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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.

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.

Mortality-to-incidence ratios (MIRs) are useful in assessing disease burdens and illustrating disparities. Unlike cancer, MIRs have not been applied to ADRDs. Therefore, we estimated and mapped the MIRs for ADRDs to show disparities in South Carolina. Using data from the South Carolina Alzheimer's Disease Registry (2017-2021), ADRD MIRs were calculated by demographic and geospatial characteristics. To account for the influence of the COVID-19 pandemic, data from 2015 to 2019 were also examined. MIRs were calculated as age-adjusted mortality rates divided by age-adjusted incidence rates. Overall, Black people and rural individuals consistently experienced higher MIRs, with the COVID-19 pandemic increasing this disparity gap. MIRs greater than 1.00 were only observed among Black people. The MIR for 31 out of 46 counties exceeded the state average. Estimating and mapping ADRDs has aided in identifying specific areas with the greatest burden of ADRD in South Carolina for targeting interventions.

Resilience has been established as a protective factor against mental health challenges (e.g., anxiety, depression). From an intersectional perspective, the stressors experienced by Black women living in rural communities are likely to have been exacerbated during the COVID-19 pandemic. This study aims to identify the challenges and stressors experienced and the resilience resources available to and relied upon by rural, Black women, during the pandemic. Individual, in-depth, structured interviews (n = 24) were conducted among Black women living in rural South Carolina, recruited by local community health workers, from January to April of 2024. All interviews were recorded, uploaded to a password-protected online drive, transcribed using Otter.ai, and verified for verbatim transcription. The data were analyzed using an abductive approach where the data were inductively coded following a thematic analysis approach and were then deductively situated within a socioecological model. Participants shared challenges and stressors (i.e., anxiety, depression, isolation, grief, visitation limitations, employment changes, increased cost of living, structural and systemic inequity, and limited access to mental healthcare) experienced throughout the pandemic. Resilience resources were also identified at the individual (i.e., religion and faith, self-care), interpersonal (i.e., generational knowledge of preparedness, social connection), organizational (i.e., workplace and religious institution pandemic policy adaptation), community (i.e., rural community norms, trusted community messengers, food and supply drives), and structural levels (i.e., emergency SNAP enrollment, stimulus checks) of the socioecological model. The present results suggest that interventions, designed to prepare and respond to public health emergencies, should leverage and amplify existing community-led resilience resources, thereby adequately tailoring efforts and ensuring they meet the needs identified by community members.

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. 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. 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. 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%. 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.

In the United States, most real-world estimates of COVID-19 vaccine effectiveness are based on data drawn from large health systems or sentinel populations. More data is needed to understand how the benefits of vaccination may vary across US populations with disparate risk profiles and policy contexts. We aimed to provide estimates of mRNA COVID-19 vaccine effectiveness against moderate and severe outcomes of COVID-19 based on state population-level data sources. Using statewide integrated administrative and clinical data and a test-negative case-control study design, we assessed mRNA COVID-19 vaccine effectiveness against SARS-CoV-2-related hospitalizations and emergency department visits among adults in South Carolina. We presented estimates of vaccine effectiveness at discrete time intervals for adults who received one, two or three doses of mRNA COVID-19 vaccine compared to adults who were unvaccinated. We also evaluated changes in vaccine effectiveness over time (waning) for the overall sample and in subgroups defined by age. We showed that while two doses of mRNA COVID-19 vaccine were initially highly effective, vaccine effectiveness waned as time elapsed since the second dose. Compared to protection against hospitalizations, protection against emergency department visits was found to wane more sharply. In all cases, a third dose of mRNA COVID-19 vaccine conferred significant gains in protection relative to waning protection after two doses. Further, over more than 120 days of follow-up, the data revealed relatively limited waning of vaccine effectiveness after a third dose of mRNA COVID-19 vaccine.

Background This study aims to investigate the incidence and dynamic risk factors for cardiovascular diseases (CVD) among people living with HIV (PLWH). Methods In this population-based statewide cohort study, we utilized integrated electronic health records data to identify adult (age ≥ 18) who were diagnosed with HIV between 2006 and 2019 and were CVD event-free at the HIV diagnosis in South Carolina. The associations of HIV-related factors and traditional risk factors with the CVD incidence were investigated during the overall study period, and by different follow-up periods (i.e., 0-5yrs, 6-10yrs 11-15yrs) using multivariable logistic regression models. Results Among 9,082 eligible participants, the incidence of CVD was 18.64 cases per 1000 person-years. Overall, conventional risk factors, such as tobacco use, hypertension, obesity, chronic kidney disease (CKD), were persistently associated with the outcome across all three groups. While HIV-related factors, such as recent CD4 count (e.g., > 350 vs. <200 cells/mm 3 : adjusted odds ratio [aOR] range: 0.18–0.25), and percent of years in retention (e.g., 31–75% vs. 0–30%: aOR range: 0.24–0.57) were associated with lower odds of CVD incidence regardless of different follow up periods. The impact of the percent of days with viral suppression gradually diminished as the follow-up period increased. Conclusions Maintaining an optimal viral suppression might prevent CVD incidence in the short term, whereas restoring immune recovery may be beneficial for reducing CVD risk regardless of the duration of HIV diagnosis. Our findings suggest the necessity of conducting more targeted interventions during different periods of HIV infection.

While prior studies have identified sociodemographic correlates of zero-dose status within populations in sub-Saharan Africa (SSA), few have applied spatial regression techniques to explore geographic variability in these relationships. We aimed to address this gap using data from Demographic and Health Surveys conducted in SSA between 2010 and 2020. Our sample comprised children aged 12-59 months in 33 countries and 329 survey regions. Data were aggregated to the first-level administrative unit prior to analysis. First, using ordinary least squares regression, we documented global relationships between theoretically important sociodemographic characteristics and zero-dose prevalence. Next, we identified patterns, i.e., geographic clustering, of zero-dose prevalence. Finally, using multiscale geographically weighted regression, we described spatial variability in relationships between sociodemographic characteristics and zero-dose prevalence. We detected 27 regions with higher than expected concentrations of zero-dose children. All but one of these hot spots were observed in 7 Western and Central African countries; only 1 was located in an Eastern African country. Regions with higher proportions of mothers with no antenatal care visits were consistently found to have higher rates of zero-dose children. In contrast, relationships between zero-dose prevalence and indicators of religious affiliation, delivery site, maternal age, maternal education, and maternal employment were found to vary locally in terms of their strength and/or direction. Study findings underscore spatial disparities in zero-dose prevalence within SSA and, further, highlight the importance of geographically informed strategies to effectively address immunization gaps. Implementing targeted interventions based on regional sociodemographic dynamics is crucial for achieving comprehensive vaccination coverage in SSA.

Electronic health record (EHR) data are widely used in public health research, including in HIV-related studies, but are limited by potential bias due to incomplete and inaccurate information, lack of generalizability, and lack of representativeness. This study explores how workflow processes among HIV health care providers (HCPs), data scientists, and state health department professionals may potentially introduce or minimize bias within EHR data. One focus group with 3 health department professionals working in HIV surveillance and 16 in-depth interviews (ie, 5 people with HIV, 5 HCPs, 5 data scientists, and 1 health department professional providing retention-in-care services) were conducted with participants purposively sampled in South Carolina from August 2023 to April 2024. All interviews were transcribed verbatim and analyzed using a constructivist grounded theory approach, where transcripts were first coded and then focused, axial, and theoretically coded. The EHR data lifecycle originates with people with HIV and HCPs in the clinical setting. Data scientists then curate EHR data and health department professionals manage and use the data for surveillance and policy decision-making. Throughout this lifecycle, the three primary stakeholders (ie, HCPs, data scientists, and health department professionals) identified challenges with EHR processes and provided their recommendations and accommodations in addressing the related challenges. HCPs reported the influence of socio-structural biases on their inquiry, interpretation, and documentation of social determinants of health (SDOH) information of people living with HIV, the influence of which is proposed to be mitigated through people living with HIV access to their EHRs. Data scientists identified limited data availability and representativeness as biasing the data they manage. Health department professionals face challenges with delayed and incomplete data, which may be addressed statistically but require consideration of the data's limitations. Overall, bias within the EHR data lifecycle persists because workflows are not intentionally structured to minimize bias and there is a diffusion of responsibility for data quality between the various stakeholders. From the perspective of various stakeholders, this study describes the EHR data lifecycle and its associated challenges as well as stakeholders' accommodations and recommendations for mitigating and eliminating bias in EHR data. Based upon these findings, studies reliant on EHR data should adequately consider its challenges and limitations. Throughout the EHR data lifecycle, bias could be reduced through an inclusive, supportive health care environment, people living with HIV verification of SDOH information, the customization of data collection systems, and EHR data inspection for completeness, accuracy, and timeliness. Future research is needed to further identify instances where bias is introduced and how it can best be mitigated and eliminated across the EHR data lifecycle. Systematic changes are necessary to reduce instances of bias between data workflows and stakeholders.

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.