electricsheepasia/asia-refugees-mpc-for-syrian-refugee-health-in-jordan

Multi-purpose cash transfers and health among vulnerable Syrian refugees in Jordan: A prospective cohort study

Publisher: Johns Hopkins School of Public Health · Source: HDX · License: cc-by · Updated: 2026-04-27

Abstract

This is the underlying data for a manuscript titled "Multi-purpose cash transfers and health among vulnerable Syrian refugees in Jordan: A prospective cohort study" to be published in PLOS Global Public Health . The manuscript presents findings from a prospective cohort study conducted from May 2018 through July 2019 to evaluate the impact of MPCs on health care-seeking and expenditures by Syrian refugees in Jordan.

Each row in this dataset represents household-level survey responses. Data was last updated on HDX on 2026-04-27. Geographic scope: JOR.

Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).

Dataset Characteristics

Variables

Geographic — pasex (Male, Female), child1cantaffordyn (No, Yes), acute1cantaffordyn (No, Yes), exphealth_usd (range 0.0001–4371.0), hhunder5yn (No, Yes) and 38 others.

Temporal — allcash1month_amt_usd (range 0.0–850.23).

Demographic — hh_id (range 11002.0–16625.0), paage (range 13.0–103.0), hhsize_cat.

Outcome / Measurement — child1allcosts_op_notrans_usd (range 0.0001–705.0), acute1allcosts_op_notrans_usd (range 0.0001–616.17), child1allcosts_op_log (range -9.2103–6.5582), acute1allcosts_op_log (range -9.2103–6.4235).

Identifier / Metadata — esa_source, esa_processed.

Other — paeducation2 (Primary, Prepartory, Secondary+), pamartialstatus (Married, Widowed, Unknown), dependratio (range 0.0–7.0), prepost, child1sector_public and 3 others.

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-refugees-all")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

Numeric Summary

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. 2 column(s) with >80% missing values were removed: childnocarecantafford, acutenocarecantafford. The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

Limitations

• —Data originates from Johns Hopkins School of Public Health and has not been independently validated by ESA.
• —Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• —The following columns have >20% missing values and should be treated with caution in modelling: child1cantaffordyn, acute1cantaffordyn, paeducation2, child1visittype_er, child1visittype_opd, child1visittype_inpatient, child1visittype_outpatient, child1rxobtainedyn....
• —Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

Citation

@dataset{hdx_asia_refugees_all,
  title     = {Multi-purpose cash transfers and health among vulnerable Syrian refugees in Jordan: A prospective cohort study},
  author    = {Johns Hopkins School of Public Health},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/mpc-for-syrian-refugee-health-in-jordan},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.