Abstract: Objectives: We examined if the guidelines for gestational weight gain (GWG) proposed by the Institute of Medicine (IOM) are the most suitable for Chilean women. Study design: Secondary analysis of records of single full-term births at the Dr. Sotero del Rio Hospital, Santiago, Chile, during 2003-2012 (n = 62,579). Methods: From clinical records, we obtained data regarding maternal age, height, prepregnancy and at delivery weights, pathologies during pregnancy such as gestational diabetes (GDM) and pre-eclampsia, gestational age at delivery, and number of infants born small for gestational age (SGA) and large for gestational age (LGA). We formulated a mathematical model (MM) to determine the GWG range that maximizes the likelihood of a healthy pregnancy (HP) if the recommendation is followed. We defined an HP as one where the mother has no complications such as pre-eclampsia, GDM, SGA, or LGA. Results: Forty-six percent of women had prepregnancy overweight or obesity. The prevalence of GDM, pre-eclampsia, SGA, and LGA were 3%, 1.2%, 9%, and 12%, respectively. An HP was present in 76% of pregnancies, 79% in the underweight group, 79% in normal weight group, 74% in the overweight group, and 67% in obese women. The GWG recommendations given by the MM (14-20 kg for underweight, 6-20 kg for normal weight, 9 -11 kg for overweight, and 6-7 kg for obese) led to higher probabilities of achieving an HP than the ones obtained with the IOM recommendations. Conclusion: The adoption of GWG recommendations based on characteristics of the Chilean population might lead to better short- and long-term health results for pregnant women. (C) 2018 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Abstract: The Marshall-Olkin (MO) copulamodel has emerged as the standard tool for capturing dependence between components in failure analysis in reliability. In this model, shocks arise at exponential random times, that affect one or several components inducing a natural correlation in the failure process. However, because the number of parameter of the model grows exponentially with the number of components, MO suffers of the “curse of dimensionality.” MO models are usually intended to be applied to design a network before its construction; therefore, it is natural to assume that only partial information about failure behavior can be gathered, mostly from similar existing networks. To construct such an MO model, we propose an optimization approach to define the shock's parameters in the MO copula, in order to match marginal failures probabilities and correlations between these failures. To deal with the exponential number of parameters of this problem, we use a column-generation technique. We also discuss additional criteria that can be incorporated to obtain a suitable model. Our computational experiments show that the resulting MO model produces a close estimation of the network reliability, especially when the correlation between component failures is significant.

Abstract: Objectives: We develop a patient prioritization scheme for treating patients infected with hepatitis C virus (HCV) and study under which scenarios it outperforms the current practices in Spain and Chile.
Study design: We use simulation to evaluate the performance of prioritization rules under two HCV patient cohorts, constructed using secondary data of public records from Chile and Spain, during 2015-2016.
Methods: We use the results of a mathematical model, which determines individual optimal HCV treatment policies as an input for constructing a patient prioritization rule, when limited resources are present. The prioritization is based on marginal analysis on cost increases and health-outcome gains. We construct the Chilean and Spanish case studies and used Monte Carlo simulation to evaluate the performance of our methodology in these two scenarios.
Results: The resulting prioritizations for the Chilean and Spanish patients are similar, despite the significant differences of both countries, in terms of epidemiological profiles and cost structures. Furthermore, when resources are scarce compared with the number of patients in need of the new drug, our prioritization significantly outperforms current practices of treating sicker patients first, both in terms of cost and healthcare indicators: for the Chilean case, we have an increase in the quality-adjusted life years (QALYs) of 0.83 with a cost reduction of 8176 euros per patient, with a budget covering 2.5% of the patients in the cohort. This difference slowly decreases when increasing the available resources, converging to the performance indicators obtained when all patients are treated immediately: for the Spanish case, we have a decrease in the QALYs of 0.17 with a cost reduction of 1134 euros per patient, with a budget covering 20% of the patients in the cohort.
Conclusion: Decision science can provide useful analytical tools for designing efficient public policies that can excel in terms of quantitative health performance indicators.