electricsheepafrica/africa-disability-sao-tome

Sao Tome and Principe - Health Indicators

Publisher: World Health Organization · Source: HDX · License: hdx-other · Updated: 2025-02-07

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Abstract

This dataset contains data from WHO's data portal 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: STP.

Curated into ML-ready Parquet format by Electric Sheep Africa.

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Dataset Characteristics

Domain	Food security and nutrition
Unit of observation	First-level administrative unit observations
Rows (total)	18,042
Columns	19 (6 numeric, 13 categorical, 0 datetime)
Train split	14,433 rows
Test split	3,608 rows
Geographic scope	STP
Publisher	World Health Organization
HDX last updated	2025-02-07

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Variables

Geographic — gho_display (Number of deaths, Distribution of causes of death among children aged < 5 years (%), Deaths per 1 000 live births), year_display (range 1961.0–2030.0), startyear (range 1961.0–2030.0), endyear (range 1961.0–2030.0), region_code (AFR, #region+code) and 4 others.

Outcome / Measurement — value.

Identifier / Metadata — gho_code (MORT_100, MORT_300, MORT_200), 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/number-of-deaths, https://www.who.int/data/gho/data/indicators/indicator-details/GHO/gho-ghe-life-tables-by-who-region-global-health-estimates, https://www.who.int/data/gho/data/indicators/indicator-details/GHO/deaths-per-1-000-live-births), numeric (range -0.0167–7199386.737), low (range -0.1915–4507.9302), high (range 0.0–5845.5972).

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Quick Start

from datasets import load_dataset

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

print(train.shape)
train.head()

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Schema

Column	Type	Null %	Range / Sample Values
gho_code	object	0.0%	MORT_100, MORT_300, MORT_200
gho_display	object	0.0%	Number of deaths, Distribution of causes of death among children aged < 5 years (%), Deaths per 1 000 live births
gho_url	object	0.0%	https://www.who.int/data/gho/data/indicators/indicator-details/GHO/number-of-deaths, https://www.who.int/data/gho/data/indicators/indicator-details/GHO/gho-ghe-life-tables-by-who-region-global-health-estimates, https://www.who.int/data/gho/data/indicators/indicator-details/GHO/deaths-per-1-000-live-births
year_display	float64	0.0%	1961.0 – 2030.0 (mean 2008.3232)
startyear	float64	0.0%	1961.0 – 2030.0 (mean 2008.3197)
endyear	float64	0.0%	1961.0 – 2030.0 (mean 2008.3232)
region_code	object	0.0%	AFR, #region+code
region_display	object	0.0%	Africa, #region+name
country_code	object	0.0%	STP, #country+code
country_display	object	0.0%	Sao Tome and Principe, #country+name
dimension_type	object	18.9%	SEX, RESIDENCEAREATYPE, AGEGROUP
dimension_code	object	18.9%	SEX_BTSX, SEX_FMLE, SEX_MLE
dimension_name	object	19.0%	Both sexes, Female, Male
numeric	float64	9.8%	-0.0167 – 7199386.737 (mean 64097.5004)
value	object	0.3%
low	float64	46.4%	-0.1915 – 4507.9302 (mean 32.4614)
high	float64	46.4%	0.0 – 5845.5972 (mean 59.1976)
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
year_display	1961.0	2030.0	2008.3232	2010.0
startyear	1961.0	2030.0	2008.3197	2010.0
endyear	1961.0	2030.0	2008.3232	2010.0
numeric	-0.0167	7199386.737	64097.5004	7.8586
low	-0.1915	4507.9302	32.4614	4.7527
high	0.0	5845.5972	59.1976	11.5

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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. 297 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.

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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 for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_disability_sao_tome,
  title     = {Sao Tome and Principe - Health Indicators},
  author    = {World Health Organization},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/who-data-for-sao-tome-and-principe},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.