Abstract: Improving Health Equity in Integrated Care through Evaluation Techniques (Society for Social Work and Research 24th Annual Conference - Reducing Racial and Economic Inequality)

Improving Health Equity in Integrated Care through Evaluation Techniques

Friday, January 17, 2020
Liberty Ballroom O, ML 4 (Marriott Marquis Washington DC)
* noted as presenting author
Tonya Hansel, PhD, MSW, Associate Professor, Tulane University, LA
Joy Osofsky, PhD, Professor, Louisiana State University Health Sciences Center New Orleans, New Orleans, LA
Howard Osofsky, PhD, Professor, Louisiana State University Health Sciences Center New Orleans, New Orleans, LA
Background and Purpose: There is a growing body of evidence supporting that integrated medical and behavioral healthcare improves health outcomes.  However, these findings focus on statistical techniques where data is combined across multiple subjects and averages are used to demonstrate significance.  Group analyses in evaluation have played an important role in emphasizing the overall effectiveness of integrated healthcare, but additional methods are needed to improve health equity and understand the unique patterns critical clinical decisions. For example, group analyses may reveal general mild improvement in mental health symptoms over time, but individual-centered analyses may reveal sub-trajectories of individuals showing substantial improvement and maintenance versus initial improvement and relapse.  Utilizing individual level analyses will provide a real world application to evaluating outcomes and improved integrated care models that can address health disparities. This study evaluated trajectories of change for patients who were seen in integrated care. Specifically we hypothesized a three-cluster grouping of depression symptoms and further explored variables, such as race, financial problems, and other that were associated with these grouping.

Method: Patients (N = 455) receiving services at five rural health clinics self-reported symptoms of depression as part of an ongoing evaluation to study the effectiveness of integrated health.  Depression was assessed using the nine item Patient Health Questionnaire. ANOVA was used to assess differences overtime and trajectories were identified with cluster analyses.  Health related factors associated with these trajectories were assessed using logistic regression.

Results: Significant overall decreases in depressive symptoms overtime were found; individual trajectories were identified and include moderate declines, steep declines, and stable high symptoms.  Financial problems, race, emergency room visits, missed appointments, chronic pain, substance use, and life stressors correctly classified trajectory membership.

Conclusion and Implications: Trajectories indicate that patients have differing treatment needs and cluster analysis as an evaluation technique may be useful in identifying what treatment works and for whom. The present study addresses a major concern for healthcare providers and emphasizes the importance of identifying health related vulnerabilities that contribute to mental health outcomes.  Sub-group analyses are a useful tool for developing more targeted treatment and improved evaluation techniques in integrated healthcare can improve health equity of patients by understanding for whom the interventions work.