Identifying At-Risk Rural Areas for Targeting Enhanced Depression Treatment
Project staff will conduct a secondary database analysis of the Statewide Inpatient Database (SID), containing the universe of hospital discharge records from all community hospitals in participating states. De-identified hospitalization data will also be collected from other national databases such as claims records from managed behavioral health plans (i.e., carve-outs) as well as encounter data from the Department of Veterans Affairs. In addition, the project will investigate the degree to which geographic areas at risk for depression-related hospitalizations can be predicted by rurality, economic factors, access to care, and demographics. Although prevalence rates have not been found to differ across rural and urban areas, it is expected that rurality is associated with hospitalization rates due to a number of reasons including poor access to outpatient specialty care and poor economic conditions. In combination with small area variation analysis methodologies, a Geographic Information System (GIS) will be used to examine spatial variation in need across geographically defined populations and to identify geographic areas of high risk. Specifically, the GIS will be used to spatially reference data from various sources and geographically join layers of spatially referenced information to create community health profiles.
Two papers will be generated from this project. The first paper will describe community-level risk factors for depression-related hospitalizations. The second paper will identify those counties in the U.S. were residents at the highest risk for a depression-related hospitalization. Future research projects will focus on developing strategies to implement evidence-based care for depression in these high risk communities.
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