Socioeconomic predictors and consequences of depression among primary care attenders with non-communicable diseases in the Western Cape, South Africa:Cohort study within a randomised trial

Background: Socioeconomic predictors and consequences of depression and its treatment were investigated in 4393 adults with specified non-communicable diseases attending 38 public sector primary care clinics in the Eden and Overberg districts of the Western Cape, South Africa.   Methods: Participants were interviewed at baseline in 2011 and 14 months later, as part of a randomised controlled trial of a guideline-based intervention to improve diagnosis and management of chronic diseases. The 10-item Center for Epidemiologic Studies Depression Scale (CESD-10) was used to assess depression symptoms, with higher scores representing more depressed mood. Results: Higher CESD-10 scores at baseline were independently associated with being less educated (p=0.004) and having lower income (p=0.003). CESD-10 scores at follow-up were higher in participants with less education (p=0.010) or receiving welfare grants (p=0.007) independent of their baseline scores. Participants with CESD-10 scores of 10 or more at baseline (56% of all participants) had 25% higher odds of being unemployed at follow-up (p=0.016), independently of baseline CESD-10 score and treatment status. Among participants with baseline CESD-10 scores of 10 or more, antidepressant medication at baseline was independently more likely in participants who had more education (p=0.002), higher income (p<0.001), or were unemployed (p=0.001). Antidepressant medication at follow up was independently more likely in participants with higher income (p=0.023), and in clinics with better access to pharmacists (p=0.053) and off-site drug delivery (p=0.013).  Conclusions: Socioeconomic disadvantage appears to be both a cause and consequence of depression, and may also be a barrier to treatment. There are opportunities for improving the prevention, diagnosis and treatment of depression in primary care in inequitable middle income countries like South Africa.  Trial registration: The trial is registered with Current Controlled Trials (ISRCTN20283604) and the Office for Human Research Protections Database (IRB00001938, FWA00001637)

Deep learning object detection as an assistance system for complex image labeling tasks

Object detection via deep learning has many promising areas of application. However, robustness and accuracy of fully automated systems are often insufficient for practical use. Integrating results from Artificial Intelligence (AI) and human intelligence in collaborative settings might bridge the gap between efficiency and accuracy. This study proves increased efficiency when supporting human intelligence through AI without negative impact on effectiveness in a fine- grained car scratch image labeling task. Based on the confirmed benefits of AI with human intelligence in the loop approaches, this contribution discusses potential practical application scenarios and envisions the implementation of assistance systems supported by computer vision