Risk Factors for Heart Disease: Google Street View Insights

Urban environment's role in heart disease risk factors.
Google Street View study unveils heart disease risks in urban areas.

Hundreds of features of the built environment, such as buildings, green spaces, pavements, and roadways, have been studied by researchers using Google Street View to examine how these elements, along with Risk Factors for Heart Disease, interact and affect coronary artery disease in residents of these areas.

These characteristics may forecast 63% of the variance in coronary heart disease risk from one place to another, according to their findings, which were published today (Thursday) in the European Heart Journal.

One of the most prevalent types of cardiovascular illness is coronary heart disease, which is caused by an accumulation of fat in the coronary arteries, cutting off the heart’s blood supply.

According to researchers, an overview of the physical environmental risk factors in both the built and natural environments can be obtained by using Google Street View. This overview can be useful not only for understanding the risk factors present in these environments but also for designing or modifying towns and cities to make them healthier places to live.

Dr. Zhuo Chen, a post-doctoral associate in Prof. Rajagopalan’s laboratory, and Profs. Sadeer Al-Kindi and Sanjay Rajagopalan from University Hospitals Harrington Heart & Vascular Institute and Case Western Reserve University, Ohio, USA, led the work.

We have always been interested in how the environment, both the built and natural environment, influences cardiovascular disease. Here in America, they say that the zip code is a better predictor of heart disease than even personal measures of health. However, to investigate how the environment influences large populations in multiple cities is no mean task. Hence, we used machine vision-based approaches to assess the links between the built environment and coronary heart disease prevalence in US cities.” said Prof. Sanjay Rajagopalan from University Hospitals Harrington Heart & Vascular Institute and Case Western Reserve University, Ohio, USA

Approximately 500,000 Google Street View photos from locations like Detroit, Michigan; Kansas City, Missouri; Cleveland, Ohio; Brownsville, Texas; Fremont, California; Bellevue, Washington State; and Denver, Colorado were included in the study. ‘Census tracts’ were used by researchers to gather data on coronary heart disease rates and Risk Factors for Heart Disease. These are places with an average population of 4,000 that are smaller than a US zip code. Convolutional neural networks are a kind of artificial intelligence that can identify and interpret patterns in images to generate predictions, and this is the method that the researchers employed.

According to the study, 63% of the variation in coronary heart disease between these tiny areas of US cities could be predicted by characteristics of the built environment seen in Google Street View photographs.

Additionally, Professor Al-Kindi said: “We also used an approach called attention mapping, which highlights some of the important regions in the image, to provide a semi-qualitative interpretation of some of the thousands of features that may be important in coronary heart disease. For instance, features like green space and walkable roads were associated with lower risk, while other features, such as poorly paved roads, were associated with higher risk. However, these findings need further investigation.

“We’ve shown that we can use computer vision approaches to help identify environmental factors influencing cardiovascular risk and this could play a role in guiding heart-healthy urban planning. The fact that we can do this at scale is something that is absolutely unique and important for urban planning.”

“With upcoming challenges including climate change and a shifting demographic, where close to 70% of the world’s population will live in urban environments, there is a compelling need to understand urban environments at scale, using computer vision approaches that can provide exquisite detail at an unparalleled level,” said Prof. Rajagopalan.

Dr. Rohan Khera of Yale University School of Medicine in the United States stated in a supporting editorial: “The association of residential location with outcomes often supersedes that of known biological risk factors. This is often summarised with the expression that a person’s postal code is a bigger determinant of their health than their genetic code. However, our ability to appropriately classify environmental risk factors has relied on population surveys that track wealth, pollution, and community resources.

“The study by Chen and colleagues presents a novel and more comprehensive evaluation of the built environment. This work creatively leverages Google’s panoramic street-view imagery that supplements its widely used map application.

“… an AI-enhanced approach to studying the physical environment and its association with cardiovascular health highlights that across our communities, measures of cardiovascular health are strongly encoded in merely the visual appearance of our neighborhoods. It is critical to use this information wisely, both in defining strategic priorities for identifying vulnerable communities and in redoubling efforts to improve cardiovascular health in communities that need it most.”

For more Information: Google Street View reveals how built environment correlates with risk of cardiovascular disease, European Society of Cardiology, https://doi.org/10.1093/eurheartj/ehae159