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Costs of Injuries Resulting from Motorcycle Crashes:
A Literature Review
Appendix A
Critical Reviews of Publications on Motorcycle Crash Costs

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Each review begins with a citation or citations to the reviewed article(s) in American Psychological Association format. Next comes the article summary, an expanded abstract edited for clarity and accuracy. The summary describes the study’s objectives, study population, data, methods, results, and conclusions, using these standard headings in this order for ease of use. The summary is followed by a critique, which indicates methodological strengths and weaknesses and assesses whether the study conclusions are merited based on the research reported. The reviewers might also note any important facts, such as the presence or absence of helmet laws in the time and place under study, that were not explicitly stated in the article. The reviewers might also highlight interesting methods, facts, or sources gleaned from an article under review. The criteria followed in writing the summary and critique of each article can be found in Appendix E.

Our comments in the “Weaknesses” section of a review do not necessarily imply fault on the part of the authors or the publications under review. Some weaknesses are unavoidable, given the current status of the data, methods, and confidentiality protections. Calling something a “weakness” does not necessarily imply that the authors could or should have done anything differently. Rather, it is intended to alert the reader to limitations on how the article’s results and conclusions should be interpreted and employed for purposes of motorcycle safety policy formulation.


Begg, D.J., Langley, J.D., & Reeder, A.I. (1994). Motorcycle crashes in New Zealand resulting in death and hospitalization. I: Introduction methods and overview. Accident Analysis and Prevention, 26(2), 157-164.

Langley, J.D., Begg, D.J., & Reeder, A.I. (1994). Motorcycle crashes resulting in death and hospitalization. II: Traffic crashes. Accident Analysis and Prevention, 26(2), 165-171.

Abstract
Objective.
The authors have written a series of three papers that describe the epidemiology of motorcycle crashes resulting in death and hospitalization in New Zealand. The first paper describes the methods used for the study, provides an overview of all crashes, and, in particular, compares traffic crashes with nontraffic crashes. The second paper focuses on traffic crashes, and the third (not reviewed here) focuses on nontraffic crashes.

Study Populations, Data, & Methods. The source of the fatality data was national mortality data files for 1978-1987 (ten years). The source of the hospitalization data was the 1988 national morbidity file, which records all public hospital discharges in New Zealand. Motorcyclists were identified by E-code and text description of cause (which New Zealand uses to supplement E-codes). AIS scores were assigned using the ICDMAP program, based on the primary injury diagnosis. Inpatient costs were extrapolated from costs for patients at the Dunedin Public Hospital for April 1988 through March 1990 (two years).

Results. Fatalities. A total of 1,175 motorcyclist fatalities were identified for the period 1978-1987, resulting in a mortality rate of 3.6 per 100,000 persons per year. Males 15-19 and 20-24 years of age had very high motorcycle traffic mortality rates (25.2 and 26.4, respectively), especially laborers (40.0) and forestry workers (32). Maori and non-Maori had similar rates. Motor vehicle traffic crashes represented 96 percent of the fatalities, and the majority (63 percent) of traffic deaths were attributable to a collision with another motor vehicle. Drivers were the victims in 88 percent of fatalities.

Hospitalizations. During 1988, a total of 2,623 motorcycle crash victims (2,222, or 85 percent, in traffic crashes) were hospitalized, resulting in a hospitalization rate of 80.4 (68.1 traffic) per 100,000 persons per year. Males 15-19 and 20-24 years of age had very high morbidity rates (464 and 462 total, 409 and 416 traffic). Maori had a higher morbidity rate than non-Maori in traffic crashes (99 vs. 61). Drivers were the victims in 86 percent of hospitalizations. For hospitalized victims, the injury locations were the lower limb (43 percent), head (23 percent), upper limb (16 percent), trunk (9 percent), internal (5 percent), and other (2 percent). Fractures were the most common injury to the lower and upper limbs (71 percent and 80 percent, respectively), and intracranial injury was the most common head injury (71 percent). The head was the site for 65 percent of severe/critical injuries (AIS>3).

The most common (40 percent) traffic crash was a collision with another motor vehicle. Collision crashes were more likely to result in lower limb injury (48 percent vs. 38 percent), have severity of AIS-3 or higher (39 percent vs. 21 percent), and result in hospital stays longer than a week (46 percent vs. 28 percent) than noncollision crashes.Whereas 29 percent of the traffic crashes were AIS-3 or higher, the comparable figure for nontraffic crashes was 19 percent.

The median length of hospital stay was 4 days. 33 patients (2 percent) died in the hospital. Of these, 24 (73 percent) had a principal condition of head injury.

There was a significant linear increase in the fatality rate between 1978 and 1988 but no comparable trend in hospitalizations. The estimated inpatient cost for all motorcycle crashes in 1988 was NZ$13,485,628. Traffic crashes represented 92 percent of the total cost. Traffic collision crashes were approximately twice as expensive as traffic noncollision crashes ($6,942 vs. $3,342), and they accounted for 61 percent of the total costs.

Conclusion. The mortality associated with motorcycles is comparable with that from a disease such as cervical cancer. Compared to cervical cancer, motorcycle crashes have received insignificant media, research, and prevention attention.

Notes
New Zealand has a compulsory helmet law covering all motorcycle drivers and passengers riding on public roads. The authors cite another study that reports 93 percent helmet use for traffic motorcyclists and 63 percent for nontraffic.

The exchange rate in 1988 was New Zealand
$1 = U.S. $0.6556.

Strengths
These articles made a point of looking at both traffic and nontraffic crashes. Most articles either ignore nontraffic crashes completely or do not separate them from traffic crashes. The articles also separated traffic collisions from noncollisions. Injuries from traffic collisions are shown to be much more costly than injuries from the other two categories.

Details on severity and body region are provided for patients hospitalized for traffic crashes.

Hospitalized survivors and fatalities are analyzed separately.

Weaknesses
There is insufficient discussion of what inpatient “costs” represent. It is unclear whether the Dunedin figures represent charges, payments, or something else. Also, according to Table 1, the estimated national cost reflects readmissions, but it is not clear whether the Dunedin mean cost per patient visit calculation also included readmissions.

The sample is not representative. It includes only fatalities and hospitalizations, thus excluding crash survivors who were not injured severely enough to be admitted to the hospital.

Some of the figures presented appear to contradict other figures in the same article. Was the head the principle body region in 23 percent (p. 167) or 19 percent (p. 168) of hospital-admitted injuries incurred in traffic crashes?

Much of the focus is on collision rates as a share of the population or sub-population. While this might be useful for demonstrating the magnitude of the problem and its demographic locus for the purpose of attracting the attention of New Zealand policymakers, it is not a useful measure for our purposes in this study.

There was no examination of the impact of helmet use.

It is not clear to what extent this New Zealand study is generalizable to the U.S. New Zealand is more rural than the U.S. Do its motorcyclists ride primarily for recreation (as in the U.S.) or for everyday transport? The demographic profile of the riders, however, looks similar to that of the U.S., with a preponderance of young males.

Interesting Finding
New Zealand, unlike most countries, uses a free-text description to complement its E-codes. The authors took advantage of this field to look more closely at cases that were E-coded with a fourth digit of 9, indicating the victim’s role in the crash as “unspecified.” ICD-9 includes fourth digits for identifying motorcycle “drivers” and “passengers,” but if the data source identifies the victim simply as a “motorcyclist,” he will often be coded as “unspecified.” The authors found 18 motorcycle fatalities (1.5 percent) and 133 motorcycle hospitalizations (5.1 percent) that had been coded as “unspecified.” This suggests that relying solely on the fourth digits of E-codes to identify motorcyclists will result in undercounts.

Estimates of Inpatient Costs

  Dunedin inpatients

Dunedin Mean
Cost/Visit

National inpatients

Estimated
national cost

Traffic collision
(E810-E815)
82
$6,942
1,197
$8,309,980

Traffic noncollision
(E816-E819)

83
$3,342
1,209
$4,039,934
Nontraffic
(E820-E825)
20
$2,617
434
$1,135,713
Total    
2,840
$13,485,628

Conclusions
Despite the flood of injury rates and the sometimes unclear distinctions between crash types, this article provided valuable information on the relative severity and costs of traffic vs. nontraffic and collision vs. noncollision motorcycle crashes. It also showed great detail on the body regions affected by motorcycle crashes.


Billheimer, J.W. (1998). Evaluation of California motorcyclist safety program.Transportation Research Record 1640, 100-109.

Abstract
Purpose & Study Population.
The California Motorcyclist Safety Program (CMSP) is a legislatively mandated, statewide program that has trained more than 100,000 motorcyclists in the 10 years since its implementation in July 1987. The program is mandatory for riders under 21 seeking a California motorcycle license. The current evaluation traces motorcycle crash trends before and after the formation of the CMSP, compares crash trends in California with those in the remainder of the United States, and analyzes the riding records of matched pairs of 2,351 trained and untrained Southern California riders.

Data & Methods. Crash trends: Historical data on motorcycle crashes, registrations, and licensing for 1977-1995 (10 years before and 9 years after implementation of CMSP), broken down by number, severity, and age, were used to plot simple graphs to show trends in annual numbers of crashes, fatalities per registered motorcycle, and crashes per licensed rider for California. California statistics were also compared with national figures for the same period.

Matched-pair analysis: On-site interviewers went to motorcyclist hang-outs in search of riders. They assembled profiles identifying riders by age, sex, years riding, miles ridden per year, and primary purpose of riding (commuting, recreation, etc.). Over five years, beginning in late 1989, interviews with 16,000 untrained motorcyclists were obtained, resulting in 2,351 pairs of motorcyclists matched by the five key factors listed above. Of these pairs, 1,139 included a rider taking the basic 16-hour course and the other 1,182 included a rider taking the 8-hour experienced rider course (ERC). The initial profiling was followed up by telephone surveys of the participants. Key methods distinguishing this study from similar previous studies were 1) to differentiate between pairs of riders who had previously ridden more than 500 miles (524 pairs) and those who had not (615 pairs), and 2) executing the first follow-up survey six months after the training course instead of waiting a full year. Aggregate crash counts and crash rates were then compared separately for those who had previously ridden 500 miles and those who had not, at the six-month, one-year, and two-year marks.

Results. Analysis of statewide crash trends indicate that fatal motorcycle crashes have dropped 69 percent since the introduction of the CMSP, falling from 840 fatal crashes per year in 1986 to 263 in 1995. If crash trends in California had paralleled those in the rest of the United States over this period, the state would have experienced an additional 124 fatalities per year. In a similar analysis truncated at 1991, just before the helmet law went into effect, California would have experienced an additional 76 fatalities and 1,333 injury crashes per year. Using these latter figures, along with costs from Peck and Healy, gives an estimate of annual savings of $113 million from the program.

In the case of novice riders with less than 500 miles of prior experience, a matched-pair analysis indicates that trained riders experience less than half the crash rate of their untrained counterparts for at least 6 months after training. Beyond 6 months, riding experience begins to have a leveling effect on the differences between the two groups. In the case of riders with more than 500 miles of experience prior to training or interviewing, no significant differences in crash rates were detected between the two groups, either before or after riders took the basic training course. There was no evidence that riders electing to enter a safety course voluntarily rode any more safely than their untrained counterparts before taking training.

Survey responses showed that recent CMSP trainees consistently reported higher usage of such protective equipment as helmets, boots, and jackets.

Of those who take the basic course, 5 percent quit riding in large part because of their poor performance in the course. If the course weeds out 3,900 unpromising riders (5 percent of all students), it probably prevents 120 crashes a year and saves $5.9 million (at $49,500/crash) B more than the $1.3 million cost of the CMSP.

Questions
What is the theoretical relationship between training and the crash rate? Should training introduce a one-time drop or an ongoing drop? If one-time, over what time period? To the extent that crashes are caused by inexperienced, untrained riders, the crash rate should be a function of the proportion of all motorcycle operators who are both inexperienced and untrained. The extent to which training is a substitute for experience could be represented as a parameter in the model.

On page 102: “A similar [multivariate time series] analysis limited to the years 1988-1991 (after the introduction of the training but before the introduction of the helmet law) also showed that training had a significant impact in reducing fatalities per 1,000 registrations.” How can a test limited to the years 1988-1991 demonstrate the impact of a change that happened in 1987, before the period being analyzed? The author probably means 1988-1991 is to be compared to 1978-1987, but this is not clear.

Why did the crash rate for riders under age 25 increase so much in 1983-86? Were more miles being ridden? Young people taking up riding at a higher rate?

Are both halves of a pair being observed simultaneously in time, or are people from different time periods being matched ex post? If the latter, the riders might not be well matched - e.g., six summer months for one might coincide to six winter months for the other (or is this even a relevant consideration in southern California?).

Useful Reference
Peck, R., and Healy, E.J. “Accident Cost and Benefit Analysis.” DMV Research Notes, Winter 95/96, Sacramento, CA, 1995.

Strengths
The article uses two different and complementary analytic approaches: 1) analysis of aggregate time series and 2) an experimental matched-pair survey that compared the traffic records of two groups of subjects.

The matched-pair study introduced two small innovations that allowed it to focus on the inexperienced riders who are most likely to benefit from training: 1) distinguishing between pairs of riders according to prior riding experience, and 2) executing the first follow-up survey after just six months instead of waiting a full year.

The study also used a relatively large sample of records from true novices (a subgroup other studies fail to tap into), which is significant because the road-experience factor may, within a very short exposure time, render potential real short-term benefits of CMSP indiscernible.

The study gives a very interesting survey finding, regarding the 16 percent of course takers who quit riding within a year, with 5 percent citing the course as the reason for no longer riding (“program as effective sieve”). If this holds, and the cost formulas used are valid and applicable, then this provides a significant benefit of the CMSP frequently overlooked by other studies.

Weaknesses
The crash trends analysis used registrations and licensed riders, rather than exposure (VMT) as the denominator in calculating rates.

The author is often vague regarding his methods. This makes it difficult to critique some of the findings. What, for example, was the method for the “multivariate time series analysis” mentioned on page 102? Was it contrasted via regressor series, explicitly via a composite ratio series, or analyzed separately, and then the two intervention coefficients tested against each other for a net difference? Or does this imply that other unmentioned series were employed as regressors, such as economic data, demographic shifts, car crash rates, etc.?

Note that, while the paper admits “no guarantee that CMSP is totally responsible for these imputed savings,” it is also possible that the effects could even be understated, if other states within the comparison series were also passing CMSP training programs during this time. In sum, it is impossible to reliably judge the time series analysis “results” other than simply giving the author the benefit of numerous possible doubts and assuming the best for the many unanswered questions.

The matched-pair study suffered from a small sample size. Even though the sample was much larger than previous comparable studies, it still included just 63 crashes. Dividing these into eight cells (by training, prior experience, and length of time after training/interview) made it almost impossible to achieve significant results. In the one instance where a significant result was achieved, one wonders if it might have been a random success.

The finding of the marginal difference in crash rates within the first six months, for the <500-mile subgroup, is based on a very small count to begin with, which by itself would be nowhere near significant (5 crashes vs. 7 crashes, out of 615 pairs). But this difference is adjusted even further apart by dividing these rare event counts by mileage exposure, which itself is imputed for a majority (63 percent) of the cases. Given that, it seems a real stretch to ascribe any significance to the p=.065 finding. (We don’t know if this is a one-tailed probability or two-tailed. With the mixed results that the other results have shown, it should use the more conservative two-tailed test.) It is even more of a stretch to make much of this questionable p=.065 result when there were 4 tests performed, with 3 of those markedly non-significant, and without even a general congruence in the directional pattern among the tests.

The “one year after” category appears to subsume the “six months after” category. This cumulative treatment is misleading, essentially double-counting the first six months. If a “second six months” category were used, it would show that untrained riders had fewer crashes in the second six months.

There is not enough information from which to judge the violation comparisons, but to his credit the author mentions that among the ERC pairs, the trained group has a lower incidence of violations pre-training, again suggesting the possibility of self-selection bias among the trained group.

It is difficult to justify a matched-pair design unless there is an a priori reason to expect that pairs of cases are likely to be statistically dependent (with correlated errors), rather than just exogenously similar on a multitude of important factors.

The study matches pairs on the basis of give of the most important factors - age, sex, years riding, miles/year, and purpose of riding - but it does not match on any measure of past driving record (e.g., moving violations) which is the factor that usually best predicts crashes.

If the state requires training for all riders under age 21, where did all the untrained riders in the study coming from? Some sort of self-selection might be embodied in this group that is willing to flout the law.

The survey follow-up found that the riding frequencies of trained and untrained riders diverged after training, with trained riders riding more. This might represent self-selection, in which case it could substantially compromise group comparability. Was the response rate (37 percent) similar between the trained and untrained groups? If one group had a higher response rate than the other, then the estimates of exposure for the groups could be biased differentially - which could be crucial (or disastrous) when the author uses those estimates to adjust crash rates for group exposures, because one group is imputed to have a 28 percent higher exposure (5,500 miles vs. 4,300 miles). If the response rates are too different, and the imputed exposures biased, then the subsequent crash rates that were analyzed would be highly questionable.

The article tells us that 16 percent of trained riders quit within one year, 5 percent because of the training course. But it does not tell us the equivalent discontinuation rates for untrained riders. This makes it hard to be sure that the 5 percent would not have discontinued anyway, finding some other convenient reason to cite. This, in turn, calls into question the estimated cost savings from the 5 percent who quit riding.

See Questions for other possible weaknesses.

Conclusions
Billheimer takes on a difficult research question. The results are instructive, but they cannot be taken as definitive. This article demonstrates the difficulty of good research on driver training.


Braddock, M., Schwartz, R., Lapidus, G., Banco, L., & Jacobs, L. (1992). A population-based study of motorcycle injury and costs. Annals of Emergency Medicine, 21(3), 273-278.

Abstract
Objective
. To provide a population-based injury and cost profile for motorcycle injury in Connecticut.

Study Population. Victims of motorcycle injuries resulting in death or hospital admission in Connecticut, 1985-89.

Data & Methods. Population-based retrospective epidemiologic review of Connecticut death certificates (1985-87), hospital discharge data (fiscal 1987-89), and police crash reports (1985-89). (The three data sources were not linked.) In the first two datasets, motorcycle cases were identified by E-code. Hospitals that E-code less than 70 percent of injury discharges were dropped. The resulting E-coded subsample included only 36 percent of Connecticut’s injury discharges, so counts were divided by 0.36 to create statewide estimates.

Hospital charges were used as a cost proxy. Estimates of payer reimbursement were assigned to cases by DRG, based on the weights, rates, and outlier definitions for one large acute care hospital in the state.

Results. Connecticut death certificates identified 112 deaths from motorcycle injuries for an annual death rate of 1.2 per 100,000 persons. Death rates were highest among 20- to 24-year-old men. Nonhelmeted motorcyclists were 3.4-fold more likely to die than were helmeted riders (P<.05). An estimated 2,833 motorcycle-related hospital discharges resulted in an annual hospitalization rate of 29.6 per 100,000 persons. Head, neck, and spinal injuries accounted for 22 percent of all injuries. Total hospital charges exceeded $29.3 million. Of this total, 12 percent was generated by operating room charges, 2 percent was computed tomography charges, and 6 percent was ICU charges. 60 percent of hospitalized patients had commercial insurance, and 29 percent were uninsured. 42 percent of charges ($12.3 million) was not reimbursed by payers to the hospitals.

Conclusion. Motorcycle injuries contribute significantly to Connecticut’s mortality, morbidity, and medical costs. This study suggests that a uniform helmet law would save an estimated 10 lives and prevent more than 90 nonfatal injuries in Connecticut each year at a cost savings to the state of $5.1 million. These data are crucial in advocating re-enactment of motorcycle helmet laws.

Note
During most of the study period, Connecticut had no helmet law. In mid-1989, a law took effect requiring riders under 18 to wear helmets.

Questions
Were costs adjusted to a common year’s dollars? If so, what year?

Strengths
This article presents estimates of both hospital charges and payer reimbursement for comparison. While the estimates are based on a number of assumptions, they are nonetheless very useful for Connecticut policymakers.

Weaknesses
The reported cost estimates appear to be based on hospital charges, rather than payments. This would bias the cost estimates upwards. On the other hand, the charges do not appear to include physician charges, which would cause the cost estimates to be understated.

Hospital reimbursement rates were based on a single hospital, whose rates were slightly higher than those of other hospitals in the state. As the authors note, this would tend to overstate the amount reimbursed to hospitals.

Crash victims with injuries too minor to require hospitalization were not included in this study.

The authors had no way of determining whether hospitalized survivors were wearing helmets at the time of their crash.

The authors point out that their sample of 112 fatalities from death certificates in 1985-87 is much smaller than the 214 reported in the same period by police crash reports. They were not able to explain the discrepancy.

In the discussion, head, neck, and spinal cord injuries are lumped together. The discussion would be better focused on head injuries only, for these are the injuries that helmets prevent. The proportion of spinal cord and neck injuries also seems very high.

The authors report that 42 percent of charges are not reimbursed by payers, but they do not discuss the reasons. Did the 29 percent of patients who were uninsured incur higher costs? Did payers refuse payment for insured patients?

E-coding was apparently not mandatory in Connecticut during the study period. Therefore, E-coding practice varied greatly between hospitals. The authors’ method for dealing with the problem (dividing by 0.36) assumes that motorcycle injuries are no more or less likely to be E-coded than other injuries. If records of motorcycle injury discharges are, in fact, more likely to be E-coded, then the estimates presented would be biased upwards. If, on the other hand, motorcycle crashes are often E-coded as motor vehicle crashes, without specifying that the victim was a motorcycle rider, then the estimates could be biased downwards.


Bray, T., Szabo, R., Timmerman, L., Yen, L., & Madison, M. (1985). Cost of Orthopaedics injuries sustained in motorcycle accidents. Journal of the American Medical Association, 254(17), 2452-2453.

Abstract
Purpose.
To assess hospital costs, insurance profiles, and costs to taxpayers of motorcycle crashes in California.

Study Population. 51 serial admissions to the orthopedic services at the University of California, Davis, Medical Center, Sacramento, for motorcycle crash trauma with open fractures were reviewed.

Data. Motorcycle cases from a large-scale study of open fractures carried out in 1980-1983 by the Department of Orthopedic Surgery were supplemented with data on BAL at admission and disposition at discharge from hospital records, plus billing and insurance information from the patient billing and collecting department.

Results. 55 percent of those tested were alcohol intoxicated at the time of admission. 75 percent carried no insurance of any kind, and for the total group, 72 percent of hospital charges for acute hospitalization ($17,704 total per patient) were charged to the state of California, with an additional 10 percent charged to other tax-based sources.

Conclusions. Care of motorcycle trauma consumes a substantial portion of public health care funds in California. This could be reduced by legislative action concerning helmet use, licensing, and rigid enforcement of compulsory insurance.

Strengths
Detailed hospital data, including payers, on a number of motorcycle injuries.

The most interesting findings concern 1) payers and 2) intoxication. The precision of both findings is somewhat compromised by the non-representativeness of the sample, but both findings are strong enough to suggest that uninsured riders and drunk riders impose large costs on the state of California.

Weaknesses
As acknowledged by the authors, these 51 open fracture cases cannot be considered a representative sample of motorcycle injuries. Open fractures account for only about a third of all motorcycle injuries, and they tend to be significantly more serious than non-fractures or closed fractures.The article, which is based on data from 1980-83, is quite dated. The 21.2-day average length of stay is extremely high; in 1991, the average length of stay for hospital-admitted motorcycle injuries was just 7.9 days. The longer length of stay represents the combined effects of 1) the sample’s bias towards more severe injuries and 2) the less cost-conscious medical regime of the earlier period. It would be very difficult to generalize these results.

The article did not mention whether any of the 51 patients died. It is presumably to be inferred that they were all alive at the time of discharge.

The article presents a finding that 55 percent of “those tested” were legally intoxicated. The authors seem to assume that the 29 patients tested were a randomly selected representative sample. But it is likely that these patients were tested because there was reason to believe they had been drinking, whereas the 22 patients not tested had probably not been drinking at all. We are not informed whether the 13 tested patients who were not intoxicated might have tested positive at lower levels. If we assume the 22 untested patients were not drinking, this would still imply that 31 percent of the patients were legally drunk, and another 25 percent may have been drinking at lower levels.

There is no indication of whether the riders were wearing helmets when they crashed. There is also no breakdown by body region or severity, but the sample size would probably not support such analysis, anyway.

The authors’ own description of their cost proxy will serve as a sufficient criticism:

The “cost of the initial hospitalization” was defined as the total hospital charges incurred during that specific admission. Not included were charges for initial emergency service; charges incurred at an initial hospital before transfer to our institution; subsequent readmission for complications, removal of hardware, or reconstructive procedures; rehabilitation costs after discharge; transfer to skilled nursing facilities or other hospitals; and professional, legal, and administrative fees.

The use of hospital charges as a proxy for cost can cause two problems. First, it will result in an overall upward bias in the medical cost estimate, since hospitals typically overcharge. Second, it will affect the distribution of costs among payers, since some payers pay 100 percent of charges, while other payers with more clout (e.g., Medicaid) might pay less than half of charges.

On the other hand, the omission of all costs other than hospital charges will bias the cost estimate in the opposite direction - downwards. The omission of professional fees - the amounts paid directly to doctors - is particularly egregious (we estimate that these fees typically add 30 percent to the cost of a hospital stay for injury). The net effect of these opposing biases is probably an underestimate of medical costs, but there is no way to be certain.


Bried, J.M., Cordasco, F.A., & Volz, R.G. (1987). Medical and economic parameters of motorcycle-induced trauma. Clinical Orthopaedics and Related Research, 233, 252-256.

Abstract
Study Population & Methods.
A retrospective study was conducted on all patients injured in a motorcycle accident who were admitted to the Arizona Health Sciences Center during a one-year period, July 1984 through June 1985. Researchers examined the paramedic report, ED report, and inpatient records.

Data & Results. The 71 hospital-admitted patients evaluated averaged 26 years of age; 79 percent were men, 75 percent were not wearing a helmet, and 24 percent were legally intoxicated. 66 percent required surgical intervention and 36 percent a second procedure. 52 percent required ICU care, (mean, 3.3 days) and 30 percent required a ventilator (mean, 6 days). There were 167 fractures, with an average of 2.4 per patient. The 27 patients requiring a blood transfusion averaged 10.5 units per patient.

Motorcyclists not wearing a helmet had an increased risk of head injury (p<.01) (see table). Those with head injuries had an increased need for intensive care (p<.0001) and a ventilator (p<.001). Patients with head injuries more commonly sustained fractures about the shoulder (p<.015) than fractures to the lower extremity (p<.005). The average hospital stay was 13 days, with average charges of $16,408 per patient. Charges were lower for motorcyclists wearing a helmet ($13,368 vs. $17,120). Charges were significantly higher in patients with a head injury ($21,945) than in patients without a head injury ($11,941). Patients sustaining a head injury were less likely to return to baseline functioning (p<.001). Of the 12 patients who became permanently impaired, none had been wearing a helmet and 10 sustained a severe head injury.

Relationship of Head Injury to Helmet Usage

  No Head Injury Head Injury Total
Non-helmeted
26 (37%)
27 (38%)
53 (75%)
Helmeted
15 (21%)
3 (4%)
18 (25%)
Total
41 (58%)
30 (42%)
71

Questions
Did the study really cover patients admitted to this hospital? How did it happen that helmet use was known for every patient?

Strengths
The cost proxy captured physician charges as well as hospital charges. And while, as the authors acknowledge, the cost estimates do not include post-hospital costs, a separate estimate of SCI rehabilitation costs is offered.

The article provided considerable detail on fractures.

The use of “baseline functioning” to operationally define permanent disability allows the authors to get at a question rarely examined in the motorcycle injury literature.

Weaknesses
The sample was small. It included only 18 non-helmeted riders.

The authors report a difference in charges of $17,120 vs. $13,368 for non-helmeted vs. helmeted, but they do not state whether this difference is statistically significant.

Raw hospital and physician charges are used as a proxy for costs. This is likely to bias the medical cost estimates upwards because hospital charges are greater than costs.

The study was not representative of the whole population of motorcycle injury victims. It captured only those victims who were injured severely enough to be admitted to a hospital. It would also have been useful to know more about the hospital where the study was performed - in particular, whether or not it is a trauma center.

Conclusions
Though short and simple, this article not only added more support to the usual arguments for the effectiveness of helmets, but it also shed light on the problem of permanent disability and suggested a negative correlation between fractures of the head/shoulders and fractures of the lower leg.

Interesting Finding
Head injuries in motorcyclists are often accompanied by fractures in the shoulder region, but rarely by fractures of the lower leg. Only 1 patient of the 71 covered by this study suffered fractures in both regions.

Interesting Reference
“In 1982, treatment for a spinal cord injury at the Phoenix Regional Spinal Cord Injury Center averaged $75,300 for a quadriplegic requiring an average stay of 184 days.” (Young, Burns, Bowen, & McCutchen. (1982). Spinal Cord Injury Statistics. Phoenix, AZ, Good Samaritan Medical Center, p. 33.)


Hell, W. & Lob, G. (1993). Typical injury patterns of motorcyclists in different crash types -- effectiveness and improvements of countermeasures. 37th Annual Proceedings of the Association for the Advancement of Automotive Medicine, 77-86.

Abstract
Purpose & Study Population.
There is a high injury rate in motorcycle crashes. In contrast to automobiles, there have been no improvements in the passive safety of motorcycles in the last few decades. The motorcyclist drives without an impact absorbing zone, and his possible safety measures are limited to a safety helmet and appropriate safety clothing. His kinematics in a crash can take many different forms. The object of this study is the investigation of 173 real-life motorcycle crashes at the scene of the accident, to get a coherent picture of the severity and injury pattern in different crash-types. Additionally, the efficiency of protective measures (147 articles of clothing and 85 crash helmets) was analyzed.

Data. 173 motorcycle crashes, involving 210 riders, in 1985-1990 in the Munich (Germany) area were documented at the scene by the Bavarian Police. This was followed up by a medical analysis in hospitals and an injury rating using AIS and ISS.

Of the 210 riders, 50 (24 percent) died. This compares to a 2 percent fatality rate for all police-reported motor vehicle crashes in Germany.

Methods. The authors did tabular analysis of their data by severity, body region, and crash type (i.e., the relative positions and directions of travel of the motorcycle and its crash opponent). They also looked at the performance of leather clothing and helmets with respect to injury severity.

Results. Of all crashes involving injuries of moderate or greater severity (AIS>=2), 43 percent involve injuries to the head, followed by lower extremities (37 percent), upper extremities (30 percent), thorax (25 percent), abdomen (16 percent), spine (12 percent), and pelvis (8 percent).

Severity and body region vary greatly depending on the crash type. Three of the eleven crash types account for 41 of the 50 fatalities: head-on collisions 1) with the front of an opposing vehicle, 2) with the side of an opposing vehicle (without fly-over), and 3) with a stationary object (e.g., tree, crash barrier). These same three crash types also have the highest rates of head injury and the highest ISS.

Two-thirds of crashes involved an opponent; the other one-third were solo. Two-thirds of crash opponents were cars, one-sixth were commercial vehicles. Most collisions with cars (90 percent) struck the front or side of the car, while a plurality of collisions with commercial vehicles (48 percent) struck the rear end. The motorcycle’s first impact point was in front in 67 percent of cases, and in the side in 29 percent.

Of 147 riders whose clothing was analyzed, 45 percent wore leather jacket and leather trousers (the article says 55 percent, but this would make the total 110 percent, and figure 4 shows that only a minority of riders wore leather trousers), 29 percent wore a leather jacket, and 26 percent wore no leather. Wearing leather safety clothing was found to be correlated with lower severity of injuries to the extremities, particularly the legs.

Of the 210 riders, 205 wore helmets. But 30 of these lost their helmets during the crash. Of 85 helmets that were investigated intensively, 16 were lost and 11 were broken in the crash. For the 58 cases where the helmets remained on riders’ heads, AIS of 0 to 2 are predominant, while more severe injuries were common when the helmet was lost. When the impact was hard enough to crack the helmet, it usually killed the victim, as well (7 of 11 cases). The most common impact regions on the helmet were the mandible (chin bar) and forehead.

Conclusions. The authors make recommendations to industry and legislators regarding the design of protective clothing, helmets, motorcycles, and other vehicles. Some of these do not derive from the data presented.

Questions
Did the data include survivors who were not medically treated? Or who were treated outside hospitals (e.g., in doctor’s offices)?

Weaknesses
The article contains no direct information on costs or payers.

Some results (e.g., body regions injured) are not separated by fatal/nonfatal (AIS 6 does not include most fatalities).

Strengths
The authors make thorough use of their data, analyzing it by 1) body region vs. severity, 2) body region vs. crash type, 3) ISS and severity vs. crash type, 4) leather clothing use vs. severity, 5) helmet performance vs. severity, and 6) helmet impact region. Several of these dimensions of motorcycle crashes (2, 3, and 4) are not commonly analyzed. Therefore, this article makes a number of unique contributions to the motorcycle crash literature.

Conclusion
With this article, Hell and Lob have made a unique and valuable contribution to the literature on motorcycle safety.


Karlson, T.A., & Quade, C.A. (1994). Head injuries associated with motorcycle use - Wisconsin, 1991. Morbidity and Mortality Weekly Report, 43(23), 423, 429-431.

Abstract
Objective.
To assess 1) the risk of head injury for motorcyclists in motor-vehicle crashes, 2) the initial inpatient hospital charges for motorcyclists with head injuries resulting from crashes, and 3) the reduction in injuries and fatalities associated with universal helmet use.

Study Population. Motorcyclists hospitalized for injury after police-reported crashes in Wisconsin in 1991.

Data & Methods. Police Accident Reports and inpatient discharge records for acute-care hospitals were linked through a probabilistic method (which calculated the likelihood that a police report and a discharge record represent the same person) using the date and location of the event, the victim’s date of birth, sex, and home Zip Code, and whether the victim was transported by ambulance. For uncertain matches, additional information was reviewed manually, including diagnoses and E-codes. About 7 percent of the automated matches were determined to be incorrect.

Head injuries were separated into three categories: 1) brain injury, 2) skull fracture without intracranial injury, and 3) concussion with only brief (less than one hour) or no loss of consciousness.

Results. Of 3,184 motorcyclists involved in police-reported crashes, 2,015 (63.3 percent) were unhelmeted and 994 (31.2 percent) were helmeted. Helmet use was unknown for 175 (5.5 percent). Of those for whom helmet use was known, 545 were hospitalized and 74 died. (See Table 1, below, for more detail.)

Of the 545 hospitalized, 187 (34.3 percent) sustained head injury. (See Table 2, below, for a detailed breakdown of these 187 cases.) Of the 2,015 unhelmeted crash victims, 7.6 percent suffered head injuries, while only 3.4 percent of the 994 helmeted victims suffered head injuries. Unhelmeted head injury victims also incurred an average of 59.2 percent greater hospital charges for the initial admission.

Conclusion. Universal helmet use by all motorcyclists in Wisconsin during 1991 potentially would have prevented 60 brain injuries, 13 skull fractures with no intracranial injury, 8 concussions, and 14 deaths. It could also have saved society more than $400,000 in medical charges for initial hospital admissions.

Note
During the study period, Wisconsin required helmet use only for motorcycle riders under 19 years old. The observed helmet use rate is 42 percent of all riders.

Questions
Did the linkage technique fail to link any crash reports with hospital records? If so, how many? How do the authors know that 7 percent of the matches were incorrect?

How did Wisconsin achieve 94.5 percent reporting on helmet use? No other state without a universal helmet law has such a high rate.

Strengths
The authors began with reliable estimates of all motorcycle crash victims in the state, and used these figures as denominators in their rate calculations.

The goals, methods, and conclusions of the analysis were consistent with the limited nature of the linked dataset, which included only hospital-admitted survivors. Since the analysis focused on relatively severe head injuries, it is likely that few relevant cases were omitted by excluding non-hospitalized injuries. The explicit, if perfunctory, analysis of fatal cases separately from nonfatal provided a necessary complement (though it could, perhaps, have been made more relevant if head injuries could have been identified among the fatalities).

Crash victims whose helmet status was unknown were accounted for explicitly (although they were not included in the analysis of head injuries or the resulting cost estimates).

Weaknesses
The presentation of the results carefully avoided showing the helmet-status breakdown of the 545 hospitalized motorcyclists whose helmet status was known. (Perhaps the space and format constraints of the MMWR prevented the raising of a question that would require a digression on the prevalence of limb injuries among helmeted motorcyclists who are hospitalized.)

Charges for initial hospital visits were used as a proxy for health care costs. Professional fees and other costs were not accounted for, which would cause costs to be underestimated. (The absence of accounting for long-term costs is particularly serious for brain injuries, which can have long-term consequences.) On the other hand, charges were not adjusted to reflect the fact that hospitals overcharge.

Table 1.
Victims of Police-Reported Motorcycle Crashes, Wisconsin 1991:
Distribution by Injury Status and Helmet Use

  Unhelmeted Helmeted Unknown Total
Total
2,015
994
175
3,184
Fatal
55
19
7
78
Hospitalized
545
32
577
Head Injured
153
34
13
200

Table 2.
Motorcycle Crash Victims Hospitalized for Head Injury, Wisconsin 1991:
Distribution, Injury Rates, and Average Charges by Injury Type and Helmet Use

  Unhelmeted Average Helmeted Average
No. Rate Average Charge No. Rate Average Charge
Brain Injury
97
4.8%
$24,705
17
1.7%
$19,642
Skull fracture w/o intracranial injury
18
0.9%
$12,373
2

0.2%

$5,419
Concussion
38
1.9%
$7,336
15
1.3%
$4,002
Total
153
7.6%
$18,940
34
3.4%
$11,897

The estimation of savings from universal helmet use assumed that the observed distribution of outcomes for helmeted riders would be replicated in the unhelmeted riders if they were to wear helmets. This might not be the case if helmet use is correlated with other riding behaviors (e.g., speed, alcohol consumption) or rider characteristics (e.g., experience).

Conclusions
This article achieves its modest goal, clearly demonstrating that helmet use is associated with lower rates of head injury and lower hospital charges for injured motorcyclists.


Kelly, P., Sanson, T., Strange, G., & Orsay, E. (1991). A prospective study of the impact of helmet usage on motorcycle trauma. Annals of Emergency Medicine, 20(8), 852-856.

Abstract
Objective.
To determine the effect of the use of a motorcycle helmet on reducing the mortality, morbidity, and health care costs resulting from motorcycle crashes.

Study Population & Data. All motorcycle crash victims presenting less than 24 hours after injury for whom helmet information was known. Data were collected during April-October 1988 (the “motorcycle season,” seven months long) at the emergency departments of eight medical centers across Illinois, including representatives from urban, rural, teaching, and community facilities.

Method. A prospective, multicenter study of all eligible motorcycle crash victims. The examining physician filled out the questionnaire for each patient and a member of the research team recorded follow-up information from the treating facility and from the patient, the family, or paramedic or police reports. County coroners were contacted for information on fatalities taken directly to the morgue.

Results. Of 398 patients, 58 (14.6 percent) were helmeted, and 340 (85.4 percent) were not. The nonhelmeted patients had higher Injury Severity Scores (11.9 vs. 7.0), sustained head/neck injuries more frequently (41.7 percent vs. 24.1 percent), and had lower Glasgow Coma Scores (13.73 vs. 14.51). 25 of the 26 fatalities were nonhelmeted patients. By logistic regression, the lack of helmet use was found to be a major risk factor for increased severity of injury, along with speed and mechanism of injury.

Health care charges were found to be 23 percent higher for nonhelmeted patients (average charges $7,208 vs. $5,852). Nonhelmeted patients were also transported by ambulance more frequently (63.4 percent vs. 46.4 percent) and more likely to be uninsured (54.6 percent vs. 44.2 percent), though none of these differences was significant at the 5 percent level.

Conclusions. Helmet use may reduce the overall severity of injury and the incidence of head injuries resulting from motorcycle crashes. A tendency toward higher health care costs was demonstrated in the nonhelmeted patients.

Questions
Mechanism of injury is said to be a predictor of injury severity. Which mechanisms are associated with more severe injuries -- vehicle-vehicle, vehicle-fixed object, overturn, or other? How did the logistic regression account for mechanism?

Data were collected only for patients whose helmet status was known. Does this mean that the questionnaire was not distributed to patients whose helmet status was unknown to the researchers a priori? Or does it mean that they dropped those patients from their sample who did not report their helmet status on the questionnaire? For how many patients was helmet status unknown?

Strengths
The study collected data on interesting and relevant factors, including a few that are often overlooked or not adequately measured: time of day, type of roadway, estimated speed, license possession, ambulance transport, and blood alcohol.

Despite data limitations, the study found four factors that are good predictors of injury severity -- speed, mechanism of injury, helmet status, and age -- all of which are highly significant.

The analysis by severity, body region, and helmet use was appropriate, though perhaps more detail could have been reported.

For at least some of the analysis, fatalities were examined separately from survivors. For other analysis, this distinction was taken into account in the severity measure.

Weaknesses
Physician charges for inpatients were not included in costs. This would tend to lead to an underestimate of medical costs.

The small sample of nonhelmeted riders (58, or 14.6 percent) made it very difficult to obtain significant results. Likewise, the sample included only 26 fatalities, and only 1 of these was nonhelmeted.

The study was not representative of the whole population of motorcycle injury victims. It captured only those victims who died or received medical treatment in an ED. Those who were injured less severely were missed.

The payers reported were apparently expected, rather than ultimate, payers. Also, more payer detail could have been reported.

No explanation was offered for the significance of age in predicting injury severity. Did it proxy for riding experience? Or for risk aversion?Interesting Finding

Figure 2 shows that helmeted riders had higher rates of limb injuries, but lower rates of external injuries (e.g., lacerations, abrasions, contusions). The former is a common finding, often resulting from the selection bias inherent in analyzing only those crash victims who were injured. But the latter result is contrary to this bias. (Does it suggest that riders who wear helmets are also more likely to wear protective clothing, which can prevent minor external injuries?)

Conclusions
This article employed good methods in collection and analysis, but the small sample of helmeted riders constrained the reporting and the conclusions. The authors were still able to obtain significant results regarding the importance of various factors in motorcycle injury severity.

The medical cost estimates show little detail. Moreover, they embody the usual problems of state-level hospital studies -- reliance on raw hospital charges and exclusion of physician charges. Nonetheless, despite the small sample, the study arrives at results similar to those from other states, pointing to the efficacy of helmet use.


Max, W., Stark, B., & Root, S. (1998). Putting a lid on injury costs: The economic impact of the California motorcycle helmet law. Journal of Trauma, Injury, Infection, and Critical Care, 45(3), 550-556.

Abstract
Objective.
To analyze the effect of California’s motorcycle helmet law, which took effect in 1992, on injury costs.

Study Population. All victims of motorcycle injuries in California, 1991-93.

Data & Methods. California state hospital discharge data, 1991-93. Motorcycle injuries (11,163) were selected by ICD-9 E-code. Cases were classified by admission date. Costs were calculated from charges using hospital-specific cost/charge ratios. Professional fees were estimated as 25 percent of hospital costs. Transfers were identified by discharge and admission dates, discharge disposition, and admission source. Unique record-linkage number were used to link readmissions, rehabilitation, and (hospital-based) nursing home care.

San Diego County cost data, 1991-93. Data on costs of ambulance transport, emergency air transport, ED visits, and follow-up ambulatory care visits were obtained from billing records. These costs were adjusted downwards by the ratio of California/San Diego per diem hospital costs.

Statewide Integrated Traffic Records System. This dataset contains all motor vehicle crash reports submitted by local and state police. Of four severity categories - killed, severe wound, other visible injuries, complaint of pain - it was assumed that the middle two were all treated in a hospital or ED. The count of hospital admissions was subtracted from this total, and the remainder were assumed to be treated in the ED and released.

Head and spinal injuries, identified by ICD-9 diagnosis, were considered separately.

Hospital costs, all direct costs, and lost productivity were analyzed separately. Total and per person costs were compared by year and by head and spinal injury status. Each outcome was compared for 1 year before and 2 years after the implementation of the helmet law.

Productivity losses from fatalities were valued at average lifetime earnings (including an imputed value for household production) by age and sex, discounted at 3 percent. Years of potential life lost by motorycycle fatalities also were estimated.

Results. Total medical care costs for motorcycle injuries were $35 million less in 1993 than in 1991, a reduction of 35 percent. Costs decreased for all payer categories, and 73 percent of the reduced hospitalization costs were attributable to reduced costs for patients with head injuries. Head injuries also accounted for a disproportionate share of the reduction in the rate (60 percent) of hospitalized injuries per 100,000 motorcyclists. Initial hospital costs for patients with head injuries averaged $18,527, compared with $10,350 for patients without head injuries. (See table 1, below, for more results.)

Conclusions. During the first 2 years of implementation of California’s helmet law, there were reduced costs for injuries and fatalities and large dollar savings to the state and other payers compared with the previous year.

Strengths
Comprehensive datasets. The California hospital discharge data capture all hospitalizations in the state, and mandatory E-coding enables selection of motorcycle riders. The Statewide Integrated Traffic Records System captures all motor vehicle crashes in the state.

Hospital-specific cost-to-charge ratios were used to convert charges to costs.

The helmet law took effect on 1 January 1992, making for a clear cut-off between pre-law and post-law periods. Data were collected for one year before and two years after law’s implementation.

The study attempts to account for all medical costs from ambulance through rehabilitation, for both hospital-admitted and non-admitted patients.

The study accounts separately for fatalities and survivors.

Some critics of California’s helmet law have attributed the fall in injury rates after passage of the law to the decline in motorcycle registrations. But Max et al. show calculations demonstrating that injury rates fell much faster than registrations between 1991 and 1992.

Weaknesses
Rates were calculated using the number of registered motorcycles, rather than VMT, as the denominator.

Professional fees were calculated as a flat 25 percent of hospital costs.

Medical expenditures were found not to be normally distributed, but the actual distribution shape was not discussed, nor were the consequences.

Why were non-fracture spinal cord injuries (ICD 952) not counted as spinal injuries?

There was no mention of the productivity losses of non-fatal injury victims. Productivity savings would therefore be underestimated.

Conclusion
The stark contrast between 1991 (before the law) and 1992-93 (after the law) in the number and cost of head injuries leaves little room for quibbling with the article’s conclusion that the helmet law saves lives, health, and money

Table 1
Results from Putting a Lid on Injury Costs:
The Economic Impact of the California Motorcycle Helmet Law

  1991 1992 1993
Number of Injuries
16,910
12,324
11,043
  Fatal
512
327
303
  Hospitalized
4,696
3,410
3,057
  Head Injury
1,468
751
663
  Other Injury
3,228
2,659
2,394
Rate per 100,000 registered motorcycles
  Injuries
2,645
2,113
1,979
  Fatal
80
56
54
  Hospitalized
735
585
548
  Head Injury
230
129
119
  Other Injury
505
456
429
Total Hospital Costs* By body location
$79,510
$50,894
$51,436
  Head Injury
$36,588
$15,890
$16,135
  Other Injury
$42,922
$35,004
$35,302
By primary expected payer
  Private Insurance
$35,111
$22,110
$22,452
  Uninsured
$16,334
$9,179
$9,447
  MediCal
$14,880
$8,623
$9,767
  CMS
$9,110
$7,894
$6,006
  Military/other federal
$4,076
$3,088
$3,764
Primary expected payer shares
  Private insurance
44.2%
43.4%
43.7%
  Uninsured
20.5%
18.0%
18.4%
  MediCal
18.7%
16.9%
19.0%
  CMS
11.5%
15.5%
11.7%
  Military/other federal
5.1%
6.1%
7.3%
Total medical costs and charges*
  Costs
$98,111
$63,087
$63,315
  Chrages
$221,309
$151,557
$136,986
Years of potential life lost
  Total
24,435
15,108
13,830
   Years lost/100,000 reg. m'cycles
3,824
2,591
2,478
Lifetime productivity losses from fatalities
(3% discount rate)
$603,232
$380,000
$345,266

 


McSwain, N.E., & Belles, A. (1990). Motorcycle helmets, medical costs and the law. Journal of Trauma 30(10), 1189-1199.

Abstract
Objective.
Since 1975, 26 states have repealed or modified their motorcycle helmet laws. Louisiana (LA) reinstated the helmet law in 1982. The medical and financial impact of repeal in Kansas (KS), reinstatement in LA (accident, fatality, and critical injury rates) have been studied through 1987. Current FARS data and studies from KS, LA, 10 states, and 5 countries are compared and reported.

Study Populations, Data, & Methods. Texas. The authors selected 99 patients transported from the scenes of motorcycle crashes by the Bexar County EMS from September 1986 to December 1987 (16 months). Eight other subjects were dropped because of unknown helmet use, or because they were transferred to or from the Medical Center Hospital, from which some data were not available. A medical student assigned AIS scores to each patient. To look at rates of fatality, hospitalization, injury, and helmet use, 6,600 Texas motorcycle collisions from the same time frame were examined.

Louisiana. 1) A detailed study examined 616 motorcycle crashes (of 704 total; including 555 injuries with 16 fatalities) in three major population centers - Lake Charles, Baton Rouge, and New Orleans - in June-September of 1981 and 1982 (two periods of four months). 2) The fatality study included all 15,741 motorcycle fatalities in Louisiana in 1981-1987 (seven years). Data sources included summary reports from the Highway Safety Commission, crash reports, hospital discharge files, coroner’s reports, and helmet observation studies.

Kansas. The methods were referenced to other articles. One of these, published by NHTSA, was “Impact of the repeal of the Kansas mandatory motorcycle helmet law: 1975-1978,” (DOT HS-805 773, and executive summary DOT HS-803 959). It outlines the following methods: Data were collected retrospectively for the months July-September of the years 1975-1978 (four periods of three months). During this period, the number of riders involved in crashes rose from 410 to 508, while the number for which helmet data were available fell from 353 to 278. Identification of motorcycle crashes and victims was carried out with the aid of various state agencies, notably the Kansas Department of Transportation, whose data included time and location of crash, type and number of vehicles involved, road conditions, number of riders, and a rough determination of the extent of injuries suffered. Hospitals in the study area helped to confirm the identities of injury victims and assign AIS severity scores to the injuries. In addition, an observational survey of 2,000 motorcycles was carried out during the July-September 1977 at representative locations in the study area. Observations were distributed among the hours of the day and days of the week.

Results. After repeal of the helmet law, the LA user rate dropped from 97 percent to 50 percent. With reinstatement, the user rate rose to 95 percent. Average hospital stay for helmeted (H) riders is 5.8, non-helmeted (NH) 11.8. Fatality rate per 1,000 motorcycle registrations is 6.2 NH, 1.6 H. Changes effected through LA helmet legislation: fatality rate was 1.17 (1981), falling to 0.44 (1987) with legislation (62 percent decrease); fatality rate per 1,000 crashes fell 28 percent, from 42.68 NH to 30.81 H; injury rate dropped from 84 percent of crashes to 73 percent; and the number of critical injuries was reduced by 44 percent (1981 to 1987). Risk of head injury: NH 2.07>H. Risk of fatality: NH 1.44>H. Crash rate is less with helmet legislation than without (19 percent KS, 48 percent LA).

The medical costs (LA 1981-1987) decreased 48.8 percent. Length of stay decreased 37 percent. The major impact hospital stay >20 days: 80 percent decline. Cost of long-term disability >30 days: 81.2 percent decrease (LA). Average disability was 26.7 days H vs. 51.1 days NH (KS); 25.5 percent H required hospitalization vs. 41.6 percent NH. Medical costs: NH 306 percent>H (KS). In Bexar Co., only 18 percent of NH riders’ charges had been paid, compared with 59 percent of H riders’. In LA, short-term disability costs per case fell from $18,314 in 1981 to $17,301 in 1982, while average long-term (>30 days) disability costs fell from $29,800 to $5,600 in the same period. In 1989 dollars, $120.8 million of additional medical care and rehabilitation expenses per year were due directly to non-use of helmets (U.S.). $4.9 billion was absorbed by the public in the form of increased taxation, higher insurance costs, and lost taxes.

Conclusion. Motorcycle helmet legislation decreases medical costs. In this era of spiraling health care costs, legislation mandating the use of protective helmets should be considered as a viable alternative to raising taxes.

Strengths
This article tapped a wide array of data from a number of original studies using data from Louisiana, Texas, and Kansas. It also summarized a wide scope of literature in the field.

The Bexar County study included potentially valuable information on ICU days and percent of charges covered. The Louisiana study included information on both short-term and long-term disability. (The helmet law appears to make a huge difference on long-term disability costs; unfortunately, there is no indication of sample size or statistical significance, so it is difficult to evaluate the result. A few high-cost patients can skew the means, especially if the sample is small, as is likely for motorcyclists in long-term care for their injuries.)

Weaknesses
The number of different studies rolled into this one paper makes for some confusion. It is not always clear which data are the source of a given result.

For Louisiana, the authors often compared 1981 with 1987, rather than with 1982. Any changes resulting from the helmet law should have occurred relatively soon after the law took effect. Stretching out the comparison period increases the likelihood of contamination by other influences. Still, the authors sometimes used 1982, giving the appearance of choosing the comparison year that gave the strongest result.

The authors attributed the falling collision rate in Louisiana to the helmet law. They offer no theoretical reason why this should be so. Moreover, there was no reduction in the first year after the law took effect; the entire reduction took place in subsequent years. It appears that other factors were at work in the state after 1982, reducing the collision rate.

Many of the numbers in the abstract are not contained in the text. Some appear contradictory, others represent different computations.

Rate comparisons use motorcycle registrations as the denominator. There is no discussion of how mileage per motorcyclist might have been changing.

Details on data and methods are often lacking, as are sample sizes and indications of significance, making it difficult to put some of the reported results in context and evaluate them.

There is no discussion of the generalizability of the results from LA, KS, and TX. Without knowing more about the motorcycle-riding populations of these states, it is difficult to apply the results more broadly.

Conclusion
The authors bit off more than they could chew in one article. The datasets assembled for this article deserve separate, more focused articles that can present the data, methods, and results in a more coherent fashion. As it stands, this article contains a lot of valuable results, but it takes a lot of work to mine them, dust them off, and evaluate them. Still, it might be worth the effort to do so if the data in this article can fill data gaps, such as disability costs.


Miller, T., Levy, D.T., Spicer, R.S., Lestina, D.C. (1998). Allocating the costs of motor vehicle crashes between vehicle types. Transportation Research Record 1635, 81-87.

Miller, T.R., Spicer, R.S., Lestina, D.C., Levy, D.T. (1999). Is it safest to travel by bicycle, car, or big truck? Journal of Crash Prevention and Injury Control, 1(1), 25-34.

Miller, T.R. (1994). Costs of safety belt and motorcycle helmet nonuse. Testimony before the Subcommittee on Surface Transportation, House Committee on Public Works and Transportation, hearing on Designating the National Highway System, as required by the Surface Transportation Efficiency Act of 1991 (ISTEA), March 3, 1994.

Abstract
The two published articles employ essentially the same methods to address similar policy questions. The testimony also uses the same methods, though they are not explicitly described. Hence, the reviews have been combined, and findings from all three sources are included here.

When different types of vehicles are involved in a crash, the harm to occupants tends to vary with the weight of the vehicles involved. In determining the appropriate level of government expenditures for traffic safety, costs in multi-vehicle crashes involving different vehicle types must be allocated between occupants and nonoccupants of a particular vehicle type.

Purpose. These studies compare highway crash incidence, injuries, and costs by vehicle type. Four methods for allocating costs among different vehicle types are considered in the first article (1998); the second (1999) includes three of these methods. The methods correspond to different perspectives, including that of occupants of a vehicle and that of society under different property right assignments. Costs based on the allocation methods for the United States as a whole and per vehicle mile are estimated and presented by vehicle type. The 1999 article also includes bicycles, railroad transportation, and air travel.

Study Population, Data, & Methods. Annual crash and injury incidence were estimated using Crashworthiness Data System (1988-1991), National Automotive Sampling System (1982-1986), General Estimates System (1992-1993), and Fatality Analysis Reporting System (1993) data. Costs were computed based on restraint use, body region, and threat-to-life severity of the injury. Costs estimated included medically related costs, emergency services, property damage, lost productivity, and quality of life. Costs were then allocated between vehicle types using the different allocation methods in order to answer comparative safety questions. The cost allocation methods are:

Results & Conclusions. The method of allocating costs to vehicle type was found to be an important determinant of the relative cost for each vehicle type. Motorcycle crashes accounted for 2-5 percent of total crash costs (using the willingness-to-pay methodology). However, motorcycle crashes account for the greatest cost per 1000 vehicle miles ($1,960 over three times greater than the cost for buses, the next most costly per mile) using the excess cost method the authors prefer.

Motor vehicle and bicycle crash costs total $389 billion annually (as reported in the 1999 article); 75 percent resulting from passenger vehicles. Motorcycles and bicycles have the highest costs per 1,000 vehicle and passenger miles; costs per victim are highest for pedestrians, bicyclists, and motorcyclists. Costs per vehicle mile for heavy trucks and passenger cars are comparable but exceed costs for light trucks. Passenger vehicle occupants are safest if a crash occurs. Light truck, other single truck, and bus occupants have the lowest cost per passenger mile, but higher costs than air and rail travelers. Motorcyclists face the greatest risks. Combination trucks may not impose an excess risk to other drivers, but their drivers face large risks.

Detailed findings from the two articles as well as the testimony are presented at the end of this review in several summary tables. (below)

Corrections
There are two corrections to the 1998 paper: The WTP cost for fatalities in Table 1 ($2,716) should read “$2,716,000.” On page 85, in the “Table 4” paragraph, the second-to-last sentence reads: “Other single vehicle truck injuries in multi-vehicle crashes may be more severe.” The word “vehicle” should be removed, so that it reads, “Other single truck injuries . . .”

Strengths
Total costs, including medical, productivity, property, and quality of life, are calculated using both human capital and willingness-to-pay methods. The cost calculations take account of injury severity, including fatal vs. nonfatal.

The studies provide a useful comparative perspective in terms of the relative risk and cost of crashes among motor vehicle types. They also point out the need to consider carefully the perspective of the analysis (cost to occupants vs. society) and hence the method for cost allocation. Whatever methodology is used to compare crash types, motorcycles are the most costly and dangerous mode of transportation of those studied in terms of cost per miles driven. A useful analysis is provided of external costs, i.e., costs not borne by the involved parties, that is useful for public policy purposes.

Weaknesses
The cost allocation methods are difficult to follow. There is no breakdown of results by helmet use or body region.

The analysis assume that the light truck category is heavier than the passenger car/van category, even though the latter includes mini-vans and sport utility vehicles, which are heavier than many pickups. The variation within these two categories is probably greater than the difference , them.

The methodologies assume that crashes are the fault of the vehicle, and leave no allocation to the driver.

The articles do not develop independent estimates of injury costs; rather they use previously published estimates. While this is not a weakness of the articles (the studies were done to allocate costs, not estimate them), it is a weakness for our purposes of reviewing the literature on crash costs.

Conclusions
These articles are useful for indicating the relative importance of motorcycle crash costs relative to the costs of other motor vehicle crash costs. The findings are unambiguous for our purposes, i.e., looking at motorcycle crashes. Whatever method used or rate calculated, motorcycles are more costly than other motor vehicle types per vehicle mile (except railroads), per person miles, per victim, and per survivor. However, motorcycles are among the least costly in terms of total costs because there are relatively few motorcycles on the road compared to cars, trucks, and other vehicles. While the cost data in this study are not presented in sufficient detail to be useful for any specific cost estimation, they provide a valuable comparative perspective and are quite useful for context.

Additional comments
Method 3 assigns excess costs to the heavier vehicle except in the case of motorcycles, reasoning that “. . . these costs are not due primarily to the weight of the other vehicle (i.e., they reflect the lack of protection afforded to riders) . . .” But similar reasoning could be applied to subcompact cars - their drivers are assuming the risk of a known hazard - collision with a much heavier vehicle. Just as methods 3 and 4 assign highway property rights to the drivers of lighter vehicles (except motorcycles), a fifth method could assign property rights to the drivers of heavier vehicles, on the grounds that their vehicle choice shows that they value these rights (or their safety) more highly.

The “Method 3” section concludes: “Assignment of costs in the case of motorcyclists and nonoccupants is also somewhat judgmental and depends upon the purpose of the analysis.” This statement applies to all vehicles and all four methods. It might have been helpful for the authors to spell out more explicitly what judgments and purposes are associated with each method. For example, the property right preference for lighter vehicles might be attributed to the desirability of avoiding a vehicular arms race, in which all drivers are encouraged to armor themselves in the heaviest vehicle available.

Summary Tables of 1993 Motorcycle Crash Costs
from articles by Miller et al.
(all costs in September 1995 dollars)

Costs of Highway Crashes by Method of Analysis

  Human Capital Willingness-to-Pay
Cost Assignment Method million $ percent million $ percent
1 Occupant Cost Method
$6,648
3.9%
$17,872
4.8%
2 Optimal Externality Method
$5,289
3.1%
$14,250
3.8%
3 Excess Cost Method
$6,787
4.0%
$18,327
4.9%
4 Heaviest Vehicle Method
$3,215
1.9%
$8,817
2.4%

Breakdown of WTP Motorcycle Cost, by Excess Cost Method

  Motorcycles All Vehicles
Annual Cost / 1,000 Vehicle Miles
$1,960
$167
Annual Cost / Registered Vehicle
$4,470
$1,939

  million $ percentage
Single Vehicle
$8,402
42.4%
Multi-Vehicle
$11,397
57.6%
Total
$19,799
100.0%

Ultimate External Costs of Highway Motorcycle Crashes...

Total $3.30 Billion
Per 1,000 vehicle miles
$391
Per registered vehicle
$891

Annual Cost of Motorcycle Crashes
by Cost Category (WTP, excess method)

Medical
$1,094 Million
Lost Work
$5,227 Million
Property Damage
$440 Million
Emergency Services
$26 Million
Quality of Life
$11,539 Million
TOTAL
$18,327 Million

Motorcycle Crash Victim Costs...

per 1,000 vehicle miles (3)
$2,031
per 1,000 person miles (1)
$1,781
per victim (1)
$129,000
per survivor (1)
$76,000
per 2-motorcycle crash (1)
$91,267
per crash (4)
$198,000

(numbers in parentheses identify method used, as per table at top of page)

Injury Costs per Motorcycle by Helmet Use, $1992

 

Public
Cost/Mile

Public
Cost/Year
Unforseen
Nonuse Cost/Year
Cyclist's Anticipated
Cost/Year
Helmeted Cyclists
$0.353
$690
$530
$885
Unhelmeted Cyclists
$0.445
$870
$745
$1,240
Helmet Use Savings
$0.092
$180
$215
$355

Annual Public Cost Savings with Universal Helmet Use, $1992

 

Public
Cost/Year
Millions

Medical
Cost/Year
Millions
Deaths Serious
Injuries
Helmeted Cyclists
$3,260
$925
2760
29,500
Unhelmeted Cyclists
$2,885
$$860
2,270
27,700
Helmet Use Savings
$375
$65
490
1,800

 


Muelleman, R.L., Mlinek, E.J., & Collicott, P.E. (1992). Motorcycle crash injuries and costs: Effect of a reenacted comprehensive helmet use law. Annals of Emergency Medicine, 21(3), 266-272.

Abstract
Objective. To document the effect of a reenacted comprehensive helmet use law on injuries and fatalities.

Study Population & Data. 671 patients reported as injured to the Nebraska Department of Roads in the period from one year before through one year after the reenactment on January 1, 1989, in two urban counties representing 40 percent of Nebraska’s population.

Methods. Retrospective before-and-after analysis.

Results. The helmet use law was temporally associated with a 26 percent decrease in the reported rate of motorcycle crashes in Nebraska compared with five other midwestern states. There were sharp declines in the number (and rates) of reported injured, hospital transports, hospital admissions, severe nonhead injuries, severe head injuries, and deaths. Serious head injuries (AIS 3) decreased 22 percent. The percentage of injured motorcyclists with serious head injuries was significantly lower among the helmeted motorcyclists (5 percent) than among the unhelmeted cyclists (14 percent) for the two years combined.

The total acute medical charges decreased by $324,648 (38 percent) after implementation of the helmet use law, consistent with the overall declines in crashes, injuries, and admissions after the law went into effect. Because the study represented 46 percent of the reported injured motorcyclists in the state, the total decrease in charges for the state may have been more than $700,000. If Rivara et al.’s estimate that acute hospitalization charges for injured motorcyclists amount to 60.5 percent of the total medical costs, then the helmet use law may have decreased total medical costs in Nebraska by more than $1.1 million.

Of the patients with known insurance status, 59 percent had private insurance, 34 percent had no insurance, and 7 percent had Medicaid or Medicare. Of the nearly $1.4 million charged over both years, 48 percent was either unpaid or paid by state funds.

Conclusion. The reenactment of a helmet use law resulted in fewer crashes, fatalities, and severe head injuries.

Acute medical charges and payments
by payer and year (1989 dollars)

  1988 1989
Collected from insurance or patient
498,352 (58%)
224,896 (42%)
Collected from Medicaid/Medicare
114,011 (13%)
53,720 (10%)
Uncollected
249,946 (29%)
259,045 (48%)
Total Change
862,309 (100%)
537,661 (100%)

Questions
What is the mechanism by which the helmet law is thought to reduce the number or rate of crashes? This benefit obviously cannot be attributed to the wearing of helmets, which reduce the severity of crash injuries, not the likelihood of crashing. Does the law simply discourage certain kinds of riders -- risk takers or the inexperienced -- from riding at all?

How were the payers and payments collected determined? Did the hospitals record expected payers and apply standard payment/charge ratios ex ante to estimate payments, or did they record actual payers and collections ex post? If it is based on hospital bills, it would be misleading, since bills indicate the expected payer, which often differs from the ultimate payer.

Strengths
The authors try to perform the right kinds of analysis, despite the fact that their sample sizes are sometimes too small to support strong conclusions.

The authors look at changes in riding patterns that coincide with the helmet law, including the percentage of riders who are licensed.

The authors implicitly acknowledge the shortcoming of hospital charges as a proxy for total medical costs by trying to extrapolate from it to a more comprehensive state-wide medical cost estimate.

The authors deal explicitly with three phenomena that are sometimes cited by anti-helmet activists: basilar skull fractures, cervical fractures, and organ donations.

Weaknesses
Crash Rate Study. It is very difficult to draw conclusions from a comparison of states, even well-chosen ones, because they differ in so many other ways (though their results are rather striking).

Clinical Injury Study. Much of the analysis, particularly the breakdowns by body region and injury type (Table 4), is based on small samples, and the results cannot be considered significant. The clearest evidence of the efficacy of helmets would come from a demonstration of a reduction in the rate of serious head injuries. But the conclusion that “serious head injuries decreased 22 percent,” based on a total of 34 cases over the two years, is particularly sensitive to sample size; a single additional head injury in 1989 would have cut this decrease by half. Given the small population, the secular decline in all motorcycle riding figures, and the decrease in unlicensed driving, a one-year post-law sample consisting of 92 injuries and 38 hospitalizations limits the strength of the conclusions that can be drawn.

The sample was restricted to victims transported to a hospital for treatment by ambulance or helicopter. Those who died at the scene or who were not injured severely enough to require emergency transport were not captured.

Hospital charges were used as a proxy for health care costs. Professional fees and other costs were not accounted for, which would cause costs to be underestimated. On the other hand, charges were not adjusted to reflect the fact that hospitals overcharge. The authors try to make a rough extrapolation from hospital charges to total medical costs, but the ratio they use does not appear to be specific to Nebraska.

Even though the hospital dataset included information on payments, as well as charges, the authors do little with it. They seem to assume that charges are real, and that when payments fall short of charges this represents a social loss. In reality, this is the usual result of hospitals’ practice of overcharging. It would also have been useful to see a the payment/charge ratios for the various payers.

Payers are broken down only by patient, not by charges. They are not broken down by helmet use. And where aggregate payments are reported, self-pay is combined with insurance, and Medicaid with Medicare; these categories should be reported separately.

Fatalities do not appear to have been separated from survivors for the analysis.

Conclusions
This article overcomes data limitations by slicing the data in enough different directions to give an overall picture that suggests the benefits of helmet use. Even though the article suffers from the common problems of 1) a sample selection process that systematically excludes some segments of the injury population and 2) reliance on hospital charges as a cost proxy, it still manages to produce a rough but reasonable estimate of statewide motorcycle injury cost savings resulting from the helmet law.


Murdock, M.A., & Waxman, K. (1991). Helmet use improves outcomes after motorcycle accidents. Western Journal of Medicine 155(4), 370-372.

Abstract
Objective. To determine the effects of motorcycle helmet use on patient outcomes.

Study Population & Data. The authors studied patient outcomes and demographic and epidemiologic variables of 474 patients injured in motorcycle collisions and treated at a Level I trauma center over a 45-month period. Of those involved in a motorcycle collision, 50 percent were not wearing a helmet, 23 percent were wearing a helmet, and in 27 percent helmet use was unknown.

Methods. For analysis, patients were separated into groups according to such variables as injury severity, body region injured, and disability. Counts and percentages of helmeted and nonhelmeted riders in various groups were compared, and differences were tested using Student’s t test or Fisher’s Exact Test.

Results. Those who were wearing a helmet had fewer and less severe head and facial injuries, required fewer days on a ventilator, and sustained no serious neck injuries. Fewer patients who wore helmets were discharged with disability (5 percent vs. 9 percent); this difference was especially sharp with respect to disabling head injuries (i.e., injuries resulting in decreased cognitive functioning) (1 percent vs. 7 percent). Average hospital charges were also lower for helmeted patients ($16,154 vs. $29,905). 53 percent of helmeted patients had third-party insurance coverage, compared with only 40 percent of nonhelmeted patients.

Conclusion. These results support the need for both increased public education regarding helmet use and mandatory helmet use legislation.

Interesting Finding
Over a 45-month period, 474 of 3,941 trauma patients (12 percent) at a Level I trauma center were involved in a motorcycle collision.

Strengths
Fatality and disability cases were examined separately, in Tables 3 and 4, though they also appear to be mixed in with the other cases for the broader analysis.

The authors’ attention to the details of severity and body region -- particularly head and neck injuries -- compensates, to a large degree, for the overall non-representativeness of the sample.

Payers were recorded at the same time as charges -- presumably at discharge.

Weaknesses
The study captured only patients treated at a Level I trauma center. This is not a representative sample of all injured riders, as it misses those who die at the scene and those who avoid significant injury.

The article does not specify the years during which the 45-month study took place, nor does it mention what year’s dollars the monetary results are reported in.

Raw hospital charges are used as a proxy for costs. This is likely to bias the medical cost estimates upwards because hospitals overcharge.

Physician charges are not included. This will result in an understatement of acute medical costs.

The authors did not take full advantage of their better-than-usual payer information. They reported no payer detail, but simply distinguished between “adequate” and “inadequate” payers, reporting the percentage of patients (not costs) with adequate payers.

Though the authors accounted explicitly for the 127 patients whose helmet use was unknown, they did not include these patients in any of their analytical calculations.

Conclusion
This article was similar in approach to that of Kelly, Sanson, Strange, and Orsay (1991), whose article was published more or less simultaneously with this one. Murdock and Waxman did not show quite the attention to detail in data collection, but their methods exploited their data well. Moreover, they were not hampered by Kelly et al.’s small sample (58) of helmeted riders, so they were able to report more significant results.

The medical cost estimates show little detail. Moreover, they embody the usual problems of state-level hospital studies -- reliance on raw hospital charges and exclusion of physician charges. Nonetheless, despite the small sample, the study arrives at results similar to those from other states, pointing to the efficacy of helmet use.


National Highway Traffic Safety Administration. (February 1996). Report to Congress: Benefits of Safety Belts and Motorcycle Helmets. (DOT HS 808 347). Washington, DC: U.S. Department of Transportation.

Johnson, S., Walker, J., & Utter, D. (1996). Crash Outcome Data Evaluation System (CODES)Project Safety Belt and Helmet Analysis. (Research Note). Washington, DC: U.S. Department of Transportation.

National Highway Traffic Safety Administration. (1996). The Crash Outcome Data Evaluation System (CODES). (DOT HS 808 338, NHTSA Technical Report). Washington, DC: U.S. Department of Transportation.

National Highway Traffic Safety Administration. (1998). Further Analysis of Motorcycle Helmet Effectiveness Using CODES Linked Data. (Research Note). Washington, DC: U.S. Department of Transportation.

Abstract
Objective. The Crash Outcome Data Evaluation System (CODES) study was undertaken in response to Section 1031(b) of ISTEA, which required NHTSA to conduct a study to determine the benefits of safety belt and motorcycle helmet use in crashes.

Study Population. The study included all persons involved in police-reported crashes in the participating states (HI, ME, MO, NY, PA, UT, and WI) during a one-year study period (1990 for HI and MO; 1992 for NY; and 1991 for the other states). The study captured those who were injured or who died and those who were not injured. Utah was excluded from the motorcycle portion of the study because its crash reports do not distinguish between unhelmeted motorcyclists and those whose helmet use was unknown. In three states with universal helmet legislation (MO, NY, and PA, which together generated 61 percent of the cases), 96 percent of crash-involved motorcyclists were wearing helmets. In the other three states, without such a law (HI, ME, and WI), the helmet-use rate was only 34 percent. Overall, 72 percent of the motorcyclists in this study were helmeted.

Data & Methods. This study employed methods whereby statewide data fr om police crash reports, emergency medical services, hospital emergency departments, hospital discharge files, claims, and other sources were linked so that those people injured in motor vehicle crashes could be followed through the health care system. Information for both the injured and uninjured was then used to determine the benefits of protective devices in motor vehicle crashes. The available financial information included inpatient charges (acute care, rehabilitation, long-term care) and estimates of actual costs using a charge-to-cost ratio. Through the cooperation of the highway safety and medical communities, this was the first time these databases were linked using a probabilistic computer algorithm. Seven states generated and analyzed the linked data upon which this report is based.

Results. The CODES study results showed that motorcycle helmets are 35 percent effective in preventing fatality, 26 percent effective in preventing injuries at least serious enough to require transport to the hospital ED, and 9 percent effective in preventing all injury. These results confirm previous NHTSA estimates. The average inpatient charge for motorcycle crash victims receiving inpatient care was $14,377 for those who used helmets, and $15,578 for those who did not, an 8 percent higher charge for those electing not to wear a helmet. Private insurance sources (including Worker’s Compensation) accounted for 63 percent of inpatient charges, public sources for 23 percent, and other sources (mostly self-pay) for 14 percent. For the private and public sources, average inpatient charges for motorcycle crash victims were 15 percent and 5 percent higher, respectively, for the unhelmeted.

Helmets cannot protect the rider from most types of injuries. But further analysis of the CODES data showed that motorcycle helmets are 67 percent effective in preventing brain injuries. In other words, unhelmeted motorcyclists were over three times as likely to suffer a brain injury as were helmeted motorcyclists. Examination of the average inpatient charges revealed that the average charge for inpatient care for a motorcyclist who sustained a brain injury is more than twice the average charge for motorcyclists receiving inpatient care for other injuries ($27,000 vs. $12,000). On average, approximately $15,000 in patient costs would be saved during the first 12 months for every injured motorcycle rider who is saved from brain injury by wearing a helmet. It is estimated that the helmet laws in MO, NY, and PA saved payers in those states nearly $1.8 million in hospital charges for brain injuries.

Conclusions. CODES demonstrated that linked, comparable data could be generated to evaluate the benefits of belts and helmets in terms of medical and financial outcome. Linkage enabled injury severity to be standardized among the CODES states. Linkage also identified previously unknown problems with missing and inaccurate data.

Number of Motorcycle Riders Contributing to the
CODES Analysis of Effectiveness of Motorcycle Helmets,
by Severity/Treatment Level

Died
351
Inpatient
1,604
Transported
2,378
Slightly Injured
3,128
Not Injured
2,892
Total
10,353

Strengths
The large sample size made possible by the CODES project greatly enhances the ability to analyze the impact of helmet usage. And linking multiple datasets, when successful, provides more comprehensive data than are commonly available from any single source.

The sample is the most comprehensive possible, including all victims of known crashes, whether injured or not. This permits much more powerful tests of helmet effectiveness than studies that omit the uninjured.

The outcome scale, which used four levels of dichotomous measures, permitted analysis by logistic regression. Unfortunately, the logistic regressions were not used to control for the other risk factors because of missing data and sample size concerns. But the method did allow separate analysis of the various severity levels, including fatality.

Weaknesses
Even though different states provided data from different years, all medical charge figures are apparently presented in nominal dollars. When results from individual states are presented separately, this is not a problem. But when summing or averaging dollar figures across states -- and therefore across years -- they should be adjusted to a common year’s dollars.

Sample sizes and measures of significance do not accompany the hospital charge figures, making it hard to judge their significance.

Monetary results were presented as charges, rather than costs. Charge/cost ratios were available, but they were only statewide aggregates, making them inappropriate for application to charges at the case level. Total charges appear to be about 40 percent higher than estimated costs.

Only inpatient facility charges are presented (including rehospitalizations and inpatient rehabilitation), even though other kinds of costs were available for some states. Professional fees, which were unavailable, were also not included.

The discussion mentions the problem of unknown helmet use, which was 38 percent in New York, but it does not explicitly state how the study dealt with the problem. However, it appears that these cases were simply omitted from the analysis.

The study discusses the difficulties of linkage and why linkage rates varied among states, but there was no discussion of possible biases that might have been introduced to the results in instances where linkage failed.

Interesting Finding
The discussion of Exhibit 14 in the first listed report focuses on the differences in charges resulting from helmet use. But the table also shows that average hospital charges were about 65 percent higher for public patients than for the privately insured. No explanation is given. Perhaps publicly insured patients, who tend to be younger, engage in more risky behavior and sustain more severe injuries. Or perhaps public patients have greater access to care and receive more services. Or perhaps high-charge states (PA) have a greater share of public payment than low-charge states (HI).

Conclusion
The large, comprehensive sample employed in this study makes it one of the most significant efforts in motorcycle safety research to date.


Nelson, D., Sklar, D. Skipper, B., & McFeeley, P.J. (1992). Motorcycle fatalities in New Mexico: The association of helmet nonuse with alcohol intoxication. Annals of Emergency Medicine, 21(3), 279-283.

Abstract
Objective. To determine the relationship among helmet use, alcohol use, and ethnicity in people killed on motorcycles.

Study Population. All decedents of motorcycle crashes in New Mexico in 1984-1988.

Data & Methods. Retrospective review of all autopsies, medical investigator reports, traffic fatality reports, and toxicological studies on fatally injured motorcyclists. The hospital bills and medical insurance status for 29 patients who died at the state’s only Level I trauma center were used as a rough gauge of the cost of health care.

Results. Nine of the helmeted drivers (18 percent) were legally intoxicated, compared with 67 of the nonhelmeted drivers (51 percent) (chi-square=15.7, P<.0001). Forty-two of the white non-Hispanic decedents (37 percent), 10 of Hispanic decedents (12 percent), and none of the 11 Native-American decedents were wearing helmets. The head and neck region was the most severely injured body region in 42 of the nonhelmeted cases (84 percent) and in 8 of the helmeted cases (50 percent) (Fisher’s exact test, P<.02).

Average health care spending for helmeted victims was estimated at $2,758, while that for nonhelmeted victims was estimated at $8,396. (See table, below, for estimates.) Thirty-three percent of helmeted victims had health insurance, but only 26 percent of nonhelmeted victims.

Conclusion. There is an association between nonuse of helmets and alcohol intoxication in fatally injured motorcyclists in New Mexico. Strategies for preventing motorcycle fatalities should address alcohol abuse and ethnicity in conjunction with helmet use.

Notes
During the study period, New Mexico mandated helmet use only for motorcycle drivers and passengers under 18 years of age. New Mexico prohibited persons from driving any vehicle, including motorcycles, with blood alcohol of more than more than 0.1 mg percent.

The paper does not explain the cost methods, but they can be partly inferred from the table. Each case is assigned a cost as follows:

Scene      $0
ED            $500
OR            $9,452
ICU           $13,960/day

Questions
Were costs adjusted to a common year’s dollars? If so, what year?

Strengths
This article examines the correlation between helmet use and alcohol intoxication, a question rarely investigated in the literature.

The analysis captures both victims who died on the scene and those who survived long enough to be treated at a hospital.

Weaknesses
The sample is limited to fatalities. It is likely, therefore, to include high proportions of both unhelmeted and drunk riders if such riders have higher fatality rates.

Medical spending and insurance status estimates are based on 29 cases treated at one hospital. The sample is small, and it is not clear how the cases are distributed among the three treatment levels. The authors are right to describe their estimate as rough.

The medical spending estimates appear to be based on hospital charges, rather than payments, which would bias the estimates upwards. On the other hand, medical spending does not appear to include physician charges, which would cause the estimates to be understated.

Five ICU cases were omitted from the cost calculations because it was feared their long stays - greater than nine days, or more than twice the average ICU stay for trauma patients - could skew the cost estimates. This reasoning seems questionable, since it entails defining outlier to include one observation in every eight in the ICU subsample. The average cost for helmeted victims would have been much higher if these cases had been included.

Cases without documented helmet use status were apparently omitted. There is no indication of how many such cases were omitted.

Estimated Medical Expenditure for Motorcycle Fatalities:
New Mexico 1984-1988

  Helmeted Cases Unhelmeted Cases
Place of Death Number Percent Medical Spending Number Percent Medical Spending
Scene
26
51.0
$0
60
38.7
$0
ED
14
27.5
$7,000
54
34.8
$27,000
OR
4
7.8
$37,808
13
8.4
$122,876
ICU
7
13.7
$95,830
28
18.1
$1,149,960
Total
51
100.0
$140,638
155
100.0
$1,299,836

  Helmeted Cases Unhelmeted Cases
Total ICU days*
7
84
Average ICU days
1
3
Average Medical Cost
$2,758
$8,396

*Two helmeted and three nonhelmeted ICU cases were dropped from the cost calculations because the authors believed that their high lengths of stay (>9 days) made them outleirs

Misinterpreted Finding
From page 282: “One argument presented in public opposition of mandatory helmet use is defined as risk compensation. This theorizes that people will become more careless and take more risks if they believe that they will be automatically protected from injury by the use of a helmet. This study does not support this theory. In fact, the nonhelmeted riders appeared to exhibit more risk-taking behavior by driving under the influence of alcohol.”

The finding does not really address the argument. The argument is that a given individual will engage in risk compensation. Thus, the authors’ comparison between two self-selected groups cannot address the assertion.


Newman, J.A., Tylko, S., & Miller T. (1994) Toward a comprehensive biomechanical injury cost model. Accident Analysis and Prevention, 26(3), 305-314.

Abstract
Purpose & Study Population. Since surrogate-based* injury assessment functions predict AIS severity distributions, and since specific AIS injuries can be costed by body region, it is theoretically possible to develop a cost model that predicts cost based on injury assessments. This article develops such a biomechanical injury cost model that utilizes surrogate-based injury assessment functions to predict the probability of occurrence and the probable cost of specific injuries to the head, thorax, abdomen, and lower extremities.

Data & Methods. The model uses cost estimates previously developed by Miller and based on the National Accident Sampling System, the National Crash Severity System, the Fatal Accident Reporting System, and the Detailed Claims Information database of the National Council on Compensation Insurance. It combines the cost estimates with available Injury Assessment Functions (or reasonable assumptions about injury assessment) to predict the probable medical and ancillary costs.

Results & Conclusions. A model of injury cost based on injury assessment functions can be developed. The application of the model is illustrated using a detailed example involving modifications to a scenario that change the severity and probability of injury and probability of death.

*The surrogate may be an anthropomorphic test device (ATD) (“crash-test dummy”), a cadaver, or a computerized victim simulation.

Comments
The term “ancillary costs” is misleading. This category would more appropriately be called “work loss and administrative costs” or “other monetary costs.”

For reasons of confidentiality of data and funding sources, the paper does not mention that it is designed for costing only motorcycle crashes. This explains the absence of discussion of upper limb and pelvis injuries, which are not measured on motorcycle ATDs.

This model has been adopted as the international standard for evaluating costs in motorcycle crash tests.

Strengths
This model has the potential to allow the effects of changes in vehicle design, crash severity, and other factors to be predicted in terms of their economic impact. It permits one to study the impact of subtle differences in design. It also is readily adapted to incorporate newer or more specific data on cost or injury assessment functions as they become available.

The model is rooted in empirical reality regarding both the effects of crashes on the body and the costs resulting from such impacts. It is designed to take full advantage of the detailed data available from crash tests.

The authors present examples that are very helpful in clarifying the application and usefulness of the model.

Weaknesses
The authors acknowledge that the data base of injury costs and injury assessment functions that they rely upon for their model is “currently of questionable precision and of limited generality.” For example, due to a lack of statistical data on the probability of injuries to the lower extremities or neck, injury determination is limited to a yes/no dichotomy and the probability of injury of a given severity cannot be made. For other injuries, probabilistic data are weak and assumptions must be made about the distribution of injury by severity. Cost data are not available for injuries to the upper limb and pelvis.

The model is designed to yield the expected (i.e., average) costs that would result from a given impact. The model clearly assumes that a wide range of physical damage levels can result, with differing degrees of probability, from a given impact. But it does not appear to calculate the standard deviation or any other measure of this variability.

The terminology in the article is a bit difficult to follow. Terms such as Cmax and VCmax are not fully explained. While they are undoubtedly clear to the engineering community among the audience, they need to be more fully described to others who might find this article useful.

The article gives us the building blocks for constructing cost estimates from crash-test-dummy data, but it does not give any summary of actual cost estimates produced by the model.

Conclusions
This article describes a model that can be refined over time to predict costs based on injury assessments. While the predictions are no better than the data upon which they are based, the potential use of the model is great.


Offner, P.J., Rivara, F.P., & Maier, R.V. (1992). The impact of motorcycle helmet use. Journal of Trauma, 32(5), 636-642.

Abstract
Objective. Mandatory motorcycle helmet-use legislation is supported by the high morbidity of motorcycle trauma and its cost to society. Opponents argue, however, that the majority of motorcycle trauma morbidity and costs are the result of injuries to body regions other than the head. Previous data do not address this argument because they fail to control for differences in non-head injury severity (i.e., kinetic impact) between helmeted and unhelmeted patients. This study investigates the impact of helmet use on the morbidity and cost of motorcycle trauma, after controlling for non-head injuries.

Study Population, Data, & Methods. A retrospective review of all motorcycle trauma patients with recorded helmet-use status admitted to Harborview Medical Center, a regional level I trauma center, from 1985 to 1989 (five years) was performed. Non-head injury severity was determined by calculating an ISS that did not include head injury. This non-head ISS was used to control for injury severity below the neck. 425 patients were identified.

Results. Stratified analysis showed that helmet use decreased the need for and duration of mechanical ventilation, the length of ICU stay, and the need for rehabilitation; and it also prevented head injury. Costs of acute care were significantly less in helmeted patients. Regression analysis, controlling for age, sex, and blood alcohol level (as well as non-head injury severity), confirmed that acute costs were 40 percent less with helmet use. Helmeted patients were more likely to have private insurance (63 percent vs. 49 percent).

Conclusion. Helmet use benefits the public good.

Mean Overall Acute Charges

  Helmet No Helmet
Hospital
$13,070
$17,173
Physician
$4,115
$5,060
Total Charges*
$17,361
$22,422

*Includes more than hospital and physician charges

Notes
No helmet law was in effect during the period of the study.

The technique of using a non-head ISS is similar to that used in Rutledge and Stutts (1993), though neither article cites the other.

Questions
Were the recorded payers actual or expected?

Is the title of Table 6, “Costs paid by public funds,” accurate? The percentages are identical to those on the “Public” line of Table 5, where they represent the percentage of patients, rather than of costs. Also, there is a footnote in Table 5 indicating significance, but the asterisk does not appear in the table. Perhaps they put the wrong numbers in Table 5.

Are the costs adjusted to a common year’s dollars? What year’s (or years’) dollars are the charges in?

Strengths
The technique of stratifying the sample by non-head ISS goes a long way towards correcting for the selection bias caused by using only data from a trauma center.

Physician fees, as well as hospital charges, were captured in the cost proxy.

The authors examined the impact of helmet use on a number of treatments and outcomes that have an impact on costs: mortality, intubation, mechanical ventilation, length of ICU stay, length of hospital stay, cervical spine injury, and rehabilitation. Some of these items -- particularly rehabilitation -- could be suggestive of long-term costs that are not captured directly by acute medical charges.

Weaknesses
The sample was drawn entirely from a level I trauma center, and was therefore not representative of all injured motorcyclists. Those who were not seriously injured and those who died at the scene were not captured.

Fatalities and survivors were not separated in the analysis, including the cost regression.

Hospital and physician charges were used as a proxy for acute medical costs. Because hospitals overcharge, this estimate of costs will be biased upwards.

The authors note that, because they omit rehabilitation and readmission costs, their results might underestimate total costs. They also suggest that, because rehabilitation is more frequently required for nonhelmeted motorcyclists, their estimate of medical cost savings from helmet use is probably low.

Fifty-nine patients whose helmet status was unknown were simply dropped from the sample.

Payers are only divided into public insurance, private insurance, and self pay. More detail -- especially splitting Medicare and Medicaid -- would have been useful.

The final sentence of the article weighs the public good of helmet use against “the small restriction of personal freedom mandatory motorcycle helmet use might represent.” While probably true, it does not follow from the analysis, since there was no investigation of the value of this loss of freedom.

Conclusion
This was a valuable article that used clever methods to get around its data limitations and presented a broader range of outcome measures than can be found elsewhere.

Interesting Finding
The protective effects of helmet appeared strongest in those with mild to moderate injury severity (ISS<16), suggesting that helmets are most effective in less severe collisions. However, this could simply be a result of the small sample size for more severe injuries, which made it harder to achieve statistical significance.

Useful Reference
The authors cite an earlier article by Rivara, Dicker, and Bergman (The public cost of motorcycle trauma, JAMA 260, 221), which found that 25 percent of costs were for rehabilitation and readmission for treatment of delayed problems. This article also found that 63 percent of motorcyclists’ medical care was paid for by public funds -- a much higher figure than the 40 percent reported in the present article.


Orsay, E., Holden, J.A., Williams, J., & Lumpkin, J.R. (1995). Motorcycle trauma in the state of Illinois: Analysis of the Illinois Department of Public Health Trauma Registry. Annals of Emergency Medicine, 26(4), 455-460.

Abstract
Objective. To assess the current morbidity and mortality of motorcycle trauma in the state of Illinois and, specifically, to assess the incidence and cost of head injury to motorcycle crash patients according to their helmet use.

Study Population. All patients involved in motorcycle crashes and subsequently taken to a Level I or Level II trauma center in Illinois and entered into the trauma registry during the study period.

Data & Methods. Retrospective, cross-sectional examination of the Illinois Department of Public Health Trauma Registry, for which data are available from July 1991 through December 1992 (18 months). Data are collected from all hospitals designated as Level I or Level II trauma centers in Illinois.

Results. Head injury, spinal injury, helmet use, demographic data, hospital charges, days in ICU, and source of payment were selected as outcome measures. During the 18-month study period, 1,231 motorcycle trauma patients were entered into the trauma registry; 18 percent were helmeted and 56 percent were nonhelmeted. In 26 percent the helmet status at the time of the crash was unknown. Thirty percent of the helmeted patients sustained head injury and 4 percent sustained spinal or vertebral injury, compared with 51 percent and 8 percent, respectively, for nonhelmeted patients. Nonhelmeted patients were significantly more likely to sustain severe (AIS 3) or critical (AIS 5) head injury (see Table 1, below). Patients with these serious head injuries incurred almost three times the hospital charges ($43,214 vs. $15,528) and used a disproportionately larger share of ICU days than those with mild or no head injuries. There was a tendency toward greater use of public funds or self-pay status (no insurance) for payment of hospital charges in nonhelmeted patients (see Table 2, below). Helmeted victims were also found to be somewhat less likely to suffer spinal injury.

If all crash victims had been wearing helmets, it is estimated that 62 fewer serious head injuries would have occurred, saving $1,716,532 in acute-care hospital charges alone.

Conclusion. Motorcycle helmet nonuse was associated with an increased incidence of serious head injury. Motorcycle trauma patients with severe or critical head injuries used a significantly greater proportion of ICU days and hospital charges than those with mild or no head injuries.

Strengths
Riders whose helmet-use status was unknown were accounted for explicitly in the preliminary data analysis. Even though these cases were dropped from later analysis, at least we can see what sorts of cases were to be omitted. (Slightly worrisome is the greater prevalence of blacks and passengers among the excluded cases.

The authors looked at the incidence of spinal cord injuries among helmeted and unhelmeted riders and found a significant association between spinal cord injury and the absence of a helmet. This addressed a question that is frequently asked and seldom answered in the motorcycle helmet literature.

Weaknesses
Hospital charges were used as a proxy for health care costs. Professional fees and other costs were not accounted for, which would cause costs to be underestimated. On the other hand, charges were not adjusted to reflect the fact that hospitals overcharge.

The sample was restricted to victims treated at trauma centers. As the authors acknowledge, it did not include those injured less seriously (not treated or treated only at a regular ED) or those injured more seriously (dead at scene). The authors report that the trauma center sample captured 782 motorcycle crashes in a one-year period, compared with 4,587 reported by the Illinois Department of Transportation -- about 17 percent.

It would have been useful to see 1) the payer distribution by injury severity, as well as by helmet status, and 2) the payer distribution weighted by cost.

The 320 patients (26 percent) with unknown helmet status were simply dropped from the analysis.

The 41 fatalities were apparently not separated from the survivors for analysis. Only 28 head injuries were classified as critical; apparently, 13 victims died from non-critical or non-head injuries (7 helmeted, 6 not), but no other details on these cases are given.

Table 1.
Incidence of Head Injury by Helmet Status

  Without Helmet With Helmet
No head injury
302 (48.9%)
141 (69.8%)
Mild Head Injury (AIS 2)
195 (31.6%)
42 (20.8%)
Severe Head Injury (AIS 3-4)
95 (15.4%)
16 (7.9%)
Critical Head Injury (AIS 5-6)
25 (4.1%)
3 (1.5%)
Total
617 (100.0%)
202 (100.0%)

Table 2
Payer Status by Helmet Status

  Without Helmet With Helmet
Public Funds
96 (14.5%)
27 (12.7%)
Private Insurance
382 (57.7%)
141 (66.2%)
Self-Pay
184 (27.8%)
45 (21.1%)
Total
662 (100.0%)
213 (100.0%)

Conclusion
This article represents a good first attempt at exploiting this statewide trauma center dataset. It features the usual selection problems associated with such datasets, as well as the shortcomings of cost studies based only on hospital charges. Nonetheless, it offers persuasive evidence regarding the efficacy of helmets and the cost savings that can result from their use. If it offers a unique contribution, it is the focus on head injuries by severity, among both helmeted and nonhelmeted riders.


Rowland, J., Rivara, F., Salzberg, P., Soderberg, R., Maier, R., & Koepsell, T. (1996). Motorcycle helmet use and injury outcome and hospitalization costs from crashes in Washington State. American Journal of Public Health, 86(1), 41-45.

Abstract
Objectives. The incidence, type, severity, and costs of crash-related injuries requiring hospitalization or resulting in death were compared for helmeted and unhelmeted motorcyclists.

Study Population, Data, & Methods. This was a retrospective cohort study of injured motorcycle drivers in Washington State in 1989. Motorcycle crash data were linked to statewide hospitalization and death data.

Results. The 2,090 crashes included in this study resulted in 409 hospitalizations (20 percent) and 59 fatalities (2.8 percent). Although unhelmeted motorcyclists were only slightly more likely to be hospitalized overall, they were more severely injured, nearly 3 times more likely to have been head injured, and nearly 4 times more likely to have been severely or critically head injured than helmeted riders. Unhelmeted riders were also more likely to be readmitted to a hospital for follow-up treatment and to die from their injuries. The average hospital stay for unhelmeted motorcyclists was longer (12.6 days vs. 9.9) and generated more charges per case ($16,460 vs. $12,689); the hospitalization charges for unhelmeted motorcyclists were 60 percent more overall ($3.5 vs. $2.2 million).

Conclusions. Helmet use is strongly associated with reduced probability and severity of injury, reduced economic impact, and a reduction in motorcyclist deaths.

Questions
The methods used to link the various datasets are not entirely clear.

On page 42, middle column, the article says, “. . . payer-specific charges were used as a proxy for costs of care,” but there was no further mention of payers in the article. What does “payer-specific” mean in this context, why does it matter, and how was it used?

Strengths
The authors begin by trying to count all motorcycle crashes in the state before narrowing the dataset for their analysis. This allows them to use these counts as denominators to estimate the percentages of crashes that result in certain severe outcomes such as hospitalization and severe head injury.

The logistic regression method controls for such factors as type of road, posted speed limit, night vs. day, wet vs. dry, and rural vs. urban, which might be correlated with helmet use. (Unfortunately, the study could not control for alcohol consumption, impact speed, or riding experience, because the state does not collect these data.)

Weaknesses
The stringent matching criteria that were used in linking records across datasets, along with the need for confidentiality, resulted in the omission of about 30 percent of all crash victims, including 14 percent who did not have a valid license. Non-licensure is probably correlated with helmet use and riding experience.

Fatalities were not separated from survivors for analysis. Even though all motorcycle crashes were identified, those not resulting in hospital admission or death were not used in the analysis, apart from inclusion in the denominators.

Hospital charges were used as a proxy for health care costs, even though the authors were aware of their inadequacy. Professional fees and other costs were not accounted for, which would cause costs to be underestimated. On the other hand, charges were not adjusted to reflect the fact that hospitals overcharge.

In their discussion section, the authors discussed some findings that were not statistically significant (differences in average cost and length of stay for unhelmeted vs. helmeted cyclists). The conclusions went a bit beyond the findings.

Interesting Finding
Unlike most articles that base their cost estimates on hospital charges, this article added the charges of follow-up visits. It was found that unhelmeted victims had a greater number of readmissions, and therefore longer total lengths of stay and higher charges. Studies that count only initial admissions would therefore underestimate the cost savings from helmet use. For our purposes, this might be the most valuable finding of this article.

Conclusions
This article tries to begin with a comprehensive count of all motorcycle crashes in Washington State before focusing on a narrower subset. This, combined with the logistic regression approach to analysis, makes for a more satisfying approach than many other articles in the motorcycle field. The authors are able to show that helmet non-use is associated with a larger risk of severe head injury. Unfortunately, the exclusion of so many cases (30 percent) makes their absolute results less trustworthy than their relative (helmet vs. no helmet) results.

The cost estimate suffers from all the usual faults of using raw hospital charges as a cost proxy. In addition, the restriction of the sample to hospitalized cases narrows the applicability of the estimate.


Rutledge, R., & Stutts, J.C. (1993). The association of helmet use with the outcome of motorcycle crash injury when controlling for crash/injury severity. Accident Analysis and Prevention, 25(3), 347-353.

Abstract
Purpose. To assess the association of helmet use with various outcomes of motorcycle crashes, controlling for overall crash severity as measured by a modified Injury Severity Score.

Study Population. Patients treated for injury at North Carolina’s 8 trauma centers, October 1987-December 1990 (39 months), who 1) were admitted to the hospital for at least 24 hours or 2) died in the ED.

Data & Methods. Information on injury severity, length of treatment, hospital charges, discharge status, and insurance status for 828 motorcycle operators and 64 passengers, identified by ICD-9 E-code, treated at 8 North Carolina trauma centers during the period October 1987-December 1990. (The data source also included information on 9,657 other roadway crash victims and 15,613 non-roadway trauma victims.) The authors assigned AIS, ISS, and modified ISS, based on ICD-9 diagnosis codes. The modified ISS was calculated excluding the AIS for head injury, since head injury is a dependent variable. (The modified ISS serves as a proxy for crash severity, as opposed to injury severity. AIS scores to body regions other than the head/neck are presumably not affected by helmet use.) Patients were then stratified into quartiles based on the modified ISS, and the various outcome measures were compared by helmet use within each quartile.

Results. For 765 patients with charge data, total hospital charges were $11,290,000 - an average of $14,700 per patient. For 460 patients with reported helmet status, hospital charges averaged $16,000 for helmeted patients and $17,000 for unhelmeted patients. These figures compare to average charges of $15,700 for patients with other transportation-related injuries and $9,179 for patients injured in non-transportation accidents.

Of 892 patients, 354 (40 percent) had no insurance coverage. The total hospital charge for these uninsured patients was over $4,000,000. The distribution of insurance coverage did not differ between helmeted and unhelmeted patients.

Of 820 patients with a reported discharge destination, 39 (4.8 percent) died, and 56 (6.8 percent) were discharged to a nursing home or rehabilitation center.

Conclusions. In crashes where the overall degree of crash severity was comparable, the risk of head injury in hospitalized motorcyclists was nearly twice as high for unhelmeted riders as it was for helmeted riders, thus confirming the protective effects of helmet use. However, there were no significant differences in various measurements of resource utilization, including days in hospital, hospital charges, and need for post-hospital rehabilitation. A higher incidence of extremity injuries among the helmeted riders may account for their failure to demonstrate consistently lower resource utilization, despite lower rates of head injury.

Questions
Was a helmet law in effect in North Carolina during the period under study (1987-90)? Probably not, based on the helmet use rates, but the legal context of motorcycling in the state should be discussed.

Where does the helmet use information in the trauma registry data come from? Is it recorded by the physician? If so, how does the physician determine this? What sort of bias might it introduce? How does it compare with the method of linking with crash reports to obtain helmet use?

The authors state that 40 percent of the sample had no insurance coverage, but they do not define what they mean by insurance. Do they consider public insurance (e.g., Medicaid and Medicare) to be insurance? It would be useful to know, since the proportion of cyclists who are uninsured is often cited as an argument for helmet laws.

Strengths
This study introduced an interesting method for addressing the selection bias caused by using only data from trauma centers.

Weaknesses
The authors settle for using raw hospital charges and do not attempt to produce a more comprehensive measure of monetary costs.

As the authors acknowledge, their modified ISS technique appears to control incompletely for crash severity, leaving a consistent, if statistically insignificant, pattern of higher frequency of extremity injuries in helmeted patients.

The fatalities are mixed with the nonfatal cases for all analysis. This might have the effect of dragging charges downwards in categories with otherwise severe injuries, such as unhelmeted victims.

The article might have been more useful if it had analyzed the data in terms of head-vs.-nonhead injuries, as well as helmeted-vs.-unhelmeted riders.

Conclusion
The study might be useful for contradicting the faulty conclusions that some readers have drawn from Stutts, Rutledge, and Martell, which relied on the same North Carolina trauma center data. By addressing a serious methodological problem with that study, the present article is able to show that helmet use is associated with a reduced likelihood of severe head injury. Unfortunately, it does not yield useful information on monetary costs.


Shankar, B.S., Ramzy, A.I., Soderstrom, C.A., Dischinger, P.C., & Clark, C.C. (1992). Helmet use, patterns of injury, medical outcome, and costs among motorcycle drivers in Maryland. Accident Analysis and Prevention, 24(4), 385-396.

Abstract
Purpose. The purpose of this study was to examine the overall morbidity and mortality resulting from motorcycle-related trauma, addressing the types and severity of injuries, the emergency medical services required, the proportion of cyclists admitted to community hospitals or trauma centers, and the costs of medical treatment. The particular focus of this paper is to address the risk of head injury as related to helmet use and to examine the costs involved in treating such injuries.

Study Population. All motorcycle drivers in police-reported traffic crashes occurring in Maryland during a one-year period (July 1987 to June 1988).

Data & Methods. All available medical and cost data were linked with police crash reports. During the study period, 1,900 motorcycle drivers were involved in crashes.

Results. The data indicated that 1) recorded helmet usage was 35 percent overall, 30 percent among fatally injured drivers, and only 16 percent among drivers with a history of drug/alcohol conviction, 2) unhelmeted drivers seen at an emergency department were almost twice as likely to have sustained head injury (40 percent) as were helmeted drivers (21 percent) (the corresponding percentages for hospitalized drivers were 55 percent and 38 percent), and 3) acute care inpatient charges (including physician fees) for unhelmeted drivers were three times those of helmeted drivers ($30,365 vs. $10,442). The data also indicated that total hospital charges for hospital-admitted drivers totaled $7,417,206, and that hospitals charged the state of Maryland an additional $1,262,936 and insurance companies an additional $2,129,789 beyond what would have been charged if these victims had worn helmets.

Conclusions. This study reemphasizes the excessive loss of life and increased disability and medical care costs that accompany non-use of helmets. It reemphasizes the burden of such injuries on the medical care system and the burden of medical care costs borne by taxpayers. These data argue strongly for the passage of helmet use laws.

Strengths
The cost calculations add professional fees to the hospital charges, which gives a broader cost measure than that used in many other articles of this sort.

Victims whose helmet use status is unknown are accounted for explicitly. In general, an effort is made to account for every motorcycle driver injured in a crash during the year of the study, including 1) victims who were not injured or not treated and 2) those who must be excluded from analysis because of missing information.

The helmet use vs. injury status analysis (Tables 4 and 5) was broken down by body part combinations that separated head and extremity injuries, allowing for individual drivers that had one, both, or neither type of injury.

Weaknesses
Charges were used as a proxy for costs, which would tend to lead to overestimation of costs.

Costs are estimated only for hospitalized victims, based on just 120 patients admitted through level I trauma centers. These costs may not be representative of all hospital-admitted victims, let alone non-admitted victims.

The authors set out to examine the cost impact of head injuries, but they did not report costs for those with and without head injuries (except in the context of multiple injuries and helmet use).

Payer shares are not reported, even though the necessary data were clearly available to the authors. Grouping Medicare and Medicaid together hides more than it reveals.

Fatal and nonfatal cases were not separated for analysis.

Interesting Result
In Table 7 (partially reproduced below), it appears that victims on public assistance had much higher average costs, whether helmeted or not, than those with no insurance or commercial insurance. Could this indicate moral hazard? Or is public assistance status correlated with the driver’s age, experience, or another relevant variable?

Insurance Status, Average Cost, and Use of Helmet (drivers)

 
Helmet Use
No Yes Unknown
No Insurance
18,780
7,546
12,578
Public Assistance
47,785
21,678
22,882
Commercial
32,005
8,855
17,686

Conclusion
This article did a lot of things right -- particularly its attempt to begin with a comprehensive survey of all motorcycle crashes in the state in the study period and its comparison of helmet use with body part injured. Its cost analysis is useful when it compares hospital charges by helmet use and payer, but its value is otherwise limited because 1) the cost estimates are based on a narrow, unrepresentative sample, and 2) charges were used as a proxy for costs.


Stutts, J.C., Rutledge, R., & Martell, C. (1991). An analysis of injury outcome and insurance status of hospitalized motorcyclists. 35th annual proceedings, Association for the Advancement of Automotive Medicine, 297-314.

Abstract
Purpose. To compare the injury experience, hospital costs, and insurance status of injured motorcyclists with that of victims of other road traffic injuries and non-road trauma.

Study Population. Patients treated at North Carolina’s 8 trauma centers, October 1987-June 1990 (33 months), who 1) were admitted to the hospital for at least 24 hours or 2) died in the ED.

Data & Methods. Information on injury severity, length of treatment, hospital charges, discharge status, and insurance status for 706 motorcycle operators and 68 passengers was compared with information on 8,961 other roadway crash victims and 15,547 non-roadway trauma victims - all identified by ICD-9 E-code - treated at 8 North Carolina trauma centers during the period October 1987-June 1990. For 309 motorcycle cases, the trauma center records were matched to the state motor vehicle crash and licensed driver history files to obtain additional information concerning the injured motorcyclist, including police-reported helmet use and license status.

Results & Conclusions. Motorcyclists and other road trauma patients experienced similar injury severity distributions and required similar levels of treatment as measured by length of hospital stay, overall hospital charges, and discharge status. Motorcyclists were more likely to be uninsured (42.7 percent of cases vs. 35.5 percent), but less likely to rely on Medicare/Medicaid (7.9 percent vs. 13.9 percent), and were just as likely as other road trauma patients to be commercially or privately insured (49.4 percent vs. 50.9 percent). However, payer shares differ by age. Among those under 16 and those over 65, motorcycle riders are more likely to be privately insured than other trauma victims. But in all age groups in the 16-to-64 age range - which include most of the people who ride motorcycles - motorcyclists are likely to be privately insured.

While fewer than half of the Trauma Registry cases could be matched to the motor vehicle crash file, the two populations of crash-involved motorcyclists were found to be similar in most respects. The reported exceptions were injury severity and alcohol involvement. Eighty-one percent of Trauma Registry cases were fatal or severe, compared with just 40 percent of the larger motorcycle crash sample. And 24.3 percent of Trauma Registry patients had alcohol involvement, compared with 14.4 percent of all cases.

Trauma center patients were as likely to be wearing a helmet (63.8 percent) as all riders in the NC crash file (63.5 percent), despite their much greater injury severity. Helmet use was not found to be associated with overall injury severity, discharge status, or insurance status.

Note
A law requiring helmet use was in effect during the study period.

Question
How many cases had unknown insurance status? (These cases were omitted from payer share calculations.)

Strengths
Linked datasets provided an unusually comprehensive level of detail for a number of motorcycle crashes resulting in severe injuries.

Weaknesses
The study used raw hospital charges, not costs or payments. Given their lower frequency of Medicare/Medicaid and higher frequency of self-pay, motorcyclists’ payment/charge ratio might have differed from that of other trauma victims.

Even though the study covered almost three years, there was apparently no effort to adjust for inflation; charges were left in nominal dollars.

The question addressed by the study, “. . . whether injured motorcyclists treated at Level I and Level II trauma centers consume a disproportionate share of public health care funds compared to other trauma victims,” is not a particularly meaningful question. A common issue in the literature is the closing of trauma centers because of the high cost of unreimbursed care. This would suggest that a more appropriate question would be whether motorcycle crash patients are consuming a disproportionate share compared to other patients in general.

Could there be a selection bias from selecting cases to go to a trauma center -- e.g., could motorcyclists be more likely to be triaged to a trauma center for less severe injuries? The finding that motorcycle crash victims are less severely injured and have lower charges than other road crash victims could result from a treatment bias in favor of sending motorcycle crash victims to a trauma center. In some states, for instance, firearm injury patients are routinely sent to a trauma center even for minor injuries. The article would benefit from a discussion of the triage criteria in North Carolina.

In this study, trauma patients were categorized by E-codes into categories, with the fourth digit used to separate motorcycle operators, motorcycle passengers, and other motor vehicle occupants. A study by McLoughlin and Romero, which specifically looked at the fourth digit, found that many times it is coded as .9 when the position of a motorcyclist on the bike (driver vs. passenger) is unknown. Thus, a number of motorcyclists might have been misclassified as other road trauma victims. This might lead to a bias in comparisons between the two groups. Furthermore, there is no general discussion of the accuracy and frequency of the use of E-coding in the database.

Many comparisons are reported in the paper with no indication of statistical significance. If 85.0 percent of injured motorcyclists are discharged home, compared with 81.5 percent of other road trauma victims, is this really a difference?

The authors mention that one-third of trauma patients had serious head injuries (page 308), but they do not follow through on the appropriate analysis. Helmets, of course, only protect against head injuries. The authors should have looked at the differences in head injury rates, severity, and costs for the helmeted vs. unhelmeted riders. (This issue might be addressed in their follow-up article, “The association of helmet use with the outcome of motorcycle crash injury when controlling for crash/injury severity,” which we will also review for this project.)

Problem finding: Helmet use rates are equal for trauma center patients and other crash victims, despite the much greater injury severity of the former. This could be a result of omitted cases - particularly victims dead on the scene. Victims who are saved from head injury by a helmet might still be severely injured in another body region and thus be sent to a trauma center.

Conclusion
The value of this study lies in its ability to inspire fruitful thought and further research. It does not, however, yield strong, useful conclusions. The overarching problem: Crash survivors treated in trauma centers are not a representative sample of motorcyclists, nor are they necessarily comparable to other trauma center patients. The relevant population would include riders dead at the scene, those treated in other settings, and those who avoid injury. If helmet use reduces the severity of injury, it might move some motorcycle crash victims out of the trauma center category into other treatment settings (or even into the uninjured category), while lack of a helmet might move others out of the trauma center category into the fatality category. A sample limited to crash victims treated in trauma centers does not permit these probable effects to be considered.

(Jane Stutts offered a similar analysis of her results in a letter to the American Motorcyclist Association, which had been misusing this paper to support its contention that helmet use is unrelated to injury cost and severity.)


Tsauo, J.Y., Hwang, J.S., Chiu, W.T., Hung, C.C., & Wang, J.D. (1999). Estimation of expected utility gained from the helmet law in Taiwan by quality-adjusted survival time. Accident Analysis and Prevention, 31, 253-263.

Abstract
Objective. This study empirically estimates the expected utility gained from the implementation of the 1997 motorcycle helmet law in Taiwan by using quality-adjusted survival time.

Study Populations, Data, & Methods. The study samples from a mandatory registry that captures all hospital admissions for “head injury” from occupants in motorcycle crashes in Taipei. Of 8,221 cases from July 1989 through June 1994 (five years), 80 cases per year were randomly selected for follow-up. Among the 400 sample cases, linkage to mortality files revealed 44 victims died. Self-interviews were completed with 99 victims using the Index of Health-Related Quality of Life (IHQL, Rosser et al. 1992), modified to fit Taiwanese society. The IHQL assesses function along dimensions of pain (none to agonizing), emotional distress (none to extremely depressed), and disability (no physical or social disability to unconscious). The responses were converted to quality-adjusted life year (QALY) loss estimates using Rosser’s weights (which were developed with the theoretically sound standard gamble method of direct utility loss measurement). The QALY losses were summed over time, without discounting to present value, to obtain what the authors called a Quality-Adjusted Survival Time (QAST). Victims were interviewed in 1995 and again in 1996 about their status on the IHQL and were asked to recall their status earlier in the post-injury period (but not pre-injury). The study also collected registry-based Glascow Outcome Scores at discharge for 7,649 of 8,221 victims.

Results. After 80 months of follow-up, the QAST of the injured population was 66.3 quality-adjusted life-months (QALMs), while that of the reference population was 78.7 QALMs. The QAST for total life expectancy was extrapolated by simulating the survival of head injury cases using life table data from the general population. The life-long utility loss of a head injury case was estimated at 4.8 QALYs. An estimated 1,300 head injury cases were prevented during the first year of enforcement of Taipei’s motorcycle helmet law, resulting in a gain of 6,240 QALYs from injuries prevented plus unmeasured additional savings from injuries reduced in severity because victims were helmeted.

Judged by Glascow Outcome Scores (GOS) at discharge, 5 percent of victims were discharged dead, 0.7 percent were in a vegetative state at discharge, 2.4 percent were disabled, 4.3 percent were independent but with sequelae, and 87.5 percent were completely recovered. Of 1,350 subjects with known helmet use, the 28 patients who wore helmets all were completely recovered at discharge (compared to an expected 24-25 if the unhelmeted outcome distribution applied). By comparison, 10 percent of the 1,322 unhelmeted patients had worse outcomes; 87 died and 36 were not fully recovered. Mean length of stay for helmet wearers was 14.3 days. For non-wearers, by GOS, mean days of stay were

1 dead
7.8
2 vegetative
85.6
3 disabled
48.5
4 independent with sequelae
31.7
5 complete recovery
17.2

Table 3 in the paper, which follows, shows mean HRQL scores by year post-injury and Glascow Outcome Score. For most intermediate years, the scores shown are based on recall in 1995. The number of responses varies by year, so the table entries are not a time series for a consistent set of respondents. For example the lower HRQL score for year 4 than year 3 among victims with Glascow Outcome Scores of 4 at discharge does not mean that some victims’ condition worsened in this time period. Rather, it stems from 1) the small sample size and 2) instability of cell sizes resulting from their sampling technique, which includes results from a year earlier only if the victim can remember his HRQL a year earlier. Among the GOS=4 victims interviewed, there might have been few that were three years post-recovery, and those four years post-recovery could not remember their HRQL of a year earlier (which would also have been included in that cell if they had remembered).

Health-Related Quality of Life (HRQL)
by Glascow Outcome Score (GOS)

Years After Injury GOS=3
(disabled)
GOS=4
(ind w/sequel)
GOS=5
(complete Recovery)
1
0.754
 
0.926
2  
0.890
0.931
3  
1.000
0.979
4  
0.962
0.965
5
0.668
0.908
0.931
6  
0.978
0.943
7    
0.906

For head-injured victims of motorcycle crashes, the probability of survival was 0.920 during the first month and 0.895 during the first year. Only 2 of the 44 deaths occurred more than one year post-injury.

Strengths
This is the only study located that directly assesses post-discharge QALY losses due to head injuries in motorcycle crashes. It also is by far the largest and most representative population with Glascow Outcome Score data.

Weaknesses
This study almost surely underestimates the QALY loss resulting from major head trauma. Use of a simple random sample of discharges (as opposed to a stratified sample) made it a study of head injuries of relatively low severity. None of the 56 victims with Glascow Outcome Scores (GOS) at discharge of 2 (vegetative state) were interviewed. Respondents included 2 of 187 discharged as disabled (GOS 3), 6 of 326 discharged as independent but with sequelae (GOS 4), and 91 of 6,695 discharged as completely recovered (GOS 5).

The survey only polled the subset of the sample who in 12/95 to 2/96 could be reached at the same phone number they had provided at the time of injury (1989-1994), 105 of 356 survivors. The 99 responses obtained represent the population who had not moved, but that population may differ considerably from the movers. The authors offer no information on normal housing mobility rates in Taiwan, so it is unclear if these injury victims differed from mobility norms. Thus the respondent group is a biased sample that probably differs from the broader victim population in unknown ways.

The Rosser scale is not very sensitive to the functional losses that result from head injury. Other QALY scales would have been more appropriate to use. This problem may have been exacerbated by the failure to use a proxy respondent if a victim had severe cognitive deficits caused by the head injury.

The applicability of this study to the US, where motorcycles primarily are recreational vehicles, is reduced by the greater importance of motorcycles as a transport mode in Taiwan and the associated differences in vehicle mix and rider mix. Taiwan motorcycle crashes and injuries undoubtedly differ from those in the US, but in largely unknown ways. Notably, one-fourth of victims studied were over age 40.

Conclusion
Despite limitations in the sample selected for follow-up and questions about comparability to the US, the results of this study can probably be taken as a lower bound on QALY losses resulting from motorcycle-related head trauma.


Wang, J.S., Knipling, R.R., & Blincoe, L.J. (May 1999). The dimensions of motor vehicle crash risk. Journal of Transportation and Statistics, 2(1), 19-43.

Abstract
Objective. A valid assessment of motor vehicle crash risks and the potential impact of safety interventions requires a precise understanding of the types of crashes involved, the types of vehicles likely to be affected, the most relevant referent to the intervention (e.g., national annual crash total, vehicle mileage, and vehicle life), and the scope of monetary crash costs to be considered.

Study Population & Data. Crash data were retrieved or derived from the General Estimates System (GES) for the five-year period 1989-93 and are intended to be representative of the population of U.S. police-reported crashes. Fatalities were adjusted to the 1989-93 levels reported in FARS.

Methods. This paper analyzes the problem of U.S. police-reported motor vehicle crashes in four dimensions: crash involvement type/role (e.g., single-vehicle roadway-departure, left-turn-across-path); subject vehicle body type (i.e., motorcycles, passenger cars, light trucks/vans, heavy combination-unit trucks, and medium/heavy single-unit trucks); type of metric (i.e., crashes, involved vehicles, persons killed/injured, and monetary cost); and problem size referent (i.e., U.S. annual, per crash, per vehicle, per driver, and per mile traveled).

The article starts with NHTSA’s official crash costs by Maximum Abbreviated Injury Scale (MAIS) severity. It uses 1982-86 data on MAIS by vehicle type and police-reported KABCO severity to estimate costs by KABCO. Incidence by crash type and KABCO severity is an average from 1989-93 GES data.

Results. Motorcycles represent a relatively small percentage of overall national crashes, but their per crash costs are high; for example, $57,190 in economic costs per police-reported crash versus $17,950 for all vehicle types combined. This reflects the relatively high vulnerability of motorcycle riders to crash injuries. In addition, motorcycles have a rate of involvement in crashes per VMT that is nearly twice that of all vehicle types combined. These two factors have a multiplicative effect in making motorcycle travel 6 to 10 times more costly per VMT than other vehicle types.

Motorcycles have low mileage exposure and relatively short operational lives (7.5 years on average). These factors make the vehicle operational life crash costs of motorcycles among the lowest of the vehicle types. From a strict per vehicle produced monetary cost-benefit perspective, this makes motorcycles a relatively unattractive platform for safety devices lasting the life of the vehicle, assuming equivalent costs and effectiveness levels. On the other hand, this type of vehicle is an extremely attractive platform for safety devices having a limited mileage life. For example, assuming comparable effectiveness, a general safety device installed for 1,000 miles on a motorcycle would produce more than 6 times the expected benefit as the same device installed for 1,000 miles on a passenger car.

Motorcycles are relatively overrepresented in single-vehicle roadway-departure (SVRD) crashes. They represent 0.8 percent of all vehicles involved in crashes but 1.2 percent of SVRD crash involvements. Furthermore, motorcycle SVRD crashes are approximately four times as severe as those of any other vehicle type. On a per mile traveled basis, motorcycle SVRD crashes are an order of magnitude more costly than those of all vehicle types combined. The per vehicle life cycle monetary costs of motorcycle SVRD crashes are actually slightly higher than the “all vehicles” average, an exception to the general rule that motorcycle life cycle crash costs are generally low compared with other vehicle types. Motorcycles are relatively underinvolved in rear-end lead vehicle stopped (RE-LVS), backing, and left-turn-across-path (LTAP) crashes. Recall that LTAP statistics reflect only crashes where the subject vehicle (SV) is turning left; motorcycles rarely play this role in LTAP crashes, but are frequently in the non-SV role (going straight), since they are often not seen by other drivers.

Conclusion. Identification of the most relevant dimensions of motor vehicle crash risk is fundamental to developing a framework for enlightened safety benefits assessment and decisionmaking.

Strengths
This article identifies the dimensions of motor vehicle crashes that are relevant to analysts and policymakers. It then takes advantage of a comprehensive dataset to provide estimates of incidence and cost for every combination of values from all four dimensions. These estimates are potentially useful to anyone doing cost-benefit analysis on highway safety interventions.

Weaknesses
Some of the cell sizes for motorcycle crashes by crash type are too small to elicit much confidence.

The costs and incidence observed in the study periods, 1982-86 and 1989-93, reflect the safety measures and practices of those periods. If such factors as traffic density, motorcycle design, helmet construction, or helmet use have changed since then, the results might no longer be directly applicable. Likewise, the results might not be directly applicable to a state where the helmet use rate differs from the 1982-86 national average. Because the study does not report the helmet use rate, it would be difficult to adapt the results to another time or place. Also, the implicit assumption that, within an AIS severity level, the distribution of injuries across body regions is the same for motorcycles as for other vehicles is a troublesome one that could skew the cost estimates.

While the study reported “fatal equivalents” (essentially costs translated from dollars to lives using a conversion factor of $3,091,420 per life), it did not report actual deaths. Fatalities were grouped with serious-to-critical nonfatal injuries.

The paper examines only selected crash types that comprise only about 30 percent of police-reported motorcycle crashes. Single-vehicle wipe-outs on the road and side-swipes are both omitted. Motorcycles crashing while backing up seem more likely to be miscodes than actual crashes.

Conclusion
By laying out a comprehensive framework for analysis of motor vehicle crashes, this article provides a useful structure that can serve as a basis for systematizing future analysis for greater comparability. Likewise, its listing of the various crash dimensions might contribute to greater thoroughness in future studies. And its copious estimates can serve as a baseline of comparison for future research that looks uses selected dimensions of the framework to explore more narrowly defined questions in greater depth.

Motorcycles Involved in These Crash Types

  All Vehicles Motorcycles Single Vehicle Roadway Departure Pedestrian/
Cyclist
Rear-End Lead Vehicle Stopped Rear-End Lead Vehicle Moving Lane Change/ Merge Backing Opposite Direction Left-Turn- Across-Path
Annual number of PR crashes
6,261,000
89,000
16,000
2,000
3,000
3,000
1,000
300
1,800
900
Annual number of this vehicle type involved in PR crashes*
10,964,000
90,000
16,000
2,000
4,000
4,000
1,000
300
1,800
1,000
Annual number of all vehicles involved in PR crashes*
10,964,000
145,000
16,000
2,000
7,000
7,000
2,000
500
4,000
1,900
Annual U.S. number of persons involved in PR crashes*
15,905,000
183,000
19,000
5,000
9,000
8,000
3,000
600
5,000
3,000
Not injured (O)*
12,278,000
90,000
3,000
1,400
6,000
5,000
2,000
400
3,000
16,000
Minor to moderate (MAIS 1-2)*
3,433,000
78,000
12,000
3,000
3,000
3,000
800
200
1,600
800
Serious to fatal (MAIS 3-fatal)*
194,000
15,000
3,000
500
300
500
100
0
700
100
Vehicle involvement rate in PR crashes
per 100 million VMT
500.41
927.65
167.65
24.07
33.51
34.14
10.29
2.74
18.21
9.65
per 1000 registered vehicles annually
59.33
21.54
3.89
0.56
0.78
0.79
0.24
0.06
0.42
0.22
Expected involvements in PR crashes
over vehicle operational life
0.7789
0.1615
0.0292
0.0042
0.0058
0.006
0.0018
0.0005
0.0032
0.0017
per driver over driving career
3.7383
                 
Annual U.S. monetary cost* (E)
$164.4B
$6.5B
$1.4B
$218M
$140M
$187M
$38M
$12M
$388M
$65M
Annual U.S. monetary cost*(C)
$431.9B
$22.6B
$5.2B
$779M
$443M
$637M
$119M
$41M
$1.4B
$218M
Average monetary cost
per PR crash* (E)
$17,950
$57,190
$71,760
$74,660
$31,400
$43,000
$27,380
$34,300
$177,420
$53,780
per PR crash* (C)
$52,610
$206,460
$263,040
$271,760
$107,430
$154,350
$93,220
$125,190
$630,630
$186,810
per VMT* (E)
7.50¢
66.52¢
14.82¢
2.25¢
1.44¢
1.93¢
0.39¢
0.13¢
3.99¢
0.67¢
per VMT* (C)
19.71¢
233.05¢
53.76¢
8.03¢
4.57¢
6.56¢
1.22¢
0.43¢
14.02¢
2.24¢
per registered vehicle annually* (E)
$890
$1,540
$340
$50
$30
$50
$10
$0
$90
$20
per registered vehicle annually* (C)
$2,340
$5,410
$1,250
$190
$110
$150
$30
$10
$330
$50
Expected monetary cost
per vehicle over opperational life* (E)d
$9,640
$10,230
$2,280
$350
$220
$300
$60
$20
$610
$100
per vehicle over opperational life* (C)d
$25,330
$35,830
$8,270
$1,230
$700
$1,010
$190
$70
$2,160
$350
per driver over driving career (E)d
$31,070
                 
per driver over driving career (C)d
$81,630
                 
Total annual U.S. fatal equivalents*
139,699
7,320
1,689
252
143
206
38
13
440
70
Average fatal equivalents per PR crash*
0.01702
0.06678
0.08509
0.08791
0.03475
0.04993
0.03015
0.04049
0.20399
0.06043

 


Weiss, A.A. (1992). The effects of helmet use on the severity of head injuries in motorcycle accidents. Journal of the American Statistical Association, 87(417), 48-56.

Abstract
Background. In 1976 the U.S. Congress removed the threat of withdrawal of certain highway funds from states that failed to enact motorcycle helmet laws. Since then, over half the states have either repealed or weakened these laws. Most researchers in the field agree that this has led to a significant increase in injuries and fatalities among motorcyclists involved in crashes. Potential limitations of many of the studies on which these conclusions are based include the facts that fatalities can result from injuries to parts of the body not protected by helmets and that other factors, such as speed and alcohol use, are not taken into account, usually because of lack of data. The former will result in a loss of power and the latter in the introduction of bias.

Objective. As well as indicating and avoiding the biases in simple comparisons and providing more accurate predictions of the effects of helmets, the multivariate model will give predictions if the values of the explanatory variables are changed.

Study Population & Data. A sample of 900 motorcycle crashes in the Los Angeles area in 1976-77. It contains detailed information, collected with the cooperation of the emergency services system, on injuries to each rider and the characteristics of each crash. A second set of data on the cost of treating 105 patients at the Harborview Medical Center (HMC) in Seattle during 1985 is employed.

Methods. This article models the severity level of head injury, rather than the fatality rate, and builds a multivariate model that includes the other factors. The basic model is an ordered probit model with heteroscedasticity in the errors. The adequacy of the model is tested by Lagrange multiplier and goodness-of-fit tests. The former include tests for the normality of the errors and the specification of the regressors.

Results & Conclusions. Predictions from the model include that helmets lead to a 42 percent increase in the number of riders with no head injury and a $1,700-per-rider decrease in the direct medical cost of treating the riders.

Questions
Is the variable DRINK (page 52) based on the BA (blood alcohol) variable, or is it an independent measure?

Strengths
The dataset employed is comprehensive in its level of detail, if not in its selection.

The author identifies and avoids many of the weaknesses of other studies in this field. He uses rigorous statistical methods, applies them soundly, and obtains detailed results. He shows good knowledge of the measures he uses, makes good decisions about when variables need to be transformed, and gives good defenses of the appropriateness of these transformations. He is very careful in noting his assumptions and exploring their implications. He clearly discusses the potential theoretical explanations for the results observed.

The author points out explicitly that his results are likely to be biased toward underestimating the benefits of helmets, because of the injury selection process.

Total Costs (1985 dollars)

  Obs Actual Predicted w/o helmet Predicted with helmet
Helmeted Riders
331
$3,964,333
$4,417,395
$3,853,439
Nonhelmeted Riders
439
$4,670,373
$5,046,795
$4,280,572
All Riders
770
$8,634,706
$9,464,190
$8,134,011

Average Costs (1985 dollars)

  Actual Predicted w/o helmet Predicted with helmet Helmet Savings
Helmeted Riders
$11,977
$13,346
$11,643
$1,703
Nonhelmeted Riders
$10,639
$11,496
$9,751
$1,745
All Riders
$11,214
$12,291
$10,564
$1,727

Weaknesses
The sample was old and small (though it was probably the best available for this purpose).

One-hundred-thirty cases with missing data were simply dropped from the analysis. It is not clear just what criteria or methods were used for dropping cases. Were these all cases with missing helmet use, or were cases dropped for other missing values? If, as it appears, listwise deletion was used (i.e., deleting an observation from all analysis if any variable relevant to any analysis is missing), could more cases have been salvaged using pairwise deletion (i.e., deleting only those cases for a given analysis that are missing a value applicable to that analysis)? Was any testing done to assess the likelihood of selection bias resulting from excluding cases with missing values? In general, more discussion of the selection process behind the dataset would have been helpful.

A more detailed explanation of the measure of the KE (kinetic energy) variable might have been helpful.

It appears that Weiss’s primary sample of 900 was not restricted to hospital-treated patients, while his costs by severity level are based only on a sample from a hospital that was a trauma center. The estimated costs are, therefore, probably high.

Cost estimates were based only on direct hospital charges. The author notes that excluded nonhospital charges (physician charges?) would have increased the total cost for HMC patients by 38 percent. On the other hand, actual costs tend to be somewhat lower than charges.

The frequencies of all non-zero head injury severities are greater for nonhelmeted riders, yet helmeted riders have greater medical costs. This probably results from the selection bias inherent in a process that tends to choose only crashes that resulted in injury, thus eliminating cases where helmets prevented injury. Helmet wearers who were captured by the sample must therefore have had limb or trunk injuries, the cost of which can be quite high, even if they are unlikely to be fatal.

Food for Thought
The author suggests that, in order to be complete, a cost-benefit analysis would need to take into account “the importance of motorcycle accident victims as sources of donor organs.”

Conclusion
The article is focused narrowly, and therefore the application of its conclusions is also narrow. But its depth compensates for its lack of breadth. By using statistical techniques not in the toolboxes of most researchers in this field, Weiss eliminates most of the usual biases in order to zero in on the benefit of helmet use in reducing injury severity.

His cost savings estimates might be useful as a relative measure of the benefit of helmet use, but the inadequacy of his cost data and methods limits the usefulness of his absolute estimates of medical costs.

It is probably a good thing that the methods are complex and the costs per case are well hidden. Otherwise, anti-helmet groups would be misusing Weiss’s article in the same manner as they misuse Stutts, Rutledge, and Martell. In this article, as in that one, nonhelmeted riders have lower medical costs because 1) fatalities at the scene are medically cheap, and 2) crash victims saved from injury by their helmets do not appear in the sample.


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