{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T03:25:44Z","timestamp":1791861944590,"version":"4.3.4"},"reference-count":71,"publisher":"SAGE Publications","issue":"13-14","license":[{"start":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T00:00:00Z","timestamp":1765497600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Neurotrauma"],"published-print":{"date-parts":[[2026,7,1]]},"abstract":"<jats:p>\n                    Traumatic brain injury (TBI) remains a leading global cause of death and disability, disproportionately impacting low- and middle-income countries (LMICs), where neurosurgical resources are often limited. In these settings, foundational gaps in health system infrastructure\u2014such as limited internet access, absence of electronic medical records (EMRs), and lack of standardized protocols\u2014impede timely diagnosis, intervention, and continuity of care. This study evaluates the relationship between health system infrastructure and neurosurgical capacity, intervention delivery, and TBI outcomes across LMICs. We conducted a systematic review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines across PubMed, Embase, and Scopus to identify studies examining TBI care and system infrastructure in LMIC institutions. Extracted data were categorized across two primary domains: (1) clinical management and patient outcomes, and (2) implementation of health system components, including EMRs, information and communication technology access, and standardized care protocols. Quantitative analysis incorporated descriptive statistics, chi-square testing, Kruskal\u2013Wallis tests, Glasgow Coma Scale-adjusted linear regression models, and machine learning classifiers to examine associations. Of the LMIC institutions reviewed, only 41% reported the presence of neurosurgical capacity. Implementation of EMRs and standardized protocols was significantly associated with increased neurosurgical capacity (odds ratio [OR] = 1.1,\n                    <jats:italic toggle=\"yes\">p<\/jats:italic>\n                    = 0.06; OR = 1.1,\n                    <jats:italic toggle=\"yes\">p<\/jats:italic>\n                    = 0.03, respectively). Among facilities with operative capacity, the median neurosurgical intervention rate was 28% (interquartile range [IQR]: 3\u201333%). Policy implementation predicted reduced post-TBI mortality (\n                    <jats:italic toggle=\"yes\">B<\/jats:italic>\n                    = \u221210.8,\n                    <jats:italic toggle=\"yes\">p<\/jats:italic>\n                    = 0.06;\n                    <jats:italic toggle=\"yes\">R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    = 0.56), with a median institutional mortality rate of 19% (IQR: 8\u201317%). Machine learning models demonstrated strong discriminatory ability to predict TBI mortality based on neurosurgical capacity and infrastructure metrics (area under the curve = 0.76). These findings highlight the potential for health system infrastructure\u2014particularly EMRs, internet access, and standardized clinical protocols\u2014to improve neurosurgical readiness and reduce preventable mortality following TBI in LMICs. Strategic investment in digital health tools and policy standardization could be a high-yield, scalable approach to closing global neurosurgical care gaps and improving TBI outcomes in resource-limited settings.\n                  <\/jats:p>","DOI":"10.1177\/08977151251406253","type":"journal-article","created":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T07:20:27Z","timestamp":1766733627000},"page":"1019-1034","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["Neurosurgical Care for Traumatic Brain Injury in Low-Resource Settings: A Multinational Review Evaluating the Influence of Health Systems Framework on Patient Outcomes"],"prefix":"10.1177","volume":"43","author":[{"given":"Caleigh S.","family":"Roach","sequence":"first","affiliation":[{"name":"Department of Neurosurgery, University of Miami Miller School of Medicine, Miami, Florida, USA."},{"name":"Miller School of Medicine, University of Miami, Miami, Florida, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacob J.","family":"Shawwa","sequence":"additional","affiliation":[{"name":"Miller School of Medicine, University of Miami, Miami, Florida, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Connor","family":"Nee","sequence":"additional","affiliation":[{"name":"Miller School of Medicine, University of Miami, Miami, Florida, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Victor M.","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, University of Miami Miller School of Medicine, Miami, Florida, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,12,12]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.3171\/2017.11.JNS171500"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1093\/jsprm\/snab002"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1227\/neu.0000000000002479"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1093\/ije\/dyn189"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.wneu.2015.12.048"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bas.2023.101747"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-4422(22)00309-X"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.3109\/02699052.2012.750742"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1227\/neu.0000000000003106"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41440-025-02227-7"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.wneu.2022.11.136"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.2196\/48256"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.1155\/2017\/8389414"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.afjem.2020.01.001"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.4314\/mmj.v29i3.4"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.wneu.2017.09.084"},{"key":"e_1_3_3_18_2","article-title":"Electronic medical record system user satisfaction and its implications for individual work performance: The case of a university teaching hospital in Rwanda","author":"Uwamungu E","year":"2024","unstructured":"Uwamungu E, , Marara A, , Munyaneza E, et al. 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