of individuals) affected;

h specifies the health outcome;

s is the weight for the scale (e.g. severity) of health effect h in individual (or sub-group) i;

d is the weight for the duration of health effect h in individual (or sub-group) i;

t  is the discount factor, applied to the timing of the health effect h in individual (or sub-group) i;

g is a weight for an effect in the population group to which individual or sub-group i belongs

k is a scaling factor, to convert the impacts into the relevant measurement units (e.g. cost of standard life).

This equation thus forms the framework for all impact analyses, and it is often informative when considering different measures to identify clearly which of these factors are allowed for (i.e. by some form of weighting) and which are ignored (and given, by default, a weighting of 1).