Challenges of Giving Numerical Values to Risks
Case Study: Detroit, Wayne County, Michigan
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Challenges of Giving Numerical Values to Risks: Detroit, Wayne County, Michigan.
Introduction
This paper will look at the challenges that arise when assigning numerical values to risks with a focus on Detroit City as an example. Risk can be loosely defined as the possibility of loss or injury (Merriam-Webster Dictionary, 2021). In the lives of humans, there are all kinds of risks always looming on the horizon right from the moment of waking to the moment of going to sleep, and even during sleep. Risks have an inherent nature of uncertainty in that most of the time, the particular risk itself is not openly known, and if known, the time when the particular risk will happen is hardly even known.
A city like Detroit is no exception when it comes to risks. In an attempt to forecast risks and curb them in a manner that minimizes loss of life and property (among other things that can be lost), risk management entities like the Federal Emergency Management Agency (FEMA) and Occupational Safety and Health Administration (OSHA) are tasked with the determination of risk level so that appropriate mitigation measures can be put in place. In the process of risk assessment, figures that reflect the degree of probability of the risk occurring are used. Unfortunately, when the risks do occur, the numbers might be found to have been insufficient in describing the risk circumstances by either underestimating or overestimating the risk.
Background of some of the potential risks that people are faced with
The potential risks can be generally categorized into three classes: natural hazards, accidental hazards, and intentional hazards. Natural hazards refer to physical phenomena that occur on their own accord in either rapid or slow onset. Natural hazards can further be grouped as climatological, geophysical, hydrological, or biological hazards. Climatological hazards include drought, resultant wildfires, and extreme temperature in certain localities. Geophysical hazards include landslides, earthquakes, tsunamis, and volcanic eruptions. Hydrological hazards include avalanches and tornadoes. Biological hazards include disease epidemics like coronavirus disease 2019 (COVID-19) and insect or animal invasions like locust plagues, etc. (International Federation of Red Cross and Red Crescent Societies, 2021).
Accidental hazards comprise injuries or losses that happen without being intended but involve human action. These are things like falls, burns, drowning, cuts, poisoning, and many others. Intentional hazards include situations whereby a malicious individual willfully causes harm to either people or property. This may include things like an active shooter, plowing into a crowd with a bus, bomb terrorism, car thefts, sexual offenses, vandalism, etc.
The challenges of giving numerical values to risks
The following are some of the challenges that are encountered when giving numerical values to risks:
• Lack of proper risk identification – some risks are so novel that no one can foresee them coming. In the field of medical epidemiology, for example, there is always a disease called disease X. A disease X can be any disease that has not been witnessed before. Recently when the coronavirus pandemic broke out, the whole world went berserk since no one knew the extent of the disease, how to prevent it, or even how to cure it. All that remained was to try conjectures that mostly no one was sure of. In this kind of situation, it is hard to even assign a numerical value to such a risk. No one can know what they don’t know.
• Lack of proper definition of the particular risk – in some cases, the particular risk is known based on previous occurrences, but it still cannot be properly defined. Since it requires one to first adequately identify the problem before it can be solved, failure to adequately define the risk automatically translates to a lack of proper mitigation.
• Lack of proper risk communication – risk analysis experts, can find themselves in a dead-end situation if they do not have a proper communication channel. For instance, if the risk terminologies have not been understood, the risk analysis experts might never make sense of some of the components of the risk that they are assessing. This is because it is highly likely that everyone will end up having a different point of view. Failure to put thoughts together can lead to misunderstandings that can create more risks rather than solve existing ones. The solution to this kind of situation is to develop a dictionary of risks, risk Assessment frameworks, and proper risk taxonomy.
• Lack of executive support in the process of risk analysis – if a risk is taken lightly by the relevant authorities, especially the political authorities, then this is a sure way of failing. For political interests, some individuals might end up underestimating a risk scale under the premise that the risk is not likely so that they don’t incur a lot of expenses on risk management strategies. This kind of mentality can lead to a poor risk level assessment.
• Lack of proper rules governing risk assessment processes – if there are no proper regulations and protocols on how risk assessments should be carried out, the process will be definitely chaotic. This is because wholesome risk analysis is a multidisciplinary endeavor. All the relevant bodies such as the weather department, the fire department, the epidemiological department, etc. –these units need to come together and join hands in structuring the risk assessment and Assessment rubric. If there are no rules to help them go about the process, the chances are that they will bring up professional quarrels that will thwart the process and thereby interfere with the risk level assessment.
• Lack of proper statistical orientation – if the risk assessment team and the society at large are no in agreement on the numerical assignment to risk levels and what they stand for, the process will be sabotaged. For instance, using a Likert scale, the number should reflect the degree of severity in an increasing or decreasing manner according to how the items for the Likert scale are designed. In some instances, the questionnaire with the Likert scale does not clearly bring out the numerical significance of a particular choice. Say, a Likert scale running from 0 to 10 whereby 0 represents a low likelihood of a tornado and 10 represents a high possibility of a tornado occurring in a place, say, Detroit City. If the significance of the 0 and the 10 are not understood, the responses will be chaotic. The risk assessment will, therefore, lose its numerical significance. In addition, reverse scoring, whereby a negating factor is involved, can make Likert scales difficult to interpret.
• Sentimentality versus objectivity – The factors that comprise the risks are often hard to empirically measure. For example, there is no exact scale that can be used to measure when accurately a fire breakout will occur or when a terrorist will attack. In this kind of scenario, the numerical affiliation assigned to the likelihood of the occurrence of the risk will be ambiguous. It might, therefore, be dependent on a person’s previous experience rather than facts, which might not accurately be representative of future events.
• The overlapping nature of risk factors – some risk factors are hard to simply put in one particular category. For example, rising temperatures can be classified as natural hazards, yet everyone knows that this can be due to global warming, which is something of human making. Given the probabilistic scheme of calculating risk levels, this kind of risk factor, which is poorly defined by categorization, can cause confusion when making calculations. Since the overall goal of risk assessment is to extrapolate one figure that is representative of the general risk status, a factor that is counted more than once can exponentially blow the risk levels out of proportion and cause unnecessary alarm. At the same time, failure to take that very risk factor into account in all its facets can cause serious underestimations, which will lead to adverse consequences in the future.
• The Domino effect – this is simply the ripple effect. Some risks, once they occur, can be a triggering factor for an avalanche of many other related risks not anticipated before. For example, when a drought occurs, the chances of random bushfires become high. When bushfires break out, the chances of burns become high. At the same time, chances of the destruction of property in the fires also rise. And the list goes on and on. The problem this kind of situation poses is that the risk values assigned to a particular risk factor in its independent setting become overruled. This, therefore, means that the previously assigned numerical risk values automatically become invalid. The overall effect is that the risk mitigation factors will also become inadequate to address the risks.
• Probabilistic uncertainties – now that it has been ascertained that there is often more than one risk in operation, a proper risk Assessment will, therefore, need to take into account all the risks active at a particular time. This risk portfolio is only relevant if all the risk items are active simultaneously. But what about when only some of the risk factors occur while others do not? This would mean that the risk portfolio has just become unreliable. This is due to the probabilistic nature of risks. Risks are more stochastic – they occur randomly. This is one of the factors that make it hard to assign numerical values to risks.
• Non-ubiquity – risk factors are not homogenous – they are no uniformly distributed. For example, the chances of fire breakouts in Detroit are not the same in all the areas of Detroit. This would make it difficult to properly assign risk a numerical value to such a risk factor in a manner that adequately describes the actual situation. The use of descriptive statistics like means and medians would therefore be inaccurate due to the homogeneity issue.
The application of numeracy of risks to Detroit City
The public safety assessment sheet (Ms. Excel) categorizes hazards into three: natural hazards, accidental hazards, and intentional hazards. The natural hazards in the assessment rubric are severe thunderstorms, tornadoes, snowfalls, blizzards, ice storms, temperature extremes, droughts, floods, tremors/earthquakes, and epidemics. The accidental hazards assessed for include burns/chemical exposures, external communications failure (i.e., blackout), driving pad accident, electrical failure, fire alarm failure, internal fires, internal flooding, internal fuel shortage, generator failure, external hazmat exposure, internal hazmat exposure, HVAC (Heating, ventilation, and air conditioning) failure, information systems failure (IT network), natural gas failure, range/firearms accident, SCBA (self-contained breathing apparatus) failure, SCBA refill failure, sewer failure, structural damage, supply shortage, and water failure.
The intentional hazards assessed include active shooter situation, arson, bomb threats, demonstration/sit-ins, disgruntled student/employee (workplace violence), forcible sex offenses, mass casualty incident, MDOP (malicious destruction of property)/damage to property, motor vehicle theft, terrorism/CBRNE (Chemical, Biological, Radiological, Nuclear, and high yield Explosives), and VIP situation. Detroit city is potentially at the risk of any of these risk portfolio elements. For example, this year, the FloodFactor website says that there is a 12% chance of property flooding in Detroit (Flood Factor™, 2021).
In the history of Detroit between 1950 and 2010, there were 1,801 thunderstorm winds, 146 floods, three blizzards, 18 cold extremes, 806 hails, 15 ice storms, two wildfires, five dense fog incidents, 12 hot extremes, 37 windstorms, two drought situations, 64 heavy snowfall situations, 42 strong winds, and more than 158 other natural hazards recorded within 50 miles radius of Detroit. There is no known volcano near Detroit. Besides, no earthquakes of 3.5 magnitudes or above have been recorded anywhere near Detroit. However, there are a total of 66 historical tornado events that had recorded a magnitude of 2 or above found in or near Detroit, MI, between 1950 and 2010 (USA.com Local Data Search, 2021).
Using the statistics above as a template, the public safety assessment sheet can be tweaked to reflect what the risk level, and hence safety situation in Detroit would look like. Detroit City has in place mitigation factors for some of the anticipated catastrophes like fire. The Detroit Fire Department (DFD) conducts regular training on how to handle fire incidents, which is a good thing. Besides, the fire department responds to an average of 4000 emergencies per month, some of which are not fire-type, but are fire-related. This includes actual fire incidents, medical emergencies, and other general emergencies. The City of Detroit’s Fire Department also has a water accident rescue program under the name Curtis Randolph, which is a fireboat. Furthermore, the fire department boasts of a quick response record of under 8 minutes. This is especially true regarding medical emergencies, whereby the city aims to attain the national standards of under 8 minutes response time from the moment a 911 call is made (City of Detroit, 2021).
According to the tweaked public safety assessment sheet (find attached), of the 13% total risk level in Detroit City, natural hazards pose more risks (20%) followed by intentional hazards (15%), and then accidental hazards (12%). In addition, the probability of overall risks occurring is at 33%, with 41% severity. In other words, the severity of the risks when they occur is higher than their probability of occurring. This literally means that more mitigation measures would be put in place to minimize the outburst of the impending risks and also to minimize their severity (Appendices).
References
Brust-Renck, P., Royer, C., & Reyna, V. (2013). Communicating Numerical Risk. Reviews Of Human Factors And Ergonomics, 8(1), 235-276. doi: 10.1177/1557234×13492980
City of Detroit. (2021). Fire Operations. Retrieved from https://detroitmi.gov/departments/detroit-fire-department/fire-operations
Crisis Equipped. (2021). These Natural Disasters Can Occur in Michigan! Are You Prepared?. Retrieved from https://crisisequipped.com/what-natural-disasters-occur-in-michigan/
Flood Factor™. (2021). Proportion of properties at risk in Wayne County. Retrieved from https://floodfactor.com/property/9208-ward-st-detroit-michigan/260064782_fsid
FutureLearn. (2021). What are the problems in implementing risk management in practice?. Retrieved from https://www.futurelearn.com/info/courses/risk-management/0/steps/39309
Hutchins, G. (2020). Risk Assessment Challenges — Accendo Reliability. Retrieved 18 February 2021, from https://accendoreliability.com/risk-assessment-challenges/
International Federation of Red Cross and Red Crescent Societies. (2021). Types of disasters: Definition of hazard. Retrieved from https://www.ifrc.org/en/what-we-do/disaster-management/about-disasters/definition-of-hazard/
Merriam-Webster Dictionary. (2021). The definition of risk. Retrieved from https://www.merriam-webster.com/dictionary/risk
USA.com Local Data Search. (2021). Detroit, MI Natural Disasters and Weather Extremes. Retrieved from http://www.usa.com/detroit-mi-natural-disasters-extremes.htm
Worksmart. (2021). What are the five steps to risk assessment?. Retrieved from https://worksmart.org.uk/health-advice/health-and-safety/hazards-and-risks/what-are-five-steps-risk-assessment
Appendices
Appendix I: Summary of events and risks – Detriot City
EVENT RISK
NATURAL HAZARD
Severe Thunderstorm 67%
Tornado 33%
Snow Fall 13%
Blizzard 46%
Ice Storm 22%
Temperature Extremes 1%
Tremor/Earthquake 0%
Flood, External 33%
Epidemic 37%
ACCIDENTAL HAZARD
Injury (Live Burn Scenario) 48%
Communications & IT Failure 41%
Electrical Failure 44%
Fire Alarm Failure 31%
Flood, Internal 28%
Sewer Failure 22%
Water Failure 24%
HVAC Failure 44%
Natural Gas Failure 28%
Driving Pad Accident 8%
Fire, Internal 12%
Hazmat Exposure, External (Lg) Train/Truck 44%
Range / Firearms Accident 22%
INTENTIONAL HAZARD
Active Shooter 43%
Bomb Threat 2%
VIP Situation (Guest Speaker / Instructor) 52%
Demonstration / Sit-Ins 3%
Disgruntled Student / Employee (Workplace Violence) 42%
Forcible Sex Offenses 7%
Mass Casualty Incident (Trauma: MV vs. Crowd)? 28%
Motor Vehicle Theft 52%
MDOP / Damage to Property 3%
Appendix II: Hazard-specific relative risks – Detroit City
Appendix II: Probability and Severity of Hazards – Detroit City
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