Abstract: The operating theater is one of the most expensive units in the hospital, representing up to 40% of the total expenses. Because of its importance, the operating room scheduling problem has been addressed from many different perspectives since the early 1960s. One of the main difficulties that
has reduced the applicability of the current results is the high variability in surgery duration, making schedule recommendations hard to implement.
In this work, we propose a time-indexed scheduling formulation to solve the operational problem. Our main contribution is that we propose the use of chance constraints related to the surgery duration's probability distribution for each surgeon to improve the scheduling performance. We show how to implement these chance constraints as linear ones in our time-indexed formulation, enhancing the performance of the resulting schedules significantly.
Through data analysis of real historical instances, we develop specific constraints that improve the schedule, reducing the need for overtime without affecting the utilization significantly. Furthermore, these constraints give the operating room manager the possibility of balancing overtime and utilization through a tunning parameter in our formulation. Finally, through simulations and the use of real instances, we report the performance for four different metrics, showing the importance of using historical data to get the right balance between the utilization and overtime.

Abstract: Purpose – The purpose of this paper is to study the optimal contact policies for customers that belong to the mass affluent market. Design/methodology/approach – The authors formulate a stochastic dynamic programming model to determine the optimal frequency of contacts in order to maximize the expected return of the company. Findings – The authors show that personalized marketing strategies provide a competitive advantage to companies that contact their customers directly through, for example, phone calls or meetings. The authors show that a threshold policy is only optimal for customers with increasing sensitivity to contact. In all other cases, optimal policies might have a less intuitive structure. The authors also study the importance of the size of the customer database and determine the optimal maximum recency when maintenance costs are present. Practical implications – Contact policies should be tailored for each company/industry individually, due to their sensitivity to customers' purchasing behavior.

Abstract: We identified factors associated with gestational weight gain (GWG) in 1,654 Chilean pregnant women with full-term pregnancies. At baseline, we collected information about sociodemographic, gyneco-obstetric, anthropometric, and health-care-related factors. We found that prepregnancy nutritional body mass index was the most important factor related to GWG above recommendations (overweight: ratio of relative risks [RRR] = 2.31, 95% confidence interval [CI, 1.73, 3.09] and obesity: RRR = 2.90, 95% CI [2.08, 4.03]). We believe that women who are overweight/obese at the beginning of pregnancy should be identified because of their higher risk, and that adequate strategies should be designed and implemented to help them achieve a healthy GWG.

Abstract: Objectives: Birth weight is an important public health indicator that reflects fetal health conditions and predicts future health. Identifying the most important factors related to birth weight would help defining preventive health strategies for both mothers and children. The objectives of this study are i. to describe, using a large birth database from a Chilean hospital, the trend of birth weight during 2002-2015, and ii. to determine factors during prenatal care associated with low and high birth weight.
Study design: This study is a secondary analysis of all single birth records at a Chilean Hospital in the southeast district of Santiago, Chile, during 2002-2015 (N = 78,931).
Methods: Sociodemographic information, clinical and obstetric history, lifestyle, and anthropometric variables were evaluated as potential predictors. Birth weight was categorized into five groups as per percentiles of weight as per gestational age. Data were extracted from clinical records. We used classification and regression tree methodology and logistic regression.
Results: The average birth weight for the period was 3316 g (SD 566), with little variation across time. Preterm births increased from 7% in 2002 to 10% in 2015, and births >40 weeks decreased from 10.7% in 2002 to 4.4% in 2015. The percentages of small and large for gestational age changed from 10.9% and 12.7% in 2002 to 9.9% and 13.9% in 2015, respectively. The predictors included in the optimal tree were body mass index, gestational weight gain, pre-eclampsia, and gestational diabetes. We found that women with a pregestational body mass index <28 kg/m(2), gestational weight gain <17 kg, and pre-eclampsia had a probability of 41% of having a small for gestational age neonate. Conversely, women with a body mass index similar to 28 kg/m(2), gestational weight gain similar to 17 kg, and gestational diabetes had a probability of 44% of having a large for gestational age neonate.
Conclusions: This study showed that the most important variables explaining birth weight are those related to maternal nutritional status. Thus, the strategies to promote a normal birth weight should aim for a normal maternal weight at the beginning of pregnancy, gestational weight gain within the recommendations, and prevention of gestational diabetes and pre-eclampsia. (C) 2021 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Abstract: Background
In 2002, Chile introduced a major health reform, designed to level out inequities in healthcare coverage, access and opportunities. In particular, the opportunity guarantees ensure a maximum time to receive the appropriate diagnosis and treatment, and thus, gender bias should not be observed.
Objective
To explore the existence of differences in the timeliness of treatment between women and men under the Chilean public health insurance system. We controlled by other observable variables, including age, insurance holder status, provider complexity and health district.
Methods
We used an individual level database that includes all interactions for the diseases covered under the national plan from 2014 to 2019. We excluded from the analysis the diseases affecting only men, women, and infants. To study the waiting time differences between women and men, we first perform a Welch two sample t-test. Then, we used a multilevel hierarchical regression model to further explore the impact of gender in waiting time. At the individual level, we included gender, insurance holder status, age, and the interaction between gender and age. For the aggregate levels, we used the specific opportunity guarantee, the type of provider, and health district.
Results
From the Welch two sample t-test, we found significant differences in waiting times between women and men, in seven opportunity guarantees. From the multilevel regression, the individual variables: holder status, ages between 35 and 49, and the interaction between gender and age for ages between 40 and 54 were statistically significant at 95% level. We remark that the major differences in waiting times between women and men were observed for individuals between ages from 40 to 54, with women waiting significantly longer.
Conclusion
Results show the existence of bias in the timeliness of treatment, proving that universal guarantees are not enough to reduce gender inequalities in health care.

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.