| |
| """ |
| 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} |
|
|
| |
| 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) |
|
|
| |
| 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 |
|
|
| |
| 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 |
|
|
| |
| 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 |
|
|
| |
| 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]) |
|
|
| |
| 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 |
|
|
| |
| 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}") |
|
|
