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Journal Home > Volume 16, Issue 2 - June 30, 2021

JAQM Volume 16, Issue 2 - June 30, 2021


Opportunity and equity gaps in Romania – what do early indicators reveal for the fall of 2020?
Ionela-Roxana GLĂVAN

The COVID-19 pandemic has not only risen hurdles for the health care industry and economy evolution, it also contributed significantly to the growing inequality. The research analyzes preliminary impact of coronavirus pandemic on the Romanian economy revealing that there is clear evidence, showing that the groups of people are not equally affected by the pandemic. An up-to date analysis and literature review are conducted using available data sources. Synthesis from Romania research records, suggests as in other European countries, that COVID-19 throws into relief economic effects which associate with inequality factors among groups. In this context, some key policy responses and measures are discussed to mitigate opportunity and equity gaps. Whether a consequence of economy, related to gender, education, health or other type, the observed inequalities in the pandemic context are pervasive.

“Seasonal Adjustment” in Google Scholar and Microsoft Academic
Andreea MIRICĂ, Octavian CEBAN, Georgiana Andreea FERARIU, Viorel CÎRNU, Bogdan Ionut CHIPER, Nicoleta Violeta VELISCA, Marian NECULA

The aim of the present paper was to explore the visibility of the concept of “seasonal adjustment” with regard to research field, using the Publish or Perish 7 tool. The online scientific databases analyzed were Google Scholar and Microsoft Academic. Furthermore, the compared analysis provides consistent findings which will help the researchers to better explore this topic and some useful recommendations regarding the use of these databases for retrieving articles on seasonal adjustment will be pointed out.

Identification of significant prognostic factors of diabetic patients along with their survival rate
Komal GOEL, Meenu GOEL, Pakhi MALHOTRA, Tarushi AGGARWAL

Diabetes mellitus, also known as diabetes, is caused when the pancreas does not produce enough insulin or when the cells of the body do not respond properly to the insulin produced. This leads to increase in blood sugar level thus causing further complications like chronic kidney disease, cardiovascular disease, diabetic ketoacidosis, etc. Symptoms often include weight loss, polyuria, slow healing of cuts, polydipsia and many more. The one way ANOVA model is an appropriate statistical method to show the various significant factors that helps in curing diabetes. In this research paper, we have demonstrated the practicality of one way ANOVA in the analysis of various prognostic factors. The main objective of this research paper is to assess and analyse the various significant prognostic factors of diabetic patients and study the interrelationship amongst them. Patients and Methods: A total sample of 1266 diabetic patients is involved in this study. These patients are classified on the basis of different factors that a diabetic patient can have, we have 107 different factors. All the statistical analysis has been performed using the IBM product in IBM SPSS software. The mean diabetic duration of patients under study is 39.6035 days (SD: 66.34122), ranging from 0 to 523 days. From the research we found many significant prognostic factors that can be considered while treating the diabetic patients. The results generated from this study also complies with the other studies on similar topic, hence we can say this study is appropriate for further uses.

Multiple Comparison in ANOVA Models using Bayes Factor

The traditional ANOVA method is used to compare multiple means followed by the multiple comparison methods to identify the significantly differing pairs on the rejection of null hypotheses. Accordingly, we identify the non-significant treatment pairs in the ANOVA model to formulate suitable reduced models. The present study is to ascertain the strength of the treatments in the model through the Bayesian approach by comparing the full and reduced models with null model based on the Bayes and shrinkage factors. Moreover, we also demonstrate the behaviour of different priors such as Zellner's g-prior, Jeffreys-Zellner- Siow prior and Hyper-g priors based on the Bayes factor. Finally, we validate using the simulated data the nature of variability in Bayes factor among the five priors considered in this study.