Impact of Armed Conflict on Traumatic Brain Injury: A Retrospective Comparative Study from Tigray, Northern Ethiopia

Impact of Armed Conflict on Traumatic Brain Injury: A Retrospective Comparative Study from Tigray, Northern Ethiopia | 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 Impact of Armed Conflict on Traumatic Brain Injury: A Retrospective Comparative Study from Tigray, Northern Ethiopia Tsega Hagos Mehari, Redaie Girmay Gebrekidan, Winner Tewelde Teklehaimanot This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7680196/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 Traumatic brain injury (TBI) is a leading cause of death and disability, with the burden amplified in conflict zones where access to imaging and surgery may be compromised. The Tigray conflict (2020–2022) severely disrupted healthcare delivery in northern Ethiopia. Objective To evaluate how the Tigray conflict influenced the epidemiology, CT imaging access, and clinical outcomes of TBI. Methods We conducted a retrospective comparative study of TBI admissions to Ayder Comprehensive Specialized Hospital over three periods: pre-conflict (Nov 2019–Oct 2020), conflict (Nov 2020–Oct 2022), and post-conflict (Nov 2023–Oct 2024). Systematic random sampling from each period yielded 547 total charts; 487 met eligibility criteria. Variables included demographics, injury mechanism, GCS, CT findings, management, and in-hospital outcomes. Chi-square, Kruskal–Wallis, and multivariable logistic regression were applied. Results Median age was 25 years (IQR 20–38), and 88.7% were male. CT availability declined during conflict (81.6% pre vs. 68.8% during conflict, p < 0.001) and rebounded post-conflict (91.6%). Severe TBI increased from 20.5% pre-conflict to 44.3% during conflict (p < 0.001). In-hospital mortality was 13.8% (24/174) pre-conflict, 11.8% (20/170) during conflict, and 6.3% (9/143) post-conflict (p = 0.02). Conflict period, Severe TBI, and ICU admission were associated with 5.5 times, 4.9 times, and 9.8 times higher odds of hospital death, respectively. Depressed skull fracture and age were not significant; their P value are 0.209 and 0.251, respectively. Conclusion Armed conflict significantly increased TBI severity and reduced imaging availability. Conflict period, severe TBI, and ICU admission were strong independent predictors of in-hospital death. traumatic brain injury conflict CT imaging mortality Tigray Ethiopia Figures Figure 1 Background TBI accounts for a major share of trauma-related deaths globally, disproportionately affecting low- and middle-income countries (LMICs) where neurosurgical capacity is limited [ 1 ]. Armed conflicts exacerbate this burden by increasing blast and penetrating injuries, damaging health infrastructure, and delaying definitive care [ 2 – 4 ]. The Tigray conflict (2020–2022) devastated health services, with > 80% of facilities nonfunctional at peak hostilities [ 5 ]. Ayder Comprehensive Specialized Hospital remained the only referral neurosurgical center for a population of > 7 million [ 6 ]. However, no prior study has quantified how the conflict altered TBI case mix, imaging access, and outcomes in this region. We hypothesized that conflict would (1) increase severe TBI cases, (2) reduce CT scan availability, and (3) worsen mortality. This study aimed to quantify these effects. Methods Study Design & Setting Retrospective comparative chart review at Ayder Comprehensive Specialized Hospital, Mekelle, Ethiopia. Study Periods Pre-conflict: Nov 2019 – Oct 2020 Conflict: Nov 2020 – Oct 2022 Post-conflict: Nov 2023 – Oct 2024 The immediate post-war year (Nov 2022–Oct 2023) was excluded because it represented a transition period with ongoing resource shortages, delayed presentations of war-related complications, and gradual restoration of health services, making it unrepresentative of either conflict or true post-conflict conditions. Participants Inclusion criteria: All patient charts of individuals of any age with a documented clinical diagnosis of traumatic brain injury (TBI) during the study periods were included, regardless of age, sex, or Glasgow Coma Scale (GCS) score. Patients with a GCS score of 15 were included if they had radiological or clinical evidence of intracranial injury or skull fracture. Exclusion criteria: Patients with isolated scalp injuries or isolated facial bone fractures without intracranial injury were excluded. Sampling & Data Collection Total of 2931 potentially eligible patients’ charts were identified in the three periods overall from emergency, neurosurgery, ICU, and operating room logs (956 from Pre-conflict, 1150 conflict, and 825 during post conflict). Given the retrospective nature of this study and the large total number of eligible TBI cases, a sample size was determined to allow for efficient extraction of the data and better resource allocation. Based on a formal sample size calculation, detecting moderate differences in continuous variables (Cohen’s f = 0.25) across three periods with 80% power at a 5% significance level would require 376 patients (approximately 126 per group). For categorical outcomes, such as mortality (Cohen’s w = 0.3), a total of 102 patients (≈approximately 34 per group) are sufficient. A systematic random selection method of sampling was made every 5th case for pre- and post-conflict periods and every 6th case for the conflict period. After exclusions of wrong diagnoses, 487 records were analyzed (174 pre-, 170 conflict, 143 post-conflict). Variables Demographics: age, sex Mechanism of injury: road traffic, fall, blast, gunshot, interpersonal violence, other Clinical severity: GCS categorized as mild (13–15), moderate (9–12), severe (≤8) Imaging: CT findings abstracted from radiology reports; classified by a radiologist blinded to study period Management: surgical vs. conservative, procedure type Outcome: in-hospital mortality, length of stay Statistical Analysis Descriptive statistics: frequencies, medians (IQR) Group comparisons: Chi-square for categorical variables; Kruskal–Wallis for GCS, Age, and length of hospital stay Multivariable logistic regression: dependent variable = death; independent variables = age, sex, period, severity, midline shift, mechanism. Model calibration was assessed using the Hosmer–Lemeshow goodness-of-fit test, and model discrimination was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) with 95% confidence intervals. An AUC > 0.7 was considered indicative of acceptable discrimination. Collinearity was also checked (VIF<2). p<0.05 considered statistically significant. Analyses conducted in SPSS v27. Results Demographics & Mechanisms A total of 487 patients were included, with a median age of 25 years (IQR 20–38) and 88.7% (n = 432) male. The distribution of injury mechanisms shifted during the conflict period, with blast injuries increasing to 16.5% and gunshot wounds to 11.2%, compared with 4.6% and 3.4%, respectively, in the pre-conflict period. Overall, road traffic injuries accounted for 17.3% (n = 84), accidental falls for 29.6% (n = 144), blast injuries for 10.3% (n = 50), and interpersonal violence with sticks or stones for 31.0% (n = 151). Imaging Availability CT imaging availability varied across periods (χ² = 25.675, p < 0.001). The proportion of CT availability declined from 81.6% to 68.8% across the pre-war to wartime periods and rose to 91.6% in the post-war period. CT Findings Among patients who underwent CT imaging (n = 385), depressed skull fracture was the most common finding, observed in 41.7% of cases. This was followed by intraparenchymal hemorrhage/contusion (36.0%) and epidural hematoma (22.8%). Midline shift was present in 19.1% of patients and was significantly more frequent during the conflict period (p < 0.01). Other CT findings included subdural hematoma (12.9%), pneumocephalus (4.4%), basal skull fracture (4.1%), and intracranial metallic foreign bodies (3.3%). Injury Severity Severe TBI proportion rose from 20.5% pre- to 44.3% during conflict (p<0.001), then declined to 31% post-conflict. Median GCS declined accordingly. Continuous Variables Median age, Glasgow Coma Scale, and length of stay differed significantly across the three periods. Median age varied significantly across groups (H = 9.3, df = 2, p = 0.010), with patients during the conflict period being younger than post-conflict patients (adjusted p = 0.008). No significant differences were observed between pre-conflict and conflict (p = 0.196) or pre- and post-conflict periods (p = 0.612). Median GCS was lower during both conflict and post-conflict periods compared with pre-conflict (H = 32.4, df = 2, p < 0.001; adjusted p < 0.001 for both) with no difference between conflict and post-conflict (adjusted p = 1.000). Median length of stay also differed across periods (H = 14.7, df = 2, p < 0.001), being shortest post-conflict compared with pre-conflict (adjusted p < 0.001) but slightly longer than during conflict (adjusted p = 0.034). There was no difference in length of stay between pre-conflict and conflict periods (adjusted p = 0.57). Table 1. Continuous variables across periods. Variable Pre=conflict Median (IQR) Conflict Median (IQR) Post-conflict Median (IQR) H (df=2) p-value Pairwise Comparisons (Bonferroni-adjusted p) Age(yrs) 25.5 (19) 24 (12) 30 (20) 9.3 0.010 War < Post (p=0.008); War = Pre (p=0.196); Pre = Post (p=0.612) GCS 15(2) 13(6) 13(6) 32.4 War (p Post (p<0.001); War = Post (p=1.000) Length of hospital stay (days) 5 (7) 5(6) 3(5) 14.7 <0.001 Post < Pre (p War (p=0.034); War = Pre (p=0.57) Management Overall, 275 patients (56.5%) underwent surgical intervention, with a similar distribution across periods. A significant association was observed between the type of surgery and study period (χ² = 25.655, p = 0.004). Burrhole drainage was less frequent during the conflict (adjusted residual −2.6) but increased post-conflict (+2.7). Decompressive craniectomy was more common during conflict (+2.5) and less frequent pre-conflict (−2.2). Craniotomy was significantly less common during conflict (−2.1). Elevation with duraplasty did not show significant variation across periods. Outcomes In-hospital mortality occurred in 48 patients overall, accounting for 37.5% pre-conflict (n = 18), 35.4% during conflict (n = 17), and 27.1% post-conflict (n = 13) (p < 0.01). Surgical intervention was performed in 275 patients (56.5%), with 90 (32.7%) pre-conflict, 89 (32.4%) during conflict, and 96 (34.9%) post-conflict. The remaining 212 patients (43.5%) received conservative treatment. Mean hospital length of stay decreased across periods, with 9.2 ± 11.7 days pre-conflict, 6.75 ± 6.8 days during conflict, and 5.91 ± 7.8 days post-conflict. Table 2. Demographics, Injury Characteristics, Imaging, Management, and Outcomes Across Study Periods Variable Pre-Conflict (n=174) Conflict (n=170) Post-Conflict (n=143) Chi-square value p-value Sex, n (%) 154 (89.5) 144 (86.7) 127 (90.1) X 2 = 1.02 0.601 Mechanism of Injury X 2 = 96 <0.001 * Blunt, n (%) 163 (94.8) 93 (56) 129 (91.5) Penetrating, n (%) 9 (5.2) 73 (44) 12 (8.5) TBI Severity X 2 = 28.9 <0.001 * Mild, n (%) 139 (80.8) 91 (54.8) 83 (58.9) Moderate, n (%) 16 (9.3) 37 (22.3) 28 (19.9) Severe, n (%) 17 (9.9) 38 (22.9) 30 (21.3) Pupillary response X 2 = 43.2 <0.001 * Abnormal reflex 18 (10.3) 61 (35.1) 58 (40.6) Normal reflex 156 (89.7) 109 (64.1) 85 (59.4) CT Performed, n (%) 140 (81.4) 116 (69.9) 129 (91.5) X 2 = 22.75 <0.001 * Surgical Intervention, n (%) 89 (51.7) 88 (53) 94 (66.7) X 2 = 8.34 0.015 * Patient was admitted to ICU X 2 = 13.97 <0.001 * Yes 44 (25.3) 22 (12.9) 16 (11.2) No 130 (74.7) 148 (87.1) 127 (88.8) Hospital Mortality, n (%) 18 (10.5) 17 (10.2%) 13 (9.2) X 2 = 0.15 0.93 *Significant at p < 0.05 Multivariable Logistic Regression In multivariable analysis, several factors were independently associated with in-hospital mortality. Patients admitted during the conflict period had 5.5 times higher odds of death compared with pre-conflict (AOR 5.53, 95% CI 1.87–16.33, p < 0.002). Severe TBI (GCS 3–8) was associated with nearly fivefold higher odds of death (AOR 4.87, 95% CI 1.75–13.59, p < 0.003). Admission to the intensive care unit carried the highest risk, with an approximate tenfold increase in odds of death (AOR 9.75, 95% CI 4.14–22.96, p < 0.001). Depressed skull fracture and age over 60 were not statistically significant predictors in the adjusted model (p = 0.209 and 0.251, respectively). Table 3. Multivariable Logistic Regression (Outcome: In-hospital death) Variable AOR (95% CI) P-value Conflict period 5.53(1.87–16.334) <0.002 Severe TBI (GCS 3–8) 4.87 (1.75–13.593) <0.003 ICU admission 9.75(4.14-22.962) 60 2.41(0.537–10.824) 0.251 Discussion This study provides a comprehensive assessment of the impact of the Tigray conflict on traumatic brain injury (TBI) epidemiology, imaging access, management, and outcomes at Ayder Comprehensive Specialized Hospital. We observed a significant increase in TBI severity during the conflict, a reduction in CT imaging availability, and higher in-hospital mortality among patients admitted during the conflict period. Severe TBI and ICU admission were strong independent predictors of mortality, whereas age and depressed skull fractures were not significant. The marked increase in severe TBIs during the conflict aligns with global evidence that armed conflicts lead to more high-energy injuries such as blasts and gunshots, overwhelming trauma care systems [7, 8]. In our study, severe TBI rose from 20.5% pre-conflict to 44.3% during the conflict, reflecting the higher acuity of injuries and the vulnerability of civilian populations in war zones. This surge is consistent with findings from other conflict-affected regions where conflict periods are associated with increased TBI severity and case fatality [9-11]. CT imaging availability decreased substantially during the conflict (from 81.6% to 68.8%). Limited imaging delays diagnosis and appropriate surgical interventions, potentially worsening outcomes. Other studies have similarly shown that reduced access to diagnostic imaging during crises directly correlates with higher mortality and poorer functional outcomes [12, 13]. The post-conflict rebound in imaging availability (91.6%) likely represents the resilience of the healthcare system at the study area. Multivariable analysis confirmed that conflict period, severe TBI, and ICU admission were independent predictors of in-hospital death. ICU admission carried nearly a tenfold increase in mortality odds, emphasizing that the sickest patients, often referred from other facilities, bear the highest risk. Age >60 years and d skull depressed skull ractures were not statistically significant, which may reflect the predominantly young civilian cohort admitted to Ayder, in contrast to populations reported in high-income settings [14-16]. While Ayder remained fully functional during the conflict as the only tertiary-level trauma center in Tigray, it primarily received referred patients who were critically ill and mostly civilians. Military hospitals handled the bulk of conflict-related TBI among combatants. Therefore, our hospital-based data likely represent only the tip of the iceberg, and the true burden of TBI in the region during the conflict may have been substantially higher. Strengths of this study include its systematic sampling across three defined periods and the detailed analysis of demographics, injury mechanisms, imaging, surgical management, and outcomes. The study provides quantifications of TBI trends in a low-resource conflict setting, offering valuable insights for trauma system planning in similar environments. Limitations include the relatively small number of mortality events (n=48), which constrained the number of variables that could be included in multivariable logistic regression. For this, we carefully selected only a limited number of clinically relevant and statistically justified variables for inclusion in the multivariable logistic regression model. Variables were chosen based on prior evidence of their association with TBI outcomes and bivariate analysis results. In addition, we assessed multicollinearity using variance inflation factors (VIF 0.7). These steps helped optimize model stability and reduce the likelihood of over-fitting. The retrospective design also limited the availability of key data such as pre-hospital delays, transport times, and post-discharge outcomes. These factors could influence mortality and were not reliably documented in the hospital records. Future research should aim to capture region-wide data, including military and civilian facilities, to better estimate the overall burden of conflict-related TBI. Prospective studies could also examine the effect of prehospital care, transport delays, and rehabilitation services on outcomes. Strengthening trauma registries and standardized documentation in low-resource and conflict settings is crucial for improving care and planning effective interventions. Conclusion The Tigray conflict significantly increased the severity of traumatic brain injuries and reduced access to critical imaging, contributing to higher in-hospital mortality. Conflict period, severe TBI, and ICU admission were independent predictors of death, highlighting the vulnerability of trauma care systems during armed conflicts. Strengthening neurosurgical capacity, ensuring timely access to imaging, and prioritizing critical care resources are essential to mitigate the impact of future conflicts on TBI outcomes. Declarations Ethics This study received ethical clearance from the Institutional Review Board of Mekelle University, College of Health Sciences (Ref. MU-CHS/IRB-2547/2025). All patient data were fully anonymized before analysis, and no personal identifiers were collected at any stage. Access to medical records was restricted to the research team, and data were stored securely in password-protected files. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Competing Interests The authors declare that they have no competing interests. Acknowledgments We thank Ayder Comprehensive Specialized Hospital for facilitating access to medical records. We also acknowledge the contributions of data collectors involved in the study. References Dewan MC, Rattani A, Gupta S, Baticulon RE, Hung YC, Punchak M, Agrawal A, Adeleye AO, Shrime MG, Rubiano AM et al : Estimating the global incidence of traumatic brain injury . J Neurosurg 2019, 130 (4):1080-1097. Jaradat JH, Altah BH, Jankhout S: The burden of neurological diseases in conflict settings: Narrative review Gaza situation . Qatar Journal of Public Health 2024, 2024 (1). Rosenfeld JV, Maas AI, Bragge P, Morganti-Kossmann MC, Manley GT, Gruen RL: Early management of severe traumatic brain injury . Lancet 2012, 380 (9847):1088-1098. 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Willett JK: Imaging in trauma in limited-resource settings: A literature review . African Journal of Emergency Medicine 2019, 9 :S21-S27. Dabas MM, Alameri AD, Mohamed NM, Mahmood R, Kim DH, Samreen M, Kim JW, Shehryar A, Gyambrah S, Bedros AW et al : Comparative Efficacy of MRI and CT in Traumatic Brain Injury: A Systematic Review . Cureus 2024, 16 (10):e72086. Eghzawi A, Alsabbah A, Gharaibeh S, Alwan I, Gharaibeh A, Goyal AV: Mortality Predictors for Adult Patients with Mild-to-Moderate Traumatic Brain Injury: A Literature Review . Neurol Int 2024, 16 (2):406-418. Miller GF, Daugherty J, Waltzman D, Sarmiento K: Predictors of traumatic brain injury morbidity and mortality: Examination of data from the national trauma data bank: Predictors of TBI morbidity & mortality . Injury 2021, 52 (5):1138-1144. Giner J, Mesa Galán L, Yus Teruel S, Guallar Espallargas MC, Pérez López C, Isla Guerrero A, Roda Frade J: Traumatic brain injury in the new millennium: new population and new management . Neurologia (Engl Ed) 2022, 37 (5):383-389. Additional Declarations No competing interests reported. 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-7680196","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":518848360,"identity":"00eb3b9b-1169-487e-9f58-330ffb762707","order_by":0,"name":"Tsega Hagos 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1","display":"","copyAsset":false,"role":"figure","size":83770,"visible":true,"origin":"","legend":"\u003cp\u003eMean GCS and Severe TBI Proportions across Study Periods. (Line graph for mean GCS with overlaid bars showing the percentage of severe TBI.)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7680196/v1/a4c6416798530c3907854811.png"},{"id":92392000,"identity":"f88938ae-12f9-4364-9208-827375100582","added_by":"auto","created_at":"2025-09-29 08:48:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1447986,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7680196/v1/88b24dba-158c-4f0e-8dae-f7d0a0367a73.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of Armed Conflict on Traumatic Brain Injury: A Retrospective Comparative Study from Tigray, Northern Ethiopia","fulltext":[{"header":"Background","content":"\u003cp\u003eTBI accounts for a major share of trauma-related deaths globally, disproportionately affecting low- and middle-income countries (LMICs) where neurosurgical capacity is limited [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Armed conflicts exacerbate this burden by increasing blast and penetrating injuries, damaging health infrastructure, and delaying definitive care [\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 Tigray conflict (2020\u0026ndash;2022) devastated health services, with \u0026gt;\u0026thinsp;80% of facilities nonfunctional at peak hostilities [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Ayder Comprehensive Specialized Hospital remained the only referral neurosurgical center for a population of \u0026gt;\u0026thinsp;7\u0026nbsp;million [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, no prior study has quantified how the conflict altered TBI case mix, imaging access, and outcomes in this region.\u003c/p\u003e\u003cp\u003eWe hypothesized that conflict would (1) increase severe TBI cases, (2) reduce CT scan availability, and (3) worsen mortality. This study aimed to quantify these effects.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy Design \u0026amp; Setting\u003c/p\u003e\n\u003cp\u003eRetrospective comparative chart review at Ayder Comprehensive Specialized Hospital, Mekelle, Ethiopia.\u003c/p\u003e\n\u003cp\u003eStudy Periods\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003ePre-conflict:\u003c/strong\u003e Nov 2019 – Oct 2020\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eConflict:\u003c/strong\u003e Nov 2020 – Oct 2022\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePost-conflict:\u003c/strong\u003e Nov 2023 – Oct 2024\u003c/li\u003e\n \u003cli\u003eThe immediate post-war year (Nov 2022–Oct 2023) was excluded because it represented a transition period with ongoing resource shortages, delayed presentations of war-related complications, and gradual restoration of health services, making it unrepresentative of either conflict or true post-conflict conditions.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInclusion criteria: All patient charts of individuals of any age with a documented clinical diagnosis of traumatic brain injury (TBI) during the study periods were included, regardless of age, sex, or Glasgow Coma Scale (GCS) score. Patients with a GCS score of 15 were included if they had radiological or clinical evidence of intracranial injury or skull fracture.\u003c/p\u003e\n\u003cp\u003eExclusion criteria: Patients with isolated scalp injuries or isolated facial bone fractures without intracranial injury were excluded.\u003c/p\u003e\n\u003cp\u003eSampling \u0026amp; Data Collection\u003c/p\u003e\n\u003cp\u003eTotal of 2931 potentially eligible patients’ charts were identified in the three periods overall from emergency, neurosurgery, ICU, and operating room logs (956 from Pre-conflict, 1150 conflict, and 825 during post conflict).\u003c/p\u003e\n\u003cp\u003eGiven the retrospective nature of this study and the large total number of eligible TBI cases, a sample size was determined to allow for efficient extraction of the data and better resource allocation.\u003c/p\u003e\n\u003cp\u003eBased on a formal sample size calculation, detecting moderate differences in continuous variables (Cohen’s f = 0.25) across three periods with 80% power at a 5% significance level would require 376 patients (approximately 126 per group). For categorical outcomes, such as mortality (Cohen’s w = 0.3), a total of 102 patients (≈approximately 34 per group) are sufficient.\u003c/p\u003e\n\u003cp\u003eA systematic random selection method of sampling was made every 5th case for pre- and post-conflict periods and every 6th case for the conflict period. After exclusions of wrong diagnoses, 487 records were analyzed (174 pre-, 170 conflict, 143 post-conflict).\u003c/p\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eDemographics: age, sex\u003c/li\u003e\n \u003cli\u003eMechanism of injury: road traffic, fall, blast, gunshot, interpersonal violence, other\u003c/li\u003e\n \u003cli\u003eClinical severity: GCS categorized as mild (13–15), moderate (9–12), severe (≤8)\u003c/li\u003e\n \u003cli\u003eImaging: CT findings abstracted from radiology reports; classified by a radiologist blinded to study period\u003c/li\u003e\n \u003cli\u003eManagement: surgical vs. conservative, procedure type\u003c/li\u003e\n \u003cli\u003eOutcome: in-hospital mortality, length of stay\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eStatistical Analysis\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eDescriptive statistics: frequencies, medians (IQR)\u003c/li\u003e\n \u003cli\u003eGroup comparisons: Chi-square for categorical variables; Kruskal–Wallis for GCS, Age, and length of hospital stay\u003c/li\u003e\n \u003cli\u003eMultivariable logistic regression: dependent variable = death; independent variables = age, sex, period, severity, midline shift, mechanism.\u0026nbsp;Model calibration was assessed using the Hosmer–Lemeshow goodness-of-fit test, and model discrimination was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) with 95% confidence intervals. An AUC \u0026gt; 0.7 was considered indicative of acceptable discrimination. Collinearity was also checked (VIF\u0026lt;2).\u0026nbsp;\u003c/li\u003e\n \u003cli\u003ep\u0026lt;0.05 considered statistically significant. Analyses conducted in SPSS v27.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Results","content":"\u003cp\u003eDemographics \u0026amp; Mechanisms\u003c/p\u003e\n\u003cp\u003eA total of 487 patients were included, with a median age of 25 years (IQR 20\u0026ndash;38) and 88.7% (n = 432) male. The distribution of injury mechanisms shifted during the conflict period, with blast injuries increasing to 16.5% and gunshot wounds to 11.2%, compared with 4.6% and 3.4%, respectively, in the pre-conflict period. Overall, road traffic injuries accounted for 17.3% (n = 84), accidental falls for 29.6% (n = 144), blast injuries for 10.3% (n = 50), and interpersonal violence with sticks or stones for 31.0% (n = 151).\u003c/p\u003e\n\u003cp\u003eImaging Availability\u003c/p\u003e\n\u003cp\u003eCT imaging availability varied across periods (\u0026chi;\u0026sup2; = 25.675, p \u0026lt; 0.001). The proportion of CT availability declined from 81.6% to 68.8% across the pre-war to wartime periods and rose to 91.6% in the post-war period.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCT Findings\u003c/p\u003e\n\u003cp\u003eAmong patients who underwent CT imaging (n = 385), depressed skull fracture was the most common finding, observed in 41.7% of cases. This was followed by intraparenchymal hemorrhage/contusion (36.0%) and epidural hematoma (22.8%). Midline shift was present in 19.1% of patients and was significantly more frequent during the conflict period (p \u0026lt; 0.01). Other CT findings included subdural hematoma (12.9%), pneumocephalus (4.4%), basal skull fracture (4.1%), and intracranial metallic foreign bodies (3.3%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInjury Severity\u003c/p\u003e\n\u003cp\u003eSevere TBI proportion rose from 20.5% pre- to 44.3% during conflict (p\u0026lt;0.001), then declined to 31% post-conflict. Median GCS declined accordingly.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContinuous Variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMedian age, Glasgow Coma Scale, and length of stay differed significantly across the three periods. Median age varied significantly across groups (H = 9.3, df = 2, p = 0.010), with patients during the conflict period being younger than post-conflict patients (adjusted p = 0.008). No significant differences were observed between pre-conflict and conflict (p = 0.196) or pre- and post-conflict periods (p = 0.612). Median GCS was lower during both conflict and post-conflict periods compared with pre-conflict (H = 32.4, df = 2, p \u0026lt; 0.001; adjusted p \u0026lt; 0.001 for both) with no difference between conflict and post-conflict (adjusted p = 1.000). Median length of stay also differed across periods (H = 14.7, df = 2, p \u0026lt; 0.001), being shortest post-conflict compared with pre-conflict (adjusted p \u0026lt; 0.001) but slightly longer than during conflict (adjusted p = 0.034). There was no difference in length of stay between pre-conflict and conflict periods (adjusted p = 0.57).\u003c/p\u003e\n\u003cp\u003eTable 1. Continuous variables across periods.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"660\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre=conflict Median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConflict Median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost-conflict Median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH \u0026nbsp; \u0026nbsp; \u0026nbsp;(df=2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePairwise Comparisons (Bonferroni-adjusted p)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge(yrs)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e25.5 (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e24 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e30 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e9.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003eWar \u0026lt; Post (p=0.008); War = Pre (p=0.196); Pre = Post (p=0.612)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGCS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e15(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e13(6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e13(6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e32.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003ePre \u0026gt; War (p\u0026lt;0.001); Pre \u0026gt; Post (p\u0026lt;0.001); War = Post (p=1.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLength of hospital stay (days)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e5 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e5(6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e3(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e14.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003ePost \u0026lt; Pre (p\u0026lt;0.001); Post \u0026gt; War (p=0.034); War = Pre (p=0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eManagement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOverall, 275 patients (56.5%) underwent surgical intervention, with a similar distribution across periods. A significant association was observed between the type of surgery and study period (\u0026chi;\u0026sup2; = 25.655, p = 0.004). Burrhole drainage was less frequent during the conflict (adjusted residual \u0026minus;2.6) but increased post-conflict (+2.7). Decompressive craniectomy was more common during conflict (+2.5) and less frequent pre-conflict (\u0026minus;2.2). Craniotomy was significantly less common during conflict (\u0026minus;2.1). Elevation with duraplasty did not show significant variation across periods.\u003c/p\u003e\n\u003cp\u003eOutcomes\u003c/p\u003e\n\u003cp\u003eIn-hospital mortality occurred in 48 patients overall, accounting for 37.5% pre-conflict (n = 18), 35.4% during conflict (n = 17), and 27.1% post-conflict (n = 13) (p \u0026lt; 0.01). Surgical intervention was performed in 275 patients (56.5%), with 90 (32.7%) pre-conflict, 89 (32.4%) during conflict, and 96 (34.9%) post-conflict. The remaining 212 patients (43.5%) received conservative treatment. Mean hospital length of stay decreased across periods, with 9.2 \u0026plusmn; 11.7 days pre-conflict, 6.75 \u0026plusmn; 6.8 days during conflict, and 5.91 \u0026plusmn; 7.8 days post-conflict.\u003c/p\u003e\n\u003cp\u003eTable 2. Demographics, Injury Characteristics, Imaging, Management, and Outcomes Across Study Periods\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"662\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-Conflict (n=174)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConflict (n=170)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost-Conflict (n=143)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChi-square value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e154 (89.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e144 (86.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e127 (90.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e = 1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.601\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMechanism of Injury\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e = 96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlunt, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e163 (94.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e93 (56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e129 (91.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Penetrating, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e9 (5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e73 (44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e12 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTBI Severity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e = 28.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMild, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e139 (80.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e91 (54.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e83 (58.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModerate, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e16 (9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e37 (22.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e28 (19.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSevere, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e17 (9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e38 (22.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e30 (21.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePupillary response\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e = 43.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbnormal reflex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e18 (10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e61 (35.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e58 (40.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNormal reflex\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e156 (89.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e109 (64.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e85 (59.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCT Performed, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e140 (81.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e116 (69.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e129 (91.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e = 22.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgical Intervention, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e89 (51.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e88 (53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e94 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e = 8.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.015\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatient was admitted to ICU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e = 13.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Yes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e44 (25.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e22 (12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e16 (11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; No\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e130 (74.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e148 (87.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e127 (88.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHospital Mortality, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e18 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e17 (10.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e13 (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e = 0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Significant at p \u0026lt; 0.05\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultivariable Logistic Regression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn multivariable analysis, several factors were independently associated with in-hospital mortality. Patients admitted during the conflict period had 5.5 times higher odds of death compared with pre-conflict (AOR 5.53, 95% CI 1.87\u0026ndash;16.33, p \u0026lt; 0.002). Severe TBI (GCS 3\u0026ndash;8) was associated with nearly fivefold higher odds of death (AOR 4.87, 95% CI 1.75\u0026ndash;13.59, p \u0026lt; 0.003). Admission to the intensive care unit carried the highest risk, with an approximate tenfold increase in odds of death (AOR 9.75, 95% CI 4.14\u0026ndash;22.96, p \u0026lt; 0.001). Depressed skull fracture and age over 60 were not statistically significant predictors in the adjusted model (p = 0.209 and 0.251, respectively).\u003c/p\u003e\n\u003cp\u003eTable 3. Multivariable Logistic Regression (Outcome: In-hospital death)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003eAOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003eConflict period\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e5.53(1.87\u0026ndash;16.334)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e\u0026lt;0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003eSevere TBI (GCS 3\u0026ndash;8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e4.87 (1.75\u0026ndash;13.593)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e\u0026lt;0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003eICU admission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e9.75(4.14-22.962)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003eDSF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 209px;\"\u003e\n \u003cp\u003e1.079\u003cstrong\u003e(\u003c/strong\u003e0.501\u0026ndash;2.326)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 209px;\"\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge \u0026gt; 60\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.41(0.537\u0026ndash;10.824)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.251\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides a comprehensive assessment of the impact of the Tigray conflict on traumatic brain injury (TBI) epidemiology, imaging access, management, and outcomes at Ayder Comprehensive Specialized Hospital. We observed a significant increase in TBI severity during the conflict, a reduction in CT imaging availability, and higher in-hospital mortality among patients admitted during the conflict period. Severe TBI and ICU admission were strong independent predictors of mortality, whereas age and depressed skull fractures were not significant.\u003c/p\u003e\n\u003cp\u003eThe marked increase in severe TBIs during the conflict aligns with global evidence that armed conflicts lead to more high-energy injuries such as blasts and gunshots, overwhelming trauma care systems [7, 8]. In our study, severe TBI rose from 20.5% pre-conflict to 44.3% during the conflict, reflecting the higher acuity of injuries and the vulnerability of civilian populations in war zones. This surge is consistent with findings from other conflict-affected regions where conflict periods are associated with increased TBI severity and case fatality [9-11].\u003c/p\u003e\n\u003cp\u003eCT imaging availability decreased substantially during the conflict (from 81.6% to 68.8%). Limited imaging delays diagnosis and appropriate surgical interventions, potentially worsening outcomes. Other studies have similarly shown that reduced access to diagnostic imaging during crises directly correlates with higher mortality and poorer functional outcomes [12, 13]. The post-conflict rebound in imaging availability (91.6%) likely represents the resilience of the healthcare system at the study area.\u003c/p\u003e\n\u003cp\u003eMultivariable analysis confirmed that conflict period, severe TBI, and ICU admission were independent predictors of in-hospital death. ICU admission carried nearly a tenfold increase in mortality odds, emphasizing that the sickest patients, often referred from other facilities, bear the highest risk. Age \u0026gt;60 years and d skull depressed skull ractures were not statistically significant, which may reflect the predominantly young civilian cohort admitted to Ayder, in contrast to populations reported in high-income settings [14-16].\u003c/p\u003e\n\u003cp\u003eWhile Ayder remained fully functional during the conflict as the only tertiary-level trauma center in Tigray, it primarily received referred patients who were critically ill and mostly civilians. Military hospitals handled the bulk of conflict-related TBI among combatants. Therefore, our hospital-based data likely represent only the tip of the iceberg, and the true burden of TBI in the region during the conflict may have been substantially higher.\u003c/p\u003e\n\u003cp\u003eStrengths of this study include its systematic sampling across three defined periods and the detailed analysis of demographics, injury mechanisms, imaging, surgical management, and outcomes. The study provides quantifications of TBI trends in a low-resource conflict setting, offering valuable insights for trauma system planning in similar environments.\u003c/p\u003e\n\u003cp\u003eLimitations include the relatively small number of mortality events (n=48), which constrained the number of variables that could be included in multivariable logistic regression. For this, we carefully selected only a limited number of clinically relevant and statistically justified variables for inclusion in the multivariable logistic regression model. Variables were chosen based on prior evidence of their association with TBI outcomes and bivariate analysis results. In addition, we assessed multicollinearity using variance inflation factors (VIF \u0026lt; 2) to ensure independence among predictors and verified model calibration with the Hosmer–Lemeshow goodness-of-fit test, while evaluating discrimination with the area under the ROC curve (AUC \u0026gt; 0.7). These steps helped optimize model stability and reduce the likelihood of over-fitting.\u003c/p\u003e\n\u003cp\u003eThe retrospective design also limited the availability of key data such as pre-hospital delays, transport times, and post-discharge outcomes. These factors could influence mortality and were not reliably documented in the hospital records.\u003c/p\u003e\n\u003cp\u003eFuture research should aim to capture region-wide data, including military and civilian facilities, to better estimate the overall burden of conflict-related TBI. Prospective studies could also examine the effect of prehospital care, transport delays, and rehabilitation services on outcomes. Strengthening trauma registries and standardized documentation in low-resource and conflict settings is crucial for improving care and planning effective interventions.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe Tigray conflict significantly increased the severity of traumatic brain injuries and reduced access to critical imaging, contributing to higher in-hospital mortality. Conflict period, severe TBI, and ICU admission were independent predictors of death, highlighting the vulnerability of trauma care systems during armed conflicts. Strengthening neurosurgical capacity, ensuring timely access to imaging, and prioritizing critical care resources are essential to mitigate the impact of future conflicts on TBI outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received ethical clearance from the Institutional Review Board of Mekelle University, College of Health Sciences (Ref. MU-CHS/IRB-2547/2025). All patient data were fully anonymized before analysis, and no personal identifiers were collected at any stage. Access to medical records was restricted to the research team, and data were stored securely in password-protected files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Ayder Comprehensive Specialized Hospital for facilitating access to medical records. We also acknowledge the contributions of data collectors involved in the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDewan MC, Rattani A, Gupta S, Baticulon RE, Hung YC, Punchak M, Agrawal A, Adeleye AO, Shrime MG, Rubiano AM\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eEstimating the global incidence of traumatic brain injury\u003c/strong\u003e. \u003cem\u003eJ Neurosurg \u003c/em\u003e2019, \u003cstrong\u003e130\u003c/strong\u003e(4):1080-1097.\u003c/li\u003e\n\u003cli\u003eJaradat JH, Altah BH, Jankhout S: \u003cstrong\u003eThe burden of neurological diseases in conflict settings: Narrative review Gaza situation\u003c/strong\u003e. \u003cem\u003eQatar Journal of Public Health \u003c/em\u003e2024, \u003cstrong\u003e2024\u003c/strong\u003e(1).\u003c/li\u003e\n\u003cli\u003eRosenfeld JV, Maas AI, Bragge P, Morganti-Kossmann MC, Manley GT, Gruen RL: \u003cstrong\u003eEarly management of severe traumatic brain injury\u003c/strong\u003e. 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\u003c/em\u003e2024, \u003cstrong\u003e16\u003c/strong\u003e(2):406-418.\u003c/li\u003e\n\u003cli\u003eMiller GF, Daugherty J, Waltzman D, Sarmiento K: \u003cstrong\u003ePredictors of traumatic brain injury morbidity and mortality: Examination of data from the national trauma data bank: Predictors of TBI morbidity \u0026amp; mortality\u003c/strong\u003e. \u003cem\u003eInjury \u003c/em\u003e2021, \u003cstrong\u003e52\u003c/strong\u003e(5):1138-1144.\u003c/li\u003e\n\u003cli\u003eGiner J, Mesa Gal\u0026aacute;n L, Yus Teruel S, Guallar Espallargas MC, P\u0026eacute;rez L\u0026oacute;pez C, Isla Guerrero A, Roda Frade J: \u003cstrong\u003eTraumatic brain injury in the new millennium: new population and new management\u003c/strong\u003e. \u003cem\u003eNeurologia (Engl Ed) \u003c/em\u003e2022, \u003cstrong\u003e37\u003c/strong\u003e(5):383-389.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"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":"traumatic brain injury, conflict, CT imaging, mortality, Tigray, Ethiopia","lastPublishedDoi":"10.21203/rs.3.rs-7680196/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7680196/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eTraumatic brain injury (TBI) is a leading cause of death and disability, with the burden amplified in conflict zones where access to imaging and surgery may be compromised. The Tigray conflict (2020\u0026ndash;2022) severely disrupted healthcare delivery in northern Ethiopia.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eTo evaluate how the Tigray conflict influenced the epidemiology, CT imaging access, and clinical outcomes of TBI.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe conducted a retrospective comparative study of TBI admissions to Ayder Comprehensive Specialized Hospital over three periods: pre-conflict (Nov 2019\u0026ndash;Oct 2020), conflict (Nov 2020\u0026ndash;Oct 2022), and post-conflict (Nov 2023\u0026ndash;Oct 2024). Systematic random sampling from each period yielded 547 total charts; 487 met eligibility criteria. Variables included demographics, injury mechanism, GCS, CT findings, management, and in-hospital outcomes. Chi-square, Kruskal\u0026ndash;Wallis, and multivariable logistic regression were applied.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eMedian age was 25 years (IQR 20\u0026ndash;38), and 88.7% were male. CT availability declined during conflict (81.6% pre vs. 68.8% during conflict, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and rebounded post-conflict (91.6%). Severe TBI increased from 20.5% pre-conflict to 44.3% during conflict (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In-hospital mortality was 13.8% (24/174) pre-conflict, 11.8% (20/170) during conflict, and 6.3% (9/143) post-conflict (p\u0026thinsp;=\u0026thinsp;0.02). Conflict period, Severe TBI, and ICU admission were associated with 5.5 times, 4.9 times, and 9.8 times higher odds of hospital death, respectively. Depressed skull fracture and age were not significant; their P value are 0.209 and 0.251, respectively.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eArmed conflict significantly increased TBI severity and reduced imaging availability. Conflict period, severe TBI, and ICU admission were strong independent predictors of in-hospital death.\u003c/p\u003e","manuscriptTitle":"Impact of Armed Conflict on Traumatic Brain Injury: A Retrospective Comparative Study from Tigray, Northern Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-23 15:43:58","doi":"10.21203/rs.3.rs-7680196/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":"c6c7fa35-ac5f-4ff1-8dfc-792cccac3a80","owner":[],"postedDate":"September 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-29T08:41:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-23 15:43:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7680196","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7680196","identity":"rs-7680196","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}