Beyond project governance. Enhancing funding and enabling financing for infrastructure in transport. Findings from the importance analysis approach

Based on the examination of the transactions made in 58 case study projects, we have developed probabilistic causation models that include relationships hypothesised from exhaustive literature reviews. These models contain relationships that relate a number of significant project variables to transport infrastructure project performance. Here, we report on the use of the Importance Analysis approach to identify the most significant factors linked to variables measuring project performance. Such an approach is used in combination of Bayesian Networks and Sensitivity Analysis. Some variables that resulted important to achieve cost, time, and revenue expectations in transport infrastructure projects are identified. These include factors other than those related to project governance but linked to the funding and financing schemes in a project and its context of implementation. Additionally, we analysed how projects in the BENEFIT database responded to the effects of the European economic crisis in 2008. The results indicated that some actions were implemented at some instances during the crisis time. Specific factors that appeared to be sufficiently robust to face the economic crisis were found

Ambulatory arterial stiffness index predict stroke in a general population

Objective The ambulatory arterial stiffness index (AASI) can be computed from individual 24-h blood pressure recordings. Methods We investigated the prognostic value of AASI and 24-h pulse pressure in a random sample of 1829 Danes, aged 40-70 years. We adjusted for sex, age, body mass index, mean arterial pressure, smoking, diabetes, ratio of total to high-density lipoprotein cholesterol, and history of cardiovascular disease with Cox regression. Results Over a median follow-up of 9.4 years, incidences of fatal and nonfatal endpoints were 40 for stroke, 150 for coronary heart disease, and 212 for cardiovascular events. In fully adjusted models, the hazard ratios associated with 1 SD increase (0.14 U) in the AASI were 1.62 (95% confidence interval, 1.14-2.28; P U 0.007) for stroke, 0.96 (0.80-1.14; P U 0.62) for coronary heart disease, and 1.06 (0.91-1.23; P U 0.49) for cardiovascular events. None of these ratios reached significance for pulse pressure (P > 0.47). The AASI still predicted stroke after excluding individuals with previous cardiovascular disease or after adjustment for systolic and/or diastolic blood pressure instead of mean arterial pressure. Conclusions In a randomly recruited European population, the AASI was a strong predictor of stroke, beyond traditional cardiovascular risk factors, including the mean arterial pressure and pulse pressure