Translating road safety into health outcomes using a quantitative impact assessment model

Dhondt, Stijn; Pirdavani, Ali; Macharis, Cathy; Bellemans, Tom; Putman, Koen

doi:10.1136/injuryprev-2011-040286

Cited by 8 publications

(13 citation statements)

References 36 publications

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“…The result is similar to the YLD rates for injuries estimated for the Rhône Département in France [16], Utrecht area in the Netherlands [15] and the Flanders and Brussels in Belgium [12]. In these three studies the YLDs per 100 000 inhabitants were 191, 120, 97, respectively.…”

Section: Resultssupporting

confidence: 83%

“…Of all the injuries, only 2% caused lifelong health effects, but these lifelong injuries caused 96% of the total YLDs. For comparison, in Dhondt et al’s [12] study in Belgium minor injuries contributed to 9% of YLDs.…”

Section: Resultsmentioning

confidence: 99%

“…The YLL rates per 100 000 inhabitants were 346, 270, 272 in Lapostolle et al, Holtslag et al and Dhondt et al [12, 15, 16], respectively, and between 200 and 350 YLLs per 100 000 inhabitants for males and between 50 and 120 YLLs per 100 000 inhabitants for females in the European burden of injury study [17]. According to European Union statistics, Sweden has the second lowest fatality rate per population due to road crashes, after the United Kingdom, which probably explains lower YLL rate in this study in comparison to previous studies [26].…”

Section: Resultsmentioning

confidence: 99%

“…The Swedish injury data was selected because it has detailed information on the transport modes of victims, injuries sustained by victims and on the location of the crash. The DALY indicator was selected because of its increasing popularity as a health indicator and because the results could be compared with the similar injury burden studies [12–17]. The data, assumptions and calculations are described below.…”

Section: Methodsmentioning

confidence: 99%

“…However, transport mode specific YLDs are rare. The Dhondt et al [12] study from Belgium is the only one, that we are aware of, that has estimated YLDs per person for different modes of transport and in that study only four victim groups (drivers, passengers, bus occupants and slow mode victims) were analysed, limiting the use of the results in the analyses with several transport modes.…”

Section: Introductionmentioning

confidence: 99%

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Severity of injuries in different modes of transport, expressed with disability-adjusted life years (DALYs)

et al. 2014

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BackgroundHealth impact assessment (HIA) studies are increasingly predicting the health effects of mode shifts in traffic. The challenge for such studies is to combine the health effects, caused by injuries, with the disease driven health effects, and to express the change in the health with a common health indicator. Disability-adjusted life year (DALY) combines years lived disabled or injured (YLD) and years of life lost (YLL) providing practical indicator to combine injuries with diseases. In this study, we estimate the average YLDs for one person injured in a transport crash to allow easy to use methods to predict health effects of transport injuries.MethodsWe calculated YLDs and YLLs for transport fatalities and injuries based on the data from the Swedish Traffic Accident Data Acquisition (STRADA). In STRADA, all the fatalities and most of the injuries in Sweden for 2007–2011 were recorded. The type of injury was recorded with the Abbreviated Injury Scale (AIS) codes. In this study these AIS codes were aggregated to injury types, and YLDs were calculated for each victim by multiplying the type of injury with the disability weight and the average duration of that injury. YLLs were calculated by multiplying the age of the victim with life expectancy of that age and gender. YLDs and YLLs were estimated separately for different gender, mode of transport and location of the crash.ResultsThe average YLDs for injured person was 14.7 for lifelong injuries and 0.012 for temporal injuries. The average YLDs per injured person for lifelong injuries for pedestrians, cyclists and car occupants were 9.4, 12.8 and 18.4, YLDs, respectively. Lifelong injuries sustained in rural areas were on average 31% more serious than injuries in urban areas.ConclusionsThe results show that shifting modes of transport will not only change the likelihood of injuries but also the severity of injuries sustained, if injured. The results of this study can be used to predict DALY changes in HIA studies that take into account mode shifts between different transport modes, and in other studies predicting the health effects of traffic injuries.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2458-14-765) contains supplementary material, which is available to authorized users.

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Section: Resultssupporting

confidence: 83%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Severity of injuries in different modes of transport, expressed with disability-adjusted life years (DALYs)

et al. 2014

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Integrated health impact assessment of travel behaviour: Model exploration and application to a fuel price increase

Dhondt

Kochan

Beckx

et al. 2013

Environment International

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Transportation policy measures often aim to change travel behaviour towards more efficient transport. While these policy measures do not necessarily target health, these could have an indirect health effect. We evaluate the health impact of a policy resulting in an increase of car fuel prices by 20% on active travel, outdoor air pollution and risk of road traffic injury. An integrated modelling chain is proposed to evaluate the health impact of this policy measure. An activity-based transport model estimated movements of people, providing whereabouts and travelled kilometres. An emission- and dispersion model provided air quality levels (elemental carbon) and a road safety model provided the number of fatal and non-fatal traffic victims. We used kilometres travelled while walking or cycling to estimate the time in active travel. Differences in health effects between the current and fuel price scenario were expressed in Disability Adjusted Life Years (DALY). A 20% fuel price increase leads to an overall gain of 1650 (1010-2330) DALY. Prevented deaths lead to a total of 1450 (890-2040) Years Life Gained (YLG), with better air quality accounting for 530 (180-880) YLG, fewer road traffic injuries for 750 (590-910) YLG and active travel for 170 (120-250) YLG. Concerning morbidity, mostly road safety led to 200 (120-290) fewer Years Lived with Disability (YLD), while air quality improvement only had a minor effect on cardiovascular hospital admissions. Air quality improvement and increased active travel mainly had an impact at older age, while traffic safety mainly affected younger and middle-aged people. This modelling approach illustrates the feasibility of a comprehensive health impact assessment of changes in travel behaviour. Our results suggest that more is needed than a policy rising car fuel prices by 20% to achieve substantial health gains. While the activity-based model gives an answer on what the effect of a proposed policy is, the focus on health may make policy integration more tangible. The model can therefore add to identifying win-win situations for both transport and health.

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Quantifying the Health Impacts of Active Travel: Assessment of Methodologies

2015

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In the past several years, active travel (walking and cycling) has been increasingly recognised as an effective means of increasing physical activity and mitigating negative external impacts of motorised transport. As a result of this recognition, a range of methodologies for quantifying the benefits and risks of active travel have emerged in the literature. This review critically assesses studies which quantify the health impacts of transport scenarios involving increased active travel. It was found that the choice of methodology and assumptions employed in such studies may have a significant impact on the results and so care must be taken when planning these studies or interpreting their results. Increases in physical activity are the most important determinant of the health impacts of active travel but different methodologies for quantifying the health impacts of this physical activity can lead to substantial differences in the scale of the impact. Therefore, further research is required into the relationship between increased physical activity and health effects. Particular attention should also be paid to ensure that health impacts are assessed for both the individuals changing their travel behaviour and all other individuals in the study area. Where relative risk relationships are used to estimate health impacts, researchers should be mindful of the specific exposures used to develop these relationships in order to prevent double counting of health impacts. Extensive sensitivity analysis is necessary to compensate for the uncertainty inherent in models for predicting health effects.

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Translating road safety into health outcomes using a quantitative impact assessment model

Abstract: With this method, the impact of traffic polices can be evaluated on both mortality and morbidity, while taking into account the variability of different injuries following a road crash. This model however still underestimates the impact due to non-fatal injuries.

Cited by 8 publications

References 36 publications

Severity of injuries in different modes of transport, expressed with disability-adjusted life years (DALYs)

Severity of injuries in different modes of transport, expressed with disability-adjusted life years (DALYs)

Integrated health impact assessment of travel behaviour: Model exploration and application to a fuel price increase

Quantifying the Health Impacts of Active Travel: Assessment of Methodologies

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