Literature Collection

BACKGROUND: Integrating digital mental health into collaborative care could address multiple mental health factors. To determine the longer-term effects of modernized collaborative care for depression on overlapping mental health factors, we analyzed data from the eIMPACT trial. METHODS: Primary care patients with depression and elevated cardiovascular disease risk (N = 216, Mage: 59 years, 78 % female, 50 % Black, 46 % with income <$10,000/year) were randomized to 12 months of the eIMPACT intervention (modernized collaborative care involving internet cognitive-behavioral therapy [iCBT], telephonic CBT, and/or select antidepressants) or usual primary care for depression. Depressive symptoms (Hopkins Symptom Checklist-20 [SCL-20] and Patient Health Questionnaire-9 [PHQ-9]), anxiety symptoms (Generalized Anxiety Disorder-7 [GAD-7]), hostility/anger/aggression (Buss-Perry Aggression Questionnaire [BPAQ]), and trait positive affect (Positive and Negative Affect Schedule-Positive Affect Subscale [PANAS-PA]) were measured at 0, 6, 12, and 24 months. RESULTS: Compared to the usual care group, the intervention group exhibited significant improvements across all outcomes. The intervention group advantage increased over the treatment period, peaked at post-treatment (12 months: SCL-20 d(adj) = -0.57, PHQ-9 d(adj) = -0.63, GAD-7 d(adj) = -0.50, BPAQ d(adj) = -0.17, PANAS-PA d(adj) = 0.41), and decreased over the follow-up period (24 months: SCL-20 d(adj) = -0.24, PHQ-9 d(adj) = -0.29, GAD-7 d(adj) = -0.20, BPAQ d(adj) = 0.06, PANAS-PA d(adj) = 0.17). CONCLUSIONS: Modernized collaborative care improved multiple mental health factors, highlighting the feasibility and longer-term benefits of blending collaborative care and digital mental health across racial, education, and income groups. Future implementation of such interventions could promote equitable access to high-quality care. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02458690.

OBJECTIVE: Polypharmacy is the use of multiple drugs. Many definitions have been established for polypharmacy, often cross-sectionally, despite it naturally changing over time. In this study, we aimed to identify clusters of older people with distinct polypharmacy trajectories over time and associated mortality risks. We then characterised the identified clusters and assessed their generalisability in two external databases. METHODS: Data were extracted from three primary care databases: the UK Clinical Practice Research Datalink (CPRD) GOLD, CPRD Aurum and the Dutch Integrated Primary Care Information (IPCI). People aged ≥65 on 1 January 2015 were included. Polypharmacy, defined as the cumulative number of prescribed ingredients, was calculated at baseline and at the end of each subsequent follow-up year (2015-19). We applied joint latent class modelling, which divides the population into clusters with different trajectories and associated mortality risks. The model was trained in GOLD and validated in Aurum and IPCI. RESULTS: Four clusters were identified and characterised based on polypharmacy baseline and rate of progression: low-steady, intermediate-slow/increasing, intermediate-fast/increasing and high-decreasing. The high-decreasing cluster had the highest average baseline polypharmacy (intercept = 23.4) and prevalence of non-cancer chronic comorbidities, whilst the intermediate-fast/increasing had the steepest polypharmacy rate of progression per year (slope = 6.4), highest baseline and cumulative incidence of cancer, and worst survival outcome. Good validation was found in Aurum and IPCI. CONCLUSION: High baseline levels and increasing levels of polypharmacy were associated with an increased mortality risk in older people. The clusters identified in this study were externally validated in two European databases, confirming their robustness and generalisability.