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Juvenile Fibromyalgia (JFM) is a complex chronic pain condition that remains poorly understood. The study aimed to expand the clinical characterization of JFM in a large representative sample of adolescents with JFM and identify psychological factors that predict pain interference.Participants were 203 adolescents (12-17 years) who completed baseline assessments for the multi-site Fibromyalgia Integrative Training for Teens (FIT Teens) randomized control trial. Participants completed the Pain and Symptom Assessment Tool (PSAT), which includes a Widespread Pain Index (WPI, 0-18 pain locations) and Symptom Severity checklist of associated somatic symptoms (SS; 0-12) based on the 2010 ACR criteria for Fibromyalgia. Participants also completed self-report measures of pain intensity, functional impairment, and psychological functioning.Participants endorsed a median of 11 painful body sites (WPI) and had a median SS score of 9. Fatigue and nonrestorative sleep were prominent features and rated as moderate to severe by 85% of participants. Additionally, neurologic, autonomic, gastroenterological, and psychological symptoms were frequently endorsed. WPI was significantly correlated with pain intensity and catastrophizing, while SS scores were associated with pain intensity and all domains of physical and psychological functioning. Depressive symptoms, fatigue, and pain catastrophizing predicted severity of pain impairment.JFM is characterized by chronic widespread pain with fatigue, nonrestorative sleep, and other somatic symptoms. However, how diffusely pain is distributed appears less important to clinical outcomes and impairment than other somatic and psychological factors highlighting the need for a broader approach to the assessment and treatment of JFM.

An extensive data collection exercise was undertaken for an area mainly covering Burkina Faso, Cote d'Ivoire and Mali. This exercise led to the development of three gridded monthly rainfall surfaces and three monthly potential evapotranspiration (PET) surfaces. Two semi-distributed hydrological models were used for the analysis. Both models utilize soil data which can be defined as being close to a Soil Water Holding Capacity (SWHC). For this, two gridded data sets were used. When applied to different catchments covering geographically close hydrographical areas, but sometimes with distinct characteristics, the models produced results that varied according to the input data sets, catchment characteristics and model structure. These preliminary results highlight the important influence of the nature and origin of input data for hydrological modelling in Africa.