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Symposium: Determinants of Cancer Disparities in Relation to Diagnosis, Treatment, and Outcome

Using area-based socioeconomic measures to study social disparities in cancer.

Harvard School of Public Health, Boston, MA

Abstract

SY09-01

Despite longstanding US evidence of socioeconomic and racial/ethnic inequalities in health, US cancer registries lack socioeconomic data, thus precluding routine monitoring and analysis of US socioeconomic inequalities in cancer and their link to racial/ethnic disparities in the burden of cancer. Also problematic is a lack of conceptual clarity regarding the definition of cancer disparities, with the most commonly employed definitions being descriptive rather than analytic. To address these empirical and conceptual gaps, this presentation will address three topics: first, it will offer a conceptual analytic definition of cancer disparities (1), premised on ecosocial theory (2–4) and its concern with how we literally embody, biologically, social inequality, thereby producing population patterns of health, disease and well-being, including health inequities. Second, it will present the methods and results of the The Public Health Disparities Geocoding Project (5–8), which geocoded and linked public health surveillance data from Massachusetts and Rhode Island to 1990 census-derived area-based socioeconomic measures (ABSMs) to determine which ABSMs, at which geographic level (census block group, census tract, ZIP Code) could validly be used to monitor socioeconomic inequalities in health. Outcomes comprised: birth, childhood lead poisoning, sexually transmitted infections, tuberculosis, non-fatal weapons-related injuries, cancer incidence, and mortality. Key findings were that in both the total population and diverse racial/ethnic-gender groups, measures of economic deprivation proved most sensitive to expected socioeconomic gradients in health, with census tract (CT) ABSMs yielding the most consistent results and maximal geocoding across outcomes, and the CT poverty measure performing as well as more complex composite measures. These results imply that geocoding and use of the CT poverty measure permits routine monitoring of US socioeconomic inequalities in health, using a common and accessible metric. Third, to demonstrate the additional utility of geocoding and using ABSMS for etiologic investigations, selected results of analyses on socioeconomic trends in breast cancer incidence will be presented (9). The central message is that the science of health disparities requires both rigorous concepts and rigorous methods, and puts to the test our understanding of determinants of population health, including cancer.