Vulnerable road-user deaths in Brazil: a Bayesian hierarchical model for spatial-temporal analysis

Abstract

Reducing the road traffic injuries burden is relevant to many sustainable development goals (SDG), in particular SDG3 – to establish good health and well-being. To describe the spatial-temporal trends and identify hotspot regions for fatal road traffic injuries, a Bayesian hierarchical Poisson model was used to analyze data on vulnerable road users (bicyclist, motorcyclist and pedestrians) in Brazil from 1999 to 2016. During the study period, mortality rates for bicyclists remained almost unchanged (0.6 per 100,000 people) but rose dramatically for motorcyclists (from 1.0 in 1999 to 6.0 per 100,000 people in 2016) and decreased for pedestrians (from 6.3 to 3.0 per 100,000 people). Spatial analyses accounting for socio-economic factors showed that the central and northeastern microregions of Brazil are hotspot areas for fatal injuries among motorcyclists while the southern areas are for pedestrians.

Keywords:

• Hierarchical Bayesian model
• road traffic
• vulnerable road users
• pedestrians
• motorcyclists
• bicyclists

Acknowledgments

The authors would like to thank Dr. Anna Dare for her help in the design stage of this study.

Disclosure statement

The authors certify that they have no financial or non-financial interest in the subject matter discussed in this manuscript. The authors alone are responsible for the views expressed in this article and they do not necessarily represent the views, decisions, or policies of the institutions with which they are affiliated.

Contributors

All the authors conceived the study and contributed to the manuscript. B.B., P.B, M.C., and H.I. performed the analyses. B.B. and M.C wrote the paper. M.S, and E.R. served as supervisors for this work, respectively in Rio de Janeiro and Toronto, and revised the manuscript. All authors read and approved the final manuscript.

Availability of data and materials

The data that support the findings of this study are available from the Brazilian Mortality Information System but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available.

Additional information

Funding

This work was supported through the Advanced Queen Elizabeth Scholars (QES) award program, which provided a scholarship to one author [B. B]. The Statistical Alliance for Vital Events (SAVE) program is a partnership between the Dalla Lana School of Public Health’s Office of Global Health & Centre for Global Health Research, St Michael’s Hospital.