ABSTRACT
Introduction: Currently, the impact of COVID-19 on sociodemographic and obstetric factors, linked to nutritional status and anemia in pregnant women, represents a significant change in management and healthcare paradigms. Objectives: To consider a multiple logistic regression model and multiple comparisons (Mann-Whitney U test) based on sociodemographic-obstetric factors, nutritional status and anemia in pregnant women before and during Covid-19. Methods: In this study, 113 medical records of pregnant women attended between November 2019 and April 2020 at a Hospital II-1 in Trujillo, Peru were analyzed. Multiple logistic regression model showed a good fit and allowed to correctly classify most of pregnant women. Results: The covariates;employment situation, obstetric history, nutritional condition and anemia were significant. Differences were observed between sociodemographic factors such as employment status, obstetric factors such as obstetric history, and factors related to nutritional status and clinical symptoms of anemia. Conclusions: The impact of COVID-19 reflected in the relationships between sociodemographic and obstetric factors with nutritional status and the anemia diagnosed in pregnant women treated in a Hospital II-1 in Trujillo, Peru, was evidenced. ©
ABSTRACT
Objective: In times of COVID-19, the relationship between the quality of pharmaceutical services and user satisfaction in health facilities has become one of the elements of the most observed global health crisis. The objective of this research was to determine the relationship between the quality of the pharmaceutical product dispensing service with user satisfaction in COVID-19 times. Method: A sample of 134 users was considered, who applied for questionnaires on the quality and satisfaction of the user with the service received. An ordinal logistic regression model was fitted on data that relate service quality to user satisfaction, reliability, responsibility, safety, empathy and tangibility. Results: It was found that in times of COVID-19, the quality of the pharmaceutical product dispensing service was valued from low to medium, and the satisfaction, reliability, responsibility, security, empathy and the tangibility of the user with the service received was rated as dissatisfied not very satisfied. The validity of the ordinal logistic regression model for the quality of the dispensing service of pharmaceutical products based on tangibility was evidenced, which confirmed the relationship between the quality of the dispensing service of pharmaceutical products in times of COVID-19 and the tangibility. Conclusions: The quality of the pharmaceutical product dispensing service was rated from low to medium, which implied a high risk of negative evaluation of the pharmaceutical product dispensing service. © 2021 Universitatea de Vest Vasile Goldis din Arad. All rights reserved.
ABSTRACT
In this paper, entropy was studied in non-linear models including exponential, Gompertz, and logistic, to estimate epidemiological parameters of interest in data from confirmed cases of infection by COVID-19 in Peru. The data related to the spread of COVID-19 in Peru comes from the information available on the INS-Peru institutional portal (2020). The Akaike information criterion (AIC) and the residual standard error (ERR) were considered to evaluate the entropy of the models. The estimation of the parameters of the models was carried out using maximum likelihood and by the Bootstrap method. The results showed that the entropy of the models is related to the information generation rate, associated with the differential in the number of tests applied. Entropy severely affected maximum likelihood estimators. The Bootstrap estimators showed better performance against EMV with the estimated peak of confirmed cases. Bootstrap estimators were significantly affected by sample size, especially when n <= 10. The results of this research suggest considering the entropy and the information generation rate (differential in the application of tests for the diagnosis of COVID-19 in Peru), as well as the use of Bootstrap estimators as an alternative to estimate parameters of epidemiological models.