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Data

Prevalence rate of hypertension in women aged 30-79

What you should know about this indicator

How is this data described by its producer?

Definition

Prevalence of hypertension (defined as having systolic blood pressure ≥140 mmHg, diastolic blood pressure ≥90 mmHg, or taking medication for hypertension) among adults aged 30-79.

Method of estimation

1,201 population-based studies that included measured blood pressure and data on blood pressure treatment in 104 million individuals aged 30-79 years were used to estimate trends in hypertension and hypertension diagnosis, treatment and control from 1990 to 2019. More information on input and data methods is available at: NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants. The Lancet S0140-6736(21)01330-1. Preferred data sources: Population-based surveys, Surveillance systems

Prevalence rate of hypertension in women aged 30-79
Prevalence of hypertension (defined as having systolic blood pressure ≥140 mmHg, diastolic blood pressure ≥90 mmHg, or taking medication for hypertension) among adults aged 30-79.
Source
World Health Organization - Global Health Observatory (2025) – processed by Our World in Data
Last updated
May 19, 2025
Next expected update
May 2026
Date range
1990–2019

Sources and processing

This data is based on the following sources

World Health Organization – Global Health Observatory

The GHO data repository is WHO's gateway to health-related statistics for its 194 Member States. It provides access to over 1000 indicators on priority health topics including mortality and burden of diseases, the Millennium Development Goals (child nutrition, child health, maternal and reproductive health, immunization, HIV/AIDS, tuberculosis, malaria, neglected diseases, water and sanitation), non communicable diseases and risk factors, epidemic-prone diseases, health systems, environmental health, violence and injuries, equity among others.

Retrieved on
May 19, 2025
Retrieved from
https://www.who.int/data/gho
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.
World Health Organization. 2025. Global Health Observatory data repository. http://www.who.int/gho/en/.

The GHO data repository is WHO's gateway to health-related statistics for its 194 Member States. It provides access to over 1000 indicators on priority health topics including mortality and burden of diseases, the Millennium Development Goals (child nutrition, child health, maternal and reproductive health, immunization, HIV/AIDS, tuberculosis, malaria, neglected diseases, water and sanitation), non communicable diseases and risk factors, epidemic-prone diseases, health systems, environmental health, violence and injuries, equity among others.

Retrieved on
May 19, 2025
Retrieved from
https://www.who.int/data/gho
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.
World Health Organization. 2025. Global Health Observatory data repository. http://www.who.int/gho/en/.

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.

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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: Prevalence rate of hypertension in women aged 30-79”. Our World in Data (2026). Data adapted from World Health Organization. Retrieved from https://archive.ourworldindata.org/20260304-094028/grapher/women-high-blood-pressure.html [online resource] (archived on March 4, 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:

World Health Organization - Global Health Observatory (2025) – processed by Our World in Data

Full citation

World Health Organization - Global Health Observatory (2025) – processed by Our World in Data. “Prevalence rate of hypertension in women aged 30-79” [dataset]. World Health Organization, “Global Health Observatory” [original data]. Retrieved April 1, 2026 from https://archive.ourworldindata.org/20260304-094028/grapher/women-high-blood-pressure.html (archived on March 4, 2026).

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

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

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