Neurosurgical Care for Traumatic Brain Injury in Low-Resource Settings: A Multinational Review Evaluating the Influence of Health Systems Framework on Patient Outcomes

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-such as limited internet access, absence of electronic medical records (EMRs), and lack of standardized protocols-impede 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-Wallis 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, <i>p</i> = 0.06; OR = 1.1, <i>p</i> = 0.03, respectively). Among facilities with operative capacity, the median neurosurgical intervention rate was 28% (interquartile range [IQR]: 3-33%). Policy implementation predicted reduced post-TBI mortality (<i>B</i> = -10.8, <i>p</i> = 0.06; <i>R</i>² = 0.56), with a median institutional mortality rate of 19% (IQR: 8-17%). 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-particularly EMRs, internet access, and standardized clinical protocols-to 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.