#!/usr/bin/env python3 """ Literature-Informed Synthetic Maternal Health & Pregnancy Complications Dataset =============================================================================== Generates realistic synthetic datasets of pregnant women attending antenatal care (ANC) in LMIC settings, with demographics, clinical measurements, risk factors, and pregnancy complications/outcomes. Target population: Pregnant women presenting for ANC or delivery in LMIC facility settings, aged 15-49. DAG (Sampling Order): 1. age (root) 2. parity, gravidity (conditional on age) 3. bmi_pre_pregnancy (root, age-adjusted) 4. hiv_status (scenario-dependent) 5. gestational_age_at_visit (root) 6. hemoglobin (conditional on scenario, GA) 7. blood_pressure (conditional on age, BMI, complication) 8. blood_glucose (conditional on BMI, age, complication) 9. proteinuria (conditional on BP, complication) 10. complication (from prevalence, conditional on risk factors) 11. risk_level (derived) References: ----------- [1] WHO (2016). WHO Recommendations on Antenatal Care for a Positive Pregnancy Experience. Geneva. [2] Say L, et al. (2014). Global causes of maternal death: a WHO systematic analysis. Lancet Global Health, 2(6):e323-333. [3] Abalos E, et al. (2013). Global and regional estimates of preeclampsia and eclampsia. Hypertension in Pregnancy, 32(sup1):36. [4] IDF (2021). IDF Diabetes Atlas, 10th edition. International Diabetes Federation. [5] Stevens GA, et al. (2013). Global, regional, and national trends in haemoglobin concentration and prevalence of anaemia. Lancet Global Health, 1(1):e16-25. [6] UNAIDS (2023). Global HIV & AIDS statistics fact sheet. [7] WHO (2023). Trends in maternal mortality 2000-2020. Geneva. [8] DHS Program. Demographic and Health Surveys, multiple countries. [9] Vogel JP, et al. (2014). Use of the Robson classification to assess caesarean section trends. Lancet Global Health, 2(5):e260-270. [10] Souza JP, et al. (2013). Moving beyond essential interventions for reduction of maternal mortality. Lancet, 381(9879):1747-1755. """ import numpy as np import pandas as pd from scipy.stats import truncnorm import argparse import os # ============================================================ # SECTION 1: Literature-Informed Parameters # ============================================================ SCENARIOS = { 'low_burden': { 'description': 'Urban LMIC with functional ANC (e.g., urban Ghana, Kenya)', 'preeclampsia_rate': 0.035, # Abalos 2013: 2-5% globally 'eclampsia_rate': 0.003, # Abalos 2013: 0.1-0.5% in good care 'gdm_rate': 0.04, # IDF 2021: 3-5% in LMIC 'hemorrhage_rate': 0.025, # Say 2014: 2-3% in facility 'severe_anemia_rate': 0.03, # Stevens 2013: 2-5% with ANC 'moderate_anemia_rate': 0.18, # Stevens 2013: 15-25% 'hiv_prevalence': 0.04, # UNAIDS 2023: varies widely 'cs_rate': 0.18, 'maternal_age_mean': 27, 'maternal_age_sd': 5.5, }, 'moderate_burden': { 'description': 'Average LMIC district (e.g., rural Uganda, Bangladesh)', 'preeclampsia_rate': 0.06, # Abalos 2013: 4-8% in LMIC 'eclampsia_rate': 0.008, # Abalos 2013: 0.5-1% in LMIC 'gdm_rate': 0.06, # IDF 2021: 5-8% in South Asia 'hemorrhage_rate': 0.04, # Say 2014: 3-5% 'severe_anemia_rate': 0.06, # Stevens 2013: 5-8% 'moderate_anemia_rate': 0.28, # Stevens 2013: 25-35% 'hiv_prevalence': 0.08, # UNAIDS: moderate prevalence setting 'cs_rate': 0.10, 'maternal_age_mean': 26, 'maternal_age_sd': 6.0, }, 'high_burden': { 'description': 'Under-resourced / high-HIV setting (e.g., rural DRC, Malawi)', 'preeclampsia_rate': 0.09, # Abalos 2013: up to 10% in SSA 'eclampsia_rate': 0.015, # Higher where ANC access poor 'gdm_rate': 0.08, # IDF 2021: rising in SSA 'hemorrhage_rate': 0.06, # Say 2014: 5-8% low-resource 'severe_anemia_rate': 0.10, # Stevens 2013: 8-15% 'moderate_anemia_rate': 0.35, # Stevens 2013: 30-45% 'hiv_prevalence': 0.18, # UNAIDS: high prevalence SSA 'cs_rate': 0.05, 'maternal_age_mean': 25, 'maternal_age_sd': 6.5, }, } # --- BMI Parameters (pre-pregnancy) --- # Source: DHS anthropometry data, LMIC women of reproductive age BMI_PARAMS = {'mean': 23.5, 'sd': 4.5, 'min': 14.0, 'max': 48.0} # --- Hemoglobin (g/dL) --- # Source: Stevens et al. (2013), WHO anemia thresholds in pregnancy # Normal ≥11.0, Mild 10.0-10.9, Moderate 7.0-9.9, Severe <7.0 HB_NORMAL = {'mean': 11.8, 'sd': 1.0} HB_MILD_ANEMIA = {'mean': 10.4, 'sd': 0.3} HB_MODERATE_ANEMIA = {'mean': 8.5, 'sd': 0.8} HB_SEVERE_ANEMIA = {'mean': 5.8, 'sd': 0.8} # --- Blood Pressure (mmHg) --- # Source: WHO ANC guidelines (2016) # Normal: SBP <120, DBP <80 # Elevated: SBP 120-139, DBP 80-89 # Hypertension: SBP ≥140 or DBP ≥90 # Severe: SBP ≥160 or DBP ≥110 BP_NORMAL = {'sbp_mean': 112, 'sbp_sd': 10, 'dbp_mean': 70, 'dbp_sd': 7} BP_PREECLAMPSIA = {'sbp_mean': 152, 'sbp_sd': 12, 'dbp_mean': 98, 'dbp_sd': 8} BP_ECLAMPSIA = {'sbp_mean': 170, 'sbp_sd': 15, 'dbp_mean': 108, 'dbp_sd': 10} # --- Blood Glucose (mg/dL) --- fasting # Source: WHO/IADPSG criteria for GDM # Normal fasting: 70-95 mg/dL # GDM: fasting ≥92 mg/dL (IADPSG) or ≥95 mg/dL (WHO) GLUCOSE_NORMAL = {'mean': 82, 'sd': 8} GLUCOSE_GDM = {'mean': 108, 'sd': 15} # ============================================================ # SECTION 2: Utility Functions # ============================================================ def trunc_normal(mean, sd, lo, hi, size, rng): a, b = (lo - mean) / sd, (hi - mean) / sd return truncnorm.rvs(a, b, loc=mean, scale=sd, size=size, random_state=rng.integers(0, 2**31)) # ============================================================ # SECTION 3: Main Generator # ============================================================ def generate_maternal_dataset(n=10000, seed=42, scenario='moderate_burden'): rng = np.random.default_rng(seed) sc = SCENARIOS[scenario] # ── Step 1: Age (root) ── age = trunc_normal(sc['maternal_age_mean'], sc['maternal_age_sd'], 15, 49, n, rng).astype(int) # ── Step 2: Gravidity & Parity ── gravidity_lambda = np.clip((age - 16) * 0.30, 0.1, 10) gravidity = rng.poisson(gravidity_lambda) gravidity = np.clip(gravidity, 1, 16) # current pregnancy counts parity = np.clip(gravidity - 1 - rng.binomial(1, 0.15, n), 0, 15) # ~15% loss rate # ── Step 3: Pre-pregnancy BMI ── bmi = trunc_normal(BMI_PARAMS['mean'], BMI_PARAMS['sd'], BMI_PARAMS['min'], BMI_PARAMS['max'], n, rng) bmi = np.round(bmi, 1) # ── Step 4: HIV status ── hiv_status = rng.random(n) < sc['hiv_prevalence'] # ── Step 5: Gestational age at visit (weeks) ── ga_at_visit = trunc_normal(28, 8, 6, 42, n, rng) ga_at_visit = np.round(ga_at_visit, 1) # ── Step 6: Assign primary complication ── # Compute risk-adjusted probabilities complication = np.array(['none'] * n, dtype=object) # Risk factors for preeclampsia: age >35, BMI >30, primigravida pe_risk = np.ones(n) pe_risk[age > 35] *= 1.8 # Say 2014: OR ~1.5-2.0 pe_risk[bmi > 30] *= 2.0 # WHO 2016: OR ~2-3 pe_risk[gravidity == 1] *= 1.5 # Primigravida OR ~1.5 pe_base = sc['preeclampsia_rate'] # Risk factors for GDM: age >30, BMI >25 gdm_risk = np.ones(n) gdm_risk[age > 30] *= 1.5 # IDF: OR ~1.5 gdm_risk[bmi > 25] *= 2.5 # IDF: OR ~2-3 gdm_base = sc['gdm_rate'] # Assign complications (mutually exclusive primary) for i in range(n): probs = { 'preeclampsia': pe_base * pe_risk[i], 'eclampsia': sc['eclampsia_rate'] * pe_risk[i], 'gestational_diabetes': gdm_base * gdm_risk[i], 'hemorrhage': sc['hemorrhage_rate'], 'severe_anemia': sc['severe_anemia_rate'], } # Normalize so total complication probability is reasonable total_comp = sum(probs.values()) p_none = max(1.0 - total_comp, 0.3) r = rng.random() cumulative = 0 assigned = False for comp, p in probs.items(): adj_p = p / (total_comp + p_none) * total_comp # keep relative rates cumulative += p / (total_comp + p_none) if r < cumulative: complication[i] = comp assigned = True break if not assigned: complication[i] = 'none' # ── Step 7: Hemoglobin (conditional on complication, scenario) ── hemoglobin = np.zeros(n) for i in range(n): if complication[i] == 'severe_anemia': hemoglobin[i] = rng.normal(HB_SEVERE_ANEMIA['mean'], HB_SEVERE_ANEMIA['sd']) elif complication[i] == 'hemorrhage': # Post-hemorrhage Hb is lower hemoglobin[i] = rng.normal(HB_MODERATE_ANEMIA['mean'], HB_MODERATE_ANEMIA['sd']) else: # Background anemia prevalence r = rng.random() if r < sc['moderate_anemia_rate']: hemoglobin[i] = rng.normal(HB_MODERATE_ANEMIA['mean'], HB_MODERATE_ANEMIA['sd']) elif r < sc['moderate_anemia_rate'] + 0.15: hemoglobin[i] = rng.normal(HB_MILD_ANEMIA['mean'], HB_MILD_ANEMIA['sd']) else: hemoglobin[i] = rng.normal(HB_NORMAL['mean'], HB_NORMAL['sd']) hemoglobin = np.clip(np.round(hemoglobin, 1), 3.0, 17.0) # Anemia classification (WHO pregnancy thresholds) anemia_status = np.where( hemoglobin < 7.0, 'severe', np.where(hemoglobin < 10.0, 'moderate', np.where(hemoglobin < 11.0, 'mild', 'none'))) # ── Step 8: Blood pressure (conditional on complication) ── systolic_bp = np.zeros(n) diastolic_bp = np.zeros(n) for i in range(n): if complication[i] == 'eclampsia': systolic_bp[i] = rng.normal(BP_ECLAMPSIA['sbp_mean'], BP_ECLAMPSIA['sbp_sd']) diastolic_bp[i] = rng.normal(BP_ECLAMPSIA['dbp_mean'], BP_ECLAMPSIA['dbp_sd']) elif complication[i] == 'preeclampsia': systolic_bp[i] = rng.normal(BP_PREECLAMPSIA['sbp_mean'], BP_PREECLAMPSIA['sbp_sd']) diastolic_bp[i] = rng.normal(BP_PREECLAMPSIA['dbp_mean'], BP_PREECLAMPSIA['dbp_sd']) else: # Age/BMI adjustment age_adj = (age[i] - 25) * 0.3 bmi_adj = (bmi[i] - 23) * 0.5 systolic_bp[i] = rng.normal(BP_NORMAL['sbp_mean'] + age_adj + bmi_adj, BP_NORMAL['sbp_sd']) diastolic_bp[i] = rng.normal(BP_NORMAL['dbp_mean'] + age_adj * 0.5 + bmi_adj * 0.3, BP_NORMAL['dbp_sd']) systolic_bp = np.clip(np.round(systolic_bp).astype(int), 70, 220) diastolic_bp = np.clip(np.round(diastolic_bp).astype(int), 40, 140) # Ensure SBP > DBP diastolic_bp = np.minimum(diastolic_bp, systolic_bp - 15) # ── Step 9: Fasting blood glucose (conditional on GDM) ── fasting_glucose = np.zeros(n) for i in range(n): if complication[i] == 'gestational_diabetes': fasting_glucose[i] = rng.normal(GLUCOSE_GDM['mean'], GLUCOSE_GDM['sd']) else: bmi_adj = (bmi[i] - 23) * 0.4 fasting_glucose[i] = rng.normal(GLUCOSE_NORMAL['mean'] + bmi_adj, GLUCOSE_NORMAL['sd']) fasting_glucose = np.clip(np.round(fasting_glucose).astype(int), 45, 250) # ── Step 10: Proteinuria (conditional on preeclampsia/eclampsia) ── # 0=none, 1=trace, 2= +1, 3= +2, 4= +3 or more proteinuria = np.zeros(n, dtype=int) for i in range(n): if complication[i] == 'eclampsia': proteinuria[i] = rng.choice([3, 4], p=[0.3, 0.7]) elif complication[i] == 'preeclampsia': proteinuria[i] = rng.choice([2, 3, 4], p=[0.3, 0.4, 0.3]) else: proteinuria[i] = rng.choice([0, 1], p=[0.85, 0.15]) # ── Step 11: Number of ANC visits ── # WHO recommends ≥8; LMIC average 4-6 anc_visits = np.clip(rng.poisson( np.where(ga_at_visit > 36, 6, np.where(ga_at_visit > 20, 4, 2)), ), 0, 15) # ── Step 12: Delivery mode ── delivery_mode = np.array(['vaginal'] * n, dtype=object) for i in range(n): p_cs = sc['cs_rate'] if complication[i] in ('eclampsia', 'preeclampsia'): p_cs = min(p_cs * 3.0, 0.60) elif complication[i] == 'hemorrhage': p_cs = min(p_cs * 2.0, 0.40) elif age[i] > 35: p_cs = min(p_cs * 1.5, 0.40) if rng.random() < p_cs: delivery_mode[i] = 'caesarean' # ── Step 13: Pregnancy outcome ── pregnancy_outcome = np.array(['live_birth'] * n, dtype=object) for i in range(n): if complication[i] == 'eclampsia': r = rng.random() if r < 0.05: # ~1-5% maternal mortality with eclampsia (Say 2014) pregnancy_outcome[i] = 'maternal_death' elif r < 0.20: pregnancy_outcome[i] = 'stillbirth' elif complication[i] == 'hemorrhage': r = rng.random() if r < 0.03: pregnancy_outcome[i] = 'maternal_death' elif r < 0.10: pregnancy_outcome[i] = 'stillbirth' elif complication[i] == 'severe_anemia': if rng.random() < 0.08: pregnancy_outcome[i] = 'stillbirth' # ── Step 14: Risk level (derived) ── risk_level = np.array(['low'] * n, dtype=object) for i in range(n): high_risk = ( complication[i] in ('eclampsia', 'severe_anemia', 'hemorrhage') or age[i] > 40 or age[i] < 16 or systolic_bp[i] >= 160 or diastolic_bp[i] >= 110 or hemoglobin[i] < 7.0 or hiv_status[i] ) moderate_risk = ( complication[i] in ('preeclampsia', 'gestational_diabetes') or age[i] > 35 or bmi[i] > 35 or parity[i] > 5 or systolic_bp[i] >= 140 or diastolic_bp[i] >= 90 or hemoglobin[i] < 10.0 ) if high_risk: risk_level[i] = 'high' elif moderate_risk: risk_level[i] = 'moderate' # ── Assemble DataFrame ── df = pd.DataFrame({ 'id': np.arange(1, n + 1), 'age_years': age, 'gravidity': gravidity, 'parity': parity, 'gestational_age_weeks': ga_at_visit, 'bmi_pre_pregnancy': bmi, 'systolic_bp_mmhg': systolic_bp, 'diastolic_bp_mmhg': diastolic_bp, 'hemoglobin_gdl': hemoglobin, 'anemia_status': anemia_status, 'fasting_glucose_mgdl': fasting_glucose, 'proteinuria': proteinuria, 'hiv_status': hiv_status.astype(int), 'anc_visits': anc_visits, 'delivery_mode': delivery_mode, 'primary_complication': complication, 'pregnancy_outcome': pregnancy_outcome, 'risk_level': risk_level, }) # ── Print summary ── print(f"\n{'='*60}") print(f"Maternal Health — {scenario} (n={n}, seed={seed})") print(f"{'='*60}") print(f"\nComplication prevalence:") for c in ['none', 'preeclampsia', 'eclampsia', 'gestational_diabetes', 'hemorrhage', 'severe_anemia']: obs = (complication == c).mean() * 100 print(f" {c:30s}: {obs:5.1f}%") print(f"\nAnemia (any, Hb<11): {(hemoglobin < 11).mean()*100:.1f}%") print(f"Severe anemia (Hb<7): {(hemoglobin < 7).mean()*100:.1f}%") print(f"Hypertension (SBP≥140|DBP≥90): " f"{((systolic_bp >= 140) | (diastolic_bp >= 90)).mean()*100:.1f}%") print(f"HIV+: {hiv_status.mean()*100:.1f}%") print(f"C-section: {(delivery_mode == 'caesarean').mean()*100:.1f}%") print(f"Stillbirth: {(pregnancy_outcome == 'stillbirth').mean()*100:.1f}%") print(f"Risk levels: low={( risk_level == 'low').mean()*100:.0f}%, " f"moderate={(risk_level == 'moderate').mean()*100:.0f}%, " f"high={(risk_level == 'high').mean()*100:.0f}%") return df # ============================================================ # SECTION 4: CLI Entry Point # ============================================================ if __name__ == '__main__': parser = argparse.ArgumentParser( description='Generate synthetic maternal health dataset') parser.add_argument('--scenario', type=str, default='moderate_burden', 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_maternal_dataset(n=args.n, seed=args.seed, scenario=sc_name) out = os.path.join('data', f'maternal_{sc_name}.csv') df.to_csv(out, index=False) print(f" → Saved to {out}\n") else: df = generate_maternal_dataset(n=args.n, seed=args.seed, scenario=args.scenario) out = args.output or os.path.join('data', f'maternal_{args.scenario}.csv') df.to_csv(out, index=False) print(f" → Saved to {out}")