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Intestinal parasites (IPs) are major concerns in most developing countries where HIV/AIDS cases are concentrated and almost 80% of AIDS patients die of AIDS-related infections. In the absence of highly active antiretroviral therapy (HAART), HIV/AIDS patients in developing countries unfortunately continue to suffer from the consequences of opportunistic and other intestinal parasites. The aim of the study was to determine the prevalence of intestinal parasites in relation to CD4+ T cells levels and anemia among HAART initiated and HAART naïve pediatric HIV patients in a Model ART center in Addis Ababa, Ethiopia. A prospective comparative cross-sectional study was conducted among HAART initiated and HAART naive pediatric HIV/AIDS patients attending a model ART center at Zewditu Memorial Hospital between August 05, 2013 and November 25, 2013. A total of 180 (79 HAART initiated and 101 HAART naïve) children were included by using consecutive sampling. Stool specimen was collected and processed using direct wet mount, formol-ether concentration and modified Ziehl-Neelsen staining techniques. A structured questionnaire was used to collect data on socio-demographic and associated risk factors. CD4+ T cells and complete blood counts were performed using BD FACScalibur and Cell-Dyn 1800, respectively. The data was analyzed by SPSS version 16 software. Logistic regressions were applied to assess any association between explanatory factors and outcome variables. P values < 0.05 were taken as statistically significant. The overall prevalence of IPs was 37.8% where 27.8% of HAART initiated and 45.5% of HAART naive pediatric HIV/AIDS patients were infected (p < 0.05). Cryptosporidium species, E. histolytica/dispar, Hook worm and Taenia species were IPs associated with CD4+ T cell counts <350 cells/μμL in HAART naive patients. The overall prevalence of anemia was 10% in HAART and 31.7% in non-HAART groups. Hook worm, S. stercoralis and H. nana were helminthes significantly associated with anemia in non-HAART patients [AOR, 95% CI: 4.5(1.3, 15.2), P< 0.05]. The prevalence of IPs in non-HAART patients was significantly associated with eating unwashed/raw fruit [AOR, 95%CI: 6.3(1.2, 25.6), P<0.05], open field defecation [AOR, 95%CI: 9.3(1.6, 53.6), P<0.05] and diarrhea [AOR, 95%CI: 5.2(1.3, 21.3), P<0.05]. IPs significantly increased in rural residents [AOR, 95%CI: 0.4(0.1, 0.9, P<0.05)]. The overall prevalence of intestinal parasites significantly differed by HAART status and cryptosporidium species were found only in HAART naïve patients with low CD4+ T cell counts. Anemia was also more prevalent and significantly associated with IPs in non-HAART patients. This study identified some environmental and associated risk factors for intestinal parasitic infections. Therefore, Public health measures should continue to emphasize the importance of environmental and personal hygiene to protect HIV/AIDS patients from infections with intestinal parasites and maximize the benefits of HAART.

Background Previous studies have implicated the role of H. pylori infection in developing the metabolic syndrome. However, findings remain contradictory, and data from developing countries are scarce. Methods We employed a cross-sectional study design to assess the relationship between H. pylori infection and metabolic syndrome among diabetic patients attending Jimma Hospital, Ethiopia. An interviewer-led questionnaire administered to study participants provided information on sociodemographic factors, and medical records were used to obtain medical history information. Metabolic parameters, including plasma glucose, triglycerides (TG), high-density lipoprotein cholesterol (HDL-c), body-mass index (BMI), waist circumference (WC), systolic blood pressure (SBP), and diastolic blood pressure (DBP) were collected. H. pylori infection status was assessed using IgG Enzyme-linked Immunosorbent Assays (ELISA). The effect of H. pylori infection on metabolic syndrome and metabolic parameters was determined using multivariate linear and logistic regressions. Results We found H. pylori infection status was positively but not significantly associated with metabolic syndrome (AOR = 1.507, 95% CI: 0.570–3.981, p  = 0.408). When the analysis was restricted to individual metabolic parameters, H. pylori positivity was significantly associated with lower HDL-c and higher SB, respectively. Conclusions Our result confirms that individual metabolic parameters, not an overall metabolic syndrome, are significantly associated with H. pylori infection. Future studies should examine the relationship between H. pylori and metabolic syndrome, considering gastrointestinal conditions such as GERD, GU, and DU.

Background Although previous epidemiological studies have examined the potential risk factors that increase the likelihood of acquiring  Helicobacter pylori  infections, most of these analyses have utilized conventional statistical models, including logistic regression, and have not benefited from advanced machine learning techniques. Objective We examined H. pylori  infection risk factors among school children using machine learning algorithms to identify important risk factors as well as to determine whether machine learning can be used to predict H. pylori infection status. Methods We applied feature selection and classification algorithms to data from a school-based cross-sectional survey in Ethiopia. The data set included 954 school children with 27 sociodemographic and lifestyle variables. We conducted five runs of tenfold cross-validation on the data. We combined the results of these runs for each combination of feature selection (e.g., Information Gain) and classification (e.g., Support Vector Machines) algorithms. Results The XGBoost classifier had the highest accuracy in predicting  H. pylori  infection status with an accuracy of 77%—a 13% improvement from the baseline accuracy of guessing the most frequent class (64% of the samples were H. Pylori negative.) K-Nearest Neighbors showed the worst performance across all classifiers. A similar performance was observed using the F1-score and area under the receiver operating curve (AUROC) classifier evaluation metrics. Among all features, place of residence (with urban residence increasing risk) was the most common risk factor for H. pylori infection, regardless of the feature selection method choice. Additionally, our machine learning algorithms identified other important risk factors for H. pylori infection, such as; electricity usage in the home, toilet type, and waste disposal location. Using a 75% cutoff for robustness, machine learning identified five of the eight significant features found by traditional multivariate logistic regression. However, when a lower robustness threshold is used, machine learning approaches identified more H. pylori risk factors than multivariate logistic regression and suggested risk factors not detected by logistic regression. Conclusion This study provides evidence that machine learning approaches are positioned to uncover H. pylori infection risk factors and predict H. pylori infection status. These approaches identify similar risk factors and predict infection with comparable accuracy to logistic regression, thus they could be used as an alternative method.

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. 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. 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. 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.

Podoconiosis is a geo-chemically induced, non-infectious, familial, chronic lymphedema of the legs that occurs among barefoot people in rural, farming communities with extreme poverty. Despite a growing body of research surrounding the disease, the pathogenesis of the disease is relatively unknown. This study aims to investigate the immunological and hematological profiles of individuals affected by podoconiosis in comparison to healthy controls. A comparative cross-sectional study was conducted in West Gojjam Zone of Ethiopia involving adult individuals clinically diagnosed with podoconiosis (n = 53) and healthy controls (n = 67) from the same area. A survey was conducted to gather information on sociodemographic, lifestyle characteristics, and clinical features of the disease. About nine ml whole blood samples were collected for hematological and immunological testing, which included IL-4, TNF-α, IL-6, IL-17, IL-10, TGF β and IFN-γ. Overall, we observed significant differences in hematological parameters between individuals with podoconiosis and healthy controls. Specifically, we found a notable reduction in white blood cell count, with an adjusted mean difference (AMD) of -1.15 (95% CI: -2.09 to -0.21; p = 0.017). Additionally, the differential white blood count showed a decrease in absolute neutrophils (AMD = -3.42, 95% CI: -4.15 to -2.69; p < 0.001) and absolute eosinophils (AMD = -0.20, 95% CI: -0.37 to -0.03; p = 0.019). Conversely, we noted an increase in absolute lymphocytes (AMD = 0.98, 95% CI: 0.50 to 1.46; p < 0.001) and monocytes (AMD = 0.54, 95% CI: 0.22 to 0.85; p = 0.001). However, we didn't observe a significant difference in cytokine profile between podoconiosis patients and healthy controls. The decrease in neutrophil counts among podoconiosis cases compared to healthy controls may provide insight into the potential disease pathogenesis, suggesting the involvement of autoimmune-related mechanisms, as it demonstrates a similar hematological profile to other autoimmune disorders.

Blood film microscopy is the gold standard approach for malaria diagnosis, and preferred method for routine patient diagnosis in health facilities. However, the inability of laboratory professionals to correctly detect and identify malaria parasites microscopically leads to an inappropriate administration of anti-malarial drugs to the patients and incorrect findings in research areas. This study was carried out to evaluate the performance of laboratory professionals in malaria diagnosis in health facilities under the Defense Health Main Department in Addis Ababa and its surroundings, Ethiopia. A cross sectional study was conducted from June to July 2015. Totally, 60 laboratory professionals out of the selected 16 health facilities were included in the study. Data were collected by distributing standardized pre-validated malaria slide-panels and self-administered questionnaires among professionals, onsite in each study facility. Sensitivity, specificity, and strength of agreement (with kappa score) in performance among the study participants against WHO-certified expert malaria microscopists were calculated. Of the 60 study participants, 8.3% (5/60) correctly read all the distributed slides in terms of parasite detection, species identification and parasite counting; whereas, each of the remaining 55(91.7%) interpreted at least two slides incorrectly. The overall sensitivity and specificity of participants' performance in detection of malaria parasites were 65.7% and 100%, respectively. Overall, fair agreement (71.4%; Kappa: 0.4) in detection of malaria parasite was observed between the study subjects and expert readers. The overall sensitivity and specificity of participants in species identification of malaria parasites were respectively 41.3% and 100%. Overall, slight agreement (51.1%; kappa: 0.04) in identification of malaria species was observed. Generally, agreement was lower in parasite detection and species identification at low parasite density and mixed infection cases. The general agreement between the study participants and expert microscopists in malaria parasite detection and species identification was very low, particularly in the cases of low-parasite density and mixed infections. Therefore, regular external quality assessments and further refreshment trainings are crucial to enhance the skill of professionals in malaria microscopy; particularly for those in non-malarious areas where exposure to malaria diagnosis is low.

Background Investigating distinct individual- and household-level risk factors for acquiring Helicobacter pylori ( H. pylori ) infection can inform disease prevention efforts and implicate possible routes of transmission. This study determined the magnitude of H. pylori infection among schoolchildren in Ziway, central Ethiopia and identified personal and household correlates of H. pylori infection in young Ethiopian children. Methods A total of 434 schoolchildren participated in this cross-sectional study. Infection status was assessed using antigen and antibody rapid tests. Demographic and lifestyle information was obtained from parents via an interviewer-led questionnaire. Univariate and multivariate logistic regressions were performed to assess the relationships between potential individual- and household-level risk factors and H. pylori infection. Results The prevalence of H. pylori infection was 65.7% (285/434). Of the personal variables assessed, the age group 10–14 years was found to be significantly associated with higher odds of H. pylori infection in univariate analysis (COR = 2.22, 95% CI: 1.06–4.66, p  = 0.03) and remained positively correlated after adjusting for confounding factors. Of the household-level factors explored, having a traditional pit or no toilet was found to be significantly associated with 3.93-fold higher odds of H. pylori infection (AOR = 3.93, 95% CI: 1.51–10.3, p  = 0.01), while the presence of smokers in the household was associated with 68% lower odds of infection (AOR = 0.32, 95% CI: 0.11–0.89, p = 0.03). Conclusion This study from a developing country provides additional evidence for older age as a personal risk factor for H. pylori infection and identifies correlations between socioeconomic and sanitation household factors and positive childhood infection status. The associations reported here support the hypothesized fecal-oralroute of transmission for H. pylori.

There 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. Our 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. We 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. 541 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. Our 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 Microscopic diagnosis of Giemsa stained thick and thin blood films by skilled microscopists has remained the standard laboratory method for the diagnosis of malaria. However, detection and identification of malaria parasites require well trained laboratory personnel. The objective of the study was to evaluate the performance of laboratory technologists and technicians in detecting and identifying malaria parasites in Hawassa town, Southern Ethiopia. Methods A cross-sectional study design was employed among a total of 80 laboratory professionals working in public and private health facilities. A standardized pre-validated slide panel and questionnaires were distributed to laboratory professionals working at eleven health facilities in Hawassa town, Southern Ethiopia. The panels included ten slides for diagnosis, [slide1: P.falciparum, 104/μl; slide 2: P.falciparum, 53404/μl; slide 3 and 4: mixed infection (both P. falciparum and P. vivax ); slide 5: P.vivax, 23503/μl; slide 6: P.vivax , 400/μl; and slides 7, 8, 9 and 10: negative slides]. Participants were asked to return the responses which were compared with expert microscopist. Agreement in detecting and identifying malaria parasites between participants and expert microscopists was estimated using the Kappa score. Results The mean age of the participants was 27 (SD = 4.1) years. More than half of the participants (56.9%) were female. Fourteen (19.4%) of the participants correctly reported all the ten distributed slides, whereas 58(80.6%) missed at least one slide. Overall, the sensitivity and specificity of participants in detection of malaria parasites were 82% and 96.5% respectively. The overall agreement between participants and reference readers on detection of malaria parasite was 88% (Kappa = 0.76) while on identification of malaria species was 74.3% (kappa = 0.63). Lower agreement on detection and identification of slides with low parasitic density and mixed infection were observed. Agreement was relatively lower for government health centers (69%; kappa = 0.56). None of the participants reported parasitic load per micro liter method. Conclusion Agreement of the participants with expert microscopist in the detection of malaria parasites was better than agreement in the identification of different species of malaria. Poor agreement was reported in detection of parasites at a low density and mixed infections.

Several factors including socio-economic and access to health facility influence burden of intestinal parasites. Epidemiological data from hard to reach areas will help to identify high-risk communities for targeted intervention. We, therefore, assessed the magnitude of intestinal parasites among Zay people residing in three islands of Lake Ziway in Ethiopia. This cross-sectional survey was conducted in March 2013 on 444 individuals aged 6 months to 85 years. Stool samples were analyzed using wet mount and formol-ether concentration methods. Data were collected using interviewer-administered questionnaire and analyzed using STATA version 10. Among the study participants, 52% (321/444) were children under 15 years. While 72.8% were positive for at least one intestinal parasite, single, dual and triple infections were found in 42.1%, 23.9% and 6.3%, respectively. Four types of intestinal parasites were detected in two children. The commonest parasites were Entamoeba histolytica/dispar (51.4%), Schistosoma mansoni (17.8%), Giardia lamblia (14.4%), Trichuris trichiura (10.8%), Taenia species (5.6%), Hymenolopis nana (4.5%), Ascaris lumbricoides (4.1%), Entrobius vermicularis (0.9%), Hookworm (0.7%), and Strongyloides stercoralis (0.2%). Remarkable proportion of study participants (51.3%) had no latrine and >85% of the islanders use the lake water for drinking, cleaning or both. About 36% had no information about waterborne and related diseases, while 31% never heard about bilharziasis. Fishing and farming were the main source of income. In the multivariate model, being in the age group > 15 years (AOR = 0.49; 95%CI = 0.28-0.85) and not using lake water for drinking or washing (AOR = 0.52; 95%CI = 0.28-0.99) had protective effect, after adjusting for education, occupation and hand wash after latrine use. The observed high rate of intestinal parasites (72.8%) in these hard to reach Islanders of Lake Ziway, warrants targeted and sustainable intervention.