Setting the scene

"Young People, Violence and Strategic Interventions in Sub-Saharan Africa (SSA)" brings together data and case studies from a range of SSA countries. While the findings are diverse, a set of themes predominate: they reveal that violence, embedded in everyday lived realities, is a complex and urgent issue that should be comprehensively probed. While moments of political and xenophobic violence are reflected upon, critical attention is firmly on young women and their grappling with sexual and gender-based violence. The COVID lens has magnified micro-struggles and long-standing structural problems showing how inequities and disadvantages have created an infrastructure through which violence has been exacerbated. There is also a focus on masculinities constructed by oppressive histories, social and economic crises, and state disregard, but with the message that marginality should not be reified, nor the gendered practices of young men oversimplified. In all the chapters, contextual specificities and insightful case studies offer thoughts and arguments about the ways in which violence manifests, what interventionist strategies are compelling, and how young people's proactive involvement in interventions could begin to address the problem of violence. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Evaluation of Online Education in the Era of COVID-19 Pandemic: A Review from Students, Parents, and Teachers' Perspectives

Qualitative content analysis is used in this study to review related online education since the outbreak of COVID-19. The aim of this study was to summarize the impact of online teaching on the education industry during the pandemic, sum up the viewpoints of all kinds of people to draw conclusions, and conclude the practical countermeasures. Based on the result of the analysis, firstly, we think that students and teachers are satisfied with online education, but parents have expressed dissatisfaction with this kind of education. Secondly, this paper lists the advantages and common problems of online teaching during study at home from different aspects. According to deficits, we summarize the solutions from three aspects: network equipment, teaching, and self-adjustment. This research is of great significance. It is not only beneficial to the development of educational platforms and personalized teaching but also helps formulate education policy to reduce the burden of education.

Three dimensions of COVID-19 risk perceptions and their socioeconomic correlates in the United States: A social media analysis.

Social media analysis provides an alternate approach to monitoring and understanding risk perceptions regarding COVID-19 over time. Our current understandings of risk perceptions regarding COVID-19 do not disentangle the three dimensions of risk perceptions (perceived susceptibility, perceived severity, and negative emotion) as the pandemic has evolved. Data are also limited regarding the impact of social determinants of health (SDOH) on COVID-19-related risk perceptions over time. To address these knowledge gaps, we extracted tweets regarding COVID-19-related risk perceptions and developed indicators for the three dimensions of risk perceptions based on over 502 million geotagged tweets posted by over 4.9 million Twitter users from January 2020 to December 2021 in the United States. We examined correlations between risk perception indicator scores and county-level SDOH. The three dimensions of risk perceptions demonstrate different trajectories. Perceived severity maintained a high level throughout the study period. Perceived susceptibility and negative emotion peaked on March 11, 2020 (COVID-19 declared global pandemic by WHO) and then declined and remained stable at lower levels until increasing once again with the Omicron period. Relative frequency of tweet posts on risk perceptions did not closely follow epidemic trends of COVID-19 (cases, deaths). Users from socioeconomically vulnerable counties showed lower attention to perceived severity and susceptibility of COVID-19 than those from wealthier counties. Examining trends in tweets regarding the multiple dimensions of risk perceptions throughout the COVID-19 pandemic can help policymakers frame in-time, tailored, and appropriate responses to prevent viral spread and encourage preventive behavior uptake in the United States.

Facilitators of Organizational Resilience Within South Carolina AIDS Service Organizations: Lessons Learned from the COVID-19 Pandemic.

HIV care services have been interrupted by the COVID-19 pandemic in many states in the U.S. including South Carolina (SC). However, many HIV care facilities demonstrated organizational resilience (i.e., the ability to maintain needed health services amid rapidly changing circumstances) by addressing challenges to maintaining care during the pandemic. This study, therefore, aims to identify key facilitators for organizational resilience among AIDS Services Organizations (ASOs) in SC. In-depth interviews were conducted among 11 leaders, from 8 ASOs, across SC during the summer of 2020. The interviews were recorded after receiving proper consent and then transcribed. Utilizing a codebook based upon the interview guide, a thematic analysis approach was utilized to analyze the data. All data management and analysis were conducted in NVivo 11.0. Our findings demonstrate several facilitators of organizational resilience, including (1) accurate and timely crisis information dissemination; (2) clear and preemptive protocols; (3) effective healthcare system policies, management, and leadership; (4) prioritization of staff psychological wellbeing; (5) stable access to personal protective equipment (PPE); (6) adequate and flexible funding; and (7) infrastructure that supports telehealth. Given the facilitators of organizational resilience among ASOs in SC during the COVID-19 pandemic, it is recommended that organizations implement and maintain coordinated and informed responses based upon preemptive protocols and emerging needs. ASO funders are encouraged to allow a flexibility in spending. The lessons learned from the participating leaders enable ASOs to develop and strengthen their organizational resilience and experience fewer disruptions in the future.

Association between immigrant concentration and mental health service utilization in the United States over time: A geospatial big data analysis.

Immigrants (foreign-born United States [US] citizens) generally have lower utilization of mental health services compared with US-born counterparts, but extant studies have not investigated the disparities in mental health service utilization within immigrant population nationwide over time. Leveraging mobile phone-based visitation data, we estimated the average mental health utilization in contiguous US census tracts in 2019, 2020, and 2021 by employing two novel outcomes: mental health service visits and visit-to-need ratio (i.e., visits per depression diagnosis). We then investigated the tract-level association between immigration concentration and mental health service utilization outcomes using mixed-effects linear regression models that accounted for spatial lag effects, time effects, and covariates. This study reveals spatial and temporal disparities in mental health service visits and visit-to-need ratio among different levels of immigrant concentration across the US, both before and during the pandemic. Tracts with higher concentrations of Latin American immigrants showed significantly lower mental health service utilization visits and visit-to-need ratio, particularly in the US West. Tracts with Asian and European immigrant concentrations experienced a more significant decline in mental health service utilization visits and visit-to-need ratio from 2019 to 2020 than those with Latin American concentrations. Meanwhile, in 2021, tracts with Latin American concentrations had the least recovery in mental health service utilization visits. The study highlights the potential of geospatial big data for mental health research and informs public health interventions.

Impact of HIV on COVID-19 Outcomes: A Propensity Score Matching Analysis with Varying Age Differences.

To exploratorily test (1) the impact of HIV and aging process among PLWH on COVID-19 outcomes; and (2) whether the effects of HIV on COVID-19 outcomes differed by immunity level. The data used in this study was retrieved from the COVID-19 positive cohort in National COVID Cohort Collaborative (N3C). Multivariable logistic regression models were conducted on populations that were matched using either exact matching or propensity score matching (PSM) with varying age difference between PLWH and non-PLWH to examine the impact of HIV and aging process on all-cause mortality and hospitalization among COVID-19 patients. Subgroup analyses by CD4 counts and viral load (VL) levels were conducted using similar approaches. Among the 2,422,864 adults with a COVID-19 diagnosis, 15,188 were PLWH. PLWH had a significantly higher odds of death compared to non-PLWH until age difference reached 6 years or more, while PLWH were still at an elevated risk of hospitalization across all matched cohorts. The odds of both severe outcomes were persistently higher among PLWH with CD4 < 200 cells/mm3. VL ≥ 200 copies/ml was only associated with higher hospitalization, regardless of the predefined age differences. Age advancement in HIV might significantly contribute to the higher risk of COVID-19 mortality and HIV infection may still impact COVID-19 hospitalization independent of the age advancement in HIV.

Protocol for developing a personalised prediction model for viral suppression among under-represented populations in the context of the COVID-19 pandemic.

INTRODUCTION: Sustained viral suppression, an indicator of long-term treatment success and mortality reduction, is one of four strategic areas of the 'Ending the HIV Epidemic' federal campaign launched in 2019. Under-represented populations, like racial or ethnic minority populations, sexual and gender minority groups, and socioeconomically disadvantaged populations, are disproportionately affected by HIV and experience a more striking virological failure. The COVID-19 pandemic might magnify the risk of incomplete viral suppression among under-represented people living with HIV (PLWH) due to interruptions in healthcare access and other worsened socioeconomic and environmental conditions. However, biomedical research rarely includes under-represented populations, resulting in biased algorithms. This proposal targets a broadly defined under-represented HIV population. It aims to develop a personalised viral suppression prediction model using machine learning (ML) techniques by incorporating multilevel factors using All of Us (AoU) data. METHODS AND ANALYSIS: This cohort study will use data from the AoU research programme, which aims to recruit a broad, diverse group of US populations historically under-represented in biomedical research. The programme harmonises data from multiple sources on an ongoing basis. It has recruited ~4800 PLWH with a series of self-reported survey data (eg, Lifestyle, Healthcare Access, COVID-19 Participant Experience) and relevant longitudinal electronic health records data. We will examine the change in viral suppression and develop personalised viral suppression prediction due to the impact of the COVID-19 pandemic using ML techniques, such as tree-based classifiers (classification and regression trees, random forest, decision tree and eXtreme Gradient Boosting), support vector machine, naïve Bayes and long short-term memory. ETHICS AND DISSEMINATION: The institutional review board approved the study at the University of South Carolina (Pro00124806) as a Non-Human Subject study. Findings will be published in peer-reviewed journals and disseminated at national and international conferences and through social media.

Rural-Urban Disparities in Hospital Admissions and Mortality Among Patients with COVID-19: Evidence from South Carolina from 2021 to 2022.

Although rural communities have been hard-hit by the COVID-19 pandemic, there is limited evidence on COVID-19 outcomes in rural America using up-to-date data. This study aimed to estimate the associations between hospital admissions and mortality and rurality among COVID-19 positive patients who sought hospital care in South Carolina. We used all-payer hospital claims, COVID-19 testing, and vaccination history data from January 2021 to January 2022 in South Carolina. We included 75,545 hospital encounters within 14 days after positive and confirmatory COVID-19 testing. Associations between hospital admissions and mortality and rurality were estimated using multivariable logistic regressions. About 42% of all encounters resulted in an inpatient hospital admission, while hospital-level mortality was 6.3%. Rural residents accounted for 31.0% of all encounters for COVID-19. After controlling for patient-level, hospital, and regional characteristics, rural residents had higher odds of overall hospital mortality (Adjusted Odds Ratio - AOR = 1.19, 95% Confidence Intervals - CI = 1.04-1.37), both as inpatients (AOR = 1.18, 95% CI = 1.05-1.34) and as outpatients (AOR = 1.63, 95% CI = 1.03-2.59). Sensitivity analyses using encounters with COVID-like illness as the primary diagnosis only and encounters from September 2021 and beyond - a period when the Delta variant was dominant and booster vaccination was available - yielded similar estimates. No significant differences were observed in inpatient hospitalizations (AOR = 1.00, 95% CI = 0.75-1.33) between rural and urban residents. Policymakers should consider community-based public health approaches to mitigate geographic disparities in health outcomes among disadvantaged population subgroups.

Incidence of Chronic Kidney Disease Following Acute Coronavirus Disease 2019 Based on South Carolina Statewide Data.

BACKGROUND: Coronavirus disease 2019 (COVID-19) was associated with severe acute illness including multiple organ failure. Acute kidney injury (AKI) was a common finding, often requiring dialysis support. OBJECTIVE: Define the incidence of new clinically identified chronic kidney disease (CKD) among patients with COVID-19 and no pre-existing kidney disease. DESIGN PARTICIPANTS: The South Carolina (SC) Department of Health and Environmental Control (DHEC) COVID-19 mandatory reporting registry of SC residents testing for COVID-19 between March 2020 and October 2021 was included. DESIGN MAIN MEASURES: The primary outcome was a new incidence of a CKD diagnosis (N18.x) in those without a pre-existing diagnosis of CKD during the follow-up period of March 2020 to January 14, 2022. Patients were stratified by severity of illness (hospitalized or not, intensive care unit needed or not). The new incidence of CKD diagnosis was examined using logistic regression and cox proportional hazards analyses. KEY RESULTS: Among patients with COVID-19 (N = 683,958) without a pre-existing CKD diagnosis, 8322 (1.2 %) were found to have a new diagnosis of CKD. The strongest predictors for subsequent CKD diagnosis were age ≥ 60 years hazard ratio (HR) 31.5 (95% confidence interval [95%CI] 25.5-38.8), and intervening (between COVID-19 and CKD diagnoses) AKI diagnosis HR 20.7 (95%CI 19.7-21.7). The presence of AKI was associated with an HR of 23.6, 95% CI 22.3-25.0, among those not hospitalized, and HR of 6.2, 95% CI 5.7-6.8 among those hospitalized, for subsequent CKD. COVID-19 was not significantly associated with subsequent CKD after accounting for the severity of illness and comorbidities. CONCLUSION: Among SC residents, COVID-19 was not associated with CKD independent from indicators of the severity of illness, especially AKI diagnosis. Kidney-specific follow-up testing may be reserved for those high-risk for CKD development. Further prospective registries should examine the long-term kidney consequences to confirm these findings.

Revealing geographic transmission pattern of COVID-19 using neighborhood-level simulation with human mobility data and SEIR model: A case study of South Carolina

Direct human physical contact accelerates COVID-19 transmission. Smartphone mobility data has emerged as a valuable data source for revealing fine-grained human mobility, which can be used to estimate the intensity of physical contact surrounding different locations. Our study applied smartphone mobility data to simulate the second wave spreading of COVID-19 in January 2021 in three major metropolitan statistical areas (Columbia, Greenville, and Charleston) in South Carolina, United States. Based on the simulation, the number of historical county-level COVID-19 cases was allocated to neighborhoods (Census block groups) and points of interest (POIs), and the transmission rate of each allocated place was estimated. The result reveals that the COVID-19 infections during the study period mainly occurred in neighborhoods (86%), and the number is approximately proportional to the neighborhood's population. Restaurants and elementary and secondary schools contributed more COVID-19 infections than other POI categories. The simulation results for the coastal tourism Charleston area show high transmission rates in POIs related to travel and leisure activities. The results suggest that neighborhood-level infectious controlling measures are critical in reducing COVID-19 infections. We also found that households of lower socioeconomic status may be an umbrella against infection due to fewer visits to places such as malls and restaurants associated with their low financial status. Control measures should be tailored to different geographic locations since transmission rates and infection counts of POI categories vary among metropolitan areas.

Revealing geographic transmission pattern of COVID-19 using neighborhood-level simulation with human mobility data and SEIR model: A case study of South Carolina.

Direct human physical contact accelerates COVID-19 transmission. Smartphone mobility data has emerged as a valuable data source for revealing fine-grained human mobility, which can be used to estimate the intensity of physical contact surrounding different locations. Our study applied smartphone mobility data to simulate the second wave spreading of COVID-19 in January 2021 in three major metropolitan statistical areas (Columbia, Greenville, and Charleston) in South Carolina, United States. Based on the simulation, the number of historical county-level COVID-19 cases was allocated to neighborhoods (Census block groups) and points of interest (POIs), and the transmission rate of each allocated place was estimated. The result reveals that the COVID-19 infections during the study period mainly occurred in neighborhoods (86%), and the number is approximately proportional to the neighborhood's population. Restaurants and elementary and secondary schools contributed more COVID-19 infections than other POI categories. The simulation results for the coastal tourism Charleston area show high transmission rates in POIs related to travel and leisure activities. The results suggest that neighborhood-level infectious controlling measures are critical in reducing COVID-19 infections. We also found that households of lower socioeconomic status may be an umbrella against infection due to fewer visits to places such as malls and restaurants associated with their low financial status. Control measures should be tailored to different geographic locations since transmission rates and infection counts of POI categories vary among metropolitan areas.

Risk for stillbirth among pregnant individuals with SARS-CoV-2 infection varied by gestational age.

BACKGROUND: Despite previous research findings on higher risks of stillbirth among pregnant individuals with SARS-CoV-2 infection, it is unclear whether the gestational timing of viral infection modulates this risk. OBJECTIVE: This study aimed to examine the association between timing of SARS-CoV-2 infection during pregnancy and risk of stillbirth. STUDY DESIGN: This retrospective cohort study used multilevel logistic regression analyses of nationwide electronic health records in the United States. Data were from 75 healthcare systems and institutes across 50 states. A total of 191,403 pregnancies of 190,738 individuals of reproductive age (15-49 years) who had childbirth between March 1, 2020 and May 31, 2021 were identified and included. The main outcome was stillbirth at ≥20 weeks of gestation. Exposures were the timing of SARS-CoV-2 infection: early pregnancy (<20 weeks), midpregnancy (21-27 weeks), the third trimester (28-43 weeks), any time before delivery, and never infected (reference). RESULTS: We identified 2342 (1.3%) pregnancies with COVID-19 in early pregnancy, 2075 (1.2%) in midpregnancy, and 12,697 (6.9%) in the third trimester. After adjusting for maternal and clinical characteristics, increased odds of stillbirth were observed among pregnant individuals with SARS-CoV-2 infection only in early pregnancy (odds ratio, 1.75, 95% confidence interval, 1.25-2.46) and midpregnancy (odds ratio, 2.09; 95% confidence interval, 1.49-2.93), as opposed to pregnant individuals who were never infected. Older age, Black race, hypertension, acute respiratory distress syndrome or acute respiratory failure, and placental abruption were found to be consistently associated with stillbirth across different trimesters. CONCLUSION: Increased risk of stillbirth was associated with COVID-19 only when pregnant individuals were infected during early and midpregnancy, and not at any time before the delivery or during the third trimester, suggesting the potential vulnerability of the fetus to SARS-CoV-2 infection in early pregnancy. Our findings underscore the importance of proactive COVID-19 prevention and timely medical intervention for individuals infected with SARS-CoV-2 during early and midpregnancy.

Efficacy and safety of COVID-19 inactivated vaccine: A meta-analysis.

Background: Inactivated vaccine is one of the primary technology types of Coronavirus Disease 2019 (COVID-19) vaccines, which has wide application in many countries, including mainland China. However, systematic evaluation of the efficacy and safety of COVID-19 inactivated vaccines remains limited. And trust in the vaccine is the key to solving vaccine hesitancy. Methods: Various academic databases were searched comprehensively for randomized controlled trials (RCTs) related to COVID-19 inactivated vaccines. The deadline for retrieval was December 2021. Study screening and data extraction were according to inclusive and exclusive criteria. Statistical analyses were performed using RevMan software 5.3 version and STATA software 16.0 version. Results: Eight studies with 79,334 subjects were included of which 48,123 had received two doses of COVID-19 inactivated vaccines, and 31,211 had received two doses of placebo. The results of the meta-analysis showed that: in terms of effectiveness evaluation, two doses of COVID-19 inactivated vaccines decreased the symptomatic infection [relative risk (RR) = 0.23, 95% confidence interval (CI) (0.18,0.30), P < 0.00001], asymptomatic infection [RR = 0.48, 95%CI (0.32, 0.74), P = 0.0008], total infection [RR = 0.32, 95%CI (0.24, 0.41), P < 0.00001] and hospitalization [RR = 0.06, 95%CI (0.01, 0.27), P = 0.0002] for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) significantly. In terms of safety assessment, two doses of COVID-19 inactivated vaccines also caused more adverse events. After two inoculations, total adverse events and systemic adverse events increased significantly [total adverse events RR = 1.14, 95%CI (1.08, 1.21), P < 0.00001; systemic adverse events RR = 1.22, 95%CI (1.09, 1.35), P = 0.0002]. The most common adverse event was pain at the injection site. Almost all local adverse reactions consisted of these events. The incidence of pain at the injection site was related to adjuvants. Using aluminum hydroxide as an adjuvant increased local pain significantly [RR = 1.97, 95%CI (1.52, 2.55), P < 0.00001]. Two doses COVID-19 inactivated vaccines did not increase serious adverse events [RR = 0.71, 95%CI (0.57, 0.90), P = 0.004]. Conclusion: Two doses of inactivated COVID-19 vaccines in people over 18 years of age effectively prevented SARS-CoV-2 infection and its associated hospitalizations. Short-term, mild to moderate adverse reactions had occurred, but serious adverse events were rare. No placebo or vaccine-related deaths had been reported. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier: 42021291250.

Mitigating the outbreak of an infectious disease over its life cycle: A diffusion-based approach.

We first qualitatively divide the cycle of an infectious disease outbreak into five distinct stages by following the adoption categorization from the diffusion theory. Next, we apply a standard mechanistic model, the susceptible-infected-recovered model, to simulate a variety of transmission scenarios and to quantify the benefits of various countermeasures. In particular, we apply the specific values of the newly infected to quantitatively divide an outbreak cycle into stages. We therefore reveal diverging patterns of countermeasures in different stages. The stage is critical in determining the evolutionary characteristics of the diffusion process. Our results show that it is necessary to employ appropriate diverse strategies in different stages over the life cycle of an infectious disease outbreak. In the early stages, we need to focus on prevention, early detection, and strict countermeasure (e.g., isolation and lockdown) for controlling an epidemic. It is better safe (i.e., stricter countermeasures) than sorry (i.e., let the virus spread out). There are two reasons why we should implement responsive and strict countermeasures in the early stages. The countermeasures are very effective, and the earlier the more total infected reduction over the whole cycle. The economic and societal burden for implementing countermeasures is relatively small due to limited affected areas, and the earlier the less burden. Both reasons change to the opposite in the late stages. The strategic focuses in the late stages become more delicate and balanced for two reasons: the same countermeasures become much less effective, and the society bears a much heavier burden. Strict countermeasures may become unnecessary, and we need to think about how to live with the infectious disease.

The impact of COVID-19 pandemic on the dynamic HIV care engagement among people with HIV: real-world evidence.

INTRODUCTION: Existing studies examining the impact of the COVID-19 pandemic on engagement in HIV care often capture cross-sectional status, while lacking longitudinal evaluations. This study examined the impact of the pandemic on the longitudinal dynamic change of retention in care and viral suppression status. METHODS: The electronic health record (EHR) data of this population-level cohort study were retrieved from the statewide electronic HIV/AIDS reporting system in South Carolina. The study population was people with HIV (PWH) who had at least one year's symmetric follow-up observation record before and after the pandemic. Multivariable generalized linear mixed regression models were employed to analyze the impact of the pandemic on these outcomes, adjusting for socio-demographic characteristics and preexisting comorbidities. RESULTS: In the adjusted models, PWH had a lower likelihood of retention in care (adjusted odds ratio [aOR]: 0.806, 95% confidence interval [CI]: 0.769, 0.844) and a higher probability of virological failure (aOR: 1.240, 95% CI: 1.169, 1.316) during the peri-pandemic period than pre-pandemic period. Results from interaction effect analysis from each cohort revealed that the negative effect of the pandemic on retention in care was more severe among PWH with high comorbidity burden than those without any comorbidity; meanwhile, a more striking virological failure was observed among PWH who reside in urban areas than in rural areas. CONCLUSION: The COVID-19 pandemic has a negative impact on retention in care and viral suppression among PWH in South Carolina, particularly for individuals with comorbidities and residing in urban areas.

Evaluation of Online Education in the Era of COVID-19 Pandemic: A Review from Students, Parents, and Teachers' Perspectives

Qualitative content analysis is used in this study to review related online education since the outbreak of COVID-19. The aim of this study was to summarize the impact of online teaching on the education industry during the pandemic, sum up the viewpoints of all kinds of people to draw conclusions, and conclude the practical countermeasures. Based on the result of the analysis, firstly, we think that students and teachers are satisfied with online education, but parents have expressed dissatisfaction with this kind of education. Secondly, this paper lists the advantages and common problems of online teaching during study at home from different aspects. According to deficits, we summarize the solutions from three aspects: network equipment, teaching, and self-adjustment. This research is of great significance. It is not only beneficial to the development of educational platforms and personalized teaching but also helps formulate education policy to reduce the burden of education.

Using Twitter Data to Estimate Prevalence of Mental Disorder Symptoms in the United States During the COVID-19 Pandemic: Ecological Cohort Study.

BACKGROUND: Existing research and national surveillance data suggested an increase of the prevalence of mental disorders during the coronavirus disease 2019 (COVID-19) pandemic. Social media, such as Twitter, could be a source of data for estimation due to its real-time nature, high availability, and large geographical coverage. However, there is a dearth of studies validating the accuracy of Twitter-based prevalence for mental disorders through the comparison with CDC-reported prevalence. OBJECTIVE: This study aims to verify the feasibility of Twitter-based prevalence for mental disorders symptoms being an instrument for prevalence estimation, where the feasibility is gauged via the correlations between Twitter-based prevalence of mental disorder symptoms (i.e., anxiety and depressive symptoms) and the one based on national surveillance data. In addition, this study aims to identify how the correlations changed over time (i.e., the temporal trend). METHODS: State-level prevalence of anxiety and depressive symptoms were retrieved from the National Household Pulse Survey (HPS) through the Centers for Disease Control and Prevention (CDC) from April 2020 to July 2021. Tweets were retrieved from the Twitter streaming API during the same period and used to estimate the prevalence of mental disorder symptoms for each state using keyword analysis. Stratified linear mixed models were employed to evaluate the correlations between the Twitter-based prevalence of mental disorder symptoms and those reported by the CDC. The magnitude and significance of model parameters were used to evaluate the correlations. Temporal trends of correlations were tested after adding the time variable to the model. Geospatial differences were compared based on random effects. RESULTS: The Pearson correlations between the overall prevalence based on CDC and Twitter for anxiety and depressive symptoms were 0.587 (P<.001) and 0.368 (P<.001), respectively. Stratified by four phases (i.e., April 2020, August 2020, October 2020, and April 2021) defined by HPS, linear mixed models showed that Twitter-based prevalence for anxiety symptoms had a positive and significant correlation with CDC-reported prevalence in phases 2 and 3 while a significant correlation for depressive symptoms was identified in phases 1 and 3. CONCLUSIONS: Positive correlations are identified between Twitter-based and CDC-reported prevalence, and temporal trends of these correlations were found. Geospatial differences in the prevalence of mental disorder symptoms were found between the northern and southern U.S. Findings from this study could inform the future investigation on leveraging social media platforms to estimate mental disorder symptoms and the provision of immediate prevention measures to improve health outcomes.

Investigating the relationships between concentrated disadvantage, place connectivity, and COVID-19 fatality in the United States over time.

BACKGROUND: Concentrated disadvantaged areas have been disproportionately affected by COVID-19 outbreak in the United States (US). Meanwhile, highly connected areas may contribute to higher human movement, leading to higher COVID-19 cases and deaths. This study examined the associations between concentrated disadvantage, place connectivity, and COVID-19 fatality in the US over time. METHODS: Concentrated disadvantage was assessed based on the spatial concentration of residents with low socioeconomic status. Place connectivity was defined as the normalized number of shared Twitter users between the county and all other counties in the contiguous US in a year (Y = 2019). COVID-19 fatality was measured as the cumulative COVID-19 deaths divided by the cumulative COVID-19 cases. Using county-level (N = 3,091) COVID-19 fatality over four time periods (up to October 31, 2021), we performed mixed-effect negative binomial regressions to examine the association between concentrated disadvantage, place connectivity, and COVID-19 fatality, considering potential state-level variations. The moderation effects of county-level place connectivity and concentrated disadvantage were analyzed. Spatially lagged variables of COVID-19 fatality were added to the models to control for the effect of spatial autocorrelations in COVID-19 fatality. RESULTS: Concentrated disadvantage was significantly associated with an increased COVID-19 fatality in four time periods (p < 0.01). More importantly, moderation analysis suggested that place connectivity significantly exacerbated the harmful effect of concentrated disadvantage on COVID-19 fatality in three periods (p < 0.01), and this significant moderation effect increased over time. The moderation effects were also significant when using place connectivity data from the previous year. CONCLUSIONS: Populations living in counties with both high concentrated disadvantage and high place connectivity may be at risk of a higher COVID-19 fatality. Greater COVID-19 fatality that occurs in concentrated disadvantaged counties may be partially due to higher human movement through place connectivity. In response to COVID-19 and other future infectious disease outbreaks, policymakers are encouraged to take advantage of historical disadvantage and place connectivity data in epidemic monitoring and surveillance of the disadvantaged areas that are highly connected, as well as targeting vulnerable populations and communities for additional intervention.

Resilience resources and coping strategies of COVID-19 female long haulers: A qualitative study.

Background: Female long haulers deal with persistent post-acute COVID-19 symptoms that have serious health implications. This study aimed to identify resilience resources at multiple socio-ecological levels for female long haulers and describe how resilience resources affect their responses to long COVID. Methods: Purposive sampling was adopted to recruit participants through social media from April to June 2021 followed by 15 semi-structured interviews. An inductive analytical approach was adopted to categorize themes by open and axial coding that were verified by peer review. Results: Female long haulers relied on resources at various socio-ecological levels to foster their resilience in response to long COVID. At the individual level, they utilized cognitive and emotional resources to increase knowledge, learn new skills, set goals, and manage emotions; behavioral resources (e.g., internal motivation and executive functioning) to perform physical, creative, and recreational activities, and adopt healthier eating habits; and spiritual resources to perform spiritual rituals and connect with God. At the social level, the support from existing relationships and/or online social support groups enhanced their social identity and provided material and informational resources. At the health systems level, the guidance from counselors and physicians and availability of clinics, medicines, and health equipment assisted them in symptom management and medication adherence. Conclusion: The resilience of female long haulers can be enhanced through (1) offering financial and health-related resources, (2) developing online social-support groups, (3) counseling and care service training for healthcare professionals, and (4) implementing more psychosocial interventions by labor organizations.

Analysis and Identification of Bioactive Compounds of Cannabinoids in Silico for Inhibition of SARS-CoV-2 and SARS-CoV.

Despite the approval of multiple vaccinations in different countries, the majority of the world's population remains unvaccinated due to discrepancies in vaccine distribution and limited production capacity. The SARS-CoV-2 RBD-ACE2 complex (receptor binding domain that binds to ACE2) could be a suitable target for the development of a vaccine or an inhibitor. Various natural products have been used against SARS-CoV-2. Here, we docked 42 active cannabinoids to the active site of the SARS-CoV-2 and SARS-CoV complex of RBD-ACE2. To ensure the flexibility and stability of the complex produced after docking, the top three ligand molecules with the best overall binding energies were further analyzed through molecular dynamic simulation (MDS). Then, we used the webserver Swissadme program and binding free energy to calculate and estimate the MMPBSA and ADME characteristics. Our results showed that luteolin, CBGVA, and CBNA were the top three molecules that interact with the SARS-CoV-2 RBD-ACE2 complex, while luteolin, stigmasterol, and CBNA had the strongest contact with that SARS-CoV. Our findings show that luteolin may be a potential inhibitor of infections caused by coronavirus-like pathogens such as COVID-19, although further in vivo and in vitro research is required.