Impact of War on Traumatic Brain Injury and Access to Imaging: A Retrospective Comparative Study from a Tertiary Referral Centre

Impact of War on Traumatic Brain Injury and Access to Imaging: A Retrospective Comparative Study from a Tertiary Referral Centre | 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 Article Impact of War on Traumatic Brain Injury and Access to Imaging: A Retrospective Comparative Study from a Tertiary Referral Centre Tsega Hagos Mehari, Redaie Girmay Gebrekidan, Winner Tewelde Teklehaimanot This is a preprint; it has not been peer reviewed by a journal.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-7759879/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Traumatic brain injury is a leading cause of death and disability, with the burden particularly severe in low and middle-income countries where access to imaging is often limited. Wars can further disrupt healthcare systems by restricting supplies, electricity, and skilled staff, making imaging access even more fragile. This study examined how the war in Tigray, northern Ethiopia, affected the care of patients with traumatic brain injury at a major referral hospital. We reviewed 487 patient charts across three periods: before the war, during the war, and after the war. The median age was 25 years, and most patients were men. Access to CT scans fell during the war compared to before, but improved again after the war. Severe traumatic brain injury became much more common during the war, while surgical operations increased most after the war. Hospital deaths were highest before and during the war, but declined after the war. In the adjusted analysis, admission during the war, severe injury, and intensive care admission were all strong predictors of death. These findings show how war strains imaging services and worsens outcomes for brain injury, highlighting the need to protect radiology and trauma systems during crises. Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Health sciences/Risk factors traumatic brain injury CT imaging war low- and middle-income countries Tigray Ethiopia Figures Figure 1 Introduction Traumatic brain injury (TBI) is a leading cause of morbidity and mortality worldwide, disproportionately affecting young adults in low- and middle-income countries (LMICs) 1-3 . Armed conflicts exacerbate this burden by increasing blast and penetrating injuries, damaging health infrastructure, and delaying definitive care 4-6 . CT imaging plays a central role in the diagnosis, triage, and surgical decision-making for TBI. 7 Yet, imaging services are often fragile in LMICs and are further disrupted during war 8,9 . The recent war in Tigray (2020–2022) devastated local health services, with >80% of facilities nonfunctional at peak hostilities 10 . Ayder Comprehensive Specialized Hospital remained the only referral neurosurgical center for a population of >7 million 11 . While descriptive reports and series from other war zones, including Syria, Gaza, and Ukraine, document shifts toward blast and penetrating mechanisms and complex imaging demands, comparative, period-based data from sub-Saharan Africa on imaging availability and its relationship to severity and outcomes remain limited 12-14 . We used this opportunity to examine how active warfare affects imaging capacity and TBI outcomes in a sub-Saharan African setting. We hypothesized that war 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-war: Nov 2019 – Oct 2020 war: Nov 2020 – Oct 2022 Post-war: 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 war or true post-war conditions. Participants Inclusion criteria: All medical charts of patients of any age with a documented clinical diagnosis of traumatic brain injury (TBI) and an available Glasgow Coma Scale (GCS) score during the study periods were included. Exclusion criteria: Patients with incomplete charts lacking a GCS score, non-traumatic causes of brain injury, or duplicate records 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-war, 1150 war, and 825 during the post-war period). Given the retrospective nature of this study and the large total number of potentially 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 approximately 376 patients (126 per group). For categorical outcomes, such as mortality (Cohen’s w = 0.3), a total of 102 patients (approximately 34 per group) would be sufficient. A systematic random selection method of sampling was used 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 analysed (174 pre-war, 170 war, 143 post-war). Variables Demographics: age, sex Mechanism of injury: road traffic, fall, blast, gunshot, interpersonal violence, other Clinical severity: GCS categorised as mild (13–15), moderate (9–12), severe (≤8) Imaging: Availability ( yes/no): CT findings among those scanned abstracted from radiology reports; 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. Ethics approval and consent to participate This study was approved by the Institutional Review Board (IRB) of Mekelle University, College of Health Sciences (Ref. MU-CHS/IRB-2547/2025). All methods were performed in accordance with the principles outlined in the Declaration of Helsinki. Given the retrospective nature of the study, the requirement for informed consent was waived by the IRB. 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 war 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-war 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 Access CT imaging access 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 war 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 war (p<0.001), then declined to 31% post-war. Median GCS declined accordingly (see Fig. 1). Continuous Variables Median age, Glasgow Coma Scale, and length of stay differed significantly across the three periods (Table 1). Median age varied significantly across groups (H = 9.3, df = 2, p = 0.010), with patients during the war period being younger than post-conflict patients (adjusted p = 0.008). No significant differences were observed between pre-war and war (p = 0.196) or pre- and post-war periods (p = 0.612). Median GCS was lower during both war and post-war periods compared with pre-war (H = 32.4, df = 2, p < 0.001; adjusted p < 0.001 for both) with no difference between war and post-war (adjusted p = 1.000). Median length of stay also differed across periods (H = 14.7, df = 2, p < 0.001), being shortest post-war compared with pre-war (adjusted p < 0.001) but slightly longer than during war (adjusted p = 0.034). There was no difference in length of stay between pre-war and war periods (adjusted p = 0.57). Table 1: Continuous variables across periods Variable Pre-war Median (IQR) War Median (IQR) Post-war 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. The proportion of surgical management differed significantly across study periods (χ² = 9.3, p = 0.009), increasing from 51.7% pre-war and 52.4% during war to 67.1% post-war (Table 2). Post-war patients were significantly more likely to undergo surgery (adjusted residual = +3.1). 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 war (adjusted residual −2.6) but increased post-war (+2.7). Decompressive craniectomy was more common during war (+2.5) and less frequent pre-war (−2.2). Craniotomy was significantly less common during war (−2.1). Elevation with duraplasty did not show significant variation across periods. Outcomes Bivariate analysis showed similar crude in-hospital mortality rates across the three periods (37.5% pre-war, 35.4% war, 27.1% post-war; p = 0.93, Table 2). However, in multivariable logistic regression, admission during the war period was independently associated with a 5.5-fold higher odds of death compared with the pre-war period (AOR 5.53, 95% CI 1.87–16.33, p < 0.002, Table 3). Mean hospital length of stay decreased across periods, with 9.2 ± 11.7 days pre-war, 6.75 ± 6.8 days during war, and 5.91 ± 7.8 days post-war. Table 2: Demographics, Injury Characteristics, Imaging, Management, and Outcomes across Study Periods Variable Pre-war (n=174) War (n=170) Post-war (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 (Table 3). Patients admitted during the war period had 5.5 times higher odds of death compared with pre-war (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 War 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 war on traumatic brain injury (TBI) epidemiology, imaging access, management, and outcomes at Ayder Comprehensive Specialized Hospital. We found a significant increase in TBI severity during the war, a reduction in CT imaging availability, and higher in-hospital mortality among patients admitted during this period. Severe TBI and ICU admission emerged as strong independent predictors of mortality, whereas age and depressed skull fractures were not statistically significant. The marked increase in severe TBIs during the war aligns with global evidence that armed conflicts lead to more high-energy injuries such as blasts and gunshots, overwhelming trauma care systems 15,16 . In our study, severe TBI rose from 20.5% pre-war to 44.3% during the war, reflecting the higher acuity of injuries and the vulnerability of civilian populations in war zones. This surge is consistent with findings from other war-affected regions where war periods are associated with increased TBI severity and case fatality 9,12,17 . CT imaging availability decreased substantially during the war (from 81.6% to 68.8%). Limited imaging delays diagnosis and appropriate surgical interventions, potentially worsening outcomes. Similar observations have been reported in LMICs, where even in non-conflict settings, access to CT is constrained by cost, infrastructure, and workforce shortages 18,19 . The post-war rebound in imaging availability (91.6%) likely represents the resilience of the healthcare system at the study area. Multivariable analysis confirmed that war 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 depressed skull fractures were not statistically significant, which may reflect the predominantly young civilian cohort admitted to our hospital, in contrast to populations reported in high-income settings 20-22 . While Ayder Comprehensive specialized Hospital 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 war 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 restricted the number of variables that could be used in multivariable logistic regression. Therefore, 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 previous evidence of their association with TBI outcomes and bivariate analysis results. Additionally, we assessed multicollinearity using variance inflation factors (VIF 0.7). These steps helped optimise model stability and minimise the risk of over-fitting. The retrospective design also limited the availability of 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 war-related TBI. Prospective studies could also examine the effect of pre-hospital care, transport delays, and rehabilitation services on outcomes. Strengthening trauma registries and standardised documentation in low-resource and war zones 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 CT imaging, contributing to higher in-hospital mortality. War 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 Availability of data and materials The dataset supporting the conclusions of this article is available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Authors’ contributions T.M. conceived and designed the study, developed the methodology, and contributed to data curation, resource acquisition, and supervision. R.G. carried out formal analysis, investigation, and validation, contributed to the original draft, and was also involved in supervision and review of the manuscript. W.T. assisted with data curation, resource provision, and supervision, and participated in reviewing and editing the manuscript. All authors contributed significantly to the study and approved the final version. References Dewan, M. C. et al. Estimating the global incidence of traumatic brain injury. J Neurosurg 130 , 1080-1097 (2019). https://doi.org:10.3171/2017.10.Jns17352 Maas, A. I. R. et al. Traumatic brain injury: integrated approaches to improve prevention, clinical care, and research. Lancet Neurol 16 , 987-1048 (2017). https://doi.org:10.1016/s1474-4422(17)30371-x Yan, J., Wang, C. & Sun, B. 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Predictors of traumatic brain injury morbidity and mortality: Examination of data from the national trauma data bank: Predictors of TBI morbidity & mortality. Injury 52 , 1138-1144 (2021). https://doi.org:10.1016/j.injury.2021.01.042 Giner, J. et al. Traumatic brain injury in the new millennium: new population and new management. Neurologia (Engl Ed) 37 , 383-389 (2022). https://doi.org:10.1016/j.nrleng.2019.03.024 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 06 Apr, 2026 Reviewers agreed at journal 08 Feb, 2026 Reviewers invited by journal 06 Feb, 2026 Editor invited by journal 06 Oct, 2025 Editor assigned by journal 05 Oct, 2025 Submission checks completed at journal 03 Oct, 2025 First submitted to journal 01 Oct, 2025 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-7759879","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":587820202,"identity":"761f87da-aac2-4f7c-a344-7c84437a4e9e","order_by":0,"name":"Tsega Hagos Mehari","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYPCCAyCC8QGCjQfwIGlhNoCz8WlD1sImQZQWe/7Dh1/8YLgjL+/e/qzqZhuDHN+NBMbHH/DZIpGWZtnD8Mxw45kzZrdz2xiMJW8kMBvgdZgEj5kBD8Nhxo0zcthAWhI33Ehgk8Crhf/8N8M/DIftN85If1YM1FIP1ML+A68Whhzmx0BbEudLJJgxA7UkGABtwR9iN9LMmGUMDidv4DljLJ1zTsJw5pmHzRJn8Ghh7z/8+OObisO289vbH37OKbOR5zuefPBDBR4tDODoAMYhNJBAUcPYgF8DMNLBkSBPUN0oGAWjYBSMWAAA/LZTKPa41ugAAAAASUVORK5CYII=","orcid":"","institution":"Mekelle University","correspondingAuthor":true,"prefix":"","firstName":"Tsega","middleName":"Hagos","lastName":"Mehari","suffix":""},{"id":587820203,"identity":"72ef6f85-2931-4853-b081-75027c15b432","order_by":1,"name":"Redaie Girmay Gebrekidan","email":"","orcid":"","institution":"Mekelle University","correspondingAuthor":false,"prefix":"","firstName":"Redaie","middleName":"Girmay","lastName":"Gebrekidan","suffix":""},{"id":587820204,"identity":"54db97b4-dcb8-464e-8af9-7aa1d0efdf9a","order_by":2,"name":"Winner Tewelde Teklehaimanot","email":"","orcid":"","institution":"Mekelle University","correspondingAuthor":false,"prefix":"","firstName":"Winner","middleName":"Tewelde","lastName":"Teklehaimanot","suffix":""}],"badges":[],"createdAt":"2025-10-01 13:23:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7759879/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7759879/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102327847,"identity":"89a88c16-255e-493b-9f67-84d6346ecdf8","added_by":"auto","created_at":"2026-02-10 14:43:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":224093,"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":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7759879/v1/b7c46f7af504f97cc3881df8.png"},{"id":102398132,"identity":"7dfea765-9427-4174-b086-6e844b6b441b","added_by":"auto","created_at":"2026-02-11 10:21:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":932788,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7759879/v1/83f5cc8b-c852-4fa4-af44-43f9e2619c9f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of War on Traumatic Brain Injury and Access to Imaging: A Retrospective Comparative Study from a Tertiary Referral Centre","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTraumatic brain injury (TBI) is a leading cause of morbidity and mortality worldwide, disproportionately affecting young adults in low- and middle-income countries (LMICs) \u003csup\u003e1-3\u003c/sup\u003e.\u0026nbsp;Armed conflicts exacerbate this burden by increasing blast and penetrating injuries, damaging health infrastructure, and delaying definitive care\u0026nbsp;\u003csup\u003e4-6\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eCT imaging plays a central role in the diagnosis, triage, and surgical decision-making for TBI.\u003csup\u003e7\u003c/sup\u003e Yet, imaging services are often fragile in LMICs and are further disrupted during war \u003csup\u003e8,9\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe recent war in Tigray (2020\u0026ndash;2022) devastated local health services, with \u0026gt;80% of facilities nonfunctional at peak hostilities \u003csup\u003e10\u003c/sup\u003e. \u0026nbsp;Ayder Comprehensive Specialized Hospital remained the only referral neurosurgical center for a population of \u0026gt;7 million \u003csup\u003e11\u003c/sup\u003e. While descriptive reports and series from other war zones, including Syria, Gaza, and Ukraine, document shifts toward blast and penetrating mechanisms and complex imaging demands, comparative, period-based data from sub-Saharan Africa on imaging availability and its relationship to severity and outcomes remain limited \u003csup\u003e12-14\u003c/sup\u003e. We used this opportunity to examine how active warfare affects imaging capacity and TBI outcomes in a sub-Saharan African setting. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe hypothesized that war 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-war:\u003c/strong\u003e Nov 2019 \u0026ndash; Oct 2020\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ewar:\u003c/strong\u003e Nov 2020 \u0026ndash; Oct 2022\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePost-war:\u003c/strong\u003e Nov 2023 \u0026ndash; Oct 2024\u003c/li\u003e\n \u003cli\u003eThe immediate post-war year (Nov 2022\u0026ndash;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 war or true post-war conditions.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInclusion criteria:\u0026nbsp;\u003c/strong\u003eAll medical charts of patients of any age with a documented clinical diagnosis of traumatic brain injury (TBI) and an available Glasgow Coma Scale (GCS) score during the study periods were included.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExclusion criteria:\u003c/strong\u003e Patients with incomplete charts lacking a GCS score, non-traumatic causes of brain injury, or duplicate records were excluded.\u003c/p\u003e\n\u003cp\u003eSampling \u0026amp; Data Collection\u003c/p\u003e\n\u003cp\u003eTotal of 2931 potentially eligible patients\u0026rsquo; charts were identified in the three periods overall from emergency, neurosurgery, ICU, and operating room logs (956 from Pre-war, 1150 war, and 825 during the post-war period).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGiven the retrospective nature of this study and the large total number of potentially eligible TBI cases, a sample size was determined to allow for efficient extraction of the data and better resource allocation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBased on a formal sample size calculation, detecting moderate differences in continuous variables (Cohen\u0026rsquo;s f = 0.25) across three periods with 80% power at a 5% significance level would require approximately 376 patients (126 per group). For categorical outcomes, such as mortality (Cohen\u0026rsquo;s w = 0.3), a total of 102 patients (approximately 34 per group) would be sufficient.\u003c/p\u003e\n\u003cp\u003eA systematic random selection method of sampling was used 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 analysed (174 pre-war, 170 war, 143 post-war).\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 categorised as mild (13\u0026ndash;15), moderate (9\u0026ndash;12), severe (\u0026le;8)\u003c/li\u003e\n \u003cli\u003eImaging: Availability ( yes/no): CT findings among those scanned abstracted from radiology reports;\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 class=\"decimal_type\"\u003e\n \u003cli\u003eDescriptive statistics: frequencies, medians (IQR)\u003c/li\u003e\n \u003cli\u003eGroup comparisons: Chi-square for categorical variables; Kruskal\u0026ndash;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\u0026ndash;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.\u0026nbsp;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\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Review Board (IRB) of Mekelle University, College of Health Sciences (Ref. MU-CHS/IRB-2547/2025). All methods were performed in accordance with the principles outlined in the Declaration of Helsinki. Given the retrospective nature of the study, the requirement for informed consent was waived by the IRB.\u003c/p\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 war 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-war 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 Access\u003c/p\u003e\n\u003cp\u003eCT imaging access 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 war 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 war (p\u0026lt;0.001), then declined to 31% post-war. Median GCS declined accordingly (see Fig. 1).\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 (Table 1). Median age varied significantly across groups (H = 9.3, df = 2, p = 0.010), with patients during the war period being younger than post-conflict patients (adjusted p = 0.008). No significant differences were observed between pre-war and war (p = 0.196) or pre- and post-war periods (p = 0.612). Median GCS was lower during both war and post-war periods compared with pre-war (H = 32.4, df = 2, p \u0026lt; 0.001; adjusted p \u0026lt; 0.001 for both) with no difference between war and post-war (adjusted p = 1.000). Median length of stay also differed across periods (H = 14.7, df = 2, p \u0026lt; 0.001), being shortest post-war compared with pre-war (adjusted p \u0026lt; 0.001) but slightly longer than during war (adjusted p = 0.034). There was no difference in length of stay between pre-war and war periods (adjusted p = 0.57).\u003c/p\u003e\n\u003cp\u003eTable 1: \u0026nbsp;Continuous variables across periods\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-war Median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWar Median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost-war Median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\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: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\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: 15px;\"\u003e\n \u003cp\u003eAge(yrs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e25.5 (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e24 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e30 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e9.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\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: 15px;\"\u003e\n \u003cp\u003eGCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e15(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e13(6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e13(6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e32.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\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: 15px;\"\u003e\n \u003cp\u003eLength of hospital stay (days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e5 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e5(6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e3(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e14.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\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\u0026nbsp;\u003cstrong\u003eManagement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOverall, 275 patients (56.5%) underwent surgical intervention. The proportion of surgical management differed significantly across study periods (\u0026chi;\u0026sup2; = 9.3, p = 0.009), increasing from 51.7% pre-war and 52.4% during war to 67.1% post-war (Table 2). Post-war patients were significantly more likely to undergo surgery (adjusted residual = +3.1). 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 war (adjusted residual \u0026minus;2.6) but increased post-war (+2.7). Decompressive craniectomy was more common during war (+2.5) and less frequent pre-war (\u0026minus;2.2). Craniotomy was significantly less common during war (\u0026minus;2.1). Elevation with duraplasty did not show significant variation across periods.\u003c/p\u003e\n\u003cp\u003eOutcomes\u003c/p\u003e\n\u003cp\u003eBivariate analysis showed similar crude in-hospital mortality rates across the three periods (37.5% pre-war, 35.4% war, 27.1% post-war; p = 0.93, Table 2). However, in multivariable logistic regression, admission during the war period was independently associated with a 5.5-fold higher odds of death compared with the pre-war period (AOR 5.53, 95% CI 1.87\u0026ndash;16.33, p \u0026lt; 0.002, Table 3). Mean hospital length of stay decreased across periods, with 9.2 \u0026plusmn; 11.7 days pre-war, 6.75 \u0026plusmn; 6.8 days during war, and 5.91 \u0026plusmn; 7.8 days post-war.\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=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-war (n=174)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWar (n=170)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost-war \u0026nbsp;(n=143)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChi-square value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\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: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e154 (89.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e144 (86.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e127 (90.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e = 1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.601\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMechanism of Injury\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e = 96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 10px;\"\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: 24px;\"\u003e\n \u003cp\u003eBlunt, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e163 (94.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e93 (56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e129 (91.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003ePenetrating, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e9 (5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e73 (44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e12 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTBI Severity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e = 28.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 10px;\"\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: 24px;\"\u003e\n \u003cp\u003eMild, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e139 (80.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e91 (54.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e83 (58.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eModerate, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e16 (9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e37 (22.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e28 (19.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eSevere, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e17 (9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e38 (22.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e30 (21.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePupillary response\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e = 43.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 10px;\"\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: 24px;\"\u003e\n \u003cp\u003eAbnormal reflex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e18 (10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e61 (35.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e58 (40.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eNormal reflex\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e156 (89.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e109 (64.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e85 (59.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCT Performed, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e140 (81.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e116 (69.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e129 (91.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e = 22.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\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: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgical Intervention, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e89 (51.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e88 (53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e94 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e = 8.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\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 colspan=\"4\" valign=\"top\" style=\"width: 74px;\"\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: 14px;\"\u003e\n \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e = 13.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\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: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e44 (25.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e22 (12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e16 (11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\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: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e130 (74.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e148 (87.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e127 (88.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\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: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHospital Mortality, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e18 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e17 (10.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e13 (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e = 0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\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 (Table 3). Patients admitted during the war period had 5.5 times higher odds of death compared with pre-war (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\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\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: 209px;\"\u003e\n \u003cp\u003eWar 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\u003eAge \u0026gt; 60\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 war on traumatic brain injury (TBI) epidemiology, imaging access, management, and outcomes at Ayder Comprehensive Specialized Hospital. We found a significant increase in TBI severity during the war, a reduction in CT imaging availability, and higher in-hospital mortality among patients admitted during this period. Severe TBI and ICU admission emerged as strong independent predictors of mortality, whereas age and depressed skull fractures were not statistically significant.\u003c/p\u003e\n\u003cp\u003eThe marked increase in severe TBIs during the war aligns with global evidence that armed conflicts lead to more high-energy injuries such as blasts and gunshots, overwhelming trauma care systems \u003csup\u003e15,16\u003c/sup\u003e. In our study, severe TBI rose from 20.5% pre-war to 44.3% during the war, reflecting the higher acuity of injuries and the vulnerability of civilian populations in war zones. This surge is consistent with findings from other war-affected regions where war periods are associated with increased TBI severity and case fatality \u003csup\u003e9,12,17\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eCT imaging availability decreased substantially during the war (from 81.6% to 68.8%). Limited imaging delays diagnosis and appropriate surgical interventions, potentially worsening outcomes. Similar observations have been reported in LMICs, where even in non-conflict settings, access to CT is constrained by cost, infrastructure, and workforce shortages \u003csup\u003e18,19\u003c/sup\u003e. The post-war 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 war 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 depressed skull fractures were not statistically significant, which may reflect the predominantly young civilian cohort admitted to our hospital, in contrast to populations reported in high-income settings \u003csup\u003e20-22\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eWhile Ayder Comprehensive specialized Hospital 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 war may have been substantially higher.\u0026nbsp;\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 restricted the number of variables that could be used in multivariable logistic regression. Therefore, 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 previous evidence of their association with TBI outcomes and bivariate analysis results. Additionally, we assessed multicollinearity using variance inflation factors (VIF \u0026lt; 2) to ensure predictors were independent and verified model calibration with the Hosmer\u0026ndash;Lemeshow goodness-of-fit test, while evaluating discrimination with the area under the ROC curve (AUC \u0026gt; 0.7). These steps helped optimise model stability and minimise the risk of over-fitting.\u003c/p\u003e\n\u003cp\u003eThe retrospective design also limited the availability of 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 war-related TBI. Prospective studies could also examine the effect of pre-hospital care, transport delays, and rehabilitation services on outcomes. Strengthening trauma registries and standardised documentation in low-resource and war zones is crucial for improving care and planning effective interventions.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe Tigray conflict significantly increased the severity of traumatic brain injuries and reduced access to CT imaging, contributing to higher in-hospital mortality. War 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\n"},{"header":"Declarations","content":"\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe dataset supporting the conclusions of this article is available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\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\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eT.M. conceived and designed the study, developed the methodology, and contributed to data curation, resource acquisition, and supervision. R.G. carried out formal analysis, investigation, and validation, contributed to the original draft, and was also involved in supervision and review of the manuscript. W.T. assisted with data curation, resource provision, and supervision, and participated in reviewing and editing the manuscript. \u0026nbsp;All authors contributed significantly to the study and approved the final version.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDewan, M. C.\u003cem\u003e et al.\u003c/em\u003e Estimating the global incidence of traumatic brain injury. \u003cem\u003eJ Neurosurg\u003c/em\u003e \u003cstrong\u003e130\u003c/strong\u003e, 1080-1097 (2019). https://doi.org:10.3171/2017.10.Jns17352\u003c/li\u003e\n\u003cli\u003eMaas, A. I. 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Predictors of traumatic brain injury morbidity and mortality: Examination of data from the national trauma data bank: Predictors of TBI morbidity \u0026amp; mortality. \u003cem\u003eInjury\u003c/em\u003e \u003cstrong\u003e52\u003c/strong\u003e, 1138-1144 (2021). https://doi.org:10.1016/j.injury.2021.01.042\u003c/li\u003e\n\u003cli\u003eGiner, J.\u003cem\u003e et al.\u003c/em\u003e Traumatic brain injury in the new millennium: new population and new management. \u003cem\u003eNeurologia (Engl Ed)\u003c/em\u003e \u003cstrong\u003e37\u003c/strong\u003e, 383-389 (2022). https://doi.org:10.1016/j.nrleng.2019.03.024\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"traumatic brain injury, CT imaging, war, low- and middle-income countries, Tigray, Ethiopia","lastPublishedDoi":"10.21203/rs.3.rs-7759879/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7759879/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Traumatic brain injury is a leading cause of death and disability, with the burden particularly severe in low and middle-income countries where access to imaging is often limited. Wars can further disrupt healthcare systems by restricting supplies, electricity, and skilled staff, making imaging access even more fragile. This study examined how the war in Tigray, northern Ethiopia, affected the care of patients with traumatic brain injury at a major referral hospital. We reviewed 487 patient charts across three periods: before the war, during the war, and after the war. The median age was 25 years, and most patients were men. Access to CT scans fell during the war compared to before, but improved again after the war. Severe traumatic brain injury became much more common during the war, while surgical operations increased most after the war. Hospital deaths were highest before and during the war, but declined after the war. In the adjusted analysis, admission during the war, severe injury, and intensive care admission were all strong predictors of death. These findings show how war strains imaging services and worsens outcomes for brain injury, highlighting the need to protect radiology and trauma systems during crises.","manuscriptTitle":"Impact of War on Traumatic Brain Injury and Access to Imaging: A Retrospective Comparative Study from a Tertiary Referral Centre","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-10 14:43:43","doi":"10.21203/rs.3.rs-7759879/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-06T20:34:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"7690682752235148795357039607523742561","date":"2026-02-08T15:57:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-06T06:28:37+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-06T14:27:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-05T09:12:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-03T06:10:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-10-01T13:17:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c6c7fa35-ac5f-4ff1-8dfc-792cccac3a80","owner":[],"postedDate":"February 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":62537012,"name":"Health sciences/Diseases"},{"id":62537013,"name":"Health sciences/Health care"},{"id":62537014,"name":"Health sciences/Medical research"},{"id":62537015,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-02-10T14:43:43+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-10 14:43:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7759879","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7759879","identity":"rs-7759879","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}