"""
Cancer Screening Programs - Sub-Saharan Africa
============================================
Based on research:
- Cervical screening uptake: 3-21% (BMC Public Health 2025)
- Breast cancer screening: 5-25% (DHS data)
- Barriers: cost, awareness, access (Screening review 2022)
- WHO 2024 Kenya National Screening Guidelines

Key Parameters:
- Cervical screening: 5-20% coverage
- Breast clinical exam: 8-30% ever screened
- Mammography: <5% in most countries

Author: Electric Sheep Africa
"""

import numpy as np, pandas as pd, argparse, os

np.random.default_rng(42)

COUNTRIES = ['Kenya', 'Uganda', 'Nigeria', 'Ghana', 'Tanzania', 'Ethiopia', 'Malawi', 'Zambia']
YEAR = {y: 0.1 for y in range(2018, 2026)}
SCREENING_TYPES = ['Cervical VIA/VIA', 'Cervical HPV', 'Clinical breast exam', 'Mammography', 'PSA test']

def sc(p, rng):
    a = np.array(list(p.values()))
    return rng.choice(list(p.keys()), p=a/a.sum())

def gen(n=5000, seed=42):
    rng = np.random.default_rng(seed)
    recs = []
    for i in range(n):
        country = sc(dict.fromkeys(COUNTRIES, 1/len(COUNTRIES)), rng)
        year = sc(YEAR, rng)
        
        # Screening rates vary by country (based on DHS data)
        screening_rates = {
            'Kenya': {'cervical': 0.18, 'breast': 0.22, 'mammogram': 0.04},
            'Uganda': {'cervical': 0.12, 'breast': 0.15, 'mammogram': 0.02},
            'Nigeria': {'cervical': 0.08, 'breast': 0.12, 'mammogram': 0.03},
            'Ghana': {'cervical': 0.15, 'breast': 0.18, 'mammogram': 0.05},
            'Tanzania': {'cervical': 0.10, 'breast': 0.12, 'mammogram': 0.02},
            'Ethiopia': {'cervical': 0.06, 'breast': 0.08, 'mammogram': 0.01},
            'Malawi': {'cervical': 0.12, 'breast': 0.10, 'mammogram': 0.01},
            'Zambia': {'cervical': 0.11, 'breast': 0.12, 'mammogram': 0.02},
        }
        rates = screening_rates.get(country, {'cervical': 0.10, 'breast': 0.12, 'mammogram': 0.02})
        
        # Determine screening history
        ever_cervical = rng.random() < rates['cervical']
        ever_breast = rng.random() < rates['breast']
        ever_mammogram = rng.random() < rates['mammogram']
        
        age = rng.integers(25, 65)
        
        recs.append({
            'participant_id': f'SCREEN-{country[:3].upper()}-{year}-{i+1:05d}',
            'country': country, 'year': year, 'age': age,
            'sex': 'F', 'urban_rural': sc({'Urban': 0.42, 'Rural': 0.58}, rng),
            'education': sc({'None': 0.08, 'Primary': 0.32, 'Secondary': 0.40, 'Tertiary': 0.20}, rng),
            'insurance': sc({'NHIF': 0.25, 'Private': 0.08, 'None': 0.67}, rng),
            'ever_cervical_screened': 'Yes' if ever_cervical else 'No',
            'cervical_screening_method': rng.choice(['VIA', 'Pap smear', 'HPV test']) if ever_cervical else 'N/A',
            'cervical_screening_result': rng.choice(['Normal', 'Abnormal', 'Inadequate']) if ever_cervical else 'N/A',
            'cervical_treatment_done': rng.choice(['Yes', 'No'], p=[0.35, 0.65]) if ever_cervical else 'N/A',
            'ever_breast_screened': 'Yes' if ever_breast else 'No',
            'breast_screening_method': rng.choice(['Clinical exam', 'Ultrasound', 'Mammography']) if ever_breast else 'N/A',
            'breast_findings': rng.choice(['Normal', 'Benign', 'Suspicious', 'Malignant']) if ever_breast else 'N/A',
            'ever_mammogram': 'Yes' if ever_mammogram else 'No',
            'mammogram_result': rng.choice(['BI-RADS 0', 'BI-RADS 1', 'BI-RADS 2', 'BI-RADS 3', 'BI-RADS 4', 'BI-RADS 5']) if ever_mammogram else 'N/A',
            'referred_for_biopsy': rng.choice(['Yes', 'No'], p=[0.12, 0.88]) if ever_mammogram else 'N/A',
            'barriers_screening': sc({'Cost': 0.28, 'Awareness': 0.32, 'Access': 0.22, 'Stigma': 0.10, 'No barriers': 0.08}, rng),
            'family_history_cancer': rng.choice(['Yes', 'No'], p=[0.08, 0.92]),
            'known_risk_factors': sc({'None': 0.45, 'Smoking': 0.15, 'Alcohol': 0.12, 'Obesity': 0.18, 'HPV': 0.10}, rng),
            'intends_to_screen': rng.choice(['Yes', 'No', 'Unsure'], p=[0.42, 0.35, 0.23]),
        })
    return pd.DataFrame(recs)

if __name__ == "__main__":
    p = argparse.ArgumentParser()
    p.add_argument('--n', type=int, default=5000)
    p.add_argument('--output', type=str, default='.')
    a = p.parse_args()
    
    for sn, m, s in [('low_burden', 0.8, 42), ('moderate_burden', 1.0, 43), ('high_burden', 1.2, 44)]:
        d = gen(int(a.n * m), s)
        d['scenario'] = sn
        d.to_csv(os.path.join(a.output, f'cancer_screening_programs_{sn}.csv'), index=False)
        print(f"Saved: cancer_screening_programs_{sn}.csv, n={len(d)}")