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. 2016 Apr 21;94(7):510–521A. doi: 10.2471/BLT.15.163121

The burden of road traffic crashes, injuries and deaths in Africa: a systematic review and meta-analysis

Incidence des accidents, traumatismes et victimes de la route en Afrique: revue systématique et méta-analyse

La tasa de colisiones, traumatismos y muertes en las carreteras africanas: una revisión sistemática y un metaanálisis

عبء حالات التصادم والإصابات والوفاة الناجمة عن حوادث المرور في أفريقيا: مراجعة منهجية وتحليل تَلوي

非洲道路交通事故、伤害和死亡负担： 系统评价和元分析

Бремя дорожно-транспортных происшествий, травматизма и смертности в результате таких происшествий в Африке: систематический обзор и метаанализ

Davies Adeloye a,✉, Jacqueline Y Thompson b, Moses A Akanbi a, Dominic Azuh a, Victoria Samuel c, Nicholas Omoregbe c, Charles K Ayo c
PMCID: PMC4933140  PMID: 27429490

Abstract

Objective

To estimate the burden of road traffic injuries and deaths for all road users and among different road user groups in Africa.

Methods

We searched MEDLINE, EMBASE, Global Health, Google Scholar, websites of African road safety agencies and organizations for registry- and population-based studies and reports on road traffic injury and death estimates in Africa, published between 1980 and 2015. Available data for all road users and by road user group were extracted and analysed. We conducted a random-effects meta-analysis and estimated pooled rates of road traffic injuries and deaths.

Findings

We identified 39 studies from 15 African countries. The estimated pooled rate for road traffic injury was 65.2 per 100 000 population (95% confidence interval, CI: 60.8–69.5) and the death rate was 16.6 per 100 000 population (95% CI: 15.2–18.0). Road traffic injury rates increased from 40.7 per 100 000 population in the 1990s to 92.9 per 100 000 population between 2010 and 2015, while death rates decreased from 19.9 per 100 000 population in the 1990s to 9.3 per 100 000 population between 2010 and 2015. The highest road traffic death rate was among motorized four-wheeler occupants at 5.9 per 100 000 population (95% CI: 4.4–7.4), closely followed by pedestrians at 3.4 per 100 000 population (95% CI: 2.5–4.2).

Conclusion

The burden of road traffic injury and death is high in Africa. Since registry-based reports underestimate the burden, a systematic collation of road traffic injury and death data is needed to determine the true burden.

Introduction

Road traffic injuries are among the leading causes of death and life-long disability globally.1 The World Health Organization (WHO) reports that about 1.24 million people die annually on the world’s roads, with 20–50 million sustaining non-fatal injuries.1,2 Globally, road traffic injuries are reported as the leading cause of death among young people aged 15–29 years and are among the top three causes of mortality among people aged 15–44 years.1 The Institute for Health Metrics and Evaluation (IHME) estimated about 907 900, 1.3 million and 1.4 million deaths from road traffic injuries in 1990, 2010 and 2013, respectively.3

In Africa, the number of road traffic injuries and deaths have been increasing over the last three decades.4 According to the 2015 Global status report on road safety, the WHO African Region had the highest rate of fatalities from road traffic injuries worldwide at 26.6 per 100 000 population for the year 2013.1,2 In 2013, over 85% of all deaths and 90% of disability adjusted life years (DALYs) lost from road traffic injuries occurred in low- and middle-income countries, which have only 47% of the world’s registered vehicles.2,3 The increased burden from road traffic injuries and deaths is partly due to economic development, which has led to an increased number of vehicles on the road.5,6 Given that air and rail transport are either expensive or unavailable in many African countries, the only widely available and affordable means of mobility in the region is road transport.1,2,7 However, the road infrastructure has not improved to the same level to accommodate the increased number of commuters and ensure their safety and as such many people are exposed daily to an unsafe road environment.1,4

The 2009 Global status report on road safety presented the first modelled regional estimate of a road traffic death rate, which was used to statistically address the underreporting of road traffic deaths by countries with an unreliable death registration system.5 In the 2009 report, Africa had the highest modelled fatality rate at 32.2 per 100 000 population, in contrast to the reported fatality rate of 7.2 per 100 000 population.5The low reported death rate reflects the problem of missing data due to non-availability of road traffic data systems, which has a direct impact on health planning including prehospital and emergency care and other responses by government agencies.

This study aimed to review existing literature on published studies, registry-based reports and unpublished articles on the burden of road traffic injuries and deaths in the African continent to generate a continent-wide estimate of road traffic injuries and deaths for all road users and by road user type (pedestrians, motorized four-wheeler occupants, motorized two–three wheeler users and cyclists).

Methods

We searched MEDLINE, EMBASE, Global Health, Google Scholar, websites of road safety agencies and relevant organizations within Africa for articles published between 1980 and 2015 (Fig. 1). The search strategy and terms are presented in Box 1 (available at: http://www.who.int/bulletin/volumes/94/7/15-163121). There was no language restriction.

Fig. 1.

Fig. 1

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Selection of studies for the review on road traffic crashes, injuries and deaths in Africa, 1980–2015

Box 1. Search strategy of published studies on the burden of road traffic crashes, injuries and deaths in Africa.

1. africa/ or exp africa, northern/ or exp algeria/ or exp egypt/ or exp libya/ or exp morocco/ or exp tunisia/ or exp “africa south of the sahara”/ or exp africa, central/ or exp cameroon/ or exp central african republic/ or exp chad/ or exp congo/ or exp “democratic republic of the congo”/ or exp equatorial guinea/ or exp gabon/ or exp africa, eastern/ or exp burundi/ or exp djibouti/ or exp eritrea/ or exp ethiopia/ or exp kenya/ or exp rwanda/ or exp somalia/ or exp sudan/ or exp tanzania/ or exp uganda/ or exp africa, southern/ or exp angola/ or exp botswana/ or exp lesotho/ or exp malawi/ or exp mozambique/ or exp namibia/ or exp south africa/ or exp swaziland/ or exp zambia/ or exp zimbabwe/ or exp africa, western/ or exp benin/ or exp burkina faso/ or exp cape verde/ or exp cote d'ivoire/ or exp gambia/ or exp ghana/ or exp guinea/ or exp guinea-bissau/ or exp liberia/ or exp mali/ or exp mauritania/ or exp niger/ or exp nigeria/ or exp senegal/ or exp sierra leone/ or exp togo/

2. exp vital statistics/ or exp incidence

3. (incidence* or prevalence* or morbidity or mortality).tw.

4. (disease adj3 burden).tw.

5. exp “cost of illness”/

6. exp quality-adjusted life years/

7. QALY.tw.

8. Disability adjusted life years.mp.

9. (initial adj2 burden).tw.

10. exp risk factors/

11. 2 or 3 or 4 or 5 or 6 or 7 or 8 or 9 or 10

12. road traffic accident*.mp. [mp = title, abstract, original title, name of substance word, subject heading word, protocol supplementary concept, rare disease supplementary concept, unique identifier]

13. RTAs.mp. [mp = title, abstract, original title, name of substance word, subject heading word, protocol supplementary concept, rare disease supplementary concept, unique identifier]

14. road traffic injur*.mp. [mp = title, abstract, original title, name of substance word, subject heading word, protocol supplementary concept, rare disease supplementary concept, unique identifier]

15. traffic crash*.mp. [mp = title, abstract, original title, name of substance word, subject heading word, protocol supplementary concept, rare disease supplementary concept, unique identifier]

16. exp Accidents, Traffic/

17. exp air bags/ or exp child restraint systems/ or exp seat belts/

18. exp motor vehicles/ or exp automobiles/ or exp motorcycles/

19. 12 or 13 or 14 or 15 or 16 or 17 or 18

20. 1 and 11 and 19

21. limit 20 to (yr = ”1980 –Current

Eligibility criteria

We included a study in the review if it met the following criteria: (i) conducted between 1980 and 2015 and that the study was done in an African country; (ii) clearly referred to road traffic crashes, injuries or deaths; (iii) referred data came from a population- or registry-based data system; (iv) registry-based hospital data with the underlying cause of death data coded in the International Classification of Disease and Related Health Problems, 10th revision (ICD-10), with codes V01–V89; (v) directly attempted to estimate the number or rate of road traffic crashes, injuries or deaths in a particular African country or the region as a whole; or (vi) provided any other information (e.g. response time, first responders) that may further help to understand the burden and determinants of road traffic crashes and policy response in the African region.

We excluded studies if they: (i) referred to deaths by other means of transportation including water, air and other unspecified transport means; (ii) were mainly reviews, viewpoints and commentaries; (iii) did not have a clearly defined study design, data capture and analysis method; and (iv) had not clearly defined and consistently applied a case definition of a road traffic crash, injury or fatality.

For this study, a crash is defined as a road traffic collision that resulted in an injury or fatality. Injury refers to non-fatal cases from a road traffic crash.5 Death is defined as a road traffic crash in which one or more persons involved in the crash died immediately or within 30 days of the crash.2 For non-fatal injuries, the case definition ranges from minor injuries with disabilities of short duration, to severe injuries with lifelong disabilities.

Quality assessment

For each full text accessed, we checked if the study method had flaws in the design and execution. For the registry-based studies, we examined the study design, completeness, the appropriateness of statistical and analytical methods employed and if the limitations were explicitly stated. For each study, we assessed if the reported sample size or study population was appropriate to provide a representative estimate and if the heterogeneities within and between population groups undermine the pooled estimates. Studies not meeting this quality assessment were excluded.

Data collection process

Available data from all selected studies were extracted twice, compiled and stored in a spreadsheet. For each study, data on the country, study period, study design, sample size, mean age and case definitions were extracted (Table 1). Reported road traffic crash, injury and death data for the overall study population and for the various categories of road users were extracted. The data were grouped by study setting and year of study, with corresponding age and sex categories.

Table 1. Studies on burden of road traffic crashes, injuries and deaths in Africa, as identified through a systematic review of the literature, 1980–2015 .

Reference Study period Country, study setting Study design Study type Source of data Type of data Case definition
Sobngwi-Tambekou8 2004–2007 Cameroon, Yaoundé-Douala Retrospective study Registry-based Police records Death Deaths within 30 days of a crash
Twagirayezu9 2005 Rwanda, Kigali Descriptive and cross-sectional Registry-based Hospital records Death and injury Deaths at the site of a road crash or injured patients presenting to a hospital
Abegaz10 2012–2013 Ethiopia, Addis-Ababa Capture–recapture Registry-based Traffic reports Death and injury Injuries and deaths resulting from a road crash
Mekonnen11 2007–2011 Ethiopia, Amhara region Retrospective and descriptive study Registry-based Police road crash records Death and injury Injuries and deaths resulting from a road crash
Bachani12 2004–2009 Kenya Retrospective and observational Registry-based Police (Kenya traffic police); vital registration (National Vital Registration System) Death Deaths within 30 days of crash
Ngallaba13 2009–2012 United Republic of Tanzania, Mwanza Retrospective design Registry-based Hospital ward register and case notes, medical record; police records Death Deaths at the site of road crash or injured patients presenting to a hospital
Zimmerman14 2011–2012 United Republic of Tanzania Cross-sectional Population-based Resident population Death Deaths within 30 days of a crash
Museru15 1990–2000 United Republic of Tanzania Retrospective and descriptive Registry-based Police records Death Deaths at crash scene and outside scene
Barengo16 1990–2001 United Republic of Tanzania, Dar es Salaam Cross-sectional Registry-based Routine police record Death and injury Injuries and deaths resulting from road crash
Kilale17 1995–1996 United Republic of Tanzania, Kiluvya-Bwawani and Chalinze-Segera Retrospective and descriptive Registry-based Police road traffic crash records from the Coast Region Traffic Office Death Death at crash scene and outside scene
Nakitto18 2004–2005 Uganda, Kawempe Cohort Population-based 35 primary schools followed for three academic school year terms Death and injury Road traffic accidents with injuries and deaths on the spot and within 30 days of a crash
Bezzaoucha19 1986 Algeria Cohort Population-based Resident population Death Fatalities defined as deaths at crash scene or outside scene
Bodalal20 2010–2011 Libya, Benghazi Retrospective Registry-based Hospital records Death and injury Deaths at the site of road crash, Injured patients presenting to hospital immediately, or delayed presentation of a previously stable patient with fresh complaints
Samuel21 2008–2009 Malawi Capture–recapture Registry-based Police and hospital records Death Deaths within 30 days of crash
Romão22 1990–2000 Mozambique Retrospective Registry-based Transport records (National Institute for Road Safety) Death and injury Injuries and deaths at the site of road crash
Olukoga23 2003 South Africa Retrospective and descriptive Registry-based Transport records (Department of Transport, Pretoria) Death Deaths within 30 days of crash
Olukoga24 2002–2003 South Africa Retrospective and descriptive Registry-based National Statistics Office (Durban municipality) Death Deaths within 30 days of crash
Meel25 1993–1999 South Africa Retrospective Registry-based Mortuary (medico-legal autopsy records) Death Deaths within 30 days of crash
Lehohla26 2001–2006 South Africa Retrospective Registry-based National Statistics Office Death and injury Injuries and deaths resulting from road crash
Hobday27 2007 South Africa, eThekwini Retrospective Registry-based Transport records (eThekwini transport authority database) Death and injury Child pedestrian injuries and deaths resulting from road crash
Kyei28 2004–2008 South Africa, Limpopo Retrospective Registry-based Transport management cooperation records Death and injury Injuries and deaths resulting from road crash
Meel29 1993–2004 South Africa, Mthatha Retrospective Registry-based Mortuary (death records and autopsies from Mthatha and Nqgeleni magisterial districts) Death Deaths within 30 days of crash
Amonkou30 2001–2011 Cote d’Ivoire, Abidjan Retrospective Registry-based Hospital records Death and injury Injuries and deaths resulting from road crash
Ackaah31 2005–2007 Ghana Retrospective and descriptive Registry-based Traffic reports, hospital records Death Fatalities where one or more persons are killed as a result of a crash and where the death occurs within 30 days of the crash
Afukaar32 1994–1998 Ghana Retrospective and descriptive Registry-based Reported traffic crash data Death and injury Death within 30 days, serious injuries (hospitalization for > 24hrs), slight injuries (hospitalization for < 24hrs)
Guerrero33 2009 Ghana Survey Population-based Resident population Death and injury Fatalities within 30 days of crash
Mock34 1999 Ghana Survey Population-based Resident population Death and injury Road crash with injuries and deaths
Aidoo35 2004–2010 Ghana Retrospective Registry-based Traffic reports Death and injury Pedestrian deaths and injuries from hit-and-run cases
Kudebong36 2004–2008 Ghana, Bolgatanga Retrospective Registry-based Traffic reports Death and injury Deaths and injuries resulting from motorcycle road crash
Mamady37 2011 Guinea Retrospective Registry-based Health ministry information Death Deaths as recorded in the country’s death notification form and coded using International Classification of Diseases, ninth revision (ICD-9)
Ezenwa38 1980–1983 Nigeria Retrospective Registry-based Police records from federal police headquarters Death Death at scene of accident and outside scene
Nigeria Federal Road Safety Corps39 2001–2013 Nigeria Surveillance Registry-based Federal Road Safety Corps data Death Death at scene of accident and outside scene
Labinjo40 2009 Nigeria Survey Population-based Resident population Death Deaths within 30 days of crash
Asogwa41 1980–1985 Nigeria Retrospective Registry-based Police records Death Accidents resulting in deaths
Balogun42 1987–1990 Nigeria, Ile-Ife Retrospective Registry-based Hospital records Death and injury Deaths at the site of road crash or injured patients presenting to hospital
Adeoye43 2006–2007 Nigeria, Ilorin Surveillance Registry-based Hospital records Death and injury Deaths at the site of road crash or injured patients presenting to hospital
Adewole44 2001–2006 Nigeria, Lagos Audit Registry-based Ambulance service records Death and injury Deaths and injuries resulting from road crash
Jinadu45 1980–1982 Nigeria, Oyo Retrospective and descriptive Registry-based Information from health and transport ministries Death and injury Deaths and injuries resulting from road crash
Aganga46 1980 Nigeria, Zaria Retrospective Registry-based Traffic reports Death and injury Deaths and injuries resulting from road crash
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Data analysis

All extracted data on road traffic crashes, injuries and deaths were converted to rates per 100 000 population. Studies were subdivided into population- and registry-based studies and analysed separately for all road users and by road user category. A random effects meta-analysis was conducted on extracted road traffic crash, injury and death rates. To give a better understanding of the data distribution and comparisons with the pooled estimates and the confidence intervals, we further presented the range, median and data points within each data set. All statistical analyses were done in Excel 2010 (Microsoft, Redmond, United States of America) and Stata version 13.1 (StataCorp. LP, College Station, United States of America).

Results

The review identified 39 studies reporting on 15 African countries (Table 1). Six were population-based and the remaining 33 were registry-based studies. Two studies were from Ethiopia,10,11 six from Ghana,31–36 nine from Nigeria,38–46 seven from South Africa23–29 and five from the United Republic of Tanzania.13–17 The remaining 10 studies were from one of the following countries: Algeria,19 Cameroon,8 Cote d’Ivoire,30 Guinea,37 Kenya,12 Libya,20 Malawi,21 Mozambique,22 Rwanda9 and Uganda.18 More than half (22) of the studies were conducted after the year 2000. The study period ranged from one year to 12 years, with a mean of 4.5 years. The full data set is available from the corresponding author.

Reported rates

From all registry-based studies, Nigeria recorded the highest and lowest total crash rate at 716.57 per 100 000 population and 2.9 per 100 000 population, in 1990 and 2011, respectively.39,42 Ethiopia recorded the highest death rate at 81.6 per 100 000 population in 2011,11 while the lowest death rate was recorded in Nigeria at 1.64 per 100 000 population in 2007.43

From the available population-based studies, Nigeria reported the highest number of road traffic injury and death rates at 4120 per 100 000 population and 160 per 100 000 population, respectively. The road traffic injury rate is the highest recorded in any single study in Africa. Algeria and Ghana also reported high road traffic injury rates at 700 and 938 per 100 000 population, respectively.19,34 Only six studies reported male and female road traffic crash estimates,14,19,21,22,29,31 with Algeria and South Africa recording the highest number of casualties.

Pooled rates

Table 2 presents the estimated pooled rates for the African continent. For total crashes, the pooled rate was 52.8 per 100 000 population, with the median at 39.7 per 100 000 population. The pooled fatal crash rate was estimated at 9.6 per 100 000 population with a median at 4.8 per 100 000 population. Pooled crash injury and death rates were estimated at 65.2 injuries and 16.6 deaths with medians of 38.9 injuries and 7.9 deaths per 100 000 population, respectively (Fig. 2 and Fig. 3).

Table 2. Pooled road traffic crash, injury and death estimates by road user type, African, 1980–2015.

Road user type Total crash ratea Fatal crash rateb Injury ratec Death rated
All road users
Pooled rate (95% CI) 52.8 (49.0–56.6) 9.6 (8.6–10.7) 65.2 (60.8–69.5) 16.6 (15.2–18.0)
Median (range of estimates) 39.7 (2.9–716.6) 4.8 (0.7–186.1) 38.9 (8.1–491.8) 7.9 (1.6–81.6)
No. of data points 49 30 59 95
Pedestrians
Pooled rate (95% CI) – – 10.8 (8.7–12.8) 3.4 (2.5–4.2)
Median (range of estimates) – – 9.14 (0.4–75.0) 2.2 (0.3–13.0)
No. of data points – – 28 21
Four-wheelers
Pooled rate (95% CI) – – 37.2 (25.7–48.7) 5.9 (4.4–7.4)
Median (range of estimates) – – 26.6 (1.4–271.0) 2.7 (0.7–63.0)
No. of data points – – 19 23
Two–three wheelers/cyclists
Pooled rate (95% CI) – – 16.1 (12.1–20.2) 1.3 (0.98–1.6)
Median (range of estimates) – – 5.8 (0.4–136.0) 0.9 (0.12–3.1)
No. of data points – – 19 22
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CI: confidence interval.

a Defined as number of all road traffic crashes (fatal and non-fatal) per 100 000 population.

b Defined as number of fatal road traffic crashes per 100 000 population.

c Defined as number of non-fatal road traffic injuries per 100 000 population.

d Defined as number of fatal road traffic injuries per 100 000 population.

Note: There were no studies with crash rate and fatal crash rate to estimate for pedestrians, four-wheelers and two–three wheelers/cyclists.

Fig. 2.

Pooled road traffic crash rate, Africa, 1980–2015

Note: In the box plot, the boxes represent the interquartile range of road traffic injury rates where the middle 50% (25–75%) of data are distributed; the bars represent road traffic injury rates outside the middle 50% (< 25% or > 75%); the dots represent specific road traffic injury rates which were a lot higher than normally observed over the study period (outliers) and the lower, middle and upper horizontal lines represent the minimum, median and maximum road traffic injury rates (excluding outliers), respectively.

Fig. 2

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Fig. 3.

Pooled fatal road traffic crash rate, Africa, 1980–2015

Note: In the box plot, the boxes represent the interquartile range of road traffic injury rates where the middle 50% (25–75%) of data are distributed; the bars represent road traffic injury rates outside the middle 50% (< 25% or > 75%); the dots represent specific road traffic injury rates which were a lot higher than normally observed over the study period (outliers) and the lower, middle and upper horizontal lines represent the minimum, median and maximum road traffic injury rates (excluding outliers), respectively.

Fig. 3

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From 1990 to 2015, road traffic injury rates increased from 40.7 to 92.9 per 100 000 population (Table 3). In contrast, death rates decreased from 19.9 to 9.27 per 100 000 population (Fig. 4 and Fig. 5). Applying these figures and using the United Nations (UN) population estimates47 for the region, the pooled estimate came to 106 000 road traffic deaths and 1.1 million injuries in 2015, compared with 126 000 deaths and 260 000 injuries in 1990.

Table 3. Ten year pooled road traffic injury and death rate estimate, Africa, 1980–2015.

Ten year range Injury ratea (95% CI) Death rateb (95% CI)
1980–1989 48.4 (44.5– 52.2) 12.6 (11.7–13.6)
1990–1999 40.7 (35.8–45.6) 19.9 (14.8–25.0)
2000–2009 75.6 (70.0–83.1) 16.5 (14.5–18.6)
2010–2015c 92.9 (84.8–101.0) 9.3 (8.2–10.3)
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CI: confidence interval.

a Defined as number of non-fatal road traffic injuries per 100 000 population.

b Defined as number of fatal road traffic injuries per 100 000 population.

c Covers only five years.

Fig. 4.

Pooled road traffic injury rate, Africa, 1980–2015

Note: In the box plot, the boxes represent the interquartile range of road traffic injury rates where the middle 50% (25–75%) of data are distributed; the bars represent road traffic injury rates outside the middle 50% (< 25% or > 75%); the dots represent specific road traffic injury rates which were a lot higher than normally observed over the study period (outliers) and the lower, middle and upper horizontal lines represent the minimum, median and maximum road traffic injury rates (excluding outliers), respectively.

Fig. 4

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Fig. 5.

Pooled road traffic death rate, Africa, 1980–2015

Note: In the box plot, the boxes represent the interquartile range of road traffic injury rates where the middle 50% (25–75%) of data are distributed; the bars represent road traffic injury rates outside the middle 50% (< 25% or > 75%); the dots represent specific road traffic injury rates which were a lot higher than normally observed over the study period (outliers) and the lower, middle and upper horizontal lines represent the minimum, median and maximum road traffic injury rates (excluding outliers), respectively.

Fig. 5

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By road user category

From individual studies, road traffic death rates among pedestrians ranged from 0.26 per 100 000 population in Nigeria in 2007 to 13 per 100 000 population in South Africa in 2003.43,24 The death rate among motorized four-wheeler occupants was lowest in Nigeria in 2007 and highest in South Africa in 1999 at 0.74 and 63 per 100 000 population, respectively.43,25 A 2007 study from Cameroon reported the lowest road traffic death rate for motorized two–three wheeler occupants and cyclists and a 2012 study from the United Republic of Tanzania reported the highest, at 0.12 and 3.12 per 100 000 population, respectively.8,13 The pooled rates showed that motorized four-wheeler occupants had the highest road traffic death rate, closely followed by pedestrians. The pooled road traffic injury and death rates among pedestrians were 10.8 and 3.4 per 100 000 population, respectively. Among motorized four-wheeler occupants, the pooled road traffic injury and death rates were 37.2 and 5.9 per 100 000 population, respectively. Among motorized two–three wheeler occupants and cyclists, the pooled injury and death rates were 16.1 and 1.3 per 100 000 population, respectively (Table 2).

Discussion

Our study reflects the difficulties that many experts have noted in describing the extent of road traffic crashes, injuries and deaths in Africa, for which modelling based on scarce and variable information, may not necessarily provide a reliable estimate.48 Moreover, registry-based reports may grossly underestimate the burden of road traffic crashes. Population-based studies consistently report a higher fatality rate.19,34,40 For example, a population-based survey conducted in Ghana in 1998 reported an injury rate of 940 per 100 000 population,34 while another registry-based study in the same country for the same year estimated 32 per 100 000 population.32 The Nigerian Federal Road Safety Corps estimated 3.7 deaths per 100 000 population for Nigeria in 2009.39 In contrast, a population-based study in the same country reported a higher estimate of 160 deaths per 100 000 population.40

The subgroup analysis showed that injury rates increased and death rates decreased between 1990 and 2015. A high road traffic injury number may reflect the effect of economic growth on the burden of road traffic injury in the region, which may be associated with increased travel and exposure to a hazardous traffic environment.49,50 However, death figures may be decreasing due to a relatively improving prehospital and emergency response system,51 as noted in Ghana, South Africa and Zambia.52, 53,54 It is important to note that many deaths may be missed or not recorded, as many of the road safety agencies tend to only record crashes, leaving the recording of deaths to health agencies.55,56

Our findings further revealed that the highest rates of casualties are among motorized four-wheeler occupants and pedestrians. A WHO report shows that 43% and 38% of road traffic deaths in the African Region occurred among motorized four-wheeler occupants and pedestrians, respectively.2 In Africa, most of these motorized four-wheeler occupants are passengers of commercial vehicles which is the commonly used means of transport.4 The high death rate among motorized four-wheeler occupants may also be due to the fact that crashes involving motorized four-wheeled vehicles are often recorded, while pedestrian crashes may be missed.4,35 However, we agree with some authors who have reported that pedestrians may be more affected in Africa due to bad road infrastructure, lack of pedestrian-friendly road signs, the way traffic is mixed with other road users and a general disregard for pedestrians by drivers.1

Meanwhile, a major challenge for the response to road crashes in Africa is the lack of reliable information and data that can inform an evidence-based public health response.49,57 Underreporting especially of vulnerable road users, poor or absent links between reporting agencies, exemptions from reporting, poor sampling techniques and varying case definitions have been indicated as limitations of reported data. The different rates of road traffic crash, injury and death reported in this study may be mostly related to surveillance system reporting errors and biases. In many African countries, there are no effective vital registration and active surveillance systems to capture the outcome of a road traffic crash1 and police data is the main source of traffic crash data.1,2 However, data from police sources tend to underreport injuries and deaths due to poor traffic police response and follow up on injured victims and varying traffic fatality definitions for real-time and chronologic data capture.4

Our study has the following limitations. Population-based studies on road traffic crashes in Africa, which would have been more reliable than registry-based studies, were not available. Population-based studies may have given insights on the extent of road users’ exposure to traffic risk, mode and frequency of road travel, distance travelled, number of road commuters and the conditions of the road. In the absence of such information, we have not based our estimates on an appropriate travel exposure denominator, thus limiting an understanding of the reasons behind the reported road traffic crash, injury and death rates and trends.

The available registry-based studies varied in their quality. They reported questionable values and trends and provided uncertain estimates. Lack of appropriate case definition for road traffic fatalities and incomplete breakdown of road traffic crash estimates by road user type were major limitations. Additionally, the non-fatal injury figures reported by the different studies varied with respect to severity and outcome. These variations could have affected our meta-analyses.

While we applied the UN population data for Africa to estimate rates where relevant national reference population data were unavailable, there were no comparable data to use for subnational studies. In addition, the data employed for this analysis were generated only from 15 countries, which is relatively small to accurately reflect the overall situation in the region. Hence, our estimates should be interpreted against these limitations.

In conclusion, our study suggests that the burden of road traffic injuries in Africa is high and there is an underestimation of road traffic fatalities. Improved road traffic injury surveillance across African countries may be useful in identifying relevant data gaps and developing contextually feasible prevention strategies in these settings.

Competing interests:

None declared.

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