Tuesday, 29 October 2013

Hazardous Events and the Haddon Matrix



Looking at hazards in different ways, through different conceptual frameworks is always useful as it tends to make you think about things, however slightly, in a different way. A framework often used in injury prevention, in road accident research and public health is the Haddon Matrix. This was devised by William Haddon back in the 1970s for use in road traffic accidents. The basic matrix is divided into 12 cells. The rows are defined by the temporal aspect of the event; pre, during and post, whilst the columns are defined as ‘host’ (you could rethink this as ‘the individual’), ‘equipment’ and two for environment; one for ‘physical’, one for ‘social’. The idea is to fill in each of the cells with key aspects that will influence or did the hazardous event. Effectively you are playing out different scenarios and filling in the cells depending on what factors you see as significant in each scenario. The framework forces you to deal systematically with the nature of the hazard and how it might play out in reality.

 
 
Figure 1 Haddon Matrix
 
The example provided is for road traffic accidents but the basis can be translated to other types of hazard. In the crash, the condition of the individual before the crash may be important for the reasons in the matrix. Each individual will have different characteristics that could be important and each can be included as appropriate. Similarly, different aspects of the equipment will be important depending on the nature of the crash and so these factors may not be clear until after the event. The environmental factors, seem to be more diffuse and provide a context, that for certain types of individual behaviour and certain equipment failings produce an environment conducive to a hazardous event. Importantly, despite the description and division of the event into these separate cells, the contents of each cell depends upon the relationships between the host, equipment and environment. For example, the social norms that permit DUI, would not be important had not the host not had a seatbelt and been drinking. The poorly designed fuel tanks only become significant when the drunk driver chase and so on.


 

Figure 2 Illustration of use of Haddon matrix

This framework does have its limitations. The recognition of important factors can be so wide ranging as to be useless in planning if extreme scenarios, with infinitesimal probabilities of occurring are considered. On the other hand, it may not be until the event happens that it becomes clear what factors are important. The matrix will probably be of most use when similar hazardous events are being considered, as similar events would be expected to have roughly similar important factors. The matrix can also be used to identify where particular factors are not relevant. In a pile-up on a foggy motorway, for example, the detailed life history of the individual in the second car in the crash may not have any significance to their survival, it is the general physical conditions that are of over-riding significance. Equipment factors, such as airbag installation, age of car, may have an impact however. In other words, the matrix might be useful to explore the topographies of different hazards or disaster; in exploring the nature or shape of the hazard and what factors dominate that landscape and which are incidental ‘bumps’ on the terrain (please excuse the landscape metaphor, but I am a physical geographer!)

The matrix framework helps to identify the factors that might be important at each stage; the Swiss cheese identifies if a particular trajectory of factors lines up to produce a disaster. The matrix helps identify the possibles, the Swiss cheese, whether these possibles are important in combination. In the case of the BP oil spill, for example, the Haddon matrix could be used to identify key pre, during and post disaster factors, such as the alleged failure in safety procedures and lack of disaster planning. The trajectory arrow of the Swiss cheese model can then be used to assess if this one failure affects the next layer, if one failure or factor then lines up with another to produce the cascade of errors that result in a disaster.