An integrated approach for evaluating the effectiveness of landslide risk reduction in unplanned communities in the Caribbean

Despite the recognition of the need for mitigation approaches to landslide risk in developing countries, the delivery of ‘on-the-ground’ measures is rarely undertaken. With respect to other ‘natural’ hazards it is widely reported that mitigation can pay. However, the lack of such an evidence-base in relation to landslides in developing countries hinders advocacy amongst decision makers for expenditure on ex-ante measures. This research addresses these limitations directly by developing and applying an integrated risk assessment and cost-benefit analysis of physical landslide mitigation measures implemented in an unplanned community in the Eastern Caribbean. In order to quantify the level of landslide risk reduction achieved, landslide hazard and vulnerability were modelled (before and after the intervention) and project costs, direct and indirect benefits were monetised. It is shown that the probability of landslide occurrence has been substantially reduced by implementing surface-water drainage measures, and that the benefits of the project outweigh the costs by a ratio of 2.7 to 1. This paper adds to the evidence base that ‘mitigation pays’ with respect to landslide risk in the most vulnerable communities – thus strengthening the argument for ex-ante measures. This integrated project evaluation methodology should be suitable for adoption as part of the community-based landslide mitigation project cycle, and it is hoped that this resource, and the results of this study, will stimulate further such programmes.Landslide modelling, Risk assessment, Cost Benefit Analysis, Developing countries, Community

Dealing with deep uncertainties in landslide modelling for disaster risk reduction under climate change

Landslides have large negative economic and societal impacts, including loss of life and damage to infrastructure. Slope stability assessment is a vital tool for landslide risk management, but high levels of uncertainty often challenge its usefulness. Uncertainties are associated with the numerical model used to assess slope stability and its parameters, with the data characterizing the geometric, geotechnic and hydrologic properties of the slope, and with hazard triggers (e.g. rainfall). Uncertainties associated with many of these factors are also likely to be exacerbated further by future climatic and socio-economic changes, such as increased urbanization and resultant land use change. In this study, we illustrate how numerical models can be used to explore the uncertain factors that influence potential future landslide hazard using a bottom-up strategy. Specifically, we link the Combined Hydrology And Stability Model (CHASM) with sensitivity analysis and Classification And Regression Trees (CART) to identify critical thresholds in slope properties and climatic (rainfall) drivers that lead to slope failure. We apply our approach to a slope in the Caribbean, an area that is naturally susceptible to landslides due to a combination of high rainfall rates, steep slopes, and highly weathered residual soils. For this particular slope, we find that uncertainties regarding some slope properties (namely thickness and effective cohesion of topsoil) are as important as the uncertainties related to future rainfall conditions. Furthermore, we show that 89 % of the expected behaviour of the studied slope can be characterized based on only two variables – the ratio of topsoil thickness to cohesion and the ratio of rainfall intensity to duration

Classifying higher education institutions by their general education requirements

General Education (GE) curricula specify requirements, most often fulfilled through coursework, that undergraduate students need to satisfy in addition to and often preceding a specialized major or program. Due to the decentralized nature of the American higher education system and lack of national requirements or guidelines, however, GE requirements vary from one institution to another. This exploratory study investigates patterns of GE requirements among a selection of 154 institutions and determines whether and how institutions could be grouped or classified by their GE requirements. Our five-dimension typology is parsimonious and meaningfully distinguishes between GE patterns giving us insightful information about the values and goals of institutions that are not communicated through our traditional categorizations

Age, Tumor Characteristics, and Treatment Regimen as Event Predictors in Ewing: A Children's Oncology Group Report

. Purpose. To associate baseline patient characteristics and relapse across consecutive COG studies. Methods. We analyzed risk factors for LESFT patients in three randomized COG trials. We evaluated age at enrollment, primary site, gender, tumor size, and treatment (as randomized). We estimated event-free survival (EFS, Kaplan-Meier) and compared risk across groups (log-rank test). Characteristics were assessed by proportional hazards regression with the characteristic of interest as the only component. Confidence intervals (CI) for RR were derived. Factors related to outcome at level 0.05 were included in a multivariate regression model. Results. Between 12/1988 and 8/2005, 1444 patients were enrolled and data current to 2001, 2004, or 2008 were used. Patients were with a median age of 12 years (0-45), 55% male and 88% Caucasian. The 5-year EFS was 68.3% ± 1.3%. In univariate analysis age, treatment, and tumor location were identified for inclusion in the multivariate model, and all remained significant (p < 0.01). Since tumor size was not collected in the last study, the other two were reanalyzed. This model identified age, treatment, tumor location, and tumor size as significant predictors. Conclusion. Age > 18 years, pelvic tumor, size > 8 cms, and chemotherapy without ifosfamide/etoposide significantly predict worse outcome. AEWS0031 is NCT00006734, INT0091 and INT0054 designed before 1993 (unregistered)