It is widely accepted that differences in risk factors, incidence, mortality, or stage of disease among populations, collectively known as health disparities, should not exist. Populations that experience disparities include racial/ethnic minorities, rural, and inner city residents, those of low socioeconomic status (SES), and LGBT groups (1). It is clear that health disparities need to be addressed to reduce the burden of cancer in the population as a whole.
Populations that experience cancer health disparities often live in defined, segregated geographic areas that have in common features that promote disparities rather than facilitate reductions in disparities in cancer outcomes. For example, lower SES areas frequently have a higher per capita burden of stores that sell tobacco products and display tobacco advertising (2). The rural area of Appalachia, Ohio, where I conduct most of my research, has (i) counties without a grocery store to purchase fresh meat, vegetables, and fruit; (ii) fewer sidewalks and health clubs to engage in exercise; (iii) counties with one or no mammography facilities; (iv) fewer colonoscopy facilities; (v) no public transportation; and (vi) poor access to state-of-the-art treatment facilities. In metropolitan Chicago, women who reside in low SES neighborhoods do not have access to quality breast cancer screening and treatment facilities (3).
In all of these situations, the impact of living in unfavorable geospatial factors is the same: poorer cancer outcomes. Moreover, residence is a marker for poorer adherence to medical advice. For example, those who live in poorer areas are less likely to stop smoking (4, 5) and find it harder to travel to a quality facility for a screening test (6). Thus, where one lives is a risk factor for disease and mortality above the usual risk factors.
Asking a patient “where do you live?” could be used to identify geographic exposures associated with cancer risk and outcomes. As there are well-defined thresholds for other vital signs, such as high blood pressure, thresholds for geographic areas could be developed and programmed into electronic medical records (EMR). There are many scales developed to assess neighborhood SES, such as the RAND N-SES (7, 8) or even the U.S. government definition of economically “distressed” and “at-risk” counties (9) that could be used to identify high-risk neighborhoods. Research needs to be conducted to construct or select a measure that could easily be implemented within an EMR, and clinicians could easily obtain a “value” for a patient with “risk” defined. As with accepted vital signs like blood pressure, this measure would need to have meaning and value in terms of predicting disease risk status. Moreover, this metric should be easy to assess and interpret and be easily reproducible.
What could a health care provider do if they learn their patient lives in a “high risk” geographical area? One answer is found in the unfunded mandate in the Affordable Care Act for patient navigators. Patient navigation, first coined by Dr. Harold Freeman (10), has demonstrated the ability to improve screening uptake (11, 12), follow-up of abnormalities (13, 14), and receipt of prompt and proper care (15, 16). Navigation works, but it is not correctly or widely implemented. With limited resources, a navigator could be assigned to assist a patient with a “vital sign” that indicates they live in a high-risk geographic area. In addition, specialized navigators could be used as community health workers to go into high-risk geographic areas to educate and direct residents to appropriate care facilities to mitigate risk for poor cancer outcomes. The time has come to accept that where we live impacts our health and do something to mitigate risks associated with living in an unfavorable neighborhood.
See all articles in this CEBP Focus section, “Multilevel Approaches to Addressing Cancer Health Disparities.”
Disclosure of Potential Conflicts of Interest
No potential conflicts of interest were disclosed.
- Received February 10, 2016.
- Accepted February 11, 2016.
- ©2016 American Association for Cancer Research.
References
Volume 25, Issue 4
