Poverty's Association With Poor Health Outcomes and Health Disparities - Health Affairs (blog)

A recent ecological study by Carl Stevens, David Schriger, Brian Raffetto, Anna Davis, David Zingmond, and Dylan H. Roby, published in the August issue of Health Affairs, showed significant associations between neighborhood poverty and diabetes-related lower extremity amputations (LEA) in the state of California, which adds to the growing evidence that where you live (not just how you live) may directly impact your health.

The authors linked data from multiple sources (i.e. California Health Information Survey, Census Bureau's American Community Survey, health facility discharge data) and used geographic information system (GIS) analyses and regression analyses to identify amputation 'hot spots' and uncovered a 10-fold variation in LEA rates between low-income and high-income neighborhoods.

Neighborhood Poverty and Health

We've long known that health system factors (e.g. insurance status) and patient factors (e.g. diet and physical activity) are related to health outcomes such as diabetes-related complications. In fact, we've known that poverty among individuals is bad for your health. This study demonstrates that poverty within neighborhoods can also contribute to worse diabetes health outcomes, which is critically important for several reasons. First, this kind of research allows policy makers, city planners, payors and researchers to identify and target 'hot spots' for interventions. The increasing trend of using technology and geographic information system (GIS) analyses will allow us to make more strategic, high-impact choices in how we allocate limited health care dollars to improve population health.

Second, it allows a shift in the paradigm of how we understand and address poverty as a social determinant of health. We can no longer exclusively have a conversation about individuals (e.g. their eligibility for Medicaid), but must begin to broaden the dialogue to include community infrastructure (e.g. safe housing, primary care facilities), resources (e.g. grocery stores, fitness centers), and the built environment (e.g. bike paths, local parks) as part of how we, as a medical community, address community health and health disparities.

What Can We Do?

Important work is being done to identify and geocode community assets, and to leverage community resources to improve diabetes outcomes. However, more research is needed to understand the complex relationships between community-level factors and health. One limitation of Stevens' study is that data regarding community assets, that may have moderated or mediated the association between poverty and amputation rates, were not available.

In a 2011 New England Journal of Medicine paper, Jens Ludwig and co-authors reported on a trial that randomized low-income residents in public housing to receive HUD vouchers (and relocate to different [e.g. higher-income] neighborhoods) or to a control group. A decade later, low-income individuals in the intervention group had lower rates of obesity and diabetes compared to control group participants, thus underscoring the importance of neighborhood poverty in determining diabetes-related health outcomes.

There is growing evidence that neighborhood poverty, and associated racial segregation within low-income neighborhoods, is associated with poor health outcomes and a contributor to health disparities. We must capitalize on recent advances in technology (e.g. GIS analyses) and current trends in health policy (e.g. global insurance payments, population health management) to address community-level factors that perpetuate poor health and health disparities within the U.S.

In Chicago, the Center for Diabetes Translation Research has been working with collaborators such as the Chicago Department of Public Health to use patient-level socio-demographic data and diabetes-related health outcome data, along with geocoded community asset data, to identify geographic 'hot spots' of preventable diabetes-related hospitalizations and explore associations between community asset distribution and hospitalization rates.

This information can allow payors, city planners and local politicians, health departments, and health systems to inform potential efforts at community-based health promotion, diabetes prevention, and population health management.

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Entities 0 Name: California Count: 2 1 Name: David Zingmond Count: 1 2 Name: LEA Count: 1 3 Name: Dylan H. Roby Count: 1 4 Name: Brian Raffetto Count: 1 5 Name: Health Affairs Count: 1 6 Name: Stevens Count: 1 7 Name: U.S. Count: 1 8 Name: HUD Count: 1 9 Name: Anna Davis Count: 1 10 Name: Chicago Department of Public Health Count: 1 11 Name: Chicago Count: 1 12 Name: American Community Count: 1 13 Name: Census Bureau Count: 1 14 Name: New England Journal of Medicine Count: 1 15 Name: Center for Diabetes Translation Research Count: 1 16 Name: David Schriger Count: 1 17 Name: Carl Stevens Count: 1 18 Name: Jens Ludwig Count: 1 Related 0 Url: http://ift.tt/1wF6Ri6 Title: 3 important lessons from HealthBeat (and 'the system is broken' isn't one of them) Description: HealthBeat 2014 is behind us, but I'd wager that many of us who were in attendance are still thinking about some of the central themes discussed. Several of them kept coming up in the sessions and in conversations in the hallways. Our health system is broken; everybody knows that.