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Background Coagulopathy and thromboembolic events are among the complications of Corona Virus disease 2019 (COVID-19). Abnormal coagulation parameters in COVID-19 patients are important prognostic factors of disease severity. The aim of this study was to analyze coagulation profiles of hospitalized COVID-19 patients in Addis Ababa, Ethiopia. Methods This prospective cross-sectional study was conducted among 455 Covid-19 patients admitted at Millennium COVID-19 care and treatment center, Addis Ababa, Ethiopia from July 1- October 23, 2020. Prothrombin Time (PT), Activated Partial Thromboplastin Time (APTT) and International normalized ratio (INR) were determined on HUMACLOT DUE PLUS® coagulation analyzer (Wiesbaden, Germany). In all statistical analysis of results, p<0.05 was defined as statistically significant. Result A prolonged prothrombin time was found in 46.8% of study participants with COVID-19 and a prolonged prothrombin time and elevated INR in 53.3% of study subjects with severe and 51% of critically COVID patients. Thrombocytopenia was detected in 22.1% of COVID-19 patients. 50.5% and 51.3% of COVID-19 patients older than 55 years had thrombocytopenia and prolonged APTT respectively. Conclusion In this study, prolonged prothrombin time and elevated INR were detected in more than 50% of severe and critical COVID-19 patients. Thrombocytopenia and prolonged APTT were dominant in COVID-19 patients older than 55 years. Thus, we recommend emphasis to be given for monitoring of platelet count, PT, APTT and INR in hospitalized and admitted COVID-19 patients.

Background Hypertension is the major public health concern; leading to cardiovascular disease. It is associated with alteration in hematological parameters which may lead to end-organ damage. Thus, this study aimed to compare hematological parameters between hypertensive and normotensive adult groups in Harar, eastern Ethiopia. Methods A comparative cross-sectional study was conducted from January to March, 2020 at Jugel and Hiwotfana Specialized University hospital, Harar, eastern Ethiopia. Convenient sampling technique was used to recruit 102 hypertensive patients from the two hospitals and 102 apparently healthy blood donors. Participant’s socio-demographic and clinical information were collected using pre-tested structured questionnaire. Blood sample were collected and analyzed by Beckman Coulter DxH 500 analyzer for complete blood count. The data were entered and analyzed using SPSS version 23. Independent t-test and Mann Whitney u-test was used for comparison between groups. Spearman’s correlation was used for correlation test. P values less than 0.05 was considered as statistically significant. Result 102 hypertensive and 102 healthy controls were enrolled in this study. The median ± IQR value of white blood cell (WBC) count, hemoglobin (Hgb), hematocrit (HCT), red cell distribution width (RDW) and mean platelet volume (MPV) were significantly higher in hypertensive group compared to apparently healthy control group. Additionally, RBC (red blood cell) count, HCT and RDW showed statistically significant positive correlations with systolic and diastolic blood pressure. WBC count and RDW were significantly and positively correlated with body mass index (BMI). Platelet (PLT) count had a significant but negative correlation (r = -0.219, P = 0.027) with duration of hypertension illness while MPV showed positive and significant correlation (r = 0.255, P = 0.010). Conclusion The median values of WBC, Hgb, HCT, RDW and MPV were significantly higher in hypertensive patient compared to apparently healthy individuals. Hence, it is important to assess hematological parameters for hypertensive individuals which may help to prevent complications associated with hematological aberrations. However, further studies are required to understand hypertensive associated changes in hematological parameters.

BackgroundThere has been a noticeable increase in the prevalence of allergy-related disorders (ARDs) in the modern era. Urbanization is believed to be a major environmental risk factor for the onset of ARDs but data from low- to middle-income countries is limited.ObjectiveOur purpose was to assess the prevalence of ARDs and atopy among a population of rural Ethiopian school children and identify environmental and lifestyle factors associated with such disorders.MethodsWe performed a cross-sectional study on 541 school-children. An interviewer-led questionnaire administered to the mothers of each participant provided information on demographic and lifestyle variables. Questions on allergic disease symptoms were based on the International Study of Asthma and Allergies in Children (ISAAC) core allergy and environmental questionnaire. Skin prick test for common allergens German cockroach (Blattella germanica) and dust mite (Dermatophagoides) was performed to define atopy. Multiple logistic regression analyses were performed to determine the odds ratio between ARDs and atopy with specific environmental and lifestyle habits.Results541 children responded to the survey questions: the majority of participants were female (60.3%) and aged 10–15 years-old. The prevalence of any ARD was 27%, while the rates of ever-having eczema, rhinitis, and wheeze was found to be 16.8%, 9.6%, and 8.6% respectively. Only 3.6% (19 school-children) tested positive for any skin sensitization. Analysis of associated factors for ARDs found that a family history of allergic disorders (AOR: 2.80; p-value<0.01), use of insecticides (AOR: 2.05; p-value<0.01), and wearing open-toed shoes (AOR: 2.19; p-value = 0.02) were all significantly associated factors. Insecticide use, river-bathing, and infection with intestinal parasites were found to be significantly associated factors for atopy. Other potential risk factors such as frequent use of soap, bacterial infection, and household crowding had no statistical significance.ConclusionOur study suggests that the prevalence of skin sensitization and ARDs in rural populations of developing countries is still relatively low. We identified several possible risk factors for further investigation. Overall, the significance of identified risk factors appears to indicate that genetic predisposition and exposure to environmental pollution are more important to the etiology of ARDs and atopy than specific lifestyle behaviors.

Background Previous epidemiological studies have examined the prevalence and risk factors for a variety of parasitic illnesses, including protozoan and soil-transmitted helminth (STH, e.g., hookworms and roundworms) infections. Despite advancements in machine learning for data analysis, the majority of these studies use traditional logistic regression to identify significant risk factors. Methods In this study, we used data from a survey of 54 risk factors for intestinal parasitosis in 954 Ethiopian school children. We investigated whether machine learning approaches can supplement traditional logistic regression in identifying intestinal parasite infection risk factors. We used feature selection methods such as InfoGain (IG), ReliefF (ReF), Joint Mutual Information (JMI), and Minimum Redundancy Maximum Relevance (MRMR). Additionally, we predicted children’s parasitic infection status using classifiers such as Logistic Regression (LR), Support Vector Machines (SVM), Random Forests (RF) and XGBoost (XGB), and compared their accuracy and area under the receiver operating characteristic curve (AUROC) scores. For optimal model training, we performed tenfold cross-validation and tuned the classifier hyperparameters. We balanced our dataset using the Synthetic Minority Oversampling (SMOTE) method. Additionally, we used association rule learning to establish a link between risk factors and parasitic infections. Key findings Our study demonstrated that machine learning could be used in conjunction with logistic regression. Using machine learning, we developed models that accurately predicted four parasitic infections: any parasitic infection at 79.9% accuracy, helminth infection at 84.9%, any STH infection at 95.9%, and protozoan infection at 94.2%. The Random Forests (RF) and Support Vector Machines (SVM) classifiers achieved the highest accuracy when top 20 risk factors were considered using Joint Mutual Information (JMI) or all features were used. The best predictors of infection were socioeconomic, demographic, and hematological characteristics. Conclusions We demonstrated that feature selection and association rule learning are useful strategies for detecting risk factors for parasite infection. Additionally, we showed that advanced classifiers might be utilized to predict children’s parasitic infection status. When combined with standard logistic regression models, machine learning techniques can identify novel risk factors and predict infection risk.