Abstract

The road traffic crash burden is significant in Brazil; calculating years of life lost and life expectancy reduction quantifies the burden of road traffic deaths to enable prioritization of this issue. Years of life lost and reduction in life expectancy were calculated using 2008 population/crash data from Brazil’s ministries of health and transport. The potential for reduction in crash mortality was calculated for hypothetical scenarios reducing death rates to those of the best-performing region and age category. In Brazil, road traffic deaths reduce the at-birth life expectancy by 0.8 years for males and by 0.2 years for females. Many years of life lost for men and woman could be averted—270,733 and 123,986, respectively—if all rates matched those of the lowest-risk region and age category. This study further characterizes the burden of motor vehicle deaths in Brazil and quantifies the potential health benefits of policies/interventions that reduce road traffic death rates to those of the best-performing subpopulations.

Introduction

Injuries are a substantial cause of morbidity and mortality in the developed and developing world, with motor vehicle crashes playing a substantial role. According to the World Health Organization (WHO 2008), road traffic crashes were responsible for a total of 1.27 million deaths globally in 2004. More than 90 % of crash fatalities occur in low-income and middle-income countries, while these account for only 48 % of registered vehicles (WHO 2009). It is predicted that if no interventions are made to improve road traffic safety, morbidity, and mortality from road traffic, crashes will increase globally by 65 % by 2020 (Peden and Toroyan 2004). In response, the United Nations General Assembly proclaimed a Decade of Action for Road Safety, from 2011–2020. This is a critical time for countries to further define the burden of road traffic mortality in quantifiable terms that will spur implementation of proven prevention strategies.

The road traffic crash burden is significant in Brazil; according to the WHO Global Burden of Disease Estimates, Brazil’s 2004 road traffic mortality rate was 21.9 per 100,000 population. A review of mortality data from 1985–2001 found that Brazil had the highest crude road traffic mortality rate of any country in the Americas, at 22.8 per 100,000; similarly, a more recent analysis by Reichenheim et al. (2011) showed that Brazil’s road traffic mortality rate of about 23 per 100,00 is higher than the world’s average as well as the average of all low- and middle-income countries (Pan American Health Organization 2004; Reichenheim et al. 2011).

Although the rate per 100,000 population is commonly used for presenting injury incidence, it does not individualize the impact of a disease/injury event. Years of life lost is calculated by subtracting the current age from the life expectancy at the age of death (Romeder and McWhinnie 1977). The life expectancy at each age is taken from a life table, which gives the average number of subsequent years of life for an individual at a given age. Years of life lost takes into account the age structure and life expectancy of the target population, giving greater weight to fatalities that occur at progressively younger ages. These characteristics allow for a more precise measurement than a broad mortality rate and therefore can be more useful in defining priorities for action.

Recent studies have assessed the burden of automobile crashes, including years of life lost because of crashes, in several European countries (Lapostolle et al. 2009; Polinder et al. 2007). Given the relatively high rates of motor vehicle crash mortality in Brazil, the aim of this article is to calculate the years of life lost from motor vehicle crashes along with the resultant reduction in expected life expectancy to better characterize the burden of motor vehicle crash fatalities. In addition, we show the potential life gains in each subpopulation if policies were implemented to reduce the road traffic mortality rate to that of the best-performing subpopulation.

Methods

Age-specific mortality rates for Brazil were taken from the 2008 data of the WHO Global Health Observatory (http://apps.who.int/ghodata/). Age-specific population data and numbers of crash deaths for 2008 were taken from the Brazilian Ministry of Health online database (DATASUS) (http://www2.datasus.gov.br). Mortality information for DATASUS is collected through the Mortality Information System (SIM). SIM employs passive surveillance of death certificates, which code cause of death according to the 10th revision of the International Classification of Diseases (ICD-10); any death appropriately coded as associated with a road traffic crash was captured in our search. The number of vehicles in 2008 was taken from the Brazilian Ministry of Transportation (DENETRAN) online database (http://www.denatran.gov.br/frota.htm).

Traditional life tables were constructed for men and women using standard methodology (Preston et al. 2001). For all age groups starting at 5 years of age, an even distribution of deaths was assumed across the age interval; that is, nax was assumed to be n / 2 (Coale et al. 1983). Because under-5 deaths are known not to occur in an even distribution in the age interval, Coale-Demeny estimates were used to calculate nax for the under-5 populations (Coale et al. 1983). We calculated years of life lost by multiplying the deaths prevented at each gender/age category by the respective life expectancy. For the purposes of this analysis, life-years were not discounted or age-weighted.

To assess the impact of road traffic crashes on life expectancy, we applied WHO mortality rates to calculate estimated deaths in each age group by gender. We subtracted the numbers of observed crash fatalities were subtracted from the calculated numbers of deaths and used the new numbers to calculate new mortality rates, which we then used in revising the life tables.

Demonstration of the potential to reduce the road traffic mortality burden by utilizing statistics from lower-burden areas or populations and extrapolating those to higher burden areas or populations can be a powerful message to policy makers that improved road traffic safety statistics are achievable. We calculated the potential decrease in burden of road traffic deaths for two hypothetical scenarios: if all five regions of the country had the same mortality rate as the region with the lowest rate in each age group, and if all age categories had the same rate as the lowest-rate age category (excluding ages too young to drive legally). We then used the new mortality numbers to calculate the revised years of life lost and changes in life expectancy.

One-way analysis of variance (ANOVA) tables were used to assess differences in crash mortality rates between genders, regions, and age categories. Pearson and Spearman correlations were used to assess the relationship between automobiles per capita and crash mortality. All statistical analyses were performed using SAS v9.2.

Results

Life tables were calculated based on 2008 data for males and females separately, and were comparable to tables published by (WHO 2012) and the Brazilian Institute of Geography and Statistics (IBGE 2011). Life expectancy at birth was 69.9 years for males and 76.9 years for females (see Table 1). According to DATASUS, there were 38,737 deaths attributable to road traffic crashes in 2008: 31,337 (81.5 %) for males and 7,108 (18.5 %) for females. The database was missing age and/or gender information on 292 deaths (0.8 %), and these were excluded from the analysis.

Road traffic deaths account for a total of 1,538,818 life-years lost: 1,230,944 (80.0 %) among males and 307,874 (20.0 %) among females (Table 1). Road traffic deaths in females account for a slightly greater proportion of the life-years lost than of the deaths because women have a longer life expectancy than men; therefore, a death at the same age accounts for more years of life lost for a woman than for a man. Men ages 20–24 lose 243,747 years of life to crash fatalities, and women of the same age range lose 46,130 years. Gender differences across age groups are significant; males consistently have greater years of life lost to crash fatalities than females, but the marked jump from 33,292 for 10- to 14-year-old males to 147,141 for 15- to 19-year-old males is significantly greater than the jump from 18,619 to 44,923 for females of the same age groups. The Central-West region (which contains Brazil’s capital city of Brasilia) has the highest 2000–2008 average mortality rate of 29.7 per 100,000 (compared with the national average of 20.7 per 100,000) (Fig. 1).

Road traffic crashes reduce the at-birth life expectancy by roughly 0.8 years (7 months) for males and 0.2 years (2 months) for females (Table 1). Crash mortality rates were significantly different between genders (p < .001), regions (p = .05), and age categories (p < .001). Automobiles per capita in each region was not significantly associated with regional crash mortality rates (p = .1260), likely because of differences in road traffic density and automobile usage that are not reflected in this figure.

If all regions of Brazil had the age- and gender-specific rates of the lowest-risk region, then 6,675 (17.4 %) deaths and 270,733 (17.6 %) years of life lost could be averted. If motor vehicle occupants of all ages had the same rates as the lowest-risk age category, at least 3,889 (10.1 %) deaths and 123,986 (8.8 %) years of life lost could be averted (Tables 2 and 3).

Discussion

In Brazil, road traffic deaths result in the loss of 0.8 years of life expectancy in newborn males and 0.2 years of life expectancy in newborn females. In total, road traffic crashes account for nearly 1.2 million and 300,000 years of life are lost for Brazilian males and females, respectively. The greatest proportion of Brazil’s 38,445 motor vehicle crash deaths occurred for males (82 %) and persons aged 20–29 (27 %). This is consistent with results seen worldwide in which males have significantly higher motor vehicle crash and death rates than females, and young adults are the highest-risk age group for motor vehicle mortality (Pan American Health Organization 2009). The South and Central-West regions of Brazil have significantly higher crash mortality rates than the North, Northeast, or Southeast regions. This is not surprising for the North and Northeast regions because these are more rural and less developed. The Southeast contains the country’s largest cities and highest number of pedestrian deaths (Reichenheim et al. 2011), and motor vehicle death rates might be lower because of the slower vehicle speeds that often occur in areas of high population density. Interestingly, the number of vehicles per capita in each region was not significantly associated with the crash mortality rate. Further epidemiologic analyses are needed to define the specific risk factors that are associated with increased crash mortality.

The WHO’s Global Disease Burden project uses an age-weighted discounted method of calculated years of life lost in the formula for disability-adjusted life-years; other authors have chosen not to make any adjustments (Hyder et al. 1998; Murray et al. 1994). Discounting uses a 3 % discount rate per year for loss of life, such that a year of healthy life is worth more than a year of healthy life at some future point. However, this method devalues residual life expectancy, particularly when death occurs at a young age, which is the case for road traffic deaths. Age-weighting is used to give greater value to years of life in the most productive time period, when people are finishing their education and working. Bonneux (2002) showed that when age-weighting is applied to a discounted life table, the calculated burden of mortality is essentially equal to an unaltered life table. Therefore, in this analysis, we chose to calculate the years of life lost without discounting or age-weighting.

This study had several limitations. First, the hypothetical impact of reducing motor vehicle deaths by age is underestimated in this analysis. Age categories that included persons too young to legally drive were excluded from the age specific analysis; therefore, the predefined age category of 15–19 years was excluded in its entirety even though the legal driving age was 18 years. Individuals aged 18–19 contributed 1,963 deaths in 2008, which represents 5.1 % of the total road traffic deaths for that year. Therefore, preventing deaths in this age group would result in a significant increase in life expectancy and reduction in years of life lost. Second, deaths among children occur as a result of crashes caused by older drivers. Therefore, a reduction in the mortality rate of older drivers would lead to reduced mortality among younger passengers and pedestrians. However, with the available data, we were not able to quantify the potential reductions in mortality in child passengers. Third, we did not provide uncertainty estimates in the data because this would require exploration beyond the scope of this analysis at the levels of data collection, population estimates, and regional representativeness. Finally, because of limitations in available information, we were not able to quantify the impact of road traffic injuries on disability or the economic impact of road traffic crashes.

Despite these limitations, this study characterizes the full extent of the burden of motor vehicle deaths in Brazil. It also describes the potential benefits that would be accrued if prevention strategies were implemented that would lower road traffic death rates to the rate for the best-performing subpopulation. This straightforward methodology described here could be employed in the future to measure the impact of traffic safety interventions. This information provides further evidence for the need for Brazil to be an active participant in the Decade of Action for Road Safety.

Acknowledgments

This work was supported by the Bloomberg Philanthropies, New York.

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