Accident Causation Model

What Does Accident Causation Model Mean?

An accident causation model is a systematic method of ascertaining the causes of an accident.

Workplaces and jobsites are complex environments with lots of people, equipment, and moving parts. When an incident occurs, this complexity can make it very difficult to identify the causes that led up to the accident. Doing so involves considering a number of potential variables, including:

• Equipment malfunctions
• Worker behavior
• Conditions of the working environment
• Safety policies and safe work procedures
• Available safety equipment

Accident causation models are attempts at explaining how accidents happen and to influence how safety professionals investigate incidents.

Safeopedia Explains Accident Causation Model

Systematic examination of causes of accidents began in the early 20th century, particularly in Herbert Heinrich’s influential work on accident causation in industrial settings. Heinrich proposed a simple and linear “domino model,” which focused on the individual’s behavior and circumstances surrounding an accident.

The 3 Basic Types of Accident Causation Models

Simple Linear Models

These simple models presume that an accident is the end result of a series of sequential events. Each event instigates the following, like a set of dominos being knocked over in a neat and ordered fashion.

These models will typically follow a sequence like this one:

• The environmental factors in the workplace
• Combined with the character traits of the workers
• Along with workers performing unsafe actions
• In the presence of a mechanical or physical hazard
• Will result in an accident

This type of model is meant to highlight the importance of control measures. By removing any of the items in the sequence, accidents can be prevented. For instance, if there is no physical hazard present, unsafe actions would not lead to an accident.

Complex Linear Models

More complex linear models keep the ordered sequence, but posit that accidents result from a number of unsafe conditions in a complex system. In other words, we can’t trace accidents back to a single event but need to consider the various factors that contributed to it. The goal is then to control these various factors to reduce the likelihood that an accident will occur.

Complex linear models of accident causation include:

• Time sequence models
• Generic epidemiological models
• Systemic models
• Reason’s “Swiss Cheese” model
• Models of system safety

Source: NASA

Complex Non-Linear Models

Non-linear models do away with the assumption that the various factors leading to an accident happen in a simple sequence. Rather, they hold that accidents are caused by variables that interact in real-time environments.

Based on these non-linear models, accidents can be prevented by not only controlling individual factors in insolation, but by understanding how they interact with each other.

Examples of non-linear models for accident causation include the Systems Theoretic Accident Model and Process (STAMP) and the Functional Resonance Accident Model (FRAM)