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Data

Share of people with diabetes

What you should know about this indicator

How is this data described by its producer?

Diabetes prevalence refers to the percentage of people ages 20-79 who have type 1 or type 2 diabetes. It is calculated by adjusting to a standard population age-structure.

Aggregation method:

Weighted average

Statistical concept and methodology:

Methodology: The data used to estimate diabetes prevalence were gathered from various sources. Most of the data were extracted from peer-reviewed publications and national health surveys, including selected WHO STEPwise approach to surveillance (WHO STEPS) studies. Additionally, data from other official sources, such as registries and reports from health regulatory bodies, were utilized, provided there was sufficient information to assess their quality. Data sources with adequate methodological information on key areas of interest, such as the method of diagnosis and sample representativeness, were included. Given the significance of age as a major determinant for diabetes prevalence, only studies with at least three age-specific estimates were considered. After selecting the data sources, the reported age- and sex-specific data in each source were smoothed using a logistic regression model.

Development relevance:

Diabetes, an important cause of ill health and a risk factor for other diseases in developed countries, is spreading rapidly in developing countries. Highest among the elderly, prevalence rates are rising among younger and productive populations in developing countries. Economic development has led to the spread of Western lifestyles and diet to developing countries, resulting in a substantial increase in diabetes. Without effective prevention and control programs, diabetes will likely continue to increase.

Limitations and exceptions:

The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information.

Source
International Diabetes Federation (Diabetes Atlas), via World Bank (2026) – processed by Our World in Data
Last updated
February 27, 2026
Next expected update
February 2027
Date range
2000–2024
Unit
% of population ages 20 to 79

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Sources and processing

This data is based on the following sources

International Diabetes Federation (Diabetes Atlas), via World Bank – World Development Indicators

The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades.WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability.The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization.WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally.Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.

Retrieved on
February 27, 2026
Retrieved from
https://data.worldbank.org/indicator/SH.STA.DIAB.ZS
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.
Diabetes Atlas, International Diabetes Federation. Indicator SH.STA.DIAB.ZS (https://data.worldbank.org/indicator/SH.STA.DIAB.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-02-27.

The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades.WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability.The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization.WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally.Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.

Retrieved on
February 27, 2026
Retrieved from
https://data.worldbank.org/indicator/SH.STA.DIAB.ZS
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.
Diabetes Atlas, International Diabetes Federation. Indicator SH.STA.DIAB.ZS (https://data.worldbank.org/indicator/SH.STA.DIAB.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-02-27.

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 people with diabetes”. Our World in Data (2026). Data adapted from International Diabetes Federation (Diabetes Atlas), via World Bank. Retrieved from https://archive.ourworldindata.org/20260512-185716/grapher/diabetes-prevalence.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:

International Diabetes Federation (Diabetes Atlas), via World Bank (2026) – processed by Our World in Data

Full citation

International Diabetes Federation (Diabetes Atlas), via World Bank (2026) – processed by Our World in Data. “Share of people with diabetes” [dataset]. International Diabetes Federation (Diabetes Atlas), via World Bank, “World Development Indicators 125” [original data]. Retrieved May 16, 2026 from https://archive.ourworldindata.org/20260512-185716/grapher/diabetes-prevalence.html (archived on May 12, 2026).

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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/diabetes-prevalence.csv?v=1&csvType=full&useColumnShortNames=false
Metadata URL (JSON format)
https://ourworldindata.org/grapher/diabetes-prevalence.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/diabetes-prevalence.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/diabetes-prevalence.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/diabetes-prevalence.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()
R
library(jsonlite)

# Fetch the data
df <- read.csv("https://ourworldindata.org/grapher/diabetes-prevalence.csv?v=1&csvType=full&useColumnShortNames=false")

# Fetch the metadata
metadata <- fromJSON("https://ourworldindata.org/grapher/diabetes-prevalence.metadata.json?v=1&csvType=full&useColumnShortNames=false")
Stata
import delimited "https://ourworldindata.org/grapher/diabetes-prevalence.csv?v=1&csvType=full&useColumnShortNames=false", encoding("utf-8") clear