Nov 1 2011
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Moving to Better Health

By Greg Duncan, PhD, and Jens Ludwig, PhD, co-winners of a 2009 Robert Wood Johnson Foundation Investigator Award in Health Policy Research.

file Greg Duncan

At different points in time, each of us has spent a blissful sabbatical year at the Russell Sage Foundation and living on the Upper East Side of Manhattan – just a few blocks from Central Park to the west and Weill Cornell Medical College to the east, with a Food Emporium right in the basement of our apartment building. Among the other striking things about the Upper East Side is how healthy people are – only 8.4 percent of residents were obese in 2003-07, the lowest rate in all of New York City. Yet just a five or ten minute ride north on the 6 train takes you to East Harlem, where nearly 30 percent of residents are obese (Black and Macinko, 2010).

file Jens Ludwig

These sorts of massive disparities across neighborhoods in health outcomes have generated long-standing concern that living in a disadvantaged neighborhood environment might causally contribute to adverse health outcomes, and so doubly-disadvantage poor families who are already at elevated risk for adverse health due to their own low incomes. Common hypotheses for why neighborhood of residence might contribute to obesity and closely related health problems such as diabetes include differential access to grocery stores that sell healthy foods, opportunities for physical activity, or medical treatment. Neighborhoods could also systematically differ with respect to social norms around health-related behaviors, or in terms of levels of psychological stress due to differences across areas in rates of crime and violence.

Empirically isolating the causal effects of neighborhood environments on health has been challenging for social scientists and medical researchers because most families have at least some degree of choice over where they live. Suppose we observe two observationally equivalent people, one living in a distressed area and the other in a more affluent area, with different health outcomes. Is the observed difference in health due to something about the neighborhood environments in which the two people are living, or instead to hard-to-measure characteristics of the two people that are related to their residential choices and directly related to health as well?

We recently published a paper in the New England Journal of Medicine (Ludwig et al., 2011) that provided the first test (of which we are aware) of how social and physical environments affect health outcomes. Drawing on data from the U.S. Department of Housing and Urban Development’s (HUD) Moving to Opportunity (MTO) experiment, we found that providing low-income women with the chance to move from high-poverty to lower-poverty urban neighborhoods was associated with reductions in rates of extreme obesity and diabetes equal to about one-fifth of the control group’s prevalence rates, which equaled 17 percent (Body Mass Index of 40 or over) and 20 percent (diabetes).

Our study suggests that over the long term, investments in improving neighborhood environments may be an important complement to medical care when it comes to preventing obesity and diabetes. Moreover these are big effects. In comparing the MTO impacts to those from other studies, it is important to keep in mind that most clinical trials in medicine usually enroll study samples that are more socio-economically advantaged than the low-income families that enrolled in MTO.

Further, different studies tend to use different measures of diabetes, and we can’t construct all of the measures other studies use because we don’t have a measure of diabetes prevalence at baseline. But with those qualifications in mind, these MTO effects are comparable in size to the long-term effects on diabetes we see from targeted lifestyle interventions or from providing people with medication that can prevent the onset of diabetes (DPP, 2009).

Our study might also help explain why the prevalence of obesity and diabetes has doubled in the U.S. since about 1980. Over this same time period, the likelihood that poor Americans live in economically disadvantaged neighborhoods has also increased. Whether our MTO findings generalize to other populations is unclear, but these results suggest the possibility that the growing exposure of Americans to distressed neighborhoods could be one reason why obesity and diabetes prevalence have been increasing in the U.S. Our MTO results might also help explain why we see disparities in obesity and diabetes prevalence between whites and minorities in the U.S., given that minorities are more likely than whites to live in high-poverty areas.

Black, Jennifer and James Macinko (2010) “The changing distribution and determinants of obesity in the neighborhoods of New York City, 2003-2007.” American Journal of Epidemiology. (Feb 19, 2010): 1-11.

Diabetes Prevention Program Research Group, “10-year follow-up of diabetes incidence and weight loss in the Diabetes Prevention Program Outcomes Study,” Lancet, 2009 (374): 1677-86.

Ludwig, Jens, Lisa Sanbonmatsu, Lisa Gennetian, Emma Adam, Greg J. Duncan, Lawrence F. Katz, Ronald C. Kessler, Jeffrey R. Kling, Stacy Tessler Lindau, Robert C. Whitaker, and Thomas W. McDade. (2011) “Neighborhoods, obesity and diabetes: A randomized social experiment.” New England Journal of Medicine. 365(16): 1509-19.

Greg Duncan is Distinguished Professor of Education at the University of California, Irvine. Jens Ludwig is the McCormick Foundation Professor of Social Service Administration, Law, and Public Policy at the University of Chicago. Duncan and Ludwig are both recipients of a 2009 RWJF Investigator Award in Health Policy Research.

Read an RWJF web story on the work of Duncan and Ludwig.

Read a related story on how nurses can play a leading role in improving the health of distressed homeowners.

Tags: HC Website Feature, Health & Health Care Policy, Investigator Awards in Health Policy Research, Medical, Nursing & Dental Workforce, Nursing, Publications, Research & Analysis, Underserved Populations, Voices from the Field