| --- |
| license: cc-by-4.0 |
| task_categories: |
| - tabular-classification |
| - tabular-regression |
| language: |
| - en |
| tags: |
| - africa |
| - health |
| - who |
| - gho |
| - "nutrition_ant_whz_ne2" |
| pretty_name: "Africa — WHO GHO: Overweight prevalence among children under 5 years of age (% weight-for-height >+2 SD), survey-based estimates" |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # 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](https://www.who.int/data/gho/data/indicators/indicator-details/GHO/NUTRITION_ANT_WHZ_NE2) |
| **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) — WHO Open Data |
|
|
| --- |
|
|
| ## 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](https://huggingface.co/electricsheepafrica) 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** | 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 |
|
|
| --- |
|
|
| ## 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. |
|
|
| --- |
|
|
| ## 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 | |
|
|
| --- |
|
|
| ## Usage |
|
|
| ```python |
| 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") |
| ``` |
|
|
| --- |
|
|
| ## Citation |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
|
|
| --- |
|
|
| _Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica) from WHO GHO open data. Original data © World Health Organization, licensed CC BY 4.0._ |
|
|
