SURVIVAL ANALYSIS OF TIME TO BLINDNESS OF GLAUCOMA PATIENTS AT FELEGE HIWOT REFERRAL HOSPITAL, BAHIR DAR, ETHIOPIA

Purpose: The purpose of this study was to identify factors that affect the time to the blindness of glaucoma patients. Design: This study was a retrospective, single-center cohort. Participants: The medical charts of 328 randomly selected glaucoma patients under the follow-up from January 2014 to December 2018 were included.  Methods: A Cox proportional hazard model was employed to identify the risk factors of the blindness of glaucoma patients. Main Outcome Measures: Coefficients of the Cox proportional hazard models. Results: The Cox proportional hazard model showed that age (HR=1.018; P = 0.0344), had high blood pressure (HR=2.813; P < 0.0001), had diabetic disease (HR=1.595; P = 0.0442), the timolol with pilocarpine medication (HR=0.554; P = 0.0043), medium duration of treatment (HR = 0.0225; P < 0.0001), long duration of treatment (HR=0.0004; P < 0.0001), the cup- disk ratio greater than 0.7 (HR= 3.699; P < 0.0001) were a significant factor with glaucoma patients. Conclusions: The age, blood pressure, diabetic disease, type of medication, duration of treatment, and cup-disk ratio were statistically significant factors on time to the blindness of glaucoma patients. Therefore, authorities and other concerned bodies give greater attention to reduce the chance of blindness of glaucoma patients by creating a focus for patients approximately irreversible blindness because of glaucoma disease

The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe

Factors Affecting Contraceptive Use in Ethiopian: A Generalized Linear Mixed Effect Model

BACKGROUND: Ethiopia is the second most populous nations in Africa. Family planning is a viable solution to control such fast-growing population. This study aimed to assess the prevalence of contraceptive use and its predictors in Ethiopia.METHODS: About 4,563 women were drawn randomly by Central Statistics Agency from its master sampling frame. The survey was conducted from January, 2014 to March, 2016 within six months’ interval for the study period. The study was conducted using secondary data collected by PMA2020/Ethiopia project. Negative Binomial regression model was employed for data analysis. The model was selected using information criterion.RESULTS: Predictors like easy access of health service, residence area, level of health institutions, regions, availability of community health volunteers, experience sharing, support from husbands, level of education and employment status of women as well as residence area significantly affected the performance of contraceptive use in Ethiopia. From the interaction effects of health centers with region and health post with number of opening days per a week were significant predictors of the contraceptive use.CONCLUSION: The performance of contraceptive use was different from one individual to another because of their experience sharing, support from their husbands, employment status and education level. A woman who got encouragement to use birth control from her husband had good performance to be effective for her contraceptive use. There should be an experience sharing/orientation, about use of birth control to protect women from unwanted pregnancy. Hence, rural women should get experience from urban women

Predictors for CD4 cell count and hemoglobin level with survival time to default for HIV positive adults under ART treatment at University of Gondar Comprehensive and Specialized Hospital, Ethiopia

Abstract Background HIV/AIDS is the most known powerful risk factor for morbidity and mortality in the world. The greatest biological markers in HIV patients are CD4 cell count and hemoglobin level, as they are independent predictors of survival of HIV patients. The objective of this study was to investigate the common socio-demographic, clinical, and behavioral Predictor’s affecting the CD4 cell count, and hemoglobin level with survival time to default from ART treatment among HIV positive adults under ART treatment at university of Gondar comprehensive and specialized hospital, North-west Ethiopia. Method This study was conducted at University of Gondar comprehensive specialized hospital by using a retrospective cohort follow up study design. The source of data in this study was secondary data obtained from patients chart. Bayesian joint models were employed to get wide-ranging information about HIV/AIDS progression. Result From a total of 403 HIV positive adults, about 44.2% were defaulted from therapy and the rest were actively followed ART treatment. The estimate of the association parameter for the current true value of CD4 cell count ( $${\alpha }_{1}$$ α 1 ), and hemoglobin level ( $${\alpha }_{2}$$ α 2 ), trend of CD4 cell count ( $${\alpha }_{2}$$ α 2 ) and hemoglobin level ( $${b}_{2}$$ b 2 ) is positive. Positive values indicating that the higher CD4 cell count and hemoglobin level is related with the higher time of defaulting from ART. Predictor’s hematocrit, weight, platelet cell count, lymphocyte count, sex, adherence, and WHO clinical stage were joint determinate risk factors affecting CD4 cell count, hemoglobin level and time to default at 5% level of significance. Conclusion Current study results revealed that hematocrit, weight, BMI, platelet cell count, lymphocyte count, sex (female), and good treatment adherence were significantly associated with higher CD4 cell count, hemoglobin level and time to default while having advanced WHO clinical stage-IV had significantly decreased CD4 cell, hemoglobin level, and time to default from treatment. Patients with HIV should be given special attention based on these important factors to improve their health and prolong their lives

Determinants of CD4 cell count change and time-to default from HAART; a comparison of separate and joint models

Abstract Background HIV has the most serious effects in Sub-Saharan African countries as compared to countries in other parts of the world. As part of these countries, Ethiopia has been affected significantly by the disease, and the burden of the disease has become worst in the Amhara Region, one of the eleven regions of the country. Being a defaulter or dropout of HIV patients from the treatment plays a significant role in treatment failure. The current research was conducted with the objective of comparing the performance of the joint and the separate modelling approaches in determining important factors that affect HIV patients’ longitudinal CD4 cell count change and time to default from treatment. Methods Longitudinal data was obtained from the records of 792 HIV adult patients at Felege-Hiwot Teaching and Specialized Hospital in Ethiopia. Two alternative approaches, namely separate and joint modeling data analyses, were conducted in the current study. Joint modeling was conducted for an analysis of the change of CD4 cell count and the time to default in the treatment. In the joint model, a generalized linear mixed effects model and Weibul survival sub-models were combined together for the repetitive measures of the CD4 cell count change and the number of follow-ups in which patients wait in the treatment. Finally, the two models were linked through their shared unobserved random effects using a shared parameter model. Results Both separate and joint modeling approach revealed a consistent result. However, the joint modeling approach was more parsimonious and fitted the given data well as compared to the separate one. Age, baseline CD4 cell count, marital status, sex, ownership of cell phone, adherence to HAART, disclosure of the disease and the number of follow-ups were important predictors for both the fluctuation of CD4 cell count and the time-to default from treatment. The inclusion of patient-specific variations in the analyses of the two outcomes improved the model significantly. Conclusion Certain groups of patients were identified in the current investigation. The groups already identified had high fluctuation in the number of CD4 cell count and defaulted from HAART without any convincing reasons. Such patients need high intervention to adhere to the prescribed medication

Long term predictors of adherence to Antiretroviral Therapy for HIV positive adults at Felege-Hiwot Teaching and Specialized Hospital, North-West Ethiopia: a transitional study

In clinical trials and practices, failure of adherence to medications is a common challenge among patients with chronic diseases. Many factors are associated with this failure. Reports of previous studies about predictors of adherence to Highly Active Antiretroviral Therapy (HAART) were not consistent. The main objective of this study was to identify predictors of long-term adherence to HAART, considering lag variables as additional predictors. Transitional modeling was used to determine the predictors of long-term adherence to HAART. A retrospective transitional study design was conducted on 792 randomly selected adult patients at Felege-Hiwot Teaching and Specialized Hospital, Bahir Dar, Ethiopia. Results revealed that the first two lag-variables ( ) were significantly associated with performance of current adherence to HAART. The increase in CD4 cell count change was significantly associated with current adherence, if patients made transition from adherent level at lag2 to non-adherent level at lag1 ( ). As a conclusion, for patients who were transferred from adherent level at lag2 to non-adherent level at lag1, their CD4 cell count changes were positively correlated with current adherence level. Due attention should thus be given to address the specific needs of each group of patients. Non-adherence to HAART in this long-term treatment program was at risk and should receive interventional action. Educational therapy during follow-ups should also be given to non-adherent patients to strengthen the era of long-term treatment

Factors affecting first month adherence due to antiretroviral therapy among HIV-positive adults at Felege Hiwot Teaching and Specialized Hospital, north-western Ethiopia; a prospective study

Abstract Background Non-adherence to Highly Active Antiretroviral Therapy (HAART) is one of the factors for treatment failure in human immunodeficiency virus (HIV) infected patients in developing countries. The main objective of this study was to identify factors for treatment failure among adult HIV patients based on the assessment of first month adherence in the study area. Methods The study was conducted using secondary data from antiretroviral unit at Felege Hiwot Teaching and Specialized Hospital. A prospective study was undertaken on 792 randomly selected adult HIV positive patients who have started HAART. The variable of interest, adherence to HAART was categorized as non-adherence if a patient had taken less than 95% of the prescribed medication and this was measured using pill counts. Descriptive statistics, Chi-square tests of association, independent samples t-test and binary logistic regression were used for data analysis. Results In first month therapy, 68.2% of the patients belong to adherence group to HAART. As age increases, a patient without cell phone was less likely to be adherent to HAART as compared to patients with cell phone (AOR = 0.661, 95% CI: (0.243, 0.964)). Compared to urban patients, rural patients were less likely to adhere to HAART (AOR = 0.995, 95% CI: (0.403, 0.999)). A patient who did not disclose his/her disease to families or communities had less probability to be adherent to HAART (AOR = 0.325, 95% CI: (0.01, 0.64)). Similarly, a patient who did not get social support (AOR = 0.42, 95% CI: (0,021, 0.473)) had less probability of adherence to HAART. The main reasons for patients to be non-adherent were forgetfulness, side effects, feeling sick and running out of medication. Conclusion This study indentified certain groups of patients who are at higher risk and who need counseling. Such groups should be targeted and tailored for improvement of adherence to HAART among HIV positive adults. The health care providers should advise the community to provide social support to HIV positive patients whenever their disease is disclosed. On the other hand, patients should disclose their disease to community to get integrated supports. HIV infected patients who are directed to start HAART should adhere the prescribed medication. For the adherence to be effective, patients who have cell phone should use them as reminder to take pills on time

Cardiac patients’ surgery outcome and associated factors in Ethiopia: application of machine learning

Abstract Introduction Cardiovascular diseases are a class of heart and blood vessel-related illnesses. In Sub-Saharan Africa, including Ethiopia, preventable heart disease continues to be a significant factor, contrasting with its presence in developed nations. Therefore, the objective of the study was to assess the prevalence of death due to cardiac disease and its risk factors among heart patients in Ethiopia. Methods The current investigation included all cardiac patients who had cardiac surgery in the country between 2012 and 2023. A total of 1520 individuals were participated in the study. Data collection took place between February 2022 and January 2023. The study design was a retrospective cohort since the study track back patients’ chart since 2012. Machine learning algorithms were applied for data analysis. For machine learning algorithms comparison, lift and AUC was applied. Results From all possible algorithms, logistic algorithm at 90%/10% was the best fit since it produces the maximum AUC value. In addition, based on the lift value of 3.33, it can be concluded that the logistic regression algorithm was performing well and providing substantial improvement over random selection. From the logistic regression machine learning algorithms, age, saturated oxygen, ejection fraction, duration of cardiac center stays after surgery, waiting time to surgery, hemoglobin, and creatinine were significant predictors of death. Conclusion Some of the predictors for the death of cardiac disease patients are identified as such special attention should be given to aged patients, for patients waiting for long periods of time to get surgery, lower saturated oxygen, higher creatinine value, lower ejection fraction and for patients with lower hemoglobin values

Time to recovery and its determinant factors among patients with COVID-19 in Assosa COVID-19 treatment center, Western Ethiopia

Abstract Background The Novel Coronavirus disease (COVID-19) pandemic has become a global threat. Determining the time to recovery from COVID-19 is intended to assist healthcare professionals in providing better care, and planning logistics. So, the study aimed to identify the factors that affect the time to recovery from COVID-19 for patients treated at Assosa COVID-19 treatment center, Benishangul Gumuz Regional State, Western Ethiopia. Methods A retrospective study design was conducted on 334 randomly selected COVID-19 patients at Assosa COVID-19 treatment center from February 2021 to July 2021. The median survival time, Kaplan–Meier survival estimate, and Log-Rank test were used to describe the data and compare the survival time between groups. The study used the Cox PH model to analyze the time to the first recovery of COVID-19 patients, where hazard ratio, p-value, and 95% CI for hazard ratio were used for testing significance. Schoenfeld and Cox-Snell residuals were used for checking the model assumption. Results The overall incidence rate was 13.79 per 100 (95% CI: 10.04, 18.95) person-days observations. The median time to recovery was 16 days. At the end of the follow-up, 77.2% of the patients had developed an event of recovery, and the rest 22.8% were censored. The mean age of patients was 45.22 years. Severe COVID-19 patients (AHR = 0.7876, 95% CI: 0.7090, 0.8748), presence of symptoms (AHR = 0.2814, 95% CI: 0.1340, 0.5914), comorbidity (AHR = 0.1627, 95% CI: 0.1396, 0.1897), ≥ 90 oxygen saturation (AHR = 3.2370, 95% CI: 2.161, 4.848), and being older age (AHR = 0.9840, 95% CI: 0.971, 0.9973) were found to have statistically significant association with the time to recovery from COVID-19. Conclusion The study concludes that severe COVID-19 patients, male patients, patients having comorbidity, older age, and patients having symptoms as poor prognostic factors of COVID-19 disease and also prolonged recovery time. Therefore, health providers in treatment centers should give strict follow-up and priority to older patients, severe COVID-19 patients, and patients having another co-morbid illness by focusing on respiratory difficulties and underlying pre-existing medical conditions to manage the disease severity and recover quickly

Determinants of Time-to-Death of Chronic Lymphocytic Leukemia Patients at Felege Hiwot Referral Hospital, Bahir Dar, Ethiopia

Background: Leukemia is a group of cancers that usually begin in the bone marrow and results in a large number of abnormal white blood cells. Chronic Lymphocytic Leukemia is the most prevalent leukemia in Western countries, with an estimated incidence rate of less than 1 to 5.5 per 100 000 people, and average age at diagnosis of 64 to 72 years. It is more common in men among Chronic Lymphocytic Leukemia patients in Ethiopia’s hospitals at Felege Hiwot Referal Hospital. Methods: A retrospective cohort research design was employed to acquire critical information from patients’ medical records in order to achieve the study’s purpose. The study comprised the medical records of 312 Chronic Lymphocytic Leukemia who were followed from January 1, 2018 to December 31, 2020. A Cox proportional hazard model was used to determine the risk factors for time to death in Chronic Lymphocytic Leukemia patients. Results: Accordingly the Cox proportional hazard model, age (Hazard Ratio = 11.36; P  < .001), sex of male (Hazard Ratio = 1.04; P  = .004), married status (Hazard Ratio = 0.03; P  = .003), medium stages of Chronic Lymphocytic Leukemia (Hazard Ratio = 1.29; P  = .024), high stages of Chronic Lymphocytic Leukemia (Hazard Ratio = 1.99; P  < .001), presence of anemia (Hazard Ratio =0.09; P  = .005), platelets (Hazard Ratio = 2.11; P  = .007), hemoglobin (Hazard Ratio = 0.02; P  < .001), lymphocytes (Hazard Ratio = 0.29; P  = .006), red blood cell (Hazard Ratio = 0.02; P  < .001), which patients with Chronic Lymphocytic Leukemia had a significant relationship with time to death. Conclusions: Age, sex, Chronic Lymphocytic Leukemia stage, anemia, platelets, hemoglobin, lymphocytes, and red blood cells were all statistically significant determinants in the time to death of Chronic Lymphocytic Leukemia patients, according to the data. As a result, healthcare providers should pay particular attention to and emphasize the identified characteristics, as well as provide frequent counseling on how to enhance the health of Chronic Lymphocytic Leukemia patients