Assessment of clinical impact of SARS-CoV-2 in people with type 1 diabetes: A cohort study.

OBJECTIVES: During the coronavirus 2019 pandemic, there had been more than 758 million COVID-19 cases as of February 13, 2023, and it is the main cause of death in many countries. Due to the variation in disease presentation, scientists determined that people living with type 2 diabetes mellitus were at higher risk of mortality. However, people living with type 1 diabetes have not been thoroughly studied, especially in extreme regions of developing countries. The objective of this study was to analyze the effects of SARS-CoV-2 pandemic restrictions on different variables in a cohort with type 1 diabetes. METHODS: This cohort-type study included pediatric and adult patients with type 1 diabetes at Regional Hospital Dr. Juan Noé Crevani in Arica, Chile. Biosocial and anthropometric factors, clinical history, self-care activities, and biochemical parameters were assessed and compared using analysis of variance and paired t tests between March 2020 and March 2021. RESULTS: A total of 150 patients were assessed during the SARS-CoV-2 pandemic in Arica, Chile. One year after the pandemic struck, the main causes for metabolic deterioration were a reduction of carbohydrate counting by an average of 8.67% (P = 0.000), a reduction of adherence to treatment by an average of 25% (P = 0.000), and a shift to telemedicine as a main health care service (P = 0.023); these factors raised hemoglobin A1c (HbA1c) levels by 1.81%, 1.78% and 0.075%, respectively. The participants' average body mass index (BMI) increased by 1.26 kg/m2 and HbA1c levels increased by 0.16% during the first year of the pandemic. Also, hospitalizations increased about 2% (P = 0.984), and there was a significant increase in carbohydrate and snack intake (P = 0.330 and P = 0.811, respectively). Children's linear growth decreased by a standard deviation of 0.035 (P = 0.648), and their physical activity decreased by 12.67% (P = 0.383). CONCLUSIONS: This study found that adherence to diabetes care was reduced during the pandemic owing to a variety of behavioral reasons and environmental changes (e.g., quarantines and food security). This affected this population's HbA1c levels, BMI, linear growth, and number of hospitalizations as main consequences. Telemedicine remains an important tool, but it must be reconsidered among all different age groups.

El Enfoque Bayesiano en las Ciencias de la Salud / The Bayesian Approach in the Health Sciences

En estadística existen dos enfoques básicos, la estadística frecuentista que es la corriente principal y la estadística bayesiana. La mayoría de los principales métodos estadísticos son frecuentistas siendo el enfoque bayesiano más desconocido entre los investigadores. En el presente artículo se exponen los fundamentos lógicos del enfoque bayesiano y su uso mediante un ejemplo de aplicación. En este contexto, más que presentar un debate entre la lógica clásica y la bayesiana, se pretende mostrar de manera introductoria las enormes posibilidades que el enfoque bayesiano puede aportar a la investigación en las Ciencias de la Salud.

In the stadistic field there are two basic approaches, the Frequentist Statistics which is the primary one, and the Bayesian Statistics. The most used statistical methods are the Frequentist methods, being the Bayesian approach the most popular among researchers. In this article, the logical basis of the Bayendian approach and its use are exposed through an application example. In this context, rather than presenting a debate between the classic and the Bayensian logic, it is intended to demonstrate in an introductory method the considerable possibilities how Bayesian approach can contribute to Health and Sciences research.

Los modelos matemáticos en epidemiología y el Covid-19 / Mathematical models in epidemiology and Covid-19

Las matemáticas en epidemiología y en general en las ciencias biológicas constituyen, además de una herramienta, una forma de pensar y estructurar descripciones, explicaciones y predicciones de procesos. Por ello, tanto en epidemiología como en otras áreas del conocimiento biológico, las matemáticas son utilizadas para modelar. El objetivo de este artículo es presentar como los modelos matemáticos se utilizan en la teoría epidemiológica. En este artículo nos centraremos en un modelo en particular, el modelo SIR, utilizado para describir la evolución de epidemias. Se presenta sus características fundamentales desde el punto de vista matemático y se discute el papel de los diferentes parámetros. Además, este modelo se aplica, a modo de ejemplo, a la evolución del Covid-19 en Chile.

Mathematics in epidemiology and in general in biological sciences constitute, in addition to a tool, a way of thinking and structuring descriptions, explanations and predictions. Thus, both in epidemiology and in other areas of biological knowledge, the mathematics is used for modeling. The objective of this article is to present how mathematical models are used in epidemiological theory. In this article will focus on a particular model, the SIR model, used to describe the evolution of epidemics. Its fundamental characteristics from the mathematical point of view are presented and the role of different parameters. In addition, this model is applied, by way of example, to the evolution of Covid-19 in Chile.

Managing slow-moving item: a zero-inflated truncated normal approach for modeling demand.

This paper proposes a slow-moving management method for a system using of intermittent demand per unit time and lead time demand of items in service enterprise inventory models. Our method uses zero-inflated truncated normal statistical distribution, which makes it possible to model intermittent demand per unit time using mixed statistical distribution. We conducted numerical experiments based on an algorithm used to forecast intermittent demand over fixed lead time to show that our proposed distributions improved the performance of the continuous review inventory model with shortages. We evaluated multi-criteria elements (total cost, fill-rate, shortage of quantity per cycle, and the adequacy of the statistical distribution of the lead time demand) for decision analysis using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). We confirmed that our method improved the performance of the inventory model in comparison to other commonly used approaches such as simple exponential smoothing and Croston's method. We found an interesting association between the intermittency of demand per unit of time, the square root of this same parameter and reorder point decisions, that could be explained using classical multiple linear regression model. We confirmed that the parameter of variability of the zero-inflated truncated normal statistical distribution used to model intermittent demand was positively related to the decision of reorder points. Our study examined a decision analysis using illustrative example. Our suggested approach is original, valuable, and, in the case of slow-moving item management for service companies, allows for the verification of decision-making using multiple criteria.