How to classify the Oldest Old according to Health Status: A study of 1160 subjects belonging to 552 90+ Italian sibships characterised by familial longevity recruited within the GeHA EU project

The health status of the oldest old, the fastest increasing population segment worldwide, progressively becomes more heterogeneous, and this peculiarity represents a major obstacle to their classification. We compared the effectiveness of four previously proposed criteria (Franceschi et al., 2000; Evert et al., 2003; Gondo et al., 2006; Andersen-Ranberg et al., 2001) in 1160 phenotypically fully characterized Italian siblings of 90 years of age and older (90+, mean age: 93 years; age range: 90–106 years) belonging to 552 sib-ships, recruited in Northern, Central and Southern Italy within the EU-funded project GEHA, followed for a six-year-survival. Main findings were: (i) ‘‘healthy’’ subjects varied within a large range, i.e. 5.2% (Gondo), 8.7% (Evert), 17.7% (Franceschi), and 28.5% (Andersen-Ranberg); (ii) Central Italy subjects showed better health than those from Northern and Southern Italy; (iii) mortality risk was correlated with health status independently of geographical areas; and (iv) 90+ males, although fewer in number, were healthier than females, but with no survival advantage. In conclusion, we identified a modified version of Andersen-Ranberg criteria, based on the concomitant assessment of two basic domains (cognitive, SMMSE; physical, ADL), called ‘‘Simple Model of Functional Status’’ (SMFS), as the most effective proxy to distinguish healthy from not-healthy subjects. This model showed that health status was correlated within sib-ships, suggesting a familial/genetic component.

Characteristics of Offshore Wind Speeds using Buoy Measurements

In the coming decade installed offshore wind capacity is expected to expand rapidly. This will be both technically and economically challenging. Precise wind resource assessment is one of the more imminent challenges. It is more difficult to assess wind power offshore than onshore due to the paucity of representative wind speed data. Offshore site-specific data is less accessible and is far more costly to collect. However, offshore wind speed data collected from sources such as wave buoys, remote sensing from satellites, national weather ships, and coastal meteorological stations and met masts on barges and platforms may be extrapolated to assess offshore wind power. This study attempts to determine the usefulness of pre-existing offshore wind speed measurements in resource assessment, and presents the results of wind resource estimation in the Atlantic Ocean and in the Irish Sea using data from two offshore meteorological buoys