Rural-Urban Differences in Maternal Responses to Childhood Fever in South East Nigeria

Childhood fevers due to malaria remain a major cause of morbidity and mortality among under-five children in Nigeria. The degree of vulnerability perceived by mothers will affect their perception of the severity and threat of their child's fever and the patterns of health care use. This study was undertaken to compare maternal responses to childhood fever in urban and rural areas of Enugu, south east Nigeria.Data was collected with pre-tested interviewer-administered questionnaires from 276 and 124 urban and rural households respectively. In each household, only one woman aged 15-49 years who had lived in each of the urban and rural communities for at least one year and had at least one child less than 5 years old was interviewed. Malaria was mentioned as the commonest cause of childhood fevers. Rural mothers were more likely to recognize danger signs and symptoms than urban mothers. Rural mothers use more of informal than formal health services, and there is more home management of the fever with urban than rural mothers. Chloroquine, ACT, SP and Paracetamol are the main drugs given at home for childhood fevers, but the rural mothers were more likely to use leftover drugs from previous treatment to treat the fevers than urban mothers. The urban respondents were also more likely to use a preventive measure. Urban mothers sought actions faster than rural mothers and the total cost of treatment was also higher in urban areas.Both urban and rural mothers are aware that malaria is the major cause of childhood fevers. Although rural mothers recognize childhood fever and danger signs better than urban mothers, the urban mothers' responses to fever seem to be better than that for rural mothers. These responses and differences may be important for geographical targeting by policy makers for malaria interventions

Development and application of computer vision systems for use in livestock production

This paper examines the feasibility of applying computer vision systems to improve health, welfare and efficiency in livestock production. Very little directly relevant literature was revealed when reviewing the subject, so it is examined from first principles. After briefly describing the value of computer vision as a sensor with powerful observational and interpretative ability, the different steps in vision system development are identified and explored. Where possible this examination is related to computer vision work on livestock as well as other biological objects which by their typically varied nature offer meaningful paradigms for the livestockrelated work. The analysis suggests that most operations in livestock production tend to be at the complex end of the spectrum of vision-related problems currently being tackled in agriculture. Hence, only applications which have a significant production or welfare effect will be viable. Another vital element necessary for success in this application is a simultaneous understanding by the system designers of a diverse set of mechanisms (the production process, the interaction between process and sensor, vision algorithm building, and software and hardware systems). This calls for a multi-disciplinary, interactive approach to develop optimal solutions.Van der Stuyft E., Schofield C.P., Randall J.M., Wambacq P., Goedseels V., "Development and application of computer vision systems for use in livestock production", Computers and electronics in agriculture, vol. 6, pp. 243-265, 1991, Elsevier Science Publishers.status: publishe

Data and knowledge management in translational research: implementation of the eTRIKS platform for the IMI OncoTrack consortium

For large international research consortia, such as those funded by the European Union’s Horizon 2020 programme or the Innovative Medicines Initiative, good data coordination practices and tools are essential for the successful collection, organization and analysis of the resulting data. Research consortia are attempting ever more ambitious science to better understand disease, by leveraging technologies such as whole genome sequencing, proteomics, patient-derived biological models and computer-based systems biology simulations