Prevalence of misconception about HIV/AIDS transmission and associated factors among reproductive age women in Ethiopia: a nationwide study.

BACKGROUND: Misconceptions and myths are still the bottlenecks for the prevention of HIV/AIDS transmission in developing countries. This study aimed to assess the prevalence and associated factors of misconception about HIV transmission among reproductive age groups using the most recently available Ethiopian Demographic and Health Surveydata. METHODS: A cross-sectional study design was done using the Ethiopian Demographic and Health Survey 2016 data set. The data analysis was conducted using  Statistical Package for Social Sciences version 25. Multivariable logistic regression analysis was done to identify associated factors of misconception about HIV/AIDS transmission. A p-value of < 0.05 and an adjusted odds ratio with a 95% confidence interval were considered to confirm a statistically significant association. RESULTS: From the sample of 11,425 reproductive-age women, the prevalence of misconception about HIV/AIDS transmission among reproductive-age women in Ethiopia was 27.47%. Women residing in rural area [AOR:1.24; 95% CI: 1.03-1.75] compared to urban resident participants, attended primary education [AOR:0.58;95%CI: 0.49-0.68], attended secondary education [AOR:0.36;95%CI:0.29-0.46], attended higher education [AOR:0.24;95%CI: 0.18-0.32] compared to those participants without education, had history of HIV test [AOR:0.77; 95%CI: 0.67-0.88] compared to their counterpart, respondents living in Amhara region [AOR:0.44:95% CI:0.35-0.54], Benishangul [AOR: 0.34; 95% CI: 0.25-0.46], SNNPR [AOR:0.50; 95% CI: 0.38-0.67], Gambela [AOR:0.57; 95% CI: 0.42-0.79], Harari [AOR:0.62; 95% CI: 0.46-0.82], Addis Ababa [AOR:0.63; 95% CI: 0.49-0.81] compared to those living in Tigray and having richest wealth status[AOR:0.57;95% CI: 1.457-4.078] compared to those whose wealth index was poorest were significantly associated with the misconception about HIV transmission. CONCLUSION: Over all the prevalence of misconception about HIV/AIDS transmission among reproductive-age women in Ethiopia was high. Residence, educational level, wealth index, region, and respondents who ever tested for HIV were significantly associated with the misconception about HIV/AIDS transmission. This high misconception could affect HIV/AIDS transmission and its prevention strategies unless timely and appropriate intervention should be taken. Strengthening strategies aimed at maximizing HIV/AIDS testing, scaling up educational status, and emphasizing regional-wide interventions might have a substantial contribution.

Spatial distribution and machine learning prediction of sexually transmitted infections and associated factors among sexually active men and women in Ethiopia, evidence from EDHS 2016.

INTRODUCTION: Sexually transmitted infections (STIs) are the major public health problem globally, affecting millions of people every day. The burden is high in the Sub-Saharan region, including Ethiopia. Besides, there is little evidence on the distribution of STIs across Ethiopian regions. Hence, having a better understanding of the infections is of great importance to lessen their burden on society. Therefore, this article aimed to assess predictors of STIs using machine learning techniques and their geographic distribution across Ethiopian regions. Assessing the predictors of STIs and their spatial distribution could help policymakers to understand the problems better and design interventions accordingly. METHODS: A community-based cross-sectional study was conducted from January 18, 2016, to June 27, 2016, using the 2016 Ethiopian Demography and Health Survey (EDHS) dataset. We applied spatial autocorrelation analysis using Global Moran's I statistics to detect latent STI clusters. Spatial scan statics was done to identify local significant clusters based on the Bernoulli model using the SaTScan™ for spatial distribution and Supervised machine learning models such as C5.0 Decision tree, Random Forest, Support Vector Machine, Naïve Bayes, and Logistic regression were applied to the 2016 EDHS dataset for STI prediction and their performances were analyzed. Association rules were done using an unsupervised machine learning algorithm. RESULTS: The spatial distribution of STI in Ethiopia was clustered across the country with a global Moran's index = 0.06 and p value = 0.04. The Random Forest algorithm was best for STI prediction with 69.48% balanced accuracy and 68.50% area under the curve. The random forest model showed that region, wealth, age category, educational level, age at first sex, working status, marital status, media access, alcohol drinking, chat chewing, and sex of the respondent were the top 11 predictors of STI in Ethiopia. CONCLUSION: Applying random forest machine learning algorithm for STI prediction in Ethiopia is the proposed model to identify the predictors of STIs.