No abstract

PurposeThe study aims to design a control chart based on an exponentially weighted moving average (EWMA) chart of Pearson's residuals of a model of negative binomial regression in order to detect possible anomalies in mortality data.Design/methodology/approachIn order to evaluate the performance of the proposed chart, the authors have considered official historical records of death of children of Ecuador. A negative binomial regression model was fitted to the data, and a chart of the Pearson residuals was designed. The parameters of the chart were obtained by simulation, as well as the performances of the charts related to changes in the mean of death.FindingsWhen the chart was plotted, outliers were detected in the deaths of children in the years 1990–1995, 2001–2006, 2013–2015, which could show that there are underreporting or an excessive growth in mortality. In the analysis of performances, the value of λ = 0.05 presented the fastest detection of changes in the mean death.Originality/valueThe proposed charts present better performances in relation to EWMA charts for deviance residuals, with a remarkable advantage of the Pearson residuals, which are much easier to interpret and calculate. Finally, the authors would like to point out that although this paper only applies control charts to Ecuadorian infant mortality, the methodology can be used to calculate mortality in any geographical area or to detect outbreaks of infectious diseases.

Obesity is a disease which affects around 650 million people worldwide and more than 4.5 million in Ecuador. These figures are alarming because it is recognized as a significant factor in the non-communicable chronic diseases, which appear in all age groups. This work focusses on finding the relations between obesity depicted by Body Mass Index (BMI) and other variables such as gender, province, age group and geographic area for adults between 19 and 59 years old. To achieve this purpose, Logistic and Poisson regression models have been applied, and these results were significant in both models. After contrast pseudo R 2 , the best model has been identified as logistic regression due to its best fit and prediction. As a result, it has been obtained that for an adult male who lives in a rural area of Guayas, the possibility of being obese decreases by 78%. Furthermore, it can be affirmed that if an individual who lives in the province of Guayas moves to the province of Pichincha, and the rest of variables remain constant, then the possibility of being obese declines by 31%.

Operating rooms are part of the most valuable resources of a hospital. Patients waiting for getting into this service could be, patients who need pre-programmed surgical interventions or patients who need to be operated urgently. This work focusses on the first case mentioned. In this context, the planning of a surgical center in a hospital becomes a critical process and at the same time a complex issue. A suitable resolution to this problem is critical to reach an efficient use of its resources and above all to increase the chances of improving the quality of life of patients. The complexity of the planning lies in the evaluation of certain variables which interact in this process. Among these are: the number of available operating theatres, the availability of doctors, the duration of surgical procedures, the exclusive use of an operating suite for some procedures. The patient priority to be operated is another variable to consider, which is based on the criteria of the doctor who evaluates the time of a patient can wait for surgery according to the diagnosis. These features have been regarded in a linear programming model to maximize the number of patients assigned to a doctor and operating room on a given day. As a result, it was found that the hospital can assist 482 patients by placing an operating room only for cases of traumatology, being greater than the number of patients treated applying the model with all the operating rooms. Furthermore, the results indicate that the allocation time of a patient was reduced, from 8 hours with the manual process to 2 hours with the proposed model.