Discrimination against HIV/AIDS patients and associated factors among women in East African countries: using the most recent DHS data (2015–2022)

Terefe, Bewuketu; Jembere, Mahlet Moges

doi:10.1186/s41043-023-00491-2

Cited by 4 publications

(1 citation statement)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The data source in this study was the Measure DHS program, which was accessed via http://www.dhsprogram.com ( 20 ) after requesting and submitting the project title and the justification of the study. The DHS data are a nationally representative household survey collected periodically in various countries.…”

Section: Methodsmentioning

confidence: 99%

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

Yehuala,

Agimas,

Derseh

et al. 2024

Front. Public Health

View full text Add to dashboard Cite

BackgroundAcute 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.MethodsThe 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.ResultIn 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.ConclusionThe 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.

show abstract

Section: Methodsmentioning

confidence: 99%

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

Yehuala,

Agimas,

Derseh

et al. 2024

Front. Public Health

View full text Add to dashboard Cite

show abstract

Knowledge, and utilization of HIV self-testing, and its associated factors among women in sub–Saharan Africa: evidence from 21 countries demographic and health survey

Terefe,

Jembere,

Reda

et al. 2024

BMC Public Health

View full text Add to dashboard Cite

Background HIV Self-Testing (HIVST) holds great significance in the fight against the HIV epidemic in Sub-Saharan Africa (SSA). It offers a convenient and confidential option for individuals to know their HIV status and seek appropriate care and support. For women in this region, where stigma, discrimination, and lack of access to healthcare services are prevalent, HIVST can empower them to take control of their health and make informed decisions. However, no study in the region has been conducted on this topic. Hence, this study aimed to fill the evidence, and population gaps by identifying women’s HIVST knowledge, and utilization, and its associated factors in SSA. Methods The data used were gathered from the most recent demographic and health surveys conducted in SSA nations between 2015 and 2022. We incorporated DHS data from 21 countries into our investigation. For our analysis, we used a weighted sample of 270,241 women overall was utilized. To handle both individual and community level factors, a multilevel logistic regression was used for the analysis. The adjusted odds ratio and its 95% confidence interval were then presented, and variables with univariate multilevel regression p-values of ≤ 0.25 and in multivariable multilevel logistic regression < 0.05 p value were considered significant factors of HIVST. Results The overall prevalence of knowledge, and utilization of HIVST among women was about 2.17 (95% CI: 2.12, 2.23) only. Women aged 25–34 years old (AOR = 1.78, 95% CI: 1.65,1.92), and 35–49 years old (AOR = 1.33, 95% CI: 1.22,1.46), primary education(AOR = 1.25, 95%CI: 1.12, 1.38), and secondary/higher education (AOR = 3.08, 95% CI: 2.79, 3.41), poorer (AOR = 1.22, 95% CI: 1.08, 1.38), middle (AOR = 1.19, 95% CI: 1.06, 1.37), richer (AOR = 1.45, 95% CI 1.45, 1.64), and richest (AOR = 1.81, 95% CI: 1.59, 2.05), employed (AOR = 1.73 05% CI: 1.62, 1.85), mass media exposure (AOR = 1.39, 95% CI: 1.31, 1.49), knew modern contraception (AOR = 2.75, 95% CI: 1.84, 4.13), health facility delivery (AOR = 1.17, 95% CI: 1.02, 1.37), being from urban (AOR = 1.53, 95% CI: 1.63, 1.73), divorced or widowed (AOR = 77, 95% CI:1.13, 1.34), have more than one sexual partners (AOR =, 95% CI: 1.24, 1.41), heard about STIs (AOR 7.47 =, 95% CI: 5.16, 10.81), high community ANC coverage (AOR = 1.46, 95% CI: 1.31, 1.63), high community mass media (AOR = 1.37 95% CI: 1.21, 1.56), Central/Southern Africa (AOR = 0.66 95% CI: 0.59,0.74), and East Africa regions (AOR = 0.87 95% CI: 0.81,0.94) were associated with the knowledge and utilization of HIVST. Conclusions The level of knowledge and utilization of HIVST among women in SSA was very low. To improve this situation, maternal health services can be enhanced. This can be achieved by facilitating institutional delivery, promoting access to modern contraception, increasing ANC coverage, empowering women’s associations, creating culturally respectful mass media content, and involving rural and economically disadvantaged women. By implementing these measures, we can enhance women’s knowledge and improve their use of HIVST.

show abstract

Comprehensive knowledge of mother-to-child HIV/AIDS transmission, prevention, and associated factors among reproductive-age women in East Africa: insights from recent demographic and national health surveys

Terefe,

Jembere,

Liyew

2024

BMC Women's Health

Self Cite

View full text Add to dashboard Cite

Background More than 90% of babies acquire HIV/AIDS through vertical transmission, primarily due to low maternal comprehensive knowledge about Mother-To-Child Transmission (MTCT) of HIV/AIDS and its prevention, which is a cornerstone for eliminating MTCT of HIV/AIDS. However, there are limitations in terms of population data and literature evidence based on recent Demographic and Health Surveys (DHS) reports in East Africa. Therefore, this study aims to assess the comprehensive knowledge and PMTCT of HIV/AIDS among women, as well as the associated factors in East Africa. Methods Our data was obtained from the most recent DHS conducted in East African countries between 2011 and 2022. For our research, we included DHS data from ten nations, resulting in a total weighted sample of 133,724 women for our investigation. A generalized linear model (GLM) with a log link and binomial family to directly estimate prevalence ratios (PR) and 95% confidence intervals (CI) for the association between the independent variables, and the outcome variable. Finally, we reported the adjusted prevalence ratios along with their corresponding 95% CIs. Factors with p-values ≤ 0.2 for univariate logistic regression and < 0.05 were considered statistically significant factors of HIV/AIDS knowledge and prevention in the final model. Results In this study, 59.41% (95% CI: 59.15–59.67) of respondents had a comprehensive knowledge about MTCT of HIV/AIDS and its prevention among reproductive-age women in East Africa. Being in the older age group, better education level, being from a rich household, employment status, having ANC follow up, institutional delivery, and modern contraception usage were associated with higher prevalence ratios of comprehensive knowledge about MTCT of HIV/AIDS and its prevention. However, being single in marital status, rural women, and traditional contraception utilization were associated with lower ratios of comprehensive knowledge about MTCT of HIV/AIDS and its prevention. Conclusion Our findings indicate a significant deficiency in comprehensive knowledge and prevention of HIV/AIDS MTCT among women in East Africa. These results emphasize the need for significant improvements in maternal-related health services. It is crucial to effectively target high-risk populations during interventions, raise awareness about this critical public health issue, and address the catastrophic consequences associated with MTCT. By implementing these measures, we can make substantial progress in reducing the transmission of HIV/AIDS from mother to child and ensuring better health outcomes for both mothers and their children.

show abstract

Discrimination against HIV/AIDS patients and associated factors among women in East African countries: using the most recent DHS data (2015–2022)

Cited by 4 publications

References 29 publications

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

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

Knowledge, and utilization of HIV self-testing, and its associated factors among women in sub–Saharan Africa: evidence from 21 countries demographic and health survey

Comprehensive knowledge of mother-to-child HIV/AIDS transmission, prevention, and associated factors among reproductive-age women in East Africa: insights from recent demographic and national health surveys

Contact Info

Product

Resources

About