Datasetpublic

electricsheepasia/asia-who-hypertension-effective-treatment-coverageamong-adults-aged

Africa — WHO GHO: Hypertension effective treatment coverage (controlled hypertension )among adults aged 30-79, age-standardized (%) Indicator code: NCD_HYP_CONTROL_A HuggingFace slug: electricsheepafrica/asia-who-hypertension-effective-treatment-coverageamong-adults-aged Source: WHO Global Health Observatory License: CC BY 4.0 — WHO Open Data Dataset Description This dataset contains country-level observations for the WHO GHO indicator "Hypertension effective… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepasia/asia-who-hypertension-effective-treatment-coverageamong-adults-aged.

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Dataset Card

Africa — WHO GHO: Hypertension effective treatment coverage (controlled hypertension )among adults aged 30-79, age-standardized (%)

Indicator code: NCD_HYP_CONTROL_A HuggingFace slug: electricsheepafrica/asia-who-hypertension-effective-treatment-coverageamong-adults-aged Source: WHO Global Health Observatory License: CC BY 4.0 — WHO Open Data

Dataset Description

This dataset contains country-level observations for the WHO GHO indicator "Hypertension effective treatment coverage (controlled hypertension )among adults aged 30-79, age-standardized (%)" (NCD_HYP_CONTROL_A) across Asian nations, spanning 1990–2019. It is part of the Electric Sheep Asia collection — a unified, ML-ready repository of African data.

Data is sourced directly from the WHO Global Health Observatory OData API and repackaged as Parquet files with a consistent schema. All values are drawn from NumericValue (the float-precision field), not the display string. Confidence interval bounds (value_low, value_high) are included where available.

Coverage


Countries	48 Asian nations
Years	1990 – 2019
Total rows	4,320
Region filter	WHO Asia regions (SEAR + WPR + EMR), filtered to Asian ISO3 codes

Countries included: AFG, ARE, ARM, AZE, BGD, BHR, BRN, BTN, CHN, CYP, GEO, IDN, IND, IRN, IRQ, ISR, JOR, JPN, KAZ, KGZ … and 28 more

Sub-dimensions

—SEX: SEXBTSX, SEXFMLE, SEX_MLE

When an indicator is stratified (e.g., by sex or age group), each unique combination of country × year × dimension produces a separate row. Filter on dim1 / dim2 for the stratum you need, or aggregate across strata.

Schema

Column	Type	Description
`indicator_code`	string	GHO indicator code (e.g., `NCD_HYP_CONTROL_A`)
`country_iso3`	string	ISO 3166-1 alpha-3 country code
`who_region`	string	WHO region code (always `AFR` here)
`year`	int	Observation year
`value_numeric`	float	Point estimate (primary ML target)
`value_low`	float	Lower confidence bound (if available)
`value_high`	float	Upper confidence bound (if available)
`value_display`	string	Formatted display string, e.g. `"58.3 [57.7–59.0]"`
`dim1_type`	string	Dimension 1 type, e.g. `SEX`, `RESIDENCEAREATYPE`
`dim1`	string	Dimension 1 value, e.g. `SEX_BTSX`, `RURAL`
`dim2_type`	string	Dimension 2 type (if present)
`dim2`	string	Dimension 2 value (if present)
`last_updated`	string	WHO data last-updated timestamp

Usage

python

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/asia-who-hypertension-effective-treatment-coverageamong-adults-aged")
df = ds["train"].to_pandas()

# Both-sexes, national level only
national = df[df.get("dim1", "").str.endswith("_BTSX") | df.get("dim1", pd.Series()).isna()]

# Time series for one country
kenya = df[df["country_iso3"] == "KEN"].sort_values("year")

Citation

bibtex

@misc{who_gho_ncd_hyp_control_a,
  title     = {WHO Global Health Observatory: Hypertension effective treatment coverage (controlled hypertension )among adults aged 30-79, age-standardized (%)},
  author    = {World Health Organization},
  year      = {2019},
  url       = {https://www.who.int/data/gho/data/indicators/indicator-details/GHO/NCD_HYP_CONTROL_A},
  note      = {Repackaged by Electric Sheep Asia}
}

Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia) from WHO GHO open data. Original data © World Health Organization, licensed CC BY 4.0.