Datasets:
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"""
Literature-Informed Oral Health & Dental Disease Dataset
=========================================================
Generates realistic synthetic records of oral health patients in
sub-Saharan Africa, including dental caries, periodontal disease,
noma, oral cancer, treatment access, and outcomes.
References (web-searched):
-----------
[1] WHO Africa 2024. Africa has largest global increase in
oral diseases. 6 major conditions: caries, periodontal,
oral cancer, oral HIV, noma, cleft lip/palate.
[2] WHO 2022. Global Oral Health Status Report. Untreated
caries is most prevalent condition globally.
[3] PubMed 2021. DMFT in East Africa: 2.57 at age 12,
4.04 at age 15. High caries burden.
[4] BMC Public Health 2021. Dental caries in adults SSA.
Limited access to dental care.
[5] WHO Africa. Noma (cancrum oris) persists in extreme
poverty. CFR 70-90% untreated. Disfiguring.
[6] PubMed 2015. Oral health South Africa: DMFT trends,
national surveys, fluoride.
[7] Dentist ratio in SSA: <1 per 100,000 population in
many countries. WHO target 1:7500.
"""
import numpy as np
import pandas as pd
import argparse
import os
SCENARIOS = {
'dental_clinic': {
'description': 'Urban dental clinic with dentist, basic '
'restorative/extraction capability, X-ray '
'(e.g., university dental clinics Nairobi, '
'Lagos, Addis Ababa)',
'dentist_available': True,
'restorative_available': True,
'xray_available': True,
'fluoride_programme': True,
'treatment_mod': 1.0,
},
'district_hospital': {
'description': 'District hospital with dental officer, '
'extraction only, no restorative '
'(e.g., district hospitals Tanzania, Malawi)',
'dentist_available': True,
'restorative_available': False,
'xray_available': False,
'fluoride_programme': False,
'treatment_mod': 0.6,
},
'rural_health_centre': {
'description': 'Rural health centre, no dental professional, '
'basic pain relief, referral '
'(e.g., rural CHCs DRC, Niger, Chad)',
'dentist_available': False,
'restorative_available': False,
'xray_available': False,
'fluoride_programme': False,
'treatment_mod': 0.2,
},
}
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_years'] = max(2, min(80, int(rng.normal(28, 18))))
rec['sex'] = rng.choice(['M', 'F'], p=[0.48, 0.52])
rec['child'] = 1 if rec['age_years'] < 18 else 0
rec['education'] = rng.choice(
['none', 'primary', 'secondary', 'tertiary'],
p=[0.20, 0.35, 0.35, 0.10])
rec['urban'] = 1 if rng.random() < 0.40 else 0
# ── 2. Risk factors ──
rec['tobacco_use'] = 0
if rec['age_years'] >= 15:
rec['tobacco_use'] = 1 if rng.random() < 0.12 else 0
rec['sugary_diet'] = 1 if rng.random() < 0.55 else 0
rec['fluoride_toothpaste'] = 1 if rng.random() < (0.50 if rec['urban'] else 0.20) else 0
rec['brushing_frequency'] = rng.choice(
['never', 'occasional', 'once_daily', 'twice_daily'],
p=[0.15, 0.25, 0.40, 0.20])
rec['hiv_positive'] = 1 if rng.random() < 0.06 else 0
rec['diabetes'] = 0
if rec['age_years'] >= 30:
rec['diabetes'] = 1 if rng.random() < 0.08 else 0
rec['malnutrition'] = 0
if rec['child']:
rec['malnutrition'] = 1 if rng.random() < 0.15 else 0
# ── 3. Dental caries [2][3] ──
caries_prob = 0.40
if rec['sugary_diet']:
caries_prob += 0.15
if rec['brushing_frequency'] in ('never', 'occasional'):
caries_prob += 0.10
if not rec['fluoride_toothpaste']:
caries_prob += 0.05
rec['dental_caries'] = 1 if rng.random() < min(caries_prob, 0.80) else 0
rec['dmft_score'] = 0
if rec['dental_caries']:
if rec['child']:
rec['dmft_score'] = max(0, min(20, int(rng.exponential(3))))
else:
rec['dmft_score'] = max(0, min(32, int(rng.exponential(5))))
rec['untreated_caries'] = 0
if rec['dental_caries']:
rec['untreated_caries'] = 1 if rng.random() < 0.80 else 0
# ── 4. Periodontal disease ──
rec['periodontal_disease'] = 0
if rec['age_years'] >= 15:
perio_prob = 0.20
if rec['tobacco_use']:
perio_prob *= 1.5
if rec['diabetes']:
perio_prob *= 1.5
if rec['hiv_positive']:
perio_prob *= 1.3
rec['periodontal_disease'] = 1 if rng.random() < min(perio_prob, 0.60) else 0
rec['periodontal_severity'] = 'none'
if rec['periodontal_disease']:
rec['periodontal_severity'] = rng.choice(
['mild', 'moderate', 'severe'],
p=[0.30, 0.45, 0.25])
rec['tooth_loss'] = 0
if rec['periodontal_severity'] == 'severe' or rec['dmft_score'] > 8:
rec['tooth_loss'] = max(0, min(20, int(rng.exponential(3))))
# ── 5. Other conditions [1][5] ──
rec['oral_cancer'] = 0
if rec['age_years'] >= 40:
oc_prob = 0.005
if rec['tobacco_use']:
oc_prob *= 3
rec['oral_cancer'] = 1 if rng.random() < oc_prob else 0
rec['noma'] = 0
if rec['child'] and rec['malnutrition']:
rec['noma'] = 1 if rng.random() < 0.005 else 0
rec['oral_hiv_manifestation'] = 0
if rec['hiv_positive']:
rec['oral_hiv_manifestation'] = 1 if rng.random() < 0.30 else 0
rec['cleft_lip_palate'] = 0
if rec['age_years'] < 10:
rec['cleft_lip_palate'] = 1 if rng.random() < 0.002 else 0
rec['dental_trauma'] = 0
if rec['age_years'] < 18:
rec['dental_trauma'] = 1 if rng.random() < 0.05 else 0
rec['dental_abscess'] = 0
if rec['untreated_caries']:
rec['dental_abscess'] = 1 if rng.random() < 0.10 else 0
rec['dental_pain'] = 0
if rec['dental_caries'] or rec['periodontal_disease'] or rec['dental_abscess']:
rec['dental_pain'] = 1 if rng.random() < 0.60 else 0
# ── 6. Treatment access [4][7] ──
rec['sought_dental_care'] = 0
if rec['dental_pain'] or rec['dental_abscess']:
rec['sought_dental_care'] = 1 if rng.random() < (0.50 * sc['treatment_mod'] + 0.10) else 0
rec['treatment_received'] = 'none'
if rec['sought_dental_care']:
if sc['restorative_available']:
rec['treatment_received'] = rng.choice(
['extraction', 'filling', 'scaling', 'antibiotics', 'pain_relief'],
p=[0.35, 0.25, 0.10, 0.15, 0.15])
elif sc['dentist_available']:
rec['treatment_received'] = rng.choice(
['extraction', 'antibiotics', 'pain_relief'],
p=[0.50, 0.25, 0.25])
else:
rec['treatment_received'] = rng.choice(
['pain_relief', 'traditional_remedy', 'referral'],
p=[0.40, 0.35, 0.25])
rec['barrier_to_care'] = 'none'
if not rec['sought_dental_care'] and rec['dental_pain']:
rec['barrier_to_care'] = rng.choice(
['cost', 'distance', 'no_dentist', 'fear',
'not_severe_enough', 'traditional_preference'],
p=[0.25, 0.20, 0.20, 0.15, 0.10, 0.10])
rec['fluoride_varnish'] = 0
if rec['child'] and sc['fluoride_programme']:
rec['fluoride_varnish'] = 1 if rng.random() < 0.20 else 0
rec['oral_health_education'] = 0
if rec['sought_dental_care'] and sc['dentist_available']:
rec['oral_health_education'] = 1 if rng.random() < 0.30 else 0
# ── 7. Outcome ──
rec['pain_resolved'] = 0
if rec['treatment_received'] not in ('none', 'referral'):
rec['pain_resolved'] = 1 if rng.random() < 0.70 else 0
rec['complication'] = 0
if rec['dental_abscess'] and rec['treatment_received'] == 'none':
rec['complication'] = 1 if rng.random() < 0.15 else 0
rec['noma_disfigurement'] = 0
if rec['noma']:
rec['noma_disfigurement'] = 1 if rng.random() < 0.80 else 0
records.append(rec)
df = pd.DataFrame(records)
print(f"\n{'='*65}")
print(f"Oral Health — {scenario} (n={n}, seed={seed})")
print(f"{'='*65}")
print(f"\n Dental caries: {df['dental_caries'].mean()*100:.1f}%")
print(f" Untreated: {df['untreated_caries'].mean()*100:.1f}%")
print(f" Periodontal: {df['periodontal_disease'].mean()*100:.1f}%")
print(f" Dental pain: {df['dental_pain'].mean()*100:.1f}%")
print(f" Sought care: {df['sought_dental_care'].mean()*100:.1f}%")
print(f" Mean DMFT: {df[df['dental_caries']==1]['dmft_score'].mean():.1f}")
return df
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description='Generate oral health 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'oral_{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'oral_{args.scenario}.csv')
df.to_csv(out, index=False)
print(f" -> Saved to {out}")
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