Trauma Care Capacity Constraints in Resource-Limited Settings: Triage and Mortality Outcomes During Acute Surge Events

Executive Summary

Hospitals in lower-middle-income countries face a structural surge ceiling that kills preventable casualties: bed occupancy is chronically near or above capacity before any mass casualty event begins, triage tools designed for high-resource field environments are imported without validation, blood supply chains are fragile at baseline, and critical care nurse-to-patient ratios are often five to ten times worse than the surge thresholds that collapse outcomes even in well-resourced systems. Evidence from academic research covering Pakistan, Rwanda, India, and comparable LMIC settings consistently shows that time to definitive care and surge capacity gap are among the strongest predictors of mass casualty mortality, yet the causal evidence linking specific triage protocols to mortality reduction in purely LMIC hospital settings remains thin. The gap is not primarily one of protocol design; it is one of physical capacity and supply logistics that no protocol can substitute.

• Global health investors and donors: Prioritize blood bank infrastructure and surgical staffing pipelines over protocol training; training without supply chains does not move mortality numbers.
• Hospital operations leaders in LMICs: Use the Brookings Health Governance Capacity Index rankings as a proxy for where private-sector partnership investment can generate the fastest surge readiness gains, particularly in Ghana, Vietnam, and South Africa, where governance scores are highest in their peer group.
• Policy stakeholders and ministries of health: Maternal and newborn health fund models, such as those documented by UNFPA across Sudan, Burkina Faso, and Rwanda, demonstrate that facility-level accountability frameworks can sustain essential care even during active conflict; the same district-led model is transferable to trauma surge planning.

The evidence base for mortality comparisons across triage protocols in LMIC mass casualty settings is sparse, but the structural constraints, chronic understaffing, absent blood banks, and pre-event bed saturation, are well-documented and represent the binding constraint on outcomes regardless of which triage system is chosen.

The Structural Ceiling That Triage Cannot Fix

The dominant framing in global health preparedness literature treats mass casualty triage analytical approach as the primary lever for mortality reduction. The Pakistan retrospective analysis covering 300 simulated MCIs challenges that framing directly: surge capacity gap and infrastructure status together account for more predictive weight in mortality outcomes than triage type. A hospital operating at 95% bed occupancy before an event, which is common in LMIC district hospitals documented by the Brookings Institution's Health Governance Capacity report, has no buffer into which a surge can expand regardless of how efficiently patients are triaged.

The CHEST Journal's Task Force for Mass Critical Care, drawing on COVID-19 ICU surge experience, identified a consistent pattern: ICUs could expand physical space far more easily than they could find qualified staff to operate expanded capacity. UK ICU data from that same analysis showed that redeployment of staff to surge areas created surgical backlogs and resulted in disproportionate healthcare worker infection rates. In LMICs, where the baseline staff-to-patient ratio is already multiple times worse than the high-income country norm, the expansion problem is not just harder but functionally different in kind. A Swedish emergency hospital study found that 53 hospitals could expand from 105 to 399 surgical teams within eight hours by activating disaster plans; the structural precondition for that quadrupling is a reserve pool of trained off-shift staff that most LMIC facilities do not maintain.

These capacity dynamics spill directly into economic risk for development finance institutions and global health investors. When a mass casualty event overwhelms a district hospital, patients who survive the acute event but require post-operative care face secondary mortality from infection, sepsis, and delayed intervention. The WHO Health Emergency and Disaster Risk Management framework, published in 2019 and referenced in hospital surge capacity literature, identifies these downstream costs as systematically underestimated in LMIC preparedness planning because they are not captured in immediate casualty counts.

Where The Blood Supply Chain Breaks

Blood logistics represent a predictable failure point that is less visible than bed counts because it does not appear in the metrics most health ministries track. The medrxiv-published City Assessment of Mass Casualty framework, drawing on LMIC hospital evaluation data, specifically weighted "written protocols and practice of making blood available at hospitals during a Mass Casualty Incident" at 5.64 on its scoring scale, one of the top-ranked hospital system gaps. Yet blood availability protocols scored below the capability to relocate non-critical patients (6.10), suggesting that even the assessment frameworks designed to identify this gap do not fully weight it against operational priorities.

The Lancet Commission on Global Surgery has established that 90% of LMICs lack adequate access to surgical and anaesthesia care, and blood availability sits at the intersection of both. The Vivekananda Memorial Hospital study in India documented a facility that met most Disease Control Priorities essential surgery criteria but had no blood bank, which is not a marginal gap: major trauma surgery without reliable blood product availability converts survivable injuries into fatalities within the operative window. A hospital that can triage correctly, move patients to the operating room quickly, and staff the procedure still loses patients it could have saved if blood is not available in the first 90 minutes.

This supply chain vulnerability translates directly into financial risk for any organization operating facilities in LMIC settings, whether NGOs, private hospital networks, or development finance beneficiaries. Blood bank infrastructure is capital-intensive but not technically complex; the binding constraint in most documented cases is recurring budget priority rather than technical capacity. The UNFPA Maternal and Newborn Health Fund 2025 Impact Report documents how in Sudan, 71 strategic facilities remained operational during active conflict by maintaining supply continuity protocols, with 50 fistula repairs performed and 60 providers trained in basic emergency obstetric care. The model demonstrates that supply continuity is achievable even in humanitarian emergencies when accountability frameworks are in place.

What Post-Event Evidence Does And Does Not Tell Us

The evidence base for LMIC-specific mass casualty outcomes contains a structural absence: nearly all published mortality data comes from post-hoc retrospective reviews of single events, not comparative studies designed to isolate the effect of one variable. The Rwanda systematic media review, published in PLOS ONE, explicitly proposed a novel methodology precisely because LMIC settings lack trauma databases, and the study noted that "MCI epidemiology is poorly studied in low- and middle-income countries lacking trauma databases." Absent those databases, what looks like evidence about triage protocol effects is often confounded by facility type, event type, casualty volume, and time of day, variables that are not controlled in the available literature.

The El Paso mass casualty incident study, published in 2025 in a peer-reviewed US setting, found that simultaneous triage-plus-care by two physicians produced survival rates equivalent to dedicated single-physician triage (p = 0.56), suggesting that triage method flexibility may be less consequential than staffing volume. This finding from a Level I US trauma center cannot be directly applied to an LMIC district hospital, but it does challenge the assumption that the specific triage protocol used is the primary determinant of who survives. What the LMIC evidence does confirm, through the Pakistan regression analysis, is that the administrative structure around triage, whether an Incident Command System is in place, whether post-incident reviews occur, is the variable most associated with durable system improvement.

The ASPR TRACIE hospital surge capacity database notes that patients admitted during high-surge periods have higher mortality than those admitted during low-surge periods, an observation that holds across income settings but is more severe in LMICs because the pre-surge baseline is already stretched. This compounds across time in ways that do not show up in immediate event mortality counts: when a mass casualty event depletes an already-thin LMIC blood supply or consumes the district hospital's last ventilator, the patients displaced by that event and unable to access care in the following days represent a mortality burden that no post-event analysis captures.

Why it matters: Finding 1 rests on the assumption that LMIC hospitals begin MCIs already full or overfull. If district hospitals routinely run below 60% occupancy, surge capacity becomes less of a binding constraint and protocol choice matters more.

Counterarguments

1. The triage protocol evidence gap may reflect publication bias, not a genuine absence of effect. Most peer-reviewed MCI triage studies have been conducted in high-income countries precisely because those settings have trauma registries that make comparative analysis possible. The Turkish Journal of Emergency Medicine review found only two hospital-based triage studies from lower-middle-income countries. If LMIC events are systematically under-studied, the observed weakness of triage protocol effects in LMIC settings may reflect data absence rather than a true null relationship. A well-powered randomized or quasi-experimental study comparing START, SALT, or resource-adapted triage protocols in a high-volume LMIC trauma center might produce effect sizes large enough to change the conclusion that structural capacity dominates protocol.

2. The Pakistan MCI dataset is simulated, not observed, which limits causal inference. The retrospective analysis covering 300 MCIs in Pakistan explicitly notes it covered "simulated mass casualty incidents," not actual historical events with verified mortality records. This is a fundamental evidential limitation: simulation calibration drives the regression coefficients, meaning the beta values for triage type, surge capacity gap, and time to care reflect model assumptions as much as empirical reality. If the simulation parameters were set conservatively, the true effect of triage type on mortality could be larger than the published coefficients suggest.

3. Rwanda's district-led accountability model, and UNFPA's humanitarian care continuity results, demonstrate that governance and supply continuity can sustain care even in severely disrupted settings, a possibility this analysis may under-weight. The UNFPA 2025 Impact Report documents that maternal death audit rates in Burkina Faso rose from 70% in 2024 to 95% in 2025 under the MNH Fund framework, and Rwanda shifted from national to district-led oversight with measurable mortality accountability gains. If governance frameworks of this type can be applied to trauma and mass casualty preparedness, the assessment that structural capacity is the binding constraint may underestimate how much accountability structure can extract from existing resources. The counterargument implies that protocol and governance investment is not wasted even in low-capacity settings; it may be the mechanism by which constrained systems self-improve over successive events.

Indicators To Watch

The table below tracks observable signals that would confirm or challenge the central assessment that structural capacity, not protocol choice, is the binding LMIC mass casualty mortality variable. Each indicator is specific enough to monitor against a named data series.

Near-term watch list: (1) WHO Global Status Report on Blood Safety (Q4 2026), which will provide updated LMIC blood service infrastructure data and allow reassessment of the blood chain vulnerability finding; (2) UNDRR Sendai Framework mid-term progress review (Q1 2027), which will include LMIC emergency management capacity data and allow the ICS correlation finding to be tested against a broader sample; (3) WHO World Health Statistics 2027 release (Q1-Q2 2027), which will update health worker density figures for the countries central to this analysis, including Pakistan, India, and the Sub-Saharan Africa region, and will be the earliest point at which the staffing constraint assumption can be reassessed.

Why it matters: These four indicators track whether the structural constraints identified in Findings 1-4 are tightening or loosening. If blood service coverage expands, health worker density rises, or ICS adoption accelerates materially within 12-24 months, the analysis priorities for donors and hospital leaders would shift toward staffing pipelines and governance systems rather than infrastructure alone.

Decision Relevance

Scenario A (~55%): Structural capacity constraints persist and dominate LMIC MCI mortality for the foreseeable planning horizon. If you advise on global health investment priorities or manage development finance portfolios with health system exposure, act now to require blood bank infrastructure and staffing pipeline metrics as explicit loan or grant conditions for LMIC hospital projects; projects that fund facility construction without addressing blood logistics and staffing ratios are likely to underperform on mortality outcomes during their first major surge event. If you lack direct health investment exposure, monitor WHO Blood Safety and World Health Statistics releases annually as the leading indicator of whether LMICs are closing the structural gap.

Scenario B (~30%): Governance and accountability frameworks prove sufficient to extract meaningfully better outcomes from existing constrained resources. The Rwanda and Burkina Faso district-led accountability evidence, together with the Pakistan ICS finding, points to this as a real possibility. If you are a hospital network operator or NGO with facilities in LMICs, invest now in Incident Command System training and post-incident review protocols; if the governance channel is as powerful as the UNFPA and Pakistan data suggest, this investment generates mortality reduction without requiring capital-intensive infrastructure. If you are a policy advisor to a health ministry, pilot district-led trauma accountability frameworks modeled on Rwanda's MPDSR methodology in two or three high-volume trauma districts and measure against baseline before scaling.

Scenario C (~15%): A validated, resource-adapted triage protocol for LMIC hospital settings is developed and proves effective in comparative trials. The Turkish Journal of Emergency Medicine review explicitly identifies the gap in hospital-based triage evidence for LMICs as a research priority. If a well-powered comparative study demonstrates that a specific protocol adapted for low-resource environments materially improves LMIC mass casualty survival rates, the current assessment that protocol choice is secondary would require revision. If you fund global health research, this represents the highest-leverage gap in the current evidence base; a single well-designed multicenter trial across three to four high-volume LMIC trauma centers could resolve the core uncertainty within three to five years.

Expert Integration

Expert Consensus Assessment

There is broad agreement among academic global surgery researchers, drawing on the Lancet Commission on Global Surgery framework and WHO HEDRM guidance, that LMIC health systems face structural barriers to MCI response that exceed protocol-level interventions. The CHEST Task Force for Mass Critical Care, while primarily focused on high-income settings, similarly concludes that staffing is the binding constraint in surge scenarios, not space.

Expert Disagreement Areas

• Triage protocol effectiveness: The Turkish Journal of Emergency Medicine review team concludes triage effectiveness in LMIC hospital-based MCI response "remains unclear," while some global surgery researchers argue that training investment in any standardized protocol improves outcomes by creating predictable decision trees. Neither position has sufficient LMIC-specific comparative evidence to prevail.
• Role of governance vs. infrastructure: The Pakistan retrospective analysis implies governance structure is important; UNFPA and Brookings research implies governance quality is a precondition for effective private-sector investment. Neither source directly addresses whether governance gains can substitute for or only complement physical infrastructure in mass casualty scenarios.

Systematic-Expert Alignment

Alignment: MIXED

This analysis aligns with expert consensus on the primacy of structural constraints and the evidence gap on LMIC triage protocols, but diverges slightly by weighting blood bank absence as a specifically identifiable single-point failure warranting dedicated intervention priority, rather than treating supply chain gaps as a general background condition.

• No directly comparable, prospective, multicenter mortality outcome data exists for LMIC hospitals comparing triage protocols during actual mass casualty events. The Pakistan dataset is simulation-derived, and the Rwanda approach uses media reviews as a proxy for trauma registries. Both are legitimate methodological innovations given data scarcity, but neither can establish causal effect sizes with the precision needed to choose one protocol over another.
• Blood supply chain failure rates during actual LMIC mass casualty events are not systematically reported. The gap is documented at baseline (blood bank absence, inadequate national blood service coverage), but how often blood unavailability is the proximate cause of a preventable death in an MCI context is unknown. If this data were collected, it would likely change the priority ranking of interventions.
• Critical care staffing ratio data for LMIC hospitals is drawn primarily from non-MCI contexts, including cancer care workload data from the ASCO Post and surgical access data from the Lancet Commission. Extrapolating these ratios to mass casualty surge scenarios assumes that the same staffing constraints apply equally under surge conditions, which may overestimate the severity of the constraint if informal community health worker mobilization occurs in some settings.
• The Ebola treatment center overcrowding data from North Kivu and Ituri (with occupancy rates documented at 131.9% in North Kivu and as high as 278% at individual centers) offers the most current documented example of LMIC health system saturation under acute surge, but this is an infectious disease context with specific IPC constraints that do not translate directly to trauma surgery scenarios.
• Conflict-affected settings such as Sudan, where UNFPA documented operational continuity of 71 strategic facilities during active fighting, represent a subset of LMIC mass casualty scenarios that may systematically differ from natural disaster or terrorist attack contexts in ways that limit generalizability across the full range of surge event types.