The Hidden Epidemic: Unveiling Associated Injuries Among Motor Traffic Accident Victims in Southern Tanzania

The Hidden Epidemic: Unveiling Associated Injuries Among Motor Traffic Accident Victims in Southern Tanzania | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Hidden Epidemic: Unveiling Associated Injuries Among Motor Traffic Accident Victims in Southern Tanzania Vulstan James Shedura, Geofrey John Ngomo, Lameck Titus Moses, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5701767/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Femoral shaft fractures (FSFs) represent a significant health burden in low- and middle-income countries, particularly among victims of motor traffic accidents (MTAs). The World Health Organization's 2018 report indicated that approximately 1.2 million deaths were attributed to motor vehicle collisions. Despite the rising incidence of MTAs in Tanzania, the current prevalence of FSFs remains unclear. We aimed to determine the prevalence and associated factors for FSFs among MTA victims in Southern Tanzania to guide interventions. Methods and findings A retrospective, quantitative, health facility-based cross-sectional study was conducted using secondary data from January to December 2023. The study included patients admitted to five referral hospitals in Southern Tanzania. Data were analyzed using Stata® Version 15.1, with associations tested through Chi-square, bivariable, and multivariable logistic regression. One hundred and fifty-four (n = 154) patients who experienced MTAs from the selected five referral hospitals in Southern Tanzania were included in this study. The median age of the study participants was 31.5 years (IQR: 22–49). The majority of patients were male (76%, 117/154) and 57.1% (88/154) were married. The majority (63.0%, 97/154) of patients were self-employed, 37.0% (57/154) were young adults (aged 18–34 years) and 55.8% (86/154) attained primary level of education. Prevalence of FSFs was 37.7%, 58/154), and the most injuries (84.9%, 90/160) were closed fractures. Head injuries was the most common associated injury (28.6%, 44/154). In addition, being a young adult (aged 18–34 years old) (aOR = 5.92, 95% CI: 1.39–25.18, p = 0.016), Male (aOR = 3.34, 95% CI: 1.20–9.33, p = 0.021), and at the summer season of the year (aOR = 3.11, 95% CI: 1.33–7.30, p = 0.009), were factors independently associated with Femoral Shaft Fractures among People Experienced MTAs in the Southern Tanzania from January to December 2023. Conclusions Femoral shaft fractures (FSFs) are a significant public health burden among motor traffic accident (MTA) victims in Southern Tanzania, with young adult males disproportionately affected. Mid-shaft fractures and associated head injuries complicate trauma management, emphasizing the need for enhanced healthcare strategies. The high prevalence of FSFs underscores the urgent need for targeted interventions, particularly for young males, and highlights the influence of age, gender, and seasonal variations in accident occurrences. Comprehensive trauma care systems and preventive measures are essential to reduce the incidence of road traffic accidents and their resulting injuries in the region. Femoral Shaft Fractures Motor Traffic Accidents Femur Fracture Prevalence Southern Tanzania Figures Figure 1 Figure 2 Figure 3 Introduction Femoral shaft fractures (FSFs) are among the most severe musculoskeletal (MSK) injuries and are a significant cause of mortality and disability, particularly in low- and middle-income countries (LMICs)[ 1 ]. The World Health Organization (WHO) reported in 2018 that motor traffic injuries caused approximately 1.2 million deaths globally, with road traffic accidents (RTAs) ranking as the ninth leading cause of morbidity and the tenth leading cause of mortality worldwide[ 1 ]. RTAs also contribute significantly to hospital admissions, with LMICs bearing 90% of global road traffic mortality and disability. FSFs are a major consequence of RTAs, with an estimated 20–50 million people globally disabled due to MSK injuries annually, of which FSFs are among the most common [ 1 , 2 ]. FSFs often result from high-energy trauma caused by RTAs, which lead to significant suffering, including disability and associated injuries such as traumatic brain injuries. Blood loss in FSFs can be substantial, with reports of 2–3 units lost per patient, which may lead to irreversible shock and death [ 3 ]. LMICs face unique challenges, such as inadequate financial resources and a focus on prevention rather than treatment, resulting in higher rates of mortality and disability among FSF patients. For example, a study in Saudi Arabia reported that one person is killed and four are injured every hour due to RTAs, with over 65% of these incidents attributed to speeding, non-compliance with traffic signals, and alcohol use[ 3 ]. RTAs impose substantial economic burdens on victims, their families, and nations. In LMICs, the lack of surgical resources and implants often results in non-surgical management of FSFs, leading to prolonged hospital stays and increased mortality rates. Even where surgical options exist, the high costs are prohibitive for many patients, compounding the burden of disability and death[ 4 ]. Studies have shown that young adult males are the most affected demographic, frequently presenting with associated injuries such as head trauma, lower limb fractures, and upper limb fractures [ 5 ]. The incidence of FSFs ranges between 15.7 and 45.5 per 100,000 people per year in LMICs. In Tanzania, the annual incidence is reported to range between 2.1 and 18.4 per 100,000 people, with FSFs being a common presentation in orthopedic departments [ 6 , 7 ]. The rise in the number of vehicles, particularly motorcycles, has contributed to the increasing prevalence of FSFs in Tanzania. Despite this alarming trend, data on the magnitude and associated factors of FSFs, especially in Southern Tanzania, remain scarce. This study aims to address this gap by determining the prevalence of FSFs and identifying associated factors among RTA victims in selected referral hospitals in Southern Tanzania. Globally, the annual incidence of FSFs from RTAs is estimated to be between 1.0 and 2.9 million, with LMICs experiencing significantly higher rates compared to high-income countries (HICs). FSFs disproportionately affect young individuals, emphasizing the need for improved access to treatment [ 2 ]. Injuries, including FSFs, account for approximately 9% of global deaths and have profound social and economic impacts on affected individuals and their families. FSFs require intensive inpatient care, further straining healthcare resources in LMICs [ 8 , 9 ]. Studies from various regions highlight the common mechanisms and demographics associated with FSFs. Motor vehicle collisions are the leading cause of FSFs in older children and adolescents, whereas falls are the primary cause in younger children[ 10 ]. In LMICs, males are disproportionately affected, often sustaining FSFs as a result of high-energy trauma. For example, a study in Newcastle reported that 62% of FSF cases involved males, many of whom had multiple injuries requiring specialized care [ 11 ]. Other studies have identified additional risk factors, such as alcohol consumption, smoking, and non-compliance with road safety measures, underscoring the need for targeted interventions to reduce the prevalence of FSFs [ 12 ]. In Tanzania, the prevalence of FSFs has been linked to an increasing number of RTAs, with males aged 21–30 years being the most affected demographic. Associated injuries, such as traumatic brain injuries, further intensify the burden. Limited surgical resources and financial constraints contribute to the high rates of disability and mortality among FSF patients. Previous studies in northern Tanzania reported that FSFs accounted for 28.6% of orthopedic cases, with the majority of injuries resulting from RTAs [ 7 ]. Despite the high burden of FSFs, data specific to Southern Tanzania are limited. This study seeks to fill this gap by assessing the prevalence and associated factors of FSFs among individuals involved in RTAs. The findings will provide valuable insights for policymakers, the Ministry of Health, and other stakeholders to develop strategies for prevention, allocate resources for treatment, and address the factors contributing to the high burden of FSFs in the region. These strategies may include funding for implants, improving access to surgical care, and strengthening road safety measures to mitigate the impact of RTAs on FSFs. FSFs represent a significant public health challenge in Tanzania, with substantial socio-economic implications. This study aims to provide a comprehensive understanding of the prevalence and associated factors of FSFs in Southern Tanzania, contributing to the development of effective interventions to reduce the burden of these injuries. Methods Study design This was a retrospective health facility-based cross-sectional study conducted between February 2024 to April 2024 by reviewing the secondary data from January to December 2023 of people experienced motor traffic accident from the selected referral hospitals in southern Tanzania. Study area This study was conducted in southern Tanzania, comprising the Lindi, Mtwara, and Ruvuma regions, which together cover an area of 146,419 square kilometers[ 13 ]. The southern zone is situated between longitudes 34°32’E and 40°26’E and latitudes 7°56’S and 11°44’S. It is bordered by the Ruvuma River and Mozambique to the south, Ruvuma region to the west, and Lindi region to the north[ 13 ]. This area is suitable for human settlement and agricultural activities, with the primary economic activities including agriculture, fishing, forestry, and beekeeping. The main cash crops grown in the region are cashew nuts and coffee, while maize, beans, cassava, and rice are the primary food crops. The transport network in the southern zone is reliable year-round, connecting the region to other parts of Tanzania via several road routes. The coastal trunk road links Mtwara, Lindi, Kilwa, and Dar es Salaam, while another major road connects Songea, Njombe, and Makambako to the Tanzania-Zambia Highway. The study was conducted in five hospitals located in the Mtwara and Lindi regions within the southern zone of Tanzania. These hospitals provide tertiary-level services in orthopedics and trauma care. The selected hospitals included the Southern Zone Referral Hospital, Ligula Regional Referral Hospital (RRH), St. Benedict’s Ndanda Council Designated Hospital (CDH), Lindi RRH, and St. Walburg’s Nyangao CDH. These facilities were purposively selected due to their accessibility and because they handle a high volume of patients with motor traffic accident-related injuries, including femoral shaft fractures (FSFs), compared to other hospitals in southern Tanzania. Study Population The study population comprised all patients (of any age) experienced motor traffic accident who attended at the Southern Zone Referral Hospital, Ligula Regional Referral Hospital (RRH), St. Benedict’s Ndanda Council Designated Hospital (CDH), Lindi RRH, and St. Walburg’s Nyangao CDH in southern Tanzania, from February 2024 to April 2024. Inclusion criteria All patients involved in motor traffic accidents who sought care at the selected study hospitals. Exclusion criteria Patients with injuries caused by factors other than motor traffic accidents, as well as those with incomplete or missing information, were excluded from the analysis. Sample size estimation and Sampling method Sample size estimation We calculated the sample size using the Kish and Leslie formula based on the data from a prevalence study conducted in Tanzania, with a prevalence rate of 10.0% for femoral shaft fractures [ 1 ]. We used a two-sided 95% confidence interval, a marginal error of 5%, and by considering a 10% non-response rate. The resulting sample size was 154. The sample size distribution per selected Hospital Figure 1 illustrates the distribution of patients who experienced accidents and sought medical care at selected hospitals within the study period. In total, 182 patients were involved in the study, with 175 of them having experienced MTAs, while the remaining 5 patients experienced other types of accidents. However, 16 patient records were excluded due to missing information, leaving 154 patients with complete record information for further analysis. Probability Proportional to size (PPS) was used to calculate the sample size for each selected hospital. Their distribution per hospital was as follows: Ligula Regional Referral Hospital ( n = 19, 12.3%), Sokoine Regional Referral Hospital ( n = 34, 22.1%), Southern Zone Referral Hospital ( n = 4, 2.6%), St. Benedict’s Ndanda Hospital ( n = 54, 35.1%), St. Walburg’s Nyangao Hospital ( n = 43, 27.9%). These hospitals were selected based on their capacity and the number of patients who experienced accidents during the study period. The distribution reflects the relative sizes of the hospitals and their contribution to the overall sample. Sampling technique Study participants were selected from the dataset using a systematic random sampling method, based on the list of participants in the sampling frame from each selected study site. The sample size for each hospital was determined using Probability Proportional to Size (PPS). Once the required number of participants for each facility was calculated, a list of all eligible patients who had experienced motor traffic accidents (the sampling frame) was compiled, and each patient record was assigned a unique number. The sampling fraction was calculated by dividing the total number of patients who had experienced motor traffic accidents (the sampling frame) by the sample size for each hospital. A random number between 1 and the calculated factor was selected to determine the starting point in the sampling frame. Participants records were then selected at regular intervals (based on the obtained factor) using systematic random sampling. Data collection procedures Prior to the actual data collection, a pre-tested paper-based structured data abstraction tool (S1) was developed and used to gather both socio-demographic and clinical data. Patients were clinically and radiographically assessed using information from their medical records. The diagnosis of FSFs was based on a review of the patient’s history, clinical examination, and radiological findings (anteroposterior and lateral X-rays) from their files. All data were extracted from the patients' medical records at each selected hospital. The information collected included the patients' demographic details, the cause of the injury, the side of injury, the anatomical site of the injury, and any associated injuries. Variables Dependant variable The dependent variable was femoral shaft fractures. This was categorized as a binary outcome: either no femoral shaft fracture or femoral shaft fracture . Independent variables The independent variables included age, sex, causes of the fracture, side of injury, anatomical site of injury, education level, economic status, area of residence, ethnicity, religion, and season of the year. Investigation tools, validity and reliability issues In this study, a structured data abstraction tool (S1) was used to collect socio-demographic and clinical information from the participants' files. The tool was validated through a pilot (pre-testing) phase before the actual data collection to ensure it would yield the desired results. A total of 35 patient files (10% of the total) were selected for pre-testing, which were included in the overall study sample. The findings from the pilot study were used to make necessary adjustments and improve the tool. To ensure reliability and accuracy, the same paper-based tools were administered to all data collectors. Only the principal investigator and trained researchers were involved in the data collection process. Both English and Swahili versions of the structured data abstraction tool were used to gather the required information from the participants. The validity of the tool was ensured by reviewing all questions with the help of research experts, who provided their input on the items to include, ensuring that the tool covered the research objectives effectively. Data processing and analysis After data collection, the data were entered into Microsoft Excel® 2019, cleaned, and checked for errors to ensure completeness (S3). The data were then double-entered and exported to Stata® version 15.1 for analysis (S3). Frequency and proportions were calculated for categorical variables, while continuous variables were summarized using the median and interquartile range (IQR) as measures of central tendency. The association between dependent and independent variables was tested using the Chi-square (X²) test. Bivariate and multivariate logistic regression models were employed to explore the relationships between dependent and independent variables. Bivariate analysis was first conducted to examine the association of each independent variable with the dependent variable. Variables with a p -value < 0.2 in bivariate analysis were included in the multivariate logistic regression analysis. Crude odds ratios (cOR) and adjusted odds ratios (aOR), along with their 95% confidence intervals (CI), were used to assess the significance of the independent variables. Variables with a p -value < 0.05 in the multivariate analysis were considered statistically significant, and 95% CI was used to evaluate the strength of the associations. Results Socio-demographic characteristics of the study participants A total of 154 patients who experienced motor traffic accidents in Southern Tanzania were enrolled in this study, with participants from five hospitals. The majority were from St. Benedict’s Ndanda Hospital (35.1%) and St. Walburg’s Nyangao Hospital (27.9%). The median age was 31.5 years (IQR: 22–49). The study participants were predominantly males (76%) and Muslim (79.2%). Occupation-wise, most were self-employed (63%), followed by students (19.5%). Regarding marital status, 57.1% were married, and 41.6% were single. Education levels varied, with 55.8% having primary education and 27.9% secondary education. Only 4.6% had no formal schooling (Table 1 ). Table 1 Socio- demographic characteristics of the study participants (N = 154). Characteristic Frequency (n) Percent (%) Study site Ligula Regional Referral Hospital 19 12.3 Sokoine Regional Referral Hospital 34 22.1 Southern Zone Referral Hospital 4 2.6 St. Benedict’s Ndanda Hospital 54 35.1 St. Walburg’s Nyangao Hospital 43 27.9 Age (in years) Median (IQR) 31.5 (22–49) ≤ 17 29 18.8 18–34 57 37.0 35–50 36 23.4 51–64 14 9.1 ≥ 65 18 11.7 Sex Female 37 24.0 Male 117 76.0 Religion status Christian 32 20.8 Muslim 122 79.2 Occupation status Employed 17 11.0 Self-employed 97 63.0 Student 30 19.5 Unemployed 10 6.5 Marital status Divorced 2 1.3 Married 88 57.1 Single 64 41.6 Education level Degree 8 5.2 Diploma 8 5.2 Certificate 2 1.3 Secondary 43 27.9 Primary 86 55.8 Didn’t attend school 7 4.6 Prevalence of FSF among patients experienced MTA in Southern Tanzania In this study, the prevalence of Femoral Shaft Fracture (FSF) among patients experienced Motor Traffic Accident in Southern Tanzania was 37.7% (58/154) (Fig. 2). The majority (29.3%, 17/58) of the patients with Femoral shaft fracture were from St. Walburg’s Nyangao Hospital and the least (1.7%, 1/58) from Southern Zone Referral Hospital (Table 2 ). The prevalence of other Femur fracture patterns was proximal femur 20.8% (32/154), and the distal femur 10.4% (16/154) (Fig. 2). Table 2 Proportion of Femoral shaft fractures among study sites in the Southern Zone referral Hospitals (N = 58). Study site Frequency (n) Proportion of FSF (%) St. Walburg’s Nyangao Hospital 17 29.3 Sokoine Regional Referral Hospital 16 27.6 Ligula Regional Referral Hospital 13 22.4 St. Benedict’s Ndanda Hospital 11 19.0 Southern Zone Referral Hospital 1 1.7 Total 58 100.0 Distribution of Femur fracture characteristics and associated injuries Out of the total 154 patients involved in motor traffic accidents, 68.8% (106 patients) had femur fractures. Of these, 14.9% had femur fractures without associated injuries, while 53.9% had both femur fractures and additional associated injuries. Another 31.2% of patients had only associated injuries without femur fractures (Fig. 3). Among the 106 patients with femur fractures, majority (84.9%) had closed fractures. Most of these fractures occurred at the mid-shaft of the femur (54.7%) and the proximal site (30.2%) (Table 3 ). Regarding the side of the fracture, 53.8% of patients had fractures on the left femur, and 46.2% on the right. A significant proportion (81.1%) had isolated femur fractures, while 18.9% had additional injuries (Table 3 ). The most common fracture pattern was transverse (54.7%), and oblique fractures (34%). Less common patterns included comminuted (8.5%) and spiral fractures (2.8%) (Table 3 ). Table 3 Distribution of Femur fracture characteristics of the studied patients (N = 106). Injury characteristic Frequency (n) Percent (%) Fracture type Closed 90 84.9 Open 16 15.1 Fracture anatomical site Proximal 32 30.2 Mid-shaft 58 54.7 Distal 16 15.1 Side of Femur fracture Left 57 53.8 Right 49 46.2 Isolated Femur fracture No 20 18.9 Yes 86 81.1 Pattern of femur fracture Transverse 58 54.7 Oblique 36 34.0 Spiral 3 2.8 Comminuted 9 8.5 In the present study, head injuries were the most common, affecting 28.6% of patients, followed by musculoskeletal injuries (26%). Less common were chest injuries (7.1%), visceral injuries (5.8%), radius fractures (2.6%), and left upper limb fractures (0.7%). A total of 14.9% of patients had no associated injuries (Table 4 ). Head injuries were slightly more common among males (29.1%) than females (27.0%), and musculoskeletal injuries were more frequent in females (35.1%) than males (23.1%). Tibia fractures were also more common in females (27.0%) compared to males (10.3%). Males had higher frequencies of chest injuries, visceral injuries, and radius fractures. A statistically significant difference was noted for the Femur fracture-associated injuries ( p = 0.041), indicating a gender-based variation (Table 4 ). Table 4 Distribution of the studied patients by associated injuries according to their Sex (N = 154). Type of associated injury Female Male Total N (%) p -value n (%) n (%) Head injury 10 (27.0) 34 (29.1) 44 (28.6) 0.041 Musculoskeletal injury 13 (35.1) 27 (23.1) 40 (26.0) Tibia fracture 10 (27.0) 12 (10.3) 22 (14.3) Chest injury 2 (5.4) 9 (7.7) 11 (7.1) Visceral injury 1 (2.7) 8 (6.8) 9 (5.8) Radius fracture 0 (0.0) 4 (3.4) 4 (2.6) Left upper limb fracture 0 (0.0) 1 (0.9) 1 (0.7) No associated injuries 1 (2.7) 22 (18.8) 23 (14.9) In the present study, head injuries were more frequent among younger patients (≤ 17 years: 37.9%, 18–34 years: 31.6%) compared to older groups. Musculoskeletal injuries were more prevalent in patients aged ≥ 65 years (50%) and the 51–64 age group (28.6%). Tibia fractures were distributed across all age groups, with a higher percentage among the 51–64 age group (28.6%). There was no significant age-related difference in the distribution of injuries ( p = 0.162) (Table 5 ). Table 5 Distribution of the studied patients by associated injuries according to their age groups (N = 154). Type of associated injury Age (in years) Total N (%) p -value ≤ 17 18–34 35–50 51–64 ≥ 65 n (%) n (%) n (%) n (%) n (%) Head injury 11 (37.9) 18 (31.6) 12 (33.3) 1 (7.1) 2 (11.1) 44 (28.6) 0.162 Musculoskeletal injury 2 (6.9) 16 (28.1) 9 (25.0) 4 (28.6) 9 (50.0) 40 (26.0) Tibia fracture 4 (13.8) 5 (8.8) 7 (19.4) 4 (28.6) 2 (11.1) 22 (14.3) Chest injury 4 (13.8) 3 (5.3) 3 (8.3) 1 (7.1) 0 (0.0) 11 (7.1) Visceral injury 4 (13.8) 3 (5.3) 1 (2.8) 1 (7.1) 0 (0.0) 9 (5.8) Radius fracture 0 (0.0) 2 (3.5) 2 (5.6) 0 (0.0) 0 (0.0) 4 (2.6) Left upper limb fracture 0 (0.0) 1 (1.8) 0 (0.0) 0 (0.0) 0 (0.0) 1 (0.7) No associated injuries 4 (13.8) 9 (15.8) 2 (5.6) 3 (21.4) 5 (27.8) 23 (14.9) Total 29 (100.0) 57 (100.0) 36 (100.0) 14 (100.0) 18 (100.0) 154 (100.0) Factors associated with FSFs among patients experienced Motor MTA in Southern Tanzania In bivariate and multivariate logistic regression analysis, the findings of this study showed that patients aged 18–34 had significantly higher odds of FSF compared to those aged ≥ 65 [aOR = 5.92 (95% CI: 1.39–25.18), p = 0.016] indicating a statistically significant association. Other age groups showed higher odds compared to the reference group (≥ 65), but these were not statistically significant (Table 6 ). Males were significantly more likely to sustain FSF than females [aOR = 3.34 (95% CI: 1.20–9.33), p = 0.021]. This suggests that males had over three times higher odds of experiencing FSF compared to females. There was no significant association between occupation and FSF. Both employed and self-employed patients did not show statistically significant differences compared to unemployed patients. While there was no statistically significant difference among marital status categories, married patients tended to have a slightly higher prevalence of FSF compared to others (Table 6 ). No significant association was found between education level and FSF, although patients with no schooling had the highest observed percentage of FSF (57.1%). Drivers had higher odds of FSF compared to pedestrians, but this was not statistically significant in either the bivariate or multivariate analysis. The occurrence of FSF was significantly higher in the summer season compared to spring [aOR = 3.11 (95% CI: 1.33–7.30), p = 0.009]. There was no significant difference between winter and spring in the occurrence of FSF (Table 6 ). Table 6 Factors associated with Femoral Shaft Fractures among patients experienced Motor Traffic Accident in Southern Tanzania (N = 154). Variable FSF status Bivariate analysis Multivariate analysis No Yes COR (95% CI) p -value AOR (95% CI) p -value Age (in years) ≤ 17 25 (86.2) 4 (13.8) 0.80 (0.16–4.08) 0.788 1.68 (0.17–16.25) 0.656 18–34 26 (45.6) 31 (54.4) 5.96 (1.55–22.87) 0.009 5.92 (1.39–25.18) 0.016* 35–50 21 (58.3) 15 (41.7) 3.57 (0.88–14.56) 0.076 3.58 (0.80-15.95) 0.094 51–64 9 (64.3) 5 (35.7) 2.78 (0.53–14.50) 0.226 2.47 (0.42–14.43) 0.317 ≥ 65 15 (83.3) 3 (16.7) 1 1 Sex Female 31 (83.8) 6 (16.2) 1 1 Male 65 (55.6) 52 (44.4) 4.13 (1.60-10.66) 0.003 3.34 (1.20–9.33) 0.021* Occupation status Employed 9 (52.9) 8 (47.1) 1.33 (0.27–6.50) 0.722 1.05 (0.17–6.43) 0.955 Self-employed 56 (57.7) 41 (42.3) 1.10 (0.29–4.14) 0.890 1.18 (0.25–5.51) 0.837 Student 25 (83.3) 5 (16.7) 0.30 (0.06–1.47) 0.137 0.38 (0.04–3.48) 0.388 Unemployed 6 (60.0) 4 (40.0) 1 1 Marital status Married 53 (60.2) 35 (39.8) 1 Divorced 1 (50.0) 1 (50.0) 1.51 (0.09–25.01) 0.772 Single 42 (65.6) 22 (34.4) 0.79 (0.41–1.55) 0.498 Education status Degree 4 (50.0) 4 (50.0) 0.75 (0.10–5.77) 0.782 Diploma 5 (62.5) 3 (37.5) 0.45 (0.06–3.57) 0.450 Secondary 27 (62.8) 16 (37.2) 0.44 (0.09–2.24) 0.326 Primary 56 (65.1) 30 (34.9) 0.40 (0.08–1.91) 0.252 Certificate 1 (50.0) 1 (50.0) 0.75 (0.03–17.51) 0.858 No school 3 (42.9) 4 (57.1) 1 Victim Category Driver 24 (49.0) 25 (51.0) 1.74 (0.74–4.06) 0.204 Passenger 47 (72.3) 18 (27.7) 0.64 (0.28–1.48) 0.295 Pedestrian 25 (62.5) 15 (37.5) 1 Season of the year Spring 40 (75.5) 13 (24.5) 1 1 Summer 50 (54.4) 42 (45.6) 2.58 (1.22–5.46) 0.013 3.11 (1.33–7.30) 0.009* Winter 6 (66.7) 3 (33.3) 1.54 (0.34–7.04) 0.579 1.64 (0.30–9.08) 0.569 * p-values of the variables significantly associated with FSF among the study participants Discussion This study revealed the prevalence of femoral shaft fractures (FSFs) among patients involved in motor traffic accidents (MTAs) in Southern Tanzania and identified the factors associated with these fractures. The findings revealed that FSFs were prevalent among MTA victims, with a rate of 37.7% (58/154), indicating a significant burden of these injuries in the region. This prevalence is consistent with previous studies that have demonstrated a high incidence of FSFs in low- and middle-income countries (LMICs) due to the high rate of road traffic accidents (RTAs) and inadequate infrastructure for trauma care[ 1 – 3 ]. The findings underscore the urgent need for targeted interventions to mitigate the risk of FSFs and improve trauma care in this region. The high prevalence of FSFs observed in this study reflects the global trend, particularly in LMICs, where RTAs are a leading cause of morbidity and mortality [ 1 , 2 ]. The World Health Organization (WHO) reported that approximately 1.2 million people die annually from road traffic injuries, and the majority of these deaths occur in LMICs[ 3 ]. Tanzania, like many other LMICs, has experienced a rise in road traffic crashes due to increased vehicle usage, poor road conditions, and inadequate enforcement of traffic laws [ 1 , 3 ]. Studies from other LMICs, such as Saudi Arabia, have similarly reported a high incidence of FSFs among RTA victims, further supporting the notion that RTAs are a major contributor to the burden of FSFs in these settings[ 3 , 4 ]. A notable finding in this study was the predominance of males (76%) among the FSF patients, with the majority falling within the age range of 18–34 years. This demographic trend aligns with previous studies that have consistently reported a higher prevalence of FSFs among young adult males[ 3 – 5 ]. The reasons for this could be attributed to the greater exposure of men to high-risk activities, such as driving and riding motorcycles, as well as their involvement in occupations that predispose them to trauma [ 4 ]. Additionally, young adults are more likely to engage in risky behaviors, such as speeding and disobeying traffic regulations, which increases their likelihood of being involved in high-energy trauma incidents [ 3 , 5 ]. In terms of fracture characteristics, the study found that the majority of FSFs occurred at the mid-shaft of the femur (54.7%), followed by fractures of the proximal and distal femur. This distribution is consistent with findings from other studies in LMICs, where mid-shaft fractures are the most common type of FSF due to the high-energy mechanisms typically involved in RTAs [ 1 , 4 ]. Mid-shaft fractures often result from direct trauma, such as in vehicle collisions, and are associated with significant morbidity and complications[ 2 ]. The high incidence of mid-shaft FSFs in this study may be linked to the severity of RTAs in the region and the lack of safety measures, such as seat belts and helmets, which could reduce the impact of trauma. Associated injuries were also prevalent among patients with FSFs, with head injuries being the most common. Approximately 28.6% of the patients in this study sustained head injuries in addition to FSFs. This finding is consistent with previous research that has shown a high rate of associated head injuries in patients with FSFs, particularly in high-energy trauma cases [ 4 , 7 ]. Head injuries are often life-threatening and can exacerbate the overall prognosis of patients with FSFs, increasing the risk of long-term disability or death [ 7 , 11 ]. The high prevalence of associated injuries, including tibia fractures and musculoskeletal injuries, further emphasizes the need for comprehensive trauma care that addresses not only the fractures but also the other serious injuries that frequently accompany them. The study also examined the factors associated with FSFs and found that young age and male gender were significant risk factors for FSFs. Patients aged 18–34 had significantly higher odds of sustaining FSFs compared to older age groups (aOR = 5.92, 95% CI: 1.39–25.18, p = 0.016). This finding is supported by previous research, which has shown that young adults are more likely to be involved in RTAs and sustain high-energy injuries, such as FSFs [ 1 , 5 ]. Males were also found to have over three times higher odds of experiencing FSFs compared to females (aOR = 3.34, 95% CI: 1.20–9.33, p = 0.021). This gender difference in FSF risk has been well-documented in the literature and is often attributed to the higher exposure of males to RTAs and high-risk activities [ 2 , 5 ]. Seasonal variation was another factor significantly associated with FSFs in this study, with a higher occurrence of FSFs during the summer season compared to spring (aOR = 3.11, 95% CI: 1.33–7.30, p = 0.009). The increased incidence of FSFs during the summer months may be related to increased travel and outdoor activities, leading to a higher likelihood of RTAs. Similar findings have been reported in other studies, where seasonal variations in trauma cases were linked to changes in weather and activity levels [ 1 – 3 ]. This suggests that public health interventions aimed at reducing RTAs and FSFs should take seasonal patterns into account when planning prevention strategies. The study findings have important implications for public health policy and trauma care in Tanzania. The high prevalence of FSFs and associated injuries highlights the need for improved trauma care infrastructure, including the availability of surgical implants and trained orthopedic surgeons, particularly in rural and underserved areas[ 1 , 6 ]. Furthermore, targeted interventions aimed at reducing RTAs, such as stricter enforcement of traffic laws, public education campaigns on road safety, and the promotion of the use of seat belts and helmets, could help reduce the incidence of FSFs and other serious injuries [ 2 – 4 ]. The present study demonstrated a high prevalence of FSFs among MTA victims in Southern Tanzania, with young males being particularly at risk. The findings underscore the urgent need for comprehensive trauma care and targeted prevention strategies to address the burden of FSFs in this region. Future research should focus on evaluating the effectiveness of these interventions and exploring additional risk factors for FSFs in different populations. The present study has several limitations; First, the retrospective nature of the study relied on secondary data, which was sometimes incomplete or inaccurately recorded, potentially leading to missing or biased information. To mitigate this, data cleaning process was employed to ensure the validity of the dataset, and incomplete records were excluded. Second, the study was conducted in selected referral hospitals, which may not fully represent the broader population of Southern Tanzania, limiting the generalizability of the findings. However, by including referral hospitals within Southern Tanzania, the study aimed to capture a more diverse sample. Lastly, recall bias may have affected the accuracy of some variables, such as the mechanism of injury or socio-economic factors. To address this, the study relied on objective data from patient files and medical records, rather than self-reported information from clinicians. These mitigation strategies helped minimize the impact of potential limitations on the study’s conclusions. Conclusion This study revealed the existing high prevalence of femoral shaft fractures (FSFs) among individuals involved in motor traffic accidents (MTAs) in Southern Tanzania. The observed FSFs burden represent a significant burden of injury in the region, highlighting an urgent public health issue that demands immediate attention. Young adult males are disproportionately affected by these injuries, underscoring the need for targeted interventions aimed at this group. The factors associated with FSFs, includes age, gender, and seasonal variations in accident occurrences. Notably, the predominance of mid-shaft fractures and the high rates of associated injuries, particularly head trauma, further complicate the clinical picture and necessitate comprehensive trauma care strategies. These findings reinforce the need for improved trauma management systems and preventive measures to reduce the incidence of both RTAs and resultant injuries in Southern Tanzania. Abbreviations CDH, Council Designated Hospital; DMOs, District Medical Officers; FSFs, Femoral Shaft Fractures; HICs, High Income Countries; IQR, Interquartile Range; LMICs, Low and Middle-Income Countries; MoH, Ministry of Health; MTAs, Motor Traffic Accidents; MTC, Motor Traffic Crush; NatHREC, National Health Research Review Committee; NIMR, National Institute for Medical Research; RMOs, Regional Medical Officers; RRH, Regional Referral Hospital; WHO, World Health Organization. Declarations Ethics statement Ethical approval for the study was granted by the National Health Research Review Committee (NatHREC) of the National Institute for Medical Research (NIMR) under identification number: NIMR/HQ/R.8a/VOL.IX/4564. Permission to conduct the study was obtained from the Regional Medical Officers (RMOs) of the selected regions (Mtwara and Lindi), as well as from the District Medical Officers (DMOs) of the selected districts: Mtwara Municipal Council (MC), Masasi District Council (DC), Lindi MC, and Lindi DC. Additionally, approval was granted by the Medical Officers in Charge (MOIs) of the Southern Zone Referral Hospital, Ligula Regional Referral Hospital, St. Benedict’s Ndanda Council Designated Hospital (CDH), Lindi Regional Referral Hospital, and St. Walburg’s Nyangao CDH. The study did not involve direct contact with patients. Confidentiality of the participants was ensured by using special identification codes, and patient names and identifying information were excluded from the dataset. Data were securely stored and maintained by the principal investigator, with access granted only to authorized personnel. Consent for publication Not applicable. Availability of data and material Data is provided within the manuscript and the supplementary information files. Competing interests All authors declare that they have no commercial or other associations that may pose a conflict of interest. Funding No funding was obtained for this study. Acknowledgments The authors thank the Ministry of Health and the Mtwara Southern Zone Referral Hospital administration for their tremendous support in conducting this research work. Special thanks to the Regional Medical Officers (RMOs) of the selected regions (Mtwara and Lindi), as well as from the District Medical Officers (DMOs) of the selected districts: Mtwara Municipal Council (MC), Masasi District Council (DC), Lindi MC, and Lindi DC. Additionally, the authors would like to acknowledge the Executive director of Southern Zone Referral Hospital, and the Medical Officers in Charge (MOIs) of Ligula Regional Referral Hospital, St. Benedict’s Ndanda Council Designated Hospital (CDH), Lindi Regional Referral Hospital, and St. Walburg’s Nyangao CDH. Author Contributions Conceptualization: Vulstan James Shedura, Geofrey John Ngomo, Lameck Titus Moses. Data curation: Vulstan James Shedura, Geofrey John Ngomo. Formal analysis: Vulstan James Shedura. Methodology: Vulstan James Shedura, Lameck Titus Moses. Resources: Herbert George Masigati. Supervision: Lameck Titus Moses, Herbert George Masigati. Writing ± original draft: Vulstan James Shedura, Geofrey John Ngomo. Writing ± review & editing: Vulstan James Shedura, Geofrey John Ngomo, Lameck Titus Moses, Herbert George Masigati. References D. Conway, P. Albright, E. Eliezer, B. Haonga, S. Morshed, and D. W. Shearer, “The burden of femoral shaft fractures in Tanzania,” Injury , vol. 50, no. 7, pp. 1371–1375, 2019, doi: 10.1016/j.injury.2019.06.005. K. J. Agarwal-harding, J. G. Meara, and S. L. M. Greenberg, “Estimating the Global Incidence of Femoral Fracture from Road Traffic Collisions,” J. BONE Jt. SURGERY,INCORPORATED , vol. 97, no. e31, pp. 1–9, 2021, [Online]. Available: http://dx.doi.org/10.2106/JBJS.N.00314 A. Sonbol et al. , “Prevalence of femoral shaft fractures and associated injuries among adults after road traffic accidents in a Saudi Arabian trauma center,” J. Musculoskelet. Surg. Res. , vol. 2, no. 2, p. 62, 2018, doi: 10.4103/jmsr.jmsr_42_17. M. B. Chagomerana, J. Tomlinson, S. Young, M. C. Hosseinipour, L. Banza, and C. N. Lee, “High morbidity and mortality after lower extremity injuries in Malawi: A prospective cohort study of 905 patients,” Int. J. Surg. , vol. 39, pp. 23–29, 2017, doi: 10.1016/j.ijsu.2017.01.047. F. M. Kalande and M. Surg, “Treatment outcomes of open femoral fractures at a county Hospital in Nakuru, Kenya,” East African Orthop. J. , vol. 12, no. 2, pp. 52–55, 2018, [Online]. Available: https://www.ajol.info/index.php/eaoj/article/view/180019 O. R. Ugezu AI, Nze IN, Ihegihu CC, Chukwuka NC, Ndukwu CU, “Management of Femoral Shaft Fractures in a Tertiary Centre , South Est Nigeria,” Afrimedic J. , vol. 6, no. 1, pp. 27–34, 2018, [Online]. Available: https://www.mendeley.com/catalogue/3f072870-e423-3abe-b6ce-9455c6117e71/?utm_source=desktop&utm_medium=1.19.4&utm_campaign=open_catalog&userDocumentId=%7Bf3c22cc9-96dc-49ac-a9ae-c946f0feaac7%7D A. P. Macha, R. Temu, F. Olotu, N. P. Seth, and H. L. Massawe, “Epidemiology and associated injuries in paediatric diaphyseal femur fractures treated at a limited resource zonal referral hospital in northern Tanzania,” BMC Musculoskelet. Disord. , vol. 23, no. 1, pp. 1–7, 2022, doi: 10.1186/s12891-022-05320-x. N. Enninghorst, D. McDougall, J. A. Evans, K. Sisak, and Z. J. Balogh, “Population-based epidemiology of femur shaft fractures,” J. Trauma Acute Care Surg. , vol. 74, no. 6, pp. 1516–1520, 2013, doi: 10.1097/TA.0b013e31828c3dc9. A. T. Schade et al. , “Epidemiology of fractures and their treatment in Malawi: Results of a multicentre prospective registry study to guide orthopaedic care planning,” PLoS One , vol. 16, no. 8 August, pp. 1–14, 2021, doi: 10.1371/journal.pone.0255052. A. Salonen, E. Laitakari, H. E. Berg, L. Felländer-Tsai, V. M. Mattila, and T. T. Huttunen, “Incidence of femoral fractures in children and adolescents in Finland and Sweden between 1998 and 2016: A binational population-based study,” Scand. J. Surg. , vol. 111, no. 1, pp. 1–8, 2022, doi: 10.1177/14574969221083133. F. Chad A Asplund, MD, MPH and M. Thomas J Mezzanotte, “Midshaft femur fractures in adults,” Up To Date , pp. 1–22, 2017, [Online]. Available: www.uptodate.com G. K. Gathecha et al. , “Prevalence and predictors of injuries in Kenya: Findings from the national STEPs survey,” BMC Public Health , vol. 18, no. Suppl 3, p. 1222, 2018, doi: 10.1186/s12889-018-6061-x. Tanzania National Bureau of Statistics and President’s Office, “Mtwara (Region, Tanzania) - Population Statistics, Charts, Map and Location,” 2022. Accessed: Oct. 10, 2024. [Online]. Available: https://www.citypopulation.de/en/tanzania/admin/09__mtwara/ Additional Declarations No competing interests reported. Supplementary Files S1File.pdf S1 Data Abstraction tool. S2File.pdf S2STROBE Checklist. S3File.zip S3 Raw data of the People Experienced MTA in Southern Tanzania. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5701767","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":433015921,"identity":"a625d019-e775-44c9-bf07-bf56d9a6fb6b","order_by":0,"name":"Vulstan James Shedura","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYFAC5gYwZQBGDDZAzNh4AL8WxgaGAwgtaWARkrQcBovh1WJwI7H584eaO/bm7M3bpCtqztutbT8MtKXGJhqPljaJA8eeJe7sOVYmeebY7eRtZxKBWo6l5Tbg0CI5I7GN4QDb4QSDGzlmkg1st5PNDgC1MDYcxqel+cOBf4ftDe6/AWr5dy7Z7PxD/Fr4JRIbJA62HWbccIPHTLKx7YCd2Q0CtvDzPGyTONt3OHHDmbRiy8a+5ASzG0BbEvD4hY09+fCHim9Ahx0/vPFmwzc7e7Pz6Q8ffKixwakFAySCVSYQqxwE7ElRPApGwSgYBSMDAABnaGwQplMb6AAAAABJRU5ErkJggg==","orcid":"","institution":"Southern Zone Referral Hospital","correspondingAuthor":true,"prefix":"","firstName":"Vulstan","middleName":"James","lastName":"Shedura","suffix":""},{"id":433015922,"identity":"a789365f-ac57-40b1-81e1-c59d8045a6f3","order_by":1,"name":"Geofrey John Ngomo","email":"","orcid":"","institution":"Southern Zone Referral Hospital","correspondingAuthor":false,"prefix":"","firstName":"Geofrey","middleName":"John","lastName":"Ngomo","suffix":""},{"id":433015923,"identity":"ac9ac2ef-dc89-401c-9987-91682b248f70","order_by":2,"name":"Lameck Titus Moses","email":"","orcid":"","institution":"Southern Zone Referral Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lameck","middleName":"Titus","lastName":"Moses","suffix":""},{"id":433015924,"identity":"46b251ed-ea8d-41d9-9c35-08f6d4722053","order_by":3,"name":"Herbert George Masigati","email":"","orcid":"","institution":"Southern Zone Referral Hospital","correspondingAuthor":false,"prefix":"","firstName":"Herbert","middleName":"George","lastName":"Masigati","suffix":""}],"badges":[],"createdAt":"2024-12-23 19:08:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5701767/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5701767/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79263592,"identity":"f1711b5e-ceb2-40fd-84f3-1f04613f5409","added_by":"auto","created_at":"2025-03-26 09:52:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":282078,"visible":true,"origin":"","legend":"\u003cp\u003eA scheme showing the data of patients experienced MTAs in selected hospitals in Southern Tanzania.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-5701767/v1/3afa7742fbe5ceb279d7407d.png"},{"id":79263591,"identity":"c27e967f-a32e-440b-a1d7-85bdc476f3f8","added_by":"auto","created_at":"2025-03-26 09:52:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":209020,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDistribution of the patients with Femur fracture according to Anatomic site of the fracture Among the Study participants (N=154).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-5701767/v1/a655bce7a669b2ba717e00bd.png"},{"id":79263596,"identity":"1784549d-9e25-4afb-bcd6-182e797a6fee","added_by":"auto","created_at":"2025-03-26 09:52:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":262008,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDistribution of type of injuries in all patients experienced Motor Traffic Accident in Southern Tanzania (N=154).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-5701767/v1/6810941ef3c1d10217e2cad8.png"},{"id":79266477,"identity":"f4feb2b5-51fa-48c5-afb6-655a0db6107e","added_by":"auto","created_at":"2025-03-26 10:08:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2425108,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5701767/v1/eb8e4820-d888-412f-a366-bcd847053fbc.pdf"},{"id":79265454,"identity":"2ef01a10-565c-43e9-8262-1cc09aba2b9a","added_by":"auto","created_at":"2025-03-26 10:00:46","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":162213,"visible":true,"origin":"","legend":"\u003cp\u003eS1 Data Abstraction tool.\u003c/p\u003e","description":"","filename":"S1File.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5701767/v1/b7dccac8aa7436584a551d64.pdf"},{"id":79263594,"identity":"eb80f680-64f8-4248-9768-4d568bd96358","added_by":"auto","created_at":"2025-03-26 09:52:46","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":88249,"visible":true,"origin":"","legend":"\u003cp\u003eS2STROBE Checklist.\u003c/p\u003e","description":"","filename":"S2File.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5701767/v1/cb515038fa4382dd65d38444.pdf"},{"id":79263595,"identity":"6108c3f3-6eea-4418-bac1-d8ee80f725fa","added_by":"auto","created_at":"2025-03-26 09:52:46","extension":"zip","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":77788,"visible":true,"origin":"","legend":"\u003cp\u003eS3 Raw data of the People Experienced MTA in Southern Tanzania.\u003c/p\u003e","description":"","filename":"S3File.zip","url":"https://assets-eu.researchsquare.com/files/rs-5701767/v1/bfe7d1cee6f9b956d9ca7aa7.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Hidden Epidemic: Unveiling Associated Injuries Among Motor Traffic Accident Victims in Southern Tanzania","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003cem\u003eFemoral shaft fractures\u003c/em\u003e (FSFs) are among the most severe musculoskeletal (MSK) injuries and are a significant cause of mortality and disability, particularly in low- and middle-income countries (LMICs)[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The World Health Organization (WHO) reported in 2018 that motor traffic injuries caused approximately 1.2\u0026nbsp;million deaths globally, with road traffic accidents (RTAs) ranking as the ninth leading cause of morbidity and the tenth leading cause of mortality worldwide[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. RTAs also contribute significantly to hospital admissions, with LMICs bearing 90% of global road traffic mortality and disability. FSFs are a major consequence of RTAs, with an estimated 20\u0026ndash;50\u0026nbsp;million people globally disabled due to MSK injuries annually, of which FSFs are among the most common [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFSFs often result from high-energy trauma caused by RTAs, which lead to significant suffering, including disability and associated injuries such as traumatic brain injuries. Blood loss in FSFs can be substantial, with reports of 2\u0026ndash;3 units lost per patient, which may lead to irreversible shock and death [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. LMICs face unique challenges, such as inadequate financial resources and a focus on prevention rather than treatment, resulting in higher rates of mortality and disability among FSF patients. For example, a study in Saudi Arabia reported that one person is killed and four are injured every hour due to RTAs, with over 65% of these incidents attributed to speeding, non-compliance with traffic signals, and alcohol use[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRTAs impose substantial economic burdens on victims, their families, and nations. In LMICs, the lack of surgical resources and implants often results in non-surgical management of FSFs, leading to prolonged hospital stays and increased mortality rates. Even where surgical options exist, the high costs are prohibitive for many patients, compounding the burden of disability and death[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Studies have shown that young adult males are the most affected demographic, frequently presenting with associated injuries such as head trauma, lower limb fractures, and upper limb fractures [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The incidence of FSFs ranges between 15.7 and 45.5 per 100,000 people per year in LMICs. In Tanzania, the annual incidence is reported to range between 2.1 and 18.4 per 100,000 people, with FSFs being a common presentation in orthopedic departments [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe rise in the number of vehicles, particularly motorcycles, has contributed to the increasing prevalence of FSFs in Tanzania. Despite this alarming trend, data on the magnitude and associated factors of FSFs, especially in Southern Tanzania, remain scarce. This study aims to address this gap by determining the prevalence of FSFs and identifying associated factors among RTA victims in selected referral hospitals in Southern Tanzania.\u003c/p\u003e \u003cp\u003eGlobally, the annual incidence of FSFs from RTAs is estimated to be between 1.0 and 2.9\u0026nbsp;million, with LMICs experiencing significantly higher rates compared to high-income countries (HICs). FSFs disproportionately affect young individuals, emphasizing the need for improved access to treatment [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Injuries, including FSFs, account for approximately 9% of global deaths and have profound social and economic impacts on affected individuals and their families. FSFs require intensive inpatient care, further straining healthcare resources in LMICs [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStudies from various regions highlight the common mechanisms and demographics associated with FSFs. Motor vehicle collisions are the leading cause of FSFs in older children and adolescents, whereas falls are the primary cause in younger children[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In LMICs, males are disproportionately affected, often sustaining FSFs as a result of high-energy trauma. For example, a study in Newcastle reported that 62% of FSF cases involved males, many of whom had multiple injuries requiring specialized care [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Other studies have identified additional risk factors, such as alcohol consumption, smoking, and non-compliance with road safety measures, underscoring the need for targeted interventions to reduce the prevalence of FSFs [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn Tanzania, the prevalence of FSFs has been linked to an increasing number of RTAs, with males aged 21\u0026ndash;30 years being the most affected demographic. Associated injuries, such as traumatic brain injuries, further intensify the burden. Limited surgical resources and financial constraints contribute to the high rates of disability and mortality among FSF patients. Previous studies in northern Tanzania reported that FSFs accounted for 28.6% of orthopedic cases, with the majority of injuries resulting from RTAs [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the high burden of FSFs, data specific to Southern Tanzania are limited. This study seeks to fill this gap by assessing the prevalence and associated factors of FSFs among individuals involved in RTAs. The findings will provide valuable insights for policymakers, the Ministry of Health, and other stakeholders to develop strategies for prevention, allocate resources for treatment, and address the factors contributing to the high burden of FSFs in the region. These strategies may include funding for implants, improving access to surgical care, and strengthening road safety measures to mitigate the impact of RTAs on FSFs.\u003c/p\u003e \u003cp\u003eFSFs represent a significant public health challenge in Tanzania, with substantial socio-economic implications. This study aims to provide a comprehensive understanding of the prevalence and associated factors of FSFs in Southern Tanzania, contributing to the development of effective interventions to reduce the burden of these injuries.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy design\u003c/h2\u003e\n \u003cp\u003eThis was a retrospective health facility-based cross-sectional study conducted between February 2024 to April 2024 by reviewing the secondary data from January to December 2023 of people experienced motor traffic accident from the selected referral hospitals in southern Tanzania.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eStudy area\u003c/h3\u003e\n\u003cp\u003eThis study was conducted in southern Tanzania, comprising the Lindi, Mtwara, and Ruvuma regions, which together cover an area of 146,419 square kilometers[\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]. The southern zone is situated between longitudes 34\u0026deg;32\u0026rsquo;E and 40\u0026deg;26\u0026rsquo;E and latitudes 7\u0026deg;56\u0026rsquo;S and 11\u0026deg;44\u0026rsquo;S. It is bordered by the Ruvuma River and Mozambique to the south, Ruvuma region to the west, and Lindi region to the north[\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]. This area is suitable for human settlement and agricultural activities, with the primary economic activities including agriculture, fishing, forestry, and beekeeping. The main cash crops grown in the region are cashew nuts and coffee, while maize, beans, cassava, and rice are the primary food crops. The transport network in the southern zone is reliable year-round, connecting the region to other parts of Tanzania via several road routes. The coastal trunk road links Mtwara, Lindi, Kilwa, and Dar es Salaam, while another major road connects Songea, Njombe, and Makambako to the Tanzania-Zambia Highway.\u003c/p\u003e\n\u003cp\u003eThe study was conducted in five hospitals located in the Mtwara and Lindi regions within the southern zone of Tanzania. These hospitals provide tertiary-level services in orthopedics and trauma care. The selected hospitals included the Southern Zone Referral Hospital, Ligula Regional Referral Hospital (RRH), St. Benedict\u0026rsquo;s Ndanda Council Designated Hospital (CDH), Lindi RRH, and St. Walburg\u0026rsquo;s Nyangao CDH. These facilities were purposively selected due to their accessibility and because they handle a high volume of patients with motor traffic accident-related injuries, including femoral shaft fractures (FSFs), compared to other hospitals in southern Tanzania.\u003c/p\u003e\n\u003ch3\u003eStudy Population\u003c/h3\u003e\n\u003cp\u003eThe study population comprised all patients (of any age) experienced motor traffic accident who attended at the Southern Zone Referral Hospital, Ligula Regional Referral Hospital (RRH), St. Benedict\u0026rsquo;s Ndanda Council Designated Hospital (CDH), Lindi RRH, and St. Walburg\u0026rsquo;s Nyangao CDH in southern Tanzania, from February 2024 to April 2024.\u003c/p\u003e\n\u003ch3\u003eInclusion criteria\u003c/h3\u003e\n\u003cp\u003eAll patients involved in motor traffic accidents who sought care at the selected study hospitals.\u003c/p\u003e\n\u003ch3\u003eExclusion criteria\u003c/h3\u003e\n\u003cp\u003ePatients with injuries caused by factors other than motor traffic accidents, as well as those with incomplete or missing information, were excluded from the analysis.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eSample size estimation and Sampling method\u003c/h2\u003e\n \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n \u003ch2\u003eSample size estimation\u003c/h2\u003e\n \u003cp\u003eWe calculated the sample size using the Kish and Leslie formula based on the data from a prevalence study conducted in Tanzania, with a prevalence rate of 10.0% for femoral shaft fractures [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. We used a two-sided 95% confidence interval, a marginal error of 5%, and by considering a 10% non-response rate. The resulting sample size was 154.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eThe sample size distribution per selected Hospital\u003c/h3\u003e\n\u003cp\u003eFigure\u0026nbsp;1 illustrates the distribution of patients who experienced accidents and sought medical care at selected hospitals within the study period. In total, 182 patients were involved in the study, with 175 of them having experienced MTAs, while the remaining 5 patients experienced other types of accidents. However, 16 patient records were excluded due to missing information, leaving 154 patients with complete record information for further analysis.\u003c/p\u003e\n\u003cp\u003eProbability Proportional to size (PPS) was used to calculate the sample size for each selected hospital. Their distribution per hospital was as follows: Ligula Regional Referral Hospital (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;19, 12.3%), Sokoine Regional Referral Hospital (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;34, 22.1%), Southern Zone Referral Hospital (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4, 2.6%), St. Benedict\u0026rsquo;s Ndanda Hospital (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;54, 35.1%), St. Walburg\u0026rsquo;s Nyangao Hospital (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;43, 27.9%). These hospitals were selected based on their capacity and the number of patients who experienced accidents during the study period. The distribution reflects the relative sizes of the hospitals and their contribution to the overall sample.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eSampling technique\u003c/h2\u003e\n \u003cp\u003eStudy participants were selected from the dataset using a systematic random sampling method, based on the list of participants in the sampling frame from each selected study site. The sample size for each hospital was determined using Probability Proportional to Size (PPS). Once the required number of participants for each facility was calculated, a list of all eligible patients who had experienced motor traffic accidents (the sampling frame) was compiled, and each patient record was assigned a unique number. The sampling fraction was calculated by dividing the total number of patients who had experienced motor traffic accidents (the sampling frame) by the sample size for each hospital. A random number between 1 and the calculated factor was selected to determine the starting point in the sampling frame. Participants records were then selected at regular intervals (based on the obtained factor) using systematic random sampling.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eData collection procedures\u003c/h2\u003e\n \u003cp\u003ePrior to the actual data collection, a pre-tested paper-based structured data abstraction tool (S1) was developed and used to gather both socio-demographic and clinical data. Patients were clinically and radiographically assessed using information from their medical records. The diagnosis of FSFs was based on a review of the patient\u0026rsquo;s history, clinical examination, and radiological findings (anteroposterior and lateral X-rays) from their files. All data were extracted from the patients\u0026apos; medical records at each selected hospital. The information collected included the patients\u0026apos; demographic details, the cause of the injury, the side of injury, the anatomical site of the injury, and any associated injuries.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eVariables\u003c/h2\u003e\n \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\n \u003ch2\u003eDependant variable\u003c/h2\u003e\n \u003cp\u003eThe dependent variable was femoral shaft fractures. This was categorized as a binary outcome: either \u003cem\u003eno femoral shaft fracture\u003c/em\u003e or \u003cem\u003efemoral shaft fracture\u003c/em\u003e.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eIndependent variables\u003c/h2\u003e\n \u003cp\u003eThe independent variables included age, sex, causes of the fracture, side of injury, anatomical site of injury, education level, economic status, area of residence, ethnicity, religion, and season of the year.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eInvestigation tools, validity and reliability issues\u003c/h2\u003e\n \u003cp\u003eIn this study, a structured data abstraction tool (S1) was used to collect socio-demographic and clinical information from the participants\u0026apos; files. The tool was validated through a pilot (pre-testing) phase before the actual data collection to ensure it would yield the desired results. A total of 35 patient files (10% of the total) were selected for pre-testing, which were included in the overall study sample. The findings from the pilot study were used to make necessary adjustments and improve the tool.\u003c/p\u003e\n \u003cp\u003eTo ensure reliability and accuracy, the same paper-based tools were administered to all data collectors. Only the principal investigator and trained researchers were involved in the data collection process. Both English and Swahili versions of the structured data abstraction tool were used to gather the required information from the participants.\u003c/p\u003e\n \u003cp\u003eThe validity of the tool was ensured by reviewing all questions with the help of research experts, who provided their input on the items to include, ensuring that the tool covered the research objectives effectively.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eData processing and analysis\u003c/h2\u003e\n \u003cp\u003eAfter data collection, the data were entered into Microsoft Excel\u0026reg; 2019, cleaned, and checked for errors to ensure completeness (S3). The data were then double-entered and exported to Stata\u0026reg; version 15.1 for analysis (S3). Frequency and proportions were calculated for categorical variables, while continuous variables were summarized using the median and interquartile range (IQR) as measures of central tendency. The association between dependent and independent variables was tested using the Chi-square (X\u0026sup2;) test. Bivariate and multivariate logistic regression models were employed to explore the relationships between dependent and independent variables. Bivariate analysis was first conducted to examine the association of each independent variable with the dependent variable. Variables with a \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.2 in bivariate analysis were included in the multivariate logistic regression analysis. Crude odds ratios (cOR) and adjusted odds ratios (aOR), along with their 95% confidence intervals (CI), were used to assess the significance of the independent variables. Variables with a \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the multivariate analysis were considered statistically significant, and 95% CI was used to evaluate the strength of the associations.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003eSocio-demographic characteristics of the study participants\u003c/h2\u003e\n \u003cp\u003eA total of 154 patients who experienced motor traffic accidents in Southern Tanzania were enrolled in this study, with participants from five hospitals. The majority were from St. Benedict\u0026rsquo;s Ndanda Hospital (35.1%) and St. Walburg\u0026rsquo;s Nyangao Hospital (27.9%). The median age was 31.5 years (IQR: 22\u0026ndash;49). The study participants were predominantly males (76%) and Muslim (79.2%). Occupation-wise, most were self-employed (63%), followed by students (19.5%). Regarding marital status, 57.1% were married, and 41.6% were single. Education levels varied, with 55.8% having primary education and 27.9% secondary education. Only 4.6% had no formal schooling (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSocio- demographic characteristics of the study participants (N\u0026thinsp;=\u0026thinsp;154).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFrequency (n)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercent (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eStudy site\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLigula Regional Referral Hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSokoine Regional Referral Hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouthern Zone Referral Hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSt. Benedict\u0026rsquo;s Ndanda Hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSt. Walburg\u0026rsquo;s Nyangao Hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (in years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMedian (IQR)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e31.5 (22\u0026ndash;49)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026ndash;34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u0026ndash;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51\u0026ndash;64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eReligion status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChristian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMuslim\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupation status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStudent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDegree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiploma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCertificate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDidn\u0026rsquo;t attend school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003ePrevalence of FSF among patients experienced MTA in Southern Tanzania\u003c/h2\u003e\n \u003cp\u003eIn this study, the prevalence of Femoral Shaft Fracture (FSF) among patients experienced Motor Traffic Accident in Southern Tanzania was 37.7% (58/154) (Fig. 2). The majority (29.3%, 17/58) of the patients with Femoral shaft fracture were from St. Walburg\u0026rsquo;s Nyangao Hospital and the least (1.7%, 1/58) from Southern Zone Referral Hospital (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The prevalence of other Femur fracture patterns was proximal femur 20.8% (32/154), and the distal femur 10.4% (16/154) (Fig. 2).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eProportion of Femoral shaft fractures among study sites in the Southern Zone referral Hospitals (N\u0026thinsp;=\u0026thinsp;58).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStudy site\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFrequency (n)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProportion of FSF (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSt. Walburg\u0026rsquo;s Nyangao Hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSokoine Regional Referral Hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLigula Regional Referral Hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSt. Benedict\u0026rsquo;s Ndanda Hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouthern Zone Referral Hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e58\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003eDistribution of Femur fracture characteristics and associated injuries\u003c/h2\u003e\n \u003cp\u003eOut of the total 154 patients involved in motor traffic accidents, 68.8% (106 patients) had femur fractures. Of these, 14.9% had femur fractures without associated injuries, while 53.9% had both femur fractures and additional associated injuries. Another 31.2% of patients had only associated injuries without femur fractures (Fig. 3).\u003c/p\u003e\n \u003cp\u003eAmong the 106 patients with femur fractures, majority (84.9%) had closed fractures. Most of these fractures occurred at the mid-shaft of the femur (54.7%) and the proximal site (30.2%) (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Regarding the side of the fracture, 53.8% of patients had fractures on the left femur, and 46.2% on the right. A significant proportion (81.1%) had isolated femur fractures, while 18.9% had additional injuries (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The most common fracture pattern was transverse (54.7%), and oblique fractures (34%). Less common patterns included comminuted (8.5%) and spiral fractures (2.8%) (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDistribution of Femur fracture characteristics of the studied patients (N\u0026thinsp;=\u0026thinsp;106).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInjury characteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFrequency (n)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercent (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eFracture type\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClosed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e84.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOpen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFracture anatomical site\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProximal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMid-shaft\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSide of Femur fracture\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIsolated Femur fracture\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePattern of femur fracture\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTransverse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOblique\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpiral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComminuted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eIn the present study, head injuries were the most common, affecting 28.6% of patients, followed by musculoskeletal injuries (26%). Less common were chest injuries (7.1%), visceral injuries (5.8%), radius fractures (2.6%), and left upper limb fractures (0.7%). A total of 14.9% of patients had no associated injuries (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Head injuries were slightly more common among males (29.1%) than females (27.0%), and musculoskeletal injuries were more frequent in females (35.1%) than males (23.1%). Tibia fractures were also more common in females (27.0%) compared to males (10.3%). Males had higher frequencies of chest injuries, visceral injuries, and radius fractures. A statistically significant difference was noted for the Femur fracture-associated injuries (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041), indicating a gender-based variation (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDistribution of the studied patients by associated injuries according to their Sex (N\u0026thinsp;=\u0026thinsp;154).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eType of associated injury\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHead injury\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (27.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 (29.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44 (28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMusculoskeletal injury\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (35.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 (26.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTibia fracture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (27.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChest injury\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVisceral injury\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRadius fracture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft upper limb fracture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo associated injuries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22 (18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (14.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eIn the present study, head injuries were more frequent among younger patients (\u0026le;\u0026thinsp;17 years: 37.9%, 18\u0026ndash;34 years: 31.6%) compared to older groups. Musculoskeletal injuries were more prevalent in patients aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years (50%) and the 51\u0026ndash;64 age group (28.6%). Tibia fractures were distributed across all age groups, with a higher percentage among the 51\u0026ndash;64 age group (28.6%). There was no significant age-related difference in the distribution of injuries (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.162) (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDistribution of the studied patients by associated injuries according to their age groups (N\u0026thinsp;=\u0026thinsp;154).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eType of associated injury\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eAge (in years)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;17\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e18\u0026ndash;34\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e35\u0026ndash;50\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e51\u0026ndash;64\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHead injury\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11 (37.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18 (31.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1 (7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44 (28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMusculoskeletal injury\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2 (6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16 (28.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4 (28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40 (26.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTibia fracture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4 (13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7 (19.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4 (28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChest injury\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4 (13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1 (7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11 (7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVisceral injury\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4 (13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1 (7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9 (5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRadius fracture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft upper limb fracture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo associated injuries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4 (13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9 (15.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3 (21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5 (27.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23 (14.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e29 (100.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e57 (100.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e36 (100.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e14 (100.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e18 (100.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e154 (100.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n \u003ch2\u003eFactors associated with FSFs among patients experienced Motor MTA in Southern Tanzania\u003c/h2\u003e\n \u003cp\u003eIn bivariate and multivariate logistic regression analysis, the findings of this study showed that patients aged 18\u0026ndash;34 had significantly higher odds of FSF compared to those aged\u0026thinsp;\u0026ge;\u0026thinsp;65 [aOR\u0026thinsp;=\u0026thinsp;5.92 (95% CI: 1.39\u0026ndash;25.18), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016] indicating a statistically significant association. Other age groups showed higher odds compared to the reference group (\u0026ge;\u0026thinsp;65), but these were not statistically significant (Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eMales were significantly more likely to sustain FSF than females [aOR\u0026thinsp;=\u0026thinsp;3.34 (95% CI: 1.20\u0026ndash;9.33), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021]. This suggests that males had over three times higher odds of experiencing FSF compared to females. There was no significant association between occupation and FSF. Both employed and self-employed patients did not show statistically significant differences compared to unemployed patients. While there was no statistically significant difference among marital status categories, married patients tended to have a slightly higher prevalence of FSF compared to others (Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eNo significant association was found between education level and FSF, although patients with no schooling had the highest observed percentage of FSF (57.1%). Drivers had higher odds of FSF compared to pedestrians, but this was not statistically significant in either the bivariate or multivariate analysis. The occurrence of FSF was significantly higher in the summer season compared to spring [aOR\u0026thinsp;=\u0026thinsp;3.11 (95% CI: 1.33\u0026ndash;7.30), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009]. There was no significant difference between winter and spring in the occurrence of FSF (Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFactors associated with Femoral Shaft Fractures among patients experienced Motor Traffic Accident in Southern Tanzania (N\u0026thinsp;=\u0026thinsp;154).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFSF status\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eBivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMultivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCOR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAOR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAge (in years)\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25 (86.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4 (13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80 (0.16\u0026ndash;4.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.68 (0.17\u0026ndash;16.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.656\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026ndash;34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26 (45.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31 (54.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.96 (1.55\u0026ndash;22.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.92 (1.39\u0026ndash;25.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.016*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u0026ndash;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21 (58.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15 (41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.57 (0.88\u0026ndash;14.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.58 (0.80-15.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51\u0026ndash;64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9 (64.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5 (35.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.78 (0.53\u0026ndash;14.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.47 (0.42\u0026ndash;14.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15 (83.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31 (83.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6 (16.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65 (55.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52 (44.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.13 (1.60-10.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.34 (1.20\u0026ndash;9.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.021*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupation status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9 (52.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8 (47.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33 (0.27\u0026ndash;6.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05 (0.17\u0026ndash;6.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.955\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56 (57.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41 (42.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10 (0.29\u0026ndash;4.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18 (0.25\u0026ndash;5.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.837\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStudent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25 (83.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30 (0.06\u0026ndash;1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38 (0.04\u0026ndash;3.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.388\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6 (60.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4 (40.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53 (60.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35 (39.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.51 (0.09\u0026ndash;25.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42 (65.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22 (34.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79 (0.41\u0026ndash;1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDegree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75 (0.10\u0026ndash;5.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiploma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5 (62.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45 (0.06\u0026ndash;3.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27 (62.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16 (37.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44 (0.09\u0026ndash;2.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56 (65.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30 (34.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.40 (0.08\u0026ndash;1.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCertificate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75 (0.03\u0026ndash;17.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3 (42.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4 (57.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eVictim Category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDriver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24 (49.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25 (51.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.74 (0.74\u0026ndash;4.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePassenger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47 (72.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18 (27.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64 (0.28\u0026ndash;1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePedestrian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25 (62.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSeason of the year\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40 (75.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13 (24.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSummer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50 (54.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42 (45.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.58 (1.22\u0026ndash;5.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.11 (1.33\u0026ndash;7.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.009*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWinter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.54 (0.34\u0026ndash;7.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.64 (0.30\u0026ndash;9.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.569\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e*\u003cem\u003ep-values of the variables significantly associated with FSF among the study participants\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study revealed the prevalence of femoral shaft fractures (FSFs) among patients involved in motor traffic accidents (MTAs) in Southern Tanzania and identified the factors associated with these fractures. The findings revealed that FSFs were prevalent among MTA victims, with a rate of 37.7% (58/154), indicating a significant burden of these injuries in the region. This prevalence is consistent with previous studies that have demonstrated a high incidence of FSFs in low- and middle-income countries (LMICs) due to the high rate of road traffic accidents (RTAs) and inadequate infrastructure for trauma care[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The findings underscore the urgent need for targeted interventions to mitigate the risk of FSFs and improve trauma care in this region.\u003c/p\u003e \u003cp\u003eThe high prevalence of FSFs observed in this study reflects the global trend, particularly in LMICs, where RTAs are a leading cause of morbidity and mortality [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The World Health Organization (WHO) reported that approximately 1.2\u0026nbsp;million people die annually from road traffic injuries, and the majority of these deaths occur in LMICs[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Tanzania, like many other LMICs, has experienced a rise in road traffic crashes due to increased vehicle usage, poor road conditions, and inadequate enforcement of traffic laws [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Studies from other LMICs, such as Saudi Arabia, have similarly reported a high incidence of FSFs among RTA victims, further supporting the notion that RTAs are a major contributor to the burden of FSFs in these settings[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA notable finding in this study was the predominance of males (76%) among the FSF patients, with the majority falling within the age range of 18\u0026ndash;34 years. This demographic trend aligns with previous studies that have consistently reported a higher prevalence of FSFs among young adult males[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The reasons for this could be attributed to the greater exposure of men to high-risk activities, such as driving and riding motorcycles, as well as their involvement in occupations that predispose them to trauma [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Additionally, young adults are more likely to engage in risky behaviors, such as speeding and disobeying traffic regulations, which increases their likelihood of being involved in high-energy trauma incidents [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn terms of fracture characteristics, the study found that the majority of FSFs occurred at the mid-shaft of the femur (54.7%), followed by fractures of the proximal and distal femur. This distribution is consistent with findings from other studies in LMICs, where mid-shaft fractures are the most common type of FSF due to the high-energy mechanisms typically involved in RTAs [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Mid-shaft fractures often result from direct trauma, such as in vehicle collisions, and are associated with significant morbidity and complications[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The high incidence of mid-shaft FSFs in this study may be linked to the severity of RTAs in the region and the lack of safety measures, such as seat belts and helmets, which could reduce the impact of trauma.\u003c/p\u003e \u003cp\u003eAssociated injuries were also prevalent among patients with FSFs, with head injuries being the most common. Approximately 28.6% of the patients in this study sustained head injuries in addition to FSFs. This finding is consistent with previous research that has shown a high rate of associated head injuries in patients with FSFs, particularly in high-energy trauma cases [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Head injuries are often life-threatening and can exacerbate the overall prognosis of patients with FSFs, increasing the risk of long-term disability or death [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The high prevalence of associated injuries, including tibia fractures and musculoskeletal injuries, further emphasizes the need for comprehensive trauma care that addresses not only the fractures but also the other serious injuries that frequently accompany them.\u003c/p\u003e \u003cp\u003eThe study also examined the factors associated with FSFs and found that young age and male gender were significant risk factors for FSFs. Patients aged 18\u0026ndash;34 had significantly higher odds of sustaining FSFs compared to older age groups (aOR\u0026thinsp;=\u0026thinsp;5.92, 95% CI: 1.39\u0026ndash;25.18, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016). This finding is supported by previous research, which has shown that young adults are more likely to be involved in RTAs and sustain high-energy injuries, such as FSFs [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Males were also found to have over three times higher odds of experiencing FSFs compared to females (aOR\u0026thinsp;=\u0026thinsp;3.34, 95% CI: 1.20\u0026ndash;9.33, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021). This gender difference in FSF risk has been well-documented in the literature and is often attributed to the higher exposure of males to RTAs and high-risk activities [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeasonal variation was another factor significantly associated with FSFs in this study, with a higher occurrence of FSFs during the summer season compared to spring (aOR\u0026thinsp;=\u0026thinsp;3.11, 95% CI: 1.33\u0026ndash;7.30, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009). The increased incidence of FSFs during the summer months may be related to increased travel and outdoor activities, leading to a higher likelihood of RTAs. Similar findings have been reported in other studies, where seasonal variations in trauma cases were linked to changes in weather and activity levels [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This suggests that public health interventions aimed at reducing RTAs and FSFs should take seasonal patterns into account when planning prevention strategies.\u003c/p\u003e \u003cp\u003eThe study findings have important implications for public health policy and trauma care in Tanzania. The high prevalence of FSFs and associated injuries highlights the need for improved trauma care infrastructure, including the availability of surgical implants and trained orthopedic surgeons, particularly in rural and underserved areas[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Furthermore, targeted interventions aimed at reducing RTAs, such as stricter enforcement of traffic laws, public education campaigns on road safety, and the promotion of the use of seat belts and helmets, could help reduce the incidence of FSFs and other serious injuries [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe present study demonstrated a high prevalence of FSFs among MTA victims in Southern Tanzania, with young males being particularly at risk. The findings underscore the urgent need for comprehensive trauma care and targeted prevention strategies to address the burden of FSFs in this region. Future research should focus on evaluating the effectiveness of these interventions and exploring additional risk factors for FSFs in different populations.\u003c/p\u003e \u003cp\u003eThe present study has several limitations; First, the retrospective nature of the study relied on secondary data, which was sometimes incomplete or inaccurately recorded, potentially leading to missing or biased information. To mitigate this, data cleaning process was employed to ensure the validity of the dataset, and incomplete records were excluded. Second, the study was conducted in selected referral hospitals, which may not fully represent the broader population of Southern Tanzania, limiting the generalizability of the findings. However, by including referral hospitals within Southern Tanzania, the study aimed to capture a more diverse sample. Lastly, recall bias may have affected the accuracy of some variables, such as the mechanism of injury or socio-economic factors. To address this, the study relied on objective data from patient files and medical records, rather than self-reported information from clinicians. These mitigation strategies helped minimize the impact of potential limitations on the study\u0026rsquo;s conclusions.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study revealed the existing high prevalence of femoral shaft fractures (FSFs) among individuals involved in motor traffic accidents (MTAs) in Southern Tanzania. The observed FSFs burden represent a significant burden of injury in the region, highlighting an urgent public health issue that demands immediate attention. Young adult males are disproportionately affected by these injuries, underscoring the need for targeted interventions aimed at this group. The factors associated with FSFs, includes age, gender, and seasonal variations in accident occurrences. Notably, the predominance of mid-shaft fractures and the high rates of associated injuries, particularly head trauma, further complicate the clinical picture and necessitate comprehensive trauma care strategies. These findings reinforce the need for improved trauma management systems and preventive measures to reduce the incidence of both RTAs and resultant injuries in Southern Tanzania.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCDH, Council Designated Hospital; DMOs, District Medical Officers; FSFs, Femoral Shaft Fractures; HICs, High Income Countries; IQR, Interquartile Range; LMICs, Low and Middle-Income Countries; MoH, Ministry of Health; MTAs, Motor Traffic Accidents; MTC, Motor Traffic Crush; NatHREC, National Health Research Review Committee; NIMR, National Institute for Medical Research; RMOs, Regional Medical Officers; RRH, Regional Referral Hospital; WHO, World Health Organization.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for the study was granted by the National Health Research Review Committee (NatHREC) of the National Institute for Medical Research (NIMR) under identification number: NIMR/HQ/R.8a/VOL.IX/4564. Permission to conduct the study was obtained from the Regional Medical Officers (RMOs) of the selected regions (Mtwara and Lindi), as well as from the District Medical Officers (DMOs) of the selected districts: Mtwara Municipal Council (MC), Masasi District Council (DC), Lindi MC, and Lindi DC. Additionally, approval was granted by the Medical Officers in Charge (MOIs) of the Southern Zone Referral Hospital, Ligula Regional Referral Hospital, St. Benedict\u0026rsquo;s Ndanda Council Designated Hospital (CDH), Lindi Regional Referral Hospital, and St. Walburg\u0026rsquo;s Nyangao CDH. The study did not involve direct contact with patients. Confidentiality of the participants was ensured by using special identification codes, and patient names and identifying information were excluded from the dataset. Data were securely stored and maintained by the principal investigator, with access granted only to authorized personnel.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is provided within the manuscript and the supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that they have no commercial or other associations that may pose a conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was obtained for this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the Ministry of Health and the Mtwara Southern Zone Referral Hospital administration for their tremendous support in conducting this research work. Special thanks to the Regional Medical Officers (RMOs) of the selected regions (Mtwara and Lindi), as well as from the District Medical Officers (DMOs) of the selected districts: Mtwara Municipal Council (MC), Masasi District Council (DC), Lindi MC, and Lindi DC. Additionally, the authors would like to acknowledge the Executive director of Southern Zone Referral Hospital, and the Medical Officers in Charge (MOIs) of Ligula Regional Referral Hospital, St. Benedict\u0026rsquo;s Ndanda Council Designated Hospital (CDH), Lindi Regional Referral Hospital, and St. Walburg\u0026rsquo;s Nyangao CDH.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConceptualization:\u0026nbsp;\u003c/strong\u003eVulstan James Shedura, Geofrey John Ngomo, Lameck Titus Moses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData curation:\u0026nbsp;\u003c/strong\u003eVulstan James Shedura, Geofrey John Ngomo.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFormal analysis:\u0026nbsp;\u003c/strong\u003eVulstan James Shedura.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology:\u0026nbsp;\u003c/strong\u003eVulstan James Shedura, Lameck Titus Moses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResources:\u0026nbsp;\u003c/strong\u003eHerbert George Masigati.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupervision:\u0026nbsp;\u003c/strong\u003eLameck Titus Moses, Herbert George Masigati.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWriting \u0026plusmn; original draft:\u0026nbsp;\u003c/strong\u003eVulstan James Shedura, Geofrey John Ngomo.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWriting \u0026plusmn; review \u0026amp; editing:\u0026nbsp;\u003c/strong\u003eVulstan James Shedura, Geofrey John Ngomo, Lameck Titus Moses, Herbert George Masigati.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eD. Conway, P. Albright, E. Eliezer, B. Haonga, S. Morshed, and D. W. Shearer, \u0026ldquo;The burden of femoral shaft fractures in Tanzania,\u0026rdquo; \u003cem\u003eInjury\u003c/em\u003e, vol. 50, no. 7, pp. 1371\u0026ndash;1375, 2019, doi: 10.1016/j.injury.2019.06.005.\u003c/li\u003e\n \u003cli\u003eK. J. Agarwal-harding, J. G. Meara, and S. L. M. Greenberg, \u0026ldquo;Estimating the Global Incidence of Femoral Fracture from Road Traffic Collisions,\u0026rdquo; \u003cem\u003eJ. BONE Jt. SURGERY,INCORPORATED\u003c/em\u003e, vol. 97, no. e31, pp. 1\u0026ndash;9, 2021, [Online]. Available: http://dx.doi.org/10.2106/JBJS.N.00314\u003c/li\u003e\n \u003cli\u003eA. Sonbol \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Prevalence of femoral shaft fractures and associated injuries among adults after road traffic accidents in a Saudi Arabian trauma center,\u0026rdquo; \u003cem\u003eJ. Musculoskelet. Surg. Res.\u003c/em\u003e, vol. 2, no. 2, p. 62, 2018, doi: 10.4103/jmsr.jmsr_42_17.\u003c/li\u003e\n \u003cli\u003eM. B. Chagomerana, J. Tomlinson, S. Young, M. C. Hosseinipour, L. Banza, and C. N. Lee, \u0026ldquo;High morbidity and mortality after lower extremity injuries in Malawi: A prospective cohort study of 905 patients,\u0026rdquo; \u003cem\u003eInt. J. Surg.\u003c/em\u003e, vol. 39, pp. 23\u0026ndash;29, 2017, doi: 10.1016/j.ijsu.2017.01.047.\u003c/li\u003e\n \u003cli\u003eF. M. Kalande and M. Surg, \u0026ldquo;Treatment outcomes of open femoral fractures at a county Hospital in Nakuru, Kenya,\u0026rdquo; \u003cem\u003eEast African Orthop. J.\u003c/em\u003e, vol. 12, no. 2, pp. 52\u0026ndash;55, 2018, [Online]. Available: https://www.ajol.info/index.php/eaoj/article/view/180019\u003c/li\u003e\n \u003cli\u003eO. R. Ugezu AI, Nze IN, Ihegihu CC, Chukwuka NC, Ndukwu CU, \u0026ldquo;Management of Femoral Shaft Fractures in a Tertiary Centre , South Est Nigeria,\u0026rdquo; \u003cem\u003eAfrimedic J.\u003c/em\u003e, vol. 6, no. 1, pp. 27\u0026ndash;34, 2018, [Online]. Available: https://www.mendeley.com/catalogue/3f072870-e423-3abe-b6ce-9455c6117e71/?utm_source=desktop\u0026amp;utm_medium=1.19.4\u0026amp;utm_campaign=open_catalog\u0026amp;userDocumentId=%7Bf3c22cc9-96dc-49ac-a9ae-c946f0feaac7%7D\u003c/li\u003e\n \u003cli\u003eA. P. Macha, R. Temu, F. Olotu, N. P. Seth, and H. L. Massawe, \u0026ldquo;Epidemiology and associated injuries in paediatric diaphyseal femur fractures treated at a limited resource zonal referral hospital in northern Tanzania,\u0026rdquo; \u003cem\u003eBMC Musculoskelet. Disord.\u003c/em\u003e, vol. 23, no. 1, pp. 1\u0026ndash;7, 2022, doi: 10.1186/s12891-022-05320-x.\u003c/li\u003e\n \u003cli\u003eN. Enninghorst, D. McDougall, J. A. Evans, K. Sisak, and Z. J. Balogh, \u0026ldquo;Population-based epidemiology of femur shaft fractures,\u0026rdquo; \u003cem\u003eJ. Trauma Acute Care Surg.\u003c/em\u003e, vol. 74, no. 6, pp. 1516\u0026ndash;1520, 2013, doi: 10.1097/TA.0b013e31828c3dc9.\u003c/li\u003e\n \u003cli\u003eA. T. Schade \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Epidemiology of fractures and their treatment in Malawi: Results of a multicentre prospective registry study to guide orthopaedic care planning,\u0026rdquo; \u003cem\u003ePLoS One\u003c/em\u003e, vol. 16, no. 8 August, pp. 1\u0026ndash;14, 2021, doi: 10.1371/journal.pone.0255052.\u003c/li\u003e\n \u003cli\u003eA. Salonen, E. Laitakari, H. E. Berg, L. Fell\u0026auml;nder-Tsai, V. M. Mattila, and T. T. Huttunen, \u0026ldquo;Incidence of femoral fractures in children and adolescents in Finland and Sweden between 1998 and 2016: A binational population-based study,\u0026rdquo; \u003cem\u003eScand. J. Surg.\u003c/em\u003e, vol. 111, no. 1, pp. 1\u0026ndash;8, 2022, doi: 10.1177/14574969221083133.\u003c/li\u003e\n \u003cli\u003eF. Chad A Asplund, MD, MPH and M. Thomas J Mezzanotte, \u0026ldquo;Midshaft femur fractures in adults,\u0026rdquo; \u003cem\u003eUp To Date\u003c/em\u003e, pp. 1\u0026ndash;22, 2017, [Online]. Available: www.uptodate.com\u003c/li\u003e\n \u003cli\u003eG. K. Gathecha \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Prevalence and predictors of injuries in Kenya: Findings from the national STEPs survey,\u0026rdquo; \u003cem\u003eBMC Public Health\u003c/em\u003e, vol. 18, no. Suppl 3, p. 1222, 2018, doi: 10.1186/s12889-018-6061-x.\u003c/li\u003e\n \u003cli\u003eTanzania National Bureau of Statistics and President\u0026rsquo;s Office, \u0026ldquo;Mtwara (Region, Tanzania) - Population Statistics, Charts, Map and Location,\u0026rdquo; 2022. Accessed: Oct. 10, 2024. [Online]. Available: https://www.citypopulation.de/en/tanzania/admin/09__mtwara/\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Femoral Shaft Fractures, Motor Traffic Accidents, Femur Fracture, Prevalence, Southern Tanzania","lastPublishedDoi":"10.21203/rs.3.rs-5701767/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5701767/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFemoral shaft fractures (FSFs) represent a significant health burden in low- and middle-income countries, particularly among victims of motor traffic accidents (MTAs). The World Health Organization's 2018 report indicated that approximately 1.2\u0026nbsp;million deaths were attributed to motor vehicle collisions. Despite the rising incidence of MTAs in Tanzania, the current prevalence of FSFs remains unclear. We aimed to determine the prevalence and associated factors for FSFs among MTA victims in Southern Tanzania to guide interventions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods and findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA retrospective, quantitative, health facility-based cross-sectional study was conducted using secondary data from January to December 2023. The study included patients admitted to five referral hospitals in Southern Tanzania. Data were analyzed using Stata® Version 15.1, with associations tested through Chi-square, bivariable, and multivariable logistic regression. One hundred and fifty-four (n = 154) patients who experienced MTAs from the selected five referral hospitals in Southern Tanzania were included in this study. The median age of the study participants was 31.5 years (IQR: 22–49). The majority of patients were male (76%, 117/154) and 57.1% (88/154) were married. The majority (63.0%, 97/154) of patients were self-employed, 37.0% (57/154) were young adults (aged 18–34 years) and 55.8% (86/154) attained primary level of education. Prevalence of FSFs was 37.7%, 58/154), and the most injuries (84.9%, 90/160) were closed fractures. Head injuries was the most common associated injury (28.6%, 44/154). In addition, being a young adult (aged 18–34 years old) (aOR = 5.92, 95% CI: 1.39–25.18, \u003cem\u003ep\u003c/em\u003e = 0.016), Male (aOR = 3.34, 95% CI: 1.20–9.33, \u003cem\u003ep\u003c/em\u003e = 0.021), and at the summer season of the year (aOR = 3.11, 95% CI: 1.33–7.30, \u003cem\u003ep\u003c/em\u003e = 0.009), were factors independently associated with Femoral Shaft Fractures among People Experienced MTAs in the Southern Tanzania from January to December 2023.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFemoral shaft fractures (FSFs) are a significant public health burden among motor traffic accident (MTA) victims in Southern Tanzania, with young adult males disproportionately affected. Mid-shaft fractures and associated head injuries complicate trauma management, emphasizing the need for enhanced healthcare strategies. The high prevalence of FSFs underscores the urgent need for targeted interventions, particularly for young males, and highlights the influence of age, gender, and seasonal variations in accident occurrences. Comprehensive trauma care systems and preventive measures are essential to reduce the incidence of road traffic accidents and their resulting injuries in the region.\u003c/p\u003e","manuscriptTitle":"The Hidden Epidemic: Unveiling Associated Injuries Among Motor Traffic Accident Victims in Southern Tanzania","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-26 09:52:41","doi":"10.21203/rs.3.rs-5701767/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1c4efe2a-72dd-446d-b6c7-463db180f6b1","owner":[],"postedDate":"March 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-26T09:52:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-26 09:52:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5701767","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5701767","identity":"rs-5701767","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}