Community-based models of care for adolescent and adult depression, suicidal behavior, anxiety, trauma, and substance use in Africa: a scoping review.

Background: Community-based care (CBC), where care is delivered outside of the traditional health facility setting, has been proposed to narrow the mental health (MH) and substance use (SU) treatment gap in Africa. Objective: This scoping review aims to comprehensively summarize CBC models addressing adolescent and adult MH (depression, anxiety, trauma, suicidal behavior) and (non-tobacco) SU problems in Africa. Methods: We searched PsycINFO, Embase, Scopus, CINAHL, and Medline Ovid. Studies and protocols were included if they reported on CBC intervention's effects on MH or SU symptoms/ diagnoses, acceptability, feasibility, or patient engagement in care, regardless of whether the intervention itself was designed specifically for MH or SU. Results: Among 11,477 screened publications, 217 were eligible. Of the unique intervention studies (n = 206), CBC models were classified into the following approaches (non-mutually exclusive): psychotherapeutic (n = 144), social (n = 81), lifestyle/physical health (n = 55), economic (n = 26), and psychopharmacological (n = 2). While quantitative results suggest possible efficacy of CBC models, description of CBC location was often poor. Fewer interventions addressed suicidal behavior (n = 12), the needs of adolescents (n = 49), or used traditional healers or religious figures as providers (n = 3). Conclusion: Many CBC models have been tested on MH and SU in Africa and should be critically appraised and meta-analyzed in subsequent reviews, where possible.

Implementing focused echocardiography and AI-supported analysis in a population-based survey in Lesotho: implications for community-based cardiovascular disease care models.

In settings where access to expert echocardiography is limited, focused echocardiography, combined with artificial intelligence (AI)-supported analysis, may improve diagnosis and monitoring of left ventricular hypertrophy (LVH). Sixteen nurses/nurse-assistants without prior experience in echocardiography underwent a 2-day hands-on intensive training to learn how to assess parasternal long axis views (PLAX) using an inexpensive hand-held ultrasound device in Lesotho, Southern Africa. Loops were stored on a cloud-drive, analyzed using deep learning algorithms at the University Hospital Basel, and afterwards confirmed by a board-certified cardiologist. The nurses/nurse-assistants obtained 756 echocardiograms. Of the 754 uploaded image files, 628 (83.3%) were evaluable by deep learning algorithms. Of those, results of 514/628 (81.9%) were confirmed by a cardiologist. Of the 126 not evaluable by the AI algorithm, 46 (36.5%) were manually evaluable. Overall, 660 (87.5%) uploaded files were evaluable and confirmed. Following short-term training of nursing cadres, a high proportion of obtained PLAX was evaluable using AI-supported analysis. This could be a basis for AI- and telemedical support in hard-to-reach areas with minimal resources.