Road traffic crashes are rapidly becoming one of the leading causes of injury and death globally. It is predicted that by 2030 crashes will become the fourth leading cause of disability-adjusted life years (DALYs) (Mathers & Loncar, [11]) and the seventh leading global cause of death (World Health Organization [WHO], [26]). The global death toll due to crashes has already escalated by 46% over the past two decades (The World Bank, [21]).
Low- and middle-income countries (LMICs) are acutely affected by this \u27hidden epidemic\u27 (Balch, [ 1]). Ninety per cent of the world\u27s crash-related deaths occur in LMICs where only 54% of its motor vehicles are registered (WHO, [25]). Furthermore, the economic toll of crashes in LMICs is concerning because nearly one half of all health care expenditures in LMICs is used to treat injuries related to motor vehicle crashes (Zakeri & Nosratnejad, [28]). This epidemic deserves urgent attention (Lin, [10]).
Research on the epidemiology of crash problems in LMICs is increasing but these research efforts predominantly report statistics. There is a paucity of qualitative research that could help to explain the statistics. Qualitative exploration has the potential to enhance crash research by describing and explicating the contexts and social processes surrounding crashes, such as the antecedents, the environments in which crashes occur and injuries are produced, and the behaviours of people which make crashes more likely (Roberts, [14]; Rothe, [16]). Qualitative research methods can spark and mobilize the ideas and efforts of affected community members, thereby optimizing crash prevention interventions. Additionally, incorporating local citizens\u27 perspectives on the nature, causes and potential solutions of traffic problems in their locale increases the likelihood that proposed solutions will be effective, wanted and beneficial (Roberts, Smith, & Bryce, [15]).
This article will review the literature to assess the extent to which qualitative methods have been implemented to research road traffic crashes in LMICs and to inform future methodological decision-making
