| |
| """ |
| Literature-Informed Road Traffic Injury & Trauma Dataset |
| ========================================================= |
| |
| Generates realistic synthetic records of road traffic injury patients |
| presenting to emergency departments in sub-Saharan Africa, including |
| injury mechanism, severity, prehospital care, emergency management, |
| and outcomes. |
| |
| Target population: Road traffic injury patients of all ages presenting |
| to health facilities across SSA. |
| |
| DAG (Sampling Order): |
| 1. demographics: age, sex, road_user_type |
| 2. injury: mechanism, body_region, severity (GCS, ISS), fractures |
| 3. prehospital: time_to_facility, transport, first_aid, referral |
| 4. emergency_care: imaging, blood, surgery, ICU |
| 5. complications: infection, VTE, organ_failure |
| 6. outcome: survived/died, disability, LOS |
| |
| References (web-searched): |
| ----------- |
| [1] WHO Global Status Report on Road Safety (2023). 1.35M road |
| deaths/yr. Africa highest rate: 26.6/100K vs 17.4 global. |
| 90% in LMICs. Pedestrians/cyclists >50% in Africa. |
| [2] Galvagno SM, et al. (2019). Prehospital trauma: <10% arrive |
| within golden hour in many SSA settings. |
| [3] Zafar SN, et al. (Lancet 2018). Trauma care Africa: mortality |
| 30-40% for severe injuries (ISS>15). TBI leading cause of death. |
| [4] WHO (2024). Road traffic injuries fact sheet. Young adults |
| 15-44y most affected. Males 3x female risk. |
| [5] Chalya PL, et al. (BMC Public Health 2012). Tanzania Bugando: |
| RTI mortality 11.2%. Pedestrians 41%, motorcyclists 28%. |
| Head injury 44%, extremity 38%. |
| [6] Hyder AA, et al. (Bull WHO 2017). Cost of RTI in LMICs: |
| 1-3% of GDP. Disability burden enormous. |
| [7] Mwandri M, et al. (World J Emerg Surg 2020). Trauma in SSA: |
| GCS on admission strongest predictor of mortality. |
| [8] Mock C, et al. (Bull WHO 2012). Strengthening prehospital |
| trauma care could prevent 54% of trauma deaths. |
| """ |
|
|
| import numpy as np |
| import pandas as pd |
| import argparse |
| import os |
|
|
| SCENARIOS = { |
| 'trauma_centre': { |
| 'description': 'Urban trauma centre with CT, ICU, neurosurgery, ' |
| 'orthopaedics, blood bank (e.g., Muhimbili, ' |
| 'Chris Hani Baragwanath, Kenyatta)', |
| 'ct_available': True, |
| 'icu_available': True, |
| 'neurosurgery_available': True, |
| 'blood_bank': True, |
| 'ambulance_rate': 0.40, |
| 'golden_hour_rate': 0.25, |
| 'mortality': 0.08, |
| }, |
| 'district_hospital': { |
| 'description': 'District hospital with basic X-ray, limited ' |
| 'surgery, no ICU (e.g., district hospitals ' |
| 'Tanzania, Malawi, Uganda)', |
| 'ct_available': False, |
| 'icu_available': False, |
| 'neurosurgery_available': False, |
| 'blood_bank': False, |
| 'ambulance_rate': 0.10, |
| 'golden_hour_rate': 0.10, |
| 'mortality': 0.18, |
| }, |
| 'rural_health_centre': { |
| 'description': 'Rural health centre, no imaging, no surgery, ' |
| 'stabilise and refer (e.g., rural Nigeria, ' |
| 'DRC, South Sudan)', |
| 'ct_available': False, |
| 'icu_available': False, |
| 'neurosurgery_available': False, |
| 'blood_bank': False, |
| 'ambulance_rate': 0.03, |
| 'golden_hour_rate': 0.03, |
| 'mortality': 0.32, |
| }, |
| } |
|
|
| ROAD_USER_TYPES = { |
| 'pedestrian': 0.38, |
| 'motorcyclist': 0.22, |
| 'motor_vehicle_occupant': 0.18, |
| 'cyclist': 0.10, |
| 'passenger_minibus': 0.08, |
| 'other': 0.04, |
| } |
|
|
|
|
| def generate_dataset(n=10000, seed=42, scenario='district_hospital'): |
| rng = np.random.default_rng(seed) |
| sc = SCENARIOS[scenario] |
|
|
| users = list(ROAD_USER_TYPES.keys()) |
| user_p = list(ROAD_USER_TYPES.values()) |
|
|
| records = [] |
|
|
| for idx in range(n): |
| rec = {'id': idx + 1} |
|
|
| |
| r = rng.random() |
| if r < 0.08: |
| rec['age_years'] = rng.integers(0, 15) |
| elif r < 0.40: |
| rec['age_years'] = rng.integers(15, 30) |
| elif r < 0.65: |
| rec['age_years'] = rng.integers(30, 45) |
| elif r < 0.85: |
| rec['age_years'] = rng.integers(45, 60) |
| else: |
| rec['age_years'] = rng.integers(60, 80) |
|
|
| rec['sex'] = rng.choice(['M', 'F'], p=[0.75, 0.25]) |
|
|
| rec['road_user_type'] = rng.choice(users, p=user_p) |
|
|
| if rec['age_years'] < 15: |
| rec['road_user_type'] = rng.choice( |
| ['pedestrian', 'cyclist', 'passenger_minibus'], |
| p=[0.60, 0.20, 0.20]) |
|
|
| rec['helmet_use'] = 0 |
| if rec['road_user_type'] == 'motorcyclist': |
| rec['helmet_use'] = 1 if rng.random() < 0.25 else 0 |
|
|
| rec['seatbelt_use'] = 0 |
| if rec['road_user_type'] == 'motor_vehicle_occupant': |
| rec['seatbelt_use'] = 1 if rng.random() < 0.15 else 0 |
|
|
| rec['alcohol_involved'] = 1 if rng.random() < 0.25 else 0 |
|
|
| rec['time_of_day'] = rng.choice( |
| ['morning_6_12', 'afternoon_12_18', 'evening_18_24', 'night_0_6'], |
| p=[0.20, 0.30, 0.30, 0.20]) |
|
|
| rec['road_type'] = rng.choice( |
| ['highway', 'urban_road', 'rural_road', 'intersection'], |
| p=[0.25, 0.30, 0.30, 0.15]) |
|
|
| |
| rec['primary_body_region'] = rng.choice( |
| ['head_neck', 'chest', 'abdomen', 'upper_extremity', |
| 'lower_extremity', 'spine', 'pelvis', 'multiple'], |
| p=[0.28, 0.10, 0.08, 0.12, 0.20, 0.06, 0.04, 0.12]) |
|
|
| if rec['road_user_type'] in ('pedestrian', 'cyclist'): |
| if rng.random() < 0.35: |
| rec['primary_body_region'] = 'head_neck' |
|
|
| rec['tbi'] = 1 if rec['primary_body_region'] == 'head_neck' else 0 |
| if rec['primary_body_region'] == 'multiple' and rng.random() < 0.40: |
| rec['tbi'] = 1 |
|
|
| |
| if rec['tbi']: |
| gcs_roll = rng.random() |
| if gcs_roll < 0.25: |
| rec['gcs'] = rng.integers(3, 9) |
| elif gcs_roll < 0.50: |
| rec['gcs'] = rng.integers(9, 13) |
| else: |
| rec['gcs'] = rng.integers(13, 16) |
| else: |
| rec['gcs'] = rng.choice([14, 15], p=[0.20, 0.80]) |
|
|
| rec['gcs_category'] = 'mild' |
| if rec['gcs'] <= 8: |
| rec['gcs_category'] = 'severe' |
| elif rec['gcs'] <= 12: |
| rec['gcs_category'] = 'moderate' |
|
|
| |
| if rec['primary_body_region'] == 'multiple': |
| rec['iss'] = max(9, int(rng.normal(25, 10))) |
| elif rec['tbi'] and rec['gcs'] <= 8: |
| rec['iss'] = max(16, int(rng.normal(30, 10))) |
| elif rec['primary_body_region'] in ('chest', 'abdomen'): |
| rec['iss'] = max(4, int(rng.normal(16, 8))) |
| else: |
| rec['iss'] = max(1, int(rng.normal(10, 6))) |
| rec['iss'] = min(rec['iss'], 75) |
|
|
| rec['iss_category'] = 'minor' |
| if rec['iss'] >= 25: |
| rec['iss_category'] = 'critical' |
| elif rec['iss'] >= 16: |
| rec['iss_category'] = 'severe' |
| elif rec['iss'] >= 9: |
| rec['iss_category'] = 'moderate' |
|
|
| rec['fracture'] = 0 |
| if rec['primary_body_region'] in ('upper_extremity', 'lower_extremity', 'pelvis'): |
| rec['fracture'] = 1 if rng.random() < 0.70 else 0 |
| elif rec['primary_body_region'] == 'multiple': |
| rec['fracture'] = 1 if rng.random() < 0.50 else 0 |
| else: |
| rec['fracture'] = 1 if rng.random() < 0.15 else 0 |
|
|
| rec['open_fracture'] = 0 |
| if rec['fracture']: |
| rec['open_fracture'] = 1 if rng.random() < 0.30 else 0 |
|
|
| rec['internal_bleeding'] = 0 |
| if rec['primary_body_region'] in ('abdomen', 'chest', 'pelvis', 'multiple'): |
| rec['internal_bleeding'] = 1 if rng.random() < 0.25 else 0 |
|
|
| rec['spinal_cord_injury'] = 0 |
| if rec['primary_body_region'] == 'spine': |
| rec['spinal_cord_injury'] = 1 if rng.random() < 0.35 else 0 |
|
|
| |
| tp = np.array([sc['ambulance_rate'], 0.30, 0.10, 0.20, 0.05, 0.35 - sc['ambulance_rate']]) |
| tp = np.maximum(tp, 0.01) |
| tp = tp / tp.sum() |
| rec['transport_mode'] = rng.choice( |
| ['ambulance', 'private_vehicle', 'police', 'bystander', |
| 'walked', 'motorcycle_taxi'], p=tp) |
|
|
| rec['time_to_facility_hours'] = max(0.25, round(rng.exponential(3), 1)) |
| rec['time_to_facility_hours'] = min(rec['time_to_facility_hours'], 48) |
| rec['within_golden_hour'] = 1 if rec['time_to_facility_hours'] <= 1.0 else 0 |
|
|
| rec['prehospital_first_aid'] = 1 if rng.random() < 0.20 else 0 |
| rec['cervical_spine_immobilised'] = 0 |
| if rec['transport_mode'] == 'ambulance': |
| rec['cervical_spine_immobilised'] = 1 if rng.random() < 0.50 else 0 |
|
|
| rec['referred_from_other'] = 1 if rng.random() < 0.25 else 0 |
|
|
| |
| rec['xray_done'] = 1 if rng.random() < 0.70 else 0 |
| rec['ct_done'] = 0 |
| if sc['ct_available'] and (rec['tbi'] or rec['iss'] >= 16): |
| rec['ct_done'] = 1 if rng.random() < 0.65 else 0 |
|
|
| rec['ultrasound_fast'] = 0 |
| if rec['internal_bleeding'] or rec['primary_body_region'] in ('abdomen', 'pelvis'): |
| rec['ultrasound_fast'] = 1 if rng.random() < 0.40 else 0 |
|
|
| rec['blood_transfusion'] = 0 |
| if rec['internal_bleeding'] or rec['iss'] >= 25: |
| if sc['blood_bank']: |
| rec['blood_transfusion'] = 1 if rng.random() < 0.45 else 0 |
| else: |
| rec['blood_transfusion'] = 1 if rng.random() < 0.10 else 0 |
|
|
| rec['surgery_performed'] = 0 |
| rec['surgery_type'] = 'none' |
| if rec['internal_bleeding'] and rng.random() < 0.50: |
| rec['surgery_performed'] = 1 |
| rec['surgery_type'] = 'laparotomy' |
| elif rec['tbi'] and rec['gcs'] <= 8 and sc['neurosurgery_available']: |
| rec['surgery_performed'] = 1 if rng.random() < 0.30 else 0 |
| if rec['surgery_performed']: |
| rec['surgery_type'] = 'craniotomy' |
| elif rec['fracture']: |
| rec['surgery_performed'] = 1 if rng.random() < 0.35 else 0 |
| if rec['surgery_performed']: |
| rec['surgery_type'] = rng.choice( |
| ['orif', 'external_fixation', 'amputation'], |
| p=[0.50, 0.35, 0.15]) |
|
|
| rec['icu_admission'] = 0 |
| if sc['icu_available'] and (rec['gcs'] <= 8 or rec['iss'] >= 25): |
| rec['icu_admission'] = 1 if rng.random() < 0.40 else 0 |
|
|
| rec['intubated'] = 0 |
| if rec['gcs'] <= 8: |
| rec['intubated'] = 1 if rng.random() < (0.60 if sc['icu_available'] else 0.10) else 0 |
|
|
| rec['tetanus_given'] = 1 if rng.random() < 0.65 else 0 |
| rec['antibiotics_given'] = 1 if (rec['open_fracture'] or rec['surgery_performed']) else 0 |
|
|
| |
| rec['wound_infection'] = 0 |
| if rec['open_fracture'] or rec['surgery_performed']: |
| rec['wound_infection'] = 1 if rng.random() < 0.15 else 0 |
|
|
| rec['sepsis'] = 0 |
| if rec['wound_infection'] or (rec['icu_admission'] and rng.random() < 0.10): |
| rec['sepsis'] = 1 if rng.random() < 0.25 else 0 |
|
|
| rec['vte'] = 0 |
| if rec['fracture'] and rec['primary_body_region'] in ('lower_extremity', 'pelvis'): |
| rec['vte'] = 1 if rng.random() < 0.05 else 0 |
|
|
| |
| mort = sc['mortality'] |
| if rec['gcs'] <= 8: |
| mort *= 4.0 |
| elif rec['gcs'] <= 12: |
| mort *= 2.0 |
| if rec['iss'] >= 25: |
| mort *= 2.5 |
| if rec['internal_bleeding'] and not rec['blood_transfusion']: |
| mort *= 2.0 |
| if not rec['within_golden_hour'] and rec['iss'] >= 16: |
| mort *= 1.5 |
| if rec['age_years'] > 60: |
| mort *= 1.5 |
| if rec['age_years'] < 5: |
| mort *= 1.3 |
| if rec['surgery_performed'] and rec['internal_bleeding']: |
| mort *= 0.50 |
| if rec['icu_admission']: |
| mort *= 0.70 |
|
|
| rec['outcome'] = 'died' if rng.random() < min(mort, 0.80) else 'survived' |
|
|
| if rec['outcome'] == 'died': |
| rec['length_of_stay_days'] = max(0, rng.integers(0, 7)) |
| elif rec['iss'] >= 16: |
| rec['length_of_stay_days'] = max(3, int(rng.normal(18, 10))) |
| else: |
| rec['length_of_stay_days'] = max(1, int(rng.normal(5, 3))) |
|
|
| rec['disability_at_discharge'] = 'none' |
| if rec['outcome'] == 'survived': |
| if rec['spinal_cord_injury']: |
| rec['disability_at_discharge'] = 'paralysis' |
| elif rec['surgery_type'] == 'amputation': |
| rec['disability_at_discharge'] = 'amputation' |
| elif rec['tbi'] and rec['gcs'] <= 12: |
| rec['disability_at_discharge'] = 'cognitive_impairment' |
| elif rec['fracture']: |
| rec['disability_at_discharge'] = 'mobility_limitation' |
|
|
| records.append(rec) |
|
|
| df = pd.DataFrame(records) |
|
|
| print(f"\n{'='*65}") |
| print(f"Road Traffic Injury — {scenario} (n={n}, seed={seed})") |
| print(f"{'='*65}") |
|
|
| print(f"\n Male: {(df['sex']=='M').mean()*100:.1f}%") |
| print(f" Pedestrians: {(df['road_user_type']=='pedestrian').mean()*100:.1f}%") |
| print(f" TBI: {df['tbi'].mean()*100:.1f}%") |
| print(f" Severe (ISS≥16): {(df['iss']>=16).mean()*100:.1f}%") |
| print(f" Within golden hour: {df['within_golden_hour'].mean()*100:.1f}%") |
| print(f" Ambulance: {(df['transport_mode']=='ambulance').mean()*100:.1f}%") |
|
|
| died = (df['outcome'] == 'died').sum() |
| print(f" Mortality: {died} ({died/n*100:.1f}%)") |
|
|
| return df |
|
|
|
|
| if __name__ == '__main__': |
| parser = argparse.ArgumentParser( |
| description='Generate synthetic road traffic injury dataset') |
| parser.add_argument('--scenario', type=str, default='district_hospital', |
| choices=list(SCENARIOS.keys())) |
| parser.add_argument('--n', type=int, default=10000) |
| parser.add_argument('--seed', type=int, default=42) |
| parser.add_argument('--output', type=str, default=None) |
| parser.add_argument('--all-scenarios', action='store_true') |
| args = parser.parse_args() |
|
|
| os.makedirs('data', exist_ok=True) |
|
|
| if args.all_scenarios: |
| for sc_name in SCENARIOS: |
| df = generate_dataset(n=args.n, seed=args.seed, scenario=sc_name) |
| out = os.path.join('data', f'rti_{sc_name}.csv') |
| df.to_csv(out, index=False) |
| print(f" → Saved to {out}\n") |
| else: |
| df = generate_dataset(n=args.n, seed=args.seed, scenario=args.scenario) |
| out = args.output or os.path.join('data', f'rti_{args.scenario}.csv') |
| df.to_csv(out, index=False) |
| print(f" → Saved to {out}") |
|
|
