Undergroundwater in many regions of the world is contaminated with high concentrations of arsenic and the resulting toxicity has created a major environmental and public health problem in the affected regions. Chronic arsenic exposure can cause many diseases, including various physical and psychological harms. Although the physical problems caused by arsenic toxicity are well reported in literature, unfortunately the consequences of arsenic exposure on mental health are not adequately studied. Therefore we conducted a review of the available literature focusing on the social consequences and detrimental effects of arsenic toxicity on mental health. Chronic arsenic exposures have serious implications for its victims (i.e. arsenicosis patients) and their families including social instability, social discrimination, refusal of victims by community and families, and marriage-related problems. Some studies conducted in arsenic affected areas revealed that arsenic exposures are associated with various neurologic problems. Chronic arsenic exposure can lead to mental retardation and developmental disabilities such as physical, cognitive, psychological, sensory and speech impairments. As health is defined by the World Health Organization as “a state of complete physical, mental and social wellbeing”, the social dimensions have a large impact on individual’s mental health. Furthermore studies in China und Bangladesh have shown that mental health problems (e.g. depression) are more common among the people affected by arsenic contamination. Our study indicates various neurological, mental and social consequences among arsenic affected victims. Further studies are recommended in arsenic-affected areas to understand the underlying mechanisms of poor mental health caused by arsenic exposure.

Whereas mobile phone-based surveillance has the potential to provide real-time validated data for disease clustering and prompt respond and investigation, little evidence is available on current practice in sub-Sahara Africa. The objective of this review was to examine mobile phone-based mHealth interventions for Public Health surveillance in the region. We conducted electronic search in MEDLINE, EMBASE, IEE Xplore, African Index Medicus (AIM), BioMed Central, PubMed Central (PMC), the Public Library of Science (PLoS) and IRIS for publications used in the review. In all, a total of nine studies were included which focused on infectious disease surveillance of malaria (n = 3), tuberculosis (n = 1) and influenza-like illnesses (n = 1) as well as on non-infectious disease surveillance of child malnutrition (n = 2), maternal health (n = 1) and routine surveillance of various diseases and symptoms (n = 1). Our review revealed that mobile phone-based surveillance projects in the sub-Saharan African countries are on small scale, fragmented and not well documented. We conclude by advocating for a strong drive for more research in the applied field as well as a better reporting of lessons learned in order to create an epistemic community to help build a more evidence-based field of practice in mHealth surveillance in the region.

The aim of this study was the development and evaluation of an algorithm-based diagnosis-tool, applicable on mobile phones, to support guardians in providing appropriate care to sick children. The algorithm was developed on the basis of the Integrated Management of Childhood Illness (IMCI) guidelines and evaluated at a hospital in Ghana. Two hundred and thirty-seven guardians applied the tool to assess their child’s symptoms. Data recorded by the tool and health records completed by a physician were compared in terms of symptom detection, disease assessment and treatment recommendation. To compare both assessments, Kappa statistics and predictive values were calculated. The tool detected the symptoms of cough, fever, diarrhoea and vomiting with good agreement to the physicians’ findings (kappa = 0.64; 0.59; 0.57 and 0.42 respectively). The disease assessment barely coincided with the physicians’ findings. The tool’s treatment recommendation correlated with the physicians’ assessments in 93 out of 237 cases (39.2% agreement, kappa = 0.11), but underestimated a child’s condition in only seven cases (3.0%). The algorithm-based tool achieved reliable symptom detection and treatment recommendations were administered conformably to the physicians’ assessment. Testing in domestic environment is envisaged.