Journal of the American College of Cardiology
August 19, 2026 · via Europe PMC
Cardiovascular Disease Mortality by County, Race and/or Ethnic Population, United States, 2000-2019.
Mensah GA, Dwyer-Lindgren L, Kelly YO, +13 more, Pérez-Stable EJ
AI summary — for doctors, generated from the abstract only
US cardiovascular mortality fell overall from 2000 to 2019, but wide gaps persisted by race, ethnicity and especially county, with the highest rates in Black populations.
What was studied
A county-level small-area estimation of age-standardised CVD mortality for five racial and ethnic groups, 3,110 counties and 21 CVD causes from US death certificate data, 2000 to 2019.
Key findings
- The age-standardised total CVD mortality rate was 237.8 per 100,000 in 2019.
- Mortality was highest in the Black population (total CVD and 12 of 17 detailed causes) and lowest in the Asian population.
- Mortality declined for total CVD and 12 of 17 causes but increased for 3 detailed causes, and varied much more between counties than states.
Clinical relevance
Highlights that where and who a patient is strongly shapes cardiovascular risk, supporting place-informed prevention and targeted resource allocation.
Who should care
Limitations
Modelled small-area estimates depend on death certificate quality and race misclassification adjustments, and the data end in 2019, before the pandemic.
Consider local and demographic disparities when planning cardiovascular prevention and outreach.
This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.
Abstract
BACKGROUND
Cardiovascular disease (CVD) remains the leading cause of death and an important contributor to U.S. health disparities.
OBJECTIVES
In this study, the authors sought to assess county-level CVD mortality and trends by race and/or ethnicity as well as by detailed CVD cause of death.
METHODS
We estimated age-standardized mortality rates (ASMRs) for 5 racial and/or ethnic groups (American Indian or Alaska Native [AIAN], Asian or Pacific Islander [Asian], Black or African American [Black], Hispanic or Latino [Latino], and White), 3,110 counties, 21 causes of CVD death, and 20 years (2000-2019) using small-area estimation methods and death certificate data from the U.S. National Vital Statistics System. Estimated mortality rates were adjusted for misreporting of race or ethnicity on death certificates and age-standardized to the 2010 census. Counties were classified into persistently high mortality, persistently low mortality, and intermediate strata based on age-adjusted CVD mortality rates, stratified by race and/or ethnicity.
RESULTS
The ASMR for total CVD was 237.8 (95% uncertainty interval: 237.2-238.4) deaths per 100,000 in 2019. Mortality was highest for the Black population for total CVD and 12 of 17 detailed CVD causes, and lowest for the Asian population for total CVD and 13 of 17 detailed causes. From 2000 to 2019, mortality declined for total CVD and 12 of 17 detailed causes; however, mortality increased for 3 detailed causes. Mortality also varied widely among counties-much more than among states-both within and among racial and/or ethnic populations. Spatial mortality patterns varied considerably by CVD cause and by race and/or ethnicity.
CONCLUSIONS
While declining CVD mortality reflects meaningful clinical and public health progress, persistent and pervasive disparities demand solutions-oriented, place-informed research to drive further progress. Critically, these pre-Covid data establish a baseline essential for isolating Covid-era-attributable shifts from preexisting trends. These granular county-level data stratified by CVD cause, race, and/or ethnicity offer a scalable, repeatable framework for agencies to tailor health interventions and rigorously track disparities over time.
DOI
10.1016/j.jacc.2026.07.010
PMID
42788921
PMCID
PMC13639261
Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.
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