COVID-19 Misinformation in Portuguese-Speaking Countries: Agreement with Content and Associated Factors

In the wake of the COVID-19 pandemic, a complex phenomenon called the “infodemic” has emerged, compromising coping with the pandemic. This study aims to estimate the prevalence of agreement with misinformation about COVID-19 and to identify associated factors. A web survey was carried out in Portuguese-speaking countries in two stages: 1. the identification of misinformation circulating in the included countries; 2. a multicentric online survey with residents of the included countries. The outcome of the study was agreement or disagreement with misinformation about COVID-19. Multivariate analyzes were conducted using the Poisson regression model with robust variance, a logarithmic link function, and 95% confidence intervals. The prevalence of agreement with misinformation about COVID-19 was 63.9%. The following factors increased the prevalence of this outcome: having a religious affiliation (aPR: 1454, 95% CI: 1393–1517), having restrictions on leisure (aPR: 1230, 95% CI: 1127–1342), practicing social isolation (aPR: 1073, 95% CI: 1030–1118), not avoiding agglomeration (aPR: 1060, 95% CI: 1005–1117), not seeking/receiving news from scientific sources (aPR: 1153, 95% CI: 1068–1245), seeking/receiving news from three or more non-scientific sources (aPR: 1114, 95% CI: 1049–1182), and giving credibility to news carried by people from social networks (aPR: 1175, 95% CI: 1104–1251). There was a high prevalence of agreement with misinformation about COVID-19. The quality, similarity, uniformity, and acceptance of the contents indicate a concentration of themes that reflect “homemade”, simple, and easy methods to avoid infection by SARS-CoV-2, compromising decision-making and ability to cope with the disease.

Modified Kibria-Lukman (MKL) estimator for the Poisson Regression Model: application and simulation

Background: Multicollinearity greatly affects the Maximum Likelihood Estimator (MLE) efficiency in both the linear regression model and the generalized linear model. Alternative estimators to the MLE include the ridge estimator, the Liu estimator and the Kibria-Lukman (KL) estimator, though literature shows that the KL estimator is preferred. Therefore, this study sought to modify the KL estimator to mitigate the Poisson Regression Model with multicollinearity. Methods: A simulation study and a real-life study was carried out and the performance of the new estimator was compared with some of the existing estimators. Results: The simulation result showed the new estimator performed more efficiently than the MLE, Poisson Ridge Regression Estimator (PRE), Poisson Liu Estimator (PLE) and the Poisson KL (PKL) estimators. The real-life application also agreed with the simulation result. Conclusions: In general, the new estimator performed more efficiently than the MLE, PRE, PLE and the PKL when multicollinearity was present.

Impact of the SARS-CoV-2 pandemic and first lockdown on pregnancy monitoring in France: the COVIMATER cross-sectional study

Background In the context of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, consultations and pregnancy monitoring examinations had to be reorganised urgently. In addition, women themselves may have postponed or cancelled their medical monitoring for organisational reasons, for fear of contracting the disease caused by SARS-CoV-2 (COVID-19) or for other reasons of their own. Delayed care can have deleterious consequences for both the mother and the child. Our objective was therefore to study the impact of the SARS-CoV-2 pandemic and the first lockdown in France on voluntary changes by pregnant women in the medical monitoring of their pregnancy and the associated factors. Methods A cross-sectional study was conducted in July 2020 using a web-questionnaire completed by 500 adult (> 18 years old) pregnant women during the first French lockdown (March–May 2020). A robust variance Poisson regression model was used to estimate adjusted prevalence ratios (aPRs). Results Almost one women of five (23.4%) reported having voluntarily postponed or foregone at least one consultation or pregnancy check-up during the lockdown. Women who were professionally inactive (aPR = 1.98, CI95%[1.24–3.16]), who had experienced serious disputes or violence during the lockdown (1.47, [1.00–2.16]), who felt they received little or no support (1.71, [1.07–2.71]), and those who changed health professionals during the lockdown (1.57, [1.04–2.36]) were all more likely to have voluntarily changed their pregnancy monitoring. Higher level of worry about the pandemic was associated with a lower probability of voluntarily changing pregnancy monitoring (0.66, [0.46–0.96]). Conclusions Our results can guide prevention and support policies for pregnant women in the current and future pandemics.

Prognostic Factors for Evolution of Non Alcoholic Fatty Liver Disease Patients Utilizing Poisson Regression and Continuous Time Markov Chains

In this article , Poisson regression model is used to relates the rates of transition among states to the covariates .

A Study of Count Regression Models for Mortality Rate

This paper discusses how overdispersed count data to be fit. Poisson regression model, Negative Binomial 1 regression model (NEGBIN 1) and Negative Binomial regression 2 (NEGBIN 2) model were proposed to fit mortality rate data. The method used is comparing the values of Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to find out which method suits the data the most. The results show that the data indeed display higher variability. Among the three models, the model preferred is NEGBIN 1 model.

Prognostic Factors for Evolution of Non Alcoholic Fatty Liver Disease Patients Utilizing Poisson Regression and Continuous Time Markov Chains

In this article , Poisson regression model is used to relates the rates of transition among states to the covariates .

Poisson Regression-Based Mean Estimator

The most frequent method for modeling count responses in numerous investigations is the Poisson regression model. Under simple random sampling, this paper offers utilizing Poisson regression-based mean estimator and discovers its associated formula of the mean square error (MSE). The MSE of the proposed estimator is compared to the MSE of traditional ratio estimators in theory. As a result of these evaluations, the proposed estimator has been proven to be more efficient than traditional estimators. Furthermore, the practical results corroborated the theoretical findings.