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Background <p>Trauma remains a major cause of death in low-resource settings, yet prospective evidence on time-to-care, triage accuracy, and critical care allocation is scarce. We quantified care delays and predictors of mortality and evaluated whether a pragmatic, physiology- and injury-based “critical status” classification is robust and scalable.</p> Methods <p>We conducted a prospective, multi-center observational study of 8,440 trauma patients presenting to emergency departments of four national referral hospitals in Tanzania (June 2023–June 2024) with 14-day follow-up. Critical status was defined by physiologic derangement—respiratory rate &lt; 8 or &gt; 30/min, oxygen saturation &lt; 90%, systolic pressure &lt; 90 mmHg, heart rate &lt; 40 or &gt; 130&#xa0;bpm, or “Painful/Unresponsive” AVPU level—or by high-risk injury requiring urgent intervention. Patients meeting ≥ 1 criterion were classified as critical, others as non-critical. Primary outcomes were 24-hour and 14-day mortality; secondary outcomes were ICU admission and length of stay. Exposures included transfer status (direct vs. interfacility) and prehospital, triage, and definitive-care delays. Analyses used Kaplan–Meier survival, Cox regression, and multivariable logistic models (adjusted odds ratios [aOR], 95% CI).</p> Results <p>Overall, 8440 traumatic patients were enrolled. Median age was 31 years (IQR 22–44); 6,393 (75.8%) were male; 5,142 (60.9%) were interfacility transfers. Overall, 3,133 (37.1%) met critical criteria and 5,307 (62.9%) were non-critical. Motor vehicle collisions caused 4,888 (57.9%) injuries. Median prehospital delay was 390&#xa0;min (IQR 200–690), longer for transfers (490 vs. 320; <i>p</i> &lt; 0.001). Overall mortality was 967(11.46%), with 255/8,440 (3.0%) occurring within the first 24&#xa0;h and an additional 712/8,440 (8.4%) occurring between 24&#xa0;h and 14 days, including 550/3,133 (17.6%) critical vs. 417/5,307 (7.9%) non-critical (HR 2.21, 95% CI 1.94–2.51). Critical status was independently associated with mortality (aOR 5.53, 95% CI 5.10–6.14, <i>p</i> &lt; 0.001), and delayed definitive care was associated with higher mortality (aOR 1.35, 95% CI 1.02–1.79; <i>p</i> = 0.04). Sensitivity analyses confirmed robustness across missing-data scenarios.</p> Conclusion <p>Trauma mortality in Tanzania remains high and may be avoidable with improved systems. A pragmatic physiology- and injury-based critical classification offers a feasible triage model for low-resource systems. Reducing transfer delays, strengthening ICU capacity, and expanding digital trauma registries could enable data-driven triage and may improve outcomes, supporting global surgical goals.</p>

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Time-critical care gaps and systemic delays linked to higher mortality in severe trauma patients in Tanzania

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月10日

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  • Cherinet D. Osebo,
  • Victoria J. Munthali,
  • Laurean J. Rwanyuma,
  • Rabi H. Ndeserua,
  • Bryson M. Ikoshi,
  • Respicious L. Boniface

摘要

Background

Trauma remains a major cause of death in low-resource settings, yet prospective evidence on time-to-care, triage accuracy, and critical care allocation is scarce. We quantified care delays and predictors of mortality and evaluated whether a pragmatic, physiology- and injury-based “critical status” classification is robust and scalable.

Methods

We conducted a prospective, multi-center observational study of 8,440 trauma patients presenting to emergency departments of four national referral hospitals in Tanzania (June 2023–June 2024) with 14-day follow-up. Critical status was defined by physiologic derangement—respiratory rate < 8 or > 30/min, oxygen saturation < 90%, systolic pressure < 90 mmHg, heart rate < 40 or > 130 bpm, or “Painful/Unresponsive” AVPU level—or by high-risk injury requiring urgent intervention. Patients meeting ≥ 1 criterion were classified as critical, others as non-critical. Primary outcomes were 24-hour and 14-day mortality; secondary outcomes were ICU admission and length of stay. Exposures included transfer status (direct vs. interfacility) and prehospital, triage, and definitive-care delays. Analyses used Kaplan–Meier survival, Cox regression, and multivariable logistic models (adjusted odds ratios [aOR], 95% CI).

Results

Overall, 8440 traumatic patients were enrolled. Median age was 31 years (IQR 22–44); 6,393 (75.8%) were male; 5,142 (60.9%) were interfacility transfers. Overall, 3,133 (37.1%) met critical criteria and 5,307 (62.9%) were non-critical. Motor vehicle collisions caused 4,888 (57.9%) injuries. Median prehospital delay was 390 min (IQR 200–690), longer for transfers (490 vs. 320; p < 0.001). Overall mortality was 967(11.46%), with 255/8,440 (3.0%) occurring within the first 24 h and an additional 712/8,440 (8.4%) occurring between 24 h and 14 days, including 550/3,133 (17.6%) critical vs. 417/5,307 (7.9%) non-critical (HR 2.21, 95% CI 1.94–2.51). Critical status was independently associated with mortality (aOR 5.53, 95% CI 5.10–6.14, p < 0.001), and delayed definitive care was associated with higher mortality (aOR 1.35, 95% CI 1.02–1.79; p = 0.04). Sensitivity analyses confirmed robustness across missing-data scenarios.

Conclusion

Trauma mortality in Tanzania remains high and may be avoidable with improved systems. A pragmatic physiology- and injury-based critical classification offers a feasible triage model for low-resource systems. Reducing transfer delays, strengthening ICU capacity, and expanding digital trauma registries could enable data-driven triage and may improve outcomes, supporting global surgical goals.