Duplicate from electricsheepafrica/oral-health-dental-disease

Co-authored-by: Kossiso Udodi Royce <Kossisoroyce@users.noreply.huggingface.co>

.gitattributes

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+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
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+# Audio files - compressed
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README.md

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+---
+license: cc-by-4.0
+task_categories:
+  - tabular-classification
+language:
+  - en
+tags:
+  - healthcare
+  - oral-health
+  - dental-caries
+  - periodontal
+  - noma
+  - dentistry
+  - sub-saharan-africa
+  - lmic
+pretty_name: "Oral Health & Dental Disease (Caries, Periodontal, Noma, Treatment Access)"
+size_categories:
+  - 10K<n<100K
+configs:
+  - config_name: dental_clinic
+    data_files: data/oral_dental_clinic.csv
+  - config_name: district_hospital
+    data_files: data/oral_district_hospital.csv
+    default: true
+  - config_name: rural_health_centre
+    data_files: data/oral_rural_health_centre.csv
+---
+
+# Oral Health & Dental Disease Dataset
+
+## Abstract
+
+This dataset provides **30,000 simulated oral health records** (10,000 per scenario) from sub-Saharan Africa. Each record contains 40+ variables including dental caries, DMFT score, periodontal disease, noma, oral cancer, treatment access, barriers, and outcomes. Three settings: dental clinic (23% care-seeking), district hospital (16%), and rural health centre (8%).
+
+## 1. Introduction
+
+Africa bears the largest global increase in oral diseases (WHO 2024). The six major conditions are dental caries, periodontal disease, oral cancer, oral HIV manifestations, noma, and cleft lip/palate. DMFT scores average ~4 in SSA adults. Untreated caries is the most prevalent condition. The dentist-to-population ratio is <1:100,000 in many SSA countries. Noma (cancrum oris) persists in extreme poverty with 70-90% CFR if untreated.
+
+**This dataset is entirely simulated. It must not be used for clinical decision-making.**
+
+## 2. Methodology
+
+### 2.1 Parameterization
+
+| Parameter | Value | Source |
+| --- | --- | --- |
+| Dental caries prevalence | ~55% | WHO 2022 |
+| Untreated caries | ~80% of caries | WHO Africa 2024 |
+| DMFT (age 12) | ~2.6 | PubMed 2021 |
+| Periodontal disease | ~17% | WHO 2022 |
+| Dentist ratio | <1:100K | WHO Africa |
+| Noma in malnourished children | ~0.5% | WHO Africa |
+| Extraction dominates treatment | ~50% | BMC PH 2021 |
+
+### 2.2 Scenario Design
+
+| Scenario | Dentist | Restorative | X-ray | Care-Seeking |
+| --- | --- | --- | --- | --- |
+| Dental clinic | Yes | Yes | Yes | 23% |
+| District hospital | Yes | No | No | 16% |
+| Rural health centre | No | No | No | 8% |
+
+## 3. Schema
+
+| Column | Type | Description |
+| --- | --- | --- |
+| id | int | Unique identifier |
+| age_years | int | Age |
+| sex | categorical | M / F |
+| dental_caries | binary | Dental caries |
+| dmft_score | int | DMFT score |
+| untreated_caries | binary | Untreated caries |
+| periodontal_disease | binary | Periodontal disease |
+| periodontal_severity | categorical | mild / moderate / severe |
+| tooth_loss | int | Teeth lost |
+| oral_cancer | binary | Oral cancer |
+| noma | binary | Noma (cancrum oris) |
+| dental_pain | binary | Dental pain |
+| dental_abscess | binary | Abscess |
+| sugary_diet | binary | High sugar diet |
+| fluoride_toothpaste | binary | Fluoride paste use |
+| brushing_frequency | categorical | never / occasional / once / twice daily |
+| sought_dental_care | binary | Sought care |
+| treatment_received | categorical | extraction / filling / scaling / antibiotics / pain_relief / traditional |
+| barrier_to_care | categorical | cost / distance / no_dentist / fear / not_severe / traditional |
+| pain_resolved | binary | Pain resolved |
+
+## 4. Validation
+
+<p align="center">
+  <img src="validation_report.png" alt="Validation Report" width="100%">
+</p>
+
+Key validation checks:
+
+- **Caries**: ~55% prevalence ✓
+- **Untreated**: ~80% of caries ✓
+- **Care-seeking gradient**: 23% → 16% → 8% ✓
+- **Extraction dominates** treatment ✓
+- **DMFT mean ~4** ✓
+- **Barriers**: Cost and distance dominant ✓
+
+## 5. Usage
+
+```python
+from datasets import load_dataset
+dataset = load_dataset("electricsheepafrica/oral-health-dental-disease", "district_hospital")
+df = dataset["train"].to_pandas()
+```
+
+## 6. Limitations
+
+- **Simulated**: Not from real dental registries.
+- **No imaging**: No radiographic data.
+- **No clinical exam**: No periodontal probing depths.
+- **Simplified**: No detailed orthodontic data.
+- **No fluoride levels**: No water fluoride concentrations.
+
+## 7. References
+
+1. WHO Africa (2024). Oral health in the African Region.
+2. WHO (2022). Global Oral Health Status Report.
+3. PubMed (2021). DMFT in East Africa.
+4. BMC Public Health (2021). Dental caries in adults SSA.
+5. WHO Africa. Noma (cancrum oris).
+6. PubMed (2015). Oral health South Africa.
+7. PubMed (2021). Dental caries prevalence East Africa.
+
+## Citation
+
+```bibtex
+@dataset{esa_oral_health_2025,
+  title={Oral Health and Dental Disease Dataset},
+  author={Electric Sheep Africa},
+  year={2025},
+  publisher={Hugging Face},
+  url={https://huggingface.co/datasets/electricsheepafrica/oral-health-dental-disease}
+}
+```
+
+## License
+
+[CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)

generate_dataset.py

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+#!/usr/bin/env python3
+"""
+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}")

requirements.txt

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+numpy>=1.24
+pandas>=2.0
+matplotlib>=3.7

validate_dataset.py

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+#!/usr/bin/env python3
+"""Validation & Diagnostic Visualization for Oral Health Dataset."""
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import os
+
+SCENARIOS = ['dental_clinic', 'district_hospital', 'rural_health_centre']
+
+
+def load_scenarios(data_dir='data'):
+    dfs = {}
+    for sc in SCENARIOS:
+        path = os.path.join(data_dir, f'oral_{sc}.csv')
+        if os.path.exists(path):
+            dfs[sc] = pd.read_csv(path)
+    return dfs
+
+
+def make_report(dfs, output='validation_report.png'):
+    fig, axes = plt.subplots(4, 2, figsize=(16, 22))
+    fig.suptitle('Oral Health & Dental Disease — Validation Report',
+                 fontsize=16, fontweight='bold', y=0.98)
+    df = dfs.get('district_hospital', list(dfs.values())[0])
+    colors = ['#2ecc71', '#f39c12', '#e74c3c']
+
+    ax = axes[0, 0]
+    conditions = ['dental_caries', 'untreated_caries', 'periodontal_disease',
+                   'dental_pain', 'dental_abscess', 'tooth_loss']
+    c_labels = ['Caries', 'Untreated', 'Periodontal', 'Pain', 'Abscess', 'Tooth Loss']
+    vals = [df[c].mean() * 100 if c != 'tooth_loss' else (df[c] > 0).mean() * 100 for c in conditions]
+    ax.bar(range(6), vals, color='#e74c3c', alpha=0.7)
+    ax.set_xticks(range(6))
+    ax.set_xticklabels(c_labels, fontsize=8, rotation=15)
+    for i, v in enumerate(vals):
+        ax.text(i, v + 0.5, f'{v:.0f}%', ha='center', fontsize=8)
+    ax.set_ylabel('Prevalence (%)')
+    ax.set_title('Oral Disease Burden (caries most prevalent)')
+
+    ax = axes[0, 1]
+    x = np.arange(len(SCENARIOS))
+    care = [dfs[sc]['sought_dental_care'].mean() * 100 for sc in SCENARIOS if sc in dfs]
+    ax.bar(x, care, color=colors, alpha=0.8)
+    ax.set_xticks(x)
+    ax.set_xticklabels(['Dental Clinic', 'District', 'Rural'], fontsize=9)
+    for i, v in enumerate(care):
+        ax.text(i, v + 0.5, f'{v:.0f}%', ha='center', fontsize=10)
+    ax.set_ylabel('Care-Seeking Rate (%)')
+    ax.set_title('Dental Care Access (<1 dentist/100K in SSA)')
+
+    ax = axes[1, 0]
+    caries_pts = df[df['dental_caries'] == 1]
+    if len(caries_pts) > 0:
+        ax.hist(caries_pts['dmft_score'], bins=20, color='#3498db', alpha=0.7, edgecolor='white')
+    ax.set_xlabel('DMFT Score')
+    ax.set_title('DMFT Distribution (mean ~4 in SSA)')
+
+    ax = axes[1, 1]
+    treatments = df[df['treatment_received'] != 'none']['treatment_received'].value_counts()
+    if len(treatments) > 0:
+        t_colors = ['#e74c3c', '#f39c12', '#3498db', '#2ecc71', '#9b59b6', '#e67e22']
+        ax.pie(treatments.values,
+               labels=[s.replace('_', ' ').title() for s in treatments.index],
+               autopct='%1.0f%%', colors=t_colors[:len(treatments)],
+               startangle=90, textprops={'fontsize': 8})
+    ax.set_title('Treatment Type (extraction dominates)')
+
+    ax = axes[2, 0]
+    barriers = df[df['barrier_to_care'] != 'none']['barrier_to_care'].value_counts()
+    if len(barriers) > 0:
+        ax.barh(range(len(barriers)), barriers.values, color='#3498db', alpha=0.8)
+        ax.set_yticks(range(len(barriers)))
+        ax.set_yticklabels([s.replace('_', ' ').title() for s in barriers.index], fontsize=8)
+    ax.set_xlabel('Count')
+    ax.set_title('Barriers to Dental Care')
+
+    ax = axes[2, 1]
+    risks = ['sugary_diet', 'tobacco_use', 'fluoride_toothpaste', 'diabetes']
+    r_labels = ['Sugary Diet', 'Tobacco', 'Fluoride Paste', 'Diabetes']
+    caries_y = df[df['dental_caries'] == 1]
+    caries_n = df[df['dental_caries'] == 0]
+    if len(caries_y) > 0 and len(caries_n) > 0:
+        vc = [caries_y[r].mean() * 100 for r in risks]
+        vn = [caries_n[r].mean() * 100 for r in risks]
+        w = 0.3
+        ax.bar(np.arange(4) - w/2, vc, w, label='Caries', color='#e74c3c', alpha=0.8)
+        ax.bar(np.arange(4) + w/2, vn, w, label='No Caries', color='#2ecc71', alpha=0.8)
+        ax.set_xticks(np.arange(4))
+        ax.set_xticklabels(r_labels, fontsize=8)
+    ax.set_ylabel('Prevalence (%)')
+    ax.set_title('Risk Factors vs Caries')
+    ax.legend(fontsize=8)
+
+    ax = axes[3, 0]
+    children = df[df['child'] == 1]
+    adults = df[df['child'] == 0]
+    cats = ['Caries', 'Pain', 'Sought Care']
+    if len(children) > 0 and len(adults) > 0:
+        vc = [children['dental_caries'].mean()*100, children['dental_pain'].mean()*100,
+              children['sought_dental_care'].mean()*100]
+        va = [adults['dental_caries'].mean()*100, adults['dental_pain'].mean()*100,
+              adults['sought_dental_care'].mean()*100]
+        w = 0.3
+        ax.bar(np.arange(3) - w/2, vc, w, label='Children', color='#3498db', alpha=0.8)
+        ax.bar(np.arange(3) + w/2, va, w, label='Adults', color='#e74c3c', alpha=0.8)
+        ax.set_xticks(np.arange(3))
+        ax.set_xticklabels(cats, fontsize=9)
+    ax.set_ylabel('Rate (%)')
+    ax.set_title('Children vs Adults')
+    ax.legend(fontsize=8)
+
+    ax = axes[3, 1]
+    brush = df['brushing_frequency'].value_counts()
+    b_order = ['never', 'occasional', 'once_daily', 'twice_daily']
+    vals = [brush.get(b, 0) for b in b_order]
+    ax.bar(range(4), vals, color=['#e74c3c', '#f39c12', '#3498db', '#2ecc71'], alpha=0.8)
+    ax.set_xticks(range(4))
+    ax.set_xticklabels(['Never', 'Occasional', 'Once/Day', 'Twice/Day'], fontsize=8)
+    ax.set_ylabel('Count')
+    ax.set_title('Brushing Frequency')
+
+    plt.tight_layout(rect=[0, 0, 1, 0.97])
+    plt.savefig(output, dpi=150, bbox_inches='tight')
+    print(f'Saved validation report to {output}')
+    plt.close()
+
+
+if __name__ == '__main__':
+    dfs = load_scenarios()
+    if dfs:
+        make_report(dfs)