#!/usr/bin/env python3 """ Literature-Informed Obstetric Fistula Dataset ============================================== Generates realistic synthetic records of obstetric fistula patients in sub-Saharan Africa, including risk factors, fistula characteristics, social consequences, surgical repair, and outcomes. References (web-searched): ----------- [1] UNFPA 2024. Obstetric fistula caused by prolonged obstructed labour without timely EmONC. SSA has 50% of global burden. 50K-100K new cases/year. [2] UNFPA WCARO 2024. West/Central Africa highest rates. Social isolation, divorce, depression common. [3] PMC 2024. Fistula repair failure in SSA. Repair success 85-90% at specialized centres. [4] PubMed 1999. Early marriage, young adolescents, premature pregnancy → VVF. Sociomedical risk factors. [5] PubMed 2018. Kitovu Hospital Uganda, highest fistula rates globally. Incidence and causative factors. [6] PubMed 2024. Systematic review repair outcomes. Proportions of successful repair in LMICs. """ import numpy as np import pandas as pd import argparse import os SCENARIOS = { 'specialized_fistula_centre': { 'description': 'Dedicated fistula repair centre with ' 'trained surgeon, physiotherapy, social ' 'support (e.g., Addis Ababa Fistula Hospital, ' 'Kitovu Uganda)', 'repair_available': True, 'specialist_surgeon': True, 'physiotherapy': True, 'social_support': True, 'repair_success': 0.90, }, 'district_hospital': { 'description': 'District hospital with visiting fistula ' 'surgeon, basic repair capability, limited ' 'follow-up (e.g., district hospitals DRC, ' 'Malawi, Tanzania)', 'repair_available': True, 'specialist_surgeon': False, 'physiotherapy': False, 'social_support': False, 'repair_success': 0.70, }, 'no_surgical_access': { 'description': 'Rural community with no surgical access, ' 'traditional remedies, referral barriers ' '(e.g., rural Niger, Chad, South Sudan)', 'repair_available': False, 'specialist_surgeon': False, 'physiotherapy': False, 'social_support': False, 'repair_success': 0.0, }, } def generate_dataset(n=10000, seed=42, scenario='district_hospital'): rng = np.random.default_rng(seed) sc = SCENARIOS[scenario] records = [] for idx in range(n): rec = {'id': idx + 1} # ── 1. Demographics ── rec['age_at_presentation'] = max(14, min(65, int(rng.normal(28, 8)))) rec['age_at_fistula_onset'] = max(13, min(rec['age_at_presentation'], int(rng.normal(20, 5)))) rec['years_living_with_fistula'] = max(0, rec['age_at_presentation'] - rec['age_at_fistula_onset']) rec['age_at_marriage'] = max(10, min(30, int(rng.normal(16, 3)))) rec['child_marriage'] = 1 if rec['age_at_marriage'] < 18 else 0 rec['age_at_first_pregnancy'] = max(rec['age_at_marriage'], min(35, int(rng.normal(17, 3)))) rec['education'] = rng.choice( ['none', 'primary', 'secondary', 'tertiary'], p=[0.45, 0.35, 0.15, 0.05]) rec['rural'] = 1 if rng.random() < 0.80 else 0 rec['parity'] = max(0, min(12, int(rng.exponential(2.5)))) rec['height_cm'] = max(140, min(180, int(rng.normal(157, 6)))) rec['short_stature'] = 1 if rec['height_cm'] < 150 else 0 rec['bmi'] = round(max(14, min(35, rng.normal(20, 3))), 1) # ── 2. Obstetric history ── rec['labour_duration_hours'] = max(6, min(120, int(rng.exponential(24) + 12))) rec['prolonged_labour'] = 1 if rec['labour_duration_hours'] > 24 else 0 rec['obstructed_labour'] = 1 if rec['labour_duration_hours'] > 18 and rng.random() < 0.85 else 0 rec['caesarean_performed'] = 0 if rec['obstructed_labour']: rec['caesarean_performed'] = 1 if rng.random() < 0.15 else 0 rec['skilled_birth_attendant'] = 1 if rng.random() < 0.20 else 0 rec['place_of_delivery'] = rng.choice( ['home', 'health_centre', 'hospital'], p=[0.60, 0.25, 0.15]) rec['stillbirth'] = 1 if rng.random() < 0.70 else 0 rec['neonatal_death'] = 0 if not rec['stillbirth']: rec['neonatal_death'] = 1 if rng.random() < 0.15 else 0 # ── 3. Fistula characteristics ── rec['fistula_type'] = rng.choice( ['vesicovaginal', 'rectovaginal', 'combined'], p=[0.80, 0.10, 0.10]) rec['fistula_size'] = rng.choice( ['small', 'medium', 'large', 'extensive'], p=[0.20, 0.35, 0.30, 0.15]) rec['urethral_involvement'] = 1 if rng.random() < 0.25 else 0 rec['circumferential_defect'] = 1 if rec['fistula_size'] == 'extensive' and rng.random() < 0.40 else 0 rec['scarring_severity'] = rng.choice( ['mild', 'moderate', 'severe'], p=[0.25, 0.40, 0.35]) rec['vaginal_stenosis'] = 1 if rec['scarring_severity'] == 'severe' and rng.random() < 0.40 else 0 rec['foot_drop'] = 1 if rec['prolonged_labour'] and rng.random() < 0.10 else 0 # ── 4. Social consequences [1][2] ── rec['divorced_separated'] = 1 if rng.random() < 0.55 else 0 rec['social_isolation'] = 1 if rng.random() < 0.65 else 0 rec['depression'] = 1 if rng.random() < 0.60 else 0 rec['anxiety'] = 1 if rng.random() < 0.45 else 0 rec['economic_impact'] = rng.choice( ['none', 'mild', 'severe'], p=[0.10, 0.30, 0.60]) rec['unable_to_work'] = 1 if rng.random() < 0.50 else 0 rec['skin_excoriation'] = 1 if rng.random() < 0.70 else 0 rec['recurrent_uti'] = 1 if rng.random() < 0.50 else 0 rec['malodour'] = 1 if rng.random() < 0.80 else 0 # ── 5. Care seeking ── rec['delay_to_presentation_years'] = rec['years_living_with_fistula'] rec['previous_repair_attempts'] = max(0, min(5, int(rng.exponential(0.5)))) rec['traditional_remedy_tried'] = 1 if rng.random() < 0.35 else 0 rec['barrier_to_care'] = rng.choice( ['cost', 'distance', 'awareness', 'stigma', 'no_service', 'none'], p=[0.20, 0.20, 0.15, 0.15, 0.20, 0.10]) rec['referred_by'] = rng.choice( ['self', 'ngo', 'health_worker', 'community', 'media'], p=[0.30, 0.25, 0.20, 0.15, 0.10]) # ── 6. Surgical repair [3][6] ── rec['repair_performed'] = 0 if sc['repair_available']: rec['repair_performed'] = 1 if rng.random() < 0.85 else 0 rec['repair_technique'] = 'none' if rec['repair_performed']: rec['repair_technique'] = rng.choice( ['transvaginal', 'transabdominal', 'combined'], p=[0.75, 0.15, 0.10]) rec['graft_used'] = 0 if rec['repair_performed'] and rec['fistula_size'] in ('large', 'extensive'): rec['graft_used'] = 1 if rng.random() < 0.20 else 0 rec['catheter_days'] = 0 if rec['repair_performed']: rec['catheter_days'] = max(7, min(28, int(rng.normal(14, 3)))) rec['repair_successful'] = 0 if rec['repair_performed']: success_prob = sc['repair_success'] if rec['fistula_size'] == 'extensive': success_prob *= 0.60 elif rec['fistula_size'] == 'large': success_prob *= 0.80 if rec['previous_repair_attempts'] > 0: success_prob *= 0.85 if rec['circumferential_defect']: success_prob *= 0.50 rec['repair_successful'] = 1 if rng.random() < success_prob else 0 rec['residual_incontinence'] = 0 if rec['repair_successful']: rec['residual_incontinence'] = 1 if rng.random() < 0.15 else 0 rec['complication_post_repair'] = 0 if rec['repair_performed']: rec['complication_post_repair'] = 1 if rng.random() < 0.10 else 0 # ── 7. Rehabilitation ── rec['physiotherapy_received'] = 0 if rec['repair_performed'] and sc['physiotherapy']: rec['physiotherapy_received'] = 1 if rng.random() < 0.70 else 0 rec['social_reintegration_support'] = 0 if rec['repair_performed'] and sc['social_support']: rec['social_reintegration_support'] = 1 if rng.random() < 0.60 else 0 rec['livelihood_support'] = 0 if rec['social_reintegration_support']: rec['livelihood_support'] = 1 if rng.random() < 0.40 else 0 rec['continence_at_3_months'] = 0 if rec['repair_successful'] and not rec['residual_incontinence']: rec['continence_at_3_months'] = 1 if rng.random() < 0.90 else 0 rec['returned_to_community'] = 0 if rec['repair_successful']: rec['returned_to_community'] = 1 if rng.random() < 0.75 else 0 records.append(rec) df = pd.DataFrame(records) print(f"\n{'='*65}") print(f"Obstetric Fistula — {scenario} (n={n}, seed={seed})") print(f"{'='*65}") print(f"\n Repair performed: {df['repair_performed'].mean()*100:.1f}%") print(f" Repair success: {df[df['repair_performed']==1]['repair_successful'].mean()*100:.1f}%" if df['repair_performed'].sum() > 0 else " No repairs") print(f" Stillbirth: {df['stillbirth'].mean()*100:.1f}%") print(f" Divorced: {df['divorced_separated'].mean()*100:.1f}%") print(f" Depression: {df['depression'].mean()*100:.1f}%") print(f" Mean years with fistula: {df['years_living_with_fistula'].mean():.1f}") return df if __name__ == '__main__': parser = argparse.ArgumentParser( description='Generate obstetric fistula 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'fistula_{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'fistula_{args.scenario}.csv') df.to_csv(out, index=False) print(f" -> Saved to {out}")