Clinician Burden and Depression Treatment: Disentangling Patient- and Clinician-Level Effects of Medical Comorbidity
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Efforts to improve primary care depression treatment have assessed strategies across heterogeneous groups of patients, but few have examined clinician-level influences on depression treatment.
To examine clinician characteristics that affect depression treatment in primary care settings, using multilevel ordinal regression modeling to disentangle patient- from clinician-level effects.
Secondary analysis from the Quality Improvement in Depression Study dataset.
The participants were 1,023 primary care patients with depression who reported on treatment in the 6-month follow-up and whose clinicians (n = 158) had at least 4 patients in the study.
Primary outcome variable was depression treatment intensity, derived from assessment of concordance with AHCPR depression treatment guidelines based on patient-reported data on their treatment. Primary independent variable was clinical practice burden for treating depression, derived from patient- and clinician-reported composite measures tested for significant association with clinician-reported practice burden.
Clinicians who treat patients with more chronic medical comorbidities perceive less burden from treating depressed patients in their practice (Spearman’s rho = −.30, p < .05). Clinicians who treat patients with more chronic medical comorbidities also provide greater intensity of depression treatment (adjusted OR = 1.44, p = .02), even after adjusting for the effects of patient-level chronic medical comorbidities (adjusted OR = 0.95, p = .45).
Clinicians who provide more chronic care also provide greater depression treatment intensity, suggesting that clinicians who care for complex patients can integrate depression care into their practice. Targeting interventions to these clinicians to enhance their ability to provide guideline-concordant depression care is a worthwhile endeavor and deserves further investigation.
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Journal of General Internal Medicine
Volume 23, Issue 11 , pp 1763-1769
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- multilevel modeling
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- Author Affiliations
- 1. Department of Family Medicine, University of Colorado Denver, Aurora, CO, USA
- 4. UCHSC, Aurora, CO, USA
- 2. Department of Medical Humanities and Social Sciences, Florida State University, Tallahassee, FL, USA
- 3. Rand Health Program, Santa Monica, CA, USA