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When it comes to making assessments about public health, the mortality rate is a very important factor. The COVID-19 pandemic has exacerbated well-known biases that affect the measurement of mortality, which varies with time and place. The COVID-19 pandemic took the world off surveillance, and since the outbreak, it has caused damage that many would have thought unthinkable in the present era. By estimating excess mortality for 2020 and 2021, we provide a thorough and consistent evaluation of the COVID-19 pandemic's effects. Excess mortality is a term used in epidemiology and public health to describe the number of fatalities from all causes during a crisis that exceeds what would be expected under 'normal' circumstances. Excess mortality has been used for thousands of years to estimate health emergencies and pandemics like the 1918 "Spanish Flu"6. Positive excess mortality occurs when actual deaths exceed previous data or recognized patterns. It could demonstrate how a pandemic affects the mortality rate. The estimates of positive excess mortality presented in this research are generated using the procedure, data, and methods described in detail in the Methods section and briefly summarized in this study. We explored different regression models in order to find the most effective factor for our estimates. We predict the pandemic period all-cause deaths in locations lacking complete reported data using the Poisson, Negative Binomial count framework. By overdispersion test, we checked the assumption of the Poisson model, and then we chose the negative binomial as a good fitting model for this analysis through Akaike Information Criteria (AIC) and Standardized residual plots, after that checking the P-value<0.05; we found some significant predictors from our choosing model Negative binomial model, and the coefficient of all predictors gave the information that some factors have a positive effect, and some has a negative effect at positive excess mortality at COVID

Reproductive coercion has been the primary focus of research on intimate partner violence against women in regard to reproductive health. While studies have taken a look at whether Intimate Partner Violence makes women more or less inclined to use contraception, not much research has been able to provide a comprehensive analysis of the connection between Intimate Partner Violence and reproductive coercion. This particular direction of research has concentrated its attention on both of these aspects when discussing reproductive coercion. It is significant to analyze these things together because it is important to fully understand the condition of reproductive coercion, reproductive choices, and the consequences that modern women are confronting. As a consequence of the negative effects of reproductive coercion on survivors' mental, physical, and emotional well-being, it is imperative that social workers be able to recognize the signs of Reproductive Coercion and provide effective interventions and advocacy on their behalf. The use of contraception in patriarchal power dynamic societies, the relationship between intimate partner violence and reproductive coercion, and the health outcome for women are all issues that could potentially be explained with the use of feminist theory and the constructionist theory that we proposed.

Breast cancer is the most lethal form of cancer that can strike women anywhere in the world. The most complex and tough undertaking in order to lower the death rate is the process of predicting a patient's likelihood of survival following breast cancer surgery. Due to the fact that this survival prediction is linked to the life of a woman, effective algorithms are required for the purpose of making the prognosis. It is of the utmost importance to accurately predict the survival status of patients who will have breast cancer surgery since this shows whether or not doing surgery is the actual approach for the specific medical scenario. Given the gravity of the situation, it is impossible to overstate how important it is to investigate new and improved methods of prediction in order to guarantee an accurate assessment of the patient's chances of survival. In this paper, we collect data and examine some models based on the survival of patients who underwent breast cancer surgery. The goal of this research is to evaluate the forecasting performance of various classification models, including the Linear regression model, logistic regression analysis, LDA, QDA, KNN, ANN, and Decision Tree. The results of the experiment on this dataset demonstrate the better performance of the came up with ANN approach, with an accuracy of 82.98 percent.

This study's primary goal is to examine the characteristics of public university canteen food service. A saying goes, "Health is riches." Therefore, it not only helps them to clear their minds but also enables them to focus on their studies, families, and careers. A model was created from the information that was provided and tested using information from a survey that was carried out at a college in northwest Pennsylvania. The findings imply that staff behavior, food quality, and price are the three key factors that affect student satisfaction. Cleanliness, responsiveness, and environment are further important factors. Considering these factors (food quality, food variety, price justice, ambiance, etc.) could help people in charge of food services provide more value and satisfaction to improve students' entire educational experience.