Data

Mothers who have lost a child under five, across birth cohorts

About this data

Source
Smith-Greenaway et al. (2023)processed by Our World in Data
Last updated
October 20, 2023
Date range
1960–1980
Unit
%

Sources and processing

Smith-Greenaway et al. – Under-five mortality exposure in low- and middle-income countries: A multi-generation and cohort perspective of sibling and offspring loss

This dataset shows estimated share of mothers who have lost a child under 5 years old, based on large-scale surveys including the Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS).

Retrieved on
October 20, 2023
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Smith-Greenaway, E., Weitzman, A., Lin, Y., & Huss, K. (2023, January 1). Under-five mortality exposure in low- and middle-income countries: A multi-generation and cohort perspective of sibling and offspring loss. https://doi.org/10.31235/osf.io/xvbkj

This dataset shows estimated share of mothers who have lost a child under 5 years old, based on large-scale surveys including the Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS).

Retrieved on
October 20, 2023
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Smith-Greenaway, E., Weitzman, A., Lin, Y., & Huss, K. (2023, January 1). Under-five mortality exposure in low- and middle-income countries: A multi-generation and cohort perspective of sibling and offspring loss. https://doi.org/10.31235/osf.io/xvbkj

All data and visualizations on Our World in Data rely on data sourced from one or several original data providers. Preparing this original data involves several processing steps. Depending on the data, this can include standardizing country names and world region definitions, converting units, calculating derived indicators such as per capita measures, as well as adding or adapting metadata such as the name or the description given to an indicator.

At the link below you can find a detailed description of the structure of our data pipeline, including links to all the code used to prepare data across Our World in Data.

Read about our data pipeline

How to cite this page

To cite this page overall, including any descriptions, FAQs or explanations of the data authored by Our World in Data, please use the following citation:

“Data Page: Mothers who have lost a child under five, across birth cohorts”. Our World in Data (2026). Data adapted from Smith-Greenaway et al.. Retrieved from https://archive.ourworldindata.org/20260512-000143/grapher/share-of-mothers-in-low--and-middle-income-countries-who-lost-a-child-under-5.html [online resource] (archived on May 12, 2026).

How to cite this data

In-line citationIf you have limited space (e.g. in data visualizations), you can use this abbreviated in-line citation:

Smith-Greenaway et al. (2023) – processed by Our World in Data

Full citation

Smith-Greenaway et al. (2023) – processed by Our World in Data. “Mothers who have lost a child under five, across birth cohorts” [dataset]. Smith-Greenaway et al., “Under-five mortality exposure in low- and middle-income countries: A multi-generation and cohort perspective of sibling and offspring loss” [original data]. Retrieved May 13, 2026 from https://archive.ourworldindata.org/20260512-000143/grapher/share-of-mothers-in-low--and-middle-income-countries-who-lost-a-child-under-5.html (archived on May 12, 2026).

Quick download

Download the data shown in this chart as a ZIP file containing a CSV file, metadata in JSON format, and a README. The CSV file can be opened in Excel, Google Sheets, and other data analysis tools.

Data API

Use these URLs to programmatically access this chart's data and configure your requests with the options below. Our documentation provides more information on how to use the API, and you can find a few code examples below.

Data URL (CSV format)
https://ourworldindata.org/grapher/share-of-mothers-in-low--and-middle-income-countries-who-lost-a-child-under-5.csv?v=1&csvType=full&useColumnShortNames=false
Metadata URL (JSON format)
https://ourworldindata.org/grapher/share-of-mothers-in-low--and-middle-income-countries-who-lost-a-child-under-5.metadata.json?v=1&csvType=full&useColumnShortNames=false

Code examples

Examples of how to load this data into different data analysis tools.

Excel / Google Sheets
=IMPORTDATA("https://ourworldindata.org/grapher/share-of-mothers-in-low--and-middle-income-countries-who-lost-a-child-under-5.csv?v=1&csvType=full&useColumnShortNames=false")
Python with Pandas
import pandas as pd
import requests

# Fetch the data.
df = pd.read_csv("https://ourworldindata.org/grapher/share-of-mothers-in-low--and-middle-income-countries-who-lost-a-child-under-5.csv?v=1&csvType=full&useColumnShortNames=false", storage_options = {'User-Agent': 'Our World In Data data fetch/1.0'})

# Fetch the metadata
metadata = requests.get("https://ourworldindata.org/grapher/share-of-mothers-in-low--and-middle-income-countries-who-lost-a-child-under-5.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()
R
library(jsonlite)

# Fetch the data
df <- read.csv("https://ourworldindata.org/grapher/share-of-mothers-in-low--and-middle-income-countries-who-lost-a-child-under-5.csv?v=1&csvType=full&useColumnShortNames=false")

# Fetch the metadata
metadata <- fromJSON("https://ourworldindata.org/grapher/share-of-mothers-in-low--and-middle-income-countries-who-lost-a-child-under-5.metadata.json?v=1&csvType=full&useColumnShortNames=false")
Stata
import delimited "https://ourworldindata.org/grapher/share-of-mothers-in-low--and-middle-income-countries-who-lost-a-child-under-5.csv?v=1&csvType=full&useColumnShortNames=false", encoding("utf-8") clear