PROCEEDINGS OF THE HUMAN FACTORS SOCIETY-3lst
ANNUAL MEETING-1987
ACCIDENT PRONENESS IN THE INDUSTRIAL SElTlNG
David L. Mayer, Scott F. Jones and Kenneth R. Laughery, Sr.
Rice University
Houston, Texas
ABSTRACT
The central notion of the accident proneness concept is that people exposed to equivalent
hazards do not have an equal number of accidents. If people were equally accident prone, one
would expect accidents to be distributed according to chance. Using accident data collected at
Shell Oil Company's Manufacturing Complex in Deer Park, Texas, the present study explored the
proneness concept for major (OSHA recordable) and minor accidents by comparing the observed
distribution of accidents to a chance distribution. The database contains information on 7131
accidents which occurred between 1981 and 1986. The methodology used to create expected
values employed a Poisson distribution and assumed that accidents are distributed randomly
among the population at risk. The minor accident data was also analyzed by job family. Chi-square
analyses of the differences between the expected and observed distributions were found to be
statistically significant, including within each job family. The data for minor accidents indicates a
striking difference between the expected and actual distributions. Many more people suffered
repeat accidents than would be predicted by chance. Approximately 3.4% of the employees
accounted for 21.5% of the accidents. While the differences for major accidents was statistically
significant, these results are not nearly so striking. The statistical effects are largely due to five
employees who were involved in three major accidents in the five year period. In the context of this
very large industrial setting, the problem of individuals having repeated minor accidents is
significant and merits attention in developing safety interventions.
INTRODUCTION
This paper presents some results of an
ongoing project dealing with industrial accident
analysis. The issue here is the distribution of
accidents across employees. Over the years this
issue has been addressed in the literature under the
label accident proneness. The concept is essentially
a question of individual differences. The central
notion of accident proneness is that people exposed
to equivalent hazards do not have-an equal number
of accidents. Accident proneness concerns whether
individual differences in a population leads to
differential numbers of accidents across the people
within that population.
If people were equally accident prone under
conditions of equal hazard exposure, one would
expect accidents to be distributed according to
chance. Thus, given equal exposure to hazards, the
existence of accident proneness reduces to a
statistical question: Are accidents among those at
risk distributed according to chance? For example, if
a population of 3000 workers were studied for a
period of time sufficient to observe and record 3000
accidents, one would not expect each employee to
have had exactly one accident. The majority of
workers would have suffered no accidents, many
would have had one accident ; fewer would have
had two, even fewer three, and so on.
The usual analytic approach to studying
accident proneness is compute a chance distribution
and compare this chance distribution with observed
data. The Poisson distribution has been accepted as
the appropriate definition of chance for this purpose.
Classic studies have employed this methodology
(Greenwood, Woods and Yule, 1919; Newbold,
1926), and more recent research has reaffirmed its
validity for this purpose (e.g. Shaw and Sichel,
1971).
Using two years of accident data collected at
Shell Oil Company's Manufacturing Complex in
Deer Park, Texas, Laughery (1984) addressed the
issue of the accident distribution across the
employee population in this manner. That study
revealed a significant difference between the
chance-predicted accident rate and the observed
distribution of accidents. A limiting factor in the study,
however, was that sufficient data did not exist to test
the proneness question within job categories.
Because the data encompassed a variety of jobs
which almost certainly differed with regard to
associated hazard level, it was not possible to
adequately separate individuals and job categories
in drawing conclusions. Another factor that could not
be explored because of the limited data was whether
or not individual differences in accident rates
(proneness) existed for both minor and major
injuries.
Since Laughery's earlier analysis, three more
years of data have been recorded for the Shell
complex. The database now contains information
about 7131 accidents which occurred during 1981
through 1986. Details of the type of information
recorded have been described elsewhere
(Laughery, Petree, Schmidt, Schwartz, Walsh and
Imig, 1983). The accidents are classified as either
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PROCEEDINGS OF THE HUMAN FACTORS SOCIETY-31st ANNUAL MEETING-1987
major or minor, where major is defined as OSHA
recordables. The present study extends the earlier
work by examining the proneness concept for the
five-year period.
The analyses included a
breakdown by major and minor injuries and by
several job families.
METHOD
As noted above, the database contains
information on 7131 accidents at a large
manufacturing complex. There are 6382 minor
injuries and 392 in the major category. The
remaining 417 incidents either involved no injury or
the injury data was missing.
The Poisson model was used to create
expected distributions for major and minor incidents,
assuming the accidents were distributed randomly
among the employee population. The procedure for
deriving these distributions is described in Sachs
(1982). Chi square tests were employed to compare
the actual and expected distributions.
In addition to separate analyses based on injury
severity, the data was partitioned into job families for
further comparisons. Five job families were defined
on the basis of similarity of tasks carried out in the
various jobs that make up a family. The specific jobs
in each family were:
Operations - operator, lab tester
Electrical crafts - electrician, instrument
mechanic
Process crafts - pipefitter, machinist
Maintenance crafts - boilermaker, welder
Miscellaneous crafts - carpenter, insulator,
painter,, garage mechanic
The number of employees in each job family and
the number of minor accidents reported for those
employees are shown in Table 1. Major accidents
were not analyzed by job family, because the
number of such incidents was too small.
Number of Employees and Accidents by Job Family
Operations
Electrical crafts
Process crafts
Maintenance crafts
Miscellaneous crafts
Number of
FmDlovees
1007
220
250
102
131
Table 2 presents the actual and expected
distributions for accidents involving minor injuries. A
chi square test showed the two distributions differed
significantly, pe.001. From the table it can be seen
that many .more employees had repeated accidents
than would be expected by chance. For example,
the expected number of people having 10 or more
accidents is virtually nil. Yet, 109 employees at this
complex experienced 10 or more, accounting for
1375 accidents.
Hence, 3.4% of the employees
accounted for 21.5% of the accidents.
Table 2
Actual and Expected Distributions for
Minor Accidents
Number of
Accidents
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17-18
19-30
Actual
Expected
1439
569
334
243
166
131
97
53
59
33
27
23
20
12
6
6
5
5
5
449
886
875
576
284
112
37
10
2.6
0.56
0.1 1
0.02
0.003
0.0005
0.0001
0.0000
0.0000
0.0000
0.0000
Table 3 presen s the actual and expec ed
distributions for all major accidents. A chi square test
showed the difference to be statistically significant,
p<.OOl. It should be noted, however, that the
statistical effect is due largely to the five employees
who had three major accidents during the five-year
period.
Table 1
Job Family
RESULTS
Number of
Accidents
2237
750
1301
613
628
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PROCEEDINGS OF THE HUMAN FACTORS SOCIETY-3lst
Table 3
Actual and Expected Distributions for
Major Accidents
Number of
Accidents
Actual
ExDected
0
1
2
3
2887
302
40
5
2860
350
21
0.99
Chi squares were also computed comparing
the actual and expected distributions of minor-injury
accidents for each of the job families. All five
outcomes were highly significant, p<.OOl.
DISCUSSION
The results support and extend Laughery's
(1984) earlier findings. Clearly, the distribution of
injury-producing accidents at this large refining and
chemical complex cannot be attributed to chance. A
relatively small number of employees accounted for
a substantial portion of the accidents.
Furthermore, the analyses showed that these
differences in individual accident rates held up within
job families. This finding supports the notion that the
effects are not simply due to differences in hazard
levels associated with various jobs.
While the difference between distributions was
statistically significant for major-injury accidents, the
largest number of incidents for any individual was
three--by five employees. It should be noted that
while only five people had a maximum of three
serious injury accidents over a five-year period (and
no one had more than three),
891 different
employees experienced three or more accidents
involving minor injuries. Indeed, from Table 2, 109
people were involved in 10 or more such incidents.
This difference does not seem consistent with some
of the ideas and data previously offered by Heinrich
(1931, 1941) about major and minor injury accidents
in industrial settings. Specifically, Heinrich has
suggested that injury severity may well be fortuitous
(a matter of luck), since similar caL'ses appear to
underlie both types of events. Lozada-Larsen and
Laughery (1987) present some data supporting this
suggestion. They also present data that is consistent
with earlier findings that indicates minor to major
injury ratios in industry are typically between 15 to 1
and 25 to 1. While the *issue here is somewhat
different, the ratio of people having three or more
minor injuries to those having three or more major
injuries is much greater-1 78 to 1.
ANNUAL M E E T I N G 1 9 8 7
more safely, in the context of situations that might
result in serious injuries, while individual differences
in safe or unsafe behaviors is more pronounced in
the context of less hazardous situations. Attention,
for example, might be one employee characteristic
that could vary in this regard. Such speculation,
however, is not entirely consistent with the notion that
similar causes underlie minor and major injury
accidents. Obviously, this particular outcome of the
present analyses merits further study.
The analysis of accident recurrence in this
manner is a statistical technique. One cannot be
content to conclude that those workers who have
experienced many accidents are accident prone. As
Cameron (1979) points out, attributing multiple
accidents to accident proneness is like saying that a
persistent sore throat is due to chronic pharyngitis.
Statistics which reveal that repeaters are responsible
for a large percentage of industrial accidents provide
a description of the problem, not an explanation.
Perhaps deeper analysis of the populdion at risk will
reveal the factors responsible for this observed
proneness.
Finally, a word about where accident
proneness fits into industEd safety. It is not our intent
to overemphasize the role of the employee in
accidents. We would strongly advocate a systems
approach to accident analysis and prevention in
which the environment, the equipment, ? t e job, g&
jhe person, are taken into account. HoNever, person
factors, be they cognitive, personality or motivational,
undoubtedly contribute to accidents and must be
taken into account. Our work on this issue to date
has shown that substantial individual differences do
exist that cannot be attributed to job categories. The
next step is to begin to sort out the person factors that
may be involved.
REFERENCES
Accident mevention: A workers educational manual.
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Cameron, C. (1973). Accident Proneness. Accident
/4nalvsis and Prevention, Vol. 7. Page 49-53.
Greenwood, M., Woods, H. M., & Yule, G. U. (1964).
A report on the incidence of industrial accidents
upon individuals with special reference to
multiple accidents.
In W. Haddon, E. A.
Suchman, and D. Klein (Eds.), Acc i d e n t
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Heinrich, H.W. (1931). Jndust rial Accident Prevention
(1st ed.). New York: McGraw-Hill.
Heinrich, H.W. (1941). Industrial Accident Prevention
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It is interesting to speculate about this
difference. Perhaps most people behave differently,
198
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the human factor in the causation of accidents.
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