DOT HS 812 013

May 2015 (Revised)

The Economic and Societal Impact
Of Motor Vehicle Crashes, 2010
(Revised)A Note on the 2015 Revision
The Economic and Societal Impact of Motor Vehicle Crashes was originally published in May 2014.
Subsequent to its publication, a coding error was discovered in the SAS program that was used to aggregate
costs to persons with multiple injuries. This error impacted estimates of medical care costs, lost work andhousehold productivity, and insurance administrative and legal costs. After evaluating the potential impactof this error, the authors determined that it was large enough to warrant a revision to the base report. This
revised report thus replaces the May 2014 report in its entirety.

DISCLAIMER
This publication is distributed by the U.S. Department of Transportation, National
Highway Traffic Safety Administration, in the interest of information exchange.
The opinions, findings, and conclusions expressed in this publication are those of
the authors and not necessarily those of the Department of Transportation or the
National Highway Traffic Safety Administration. The United States Government
assumes no liability for its contents or use thereof. If trade or manufacturers’ names
or products are mentioned, it is because they are considered essential to the objectof the publication and should not be construed as an endorsement. The UnitedStates Government does not endorse products or manufacturers.

Suggested APA Format Citation:
Blincoe, L. J., Miller, T. R., Zaloshnja, E., & Lawrence, B. A. (2015, May).
The economic and societal impact of motor vehicle crashes, 2010. (Revised)(Report No. DOT HS 812 013). Washington, DC: National Highway Traffic
Safety Administration.

Technical Report Documentation Page
1. Report No.
2. Government Accession No.
DOT HS 812 013
4. Title and Subtitle
The Economic and Societal Impact of Motor Vehicle Crashes, 2010 (Revised)
7. Authors
Lawrence Blincoe, Ted R. Miller, Ph.D., Eduard Zaloshnja, Ph.D., Bruce A.
Lawrence, Ph.D.
9. Performing Organization Name and Address
National Center for Statistics and Analysis
National Highway Traffic Safety Administration
Washington, DC 20590
12. Sponsoring Agency Name and Address
U.S. Department of Transportation
National Highway Traffic Safety Administration
1200 New Jersey Avenue SE.
Washington, DC 20590
15. Supplementary Notes

3. Recipient’s Catalog No.
5. Report Date
May 2015
6. Performing Organization Code
8. Performing Organization Report No.

10. Work Unit No. (TRAIS)
11. Contract or Grant No.
13. Type of Report and Period Covered
NHTSA Technical Report
14. Sponsoring Agency Code

16. Abstract
In 2010, there were 32,999 people killed, 3.9 million were injured, and 24 million vehicles were damaged in motor vehicle crashes
in the United States. The economic costs of these crashes totaled $242 billion. Included in these losses are lost productivity,
medical costs, legal and court costs, emergency service costs (EMS), insurance administration costs, congestion costs, property
damage, and workplace losses. The $242 billion cost of motor vehicle crashes represents the equivalent of nearly $784 for eachof the 308.7 million people living in the United States, and 1.6 percent of the $14.96 trillion real U.S. Gross Domestic Product for2010. These figures include both police‐reported and unreported crashes. When quality of life valuations are considered, thetotal value of societal harm from motor vehicle crashes in 2010 was $836 billion. Lost market and household productivity
accounted for $77 billion of the total $242 billion economic costs, while property damage accounted for $76 billion. Medical
expenses totaled $23 billion. Congestion caused by crashes, including travel delay, excess fuel consumption, greenhouse gases
and criteria pollutants accounted for $28 billion. Each fatality resulted in an average discounted lifetime cost of $1.4 million.
Public revenues paid for roughly 7 percent of all motor vehicle crash costs, costing tax payers $18 billion in 2010, the equivalentof over $156 in added taxes for every household in the United States. Alcohol involved crashes accounted for $52 billion or 22
percent of all economic costs, and 84 percent of these costs occurred in crashes where a driver or non‐occupant had a blood
alcohol concentration (BAC) of .08 grams per deciliter or greater. Alcohol was the cause of the crash in roughly 82 percent ofthese cases, causing $43 billion in costs. Crashes in which alcohol levels are BAC of .08 or higher are responsible for over 90
percent of the economic costs and societal harm that occurs in crashes attributable to alcohol use. Crashes in which police
indicate that at least one driver was exceeding the legal speed limit or driving too fast for conditions cost $52 billion in 2010. Seat
belt use prevented 12,500 fatalities, 308,000 serious injuries, and $50 billion in injury related costs in 2010, but the failure of a
substantial portion of the driving population to buckle up caused 3,350 unnecessary fatalities, 54,300 serious injuries, and costsociety $10 billion in easily preventable injury related costs. Crashes in which at least one driver was identified as being
distracted cost $40 billion in 2010. The report also includes data on the costs associated with motorcycle crashes, failure to wear
motorcycle helmets, pedestrian crash, bicyclist crashes, and numerous different roadway designation crashes.
18. Distribution Statement
17. Key Words
Document is available to the public from the National
Economic, cost, harm, comprehensive cost, motor vehicle, crashes,Technical Information Service www.ntis.gov.
traffic, accidents, alcohol, speed, seat belts, distraction, helmet,
motorcycle, congestion, travel delay, source of payment, fatalities,
injuries, unreported crashes, property damage, pedestrian,bicyclist, interstate intersection, roadway departure
19. Security Classif. (Of this report)
20. Security Classif. (Of this
21. No. of Pages 22. Price
page)
Unclassified
304
Unclassified
Form DOT F 1700.7 (8-72)
Reproduction of completed page authorizediTable of Contents
Executive Summary ....................................................................................................................................... 1
1. Introduction and Summary Tables ............................................................................................................ 5
2. Human Capital Costs ............................................................................................................................... 22
3. Congestion Impacts................................................................................................................................. 50
4. Lost Quality-of-life................................................................................................................................. 113
5. Incidence ............................................................................................................................................... 121
6. State Costs............................................................................................................................................. 144
7. Alcohol................................................................................................................................................... 147
8. Speeding ................................................................................................................................................ 169
9. Distracted Driving ................................................................................................................................. 172
10. Motorcycle Crashes ............................................................................................................................. 177
11. Seat Belt Use ....................................................................................................................................... 193
12. Cashes by Roadway Location .............................................................................................................. 204
13. Other Special Interest Crash Scenarios ............................................................................................... 224a) Intersection Crashes................................................................................................................. 225b) Interstate Highway Crashes ..................................................................................................... 226c) Single-Vehicle Crashes.............................................................................................................. 226d) Roadway Departure Crashes................................................................................................... 226e) Pedestrian/bicyclist Crashes .................................................................................................... 226f) Pedestrian Crashes ................................................................................................................... 226g) Bicyclist Crashes ....................................................................................................................... 226
14. Source of Payment .............................................................................................................................. 235
Appendix A: Sensitivity Analysis, Value of a Statistical Life ...................................................................... 241
Appendix B: Costs by Body Region ............................................................................................................ 245

ii

Appendix C: KABCO/MAIS Translators ...................................................................................................... 248
Appendix D: KABCO Unit Costs.................................................................................................................. 251
Appendix E: Estimating the Costs of Motor Vehicle Traffic Injuries in the United States From
Health Care Files ....................................................................................................................................... 252
Supplement to Appendix E: Short-Term Follow-Up Cost Factors .............................................................. 266
Appendix F: Unit Costs and Standard Errors at Different Discount Rates................................................. 268
Appendix G: Definitions............................................................................................................................. 287
Bibliography .............................................................................................................................................. 288

iii

Executive Summary
In 2010 the total economic cost of motor vehicle crashes in the United States was $242 billion. This
represents the present value of lifetime economic costs for 32,999 fatalities, 3.9 million non-fatal
injuries, and 24 million damaged vehicles. These figures include both police-reported and unreported
crashes. When quality-of-life valuations are considered, the total value of societal harm from motor
vehicle crashes in 2010 was $836 billion.
All costs in this report are expressed in year 2010 economics using a 3-percent discount rate. Nonfatal
injury costs are stratified by severity level based on the Abbreviated Injury Scale, 1 but unit costs basedon the KABCO scale commonly used in police reports are also supplied in an appendix. The cost
components include productivity losses, property damage, medical costs, rehabilitation costs,
congestion costs, legal and court costs, emergency services such as medical, police, and fire services,
insurance administration costs, and the costs to employers. Values for more intangible consequencessuch as physical pain or lost quality-of-life are also examined in estimates of comprehensive costs, which
include both economic cost components and quality-of-life valuations.
Economic Impact of Crashes■ The economic cost of motor vehicle crashes that occurred in 2010 totaled $242 billion. This isequivalent to approximately $784 for every person living in the United States and 1.6 percent of the
U.S. Gross Domestic Product.
■ The lifetime economic cost to society for each fatality is $1.4 million. Over 90 percent of this amountis attributable to lost workplace and household productivity and legal costs.
■ Each critically injured survivor (using the MAIS 5 scale) cost an average of $1.0 million. Medical costs
and lost productivity accounted for 82 percent of the cost for this most serious level of non-fatal
injury.
■ Lost workplace productivity costs totaled $57.6 billion, which equaled 24 percent of the total costs.
Lost household productivity totaled $19.7 billion, representing 8 percent of the total economic
costs.

1

The Abbreviated Injury Scale (AIS) is an anatomically based, consensus-derived global severity scoring systemthat classifies each injury by body region according to its relative importance on a 6-point ordinal scale (1=minor
and 6=maximal). AIS is the basis for the Injury Severity Score (ISS) calculation of the multiply injured patient. The
AIS was developed by the Association for the Advancement of Automotive Medicine (AAAM) See
www.aaam1.org/ais/ for further information.

1

■ Total property damage costs for all crash types (fatal, injury, and property damage only [PDO])
totaled $76.1 billion and accounted for 31 percent of all economic costs.
■ Property-damage-only crashes (in which vehicles were damaged but nobody was injured) cost $71.5
billion and accounted for 30 percent of total economic motor vehicle crash costs.
■ Present and future medical costs due to injuries occurring in 2010 were $23.4 billion, representing
10 percent of the total costs. Medical costs accounted for 21 percent of costs from non-fatal
injuries.
■ Congestion costs, including travel delay, added fuel usage, and adverse environmental impacts cost
$28 billion, or 12 percent of total economic crash costs.
■ Police-reported crashes account for 83 percent of the economic costs and 89 percent of totalsocietal harm that occurs from traffic crashes. Crashes that are not reported to the police accountfor 17 percent of economic costs and 11 percent of total societal harm.
■ Approximately 7 percent of all motor vehicle crash costs are paid from public revenues. Federal
revenues accounted for 4 percent and States and localities paid for approximately 3 percent. An
additional 1 percent is from programs that are heavily subsidized by public revenues, but for which
the exact source could not be determined. Private insurers pay approximately 54 percent of all
costs. Individual crash victims pay approximately 23 percent while third parties such as uninvolved
motorists delayed in traffic, charities, and health care providers pay about 16 percent. Overall, those
not directly involved in crashes pay for over three-quarters of all crash costs, primarily throughinsurance premiums, taxes and congestion related costs such as travel delay, excess fuelconsumption, and increased environmental impacts. In 2010 these costs, borne by society rather
than by crash victims, totaled over $187 billion.
Incidence of Crashes■ Some 3.9 million people were injured in 13.6 million motor vehicle crashes in 2010, including 32,999
fatalities. Twenty-four percent of these injuries occurred in crashes that were not reported to police.
■ About 23.9 million vehicles were damaged in motor vehicle crashes in 2010; 18.5 million or 77
percent of these vehicles were damaged in incidents that incurred property damage only. The
remaining 23 percent involved injuries to occupants of the vehicle, or to nonoccupants such aspedestrians or bicyclists.
■ Approximately 60 percent of property-damage-only crashes and 24 percent of all injury crashes are
not reported to the police. Unreported injury crashes tend to involve only minor or moderate
injuries.

2

Alcohol Involvement in Crashes■ Alcohol-involved crashes resulted in 13,323 fatalities, 430,000 nonfatal injuries, and $52.5 billion ineconomic costs in 2010, accounting for 22 percent of all crash costs.
■ Crashes involving drivers or nonoccupants with a blood alcohol concentration of .08 grams per
deciliter or higher (the legal definition of impairment in all States) accounted for 84 percent of thetotal economic cost of all alcohol-involved crashes.
■ The impact of alcohol involvement increases with injury severity. Alcohol-involved crashes
accounted for 14 percent of property-damage-only crash costs, 17 percent of nonfatal injury crashcosts; and 48 percent of fatal injury crash costs.
■ Although drinking drivers may experience impaired judgment, perceptions, and reaction times, not
all crashes in which alcohol was present were caused by alcohol. Crashes in which alcohol was thecause resulted in 11,226 fatalities, 326,000 nonfatal injuries, and $43.2 billion in economic costs.
This is approximately 84 percent of the alcohol-related fatalities and 82 percent of alcohol-related
economic costs. It represents 34 percent of all fatalities and 18 percent of all costs from motor
vehicle crashes.
Impact of Speed-Related Crashes■ Crashes in which at least one driver was exceeding the legal speed limit or driving too fast forconditions cost $52 billion in 2010.
■ Speed-related crashes are associated with 10,536 fatalities, 800,000 nonfatal injuries and damage to
3.0 million vehicles in property-damage-only crashes. This represents 32 percent of all fatalities; 20
percent of all nonfatal injuries, and 16 percent of all property-damage-only crashes.
■ Speed-related crashes cost an average of $168 for every person in the United States.
Seat Belt Use■ In 2010, seat belts prevented 12,500 fatalities and 308,000 serious injuries, saving $50 billion in
medical care, lost productivity, and other injury-related costs.
■ Seat belt non-use represents an enormous lost opportunity for injury prevention. In 2010 alone,over 3,350 people were killed and 54,300 were seriously injured unnecessarily because they failed
to wear their seat belts, costing society $10.43 billion.
■ Over the past 36 years, since FARS started collecting data in 1975, seat belts have prevented over
280,000 fatalities and 7.2 million serious injuries. This saved society $1.2 trillion in medical care, lost
productivity, and other injury-related economic costs. During the same time period, nearly 367,000
additional fatalities and 5.8 million additional serious injuries could have been prevented by seatbelts if all occupants had used them. This represents an economic loss of roughly $1.1 trillion in
unnecessary expenses and lost productivity.
3Distracted Driving Crashes■ Crashes in which at least one driver was identified as being distracted resulted in 3,267 fatalities,
735,000 nonfatal injuries and damaged 3.3 million vehicles in property-damage-only crashes in2010. This represents about 10 percent of all motor vehicle fatalities and 18 percent of all nonfatal
crashes. These crashes cost $39.7 billion in 2010, roughly 16 percent of all economic costs frommotor vehicle crashes.
Societal Impacts of Crashes (Comprehensive Costs)■ The value of societal harm from motor vehicle crashes, which includes both economic impacts andvaluation for lost quality-of-life, was $836 billion in 2010. Seventy-one percent of this value
represents lost quality-of-life, while 29 percent is economic impacts.
■ The lifetime comprehensive cost to society for each fatality is $9.1 million. Eighty-five percent of thisamount is attributable to lost quality-of-life.
■ Each critically injured survivor (MAIS 5) has comprehensive costs that average of $5.6 million. Lost
quality-of-life accounted for 82 percent of the total harm for this most serious level of non-fatal
injury.
■ Alcohol-involved crashes resulted in $236 billion in comprehensive costs in 2010, accounting for28 percent of all societal harm from motor vehicle crashes. Eighty-five percent of these costs
occurred in crashes where one driver had a BAC of .08 g/dL or greater.
■ Although drinking drivers may experience impaired judgment, perceptions, and reaction times, not
all crashes in which alcohol was present were caused by alcohol. Crashes in which alcohol was thecause resulted in $194 billion in societal harm in 2010. This represents 23 percent of all societal
harm from motor vehicle crashes. Ninety-four percent of societal harm from crashes caused byalcohol occurs in crashes where drivers had BACs of .08 or greater.
■ Crashes in which at least one driver was exceeding the legal speed limit or driving too fast forconditions caused $203 billion in comprehensive costs in 2010. This represents 24 percent of allsocietal harm from motor vehicle crashes.
■ Crashes in which at least one driver was identified as being distracted caused $123 billion incomprehensive costs in 2010, causing roughly 15 percent of all societal harm from motor vehicle
crashes.
■ In 2010, seat belts prevented $329 billion in comprehensive costs to society. Over the last 36
years, seat belts have prevented $7.6 trillion in societal harm, resulting in lower economic costs tosociety and improved quality-of-life for millions of motor vehicle occupants.

4

1. Introduction
In 2010, there were 32,999 people killed, 3.9 million were injured, and 24 million vehicles weredamaged in motor vehicle crashes in the United States. The economic costs of these crashes totaled$242 billion. Included in these losses are lost productivity, medical costs, legal and court costs,
emergency service costs (EMS), insurance administration costs, congestion costs, property damage, andworkplace losses. The $242 billion cost of motor vehicle crashes represents the equivalent of$784 for each of the 308.7 million people living in the United States, and 1.6 percent of the $14.96
trillion real Gross Domestic Product for 2010.
All levels of society -- the individual crash victims and their families, their employers, and society at large-- are affected by motor vehicle crashes in many ways. The cost of medical care is borne by theindividual in the form of payments for insurance, deductibles, uncovered costs, and uninsured expenses.
It is borne by society through higher insurance premiums and through the diversion of medical
resources away from other medical needs, such as medical research, disease prevention and control,and basic public health needs. There are also significant costs associated with the lost productivity
experienced by an individual and others when the victim dies prematurely or experiences a short or
long-term disability. The victim’s dependents suffer immediate economic hardship in the loss of thevictim’s income and other contributions, while society is burdened by the necessity to support thevictim or their dependents and through foregone contributions to the Nation’s productivity. Aside fromthese economic consequences, victims suffer from physical pain, disability, and emotional impacts thatcan greatly reduce the quality of their lives.
This report examines these and other costs resulting from motor vehicle crashes. The purpose ofpresenting these costs is to place in perspective the economic losses and societal harm that result fromthese crashes, and to provide information to government and private sector officials for use instructuring programs to reduce or prevent these losses.
Economic Impacts:
Total economic costs are summarized in Table 1-1. The total economic cost of motor vehicle crashes in2010 is estimated to have been $242.0 billion. Of this total, medical costs were responsible for $23.4
billion, property damage losses for $76.1 billion, lost productivity (both market and household) for $77.4
billion, and congestion impacts for $28 billion. All other crash related costs totaled $37 billion.
The most significant costs were property damage and lost market productivity, which accounted for 31
and 24 percent, respectively, of the total economic costs in 2010. For lost productivity, these high costs
are a function of the level of disability that has been documented for crashes involving injury and death.
For property damage, costs are primarily a function of the very high incidence of minor crashes in which
injury does not occur or is negligible. Medical care costs and emergency services (which include police

5and fire services) are responsible for about 10 percent of the total. Travel delay, added fuelconsumption, and pollution impacts caused by congestion at the crash site accounts for nearly 12
percent.
The value of lost household productivity accounts for 8 percent of total costs. Legal and court costs
account for 5 percent and insurance administration costs for about 9 percent of the total. These costs
are summarized in Tables 1-1, 1-2, and Figure 1-A. The incidence of injuries and crashes that produced
these costs is summarized in Table 3.
Approximately 8 percent of all motor vehicle crash costs are paid from public revenues. Federal
revenues accounted for 4 percent and States and localities paid for approximately 3 percent. An
additional 1 percent is from programs that are heavily subsidized by public revenues, but for which theexact source could not be determined. Private insurers pay approximately 54 percent of all costs.
Individual crash victims pay approximately 23 percent while third parties such as uninvolved motorists
delayed in traffic, charities, and health care providers pay about 16 percent. Overall, those not directly
involved in crashes pay for over three-quarters of all crash costs, primarily through insurance
premiums, taxes and congestion related costs such as travel delay, excess fuel consumption, andincreased environmental impacts. In 2010 these costs, borne by society rather than by crash victims,
totaled $187 billion. Figure 1-A summarizes these illustrates these cost distributions.
From Table 1-3, over half of all PDO crashes and about a quarter of all non-fatal injury crashes are notreported to police. However, analyses of safety countermeasures frequently rely only on police-reported
incidence data. Crashes that get reported to police are likely to be more severe than unreported crashes
because vehicles are more likely to require towing and occupants are more likely to require
hospitalization or emergency services. These crashes are typically also likely to require more time toinvestigate and clear from roadways than unreported crashes. Analysis based solely on police-reported
crashes should thus be based on unit costs that are specific to police-reported crashes. For injury related
costs, this is more or less automatically accounted for by the shift in the injury severity profile.
Unreported crashes have a lower average severity profile than do reported crashes. However, for noninjury related cost components – property damage and congestion costs – there is no profile to shift. In
addition, police-reported crashes have higher response rates for emergency services.
For this report, costs specific to police-reported and unreported crashes have been developed. The
results of this analysis are presented in Tables 1-4, 1-5, 1-6, and 1-7. The differences seem negligible atthe more severe injury levels due to the overwhelming costs of factors such as lost productivity andmedical care which do not vary by reporting status, except through the shift in injury profiles. However,at lower severity levels the unit costs are significant. For PDO vehicles and MAIS0 cases, police-reported
crashes have costs that are three times those of unreported crashes. For minor (MAIS1) injuries,
reported crashes cost 40 percent more than unreported crashes. These ratios decline as injury severity
increases. Note that for MAIS4s, MAIS5s, and fatalities, property damage costs are identical under bothreported and unreported cases. Virtually all injuries at these levels are believed to be reported to police,
and the original property damage cost estimate is thus assumed to represent police-reported cases.
These same costs are thus listed under both scenarios.
6Figure 1-B shows the proportion of each cost category that is accounted for by police-reported crashes.
For most categories, the portions vary due to the differing proportions of incidence across the various
injury levels. For congestion, property damage, and emergency services, differing unit costs are involved
as well. Overall, police-reported crashes are estimated to account for 83 percent of the economic costs
that are incurred from traffic crashes.
NHTSA last examined the cost of motor vehicle crashes in 2002. At that time the report was based on
2000 data. The current report indicates a total cost from traffic crashes in 2010 of $242.0 billion,
approximately 5 percent higher than our previous estimate of $230.6 billion in 2000 1. The difference inthese estimates is attributable to a number of factors. Inflation accounts for an overall rise in the cost of
goods and services of approximately 27 percent, but incidence of fatalities and injuries has declined
over the past decade due to a combination of technological, behavioral, infrastructure, and economic
factors. In 2010 there were 32,999 fatalities in motor vehicle crashes, a decline of 21 percent from 2000.
At the same time, the number of police-reported injuries reported in the General Estimates System(GES) dropped by 30 percent. These declines reflect significant safety improvements in the on-roadvehicle fleet. Since 2000 a number of significant technological safety improvements have been phased into the vehicle fleet. These include advanced air bags, better side impact protection, tire pressure
monitoring systems, interior padding, improved seat belts, improved vehicle conspicuity, antilock brakesystems, and electronic stability control. Seat belt use has also increased over this decade, rising from 73
percent in 2000 to 85 percent in 2010, due in part to enforcement of primary belt use laws and to public
education programs that educate drivers to the importance of belt use. These and other factors such as
improved roadways have helped drive the fatality rate/hundred million VMT down from 1.53 deaths per
hundred million vehicle miles travelled (VMT) to 1.11 in 2010. The economic recession likely had some
impact as well, although VMT did not decline significantly. However, fatalities usually decline during
periods of economic uncertainty, possibly due to a more sober attitude on the part of drivers. Cost shifts
also occurred because most cost factors were re-examined based on more recent data sources and thiscaused shifts in unit costs that impacted the overall estimate in a variety of ways. The specifics of thesechanges are described in the body of this report. Note also that lifetime cost impacts such as lost
productivity and medical care for serious injury are measured using a 3-percent discount rate, whereas
the previous report used a 4-percent rate. The shift to the 3-percent rate reflects lower real investment
returns over the past decade and has been established as the appropriate value to represent the social
rate of time preference by the Office of Management and Budget. This accounts for a small portion of
the difference in costs as well.
Alcohol consumption remains a major cause of motor vehicle crashes; 2010 data shows that alcohol
involved crashes declined slightly in incidence. Historically, approximately half of all motor vehicle
fatalities have occurred in crashes where the drivers or nonoccupants had been drinking, but thisnumber has gradually declined in recent years to about 40 percent. Alcohol is involved in crashes that
account for 22 percent of all economic costs, with 84 percent of these costs involving crashes where adriver or nonoccupant was legally intoxicated (illegal per se), defined as a blood alcohol concentration(BAC) of .08 grams per deciliter (g/dL) or higher.
1

The two numbers are not directly comparable because the previous of $231 billion has not been adjusted for thecoding error corrected in the revised version.

7

The report indicates that while alcohol-involved crashes are more costly than in 2000, they account for asmaller portion of the overall crash cost. This reflects the impact of efforts at Federal, State, and local
levels to reduce the incidence of drunk driving. The report also estimates the portion of alcohol-involved
crash costs that were actually caused by impaired driving. Although drinking drivers may experience
impaired judgment, perceptions and reaction times, not all crashes in which alcohol was present werecaused by alcohol. For example, a driver with a BAC of .04 g/dL could be stopped at a light and run into
by a texting driver. Crashes caused by alcohol accounted for 82 percent of all economic costs fromcrashes where at least one driver or nonoccupant had been drinking. The portion attributable to alcoholrises dramatically as BAC increases, with only 6 percent attribution at low BAC levels (BAC=.01 to .04), but
94 percent attribution at legally impaired (illegal per se) levels (BAC>=.08). Crashes caused by legally
impaired drivers with BACs in excess of .08 g/dL account for over 95 percent of the economic andsocietal harm that results from all alcohol caused crashes.
The report also analyzes the impact of seat belt use as well as the cost the Nation incurs from failure towear seat belts. Over the last 35 years, seat belts have prevented over 280,000 fatalities and 7.2 million
serious nonfatal injuries, which saved $1.2 trillion in economic costs (in 2010 dollars). During this same
period, the failure of a substantial portion of the driving population to wear belts caused 367,000
unnecessary deaths and 5.8 million nonfatal injuries, costing the Nation $1.1 trillion in preventable
medical costs, lost productivity, and other injury related expenditures.
The Abbreviated Injury Scale (AIS) used in this report provides the basis for stratifying societal costs byinjury severity. Significant sources of economic loss, such as medical costs and lost productivity, arehighly dependent on injury outcome. AIS codes are primarily oriented toward the immediate threat tolife resulting from the injury, and are estimated soon after a crash occurs. Although the more serious
injuries tend to have more serious outcomes, AIS codes are not always accurate predictors of long-term
injury outcomes. Some injuries with low AIS codes, such as lower extremity injuries, can actually result inserious and expensive long-term outcomes. There is currently no incidence database organized by injury
outcome. The development and use of such a database could improve the accuracy of economic costestimates, and might result in a significant shift in the relative number of injuries regarded as serious.
This report focuses on “average” costs for injuries of different severity. While this approach is valid forcomputing combined costs at a nationwide level, the costs of individual cases at different injury levels
can vary quite dramatically. The average costs outlined in this report are significant; however, inindividual cases they can be exceeded by a factor of three or more. There is considerable evidence toindicate that the most serious injuries are not adequately covered by insurance. Depending on thefinancial ability and insurance coverage of the individual crash victims, the medical and rehabilitation
costs, as well as the loss in wages resulting from serious injury, can be catastrophic to the victim’s
economic wellbeing in addition to their physical and emotional condition.
When using this report for the analysis of crash impact and injury countermeasures, it is important toinclude only those cost elements that are applicable to the specific programs addressed. For example,
programs that encourage seat belt use may reduce costs associated with injuries, but would not have aneffect on property-damage or congestion costs. Therefore, careful consideration should be given to the8nature of the benefits from any proposal before incorporating the results of this report into analyses orrecommendations. Economic costs represent only one aspect of the consequences of motor vehicle
crashes. People injured in these crashes often suffer physical pain and emotional anguish that is beyond
any economic recompense. The permanent disability of spinal cord damage, loss of mobility, loss ofeyesight, and serious brain injury can profoundly limit a person’s life, and can result in dependence onothers for routine physical care. More common, but less serious injuries, can cause physical pain andlimit a victim’s physical activities for years after the crash. Serious burns or lacerations can lead to longterm discomfort and the emotional trauma associated with permanent disfigurement. For an individual,
these non-monetary outcomes can be the most devastating aspect of a motor vehicle crash.
The family and friends of the victim feel the psychological repercussions of the victim’s injury acutely as
well. Caring for an injured family member can be very demanding for others in the family, resulting ineconomic loss and emotional burdens for all parties concerned. It can change the very nature of theirfamily life; the emotional difficulties of the victim can affect other family members and the cohesivenessof the family unit. When a crash leads to death, the emotional damage is even more intense, affecting
family and friends for years afterward and sometimes leading to the breakup of previously stable family
units.
Action taken by society to alleviate the individual suffering of its members can be justified in and of
itself; in order to increase the overall quality-of-life for individual citizens. In this context, economic
benefits from such actions are useful to determine the net cost to society of programs that are primarily
based on humane considerations. If the focus of policy decisions was purely on the economic
consequences of motor vehicle crashes, the most tragic, and, in both individual and societal terms,
possibly the most costly aspect of such crashes would be overlooked.
Societal Impacts:
Previous versions of this report have focused on the economic impact of motor vehicle crashes – thesocietal losses that can be directly measured in economic terms. However, these costs do not represent
the more intangible consequences of these events and should not, therefore, be used alone to produce
cost-benefit ratios. Measurement of the dollar value of intangible consequences such as pain andsuffering has been undertaken in numerous studies. These studies have estimated values based on
wages for high-risk occupations and prices paid in the market place for safety products, among othermeasurement techniques. These “willingness to pay” based estimates of how society values risk
reduction capture valuations not associated with direct monetary consequences. Most researchers
agree that the value of fatal risk reduction falls in the range of $5 to $15 million per life saved. In thisstudy, comprehensive costs, which include both the economic impacts of crashes and valuation of lost
quality-of-life, are also examined. Comprehensive costs represent the value of the total societal harmthat results from traffic crashes. The basis for these estimates is the most recent guidance issued by the
U.S. Department of Transportation for valuing risk reduction. This guidance, which was issued in
February 2013, establishes a new value of a statistical life (VSL) at $9.1 million in 2012 economics ($8.86
million in 2010 economics). In addition, it establishes new relative disutility factors stratified by injury
severity level to estimate the lost quality-of-life for nonfatal injuries. These factors were derived in a9

research contract designed specifically for this current cost study. More detailed discussion ofcomprehensive costs is included in Chapter 4 of this report. The total societal harm from motor vehicle
crashes as measured by comprehensive costs, is shown in Tables 1-4 and 1-5, and Figure 1-C.
From Table 1-4, the total societal harm from motor vehicle crashes in 2010 is estimated to have been
$836 billion, roughly three and a half times the value measured by economic impacts alone. Of this total,
71 percent represents lost quality-of-life, dwarfing the contribution of all other cost categories. Thishighlights the importance of accounting for all societal impacts when measuring costs and benefits frommotor vehicle safety countermeasures. However, the literature on VSL estimates indicates a wide rangeof measured estimates of VSLs – some as low as a few million dollars, some as high as over $30 million.
The U. S. DOT guidance memorandum (U.S. Department of Transportation (2013), Guidance on
Treatment of the Economic Value of a Statistical Life in U.S. Department of Transportation Analyses,
Memorandum from the Office of the Secretary of Transportation, U.S. Department of Transportation.
Available at: http://www.dot.gov/sites/dot.gov/files/docs/VSL%20Guidance_2013.) discusses a feasible
range of VSLs for sensitivity analysis from $5.2 million to $12.9 million. There is thus far more
uncertainty regarding the accuracy of estimates of lost quality-of-life than there is regarding economic
costs. In Appendix A comprehensive costs are estimated based on this range. The results indicate afeasible range of societal harm from motor vehicle crashes of from $546 billion to $1.12 trillion in 2010,
with lost quality-of-life accounting for between roughly half and three-quarters of all societal harm
respectively.
Tables 10 to 13 examine the comprehensive costs of police-reported and unreported crashes. Roughly
89 percent of aggregate societal harm from motor vehicle crashes occurs in police-reported crashes.
This is somewhat higher than the 83 percent for economic costs. The difference is due to the impact of
quality-of-life valuations on fatalities and the most serious injuries (MAIS4+), which are all policereported.
Overview:
Table 1-14 summarizes both the economic and comprehensive costs of selected crash categories
examined in this study. Nonfatal injuries were the most costly severity outcome, accounting for roughly
half of both economic costs and societal harm. Damage to vehicles in which no injury occurred was thesecond highest economic cost outcome due to the high frequency of these low impact crashes.
However, in terms of societal harm, fatalities were the second most costly outcome due to the inclusionof lost quality-of-life for the life years that fatal crash victims lose.
This report examined five different types of adverse driver behavior - alcohol use, speeding, distracted
driving, failure to wear seat belts, and riding a motorcycle without a helmet. The most costly of these
involved alcohol use. Alcohol-involved crashes, in which drivers or pedestrians had some level of alcoholin their bloodstreams, accounted for 22 percent of economic costs and 28 percent of societal harm.
However, crashes in which alcohol was a likely cause of the crashes accounted for 18 percent of
economic costs and 23 percent of societal harm. Over 90 percent of this toll occurred in crashes where
the drivers were legally intoxicated.

10

Crashes in which one or more drivers were exceeding the legal speed limit or driving too fast forconditions caused 22 percent of economic costs and 24 percent of societal harm. The extent to which
speed actually caused these crashes is uncertain, but higher speeds leave less time for drivers to react toemergency situations.
Distracted driving, which includes talking on cell phones, texting, eating, and other non-driving activities,
was a factor in crashes that caused 16 percent of economic costs and 15 percent of societal harm.
However, distracted driving is difficult to detect and it is likely that distraction plays an even larger rolein causing crashes and their resulting impacts on society.
The failure of some vehicle occupants to use their seat belts accounts for roughly 4 percent of economic
costs and 8 percent of societal harm. While these portions seem relatively small, they represent
economic costs of $10 billion and societal harm of $69 billion annually. Likewise, failure to wear
motorcycle helmets causes a small portion of the overall total, but has serious economic and quality-oflife consequences for the injured riders and their families.
Injuries to non-occupants also have significant economic and societal impacts. Motorcyclist injuries cause
5 percent of the economic costs and 8 percent of societal harm from traffic crashes. Injuries topedestrians and bicyclists cause 7 percent of the economic costs and 10 percent of the societal harm.
The report also examines crash costs for various roadway types and crash configurations. Among its
findings, crashes on interstate highways account for roughly 10 percent of both economic costs andsocietal harm, while the more frequent but generally less serious crashes at intersections account for 50
percent of economic costs and 44 percent of societal harm. Crashes on urban roadways account forroughly 62 percent of all economic and 56 percent of all societal harm, while crashes on rural roadways
account for roughly 38 percent of economic impacts and 44 percent of societal harm.
Table 1-1. Summary of Total Economic Costs, (Millions of 2010 Dollars)
PDO
Vehicle

MAIS0

MAIS1

MAIS2

MAIS3

MAIS4

MAIS5

Household

$0
$518
$0
$1,111

$0
$96
$0
$206

$9,682$308$9,430$2,982$3,879$66$6,557$2,407$4,898$42$6,481$2,286$2,329$14$2,406$641$2,209$373$5$30$1,941 $30,797$548 $9,567

Ins. Admin.

$3,535$655$11,408$1,578$1,548$482$417

Workplace$1,148$211$1,180

$896

$582

$109

$0

$0

$4,089$1,135$1,249$456

Subtotal$6,311$1,169$39,079$16,519$17,087$6,437

Congestion$19,934$3,483$3,836

$405

$144

$26

$9

$189

$28,027

11.6%

Prop. Dmg.

$45,235$8,378$18,694$1,957$1,096

$279

$87

$370

$76,096

31.4%

Subtotal
Total$65,169 $11,861$71,480 $13,030
29.5%
5.4%

$22,530$61,608
25.5%$2,363$18,8817.8%$1,241$18,3277.6%$305$6,7422.8%

MedicalEMS
Market Prd.

Legal

% Total

11

Total

%
Total

$23,372$1,079$57,612$19,748

9.7%
0.4%
23.8%
8.2%

$935

$20,559

8.5%

$64

$389

$4,5771.9%$475$3,514$10,918

4.5%$5,660 $45,604 $137,865

57.0%

Fatal

$96
$559 $104,123 43.0%$5,755 $46,163 $241,988 100.0%
2.4%
19.1% 100.0%
0.0%

Figure 1-A. Components of Total Economic Costs

Components of Total Economic Costs
Medical10%

Prop. Damage
31%

Congestion
12%

Legal
5%

Insurance
8%

EMS
0%
MarketProductivity
24%
HH Prod8%

Workplace2%

Table 1-2. Summary of Unit Costs and Police-Reported and Unreported Crashes, 2010 Dollars
PDO
Vehicle

MAIS0

$0
$28
$0
$60
$191
$62

$0
$21
$0
$45
$143
$46

Subtotal Injury

$0
$341

Congestion

MAIS2

MAIS3

$2,799$89$2,726$862$3,298$341$11,453$194$19,359$7,106$4,659$2,644$48,620 $136,317
$416
$838
$64,338 $140,816$22,688 $37,541$15,371 $28,228$5,776$6,361$0$255$1,182$11,297$3,351 $12,402 $26,668$48,766 $169,611 $376,769$1,077$760$1,109$1,197$1,434$1,511$1,529$5,720

Prop. Damage$2,444$1,828$5,404$5,778$10,882$16,328$15,092$11,212Subtotal Non-Inj.

$3,521$2,588$6,513$6,975$12,316$17,839$16,621$16,932

Total$3,862$2,843$17,810$55,741 $181,927 $394,608 $1,001,089 $1,398,916

Medical Care
EMS
Market Prod.
Household Prod.
Insurance Adm.
Workplace Costs
Legal Costs

MAIS1

MAIS4

MAIS5

Fatal

$384,273$855$337,607$95,407$72,525$11,091$11,317$902$933,262$289,910$28,322$11,783$82,710$106,488$984,468 $1,381,984

Note: Unit costs are expressed on a per-person basis for all injury levels. PDO costs are
expressed on a per-damaged-vehicle basis.

12Figure 1-B. Source of Payment for Motor Vehicle Crash Costs

Source of Payment for Motor Vehicle Crash
Costs
23%

7%

Government
Private Insurer

16%

54%

Other
Self

Table 1-3. Incidence Summary – 2010 Total Police-Reported and Unreported Injuries

Severity

Policereported

Not Policereported

Total

Percent
Unreported

VehiclesInjury Vehicles

3,225,839

2,121,769

5,347,608

39.7%

PDO Vehicles

7,454,761

11,053,871

18,508,632

59.7%

Total Vehicles

10,680,601
13,175,640
People in Injury Crashes

23,856,241

55.2%

MAIS0

2,147,857
2,578,993
271,160
96,397
17,086
5,749
32,9995,150,241
3,002,385

4,583,265
3,459,200
338,730
100,740
17,086
5,749
32,9998,537,770
3,954,504

53.1%

10,565,514
2,969,963
30,296
13,565,773

59.7%

MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
Fatal
Total
Total Injuries

2,435,409
880,207
67,5704,343
00
03,387,528
952,120

25.4%
19.9%
4.3%
0.0%
0.0%
0.0%
39.7%
24.1%

Crashes
PDO
InjuryFatal
Total Crashes

4,255,495
1,791,572
30,296
6,077,362

6,310,019
1,178,391
0
7,488,411
13

39.7%
0.0%
55.2%

Table 1-4. Summary of Unit Costs, Police-Reported Crashes, 2010 Dollars
PDO
Vehicle
$0

MAIS
$0

MAIS1
$2,799MAIS2$11,453

MAIS3$48,620MAIS4$136,317

MAIS5$384,273

Fatal$11,317

$59

$38

$109

$221

$416

$838

$855

$902

$0

$0

$2,726$19,359$64,338$140,816$337,607$933,262

$60

$45

$862

$7,106$22,688$37,541$95,407$289,910

$191

$143

$3,298$4,659$15,371$28,228$72,525$28,322

Workplace

$62

$46

$341

$2,644$5,776$6,361$11,091$11,783

Legal Costs
Subtotal$0$0$1,182$3,351$12,402$26,668$82,710$106,488

$372

$272

$11,317$48,793$169,611$376,769$984,468$1,381,984

Congestion$2,104$1,416$1,426$1,450$1,490$1,511$1,529$5,720Prop.
Damage$3,599$2,692$7,959$8,510$16,027$16,328$15,092$11,212$5,704$4,108$9,385$9,960$17,517$17,839$16,621$16,932$6,076$4,30$20,701$58,754$187,128$394,608$1,001,089$1,398,916

MedicalEMS
MarketHouseholdInsurance

Subtotal
Total

Note: Unit costs are expressed on a per-person basis for all injury levels. PDO costs are
expressed on a per-damaged-vehicle basis.
Table 1-5. Summary of Unit Costs, Unreported Crashes, 2010 Dollars

MedicalEMS
MarketHouseholdInsurance
Workplace
Legal Costs
Subtotal
Congestion
Prop. Damage
Subtotal
Total

PDO
Vehicle
$0

MAIS
$0

MAIS1
$2,799MAIS2$11,453

MAIS3$48,620

MAIS4*$136,317

MAIS5*$384,273

Fatal*$11,317

$7

$6

$32

$84

$416

$838

$855

$902

$0

$0

$2,726$19,359$64,338$140,816$337,607$933,262

$60

$45

$862

$7,106$22,688$37,541$95,407$289,910

$191

$143

$3,298$4,659$15,371$28,228$72,525$28,322

$62

$46

$341

$2,644$5,776$6,361$11,091$11,783$0$0$1,182$3,351$12,402$26,668$82,710$106,488$320$384$1,224$1,609$1,928

$240
$180
$916
$1,096$1,337$11,240$180$2,707$2,888$14,127$48,656$180$2,894$3,075$51,731$169,611$180$5,451$5,632$175,243$376,769$180$16,328$16,508$393,277$984,468$180$15,092$15,272$999,740$1,381,984$458$11,212$11,670$1,393,654*Note: Unit costs are expressed on a per-person basis for all injury levels. PDO costs are
expressed on a per-damaged-vehicle basis. Generally, all MAIS 4, 5, and fatal injuries are
believed to be police-reported. Values are still included here for reference to cover any
exceptional case where unreported crashes might be found for these injury severity categories.

14

Table 1-6. Summary of Total Economic Costs in Police-Reported Crashes, Millions of 2010 Dollars

MedicalEMS
MarketHouseholdInsurance
Workplace
Legal Costs
Subtotal
Congestion
Prop. Damage
Subtotal
Total% Total

PDO
Vehicle MAIS0
$0
$0
$443
$81
$0
$0
$447
$97
$1,424
$307
$462
$99
$0
$0
$2,776$583$15,687 $3,042$26,833 $5,783$42,521 $8,825$45,297 $9,408
22.6%
4.7%

MAIS1
$7,219$280$7,030$2,223$8,506$879$3,048$29,185$3,677$20,526$24,203$53,389
26.6%

MAIS2
$3,106$60$5,249$1,927$1,263$717$909$13,231$393$2,308$2,701$15,932
7.9%

MAIS3 MAIS4 MAIS5$4,687 $2,329 $2,209
$40
$14
$5
$6,202 $2,406 $1,941$2,187$641$548$1,482
$482
$417
$557
$109
$64
$1,196$456$475$16,350 $6,437 $5,660
$144
$26
$9
$1,545$279$87$1,689$305$96$18,039 $6,742 $5,755
9.0%
3.4%
2.9%

Fatal
$373
$30
$30,797$9,567$935$389$3,514$45,604
$189
$370
$559
$46,163
23.0%

Total
$19,923$952$53,625$17,638$14,815$3,275$9,598$119,826$23,167$57,730$80,898$200,724
100.0%

%
Total
9.9%
0.5%
26.7%
8.8%
7.4%
1.6%
4.8%
59.7%
11.5%
28.8%
40.3%
100.0%
0.0%

Table 1-7. Summary of Total Economic Costs in Unreported Crashes, Millions of 2010 Dollars
PDO
Vehicle MAIS0$0$0
Medical
$76
$16
EMS
$0
$0
Market$663$110
Household$2,111$348
Insurance$685$112
Workplace$0$0Legal Costs$3,536$586
Subtotal$4,248$439
Congestion
Prop. Damage $13,534 $2,230$17,782 $2,670
Subtotal$21,317 $3,255
Total
51.7%
7.9%
% Total

MAIS1 MAIS2 MAIS3 MAIS4 MAIS5 Fatal$2,464
$774
$211
$0
$0
$0
$28
$6
$2
$0
$0
$0
$2,399 $1,308
$279
$0
$0
$0
$759
$480
$99
$0
$0
$0
$2,903
$315
$67
$0
$0
$0
$300
$179
$25
$0
$0
$0
$1,040
$226
$54
$0
$0
$0
$9,893 $3,288
$737
$0
$0
$0
$159
$12
$1
$0
$0
$0
$2,383
$196
$24
$0
$0
$0
$2,542
$208
$24
$0
$0
$0
$12,435 $3,495
$761
$0
$0
$0
30.1%
8.5%
1.8%
0.0%
0.0% 0.0%

15

Total
$3,449$127$3,987$2,110$5,744$1,301$1,321$18,039$4,859$18,366$23,225$41,264100.0%% Total
8.4%
0.3%
9.7%
5.1%
13.9%
3.2%
3.2%
43.7%
11.8%
44.5%
56.3%
100.0%
0.0%

Figure 1-C. Percentage of Total Costs from Police-Reported Crashes

Percent of Total Costs from Police Reported
Crashes
83%

Economic Total

89%

Comprehensive Total

92%

Quality of Life76%

Property Damage

83%

Congestion

88%

Legal Costs
Workplace Costs

72%

Insurance Admin.

72%
89%

Household Productivity

93%

Market Productivity

88%

Emergency Services

85%

MedicalTable 1-8. Summary of Total Comprehensive Costs, Reported and Unreported Crashes, Millions of2010 Dollars
PDO
Vehicle

MAIS0

MAIS1

MAIS2

MAIS3

MAIS4

MAIS5

Medical$0$0$9,682$3,879$4,898$2,329$2,209

EMS

$518

$96

$308

$66

$42

$14

$5

$0
$1,111$3,535$1,148$0$6,311$19,934$45,235$65,169$71,480$0$71,480
8.6%

$0
$206
$655
$211
$0
$1,169$3,483$8,378$11,861$13,030$0$13,030
1.6%$9,430$2,982$11,408$1,180$4,089$39,079$3,836$18,694$22,530$61,608$80,395$142,004
17.0%$6,557$2,407$1,578$896$1,135$16,519$405$1,957$2,363$18,881$115,464$134,345
16.1%$6,481$2,286$1,548$582$1,249$17,087$144$1,096$1,241$18,327$81,166$99,493
11.9%$2,406
$641
$482
$109
$456
$6,437
$26
$279
$305
$6,742$34,812$41,555
5.0%$1,941
$548
$417
$64
$475
$5,660$9$87$96$5,755$26,322$32,077
3.8%

Market Prod.
HouseholdInsurance
Workplace
Legal Costs
Subtotal
Congestion
Prop. Damage
Subtotal
Total
QALYs
Comp. Total% Total

16

Fatal
$373

Total

% Total$23,3722.8%$30$1,0790.1%$30,797$9,567$935$389$3,514$45,604
$189
$370
$559
$46,163$255,646$301,809
36.1%$57,612$19,748$20,559$4,577$10,918$137,865$28,027$76,096$104,123$241,988$593,805$835,793
100.0%

6.9%
2.4%
2.5%
0.5%
1.3%
16.5%
3.4%
9.1%
12.5%
29.0%
71.0%
100.0%
0.0%

Table 1-9. Summary of Comprehensive Unit Costs, Reported and Unreported Crashes, 2010 Dollars

MedicalEMS
MarketHouseholdInsurance
Workplace
Legal Costs
Subtotal
Congestion
Prop.
DamageSubtotal
Total Econ.
QALYs
Comp.Total

PDO
Vehicle MAIS0
$0
$0

MAIS1
$2,799MAIS2$11,453

MAIS3$48,620MAIS4$136,317

MAIS5$384,273

Fatal$11,317

$28

$21

$89

$194

$416

$838

$855

$902

$0

$0

$2,726$19,359$64,338$140,816$337,607$933,262

$60
$191
$62
$0
$341
$1,077$2,444

$45
$143
$46
$0
$255
$760
$1,828$862$3,298$341$1,182$11,297$1,109$5,404$7,106$4,659$2,644$3,351$48,766$1,197$5,778$22,688$15,371$5,776$12,402$169,611$1,434$10,882$37,541$28,228$6,361$26,668$376,769$1,511$16,328$95,407$72,525$11,091$82,710$984,468$1,529$15,092$289,910$28,322$11,783$106,488$1,381,984$5,720$11,212$3,521$3,862$0$3,862$2,588$2,843$0$2,843$6,513$17,810$23,241$41,051$6,975$55,741$340,872$396,613$12,316$181,927$805,697$987,624$17,839$394,608$2,037,483$2,432,091$16,621$1,001,089$4,578,525$5,579,614$16,932$1,398,916$7,747,082$9,145,998

Note: Unit costs are expressed on a per-person basis for all injury levels. PDO costs are
expressed on a per-damaged-vehicle basis.

17

Figure 1-D. Components of Comprehensive Costs

Components of Comprehensive Costs
Medical EMS Market Productivity
3%
7% HH Prod
0%2%

Insurance
3%

Workplace
1%
Legal
1%
Congestion
3%
Prop. Damage
9%

Lost Quality of Life
71%

Table 1-10. Summary of Comprehensive Unit Costs, Police-Reported Crashes, 2010 Dollars

MedicalEMS
MarketHouseholdInsurance
Workplace
Legal Costs
Subtotal
Congestion
Prop. Damage
Subtotal
Total Economic
QALYs
Total Compr.

PDO
Vehicle MAIS0 MAIS1$0$0$2,799MAIS2$11,453

MAIS3$48,620MAIS4$136,317

MAIS5$384,273

Fatal$11,317

$59

$38

$109

$221

$416

$838

$855

$902

$0

$0

$2,726$19,359$64,338$140,816$337,607$933,262

$60
$191
$62
$0
$372
$2,104$3,599$5,704$6,076$0$6,076

$45
$143
$46
$0
$272
$1,416$2,692$4,108$4,380$0$4,380$862$3,298$341$1,182$11,317$1,426$7,959$9,385$20,701$23,241$43,942$7,106$4,659$2,644$3,351$48,793$1,450$8,510$9,960$58,754$340,872$399,626$22,688$15,371$5,776$12,402$169,611$1,490$16,027$17,517$187,128$805,697$992,825$37,541$28,228$6,361$26,668$376,769$1,511$16,328$17,839$394,608$2,037,483$2,432,091$95,407$72,525$11,091$82,710$984,468$1,529$15,092$16,621$1,001,089$4,578,525$5,579,614$289,910$28,322$11,783$106,488$1,381,984$5,720$11,212$16,932$1,398,916$7,747,082$9,145,998

Note: Unit costs are expressed on a per-person basis for all injury levels. PDO costs are
expressed on a per-damaged-vehicle basis.

18

Table 1-11. Summary of Comprehensive Unit Costs, Unreported Crashes, 2010 Dollars

Medical

PDO
Vehicle MAIS0
MAIS1
$0
$0
$2,799MAIS3$48,620

MAIS4*$136,317

MAIS5*$384,273

Fatal*$11,317

$7

$6

$32

$84

$416

$838

$855

$902

$0

$0

$2,726$19,359$64,338$140,816$337,607$933,262

$60
$191
$62
$0
$320
$384
$1,224$1,609$1,928$0$1,928

$45
$143
$46
$0
$240
$180
$916
$1,096$1,337$0$1,337$862$3,298$341$1,182$11,240$180$2,707$2,888$14,127$23,241$37,368$7,106$4,659$2,644$3,351$48,656$180$2,894$3,075$51,731$340,872$392,603$22,688$15,371$5,776$12,402$169,611$180$5,451$5,632$175,243$805,697$980,940$37,541$28,228$6,361$26,668$376,769$180$16,328$16,508$393,277$2,037,483$2,430,760$95,407$72,525$11,091$82,710$984,468$180$15,092$15,272$999,740$4,578,525$5,578,265$289,910$28,322$11,783$106,488$1,381,984$458$11,212$11,670$1,393,654$7,747,082$9,140,736

EMS
MarketHouseholdInsurance
Workplace
Legal Costs
Subtotal
Congestion
Prop. Damage
Subtotal
Total Economic
QALYs
Comp. Total

MAIS2$11,453*Note: Unit costs are expressed on a per-person basis for all injury levels. PDO costs are
expressed on a per-damaged-vehicle basis. Generally, all MAIS 4, 5, and fatal injuries are believed
to be police-reported. Values are still included here for reference to cover any exceptional casewhere unreported crashes might be found for these injury severity categories.

Table 1-12. Summary of Total Comprehensive Costs, Police-Reported Crashes, Millions of 2010 Dollars)%
MAIS0 MAIS1
MAIS2
MAIS3
MAIS4 MAIS5 Fatal
Total
Total
$0
$0
$7,219$3,106$4,687 $2,329 $2,209$373 $19,923
2.7%
$443
$81
$280
$60
$40
$14
$5
$30
$952
0.1%
$0
$0
$7,030$5,249$6,202 $2,406 $1,941 $30,797 $53,625
7.2%
$447
$97
$2,223$1,927$2,187$641$548$9,567 $17,638
2.4%$1,424$307$8,506$1,263$1,482$482$417$935 $14,815
2.0%
$462
$99
$879
$717
$557
$109
$64
$389
$3,2750.4%$0$0$3,048$909$1,196$456$475$3,514$9,598
1.3%$2,776$583 $29,185 $13,231 $16,350 $6,437 $5,660 $45,604 $119,826
16.0%$15,687 $3,042$3,677
$393
$144
$26
$9
$189 $23,167
3.1%$26,833 $5,783 $20,526$2,308$1,545$279$87$370 $57,730
7.7%$42,521 $8,825 $24,203$2,701$1,689$305$96$559 $80,898
10.8%$45,297 $9,408 $53,389 $15,932 $18,039 $6,742 $5,755 $46,163 $200,724
26.9%$0$0 $59,938 $92,431 $77,667 $34,812 $26,322 $255,646 $546,816
73.1%$45,297 $9,408 $113,327 $108,363 $95,705 $41,555 $32,077 $301,809 $747,540 100.0%
6.1%
1.3%
15.2%
14.5%
12.8%
5.6%
4.3%
40.4% 100.0%
0.0%

PDO
MedicalEMS
MarketHouseholdInsurance
Workplace
Legal Costs
Subtotal
Congestion
Prop. Damage
Subtotal
Total Economic
QALYs
Comp.Total% Total19

Table 1-13. Summary of Total Comprehensive Costs, Unreported Crashes (Millions of 2010 Dollars)
MedicalEMS
MarketHouseholdInsurance
Workplace
Legal Costs
Subtotal
Congestion
Prop. Damage
Subtotal
Total
QALYs
Comp.Total% Total

PDO

$0
$76
$0
$663
$2,111$685$0$3,536$4,248$13,534$17,782$21,317$0$21,31724.2%

MAIS0 MAIS1MAIS2MAIS3 MAIS4 MAIS5 Fatal Total% Total$0$2,464
$774
$211
$0
$0
$0
$3,449
3.9%
$16
$28
$6
$2
$0
$0
$0
$127
0.1%
$0
$2,399$1,308
$279
$0
$0
$0
$3,987
4.5%
$110
$759
$480
$99
$0
$0
$0
$2,1102.4%$348$2,903
$315
$67
$0
$0
$0
$5,744
6.5%
$112
$300
$179
$25
$0
$0
$0
$1,3011.5%$0$1,040
$226
$54
$0
$0
$0
$1,321
1.5%$586$9,893$3,288$737$0$0$0 $18,039
20.4%
$439
$159
$12
$1
$0
$0
$0
$4,8595.5%$2,230$2,383
$196
$24
$0
$0
$0 $18,366
20.8%$2,670$2,542
$208
$24
$0
$0
$0 $23,225
26.3%$3,255 $12,435$3,495$761$0$0$0 $41,264
46.8%$0 $20,457 $23,033 $3,499$0$0$0 $46,989
53.2%$3,255 $32,892 $26,528 $4,260$0$0$0 $88,252 100.0%
3.7%
37.3%
30.1%
4.8%
0.0%
0.0% 0.0% 100.0%
0.0%

20

Table 1‐14. Economic and Societal Costs for Selected Crash Types

Outcome Severity:
Fatalities
Nonfatal Injuries
PDO Vehicles
Uninjured (MAIS0)
Total

Economic
Cost(Millions of2010 Dollars)% TotalComprehensive
Cost(Millions of2010 Dollars)% Total$46,163$111,314$71,480$13,030$241,988

19.1%
46.0%
29.5%
5.4%
100.0%$301,809$449,473$71,480$13,030$835,793

36.1%
53.8%
8.6%
1.6%
100.0%

Adverse Driver Behavior:
Seat Belt Non-use
Helmet non-use
Distraction
Alcohol Involvement
Alcohol Causation
Speed$10,435$1,215$39,700$52,497$43,154$51,964

4.3%
0.5%
16.4%
21.7%
17.8%
21.5%

$68,600$7,592$123,390$235,738$193,642$203,228

8.2%
0.9%
14.8%
28.2%
23.2%
24.3%

Nonoccupants:
Motorcycles
Pedestrian/Cyclist$12,893$15,805

5.3%
6.5%$65,735$86,559

7.9%
10.4%

Crash Types:
Roadway Departure Crashes
Single-Vehicle Crashes$64,443$76,264

26.6%
31.5%

$298,152$344,712

35.7%
41.2%

Crash Location:
Interstate Highway Crashes
Intersection Crashes
Urban Roadways
Rural Roadways$25,225$120,336$149,014$92,974

10.4%
49.7%
61.6%
38.4%

$85,445$371,314$469,525$366,268

10.2%
44.4%
56.2%
43.8%

21

2. Human Capital Costs
Estimating the cost of a crash requires estimates of the number of people and vehicles involved in thecrash, the severity of each person’s injuries, and the costs of those injuries. The first section of thischapter describes the methods used to estimate the incidence and severity of motor vehicle crashes.
The succeeding sections explain how the unit costs of injuries were estimated and present thoseestimates.
I. Crash Data and Severity Estimation
Crash databases do not accurately describe the severity of motor vehicle crashes. Accordingly, we madeseveral adjustments to more accurately reflect the severity of crashes. To estimate injury incidence andseverity, we followed procedures developed by Miller and Blincoe (1994) and Miller, Galbraith, et al.
(1995) and later applied in Blincoe (1996); Miller, Levy, et al. (1998); Miller, Lestina, and Spicer (1998);
Miller, Spicer, et al. (1999); Blincoe et al. (2002); and Zaloshnja et al. (2004). Below we summarize theprocedures and describe the adjustments.
NHTSA’s General Estimates System (GES) provides a sample of U.S. crashes by police-reported severity
for all crash types. GES records injury severity by crash victim on the KABCO scale (National Safety
Council, 1990) from police crash reports. Police reports in almost every State use KABCO to classify crash
victims as K–killed, A–incapacitating injury, B–non-incapacitating injury, C–possible injury, or O–noapparent injury.
KABCO ratings are coarse and inconsistently coded between States and over time. The codes are
selected by police officers without medical training, typically without benefit of a hands-on examination.
Some victims are transported from the scene before the police officer who completes the crash reporteven arrives. Miller, Viner, et al. (1991) and Blincoe and Faigin (1992) documented the great diversity in
KABCO coding across cases. O’Day (1993) more carefully quantified the wide variability in use of the Ainjury code between States. Viner and Conley (1994) explained the contribution to this variability ofdiffering State definitions of A-injury. Miller, Whiting, et al. (1987) found that police-reported injury
counts by KABCO severity systematically varied between States because of differing State crash
reporting thresholds (the rules governing which crashes should be reported to the police). Miller andBlincoe (1994) found that State reporting thresholds often changed over time.
Thus police reporting does not accurately describe injuries medically. To minimize the effects ofvariability in severity definitions by State, reporting threshold, and police perception of injury severity,
we turned to NHTSA data sets that included both police-reported KABCO and medical descriptions of
injury in the Occupant Injury Coding system (OIC; AAAM, 1990, 1985). OIC codes include AIS severity
score and body region, plus more detailed injury descriptors. We used both 2008–2010 Crashworthiness
Data System (CDS) and 1984–1986 National Accident Sampling System (NASS; NHTSA, 1987) data. CDS
describes injuries to passenger vehicle occupants involved in tow-away crashes. The 1984–1986 NASS
data provide the most recent medical description available of injuries to medium/heavy truck and bus
occupants, nonoccupants, and other non-CDS crash victims. The NASS data was coded with the 1980
version of AIS, which differs slightly from the 1985 version; but NHTSA made most AIS-85 changes wellbefore their formal adoption. CDS data was coded in AIS-90/98 with coding shifting to AIS-2005, Update
2008, in 2011. We differentiated our analysis of the two versions of AIS because AIS-90/98 scores and
OIC codes differ greatly from codes and scores in AIS-85, especially for brain and severe lower limb

22

injury. Garthe, Ferguson, and Early (1996) find that AIS scores shifted for roughly 25 percent of all OICs
between AIS-85 and AIS-90/98.
We used weighted, annualized 2008–2010 GES counts to reweight the CDS and NASS data so that they
represent the estimated GES injury victim counts in motor vehicle crashes during 2008–2010. In applying
the GES counts to adjust old NASS weights at the person level, we controlled for police-reported injury
severity, restraint use, alcohol involvement, and occupant type (CDS occupant, non-CDS occupant, andnonoccupant). All cells had at least 10 cases. Weighting the NASS data to GES restraint use and alcohol
involvement levels updates the NASS injury profile to reflect contemporary belt use and alcoholinvolvement levels, although it is imperfect in terms of its representation of airbag use in non-tow-away
crashes. At the completion of the weighting process, we had a hybrid CDS/NASS casualty-level file—that
is, we had an appropriately reweighted NASS record for each injury victim in each non-CDS crash.
Similarly, we reweighted the 2008–2010 CDS file to match GES counts in order to get appropriately
weighted unit records for the CDS sample strata.
Unit Cost Estimates
The second step required to estimate average crash costs was to generate costs per crash victim by
maximum AIS (MAIS), body part, and whether the victim suffered a fracture or dislocation. A 41-level
body part descriptor was created based on information provided by the NASS/CDS variables describing
the body region, system/organ, lesion, and aspect of each injury. Burns were classified as a separate
category due to the lack of location information for burn injuries.
The sections that follow describe unit medical costs, work loss costs, and selected ancillary costs.
Appendix A describes the costing methods. Medical and work loss costs cover three mutually exclusive
categories that reflect injury severity: (1) injuries resulting in death, including post-injury deaths in ahealthcare setting; (2) injuries resulting in hospitalization with survival to discharge; and (3) injuries
requiring an emergency department visit not resulting in hospitalization (ED-treated injuries). For
injuries treated only in doctor’s offices or outpatient departments, we used prior estimates of unit costs(Finkelstein et al., 2006), properly inflated. To estimate mean costs across all surviving crash victims, we
needed to add costs for cases treated only in physicians’ offices or outpatient departments to the costfor cases treated in hospital emergency departments or admitted to hospitals. To do so, we multiplied
unit costs for ED-treated injuries by body part and nature of injury (as per the Barell injury-diagnosis
matrix) times ratios of ED-treated injuries versus injuries treated only in doctor’s offices or outpatient
departments found in Finkelstein et al. (2006). We then took averages across treatment settings. We
computed costs from a societal perspective, which means we included all costs regardless of who paid
for them.
We estimated mean costs per surviving victim by maximum AIS (MAIS), body part, andfracture/dislocation involvement from combined Healthcare Cost and Utilization Project (HCUP)
Nationwide Inpatient Sample (NIS) and Nationwide Emergency Department Sample (NEDS) files. (Fordescriptions of these files, see Appendix E.) We used ICDMAP-90 software (Johns Hopkins University and
Tri-Analytics Inc., 1997) to assign MAIS-90 scores to cases. 2 We assigned AIS-85 scores with mappings
developed by Miller et al. (1991). After assigning AIS scores to each injury, we determined the MAIS foreach person. We estimated standard errors of means with the SURVEYMEANS command in SAS 9.2,

2

Costs for AIS98 are essentially the same as for AIS90. No data exists to estimate costs accurately for more recent
AIS codes except through equivalency tables to older coding. Similarly detailed incidence data for estimating costs
in non-CDS strata for MAIS versions other than MAIS85 (notably for heavy truck occupant injury) do not exist.

23which accounts for sample stratification. Appendix B presents unit costs and standard errors at different
discount rates.
Merging HCUP-Based Costs Onto the Reweighted NASS/CDS Injury File
Typically, motor vehicle crash patients suffer multiple injuries. In the HCUP-based data, when a victim
had two injuries of maximum AIS, we assigned the body part of the more costly injury. In merging costs
onto the re-weighted NASS/CDS injury level file (NASS/CDS lists up to six injuries per injury victim) wemerged medical and work loss costs separately. In each case, we assigned the cost for the injury withthe highest cost for that cost component. Thus if a victim’s ruptured spleen had the highest medical costand her broken leg had the highest work loss cost, this hybrid set of costs was assigned to the case. This
will result in conservative cost estimates since it assumes that secondary injury conditions do not resultin additional costs.
To estimate the standard error of the mean cost per victim in the reweighted NASS/CDS file we used thefollowing procedure. Based on the standard errors estimated from the HCUP files we estimated theupper and lower levels of the confidence interval for the unit costs at significance level α=0.1. We thenmerged these two levels onto the reweighted NASS/CDS casualty-level file, following the sameprocedures as above. For each level we estimated the upper and lower levels of the confidence interval
for the unit costs at significance level α=0.1, separately for CDS and non-CDS strata. Again, to estimate
these intervals, we used the SURVEYMEANS command in SAS 9.2, which takes into account the sample
stratification. At the end of the process, we had a combined confidence interval at significance level
α=0.01 (0.1×0.1=0.01); or, to put it differently, the 99-percent confidence interval of the mean unit
costs. Assuming a normal distribution of the combined sampling errors, we estimated implied standard
errors based on the 99-percent confidence interval of the mean unit costs, by dividing the difference
between the 99-percent upper limit and the mean by 2.7045 (the multiplier of the standard error for the
99% confidence interval, assuming a normal distribution for the sampling errors).
Unit Costs Estimated from the Reweighted NASS/CDS File
Table 2-1 presents NASS/CDS crash costs per surviving victim at a 3-percent discount rate by MAIS
separately for CDS and non-CDS strata. A paucity of MAIS-6 cases dictated collapsing MAIS-5 and MAIS-6
into a single category. Unit costs generally are higher for crash survivors in CDS than non-CDS strata. The
difference results in part from differences between the 1985 and 1990 versions of the AIS coding system(Zaloshnja et al., 2001). However, comparing HCUP-based unit cost estimates by MAIS (Table 2-2), witheach crash survivor scored both in AIS-85 and AIS-90 (i.e., keeping the injury mix constant), indicatesthat the non-CDS strata injury mix drives the cost difference. Table 2-3 presents the NASS/CDS crashcosts per surviving victim and fatality at 3 percent and 4-percent discount rates by MAIS, regardless of
the AIS version. At a 3-percent discount rate, the average crash fatality involves an estimated $11,317 inmedical spending (with a standard error of $100 based on 33,932 crash deaths in 2010), $933,262 inwage and fringe benefit losses (standard error $3,282 based on 32,885 crash deaths in 2010), and$289,910 in household work losses (standard error $631, also based on 32,885 crash deaths in 2010).
Tables 2-4 to 2-6 present NASS/CDS crash costs per surviving victim at 3-percent discount rate by body
region, fracture/dislocation involvement, and MAIS. Appendix B provides detailed unit costs by bodypart, fracture/dislocation involvement, and MAIS, at different discount rates.
A major limitation of the costs presented is that some cost components are unavoidably quite old. In
particular, no recent source exists for the percentage of lifetime medical costs that is incurred more24

than 18 months post-injury, probabilities of permanent disability by detailed diagnosis and whether
hospital admitted, or the ratio of household work days lost to wage work days lost.
Table 2-1. 2008–2010 NASS/CDS-based crash costs per surviving victim
at 3-percent discount rate by MAIS (2010 Dollars)
Non-CDS strata (in AIS85 scale)
MAIS

Impliedstd. error

Mean

CDS stratum (in AIS-90 scale)
Implied99% Conf.
Mean
std.
intervalerror

99% Conf.
interval

Medical cost per victim
1

2,713

63

2,541

2,884

2,794

606

1,156

4,432

2

11,122980

8,472

13,772

11,596

1,896

6,470

16,723

3

53,837

4,332

42,12265,552

44,9182,452

38,28751,5494

129,678

12,813

95,027

164,330

138,097

12,481

104,342

171,851

5&6

503,638

68,603

318,103689,172

345,924

14,759306,009385,838

Wage loss per victim
1

2,661

138

2,286

3,035

2,753

402

1,667

3,839

2

18,922

1,591

14,61923,224

19,1802,445

12,56825,793

3

69,906

5,697

54,50085,313

61,9204,024

51,03772,803

4

116,862

8,364

94,243

139,481

144,629

13,256

108,779

180,479

5&6

439,191

56,916285,262

593,119299,647

12,809265,006

334,288

Household productivity loss per victim
1

850

54

703

997

869

126

527

1,211

2

7,000

543

5,532

8,467

7,142

730

5,167

9,118

3

24,807

1,496

20,76228,852

21,491797

19,33523,647

4

34,4452,464

27,781

41,10937,9902,675

30,75545,226

5&6

119,759

7,392

99,767

139,751

87,5972,359

81,216

93,97825

Table 2-2. HCUP-based crash costs per surviving victim at 3-percent discount rate by MAIS;
AIS-85 versus AIS-90 (2010 dollars)
Cost category
Medical cost per victim
Earnings loss per victim
Household production loss per
victimScored in
AIS-85
MAIS 1
2,615
2,657

MAIS 2
Medical cost per victim
Earnings loss per victim
Household production loss per
victim
MAIS 3
Medical cost per victim
Earnings loss per victim
Household production loss per
victim
MAIS 4
Medical cost per victim
Earnings loss per victim
Household production loss per
victim
MAIS 5
Medical cost per victim
Earnings loss per victim
Household production loss per
victimScored in
AIS-90
2,650
2,533

977

936

11,98819,723

10,632
19,068

7,192

7,026

53,88969,661

41,23956,20323,827

20,325

122,721106,901

117,263
134,794

38,45943,961

504,975
429,326

335,608
283,893

120,074

81,040

N/A
N/A

482,964
387,240

N/A

112,880

MAIS 6
Medical cost per victim
Earnings loss per victim
Household production loss per
victim26

Table 2-3. 2008–2010 NASS/CDS-based crash costs per victim
at 3-percent and 4-percent discount rates by MAIS

MAIS
1
2
3
4
5&6
Fatality

Medical costs

Earnings loss

Discounted Discounted Discounted Discounted
@ 3%
@ 4%
@ 3%
@ 4%
2,782
2,782
2,726
2,369
11,347
11,347
19,35916,73948,39048,39064,33856,375136,035
135,355
140,816
119,235

Household production
loss
Discounted Discounted
@ 3%
@ 4%
862
760
7,106
6,154
22,688
19,69337,541
31,832

384,011380,298

337,607318,291

95,40791,016

11,317

11,317933,262799,270289,910

246,559

27

Table 2-4. 2008–2010 NASS/CDS-based medical costs per surviving victim
at a 3-percent discount rate, by body region and MAIS (2010 dollars) 3
Body
regionFracture/
dislocation

SpinalCord
InjuryNo

Traumatic
Brain
Injury

No

No
Lower
extremity
Yes

Upper
extremity

No
Yes

3

Non-CDS strata (in AIS-85 scale)
ImpliedMAIS
99% confidence
Mean
std.
intervalerro
249,756 73,879
49,951 449,562
3
460,668 119,235 138,200 783,136
41,850 987,863 997,871
5&6 992,867
3,977
72
3,781
4,172
1
5,318
25
5,251
5,386
2
68,5552,841
60,872
76,2383
163,065
4,822 150,025 176,105
4
5&6 408,684 15,499 366,768 450,600
1,876
22
1,817
1,935
1
15,282989
12,608
17,9562
37,2642,920
29,36545,162
3
168,892 23,093 106,437 231,347
4
N/A
N/A
N/A
N/A
5
2,244
126
1,903
2,585
1
14,696696
12,813
16,5792
60,3281,597
56,00864,6483
62,066 15,961
18,901 105,232
4
N/A
N/A
N/A
N/A
5
1,564
19
1,513
1,615
1
3,943
206
3,385
4,501
2
36,403
5,635
21,16451,643
3
2,035
87
1,800
2,271
1

CDS stratum (in AIS-90 scale)
Implied99% confidence
Mean
std.
intervalerro
123,640 30,238
41,863
205,417
321,751 13,129
286,243
357,259
888,6908,339
866,137
911,244
2,316
11
2,286
2,347
4,123
765
2,054
6,191
23,313963
20,70825,91881,461
1,693
76,881
86,040284,799 11,496
253,708
315,891
1,599
50
1,464
1,734
3,213
464
1,959
4,467
40,095
4,585
27,69652,49489,323 12,226
56,258122,388
N/A
N/A
N/A
N/A
1,450
58
1,294
1,606
9,972
1,805
5,090
14,85338,7901,658
34,30643,27475,424
2,329
69,126
81,723
149,205 11,420
118,320
180,089
1,100
13
1,065
1,135
4,015
1,405
216
7,813
23,463
5,611
8,288
38,638
1,470
46
1,344
1,595

Note that some MAIS level injuries do not exist for certain body regions. These are noted as NA in this table.

28

Body
regionFracture/
MAIS
dislocation

No
Trunk/
abdomen
Yes

No
Other
head
Yes

Burns

No

Minor
external

No

2
3
1
2
3
4
5
1
2
3
4
5
1
2
3
1
2
3
4
1
2
5
1

Non-CDS strata (in AIS-85 scale)
Implied99% confidence
Mean
std.
interval
error
6,056
223
5,452
6,660
42,865
1,331
39,26546,465
3,079
97
2,818
3,341
16,0304,742
3,206
28,85456,414 13,685
19,40593,424
94,023 29,822
13,370 174,677
164,531 30,605
81,760 247,303
4,455
61
4,290
4,619
9,685
446
8,479
10,891
26,277
1,151
23,16529,390107,895
6,259
90,967 124,823
N/A
N/A
N/A
N/A
2,298
12
2,267
2,330
5,614
726
3,649
7,579
28,532
4,580
16,14540,918
4,455
61
4,290
4,619
14,084624
12,396
15,772
89,621
5,368
75,102 104,140
235,940 28,214 159,634 312,245
1,894
118
1,576
2,212
17,532
2,857
9,806
25,259256,351 19,737 202,971 309,730
3,321

32

3,235

29

3,408

CDS stratum (in AIS-90 scale)
Implied99% confidence
Mean
std.
interval
error
2,923
795
771
5,074
23,644875
21,27826,010
7,430
1,733
2,744
12,117
6,923
2,200
974
12,87336,702
4,982
23,23050,17555,1497,074
36,01674,281
155,662 26,241
84,693
226,631
2,099
159
1,669
2,529
6,405
1,078
3,490
9,319
44,571
2,324
38,28550,85743,0832,774
35,58050,585
167,832 49,036
35,214300,449
2,371
37
2,270
2,471
7,330
2,217
1,333
13,32722,536
2,154
16,70928,362
4,418
67
4,235
4,600
10,265987
7,596
12,93558,512
5,255
44,29972,725
119,556
2,207
113,586
125,525
1,502
252
820
2,183
11,981
1,924
6,779
17,184
N/A
N/A
N/A
N/A
3,906

35

3,812

3,999

Table 2-5. 2008–2010 NASS/CDS-based earnings loss per surviving victim
at a 3-percent discount rate, by body region and MAIS (2010 dollars) 4
Body
regionFracture/
dislocation

SpinalCord
InjuryNo

Traumatic
Brain
Injury

No

No
Lower
extremity
Yes

Upper
extremity
4

No

Non-CDS strata (in AIS-85 scale)
ImpliedMAIS
99% confidence
Mean
std.
intervalerro
86,775 20,282
31,922 141,628
3
180,949 20,632 125,151 236,746
4
5&6 159,355 19,941 105,425 213,284
3,868
171
3,407
4,329
1
6,763
68
6,579
6,947
2
121,462
1,648
117,005
125,9193
138,407
3,046 130,170 146,644
4
5&6 650,180 14,279 611,562 688,798
1,374
20
1,320
1,428
1
22,035
1,587
17,743
26,3282
22,742
3,500
13,27832,207
3
199,595
9,437 174,074 225,116
4
N/A
N/A
N/A
N/A
5
3,411
121
3,084
3,738
1
25,893848
23,59828,187
2
70,6791,135
67,60973,7493
82,936 19,082
31,330 134,543
4
N/A
N/A
N/A
N/A
5
1,284
24
1,220
1,349
1
5,021
456
3,789
6,253
2

CDS stratum (in AIS-90 scale)
Implied99% confidence
Mean
std.
intervalerro
114,544 24,924
47,138 181,950
167,295
7,824 146,135 188,455
96,252 23,866
31,707 160,797
3,000
44
2,882
3,119
12,004
1,600
7,678
16,33045,526916
43,05048,003
139,974
2,043 134,448 145,499
312,429
6,986 293,537 331,322
1,959
132
1,602
2,316
7,829
1,028
5,049
10,60948,384
5,411
33,75263,017
171,534
9,353 146,238 196,829
N/A
N/A
N/A
N/A
3,204
49
3,072
3,336
26,396
4,124
15,24237,551
41,5772,182
35,67547,47869,7501,788
64,916
74,58467,009 13,353
30,895 103,123
1,310
17
1,263
1,357
8,297
2,270
2,158
14,436Note that some MAIS level injuries do not exist for certain body regions. These are noted as NA in this table.

30

Body
regionFracture/
MAIS
dislocation

Yes

No
Trunk/
abdomen
Yes

No
Other
head
Yes

Burns

No

Minor
external

No

3
1
2
3
1
2
3
4
5
1
2
3
4
5
1
2
3
1
2
3
4
1
2
5
1

Non-CDS strata (in AIS-85 scale)
Implied99% confidence
Mean
std.
intervalerror
67,146 11,737
35,402
98,889
6,193
96
5,933
6,454
14,333587
12,745
15,922
54,145
1,662
49,65058,640
4,936
170
4,476
5,396
29,517
7,693
8,712
50,322
74,187 24,922
6,786 141,588
77,360 19,559
24,463 130,258
312,890 45,887 188,790 436,990
4,269
50
4,134
4,403
21,745660
19,96023,53041,374
1,529
37,23945,51054,377
7,190
34,93173,823
N/A
N/A
N/A
N/A
2,315
20
2,262
2,368
14,351
4,387
2,486
26,216
31,971
8,199
9,798
54,144
4,269
50
4,134
4,403
14,247368
13,251
15,242
105,432
5,555
90,409 120,455
169,065 14,923 128,706 209,425
3,671
153
3,256
4,086
8,971
539
7,512
10,430186,896 21,196 129,573 244,219
1,686

18

1,636

31

1,735

CDS stratum (in AIS-90 scale)
Implied99% confidence
Mean
std.
intervalerror
35,543 10,165
8,051
63,034
5,883
181
5,393
6,374
8,777
1,722
4,121
13,43332,4992,189
26,57938,41938,2884,546
25,99350,583
17,344
3,978
6,585
28,10355,974 10,583
27,35384,59576,042 11,507
44,922 107,161
154,110 16,808 108,653 199,567
3,967
293
3,173
4,760
17,252
2,315
10,99023,51455,906
4,873
42,72669,08654,605
5,802
38,91470,295
225,475 74,988
22,673 428,278
3,888
185
3,387
4,388
15,526
1,290
12,03919,01435,3993,061
27,12043,678
6,183
88
5,946
6,420
16,764
1,998
11,35922,16859,1762,852
51,463
66,888
164,710
2,055 159,153 170,267
3,343
116
3,031
3,656
7,995
680
6,155
9,834
N/A
N/A
N/A
N/A
1,272

18

1,224

1,319

Table 2-6. 2008–2010 NASS/CDS-based household production per surviving victim
at 3-percent discount rate, by body region and MAIS (2010 dollars) 5
Body
regionFracture/
dislocation

SpinalCord
InjuryNo

Traumatic
Brain
Injury

No

No
Lower
extremity
Yes

Upper
extremity
5

No

Non-CDS strata (in AIS-85 scale)
ImpliedMAIS
99% confidence
Mean
std.
intervalerro
28,776
6,719
10,606
46,947
3
56,461
8,464
33,57079,353
4
78,238404
77,146
79,329
5&6
1,123
41
1,012
1,233
1
2,739
25
2,673
2,806
2
43,518453
42,29344,743
3
52,940748
50,91754,962
4
105,901
2,098
100,229
111,574
5&6
523
6
507
538
1
7,885
533
6,444
9,325
2
14,731936
12,19917,262
3
58,5391,690
53,96963,108
4
N/A
N/A
N/A
N/A
5
1,316
47
1,188
1,444
1
11,198308
10,365
12,0302
27,983604
26,34829,617
3
29,085
4,373
17,25740,912
4
N/A
N/A
N/A
N/A
5
527
9
503
551
1
1,856
145
1,463
2,249
2

CDS stratum (in AIS-90 scale)
Implied99% confidence
Mean
std.
intervalerro
74,241 15,713
31,746 116,737
68,4262,185
62,51774,33475,618
1,788
70,78280,454
1,259
17
1,212
1,306
5,591
676
3,764
7,419
22,061
338
21,14822,97554,059554
52,56055,55897,962
1,864
92,922 103,002
969
62
803
1,136
3,693
440
2,504
4,882
25,5272,115
19,80831,24657,723
1,609
53,37062,075
N/A
N/A
N/A
N/A
1,482
16
1,438
1,526
14,713
1,349
11,064
18,361
20,136476
18,84821,42428,966647
27,21630,71732,135
3,548
22,53841,731
619
7
600
639
5,438
1,338
1,819
9,057

Note that some MAIS level injuries do not exist for certain body regions. These are noted as NA in this table.

32

Body
regionFracture/
MAIS
dislocation

Yes

No
Trunk/
abdomen
Yes

No
Other
head
Yes

Burns

No

Minor
external

No

3
1
2
3
1
2
3
4
5
1
2
3
4
5
1
2
3
1
2
3
4
1
2
5
1

Non-CDS strata (in AIS-85 scale)
Implied99% confidence
Mean
std.
intervalerror
24,0852,356
17,71430,456
2,137
30
2,055
2,219
8,130
663
6,337
9,923
24,4781,303
20,95528,001
2,179
60
2,018
2,341
10,6242,280
4,459
16,79029,075
7,398
9,068
49,08228,6548,219
6,426
50,882
55,636
3,071
47,331
63,941
1,328
12
1,295
1,362
10,177226
9,567
10,787
18,784657
17,00820,56023,9881,841
19,01028,966
N/A
N/A
N/A
N/A
768
6
752
784
4,015
1,006
1,296
6,735
12,867
2,930
4,943
20,791
1,325
10
1,297
1,353
5,078
121
4,750
5,406
36,121
833
33,86938,37247,582
3,316
38,61556,549
1,307
43
1,190
1,425
3,091
116
2,777
3,406
42,3406,500
24,761
59,919
733

7

714

33

751

CDS stratum (in AIS-90 scale)
Implied99% confidence
Mean
std.
intervalerror
22,750
4,628
10,23235,267
2,588
79
2,375
2,801
5,075
677
3,245
6,905
22,137805
19,961
24,314
13,895
1,718
9,248
18,542
9,587
2,060
4,015
15,15930,656
5,697
15,24846,06437,057
4,413
25,123
48,99077,9008,103
55,987
99,813
1,851
51
1,712
1,989
8,848
894
6,430
11,26630,048
1,073
27,14732,94924,864
1,197
21,62828,10189,427
4,749
76,585 102,269
1,592
49
1,460
1,724
11,128
3,637
1,291
20,965
12,926367
11,934
13,918
2,278
21
2,222
2,334
8,026
812
5,830
10,221
28,276
1,067
25,38931,16354,152629
52,45255,852
1,479
43
1,363
1,596
3,568
188
3,058
4,077
N/A
N/A
N/A
N/A
531

6

515

547

II. Property Damage, Insurance, and Legal Costs
Some crash costs are most easily estimated from insurance data. These include not only insurance
claims processing and legal costs but also costs of property damage. Insurance data also is a critical
input when analyzing who pays the costs of crashes.
To analyze the insurance-related costs, we purchased data from the Insurance Services Office (ISO). ISO
is a data-pooling organization that aggregates claims data from a large cross-section of auto insurers.
Data we bought detailed insurance premiums collected and claims paid by selected insurers in 2009. We
used those data in conjunction with national insurance statistics and crash data to analyze (1) property
damage costs per crash, (2) numbers of people receiving insurance claims payments due to crash injury,and (3) transaction costs of compensation through the insurance and legal systems. This chapter
describes the data we purchased, our analyses of them, and what they showed.
Auto Insurance Data Description and Loss Cost Computations
ISO structured its data report around a spreadsheet developed by the Motorcycle Insurance Committeeof the National Association of Independent Insurers (NAII, Miller and Lawrence, 2003). ISO was able tobreak out data only by motorcycle versus other personal auto versus commercial auto, with commercial
auto decomposed by vehicle type. They provided data on seven categories of insurance coverage:•

Bodily injury liability (coverage if the policyholder’s vehicle injures someone; mandatory in mostStates; in no-fault insurance States this coverage compensates losses that exceed the no-fault
threshold). For motorcycles, some companies separated passenger liability coverage from otherbodily injury coverage.

•

Property damage liability (coverage if the policyholder’s vehicle damages or destroys someone
else’s property; mandatory in many States).

•

Own medical payments (coverage for the policyholder’s own injury treatment costs up to amodest ceiling, typically $1,000; often mandatory in States without no-fault insurance).

•

Personal injury protection (no-fault coverage for the policyholder’s own losses up to a modest
ceiling, typically $15,000 to $25,000; mandatory in some States).

•

Collision (coverage for damage to the policyholder’s vehicle when the policyholder is at fault in
the crash or no one is; typically required by the lender if vehicle purchase was financed).

•

Comprehensive (coverage for theft or non-crash damage to the policyholder’s vehicle; typicallyrequired by the lender if vehicle purchase was financed).

•

Uninsured and underinsured motorist (coverage for injuries to the policyholder and otheroccupants of the policyholder’s vehicle, as well as the policyholder’s property damage when adriver without insurance is at fault or when the at-fault driver has too little insurance to fullycompensate the policyholder’s losses; mandatory in many States).

For each category, we obtained four data items for policies written in 2009. Coverage in a policy is for a
maximum of one year:•

Earned exposure (the number of vehicles covered by insurance for this risk).

•

Earned premiums (how much policyholders paid for this coverage, net of any dividends orrebates to policyholders).

34•

Incurred losses (the amount paid or reserved for future payment of claims against the policies,
including amounts that will be paid by reinsurers).

•

Incurred claim count (the number of damage claims that the insurance paid for or anticipates
paying for as lawsuits and other disputes are resolved).

From the data collected, by vehicle and coverage type, we computed:•

Claims per 1,000 covers (incurred claim count divided by earned exposure, i.e., the number ofclaims filed per 1,000 policies that offer the specific coverage).

•

Claim severity (incurred losses divided by incurred claim count, i.e., the average payments per
claim paid).

•

Average loss cost (incurred losses divided by earned exposure, a measure influenced by both thefrequency of claims and claim severity, i.e., losses per cover).

•

Percent of total losses (by vehicle type, incurred losses for each coverage divided by total
incurred losses for all coverages).

•

Loss ratio (the ratio of incurred losses to earned premiums, i.e., the percentage of premiums
that is paid to settle claims).

We used National data on premiums written and loss ratios (Glenn, 2010) to estimate coverage andrepresentativeness of ISO data and to factor up ISO data to National estimates. As Table 7 shows, ISOdata include 26.2 percent of private passenger auto premiums and 26.2 percent of commercial autopremiums. Unlike in 1998–1999, when Miller and Lawrence (2000) found losses in ISO data was typicalof all auto policies, the loss ratios in 2009 ISO private passenger and commercial auto liability data arelower than the National averages. Loss ratios still are comparable to National averages for othercoverages.
Table 2-8 summarizes premiums and exposures earned and policy results.
Property Damage Costs
Across commercial and personal lines, property damage averaged $2,547 per liability claim for damage toother vehicles and $3,122 per collision claim for damage to the insured’s vehicle, with an overall averageof $2,840. In general, own collision is subject to a deductible but liability is first-dollar coverage. So why isthe average insurance payment higher under own collision? Because people do not file small claims
below their deductible from fear of increased insurance rates, or with a claim amount below theirdeductible.
We estimated how often people do not claim for property damage. Ratioing the number of earned
exposures from Table 2-8 indicates that 78.3 percent (47.3 million collision policies/60.4 million liability
policies) of insured drivers carry collision coverage. About one-third of crashes are single-vehicle
crashes, with the large majority of the rest involving two vehicles. So if all crashes with meaningfuldamage led to claims, we would expect to see 1.566 (2×0.783) times as many own-vehicle claims as
liability claims. Indeed, this multiplier could be even higher since drivers share fault in some crashes. The
actual ratio is 1.044. The remaining 0.522 smaller claims are not filed.
Damage costs per own damaged vehicle average $3,122 plus deductible. A Web site that specializes ininsurance quotes (www.carinsurance.com/kb/content24628.aspx, accessed July 21, 2012) States that
insurance professionals suggest collision policies typically carry a $500 deductible but itself estimates alower $375 average. With a $375 deductible, costs would average $3,497 per vehicle damaged
sufficiently to prompt a collision claim in 2009. We assume the $2,547 average in liability claims should
35

apply to own damage as well. Then for the remaining one-third of damaged vehicles (1−1.044/1.566 =
0.3335), costs would average $648 ([$2,547−$3,497×.6665]/0.3335). Presumably that lower property
damage cost is representative of damage in unreported crashes. Across all vehicles with property
damage compensated by insurers, damage costs per vehicle would average $3,032($2,840+$375×2,303,872/4,511,166).
Blincoe and Luchter (1983) provide ratios that can be applied to average property damage in reported
crashes to estimate damage by crash severity. Table 2-10 shows those factors and applies them toestimate property damage per vehicle by MAIS crash severity. It provides both cost per vehicle and cost
per crash, as well as associated incidence estimates. In this table, we computed vehicles in insurerreported property damage crashes as vehicles with property damage compensated (computed as
vehicles with claims in ISO divided by the percentage of property damage claims costs in ISO) minus
vehicles where someone had an injury reported in the CDS/GES aggregate file. Multiplying vehicle
involvements times cost per damaged vehicle indicates that $59.9 billion in damage occurred in thosecrashes, generating $54.2 billion in insurance payments. By comparison, the insurance data indicated$53.5 billion was paid. The close agreement of these numbers suggests that the severity allocation
factors, although old, still are reasonably accurate.
Property Damage Cost per Crash. Insurance compensated $60 billion in crash damage in 2009 (Table 210). Because NHTSA no longer collects this information, Table 2-10 uses number of vehicles per crash by
MAIS from 1984–1986 NASS and 2009 FARS to compute property damage costs per crash from costs per
vehicle. Table 2-11 shows that 2009 GES estimates of vehicles per crash by police-reported KABCO
severity are virtually identical to the 1984–1986 NASS estimates. These ratios have remained remarkably
stable over time. For unknown reasons, the GES/NASS ratio for fatal crashes of 1.63–1.66 is much higher
than the ratio of 1.48 for 2009 from FARS. We used the FARS ratio.
Number of Claims That Auto Insurance Compensates for Injury
ISO includes 4,511,166 property damage claims (Table 2-8). The percentage of property damage
premiums covered by ISO insurers is slightly higher than the percentage of claims paid (Table 2-7). Thus,
drivers covered by these insurers either (1) have slightly lower crash risks than other insureds, (2) suffer
slightly less damage per crash, or (3) buy slightly more costly insurance. Depending on which of these
possibilities is correct, insurers paid for damage to 18.3–18.8 million crashed vehicles in 2009. Similarly,
exclusive of uninsured motorist coverage, auto insurers paid 4.62–5.14 million injury claims in 2009. We
computed these ranges as ISO claims incurred divided by percentage of premiums or claims payments inISO.
Some own-medical claims and no-fault claims, however, are for injuries that also generate bodily injury
claims. Roughly one-third of crashes involve a single vehicle. Thus at most one-third of drivers (half ofdrivers in multi-vehicle crashes) might be in crashes where another driver was at fault. Those drivers
generally would receive bodily injury compensation as their insurer recovered own-medical losses fromthe at-fault driver’s insurance. Because some bodily injury claims are for recovery above no-fault limits,
we assume 10 percent of no-fault claims also involve a bodily injury claim. Reducing own-medical claims
by one-third and no-fault claims by 10 percent suggests liability insurance compensated 4.2–4.7 million
injured people in 2009. This estimate is incomplete.
Despite preponderant State laws mandating liability coverage, an estimated 13.8 percent of U.S. drivers
are uninsured (Insurance Research Council, 2011). Uninsured motorist coverage compensates bodily
injury and in some States, either by mandate or at buyer option, property damage. A single claim cancapture both categories of losses. This coverage is not mandatory everywhere; only 89 percent of
personal auto liability insurance buyers purchased it in 2009. It probably compensated another 0.25 to36

0.28 million injury claims for insured drivers (89% with coverage × 13.8% of drivers uninsured × 561,680
ISO bodily injury claims against insured drivers/24.3%–27.1% of all bodily injury claims in the ISO data).
That brings the total number of auto insurance compensation claims for injury in 2009 to 4.45 to
5.0 million.
Suppose uninsured drivers had average crash risks and carried auto insurance. Then 4.9 to 5.45 million
auto insurance compensation claims would have been paid covering medical costs, earnings losses, orlost household production in 2009 (4.2–4.7 million/86.2% insured).
Comparison to Other Crash Injury Counts. How does this number compare with estimates from NHTSA
data systems and health care administrative system? At the person level, we reweighted NHTSA’s CDS
and old NASS data for non-CDS strata using 2007–2010 GES data. The weights were matched by policereported injury severity, belt use, alcohol-involvement in the crash, single- versus multiple-vehicle crash,and occupant/nonoccupant. We then applied GES and police underreporting rates by MAIS fromNHTSA’s main report. We added fatalities from FARS. The resulting incidence estimate for people
injured in crashes was 3,954,503 (2,391,766 from CDS and FARS, plus 1,562,737 from non-CDS strata),
including 3,002,384 injured people included in police-reported crashes. Table 2-9 summarizes theestimates. It also uses the cost estimates in Table 2-2 to estimate total cost of these injuries. Since even
our lower bound is far higher, computations in the remainder of this report use the 4.9 million lower
bound.
HCUP NIS and NEDS offer a further estimate of nonfatal crash injury incidence. They indicate that
2,735,916 people were treated and released for crash injuries in 2007 and 221,366 crash survivors wereadmitted to hospital in 2008. Adding survivors treated only in physician offices and clinics based on
factors from Finkelstein et al. (2006), we estimate 3.6 million crash survivors were medically treatedannually in 2007–2008. The comparable total for 2000 was 4.2 million.
Portion of Injured People Compensated. Blincoe et al. (2002) adopt estimates from Miller et al. (1991)
that auto liability policy limits average $148,000 per person injured ($210,000 inflated to 2010 dollars)
and that 55 percent of those suffering moderate (MAIS-2) to fatal injuries make a claim. Since States
have not been shifting liability regimes (e.g., changing to no-fault insurance or raising minimum liability
coverage requirements), these factors probably are unchanged. Among the remaining 45%, roughly half
are covered by no-fault coverage up to an average of perhaps $25,000 and 90% of the remainder by anaverage of perhaps $3,500 in own-medical coverage. Of the $50.017 billion ($75.612 billion in premiums
× .651 loss ratio) in insurance compensation for injury, they suggest $25.957 billion ($7.908 billion
medical + $16.049 billion work) from Table 9) pays for medical and earnings/household production
losses of people with MAIS 2+ injuries. If liability insurance also covered 55% of MAIS-1 injuries, MAIS-1
compensation would total $18,922 billion ($9.335 billion medical + $9.587 billion work), bringing total
legitimate compensation to $42.879 billion or 85.7% of total compensation. The remaining 14.3% would
pay for fraudulent and built up or inflated claims. That percentage lies in industry’s estimated 13% to
18% range for fraud and build-up (Insurance Research Council, 2008).
Uniform 55% liability compensation seems more credible in today’s insurance market than theestimated 24.9% of MAIS-1 injuries covered in 1990 (Miller et al., 1991). The average property damage
liability claim rose from roughly $1,600 in 1998–1999 to $2,547 in 2009. As property damage amounts
rise, people are more likely to claim rather than settle informally outside the insurance system. Minor
injuries they otherwise might have ignored probably would be divulged and compensated as part of theprocess.

37

Insurance Administration and Legal Costs per Person
We estimated insurance administration and legal costs from medical, work loss, and property damage
costs using equations from Blincoe et al. (2000). We modified the equations to incorporate the 55
percent estimate of people with MAIS-1 injuries compensated . In addition, we adopted a more updated
policy limit of $210,000 and expected life insurance payout of $59,680 and an expected Worker’s
Compensation payout of $100,000 for fatalities that occur on the job. Insurance Administration costs
reflect administrative costs from a variety of insurance coverages. These include medical expense
claims, liability claims, disability claims, Worker’s Compensation, welfare payments, sick leave, property
damage claims, and life insurance. The derivation and sources for administrative cost rates are detailedin Miller, Viner, et al., 1991. Table 2-12 shows the resulting administrative cost estimates for each injury
severity level.

38

Table 2-7. Policyholders in 2009 pooled, multi-insurer Insurance Services Office (ISO) data
as a percentage of insured vehicles, and representativeness of loss ratios in ISO data
Premiums Written

Loss Ratio% of
Losses in
ISO Data

In ISO Data

% ofPremiums in
ISO Data

Nationally

In ISO
Data

Private Passenger Auto Liability
Private Passenger Property Damage
Total Private Passenger$96,498,093,000 $26,303,105,214$64,821,016,000 $15,941,256,094$161,319,109,000 $42,244,361,308

27.3%
24.6%
26.2%

68.9
58.3
64.6

58.3
56.7
57.7

23.1%
23.9%
23.4%

Commercial Auto Liability
Commercial Property Damage
Total Commercial$18,394,752,000$5,656,866,000$24,051,618,000$4,864,048,399$1,430,695,559$6,294,743,958

26.4%
25.3%
26.2%

53.1
54.0
53.3

50.4
52.9
50.9

25.1%
24.8%
25.0%

All Auto Liability
All Own Property Damage
Grand Total$114,892,845,000 $31,167,153,613$70,477,882,000 $17,371,951,653$185,370,727,000 $48,539,105,266

27.1%
24.6%
26.2%

66.4
58.0
63.2

57.1
56.4
56.8

23.3%
24.0%
23.6%

All Bodily Injury
All Property Damage *$76,831,734,421 $20,831,443,918$108,538,992,579 $27,707,661,348

27.1%
25.5%

65.1
62.0

58.5
57.4

24.3%
23.6%

Coverage

Nationally* Includes comprehensive (non-crash) coverage

39

Table 2-8. Earned premiums, exposures, claims, and losses by auto insurance line and coveragein 2009 pooled, multi-insurer Insurance Services Office data
EarnedExposure(Car
Years)

Incurred
ClaimsClaimsper
1,000
Covers53,294,052

561,680

53,465,036

Incurred Losses

Average
Cost per
Claim

Average
Loss
Cost

% of
Total
Losses

Loss
Ratio

10.5

$5,933,591,572$10,564

$111

38.7%

55.6

2,023,212

37.8$4,986,741,517$2,465

$93

32.5%

63.9

23,026,608

429,12918.6$2,498,591,166$5,822

$109

16.3%

73.2

26,681,405

200,499

7.5$522,840,594$2,608

$20

3.4%

71.0

47,639,390

180,508

3.8$1,397,283,791$7,741

$29

9.1%

37.9

53,465,036

3,395,028

63.5$15,339,048,640$4,518$287

100.0%58.3

PERSONAL AUTO PHYSICAL DAMAGE
Collision$11,511,031,389 41,708,624$4,430,224,705 42,403,787
Comprehensive$15,941,256,094 42,403,787
Total

2,173,779
2,393,853
4,567,632

52.1
56.5
107.7

$6,634,847,526$2,399,668,164$9,034,515,690$3,052$1,002$1,978

$159
$57
$213

43.3%
15.6%
58.9%

57.6
54.8
56.9

53,365

7.7$1,763,305,023$33,042

$255

11.5%

50.3

184,08226.6$634,507,411$3,447

$92

4.1%

50.3

7,690
245,137

3.4
15.2$51,483,863$2,449,296,297$6,695$9,992

$22
$152

0.3%
16.0%

51.1
50.4

130,093
92,322
222,415

23.3
17.0
39.9

$557,378,289$199,488,341$756,866,630$4,284$2,161$3,403

$100
$37
$136

3.6%
1.3%
4.9%

51.5
57.1
52.9

Coverage
Earned Premium
PERSONAL LIABILITY
Bodily Injury$10,664,642,098
Property$7,804,414,085
DamagePersonal Injury$3,414,579,139
Protection
Medical$736,154,211
Payments
Uninsured/Under$3,683,315,681
insured Motorist
Total$26,303,105,214

COMMERCIAL LIABILITY
Bodily Injury$3,502,825,417
6,921,091
Property$1,260,456,165
6,921,091
DamageNo Fault$100,766,817
2,295,294
Total$4,864,048,399 16,137,476
COMMERCIAL AUTO PHYSICAL DAMAGE
Collision$1,081,294,659
5,573,201
Comprehensive$349,400,900
5,422,795
Total$1,430,695,559
5,573,20140

Coverage

Earned Premium

EarnedExposure(Car
Years)

Incurred
ClaimsClaimsper
1,000
CoversIncurred Losses

Average
Cost per
Claim

Average
Loss
Cost

% of
Total
LossesLoss
Ratio

ALL POLICIES EXCEPT UNINSURED MOTORIST AND COMPREHENSIVE
Property
DamageLiability
Own(Deductible)$21,657,196,298$9,064,870,250

107,667,952
60,386,127

4,511,166
2,207,294

41.9
36.6

$12,813,474,743$5,621,248,928$2,840$2,547

$119
$93

83.5%
36.6%

59.2
62.0

47,281,825

2,303,`872

48.7$7,192,225,815$3,122

$152

46.9%

57.1

112,218,450$18,418,967,682
1,252,363
11.2 $10,769,812,218
Personal lines coverage includes private passenger vehicles and motorcycles.

$8,600

$96

70.2%

58.5

$12,592,326,048

Bodily Injury

41

Table 2-9. Estimated people injured in crashes and compensated losses (2010 dollars)by injury severity and crash strata

MAIS
1
2
3
4
5&6
Fatal
All

CDS
Cases
1,364,841
179,341
56,41913,1293,932
32,9991,650,661

Cases
Adjustedfor
Underreporting
2,026,599
248,00965,272
14,534
4,353
32,9992,391,766

Paid/
Cost *
Non-CDS(millions)
Person*Cases$6,417 $13,004
964,805$37,919$9,40465,602$128,329$8,37630,658$210,000$3,052
2,305$210,000$914
1,261$210,000$6,930$41,680 1,064,631

Cases
Adjustedfor
Actually
UnderPaid/
Cost *
Paid(millions)millions
reporting
Person*1,432,601$6,224$8,916 $18,922
90,720$37,043$3,361$8,92635,468 $148,550$5,269$8,2312,552 $210,000$536$2,097
1,396 $210,000
$293
$706
$210,000$3,997
1,562,737$18,375 $42,879* If 100% claimed ; in reality 55% claim , exclusive of no-fault and own medical claims. other costs. Totals
were computed before rounding.
Table 2-10. Property damage in crashes reported to insurers, 2009*
MAIS
0
1
2
3
4
5
6
1-6
All

Fraction
Property
Property
Total Property
VehiclesCrashesof MeanDamage/Vehicle
Damage/Crash
DamageCost
0.8981$2,723 16,936,098
9,733,390$4,738 $46,117,102,074
1.9172$5,813
1,784,195
969,671$10,696 $10,371,421,164
2.2420$6,798230,173
133,048$11,760 $1,564,684,072
3.5032$10,62278,673
46,278$18,057$835,637,812
4.7898$14,523
18,79911,463$23,817$273,017,849
4.7898$14,523
7,734
4,834$23,236$112,314,709
4.7898$14,523
45,63230,797$21,518$662,699,017
2.1051$6,383
2,165,206
1,196,091$11,554 $13,819,774,624
1.0000$3,032 19,101,304 10,929,481$5,299 $59,936,876,698* Excludes comprehensive (non-crash) coverage
Table 2-11. Vehicles per crash by police-reported crash severity
Crash Severity
GES 2009 NASS 1982-86
O – Property Damage Only
1.75
1.75
C – Possible Injury
1.94
1.93
B – Non-incapacitating Injury
1.76
1.75
A – Incapacitating Injury
1.71
1.74
K – Fatal Injury
1.66
1.63
All
1.78
1.76

42

Table 2-12. Insurance administration and legal costs per person by MAIS severity(2010 dollars, computed at a 3% discount rate)
MAIS
0
1
2
3
4
5
Fatal

Insurance Administration
Legal
$143
$0
$ 3,298$1,182$ 4,659$3,351$15,371$12,402$28,228$26,668$72,525$82,710$28,322 $106,488

III. Miscellaneous Costs
In this brief chapter we examine various costs not covered in the previous chapters, including thoseincurred by State and local governments, such as crash-related damage to public property and public
services like police and fire department attendance at crash sites.
Adding Roadside Furniture Damage to Property Damage
The insurance data suggest property damage averages $3,032 per crash-involved vehicle damaged
seriously enough to prompt an insurance claim, with 18.3 to 18.8 million vehicles damaged that
extensively in 2009. These estimates exclude most costs of damage to signs, lampposts, guardrails, andother roadside furniture. State and local governments absorb the roadside furniture costs not coveredby insurance.
Estimated costs of roadside furniture damage by crash severity came from 1,462 crashes in 2008 tracked
by the Missouri Claims Recovery Department. The data excluded costs not recovered from at-fault
drivers and their insurers. As Table 2-13 shows, the costs average $59 per fatal crash and $47 per injury
crash. These results, based on a single year in a single State, should be treated with caution.
Public Services
Public services costs are paid almost entirely by State and local government. Using the data underlying
the crash cost estimates (Miller et al., 1991), we separated out EMS, police, fire, vocational
rehabilitation, and court costs.
The States of Missouri and Washington provided average incident management costs. In 2009 dollars,
the estimated mean cost per crash attendance was $82 for 315 crashes in Missouri and $125 for 3,880
crashes in Washington (assuming the response rate to serious injury [A] crashes was 60% of the
response rate to fatal [K] crashes). We adopted Washington State’s estimate because the data wasmuch more complete than the Missouri data. Using data on the percentage of crashes attended, we
broke the estimate down by police-reported crash severity.
To break the costs of incident management (and vehicle and roadside furniture damage) down into cost
per person involved in a crash by injury severity, we followed the method used by Miller, Viner,
Rossman, et al. (1991). We first cross-tabulated the number of people in a crash by the AbbreviatedInjury Scale (AIS) severity of their maximum injury (MAIS), and by the maximum MAIS of anyone in thecrash (AAIS). Second, we used that cross-tabulation to iteratively estimate costs by MAIS. We firstdivided the cost for a property damage only (PDO) crash by the uninjured people involved in a PDO
crash to get a cost per uninjured person. Next, we used that cost per uninjured person to compute thecost of an MAIS-1 crash net of the costs associated with uninjured people. Dividing by the number of
MAIS-1 injury victims in a crash then yields the cost per MAIS-1 victim. This process was repeated43sequentially to compute costs for all MAIS levels. We also counted the number of vehicles per crash by
MAIS.
Table 2-14 shows the resulting estimated costs per person injured by MAIS severity, as well as estimatesfor police, fire department, and vocational rehabilitation inflated from prior NHTSA crash cost studies.
These factors are small, but the limited geographic coverage of the data underpinning them and the ageof some of them mean their uncertainty is wide. A recent National Cooperative Highway Research
Program project charged with updating most of these costs was unable to obtain data from additional
jurisdictions.
Table 2-13. Crashes by severity, portion involving roadside furniture damage,
costs per crash with costs and cost per crash, Missouri, 2008
Severity Crashes With Furniture Damage $/Crash with Costs Cost/Crash
Fatal
619
102
$356
$59
Injury21,055
2,178
$452
$47
Table 2-14. Selected ancillary crash costs per person by MAIS (2010 dollars)
MAIS
0
1
2
3
4
5
Fatal

Vehicle Roadside
Incident
VocationalFire
Damage Furniture Management Rehabilitation Department Police$1,816
$12
$1.60
$0
$7
$12
$5,382
$22
$0.60
$17
$9
$79
$5,756
$22
$0.30
$106
$95
$99
$10,860
$22
$81
$230
$227
$108
$16,306
$22
$81
$282
$639
$118
$15,070
$22
$78
$262
$651
$126
$11,180
$32
$112
$0
$543
$247

Motor vehicle crashes also result in added societal costs due to congestion and workplace disruption.
Congestion costs, which include travel delay, excess fuel consumption, and added greenhouse gases andcriteria pollutants are examined in a separate chapter of this report. Workplace costs were estimated byadjusting the workplace costs from Blincoe et al., 2002 to 2010 levels using the employment cost index
for total compensation published by the Bureau of Labor Statistics. Table 2-15 summarizes the unit costs
by injury severity and cost component for 2010. All injury unit costs are expressed on a per person
injured basis. The costs for PDO’s are expressed on a per-damaged-vehicle basis. Medical costs include
both medical care from Table 3 and vocational rehabilitation costs from Table 2-14. Property damage
costs include both vehicle damage and roadside furniture from Table 2-14. Emergency Services includes
Incident management, Fire Department, and Police from Table 2-14. Market and Household Productivity
are from Table 3. Legal and Insurance Administration costs are from Table 2-12.
Each fatality results in economic impacts of roughly $1.4 million, due primarily to lost productivity andlegal costs. MAIS 5 injuries are almost as costly at $1.1 million. The most costly impact for these mostserious of survivor injuries is the cost of medical care, but there are also significant costs from lost
productivity, legal costs, and insurance administrative costs. For all cost categories, injury costs gradually
decline as severity decreases.

44

Table 2-15. Summary of Unit Costs, Police-Reported and Unreported Crashes, 2010,
3-Percent Discount Rate, 2010 Dollars
PDO

MAIS0

MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
Fatal
$2,782$11,347$48,390 $136,035$384,011$11,317
$89
$194
$416
$838
$855
$902

Medical

$0

$0

EMS

$28

$21

Market$0$0$2,726$19,359$64,338$140,816$337,607$933,262$7,106$22,688$37,541$95,407$289,910

Household

$60

$45

$862

Insurance

$191

$143

$3,935$9,370$24,348$37,372$79,967$28,322

Workplace

$62

$46

$341

$2,644$5,776$6,361$11,091$11,783$0$341$1,077$2,444$3,521$3,862

$0
$255
$760
$1,828$2,588$2,843$1,410$12,145$1,109$5,404$6,513$18,658$6,739$56,758$1,197$5,778$6,975$63,733$19,645$185,601$1,434$10,882$12,316$197,917$35,307$394,270$1,511$16,328$17,839$412,109$91,197$1,000,135$1,529$15,092$16,621$1,016,756$106,488$1,381,984$5,720$11,212$16,932$1,398,916

Legal Costs
Subtotal
Congestion
Prop. Damage
Subtotal
Total

Note: Unit costs are expressed on a per-person basis for all injury levels. PDO costs are expressed onaper-damaged-vehicle basis.
IV. Police-Reported Versus Unreported Crash Costs

As noted in Chapter 5, over half of all PDO crashes and about a quarter of all non-fatal injury crashes are
not reported to police. However, analyses of safety countermeasures frequently rely only on policereported incidence data. Crashes that get reported to police are likely to be more severe than unreported
crashes because vehicles are more likely to require towing and occupants are more likely to require
hospitalization or emergency services. These crashes are typically also likely to require more time toinvestigate and clear from roadways than unreported crashes. Analysis based solely on police- reported
crashes should thus be based on unit costs that are specific to police-reported crashes. For injury related
costs, this is more or less automatically accounted for by the shift in the injury severity profile.
Unreported crashes have a lower average severity profile than do reported crashes. However, for noninjury related cost components – property damage and congestion costs – there is no profile to shift. In
addition, emergency services have higher involvement rates for police-reported crashes.
A separate set of costs was developed in Chapter 3 for police-reported and unreported congestion costs.
To estimate separate costs for property damage, we used property damage cost data from the MDAC
survey. Data was derived separately for reported and unreported crashes. Table 2-16 lists the results.
The mean property damage cost of a crashed vehicle in the MDAC survey was $4,476. However, themean property damage cost for vehicles in crashes reported to the police was $5,607, and the mean cost
for a vehicle in crashes not reported to the police was $1,907. To estimate separate unit costs for vehicles
in each crash type, we took the ratio of each crash type to the mean overall cost and applied thesefactors to the average property damage cost previously derived from insurance data. Since these ratios
were derived independently from both the main incidence and property damage analyses, a further
adjustment was made to normalize the unit costs so that the sum of reported and unreported crashes
matched the overall totals.6 A similar approach was used for emergency services. Emergency Services
consists of separate police, fire, and incident management components. Each component was distributed
assuming that unit costs per case were identical for both reported and unreported cases of a specific45

severity for any case for which police, fire, or incident management teams actually responded. The
difference in unit costs for reported and unreported cases is thus a function of differing response rates.
For police-reported cases, response rates are assumed to be 100 percent by definition. This is confirmedby the 100 percent rates reported in the MDAC survey for police-reported cases. For unreported cases,
MDAC survey police response rates were reported to be 100 percent for all injury cases MAIS 3 andgreater. Police response rates for unreported MAIS 0, MAIS1, MAIS2, and PDO cases were reported to be
17.1 percent, 29.2 percent, 37.8 percent, 11.5 percent respectively.
Fire response is assumed to be a subset of police response cases. Fire response rates derived from
Blincoe et al. (1992) were thus assumed for police-reported cases, and were further modified by the
relative unreported/reported police response rate in the MDAC survey for unreported cases.
Incident management response rates were estimated based on data from Washington State cited in
NCHRP Working Paper 4 (Bahar & Miller, 2010), which indicate response rates of 23.2 percent for K and
A injuries, 2.3 percent for B and C injuries, and 5.9 percent for O injuries. In order to translate these into
equivalent MAIS levels, a KABCO/MAIS injury matrix was established. In order to reflect the fact thatwithin each KABCO level, incident response rates were likely to be more heavily weighted towards themore serious crashes, the initial incidence matrix was modified by applying the relative Fire Department
response rates across MAIS severities as a model proxy. For each MAIS category, relative weights werethen computed across the 5 KABCO categories, and these weights were applied to the corresponding
average incident management response rates and then summed to calculate an average response rate
by MAIS severity level. These rates were assumed to represent police-reported cases. As with Fire
response, they were further modified using the relative unreported/reported police response rate from
the MDAC survey to estimate incident management response rates for unreported cases. Table 2-17
summarizes the inputs and results of this process for each EMS component.
The results of this analysis for congestion, property damage, and emergency services are presented inTables 2-18 and 2-19 together with the other cost components that did not vary by reporting status. The
differences seem negligible at the more severe injury levels due to the overwhelming costs of factors
such as lost productivity and medical care which do not vary by reporting status, except through theshift in injury profiles. However, at lower severity levels the unit costs are significant. For PDO vehicles
and MAIS0s, police-reported crashes have costs that are three times those of unreported crashes. For
minor (MAIS1) injuries, reported crashes cost 40 percent more than unreported crashes. These ratios
decline as injury severity increases. Note that for MAIS4s, MAIS5s, and Fatalities, property damage costs
are identical under both reported and unreported cases. All injuries at these levels are believed to bereported to police, and the original property damage cost estimate is thus assumed to represent policereported cases. These same costs are thus listed under both scenarios
6This consisted of calculating a simple normalizing factor by comparing the results of the main analysis to the sumof the separately calculated reported and unreported analyses. This factor was then applied back to the unit costs.
This process maintains the relative differences found in the MDAC analysis, while remaining consistent with theoriginal unit costs and incidence totals, which were derived from a more robust data set.

46

Table 2-16. Per Vehicle Property Damage in MDAC Survey

StatisticAll

NumberMeanMedianSE of Mean
95% LCL of Mean
95% UCL of Mean
Minimum
25th Percentile
75th Percentile
Maximum
Mean Ratio to All

1847$4,476$1,698$846$2,816$6,136$0$576$3,685$310,000
1.000

All crashes
Reported
Unreported
1256$5,607$2,000$1,200$3,251$7,962$0$884$4,265$310,000
1.253

47

591
$1,907$762$408$1,107$2,708$0$241$1,755$300,000
0.426

Table 2-17. Summary of Police-Reported and Unreported Emergency Services Unit Costs
Response Rates
Reported
Unreported
Crashes
Crashes

Fatal
MAIS 0
MAIS 1
MAIS 2MAIS 3MAIS 4MAIS 5PDO

Fatal
MAIS 0
MAIS 1
MAIS 2MAIS 3MAIS 4MAIS 5PDO

Fatal
MAIS 0
MAIS 1
MAIS 2MAIS 3MAIS 4MAIS 5PDO

Fatal
MAIS 0
MAIS 1
MAIS 2MAIS 3MAIS 4MAIS 5PDO

100.00%100.00%100.00%100.00%100.00%100.00%100.00%100.00%95.00%
1.00%
1.00%
15.00%
35.00%
90.00%
95.00%
1.00%

22.45%
5.80%
5.65%
9.78%
15.67%
17.85%
20.49%
5.80%

Average Unit
Cost

Percent
Unreported

Unit Costs
Reported Unreported
Crashes
Crashes100.00%17.16%
29.17%
37.84%
100.00%100.00%100.00%11.54%

Police Response$247.00
0.00%$12.0053.14%$79.0025.45%$99.0019.95%$108.00
4.31%$118.00
0.00%$126.00
0.00%$17.0059.72%$247.00$21.44$96.37$113.01$108.00$118.00$126.00$36.04$247.00$3.68$28.11$42.76$108.00$118.00$126.00$4.16

95.00%
0.17%
0.29%
5.68%
35.00%
90.00%
95.00%
0.12%

Fire Department Response$543.00
0.00%$7.00
53.14%$9.00
25.45%$95.0019.95%$227.00
4.31%$639.00
0.00%$651.00
0.00%$9.00
59.72%$543.00$12.50$10.98$108.45$227.00$639.00$651.00$19.08$543.00$2.15$3.20$41.04$227.00$639.00$651.00$2.20

22.45%
1.00%
1.65%
3.70%
15.67%
17.85%
20.49%
0.67%

Incident Management
Response$112.00
0.00%$2.00
53.14%$1.00
25.45%$0.00
19.95%$81.004.31%$81.000.00%$78.000.00%$2.00
59.72%$112.00
$3.57
$1.22
$0.00
$81.00$81.00$78.00$4.24$112.00
$0.61
$0.36
$0.00
$81.00$81.00$78.00$0.49

Total Emergency Services$902.00
0.00%$21.0053.14%$89.0025.45%$194.00
19.95%$416.00
4.31%$838.00
0.00%$855.00
0.00%$28.0059.72%$902.00$37.51$108.57$221.46$416.00$838.00$855.00$59.36$902.00$6.44$31.67$83.80$416.00$838.00$855.00$6.85

48

Table 2-18. Summary of Unit Costs, Police-Reported Crashes, 3-percent discount Rate (2010 Dollars)

MedicalEMS
MarketHouseholdInsurance
Workplace
Legal Costs
Subtotal
Congestion
Prop. Damage
Subtotal
Total

PDO
Vehicle MAIS0 MAIS1$0$0$2,799MAIS2$11,453

MAIS3$48,620MAIS4$136,317

MAIS5$384,273

Fatal$11,317

$59

$38

$109

$221

$416

$838

$855

$902

$0

$0

$2,726$19,359$64,338$140,816$337,607$933,262

$60

$45

$862

$7,106$22,688$37,541$95,407$289,910

$191

$143

$3,298$4,659$15,371$28,228$72,525$28,322

$62

$46

$341

$2,644$5,776$6,361$11,091$11,783$0$0$1,182$3,351$12,402$26,668$82,710$106,488$372$2,104$3,599$5,704$6,076$272$1,416$2,692$4,108$4,380$11,317$1,426$7,959$9,385$20,701$48,793$1,450$8,510$9,960$58,754$169,611$1,490$16,027$17,517$187,128$376,769$1,511$16,328$17,839$394,608$984,468$1,529$15,092$16,621$1,001,089$1,381,984$5,720$11,212$16,932$1,398,916

Note: Unit costs are expressed on a per-person basis for all injury levels. PDO costs are expressedon a per-damaged-vehicle basis.

Table 2-19. Summary of Unit Costs, Unreported Crashes, 3-percent discount Rate (2010 Dollars)

MedicalEMS
MarketHouseholdInsurance
Workplace
Legal Costs
Subtotal
Congestion
Prop. Damage
Subtotal
Total

PDO
Vehicle*
$0

MAIS0
$0

MAIS1
$2,799

$7

$6

$32

$0

$0

$60

MAIS4*$136,317

MAIS5*$384,273

Fatal*$11,317

$416

$838

$855

$902

$2,726$19,359 $64,338$140,816$337,607$933,262

$45

$862

$7,106 $22,688$37,541$95,407$289,910

$191

$143

$3,298$4,659 $15,371$28,228$72,525$28,322

$62

$46

$341

$2,644$5,776$6,361$11,091$11,783$0$0$1,182$3,351 $12,402$26,668$82,710$106,488$240 $11,240
$180
$180
$916
$2,707$1,096$2,888$1,337 $14,127$48,656 $169,61
$180
$180
$2,894 $5,451$3,075 $5,632$51,731 $175,24$376,769$180$16,328$16,508$393,277$984,468$180$15,092$15,272$999,740$1,381,984$458$11,212$11,670$1,393,654

$320
$384
$1,224$1,609$1,928

MAIS2
MAIS3$11,453 $48,620$84

Note: Unit costs are expressed on a per-person basis for all injury levels. PDO costs are expressed on a
per-damaged-vehicle basis. Generally, all MAIS 4, 5, and fatal injuries are believed to be policereported. Values are still included here for reference to cover any exceptional case where unreported
crashes might be found for these injury severity categories.

49

3. Congestion Impacts
Motor vehicle crashes result in significant time delays to other motorists who are inconvenienced bylane closures, police, fire, or emergency services activity, detours, and general traffic slowdowns
resulting from rubbernecking and chain reaction braking. This results in a significant time penalty for
those affected, which can be valued based on wage rates and the value people place on their free time.
It also results in wasted fuel, increased greenhouse gas production, and increased pollution as engines
idle while drivers are caught in traffic jams and slowdowns. These impacts affect drivers’ transportation
costs and negatively impact the health and economic welfare of the Nation.
Assessing congestion costs is difficult because virtually every crash occurs under unique circumstances.
Differences in crash severity, vehicle involvement, roadway type, time of day, traffic density, emergency
services response time, weather, hazardous material spillage, lane configurations, driver behavior, andother variables can influence the extent of congestion and the resulting societal impacts. While there
are a number of studies that document the impact of crashes on roadway congestion, most of these
focus very narrowly on impacts for a specific roadway, and in most cases, these roadways are urban
interstates.
A few studies have attempted to project congestion impacts from crashes at a higher level. Chin,
Franzese, Greene, Hwang, & Gibson (2004), used traffic engineering modeling methods to derive
estimates of delay impacts. Nationally for freeways and principal arterials. Zaloshnja, Miller, and Spicer(2000) used relative traffic density data to scale results from a study of urban interstates in MinneapolisSt.Paul to estimate the delay hours for police-reported crashes involving trucks and buses with a grossvehicle weight rating over 10,000 pounds across six different urban and rural roadway categories.
More recently, the Federal Motor Carrier Safety Administration (FMCSA) contracted with the US
DOT/Volpe Center in Cambridge, MA to produce a simulation based estimate of the per-crash impacts ofcongestion from commercial vehicle crashes (Hagemann et al., 2013). This study involved traffic
simulation measurements using TSIS-CORSIM, a micro-simulation tool developed by the University of
Florida McTrans Center. TSIS-CORSIM simulates traffic responses to specific roadway and crash
scenarios and produces estimates of aggregate vehicle delay hours and added fuel consumption. The
authors of the study then linked the TSIS-CORSIM results to the Environmental Protection Agency’sMotor Vehicle Emission Simulator (MOVES) model to produce estimates of greenhouse gas and criteria
pollutant emissions. The estimation process involved Monte Carlo simulations of 77 different crash
scenarios in order to capture the variety of possible outcomes across numerous sets of crash
circumstances. These results were then weighted based on nationwide crash incidence, producing
average impacts for crashes on 5 different categories of roadways varying by three different crashseverities (fatal crashes, injury crashes, property-damage-only crashes). While any simulation process issubject to uncertainty, the FMCSA study is arguably the most sophisticated attempt thus far to estimate
nationwide congestion costs from crashes. However, the FMCSA study’s focus on commercial vehicle50

crashes limits its applicability to this current effort, which examines all motor vehicle crashes.
Commercial vehicle crashes make up only about 5 percent of all police-reported crashes nationwide.
More importantly, they typically have more serious congestion consequences than other crashes. This
results from several factors, most notably, that they are more likely to involve lane closings, that theytake longer to clear from the roadway (especially in the case of hazardous waste or cargo spillage), andthat they are more likely to occur during normal weekday hours, when traffic density is highest, and lesslikely to occur on weekends and at night when traffic density is lighter.
The approach taken in this study involves a synthesis of past approaches. It uses empirical data derived
from both current data sources and previous literature to develop a basic congestion model. This model
will estimate the congestion impacts from lane closings, rubbernecking, and subsequent traffic dispersal
across the same roadway categories examined in the FMCSA study. The model is run once with data andassumptions appropriate for the universe of all crashes, and then again with data appropriate forcommercial vehicle crashes. The results of these two sets of outputs are then used to computenormalizing factors that can be applied to the FMCSA results for commercial vehicle crashes, to derive
an estimate that is more representative of the overall universe of traffic crashes. This linkage to the
FMCSA report is motivated by the ability of its simulation methods to capture several aspects that are
not easily estimated using more conventional approaches. These include the impact of detours, andmore importantly, the ability to capture non-linear impacts that cause disproportionate congestion
under extreme circumstances that cannot be reflected using average input values.
Methodology:
The primary measure of lost capacity due to congestion is vehicle hours. In its most literal sense “Vehicle
Hours” represents the sum of the net delay encountered by each individual affected motorist. In
practice, it is measured more broadly by determining the change in capacity over specific roadway
segments during the course of a crash event and its aftermath. Estimating lost vehicle hours nationwide
thus requires measures of crash event durations, roadway capacity, lane closures, and capacity losses.
These factors influence various aspects of congestion delay. These include delay caused by lane closings,
delay caused by rubbernecking in lanes travelling in the direction of the crash, delay caused byrubbernecking in lanes travelling in the opposite direction of the crash, delay caused by dispersal of theremaining traffic queue after the crash is cleared from the road, and delay caused by detours. Each ofthese factors will be addressed separately.
Lane Closings
Capacity losses due to lane closure is a function of traffic density on the affected roadway at the time of
the crash, the duration of the crash event, the probability of and extent of lane closures, andproportional reduction in travel capacity through the roadway that results from those lane closures.
These factors combine in a direct multiplicative relationship to determine lost capacity due to laneclosures as follows:51

VC = AAHT*CD*PLC*RCL
where:
VC = Vehicle capacity lost due to lane closure
AAHT = Average annual hourly traffic (vehicles) past the crash site during the time affected by the crash
CD = crash duration7
PLC = Probability-of-lane closure
RCL = Reduced capacity (%) in the direction of the crash given lane closure.

Annual Average Hourly Traffic
Annual average hourly traffic (AAHT) is the average number of vehicles that would pass the crash site
during the time affected by the crash. AAHT can be derived by merging together data regarding roadway
capacity, travel profiles, and crash occurrence profiles. The initial basis for computing AAHT is the annual
average daily traffic (AADT) 8 data collected by the Federal Highway Administration through their
Highway Performance Monitoring System (HPMS). The HPMS is the most comprehensive source of data
about the use of the Nation’s roadways. It currently collects data from over 110,000 highway sample
segments. HPMS monitors roadway traffic to produce AADT statistics for the various roadway types
across the country.
The impact of congestion is largely a function of traffic density, and this can vary significantly by roadtype. For this analysis, AADT estimates were collected for the following roadway types:
Urban Interstate/Expressway,
Urban Arterial,
Urban Other,
Rural Interstate/Principal Arterial, andRural Other.
All U.S. roadway types are collapsed into these five broader categories, which match those used in the
FMCSA heavy truck crash report. They were chosen for this report to facilitate the utilization of somedata elements in the FMCSA report, which were stratified using these roadway categories.
7

For crashes with lane closings, crash duration is a proxy for the time lanes are closed. This assumes the crash
immediately blocks the traffic lanes or lanes, and that police or emergency vehicle presence remains throughout
the timeframe when vehicles are removed.
8
AADT is the average number of vehicles that travel on a roadway over the course of a day.

52

The AADT data obtained from FHWA for these categories are summarized in the first column of Table 31. 9 These represent the mean VMT weighted AADT counts across all roadways within the five basic
roadway categories. These five categories collapse nine categories that are collected under the HPMS
system. The groupings are combined as follows:
Urban Interstate/Expressway includes the two roadway functional categories “urban principal arterial urban interstate” and “urban principal arterial- other freeways and expressways..”
Urban Arterial includes the two roadway functional categories “urban principal arterial – other” and“urban minor arterial.”
Urban Other includes the two roadway functional categories “urban collector” and “urban local..”
Rural Interstate/Principal Arterial includes the two roadway functional categories “rural principal arterial- rural interstate” and “rural other principal arterial..”
Rural Other includes “rural minor arterials,,” “rural major collectors,,” “rural local,” and “rural minor
collectors..”
The mean VMT weighted AADTs were calculated using the following formula:∑ 𝐴𝐴𝐴𝐴 ∗ 𝐴𝐴𝐴𝐴 ∗ 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆ℎ∑ 𝐴𝐴𝐴𝐴 ∗ 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆ℎ

However, data for three of the HPMS categories, “urban local,,” “rural local,” and “rural minor
collectors” were only collected in summary format from the States and could not be directly weighted
using this method. HPMS does calculate AADT values that include these 3 roadway functional categories
using a standard method that does not weight by VMT. 10 The impact of these segments was therefore
estimated by comparing the ratio of the Urban Other and Rural Other AADTs both including andexcluding these 3 categories. This ratio was applied to the mean VMT weighted AADTs for Urban Other
and Rural Other that excluded these 3 categories to produce the estimates noted in Table 3-1.
As would be expected, urban roadways experience far greater travel densities than rural roadways do,
and interstates and freeways are more travelled than other roadway categories. The obvious implicationof this is that crashes that occur on urban roadways will affect more vehicles than those that occur onrural roadways, as will those that occur on interstates and freeways.

9

This data represents annual average AADT over the 2005-2008 period, the latest data available at the time ofthis analysis.
10∑ 𝐴𝐴𝐴𝐴 ∗ 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆ℎ∑ 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆ℎ53

Table 3-1. Annual Average Daily Traffic by Roadway Category, 2005-2008
Roadway Functional Class
Urban Interstate/ Expressway

AADT
113,814

Urban Arterial
Urban Other

23,996
2,908

Rural Interstate/Principal Arterial

25,579Rural Other

1,502

AADT statistics represent the average number of vehicles that travel a roadway segment over thecourse of a day. However, travel within a given day is not evenly distributed, and travel patterns vary on
weekends from those that occur on weekends. Festin (1996) documented daily travel patterns over the
24-hour cycle on both weekdays and weekends using data obtained from 5,000 Automatic Traffic
Recorder (ATR) sites nationwide. Figures 3-A and 3-B illustrate the average traffic density patterns forweekday and weekend travel.

54

Figure 3-A. % Daily Traffic, Weekday

Figure 3-B. % Daily Traffic, Weekend55

A typical weekday pattern starts at a low point between 2 and 4 a.m., and then starts a rapid
acceleration through about 8 a.m. as workers commute to jobs. It then drops into a mid-morning lull,
before picking up in the afternoon and peaking during the evening rush hour, after which it rapidly
declines. By contrast, on weekends traffic displays a gradual rise from about 4 a.m. until it peaks
between noon and 4 p.m., after which it declines steadily into the evening. These patterns are importantbecause they determine the density of traffic that will be present at different times of day or days of theweek, and crashes that occur during peak hours will cause considerably more congestion than those that
occur during off-peak hours.
Crash frequencies also vary by time of day and day of week, as well as by crash type. Table 3-2 shows
the frequency of crashes by time of day for weekends and weekdays, for crashes of different severity
compiled from NHTSA’s FARS and GES databases for the years 2005-2010. In Figures 3-C and 3-D thisdata is charted for each crash severity level against the traffic density profiles illustrated in Figures 3-A
and 3-B.
It is apparent from these figures that while crash frequencies may follow a somewhat similar pattern totraffic density, they diverge in ways that have implications for congestion impacts. Moreover, thisdivergence is more pronounced on weekends.
During weekdays, crash patterns follow the general shape of traffic density patterns. This is expected
since traffic density is a measure of exposure. In the early morning hours, injury and PDO crashes follow
traffic density patterns very closely. The highest number of these crashes occur during the mid-day
timeframe up to the end of rush hour, when traffic density is highest. After peak rush hour, they occur
at a rate that is above traffic density. Fatal crashes follow a similar pattern, but generally occur at rates
below traffic density during the day but above density in the early morning hours and after rush hourwhen traffic density is relatively light.
On weekends, a significant proportion of all three crash types occur between midnight and 8 a.m., whentraffic congestion is relatively light. This is especially true for fatal crashes, with a particularconcentration of fatalities in the midnight to 6 a.m. timeframe. This likely reflects the increased
consumption of alcohol on weekend evenings, with its resulting toll on driver skills and judgment. For
the remaining hours of the weekend, crash frequencies are lower than would be expected based solely
on exposure, but when they occur, they occur during times of relatively high traffic density.

56

Table 3-2. Distribution of Daily Traffic and Crash Occurrences by Crash SeverityTime
12 Midnight
1 a.m.
2 a.m.
3 a.m.
4 a.m.
5 a.m.
6 a.m.
7 a.m.
8 a.m.
9 a.m.
10 a.m.
11 a.m.
12 noon
1 p.m.
2 p.m.
3 p.m.
4 p.m.
5 p.m.
6 p.m.
7 p.m.
8 p.m.
9 p.m.
10 p.m.
11 p.m.
Total

Weekday Crashes
Fatal
Injury
PDO
Crash
Crash
Crash

Weekend Crashes
Fatal
Injury
PDO
Crash
Crash
Crash

0.97%
0.62%
0.47%
0.44%
0.67%

3.91%
3.15%
3.22%
2.48%
1.84%

1.57%
1.13%
1.06%
0.88%
0.59%

1.50%
1.05%
0.95%
0.76%
0.63%

5.04%
5.94%
7.50%
6.28%
4.17%

3.22%
3.08%
3.78%
3.04%
2.09%

3.47%
2.98%
3.37%
2.94%
2.01%

1.76%
4.33%
6.66%
5.85%
5.07%
5.18%
5.58%
5.85%
5.92%
6.39%
7.28%
7.90%
7.79%
5.88%
4.38%
3.54%
3.16%
2.49%
1.82%

2.13%
3.44%
4.08%
3.77%
3.18%

0.78%
1.77%
3.78%
6.46%
4.78%

0.83%
1.92%
4.35%
6.97%
4.83%

3.05%
2.67%
2.39%
2.07%
2.22%

1.71%
1.75%
1.68%
2.32%
3.02%

1.64%
1.68%
2.09%
2.57%
3.17%

3.31%
3.68%
4.03%
4.28%
4.82%
5.47%
5.77%
5.68%
5.81%
5.55%
5.28%
5.30%

3.86%
4.40%
5.36%
6.01%
6.14%
7.82%
8.59%
8.87%
7.86%
5.37%
4.05%
3.47%

4.24%
4.27%
5.65%
6.00%
6.02%
7.57%
8.52%
9.23%
7.91%
5.14%
3.57%
3.23%

2.45%
2.82%
3.31%
3.71%
3.99%
4.31%
4.55%
4.74%
5.08%
5.10%
4.94%
4.89%

3.81%
4.79%
5.68%
6.26%
6.53%
6.74%
7.05%
6.34%
6.28%
5.38%
4.47%
4.07%

4.12%
4.77%
5.78%
6.33%
6.32%
6.34%
6.06%
6.55%
6.15%
5.58%
4.65%
4.31%

5.08%
4.72%
100.00% 100.00%

3.01%
2.37%
100.00%

2.72%
4.54%
2.13%
4.23%
100.00% 100.00%

3.71%
3.19%
100.00%

3.89%
3.22%
100.00%% Daily
Traffic

57

Figure 3-C. Weekday Crash Frequencies Versus Congestion

58

Figure 3-D. Weekend Crash Frequencies Versus Congestion

Given these different relationships between traffic density and crash occurrence on weekdays versus
weekends, the two time frames must be analyzed separately. Therefore, to derive AAHT for trafficcrashes, AADT was first split into weekday and weekend values using factors developed in the FMCSA
study. HPMS does not publish separate AADTs for weekdays and weekends, so FMCSA analyzed ATR
data to derive ratios between the weekend, weekday, and total sample means. These provided scaling
factors that were used to transform the overall AADT number into separate weekend and weekday
AADTs. These scaled AADT values were then weighted to reflect the relative frequency of crashes on
weekdays and weekends 11, and the resulting AADT totals were used to derive overall ratio weights toapply to the AAHT results.
AAHT was then calculated for each crash severity and roadway type by weighting the hourly distributionof travel density by the portion of crashes that occur during each specific hour. For example, about 1%of average weekday travel occurs around midnight on weekdays, when the weighted AAHT on urban
interstates/freeways is 1166 vehicles, and about 4 percent of weekday fatal crashes occur; but on
weekends about 2 percent of fatal crashes occur around midnight, when AAHT is 1925 and about 5
percent of weekend fatal crashes occur. Summing these aggregates across the full 24-hour period gives
an exposure adjusted AAHT value as follows:11This data indicated that 65 percent of fatal crashes, 75 percent of injury crashes, and 77 percent of PDO crashes
occurred on weekdays. The remainders, 35 percent of fatal crashes, 25 percent of injury crashes, and 23 percent
of PDO crashes occurred on weekends.

59

�

24

ℎ=1

𝐴𝐴𝐴𝐴(ℎ ) ∗ 𝐶𝐶(ℎ)

where AADT(h) = Average annual daily travel during the specific hour of the day
Cf(h) = Proportional crash frequency during the specific hour of the day.
Table 3-3 lists the results of this analysis. The simple average AAHT, which is computed as the AADT/24
hours, is shown in the second column for comparison purposes only. The results indicate that on
average, crashes of all severity categories tend to occur during times when travel density is somewhat
higher than average. However, the more serious injury (and especially fatal) crashes are more likely than
PDOs to occur during late night hours when travel density is relatively low. The exposure adjusted AAHT
is thus highest for the least serious crashes, PDOs, and the lowest for the most serious crashes, fatal
crashes.
Table 3-3. Crash Exposure Adjusted AAHT by Roadway Class and Crash Severity

Roadway Category

AADT

Urban Interstate Expressway
Urban Arterial
Urban Other
Rural Interstate/Principal Arterial
Rural Other

113814
23996
2908
25579
1502

SimpleAverage

Crash Exposure Adjusted AAHT

AAHT

Fatal

4742
1000
121
1066
63

4934
1040
126
1114
65

Injury
6218
1311
159
1392
82

PDO
6300
1328
161
1407
83

Crash Duration (CD)Comparisons across studies of travel delay often focus on different issues and produce conflicting
results. One metric that is common to many studies is crash duration – the time during which the crashaffects travel on the roadway. Estimates of vehicle hours spent in congestion caused by crashes are
partially a function of the time that passes from the onset of the crash until the crash is fully cleared
from the roadway. Various estimates of these time intervals have been derived by authors examining
crash data across different locations. A sampling of these estimates is shown in Table 3-4.

60

Table 3-4. Crash Duration Estimates (Minutes)
Crash
Type

Study
Zaloshnja, Miller, and Spicer(5 U.S. urban freeways, 1989 - Unk # observations)
FMCSA (2012)(Pennsylvania, 2006-2008 - 23,388 observations)
Giuliano (1988)(LA I-10 freeway, 1983-85 - 270 obs)
Wu, Kachroo, and Ozbay (1998)(Northern VA, 1997 - 33 observations)Boyles, Fajardo, and Waller (2006)(Atlanta, 2004 - 2,970 observations)
Lan and Hu (1999)12 (3,877 observations)(Minnesota Urban freeways, 1994-95)
Skabardonis, Chira-Chavala, and Rydzewski (1998)(CA I-880 freeway 1995 - 92 observations)

PDO

Injury

Fatal

All

All

49

86

233

60

Truck

35

55

216

44

All

44

56

49

All

27

50

34

All

42

All

39

All

40

63

43

Simple Average (minutes)=

39

62

224

44

Simple Average (hours)=

0.65

1.04

3.74

0.74

The results indicate a range of durations for PDOs of from 27 to 49 minutes, and for injury crashes offrom 50 to 86 minutes. PDOs are by far the most common event and there is close agreement between
the Zaloshnja et al., Giuliano and Skabardonis, et al. PDO estimates as well as the FMCSA, Boyles et al.,Lan and Hu, and the Skabardonis et al. overall estimates, which are dominated by PDOs. The injury
estimates for Giuliano, Wu et al., and FMCSA are also similar, but Zaloshnja et al. found significantly
higher durations for injury crashes than the other studies. The differences in these findings may reflect
the differences in the localities and roadways that they examined. Zaloshnja et al. based their estimateson data provided for five different police jurisdictions in 1989. 13 They represent the time police spent at
the crash and they include all crash and roadway types within those jurisdictions. Giuliano examinedcrashes that occurred in a section of a major Los Angeles Freeway in 1983-85. The Giuliano values were
subsequently used in reports by Chin et al. (2002). Wu and Kachroo examined crashes in Northern
Virginia in 1997, and Boyles, Fjardo, and Waller used police logs from the Georgia Department ofTransportation in 2004. Lan and Hu examined urban freeways in the Twin Cities area of Minnesota
during 1994-95. Skabardonis, Chia-Chavala, and Rydzewski (1998) examined crashes on California’s I-880
freeway in 1995.

12

Personal communication in 2000 from Patricia S. Hu to Ted R. Miller regarding data from 1999 study conductedby Chang-Jen Lan and Patricia S. Hu of urban freeways in Minneapolis-St. Paul.
13
The jurisdictions included Dade County, FL, Lakewood, CO, Montgomery County, MD, San Antonio, TX, and
San Jose, CA.

61

There are only two studies that estimate durations for fatal crashes, but agreement is fairly close. We
note that the FMCSA study specifically examined crash data for commercial vehicles while the Zaloshnja
et al. study collected data for all crash types. One might expect that time intervals to clear heavy truck
crashes might be longer, but this is not apparent from these two samples. Further, the FMCSA study
time intervals, which represent data from 23,388 crashes in Pennsylvania from 2006 to 2008, represent
time intervals during which the road was closed due to the crash, whereas the Zaloshnja estimates
represent the time interval during which police were present at the crash. Other factors such as local
differences and reporting parameters may obscure such differences. However, it is also possible that thetime difference may not be significant. Studies that examine crashes both with and without trucks havegenerally found more vehicle hours of delay in heavy truck crashes. 14 However, this is often a function of
more lane closures due to the size of the vehicle as well as more equipment needed to remove them. In
addition, in the rare case of hazardous waste or other cargo spillage, multiple lanes may be blocked byspilled cargo or fuel.
Each of these studies focused on specific locations and since they do not overlap, it is not surprising that
there is variation in their results. These results reflect differences in traffic density, which is specific todifferent roadway types and cities, and which impacts police, rescue, and cleanup operation response
times. It also reflects differences in the metric used, which varies from empirical observations to police
logs. It’s unclear whether police presence on the scene would exceed the timeframe when the crash has
been cleared. There is likely to be a delay between the crash occurrence and the police response.
Conversely, in some cases police may remain at a crash site to complete crash reports after the vehicles
have been cleared from the road.
The definition of crash duration can vary by study. The universe of events that reflect the full impacts of acrash begins with the crash incident and ends when traffic patterns return to normal. Within this interval,
a number of events occur that influence the impact on traffic congestion. The first of these is the arrivalof law enforcement or emergency service personnel at the scene. This usually occurs a short period oftime after the incident is detected by State or local traffic or emergency systems. While detection usually
involves some level of delay from the time of the incident, with the proliferation of cell
phones, this interval is becoming increasingly shorter. Arrival of emergency service or police can result in
an increase in congestion since these vehicles may block additional lanes while at the crash site.
Subsequently, police may formally close down travel lanes and/or move crashed vehicles off the
roadway to relieve congestion. Once the vehicle is removed from the scene and emergency personnel
and police have left, traffic will gradually return to normal.
Figure 3-E illustrates the general impact on traffic flow over time from a crash event. 15

14

For example, Lan and Hu found roughly twice the delay hours in crashes involving heavy trucks compared tothose that did not involve heavy trucks. Personal communication cited in Zaloshnja et al., 2000.
15
Figure E simplifies how delay caused by crashes, particularly major crashes, accumulates, since it ignores both themodification of the reduced capacity made when the crash-involved vehicles are moved to the shoulder and thefact that traffic demand is not usually constant during a major crash. However, it is used here only to describe thebasic congestion dynamic.

62

Figure 3-E
25

C

Cumulative Vehicle Flow

20

15

10

B

5

A
0
1

2

3

4

5

6

7

8

9

10 11 12 13 14 15 16 17 18 19 20 21
Time from Crash

In Figure 3-E, the X axis represents time and the Y axis represents cumulative vehicle flow. Line AC
represents the normal vehicle flow rate - the rate at which traffic would normally proceed through theroad segment at the time of the crash. Line AB represents the reduced rate of traffic flow that resultsfrom the crash. This rate continues until time B, which marks the point at which the crash is cleared andthere are no longer any lane blockages. Line BC represents the return to normal roadway capacity, which
would define the rate at which traffic flow would return to its pre-crash pattern, a period during which theresidual aspects of both lane blockage and rubbernecking are gradually cleared out. At time point C, theimpacts of the crash are over, and there is no more congestion due to the crash.
The studies by Giuliano and by Zaloshnja et al reflect time at the scene from police records. Lan and Hu’s
estimate reflects the time from detection until the crash was cleared from the roadway. Boyles et al used
incident logs from the GA Dept of Transportation, while Wu et al used a combination of video and incident
logs, which commenced at the time a crash was detected. Skabardonis et al used California Highway
Patrol incident logs. Most of these sources thus appear to measure a time period commencing shortly
after the crash occurs (either time from detection or time of arrival at the scene by police) and stopping
when the crash is cleared and police or emergency vehicles have left the scene. While there appear to bepossible minor differences among these sources, in a very rough sense, they approximate the time fromwhen the emergency response arrives at the scene until the crashed vehicles have been removed and theemergency response vehicles have departed. From Figure E above, they thus cover a time commencing
shortly after the crash occurs and extending to time B, when the roadway is basically cleared of any
evidence of the crash. They do not cover the recovery phase during which the roadway gradually returns
to normal after the incident has been removed. An estimate of this queue dispersal impact will thus be63

made separately based on the results of the following analysis of impacts during the accident duration
phase. See Post-Crash Duration Queue Dispersal later in this report for further discussion and analysis of
the impacts of this phase of crash impacts.
Most of the studies noted in Table 3-4 share the basic limitation of being conducted only on specificurban freeways during relatively short timeframes. The exception is the FMCSA report, which examinedcrashes on all roadway types in Pennsylvania from 2006 to 2008. However, the FMCSA report was
confined to heavy truck crashes, and wasn’t aggregated by lane blockage. The framework for this current
analysis is specific to five different roadway types, and is intended to be representative of all roadwaycrashes. To accommodate this structure, we obtained data for all crash types from the Pennsylvania DOT
traffic records database for the years 2006-2010. The Pennsylvania DOT data represent a complete
census of police-reported crashes for this time period, broken out by roadway
type, roadway blockage status, crash severity, and crash duration. This data describes the characteristicsof roughly 418,000 crashes that occurred in Pennsylvania during this timeframe. From this data, we
obtained counts of crashes stratified into the 7 duration categories coded in the Pennsylvania DOT
database. An example of this output is shown in Table 3-5.

64

Table 3-5. 2006-2010 PA Statewide Crashes by Roadway Closure and Severity on Urban Interstate
Expressways
Unknown
Time/None< 30
Minutes

30-60
Minutes

1-3
Hours

3-6
Hours

6-9
Hours

>9
Hours

Fatal
Injury
PDO
All

4
27
19
50

7
1,667
1,303
2,977

28
1,850
1,036
2,914

131
685
351
1,167

100
146
68
314

24
26
17
67

7
20
11
38

301
4,421
2,805
7,527

Fatal
Injury
PDO
All
Fatal
Injury
PDO
All
Fatal
Injury
PDO
All

36
9,195
16,291
25,522
2
121
101
224
0
4
4
8

0
0
0
0
3
3,606
3,754
7,363
0
2
0
2

0
0
0
0
12
3,064
2,295
5,371
0
1
0
1

0
0
0
0
54
555
347
956
0
0
0
0

0
0
0
0
24
32
35
91
0
0
0
0

0
0
0
0
3
6
5
14
0
0
0
0

0
0
0
0
1
2
5
8
0
0
0
0

36
9,195
16,291
25,522
99
7,386
6,542
14,027
0
7
4
11

Fatal

42

10

40

185

124

27

8

436

Injury

9,347

5,275

4,915

1,240

178

32

22

21,009PDO

16,416

5,057

3,331

698

103

22

16

25,643

All

25,805

10,342

8,286

2,123

405

81

46

47,088

ClosureSeverity

Fully

None

Partially

Unknown

Totals

Total

From this data, we computed the proportion of crashes that occurred under each duration category,
and applied that proportion to the average duration of crashes in that category. An example of thisprocess is shown in Table 3-6 for crashes with full road closure.

65

Table 3-6. PA Crash Duration, Urban Interstate Expressways, 2006-2010,
Full Roadway Closure (Minutes)
< 30
30-60
1-3
3-6
6-9
Severity
minutesminutes
Hours
Hours
Hours

>9 Hours

Total

Fatal

2.36%

9.43%

44.11%

33.67%

8.08%

2.36%

100.00%Injury37.94%

42.10%

15.59%

3.32%

0.59%

0.46%

100.00%PDO

46.77%

37.19%

12.60%

2.44%

0.61%

0.39%

100.00%All

39.82%

38.97%

15.61%

0.90%

0.51%

100.00%

Severity

15

45

90

4.20%
Minutes
240

420

540

Total

Fatal

0.35

4.24

39.70

80.81

33.94

12.73

171.77

Injury

5.69

18.95

14.03

7.97

2.49

2.46

51.59

PDO

7.02

16.73

11.34

5.86

2.56

2.13

45.64

All

5.97

17.54

14.05

10.08

3.76

2.74

54.14

The table indicates that, for fatal crashes on urban interstate expressways where there was full roadway
closure, the average incident duration was 172 minutes. For injury crashes, the average duration was 52
minutes, and for PDO crashes, the average duration was 46 minutes. Note that the averages from someduration categories are skewed within the duration. This reflects weighting of cases within eachcategory. The weighted time values chosen to represent each category were adopted from FMCSA,
based on data from crashes in Kentucky. Table 3-7 summarizes the results for each roadway type andcrash severity.

66

Table 3-7. Average Roadway Closure Times by Crash Severity and Roadway Type
Fatal Crashes
Roadway Type
Urban Int/Expressway
Urban Arterial
Urban Other
Rural Int/Arterial
Rural Other

Closure (minutes)
Full
Partial Combined
171.77 134.07
162.44161.01
95.33
151.80159.9956.25
152.80175.84 118.05
161.74146.8983.40
139.14

Closure (hours)
Full
PartialCombined
2.86
2.23
2.71
2.68
1.59
2.53
2.67
0.94
2.55
2.93
1.97
2.70
2.45
1.39
2.32

Roadway Type
Urban Int/Expressway
Urban Arterial
Urban Other
Rural Int/Arterial
Rural Other

Injury Crashes
Closure (minutes)
Closure (hours)
Full
Partial Combined
FullPartialCombined
51.59
34.85
41.12
0.86
0.58
0.69
47.94
29.51
37.18
0.80
0.49
0.62
52.89
31.54
43.31
0.88
0.53
0.72
74.22
47.72
57.71
1.24
0.80
0.96
60.58
41.79
51.27
1.01
0.70
0.85
PDO Crashes
Closure (minutes)

Roadway Type
Urban Int/Expressway
Urban Arterial
Urban Other
Rural Int/Arterial
Rural Other

Full
45.64
43.48
50.26
74.86
58.63

Partial
31.67
27.51
28.94
44.00
38.57

Combined
35.87
32.26
37.39
52.26
45.99

Closure (hours)
Full
0.76
0.72
0.84
1.25
0.98

PartialCombined
0.53
0.60
0.46
0.54
0.48
0.62
0.73
0.87
0.64
0.77

As previously noted, the duration times in Tables 3-5, 3-6, and 3-7 represent the time spent by police at
the crash site. However, crashes will begin to influence congestion from the time of their occurrence.
There is typically a delay between the crash occurrence and the arrival of emergency personnel such aspolice or ambulance. A review of online studies or articles focused on local police or EMS response times
indicates that responses can typically take 5 to 20 minutes, depending on the nature of the jurisdiction,with longer response times generally occurring in rural areas. We did not find studies that examine
National average response times. To account for this time lag, we added 5 minutes to the duration times
for urban roadway crashes and 10 minutes to rural roadway crashes. We believe these may beconservative estimates, since 5 minutes appears to be a minimum response time except in very small
jurisdictions, and many rural response times exceeded 10 minutes. In addition, published response
times are typically based on the elapsed time between emergency notification or dispatch and EMS or
67

police arrival at the scene. The lag between the crash and such notification is thus not accounted for inthese studies. As noted previously, the proliferation of cell phones and other portable communication
devices likely minimizes the time between crash events and emergency notification. Table 3-8 modifies
Table 3-7 to include these average response time lag estimates.
Table 3-8. Average Roadway Closure Times by Crash Severity and Roadway
Type, Adjusted for Response Lag
Fatal Crashes
Closure (minutes)

Closure (hours)

Roadway Type

Full

PartialCombined

Urban Int/Expressway
Urban Arterial
Urban Other
Rural Int/Arterial
Rural Other

176.77166.01164.99185.84156.89139.07100.3361.25
128.0593.40

167.44156.80157.80171.74149.14

Full
2.95
2.77
2.75
3.10
2.61

PartialCombined

2.32
1.67
1.02
2.13
1.56

2.79
2.61
2.63
2.86
2.49

Injury Crashes
Closure (minutes)

Closure (hours)

Roadway Type

Full

PartialCombined

Urban Int/Expressway
Urban Arterial
Urban Other
Rural Int/Arterial
Rural Other

56.59
52.94
57.89
84.22
70.58

39.85
34.51
36.54
57.72
51.79

46.12
42.18
48.31
67.71
61.27

Full
0.94
0.88
0.96
1.40
1.18

PartialCombined

0.66
0.58
0.61
0.96
0.86

0.77
0.70
0.81
1.13
1.02

PDO Crashes
Roadway Type

Closure (minutes)
Full
Partial Combined

Urban Int/Expressway
Urban Arterial

50.64
48.48

36.67
32.51

40.87
37.26

0.84
0.81

0.61
0.54

0.68
0.62

Urban Other

55.26

33.94

42.39

0.92

0.57

0.71

Rural Int/Arterial

84.86

54.00

62.26

1.41

0.90

1.04

Rural Other

68.63

48.57

55.99

1.14

0.81

0.93

Full

Closure (hours)
PartialCombined

The average crash duration times in Table 3-8 served as the basis for each congestion aspect in thisanalysis. For crashes where there was lane closure, the weighted combined duration for both full andpartial closure is applicable. It seems likely that crashes that do not require lane closure would disrupt
traffic for a shorter duration than those that do. Directionally, this is confirmed by the significant
decrease in duration found across all roadway types when comparing cases that involved full closure to68those that involved only partial closure. The PA database does not include duration information forcrashes that did not involve road closure, and the other studies cited in Table 4 did not discriminate
between crashes with lane closure and those without. Since we lack an estimate for these crashes, forthis analysis, it will be assumed that crashes that did not require road closure have similar durations tothose that required only partial closure. We note that this may produce an upward bias for this segmentof the analysis, but this data is the closest match available. However, the overall average crash duration(across all crash types) resulting from this assumption fits well within the overall averages found in otherstudies. Within individual categories, for fatal crashes the current study estimates a shorter duration thanthe Miller and FMCSA studies. However, the Miller study reflects only urban freeways whereas this study
represents all roadway types, so this might be expected. Likewise, the FMCSA study represents only
heavy truck crashes, whereas this study represents all crash types, so a shorter duration might be
expected. The current study’s injury estimate is lower than those found in previous studies, butreasonably close to three of the five studies that examined injury crashes. Again, the biggest differenceis with the Miller study that examined only urban crashes, but most other studies were also based
primarily on urban freeways. For PDO crashes, this study’s duration estimate fits well within the range ofother studies findings, as does the overall average for all crash types. Overall, the PA data used for thisstudy is the most recent data source, represents the broadest group of roadways and crash types, and isbased on the largest number of observations. Table 9 summarizes the various duration estimates fromthese studies and those used in this study.
Table 3-9. Summary of Crash Duration Estimates (Minutes)
Study
Zaloshnja, Miller, and Spicer(5 U.S. Urban freeways, 1989)
FMCSA (2012)(Pennsylvania, 2006-2008)
Giuliano (1988)(LA I-10 freeway, 1983-85)
Wu, Kachroo, and Ozbay (1998)(Northern VA, 1997)
Boyles, Fajardo, and Waller (2006)(Atlanta, 2004)
Lan and Hu (1999)(Minnesota Urban freeways, 1994-95)
Skabardonis, Chira-Chavala, andRydzewski (1998) (CA I-880 frwy 1995)
NHTSA 2013 (this study)(Pennsylvania 2006-2010)

Crash TypeNo. Obs.

PDO

Injury

Fatal

All

All

Unknown

49

86

233

60

Truck

23,388

35

55

216

44

All

270

44

56

49

All

33

27

50

34

All

2,970

42

All

3877

39

All

92

40

63

All

418,000

41

45

43
151

43

The final crash duration times are summarized in Table 10. The closure values will be used for calculating
the impacts of crashes with lane closings. The no-closure values will be used for impacts resulting from69

crashes without lane closings. The combined values, which are derived using PA frequency weights, will
be used for calculating opposite direction rubbernecking estimates, which can occur for all crash types.
Table 3-10. Crash Duration by Roadway Type and Crash Severity (Hours)

Roadway Type

Fatal Crashes
No
ClosureClosureInjury Crashes
No
ClosureClosurePDO Crashes
No
ClosureClosureUrban Int/Express
Urban Arterial
Urban Other
Rural Int/Arterial
Rural OtherAll Roadways
Roadway Type
Urban Int/Express
Urban Arterial
Urban Other
Rural Int/Arterial
Rural OtherAll Roadways

2.79
2.61
2.63
2.86
2.49
2.71
Combined
2.75
2.48
2.50
2.79
2.39
2.61

0.77
0.70
0.81
1.13
1.02
0.97
Combined
0.72
0.64
0.73
1.05
0.95
0.90

0.68
0.62
0.71
1.04
0.93
0.86
Combined
0.64
0.58
0.63
0.94
0.86
0.78

2.32
1.67
1.02
2.13
1.56
1.85

0.66
0.58
0.61
0.96
0.86
0.81

0.61
0.54
0.57
0.90
0.81
0.74

Probability of Lane Closure
When crashes occur, some vehicles will remain in the roadway while others will end up on the side of
the roadway where they won’t directly block traffic. Roadways can be blocked by vehicles that come torest either entirely or partially within the roadway travel lanes, or by debris from the crash (vehicle
parts, cargo, damaged roadway structures, etc.). Roadway blockage can also result when police or
emergency equipment responds to the crash. Lane blockage can result from formal lane closures set up
by police or from de facto closures due to the presence of crashed vehicles. In many crashes, both will
occur. Nearly all crashes result in some sort of delay, either through blockage or due to rubbernecking,but a closed lane clearly has a bigger impact. The extent to which lanes are blocked thus has a direct
impact on the amount of congestion that results from a crash.
Chin et al. (2004), developed estimates of the probability-of-lane closure (PLC) from 1998 FARS data. For
crashes involving single vehicles, the probability that a fatal crash would not cause a lane closure wasderived assuming that any crash that was located on the roadway facility would cause lane closure, but
those that were located outside the facility right of way or classified as off-road crashes would not. The
probability of a fatal crash not closing a lane was computed as the ratio of these off roadway crashes tothe total number of crashes for which the location was known. This resulted in an estimated probability
that lanes would not be closed of 10.8 percent, leaving a probability of closure of 89.2 percent. Chin70

adopted this same rate for non-fatal injuries as well. 16 Chin assumed that fatal or injury crashes involving
two or more vehicles would always involve lane closure. For PDO crashes, Chin used data derived from
Giuliano (1989) in her study of incident data on LA freeways. This data indicated a 60 percent
probability-of-lane closure in non-injury crashes. Chin overrode these probabilities if more than threecars or more than one truck was involved in the crash. Under these circumstances, Chin assumed that
there would be lane closures even if there was no injury.
For this study, we use the actual crash records from the same Pennsylvania database that produced
estimates of crash duration. Based on this data, the proportion of police-reported crashes that involved
lane closings is summarized in Table 3-11. The fatal crash estimates show close agreement with Chin’s
89 percent closure rate estimate based on roadway and crash involvement characteristics. However,they indicate a lower closure rate for injury crashes, which differs from Chin’s assumption that the rates
would be similar for both fatal and injury crashes. Likewise, the rates of lane closure for PDOs are lower
than the 60 percent rate that Giuliano derived in 1989 for LA freeways. This difference likely reflects thebroader scope of the Pennsylvania, all-roadway data used in the current analysis.
Table 3-11. Portion of Police-Reported Crashes Involving Lane Closure, PA Crashes 2006-2010
Roadway Type
Urban Interstate/ Expressway
Urban Arterial
Urban Other
Rural Interstate/Principal Arterial
Rural Other

Fatal
92%
86%
92%
90%
90%

Injury
56%
52%
60%
55%
57%

PDO
36%
42%
47%
29%
39%

Reduced Capacity
Given that some level of closure occurs, how does this affect roadway capacity? Logically, this is afunction of both the number of blocked lanes and the number of lanes available. This issue wasoriginally examined by Blumentritt, Ross, Glazer, Pinnell, and McCasland (1981). Blumentritt andcolleagues obtained data from 10 agencies that operate freeway ramp metering installations toestimate the capacity reduction that results from lane closures as a function of the number of lanes on
the roadway and the number of lanes closed. Blumentritt and his group limited their study to up to twolane closures. Chin et al. (2004) expanded his estimates to include up to four lanes. Table 3-12 below
summarizes the values derived by Chin.

16

Similar data on crash location was not available in nonfatal injury databases (GES and CDS).

71

Table 3-12. Reduced Capacity Due to Freeway Crashes (Normal Capacity = 1.0)
Number of Freeway Lanes (One Direction)
1
2
3
4
5+
Effect of Crash
Vehicle on Shoulder
0.45
0.75
0.84
0.89
0.93
1 lane blocked
0.00
0.32
0.53
0.56
0.75
2 lanes blocked
NA
0.00
0.22
0.34
0.50
3 lanes blocked
4 lanes blocked

NA
NA

NA
0.00
0.15
NA
NA
0.00
Source: Chin et al., 2004, Table 3-7

0.20
0.10

This analysis adopts Chin’s estimates of reduced capacity. To use this data, frequency weights must bederived that reflect the lane profile of motor vehicle crashes. For this purpose 2008-2009 FARS data wasused. FARS is the only nationwide database containing information on the number of lanes in the
roadway by roadway type. However, FARS codes lane data differently for divided and undivided
highways. On undivided highways, the FARS lane count represents lanes going in both directions. On
divided highways, FARS lane counts represent only the lanes going in the direction of travel that thecrash occurred in. Table 3-12 uses unidirectional stratification, i.e., it expresses capacity reduction basedon lanes going in one direction only. FARS data for undivided highways must thus be adjusted tonormalize them to a single direction basis. This was done by dividing the lane count for undivided
highways by two and re-assigning them accordingly. While there are many undivided roadway segments
where there are odd numbers of lanes, such as two lanes in one direction and one in the other, data is
not available to determine which side the crash occurred in on these odd number of lanes segments.
While not precise, halving the two-way lane count assumes that over the universe of crashes on suchroads, about half would occur in either direction. The practical application of this would result, forexample, in assigning all fatalities coded in FARS as occurring in two-lane, undivided expressways as
occurring in roadways where there was a single lane in each direction. In addition, half the fatalities thatwere coded as occurring in three-lane undivided expressways would be assigned to roadways where
there was a single lane in each direction.
Chin also derived probability distributions of the number of lanes closed, given that there was somelevel of lane closure. These distributions are shown in Table 3-13.

72

Table 3-13. Probability Distribution of the Number of Lanes Closed Given Lane Closure, by Vehicle
Involvement
Number of Vehicles
Involved
I Vehicle
2 Vehicles
3 Vehicles

More Than 3 Vehicles

Type of Vehicles Involved
Any Type
2 Cars, or 1 car and 1 truck
2 trucks
3 cars, or 2 cars and 1 truck
1 car and 2 trucks or 3
trucksAny type

1
0.997
0.950
0.001
0.500

Lanes Closed
2
0.001
0.048
0.997
0.450

3
0.001
0.001
0.001
0.049

4+
0.001
0.001
0.001
0.001

0.001
0.001

0.600
0.099

0.300
0.800

0.099
0.100

In order to determine incidence of lane closure, FARS data was examined consistent with the vehicles
involved categories used in Table 3-13. Data was collected separately for divided roadways, two
direction undivided roadways, and single direction undivided roadways. Counts for the number ofcrashes that occurred under each lane category in one direction were determined by adding the totals
for divided roadways, single direction undivided roadways, and half of the crashes for two directional
undivided roadways. This produced total counts of crashes by lane counts for each vehicle involvement
category, which were in turn used to determine the relative frequency of crashes for each cell. An
example of these results for Urban Interstates/Expressways 17 is shown in Table 3-14.
Table 3-14. Crash Distribution Within Lane Categories, Urban Interstate/Expressway
Number of Vehicles
Involved

Lanes in Direction of Crash
2
3
4+

Type of Vehicles Involved

1

I Vehicle
2 Vehicles

Any Type
2 Cars, or 1 car and 1 truck
2 trucks

0.700
0.253
0.002

0.594
0.308
0.007

0.562
0.305
0.006

0.489
0.345
0.005

3 Vehicles

3 cars, or 2 cars and 1 truck
1 car and 2 trucks or 3 trucks
Any type

0.034
0.001
0.009

0.057
0.005
0.029

0.071
0.006
0.051

0.095
0.007
0.058

1.000

1.000

1.000

1.000

More than 3 Vehicles
All

The probability distributions from Table 3-13 were combined with the crash involvement frequencies
derived in Table 3-14 to determine the distribution of lane blockage for roadways of various lane counts.
The results are shown in Table 3-15.

17

Separate calculations were required for each roadway type to link with AAHT from Table 3-3.

73

Table 3-15. Distribution of Lane Blockage, Given at Least One Lane Blocked,
Urban Interstate/Expressway
Effect of Crash
1 lane blocked
2 lanes blocked
3 lanes blocked
4 lanes blocked

1
1.00
NA
NA
NA

All

1.00

Number of Freeway Lanes (One Direction)
2
3
4
5+
0.91
0.89
0.86
0.86
0.09
0.06
0.08
0.08
NA
0.05
0.05
0.05
NA
NA
0.01
0.01
1.00

1.00

1.00

1.00

So, for example, based on the relative frequency of crashes by roadway width (lane count) on a roadway
with two lanes in the direction of travel of the crashed vehicle, 91 percent of the time only one lane isblocked, but 9 percent of the time two lanes are blocked.
The results from Table 3-15 were then combined with the data from Table 3-12 and the lane frequency
counts that were behind the proportions in Table 3-14 to determine the weighted average capacity
reduction percentage for each roadway category. As previously mentioned, lane information by
roadway type is not available in GES or CDS data bases. Therefore, the blockage estimates derived fromFARS will be used for both nonfatal injury and PDO crashes as well. The results are shown in Table 3-16.
Table 3-16 summarizes the inputs and results of this analysis of the congestion impacts of lane closure.
The results indicate relative impacts that reflect differences in both traffic density and crash severity.
Fatal crashes have the greatest impact on congestion followed by nonfatal injury crashes and PDO
crashes. This reflects the added duration of the crash events as well as the added rate of lane closurethat results from emergency and police response to the more serious crashes involving death or injury.
Within each severity category, urban interstates/expressway experience the highest per-crashcongestion impact, followed by urban arterials and rural interstates/principal arterials. These impacts
reflect the much higher traffic density found in urban roadways, and to a lesser extent on ruralinterstates.

74

Table 3-16.
Derivation of Average Vehicle Capacity Reduction,18 Crashes With at Least One Lane Closed
Rural
Urban
Interstate/
Interstates/ Urban
Urban Principal
Rural
Expressways Arterials Other Arterials
Other
Fatal Crashes
AADT

113,814

23,996

2,908

25,5791,502

AAHT
AAHT - one way
Crash duration (hours)% w/at least 1 lane closed% Blockage
Avg. vehicle capacity reduction
Injury Crashes

4,934
2,467
2.79
91.7%
62.0%
3,913

1,040
520
2.61
86.1%
75.8%
887

126
63
2.63
91.9%
89.1%
136

1,114
557
2.86
90.3%
78.9%
1,135

65
33
2.49
89.8%
97.2%
71

AADT
AAHT
AAHT - one way
Crash duration (hours)% w/at least 1 lane closed% Blockage
Avg. vehicle capacity reduction
PDO Crashes

113,814
6,218
3,109
0.77
56.2%
62.0%
832

23,996
1,311
655
0.70
51.6%
75.8%
180

2,908
159
79
0.81
60.3%
89.1%
34

25,579
1,392
696
1.13
54.9%
78.9%
340

1,502
82
41
1.02
57.4%
97.2%
23

AADT
AAHT
AAHT - one way
Crash duration (hours)% w/at least 1 lane closed% Blockage
Avg. vehicle capacity reduction

113,814
6,300
3,150
0.68
36.5%
62.0%
485

23,996
1,328
664
0.62
42.3%
75.8%
132

2,908
161
80
0.71
47.3%
89.1%
24

25,579
1,407
703
1.04
29.4%
78.9%
169

1,502
83
41
0.93
39.4%
97.2%
15

Rubbernecking:Rubbernecking occurs when drivers slow down as they pass an unexpected highway incident such as anaccident or broken down vehicle. The term is most commonly used to describe unnecessary slowing on
the part of drivers, but as used here it encompasses any slowing that affects capacity when a lane isn’t
blocked, regardless of specific motivation. This slowing can occur because the drivers of passing vehicles
18

Note: Average Vehicle Capacity Reduction refers to the reduction in the number of vehicles that would havepassed the crash site during the duration of the crash, had the crash not occurred.

75

are curious, or because the presence of the crashed vehicle makes them cautious, or because they must
respond to braking of other vehicles ahead of them. Under moderate or heavy congestion levels, once
an initial vehicle brakes, it can cause a corresponding wave of reactive braking to ripple back throughtraffic, slowing all subsequent drivers until they pass the crash site. Within the context of crashes,rubbernecking is caused by the presence of crashed vehicles or police or emergency equipment at thescene that is not directly blocking the travel lane. Rubbernecking can occur in the direction of travel forthe crashed vehicle when vehicles are removed to the side of the road, or in open lanes when vehicles
are blocking only a portion of the roadway. Rubbernecking can also affect traffic going in the opposite
direction from the crash affected lanes.
The aggregate reduced capacity estimates used in the previous analysis of lane closures essentially
include the impact of both rubbernecking and slowed traffic in cases where lanes are closed.
Rubbernecking within same direction lanes for crashes with lane closure thus does not have to be
separately estimated. This section will therefore focus on rubbernecking in same direction lanes forcases where there is no lane closure, as well as rubbernecking impacts in opposite direction lanes. Theseestimates will be calculated as follows.
VCL = (AAHT*CD*(1-PLC)*RCR) + (AAHT*CD*RO*RCO)
where:
VCL = Vehicle capacity lost due to rubbernecking when no lane closure and in opposite direction of
travelAAHT = Average annual hourly travel (vehicles) past the crash site during the time affected by the crash
CD = crash duration
PLC = Probability-of-lane closure
RCR = Reduced capacity when no lanes blocked
RO = Rate of rubbernecking in opposite lane
RCO = Reduced capacity due to rubbernecking in opposite lane

Frequency of Crashes With No Lane Closure (1-PLC)
AAHT, CD, and PLC were derived in the previous discussion of congestion due to lane closures. The
frequency of crashes with no lane closures is derived as 1-PLC. These represent crashes that occur far
enough outside the travelled roadway, or are of a minor enough severity, that neither the involved
vehicles nor police or emergency response vehicles and operations will result in lane blockage or
closure.

76

Reduced Capacity When No Lanes Blocked (RCR)
RCR is derived from the “Vehicle on Shoulder” row in Table 3-12. This represents the reduced capacitythat results when there is no lane closure. Essentially, this represents the impact of rubbernecking as
vehicles slow down to pass the crashed vehicles on the side of the road. For each roadway width(number of lanes), RCR is calculated as the product of the reduced capacity when there are vehicles on
the shoulder and the relative frequency of crashes on roadways with the specified width for the lane(originally developed as described under the previous lane closure discussion). So, for example, forurban interstate/expressways, a vehicle on the shoulder of a two-lane (in one direction) roadway will
reduce roadway capacity by 25 percent (1- the value in Table 12). 34.7 percent of all urban
interstate/expressway crashes occur under these two lane conditions. The product of these numbers
added to these products for each lane count determines the average reduced capacity across all
roadway widths that are urban interstates/expressways. The results are shown for all roadway
categories in Table 3-17.
Rate of Rubbernecking in Opposite Lane (RO)There are very little data available regarding the incidence of rubbernecking in opposite direction lanes.
This analysis is based on a study conducted by Masinick and Teng in 2004. That study was the first toevaluate the rubbernecking impact of accidents in traffic in the opposite direction based on archived
traffic and accident data. Masinick and Teng examined archived data from the year 2000 on roadway
occupancy behavior on a 10-mile section of freeway in the Hampton Roads, Virginia, area. They judged
each crash based on particular criteria to determine whether it would be considered to have asignificant impact on roadway capacity during the crash. They defined a significant impact as one inwhich they observed a sharp increase in segment occupancy soon after the crash occurred, with thisincrease held fairly constant for the duration of the incident, after which occupancy rates returned tonormal. On this basis, they determined that there were 201 crashes that had a significant impact on
traffic flow in the direction of the accident, 102 that had a significant impact in the opposite direction,and 84 that had this impact in both directions. The discrepancy between the 84 and 102 opposite
direction crashes was a function of greater volume in the opposite direction (e.g., a rush hour crash that
occurred in the opposite direction of rush hour traffic), or possibly of roadway design that produced
more significant impacts in one direction. The ratio of opposite direction rubbernecking was thus either
51 percent or 42 percent of the rate in the crash direction, depending on whether these factors areaccounted for. This study uses the .51 ratio that includes all cases, since both factors are legitimate
causes of rubbernecking attributable to the crash.
A more recent study of rubbernecking by Knoop, Hoogendoorn, and van Zuylen (2008) found reverse
direction rubbernecking in one of two crashes that they studied in detail (a 50% ratio), but this is far too
small a sample to draw conclusions from. Note that Masinick and Teng (2004) found only 24 percent of
the crashes they observed involved a significant impact on traffic flow in the direction of the crash. This
contrasts with the data in Chin et al. (2004), which found significantly higher rates just for lane closure.
Data from NHTSA’s FARS and GES databases indicate that roughly 75 percent of all crashes occur in the
roadway, where they are almost certain to cause traffic disruption due to lane blockage. Another 20

77percent occur in areas immediately adjacent to the roadway such as shoulders, medians, and roadsides,
where lane closure is possible in cases where police or emergency equipment respond at the scene, andwhere rubbernecking is likely even if lanes remain open. These are also likely to cause some level ofcongestion. Finally, data taken directly from the from PA accident database indicate higher rates of laneclosure for police-reported crashes than were found for rubbernecking in the Hampton Roads study.
Under these circumstances, it seems likely that the lower rate found by Masinick and Teng (2004) may
be a function of the specific traffic flow characteristics of the Hampton Roads freeway system theyobserved, or possibly their definition of a serious traffic impact. However, we should note that themethod used by Chin et al. to decide which crash locations were likely to involve lane closure also
involved a degree of subjective judgment.
Reduced Capacity due to Rubbernecking in the Opposite Lane (RCO)
As with the rate of rubbernecking in opposite lanes, there are only sparse data regarding the capacity
reduction that is caused by such actions. The study cited above by Masinick and Teng (2004) also
estimated average capacity reduction in their Hampton Roads study area. They found an averagecapacity reduction of 12.7 percent for all crashes. More recently, Knoop, Hoogendoorn, and van Zuylen(2008) conducted an aerial study (using a helicopter) of the impact of traffic crashes on opposite
direction traffic flows. The authors found a 50-percent reduction in traffic flows in the opposite directionof one of the crashes. The differences between these two studies’ results are difficult to interpret, but
may be related to the specific locations. Hampton Roads, Virginia, is not a particularly congested areaand may not be typical for urban roadways in most metropolitan areas. Likewise, although the Knoop etal. study appears to be quite accurate due to complete video documentation of the crash impacts, it
represents only one crash on a divided rural interstate in Holland (a second crash was also observed but
it occurred on a roadway where there was insufficient traffic to cause opposite direction
rubbernecking.)
An alternative basis used in this study is to use the percent reduction that was derived for same
direction rubbernecking (RCR). This produced estimates of reduced capacity that averaged 20 to24
percent, depending on roadway type. RCR was derived from data reflecting capacity reductions incrashes where lanes are not blocked, and thus represents a rubbernecking impact, albeit not specificallyin opposite direction lanes. The rationale for this choice is that RCR is ultimately based on a sample ofcrash impacts (from Blumentritt et al., modified by Chin et al.) that represent a more diverse group of 28
roadways spread across 10 different States. This is a broader sample than the two narrower studies
cited above. We also note that the RCR estimates are between the two extremes found in the twostudies that specifically addressed opposite direction rubbernecking. Moreover, Knoop Hoogendoorn,
and van Zuylen found similar impacts due to rubbernecking in both directions.
Table 3-17 summarizes the inputs and estimated reduced capacity from rubbernecking in crashes wheresame direction lanes were not closed and in opposite direction lanes for all crashes. As with laneclosures, the results indicate relative impacts that reflect differences in both traffic density and crashseverity. Fatal crashes have the greatest impact on congestion, followed by nonfatal injury crashes and
PDO crashes. This reflects the added duration of the crash events to the more serious crashes involving
78

death or injury. Within each severity category, urban interstates/expressways experience the highest
per-crash congestion impact, followed by urban arterials and rural interstates/principal arterials. These
impacts reflect the much higher traffic density found in urban roadways, and to a lesser extent on ruralinterstates.
Table 3-17. Congestion Impacts of Rubbernecking in Open Lane Crashes and Opposite Directions(per Crash)
Rural
Urban
Interstate/
Interstates/ Urban
Urban Principal
Rural
Expressways Arterials Other Arterials
Other
Fatal Crashes
AAHT - 1 direction
Crash duration (hours)% Same direction rubbernecking
Capacity reduction %S.D. Vehicle capacity reduction% Opposite direction
rubbernecking

2,467
2.79
0.083
0.23
108
50.7%
787

520
63
2.61
2.63
0.139 0.081
0.33
0.45
40
2
50.7% 50.7%
217
36

557
33
2.86
2.49
0.097 0.102
0.35
0.52
41
3
50.7% 50.7%
279
21

3,109
0.77
0.438
0.23
207
50.7%
261

655
79
0.70
0.81
0.484 0.397
0.33
0.45
61
9
50.7% 50.7%
71
13

696
41
1.13
1.02
0.451 0.426
0.35
0.52
107
8
50.7% 50.7%
132
10

3,150
0.68
0.635
0.23
280
50.7%
233

664
80
0.62
0.71
0.577 0.527
0.33
0.45
69
11
50.7% 50.7%
64
12

703
41
1.04
0.93
0.706 0.606
0.35
0.52
158
11
50.7% 50.7%
119
9

Injury Crashes
AAHT - 1 direction
Crash duration (hours)% Same direction rubbernecking
Capacity reduction %S.D. vehicle capacity reduction% Opposite direction
rubbernecking
PDO Crashes
AAHT - 1 direction
Crash duration (hours)% Same direction rubbernecking
Capacity reduction %S.D. Vehicle capacity reduction% Opposite direction
rubbernecking

The vehicle capacity reduction estimates in Tables 3-16 and 3-17 represent the number of vehicles thatwere prevented from passing the crash site due to congestion during the duration of the crash. However,
the impacts of congestion are linked not to vehicles but to vehicle hours of delay. The value of travel
delay to vehicle occupants as well as the impact on fuel consumption, greenhouse gases and criteria
pollutants are all a function of the aggregate time that vehicles are delayed. To estimate delay
79hours we assume a constant rate of arrival by vehicles throughout the duration of the crash. Under thisassumption, average delay hours are calculated as:�𝑣

𝐷𝐷−� �∗𝑛𝑣
𝑛=1

Where D = crash duration and v = vehicle capacity reduction during D.
This produces an estimate that assumes vehicle arrivals beginning at D/v, or the full interval between
vehicle arrivals after the crash occurs. An alternate assumption could be made reflecting vehicle arrival
beginning at the time of the crash by modifying the equation to multiply by n-1 instead of n. However, it
is likely that the arrival of vehicles will be spread randomly across this interval. To reflect this, vehicle
hours are estimated as a simple average of the product of the number of vehicles affected and crash
duration or Dv/2. This produces an estimate midway between the two more extreme assumptions.
Table 3-18 summarizes the estimates of vehicle delay hours for congestion during crash duration,
including lane blockage and rubbernecking, but excluding detours. Note that these calculations are
based on the portion of vehicles delayed that did not detour to avoid congestion. Discussion of themethods used to calculate this portion is included in the next section.
Table 3-18. Vehicle Delay Hours from Congestion During Crash Duration
Rural
Urban
Interstate/
Interstates/ Urban
Urban Principal
Rural
Expressways Arterials Other Arterials
Other
Fatal Crashes
Total vehicle capacity reduction
Vehicles that didn't detour
Crash duration (hours)
Vehicle delay hours, non-detours

4,386
3,223
2.79
4,497

986
926
2.61
1,210

161
157
2.63
206

1,308
1,188
2.86
1,700

85
84
2.49
104

Injury Crashes
Total vehicle capacity reduction
Vehicles that didn't detour
Crash duration (hours)
Vehicle delay hours, non-detours

819
793

193
190

38
38

366
357

27
27

0.77
305

0.70
67

0.81
15

1.13
201

1.02
14

PDO Crashes
Total vehicle capacity reduction
Vehicles that didn't detour
Crash duration (hours)
Vehicle delay hours, non-detours

587
573
0.68
195

160
157
0.62
49

29
29
0.71
10

280
275
1.04
143

22
22
0.93
10

80

Post-Crash Duration Queue Dispersal:
The previous sections analyze the impacts of congestion caused by both lane blockage andrubbernecking during the period after the crash until the crashed vehicles have been removed and theemergency response vehicles have departed. Once the roadway is cleared of damaged vehicles, crashdebris, and emergency vehicles, a residual queue of vehicles may still be present, which will then rapidly
disperse past the accident scene. There are no studies which focus on this particular aspect ofcongestion activity. Therefore, an estimate of vehicle dispersal time will be made based on the results ofthe previous analysis of impacts during the accident duration phase.
Dispersal time is a function of both time spent waiting in queue before proceeding and the added timespent accelerating to cruising speed. Time spent waiting in queue is a relatively straightforward
calculation, but acceleration time requires consideration of acceleration, velocity, and the distancetravelled while accelerating. The interaction between these factors is described in the following
relationship.19
1𝑠 − 𝑠0 = 𝑣0 𝑡 + 𝑎𝑡 22

where:

s = distancev = velocityt = time (duration)a = acceleration
Based on this relationship, the formula to estimate initial queue dispersal time is as follows.
𝑘𝑑 (𝑛 − 1)𝑑�(𝑆 − 1)𝑖 + �2(𝑆 − 1) −
≤𝑆
𝑐
𝑎

𝑛=1

𝑛

� (𝑆 − 1)𝑖 +𝑛=𝑘+1

where:(𝑆 − 1)𝑑 −𝑐

𝑎𝑆 2
2 + 𝑆 − (𝑆 − 1)𝑑 > 𝑆𝑐k = number of vehicles in queue at end of crash durationi = interval (seconds) between initiated acceleration of each subsequent vehicle in queued = average total distance (feet) between the front bumper of each subsequent vehicle in queuea = average vehicle acceleration rate (ft/sec sq)
19This basic relationship is commonly found in engineering texts. See for example, Brach and Brach, 2005.

81

c = average cruising speed (ft/sec) over distance travelled
t = average acceleration time to cruising speed (s/a) (seconds)
The sum of the results from the above formulas was divided by the average number of lanes on the
roadway in the direction of the congestion impact to determine total vehicle hours of delay for eachroadway crash scenario.
This formula defines queue dispersal time as the total time required to dissipate all traffic existing in thequeue at the end of the crash duration period, plus the time required to dissipate any additional trafficthat accumulates while the initial queue is dispersing. Dispersal time for each vehicle is defined as thetime spent waiting for vehicles ahead in the queue to move so that it can proceed, plus the difference
between the time it takes for the vehicle to accelerate up to the average cruising speed it would havebeen traveling if the crash had not occurred, and the time it would have taken to travel the distancecovered while accelerating to cruising speed, at that cruising speed. For example, a vehicle that is 20
cars back in the queue may have to wait 40 seconds before the traffic in front of it has cleared enoughfor it to begin to accelerate through the now-open roadway. In addition, the vehicle loses time because
it must accelerate back up to normal cruising speed over a distance of, for example, 350 feet, whereas
had the crash not occurred, it would have cruised through this same distance in a shorter time period at
cruising speed.
The first formula measures the difference in acceleration versus cruising time only up to the point where
the crash occurred. Once vehicles cross this point they may still experience minor delay if they have not
yet achieved cruising speed, but they would no longer influence the accumulation of secondary queues.
This formula is thus applied to each vehicle in the queue that has not yet reached cruising speed at thetime it crosses the crash site when determining the delay under which secondary queues can occur.
Once vehicles in queue reach cruising speed prior to crossing the accident site, the second more generalformula that measures time lost over the full distance required to achieve cruising speed is applied. This
formula is also applied when measuring the full time loss for all vehicles in queue.
In the formula, k is assumed to equal the number of vehicles that were unable to pass through the crashsite during the crash interval, which was calculated in the previous section, adjusted to reflect vehicles
that would have diverted to secondary routes to avoid the delays caused by the crash. A number ofstudies have examined the tendency of drivers to divert to alternate routes when they are faced withtraffic congestion. Most such studies are based on reactions to changeable roadway messaging systems(CMS). Harder, Bloomfield, and Chihak (2004) performed a study with 120 drivers using a driving
simulator in which 56 percent of participants diverted to an alternate route when a CMS indicated that acrash had occurred in the roadway ahead and that drivers should use an upcoming exit. Anothersimulator study by Srinivasan and Krishnamurthy (2003) found that that CMSs can reduce congestion
levels by 40 percent. Chatterjee and McDonald (2004) examined studies of the effectiveness of CMSs in
six European countries found that an average of 8 percent of drivers diverted from their intended routes
based on the information they received from CMS. Horowitz, Weisser, and Notbohm (2003) evaluated82

diversion from a rural work zone and found diversions of 7 percent to 10 percent, depending on the dayof the week.
For this study, diversion was based on a 1996 study by Yim and Ygnace (1996) of drivers’ responses toincreasing congestion on Paris roadways. Yim and Ygnace derived probabilities that drivers would divert
to an alternate roadway based on volume to capacity ratios. Although capacity information is notavailable for the roadways included in this study, Yim and Ygnace did include diversion rate results for avariety of queue length backups. These results were used to create a regression model whichapproximates the relationship found in their study. 20 This data, together with the regression model, areshown in Figure 3-F. The model predicts roughly 20 percent of vehicles would divert when queues reach2 miles in length.
Figure 3-F. Probability Of Diversion

20

Although there were only 5 data points, we chose a curvilinear model because there is a natural expectation that thedecision to divert from a traffic queue would increase in this manner. The longer the queue, the more likely it is thata detour would provide a more efficient route. We also note that although there are a limited number of data points,
they are visually consistent with this type of model. Yim and Ygnace only measured diversion on backups of up to
2.5 miles. The curvilinear model only affects queue lengths greater than 2.5 miles under the fatal crash on urban
interState expressway scenario. Under these dense traffic circumstances, queues well in excess of 2.5 miles canaccumulate. In an urban environment, however, there are typically many opportunities to detour to other routes, so
we would expect a relatively high rate of congestion avoidance. It is possible that a curvilinear model could overstate
diversion under more extreme conditions. We note that if this model does overestimate the rate ofdiversion to alternate routes for these extreme conditions, its net impact is to provide a conservative estimate ofoverall congestion impacts, since those alternate routes would be more efficient than the alternative of waiting forthe main roadway to be cleared.

83

The interval “i” is an assumed value of 1.75 seconds, based on personal observations of vehicles
progressing through a busy intersection during rush hour in Northern Virginia. Commencing with eachgreen light at this intersection, the vehicles that advanced through lengthy queue dispersions werecounted and timed to produce an estimate of about 1.75 seconds delay between each vehicle.21 The
distance between vehicles’ front bumpers, d, is an assumed value of 24 feet, based on an examination ofvehicle lengths in Ward’s Automotive Reports Yearbook indicating roughly 200 inches across all lightvehicle types, or 16.7 feet, plus an allowance for spacing. The acceleration rate, a, is assumed to be 10ft/ sec2, or roughly 0.3g. 22 Distance travelled in ft/sec at cruising speed is a function of the assumed
roadway cruising speeds, which vary by roadway as follows.
Urban Interstates/Expressways – 55 mph (81 ft/sec)
Urban Arterials – 45 mph (66 ft/sec)
Urban Other – 35 mph (51 ft/sec)
Rural Interstates/Arterials – 55 mph (81 ft/sec)
Rural Other – 35 mph (51 ft/sec)
Secondary queues are estimated by measuring the number of vehicles that would join the initial queue
before it disperses, which is a function of the arrival rate (AAHT) of additional vehicles during the time ittakes for the queue to disperse. These vehicles then disperse while additional queues arrive. The
process is repeated until the number of additional vehicles arriving before the previous queue disperses
queue drops below 1, at which time the congestion impacts of the crash will have been completed. Notethat AAHT for queue dispersal is different from AAHT for the initial crash since it occurs after the crash
duration has ended. Queue dispersal AAHTs were calculated by repeating the process previously
described for calculating AAHT, but with offsets of 1 hour for injury and PDO crashes, and 3 hours forfatal crashes. These offsets are based on the closest full hour increment to the crash durations for eachseverity category. This resulted in a small shift (1-2% decrease on weekdays and 1% decrease on
weekends) in average AAHT levels one hour after the time of the crash, with more significant but still
modest increases (2-5% on weekdays and 1-4% on weekends) 3 hours after the crash, reflecting the
dynamics of daily driving cycles.

21

20 light cycles were observed. This average reflects a variety of observed response behaviors. Most drivers
began their acceleration shortly after the vehicle in front of them began to move, but some drivers began toaccelerate at virtually the same time as the frontward vehicle, anticipating the forward driver’s response andtaking advantage of the gap between vehicles as a safety margin for their own simultaneous movement. Offsettingthese more efficient behaviors were relatively large time delays caused by drivers who were distracted by cell
phones, texting, or other less attentive activities.
22
Estimate based on engineering judgment. Under this assumption a vehicle would move about 5 feet in the firstsecond and an additional 15 feet in the next second or roughly 20 feet after 2 seconds.

84

Table 3-19 summarizes the estimated vehicle hours calculated for queue dispersal for fatal crashes. Note
that, unlike the previous aspects of delay, queue dispersion was directly calculated in terms of vehicle
hours of travel delay rather than vehicles delayed.
Table 3-19. Queue Dispersal Travel Delay Summary, Impact per Fatal Crash by Functional
Roadway Type
Urban
Interstates/
Expressways

Urban
Arterials

Urban
Other

Rural
Interstat/
Principal
Arterials

2,169
55
2.85

457
45
1.99

55
35
1.45

490
55
1.89

29
35
1.11

Initial Vehicles in Queue
E.T. - Initial Queue Dispersal (min)Vehicle Hours, Initial Queue
Vehicle Hours, Secondary Queues
Total Vehicle Hours
Results - opposite direction of
crash

2,475
25.30
522.2973.14
595.43

714
10.49
43.55
0.81
44.36

122
2.45
1.28
0.02
1.30

917
14.17
71.86
1.76
73.62

63
1.66
0.35
0.02
0.37

Initial Vehicles in Queue
E.T. - Initial Queue Dispersal (min)Vehicle Hours, Initial Queue
Vehicle Hours, Secondary Queues
Total Vehicle Hours

748
7.65
47.85
7.31
55.16

212
3.12
3.87
0.09
3.96

35
0.71
0.11
0.02
0.12

271
4.20
6.33
0.17
6.50

21
0.56
0.04
0.02
0.06

Results - Total
Initial Vehicles in Queue
E.T. - Initial Queue Dispersal (min)Vehicle Hours, Initial Queue
Vehicle Hours, Secondary Queues
Total Vehicle Hours, Queue Disp.

3,223
25.30
570.1480.45
650.59

926
10.49
47.42
0.90
48.32

157
2.45
1.39
0.03
1.42

1,188
14.17
78.18
1.93
80.11

84
1.66
0.39
0.04
0.43

InputsAAHT (one way)
Average Speed
Average # Lanes (one way)
Results - direction of crash

85

Rural
Other

Table 3-20 summarizes total vehicle hours of delay per crash during both crash duration and queue
dispersal by crash severity and roadway functional category.
Table 3-20. Summary of Vehicle Delay Hours During Crash Duration and Queue Dispersal

Fatal Crashes
Crash Duration (Hours)
Vehicle Hours, Crash Duration
Vehicle Hours, Queue Dispersal
Total Vehicle Hours/Crash
Injury Crashes
Crash Duration (Hours)
Vehicle Hours, Crash Duration
Vehicle Hours, Queue Dispersal
Total Vehicle Hours/CrashPDO Crashes
Crash Duration (Hours)
Vehicle Hours, Crash Duration
Vehicle Hours, Queue Dispersal
Total Vehicle Hours/Crash

Urban
Other

Rural
Interstate/
Principal
Arterials

Rural
Other

2.61
1,209.94
48.32
1,258.26

2.63
206.461.42
207.882.86
1,700.19
80.11
1,780.31

2.49
104.400.43
104.820.77
304.7640.53
345.29

0.70
66.79
1.77
68.56

0.81
15.30
0.10
15.40

1.13
201.436.25
207.68

1.02
13.79
0.08
13.86

0.68
195.1319.86
215.00

0.62
48.75
1.19
49.94

0.71
10.24
0.07
10.32

1.04
142.683.57
146.25

0.93
10.26
0.07
10.33

Urban
Interstates/
Expressways

Urban
Arterials

2.79
4,497.11
650.595,147.70

Detours and Non-linear Congestion Impacts
The methods described in the above sections account for most congestion impacts that result fromtraffic crashes. However, they do not account for the added impacts of vehicles that detour to alternate
routes to avoid waiting in traffic congestion. In addition, they are based on average traffic densities foreach roadway type. Delay impacts can occur disproportionately due to excess roadway capacity under
low density traffic conditions or inadequate capacity under highly congested conditions. Moreover,average roadway capacity data is not collected under the HPMS, and thus could not be used inassociation with AADT statistics to predict average delay impacts. The current model thus assumes thatchanges in roadway capacity produce proportional changes in vehicle travel. This assumption is likely to
produce an overestimate of travel delay in cases where roadway capacity is sufficient to handle existing
traffic density even under diminished capacity. Thus, an estimate based solely on an average traffic
density may not adequately capture the full impacts of congestion under all density conditions. This
type of dynamic can only be measured through direct observation or estimated through traffic
simulation measurements such as TSIS-CORSIM.
An advantage of using simulations, as was done with the recent FMCSA study, is that it can capture
these conditions. FMCSA did attempt to measure the impacts of detouring, albeit with limited
86

assumptions regarding the scope of potential detour routes. In addition, the FMCSA study examineda variety of traffic density scenarios under each roadway type, which enabled the capture ofdisproportionate impacts of congestion under high traffic density conditions. Moreover, the TSISCORSIM model examines a variety of scenarios that recognize the interaction of reduced roadway
capacity with traffic density. However, the FMCSA study specifically examined commercial vehicle
crashes, which primarily involve heavy trucks. Commercial truck crashes differ from other crashes in a
number of ways. These include:1) Truck crashes are more likely to result in lane closings. This is a function of the size and functionof commercial vehicles. An overturned or jackknifed truck trailer can block many more lanes
than a normal passenger vehicle. Moreover, if the trucks cargo spills, this can also spread over alarger area, and close more lanes.
2) Truck crashes close off roadways for a longer duration than non-truck crashes. This is a functionof both the added lane closings, the need to clean up spilled cargo (and occasionally hazardous
waste), and the added difficulty of clearing a larger vehicle and often its trailer from the
roadway.
3) The diurnal profile of truck crashes is different from non-truck crashes. Commercial deliveries areless likely to occur during evening and weekend hours. The incidence of truck crashes is thus
relatively underrepresented in late night and weekend hours compared to passenger vehicles.
Because driving patterns and crash incidence have different frequency profiles during thesetimes, the traffic density during a truck crash will differ, on average, from that in passenger
vehicle crashes. This is most noticeable for fatal crashes, where late night weekend crashes
involving passenger vehicles make up a much larger portion of fatal crashes involving passenger
vehicles than heavy trucks. Since roadway densities are relatively light during these hours, theaverage density for a fatal truck crash is noticeably higher than that of a passenger vehicle crash.
Because of these factors, the results obtained in the FMCSA report are not representative of the broader
universe of crashes that are addressed in this current study. 23 However, as noted above, the FMCSA
report has the clear advantage of being able to address detour behavior, the interaction of traffic
density with roadway capacity, and the impact of non–linear congestion effects. To adjust for theseeffects, results derived from the methods described previously for this report were re-computed using
data that is specific to commercial truck crashes for crash duration, probability of lane closings, anddiurnal crash profile. These results were then compared to the results already computed for all crashtypes, and the resulting factor was applied to the FMCSA results to derive an estimate of the overall
impacts of congestion on all crash types. This approach essentially assumes that the FMCSA estimate forcommercial trucks is a more complete estimate since it captures aspects not yet measured in thecurrent effort (detours, capacity, and non-linear congestion impacts). In essence, the FMCSA estimate
for commercial trucks is normalized down to reflect the impacts that would be expected for the fulluniverse of crashes, which generally have less serious consequences for congestion.

23

Based on 2010 GES data, commercial trucks are involved in about 5 percent of all police-reported motor vehicle
crashes, and make up about 3 percent of all vehicles involved in police-reported motor vehicle crashes. They thus
represent a relatively small subset of the universe of crashes addressed in this study.

87

An additional adjustment was made to reflect the fact that the FMCSA estimates were computed forroads with a specific number of lanes. This was necessary because the simulation required specific roaddesign parameters. However, although the lane profile was selected to represent a typical roadway
within each roadway category, the average number of lanes in most of these categories is somewhat
different from the specifications used for the FMCSA simulations. AADT for two categories, Urban Other
and Rural Other, was also adjusted to reflect the inclusion of several categories of roadways for which
average AADT information could not be weighted by VMT. These include urban local, rural local, andrural minor collectors. This data was provided in summary formats which exclude the possibility ofweighting by VMT. Therefore, they are not included in the FMCSA estimates. An alternate weighting
method based on segment length is used for FHWA’s published averages. We obtained estimates forboth Urban Other and Rural Other roadway types from FHWA both including and excluding these 3
minor roadways types. From these, we computed a ratio to adjust the VMT weighted AADT values toinclude these minor roadways.
The adjusted all lane AADT was thus used for the all crash calculations, while the FMCSA AADT value wasused for the commercial truck calculations. The resulting ratios thus represent both the different
characteristics of commercial versus all crashes as well as the correction for all lane and all roadway
AADT counts in the two “Other” categories.
Table 3-21 summarizes the normalization process and the final vehicle delay hours estimates for all
crashes across the three crash severity and five roadway types. As would be expected, the results
indicate lower average delay hours per crash for all crashes compared to the heavy vehicle crashes
examined in the FMCSA report. The difference is least pronounced for urban interstates, and mostpronounced for minor roadways. Note however, that a significant portion of the difference for the two“other” categories is due to inclusion of local roads in this study, which were not included in the truck
study. We note that Lan and Hu (1999) in their study of urban interstate crashes in Minneapolis-St Paul,
found crashes that did not involve heavy trucks had roughly 47 percent the delay hours that occurred inheavy-truck crashes. 24 This study finds ratios for urban interstates ranging from 34 percent to 60
percent, depending on the crash severity. The ratios for other roadway categories are smaller, in partdue to the previously mentioned inclusion of local roadways, and in part due to the disproportionate
impact that heavy-truck crashes can have on roadways with less capacity.

24

Personal communication to Ted Miller cited in Zaloshnja, Miller, and Spicer, 2000.

88

Table 3-21. Vehicle Delay Hours by Crash Severity and Roadway Type, Average for All Crashes
Urban
Interstates/ Urban
Expressways Arterials
Fatal Crashes
Total Vehicle Hours, All Crashes
Total Vehicle Hours, Truck Crashes
Ratio, All/Truck
FMCSA Truck Vehicle Hours
All Crashes Vehicle Hours

5,147.70
8,590.02
0.60
6,729.00
4,032.45

Injury Crashes
Total Vehicle Hours, All Crashes
Total Vehicle Hours, Truck Crashes
Ratio, All/Truck
FMCSA Truck Vehicle Hours
All Crashes Vehicle Hours

345.291,022.25
0.34
2,522.00
851.8568.56
145.660.47
137.0064.48

PDO Crashes
Total Vehicle Hours, All Crashes
Total Vehicle Hours, Truck Crashes
Ratio, All/Truck
FMCSA Truck Vehicle Hours
All Crashes Vehicle Hours

215.00636.060.34
2,144.00
724.71

49.94
139.400.36
109.00
39.05

Urban
Other

1,258.26
207.882,094.75 1,400.59
0.60
0.15
483.00291.00290.13
43.19

RuralInterstate/
Principal
Arterials

Rural
Other

1,780.31
3,294.58
0.54
464.00250.73104.82617.33
0.17
99.00
16.81

15.40
87.83
0.18
108.0018.94

207.68711.470.29
159.0046.41

13.86
108.99
0.13
34.00
4.32

10.32
82.49
0.13
91.00
11.38

146.25415.830.35
134.00
47.13

10.33
81.13
0.13
28.00
3.57

Environmental and Resource Impacts
Motor vehicle crashes result in significant time delays to other motorists who are inconvenienced by roadblockage due to lane closures, police, fire, or emergency services activity, and general traffic slowdowns
resulting from rubbernecking and chain reaction braking. This results in a significant time penalty for
those affected, which can be valued based on wage rates and the value people place on their free time.
However, it also results in wasted fuel, increased greenhouse gas production, and increased criteria
pollutant emissions as engines idle while drivers are caught in traffic jams and slowdowns. These impacts
are also created when drivers are forced to detour around a crash. Such detours can be amatter of blocks or miles, but in either case, more fuel is burned by other motorists as a direct result of
the initial crash.
Unlike lost time, which is a function of the number of people affected by the crash, these resource andenvironmental impacts are a function of the number of vehicles affected in the crash. The reduced
capacity impacts previously derived are presented in vehicle hours. Based on the previous analysis,89

individual crashes can cause an average of from 4 to 4,000 vehicle hours of delay depending on thecircumstances and severity of the crash.
Fuel Burned by Vehicles Delayed in Traffic
Traffic jams usually involve a combination of idle time and very slow forward progress through the
distance affected by the crash, followed by either a return to normal speeds or possibly to higher thannormal speeds while drivers attempt to make up for lost time once they are past the crash site. Much of
the time wasted in traffic jams caused by crashes is essentially spent at idle speeds. Technically, idle
conditions occur when the motor is running but the car is not moving forward, however, the vast
majority of cars in the US are equipped with automatic transmissions and inching forward in a traffic jam
often involves nothing more than releasing the brake, which allows low speed acceleration through theautomatic transmission drive trains. The portion of time drivers spend idling in traffic crashes, and the
portion spent moving forward at low speeds in stop-start conditions is not known. However, an upperbound of the impact of idling can be derived assuming that all of the vehicle hours lost in traffic crashes
is spent in an idle condition – either sitting still or inching forward without applying the gas pedal.
There is very little research available on idling fuel consumption. Researchers at Argonne National
Laboratory (Gaines, Levinson, and McConnell, 2010) measured the fuel consumption rate for a variety ofengines while at idle. Argonne researchers compiled these performance data into engine size groupings
to produce plots of fuel consumption as a function of idling time. Fuel consumption is linear with timeand increases with engine size. The resulting relationships are shown in Figure 3-G.
Figure 3-G. Idling Fuel Consumption With Respect to Idling Time90

The relationships depicted in Figure 3-G indicate that idling consumes 0.165 gallons/hour for I-liter
engines, 0.493 gallons/hour for 3-liter engines, and 0.822 gallons/hour in 5-liter engines. The average
engine size for passenger vehicles in the US has changed over time as light trucks and SUVs, which carrylarger displacement engines, became more popular. At the same time, engine size for specific vehicles
tended to decrease as more advanced and powerful small engines allowed for better fuel economy. In
estimating the impact of idling on a fleet of vehicles, the relevant metric is exposure rather than
production. Newer vehicles are driven more miles than older vehicles, and are thus more likely to becaught up in congestion resulting from traffic crashes. To estimate the exposure adjusted engine size ofthe on-road passenger vehicle fleet, average engine size for six different passenger car body types andnine different light truck types were obtained from EPA’s light duty fuel economy trends database (EPA,2010) and combined with survival probability and VMT data by vehicle age derived by NHTSA (Lu, 2006).
The results indicate an average on-road exposure adjusted displacement of 2.70 liters for passenger carsand 3.94 liters for light trucks, vans, and SUVs, with an average displacement of 3.31 liters for all
passenger vehicles. Similar data was not available for heavy trucks and buses, which are much lessprevalent than passenger vehicles. From the values in Figure 3-G, an imputed value of 0.542 gallons offuel wasted for each hour of idling was derived for the average 3.3-liter engine. 25 This added fuel has
societal cost implications through both out of pocket expenses and added health risks due to increased
criteria pollutant emissions and greenhouse gases.

Resource Cost Impacts – Greenhouse Gases and Criteria Pollutants:
As previously discussed, driver response to crashes is a complex mix of slowing down, idling,
accelerating, and often, seeking other alternate routes to detour around traffic congestion. The impact ofthese interactions on fuel consumption and emissions is difficult to quantify, even for properly observed
crash samples. In their report on heavy truck crashes, FMCSA simulated traffic responses using
the TSIS-CORSIM model developed by the University of Florida McTrans Center in 2010.26 They thenlinked these results to EPA’s Motor Vehicle Emission Simulator (MOVES) (EPA, 2009) in order to estimate
the full range of fuel consumption and emission impacts from traffic congestion resulting from the heavy
truck crashes in their model. Truck crashes typically involve more lane closures and take more time toclear than other crashes. They thus reflect both longer crash durations and a higher rate of lane closure
than would be estimated in this study. However, these factors can be largely muted by normalizing theresults to a common per-vehicle-hour basis. This can be done using data in the FMCSA report. For thisstudy, the fuel consumption and emissions impacts per vehicle hour from the FMCSA study will beapplied to the vehicle hour estimates derived for the general crash population.
The environmental impacts estimated both here and in the FMCSA report reflect emissions of bothgreenhouse gases and criteria pollutants. These emissions are described below. The descriptions arequoted directly from the FMCSA report, but were originally taken from a variety of EPA sources.

25
26This does not account for assessor and air conditioning loads, which would increase consumption.
http://mctrans.ce.ufl.edu/featured/TSIS/91

Carbon dioxide is a greenhouse gas emitted naturally through the carbon
cycle and through human activities like fossil fuel combustion. Since the Industrial
Revolution in the 1700s, human activities, such as burning oil, coal, and gas, haveincreased CO2 concentrations in the atmosphere. The release of greenhouse gases andaerosols resulting from human activities are changing the amount of radiation coming
into and leaving the atmosphere, likely contributing to changes in climate.
Carbon monoxide is a colorless, odorless gas emitted from combustion processes.
Nationally, the majority of CO emissions to ambient air come from mobile
sources. CO can cause harmful health effects by reducing oxygen delivery to thebody’s organs (like the heart and brain) and tissues. At high levels, CO cancause death.
Hydrocarbon and volatile organic compounds are a group ofchemical compounds composed of carbon and hydrogen. When in gaseous form,hydrocarbons (HC) are called volatile organic compounds (VOCs). They are generated
via incomplete gasoline combustion or are petrochemical industry by-products.
HC/VOCs include methane, gasoline and diesel vapors, benzene, formaldehyde, butadiene,
and acetaldehyde. All HC/VOCs are carcinogenic to some extent, fatal at highconcentrations, harmful to crops, and bio-accumulate within the food chain. All
HC/VOCs contribute to smog, ground level ozone, and acid rain formation.
Nitrous oxides (NOx) are a group of highly reactive gases that include nitrogendioxide (NO2), nitrous acid, and nitric acid. NO2 forms quickly from emissions fromcars, trucks, and buses. In addition to contributing to the formation of ground-levelozone, and fine particle pollution, NO2 is linked to a number of adverse effects to therespiratory system.
Particulate matter (PM) is a complex mixture of extremely small particles and liquid
droplets. Particle pollution is made up of a number of components, including
acids (such as nitrates and sulfates), organic chemicals, metals, and soil or dust particles.
The size of particles is directly linked to their potential to cause health problems; particles
that are 10 micrometers in diameter or smaller generally pass through the throat andnose and enter the lungs. Once inhaled, these particles can affect the heart and lungs
and cause serious health effects. EPA groups particulate pollution in two categories:• Particulate matter smaller than 10 micrometers (PM10), and larger than 2.5
micrometers in diameter; a size referred to as “inhalable coarse particles.”• Particulate matter smaller than 2.5 micrometers in diameter (PM2.5), also
known as “fine particle” emissions. These particles can be directly emitted
from automobiles and react in the air.
Sulfur dioxide is one of a group of highly reactive gases known as “oxides ofsulfur.” The largest source of SO2 emissions occur from fossil fuel combustion andthe pollutant is linked to a number of adverse effects on the respiratory system.
The FMCSA report examined previous research to assign a societal cost to the health and environmental
effects of these emissions. The values used in the FMCSA report are shown in Table 3-22.

92

Table 3-22. Emissions Costs Used in FMCSA Report
Cost per Short Ton
Emission(2010 Dollars)
Source
CO2
CO
NOx
PM10
PM2.5
SO2
VOC

$21
$145
$12,000$46,094$270,000$67,000$2,800Interagency Working Group on Social Cost of Carbon
McCubbin and Delucchi (1999)
Fann et al. (2009)
McCubbin and Delucchi (1999)
Pope et al. (2003)
Fann et al. (2009)
Fann et al. (2009)

Table 3-23 illustrates the net emissions/fatal crash calculated in the FMCSA report stratified by facility
type. Both Tables 3-22 and 3-23 were taken directly from the FMCSA report.
Table 3-23. Estimated Net Tailpipe Emissions/Crash by Facility Type, FMCSA Fatal Commercial Vehicle
Crashes (Short Tons)
Facility Type
Urban
Interstate/Expressway
Urban Arterial
Urban Other
Rural Interstate/Principal
Arterials
Rural Other
Average for All Facility
Types

CO2

CO

NOx

PM10

PM2.5

SO2

Total
HC

VOC

32.01443

0.22563

0.058160.00492

0.004750.00058

0.02456

0.024218.24277

0.052080.014740.000690.000650.000140.00355

0.003442.556290.01568

0.00470.000180.000170.00005

0.00090.00088

5.34325

0.035120.020440.001120.00108

0.00008

0.002420.00238

2.11074

0.01280.006780.000350.00033

0.000030.000830.0008110.20434

0.069220.021020.001450.00140.000180.00652

0.00641

Table 3-24 shows the estimated value/fatal crash of the emissions found in the FMCSA report. It
represents the product of the values in Table 3-22 and the emissions in Table 23.
Table 3-25 shows the net emissions/vehicle hour in fatal crashes found in the FMCSA report. It wasderived by dividing the net emissions/ fatal crash by the total vehicle hours fatal/crash listed in Table 335 of the FMCSA report. Those hours were 6,729 for Urban Interstate/Expressways, 483 for Urban
Arterials, 291 for Urban Other, 464 for Rural Interstate/Principal Arterials, 99 for Rural Other, and 1,626
for all facility types.

93

Table 3-24. Estimated Value of Net Tailpipe Emissions/Crash, FMCSA Fatal Commercial Vehicle
Crashes (2010 Dollars)
Urban Interstate/Expressway

$672

$33

$698

$227

$1,283

$39

Total
HC
$0

Urban Arterial

$173

$8

$177

$32

$176

$9

$0

$10

$584

Urban Other

$54

$2

$56

$8

$46

$3

$0

$2

$172

Rural Interstate/Principal Arterials

$112

$5

$245

$52

$292

$5

$0

$7

$718

Rural Other

$44

$2

$81

$16

$89

$2

$0

$2

$237

Average for All Facility Types

$214

$10

$252

$67

$378

$12

$0

$18

$951

Facility Type

CO2

CO

NOx

PM10

PM2.5

SO2

VOC

All

$68

$3,019

Table 3-25. Estimated Net Tailpipe Emissions/Vehicle Hour by Facility Type, All Fatal Crashes(Short Tons)
Facility Type

CO2

CO

NOx

PM10

PM2.5

SO2

Total
HC

VOC

Urban Interstate/Expressway

4.76E-03

3.35E-05

8.64E-06

7.31E-07

7.06E-07

8.62E-08

3.65E-06

3.60E-06

Urban Arterial

1.71E-02

1.08E-04

3.05E-05

1.43E-06

1.35E-06

2.90E-07

7.35E-06

7.12E-06

Urban Other

8.78E-03

5.39E-05

1.62E-05

6.19E-07

5.84E-07

1.72E-07

3.09E-06

3.02E-06

Rural Interstate/Principal
Arterials

1.15E-02

7.57E-05

4.41E-05

2.41E-06

2.33E-06

1.72E-07

5.22E-06

5.13E-06

Rural Other

2.13E-02

1.29E-04

6.85E-05

3.54E-06

3.33E-06

3.03E-07

8.38E-06

8.18E-06

Average for All Facility Types

6.27E-03

4.25E-05

1.29E-05

8.91E-07

8.60E-07

1.11E-07

4.01E-06

3.94E-06

Table 3-26 lists the estimated net emissions per crash by facility type for all fatal vehicle crashes. These
values were derived by multiplying the net emissions per vehicle hour from Table 3-25 by the estimated“all crashes vehicle hours” previously derived in Table 3-21.

94

Table 3-26. Estimated Net Tailpipe Emissions/Crash by Facility Type, All Fatal Crashes (Short Tons)
Facility Type

CO2

CO

NOx

PM10

PM2.5

SO2

Total
HC

VOC

Urban
Interstate/Expressway

19.185129

0.135212

0.034853

0.002948

0.002847

0.000348

0.014718

0.014508

Urban Arterial

4.951222

0.031283

0.008854

0.000414

0.000390

0.000084

0.002132

0.002066

Urban Other

0.379413

0.002327

0.000698

0.000027

0.000025

0.000007

0.000134

0.000131

RuralInterstate/Principal
Arterials

2.887361

0.0189780.011045

0.000605

0.000584

0.000043

0.001308

0.001286

Rural Other

0.358411

0.002173

0.001151

0.000059

0.000056

0.000005

0.0001410.000138

Average for All Road
Types

3.695575

0.024857

0.007931

0.000522

0.000501

0.000064

0.002248

0.002207

The emissions derived above were based on the emissions resulting from linkage of the TSIS-CORSIM
model results to EPA’s MOVES model. MOVES models emissions from mobile sources including exhaustemissions (tailpipe), evaporative emissions (both running and parked, including leaks and diurnal
emissions), and refueling emissions (vapor displacement and spillage). However, in order to produce anddistribute the added fuel burned due to congestion from traffic crashes, additional “upstream”
emissions are produced. Upstream emissions from fuel extraction, production, and distribution are notcurrently modeled by MOVES. These emissions must thus be estimated separately. To estimate theseemissions, we adopt upstream emissions/gallon values used by NHTSA and EPA in their analysis of 20172025 Corporate Average Fuel Economy Standards (CAFE). In that study, the agencies analyzed the
upstream emissions associated with production of each gallon of fuel. These values were derived
separately for gasoline and diesel fuel for each pollutant, and then combined based on the 2010 relative
highway consumption of these fuels. 27 In the CAFE analysis, values for all pollutants except CO2 were
measured in short tons. CO2 was measured in metric tons. However, for this analysis, CO2 was converted
to short tons for consistency with the approach taken in the FMCSA report. Table 3-27 summarizes the
upstream emissions per gallon of gasoline and diesel, as well as for the combined fuels. Note that there
are no values for PM10 or Total HC. Total HC was not estimated in the CAFE rulemaking nor valued in
the FMCSA study. Most damage from particulate matter is caused by the finer particles in PM2.5, anddamage caused by the larger particles is uncertain. For consistency with the recent CAFE studies, thisstudy does not value PM10.
27

Derived from the updated data used in production of FHWA publication “Highway Statistics, 2010.”
See www.fhwa.dot.gov/policyinformation/statistics/2010/mf27.cfm. Accessed on January 8, 2012. Thisdata indicates 78.5 percent of fuel used by motor vehicles is gasoline, and 21.5 percent are alternative
fuels, primarily diesel.

95

Table 3-27. Emissions per Gallon of Fuel (Short Tons)
Pollutant
CO2
CO
NOx
PM2.5
SO2
VOC

Gasoline
0.002274964
0.000000541
0.000001772
0.000000199
0.000001014
0.000002758

DieselCombined

0.002290422
0.000000556
0.000001849
0.000000193
0.000001025
0.000000425

0.002278293
0.000000544
0.000001789
0.000000198
0.000001016
0.000002255

To estimate upstream emissions, the combined upstream emissions/gallon from Table 3-27 wereapplied to the net added gallons of fuel used due to congestion. Added fuel usage is discussed in afollowing section of this study (see Table 33). The results for fatal crashes are shown in Table 3-28.
Table 3-28. Estimated Upstream Emissions/Crash by Facility Type, Short Tons, All Fatal Crashes
Facility Type

CO2

CO

NOx

PM10

PM2.5

SO2

Total
HC

VOC

Urban
Interstate/Expressway

4.445342

0.001061

0.003490

0.000000

0.000386

0.001982

0.000000

0.004401

Urban Arterial

1.147236

0.000274

0.000901

0.000000

0.000100

0.000512

0.000000

0.001136

Urban Other

0.087913

0.000021

0.000069

0.000000

0.000008

0.000039

0.000000

0.000087

Rural
Interstate/Principal
Arterials

0.669024

0.000160

0.000525

0.000000

0.000058

0.000298

0.000000

0.000662

Rural Other

0.083047

0.000020

0.000065

0.000000

0.000007

0.000037

0.000000

0.000082

Average for All Road
Types

0.856293

0.0002040.000672

0.000000

0.000074

0.000382

0.000000

0.000848

Table 3-22 above listed the emissions values originally used in the FMCSA study. For all criteria pollutantsin this analysis, we have adopted values consistent with the values used by NHTSA and EPA in their mostrecent analysis of CAFE for model years 2017-2025 (NCSA, 2012). However, those values reflect
projected real growth in the value of reducing pollutants over the course of that rulemaking, which
covers production from 2017 forward. To derive estimates of these values for 2010, the base year of thisreport, we interpolated back based on the rate of growth for each pollutant implied by base EPA
estimates for 2015, 2020, and 2030. The implied growth rate over these years was roughly 1.6 to 1.9
percent, depending on the pollutant. These values are listed in Table 3-29, together with those appliedin the FMCSA report and the published values from the 2017-2025 CAFE rulemaking that reflect
conditions in roughly 2030. The source of the original criteria pollutant 2015, 2020, and 2030 values

96

used for these imputations was Pope, Arden, Burnett, and Thun (2002). All values assume a 3-percent
discount rate.
The original source of the CO2 value used in the MY 2017-2025 CAFE analysis was the Interagency
Working Group on Social Cost of Carbon (IWG, 2010). Discussion of the 2010 CO2 value is included in thepreviously cited MY2017-2025 CAFE FRIA. However, in May 2013 the IWG published revised guidance on
the social cost of carbon based on improved models of the impacts of climate change (IWG, 2013).
These revised models now address explicit assessments of damages from sea level changes as well asother updates and improvements. This study adopts the revised SCC value from that paper for 2010($33 in 2007 dollars), adjusted to 2010 economics.
Note that we do not apply values for PM10 or CO. NHTSA and EPA did not include values for these two
pollutants in their 2017-2025 CAFE analyses. PM10 was not included because virtually all the adverse
health impacts from PM arise from fine particulates, defined as the fraction that is less than 2.5 microns
in diameter (hence the notation PM2.5). These account for most of the particulate matter whenmeasured by number of particles, although a much smaller fraction of it when measured by total massof particulate emissions. So while particulate matter between 2.5 and 10 microns accounts for most of
the mass, it does little of the health damage. Likewise, CO was not valued by NHTSA and EPA because atcurrent exposure levels, there is no evidence that CO causes any adverse health impacts.
Table 3-29 summarizes the values used for tailpipe emissions in this study, as well as those used in the
FMCSA and 2017-2025 CAFE studies. These same values were applied to upstream emissions except for
NOx and PM2.5. EPA has determined that the different populations exposed to upstream emissions faceslightly different health impacts than those exposed through tailpipe emissions. Upstream emissions forNOx are valued at $4,481 and those for PM2.5 are valued at $211,602.

Table 3-29. Tailpipe Emissions Costs (2010 Dollars)
Emission Type
CO2 (central value)
CO
NOx
PM10
PM2.5
SO2
VOC

FMCSA
Report$21$145$12,000$46,094$270,000$67,000$2,800This Study
CAFE 2017-2025 x2 2010
Values 282030 values
$34
$35
$0
$0
$6,700$4,646$0$0$306,500$254,015$39,600$27,300$1,700$1,12228

Note that separate values are applied to upstream NOx and PM2.5 emissions. EPA has determined that thedifferent populations exposed to upstream emissions face slightly different health impacts than those exposed
through tailpipe emissions. Upstream emissions for NOx are valued at $4,481 and those for PM2.5 are valued
at $211,602. Also note that the CO2 value used in the CAFE analysis was $22.22 in 2010 dollars per metric ton.
This was converted to a short ton value of $20.16 to derive a number compatible with the FMCSA basis.

97

Table 3-30 summarizes the total cost/fatal crash for the various emissions categories. These values
represent the product of the values in Table 3-29 and the corresponding emissions categories in Table
3-26. These same values are listed for Injury Crashes and PDO crashes, respectively, in Tables 3-31 and3-32.
Table 3-30. Estimated Value of Net Emissions/Crash by Facility Type, All Fatal Crashes (2010 Dollars)
Facility Type

CO2

CO

NOx

PM10

PM2.5

SO2

VOC

Urban
Interstate/Expressway$743.98$0.00$177.56$0.00$804.75$63.61$21.22

Urban Arterial$192.00$0.00$45.17$0.00$120.26$16.26$3.59

Urban Other$14.71

$0.00

$3.55

$0.00

$8.02

$1.27

$0.24

Rural
Interstate/Principal
Arterials$111.97$0.00$53.67$0.00$160.54

$9.33

$2.19

Rural Other$13.90

$0.00

$5.64

$0.00

$15.76

$1.15

$0.25

Average for All Road
Types

$143.31$0.00$39.86$0.00$143.04$12.16$3.43

Table 3-31. Estimated Value of Net Emissions/Crash by Facility Type, All Injury Crashes (2010 Dollars)
Facility Type

CO2

CO

NOx

PM10

PM2.5

SO2

VOC

Urban
Interstate/Expressway$157.18$0.00$37.51$0.00$169.98$13.46$4.48

Urban Arterial$42.58$0.00$10.01$0.00$26.20

$3.61

$0.79

Urban Other

$6.45

$0.00

$1.55

$0.00

$3.38

$0.56

$0.11

Rural
Interstate/Principal
Arterials$20.65

$0.00

$9.90

$0.00

$29.70

$1.74

$0.40

Rural Other

$3.53

$0.00

$1.43

$0.00

$3.94

$0.29

$0.06

Average for All Road
Types

$30.81

$0.00

$8.45

$0.00

$30.06

$2.62

$0.73

98

Table 3-32. Estimated Value of Net Emissions/Crash by Facility Type, All PDO Crashes (2010 Dollars)
Facility Type

CO2

CO

NOx

PM10

PM2.5

SO2

VOC

Urban
Interstate/Expressway$133.68$0.00$31.91$0.00$144.33$11.39$3.81

Urban Arterial$25.80

$0.00

$6.07

$0.00

$16.48

$2.17

$0.48

Urban Other

$3.89

$0.00

$0.94

$0.00

$2.02

$0.32

$0.07

Rural
Interstate/Principal
Arterials$21.01$0.00$10.07$0.00$30.00

$1.72

$0.41

Rural Other

$2.95

$0.00

$1.20

$0.00

$3.23

$0.25

$0.05

Average for All Road
Types

$24.41

$0.00

$6.92

$0.00

$25.03

$2.06

$0.59

Excess Fuel Consumption:
Fuel consumption and CO2 production are directly related. EPA’s Clean Energy Web site(www.epa.gov/cleanenergy/) states that “To obtain the number of grams of CO2 emitted per gallon ofgasoline combusted, the heat content of the fuel per gallon is multiplied by the kg CO2 per heat content
of the fuel. The average heat content per gallon of gasoline is 0.125 mmbtu/gallon and the average
emissions per heat content of gasoline is 71.35 kg CO2/mmbtu.”
This produces the following relationship0.125 mmbtu/gallon * 71.35 kg CO2/mmbtu * 1 metric ton/1,000 kg = 8.92*10-3 metric tons CO2/gallonof gasoline.
So, there are .00892 metric tons of CO2 in every gallon of gasoline.
Since the FMCSA emissions values are expressed in short tons, we must convert this relationship to ashort ton basis. 1 short ton = 0.90718474 metric tons. Therefore, there are .00892/.90718474 =.00098326 short tons of CO2 in every gallon of gasoline. Therefore 101.70233 gallons of gasoline
contains 1 short ton of CO2. 29

29

Note that a small portion of the on-road fleet are diesels, which have different emission characteristics thangasoline engines. Diesel has a slightly higher energy content than gasoline (138,700 BTU/gal versus 125,000) andthus burns about 10 percent more CO2/gallon. However, they are also more efficient and thus waste somewhat
less fuel. These two factors are partially offsetting. This analysis is based on the published EPA values for gasoline.

99

The average fuel price in 2010 was roughly $2.46/gallon. 30 In Tables 3-33, 3-34, and 3-35, the CO2
emissions previously estimated are converted to gallon equivalents and valued using this average cost of$2.46/gallon.
Table 3-33. Net Increase in and Cost of Fuel Consumption, Fatal Crashes
CO2 (short tons)

Gallons/fuelValue (2010
Dollars)

Urban Interstate/Expressway

19.185129

1951$4,800Urban Arterial

4.951222

504$1,239Urban Other

0.379413

39$95

Rural Interstate/Principal Arterials

2.887361

294

$722

Rural Other

0.358411

36$90

Average All Roadway Types

3.695575

376

$925

Facility Type

Table 3-34. Net Increase in and Cost of Fuel Consumption, Injury Crashes
CO2 (short tons)

Gallons/fuelValue (2010
Dollars)

Urban Interstate/Expressway

4.053262

412$1,014

Urban Arterial

1.098054

112

$275

Urban Other

0.166285

17$42

Rural Interstate/Principal Arterials

0.532603

54$133

Rural Other

0.091012

9$23

Average All Roadway Types

0.794605

81$199

Facility Type30

Excludes State and local taxes, which are a transfer payment from one segment of society to another, and thus
are not counted as a societal cost. The $2.46 price results from the FMCSA simulation in which average gasoline
prices were $2.43/gallon and average diesel prices were $2.52.

100

Table 3-35. Net Increase in and Cost of Fuel Consumption, PDO Crashes
CO2 (short tons)

Gallons/fuelValue (2010
Dollars)

Urban Interstate/Expressway

3.447093

351

$862

Urban Arterial

0.665271

68$166

Urban Other

0.100257

10$25Rural Interstate/Principal Arterials

0.541695

55$136

Rural Other

0.075970

8$19

Average All Roadway Types

0.629492

64$157

Facility Type

Value of Travel Time:The added time spent by vehicle occupants stuck in or detouring around traffic at a crash site is an
opportunity cost that represents a real cost to society. While the ability to travel is a valued asset that
improves quality-of-life, consumers generally seek to minimize the time spent travelling because it
reduces their opportunities to engage in more lucrative or enjoyable pursuits. Time spent travelling
could instead be dedicated to production, which would yield monetary benefits to the travelers, theiremployers, or both. Alternately, it could be spent in recreation or other activities which the traveler
would preferably choose to engage in. Finally, the conditions associated with traffic congestion anddelay can cause frustration and tension which in themselves have a negative impact on vehicle
occupants.
The USDOT has issued general guidance regarding valuing travel time. 31 This guidance lays out guidelines
for valuing travel time under various surface modes, and for both business and personal travel.
Generally, business travel is valued using wage rates while personal travel is valued using a variable
percentage of wage rates, depending on mode and on whether travel is local or intercity. Based on thisguidance and updated wage data from the U.S. Bureau of Labor Statistics, FMCSA derived averagevalues of travel time by roadway type for their commercial vehicle study. These values, which are shownin Table 36 below, were weighted according to the prevalence of vehicle types on the roadway as well as
average occupancy and are thus applicable for this study as well.

31

Memorandum to Secretarial Officers and Modal Administrators, “Revised Departmental Guidance on Valuationof Travel Time in Economic Analysis” from Polly Trottenberg, Assistant Secretary for Transportation Policy,
Prepared by Peter Belenky, Economist, September 29, 2011.

102

In Table 3-37 these values are combined with the total vehicle hours/crash from Table 3-21 to derive thetravel time cost/crash by crash severity and roadway type. Average costs across all roadway types were
derived based on FARS incidence for each roadway type for fatal crashes. These roadway definitions are
not available in NHTSA’s injury databases. Therefore, for nonfatal crashes, relative incidence by roadway
type from the previously described PA database was normalized to the fatal crash weights from FARS toestablish relative weights for injury and PDO crashes. These same weights, shown in Table 3-37a, areapplied to all subsequent average cost calculations across the 5 roadway types.
Table 3-36. Average Value of Travel per Hour by Road Type (2010 Dollars)Road Type

Average VOTUrban Interstate/Expressway
Urban Arterial
Urban Other
Rural Interstate/Principal Arterial
Rural Other
Total Rural and Urban$24.28$23.91$23.88$26.05$24.78$24.34

Table 3-37. Value of Travel Time/Crash, by Crash Severity and Roadway Type, All Crashes

Urban
Other

Rural
Interstate/
Principal
Arterials

Rural
Other

Average
All
RoadwayTypes$23.91$23.88$26.05$24.78$24.344,032.45

290.1343.19

250.7316.81

527.01$97,908$6,937$1,031$6,532$417$12,855

Urban
Interstates/
Expressways

Urban
Arterials$24.28

Vehicle Hours/Crash
Total Cost/Crash

VOT/Vehicle Hour
Fatal Crashes

Injury Crashes
Vehicle Hours/Crash

851.85

64.48

18.94

46.41

4.32

140.45

Total Cost/Crash$20,683$1,542$452$1,209$107$3,409Vehicle Hours/Crash

724.71

39.05

11.38

47.13

3.57

138.77

Total Cost/Crash$17,596

$934

$272

$1,228$88$3,376PDO Crashes

Table 3-37a. Relative Incidence Weights Among Roadway Types
Urban Interstates/Expressways
Urban Arterials
Urban Other
Rural Interstates/Principal Arterials
Rural Other
Total

Fatal
0.10
0.21
0.13
0.18
0.37
1.00

103

Injury
0.13
0.37
0.17
0.11
0.23
1.00

PDO
0.16
0.30
0.18
0.14
0.22
1.00

Congestion Cost Summary
Table 3-38 summarizes the various costs that are estimated to result from congestion caused by policereported motor vehicle crashes. Total costs range from $14,121for fatal crashes to $3,673 for PDO
crashes. The largest loss results from the opportunity cost of delay for vehicle occupants, but there arealso significant impacts due to wasted fuel. Greenhouse gases and criteria pollutants are the least costly
impact, but are still important given the large number of crashes that occur annually.
Table 3-38. Summary of Congestion Costs/Crash Due to Time Delay, Excess Fuel Burned, and Pollution
Police-Reported Crashes

Urban
Interstates/
Expressways

Urban
Arterials

Urban
Other

$192

$15

Rural
Interstate/
Principal
Rural
Arterials
Other

Average
All
Roadway
Types

Fatal Crashes
CO2
CO
NOx
PM10
PM2.5
SO2
VOC
Total Emissions
Excess Fuel Burned
Value of Time
Total Congestion
Costs
Injury Crashes
CO2
CO
NOx
PM10
PM2.5
SO2
VOC
Total Emissions
Excess Fuel Burned
Value of Time
Total Congestion
Costs

$744

$112

$14

$143

$0

$0

$0

$0

$0

$0

$178

$45

$4

$54

$6

$40

$0

$0

$0

$0

$0

$0

$805

$120

$8

$161

$16

$143

$64

$16

$1

$9

$1

$12

$21

$4

$0

$2

$0

$3

$1,811

$377

$28

$338

$37

$342

$4,800$1,239

$95

$722

$90

$925

$97,908$6,937$1,031$6,532$417$12,855$104,519$8,553$1,154$7,592$543$14,121

$157

$43

$6

$21

$4

$40

$0

$0

$0

$0

$0

$0

$38

$10

$2

$10

$1

$10

$0

$0

$0

$0

$0

$0

$170

$26

$3

$30

$4

$36

$13

$4

$1

$2

$0

$3

$4

$1

$0

$0

$0

$1

$383

$83

$12

$62

$9

$90

$1,014

$275

$42

$133

$23

$255

$20,683$1,542$452$1,209$107$3,409$22,080$1,900$506$1,405$139$3,755104

PDO Crashes
CO2
CO
NOx
PM10
PM2.5
SO2
VOC
Total Emissions
Excess Fuel Burned
Value of Time
Total Congestion Costs

$134

$26

$4

$21

$3

$34

$0

$0

$0

$0

$0

$0

$32

$6

$1

$10

$1

$9

$0

$0

$0

$0

$0

$0

$144

$16

$2

$30

$3

$34

$11

$2

$0

$2

$0

$3

$4

$0

$0

$0

$0

$1

$325

$51

$7

$63

$8

$80

$862

$166

$25

$136

$19

$217

$17,596

$934

$272

$1,228$88$3,376$18,783$1,151$304$1,426$115$3,673Congestion costs have been estimated separately for fatal, injury, and PDO crashes. However, thisreport is primarily stratified according to injury severity for all injury crashes. As discussed previously,
within injury crashes there are 5 nonfatal categories. For any given crash, congestion costs are afunction of crash circumstances rather than injury severity. This implies an equal distribution ofcongestion costs among all involved parties, regardless of whether they died, were injured or were
uninjured. To distribute costs among crash involved people for fatal crashes, the average cost/crash forfatal crash was divided by the average number of involved people /fatal crash. This data was obtainedby examining FARS data for 2009 to 2011. From this data, the KABCO injury profile was obtained and run
through an MAIS translator to reveal the average MAIS profile among fatal crashes. By definition, allfatalities occur in fatal crashes, so the average congestion cost per fatality was taken directly from theanalysis of FARS crashes. The same approach was also applied to injury crashes. However, nonfatal
injuries occur in both fatal and nonfatal injury crashes. The two nonfatal injury profiles were therefore
weighted together based on the relative incidence of each injury severity in fatal or injury crashes. Since
fatal crashes are relatively rare, the nonfatal injury crash estimate was heavily weighted towards thecosts from injury crashes. Table 3-39 lists the weights, injuries per crash, and resulting congestion costs
per injury for each injury severity for both fatal and injury crashes.
Table 3-39. Allocation of Congestion Costs Across Involved People in Fatal and Injury Crashes
Fatal Crashes
Injury Crashes% of All% of All
MAIS
Injuries
Injuries/Crash Cost/Person Injuries Injuries/Crash Cost/Person$5,720$1,38000.00830.60520.99171.4342$5,720$1,38010.01050.58740.98951.1015$5,720$1,3802
0.01620.10930.98380.1318$5,720$1,3803
0.02540.05390.9746
0.0410$5,720$1,38040.03020.0136
0.96980.0086$5,720$1,38050.03430.00550.96570.0031$5,720$1,380Fatal
1.00001.09370.00000.0000$14,121$3,755
Crash
0.01772.46870.98232.7202

105

PDO crashes are expressed on a per damaged vehicle basis. Therefore the unit cost for PDO crashes was
divided by the average number of vehicles damaged in PDO crashes. Again, this data was derived from2009-2011 GES records, which indicated an average of 1.75 vehicles/PDO crash.
The results are summarized in Table 40. The nonfatal injury MAIS levels (MAIS 0 to 5) are the weighted
average of these costs from fatal and injury crashes noted in the previous table. Congestion costs fornonfatal injuries decline gradually as injury severity decreases because a larger portion of less severe
injuries occur in injury crashes, resulting in more weight being given to the less costly injury crashes.
Note that the PDO unit cost is higher than nonfatal injury costs because it is expressed on a per vehicle
basis. If it were adjusted for vehicle occupants, it would decline to $911/person.
Table 3-40. Final Congestion Cost/Severity Unit ($2010), Police-Reported Crashes
Severity
Cost/Injured32$1,416MAIS0$1,426MAIS1$1,450MAIS2
MAIS3$1,490MAIS4$1,511$1,529MAIS5$5,720Fatal$2,104PDO
Unreported Crashes:
Most crashes that involve either serious injury or significant roadway blockage are reported to police, byeither the involved parties or by passing motorists. Police reports are filed in those cases where police
respond to the crash and the crash severity passes a certain threshold, usually a specific amount ofproperty damage, which varies by state. However, because they typically do not involve police or
emergency vehicle presence, unreported crashes, even of the same nominal severity category, areunlikely to cause the same congestion impacts as police-reported crashes. Unfortunately, we wereunable to find any research that directly addresses the issue of congestion caused by unreported
crashes. To estimate these impacts, we assume that unreported crashes would have only half theprobability of a lane being blocked and would be present on the roadway (crash duration) for only half
as long as a police-reported crash. In addition we assume that the proportion of roadway blockage andprobability of opposite direction rubbernecking is only half that of police-reported crashes. 33 These32

For all MAIS and fatal injury categories, costs are expressed on a per injured person basis. For PDO crashes, costs are
expressed on a per damaged vehicle basis.
33
We acknowledge that the selection of “half” as a factor to reflect the nature off unreported crashes is somewhat
arbitrary. However, lacking specific data, we are hesitant to select values that imply that unreported crashes would

106

assumptions are based on the likelihood that any formal lane closing would require police presence andany significant informal lane closing (due to vehicle obstruction) would draw police attention and thus
could become a reported crash. Nonetheless, unreported crashes would likely involve at least somelevel of temporary lane blockage and would cause rubbernecking until the vehicles are removed or
driven away. An example might be a low speed crash in which one vehicle rear-ends another at astoplight. If the damage is minor, the two drivers may contact their insurance companies, exchange
insurance information and then drive away, but during the period they were examining their vehicles fordamage and exchanging information the vehicles would have blocked the lane they were in. Alternately,
this same crash might draw police attention, but, if the damage is minor, police may not file a formal
report, and it would thus be an unreported crash. We note that all fatal and serious injury crashes arereported to the police. Therefore, only the minor injury and PDO congestion estimates are relevant tothis estimate.
The impact of these assumptions is noted in Table 3-41. These assumptions imply that on average,
unreported injury crashes result in congestion impacts that are roughly 15 percent of the impacts that
occur in police-reported injury crashes, and unreported PDO crashes produce congestion impacts thatare roughly 20 percent of the impacts that occur in police-reported PDO crashes.

have impacts that are more nearly like those of police-reported crashes or closer to zero. We view half as thebest way to minimize potential error. Directionally, we only know unreported crashes would cause some level ofcongestion but that it is less than reported crashes.

107

Table 3-41. Summary of Congestion Costs/Crash Due to Time Delay, Excess Fuel Burned, and Pollution
Unreported Crashes

Fatal Crashes
CO2
CO
NOx
PM10
PM2.5
SO2
VOC
Total Emissions
Excess Fuel Burned
Value of Time
Total Congestion Costs
Injury Crashes
CO2
CO
NOx
PM10
PM2.5
SO2
VOC
Total Emissions
Excess Fuel Burned
Value of Time
Total Congestion Costs
PDO Crashes
CO2
CO
NOx
PM10
PM2.5
SO2
VOC
Total Emissions
Excess Fuel Burned
Value of Time
Total Congestion Costs

RuralInterstate/
Principal
Arterials

Rural
Other

Average
All
Roadway
Types

$0.84
$0.00
$0.20
$0.00
$0.46
$0.07
$0.01
$1.59
$5.43
$58.97$65.98

$8.44
$0.00
$4.04
$0.00
$12.10$0.70$0.16$25.44$54.43$492.11$571.97

$1.00
$0.00
$0.40
$0.00
$1.13
$0.08
$0.02
$2.63
$6.42
$29.83$38.88$11.14
$0.00
$3.08
$0.00
$11.23$0.95$0.27$26.67$71.89$1,031.44$1,130.00

$6.24
$0.00
$1.47
$0.00
$3.84
$0.53
$0.12
$12.18$40.23$225.75$278.16

$0.86
$0.00
$0.21
$0.00
$0.45
$0.08
$0.01
$1.61
$5.54
$60.23$67.38

$2.98
$0.00
$1.43
$0.00
$4.28
$0.25
$0.06
$9.00
$19.22$174.42$202.64

$0.53
$0.00
$0.22
$0.00
$0.59
$0.04
$0.01
$1.39
$3.43
$16.15$20.97

$5.37
$0.00
$1.37
$0.00
$4.78
$0.46
$0.12
$12.10$34.65$443.93$490.68

$4.51
$0.00
$1.06
$0.00
$2.88
$0.38
$0.08
$8.92
$29.10$163.25$201.27

$0.68
$0.00
$0.16
$0.00
$0.35
$0.06
$0.01
$1.26
$4.39
$47.53$53.18

$4.28
$0.00
$2.05
$0.00
$6.11
$0.35
$0.08
$12.88$27.62$250.23$290.73

$0.55
$0.00
$0.22
$0.00
$0.60
$0.05
$0.01
$1.42
$3.52
$16.36$21.30

$6.13
$0.00
$1.62
$0.00
$6.17
$0.52
$0.15
$14.59$39.54$616.56$670.69

Urban
Interstates/
Expressways

Urban
Arterials

Urban
Other

$61.38$0.00$14.65$0.00$66.39$5.25$1.75$149.42$396.01$8,077.71$8,623.14$13.81
$0.00
$3.25
$0.00
$8.65
$1.17
$0.26
$27.13$89.07$498.80$615.00$19.76
$0.00
$4.72
$0.00
$21.37$1.69$0.56$48.10$127.49$2,600.27$2,775.86$24.36
$0.00
$5.82
$0.00
$26.30$2.07$0.69$59.25$157.17$3,206.84$3,423.26

108

The final MAIS distribution for unreported crashes, which is summarized in Table 3-42, is based on the
average person involvement rates from police-reported crashes. As previously discussed, it is possiblethat unreported crashes have lower person involvement rates than reported crashes, since the presenceof more than one driver is likely to increase the chances of the crash being reported. We do not havedata on involvement rates for unreported crashes, but it is likely that basing these unit costs on policereported rates produces a conservative estimate of these costs for unreported crashes. Note that there
is no need to average congestion costs from both fatal and nonfatal crashes when allocating nonfatal
injury costs because all fatal crashes are reported to police. Although costs are shown for each injury
category, virtually all unreported crashes involve either minor injury or property damage only.
Table 3-42. Final Congestion Cost/Severity Unit ($2010), Unreported Crashes
Cost/Crash Injured/Crash Cost/Injured
$491
$180
MAIS0
2.72
$491
$180
2.72
MAIS1
$491
$180
2.72
MAIS2
$491
$180
2.72
MAIS3
$491
$180
MAIS4
2.72
$491
$180
2.72
MAIS5
$1,130
$458
2.47
Fatal
$671
$384
PDO
1.75

Average and Total Congestion Costs, Reported and Unreported Crashes:
The average cost/crash across both police-reported and unreported crashes was calculated by weighting
each category’s costs according to the relative incidence within each severity category. For all injury
categories this was based on the incidence of injured people. For PDOs, it is based on the incidence ofdamaged vehicles. These definitions are consistent with the stratification used throughout this report.
Incidence was derived from the incidence chapter of this report. Table 3-43 summarizes this process andits results. Table 3-44 summarizes the total costs of congestion. In 2010, motor vehicle crashes are
estimated to have caused $28 billion in travel delay, excess fuel consumption, and health and othereconomic impacts from added criteria pollutants and greenhouse gases.

109

Table 3-43. Average Congestion Costs for All Crashes
Incidence

MAIS0
MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
Fatal
PDO

Unit Costs (2010 Dollars)
PoliceReported

Unreported

Combined

46.86%
74.55%$1,416$1,426

$180
$180

$760
$1,109338,730

80.05%$1,450$180$1,197100,740

95.69%$1,490$180$1,4340

17,086100.00%$1,511$180$1,511

5,749

0

5,749

100.00%$1,529$180$1,52932999

032,999100.00%$5,720$458$5,7207,454,761

11,053,871

18,508,632

40.28%$2,104$384$1,077

PoliceReported

Unreported

Total

%PR

2,147,857
2,578,993

2,435,409
880,2074,583,265
3,459,200

271,160

67,57096,397

4,343

17,086

Table 3-44. Total Congestion Costs, 2010
Police-Reported

Unreported

Combined

MAIS0
MAIS1

$3,042,349,414$439,378,392$3,481,727,806$3,677,250,555$158,800,405$3,836,050,960

MAIS2
MAIS3

$393,360,852$12,190,477$405,551,329$143,696,818$783,532$144,480,350

MAIS4$25,826,478$0$25,826,478

MAIS5
Fatal

$8,791,521$0$8,791,521$188,789,259$0$188,789,259

PDO$15,687,384,220$4,248,011,358$19,935,395,578

Total$23,167,449,117$4,859,164,164$28,026,613,281

Discussion:
While this analysis is designed to represent average nationwide experience, it is substantially influenced
by data collected from Pennsylvania. The Pennsylvania traffic records database contains information on
all police-reported crashes reported to the State. This data was used to estimate a number of criticalfactors in this analysis, including crash duration and the probability-of-lane closings by roadway type.
The use of this database was largely a function of availability. Very few States maintain publicallyavailable traffic records databases at this level of detail. Nonetheless, using a single State’s experience asa proxy for a nationwide projection raises the question of how representative the crash experience inthat State is of average nationwide experience. To consider this issue, we examined data contained in
FHWA’s publication Highway Statistics 2009. 34 FHWA gathered data for the specific purpose of34

See www.fhwa.dot.gov/policyinformation/statistics/2008/pdf/ps1.pdf All values are for 2008 except percent truck
VMT, which was last published for 2005.

110

identifying “peer States” with similar characteristics that might influence various roadway measures.
This data is presented in a table PS-1 titled “Selected Measures for Identifying Peer States.” These
measures include the urban/rural breakdown of net land area, population, annual VMT and lane miles,
as well as personal income, gross State product per capita, annual VMT per capita, AADT per lane mile,
and the proportion of VMT accounted for by trucks. In table 45 we summarize this data for both
Pennsylvania and the country as a whole.
Overall, Pennsylvania appears to be quite similar to the country as a whole in most of the categories thatare examined. The only category where there is a significant difference is in the urban portion of landarea, where Pennsylvania has a noticeably larger proportion of land designated as urban then the
United States as whole. However, this is arguably the least relevant metric for purposes of roadwaycrash analysis, and it is largely offset by the close matches in more relevant measures such as population,
VMT, truck travel, and AADT. Close matches are also found for the two wealth measures – personal
income and gross State product per capita. These measures are less relevant than roadway measures,
but are still important in that they influence the types of vehicles and transportation systems that
residents choose to invest in. Overall, Pennsylvania’s characteristics appear to be a reasonably close
match to those of the country as a whole, and there do not appear to be any significant contra- indicatorsin this data for using statewide Pennsylvania experience for crash duration and lane closings
as a proxy for the United States.
Table 3-45. Demographic and Roadway Characteristics, Pennsylvania Versus United States, 2008 35

Population
Annual VMTLane Miles
Net Land Area
% Truck VMT Rural% Truck VMT Urban
Personal Income
Gross State Product/Capita
Annual VMT/Capita
AADT/Lane, Rural
AADT/Lane, Urban
AADT/Lane, All

Percent
Urban
U.S.
Total

Percent
Urban
Pennsylvania

79%
67%
28%
5%

77%
64%
38%
28%

Value
U.S.
Total

Value
Pennsylvania

12%
5%$38,615$46,593
9,728
1,456
6,744
2,771

15%
7%
$38,793$44,448
8,664
1,327
5,097
2,499

Nonetheless, we note that this study, like all studies of this nature, is inherently dependent on theaccuracy of the inputs and assumptions that are adopted. We have attempted to be as transparent as
possible regarding the variation in various data sources and the basis we have used for our
methodology. However, we do not claim that the end result is definitive. With 14 million traffic crashes

35

All values are for 2008 except percent truck VMT, which was last published for 2005.

111

every year, validation of this or any model of congestion impacts would require a massive data gathering
effort that would not be practical or affordable. There is a reason why most studies of traffic crashes
only focus on one aspect of the crash in one jurisdiction. There is clearly room for speculation regarding
confidence intervals around our results, but since this data was not statistically derived, assignment ofsuch intervals would be arbitrary. Further, this study is directly linked to the TSIS-CORSIM model,
discussed in the body of this analysis. While we believe that this model is a step forward in estimating
traffic dynamics, any faults it contains will spill over into this study as well.
Finally, we note that The Texas Transportation Institute (TTI) makes annual estimates of traffic
congestion costs. However, the TTI study examines the cost of congestion from all sources, which isprimarily a function of routine rush hour delays and normal traffic congestion. It is thus not directlycomparable to this study, which only measures the costs of congestion caused by traffic crashes. We
note that the TTI estimate of total congestion costs is $120 billion in 2010, compared to our estimate of$28 billion for crash-caused congestion. It might be tempting to ratio these costs and conclude that
traffic crashes are responsible for 23 percent of total congestion costs, but we would discourage thisbecause the two studies use different methodologies as well as different inputs. For example, this study
uses values based on the current USDOT guidance on valuing travel time, weighted by vehicle types on
the roadway as well as average occupancy, which sets the cost per vehicle hour of delay at $24. The TTI
study uses a value of roughly $21/hour of vehicle delay. To draw conclusions regarding the relative
impact of traffic crashes to congestion, common methods and inputs would be required. Thus, although
the results of the two studies do not necessarily appear inconsistent, we do not draw conclusions basedon a comparison of these two studies results.

112

4. Lost Quality-of-Life
The human capital costs documented in the first chapter represent the tangible losses that result frommotor vehicle crashes. They define the value of resources that are used or that would be required torestore crash victims, to the extent possible, to their pre-crash physical and financial status. These areresources have been diverted from other more productive uses to merely maintain the status quo.
These costs, which can be estimated in a fairly direct manner through empirical measurements, include
medical care, lost productivity, legal and court costs, insurance administrative costs, legal costs,
workplace costs, travel delay, and property damage.
However, in cases of serious injury or death, medical care cannot fully restore victims to their pre-crashstatus and human capital costs fail to capture the relatively intangible value of lost quality-of-life that
results from these injuries. In the case of death, victims are deprived of their entire remaining lifespan.
In the case of serious injury, the impact on the lives of crash victims can involve extended or evenlifelong impairment or physical pain, which can interfere with or prevent even the most basic living
functions. These more intangible effects can be valued using studies that examine the willingness of
consumers to pay to avoid risk of death or injury. Assessing the value of these impacts provides a more
complete basis for quantifying the harmful impacts of motor vehicle crashes on society.
Value of a Statistical Life:The value of a statistical life (VSL) is a measure of the implied value consumers place on their lives asrevealed by the price they are willing to pay to avoid risk of death. A wide range of estimates of the valueof VSL have been derived from numerous studies conducted over the past three decades. These“willingness to pay” studies (WTP) are most frequently based on wage rate differentials for risky jobs, oron studies of the prices consumers pay for products that reduce their risk of being fatally injured. The
individual studies are too numerous to document here, but a number of authors have attempted toevaluate these studies as a group through systematic reviews or meta-analysis, which applies
normalizing parameters and statistical weighting techniques to draw conclusions from related studies.
In 1990, Miller conducted a systematic review of 67 of these studies. In this study, Milller selected 47
studies that were the most methodologically sound, adjusted them to a common discount rate, andmade adjustments for errors in perceived risk levels. The VSLs found in these 47 studies had both amean and median value of $2.2 million in 1988 dollars with a standard deviation of $0.65 million. In
2000, Miller published another meta-analysis examining VSL estimates across differing countries. In thisstudy he examined 68 studies, including the original 47 he had examined in 1990. Based on this study,
Miller estimated the VSL in the United States to be $3.67 million in 1995 dollars.
Viscusi has also published a number of WTP reviews. In 1993 Viscusi found that most VSL estimates are
clustered in the $3 million to $7 million range. In 2003, Viscusi and Aldy published a worldwide review of
VSL studies and estimated a median value of $7 million in 2000 dollars. In 2004 Viscusi published his
own estimate of WTP based on wage-risk premiums resulting in a $5 million VSL (using 2000 dollars).

113

Other reviews include those by Mrozek and Taylor (2002), who found VSL estimates ranging from $1.5
million to $2.5 million in 1998 dollars, and a 2003 meta-analysis by Kochi, Hubbell, and Kramer (2006),
which produced a mean VSL estimate of $5.4 million in 2000 dollars.
It is apparent that there are a wide range of estimates regarding the implied VSL from WTP studies. This
range is reflected in guidance supplied by the Office of Management and Budget in Circular A-4, issued
on September 17, 2003, which recommends values between $1 million and $10 million be used bygovernment agencies when evaluating the impacts of proposed regulations that affect fatality risk. In
recent years, government agencies such as NHTSA, the FDA, EPA, the Consumer Product Safety
Commission, the Department of Agriculture, and the Occupational Safety and Health Administration
have used values ranging from $5 to $7 million in evaluating their regulations.
In February 2008, based on a review of the studies cited above, the Office of the Secretary of the
Department of Transportation issued guidance setting a VSL of $5.8 million for use in Departmental
regulatory programs (T. Duvall, Assistant Secretary for Transportation Policy, & D.J. Gribbon, General
Council. Treatment of the Economic Value of a Statistical Life in Departmental Analysis. Memorandum to
Secretarial Officers and Modal Administrators, Department of Transportation, February 5, 2008). Thisvalue was updated for inflation twice, most recently in July 2011 to a value of $6.2 million (P.
Trottenberg, Assistant Secretary for Transportation Policy, & R. S. Rivkin, General Council. “Treatment of
the Economic Value of a Statistical Life in Departmental Analysis – 2011 Interim Adjustment.”
Memorandum to Secretarial Officers and Modal Administrators, July 19, 2011).
In February 2013, USDOT again updated their VSL guidance to a value of $9.1 million in 2012 dollars (P.
Trottenberg, Assistant Secretary for Transportation Policy, & R. S. Rivkin, General Council. “Treatment of
the Economic Value of a Statistical Life in Departmental Analysis – 2011 Interim Adjustment.”
Memorandum to Secretarial Officers and Modal Administrators, July 19, 2011). This latest update wasbased exclusively on studies that used the Census of Fatal Occupational Injuries, a complete census ofoccupational fatalities conducted by the Bureau of Labor Statistics. For a variety of reasons outlined inthat guidance, USDOT considered studies based on this data to be superior to those that used othersources. This study adopts this current guidance for assessing the monetary value of fatalities caused by
motor vehicle crashes. However, we also acknowledge the uncertainty evident in the wide range ofresults that are found in the literature. Since this study examines 2010 in detail, we obtained the 2010based VSL, adjusted for both economics and real changes in wages, from OST. This value, $8.86 million,
will be used in this report. However, the literature on VSL estimates indicates a wide range of measured
estimates of VSLs – some as low as a few million dollars, some as high as over $30 million. The U. S. DOT
guidance memorandum (U.S. Department of Transportation (1997), Departmental Guidance for the
Valuation of Travel Time in Economic Analysis. Memorandum from the Office of the Secretary ofTransportation, U.S. Department of Transportation. Available at http://ostpxweb.dot.gov/policy/Data/VOT97guide.pdf) discusses a feasible range of VSLs for sensitivity analysis from $5.2 million to$12.9 million. Appendix A provides a sensitivity analysis consistent with this range.
Lost Quality-of-Life for Nonfatal Injuries:
While WTP studies can be used to value loss of life, nonfatal injuries, which are a far more prevalent114

occurrence in motor vehicle crashes, require a more complex examination to reflect the diversity ofpossible outcomes. When a life is lost prematurely in a motor vehicle crash, the victim loses all of his
remaining life, and this can be quantified in terms of life years by comparing the victim’s age atdeath to expected remaining lifespan. However, when the victim is injured but survives, the loss tothe victim is a direct function of the extent to which the victim is disabled or made to suffer throughphysical pain or emotional distress, as well as the duration through which these impacts occur.
The metric commonly used to value these nonfatal injury losses is the quality-adjusted life year (QALY).
A QALY is a health outcome measure that assigns a value of 1 to a year of perfect health and a value of 0
to death (Gold, Stegel, Russel & Weinstein, 1996). QALY loss is determined by the duration and severityof the health problem, with a full year of QALY loss being equivalent to the loss of a full year of life inperfect health. QALYs are used in evaluating the outcomes of clinical trials of medical interventions, inapproval of pharmaceuticals, and in studies of the return on investment in preventive health and safetymeasures (Miller, 2000). NHTSA routinely uses QALY based valuations to determine the relative value ofnonfatal injuries when measuring the cost effectiveness of regulatory alternatives. The QALY valuations
used by NHTSA were originally derived from work by Miller, Pindus, Douglass and Rossman) (1995).
These values were adopted for the previous report on the cost of crashes issued by NHTSA (Blincoe etal.) in 2002, and incorporated in subsequent regulatory evaluations conducted by NHTSA. Miller, Pindus,
Douglass and Rossman based their QALY valuations on the Injury Impairment Index (III). The III is basedon physician estimates of impairment across six functional dimensions (cognitive, mobility,
bending/grasping/lifting, sensory, pain, and cosmetic), originally developed for physician use by Hirsh etal. (1983), but subsequently enhanced to include permanent and partial work related disability by Miller,
Pindus, Douglass and Rossman (1995).
It has been over 15 years since the 1995 study by Miller and his colleagues and NHTSA was concernedthat subsequent advances in medical technology could have shifted the relative values of QALYs
associated with motor vehicle injuries. In preparation for this current study, NHTSA contracted with the
Pacific Institute for Research and Evaluation to re-examine the III injury preference weights based on themost recent literature and reflect any changes that may have occurred due to shifts in the injury casemix. The resulting study by Spicer and Miller (2010) provides the basis for the nonfatal injury QALY
values used in this report. The report found slightly different QALY values for all injury levels, reflecting
both the revised preference weights and the larger and more recent database examined in the newreport. The results of this effort are summarized by MAIS injury severity level for a variety of discountrate assumptions in Table 5-1.
Table 4-1. QALY Values for Injured Survivors by Discount Rate and MAIS
Discount
Rate
InjurySeverity
MAIS1
MAIS2
MAIS3
MAIS4
MAIS5

0%

3%

4%

7%

10%

0.3%
3.5%
10.1%
25.5%
58.3%

0.3%
4.4%
10.4%
26.3%
59.1%

0.3%
4.7%
10.5%
26.6%
59.3%

0.4%
5.6%
10.8%
27.5%
60.1%

0.4%
6.5%
11.1%
28.4%
60.9%

115

QALY values for the most serious injuries (MAIS5) are thus roughly 60 percent of a full remaining life,
while minor injuries (MAIS1) are valued at less than 1 percent of a full remaining life. QALYs riseprogressively with the severity of the injury. This reflects both the severity and longevity of injury
consequences at each severity level. For example, serious brain injury, spinal cord injury, and otherinjuries likely to involve long term debilitation are typically classified in the higher MAIS categories, whileless debilitating injuries with shorter recovery times tend to be classified in the lower MAIS categories.
Note that the impact of discount rates on QALY values is relatively limited. Shifts in discount rates affectboth the MAIS levels and the full life values, which minimizes the relative impact on QALYs.
Although the impact of discount rates on QALYs is minor, a single value must still be adopted for thisanalysis. Ideally, QALY values would reflect the discount rate implicit in consumer valuations used tomeasure the VSL, which these QALYs will be applied to. Estimates of this rate vary as widely as estimates
for the VSL. Aldy and Viscusi (2007) cite a range of implicit discount rates of from 1 to 17 percent acrossfive different studies that examined VSLs or VSLYs. 36 Hartwick (2008) derived implicit discount rates ofbetween 3 percent and 4 percent for people who die from ages 30 to 40 with a VSL of $6.3 million.
Based on 2007-2009 FARS data, the median age of a person killed in a motor vehicle crash is 38. On thisbasis, either a 3-percent or a 4-percent discount rate appears to be appropriate, and the difference in
QALYs between these two rates is extremely small. The USDOT has adopted QALYs based on a 4-percent
discount rate as an intermediate value between the 3-percent and 7-percent rates recommended by
OMB (Trottenberg & Rivkin, 2013). Since this report is based on a 3-percent discount rate, we use the 3
percent values to retain consistency with the rest of the report. A separate table consistent with the
OMB recommendation (i.e., with nonfatal injury QALYs based on a 4-percent discount rate), can bederived using Table 4-1 above.
Comprehensive Costs:
The VSL and QALY measures discussed in the previous section represent an average valuation of the lost
quality-of-life that would be lost to crash victims. However, it does not include the economic costs that
result from an unexpected event such as death or injury resulting from a motor vehicle crash. Those
costs, which include medical care, legal costs, emergency services, insurance administrative costs,
workplace costs, congestion impacts, and property damage, were previously estimated in Chapters 2
and 3 of this study. The full societal impact of crashes includes both the intangible impacts represented
by VSL and QALY estimates, and the economic impacts that result directly from the crash. Combining
these impacts – the direct economic costs that result from the crash and the value of lost quality-of-life
experienced by injured crash victims, results in a measure of the comprehensive cost to society fromdeath or injury.
The economic cost estimates developed previously include lost market and household productivity. WTP
based valuations of life, which encompass the entire expected life experience of consumers,
theoretically encompass after-tax wages (the portion of wages actually received by the employee) and36

VSLY is the value of a statistical life year – a single year of remaining statistical life rather than the full value ofall remaining life years as measured by VSL.

116

household productivity.37 Since these measures are hypothetically already included under WTP
valuations, combining measures of economic costs and lost quality-of-life requires an adjustment toavoid double counting these components. In Table 4-2 below, the components that make upcomprehensive costs are listed in the left column. These consist of the various economic cost
components with an additional line for QALYs. Because lost after- tax market and householdproductivity are separate line items that are implicitly included in QALYs, the QALY values are reducedby these values so that the separate components can be added to produce the total comprehensive costfor each injury severity level.
Comprehensive costs have been used by NHTSA and other agencies to evaluate regulatory programs forover a decade. They provide a convenient basis for measuring the full societal benefits of regulations
against their costs, and they are the appropriate basis for valuing benefits in a cost-benefit context
where societal impacts are the overriding concern. However, in some circumstances, users may wish tomeasure only the tangible economic value of goods and services lost and out of pocket expenses
incurred that result of motor vehicle crashes. Economic impacts are commonly considered bypolicymakers and public interest groups when safety issues are being debated. These more tangible
economic costs are both more easily understood and more reliably measured than lost quality-of-life,
which, as noted previously, is subject to a wide range of estimates and the uncertainty implicit in thisrange. This report provides estimates of impacts under both bases to facilitate either approach.
Table 4-2 summarizes the total unit cost of crashes stratified by injury level and cost category, andFigure A illustrates the relative contribution of economic costs and quality-of-life to the total
comprehensive cost for each injury severity level. The total Comprehensive cost for a fatality is $9.1
million, with roughly 97 percent of this due to components that influence the VSL (QALY and lost
productivity), but roughly 85 percent representing lost quality-of-life in excess of these factors. The
portion of total comprehensive costs represented by economic costs decreases as the severity of the
injury increases. Economic costs represent 15 percent of fatal comprehensive costs, 14-18 percent of
the more serious nonfatal injury costs, 43 percent of minor injury costs, and 100 percent of MAIS0 and
PDO costs. This reflects the relatively small values for lost quality-of-life found for less severe injuries.
The “Subtotal Injury” line represents components associated with injuries. Costs on this line are thus
useful in analyzing the economic benefits of safety countermeasures that prevent injury in the event ofa crash. The “Economic Total” line is useful for estimating the economic benefits from countermeasuresthat prevent crashes from occurring. To examine the total societal harm prevented by either
countermeasure type, the value on the QALY line should be added to the appropriate economic values.

37

After-tax market productivity is inherent to VSLs because it determines the individual’s valuation of theirpotential material consumption. Household productivity is inherent to VSLs because it is a routine activity that ispart of life experience. Both aspects are potentially threatened by behaviors that increase risk, and are thus
inherently already reflected in the VSL.

117

Over half of all PDO crashes and about a quarter of all non-fatal injury crashes are not reported topolice. However, analyses of safety countermeasures frequently rely only on police-reported incidence
data. Crashes that get reported to police are likely to be more severe than unreported crashes because
vehicles are more likely to require towing and occupants are more likely to require hospitalization or
emergency services. These crashes are typically also likely to require more time to investigate and clear
from roadways than unreported crashes. Analysis based solely on police-reported crashes should thus
be based on unit costs that are specific to police-reported crashes. For injury related costs, this is moreor less automatically accounted for by the shift in the injury severity profile. Unreported crashes have alower average severity profile than do reported crashes. However, for non-injury related cost
components – property damage and congestion costs – there is no profile to shift. In addition,
emergency services have higher involvement rates for police-reported crashes.
For this report, costs specific to police-reported and unreported crashes have been developed. Thechanges in unit costs are all due to economic cost factors and these are discussed in detail in the Human
Capital chapter. The results of this analysis on comprehensive costs are presented in Tables 4-3 and 4-4.
The differences seem negligible at the more severe injury levels due to the overwhelming costs of factors
such as lost productivity and medical care which do not vary by reporting status, except through the shift
in injury profiles. However, at lower severity levels the unit costs are significant. For PDO vehicles and
MAIS 0s, police-reported crashes have costs that are three times those of unreported crashes. For minor(MAIS1) injuries, reported crashes cost 16 percent more than unreported crashes. These ratios decline as
injury severity increases. Note that for MAIS 4s, MAIS5s, and Fatalities, property damage costs are
identical under both reported and unreported cases. All injuries at these levels are believed to bereported to police, and the original property damage cost estimate is thus assumed to represent policereported cases. These same costs are thus listed under both scenarios.
Table 4-2. Comprehensive Unit Costs, Reported and Unreported Crashes (2010 Dollars)
MedicalEMS
Market Prod.
Household Prod.
Insurance Adm.
Workplace
Legal Costs
Subtotal
Congestion
Prop. Damage
Subtotal
Total Economic
QALYs
Comp.Total

PDO
MAIS0
$0
$0

MAIS1
$2,799MAIS2$11,453

MAIS3$48,620MAIS4$136,317

MAIS5$384,273

Fatal$11,317

$28

$21

$89

$194

$416

$838

$855

$902

$0

$0

$2,726$19,359$64,338$140,816$337,607$933,262

$60
$191
$62
$0
$341
$1,077$2,444$3,521$3,862$0$3,862

$45
$143
$46
$0
$255
$760
$1,828$2,588$2,843$0$2,843$862$3,298$341$1,182$11,297$1,109$5,404$6,513$17,810$23,241$41,051$7,106$4,659$2,644$3,351$48,766$1,197$5,778$6,975$55,741$340,872$396,613$22,688$15,371$5,776$12,402$169,611$1,434$10,882$12,316$181,927$805,697$987,624$37,541$28,228$6,361$26,668$376,769$1,511$16,328$17,839$394,608$2,037,483$2,432,091$95,407$72,525$11,091$82,710$984,468$1,529$15,092$16,621$1,001,089$4,578,525$5,579,614$289,910$28,322$11,783$106,488$1,381,984$5,720$11,212$16,932$1,398,916$7,747,082$9,145,998

Note: Unit costs are on a per-person basis for all injury levels. PDO costs are on a per-damaged-vehicle
basis.

118

Figure 4-A. Relative Distribution of Comprehensive Costs
100%
80%
60%
40%
20%
0%

PDO

MAIS0

MAIS1

MAIS2

Economic Costs

MAIS3

MAIS4

MAIS5 Fatality

Quality of Life

Table 4-3. Comprehensive Unit Costs, Police-Reported Crashes (2010 Dollars)
MedicalEMS
MarketHouseholdInsurance
Workplace
Legal Costs
Subtotal
Congestion
Prop. Damage
Subtotal
Total
QALYs
Total

PDO
$0

MAIS0
$0

MAIS1
$2,799MAIS2$11,453

MAIS3$48,620MAIS4$136,317

MAIS5$384,273

Fatal$11,317

$59

$38

$109

$221

$416

$838

$855

$902

$0

$0

$2,726$19,359$64,338$140,816$337,607$933,262

$60
$191
$62
$0
$372
$2,104$3,599$5,704$6,076$0$6,076

$45
$143
$46
$0
$272
$1,416$2,692$4,108$4,380$0$4,380$862$3,298$341$1,182$11,317$1,426$7,959$9,385$20,701$23,241$43,942$7,106$4,659$2,644$3,351$48,793$1,450$8,510$9,960$58,754$340,872$399,626$22,688$15,371$5,776$12,402$169,611$1,490$16,027$17,517$187,128$805,697$992,825$37,541$28,228$6,361$26,668$376,769$1,511$16,328$17,839$394,608$2,037,483$2,432,091$95,407$72,525$11,091$82,710$984,468$1,529$15,092$16,621$1,001,089$4,578,525$5,579,614$289,910$28,322$11,783$106,488$1,381,984$5,720$11,212$16,932$1,398,916$7,747,082$9,145,998

Note: Unit costs are on a per-person basis for all injury levels. PDP costs are on a per-damaged-vehicle
basis.

119

Table 4-4. Comprehensive Unit Costs, Unreported Crashes (2010 Dollars)

MedicalEMS
MarketHouseholdInsurance
Workplace
Legal Costs
Subtotal
Congestion
Prop. Damage
Subtotal
Total
QALYs
Comp. Total

PDO
$0

MAIS0
$0

MAIS1
$2,799MAIS2$11,453

MAIS3$48,620MAIS4$136,317

MAIS5$384,273

Fatal$11,317

$7

$6

$32

$84

$416

$838

$855

$902

$0

$0

$2,726$19,359$64,338$140,816$337,607$933,262

$60
$191
$62
$0
$320
$384
$1,224$1,609$1,928$0$1,928

$45
$143
$46
$0
$240
$180
$916
$1,096$1,337$0$1,337$862$3,298$341$1,182$11,240$180$2,707$2,888$14,127$23,241$37,368$7,106$4,659$2,644$3,351$48,656$180$2,894$3,075$51,731$340,872$392,603$22,688$15,371$5,776$12,402$169,611$180$5,451$5,632$175,243$805,697$980,940$37,541$28,228$6,361$26,668$376,769$180$16,328$16,508$393,277$2,037,483$2,430,760$95,407$72,525$11,091$82,710$984,468$180$15,092$15,272$999,740$4,578,525$5,578,265$289,910$28,322$11,783$106,488$1,381,984$458$11,212$11,670$1,393,654$7,747,082$9,140,736

Note: Unit costs are on a per-person basis for all injury levels. PDP costs are on a per-damaged-vehicle
basis.

120

5. Incidence
Crash costs are driven by the incidence of fatalities, injuries, and damaged vehicles that result frommotor vehicle crashes. Most serious crashes are reported in police records within individual States andjurisdictions, but many less serious crashes are either not reported to police, or are reported but notrecorded because their severity falls below a local reporting threshold. In this section we estimate theincidence of both the police-reported and unreported crashes that occur annually on our roadways.
Fatalities:
The incidence of fatalities that result from motor vehicle crashes is derived from the Fatality Analysis
Reporting System (FARS). FARS is an annual census of all fatal roadway crashes. FARS collects data on all
fatal traffic crashes within the 50 States, the District of Columbia, and Puerto Rico. To be included in
FARS, a crash must involve a motor vehicle travelling on a roadway customarily open to the public andresult in the death of a person (occupant of a vehicle or a nonoccupant) within 30 days of the crash.
FARS collects information on over 100 different coded data elements that characterize the crash, thevehicle, and the people involved.
Over the past decade, fatal crashes have declined due to a variety of factors including safer vehicles,
safer roadways, improved driver behavior such as increased seat belt use and decreased impaired
driving, increased congestion, which reduces travel speeds, more use of mass transit, and, since 2007,
reduced economic activity. In 2000, when NHTSA last examined this issue, there were roughly 42,000
fatalities from motor vehicle crashes. By 2010, that total has dropped to roughly 33,000 – a 21-percent
decline. This is an encouraging trend, and is even more impressive in light of the increased populationand generally rising rates of travel over time. Some aspects of this decline are likely to remain and evenaccelerate as the older on-road fleet is replaced by more modern vehicles with advanced safety features
such as electronic stability control and advanced air bag systems. However, as the economy rebounds
from the recession, increased economic activity is likely to offset some of this progress.
Table 5-1 and Figure 5-A illustrate the trend in fatalities over the past 60 years, along with the trend in
the fatality rates. Over this period, fatality rates have exhibited steady decline. Fatality counts rose
during the 1960s in response to rapid increase in the driving population associated at least in part by the
demographic shift of the baby boom generation into the driving cohort. For the first half of the past
decade, fatality counts were relatively steady while fatality rates continued their decline, but anoticeable decline in fatalities occurred beginning in 2006-2007, which accelerated in the following
years as the country weathered the recession. Although an economic recovery has been in place forseveral years, 2011 fatalities continued to decline to their lowest level since 1949. However, preliminary
data for 2012 indicate that fatalities may now begin to increase from these historic low points.

121

Table 5-1. Fatalities and Fatality Rates, 1949-2011

Year
1949
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980

Fatality
Rate
per
100M
Fatalities VMT
30,246
7.13
33,186
7.24
35,3097.19
36,088
7.03
36,1906.65
33,8906.03
36,6886.06
37,965
6.05
36,932
5.71
35,331
5.32
36,223
5.17
36,3995.06
36,285
4.92
38,9805.08
41,7235.18
45,645
5.39
47,0895.3
50,894
5.5
50,724
5.26
52,725
5.19
53,543
5.04
52,627
4.74
52,542
4.46
54,5894.33
54,052
4.12
45,196
3.53
44,525
3.35
45,523
3.25
47,8783.26
50,331
3.26
51,093
3.34
51,091
3.35

Year
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011

Fatality
Rate
per
100M
Fatalities VMT49,301
3.17
43,945
2.76
42,5892.58
44,257
2.57
43,8252.47
46,0872.51
46,3902.41
47,0872.32
45,5822.17
44,5992.08
41,508
1.91
39,2501.75
40,1501.75
40,716
1.73
41,8171.73
42,065
1.69
42,013
1.64
41,501
1.58
41,717
1.55
41,945
1.53
42,1961.51
43,0051.51
42,884
1.48
42,836
1.44
43,5101.46
42,708
1.42
41,2591.36
37,423
1.26
33,8831.15
32,9991.11
32,3671.1

122

Figure 5-A. Fatalities and Fatality Rate, by Year

Nonfatal Police-Reported Injuries:
While FARS provides a dependable census of all fatal crashes, there is no equivalent data source fornonfatal injuries. Nonfatal injuries are estimated in several NHTSA databases. The Crashworthiness Data
System (CDS) is a nationally representative sample of roughly 5,000 crashes annually. CDS contains
detailed information on police-reported injuries incurred by passengers of towed passenger vehicles.
CDS employs trained crash investigators to obtain data from police-reported crash sites, studying
evidence such as skid marks, fluid spills, broken glass, and bent guard rails. They locate the vehicles
involved, photograph them, measure the crash damage, and identify interior locations that were struckby the occupants. These researchers follow up on their on-site investigations by interviewing crash
victims and reviewing medical records to determine the nature and severity of injuries. This enables
researchers to properly categorize injury severity based on the Abbreviated Injury Scale (AIS), the basis
for stratifying injury severity in this report. Crashes covered by the CDS represent about 62 percent of allpolice-reported injuries and typically involve the most serious injuries to vehicle occupants.
Injuries that occur in non-tow-away crashes, to occupants of large trucks, buses, motorcycles, bicyclists,or to pedestrians, are not included in CDS. The incidence of these injuries can be derived from theGeneral Estimates System (GES). Data for GES come from a nationally representative sample of policereported motor vehicle crashes of all severity and vehicle types. In order for a crash to be eligible for the
GES sample a police accident report (PAR) must be completed, it must involve at least one motor vehicle
traveling on a traffic way, and it must have resulted in property damage, injury, or death.

123

These accident reports are chosen from 60 areas that reflect the geography, roadway mileage,
population, and traffic density of the GES data collectors make weekly visits to approximately 400 police
jurisdictions in the 60 areas across the United States, where they randomly sample about 50,000 PARseach year. No other data are collected beyond the selected PARs. As a result, the only severity
stratification in GES is that obtained from the PAR. In most States this is typically based on what iscommonly known as the KABCO system. Police at the scene of the crash make their best determination of
the status of each involved driver or occupant or pedestrian and categorize it as either killed (K),incapacitating injury (A), non-incapacitating injury (B), possible injury (C), or uninjured (O). Unlike the AIS
severity stratification that can be obtained from CDS which is derived from medical records, these
designations reflect only the initial opinion of responders who are not medical specialists. The KABCO
results from GES thus provide only vague and sometimes inaccurate information regarding injury
severity.
To address this problem, translators have been developed to convert KABCO ratings into specific MAIS
ratings. These translators are developed from 1982-1986 data from the National Accident Sampling
System (NASS). NASS was the primary injury data system used by NHTSA through 1986. It was replacedin 1989 by the current GES and CDS systems. Both NASS and CDS contain severity designations on MAIS
and KABCO bases, which allows for an examination of the actual injury severity levels that are containedin each KABCO category. An example of these translators is shown in Table 5-2 for Non-CDS cases
involving nonoccupants and motorcyclists. The results indicate the importance of expressing injuries on
an MAIS basis rather than relying on the KABCO ratings from PARs. About 36 percent of the cases thatpolice coded as uninjured were actually injured. 4.3 percent of the cases coded as possible injury wereactually uninjured, as were 2.2 percent of cases coded Non-incapacitating, 0.5 percent of those coded asincapacitating, and 3.2 percent of those coded injured, but severity unknown. In addition, 21.6 percentof those coded Unknown (if injured) were uninjured while 78.4 percent were injured. There are alsosignificant differences in the distribution of severities among those who are injured. 30.1 percent ofcases coded as incapacitating, the most severe injury category under the KABCO system, actuallyexperienced only a minor injury (MAIS1) and another 27.8 percent only experienced a moderate injury(MAIS2).
Table 5-2. Translator for Non-CDS Cases, Nonoccupants and Motorcyclists
Translator for Non-CDS Cases, Nonoccupants and Motorcyclists
MAIS
MAIS0
MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
ISU
Fatality
Total

O
0.640
0.308
0.044
0.004
0.001
0.000
0.004
0.000
1.000

C
0.043
0.572
0.164
0.040
0.001
0.001
0.179
0.000
1.000

B
0.022
0.618
0.158
0.059
0.002
0.000
0.140
0.000
1.000

A
0.005
0.301
0.278
0.270
0.025
0.026
0.089
0.006
1.000

124

ISU
0.032
0.433
0.085
0.042
0.005
0.000
0.403
0.000
1.000

Unknown
0.216
0.469
0.117
0.064
0.008
0.004
0.098
0.024
1.000

Although CDS contains the more accurate MAIS injury severity estimates, its smaller sample size makes
it a less reliable indicator of aggregate incidence. To derive a National non-fatal injury profile for 2010,
CDS was used to establish an initial incidence and distribution for cases fitting the CDS profile – crashes
involving at least one towed passenger vehicle. These cases were then increased by the ratio of CDS
equivalent injury cases from the GES to the CDS total. This normalization process acknowledges thesmaller standard error that results from the more robust sample that GES uses.
A different approach was used for cases not covered by CDS. Non-CDS cases were isolated from the
1982-86 NASS files and split according to their seat belt status. Belt status was examined separately
because belts have a significant impact on injury profiles and belt use has increased significantly sincethe 1982-86 period. Four separate categories were examined: belted occupants, unbelted occupants,
unknown belt status, and nonoccupants including motorcyclists. A separate translator was developedfor each of these categories. These translators were applied to their corresponding non-CDS equivalent
cases from the 2010 GES file to estimate total non-CDS equivalent injuries by MAIS level.
The combined CDS and non-CDS cases represent police-reported injuries as estimated in these systems.
While the data systems noted previously estimate National level totals of injuries based on samples,
individual States collect police-reported injury totals from the various jurisdictions within that State.
State data systems thus provide a potential census of all crashes for which a police report was filed. At
one time this data was gathered together and published by FHWA, however, FHWA no longer compiles
this data so they must be obtained from other sources.
Since the early 1980s, NHTSA has been obtaining from various States computer data files coded fromdata recorded on police accident reports (PARs). A PAR is completed by a police officer at a motor
vehicle traffic crash scene and contains information describing characteristics of the crash, the vehicles,
and the people involved. The data recorded on these forms are computerized into a central crash data
file at the State level. Information will vary from State to State because each State has different datacollection and reporting standards. NHTSA refers to the collection of these computerized State crashdata files as the State Data System (SDS).
The State crash data files are requested annually from the State agencies that maintain the files. In most
instances, this agency is the State police, the State Highway Safety Department, or the State Departmentof Transportation. The data is received in various formats and converted to Statistical Analysis System(SAS) data files. The SAS files are placed on NHTSA’s Local Area Network, where they are available forthe analytical needs of the NHTSA staff. The State crash data files in SDS are not available for researchoutside NHTSA unless permission has been granted from the State to release the crash data. The Statecrash data files are obtained to support NHTSA’s efforts to identify traffic safety problems, to help
develop and implement vehicle and driver countermeasures, to evaluate motor vehicle standards, andto study crash avoidance issues, crashworthiness issues, and regulations.
Because only 34 States participate in this system, SDS data was supplemented by directly contacting oraccessing the Web sites of non-participating States.
Previous analysis comparing State police reports to GES counts have found that actual police-reported
injuries exceed those accounted for in the GES by 10 to 15 percent (Blincoe & Faigin, 1992, Blincoe et al.,2002). These previous analyses have focused on the difference in State injury counts and GES estimates.
A similar attempt was made to examine these counts for the current analysis, however, it was foundthat State injury reporting practices have now become too dissimilar and fragmented to produce a
reliable injury count for this comparison. For example, definitions of specific injury levels often overlap

125between States, hospital follow-up requirements vary by jurisdiction, and use of the “Unknown” severity
appears to vary by jurisdiction as well. Instead, a comparison was made between total police-reported
crashes in all States to those derived from GES. 38 Due to significant widespread delays in finalizing
records within many State reporting systems, the latest year for which complete crash counts wereavailable from most States was 2009.39 Based on this data, there were 6,085,916 police-reported
crashes in State records for 2009. These counts are shown in Table 5-3. By contrast, the 2009 GES
estimated a total of 5,497,506 crashes in 2009. The ratio of State to GES crashes is thus 1.107, implyingthat GES understated total crashes by 10.7 percent. This is consistent with past estimates based on
injury counts, which were in the 10- to 15-percent range. Our final estimate of police-reported injuries isderived by inflating the non-fatal injury profile derived above by this 1.107 factor. The results are shownin Table 5-4.

38

Bondy, N., Validation of the National Estimates Produced From NASS GES, NHTSA Research Note, 2014.
Note that even for 2009 data was not available for Montana. Data from 2008 was substituted for Montana.
Total crashes for Colorado and Hawaii were estimated based on trends in fatal crashes. This should not
significantly impact the accuracy of this analysis.
39

126

Table 5-3. Motor Vehicle Police-Reported Traffic Crashes by State
Motor Vehicle Police-Reported Traffic Crashes by State
State# Crashes

Year

Alabama

1235032009

SDS

Alaska

12890

2009

ST Web site

Arizona

1067672009

Arkansas

62808

California

SourceState# Crashes

Year

Montana

21971

2008

SDS

Nebraska

34664

2009

ST Web site

Nevada

53151

2009

2009

SDS

New Hampshire

33265

2009

SDS
ST Website
ST Website

4262282009

SDS

New Jersey

3012332009

SDS

Colorado

1050002009

SDS

New Mexico

46213

2009

SDS

Connecticut

1037192009

SDS

New York

3149742009

SDS

Delaware

16723

2009

SDS

North Carolina

209695

2009

SDS

DC

16841

2009

ST Web site

North Dakota

17686

2009

SDS

Florida235803

2009

SDS

Ohio

2990402009

Georgia

3185312009

SDS

Oklahoma

71218

2009

Hawaii

10000

2009

ST Web site

Oregon

41271

2009

SDS
ST Website
ST Web
site

Idaho

22992

2009

ST Web site

Pennsylvania

1212982009

Illinois2924372009

SDS

Rhode Island

41788

2009

Indiana

1899832009

SDS

South Carolina

106864

2009

Iowa

55488

2009

SDS

South Dakota

16994

2009

Kansas

61119

2009

SDS

Tennessee

1550992009

Kentucky126237

2009

SDS

Texas

428667

2009

Louisiana

155857

2009

SDS

Utah

51367

2009

Maine

33118

2009

ST Web site

Vermont

12640

2009

SDS
ST Website
ST Website

Maryland

96391

2009

SDS

Virginia1167422009

SDS

Massachusett

1363842009

ST Web site

Washington

1100702009

Michigan

2934032009

SDS

West Virginia

39906

2009

SDS
ST Website

Minnesota

73498

2009

SDS

Wisconsin

1217362009

SDS

Mississippi

74122

2009

ST Web site

Wyoming

15507

2009

SDS

Missouri153015

2009

SDS

TOTAL

6085916

127

SourceSDS
ST Website
SDS
ST Website
ST Website

Table 5-4. Estimated Police-Reported Non-Fatal Injury Profile, 2010

Severity
MAIS0
MAIS1
MAIS2
MAIS3
MAIS4
MAIS5

CDS

8928991364841
179341
56419
13129
3932

Non-CDS
Unbelted

Non-CDS
BeltedNon-CDS
Unk Belt

GES
Nonoccupant

50425
156165
40345
20587
1996
1227

19401942329646244943
87077
15434
5193

54730
50678
4360
566
76
34

838576684262
17559
9291
233

103563
73701
3338
214

Total

Adjusted forStates21478572578993271160
96397
17086
5749

Total
Total
Injuries

25105621104431549921

18081627074546224885117242

161766355713

71134577253

22032026822942969386% Total60.31%

2.08%

26.52%

2.88%

8.21%

100.00%An estimated 2.97 million injuries were documented in police reports in 2010. This is a decrease fromthe 4.1 million estimated for 2000 in the previous version of this report. This decline is illustrated in
Figure 5-B. Note however that Figure B only reflects the raw injury counts from the GES system. It does
not reflect adjustments for MAIS injury translation or GES undercounting. Nonetheless, it does illustrate
the steady decrease in injuries and injury rates over the past decade. This decrease reflects a variety offactors in including safer vehicles, safer roads, increased belt use, increased enforcement of alcohol
countermeasures, and, after 2007, an economic slowdown that reduced driving and exposure.

128

Figure 5-B. People Injured and Injury Rate, by Year

Unreported Crashes and Injuries
The primary basis for incidence estimates used in this report are databases maintained by NHTSA thatexamine police-reported crashes. As discussed above, FARS is a census of all fatal crashes, while GES and
CDS sample a broader set of police-reported crashes, including nonfatal crashes as well. These sources
provide a basis for estimating the incidence of all police-reported crashes nationwide. However, asignificant number of crashes are not reported to police.

129

In previous NHTSA analysis of this issue (Blincoe & Faigin, 1992), unreported injury crashes were
estimated from data derived from Rice (1989), and Miller (1991) from the National Health Interview
Survey (NHIS), while unreported property damage crashes were estimated based on a comparison of
insurance claims data to National estimates of police-reported PDOs (Blincoe & Faigin, 1992) . In
subsequent NHTSA analysis, (Blincoe 1996, Blincoe et al., 2002), the unreported PDO estimate wasretained but the unreported injury basis was derived from a study by Greenblatt, Merrin, andMorgenstein (1981). The switch in the unreported injury basis occurred because the Department of
Health and Human Services announced that it had discovered a programming error that affected all
motor vehicle injury estimates in the NHIS from 1982 through 1994. The most recent estimates of
unreported crashes were thus based on injury survey data that is currently over 30 years old, and PDO
insurance data that is over 20 years old.
NHTSA was concerned that changes in police reporting practices, insurance coverage, vehicle costs,
litigation practices, real incomes, and the proliferation of cell phones may have shifted the unreported
crash proportion over the past two to three decades. To address this concern, NHTSA contracted with
M. Davis and Company (MDAC) to conduct a comprehensive nationally representative survey ofhouseholds to determine the relative incidence of reported and unreported crashes. In late 2009 andthe first half of 2010, MDAC conducted interviews with roughly 2,300 households where the respondent
had experienced a motor vehicle crash during the previous 12 months. The interviews addressed therate of reporting to police, the rate of reporting to insurance agencies, the severity of the crash, thelocation of vehicle damage, the types of injuries experienced in the crash, the cost of medical care,vehicle repair costs, the reasons why the crash was not reported, the crash location, and the number ofvehicles involved in the crash. Most data elements were stratified separately for injury crashes and PDO
crashes.
The results of the survey are contained in a report (NHTSA, 2011c) that details both methods and survey
results. Some of the findings include:
•
•
•
•
•
•
•

29.3 percent of all crashes are not reported to police;
15.4 percent of injury crashes are not reported to police;
35.6 percent of property-damage-only crashes are not reported to police;
18.5 percent of all crashes are not reported to insurance companies;
12.3 percent of injury crashes are not reported to insurance companies;
20.8 percent of property-damage-only crashes are not reported to insurance companies;
Medical care costs for police-reported crashes are roughly 9 times higher than for unreported
crashes; and• Vehicle repair costs for police-reported crashes are roughly 5 times higher than for unreported
crashes.
Respondents gave a variety of reasons why they did not report the crashes they were in. The mostcommon was that the extent of vehicle damage or injury was not serious/severe enough to warrant
reporting the crash, which together made up 56.7 percent of the responses for unreported injury
crashes and 71.7 percent of the responses for PDO crashes. None of the other 10 possible responses

130was offered by more than 8 percent of the respondents. However, the survey data also indicate thatinvolvement of other vehicles was a large determinant for reporting. Single-vehicle crashes represent
only 15.6 percent of reported injury crashes but 27.0 percent of unreported injury crashes. This
discrepancy is even more pronounced for property damage crashes. For PDOs, single-vehicle crashes
represent 14.7 percent of reported crashes, but 39 percent of unreported crashes. Thus, the presence ofa second driver, which automatically raises the issue of fault, increases the likelihood that crashes will bereported. In single-vehicle crashes, especially in cases where there is no injury, the driver has no
incentive to report a crash in which they were clearly at fault.
The unreported rates found in the current survey differ from estimates used in previous studies. For
example, in both the 2002 and 1996 reports, it was estimated that 21.4 percent of injury crashes and 48
percent of PDO crashes are not reported to the police. These estimates reflect data from two to three
decades ago. The current survey indicates that 15.4 percent of injury crashes and 35.6 percent of PDO
crashes are not reported to police.
It is not clear to what extent the change in non-reporting rates represents an actual shift in reporting
habits as opposed to a more accurate estimate. We note that the MDAC survey, in addition to being
more current, is also much larger than the 1981 survey that was the basis for previous injury crashestimates, which surveyed only 279 households compared to 2,299 in the MDAC survey. However, it
seems likely that this difference represents, at least in part, an actual change. The proliferation of cell
phones makes reporting easy, not just for the crash involved driver, but also for the other drivers who
witness the crash or see a disabled vehicle. It is likely that in many cases where a driver might previously
have chosen not to report a crash to police, other passing drivers have already notified police andforeclosed their option not to report.
A limitation of this survey is that it only reflects the knowledge of the crash involved drivers. Reporting acrash to police does not assure that a police accident report will actually be filed. Individual police
jurisdictions typically have reporting thresholds, especially for crashes that only involve property
damage. Consumers may report crashes, but if police determine that the crashes do not meet thedamage threshold, they may not file an accident report. Reporting thresholds vary by State andsometimes by jurisdiction. Table 5 lists damage reporting thresholds by State.

131

Table 5-5. State PDO Reporting Thresholds
PDO ReportingState
Thresholds
Alabama$250
Alaska$2,000
Arizona
Not required
Arkansas$1,000
California$750
Colorado
Not required
Connecticut
Not required
D.C.
Not required
Delaware$500
Florida$500
Georgia$500
Hawaii$3,000Idaho$1,500Illinois$1,500Indiana$1,000Iowa$1,000Kansas$1,000Kentucky$500
Louisiana
All Crashes
Maine$1,000MarylandNot required
Massachusetts$1,000Michigan$1,000Minnesota
Not required
Mississippi
Not required

State
MissouriMontana
Nebraska
NevadaNew Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
OregonPennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming

PDO Reporting
Thresholds$500$1,000$1,000$750$1,000$500$500$1,000$1,000$1,000$400$300$1,500
towed vehicles$1,000$1,000$1,000$400$1,000$1,000$1,000$1,000$700$500$1,000$1,000Source: http://dmvanswers.com/questions/356/When-do-I-have-to-file-an-accident-reportaccessed on April 6, 2012.

Damage thresholds vary from $250 to $3,000, but seven States have no requirement for reporting
crashes unless there is bodily injury, while one State requires reports only for tow-aways and one Staterequires all crashes to be reported. The most common threshold is $1,000. These thresholds are
established for individual drivers, but police are likely to take these thresholds into account when
deciding whether to file a crash report for PDO crashes, although special circumstances such as adverse
weather conditions or natural disasters can influence police decisions as well.

132

In an effort to further understand the relationship between crash incidence and police reports, we
queried a number of police jurisdictions that participate as primary sampling units for the National
Accident Sampling System (NASS) regarding the relationship between police accident responses andaccident reports. Only six jurisdictions responded with data, most of which was already publicallyavailable. The results are summarized in Table 5-6.
Table 5-6. Sample Data of Various Jurisdictions Accident Reporting Rates
WrittenAccident Written Report Unreported
Responses Reports
Rate
Rate
Wake County, NC
1,086
8
0.7%
99.3%
4,546
1,719
37.8%
62.2%
Knox County, TN
866
633
73.1%
26.9%
Golden, CO
1,695
1,139
67.2%
32.8%
Muskegon, MI
4,269
4,064
95.2%
4.8%
Cary, NC
Henrico County, VA
12,522
6,474
51.7%
48.3%
54.3%
45.7%
Simple Average
The reporting rates across this small sample vary widely. Often crashes occur in locales where both Stateand local police have jurisdiction, and it is likely that in those jurisdictions where the reporting rate isextremely low, many of the missing crashes were documented in reports by State police. A simple
average of these six jurisdictions indicates that nearly half of all crashes that police investigate do not
get into police reports that would be sampled by NHTSA to estimate nationwide police-reported crashtotals, but this proportion is likely over stated because of the aforementioned jurisdictional overlap. 40
This sample is too small to use for National projections, but this does give an indication that the numberof crashes in police records can differ significantly from the number that police respond to.
There are thus two bases for non-police reported crashes: crashes that are not reported to police, andcrashes that are reported to police, but that are not documented in police records. The MDAC survey isuseful for estimating crashes not reported to police, but not for those that police don’t document.
Table 5-7 summarizes National estimates of injured people in injury crashes and drivers of PDO vehicles
from the MDAC study.

40 Elimination of the extreme results for Wake County, for example, would by itself reduce theunreported average to 35 percent.

133

Table 5-7. Estimated Injured People and Drivers of PDOs From MDAC Study, by Police Reporting
StatusInjuries, or Drivers for PDOs
Type of Crash
all crashes
vehicle damage only
injury crashes
injured as driverinjured as passenger
injured as pedestrian

Reported Unreported Unknown Reported +Numberto Police to Police
StatusUnreported
20535814 14212974
5893978
428862
20106952
14178900
89110474932537
33531613843584
6356914530192796144193546
62633684073484361385440938250249
40232361475318
1280366
15947435478
1439840
8081124077073925867819
800293

These estimates exceed those that would be derived from GES by significant margins. MDAC estimatesnearly three times as many injured people as does GES, and 80 percent more PDO involved vehicles thandoes GES. Within the MDAC study itself, the survey estimates indicate 20 percent more injuries, and 59
percent more PDO vehicles than are reported to police. Since the MDAC survey includes both reported
and unreported crashes, this might be expected. However, the police reported subset from the MDAC
survey also exceeds the GES estimate by a significant margin. For injury crashes, the MDAC police
reported total is more than double the GES police-reported total. Since police are universally required toreport all injury crashes that they investigate, it might be expected that National estimates fromindependent surveys using valid methodologies would yield somewhat similar results, but this is not thecase here. There are several possible explanations for these differences.
• Recall perception bias – the survey was designed to collect injuries that had occurred within thepast 12 months. If survey respondents underestimate the time that has elapsed since an injury,
they would overstate the frequency of injuries within this time frame.
• Delayed injury recognition – drivers may report a crash to police, but at the time when queried
as to whether they were injured, they may have said no, only to find later that day or somedays later that they had actually sustained a minor non-debilitating injury (such as chestbruising from the seat belt or minor whiplash) . At the time of the survey, however,
respondents remember that they were, in fact, injured. Under these circumstances, many of
the crashes that survey respondents now characterize as injury crashes may have been
considered by police to be PDO crashes. This would cause an underestimation in police records
of injury crashes, and an overestimation in police records of PDO crashes.

134• Motorist initiated reports – injury crashes that are investigated by police are typically included inany count of police-reported injury crashes. However, drivers can report crashes to police after
the crash as well. This is often done for insurance purposes when police were not present at thecrash scene. While many States require motorists to fill out reports if they are injured in a
motor vehicle crash, the treatment of these reports in compiling injury data varies by State, andeven by local jurisdiction. Since the GES system is confined to police reports, these types of
injuries may not be included in the GES total and to some extent may be missing from State
compilations as well. In a previous examination of this issue (Blincoe & Faigin, 1992), motorist
reports were reflected in published injury counts in 8 of 22 States that were queried, and in the
8 States that did count motorist reports, they accounted for a highly variable portion of theState injury counts (ranging from less than 1 percent to 39 percent of total State injury counts.)
In the remaining 14 States, motorist reports were not included in injury counts at all. These
cases would show up as police-reported injury cases in the MDAC survey, but would be missing
entirely from GES and to a lesser extent from State data.
• Unknown survey design weaknesses or data collection inaccuracies. Telephone surveys are only
as accurate as their participant’s responses, and GES is known to underestimate fatal crashes,
although it is unclear whether this affects estimates of more common injury or PDO crashes.
Within the MDAC survey roughly 33 percent of police-reported injuries and 68 percent of unreported
injuries did not seek medical care for their injuries. Another 16 percent of police-reported injuries and 5
percent of unreported injuries had zero medical cost. These could be considered to be cases where
injuries were so minor that no treatment was sought, 41 and which may not even be noticed at the crashscene. What is notable here is that this proportion is much higher for cases that were reported to thepolice, the opposite of what would be expected given that logically, less serious crashes are more likely
to go unreported. Again, assuming that no treatment was sought in these cases, they might beconsidered a proxy for the minimum rate of delayed injury recognition. Other cases that did experience
medical costs might also reflect this as well, since latent discovery of injuries such as whiplash could
result in subsequent visits to doctors or hospitals.
It is also worth noting that the ratio of total injuries to police-reported injuries internal to the MDAC
study is similar to the results previously obtained from the 1981 study (1.20 in MDAC versus 1.21 inGreenblatt et al.). In previous studies, this markup was applied directly to the police-reported total
obtained from NHTSA databases. In this case, with the separate MDAC police-reported total indicating ahigher level of these injuries as well, the option exists to recognize some level of misapportionment of
injury versus PDO cases within police files due to delayed injury recognition. The other two possible
explanations – recall perception bias and/or unknown survey weaknesses or data inaccuracies, are notmeasurable.
We also examined National estimates of injuries from several other sources. The National HealthInterview Survey (NHIS) is a cross-sectional household interview survey designed to monitor the health41

It is also possible that respondents were not accurately portraying their true medical costs, or that all of theircosts were covered by insurance and they misunderstood the question. However, since most policies contain atleast some kind of deductible and/or co-pay, this later case seems unlikely.

135

of the U.S. population. It captures information on a variety of health issues, including injuries from motor
vehicle crashes. Motor vehicle injuries captured in the NHIS were those for which medical treatment wassought, including both police-reported and unreported crashes. In 2010, the NHIS estimate for motor
vehicle injuries for which medical treatment was sought totaled 2.5 million. Of these, roughly 1.7 million
cases were treated in emergency rooms (ER).
Another source of injury data is the National Electronic Injury Surveillance System – All Injury Program(NEISS). NEISS surveys 66 U.S. hospital emergency departments to produce National estimates of
nonfatal injuries, including motor vehicle injuries, treated in ERs. In 2010, NEISS estimated that there
were 3.3 million motor vehicle injuries treated in an ER.
Table 5-8 summarizes the various injury estimates from these 5 sources.
Table 5-8. 2010 Injury Estimates2010 Injury Estimates

SourceGES/CDS
State PARS/GES PARS
NHIS
NEISS
MDAC Total
MDAC PRMDAC NOT PR

AllPoliceReported
2,682,294
2,969,386

5,381,113
5,381,113

Not
PoliceReported

975,801
975,801

Any
MedicalTreatment

ER
MedicalTreatment

No
MedicalTreatment

2,499,016

1,708,741
3,258,889
2,973,040
2,750,879
223,8412,435,409
1,775,767
659,641

3,922,216
3,605,346
316,159

There are thus 5 separate categories of injuries estimated across these 5 data sources. The MDAC study
is based on driver responses, and thus hypothetically covers the universe of possible injury reporting
circumstances. Subsets of the MDAC study are thus comparable to their corresponding subsets fromother sources.
GES, MDAC, and State Data provide estimates for police-reported injuries, but MDAC includes police
reporting by drivers, which may or may not have resulted in a police report being filed, while GES
reflects actual police reports only and State data counts reflect actual police reports as well as somemotorist reports. The large difference between MDAC and the other sources likely reflects the factors
discussed above, including recall perception bias, delayed injury recognition, and motorist reports.
The difference between the GES estimate and State counts continues the historic pattern that has beenrecognized since the 1990s. This pattern was discussed in detail in a previous NHTSA report (Blincoe &Faigin, 1992, pp. III-28-III-65) where differences ranging from 6 to 18 percent were documented
between 1983 and 1990. These differences persisted through the 1996-1999 period when the average
difference over the 4 year span was found to be 13 percent (Blincoe et al., 2002). The current (as of2010) difference is 10.7 percent. As noted previously, due to inconsistencies among State data bases,

136

the current difference is based on crash ratios rather than injury ratios. Blincoe and Faigin found avariety of causes for the difference between GES and State data including the inclusion of motorist
reports in some State totals, misapportioned Injury/PDO cases (which may be caused by delayed injury
recognition), undercounted police reports, and undercounted injuries/crash.
Estimates of medically treated injuries are available from the MDAC and NHIS surveys. There isconsiderable disagreement between the two surveys with the MDAC survey estimating 57 percent more
medically treated injuries than NHIS. There does not appear to be an obvious theoretical reason for thisdifference. 42 Both surveys result from personal interviews and injuries severe enough to require medical
treatment should be memorable enough to assume a reasonable level of accuracy on the part ofrespondents. There may be unknown design weaknesses or weighting issues that affect one or bothstudies. However, when the ER Treatment totals from these two studies are compared along with the
NEISS estimate of ER treated injuries, two of the sources, MDAC and NEISS, are in fundamental
agreement. The MDAC estimates 3.0 million ER injuries while NEISS estimates 3.3 million. By contrast,
NHIS estimates only 1.7 million ER injuries or roughly half those found in MDAC and NEISS. In fact, bothof these studies estimate more ER treated injuries than NHIS estimates for all treatment sources
combined. Even the upper confidence bound for the NHIS is outside the confidence interval for either
the MDAC or the NEISS surveys. There is thus reasonable agreement between two of the three surveys
regarding ER treated injuries, which lends some credence to their findings and suggests the conflicting
estimate from NHIS may be understated.
The MDAC estimate of injuries for which there was no medical treatment can be divided by policereported status. Respondents indicated that roughly two-thirds of all injuries that were not reported tothe police and about one-third of injuries that were reported to the police were not serious enough towarrant treatment. Overall, 38 percent of all injuries were so minor that drivers did not seek medical
treatment.
A feasible characterization for the conflicting injury estimates among the various data sources ispresented in Table 5-9. This table adopts the basic MDAC estimate, which encompasses the universe of
injury categories, and interprets the differences between this estimate and the State data counts. The
2.97 million injuries estimated to be counted in State data files represent injuries included in police
reports, supplemented in some States by motorist reports. All injuries in this category represent cases
where the motorist indicated that the crash was reported to police and an injury was involved. The 1.8
million MDAC police-reported injuries for which no medical treatment was sought are likely to be minor
injuries. Logically, they are consistent with a subset of “delayed injury recognition” cases, where drivers
reported the crash to police but did not initially recognize that they were injured. The crash was thus
initially reported to police as a PDO crash, and therefore did not get into the State injury counts. These
two categories account for 4.7 million of the 5.4 million police-reported injuries in the MDAC survey. The
remaining 635,000 injuries may represent a combination of motorist reports that are not included in
42

NHIS covers the civilian, non-institutionalized population living in households, so it would not include military,
institutionalized, or homeless population. Neither institutionalized nor homeless would be included in the MDAC
survey either, but military living in households could be. This might account for a small portion of the difference
between these surveys.

137

State data totals and delayed recognition cases that eventually did seek medical treatment. An
additional 975,000 injuries are not reported to police. As noted above, about two-thirds of these injuries
were minor enough that the drivers did not seek medical treatment. In all, a total of 6.4 million people
are injured to some extent, but 2.4 million, or 38 percent of these injuries, required no medical
treatment, while the remaining 3.9 million injuries did require medical treatment.
It should be acknowledged that the above characterization is uncertain. There may be alternate
explanations for portions of each category. In addition, since the MDAC data is derived from a survey,
these estimates cannot be regarded with the certainty that is available in a true census such as FARS.
The ratio of total injuries to driver reported injuries from MDAC is 1.18 slightly below the 1.27 ratio
found in the previous study. However, the ratio of total injuries to police-reported injuries from Statedata is 2.20. The difference primarily represents factors discussed above including delayed injury
recognition, motorist reports, etc. The largest portion of the difference represents minor injuries forwhich no medical treatment was sought. Thus, although there is a significant difference between theestimates from the MDAC survey and police reports, most of this difference involves cases where injury
and thus costs, were minimal.
Table 5-9. Summary of Motor Vehicle Injuries Compiled From Various Sources
Summary of Motor Vehicle Injuries Compiled From Various Sources
2,969,386 State Data police-reported Injuries (PARs partially supplemented by
motorist reports in some States)
1,775,767 Minor injuries possibly initially reported to police as PDOs, notreatment sought
635,960 Motorist reports not included in State PAR counts and minor injuries
reported to police as PDO that later sought treatment
5,381,113 Total injuries reported by drivers
975,801 Injuries not reported to police or on motorist reports
6,356,914 Total Injuries based on MDAC
1.18 Ratio of total injuries to all driver reported injuries
2.14 Ratio of total injuries to police reports
1.34 Ratio of total injuries to driver reported injuries on police reports
1.81 Ratio of all driver reported injuries to police-reported injuries

As noted above, the survey found that unreported injuries were significantly less expensive to treat andthat repair damage from unreported crashes was significantly less costly than for reported crashes. The
study also documented the most serious injury and body region of these injuries. In the MDAC study,
survey participants that were injured were asked a series of questions about their injury, the symptoms
and level of treatment they received. These questions were designed so that a lay person would easily
understand and be able to respond yes or no, yet the responses could be used to determine the AISlevel of the injury. Using a probability algorithm based on injury characteristics in the Abbreviated Injury
Scale (AIS), NHTSA examined the records for each injury case to assign the injury an MAIS designation

138

consistent with the coding structure used for nonfatal injuries throughout this analysis. Based on this, aseparate unreported rate was derived for each nonfatal MAIS severity category. From Table 8 above, the2,435,409 minor injuries for which no medical treatment was sought will be treated as MAIS 0s. 43
659,641 of these were not reported to the police, while the remainder were arguably not in police
records due to misreporting or misclassification as PDOs. The 316,159 injuries that were not reported topolice but that did receive medical treatment were distributed according to the injury profile of nonhospitalized non-police-reported cases in the MDAC study, while the 635,960 cases that were reported
by drivers but not reflected in PAR counts were distributed according to the injury profile of nonhospitalized police-reported cases in the MDAC study. Table 5-10 summarizes the unreported crash
rates adopted for nonfatal injuries in this study.

Table 5-10. Nonfatal Injury Summary by Police Report Status

Severity
MAIS0
MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
Total
Total Injuries% Total

PoliceReported

Not PoliceReported

Total

Percent
Unreported

21478572578993271160
96397
17086
5749

2435409 4583265
880207 3459200
67570
3387304343 100740
0
17086
0
5749

53.1%
25.4%
19.9%
4.3%
0.0%
0.0%

511724229693863387528 8504771
952120 3921505

39.8%
24.3%

75.7%

24.3%

100.0%

Property-Damage-Only Crashes
While crashes that involve death or injury produce the most serious consequences, they are relatively
rare events. The vast majority of crashes are low-speed crashes that damage vehicles but leave vehicle
occupants unharmed. Although these crashes impose a lower unit cost on society, their frequency
makes this the most costly single type of crash overall.
Although police records include a large number of PDO crashes, they tend to be significantly
undercounted in police records due to a variety of factors including relatively high reporting thresholds

43

MAIS 0 injuries are allocated a portion of crash costs, primarily congestion and property damage.
However, they do not have medical care costs or associated lost market productivity. Allocation of thesecases, for which no medical treatment was sought, to MAIS0, is thus done to more accurately accountfor their lower costs.
139

in various States, as well as the failure of drivers to report them to police. A full analysis of PDOs must
therefore address not only police records but other sources as well.
The starting point for our estimate of PDOs is police reports. Because injury is not involved, the primary
cost from PDO crashes is damage to the vehicle. Therefore, PDOs are analyzed on a per-damagedvehicle basis. Data from the GES for 2010 indicate that there were 7,819,632 vehicles damaged without
injury caused to either the vehicle’s occupants or to pedestrians. Of these, 6,734,006 occurred in crashes
where nobody was injured, while 1,085,626 occurred in crashes where an injury occurred, but not to thevehicle’s occupant. These later cases are classified in this current report as MAIS 0 injuries so they will
not be addressed as PDOs. The PDO category thus will ultimately represent only vehicles damaged in
PDO crashes. However, a variety of other sources combine undamaged vehicles in both PDOs and injury
crashes. Therefore, the initial analysis will treat these cases as a group.
Table 5-11 lists PDO estimates from three sources. The first is the GES police-report-based estimate
previously discussed. This estimate is modified using the same 10.7 percent markup factor previously
discussed for non-fatal injuries to reflect the undercounting of State police reports inherent in GES files.
These sources imply a total of 8.7 million vehicles were damaged in crashes where the occupants were
not injured, with 86 percent of these occurring in PDO crashes.
The second source is the MDAC survey, which gathered data on police and insurance reporting for bothinjuries and for damaged vehicles. MDAC found a total of 9.1 million vehicles damaged in crashes where
the driver or occupant of that vehicle wasn’t injured that were reported to police, and an additional 5.1
million that were not reported to police. MDAC also reported that of these 14.2 million cases, 11.2
million were reported to insurance companies and 3.0 million were not. MDAC published a tableillustrating the interaction of these cases. Table 12 reproduces this table, which indicates that 64.2
percent of all cases were reported to police while 78.8 percent were reported to insurance and 58.4
percent were reported to both police and insurance.
The third source is insurance data gathered for this report and described previously in Chapter 2 (seeTable 2-10 in Chapter 2). This data indicate that there were 16.9 million vehicle claims in crashes wherethe vehicle owners were not injured. These are shown in the “Insurance Reported” column of Table 511. Because this data was gleaned from actual insurance records, we consider them to be the moreaccurate estimate of insurance reported PDOs. Crashes not reported to insurance companies must be
estimated from other sources. Using the proportions specified in the MDAC study, we estimate that,based on these insurance data, a total of 21.5 million vehicles were actually damaged in crashes where
the occupants were not injured, with 4.6 million of them not reported to insurance. From these samedata we can also estimate that 13.8 million of these vehicles were reported to police by their drivers,
while another 7.7 million were not.
As noted previously in the discussion of injuries, for a variety of reasons actual police report counts do
not match driver’s responses. Specifically, even though a driver may report a crash to police, the police
may not fill out an accident report if the crash damage is below a certain threshold. Report writing rates
in a small sample of jurisdictions averaged only 45 percent. For this analysis, we assume the National140

police-reported PDO counts produced by GES are reasonably representative of the cases for which areport is actually made. Further, we assume that the best estimate of total PDO vehicles is derived frominsurance data. In Table 5-11, the row marked “Insurance Adjusted for PR” re-distributes the difference
between the GES police-reported counts and the implied police-reported counts derived from insurance
data to non-police-reported status. Essentially, these 5.1 million cases are considered to be cases where
drivers reported crashes to police, but where police did not fill out a report. The results indicate that 40
percent of PDOs are reflected in police reports while 60 percent are not, and that 37 percent of cases
Table 5-11. PDO Vehicle Summary

PDO Vehicles
Not
PolicePoliceReported Reported

SourceNot
Insurance Insurance
Reported Reported

Total

GES

7819632

7819632

State/GES Adjustment

86565848656584

MDAC
Insurance (implied from
MDAC)

9126888505201211224825

2954075

14178900

13798192

769431916936098

455641221492510

Insurance adjusted for PR

865658412835926

16936098

455641221492510

40.3%

59.7%

78.8%

21.2%

100.0%

Percent of Total

Table 5-12. MDAC PDO Reporting Status
PDO Reported to Police?
ReportedtoInsurance?

Yes

No

Total

Yes

58.4%

20.4%

78.8%

No

5.8%

15.3%

21.2%

Total

64.2%

35.8%

100.0%

A final adjustment to the PDO estimate was made to accomplish the exclusion of MAIS 0 cases referred
to earlier in this section. GES data from 2010 indicates that 86.12 percent of PDO vehicles occurred in
PDO crashes, but the remaining 13.8 percent represent vehicles with uninjured occupants in injury
crashes. Since these cases are already counted as MAIS 0 injuries, they were removed from the PDO
total. No separate accounting for the nature of the crash was possible from either the MDAC study or
the insurance data, so the GES proportion was applied to all PDO categories. The results are summarizedin Table 5-13. This data indicates that roughly 7.5 million vehicles were damaged in PDO crashes thatwere documented in police reports, but another 11.0 million occurred which were not reflected in police
reports, for a total of 18.5 million vehicles damaged in all property damage-only crashes. Roughly 40

141percent of all PDO crashes are thus reflected in police reports. A combination of factors previously
discussed, including crashes unreported by drivers and high damage reporting thresholds within police
jurisdictions are responsible for this low reporting rate.

Table 5-13. PDO Vehicle Summary Adjusted to Remove MAIS 0 Cases
PDO Vehicles

Source

Not
PolicePoliceReported Reported

Not
Insurance Insurance
Reported Reported

Total

GES

6734006

6734006

State/GES Adjustment

74547617454761

MDAC
Insurance (implied from
MDAC)

78597714350624

96664452543950

12210395

11882542

662609014584802

3923830

18508632

Insurance adjusted for PR

745476111053871

14584802

3923830

18508632

40.3%

59.7%

78.8%

21.2%

100.0%

Percent of Total

Overall, the 40 percent reported rate for PDOs is the lowest among all injury severity categories. This is
expected given the relatively minor nature of these crashes. Figure 5-C illustrates the reporting rates ofeach severity level. MAIS 0 injuries (uninjured people in injury crashes) have a reporting rate of only 47
percent, while 73 percent of MAIS1s, 79 percent of MAIS2s, and 95 percent of MAIS3s are reflected inpolice records. All of the more serious MAIS4 and MAIS5 injuries, as well as all fatalities, are estimatedto be accounted for in police records. Table 5-14 summarizes reported and unreported incidence for all
severity categories.

142

Figure 5-C. Distribution of Police-Reported/Unreported Injuries

Table 5-14. 2010 Incidence Summary

Severity
MAIS0
MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
Fatalities
Total
Total
Injuries

PoliceReported

Not PoliceReported

Total

2,147,857
2,578,993
271,160
96,397
17,086
5,749
32,9995,150,241

2,435,409
880,207
67,5704,343
00
03,387,528

4,583,265
3,459,200
338,730
100,740
17,086
5,749
32,9998,537,770

3,002,385

952,120

3,954,504

Percent
Unreported

53.1%
25.4%
19.9%
4.3%
0.0%
0.0%
0.0%
39.7%

24.1%
59.7%
PDO
7,454,761
11,053,871 18,508,632
Note: All injury categories reflect injured people. PDO reflects damaged vehicles

1436. State Costs
In recent years, States have continued to increase their involvement in establishing and enforcing laws
related to motor vehicle safety. This is due, in part, to Federal legislation enacted to promote highway
safety such as The Safe, Accountable, Flexible, Efficient Transportation Equity Act: A Legacy for Users(SAFETEA-LU) which was enacted in 2005 and which provided one time grants to States that enacted
and are enforcing a conforming primary seat belt law for all passenger motor vehicles. SAFETEA-LU
authorized a total of $770 million in grant money over a six-year period to address roadway and driver
behavioral safety activities, especially those designed to increase belt use.
State legislators are often interested in the societal and economic cost of motor vehicle injury as they
consider new traffic safety laws, changes to existing laws and funding for enforcement of the laws. This
information can assist them in making the case to their constituencies as to the relevance of the laws
designed to make the population safer.
A State-specific distribution of total economic costs has been prepared as follows:•••

The year 2010 fatalities were obtained by State from FARS. The portion of total National
fatalities in each State was then applied directly to the total fatality cost ($46.2 billion).
State crash incidence data was obtained from individual States for 2008-2010. In cases wheredata was not available, a factor based on the trend in fatalities within the State was used toestimate crashes from the last years for which complete data was available. The portion of total
National crashes in each State was applied to the total cost of all nonfatal injuries, PDOs, anduninjured occupants ($195.8 billion).
The total costs for each State were then adjusted to reflect locality cost differences based on theratio of costs in each State to the National total. Medical costs were adjusted based on dataobtained from the ACCRA Cost of Living Index and cited by Miller and Galbraith (1995). Lost
productivity, travel delay and workplace costs were adjusted based on 2010 per-capita income.
Insurance administration and legal costs were adjusted using a combination of these two
inflators weighted according to the relative weight of medical and lost productivity
administrative costs. All other cost categories were adjusted using a composite index developedby ACCRA (also provided by Miller).

These four adjustment factors were applied separately to the fatal and nonfatal costs for each State.
Weights to combine each factor were derived separately from the relative importance of each cost
category to nationwide fatal and nonfatal total costs. The sum of fatal and nonfatal costs for each Statewas then adjusted to force the sum of all States’ costs to equal the National total.
The results of this analysis are depicted in Table 6-1. There is considerable variation in costs among theStates with New York, for example, having costs that are 17 times higher than those for Idaho. This is
primarily due to the higher incidence of death and injury in New York (a function of population), but also
to the higher cost levels in that State. However, as noted by Miller and Galbraith (1995), costcomparisons between States that are based on State injury totals can be misleading because injury
totals do not capture differences in nonfatal injury severity between States. This would tend to understate
costs in rural States relative to urban States, which typically have lower average speeds and consequently
less severe injuries. Ideally, State costs would be based on individual State injury profiles, but these are
not available for many States.

144

Differences between States may also result from different reporting practices that result in more or lesscomplete recording of injuries from State to State. Differences in roadway characteristics and state of
repair may account for some of this discrepancy, though it seems likely that variation in injury reportingis also a contributing factor. Finally, the impact of crash costs must be viewed in the context of eachState’s economy. Smaller, less populated States may have lower absolute costs, but they may also havefewer resources available to address these costs. A significant portion of these costs is borne by thegeneral public through State and local revenue, or through private insurance plans. The per capita costs
for each State vary from roughly $400 to $1,500 compared to the nationwide average of $784. This
represents 1.0 to 3.6 percent of the per capita income for each State, with an overall average of 1.9
percent.
Table 6-1. Estimated 2010 Economic Costs Due to Motor Vehicle Crashes by StateState
ALABAMAALASKA
ARIZONA
ARKANSASCALIFORNIACOLORADOCONNECTICUT
DELAWARE
DIST. OF COL.
FLORIDAGEORGIAHAWAIIIDAHOILLINOISINDIANAIOWAKANSASKENTUCKYLOUISIANA
MAINE
MARYLANDMASSACHUSETTSMICHIGANMINNESOTAMISSISSIPPIMISSOURIMONTANANEBRASKA
NEVADANEW HAMPSHIRE(Millions 2010
Dollars)$4,473$592$4,183$2,386$19,998$4,173$4,880$684$859$10,750$10,787$577$886$10,885$6,375$2,188$2,445$4,363$5,691$1,303$4,476$5,835$9,599$3,057$2,718$5,560$898$1,295$1,978$1,374% Total
1.8%
0.2%
1.7%
1.0%
8.3%
1.7%
2.0%
0.3%
0.4%
4.4%
4.5%
0.2%
0.4%
4.5%
2.6%
0.9%
1.0%
1.8%
2.4%
0.5%
1.8%
2.4%
4.0%
1.3%
1.1%
2.3%
0.4%
0.5%
0.8%
0.6%

145

Cost perCapita
$936
$833
$654
$818
$537
$830
$1,365$761$1,427$572$1,113
$425
$565
$848
$983
$718
$857
$1,005$1,255
$981
$775
$891
$971
$576
$916
$928
$908
$709
$732
$1,044% per Capita
Personal Income
2.8%
1.9%
1.9%
2.5%
1.2%
1.9%
2.4%
1.9%
2.0%
1.5%
3.1%
1.0%
1.8%
2.0%
2.8%
1.9%
2.2%
3.0%
3.3%
2.6%
1.6%
1.7%
2.7%
1.3%
2.9%
2.5%
2.6%
1.8%
2.0%
2.4%

NEW JERSEYNEW MEXICONEW YORKNORTH CAROLINANORTH DAKOTAOHIO
OKLAHOMA
OREGONPENNSYLVANIARHODE ISLAND
SOUTH CAROLINA
SOUTH DAKOTATENNESSEE
TEXAS
UTAH
VERMONTVIRGINIAWASHINGTONWEST VIRGINIA
WISCONSINWYOMING
Total$12,813$1,769$15,246$7,909$706$10,125$2,910$1,768$5,851$1,599$4,045$720$5,667$17,044$1,725$538$4,998$4,469
$1,482$4,546$788$241,988

5.3%
0.7%
6.3%
3.3%
0.3%
4.2%
1.2%
0.7%
2.4%
0.7%
1.7%
0.3%
2.3%
7.0%
0.7%
0.2%
2.1%
1.8%
0.6%
1.9%
0.3%
100.0%

146$1,457
$859
$787
$829
$1,049
$878
$776
$461
$461
$1,519
$875
$885
$893
$678
$624
$860
$625
$665
$800
$799
$1,398
$784

2.9%
2.5%
1.6%
2.3%
2.6%
2.4%
2.1%
1.2%
1.1%
3.6%
2.6%
2.3%
2.5%
1.7%
1.9%
2.1%
1.4%
1.5%
2.5%
2.1%
2.9%
1.9%

7. Alcohol
Alcohol consumption is a major cause of motor vehicle crashes and injury. Over the past two decades,
about 40 percent of all motor vehicle fatalities occur in crashes in which a driver or nonoccupant has
consumed a measurable level of alcohol prior to the crash, and of these cases, 86 percent involved alevel of consumption which met the current typical legal definition for intoxication or impairment, ablood alcohol concentration of .08 grams per deciliter or higher. Over the past two decades, there has
been an increased awareness of the problems caused by impaired driving. Many groups from NHTSA to
Mothers Against Drunk Driving (MADD), Students Against Destructive Decisions (SADD), and State andlocal agencies, have promoted the enactment of laws and implemented public awareness campaigns toassist in combating this problem. Legal measures such as administrative license revocation/suspension
have been enacted in numerous States. As a result, there has been a marked decrease in the number offatalities resulting from alcohol-involved crashes. Table 7-1 displays the share of fatalities associatedwith alcohol involvement (BAC>.01 g/dL) and the current definition of legal intoxication (illegal per se,.08 g/dL) since 1982. Alcohol involvement in fatal crashes has declined from 60 percent of all fatalities in1982 to roughly 40 percent in 2010, while legal intoxication (defined as a BAC of .08 g/dL or greater) has
declined from 53 percent to 35 percent over the same period. While these declines are encouraging,alcohol still remains a significant causative factor in motor vehicle crashes.
All 50 States, the District of Columbia, and Puerto Rico define legal intoxication, the level at which DWI
convictions can be made, as having a BAC of .08 or higher. FARS data indicates that fatalities involving
legally intoxicated drivers or nonoccupants account for 86 percent of the fatalities arising from all levelsof alcohol involvement.

Fatalities:
FARS provides detailed information about all traffic fatalities that occur within 30 days of a crash on a
public road. Each case is investigated and documentation regarding alcohol involvement is included.
Alcohol involvement can be indicated either by the judgment of the investigating police officers or by
the results of administered BAC tests. Cases where either of these factors is positive are taken as
alcohol-involved and any fatalities that result from these crashes are considered to be alcohol-involved
fatalities. In addition, there are a large number of cases where alcohol involvement is unknown. In 1986,
NHTSA’s National Center for Statistics and Analysis (NCSA) developed an algorithm based on
discriminant analysis of crash characteristics that estimates the BAC level for these cases (Klein, 1986). In
1998, NHTSA developed a more sophisticated technique to accomplish these estimates using multiple
imputation (Rubin, Schafer, & Subramanian, 1998), and substituted this method beginning with the2001 FARS file. NHTSA has recomputed previous FARS files using this method and alcohol involvement
rates based on the new method are routinely published by NHTSA and used in this report. The total
number of alcohol-involved fatalities by BAC level is shown in Table 7-1 from 1982 through 2011. In

147

2010, about 86 percent of all fatalities that occurred in alcohol-involved crashes were in cases where adriver or pedestrian had a BAC of .08 or higher.

Table 7-1. Alcohol-Involved and Intoxicated Traffic Fatalities, Highest BAC in CRASH
Year
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011

Total
Number Percent
43,945100%
42,589100%
44,257100%
43,825100%
46,087100%
46,390100%
47,087100%
45,582100%
44,599100%
41,508100%
39,250100%
40,150100%
40,716100%
41,817100%
42,065100%
42,013100%
41,501
100%
41,717100%
41,945100%
42,196100%
43,005100%
42,884100%
42,836100%
43,510100%
42,708100%
41,259100%
37,423100%
33,883100%
32,999100%
32,367100%

BAC=.00
Number Percent
17,77340%
17,95542%
19,49644%
20,65947%
21,07046%
22,29748%
23,25449%
23,15951%
22,012
49%
21,34951%
20,96053%
22,242
55%
23,40957%
24,08558%
24,31658%
25,302
60%
24,82860%
25,14560%
24,56559%
24,79659%
25,48159%
25,77960%
25,91861%
25,92060%
24,97058%
24,101
58%
21,97459%
19,70458%
19,67660%
19,21259%

BAC=.01 to .07
Number Percent
2,927
7%
2,594
6%
3,046
7%
3,081
7%
3,546
8%
3,398
7%
3,234
7%
2,893
6%
2,980
7%
2,560
6%
2,443
6%
2,361
6%
2,322
6%
2,490
6%
2,486
6%
2,290
5%
2,465
6%
2,321
6%
2,511
6%
2,542
6%
2,432
6%
2,427
6%
2,325
5%
2,489
6%
2,594
6%
2,554
6%
2,191
6%
2,031
6%
1,861
6%
1,758
5%

BAC=.08+
Number Percent
23,24653%
22,041
52%
21,71549%
20,08646%
21,47147%
20,69645%
20,59944%
19,53143%
19,60744%
17,59942%
15,84740%
15,54739%
14,98537%
15,24236%
15,26336%
14,421
34%
14,20734%
14,25034%
14,87035%
14,85835%
15,09335%
14,67834%
14,59334%
15,10235%
15,14435%
14,60335%
13,25835%
12,14936%
11,462
35%
11,39735%

BAC=.01+
Number Percent
26,17360%
24,63558%
24,762
56%
23,16753%
25,01754%
24,09452%
23,83351%
22,42449%
22,58751%
20,15949%
18,29047%
17,90845%
17,30843%
17,73242%
17,74942%
16,71140%
16,67340%
16,57240%
17,38041%
17,40041%
17,524
41%
17,10540%
16,91939%
17,59040%
17,73842%
17,15842%
15,44941%
14,17942%
13,32340%
13,155
41%

Alcohol use by drivers is the focus of most behavioral programs and State laws. Drivers are involved in
the vast majority of alcohol-related traffic crashes, but a significant number of crashes occur where
pedestrians or bicyclist alcohol use was indicated, while drivers were not drinking. Table 7-2 summarizes
the incidence of alcohol-related crashes based on driver BAC, while Table 7-3 shows the incidence of
fatalities where pedestrians or bicyclists were using alcohol, but not drivers. In 2010, 85 percent of all148

fatalities that occurred in alcohol-involved crashes were in cases where a driver had a BAC of .08 or
higher. About 5 percent of all alcohol-related traffic fatalities involve alcohol use by pedestrians rather
than motor vehicle drivers. Of these cases, over 90 percent involve alcohol impairment (BAC = .08 or
higher) on the part of the pedestrian.

Table 7-2. Alcohol-Involved and Intoxicated Traffic Fatalities, Highest Driver BAC
Year
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011

Total*

Number

43,94542,58944,257
43,82546,087
46,39047,08745,582
44,59941,50839,25040,15040,716
41,81742,06542,01341,501
41,717
41,94542,19643,00542,884
42,83643,51042,70841,25937,42333,88332,99932,367

Percent

100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%

BAC=.00

Number

19,771
19,787
21,42922,58922,89624,18625,16425,152
23,82323,02522,72623,97924,94825,76826,052
26,902
26,477
26,79826,082
26,33427,08027,32827,41327,423
26,63325,611
23,49921,051
21,00520,752

Percent

45%
46%
48%
52%
50%
52%
53%
55%
53%
55%
58%
60%
61%
62%
62%
64%
64%
64%
62%
62%
63%
64%
64%
63%
62%
62%
63%
62%
64%
64%

BAC=.01 to .07

Number

2,912
2,588
3,007
2,974
3,487
3,238
3,156
2,793
2,901
2,480
2,352
2,300
2,236
2,416
2,415
2,216
2,353
2,235
2,422
2,441
2,321
2,327
2,212
2,404
2,479
2,494
2,115
1,972
1,771
1,633

149

Percent

7%
6%
7%
7%
8%
7%
7%
6%
7%
6%
6%
6%
5%
6%
6%
5%
6%
5%
6%
6%
5%
5%
5%
6%
6%
6%
6%
6%
5%
5%

BAC=.08+

Number21,11320,051
19,63818,125
19,554
18,813
18,611
17,521
17,705
15,827
14,04913,73913,39013,47813,451
12,757
12,546
12,555
13,324
13,29013,472
13,096
13,09913,582
13,491
13,041
11,711
10,75910,136
9,878

Percent

48%
47%
44%
41%
42%
41%
40%
38%
40%
38%
36%
34%
33%
32%
32%
30%
30%
30%
32%
31%
31%
31%
31%
31%
32%
32%
31%
32%
31%
31%

BAC=.01+

Number

24,025
22,63922,64521,09823,04122,051
21,767
20,31420,60718,307
16,401
16,03915,626
15,893
15,866
14,973
14,89914,79015,746
15,731
15,793
15,423
15,311
15,985
15,97015,534
13,826
12,731
11,906
11,510Percent

55%
53%
51%
48%
50%
48%
46%
45%
46%
44%
42%
40%
38%
38%
38%
36%
36%
35%
38%
37%
37%
36%
36%
37%
37%
38%
37%
38%
36%
36%

Table 7-3. Pedestrian and Bicyclist Alcohol Use Related Traffic Fatalities

Year
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011

Total Including
Occupants
Number
Percent
43,945100%
42,589100%
44,257100%
43,825100%
46,087100%
46,390100%
47,087100%
45,582100%
44,599100%
41,508100%
39,250100%
40,150100%
40,716100%
41,817100%
42,065100%
42,013100%
41,501
100%
41,717100%
41,945100%
42,196100%
43,005100%
42,884100%
42,836100%
43,510100%
42,708100%
41,259100%
37,423100%
33,883100%
32,999100%
32,367100%

BAC=.01 to .07
Number
15
6
39
107
59
160
78
100
79
80
91
61
86
74
71
74
112
86
89
101
111
100
113
85
115
60
76
59
90
125

Percent
0.0%
0.0%
0.1%
0.2%
0.1%
0.3%
0.2%
0.2%
0.2%
0.2%
0.2%
0.2%
0.2%
0.2%
0.2%
0.2%
0.3%
0.2%
0.2%
0.2%
0.3%
0.2%
0.3%
0.2%
0.3%
0.1%
0.2%
0.2%
0.3%
0.4%

BAC=.08+
Number
2,133
1,990
2,077
1,961
1,917
1,883
1,988
2,010
1,902
1,772
1,798
1,808
1,595
1,764
1,812
1,664
1,661
1,695
1,546
1,568
1,621
1,582
1,494
1,520
1,653
1,562
1,547
1,390
1,326
1,519

Percent
5%
5%
5%
4%
4%
4%
4%
4%
4%
4%
5%
5%
4%
4%
4%
4%
4%
4%
4%
4%
4%
4%
3%
3%
4%
4%
4%
4%
4%
5%

BAC=.01+
Number
2,148
1,996
2,117
2,069
1,976
2,043
2,066
2,110
1,980
1,852
1,889
1,869
1,682
1,839
1,883
1,738
1,774
1,782
1,634
1,669
1,731
1,682
1,608
1,605
1,768
1,624
1,623
1,448
1,417
1,645

Percent
5%
5%
5%
5%
4%
4%
4%
5%
4%
4%
5%
5%
4%
4%
4%
4%
4%
4%
4%
4%
4%
4%
4%
4%
4%
4%
4%
4%
4%
5%

Figure 7-A illustrates the historical trend of overall fatalities plotted against alcohol-related and alcohol
impaired fatalities. Their general trends are similar, but there was a noticeable decline in alcohol-related
fatalities as a proportion of total fatalities during the 1990s. Overall alcohol-related fatalities declined
from 60 percent of total fatalities in 1982 to about 40 percent by 1997. Since that time, the proportion
has remained roughly constant. A similar trend is evident for fatalities in crashes involving alcohol
impairment. Alcohol impaired fatalities declined from 53 percent of all fatalities in 1982 to about 34
percent in 1997, and have remained at roughly 35 percent through 2010.

150

Figure 7-A. Historical Trend of Fatalities, Alcohol-Involved Fatalities, and Alcohol-Impaired Fatalities

Nonfatal Injuries:
NHTSA collects crash data though a two-tiered system, a system that was redesigned in 1988 to replace
the former NASS; the NASS Crashworthiness Data System and the General Estimates System comprise
this new method.
The CDS is a probability sample of a subset of police-reported crashes in the United States It offers
detailed data on a representative, random sample of thousands of minor, serious, and fatal crashes. The
crash in question must be police-reported and must involve property damage and/or personal injury
resulting from the crash in order to qualify as a CDS case. It must also include a towed passenger car or
light truck or van in transport on a public road or highway. Injuries in vehicles meeting these criteria are
analyzed at a level of detail not found in the broader GES.
In contrast, the GES collects data on a sample of all police-reported crashes, without a specific set ofvehicle and severity criteria. Although GES collects data on a broader array of crashes, it collects lessinformation on each crash, limiting possible analysis of alcohol involvement. Cases are restricted to a151

simple “yes,” “no,” or “unknown” alcohol indication on the police crash report, as observed by thereporting police office. Actual BAC test results are not available through the GES sample.
The GES provides a sample of U.S. crashes by police-reported severity for all crash types. GES records
injury severity by person on the KABCO scale (National Safety Council, 1990) from police crash reports as
discussed at the beginning of Chapter 2.
KABCO ratings are coarse and inconsistently coded between States and over time. The codes are
selected by police officers without medical training, typically without benefit of a hands-on examination.
Some of the injured are transported from the scene before the police officer who completes the crash
report even arrives. Miller, Viner, et al. (1991) and Blincoe and Faigin (1992) documented great diversity
in KABCO coding across cases. O’Day (1993) more carefully quantified variability in use of the A-injury
code between States. Viner and Conley (1994) probed how differing State definitions of A-injury
contributed to this variability. Miller, Whiting, et al. (1987) found police-reported injury counts by
KABCO severity systematically varied between States because of differing State crash reporting
thresholds (rules governing which crashes should be reported to the police). Miller and Blincoe (1994)
found that State reporting thresholds often changed over time.
Thus police reports inaccurately describe injuries medically and crash databases inaccurately describe
motor vehicle crash severity. We adopted a widely used method to refine crash and injury severity.
Developed by Miller and Blincoe (1994), numerous studies have used this method, notably in impaireddriving cost estimates in Blincoe (1996); Miller, Lestina, and Spicer (1998); Blincoe et al. (2002); andZaloshnja and Miller (2009).
To minimize the effects of variability in severity definitions by State, reporting threshold, and police
perception of injury severity, the method uses NHTSA data sets that include both police-reported KABCO
and medical descriptions of injury in the Occupant Injury Coding system (OIC; AAAM, 1990, 1985). OIC
codes include AIS severity score and body region, plus more detailed injury descriptors. We used both
2008–2010 CDS and 1984–1986 NASS data (NASS; NHTSA, 1987). CDS describes injuries to passenger
vehicle occupants involved in tow-away crashes. The 1984–1986 NASS data provides the most recent
medical description available of injuries to medium/heavy truck and bus occupants, nonoccupants, andothers in non-CDS crashes. The NASS data was coded with the 1980 version of AIS, which differs slightly
from the 1985 version; but NHTSA made most AIS 85 changes well before their formal adoption. CDS
data was coded in AIS 90/98 with coding shifting to AIS 2005 Update 2008 in 2011. We differentiatedour analysis of the two versions of AIS because AIS 90/98 scores and OIC codes differ greatly from codesand scores in AIS 85, especially for brain and severe lower limb injury. Garthe, Ferguson, and Early(1996) find that AIS scores shifted for roughly 25 percent of all OICs between AIS 85 and AIS 90/98.
We used 2008–2010 CDS and GES non-CDS weights to weight the CDS and NASS data, respectively, sothat they represent estimated counts of people injured in motor vehicle crashes during 2008–2010. In
applying the GES weights to old NASS, we controlled for police-reported injury severity, restraint use,alcohol involvement, and occupant type (CDS occupant, non-CDS occupant, and nonoccupant). Weighting
NASS data to GES restraint use and alcohol involvement levels updates the NASS injury profile152

to reflect contemporary belt use and alcohol-involvement levels, although it is imperfect in terms of its
representation of airbag use in non-tow-away crashes. At completion of the weighting process, we had ahybrid CDS/NASS casualty-level file—that is, we had an appropriately reweighted NASS record for eachinjured survivor in each non-CDS crash. Similarly, we reweighted the 2008–2010 CDS file to match GES
counts in order to get appropriately weighted unit records for CDS sample strata. From this file weobtained counts of alcohol cases based on all indicators of alcohol use to obtain an initial count of
alcohol involved crashes from police-reported crashes. The results are shown in the upper part of Table
4 below:
Table 7-4. Alcohol Involvement Identified in Police-Reported Crashes

Alcohol Involvement in Police-Reported Crashes
Initially Derived from
CDS/GES
Total
Injuryseverity
Incidence
PDO
6,187,743
MAIS0
1,782,823
MAIS1
2,204,294
MAIS2
220,982
MAIS3
74,235MAIS413,131
MAIS5
3,861
Fatal
32,999

Alcohol
Percentage
Involved
Alcohol Involved
410,414
6.63%
118,235
6.63%
104,230
4.73%
17,7838.05%
8,455
11.39%
1,574
11.99%
574
14.86%
13,32340.37%

Adjusted for GES Undercount and Unreported
Total
Alcohol
Percentage
Injuryseverity
Incidence
Involved
Alcohol Involved
PDO
17,007,212
1,128,037
6.63%
MAIS0
4,211,513
279,302
6.63%
MAIS1
3,273,070
154,7674.73%
MAIS2
305,594
24,5928.05%
MAIS385,8839,781
11.39%
MAIS4
14,5371,742
11.99%
MAIS5
4,274
635
14.86%
Fatal
32,99913,32340.37%

As noted in chapter 5, GES has historically undercounted police-reported crashes on the order of 10 to
13 percent. Our most recent analysis indicates an undercounting of roughly 10.7 percent for 2010. We
therefore multiplied incidence by 1.107 to adjust for systematic undercounting in GES of police crash
reports. Also as previously noted, a significant portion of crashes are not reported to police. We assume
that these underreporting rates apply to alcohol-involved crashes as well as to overall crashes. We thus
divided by estimated fractions reported to the police: 1.0 for people with critical to fatal injuries, 0.953

153

for people with MAIS3 injuries, 0.794 for MAIS2, 0.725 for MAIS1, 0.469 for uninjured people in injury
crashes, and 0.406 for crashes without injuries. 44 The results of these adjustments are shown in thelower half of Table 7-4.

Underreported Alcohol:
Although police accident reports typically include an indication of whether alcohol was involved, thenature of accident investigations often precludes an accurate assessment of alcohol involvement at thecrash site. Police underreporting of alcohol involvement has been well documented in numerous
studies. Typically, studies on underreporting compare the results of BAC tests administered in medicalcare facilities to police reports of alcohol involvement. In a 1982 study of injured drivers, Terhune foundthat police correctly identified 42 percent of drivers who had been drinking. These rates of identification
improved at higher BAC levels, ranging from only 18.5 percent of those with BACs of .01to .09, to 48.9
percent for those with BACs of .10 or greater. In a 1990 study, Soderstrom, Birschbach, and Dischinger
found that police correctly identified alcohol use in 71 percent of legally intoxicated, injured drivers.
Earlier studies by Maull, Kunning, and Hickman in 1984 and Dischinger and Cowley in 1989, found thatpolice correctly identified 57.1 percent and 51.7 percent of intoxicated drivers, respectively. The
Dischinger and Cowley study also found a lower identification rate for “involved but not intoxicated”
drivers of 28.6 percent. In a 1991 study of injured motorcycle drivers, Soderstrom, Birschbach, andDischinger found that police correctly identified only half the drivers with positive alcohol readings later
identified by the hospital.
These early studies demonstrate that during the late 1980s and early 1990s, the police were identifying
approximately half of all legally intoxicated drivers, and about one quarter of all drivers who werealcohol involved, but not legally intoxicated. It is clear from the studies that police are more accurate in
identifying alcohol involvement as the BAC rate increases. This may reflect the more obvious nature of
impaired behavior on the part of drivers who have higher BAC levels, as well as a tendency to investigate
more thoroughly the more serious crashes that result from higher BACs.
In several previous versions of this report (Blincoe & Faigin, 1992, and Blincoe, 1996) the studies cited
above were used to estimate the impact of police underreporting of alcohol involvement. In the mostrecent version (Blincoe et al., 2002), more updated information was used. However, those studies areover a decade old, and when applied to current data, they produced results that imply a higher rate ofalcohol involvement in less severe injuries than in fatalities and more severe injuries. This is bothcounter-intuitive and at odds with historical alcohol involvement patterns. Moreover, over the last
decade there has been a concerted effort on the part of Federal, State and local governments to reduce
alcohol-related crashes, and this may have improved the rate of alcohol reporting during accident
investigations. Data that was more recent was therefore needed to make this adjustment for 2010 data.

44

For incidence purposes, we used only the 2010 portion of the reweighted hybrid CDS/NASS casualty-level file andthe 2010 FARS file154

The Crash Outcome Data Evaluation System (CODES) is a system that links existing crash and injury dataso that specific person, vehicle, and event characteristics can be matched to their medical and financial
outcomes. At the time of the 2002 study there were 25 States participating in this program and 17 ofthese States are part of a data network supporting NHTSA highway safety programs. An effort was madeto contact all States participating in NHTSA’s CODES project to determine whether data was available
that could be used to estimate current alcohol reporting rates. For a variety of reasons, only one State,
Maryland, had data that was properly linked to allow a comparison between alcohol assessments inpolice reports and actual measured BACs. The Maryland data represented 2,070 cases admitted to the R
Adams Cowley Shock Trauma Center between 1997 and 1999. The basis for this data was thus similar to
most of the studies cited above from the late 80s and early 90s.
An analysis of this data indicated that police were correctly identifying 74 percent of all alcohol involved
cases where BACs equaled or exceeded .10 g/dL, and 46 percent of all cases where BACs were positive,
but less than .10. This represents a significant improvement from the corresponding rates of only 55
percent and 27 percent that were found in the earlier studies. This was consistent with the expectationthat reporting rates have improved, and, when applied to police-reported rates in the NHTSA data bases,
the more recent factors produce overall estimates that are consistent with FARS rates of involvement for
fatal crashes. However, although this data produce logical results, they were gathered from only one
State and there are no data to confirm whether the Maryland experience is typical of the Nation. Theseestimates were thus subject to the caveat that these results have not been verified by broader studies
from more diverse regions. One of the previous studies (Soderstrom, Birschbach, &Dischinger, 1990) was conducted at this same facility and found a higher rate of alcohol recognition thanthe other studies previously discussed. A second caveat is that, because this data was collected at a
trauma unit, they may reflect the more serious cases rather than a sample of all injury levels. There are
two different, somewhat offsetting biases that could result from this. Trauma unit cases are more likely
to involve emergency transport and treatment which may occur before police are able to gain access todrivers to determine alcohol involvement. This could result in police missing a larger portion of trauma
unit cases. On the other hand, the severity of the crash may prompt a more thorough investigation by thepolice, resulting in a higher rate of correct alcohol identification. It is not clear what the net effect ofthese biases would be.
Given these caveats, this current paper is based on a more recent study that analyzed what portion of
U.S. nonfatal crashes are alcohol-involved and how well police and hospitals detect involvement (Miller
et al., 2012). In that study, a capture recapture model estimated alcohol involvement from levels
detected by police and hospitals and the extent of detection overlap. The authors analyzed 550,933
Crash Outcome Data Evaluation System driver records from 2006-2008 police crash report censuses
probabilistically linked to hospital inpatient and emergency department (ED) discharge censuses forConnecticut, Kentucky (admissions only), Maryland, Nebraska, New York, South Carolina, and Utah. They
then computed National estimates from NHTSA’s General Estimates System.
Nationally an estimated 7.5 percent of drivers in nonfatal crashes and 12.9 percent of nonfatal crashes
were alcohol-involved. (Crashes often involve multiple drivers but rarely are two alcohol-involved.)
Police correctly identified an estimated 32 percent of alcohol-involved drivers in non-fatal crashes

155

including 48 percent in injury crashes. Excluding Kentucky, police in the six States reported 47 percent of
alcohol involvement for cases treated in EDs and released and 39 percent for admitted cases. In
contrast, hospitals reported 28 percent of involvement for ED cases and 51 percent for admitted cases.
Underreporting varied widely between States. Police-reported alcohol involvement for 44 percent of
those who hospitals reported were alcohol-involved, while hospitals reported alcohol involvement for
33 percent of those who police reported were alcohol-involved. Police alcohol reporting completenessrose with police-reported driver injury severity. At least one system reported 62 percent of alcohol
involvement. Based on the combined results from the 6 States that had both admitted and ED data,
police records account for 30 to45 percent of total actual alcohol involvement, depending on injury
severity. These rates and the resulting estimates of alcohol involvement are summarized in Table 7-5.
Note that although fatalities are listed in Table 7-5, they were not examined in the capture-recapture
analysis. As noted previously fatal crashes are investigated much more thoroughly than nonfatal crashes
and NHTSA’s FARS, through both documentation of police and medical records and through modelingfor unreported cases, is believed to account for all alcohol involvement in fatal crashes.
Table 7-5. Total Alcohol Involvement Adjusted for Unreported Cases
Injuryseverity
PDO
MAIS0
MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
Fatal

Total
Incidence
18,508,632
4,583,265
3,459,200
338,730
100,740
17,086
5,749
32,999Percent
Identified
42.90%
42.90%
45.40%
42.60%
39.70%
40.60%
30.10%
100.00%Alcohol
Involved
2,629,458
651,054
340,897
57,72824,6384,292
2,110
13,323

Percent
Involved
14.21%
14.21%
9.85%
17.04%
24.46%
25.12%
36.70%
40.37%

BAC Levels:
BAC levels are difficult to determine from injury data. Although there are some indications of BAC
included in CDS data, the GES has no such indicators. To determine BAC levels, an initial assessment wasmade that virtually all police-reported BACs for nonfatal crashes represent BACs that are at the .05 BAC
level or higher. It is illegal per se in every State to drive a motor vehicle with a BAC of .08 or higher.
Some State laws establish lesser included offenses at lower BAC levels (most typically at .05 BAC). Unlessa crash involves a fatality, police generally do not test or use the alcohol checkbox unless they suspect
the driver might be near these levels. In fact, except for fatal crashes, some States do not even allow
testing unless a BAC over .08 is suspected. Low BAC levels (especially below .05) are thus unlikely to beregistered in police records. An examination of available data from NHTSA’s CDS and NASS data systems
bears this out. For nonfatal crashes, less than half of 1 percent of nonfatal injuries were recorded as
BACS being between .01 and.04 g/dL. However, this primarily represents a limitation in data gatheringrather than an indication of near complete absence of crashes at these lower BAC levels. An estimate ofcrashes at these BAC levels was thus derived from crash probabilities.

156

Subcategories of BAC levels were calculated as a function of odds ratios for crashes at each specific BAC
level compared to exposure at those levels. Odds ratios were derived from a study of relative crash risk
conducted by Dunlap and Associates (Blomberg, Peck, Moskowitz, Burns and Fiorentino, 2005). In thisstudy over 2,800 crashes and nearly 15,000 drivers in Long Beach, California and Fort Lauderdale, Florida
were sampled to determine the relative risk of crashes at different BAC levels. Logistic regression
techniques were used to create a relative risk model which indicated a notable dose-response
relationship beginning at 0.04 percent BAC and increasing exponentially at >=.10 percent BAC. The
results of this model are summarized in Figure 7-B below:
Figure 7-B. Relative Risk of Crash by Blood Alcohol Concentration (Source: Blomberg, Peck,
Moskowitz, Burns & Fiorentino, 2005)

The authors found some level of added crash risk beginning at roughly .04 BAC, but this risk rises
noticeably at .08 BAC and rises exponentially from .10 BAC and beyond. For example, at .04 BAC the riskof a crash is 18 percent higher than at zero BAC, but at .08 BAC the risk of a crash is 2.69 times as highand at .10 BAC it is 4.79 times as high. To determine BAC distributions, the relative risk ratios of eachindividual BAC category were combined with exposure data from the same study to estimate therelative risk factor for each grouped BAC category. These grouped relative risk factors were then
combined with National exposure data from Lacey et al. to determine the distribution of each grouped
BAC category as follows:.

157

rn*en/ry*ey
where: rn = relative risk ratio of specific BAC category
en = exposure of specific BAC categoryry = relative risk of broader BAC category
ey = exposure of broader BAC category
The broader categories are those derived above for nonfatal injuries, which were all assumed to be
BAC>=.05, and the difference between these and the total incidence, which represent 0-.04 BAC.
Essentially, this divides alcohol BAC cases into two broad categories at the .05 BAC level. The .08+BAC
category was then derived using the above formula from the >=.05 BAC total and the .01-.04 BAC
category was derived from the <.05 BAC category. The inputs used for each category and the resulting
BAC distributions are shown in Table 7-6.
Table 7-6. Incidence Stratified by Highest Driver or Nonoccupant BAC and Injury Severity
Injury severity
PDO
MAIS0
MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
Fatal
Total
% of CrashInvolved People% of Miles Driven
Relative Risk

BAC= 0
BAC=.01-.04 BAC=.05-.07
BAC>=.08
BAC=.01+
Total
15,879,174
341,369
162,584
2,125,505
2,629,458
18,508,632
3,932,211
84,53440,255526,265
651,054
4,583,265
3,118,303
67,037
19,459254,401
340,8973,459,200
281,002
6,041
3,672
48,01557,728
338,730
76,102
1,636
1,634
21,36824,638100,740
12,794
275
286
3,731
4,292
17,086
3,639
78
144
1,888
2,110
5,749
19,676
1,002
859
11,462
13,32332,99923,322,901
501,972
228,8932,992,635
3,723,500
27,046,401
86.23%
97.18%
1.00001.86%
1.96%
1.06450.85%
0.39%
1.658111.06%
0.47%
17.9870

13.77%
2.82%
4.7477100%
100%

The results illustrate the disproportionate impact that high BACs have on crash incidence. Less than 1
percent of overall miles are driven by impaired drivers (.08+ BAC), but they account for over 11 percent
of all vehicle crashes, and over 80 percent of all alcohol related crashes, including 86 percent of all
fatalities.
Figure 7-C illustrates the relative incidence of alcohol impaired and not impaired crashes to all crashes.
Alcohol involved crashes account for 40 percent of all fatal crashes. There is a clear trend towards
increased alcohol involvement as injury severity increases. This figure illustrates the fact that alcohol not
only increases the likelihood of crashes, but their severity as well.
Figure 7-D illustrates the relative incidence of crashes at various BAC levels. The vast majority of all
alcohol related crashes occur at legally impaired BAC levels of .08 and above.

158

Figure 7-C. Relative Incidence of Impaired and Unimpaired BAC Levels to All Crashes

Figure 7-D. Relative Incidence of BAC Levels in Alcohol Involved Crashes by Injury Severity

159

Alcohol-Involved Crash Costs:
The costs of alcohol-involved crashes tend to exceed those of non-alcohol-involved crashes due to avariety of factors. The first is a general tendency toward greater relative severity of alcohol-involved
crashes. For all crashes, fatalities are approximately 0.8 percent of injured survivors. This rate nearlyquadruples for crashes involving alcohol. Similarly, the rate for critical injuries (MAIS 5) triples foralcohol cases and for severe injuries (MAIS 4) it more than doubles. The more severe and expensive
injuries represent a much higher portion of alcohol-involved cases. A second factor is demographics.
Males are disproportionately represented in alcohol-involved crashes and this makes the cost for eachalcohol-involved case higher. This occurs because males have higher earnings and participation in thework force than females; thus there is a higher lost productivity cost associated with these crashes. In
non-alcohol-involved crashes, the gender distribution is more evenly distributed. In addition, the victimsof alcohol-involved crashes tend to be of an age group where lost productivity is maximized by thediscounting process.
Unit costs specific to alcohol-involved crashes were developed by extracting cases with police-reported
alcohol from the previously discussed file based on 2008-2010 weights. As noted above, virtually all ofthese cases represent crashes with BACs of 0.5 or greater. Unit costs for these crashes were thus
weighted by the relative incidence of 0.05 BAC+ cases within all positive BAC cases. The unit costs ofcases with BACs of 0.0-0.04 were then derived as a function of the relative incidence and cost of the
0.05+BAC crashes and All Crashes as follows:b=(cz-ax)/y
where: b=unit cost in crashes with BAC<0.05
c=average unit cost of all crashes
z=incidence of all crashes
a = unit cost of crashes with BAC>=0.05
x=incidence of crashes with BAC>=0.05
y = incidence of crashes with BAC<0.05
The results of this process are shown in Tables 7-7, 7-8, and 7-9160

Table 7-7. Average Unit Costs, BAC>=.05 injuries, and BAC > .00 Fatalities (2010 Dollars)
PDO MAIS0 MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
$0
$0 $3,052 $12,224$51,487 $139,520 $392,163
$28
$21
$89
$194
$416
$838
$855
Emergency Services$0$0$2,999$20,309$68,027$149,858$346,818
Market Productivity
$60
$45
$963
$7,397$24,283$39,145$98,395
Household Productivity$191$143 $3,994$5,545$16,355$29,874$74,198
Insurance Admin.
$62
$46
$341
$2,644$5,776$6,361$11,091
Workplace Costs$0$0 $1,474$4,076$13,229$28,359$86,358
Legal Costs
$341
$255
$12,912$52,388$179,572$393,955$1,009,878
Injury Subtotal$1,077$760 $1,109$1,197$1,434$1,511$1,529Congestion Costs$2,444 $1,828 $5,404$5,778$10,882$16,328$15,092
Property Damage$3,862$2,843$19,425$59,363$191,888$411,794$1,026,499
Economic Subtotal$0$0 $24,692 $365,054 $869,987 $2,117,774 $4,994,639
QALYs$3,862 $2,843 $44,117 $424,417 $1,061,875 $2,529,568 $6,021,138
Comprehensive Total
MedicalFatal$11,317$902$1,156,859$315,326$28,322$11,783$106,488$1,630,997$5,720$11,212$1,647,929$8,495,097$10,143,026*Note: Unit costs are expressed on a per-person basis for all injury levels. PDO costs are
expressed on a per-damaged-vehicle basis.

Table 7-8. Average Unit Costs, BAC=.00-0.04 injuries, and BAC = .00 Fatalities (2010 Dollars)
PDO
MAIS0 MAIS1 MAIS2
MAIS3
MAIS4
MAIS5
Fatal
$0
$0 $2,777 $11,314$47,772 $135,333 $379,960$11,317
$28
$21
$89
$194
$416
$838
$855
$902
$0
$0 $2,703 $19,188$63,247 $138,037 $332,571$781,860
Market Productivity
$60
$45
$853
$7,054$22,216$37,048$93,774$272,700
Household Productivity$191$143 $3,238$4,499$15,080$27,722$71,610$28,322
Insurance Admin.
$62
$46
$341
$2,644$5,776$6,361$11,091$11,783
Workplace Costs$0$0$1,157$3,220$12,157$26,148$80,715$106,488
Legal Costs$341$255 $11,158 $48,114 $166,663 $371,487 $970,577$1,213,372
Injury Subtotal$1,077$760 $1,109$1,197$1,434$1,511$1,529$5,720Congestion Costs$2,444$1,828$5,404$5,778$10,882$16,328$15,092$11,212
Property Damage$3,862 $2,843 $17,671 $55,089 $178,979 $389,326 $987,198$1,230,304
Economic Subtotal$0$0 $23,116 $336,518 $786,674 $2,012,806 $4,351,042$7,240,587
QALYs$3,862$2,843$40,787$391,606$965,653$2,402,133$5,338,239$8,470,891
Comprehensive Total
MedicalEmergency Services*Note: Unit costs are expressed on a per-person basis for all injury levels. PDO costs are
expressed on a per-damaged-vehicle basis.

161

Table 7-9. Average Unit Costs, All Positive BAC Injuries and Fatalities (2010 Dollars)
PDO MAIS0 MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
$0
$0
$2,998$12,128$51,240$139,252$391,712
Medical
$28
$21
$89
$194
$416
$838
$855
Emergency Services$0$0 $2,941 $20,192$67,709 $149,100 $346,292
Market Productivity
$60
$45
$941
$7,361$24,146$39,011$98,224
Household Productivity$191$143 $3,845$5,436$16,270$29,736$74,102
Insurance Admin.
$62
$46
$341
$2,644$5,776$6,361$11,091
Workplace Costs$0$0 $1,412$3,986$13,158$28,217$86,150
Legal Costs$341$255 $12,567 $51,941 $178,715 $392,515 $1,008,426
Injury Subtotal$1,077$760 $1,109$1,197$1,434$1,511$1,529Congestion Costs$2,444$1,828$5,404$5,778$10,882$16,328$15,092
Property Damage$3,862 $2,843 $19,080 $58,916 $191,031 $410,354 $1,025,047
Economic Subtotal$0$0 $24,382 $362,068 $864,455 $2,111,048 $4,970,847
QALYs$3,862 $2,843 $43,462 $420,984 $1,055,486 $2,521,402 $5,995,894
Comprehensive Total

Fatal$11,317$902$1,156,859$315,326$28,322$11,783$106,488$1,630,997$5,720$11,212$1,647,929$8,495,097$10,143,026*Note: Unit costs are expressed on a per-person basis for all injury levels. PDO costs are
expressed on a per-damaged-vehicle basis.

Table 7-10 lists the aggregate 2010 costs of alcohol related crashes, and Table 7-11 lists the proportionof total economic crash costs that each BAC level represents. Alcohol is involved in crashes that accountfor 14 percent of the costs of PDO crashes, 17 percent of the costs that result from nonfatal injuries and48 percent of the costs that result from fatalities. Overall, these crashes are responsible for 22 percent
of total economic costs. The impact of alcohol-involved crashes on overall costs is thus higher thanwould be indicated by the alcohol-involved incidence rates. Overall, alcohol involved crashes cost $52
billion in economic costs in 2010, with 84 percent of this or $44 billion, occurring in crashes where thehighest BAC was >=.08.
Table 7-10. Summary of Total Economic Costs by BAC Level (Millions of 2010 Dollars)
PDO
MAIS0
MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
Fatal
Total
% Total Alcohol Costs% Total

BAC= 0$61,325$11,179$55,104$15,480$13,621$4,981$3,592$24,207$189,491
NA
78.31%

BAC=.01-.04$1,318$240$1,185
$333
$293
$107
$77
$1,651$5,204
9.91%
2.15%

162

BAC=.05-.07
$628
$114
$378
$218
$314
$118
$148
$1,416$3,3336.35%
1.38%

BAC>=.08$8,209$1,496$4,942$2,850$4,100$1,536$1,938$18,889$43,960
83.74%
18.17%

BAC=.01+$10,155$1,851$6,504$3,401$4,707$1,761$2,163$21,955$52,497
100.00%21.69%

Total$71,480$13,030$61,608$18,881$18,327$6,742$5,755$46,163$241,988NA
100.00%Table 7-11. Percent of Economic Injury Costs by Alcohol Involvement Rate

PDO
MAIS0
MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
Fatal
Total

BAC= 0 BAC=.01-.04 BAC=.05-.07 BAC>=.08 BAC=.01+
85.79%
1.84%
0.88%
11.48%
14.21%
85.79%
1.84%
0.88%
11.48%
14.21%
89.44%
1.92%
0.61%
8.02%
10.56%
81.99%
1.76%
1.15%
15.10%
18.01%
74.32%
1.60%
1.71%
22.37%
25.68%
73.88%
1.59%
1.74%
22.79%
26.12%
62.42%
1.34%
2.57%
33.67%
37.58%
52.44%
3.58%
3.07%
40.92%
47.56%
78.31%
2.15%
1.38%
18.17%
21.69%

Total
100.00%100.00%100.00%100.00%100.00%100.00%100.00%100.00%100.00%

Table 7-12 lists the aggregate 2010 comprehensive costs of alcohol related crashes, and Table 7-13 lists
the proportion of total comprehensive crash costs that each BAC level represents. Alcohol is involved incrashes that account for 14 percent of the societal harm of PDO crashes, 20 percent of the harm that
result from nonfatal injuries, and 45 percent of the harm that result from fatalities. All alcohol involved
crashes are responsible for 28 percent of total societal harm from motor vehicle crashes, but crashes
with BAC>=.08 are responsible for 85 percent of this or 24 percent. The impact of alcohol-involved
crashes on overall costs is thus higher than would be indicated by the alcohol-involved incidence rates.
Overall, alcohol involved crashes cost $236 billion in comprehensive societal costs in 2010, with 85
percent of this or $201 billion, occurring in crashes where the highest BAC was >=.08.
Table 7-12. Total Comprehensive Costs by BAC Level (Millions of 2010 Dollars)

PDO
MAIS0
MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
Fatal
Total
% Total Alcohol Costs% Total

BAC= 0
BAC=.01-.04 BAC=.05-.07 BAC>=.08 BAC=.01+$61,325$1,318$628$8,209$10,155$11,179$240$114$1,496$1,851$127,188$2,734$858$11,223$14,816$110,042$2,366$1,559$20,378$24,303$73,488$1,580$1,735$22,690$26,005$30,734$661$723$9,438$10,821$19,426$416$867$11,368$12,651$166,673$10,163$8,713$116,259$135,136$600,055$19,479$15,197$201,062$235,738
NA
8.26%
6.45%
85.29%
100.00%71.79%
2.33%
1.82%
24.06%
28.21%

163

Total$71,480$13,030$142,004$134,345$99,493$41,555$32,077$301,809$835,793
NA
100.00%Table 7-13. Percent of Comprehensive Injury Costs by Alcohol Involvement Rate

PDO
MAIS0
MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
Fatal
Total

BAC= 0 BAC=.01-.04 BAC=.05-.07 BAC>=.08 BAC=.01+
85.79%
1.84%
0.88%
11.48%
14.21%
85.79%
1.84%
0.88%
11.48%
14.21%
89.57%
1.93%
0.60%
7.90%
10.43%
81.91%
1.76%
1.16%
15.17%
18.09%
73.86%
1.59%
1.74%
22.81%
26.14%
73.96%
1.59%
1.74%
22.71%
26.04%
60.56%
1.30%
2.70%
35.44%
39.44%
55.22%
3.37%
2.89%
38.52%
44.78%
71.79%
2.33%
1.82%
24.06%
28.21%

Total
100.00%100.00%100.00%100.00%100.00%100.00%100.00%100.00%100.00%Alcohol Crash Causation:
Inebriated drivers often experience impaired perceptions that can lead to risky behavior such as
speeding, reckless driving, and failure to wear seat belts. They also experience reduced reaction times,
which can make it more difficult for them to perform defensive safety maneuvers. As a result, there is a
general tendency to equate the presence of alcohol with crash causation. However, there are clearlysome instances in which crashes would occur regardless of whether the driver had consumed alcohol.
For example, if a distracted texting driver were to run into a driver with a positive BAC who was stopped
at a red light, a police investigation or medical records might record that the struck driver had a positive
BAC, even though that driver was not at fault. In this case, the crash would be recorded as alcoholinvolved, even though alcohol was not a causative factor.
Miller, Spicer and Levy (1999) estimated the percentages of alcohol-related crashes that are actually
attributable to alcohol. In this study they examined the probability of crash involvement for drivers
based on their BAC level and then removed the normal risk of crash involvement without alcohol fromthe overall risk found for drivers with positive BACs. Their study found that 94 percent of crashes at BACs
of .10 or higher, and 31 percent of crashes with positive BACs less than .10, were actually caused byalcohol. The remaining crashes were due to bad weather, poor road conditions, non-drinking drivers,etc. Currently .08 BAC is considered to be the definition of “illegal per se” alcohol impairment rather
than 0.10. More recently, Blomberg et al. (2005) examined the relative crash risk of drinking and nondrinking drivers. The methods and results of this study were discussed previously (see Figure B above).
Table 6 displayed the relative risk for various BAC categories that were derived from Blomberg andcolleagues’ BAC specific risk factors. These factors can be used to estimate the incidence of crashes
where alcohol consumption actually contributed to the crash occurrence across the various BAC
groupings examined in this report. These proportions were estimated as the ratio of the added risk in an
alcohol involved crash to the total risk in this crash. Specifically:164y=(r-1)/r
where: y = proportion of BAC + crashes that are attributable to alcohol.
r= relative risk ratio of specific BAC category

Table 7-14 and Figures 7-E and 7-F illustrate the results of this process. The second to the last row inTable 7-14 lists the relative risk calculated from data in Dunlop, while the last row lists the proportion of
injuries in each BAC category that are attributable to alcohol. Roughly 6 percent of BAC = .01-.04
injuries, 40 percent of BA = .05-.07 injuries, and 94 percent of BAC>= .08 injuries are attributable toalcohol. The increasing proportions are expected since higher BAC levels cause more inebriation, with its
associated reduction in awareness and motor skills. Overall, about 79 percent of injuries from crashes
recorded as alcohol-involved can be attributed to alcohol as a causative factor. This is roughly the same
percentage calculated in Blincoe et al., 2002 (80.8 percent), which was based on the earlier Miller,
Spicer, and Levy analysis. Alcohol thus appears to be a causative factor in roughly 80 percent of cases
coded as alcohol-involved, but is irrelevant to crash causation in the other 20 percent of cases.

Table 7-14. Injuries Attributable to Alcohol Use by BAC Level
Injury severity
PDO
MAIS0
MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
Fatal
Total
Relative Risk
% Attributable to Alcohol

BAC=.01-.04
20,688
5,123
4,063
366
99
17
5
61
30,421
1.0645
6.06%

BAC=.05-.079
64,52915,977
7,723
1,458
649
113
57
341
90,8461.658139.69%

165

BAC>=.08
2,007,336
497,007240,257
45,34620,1803,524
1,783
10,8252,826,257
17.9870
94.44%

BAC=.01+
2,092,553
518,107
252,043
47,16920,9283,654
1,845
11,226
2,947,525
4.747779.16%

Figure 7-E. Percent of Positive BAC Crashes Attributable to Alcohol by BAC Level

Figure 7-F. Percent of Injuries Attributable to Alcohol by Injury Severity Level

To estimate the economic cost of crashes actually attributable to alcohol, the incidence from Table 7-14
was combined with the unit costs from Tables 7-7 and 7-8. The results, summarized in Table 7-15,
indicate that alcohol causes crashes that result in roughly $43 billion in economic costs annually. Thisaccounts for 82 percent of the crash costs associated with crashes that are considered alcohol-involved.

166

It represents 18 percent of all crash costs (including those without alcohol involvement), accounting for
11 percent of PDO costs, 14 percent of nonfatal injury costs, and 40 percent of fatality costs.
Table 7-15. Economic Crash Costs Attributable to Alcohol Use by BAC Level (Millions of 2010 Dollars)
Injury severity
PDO
MAIS0
MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
Fatal
Total
% of Total Alcohol Involved Costs
Attributable to Alcohol% of Total Costs Attributable to Alcohol

BAC=.01-.04 BAC=.05-.079 BAC>=.08 BAC=.01+$80$249$7,752$8,081$15$45$1,413$1,473$72$150$4,667$4,889$20$87$2,692$2,799$18$124$3,872$4,015$6$47$1,451$1,504$5$59$1,830$1,894$100$562$17,838$18,500$315$1,323$41,516$43,1546.06%
39.69%
94.44%
82.20%
0.13%

0.55%

17.16%

17.83%

Table 7-16. Percent of Total Economic Costs Attributable to Alcohol
Injuryseverity
PDO
MAIS0
MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
Fatal
Total

BAC=.01-.04
0.11%
0.11%
0.12%
0.11%
0.10%
0.10%
0.08%
0.22%
0.13%

BAC=.05-.079
0.35%
0.35%
0.24%
0.46%
0.68%
0.69%
1.02%
1.22%
0.55%

BAC>=.08
10.85%
10.84%
7.58%
14.26%
21.13%
21.52%
31.80%
38.64%
17.16%

BAC=.01+
11.31%
11.30%
7.94%
14.82%
21.90%
22.31%
32.90%
40.08%
17.83%

To estimate the comprehensive cost of crashes actually attributable to alcohol, the incidence from Table
7-14 was combined with the unit costs from Tables 7-7 and 7-8. The results, summarized in Table 7-17
and 7-18, indicate that alcohol causes crashes that result in roughly $194 billion in comprehensive
societal costs annually. This accounts for 82 percent of the comprehensive crash costs associated withcrashes that are considered alcohol-involved. It represents 23 percent of all crash costs (including those
without alcohol involvement, accounting for 11 percent of societal harm from PDOs, 16 percent of harmfrom nonfatal injuries, and 37 percent of harm from fatalities.

167

Total
$71,480$13,030$61,608$18,881$18,327$6,742$5,755$46,163$241,988

Table 7-17. Comprehensive Crash costs Attributable to Alcohol Use by BAC Level (Millions of 2010
Dollars)
Injury severity
PDO
MAIS0
MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
Fatal
Total
% of Total Alcohol Involved Costs
Attributable to Alcohol% of Total Costs Attributable to Alcohol

BAC=.01-.04
BAC=.05-.079 BAC>=.08 BAC=.01+$80$249$7,752$8,081$15$45$1,413$1,473$166$341$10,599$11,106$143$619$19,245$20,007$96$689$21,429$22,213$40$287$8,913$9,240$25$344$10,736$11,105$616$4$109,796$110,416$1,180$2,578$189,884$193,642
6.06%
16.96%
94.44%
82.14%
0.14%

0.31%

22.72%

Table 7-18. Percent of Total Comprehensive Costs Attributable to Alcohol
Injuryseverity
PDO
MAIS0
MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
Fatal
Total

BAC=.01-.04 BAC=.05-.079 BAC>=.08
BAC=.01+
0.11%
0.35%
10.85%
11.31%
0.11%
0.35%
10.84%
11.30%
0.12%
0.24%
7.46%
7.82%
0.11%
0.46%
14.33%
14.89%
0.10%
0.69%
21.54%
22.33%
0.10%
0.69%
21.45%
22.24%
0.08%
1.07%
33.47%
34.62%
0.20%
0.00%
36.38%
36.58%
0.14%
0.31%
22.72%
23.17%

168

23.17%

Total$71,480$13,030$142,004$134,345$99,493$41,555$32,077$301,809$835,793

8. Speeding
Excess speed can contribute to both the frequency and severity of motor vehicle crashes. At higher
speeds, additional time is required to stop a vehicle and more distance is traveled before corrective
maneuvers can be implemented. Speeding reduces a driver’s ability to react to emergencies created bydriver inattention; by unsafe maneuvers of other vehicles; by roadway hazards; by vehicle system
failures (such as tire blowouts); or by hazardous weather conditions. The fact that a vehicle wasexceeding the speed limit does not necessarily mean that this was the cause of the crash, but theprobability of avoiding the crash would likely be greater had the driver or drivers been traveling at
slower speeds.
A speed-related crash is defined as any crash in which the police indicate that one or more drivers
involved was exceeding the posted speed limit, driving too fast for conditions, driving at a speed greaterthan reasonable or prudent, exceeding a special speed limit or zone, or racing. FARS data indicate that in2010, a total of 10,536 fatalities, representing 32 percent of all motor vehicle fatalities, occurred in
speed-related crashes.
To estimate the cost of these crashes, we examined the relative incidence of each injury severity levelthat was represented by crashes that were speed related. These estimates reflect the relative
proportions of specific injury severities that occur under each scenario. GES was used for each non-fatalcase, while FARS was used for each fatal case. Each case in FARS contained information regarding
speeding status, so the proportion of fatalities that occurred under each scenario was obtained directlyfrom the FARS database. For nonfatal injuries and PDOs, GES data was queried to determine whether
the case fell under the scenario or not. However, GES data is only recorded using the KABCO severity
system, whereas this report is based on the Abbreviated Injury Scale. To translate GES data to an MAIS
basis, we used a variety of KABCO/MAIS translators. For CDS equivalent crashes, we used a current
translator derived from 2000-2008 CDS data. Since this data is relatively recent, they reflect roughly
current levels of seat belt usage. For non-CDS cases, the only available data from which to developtranslators were contained in the 1982-1986 NASS files. Seat belt use has increased dramatically since
this time. Observed belt use during this period ranged from roughly 10-37 percent as public awarenessof the importance of belt use and belt use laws were just beginning to take hold in 1986. Belt use hassince risen dramatically, and has been between 80 and 85 percent since 2004. Belt use can influence
injury reporting significantly in a number of ways. It changes the nature of injuries by preventing many
more visible injuries (such as head/face contact with the windshield) but replaces them with often lessvisible (and also typically less serious) abdominal injuries such as bruising caused by pressure from thebelt across the torso. This can influence the relationship between the KABCO reported injury severity
and the corresponding MAIS injury level. For this reason, separate translators were developed from the
1982-86 NASS data for non-CDS cases where the victim was belted, unbelted, unknown belted status,and for nonoccupants/motorcyclists. These translators are presented in Tables 13-1 through 13-5 in
Chapter 13.

169

2010 GES KABCO incidence counts were obtained both for speed involved and uninvolved cases.
Consistent with NHTSA publication practice, cases where speed involvement was unknown weregrouped with the uninvolved cases. Thus, one set of incidence counts was obtained for speed involved,
and another for all other crashes. Each of this data sets was run through its corresponding translator to
produce a set of MAIS based injury counts. These counts from each grouping (CDS equivalent cases,
belted non-CDS cases, unbelted non-CDS cases, unknown belt status non-CDS cases, andnonoccupant/motorcycle cases) were added together to produce a total MAIS injury profile for eachscenario. The percentage of each MAIS injury incidence that was appropriate to each scenario was thencalculated as:
x=a/(a+b)
where x is the percentage of incidence attributable to speed related crashes
a = the incidence of speed related crashes
b = the incidence of crashes not related to speed, including those where the speed related variable wascoded unknown
The speed attributable portion of each MAIS level was then multiplied by the total cost of all 2010
crashes for that MAIS level and the MAIS level results were summed to produce the total cost of eachcrash scenario. MAIS0 portions were calculated using the same procedure described elsewhere in thisreport for Urban/Rural crashes, based on the relative incidence of MAIS 0 cases in injury crashes. The
PDO portion was based on a direct count of PDO vehicles from each crash scenario compared to those
not in that scenario.
The results of this process are summarized in Tables 8-1 and 8-2 for economic and comprehensive costs.
Speed related crashes resulted in 10,536 fatalities, over 800,000 nonfatal injuries, and over 3 million
PDO damaged vehicles in 2010. This represents 32 percent of all fatalities and roughly 20 percent of all
nonfatal crashes (including both nonfatal injury and PDO). Speed related crashes caused $52 billion ineconomic costs and $203 billion in comprehensive costs, accounting for 21 percent of all economic costs
and 24 percent of all societal harm (measured as comprehensive costs) from motor vehicle crashes.

170

Table 8-1. Economic Costs of Speed Related Crashes (Millions of 2010 Dollars)

PDO Vehicles
MAIS0
MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
Fatalities
Total
Percent of
Total

%
Speed
Relate16.28%
20.54%

Incidence
Total Economic Crash Costs
Speed
Speed
Total
related
Total
RelatedOther
18,508,632
3,013,887$71,480$11,640$59,841
4,583,265
941,619$13,030$2,677$10,353

20.51%

3,459,200

709,566$61,608$12,637$48,971

20.00%

338,730

67,733$18,881$3,776$15,106

20.08%

100,740

20,234$18,327$3,681$14,646

22.34%

17,0863,816$6,742$1,506$5,23622.72%

5,749

1,306

$5,755$1,308$4,44831.93%

32,99910,536$46,163$14,740$31,423

17.63%

27,046,402
100.00%4,768,697
17.63%$241,988
100.00%$51,964
21.47%$190,024
78.53%

Table 8-2. Comprehensive Costs of Speed Related Crashes (Millions of 2010 Dollars)

PDO Vehicles
MAIS0
MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
Fatalities
Total
Percent of
Total

%
Speed
Relate16.28%

Incidence
Total Comprehensive Crash Costs
Speed
Speed
Total
Total
Other
relateRelated
18,508,632
3,013,887$71,480$11,640$59,841

20.54%

4,583,265

941,619$13,030$2,677$10,353

20.51%

3,459,200

709,566$142,004$29,128$112,875

20.00%

338,730

67,733$134,345$26,864$107,481

20.08%

100,740

20,234$99,493$19,983$79,51022.34%

17,086

3,816$41,555$9,281$32,273

22.72%
31.93%
17.63%

5,749
32,99927,046,402
100.00%1,306
10,536
4,768,697
17.63%$32,077$301,809$835,793
100.00%$7,288$96,366$203,228
24.32%$24,789$205,443$632,565
75.68%

One note of caution is in order when using these estimates - there is a significant overlap between
alcohol involvement and speed. Many speed-related crashes involved alcohol and vice-versa. These two
estimates should not be added together in order to account for the portion of costs that represent the
combined factors of speed and alcohol. This same caveat applies to many of the other scenarios
examined in this report, as multiple factors can be involved in any given crash.

171

9. Distracted Driving
Driver error has long been recognized as the primary cause of motor vehicle crashes. In a landmark 1979
Tri-Level study by the University of Indiana (Teat et al., 1999), human factors such as speeding,inattention, distraction, and performance errors were found to be a factor in 92.6 percent of all crashes.
The Tri-Level study found that inattention was a definite crash cause in roughly 9.8 percent and aprobable cause in 15.0 percent of crashes. It also found that “internal distraction” was a definite cause
in 5.7 percent of crashes and a probable cause in 9.0 percent. More recently, the National Motor Vehicle
Crash Causation Survey (NMVCCS, NHTSA, 2008, July) sponsored by NHTSA found that driver related
factors were the primary cause in 95.4 percent of crashes. Driver factors include both performanceerrors and errors related to non-driving activities, which typically involve distraction, inattention,
inadequate surveillance, etc. Distraction, including interior distraction, exterior distraction, andinattention, was involved in about 17.7 percent of all cases where the critical pre-crash event was
attributed to drivers. With vehicles traveling and interacting with other vehicles at high speeds, evenmomentary distraction can result in a crash.
For the National databases, FARS and GES, NHTSA essentially defines distraction to include both interior
and exterior sources of distraction including inattentive driving. Types of distraction include talking oncell phones, texting, talking to other passengers, adjusting interior devices such as radios or mirrors,
eating or drinking, diverting your attention to an exterior object, person , or event, or being lost inthought. All of these activities can potentially distract drivers from the task of safely driving an
automobile. Data indicate that distracted driving is playing a substantial role in motor vehicle crashes:45•

In 2010, about 10 percent of fatal crashes were reported as distraction-affected crashes.

•

Eighteen percent of injury crashes in 2010 were reported as distraction-affected crashes.

•

In 2010, there were 3,267 people killed in crashes involving distracted drivers and an estimatedadditional 735,000 were injured in motor vehicle crashes involving distracted drivers.

•

Of those people killed in distraction-affected crashes, 419 occurred in crashes in which at least
one of the drivers was using a cell phone (13 percent of fatalities in distraction-affected crashes)at the time of the crash. Use of a cell phone includes talking/listening to a cell phone,dialing/texting a cell phone, or other cell-phone-related activities. 46•

Of those injured in distraction-affected crashes, an estimated 27,000 were injured in crashes
that involved the use of cell phones at the time of the crashes (5 percent of injured people indistraction-affected crashes).

45

See for example, NHTSA, 2012, September. That publication was based on preliminary data files, whereas thiscurrent analysis is based on final 2010 FARS and GES files, and translates KABCO injuries into MAIS equivalents.
This results in small but noticeable differences between the previously published data and this current analysis.
46
This definition is different than previous years and cannot be compared directly to cell phone involvement prior to2010. See NHTSA, 2012, September for further details.

172

•

Eleven percent of all drivers under age 20 involved in fatal crashes were reported as distracted
at the time of the crashes. This age group has the largest proportion of drivers who were
distracted (NHTSA, 2012, September).

•

For drivers under age 20 involved in fatal crashes, 19 percent of the distracted drivers were
distracted by the use of cell phones (NHTSA, 2012, September).

To estimate the cost of distracted driving crashes, we examined the relative incidence of each injury
severity level that was represented by distraction affected crashes. These incidence estimates reflect therelative proportions of specific injury severities that occur in crashes involving distraction. FARS wasused for each fatal case. For nonfatal injuries, the rate of distraction involvement is taken from GES andapplied to the total incidence estimates previously derived. Application of these rates rather than direct
counts is required to cover the various incidence cases not covered by GES, including both theadjustment for GES undercounting of police-reported crashes as well as unreported crashes.
GES data is only recorded using the KABCO severity system, whereas this report is based on the
Abbreviated Injury Scale. To translate GES data to an MAIS basis, we used a variety of KABCO/MAIS
translators. For CDS equivalent crashes, we used a current translator derived from 2000-2008 CDS data.
Since this data is relatively recent, they reflect roughly current levels of seat belt usage. For non-CDS
cases, the only available data from which to develop translators were contained in the 1982-1986 NASS
files. Seat belt use has increased dramatically since this time. Observed belt use during this period
ranged from roughly 10-37 percent as public awareness of the importance of belt use and belt use laws
were just beginning to take hold in 1986. Belt use has since risen dramatically, and has been between 80
and 85 percent since 2004. Belt use can influence injury reporting significantly in a number of ways. Itchanges the nature of injuries by preventing many more visible injuries (such as head/face contact withthe windshield) but replaces them with often less visible (and also typically less serious) shoulder andabdominal injuries such as bruising caused by pressure from the belt across the torso. This can influence
the relationship between the KABCO reported injury severity and the corresponding MAIS injury level.
For this reason, separate translators were developed from the 1982-86 NASS data for non-CDS cases
where the victim was belted, unbelted, unknown belted status, and for nonoccupants/motorcyclists.
These translators are presented in Tables 13-1 through 13-5 in Chapter 13.
2010 GES KABCO incidence counts were obtained for each distraction status (distracted, not distracted,
unknown if distracted). Each of these data sets was divided according to belt use and occupancy status
and run through its corresponding translator to produce a set of MAIS based injury counts. These counts
from each grouping (CDS equivalent cases, belted non-CDS cases, unbelted non-CDS cases, unknown
belt status non-CDS cases, and nonoccupant/motorcycle cases) were added together to produce a total
MAIS injury profile for each distraction scenario. The percentage of each MAIS injury incidence that
resulted from a distraction-affected crash was then calculated as:173

x=a/(a+b)
where x = the percentage of incidence attributable to a distraction-affected crash at each injury severitylevel
a = the incidence of distraction injuriesb = the incidence of injuries that were not specifically coded as being distraction related (includes “notdistracted” and Unknown).
The distraction-attributable portion of each injury severity level was then multiplied by the total cost of
all 2010 crashes for that severity level and the results were summed to produce the total cost of
distraction-affected crashes. MAIS0 portions were calculated using the same procedure described
previously for Urban/Rural crashes, based on the relative incidence of MAIS 0 cases in injury crashes.
The PDO portion was based on a direct count of PDO vehicles from the 2010 GES crashes involving
distraction compared to those that did not.
The results of this analysis are summarized in Tables 9-1 and 9-2 for economic and comprehensive
societal costs. Distracted driving is identified as a factor for roughly 10 percent of all fatalities and 18
percent of all crashes overall. In 2010 distraction-affected crashes caused $40 billion in economic costs
and are responsible for 16 percent of all economic impacts from motor vehicle crashes. They caused$123 billion in societal harm (as measured by comprehensive costs), representing roughly 15 percent of
total harm caused by motor vehicle crashes.
These estimates are almost certainly conservative because they are based only on identified distraction
cases. Police records frequently fail to identify whether or not distraction was involved in the crash.
Roughly 21 percent of all fatal crashes and 7 percent of all nonfatal crashes were coded in GES as“Distraction Unknown.” 47 Although it is likely that a portion of these cases could involve distraction,none of them are distributed to distraction in this analysis.
In previous publications NHTSA has noted that there are limitations to the collection and reporting of
FARS and GES data with regard to driver distraction (NHTSA, 2012, September).The data for FARS and
GES are based on PARs and investigations conducted after the crash has occurred. One significant
challenge for collection of distracted driving data is the PAR itself. Police accident reports vary acrossjurisdictions, thus creating potential inconsistencies in reporting. Many variables on the police accident
report are nearly universal, but distraction is not one of those variables. Some police accident reports
identify distraction as a distinct reporting field, while others do not have such a field and identificationof distraction is based upon the narrative portion of the report. The variation in reporting forms
contributes to variation in the reported number of distraction-affected crashes. Any National or State
count of distraction-affected crashes should be interpreted with this limitation in mind due to potential
under-reporting in some States/primary sampling units and over-reporting in others.

47

The discrepancy between the rates for fatal and nonfatal crashes may be a function of police inability to interview
survivors in fatal crashes where drivers or occupants are deceased.

174

There are several potential reasons for underreporting of distraction-affected crashes.
• There are negative implications associated with distracted driving—especially in conjunction
with a crash. Survey research shows that self-reporting of negative behavior is lower than actual
occurrence of that negative behavior. There is no reason to believe that self-reporting of
distracted driving to a law enforcement officer would differ. The inference is that the reported
driver distraction during crashes is lower than the actual occurrence.
• If a driver fatality occurs in the crash, law enforcement must rely on the crash investigation inorder to report on whether driver distraction was involved. Law enforcement may not haveinformation to indicate distraction. For example, some forms of distraction such as cognitive
distraction (lost in thought) are impossible to identify. These investigations often rely on witness
account and these accounts are often not available, especially in fatal crashes.
Another concern is the speed at which technologies are changing and the difficulty in updating the PAR
to accommodate these changes. Without broad-sweeping changes to the PAR to incorporate newtechnologies and features of technologies, it is difficult to capture the data that involve interaction withthese devices.
In the reporting of distraction-affected crashes, oftentimes external distractions are identified as a
distinct type of distraction. Some of the scenarios captured under external distractions might actually be
related to the task of driving (e.g., looking at a street sign). However, the crash reports may not
differentiate these driving-related tasks from other external distractions (looking at previous crash or
billboard). Currently, the category of external distractions is included in the counts of distractionaffected crashes.
Table 9-1. Economic Cost of Identified Distracted Driving Crashes (Millions of 2010 Dollars)

PDO Vehicles
MAIS0
MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
Fatalities
Total
Percent of
Total%
Distracte
17.81%

Incidence

Total Economic Crash Costs

Total
Distracted Total
Distracted
18,508,632
3,295,716$71,480$12,728

Other$58,752

18.66%

4,583,265

855,361$13,030$2,432$10,598

18.96%

3,459,200

656,014$61,608$11,684$49,925

17.20%

338,730

58,272$18,881$3,248$15,633

16.46%

100,740

16,586$18,327$3,017$15,31016.66%

17,0862,847$6,742$1,124$5,61915.60%
9.90%

5,749
32,999897
3,267$5,755$46,163$898$4,570$4,858$41,59318.08%

27,046,402

4,888,960$241,988$39,700$202,287

100.00%18.08%

100.00%16.41%

83.59%

175

Table 9-2. Comprehensive Cost of Identified Distracted Driving Crashes (Millions of 2010 Dollars)

PDO Vehicles
MAIS0
MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
Fatalities
Total
Percent of
Total%
Distracte
17.81%

Total Comprehensive Crash
Costs

Incidence
Total
Distracted
18,508,632
3,295,716

Total
Distracted$71,480$12,728

Other$58,752

18.66%

4,583,265

855,361$13,030$2,432$10,598

18.96%

3,459,200

656,014$142,004$26,930$115,074

17.20%

338,730

58,272$134,345$23,111$111,233

16.46%
16.66%

100,740
17,086

16,586
2,847$99,493$41,555$16,381$6,925$83,113$34,630

15.60%

5,749

897$32,077$5,003$27,074

9.90%
18.08%

32,99927,046,402
100.00%3,267
4,888,960
18.08%$301,809$835,793
100.00%$29,880$123,390
14.76%$271,929$712,403
85.24%

176

10.

Motorcycle Crashes

Motorcycles are the most hazardous form of motor vehicle transportation. The lack of external
protection provided by vehicle structure, the lack of internal protection provided by seat belts and air
bags, their speed capability, the propensity for riders to become airborne through ejection, and therelative instability inherent with riding a two-wheeled vehicle all contribute to making the motorcycle
the most risky passenger vehicle. In 2010, 4,518 motorcyclists were killed and 96,000 48 were injured inpolice-reported crashes on our Nation’s roadways. This represents 14 percent of all traffic fatalities and3 percent of all police-reported injuries. Motorcycles accounted for only 0.6 percent of all vehicle miles
traveled in 2010. Per vehicle mile traveled in 2010, a motorcyclist was about 30 times more likely than apassenger car occupant to die in a motor vehicle traffic crash and 5 times more likely to be injured. The
difference in these proportions reflects the more severe injury profile that results from motorcycle
crashes.
Over the past several decades motorcycle fatalities and injuries have generally increased relative tothose in other vehicle types. Figure 1 shows the fatality rate/100,000 registered vehicles by vehicle
type.49 The rates for passenger cars, light trucks, and heavy trucks declined steadily from 1995 through2006. 50 The recession that occurred in 2007 caused a dramatic decline in fatality rates for heavy trucks,
and a less severe but still noticeable decline in the rates for passenger cars and light trucks. The heavy
truck rate began increasing in 2010 as the economy began to rebound. By contrast, the motorcycle
fatality rate climbed steadily from the mid-1990s through 2006 as middle-aged baby boomers showed
increased interest in motorcycle riding (Blincoe & Shankar, 2007). Motorcycle fatality rates were also
affected by the recession and declined sharply from 2007 through 2009, but have since stabilized. Whatis most apparent from Figure 10-A is the magnitude of the fatality rate for motorcycles when comparedwith other vehicles.
Figure 10-B illustrates the percentage of occupant fatalities by vehicle type. The portions of fatalities
represented by motorcycles and light trucks have been increasing since 1995, while the portions
represented by passenger cars and heavy trucks have declined. The light truck increase is explained by
the increasing sales of these vehicles relative to other types. However, as shown in Table 10-1, thefatality rate for these vehicles has actually been declining while for motorcycles it has increased overall.
Light trucks have benefitted from a variety of occupant protection safety standards such as air bags,
48

There were 81,979 injuries estimated in the 2010 GES. These were adjusted using the same 10.7 percent markup
factor discussed in the Incidence chapter to reflect undercounting in GES compared to State total police-reported
crashes. It is assumed this same level of undercounting applies to all crash types. This adjustment produces a total of
90,753 nonfatal injuries. However, further adjustment to reflect the more accurate MAIS coding structure indicatesover 96,000 nonfatal injuries. See further discussion in this section.
49
Although VMT is the preferable basis for fatality rates, motorcycle fatality VMT was not recorded reliably until
2007, therefore rate comparisons are based on vehicle registrations.
50
The heavy truck fatality rate per registered vehicle is much higher than the passenger car and light truck rates
because heavy trucks drive many more miles per year.

177increased seat belt use, and side door beams that cannot be installed in motorcycles. The increase in the
portion of fatalities represented by motorcycles thus represents both their increased popularity and therelative safety improvements made in other vehicle types, but not in motorcycles. If these trends continue
motorcycle riders will make up an increasing share of occupant fatalities.
Figure 10-A. Fatality Rates per 100,000 Registered Vehicles by Vehicle Type
80
70
60
50
PC
40

LTV
HT

30

Motorcycle

20
10
0
2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

178

Figure 10-B. Portions of Occupant Fatalities Represented by Vehicle Type60.00%

50.00%

40.00%
PC
30.00%LTVHT
Motorcycle

20.00%

10.00%

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

0.00%

Table 10-1 lists the history of motorcycle fatalities and injuries along with fatality and injury rates from1975 through 2011. Fatalities are taken directly from NHTSA’s FARS database while injury totals
represent the sum of all A, B, and C injuries from NHTSA’s GES system. As noted elsewhere, these
KABCO based injury counts are not consistent with the Abbreviated Injury Scale used to stratify injury inthis report. They must therefore be adjusted using a KABCO/MAIS translator. This translator was derived
from motorcycle crashes contained in the 1982-1986 NASS – the only database containing both KABCO
and MAIS information that also has motorcycle crashes.
Nonfatal injuries were further adjusted to reflect the undercounting of police-reported crashes inherent
in the GES database. This 10.7 percent adjustment represents the difference between total State policereported crashes and the GES total. It is discussed in detail in the Incidence chapter.
A final adjustment was made to nonfatal injuries to represent unreported crashes. We know of nostudies that indicate the extent to which motorcycle crashes go unreported, but we have no reason tobelieve that there is no underreporting for this vehicle type. 51 It’s possible that the rates are different
due to post-crash vehicle drivability, insurance coverage rates, the prevalence of single-vehicle crashes,or the different nature of motorcycle injuries, but we have no data to quantify how any such differences
would impact police reporting for motorcycle crashes. For this study, we assume that underreporting
rates are the same for motorcycles as for all vehicles.

179

The results of this process are summarized in Table 10-2. In 2010 it is estimated that there were 4519
motorcycle riders killed in crashes. An additional 96,000 were injured in police-reported crashes while
27,000 were injured in unreported crashes. Overall, an estimated 123,000 motorcyclists were injured incrashes, roughly 41,000 of them seriously (MAIS2-5).

51

Motorcycles were included in the MDAVIS survey discussed in the Incidence chapter. However, the survey only
contained 6 motorcycle cases. The weighted police reporting rate for those cases (53%) was almost identical to theoverall rate for all crashes (54%), but these are too few cases to rely on for a separate motorcycle reporting rate.

180

Table 10-1. Motorcyclist Fatalities, Injuries, and Casualty Rates, 1975-2011
Year

Registered
Motorcycles

VehicleMiles
Traveled-millions

Motorcycle
Rider
Fatalities

FatalityRate
per100,000
RegistrationsFatality
Rate per
100
Million
VMT

1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002

4,964,070
4,933,332
4,933,256
4,867,855
5,422,132
5,693,940
5,831,132
5,753,858
5,585,112
5,479,822
5,444,404
5,198,993
4,885,772
4,584,284
4,420,420
4,259,462
4,177,365
4,065,118
3,977,856
3,756,555
3,897,191
3,871,599
3,826,373
3,879,450
4,152,433
4,346,068
4,903,056
5,004,156

5,629
6,003
6,349
7,158
8,637
10,214
10,690
9,910
8,760
8,784
9,086
9,397
9,506
10,024
10,371
9,557
9,178
9,557
9,906
10,2409,797
9,920
10,081
10,283
10,584
10,469
9,633
9,552

3,189
3,312
4,104
4,577
4,894
5,144
4,906
4,453
4,265
4,608
4,564
4,566
4,036
3,662
3,141
3,244
2,806
2,395
2,449
2,320
2,227
2,161
2,116
2,294
2,483
2,897
3,197
3,270

64.24
67.14
83.19
94.02
90.26
90.34
84.13
77.39
76.36
84.09
83.83
87.82
82.61
79.88
71.06
76.16
67.17
58.92
61.57
61.76
57.14
55.82
55.3
59.13
59.8
66.66
65.2
65.35

56.65
55.17
64.64
63.94
56.66
50.36
45.89
44.93
48.69
52.46
50.23
48.59
42.46
36.53
30.29
33.94
30.57
25.06
24.72
22.66
22.73
21.78
20.99
22.31
23.46
27.67
33.19
34.23

2003

5,370,035

9,576

3,714

69.16

38.78

67,103

1,250

701

2004

5,767,934

10,122

4,028

69.83

39.79

76,379

1,324

755

2005

6,227,146

10,454

4,576

73.48

43.77

87,335

1,402

835

2006

6,678,958

12,049

4,837

72.42

40.14

87,652

1,312

727

2007

7,138,476

21,396

5,174

72.48

24.18

102,994

1,443

481

2008

7,752,926

20,811

5,312

68.52

25.52

95,986

1,238

461

2009

7,929,724

20,822

4,469

56.36

21.46

89,607

1,130

430

2010

8,009,503

18,513

4,518

56.41

24.4

81,979

1,024

443

2011

8,437,502

18,500

4,612

54.66

24.93

81,399965

440

Source: Traffic Safety Facts, 2011, Table 10, NHTSA, DOT HS 811 754

181

Motorcycle
RidersInjuredInjuryRate
per100,000
RegistrationsInjuryRate per
100
Million
VMT

-

-

-

105,168
83,43584,28580,43565,09959,436
57,40557,48055,281
52,574
48,97449,98657,72360,23664,713

2,294
1,888
1,979
1,925
1,601
1,494
1,528
1,475
1,428
1,374
1,262
1,204
1,328
1,229
1,293

1,049
805
882
876
681
600
561
587
557
522
476
472
551
625
677

Table 10-2. Motorcycle Riders, Incidence Summary, 2010

MAIS 0
MAIS 1
MAIS 2MAIS 3MAIS 4MAIS 5
Nonfatal Injury Total
Fatal
PDO Vehicle

GES
Translated
2,954
55,075
19,065
10,978808
746
86,6734519
9,518

Adjusted toState Total% Unreported
3,270
53.14%
60,97025.45%
21,10519.95%
12,153
4.31%
895
0.00%
826
0.00%
95,95021.72%
4,519
0.00%
10,53659.72%# Unreported Total
3,708
6,977
20,80981,7795,259
26,365548
12,7010895
0826
26,616
122,566
0
4,519
15,623
26,160The crash environment faced by motorcyclists results in an injury profile skewed more towards serious
injuries than the typical crash in a passenger car or light truck. Minor injuries (MAIS1) represent over 87
percent of injuries for the general crash population, but they represent only 67 percent of motorcyclist
injuries. The more serious MAIS 2-5 injuries represent 33 percent of motorcyclist injuries, compared toonly 13 percent for the general crash population. In addition, within each MAIS category, the type of
injuries typically received is different for motorcyclists. For example, motorcyclists, especially those who
do not wear helmets, are more likely to receive head injuries than are their counterparts in regularpassenger vehicles. Lower extremity injuries are also more likely since a crashed motorcycle is likely tofall over and crush lower limbs. These differences produce different average injury costs within each
MAIS category. To assess these injuries, we isolated crash records for motorcycle occupants on the crash
file described in the Incidence chapter. The resulting average unit costs are shown in Table 3. Also in
Table 3, these motorcycle occupant specific costs are combined with injury incidence from Table 2 toestimate the total costs associated with motorcycle crashes.
In 2010, motorcycle crashes cost $12.9 billion in economic impacts, and $66 billion in societal harm as
measured by comprehensive costs. Compared to other motor vehicle crashes, these costs are
disproportionately caused by fatalities and serious injuries.

182

Table 10-3. Motorcycle Rider Unit Costs and Total Costs (2010 Dollars)

MAIS 0
MAIS 1
MAIS 2MAIS 3MAIS 4

Total
Unit
Economic
Unit
Total
Economic Costs
Comprehensive Comprehensive
Incidence Costs(Millions)
Costs
Costs (Millions)
6,977$2,975$21$2,975$21
81,779$18,347$1,500$31,540$2,57926,365$48,538$1,280$222,776$5,873
12,701$184,835$2,348$760,708$9,662895$337,483$302$1,756,197$1,571826$1,347,036$1,113$5,885,099$4,863

4,519$1,381,645$6,244$9,090,622$41,081

26,160$3,272$86$3,272$86

MAIS 5
Fatal
PDO
Total

$12,893$65,735

Impacts of Helmet Use
Motorcycle helmet usage is the most important action that motorcycle riders can take to protect
themselves in the event of a crash. Helmets reduce the chance of fatal injury by 37 percent for
motorcycle operators and by 41 percent for passengers (Deutermann, 2004). They reduce the chance ofnonfatal serious injury by 13 percent and minor injury by 8 percent (Blincoe, 1988). Unfortunately, only
about two-thirds of motorcycle riders currently wear helmets. This causes unnecessary loss of life andcritical injury, as well as considerable preventable economic loss to society. Figure 3 illustrates thehistorical trend in motorcycle rider helmet use from 1994 through 2011.
Helmet use peaked in 2000, but then declined after a number of States repealed their helmet use laws.
It reached a nadir in 2005, but has since slowly increased due to a number of factors including publicawareness and possibly shifting attitudes associated with the age of riders. Note that there was a
noticeable decline in observed use in 2010, but this was followed by a return to 2009 levels in 2011. The
2010 drop was not mirrored by a similar drop in use in crash data, raising the possibility that theobservation survey recorded a less representative sample that year. In any case, calculations used in thisanalysis are based on police-reported use in crashes, not the NOPUS survey.

183

Figure 10-C. Observed Helmet Use

Source: National Occupant Protection Use Survey (NOPUS), 1994-2011 data
NHTSA has published historical estimates of lives that have been saved by helmets, as well as those that
could have been saved, but were instead lost due to helmet nonuse. These estimates are shown in Table
10-4. To determine the cost impact of these savings, similar estimates must be derived for nonfatal
injuries. The methods used to estimate savings from helmet use have been established in a number ofstudies. 52 Different approaches apply depending on the type of data available. For this study we based
our calculations on methods used in the NHTSA 2011 Research Note because we were able to develop
separate incidence for helmeted and unhelmeted riders and it contains the most up to dateeffectiveness estimates.
A first step in this process was to develop separate helmeted and unhelmeted incidence profiles using
the same methods and translators as were used for the overall incidence estimate, but specific to caseswith known helmet status. We then distributed cases with Unknown helmet status according to the
known cases. The results are shown in Tables 10-4 and 10-5.

52

See NHTSA, 1988, Blincoe, 1994, and NHTSA, 2011, March.

184

Table 10-4. Helmeted Motorcycle Injured Riders, Incidence Summary, 2010
GES
Translated

Adj toState
Total% Unreported# Unreported

Total

MAIS 0
MAIS 1

2,126
37,9492,353
42,01153.14%
25.45%

2,668
14,3385,021
56,350

MAIS 2

12,81014,18119.95%

3,534

17,715

MAIS 3

7,129

7,892

4.31%

356

8,247

MAIS 4MAIS 5
Nonfatal Injury Total
Fatal
PDO

515
472
58,875
2,636
6,849

570
523
65,176
2,636
7,582

0.00%
0.00%
21.72%
0.00%
59.72%

0
0
18,2280
11,243570
523
83,404
2,636
18,825

Table 10-5. Unhelmeted Motorcycle Injured Riders, Incidence Summary, 2010
GES
Translated

Adj toState
Total% Unreported# Unreported

Total

MAIS 0828

917

53.14%

1,040

1,956

MAIS 1

17,126

18,95925.45%

6,471

25,430

MAIS 2

6,255

6,924

19.95%

1,725

8,650

MAIS 3

3,850

4,262

4.31%

192

4,454

MAIS 4MAIS 5
Nonfatal Injury Total
Fatal
PDO

293
274
27,798
1,883
2,669

325
304
30,773
1,883
2,954

0.00%
0.00%
21.72%
0.00%
59.72%

0
0
8,388
0
4,380

325
304
39,162
1,883
7,334

Unhelmeted motorcycle crash victims have more severe injuries than do those who wear helmets.
These injuries are also more expensive to treat and result in more lost quality-of-life. To determine
benefits from helmet use, we isolated crash records separately for helmeted and unhelmeted
motorcycle occupants on the crash file described in section 2. The resulting average unit costs areshown in Tables 10-6 and 10-7. Also in these tables, these motorcycle occupant helmet status specificcosts are combined with injury incidence from Tables 4 and 5 to estimate the total costs associated withhelmeted and unhelmeted motorcycle riders. Unhelmeted riders make up 32 percent of all motorcycle
injuries, but, due to their more serious injury profile, account for 39 percent of total economic costs and40 percent of total comprehensive costs caused by these crashes.

185

Table 10-6. Helmeted Motorcycle Rider Unit Costs and Total Costs (2010 Dollars)

MAIS 0
MAIS 1
MAIS 2MAIS 3MAIS 4MAIS 5Fatal
PDO Vehicle
Total

Incidence
5,021
56,35017,715
8,247
570
523
2,636
18,825

Total
Unit
EconomiUnit
Economic Costs
Comprehensiv
Total Comprehensivec Costs(Millions)e Costs
Costs (Millions)$2,975$15$2,975$15$18,079$1,019$30,915$1,742$48,186$854$220,580$3,908$184,941$1,525$759,107$6,261$328,872$187$1,701,424$969$1,190,011$622$4,909,241$2,566$1,381,645$3,641$9,090,622$23,959$3,272$62$3,272$62$7,925$39,481

Table 10-7. Unhelmeted Motorcycle Rider Unit Costs and Total Costs (2010 Dollars)

MAIS 0
MAIS 1
MAIS 2MAIS 3MAIS 4MAIS 5Fatal
PDO Vehicle
Total

Incidence
1,956
25,430
8,650
4,454
325
304
1,883
7,334

Total
Unit
Unit
EconomiEconomiComprehensiv
Total Comprehensivec Costs
c Costse Costs
Costs (Millions)(Millions)$2,975$6$2,975$6$18,941$482$32,926$837$49,258$426$227,273$1,966$184,639$822$763,673$3,401$352,587$115$1,852,270$602$1,617,283$491$7,564,608$2,297$1,381,645$2,602$9,090,622$17,122$3,272$24$3,272$24$4,968$26,254

Using methods described in NHTSA 2011, the lives saved, serious (MAIS 2-5) injuries and minor (MAIS 1)
injuries avoided due to helmet use and non-use were calculated and combined with the unit costs from
Table 10-7 to derive estimates of the economic impact of helmet use and non-use from 1975 through2010. The results are summarized in Tables 8 and 9. Over this 36 year period, motorcycle helmets havesaved over $60 billion in economic costs. Another $48 billion in potential economic savings was lost due
to the refusal of some riders to wear helmets. Helmets are currently saving $2.7 billion in economic
costs annually. As shown in Figures 4 and 5, the gap between potential benefits and achieved benefits
has grown smaller over time, but there is still considerable progress to be made if all motorcycle riders
can be persuaded to wear helmets.

186

Table 10-8. Economic Benefits of Helmet Use, 1975-2010

Year
1975

Lives
MAIS 2-5
MAIS 2-5
Saved
Cost/Fatality
Injuries
Cost/Injury
byCurrent$
Prevented
Current$
Helmet823$336,380
3,088$30,810MAIS 1
Injuries
Prevented

MAIS1
Cost/Injury
Current$

Current$(Millions)2010
Dollars(Millions)

3,743$2,934$383$1,552

1976

788$355,763

2,955$32,585

3,582$3,103$388$1,486

1977
1978

970
900

$378,897$407,658

3,648
3,382

$34,704$37,339

4,422
4,100

$3,304$3,555$509$508$1,831$1,6981979885$453,926

3,318$41,576

4,022$3,959$556$1,6691980

871$515,200

3,289$47,189

3,986$4,493$622$1,646

1981

843$568,345

3,171$52,056

3,843$4,957$663$1,591

1982

816$603,359

3,071$55,263

3,722$5,262$682$1,5401983

735$622,741

2,765$57,039

3,351$5,431$634$1,387

1984

813$649,627

3,066$59,501

3,716$5,665$732$1,535

1985

788$672,761

2,960$61,620

3,588$5,867$734$1,4871986

807$685,266

3,056$62,765

3,704$5,976$767$1,526

1987

667$710,275

2,521$65,056

3,055$6,194$657$1,2601988

622$739,662

2,283$67,748

2,767$6,451$633$1,166

1989

561$775,301

2,006$71,0122,431$6,762$594$1,044

1990

655$817,192

2,271$74,849

2,753$7,127$725$1,2091991

595$851,580

2,018$77,9992,446$7,427$682$1,092

1992

641$877,215

2,118$80,347

2,567$7,650$752$1,1691993

671$903,475

2,156$82,7522,614$7,879$805$1,215

1994

625$926,609

1,960$84,871

2,376$8,081$765$1,125

1995

624$952,869

1,913$87,276

2,319$8,310$781$1,117

1996

617$981,005

1,846$89,853

2,237$8,555$790$1,098

1997

627$1,003,514

1,837$91,9152,226$8,752$818$1,1111998

660$1,019,145

1,888$93,346

2,289$8,888$869$1,163

1999

745$1,041,654

2,090$95,408

2,533$9,084$998$1,307

2000

872$1,076,667

2,390$98,615

2,896$9,390$1,202$1,522

2001

947$1,107,304

2,547$101,421

3,087$9,657$1,337$1,646

2002

992$1,124,811

2,615$103,025

3,170$9,810$1,416$1,717

2003

1,173

$1,150,446

3,022$105,373

3,663$10,033$1,705$2,020

2004

1,324

$1,181,083

3,415$108,179

4,140$10,300$1,976$2,281

2005

1,554

$1,221,098

4,013$111,844

4,865$10,649$2,398$2,678

2006

1,667

$1,260,489

4,313$115,452

5,228$10,993$2,657$2,874

2007

1,788

$1,296,390

4,627$118,740

5,608$11,306$2,931$3,082

2008

1,836

$1,346,166

4,748$123,299

5,755$11,740$3,125$3,164

2009

1,486

$1,341,376

3,844$122,861

4,659$11,698$2,520$2,561

2010

1,556

$1,363,378

4,010$124,876

4,860$11,890$2,680$2,680$40,989$60,250

Total

33,544104,221

126,323

187

Table 10-9. Economic Benefits Forgone by Helmet Nonuse
Lives
Lost
Due toHelmetCost/Fatality
Year Nonuse
Current$
1975
1,164$336,380

MAIS 2-5
Injuries
Caused
MAIS 2-5
byCost/Injury
HelmetCurrent$
Nonuse1,114$30,810MAIS 1
Injuries
MAIS 1
Cause
Cost/Injury
d byCurrent$
Helmet1,270$2,9342010
Current $Dollars(Millions) (Millions)$430$1,741

1976

1,189

$355,763

1,313$32,585

1,496$3,103$470$1,803

1977
1978

1,472
1,588

$378,897$407,658

1,636
2,248

$34,704$37,339

1,864
2,562

$3,304$3,555$621$740$2,233$2,476

1979

1,676

$453,926

2,588$41,576

2,950$3,959$880$2,643

1980

1,744

$515,200

2,843$47,189

3,240$4,493$1,047$2,771

1981

1,667

$568,345

2,691$52,056

3,067$4,957$1,103$2,645

1982

1,528

$603,359

2,323$55,2632,647$5,262$1,064$2,405

1983

1,450

$622,741

2,334$57,039

2,660$5,431$1,051$2,3001984

759$649,627

2,473$59,501

2,819$5,665$656$1,377

1985

764$672,761

2,497$61,620

2,845$5,867$685$1,387

1986

751$685,266

2,439$62,765

2,780$5,976$684$1,362

1987

697$710,275

2,268$65,056

2,584$6,194$659$1,264

1988

644$739,662

2,060$67,748

2,348$6,451$631$1,163

1989

553$775,301

1,738$71,012

1,980$6,762

$566

$995

1990

541

$817,192

1,671$74,849

1,905$7,127

$581

$969

1991

467

$851,580

1,412$77,999

1,610$7,427

$520

$832

1992

323

$877,215

961$80,347

1,096$7,650

$369

$573

1993

336

$903,475

989$82,752

1,127$7,879

$394

$595

1994

339

$926,609

988$84,871

1,125$8,081

$407

$599

1995

326

$952,869

928$87,276

1,058$8,310

$400

$573

1996

324

$981,005

908$89,853

1,034$8,555

$408

$567

1997

315

$1,003,514

871$91,915992$8,752

$405

$550

1998

369

$1,019,145

1,007$93,346

1,148$8,888

$480

$643

1999

396

$1,041,654

1,062$95,408

1,210$9,084

$525

$687

2000

478

$1,076,667

1,269$98,615

1,446$9,390

$653

$827

2001

558

$1,107,304

1,457$101,421

1,660$9,657

$782

$962

2002

576

$1,124,811

1,483$103,025

1,690$9,810

$817

$991

2003

651

$1,150,446

1,653$105,373

1,883$10,033$942$1,116

2004

673$1,181,083

1,707$108,179

1,946$10,300$1,000$1,154

2005

731$1,221,098

1,857$111,844

2,117$10,649$1,123$1,254

2006

756$1,260,489

1,919$115,452

2,187$10,993$1,199$1,296

2007

805$1,296,390

2,045$118,740

2,331$11,306$1,313$1,381

2008

827$1,346,166

2,101$123,299

2,394$11,740$1,400$1,418

2009733$1,341,376

1,861$122,861

2,120$11,698$1,237$1,257

2010

708$1,363,378

1,804$124,876

2,056$11,890

Total

28,87862,52071,248188$1,215$1,215$27,456$48,026

Figure 10-4. Realized and Unrealized Fatality Benefits From Motorcycle Helmet Use

189

Figure 10-5. Realized and Unrealized Economic Benefits from Motorcycle Helmet Use

Realized and Unrealized Economic Benefits
from Motorcycle Helmet Use
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009
Realized Benefits

Unrealized Benefits

In Table 10-10, the societal impact of helmet use over this same time period is shown. Over $368 billion
in societal harm, as measured by comprehensive costs, has been averted over the past 36 years due tomotorcycle helmet use. Over this same period, an additional $301 billion in societal harm could havebeen prevented had all motorcycle riders worn helmets. Motorcycle helmets are currently preventing$17 billion in societal harm annually, but another $8 billion in harm could be prevented if all riders were
to wear their helmets.

190

Table 10-10. Comprehensive Societal Benefits of Helmet Use, 1975-2010
Year

Lives
Saved
by
Helmets

Cost/Fatality
Current$

MAIS 2-5
Injuries
Prevented

MAIS 2-5
Cost/Injury
Current$

MAIS 1
Injuries
Prevented

MAIS 1
Cost/Injury
Current$

Current $(Millions)2010
Dollars(Millions)

1975

823

$2,238,383

3,088$146,015

3,743$5,099$2,312$9,371

1976

788$2,367,360

2,955$154,428

3,582$5,393$2,341$8,972

1977
1978

970
900

$2,521,301$2,712,687

3,648
3,382

$164,470$176,955

4,422
4,100

$5,744$6,180$3,071$3,065$11,050$10,252

1979

885$3,020,568

3,318$197,039

4,022$6,881$3,355$10,076

1980

871$3,428,303

3,289$223,636

3,986$7,810$3,753$9,931

1981

843$3,781,951

3,171$246,706

3,843$8,616$4,003$9,6041982

816$4,014,943

3,071$261,904

3,722$9,147$4,115$9,297

1983

735$4,143,920

2,765$270,318

3,351$9,441$3,825$8,374

1984

813$4,322,824

3,066$281,988

3,716$9,848$4,416$9,267

1985

788$4,476,765

2,960$292,030

3,588$10,199$4,429$8,975

1986

807$4,559,976

3,056$297,458

3,704$10,389$4,627$9,207

1987

667$4,726,399

2,521$308,314

3,055$10,768$3,963$7,606

1988

622$4,921,945

2,283$321,070

2,767$11,213$3,826$7,051

1989

561$5,159,097

2,006$336,540

2,431$11,753$3,598$6,327

1990

655$5,437,855

2,271$354,724

2,753$12,389$4,401$7,343

1991

595$5,666,686

2,018$369,651

2,446$12,910$4,149$6,643

1992

641$5,837,269

2,118$380,779

2,567$13,298$4,582$7,122

1993

671$6,012,013

2,156$392,178

2,614$13,697$4,916$7,418

1994

625$6,165,953

1,960$402,2202,376$14,047$4,676$6,8801995

624$6,340,697

1,913$413,619

2,319$14,445$4,781$6,841

1996

617$6,527,922

1,846$425,832

2,237$14,872$4,847$6,7361997

627$6,677,703

1,837$435,602

2,226$15,213$5,021$6,821

1998

660$6,781,717

1,888$442,387

2,289$15,450$5,347$7,153

1999

745$6,931,497

2,090$452,158

2,533$15,791$6,149$8,048

2000

872$7,164,488

2,390$467,356

2,896$16,322$7,411$9,385

2001

947$7,368,356

2,547$480,655

3,087$16,787$8,254$10,162

2002

992$7,484,852

2,615$488,254

3,170$17,052$8,756$10,613

2003

1,173

$7,655,435

3,022$499,382

3,663$17,441$10,553$12,506

2004

1,324

$7,859,302

3,415$512,681

4,140$17,905$12,231$14,119

2005

1,554

$8,125,578

4,013$530,051

4,865$18,512$14,845$16,574

2006

1,667

$8,387,694

4,313$547,149

5,228$19,109$16,442$17,784

2007

1,788

$8,626,593

4,627$562,733

5,608$19,653$18,138$19,075

2008

1,836

$8,957,816

4,748$584,339

5,755$20,408$19,338$19,586

2009

1,486

$8,925,946

3,844$582,260

4,659$20,335$15,597$15,853

2010

1,556

$9,072,356

4,010$591,811

4,860$20,669$16,590$16,590

Total

33,544$251,722$368,613

104,221

126,323

191

Table 10-11. Comprehensive Benefits Forgone by Helmet Nonuse
Year

Lives
Lost
Due toHelmetNonuseCost/Fatality
Current$

1975

1,164

MAIS 2-5
MAIS 2-5
Injuries Cost/Injury
CausedCurrent$
byHelmetNonus$2,238,383
1,114$146,015

MAIS 1
MAIS 1
Injuries Cost/Injury
CausedCurrent$
byHelmetNonus
3,743$5,099

1976

1,189

$2,367,360

1,313$154,428

3,582

1977
1978

1,472
1,588

$2,521,301$2,712,687

1,636
2,248

$164,470$176,955

1979

1,676

$3,020,568

2,588

1980

1,744

$3,428,303

2,843

1981

1,667

$3,781,951

1982

1,528

1983

Current $(Millions)2010
Dollars(Millions)$2,787$11,297$5,393$3,037$11,638

4,422
4,100

$5,744$6,180$4,006$4,731$14,414$15,822$197,039

4,022$6,881$5,600$16,820$223,636

3,986$7,810$6,646$17,587

2,691$246,706

3,843$8,616$7,001$16,796$4,014,943

2,323$261,904

3,722$9,147$6,777$15,3141,450$4,143,920

2,334$270,318

3,351$9,441$6,671$14,606

1984

759$4,322,824

2,473$281,988

3,716$9,848$4,015$8,426

1985

764$4,476,765

2,497$292,030

3,588$10,199$4,186$8,483

1986

751$4,559,976

2,439$297,458

3,704$10,389$4,189$8,334

1987

697$4,726,399

2,268$308,314

3,055$10,768$4,026$7,7291988

644$4,921,945

2,060$321,070

2,767$11,213$3,862$7,1191989

553$5,159,097

1,738$336,540

2,431$11,753$3,466$6,096

1990

541$5,437,855

1,671$354,724

2,753$12,389$3,569$5,954

1991

467$5,666,686

1,412$369,651

2,446$12,910$3,200$5,123

1992

323$5,837,269

961$380,779

2,567$13,298$2,286$3,552

1993

336$6,012,013

989$392,178

2,614$13,697$2,444$3,687

1994

339$6,165,953

988$402,2202,376$14,047$2,521$3,7091995

326$6,340,697

928$413,619

2,319$14,445$2,485$3,555

1996

324$6,527,922

908$425,832

2,237$14,872$2,535$3,523

1997

315$6,677,703

871$435,602

2,226$15,213$2,517$3,4191998

369$6,781,717

1,007$442,387

2,289$15,450$2,983$3,991

1999

396$6,931,497

1,062$452,158

2,533$15,791$3,265$4,274

2000

478$7,164,488

1,269$467,356

2,896$16,322$4,065$5,148

2001

558$7,368,356

1,457$480,655

3,087$16,787$4,863$5,988

2002

576$7,484,852

1,483$488,254

3,170$17,052$5,090$6,1692003

651$7,655,435

1,653$499,382

3,663$17,441$5,873$6,9602004

673$7,859,302

1,707$512,681

4,140$17,905$6,239$7,202

2005

731$8,125,578

1,857$530,051

4,865$18,512$7,014$7,832

2006

756$8,387,694

1,919$547,149

5,228$19,109$7,491$8,103

2007

805$8,626,593

2,045$562,733

5,608$19,653$8,205$8,6292008

827$8,957,816

2,101$584,339

5,755$20,408$8,753$8,865

2009733$8,925,946

1,861$582,260

4,659$20,335$7,721$7,847

2010

708$9,072,356

1,804$591,811

4,860$20,669$7,592$7,592

Total

28,878$171,711$301,602

62,520126,323

192

11.

Seat Belt Use

When properly fastened, seat belts provide significant protection to vehicle occupants involved in acrash. The simple act of buckling a seat belt can improve an occupant’s chance of surviving a potentiallyfatal crash by from 44 to 73 percent, depending on the type of vehicle and seating position involved.
They are also highly effective against serious nonfatal injuries. Belts reduce the chance of receiving an
MAIS 2-5 injury (moderate to critical) by 49 to 78 percent.
The effectiveness of seat belts is a function of vehicle type, restraint type, and seat position. Table 11-1
shows the estimated effectiveness of seat belts for various seating positions for passenger cars and forlight trucks, vans, and sports utility vehicles (LTVs).
Table 11-1. Effectiveness of Seat Belts against Fatalities and Serious Injuries
Percent EffectivenessLap Belts

Lap/Shoulder Belts

Passenger Cars, Front Seat
Fatalities
MAIS 2-5 Injuries

35
30

45
50

Passenger Cars, Rear Seat
Fatalities
MAIS 2-5 Injuries

32
37

44
49

Light Trucks, Front Seat
Fatalities
MAIS 2-5 Injuries

50
55

60
65

Light Trucks, Rear Seat
Fatalities
MAIS 2-5 Injuries

63
68

73
78

Sources: Kahane, 2000; Morgan, 1999; NHTSA 1984

Although all passenger vehicles have been equipped with seat belts since 1968, a sizable minority ofvehicle occupants still neglect to use these devices. As of 2012, about 86 percent of occupants wear
their seat belts. Usage has risen steadily throughout the last two decades, largely in response to public
education programs sponsored by State and Federal safety agencies, as well as private consumer andsafety advocacy groups. A major factor in this increase has been the passage of seat belt use laws. As of
2001, all States except New Hampshire had some form of adult usage law. These laws can take the formof either primary enforcement laws, under which police can stop drivers specifically for failing to wear
seat belts, or secondary laws, under which fines can only be levied if a driver is stopped for some otheroffense. Primary enforcement laws are far more effective in increasing seat belt use. Experience in a
number of States indicates that usage rates rise from 10-15 percentage points when primary laws are
passed. For example, usage in California jumped from 70 percent to 82 percent when a primary law was
passed in 1993. Similar impacts occurred in Louisiana where usage rose 18 points, in Georgia whereusage rose 17 points, in Maryland where usage rose 13 points, and in the District of Columbia where193usage rose 24 points when they combined a new primary enforcement law with penalty points. Overall,States with primary belt use laws have an average belt use rate that is 12 percentage points higher thanStates with only secondary enforcement (NHTSA, 2012, November). Figure 11-A illustrates thenationwide trend in seat belt use rates from 1983 through 2010.

Figure 11-A. Observed Belt Use Rate

By combining seat belt use rates with effectiveness rates and National injury counts, an estimate can bemade of the impact of seat belts on fatality and casualty rates. The basic methods for these calculations
are well documented (Partyka & Womble, 1989, Blincoe, 1994, Wang & Blincoe, 2001, Wang & Blincoe,
2003, Glassbrenner, 2007). The effect of increases in seat belt use on fatalities is curvilinear, i.e., themore the observed usage rate in the general population approaches 100 percent, the more lives are
saved for each incremental point increase. This occurs because those who are most resistant to bucklingup tend to be in high-risk groups such as impaired drivers or people who are risk takers in general. These
people are more likely to be involved in serious crashes and are thus more likely to actually benefit fromwearing their belts. Belt use by people involved in potentially fatal crashes (UPFC) tends to be lower
than observed use for these same reasons. Figure 11-B illustrates the relationship between use inpotentially fatal crashes as well as lives saved and increasing rates of observed seat belt usage.

194

Figure 11-B. UPFC and Percentage of Lives Saved as a Function of Observed Belt Use

Table 11-2 lists the historical and cumulative impact of seat belt use on motor vehicle casualties.
Through 2010, seat belts have saved 280,000 lives and prevented 7.2 million serious nonfatal policereported injuries. At current (2010) use rates, they are preventing 12,500 fatalities and 308,000 serious(MAIS 2-5) police-reported nonfatal injuries annually.
The failure of a large segment of the driving population to wear their belts also has significant safety
implications. If all occupants had used seat belts properly, many more lives would have been saved.
Table 11-2 also lists the potential safety benefits that could have been realized since 1975 had all
occupants worn their seat belts. Over this period, passenger vehicles were equipped with devices that
could have saved over 367,000 additional lives and prevented 5.8 million additional serious policereported injuries53 if all vehicle occupants had taken a few seconds to buckle their seat belts. At current(2010) belt use rates, an additional 3,353 fatalities and 54,000 serious injuries could be prevented every

53

This analysis includes only police-reported injuries. About 20 percent of MAIS2 injuries and 4 percent of MAIS3
injuries are estimated to be unreported. Belt use rates are unknown for unreported crashes. If belt use rates for
unreported crashes are similar to reported crashes, benefits for each of these two categories would increase
proportionally. All MAIS 4, 5, and Fatal injuries are estimated to be reported to police.

195year if all passengers were to wear their seat belts. This represents an enormous lost opportunity for
injury prevention.
Table 11-2. Achieved and Potential Impact of Seat Belt Use on Fatalities and Serious Injuries,
1975-2010

Year

Lives
Saved
by Seat
Belts

Fatalities
Preventable@100%
Usage*

Lives Lost
Due to Belt
Nonuse

MAIS 2-5 PR
Injuries
Prevented

MAIS 2-5 PR
Injuries
Preventable@ 100%Usage

PR MAIS 2-5
Benefits
Lost toNonuse

1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Total

978
796
682
679
594
575
548
678
809
1,197
2,435
4,094
5,141
5,959
6,333
6,592
6,838
7,020
7,773
9,219
9,882
10,710
11,259
11,68011,941
12,882
13,295
14,26415,095
15,54815,68815,45815,223
13,312
12,763
12,546
280,486

14,27914,647
15,142
16,22016,32016,305
15,77013,92813,722
14,424
14,943
16,822
17,81918,633
18,58918,353
17,65017,215
17,98518,726
19,663
20,16920,355
20,37020,75021,12721,311
21,101
21,24621,422
21,35520,926
20,271
17,483
16,463
15,899647,403

13,301
13,851
14,46015,541
15,726
15,73015,222
13,25012,913
13,227
12,50812,72812,67812,674
12,256
11,761
10,812
10,195
10,212
9,507
9,781
9,459
9,096
8,690
8,809
8,245
8,016
6,837
6,151
5,874
5,667
5,468
5,048
4,171
3,700
3,353
366,917

35,11828,75425,31122,01622,254
21,602
20,59625,37835,44836,72856,778
119,801
146,489
160,765
167,795
179,344
216,513233,096
266,990284,688314,151
318,593313,258
290,042
317,209343,460
319,006
329,791
341,224
339,907348,798
319,550
318,772
303,555
300,994
307,958
7,231,732

259,709262,473274,272
253,280
294,178
292,407
281,042
253,185
253,197262,346
270,373
323,788348,782
357,255364,771
366,009366,971375,961
404,530
424,908
461,986
468,519453,998
420,351
453,155
470,494436,995
439,722431,929424,884
425,363394,506
388,747
365,729358,326
362,303
13,046,444

224,591
233,719
248,961
231,264
271,924
270,805
260,446
227,807
217,750
225,617
213,595
203,986
202,294
196,490
196,976
186,665
150,458
142,865
137,540
140,220
147,836
149,926
140,739
130,309
135,947
127,033
117,989
109,930
90,70584,97776,56574,956
69,97462,17457,332
54,3455,814,711

196

Seat belt use has also had a significant economic impact. Table 11-3 lists the economic savings that haveresulted from seat belt use over the 36 years. Since 1975, about $1.2 trillion in economic costs (2010
dollars) have been saved due to seat belt use. At 2010 usage rates, seat belts saved society an estimated$50 billion annually in medical care, lost productivity, and other injury related costs. Table 11-4 lists thepotential economic savings that were lost due to nonuse. These lost savings could be viewed as costs ofseat belt nonuse. Since 1975, nearly $1.1 trillion in unnecessary economic costs (2010 dollars) have been
incurred due to seat belt nonuse. At current usage rates, the needless deaths and injuries that result
from nonuse continue to cost society an estimated $10 billion annually in medical care, lost productivity,and other injury related costs. 54

54

Prior years’ unit costs were estimated by deflating the 2010 unit costs using the CPI annual average All Items
index.

197

Table 11-3. Impact of Historical Seat belt Use on Economic Costs

Total Cost Savings(Millions)

Year

Lives
Saved
by Seat
Belts

1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Total

978
796
682
679
594
575
548
678
809
1,197
2,435
4,094
5,141
5,959
6,333
6,592
6,838
7,020
7,773
9,219
9,882
10,710
11,259
11,68011,941
12,882
13,295
14,26415,095
15,54815,68815,45815,223
13,312
12,763
12,582
280,522

Cost/Fatality
Current$$340,971$360,618$384,068$413,221$460,121$522,230$576,101$611,593$631,240$658,492$681,942$694,617$719,968$749,756$785,881$828,344$863,201$889,186$915,805$939,254$965,873$994,393$1,017,209$1,033,053$1,055,869$1,091,360$1,122,415$1,140,161$1,166,146$1,197,201$1,237,762$1,277,690$1,314,081$1,364,536$1,359,681$1,381,984

MAIS 2-5
Injuries
Prevented

MAIS 2-5
Cost/Injury
Current$

35,11828,75425,31122,01622,254
21,602
20,59625,37835,44836,72856,778
119,801
146,489
160,765
167,795
179,344
216,513233,096
266,990284,688314,151
318,593313,258
290,042
317,209343,460
319,006
329,791
341,224
339,907348,798
319,550
318,772
303,555
300,994
307,958
7,231,732$26,336$27,853$29,664$31,916$35,538$40,336$44,496$47,238$48,755$50,860$52,671$53,650$55,608$57,909$60,699$63,979$66,671$68,678$70,734$72,545$74,601$76,804$78,566$79,790$81,552$84,293$86,692$88,063$90,070$92,468$95,601$98,685$101,496$105,393$105,018$106,740

198

Current $$1,258$1,088$1,013$983$1,064$1,172$1,232$1,613$2,239$2,656$4,651$9,271$11,847$13,777$15,162$16,935$20,338$22,251$26,004$29,312$32,981$35,119$36,064$35,209$38,477$43,010$42,578$45,306$48,337$50,045$52,763$51,285$52,358$50,157$48,963$50,260$896,779

2010
Dollars$5,100$4,169$3,644$3,288$3,196$3,100$2,956$3,646$4,902$5,575$9,426$18,445$22,741$25,395$26,663$28,253$32,561$34,582$39,241$43,128$47,189$48,808$48,997$47,101$50,361$54,464$52,424$54,915$57,283$57,769$58,911$55,472$55,064$50,798$49,766$50,260$1,159,594

Table 11-4. Impact of Potential Seat belt Use on Economic Costs

Year

1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Total

Lives
Lost Dueto Belt
Nonuse13,301
13,851
14,46015,541
15,726
15,73015,222
13,25012,913
13,227
12,50812,72812,67812,674
12,256
11,761
10,812
10,195
10,212
9,507
9,781
9,459
9,096
8,690
8,809
8,245
8,016
6,837
6,151
5,874
5,667
5,468
5,048
4,171
3,700
3,353
366,917

Cost/Fatality
Current$$340,971$360,618$384,068$413,221$460,121$522,230$576,101$611,593$631,240$658,492$681,942$694,617$719,968$749,756$785,881$828,344$863,201$889,186$915,805$939,254$965,873$994,393$1,017,209$1,033,053$1,055,869$1,091,360$1,122,415$1,140,161$1,166,146$1,197,201$1,237,762$1,277,690$1,314,081$1,364,536$1,359,681$1,381,984

MAIS 2-5
InjuryBenefits
Lost Due toBelt Nonuse

224,591
233,719
248,961
231,264
271,924
270,805
260,446
227,807
217,750
225,617
213,595
203,986
202,294
196,490
196,976
186,665
150,458
142,865
137,540
140,220
147,836
149,926
140,739
130,309
135,947
127,033
117,989
109,930
90,70584,97776,56574,956
69,97462,17457,332
54,3455,814,711

199

MAIS 2-5
Cost/Injury
Current$$26,336$27,853$29,664$31,916$35,538$40,336$44,496$47,238$48,755$50,860$52,671$53,650$55,608$57,909$60,699$63,979$66,671$68,678$70,734$72,545$74,601$76,804$78,566$79,790$81,552$84,293$86,692$88,063$90,070$92,468$95,601$98,685$101,496$105,393$105,018$106,740

Cost Savings
Forgone (Millions)
Current $2010
Dollars$10,450$11,505$12,939$13,803$16,900$19,138$20,358$18,865$18,768$20,185$19,780$19,785$20,377$20,881$21,588$21,685$19,364$18,877$19,081$19,102$20,476$20,921$20,310$19,375$20,388$19,706$19,226$17,476$15,343$14,890$14,334$14,383$13,736$12,244$11,052$10,435$627,722$42,355$44,089$46,558$46,163$50,758$50,644$48,837$42,628$41,088$42,362$40,085$39,363$39,114$38,489$37,963$36,178$31,002$29,339$28,794$28,106$29,297$29,075$27,593$25,919$26,685$24,954$23,672$21,183$18,182$17,188$16,004$15,558$14,445$12,401$11,233$10,435$1,127,737

Figure 11-C compares the portion of potential seat belt fatality benefits to those that could be achieved
if observed belt use rose to 100 percent. For nearly 2 decades, between 1975 and 1985, belt use was so
low that less than 10 percent of potential safety benefits were actually achieved. However, belt use andits corresponding life-saving benefits increased dramatically over the past 25 years, and by 2010, 79
percent of potential safety benefits were being realized.
Figure 11-C. Realized and Unrealized Fatality Benefits from Safety Belt Use

Realized and Unrealized Fatality Benefits from
Safety Belt Use
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009
Realized Benefits

Unrealized Benefits

Figure 11-D compares the achieved economic benefits from seat belt use to those that could have beenachieved if observed belt use was 100 percent. Cost impacts (which include impacts to both fatalities
and nonfatal injuries) roughly parallel the pattern seen for fatalities, with less than 10 percent ofpotential economic benefits being realized between 1975 and 1985, but with significant growth in later
years due to increases in belt use. By 2010, some 83 percent of potential economic benefits were being
realized.

200

Figure 11-D. Realized and Unrealized Economic Benefits from Safety
Belt Use

Realized and Unrealized Economic Benefits from
Safety Belt Use
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009
Realized Benefits

Unrealized Benefits

Table 11-5 lists the comprehensive impact of seat belt use. This table reflects the combined impact ofboth economic cost savings and valuations for lost quality-of-life (see Chapter 4). The comprehensive
societal benefits from seat belt use are enormous. From 1975 to 2010, seat belts have prevented $7.6
trillion in societal harm as measured by comprehensive costs, and they are currently preventing $330
billion in societal harm annually.

201

Table 11-5. Comprehensive Benefits from Seat Belt Use
Total Cost Savings(Millions)
Year

Total

1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010

Lives
Saved by
Seat
Belts

978
796
682
679
594
575
548
678
809
1,197
2,435
4,094
5,141
5,959
6,333
6,592
6,838
7,020
7,773
9,219
9,882
10,710
11,259
11,68011,941
12,882
13,295
14,26415,095
15,54815,68815,45815,223
13,312
12,763
12,582
280,522

Cost/Fatality
Current$$2,252,374$2,382,158$2,537,061$2,729,643$3,039,449$3,449,733$3,805,592$4,040,040$4,169,823$4,349,846$4,504,749$4,588,480$4,755,943$4,952,712$5,191,346$5,471,846$5,702,108$5,873,757$6,049,593$6,204,496$6,380,332$6,568,728$6,719,444$6,824,108$6,974,825$7,209,273$7,414,415$7,531,639$7,703,288$7,908,430$8,176,370$8,440,124$8,680,517$9,013,810$8,981,741$9,129,066

MAIS 2-5
Injuries
Prevented

MAIS 2-5
Cost/Injury
Current$

35,11828,75425,31122,01622,254
21,602
20,59625,37835,44836,72856,778
119,801
146,489
160,765
167,795
179,344
216,513233,096
266,990284,688314,151
318,593313,258
290,042
317,209343,460
319,006
329,791
341,224
339,907348,798
319,550
318,772
303,555
300,994
307,958
7,231,732$172,473$182,411$194,272$209,019$232,742$264,159$291,409$309,361$319,299$333,084$344,946$351,357$364,181$379,248$397,521$419,000$436,632$449,776$463,240$475,102$488,566$502,993$514,534$522,548$534,089$552,042$567,750$576,726$589,870$605,579$626,096$646,293$664,700$690,222$687,766$699,048

202

Current $2010
Dollars$8,260$7,141$6,648$6,455$6,985$7,690$8,087$10,590$14,692$17,440$30,555$60,878$77,799$90,483$99,579$111,216$133,528$146,075$170,704$192,455$216,534$230,601$236,836$231,267$252,704$282,474$279,691$297,631$317,559$328,801$346,652$336,990$344,032$329,512$321,648$330,139$5,890,327$33,477$27,367$23,920$21,589$20,979$20,350$19,400$23,930$32,165$36,602$61,920$121,121$149,335$166,782$175,111$185,549$213,777$227,031$257,599$283,171$309,820$320,484$321,767$309,381$330,754$357,696$344,372$360,757$376,335$379,550$387,043$364,498$361,809$333,725$326,924$330,139$7,616,228

Table 11-6 lists the unnecessary societal harm (as measured by comprehensive costs) that resulted fromfailure of occupants to wear seat belts. These lost potential savings can be viewed as the societal cost ofseat belt nonuse. Since 1975, some $7.4 trillion in unnecessary societal harm (2010 Dollars) has been
incurred due to seat belt nonuse. At current usage rates, the needless deaths and injuries that result
from nonuse continue to cost society an estimated $69 billion annually in lost quality-of-life, medicalcare, lost productivity, and other injury related costs.
Table 11-6. Impact of Potential Seat Belt Use on Societal Harm

Year

Total

1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010

Lives Lost
Due toBelt
Nonuse13,301
13,851
14,46015,541
15,726
15,73015,222
13,25012,913
13,227
12,50812,72812,67812,674
12,256
11,761
10,812
10,195
10,212
9,507
9,781
9,459
9,096
8,690
8,809
8,245
8,016
6,837
6,151
5,874
5,667
5,468
5,048
4,171
3,700
3,353
366,917

Cost/Fatality
Current$$2,252,374$2,382,158$2,537,061$2,729,643$3,039,449$3,449,733$3,805,592$4,040,040$4,169,823$4,349,846$4,504,749$4,588,480$4,755,943$4,952,712$5,191,346$5,471,846$5,702,108$5,873,757$6,049,593$6,204,496$6,380,332$6,568,728$6,719,444$6,824,108$6,974,825$7,209,273$7,414,415$7,531,639$7,703,288$7,908,430$8,176,370$8,440,124$8,680,517$9,013,810$8,981,741$9,129,066

MAIS 2-5
InjuryBenefits
Lost
Due
to Belt

224,591
233,719
248,961
231,264
271,924
270,805
260,446
227,807
217,750
225,617
213,595
203,986
202,294
196,490
196,976
186,665
150,458
142,865
137,540
140,220
147,836
149,926
140,739
130,309
135,947
127,033
117,989
109,930
90,70584,97776,56574,956
69,97462,17457,332
54,3455,814,711

203

Cost Savings Forgone(Millions)
MAIS 2-5
Cost/Injury
Current$$172,473$182,411$194,272$209,019$232,742$264,159$291,409$309,361$319,299$333,084$344,946$351,357$364,181$379,248$397,521$419,000$436,632$449,776$463,240$475,102$488,566$502,993$514,534$522,548$534,089$552,042$567,750$576,726$589,870$605,579$626,096$646,293$664,700$690,222$687,766$699,048

Current $$68,695$75,628$85,052$90,760$111,087$125,800$133,825$124,005$123,372$132,685$130,024$130,074$133,967$137,289$141,927$142,567$127,346$124,140$125,493$125,605$134,634$137,545$133,535$127,394$134,049$129,568$126,422$114,894$100,887$97,914$94,273$94,594$90,331$80,510$72,664$68,600$4,127,156

2010
Dollars$278,425$289,827$306,042$303,539$333,652$332,906$321,027$280,208$270,101$278,467$263,499$258,791$257,151$253,058$249,582$237,854$203,881$192,940$189,373$184,810$192,636$191,157$181,421$170,424$175,451$164,071$155,658$139,262$119,560$113,027$105,257$102,316$94,999$81,540$73,856$68,600$7,414,369

12.

Crashes by Roadway Location

Urban roadway environments are characterized by high population densities. This typically produceshigher traffic volumes and, on average, lower average travel speeds than are found in more rural areas.
These conditions affect crash impacts in a variety of ways. Slower travel speeds can reduce the severityof crashes when they occur, but higher traffic volume creates more opportunities for exposure to
distracted or alcohol impaired drivers, as well as more complex driving interactions in general. Higher
traffic volume also means that when crashes do occur, they will have more impact on uninvolved drivers
and cause more aggregate travel delay and pollution. By contrast, the higher speeds typicallyencountered on less congested rural roadways can lead to more serious injury outcomes in the event ofa crash.
The categorization of any specific crash locale as urban or rural is a function of the definition that isassumed for these designations. According to the Washington Post, within the U.S. government there
are at least 15 different official definitions of the word “rural (Fahrenthold, 2013).” The Department ofAgriculture alone has 11 different definitions depending on the specific program that the definition
relates to. Most definitions seem to be based on absolute population size: for example, “fewer than
50,000 inhabitants and not located next to an urban area” or “20,000 or fewer inhabitants,” or “10,000
or fewer inhabitants,” or “5,000 or fewer inhabitants.” In some cases, these definitions are based onarea as well, such as “less than 20 people per square mile.” The Federal Highway Administration (FHWA)
is a primary user of crash cost information related to roadway systems. FHWA uses this data to allocate
resources towards improving safety on the U.S. roadways. The definition adopted for this study is thatused by the FHWA to define the Nation’s roadway system. FHWA’s roadway designations were designed
to be consistent with designations used by the U.S. Census Bureau. Urban areas are defined in theFederal aid highway law (Section 101 of Title 23, U.S. Code) as follows:"The term 'urban area' means an urbanized area or, in the case of an urbanized area encompassing
more than one State, that part of the urbanized area in each such State, or an urban place as
designated by the Bureau of the Census having a population of five thousand or more and notwithin any urbanized area, within boundaries to be fixed by responsible State and local officials incooperation with each other, subject to approval by the Secretary. Such boundaries shall, as aminimum, encompass the entire urban place designated by the Bureau of the Census… .
Small urban areas are those urban places, as designated by the Bureau of the Census having a
population of five thousand (5,000) or more and not within any urbanized area.
Urbanized areas are designated as such by the Bureau of the Census.
Rural areas comprise the areas outside the boundaries of small urban and urbanized areas, as
defined above.”

204

NHTSA’s FARS system collects geospatial coordinates which permit the exact identification of crashes andallow for the overlay of theses crashes on the roadway land use designation map defined by FHWA. The
urban/rural breakout of fatal crashes can thus be derived directly from the FARS database. Table 121 below lists this profile over the past 14 years. Over this period, there has been a very gradual decline
in the portion of fatalities that occur in rural jurisdictions, from roughly 61 percent to 55 percent.
Table 12-1. Traffic Fatalities with Known Urban/Rural Designation
1998
1999
2000
2001
2002
2003

Urban
16219
16058
16113
16988
17013
17783

%Urban
39.2%
38.6%
39.3%
40.3%
39.6%
41.6%

Rural
25185
25548
24838
25150
25896
24957

%Rural
60.8%
61.4%
60.7%
59.7%
60.4%
58.4%

2004

17581

41.1%

25179

58.9%

2005

18627

43.1%

24587

56.9%

2006

18791

44.3%

23646

55.7%

2007

17908

43.5%

23254

56.5%

2008
2009
2010
2011

16218
14,501
14,65914,464

43.6%
42.9%
44.8%
44.9%

20987
19,32318,08917,762

56.4%
57.1%
55.2%
55.1%

Urban/rural designations for nonfatal crashes are more elusive. There are no definitive sources or
surveys designed specifically to produce a nationally representative break out of urban and ruralcrashes. Until it was discontinued in 1997, the GES included a specific urban/rural variable. However,this variable was not directly linked to the crash itself. Rather, it represented the pre-determined
urban/rural proportion of the general population that was covered by the primary sampling unit (PSU)
from which each case was drawn. Thus, use of this variable assumes that crashes occur proportionallyaccording to the population spread.
A second possible indicator of urban/rural status is the Land Use Variable that has been collected in GES
since 1988 (except 2009). This variable categorizes land use based on population size within the specificpolice jurisdictions from which crash records are drawn. Each PSU has multiple police jurisdictions.
Therefore, this variable represents a finer definition than the Urban/Rural variable which reflected
populations at the PSU level. The categories included under land use are:Within an area of population 25,000-50,000;
Within an area of population 50,000-100,000;
Within an area of population 100,000 +;Other area; andUnknown area.

205

This variable quantifies populations, but fails to define urban/rural. A small area with a population of
25,000 might be considered urban whereas a large area with the same population might be considered
rural. Generally, since these areas are all specific police jurisdictions, the size of the area is somewhat
limited and, although there would be exceptions, we might expect that most police jurisdictions with
25,000 or more population would be considered urban. By the same logic, the “Other Area” category ismore likely to represent rural roadways, but may also include some urban areas as defined by FHWA.
A third possible source for insight into the urban/rural breakdown of nonfatal crashes is the National
Highway System variable collected in GES from 1995-1998. This variable has 20 possible selections basedon the type of roadway on which the crash occurred and whether that roadway was urban or rural. The
urban/rural designation for these roadways reflects the characteristics of the surrounding land use area.
The selections for this variable for roadways with known urban/rural characteristics are as follows:
National Highway System Variables
Urban
1 = Eisenhower Interstate (EIS)
2= Congressional High Priority Route
3 = STRAHNET Route
4 = STRAHNET Major Connector
5 = Other NHS Route
9 = Unknown Urban Route
Rural
11 = Eisenhower Interstate (EIS)
12 = Congressional High Priority Route
13 = STRAHNET Route
14 = STRAHNET Major Connector
15 = Other NHS Route19 = Unknown Rural RouteOnly about 15 percent of the crashes in the 1994-1998 GES occurred on roadways in the NHS, so thisdata represents only a sample of all crashes and use of these variables to represent nationwide
distributions assumes that the urban/rural distribution for crashes on all roadways is similar to that on
NHS roadways.
A fourth possible source of urban/rural designations is State data files collected in NHTSA’s State Data
System (SDS). Many States do not collect urban/rural information, but a subsample of the 34 States in
the SDS system do have urban/rural indicators. NHTSA found 10 States with urban/rural indicators
within their data sets in 2008 or later. Table 12-2 summarizes the average rural portion of crashes ineach State by injury severity based on the designations found in State files for all available years withineach State from 2008-2011. Table 12-3 presents the relative rates of rural proportions according toinjury severity for each State and the average across all States.

206

Table 12-2. Portion of Injuries Occurring in Rural Jurisdictions, by Injury Severity
State

Injury Severity
B
C

O

Total

K

A

Arkansas67.26%

57.20%

31.74%

18.94%

14.22%

16.88%

Florida61.81%

56.63%

46.77%

44.97%

39.44%

42.08%

Illinois49.90%

32.21%

21.20%

9.19%

11.36%

12.11%

KansasMinnesota

69.17%
76.98%

53.82%
62.18%

40.31%
48.26%

25.86%
33.93%

27.62%
24.50%

28.69%
26.98%

Missouri73.40%

64.55%

46.56%

28.15%

30.74%

32.13%

Nebraska

91.34%

66.07%

51.94%

35.52%

37.15%

38.35%

Texas

28.77%

20.60%

14.12%

7.93%

9.13%

9.44%

Washington

66.73%

54.57%

48.83%

23.02%

21.75%

23.74%

Wisconsin

78.57%

65.10%

52.74%

35.24%

36.66%

37.74%

Average

66.39%

53.29%

40.25%

26.27%

25.26%

26.81%

Table 12-3. Rural Proportions Relative to Fatal Proportions by Injury Severity
State

K

Injury Severity
A
B
C

O

Total

Arkansas1.00000.85040.47200.28160.21140.2510Florida1.00000.91610.75660.7274

0.63800.6808Illinois

1.00000.64550.42500.18410.22770.2428KansasMinnesota

1.00001.00000.77810.80770.58280.62690.37390.44080.39930.31830.41480.3505

Missouri1.00000.87950.63430.38350.41880.4378Nebraska

1.00000.72330.5686

0.38880.40670.4199Texas

1.00000.71610.49090.27560.31730.3282

Washington

1.00000.81770.73170.34490.32590.3557

Wisconsin

1.00000.82850.67120.4486

0.46660.4803

Average1.00000.79630.59600.38490.37300.3962A final source for urban/rural definitions for injuries is the National Motor Vehicle Crash Causation
Survey (NMVCSS). In 2008 NHTSA’s National Center for Statistics and Analysis completed a nationwide
survey of crashes involving light passenger vehicles, with a focus on the factors related to pre-crash
events. This Nationally representative sample of crashes was investigated from 2005 to 2007. NMVCCS
investigated a total of 6,950 crashes during the 3-year period from January 2005 to December 2007.
However, the final report was based on a nationally representative sample of 5,471 crashes that were
investigated during a 2 ½- year period from July 3, 2005, to December 31, 2007. The remaining 1,479
crashes were investigated but were not used because (1) these crashes were investigated during the207transition period from January 1, 2005, to July 2, 2005, when the data collection effort was being phasedin, or (2) these crashes were investigated after the phase-in period, but ultimately determined not tomeet the requisite sample selection criteria. Each investigated crash involved at least one lightpassenger vehicle that was towed due to damage. Data was collected on at least 600 data elements tocapture information related to the drivers, vehicles, roadways, and environment. In addition, the
NMVCCS database includes crash narratives, photographs, schematic diagrams, vehicle information, as
well as event data recorder (EDR) data, when available. An important feature of NMVCCS relevant tothis study is the fact that each crash location was recorded using geo-spatial devices. For this study,
these coordinates were overlain on the previously discussed roadway land use designation map
developed by FHWA to produce urban and rural designations for each crash.
As noted above, the higher average speeds encountered in less congested rural areas result in generally
more severe crash outcomes. Based on this, we would expect to see a higher rural proportion of more
serious crashes. To estimate the urban/rural portions for nonfatal crashes, we examined the relative
proportions of these factors by injury severity level across the 5 sources cited above. As expected, therural proportion of crashes was highest in the most serious crashes and declined fairly steadily as crashseverity diminished. However, the absolute portions of fatal crashes that were rural in these databases
differed significantly from the rural rates that were found in FARS. It is uncertain why these differences
occur, but it is possible that whatever is biasing the fatality number in these sources is also biasing thenonfatal injuries as well. This would seem to be the case given that the value of all nonfatal injury
severity levels seems consistent with the absolute value of the fatal injury proportions measured in eachdata source. Generally speaking, the sources with rural fatality portion that are higher also have nonfatal
injury rural portions that are higher. For this analysis we adopted this assumption (that both fatal andinjury biases are similar) and normalize the results of each source to the known rural portion from FARS.
That is, we assume the FARS urban/rural distribution is correct, but accept the relative ratios among
injury severity from the 5 sources. The results are displayed in Table 12-4.

208

Table 12-4. Derivation of Estimated Urban/Rural Proportions of Crashes by Injury Severity
No
Injury(O)

Possible
Injury (C)Nonincapacitating
Injury (B)Incapacitating
Injury (A)Fatal
Injury (K)Estimated Percentage Rural
1994-1996 GES Urban/Rural

23.8%

21.3%

25.8%

29.5%

33.0%

2010-2011 GES Land Use
1995-1998 National Highway System
SDS States w/Urban/Rural (10 States)
NMVCCS

29.9%
39.0%
25.3%
29.7%

33.2%
30.2%
26.3%
18.6%

41.1%
46.2%
40.2%
27.6%

42.7%
66.2%
53.3%
42.8%

50.0%
70.2%
66.4%
49.2%

Ratio/Fatal
1994-1996 GES Urban/Rural
2010-2011 GES Land Use
1995-1998 National Highway System
SDS States w/Urban/Rural (10 States)
NMVCCS

0.72020.59850.55530.37300.60300.6454
0.66300.42950.38490.3776

0.78270.82080.65880.59600.55980.89550.85430.94330.79630.86931.00001.00001.00001.00001.0000Normalized to 2010 FARS
1994-1996 GES Urban/Rural
2010-2011 GES Land Use
1995-1998 National Highway System
SDS States w/Urban/Rural (10 States)
NMVCCS

39.8%
33.1%
30.7%
21.0%
33.3%

35.6%
36.6%
23.7%
21.9%
20.9%

43.2%
45.3%
36.4%
33.5%
30.9%

49.5%
47.2%
52.1%
44.3%
48.0%

55.2%
55.2%
55.2%
55.2%
55.2%

Average of 5 Methods

31.6%

27.7%

37.9%

48.2%

55.2%

A limitation common to all 5 sources is that injury severity is only coded in the KABCO system. As
previously mentioned, this report stratifies injury severity using the more precise MAIS basis. Previously
the derivation and use of KABCO-MAIS translators was discussed. In order to derive urban/ruralproportions under MAIS, a reverse translator was applied to the urban and rural KABCO injury
distributions (see Table 12-5 below). This translator was derived from the same historical data bases as
the previously discussed translators. However, since it will be applied to known nonfatal injuries only, it
was normalized to remove categories of Unknowns and fatalities. Table 12-6 shows the resulting
KABCO/MAIS matrix. Table 12-7 shows the estimated rural incidence counts for each MAIS level derived
by applying the KABCO injury- severity- specific rural percentage (from Table 12-4 above) to thecorresponding incidence counts in Table 12-6. The resulting MAIS totals were then used to obtain rates
for urban and rural crashes for each MAIS severity level, and these rates were applied to the nationwide
2010 incidence data previously derived in the Incidence chapter to estimate total nationwide urban andrural crash incidence.

209

Table 12-5. Reverse Translator for MAIS to KABCO Application to Injured Survivors
O

C

B

A

MAIS
0
1

No Injury

Possible Injury

Non- Incapacitating

Incapacitating

0.96507

0.028270.005380.00128

1.00000

0.330420.363140.216220.090221.00000

2
3
4
50.08384

0.31360

0.28566

0.31690

1.00000

0.00984

0.146650.233280.61024

1.00000

0.001900.08278

0.19400

0.72132

1.00000

0.03750

0.021340.084510.85665

1.00000

Total

Table 12-6. KABCO Incidence Counts from MAIS Injured Survivors
O
MAIS
0
1
2
3
4
5

C
Possible
InjuryNo Injury

B
Non
IncapacitatingA
Incapacitating

Total

4,423,168

129,580

24,6505,867

4,583,265

1,142,986

1,256,178

747,956

312,080

3,459,200

28,400106,224

96,761

107,344338,730991

14,77323,50061,476

100,740

32

1,414

3,315

12,32417,086

216

123

486

4,925

5,749

Table 12-7. Rural KABCO Incidence Counts from MAIS Injured Survivors

MAIS
0
1
2
3
4
5

O

C

B

No Injury

Possible Injury

1,502,706

38,689360,812
8,965
313
10
68

A

Non
Incapacitating

Incapacitating

Total Rural% Rural10,0473,045

1,554,487

33.92%

348,498
29,4704,098
392

283,270
36,6468,900
1,255

150,490
51,76329,645
5,943

1,143,070
126,844
42,956
7,601

33.04%
37.45%
42.64%
44.49%

34

184

2,375

2,661

46.29%

Cases designated as O-Uninjured in KABCO records are likely to be predominately PDO crashes. In
addition, they would include cases where uninjured people were involved in crashes that did produce
injury, which are categorized as MAIS 0 in this study. Since PDOs are counted separately, the rural
portion for MAIS 0 injuries should equal the weighted average rural portion of MAIS 0 incidence in injury
crashes. To estimate rural MAIS 0 incidence we examined the frequency of uninjured occupants in injury
crashes by MAIS level. Data from the 2009-2011 CDS and 1982-86 NASS were examined to determine

210

ratios of uninjured occupant frequencies. These are the only two databases with MAIS stratification.
Neither database is ideal – CDS represents tow-away crashes for light vehicles while the NASS data,
which include all crash types, are old. However, both sources gave very similar frequencies. These areshown in Table 12-8. Using these frequencies, we calculated a weighted average rural portion across all
injury severity categories. We then assumed that the frequency of PDOs versus MAIS 0 was similar to
the relative frequency of these cases nationwide. This would imply that 80.1 percent of these cases
were PDOs and 19.9 percent were MAIS0s. Using these weights, we derived the rural portion of MAIS0s.
We then imputed the PDO portion. The results, which are shown in the lower half of Table 12-8, arenearly identical for either data source. We chose to base our estimate on the NASS data because it
includes all crash types, but the difference if we used CDS would be insignificant.
Table 12-8. Derivation of Rural Incidence Percentage for MAIS 0 and PDO
2009-11 CDS

1982-86 NASS

0.86620.10290.02300.00440.00060.00290.87220.08750.02840.00330.00150.007031.57%

31.57%

MAIS 0 in Injury Crashes

33.84%

33.92%

PDO

31.00%

30.99%

Distribution of MAIS 0 by Crash Severity
MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
Fatal
Imputed % Rural
All KABCO O Injuries

As noted previously, for fatalities, the urban/rural designation contained in the FARS files is useddirectly. The resulting urban/rural incidence counts are illustrated in Figure 12-A and shown in Table
12-9 for each injury severity category.

211

Figure 12-A. Rural Percentage of Motor Vehicle Injury by Injury Severity

Table 12-9. Urban/Rural Incidence Summary
MAIS0
MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
Fatal
PDO

Urban
3,028,778
2,316,130
211,886
57,784
9,485
3,088
14,771
12,773,589% Urban
66.08%
66.96%
62.55%
57.36%
55.51%
53.71%
44.76%
69.01%

Rural
1,554,487
1,143,070
126,844
42,956
7,601
2,661
18,2285,735,043% Rural33.92%
33.04%
37.45%
42.64%
44.49%
46.29%
55.24%
30.99%

Total
4,583,265
3,459,200
338,730
100,740
17,086
5,749
32,99918,508,632

In Table 12-10, the incidence from Table 12-9 is combined with the per-unit economic costs of crashes
from Table 2-15 in chapter 2. In Table 12-11, incidence is combined with the per-unit comprehensive
costs from Table 4-2 in chapter 4. The results indicate that urban crashes cost an estimated $149 billion
and rural crashes cost $93 billion in 2010. Roughly 62 percent of all economic crash costs thus occur in
urban areas while 38 percent occur in rural areas. From table 12-11, urban crashes caused $470 billion
and rural crashes caused $366 billion in societal harm in 2010. Comparing Tables 12-9, 12-10, and 1211, the rural portion of incidence was 32 percent, but the rural portion rises to 38 percent for economic
costs and 44 percent for comprehensive costs. This is a reflection of the more severe injury profile
associated with rural crashes, which have larger proportions of more costly and debilitating injuries.
Nonetheless, the higher frequency of crashes in urban areas results in urban crashes causing the
majority of all injury incidence, economic costs, and comprehensive costs.

212

Table 12-10. Urban/Rural Economic Cost Summary, (Millions of 2010 Dollars)
MAIS0
MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
Fatal
PDO
Total Economic Costs

Urban% Urban$8,61166.08%$41,250
66.96%$11,81162.55%$10,51257.36%$3,74355.51%$3,09153.71%$20,664
44.76%$49,332
69.01%$149,014
61.58%Rural% Rural$4,41933.92%$20,358
33.04%$7,07037.45%$7,81542.64%$2,99944.49%$2,66446.29%$25,49955.24%$22,149
30.99%$92,974
38.42%

Total$13,030$61,608$18,881$18,327$6,742$5,755$46,163$71,480$241,988

Table 12-11. Urban/Rural Comprehensive Cost Summary (Millions of 2010 Dollars)

MAIS0
MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
Fatal
PDO
Total Comprehensive
Costs

Urban
$8,611$95,079$84,037$57,069$23,068$17,230$135,099$49,332$469,525%
Urban
66.08%
66.96%
62.55%
57.36%
55.51%
53.71%
44.76%
69.01%
56.18%

Rural$4,419$46,924$50,308$42,424$18,486$14,847$166,710$22,149$366,268%
Rural
33.92%
33.04%
37.45%
42.64%
44.49%
46.29%
55.24%
30.99%
43.82%

Total$13,030$142,004$134,345$99,493$41,555$32,077$301,809$71,480$835,793

A further breakdown of these crash cost estimates was made by roadway classification. Data on
roadway crash costs is useful for highway safety planning and allocation of limited roadway construction
funds. For this analysis, roadways were divided into the following 5 classifications.
4-lane divided roadways
Greater than 4-lane divided roadways
2-lane undivided roadways
Multi-lane undivided roadways
All other roadways
The lane count designations in the above categories include lanes in both directions. Thus, for example,
4 lane divided roadways would include 2 lanes in each direction. These categories and designations were213

selected based on discussions with FHWA staff regarding the most useful categories for planningpurposes.
As previously noted, NHTSA’s FARS system collects geospatial coordinates which permit the exact
identification of crashes and allow for the overlay of these crashes on the roadway land use designation
maps defined by FHWA, or, for some roadway designations, individual States. The urban/rural androadway designation breakout of fatal crashes can thus be derived directly from the FARS database.
For nonfatal injuries and PDOs, roadway designations are available within NHTSA’s GES data system, but
as noted earlier, urban and rural designations for these roadways are not collected in GES. To stratify
these nonfatal impacts by roadway category, we first examined the roadway designation proportions
within GES under the 5 categories discussed above. All cases were stratified by their coded roadway
type and lumped under the appropriate category. The approach we used involved determining
proportions of cases that occurred under each roadway type from the data files and then applying these
proportions to the total urban and rural costs already derived. Cases where the roadway designation
was unknown were thus ignored, because redistributing these cases across known roadway cases would
not alter the proportions assigned to that roadway type. In other words, we used the police-reported
cases with known roadway types to determine the proportions of crashes that occurred on eachroadway type, and then applied that proportion to the total costs of urban and rural crashes.
Because GES is stratified only by KABCO, data were organized into the same 5 categories noted inprevious sections of this report to be run through KABCO/MAIS translators to produce an MAIS based
injury profile. Those categories are CDS equivalent cases, Unbelted Non-CDS cases, Belted Non-CDS
cases, Unknown Belt Use Non-CDS cases, and Motorcycle/nonoccupant cases. The MAIS injury totals
from each of these cases were then combined to form a full MAIS injury profile representing all 5
translator scenarios. This was done separately for each of the 5 roadway types.
Because GES records do not include an urban/rural designation, this feature was derived from thedatabase we created from NMVCCS discussed above. NMVCCS cases were stratified within one of the 5
roadway designations for both urban and rural crashes. The proportions of each Roadway Type thatwere Urban and Rural were calculated within each KABCO injury severity level. Table 12-12 lists the
NMVCCS case distributions that resulted from this process. These proportions were then applied to eachtranslated MAIS case total that was derived from the corresponding KABCO distribution.

214

Table 12-12. NMVCCS Cases, Percentages Urban Versus Rural by Roadway TypeA Injuries

Greater than 4 lanes divided
Multi-lane undivided

Rural

Urban

Total

13.11%

86.89%

100.00%26.63%

73.37%

100.00%

Four lanes divided

64.59%

35.41%

100.00%Two-lane undivided
Other
Total

67.79%

32.21%

100.00%37.20%

62.80%

100.00%42.88%

57.12%

100.00%Greater than 4 lanes divided

14.14%

85.86%

100.00%Multi-lane undivided
Four lanes divided
Two-lane undivided
Other
Total

13.14%

86.86%

100.00%43.27%

56.73%

100.00%43.67%

56.33%

100.00%14.30%

85.70%

100.00%27.82%

72.18%

100.00%9.42%

90.58%

100.00%12.70%

87.30%

100.00%24.34%

75.66%

100.00%Two-lane undivided
Other

30.74%

69.26%

100.00%10.11%

89.89%

100.00%Total

18.77%

81.23%

100.00%Greater than 4 lanes divided

23.14%

76.86%

100.00%Multi-lane undivided
Four lanes divided
Two-lane undivided
Other
Total

14.82%

85.18%

100.00%67.71%

32.29%

100.00%34.91%

65.09%

100.00%20.19%

79.81%

100.00%29.90%

70.10%

100.00%Greater than 4 lanes divided
Multi-lane undivided

4.98%

95.02%

100.00%5.11%

94.89%

100.00%

Four lanes divided

31.20%

68.80%

100.00%Two-lane undivided

0.79%

99.21%

100.00%

Other
Total

0.00%

100.00%100.00%4.72%

95.28%

100.00%B Injuries

C Injuries

Greater than 4 lanes divided
Multi-lane undivided
Four lanes divided

Uninjured

Injured Severity Unknown

215

This process produced separate tables that define the proportions of all crashes that occur on different
roadway types by injury severity for urban and rural locations. Tables 12-13 and 12-14 summarize theseresults.
Table 12-13. Proportions of Fatalities, Injuries and PDOV by Roadway Designation in Rural Crashes
Divided Roadways

Undivided Roadways

4 Lanes>4 Lanes

2 Lanes>2 Lanes

All Other

Total

MAIS0

31.47%

13.29%

39.42%

10.96%

4.87%

100.00%MAIS123.45%

8.26%

54.16%

10.58%

3.55%

100.00%MAIS222.13%

5.94%

59.17%

9.22%

3.53%

100.00%MAIS321.68%

4.89%

60.65%

9.07%

3.72%

100.00%MAIS422.99%

4.75%

60.18%

8.81%

3.28%

100.00%MAIS522.57%

4.14%

60.98%

8.73%

3.58%

100.00%Fatal

15.74%

7.09%

71.96%

4.17%

1.03%

100.00%PDOV

32.35%

10.53%

41.50%

9.80%

5.82%

100.00%

Table 12-14. Proportions of Fatalities, Injuries and PDOV by Roadway Designation in Urban Crashes
Divided Roadways

Undivided Roadways

4 Lanes>4 Lanes

2 Lanes>2 Lanes

All Other

Total

MAIS0

8.98%

21.74%

32.24%

27.84%

9.20%

100.00%MAIS113.80%

20.52%

33.15%

24.21%

8.32%

100.00%MAIS213.86%

19.62%

35.00%

23.11%

8.40%

100.00%MAIS313.58%

20.14%

33.92%

23.99%

8.38%

100.00%MAIS413.99%

21.72%

33.21%

24.06%

7.02%

100.00%MAIS512.23%

20.98%

35.57%

23.73%

7.48%

100.00%Fatal

17.21%

22.66%

37.09%

18.21%

4.83%

100.00%

PDOV

9.58%

17.89%

35.25%

25.87%

11.42%

100.00%

The resulting estimates indicate significant differences in the proportions of crashes that occur on
various roadway types in rural versus urban settings. In rural settings over half of all injuries and 40
percent of PDOs occur on 2-lane undivided roadways, and over 20 percent of injuries and 30 percent of
PDOs occur on 4-lane divided roadways. These two roadway types account for roughly 80 percent of all
injuries and over 70 percent of all PDOs in rural settings. By contrast, in urban settings injury incidence isspread more evenly, with over 30 percent occurring on 2-lane undivided roadways, about 24 percent on
undivided roadways with more than 2 lanes, about 20 percent on divided roadways with more than 4lanes, and about 14 percent on 4-lane divided roadways. This might be expected given that the roadway

216

infrastructure in rural areas is typically designed for a lower population density. Rural occupants travel
exposure is more likely to occur on roadways with fewer lanes.
The proportions in Table 12-13 were then applied to the total economic costs of rural crashes from
Table 12-10, and the proportions in Table 12-14 were applied to the total economic costs of urban
crashes in Table 12-10 to distribute these costs by roadway type. This same process was then repeated
for the comprehensive costs in Table 12-11. The results are shown in Tables 12-15 and 12-16 foreconomic costs, and Tables 12-17 and 12-18 for comprehensive costs. Figures 12-B through 12-G
illustrate the distribution of costs across roadways.
Table 12-15. Estimated Economic Cost of Crashes in Rural Areas by Roadway Designation(Millions 2010 Dollars)
Divided Highways

MAIS0
MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
Fatal
PDOV
Total
% Rural% All

4 Lanes >4 Lanes$1,391$587

Undivided
Highways
2
>2
Lanes
Lanes
$1,742
$484

All
Other
$215

Total
$4,419$4,774$1,681$11,026$2,154$723$20,358$1,565$420$4,184$652$250$7,070$1,694$382$4,740$709$290$7,815$689$142$1,805$264$98$2,999$601$110$1,625$233$95$2,664$4,014$1,809$18,350$1,062$263$25,499$7,164$2,332$9,192$2,172$1,289$22,149$21,893$7,464$52,663$7,730$3,224$92,974

23.55%

8.03%

56.64%

8.31%

3.47%

100.00%

9.05%

3.08%

21.76%

3.19%

1.33%

38.42%

217

Table 12-16. Estimated Economic Cost of Crashes in Urban Areas by Roadway Designation(Millions 2010 Dollars)
Divided Highways

MAIS0
MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
Fatal
PDOV
Total
% Rural% All

Undivided
Highways
2
>2
Lanes
Lanes
$2,777$2,397

All
Other
$792

Total
$8,611

4 Lanes$773>4 Lanes$1,872$5,693$8,466$13,675$9,985$3,432$41,250$1,638$2,317$4,134$2,730$992$11,811$1,427$2,117$3,566$2,522$881$10,512

$523

$813

$1,243$901$263$3,743$378$649$1,100$734$231$3,091$3,556$4,683$7,665$3,762$997$20,664$4,725$8,823$17,388$12,761$5,634$49,332$18,713$29,739$51,547$35,791$13,224$149,014

12.56%

19.96%

34.59%

24.02%

8.87%

100.00%7.73%

12.29%

21.30%

14.79%

5.46%

61.58%

Table 12-17. Estimated Comprehensive Cost of Crashes in Rural Areas by Roadway Designation(Millions 2010 Dollars)

4 Lanes >4 Lanes$1,391$587

Undivided
Highways>2
2 Lanes
Lanes$1,742
$484

All
Other
$215

Total
$4,419$11,003$3,875$25,413$4,966$1,667$46,924$11,136$2,990$29,768$4,638$1,775$50,308$9,196$2,073$25,731$3,848$1,577$42,424$4,249$878$11,126$1,628$605$18,486$3,351$614$9,054$1,296$531$14,847$26,246$11,826$119,969$6,946$1,723$166,710$7,164$2,332$9,192$2,172$1,289$22,149$73,736$25,176$231,997$25,978$9,382$366,268

20.13%

6.87%

63.34%

7.09%

2.56%

100.00%

8.82%

3.01%

27.76%

3.11%

1.12%

43.82%

Divided Highways

MAIS0
MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
Fatal
PDOV
Total
% Rural% All

218

Table 12-18. Estimated Comprehensive Cost of Crashes in Urban Areas by Roadway Designation(Millions 2010 Dollars)
Divided Highways

MAIS0
MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
Fatal
PDOV
Total
% Rural% All

Undivided
Highways>2
2 Lanes
Lanes$2,777$2,397

All
Other
$792

Total
$8,611

4 Lanes$773>4 Lanes$1,872$13,121$19,513$31,521$23,014$7,910$95,079$11,651$16,488$29,413$19,423$7,062$84,037$7,747$11,492$19,358$13,689$4,783$57,069$3,226$5,009$7,661$5,551$1,620$23,068$2,107$3,616$6,130$4,088$1,289$17,230$23,251$30,615$50,114$24,598$6,521$135,099$4,725$8,823$17,388$12,761$5,634$49,332$66,602$97,427$164,360$105,522$35,613$469,525

14.19%

20.75%

35.01%

22.47%

7.58%

100.00%

7.97%

11.66%

19.67%

12.63%

4.26%

56.18%

Figure 12-B. Distribution of Economic Costs, Rural Roadway Crashes

Distribution of Economic Costs, Rural
Roadway Crashes
3%
8%

24%

4 Lane Divided
8%>4 Lane Divided
2 Lane Undivided

57%>2 Lane Undivided
All Other219

Figure 12-C. Distribution of Economic Costs, Urban Roadway Crashes

Distribution of Economic Costs, Urban
Roadway Crashes
9%

12%

24%

20%

4 Lane Divided>4 Lane Divided
2 Lane Undivided>2 Lane Undivided

35%

All Other

Figure 12-D. Distribution of Economic Costs by Roadway Type220

Figure 12-E. Distribution of Comprehensive Costs, Rural Roadway Crashes

Figure 12-F. Distribution of Comprehensive Costs, Urban Roadway Crashes

221

Figure 12-G. Distribution of Comprehensive Costs by Roadway Type

Distribution of Comprehensive Costs by
Roadway Type

12%

4%

Rural 4 Lane Divided

3%

9%

Rural >4 Lane Divided
Rural 2 Lane Undivided
28%

20%

Rural >2 Lane Undivided
RuralAll Other
Urban 4 Lane Divided

12%

Urban >4 Lane Divided

8%

Urban 2 Lane Undivided
1%

3%

Urban >2 Lane Undivided
Urban All Other

These data indicate that the largest impact on society from motor vehicle crashes, whether measured byeconomic costs or by the societal harm implied in comprehensive costs, occurs on 2-lane undivided
roadways in both rural and urban settings. In rural areas, this is likely a function of both exposure, sincethese roadways are the most common type, and the relatively serious injury profile that occurs on rural
2-lane undivided roadways, which lack dividers to separate vehicles traveling at relatively high speeds
compared to those in urban areas where congestion slows down traffic. In urban areas, exposure is theprimary cause of this disproportionate impact. Figures 12-H and 12-I show the relative portion of all
motor vehicle injuries that are in the most serious injury categories – MAIS3, MAIS4, MAIS5, and Fatal.
In rural crashes, this portion is significantly higher on 2-Lane undivided roadways than on other types,
and it is higher for all rural roadways than for any urban roadways – an indication of the impact thathigher travel speeds have on injury profiles.
Significant economic impact also occurs on urban divided highways (both lane count categories), and in
urban undivided highways with more than two lanes. These impacts are primarily exposure driven,
although high speed travel on urban divided roadways does contribute to a relatively severe injury
profile as well.

222

Figure 12-H. Portion of Rural Injuries MAIS3+

Figure 12-I. Portion of Urban Injuries MAIS3+

223

13.

Other Special Interest Crash Scenarios

Motor vehicle crashes cost society hundreds of billions of dollars in medical care, lost productivity, legal
costs, congestion, and other economic impacts. The cost of crashes is even higher when pain, suffering,and lost quality-of-life are taken into account. Federal, State, local, and private organizations areconstantly striving to reduce these impacts through motor vehicle safety regulations, behavioral
programs such as alcohol and seat belt laws, roadway improvements, traffic control measures, andpublic information and educational programs. Efforts to address the impacts of motor vehicle crashes
are normally focused on specific types of crashes, based on locality, roadway type, crash causation, or
victim characteristics. For this report we have estimated crash costs for a number of specific crash
scenarios which are commonly of interest to organizations interested in improving vehicle safety. These
include seat belt use, impaired driving, speeding, and distracted driving, urban/rural crashes, and Statespecific costs, each of which is examined in a separate chapter due to the complexity of themethodology required. However, a number of additional crash scenarios were estimated based on afairly straightforward examination of the data in NHTSA’s two primary databases, FARS and GES. These
include, crashes on interstate highways, crashes at intersections, single-vehicle crashes, roadway
departure crashes, and pedestrian/bicyclist crashes.
To estimate the cost of these crashes, we examined the relative incidence of each injury severity levelthat was represented by crashes that matched each scenario. These estimates reflect the relative
proportions of specific injury severities that occur under each scenario. GES was used for each non-fatalcase, while FARS was used for each fatal case. Each case in FARS contained information regarding thestatus of the specific scenario, so the proportion of fatalities that occurred under each scenario was
obtained directly from the FARS database. For nonfatal injuries and PDOs, GES data was queried todetermine whether the case fell under the scenario or not. However, GES data is only recorded using the
KABCO severity system, whereas this report is based on the Abbreviated Injury Scale. To translate GES
data to an MAIS basis, we used a variety of KABCO/MAIS translators. For CDS equivalent crashes, weused a current translator derived from 2000-2008 CDS data. Since this data is relatively recent, it reflects
roughly current levels of seat belt usage. For non-CDS cases, the only available data from which todevelop translators were contained in the 1982-1986 NASS files. Seat belt use has increased
dramatically since this time. Observed belt use during this period ranged from roughly 10-37 percent as
public awareness of the importance of belt use and belt use laws were just beginning to take hold in
1986. Belt use has since risen dramatically, and has been between 80 and 85 percent since 2004. Beltuse can influence injury reporting significantly in a number of ways. It changes the nature of injuries by
preventing many more visible injuries (such as head/face contact with the windshield) but replaces themwith often less visible (and also typically less serious) abdominal injuries such as bruising caused bypressure from the belt across the torso. This can influence the relationship between the KABCO
reported injury severity and the corresponding MAIS injury level. For this reason, separate translators
were developed from the 1982-86 NASS data for non-CDS cases where the victim was belted, unbelted,

224

unknown belted status, and for nonoccupants/motorcyclists. These translators are presented in Tables
13-1 through 13-5.
2010 GES KABCO incidence counts were obtained for each scenario, both for the cases that met the
scenario definition, and for all other cases. So, for example, one set of incidence counts was obtained forintersection crashes, and another for all other crashes. Each of these data sets was run through its
corresponding translator to produce a set of MAIS based injury counts. These counts from each grouping(CDS equivalent cases, belted non-CDS cases, unbelted non-CDS cases, unknown belt status non-CDS
cases, and nonoccupant/motorcycle cases) were added together to produce a total MAIS injury profile
for each scenario. The process was repeated for each “Other” category (e.g., all non-intersection
crashes). The percentage of each MAIS injury incidence that was appropriate to each scenario was thencalculated as:
x=a/(a+b)
where x is the percentage of incidence attributable to the specific crash scenario
a = the incidence of the specific crash scenariob = the incidence of each case not attributable to the specific crash scenario.
The attributable portion of each MAIS level was then multiplied by the total cost of all 2010 crashes forthat MAIS level and the MAIS level results were summed to produce the total cost of each crash
scenario. MAIS0 portions were calculated using the same procedure described previously for
Urban/Rural crashes, based on the relative incidence of MAIS 0 cases in injury crashes. The PDO portion
was based on a direct count of PDO vehicles from each crash scenario compared to those not in that
scenario. For the interstate highway crash scenario, congestion costs were modified based on data in
Chapter 3 to reflect congestion impacts specific to interstate highways, which have far more serious
congestion impacts. This data indicates that crashes on interstates cause roughly three times theaverage congestion costs across all roadways.
The results of this process are summarized for each scenario in Tables 6 to 15 for both economic costs
and comprehensive costs. Note that these categories are not exclusive or additive, since some crashes
qualify under more than one category.
Intersection Crashes: Intersection crashes resulted in 8,682 fatalities, over 2.2 million injuries, and over
10 million PDO damaged vehicles in 2010. 55 This represents 26 percent of all fatalities and roughly 55
percent of all nonfatal crashes (including both nonfatal injury and PDO). Intersection crashes caused$120 billion in economic costs and $371 billion in comprehensive costs, accounting for 50 percent of all
economic costs and 44 percent of all societal harm (measured as comprehensive costs) from motor
vehicle crashes.

55

Intersection crashes includes crashes that occur at normal roadway intersections, at driveway or alleyway
intersections, and at some highway interchanges.

225

Interststate Highway Crashes: Crashes on interstate highways resulted in 4,288 fatalities, over 300,000
injuries, and 1.4 million PDO damaged vehicles in 2010. This represents 13 percent of all fatalities androughly 8 percent of all nonfatal crashes (including both nonfatal injury and PDO). Interstate highway
crashes caused $25 billion in economic costs and $85 billion in comprehensive costs, accounting forroughly 10 percent of both economic costs and societal harm (measured as comprehensive costs).
Single-Vehicle Crashes: Single-vehicle crashes resulted in 19,241fatalities, 962,000 injuries, and nearly
3.2 million PDO damaged vehicles in 2010. This represents 58 percent of all fatalities and roughly 20
percent of all nonfatal crashes (including both nonfatal injuries and PDO). Single-vehicle crashes caused$76 billion in economic costs and $345 billion in comprehensive costs, accounting for 32 percent of all
economic costs, and 41 percent of all societal harm (measured as comprehensive costs).
Roadway Departure Crashes: Roadway departure crashes resulted in 18,850 fatalities, 795,000 injuries,
and over 2.4 million PDO damaged vehicles in 2010.56 This represents 57 percent of all fatalities androughly 16 percent of all nonfatal crashes (including both nonfatal injury and PDO). Roadway departurecrashes caused $64 billion in economic costs and $298 billion in comprehensive costs, accounting for 27
percent of all economic costs, and 36 percent of all societal harm (measured as comprehensive costs).
Pedestrian/bicyclist Crashes: Pedestrian/bicyclist crashes resulted in 5,123 fatalities, 189,000 injuries,
and over 13,000 PDO damaged vehicles in 2010. 57 This represents 16 percent of all fatalities and roughly
2 percent of all nonfatal crashes (including both nonfatal injury and PDO). These crashes caused $16
billion in economic costs and $87 billion in comprehensive costs, accounting for 7 percent of all
economic costs, and 10 percent of all societal harm (measured as comprehensive costs).
Pedestrian Crashes: Pedestrian crashes resulted in 4,372 fatalities, 110,000 injuries, and 4,370 PDO
damaged vehicles in 2010.58 This represents 13 percent of all fatalities and roughly 1 percent of all
nonfatal crashes (including both nonfatal injury and PDO). These crashes caused $11 billion in economic
costs and $65 billion in comprehensive costs, accounting for 5 percent of all economic costs, and 8
percent of all societal harm (measured as comprehensive costs).
Bicyclist Crashes: Bicyclist crashes resulted in 632 fatalities, 79,000 injuries, and 9.078 PDO damaged
vehicles in 2010. 59 This represents 2 percent of all fatalities and roughly 1 percent of all nonfatal crashes
56

Roadway departure crashes are defined as crashes in which a vehicle crosses an edge line, a centerline, orotherwise leaves the travelled way. This includes crashes where the first event in the sequence coded for any
involved vehicle is run off the road to either the right or left, cross a median, cross a center line, and hit a permanent
fixed object, or become airborne, or re-entered the roadway.
57
This category includes all crashes where a pedestrian or bicyclist was involved. This includes cases where a driverswerves to avoid a pedestrian or bicyclist and crashes his vehicle, causing property damage or injury to the vehicle
occupants. It thus includes counts of all fatalities, injuries, or property damage that occur in crashes where apedestrian or bicyclist was involved, regardless of whether the pedestrian or bicyclist was struck or injured.
58
This category includes all crashes where a pedestrian was involved. This includes cases where a driver swerves toavoid a pedestrian and crashes his vehicle, causing property damage or injury to the vehicle occupants. It thus
includes counts of all fatalities, injuries, or property damage that occur in crashes where a pedestrian was involved,
regardless of whether the pedestrian was struck or injured.
59
This category includes all crashes where a bicyclist was involved. This includes cases where a driver swerves toavoid a bicyclist and crashes his vehicle, causing property damage or injury to the vehicle occupants. It thus includes

226

(including both nonfatal injury and PDO). These crashes caused $4 billion in economic costs and $22
billion in comprehensive costs, accounting for 2 percent of all economic costs, and 3 percent of allsocietal harm (measured as comprehensive costs).
Table 13-1. KABCO/MAIS Translator for CDS Equivalent Cases
O

B
Nonincapacitating

A

K

No Injury

C
Possible
InjuryIncapacitating

00.81910.21880.09061

0.17590.701420.0047

3
4
5
Fatal
Total

MAIS

KilledInjuredSeverity
Unknown

Unknown if
Injured

0.03760.00320.24290.59350.75180.57820.01100.59610.3751

0.06740.11130.19240.00190.10390.02080.00020.01010.03480.12590.00410.04060.00910.00000.00210.00850.04440.00270.00470.00080.00010.00001.000

0.00010.00011.000

0.00140.00151.000

0.01710.00431.000

0.0007
0.97651.000

0.01170.00001.000

0.00060.0001
1.000

Table 13-2. KABCO/MAIS Translator for Non-CDS Equivalent Cases, Unbelted
O
No Injury

C
Possible
InjuryB
Nonincapacitating

00.95910.2655

1

0.038820.0011

3

A
Incapacitating

InjuredSeverity
Unknown

Unknown
if Injured

0.07770.0394

0.10820.9196

0.43600.66670.50330.3931

0.06810.03890.06400.15170.03450.00040.00000.00450.0041

0.05120.00000.001340.00000.00000.00080.01160.00000.000050.00000.00000.00000.00610.00000.000070.00100.25450.18630.2347

0.46150.0106

Fatality
Total

0.00001.00000.00041.00000.00051.00000.00201.00000.00271.00000.00001.0000MAIS

counts of all fatalities, injuries, or property damage that occur in crashes where a bicyclist was involved, regardlessof whether the bicyclist was struck or injured.

227

Table 13-3. KABCO/MAIS Translator for Non-CDS Equivalent Cases, Belted
O
MAIS

No Injury

C
Possible
InjuryB
NonincapacitatingA

00.94910.30020.15580.09620.51150.91641

0.04940.48880.65440.54180.19480.059820.00060.01540.01920.1966

0.06550.000030.00010.01240.00820.05050.01790.000040.00000.00010.00120.00730.00000.000050.00000.00000.00000.00000.00000.00007

0.00090.18310.16110.10760.21040.0239Fatality
Total

0.00001.00000.00001.00000.00001.00000.00001.00000.00001.00000.00001.0000

Incapacitating

InjuredSeverity
Unknown

Unknown
if Injured

Table 13-4. KABCO/MAIS Translator for Non-CDS Equivalent Cases, Unknown Belt Use
O
No Injury

C
Possible
InjuryB
Nonincapacitating

00.99600.107810.0001

2

A
Incapacitating

InjuredSeverity
Unknown

Unknown
if Injured

0.09080.02520.04130.94760.11610.49710.26870.18660.00500.00000.00570.00670.14910.00670.000030.00000.00000.00000.13050.00000.000040.00000.00000.00000.00000.00000.000050.00000.00000.00000.00000.00000.00007

0.00380.77040.40530.42650.76540.0475

Fatality
Total

0.00001.00000.00001.00000.00001.00000.00001.00000.00001.00000.00001.0000

MAIS

228

Table 13-5. KABCO/MAIS Translator for Nonoccupants and Motorcyclists
O
No
InjuryC
Possible
InjuryB
NonincapacitatingA
Incapacitating

00.64030.04290.02210.0050InjuredSeverity
Unknown
0.032410.30790.57240.61800.30130.4332

0.46862

0.04350.16360.15770.27760.08470.117230.00350.03970.05930.2701

0.0422

0.063940.00050.00120.00210.02480.00450.008350.00000.00140.00030.02630.00000.003770.00440.17880.14020.08900.40290.0981

Fatality
Total

0.00001.00000.00001.00000.00021.00000.00581.00000.00001.00000.02381.0000

MAIS

Unknown
if Injured
0.2164

Table 13-6. Economic Costs of Intersection Crashes, (Millions of 2010 Dollars)

PDO Vehicles
MAIS0
MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
Fatalities
Total
Percent of Total%
Intersectio
54.72%
56.68%
57.19%
55.45%
53.24%
50.28%
49.39%
26.31%
55.33%

Incidence
Total
Intersection
18,508,632
10,127,014
4,583,265
2,597,665
3,459,200
1,978,467
338,730
187,814
100,740
53,632
17,086
8,591
5,749
2,839
32,9998,682
27,046,402
14,964,704
100.00%55.33%

Total Economic Crash Costs
Total
Intersection
Other$71,480$39,111$32,370$13,030$7,385$5,645$61,608$35,236$26,372$18,881$10,469$8,412$18,327$9,757$8,570$6,742$3,390$3,352$5,755$2,842$2,913$46,163$12,145$34,017$241,988$120,336 $121,652
100.00%49.73%
50.27%

Table 13-7. Comprehensive Costs of Intersection Crashes, (Millions of 2010 Dollars)

PDO Vehicles
MAIS0
MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
Fatalities
Total
Percent of Total%
Intersectio
54.72%
56.68%
57.19%
55.45%
53.24%
50.28%
49.39%
26.31%
55.33%

Incidence
Total Comprehensive Crash Costs
Total
Intersection Total
Intersection
Other
18,508,632
10,127,014$71,480$39,111$32,370
4,583,265
2,597,665$13,030$7,385$5,6453,459,200
1,978,467 $142,004$81,218$60,786
338,730
187,814 $134,345$74,490$59,855
100,740
53,632$99,493$52,968$46,525
17,086
8,591$41,555$20,894$20,661
5,749
2,839$32,077$15,842$16,235
32,9998,682 $301,809$79,406 $222,403
27,046,402
14,964,704 $835,793$371,314 $464,479
100.00%55.33% 100.00%
44.43%
55.57%

229

Table 13-8. Economic Costs of Interstate Highway Crashes, (Millions of 2010 Dollars)

PDO Vehicles
MAIS0
MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
Fatalities
Total
Percent of Total%
Interstat
7.62%
8.11%
8.11%
7.74%
7.66%
8.94%
8.44%
12.99%
7.78%

Incidence
Interstate
Total
Interstate Unit Costs
18,508,632 1,411,077$5,517
4,583,265
371,519$4,404
3,459,200
280,704$20,310
338,730
26,203$58,472
100,740
7,713$185,281
17,086
1,527$398,163
5,749
485 $1,004,685
32,9994,288 $1,410,277
27,046,402 2,103,517
100.00%7.78%

Total Economic Crash Costs
Total
Interstate
Other$71,480$7,784$63,696$13,030$1,636$11,394$61,608$5,701$55,907$18,881$1,532$17,349$18,327$1,429$16,898$6,742$608$6,134$5,755$488$5,268$46,163$6,047$40,116$241,988$25,225 $216,763
100.00%10.42%
89.58%

Table 13-9. Comprehensive Costs of Interstate Highway Crashes, (millions of 2010$)

PDO Vehicles
MAIS0
MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
Fatalities
Total
Percent of Total%
Interstat
7.62%
8.11%
8.11%
7.74%
7.66%
8.94%
8.44%
12.99%
7.78%

Incidence
Interstate
Total
Interstate Unit Costs
18,508,632 1,411,077$5,517
4,583,265
371,519$4,404
3,459,200
280,704$43,551
338,730
26,203$399,344
100,740
7,713$990,97817,086
1,527 $2,435,646
5,749
485 $5,583,210
32,9994,288 $9,157,359
27,046,402 2,103,517
100.00%7.78%

Total Comprehensive Crash
Costs
Total
Interstate
Other$71,480$7,784$63,696$13,030$1,636$11,394$142,004$12,225 $129,779$134,345$10,464 $123,881$99,493$7,644$91,850$41,555$3,719$37,835$32,077$2,710$29,367$301,809$39,263 $262,546$835,793$85,445 $750,348
100.00%10.22%
89.78%

Table 10. Economic Costs of Single-Vehicle Crashes, (Millions of 2010 Dollars)

PDO Vehicles
MAIS0
MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
Fatalities
Total
Percent of Total%
Single17.22%
24.83%
22.82%
35.41%
42.87%
38.94%
44.85%
58.31%
19.62%

Incidence
Total Economic Crash Costs
SingleSingleTotal
Total
Other
VehiclVehicl
18,508,632
3,186,682$71,480$12,307$59,173
4,583,265
1,137,803$13,030$3,235$9,7953,459,200
789,271$61,608$14,057$47,551
338,730
119,945$18,881$6,686$12,195
100,740
43,187$18,327$7,857$10,470
17,086
6,653$6,742$2,626$4,117
5,749
2,578$5,755$2,581$3,17432,99919,241$46,163$26,917$19,246
27,046,402
5,305,360$241,988$76,264$165,723
100.00%19.62%
100.00%31.52%
68.48%

230

Table 13-11. Comprehensive Costs of Single-Vehicle Crashes, (Millions of 2010 Dollars)

PDO Vehicles
MAIS0
MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
Fatalities
Total
Percent of Total%
Single17.22%
24.83%
22.82%
35.41%
42.87%
38.94%
44.85%
58.31%
19.62%

Incidence
SingleTotal
Vehicle18,508,632
3,186,682
4,583,265
1,137,803
3,459,200
789,271338,730
119,945
100,740
43,187
17,086
6,653
5,749
2,578
32,99919,241
27,046,402
5,305,360
100.00%19.62%

Total Comprehensive Crash Costs
SingleTotal
VehicleOther$71,480$12,307$59,173$13,030$3,235$9,795$142,004$32,400$109,603$134,345$47,572$86,773$99,493$42,653$56,841$41,555$16,182$25,373$32,077$14,385$17,692$301,809$175,978$125,831$835,793$344,712$491,081
100.00%41.24%
58.76%

Table 13-12. Economic Costs of Roadway Departure Crashes, (Millions of 2010 Dollars)

PDO Vehicles
MAIS0
MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
Fatalities
Total
Percent of Total% Roadway
Departure
13.17%
20.54%
19.47%
25.45%
27.16%
34.13%
33.11%
57.12%
15.50%

Incidence
Roadway
Total
Departure
18,508,632
2,437,564
4,583,265
941,5213,459,200
673,382338,730
86,193
100,740
27,366
17,086
5,831
5,749
1,904
32,99918,85027,046,402
4,192,611
100.00%15.50%

Total Economic Crash Costs
Roadway
Total
Departure
Other$71,480$9,414$62,066$13,030$2,677$10,353$61,608$11,993$49,615$18,881$4,805$14,077$18,327$4,979$13,349$6,742$2,301$4,441$5,755$1,906$3,850$46,163$26,370$19,793$241,988$64,443 $177,545
100.00%26.63%
73.37%

Table 13-13. Comprehensive Costs of Roadway Departure Crashes, (Millions of 2010 Dollars)

PDO Vehicles
MAIS0
MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
Fatalities
Total
Percent of Total% Roadway
Departure
13.17%
20.54%
19.47%
25.45%
27.16%
34.13%
33.11%
57.12%
15.50%

Incidence
Roadway
Total
Departure
18,508,632
2,437,564
4,583,265
941,5213,459,200
673,382338,730
86,193
100,740
27,366
17,086
5,831
5,749
1,904
32,99918,85027,046,402
4,192,611
100.00%15.50%

231

Total Comprehensive Crash Costs
Roadway
Total
Departure
Other$71,480$9,414$62,066$13,030$2,677$10,353$142,004$27,643 $114,361$134,345$34,185 $100,159$99,493$27,027$72,466$41,555$14,183$27,372$32,077$10,621$21,456$301,809$172,402 $129,407$835,793$298,152 $537,641
100.00%35.67%
64.33%

Table 13-14. Economic Costs of Pedestrian/Bicyclist Crashes, (Millions of 2010 Dollars)

PDO Vehicles
MAIS0
MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
Fatalities
Total
Percent of Total% Pedestrian/Bicyclist
0.07%
4.93%
3.99%
10.39%
14.04%
7.06%
11.26%
15.15%
1.60%

Incidence
Total Economic Crash Costs
Pedestrian/Pedestrian/
Total
Bicyclist
Total
Bicyclist
Other
18,508,632
13,449$71,480$52$71,428
4,583,265
226,076$13,030$643$12,3873,459,200
137,926$61,608$2,456$59,152
338,730
35,180$18,881$1,961$16,920
100,740
14,147$18,327$2,574$15,754
17,086
1,207$6,742$476$6,266
5,749
648$5,755$648$5,10732,9995,000$46,163$6,995$39,168
27,046,402
433,632 $241,988$15,805 $226,183
100.00%1.60%

100.00%6.53%

93.47%

Table 13-15. Comprehensive Costs of Pedestrian/Bicyclist Crashes, (Millions of 2010 Dollars)

PDO Vehicles
MAIS0
MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
Fatalities
Total
Percent of Total% Pedestrian/Bicyclist
0.07%
4.93%
3.99%
10.39%
14.04%
7.06%
11.26%
15.15%
1.60%

Incidence
Total Comprehensive Crash Costs
Pedestrian/Pedestrian/
Total
Bicyclist
Total
Bicyclist
Other
18,508,632
13,449$71,480$52$71,428
4,583,265
226,076$13,030$643$12,3873,459,200
137,926$142,004$5,662 $136,342
338,730
35,180$134,345$13,953 $120,392
100,740
14,147$99,493$13,971$85,522
17,086
1,207$41,555$2,935$38,620
5,749
648$32,077$3,613$28,464
32,9995,000$301,809$45,730 $256,079
27,046,402
433,632$835,793$86,559 $749,234
100.00%1.60%
100.00%10.36%
89.64%

Table 13-16. Economic Cost of Pedestrian Crashes, (Millions of 2010 Dollars)

PDO Vehicles
MAIS0
MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
Fatalities
Total
Percent of Total% Pedestrian
0.02%
2.91%
2.29%
6.16%
8.78%
4.65%
7.71%
13.25%
0.93%

Incidence
Total
Pedestrian
18,508,632
4,370
4,583,265
133,1543,459,200
79,183
338,730
20,863
100,740
8,844
17,086
794
5,749
443
32,9994,372
27,046,402
252,024100.00%0.93%

232

Total Economic Crash Costs
Total
Pedestrian
Other$71,480$17$71,463$13,030$379$12,652$61,608$1,410$60,198$18,881$1,163$17,718$18,327$1,609$16,718$6,742$313$6,429$5,755$444$5,311$46,163$6,116$40,047$241,988$11,451 $230,537
100.00%4.73%
95.27%

Table 13-17. Comprehensive Cost of Pedestrian Crashes, (Millions of 2010 Dollars)

PDO Vehicles
MAIS0
MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
Fatalities
Total
Percent of Total% Pedestrian
0.02%
2.91%
2.29%
6.16%
8.78%
4.65%
7.71%
13.25%
0.93%

Incidence
Total Comprehensive Crash Costs
Total
Pedestrian Total
Pedestrian
Other
18,508,632
4,370$71,480$17$71,463
4,583,265
133,154$13,030$379$12,652
3,459,200
79,183$142,004$3,251$138,753
338,730
20,863$134,345$8,275$126,070
100,740
8,844$99,493$8,735$90,758
17,086794$41,555$1,930$39,624
5,749
443$32,077$2,474$29,603
32,9994,372$301,809$39,986$261,822
27,046,402
252,024$835,793$65,046$770,747
100.00%0.93%
100.00%7.78%
92.22%

233

Table 13-18. Economic Cost of Bicyclist Crashes, (Millions of 2010 Dollars)

PDO Vehicles
MAIS0
MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
Fatalities
Total
Percent of Total% Bicyclist
0.05%
2.04%
1.70%
4.27%
5.32%
2.44%
3.60%
1.92%
0.67%

Incidence
Total
Bicyclist
18,508,632
9,078
4,583,265
93,3903,459,200
58,920338,730
14,465
100,740
5,363
17,086
417
5,749
207
32,999632
27,046,402
182,472
100.00%0.67%

Total Economic Crash Costs
Total
Bicyclist
Other$71,480$35$71,445$13,030$266$12,765$61,608$1,049$60,559$18,881$806$18,075$18,327$976$17,352$6,742$164$6,578$5,755$207$5,548$46,163$884$45,279$241,988$4,388 $237,600
100.00%1.81%
98.19%

Table 13-19. Comprehensive Cost of Bicyclist Crashes, (Millions of 2010 Dollars)

PDO Vehicles
MAIS0
MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
Fatalities
Total
Percent of
Total% Bicyclist
0.05%
2.04%
1.70%
4.27%
5.32%
2.44%
3.60%
1.92%
0.67%

Total Comprehensive Crash
Incidence
Costs
Total
Bicyclist Total
Bicyclist
Other
18,508,632
9,078$71,480$35$71,445
4,583,265
93,390$13,030$266$12,765
3,459,200
58,920 $142,004$2,419 $139,585
338,730
14,465 $134,345$5,737 $128,608
100,740
5,363$99,493$5,297$94,196
17,086417$41,555$1,013$40,541
5,749
207$32,077$1,156$30,92132,999632 $301,809$5,780 $296,029
27,046,402
182,472 $835,793$21,703 $814,090
100.00%0.67% 100.00%
2.60%
97.40%

234

14.

Source of Payment

The economic toll of motor vehicle crashes is borne by society through a variety of payment mechanisms.
The most common of these are private insurance plans such as Blue Cross-Blue Shield, HMOs,
commercial insurance policies, or worker’s compensation. Medicare is the primary payer for people over
the age of 65. When these sources are not available, government programs such as Medicaid may
provide coverage for those who meet eligibility requirements. A host of other Federal, State, and local
programs such as CHAMPVA, Tricare, Title 5, and Indian Health Services also provide health carecoverage for specific groups. Expenses not covered by private or governmental sources must be paid outof-pocket by individuals, or absorbed as losses by health care providers.
Blincoe (1996) provided estimates of sources of payment for motor vehicle crashes that combined
analysis of CODES data with previous estimates developed by the Urban Institute (Miller et al., 1991).
This data was also used in the previous version of this current study (Blincoe et al., 2002). For thiscurrent report, data from Blincoe (1996) were carried forward for insurance administration, workplacecosts, legal costs, and congestion while new estimates of source of payment were developed for medicalcare, lost productivity, and property damage. Blincoe also estimated values for emergency services.
However, in that study ambulance costs were included under emergency services, while for this current
study, ambulance costs are included under medical care. Ambulance costs had been distributed acrossall payer categories in the same proportion as medical care. To adjust for this, the impact of ambulancecosts was removed from the EMS distribution. This results in 100 percent of emergency service costs
being born by States and localities (primarily localities). However, we note that over the past few years,
primarily in response to budget tightening that has resulted from the economic downturn, many local
fire departments and other EMS operators have begun charging insurance companies and in some cases
individuals for their services. This practice has become controversial and has been banned in a numberof States. Nonetheless, it is likely that some portion of emergency services are in fact now being paid
through insurance, and possibly by individuals. It is not certain whether in the long run this practice will
become more widespread or will decline due to legislative actions. As this is a relatively recent practice,
to date we know of no studies that have examined these impacts on an aggregate basis.
We have also added an additional payer category, “unspecified government,” to this study. This
category represents programs that are funded primarily by government revenues, but that are lumped
together in HCUP data and that therefore cannot be individually identified as belonging to either Stateor Federal categories. In addition, some of these programs are partially funded by participants throughsubsidized premium charges. Programs in these categories include Veterans’ Administration, Tricare,
Title 5, Indian Health Services, and State and local health care programs. These are programs that cover
medical care for service personal and their families, veterans’, Native Americans, and State and local
employees. In previous studies, these costs were lumped under the “Other” category. We havecategorized them with government programs because they are either entirely supported by, or heavily

235subsidized by, tax dollars, but some unknown portion of these costs are paid by individual insurance
premiums.
Following are discussions of the derivation of revised source of payment estimates for medical care, lost
productivity, and property damage.
Medical Costs
Miller et al. (2011) provide factors for computing the percentage of crash costs paid by State/local andFederal governments. Table 1, drawn from that paper, is built from the million-record 2007 Healthcare
Cost and Utilization Project (HCUP) Nationwide Inpatient Sample (NIS). Medical costs were estimatedusing hospital charges, as recorded in the NIS, with hospital-specific cost-to-charge ratios supplied by
HCUP. The NIS also records the expected payer for each hospital stay, which allowed estimation of theamount paid by payer. Medicaid paid an estimated 15.8 percent of hospital costs for motor vehicle
crashes and Medicare paid 7.3 percent.
Zaloshnja and Miller (2012) analyzed Medicaid claims and HCUP data from 14 States. They estimatedthat 22 percent of adults ages 19 to 64 with hospital-admitted crash injuries covered by Medicaid (2.85%
of all those admitted) became Medicaid-eligible because earnings losses and medical bills resulting fromcrash injury left them indigent or disabled. The crash resulted in Medicaid paying all their medical bills,
not just their injury bill. Zaloshnja and Miller (2012) further estimate that 35 percent of those who
convert to Medicaid to pay hospital bills stay on Medicaid indefinitely. The present value of their lifetime
Medicaid health care costs averages $316,000 (computed following the article’s methods but
substituting fiscal year 2009 Medicaid spending of $15,840 per disabled recipient from:www.statehealthfacts.org/comparetable.jsp?ind=183&cat=4.)
Beyond crash medical costs, that adds an estimated $3,152 ($316,000×0.0285×0.35) in Medicaid costs
for other care to government’s crash bill per hospital-admitted non-elderly crash survivor. With roughly
5.4 percent of medically treated crash survivors admitted, added government cost due to Medicaid
conversion is $170 per injured crash survivor.
The division of Medicaid costs between the Federal and State levels varies by State. On average, States
paid 35.2 percent in Fiscal Year 2011 ( Office of the Assistant Secretary for Planning and Evaluation,2012).
Among the elderly, HCUP data show private insurance paid 50.0 percent of the medical cost of crashinjuries but only 7.4 percent of the cost of other injuries. The 42.6 percent differential presumably is due
to medical costs borne by or recovered from auto insurers. Assuming this percentage applies to injuries
not requiring hospital admission is aggressive. Health insurers are less likely to pursue recovery throughsubrogation for smaller medical bills. Nevertheless, if we make that assumption, an estimated $14.85
billion in medical costs would be compensated by auto insurance. Since available auto insurance data do
not decompose compensation for medical costs versus work losses (or even quality-of-life), we used the42.6 percent compensation for medical care to compute the compensation level for wage andhousehold work loss.

236

Table 14-1. Primary payer for medical costs of hospital-admitted road crash injuries,
by age, United States, as per 2007 HCUP-NIS
Payer*
Medicare
Medicaid
PrivateSelf
Charity
Other
Unknown

Ages
0–18
0.4%
30.6%
59.4%
4.3%
0.3%
4.5%
0.5%

Ages
19–64
2.3%
12.1%
44.4%
10.5%
1.2%
7.7%
0.2%

Ages
≥65
40.5%
2.2%
50.0%
2.8%
0.0%
4.4%
0.2%

All
Ages
7.3%
15.8%
56.1%
10.9%
1.2%
8.5%
0.3%

*Private includes auto insurance, private health insurance, HMO/managed care, and workers’
compensation. Self-pay and charity care ultimately may shift to Medicare or Medicaid. Other includes
CHAMPVA, TRICARE, Indian Health Services, Title 5, and State and local government health careprograms.

Productivity (Work) Losses
Estimated annual productivity losses in 2008–2010 were $92.3 billion, of which 76.8 percent was wages
and fringe benefits. The remainder was lost household productivity. Above, we estimated that auto
insurance compensation net of fraud was $45.516 billion ($50.017 billion* 91 percent). Subtracting the$14.850 billion in medical costs compensated ($34,860 billion* 42.6 percent), $30.666 billion in
productivity losses is compensated, which equates to 33.2 percent of those losses.
From data on State income and sales tax and Federal income tax revenues, Miller et al. (2011) estimatethat 4.1 percent of total productivity losses (5.27 percent of wage and fringe benefit losses) are State tax
losses and 7.3 percent (9.4 percent of wage-related loss) are Federal tax losses. From old survey data,
they estimate that the safety net compensates another 1.3 percent (1.65 percent of wage-related loss).
Miller et al. (2010) estimated the costs of crashes to employers. Inflating their estimates to 2010 dollars,
workers’ compensation covers $2.2 billion in losses (1.2 percent of total compensation), disability
insurance covers $0.85 billion (0.5 percent), and sick leave $5.6 billion (2.9 percent).
American Life Insurance Council (2012) provides data on life insurance policies in force by type. Table 2
starts from those data. (In this table, group policies generally are employment-related.) Dividing theamount of coverage by policies in force yields the average payment per premium. Multiplying coverage
per policy times the percentage of the U.S. population with policies of the given type yields expectedpayout per policy. The average death in 2010 generated an estimated $59,680 in life insurance
payments. With 33,012 crash deaths, life insurance payments totaled $1.97 billion (1.0 percent of
productivity loss).
Together, these sources absorb an estimated 44.5 percent-45.6 percent of the productivity losses. The
remaining 54.4 percent-55.5 percent are paid by people injured in crashes and their families.

237

Table 14-2. Life insurance coverage and policy amounts, United States, 2010
1,000s of Policies Amount/Policy Paid/Death
Individual
151,787$69,067$33,955
Group
109,462$71,537$25,363
Credit23,086$4,843$362
All284,335$64,804$59,680
Source: Computed from data in American Life Insurance Council (2012).
Property Damage
In the Unit Cost chapter we estimated that insurers paid out $53.5 billion in property damage insurance
claims for motor vehicle accidents in 2009. Private Passenger Physical Damage losses, which include
both collision and comprehensive insurance, declined slightly in 2010 from $39.7 billion to $39.4 billion.
Liability losses, which include both bodily injury and property damage, increased slightly in 2010 from72.1 billion to 72.9 billion. It thus appears that overall, property damage claims for motor vehicle
crashes were roughly flat from 2009 to 2010. Using this assumption, we estimate that private insurers
covered 70.31 percent of all property damage in 2010 ($53.5 billion in reimbursements/$76.1 billion
total property damage). The remaining 29.7 percent was paid out of pocket by crash involved drivers.
Results
Table 14-3 shows the distribution of the portion of crash related costs that are borne by private insurers,
governmental sources, individual crash victims, and other sources. These distributions are quite variable
depending on the nature of the cost category. Private Insurers are the primary source of payment for
medical care, insurance administration, legal costs, and property damage, but tax revenues cover asignificant portion of medical care, emergency services, and lost market productivity. Third parties
absorb all workplace costs and congestion costs. Individual accident victims pay a modest portion of
medical care, and absorb significant portions of both market and household productivity losses, as wellas property damage.
Table 14-3. Distribution of Source of Payment for Economic Costs by Cost Category

MedicalEmergency Serv.
Market Prod.
Household Prod.
Insurance Admin.
Workplace Costs
Legal Costs
Congestion Costs
Property Damage

Federal17.54%
0.00%
10.44%
0.00%
0.89%
0.00%
0.00%
0.00%
0.00%

State
5.56%
100.00%
6.18%
0.00%
0.51%
0.00%
0.00%
0.00%
0.00%

Unspecified
Government
8.50%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%

Total
Government
31.60%
100.00%16.62%
0.00%
1.40%
0.00%
0.00%
0.00%
0.00%

238

PrivateInsurer
56.10%
0.00%
35.95%
33.14%
98.60%
0.00%
100.00%0.00%
70.31%

Other
1.20%
0.00%
7.98%
0.00%
0.00%
100.00%0.00%
100.00%0.00%

Self
11.10%
0.00%
39.45%
66.86%
0.00%
0.00%
0.00%
0.00%
29.69%

Total
100.00%100.00%100.00%100.00%100.00%100.00%100.00%100.00%100.00%In Table 14-4, total costs are distributed according to the proportions listed in Table 14-3. The results
indicate that approximately $18 billion, or 8 percent of all costs are borne by public sources, with
Federal revenues accounting for 4.3 percent and States accounting for 2.5 percent, with another
0.8 percent borne by a number of State and Federal programs that could not be broken out by
government source. Public expenditures for economic harm caused by motor vehicle crashes are theequivalent of $156 in added taxes for every household in the United States. 60 State and local
government pay almost all costs of police, fire, emergency medical, vocational rehabilitation, victim
assistance, and coroner services; incident management; and roadside furniture damage. They shareforegone taxes, welfare, and public medical payments. As employers, they bear their share of costs thatfall on employers, including private medical care, disability compensation, property damage, auto
liability insurance payments, insurance claims processing expenses, and workplace disruption/rehiring
expenses.
Private insurers paid $130 billion, or 54 percent, while individual crash victims absorbed $55 billion or 23
percent. Other sources, including third parties impacted by traffic congestion from accidents, employers
who pay for sick leave and workplace disruption, and health care providers and charities who absorb
unpaid charges for medical care, absorbed $38 billion (16 percent) of the total economic cost.
Table 14-4. Source of Payment of Economic Costs by Cost Category, 2010 Motor Vehicle Crash Costs(Millions of 2010 Dollars)
Federal$4,099

MedicalEmergency
Services
MarketProductivity
HouseholdProductivity
Insurance Admin.

Unspecified
Total
State
Government Government$1,300$1,987$7,385

Insurer$13,111

Other
$280

Self
$2,594

Total$23,372$0$1,079$0$1,079$0$0$0$1,079$6,023$3,603$0$9,626$24,808$5,600$17,578$57,612

$0

$0

$0

$0

$7,828$0$11,920$19,748

$183

$105

$0

$288

$20,272$0$0$20,559

Workplace Costs

$0

$0

$0

$0

$0

$4,577$0$4,577

Legal Costs

$0

$0

$0

$0

$10,918$0$0$10,918

Congestion Costs

$0

$0

$0

$0

$0

$28,027$0$28,027

Property Damage

$0

$0

$0

$0

$53,500$0$22,596$76,096

Total$10,305$6,087$1,987$18,378$130,437$38,484$54,688$241,988% Total

4.26%

2.52%

0.82%

7.59%

53.90%

15.90%

22.60%

100.00%60

Based on 117,538,000 households in the U.S. in 2010 (Source: Statistical Abstract of the United States, 2012)239

Figure 14-A. Source of Payment for Motor Vehicle Crash Costs

Source of Payment for Motor Vehicle
Crash Costs
23%

7%
Government

16%

Private Insurer
54%

Other
Self

To some extent it is illusory to disaggregate costs across payment categories because ultimately, it isindividuals who pay for these costs through insurance premiums, taxes, direct out-of-pocket cost, or
higher charges for medical care. A real distinction can be made, however, between costs borne by those
directly involved in the crashes and costs that are absorbed by the rest of society. Costs paid out ofFederal or State revenues are funded by taxes from the general public. Similarly, costs borne by private
insurance companies are funded by insurance premiums paid by policyholders, most of whom are not
involved in crashes. Even unpaid charges, which are absorbed by health care providers are ultimatelytranslated into higher costs that are borne by a smaller segment of the general public – users of healthcare facilities. From this perspective, perhaps the most significant point from Table 14-4 is that society atlarge picks up over three-quarters of all crash costs that are incurred by individual motor vehicle crash
victims.

240

Appendix A
Sensitivity Analysis, Value of a Statistical Life
Previous versions of this report have focused on the economic impact of motor vehicle crashes – thesocietal losses that can be directly measured in economic terms. However, these costs do not represent
the more intangible consequences of these events and should not, therefore, be used alone to produce
cost-benefit ratios. Measurement of the dollar value of intangible consequences such as pain andsuffering has been undertaken in numerous studies. These studies have estimated values based on
wages for high-risk occupations and prices paid in the market place for safety products, among othermeasurement techniques. These “willingness to pay” based estimates of how society values risk
reduction capture valuations not associated with direct monetary consequences. In this study,
comprehensive costs, which include both the economic impacts of crashes and valuation of lost qualityof-life, are also examined. Comprehensive costs represent the value of the total societal harm that
results from traffic crashes. The basis for these estimates is the most recent guidance issued by the U.S.
Department of Transportation for valuing risk reduction. This guidance, which was issued in February
2013, establishes a new value of a statistical life (VSL) at $9.1 million in 2012 economics ($8.86 million in2010 economics). In addition, it establishes new relative disutility factors stratified by injury severity
level to estimate the lost quality-of-life for nonfatal injuries. These factors were derived in a researchcontract designed specifically for this current cost study. More detailed discussion of comprehensive
costs is included in Chapter 4 of this report.
From Table 8 in chapter 1, the total societal harm from motor vehicle crashes in 2010 is estimated tohave been $836 billion, roughly three and a half times the value measured by economic impacts alone.
Of this total, 71 percent represents lost quality-of-life, dwarfing the contribution of all other cost
categories. This highlights the importance of accounting for all societal impacts when measuring costs
and benefits from motor vehicle safety countermeasures. However, the literature on VSL estimatesindicates a wide range of measured estimates of VSLs – some as low as a few million dollars, some ashigh as over $30 million. The U. S. DOT guidance memorandum discusses a feasible range of VSLs forsensitivity analysis in 2012 dollars from $5.2 million to $12.9 million. There is thus far more uncertainty
regarding the accuracy of estimates of lost quality-of-life than there is regarding economic costs. In thisappendix, comprehensive costs are estimated based on this range adjusted to the 2010 basis of an $8.86
million VSL ($5.1 million and $12.6 million, computed by applying the ratio of the 2010 VSL to the 2012
VSL to each end of the range and rounding to the nearest tenth). The results indicate a feasible range ofsocietal harm from motor vehicle crashes of from $545 billion to $1.12 trillion in 2010, with lost qualityof-life accounting for between 56 and 78 percent of all societal harm respectively. The central value usedin this report, $836 billion, should thus be viewed with this range in mind. Although the USDOT values
were not selected statistically, they imply a central value with the equivalent of a confidence interval of
approximately +-33 percent.

241

Table A-1. Total Comprehensive Costs, $5.1 Million VSL (Millions of 2010 Dollars)
PDO

MedicalEMS
MarketHouseholdInsurance
Workplace
Legal Costs
Subtotal
Congestion
Prop. Damage
Subtotal
Economic Total
QALYs
Comp.Total% Total$0$518$0$1,111$3,535$1,148$0$6,311$19,934$45,235$65,169$71,480$0$71,480
13.1%

MAIS0 MAIS1$0$9,682$96$308$0$9,430$206$2,982$655 $11,408$211$1,180$0$4,089$1,169 $39,079$3,483$3,836$8,378 $18,694$11,861 $22,530$13,030 $61,608$0 $40,992$13,030 $102,600
2.4%
18.8%

MAIS2
$3,879$66$6,557$2,407$1,578$896$1,135$16,519$405$1,957$2,363$18,881$58,870$77,751
14.3%

MAIS3 MAIS4 MAIS5 Fatal
Total% Total$4,898 $2,329 $2,209$373$23,372
4.3%
$42
$14
$5
$30
$1,0790.2%$6,481 $2,406 $1,941 $30,797$57,61210.6%$2,286$641$548$9,567$19,748
3.6%$1,548
$482
$417
$935
$20,559
3.8%
$582
$109
$64
$389
$4,5770.8%$1,249$456$475$3,514$10,9182.0%$17,087 $6,437 $5,660 $45,604 $137,865
25.3%
$144
$26
$9
$189
$28,027
5.1%$1,096
$279
$87
$370
$76,09614.0%$1,241$305$96$559 $104,123
19.1%$18,327 $6,742 $5,755 $46,163 $241,988
44.4%$41,383 $17,750 $13,421 $130,344 $302,759
55.6%$59,711 $24,492 $19,176 $176,507 $544,747 100.0%
11.0%
4.5%
3.5%
32.4%
100.0%
0.0%

Table A-2. Comprehensive Unit Costs, $5.1 Million VSL (2010 Dollars)

MedicalEMS
MarketHouseholdInsurance
Workplace
Legal Costs
Subtotal
Congestion
Prop. Damage
Subtotal
Economic Total
QALYs
Comp.Total

PDO
MAIS0
$0
$0

MAIS1
$2,799MAIS2$11,453

MAIS3$48,620MAIS4$136,317

MAIS5$384,273

Fatal$11,317

$28

$21

$89

$194

$416

$838

$855

$902

$0

$0

$2,726$19,359$64,338$140,816$337,607$933,262

$60
$191
$62
$0
$341
$1,077$2,444$3,521$3,862$0$3,862

$45
$143
$46
$0
$255
$760
$1,828$2,588$2,843$0$2,843$862$7,106 $22,688$37,541$95,407$3,298$4,659 $15,371$28,228$72,525$341$2,644$5,776$6,361$11,091$1,182$3,351 $12,402$26,668$82,710$11,297 $48,766 $169,611$376,769$984,468$1,109$1,197$1,434$1,511$1,529$5,404$5,778 $10,882$16,328$15,092$6,513$6,975 $12,316$17,839$16,621$17,810 $55,741 $181,927$394,608 $1,001,089$11,850 $173,797 $410,794 $1,038,834 $2,334,414$29,660 $229,538 $592,721 $1,433,442 $3,335,503$289,910$28,322$11,783$106,488$1,381,984$5,720$11,212$16,932$1,398,916$3,949,939$5,348,855*Note: Unit costs are expressed on a per-person basis for all injury levels. PDO costs are
expressed on a per-damaged-vehicle basis.

242

Figure A-A. Components of Comprehensive Costs, $5.1 Million VSL

Components of Comprehensive Costs, $5.1
Million VSL
Medical4%EMS
0%Market Productivity
10%
HH Prod
4% Insurance
4%

Workplace
Legal 1%
2%

Lost Quality of Life
56%
Prop. Damage
14%

Congestion
5%

Table A-3. Total Comprehensive Costs, $12.6 Million VSL (Millions of 2010 Dollars)

MedicalEMS
MarketHouseholdInsurance
Workplace
Legal Costs
Subtotal
Congestion
Prop. Damage
Subtotal
Economic Total
QALYs
Comp.Total% Total

PDO
$0
$518
$0
$1,111$3,535$1,148$0$6,311$19,934$45,235$65,169$71,480$0$71,480
6.4%

%
MAIS0
MAIS1
MAIS2
MAIS3 MAIS4 MAIS5
Fatal
Total
Total
$0
$9,682$3,879$4,898 $2,329 $2,209$373$23,372
2.1%
$96
$308
$66
$42
$14
$5
$30
$1,0790.1%$0$9,430$6,557$6,481 $2,406 $1,941 $30,797$57,6125.1%$206$2,982$2,407$2,286$641$548$9,567$19,7481.8%$655 $11,408$1,578$1,548
$482
$417
$935
$20,5591.8%$211$1,180
$896
$582
$109
$64
$389
$4,5770.4%$0$4,089$1,135$1,249$456$475$3,514$10,918
1.0%$1,169 $39,079 $16,519 $17,087 $6,437 $5,660 $45,604 $137,865 12.3%$3,483$3,836
$405
$144
$26
$9
$189
$28,027
2.5%$8,378 $18,694$1,957$1,096
$279
$87
$370
$76,096
6.8%$11,861 $22,530$2,363$1,241$305$96$559 $104,123
9.3%$13,030 $61,608 $18,881 $18,327 $6,742 $5,755 $46,163 $241,988 21.6%$0 $118,792 $170,605 $119,928 $51,438 $38,893 $377,735 $877,392 78.4%$13,030 $180,401 $189,487 $138,256 $58,180 $44,648 $423,898 $1,119,379 100.0%
1.2%
16.1%
16.9%
12.4%
5.2%
4.0%
37.9%
100.0%
0.0%

243

Table A-4. Comprehensive Unit Costs, $12.6 Million VSL (2010 Dollars)
PDO
MedicalEMS
MarketHouseholdInsurance
Workplace
Legal Costs
Subtotal
Congestion
Prop.
DamageSubtotal
Economic
Total
QALYs
Comp.Total$0

MAIS0 MAIS1
MAIS2
MAIS3
MAIS4
MAIS5
Fatal
$0
$2,799$11,453$48,620$136,317$384,273$11,317

$28

$21

$89

$194

$416

$838

$855

$902

$0

$0

$2,726$19,359$64,338$140,816$337,607$933,262

$60
$191
$62
$0
$341
$1,077$2,444

$45
$143
$46
$0
$255
$760
$1,828$862$3,298$341$1,182$11,297$1,109$5,404$7,106$4,659$2,644$3,351$48,766$1,197$5,778$22,688$15,371$5,776$12,402$169,611$1,434$10,882$37,541$28,228$6,361$26,668$376,769$1,511$16,328$95,407$72,525$11,091$82,710$984,468$1,529$15,092$289,910$28,322$11,783$106,488$1,381,984$5,720$11,212$3,521$3,862$2,588$2,843$6,513$17,810$6,975$55,741$12,316$181,927$17,839$394,608$16,621$1,001,089$16,932$1,398,916$0$3,862$0$2,843$34,341$52,151$503,662$559,403$1,190,474$1,372,401$3,010,525$3,405,133$6,765,095$7,766,184$11,446,862$12,845,778*Note: Unit costs are expressed on a per-person basis for all injury levels. PDO costs are expressed on a
per-damaged-vehicle basis.
Figure A-B. Components of Comprehensive Costs, $12.6 Million VSL

Components of Comprehensive Costs, $12.6 Million VSL
Medical
2%

EMS Market Productivity Insurance
Workplace
5% HH Prod
0%
2%
0%
Legal
2%
1%
Congestion
3%
Prop. Damage
7%

Lost Quality of Life
78%

244

Appendix B Costs by Body Region
Table B‐1. Economic Costs by Body Region
MAIS

MedicalEmerg Wage
Svcs

Household Prod

Insur
Admin

Work-

Legal

Congestion

Prop Dam

-

-

-

Total

place

SCI
1

-

2

-

3

-

168,272

-

416

-

-

-

-

223,426-

78,787--53,380

-

5,776

-

43,069

-

1,434

-

-

10,882-585,443

342,782838

354,535

94,51871,071

6,361

67,144

1,511

16,328955,088914,249855

803,540

227,077

172,616

11,091

196,857

1,529

15,092

2,342,908

3,021
4,549
39,472
93,932
315,144

89
194
416
838
855

2,943
7,580
52,17596,940276,831931
2,782
18,39925,84478,231

BRAIN
3,561
341
1,824
2,644
12,465
5,776
19,4336,361
59,469
11,091

1,276
1,312
10,05818,35967,820

1,109
1,197
1,434
1,511
1,529

5,394
5,778
10,882
16,32815,092

18,66527,861
151,077
279,546
826,062

1
2
3
4
5

1,763
13,94742,966
87,600149,467

89
194
416
838
855

1,711
23,61556,821
90,387
131,175

742
4,088
10,953
17,11832,136

1,109
1,197
1,434
1,511
1,529

5,394
5,778
10,882
16,32815,092

13,76065,815
162,860
262,359
406,593

1
2
3
4

1,515
3,985
29,445

89
194
416

1,468
6,617
38,844

636
1,145
7,488

1,109
1,197
1,434

5,394
5,778
10,882

12,79225,582
117,263

-

-

-

-

-

-

-

-

-

-

5

-

-

-

-

-

-

-

-

-

-

4
5
1
2
3
4
5

1
2
3
4
5
1
2
3
4
5

89

4,264

12,42941,11657,141
163,086

194
416
838
855

21,02454,36258,857
143,148

3,404
12,18547,286137,221

89
194
416
838

3,319
20,60962,564
141,751

-

-

1,681
13,574

1
2
3

4,369

-

-

89
194
-

LOWER EXTREMITY
541
2,070
341
8,668
5,684
2,644
20,037
13,575
5,776
24,097
18,1196,361
37,07028,179
11,091
UPPER EXTREMETY
464
1,776
341
2,429
1,593
2,644
13,6989,280
5,776

TRUNK, ABDOMEN
1,349
5,159
341
7,717
5,060
2,644
19,17012,988
5,776
15,691
11,7996,361
40,45330,751
11,091
FACE/OTHER HEAD/NECK
1,050
4,015
341
7,564
4,960
2,644
22,062
14,948
5,776
37,79028,416
6,361
-

1,630
22,978
-

-

-

-

245

1,109

5,394

23,922

3,639
10,479
11,14735,069

1,197
1,434
1,511
1,529

5,778
10,882
16,32815,092

59,682
156,623
179,672
441,074

1,439
3,567
12,06026,846

1,109
1,197
1,434
1,511

5,394
5,778
10,882
16,32820,15958,699177,429
397,062

-

BURNS
1,973
341
5,530
2,644

516
8,434
-

-

1,849

-

707
3,978
-

-

1,109
1,197
-

-

5,394
5,778
-

13,43964,307-

4-

5

256,613

1

3,681

-

855

89

-

225,373

3,590-

63,6901,135-

48,415-

11,091-MINOR EXTERNAL
4,343
341

55,214

-

1,556

1,529

1,109

-

15,092

5,394

Table B‐2. QALY Values by MAIS, Body Region and Injury Status (2010 Dollars)*
Maximum AIS(MAIS)
AIS 1
AIS 2
AIS 3
AIS 4
AIS 5
AIS 1
AIS 2
AIS 3
AIS 1
AIS 2
AIS 1
AIS 2
AIS 3
AIS 4
AIS 5
AIS 1
AIS 2
AIS 3
AIS 4
AIS 5
AIS 3
AIS 4
AIS 5
AIS 1
AIS 2
AIS 3
AIS 1
AIS 2
AIS 3
AIS 4
AIS 1
AIS 2
AIS 2
AIS 3
AIS 4
AIS 1
AIS 2
AIS 3

Fracture or
Dislocation
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes

Body Region
Head
Head
Head
Head
Head
Face
Face
Face
Neck
Neck
ThoraxThoraxThoraxThoraxThoraxAbdomen/Pelvis
Abdomen/Pelvis
Abdomen/Pelvis
Abdomen/Pelvis
Abdomen/Pelvis
Spinal Cord
Spinal Cord
Spinal Cord
Upper Extremity
Upper Extremity
Upper Extremity
Lower Extremity
Lower Extremity
Lower Extremity
Lower Extremity
Burns/Other
Burns/Other
Head
Head
Head
Face
Face
Face

Not
Hospitalized$59,331$426,278$438,371$0$0$10,203$0$0$0$0$5,291$28,721$24,564$0$0$0$24,942
$0
$0
$0
$0
$0
$0
$9,826$43,837$0$10,203$66,512$0$0$21,919$0$1,104,998$0$0$622,790$18,140$0246

Hospitalized$489,767$750,522$1,559,620$3,351,274$5,244,208$403,604$945,900$865,784$462,557$1,183,981$125,087$440,261$196,133$352,209$572,528$76,337$193,110$315,930$458,023$265,290$2,092,468$5,870,777$6,837,083$143,982$468,982$720,290$169,302$498,081$741,830$1,228,196$232,412$757,325$1,671,480$1,795,056$1,782,585$205,203$919,446$899,039

All$93,721$545,697$1,270,899$3,351,274$5,244,208$17,762$945,900$865,784$462,557$1,183,981$10,581$119,041$173,837$352,209$572,528$180,639$136,802$315,930$458,023$265,290$2,061,102$5,870,777$6,837,083$13,605$177,238$874,475$15,116$82,006$597,092$1,228,196$23,430$757,325$1,489,329$1,795,056$1,782,585$586,889$287,587$899,039-

677,871

21,238AIS 1
AIS 2
AIS 3
AIS 4
AIS 1
AIS 2
AIS 3
AIS 1
AIS 2
AIS 3
AIS 4

Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes

ThoraxThoraxThoraxThoraxUpper Extremity
Upper Extremity
Upper Extremity
Lower Extremity
Lower Extremity
Lower Extremity
Lower Extremity

$0
$0
$0
$0
$26,453$53,663$0$9,826$198,779$828,371$0$167,413$311,017$397,180$581,220$298,546$452,354$911,132$0$535,116$732,005$1,506,713$167,413$311,017$397,180$581,220$26,831$137,180$911,132$9,826$318,197$747,121$1,506,713*Derived from QALY values presented in Table 5 in Spicer, Miller, Hendrie, and Blincoe(2011).

247

Appendix C
KABCO/MAIS Translators
Throughout this analysis translators developed from historical data records are used to translate nonfatal injury severity estimates based on police records using a KABCO scale, into the more precise
Abbreviated Injury Scale measure. For nonfatal injuries and PDOs, GES data was frequently the basis forincidence. However, GES data is only recorded using the KABCO severity system, whereas this report isbased on the Abbreviated Injury Scale. To translate GES data to an MAIS (Maximum Abbreviated Injury
Scale) basis, we used a variety of KABCO/MAIS translators. For CDS equivalent crashes, we used acurrent translator derived from 2000-2008 CDS data. Since this data is relatively recent, it reflects
roughly current levels of seat belt usage. For non-CDS cases, the only available data from which todevelop translators were contained in the 1982-1986 NASS files. Seat belt use has increased
dramatically since this time. Observed belt use during this period ranged from roughly 10 to 37 percent
as public awareness of the importance of belt use and belt use laws were just beginning to take hold in
1986. Belt use has since risen dramatically, and has been between 80 and 85 percent since 2004. Belt use
can influence injury reporting significantly in a number of ways. It changes the nature of injuries by
preventing many more visible injuries (such as head/face contact with the windshield) but replaces themwith often less visible (and also typically less serious) abdominal injuries such as bruising caused bypressure from the belt across the torso. This can influence the relationship between the KABCO
reported injury severity and the corresponding MAIS injury level. For this reason, separate translators
were developed from the 1982-86 NASS data for non-CDS cases where the victim was belted, unbelted,
unknown belted status, and for nonoccupants/motorcyclists. These translators are presented in Tables
C-1 through C-5 below.
Table C-1 All CDS Equivalent Cases
O
No
InjuryMAISC
Possible
InjuryB
Non-incapacitating
InjuryA
Incapacitating
InjuryInjured Severity
Unknown

Unknown
if Injured

00.81910.21880.0906

0.03760.00320.242910.17590.70140.75180.57820.01100.5961

20.00470.06740.11130.19240.00190.103930.00020.01010.03480.12590.00410.040640.00000.00210.00850.04440.00270.004750.00010.00010.00140.01710.00070.0117

Fatality
Total

0.00001.00000.00011.00000.00151.00000.00431.00000.97651.00000.00001.0000248

Table C-2. Non-CDS, Unbelted
O
No
InjuryMAISC
Possible
InjuryB
Non-incapacitating
InjuryA
Incapacitating
InjuryInjured Severity
Unknown

Unknown
if Injured

00.95910.26550.07770.0394

0.10820.91961

0.03880.43600.66670.50330.3931

0.068120.00110.03890.06400.15170.03450.000430.00000.00450.0041

0.05120.00000.001340.00000.00000.00080.01160.00000.000050.00000.00000.00000.00610.00000.000070.00100.25450.18630.2347

0.46150.0106

Fatality
Total

0.00001.00000.00041.00000.00051.00000.00201.00000.00271.00000.00001.0000B
Non-incapacitating
InjuryA
Incapacitating
InjuryInjured Severity
Unknown

Unknown
if Injured

Table C-3. Non-CDS, Belted
O
No
InjuryMAISC
Possible
Injury00.94910.30020.15580.09620.51150.91641

0.04940.48880.65440.54180.19480.059820.00060.01540.01920.1966

0.06550.000030.00010.01240.00820.05050.01790.000040.00000.00010.00120.00730.00000.000050.00000.00000.00000.00000.00000.00007

0.00090.18310.16110.10760.21040.0239Fatality
Total

0.00001.00000.00001.00000.00001.00000.00001.00000.00001.00000.00001.0000B
Non-incapacitating
InjuryA
Incapacitating
InjuryInjured Severity
Unknown

Unknown
if Injured

Table C-4. Non-CDS, Unknown if Belted
O
No
InjuryMAISC
Possible
Injury00.99600.10780.09080.02520.04130.94761

0.00010.11610.49710.26870.18660.005020.00000.00570.00670.14910.00670.000030.00000.00000.00000.13050.00000.000040.00000.00000.00000.00000.00000.000050.00000.00000.00000.00000.00000.00007

0.00380.77040.40530.42650.76540.0475

Fatality
Total

0.00001.00000.00001.00000.00001.00000.00001.00000.00001.00000.00001.0000249

Table C-5. Non-CDS, Nonoccupants/Motorcyclists
O
No
InjuryMAISC
Possible
InjuryB
Non-incapacitating
InjuryA
Incapacitating
InjuryInjured Severity
Unknown

Unknown
if Injured

00.73700.10570.02210.00590.02540.21451

0.23410.60670.64390.2886

0.54830.5992

2

0.02180.09720.14550.28390.17500.060930.00470.0261

0.0516

0.25830.04620.083040.00040.00270.00240.03150.00230.000050.00000.00080.00050.02340.00210.00007

0.00200.16080.1334

0.09650.18730.0424

Fatality
Total

0.00001.00000.00001.00000.00071.00000.01191.00000.01331.00000.00001.0000250

Appendix D:
KABCO Unit Costs
Police reports are generally coded using a generalized injury severity estimate commonly known as the
KABCO scale. Within this scale, injuries are typically coded under one of the following categories:
K = Killed
A = incapacitating injury
B = non-incapacitating injury
C= complaint of pain
O = No injury
This very general scale is used by police officers on the scene and represents their judgment regarding
injury severity. While police do their best to accurately judge each case, they do not have the training or
diagnostic skills or equipment to determine more precise estimates of actual injury. As noted elsewherein this report, translators developed from data systems that collected both KABCO and MAIS injury
severities indicate that KABCO ratings are not very meaningful. Large numbers of crash victims are
miscoded regarding their actual injury levels, with many people coded as A – incapacitating injury
actually being uninjured and many coded as uninjured actually experiencing injuries. For this reason webelieve that analysis of motor vehicle injuries is more meaningful when injuries are first expressed,
either directly or through a translator, using the Abbreviated Injury Scale. Nonetheless, we recognizethat in many cases police-reported data will be used directly with KABCO designations. For this reason,
we have supplied a KABCO based unit cost table in this appendix. Note that there is a QALY value
associated with O category KABCO injuries. This reflects the fact that many O injuries are actually
injured.
Table D-1. KABCO NONFATAL INJURY UNIT COSTS

Injury Subtotal

0$2,571$20$2,184$710$2,240$7$56$7,789C$4,393$45$5,096$1,562$3,648$208$1,125$16,078

B$4,981$56$6,465$1,966$3,670$1,459$1,684$20,282A$21,189$122$24,403$7,182$11,751$3,941$8,557$77,145

Congestion
Prop. Damage
Crash Subtotal
Economic Cost Total
QALYs
Comprehensive Total$1,026$1,009$995$1,385$1,624$2,650$10,439$31,859$42,298$2,407$3,416$19,494$108,274$127,768$2,465$3,460$23,742$252,268$276,010$3,518$4,903$82,048$919,158$1,001,206

MedicalEMS
MarketHouseholdInsurance
Workplace
Legal Costs

251

Appendix E
Estimating the Costs of Motor Vehicle Traffic Injuries in the United States From
Healthcare Files
Data
The medical and work-loss costs of each injury death were estimated using 2008 data from the National
Vital Statistics System (NVSS). The unit medical costs of non-fatal injuries were built primarily from the2008 Nationwide Inpatient Sample and the 2003 State Emergency Department Databases (SEDD), bothfrom the Healthcare Cost and Utilization Project. The information on initial treatment costs from the NIS
and the SEDD was augmented with data from Finkelstein et al. (2006) including cost data on emergency
transportation, physician fees, rehabilitation, and long-term treatment. The SEDD-based costs weremerged onto another HCUP dataset, the 2007 Nationwide Emergency Department Sample (NEDS),
which, unlike the SEDD, is nationally representative, and therefore more appropriate for this project.
The 2008 NIS and the 2007 NEDS also served as the basis for the estimation of lifetime work loss costs,
again supplemented by information from Finkelstein et al. (2006).
From the costed 2008 NVSS, 2008 NIS, and 2007 NEDS, we selected the acute injury cases that werecause-coded as unintentional motor vehicle traffic crashes. To avoid double counting, we excluded
fatalities from the NIS and the NEDS and transfers from the NIS. (The NEDS already excludes ED visitsthat result in admission as an inpatient.) We also dropped a number of duplicate cases reported by one
hospital in the NIS.
Year of Dollars, Inflator Series, and Discount Rate
All costs are reported in 2010 dollars. Individual cost elements used in developing the cost module camefrom datasets belonging to different time periods and were inflated to 2010 dollars. Health care costs inearlier year’s dollars were inflated using the medical care component of the Consumer Price Index(CPI−Medical). Work loss costs were inflated using the index that DoT uses for inflating the value of astatistical life: CPI × ECI 0.55, where CPI is the consumer price index, ECI is the employment cost index,
and 0.55 is the estimated income elasticity. Work-loss costs more than one year post-injury werediscounted to present value using the 3-percent discount rate recommended by the Panel on CostEffectiveness in Health and Medicine (Gold, Siegel, Russell, & Weinstein, 1996) and by Haddix, Teutsch,
and Corso (2003). For sensitivity analysis, we also estimated costs using alternative discount rates of 0
percent, 2 percent, 4 percent, 7 percent, and 10 percent.
Lifetime Medical Costs of Injuries
For some injuries, medical treatment and corresponding costs may persist for years or even decades
after the initial injury. The medical costs presented in this study include costs associated with treatment
for physical injuries only, as data required to estimate costs for mental health and psychologicaltreatment were not available.
Fatal Injuries:
Fatal medical costs were calculated using costs per case by place of death from Finkelstein et al. (2006),
expanded to include deaths in hospice. Costs were computed separately for six different places of death
identified in the 2008 NVSS data:•
On-scene/at home.

252

•
•
•
•
•

Dead on arrival at a hospital (DOA).
In a hospital emergency department (ED).
In a hospital after inpatient admission.
In a nursing home.
In a hospice.

The medical costs incurred, depending on the place of death, might include charges for coroner/medical
examiner, emergency medical transport, ED visit, and stays in a hospital, nursing home, or hospice.
Table E-1 summarizes the components included for each place of death.
Table E-1. Data and Methods for Estimating Medical Costs of Fatal Injuries
Place of Death

Cost Categories

Description, Unit Cost
(2010 U.S. $)Source of Data

On scene/at home

Coroner/ME(C/ME)$43 admin, plus $1,619 if
autopsy (C/ME)

Dead on arrival(DOA) at hospital

C/ME + Transport(T)$43[+$1,619] (C/ ME) +$315 (T)

Hickman, Hughes, Strom &Ropero-Miller (2007)(C/ME)
1999 Medicare 5 percent
Sample (T)In ED

C/ME + T + ED

$43[+$1,619] (C/ME) +$315 (T) + Avg. cost for
fatalities in ED by externalcause and age (ED)In hospital after
admission

C/ME + T + Fatal$43[+$1,619] (C/ME) +inpatient total (FIT) $315 (T) + Avg. cost for
fatalities in hospital by
select diagnosis andmechanism group (FIT)2008 HCUP-NIS for hospital
facilities costs, 1996-97
MarketScan data for nonfacility costs (FIT)In nursing home

C/ME + 2×T + Nonfatal inpatient
total (NIT) + Costof nursing home
stay ending indeath (NH)$43[+$1,619] (C/ME) +$630 (2×T) + Avg.
inpatient costs fordischarges to NH bydiagnosis and mechanism(NIT) + Avg. days in NH bybody region × $197
cost/day (NH)2008 HCUP-NIS and 1996–
97 MarketScan data (NIT),
days in NH estimated from2004 National Nursing
Home Survey (NH),cost/day from Genworth
2007 Cost of Care Survey

In hospice

C/ME + 2×T + Nonfatal inpatient
total (NIT) + Costof hospice stayending in death(HSP)$43[+$1,619] (C/ME) +$630 (2×T) + Avg.
inpatient costs fordischarges to hospice bymechanism and body
region (NIT) + $6,258(HSP)

2008 HCUP-NIS and 1996–
97 MarketScan data (NIT),
2007 National Hospice andHome Care Survey (HSP)

Estimated using fatalities in
the 2007 HCUP-NEDS (ED)All fatalities were assigned coroner/medical examiner costs—a $43 administrative fee, plus an additional$1,619 if an autopsy was performed, as indicated by the NVSS autopsy variable. For cases where this253

variable was missing, we used autopsy probabilities by external cause group (46.0 percent for motor
vehicle deaths) based on Hoyert (2011). We estimated the coroner costs from Hickman, Hughes, Strom& Ropero-Miller (2007). This survey-based document provides the costs and workload of all U.S. medical
examiner and coroner offices except in Louisiana. We calculated the $1,619 cost per accepted fatality
under the arbitrary assumption that 5 percent of the office budget is used to determine which cases to
accept, keep records about those determinations, and handle public relations and education requests
unrelated to specific deaths.
DOAs and all deaths in the hospital, whether in the ED or as an inpatient, also received the cost of a oneway transport, which was based on average ambulance transport costs for injured patients in the 1999
Medicare 5 percent sample. Deaths in a nursing home or hospice were assigned the cost of two
emergency transports—one from the scene to the hospital, and a second from the hospital to thefacility where death eventually occurred. (This component is described in greater detail below, in thesection on medical costs of hospital-admitted injuries.)
For deaths in the ED, Finkelstein et al. (2006) added average costs for injury fatalities in the ED by injury
mechanism and age group, computed from 363 injury deaths in EDs in 1997 in three States. We applied
this method to the fatal cases in the newer, larger 2007 HCUP-NEDS. Of the 4,336 fatalities in the NEDS,
we were able to estimate costs for 3,623. AHRQ has not produced cost-to-charge ratios for the NEDS,
but we were able to estimate cost-to-charge ratios for most facilities and sampling strata, as described
below in the section on medical costs of ED-treated injuries. Therefore, we were able to estimate costsat the case level by multiplying hospital charges times our estimated cost-to-charge ratios. Average costs
were computed by injury mechanism and, where sample size permitted, by age, intent, or body region
injured.
For deaths in the hospital, costs of an inpatient admission that ended in death were added to thetransport and medical examiner/coroner costs. These inpatient costs were computed by injury
mechanism (fall, motor vehicle, poisoning, suffocation, other), nature of injury (fracture or dislocation,internal organ injury, other), body region (brain, spinal cord, trunk, other) and age group (0–9, 10–24,
25–49, 50–69, 70–84, 85 and older), from 16,004 injury-related cases in the 2008 HCUP-NIS in which thepatient died in the hospital. For each case, the facility cost was estimated by multiplying the hospitalcharge times a facility-specific cost-to-charge ratio. This product, in turn, was multiplied times anotherfactor to account for non-facility services—i.e., professional services used while in the hospital yet notincluded in the admissions billing (e.g., surgeon, anesthesia, physical therapy). These non-facility factors
were based on Medstat’s 1996 and 1997 MarketScan Commercial Claims and Encounters Database. This
database contains an inpatient hospital admissions file, which summarizes each hospital admission,
including total payments, facility payments, length of stay, and detailed diagnosis data. After removing
non-fee-for-service claims and claims without a diagnosis of injury, a file of 19,247 inpatient injury
admissions was created. Using these records, we calculated the mean ratio of total medical costs during
the inpatient stay to facilities costs by body region as presented in the Barell injury-diagnosis matrix. The
ratios of total costs to facilities costs ranged from 1.03 to 1.39, with an overall average of 1.26. The
HCUP-NIS cost estimate for each admission was multiplied times the ratio for the corresponding body
region to yield estimated total inpatient costs for each injury admission in the HCUP-NIS. The non-facility
costs of non-fatal hospital admissions were estimated using this same approach (see below). Averagecosts by selected mechanism, diagnosis, and age group, as described above, were computed, and these
averages were applied to the corresponding cases of the 2008 NVSS data.

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Deaths in a nursing home or hospice were assumed to be preceded by a stay in an acute care hospital.
The method described in the previous paragraph was used to estimate hospital costs for each patient in
the 2008 HCUP-NIS who was discharged to a hospice (3,336 cases) and each patient who was discharged
to a nursing home following a severe (AIS≥4) injury (4,327 cases). Because of the small samples, fewer
diagnosis/mechanism groups were used than for deaths in hospital, and there was no age breakdown.
This cost was added to the usual coroner cost plus twice the usual emergency transport cost. Patients
who died in a nursing home or hospice were assumed to have been transported by ambulance twice—first to the hospital, and then to the nursing home or hospice. The final component of medical costs forthese deaths was the cost of the terminal stay in the nursing home or hospice.
For deaths in a nursing home, the cost of the nursing home stay was calculated as cost per day times thelength of stay (LoS) in the nursing home. The average cost per day of nursing home care was taken fromthe Genworth Financial 2007 Cost of Care Survey and inflated to 2010 dollars. The average LoS in anursing home was estimated by body region injured (head or neck, trunk, upper limb, hip, upper leg orknee, lower leg or foot, other) from 1,234 resident cases with an admitting diagnosis of injury from the2004 National Nursing Home Survey (NNHS). Since the NNHS is based on a survey of residents rather
than discharge data, it did not allow us to identify patients whose stay ended in death. Moreover, it
provided only the LoS as of the survey date, not the final LoS. To estimate the average complete LoS, we
assumed that each surveyed resident represented a nursing home bed that was always filled with apatient identical to the survey respondent. We further assumed that each patient was surveyed at the
midpoint of the nursing home stay, unless this would have resulted in a LoS of less than 13 days, which
we imposed as a minimum, based on sensitivity analysis of the nursing home data. This allowed us toaccount for the many residents with a short LoS who would have passed through the nursing home
before and after the survey date while residents with a longer LoS remained.
The cost of a terminal hospice stay was estimated using data from the 2007 National Home Health andHospice Care Survey. This dataset, unlike the NNHS, was based on discharge data, including bothcharges and payments. Only eight cases involved injury, and just five of these ended in death. We
computed the average total payment for these five cases.
These costing methods were applied to the deaths in the 2008 NVSS data at the case level using theplace of death variable, which specifies where the death occurred, to produce the fatal medical costs.
Hospitalized Injuries:
The hospitalized injury costing methods in Finkelstein et al. (2006) were applied to 2008 acute carecosts. An overview of the approach is presented in Table E-2. The details are provided in the following
sections.
Table E-2. Data and Methods for Estimating Medical Costs of
Non-Fatal Injuries Requiring Hospitalization
Cost Category

Description, Unit Cost (2010 U.S. $)Source/Notes

Facilities
component of
inpatient stay

Inpatient facility charges for the casemultiplied by inpatient cost-to-charge
ratio for the facility

2010 NIS for charges; cost-to-charge
ratios from AHRQ

255

Cost Category

Description, Unit Cost (2010 U.S. $)Source/Notes

Non-facilities
component of
inpatient stay

Estimated by comparing ratio of total
costs to facilities costs by Barell bodypart2010-11 MarketScan commercial claims
data

Hospital
readmissions

Readmission rates by age group andBarell diagnosis group2007 SID analysis, reported by
Zaloshnja et al. (2011)

Short- to mediumterm follow-up
costs

Estimated as the ratio of total costs inmonths 1–18 (on average) to total
inpatient costs by 16 diagnosis groups,
excluding costs of readmission in thefirst 6 months

1996–99 MEPS

Follow-up costs
beyond 18
months, up to 7
years

Estimated using ratios of total lifetime
costs to 18-month costs for 17
diagnosis groups

1979–88 Detailed Claim Information(DCI) data from workers’
compensation claims

Long-term costs
beyond 7 years for
SCI and TBI

SCI: All post-discharge costs wererecomputed using the ratio of pre-to
post-discharge costs

1986 survey data reported in
Berkowitz et al. (1990)

TBI: Post-7-year costs estimated at 75
percent of SCI costs
Hospitalrehabilitation costs

Probabilities and average costs ofrehabilitation estimated for 11 injury
diagnosis groups

Probabilities from 1997 CA, MD, & PA
hospital data; costs estimated using
Prospective Payment System
reimbursement amounts, as reported
in Miller et al. (2006)

Nursing homecosts

Cost/day in NH ($208) times estimatedaverage length of stay by 7 body
regions for patients discharged to NH

Cost/day from Genworth 2007 Cost ofCare Survey; length of stay estimatedfrom 2004 National Nursing Home
SurveyTransport

Half of mean ambulance claims ($350)
applied to each case

Mean ambulance claims for cases fromthe 1999 Medicare 5 percent sample
with an injury E-code

Claimsadministration

Insurance overhead percentages bypayer

Woolhandler et al. (2003)

Total inpatient costs (facility and non-facility)
The 2010 NIS included the inpatient facility charge for each admission. For each record in the NIS, thischarge was multiplied times the 2010 Medicare cost-to-charge ratios provided by AHRQ. These ratios
are hospital specific for 63 percent of the acute injury records in the 2010 NIS. For hospitals whose
facility-specific ratio could not be calculated, a weighted group average ratio specific to the hospital’s
State, ownership, urban/rural location, and number of beds was used as recommended by AHRQ
(Friedman, De La Mare, Andrews, & McKenzie, 2002). For Kaiser hospitals in California, which do not

256report charges, we computed the average facility cost by sampling stratum and diagnosis for California
hospitals in the 2010 NIS. These estimates of facility costs for each hospital admission were thenmultiplied times a ratio of total inpatient costs to facility costs to obtain the total cost of the admission,
including non-facility costs, i.e., payments to professionals such as surgeons, anesthesiologists, andtherapists who bill separately from the hospital itself. This factor is discussed in detail above, in theparagraph on medical costs of deaths in hospital.
In order to account for follow-up admissions, we used readmission rates based on HCUP’s 2007 State
Inpatient Databases (SID) from 13 States (AZ, CA, FL, MO, NE, NH, NV, NY, NC, SC, TN, UT, and WA), as
reported by Zaloshnja et al. (2011). The SID covers all inpatient stays in participating States. In 2007
AHRQ tracked revisits for inpatients in these 13 States, providing a rare look at follow-up
hospitalizations. Zaloshnja et al. computed readmission rates by Barell nature of injury and body partand age group (0–14, 15–29, 30–74, 75+). Readmission rates averaged 4.3 percent but ranged as high as
21 percent (for hip fractures, ages 75+). We assumed that, on average, follow-up admissions have thesame costs as initial admissions. (We are forced to make this assumption because the NIS does not allow
us to distinguish initial from follow-up admissions with any precision.) We divided the total inpatient
cost of each case by (1−r), where r is the readmission rate, to factor up hospital costs for readmissions.
Short- to medium-term follow-up costs. To develop estimates of short- to medium-term costs forinjuries requiring an inpatient admission, Finkelstein et al. (2006) multiplied total inpatient costs foreach record in HCUP-NIS/Marketscan (as derived above) times the ratio of all costs in the first 18
months after injury, on average, (including costs for inpatient services, ED visits, ambulatory care,prescription drugs, home health care, vision aids, dental visits, and medical devices) to the total
inpatient costs (including initial admissions and readmissions) for injury by diagnosis and mechanism of
injury. These ratios were derived from 1996–99 MEPS data. MEPS is a nationally representative surveyof the civilian non-institutionalized population that quantifies individuals’ use of health services andcorresponding medical expenditures for two consecutive years following enrollment. Because the MEPS
analysis was limited to injuries of admitted patients with at least 12 months of follow-up and the MEPS
data include costs for up to 24 months, the MEPS sample captures injuries with an average of 18 monthsof post-injury treatment.
Although MEPS is the best source of available data for capturing nationally representative injury costs
across treatment settings (e.g., hospitals, physician’s office, pharmacy), even after pooling four years ofdata the sample size for many injuries with low incidence rates was small. Therefore, to obtain robust
direct cost estimates, injuries were collapsed into broad categories prior to quantifying average costs.
Records were collapsed into ICD-9 diagnosis groupings based on the following guidelines (in priority
order):
1.
Groupings must be comprehensive, covering all injury diagnoses (including those for which
MEPS lacks cases).
2.
Groupings need to balance the goals of diagnosis-level detail and reasonable cell sizes. In someinstances, cell samples as small as 5 were accepted in order to avoid combining radically dissimilar
diagnoses into a single group.
3.
Groupings should be similar, either in nature of injury (e.g., sprain, fracture) or in body region, if
not in both.
4.
Total injury costs (or the ratio of total injury costs to hospitalization costs for admitted injuries)
should be similar in magnitude across diagnoses within each grouping.

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Using the MEPS data grouped according to these criteria, we calculated the average ratio of 18-monthcosts to total inpatient costs (including inpatient facility and non-facility fees) for 15 injury-specificdiagnosis groups, ranging in size from 5 to 61 unweighted cases. The ratios ranged from 1.02 to 2.12,
with an overall average of 1.26 (see Supplement, Table A). The ratios were then multiplied times thecorresponding inpatient cost estimates detailed in the preceding section to arrive at 18-month costs forinjuries requiring an inpatient admission.
Long-term follow-up costs. While short- to medium-term costs capture the majority of costs for mostinjuries, some injuries continue to require treatment and costs beyond 18 months. Rice et al. (1989)
estimated long-term medical costs from costs in the first six months using multipliers derived fromlongitudinal 1979–88 Detailed Claim Information (DCI) data on 463,174 workers’ compensation claims
spread across 16 States. The DCI file was unique: nothing similar in size, geographic spread, and duration
has become available subsequently. Because occupational injury includes a full spectrum of externalcauses (e.g., motor vehicle crash, violence, fall), the DCI data by diagnosis presumably captured the
medical spending pattern for an injury to a working-age adult reasonably accurately. Their applicability
to childhood injuries was questionable. To address this concern, Miller, Romano, and Spicer (Miller,
Romano, & Spicer, 2000b) analyzed the 30-month cost patterns (long-term costs were not available) of
adult versus child injury using 1987–89 MarketScan data on private health insurance claims. They foundthat the ratios of 30-month costs to initial hospitalization costs for children’s episodes by diagnosis did
not differ significantly from the comparable ratios for adults. By diagnosis, the ratios for children ranged
from 95 percent to 105 percent of the ratios for adults. Thus, it is reasonable to apply the DCI estimates
to childhood injury cases.
Costs beyond 18 months were not inconsequential for some injuries. For lack of a better alternative,
following Finkelstein et al. (Finkelstein, et al., 2006), we used ratios computed from the DCI expenditurepatterns to adjust estimates of costs in the first 18 months to arrive at estimates of the total medical
costs (including long-term) associated with injuries. This method implicitly assumed that while treatment
costs varied over time, the ratio of lifetime costs to 18-month costs had remained constant between thetime the DCI data was reported and 2009. The 18-month cost estimates from the previous section were
multiplied times the ratio of lifetime costs to the costs in months 1–18 by Barell nature of injury(fracture, other) and body region. Although the DCI ratios varied by injury diagnosis, on average, at a 3percent discount rate, 77 percent of the costs for admitted cases were incurred in months 1–18 (Miller
et al., 2000a). The average long-term multiplier for admitted cases was 1.30.
Long-term costs of spinal cord injuries (SCI) and traumatic brain injuries (TBI). These estimatesincorporate long-term SCI and TBI costs from Berkowitz et al. (1990). For several types of injuries, andespecially for SCI and TBI, a substantial portion of the total medical costs occur more than seven years
after the injury is sustained. For severe SCI (i.e., quadriplegia or paraplegia), the ratio of lifetime costs tocosts of the initial admission (including emergency transport) was used to factor up the cost of the initialadmission. Ratios were computed separately for complete quadriplegia, partial quadriplegia, complete
paraplegia, and partial paraplegia, as inferred from the primary injury diagnosis. (This special procedure
for severe SCI cases bypasses the medium- and long-term cost methods described in previous sections.)
This ratio was generated from data collected by Berkowitz et al. (1990), who surveyed a nationally
representative sample of SCI survivors and their families in 1986 and collected data on 758 SCI victims,
including those residing in institutions, those living at home, and those in independent living centers.
The respondents (victims, families, or guardians) provided details of care payments during the past year,
including payments for medical, hospital, prescription, vocational rehabilitation, durable medical
equipment, environmental modification, personal assistant, and custodial care. The long-term cost258

estimates for SCI rely on the assumption that the now-dated Berkowitz data on medical costs by year
post-injury mirror the expected lifetime costs for recent SCI victims.
Quantifying long-term costs for TBI is more problematic. Most TBI programs do not have longitudinal
data on TBI costs. However, Miller et al. (2004) estimated inpatient rehabilitation costs by diagnosis
group, including SCI and TBI, finding that among patients receiving rehabilitation, the cost per case for
TBI averaged 75 percent of the cost for SCI. TBI patients, however, were far less likely to receive
inpatient rehabilitation (6 percent versus 31 percent). Finkelstein et al. (2006) assumed the TBI patients
who received inpatient rehabilitation would follow the same cost pattern more than seven years postinjury as the SCI patients, but with costs equal to 75 percent of SCI levels. Again using the Berkowitz
data, we estimated that, at a 3-percent discount rate, 46.92 percent of the medical costs of TBI are
incurred in the first seven years. Therefore, we divided the seven-year costs by this percentage to arrive
at lifetime medical costs of TBI. As with other long-term costs, we replicated this process for otherdiscount rates to facilitate sensitivity analysis.
For very severe burns, amputations, and other non-SCI, non-TBI injuries requiring lifetime medical care,lack of available data will bias our lifetime cost estimates downwards.
Inpatient rehabilitation costs. Costs of inpatient rehabilitation were estimated using direct costs
developed for 11 injury diagnosis groups by Miller et al. (2004). These costs came from the Health Care
Financing Administration (HCFA, now the Center for Medicare and Medicaid Services, CMS) Prospective
Payment System (PPS) reimbursement schedule that governs payments for all U.S. inpatient
rehabilitation including professional fees. Miller et al. (2004) used PPS data on lengths of stay and cost
per day to develop direct cost estimates of rehabilitative treatment. They used data from California,
Maryland, and Pennsylvania hospital discharge systems to compute the probability of rehabilitation foreach PPS diagnosis and mechanism group. The product of the probability of rehabilitation and the direct
cost estimate of rehabilitation developed by Miller et al. (2004) were added to the HCUPNIS/Marketscan-based cost estimates.
Transport costs. None of the data sets and analyses of non-fatal hospitalized injuries described above
include transportation costs. We incorporate transportation costs from Finkelstein et al. (2006), who
arbitrarily assumed that half of non-fatal injuries requiring a hospital admission also required a one-way
trip via ambulance to the hospital. For each injury case, the costs include half of the one-way average
emergency transport costs based on 1999 average transport costs for Medicare beneficiaries with an E
code on an ambulance claim. There were 15,579 E-coded ambulance claims (including air ambulance) in
the Medicare 5 percent sample, with an average cost of $350 in 2010 dollars. After our report wasessentially completed, GAO (2012) reported that median cost per transport covered by Medicare was$429 in 2010. Incorporating that estimate would raise the ambulance cost component of our medical
cost estimate from $175 to $214.
The assumed 50 percent transport rate may be conservative. The National Pediatric Trauma Registry,
which captures admitted serious injuries, showed that from 4/1/1994 to 11/5/2001, 58.4 percent of
48,288 pediatric patients arrived by ambulance (National Pediatric Trauma Registry, 2002).
Claims administration. To estimate the claims processing expenses incurred by private insurers andgovernment payers like Medicare and Medicaid, we drew on insurance overhead rates published by
Woolhandler et al. (2003): 11.7 percent for private insurers, 3.6 percent for Medicare, and 6.8 percent
for Medicaid. For each case, we applied the rate corresponding to the primary expected payer coded in
the hospital record to all medical costs detailed above. When the listed payer was workers’

259

compensation we applied the private insurance rate, and when it was another government program weapplied the Medicaid rate. For cases coded as charity or self-pay, we assumed there were no claims
processing expenses. And when the payer was missing we applied an average rate by sex and age group.
Injuries Treated in an Emergency Department
Table E-3 summarizes the approach for quantifying costs of non-fatal injuries treated in EDs andreleased without inpatient admission.
Table E-3. Data and Methods for Estimating Medical Costs of Non-Fatal,
Non-Admitted Injuries Treated in Emergency Departments
Cost Category

Description, Unit Cost
(2010 U.S. $)Source/Notes

ED visit

ED facility payments

2010-11 Marketscan commercial
claims data

Follow-up visits andmedication,months 1–18

Estimated as the ratio of all costsin the first 18 months after injury
to costs of the initial ED visit bydiagnosis grouping

1996–99 MEPS

Follow-up costs
beyond 18 months

Estimated using ratios of total
lifetime costs to 18 month costs
for 17 diagnosis groups

1979–88 Detailed Claim
Information (DCI) data fromworkers’ compensation claims;
adjustment factor for youth from
Miller et al. (2000a)

Emergencytransport

50 percent of ED visits assumed tohave transport costs of $350

Mean cost estimated using 1999
Medicare ambulance claims withan injury E code

Claimsadministration

Insurance overhead percentages
by payer

Woolhandler et al. (2003)

We used initial visit cost estimates for ED-treated, non-admitted, non- fatal injuries from 2010-11
Marketscan commercial claims data, which show total payments including copay and deductible.
As with costs for hospitalized injuries, the costs of the initial visit were factored up by 1996–99 MEPSbased ratios for 51 categories of non-admitted injuries treated in the ED to account for follow-up visits
and medication in the first 18 months post-injury. The ratios ranged from 1.02 to 5.44, with an overall
average of 1.78 (see Supplement, Table B). For follow-up costs beyond 18 months, average costs were
estimated using ratios from DCI expenditure patterns and implicitly assuming that the ratio of lifetime
costs to 18-month costs had remained constant between the time DCI data was reported and 2007.
These long-term costs were calculated by multiplying the DCI ratios of lifetime costs to 18-month costs
times the 18-month costs by diagnosis group. At a 3-percent discount rate, 88 percent of the costs for
non-admitted cases occurred in months 1–18 and the average multiplier was 1.14 (Miller et al., 2000a).
For age groups not represented in the DCI, the costs were adjusted using ratios from Miller et al. (2000b).
As with hospital costs, half of patients were assumed to receive emergency transport, so half of theaverage one-way emergency transport cost was added to the medical cost of each case (see “Transport260

costs” section above for details). Finally, we added claims administration costs from Woolhandler et al.
(2003), as described above under hospitalized medical costs.
Using the augmented Marketscan data, we computed mean medical costs by diagnosis. We then merged
these mean costs onto the 2010 HCUP Nationwide Emergency Department Sample (NEDS), a multi-State
sample of patients treated in a hospital ED and not subsequently admitted as inpatients. The NEDS isnationally representative. It could not be costed directly, however, because many cases do not indicate
charges, AHRQ has not made cost-to-charge ratios available for the NEDS, and the charges that areincluded exclude professional fees.
Lifetime Work Losses Due to Injuries
Injuries can result in both temporary and permanent disability. When this occurs, injury victims may losepart or all of their productivity potential. Work losses due to injury may include lost earnings andaccompanying fringe benefits, plus the lost ability to perform one’s normal household responsibilities.
For non-fatal injuries, work losses represent the value of goods and services not produced because of
injury-related illness and disability. To the degree that injuries prevent or deter individuals fromproducing goods and services in the marketplace, the public sector, or the household, the value of theselosses is a cost borne by society.
Fatal work losses represent the value of goods and services never produced because of injury-relatedpremature death. These work loss costs were estimated by applying expected lifetime earnings by age
and sex to the all deaths from injury sustained in 2008, including an imputed value for lost household
services.
Consistent with the human capital approach for quantifying the burden of injuries (Rice et al., 1989),
estimates of non-fatal work losses involve applying average earnings to work-years lost and the value ofhousekeeping services to time lost in home production. Non-fatal injuries may result in both short-termwork loss and in lifetime work losses. The latter includes the value of output lost by people disabled inlater years as a result of injury sustained in 2008 (or 2007 for ED-treated, non-fatal injuries).
All work loss estimates were inflated from Finkelstein et al. (2006). Non-fatal work losses were stratified
into two categories: short-term losses, which represent lost earnings and accompanying fringe benefits
and household services occurring in the first six months after an injury, and long-term losses, which
represent the respective earnings and household loss occurring after six months from the time of the
injury. The decision to use six months as the transition point between short-term and long-term work
losses was driven by the availability of data on duration of work loss.
Because men earn higher earnings than women, even in the same job (Bureau of Labor Statistics, 2001)
or for injuries with the same prevalence between men and women, the work loss estimates were greaterfor men. Finkelstein et al. (2006) view this as more of a shortcoming of the labor market than an
inherent problem with the human capital approach. Regardless, this undervaluation of women’s labor isreflected in the estimates.
Fatal Injuries:
For someone of a given sex and age who sustained a fatal injury, Finkelstein et al. (2006) summed thesex-specific probability of surviving to each subsequent year of age times sex-specific expected earnings
for someone of that age. We followed this method using updated data (Arias, 2012). We used thisformula with money earnings data by sex and year of age derived from the March Supplement of theCurrent Population Survey, averaged across a full business cycle from 2002 through 2009. We inflated all
earnings figures to 2010 dollars using the Employment Cost Index–Wages & Salaries, All Civilian. We
261

added fringe benefits of 23.33 percent of wages based on the average ratio of wage supplements towages for 2002–09 from the National income accounts (Economic Report of the President, various
years, Table B-28). Earnings, including salary and the value of fringe benefits, at future ages were
adjusted upwards to account for a historical 1 percent work growth rate (Haddix et al., 2003) and thendiscounted to present value using a 3-percent discount rate. (For sensitivity analysis, parallel estimates
were constructed using discount rates of 0 percent, 2 percent, 4 percent, 7 percent, and 10 percent).
Parallel calculations valued lost household work. Estimates of the value of household work are also
included in Haddix et al. (2003). Historically, productivity growth in household production has been
negligible, so Finkelstein et al. (2006) did not adjust for it. In all cases, they assumed that the probabilityof surviving past the age of 102 is zero. In equation form, lifetime earnings for someone of age a and sexb (Earna,b) is computed aswhere Pa,b(k) = the probability that someone of age a and sex b will live until age k; Yk,b = the averagevalue of annual earnings (including fringe benefits) or of annual household production at age k forsomeone of sex b; g = the productivity growth rate (0.01 for earnings, 0.00 for household production);and d is the discount rate (usually 0.03, but allowed to vary for sensitivity analysis).
These costing methods were applied to each case in the 2008 NVSS data to produce the fatal work loss
costs to be used in our estimates.
Non-Fatal Injuries
For non-fatal injuries, work loss estimates included the sum of the value of wage and household work
lost due to short-term disability in the acute recovery phase and of the value of wage and household
work lost due to permanent or long-term disability for the subset of injuries that cause lasting
impairments that restrict work choices or preclude return to work.
Short-term work losses. Finkelstein et al. (2006) quantified temporary or short-term work loss for nonfatal injuries using the approach presented in Lawrence et al. (2000). Lawrence et al. combined theprobability of an injury resulting in lost workdays from 1987–96 National Health Interview Survey (NHIS)
data with the mean work days lost (conditional on having missed at least one day) per injury estimatedfrom the 1993 Annual Survey of Occupational Injury and Illness reported by the Bureau of Labor Statistics(BLS). This data was sent to BLS by employers through a mandatory reporting system. Employers
reported work loss from date of occupational injury to the end of the calendar year for asample of approximately 600,000 injury victims. All cases reported involved at least one day of work loss
beyond the date of the injury. Moreover, if a worker still was out of work at the time the employer report
was due to BLS, the report would undercount work days lost. On average, BLS work-loss reports cover six
months post injury. Lawrence et al. (2000) used a Weibull regression model to estimate the total
duration of work loss for cases still open at the end of the survey reporting period. These results werecombined with those of the closed cases to estimate average work loss, conditional on having missed atleast one day of work. These BLS-based estimates were then combined with the pooled 1987–
96 NHIS data on probability of work loss to compute mean work loss including cases without work loss.
Although BLS uses a detailed two-column coding system (body part, nature of injury), Finkelstein et al.
(2006) were able to map their codes to the ICD-9-CM codes.
Although the BLS data is limited to injuries that occur on the job, Finkelstein et al.’s (2006) separate analysis
of 1996–99 MEPS data (based on a much smaller sample) found that the duration of work loss did not differ
significantly by whether or not the injury occurred on the job. This suggested that the BLS- NHIS work loss262

estimates could credibly be applied to estimate work loss associated with non-work- related injuries.
Analysis of the MEPS data revealed that work loss was roughly five times longer for hospitalized injuries
than for non-hospitalized injuries with work loss. Using this ratio, Finkelstein et al. (2006) decomposed
work-loss durations into separate estimates for admitted and non-admitted injuries.
To place a monetary value on temporary wage work loss, the estimated days of work lost were
multiplied times average earnings per day of work, given the victim’s age and sex, from the Current
Population Survey, as described above in the section on fatal injuries.
Household workdays lost were estimated as 90 percent of wage workdays lost, based on findings froman unpublished nationally representative survey on household work losses following injury (S. Marquis,
the Rand Corporation, personal communication, 1992). This ratio and the value of household work usedin Haddix et al. (2003), were used to impute a value to household work lost. Haddix et al. (2003) valued
household production lost using replacement cost. They started with national survey data on the
average amount and nature of housework that people do by age group and sex, for example the hours
that a woman 30–34 years old spends on cooking and on cleaning. They valued the cost of replacingthese hours using BLS data on average wage rate by occupation (e.g., for cooks and maids).
Long-term work losses. Finkelstein et al. (2006) considered permanent total disability and permanent
partial disability separately. For permanent total disability, the present value of age-and-sex-specificlifetime earnings and household production from the fatality analysis were multiplied times theprobability of permanent total disability for each type of injury. For permanent partial disability, theearnings estimate times the probability of permanent partial disability was multiplied times anadditional factor identifying the extent of disability resulting from that type of injury. The total andpartial disability costs were then summed to compute the net work loss associated with permanent
disability.
The probabilities of permanent total and partial disability by diagnosis and admission status came from
Miller, Pindus, et al. (1995) and were based on pooled multi-State workers’ compensation data from the
1979–88 Detailed Claims Information (DCI) database of the National Council on Compensation Insurance(NCCI). The disability percentage (i.e., the average extent of disability) by diagnosis came from Lawrence
et al. (2000) and was based on 1992–96 DCI data. DCI records the disability status for each sampledcase. Following Rice et al. (1989), Finkelstein et al. (2006) assumed that these probabilities do not vary
according to whether the injury occurred on the job and that these probabilities have not changed
significantly over time. This method also assumes that the probability that an injury (e.g., a skull
fracture) will cause someone never to do wage or household work again is the same for children, adults,
and the elderly (though the years of work lost obviously will vary with the age of onset) and that people
will experience the same percentage reduction in household work ability that they experience in wage
work ability.
To verify that the DCI data produce reasonable estimates, Finkelstein et al. (2006) conducted a literature
review to compare their estimates to those from other sources. Because of the paucity of data on thissubject, they identified only a few sources of published disability estimates, and these were generally
dated and limited to specific populations. Based on the limited information available, the DCI datasuggested similar probabilities of permanent disability to the other studies of long-term work loss.
Although dated and restricted to occupational injury, the DCI data have several advantages thatoutweigh their disadvantages. As a result of their large sample, the DCI data can be used to computeprobabilities for a far wider range of specific diagnoses than all the disability studies in the literature263

combined. Despite its restriction to occupational injury, the DCI sample also is more representative of
the mix of injuries admitted to hospitals than the few studies in the literature, notably those which are
restricted to patients triaged to trauma centers. The DCI data also are virtually the only source of
information about permanent disability due to injuries not admitted to the hospital. The sample
includes 318,885 medically treated, non-admitted patients with valid lost-work claims in workers’
compensation. Averaged across all injuries, the estimated percentage of lifetime productivity potential
lost due to permanent injury-related disability was 0.26 percent per injury.
For hospital-admitted cases of traumatic brain injury (TBI), we computed modified disability
probabilities using a logistic regression model developed by Selassie et al. (2008). The model tookaccount of the severity of TBI (as per the Barell matrix, which distinguishes three types of TBI), thepresence of comorbid conditions, whether the patient was transferred from the acute hospital toanother medical facility, and the patient’s age and sex. This new disability probability was thendecomposed into separate probabilities of total and partial disability according to the total/partial
ratio of the old disability probabilities. In cases where the TBI diagnosis was a secondary diagnosis,
the new probability was kept only if it exceeded the old probability based on the non-TBI primary
injury diagnosis.
Calculating total work loss costs. The work loss costs were computed as described for all non-fatal acuteinjury cases in the 2008 NIS and the 2007 NEDS. Short- and long-term costs were summed to computetotal work loss costs.
Limitations of Methods for Medical and Work Loss Estimates
These cost estimates are subject to several limitations. First, the estimates focus exclusively on medical
costs and work loss costs. They do not account for non-health costs (e.g., criminal justice, educational
impacts, property damage etc.), pain and suffering, quality-of-life loss, or injury costs borne by family
and caretakers. Also excluded are costs due to psychological treatment, e.g., for PTSD.
Second, a major limitation was the requirement to use data from a multitude of sources. Although thesewere the best available data at the time of the analysis, some sources are old, others are based on nonrepresentative samples, and all are subject to reporting and measurement error. These factors may haveincorporated significant bias into the cost estimates. The costing approach was designed to minimize thepotential bias. More current and nationally representative data would have been preferable but were
not available.
Third, combining factors from multiple data sets (sometimes with only a published mean estimatesavailable) and unavoidable assumptions about data sets being representative make it impossible togenerate standard errors around the cost estimates.
The methods for estimating work loss costs had many additional limitations. Because women, theelderly, and children have lower average earnings, the human capital approach applied undervalued
injuries to these groups. The approach also placed lower values on the work of full-time homemakers
than the work of people participating in the labor market, which further depressed the value placed on
women’s losses relative to men’s losses. It also undervalued disability among those of retirement age,and did not value temporary disability among children, as they had not yet entered the labor force.
Discounting future work losses to present value meant that the loss of a lifetime of work by a 2-year-old
was considered equivalent to loss of a lifetime of work by a 43-year-old. Although the child loses many
more years of work, those years are far in the future and heavily discounted. The work loss costcalculations are also based on a year 2008 life table, which essentially assumes that life expectancy
264

would have remained constant over each person’s expected lifespan absent injury. Moreover, victims ofserious and fatal injury may tend to be risk-takers (for example, thrill-seekers, heavy drinkers, or drug
abusers) whose life expectancy may be shorter than for the average population, which would further
bias the results. And, as noted above, some of the estimates are computed using fairly dated data thatare based on a working population. Additionally, the estimates exclude the ability to work lost by people
other than the injured person. These losses may include the time family, friends, and professionals
spend caring for the injured, time spent investigating the injury, and worker retraining. All theselimitations suggest that the costs and especially the available standard error information should beinterpreted with caution.

265

Supplement to Appendix E
Short-Term Follow-Up Cost Factors
A. Multipliers for Short-Term Follow-Up Costs for Hospital-Admitted Patients
The 16 diagnosis groups and associated multipliers that were used for estimating short-term follow up
costs for admitted patients were as follows:
Group
No.

ICD-9-CM Diagnosis Codes

Average ratio of all
costs in the first 18
months after injury tototal inpatient costs

1802, 830

1.02

2800, 801,803, 804, 850–854

1.38

3

806, 952

2.12

4805, 807–809, 839

1.10

5

810–819, 831–834

1.26

6

820, 835

1.35

7

821–829, 836–838

1.43

8

840–848

1.67

9

860–8691.12

10

870–904

1.12

11

910–9291.24

12

930–939, 950–958, 990–995

1.97

13

940–949

1.13

14

959

1.16

15

960–989

1.02

16

Other

1.03

All

1.26

266

B. Multipliers for Short-Term Follow-Up Costs for ED-Treated Patients
The 51 diagnosis groups and associated multipliers that were used for estimating short-term follow-up
costs for injuries treated in emergency departments and released were as follows:
Group ICD-9-CM
No. Diagnosis Codes

Average ratio of all
costs in the first 18
months after injury
to total ED visit
costsGroup ICD-9-CM
No.
Diagnosis Codes

Average ratio of all
costs in the first 18
months after injury
to total ED visit
costs

1

802, 830

2.47

27

851–8541.38

2800, 801, 803, 804

1.19

28

860–8691.04

3

805–8091.40

29

870–8741.15

4

810–8113.40

30

875–879

1.09

5

812

3.95

31

880–881

1.82

6

813

1.43

32

882

1.28

7

814

2.83

33

883

1.28

8

815–8171.75

34

884–8871.45

9818–8191.77

35

890–891, 894–897

1.35

10

820–8222.01

36

892–893

1.18

11

823

2.31

37

900–904

2.73

12

824

2.19

38

910–919

1.29

13

825

1.77

39

920

1.02

14

826

1.69

40

921

1.33

15

827–829

1.38

41

922

1.32

16

831

2.44

42

923

1.28

17

832–833

3.96

43

924

1.49

18

834

1.36

44

925–9291.53

19

835–8391.27

45

930–934

1.11

20

840

5.44

46

935–9391.74

21

841–8421.22

47940–949

1.93

22

843–8442.25

48

950–958, 990–995

1.11

23

845

1.34

49

959

2.00

24

846–8471.83

50960–988

1.11

25

848

1.62

51

989

1.12

26

850

1.16

All

1.78

267

Appendix F
Unit Costs and Standard Errors at Different Discount Rates

Table F-1. 2008 crash costs per fatal victim at different discount rates (2010 dollars)
Cost component
Medical3%11,3170%
11,317

Wage loss
Household production

933,262
289,910

1,647,638
544,672268

Discount Rate2%
4%
11,317
11,317
1,107,209
348,275

799,270
246,559

7%
11,317

10%
11,317543,031
167,221403,145
125,389

Table F-2. 2007–2008 HCUP-based medical unit costs at different discount rates,
AIS-90 (2010 dollars)
Body part

Skull

Fracture/
Disclocation

Yes

Brain/
intracarnial

No

Ear

No

Eye & adnexa

No

No
Nouse/mouth/
face/scalp/
neck
Yes

Neck/internalorgans/bloodvessels

Neck-spinal
cord

No

No

No
Shoulder/clavicule/scapula/upper arm
Yes

No
Elbow
Yes

Forearm

Yes

No
Wrist/hand/finger/thumb
Yes

Mais
-90

Discount rate 3%

Discount rate 0%

Discount rate 2%

Discount rate 4%

Discount rate 7%

Discount rate 10%

Mean

Mean

Mean

Mean

Mean

Mean

Std error

Std error

Std error

Std error

Std error

Std error

1

18,7573,320

18,7573,320

18,7573,320

18,7573,320

18,7573,320

18,7572

13,782476

13,782476

13,782476

13,782476

13,782476

13,782

3,320
476

3

25,449981

25,449981

25,449981

25,449981

25,449981

25,449981

4165,079

5,008

173,368

5,267

167,283

5,077

163,266

4,951

159,633

4,838

157,601

4,774

5

518,766

25,194545,406

26,501

525,848

25,542512,939

24,908501,265

24,335494,735

24,015

1

4,026

32

4,026

32

4,026

32

4,026

32

4,026

32

4,026

32

2

9,438

77

9,438

77

9,438

77

9,438

77

9,438

77

9,438

77

3

37,7691,536

37,7691,536

37,7691,536

37,7691,536

37,7691,536

37,7691,536

4

112,479

3,842

118,154

4,041

113,9883,895

111,237

3,799

108,750

3,712

107,359

3,663

5

301,715

18,793

317,157

19,765

305,820

19,052

298,337

18,581

291,573

18,154

287,787

17,9162

21,204

2,189

21,204

2,189

21,204

2,189

21,204

2,189

21,204

2,189

21,204

2,189

1

4,050

127

4,050

127

4,050

127

4,050

127

4,050

127

4,050

127

2

11,952916

11,952916

11,952916

11,952916

11,952916

11,952916

312,954

1,219

12,954

1,219

12,954

1,219

12,954

1,219

12,954

1,219

12,9541,219

424,967

9,463

24,967

9,463

24,967

9,463

24,967

9,463

24,967

9,463

24,967

9,463

1

3,304

21

3,304

21

3,304

21

3,304

21

3,304

21

3,304

21

2

5,604

99

5,604

99

5,604

99

5,604

99

5,604

99

5,604

99
2,165

3

20,973

2,165

20,973

2,165

20,973

2,165

20,973

2,165

20,973

2,165

20,973

4

25,271

9,578

25,271

9,578

25,271

9,578

25,271

9,578

25,271

9,578

25,2719,578

5

903,411

93,247 1,150,108

118,710

971,949

100,321845,288

87,248716,625

73,968633,098

65,346

1

6,833

172

6,833

172

6,833

172

6,833

172

6,833

172

6,833

172

2

27,763

1,934

27,763

1,934

27,763

1,934

27,763

1,934

27,763

1,934

27,763

1,934

3

101,472

12,795

101,472

12,795

101,472

12,795

101,472

12,795

101,472

12,795

101,472

12,795

4

56,24910,50256,273

10,50256,255

10,502

56,244

10,502

56,233

10,50256,227

10,502

5

612,447

68,056

744,500

85,063648,282

72,679582,591

64,222518,272

56,119477,808

51,252

2

16,388

1,368

16,388

1,368

16,388

1,368

16,388

1,368

16,388

1,368

16,3881,368

357,602

16,731

57,602

16,731

57,602

16,731

57,602

16,731

57,602

16,731

57,602

16,731

4

41,173

5,898

41,173

5,898

41,173

5,898

41,173

5,898

41,173

5,898

41,1735,898

564,824

5,755

64,824

5,755

64,824

5,755

64,824

5,755

64,824

5,755

64,8245,755

375,347

4,359

75,347

4,198

75,347

4,314

75,347

4,398

75,347

4,495

75,3474,573

4506,048

11,827643,751

11,796544,307

11,817473,602

11,835

401,763

11,857355,104

11,8735

1,010,432

71,947 1,088,512

62,327944,21755,120797,641

47,580702,485

42,633

1

1,881

17

1,881

17

1,881

17

1,881

17

1,881

17

1,881

17

2

3,240

85

3,240

85

3,240

85

3,240

85

3,240

85

3,240

85

3

35,2896,512

35,2896,512

35,2896,512

35,2896,512

35,2896,512

35,2896,512

429,132

2,155

29,132

2,155

29,132

2,155

29,132

2,155

29,132

2,155

29,132

2,155

58,461 1,291,475

2

7,326

135

7,326

135

7,326

135

7,326

135

7,326

135

7,326

135

3

37,863

1,651

37,863

1,651

37,863

1,651

37,863

1,651

37,863

1,651

37,8631,651

461,8106,368

61,8106,368

61,8106,368

61,8106,368

61,8106,368

61,8106,368

5167,668

26,157

167,668

26,157

167,668

26,157

167,668

26,157

167,668

26,157

167,668

26,157

1

2,549

38

2,549

38

2,549

38

2,549

38

2,549

38

2,549

38

2

5,574

266

5,574

266

5,574

266

5,574

266

5,574

266

5,574

266

3

13,6281,434

13,6281,434

13,6281,434

13,6281,434

13,6281,434

13,6281,434

463,4999,906

63,4999,906

63,4999,906

63,4999,906

63,4999,906

63,499

9,906

2

7,641

139

7,641

139

7,641

139

7,641

139

7,641

139

7,641

139

3

41,842

1,846

41,842

1,846

41,842

1,846

41,842

1,846

41,842

1,846

41,842

1,846

469,338

8,079

69,338

8,079

69,338

8,079

69,338

8,079

69,338

8,079

69,3388,079

5257,647

43,493

257,647

43,493

257,647

43,493

257,647

43,493

257,647

43,493

257,647

43,493

2

7,941

350

7,941

350

7,941

350

7,941

350

7,941

350

7,941

350

3

28,4283,312

28,4283,312

28,4283,312

28,4283,312

28,4283,312

28,428

3,312

1

1,960

22

1,960

22

1,960

22

1,960

22

1,960

22

1,960

22

2

4,140

171

4,140

171

4,140

171

4,140

171

4,140

171

4,140

171

3

13,3982,259

13,3982,259

13,3982,259

13,3982,259

13,3982,259

13,3982,259

414,0862,890

14,0862,890

14,0862,890

14,0862,890

14,0862,890

14,086

2,890

1

2,964

76

2,964

76

2,964

76

2,964

76

2,964

76

2,964

76

2

4,130

126

4,130

126

4,130

126

4,130

126

4,130

126

4,130

126

3

37,741

3,981

37,741

3,981

37,741

3,981

37,741

3,981

37,741

3,981

37,7413,981

443,136

12,547

43,136

12,547

43,136

12,547

43,136

12,547

43,136

12,547

43,136

12,5475

150,501

43,775150,501

43,775150,501

43,775150,501

43,775150,501

43,775150,501

43,775269

Table F-2 (continued)
Body partUpper
ext/mult/unsp

Fracture/
Disclocation

No

Yes
Chest/abdomNo

No

Ribs/sternum
Yes

Back
(including
vertevrae)

No
Yes

Trunk -spinal
cord

No

Trunk, superf

No

No
Trunk,
multiple/unsp
ecifiedYes

Thoracicorgs/bloodvessels

Liver

No

No

Mais
-90

Discount rate 3%

Discount rate 0%

Discount rate 2%

Discount rate 4%

Discount rate 7%

Discount rate 10%

Mean

Mean

Mean

Mean

Mean

Mean

Std error

Std error

Std error

Std error

Std error

Std error

1

2,517

56

2,517

56

2,517

56

2,517

56

2,517

56

2,517

56

2

14,6281,354

14,6281,354

14,6281,354

14,6281,354

14,6281,354

14,6281,354

345,1806,163

45,1806,163

45,1806,163

45,1806,163

45,1806,163

45,1806,163

454,161

5,426

54,161

5,426

54,161

5,426

54,161

5,426

54,161

5,426

54,1615,426

568,0586,818

68,0586,818

68,0586,818

68,0586,818

68,0586,818

68,058

6,818

2

2,179

402

2,179

402

2,179

402

2,179

402

2,179

402

2,179

402

3

5,372

538

5,372

538

5,372

538

5,372

538

5,372

538

5,372

538

2

10,879746

10,879746

10,879746

10,879746

10,879746

10,879

746

1

1,991

6

1,991

6

1,991

6

1,991

6

1,991

6

1,991

6

2

4,386

92

4,386

92

4,386

92

4,386

92

4,386

92

4,386

92

3

14,2303,173

14,2303,173

14,2303,173

14,2303,173

14,2303,173

14,2303,173

440,937

13,77340,937

13,77340,937

13,77340,937

13,77340,937

13,77340,937

13,773

1

6,268

677

6,268

677

6,268

677

6,268

677

6,268

677

6,268

677

2

17,206335

17,206335

17,206335

17,206335

17,206335

17,206335

349,5693,512

49,5693,512

49,5693,512

49,5693,512

49,5693,512

49,5693,512

465,7705,066

65,7705,066

65,7705,066

65,7705,066

65,7705,066

65,7705,066

5104,534

14,416

104,534

14,416

104,534

14,416

104,534

14,416

104,534

14,416

104,534

14,416

2

6,631

924

6,631

924

6,631

924

6,631

924

6,631

924

6,631

924

3

90,395

12,59090,395

12,59090,395

12,59090,395

12,59090,395

12,59090,395

12,5902

15,679445

15,679445

15,679445

15,679445

15,679445

15,679445

374,9794,472

74,9794,472

74,9794,472

74,9794,472

74,9794,472

74,9794,472

240,6493,178

40,6493,178

40,6493,178

40,6493,178

40,6493,178

40,6493,178

3

214,216

7,144

214,216

7,144

214,216

7,144

214,216

7,144

214,216

7,144

214,216

7,144

4

353,452

27,636353,452

27,636353,452

27,636353,452

27,636353,452

27,636353,452

27,636

5484,950

16,172570,686

16,709507,088

16,282

467,227

16,098431,490

15,991

410,841

15,955230,935

7,985

30,935

7,985

30,935

7,985

30,935

7,985

30,935

7,985

30,935

7,985

1

2,656

17

2,656

17

2,656

17

2,656

17

2,656

17

2,656

17

2

5,872

342

5,872

342

5,872

342

5,872

342

5,872

342

5,872

342

3

19,576566

19,576566

19,576566

19,576566

19,576566

19,576566

447,6072,640

47,6072,640

47,6072,640

47,6072,640

47,6072,640

47,6072,640

5243,52058,876

264,328

79,519249,30164,601

238,61754,031

227,765

43,351

220,720

36,472

1

5,879

196

5,879

196

5,879

196

5,879

196

5,879

196

5,879

196

2

9,484

215

9,484

215

9,484

215

9,484

215

9,484

215

9,484

215

3

18,707640

18,707640

18,707640

18,707640

18,707640

18,707640

425,878

1,428

25,878

1,428

25,878

1,428

25,878

1,428

25,878

1,428

25,8781,428

5520,954

27,491

606,830

27,878543,128

27,544503,20227,472

467,406

27,498446,724

27,553

3

29,481

3,038

29,481

3,038

29,481

3,038

29,481

3,038

29,481

3,038

29,4813,038

4

54,0798,456

54,0798,456

54,0798,456

54,0798,456

54,0798,456

54,0798,456

5138,641

13,290138,641

13,290138,641

13,290138,641

13,290138,641

13,290138,641

13,290

1

5,193

74

5,193

74

5,193

74

5,193

74

5,193

74

5,193

74

2

20,571

732

20,571

732

20,571

732

20,571

732

20,571

732

20,571732

341,506

2,902

41,506

2,902

41,506

2,902

41,506

2,902

41,506

2,902

41,5062,902

457,956

3,811

57,956

3,811

57,956

3,811

57,956

3,811

57,956

3,811

57,9563,811

572,0995,798

72,0995,798

72,0995,798

72,0995,798

72,0995,798

72,0995,798

SpleenNo

444,372

5,558

44,372

5,558

44,372

5,558

44,372

5,558

44,372

5,558

44,3725,558

329,097

9,433

29,097

9,433

29,097

9,433

29,097

9,433

29,097

9,433

29,097

9,433

KidneyNo

485,94336,62785,94336,62785,94336,62785,94336,62785,94336,62785,94336,627

5

152,037

17,174

152,037

17,174

152,037

17,174

152,037

17,174

152,037

17,174

152,037

17,174

3

28,627

7,106

28,627

7,106

28,627

7,106

28,627

7,106

28,627

7,106

28,6277,106

482,902

17,86682,902

17,86682,902

17,86682,902

17,86682,902

17,86682,902

17,866

5

145,075

29,071145,075

29,071145,075

29,071145,075

29,071145,075

29,071145,075

29,071

2

15,684312

15,684312

15,684312

15,684312

15,684312

15,684312

382,518

14,77582,518

14,77582,518

14,77582,518

14,77582,518

14,77582,518

14,775

4

18,677

3,344

18,677

3,344

18,677

3,344

18,677

3,344

18,677

3,344

18,677

3,344

Gastrointestinal

Genitourinary

No

No

270

Table F-2 (continued)
Body part

Fracture/
DisclocationNo
Hip/thighYes

L. extremity
superfic

No

No
Knee
Yes

No
Lower leg
Yes

No
Ankle/foot/
toes
Yes

Burns

Min. extern.

No

No

Mais90Discount rate 3%

Discount rate 0%

Discount rate 2%

Discount rate 4%

Discount rate 7%

Discount rate 10%

Mean

Mean

Mean

Mean

Mean

Mean

Std error

Std error

Std error

Std error

Std error

Std error

1

2,117

29

2,117

29

2,117

29

2,117

29

2,117

29

2,117

29

2

9,312

430

9,312

430

9,312

430

9,312

430

9,312

430

9,312

430

3

43,514

6,722

43,514

6,722

43,514

6,722

43,514

6,722

43,514

6,722

43,5146,722

437,757

7,474

37,757

7,474

37,757

7,474

37,757

7,474

37,757

7,474

37,7577,474

2

27,7891,096

27,7891,096

27,7891,096

27,7891,096

27,7891,096

27,7891,096

352,516913

52,516913

52,516913

52,516913

52,516913

52,516913

4104,143

5,271

104,143

5,271

104,143

5,271

104,143

5,271

104,143

5,271

104,1435,271

5189,868

22,419189,868

22,419189,868

22,419189,868

22,419189,868

22,419189,868

22,419

1

3,483

82

3,483

82

3,483

82

3,483

82

3,483

82

3,483

82

2

2,478

81

2,478

81

2,478

81

2,478

81

2,478

81

2,478

81

1

1,841

22

1,841

22

1,841

22

1,841

22

1,841

22

1,841

22

2

7,586

597

7,586

597

7,586

597

7,586

597

7,586

597

7,586

597

3

25,2493,611

25,2493,611

25,2493,611

25,2493,611

25,2493,611

25,2493,611

4

123,335

25,780123,335

25,780123,335

25,780123,335

25,780123,335

25,780123,335

25,7802

16,106610

16,106610

16,106610

16,106610

16,106610

16,106610

343,3342,515

43,3342,515

43,3342,515

43,3342,515

43,3342,515

43,3342,515

455,49619,15855,49619,15855,49619,15855,49619,15855,49619,15855,49619,158

1

2,024

30

2,024

30

2,024

30

2,024

30

2,024

30

2,024

30

2

4,798

455

4,798

455

4,798

455

4,798

455

4,798

455

4,798

455
11,855

3

115,358

11,855

115,358

11,855

115,358

11,855

115,358

11,855

115,358

11,855

115,3582

23,320296

23,320296

23,320296

23,320296

23,320296

23,320296

372,0102,019

72,0102,019

72,0102,019

72,0102,019

72,0102,019

72,010

2,019

1

2,084

59

2,084

59

2,084

59

2,084

59

2,084

59

2,084

59

2

7,944

929

7,944

929

7,944

929

7,944

929

7,944

929

7,944

929
11,193

3

55,342

11,19355,342

11,19355,342

11,19355,342

11,19355,342

11,19355,342

1

3,016

126

3,016

126

3,016

126

3,016

126

3,016

126

3,016

126

2

10,487248

10,487248

10,487248

10,487248

10,487248

10,487248

366,5995,052

66,5995,052

66,5995,052

66,5995,052

66,5995,052

66,599

5,052

1

1,502

252

1,502

252

1,502

252

1,502

252

1,502

252

1,502

252

2

11,981

1,924

11,981

1,924

11,981

1,924

11,981

1,924

11,981

1,924

11,9811,924

3104,193

16,263

104,193

16,263

104,193

16,263

104,193

16,263

104,193

16,263

104,193

16,263

4

117,787

12,179117,787

12,179117,787

12,179117,787

12,179117,787

12,179117,787

12,1795

256,351

39,483

256,351

39,483

256,351

39,483

256,351

39,483

256,351

39,483

256,351

39,483

1

3,906

35

3,906

35

3,906

35

3,906

35

3,906

35

3,906

35

271

Table F-3. 2007–2008 HCUP-based unit earnings loss at different discount rates,
AIS-90 (2010 dollars)
Body part

Skull

Brain/
intracarnial

Ear

Eye & adnexa

Fracture/
Disclocation

Yes

No

No

No

No
Nouse/mouth/
face/scalp/
neck
Yes

Neck/internalorgans/bloodvesselsNo

Neck-spinal
cord

No

No
Shoulder/clavicule/scapula/upper arm
Yes

No
Elbow
Yes

Forearm

Yes

No
Wrist/hand/finger/thumb
Yes

Mais90Discount rate 3%

Discount rate 0%

Discount rate 2%

Discount rate 4%

Discount rate 7%

Discount rate 10%

Mean

Mean

Std error

Mean

Mean

Mean

Mean

1

101,254

40,197208,384

100,058

126,152

53,43882,82630,83350,07915,712

34,1552

20,1401,156

36,184

2,529

23,881

1,452

17,343948

12,244

603

9,632

446

3

53,2052,271

90,326

4,413

62,111

2,741

46,4101,931

33,587

1,332

26,685

1,028

4

199,239

4,084

356,876

8,087

236,984

4,970

170,526

3,447

116,720

2,341

88,1351,796

5343,941

12,829608,546

26,478408,531

15,865294,201

10,656

199,480

6,993

148,394

5,295

1

3,316

80

5,446

152

3,820

96

2,935

68

2,231

47

1,864

36

2

15,256297

25,635534

17,782352

13,312

255

9,601

178

7,580

137

3

97,217

3,213

162,625

5,932

113,340

3,846

84,6732,740

60,2601,873

46,5881,419

4

169,317

4,062

291,603

7,744

199,249

4,911

146,174

3,434

101,651

2,307

77,1331,732

5357,818

13,114652,040

26,604

428,754

16,145

303,647

10,927201,702

7,169

147,4345,364

2

131,454

10,739180,317

15,449144,847

11,913

120,190

9,815

95,456

7,934

79,445

6,760

1

4,472

241

8,261

470

5,362

293

3,806

204

2,588

139

1,963

106

2

25,183

3,660

47,344

6,721

30,3584,420

21,2793,111

14,0792,142

10,338

1,644

Std error

Std error

Std error

Std error

Std error
9,662

3

43,568

9,631

67,006

17,68949,628

11,60338,6898,123

28,6895,325

22,7313,916

4205,254

46,577

260,545

67,568221,118

52,519191,379

41,475

158,751

30,063

135,467

22,738

1

2,530

25

4,444

49

2,976

30

2,199

21

1,598

15

1,293

12

2

8,724

284

15,344577

10,290

341

7,544

247

5,363

193

4,226

168

3

56,018

4,576

92,185

7,898

64,927

5,340

49,085

4,008

35,575

2,957

27,9832,381

4135,92230,844249,108

64,601

163,248

38,774

115,091

24,942

76,173

14,42555,8069,367

536,761

3,003

45,767

3,921

39,241

3,227

34,6582,830

29,9192,487

26,675

2,270

1

4,913

114

8,349

198

5,739

132

4,285

102

3,109

79

2,490

68

2

32,397

1,100

56,387

1,921

38,1271,260

28,052994

19,978820

15,758728

3126,536

8,092

206,428

14,104

146,575

9,521

110,769

7,008

79,636

4,975

61,9713,875

493,8278,580

149,040

15,599107,487

10,164

83,181

7,438

62,442

5,486

50,8414,548

5607,915

89,625 1,074,785

143,201

722,554

103,384

519,278

78,591

349,465

56,121

257,208

42,881

2

59,08934,244

104,536

72,57969,86343,22651,001

27,57036,29915,681

28,9339,933

3194,184

4,203

329,865

5,160

228,272

4,507

167,408

3,921

114,977

3,206

85,8772,668

444,558

7,956

57,006

7,165

47,707

7,823

42,053

7,997

36,983

7,813

33,9647,456

598,833

21,231

127,661

26,267

106,736

22,72392,15019,89377,24016,646

67,26014,277

3

187,945

18,755315,924

33,881

220,364

22,431

162,335

15,949111,773

10,727

83,4478,017

4232,096

11,144

382,645

21,907

269,043

13,547203,081

9,411

145,883

6,463

113,404

5,074

523,2082,316

25,1752,700

23,827

2,425

22,621

2,222

21,032

2,018

19,666

1,889

1

2,540

19

3,264

25

2,717

20

2,403

18

2,140

17

1,997

16

2

7,992

115

10,808

148

8,684

120

7,457

112

6,427

108

5,864

107

3

23,595

2,891

41,248

8,267

27,533

4,045

20,5972,071

15,009870

12,092755

456,084

4,487

75,026

6,319

60,888

4,948

52,271

4,128

44,617

3,446

40,1573,092

2

14,629150

21,977

235

16,408166

13,264

139

10,669

122

9,263

114

3

58,246

1,537

88,963

2,612

65,656

1,753

52,554

1,394

41,7201,174

35,8411,074

4

91,1107,331

136,972

13,194102,477

8,688

82,234

6,337

64,886

4,617

55,1363,815

5109,793

37,567

206,727

58,981

132,457

43,24892,90832,97762,44023,73747,151

18,520

1

1,517

25

2,232

40

1,689

29

1,386

23

1,141

19

1,012

17

2

4,927

618

7,429

982

5,540

708

4,454

549

3,545

415

3,049

344

3

21,785

1,804

33,374

2,448

24,517

1,931

19,7291,719

15,9271,580

13,9291,507

446,92916,057

66,58818,99851,988

16,97442,855

15,211

34,452

13,09729,366

11,5352

21,513232

33,408399

24,393266

19,302

209

15,102

174

12,832159

377,578

1,426

115,535

2,431

86,892

1,625

70,343

1,298

56,328

1,115

48,5531,037

4109,157

6,971

167,999

13,625

123,643

8,413

97,908

5,979

76,1194,449

64,011

3,829

5

251,124

63,961

430,935

102,419

295,453

73,962

216,792

55,895151,047

39,417

115,578

29,745

2

25,073461

30,290637

26,454495

23,939442

21,530426

20,008427

342,8952,739

46,032

3,733

43,816

2,981

42,0692,569

40,054

2,341

38,535

2,305

1

935

17

1,267

31

1,014

20

874

15

760

12

700

11

2

12,331

767

21,645

1,564

14,532

943

10,674

640

7,611

423

6,012

321

3

29,4993,601

51,227

6,727

34,7104,340

25,5383,049

18,143

2,053

14,2581,561

4

105,734

41,889234,680

80,480134,272

51,30785,294

34,66450,68021,08434,81013,977

1

6,795

131

11,251

236

7,860

153

5,985

117

4,472

93

3,671

82

2

12,601

188

19,949327

14,389217

11,228

167

8,618

136

7,213

122

3

60,851

4,636

90,084

6,936

68,0485,103

55,2634,319

44,441

3,793

38,4353,520

4160,735

15,688260,581

29,705

185,635

18,759141,273

13,511

103,393

9,841

82,4438,080

5166,184

16,220342,392

39,031

207,634

20,982

135,284

12,93879,9107,606

52,818

5,177

272

Table F-3 (continued)
Body part

Upper ext/
mult/unsp

Fracture/
Disclocation

No

Yes
Chest/abdomNo

No

Ribs/sternum
Yes

Back (including
vertevrae)

Trunk -spinal
cord
Trunk, superf

No
Yes

No

No

No
Trunk,
multiple/
unspecified
Yes

Thoracicorgs/bloodvessels

Liver

SpleenKidneyGastrointestinal

Genitourinary

No

No

No
No

No

No

Mais90Discount rate 3%

Discount rate 0%

Discount rate 2%

Discount rate 4%

Discount rate 7%

Mean

Mean

Mean

Mean

Mean

Std error

Std error

Std error

Std error

Discount rate 10%

Std error

Mean

Std error

1

1,059

41

1,502

76

1,165

49

979

36

828

28

749

25

2

19,704

1,527

33,346

2,967

23,0201,844

17,1591,304

12,339

944

9,759

787

3

93,044

11,783

153,015

22,683

107,861

14,32081,5339,909

59,325

6,617

47,1535,050

4183,943

6,215

327,465

6,896

219,3216,524

156,556

5,888

104,172

4,981

75,9874,282

5234,397

7,919

312,379

6,579

255,892

7,612

216,2298,132

176,015

8,416

149,685

8,435

2

8,751

254

14,782426

10,198

283

7,652

239

5,608

224

4,543

218

3

10,859367

18,863397

12,813

381

9,359

352

6,536

313

5,057

285

2

29,6008,922

48,312

15,43034,166

10,53326,093

7,679

19,4705,345

15,963

4,154

1

3,214

6

3,382

6

3,255

6

3,182

6

3,120

6

3,086

6

2

9,062

196

11,107

274

9,580

213

8,650

185

7,823

164

7,339

153

3

32,224

9,354

42,774

13,39635,016

10,44029,934

8,453

25,095

6,519

22,0995,305

451,3592,212

94,887

9,494

60,544

3,367

44,9801,818

34,5992,147

29,971

2,477

1

6,085

671

8,446

1,050

6,687

754

5,605

613

4,636

516

4,068

469

2

32,477465

47,339742

36,162527

29,591

420

23,926342

20,726304

379,052

4,203

126,446

7,380

90,445

4,929

70,2293,670

53,257

2,733

43,9582,273

464,352

4,159

94,188

7,052

71,861

4,827

58,421

3,665

46,5992,775

39,7752,313

5150,350

23,399222,725

20,035

168,864

23,090135,544

23,280105,451

21,74087,627

19,742

2

83,593

4,320

114,545

6,336

91,8594,828

76,7803,919

62,3373,122

53,4132,669

366,658

3,445

105,784

5,851

76,644

4,029

58,703

2,996

42,711

2,139

33,4571,672

235,691599

52,685991

39,909685

32,390538

25,918436

22,268387

3110,493

4,120

181,091

7,585

128,133

4,923

96,664

3,523

69,562

2,434

54,391

1,867

2

183,583

12,075329,811

28,168219,711

15,646

155,598

9,566

102,110

5,595

73,4484,009

3225,969

6,293

518,963

17,777

290,507

8,666

180,052

4,704

103,482

2,400

69,3631,673

4239,679

15,764410,01335,018281,662

20,058207,091

12,732

144,147

7,898

109,4475,975

5457,776

12,749928,117

31,793568,735

16,965

374,797

9,793

225,108

5,222

151,0243,642

293,604

15,447

138,392

13,239104,985

14,67884,54915,92966,307

16,10255,641

15,392

1

1,715

11

2,042

15

1,794

12

1,654

11

1,539

10

1,476

10

2

5,197

376

7,037

596

5,650

427

4,844

337

4,158

266

3,775

228

3

21,170628

30,146

1,089

23,343733

19,490550

16,264411

14,492340

440,5792,302

58,986

4,122

45,103

2,725

37,027

1,985

30,015

1,403

26,014

1,101
8,159

5

91,022

29,721

161,490

61,938108,038

37,40578,05923,93253,906

13,39841,333

1

5,191

117

6,702

155

5,576

126

4,882

111

4,252

98

3,876

91

2

12,338337

15,383461

13,121367

11,703313

10,377

266

9,562

240

3

23,468573

29,562812

25,000629

22,231

530

19,670450

18,110406

432,686

1,413

41,384

2,216

34,912

1,599

30,882

1,275

27,133

1,023

24,841890

5456,219

14,826913,228

33,506564,442

18,815375,046

12,132

227,850

8,161

154,4136,633

3

16,7492,351

22,895

3,470

18,274

2,633

15,556

2,127

13,222

1,684

11,9071,434

451,683

9,925

74,857

17,32957,527

11,75547,0698,503

37,903

5,778

32,6594,313

585,701

17,060135,566

31,586

98,17920,50875,884

14,47256,454

9,766

45,348

7,389

1

1,727

38

2,583

67

1,927

44

1,579

34

1,310

28

1,174

26

2

20,343671

30,397

1,348

22,733813

18,532574

15,167422

13,400356

328,8481,774

45,923

3,427

32,9082,150

25,768

1,499

20,032

1,023

17,020797

443,3393,139

70,383

6,102

49,8893,826

38,323

2,633

28,845

1,743

23,7731,317

551,5685,171

83,386

9,791

59,277

6,232

45,657

4,387

34,452

2,983

28,4242,275

475,795

13,539126,250

30,311

88,08917,363

66,342

10,772

48,385

6,128

38,7174,171

3

29,765

16,04748,44930,86434,338

19,63526,244

13,316

19,567

8,262

16,0055,694

446,9191,868

63,1193,107

51,2372,173

43,354

1,633

35,7301,186

30,926950

5108,866

9,586

178,722

18,484

126,179

11,73595,354

7,952

68,9794,931

54,1863,392

317,153

4,031

26,3496,654

19,3584,598

15,473

3,631

12,321

2,946

10,6362,591

485,257

13,959145,053

27,32899,84517,02974,03911,71052,766

7,754

41,3995,825

5

109,586

15,016

191,365

33,841

129,425

19,501

94,425

11,65066,0485,586

51,2082,643

237,04010,82060,157

20,76942,661

13,25032,736

8,956

24,647

5,451

20,4013,618

3

62,783

20,146

105,482

37,29073,282

24,20054,654

17,11039,037

11,60130,5058,803

414,555

4,670

16,773

5,929

15,203

5,020

13,9804,377

12,591

3,742

11,561

3,336

273

Table F-3 (continued)
Body part

Fracture/
DisclocationNo
Hip/thighYes

L. extremity
superfic

Discount rate 0%

Discount rate 2%

Discount rate 4%

Discount rate 7%

Mean

Mean

Mean

Mean

Std error

Std error

Std error

Std error

Discount rate 10%

Std error

Mean

Std error

1

1,997

24

2,329

32

2,078

26

1,936

24

1,819

22

1,755

22

2

15,201

795

23,477

1,643

17,178972

13,703679

10,929

522

9,493

474

3

50,297

7,622

74,582

13,11056,364

8,944

45,542

6,618

36,235

4,761

31,0293,813

461,7459,215

92,401

12,15069,26810,06055,8808,473

44,338

6,741

37,6975,557

261,1581,287

102,069

2,188

70,785

1,480

53,906

1,148

40,509903

33,507776

348,651

565

76,887

97855,439655

43,442500

33,563391

28,241339

484,372

3,544

136,151

6,324

96,977

4,174

74,6683,084

56,131

2,271

46,0341,861

577,436

5,883

129,697

11,627

90,2007,117

67,6105,041

48,936

3,758

38,917

3,249

1

1,813

27

2,668

45

2,017

31

1,658

25

1,371

22

1,220

20

No

2

4,088

81

4,994

136

4,307

92

3,920

74

3,604

63

3,434

59

1

643

11

708

16

660

12

631

10

605

9

591

8

2

9,919

396

13,158517

10,726

421

9,288

379

8,051

349

7,356

332

Knee
Yes

No
Lower leg
Yes

No
Ankle/
foot/toes
Yes

Min. extern.

Discount rate 3%
Mean

No

No

Burns

Mais90NoNo

328,2891,986

38,0793,006

30,7942,250

26,2891,774

22,234

1,340

19,8541,085

4207,493

17,278

270,507

21,158

223,799

18,261

194,347

16,482

167,327

14,787

151,152

13,6872

37,823

1,039

56,142

1,772

42,361

1,201

34,269925

27,293735

23,365647

371,016

2,124

110,406

3,637

80,6282,450

63,5691,897

49,236

1,529

41,3701,360

4145,640

33,293

232,683

51,076167,263

37,934

128,762

29,527

95,883

21,71077,58916,990

1

842

11

1,004

18

880

12

813

10

759

9

730

8

2

8,430

489

12,065

812

9,325

558

7,733

439

6,376

353

5,615

310

3

155,614

10,391

210,369

14,920169,370

11,265

144,765

9,843

123,210

9,095

110,8258,812

242,397299

61,553480

47,096334

38,754276

31,702242

27,780227

384,295

1,258

124,1462,140

94,044

1,438

76,744

1,138

62,178953

54,132

871

1

2,018

39

3,101

69

2,276

45

1,823

35

1,461

28

1,272

26

2

13,158728

21,6001,576

15,133880

11,678

637

8,963

513

7,550

460
4,073

3

68,161

3,704

103,849

10,92977,022

4,947

61,244

3,247

47,751

3,621

40,214

1

3,478

88

4,887

144

3,819

98

3,216

81

2,722

73

2,456

71

2

23,178303

33,536477

25,734337

21,192

280

17,343245

15,211

229
2,702

3

76,207

4,454

110,843

7,986

84,843

5,234

69,446

3,909

56,1983,053

48,754

1

3,343

116

5,676

228

3,902

141

2,919

98

2,127

67

1,710

52

2

7,995

680

13,549

1,296

9,329

821

6,977

577

5,069

399

4,057

315

3

43,967

11,276

72,41921,46350,957

13,73938,5599,404

28,191

5,943

22,5544,187

454,001

15,203

99,81032,79464,91019,29245,782

12,18530,765

6,894

23,1904,440

568,202

34,205

109,920

51,64078,522

38,62060,14630,67944,37823,45635,481

19,085

1

1,272

18

1,727

28

1,381

20

1,188

16

1,030

14

946

13

274

Table F-4. 2007–2008 HCUP-based unit household production loss at different
discount rates, AIS-90 (2010 dollars)
Body part

Skull

Brain/
intracarnial

Ear

Eye & adnexa

Fracture/
Disclocation

Yes

No

No

No

No
Nouse/
mouth/face/scalp/neck
Yes

Neck/internalorgans/bloodvessels

Neck-spinal
cord

No

No

No
Shoulder/clavicule/scapula/upper arm
Yes

No
Elbow
Yes

Forearm

Yes

No
Wrist/hand/finger/thumb
Yes

Mais90Discount rate 3%

Discount rate 0%

Discount rate 2%

Discount rate 4%

Discount rate 7%

Discount rate 10%

Mean

Mean

Std error

Mean

Mean

Mean

Mean

1

27,642

5,900

54,184

14,91633,4907,754

23,415

4,632

15,982

2,608

12,229

2

6,100

294

11,126

634

7,213

364

5,293

246

3,866

165

3,147

126

3

15,571

579

27,251

1,144

18,228699

13,608493

10,034

345

8,159

270

4

57,316

1,019

108,605

2,163

68,851

1,255

48,861855

33,702579

25,921445

595,9193,018

183,850

6,807

115,831

3,793

81,2852,487

55,027

1,624

41,582

1,226

Std error

Std error

Std error

Std error

Std error
1,729

1

947

21

1,545

44

1,081

26

849

18

675

12

586

10

2

5,317

91

9,317

173

6,235

109

4,633

78

3,377

54

2,711

41

3

34,951880

60,856

1,719

41,0301,064

30,353

747

21,695511

16,959389

458,192981

103,561

2,046

68,7391,207

50,271820

35,491545

27,482410

5104,601

3,262

204,120

7,218

127,031

4,088

88,153

2,685

58,694

1,722

43,6151,264

2

27,042

1,913

43,497

3,116

31,126

2,204

23,8601,688

17,693

1,248

14,287

1,002

1

1,172

61

2,221

122

1,403

73

1,005

52

713

37

566

29

2

7,179

614

14,253

1,753

8,730

814

6,061

497

4,099

353

3,113

303

3

16,7991,475

28,013

4,226

19,4972,019

14,728

1,134

10,744

729

8,512

631

4

62,058

10,90993,685

18,96370,41912,90055,222

9,372

40,903

6,447

32,175

4,867

1

686

6

1,200

11

799

7

605

5

463

4

393

3

2

2,793

82

5,107

163

3,308

98

2,419

71

1,757

54

1,422

45

3

19,444

1,376

34,0782,441

22,858

1,619

16,878

1,194

12,105

854

9,545

669

4

49,903

8,772

96,05819,44460,277

11,042

42,314

7,181

28,767

4,534

21,855

3,306
4,046

5

76,621

5,421

90,0416,450

80,5595,705

73,094

5,170

64,391

4,544

57,707

1

1,247

19

2,180

36

1,455

22

1,095

16

826

13

690

12

2

9,814

358

17,592583

11,553

403

8,547

328

6,297

273

5,157

243

3

42,5482,030

74,911

3,704

50,1072,406

36,8691,752

26,335

1,245

20,707976

432,882

3,069

55,534

5,755

38,123

3,661

28,9692,640

21,772

1,882

17,9731,501

5141,350

15,778

268,638

28,931

170,480

18,823

119,795

13,50680,728

9,345

60,496

7,167

2

19,966

4,609

30,546

12,21822,341

6,232

18,227

3,475

15,115

1,631

13,512876

3102,949

3,570

191,260

5,852

123,182

4,142

87,9983,121

61,0182,243

47,1071,753

4

12,053

2,633

16,111

3,320

13,041

2,845

11,286

2,443

9,790

1,997

8,942

1,695

5

27,674

4,971

40,271

7,642

30,8995,673

25,097

4,402

19,8693,233

16,7952,544

3111,1278,206

208,089

16,613

133,243

10,002

94,826

6,934

65,483

4,756

50,3773,682

474,393

2,648

130,356

5,566

87,532

3,247

64,476

2,236

45,915

1,554

35,8761,223

583,6206,174

96,535

7,707

87,534

6,571

80,037

5,853

70,9395,152

63,761

4,660

1

912

5

1,168

6

971

5

869

5

791

5

750

5

2

2,514

29

3,394

40

2,716

31

2,364

28

2,092

26

1,949

25

3

8,062

493

13,431

1,614

9,269

721

7,180

346

5,604

190

4,798

206

4

23,2902,122

31,391

1,583

25,282

2,057

21,7322,130

18,643

2,006

16,839

1,827

2

5,622

39

8,569

66

6,294

44

5,125

37

4,221

34

3,749

33

3

19,467386

30,062663

21,892438

17,669351

14,391298

12,674273

432,888

1,745

49,7862,947

36,883

1,995

29,856

1,572

24,128

1,277

20,9901,131

536,862

7,492

63,841

12,32442,941

8,655

32,401

6,608

24,3884,950

20,253

4,069

1

486

6

706

10

536

7

450

6

386

5

353

4

2

1,802

155

2,737

250

2,014

176

1,646

140

1,366

115

1,222

103

3

6,890

602

10,552

821

7,720

652

6,277

564

5,165

492

4,586

452

4

17,691

3,596

26,5795,131

19,8544,001

16,021

3,267

12,7882,596

10,972

2,204

2

8,323

51

12,920

90

9,376

57

7,542

48

6,118

43

5,370

41

3

27,513374

41,396606

30,750415

25,083349

20,572309

18,156288

438,9572,357

61,8044,700

44,263

2,823

34,977

2,043

27,587

1,546

23,6241,319

548,89911,66590,18821,70658,237

14,032

42,0709,885

29,984

6,612

23,9504,906

216,975

231

21,778319

18,227

246

15,959222

13,845208

12,560199

333,446

2,023

41,842

3,182

35,6782,275

31,613

1,847

27,7491,555

25,374

1,417

1

313

3

406

6

334

4

298

3

270

3

256

3

2

3,765

193

6,760

410

4,433

239

3,279

160

2,414

107

1,975

82

3

10,813862

19,022

1,660

12,660

1,029

9,458

745

7,026

549

5,775

454

4

30,494

6,932

79,29815,63840,126

8,875

24,0505,525

14,045

3,073

9,830

1,870

1

1,939

30

3,307

61

2,244

36

1,716

26

1,319

20

1,118

18

2

4,353

40

6,876

66

4,929

44

3,926

38

3,152

35

2,750

34

3

21,730754

33,467

1,727

24,430916

19,716664

16,010567

14,042540

443,6282,694

72,217

5,055

50,3103,125

38,6152,426

29,351

2,045

24,442

1,884

598,192

6,063

213,651

14,954

123,006

7,639

80,606

5,064

50,705

3,532

36,366

2,804

275

Table F-4 (continued)
Body part

Upper ext/
mult/unsp

Fracture/
Disclocation

No

Yes
Chest/abdomNo

No

Ribs/sternum
Yes

Back
(including
vertevrae)

No
Yes

Trunk -spinal
cord

No

Trunk, superf

No

No
Trunk,
multiple/
unspecified
Yes

Thoracicorgs/bloodvessels

Liver

No

No

Mais90Discount rate 3%

Discount rate 0%

Discount rate 2%

Discount rate 4%

Discount rate 7%

Mean

Mean

Mean

Mean

Mean

Std error

Std error

Std error

Std error

Discount rate 10%

Std error

Mean

Std error

1

346

9

467

15

373

10

326

8

290

7

271

6

2

6,499

488

11,095

718

7,560

544

5,709

444

4,268

353

3,511

296

3

25,2292,816

43,425

5,402

29,407

3,378

22,1302,415

16,503

1,728

13,5791,396

443,2409,139

82,984

15,67352,240

10,66436,652

7,999

24,961

5,904

19,0944,799

549,214

10,402

76,51814,45256,034

11,43843,8799,576

33,468

7,916

27,673

6,955

2

3,368

83

5,695

192

3,886

101

2,992

73

2,329

63

1,997

61

3

2,143

453

3,870

731

2,529

516

1,863

407

1,375

325

1,136

285
695

2

6,278

1,552

10,560

3,016

7,243

1,881

5,573

1,313

4,324

897

3,697

1

1,301

2

1,367

2

1,316

2

1,290

2

1,270

2

1,259

2

2

3,928

55

4,917

82

4,162

61

3,750

51

3,415

45

3,233

42

3

9,244

1,647

12,921

2,795

10,138

1,928

8,555

1,431

7,236

1,025

6,513

814

4

14,989338

26,185

1,381

17,345445

13,334331

10,535

382

9,189

398
78

1

1,864

77

2,651

113

2,053

80

1,719

77

1,442

78

1,288

2

13,282

122

19,332

209

14,702

139

12,210110

10,202

90

9,112

81

3

28,887

1,146

46,3472,182

32,895

1,366

25,911

992

20,472735

17,617617

424,876

1,286

36,961

2,254

27,775

1,491

22,6501,140

18,359884

15,942750

550,534

1,249

83,371

6,498

58,331

2,198

44,609679

33,4271

27,357139

216,846538

25,979923

19,059618

15,152481

11,948380

10,226326395

325,348809

44,985

1,599

29,914970

21,936696

15,671

498

12,3852

14,558129

21,879246

16,267150

13,271

115

10,874

97

9,582

90

3

48,0081,103

85,1482,217

56,565

1,349

41,651926

30,060615

23,996459

233,4862,152

66,4955,420

40,9352,808

28,053

1,711

18,4691,025

13,703733

382,144

2,451

203,6296,876

106,625

3,250

65,501

1,946

38,874

1,208

27,081903

488,5395,689

165,921

13,525106,2137,286

75,4604,603

51,7072,870

39,3472,104

599,7382,976

211,701

7,149

124,223

3,787

82,223

2,442

52,1401,621

37,6221,255

223,702

3,813

39,4274,315

27,365

4,041

20,956

3,577

15,883

2,958

13,201

2,522

1

703

3

819

4

730

3

683

3

647

3

628

3

2

2,261

108

3,060

210

2,445

129

2,123

92

1,869

67

1,733

56

3

7,785

185

11,035

333

8,536

217

7,225

161

6,190

119

5,637

98

4

13,789632

20,674

1,188

15,419756

12,551541

10,205

379

8,916

295

5

47,198

8,245

83,29819,22455,382

10,502

41,150

6,732

30,082

4,458

24,156

3,565

1

1,848

16

2,406

25

1,984

17

1,744

15

1,542

14

1,428

13

2

5,788

77

7,254

124

6,151

87

5,503

70

4,937

58

4,606

52

3

10,148134

12,933

207

10,835

150

9,610

122

8,544

101

7,925

90

4

13,145464

17,499810

14,208540

12,320408

10,704

308

9,783

257

5

97,952

1,237

205,493

3,513

121,570

1,515

81,001

1,145

51,725

1,091

37,480

1,029

3

7,981

2,111

11,099

2,916

8,747

2,332

7,383

1,926

6,194

1,520

5,501

1,257

4

15,497

1,932

24,724

3,609

17,6382,314

13,895

1,650

10,938

1,145

9,369

888

5

31,706

5,336

54,75810,85037,034

6,548

27,728

4,465

20,397

2,947

16,498

2,198

1

655

10

943

16

718

11

609

9

528

9

488

8

2

7,458

197

11,070

362

8,268

232

6,866

173

5,804

131

5,260

111

3

9,868

486

15,645912

11,168

579

8,916

419

7,209

303

6,332

245

4

16,38997826,876

1,885

18,794

1,176

14,601

835

11,319

584

9,578

456

5

18,0401,683

30,3573,401

20,8292,053

15,985

1,418

12,269961

10,338734

SpleenNo

432,562

4,743

55,216

8,876

37,795

5,666

28,653

4,068

21,4302,860

17,5662,243

316,502

8,424

29,113

18,105

19,343

10,535

14,418

6,916

10,677

4,322

8,746

3,058

KidneyNo

420,676222

32,30842

23,501164

18,506263

14,374331

12,129361

540,3385,607

72,455

11,796

47,661

6,963

34,915

4,634

25,0272,950

19,8202,124

Gastrointestinal

Genitourinary

No

No

3

7,620

2,534

11,571

3,897

8,551

2,884

6,915

2,252

5,588

1,681

4,867

1,347

4

30,194

4,555

55,667

9,246

35,9205,574

26,0093,825

18,5702,556

14,8041,929

559,513

7,100

115,849

19,087

71,981

9,626

50,484

5,351

34,5782,538

26,549

1,328

2

9,075

946

15,2292,981

10,440

1,393

8,087

627

6,360

85

5,507

170

3

18,9496,158

35,784

12,842

22,725

7,619

16,191

5,110

11,280

3,291

8,787

2,394

4

12,046

3,915

17,0596,122

13,360

4,479

10,980

3,465

8,771

2,559

7,445

2,028

276

Table F-4 (continued)
Body part

Fracture/
DisclocationNo
Hip/thighYes

L. extremity
superfic

Discount rate 0%

Discount rate 2%

Discount rate 4%

Discount rate 7%

Mean

Mean

Mean

Mean

Std error

Std error

Std error

Std error

Discount rate 10%

Std error

Mean

Std error

1

757

7

869

8

782

7

738

7

703

7

685

7

2

5,150

200

7,796

389

5,742

236

4,720

176

3,961

142

3,579

129

3

13,356

1,457

20,6082,705

15,034

1,733

12,105

1,258

9,814

916

8,610

752

4

24,505

3,336

40,355

6,039

28,132

3,962

21,8082,867

16,8542,000

14,233

1,546

2

20,133351

34,148614

23,256403

17,857315

13,808256

11,746226

318,251

164

28,655304

20,621

193

16,499145

13,311

113

11,64298

430,2201,106

50,6732,131

34,885

1,324

26,766953

20,477691

17,183

563
2,031

5

32,027

3,826

53,9897,417

37,0294,571

28,3303,309

21,627

2,447

18,129

1

574

7

826

11

631

7

533

6

459

5

420

5

No

2

1,658

23

1,944

34

1,723

25

1,610

22

1,522

20

1,476

19

Knee
Yes

No
Lower leg
Yes

No
Ankle/foot/
toes
Yes

Min. extern.

Discount rate 3%
Mean

No

No

Burns

Mais90

No

No

1

293

2

321

4

300

3

288

2

279

2

273

2

2

3,633

110

4,921

166

3,931

121

3,410

102

3,000

89

2,783

82

3

10,362771

15,184

1,080

11,490

844

9,513

713

7,938

597

7,099

525

4

59,381

2,528

82,7703,267

65,0052,683

55,082

2,420

46,926

2,242

42,4872,162

2

14,505

222

21,375387

16,122

254

13,286202

10,998

172

9,760

159

3

27,927629

44,5991,216

31,748746

25,092550

19,916427

17,196372

440,301

5,607

65,474

9,744

46,134

6,542

35,952

4,934

27,997

3,799

23,839

3,286

1

318

3

370

4

329

3

309

3

294

2

286

2

2

3,098

107

4,534

169

3,432

118

2,847

99

2,379

84

2,128

77
1,983

3

43,522

2,244

62,441

2,874

47,997

2,352

40,132

2,174

33,7702,052

30,3382

15,39975

22,830123

17,123

83

14,10870

11,71963

10,44460

327,847323

41,988566

31,113369

25,412

293

20,939247

18,576

226

1

642

10

980

18

718

12

587

9

489

7

440

7

2

4,305

248

7,325

553

4,973

307

3,820

209

2,962

148

2,529

121
1,042

3

17,881

1,918

29,225

3,997

20,494

2,332

15,9401,643

12,3991,222

10,553

1

1,103

20

1,539

24

1,202

20

1,030

19

899

19

831

18

2

9,028

83

13,263135

10,006

92

8,300

77

6,964

69

6,260

65

3

27,145

1,454

41,1762,865

30,3991,747

24,712

1,253

20,225931

17,845

790

1

1,479

43

2,603

88

1,730

53

1,297

36

974

25

810

20

2

3,568

188

6,496

399

4,215

230

3,099

161

2,278

116

1,868

97

3

14,964

6,757

27,892

14,103

17,846

8,372

12,868

5,594

9,161

3,573

7,289

2,574

4

17,943

6,787

33,382

14,113

21,357

8,398

15,476

5,628

11,163

3,619

9,014

2,633

5

27,945

11,83850,11823,59532,963

14,44424,2469,950

17,545

6,618

14,041

4,943

1

531

6

709

10

571

7

501

6

448

5

421

5

277

Table F-5. 2007–2008 HCUP-based medical unit costs at different discount rates,
AIS-85 (2010 dollars)
Body part

Skull

Brain/
intracarnial

Ear

Eye & adnexa

Fracture/
MaisDisclocation
85

Yes

No

No

No

No
Nouse/
mouth/face/s
calp/neck
Yes

Neck/
internalorgans/blood vessels

No

Neck-spinal
cord

No

No
Shoulder/clavicule/scapula/upper arm

Yes

No
Elbow
Yes

Forearm

Wrist/hand/fi
nger/
thumb

Yes

No

Yes

Discount rate 3%

Discount rate 0%

Discount rate 2%

Discount rate 4%

Discount rate 7%

Discount rate 10%

Mean

Mean

Mean

Mean

Mean

Mean

Std error

Std error

Std error

Std error

Std error

Std error

1

8,634

1,494

8,634

1,494

8,634

1,494

8,634

1,494

8,634

1,494

8,634

2

19,704594

19,704594

19,704594

19,704594

19,704594

19,704

1,494
594

3

115,206

3,622

120,252

3,812

116,547

3,672

114,1023,580

111,890

3,497

110,653

3,451

4

184,508

13,229193,352

13,925

186,860

13,414

182,575

13,077

178,699

12,771

176,532

12,601

5555,437

27,319583,766

28,740562,968

27,696549,241

27,007536,828

26,385529,883

26,036

1

4,730

141

4,730

141

4,730

141

4,730

141

4,730

141

4,730

141

2

10,28780

10,28780

10,28780

10,28780

10,28780

10,28780

376,7282,570

79,232

2,689

77,3932,601

76,181

2,544

75,083

2,492

74,4692,463

4110,2315,354

115,675

5,630

111,679

5,427

109,041

5,293

106,655

5,172

105,3205,105

5440,41127,316462,965

28,732446,407

27,692435,477

27,006425,595

26,386420,066

26,0382

35,3845,020

35,3845,020

35,3845,020

35,3845,020

35,3845,020

35,384

5,020

1

3,798

121

3,798

121

3,798

121

3,798

121

3,798

121

3,798

121

2

14,611773

14,611773

14,611773

14,611773

14,611773

14,611773

317,6672,506

17,6672,506

17,6672,506

17,6672,506

17,6672,506

17,6672,506

434,501

3,063

34,501

3,063

34,501

3,063

34,501

3,063

34,501

3,063

34,501

3,063

1

3,504

23

3,504

23

3,504

23

3,504

23

3,504

23

3,504

23

2

7,724

100

7,724

100

7,724

100

7,724

100

7,724

100

7,724

100

3

18,691

1,659

18,691

1,659

18,691

1,659

18,691

1,659

18,691

1,659

18,6911,659

435,332

9,467

35,332

9,467

35,332

9,467

35,332

9,467

35,332

9,467

35,332

9,467

1

6,811

153

6,811

153

6,811

153

6,811

153

6,811

153

6,811

153

2

26,572

1,622

26,572

1,622

26,572

1,622

26,572

1,622

26,572

1,622

26,572

1,622

387,0599,001

87,7189,211

87,242

9,055

86,904

8,958

86,561

8,870

86,3388,820

4122,197

30,861

122,197

30,861

122,197

30,861

122,197

30,861

122,197

30,861

122,197

30,86152,602

5

662,441

69,024805,135

86,096

701,12973,636630,230

65,221560,915

57,278517,366

1

2,862

229

2,862

229

2,862

229

2,862

229

2,862

229

2,862

229

2

20,275

1,625

20,275

1,625

20,275

1,625

20,275

1,625

20,275

1,625

20,2751,625

332,726

6,664

32,726

6,664

32,726

6,664

32,726

6,664

32,726

6,664

32,726

6,664

4

195,457

39,802

195,457

39,802

195,457

39,802

195,457

39,802

195,457

39,802

195,457

39,802

3

311,704

268,652

372,060

329,006

328,474

285,420

297,483

254,431265,996

222,943

245,545

202,4934

416,938

13,287

553,614

14,284454,911

13,544

384,733

13,081

313,429

12,671

267,117

12,4405

1,121,652

40,671 1,051,528

40,537896,298

40,400795,523

40,311

1

2,012

18

2,012

18

2,012

18

2,012

18

2,012

18

2,012

18

2

5,382

123

5,382

123

5,382

123

5,382

123

5,382

123

5,382

123

3

32,1289,540

32,1289,540

32,1289,540

32,1289,540

32,1289,540

32,1289,540

2

11,771

183

11,771

183

11,771

183

11,771

183

11,771

183

11,771183

346,951

1,927

46,951

1,927

46,951

1,927

46,951

1,927

46,951

1,927

46,951

1,927

497,744

12,26097,744

12,26097,744

12,26097,744

12,26097,744

12,26097,744

12,2605

59,624

7,479

59,624

7,479

59,624

7,479

59,624

7,479

59,624

7,479

59,624

7,479

1

2,409

38

2,409

38

2,409

38

2,409

38

2,409

38

2,409

38

2

8,601

257

8,601

257

8,601

257

8,601

257

8,601

257

8,601

257

40,598 1,419,289

40,861 1,204,342

3

20,5101,383

20,5101,383

20,5101,383

20,5101,383

20,5101,383

20,5101,383

454,214

14,95954,214

14,95954,214

14,95954,214

14,95954,214

14,95954,214

14,9592

11,977218

11,977218

11,977218

11,977218

11,977218

11,977218

348,7291,883

48,7291,883

48,7291,883

48,7291,883

48,7291,883

48,7291,883

482,89917,65982,89917,65982,89917,65982,89917,65982,89917,65982,89917,6592

15,596

3,322

15,596

3,322

15,596

3,322

15,596

3,322

15,596

3,322

15,5963,322

359,025

6,802

59,025

6,802

59,025

6,802

59,025

6,802

59,025

6,802

59,025

6,802

1

2,046

22

2,046

22

2,046

22

2,046

22

2,046

22

2,046

22

2

6,191

213

6,191

213

6,191

213

6,191

213

6,191

213

6,191

213

3

14,332

1,651

14,332

1,651

14,332

1,651

14,332

1,651

14,332

1,651

14,332

1,651

1

2,897

73

2,897

73

2,897

73

2,897

73

2,897

73

2,897

73

2

6,774

180

6,774

180

6,774

180

6,774

180

6,774

180

6,774

180

3

38,296

3,622

38,296

3,622

38,296

3,622

38,296

3,622

38,296

3,622

38,296

3,622

278

Table F-5 (continued)
Body part

Upper ext/
mult/unsp

Fracture/
MaisDisclocation
85

No

Yes
Chest/abdomNo

No
Ribs/sternum
Yes

Back
(including
vertevrae)

No

Yes

Trunk -spinal
cord

No

Trunk, superf

No

No
Trunk,
multiple/
unspecified
Yes

Thoracicorgs/bloodvessels

Liver

No

No

SpleenNo

KidneyNo

Gastrointestinal

GenitourinaryNo

No

Discount rate 3%

Discount rate 0%

Discount rate 2%

Discount rate 4%

Discount rate 7%

Mean

Mean

Mean

Mean

Mean

Std error

Std error

Std error

Std error

Discount rate 10%

Std error

Mean

Std error

1

2,575

60

2,575

60

2,575

60

2,575

60

2,575

60

2,575

60

2

12,503

1,215

12,503

1,215

12,503

1,215

12,503

1,215

12,503

1,215

12,5031,215

345,591

4,372

45,591

4,372

45,591

4,372

45,591

4,372

45,591

4,372

45,5914,372

428,397

11,31928,397

11,31928,397

11,31928,397

11,31928,397

11,31928,397

11,319
671

2

3,636

671

3,636

671

3,636

671

3,636

671

3,636

671

3,636

3

6,537

88

6,537

88

6,537

88

6,537

88

6,537

88

6,537

88

2

13,0291,041

13,0291,041

13,0291,041

13,0291,041

13,0291,041

13,029

1,041

1

2,153

6

2,153

6

2,153

6

2,153

6

2,153

6

2,153

6

2

5,988

80

5,988

80

5,988

80

5,988

80

5,988

80

5,988

80

3

9,350

1,109

9,350

1,109

9,350

1,109

9,350

1,109

9,350

1,109

9,350

1,109

4

34,910

6,785

34,910

6,785

34,910

6,785

34,910

6,785

34,910

6,785

34,9106,785

220,872547

20,872547

20,872547

20,872547

20,872547

20,872547

325,841651

25,841651

25,841651

25,841651

25,841651

25,841651

472,936

6,890

72,936

6,890

72,936

6,890

72,936

6,890

72,936

6,890

72,936

6,890
17,127

5

181,413

17,127

181,413

17,127

181,413

17,127

181,413

17,127

181,413

17,127

181,413

2

8,271

71

8,271

71

8,271

71

8,271

71

8,271

71

8,271

71

3

98,592

9,308

98,592

9,308

98,592

9,308

98,592

9,308

98,592

9,308

98,5929,308

4135,635

1,169

135,635

1,169

135,635

1,169

135,635

1,169

135,635

1,169

135,635

1,169

2

17,8491,302

17,8491,302

17,8491,302

17,8491,302

17,8491,302

17,8491,302

331,8101,310

31,8101,310

31,8101,310

31,8101,310

31,8101,310

31,8101,310

456,982

13,24356,982

13,24356,982

13,24356,982

13,24356,982

13,24356,982

13,243

3498,881

23,417

498,881

23,417

498,881

23,417

498,881

23,417

498,881

23,417

498,881

23,417

4346,125

33,874

346,125

33,874

346,125

33,874

346,125

33,874

346,125

33,874

346,125

33,874

5

509,528

16,899601,592

17,750533,300

17,101

490,498

16,747452,122

16,469429,949

16,3262

31,126

2,690

31,126

2,690

31,126

2,690

31,126

2,690

31,126

2,690

31,126

2,690

1

2,875

18

2,875

18

2,875

18

2,875

18

2,875

18

2,875

18

2

8,243

357

8,243

357

8,243

357

8,243

357

8,243

357

8,243

357

3

25,993793

25,993793

25,993793

25,993793

25,993793

25,993793

453,872

3,533

53,872

3,533

53,872

3,533

53,872

3,533

53,872

3,533

53,8723,533

5244,697

54,914

262,616

71,340249,675

59,391

240,476

51,190231,130

43,274225,063

38,479

1

6,310

222

6,310

222

6,310

222

6,310

222

6,310

222

6,310

222

2

15,586383

15,586383

15,586383

15,586383

15,586383

15,586383

323,336652

23,336652

23,336652

23,336652

23,336652

23,336652

456,433

14,41956,433

14,41956,433

14,41956,433

14,41956,433

14,41956,433

14,4195545,070

28,968

638,670

30,002569,238

29,210525,72128,785

486,705

28,454464,162

28,284

3

60,1096,993

60,1096,993

60,1096,993

60,1096,993

60,1096,993

60,1096,993

460,63813,522

60,63813,522

60,63813,522

60,63813,522

60,63813,522

60,63813,522

5

180,536

20,629180,536

20,629180,536

20,629180,536

20,629180,536

20,629180,536

20,629

1

5,532

75

5,532

75

5,532

75

5,532

75

5,532

75

5,532

75

2

28,365894

28,365894

28,365894

28,365894

28,365894

28,365894

349,3352,445

49,3352,445

49,3352,445

49,3352,445

49,3352,445

49,3352,445

462,348

4,258

62,348

4,258

62,348

4,258

62,348

4,258

62,348

4,258

62,3484,258

5121,203

13,796

121,203

13,796

121,203

13,796

121,203

13,796

121,203

13,796

121,203

13,796

3

51,2693,893

51,2693,893

51,2693,893

51,2693,893

51,2693,893

51,2693,893

449,143

14,90649,143

14,90649,143

14,90649,143

14,90649,143

14,90649,143

14,906

3

46,701

11,757

46,701

11,757

46,701

11,757

46,701

11,757

46,701

11,757

46,701

11,757

4

70,127

18,511

70,127

18,511

70,127

18,511

70,127

18,511

70,127

18,511

70,127

18,511

5

229,902

22,952229,902

22,952229,902

22,952229,902

22,952229,902

22,952229,902

22,952

2

114,81178,056

114,81178,056

114,81178,056

114,81178,056

114,81178,056

114,81178,056

3

67,792

12,61567,792

12,61567,792

12,61567,792

12,61567,792

12,61567,792

12,615

4

65,746

14,29765,746

14,29765,746

14,29765,746

14,29765,746

14,29765,746

14,297

5

219,586

57,340219,586

57,340219,586

57,340219,586

57,340219,586

57,340219,586

57,3402

32,644

3,137

32,644

3,137

32,644

3,137

32,644

3,137

32,644

3,137

32,644

3,137

3

104,729

16,861

104,729

16,861

104,729

16,861

104,729

16,861

104,729

16,861

104,729

16,861

4

18,7903,025

18,7903,025

18,7903,025

18,7903,025

18,7903,025

18,7903,025

279

Table F-5 (continued)
Body part

Fracture/
MaisDisclocation
85

No
Hip/thighYes

L. extremity
superfic

No

No
Knee
Yes

No
Lower leg
Yes

No
Ankle/foot/
toes
Yes

Burns

Min. extern.

No

No

Discount rate 3%

Discount rate 0%

Discount rate 2%

Discount rate 4%

Discount rate 7%

Mean

Mean

Mean

Mean

Mean

Std error

Std error

Std error

Std error

Discount rate 10%

Std error

Mean

Std error

1

2,279

31

2,279

31

2,279

31

2,279

31

2,279

31

2,279

31

2

9,154

530

9,154

530

9,154

530

9,154

530

9,154

530

9,154

530

3

26,5472,536

26,5472,536

26,5472,536

26,5472,536

26,5472,536

26,5472,536

433,4305,924

33,4305,924

33,4305,924

33,4305,924

33,4305,924

33,4305,924

238,411

1,328

38,411

1,328

38,411

1,328

38,411

1,328

38,411

1,328

38,411

1,328

3

71,364

1,203

71,364

1,203

71,364

1,203

71,364

1,203

71,364

1,203

71,3641,203

4130,799

8,917

130,799

8,917

130,799

8,917

130,799

8,917

130,799

8,917

130,799

8,917

5

204,548

54,233

204,548

54,233

204,548

54,233

204,548

54,233

204,548

54,233

204,548

54,233

1

2,880

50

2,880

50

2,880

50

2,880

50

2,880

50

2,880

50

2

4,932

134

4,932

134

4,932

134

4,932

134

4,932

134

4,932

134

1

2,007

24

2,007

24

2,007

24

2,007

24

2,007

24

2,007

24

2

6,456

581

6,456

581

6,456

581

6,456

581

6,456

581

6,456

581

3

17,276

1,813

17,276

1,813

17,276

1,813

17,276

1,813

17,276

1,813

17,2761,813

4114,170

23,689114,170

23,689114,170

23,689114,170

23,689114,170

23,689114,170

23,6892

19,775665

19,775665

19,775665

19,775665

19,775665

19,775665

363,392

3,700

63,392

3,700

63,392

3,700

63,392

3,700

63,392

3,700

63,3923,700

441,957

12,39941,957

12,39941,957

12,39941,957

12,39941,957

12,39941,957

12,399

1

2,204

32

2,204

32

2,204

32

2,204

32

2,204

32

2,204

32

2

4,553

303

4,553

303

4,553

303

4,553

303

4,553

303

4,553

303

3

39,743

4,432

39,743

4,432

39,743

4,432

39,743

4,432

39,743

4,432

39,7434,432

220,054277

20,054277

20,054277

20,054277

20,054277

20,054277

372,642

1,552

72,642

1,552

72,642

1,552

72,642

1,552

72,642

1,552

72,642

1,552

1

1,989

47

1,989

47

1,989

47

1,989

47

1,989

47

1,989

47

2

11,547915

11,547915

11,547915

11,547915

11,547915

11,547915

310,802

1,519

10,802

1,519

10,802

1,519

10,802

1,519

10,802

1,519

10,802

1,519

1

3,090

136

3,090

136

3,090

136

3,090

136

3,090

136

3,090

136

2

16,216407

16,216407

16,216407

16,216407

16,216407

16,216407

362,891

3,494

62,891

3,494

62,891

3,494

62,891

3,494

62,891

3,494

62,891

3,494
28,172

4

75,95428,17275,95428,17275,95428,17275,95428,17275,95428,17275,954

1

1,894

118

1,894

118

1,894

118

1,894

118

1,894

118

1,894

118

2

17,532

2,857

17,532

2,857

17,532

2,857

17,532

2,857

17,532

2,857

17,5322,857

3107,150

13,483

107,150

13,483

107,150

13,483

107,150

13,483

107,150

13,483

107,150

13,483

4

123,242

23,507

123,242

23,507

123,242

23,507

123,242

23,507

123,242

23,507

123,242

23,507

5

256,351

19,737

256,351

19,737

256,351

19,737

256,351

19,737

256,351

19,737

256,351

19,737

1

3,321

32

3,321

32

3,321

32

3,321

32

3,321

32

3,321

32

280

Table F-6. 2007–2008 HCUP-based unit earnings loss at different discount rates,
AIS-85 (2010 dollars)
Body part

Skull

Brain/
intracarnial

Ear

Eye & adnexa

Fracture/
Disclocation

Yes

No

No

No

No
Nouse/mouth/
face/scalp/neck
Yes

Neck/internalorgans/bloodvesselsNo

Neck-spinal
cord

No

No
Shoulder/clavicule/scapula/upper
arm

Yes

No
Elbow
Yes

Forearm

Yes

No
Wrist/hand/finger/thumb
Yes

Mais85

Discount rate 3%

Discount rate 0%

Discount rate 2%

Discount rate 4%

Discount rate 7%

Discount rate 10%

Mean

Mean

Mean

Mean

Mean

Mean

Std error

Std error

Std error

Std error

Std error

Std error

1

15,946

95

23,961400

17,991

170

14,31438

11,013

72

9,081

2

25,487

1,258

44,4152,596

29,934

1,546

22,143

1,055

15,983721

12,783124567

3143,505

3,046

255,397

6,009

170,259

3,706

123,170

2,568

85,107

1,732

64,9031,318

4188,117

10,184

331,664

19,217

222,731

12,205

161,634

8,707

111,518

6,055

84,5384,675

5396,985

15,034703,345

31,111

471,713

18,624339,470

12,461230,034

8,118

171,070

6,115

1

4,178

303

7,498

597

4,969

372

3,580

251

2,471

159

1,893

113

2

13,762

227

22,972415

15,985270

12,060

194

8,839

135

7,105

103

3

145,003

3,234

250,176

6,133

170,723

3,906

125,133

2,736

86,966

1,835

66,013

1,373
2,243

4

139,075

5,028

229,643

9,321

161,514

6,022

121,544

4,289

87,175

2,944

67,701

5450,287

16,175

819,466

34,138539,113

20,123

382,520

13,387

255,0788,788

187,175

6,693

2

204,872

26,310281,024

44,419225,744

30,847

187,315

22,831

148,769

16,513

123,815

13,485

1

3,057

148

5,564

307

3,642

183

2,620

123

1,826

81

1,422

61

2

34,3062,743

65,4995,11941,647

3,269

28,7982,359

18,712

1,663

13,5341,289

348,8706,276

80,187

12,67456,318

7,696

42,987

5,239

31,283

3,472

24,5512,674

4

297,186

22,453379,046

31,286

320,654

24,782

276,676

20,568228,509

16,678194,201

14,252

1

2,487

26

4,352

51

2,920

31

2,166

22

1,585

16

1,291

13

2

11,182

230

20,025476

13,266

278

9,616

198

6,739

150

5,252

128

3

46,7403,531

78,091

6,446

54,442

4,208

40,772

3,031

29,2492,135

22,8841,679

4118,127

18,168

194,00337,598

136,959

22,624103,405

14,90774,552

9,218

58,260

6,504

1

4,611

88

7,876

174

5,392

106

4,018

77

2,915

59

2,339

52

2

29,163

1,104

51,101

1,830

34,3981,255

25,200997

17,851805

14,022698

3106,458

6,332

176,610

11,656

123,840

7,566

92,9105,419

66,565

3,778

51,9172,940

4142,692

14,620221,195

24,335

162,752

17,024

126,680

12,74594,306

9,097

75,3477,056

5735,358

102,377 1,293,560

167,321872,688

118,963

629,013

89,144

424,762

62,464313,407

47,000

1

1,798

354

4,064

647

2,297

423

1,444

303

859

208

604

162

2

89,66017,671

176,534

28,087110,166

20,29974,29815,56746,435

11,27332,4838,728

338,167

14,73954,471

18,38142,50815,846

34,585

13,742

26,923

11,321

22,0849,573

4227,431

87,828362,118

122,197

262,080

97,694

199,692

79,348

143,562

60,369110,905

48,076

3

81,07865,543112,047

91,64089,61772,73573,862

59,46457,88446,01447,41837,213

4

169,458

13,393

240,601

23,435

188,143

15,741

154,167

11,629

121,760

8,339

101,3736,554

5

144,340

16,158

202,651

19,145160,044

17,041

131,293

15,366103,155

13,42485,228

11,959

1

2,717

21

3,475

27

2,903

22

2,574

20

2,299

18

2,148

17

2

11,247207

15,364339

12,256233

10,467

191

8,973

171

8,159

164

3

30,1042,604

45,516

4,688

33,714

3,102

27,313

2,288

21,9581,890

19,041

1,785

2

24,227

253

36,498409

27,196283

21,949233

17,619202

15,275189

364,321

1,679

96,901

2,895

72,246

1,934

58,2201,504

46,553

1,227

40,1781,102

487,144

8,696

137,365

16,28799,451

10,367

77,6247,534

59,304

5,716

49,222

4,979

5

202,550

20,213

347,518

41,205

237,456

24,753

175,888

17,071

125,554

12,102

98,473

9,961

1

1,352

23

1,973

38

1,501

26

1,239

21

1,029

17

918

15

2

7,256

350

11,031

548

8,170

396

6,556

315

5,230

252

4,519

220

3

22,884

1,708

34,1382,345

25,574

1,838

20,837

1,620

16,983

1,474

14,9131,398

458,38825,345

81,191

27,64064,32326,35753,561

24,28043,432

21,291

37,152

18,9072

31,989355

49,727611

36,279408

28,701320

22,459266

19,090243

383,967

1,434

125,808

2,482

94,245

1,646

75,981

1,296

60,517

1,092

51,9431,005

4

114,710

13,097

176,951

23,852130,257

15,607

102,530

11,25078,6798,029

65,3246,515

247,231

5,393

54,045

7,285

49,084

5,881

45,671

5,011

42,214

4,308

39,8953,979

357,171

4,165

61,292

5,255

58,382

4,453

56,091

3,937

53,453

3,513

51,470

3,322

1

858

16

1,136

29

924

18

808

13

716

10

668

9

2

13,848878

24,5891,798

16,381

1,083

11,942

730

8,426

471

6,598

345
1,186

3

33,191

2,418

53,9294,624

38,184

2,913

29,3802,062

22,206

1,460

18,381

1

6,728

134

11,183

244

7,789

157

5,924

118

4,427

92

3,638

80

2

19,557285

30,962497

22,333330

17,424254

13,367207

11,180186

366,693

5,336

103,741

9,532

75,805

6,299

59,633

4,632

46,0483,419

38,6092,854

281

Table F-6 (continued)
Body part

Upper ext/
mult/unsp

Fracture/
Disclocation

No

Yes
Chest/abdomNo

No
Ribs/sternum
Yes

No
Back (including
vertevrae)
Yes

Trunk -spinal
cord

No

Trunk, superf

No

No
Trunk, multiple/
unspecified
Yes

Thoracicorgs/bloodvessels

Liver

SpleenKidneyGastrointestinal

Genitourinary

No

No

No

No

No

No

Mais85

Discount rate 3%

Discount rate 0%

Discount rate 2%

Discount rate 4%

Discount rate 7%

Discount rate 10%

Mean

Mean

Mean

Mean

Mean

Mean

Std error

Std error

Std error

Std error

Std error

Std error

1

848

23

1,122

43

914

27

798

20

705

16

655

14

2

14,362863

22,766

1,566

16,3991,001

12,799

771

9,834

626

8,238

558

3

88,878

8,444

151,416

15,345104,212

10,03077,0397,280

54,415

5,233

42,1784,224

461,856

12,967

104,600

14,39072,601

13,61353,41812,28536,917

10,39327,7898,935

213,638397

23,038664

15,893441

11,925

373

8,739

349

7,080

339

3

11,925

193

20,716378

14,071

234

10,278

164

7,178

111

5,553

84

2

28,897864

43,425

5,025

32,435

1,877

26,179177

21,028

1,393

18,271

2,111

1

3,477

6

3,658

7

3,521

6

3,442

6

3,376

6

3,339

6

2

9,087

147

10,560

193

9,463

158

8,787

140

8,180

127

7,824

119

3

17,111

2,178

23,6983,213

18,7152,441

15,8681,966

13,456

1,526

12,0981,257

470,195

4,334

104,6226,106

77,813

4,678

64,6694,117

54,867

3,814

49,8283,681

243,407997

62,594

1,604

48,191

1,134

39,646898

32,222726

27,998641

340,334738

60,302

1,248

45,257853

36,497655

29,021

512

24,843443

453,5904,775

74,922

7,390

58,945

5,402

49,291

4,290

40,4883,345

35,217

2,804
8,361

5

238,628

14,236

302,088

20,547

256,688

15,906

222,940

12,872

186,519

10,042

161,0092

51,717

3,415

80,143

9,760

59,131

4,901

45,7242,328

33,434501

26,181250

368,583

4,091

108,773

7,398

78,841

4,885

60,414

3,488

43,9942,369

34,5001,792

4

175,844

11,612

272,355

33,167200,641

16,629155,989

7,942

115,686

1,732

91,988879

244,2892,130

69,551

3,784

50,426

2,455

39,574

1,916

30,591

1,584

25,7171,431

353,111

1,083

82,366

1,960

60,3991,285

47,397934

36,184668

29,879534

445,266

11,771

67,94521,71050,957

14,15240,785

9,969

31,9186,654

26,8634,971

3299,393

15,380524,398

48,587354,218

21,876257,188

11,647

176,696

8,780

133,0028,731

4

243,617

19,751

419,305

42,188286,906

24,860210,040

16,055

145,313

9,827

109,7827,082

5529,808

14,644 1,076,520

37,313

658,686

19,671

433,487

11,112259,908

5,658

174,1293,845

2

69,0392,060

99,71812,321

76,5804,572

63,194

136

51,924

3,424

45,724

5,198

1

1,845

13

2,185

16

1,928

13

1,782

12

1,661

11

1,596

11

2

5,732

220

7,998

315

6,278

241

5,314

205

4,523

178

4,095

163

3

26,130761

36,894

1,307

28,767889

24,078666

20,093490

17,872400

433,986

2,775

52,0505,155

38,282

3,304

30,657

2,388

24,207

1,696

20,608

1,343
8,551

5

115,190

28,678

209,705

59,361

137,994

35,95897,82823,22065,524

13,371

48,757

1

5,486

126

7,089

168

5,894

136

5,158

119

4,490

106

4,091

98

2

18,342509

22,810689

19,485553

17,413474

15,473406

14,279367

327,788588

35,090866

29,647653

26,287

540

23,175450

21,279401

436,573

13,43354,35825,50840,63816,23633,392

11,271

27,177

7,174

23,7045,022

5524,231

16,407 1,053,560

39,242

649,396

21,282

430,456

13,144

260,737

8,563

176,295

7,033

3

41,188

8,045

58,28013,85745,4859,483

37,802

6,927

31,104

4,781

27,2953,626

426,262

6,205

35,356

7,896

28,535

6,667

24,463

5,811

20,837

4,915

18,6804,294

5

178,534

18,997

288,663

35,273

206,134

22,843

156,831

16,116

114,005

10,89389,702

8,269

1

1,611

26

2,387

49

1,791

30

1,478

24

1,238

21

1,117

20

2

27,151

859

40,631

1,807

30,347

1,054

24,739729

20,283534

17,966457

336,338

1,775

57,983

3,467

41,5102,162

32,4001,493

25,023

1,002

21,124772

438,4442,888

60,4405,183

43,8253,430

34,2882,479

26,317

1,728

21,9601,339

5

107,599

11,933

199,649

26,950129,277

15,291

91,3489,519

61,6875,430

46,5573,579

387,99527,824

157,448

62,336

104,60935,75975,41822,01752,188

11,95840,2307,347

475,113464

112,738

282

84,634259

67,570636

52,486

1,012

43,7401,248

392,80833,777

162,893

74,672

109,598

43,23580,074

26,821

56,435

14,665

44,1328,995

416,977443

25,6081,185

19,044589

15,397349

12,381

265

10,716299

5169,232

11,924262,450

24,277

193,047

14,888150,224

9,683

111,766

5,572

89,1993,500
13,6982

49,393

19,58664,05023,61153,092

20,78946,43918,49740,324

15,78436,492

3

50,773

4,995

85,745

11,34459,206

6,411

44,3493,999

32,371

2,469

26,1301,962

499,081

31,987

153,558

56,528112,806

37,951

88,256

27,40866,821

18,71754,643

14,0388,306

5159,899

35,013

308,531

81,772

195,30945,650133,21327,30084,441

14,16859,8262

52,43915,09985,841

28,66260,543

18,40546,241

12,56834,631

7,844

28,5585,420

3120,057

34,467200,532

62,638

139,915

41,105

104,645

29,50474,921

20,49158,60815,867

4

14,644

4,204

16,876

5,271

15,296

4,494

14,066

3,966

12,668

3,465

11,633

3,149

282

Table F-6 (continued)
Body part

Fracture/
DisclocationNo
Hip/thighYes

L. extremity
superfic

No

No
Knee
Yes

No
Lower leg
Yes

No
Ankle/foot/toes
Yes

Burns

Min. extern.

No

No

Mais85

Discount rate 3%

Discount rate 0%

Discount rate 2%

Discount rate 4%

Discount rate 7%

Discount rate 10%

Mean

Mean

Mean

Mean

Mean

Mean

Std error

Std error

Std error

Std error

Std error

Std error

1

2,128

26

2,457

31

2,208

27

2,067

25

1,949

24

1,885

23

2

9,264

944

14,086

2,100

10,381

1,191

8,437

779

6,958

548

6,226

480

3

38,5992,707

58,7504,429

43,553

3,117

34,764

2,397

27,396

1,821

23,3781,519

449,197

6,510

75,14910,20055,3887,388

44,475

5,829

35,501

4,479

30,5533,678

258,494

1,169

92,703

2,082

66,711

1,366

52,1791,031

40,143801

33,605691

358,062705

93,463

1,229

66,526821

51,605622

39,466479

32,999409

471,494

4,301

110,792

7,324

81,126

4,988

64,034

3,796

49,6202,886

41,6292,409

583,136

4,681

130,809

8,399

95,176

5,147

73,623

4,497

54,751

4,346

44,026

4,215

1

1,470

30

2,130

49

1,626

33

1,353

27

1,137

24

1,025

22

2

5,367

84

6,978

134

5,755

94

5,072

77

4,516

67

4,220

63

1

700

12

770

17

717

13

686

11

658

9

642

9

2

6,291

404

7,961

523

6,712

430

5,958

384

5,295

348

4,915

328

3

20,421

1,233

27,4901,505

22,191

1,299

19,0301,181

16,281

1,069

14,721995

4200,558

14,389263,013

17,633

216,642

15,152

187,645

13,800161,283

12,621

145,659

11,8902

42,834

1,112

64,717

1,905

48,211

1,288

38,640989

30,473786

25,923693

384,9002,782

130,132

4,907

96,065

3,247

76,222

2,457

59,4201,931

50,124

1,696
11,967

4

83,33722,034112,469

32,36991,04624,78777,01019,766

63,617

14,95255,289

1

919

12

1,086

18

959

13

889

11

833

10

803

9

2

2,172

131

2,805

323

2,319

168

2,063

109

1,866

86

1,767

83

3

55,205

3,141

77,8894,607

60,842

3,444

50,795

2,935

42,1302,609

37,2212,460

232,400253

46,779400

35,923281

29,670235

24,387208

21,450195

396,7291,128

142,474

1,887

107,979

1,287

87,9881,019

71,039845

61,612

763

1

1,899

40

2,951

73

2,149

47

1,710

35

1,360

26

1,178

22

2

14,273966

23,031

2,536

16,2891,235

12,779818

10,078

662

8,694

614
471

3

16,183634

25,485

1,162

18,419732

14,475575

11,253

501

9,521

1

3,556

94

4,948

147

3,894

104

3,295

89

2,800

82

2,533

79

2

34,530464

50,046731

38,354517

31,560429

25,812375

22,632351

375,556

3,284

110,548

6,062

84,251

3,893

68,7672,863

55,513

2,213

48,097

1,954

4

108,786

9,857

155,604

10,682

120,680

10,03899,341

9,696

80,4299,240

69,513

8,812

1

3,671

153

6,238

292

4,287

185

3,203

131

2,326

90

1,864

69

2

8,971

539

15,055

1,064

10,429

656

7,861

456

5,786

318

4,690

258

3

49,944

9,614

87,17821,45558,90812,326

43,121

7,638

30,402

4,270

23,7662,791

446,94812,017

83,97322,48855,85814,56940,177

10,063

27,611

6,420

21,1224,563

5186,896

21,196295,857

26,851

214,439

22,899165,090

19,674

121,549

16,061

96,434

13,530

1

1,686

18

2,110

27

1,788

20

1,607

17

1,460

15

1,381

14

283

Table F-7. 2007–2008 HCUP-based unit household production loss at different
discount rates, AIS-85 (2010 dollars)
Body part

Skull

Brain/
intracarnial

Ear

Eye & adnexa

Fracture/
Disclocation

Yes

No

No

No

No
Nouse/mouth/f
ace/scalp/neck
Yes

Neck/internalorgans/bloodvessels

Neck-spinal
cord

No

No

No
Shoulder/clavic
ule/scapula/up
per arm

Yes

No
Elbow
Yes

Forearm

Yes

No
Wrist/hand/finger/thumb
Yes

Mais85

Discount rate 3%

Discount rate 0%

Discount rate 2%

Discount rate 4%

Discount rate 7%

Mean

Mean

Mean

Mean

Mean

Std error

Std error

Std error

Std error

Discount rate 10%

Std error

Mean

Std error

1

3,741

27

6,437

150

4,361

52

3,281

10

2,444

16

2,012

26

2

7,705

300

13,843

704

9,064

380

6,719

248

4,977

166

4,097

130

3

40,634763

75,757

1,580

48,548935

34,825642

24,381436

18,998334

452,0582,430

98,8055,171

62,5752,999

44,351

2,033

30,552

1,364

23,4891,039

5111,019

3,562

212,974

8,039

134,094

4,479

94,066

2,932

63,655

1,908

48,085

1,438

1

988

57

1,805

113

1,171

69

854

48

615

32

493

24

2

4,813

68

8,264

131

5,600

82

4,230

58

3,168

40

2,609

31

3

47,853819

86,132

1,671

56,711

1,002

41,224688

28,941

460

22,353345

453,655

1,245

90,6662,497

62,456

1,515

46,934

1,051

34,062714

26,850543

5131,126

4,248

257,414

9,852

159,567

5,395

110,281

3,462

72,9762,182

53,914

1,592

2

41,4205,085

66,6259,183

47,676

036,546

4,390

27,1003,127

21,883

2,473

1

782

28

1,445

71

927

36

678

22

496

13

406

10

2

9,832

556

20,033

1,214

12,058

679

8,234

475

5,453

346

4,077

282

3

20,494

2,516

34,105

4,701

23,757

3,002

17,993

2,161

13,187

1,522

10,492

1,186

488,0586,753

134,265

11,213

100,236

0

78,126

5,883

57,412

4,153

44,863

3,150

1

671

6

1,164

12

779

7

594

5

459

4

392

3

2

3,446

65

6,365

132

4,093

78

2,977

56

2,151

42

1,736

36

3

15,2191,167

27,2862,279

17,992

1,411

13,158

991

9,381

679

7,391

519

4

47,404

3,595

81,931

7,638

55,592

4,443

41,190

3,002

29,504

2,008

23,186

1,539

1

1,168

13

2,040

29

1,363

16

1,027

12

777

11

651

10

2

8,977

311

16,077502

10,563

350

7,823

283

5,777

233

4,742

206

3

38,342

2,018

68,185

3,889

45,236

2,428

33,203

1,722

23,772

1,197

18,798928

442,4702,954

71,6995,398

49,403

3,505

37,2082,549

27,3001,815

21,925

1,429
7,748

5

164,014

16,823308,152

30,561

197,197

20,034

139,366

14,423

94,456

10,03571,056

1

764

95

1,832

205

982

0

615

78

374

50

267

37

2

20,613

2,552

41,641

4,657

25,237

3,028

17,293

2,200

11,547

1,559

8,749

1,226

3

14,148

4,852

23,195

7,839

16,374

5,608

12,422

4,256

9,091

3,079

7,253

2,415

4

45,203

15,503

81,82827,65553,8140

38,736

13,271

26,841

9,089

20,6306,870

319,472

11,39630,485

20,22922,23813,58817,2999,690

13,013

6,407

10,5824,623

458,4992,146

90,106

4,320

66,464

2,613

52,1801,815

39,453

1,248

31,966970

5118,087

6,845

150,725

10,144

126,949

7,754

110,598

6,078

93,683

4,395

81,920

3,314

1

956

5

1,219

6

1,016

5

911

5

831

5

788

5

2

3,574

47

4,881

70

3,873

51

3,353

45

2,951

41

2,743

40

3

9,602

587

14,406

1,098

10,708

677

8,778

533

7,269

462

6,472

434

2

8,564

62

13,056

104

9,588

69

7,806

58

6,429

53

5,709

51

3

21,752416

33,400718

24,428475

19,762377

16,115314

14,191285

433,7832,448

52,475

3,635

38,162

2,686

30,4782,279

24,281

1,959

20,9131,770

542,692

3,094

77,8985,396

50,722036,755

2,748

25,996

2,098

20,431

1,729

1

452

6

649

10

496

7

420

5

362

4

333

4

2

2,355

94

3,595

145

2,636

105

2,148

87

1,774

73

1,581

67

3

7,705

519

11,428

710

8,559

562

7,071

487

5,909

426

5,296

393

4

21,2005,816

32,9507,909

24,0696,403

18,9795,328

14,6844,300

12,2843,686

212,28776

19,090137

13,843

86

11,133

71

9,029

64

7,925

62

3

29,652397

44,920677

33,208449

26,983363

22,036311

19,392285

439,252

3,422

61,407

5,009

44,4443,790

35,3403,139

28,051

2,581

24,1312,251

234,8893,041

44,2883,613

37,368032,862

2,919

28,6102,633

26,0012,426

343,5272,319

54,2393,774

46,367

041,201

2,124

36,3101,839

33,316

1,732

1

292

2

365

5

308

3

280

2

258

2

247

2

2

4,254

223

7,741

472

5,029

276

3,690

185

2,692

122

2,187

91

3

11,399607

19,0601,326

13,123741

10,136

522

7,871

399

6,706

349

1

1,900

31

3,231

63

2,197

37

1,684

27

1,298

21

1,102

18

2

6,602

59

10,469

97

7,484

65

5,949

56

4,766

52

4,151

50

3

22,063845

34,8971,770

25,011

1,019

19,870737

15,861591

13,751

540

284

Table F-7 (continued)
Body partUpper
ext/mult/unsp

Fracture/
Disclocation

No

Yes
Chest/abdomNo

No
Ribs/sternum
Yes

No
Back (including
vertevrae)
Yes

Trunk -spinal
cord

No

Trunk, superf

No

No
Trunk,
multiple/unspe
cifiedYes

Thoracicorgs/bloodvessels

Liver

SpleenKidneyGastrointestinal

Genitourinary

No

No

No

No

No

No

Mais85

Discount rate 3%

Discount rate 0%

Discount rate 2%

Discount rate 4%

Discount rate 7%

Mean

Mean

Mean

Mean

Mean

Std error

Std error

Std error

Std error

Discount rate 10%

Std error

Mean

Std error

1

296

6

376

10

314

6

283

5

260

4

248

4

2

4,428

229

7,152

373

5,050

259

3,970

208

3,141

169

2,713

149

3

25,1202,061

44,184

3,734

29,502

2,424

21,872

1,799

15,973

1,334

12,9031,094

428,72919,36452,07433,20934,082

22,594

24,774

16,94917,646

12,50913,982

10,167

2

5,159

127

8,723

295

5,952

155

4,583

111

3,568

96

3,058

94

3

2,382

23

4,302

52

2,811

0

2,071

19

1,529

13

1,263

11

2

6,458

85

9,888

641

7,239

101

5,883

168

4,848

305

4,317

355

1

1,371

2

1,440

2

1,387

2

1,360

2

1,338

2

1,327

2

2

4,128

40

4,858

58

4,301

43

3,997

37

3,750

33

3,616

31

3

6,309

439

8,618

709

6,850

503

5,900

390

5,135

300

4,723

253

4

24,435998

36,013

1,337

27,037

1,067

22,521

948

19,046849

17,231789

2

16,361

234

23,615419

18,069272

15,070208

12,648166

11,333147

316,400202

24,466361

18,283234

14,986179

12,351143

10,931126

423,756

1,561

33,946

2,547

26,251

1,778

21,812

1,402

17,9691,107

15,735943

593,4093,003

141,845

4,868

105,921083,3682,678

62,9642,040

51,020

1,662

2

9,628

657

16,9992,148

11,372

0

8,316

469

5,895

194

4,630

88

3

25,987835

46,2181,586

30,684993

22,482722

16,069521

12,719414

474,5605,091

128,285

16,21287,267

7,276

64,9293,660

46,805

1,541

36,943699

217,962570

28,5591,113

20,389674

16,165503

12,891

401

11,176356

322,391286

36,606575

25,685349

19,930242

15,400166

13,000131

420,001

5,220

31,246

9,738

22,6506,260

17,9994,449

14,244

3,053

12,2062,335

3

120,373

8,762

228,582

21,467

144,974

11,188102,212

7,177

69,344

4,772

52,3133,739

483,034

5,297

155,776

12,541

99,637

6,769

70,7604,299

48,513

2,717

36,9662,020

5114,800

3,449

243,881

8,337

143,015

4,397

94,6202,827

59,9801,873

43,2681,451

2

22,201

164

37,156

1,554

25,536

189

19,771

401

15,467749

13,298

872

1

740

3

859

4

767

3

720

3

682

3

662

3

2

2,460

71

3,336

109

2,660

79

2,311

64

2,039

53

1,894

47

3

9,218

216

13,185391

10,141

255

8,526

187

7,239

137

6,548

111

4

13,709800

20,556

1,530

15,318960

12,491684

10,191

477

8,930

371

5

52,2995,023

92,836

14,35861,457

6,891

45,553

3,837

33,3092,433

26,839

2,200

1

1,921

17

2,493

26

2,060

18

1,814

16

1,606

15

1,488

14

2

8,470

94

10,572

143

8,991

104

8,060

87

7,242

76

6,765

70

3

11,738162

15,171

268

12,582

185

11,079

145

9,782

114

9,033

99

4

14,637

2,509

20,522

5,079

16,015

3,081

13,596

2,097

11,637

1,389

10,561

1,053

5

112,311

1,034

235,945

3,810

139,441

1,323

92,8491,016

59,2591,133

42,9261,125

315,132

3,281

23,074

6,078

17,004

3,901

13,714

2,826

11,030

2,001

9,555

1,561

4

9,136

1,517

12,637

2,291

9,970

1,717

8,499

1,355

7,284

1,028

6,612

841

5

61,717

8,554

109,532

17,69072,771

10,57153,468

7,097

38,285

4,548

30,228

3,280

1

623

8

888

11

682

8

581

8

507

7

470

7

2

10,064256

14,832

470

11,132

300

9,282

225

7,885

174

7,169

150

3

12,354501

19,776956

14,031600

11,119

430

8,887

305

7,729

243

4

14,765980

23,888

1,826

16,864

1,169

13,203842

10,332

594

8,810

466

5

42,481

6,775

81,087

15,94950,975

8,681

36,346

5,453

25,581

3,280

20,1852,284

337,148

7,788

67,906

15,71944,034

9,518

32,125

6,555

23,193

4,451

18,6443,449

432,0083,580

48,4006,644

36,056

4,330

28,840

3,007

22,5691,947

18,9381,415

336,626

10,48565,97323,15443,306

13,216

31,6968,550

22,772

5,272

18,139

3,713

4

7,526

82

11,145

443

8,343

128

6,926

95

5,843

170

5,282

210

5

65,762

7,643

117,137

16,51977,575

9,578

56,9696,260

40,802

3,876

32,2122,713

233,52818,46443,42324,522

36,176

20,21031,317

16,93826,462

13,362

23,254

10,844

3

19,783

4,006

36,414

8,209

23,462

4,922

17,1253,350

12,476

2,217

10,1691,665

433,208

7,427

56,206

13,70938,6008,879

29,1496,342

21,604

4,339

17,5743,274

574,473

11,636

149,889

25,58991,06014,62462,538

9,525

41,792

5,960

31,5624,274

213,4861,305

22,871

4,107

15,5591,918

11,991

868

9,386

136

8,104

230

3

36,133

5,780

67,75911,264

43,2596,998

30,913

4,892

21,5793,309

16,8192,501

412,3081,969

17,4302,897

13,650011,218

1,775

8,961

1,374

7,607

1,131

285

Table F-7 (continued)
Body part

Fracture/
DisclocationNo
Hip/thighYes

L. extremity
superfic

No

No
Knee
Yes

No
Lower leg
Yes

No
Ankle/foot/toes
Yes

Burns

Min. extern.

No

No

Mais85

Discount rate 3%

Discount rate 0%

Discount rate 2%

Discount rate 4%

Discount rate 7%

Mean

Mean

Mean

Mean

Mean

Std error

Std error

Std error

Std error

Discount rate 10%

Std error

Mean

Std error

1

793

8

906

9

819

8

774

8

739

7

721

7

2

2,997

177

4,208

388

3,263

217

2,805

151

2,470

117

2,304

106

3

12,185726

19,0601,292

13,753854

11,028

632

8,943

466

7,870

384

4

19,8001,525

32,3703,585

22,621

1,943

17,726

1,239

13,963782

11,990589

224,505294

38,726580

27,756348

22,093259

17,679208

15,349186

320,168198

32,447366

22,942

233

18,126174

14,446

134

12,542115

429,184

1,632

47,8103,274

33,443

1,977

26,022

1,392

20,237988

17,182797

555,646

6,385

92,041

12,73864,0897,742

49,331

5,425

37,663

3,789

31,437

2,997

1

455

7

648

11

498

7

424

6

369

6

342

5

2

2,150

25

2,701

36

2,275

27

2,057

23

1,890

21

1,803

20

1

310

3

340

5

317

3

305

2

295

2

289

2

2

2,598

97

3,360

137

2,775

105

2,465

91

2,219

81

2,088

77

3

6,921

331

9,703

485

7,568

365

6,437

305

5,543

256

5,068

229

4

59,3302,598

82,732

3,451

64,9352,789

55,0562,456

46,9772,188

42,5952,039

224,500346

36,752627

27,359399

22,351

312

18,358262

16,213241

330,926883

49,288

1,703

35,146

1,050

27,788768

22,044585

19,019501

429,4785,094

42,423

8,993

32,624

6,020

27,056

4,394

22,417

3,102

19,862

2,430

1

340

3

394

4

352

3

331

3

315

3

307

3

2

809

31

975

51

846

33

782

30

734

29

709

29
570

3

17,455717

25,8601,027

19,426

777

15,971

675

13,200605

11,711

2

21,328110

31,397

176

23,664121

19,577104

16,33595

14,60292

329,287257

44,332441

32,776292

26,677

233

21,862

195

19,304

178

1

613

11

943

19

687

12

560

9

465

7

417

6

2

4,927

417

8,083

941

5,622

518

4,423

350

3,532

244

3,081

197

3

4,790

210

7,910

414

5,492

250

4,276

183

3,356

140

2,885

121

1

1,106

20

1,522

20

1,201

20

1,036

21

909

20

844

20

2

13,217126

19,444206

14,654141

12,147118

10,187

105

9,154

99

3

27,982

1,037

42,065

1,882

31,261

1,212

25,525916

20,977717

18,555625

427,8672,049

43,834

2,906

31,572

2,206

25,103

1,940

20,043

1,740

17,402

1,623

1

1,307

43

2,304

88

1,530

53

1,145

37

858

25

712

19

2

3,091

116

5,488

258

3,623

144

2,706

98

2,027

70

1,687

59

3

11,987

1,618

21,628

3,918

14,132

2,100

10,428

1,286

7,679

756

6,298

535

4

24,281

7,724

46,70915,781

29,237

9,509

20,7036,432

14,4484,172

11,3383,049

542,3406,500

76,756

8,120

50,176

7,086

36,543

5,961

25,982

4,718

20,425

3,927

1

733

7

903

11

771

8

705

6

654

5

627

5

286

Appendix G:
Definitions
Comprehensive Costs: Comprehensive costs are a measure of total societal harm that results from trafficcrashes. They present the value of lost quality-of-life as measured by society’s willingness to pay to
avoid risk, together with the economic impacts that result from death or injury in traffic crashes.
Congestion Costs: The value of travel delay, added fuel usage, greenhouse gas and criteria pollutants
that result from congestion that results from motor vehicle crashes.
Economic Costs: The monetary impact of traffic crashes resulting from goods and services expended torespond to the crash, treat injuries, repair or replace damaged property, litigate restitution, administer
insurance programs, and retrain or replace injured employees. Economic costs also include the healthand environmental impacts that result from congestion, the value of workplace and householdproductivity that is lost due to death and injury, and the value of productivity and added travel time thatis incurred by uninvolved motorists due to congestion from traffic crashes.
Emergency Services: Police and fire department response costs.
Household Productivity: The present value of lost productive household activity, valued at the market
price for hiring a person to accomplish the same tasks.
Insurance Administration: The administrative costs associated with processing insurance claims resulting
from motor vehicle crashes and defense attorney costs.
Legal Costs: The legal fees and court costs associated with civil litigation resulting from traffic crashes.
Market Productivity: The present discounted value (using a 3-percent discount rate for 2010 dollars) of
the lost wages and benefits over the victim’s remaining life span.
Medical Care: The cost of all medical treatment associated with motor vehicle injuries including that given
during ambulance transport. Medical costs include emergency room and inpatient costs, follow-up visits,
physical therapy, rehabilitation, prescriptions, prosthetic devices, and home modifications..
Property Damage: The value of vehicles, cargo, roadways and other items damaged in traffic crashes.
Travel delay: The value of travel time delay for people who are not involved in traffic crashes, but who
are delayed in the resulting traffic congestion from these crashes.
Vocational Rehabilitation: The cost of job or career retraining required as a result of disability caused by
motor vehicle injuries.
Workplace Costs: The costs of workplace disruption that is due to the loss or absence of an employee.
This includes the cost of retraining new employees, overtime required to accomplish work of the injured
employee, and the administrative costs of processing personnel changes.

287

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