Assessing the survival time of women with breast cancer in Northwestern Ethiopia: using the Bayesian approach.

BACKGROUND: Despite the significant weight of difficulty, Ethiopia's survival rate and mortality predictors have not yet been identified. Finding out what influences outpatient breast cancer patients' survival time was the major goal of this study. METHODS: A retrospective study was conducted on outpatients with breast cancer. In order to accomplish the goal, 382 outpatients with breast cancer were included in the study using information obtained from the medical records of patients registered at the University of Gondar referral hospital in Gondar, Ethiopia, between May 15, 2016, and May 15, 2020. In order to compare survival functions, Kaplan-Meier plots and the log-rank test were used. The Cox-PH model and Bayesian parametric survival models were then used to examine the survival time of breast cancer outpatients. The use of integrated layered Laplace approximation techniques has been made. RESULTS: The study included 382 outpatients with breast cancer in total, and 148 (38.7%) patients died. 42 months was the estimated median patient survival time. The Bayesian Weibull accelerated failure time model was determined to be suitable using model selection criteria. Stage, grade 2, 3, and 4, co-morbid, histological type, FIGO stage, chemotherapy, metastatic number 1, 2, and >=3, and tumour size all have a sizable impact on the survival time of outpatients with breast cancer, according to the results of this model. The breast cancer outpatient survival time was correctly predicted by the Bayesian Weibull accelerated failure time model. CONCLUSIONS: Compared to high- and middle-income countries, the overall survival rate was lower. Notable variables influencing the length of survival following a breast cancer diagnosis were weight loss, invasive medullar histology, comorbid disease, a large tumour size, an increase in metastases, an increase in the International Federation of Gynaecologists and Obstetricians stage, an increase in grade, lymphatic vascular space invasion, positive regional nodes, and late stages of cancer. The authors advise that it is preferable to increase the number of early screening programmes and treatment centres for breast cancer and to work with the public media to raise knowledge of the disease's prevention, screening, and treatment choices.

Predictors of cervical tumour size for outpatients with cervical cancer at the University of Gondar referral hospital: a retrospective study design.

BACKGROUND: Cervical cancer is one of the most serious threats to women's lives. Modelling the change in tumour size over time for outpatients with cervical cancer was the study's main goal. METHODS: A hospital conducted a retrospective cohort study with outpatients who had cervical cancer. The information about the tumour size was taken from the patient's chart and all patient data records between May 20, 2017, and May 20, 2021. The data cover 322 cervical cancer outpatients' basic demographic and medical information. When analysing longitudinal data, the linear mixed effect model and the connection between tumour sizes in outpatients were taken into consideration. A linear mixed model, a random intercept model, and a slope model were used to fit the data. RESULT: A sample of 322 cervical cancer outpatients was examined, and 148 (or 46% of the outpatients) tested positive for HIV. The linear mixed model with a first-order autoregressive covariance structure revealed that a change in time of one month led to a 0.009 cm2 reduction in tumour size. For every kilogramme more in weight, the tumour size change in cervical cancer patients decreased considerably by 0.0098 cm2. The tumour size change in the cervical cancer patient who was HIV-positive was 0.4360 cm squared greater than that in the HIV-negative outpatients. CONCLUSION: As a consequence, there was a significant association between the longitudinal change in tumour size and the predictor variables visit time, therapy, patient weight, cancer stage, HIV, oral contraceptive use, history of abortion, and smoking status.

Panel data analysis of profitability and employment growth of medium and large size industries in Ethiopia.

Manufacturing industries are an asset or value added part of an economy, intimately linked with industrial and engineering design, and engaged in producing significant economic productions that can assist the national growth and development. The aim of the study was to estimate the profitability and employment growth of medium and large size industries in Ethiopia. The variables were chosen based on theoretical and experimental literature findings. In this study, an explanatory study design was implemented in carrying out this research with the prearrangement of secondary data collected from the panel data set of medium and large size manufacturing industries conducted annually by the central statistics agency over the period 2002-2011 E.C. The study employed panel data estimation methods to analyze the influence of medium and large size manufacturing industries on profitability and employment growth. Three panel data models are used: pooled ordinary least squares, fixed effect, and random effect estimation method. The Hausman test revealed the random model was the best fit for both profitability and employment growth. The diagnosticstest: normality, multicollinearity, heteroscedasticity, and autocorrelation tests were conducted on the data. The results obtained indicate that employment growth and profitability in Ethiopia are generally driven by medium and large-size manufacturing industries. The random model result shows that ownership, firm-size, advertising intensity and import intensity have a positive influence and substantial effect on profitability. But, the government has significant effect and a negative influence on profitability. The random model shows that government, advertising intensity and firm-size have significant effect and positive influence on employment growth. Nevertheless, import has significant effect and a negative influence on employment growth. Eventually, the employment growth and profitability have a positive influence and insignificant effect and the authors suggested further research in the areas by taking into account additional variables and newly emerging industries.