Local Food Environments and Disparities in Ovarian Cancer Survival (CEED Project 1)


Background: Factors in the Chicago area responsible for disparities in ovarian cancer survival between African Americans and whites are not known. Prior research has identified a survival advantage conferred by increased fruit/vegetable intake, and demonstrated that food networks at the community level influence dietary intake. In Chicago, areas without adequate supermarket coverage are located in predominantly African American neighborhoods on the South Side.

Objective: This study investigates the role of the local food environment, an important neighborhood-level determinant of health, in ovarian cancer-specific survival disparities in Chicago. This will be accomplished by the following: 

  1. Assess in an existing cohort of more than 6,000 older adults whether census tract-level factors that describe neighborhood food networks influence individual dietary intake in Chicago, and if food networks differentially influence dietary intake by race. Existing data from the Chicago Healthy Aging Project, a population-based, biracial cohort study of older adults will be used with multi-level logistic regression to assess the effect of neighborhood-level grocery store density on the adequacy of fruit/vegetable intake.  Data will include completed food frequency questionnaires validated in the cohort, and neighborhood-level grocery store data identified through a systematic neighborhood survey. 
  2. Assess whether ovarian cancer survival disparities are associated with inadequacies in the local food environment. A population-based dataset of more than 5,000 ovarian cancer cases will be assembled that includes demographics and tumor characteristics, census tract-level measures of local food environments and socioeconomic deprivation, and cause-specific mortality. Data will be obtained from the Illinois State Cancer Registry (cases diagnosed 1995–2008), the 1990 and 2000 U.S. Census, Dun and Bradstreet (grocery store data), Social Security Administration and the National Death Index.
  3. Use census tract-level data on food environments to develop models that predict geographical areas within the Chicago area that are future “hotspots” for ovarian cancer. Spatial scan statistics will be used to detect geographical areas with higher-than-expected ovarian cancer prevalence, and logistic regression models identify area-level characteristics that predict patterns.

Affiliated Center/Program

This study is a project within a center grant:

Center of Excellence in Eliminating Disparities
Start date
End date
About this grant

The funding period of this grant was extended by one year with no additional money. 

Related publications

Dolecek TA, McCarthy BJ, Joslin CE, Peterson CE, Kim S, Freels SA, Davis FG. Prediagnosis food patterns are associated with length of survival from epithelial ovarian cancer. J Am Diet Assoc. 2010;110:369-382. [See abstract.]

Kim S, Dolecek TA, Davis FG. Racial differences in stage at diagnosis and survival from epithelial ovarian cancer: A fundamental cause of disease approach. Soc Sci Med. 2010 Jul;71(2):274-81. [See abstract.]