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Data

Share of mothers who have lost a child under five

About this data

Source
Smith-Greenaway et al. (2021) – processed by Our World in Data
Last updated
November 26, 2024
Date range
2010–2018
Unit
per 1000 mothers

Sources and processing

This data is based on the following sources

Smith-Greenaway et al. – Global burden of maternal bereavement: indicators of the cumulative prevalence of child loss

This dataset shows survey data and estimates of maternal offspring loss across countries. This includes mothers who have lost an infant, child under 5 years old, or offspring. These are given as a rate per 1000 women in the age group. Underlying data comes from large-scale surveys (such as the Demographic and Health Surveys and Multiple Indicator Cluster Surveys) conducted in many low- and middle-income countries. For countries lacking data, these are estimated using an indirect approach that combines formal kinship models and life-table methods in an additional 81 countries. Citation: Smith-Greenaway, E., Alburez-Gutierrez, D., Trinitapoli, J., & Zagheni, E. (2021). Global burden of maternal bereavement: Indicators of the cumulative prevalence of child loss. BMJ Global Health, 6(4), e004837. https://doi.org/10.1136/bmjgh-2020-004837

Retrieved on
November 26, 2024
Retrieved from
https://gh.bmj.com/content/6/4/e004837.abstract
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., Alburez-Gutierrez, D., Trinitapoli, J., & Zagheni, E. (2021). Global burden of maternal bereavement: Indicators of the cumulative prevalence of child loss. BMJ Global Health, 6(4), e004837.

This dataset shows survey data and estimates of maternal offspring loss across countries. This includes mothers who have lost an infant, child under 5 years old, or offspring. These are given as a rate per 1000 women in the age group. Underlying data comes from large-scale surveys (such as the Demographic and Health Surveys and Multiple Indicator Cluster Surveys) conducted in many low- and middle-income countries. For countries lacking data, these are estimated using an indirect approach that combines formal kinship models and life-table methods in an additional 81 countries. Citation: Smith-Greenaway, E., Alburez-Gutierrez, D., Trinitapoli, J., & Zagheni, E. (2021). Global burden of maternal bereavement: Indicators of the cumulative prevalence of child loss. BMJ Global Health, 6(4), e004837. https://doi.org/10.1136/bmjgh-2020-004837

Retrieved on
November 26, 2024
Retrieved from
https://gh.bmj.com/content/6/4/e004837.abstract
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., Alburez-Gutierrez, D., Trinitapoli, J., & Zagheni, E. (2021). Global burden of maternal bereavement: Indicators of the cumulative prevalence of child loss. BMJ Global Health, 6(4), e004837.

How we process data at Our World in Data

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

Reuse this work

  • All data produced by third-party providers and made available by Our World in Data are subject to the license terms from the original providers. Our work would not be possible without the data providers we rely on, so we ask you to always cite them appropriately (see below). This is crucial to allow data providers to continue doing their work, enhancing, maintaining and updating valuable data.
  • All data, visualizations, and code produced by Our World in Data are completely open access under the Creative Commons BY license. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.

Citations

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: Share of mothers who have lost a child under five”. Our World in Data (2026). Data adapted from Smith-Greenaway et al.. Retrieved from https://archive.ourworldindata.org/20260512-000143/grapher/share-of-mothers-who-have-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. (2021) – processed by Our World in Data

Full citation

Smith-Greenaway et al. (2021) – processed by Our World in Data. “Share of mothers who have lost a child under five” [dataset]. Smith-Greenaway et al., “Global burden of maternal bereavement: indicators of the cumulative prevalence of child loss” [original data]. Retrieved May 17, 2026 from https://archive.ourworldindata.org/20260512-000143/grapher/share-of-mothers-who-have-lost-a-child-under-5.html (archived on May 12, 2026).

Download

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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.

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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-who-have-lost-a-child-under-5.csv?v=1&csvType=full&useColumnShortNames=false
Metadata URL (JSON format)
https://ourworldindata.org/grapher/share-of-mothers-who-have-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-who-have-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-who-have-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-who-have-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-who-have-lost-a-child-under-5.csv?v=1&csvType=full&useColumnShortNames=false")

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