electricsheepasia/asia-hypertension-ht-dm-care-and-medication-in-lebanon

Health Service Utilization and Adherence to Medication for Hypertension and Diabetes Among Syrian Refugees and Affected Host Communities in Lebanon

Publisher: Johns Hopkins School of Public Health · Source: HDX · License: cc-by · Updated: 2021-09-23

Abstract

This is the underlying data for a manuscript published in the Journal of Diabetes & Metabolic Disorders (DOI : 10.1007/s40200-020-00638-6). The manuscript presents findings from a 2015 survey of Syrian refugees and Lebanese host communities to characterize care-seeking, health service utilization and spending, and medication prescribing and adherence for hypertension and diabetes.

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2021-09-23. Geographic scope: LBN.

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

Dataset Characteristics

Variables

Geographic — hh_headsex (Male, Female), anyht (No, Yes, Don't know), anydm (No, Yes, Don't know), region (North, Bekaa, BML), htcarereceivedyn and 10 others.

Temporal — htcare3months, htcare6months.

Demographic — hh_headeducation2rev, femalehead.

Identifier / Metadata — esa_source, esa_processed.

Other — cluster (range 1.0–149.0), result (Refugee, Lebanese), htmdvisit (1-2months, <1 month, 3-6 months), htmedperscribe (in Lebanon, in Syria, no), htmedcurrent (Yes, No, Don't know) and 4 others.

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-hypertension-ht-dm-care-and-medication-in-lebanon")
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. 28 column(s) with >80% missing values were removed: hthealthcarelocation, hthealthcarelocationreason, htconsultpayyn, diabetesmdvisit, diabeteshealthcarelocation, diabeteshealthcarelocationreason.... 419 exact duplicate rows were removed. 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: htmdvisit, htmedperscribe, htmedcurrent, htmedstopped, htcarereceivedyn, htmedstoppedyn, yeararrivehostcountry_cat, htcare3months....
• —Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

Citation

@dataset{hdx_asia_hypertension_ht_dm_care_and_medication_in_lebanon,
  title     = {Health Service Utilization and Adherence to Medication for Hypertension and Diabetes Among Syrian Refugees and Affected Host Communities in Lebanon},
  author    = {Johns Hopkins School of Public Health},
  year      = {2021},
  url       = {https://data.humdata.org/dataset/ht-dm-care-and-medication-in-lebanon},
  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.