Background and Aims: Cervical cancer is the fourth most common cause of cancerrelated death in the world. The objective of this study was to determine factors that affect the longitudinal change of tumor size and the time to death of outpat Methods: A retrospective follow-up study was carried out among 322 randomly selected patients with cervical cancer at the University of Gondar Referral Hospital from May 15, 2018 to May 15, 2022. Data were extracted from the patient's chart from all patients' data records. Kaplan-Meier estimator, log-rank test, the Cox proportional-hazard model, and the joint model for the two response variables simultaneously were used.Results: Among 322 outpatients with cervical cancer, 148 (46%) of them were human immunodeficiency virus (HIV) positive and 107 (33.3%) of them died. The results of joint and separate models show that there is an association between survival and the longitudinal data in the analysis; it indicates that there is a dependency between longitudinal terms of cervical tumor size and time-to-death events. A unit centimeter square rise in tumor size, corresponding to an exp (0.8502) = 2.34 times, significantly raised the mortality risk. Conclusion:The study showed that HIV, stage of cancer, treatment, weight, history of abortion, oral contraceptive use, smoking status, and visit time were statistically significant factors for the two outcomes jointly.Implications: As a result, adequate health services and adequate resource allocations are critical for cervical cancer control and prevention programs. Therefore, the government should provide adequate funding and well-trained health professionals to hospitals to sustain screening programs with appropriate coverage of cervical cancer patient treatments.

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Background: Diabetes Mellitus (DM) is a chronic, progressive disease characterized by elevated levels of blood glucose. Despite the fact that most international association/organization gave attention toward diabetes control and prevention by healthy professional, still diabetes and its complication such as cardio vascular, blood vessels, eyes, kidneys and nerves become a major cause of premature death and disability across the world. The overall aim of this study is assessing fasting blood sugar variation over time and its determinant among diabetic patients. Methods: Data were obtained from Adama Hospital Medical College diabetic patients who have been active in the follow-up treatment from September 1, 2018 to August 30, 2019. The data consists of basic demographic and clinical characteristics of 312 DM patients were selected using simple random sampling techniques and of whom 177 were males and the rest 135 were females. The linear mixed effect model for longitudinal data analysis was used by taking the correlation between Fasting Blood Sugar (FBS) level of patients into account. Linear mixed model, random intercept and slope models were used for feting the data. Results: The results from the linear mixed model with unstructured co-variance structure showed that for one-month change in time decreases log FBS level by 0.0111267 mg/dl. While a unit increase in Body Mass Index (BMI) of a patient on treatment, the log FBS level was increased by 0.0434 mg/dl. Similarly, a unit increase in Diastolic Blood Pressure (DBP), the log FBS level was increased by 0.0004749 mg/dl. Being tertiary and secondary level of education decreases logFBS level by 0.0058844 and 0.0055161 respectively compared with patients with no education.Conclusion: Age, Educational status, Dietary type, Drug type, History of hypertension, BMI, DBP, Time, interaction effect of Age, history of hypertension, Dietary type, other comorbidity at baseline with time were the significant determinant for the change in mean FBS level of the diabetes patients over time. Based on the findings of our study and WHO recommendation, maintaining of healthy body weight, by taking healthy diet along with lower blood glucose level is essential to control blood sugar in body and to prevent long term complication.

BackgroundThe Poisson regression model is useful for analyse count data, but, when the observations are correlated the Poisson estimate will be biased. Whereas, when the over-dispersion and heterogeneity problems occur the imposition of the Poisson model underestimate the standard error and overestimate the significance of the regression parameters. Therefore, the objective of this paper was to develop a test statistic to model and predict clustered count response data via the application and simulation data.MethodsThis paper concentrated on the clustered count data model to take into account heterogeneity. Accordingly, we developed a score test based on the multilevel Poisson model for testing heterogeneity with the alternative Poisson regression model. In addition, for the model application, we used the EDHS children`s data. Therefore, to evaluate the proposed model, we used both simulation and application data.ResultsSimulation results showed that the proposed score test has high power to predict and used to control heterogeneity between groups. Oromia, Amhara, and SNNPR are among the regions with the highest child mortality rates (Table 1). The results indicated that women who made marriage a mean age of 16 years and gave birth to the first child a mean age of 18 years and 8 months. Table 1 showed that 81% of all child deaths have recorded in rural areas. 78% of child families were illiterate, as a result, 75% of children don't have access to latrines and drinking water. Rivers and open-source waters are the common sources of drinking water, which comprised 79% of the total water supply. Therefore, from the research finding, it is possible to conclude that most child mortality is due to scarcity of water.ConclusionThe Power of test estimates indicated that the proposed method was better than the existing models. All covariant and dummy explanatory variables have a significant effect on the deaths of children. Hence, the multilevel Poisson model results indicated that there exists high variability among regions for the deaths of children. Therefore, this work suggested that the applications of the random-effects model provided a simple and robust means to predict the count response data model.

Background: Preeclampsia is a hypertensive disorder of pregnancy that affects 2-8% of pregnant women. It is the major cause of maternal and perinatal morbidity and mortality worldwide. The purpose of this study was to identify factors associated with hypertension measurements and time-to-onset of preeclampsia among pregnant women attending antenatal care service at Arerti Primary Hospital. Methodology: A retrospective longitudinal study design was employed on a total of 201 pregnant women attending the antenatal clinic of Arerti Primary Hospital between September 2018 and June 2019. A closed-form sample size formula for estimating the effect of the longitudinal data on time-to-event was used. To analyze our data we employed descriptive method, linear mixed effect model, Cox-PH model and joint models for longitudinal and survival outcomes.Relevantdemographicandclinicalcovariateswereincludedinsubmodels. Results: This study revealed that baseline age, visiting times, weight, diabetes, history of PE and parity had significantly associated with mean change in the BP measurements. From the Cox model result, age, weight, history of PE and marital status were associated with a significant hazard of developing preeclampsia. The univariate joint models reveal that the each longitudinal BP measurements are significantly associated with hazard of developing preeclampsia. Form the bi-ariate joint model; only DBP is significantly associated with risk of developing PE. Conclusion: As the result obtained in this study, we summarized that, age, weight, history of PE and marital status had a significant effect on time to developing preeclampsia. Furthermore, due to significance of association between the longitudinal BP measurements and time to onset of preeclampsia, joint model analysis was suggested as it incorporates all information simultaneously and provides valid and efficient inferences over separate models analysis.