electricsheepafrica/africa-who-overweight-prevalence-among-children-under-5-years-of-age-nantwhzne2

Africa — WHO GHO: Overweight prevalence among children under 5 years of age (% weight-for-height >+2 SD), survey-based estimates

Indicator code: NUTRITION_ANT_WHZ_NE2 HuggingFace slug: electricsheepafrica/africa-who-overweight-prevalence-among-children-under-5-years-of-age-nantwhzne2 Source: WHO Global Health Observatory License: CC BY 4.0 — WHO Open Data

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Dataset Description

This dataset contains country-level observations for the WHO GHO indicator "Overweight prevalence among children under 5 years of age (% weight-for-height >+2 SD), survey-based estimates" (NUTRITION_ANT_WHZ_NE2) across African nations, spanning 1985–2024. It is part of the Electric Sheep Africa 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.

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Coverage

Countries	47 African nations
Years	1985 – 2024
Total rows	26,324
Region filter	WHO AFRO (ParentLocationCode = 'AFR')

Countries included: AGO, BDI, BEN, BFA, BWA, CAF, CIV, CMR, COD, COG, COM, CPV, DZA, ERI, ETH, GAB, GHA, GIN, GMB, GNB … and 27 more

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Sub-dimensions

• AGEGROUP: AGEGROUP_MONTHS0-11, AGEGROUP_MONTHS0-23, AGEGROUP_MONTHS0-5, AGEGROUP_MONTHS12-17, AGEGROUP_MONTHS12-23, AGEGROUP_MONTHS12-59, AGEGROUP_MONTHS18-23, AGEGROUP_MONTHS24-29, AGEGROUP_MONTHS24-35, AGEGROUP_MONTHS24-59 …
• EDUCATIONLEVEL: EDUCATIONLEVEL_EDUC_HIGHER, EDUCATIONLEVEL_EDUC_NONE_PRIMARY, EDUCATIONLEVEL_EDUC_SECONDARY, EDUCATIONLEVEL_NHLM, EDUCATIONLEVEL_PRLM, EDUCATIONLEVEL_SHLM, EDUCATIONLEVEL_TOTL
• HOUSEHOLDWEALTH: HOUSEHOLDWEALTH_TOTL, HOUSEHOLDWEALTH_WEALTH_BOTTOM40, HOUSEHOLDWEALTH_WEALTH_BOTTOM60, HOUSEHOLDWEALTH_WEALTH_BOTTOM80, HOUSEHOLDWEALTH_WEALTH_MIDDLE60, HOUSEHOLDWEALTH_WEALTH_TOP40, HOUSEHOLDWEALTH_WEALTH_TOP60, HOUSEHOLDWEALTH_WEALTH_TOP80
• RESIDENCEAREATYPE: RESIDENCEAREATYPE_RUR, RESIDENCEAREATYPE_TOTL, RESIDENCEAREATYPE_URB
• SEX: SEX_BTSX, SEX_FMLE, SEX_MLE
• WEALTHDECILE: WEALTHDECILE_TOTL, WEALTHDECILE_WEALTHDECILE01, WEALTHDECILE_WEALTHDECILE02, WEALTHDECILE_WEALTHDECILE03, WEALTHDECILE_WEALTHDECILE04, WEALTHDECILE_WEALTHDECILE05, WEALTHDECILE_WEALTHDECILE06, WEALTHDECILE_WEALTHDECILE07, WEALTHDECILE_WEALTHDECILE08, WEALTHDECILE_WEALTHDECILE09 …
• WEALTHQUINTILE: WEALTHQUINTILE_TOTL, WEALTHQUINTILE_WQ1, WEALTHQUINTILE_WQ2, WEALTHQUINTILE_WQ3, WEALTHQUINTILE_WQ4, WEALTHQUINTILE_WQ5
• WEALTHTERCILE: WEALTHTERCILE_TOTL, WEALTHTERCILE_WEALTHTERCILE1, WEALTHTERCILE_WEALTHTERCILE2, WEALTHTERCILE_WEALTHTERCILE3

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.

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Schema

Column	Type	Description
indicator_code	string	GHO indicator code (e.g., NUTRITION_ANT_WHZ_NE2)
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

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Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-who-overweight-prevalence-among-children-under-5-years-of-age-nantwhzne2")
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")

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Citation

@misc{who_gho_nutrition_ant_whz_ne2,
  title     = {WHO Global Health Observatory: Overweight prevalence among children under 5 years of age (% weight-for-height >+2 SD), survey-based estimates},
  author    = {World Health Organization},
  year      = {2024},
  url       = {https://www.who.int/data/gho/data/indicators/indicator-details/GHO/NUTRITION_ANT_WHZ_NE2},
  note      = {Repackaged by Electric Sheep Africa}
}

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Repackaged by Electric Sheep Africa from WHO GHO open data. Original data © World Health Organization, licensed CC BY 4.0.