There’s lots of misinformation spread by people who believe the myths that helmets mean reduced cycling rates.
Here’s the science: Olivier JACRS 2014 helmet (PDF 664KB – If you’re worried about clicking, below is the plain text version unformated and no pics – not as good, but still useful).
Journal of the Australasian College of Road Safety – Volume 25 No.4, 2014
Anti-helmet arguments: lies, damned lies and flawed
by Jake Olivier, 1 Joanna JJ Wang 1,2 , Scott Walter 3 and Raphael Grzebieta 2
School of Mathematics and Statistics, University of New South Wales
Transport and Road Safety (TARS) Research, University of New South Wales
Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, University of New South Wales
Bicycle helmets are designed to mitigate head injury during
a collision. In the early 1990’s, Australia and New Zealand
mandated helmet wearing for cyclists in an effort to
increase helmet usage. Since that time, helmets and helmet
laws have been portrayed as a failure in the peer-reviewed
literature, by the media and various advocacy groups. Many
of these criticisms claim helmets are ineffective, helmet
laws deter cycling, helmet wearing increases the risk of an
accident, no evidence helmet laws reduce head injuries at
a population level, and helmet laws result in a net health
reduction. This paper reviews the data and methods used
to support these arguments and shows they are statistically
flawed. When the majority of evidence against helmets or
mandatory helmet legislation (MHL) is carefully scrutinised
it appears overstated, misleading or invalid. Moreover,
much of the statistical analysis has been conducted by
people with known affiliations with anti-helmet or anti-
Bicycle helmets, Bicycle helmet legislation, Statistical
Use of the helmet is the most controversial topic in all
issues discussed in cycling. Media discussions about
cycling safety often devolve into a debate about helmets
. To date, a substantial body of research has been
published both in favour and against bicycle helmet use
and mandatory helmet legislation (MHL). It is important to
note there are two distinct but related debates with regards
to bicycle helmets. One is centred on the helmet itself
and its effectiveness in a crash. The other debate focuses
on whether governments should mandate their use. It is
not uncommon for an individual to favour helmet use but
oppose government mandated use of helmets.
Research evidence supportive of helmet use notes a
protective effect in mitigating head injuries while research
opposed argues helmet use increases the likelihood of
rotational head injuries, increases risky behaviour and is
associated with closer motor vehicle overtaking. Research
evidence supportive of MHL notes declines in bicycle
related head injury coinciding with an increase in helmet
wearing at the time of the law. On the other hand, research
opposed to MHL argues declines in head injury are due
to less cycling as MHL is a cycling deterrent and also
claims there is an absence of population-level evidence
demonstrating a benefit. MHL opponents further argue
the combination of deterred cycling, increased risk per
cyclist due to fewer cyclists and risk compensation leads
to a negative health benefit. Note that this final argument is
dependent on the other arguments holding true.
This manuscript will demonstrate the primary arguments
against helmet use and/or MHL are statistically flawed.
In turn, we will discuss the arguments (1) helmets are
ineffective, (2) helmet laws deter cycling, (3) helmet
wearing increases the risk of a crash, (4) no evidence
helmet laws reduce head injuries at a population level
and (5) helmet laws result in a net health reduction. These
are the core arguments found on anti-helmet advocacy
websites (Bicycle Helmet Research Foundation, http://
http://www.cyclehelmets.org/; Cyclists rights Action Group,
http://crag.asn.au/; Helmet Freedom, http://helmetfreedom.
org/; Freestyle Cyclists, http://www.freestylecyclists.
org/; Transport and Health Study Group, http://www.
transportandhealth.org.uk/) and even cycling organisations
(Bicycle NSW, http://bicyclensw.org.au/advocacy/;
European Cyclists’ Federation, http://www.ecf.com/).Journal of the Australasian College of Road Safety – Volume 25 No.4, 2014
Helmets are ineffective
There is substantial biomechanical evidence using test
dummies that helmet use will lessen the kinetic energy
to the head when struck in a collision [for example, see
McIntosh, Lai and Schilter, . Randomised controlled
trials are not ethically possible to assess the potential
association between helmet wearing and head injury;
therefore, most human subjects’ research on helmet efficacy
comes from observational studies. There have been many
case-control studies that assess the association between
helmet wearing and head injury and, to date, there has
been a Cochrane review , a meta-analysis  and three
versions of a re-analysis of the meta-analysis [37, 38].
In each case, the odds of a head injury were significantly
diminished for cyclists wearing helmets versus those that
Curnow [26, 27] suggested helmets exacerbate rotational
injuries; the more serious being diffuse axonal injury
(DAI). Although Curnow only hypothesised the DAI/
helmet link unsupported by any real world or experimental
evidence, some have taken this as fact [11, 13, 42, 94, 82,
83, 14]. There is, however, no existing evidence to support
the DAI hypothesis. McIntosh, Lai and Schilter  found,
when testing oblique impacts on dummies to simulate
head rotation, helmet wearing did not increase angular
acceleration, a result unsupportive of Curnow’s hypothesis.
In a study by Dinh et al. , using trauma registry data
from seven Sydney area hospitals over one calendar year,
110 cyclists were identified and none were diagnosed with
DAI regardless of helmet wearing. Walter et al. , using
linked police and hospitalisation data in New South Wales
(NSW) from 2001-2009, reported at most 12 possible DAI
cases out of 6,745 cyclists in a motor vehicle collision.
Seven of the twelve cyclists were unhelmeted. These results
suggest the incidence of DAI among cyclists appears to
be rare and unrelated to helmet wearing. Additionally,
computer simulated studies of bicycle crashes found no
evidence helmets increased the likelihood of neck injury
among adults  nor was there evidence helmets increased
the severity of brain or neck injury in children .
In addition to head injuries, Elvik  performed separate
analyses by combining head, face and neck injuries. The
results from a random effects model adjusting for potential
publication bias estimate a small, slightly significant
benefit to helmet wearing to protect the head, face or
neck (OR: 0.85, 95% CI: 0.74-0.98). However, due to
data and analytic errors, Elvik published a full length
corrigendum to this article reporting a slightly different
estimate (OR: 0.82, 95% CI: 0.72-0.93). More errors were
found in Elvik’s correction , which led to a correction
of the corrigendum . The current version estimates a
substantially larger overall benefit of helmet wearing (OR:
0.67, 95% CI: 0.56-0.82) to protect the head, neck and
face. With regards to head injury alone, which helmets are
designed to mitigate, Elvik  estimates an even greater
reduction in the odds (OR: 0.50, 95% CI: 0.39-0.65).
Additionally, Elvik [37, 38] reported an increasing
time trend for odds ratio estimates of helmet efficacy
and suggested his summary estimate, OR=0.50, fit the
trend “remarkably well.” However, it is unclear if a time
trend truly exists as more recent studies have estimated
substantial reductions in head injury associated with
helmet wearing that do not follow this pattern. Dinh et al
 estimate an odds ratio of 0.19 (95% CI: 0.06-0.59)
for intracranial bleeding or skull fractures, Amoros et al
 report an odds ratio of 0.34 (95% CI: 0.15-0.65) for
serious head injuries (AIS3+) in urban areas, Dinh et al
 estimate an odds ratio of 0.18 for head injuries in a
trauma registry (95% CI: 0.07-0.48) and Bambach et al 
report an odds ratio of 0.26 (95% CI: 0.15-0.45) comparing
severe versus possible minor head injury (survival risk
ratio ≤ 0.854). In a technical report cited by Elvik  but
not included in his meta-analysis, Amoros et al.  report
an odds ratio of 0.29 (95% CI: 0.13-0.56) for serious head
Helmet laws deter cycling
Using NSW and Victorian data, Robinson  concluded
the impact of MHL in Australia was to reduce cycling
numbers and not reduce head injuries. Some recent
researchers have taken MHL as a cycling deterrent as fact
and present no supportive evidence [83, 90]. It should be
noted, however, that Robinson omits important, relevant
data and other information from her analyses.
When describing cycling count data in NSW for children
(< 16 years), Robinson  states
“Comparable figures were not available for adults”
and, in a related paper, Robinson  states
“all available long and short term data show cycling
is less popular than would have been expected without
Cycling count data for adults does, in fact, exist for NSW
before and after MHL. Additionally, Robinson  omits
NSW cycling counts for children from October 1990 in her
Prior to MHL in NSW, the Roads and Traffic Authority
commissioned a series of helmet wearing surveys with data
collected at road intersections and recreation areas for all
ages as well as school gates for children only [106, 107,
108, 92). Note counts were not taken at recreation areas
in the 1990 report. The counts of adult cyclists from these
reports are summarised in Table 1. MHL became effective
for NSW adults on 1 January 1991.
11Journal of the Australasian College of Road Safety – Volume 25 No.4, 2014
Comparing the October 1990 and April 1991 counts,
there was a 7% increase in adult cycling counts at road
intersections spurred by a large increase in Sydney (+22%)
but a decline in rural areas (-10%). Thereafter, counts at
road intersections declined; however, counts in recreational
areas increased substantially from the second to fourth
surveys (+141%) and the absolute decrease in road
intersection counts was smaller than the absolute increase
in counts at recreation areas. In their summary of the
effect of helmet legislation on bicycle ridership, Smith and
Milthorpe  found “no drop in adult ridership following
With regards to children cycling, Smith and Milthorpe 
noted a decline but concluded
“The unevenness in the change in ridership – up at
some sites, down in others – makes it difficult to draw
conclusions about trends.”
Table 1. Counts of adult cyclists in NSW from RTA
surveys (*adult recreation cycling not separated by
Oct 90 April 91*
April 92 April 93
It may be argued that cycling counts in October are not
comparable to those in April. However, these two months
have similar weather patterns for Sydney in terms of
average high temperature (22.1 0 C vs. 22.4 0 C) and average
number of rainy days (8.0 vs 9.0) according to the Bureau
of Meteorology . They do differ in terms of rainfall
(77.1mm vs. 127.2mm); however, this would contribute to
a decline in post-MHL adult cycling since weather is often
cited as a cycling deterrent. Additionally, Olivier et al.
 found no significant difference in cycling related head
injury hospitalisations between those months in the
pre-MHL period for adults.
Caution should be taken when interpreting statistical
results using this survey data whether supportive or
opposed to helmet legislation. There is only one pre-law
observation making it impossible to control for existing
trends. Smith and Milthorpe  note the surveys were
designed to estimate helmet wearing in NSW and not
to estimate cycling exposure. A recent article found that
direct observation of cyclists could lead to biased trend
estimates if precipitation, temperature and day of the week
are not taken into account in the analysis . Also, over a
forty-eight month period, data was only collected over four
months (akin to an 8.3% response rate). However, the use
of the NSW helmet use surveys only support Robinson’s
conclusions when the data and its limitations are not
considered in full.
A series of Victorian cycling surveys found results similar
to those in NSW. Cameron et al.  report a 3% drop
in young children (aged 5-11 years), a 43% decrease in
older children (aged 12-17 years) and a 44% increase in
adult cycling comparing surveys from 1987/88 and 1991.
The authors conclude for all ages “bicycle use was higher
during the post-law years than it was in 1987-88”.
Marshall and White , in a report assessing the South
Australian (SA) MHL, give estimated changes in cycling
exposure. This work is cited by Robinson ; however,
she does not mention survey results of cycling exposure.
Using data from approximately 3000 households before
(1990) and after (1993) helmet legislation, the authors
found no significant declines in cycling exposure regardless
of age, gender or level of urbanisation. Marshall and
White  also report a 2.9% increase in counts of cyclists
into Adelaide following MHL. Another survey of helmet
wearing among SA schoolchildren did note a 38.1% decline
of cycling to school from observational surveys of helmet
wearing in 1988 and 1994. This is inconsistent with the
other SA surveys; however, the authors note only 20% of
those aged 15 years of age or younger reported cycling to
There is evidence cycling was declining prior to helmet
legislation in Australia and New Zealand (NZ). The mode
share for cycling in Australian metropolitan areas peaked at
approximately 8-9% in the early 1940’s . Since that time,
travel by private vehicle steadily increased, plateauing just
under 90% mode share while active transport modes (i.e.,
cycling, walking and public transport) steadily declined
during that period. With regards to New Zealand, Tin Tin,
Woodward and Ameratunga  noted commuting by
bicycle was in decline since 1986, eight years prior to the
NZ helmet law.
In Ontario, Canada, Macpherson, Parkin and To 
reported no declines in children cycling (5-14 years) after
the introduction of helmet legislation. In another Canadian
study, Dennis et al.  found no evidence of declines in
cycling in provinces that introduced helmet legislation.
Current opinions in Australia regarding bicycle helmets
suggest it is a minor issue with more important concerns
regarding cycling. Recent surveys list helmet wearing asJournal of the Australasian College of Road Safety – Volume 25 No.4, 2014
the 10th and 13th most common barrier to cycling among
current and non-cyclists respectively . This survey
allowed for multiple responses making it difficult to
ascertain the primary deterrent to cycling; however, helmet
wearing comprised approximately 4% of all responses.
In a survey of Australian women regarding encouraging
women to cycle more, 4.1% gave the repeal of the helmet
law as their main response . In both surveys, the lack of
cycling infrastructure and safety concerns were much more
Rissel and Wen  report significantly more people would
cycle without helmet legislation. However, Olivier et al.
 note the authors misinterpreted their statistical results
by confusing between group comparisons with prevalence
estimates. Their results actually indicated most Australians
would not cycle more. Further, since Rissel and Wen’s
survey only concerned helmets as a cycling deterrent, it is
unclear if those indicating they would cycle more without
helmet legislation would not be further deterred due to
other, more often cited factors such as lack of cycling
infrastructure or concerns regarding safety.
Although the evidence is weak or mixed with regards to the
argument helmet legislation deters cycling, this hypothesis
cannot be fully rejected. However, it is important to note
this is not a phenomenon unique to countries with such
legislation. Cycling has decreased 17% in Denmark from
1990 to 2008  and there was a decrease in on-road
cycling of 19% in the United Kingdom from 1989/90 to
It has been argued that increasing the number of cyclists
will lower the number of cycling injuries per cyclist .
This is often called the safety in numbers (SiN) effect
and is a variation of Smeed’s Law. Robinson , using
her estimates of the deterrent effects of MHL, further
hypothesised helmet legislation could increase the number
of injuries per cyclist. The mathematical representation of
SiN for cyclists is
∝ C − 0 . 6
where I represents the number of injuries and C is the
amount of cycling.
As noted above, very little cycling exposure data exists at
the time of helmet legislation in the early 1990’s. Yearly
estimates of cycling participation does exist beginning in
2001 as part of the Participation in Exercise, Recreation
and Sport (ERASS) surveys from the Australian Bureau of
Equation (1) can be reformulated as
I = I 0
0 . 4
where I 0 and C 0 are initial values for injuries and amount of
cycling respectively. Using NSW hospitalisation data ,
Figure 1 gives actual and expected head and arm injuries
for 2001-2010 using equation (2) and 2001 injury and
cycling participants as initial values.
The results are not supportive of SiN as the observed
injuries differ substantially from expected (chi-square
test, p<0.001 in each case). Additionally, using the counts
of head/arm injuries and ERASS cycling estimates, the
exponent is estimated to be 0.94 (95% CI: 0.59-1.30).
Therefore, this data suggest a proportional change in
cycling is associated with a similar change in the proportion
of cycling-related injury and is not supportive of the SiN
effect for cycling.
Although the counts of observed and expected injuries
diverge immediately, they seem to converge after 2006. In
fact, observed head injuries are less than expected by 2010.
This change coincides with increased cycling expenditures
in NSW  suggesting segregated cycling infrastructure
and helmet legislation, not safety in numbers, are major
causal factors in cycling safety. In other words, the safety
in numbers effect may be a consequence of an existing
safe cycling environment. Other authors  have further
questioned the use of SiN in determining transportation
policy due to the lack of supportive evidence.
The increase in cycling injuries is also consistent with
increased cycling per person (measured in either time or
distance). The ERASS surveys estimate a 45% increase in
Australians cycling from 2001 to 2010, although these are
participation rates and not actual amounts of cycling [6, 7].
However, this would indicate the amount of cycling (not
just participation) can increase in jurisdictions with helmet
legislation which runs counter to most arguments against
helmet legislation. In fact, a key assumption by de Jong
 is the kilometres cycled per person can only decrease
with helmet legislation.
Helmet wearing increases the risk of a crash
Robinson [85, 88] suggested a cyclist’s perception of risk
is modified when wearing a helmet and, as a consequence,
will exhibit riskier behaviour when wearing a helmet. This
is often termed risk compensation or risk homeostasis. In
a criticism of a Cochrane Review assessing the protective
effect of bicycle helmets , Adams and Hillman  argue
in favour of risk compensation. Adams  has made similar
arguments around seat belts in motor vehicles. However,
there is scant evidence to support this theory.
13Journal of the Australasian College of Road Safety – Volume 25 No.4, 2014
Figure 1. Actual and expected NSW cycling hospitalisations
(2001-2010) for (a) head and arm injuries and (b) head only
A series of Norwegian studies, in an effort to measure risk
compensation for helmet wearing, recruited cyclists who
either usually wear or not wear helmets. Their primary
outcome was average speed while wearing or not wearing
a helmet and a measure of psychophysiological relaxation.
For usual helmet wearers, Phillips, Fyhri and Sagberg
 report lower cycling speeds and increased heart rate
variability when not wearing a helmet. No significant
differences were found for non-helmet users. A plot of this
relationship is given in Fyhri and Phillips  which has
been reproduced below in the left panel of Figure 2. The
authors urge caution regarding helmet legislation in light of
These results, and particularly their figure, are misleading
as it conveys a temporal ordering that does not exist. This
figure gives the impression a cyclist who usually wears a
helmet will increase speed when wearing a helmet. The
correct temporal ordering here is the reverse for usual
helmet wearers and the correct ordering is given in the
right panel of Figure 2. When plotted correctly, their results
demonstrate a decrease in cycling speed when a cyclist
moves from their usual condition (helmet use or non-use) to
the treatment condition (non-use or helmet use). This is also
true for their psychological relaxation results, i.e., declines
in both groups when subjected to the treatment condition.
Further, it is unclear if increased speed is a valid measure of
risk compensation for bicycle helmet use. Through the use
of computer simulation of bicycle crashes, helmet use was
found to increase in protection as cycling speed increased
thereby negating any potential effect of risk compensation
More importantly, helmet promotion and helmet legislation
have a clear temporal ordering: usual non-wearers are urged
or mandated to put on a helmet. In this situation, the authors
report no significant changes in speed or psychological
relaxation when a non-user wears a helmet, so their results
do not support risk compensation theory as it relates to
helmet promotion or legislation. On the other hand, results
from case-control studies give evidence non-helmet users in
a crash were more likely to exhibit illegal behaviour [52, 9].
One of the NSW helmet wearing surveys  examined
whether helmet legislation may have influenced levels
of compliance with other regulations governing the use
of bicycles on the road. The data estimated a decrease in
certain illegal behaviour by NSW adults including riding
on the wrong part of the road or riding on the footpath
following MHL. There was also no evidence that dangerous
riding behaviour, such as doubling, riding ‘no hands’ or
‘no feet’ or riding more than three abreast, increased after
the law. The report concluded that “the evidence available
provides no support for the risk hypothesis.”
Thompson, Thompson and Rivara  have called
for a systematic review of the evidence surrounding
bicycle helmets and risk compensation. In their view, the
“empirical evidence to support the risk compensation
theory is limited if not absent.” In a response, Adams and
Hillman  argue such a review would be difficult due
to the “tens of thousands of articles that have a bearing
on risk compensation”. A search using the phrase “risk
compensation” turned up 147 articles on Medline, 322
articles on Scopus and 343 articles on Web of Science (14
August 2014). The number of articles reduced dramatically
when the phrase “bicycle helmet” was added to the searchJournal of the Australasian College of Road Safety – Volume 25 No.4, 2014
Figure 2. Cycling speed with and without helmet wearing for regular helmet users and non-users
with (a) incorrect and (b) correct temporal ordering (source: Fyhri & Phillips )
– one for Medline, nine for Scopus and six for Web of
Science. Note that four of the nine Scopus articles are
opinion pieces co-authored by Adams or Hillman.
In a study of driver behaviour towards cyclists, Walker
 reported significantly less overtaking distance when
wearing a helmet versus not. Although not an example of
classical risk compensation, the implication is the cyclist’s
environment is riskier when wearing a helmet.
It is known that lateral forces are increased as a result of air
turbulence when vehicles get nearer a cyclist. This is often
the basis for the one metre rule, or similar three foot rule
in the US, for safe overtaking . Further, on his website,
Walker  supports the categorisation of his data using
the one metre rule stating “this is perhaps the clearest
way to illustrate the effect of helmet wearing.” However,
using data available on his website, Olivier and Walter
 demonstrated the association between helmet wearing
and unsafe passing distances (< 1m) is non-significant
(OR=1.3, p=0.182) and this effect is reduced when adjusted
for vehicle size, city of occurrence and distance to the
kerb (aOR=1.1, p=0.540). This result is not due to lack of
statistical power since the sample size of the original study
was based on 98% power. Walker, Garrard and Jowitt 
found no evidence overtaking distance was associated with
helmet wearing in a follow-up study.
jurisdictions with helmet legislation. Both authors cite a
study by Hendrie et al.  using Western Australian (WA)
data to support their arguments, yet each fail to note the
paper found a significant decline in the ratio of cycling to
pedestrian head injury at the time of the WA helmet law.
Comparing head and arm injury hospitalisations in NSW,
Voukelatos and Rissel  concluded helmet legislation
did not lead to a greater reduction in head injuries beyond
an overall declining trend in cycling injuries. However,
serious data issues were identified in this study 
and the article was later retracted by the journal .
Subsequently, however, the results from the retracted paper
have been used as evidence against helmet legislation .
Additionally, Gillham  uses the incorrect data reported
by Voukelatos and Rissel  as the basis for arguing
against conclusions drawn from subsequent analyses by
Walter et al.  using the same source data while also
hosting the original, retracted article (http://www.cycle-
No evidence helmet laws reduce head
injuries at a population level Mindell, Wardlaw and Franklin  combined figures
found in Walter et al.  and state “it is difficult to
discern any particular reduction in head injuries to cyclists
(black) compared with pedestrians (grey), although the data
are rather “noisy”.” Their plot is given in Figure 3. Note
that these plots do not correspond to the actual data. In fact,
the time series of head/arm and head/leg ratios for cyclists
and pedestrians respectively do not overlap at all and
exhibit differing amounts of variability or “noise”.
Although helmet use has been shown to be beneficial
in a cycling crash, Robinson  and Rissel  argue
a population level effect has not been detected for The correct plots are given in Figure 4. To reproduce
the plots in Mindell, Wardlaw and Franklin , the
height and variability of each time series would need to
15Journal of the Australasian College of Road Safety – Volume 25 No.4, 2014
be adjusted producing time series that are ultimately no
longer comparable. This is a clear case of manipulating the
presentation of data to lend support to an existing policy in
opposition to helmet legislation .
Relative to the other time series plots, there would appear
to be less variability (i.e., “noise”) in the head/arm ratio
for cyclists and the head/leg ratio for pedestrians. By
contrast, there is more “noise” in the comparison between
cycling head and leg injuries. This suggests cycling arm
and pedestrian leg injuries are better comparators with
their respective primary outcomes (i.e., head injury). With
regards to cycling injury, this is supported numerically as
the within-month correlation is higher comparing cycling
head injuries to arm injuries as opposed to leg injuries
. Further, Figure 5 gives a plot of the head/arm injury
ratio and the estimated counterfactual, i.e., the trend without
the effect of the helmet law. This plot demonstrates a
clear level shift in the head/arm ratio for cyclists after the
helmet law as 89% (16/18) of monthly ratios are below the
called the “signal” to “noise” ratio. Importantly, Ramsay et
al. , in a systematic review of studies using interrupted
time series designs, found over 40% of studies in which
the data was not analysed or analysed inappropriately,
the original conclusions were reversed when appropriate
statistical methods were used.
A numerical analysis of the NSW hospitalisation data for
cycling and pedestrian head injuries is given in Table 2.
Walter et al.  validated the fit of their model through
inspection of the deviance residuals which included
checking for residual autocorrelation. Furthermore, this
study meets all the quality criteria for interrupted time
series designs proposed by Ramsay et al. . Additional
resources for properly assessing population-based
interventions through interrupted time series designs are
Wagner et al. , Shadish, Cook and Campbell  and
French and Heagerty .
Table 2: Ratio of head to limb injury hospitalisations
in NSW for cyclists and pedestrians immediately before
and after mandatory helmet legislation (source: Walter
et al., )
Note that the p-values given are substantially lower when
the within-month correlation between head and limb
injuries is part of the model or the most parsimonious
model is chosen . For each type of ratio, there is a
significant change with the helmet law for cyclists but not
for pedestrians. In fact, there is an estimated increase in the
head/arm ratio for pedestrians while there is a substantial
decrease for cyclists. These results point to a small amount
of “noise” relative to “signal” in the NSW hospitalisation
data for cycling head injuries around the helmet law.
Figure 3. Time series of the ratio of head to limb injuries for bicycle
and pedestrian related hospitalisation in NSW
(source: Mindell et al. )
Although graphical displays of data are an efficient method
for presenting a study’s results, they can also be misleading
as demonstrated above. Additionally, a determination that
data is “noisy” should be assessed objectively by comparing
an observed effect to an estimate of variance, sometimes
There is a drawback of strictly analysing the ratio of one
injury to another. Specifically, the ratio between them may
vary over time, yet it will be unclear whether it is due to
changes in one or both. A more appropriate analysis, and
perhaps time series plot, would be to estimate them as part
of a joint model. Separate time series plots of cycling head
and arm injury hospitalisations in NSW for the eighteen
month period around the helmet law and the following two
decades are given in Figure 6.Journal of the Australasian College of Road Safety – Volume 25 No.4, 2014
Figure 4. Time series of the ratio of head to limb injuries for bicycle and pedestrian related hospitalisation in NSW
(source: NSW Department of Health)
January 1994 and Clarke’s comparison ignores intermediate
injury data for 1996-1999 and estimates of helmet wearing.
There is a 17% decline in overall cycling injury comparing
1988-1991 with 1996-1999 data as well as a 53% decline
in serious cycling injury (AIS: 3+). This time period also
corresponds to an increase in helmet wearing (see Figure 7).
Figure 5. Time series of the ratio of head to arm bicycle injury
hospitalisations in NSW and the expected ratio without the helmet law
(source: NSW Department of Health)
In the eighteen month period before the helmet law, the
head injury rate is consistently higher than the arm injury
rate while the opposite holds in the subsequent eighteen
month period. There is a clear divergence between these
injury rates over the next twenty years using yearly
In a review of New Zealand data found in Tin Tin,
Woodward and Ameratunga , Clarke  argues the
NZ helmet law is associated with an increased injury risk
of 20-32%. This conclusion comes from comparing overall
injuries per million hours cycling in the periods 1988-1991
and 2003-2007. The NZ helmet law was effective from 1
Although helmet use is a targeted intervention (i.e., a
helmet will only protect the head), Clarke did not analyse
head injuries separately and instead combined all cycling
related injury . Missing from Clarke’s study was
a 67% decline in serious traumatic brain injury (TBI)
comparing 1988-1991 and 1996-1999 data. Further, when
contrasted with increases in helmet wearing, there is a
decline in both injuries overall and serious TBI alone.
While there is an increase in overall cycling injury
comparing 1996-1999 and 2003-2007 data, there is only
a slight increase in TBI. During this period, estimates of
helmet wearing in NZ have remained steady indicating
any changes in the injury trends are unrelated to helmet
Helmet legislation has also been shown to be beneficial in
other jurisdictions. This includes reductions in cycling head
injury or fatality for children under 18 years in Alberta,
Canada , children under 16 years in Ontario, Canada
, Canadian children aged 5-19 years in provinces with
helmet legislation , children under 16 years in the US
[43, 64], children 17 years or under in California , male
children under the age of 15 in Sweden  and cyclists
in Spain . A Cochrane Review has also found helmet
legislation to be beneficial at decreasing cycling head injury
17Journal of the Australasian College of Road Safety – Volume 25 No.4, 2014
Figure 6. Cyclist head and arm injury hospitalisations in NSW during (a) the 36 month period around
the helmet law and (b) 20 years post-MHL (source: NSW Department of Health)
Figure 7. Overall cycling-related injuries and serious traumatic brain injury (TBI) per one million hours travelling
and estimated helmet wearing rates in New Zealand (source: Tin Tin et al., , New Zealand Ministry of Transport, )
Helmet laws result in a net health reduction
It is often argued the deterrent effects of MHL, and
subsequent increase in injury risk per cyclist through
safety in numbers, leads to a net reduction in health. In a
study regarding the health impact of MHL, de Jong 
concludes MHL is only overall beneficial under “relatively
Among de Jong’s assumptions is helmet legislation can only
lead to declines in cycling. As support for this assumption,
de Jong notes, without citation, motorcyclists do not
like helmets, so it is “safe to assume the same is true for
bicyclists”. He also points to Robinson [85, 88, 89] as the
“main statistical studies” on the subject. As demonstrated
above, there is no evidence adult cycling diminished with
helmet legislation in NSW, South Australia or Victoria and
the safety in numbers hypothesis is not supported using
available NSW data. There is also little evidence helmet use
increases the risk of DAI or an increase in risky behaviour.
Therefore, the belief that helmet legislation will not lead
to less cycling or helmet use will not increase the risk ofJournal of the Australasian College of Road Safety – Volume 25 No.4, 2014
injury are reasonable assumptions. Under those conditions,
de Jong’s model will always demonstrate a net benefit to
With regards to Australia, de Jong used model parameters
based on data from other nations. So, it is unclear if
any of his results are applicable to cycling in Australia.
Additionally, Newbold  found a benefit to helmet
legislation using de Jong’s model using parameters relevant
to the United States. In an unrelated assessment, Elvik
and colleagues  found a positive cost-benefit ratio for
helmet legislation under most scenarios.
In this paper, we discuss common arguments against the
use of bicycle helmet use and adoption of a government
mandated helmet law. As demonstrated, these arguments
are not supported by available data (DAI hypothesis, safety
in numbers); rely on the omission of key data (deterrent
effects of legislation, lack of population level effects); or
the misrepresentation of data (risk compensation, lack
of population level effects). The hypothesis that helmet
legislation leads to a net health disbenefit, or the related
obesity link (for example, see Rissel, ), is dependent on
these arguments and is therefore not supported by available
This is not the first paper critical of methods used in anti-
helmet arguments. Other work not cited above has pointed
to common fallacies in the literature portraying bicycle
helmets or helmet laws negatively [25, 46, 45, 81, 70, 15,
99, 19, 78, 75].
Many of the authors arguing against helmets cited in this
paper belong to anti-helmet advocacy groups. Adams,
Curnow, Franklin, Gillham, Hillman, Robinson and
Wardlaw are members of the Bicycle Helmet Research
Foundation . Curnow and Gillham also maintain their
own websites dedicated to anti-helmet advocacy [24,
42]. Mindell is vice-chair of the Transport and Health
Study Group whose objectives include “To promote a
more balanced approach to cycle safety and oppose cycle
helmet legislation” . The THSG is affiliated with a
new Elsevier journal with Mindell as editor-in-chief with
Rissel and Wardlaw as members of the editorial board .
Additionally, Rissel has participated in anti-helmet protests
Quite often arguments against helmet legislation are framed
as an all-or-nothing safety intervention strategy that is
in direct competition with creating segregated cycling
infrastructure. In other words, it is believed a government
will support one but not both. To wit, Ian Walker in a
recent New York Times article states “Any solution to
bicyclist safety should focus on preventing collisions from
taking place, not seeking to minimize the damage after a
collision has occurred” . This strategy runs counter
to the safe system approach supported by government
and safety advocacy groups, where personal protection
is seen as a critical component of the whole system to
reducing vulnerable road user (cyclist and motorcyclist)
injuries. There is also little support for focussing on injury
avoidance alone in the injury record. In NSW from 1991
to 2010, only 12% and 23% of bicycle related head injury
hospitalisations for children and adults respectively involve
a motor vehicle. The goal of the safe system approach, on
the other hand, would be to minimise the risk of a crash
(crash avoidance) and to minimise the risk of injury when a
crash occurs (personal protection), i.e., a holistic approach
is used to reduce road trauma.
There are other anti-helmet arguments we have not
addressed. A Straw Man is often posited that helmet use is
not mandated for pedestrians, so it should not be applied to
cyclists. This argument has appeal on the surface; however,
a similar argument could be made regarding seat belt
legislation. A similarly structured argument might be “seat
belts are not required for cyclists who are often injured
falling off a bicycle, so it should not apply to drivers or
passengers.” Another argument is that helmet legislation
impedes personal freedoms . In a democratic society,
this is a valid argument for an individual. However, helmet
legislation would be valid for a democratic society with
support from the majority. An estimated 94% of Australians
support helmet legislation . Consideration should also
be given in jurisdictions with government funded health
care as the cost of cycling injuries is shared by all tax
payers. Olivier et al.  point out that presently more than
700 head injury hospitalisations are currently being avoided
with the associated reduced health burden cost saving on
the order of around a third of a billion dollars saved each
year by taxpayers.
This paper does not suggest research in favour of helmets
is not without flaws. For example, Robinson  was
critical of Povey et al.  for not fitting time trends in
their assessment of the New Zealand helmet law. Povey et
al. fit the log of the ratio of head injuries to limb fractures
with estimates of helmet wearing for years 1990-1996.
Observations taken over time can exhibit serial dependence
and failure to account for this interdependence can lead
to invalid inferences. The model used by Povey et al.
assumes independence, serial or otherwise. Fitting time
trends is an indirect method for accounting for serial
dependence and there are more direct statistical methods
for this purpose, for example, autocorrelated regression or
autoregressive integrated moving average models (see, for
example, McDowall et al. ). At issue with the Povey
et al. analysis is whether their model assumptions were
justified, specifically serially independent observations.
Neither Povey et al.  nor Robinson  assessed
serial dependence in the NZ data and there are other
19Journal of the Australasian College of Road Safety – Volume 25 No.4, 2014
methodological issues in much of the research assessing
the NZ law . Importantly, the Durbin-Watson statistic
for this data is 1.8 indicating an independence assumption
is reasonable and, therefore, the results of the Povey et
al.  analysis are valid. So, Robinson’s concerns were
reasonable, although her specific criticism was not.
7. Australian Bureau of Statistics. (2010). Participation in
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9. Bambach, M.R., Mitchell, R.J., Grzebieta, R.H. & Olivier,
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While there is much conflicting evidence related to helmets
and MHL efficacy, when brought under statistical scrutiny
the majority of evidence against helmets or MHL appears
overstated, misleading or invalid. Moreover, much of it has
been conducted by people with known affiliations with anti-
helmet or anti-MHL organisations. Ultimately, this body
of work distorts our understanding of the mechanisms by
which helmet wearing protects the heads of cyclists and the
factors related to the success or failure of helmet legislation.
Future research should exercise caution regarding the
validity of the anti-helmet arguments discussed in this
paper unless, of course, they are supported by robust data
and analyses from the peer-reviewed literature. We further
caution against the use of advocacy groups, such as those
listed above, as a resource for shaping road safety policy.
The authors wish to thank the NSW Ministry of Health,
Centre for Epidemiology and Evidence for providing the
data analysed in this study. An early version of this article
appears in the 2013 Proceedings of the Australasian College
of Road Safety Conference.
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