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.

The Effect of COVID-19 on Routine HIV Care Services from Health Facilities in Northwest Ethiopia.

BACKGROUND: There is mounting evidence that the risk of death from COVID-19 among people with HIV could be as much as twice that of the general population. Recent evidence revealed that HIV services has been decreased by 75% and the problem is much more extensive in Ethiopia since most of the logistics for HIV services and fund donated by the good will of NGOs. Understanding the impact of COVID-19 on HIV services is a crucial first step to draw appropriate intervention. Thus, this study aimed to assess the impact of COVID-19 pandemic on HIV services in northwest Ethiopia. METHODS: An institution-based repeated cross-sectional study was conducted in Gondar city in August 2021. The DHIS-2 system, operated by FMOH contains data from all the nine health facilities for HIV care was used to extract data from the central repository. Excel data was exported to STATA 14 for analysis. We calculated indicators of HIV services, representing the 12 months pre-COVID 19 (2019) and 16 months during the COVID-19 period (2020 and 2021). ANOVA was used to detect the presence of significant mean differences between those periods. Assumptions of ANOVA was checked. The statistical significance was declared at 95% confidence interval (CI), p-value less than 0.05. RESULTS: The mean difference was significant within HIV_VCT, HIV_PICT, ART between the years 2019 before COVID-19 and 2020 during COVID-19 (p-value < 0.05). HIV_VCT, ART variability was substantial between the years 2019 and 2021 (p-value < 0.05). CONCLUSION: COVID-19 seriously affected all aspects of HIV service uptake such as HIV VCT, HIV PICT, ART, newly started ART, TB screening, and lost to ART follow-up. This study urges optimizing ART delivery mitigation with the ongoing COVID-19 in Ethiopia and beyond, in order to maintain progress toward HIV epidemic control.