Browsing by Author "Teshome, Birhanu(PhD)"
Item Analysis of Risk Factors for Under-Five Child Malnutrition in Ethiopia: Multi-Level Approach(Addis Abeba university, 2016-06) Workineh, Sintayehu; Teshome, Birhanu(PhD)Although the problem of malnutrition affects the entire population, children are more vulnerable because it reduces their physical growth, proper organ formulation and cognitive development and weakens immune system. Under-nutrition contributes half of all deaths in children underfive in Africa and Asia. The solution of this big public health problem is essential. From this perspective, this study aimed to explore risk factors for under-five child malnutrition in Ethiopia. The data for the study were taken from Ethiopian Mini Demographic and Health Survey (EMDHS) of year 2014. To achieve objectives of the study, two indicators (stunting and wasting) were studied. The two indicators were treated separately due to their biological differences. Statistical models that handle the complexities of correlated data were employed. Generalized Estimating Equations (GEEs), Alternating Logistic Regression (ALR), Proportional Odds Model (POM), Partial Proportional Odds Model (PPOM) and baseline category logit models were used for analysis. Results showed that mothers with less educational level, preceding birth interval less than 24 months, from Tigray, Affar and SNNP regions were associated with higher probability of stunting. Child age in months had positive effect on child stunting. Children from non-educated mothers, from Affar and Somali regions had higher chance of wasting. Two-level ALR analysis indicated strong association ( ) between two children from the same households. In conclusion, results suggest that region, mother’s education level, wealth index, preceding birth interval and child age were associated with stunting, while region, mother’s education level, mother’s current marital status, sex and age of child in months were associated with wasting among children under-five in Ethiopia. To reduce under-five child malnutrition, some crucial steps regarding educating mothers and improving economic situation of population as well as supplementary feeding programs should be considered. Key Words: Alternating Logistic Regression (ALR), Generalized Estimating Equations (GEEs), Proportional Odds Model (POM), Partial Proportional Odds Model (PPOM) Baseline Category Logit, Stunting, WastingItem Application of Longitudinal Count Data Models to Progression of CD4 Count: A Case of Debre Markos Referral Hospital(Addis Abeba university, 2017-06) Desyebelew, Belay; Teshome, Birhanu(PhD)Even though the world is ghting HIV disease in unity and patients are getting antiretroviral therapy treatment, HIV disease continues to be a serious health issue for parts of the world and large number of AIDS related deaths are being registered every year. A number of studies have been conducted to assess factors related with the progression of the disease using surrogate end- points like CD4 cell count. The main objective of this study was to make use of appropriate statistical models to analyze CD4 cell counts data and identify associated risk factors a ecting the CD4 cell progression of patients under ART tra etment in Debre Markos Re eral Hospital. In this longitudinal retrospective cohort based study, data was collected from 445 HIV patients reg- istered for ART treatment between September, 2005 and August, 2014 in the Hospital. Poisson, Poisson-Gamma, Poisson-Normal, and Poisson-Gamma-Normal models were applied to account for overdispersion and correlation in the data. Poisson-Gamma-Normal model with random in- tercept was selected as a best model to t the data based on di erent model selection criteria. The ndings of the study revealed that time in months, sex of patients, baseline WHO stage and baseline CD4 cell count were found to be signi cant factors for progression of HIV patients' CD4 cell count. Patients who started ART at higher baseline CD4 counts evolved higher than those who started at lower CD4 counts. Therefore, patients should start ART treatment early to increase their CD4 cell count progression. Keywords: CD4 count, Longitudinal data analysis, Poisson-Normal Model, Poisson-Gamma- Normal model, Antiretroviral therapy (ART), HIV/AIDSItem A Joint Modeling of Longitudinal and Survival Data With Application to HIV-Infected Patients under HAART Follow-Up: a Case of Mekelle General Hospital, Ethiopia(Addis Abeba university, 2017-06) Boja, Getu; Teshome, Birhanu(PhD)Despite tremendous progress in the control of the global HIV epidemic, the burden of HIV is still severe in Sub-Saharan Africa. Longitudinal and survival data frequently observed together in practice and useful for analysis of HIV related data. The separate analyses of longitudinal and survival endpoints may not be adequate and could lead to ine cient estimation or biased results. Joint modeling approaches correct for this bias by accounting for the association between the two responses. The main purpose of this study was to jointly model and analyze longitu- dinal and survival endpoints with application to retrospective cohort data of 469 HIV-infected patients under HAART follow-up in Mekelle General Hospital, Tigray, Ethiopia. The analysis consists of exploratory data analysis and tting three di erent models namely; a linear mixed e ects model for the longitudinal data, a semi-parametric survival model for the time-to-event data and a joint modeling of the two responses via shared random-e ects approach. The results of both the separate and joint analyses are consistent. However, the use of a joint analysis compared to independent models shows a reduction in the standard errors which indicates that more adequate and e cient inferences can be made by using joint model estimates. The esti- mated association parameter ( ) in the joint model is -0.138 (with 95% CI: -0.196 -0.079) and statistically signi cant (p value < 0:0001). This indicates that there is strong evidence of association between the e ect of the longitudinal biomarker to the risk of death. The results indicates that higher initial values of CD4 cells is associated with a better survival. Further- more, patients with lower initial weight, being male, late WHO clinical stage, being ambulatory and bedridden were associated with higher risk of death. Future extension of this research could possibly be to account for missing data and attempt should be given to health workers and data clerks working with patients under HAART to improve the quality of the data records of patients. Keywords: HAART, HIV/AIDS Data, Joint Modeling, Longitudinal Data Analysis, Survival Data AnalysisItem Modeling CD4 + Cell Counts of HIV-Positive Patients Following Antiretroviral Therapy (ART): A Case of Yekatit 12 Hospital, Addis Ababa(Addis Abeba university, 2016-06) Tadesse, Kebadu; Teshome, Birhanu(PhD)HIV infection leads to severe depletion of CD4+ counts with subsequent reduced levels of circulating CD4+ lymphocytes in the peripheral blood. CD4+ counts are the primary laboratory markers used to track the progression of HIV to AIDS. It is the strongest predictor for risk of death and AIDS (DHHS, 2011) at the time of initiating therapy and used to decide when to start ART treatment. The main objective of this study was to model the progression of CD4+ cell counts of HIV- positive patients following ART. A longitudinal data was obtained from ART case unit of HIV positive patients in Yekatit 12 Hospital who were following ART from September 10, 2009 to September 9, 2012 G.C and followed until January 9, 2016 G.C, Addis Ababa. The data was extracted from SMART CARE of patients chart and patients with at least three times of measurements and those in first line ART regimens were included. The linear mixed model analysis was applied taking the correlation between CD4+ counts of patients into account. We included 217 patients of whom 141 (65%) were females and only 51.61% of patients were followed until the last follow up time inducing 48.39% missingness. The results from the linear mixed model with AR(1) covariance structure showed that baseline CD4+ count, WHO clinical stage, time and the interaction effects of sex, Age, baseline CD4+ count and regimen class with time were statistically significant factors for the progression of CD4+ count at a square root level over time. Results from the full likelihood and multiple imputation approaches were found to be almost identical for covariates that were significant. The mean CD4+ count showed an increasing progress over time after patients were initiated on ART. We recommend that patients should be initiated to ART regimens at an early WHO clinical stages, at a higher CD4+ count and ART regimens should be taken continuously to increase the progress of CD4+ count. KEY WORDS: ART, CD4+, Linear Mixed Model, Longitudinal Data, Missing Data, AR(1), ML, MI, MARItem Survival Analysis of Recurrent Events: an Application to Diabetes Mellitus Patients in The Case of Menellik li Referral Hospital(Addis Abeba university, 2016-06) Derebew, Bizuwork; Teshome, Birhanu(PhD)Diabetes mellitus is a group of metabolic diseases characterized by elevated blood glucose levels (hyperglycemia) resulting from defects in insulin discharge, insulin action or both. Recovery to normal blood sugar level in DM patients often is recurrent and correlation between events needs to be taken into account during analysis of such data. The main objectives of this research were to analysis time to recovery of DM patients and make a comparison between standard Cox-proportional Hazard (PH) and Frailty models. To achieve the objectives of the study, a retrospective data were obtained from Menellik II Referral Hospital chronic patient’s clinic. All diabetes patients of over 15 years of age and who were under treatment between 2009 and 2015 were included in the study. Unmeasured shared similarities due to the impact of multiple events were modeled using a random effect (Frailty) term. The Likelihood Cross Validation (LCV) criteria were used for comparison between the Standard Cox PH and Frailty model. Shared Log- Normal Frailty model had a minimum value of LCV than the Cox PH and shared Gamma Frailty models. Hence, the shared log-normal Frailty model was chosen for analysis of the recurrent event of time to recovery of DM patients in Menellik II Referral Hospital. The median recovery time of DM patients was 32 weeks. The patient’s sex and Regimen groups at baseline were significantly associated with recurrent event of time to recovery of DM patients. Key Words: Recurrent events, Frailty, Gamma, Log-normal, LCV, Penalized Marginal Likelihood