2013 Australasian College of Road Safety Conference A Safe System: The Road Safety Discussion Adelaide

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1 2013 Australasan College of Road Safety Conference A Safe System: The Road Safety Dscusson Adelade An evaluaton of the methods used to assess the effectveness of mandatory bcycle helmet legslaton n New Zealand Wang, J.J.J. 1,2 Grzebeta, R. 1 Walter, S. 3 & Olver, J. 2 1 Transport and Road Safety, Unversty of New South Wales 2 School of Mathematcs and Statstcs, Unversty of New South Wales 3 Centre of Health Systems and Safety Research, Unversty of New South Wales Abstract Mandatory helmet legslaton (MHL) was ntroduced n New Zealand (NZ) n January Prevous studes have shown a sgnfcant reducton n cyclng head njury assocated wth MHL; however, one analyss has suggested a dmnshng return n head njury reducton wth ncreased helmet wearng rates. The am of ths study s to crtcally assess the valdty of methods and conclusons from studes evaluatng the effect of MHL on head njury n NZ. We emphasse the mportance of accurately and objectvely presentng data and the need for a proper subsequent analyss for vald nference. Ths plays a paramount role n the communcaton of research fndngs as they heavly nfluence the publc percepton of road safety and the effectveness of polcy nterventons. Keywords Mandatory helmet legslaton, New Zealand, head njures, statstcal methods, tme trends Introducton The New Zealand helmet legslaton for cyclsts came nto effect on 1 January The legslaton requres all cyclsts to wear a standard approved cycle helmet for all on-road cyclng (Cycles: Road rules and equpment (Factsheet1), 2013). Several case-control studes n the past have shown that cycle helmets do reduce the rsk of a bran njury (Bambach et al., 2013). Smlarly, recent bomechancal testng has shown clear evdence that bcycle helmets reduce substantally potental njury to the head from both lnear and angular mpact acceleratons (McIntosh et al, 2013). Moreover, a recent nvestgaton by the authors nvestgatng the long-term trends n cyclst head and arm njures, ndcate the ntal observed beneft of the mandatory helmet law n New South Wales has been mantaned over the ensung decades (Olver et al, 2013). Wth ths n mnd, the am of the legslaton s to ncrease the helmet wearng rate, n an effort to reduce head njures to cyclsts. Pror to the law, voluntary helmet use had been wdely promoted n New Zealand. Ths ncludes natonal and local publcty and awareness campagns startng n the late 1980s. As a result, voluntary helmet wearng rates ncreased steadly durng that tme. The helmet wearng rate ncreased from vrtually zero n 1986 to 84%, 62% and 39% n September 1992 for prmary school chldren (5-12 years of age), secondary school chldren (13-18 years) and adult commuters (over 18 years), respectvely (Scuffham, et al., 2000). Just after the legslaton, helmet wearng rates ncreased to above 90% for all cyclst age groups. Note that the data used n each of the referenced studes n ths paper are from hosptalsaton or coronal data. None of these data sources nclude nformaton on helmet wearng. However, there exst helmet wearng estmates for the general cyclng populaton from yearly studes n New Zealand. 1

2 2013 Australasan College of Road Safety Conference A Safe System: The Road Safety Dscusson Adelade Research has been conducted to examne the assocaton between helmet wearng rates and rates of head njury to cyclsts n New Zealand. Scuffham and Langley (1999) studed the effect of voluntary helmet wearng on serous head njury to cyclsts. Ther results revealed the ncrease n helmet wearng rates had lttle assocaton wth the percentage of head njures to njured cyclsts for all three age groups. A later study by Povey et al. (1999) assessed the effect of cycle helmet wearng on hosptalsed njures between 1990 and 1996, usng cyclst lmb fractures as a measure of exposure. They reported that the ncrease n helmet wearng assocated wth the mandatory helmet law accounted for a 20% reducton n head njures to cyclsts nvolved n motor vehcle crashes and between 24 and 32% n non-motor vehcle crashes. Scuffham et al. (2000) extended the study by Scuffham and Langley (1999) by usng a longer tme frame ( ) and a general fndng was helmet wearng sgnfcantly reduced head njures to cyclsts n all age groups. In partcular, they estmated that the helmet law averted 139 head njures over a 3-year perod. Sandar Tn Tn et al. (2010) nvestgated exposure-based rates of on-road njures to cyclsts that resulted n death or hosptal npatent treatment over the perod Ther analyss showed the rates of traumatc bran njures were lower n and as compared to whle there was an ncreasng trend n the rates of njures to other body parts. Clarke (2012) revewed publcally avalable data, ncludng the njury data n Sandar Tn Tn et al. (2010), to evaluate the effcacy of the New Zealand bcycle helmet law n terms of safety, health, law enforcement, accdent compensaton, envronmental ssues and cvl lbertes. One of the conclusons of Clarke s study was that the New Zealand helmet law reduced cyclng usage by 51% and has contrbuted to 53 premature deaths per year due to reluctance to cycle and lack of exercse. Whle conclusons from prevous research regardng the effectveness of the New Zealand helmet law are mxed, t s mportant to crtcally assess the methodologes used n those analyses snce flawed statstcal methods and arguments lead to erroneous results and hence undermne conclusons based on these results. Robnson (2001) revewed the results of Povey et al. (1999) and suggested the apparent reducton n head njury was not due to ncreased helmet wearng, but rather an artefact caused by falure to ft tme trends n ther model. Olver (2012), n a commentary regardng Clarke s study (2012), noted the author faled to meet any of the necessary crtera to establsh a causal relatonshp and hence the orgnal conclusons were not fully supported. The purpose of ths paper s to revew studes assessng the effectveness of the New Zealand bcycle helmet law and crtcally evaluate the statstcal methods used n some of the analyses. We conclude wth general recommendatons for the choce of data and the use of approprate statstcal models for future research on ths topc. Methodology Ths evaluaton revews some exstng studes on the assocaton of helmet wearng and the mandatory helmet law wth head njures to cyclsts n New Zealand. We dscuss the sutablty of the statstcal approaches employed n these studes and suggest alternatve approaches where possble. We chose studes based on Google Scholar and Pubmed searches usng the keywords New Zealand bcycle helmet. Studes that dd not analyse New Zealand data or were unrelated to bcycle helmet use and cyclng head njury were excluded. 2

3 2013 Australasan College of Road Safety Conference A Safe System: The Road Safety Dscusson Adelade Results and Dscussons Povey et al. (1999) and Robnson (2001) Povey et al. (1999) examned the effect of helmet wearng on hosptalsed head njures between 1990 and 1996, usng cyclst lmb fractures as a measure of cyclng exposure. Cyclng njures from motor vehcle and non-motor vehcle crashes were analysed separately. Addtonally, njures from non-motor crashes were further broken down nto three age groups: prmary school age (5-12 years), secondary school age (13-18) and adult (age 19 and above). For statstcal analyss, a log-lnear model was ftted to the rato of head to lmb njures and the proporton of surveyed cyclsts who were wearng a helmet was used as an explanatory varable as below ln( HEAD / LIMB ) = α + β( HELMET ) + ε (1) where the errors ε are assumed to be ndependent, dentcally dstrbuted normal random varables. The authors estmated 24%, 32% and 28% reducton n head njury due to the helmet law for prmary, secondary and adult cyclsts n non-motor vehcle crashes. For motor vehcle crashes, the estmated reducton was 20% overall. Robnson (2001) suggested the effects estmated n Povey et al. s study were an artefact caused by falure to ft tme trends n the above model. It s mportant to note here the potental weakness of Povey s analyss s the assumpton of ndependent errors for sequentally collected data. However, nether Povey et al. nor Robnson appears to have checked the valdty of ths assumpton. For the case where the errors are serally correlated, t s possble to adequately model equaton (1) wthout tme trends by assumng an autocorrelaton error structure, for example, we can replace the error by v = φ 1 + ε, (2) v and use readly avalable estmaton methods. Fttng tme trends does not drectly address ths ssue for serally correlated data. Usng the resduals from model (1), we found lttle to no seral autocorrelaton (Durbn Watson statstc s 1.8). To demonstrate the potental bas assocated wth falure to ft tme trends, Robnson created some hypothetcal data contanng no effect of helmet wearng. The data contans only a lnear trend n whch the rato of head to lmb njures falls by 0.1 every year. Robnson then fts the followng lnear model for ths data: rato = α + β( HELMET ) + ε (3) and obtaned a hghly sgnfcant estmate for β, whch Robnson clamed was spurous. However, ths result s not as spurous as Robnson clamed, snce the varable HELMET s hghly correlated wth tme (r=0.90). To nvestgate ths further, we regress HELMET on TIME, and obtaned a statstcally sgnfcant estmate for the slope (p=0.0056). Hence, even the hypothetcal data contans no effect of helmet wearng by constructon, the varable 3

4 2013 Australasan College of Road Safety Conference A Safe System: The Road Safety Dscusson Adelade HELMET s hghly sgnfcant n predctng the rato of head to lmb njures because both the varable and the outcome can be predcted usng a lnear functon of TIME. We also found the correlaton between HELMET and the hypothetcal data s the same as the one between HELMET and TIME. Ths result s not surprsng as the tme dependent component n the varable HELMET s used to account for the varablty n the data. The remanng effect of HELMET does not mprove the ft of the model as a smple lnear model fts the data (whch s nothng else but a straght lne) perfectly (R 2 =1, resdual S.E.=0). The left graph n Fgure 1 plots the hypothetcal rato of head to lmb njures n Robnson s example and the ftted values usng HELMET as the only explanatory varable. The plot on the rght n Fgure 1 llustrates much of the varablty n yearly helmet wearng percentages s explaned by a lnear model n tme. Fgure 1 (a): hypothetcal rato of head to lmb njures and ftted values (b): smple lnear regresson of helmet wearng percentage on tme Although the example by Robnson ams to demonstrate that gnored tme trends result n bases from the model, the approach adopted requres more careful consderaton. Frstly, the hypothetcal data n Robnson s example s not smulated data as stated n the paper because they are not randomly drawn from a data-generatng process or model. As a result, there s no random error assocated wth each observaton. Hence, a completely determnstc mathematcal model s suffcent for ths data, and there s no need to ft model (3). Moreover, a determnstc model assumes that the model parameters are known and the outcome s certan gven a fxed nput value, whch s never the case n real applcatons. Hence the hypothetcal data used n Robnson s example s rather napproprate n ths stuaton. The orgnal study by Povey et al. used a lnear model for the log of head to lmb njury counts. 4

5 2013 Australasan College of Road Safety Conference A Safe System: The Road Safety Dscusson Adelade Robnson s example should be at least consstent n modellng the log of that rato, nstead of modellng the ratos themselves. Robnson argued that trends, f present, should be common to all cyclsts. Hence the mean head to lmb rato for prmary and secondary school chldren s used as an estmate of trend. It was then shown, that the mean squared error (MSE), calculated as sum[predctedactual] 2 /[number of cases] (rather than mean[predcted-actual] 2 /[number of cases] as stated n Robnson, 2001) was much better when based on predctons from trends as compared to predctons from helmet wearng rates from the model of Povey et al. (1999). However, the most ntutve approach to account for tme trend would be to nclude tme as a varable n the model. We then ft a smple lnear regresson model to the rato of adult head to lmb njures usng tme as the only varable. The ftted values are reported n Table 1. The MSE, based on predcton from only a lnear tme trend, s better than both MSEs n Povey et al. and Robnson s studes. Hence n terms of MSE as a measure of the model s ft to the observed data, the lnear tme trend model provdes a better ft than the one usng estmated trend gven by the mean head to lmb rato for prmary and secondary school chldren. Note that provded the data gven n Table 1 of Robnson (2001) were correct, we could not reproduce the results gven n Table 1 of Povey et al. (1999) usng the SAS procedure GENMOD. Hence the predcted values reported n Table 1 under Povey et al. are what we obtaned from estmatng model (3). Note also that the orgnal model n Povey et al. (1999) s for the log rato of head to lmb njures. However, n subsecton 4.1 of the paper, the authors mentoned that the above model was ftted to each of these three data seres n Fgure 4 from ther paper, whch shows the proporton of head njured (head/(head+lmb) 100%). Table 1 Rato of head to lmb njures accordng to the model of Povey et al. (1999), from fttng a trend derved from the rato of head to lmb njures n chldren (Robnson, 2001) and from fttng a lnear tme trend Year Rato of head/lmb (R=HI/L) Predcton of R Povey et al. Robnson Tme trend MSE Changes Helmet wearng (%) Based on Povey et al. s model (Model (1)), we examne the effect of addng a lnear tme trend n the log-lnear model as follows: ln( HEAD / LIMB ) = α + β( HELMET ) + γ ( TIME ) + ε, (4) 5

6 2013 Australasan College of Road Safety Conference A Safe System: The Road Safety Dscusson Adelade where TIME conssts of the numbered consecutve tme ponts n the seres (TIME=1,2,,7). We ft the above model usng the only avalable data, namely adult cyclng njures not nvolvng a motor vehcle, usng the lm functon n R (R Core Team, 2012). Results are reported n Table 2. Agan, note that the results under Povey (Model 3) are what we estmated from estmatng Model 3, nstead of the ones taken from Povey et al. (1999). We found a sgnfcant negatve estmate of the varable TIME and the varable HELMET becomes hghly nsgnfcant n the presence of a lnear tme trend. The model ncludng a tme trend provdes a much better ft to the data n terms of adjusted multple R 2, estmated resdual standard error and Akake nformaton crteron (AIC). Gven ths result, we are nterested n knowng the relatve performance of the model nvolvng only the tme trend. Hence we ft a thrd model to the data: ln( HEAD / LIMB ) = α + δ ( TIME ) + ε, (5) Results are gven n Table 2. Although the estmate of TIME does not change much from Model 4, a much smaller p-value s obtaned, ndcatng that t s a hghly sgnfcant varable n predctng the rato of head to lmb njures for adult non-motor crashes. Moreover, Model 5 provdes a superor ft as compared to Model 4, n terms of multple R 2, resdual S.E. and AIC. Hence for ths data set, addng the varable HELMET to a model whch contans a TIME varable does not mprove the ft of the model. The orgnal data, wth the ftted values under the three models are shown n Fgure 2. It can be seen from the plot that the ftted values usng Model 4 and Model 5 are almost ndstngushable and the addton of HELMET does not result n very dfferent ftted values. Table 2 Parameter estmates under three models Povey (Model 3) Model 4 Model 5 Estmate of α 0.34 (0.07, 0.61) 0.37 (0.17, 0.50) 0.34 (0.22, 0.46) p-value Estmate of β (-1.02, -0.20) (-0.54, 0.60) p-value Estmate of γ (-0.17, -0.02) p-value Estmate of δ (-0.12, -0.06) p-value Adjusted R Resdual S.E AIC The problem wth fttng a model wth both HELMET and TIME s mult- collnearty: the predctor varables HELMET and TIME are hghly correlated. We beleve ths s the case here because a multple regresson fnds an nsgnfcant estmated coeffcent of HELMET and yet a smple lnear regresson on ths varable shows the estmate s sgnfcantly dfferent from zero. One of the consequences of mult- collnearty s that 6

7 2013 Australasan College of Road Safety Conference A Safe System: The Road Safety Dscusson Adelade whle controllng for other varables (for example TIME), the estmate of a varable (such as HELMET) tends to be less precse, hence ts nfluence on the dependent varable cannot be as accurately estmated. An alternatve method such as rdge regresson can be used to cope wth the problem of mult- collnearty. However, the major problem here s the lack of data to carry out accurate statstcal nference: the degrees of freedom are 5, 4 and 5 for Models 3-5 respectvely. In other words, these are the effectve sample szes or the amount of useful nformaton we have for each model. Fgure 2 Plot of the log rato of head to lmb njures and comparson of ftted values under three models To check for resdual ndependence and normalty assumptons, we produced resdual plots as well as normal quantle plots for each of the three models as shown n Fgure 3. There seems to be some pattern n the resdual plots for models 4 and 5, hence the resdual ndependence assumpton may not be satsfed n these cases. In fact, snce many road safety data consst of sets of observatons that are sequentally ordered over tme, ssues arse n the analyss of these data snce they may not be ndependent. A general approach for dealng wth ths type of data s to employ specalsed tme seres models such as ARIMA-type and state space models. However, the use of more dedcated tme seres s dscouraged by lack of data n ths case. For a dscusson of approprate tme seres analyss technques used n road safety research, see Commandeur et al. (2012). Generally speakng, we recommend the use of monthly data rather than yearly data when monthly data are avalable. Ths s because monthly data contan more nformaton and are able to capture partcular characterstcs (such as seasonal patterns) than the yearly data where these effects are averaged out. In terms of assessng the effect of cycle helmet wearng on hosptalsed head njures, ths data set s lmted. The data conssts of adult non-motor vehcle njures; hence certan proporton of ths group may be crashes that occurred off-road ncludng recreatonal cyclng, whereas the cyclng helmet law apples to all on-road cyclsts n New Zealand. Therefore a more nformatve data set s perhaps the motor vehcle njury counts. Furthermore, t s mportant to dstngush tme trends from the effects of explanatory varables, n ths case, 7

8 2013 Australasan College of Road Safety Conference A Safe System: The Road Safety Dscusson Adelade helmet wearng. However, how to properly account for tme trends n a model remans an open queston. In fact, Robnson (2001) rased a concern regardng the analyss of Povey et al. (1999). However, the man ssue s not so much about modfyng the model structure, for example, addng an addtonal varable to model a lnear tme trend, but more about checkng for model assumpton, whch both Povey et al. and Robnson have omtted. After checkng the resdual plots and test for seral correlaton, t seems that the model assumptons for Model (4) are satsfed and hence the results and conclusons n Povey et al. s analyss are vald. Nonetheless, we showed that a model wth a lnear tme trend outperformed the one wth helmet wearng rate as the explanatory varable. We also showed that ncludng both tme trend and helmet wearng rate n the model may cause a mult-collnearty problem snce they are hghly correlated. Clarke (2012) Fgure 3 Resdual plots and quantle plots for three models Clarke compared hghly aggregated data from before and after the helmet law was ntroduced. He found that from 1989 to 1990 (four years before the helmet law) to (13 years after the law), there was a 51% drop n the average number of hours cycled per person. He clamed that ths sgnfcant drop was completely attrbutable to helmet law. Frstly, observatons on adjacent dates are more correlated than dates farther apart. That s, njures and cyclng rates near the ntroducton of the helmet law are more lkely to be nfluenced by the law. However, no data are presented around the date of the helmet law n New Zealand and the concluson that the helmet law dscouraged cyclng to a sgnfcant 8

9 2013 Australasan College of Road Safety Conference A Safe System: The Road Safety Dscusson Adelade extent s undermned by comparng data from tme perods that are not near the mandatory helmet law date. To analyse the effect of an nterventon, t s mportant to effectvely estmate the trend before and after the nterventon n order to assess whether or not any observed decrease/ncrease around the tme of the nterventon s part of a longer downward/upward trend. Hence t s mportant to account for background trend and the estmaton of trends cannot be acheved by smply comparng two ponts n tme on ether sde of the law. In fact, prevous studes have noted a declne n rdershp back to 1986 for commuters (Tn Tn, 2009) whch began long before the mandatory helmet law n 1994 and before the substantal ncrease n helmet wearng that began n 1990 (Povey et al., 1999). Ths downward trend s not n any form captured n Clarke s analyss. Compared wth the pre-law perod , Clarke showed that cyclsts had a 20% hgher accdent rate (adjusted for mllons of hours spent travellng) by Clarke then assocates ths ncrease n overall accdent rate wth the helmet use. Reasons provded nclude Safety n Numbers, rsk compensaton and balance and rdng stablty aspects. However, when we compare the pre-law data to a perod that s more relevant to the mandatory helmet law, that s, , there are substantal drops n cyclst njures overall (-17%) and serous njures wth an abbrevated njury score (AIS) greater than or equal to 3 (-53%) after adjustng for mllons of hours spent travellng. The artcle notes the rato of cyclst and pedestran fataltes s 0.24 for the fve year perod pre-law. A comparson s then made wth the four year perod (rato: 0.27). Frstly the rato should be 0.26 as the cyclsts deaths were 39 nstead of the 41 stated n the artcle. Hence ncreasng the totals to equate pre law levels of cyclng (snce the average hours cyclng reduced by 51%), would gve total of 80 and the rato s 47% nstead of the 49% calculated n the artcle. However when we choose a tme perod that s closer to the helmet law, say , the rato of cyclst to pedestran fataltes s 0.22 and after adjustng for changes to numbers of hours cycled and walked (average hours walked ncreased by 2% and cyclng reduced by 40%), the rato s 38%. Addtonally, there s a 23% declne n cyclst fataltes n the mmedate three years post-law ( ) compared wth the precedng three years pre-law ( ) whch s not dscussed n the artcle. Clarke also fals to dscuss that the change n the cyclst to pedestran fatalty rato s nfluenced by changes n general regardng pedestran and cyclst safety envronment and not the helmet law or helmet wearng alone. The author does not address possble confoundng factors and attrbutes all declnes n cyclng rates and ncreases n cyclng fataltes/njures to the helmet law. In the artcle Clarke sourced for bcycle njures, Tn Tn et al. (2010) lsts several reasons apart from the helmet law for declnes n cyclng rates and ncreases n njures. These nclude the lack of a cyclng focus n the New Zealand road safety agenda, an ncrease n chldren beng drven to school due to parental concerns of safety and pre-law declne n cyclng rates. Hence choosng arbtrary tme perods on ether sde of the law and drawng a concluson that the hgher accdent rate s assocated wth helmet use based on computng ratos rather than statstcal nference, may convey msleadng nformaton and deas on the mpact of polcy nterventons, for nstance, the mandatory helmet law n New Zealand. Furthermore, the helmet laws are enacted to ncrease helmet wearng n an attempt to mtgate bcycle related head njures and do not offer njury protecton to other body parts. However, 9

10 2013 Australasan College of Road Safety Conference A Safe System: The Road Safety Dscusson Adelade the fataltes and njury counts presented n Clarke s paper are for all bcycle related njures and hence trends n head njures/fataltes before and after the helmet law cannot be estmated usng ths data. The exposure data came from the Land Transport Safety Authorty collected n the perods , and and the Ongong New Zealand Household Survey was collected n As a result, njury rates relatve to the amount of cyclng are only estmable durng those years. Ths study shed no lght on the cyclng envronment n a four year wndow around the mandatory helmet law. In concluson, due to weakness n the analyss and choce of data partcularly the four year absence of data around the tme helmet laws were ntroduced, the concluson that the mandatory helmet law halved the number of cyclsts and contrbuted to 53 deaths each year s hghly questonable f not msleadng. Scuffham and Langley (1997) The authors examned the serous njury trends for three age groups of cyclsts admtted to publc hosptals between 1980 and 1992; twelve months before the ntroducton of helmet legslaton. The njury data were grouped nto correspondng sx-month ntervals centred around the month of the helmet survey, totallng a sample sze of 26. A Posson regresson model s constructed for the number of njured cyclsts wth a head njury and the total number of cyclsts admtted s used as the offset. Covarates ncluded n the lnear part of the regresson model nclude admsson polcy varable, helmet wearng varable and tme. The admsson polcy varable, defned as the number of head njured non-cyclsts dvded by the total number of njured non-cyclsts, s used to account any possble changes n hosptal admsson polces for head and non-head njures; helmet wearng s represented as a categorcal varable ndcatng the helmet wearng perod: no helmets ( ), some helmet wearng (1986-md 1989) and a lot of wearng helmets (md ). Tme was a contnuous varable representng any underlyng tme trend to capture any gradual change n rsk of head njury. The results ndcated that there was no sgnfcant dfference n the underlyng downward trend between each age group and the only sgnfcant varable n the model s tme. Also there was no sgnfcant dfference n cyclsts head njures n the perods of no helmets or some helmets compared to a lot of helmets when helmet wearng was hgh. Hence the authors concluded that the downward trend n head njures was smply because of the underlyng tme trend and was ndependent of helmet wearng. Ths result s not surprsng when lookng at the percentage of head njures n Fgure 2 n Scuffham and Langley (1997). From Fgure 2, we observe that the head njury percentages are declnng gradually over tme for all three age groups. Throughout the sample perod, we do not observe any abrupt changes to the percentage of head njures. Hence the changes n head njures n ths case, may well be explaned by just a temporal trend. Ths s n clear contrast wth the study conducted on hosptal admsson counts of cyclst head njures from New South Wales, Australa (Walter et al., 2011), where there was an abrupt and sgnfcant reducton n head njury rates compared to arm njury rates mmedately followng the ntroducton of mandatory helmet legslaton. To examne the effect of the helmet law, the authors choose to model t as a contnuous functon of helmet wearng rather as a step functon because helmet wearng proporton had been ncreasng steadly n the years leadng up to the legslaton due to the promoton of voluntary helmet use n New Zealand. On the other hand, the study by Walter et al. (2011) 10

11 2013 Australasan College of Road Safety Conference A Safe System: The Road Safety Dscusson Adelade used an ndcator varable to represent the helmet legslaton as the helmet wearng rate n New South Wales ncreased from approxmately 20% to more than 60% among chldren and over 70% for adults wthn two months of the legslaton comng nto effect. The problem wth ncludng both helmet wearng and tme n the Scuffham and Langley (1997) regresson model s agan mult-collnearty. Removng the tme trend for nstance, may result n a sgnfcant estmate of the helmet wearng varable. The result of the Scuffham and Langley (1997) study showed that ncreasng helmet wearng pre-law n New Zealand has had lttle assocaton wth serous head njures to cyclsts, whch s nconsstent wth the results of some case control studes (Thompson et al. (1989), Thomas et al. (1994)). Scuffham and Langley (1997) also compared ther results wth a Vctoran study (Cameron et al., 1994) who suggested that apart from helmet wearng rate, other factors such as major ntatves at drnk/drvng and speed reducton may also reduce the number of cyclsts nvolved n crashes. Scuffham et al. (2000) Ths study used a smlar model as the one n Scuffham and Langley (1997). However, a negatve bnomal was assumed for the dependent varable n place of the usual Posson dstrbuton. Independent varables nclude hosptal admsson varable and helmet wearng rate, whereas temporal trend was not ncluded n the model. The results ndcated the helmet wearng rate varable was negatve and sgnfcant, and that a 1% ncrease n the helmet wearng rate was assocated wth a reducton n overall head njures by 0.43% for all age groups. The authors explaned that the reason for not ncludng a lnear varable to account for observed trends s that the addton of a tme-trend varable caused the helmet wearng proporton to become nsgnfcant. That s, a tme-trend varable swamped the real effect. However, what s consdered as real effect s not so clear. If addng a lnear tme varable caused the helmet wearng varable to become nsgnfcant, then perhaps much of the varaton n the outcome seres (.e., the number of cyclsts wth head njury) can be modelled just by a lnear temporal trend snce the helmet wearng rate tself, can be modelled as a functon of tme. Hence, the separaton of helmet wearng effect from the background temporal trend s the key ssue. As mentoned by Scuffham et al. (2000), there was a substantal seasonal pattern for cyclst head and non-head njures as seen n Fgure 2. However, ths seasonal pattern was not explctly modelled and nether covarates (helmet wearng rate and hosptal admsson polcy varable) are able to take seasonalty nto account. One way to account for seasonalty s to ntroduce dummy varables to capture seasonal fluctuatons n the data seres. However, to account for monthly varaton for example, an extra 11 dummy varables needs to be added to the model and there must be suffcent data to make t reasonable n terms of degrees of freedom. Another way s to seasonally adjust the data usng for example, the X11 method, pror to fttng a model, as was done n Bernat et al. (2004) and Walter et al. (2011). Concluson In evaluatons of mandatory helmet laws, researchers need to ensure that ther conclusons are not based on statstcally flawed analyses and arguments. As an example, based on peerrevewed studes from several countres, Attewell et al. (2001) performed a formal metaanalyss and concluded that there s a statstcally sgnfcant effect of bcycle helmets n preventng serous njury and even death. Elvk (2011), however, n a re-analyss of Attewell 11

12 2013 Australasan College of Road Safety Conference A Safe System: The Road Safety Dscusson Adelade et al. (2001) reported nflated estmates of the effects of helmets due to ther falure to control for publcaton bas and tme trend bas. However, due to data and analytc errors, Elvk (n press) has publshed a full length corrgendum to ths paper. Subsequently, Churches (2013) reported dffculty n reproducng Elvk s (n press) results and estmated a substantally larger beneft of helmet wearng, usng the same data as Elvk. To non-specalsts, they may not be aware of the statstcal ssues nvolved n reachng certan conclusons. In fact, many statstcally flawed studes have been cted extensvely n publc debate over bcycle helmet laws (Olver et al., submtted). These studes may have nfluenced how the meda, publc and polcymakers perceve bcycle helmets and mandatory helmet laws. The assessment of an nterventon through the analyss of routnely collected observatonal data s non-trval and requres careful model specfcaton and statstcal analyss. As shown n ths study, falure to address mportant ssues such as checkng model assumptons, multcollnearty between explanatory varables, etc., can result n msleadng or ncorrect conclusons. References Attewell, R.G., Glase, K. & McFadden, M. (2001). Bcycle helmet effcacy: a meta-analyss. Accdent Analyss and Preventon, 33, Bambach M.R., Mtchell R.J., Grzebeta R.H., Olver J., (2013) The effectveness of helmets n bcycle collsons wth motor vehcles: A case control study, Accdent Analyss and Preventon 53 (2013) Bernat, D.H., Dunsmur, W.T.M. & Wagenaar, A.C. (2004). Effects of lowerng the legal BAC to 0.08 on sngle-vehcle-nghttme fatal traffc crashes n 19 jursdctons. Accdent Analyss and Preventon, 36(6), Cameron, M.H., Vulcan, A.P., Fnch, C.F. & Newstead, S.V. (1994). Mandatory bcycle helmet use followng a decade of helmet promoton n Vctora Australa - an evaluaton. Accdent Analyss & Preventon, 26, Churches, T. (2013). The benefts of reproducble research: a publc health example. Avalable at: (accessed ) Clark, C.F. (2012). Evaluaton of New Zealand s bcycle helmet law. The New Zealand Medcal Journal, 125(1349), Commandeur, J.J.F., Bjleveld, F.D., Bergel-Hayat, R., Antonou, C., Yanns, G. & Papadmtrou, E. (2012). On statstcal nference n tme seres analyss of the evoluton of road safety. Accdent Analyss & Preventon, Cycles: Road rules and equpment (Factsheet1) (2013). Retreved from Elvk, R. (2011). Publcaton bas and tme-trend bas n meta-analyss of bcycle helmet effcacy: a re-analyss of Attewell, Glase and McFadden, Accdent Analyss and Preventon, 43,

13 2013 Australasan College of Road Safety Conference A Safe System: The Road Safety Dscusson Adelade Elvk, R. (n press). Corrgendum to: Publcaton bas and tme-trend bas n meta-analyss of bcycle helmet effcacy: A re-analyss of Attewell, Glase and McFadden, Accdent Analyss and Preventon. McIntosh A.S., La A. and Schlter E. (2013) Bcycle Helmets: Head Impact Dynamcs n Helmeted and Unhelmeted Oblque Impact Tests, Traffc Injury Preventon, 14:5, , DOI: / Olver, J. (2012). Don t blame mandatory helmets for cyclst deaths n New Zealand. The Conversaton. Avalable at: Olver, J., Grzebeta, R., Wang, J.J.J. & Walter, S. (submtted). Statstcal Errors n Ant- Helmet Arguments. Proceedngs of the 2013 Australasan College of Road Safety Conference. Olver, J., Walter, S. and Grzebeta, R. (2013). Long term bcycle related head njury trends for New South Wales, Australa followng mandatory helmet legslaton. Accdent Analyss & Preventon, 50, Povey, L.J., Frth, W.J. & Graham, P. G. (1999). Cycle helmet effectveness n New Zealand. Accdent Analyss & Preventon, 31, R Core Team (2012). R: A language and envronment for statstcal computng. R Foundaton for Statstcal Computng, Venna, Austra. ISBN , URL Robnson, D.L. (2001). Changes n head njury wth the New Zealand bcycle helmet law. Accdent Analyss & Preventon, 33, Scuffham, P. & Langley, J.D. (1997). Trends n cycle njury n New Zealand under voluntary helmet use. Accdent Analyss & Preventon, 29, 1-9. Scuffham, P., Alsop, J., Cryer, C. & Langley, J.D. (2000). Head njures to bcyclsts and the New Zealand bcycle helmet law. Accdent Analyss & Preventon, 32, Thomas, S., Acton, C. & Nxon, J. (1994). Effectveness of bcycle helmets n preventng head njury n chldren: case-control study. Brtsh Medcal Journal, 308, Thompson, R.S., Rvara, F.P. & Thompson, D.C. (1989). A case-control study of the effectveness of bcycle safety helmets. New England Journal of Medcne, 320, Tn Tn, S., Woodward, A., Thornley, S. & Ameratunga, S. (2009). Cyclng and walkng to walk n New Zealand, : regonal and ndvdual dfferences, and ponters to effectve nterventons. Internatonal Journal of Behavoral Nutrton and Physcal Actvty, 6(1):64. Tn Tn, S., Woodward, A. & Ameratunga, S. (2010). Injures to pedal cyclsts on New Zealand roads, BMC Publc Health, 10:

14 2013 Australasan College of Road Safety Conference A Safe System: The Road Safety Dscusson Adelade Walter, S.R., Olver, J., Churches, T. & Grzebeta, R. (2011). The mpacts of compulsory cycle helmet legslaton on cyclst head njures n New South Wales, Australa. Accdent Analyss and Preventon, 43,