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.

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.

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.

Curating a knowledge base for individuals with coinfection of HIV and SARS-CoV-2: a study protocol of EHR-based data mining and clinical implementation.

INTRODUCTION: Despite a higher risk of severe COVID-19 disease in individuals with HIV, the interactions between SARS-CoV-2 and HIV infections remain unclear. To delineate these interactions, multicentre Electronic Health Records (EHR) hold existing promise to provide full-spectrum and longitudinal clinical data, demographics and sociobehavioural data at individual level. Presently, a comprehensive EHR-based cohort for the HIV/SARS-CoV-2 coinfection has not been established; EHR integration and data mining methods tailored for studying the coinfection are urgently needed yet remain underdeveloped. METHODS AND ANALYSIS: The overarching goal of this exploratory/developmental study is to establish an EHR-based cohort for individuals with HIV/SARS-CoV-2 coinfection and perform large-scale EHR-based data mining to examine the interactions between HIV and SARS-CoV-2 infections and systematically identify and validate factors contributing to the severe clinical course of the coinfection. We will use a nationwide EHR database in the USA, namely, National COVID Cohort Collaborative (N3C). Ultimately, collected clinical evidence will be implemented and used to pilot test a clinical decision support prototype to assist providers in screening and referral of at-risk patients in real-world clinics. ETHICS AND DISSEMINATION: The study was approved by the institutional review boards at the University of South Carolina (Pro00121828) as non-human subject study. Study findings will be presented at academic conferences and published in peer-reviewed journals. This study will disseminate urgently needed clinical evidence for guiding clinical practice for individuals with the coinfection at Prisma Health, a healthcare system in collaboration.

COVID-19 vaccine effectiveness among people living with and without HIV in South Carolina, USA: protocol of a population-based cohort study.

INTRODUCTION: Despite the effectiveness of COVID-19 vaccines in preventing severe COVID-19 outcomes, a small percentage of fully vaccinated persons will develop symptomatic or asymptomatic infections with SARS-CoV-2, which is referred to as 'breakthrough COVID-19'. People living with HIV (PLWH) appear to have an elevated risk of severe COVID-19 outcomes, yet the effectiveness of the COVID-19 vaccine in this population remains unclear due to the limited research efforts in this population in the real world. This study aims to characterise and compare the breakthrough COVID-19 (eg, prevalence and disease severity) between PLWH and non-PLWH and then examine whether HIV markers play a role in COVID-19 vaccine effectiveness within the PLWH population. METHODS AND ANALYSIS: This cohort study will merge electronic health records data from multiple data sources in South Carolina (SC), including the 'HIV Cohort' (n=12 203) identified from the statewide Enhanced HIV/AIDS Reporting System, 'Vaccine Cohort' from the Statewide Immunisation Online Network which provides patient-level immunisation records (n=~1.71 million), and 'COVID-19 Cohort' which includes healthcare encounters and COVID-19 diagnosis information for all individuals who were tested for COVID-19 (n=~3.41 million). The PLWH will be matched with a comparison group of non-PLWH by the propensity score matching method. To distinguish the role of immunity level in affecting the vaccine effectiveness, we will conduct subgroup analyses to compare the outcome of virally controlled and immunosuppressed PLWH with non-PLWH. Conditional logistic regression and generalised linear models will be employed to analyse the relationship between HIV status and protection durability by adjusting for potential confounders. ETHICS AND DISSEMINATION: The study was approved by the Institutional Review Board at the University of South Carolina (Pro00117583) as a Non-Human Subject study. The study's findings will be published in peer-reviewed journals and disseminated at national and international conferences and through social media.

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.

The impact of COVID-19 on older adults living with HIV: HIV care and psychosocial effects

This reprinted chapter originally appeared in Journal of Gerontological Social Work, 2020, 63[6-7], 602-606. (The following of the original article appeared in record 2020-58061-001.) COVID-19 continues to have a detrimental impact worldwide. Older adults living with HIV are a vulnerable group. COVID-19 may have an effect on HIV treatment outcomes and psychosocial health among older adults living with HIV. Social workers and health-care providers should be aware of the potential longitudinal impact of COVID-19 on this vulnerable population. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Associations between HIV infection and clinical spectrum of COVID-19: a population level analysis based on US National COVID Cohort Collaborative (N3C) data.

BACKGROUND: Evidence of whether people living with HIV are at elevated risk of adverse COVID-19 outcomes is inconclusive. We aimed to investigate this association using the population-based National COVID Cohort Collaborative (N3C) data in the USA. METHODS: We included all adult (aged ≥18 years) COVID-19 cases with any health-care encounter from 54 clinical sites in the USA, with data being deposited into the N3C. The outcomes were COVID-19 disease severity, hospitalisation, and mortality. Encounters in the same health-care system beginning on or after January 1, 2018, were also included to provide information about pre-existing health conditions (eg, comorbidities). Logistic regression models were employed to estimate the association of HIV infection and HIV markers (CD4 cell count, viral load) with hospitalisation, mortality, and clinical severity of COVID-19 (multinomial). The models were initially adjusted for demographic characteristics, then subsequently adjusted for smoking, obesity, and a broad range of comorbidities. Interaction terms were added to assess moderation effects by demographic characteristics. FINDINGS: In the harmonised N3C data release set from Jan 1, 2020, to May 8, 2021, there were 1 436 622 adult COVID-19 cases, of these, 13 170 individuals had HIV infection. A total of 26 130 COVID-19 related deaths occurred, with 445 among people with HIV. After adjusting for all the covariates, people with HIV had higher odds of COVID-19 death (adjusted odds ratio 1·29, 95% CI 1·16-1·44) and hospitalisation (1·20, 1·15-1·26), but lower odds of mild or moderate COVID-19 (0·61, 0·59-0·64) than people without HIV. Interaction terms revealed that the elevated odds were higher among older age groups, male, Black, African American, Hispanic, or Latinx adults. A lower CD4 cell count (<200 cells per µL) was associated with all the adverse COVID-19 outcomes, while viral suppression was only associated with reduced hospitalisation. INTERPRETATION: Given the COVID-19 pandemic's exacerbating effects on health inequities, public health and clinical communities must strengthen services and support to prevent aggravated COVID-19 outcomes among people with HIV, particularly for those with pronounced immunodeficiency. FUNDING: National Center for Advancing Translational Sciences, National Institute of Allergy and Infectious Diseases, National Institutes of Health, USA.

Change in Antimicrobial Use During COVID-19 Pandemic in South Carolina Hospitals: A Multicenter Observational Cohort Study.

OBJECTIVES: This retrospective cohort study examined the impact of the pandemic on antimicrobial use (AU) in South Carolina hospitals. METHODS: Antimicrobial use in days of therapy (DOT) per 1000 days-present was evaluated in 17 hospitals in South Carolina. Matched-pairs mean difference was used to compare AU during the pandemic (March-June 2020) with that during the same months in 2019 in hospitals that did and did not admit patients with COVID-19. RESULTS: There was a 6.6% increase in overall AU in the seven hospitals that admitted patients with COVID-19 (from 530.9 to 565.8; mean difference (MD) 34.9 DOT/1000 days-present; 95% CI 4.3, 65.6; P = 0.03). There was no significant change in overall AU in the remaining 10 hospitals that did not admit patients with COVID-19 (MD 6.0 DOT/1000 days-present; 95% CI -55.5, 67.6; P = 0.83). Most of the increase in AU in the seven hospitals that admitted patients with COVID-19 was observed in broad-spectrum antimicrobial agents. A 16.4% increase was observed in agents predominantly used for hospital-onset infections (from 122.3 to 142.5; MD 20.1 DOT/1000 days-present; 95% CI 11.1, 29.1; P = 0.002). There was also a 9.9% increase in the use of anti-methicillin-resistant Staphylococcus aureus (MRSA) agents (from 66.7 to 73.3; MD 6.6 DOT/1000 days-present; 95% CI 2.3, 10.8; P = 0.01). CONCLUSION: The COVID-19 pandemic appears to drive overall and broad-spectrum antimicrobial use in South Carolina hospitals admitting patients with COVID-19. Additional antimicrobial stewardship resources are needed to curtail excessive antimicrobial use in hospitals to prevent subsequent increases in antimicrobial resistance and Clostridioides difficile infection rates, given the continuing nature of the pandemic.

Demographic Disparities in Clinical Outcomes of COVID-19: Data From a Statewide Cohort in South Carolina.

BACKGROUND: Current literature examining the clinical characteristics of coronavirus disease 2019 (COVID-19) patients under-represent COVID-19 cases who were either asymptomatic or had mild symptoms. METHODS: We analyzed statewide data from 280 177 COVID-19 cases from various health care facilities during March 4-December 31, 2020. Each COVID-19 case was reported using the standardized Case Report Form (CRF), which collected information on demographic characteristics, symptoms, hospitalization, and death. We used multivariable logistic regression to analyze the associations between sociodemographics and disease severity, hospitalization, and mortality. RESULTS: Among a total of 280 177 COVID-19 cases, 5.2% (14 451) were hospitalized and 1.9% (5308) died. Older adults, males, and Black individuals had higher odds of hospitalization and death from COVID-19 (all P < 0.0001). In particular, individuals residing in rural areas experienced a high risk of death (odds ratio [OR], 1.16; 95% CI, 1.08-1.25). Regarding disease severity, older adults (OR, 1.06; 95% CI, 1.03-1.10) and Hispanic or Latino patients (OR, 2.06; 95% CI, 1.95-2.18) had higher odds of experiencing moderate/severe symptoms, while male and Asian patients, compared with White patients, had lower odds of experiencing moderate/severe symptoms. CONCLUSIONS: As the first statewide population-based study using data from multiple health care systems with a long follow-up period in the United States, we provide a more generalizable picture of COVID-19 symptoms and clinical outcomes. The findings from this study reinforce the fact that rural residence and racial/ethnic social determinants of health, unfortunately, remain predictors of adverse health outcomes for COVID-19 patients.

Telehealth for HIV Care Services in South Carolina: Utilization, Barriers, and Promotion Strategies During the COVID-19 Pandemic.

To ensure continuing HIV care services during the COVID-19 pandemic, telehealth has been recommended and implemented in numerous HIV-related facilities. This study aims to understand telehealth utilization for HIV care services in South Carolina (SC), identify barriers to telehealth during COVID-19, and investigate strategies to facilitate remote HIV care delivery. In-depth interviews with 11 management personnel from 8 HIV-related facilities in SC were analyzed using thematic analysis. Utilizations of telehealth were diverse in delivering medical and non-medical HIV care services. Barriers included technological challenges, digital literacy, client/provider experiences, low socio-economic status of client population, and reimbursement issues. Various strategies were mentioned for promoting telehealth utilization, from client empowerment, provider training to improved organizational readiness. For successful telehealth use during and after COVID-19, it is necessary to continue efforts to promote telehealth and remove barriers to telehealth by implementing inclusive multi-level strategies for non-technologically savvy or disadvantaged populations living with HIV.

Spatial-Temporal Relationship Between Population Mobility and COVID-19 Outbreaks in South Carolina: Time Series Forecasting Analysis.

BACKGROUND: Population mobility is closely associated with COVID-19 transmission, and it could be used as a proximal indicator to predict future outbreaks, which could inform proactive nonpharmaceutical interventions for disease control. South Carolina is one of the US states that reopened early, following which it experienced a sharp increase in COVID-19 cases. OBJECTIVE: The aims of this study are to examine the spatial-temporal relationship between population mobility and COVID-19 outbreaks and use population mobility data to predict daily new cases at both the state and county level in South Carolina. METHODS: This longitudinal study used disease surveillance data and Twitter-based population mobility data from March 6 to November 11, 2020, in South Carolina and its five counties with the largest number of cumulative confirmed COVID-19 cases. Population mobility was assessed based on the number of Twitter users with a travel distance greater than 0.5 miles. A Poisson count time series model was employed for COVID-19 forecasting. RESULTS: Population mobility was positively associated with state-level daily COVID-19 incidence as well as incidence in the top five counties (ie, Charleston, Greenville, Horry, Spartanburg, and Richland). At the state level, the final model with a time window within the last 7 days had the smallest prediction error, and the prediction accuracy was as high as 98.7%, 90.9%, and 81.6% for the next 3, 7, and 14 days, respectively. Among Charleston, Greenville, Horry, Spartanburg, and Richland counties, the best predictive models were established based on their observations in the last 9, 14, 28, 20, and 9 days, respectively. The 14-day prediction accuracy ranged from 60.3%-74.5%. CONCLUSIONS: Using Twitter-based population mobility data could provide acceptable predictions of COVID-19 daily new cases at both the state and county level in South Carolina. Population mobility measured via social media data could inform proactive measures and resource relocations to curb disease outbreaks and their negative influences.

Monitoring the Spatial Spread of COVID-19 and Effectiveness of Control Measures Through Human Movement Data: Proposal for a Predictive Model Using Big Data Analytics.

BACKGROUND: Human movement is one of the forces that drive the spatial spread of infectious diseases. To date, reducing and tracking human movement during the COVID-19 pandemic has proven effective in limiting the spread of the virus. Existing methods for monitoring and modeling the spatial spread of infectious diseases rely on various data sources as proxies of human movement, such as airline travel data, mobile phone data, and banknote tracking. However, intrinsic limitations of these data sources prevent us from systematic monitoring and analyses of human movement on different spatial scales (from local to global). OBJECTIVE: Big data from social media such as geotagged tweets have been widely used in human mobility studies, yet more research is needed to validate the capabilities and limitations of using such data for studying human movement at different geographic scales (eg, from local to global) in the context of global infectious disease transmission. This study aims to develop a novel data-driven public health approach using big data from Twitter coupled with other human mobility data sources and artificial intelligence to monitor and analyze human movement at different spatial scales (from global to regional to local). METHODS: We will first develop a database with optimized spatiotemporal indexing to store and manage the multisource data sets collected in this project. This database will be connected to our in-house Hadoop computing cluster for efficient big data computing and analytics. We will then develop innovative data models, predictive models, and computing algorithms to effectively extract and analyze human movement patterns using geotagged big data from Twitter and other human mobility data sources, with the goal of enhancing situational awareness and risk prediction in public health emergency response and disease surveillance systems. RESULTS: This project was funded as of May 2020. We have started the data collection, processing, and analysis for the project. CONCLUSIONS: Research findings can help government officials, public health managers, emergency responders, and researchers answer critical questions during the pandemic regarding the current and future infectious risk of a state, county, or community and the effectiveness of social/physical distancing practices in curtailing the spread of the virus. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/24432.

Disparity in HIV Service Interruption in the Outbreak of COVID-19 in South Carolina.

To examine HIV service interruptions during the COIVD-19 outbreak in South Carolina (SC) and identify geospatial and socioeconomic correlates of such interruptions, we collected qualitative, geospatial, and quantitative data from 27 Ryan White HIV clinics in SC in March, 2020. HIV service interruptions were categorized (none, minimal, partial, and complete interruption) and analyzed for geospatial heterogeneity. Nearly 56% of the HIV clinics were partially interrupted and 26% were completely closed. Geospatial heterogeneity of service interruption existed but did not exactly overlap with the geospatial pattern of COVID-19 outbreak. The percentage of uninsured in the service catchment areas was significantly correlated with HIV service interruption (F = 3.987, P = .02). This mixed-method study demonstrated the disparity of HIV service interruptions in the COVID-19 in SC and suggested a contribution of existing socioeconomic gaps to this disparity. These findings may inform the resources allocation and future strategies to respond to public health emergencies.