Estimating Effectiveness of the Control of Violence and Socioeconomic Development in Colombia: An Application of Dynamic Data Envelopment Analysis and Data Panel Approach

This paper develops an index to evaluate the level of effectiveness of the control of violence based on the data envelopment analysis approach. The index is used to examine the grade of effectiveness of the control of violence at the level of Colombian departments between 1993 and 2007. Comparing the results across Colombian departments, we find that the majority of departments show improvement in their scores of effectiveness. A second stage of the regression model reveals that departments with a higher gross domestic product and higher education and employment are more effective in the control of violence, whereas departments with higher political violence, unemployment rates, unsatisfied basic needs, a displaced population, and hectares cultivated with coca show lower effectiveness in the control of violence. All these findings are of particular interest in the formulation and development of policies against violence, taking into account that organised forms of violence, such as drug trafficking, impede the adequate effectiveness of its control. Moreover, violence decreases social investments, generating alterations in social services that produce long-run deterioration in faith in the government’s ability to govern, which should become an incentive to further violence

Influence of ultra-low dose Aprotinin on thoracic surgical operations: a prospective randomized trial

<p>Abstract</p> <p>Background</p> <p>The blood saving effect of aprotinin has been well documented in cardiac surgery. In thoracic surgery, very few recent studies, using rather high doses of aprotinin, have shown a similar result. In a randomized prospective trial, we have tested the influence of aprotinin using an ultra-low dose drug regime.</p> <p>Methods</p> <p>Fifty-nine patients, mean age 58 ± 13.25 years (mean ± SD) undergoing general thoracic procedures were randomized into placebo (Group A) and treatment group (Group B). The group B (n = 29) received 500.000 IU of aprotinin after induction to anesthesia and a repeat dose immediately after chest closure. A detailed protocol with several laboratory parameters was recorded. Patients were transfused when perioperative Ht was less than 26%.</p> <p>Results</p> <p>The two groups were similar in terms of age, gender, diagnosis, pathology, co-morbidity and operations performed. The mean drainage of the first and second postoperative day in group B was significantly reduced (412.6 ± 199.2 vs. 764.3 ± 213.9 ml, p < 0.000, and 248.3 ± 178.5 vs. 455.0 ± 274.6, p < 0.001). Similarly, the need for fresh frozen plasma transfusion was lower in group B, p < 0.035. Both the operation time and the hospital stay were also less for group B but without reaching statistical significance (84.6 ± 35.2 vs 101.2 ± 52.45 min. and 5.8 ± 1.6 vs 7.2 ± 3.6 days respectively, p < 0.064). The overall transfusion rate did not differ significantly. No side effects of aprotinin were noted.</p> <p>Conclusion</p> <p>The perioperative ultra-low dose aprotinin administration was associated with a reduction of total blood losses and blood product requirements. We therefore consider the use of aprotinin safe and effective in major thoracic surgery.</p

Antiphospholipid syndrome; its implication in cardiovascular diseases: a review

Antiphospholipid syndrome (APLS) is a rare syndrome mainly characterized by several hyper-coagulable complications and therefore, implicated in the operated cardiac surgery patient. APLS comprises clinical features such as arterial or venous thromboses, valve disease, coronary artery disease, intracardiac thrombus formation, pulmonary hypertension and dilated cardiomyopathy. The most commonly affected valve is the mitral, followed by the aortic and tricuspid valve. For APLS diagnosis essential is the detection of so-called antiphospholipid antibodies (aPL) as anticardiolipin antibodies (aCL) or lupus anticoagulant (LA). Minor alterations in the anticoagulation, infection, and surgical stress may trigger widespread thrombosis. The incidence of thrombosis is highest during the following perioperative periods: preoperatively during the withdrawal of warfarin, postoperatively during the period of hypercoagulability despite warfarin or heparin therapy, or postoperatively before adequate anticoagulation achievement. Cardiac valvular pathology includes irregular thickening of the valve leaflets due to deposition of immune complexes that may lead to vegetations and valve dysfunction; a significant risk factor for stroke. Patients with APLS are at increased risk for thrombosis and adequate anticoagulation is of vital importance during cardiopulmonary bypass (CPB). A successful outcome requires multidisciplinary management in order to prevent thrombotic or bleeding complications and to manage perioperative anticoagulation. More work and reporting on anticoagulation management and adjuvant therapy in patients with APLS during extracorporeal circulation are necessary

Robust ordinal regression in preference learning and ranking

Multiple Criteria Decision Aiding (MCDA) offers a diversity of approaches designed for providing the decision maker (DM) with a recommendation concerning a set of alternatives (items, actions) evaluated from multiple points of view, called criteria. This paper aims at drawing attention of the Machine Learning (ML) community upon recent advances in a representative MCDA methodology, called Robust Ordinal Regression (ROR). ROR learns by examples in order to rank a set of alternatives, thus considering a similar problem as Preference Learning (ML-PL) does. However, ROR implements the interactive preference construction paradigm, which should be perceived as a mutual learning of the model and the DM. The paper clarifies the specific interpretation of the concept of preference learning adopted in ROR and MCDA, comparing it to the usual concept of preference learning considered within ML. This comparison concerns a structure of the considered problem, types of admitted preference information, a character of the employed preference models, ways of exploiting them, and techniques to arrive at a final ranking

Data envelopment analysis with nonlinear virtual inputs and outputs

An underlying assumption in DEA is that the weights coupled with the ratio scales of the inputs and outputs imply linear value functions. In this paper, we present a general modeling approach to deal with outputs and/or inputs that are characterized by nonlinear value functions. To this end, we represent the nonlinear virtual outputs and/or inputs in a piece-wise linear fashion. We give the CCR model that can assess the efficiency of the units in the presence of nonlinear virtual inputs and outputs. Further, we extend the models with the assurance region approach to deal with concave output and convex input value functions. Actually, our formulations indicate a transformation of the original data set to an augmented data set where standard DEA models can then be applied, remaining thus in the grounds of the standard DEA methodology. To underline the usefulness of such a new development, we revisit a previous work of one of the authors dealing with the assessment of the human development index on the light of DEA.Data envelopment analysis Nonlinear value functions