electricsheepafrica/africa-disability-burkina-faso

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- tabular-classification
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- disability
- disease
- environment
- health
- hxl
- indicators
- malaria
- maternity
- bfa
pretty_name: "Burkina Faso - Health Indicators"
dataset_info:
splits:
- name: train
num_examples: 17471
- name: test
num_examples: 4367
---

# Burkina Faso - Health Indicators

**Publisher:** World Health Organization · **Source:** [HDX](https://data.humdata.org/dataset/who-data-for-burkina-faso) · **License:** `hdx-other` · **Updated:** 2025-02-07

---

## Abstract

This dataset contains data from WHO's [data portal](https://www.who.int/gho/en/) covering the following categories:

Air pollution, Antimicrobial resistance (AMR), Assistive technology, Child mortality, Dementia diagnosis, treatment and care, Dementia policy and legislation, Environment and health, Foodborne Diseases Estimates, Global Dementia Observatory (GDO), Global Health Estimates: Life expectancy and leading causes of death and disability, Global Information System on Alcohol and Health, HIV, Health Inequality Monitor, Health financing, Health systems, Health taxes, Health workforce, Hepatitis, Immunization coverage and vaccine-preventable diseases, International Health Regulations (2005) monitoring framework, Malaria, Maternal and reproductive health, Mental health, Neglected tropical diseases, Noncommunicable diseases, Nutrition, Oral Health, Priority health technologies, Resources for Substance Use Disorders, Road Safety, SDG Target 3.8 | Achieve universal health coverage (UHC), Sexually Transmitted Infections, Tobacco control, Tuberculosis, Vaccine-preventable communicable diseases, Violence against women, Violence prevention, Water, sanitation and hygiene (WASH), Women and health, World Health Statistics.

For links to individual indicator metadata, see resource descriptions.

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2025-02-07. Geographic scope: **BFA**.

*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Food security and nutrition |
| **Unit of observation** | First-level administrative unit observations |
| **Rows (total)** | 21,839 |
| **Columns** | 19 (6 numeric, 13 categorical, 0 datetime) |
| **Train split** | 17,471 rows |
| **Test split** | 4,367 rows |
| **Geographic scope** | BFA |
| **Publisher** | World Health Organization |
| **HDX last updated** | 2025-02-07 |

---

## Variables

**Geographic** — `gho_display` (Wasting prevalence among children under 5 years of age (% weight-for-height <-2 SD), survey-based estimates, Underweight prevalence among children under 5 years of age (% weight-for-age <-2 SD), survey-based estimates, Severe wasted prevalence among children under 5 years of age (% weight-for-height <-3 SD), survey-based estimates), `year_display` (range 1944.0–2030.0), `startyear` (range 1944.0–2030.0), `endyear` (range 1944.0–2030.0), `region_code` (AFR, #region+code) and 4 others.

**Outcome / Measurement** — `value`.

**Identifier / Metadata** — `gho_code` (NUTRITION_WH_2, NUTRITION_WA_2, NUTRITION_WH_3), `dimension_code` (SEX_BTSX, SEX_FMLE, SEX_MLE), `dimension_name` (Both sexes, Female, Male), `esa_source`, `esa_processed`.

**Other** — `gho_url` (https://www.who.int/data/gho/data/indicators/indicator-details/GHO/gho-jme-country-children-aged-5-years-wasted-br-%28-weight-for-height--2-sd%29, https://www.who.int/data/gho/data/indicators/indicator-details/GHO/gho-jme-country-children-aged-5-years-underweight-%28-weight-for-age--2-sd%29-%28-%29, https://www.who.int/data/gho/data/indicators/indicator-details/GHO/gho-jme-country-children-aged-5-years-severe-wasting-br-%28-weight-for-height--3-sd%29), `numeric` (range 0.0–19229686.0), `low` (range 0.0–511348.5), `high` (range 0.0–702181.75).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-disability-burkina-faso")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

print(train.shape)
train.head()
```

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `gho_code` | object | 0.0% | NUTRITION_WH_2, NUTRITION_WA_2, NUTRITION_WH_3 |
| `gho_display` | object | 0.0% | Wasting prevalence among children under 5 years of age (% weight-for-height <-2 SD), survey-based estimates, Underweight prevalence among children under 5 years of age (% weight-for-age <-2 SD), survey-based estimates, Severe wasted prevalence among children under 5 years of age (% weight-for-height <-3 SD), survey-based estimates |
| `gho_url` | object | 0.0% | https://www.who.int/data/gho/data/indicators/indicator-details/GHO/gho-jme-country-children-aged-5-years-wasted-br-%28-weight-for-height--2-sd%29, https://www.who.int/data/gho/data/indicators/indicator-details/GHO/gho-jme-country-children-aged-5-years-underweight-%28-weight-for-age--2-sd%29-%28-%29, https://www.who.int/data/gho/data/indicators/indicator-details/GHO/gho-jme-country-children-aged-5-years-severe-wasting-br-%28-weight-for-height--3-sd%29 |
| `year_display` | float64 | 0.0% | 1944.0 – 2030.0 (mean 2008.3545) |
| `startyear` | float64 | 0.0% | 1944.0 – 2030.0 (mean 2008.3499) |
| `endyear` | float64 | 0.0% | 1944.0 – 2030.0 (mean 2008.3545) |
| `region_code` | object | 0.0% | AFR, #region+code |
| `region_display` | object | 0.0% | Africa, #region+name |
| `country_code` | object | 0.0% | BFA, #country+code |
| `country_display` | object | 0.0% | Burkina Faso, #country+name |
| `dimension_type` | object | 17.6% | SEX, AGEGROUP, RESIDENCEAREATYPE |
| `dimension_code` | object | 17.6% | SEX_BTSX, SEX_FMLE, SEX_MLE |
| `dimension_name` | object | 17.6% | Both sexes, Female, Male |
| `numeric` | float64 | 8.1% | 0.0 – 19229686.0 (mean 86286.2058) |
| `value` | object | 0.1% | |
| `low` | float64 | 41.9% | 0.0 – 511348.5 (mean 2174.5784) |
| `high` | float64 | 41.9% | 0.0 – 702181.75 (mean 3465.6546) |
| `esa_source` | object | 0.0% | |
| `esa_processed` | object | 0.0% | |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `year_display` | 1944.0 | 2030.0 | 2008.3545 | 2010.0 |
| `startyear` | 1944.0 | 2030.0 | 2008.3499 | 2010.0 |
| `endyear` | 1944.0 | 2030.0 | 2008.3545 | 2010.0 |
| `numeric` | 0.0 | 19229686.0 | 86286.2058 | 13.746 |
| `low` | 0.0 | 511348.5 | 2174.5784 | 9.3 |
| `high` | 0.0 | 702181.75 | 3465.6546 | 15.4661 |

---

## Curation

Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (`N/A`, `null`, `none`, `-`, `unknown`, `no data`, `#N/A`) were unified to `NaN`. 195 exact duplicate rows were removed. 6 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

---

## Limitations

- Data originates from World Health Organization and has not been independently validated by ESA.
- Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
- The following columns have >20% missing values and should be treated with caution in modelling: `low`, `high`.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/who-data-for-burkina-faso) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_disability_burkina_faso,
title = {Burkina Faso - Health Indicators},
author = {World Health Organization},
year = {2025},
url = {https://data.humdata.org/dataset/who-data-for-burkina-faso},
note = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}
```

---

*[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.*