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Tuberculosis disease burden remains a fundamental global public health concern for decades. The disease may not uniformly distributed with certain geographical areas recording higher notification rate than others. However, the Ethiopian national TB control program does not provide services based on those areas with the greatest notifications but rather on a uniform strategy. Therefore, this study aimed to assess the spatial distribution and presence of the spatio-temporal clustering of the disease in different geographic settings over 8 years in the East Hararge Zone. A retrospective space-time and spatial analysis were carried out at districts of East Hararghe zone based on a total of 34,564 notified TB cases during the study period. The study identified different case notification rate over districts and clustering effects for the purely spatial and spatiotemporal with different estimated relative risks. The study recommends national tuberculosis control program to give attention to highly observed case notification rates specially Babile, Haramaya and Jarso districs of East Hararge Zone to have effective TB intervention in the study area.

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Background: Pregnancy termination commonly known as abortion is the preventable causes for the maternal mortality worldwide that largely forgotten. About 45 % of these pregnancy terminations are unsafe causing death of more than 22,000 women every year and remains major public health problems in developing countries including Ethiopia. This study was also aimed to model and investigate risk factors associated with time to pregnancy termination in Ethiopia by applying survival model considering the clustering effects.Methods: The study considered 15,683 reproductive age group women from 2016 Ethiopian Demographic and Health Survey data. Kaplan-Meier(KM) was employed to estimate the survival curve and this estimated KM survival curve estimated for different groups were tested based on log rank test. To come up with appropriate model for the time to pregnancy termination and the associated risk factors both semi-parametric and parametric survival model with no frailty effects as wells as with shared frailty effects which handles random effects were employed and compared based AIC and BIC of the fitted models.Results: The result of the study showed generalized gamma and lognormal survival models were appropriate models compared with semi-parametric and other candidate parametric models.Fitting these survival model with frailty showed the improvement of the models which was an indication for the presence of unobservable random effects in clusters. Regarding the frailty models comparison, log normal with gamma frailty model was considered as appropriate model for fitting time to pregnancy termination model in Ethiopia compared with other candidate frailty models. Furthermore, the selected frailty model result showed that age of women, ever trying to avoid pregnancy, contraceptive method use, age at first sex, total number of children ever born and place of residence were the identified risk factors for the time to pregnancy termination at 5% level of significance.Conclusions: Based on the finding of this study, starting sex at early age, residing urban areas, having lower number of children, being in married marital status group, chewing chat and do not using contraceptive methods were the risk factors that results pregnancy termination at early age that needs serious consideration to prevent the problem in Ethiopia.

Background: Personalised or stratified medicine has played an increasingly important role in improving bio-medical care in recent years. A Bayesian joint modelling approach to dynamic prediction of HIV progression and mortality allows such individualised predictions to be made for HIV patients, based on monitoring of their CD4 counts. This study aims to provide predictions of patient-specific trajectories of HIV disease progression and survival.Methods: Longitudinal data on 254 HIV/AIDS patients who received ART between 2009 and 2014, and who had at least one CD4 count observed, were employed in a Bayesian joint model of disease progression, as measured by CD4 counts, and survival, to obtain individualised dynamic predictions of both processes that were updated at each visit time in the follow-up period. Different forms of association structure that relate the longitudinal CD4 biomarker and time to death were assessed; and predictions were averaged over the different models using Bayesian model averaging.Results: A total of 254 subjects were observed in the dataset with a median age of 30 years (interquartile range, IQR, 26–38). The individual follow-up times ranged from 1 to 120 months, with a median of 22 months and IQR 7 -39 months. The median baseline CD4 count was 129 cells/mm3 (IQR 61–247 cells/mm3). From the joint model with highest posterior weight, subjects whose functional status was working were significantly associated with a higher baseline CD4 count (β = 1.86; 95% CI: 0.65 3.04) whereas subjects who were bedridden were significantly associated with a lower baseline CD4 count (estimated effect β = -3.54; 95% CI: -5.65, -1.39), compared to ambulatory patients. A unit increase in weight of the individual increased the mean square root CD4 measurement by 0.06. The estimates of the association structure parameters from all three models considered indicated that the HIV mortality hazard at any time point is associated with the current underlying value of the CD4 count at the same time point. The model with highest posterior weight also had a time-dependent slope, indicating that HIV mortality is also associated with the rate of change in CD4 count. From both the model-averaged predictions and the highest posterior weight model alone, an increase in CD4 count was predicted at different visit times from the dynamic predictions. It was also found that there was an increase in the width of prediction intervals as time progressed.Conclusions: Functional status, weight and alcohol intake are important contributing factors that affect the mean square root of CD4 measurements. For this particular dataset, model averaging the dynamic predictions resulted in only one of the hypothesised association structures having non-zero weight at the majority of time points for each individual. The predictions were therefore similar whether we averaged them over models or derived them from the highest posterior weight model alone. We also observed that the parameter estimates in the both the CD4 count and survival sub-models showed slight variability between the postulated association structures.