README.md

---
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/)