Identifying At-Risk Rural Areas for Targeting Enhanced Depression Treatment

Research center:
Lead researcher:
Project funded:
September 2004
Project completed:
December 2005
This project will identify rural areas that should be targeted for early adoption of evidence-based depression treatments based on community need. The goal is to provide health plans with a scientifically-based method to identify counties in greatest need and to inform national, regional, and local decision-makers about distributing scarce resources to areas which would most benefit from enhanced depression treatment. The results of this research will benefit health plans that cover these communities, particularly if adoption of evidence-based depression treatments can reduce the elevated rates of hospitalization observed in depressed rural residents. The rate of depression-related hospitalizations is the best nationally available proxy for a population's need for enhanced depression care programs.

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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