The purpose of this review is to determine the pooled prevalence of RTA and their associated factors among public transportation in Africa. The study’s accuracy and comprehensiveness were ensured using the PRISMA 2020 checklist (12) (Supplementary Table S1). The review protocol has been submitted to an international register for systematic reviews to ensure accountability.

Various databases were used to conduct a comprehensive search for relevant studies, including, PubMed, Cochrane Library, Google Scholar, Science Direct, and African Journals Online. The literature search was limited to studies published in English that examined the pooled prevalence of RTA among drivers of public transportation in Africa. To ensure comprehensive coverage of the literature, the reference lists of the included studies were carefully checked. A systematic approach was followed to conduct an advanced search on PubMed. Initially, search terms were formulated for eight areas: “road traffic accidents,” “prevalence,” “accidents,” “road traffics,” “drivers” “public transportation” “Associated Factors” and “Africa.” These keywords were retrieved from Google Scholar and then individually searched in PubMed to identify relevant MeSH terms within the MeSH hierarchy tree. These terms were then combined using advanced Boolean search logic, specifically using the “AND” and “OR” operators to effectively bring the concepts together. These search terms were selected based on the PECCO-Principles selected to ensure retrieval of relevant articles from the above databases. All searches were limited to papers written in English and the last search in all databases was performed on the 22th July 2024.

P=Population: drivers. E = Exposure: The level of exposure plays a crucial role in influencing the adherence to road traffic accidents by drivers in Africa. These factors include driving experiences, chawing Khat while driving, alcohol use, and receiving mobile phone calls while driving. C=Context: Africa. C=Condition: public transportation.

O=Outcome measurement: The main objective of the research was to assess the prevalence of road traffic accidents among drivers of public transportation in Africa. Furthermore, the study sought to investigate the predictors of road traffic accident involvement.

This study included studies that met specific criteria. These criteria included having a population of drivers, focusing on the prevalence of road traffic accidents and their associated factors. The studies were conducted exclusively in Africa and were published in English. However, certain primary studies were excluded for various reasons. These reasons included a lack of information on the prevalence of road traffic accidents, unavailability of the full text, low-quality score, inability to access the full text after multiple attempts to contact the corresponding author, and Non-primary studies, such as review articles, conference papers, and editorials, were excluded.

Three authors (YAA, KAG, and BG) independently assessed the standard of the studies using Joanna Briggs Institute (JBI) standardized quality assessment checklist (13). The disagreements raised during the quality assessment were resolved through a discussion led by the third author (BWA). Eventually, the dispute was settled and an agreement was reached. The critical analysis checklist contains eight parameters with the options Yes, No, Unclear, and Not Applicable. Studies were considered low risk when they scored 50% and above on the quality assessment indicators, as reported in a Supplementary Table S2.

We employed the instruments developed by Hoy et al. (14) to evaluate both internal and external validity based on ten established criteria, aiming to assess the potential risk of bias. The potential biases were categorized as either low (with a total score of 6 to 8), moderate (with a total score of 3 or 5), or high (with a total score of 0 to 2). Ultimately, only articles with minimal risk of biases were included in this comprehensive evaluation (Supplementary Table S3).

Four authors (YAA, KAG, BG, and BWA) working independently abstracted the relevant data from studies by using a standardized Microsoft Excel spreadsheet. The JBI tool for prevalence studies was used as a guideline for data extraction from the finally selected articles. The data extraction tool contains information on the author’s name, year of the study, title, year study was conducted and year of publication, country, sub-region, study design, and type, sample size, and the outcome measured, prevalence rate, Information regarding the publication status, and the study quality scores. Moreover, for the factors, a separate data extraction tool was prepared, and it contains information on the author’s name, year of publication, and factors like driving experiences, chewing khat while driving, alcohol use, and receiving mobile phone calls while driving.

After extracting all relevant insights into a Microsoft Excel spreadsheet, the data was then transferred to STATA software version 14 for comprehensive analysis. The combined prevalence of road traffic accidents was determined using a 95% confidence interval. Forest plots were used to show the magnitude of road traffic accidents among public transportation in Africa. Due to its help in minimizing the heterogeneity of included studies, the random effect model of analysis was used as a method of meta-analysis. Furthermore, to assess the presence of publication bias, a funnel plot was used and a more objective assessment was performed by implementing Begg and Eggers regression tests, with a significance level of p < 0.05 indicating the possible presence of publication bias.

Sub-group analyses were also conducted by different study characteristics such as sub-regions of Africa (Northern, South, Western, Central, and Eastern Africa), study design (cross-sectional or retrospective study), and country. Moreover, the meta-analysis regression was conducted to identify the sources of heterogeneity among studies. It was conducted using the following study-level covariates: sample size, publication year, and sub-region of included studies. The different factors associated with RTA were presented using odds ratios (OR) with a 95% confidence interval (CI). A sensitivity analysis was executed to see the effect of a single study on the overall prevalence of the meta-analysis estimate.

This systematic review included published studies conducted on the prevalence of road traffic accidents among drivers in Africa. A total of 1,894 records were retrieved through electronic database searching. From these, 996 duplicated records were excluded, and from 898 articles screened using their titles and abstracts, 801 were excluded. Ninety-seven (97), full-text articles were assessed for eligibility. From these, 52 full-text articles were excluded from prior criteria, and finally, 45 full-text primary articles were selected for quantitative analysis (Figure 1).

Forty- five (15–59) African countries were represented in this review with a total of 15,968 study participants. From all, 25 of the studies were from Eastern Africa (15–18, 22–24, 30–34, 39–44, 53–59), 12 were from Western African countries (12–21, 25–29, 35–38), 6 were from Northern Africa (45, 46, 48–51), one study from Southern, and one study from Central African countries (47, 52). The sample size of the included studies ranged from a minimum of 32 in a study conducted in Egypt (45) to a maximum of 862 in a study conducted in Sudan (51) of the included studies, thirty-seven were cross-sectional (15, 16, 18–20, 22–36, 38–41, 43, 44, 46, 47, 49–51, 53, 55–59), while, eight studies followed retrospective study design (17, 21, 37, 42, 45, 48, 52, 54) (Table 1).

A comprehensive analysis was conducted on a sample of 15,968 participants to determine the pooled prevalence of road traffic accidents (RTA) in Africa. The study included a total of twenty-five research studies in East Africa, twelve studies in West Africa, six in North Africa, one study in Southern Africa, and one study in Central African countries.

The overall pooled prevalence of road traffic accidents (RTA) in Africa was determined using the random-effect model with Mantel-Hanenszel heterogeneity at 38.83% (95%CI: 33.54, 44.12). This estimate was statistically significant with a p value of less than 0.001. Furthermore, heterogeneity between studies was found to be high with an I² value of 98.3%, (Figure 2).

Before conducting a meta-analysis on the effect sizes of the studies included, it was necessary to assess the presence of statistical variability among these studies. This analysis focused on the study design, country, and sub-region. As a result, based on country, the highest pooled estimates of road traffic accidents were seen in Congo at 67.90% (95% CI: 59.99, 75.81). Whereas, the lowest pooled estimate was seen in Libya at 15.80% (95% CI: 10.99, 20.61).

On the other hand, in a sub-group analysis based on sub-region, the highest pooled estimates of road traffic accidents were seen in Central Africa at 67.90% (95% CI:59.99,75.80). Whereas, the lowest pooled estimate was seen in North Africa at 24.92% (95%CI: 13.90, 35.95). In addition, a sub-group analysis was conducted on studies using different types of study design, including cross-sectional, and retrospective study design. The prevalence of road traffic accidents in these studies was found to be 37.17% (95% CI: 31.37, 42.95), and 46.84% (95% CI: 33.79, 59.89), respectively (Table 2).

To examine the possible influence of individual studies on the overall pooled prevalence effect, a leave-one-out sensitivity analysis was performed. The results showed that no specific study had a significant impact on the overall pooled prevalence of road traffic accidents in Africa. The results showed that the combined effect remained significantly unchanged even after excluding a specific study (Table 3).

Meta-regression, along with sub-group and sensitivity analysis, was used to identify sources of heterogeneity, but no significant variables were found. Therefore, heterogeneity can be explained by other factors not included in this review (Table 4).

The presence of publication bias was assessed using a funnel plot and the application of the Egger’s and Begg’s regression test at a significant level of 5%. The symmetrical arrangement of the included studies, as shown in the funnel plot, indicated the absence of publication bias. Furthermore, there was no statistical evidence to support the existence of publication bias, and Begg’s and Egger’s tests yielded p-values of 0.43 and 0.23, respectively, which were not statistically significant. Consequently, the test results provide no evidence of a small study effect (Figure 3).

We performed a meta-analysis to identify associated factors for road traffic accidents using the random effects model. During the extraction process, we planned to show the association of each factor with the outcome variable. Therefore, we examined the pooled effect of four factors on the outcome variable such as driving experience, chewing Khat while driving, alcohol use, and receiving mobile Phone calls while driving.

In this meta-analysis, factors associated with road traffic accidents (RTA) were assessed using thirteen studies (15, 16, 18, 19, 23, 24, 26, 31, 32, 41, 53, 56, 58). Among thirteen articles, the findings of seven studies (15, 16, 19, 26, 31, 53, 58) revealed that road traffic accidents were significantly associated with driving experiences. As a result, the probability of road traffic accidents was 2.64 times greater for public transportation lacking driving experience compared to those who possess such experience (AOR: 2.64, 95% CI:1.70, 3.34) (Figure 4). Higher heterogeneity was observed across studies (I² = 92.40%, p = 0.00), for this reason, we used a random effects model.

The relationship between road traffic accidents and chewing Khat while driving has been assessed in six studies (18, 23, 41, 53, 56, 58). Drivers who chewed Khat while driving were 4.69 times (AOR = 4.69, CI: 2.80,7.85) more likely to be at risk of road traffic accidents as compared to taxi drivers who did not chew Khat while driving (Figure 5). Moderate heterogeneity was observed across studies (I² = 60.60%, p = 0.02), for this reason, we used a random effects model.

The relationship between road traffic accidents and alcohol use has been assessed in four studies (24, 31, 32, 53). The result showed that the combined effect of alcohol use was significantly associated with RTA. Drivers who used alcohol while driving were 2.72 times (AOR = 2.72, CI: 1.49, 4.97) more likely to be at risk of road traffic accidents (RTA) as compared to taxi drivers who did not use alcohol while driving (Figure 6). Moderate heterogeneity was observed across studies (I² = 72.60%, p = 0.01), for this reason, we used a random effects model.

Finally, the four studies showed that road traffic accidents were significantly associated with receiving mobile Phone calls while driving (18, 24, 31, 41). Drivers who received mobile Phone calls while driving were 2.37 times (AOR = 2.37, CI: 1.42, 3.95) more likely to be at risk of road traffic accidents (RTA) as compared to taxi drivers who did not receive mobile Phone calls while driving. Because heterogeneity was moderate, we used a random effects model (I² = 69.7%, p = 0.01) (Figure 7).