|Grant Number:||5R03CA091734-02 Interpret this number|
|Primary Investigator:||Schootman, Mario|
|Project Title:||Gis, Multilevel Modeling and Breast Cancer Screening|
Certain segments of the population (e.g., women of low income and those without health insurance) are less likely screened for breast cancer despite its proven effectiveness to reduce the risks heath from this disease. although differences exist between rural and urban populations that are associated with use of screening, rural urban screening differences need further investigation. Most studies that have shown rural women are less likely screened are several years old. these studies have typically asked screening differences at the county level and not used lower levels of geography such as Census tracts. they ave typically used only individual level factors as potential barriers to screening and ignored the influence of ecological factors (e.g., area deprivation and income inequality) using multilevel models. Finally, these studies did not have the added advantage of using newly developed methods, such as geographic information systems (GIS). Other studies have not found rural-urban screening differences. As a result, it is not clear if rural women are less likely screened when taking a more rigorous approach using multilevel modeling in connection with GIS. In the proposed study, we will use a one-stage stratified cluster sampling method and multilevel approach in connection with a GIS to describe rural urban screening differences and identify ecological barriers to early detection. Following the random selection of Census tracts stratified by their rurality, we will use the Mitofsky-Waksberg random digit dating method to identify women aged 50-69. Eligible women will be interviewed by telephone about their breast cancer screening and individual level factors including demographics, access to health care, and psychological barriers. Ecological factors will be obtained from existing data sources and include social disorganization, area deprivation, country mammography rates, physician distribution, and availability of mammography facilities. Multilevel modeling and GIS facilitate new and exciting opportunities that could identify new barriers to screening, which may be used to reduce breast cancer mortality by implementing multilevel interventions. Few studies focusing on breast cancer screening have taken this approach, which we, and others feel is warranted.