COVID, Volume 4, Issue 10 (October 2024) – 7 articles

Background. People living with HIV (PWH) frequently have co-morbid substance use disorders that may have been impacted by the COVID-19 pandemic. This study examined associations between COVID-related stress and increased substance use among PWH in Washington State. Methods. Between August 2020 and March 2021, we conducted an online survey of 397 PWH in western Washington. Logistic regression was used to analyze associations between a COVID-19 stress score and four self-reported outcomes: increased alcohol use, increased cigarette smoking, increased marijuana use, and increased use of illicit substances. Results. Thirty-five (38.0%) of 92 participants who smoked, 61 (23.4%) of 261 participants who used alcohol, 15 (14.6%) of 103 participants who used marijuana, and 35 (33.0%) of 102 participants who used illicit substances reported increased use of these substances. Higher COVID-19 stress scores were associated with higher odds of increased cigarette smoking (adjusted odds ratio [aOR] = 1.15, 95% confidence interval [CI]: 1.04–1.27), even after adjustment for anxiety and depressive symptoms (aOR 1.14, 95%CI: 1.03–1.27). COVID-19 stress was not associated with an increased use of alcohol, marijuana, or illicit substances. Conclusions. COVID-19-related stress was associated with self-reported increased cigarette smoking among PWH in western Washington during the pandemic. Full article

The COVID-19 pandemic has had profound impact, necessitating a deeper understanding of factors influencing virus transmission. The negative impacts have weakened the economy and changed billions of lives around the world. COVID-19 is a new virus, and a lot of studies have tried to investigate its effect on, for example, the economy or environment. This research reveals new approaches to recognizing and stopping the spread of this virus with its connection to weather conditions and relevant parameters. By analyzing how temperature and humidity affect COVID-19 spread, alongside evaluating the effectiveness of initial public policies, this study addresses the critical gap in research by investigating the interplay between climate conditions and government regulations during the early stages of the pandemic in South Korea. This dual approach provides a comprehensive framework for understanding how environmental and policy factors jointly influence pandemic dynamics, offering valuable lessons for future global health crises. Although it focuses only on the first phase of South Korea COVID-19 regulations, outcomes show that these regulations were notably effective against the COVID-19 pandemic. The outcomes prove that higher temperature and higher relative humidity lead to lower transmission. Hence, based on the results during winter, the number of infections would be expected to speed up again. Full article

Several websites have offered patients opportunities to find out whether they meet the case definitions for myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). The current study describes a new online screener that can be completed by individuals who might like to determine if they meet the current ME/CFS criteria. The website is available for anyone to use, and the feedback is more comprehensive than other sites’, particularly in providing data on how the participants’ data compares with a large ME/CFS patient population, as well as whether the current ME and ME/CFS case definitions are met. Full article

This study in Singapore analysed the incubation periods of the following SARS-CoV-2 variants: Wuhan-Hu-1, Delta, and Omicron. Three pandemic waves were examined: Wuhan-Hu-1 (January 2020–March 2021), Delta (May–October 2021), and Omicron (January–June 2022). Data from the SingHealth COVID-19 registry, covering patients from 23 January 2020 to 10 June 2022, were used to calculate incubation periods during the three time periods. The study found median incubation periods of 11 days for Wuhan-Hu-1, 3 days for Delta, and 3 days for Omicron (p-value: <0.001). This study highlighted the impact of different containment measures and the importance of robust EMR systems for tracking and managing infectious diseases. Key challenges included accurate contact tracing and IT infrastructure capabilities. The findings support the use of EMR data for future infectious disease preparedness in Singapore. Full article

As a framework for understanding the structural factors that affect health, the social determinants of health (SDoH) have particular significance during the developmental stage of adolescence. When the global coronavirus pandemic (COVID-19) began, public health measures (PHMs) implemented to curb its spread shifted adolescents’ daily lives and routines, initiating changes to their mental health. The purpose of this study was to apply the SDoH to investigating the impacts of the pandemic-related PHMs on the mental health of adolescents in Canada. Using a youth engagement approach, interviews were conducted with 33 adolescents aged 14–19 years from two sites in Alberta, Canada. Participants shared their experiences of adjusting to the PHMs and how these shaped their mental health. Findings indicate that PHMs particularly affected the social determinants of education, access to health services, employment and income security, and social support amongst adolescents as online schooling, loss of connection with peers, income instability, and limited health services affected their mental health. Most commonly, adolescents expressed feeling greater anxiety, depression, or loneliness as the SDoH shifted with the PHMs. As we continue to understand the mental health impacts of the pandemic, the SDoH framework can be used to identify salient social determinants and evaluate these determinants post-pandemic. This study draws attention to the need for policies and programs that protect access to key SDoH at such a critical life stage as adolescence and promote their mental health resilience in shifting SDoH contexts. Full article

As the COVID-19 pandemic began spreading worldwide in late 2019 and early 2020, many vaccine candidates were developed to combat the disease. However, new COVID-19 variants such as Omicron and Delta continue to emerge globally despite advancements in vaccine technology, leaving certain countries and variants more vulnerable than others to future outbreaks of these variants. This research aims to analyze the susceptibility of different countries to a COVID-19 outbreak, present the first visualization of the spread of COVID-19, and predict which countries are at greater risk for future outbreaks of new variants based on various factors. We created interactive maps to understand the pandemic’s spread and identify high-risk countries based on their vaccination percentages. Then we employed binary classification, K-nearest neighbors (KNN), and neural network machine learning models to predict each country’s risk factor. The risk factor determines whether a country is safe from a new COVID-19 variant based on vaccine percentage and government stringency. The neural network achieved the highest accuracy, classifying countries as high risk or low risk with 94% accuracy. Inspired by the Albert Barabasi model, we graphed connections between countries based on vaccination percentages. These graphs illustrate the correlation between the two countries and better demonstrate how their vaccination rates relate to the probability of a new COVID-19 outbreak. Full article

Objective: To summarize the prevalence of Long COVID symptoms among the general population reported in published articles from the pre-omicron SARS-CoV2 era. This narrative review examined 21 symptoms. Methods: A PubMed/manual search returned 114 articles on general Long COVID symptoms. Manuscripts were excluded if they were not research studies, did not report symptom prevalence, or used a pediatric population. Ninety-eight studies were selected for review and fifty-nine met the criteria for inclusion. The risk of bias was assessed with the Hoy critical appraisal tool. Results: After excluding studies with a high risk of bias, meta-analysis of prevalence for 21 symptom categories ranged from 2.6–28.7% in studies based on surveys to 0.3–7.1% in studies based on electronic health record data. Conclusions: Long COVID symptom studies are limited by the variability in study design and representation of the general population. Further research is needed to effectively cluster symptoms in meaningful ways that enable focused treatment. Full article