Literature Collection

BACKGROUND: Depression and hearing loss (HL) commonly occur in the aging population and may arise from shared mechanisms. AIMS: To investigate the observational associations between depression and HL. STUDY DESIGN: Observational study. METHODS: Adults aged ≥ 55 years from three nationally representative study cohorts were included: the National Health and Nutrition Examination Survey, the Health and Retirement Study, and the English Longitudinal Study of Ageing. Multiple linear regression was applied to examine the association between depressive severity and audiometric thresholds. Cox regression models were applied to evaluate the associations between depressive symptoms and HL. RESULTS: Cross-sectional analyses revealed that depression was significantly associated with higher pure-tone average thresholds. In pooled longitudinal analyses of 6,956 participants, individuals with baseline depression exhibited a higher incidence of HL when compared to their non-depressed counterparts. Longitudinal trajectory analyses identified three significant patterns: increasing [hazard ratio (HR) 1.48, 95% confidence interval (CI) 1.09-2.21] and fluctuating (HR 1.25, 95% CI 1.12-1.39) depressive symptom trajectories as independent predictors of HL, whereas decreasing trajectories indicated no significant association. CONCLUSION: Depression and specific longitudinal trajectories are associated with elevated risk of HL. To further understand this association, integrated care models that synergistically address depression and HL in older adults are warranted.

BACKGROUND: Syringe exchange programs are uniquely positioned to offer treatment services to interested clients. Prevention Point Philadelphia recently expanded to offer buprenorphine maintenance treatment through its Stabilization, Treatment, and Engagement Program (STEP). OBJECTIVE: To describe the STEP model of care and report treatment outcomes. METHODS: Retrospective chart review of patients enrolled in STEP (October 2011 to August 2014). The main outcome measure was time retained in treatment, defined as time from treatment initiation to treatment failure. Secondary outcome measures were buprenorphine and opiate use, from urine toxicology screens. Retention in treatment was analyzed using Kaplan-Meier survival estimates; patients who remained in treatment at the end of the study period were censored on that day. The percentage of patients who were positive for buprenorphine and opiates in each month of treatment was calculated. RESULTS: Of the 124 patients enrolled in STEP, the median age was 41 (range: 21-63) and 80% reported injection heroin use. Comorbidities were common: 33% had human immunodeficiency virus (HIV) infection, most reported anxiety (78%) or depression (71%), and 20% were homeless. The most common program outcomes were unplanned self-discharge (n = 29; 23%), incarceration (n = 20; 16%), and administrative discharge (n = 19; 15%). The percentages of patients retained in treatment at 3, 6, 9, and 12 months were 77%, 65%, 59%, and 56%, respectively. Among those retained, the percentages with a positive buprenorphine screen at 3, 6, 9, and 12 months were 88%, 100%, 96%, and 95%, respectively. The percentages with a positive opiates screen were 19%, 13%, 17%, and 16%, respectively. CONCLUSIONS: With a program that blended organizational and community resources, retention in buprenorphine maintenance treatment was comparable to retention rates reported from other settings. Further research should directly compare treatment outcomes in syringe exchange program-based settings versus primary care and specialty settings.

BACKGROUND: Integrating advanced machine-learning (ML) algorithms into clinical practice is challenging and requires interdisciplinary collaboration to develop transparent, interpretable, and ethically sound clinical decision support (CDS) tools. We aimed to design a ML-driven CDS tool to predict opioid overdose risk and gather feedback for its integration into the University of Florida Health (UFHealth) electronic health record (EHR) system. METHODS: We used user-centered design methods to integrate the ML algorithm into the EHR system. The backend and UI design sub-teams collaborated closely, both informed by user feedback sessions. We conducted seven user feedback sessions with five UF Health primary care physicians (PCPs) to explore aspects of CDS tools, including workflow, risk display, and risk mitigation strategies. After customizing the tool based on PCPs' feedback, we held two rounds of one-on-one usability testing sessions with 8 additional PCPs to gather feedback on prototype alerts. These sessions informed iterative UI design and backend processes, including alert frequency and reappearance circumstances. RESULTS: The backend process development identified needs and requirements from our team, information technology, UFHealth, and PCPs. Thirteen PCPs (male = 62%, White = 85%) participated across 7 user feedback sessions and 8 usability testing sessions. During the user feedback sessions, PCPs (n = 5) identified flaws such as the term "high risk" of overdose potentially leading to unintended consequences (e.g., immediate addiction services referrals), offered suggestions, and expressed trust in the tool. In the first usability testing session, PCPs (n = 4) emphasized the need for natural risk presentation (e.g., 1 in 200) and suggested displaying the alert multiple times yearly for at-risk patients. Another 4 PCPs in the second usability testing session valued the UFHealth-specific alert for managing new or unfamiliar patients, expressed concerns about PCPs' workload when prescribing to high-risk patients, and recommended incorporating the details page into training sessions to enhance usability. CONCLUSIONS: The final backend process for our CDS alert aligns with PCP needs and UFHealth standards. Integrating feedback from PCPs in the early development phase of our ML-driven CDS tool helped identify barriers and facilitators in the CDS integration process. This collaborative approach yielded a refined prototype aimed at minimizing unintended consequences and enhancing usability.