This report was originally written as a term paper for the Road Safety graduate course in Civil Engineering at Ryerson University.
Summary: Based on the evidence reviewed in this paper, it is concluded that mandatory helmet law (MHL) is not an effective policy for reducing head injuries among cyclists. The adverse effects of MHL are too great for it to be effective policy, particularly regarding the potential for reduction in cycling rates. As the best available evidence suggests that helmets are beneficial for reducing risk of head injury in the event of a crash, governments should consider using promotional tactics to increase the use of helmets, but should not mandate or enforce their use. Helmets are a last-resort form of safety, as they only provide benefit to the user in the event of a crash. Excessive focus by governments and policymakers on increasing helmet use fails to acknowledge the wider issue of addressing the source of bicycle crashes and the position of cyclists as vulnerable road users.
Many studies have identified that, compared to other road users, cyclists have a disproportionately high rate of injury and death (Ohlin, Strandroth and Tingvall 2017). As such, though cycling fatalities are quite low in absolute terms, it is of high interest of governments to reduce the fatality and injury rates of cyclists.
Cycling crashes occur from a variety of sources. A survey of cyclists in Europe found that 50 percent of bicycle crashes are single-vehicle accidents, and 13 percent involve a motor vehicle. Further, less than 10 percent of crashes were reported to the police, and only one third of crashes with motor vehicles were reported (Bogerd, et al. 2015).
One of the most common places of injury is the head, and it is also the most severe. In the US, head injuries account for three quarters of cycling deaths, and two thirds of all cyclist hospital admissions (Thompson, Rivara and Thompson 1999). In response to this, the use of safety helmets for protecting against head injuries have been promoted and encouraged, which has led to a steady increase in helmet usage rates over time (Cameron, et al. 1994). Still though, in many parts of the world, helmet usage is very low (Bogerd, et al. 2015).
The natural evolution of encouraging helmet use is mandating it legislatively, through the introduction of a mandatory helmet law (MHL). The objective of this paper is to review the efficacy of mandatory helmet law as a policy to reduce bicyclist head injuries. This paper defines MHL as one that exists at any level of government and applies to an entire population (all ages). This paper will explore some jurisdictions where MHL applies only to children, but will assess the value of an all-ages MHL.
The logic of Mandatory Helmet Law is simple: if helmets are found to reduce the risk of head injury in the event of a crash, then it is theorized that higher rates of helmet usage would indirectly lead to an overall reduction in head injuries. Legislating helmet use does not have a direct causal effect on head injuries; rather it encourages more of what might be considered a safe behaviour (Oliver, Wang and Grzebieta 2014). Thus, further questions can be developed for evaluation of MHL:
- What is the role of helmets in reducing head injuries?
- Are helmeted cyclists at a lower overall risk of head injury than non-helmeted cyclists?
- Does MHL lead to increased helmet usage rates?
- Is MHL effective in reducing head injury rates over time?
- Do the benefits of MHL outweigh the adverse effects?
This paper seeks to answer the above questions, concluding with a recommendation on whether MHL should be used as a policy for reducing head injury rates.
Government websites frequently communicate strong messages regarding bicycle helmets and safety. The New Zealand Transport Agency website states “The most common cyclist injuries that cause death are head injuries, so protecting your head is important” (New Zealand Transport Agency 2014). The Finland government creates a strong call-to-action with its website message “A cycle helmet is the only safety device that protects the rider from head injury. Severe brain injury may result even from just falling” (Liikenneturva 2017).
Even in jurisdictions where bicycle helmets are not mandated by law, it is common for governments to stress their importance and encourage their use. In Ontario, the Ministry of Transportation states that “a helmet can greatly reduce the risk of permanent injury or death if you fall or collide. It is strongly recommended that all riders wear helmets” (Ontario Ministry of Transportation 2017). The cycling safety tips section of the City of Toronto website tells riders to “wear an approved helmet for safety” (City of Toronto 2017).
The use of legislation to promote helmet use varies significantly across the world. As an added complexity, depending on the jurisdiction helmets can be mandated at the city, province, state, or national level. While Canada has no national cycling helmet law, legislation varies by province. Nova Scotia, Prince Edward Island, New Brunswick, and British Columbia all have MHLs for all ages of cyclists, while Alberta and Ontario require helmets for those under 18. The remaining provinces have no helmet legislation (Casey 2010). One suburban municipality in Alberta, St. Albert, introduced a community-wide helmet law for all ages in 2006 (Karkhaneh, Rowe, et al. 2011).
In the United States, where there is no federal helmet law, application of all-ages laws at the state level is inconsistent. Still, laws at the community or state level generally apply to children up to the ages of 14-18. Washington state is an exception, which mandates helmets for cyclists of all ages (Helmets.org 2017).
Helmets are mandatory for all ages at the national level in just four countries – Australia, New Zealand, Finland, and Dubai, introduced in 1989, 1994, 2003, and 2010, respectively. Finland is an outlier, however, in that there is no fine associated with breaking the helmet law (Helmets.org 2017).
Rates of helmet use are an important consideration behind MHL, as it gives an idea of the level of acceptance or resistance that can be expected if a law were imposed. Variance within a population can also draw attention to detailed considerations, such as a disproportionate impact on certain cultural or socioeconomic groups.
Similar to helmet regulation, helmet usage rates vary widely in the global context. In Europe, national helmet usage rates range from as low as 3 percent in Italy to 50 percent in Norway, and are generally higher among children, in part due to many countries having MHL for children (Bogerd, et al. 2015). In Amsterdam, just 0.1 percent of cyclists wear helmets (Osberg and Stiles 1998), while in Queensland, Australia, observed helmet compliance is 98 percent (Debnath, et al. 2016). In the US, 29 percent of adults and 42 percent of children always wear a helmet when cycling (Jewett, et al. 2016). In Vancouver, Canada, where cycling rates are relatively high in the North American context, and mandatory helmet law exists, prevalence of use is 78 percent (Zanotto and Winters 2017).
Table 1 – Helmet usage rates in select regions
|Region||Helmet Usage||Survey Type|
|US (adults)||29%||Respondent survey, always use a helmet|
|US (children)||42%||Respondent survey, always use a helmet|
Helmet use is also not homogenous within a population. The same Vancouver study also found higher usage rates along personal bike users compared to bikeshare users, as well as higher rates on weekday mornings (suggesting commuter helmet usage rates are higher). It was also found that women had 54 percent higher odds of wearing a helmet compared to men (Zanotto and Winters 2017). A study of European cyclists found that while 38 percent of racing bike users wore helmets all of the time, only 11 percent of city bike users did (Bogerd, et al. 2015). In Queensland, among those more likely to wear helmets were group riders, road bike riders, and those wearing Lycra. In the United States, helmet usage rates were found to be lower for persons making less than $40,000 per year. Children were more likely to wear helmets if they lived in a state with MHL for children, and if their parents always wore a helmet (Jewett, et al. 2016).
Often overlooked by medical studies are the reasons why bicyclists forego the use of helmets. This is of important consideration when evaluating the potential of MHL, because of the potential implications of mandating helmet use among these individuals. For example, it is noted above that low-income persons are less likely to bicycle with a helmet. Mandating helmets without the consideration of this could result in the policy being highly regressive.
Convenience is a common theme for choosing to not wear a helmet, which includes inconvenience of purchasing, wearing, and carrying a helmet throughout the day (Fishman, et al. 2014). Compulsory helmet use can also inhibit the spontaneity of bicycle travel, especially for bike sharing systems. In a survey asking Melbourne residents about their barriers to using Melbourne Bike Share, having to carry a helmet around the city was a common open-ended response (Fishman, et al. 2014). This supports the above-mentioned finding from Vancouver that bikeshare users are less likely to wear a helmet (Zanotto and Winters 2017).
A survey of European cyclists found that most cyclists agree that their safety is improved with helmets, and even agreed that wearing one improved their own sense of safety (Bogerd, et al. 2015). Despite this, the majority choose not to wear them for reasons including carelessness, length of trip, discomfort, and temperature. Many helmet users feel that wearing one makes them sweat more (Jansch and Otte 2014). Given the fact that individuals acknowledge the benefits of helmets and still choose not to wear one is evidence that at the individual level, there is clearly a trade-off between safety, convenience, and comfort when choosing whether to wear a helmet.
Social norms also appear to have a strong impact on decisions to wear a helmet. In the Netherlands, a survey of Dutch pediatricians found while most agreed that helmets were effective in reducing head injury to bicyclists and 82 percent agreed that usage should be advised for children, only 2 percent of the participants cycle with helmets. Barriers to personal use included: never thought about it, poor social acceptance, nobody does it, and uncomfortable (Villamor, Hammer and Martinez-Olaizola 2008). A strong culture of non-use of helmets, such as in the Netherlands, is likely self-reinforcing, given that even healthcare professionals who believe in the efficacy of helmets are unlikely to wear helmets themselves.
Finally, it is also important to note why individuals who choose to wear a helmet do so. A study of college students in the US found that pressure from peers and family members was a significant influencer, as well as personal safety perception (Ross, et al. 2010). A survey of European bicyclists found that the top reasons for choosing to wear a helmet were feelings of safety, and a need to protect themselves from other road users (Jansch and Otte 2014).
In summary, bicyclists in general seem to be aware of the risks of cycling as well as the benefits of helmets. Commonly-mentioned disadvantages of helmets are that they are inconvenient to carry around, uncomfortable, and can prevent spontaneous trips. Social norms and others also have a significant influence on an individual’s choice to wear a helmet. Given all of this information, there are clear reasons to wear and to not wear a helmet from an individual’s perspective, and currently cyclists appear to be making conscious, informed decisions when deciding whether or not to wear one.
There is strong and clear evidence that, in the event of a head impact while cycling, the use of a helmet dramatically reduces risk of head injury and death. In a review of five case-control studies, (Thompson, Rivara and Thompson 1999) found that in the event of a head impact in a bicycle crash, helmets provide a 63-88 percent reduction in the risk of head, brain, and severe brain injury for all ages of bicyclists. Given the reduction in risk in the case of a head injury, combined with the high proportion of head injuries in terms of cyclist injuries and deaths, it is a common assertion of researchers that helmets increase cyclist safety (Bogerd, et al. 2015), and therefore helmet use should be encouraged if not regulated, drawing comparisons to seatbelts in vehicles (Attewell, Glase and McFadden 2001).
These findings have led researchers, especially medical professionals, to conclude that higher helmet use should be a “no-brainer” for governments, especially those with public healthcare systems like Canada, where severe brain injuries can cost the medical system upwards of $400,000 at the time of the injury (Casey 2010). The argument is sometimes made on the basis of saving the healthcare system (and thereby society) significant funds in exchange for the relatively low cost of a helmet for an individual (Casey 2010). It is further argued that societies with extensive social security systems put less burden of crash risks on the individual, which may lead people to value helmets less when deciding whether to wear one (Sieg 2016).
As mentioned above, it is virtually undisputed that in the event of a head impact in a crash, helmets significantly reduce risk of head injury (Attewell, Glase and McFadden 2001); however, this is not the same as concluding that helmets improve safety or reduce head injuries.
In the case of assessing the efficacy of helmets, the goal of a research study is to determine whether the control variable (helmet or no helmet) has an impact on the outcome (head injuries). The most robust way of measuring this is through a randomized control trial, where cyclists are randomly selected to wear a helmet or not, with their exposure and behaviour controlled and studied over the study period. There are, however, several limitations to using randomized control trial in this application. First, injury rates are so low that a very large sample would be required. Second, there are ethical risks as the experiment may expose cyclists to undue harm. Third, participants would be required to have an unbiased opinion of the effectiveness of helmets, and given the significant amount of promotional efforts encouraging helmet use, this would be quite difficult (Sheikh, Cook and Ashcroft 2004).
One available alternative to randomized control trials is the use of case-control studies, which compare two existing groups, differing in outcome, on the basis of some supposed causal attribute (Wikipedia 2017). In the case of helmets, the supposed causal attribute is use of a helmet, and the outcome studied is head injuries. Because full control is not possible in a case-control study, results are reported in the form of an odds ratio (OR). In the case of bicycle helmets, the OR might be represented as:
There is an additional limitation to this approach, however: the real OR cannot be calculated in most cases because information on the number of cyclists without head injuries is generally unavailable (Zeegers 2015). Gathering such information would require full knowledge of all cycling trips, or a representative sample. As an alternative, researchers often report results using an alternative odds ratio (ORalt), where rates of injured cyclists with non-head injuries is often used as a proxy.
While ORalt can be used as a measure of comparative risk (i.e. risk given the condition of a crash), it is often misinterpreted as the relative risk (Olivier and Radun 2017), or risk ratio, that is:
The conclusion is then drawn from the ORalt that cyclists wearing helmets are significantly less likely to suffer head injuries than those not wearing helmets. The limitation of relying on hospital data is that the study population is people who have already been involved in a crash, so there is no opportunity to control for the relative risk of wearing versus not wearing a helmet.
The central assumption behind this method is that prevalence of non-head injuries (ex. arm, leg) among cyclists is independent of helmet use. Considering that helmet use varies significantly within and between cycling populations (Zanotto and Winters 2017), it is natural to challenge this assumption. Risk exposure can be measured in terms of distance travelled under each condition. Thus, the question remains: on a per-kilometre basis, are helmeted cyclists more likely to be involved in a crash than non-helmeted cyclists?
If data is available on the average daily kilometres cycled with and without a helmet among the study population, the exposure rate (ER) can be calculated as the ratio of the two:
The ER can then be used to convert the adjusted odds ratio into the relative risk of cycling with versus without a helmet. The remainder of this section will discuss a study by (Zeegers 2015) which attempted to apply this method to existing studies, followed by a counter-assessment by (Olivier and Radun 2017) which found a conflicting result.
Upon attempting to estimate risk ratio for helmet use from existing studies, Zeegers found that in most instances, exposure rates were unavailable. Three studies were found, however, where exposure rates could be reasonably estimated, thus allowing the estimation of risk ratio based on the alternative odds ratio.
In the Netherlands, where helmet use is quite low, and directional helmet usage data is available, Zeegers estimates the ER to be ~0.05; that is, for every 100km travelled by bicycle, 5km are made by helmeted cyclists. Case-control study data on head injury and other injuries then shows that helmeted cyclists account for five percent of head injuries. The logic follows that if helmeted cyclists account for five percent of kilometres cycled and five percent of head injuries, then their relative risk of head injury for non-helmeted cyclists is no higher than that of helmeted cyclists. Zeegers concludes that the previous studies overestimated the risk ratio for bicycle helmet use, and that in fact the null hypothesis that risk ratio is the same for helmeted and non-helmeted cyclists is equal cannot be rejected (Zeegers 2015).
Zeegers repeated this method for two additional studies in which exposure rates could be estimated, in Victoria, Australia and Seattle, USA. In all three cases, Zeegers finds the risk ratio to be overstated for both head and non-head injuries. Based on the three cases, Zeegers concludes that:
- Case-control studies on helmet effectiveness which use odds ratios based on victim data are not valid, and likely overestimate the effectiveness of helmets in preventing head and overall injury; and
- future studies should include observations of helmet use among non-injured cyclists to adequately control for exposure.
The conclusion to draw from Zeegers analysis is not that an individual will be safer or equally safe without a helmet, but rather that at the population-level, cyclists wearing helmets appear to have a higher risk exposure which offsets the benefit of a helmet. An individual who wears a helmet without changing their behaviour is still likely to reduce their risk, but as mentioned above, this involves a certain level of trade-off of comfort and convenience (Fishman, et al. 2014).
Zeegers’ conclusion that common assumptions around helmet efficacy may be invalid prompted significant conversation within and beyond the academic community. Two years after the publication of Zeegers’ work, Oliver & Radun published a direct response, in a paper titled Bicycle Helmet Effectiveness is not Overstated. The authors express agreement with Zeegers’ points regarding lack of understanding of risk exposure, but ultimately conclude that “the best available evidence suggests that helmet use is an effective measure of reducing cycling head injury” (Olivier and Radun 2017). Oliver & Radun’s arguments are summarized in this section.
Acknowledging that risk exposure among types of cyclists is likely to differ, another alternative to case-control studies is cohort studies. Cohort studies track individuals over time to assess the interactions of various factors in risk exposure (for example, sport cycling versus leisure cycling). Cohort studies are only as good as the data collected however, and rely on reported information from participants, and are thus subject to recall bias (for example, recall of kilometres cycled with and without a helmet) (MacGill 2016).
Helmet use is just one of the many potential factors affecting risk exposure, which could also include age and type of cyclist (as discussed above); however, attempting to classify types of cyclists has proven to be difficult (Olivier and Radun 2017). Finally, in studies assessing helmet effectiveness, exposure data is often unavailable, making cohort studies impossible.
Upon a review of the same cases studied by Zeegers, Oliver & Radun recalculated relative risk and find it to be better than or similar to the odds ratios in each case. This conclusion is supported with the arguments that bicycle crashes are often underreported, and that improper wear of a helmet has a potential to soften the overall benefits of helmets when proper use cannot be isolated for each crash.
Oliver & Radun conclude that available research is insufficient for answering “whether helmet use is associated with a crash or fall and whether helmet effectiveness differs for different cycling types” (Olivier and Radun 2017), and that future research on this would be valuable, in the form of a cohort study. It is also noted that agreement has not been achieved on classifying different types of cyclists, so more work is required to do so in a way that could capture different risk exposures. Finally, given the available evidence in the form of odds ratios, Oliver & Radun support the conclusion that helmet use is an effective way to reduce head injuries should prevail until sufficient contrary evidence is available, and strategies to increase helmet use should be implemented (Olivier and Radun 2017).
While both studies agree that case-control studies are not ideal for assessing helmet effectiveness, both assert different conclusions based on the same studies, which should be a reminder of how easily data can be interpreted in different ways, especially when there is potential for researcher bias. Zeegers concludes, based on estimates of risk ratios, that helmeted cyclists are no less likely to experience head injuries than non-helmeted cyclists. Oliver & Radun’s analysis concludes that adjusted odds ratio and relative risk are similar in each of the studies, supporting the claim that helmet use reduces risk of head injury. Clearly there is a need for quality cohort studies to settle this uncertainty.
To assess the impact that MHL has on helmet usage rates, time-series studies are often used, which measure the relative rate of head injuries before and after MHL is introduced, using injury rates from a comparison group as a baseline. Comparison groups are those which are unaffected by the intervention (MHL), such as pedestrian injury rates, or non-head injury rates among bicyclists.
MHL was introduced in Nova Scotia in 1997. The new law was supported with an extensive media campaign, as well as enforcement by police through the issuance of $25 tickets. Researchers passively observed cyclists in Halifax annually over a five-year period, from 1995 to 1999 to assess the before-after impacts of the MHL. The researchers found that usage rates increased significantly, from below 40 percent before, to 75 percent in 1997 during the time of the media campaign, to over 80 percent in the two years following, when enforcement had begun (LeBlanc, Beattie and Culligan 2002).
In July 1990, MHL was introduced nation-wide in Australia. Over a period of eight years prior to the law and one year following, researchers observed helmet usage rates among both commuter and recreational cyclists. While helmet usage rates had been trending upwards in the years leading up to the MHL, rising from 6 percent in 1983 to 36 percent in 1990, the first year after the introduction of the law showed a significant spike in usage to 73 percent, which was validated with a basic logistic regression model developed with year and MHL as variables for predicting helmet usage. Using the model, researchers concluded that the introduction of MHL led to an increase in helmet usage of 1.8 times what would have been expected based on the historical trend (Cameron, et al. 1994). Worth noting is that national promotional efforts to increase usage rates were carried out throughout the 1980s, which included subsidized helmets for schoolchildren, media campaigns, helmet purchase rebates, and improvements to the comfort of helmets. These likely were the main contributors of increased adoption prior to the law. Upon introduction of the law, enforcement was conducted, with the penalty being a $15 ticket, however overall enforcement rates were relatively moderate (Cameron, et al. 1994).
In May 2002, MHL for all bicyclists younger than 18 was passed in the province of Alberta. Four years later, in February 2006, the suburban Alberta community of St. Albert passed a local all-ages MHL, which came into effect in July 2006. The impact of these measures on helmet prevalence (HP) was measured through observational counts, using local data from a province-wide study in 2000, compared to researcher-collected data in Summer 2006. No mention was made to the level of promotion made regarding the policy changes. Enforcement was carried out, with 130 tickets being issued to cyclists of all ages between 2006 and 2008 (Karkhaneh, Rowe, et al. 2011). Researchers found that HP among children increased 53 percent and adolescent usage increased six-fold, while adult usage increase was not statistically significant. A major limitation of this study is that observations were conducted during the summer of implementation of the policy, and that St. Albert appears to have a very low cycling rate; researchers observed just 225 cyclists over 18 sites over the course of the summer (Karkhaneh, Rowe, et al. 2011). While this may not limit the research findings, it does limit the usefulness of the findings for application to larger metropolitan areas.
Based on the reviewed examples, it can be concluded that MHL increases helmet usage rates substantially, even with relatively minor rates of enforcement and penalties. There is also evidence from the case of Melbourne that helmet use can be increased significantly without MHL through the use of promotion, subsidies, and programs (Cameron, et al. 1994).
In order to effectively estimate the affect of a treatment, in this case MHL, studies must account for (Persaud 2017):
- Causal factors which influence head injury rates (ex. cycling participation rates, availability of infrastructure, travel speed, etc.)
- Other factors that affect head injury rates for which the influence is not known (ex. helmet use)
- Selection bias (regression to the mean) involved in the selection of jurisdictions for implementing MHL (i.e. areas where MHL is implemented may have had disproportionately high rates of head injuries, spurring the policy response)
- Changes to the extent of bicyclist head injury reporting (police reports, insurance reports, hospital reports, etc.)
A naïve before-after comparison, for example, would look at the absolute number of head injuries for a jurisdiction before and following introduction of MHL. Using this approach to assess the results of MHL in Victoria, Australia, researchers found that the number of head-injured bicyclists fell 48% in the first year after the law (Cameron, et al. 1994). However, this analysis lacked sufficient data to control for the influence of helmet-usage rates (though there was a measured increase in parts of the province) and for changes in cycling rates as a result of the law. In their conclusion, researchers also failed to acknowledge that head injuries had been falling steadily for eight years preceding the introduction of the law, as well as head injuries as a proportion of all bicyclist injuries (Cameron, et al. 1994).
A study on the impacts of MHL in Halifax, Nova Scotia was conducted with a similar shortfall. The proportion of cyclist head injuries of all cyclist injuries was found to decrease by half following the law, at the 94% significance level. However, the study failed to assess the impact on cycling rates, and therefore the exposure rate (LeBlanc, Beattie and Culligan 2002).
In a meta-analysis of existing literature, (Oliver, Wang and Grzebieta 2014) reviewed four studies which evaluated the effects of New Zealand’s MHL. Three of the studies attempted to control for exposure rate through a control group, though they were all different, and each qualitatively selected, and failed to adequately control for confounding (Oliver, Wang and Grzebieta 2014). Though the authors applied a disciplined analysis to selecting which papers to review, they ultimately concluded that “more methodologically sound evaluations with rigorous statistical methods for data analysis are urgently needed to assess the impact of helmet legislation on cycling head injuries in New Zealand”.
In conclusion, it appears that a methodologically-sound study on the effects of MHL has not yet been conducted, and as such, conclusions cannot be reasonably drawn regarding the effectiveness of MHL in reducing head injury rates.
As mentioned above, this is often the result of a lack of sufficient data collection to control for influencing factors. An ideal study would consider:
- The impacts of causal factors including cycling participation rates, helmet usage rates, and cycling trip purpose (leisure, sport, commute, etc.)
- A comparison jurisdiction, in which helmet laws were not introduced, where other laws, infrastructure, and other exposure variables are similar
As safety concerns are the common motivation behind introduction of MHL, selection bias is also likely to play a role in where MHL is implemented. Because of this, it is possible that the count of head injuries prior to implementation of MHL is not a sensible base estimate of the expected crash count following implementation (Persaud 2017).
Mandatory helmet laws have received much criticism since being introduced, from researchers, advocates, and bicyclists alike. This is an important consideration when considering a balanced policy decision.
Perhaps the most frequently-mentioned counterargument to MHL is that mandating helmet use will lead to a decrease in cycling rates, thus discouraging a behaviour that is beneficial for both individuals and society (Sieg 2016). As part of the development and measurement of impacts of MHL in Australia, large-scale roadside counts were conducted along key cycling routes before and after implementation of MHL. Though not necessarily representative of the entire cycling population, these counts found a 20 to 36 percent reduction in cycling volumes after the implementation of MHL. The absolute decrease in cyclists was greater than the absolute increase in helmet-wearing cyclists (Robinson 2003).
A study exploring the potential effects of removing MHL in Sydney, Australia found that nearly a quarter of respondents stated they would bicycle more if the law was repealed (Rissel and Wen 2011). This study has been used to predict the increase in participation if MHL were repealed (Sieg 2016), however caution should be exercised as stated preference is not always a strong predictor of behaviours.
Given the generally-accepted benefits of cycling and the desire of governments to encourage it, it is important to consider the level of enforcement efforts given to helmet laws. While issuance and severity of fines reviewed in Nova Scotia, Melbourne, and St. Albert were generally quite modest, more recent enforcement of MHL in Australia has been significantly higher. In 2003, Queensland was issuing 23,000 bicycle helmet offence notices annually; on a per-kilometre basis, cyclists were three times more likely to receive a helmet offence than other road users for all other offences, including speeding and drunk driving (Robinson 2003). In March 2016, New South Wales quadrupled the penalty for not wearing a helmet, to $319. Enforcement issued 9,760 infringements in the first 12 months, collecting nearly $2 million in revenue (O’Sullivan 2017). The higher fines appear to have had a significant adverse effect on cycling participation, with only 12.5 percent of the NSW population reporting riding in a typical week in 2017, compared with 17 percent in 2015 (O’Sullivan 2017).
Qualitative studies have suggested that helmeted cyclists are perceived by drivers as more proper and responsible by motorists, and it is theorised that this translates to motorists passing at a closer distance, given that they have more confidence in the cyclist’s abilities (Walker, Garrard and Jowitt 2014). A naturalistic experiment in the UK found evidence in support of this, specifically finding that motorists overtook a helmet-wearing cyclist at a closer proximity than a non-helmeted cyclist (Walker 2007).
It has been speculated that at the individual level, helmet users take more risks, potentially offsetting the protective effects of helmets. This has been supported by evidence that helmeted cyclists more frequently experience non-head injuries (Attewell, Glase and McFadden 2001). However, the opposite has also been found, which is that helmet users experience lower rates of non-head injuries. This evidence has then been used to suggest that helmeted cyclists are less risk-taking (Attewell, Glase and McFadden 2001). There is no clear conclusion on the effect of helmet usage on risk-taking, but it should be considered as a potentially confounding factor in future studies (Attewell, Glase and McFadden 2001).
Mandatory helmet laws, especially for adult populations, can be viewed as paternalistic; that is, a restriction on the freedoms of individuals in the name of a higher societal interest (Sheikh, Cook and Ashcroft 2004). Given the legitimate reasons why individuals choose to not wear helmets discussed above (Fishman, et al. 2014), and the strength of social norms on influencing helmet use (Villamor, Hammer and Martinez-Olaizola 2008), there is significant potential for resistance to proposed MHL, as is the case in the United Kingdom where MHL is currently being considered (Boardman 2017). The extent to which government should take involvement in such behaviours should be carefully considered. For example, should citizens also be required to wear helmets when using public skating rinks, a popular winter activity in Canada?
Many conclusions can be drawn from this review. First, there is clear evidence that helmets significantly reduce likelihood of head injury (by 67 percent or more) in the event of a crash. Second, there is also clear evidence that introduction of MHL, combined with promotion and modest enforcement, leads to a significant increase in helmet usage rates (by 40 percentage points or more).
What is less clear, and more disputed, is how risk exposure rates vary by helmet usage. This review has demonstrated that helmet usage varies significantly within a cycling population. For example, Lycra-clad cyclists, children, and cyclists riding sport bikes have higher rates of helmet usage; it can be theoretically argued that these subsets have higher risk exposure rates, due to higher speeds in the case of Lycra-clad cyclists, and less coordination in the case of child cyclists. This has the potential to distort the conversion of adjusted odds ratios to risk ratios in case-control studies.
The current prevailing conclusion by medical professionals is that increased helmet usage corresponds with lower head injury rates; however, this has yet to be proven by a sound cohort study. If, for example, helmeted cyclists are found to be more likely to be involved in a crash, the resulting risk exposure rate could reduce or eliminate the safety benefits of helmets. Still, at the individual level, cyclists are likely to be safer wearing a helmet, though there have been findings that even challenge this assumption.
Time-series studies allow for the evaluation of the affect of MHL on rates of head injuries within jurisdictions where the law has been implemented. While at first glance, the studies reviewed in this paper suggest that MHL lead to a decrease in head injury rates, the studies were found to lack academic rigor. Studies failed to control for confounding variables such as changes in cycling rates, variations in helmet usage, and did not manage potential for selection bias. More rigorous studies are needed before conclusions can be drawn on the effects of existing MHLs.
Finally, there are many adverse impacts associated with MHL which must be considered. Though generally cyclists are aware of the benefits of helmets, many make conscious decisions to not wear one, due to a combination of factors including comfort, convenience, spontaneity, and social norms. Low-income populations have been found to be less likely to wear helmets, so imposing penalties for non-use could be a regressive policy. MHL has been shown to have a negative impact on cycling participation rates, which contradicts governments objectives to promote higher rates of cycling for its individual and societal benefits. Disproportionately heavy rates of enforcement of helmet laws can further stifle cycling participation. There is also an argument to be made on whether MHL crosses a line in terms of government imposing unnecessary burden on individuals in the name of societal benefit.
Based on the evidence reviewed in this paper, it is concluded that mandatory helmet law is not an effective policy for reducing head injuries among cyclists. The adverse effects of MHL are too great for it to be effective policy, particularly regarding the potential for reduction in cycling rates. As the best available evidence suggests that helmets are beneficial for reducing risk of head injury in the event of a crash, governments should consider using promotional tactics to increase the use of helmets, but should not mandate or enforce their use. Helmets are a last-resort form of safety, as they only provide benefit to the user in the event of a crash. Excessive focus by governments and policymakers on increasing helmet use fails to acknowledge the wider issue of addressing the source of bicycle crashes and the position of cyclists as vulnerable road users.
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