Addressing depression and comorbid health conditions through solution-focused brief therapy in an integrated care setting: a randomized clinical trial.

BACKGROUND: Co-occurring physical and mental health conditions are common, but effective and sustainable interventions are needed for primary care settings. PURPOSE: Our paper analyzes the effectiveness of a Solution-Focused Brief Therapy (SFBT) intervention for treating depression and co-occurring health conditions in primary care. We hypothesized that individuals receiving the SFBT intervention would have statistically significant reductions in depressive and anxiety symptoms, systolic blood pressure (SBP), hemoglobin A1C (HbA1c), and body mass index (BMI) when compared to those in the control group. Additionally, we hypothesized that the SFBT group would have increased well-being scores compared to the control group. METHODS: A randomized clinical trial was conducted at a rural federally qualified health center. Eligible participants scored ≥ 10 on the Patient Health Questionnaire (PHQ-9) and met criteria for co-occurring health conditions (hypertension, obesity, diabetes) evidenced by chart review. SFBT participants (n = 40) received three SFBT interventions over three weeks in addition to treatment as usual (TAU). The control group (n = 40) received TAU over three weeks. Measures included depression (PHQ-9) and anxiety (GAD-7), well-being (Human Flourishing Index), and SFBT scores, along with physical health outcomes (blood pressure, body mass index, and hemoglobin A1c). RESULTS: Of 80 consented participants, 69 completed all measures and were included in the final analysis. 80% identified as female and the mean age was 38.1 years (SD = 14.5). Most participants were white (72%) followed by Hispanic (15%) and Black (13%). When compared to TAU, SFBT intervention participants had significantly greater reductions in depression (baseline: M = 18.17, SD = 3.97, outcome: M = 9.71, SD = 3.71) and anxiety (baseline: M = 14.69, SD = 4.9, outcome: M = 8.43, SD = 3.79). SFBT intervention participants also had significantly increased well-being scores (baseline: M = 58.37, SD = 16.36, outcome: M = 73.43, SD = 14.70) when compared to TAU. Changes in BMI and blood pressure were not statistically significant. CONCLUSION: The SFBT intervention demonstrated efficacy in reducing depressive and anxiety symptoms and increasing well-being but did not affect cardio-metabolic parameters over a short period of intervention. TRIAL REGISTRATION: The study was pre-registered at ClinicalTrials.gov Identifier: NCT05838222 on 4/20/2023. *M = Mean, SD = Standard deviation.

Social determinants of health and diabetes: using a nationally representative sample to determine which social determinant of health model best predicts diabetes risk.

OBJECTIVES: Social determinants of health (SDOH) research demonstrates poverty, access to healthcare, discrimination, and environmental factors influence health outcomes. Several models are commonly used to assess SDOH, yet there is limited understanding of how these models differ regarding their ability to predict the influence of social determinants on diabetes risk. This study compares the utility of four SDOH models for predicting diabetes disparities. STUDY DESIGN: We utilized The National Longitudinal Study of Adolescent to Adulthood (Add Health) to compare SDOH models and their ability to predict risk of diabetes and obesity. METHODS: Previous literature has identified the World Health Organization (WHO), Healthy People, County Health Rankings, and Kaiser Family Foundation as the conventional SDOH models. We used these models to operationalize SDOH using the Add Health dataset. Add Health data were used to perform logistic regressions for HbA1c and linear regressions for body mass index (BMI). RESULTS: The Kaiser model accounted for the largest proportion of variance (19%) in BMI. Race/ethnicity was a consistent factor predicting BMI across models. Regarding HbA1c, the Kaiser model also accounted for the largest proportion of variance (17%). Race/ethnicity and wealth was a consistent factor predicting HbA1c across models. CONCLUSION: Policy and practice interventions should consider these factors when screening for and addressing the effects of SDOH on diabetes risk. Specific SDOH models can be constructed for diabetes based on which determinants have the largest predictive value.