The Lausanne cohort Lc65+: a population-based prospective study of the manifestations, determinants and outcomes of frailty

BACKGROUND: Frailty is a relatively new geriatric concept referring to an increased vulnerability to stressors. Various definitions have been proposed, as well as a range of multidimensional instruments for its measurement. More recently, a frailty phenotype that predicts a range of adverse outcomes has been described. Understanding frailty is a particular challenge both from a clinical and a public health perspective because it may be a reversible precursor of functional dependence. The Lausanne cohort Lc65+ is a longitudinal study specifically designed to investigate the manifestations of frailty from its first signs in the youngest old, identify medical and psychosocial determinants, and describe its evolution and related outcomes. METHODS/DESIGN: The Lc65+ cohort was launched in 2004 with the random selection of 3054 eligible individuals aged 65 to 70 (birth year 1934-1938) in the non-institutionalized population of Lausanne (Switzerland). The baseline data collection was completed among 1422 participants in 2004-2005 through questionnaires, examination and performance tests. It comprised a wide range of medical and psychosocial dimensions, including a life course history of adverse events. Outcomes measures comprise subjective health, limitations in activities of daily living, mobility impairments, development of medical conditions or chronic health problems, falls, institutionalization, health services utilization, and death. Two additional random samples of 65-70 years old subjects will be surveyed in 2009 (birth year 1939-1943) and in 2014 (birth year 1944-1948). DISCUSSION: The Lc65+ study focuses on the sequence "Determinants --> Components --> Consequences" of frailty. It currently provides information on health in the youngest old and will allow comparisons to be made between the profiles of aging individuals born before, during and at the end of the Second World War

Structural damage detection using quantile regression

Structural health monitoring is an important emerging engineering discipline in the UK and the world. Structural failure without warning is recognised as a significant hazard in the service life of a structure. Thus there is a need to provide a clear guidance to determine the cutoff line for operation, repair and maintenance. A quantile regression approach has been proposed for structural damage detection using vibration data (accelerations). This method is based on a sequence of quantile autoregressive time series models and the differences between two distributions associated with the residual series of the undamaged and damaged structures are studied at different quantile levels. This new approach is based on the information on damages at any quantile levels, not just at a mean level that is commonly used in the literature. In addition, it does not depend on the distribution of the error term. This is a very useful feature as in practice it can be very difficult to assume a proper distribution for the error term of the model. The performance of the developed method is investigated via extensive simulation studies to detect single-damage and multi-damage scenarios with input and output measurement noise. The proposed method is further substantiated experimentally using an eight-storey steel plane frame model subjected to shaker excitation. Both numerical and experimental results have shown that the proposed method gives reasonably accurate damage identification, including both damage existence and location