Program in Cardiovascular Health Metrics
Advancing cardiovascular health through innovative research, global collaboration, and actionable insights for healthier communities worldwide
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Key findings
Cardiovascular diseases (CVDs) constitute the leading cause of global mortality and are a major contributor to health loss worldwide.
CVDs includes ischemic heart disease, stroke, heart failure, peripheral and aortic arterial disease, arrhythmias, and valvular diseases.
In 2022 alone, CVD caused an estimated 19.8 million deaths worldwide.
This corresponds to 396 million years of life lost and another 44.9 million years lived with disability.
Factsheets
For a concise overview of each cardiovascular cause, impairment, and risk factor modeled in the GBD study and the health loss related to each, please review our two-page factsheets.
Download data
Our cardiovascular disease data are the latest estimates available and may differ from previous estimates published in journals.
Global Burden of Cardiovascular Diseases and Risks, 1990-2022
United States Maternal Mortality Ratio Estimates by Race and Ethnicity 1999-2019
United States Mortality Rates and Life Expectancy by State, Race, and Ethnicity 1990-2019
Global Burden of Disease Study (GBD) Burden of Proof and Risk-Outcome Scores
Download all cardiovascular disease data from the GBD Results tool.
Which data sources did you use?
Our data sources include scientific literature, registries, surveys, and administrative records in our online data catalog, the Global Health Data Exchange (GHDx).
- Scientific literature includes results from population representative trials and other studies of cardiovascular health outcomes.
- Registries refer to comprehensive collections of information related to cardiovascular diseases and treatments, systematically gathered and maintained for clinical, research, and quality improvement purposes.
- Survey data comes from reputable sources including the Demographics and Health Survey, WHO, censuses, and others.
- Administrative data refers to official data reported by health care organizations, institutions, or government agencies.
For more information, please review our detailed methodological information for each cause, impairment, or risk factor with these appendices.
How do you address lower-quality data or gaps in data?
We check for data quality issues before running our models both by analyzing the data and by looking at external sources of information about the data. Where possible, we use statistical techniques to quantify and adjust for biases in the data.
In some cases, when there are significant concerns about data quality, those sources may be excluded from the models. Our models use trends in time and predictive covariates to produce estimates of CVDs for locations and years in which no data are available.
We also produce estimates of the uncertainty for our predictions – when data are sparse or conflicting, our estimates are more uncertain.
While these models provide valuable insights into cardiovascular health in settings where data are missing or of lower quality, they aren’t a replacement for high-quality data. We support the broader efforts of the cardiovascular health metrics community to strengthen data quality.
How can I contribute data or share feedback on estimates?
To learn how you can contribute to the estimation of CVDs, email us at [email protected].