"Social and environmental factors have complex, non-linear interactions with cardiovascular disease," said Chunara. "Machine learning can be particularly useful in capturing these intricate relationships."

The researchers analyzed existing research on machine learning and cardiovascular disease risk, screening more than 1,600 articles and ultimately focusing on 48 peer-reviewed studies published in journals between 1995 and 2020.

They found that including social determinants of health in machine learning models improved the ability to predict cardiovascular outcomes like rehospitalization, heart failure, and stroke. However, these models did not typically include the full list of community-level or environmental variables that are important in cardiovascular disease risk. Some studies did include additional factors such as income, marital status, social isolation, pollution, and health insurance, but only five studies considered environmental factors such as the walkability of a community and the availability of resources like grocery stores.

The researchers also noted the lack of geographic diversity in the studies, as the majority used data from the United States, countries in Europe, and China, neglecting many parts of the world experiencing increases in cardiovascular disease.

"If you only do research in places like the United States or Europe, you'll miss how social determinants and other environmental factors related to cardiovascular risk interact in different settings and the knowledge generated will be limited," said Chunara.

"Our study shows that there is room to more systematically and comprehensively incorporate social determinants of health into cardiovascular disease statistical risk prediction models," said Stephanie Cook, assistant professor of biostatistics at NYU School of Global Public Health and a study author. "In recent years, there has been a growing emphasis on capturing data on social determinants of health--such as employment, education, food, and social support--in electronic health records, which creates an opportunity to use these variables in machine learning studies and further improve the performance of risk prediction, particularly for vulnerable groups."

"Including social determinants of health in machine learning models can help us to disentangle where disparities are rooted and bring attention to where in the risk structure we should intervene," added Chunara. "For example, it can improve clinical practice by helping health professionals identify patients in need of referral to community resources like housing services and broadly reinforces the intricate synergy between the health of individuals and our environmental resources."

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