Clinical prediction of occupational and non-specific low back pain

Non-specific Occupational Low Back Pain (NOLBP) is a health condition that generates a high absenteeism and disability. Due to multifactorial causes is difficult to determine accurate diagnosis and prognosis. The clinical prediction of NOLBP is identified as a series of models that integrate a multivariate analysis to determine early diagnosis, course, and occupational impact of this health condition. Objective: to identify predictor factors of NOLBP, and the type of material referred to in the scientific evidence and establish the scopes of the prediction. Materials and method: the title search was conducted in the databases PubMed, Science Direct, and Ebsco Springer, between1985 and 2012. The selected articles were classified through a bibliometric analysis allowing to define the most relevant ones. Results: 101 titles met the established criteria, but only 43 metthe purpose of the review. As for NOLBP prediction, the studies varied in relation to the factors for example: diagnosis, transition of lumbar pain from acute to chronic, absenteeism from work, disability and return to work. Conclusion: clinical prediction is considered as a strategic to determine course and prognostic of NOLBP, and to determine the characteristics that increase the risk of chronicity in workers with this health condition. Likewise, clinical prediction rules are tools that aim to facilitate decision making about the evaluation, diagnosis, prognosis and intervention for low back pain, which should incorporate risk factors of physical, psychological and social.

Inequality of opportunity in educational achievement in Latin America: evidence from PISA 2006-2009

We assess inequality of opportunity in educational achievement in six Latin American countries, employing two waves of PISA data (2006 and 2009). By means of a non-parametric approach using a decomposable inequality index, GE(0), we rank countries according to their degree of inequality of opportunity. We work with alternative characterizations of types: school type (public or private), gender, parental education, and combinations of those variables. We calculate incremental contributions of each set of circumstances to inequality. We provide rankings of countries based on unconditional inequalities (using conventional indices) and on conditional inequalities (EOp indices), and the two sets of rankings do not always coincide. Inequality of opportunities range from less than 1% to up to 27%, with substantial heterogeneity according to the year, the country, the subject and the specificication of circumstances. Robustness checks based on bootstrap and the use of an alternative index confirm most of the initial results.

Estimating and Forecasting the Term Structure of Interest Rates:US and Colombia Analysis

In this paper we use the most representative models that exist in the literature on term structure of interest rates. In particular, we explore affine one factor models and polynomial-type approximations such as Nelson and Siegel. Our empirical application considers monthly data of USA and Colombia for estimation and forecasting. We find that affine models do not provide adequate performance either in-sample or out-of-sample. On the contrary, parsimonious models such as Nelson and Siegel have adequate results in-sample, however out-of-sample they are not able to systematically improve upon random walk base forecast.