Lack of measured data on the key phenomena of interest often means that proxies have to be used in studies of environment and health.  Proxies such as distance from source or source intensity (e.g. distance to roads, road traffic volume) are widely employed in epidemiological studies to serve as exposure measures, where direct observations are unavailable.  Likewise, measures of socio-economic status or education are used to represent the complex (but unmeasured) set of behavioural and contextual factors that help to determine susceptibility to environmental risk factors.

Proxies are similarly useful in carrying out screening studies for integrated impact assessment: their ready availability and ease of acquisition means that approximations may be made quickly and cheaply.  They are especially valuable to represent changes in exposure. 

As examples, we might use:

• regional land use as a proxy for exposure to pesticides or other contaminants from agriculture;
• population density as a proxy for exposure to urban air pollutants;
• occupation category as a proxy for exposures to industrial pollutants.

In defining proxies, we need to be cautious.  Simple statistical association with the phenomena they are intended to represent (the objective) is often not sufficient, for the proxies are often used to indicate how the system will change in response to changing conditions or interventions (i.e.  under different scenarios).  This may involve extrapolating beyond the limits of existing data: in that range, the statistical relationships previously observed may no longer be valid.   Statistical association also does not imply causality. 

To be considered reliable, therefore, the proxies should be functionally related to the phenomena of interest. Even then, care is needed in using proxies, for the relationships involved are not always what is assumed, and may vary from one metric to another (see the example of traffic-related air pollution).  

Confirmation of the association between the proxy and its objective is vital. If the proxies for exposure are to be used to provide estimates of potential health impacts, it is also important that a relevant exposure-response function is available (or can be deduced).