Predicting the individualized risk of human immunodeficiency virus infection among sexually active women in Ethiopia using a nomogram: prediction model development and validation.

Introduction: Women are more vulnerable to HIV infection due to biological and socioeconomic reasons. Developing a predictive model for these vulnerable populations to estimate individualized risk for HIV infection is relevant for targeted preventive interventions. The objective of the study was to develop and validate a risk prediction model that allows easy estimations of HIV infection risk among sexually active women in Ethiopia. Methods: Data from the 2016 Ethiopian Demographic and Health Survey, which comprised 10,253 representative sexually active women, were used for model development. Variables were selected using the least absolute shrinkage and selection operator (LASSO). Variables selected by LASSO were incorporated into the multivariable mixed-effect logistic regression model. Based on the multivariable model, an easy-to-use nomogram was developed to facilitate its applicability. The performance of the nomogram was evaluated using discrimination and calibration abilities, Brier score, sensitivity, and specificity. Internal validation was carried out using the bootstrapping method. Results: The model selected seven predictors of HIV infection, namely, age, education, marital status, sex of the household head, age at first sex, multiple sexual partners during their lifetime, and residence. The nomogram had a discriminatory power of 89.7% (95% CI: 88.0, 91.5) and a calibration p-value of 0.536. In addition, the sensitivity and specificity of the nomogram were 74.1% (95% CI: 68.4, 79.2) and 80.9% (95% CI: 80.2, 81.7), respectively. The internally validated model had a discriminatory ability of 89.4% (95% CI: 87.7, 91.1) and a calibration p-value of 0.195. Sensitivity and specificity after validation were 72.9% (95% CI: 67.2, 78.2) and 80.1% (95% CI: 79.3, 80.9), respectively. Conclusion: A new prediction model that quantifies the individualized risk of HIV infection has been developed in the form of a nomogram and internally validated. It has very good discriminatory power and good calibration ability. This model can facilitate the identification of sexually active women at high risk of HIV infection for targeted preventive measures.

Machine learning algorithms to predict healthcare-seeking behaviors of mothers for acute respiratory infections and their determinants among children under five in sub-Saharan Africa.

Background: Acute respiratory infections (ARIs) are the leading cause of death in children under the age of 5 globally. Maternal healthcare-seeking behavior may help minimize mortality associated with ARIs since they make decisions about the kind and frequency of healthcare services for their children. Therefore, this study aimed to predict the absence of maternal healthcare-seeking behavior and identify its associated factors among children under the age 5 in sub-Saharan Africa (SSA) using machine learning models. Methods: The sub-Saharan African countries' demographic health survey was the source of the dataset. We used a weighted sample of 16,832 under-five children in this study. The data were processed using Python (version 3.9), and machine learning models such as extreme gradient boosting (XGB), random forest, decision tree, logistic regression, and Naïve Bayes were applied. In this study, we used evaluation metrics, including the AUC ROC curve, accuracy, precision, recall, and F-measure, to assess the performance of the predictive models. Result: In this study, a weighted sample of 16,832 under-five children was used in the final analysis. Among the proposed machine learning models, the random forest (RF) was the best-predicted model with an accuracy of 88.89%, a precision of 89.5%, an F-measure of 83%, an AUC ROC curve of 95.8%, and a recall of 77.6% in predicting the absence of mothers' healthcare-seeking behavior for ARIs. The accuracy for Naïve Bayes was the lowest (66.41%) when compared to other proposed models. No media exposure, living in rural areas, not breastfeeding, poor wealth status, home delivery, no ANC visit, no maternal education, mothers' age group of 35-49 years, and distance to health facilities were significant predictors for the absence of mothers' healthcare-seeking behaviors for ARIs. On the other hand, undernourished children with stunting, underweight, and wasting status, diarrhea, birth size, married women, being a male or female sex child, and having a maternal occupation were significantly associated with good maternal healthcare-seeking behaviors for ARIs among under-five children. Conclusion: The RF model provides greater predictive power for estimating mothers' healthcare-seeking behaviors based on ARI risk factors. Machine learning could help achieve early prediction and intervention in children with high-risk ARIs. This leads to a recommendation for policy direction to reduce child mortality due to ARIs in sub-Saharan countries.

Incidence rate of mortality and its predictors among tuberculosis and human immunodeficiency virus coinfected patients on antiretroviral therapy in Ethiopia: systematic review and meta-analysis.

Background: Tuberculosis (TB) is the leading cause of death among HIV-infected adults and children globally. Therefore, this study was aimed at determining the pooled mortality rate and its predictors among TB/HIV-coinfected patients in Ethiopia. Methods: Extensive database searching was done via PubMed, EMBASE, SCOPUS, ScienceDirect, Google Scholar, and Google from the time of idea conception on March 1, 2023, to the last search via Google on March 31, 2023. A meta-analysis was performed using the random-effects model to determine the pooled mortality rate and its predictors among TB/HIV-coinfected patients. Heterogeneity was handled using subgroup analysis, meta-regression, and sensitivity analysis. Results: Out of 2,100 records, 18 articles were included, with 26,291 total patients. The pooled incidence rate of mortality among TB/HIV patients was 12.49 (95% CI: 9.24-15.74) per 100 person-years observation (PYO); I2 = 96.9%. The mortality rate among children and adults was 5.10 per 100 PYO (95% CI: 2.15-8.01; I2 = 84.6%) and 15.78 per 100 PYO (95% CI: 10.84-20.73; I2 = 97.7%), respectively. Age ≥ 45 (pooled hazard ratios (PHR) 2.58, 95% CI: 2.00- 3.31), unemployed (PHR 2.17, 95% CI: 1.37-3.46), not HIV-disclosed (PHR = 2.79, 95% CI: 1.65-4.70), bedridden (PHR 5.89, 95% CI: 3.43-10.12), OI (PHR 3.5, 95% CI: 2.16-5.66), WHO stage IV (PHR 3.16, 95% CI: 2.18-4.58), BMI < 18.5 (PHR 4.11, 95% CI: 2.28-7.40), anemia (PHR 4.43, 95% CI: 2.73-7.18), EPTB 5.78, 95% CI: 2.61-12.78 significantly affected the mortality. The effect of TB on mortality was 1.95 times higher (PHR 1.95, 95% CI: 1.19-3.20; I2 = 0) than in TB-free individuals. Conclusions: The mortality rate among TB/HIV-coinfected patients in Ethiopia was higher compared with many African countries. Many clinical factors were identified as significant risk factors for mortality. Therefore, TB/HIV program managers and clinicians need to design an intervention early.

Factors associated with the co-utilization of oral rehydration solution and zinc for treating diarrhea among under-five children in 35 sub-saharan Africa countries: a generalized linear mixed effect modeling with robust error variance.

INTRODUCTION: Even though childhood diarrhea is treated with a simple treatment solution, it continues to be one of the leading causes of under-five child mortality and malnutrition globally. In resource-limited settings such as Sub-Saharan Africa (SSA), the combination of oral rehydration salts (ORS) and zinc is regarded as an effective treatment for diarrhea; however, its utilization is very low. The purpose of this study was to determine the proportion and associated factors of co-utilization of ORS and zinc among under-five children with diarrhea in SSA. METHODS: The proportion and associated factors of co-utilization of ORS and zinc among under-five children with diarrhea in SSA were determined using secondary data analysis of recent Demographic and Health Surveys (DHS) of 35 SSA countries. The study included a total of 44,341 under-five children with diarrhea in weighted samples. A generalized linear mixed-effects model with robust error variance was used. For the variables included in the final model, adjusted prevalence ratios (aPR) with 95% confidence intervals (CI) were estimated. A model with the lowest deviance value were considered as the best-fitted model. RESULT: The pooled proportion of co-utilization of ORS and zinc for the treatment of diarrhea among under five children in SSA countries was 43.58% with a 95% CI (43.15%, 44.01%). Sex of the child, maternal age, residence, maternal educational and employment status, wealth index, media exposure, perceived distance to health facility and insurance coverage were statistically significant determinants of ORS and Zinc co-utilization for treating diarrhea among under five children in SSA. CONCLUSION: Only less than half of under-five children with diarrhea in SSA were treated with a combination of ORS and zinc. Thus, strengthening information dissemination through mass media, and community-level health education programs are important to scale up the utilization of the recommended combination treatment. Furthermore, increasing health insurance coverage, and establishing strategies to address the community with difficulty in accessing health facilities is also crucial in improving the use of the treatment.

Geographical variation in hotspots of stunting among under-five children in Ethiopia: A geographically weighted regression and multilevel robust Poisson regression analysis.

INTRODUCTION: Childhood stunting is a global public health concern, associated with both short and long-term consequences, including high child morbidity and mortality, poor development and learning capacity, increased vulnerability for infectious and non-infectious disease. The prevalence of stunting varies significantly throughout Ethiopian regions. Therefore, this study aimed to assess the geographical variation in predictors of stunting among children under the age of five in Ethiopia using 2019 Ethiopian Demographic and Health Survey. METHOD: The current analysis was based on data from the 2019 mini Ethiopian Demographic and Health Survey (EDHS). A total of 5,490 children under the age of five were included in the weighted sample. Descriptive and inferential analysis was done using STATA 17. For the spatial analysis, ArcGIS 10.7 were used. Spatial regression was used to identify the variables associated with stunting hotspots, and adjusted R2 and Corrected Akaike Information Criteria (AICc) were used to compare the models. As the prevalence of stunting was over 10%, a multilevel robust Poisson regression was conducted. In the bivariable analysis, variables having a p-value < 0.2 were considered for the multivariable analysis. In the multivariable multilevel robust Poisson regression analysis, the adjusted prevalence ratio with the 95% confidence interval is presented to show the statistical significance and strength of the association. RESULT: The prevalence of stunting was 33.58% (95%CI: 32.34%, 34.84%) with a clustered geographic pattern (Moran's I = 0.40, p<0.001). significant hotspot areas of stunting were identified in the west and south Afar, Tigray, Amhara and east SNNPR regions. In the local model, no maternal education, poverty, child age 6-23 months and male headed household were predictors associated with spatial variation of stunting among under five children in Ethiopia. In the multivariable multilevel robust Poisson regression the prevalence of stunting among children whose mother's age is >40 (APR = 0.74, 95%CI: 0.55, 0.99). Children whose mother had secondary (APR = 0.74, 95%CI: 0.60, 0.91) and higher (APR = 0.61, 95%CI: 0.44, 0.84) educational status, household wealth status (APR = 0.87, 95%CI: 0.76, 0.99), child aged 6-23 months (APR = 1.87, 95%CI: 1.53, 2.28) were all significantly associated with stunting. CONCLUSION: In Ethiopia, under-five children suffering from stunting have been found to exhibit a spatially clustered pattern. Maternal education, wealth index, birth interval and child age were determining factors of spatial variation of stunting. As a result, a detailed map of stunting hotspots and determinants among children under the age of five aid program planners and decision-makers in designing targeted public health measures.

Prediction of incomplete immunization among under-five children in East Africa from recent demographic and health surveys: a machine learning approach.

The World Health Organization as part of the goal of universal vaccination coverage by 2030 for all individuals. The global under-five mortality rate declined from 59% in 1990 to 38% in 2019, due to high immunization coverage. Despite the significant improvements in immunization coverage, about 20 million children were either unvaccinated or had incomplete immunization, making them more susceptible to mortality and morbidity. This study aimed to identify predictors of incomplete vaccination among children under-5 years in East Africa. An analysis of secondary data from six east African countries using Demographic and Health Survey dataset from 2016 to the recent 2021 was performed. A total weighted sample of 27,806 children aged (12-35) months was included in this study. Data were extracted using STATA version 17 statistical software and imported to a Jupyter notebook for further analysis. A supervised machine learning algorithm was implemented using different classification models. All analysis and calculations were performed using Python 3 programming language in Jupyter Notebook using imblearn, sklearn, XGBoost, and shap packages. XGBoost classifier demonstrated the best performance with accuracy (79.01%), recall (89.88%), F1-score (81.10%), precision (73.89%), and AUC 86%. Predictors of incomplete immunization are identified using XGBoost models with help of Shapely additive eXplanation. This study revealed that the number of living children during birth, antenatal care follow-up, maternal age, place of delivery, birth order, preceding birth interval and mothers' occupation were the top predicting factors of incomplete immunization. Thus, family planning programs should prioritize the number of living children during birth and the preceding birth interval by enhancing maternal education. In conclusion promoting institutional delivery and increasing the number of antenatal care follow-ups by more than fourfold is encouraged.

Determinants of normal haemoglobin concentration among under-five children in Sub-Saharan Africa: a Positive deviance inquiry using cross-sectional study design.

BACKGROUND: Low haemoglobin level in children is linked with short-term and long-term consequences including developmental delay. Globally, over half of the children under the age of five years had low haemoglobin concentration. However, there is limited research on the prevalence and determinants of normal haemoglobin concentration among under-five children in sub-Saharan Africa. OBJECTIVE: To assess determinants of normal haemoglobin concentration among under-five children in SSA. DESIGN: Cross-sectional study design using a positive deviance approach SETTING: 33 SSA countries. PARTICIPANTS: 129 408 children aged 6-59 months PRIMARY AND SECONDARY OUTCOME MEASURES: A multilevel Poisson regression model with robust variance was fitted to identify determinants of normal haemoglobin concentration. An adjusted prevalence ratio with a 95% CI was reported to declare the statistical significance. RESULT: The pooled prevalence of normal haemoglobin concentration among under-five children in SSA was 34.9% (95% CI: 34.6% to 35.1%). High maternal education, middle and rich household wealth, female child, frequent antenatal care visits, non-anaemic mothers, taking anthelmintic drugs and normal nutritional status were associated with increased odds of normal haemoglobin concentration. On the other hand, higher birth order, having fever and diarrhoea, rural residence were associated with lower odds of normal haemoglobin levels. CONCLUSION: According to our finding, only four out of 10 under-five children in SSA had a normal haemoglobin level. This finding proved that anaemia among children in SSA remains a serious public health concern. Therefore, improving maternal education, provision of drugs for an intestinal parasite and early detection and treatment of maternal anaemia, febrile illness and diarrhoeal disease is important.