There is abundant evidence of persistent differences in cardiovascular disease (CVD) morbidity and mortality by socioeconomic status (SES). The determinants of SES-related differences in CVD outcomes and risk factors have not been fully established. Previous work in this area has focused predominantly on individual-level SES indicators, but recently attention has shifted to the role of neighborhood or community-level variables in shaping health outcomes, independently of individual-level SES. Several epidemiologic studies have suggested that neighborhood characteristics may influence the distribution of disease risk, but the role of both neighborhood-level and individual-level SES variables in shaping individual-level outcomes and risk factors has been rarely addressed in epidemiologic studies of CVD. This application has the broad objective of investigating the contributions of neighborhood environments to the distribution of CVD risk across different age ranges and racial/ethnic groups, using data from three ongoing cohort studies of cardiovascular disease: the Coronary Artery Disease Risk Development in Young Adults (CARDIA) Study, the Atherosclerosis Risk in Communities (ARIC) Study, and the Cardiovascular Health Study (CHS). Associations of neighborhood socioenvironmental characteristics with CVD prevalence and incidence in middle-aged and elderly populations will be investigated using data from the ARIC Study and CHS. Associations of neighborhood socioenvironmental characteristics with CVD risk factors and risk factor trends in young and middle-aged adults will be investigated using data from the CARDIA and ARIC studies. CARDIA and ARIC data will also be used to explore the contributions of neighborhood characteristics to racial differences in CVD risk factors. Census defined areas will be used as proxies for neighborhoods. Participants will be linked to their census-tract and block-group of residence using their home address, and neighborhood characteristics will be obtained from the 1990 U.S. Census. The three data sets will be analyzed separately. After exploratory and descriptive analyses, regression models will be used to investigate associations of neighborhood characteristics with the outcomes before and after controlling for individual-level SES and other relevant covariates. Appropriate statistical methods (mixed effects models) will be used to account for the multilevel structure of the data (individuals nested within neighborhoods and repeated measures nested within individuals), and the potential violations of the assumption of independence of observations that may arise from it. The investigators point ut that the proposed study builds on existing cohort studies of cardiovascular disease to explore hypotheses relevant to understanding the factors contributing to SES differences in CVD risk.
Showing the most recent 10 out of 13 publications
