Sarah Holder is a staff writer at CityLab covering local policy, housing, labor, and technology.
“Your neighborhood shouldn’t influence your odds of seeing your grandchildren grow up,” says a researcher for NYU’s new analysis of City Health Dashboard data.
Two children are born and settle down in New York City. They live parallel lives, separated by a few blocks and a handle of corner bodegas. But the one who grows old on the Upper East Side lives to be nearly 90; and the one in East Harlem dies at 71. What happened?
The glaring divergence in life expectancy for these hypothetical—but all-too-typical—Manhattan residents is strongly associated with segregation, according to researchers in the Department of Population Health at NYU School of Medicine.
Using data from the City Health Dashboard, a national database that compares 500 cities with populations of 66,000-plus on a series of health metrics, the NYU research team identified 56 cities with the largest life expectancy gaps between census tracts. Cities whose residents had the greatest variance between lifetimes—sometimes by 20 to 30 years—were also the places where racial and ethnic lines between neighborhoods are most stark.
“We see life expectancy in many ways as a summary measure of impacts of many other drivers of health outcomes,” said Marc Gourevitch, the Muriel G. and George W. Singer Professor and chair of the Department of Population Health at NYU Langone, and the principal architect of the City Health Dashboard. “Rather than looking at a specific one, which is also valuable—like cardiovascular mortality—life expectancy really folds in not only a variety of health conditions, but also of determinants of health.”
Some of America’s largest cities also have particularly large life expectancy gaps: Chicago, Washington, D.C., and New York City, among others. All of the cities with the highest life expectancy gaps had neighborhood racial-ethnic segregation scores more than five times that of the cities with the smallest gaps.
Those cities where life expectancy gaps are less pronounced also tend to be fairly small: the Indianapolis suburb of Fishers, Indiana; Cicero, Illinois; Lynwood and Livermore, California; and Meridian, Idaho. All of these cities hover between 4.5 and 5 years’ difference in life expectancy between tracts.
Size is only part of the explanation, however. Huge differences in life expectancy are visible even among America’s largest cities, such as comparing Chicago (one of the most segregated cities in the country, where neighborhood life expectancies range between 60 and 90 years old) and San Jose (the most-integrated metropolitan region in the Bay Area, at least, where the worst-off neighborhoods still have average lifespans near 80).
“We think that there’s a relationship there that’s more than coincidental,” said Gourevitch, of the magnitude of racial and ethnic segregation and the life expectancy gaps.
The segregation of census tracts alone, however, only tells part of the story. A CityLab analysis of the City Health Dashboard data found a host of socioeconomic and health factors are associated with neighborhood lifespan, beyond just race.
Poverty concentration has already been identified as one of the most potent determinants of life expectancy: a Harvard analysis found that residents of rich neighborhoods live on average 15 years longer than residents of poor neighborhoods, and that even lower-income residents of wealthy neighborhoods live longer than others in poorer ones. CityLab’s analysis found that poverty alone could explain a third of the variation in neighborhood life expectancy. If a resident from the Upper East Side lives 30 years longer than one from East Harlem, it could have a lot to do with the fact that on the UES, median income reaches $124,000. East Harlem’s is closer to $34,000.
But these income disparities often correlate with ethnic and racial disparities within cities. And these poorer, browner, neighborhoods frequently suffer disinvestment in local business, weak schools, spatial mismatch between jobs and housing, violence, and proximity to environmental risk factors. These, in turn, breed health disparities: Children in historically “redlined” California neighborhoods (those that were intentionally segregated as a result of New Deal mortgage lending) are more likely to breathe in diesel particulate matter and go to the emergency room for asthma-related problems. And across the U.S., people of color are more likely to live near particulate matter pollutants and, relatedly, are more often exposed to polluted air than white residents—air that can cause cancer, lung disease, and heart attacks in those who breathe it in.
It’s the combination of these three factors—race, place, and income—that in many places are decisive in who ails, and who gets better. The City Health Dashboard already maps all the granular effects of environmental risks. But NYU’s research sheds new light on where they’re compounded, and to what effect.
“Your neighborhood shouldn’t influence your odds of seeing your grandchildren grow up,” Gourevitch said in a statement. Right now, it does.
The researchers highlighted other limitations. Though they produce a life expectancy for each neighborhood, most people don’t live their whole lives without moving from the place they were born. And it’s true that lives can be cut short in any city for any number of place-specific reasons; researchers note that historical context, too, should be factored into any further investigation. CityLab’s analysis found that specific health issues such as rates of diabetes or smoking were associated with lower life expectancies.
But it’s the long legacy of racism, and exclusionary place-making policies that created what now looks like spatial determinism. Investing in and making affordable housing safer, raising the minimum wage, improving the quality of primary education, and stopping gun violence are all city-level interventions that could help shrink these expectancy gaps. So could breaking the barriers between neighborhoods—with re-zoning policies, housing interventions, or the like.
“None of these come at no cost, and none of them will turn around life expectancy numbers overnight,” said Gourevitch. “But life expectancy takes a lifetime to develop.”