electricsheepasia/asia-malnutrition-afghanistan-malnutrition-prevalence

Afghanistan - Malnutrition Prevalence

Publisher: OCHA Afghanistan · Source: HDX · License: cc-by · Updated: 2025-05-05

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Abstract

Afghanistan prevalence of malnutrition, SAM, MAM and GAM.

Each row in this dataset represents tabular records. Data was last updated on HDX on 2025-05-05. Geographic scope: AFG.

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

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Dataset Characteristics

Domain	Humanitarian and development data
Unit of observation	Tabular records
Rows (total)	35
Columns	32 (16 numeric, 16 categorical, 0 datetime)
Train split	28 rows
Test split	7 rows
Geographic scope	AFG
Publisher	OCHA Afghanistan
HDX last updated	2025-05-05

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Variables

Geographic — wasting_by_weight_for_height_z_score_oedema_criteria (range 486.0–1271.0), underweight_by_weight_for_age_z_score_criteria (range 34.0–1276.0), stunting_by_height_for_age_z_score_criteria (range 473.0–1262.0).

Identifier / Metadata — unnamed_0 (Province, Parwan, Kabul Rural), unnamed_1 (AF01, PROV_CODE, AF03), unnamed_3 (range 1.0–29.0), unnamed_4 (1.8 (1.1-3.0), % (95% CI), 2.1 (1.3-3.4)), unnamed_5 (range 9.0–127.0) and 23 others.

Other — combined_gam_and_sam_based_on_whz_and_muac (range 494.0–1282.0).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-malnutrition-afghanistan-malnutrition-prevalence")
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
unnamed_0	object	5.7%	Province, Parwan, Kabul Rural
unnamed_1	object	5.7%	AF01, PROV_CODE, AF03
wasting_by_weight_for_height_z_score_oedema_criteria	float64	8.6%	486.0 – 1271.0 (mean 826.1875)
unnamed_3	float64	8.6%	1.0 – 29.0 (mean 13.6875)
unnamed_4	object	5.7%	1.8 (1.1-3.0), % (95% CI), 2.1 (1.3-3.4)
unnamed_5	float64	8.6%	9.0 – 127.0 (mean 69.625)
unnamed_6	object	5.7%	% (95% CI), 8.7 (7.0-10.8), 10.5 (8.2-13.4)
unnamed_7	float64	8.6%	10.0 – 153.0 (mean 83.0938)
unnamed_8	object	5.7%	% (95% CI), 10.5 (8.5-12.9), 12.6 (9.8-16.0)
combined_gam_and_sam_based_on_whz_and_muac	float64	8.6%	494.0 – 1282.0 (mean 837.1875)
unnamed_10	float64	8.6%	5.0 – 48.0 (mean 26.8438)
unnamed_11	object	5.7%	% (95% CI), 2.2 (1.5-3.9), 4.1 (3.0-6.4)
unnamed_12	float64	8.6%	13.0 – 171.0 (mean 94.3438)
unnamed_13	object	5.7%	% (95% CI), 10.0 (8.1-12.1), 12.0 (9.7-14.3)
unnamed_14	float64	8.6%	20.0 – 215.0 (mean 121.1875)
unnamed_15	object	5.7%	% (95% CI), 12.2 (10.9-15.8), 16.1 (14.1-21.0)
underweight_by_weight_for_age_z_score_criteria	float64	8.6%	34.0 – 1276.0 (mean 806.625)
unnamed_17	float64	8.6%	14.0 – 130.0 (mean 51.4375)
unnamed_18	object	5.7%	% (95% CI), 6.0 (4.5-7.9), 7.2 (5.4-9.4)
unnamed_19	float64	8.6%	66.0 – 259.0 (mean 147.625)
unnamed_20	object	5.7%	% (95% CI), 17.5 (14.9-20.4), 15.8 (12.9-19.1)
unnamed_21	float64	8.6%	80.0 – 353.0 (mean 199.0625)
unnamed_22	object	5.7%
stunting_by_height_for_age_z_score_criteria	float64	11.4%	473.0 – 1262.0 (mean 819.129)
unnamed_24	float64	8.6%	30.0 – 217.0 (mean 91.1875)
unnamed_25	object	5.7%
unnamed_26	float64	8.6%	113.0 – 361.0 (mean 196.5312)
unnamed_27	object	5.7%
unnamed_28	float64	8.6%	182.0 – 501.0 (mean 287.7188)
unnamed_29	object	5.7%
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
wasting_by_weight_for_height_z_score_oedema_criteria	486.0	1271.0	826.1875	771.5
unnamed_3	1.0	29.0	13.6875	12.0
unnamed_5	9.0	127.0	69.625	68.0
unnamed_7	10.0	153.0	83.0938	81.5
combined_gam_and_sam_based_on_whz_and_muac	494.0	1282.0	837.1875	783.0
unnamed_10	5.0	48.0	26.8438	27.0
unnamed_12	13.0	171.0	94.3438	91.5
unnamed_14	20.0	215.0	121.1875	116.5
underweight_by_weight_for_age_z_score_criteria	34.0	1276.0	806.625	767.0
unnamed_17	14.0	130.0	51.4375	48.5
unnamed_19	66.0	259.0	147.625	135.5
unnamed_21	80.0	353.0	199.0625	179.0
stunting_by_height_for_age_z_score_criteria	473.0	1262.0	819.129	752.0
unnamed_24	30.0	217.0	91.1875	77.0
unnamed_26	113.0	361.0	196.5312	175.5

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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. 16 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 OCHA Afghanistan and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_asia_malnutrition_afghanistan_malnutrition_prevalence,
  title     = {Afghanistan - Malnutrition Prevalence},
  author    = {OCHA Afghanistan},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/afghanistan-malnutrition-prevalence},
  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.