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.

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.

Effects of coronavirus disease 2019 vaccination on seizures in patients with epilepsy.

BACKGROUND: Given that seizures may be triggered by vaccination, this study aimed to evaluate the risk and correlative factors of seizures in patients with epilepsy (PWE) after being vaccinated against coronavirus disease 2019 (COVID-19). METHODS: This study retrospectively enrolled PWE who were vaccinated against COVID-19 in the epilepsy centers of 11 hospitals in China. We divided the PWE into two groups as follows: (1) patients who developed seizures within 14 days of vaccination were assigned to the SAV (with seizures after vaccination) group; (2) patients who were seizure-free within 14 days of vaccination were assigned to the SFAV (seizure-free after vaccination) group. To identify potential risk factors for seizure reccurence, the binary logistic regression analysis was performed. Besides, 67 PWE who had not been vaccinated were also included for elucidating the effects of vaccination on seizures recurrence, and binary logistic regression analysis was performed to determine whether vaccination would affect the recurrence rate of PWE who had drug reduction or withdrawal. RESULTS: The study included a total of 407 patients; of which, 48 (11.8%) developed seizures within 14 days after vaccination (SAV group), whereas 359 (88.2%) remained seizure-free (SFAV group). The binary logistic regression analysis revealed that duration of seizure freedom ( P  < 0.001) and withdrawal from anti-seizure medications (ASMs) or reduction in their dosage during the peri-vaccination period were significantly associated with the recurrence of seizures (odds ratio = 7.384, 95% confidence interval = 1.732-31.488, P  = 0.007). In addition, 32 of 33 patients (97.0%) who were seizure-free for more than three months before vaccination and had a normal electroencephalogram before vaccination did not have any seizures within 14 days of vaccination. A total of 92 (22.6%) patients experienced non-epileptic adverse reactions after vaccination. Binary logistic regression analysis results showed that vaccine did not significantly affect the recurrence rate of PWE who had the behavior of ASMs dose reduction or withdrawal ( P  = 0.143). CONCLUSIONS: PWE need protection from the COVID-19 vaccine. PWE who are seizure-free for >3 months before vaccination should be vaccinated. Whether the remaining PWE should be vaccinated depends on the local prevalence of COVID-19. Finally, PWE should avoid discontinuing ASMs or reducing their dosage during the peri-vaccination period.

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.

Perceived COVID-19 threat, perceived healthcare system inequities, personal experiences of healthcare discrimination and their associations with COVID-19 preventive behavioral intentions among college students in the U.S.

College students are often reluctant to follow U.S. preventive guidelines to lower their risk of COVID-19 infection, despite an increased risk of transmission in college settings. Prior research suggested that college students who perceived greater COVID-19 severity and susceptibility (i.e., COVID-19 threat) were more likely to engage in COVID-19 preventive behaviors, yet there is limited research examining whether perceived COVID-19 threat, perceived U.S. healthcare system inequities, and personal experiences of healthcare discrimination collectively influence college students' COVID-19 preventive behaviors. This study identified latent classes of perceived COVID-19 threat, perceived U.S. healthcare system inequities, and personal experiences of healthcare discrimination, examined whether latent classes were associated with COVID-19 preventive behavioral intentions, and assessed whether latent class membership varied across racial/ethnic groups.Students from the University of Maryland, College Park (N = 432) completed the Weighing Factors in COVID-19 Health Decisions survey (December 2020-December 2021). Latent class analysis identified latent classes based on perceived COVID-19 threat, perceived U.S. healthcare system inequities, and personal experiences of healthcare discrimination. Regression analyses examined associations between the latent classes and COVID-19 preventive behavioral intentions (i.e., social distancing, mask-wearing, COVID-19 vaccination) and whether latent class membership varied across racial/ethnic groups.Students in Latent Class 1 (27.3% of the sample) had high perceived COVID-19 threat and U.S. healthcare system inequities and medium probability of experiencing personal healthcare discrimination. Students in Latent Class 1 had higher social distancing, mask-wearing, and vaccination intentions compared to other latent classes. Compared to Latent Class 4 (reference group), students in Latent Class 1 had higher odds of identifying as Hispanic or Latino, Non-Hispanic Asian, Non-Hispanic Black or African American, and Non-Hispanic Multiracial versus Non-Hispanic White.Latent classes of higher perceived COVID-19 threat, perceived U.S. healthcare system inequities, and personal experiences of healthcare discrimination were associated with higher COVID-19 preventive behavioral intentions and latent class membership varied across racial/ethnic groups. Interventions should emphasize the importance of COVID-19 preventive behaviors among students who perceive lower COVID-19 threat.

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.

Social Life of Females with Persistent COVID-19 Symptoms: A Qualitative Study.

Persistent COVID-19 symptoms (long COVID) may bring challenges to long haulers' social lives. Females may endure more profound impacts given their special social roles and existing structural inequality. This study explores the effects of long COVID on the social life of female long haulers. We conducted semi-structured interviews via Zoom between April and June 2021 with 15 female long haulers in the United States, purposely recruited from Facebook and Slack groups and organization websites related to long COVID. Interviews were audio-recorded and transcribed verbatim with consent. The interview data were managed using MAXQDA and examined by thematic analysis. Long COVID negatively affected female long haulers' social lives by causing physical limitations, economic issues, altered social relationships, social roles' conflicts, and social stigma. Long COVID prevented female long haulers' recovery process. Physical limitations altered their perceptions on body, and family-work conflicts caused tremendous stress. They also experienced internalized stigma and job insecurities. This study provides insights into challenges that COVID-19 female long haulers could face in their return to normal social life, underscoring the vulnerability of females affected by long COVID due to significant alterations in their social lives. Shifting to new methods of communication, especially social media, diminished the adverse effects of long COVID (e.g., social isolation).

How Different Pre-existing Mental Disorders and Their Co-occurrence Affects COVID-19 Clinical Outcomes? A Real-World Data Study in the Southern United States

Background Although a psychiatric history might be an independent risk factor for COVID-19 infection and mortality, no studies have systematically investigated how different clusters of pre-existing mental disorders may affect COVID-19 clinical outcomes or showed how the coexistence of mental disorder clusters is related to COVID-19 clinical outcomes. Methods Using a retrospective cohort study design, a total of 476,775 adult patients with lab-confirmed and probable COVID-19 between March 06, 2020 and April 14, 2021 in South Carolina, United States were included in the current study. The electronic health record data of COVID-19 patients were linked to all payer-based claims data through the SC Revenue and Fiscal Affairs Office. Pre-existing mental disorder diagnoses from Jan 2, 2019 to Jan 14, 2021 were extracted from the patients' healthcare utilization data via ICD-10 codes. Results There is an elevated risk of COVID-19-related hospitalization and death among participants with pre-existing mental disorders adjusting for key socio-demographic and comorbidity covariates. Co-occurrence of any two clusters was positively associated with COVID-19-related hospitalization and death. The odds ratio of being hospitalized was 1.26 (95% CI: 1.151, 1.383) for patients with internalizing and externalizing disorders, 1.65 (95% CI: 1.298, 2.092) for internalizing and thought disorders, 1.76 (95% CI: 1.217, 2.542) for externalizing and thought disorders, and 1.64 (95% CI: 1.274, 2.118) for three clusters of mental disorders. Conclusions Pre-existing internalizing disorders and thought disorders are positively related to COVID-19 hospitalization and death. Co-occurrence of any two clusters of mental disorders have elevated risk of COVID-19-related hospitalization and death compared to those with a single cluster.

Listening to Voices from African American Communities in the Southern States about COVID-19 Vaccine Information and Communication: A Qualitative Study.

The high uptake of COVID-19 vaccines is one of the most promising measures to control the pandemic. However, some African American (AA) communities exhibit vaccination hesitancy due to mis- or disinformation. It is important to understand the challenges in accessing reliable COVID-19 vaccine information and to develop feasible health communication interventions based on voices from AA communities. We conducted 2 focus group discussions (FGDs) among 18 community stakeholders recruited from 3 counties in South Carolina on 8 October and 29 October 2021. The FGDs were conducted online via Zoom meetings. The FGD data were managed and thematically analyzed using NVivo 12. Participants worked primarily in colleges, churches, and health agencies. We found that the challenges of accessing reliable vaccine information in AA communities primarily included structural barriers, information barriers, and a lack of trust. Community stakeholders recommended recruiting trusted messengers, using social events to reach target populations, and conducting health communication campaigns through open dialogue among stakeholders. Health communication interventions directed at COVID-19 vaccine uptake should be grounded in ongoing community engagement, trust-building activities, and transparent communication about vaccine development. Tailoring health communication interventions to different groups may help reduce misinformation spread and thus promote vaccination in AA communities in the southern states.

Multilevel determinants of racial/ethnic disparities in severe maternal morbidity and mortality in the context of the COVID-19 pandemic in the USA: protocol for a concurrent triangulation, mixed-methods study.

INTRODUCTION: The COVID-19 pandemic has affected communities of colour the hardest. Non-Hispanic black and Hispanic pregnant women appear to have disproportionate SARS-CoV-2 infection and death rates. METHODS AND ANALYSIS: We will use the socioecological framework and employ a concurrent triangulation, mixed-methods study design to achieve three specific aims: (1) examine the impacts of the COVID-19 pandemic on racial/ethnic disparities in severe maternal morbidity and mortality (SMMM); (2) explore how social contexts (eg, racial/ethnic residential segregation) have contributed to the widening of racial/ethnic disparities in SMMM during the pandemic and identify distinct mediating pathways through maternity care and mental health; and (3) determine the role of social contextual factors on racial/ethnic disparities in pregnancy-related morbidities using machine learning algorithms. We will leverage an existing South Carolina COVID-19 Cohort by creating a pregnancy cohort that links COVID-19 testing data, electronic health records (EHRs), vital records data, healthcare utilisation data and billing data for all births in South Carolina (SC) between 2018 and 2021 (>200 000 births). We will also conduct similar analyses using EHR data from the National COVID-19 Cohort Collaborative including >270 000 women who had a childbirth between 2018 and 2021 in the USA. We will use a convergent parallel design which includes a quantitative analysis of data from the 2018-2021 SC Pregnancy Risk Assessment and Monitoring System (unweighted n>2000) and in-depth interviews of 40 postpartum women and 10 maternal care providers to identify distinct mediating pathways. ETHICS AND DISSEMINATION: The study was approved by institutional review boards at the University of SC (Pro00115169) and the SC Department of Health and Environmental Control (DHEC IRB.21-030). Informed consent will be provided by the participants in the in-depth interviews. Study findings will be disseminated with key stakeholders including patients, presented at academic conferences and published in peer-reviewed journals.

Examining Social Determinants of Health During a Pandemic: Clinical Application of Z Codes Before and During COVID-19.

Recognition of the impact of social determinants of health (SDoH) on healthcare outcomes, healthcare service utilization, and population health has prompted a global shift in focus to patient social needs and lived experiences in assessment and treatment. The International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) provides a list of non-billable "Z codes" specific to SDoH for use in electronic health records. Using population-level analysis, this study aims to examine clinical application of Z codes in South Carolina before and during the COVID-19 pandemic. The study population consists of South Carolina residents who had a healthcare visit and had their COVID-19 test result reported to the state's Department of Health and Environmental Control before January 14, 2021. Of the 1,190,531 individuals in the overall sample, Z codes were used only for 14,665 (1.23%) of the patients, including 2,536 (0.97%) COVID-positive patients and 12,129 (1.30%) COVID-negative patients. Compared with hospitals that did not use Z codes, those that did were significantly more likely to have higher bed capacity (p = 0.017) and to be teaching hospitals (p = 0.03), although this was significant only among COVID-19 positive individuals. Those at inpatient visits were most likely to receive Z codes (OR: 5.26; 95% CI: 5.14, 5.38; p < 0.0001) compared to those at outpatient visits (OR: 0.07; 95%CI: 0.06, 0.07; p < 0.0001). There was a slight increase of Z code use from 2019 to 2020 (OR: 1.33, 95% CI: 1.30, 1.36; p < 0.0001), which was still significant when stratified by facility type across time. As one of the first studies to examine Z code use among a large patient population, findings clearly indicate underutilization by providers. Additional study is needed to understand the potentially long-lasting health effects related to SDoH among underserved populations.

COVID-19 vaccine acceptance among college students in South Carolina: do information sources and trust in information matter?

BACKGROUND: For college students who are exposed to multimedia, the sources of COVID-19 vaccine information and their trust in these sources may play a role in shaping the vaccine acceptance spectrum (refusal, hesitancy, and acceptance). METHODS: Based on an online survey among 1,062 college students in South Carolina, we investigated vaccine information sources among college students and examined how COVID-19 vaccine acceptance was associated with information source and trust level in each source. RESULTS: The top three sources of COVID-19 vaccine information were health agencies, mass media, and personal social networks. Trust in mass media, health agencies, scientists, and pharmaceutical companies was negatively associated with vaccine refusal. Trust in government and scientists was negatively associated with vaccine hesitancy. DISCUSSION: Our findings highlight the importance of restoring trust in government, healthcare system, scientists, and pharmaceutical industries in the COVID-19 era.

Social Capital, Urbanization Level, and COVID-19 Vaccination Uptake in the United States: A National Level Analysis.

Vaccination remains the most promising mitigation strategy for the COVID-19 pandemic. However, existing literature shows significant disparities in vaccination uptake in the United States. Using publicly available national-level data, we aimed to explore if county-level social capital can further explain disparities in vaccination uptake rates when adjusting for demographic and social determinants of health (SDOH) variables, and if association between social capital and vaccination uptake may vary by urbanization level. Bivariate analyses and a hierarchical multivariable quasi-binomial regression analysis were conducted, where the regression analysis was stratified by urban-rural status. The current study suggests that social capital contributes significantly to the disparities of vaccination uptake in the US. The results of the stratification analysis show common predictors of vaccine uptake but also suggest various patterns based on urbanization level regarding the associations of COVID-19 vaccination uptake with SDOH and social capital factors. The study provides a new perspective to address disparities in vaccination uptake through fostering social capital within communities; which may inform tailored public health intervention efforts to enhance social capital and promote vaccination uptake.

Health Care Worker Burnout During the COVID-19 Pandemic in HIV Clinics in Guangxi, China: A Cross-sectional Analysis of Risk and Protective Factors.

ABSTRACT: Dual epidemics of coronavirus disease 2019 (COVID-19) and HIV cause burnout among HIV health care workers. Guided by a socioecological framework, we investigated risk and protective factors of their burnout in Guangxi, China based on an online survey (N = 1,029) from April to May 2020. Descriptive and bivariate analyses and hierarchical regression were conducted. COVID-19-related stressors (B = 0.648 [0.482-0.816], p < .001), challenges while delivering HIV services (B = 0.236 [0.174-0.298], p < .001), and working in province/city-level institutes (B = 2.302, [0.828-3.776], p =.002) were risk factors. Protective factors were resilience (B = -0.596 [-0.687 to -0.504], p < .001), workplace social support (B = -0.410 [-0.717 to -0.044], p =.03), and institutional responsiveness to COVID-19 (B = -0.138 [-0.205 to -0.071], p < .001), respectively. Burnout-related interventions may benefit from promoting resilience, creating a supportive work environment, and strengthening institutional response to public health emergencies.

Attitudes to Being Vaccinated Against COVID-19: A Survey of People With Epilepsy in China.

Objective: We conducted a survey to assess vaccination coverage, vaccination willingness, and variables associated with vaccination hesitancy to provide evidence on coronavirus disease (COVID-19) vaccination strategies. Methods: This anonymous questionnaire study conducted a multicenter, cross-sectional survey of outpatients and inpatients with epilepsy (PWE) registered in epilepsy clinics, in 2021, in 10 hospitals in seven cities of Shandong Province. Results: A total of 600 questionnaires were distributed, and 557 valid questionnaires were returned. A total of 130 people were vaccinated against COVID-19. Among 427 unvaccinated participants, 69.32% (296/427) were willing to receive the COVID-19 vaccine in the future, and the remaining 30.68% (131/427) were unwilling to receive vaccination. Most (89.9%) of the participants believed that the role of vaccination was crucial in response to the spread of COVID-19. A significant association was found between willingness to receive the COVID-19 vaccine and the following variables: age, marital status, level of education, occupation, residence, seizure type, and seizure control after antiepileptic drug therapy. It is noteworthy that education level, living in urban areas, and seizure freedom were significantly related to willingness to receive COVID-19 vaccination. Conclusions: Vaccination is a key measure for the prevention and control of COVID-19, and most PWE are willing to be vaccinated. Vaccine safety, effectiveness, and accessibility are essential in combatting vaccine hesitation and increasing vaccination rates.

HIV Service Interruptions During the COVID-19 Pandemic in China: The Role of COVID-19 Challenges and Institutional Response from Healthcare Professional's Perspective.

During the COVID-19 pandemic, HIV-related services have been unavoidably disrupted and impacted. However, the nature and scope of HIV service disruptions due to COVID-19 has rarely been characterized in China. A cross-sectional online survey was conducted among 1029 HIV healthcare providers in Guangxi, China, from April to May 2020. Latent class analysis (LCA) was first used to identify HIV service disruption levels, then hierarchical multilevel logistic regression was conducted to analyze the relationships of COVID-19 challenges, institutional responses, and HIV service disruption levels adjusting for the clustering effect of institutional ownership levels. Four classes of HIV service disruption were identified, with 22.0% complete disruption, 15.4% moderate disruption, 21.9% minor disruption, and 40.7% almost no disruption. COVID-19 challenges were positively associated with the probabilities of service disruption levels. Institutional responses were negatively associated with the probabilities of being classified as "minor disruption" and moderated the association of COVID-19 challenges with complete and moderate disruptions compared with no disruption group. To maintain continuity of core HIV services in face of a pandemic, building a resilient health care system with adequate preparedness is necessary.