Dataset of 100-year flood susceptibility maps for the continental U.S. derived with a geomorphic method
Efficient strategies for preparing communities to protect against, respond to, recover from, and mitigate flood hazard are often hampered by the lack of information about the position and extent of flood-prone areas. Hydrologic and hydraulic analyses allow to obtain detailed flood hazard maps, but are a computationally intensive exercise requiring a significant amount of input data, which are rarely available both in developing and developed countries. As a consequence, even in data-rich environments, official flood hazard graduations are often affected by extensive gaps. In the U.S., for instance, the detailed floodplain delineation produced by the Federal Emergency Management Agency (FEMA) is incomplete, with many counties having no floodplain mapping at all. In this article we present a mapping dataset containing 100-year flood susceptibility maps for the continental U.S. with a 90 m resolution. They have been obtained performing a linear binary classification based on the Geomorphic Flood Index (GFI). To the best knowledge of the authors, there are no available flood-prone areas maps for the entire continental U.S. with resolution lower that 30׳׳×30׳׳ (approximatively 1 km at the equator).
A digital elevation model based method for a rapid estimation of flood inundation depth
In recent years, the acquisition of data from multiple sources, together with improvements in computational capabilities, has allowed to improve our understanding on natural hazard through new approaches based on machine learning and Big Data analytics. This has given new potential to flood risk mapping, allowing the automatic extraction of flood prone areas using digital elevation model (DEM) based geomorphic approaches. Most of the proposed geomorphic approaches are conceived mainly for the identification of flood extent. In this article, the DEM‐based method based on a geomorphic descriptor—the geomorphic flood index (GFI)—has been further exploited to predict inundation depth, which is useful for quantifying flood induced damages. The new procedure is applied on a case study located in southern Italy, obtaining satisfactory performances. In particular, the inundation depths are very similar to the ones obtained by hydraulic simulations, with a root‐mean‐square error (RMSE) = 0.335 m, in the domain where 2D dynamics prevail. The reduced computational effort and the general availability of the required data make the method suitable for applications over large and data‐sparse areas, opening new horizons for flood risk assessment at national/continental/global scale.
Study on essential drug use status and its influencing factors among cerebral infarction inpatients in county level hospitals of Anhui Province, China
Background and purpose Drug costs is one of the main components of hospitalization expenditure for cerebral infarction inpatients. In China, the National Essential Medicine System (NEMS) was created to relieve the heavy drug-cost burden for patients. The objective of this study was to investigate essential drug-use status and its influencing factors among cerebral infarction inpatients in county-level hospitals of Anhui province, China. Methods Three county-level hospitals were selected through a multi-stage cluster random sampling method. The hospitalization cost data of cerebral infarction inpatients in the three hospitals were extracted from the Anhui provincial information platform of the New Rural Cooperative Insurance System (NCMS), and whether the proportion of essential drug cost in the total drug cost reached the median value of 33.05% which was set as the evaluation index for essential drug-use status. Questionnaires for hospitals and physicians were designed and given to them to assess influencing factors. Results We retrieved the cost data of 2,189 inpatients from the NCMS platform and investigated 51 corresponding physicians in total. The drug costs accounted for 52.6% of the total hospitalization cost, and essential drug costs alone accounted for 37.0% of the total drug costs. The essential drug-cost proportion was high among physicians with a higher recognition degree on NEMS, older age, lower final academic degree, longer work experience and lower professional title. Married physicians and those with tight organizational affiliation also prescribed more essential drugs. Conclusions Increasing the proportion of essential drugs was an effective way to reduce the disease burden for cerebral infarction patients. Perfecting the NEMS, increasing government investment, reinforcing education and propaganda, and formulating relevant incentive and restrictive mechanisms were all effective ways to promote and increase the number of essential drug prescriptions written by physicians.