How Geographic Data Supports Population Health During COVID-19

Although COVID-19 has spread to communities around the world, as the pandemic wears on it’s clear that the virus has impacted some populations more than others.

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Studies have demonstrated the disproportionate effects COVID-19 has had on minority communities, individuals with comorbidities, and older adults. These trends have led researchers to increasingly examine geographic data and pinpoint where to target population health efforts.

“There has been a fair amount of conversation about COVID-19 being an equal opportunity killer. But frankly, that’s just not true,” Helen Dowling, MPH, data in action coordinator for the Public Health Alliance of Southern California, recently told HealthITAnalytics.

“There are communities that are dying at much higher rates than others, and it’s really important that we look at the data, spell it out by race and ethnicity, and focus on how we can improve the communities where these individuals live.”

To better understand where to direct resources, researchers at NYU Grossman School of Medicine recently developed a city-oriented COVID Local Risk Index. The tool calculates COVID-19 risk down to the hyperlocal, neighborhood level by relying on key health, economic, and social data at the census tract level. The index also allows for comparison of COVID-19 risk across other cities and between neighborhoods.

“While COVID-19 affects every community, we know its harm is disproportionately greater in certain groups, including people of color, those with underlying health conditions, older people, and frontline workers with low incomes,” said Marc N. Gourevitch, MD, MPH, the Muriel G. and George W. Singer Professor of Population Health and chair of the Department of Population Health at NYU Langone, as well as the principal architect of the City Health Dashboard.

“We also know that there can be huge variations in risk level between neighborhoods in the same city, sometimes separated by less than a mile or two. Even cities with lower cases of COVID-19 can have individual neighborhoods with populations at higher risk. Having access to this hyperlocal data is critical for leaders needing to make urgent decisions about re-opening and deploying resources amidst the pandemic.”

The COVID Local Risk Index incorporates data from multiple sources that are grouped into three overarching themes to determine communities with highest risk of being severely affected by COVID-19. The first is social vulnerability drawn from the CDC’s Social Vulnerability Index, which incorporates variables like income and overcrowded housing.

The second theme is COVID-19 relevant chronic health conditions, such as obesity, coronary heart disease, high blood pressure, diabetes, and chronic kidney disease. The Index also considers COVID-19 relevant demographics, including age and minority status.

The COVID Local Risk Index is now available on the City Health Dashboard, an online resource with community-level health, social, and economic data for 750 cities across the US.

“The City Health Dashboard’s new data on COVID-19 local risks provides a map for mayors and city managers on where to focus resources to improve health outcomes,” said David Eichenthal, executive director of the National Resource Network and former city official in New York City and Chattanooga, Tennessee.

“At the local level, we’ve known for years that these health disparities exist from city to city and neighborhood to neighborhood. But the dashboard, for the first time, provides city leaders with vital information to take on these challenges to help combat the worst effects of the pandemic and create more equitable communities.”

Other institutions have implemented similar tactics to track COVID-19 risk down to a granular level. Parkland Center for Clinical Innovation (PCCI) recently developed a big data analytics dashboard to accurately identify communities at high risk for COVID-19 infection.

The dashboard has helped leaders identify factors that could lead to improved population health initiatives.

“These kinds of precise data insights will help us understand communities and populations at greatest risk to COVID-19 and how to prioritize and tailor community interventions in order to proactively manage current and future outbreaks or other community-wide interventions,” said Steve Miff, PhD, President and CEO of PCCI.

Geographic data has also helped organizations determine best practices when it comes to social distancing measures. Researchers at the University of Texas at Austin studied cities throughout China and analyzed when first cases were detected, when social distancing measures were implemented, and when the outbreak was considered contained.

The results showed that every day a city delayed in implementing social distancing measures after the appearance of a first case added 2.4 days to the length of an outbreak.

The findings could have important implications for maintaining population health as the US begins to reopen.

“Every day saves time, saves effort, saves people becoming infected and probably saves lives,” said Lauren Ancel Meyers, a professor of integrative biology who leads the UT Austin COVID-19 Modeling Consortium. “This is particularly important as we think about the coming weeks and months.”