| """ |
| 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 = { |
| '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}) |
| |
| |
| 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)}") |
|
|
