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Given the regional surface network of the Umbria region, a mountainous area located in central Italy, the observed hourly temperature time series from 2010 to 2017 were analysed by applying basic and extended quality control procedures following World Meteorological Organization (WMO) standards. The validation procedure consisted of automatic quality control, producing validated data with metadata subsequently recorded in the NetCDF format. After these controls, data were checked manually and an extended procedure was applied to reconstruct the temperature time series for missing data. The spatiotemporal method used to reconstruct the data was a linear interpolation for 1 hr gaps and the empirical orthogonal function (EOF) algorithm for gaps ≥ 2 hr. The introduction of a complete and homogeneous data set of hourly reanalysis ERA5 (from the European Center for Medium-Range Weather Forecasts-ECMWF) allowed for the reconstruction of the longest gaps with statistical and physical consistency. The final product of the study is a continuous station time series of hourly temperatures that will be available to the public by the end of 2020; a daily version of the original time series is already available on the regional website. [ABSTRACT FROM AUTHOR]

publisher‐imprint‐name Springer volume‐issue‐count 1 issue‐article‐count 0 issue‐toc‐levels 0 issue‐pricelist‐year 2016 issue‐copyright‐holder The Author(s) issue‐copyright‐year 2016 article‐contains‐esm No article‐numbering‐style Unnumbered article‐registration‐date‐year 2016 article‐registration‐date‐month 6 article‐registration‐date‐day 27 article‐toc‐levels 0 toc‐levels 0 volume‐type Regular journal‐product ArchiveJournal numbering‐style Unnumbered article‐grants‐type OpenChoice metadata‐grant OpenAccess abstract‐grant OpenAccess bodypdf‐grant OpenAccess bodyhtml‐grant OpenAccess bibliography‐grant OpenAccess esm‐grant OpenAccess online‐first false pdf‐file‐reference BodyRef/PDF/40169_2016_Article_105.pdf target‐type OnlinePDF issue‐type Regular article‐type OriginalPaper journal‐subject‐primary Medicine & Public Health journal‐subject‐secondary Medicine/Public Health, general journal‐subject‐collection SC11 open‐access true --> Background: Frailty has been defined in different ways and several diagnostic tools exist, but most of them are not applicable in routine primary care. Nonetheless, general practitioners (GPs) have a natural advantage in identifying frailty, due to their continued access to patients, patient‐centered approach and training. GPs have also an advantage in conducting population‐based evaluation as consequence of their role of gatekeepers of the health care system. This paper aims to identify those socio‐demographic and clinical profiles and the relative information sources that, from the GPs' perspective, act as frailty markers, not solely as a diagnosis of state but as the ability to identify a patient's trajectory, over time, through the aging process. Methods: This study was performed as a survey within a population aged 75 and over, attending 148 GPs in Italy. A total of 23,996 patients were classified by GPs in distinct frailty status, without the use of a specific evaluation tool, but only referring to general indications. Co‐morbidity was objectively assessed by a record‐linkage with previous hospitalizations, in order to assess the occurrence of previous illnesses that could be associated with the likelihood of being identified as frails or at risk. The methodological approach is based on social network analysis (SNA), suited to explore relational aspects of complex phenomena. Results: Our findings reveal that GPs are able to perform low cost population‐based evaluation, by exploiting the advantages of their approach to patients, combined with the information derived from their daily practice and from other sources currently available. Conclusion: We believe that informative integration among different sources of available data can provide a comprehensive picture of the health state of patients in a shorter time and at lower cost. The identification of limited patient trajectories based on these observations can enable the development of critical biomarkers/diagnostics and prognostic indicators that will enhance patient care and potentially reduce inappropriate healthcare use. We also believe that network analysis is an extremely flexible research tool and a rich theoretical paradigm, and it may be used in the healthcare planning. [ABSTRACT FROM AUTHOR]