--- license: cc-by-4.0 task_categories: - tabular-classification language: - en tags: - healthcare - trauma - road-traffic-injury - emergency - prehospital - GCS - ISS - TBI - sub-saharan-africa - lmic pretty_name: "Road Traffic Injury & Trauma (GCS, ISS, Prehospital, Emergency Care)" size_categories: - 10K50% of road deaths in Africa. Males aged 15-44 are most affected, with a 3:1 male-to-female ratio. Less than 10% of trauma patients arrive within the golden hour in many SSA settings. Strengthening prehospital trauma care could prevent 54% of trauma deaths (Mock et al., Bull WHO 2012). **This dataset is entirely simulated. It must not be used for clinical decision-making.** ## 2. Methodology ### 2.1 Parameterization | Parameter | Value | Source | | --- | --- | --- | | RTI death rate (Africa) | 26.6/100K | WHO Global Status Report 2023 | | Pedestrian proportion | >50% of deaths | WHO 2023 | | Male:Female ratio | 3:1 | WHO 2024 | | Golden hour arrival (SSA) | <10% | Galvagno et al., 2019 | | Severe injury mortality (SSA) | 30-40% (ISS>15) | Zafar et al., Lancet 2018 | | Head injury proportion | 44% | Chalya et al., BMC Pub Health 2012 | | Prehospital prevention potential | 54% deaths preventable | Mock et al., Bull WHO 2012 | ### 2.2 Scenario Design | Scenario | Facility | CT | ICU | Ambulance | Mortality | | --- | --- | --- | --- | --- | --- | | Trauma centre | Urban, neurosurgery | Yes | Yes | 38% | 18.6% | | District hospital | Basic X-ray | No | No | 10% | 33.8% | | Rural health centre | No imaging | No | No | 3% | 48.2% | ## 3. Schema | Column | Type | Description | | --- | --- | --- | | id | int | Unique identifier | | age_years | int | Patient age | | sex | categorical | M / F | | road_user_type | categorical | pedestrian / motorcyclist / motor_vehicle_occupant / cyclist / passenger_minibus | | helmet_use | binary | Helmet worn (motorcyclist) | | seatbelt_use | binary | Seatbelt worn (vehicle occupant) | | alcohol_involved | binary | Alcohol involved | | time_of_day | categorical | morning / afternoon / evening / night | | road_type | categorical | highway / urban_road / rural_road / intersection | | primary_body_region | categorical | head_neck / chest / abdomen / upper/lower_extremity / spine / pelvis / multiple | | tbi | binary | Traumatic brain injury | | gcs | int | Glasgow Coma Scale (3-15) | | gcs_category | categorical | severe / moderate / mild | | iss | int | Injury Severity Score (1-75) | | iss_category | categorical | minor / moderate / severe / critical | | fracture | binary | Fracture present | | open_fracture | binary | Open fracture | | internal_bleeding | binary | Internal bleeding | | spinal_cord_injury | binary | Spinal cord injury | | transport_mode | categorical | ambulance / private_vehicle / police / bystander / walked / motorcycle_taxi | | time_to_facility_hours | float | Time from injury to facility | | within_golden_hour | binary | Arrived within 1 hour | | prehospital_first_aid | binary | First aid given | | cervical_spine_immobilised | binary | C-spine immobilised | | referred_from_other | binary | Referred from another facility | | xray_done | binary | X-ray performed | | ct_done | binary | CT scan performed | | ultrasound_fast | binary | FAST ultrasound | | blood_transfusion | binary | Blood transfusion | | surgery_performed | binary | Surgery performed | | surgery_type | categorical | laparotomy / craniotomy / orif / external_fixation / amputation | | icu_admission | binary | ICU admission | | intubated | binary | Intubated | | tetanus_given | binary | Tetanus prophylaxis | | antibiotics_given | binary | Antibiotics | | wound_infection | binary | Wound infection | | sepsis | binary | Sepsis | | vte | binary | VTE | | outcome | categorical | survived / died | | length_of_stay_days | int | Hospital stay | | disability_at_discharge | categorical | none / paralysis / amputation / cognitive_impairment / mobility_limitation | ## 4. Validation

Validation Report

Key validation checks: - **Mortality gradient**: 18.6% → 33.8% → 48.2%, reflecting infrastructure gap ✓ - **Male predominance**: 75%, consistent with WHO 3:1 ratio ✓ - **Pedestrians most common**: ~40%, consistent with WHO Africa data ✓ - **GCS dose-response**: Severe GCS mortality >> mild ✓ - **Golden hour**: Only 3-30% arrive within 1 hour ✓ - **Head injury leading**: ~45% TBI, consistent with Chalya 2012 ✓ ## 5. Usage ```python from datasets import load_dataset dataset = load_dataset("electricsheepafrica/road-traffic-injury-trauma", "district_hospital") df = dataset["train"].to_pandas() ``` ```bash python generate_dataset.py --all-scenarios --n 10000 --seed 42 ``` ## 6. Limitations - **Simulated**: Not derived from real trauma registries. - **No imaging**: CT/X-ray findings are binary, no images. - **Simplified ISS**: Estimated, not calculated from individual AIS scores. - **No rehabilitation**: Discharge snapshot only, no long-term follow-up. - **No cost data**: No financial burden or catastrophic expenditure. ## 7. References 1. WHO (2023). Global Status Report on Road Safety. 2. Galvagno SM, et al. (2019). Prehospital trauma care SSA. 3. Zafar SN, et al. (2018). Trauma care Africa. *Lancet*, 391(10127):1308. 4. WHO (2024). Road traffic injuries fact sheet. 5. Chalya PL, et al. (2012). RTI Tanzania Bugando. *BMC Public Health*, 12:501. 6. Hyder AA, et al. (2017). Cost of RTI in LMICs. *Bull WHO*, 95(5):326. 7. Mwandri M, et al. (2020). Trauma SSA. *World J Emerg Surg*, 15:17. 8. Mock C, et al. (2012). Prehospital trauma care. *Bull WHO*, 90(8):577. ## Citation ```bibtex @dataset{esa_rti_2025, title={Road Traffic Injury and Trauma Dataset}, author={Electric Sheep Africa}, year={2025}, publisher={Hugging Face}, url={https://huggingface.co/datasets/electricsheepafrica/road-traffic-injury-trauma} } ``` ## License [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)