53 extractive question-and-answer pairs built from Share of people with diabetes | Our World in Data, published by ourworldindata.org. Every answer is a verbatim span of text the source prints, and each row carries the passage it sits in, its offset in that passage, the source quote, the page and the location in the document, so any row can be checked against the original. 37 of the 53 pairs (69.8%) are explanatory questions and 16 restate a figure. 100.00% of rows pass the corpus quality gate.
Use the API URL with your free DD token in notebooks, scripts, and pipelines.
https://www.desidata.in/api/datasets/share-of-people-with-diabetes-question-and-answer-dataset/downloadDataset downloads are free. For Python or API downloads, sign in once and create a free DD token; set it as DD_TOKEN or save it in your notebook's secrets. Requests are linked to your account so your download history and counts stay accurate.
# One-time install: pip install desidata
# Set DD_TOKEN in your environment first (create a free token in Profile & settings).
import desidata
df = desidata.load("share-of-people-with-diabetes-question-and-answer-dataset")
df.head()Sign in with Google to download.
Usable for analysis, but expect some cleaning before you rely on it.
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First 10 of 53 rows
| question | answer | context | answer_start | question_type | knowledge_quality_score | source_quote | source_page | source_location | confidence | validation_status |
|---|---|---|---|---|---|---|---|---|---|---|
| What is the World Development Indicators (WDI) database? | a comprehensive collection of global development data, providing key economic, social, and environmental statistics | 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. | 81 | definition | 1.000 | 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. | p[7] | 0.700 | valid | |
| What approach from the WHO was used for surveillance in the studies? | WHO STEPwise approach to surveillance (WHO STEPS) | Most of the data were extracted from peer-reviewed publications and national health surveys, including selected WHO STEPwise approach to surveillance (WHO STEPS) studies. 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. //data.worldbank.org/indicator/SH.STA.DIAB.ZS | 112 | definition | 1.000 | Most of the data were extracted from peer-reviewed publications and national health surveys, including selected WHO STEPwise approach to surveillance (WHO STEPS) studies. | p[15] | 0.700 | valid | |
| What does diabetes prevalence measure? | the percentage of people ages 20-79 who have type 1 or type 2 diabetes | 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. 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. | 202 | definition | 0.980 | Diabetes prevalence refers to the percentage of people ages 20-79 who have type 1 or type 2 diabetes. | p[0] | 0.850 | valid | |
| What information is provided in the documentation? | how to use the API | Includes only the entities and time points currently visible in the chart Use these URLs to programmatically access this chart's data and configure your requests with the options below.Our documentation provides more informationon how to use the API, and you can find a few code examples below. Examples of how to load this data into different data analysis tools. //data.worldbank.org/indicator/SH.STA.DIAB.ZS //archive.ourworldindata.org/20260910-011030/grapher/diabetes-prevalence.html International Diabetes Federation (Diabetes Atlas), via World Bank (2026)–processedby Our World in Data % of population ages 20 to 79 2000-2024 2026-07-27 2027-01-23 Mojmír VinklerandPablo Arriagada The data used to estimate diabetes prevalence were gathered from various sources. | 231 | definition | 0.980 | Includes only the entities and time points currently visible in the chart Use these URLs to programmatically access this chart's data and configure your requests with the options below.Our documentation provides more informationon how to use the API, and you can find a few code examples below. | p[15] | 0.700 | valid | |
| What does diabetes prevalence refer to? | the percentage of people ages 20-79 who have type 1 or type 2 diabetes | 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. 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. | 655 | definition | 0.980 | Diabetes prevalence refers to the percentage of people ages 20-79 who have type 1 or type 2 diabetes. | p[0] | 0.700 | valid | |
| What has led to the rapid increase in diabetes in developing countries? | Economic development has led to the spread of Western lifestyles and diet | After selecting the data sources, the reported age- and sex-specific data in each source were smoothed using a logistic regression model. 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. 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. | 409 | relationship | 0.970 | 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. | ||||
| What requirement must be fulfilled to use, share, or reproduce the data? | provided the source and authors are credited | You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited. 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. Includes all entities and time points | 79 | relationship | 0.970 | You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited. | p[7] | 0.700 | valid | |
| What were the inclusion criteria for data sources used to estimate diabetes prevalence? | adequate methodological information on key areas of interest, such as the method of diagnosis and sample representativeness | Most of the data were extracted from peer-reviewed publications and national health surveys, including selected WHO STEPwise approach to surveillance (WHO STEPS) studies. 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. //data.worldbank.org/indicator/SH.STA.DIAB.ZS | 367 | relationship | 0.970 | Data sources with adequate methodological information on key areas of interest, such as the method of diagnosis and sample representativeness, were included. | p[15] | |||
| What was the reason for considering studies with at least three age-specific estimates? | Given the significance of age as a major determinant for diabetes prevalence | Most of the data were extracted from peer-reviewed publications and national health surveys, including selected WHO STEPwise approach to surveillance (WHO STEPS) studies. 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. //data.worldbank.org/indicator/SH.STA.DIAB.ZS | 507 | relationship | 0.970 | 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. | ||||
| Why were studies with at least three age-specific estimates considered for diabetes prevalence data? | Given the significance of age as a major determinant for diabetes prevalence | Weighted average 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. 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. | 619 | relationship | 0.970 | 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. |
Read straight from the file — download or use the API URL for the full dataset.
| p[0] |
| 0.700 |
| valid |
| 0.700 |
| valid |
| p[15] |
| 0.700 |
| valid |
| p[0] |
| 0.700 |
| valid |