The 130th Annual Meeting of APHA

Abstract #35411

Nancy Krieger, PhD¹, Pamela D Waterman, MPH², Jarvis T Chen, ScD², Mah-Jabeen Soobader, PhD³, S.V. Subramanian, PhD¹, and Rosa Carson, BA⁴. (1) Dept of Health & Social Behavior, Harvard School of Public Health, 677 Huntington Ave, Boston, MA 02115, 617-432-1571, nkrieger@hsph.harvard.edu, (2) Dept of Health and Social Behavior, Harvard School of Public Health, Landmark Center, 401 Park Drive, Room 403-M, Boston, MA 02215, (3) School of Public Health, Dept of Society, Human Development & Health, Harvard University, Landmark Center, 401 Park Drive, Room 403-B, Boston, MA 02215, (4) Harvard School of Public Health, Landmark Center, 401 Park Drive, Room 443, Boston, MA 02215

Despite the promise of geocoding and use of area-based socioeconomic measures (ABSMs) to overcome the paucity of socioeconomic data in US public health surveillance systems, no consensus exists as to which measures should be used, at which level of geography. The present study generated diverse single-variable and composite ABSMs at the census tract, block group, or ZIP Code level for Massachusetts (1990 population=6,016,425) and Rhode Island (1990 population=1,003,464) to investigate their associations with diverse health outcomes routinely monitored by state health departments. Outcomes included: mortality, cancer incidence, low birthweight, childhood lead poisoning, infectious disease (sexually transmitted diseases; tuberculosis) and non-fatal weapons-related injuries. Analyses of the mortality, cancer incidence, low birthweight, and childhood lead poisoning data so far indicate that: (a) block group and tract ABSMs perform comparably within and across both states, whereas ZIP Code ABSMs perform less consistently, often yielding estimates that were less than or at times contrary to socioeconomic gradients observed with tract and block group measures; (b) measures of economic poverty (e.g., % below the US poverty line) consistently detected the starkest gradients, often capturing disparities unobserved with measures of only education and wealth; and (c) similar gradients were detected with categories generated by quintiles and by a priori categorical cutpoints. These results suggest that efforts to monitor US socioeconomic inequalities in health will be best served by tract or block group measures of economic poverty that are easily understood, hence based on readily interpretable variables with a priori categorical cut-points.

Learning Objectives: At the conclusion of this presentation, participants will be able to

• Define what is meant by an "area-based socioeconomic measure" (ABSM).
• Identify the types of areas delimited by census block groups, census tracts, and ZIP codes.
• Explain why both the choice of type of ABSM and the geographic level at which it is measured matters for monitoring socioeconomic inequalities in health.

Keywords: Social Class Measurement, Data/Surveillance

Presenting author's disclosure statement:
I do not have any significant financial interest/arrangement or affiliation with any organization/institution whose products or services are being discussed in this session.