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Transforming the understanding
and treatment of mental illnesses.

Addressing Mental Health Disparities Research Gaps: Aggregating and Mining Existing Data Sets for Secondary Analyses

Presenter:

Eve E. Reider, Ph.D.
Division of Services and Intervention Research

Goal:

This initiative seeks to encourage theory-based research targeting the reduction and elimination of mental health disparities in the United States. Given that there are many existing, untapped data resources, the purpose of this initiative is to provide an opportunity for investigators to integrate and analyze datasets in a cost-efficient manner to yield novel findings, identify key scientific gaps, and inform future research investments.

Rationale:

The National Institute on Minority Health and Health Disparities defines a health disparity (HD) as a health difference that adversely affects disadvantaged populations. The National Institutes of Health define health disparity populations as racial and ethnic minority populations, less privileged socioeconomic status (SES) populations, underserved rural populations, sexual and gender minorities (SGM), and any subpopulations that could be characterized by two or more of these descriptions.

The National Institute of Mental Health (NIMH) supports a research agenda aimed at understanding and reducing mental health disparities and increasing health equity. While it is well known that mental health disparities exist, understanding the underlying mechanisms and identifying strategies to address them have proven elusive, in part because many studies do not include enough individuals from health disparity populations to identify statistically significant differences between subgroups and explore associated mechanisms. Aggregating data sets allow the opportunity to examine subgroups and low base rate behaviors, which is often not possible with individual studies. Previous initiatives that focused on aggregating data sets (e.g., addressing suicide research gaps, long-term and cross-over effects of prevention interventions) were quite successful; however, they were not focused on mental health disparities. Selected studies funded from these funding opportunity announcements (FOAs) demonstrated the potential of aggregated data sets for examining health disparities.

Priority areas include: 1) examining mechanisms by which various factors at multiple levels (e.g., policy, society, community, school, family, individual) contribute to, exacerbate, or reduce disparities across development to inform understanding of disparities in risk and etiology of mental disorders; 2) facilitating the development and refinement of preventive and therapeutic interventions for differentially affected individuals; 3) seeking to inform targets and timing of services interventions to address disparities in access, engagement, quality and outcomes of mental health services.  Sharing harmonized data sets will be essential.

The ultimate goal of this research is to contribute information that may help reduce health disparities and increase health equity.