Citclops: A next-generation sensor system for the monitoring of natural waters and a citizens' observatory for the assessment of ecosystems' status.

The European-Commission-funded project 'Citclops' (Citizens' observatory for coast and ocean optical monitoring) developed methods, tools and sensors, which can be used by citizens to monitor natural waters, with a strong focus on long-term data series related to environmental sciences. The new sensors, based on optical technologies, respond to a number of scientific, technical and societal objectives, ranging from more precise monitoring of key environmental descriptors of the aquatic environment (water colour, transparency and fluorescence) to an improved management of data collected with citizen participation. The sensors were tested, calibrated, integrated on several platforms, scientifically validated and demonstrated in the field. The new methods and tools were tested in a citizen-science context. The general conclusion is that citizens are valuable contributors in quality and quantity to the objective of collecting, integrating and analysing fragmented and diverse environmental data. An integration of these data into data-analysis tools has a large potential to support authoritative monitoring and decision-making. In this paper, the project's objectives, results, technical achievements and lessons learned are presented.

Proposals for enhanced health risk assessment and stratification in an integrated care scenario.

OBJECTIVES: Population-based health risk assessment and stratification are considered highly relevant for large-scale implementation of integrated care by facilitating services design and case identification. The principal objective of the study was to analyse five health-risk assessment strategies and health indicators used in the five regions participating in the Advancing Care Coordination and Telehealth Deployment (ACT) programme (http://www.act-programme.eu). The second purpose was to elaborate on strategies toward enhanced health risk predictive modelling in the clinical scenario. SETTINGS: The five ACT regions: Scotland (UK), Basque Country (ES), Catalonia (ES), Lombardy (I) and Groningen (NL). PARTICIPANTS: Responsible teams for regional data management in the five ACT regions. PRIMARY AND SECONDARY OUTCOME MEASURES: We characterised and compared risk assessment strategies among ACT regions by analysing operational health risk predictive modelling tools for population-based stratification, as well as available health indicators at regional level. The analysis of the risk assessment tool deployed in Catalonia in 2015 (GMAs, Adjusted Morbidity Groups) was used as a basis to propose how population-based analytics could contribute to clinical risk prediction. RESULTS: There was consensus on the need for a population health approach to generate health risk predictive modelling. However, this strategy was fully in place only in two ACT regions: Basque Country and Catalonia. We found marked differences among regions in health risk predictive modelling tools and health indicators, and identified key factors constraining their comparability. The research proposes means to overcome current limitations and the use of population-based health risk prediction for enhanced clinical risk assessment. CONCLUSIONS: The results indicate the need for further efforts to improve both comparability and flexibility of current population-based health risk predictive modelling approaches. Applicability and impact of the proposals for enhanced clinical risk assessment require prospective evaluation.

Algorithm for automatic forced spirometry quality assessment: technological developments.

We hypothesized that the implementation of automatic real-time assessment of quality of forced spirometry (FS) may significantly enhance the potential for extensive deployment of a FS program in the community. Recent studies have demonstrated that the application of quality criteria defined by the ATS/ERS (American Thoracic Society/European Respiratory Society) in commercially available equipment with automatic quality assessment can be markedly improved. To this end, an algorithm for assessing quality of FS automatically was reported. The current research describes the mathematical developments of the algorithm. An innovative analysis of the shape of the spirometric curve, adding 23 new metrics to the traditional 4 recommended by ATS/ERS, was done. The algorithm was created through a two-step iterative process including: (1) an initial version using the standard FS curves recommended by the ATS; and, (2) a refined version using curves from patients. In each of these steps the results were assessed against one expert's opinion. Finally, an independent set of FS curves from 291 patients was used for validation purposes. The novel mathematical approach to characterize the FS curves led to appropriate FS classification with high specificity (95%) and sensitivity (96%). The results constitute the basis for a successful transfer of FS testing to non-specialized professionals in the community.