Pilot--Health care utilization for chronically ill adult

Charles R. Drew University Of Med &Sci, Los Angeles CA

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Abstract

The Effects of Individual Social Position and the Neighborhood Environment on Health Behaviors and Health Care Utilization for Adults with Chronic Illness Social position (race/ethnicity, income wealth, education, etc.) relates to health, such that the poor and minorities have worse health than do those who are more socio-economically advantaged and white. Research suggests that neighborhoods also impact health such that living in poor communities has negative effects on health status. Social position and neighborhood factors may exert their influence on health through two mechanisms, health behaviors and health care utilization. Health behaviors, such as smoking, exercise, and diet, are strongly related to health outcomes. In fact, half of all preventable deaths in the United States are related to engaging in high-risk behaviors. According to the CDC, health care utilization accounts for somewhere between 10%-20% of health outcomes. We propose to analyze the relationship between individual and neighborhood characteristics and health behaviors and health care use among participants in The Los Angeles Family and Neighborhood Survey (L.A. FANS). L.A.FANS is based in a representative sample of 65 neighborhoods in Los Angeles County, with an oversample in poor neighborhoods. Approximately 45 adult respondents (and other household members) were interviewed in each neighborhood for a total of approximately 2600 adult interviews. Almost 70% of the adult sample are members of minority ethnic groups. L.A. FANS collected extensive data on social position and the neighborhoods in which respondents lived. Data can be linked to census block information on the respondents. Both neighborhood and individual level data will be used to evaluate the relationship between community and individual characteristics, on one hand, and health behavior and health care utilization on the other. We will conduct both stratified and multivariate analyses to compare these associations for respondents reporting chronic conditions with those reporting no chronic conditions. We do this because we anticipate that health care utilization should be different between these two groups. This data set was explicitly designed to use multilevel modeling to identify the differential contributions of individual and community characteristics for health and health related behaviors.