Workplace Injuries: Difference of Degrees

The Heinrich Triangle has been a foundational piece of most safety efforts since the concept was developed in the 1930s. However, some recent research addressed in an article by Donald Martin and Alison Black in the September 2015 issue of Professional Safety  magazine seems to suggest that the Heinrich Triangle was actually a bit too simplistic.

Apples vs. Oranges?

Heinrich was a big believer that all incidents had similar contributing factors, which meant he took a quantitative approach to his Triangle – the fewer incidents you have, naturally the fewer incidents you will have that could cause serious injuries and fatalities (SIFs).

However, as Martin and Black discovered in analyzing the data from a research sample of six major corporations (all with more than 100,000 employees) about more than 1,000 incidents over a two-year period, was that Heinrich’s Triangle did not take into account the possibility that the factors that go into a SIF incident are actually quite a bit different than those that go into minor incidents.

This is one explanation for why the trending numbers in decreased incidents is three times faster than the rate of decrease of SIF incidents. What does this mean for us safety professionals? While before our concern was reducing the number of incidents in the first place, now have the challenge of trying to understand why each incident now has an increased risk of being a SIF incident compared to 10 or 20 years ago.

With the “quality” of the incident increasing in recent years, Martin and Black wrote in their article about some research that delved into why major incidents are not declining nearly as fast as the overall incident rate, and what might be driving this disparity. And what they have found is that what goes into a major incident is fundamentally different than what goes into all other incidents.

The Qualitative Findings

The various incidents were placed into two groups: Group 1 were all incidents that had SIF potential according to the context around each of the incidents (the probabilities were covered in a previous post; the research found that fully one-fifth of all incidents reported had a high risk of being a SIF incident, even if it wasn’t). The second group consisted of non-SIF incidents that had low SIF risk.

In a qualitative test, researchers looked at a random sample of incidents and looked into root causes of each of the incidents. What they found were a couple fo stark themes:

1. 42 percent of SIFs (but zero non-SIFs) were caused by breakdowns in life-saving procedures and/or protocols;
2. 74 percent of non-SIFs (and only 11 percent of SIFs) had connections to human action that was not related to a life-saving procedure (perhaps human error as an example).

The Quantitative Findings

A sample of about 450 cases were put under a quantitative microscope. These cases were split up into two similar groups as in the qualitative study – one of SIF and non-SIF incidents that had a high risk to be a SIF; and a second non-SIF incident group that had very low risk of potential to be a SIF.

A qualitative process is much more complicated to explain here, but a mathematical model was developed based on the variables and data gathered from the 450 sampled incidents, and what came out were these results:

1. The model was able to determine SIF incidents with more than 80-percent accuracy, and correctly classified all incidents at a 78-percent clip.
2. There were two distinct variables that were decidedly more correlated with the Group 1, or high-risk SIF, incidents than with Group 2 incidents: the type of activities and work environments (including deviations from standard procedures and/or drift), and the types of exposure sources and safety controls that were in place.  The first variable accounted for about 91 percent of all injuries from that variable, and 100 percent of the injuries deemed from the latter variable were Group 1.

The Bottom Line

We can bring both of these analyses together and through observing the results, the same general conclusions were reached:

• Factors that contributed to SIF and non-SIF incidents were very different;
• SIF precursors (warning signs) can be found in minor incidents that have SIF potential; and
• Life-saving programs and protocols with high integrity and reliability are extremely important.

Next time, we will go into some examples of these SIF precursors, and then we’ll follow with a post about how our perception needs to change in regards to SIFs and how this research can lead us in our paradigm shift.