Disparities in Heart Failure Rates Found Among Asian American Subpopulations

A new data analysis of heart failure among Asian Americans showed that disparities exist between ethnic subgroups, and the differences between Southeast and East Asian patients were larger than those between Black and white patients, according to a study in JAMA Network Open.

In this video interview, study authors Qing Zeng-Treitler, PhD, and Adrienne Ngar-Yee Poon, MD, MPH, both of George Washington University in Washington, D.C., discuss the findings and implications.

The following is a transcript of their remarks:

Zeng-Treitler: Heart failure is a leading cause of death in the U.S. Asian Americans constitute approximately 7.2% of the U.S. population and are the fastest growing racial ethnic community. At the same time, the current evidence on the burdens of heart failure in Asian American populations, especially Asian American subpopulations, is limited and inconsistent.

The objective of this study was to assess and compare incidents and prevalence of heart failure in Asian American subgroups. We did an incidence and prevalence analysis using the Cerner Real-World Database. We had a fairly large sample size for the incidence analysis, the sample size was 6.8 million, and for the prevalence analysis, the prevalence was 13 million.

So using the 2015 population as the standard, we analyzed the age- and sex-standard heart failure incidences in Southeast Asian, South Asian, and East Asian [subgroups], and we also compared these rates with white patients and Black patients.

What we found is that the disparities between Southeast and East Asian patients were larger than those between Black and white patients, with the estimates in the Southeast Asian patients being similar to those of Black patients.

Poon: Cardiovascular disease is the second leading cause of death among Asian Americans. Given the broad diversity of socioeconomic and sociocultural backgrounds of the Asian American communities, it’s crucial to examine the disparities in heart failure incidence and prevalence among different ethnic subgroups.

The clinical implication of our study is that risk assessment and diagnostic decisions relating to heart failure should not regard Asian Americans as a monolithic group, but instead consider individual ethnicities and their respective cardiovascular risk factors. This study also raises concern for under-recognition and under-screening of cardiovascular diseases at earlier stages that would support prevention of cardiovascular disease and heart failure.

First, our results highlight the importance of aggregating Asian American health data. This work was exploratory in terms of assessing the availability of ethnicity-level information in [electronic health record] data. That can provide a wealth of information. Most of the data is not collected in this aggregated format. One major policy change would be to standardize systems of race and ethnicity standards, perhaps matching the new federal standards, so that we can have actual meaningful research going forward.

Community engagement is also really important for Asian American health equity research. Trying to understand the upstream factors for cardiovascular disease is crucial.

And last, it’s really important to support expanded research funding for Asian American health research, because so much of these disparities are invisible and there’s limited funding. It’s hard to make the disparities apparent and also attract interest for research funding, so I think it’s really important to try to break that cycle and increase that funding to be able to grow meaningful research.

Zeng-Treitler: One thing I want to add is that AI [artificial intelligence] is propelling precision medicine forward. Recent ethnicity data often uses surrogates for genetic makeup, or SDOH [social determinants of health], which can sometimes cause more biases. For example, without nuanced data, AI systems can mistakenly underestimate or overestimate heart failure risks for some subgroups, which can then lead to less optimal treatment decisions.

To have trustworthy and fair AI, we need more nuanced data.