Increased reproductive tract infections among secondary school girls during the COVID-19 pandemic: associations with pandemic-related stress, mental health, and domestic safety

Background: Kenya, like many countries, shuttered schools during COVID-19, with subsequent increases in poor mental health, sexual activity, and pregnancy. Aim: We sought to understand how the COVID-19 pandemic may mediate the risk of reproductive tract infections. Methods: We analyzed data from a cohort of 436 secondary schoolgirls in western Kenya. Baseline and 6-, 12-, and 18-month study visits occurred from April 2018 to December 2019 (pre–COVID-19), and 30-, 36-, and 48-month study visits occurred from September 2020 to July 2022 (COVID-19 period). Participants self-completed a survey for sociodemographics and sexual activity and provided self-collected vaginal swabs for bacterial vaginosis (BV) testing, with sexually transmitted infection (STI) testing at annual visits. We hypothesized that greater COVID-19–related stress would mediate risk via mental health, feeling safe inside the home, and sexual exposure, given the pandemic mitigation–related impacts of school closures on these factors. COVID-19–related stress was measured with a standardized scale and dichotomized at the highest quartile. Mixed effects modeling quantified how BV and STI changed over time. Longitudinal mediation analysis quantified how the relationship between COVID-19 stress and increased BV was mediated. Outcomes: Analysis outcomes were BV and STI. Results: BV and STI prevalence increased from 12.1% and 10.7% pre–COVID-19 to 24.5% and 18.1% during COVID-19, respectively. This equated to 26% (95% CI, 1.00–1.59) and 36% (95% CI, 0.98–1.88) higher relative prevalence of BV and STIs in the COVID-19 vs pre–COVID-19 periods, adjusted for numerous sociodemographic and behavioral factors. Higher COVID-19–related stress was associated with elevated depressive symptoms and feeling less safe inside the home, which were each associated with a greater likelihood of having a boyfriend. In mediation analyses, the direct effect of COVID-19–related stress on BV was small and nonsignificant, indicating that the increased BV was due to the constellation of factors that were affected during the COVID-19 pandemic. Clinical Translation: These results highlight factors to help maintain reproductive health for adolescent girls in future crises, such as anticipating and mitigating mental health impacts, domestic safety concerns, and maintaining sexual health services. Strengths and Limitations: Impacts of the COVID-19 pandemic on drivers of reproductive tract health among those who did not attend school or who live in different settings may differ. Conclusions: In this cohort of adolescent girls, BV and STIs increased following COVID-19–related school closures, and risk was mediated by depressive symptoms and feeling less safe in the home, which led to a higher likelihood of sexual exposures

Sexually Transmitted Disease–Related Reddit Posts During the COVID-19 Pandemic: Latent Dirichlet Allocation Analysis

BackgroundSexually transmitted diseases (STDs) are common and costly, impacting approximately 1 in 5 people annually. Reddit, the sixth most used internet site in the world, is a user-generated social media discussion platform that may be useful in monitoring discussion about STD symptoms and exposure. ObjectiveThis study sought to define and identify patterns and insights into STD-related discussions on Reddit over the course of the COVID-19 pandemic. MethodsWe extracted posts from Reddit from March 2019 through July 2021. We used a topic modeling method, Latent Dirichlet Allocation, to identify the most common topics discussed in the Reddit posts. We then used word clouds, qualitative topic labeling, and spline regression to characterize the content and distribution of the topics observed. ResultsOur extraction resulted in 24,311 total posts. Latent Dirichlet Allocation topic modeling showed that with 8 topics for each time period, we achieved high coherence values (pre–COVID-19=0.41, prevaccination=0.42, and postvaccination=0.44). Although most topic categories remained the same over time, the relative proportion of topics changed and new topics emerged. Spline regression revealed that some key terms had variability in the percentage of posts that coincided with pre–COVID-19 and post–COVID-19 periods, whereas others were uniform across the study periods. ConclusionsOur study’s use of Reddit is a novel way to gain insights into STD symptoms experienced, potential exposures, testing decisions, common questions, and behavior patterns (eg, during lockdown periods). For example, reduction in STD screening may result in observed negative health outcomes due to missed cases, which also impacts onward transmission. As Reddit use is anonymous, users may discuss sensitive topics with greater detail and more freely than in clinical encounters. Data from anonymous Reddit posts may be leveraged to enhance the understanding of the distribution of disease and need for targeted outreach or screening programs. This study provides evidence in favor of establishing Reddit as having feasibility and utility to enhance the understanding of sexual behaviors, STD experiences, and needed health engagement with the public

Machine learning and deep learning for phishing email classification using one-hot encoding

Representation of text is a significant task in Natural Language Processing (NLP) and in recent years Deep Learning (DL) and Machine Learning (ML) have been widely used in various NLP tasks like topic classification, sentiment analysis and language translation. Until very recently, little work has been devoted to semantic analysis in phishing detection or phishing email detection. The novelty of this study is in using deep semantic analysis to capture inherent characteristics of the text body. One-hot encoding was used with DL and ML techniques to classify emails as phishing or non-phishing. A comparison of various parameters and hyperparameters was performed for DL. The results of various ML models, Naïve Bayes, SVM, Decision Tree, as well as DL models, Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM), were presented. The DL models performed better than the ML models in terms of accuracy, but the ML models performed better than the DL models in terms of computation time. CNN with Word Embedding performed the best in terms of accuracy (96.34%), demonstrating the effectiveness of semantic analysis in phishing email detection.Journal ArticlePublishe

Quality Analysis of Streaming Audio over Mobile Networks

This paper utilizes open source software to analyze the quality of audio streamed over mobile cellular networks. Industry conventions have been developed to assess audio quality such as the Mean Opinion Score or MOS scale described in the International Telecommunications Union ITU P.800.1 document. The MOS scale is a subjective assessment based on the listener’s experience. To eliminate the use of a trained audio listener we automate an estimated MOS calculation by measuring the packet loss, average latency and jitter over the network transport path. The network under test is a pilot network to replace the dedicated analog circuit from the broadcast center to a radio transmitter. We intend to automate the logging of an objective quality assessment using MOS, cellular router and decoder measurements with confirmation using automatically generated visual representations of sample audio received. These visual representations will aid in manual confirmation of poor MOS scores with audio samples available for more in-depth review.Final article publishedJournal ArticlePermission granted to the University of West Florida to digitize and display this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires the permission of the copyright holder