Antenatal depression and its predictors among HIV positive women in Sub-Saharan Africa; a systematic review and meta-analysis

BackgroundAntenatal depression in Human Immunodeficiency Virus (HIV) positive pregnant women can have significant adverse effects on both the mother and newborns, yet it is often overlooked in pregnancy care in Sub-Saharan Africa (SSA). Despite this, there is limited data on the combined prevalence of antenatal depression and its predictors among HIV-positive women in the region.ObjectiveTo assess the pooled prevalence of antenatal depression and its associated factors among HIV-positive women in SSA.MethodsAll primary cross-sectional studies published before 1st January/2024, were included. We conducted searches in relevant databases; PubMed, HINARI, Web of Science, PsycINFO, Psychiatry Online, ScienceDirect, and Google Scholar. The Joanna Briggs Institute checklist was used to critically appraise the selected studies. To assess heterogeneity among the studies, we utilized the I2 test. Publication bias was evaluated using a funnel plot and Egger’s test. The forest plot was used to present the combined proportion of antenatal depression and odds ratio, along with a 95% confidence interval.ResultsThe pooled prevalence of antenatal depression among HIV-positive women in Sub-Saharan Africa was found to be 30.6% (95% CI, 19.8%-41.3%). Factors significantly associated with antenatal depression among HIV-positive women in SSA included being unmarried (AOR: 3.09, 95% CI: 1.57 – 6.07), having a previous history of depression (AOR: 2.97, 95% CI: 1.79 – 4.91), experiencing intimate partner violence (IPV) (AOR: 2.11, 95% CI: 1.44 – 3.09), and experiencing stigma (AOR: 1.36, 95% CI: 1.05 – 1.76).ConclusionHigh prevalence of antenatal depression among HIV-positive women in SSA underscores the need for prioritizing identification and management. Interventions addressing factors like IPV and stigma, along with training for healthcare providers in recognizing symptoms and providing support, are recommended.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier CRD42024508236

Machine learning prediction of adolescent HIV testing services in Ethiopia

BackgroundDespite endeavors to achieve the Joint United Nations Programme on HIV/AIDS 95-95-95 fast track targets established in 2014 for HIV prevention, progress has fallen short. Hence, it is imperative to identify factors that can serve as predictors of an adolescent’s HIV status. This identification would enable the implementation of targeted screening interventions and the enhancement of healthcare services. Our primary objective was to identify these predictors to facilitate the improvement of HIV testing services for adolescents in Ethiopia.MethodsA study was conducted by utilizing eight different machine learning techniques to develop models using demographic and health data from 4,502 adolescent respondents. The dataset consisted of 31 variables and variable selection was done using different selection methods. To train and validate the models, the data was randomly split into 80% for training and validation, and 20% for testing. The algorithms were evaluated, and the one with the highest accuracy and mean f1 score was selected for further training using the most predictive variables.ResultsThe J48 decision tree algorithm has proven to be remarkably successful in accurately detecting HIV positivity, outperforming seven other algorithms with an impressive accuracy rate of 81.29% and a Receiver Operating Characteristic (ROC) curve of 86.3%. The algorithm owes its success to its remarkable capability to identify crucial predictor features, with the top five being age, knowledge of HIV testing locations, age at first sexual encounter, recent sexual activity, and exposure to family planning. Interestingly, the model’s performance witnessed a significant improvement when utilizing only twenty variables as opposed to including all variables.ConclusionOur research findings indicate that the J48 decision tree algorithm, when combined with demographic and health-related data, is a highly effective tool for identifying potential predictors of HIV testing. This approach allows us to accurately predict which adolescents are at a high risk of infection, enabling the implementation of targeted screening strategies for early detection and intervention. To improve the testing status of adolescents in the country, we recommend considering demographic factors such as age, age at first sexual encounter, exposure to family planning, recent sexual activity, and other identified predictors

Determinants of Cervical Cancer Screening Among Women Aged 30–49 Years Old in Four African Countries: A Cross-Sectional Secondary Data Analysis

Background Early-stage cervical cancer screening is essential for providing women with a better chance of receiving effective treatment for precancerous and cancer stages. Delay in cervical cancer screening results in late presentation and cancer metastasis. National-level cervical cancer screening in resource-limited countries was scarce and not well studied in Africa based on national data specifically in Kenya, Cameroon, Nambia, and Zimbabwe. Objective To determine the prevalence and determinants of cervical cancer screening among eligible women in Kenya, Cameroon, Nambia, and Zimbabwe. Methods This study analyzed demographic and health survey data from Kenya, Cameroon, Nambia, and Zimbabwe. The data were extracted and analyzed by STATA version 15 and further analysis was done. Intraclass correlation coefficient, median odds ratio, and proportional change in variance were calculated to check the appropriateness of multilevel analysis. Variables with P -value =35 [AOR = 5.27; 95% CI 1.29-21.52], condom use [AOR = 1.79; 95% CI 1.46,2.19], husbands having worked [AOR = 1.5; 95% CI 1.08,2.11], rich household wealth [AOR = 1.43; 95% CI 1.13,1.8], and having health insurance [AOR = 2.2; 95% CI 1.8,2.7]. Conclusion The prevalence of cervical cancer screening in Kenya, Cameroon, Nambia, and Zimbabwe was low as compared to World Health Organization (WHO) recommendations. Age, residence, work status, smoking status, women’s age at first birth, condom use, husbands having work, wealth status, and health insurance were the identified determinants of cervical cancer screening. Programme and policy interventions could address younger, rural residence women, poor wealth status women, women without work, and those who never use health insurance for the uptake of cervical cancer screening

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BackgroundDespite endeavors to achieve the Joint United Nations Programme on HIV/AIDS 95-95-95 fast track targets established in 2014 for HIV prevention, progress has fallen short. Hence, it is imperative to identify factors that can serve as predictors of an adolescent’s HIV status. This identification would enable the implementation of targeted screening interventions and the enhancement of healthcare services. Our primary objective was to identify these predictors to facilitate the improvement of HIV testing services for adolescents in Ethiopia.MethodsA study was conducted by utilizing eight different machine learning techniques to develop models using demographic and health data from 4,502 adolescent respondents. The dataset consisted of 31 variables and variable selection was done using different selection methods. To train and validate the models, the data was randomly split into 80% for training and validation, and 20% for testing. The algorithms were evaluated, and the one with the highest accuracy and mean f1 score was selected for further training using the most predictive variables.ResultsThe J48 decision tree algorithm has proven to be remarkably successful in accurately detecting HIV positivity, outperforming seven other algorithms with an impressive accuracy rate of 81.29% and a Receiver Operating Characteristic (ROC) curve of 86.3%. The algorithm owes its success to its remarkable capability to identify crucial predictor features, with the top five being age, knowledge of HIV testing locations, age at first sexual encounter, recent sexual activity, and exposure to family planning. Interestingly, the model’s performance witnessed a significant improvement when utilizing only twenty variables as opposed to including all variables.ConclusionOur research findings indicate that the J48 decision tree algorithm, when combined with demographic and health-related data, is a highly effective tool for identifying potential predictors of HIV testing. This approach allows us to accurately predict which adolescents are at a high risk of infection, enabling the implementation of targeted screening strategies for early detection and intervention. To improve the testing status of adolescents in the country, we recommend considering demographic factors such as age, age at first sexual encounter, exposure to family planning, recent sexual activity, and other identified predictors.</p

Dental caries and associated factors among preschool children in Southwest Ethiopia: a cross-sectional study

Background Dental caries is a global public health problem, especially for young children. This study aimed to assess the prevalence of dental caries and its associated factors among preschool children in Mizan Aman town, Southwest Ethiopia.Methods A school-based cross-sectional study was conducted from 1 October to 1 December 2022. A total of 354 children and their parents participated using simple random sampling techniques. Data were collected through an oral clinical examination, interviewing the parents and measuring the anthropometry of the children.Results The prevalence of dental caries was 36.4% (95% CI 31.2% to 41.8%). Night feeding (adjusted OR (AOR)=3.98, 95% CI 1.56 to 10.15), children who did not brush their teeth under parental supervision (AOR=2.98, 95% CI 1.60 to 5.57), body mass index (AOR=3.48, 95% CI 1.30 to 9.41) and history of dental visits (AOR=3.05, 95% CI 1.61 to 5.81) were significantly associated with dental caries.Conclusion The prevalence of dental caries in preschool children was found to be high. Children who did not brush their teeth under parental supervision, who had experience of night feeding, who had a high body mass index and who had a history of dental visits were at risk for dental caries. Prevention of those identified modifiable risk factors should be considered to reduce dental caries

Machine learning algorithms for predicting COVID-19 mortality in Ethiopia

Abstract Background Coronavirus disease 2019 (COVID-19), a global public health crisis, continues to pose challenges despite preventive measures. The daily rise in COVID-19 cases is concerning, and the testing process is both time-consuming and costly. While several models have been created to predict mortality in COVID-19 patients, only a few have shown sufficient accuracy. Machine learning algorithms offer a promising approach to data-driven prediction of clinical outcomes, surpassing traditional statistical modeling. Leveraging machine learning (ML) algorithms could potentially provide a solution for predicting mortality in hospitalized COVID-19 patients in Ethiopia. Therefore, the aim of this study is to develop and validate machine-learning models for accurately predicting mortality in COVID-19 hospitalized patients in Ethiopia. Methods Our study involved analyzing electronic medical records of COVID-19 patients who were admitted to public hospitals in Ethiopia. Specifically, we developed seven different machine learning models to predict COVID-19 patient mortality. These models included J48 decision tree, random forest (RF), k-nearest neighborhood (k-NN), multi-layer perceptron (MLP), Naïve Bayes (NB), eXtreme gradient boosting (XGBoost), and logistic regression (LR). We then compared the performance of these models using data from a cohort of 696 patients through statistical analysis. To evaluate the effectiveness of the models, we utilized metrics derived from the confusion matrix such as sensitivity, specificity, precision, and receiver operating characteristic (ROC). Results The study included a total of 696 patients, with a higher number of females (440 patients, accounting for 63.2%) compared to males. The median age of the participants was 35.0 years old, with an interquartile range of 18–79. After conducting different feature selection procedures, 23 features were examined, and identified as predictors of mortality, and it was determined that gender, Intensive care unit (ICU) admission, and alcohol drinking/addiction were the top three predictors of COVID-19 mortality. On the other hand, loss of smell, loss of taste, and hypertension were identified as the three lowest predictors of COVID-19 mortality. The experimental results revealed that the k-nearest neighbor (k-NN) algorithm outperformed than other machine learning algorithms, achieving an accuracy of 95.25%, sensitivity of 95.30%, precision of 92.7%, specificity of 93.30%, F1 score 93.98% and a receiver operating characteristic (ROC) score of 96.90%. These findings highlight the effectiveness of the k-NN algorithm in predicting COVID-19 outcomes based on the selected features. Conclusion Our study has developed an innovative model that utilizes hospital data to accurately predict the mortality risk of COVID-19 patients. The main objective of this model is to prioritize early treatment for high-risk patients and optimize strained healthcare systems during the ongoing pandemic. By integrating machine learning with comprehensive hospital databases, our model effectively classifies patients' mortality risk, enabling targeted medical interventions and improved resource management. Among the various methods tested, the K-nearest neighbors (KNN) algorithm demonstrated the highest accuracy, allowing for early identification of high-risk patients. Through KNN feature identification, we identified 23 predictors that significantly contribute to predicting COVID-19 mortality. The top five predictors are gender (female), intensive care unit (ICU) admission, alcohol drinking, smoking, and symptoms of headache and chills. This advancement holds great promise in enhancing healthcare outcomes and decision-making during the pandemic. By providing services and prioritizing patients based on the identified predictors, healthcare facilities and providers can improve the chances of survival for individuals. This model provides valuable insights that can guide healthcare professionals in allocating resources and delivering appropriate care to those at highest risk

The pooled prevalence of attention-deficit/hyperactivity disorder among children and adolescents in Ethiopia: A systematic review and meta-analysis.

BackgroundAttention-deficit/hyperactivity disorder is one of the most common childhood neurobehavioral disorders, which has a serious negative effect on educational achievement, peer relationships, social functioning, behavior, and self-esteem of children. However, the pooled prevalence of attention-deficit/hyperactivity disorder is not well known in Ethiopia. Therefore, the main objective of this systematic review and meta-analysis is to estimate the pooled prevalence of attention-deficit/hyperactivity disorder among children and adolescents in Ethiopia.MethodsPubMed, HINARI, Science Direct, Psych INFO, Google Scholar, African Journals Online, and cross-referenced were searched to identify relevant articles. Quality appraisal was done using the Joanna Briggs Institute checklist. Heterogeneity was tested using the I-square statistics. Publication bias was tested using a funnel plot visual inspection. Further, trim and fill analysis was done to correct publication bias.Forest plots and tables were used to present results. The random effect model was used to compute the pooled prevalence of attention-deficit/hyperactivity disorder among children and adolescents.ResultsThe overall pooled prevalence of attention-deficit/hyperactivity disorder among children and adolescents in Ethiopia was 14.2% (95% CI: 8.48, 22.83). Being male (OR: 2.19, 95% CI: 1.54; 3.12), being aged 6-11 years (OR: 3.67, 95% CI: 1.98; 6.83), low family socioeconomic status (OR: 3.45 95% CI: 2.17; 5.47), maternal complication during pregnancy (OR: 3.29, 95% CI: 1.97; 5.51) and family history of mental illness (OR: 3.83, 95% CI:2.17; 6.77) were factors associated with a higher odds of attention-deficit/hyperactivity disorder among children and adolescents.ConclusionsThe overall pooled prevalence of attention-deficit/hyperactivity disorder among children and adolescents is high in Ethiopia as compared to previous literature. To reduce the prevalence of attention-deficit/hyperactivity disorder among children and adolescents, emphasis has to be given to prevention, early detection, and management of pregnancy-related complications. Moreover, parents with mental illness should be supported and properly treated to reduce the impact of hostile parenting on their child's health.Trial registrationRegistered in PROSPERO with ID: CRD42024536334

Incidence rate of tuberculosis among HIV infected children in Ethiopia: systematic review and meta-analysis

Abstract Background Tuberculosis is one the leading causes of death from a single infectious disease, caused by the bacillus mycobacterium tuberculosis. In Ethiopia, even though several primary studies have been conducted on the incidence of tuberculosis among HIV-infected children, the pooled incidence rate of tuberculosis among HIV-infected children (aged 0–14 years) is unknown. Therefore, the main objectives of this systematic review and meta-analysis are to estimate the pooled incidence rate of tuberculosis among HIV-infected children and its predictors in Ethiopia. Method International electronic databases such as PubMed, HINARI, Science Direct, Google Scholar, and African Journals Online were searched using different search engines.  Quality of primary studies was checked using the Joanna Briggs Institute checklist. The heterogeneity of studies was tested using I-square statistics. Publication bias was tested using a funnel plot and Egger’s test. Forest plots and tables were used to present the results. The random effect model was used to estimate the pooled incidence of tuberculosis among children living with HIV. Result A total of 13 studies were included in this systematic review and meta-analysis. The pooled incidence of tuberculosis among HIV-infected children was 3.77 (95% CI: 2.83, 5.02) per 100-person-year observations. Advanced HIV disease (HR: 2.72, 95% CI: 1.9; 3.88), didn’t receive complete vaccination (HR: 4.40, 95% CI: 2.16; 8.82), stunting (HR: 2.34, 95% CI: 1.64, 3.33), underweight (HR: 2.30, 95% CI: 1.61; 3.22), didn’t receive Isoniazid preventive therapy (HR: 3.64, 95% CI: 2.22, 5.96), anemia (HR: 3.04, 95% CI: 2.34; 3.98), fair or poor antiretroviral therapy adherence (HR: 2.50, 95% CI: 1.84; 3.40) and didn’t receive cotrimoxazole preventive therapy (HR: 3.20, 95% CI: 2.26; 4.40) were predictors of tuberculosis coinfection among HIV infected children. Conclusion This systematic review and meta-analysis concluded that the overall pooled incidence rate of tuberculosis among HIV-infected children was high in Ethiopia as compared to the END TB strategy targets. Therefore, emphasis has to be given to drug adherence (ART and Isoniazid) and nutritional counseling. Moreover, early diagnosis and treatment of malnutrition and anemia are critical to reduce the risk of TB coinfection. Registration Registered in PROSPERO with ID: CRD42023474956