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.

Quality of life among patients with the common chronic disease during COVID-19 pandemic in Northwest Ethiopia: A structural equation modelling.

BACKGROUND: Improving Quality of Life (QoL) for patients with chronic diseases is a critical step in controlling disease progression and preventing complications. The COVID-19 pandemic has hampered chronic disease management, lowering patients' quality of life. Thus, we aimed to assess the quality of life and its determinants in patients with common chronic diseases, in Northwest Ethiopia during the COVID-19 pandemic. METHODS: A cross-sectional study was conducted among 1815 randomly selected chronic patients with common chronic diseases. A standardized WHOQOL BREF tool was used, and electronic data collection was employed with the kobo toolbox data collection server. Overall QoL and the domains of Health-Related Quality of life (HRQoL) were determined. Structural equation modelling was done to estimate independent variables' direct and indirect effects. Path coefficients with a 95% confidence interval were reported. RESULTS: About one in third, (33.35%) and 11.43% of the study participants had co-morbid conditions and identified complications, respectively. The mean score of QoL was 56.3 ranging from 14.59 and 98.95. The environmental domain was the most affected domain of HRQoL with a mean score of 52.18. Age, psychological, and environmental domains of HRQoL had a direct positive effect on the overall QoL while the physical and social relationships domains had an indirect positive effect. On the other hand, the number of medications taken, the presence of comorbidity, and complications had a direct negative impact on overall QoL. Furthermore, both rural residency and the presence of complications had an indirect negative effect on overall QoL via the mediator variables of environmental and physical health, respectively. CONCLUSION: The quality of life was compromised in chronic disease patients. During the COVID-19 pandemic, the environmental domain of HRQoL was the most affected. Several socio-demographic and clinical factors had an impact on QoL, either directly or indirectly. These findings highlighted the importance of paying special attention to rural residents, patients with complications, patients taking a higher number of medications, and patients with comorbidity.