Abstract
Introduction
Trauma in motorcycle riders usually comes with high injury severity and significant mortality and morbidity. The unique vulnerability of motorcyclists results in a high incidence of severe injuries and fatalities. Efforts to improve outcomes require profound understanding of causes and patterns of trauma, as well as trauma systems improvements focused on at-risk demographics.
Method
This is a cross-sectional study designed to investigate the trauma type and treatment outcomes in motorcycle accident victims admitted to the largest trauma center in southern Iran, Shiraz, during 2022–2024. Patients who were injured in a motorcycle accident were enrolled in the study. Patients’ data were collected from the Shiraz Trauma Registry, a branch of the National Trauma Registry of Iran (NTRI). Demographic, prehospital and posthospital vital signs and clinical properties, aside from injury patterns of the patients were investigated.
Results
In total, 3310 patients were enrolled in the study. Deceased patients had higher median age compared to the survived ones (p-value < 0.01). patients transported by EMS had significantly higher mortality rates (p-value < 0.01). Logistic regression models revealed that prehospital GCS (p-value < 0.01, OR = 0.7), SBP in ER (p-value = 0.04, OR = 0.98), SpO2 in ER (p-value = 0.02, OR = 0.94), GCS in ER (p-value < 0.01, OR = 0.7), and ISS (p-value < 0.01, OR = 1.1) were significantly associated with mortality in the patients. Injury to head (p-value < 0.01, OR = 5.2), face (p-value < 0.01, OR = 0.3), thorax (p-value = 0.03, OR = 1.9), upper extremity (p-value = 0.03, OR = 0.4), and lower extremity (p-value < 0.01, OR = 0.3) had significantly contributed to mortality.
Conclusion
This study from Southern Iran reveals young male riders bear a disproportionate burden of motorcycle accident mortality, primarily linked to critical head and thoracic injuries and high Injury Severity Scores. While extremity and facial injuries are common, they are less associated with death. The findings stress the importance of strict helmet laws, early use of trauma scoring for triage, and prioritizing critical care for severe cases. Enhancing prevention, pre-hospital care, and trauma system capacity are key to reducing deaths and improving outcomes in the region.
Keywords: Injuries, Motor vehicles, Mortality
Introduction
Motorcycle-related accidents represent a significant public health concern worldwide, contributing substantially to trauma admissions and mortality rates. The unique vulnerability of motorcyclists—due to limited physical protection compared to occupants of enclosed vehicles—results in a high incidence of severe injuries and fatalities.
Recent data indicate that motorcycle trauma disproportionately affects specific demographic groups, with notable patterns in age, gender, and socioeconomic status. The majority of victims are young men, with mean ages often reported around 24–35 years and most incidents involving riders in their twenties and thirties. Males are consistently overrepresented, often comprising more than 70% of motorcycle trauma cases [1–3]. Socioeconomic and behavioral risk factors, including lack of valid driver’s licensing, play a prominent role. For instance, studies show that only a small proportion of motorcycle accident victims possess a valid license, highlighting a need for better regulatory enforcement [2, 3].
Injury patterns in motorcycle trauma reflect the vulnerability of riders. Extremity, head, and thoracic injuries are most reported injuries, with extremities and head/face injuries prevailing as the primary sites of trauma [1, 2, 4]. Severity scores also indicate that while many patients may survive, a notable proportion suffer significant disabilities, and mortality rates remain considerable in hospital cohorts (ranging from 3% to 17%) [2, 3].
Contributing factors to mortality have been explored extensively in recent studies. Helmet use emerges as the single most effective protective factor—yet rates of helmet adoption remain low in many regions. Lack of helmet usage correlates with more severe head injuries and higher mortality [1, 2]. Universal helmet legislation has been shown to reduce motorcycle crash mortality substantially, with recommendations for policies mandating helmet use to further decrease fatalities [5, 6]. Alcohol use prior to crashes is another major factor, with affected riders experiencing increased injury severity, higher injury severity scores, and elevated risk of death [7, 8]. Younger age also predicts a higher risk of severe injury, particularly among underage or early-licensure riders [9].
Overall, trauma in motorcycle riders remains defined by high injury severity and significant mortality— predominantly among young, unlicensed, helmetless males engaging in risky behaviors. Efforts to improve outcomes require targeted interventions, including robust helmet legislation, enhanced licensing enforcement, and trauma system improvements focused on at-risk demographics. In this study, we aimed to investigate trauma type and treatment outcomes in motorcycle accident patients admitted to the largest trauma center in Southern Iran during 2022–2024.
Method
Study design
This is a cross-sectional study designed to investigate the trauma type and treatment outcomes in motorcycle accident victims admitted to the largest trauma center in southern Iran, Shiraz, during 2022–2024. STROBE guideline is utilized in writing this manuscript to enhance the accuracy of the reporting [10].
Participants
All the patients who were injured in a motorcycle accident (either as passenger or driver of the motorcycle) and admitted to the largest trauma center in southern Iran, Shiraz (Rajaie hospital), during 2022–2024 were enrolled in the study. Exclusion criteria were as follows: patients who died before reaching the hospital, those with incomplete or missing data, and individuals who left the hospital without completing their treatment and therefore, had unknown outcome status.
Data sources
Patients’ data were collected from the Shiraz Trauma Registry, a branch of the National Trauma Registry of Iran (NTRI) with ethical approval code IR.SUMS.REC.1401.183. Ethics committee have approved this study with approval code IR.SUMS.MED.REC.1403.802. Moreover, the committee have waivered the need for informed consent from the patients due to the retrospective nature of the manuscript.
Variables
Demographic characteristics, educational status, position of the patient, time and season of the accident, method of transfer to hospital, vital signs (both before and after the emergency room admission), injury mechanism and type, length of hospital stay, outcomes, need for emergency surgery, frequency of intensive care unit (ICU) admission, frequency of multiple trauma (defined as AIS > 3 for two or more body regions), distribution of injury site (based on AIS), severity of injury (defined as ISS > 15), and the relationship of these variables to the mortality rate of patients were evaluated in the study.
Statistical analysis
Data normality was assessed using the Kolmogorov-Smirnov test. Quantitative data were presented as mean and standard deviation for parametric variables, and median and interquartile range for non-parametric variables. Qualitative data were reported as numbers and percentages. Chi-square tests were used to analyze quantitative variables. Cases with missing values in variables required for multivariable analyses were excluded by complete-case analysis. Because missing observations represented a very small proportion of the registry and no variable showed substantial missingness, no imputation procedures were performed. Variables considered clinically relevant or associated with mortality in the univariable analyses (p < 0.20) were entered into the initial multivariable logistic regression model. Before model fitting, multicollinearity was assessed using variance inflation factor (VIF < 5) and tolerance (> 0.1). A backward Wald elimination procedure was then applied using an entry probability of 0.05 and a removal probability of 0.01. Variables remaining in the final model with p < 0.05 were considered independently associated with mortality. Hosmer-Lemeshow goodness of fit test was used for evaluating model fit. Data analysis was conducted using IBM SPSS version 27.0 and MedCalc.
Results
In total, 3310 patients were enrolled in the study, of which 59 had died and 3251 survived. The median (interquartile range) age of the patients was 26 (19–40). Deceased patients had higher median age compared to the survived ones (p-value < 0.01). 4.4% of the patients were females. Nearly half of the patients (49.5%) had pre-diploma degrees (elementary, junior high school, and high school), and education had no significant effect on mortality rate among the population (p-value = 0.65) (Table 1).
Table 1.
Demographic data of the study population
| Outcome | p-value | |||
|---|---|---|---|---|
| Total | Deceased, N = 59 | Survived, N = 3251 | ||
| Age, median (IQR) | 26 (19–40) | 33 (21–51) | 25 (19–40) | < 0.01 |
| Gender, N (%) | 0.78 | |||
| Male | 3166 (95.6) | 56 | 3110 | |
| Female | 144 (4.4) | 3 | 141 | |
| Education, N (%) | 0.65 | |||
| Illiterate | 236 (7.1) | 2 (3.4) | 234 (7.2) | |
| Pre-diploma | 1638 (49.5) | 32 (54.2) | 1606 (49.4) | |
| Diploma | 1298 (39.2) | 22 (37.3) | 1276 (39.2) | |
| Academic | 138 (4.2) | 3 (5.1) | 135 (4.2) | |
IQR, interquartile range
EMS was the most frequent method of transportation (72%), and patients transported by this method had significantly higher mortality rates (p < 0.01) compared to other methods. Most of the patients were motorcycle drivers (86%); however, position of the patient had no relation to mortality (p = 0.21). time of the accident during the day and the season in which the accident happened had no significant relation to mortality among the population (p-value = 0.39 and 0.97, respectively) (Table 2).
Table 2.
Prehospital and accident-related data of the patients
| Total | Outcome | p-value | ||
|---|---|---|---|---|
| Deceased, N = 59 | Survived, N = 3251 | |||
| Transfer to hospital, N (%) | < 0.01 | |||
| By EMS | 2384 (72) | 51 (86.4) | 2333 (71.8) | |
| By self | 795 (24) | 2 (3.4) | 793 (24.4) | |
| Other methods | 131 (4) | 6 (10.2) | 125 (3.8) | |
| Position of the patient, N (%) | 0.21 | |||
| Driver | 2845 (86) | 54 (91.5) | 2791 (85.9) | |
| Passenger | 465 (14) | 5 (8.5) | 460 (14.1) | |
| Time of accident, N (%) | 0.39 | |||
| Morning (4–12) | 949 (28.7) | 15 (25.4) | 934 (28.7) | |
| Afternoon (13–17) | 780 (23.6) | 19 (32.2) | 761 (23.4) | |
| Evening (18–21) | 889 (26.9) | 16 (27.1) | 873 (26.9) | |
| Night (22 − 4) | 692 (20.9) | 9 (15.3) | 683 (21) | |
| Season of accident, N (%) | 0.97 | |||
| Spring | 1059 (32) | 19 (32.2) | 1040 (32) | |
| Summer | 578 (17.5) | 9 (15.3) | 569 (17.5) | |
| Autumn | 796 (24) | 15 (25.4) | 781 (24) | |
| Winter | 877 (26.5) | 16 (27.1) | 861 (26.5) | |
IQR, interquartile range; EMS, emergency medical services
A backward stepwise Wald logistic regression procedure was applied. The models have revealed that prehospital GCS (p-value < 0.01, OR = 0.7), SBP in ER (p-value = 0.04, OR = 0.98), SpO2 in ER (p-value = 0.02, OR = 0.94), GCS in ER (p-value < 0.01, OR = 0.7), and ISS (p-value < 0.01, OR = 1.1) were significantly associated with mortality in the patients (Table 3).
Table 3.
Logistic regression analysis* of clinical data of the study population
| Outcome | P-value | Odds ratio, (95% CI) | |||
|---|---|---|---|---|---|
| Total | Deceased, N = 59 | Survived, N = 3251 | |||
| Pre-hospital Data, median (IQR)** | |||||
| Systolic blood pressure | 125 (118–136) | 95 (90-133.8) | 126 (119–136) | 0.29 | 1 (0.9–1.01) |
| Respiratory rate | 16 (16–18) | 16 (10–18) | 16 (16–18) | 0.49 | 0.9 (0.9–1.1) |
| Puls rate | 88 (80-94.3) | 105 (79.3–120) | 88 (80–94) | 0.97 | 1 (0.9–1.2) |
| SpO2 | 96 (94–97) | 90 (85-93.8) | 96 (95–97) | 0.05 | 0.97 (0.94–0.99) |
| GCS | 15 (15–15) | 8 (4–15) | 15 (15–15) | < 0.01 | 0.7 (0.63–0.73) |
| ER data, median (IQR)¥ | |||||
| Systolic blood pressure | 131 (121–142) | 100 (90–128) | 132 (121–142) | 0.04 | 0.98 (0.96–0.99) |
| Respiratory rate | 16 (16–18) | 14 (10–18) | 16 (16–18) | 0.7 | 0.9 (0.8–1.01) |
| Puls rate | 88 (79–100) | 103 (75–120) | 88 (79–100) | 0.8 | 1 (0.9–1.02) |
| Temperature | 37 (36.5–37) | 36.3 (36–37) | 37 (36–37) | 0.8 | 1 (0.5–1.8) |
| SpO2 | 96 (95–96) | 90 (88–94) | 96 (95–96) | 0.02 | 0.94 (0.9–0.99) |
| GCS | 15 (15–15) | 6 (3–12) | 15 (15–15) | < 0.01 | 0.7 (0.65–0.77) |
| ISS, median (IQR) | 5 (4–10) | 25 (13–34) | 5 (4–10) | < 0.01 | 1.1 (1.08–1.13) |
*Backward Wald logistic regression results for clinical and demographic variables. Only the variables remaining in the final model are reported in this table. Other variables were insignificant and therefore, removed from the final model
**The final model demonstrated acceptable calibration (Hosmer–Lemeshow p=0.65) and explained approximately 33% of the variation in mortality (Nagelkerke R2 = 0.33)
¥The final model demonstrated acceptable calibration (Hosmer–Lemeshow p=0.45) and explained approximately 39% of the variation in mortality (Nagelkerke R2 = 0.39)
IQR, interquartile range; GCS, Glasgow coma scale; ISS, injury severity score; ER, emergency room
All the patients have suffered blunt trauma. 64.7% of the patients underwent surgery without significant variation among the study groups (p-value = 0.16). Mortality rate was higher among the patients admitted to ICU (p-value < 0.01). Most patients spent less than one week in the hospital (61.1% 2 to 7 days and 17.5% less than 2 days), and the length of hospital stay was insignificant in predicting mortality rate (p-value = 0.13) (Table 4).
Table 4.
The relation between clinical characteristics of trauma and mortality in patients
| Total | Outcome | P-value | ||
|---|---|---|---|---|
| Deceased, N = 59 | Survived, N = 3251 | |||
| Surgery, N (%) | 2140 (64.7) | 33 (55.9) | 2107 (64.8) | 0.16 |
| ICU admission, N (%) | 331 (10) | 36 (61) | 295 (9.1) | < 0.01 |
| LOS, median days (IQR) | 4 (2–7) | 4 (0–11) | 4 (2–7) | 0.13 |
| Short (< 2 days) | 579 (17.5) | 25 (42.4) | 554 (17) | |
| Medium (2–7 days) | 2024 (61.1) | 14 (23.7) | 2010(61.8) | |
| Long (> 7 days) | 707 (21.4) | 20 (33.9) | 687 (21.2) | |
Face injury (48.7%), Head injury (45.9%), and upper extremity injury (40.3%) were the most frequent traumas in motorcyclists (Fig. 1). Logistic regression analysis has shown that injury to head (p-value < 0.01, OR = 5.2), face (p-value < 0.01, OR = 0.3), thorax (p-value = 0.03, OR = 1.9), upper extremity (p-value = 0.03, OR = 0.4), and lower extremity (p-value < 0.01, OR = 0.3) were independently associated with mortality. Multiple traumas (defined as two or more body parts with AIS severity score of 3 or higher) and major traumas (ISS > 15) were significantly associated with mortality (p-value < 0.01 and < 0.01, OR = 2.5 and 20, respectively) (Table 5).
Fig. 1.
Frequency of injured body regions
Table 5.
Frequency and logistic regression analysis¥ of injured body regions
| Outcome | P-value | Odds ratio, (95% CI) | ||
|---|---|---|---|---|
| Deceased, N=59 | Survived, N=3251 | |||
| Affected body region, N (%)* | ||||
| Head | 54 (91.5) | 1465 (45.1) | <0.01 | 5.2 (2-13.6) |
| Face | 6 (10.2) | 1606 (49.4) | <0.01 | 0.3 (0.1-0.6) |
| Neck | 2 (3.4) | 45 (1.4) | 0.35 | 2 (0.5-8.9) |
| Thorax | 23 (39) | 556 (17.1) | 0.03 | 1.9 (1.1-3.2) |
| Abdomen | 6 (10.2) | 146 (4.5) | 0.12 | 2 (0.8-5) |
| Spine | 4 (6.8) | 244 (7.5) | 0.54 | 0.7 (0.3-2) |
| Upper extremity | 7 (11.9) | 1326 (40.8) | 0.03 | 0.4 (0.2-0.9) |
| Lower extremity | 8 (13.6) | 1287 (39.6) | <0.01 | 0.3 (0.1-0.7) |
| Multiple traumas, N (%) ** | 56 (94.9) | 2503 (77) | <0.01 | 2.5 (0.8-8.2) |
| Major trauma, N (%)*** | 42 (71.2) | 358 (11) | <0.01 | 20 (11-35.5) |
¥ Backward Wald logistic regression results for clinical and demographic variables. Only the variables remaining in the final model are reported in this table. Other variables were insignificant and therefore, removed from the final model
* Based on AIS; the reported numbers and percentages are not case-based and represent the cumulative frequency of injuries across the study population; therefore, it does not add up to 100 percent
** Defined as two or more body parts with AIS severity score of 3 or higher
*** Defined as ISS>15
IQR, interquartile range; GCS, Glasgow coma scale; ISS, injury severity score
Discussion
This cross-sectional analysis of 3310 motorcycle accident patients across 2022–2024 at the largest trauma center in Southern Iran reveals that age, low prehospital GCS, physiological instability at admission (hypotension, hypoxemia, low GCS), and higher Injury Severity Scores (ISS) independently predict mortality. Severe head and thoracic injuries, and multiple-region trauma were also significantly associated with fatal outcomes.
Our results are similar with those from a well-characterized retrospective cohort in Italy, where head (OR 3.06), thoracic (OR 1.82), and abdominal trauma independently predicted death, while facial injuries did not directly contribute to mortality, aligning with our finding that facial trauma may indicate survivable but distracting injury not directly fatal [11]. Further validation comes from a single-center study in 2020, which showed that both age and injury site significantly impact mortality in motorcycle trauma, in particular, head, chest, abdominal, and pelvic injuries elevated risk, consistent with our ISS-based observations [4].
In broader trauma populations (not motorcycle-specific), pre-hospital hypotension and neurological compromise were also independently linked to mortality, reinforcing the importance of early vital sign assessment - again supporting the prognostic value of SBP, GCS, and injury pattern in our cohort [12, 13]. Finally, thoracic injuries have been increasingly recognized as critical contributors to trauma mortality. A Scandinavian registry analysis reported that thoracic trauma - especially when concurrent with head or limb injuries, accounted for a significant portion of fatal outcomes, emphasizing the complexity of chest trauma as seen in our findings [14].
Our findings support the use of admission SBP, SpO₂, and GCS during early emergency department triage to identify patients at increased risk of mortality, particularly for assessing motorcycle crash victims. Given the demonstrated validity of ISS and alternative scoring systems like RTS or TRISS in predicting outcomes, especially in resource-limited settings, emergency systems should integrate such tools to effectively triage and manage high-risk cases [15, 16]. Moreover, prehospital vital signs may show weaker associations with mortality. the discrepancy likely reflects differences in measurement context; prehospital vitals are subject to greater variability and incomplete stabilization, whereas ER vitals are recorded under more standardized conditions and may more accurately represent early physiologic severity. Additionally, prehospital and ED values do not always agree well, especially for respiratory rate and blood pressure, which further supports why ED measurements may perform better statistically [17, 18].
With head injuries being the strongest mortality risk factor, enforcing helmet legislation and promoting advanced helmet designs remain top priorities. The evolving burden of thoracic trauma seen in the literature highlights the need to explore protective gear that also mitigates chest injuries, such as structural jackets or airbag vests [19]. Extremity injuries, particularly upper-extremity injuries, have been associated with lower mortality in some trauma cohorts [20, 21], whereas facial injury data do not consistently show a mortality reduction, in contrast to our findings [22]. Some studies suggest that lower mortality associated with distal upper-extremity injury may reflect a crumple-zone effect, in which the injured limb dissipates impact forces and reduces fatal head or neck trauma [23]. The rising frequency and severity of thoracic trauma in motorcyclists, as global data suggest, point toward enhanced diagnostics (e.g., focused chest imaging), aggressive respiratory support, and early intervention strategies, such as pain control and ventilation protocols [14, 24].
Seasonal patterns reveal that severe injuries are more likely during the rainy season, especially in motorcycle-to-motorcycle crashes and weekend incidents, with fatal injuries also linked to speeding during rainy periods. Conversely, the dry season sees a higher risk of severe injury among riders compared to pillion passengers, particularly on national roads. Regarding time of day, most motorcycle crashes and injuries occur during daylight hours, particularly between 6 pm and 9 pm, corresponding with increased traffic density and human activity [25, 26]. Our findings did not show any differences between timing of injury and mortality rate of the patients. This could be due to geographical inconsistencies among the studies. Injury severity scores indicate riders sustain more serious injuries than passengers or other road users, with riders disproportionately represented among fatal and critical injuries. Injury patterns also differ by motorcycle type, with standard motorcycles associated with a higher risk of severe trauma compared to scooters, especially for lower extremity injuries. Key predictors of trauma severity include motorcycle type, speed, helmet use, and rider age. Together, these findings underscore the complex relationship between environmental factors, timing, and rider characteristics in motorcycle accident injuries [27, 28].
Strengths and limitations
This study included a large cohort of motorcycle trauma patients from the largest trauma referral center in southern Iran and evaluated both physiological and anatomical predictors of mortality, using multivariable regression analysis. Nevertheless, several limitations should be considered. First, the cross-sectional design precludes causal inference. Additionally, although the overall sample size was large, the number of mortality events was relatively small compared with the number of candidate predictors entered the regression models. This relatively low event-to-variable ratio may have increased the risk of model overfitting and reduced the stability of the estimated odds ratios, despite the use of backward variable selection and multicollinearity assessment. Therefore, the regression findings should be interpreted with appropriate caution and ideally validated in larger multicenter cohorts. Finally, the absence of information on helmet use, prehospital interventions, and other potentially important confounders, together with the single-center design, may limit the generalizability of our findings.
Conclusion
This cross-sectional study of motorcycle accident victims in Southern Iran highlights the disproportionate burden on young male riders and underscores the critical role of head and thoracic injuries, elevated Injury Severity Score, and adverse physiological parameters in predicting mortality. While extremity and facial injuries were common, they were not strongly associated with fatal outcomes. These findings reaffirm the necessity of strict helmet enforcement, early triage using validated trauma scores, and prioritization of critical care resources for patients with high-risk injury patterns. Strengthening preventive measures, optimizing pre-hospital care, and expanding trauma system capacity remain essential steps to reduce mortality and improve outcomes for motorcycle crash victims in the region.
Acknowledgements
The authors thank the Research Vice-Chancellor of Shiraz University of Medical Sciences for supporting this study. (Grant Number 31357).
Author contributions
FF and MK: study conception and design, data acquisition, data analysis, drafting and revision. EK&HR: study conception and design, data interpretation, drafting and revision.
Funding
The Shiraz University of Medical Science supported this study in the context of a dissertation project (Grant Number 31357).
Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
Research Ethics Committees of School of Medicine - Shiraz University of Medical Sciences, Shiraz, Iran (Approval No. IR.SUMS.MED.REC.1403.802) have approved this study. All procedures performed in this study involving human participants were conducted in accordance with the ethical standards of the institutional research committee and with the Declaration of Helsinki. Because this was a retrospective registry-based study using existing anonymized data, the requirement for informed consent was waived by the Research Ethics Committee.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.