electricsheepafrica/africa-sira-disability-and-older-age-dataset-metuge-and-pemba-cabo-delgado-mozambique-may-2024

Mozambique: Survey for Inclusive Rapid Assessment (SIRA)

Publisher: Light for the World · Source: HDX · License: cc-by · Updated: 2025-07-22

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Abstract

SIRA Dataset for disability and older age - Cabo Delgado, Mozambique

Background

The Survey for Inclusive Rapid Assessment (SIRA) Dataset for disability and older age was collected in May 2024 in Pemba (urban) and Metuge (rural) localities of Cabo Delgado, Mozambique, as part of the Data that Matters project funded by Elrha. Stratified clustered random sampling was used in this survey, the aim of which is to assess the barriers and enablers people face in accessing humanitarian assistance, in particular persons with disabilities and older persons.

The Data

The dataset consists of:

• SIRA Dataset #1. Household-level data: locality, household size, displacement status, registration
• SIRA Dataset #2. Individual-level data: sex, age, education, health, employment
• SIRA Dataset #3. Individual-level data: Washington Group (WG) questions taken from short (WG_SS), extended (WG-ES) of questions and child functioning modules (0-4 and 5-17 years). Questions include functioning domains associated with mental health.
• SIRA Dataset #4. Individual-level data: barriers in accessing humanitarian assistance for i) distributions, ii) services, iii) livelihood opportunities, iv) sexual, maternal and reproductive health, v) safety and security.

The datasets can be merged via the respondent identifier "indID", with "hhID" allowing to group individuals linked to a common household.

Each row in this dataset represents household-level survey responses. Data was last updated on HDX on 2025-07-22. Geographic scope: MOZ.

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

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Dataset Characteristics

Domain	Public health
Unit of observation	Household-level survey responses
Rows (total)	23,011
Columns	9 (0 numeric, 9 categorical, 0 datetime)
Train split	18,408 rows
Test split	4,602 rows
Geographic scope	MOZ
Publisher	Light for the World
HDX last updated	2025-07-22

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Variables

Geographic — respsex (2. Female, 1. Male), barrierwhy (2. Fear of attack, harrassement, aggression, 16. I don't face any of these barriers, 1. I do not know where the service or support is available or who can help).

Demographic — hhid (cyojyxolvhxlq6hb, c83s42tlvumb61w3, chi8xz2lvm8328ea), respage (16 to 50, 51 and above, 0 to 15).

Identifier / Metadata — indid (cjfmoimlvz25w5u9, cpjg383lvw0sz7ka, cr2t7iylvxkz4t24), esa_source (HDX), esa_processed (2026-04-18).

Other — barrierwhich (SRH, Services, Fear), barrierwhere (1. Food, 2. Family planning, 1. Water).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-sira-disability-and-older-age-dataset-metuge-and-pemba-cabo-delgado-mozambique-may-2024")
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
hhid	object	0.0%	cyojyxolvhxlq6hb, c83s42tlvumb61w3, chi8xz2lvm8328ea
indid	object	0.0%	cjfmoimlvz25w5u9, cpjg383lvw0sz7ka, cr2t7iylvxkz4t24
respsex	object	0.0%	2. Female, 1. Male
respage	object	0.0%	16 to 50, 51 and above, 0 to 15
barrierwhich	object	0.0%	SRH, Services, Fear
barrierwhere	object	0.0%	1. Food, 2. Family planning, 1. Water
barrierwhy	object	0.0%	2. Fear of attack, harrassement, aggression, 16. I don't face any of these barriers, 1. I do not know where the service or support is available or who can help
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-18

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Numeric Summary

Column	Min	Max	Mean	Median
No numeric columns.

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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. 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 Light for the World 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_africa_sira_disability_and_older_age_dataset_metuge_and_pemba_cabo_delgado_mozambique_may_2024,
  title     = {Mozambique: Survey for Inclusive Rapid Assessment (SIRA)},
  author    = {Light for the World},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/sira-disability-and-older-age-dataset-metuge-and-pemba-cabo-delgado-mozambique-may-2024},
  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.