Analyzing the Risk of Daily Life-Revisited
for Consumer and Recreational Products
Kevin C. Breen, PE
William J. Fischer, PE
Zednek Hejzlar, Ph.D., CSP
ESI
12750 Commonwealth Drive
Ft. Myers, FL 33913
(239) 482-0500
(239) 482-2941 [fax]
[email protected]
Return to course materials table of contents
Kevin C. Breen, PE, is a Managing Principal and Director of Marine and Automotive Research for Engineering Systems Inc. For more than 30 years, Mr. Breen has
been actively involved in the analysis of transportation equipment accidents and injuries, product performance analysis, human factors studies, risk analysis and research.
Included in this experience have been studies relating to vehicle equipment handling
characteristics, crash performance, component analysis, warning/communication and
novice/Human Factors research. He has been actively involved in testing, research,
and the development of standards for watercraft and special purpose vehicles. The
results of these studies have been presented in regulatory proceedings as well as
litigation proceedings in the U.S. and abroad.
William J. Fischer, PE, is a degreed Civil Engineer with ESI in Ft. Myers, Florida.
Mr. Fischer has extensive experience in analysis of vehicle accidents, testing, research
and the operation of on and off-road motorcycle based vehicles. In addition, he has
conducted research and testing related to the performance of personal watercraft and
recreational marine vessels. His professional experience includes collision reconstruction and dynamics analysis, geometric roadway design, traffic control devices, traffic
operations and driver factors risk analysis, and product research.
Zednek Hejzlar, Ph.D., CSP, is a Senior Managing Consultant with ESI, a professional engineering consulting firm and laboratory headquartered in Aurora, Illinois.
ESI is a multi-disciplinary company that provides professional scientific and engineering services to industrial, legal and insurance firms, government agencies and
trade organizations, and provides consultants to other engineering firms. The laboratory capabilities are supplemented by cooperative agreements with other recognized
facilities to provide a wide range of technical support capabilities, including chemical
metallurgical, materials, aeronautical, mechanical, structural, electrical, safety, automotive and audio/visual services. Projects ranging from simple failure investigations
to complex engineering studies are undertaken.
Analyzing the Risk of Daily Life-Revisited
for Consumer and Recreational Products
Table of Contents
I.
II.
III.
IV.
Background.............................................................................................................................................. 79
Discussion................................................................................................................................................ 79
Conclusions.............................................................................................................................................. 88
References................................................................................................................................................ 88
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Analyzing the Risk of Daily Life-Revisited for
Consumer and Recreational Products
I. Background
In 1979, Richard Wilson of MIT published an article entitled, Analyzing the Risk of Daily Life. In that
article, using a series of anecdotes, Wilson goes through a daily routine assessing the trade-offs with respect to
every day activity decisions and their relative risk. The risks he considers were health and safety risk decisions.
To a large extent, this article humorously points out that in our day to day activities there is some level of risk
associated with most everything. In fact, according to Consumer Product Safety Commission (NEISS 20002009) data several thousand people are injured each year using first aid kits, not to mention the hundreds of
thousands injured while using beds, stairs, and everyday products.
The reality of the risk of daily activities, risk perception, and risk acceptance are a part of virtually
everyone’s daily life choices and are a part of the considerations made by regulators, designers, and scientist as
a part of overall safety evaluations. Data and a scientific approach to considering risk becomes a series of evaluations of user decisions, trade-offs, and consequences.
The level of societal risk acceptance is not a fixed threshold rather, it has evolved over time. For example, in the early 1900s there were considerations of banning football as it was deemed too dangerous. Today,
there are more than a hundred thousand of people injured, to various levels, each year participating in football
as a sport. Yet, the popularity continues to grow. Similarly, in the 1990s there were efforts to regulate reducing
the risk of head injuries for bicyclist by requiring the use of helmets by riders below a certain age in certain
states. A part of the consequence of these requirements was a period of time where some individuals chose not
to ride bicycles as opposed to wearing the helmet as required by law.
Included in the discussion of the risk of daily activities are the concepts of risk perception and risk
acceptance. Although common sense would dictate that they are closely related, they are, or can be, different
depending on the individual or population group that is trying to apply these concepts.
The purpose of this study is to review of the history of risk analysis, risk acceptance and review current information regarding risk data. In addition, an overview analysis of risk factors and their role in risk analysis is presented. This study also looks at recreational product use in a separate category given the fact of life
that use of recreational products or participation in recreational activities is a voluntary choice.
II. Discussion
The consideration of the scientific analysis of risk and risk acceptance is not new technology rather
has evolved over time. Webster’s New Collegiate Dictionary (1975) defines risk as, “the probability of a loss.”
Singleton & Hovden (1987) have discussed and defined risk using various concepts that include:
1) Risk is the probability of a loss
2) Risk is the size of the possible loss
3) Risk is a function, mostly the product of probability and size of loss
4) Risk is equal to the variance of the probability distribution of all possible consequences of risky
course of action
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5) Risk is the semi variance of the distribution of all consequences, taken over negative consequences only and with respect to some adopted reference value
6) Risk is a weighted linear combination of the variance of and the expected value of the distribution of all possible consequences
A common characteristic of these definitions of risk is that they refer to risk in abstract terms – therefore the various objective aspects of the risky events cannot be measured but have to come from expert judgment. The concept of objective risk is complex and research typically also looks at factors which affect risk
judgments and risk acceptance.
Risk is typically quantified in the context of risk assessment. Risk assessment is the determination of
quantitative or qualitative value of risk related to a concrete situation and a recognized threat (hazard). Quantitative risk assessment requires calculations of two components of risk: R, the magnitude of the potential loss L,
and the probability p, that the loss will occur. In this context risk assessment consists of an objective evaluation
of risk in which assumptions and uncertainties are clearly considered and presented.
A risk with a large potential loss and a low probability of occurring is often treated differently from
one with a low potential loss and a high likelihood of occurring. Probability, however quantifies expectation,
not the human thought process, decision making, and choice framework (Viscusi & Magat, 1987).
The concept of risk as a probability is a mathematical approach to draw conclusions about the likelihood that an event will occur or to evaluate historical data. In this approach, events are measured based on the
historical data that can then be used to estimate future events only with the underlying assumption that similar
conditions are prevalent for both categories; past and future. Changes in the conditions or situation can result
in a change in estimated future risk levels based on historical data.
Risk analysis is widely used and accepted in many regulatory fields. In the context of public health,
risk assessment is the process of quantifying the probability of a harmful effect to individuals or populations from certain human activities. In most countries, the use of specific chemicals, or the operations of specific facilities (e.g., power plants, manufacturing plants) is not allowed unless it can be shown that they do not
increase the risk of death or illness above a specific threshold.
For example, the American Food and Drug Administration (FDA) regulate food safety through risk
assessment. The FDA required in 1973 that cancer-causing compounds must not be present in meat at concentrations that would cause a cancer risk greater than one in a million lifetimes.
The U.S. Environmental Protection Agency (EPA) provides basic information about environmental
risk assessments for the public via its risk assessment portal. The EPA considers risk to be the chance of harmful effects to human health or to ecological systems resulting from exposure to an environmental stressor. A
risk level of one in a million implies a likelihood that up to one person, out of one million equally exposed
people would contract cancer if exposed continuously (24 hours per day) to the specific concentration over
70 years (an assumed lifetime). This would be in addition to those cancer cases that would normally occur in
an unexposed population of one million people. Note that this assessment looks at lifetime cancer risks, which
should not be confused with or compared to annual cancer risk estimates.
The concept of one in a million as a threshold has been examined at several levels (Fruendenburg,
1988). However, in situations where people make decisions concerning risk and the risk environments are varied this analysis becomes more complex. Assume that a goal is a risk of one in a million. But now assume that
the risk can reasonably be divided into three categories of environments or individual choice factors; one category that has a lower than average risk, one that has an average risk, and one that has a higher than average
risk. If we assume for our model that 80 percent of the situations involved are the target one in a million risks;
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but that ten percent are 1 in 1,000 and ten percent are 1 in a billion. The resulting risk to the entire population
becomes 1 in 10,000.
RISK = 0.1 x 10 -9 + 0.8 x 10 -6 + 0.1 x 10 -3 = .0001008001
or = 1:10,000
In Juran’s Quality Control Handbook 4th ed. (1988), Juran & Gryna provide historical perspective on
risk in our daily lives. In essence, it’s concluded that human adaptation to natural environment has been based
on two major areas of response:
1) Human sensing – The human senses are used to judge the quality of natural goods and services.
2) Lessons learned – The experience of the past; i.e., when to plant, what berries are poisonous.
Given this as a background, he observes that human beings in most primitive societies lived short life
spans due to hard working conditions, malnutrition, disease, natural disasters. To protect themselves against
these risks they created non-natural aids to their mental and physical capabilities: division of labor, shelter,
community forms such as villages, tools, weapons and processed natural material in goods such as pottery and
textiles.
As a result, we in modern society live longer and more varied lives. The non-natural goods and
services have created new dependencies and thus new potential risk. The extent of the risk depends, in part,
on the quality built into those non-natural goods and services. As early as 1970, the concept of, a quantitative basis for separating risk on matters of safety that any hour of human life should be a safe as any other
hour, has been suggested. This concept carries several questions in defining what the threshold level of risk
should be and should all efforts then be made to avoid decision or activities that avoid risk (Juran & Gryna,
1988).
But from a practical standpoint does this allow our society to flourish and prosper? For example, the
early settlers and space explorers took on risk that was beyond that of many of their counter parts to provide a
better life and to expand the world they and we now live in. As the length and quality of life has increased society has become increasing concerned in avoiding risks (Risk Perception Research, 2004)
Clearly individual choices make a significant difference in risk assessments. An excellent example
can be considered when looking at choices in risk taking in recreational or sporting activities. Dr. Frank Farley
has proposed that there are those who love life and love to experiment with life that he terms as Type-T personalities (Groves, 1988). Type-T personalities are “thrill seekers” as compared to Type-t personalities, “thrill
dodgers.” He further points out that the U.S. embraces Type-T personality and has been led by Type-T people
making and taking higher risk choices. Today, there are in fact, “thrill seeking sports” where winning is not
enough, risk to body and self esteem must also be involved. Successful risk takers become experts at controlling
the odds; but, sometimes fail.
The risk associated with various choices in recreational activities is not the same; nor is the risk uniform or a stepped function. As an example, at opposite ends of the spectrum there are people who chose contact sports such as boxing or football for recreational purposes while others chose less aggressive activities such
as golf and fishing. CPSC data demonstrates that there is a risk associated with each choice of form or recreational activity. This naturally leads to a discussion of risk perception and risk acceptance.
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Similar trends are apparent in consumer products and every day activities. CPSC data shows that
virtually every product or activity that is encountered in a typical day has some level of risk. Yet each day we
“expose” ourselves to these risks.
Risk comparisons have shown to be helpful in providing context and perspective of absolute or relative risk and risk choices. However, comparisons need to be presented in a manner that provides one of several
inputs on risk decisions. Most useful risk comparisons are those that convey the magnitude of a particular risk
compared to other alternatives. Multiple comparisons may avoid incorrect assumptions or conclusions about
risk. For example, clearly, playing football poses a greater risk than playing golf. However, where does each of
these examples fit in the continuum of recreational activity choices?
Generally, people do not perceive quantitative risk, rather they perceive various features of decisions
and this leads to their perception or feelings of risk. As a result, there is limited value in asking if risk is cor82 v Product Liability Conference v April 2011
rectly perceived. As a result, it has been suggested that an alternative is to evaluate and compare risk estimates
that are obtained from data and various formulae and intuitive risk estimates (Skjong & Eknes).
Researchers, (Pan American Health Organization, 2004) have conducted studies of the characteristics
of risk that do influence perception. The conditions that were found to have the largest influence included:
1) Fear/dread,
2) Our ability to control the risk,
3) If the risk is natural or man-made,
4) What choices we have,
5) Concerns of special portions of the population,
6) New and unknown risk,
7) Awareness,
8) Personal impact,
9) Cost/benefit ratio,
10) Confidence, memory/time /space, and
11) The process involved.
This research has also identified significant differences in how risk is perceived by various groups
within the population such as gender, age, and individual support roles. However, the psychology and how the
psychological (psychometric) paradigm is not well based on empirical data. The demand for risk mitigation
is reported as being related primarily to the seriousness of consequences of hazard, not the risk of an accident
or the riskiness of the activity. The ratio of frequency of events to severity of events is a criterion that has often
been discussed (Skjong & Eknes).
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This quantification leads to a discussion of exposure based risk analysis. Clearly, a factor in risk perception is related to the number of the population, or amount to time individuals are exposed to the risk.
The concept of quantifying per hour or other measure of exposure to risk is a useful, accepted scientific tool that is widely utilized by safety and risk management professionals across many industries. Traffic
data is analyzed for trends on the basis of accidents or loss events per million miles of travel (NHTSA, 2009).
The Coast Guard reports recreational boating fatalities on the basis of number of registered vessels (USCG
2000-2008). The Department of Labor uses incident rates per hour work, the National Safety Council and International Association of Amusement Parks and Attractions estimate injury rates per million attendees and per
million patron rides. Health risk assessments that serve as the basis for regulatory review are evaluated based
on exposure, or potential of an event per population (NSC, 2009).
Expressing and evaluating the tolerable amount of risk or risk acceptance is very difficult (Explaining Risk Perception, 2004). The acceptable or tolerable amount of risk for an event or technology is an evolution over time. The regulation aspect of risk for any event or technology, defines a value that is ultimately
agreed upon by professionals in that particular field. For example, a particular percentile of compliance may
be justified for design based on the population expressed in normal or other defined distributions (Forscher,
1986).
Objective risk estimates are average values based on summary statistics. As shown by research the
majority of drivers tend not to evaluate their own traffic risk with that of the average person, because they
believe themselves to be more skillful and safer than average. In other words, what are the odds that “X” will
happen to me, today or any time of immediate concern? Understanding risk is not sufficient as organizations
or individuals do not chose between risks. Rather, they generally chose between options, each of which presents
some level of risk. (Wilson & Crouch)
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Similar data has been used historically to compare risk of various activities or products that may
affect the overall risk. As noted below, there are different risk levels based on activities that comprise a continuum as opposed to a step or threshold function. In other words, there are degrees of risk. This clearly demonstrates that risk and safety are systems, not isolated properties, which have wide reaching factors that affect the
level of risk or perceived safety (Wilson & Crouch)
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Safety can be defined as, “that state for which risk is judged to be acceptable” (Christensen & Manuele, 1999). Designing to minimize risk, to an acceptable risk, is a goal of this concept. This does not imply that
designing to zero risk is a requirement as this may well be impossible. The inherent risk associated with participating in any activity is a recognized concept and addressed in ASTM International standards. As an example,
ASTM 770 Standard Practice for Amusement Rides and Devices states: “There are inherent risk in the participation in or on any amusement ride, device, or attraction” (ASTM, 2005). Patrons of an amusement ride, device,
or attraction accept the risk inherent in such participation of which the ordinary, prudent person is or should
be aware.
In general, regardless of what actions are taken to avoid, eliminate, or control hazards some residual
risk will exist given the human element. Most researchers agree that safety involves personal value judgments,
making safety a very personalized concept. Therefore, acceptable risk is also a personal choice or value. Any
measure of risk involves making value judgments and decisions (Forscher, 1986).
Society and regulatory groups also make choices concerning acceptable risk levels. For example, traffic schemes are designed based on an 85th percentile reaction time data for drivers (Roess, Prasses, & McShane,
2004). Assuming that the reaction time data is a bell shaped curve, this would result in 7.5 percent of the population having reaction time greater than the desired level for the traffic design. In the context of recreational
and consumer activities a wide range of risk levels based on current data is considered acceptable and in many
instances the product or activity is regulated by standards.
In most situations, there are groups of participants in various activities within a population who are
exposed to similar risk and some are injured while others are not. For example, nearly 12 million people participate in football and “only” 450,000 sustain injuries according to CPSC data. What is the difference between the
450,000 who are injured and the 11,550, 000 participants who are not injured?
Similar comparisons are noted for other recreational activities. This trend is also common in everyday
life from the driving cars without being involved in an accident to walking in public areas and not slipping/tripping and falling.
Generally, a portion of the risk is simply chance while a significant portion may involve individual
choices and real time decisions; a running back is more likely to be injured than a punter. Walking on a wet or
icy surface is more difficult than a clear dry surface. However, in many situations this analysis is not as straight
forward or obvious. Understanding the differences between the injured versus the non-injured populations
requires a detailed analysis of the accident or injury patterns using scientific principles.
This analysis technique involves a systematic evaluation of factors that has historically been divided
into human-machine-environment or the “Haddon Matrix” (Baker, 1975). The objective of this analysis is to
look at the factors that are involve in accident or injury events and compare them to similar use of the product
or activity where no injury or accident has occurred.
As an example, in 1989 the CPSC conducted a study of All-Terrain Vehicle (ATV) accidents using a
systematic approach (CFR, 1991). The historical CPSC data showed that accidents resulting in a reported injury
occurred to nominally one-half of one percent of riders. The 1989 study found that ~83 percent of those accidents studied were the result of operator error or misuse; factors such as use of alcohol, under-aged drivers,
riding in inappropriate areas, etc. A similar study in the automotive area found a similar result (Treat & Jocelyn,
1988).
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A detailed analysis of the “underlying facts” that result in incidents or accidents is a very useful tool to
determine which factors are more significant in accident/incident events compared to similar activities where
no accidents or incidents occur. In other words, the following questions, and others are addressed:
1) What factors the individual accident made it different from any similar activity where no accident
occurred?
2) Are the accidents in fact different or similar in nature?
3) Are there a unique set of factors that are required to come together for a situation resulting in an
accident?
4) Are there aspects of the accident pattern that are within the normal distribution of any given factor or are the factors in the extremes?
5) Does the activity have some level of accident occurrence due to chance or the inherent nature of
the activity?
6) Does the rate of the non-extreme pattern accidents fall within the normal distribution levels?
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Why should risk be so important? Researchers have seen risk to be a dominating factor in accounting
for attitude, benefits being much less important. In related studies, we found that people are more easily sensitized to risk than to safety and have been found to be more influenced by negative expectations than by positive ones. People seem to be more eager to avoid risk than to pursue chances or odds.
Successful risk communication does not always lead to better decisions because risk communication
is only part of the risk management process (NRC, 1989). People do not necessarily all share common interest
or values, as a result, better understanding of risk will not lead them all in the same direction.
A review of current and historical literature in the field of risk perception and acceptance demonstrates that individual choices are critical in assessing the levels of acceptable or threshold levels of risk. The
historical research also demonstrates that many converging factors are involved in risk analysis and suggest
that analysis without an adequate understanding of individual situations and choices may not be adequate.
III. Conclusions
A review of various studies of risk over a period of more than 30 years indicates that the analysis of
risk has evolved into a valid accepted scientific process that is used by various governmental and regulatory
agencies to make decision concerning many aspects of daily life. All of the studies and data continue to reflect
that there is some level of risk inherent in most if not all activities. The concept of an absolute “zero” risk level
is not practical as analysis of risk should consider exposure, situations, choices, and comparisons of other relevant options.
Clearly risk involves a wide range of individual choices and acceptance levels of risk. This is especially
true in recreational activities and products. Given the wide ranges of individual choices there is no absolute
level of risk that can be defined as acceptable versus not acceptable. Society’s acceptance of risk is an evolving
process and individuals within any group have wide ranges of risk acceptance. Risk data must be put into perspective of exposure and into context of choices to have relevance.
Understanding and properly evaluating the underlying factors involved in accidents and losses are an
important step of identifying risk factors that are significant in situations where the activity does and does not
result in an accident or loss. This analysis requires an understanding of the range of distribution of activity in
all of the aspects of the accident matrix.
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