Factors associated with road traffic injury severity among victims retrieved by pre-hospital emergency services in Rwanda

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n.callMethod.apply(n,arguments):n.queue.push(arguments)} ;if(!f._fbq)f._fbq=n; n.push=n;n.loaded=!0;n.version='2.0';n.queue=[];t=b.createElement(e);t.async=!0; t.src=v;s=b.getElementsByTagName(e)[0];s.parentNode.insertBefore(t,s)}(window, document,'script','https://connect.facebook.net/en_US/fbevents.js'); fbq('init', '1641728616063202'); fbq('track', "PixelInitialized", {}); (function(h,o,t,j,a,r){ h.hj=h.hj||function(){(h.hj.q=h.hj.q||[]).push(arguments)}; h._hjSettings={hjid:2318163,hjsv:6}; a=o.getElementsByTagName('head')[0]; r=o.createElement('script');r.async=1; r.src=t+h._hjSettings.hjid+j+h._hjSettings.hjsv; a.appendChild(r); })(window,document,'https://static.hotjar.com/c/hotjar-','.js?sv='); search file_upload Submit your research search menu close search Browse Gateways & Collections How to Publish Submit your Research My Submissions Article Guidelines Article Guidelines (New Versions) Open Data, Software and Code Guidelines Open Data and Accessible Source Materials Guidelines (HSS) Open Data, Software and Code Guidelines (PSE) Prepublication Checks Production Process Posters and Slides Guidelines Document Guidelines Article Processing Charges Peer Review Finding Article Reviewers About How it Works For Reviewers Our Advisors Policies Glossary FAQs For Developers Newsroom Contact My Research Submissions Content and Tracking Alerts My Details Sign In file_upload Submit your research { "@context": "https://schema.org", "@type": "ScholarlyArticle", "mainEntityOfPage": { "@type": "WebPage", "@id": "https://f1000research.com/articles/14-1282" }, "headline": "Factors associated with road traffic injury severity among victims retrieved by pre-hospital emergency...", "datePublished": "2025-11-19T15:44:42", "dateModified": "2026-02-05T10:40:35", "author": [ { "@type": "Person", "name": "Eric Uwitonze" }, { "@type": "Person", "name": "Emmanuel Biracyaza" } ], "publisher": { "@type": "Organization", "name": "F1000Research", "logo": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 480, "width": 60 } }, "image": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 1200, "width": 150 }, "description": " Background Traumatic injuries remain critical public health concerns, placing psychosocial and economic burdens on individuals, families, and healthcare systems. Despite being a leading cause of morbidity and mortality globally, especially in low-and middle-income countries, few studies have examined predictors of injury severity in pre-hospital settings. Most research focuses on injury incidence, with limited attention to pre-hospital factors. We aimed assessing the prevalence of severe injuries and their associated factors among patients managed by pre-hospital emergency services. Methods This cross-sectional study utilized medical registry data from 1,162 RTI victims. Demographic, epidemiological, and clinical information were collected, with injuries categorized as severe or non-severe based on the Injury Severity Score. Bivariate and multivariable logistic regression models were conducted to indicate associated factors of severe injury. Results Among 1162 victims, 165 (14%) experienced severe injury. Our results showed that females were less likely to experience severe injury (aOR=0.47, 95%CI:0.26–0.79) than males. Regarding trauma mechanism, car-to-pedestrian collisions (aOR=2.3, 95%CI:1.25–4.1), car-to-motorcycle collisions considerably increased the likelihoods of severe injury (aOR=3.88, 95%CI:1.16–13.05) compared to car-only crashes. Alcohol users were more likely to experience severe injury (aOR=3.37; 95%CI: 2.04-5.56) than non-users. Those who travelled distance ranged 21-40 km had higher likelihoods (aOR=2.91, 95%CI:1.27–6.63), while those with more than 40 km faced higher likelihoods of severe injury (aOR=2.64, 95% CI:1.11–6.25) than individuals with less than 20 km to reach to a healthcare facility. Those with extremity injuries (aOR=0.28, 95% CI:0.15–0.52), chest injuries (aOR=0.40, 95%CI:0.23–0.71, p=.002) had lower likelihoods of severe injury than those with head trauma. Conclusion This study provides valuable insights into the factors influencing injury severity in the pre-hospital setting. The findings underscore the importance of strengthening early identification and rapid stabilization of high-risk patients during pre-hospital care. Future research using prospective longitudinal designs is recommended to confirm causality. " } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/14-1282/v2", "name": "Factors associated with road traffic injury severity among victims..." } } ] } Home Browse Factors associated with road traffic injury severity among victims... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Uwitonze E and Biracyaza E. Factors associated with road traffic injury severity among victims retrieved by pre-hospital emergency services in Rwanda [version 2; peer review: 2 approved] . F1000Research 2026, 14 :1282 ( https://doi.org/10.12688/f1000research.172231.2 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Revised Factors associated with road traffic injury severity among victims retrieved by pre-hospital emergency services in Rwanda [version 2; peer review: 2 approved] Eric Uwitonze https://orcid.org/0009-0006-4472-4758 1,2 , Emmanuel Biracyaza https://orcid.org/0000-0001-7494-2779 3,4 Eric Uwitonze https://orcid.org/0009-0006-4472-4758 1,2 , Emmanuel Biracyaza https://orcid.org/0000-0001-7494-2779 3,4 PUBLISHED 05 Feb 2026 Author details Author details 1 Department of Epidemiology and Biostatistics, College of Medicine and Health Sciences Huye, University of Rwanda, Butare, Southern Province, Rwanda 2 Emergency Medical Service (EMS) Division, Rwanda Biomedical Centre, Kigali, Rwanda 3 School of Rehabilitation, Universite de Montreal, Montreal, Québec, Canada 4 Centre for Interdisciplinary Research in Rehabilitation of Greater Montréal, Montréal, Canada Eric Uwitonze Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Emmanuel Biracyaza Roles: Conceptualization, Formal Analysis, Methodology, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS Abstract Background Traumatic injuries remain critical public health concerns, placing psychosocial and economic burdens on individuals, families, and healthcare systems. Despite being a leading cause of morbidity and mortality globally, especially in low-and middle-income countries, few studies have examined predictors of injury severity in pre-hospital settings. Most research focuses on injury incidence, with limited attention to pre-hospital factors. We aimed assessing the prevalence of severe injuries and their associated factors among patients managed by pre-hospital emergency services. Methods This cross-sectional study utilized medical registry data from 1,162 RTI victims. Demographic, epidemiological, and clinical information were collected, with injuries categorized as severe or non-severe based on the Injury Severity Score. Bivariate and multivariable logistic regression models were conducted to indicate associated factors of severe injury. Results Among 1162 victims, 165 (14%) experienced severe injury. Our results showed that females were less likely to experience severe injury (aOR=0.47, 95%CI:0.26–0.79) than males. Regarding trauma mechanism, car-to-pedestrian collisions (aOR=2.3, 95%CI:1.25–4.1), car-to-motorcycle collisions considerably increased the likelihoods of severe injury (aOR=3.88, 95%CI:1.16–13.05) compared to car-only crashes. Alcohol users were more likely to experience severe injury (aOR=3.37; 95%CI: 2.04-5.56) than non-users. Those who travelled distance ranged 21-40 km had higher likelihoods (aOR=2.91, 95%CI:1.27–6.63), while those with more than 40 km faced higher likelihoods of severe injury (aOR=2.64, 95% CI:1.11–6.25) than individuals with less than 20 km to reach to a healthcare facility. Those with extremity injuries (aOR=0.28, 95% CI:0.15–0.52), chest injuries (aOR=0.40, 95%CI:0.23–0.71, p=.002) had lower likelihoods of severe injury than those with head trauma. Conclusion This study provides valuable insights into the factors influencing injury severity in the pre-hospital setting. The findings underscore the importance of strengthening early identification and rapid stabilization of high-risk patients during pre-hospital care. Future research using prospective longitudinal designs is recommended to confirm causality. READ ALL READ LESS Keywords Road Traffic Injuries; Emergency care; Severe injury; Predictors; Public health Corresponding Author(s) Eric Uwitonze ( [email protected] ) Close Corresponding author: Eric Uwitonze Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2026 Uwitonze E and Biracyaza E. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Uwitonze E and Biracyaza E. Factors associated with road traffic injury severity among victims retrieved by pre-hospital emergency services in Rwanda [version 2; peer review: 2 approved] . F1000Research 2026, 14 :1282 ( https://doi.org/10.12688/f1000research.172231.2 ) First published: 19 Nov 2025, 14 :1282 ( https://doi.org/10.12688/f1000research.172231.1 ) Latest published: 05 Feb 2026, 14 :1282 ( https://doi.org/10.12688/f1000research.172231.2 ) Revised Amendments from Version 1 The major differences between the current version of the article and the previously published version stem from the careful integration of the reviewer’s methodological comments. In the revised version, we substantially strengthened the discussion of missing data and potential selection bias, expanded and clarified the Limitations section, and provided a more explicit justification of key methodological choices, including the injury severity threshold (ISS >9), the selection of predictive variables, and the categorization of distance to care. We also clarified the operational relevance of pre-hospital variables and the assessment of alcohol consumption. Overall, the revised manuscript offers improved methodological transparency, contextual justification, and interpretative rigor compared to the original version. we really appreciate the constructive comments from the reviewer. The major differences between the current version of the article and the previously published version stem from the careful integration of the reviewer’s methodological comments. In the revised version, we substantially strengthened the discussion of missing data and potential selection bias, expanded and clarified the Limitations section, and provided a more explicit justification of key methodological choices, including the injury severity threshold (ISS >9), the selection of predictive variables, and the categorization of distance to care. We also clarified the operational relevance of pre-hospital variables and the assessment of alcohol consumption. Overall, the revised manuscript offers improved methodological transparency, contextual justification, and interpretative rigor compared to the original version. we really appreciate the constructive comments from the reviewer. See the authors' detailed response to the review by Emmanuel Bonnet READ REVIEWER RESPONSES Background Road traffic injuries (RTIs) represent a major public health and socio-economic challenge worldwide, responsible for approximately 1.19 million deaths annually in 2021, 1 a decrease from 1.35 million in 2016. 2 – 4 Nearly 90% of these fatalities occur in low- and middle-income countries (LMICs), where road safety systems remain underdeveloped. RTIs are now the eighth leading cause of death globally and a major contributor to disability-adjusted life years (DALYs) lost. 5 , 6 More than half of these issues involve vulnerable road users, including pedestrians, cyclists, and motorcyclists. 7 Without stronger prevention measures, the World Health Organization projects a 40% increase in global RTIs, particularly in LMICs, with RTIs expected to become the fifth leading cause of death by 2030. 6 , 8 , 9 Although high-income countries have made significant progress in reducing RTI fatalities, 10 low- and middle-income countries (LMICs) continue to face disproportionately high rates of traffic crashes and related deaths. Several contributing factors have been identified, including rapid motorization, substandard road infrastructure, excessive speeding, weak enforcement of traffic regulations, limited access to trauma care, and insufficient pre-hospital emergency services. 11 – 13 Individual-level factors such as being male, young age, and low educational attainment are also associated with increased injury severity, along with clinical factors such as multiple injuries, head, neck, or spinal trauma, and the victim’s role in traffic (driver, passenger, or pedestrian). Additional environmental and contextual risks include long travel distances to healthcare facilities, seasonal hazards like rain, and lack of protective gear. Characteristics of the crash itself such as location, type, time of occurrence, and absence of safety equipment (e.g., helmets, seat belts, airbags) also play a role. 14 – 18 Furthermore, behavioral factors, particularly alcohol consumption, and underlying health conditions, such as chronic illnesses (e.g., diabetes), have been linked to the likelihood of sustaining severe injuries. 12 , 19 – 21 Like other LMICs, Rwanda faces a growing burden of RTIs, which pose a significant challenge to both public health and economic development. 22 Data from the Rwanda National Police and Ministry of Infrastructure identify road traffic crashes as one of the leading causes of injury-related deaths and hospitalizations. 23 , 24 Recent studies show that motorcyclists and pedestrians are disproportionately affected, and thousands of Rwandans suffer severe trauma from road crashes every year. The resulting health consequences translate into a substantial DALY burden and overwhelm Rwanda’s already stretched healthcare system. 22 Despite national efforts to improve road safety in Rwanda, 23 – 25 critical gaps persist particularly in pre-hospital emergency care, road infrastructure, and strategies addressing the root causes of road RTIs. These shortcomings continue to undermine progress and contribute to avoidable injuries and deaths. There is limited evidence in Rwanda and similar settings regarding predictors of injury severity in the pre-hospital context, which is crucial for improving early trauma care and outcomes. Given this gap, this study aimed to assess the prevalence of severe injuries and determine their associated factors among RTI victims managed by pre-hospital emergency services in Rwanda. The findings are expected to support national policy reforms, improve emergency response systems, and inform targeted, evidence-based injury prevention strategies ultimately contributing to reduced mortality and improved trauma care from the point of first contact. Methods and materials Study design This study employed a retrospective cross-sectional design using secondary data from the registries of Emergency Medical Services (EMS), known as Service d’Aide Médicale d’Urgence (SAMU). It encompassed all patients attended by pre-hospital emergency ambulance services. Study setting and data source Rwanda, a landlocked country in East Africa, has a population of approximately 14 million, with a majority being young people. It is one of the most densely populated nations in Africa, covering an area of 26,338km 2 . 26 The study was conducted within the SAMU, the national pre-hospital emergency care division of the Ministry of Health in Rwanda. SAMU operates in Kigali, capital city of Rwanda and provides emergency transportation and pre-hospital care for trauma victims, including those injured in RTIs. Patient information is systematically documented in a computerized registry developed in partnership with Virginia Commonwealth University, which captures demographic and clinical data relevant to emergency medical care. 27 This institution has a diverse team of healthcare professionals, including nurses and anesthetists, all of whom receive training in emergency care and first aid to ensure the delivery of high-quality services. Depending on the severity of the case, their main services also include patient transportation, pre-hospital care, first aid, and client transfer. The drivers, who are also trained in first aid, play an important role by providing safe, timely, and high-quality support during patient transport. 28 Participants, sampling, and eligibility criteria We conducted this research using the SAMU pre-hospital registry to include all patients whose primary injury mechanism was RTI between January 1 and December 31, 2020. Eligible cases included individuals injured as motor vehicle occupants, motorcyclists, bicyclists, pedestrians, or users of animal-drawn carts on roads. From 2,062 identified cases, 900 were excluded due to missing demographic or clinical data, resulting in a final analytic sample of 1,162 patients (56.5%). Inclusion criteria required that the registry listed RTI as the main cause of injury and that patient records contained complete demographic and clinical information; cases with injuries unrelated to RTIs or with incomplete data were excluded. Furthermore, a census sampling approach was used including all eligible cases within the study period to provide more comprehensive coverage of the target population and minimize sampling bias. Study variables The study variables in this study were categorized as dependent and independent. The dependent variable was injury severity, classified using the Injury Severity Score (ISS). This ISS was used as an anatomical scoring system that provides overall scores for patients with multiple injuries. 29 The ISS is derived from the Abbreviated Injury Scale (AIS) by selecting the three most severely injured body regions, squaring the AIS scores for these regions, and summing them to produce a total score ranging from 0 to 75. 30 In this research, injury severity was dichotomized as either severe (ISS ≥ 9) or non-severe (ISS <9). 31 This cutoff was chosen based on established thresholds in trauma research, where an ISS of 9 or higher is commonly used to indicate clinically significant injuries requiring advanced medical attention. In the pre-hospital context of Rwanda, patients with ISS >9 represent clinically significant injuries requiring urgent care, and this threshold is commonly used in EMS and LMIC studies to ensure adequate subgroup sizes for analysis. 32 – 34 The independent variables included a range of socio-demographic factors (e.g., age, gender, and health insurance status), clinical or health-related factors (e.g., primary complaints, trauma mechanism, time of collision), behavioral factors such as alcohol use (alcohol consumption was assessed at first SAMU contact using standardized alcohol testing instruments where available, complemented by patient self-report or EMS personnel observation like smell of alcohol), as recorded in the SAMU registry), and environmental factors including distance from the accident site to the health facility. These variables were analyzed to explore their association with injury severity. The head injury or traumatic brain injury (TBI) measured using Glasgow Coma Scale (GCS) to indicate the level of consciousness. While this instrument shows severity of brain injury, we categorised the patients who experienced head injury as severe (≤8) TBI or non-severe injury (>8). In addition, the classification of variables such as trauma mechanism and time of collision was guided by the structure of EMS records and prior studies conducted in sub-Saharan Africa. 11 , 35 , 36 These elements are already standardized within the EMS reporting system, ensuring consistency in data extraction and analysis. While previous studies did not categorize distance to health facilities, we created three distance-based categories to reflect access challenges. Distances over 5 km were considered significant due to their potential to delay care, aligning with national goals to improve timely access to emergency services. 37 , 38 Last, chief complaints recorded by SAMU at first patient contact were collected. These complaints reflect the initial clinical presentation before anatomical scoring and allow analysis of pre-hospital indicators associated with higher injury severity, which could inform triage and resource allocation decision. Data collection and materials Data was collected from SAMU’s electronic data registry between July 13 and November 30, 2022. SAMU staff collected demographic and clinical data using standardized client forms, which were later entered into an electronic registry. Data collection was supervised by SAMU’s pre-hospital team leader and overseen by the study’s primary author. Enumerators received two days of training on the data collection process to ensure accuracy. Data from the electronic registry was exported to the Statistical Package for the Social Sciences (SPSS) software, version 28, for statistical analysis. Data analysis Following data cleaning and the removal of incomplete variables, the analysis was conducted in two stages: descriptive and analytical. In the descriptive analysis, statistical parameters such as mean, standard deviation, frequency, and percentage were used to summarize the data. Cross-tabulations were performed to explore the distribution of injury severity across the independent variables. For the analytical phase, bivariate logistic regression was performed to calculate crude odds ratios (cORs) and assess associations between injury severity and each independent variable. Variables found to be statistically significant in the bivariate analysis were included in multivariable logistic regression models using a forward selection method. This approach allowed for the calculation of adjusted odds ratios (aOR) with 95% confidence intervals (CI) for each significant variable, identifying factors associated with severe injury. To address potential multicollinearity, the variance of inflation factor (VIF) was assessed for all independent variables. A VIF threshold of 5 was used; variables with VIF values exceeding this cutoff were considered to exhibit high multicollinearity and were subsequently reviewed or excluded from the model to ensure robustness. 39 , 40 Ethics This research did not involve direct interaction with human participants or the collection of new data from individuals. Instead, it employed secondary data obtained from existing medical records and laboratory logbooks. Ethical approval for the study was obtained from the Institutional Review Board of the College of Medicine and Health Sciences, University of Rwanda (No: 233/CMHS IRB/2021). As the study relied solely on secondary data, informed consent from participants was not required. However, access to the dataset was granted after obtaining permission from the Emergency Medical Services (EMS) and data custodians who oversee records of patients. All data were anonymized prior to analysis to ensure confidentiality and privacy. All methods were conducted in accordance with the ethical standards outlined in the Declaration of Helsinki. 41 Results Descriptive analysis The participants of this study had a mean age of 31.64 years (SD = 11.1), with the largest age group (38%) falling between 20 and 29 years, highlighting a high burden of RTIs among young adults. A substantial majority (78.4%) were male, and over half (55.9%) were uninsured at the time of injury. Most collisions occurred in the afternoon (2:00 pm–7:59 pm; 28.9%), followed closely by morning to early afternoon incidents (8:00 am–1:59 pm; 27.1%), indicating peak risk during active daytime hours. Alcohol use was reported in 24.5% of cases, while 54.9% of participants had no history of alcohol users, pointing to behavioral risk factors among a considerable segment of the population. Regarding clinical presentation, extremity injuries were the most common (46%), followed by TBI at 22.2%, indicating high prevalence of both types. On average, individuals traveled 15.8 kilometers (SD=12.45) to access health services, with 68.8% residing within a 0–20 km radius, suggesting moderate geographic access to care ( Table 1 ). Table 1. Description of the participants and all variables N=1162. Variables Frequency %age Gender Males 911 78.4 Females 251 21.6 Age of participants <20 years 107 9.2 20-29 years 442 38.0 30-39 years 390 33.6 40-49 years 153 13.2 50 years and above 70 6.0 Health insurance No 649 55.9 Yes 513 44.1 Time of collision 8:00 am to 1:59 pm 315 27.1 2:00 pm to 7:59 pm 336 28.9 8:00 pm to 1:59 am 292 25.1 2:00 am to 7:59 am 219 18.8 Chief complaints TBI/head injury 258 22.2 Extremity injury 535 46 Chest pain 48 4.1 Abdominal pain/vomiting or nausea 15 1.3 Altered mental status 20 1.7 Other 286 24.6 Trauma mechanism Car only 47 4 Moto to moto 150 12.9 Bike only 23 2.0 Car to pedestrian 124 10.7 Car to moto 358 30.8 Moto only 63 5.4 Bike to pedestrian 215 18.5 Car to car 32 2.8 Bike to moto 80 6.9 Bike to car 70 6.0 Substance use No 638 54.9 Yes 285 24.5 Unknown 239 20.6 Distance 0-20 km 799 68.8 21-40 km 334 28.7 More than 40 km 29 2.5 Prevalence of severe injury by demographic and clinical information A total of 1162 victims were considered that the prevalence of severe injury was 14% (n=165), with males accounting for 82.4% of these cases ( Figure 1 ). Figure 1. Prevalence of severe trauma. The highest prevalence was observed among individuals aged 30–39 years (35.8%) and 20–29 years (35.2%). Individuals without health insurance had a significantly higher prevalence of severe injury (61.2%). RTIs occurring between 8:00am and 1:59pm had the highest prevalence of severe injury (38.2%). Extremity injuries were the most common severe complaint (40.6%), followed by traumatic brain injuries (35.8%). Those who were not alcohol users had a slightly higher prevalence of severe injury compared to those who used (35.2% vs 31.5%). Among the mechanisms of injury, car-to-motorcycle collisions had the highest prevalence of severe injury (31.5%) ( Table 2 ). Table 2. Prevalence of severe injury by demographics and clinical characteristics (N=1162). Variables Severe injury Not severe injury N % N % Total A. Socio-demographic characteristics Gender Males 136 82.4 775 77.7 911 Females 29 17.6 222 22.3 251 Age of participants <20 years 18 10.9 89 8.9 107 20-29 years 58 35.2 384 38.5 442 30-39 years 59 35.8 331 33.2 390 40-49 years 18 10.9 135 13.5 153 50 years and above 12 7.3 58 5.8 70 Health insurance No 101 61.2 548 55.0 649 Yes 64 38.8 449 45.0 513 Time of collision 8 am to 1:59 pm 63 38.2 268 26.9 331 2 pm to 7:59 pm 41 24.8 296 29.7 337 8 pm to 1:59 am 40 24.2 237 23.8 277 2 am to 7:59 am 21 12.7 196 19.7 217 Primary complaints TBI/head injury 59 35.8 199 20.0 258 Extremity injury 67 40.6 468 46.9 535 Chest pain 3 1.8 45 4.5 48 Abdominal pain/vomiting or nausea 1 0.6 14 1.4 15 Altered mental status 12 7.3 8 0.8 20 Other 23 13.9 263 26.4 286 Trauma mechanism Car only 5 3.0 42 4.2 47 Moto to motor 15 9.1 135 13.5 150 Bike only 1 0.6 22 2.2 23 Car to pedestrian 30 18.2 94 9.4 124 Car to motor 52 31.5 306 30.7 358 Moto only 10 6.1 53 5.3 63 Bike to pedestrian 20 12.1 195 19.6 215 Car to car 5 3.0 27 2.7 32 Bike to motor 9 5.5 71 7.1 80 Bike to car 18 10.9 52 5.2 70 Alcohol users No 58 35.2 580 58.2 638 Yes 52 31.5 233 23.4 285 Unknown 55 33.3 184 18.5 239 Distance 0-20 km 108 65.5 691 69.3 799 21-40 km 45 27.3 282 28.3 327 More than 40 km 12 7.3 24 2.4 36 Bivariate and multivariate logistic regression analyses for the associated factors of severe injury The findings from bivariate logistic regression analysis demonstrated that gender time of collision, primary complaints, trauma mechanism, alcohol use, and distance to the treatment health facility were significant factors of severe injury among the pre-hospital emergency patients. All significant variables in bivariate logistic regressions were transported into multivariate logistic regression models. After adjusting for other variables, females had lower odds to experience severe injury compared to males (aOR=0.47, 95%CI: 0.26–0.79, p=0.032). In terms of trauma mechanism, we found that car-to-pedestrian collisions had more than twice the odds of severe injury (aOR=2.3, 95% CI: 1.25–4.1, p=0.007) compared to car-only accidents. Furthermore, car-to-motorcycle collisions posed an even greater risk, with nearly four times the odds of experiencing severe injury (aOR=3.88, 95% CI: 1.16–13.05, p=0.007) relative to car-only crashes. Alcohol use was also a strong predictor of severe injury, with alcohol users being more than three times as likely to experience severe injuries compared to non-users (aOR=3.37, 95% CI: 2.04–5.56, p<0.001). Regarding the timing of collisions, individuals involved in RTIs between 2:00 pm and 7:59 pm (aOR=0.52, 95% CI: 0.25–0.72, p=0.02) and 8:00 pm to 1:59 am (aOR=0.25, 95%CI: 0.04–0.81, p =0.012) were significantly less likely to experience severe injury compared to those involved in RTIs between 8:00 am and 1:59 pm. Additionally, the distance to healthcare facilities played a critical role in injury severity, with individuals traveling between 21 and 40 km after an RTI having nearly three times the odds of severe injury (aOR=2.91, 95%CI: 1.27–6.63, p=0.011), and those traveling over 40 km facing a similarly elevated risk (aOR=2.64, 95% CI: 1.11–6.25, p=0.028) compared to those within 0–20 km of a healthcare facility. These findings highlight the strong associations between severe injury and collision type, alcohol use, time of occurrence, and access to healthcare, underscoring the urgent need for targeted trauma prevention strategies to address these risk factors. In addition, the individuals who sustained extremity injuries (aOR=0.28, 95% CI: 0.15–0.52, p<.001), chest injuries (aOR=0.40, 95% CI: 0.23–0.71, p=.002), or other types of injuries (aOR=0.16, 95% CI: 0.05–0.57, p=.004) were significantly less likely to suffer from severe injury compared to those with TBI or head trauma ( Table 3 ). Table 3. Bivariate and multivariate logistic regression analyses of factors associated with severe injury among pre-hospital emergency patients (n=1162). Variables Bivariate analysis; 95% CI p-value Multivariate analysis, 95% CI p-value N (%) COR Lower bound Upper bound aOR Lower bound Upper bound A. Socio-demographic characteristics Gender Males 911 (78.4) 1 1 Females 251 (21.6) 0.61 0.21 0.94 0.002 ** 0.47 0.26 0.79 0.036 * Age of participants <20 years 107 (9.2 1 0.639 20-29 years 442 (38) 1.02 0.46 2.28 0.956 30-39 years 390 (33.6) 1.37 0.69 2.7 0.365 40-49 years 153 (13.2) 1.16 0.59 2.29 0.668 50 years and above 70 (6) 1.55 0.7 3.43 0.277 B. Clinical or health related characteristics Health insurance No 649 (55.9) 1 Yes 513 (44.1) 0.77 0.55 1.08 0.135 Time of collision 8:00 am to 1:59 pm 315 (27.1) 1 0.012 * 1 2:00 pm to 7:59 pm 336 (28.9) 0.46 0.27 0.77 0.003 ** 0.52 0.25 0.72 0.02 * 8:00 pm to 1:59 am 292(25.1) 0.77 0.44 1.35 0.365 0.25 0.04 0.81 0.012 * 2:00 am to 7:59 am 219 (18.8) 0.64 0.36 1.11 0.112 0.57 0.12 2.18 0.331 Chief complaints TBI/head injury 258 (22.2) 1 <0.001 *** 1 <.001 *** Extremity injury 535 (46) 0.3 0.18 0.49 <0.001 *** 0.28 0.15 0.52 <.001 *** Chest pain 48 (4.1) 0.61 0.37 1 0.052 0.4 0.23 0.71 .002 ** Abdominal pain/vomiting/nausea 15 (1.3) 1.31 0.38 4.55 0.669 0.85 0.23 3.1 0.803 Altered mental status 20 (1.7) 1.22 0.15 9.73 0.848 1.3 0.13 12.7 0.824 Other 286 (24.6) 0.06 0.02 0.16 <0.001 *** 0.16 0.05 0.57 .004 ** Trauma mechanism Car only 47 (4) 1 0.002 ** 1 .011 * Moto to motor 150 (12.9) 2.91 1 8.49 0.051 1.84 0.52 6.52 0.346 Bike only 23 (2) 3.12 1.46 6.64 0.003 ** 1.78 0.71 4.47 0.223 Car to pedestrian 124 (10.7) 7.62 0.96 60.62 0.055 2.3 1.25 4.1 .007 ** Car to motor 358 (30.8) 1.08 0.55 2.13 0.814 3.88 1.16 13.05 .024 * Moto only 63 (5.4) 2.04 1.11 3.75 0.023 * 1.06 0.49 2.29 0.73 Bike to pedestrian 215 (18.5) 1.83 0.77 4.35 0.168 1.15 0.4 3.32 0.839 Car to car 32 (2.8) 3.38 1.67 6.84 <0.001 *** 2.07 0.87 4.93 0.102 Bike to motor 80 (6.9) 1.87 0.63 5.58 0.263 1 0.29 3.48 0.998 Bike to car 70 (6) 2.73 1.14 6.56 0.025 * 1.3 0.47 3.68 0.611 Alcohol users No 638 (54.9) 1 <0.001 *** 1 <.001 *** Yes 285 (24.5) 2.99 2 4.48 <0.001 *** 3.37 2.04 5.56 <.001 *** Unknown 239 (20.6) 1.34 0.88 2.05 0.178 1.55 0.91 2.64 0.111 Distance to the treating health facility 0-20 km 799 (68.8) 1 0.006 ** 1 .04 * 21-40 km 334 (28.7) 3.2 1.55 6.59 0.002 ** 2.91 1.27 6.63 .011 * More than 40 km 29 (2.5) 3.13 1.46 6.71 0.003 ** 2.64 1.11 6.25 .028 * * Emboldened values represent statistically significant at p<0.05. ** Emboldened values represent statistically significant at p<0.01. *** Indicated the variables statistical significance at p<0.001. Discussion This study aimed to assess the prevalence of severe injury and its associated factors among RTI victims who received pre-hospital care. Our findings revealed that 14% of cases involved severe injuries, which is comparable to studies conducted in Australia (10%), Ethiopia (13.8%), and Kenya (19%). 1 , 42 – 44 However, other studies, particularly in Tanzania and Ethiopia, reported a significantly higher prevalence, exceeding 36.4%. 20 These variations can be explained by differences in study methodologies. Our research relied on secondary data analysis and trauma registries from pre-hospital emergency care services, which primarily respond to RTI emergencies. The role of SAMU includes rescuing, providing immediate care, and transporting severely injured individuals to health facilities, mainly in Kigali, based on injury severity. We found that younger individuals were not significantly more likely to suffer from severe injury, which contrasts with previous research 43 , 45 that identified youth as a high-risk group due to factors such as risk-taking, inexperience, and unsafe driving behaviors. Despite not being a predictor of severe injury in our analysis, young adults (ages 20–39) still accounted for around 70% of all RTI cases, highlighting their greater involvement in traffic incidents overall. This underscores their vulnerability and the need for targeted preventive efforts. Additionally, consistent with earlier studies, 43 , 44 , 46 our findings confirmed that females had a lower likelihood of sustaining severe injuries compared to males, likely due to men’s increased exposure to high-risk driving situations and their higher participation in professional driving. Our multivariable logistic regression analysis showed that collisions involving cars and pedestrians or cars and motorcyclists were associated with significantly higher odds of severe injury compared to car-only crashes. This aligns with existing literature indicating that vulnerable road users, such as pedestrians and motorcyclists, face increased odds of severe injury, which may be partly explained by their limited physical protection and greater exposure to traffic hazards. 16 , 47 , 48 These increased odds could be attributed to the insufficient or lack of physical protection for pedestrians, cyclists, and motorcyclists, which makes them more susceptible to severe injuries in road traffic incidents. Additionally, inadequate knowledge of road traffic rules among some vehicle users, coupled with frequent traffic violations and discourteous behavior, may further increase the odds of accidents. Consistent with prior studies, 11 we also found that alcohol use was associated with nearly threefold higher odds of sustaining severe injuries to themselves or others compared to sober drivers. These results reinforce evidence that alcohol use substantially increases the odds of severe injury. 49 – 51 Our study identified that road traffic injuries occurring between 2:00 pm and 1:59 am (afternoon to early night) were less likely to result in severe injuries compared to those between 8:00 am and 1:59 pm (morning to early afternoon). This finding contrasts with several studies from sub-Saharan Africa, which report higher injury severity during evening and night hours. For instance, a study in Thika, Kenya, found that night-time crashes were associated with increased injury severity. 44 Similarly, research in Adama, Ethiopia, indicated that night-time injuries significantly elevated the risk of death. These findings align with a study from the Oromia region of Ethiopia, which reported fewer fatalities during evening and night hours compared to the afternoon. 15 , 52 These discrepancies between our results and the previous studies may stem from differences in traffic volume, road user behavior, and environmental conditions across regions. In our context, higher traffic density during morning hours could contribute to increased injury severity. In contrast, in settings where night-time driving is more hazardous due to factors like poor visibility or inadequate lighting, evening and night crashes may result in more severe outcomes. Therefore, contextual factors such as local traffic patterns, infrastructure, and enforcement of road safety measures likely influence the relationship between time of day and injury severity. 52 , 53 The study demonstrated that individuals presenting with extremity injuries had a lower likelihood of experiencing severe injury compared to those with TBI or head trauma. This distinction is clinically significant, as injuries to the brain, neck, and spinal cord often involve vital structures and can lead to rapid deterioration, long-term disability, or death—even when external signs may appear less dramatic than in cases of open fractures or limb trauma. These findings are consistent with a previous study that reported head, neck and spinal cord injury as the major factors of severe injury. 1 Additionally, a greater distance to a hospital increased the likelihood of sustaining severe injuries, with individuals traveling 21-40 km and those traveling more than 40 km being determinants of severe injuries compared to those traveling less than 21 km for definitive healthcare services. This likely reflects both delays in reaching care and potential deterioration during transit especially in contexts with limited prehospital stabilization and transport infrastructure. These results collaborate with the previous studies that reported the long distance is a risk factor of severe injury. 45 , 54 Study strengths and limitations This study has some notable strengths. It utilized a large registry dataset covering pre-hospital care in Kigali, which enhances statistical power and supports broader generalization of the findings. Additionally, this study addresses a less-researched topic in Rwanda, shedding light on a significant public health issue that necessitates the implementation of targeted strategies. The insights gained from this study can inform policy decisions and interventions aimed at reducing the burden of road traffic injuries and improving road safety measures. Despite its strengths, this study has several limitations that warrant discussion. First, the use of secondary data from registries restricted our ability to include certain key predictors, such as pre-hospital care details (e.g. care provided at the scene, mode of transport to the hospital, use of spinal immobilization or trauma protocols), socio-demographic factors (e.g., marital status, education, employment status, and type of residence), and hospital-related variables (such as length of hospital stay, surgical interventions). Additionally, safety-related variables, such as adherence to traffic regulations, data on safety device usage, seatbelt and helmet utilization, and the duration of possessing a driver’s license before the accident, were not documented, limiting our understanding of factors influencing injury severity. Second, environmental factors, including road type and accident location, were not captured, which may have influenced injury outcomes. Third, the classification of distance to the health facility was not based on established evidence, which might have introduced bias through possible over- or underestimation of its association with injury severity. Fourth, the retrospective cross-sectional design prevents the establishment of causal relationships between the identified predictors and injury severity. Finally, a substantial proportion of cases were excluded because of missing data in the SAMU registry, which might have introduced selection bias, particularly if patients with more severe injuries were more likely to have incomplete records in the pre-hospital emergency context. Public health implications The findings of this study highlight an urgent need for public health interventions to address road RTIs and their associated consequences. The high prevalence of severe injury among alcohol users reinforces the importance of targeted awareness campaigns focused on reducing alcohol and alcohol use, particularly among young male drivers, who represented a significant proportion of severe cases. Behavioral change programs tailored for this demographic could play a critical role in reducing high-risk behaviors on the road. Moreover, the elevated risk of severe injury in pedestrian and motorcycle-related collisions underscores the vulnerability of these road users. Policy interventions such as creating pedestrian-friendly infrastructure, implementing protected lanes for cyclists and motorcyclists, and enforcing speed reduction zones in high-traffic areas are essential to safeguard these populations. The study also found that injury severity was influenced by the time of day, suggesting a need for improved traffic regulation and law enforcement during high-risk periods, particularly in the early morning and evening hours. Finally, access to timely medical care remains a critical issue, especially for those living in rural or remote areas. Strengthening pre-hospital emergency systems and decentralizing trauma care services to underserved regions will be key to improving trauma outcomes and reducing preventable deaths. These findings highlight the importance of prioritizing high-risk patients within emergency medical services and developing targeted prevention and trauma care strategies to improve patient outcomes and reduce the overall burden of severe injuries. These implications call for an integrated public health approach that combines education, policy enforcement, and infrastructure development. By prioritizing these measures, policymakers and stakeholders can make significant strides in reducing the burden of RTIs and promoting safer road environments. Future directions To expand upon the findings of this study, future research should prioritize longitudinal and prospective designs to establish causal links between various risk factors and severe injury outcomes. A more comprehensive investigation of socio-demographic and environmental factors will offer deeper insights into their influence on road traffic injuries. Additionally, exploring healthcare-related aspects including hospital-specific factors could enhance understanding of their effects on injury severity, recovery, and disability. Given the limited understanding of how prehospital emergency care influences trauma severity, the integration of innovative methodologies like participatory action research (PAR) could prove particularly valuable in preventing RTIs and their outcomes. 55 By actively engaging community members, stakeholders, and policymakers throughout the research process, PAR fosters a collaborative exploration of local challenges and facilitates the co-creation of interventions aiming sustainable changes especially reducing RTIs. Moreover, employing mixed methods in future research can provide a holistic understanding of the complexities surrounding road traffic injuries. While quantitative data can reveal statistical trends in injury prevalence and associated factors, qualitative methods can uncover the lived experiences, perceptions, and behaviors of road users. This integrative approach allows for a nuanced analysis of underlying issues and barriers to road safety, informing more effective public health interventions. Lastly, collaborative efforts among public health officials, policymakers, and community stakeholders are essential for devising and implementing strategies to reduce road traffic injuries and promote safer road practices. By incorporating PAR and mixed methods, future research can not only guide policy development but also empower road users to actively participate in creating safer environments. Conclusion This study identified several factors associated with severe injury, including traveling distance to emergency services, gender, alcohol use, time of day, injury type, and mechanisms of trauma. These findings highlight the importance of targeted and context-specific interventions to reduce the risk of severe injury. Improving access to prehospital emergency care, enforcing regulations on alcohol use, and promoting safer road behaviors are practical measures that could enhance injury prevention efforts. Additionally, strengthening trauma care systems and data collection practices particularly in resource-limited settings can support more timely and effective emergency responses. Clinically, the identified predictors of severe injury can support improved triage and risk stratification in emergency departments. Incorporating these factors into clinical assessment tools may help providers rapidly identify and prioritize high-risk patients, especially in resource-constrained settings where timely intervention is critical. Future research, particularly using longitudinal designs, is recommended to further investigate causal relationships and inform evidence-based policy and clinical practice. Data availability The data analyzed and reported in this study are fully included in the manuscript. Additional datasets used or generated during the study can be obtained from the corresponding author upon reasonable request. The dataset can be accessed via https://doi.org/10.5281/zenodo.17489912 . 56 References 1. Robera OF, Fite RO, Mesele M, et al. : Severity of Injury and Associated Factors among Injured Patients Who Visited the Emergency Department at Wolaita Sodo Teaching and Referral Hospital, Ethiopia. Ethiop. J. Health Sci. 2020; 30 (2): 189–198. PubMed Abstract | Publisher Full Text | Free Full Text 2. Masoumi K, Forouzan A, Barzegari H, et al. : Effective Factors in Severity of Traffic Accident-Related Traumas; an Epidemiologic Study Based on the Haddon Matrix. Emerg. 2016; 4 (2): 78–82. 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Publisher Full Text Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 19 Nov 2025 ADD YOUR COMMENT Comment Author details Author details 1 Department of Epidemiology and Biostatistics, College of Medicine and Health Sciences Huye, University of Rwanda, Butare, Southern Province, Rwanda 2 Emergency Medical Service (EMS) Division, Rwanda Biomedical Centre, Kigali, Rwanda 3 School of Rehabilitation, Universite de Montreal, Montreal, Québec, Canada 4 Centre for Interdisciplinary Research in Rehabilitation of Greater Montréal, Montréal, Canada Eric Uwitonze Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Emmanuel Biracyaza Roles: Conceptualization, Formal Analysis, Methodology, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (2) version 2 Revised Published: 05 Feb 2026, 14:1282 https://doi.org/10.12688/f1000research.172231.2 version 1 Published: 19 Nov 2025, 14:1282 https://doi.org/10.12688/f1000research.172231.1 Copyright © 2026 Uwitonze E and Biracyaza E. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Uwitonze E and Biracyaza E. Factors associated with road traffic injury severity among victims retrieved by pre-hospital emergency services in Rwanda [version 2; peer review: 2 approved] . F1000Research 2026, 14 :1282 ( https://doi.org/10.12688/f1000research.172231.2 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 2 VERSION 2 PUBLISHED 05 Feb 2026 Revised Views 0 Cite How to cite this report: Tahir MN. Reviewer Report For: Factors associated with road traffic injury severity among victims retrieved by pre-hospital emergency services in Rwanda [version 2; peer review: 2 approved] . F1000Research 2026, 14 :1282 ( https://doi.org/10.5256/f1000research.195770.r481760 ) The direct URL for this report is: https://f1000research.com/articles/14-1282/v2#referee-response-481760 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 09 May 2026 Muhammed Navid Tahir , University of the Punjab, Lahore, Pakistan Approved VIEWS 0 https://doi.org/10.5256/f1000research.195770.r481760 Though study does not have novel idea/subject yet it has some strenghts such as it is originated from destination where there is limited research especially in road safety and injury prevention area. Moreover authors have strengthened methodology part, and provided ... Continue reading READ ALL Though study does not have novel idea/subject yet it has some strenghts such as it is originated from destination where there is limited research especially in road safety and injury prevention area. Moreover authors have strengthened methodology part, and provided a more explicit justification of key methodological choices, including the injury severity threshold (ISS >9), the selection of predictive variables, and the categorization of distance to care. Therefore this version of manuscript offers improved methodological and interpretative rigor. Study also provides public health implications Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: road safety, injury prevention, public health, prevention of non-communicable diseases, pre-hospital care, I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Tahir MN. Reviewer Report For: Factors associated with road traffic injury severity among victims retrieved by pre-hospital emergency services in Rwanda [version 2; peer review: 2 approved] . F1000Research 2026, 14 :1282 ( https://doi.org/10.5256/f1000research.195770.r481760 ) The direct URL for this report is: https://f1000research.com/articles/14-1282/v2#referee-response-481760 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Bonnet E. Reviewer Report For: Factors associated with road traffic injury severity among victims retrieved by pre-hospital emergency services in Rwanda [version 2; peer review: 2 approved] . F1000Research 2026, 14 :1282 ( https://doi.org/10.5256/f1000research.195770.r455827 ) The direct URL for this report is: https://f1000research.com/articles/14-1282/v2#referee-response-455827 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 20 Feb 2026 Emmanuel Bonnet , Institut de Recherche pour le Développement, CNRS Université Paris 1 Panthéon- Sorbonne, Île-de-France, France Approved VIEWS 0 https://doi.org/10.5256/f1000research.195770.r455827 I thank the authors for their careful and detailed responses to the comments previously raised. The revised version has improved in clarity and methodological transparency. The authors have provided satisfactory elements regarding the high exclusion rate by ... Continue reading READ ALL I thank the authors for their careful and detailed responses to the comments previously raised. The revised version has improved in clarity and methodological transparency. The authors have provided satisfactory elements regarding the high exclusion rate by acknowledging its potential implications and contextualizing pre-hospital data collection in Rwanda. Although residual bias cannot be entirely ruled out, this limitation is now explicitly discussed. The justification for the ISS > 9 threshold is better argued and more clearly situated within the context of pre-hospital care and low-resource settings, which strengthens the coherence of the severity classification. The clarifications provided regarding the use of chief complaints as predictive variables are acceptable, particularly from a pre-hospital triage perspective. The methodological choices are now sufficiently explained. The categorization of distance variables and the assessment of alcohol consumption have also been clarified; this remains not entirely satisfactory, but I believe it is unlikely that further improvement is possible at this stage. Overall, the authors have responded constructively to the comments, and the manuscript has improved accordingly. At this stage, I consider that the concerns raised have been appropriately addressed and that it is probably not possible to go further given the available data. Competing Interests: No competing interests were disclosed. Reviewer Expertise: Health geographer I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Bonnet E. Reviewer Report For: Factors associated with road traffic injury severity among victims retrieved by pre-hospital emergency services in Rwanda [version 2; peer review: 2 approved] . F1000Research 2026, 14 :1282 ( https://doi.org/10.5256/f1000research.195770.r455827 ) The direct URL for this report is: https://f1000research.com/articles/14-1282/v2#referee-response-455827 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Version 1 VERSION 1 PUBLISHED 19 Nov 2025 Views 0 Cite How to cite this report: Bonnet E. Reviewer Report For: Factors associated with road traffic injury severity among victims retrieved by pre-hospital emergency services in Rwanda [version 2; peer review: 2 approved] . F1000Research 2026, 14 :1282 ( https://doi.org/10.5256/f1000research.189946.r434978 ) The direct URL for this report is: https://f1000research.com/articles/14-1282/v1#referee-response-434978 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 04 Dec 2025 Emmanuel Bonnet , Institut de Recherche pour le Développement, CNRS Université Paris 1 Panthéon- Sorbonne, Île-de-France, France Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.189946.r434978 This article examines the factors associated with the severity of road traffic injuries (RTIs) among victims managed by the SAMU (Emergency Medical Service) in Rwanda in 2020. The study is relevant as it addresses a major public ... Continue reading READ ALL This article examines the factors associated with the severity of road traffic injuries (RTIs) among victims managed by the SAMU (Emergency Medical Service) in Rwanda in 2020. The study is relevant as it addresses a major public health issue in a Low- and Middle-Income Country (LMIC), focusing on the pre-hospital phase, which is often under-researched. However, there are elements that need clarification and improvement, notably a potential selection bias (high exclusion rate) and debatable choices regarding predictive variables. These points must be addressed before full approval. Regarding the methodological aspects: The use of the SAMU electronic registry is a sound approach to obtaining real-world clinical data and is particularly valuable given its rarity on the continent. The use of the Injury Severity Score (ISS) to classify severity is also suitable for the African context. However, I have a few questions/concerns: The authors indicate that out of 2,062 identified cases, 900 were excluded due to missing data, leaving only 1,162 patients for analysis. This represents an exclusion rate of nearly 44%. It is necessary to specify whether this missing data is random or not. Could patients who died before arrival or those with very severe injuries be overrepresented in the missing data? The urgency of care could explain the incompleteness of the information. The authors must compare, if possible, the baseline characteristics of the excluded vs. included patients, or at least discuss this bias more deeply in the limitations section. Otherwise, there is a significant selection bias. 2. The study dichotomizes severity at an ISS > 9. Although justified by the authors, an ISS of 9 is often considered "moderate." A threshold of 15 (major trauma) is more common in international literature to define a "severe injury." The authors should further justify this choice by citing references such as: Copes WS, Champion HR, Sacco WJ, Lawnick MM, Keast SL, Bain LW. "The Injury Severity Score revisited." The Journal of Trauma. 1988;28(1):69-77. 3. In the logistic regression (Table 3), the authors include "Chief complaints" (e.g., TBI/head injury, Extremity injury) as independent variables to predict "Severity." I find this surprising because, inherently, a TBI predicts a high score. The ISS is calculated based on anatomical injuries. The authors need to provide further justification for their choice of independent variables. 4. The correlation between a distance >20km and severity is a very interesting result. However, the categorization (0-20, 21-40, >40) seems arbitrary, as mentioned in the limitations; would it be possible to modify this to allow for a finer analysis? 5.Regarding alcohol, it should be specified how consumption was assessed. 6. In the limitations section, the issue regarding missing data mentioned above must be detailed. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Health geographer I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Bonnet E. Reviewer Report For: Factors associated with road traffic injury severity among victims retrieved by pre-hospital emergency services in Rwanda [version 2; peer review: 2 approved] . F1000Research 2026, 14 :1282 ( https://doi.org/10.5256/f1000research.189946.r434978 ) The direct URL for this report is: https://f1000research.com/articles/14-1282/v1#referee-response-434978 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 05 Feb 2026 Eric Uwitonze , Department of Epidemiology and Biostatistics, College of Medicine and Health Sciences Huye, University of Rwanda, Butare, Rwanda 05 Feb 2026 Author Response Report for reviewers This article examines the factors associated with the severity of road traffic injuries (RTIs) among victims managed by the SAMU (Emergency Medical Service) in Rwanda in 2020. ... Continue reading Report for reviewers This article examines the factors associated with the severity of road traffic injuries (RTIs) among victims managed by the SAMU (Emergency Medical Service) in Rwanda in 2020. The study is relevant as it addresses a major public health issue in a Low- and Middle-Income Country (LMIC), focusing on the pre-hospital phase, which is often under-researched. However, there are elements that need clarification and improvement, notably a potential selection bias (high exclusion rate) and debatable choices regarding predictive variables. These points must be addressed before full approval. Answer: We thank the reviewer for the careful assessment of our manuscript and for raising important methodological concerns. Regarding the high exclusion rate and potential selection bias, we acknowledge this as a key limitation of our study. The SAMU data management system in Rwanda in 2020 was still evolving and was not sufficiently robust to ensure complete and systematic recording of all clinically relevant variables. Consequently, a considerable number of records contained missing or incomplete information on essential variables, which led to their exclusion from the analysis. This situation is understandable in a pre-hospital emergency context in a low-resource setting, where the priority is rapid life-saving care rather than detailed data documentation. This challenge is not unique to our study. Previous research has similarly reported limitations in the use of SAMU datasets due to incomplete and inconsistent reporting, which has restricted broader use of these data for research purposes. Our study therefore represents one of the few attempts to analyze RTI severity using SAMU data despite these constraints. To minimize bias, exclusions were based strictly on objective criteria related to data completeness, rather than on injury severity or patient characteristics. While we cannot entirely rule out selection bias, there is no indication that excluded cases differed systematically from included cases in a way that would invalidate the observed associations. We have explicitly acknowledged this limitation in the manuscript and advised caution in interpreting and generalizing the findings. Regarding the selection of predictive variables, our choices were guided by (1) the consistent availability of variables in the SAMU database, (2) their clinical and operational relevance in the pre-hospital setting, and (3) evidence from the existing literature on road traffic injuries in LMICs. Although additional variables could potentially improve prediction of injury severity, they were not reliably recorded, and their inclusion would have introduced further bias. Finally, by explicitly recognizing these data limitations, we have translated them into a practical recommendation in the manuscript. We emphasize the need for improved and standardized reporting of all cases managed by SAMU, including more complete documentation of clinical and contextual variables. Strengthening routine EMS data collection will not only enhance patient care and system monitoring but also enable higher-quality research and more robust evidence to inform injury prevention and emergency care policies in Rwanda. Regarding the methodological aspects: The use of the SAMU electronic registry is a sound approach to obtaining real-world clinical data and is particularly valuable given its rarity on the continent. The use of the Injury Severity Score (ISS) to classify severity is also suitable for the African context. However, I have a few questions/concerns: 1. The authors indicate that out of 2,062 identified cases, 900 were excluded due to missing data, leaving only 1,162 patients for analysis. This represents an exclusion rate of nearly 44%. It is necessary to specify whether this missing data is random or not. Could patients who died before arrival or those with very severe injuries be overrepresented in the missing data? The urgency of care could explain the incompleteness of the information. The authors must compare, if possible, the baseline characteristics of the excluded vs. included patients, or at least discuss this bias more deeply in the limitations section. Otherwise, there is a significant selection bias. Answer: We acknowledge the high exclusion rate as an important limitation. Missing data largely reflect the operational realities of pre-hospital emergency care in Rwanda, where documentation may be incomplete in high-urgency cases. The SAMU registry does not systematically capture pre-hospital outcomes or allow formal assessment of whether missingness was random, limiting comparisons between excluded and included patients. Exclusions were based solely on objective data completeness criteria. We now discuss the potential for non-random missingness and selection bias more explicitly in the Limitations section and recommend improved standardized reporting within SAMU. 2. The study dichotomizes severity at an ISS > 9. Although justified by the authors, an ISS of 9 is often considered "moderate." A threshold of 15 (major trauma) is more common in international literature to define a "severe injury." The authors should further justify this choice by citing references such as: Copes WS, Champion HR, Sacco WJ, Lawnick MM, Keast SL, Bain LW. "The Injury Severity Score revisited." The Journal of Trauma. 1988;28(1):69-77. Answer: We thank the reviewer for this important methodological point. We acknowledge that ISS 15 is commonly used in international literature to define major trauma, particularly in high-income hospital-based trauma registries (e.g., Copes et al., 1988). However, in the pre-hospital context and LMIC settings, lower ISS thresholds (ISS >9) have frequently been used to distinguish clinically meaningful moderate-to-severe injuries requiring urgent care, referral, or advanced management. In Rwanda’s SAMU setting, patients with ISS >9 already represent a group with substantially higher clinical acuity and resource needs. Moreover, using ISS ≥15 would have resulted in a small subgroup, limiting statistical power and interpretability. We have clarified and strengthened this justification in the Methods and Discussion sections, while acknowledging this choice as a contextual consideration. To do this classification, we referred to the local evidence/national standards that showed those with more than 9 ISS have severe injuries. We found there are many studies that contrast the classifications of Copes et al. (1988). See the following studies used as references: Traisathit, P., Chittawatanarat, K., Chandacham, K. et al. Trauma referral audit impact assessment on the outcomes of injured patients via an interrupted time-series analysis: an 11-year before-and-after study of trauma cases at the Maharaj Nakorn Chiang Mai hospital, Thailand. BMC Emerg Med 25, 64 (2025). https://doi.org/10.1186/s12873-025-01220-0 ) Abajas-Bustillo R, Amo-Setién FJ, Leal-Costa C, Ortego-Mate MDC, Seguí-Gómez M, Durá-Ros MJ, Zonfrillo MR. Comparison of injury severity scores (ISS) obtained by manual coding versus "Two-step conversion" from ICD-9-CM. PLoS One. 2019 May 1;14(5):e0216206. https://doi.org/10.1371/journal.pone.0216206. Reith, G., Lefering, R., Wafaisade, A. et al. Injury pattern, outcome and characteristics of severely injured pedestrian. Scand J Trauma Resusc Emerg Med 23, 56 (2015). https://doi.org/10.1186/s13049-015-0137-8 (These support the use of ISS >9 to distinguish clinically important injury severity levels when conventional major trauma cutoffs (e.g., ISS ≥15 or ≥16) may be less practical due to sample size or context, especially in pre-hospital and LMIC RTI research). 3.In the logistic regression (Table 3), the authors include "Chief complaints" (e.g., TBI/head injury, Extremity injury) as independent variables to predict "Severity." I find this surprising because, inherently, a TBI predicts a high score. The ISS is calculated based on anatomical injuries. The authors need to provide further justification for their choice of independent variables. Answer : We thank the reviewer for this insightful comment. We acknowledge that ISS is anatomically based and that certain injuries, such as TBI, inherently contribute to higher ISS scores. However, in the pre-hospital EMS context, “chief complaints” recorded by SAMU reflect the initial clinical presentation before detailed anatomical scoring is available. Including these variables allows us to examine which pre-hospital observable injuries or complaints are associated with higher eventual ISS, providing insight into early triage and resource allocation. We have clarified this rationale in the Methods section and emphasized that these variables represent pre-hospital indicators rather than post-hoc predictors, highlighting their operational relevance. Predictive variables were selected based on their consistent availability in the SAMU registry, clinical relevance in the pre-hospital setting, and prior literature on road traffic injury severity in LMICs. Variables not consistently recorded were excluded to maintain analytical rigor and reduce bias. 4. The correlation between a distance >20km and severity is a very interesting result. However, the categorization (0-20, 21-40, >40) seems arbitrary, as mentioned in the limitations; would it be possible to modify this to allow for a finer analysis? Answer: We thank the reviewer for highlighting this important finding. We acknowledge that these cutoffs may appear arbitrary and limit granularity. Unfortunately, distance was not consistently recorded as a continuous variable, which precluded a finer or alternative categorization (this is because our dependent variables are also categorical variable). Distances were grouped into three categories (0–20, 21–40, >40 km) informed by the distribution of SAMU data and national geographic and service delivery considerations; although prior studies did not categorize distance, these groupings were designed to capture meaningful access challenges, as distances beyond short travel ranges are known to delay care and align with national objectives to improve timely access to emergency services. 5.Regarding alcohol, it should be specified how consumption was assessed. Answer: Alcohol consumption was assessed based on information recorded in the SAMU registry at first patient contact, including patient self-report or observation by the attending EMS personnel (e.g., smell of alcohol or reported recent intake). However, SAMU has standardized instruments (alcohol test) used to measure the level of alcohol. We have clarified this assessment method in the Methods section (see page 4, subsection of study variables). 6. In the limitations section, the issue regarding missing data mentioned above must be detailed. Answer : We have expanded the Limitations section to provide a more detailed discussion of missing data, explaining its extent, likely causes related to the pre-hospital emergency context, the resulting exclusions, and the potential for non-random missingness and selection bias, as well as their implications for interpretation and generalizability of the findings (see page 16). Report for reviewers This article examines the factors associated with the severity of road traffic injuries (RTIs) among victims managed by the SAMU (Emergency Medical Service) in Rwanda in 2020. The study is relevant as it addresses a major public health issue in a Low- and Middle-Income Country (LMIC), focusing on the pre-hospital phase, which is often under-researched. However, there are elements that need clarification and improvement, notably a potential selection bias (high exclusion rate) and debatable choices regarding predictive variables. These points must be addressed before full approval. Answer: We thank the reviewer for the careful assessment of our manuscript and for raising important methodological concerns. Regarding the high exclusion rate and potential selection bias, we acknowledge this as a key limitation of our study. The SAMU data management system in Rwanda in 2020 was still evolving and was not sufficiently robust to ensure complete and systematic recording of all clinically relevant variables. Consequently, a considerable number of records contained missing or incomplete information on essential variables, which led to their exclusion from the analysis. This situation is understandable in a pre-hospital emergency context in a low-resource setting, where the priority is rapid life-saving care rather than detailed data documentation. This challenge is not unique to our study. Previous research has similarly reported limitations in the use of SAMU datasets due to incomplete and inconsistent reporting, which has restricted broader use of these data for research purposes. Our study therefore represents one of the few attempts to analyze RTI severity using SAMU data despite these constraints. To minimize bias, exclusions were based strictly on objective criteria related to data completeness, rather than on injury severity or patient characteristics. While we cannot entirely rule out selection bias, there is no indication that excluded cases differed systematically from included cases in a way that would invalidate the observed associations. We have explicitly acknowledged this limitation in the manuscript and advised caution in interpreting and generalizing the findings. Regarding the selection of predictive variables, our choices were guided by (1) the consistent availability of variables in the SAMU database, (2) their clinical and operational relevance in the pre-hospital setting, and (3) evidence from the existing literature on road traffic injuries in LMICs. Although additional variables could potentially improve prediction of injury severity, they were not reliably recorded, and their inclusion would have introduced further bias. Finally, by explicitly recognizing these data limitations, we have translated them into a practical recommendation in the manuscript. We emphasize the need for improved and standardized reporting of all cases managed by SAMU, including more complete documentation of clinical and contextual variables. Strengthening routine EMS data collection will not only enhance patient care and system monitoring but also enable higher-quality research and more robust evidence to inform injury prevention and emergency care policies in Rwanda. Regarding the methodological aspects: The use of the SAMU electronic registry is a sound approach to obtaining real-world clinical data and is particularly valuable given its rarity on the continent. The use of the Injury Severity Score (ISS) to classify severity is also suitable for the African context. However, I have a few questions/concerns: 1. The authors indicate that out of 2,062 identified cases, 900 were excluded due to missing data, leaving only 1,162 patients for analysis. This represents an exclusion rate of nearly 44%. It is necessary to specify whether this missing data is random or not. Could patients who died before arrival or those with very severe injuries be overrepresented in the missing data? The urgency of care could explain the incompleteness of the information. The authors must compare, if possible, the baseline characteristics of the excluded vs. included patients, or at least discuss this bias more deeply in the limitations section. Otherwise, there is a significant selection bias. Answer: We acknowledge the high exclusion rate as an important limitation. Missing data largely reflect the operational realities of pre-hospital emergency care in Rwanda, where documentation may be incomplete in high-urgency cases. The SAMU registry does not systematically capture pre-hospital outcomes or allow formal assessment of whether missingness was random, limiting comparisons between excluded and included patients. Exclusions were based solely on objective data completeness criteria. We now discuss the potential for non-random missingness and selection bias more explicitly in the Limitations section and recommend improved standardized reporting within SAMU. 2. The study dichotomizes severity at an ISS > 9. Although justified by the authors, an ISS of 9 is often considered "moderate." A threshold of 15 (major trauma) is more common in international literature to define a "severe injury." The authors should further justify this choice by citing references such as: Copes WS, Champion HR, Sacco WJ, Lawnick MM, Keast SL, Bain LW. "The Injury Severity Score revisited." The Journal of Trauma. 1988;28(1):69-77. Answer: We thank the reviewer for this important methodological point. We acknowledge that ISS 15 is commonly used in international literature to define major trauma, particularly in high-income hospital-based trauma registries (e.g., Copes et al., 1988). However, in the pre-hospital context and LMIC settings, lower ISS thresholds (ISS >9) have frequently been used to distinguish clinically meaningful moderate-to-severe injuries requiring urgent care, referral, or advanced management. In Rwanda’s SAMU setting, patients with ISS >9 already represent a group with substantially higher clinical acuity and resource needs. Moreover, using ISS ≥15 would have resulted in a small subgroup, limiting statistical power and interpretability. We have clarified and strengthened this justification in the Methods and Discussion sections, while acknowledging this choice as a contextual consideration. To do this classification, we referred to the local evidence/national standards that showed those with more than 9 ISS have severe injuries. We found there are many studies that contrast the classifications of Copes et al. (1988). See the following studies used as references: Traisathit, P., Chittawatanarat, K., Chandacham, K. et al. Trauma referral audit impact assessment on the outcomes of injured patients via an interrupted time-series analysis: an 11-year before-and-after study of trauma cases at the Maharaj Nakorn Chiang Mai hospital, Thailand. BMC Emerg Med 25, 64 (2025). https://doi.org/10.1186/s12873-025-01220-0 ) Abajas-Bustillo R, Amo-Setién FJ, Leal-Costa C, Ortego-Mate MDC, Seguí-Gómez M, Durá-Ros MJ, Zonfrillo MR. Comparison of injury severity scores (ISS) obtained by manual coding versus "Two-step conversion" from ICD-9-CM. PLoS One. 2019 May 1;14(5):e0216206. https://doi.org/10.1371/journal.pone.0216206. Reith, G., Lefering, R., Wafaisade, A. et al. Injury pattern, outcome and characteristics of severely injured pedestrian. Scand J Trauma Resusc Emerg Med 23, 56 (2015). https://doi.org/10.1186/s13049-015-0137-8 (These support the use of ISS >9 to distinguish clinically important injury severity levels when conventional major trauma cutoffs (e.g., ISS ≥15 or ≥16) may be less practical due to sample size or context, especially in pre-hospital and LMIC RTI research). 3.In the logistic regression (Table 3), the authors include "Chief complaints" (e.g., TBI/head injury, Extremity injury) as independent variables to predict "Severity." I find this surprising because, inherently, a TBI predicts a high score. The ISS is calculated based on anatomical injuries. The authors need to provide further justification for their choice of independent variables. Answer : We thank the reviewer for this insightful comment. We acknowledge that ISS is anatomically based and that certain injuries, such as TBI, inherently contribute to higher ISS scores. However, in the pre-hospital EMS context, “chief complaints” recorded by SAMU reflect the initial clinical presentation before detailed anatomical scoring is available. Including these variables allows us to examine which pre-hospital observable injuries or complaints are associated with higher eventual ISS, providing insight into early triage and resource allocation. We have clarified this rationale in the Methods section and emphasized that these variables represent pre-hospital indicators rather than post-hoc predictors, highlighting their operational relevance. Predictive variables were selected based on their consistent availability in the SAMU registry, clinical relevance in the pre-hospital setting, and prior literature on road traffic injury severity in LMICs. Variables not consistently recorded were excluded to maintain analytical rigor and reduce bias. 4. The correlation between a distance >20km and severity is a very interesting result. However, the categorization (0-20, 21-40, >40) seems arbitrary, as mentioned in the limitations; would it be possible to modify this to allow for a finer analysis? Answer: We thank the reviewer for highlighting this important finding. We acknowledge that these cutoffs may appear arbitrary and limit granularity. Unfortunately, distance was not consistently recorded as a continuous variable, which precluded a finer or alternative categorization (this is because our dependent variables are also categorical variable). Distances were grouped into three categories (0–20, 21–40, >40 km) informed by the distribution of SAMU data and national geographic and service delivery considerations; although prior studies did not categorize distance, these groupings were designed to capture meaningful access challenges, as distances beyond short travel ranges are known to delay care and align with national objectives to improve timely access to emergency services. 5.Regarding alcohol, it should be specified how consumption was assessed. Answer: Alcohol consumption was assessed based on information recorded in the SAMU registry at first patient contact, including patient self-report or observation by the attending EMS personnel (e.g., smell of alcohol or reported recent intake). However, SAMU has standardized instruments (alcohol test) used to measure the level of alcohol. We have clarified this assessment method in the Methods section (see page 4, subsection of study variables). 6. In the limitations section, the issue regarding missing data mentioned above must be detailed. Answer : We have expanded the Limitations section to provide a more detailed discussion of missing data, explaining its extent, likely causes related to the pre-hospital emergency context, the resulting exclusions, and the potential for non-random missingness and selection bias, as well as their implications for interpretation and generalizability of the findings (see page 16). Competing Interests: We declare no competing interests. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 05 Feb 2026 Eric Uwitonze , Department of Epidemiology and Biostatistics, College of Medicine and Health Sciences Huye, University of Rwanda, Butare, Rwanda 05 Feb 2026 Author Response Report for reviewers This article examines the factors associated with the severity of road traffic injuries (RTIs) among victims managed by the SAMU (Emergency Medical Service) in Rwanda in 2020. ... Continue reading Report for reviewers This article examines the factors associated with the severity of road traffic injuries (RTIs) among victims managed by the SAMU (Emergency Medical Service) in Rwanda in 2020. The study is relevant as it addresses a major public health issue in a Low- and Middle-Income Country (LMIC), focusing on the pre-hospital phase, which is often under-researched. However, there are elements that need clarification and improvement, notably a potential selection bias (high exclusion rate) and debatable choices regarding predictive variables. These points must be addressed before full approval. Answer: We thank the reviewer for the careful assessment of our manuscript and for raising important methodological concerns. Regarding the high exclusion rate and potential selection bias, we acknowledge this as a key limitation of our study. The SAMU data management system in Rwanda in 2020 was still evolving and was not sufficiently robust to ensure complete and systematic recording of all clinically relevant variables. Consequently, a considerable number of records contained missing or incomplete information on essential variables, which led to their exclusion from the analysis. This situation is understandable in a pre-hospital emergency context in a low-resource setting, where the priority is rapid life-saving care rather than detailed data documentation. This challenge is not unique to our study. Previous research has similarly reported limitations in the use of SAMU datasets due to incomplete and inconsistent reporting, which has restricted broader use of these data for research purposes. Our study therefore represents one of the few attempts to analyze RTI severity using SAMU data despite these constraints. To minimize bias, exclusions were based strictly on objective criteria related to data completeness, rather than on injury severity or patient characteristics. While we cannot entirely rule out selection bias, there is no indication that excluded cases differed systematically from included cases in a way that would invalidate the observed associations. We have explicitly acknowledged this limitation in the manuscript and advised caution in interpreting and generalizing the findings. Regarding the selection of predictive variables, our choices were guided by (1) the consistent availability of variables in the SAMU database, (2) their clinical and operational relevance in the pre-hospital setting, and (3) evidence from the existing literature on road traffic injuries in LMICs. Although additional variables could potentially improve prediction of injury severity, they were not reliably recorded, and their inclusion would have introduced further bias. Finally, by explicitly recognizing these data limitations, we have translated them into a practical recommendation in the manuscript. We emphasize the need for improved and standardized reporting of all cases managed by SAMU, including more complete documentation of clinical and contextual variables. Strengthening routine EMS data collection will not only enhance patient care and system monitoring but also enable higher-quality research and more robust evidence to inform injury prevention and emergency care policies in Rwanda. Regarding the methodological aspects: The use of the SAMU electronic registry is a sound approach to obtaining real-world clinical data and is particularly valuable given its rarity on the continent. The use of the Injury Severity Score (ISS) to classify severity is also suitable for the African context. However, I have a few questions/concerns: 1. The authors indicate that out of 2,062 identified cases, 900 were excluded due to missing data, leaving only 1,162 patients for analysis. This represents an exclusion rate of nearly 44%. It is necessary to specify whether this missing data is random or not. Could patients who died before arrival or those with very severe injuries be overrepresented in the missing data? The urgency of care could explain the incompleteness of the information. The authors must compare, if possible, the baseline characteristics of the excluded vs. included patients, or at least discuss this bias more deeply in the limitations section. Otherwise, there is a significant selection bias. Answer: We acknowledge the high exclusion rate as an important limitation. Missing data largely reflect the operational realities of pre-hospital emergency care in Rwanda, where documentation may be incomplete in high-urgency cases. The SAMU registry does not systematically capture pre-hospital outcomes or allow formal assessment of whether missingness was random, limiting comparisons between excluded and included patients. Exclusions were based solely on objective data completeness criteria. We now discuss the potential for non-random missingness and selection bias more explicitly in the Limitations section and recommend improved standardized reporting within SAMU. 2. The study dichotomizes severity at an ISS > 9. Although justified by the authors, an ISS of 9 is often considered "moderate." A threshold of 15 (major trauma) is more common in international literature to define a "severe injury." The authors should further justify this choice by citing references such as: Copes WS, Champion HR, Sacco WJ, Lawnick MM, Keast SL, Bain LW. "The Injury Severity Score revisited." The Journal of Trauma. 1988;28(1):69-77. Answer: We thank the reviewer for this important methodological point. We acknowledge that ISS 15 is commonly used in international literature to define major trauma, particularly in high-income hospital-based trauma registries (e.g., Copes et al., 1988). However, in the pre-hospital context and LMIC settings, lower ISS thresholds (ISS >9) have frequently been used to distinguish clinically meaningful moderate-to-severe injuries requiring urgent care, referral, or advanced management. In Rwanda’s SAMU setting, patients with ISS >9 already represent a group with substantially higher clinical acuity and resource needs. Moreover, using ISS ≥15 would have resulted in a small subgroup, limiting statistical power and interpretability. We have clarified and strengthened this justification in the Methods and Discussion sections, while acknowledging this choice as a contextual consideration. To do this classification, we referred to the local evidence/national standards that showed those with more than 9 ISS have severe injuries. We found there are many studies that contrast the classifications of Copes et al. (1988). See the following studies used as references: Traisathit, P., Chittawatanarat, K., Chandacham, K. et al. Trauma referral audit impact assessment on the outcomes of injured patients via an interrupted time-series analysis: an 11-year before-and-after study of trauma cases at the Maharaj Nakorn Chiang Mai hospital, Thailand. BMC Emerg Med 25, 64 (2025). https://doi.org/10.1186/s12873-025-01220-0 ) Abajas-Bustillo R, Amo-Setién FJ, Leal-Costa C, Ortego-Mate MDC, Seguí-Gómez M, Durá-Ros MJ, Zonfrillo MR. Comparison of injury severity scores (ISS) obtained by manual coding versus "Two-step conversion" from ICD-9-CM. PLoS One. 2019 May 1;14(5):e0216206. https://doi.org/10.1371/journal.pone.0216206. Reith, G., Lefering, R., Wafaisade, A. et al. Injury pattern, outcome and characteristics of severely injured pedestrian. Scand J Trauma Resusc Emerg Med 23, 56 (2015). https://doi.org/10.1186/s13049-015-0137-8 (These support the use of ISS >9 to distinguish clinically important injury severity levels when conventional major trauma cutoffs (e.g., ISS ≥15 or ≥16) may be less practical due to sample size or context, especially in pre-hospital and LMIC RTI research). 3.In the logistic regression (Table 3), the authors include "Chief complaints" (e.g., TBI/head injury, Extremity injury) as independent variables to predict "Severity." I find this surprising because, inherently, a TBI predicts a high score. The ISS is calculated based on anatomical injuries. The authors need to provide further justification for their choice of independent variables. Answer : We thank the reviewer for this insightful comment. We acknowledge that ISS is anatomically based and that certain injuries, such as TBI, inherently contribute to higher ISS scores. However, in the pre-hospital EMS context, “chief complaints” recorded by SAMU reflect the initial clinical presentation before detailed anatomical scoring is available. Including these variables allows us to examine which pre-hospital observable injuries or complaints are associated with higher eventual ISS, providing insight into early triage and resource allocation. We have clarified this rationale in the Methods section and emphasized that these variables represent pre-hospital indicators rather than post-hoc predictors, highlighting their operational relevance. Predictive variables were selected based on their consistent availability in the SAMU registry, clinical relevance in the pre-hospital setting, and prior literature on road traffic injury severity in LMICs. Variables not consistently recorded were excluded to maintain analytical rigor and reduce bias. 4. The correlation between a distance >20km and severity is a very interesting result. However, the categorization (0-20, 21-40, >40) seems arbitrary, as mentioned in the limitations; would it be possible to modify this to allow for a finer analysis? Answer: We thank the reviewer for highlighting this important finding. We acknowledge that these cutoffs may appear arbitrary and limit granularity. Unfortunately, distance was not consistently recorded as a continuous variable, which precluded a finer or alternative categorization (this is because our dependent variables are also categorical variable). Distances were grouped into three categories (0–20, 21–40, >40 km) informed by the distribution of SAMU data and national geographic and service delivery considerations; although prior studies did not categorize distance, these groupings were designed to capture meaningful access challenges, as distances beyond short travel ranges are known to delay care and align with national objectives to improve timely access to emergency services. 5.Regarding alcohol, it should be specified how consumption was assessed. Answer: Alcohol consumption was assessed based on information recorded in the SAMU registry at first patient contact, including patient self-report or observation by the attending EMS personnel (e.g., smell of alcohol or reported recent intake). However, SAMU has standardized instruments (alcohol test) used to measure the level of alcohol. We have clarified this assessment method in the Methods section (see page 4, subsection of study variables). 6. In the limitations section, the issue regarding missing data mentioned above must be detailed. Answer : We have expanded the Limitations section to provide a more detailed discussion of missing data, explaining its extent, likely causes related to the pre-hospital emergency context, the resulting exclusions, and the potential for non-random missingness and selection bias, as well as their implications for interpretation and generalizability of the findings (see page 16). Report for reviewers This article examines the factors associated with the severity of road traffic injuries (RTIs) among victims managed by the SAMU (Emergency Medical Service) in Rwanda in 2020. The study is relevant as it addresses a major public health issue in a Low- and Middle-Income Country (LMIC), focusing on the pre-hospital phase, which is often under-researched. However, there are elements that need clarification and improvement, notably a potential selection bias (high exclusion rate) and debatable choices regarding predictive variables. These points must be addressed before full approval. Answer: We thank the reviewer for the careful assessment of our manuscript and for raising important methodological concerns. Regarding the high exclusion rate and potential selection bias, we acknowledge this as a key limitation of our study. The SAMU data management system in Rwanda in 2020 was still evolving and was not sufficiently robust to ensure complete and systematic recording of all clinically relevant variables. Consequently, a considerable number of records contained missing or incomplete information on essential variables, which led to their exclusion from the analysis. This situation is understandable in a pre-hospital emergency context in a low-resource setting, where the priority is rapid life-saving care rather than detailed data documentation. This challenge is not unique to our study. Previous research has similarly reported limitations in the use of SAMU datasets due to incomplete and inconsistent reporting, which has restricted broader use of these data for research purposes. Our study therefore represents one of the few attempts to analyze RTI severity using SAMU data despite these constraints. To minimize bias, exclusions were based strictly on objective criteria related to data completeness, rather than on injury severity or patient characteristics. While we cannot entirely rule out selection bias, there is no indication that excluded cases differed systematically from included cases in a way that would invalidate the observed associations. We have explicitly acknowledged this limitation in the manuscript and advised caution in interpreting and generalizing the findings. Regarding the selection of predictive variables, our choices were guided by (1) the consistent availability of variables in the SAMU database, (2) their clinical and operational relevance in the pre-hospital setting, and (3) evidence from the existing literature on road traffic injuries in LMICs. Although additional variables could potentially improve prediction of injury severity, they were not reliably recorded, and their inclusion would have introduced further bias. Finally, by explicitly recognizing these data limitations, we have translated them into a practical recommendation in the manuscript. We emphasize the need for improved and standardized reporting of all cases managed by SAMU, including more complete documentation of clinical and contextual variables. Strengthening routine EMS data collection will not only enhance patient care and system monitoring but also enable higher-quality research and more robust evidence to inform injury prevention and emergency care policies in Rwanda. Regarding the methodological aspects: The use of the SAMU electronic registry is a sound approach to obtaining real-world clinical data and is particularly valuable given its rarity on the continent. The use of the Injury Severity Score (ISS) to classify severity is also suitable for the African context. However, I have a few questions/concerns: 1. The authors indicate that out of 2,062 identified cases, 900 were excluded due to missing data, leaving only 1,162 patients for analysis. This represents an exclusion rate of nearly 44%. It is necessary to specify whether this missing data is random or not. Could patients who died before arrival or those with very severe injuries be overrepresented in the missing data? The urgency of care could explain the incompleteness of the information. The authors must compare, if possible, the baseline characteristics of the excluded vs. included patients, or at least discuss this bias more deeply in the limitations section. Otherwise, there is a significant selection bias. Answer: We acknowledge the high exclusion rate as an important limitation. Missing data largely reflect the operational realities of pre-hospital emergency care in Rwanda, where documentation may be incomplete in high-urgency cases. The SAMU registry does not systematically capture pre-hospital outcomes or allow formal assessment of whether missingness was random, limiting comparisons between excluded and included patients. Exclusions were based solely on objective data completeness criteria. We now discuss the potential for non-random missingness and selection bias more explicitly in the Limitations section and recommend improved standardized reporting within SAMU. 2. The study dichotomizes severity at an ISS > 9. Although justified by the authors, an ISS of 9 is often considered "moderate." A threshold of 15 (major trauma) is more common in international literature to define a "severe injury." The authors should further justify this choice by citing references such as: Copes WS, Champion HR, Sacco WJ, Lawnick MM, Keast SL, Bain LW. "The Injury Severity Score revisited." The Journal of Trauma. 1988;28(1):69-77. Answer: We thank the reviewer for this important methodological point. We acknowledge that ISS 15 is commonly used in international literature to define major trauma, particularly in high-income hospital-based trauma registries (e.g., Copes et al., 1988). However, in the pre-hospital context and LMIC settings, lower ISS thresholds (ISS >9) have frequently been used to distinguish clinically meaningful moderate-to-severe injuries requiring urgent care, referral, or advanced management. In Rwanda’s SAMU setting, patients with ISS >9 already represent a group with substantially higher clinical acuity and resource needs. Moreover, using ISS ≥15 would have resulted in a small subgroup, limiting statistical power and interpretability. We have clarified and strengthened this justification in the Methods and Discussion sections, while acknowledging this choice as a contextual consideration. To do this classification, we referred to the local evidence/national standards that showed those with more than 9 ISS have severe injuries. We found there are many studies that contrast the classifications of Copes et al. (1988). See the following studies used as references: Traisathit, P., Chittawatanarat, K., Chandacham, K. et al. Trauma referral audit impact assessment on the outcomes of injured patients via an interrupted time-series analysis: an 11-year before-and-after study of trauma cases at the Maharaj Nakorn Chiang Mai hospital, Thailand. BMC Emerg Med 25, 64 (2025). https://doi.org/10.1186/s12873-025-01220-0 ) Abajas-Bustillo R, Amo-Setién FJ, Leal-Costa C, Ortego-Mate MDC, Seguí-Gómez M, Durá-Ros MJ, Zonfrillo MR. Comparison of injury severity scores (ISS) obtained by manual coding versus "Two-step conversion" from ICD-9-CM. PLoS One. 2019 May 1;14(5):e0216206. https://doi.org/10.1371/journal.pone.0216206. Reith, G., Lefering, R., Wafaisade, A. et al. Injury pattern, outcome and characteristics of severely injured pedestrian. Scand J Trauma Resusc Emerg Med 23, 56 (2015). https://doi.org/10.1186/s13049-015-0137-8 (These support the use of ISS >9 to distinguish clinically important injury severity levels when conventional major trauma cutoffs (e.g., ISS ≥15 or ≥16) may be less practical due to sample size or context, especially in pre-hospital and LMIC RTI research). 3.In the logistic regression (Table 3), the authors include "Chief complaints" (e.g., TBI/head injury, Extremity injury) as independent variables to predict "Severity." I find this surprising because, inherently, a TBI predicts a high score. The ISS is calculated based on anatomical injuries. The authors need to provide further justification for their choice of independent variables. Answer : We thank the reviewer for this insightful comment. We acknowledge that ISS is anatomically based and that certain injuries, such as TBI, inherently contribute to higher ISS scores. However, in the pre-hospital EMS context, “chief complaints” recorded by SAMU reflect the initial clinical presentation before detailed anatomical scoring is available. Including these variables allows us to examine which pre-hospital observable injuries or complaints are associated with higher eventual ISS, providing insight into early triage and resource allocation. We have clarified this rationale in the Methods section and emphasized that these variables represent pre-hospital indicators rather than post-hoc predictors, highlighting their operational relevance. Predictive variables were selected based on their consistent availability in the SAMU registry, clinical relevance in the pre-hospital setting, and prior literature on road traffic injury severity in LMICs. Variables not consistently recorded were excluded to maintain analytical rigor and reduce bias. 4. The correlation between a distance >20km and severity is a very interesting result. However, the categorization (0-20, 21-40, >40) seems arbitrary, as mentioned in the limitations; would it be possible to modify this to allow for a finer analysis? Answer: We thank the reviewer for highlighting this important finding. We acknowledge that these cutoffs may appear arbitrary and limit granularity. Unfortunately, distance was not consistently recorded as a continuous variable, which precluded a finer or alternative categorization (this is because our dependent variables are also categorical variable). Distances were grouped into three categories (0–20, 21–40, >40 km) informed by the distribution of SAMU data and national geographic and service delivery considerations; although prior studies did not categorize distance, these groupings were designed to capture meaningful access challenges, as distances beyond short travel ranges are known to delay care and align with national objectives to improve timely access to emergency services. 5.Regarding alcohol, it should be specified how consumption was assessed. Answer: Alcohol consumption was assessed based on information recorded in the SAMU registry at first patient contact, including patient self-report or observation by the attending EMS personnel (e.g., smell of alcohol or reported recent intake). However, SAMU has standardized instruments (alcohol test) used to measure the level of alcohol. We have clarified this assessment method in the Methods section (see page 4, subsection of study variables). 6. In the limitations section, the issue regarding missing data mentioned above must be detailed. Answer : We have expanded the Limitations section to provide a more detailed discussion of missing data, explaining its extent, likely causes related to the pre-hospital emergency context, the resulting exclusions, and the potential for non-random missingness and selection bias, as well as their implications for interpretation and generalizability of the findings (see page 16). Competing Interests: We declare no competing interests. Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 19 Nov 2025 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 Version 2 (revision) 05 Feb 26 read read Version 1 19 Nov 25 read Emmanuel Bonnet , Institut de Recherche pour le Développement, CNRS Université Paris 1 Panthéon- Sorbonne, Île-de-France, France Muhammed Navid Tahir , University of the Punjab, Lahore, Pakistan Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Tahir M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 09 May 2026 | for Version 2 Muhammed Navid Tahir , University of the Punjab, Lahore, Pakistan 0 Views copyright © 2026 Tahir M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Though study does not have novel idea/subject yet it has some strenghts such as it is originated from destination where there is limited research especially in road safety and injury prevention area. Moreover authors have strengthened methodology part, and provided a more explicit justification of key methodological choices, including the injury severity threshold (ISS >9), the selection of predictive variables, and the categorization of distance to care. Therefore this version of manuscript offers improved methodological and interpretative rigor. Study also provides public health implications Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise road safety, injury prevention, public health, prevention of non-communicable diseases, pre-hospital care, I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Tahir MN. Peer Review Report For: Factors associated with road traffic injury severity among victims retrieved by pre-hospital emergency services in Rwanda [version 2; peer review: 2 approved] . F1000Research 2026, 14 :1282 ( https://doi.org/10.5256/f1000research.195770.r481760) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1282/v2#referee-response-481760 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Bonnet E. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 20 Feb 2026 | for Version 2 Emmanuel Bonnet , Institut de Recherche pour le Développement, CNRS Université Paris 1 Panthéon- Sorbonne, Île-de-France, France 0 Views copyright © 2026 Bonnet E. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions I thank the authors for their careful and detailed responses to the comments previously raised. The revised version has improved in clarity and methodological transparency. The authors have provided satisfactory elements regarding the high exclusion rate by acknowledging its potential implications and contextualizing pre-hospital data collection in Rwanda. Although residual bias cannot be entirely ruled out, this limitation is now explicitly discussed. The justification for the ISS > 9 threshold is better argued and more clearly situated within the context of pre-hospital care and low-resource settings, which strengthens the coherence of the severity classification. The clarifications provided regarding the use of chief complaints as predictive variables are acceptable, particularly from a pre-hospital triage perspective. The methodological choices are now sufficiently explained. The categorization of distance variables and the assessment of alcohol consumption have also been clarified; this remains not entirely satisfactory, but I believe it is unlikely that further improvement is possible at this stage. Overall, the authors have responded constructively to the comments, and the manuscript has improved accordingly. At this stage, I consider that the concerns raised have been appropriately addressed and that it is probably not possible to go further given the available data. Competing Interests No competing interests were disclosed. Reviewer Expertise Health geographer I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Bonnet E. Peer Review Report For: Factors associated with road traffic injury severity among victims retrieved by pre-hospital emergency services in Rwanda [version 2; peer review: 2 approved] . F1000Research 2026, 14 :1282 ( https://doi.org/10.5256/f1000research.195770.r455827) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1282/v2#referee-response-455827 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Bonnet E. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 04 Dec 2025 | for Version 1 Emmanuel Bonnet , Institut de Recherche pour le Développement, CNRS Université Paris 1 Panthéon- Sorbonne, Île-de-France, France 0 Views copyright © 2025 Bonnet E. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This article examines the factors associated with the severity of road traffic injuries (RTIs) among victims managed by the SAMU (Emergency Medical Service) in Rwanda in 2020. The study is relevant as it addresses a major public health issue in a Low- and Middle-Income Country (LMIC), focusing on the pre-hospital phase, which is often under-researched. However, there are elements that need clarification and improvement, notably a potential selection bias (high exclusion rate) and debatable choices regarding predictive variables. These points must be addressed before full approval. Regarding the methodological aspects: The use of the SAMU electronic registry is a sound approach to obtaining real-world clinical data and is particularly valuable given its rarity on the continent. The use of the Injury Severity Score (ISS) to classify severity is also suitable for the African context. However, I have a few questions/concerns: The authors indicate that out of 2,062 identified cases, 900 were excluded due to missing data, leaving only 1,162 patients for analysis. This represents an exclusion rate of nearly 44%. It is necessary to specify whether this missing data is random or not. Could patients who died before arrival or those with very severe injuries be overrepresented in the missing data? The urgency of care could explain the incompleteness of the information. The authors must compare, if possible, the baseline characteristics of the excluded vs. included patients, or at least discuss this bias more deeply in the limitations section. Otherwise, there is a significant selection bias. 2. The study dichotomizes severity at an ISS > 9. Although justified by the authors, an ISS of 9 is often considered "moderate." A threshold of 15 (major trauma) is more common in international literature to define a "severe injury." The authors should further justify this choice by citing references such as: Copes WS, Champion HR, Sacco WJ, Lawnick MM, Keast SL, Bain LW. "The Injury Severity Score revisited." The Journal of Trauma. 1988;28(1):69-77. 3. In the logistic regression (Table 3), the authors include "Chief complaints" (e.g., TBI/head injury, Extremity injury) as independent variables to predict "Severity." I find this surprising because, inherently, a TBI predicts a high score. The ISS is calculated based on anatomical injuries. The authors need to provide further justification for their choice of independent variables. 4. The correlation between a distance >20km and severity is a very interesting result. However, the categorization (0-20, 21-40, >40) seems arbitrary, as mentioned in the limitations; would it be possible to modify this to allow for a finer analysis? 5.Regarding alcohol, it should be specified how consumption was assessed. 6. In the limitations section, the issue regarding missing data mentioned above must be detailed. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Health geographer I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 05 Feb 2026 Eric Uwitonze, Department of Epidemiology and Biostatistics, College of Medicine and Health Sciences Huye, University of Rwanda, Butare, Rwanda Report for reviewers This article examines the factors associated with the severity of road traffic injuries (RTIs) among victims managed by the SAMU (Emergency Medical Service) in Rwanda in 2020. The study is relevant as it addresses a major public health issue in a Low- and Middle-Income Country (LMIC), focusing on the pre-hospital phase, which is often under-researched. However, there are elements that need clarification and improvement, notably a potential selection bias (high exclusion rate) and debatable choices regarding predictive variables. These points must be addressed before full approval. Answer: We thank the reviewer for the careful assessment of our manuscript and for raising important methodological concerns. Regarding the high exclusion rate and potential selection bias, we acknowledge this as a key limitation of our study. The SAMU data management system in Rwanda in 2020 was still evolving and was not sufficiently robust to ensure complete and systematic recording of all clinically relevant variables. Consequently, a considerable number of records contained missing or incomplete information on essential variables, which led to their exclusion from the analysis. This situation is understandable in a pre-hospital emergency context in a low-resource setting, where the priority is rapid life-saving care rather than detailed data documentation. This challenge is not unique to our study. Previous research has similarly reported limitations in the use of SAMU datasets due to incomplete and inconsistent reporting, which has restricted broader use of these data for research purposes. Our study therefore represents one of the few attempts to analyze RTI severity using SAMU data despite these constraints. To minimize bias, exclusions were based strictly on objective criteria related to data completeness, rather than on injury severity or patient characteristics. While we cannot entirely rule out selection bias, there is no indication that excluded cases differed systematically from included cases in a way that would invalidate the observed associations. We have explicitly acknowledged this limitation in the manuscript and advised caution in interpreting and generalizing the findings. Regarding the selection of predictive variables, our choices were guided by (1) the consistent availability of variables in the SAMU database, (2) their clinical and operational relevance in the pre-hospital setting, and (3) evidence from the existing literature on road traffic injuries in LMICs. Although additional variables could potentially improve prediction of injury severity, they were not reliably recorded, and their inclusion would have introduced further bias. Finally, by explicitly recognizing these data limitations, we have translated them into a practical recommendation in the manuscript. We emphasize the need for improved and standardized reporting of all cases managed by SAMU, including more complete documentation of clinical and contextual variables. Strengthening routine EMS data collection will not only enhance patient care and system monitoring but also enable higher-quality research and more robust evidence to inform injury prevention and emergency care policies in Rwanda. Regarding the methodological aspects: The use of the SAMU electronic registry is a sound approach to obtaining real-world clinical data and is particularly valuable given its rarity on the continent. The use of the Injury Severity Score (ISS) to classify severity is also suitable for the African context. However, I have a few questions/concerns: 1. The authors indicate that out of 2,062 identified cases, 900 were excluded due to missing data, leaving only 1,162 patients for analysis. This represents an exclusion rate of nearly 44%. It is necessary to specify whether this missing data is random or not. Could patients who died before arrival or those with very severe injuries be overrepresented in the missing data? The urgency of care could explain the incompleteness of the information. The authors must compare, if possible, the baseline characteristics of the excluded vs. included patients, or at least discuss this bias more deeply in the limitations section. Otherwise, there is a significant selection bias. Answer: We acknowledge the high exclusion rate as an important limitation. Missing data largely reflect the operational realities of pre-hospital emergency care in Rwanda, where documentation may be incomplete in high-urgency cases. The SAMU registry does not systematically capture pre-hospital outcomes or allow formal assessment of whether missingness was random, limiting comparisons between excluded and included patients. Exclusions were based solely on objective data completeness criteria. We now discuss the potential for non-random missingness and selection bias more explicitly in the Limitations section and recommend improved standardized reporting within SAMU. 2. The study dichotomizes severity at an ISS > 9. Although justified by the authors, an ISS of 9 is often considered "moderate." A threshold of 15 (major trauma) is more common in international literature to define a "severe injury." The authors should further justify this choice by citing references such as: Copes WS, Champion HR, Sacco WJ, Lawnick MM, Keast SL, Bain LW. "The Injury Severity Score revisited." The Journal of Trauma. 1988;28(1):69-77. Answer: We thank the reviewer for this important methodological point. We acknowledge that ISS 15 is commonly used in international literature to define major trauma, particularly in high-income hospital-based trauma registries (e.g., Copes et al., 1988). However, in the pre-hospital context and LMIC settings, lower ISS thresholds (ISS >9) have frequently been used to distinguish clinically meaningful moderate-to-severe injuries requiring urgent care, referral, or advanced management. In Rwanda’s SAMU setting, patients with ISS >9 already represent a group with substantially higher clinical acuity and resource needs. Moreover, using ISS ≥15 would have resulted in a small subgroup, limiting statistical power and interpretability. We have clarified and strengthened this justification in the Methods and Discussion sections, while acknowledging this choice as a contextual consideration. To do this classification, we referred to the local evidence/national standards that showed those with more than 9 ISS have severe injuries. We found there are many studies that contrast the classifications of Copes et al. (1988). See the following studies used as references: Traisathit, P., Chittawatanarat, K., Chandacham, K. et al. Trauma referral audit impact assessment on the outcomes of injured patients via an interrupted time-series analysis: an 11-year before-and-after study of trauma cases at the Maharaj Nakorn Chiang Mai hospital, Thailand. BMC Emerg Med 25, 64 (2025). https://doi.org/10.1186/s12873-025-01220-0 ) Abajas-Bustillo R, Amo-Setién FJ, Leal-Costa C, Ortego-Mate MDC, Seguí-Gómez M, Durá-Ros MJ, Zonfrillo MR. Comparison of injury severity scores (ISS) obtained by manual coding versus "Two-step conversion" from ICD-9-CM. PLoS One. 2019 May 1;14(5):e0216206. https://doi.org/10.1371/journal.pone.0216206. Reith, G., Lefering, R., Wafaisade, A. et al. Injury pattern, outcome and characteristics of severely injured pedestrian. Scand J Trauma Resusc Emerg Med 23, 56 (2015). https://doi.org/10.1186/s13049-015-0137-8 (These support the use of ISS >9 to distinguish clinically important injury severity levels when conventional major trauma cutoffs (e.g., ISS ≥15 or ≥16) may be less practical due to sample size or context, especially in pre-hospital and LMIC RTI research). 3.In the logistic regression (Table 3), the authors include "Chief complaints" (e.g., TBI/head injury, Extremity injury) as independent variables to predict "Severity." I find this surprising because, inherently, a TBI predicts a high score. The ISS is calculated based on anatomical injuries. The authors need to provide further justification for their choice of independent variables. Answer : We thank the reviewer for this insightful comment. We acknowledge that ISS is anatomically based and that certain injuries, such as TBI, inherently contribute to higher ISS scores. However, in the pre-hospital EMS context, “chief complaints” recorded by SAMU reflect the initial clinical presentation before detailed anatomical scoring is available. Including these variables allows us to examine which pre-hospital observable injuries or complaints are associated with higher eventual ISS, providing insight into early triage and resource allocation. We have clarified this rationale in the Methods section and emphasized that these variables represent pre-hospital indicators rather than post-hoc predictors, highlighting their operational relevance. Predictive variables were selected based on their consistent availability in the SAMU registry, clinical relevance in the pre-hospital setting, and prior literature on road traffic injury severity in LMICs. Variables not consistently recorded were excluded to maintain analytical rigor and reduce bias. 4. The correlation between a distance >20km and severity is a very interesting result. However, the categorization (0-20, 21-40, >40) seems arbitrary, as mentioned in the limitations; would it be possible to modify this to allow for a finer analysis? Answer: We thank the reviewer for highlighting this important finding. We acknowledge that these cutoffs may appear arbitrary and limit granularity. Unfortunately, distance was not consistently recorded as a continuous variable, which precluded a finer or alternative categorization (this is because our dependent variables are also categorical variable). Distances were grouped into three categories (0–20, 21–40, >40 km) informed by the distribution of SAMU data and national geographic and service delivery considerations; although prior studies did not categorize distance, these groupings were designed to capture meaningful access challenges, as distances beyond short travel ranges are known to delay care and align with national objectives to improve timely access to emergency services. 5.Regarding alcohol, it should be specified how consumption was assessed. Answer: Alcohol consumption was assessed based on information recorded in the SAMU registry at first patient contact, including patient self-report or observation by the attending EMS personnel (e.g., smell of alcohol or reported recent intake). However, SAMU has standardized instruments (alcohol test) used to measure the level of alcohol. We have clarified this assessment method in the Methods section (see page 4, subsection of study variables). 6. In the limitations section, the issue regarding missing data mentioned above must be detailed. Answer : We have expanded the Limitations section to provide a more detailed discussion of missing data, explaining its extent, likely causes related to the pre-hospital emergency context, the resulting exclusions, and the potential for non-random missingness and selection bias, as well as their implications for interpretation and generalizability of the findings (see page 16). View more View less Competing Interests We declare no competing interests. reply Respond Report a concern Bonnet E. Peer Review Report For: Factors associated with road traffic injury severity among victims retrieved by pre-hospital emergency services in Rwanda [version 2; peer review: 2 approved] . F1000Research 2026, 14 :1282 ( https://doi.org/10.5256/f1000research.189946.r434978) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. 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