David Bartels

Building national estimates of the burden of road traffic injuries in developing countries from all available data sources: Iran.

Objective: To use a range of existing information sources to develop a national snapshot of the burden of road traffic injuries in one developing country—Iran. Methods: The distribution of deaths was estimated by using data from the national death registration system, hospital admissions and outpatient visits from a time-limited hospital registry in 12 of 30 provinces, and injuries that received no institutional care using the 2000 demographic and health survey. Results were extrapolated to national annual incidence of health burden differentiated by age, sex, external cause, nature of injuries and institutional care. Results: In 2005, 30 721 Iranians died annually in road traffic crashes and over one million were injured. The death rate (44 per 100 000) is the highest of any country in the world for which reliable estimates are available. Road traffic injuries are the third leading cause of death in Iran. While young adults are at high risk in non-fatal crashes, the elderly have the highest total death rates, largely due to pedestrian crashes. While car occupants lead the death count, motorised two-wheeler riders dominate hospital admissions, outpatient visits and health burden. Conclusions: Reliable estimates of the burden of road traffic injuries are an essential input for rational priority setting. Most low income countries are unlikely to have national injury surveillance systems for several decades. Thus national estimates of the burden of injuries should be built by collating information from all existing information sources by appropriately correcting for source specific shortcomings.

Methods for developing country level estimates of the incidence of deaths and non-fatal injuries from road traffic crashes

The estimates of the incidence of deaths and non-fatal injuries from road traffic crashes are essential inputs for prioritising national health and transport policies. This article sketches a methodology for assembling such estimates at the country level by piecing together data from a wide array of sources that include death registers, hospital records, funeral records, health surveys and police reports. Using examples of the types of data sources available in four developing countries (Iran, Mexico, Ghana and India), methods are proposed for making these consistent and extrapolating to estimates of injury incidence at the country level. This requires filling information gaps, mapping from varying case definitions, deriving population-based incidence estimates from sources that may not track denominator populations, and appropriately reapportioning cases assigned to poorly specified causes. The principles proposed here will form the methodological basis for a series of country reports to be published in the future.

Estimating the incidence of road traffic fatalities and injuries in Sri Lanka using multiple data sources

We used data from multiple sources to estimate the incidence of fatal and non-fatal road traffic injuries in Sri Lanka in 2005. We validated the accuracy of the data from the national traffic police by comparing with estimates based on national death registration. For estimating the incidence and patterns of non-fatal injuries, we used a nationally represented health survey (World Health Survey), and data on hospital admissions from a rural setting (Galle district). We estimate that in the year 2005, approximately 2300 people died in Sri Lanka due to road traffic crashes, approximately 300,000 were injured in non-fatal crashes and approximately 140,000 received care for their injuries at hospitals. While the road traffic death rate in Sri Lanka is low compared with other low-income countries, it has been steadily rising for several years. Although young adults are at high risk in non-fatal crashes, the elderly have the highest death rate. Pedestrians and bicyclists account for more than half of all road traffic deaths and riders of motorised two-wheelers accounted for an additional 13%. The government of Sri Lanka should act immediately to stop the needless loss of life by implementing the recommendations of the 2004 World Report on Road Traffic Injury Prevention.

Incidence of road injuries in Mexico: country report

We used data from various sources to triangulate to a national snapshot of the incidence of fatal and non-fatal road traffic injuries in Mexico in 2005. Data sources used include national death registration data, national hospital discharge data and a nationally representative health survey. We estimate that in 2005, 19,389 people died due to injuries and nearly one million were injured in road traffic crashes. While deaths in high-income countries are declining, this is not the case in Mexico. Young adult males are the demographic at the highest risk in non-fatal crashes, but the elderly have the highest road death rates primarily due to pedestrian crashes. Pedestrians alone comprise nearly half (48%) of all deaths. Cars pose a substantial threat to occupants (38% of deaths and 39% of hospital admissions) and to other road users.

Improving the quality of road injury statistics by using regression models to redistribute ill-defined events

Objective To test the predictive ability of multinomial regression method in obtaining category of death distribution for cases with unknown/ill-defined mortality codes. Methods The authors evaluated the performance of the multinomial regression model by fitting the model to trial datasets from 2004 Mexican vital registration data. To predict category of death, the regression method makes use of explanatory variables, such as gender, age, place of crash, place of residence, education and insurance type. The authors compared the results of a full model regression with those of a reduced model that only contained gender and age as explanatory variables. For this comparison, the authors constructed two forms of data: dummy variable adjustment method and case-wise deleted method. The comparison was made through estimated area under the curve (AUC) for each outcome variable. Results The full model significantly outperformed the gender-age (reduced) model using both datasets. In the case-wise deleted method, the AUC was increased from 0.55 to 0.7 for the reduced model and from 0.64 to 0.84 for the full model. Improvement in AUC using the dummy variable adjustment method was less significant. Conclusions To predict ill-defined categories of death, adding relevant explanatory variables to gender and age is recommended. Multiple imputations may perform even better than this model especially when significant portion of the data are missing.