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Background: The aim was to describe the population of patients with moderate rheumatoid arthritis (RA) in the United Kingdom and the burden of disease from the perspectives of the patient, caregiver, and health service., Methods: In this descriptive study, retrospective patient-level data were extracted from hospital medical records to assess healthcare resource utilisation and validated outcome measures were administered via questionnaire to patients with moderate RA (Disease Activity Score [DAS28] between 3.2 and 5.1) from eight secondary care centres, and their caregivers. Patient-reported outcome instruments were scored according to licensed manuals., Results: Outcome measures were completed by 102 patients and 38 caregivers. The mean EuroQoL-5 dimension-5 level crosswalk index value for patients was 0.62 (SD 0.24) compared to an England population norm of 0.82. Mean pain VAS score was 37.7 (SD 24.0) and mean Health Assessment Questionnaire Disability Index was 1.1 (SD 0.8). In employed patients who completed the Work Productivity and Activity Impairment questionnaire (n = 26), a mean 29% (SD 26%) reduction in work productivity was recorded. Patients experienced significant fatigue as a result of their RA (median Functional Assessment of Chronic Illness Therapy fatigue score 17.2 of a possible 52, interquartile range [IQR] 11.0-28.8). Over 50% of caregivers reported providing > 7 h of support care per week to the patient with RA, and 16 and 11% took paid/unpaid leave or reduced working hours, respectively. Mean Caregiver Reaction Assessment subscale scores were 1.9 (SD 0.9) for finance, 1.7 (SD 0.8) for health, 2.3 (SD 1.0) for schedule disruption, and 1.9 (SD 0.8) for family support. Patients had a mean 5.5 (SD 4.1) outpatient attendances and a median 9.0 (IQR 2.0-20.0) diagnostic and monitoring tests in the 12 months prior to enrolment., Conclusions: This study shows that moderate RA has a considerable impact on healthcare resources and on patients' and caregivers' lives. There is scope to improve the management of patients with moderate RA.

Conservation increasingly operates at the landscape scale. For this to be effective, we need landscape scale information on species distributions and the environmental factors that underpin them. Species records are becoming increasingly available via data centres and online portals, but they are often patchy and biased. We demonstrate how such data can yield useful habitat suitability models, using bat roost records as an example. We analysed the effects of environmental variables at eight spatial scales (500 m - 6 km) on roost selection by eight bat species (Pipistrellus pipistrellus, P. pygmaeus, Nyctalus noctula, Myotis mystacinus, M. brandtii, M. nattereri, M. daubentonii, and Plecotus auritus) using the presence-only modelling software MaxEnt. Modelling was carried out on a selection of 418 data centre roost records from the Lake District National Park, UK. Target group pseudoabsences were selected to reduce the impact of sampling bias. Multi-scale models, combining variables measured at their best performing spatial scales, were used to predict roosting habitat suitability, yielding models with useful predictive abilities. Small areas of deciduous woodland consistently increased roosting habitat suitability, but other habitat associations varied between species and scales. Pipistrellus were positively related to built environments at small scales, and depended on large-scale woodland availability. The other, more specialist, species were highly sensitive to human-altered landscapes, avoiding even small rural towns. The strength of many relationships at large scales suggests that bats are sensitive to habitat modifications far from the roost itself. The fine resolution, large extent maps will aid targeted decision-making by conservationists and planners. We have made available an ArcGIS toolbox that automates the production of multi-scale variables, to facilitate the application of our methods to other taxa and locations. Habitat suitability modelling has the potential to become a standard tool for supporting landscape-scale decision-making as relevant data and open source, user-friendly, and peer-reviewed software become widely available.