Alcohol, bicycling, and head and brain injury: a study of impaired cyclists' riding patterns R1

ArticleinThe American journal of emergency medicine 28(1):68-72 · January 2010with35 Reads
Impact Factor: 1.27 · DOI: 10.1016/j.ajem.2008.09.011 · Source: PubMed
Abstract

The aim of the study was to examine the interactions between alcohol, bicycle helmet use, experience level, riding environment, head and brain injury, insurance status, and hospital charges in a medium-sized city without an adult helmet law. A study of adult bicycle accident victims presenting to a regional trauma center over a 1-year period was undertaken. Data were collected at the bedside regarding helmet use, alcohol use, experience level, location and type of accident and prevailing vehicle speed (for road accidents), and presence and degree of head or brain injury. Two hundred patients 18 years or older were enrolled from December 2006 through November 2007. Alcohol use showed a strong correlation with head injury (odds ratio, 3.23; 95% confidence interval, 1.57-6.63; P = .001). Impaired riders were less experienced, less likely to have medical insurance, rarely wore helmets, were more likely to ride at night and in slower speed zones such as city streets, and their hospital charges were double (all P values <.05). Alcohol use leads to a host of unsafe bicycling practices, increased head and brain injuries, and costs to the cyclist and community. The interrelated characteristics of the riding patterns of the cyclists who use alcohol might help target interventions.

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Brief Report
Alcohol, bicycling, and head and brain injury: a study of
impaired cyclists' riding patterns R1
Patrick Crocker DO
b
, Omid Zad MD
a
, Truman Milling MD
a,
, Karla A. Lawson PhD
b
a
University Medical Center at Brackenridge, Austin, TX 78731, USA
b
Dell Children's Medical Center of Central Texas, Austin, TX 78723, USA
Received 8 August 2008; revised 3 September 2008; accepted 3 September 2008
Abstract
Objective: The aim of the study was to examine the interactions between alcohol, bicycle helmet use,
experience level, riding environment, head and brain injury, insurance status, and hospital charges in a
medium-sized city without an adult helmet law.
Methods: A study of adult bicycle accident victims presenting to a regional trauma center over a 1-year
period was undertaken. Data were collected at the bedside regarding helmet use, alcohol use, experience
level, location and type of accident and prevailing vehicle speed (for road accidents), and presence and
degree of head or brain injury.
Results: Two hundred patients 18 years or older were enrolled from December 2006 through November
2007. Alcohol use showed a strong correlation with head injury (odds ratio, 3.23; 95% confidence
interval, 1.57-6.63; P = .001). Impaired riders were less experienced, less likely to have medical
insurance, rarely wore helmets, were more likely to ride at night and in slower speed zones such as city
streets, and their hospital charges were double (all P values b.05).
Conclusions: Alcohol use leads to a host of unsafe bicycling practices, increased head and brain injuries,
and costs to the cyclist and community. The interrelated characteristics of the riding patterns of the
cyclists who use alcohol might help target interventions.
© 2010 Elsevier Inc. All rights reserved.
1. Introduction
The National Safety Council estimates that 35.6 million
Americans ride bicycles [1]. About 480 000 of those end up in
emergency departments for injuries, and 20 000 are admitted to
the hospital [1,2]. Head injury accounts for about a third of
bicycle-related injuries, and these victims are more likely to die
[3]. Those that survive have more long-term sequela [3].
However, the riding habits, experience level, and riding
environment of riders who have been drinking alcohol have not
been fully characterized. Understanding when, where, and how
these cyclists come to be head-injured has important implica-
tions for public safety campaigns targeting interventions to
reduce morbidity and mortality from bicycle accidents.
Our goal was to study a group of bicycle accident victims
presenting to a regional trauma center and examine which
characteristics correlated with alcohol use and head and/or
brain injury.
Corresponding author.
E-mail address: tjmilling@yahoo.com (T. Milling).
www.elsevier.com/locate/ajem
0735-6757/$ see front matter © 2010 Elsevier Inc. All rights reserved.
doi:10.1016/j.ajem.2008.09.011
American Journal of Emergency Medicine (2010) 28,6872
Page 1
2. Methods
This was a consecutive cross-sectional study of all bicycle
accidents involving adults (18 years or older) presenting to a
regional trauma center from December 1, 2006, to November
30, 2007. By community standard, ambulances bring all
patients with more than trivial traumatic injury to this
hospital. The hospital is level II per the American College of
Table 1 Characteristics of study subjects according to injury status
Characteristics No head Injury Head injury
a
P
b
No. of patients 126 72
Mean age (y)0 missing 34.5 ± 16.6 36.3 ± 13.9 .34
Male (%)0 missing 103 (81.8) 56 (77.8) .50/.58
Skill level (%)32 missing
Beginner 12 (11.2) 8 (13.6) .40/.41
Intermediate 53 (49.5) 34 (57.6)
Expert 42 (39.3) 17 (28.8)
Helmet use (%)7 missing
Yes 48 (40.0) 22 (31.0) .21/.22
No 72 (60.0) 49 (69.0)
Accident type (%)3 missing
Single Bike 71 (56.8) 46 (65.7) .37/.35
Bike vs auto 50 (40.0) 21 (30.0)
Bike vs bike 4 (3.2) 3 (4.3)
Alcohol use (%)9 missing
Yes 17 (13.9) 23 (34.3) .001/.001
No 105 (86.1) 44 (65.7)
Road conditions (%)23 missing
Dry 96 (87.3) 60 (92.3) .30/.45
Wet 14 (12.7) 5 (7.7)
Disposition0 missing
Discharged from ED 114 (90.5) 50 (69.4) .001/b.001
Admitted 11 (8.7) 21 (29.2)
Eloped 1 (0.79) 1 (1.4)
Mean hospital charges ($)1 missing 5,247 ± 7,375 17,537 ± 36,751 .0004
Location (%)17 missing
Street 88 (77.2) 52 (77.6) .97/.98
Off-road 7 (6.1) 3 (4.5)
Developed trail 10 (8.8) 6 (9.0)
Highway 9 (7.9) 6 (9.0)
Speed limit at location (%)55 missing
0-25 mph 32 (35.2) 23 (44.2) .56/.57
26-45 mph 49 (54.9) 24 (46.2)
46-65 mph 10 (11.0) 5 (9.6)
Weather conditions (%)5 missing
Clear 88 (72.7) 55 (76.4) .48/.58
Cloudy 27 (22.3) 14 (19.4)
Rainy 6 (5.0) 2 (2.8)
Foggy 0 (0.0) 1 (1.4)
Time of accident (%)
05:01
AM-09:00 am 12 (10.1) 6 (8.5) .76/.79
09:01
AM-04:00 pm 44 (37.0) 22 (31.0)
04:01
PM-08:00 pm 36 (30.3) 23 (32.4)
08:01
PM-05:00 am 27 (22.7) 20 (28.2)
Insurance status0 missing
None or assistance insurance
c
73 (57.9) 35 (48.6) .21/.24
Private insurance 53 (42.1) 37 (51.4)
a
Head injury was defined as any minor (headaches and concussions), mild (GCS score 13-15), moderate (GCS score 9-12), or severe brain injury (GCS
score 8).
b
Group means and frequencies were compared by 2-sided Student t tests or by χ
2
tests/Fisher exact test, respectively. Statistical significance was set at
P b .05.
c
Medicaid (n = 3), Medicare (n = 4), county or state medical assistance program (n = 13), and self-pay/uninsured (n = 88).
69Alcohol, bicycling, and head and brain injury
Page 2
Surgeons, although it is the only trauma center serving an
11-county region anchored by a medium-sized city (greater
metropolitan area population 1.2 million), and it sees 80 000
patients per year. The protocol was approved by the
Brackenridge Hospital Institutional Review Board.
Data collection was performed at the bedside by a nurse
and/or study coordinator. Prospectively defined data points
were helmet use, type of helmet, skill level (self-described),
accident type, alcohol use immediately before or during ride
(self-reported in conscious patients and confirmed with
serum alcohol level in conscious and unconscious), road
type, street address, weather conditions , time, type of
bicycle, and head or brain injury. Head injury was defined
as any injury to scalp or skull. Brain injury was defined by
Glasgow Coma Scale (GCS) score: mild, 13-15; moderate, 9-
12; severe, 8 or less. Mild brain injuries with GCS score of
15 were defined as those who required a head CT in the
opinion of the treating physician (such as for prolonged loss
of consciousness before arrival). Hospital charges were
culled from a finance database. Data were entered into a
Microsoft Excel worksheet and imported into STATA.
Statistical analysis was performed using the Student t test
for continuous variables and χ
2
and/or Fisher exact test for
categorical analysis and reported with P values, for which
Table 2 Serum ETOH level, head/brain injury, and hospital charge for 40 patients who used alcohol
ID Age (y) Serum ETOH (mg/dL) Injury Disposition Hospital charge ($)
7 47 SR No injury Admitted 47 643
31 36 SR Head injury Discharged 1725
33 34 373 Head injury Discharged 20 941
40 46 327 No injury Discharged 11 547
44 40 182 No injury Admitted 22 680
45 44 274 Head injury Discharged 8371
50 61 SR Head injury Discharged 3303
56 50 SR No injury Discharged 4772
60 59 146 Severe brain injury Admitted 181 839
67 23 SR No injury Discharged 2569
71 23 227 Head injury Discharged 297
91 20 SR Head injury Discharged 8429
92 30 SR Head injury Discharged 2460
95 35 SR No injury Discharged 2212
97 46 SR No injury Discharged 5139
102 27 SR Head injury Discharged 12 399
106 36 SR Head injury Discharged 9772
110 33 302 Mild brain injury Discharged 6607
111 28 SR Head injury Discharged 6658
112 26 296 Mild brain injury Admitted 17 652
118 44 321 No injury Discharged 803
120 41 SR No injury Discharged 857
129 44 SR No injury Discharged 7909
133 25 SR Head injury Discharged 2370
135 24 SR Head injury Discharged 1364
137 27 285 Severe brain injury Admitted 105 961
139 25 SR No injury Discharged 5093
142 25 275 Mild brain injury Discharged 5904
148 24 45 Mild brain injury Admitted 35 948
149 48 112 Mild brain injury Discharged 8876
161 48 SR No injury Discharged 9685
162 46 167 No injury Discharged 9341
163 32 SR Head injury Discharged 1496
184 24 SR No injury Discharged 1140
186
a
55 SR Head injury Admitted 6239
192 57 SR No injury Discharged 1372
193 22 SR Head injury Discharged 1108
198 26 159 Head injury Discharged 7956
205 50 SR No injury Discharged 600
213 32 SR No injury Discharged 1940
SR indicates self-report.
a
The only patient with helmet in alcohol use group.
70 P. Crocker et al.
Page 3
.05 or less was considered significant. Univariate logistic
regression was used to determine the odds of head/brain
injury in those who did and did not use alcohol.
3. Results
A total of 200 patients were enrolled during the study
period. Information on helmet use was available in all but 8
(4%) riders and alcohol use in all but 10 (5%). Head injury
data were unavailable for 2 patients (1.0%).
Median age was 32 (range, 18-67), and the population
was 19.5% female.
Seventy-two patients had a head or brain injury (52 head
injury; 17 mild brain injury; 1 moderate brain injury; 2 severe
brain injury).
The subjects were divided by head or brain injury and the
degree thereof, and variables were examined for their
correlation thereto (see Table 1). Alcohol use showed the
strongest association in logistic regression modeling (odds
ratio, 3.23; 95% confidence interval, 1.57-6.63; P b .001).
There were 40 riders who consumed alcohol. Serum alcohol
levels were available in 15 patients. All but 1 of those were
above 80 mg/dL, the state legal limit for driving a car. Twenty-
five patients reported alcohol use at the bedside (see Table 2).
The cohort of riders who used alcohol was then examined
for correlating characteristics (see Table 3).
4. Limitations
In an analysis of this many variables, it is always possible
that some statistically significant correlations will be found
by chance. We believe that the strength of the correlations
and the fact that these variables were chosen for previously
reported significance or high clinical relevance mitigate
against this possibility. Also, hospital charges are not formal
costs and represent only a rough estimate of the economic
impact of acute care in a bicycle accident. In addition,
because alcohol use was partially examined by self-report,
some patients may have been missed and categorized as
nonalcohol using. Finally, GCS is a measure of level of
consciousness and only an indirect assessment of brain
injury, although all 3 patients with moderate to severe brain
injury defined by GCS had CT findings of brain injury as
well. The treating physician's assessment that a patient
needed a CT is also somewhat subjective but was the best
available marker for a clinical determination of severity.
5. Discussion
There have been several reports on the link between
alcohol use, helmet non-use, and bicycling injury [4-10].
Prior research indicates that riding a bicycle is a much more
complex psychomotor task than driving a car, and alcohol
has a greater negative impact on riding abilities than driving
Table 3 Characteristics of study subjects according to alcohol
use
Characteristics Using alcohol P
a
Yes No
No. of patients 40 150
Mean age (y) 36.6 ± 11.7 34.8 ± 12.6 .41
Male (%) 32 (80.0) 120 (80.0) 1.00
Skill level (%)
Beginner 4 (12.1) 16 (11.9) .016
Intermediate 24 (72.7) 63 (47.0)
Expert 5 (15.2) 55 (41.0)
Head injury (%)
Yes 17 (42.5) 105 (70.5) .001
No 23 (57.5) 44 (29.5)
Accident type (%)
Single bike 26 (66.7) 86 (57.7) .59
Bike vs auto 12 (30.8) 57 (38.3)
Bike vs bike 1 (2.6) 6 (4.0)
Helmet (%)
Yes 1 (2.6) 65 (44.2) b.0001
No 38 (97.4) 82 (55.8)
Road conditions (%)
Dry 28 (77.8) 125 (91.2) .025
Wet 8 (22.2) 12 (8.8)
Disposition
Discharged from ED 33 (82.5) 124 (82.7) .75
Admitted 7 (17.5) 24 (16.0)
Eloped 0 (0.0) 2 (1.3)
Mean hospital
charges ($)
14,825 ± 32,631 7,059 ± 9,118 .011
Location (%)
Street 38 (97.4) 101 (72.1) .009
Off-road 0 (0.0) 10 (7.1)
Developed trail 1 (2.6) 14 (10.0)
Highway 0 (0.0) 15 (10.7)
Speed limit at location (%)
0-25 mph 19 (55.9) 36 (33.6) .015
26-45 mph 15 (44.1) 56 (52.3)
46-65 mph 0 (1.0) 15 (14.0)
Weather conditions (%)
Clear 26 (68.4) 114 (77.0) .17
Cloudy 8 (21.1) 29 (19.6)
Rainy 4 (10.5) 5 (3.4)
Time of accident (%)
05:01
AM-09:00 am 1 (2.6) 15 (10.3) b.0001
09:01
AM-04:00 pm 3 (7.7) 60 (41.4)
04:01
PM-08:00 pm 11 (28.2) 46 (31.7)
08:01
PM-05:00 am 24 (61.5) 24 (16.6)
Insurance status
None or assistance
insurance
30 (75) 73 (48.7) .003
Private insurance 10 (25%) 77 (51.3)
a
Means and frequencies were compared by 2-sided Student t tests
and χ
2
tests, respectively. Statistical significance was set at P b .05.
71Alcohol, bicycling, and head and brain injury
Page 4
abilities [11,12]. Our data showing a more than tripled risk of
head/brain injury support this finding.
A well-designed analysis from Maryland found a similar
odds ratio (5.6) for any injury in riders under the influence of
alcohol, but the study specifically excluded enrollment at
night out of concern for the safety of researchers enrolling
controls [13] , although 2 prior reports found that nighttime
riders are more likely to be impaired [4,8]. These retro-
spective reports also found a strong correlation between
alcohol and bicycling injury. Another report from Sweden
using telephone interviews attempted to characterize alcohol
use and riders' environment in Goteborg, that nation's second
largest city (population 500 000) [10], but these data are
difficult to extrapolate to American cities because of likely
differences in riding and driving patterns. No detailed,
prospective report has bee n published fully describing
where alcohol-using bicyclists are likely to ride and thus
where interventions targeted at reducing drunken riding are
most likely to succeed.
From our data, a number of strong associations arise with
riders who use alcohol. Only 1 of the 40 riders wore a helmet.
Local laws require only riders younger than 18 years to wear
one, and Texas has no state law. Only 21 states and the
District of Columbia have state laws, all applying to minors
only, although there exists a patchwork of 186 local laws,
some applying to adults, in many states [14]. (The Bicycle
Helmet Safety Institute publishes a yearly review of helmets
meeting impact safety criteria [14], and it identified the Bell
Citi model as one of the safest and best value.)
Impaired riders were less experienced by self report,
impervious to adverse road conditions and unlikely to have
medical insurance, leaving society to bear the financial
burden of their care. With rare exception, they rode in the
evening or at night on city streets with speed limits less than
45 mph. Under local laws, impaired cyclists can only be
arrested for public intoxication and not the more serious
charge of driving while intoxicated. Our data support a call
for national and/or state legislation specifically addressing
cycling while intoxicated and imposing much stronger
penalties for this hazardous activity. Although our data set
did not find significance in relative risk of cycling without
helmet (most likely due to small sample size), it did show a
trend consistent with previous studies that form an over-
whelming body of evidence that helmets prevent injuries
and save lives, and laws should be passed to encourage
cyclists to wear helmets.
6. Conclusion
Riders who use alcohol appear to exhibit predictable and
unsafe riding patterns in this mid-sized American city, and,
confirming prior reports, are much more likely to have head
or brain injury, lack medical insurance, and generate
increased hospital charges. Awareness of these interrelated
characteristics may lead to more successful interventions.
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