National COVID-19 Research Articles

Finding Long-COVID: temporal topic modeling of electronic health records from the N3C and RECOVER programs

Post-Acute Sequelae of SARS-CoV-2 infection (PASC), also known as Long-COVID, encompasses a variety of complex and varied outcomes following COVID-19 infection that are still poorly understood. We clustered over 600 million condition diagnoses from 14 million patients available through the National COVID Cohort Collaborative (N3C), generating hundreds of highly detailed clinical phenotypes. Assessing patient clinical trajectories using these clusters allowed us to identify individual conditions and phenotypes strongly increased after acute infection. We found many conditions increased in COVID-19 patients compared to controls, and using a novel method to associate patients with clusters over time, we additionally found phenotypes specific to patient sex, age, wave of infection, and PASC diagnosis status. While many of these results reflect known PASC symptoms, the resolution provided by this unprecedented data scale suggests avenues for improved diagnostics and mechanistic understanding of this multifaceted disease.

Completeness and Timeliness of Vietnam's National COVID-19 Reporting System Among Schoolchildren in Thai Nguyen City, Vietnam During the Omicron Variant Epidemic.

Completeness and Timeliness of Vietnam's National COVID-19 Reporting System Among Schoolchildren in Thai Nguyen City, Vietnam During the Omicron Variant Epidemic.

Wastewater-based surveillance for Hepatitis A virus, Enterovirus, Poliovirus, and SARS-CoV-2 in São Tomé and Príncipe: A pilot study

BackgroundWastewater-based surveillance is a valuable tool for monitoring pathogen transmission in communities, especially in regions where formal surveillance systems are limited. AimThe aim of this study was to implement and evaluate a wastewater-based monitoring system for viral pathogens in São Tomé and Príncipe. MethodsA total of 122 water samples were collected bi-weekly from June 2022 to July 2023 at six locations in São Tomé city and analysed for molecular detection of Hepatitis A Virus (HAV), Enterovirus (EV), Poliovirus (PV), SARS-CoV-2, as well as JC-Polyomavirus (JCPyV) and pepper mild mottle virus (PMMoV) as indicators of human contamination. Prevalence was analysed per pathogen and across sampling locations. Results for SARS-CoV-2 were assessed together with notifications from national COVID-19 surveillance. Further, we estimated resources needed to establish a wastewater-based approach to assess community-level transmission of viral pathogens. ResultsAll 122 and 117 samples were found positive for PMMoV and JCPyV, respectively, demonstrating a high level of human contamination at all sampling locations. The prevalence of HAV and EV ranged from 0 % to 59 % and 56 % respectively. Consistent with national surveillance data the highest proportion of SARS-CoV-2 positive water samples coincides with the highest number of COVID-19 cases reported during the study, demonstrating the potential of wastewater-based surveillance to identify signals. In addition, for SARS-CoV-2 this approach provided evidence of continuous circulation of the virus in the community, most importantly during weeks when no COVID-19 cases were reported. ConclusionOur findings provide evidence of high transmission of HAV and EV in communities in São Tomé and continuous circulation of SARS-CoV-2, even in weeks without COVID-19 case notifications. This study demonstrates that monitoring of viral pathogens in humanly impacted open water streams and sewage tanks is a valuable tool to complement clinical surveillance in resource-limited settings.

An Equitable Communication and Dissemination Model for Linking Culturally Diverse Communities to COVID-19 Services.

In 2020, the Office of Minority Health (OMH) funded the establishment of the National COVID-19 Resiliency Network (NCRN) to engage priority communities that included African American, American Indian, Alaska Native, Asian, Hispanic, Native Hawaiian, and Pacific Islander persons to aggregate culturally relevant messages, education tools, and national data sources. A mobile application and Web site supported a culturally sensitive, equity-focused communication and dissemination initiative to link communities to COVID-19 clinical, social, and behavioral health services to address health determinants. (Am J Public Health. Published online ahead of print October 16, 2024:e1-e5. https://doi.org/10.2105/AJPH.2024.307813).

Spectrum of COVID-19 cases in Arkhangelsk, Northwest Russia: Findings from a population-based study linking serosurvey, registry data, and self-reports of symptoms.

The spectrum of COVID-19 manifestations makes it challenging to estimate the exact proportion of people who had the infection in a population, with the proportion of asymptomatic cases likely being underestimated. We aimed to assess and describe the spectrum of COVID-19 cases in a sample of adult population aged 40-74 years in Arkhangelsk, Northwest Russia, a year after the start of the pandemic. A population-based survey conducted between February 24, 2021 and June 30, 2021 with an unvaccinated sample aged 40-74 years (N = 1089) combined a serological survey data, national COVID-19 case registry, and self-reported data on COVID-19 experience and symptoms. Based on the agreement between these sources, we classified the study participants as non-infected and previously infected (asymptomatic, non-hospitalized and hospitalized symptomatic) cases, and compared these groups regarding demographics, lifestyle and health characteristics. After a year of the pandemic in Arkhangelsk, 59.7% 95% confidence intervals (CI) (56.7; 62.6) of the surveyed population had had COVID-19. Among those who had been infected, symptomatic cases comprised 47.1% 95% CI (43.2; 51.0), with 8.6% 95% CI (6.6; 11.1) of them having been hospitalized. Of the asymptomatic cases, 96.2% were not captured by the healthcare system. Older age was positively associated, while smoking showed a negative association with symptomatic COVID-19. Individuals older than 65 years, and those with poor self-rated health were more likely to be hospitalized. More than half of the infected individuals were not captured by the healthcare-based registry, mainly those with asymptomatic infections. COVID-19 severity was positively associated with older age and poor self-rated health, and inversely associated with smoking. Combining different sources of surveillance data could reduce the number of unidentified asymptomatic cases and enhance surveillance for emerging infections.

Tree-based classification model for Long-COVID infection prediction with age stratification using data from the National COVID Cohort Collaborative.

We propose and validate a domain knowledge-driven classification model for diagnosing post-acute sequelae of SARS-CoV-2 infection (PASC), also known as Long COVID, using Electronic Health Records (EHRs) data. We developed a robust model that incorporates features strongly indicative of PASC or associated with the severity of COVID-19 symptoms as identified in our literature review. The XGBoost tree-based architecture was chosen for its ability to handle class-imbalanced data and its potential for high interpretability. Using the training data provided by the Long COVID Computation Challenge (L3C), which was a sample of the National COVID Cohort Collaborative (N3C), our models were fine-tuned and calibrated to optimize Area Under the Receiver Operating characteristic curve (AUROC) and the F1 score, following best practices for the class-imbalanced N3C data. Our age-stratified classification model demonstrated strong performance with an average 5-fold cross-validated AUROC of 0.844 and F1 score of 0.539 across the young adult, mid-aged, and older-aged populations in the training data. In an independent testing dataset, which was made available after the challenge was over, we achieved an overall AUROC score of 0.814 and F1 score of 0.545. The results demonstrated the utility of knowledge-driven feature engineering in a sparse EHR data and demographic stratification in model development to diagnose a complex and heterogeneously presenting condition like PASC. The model's architecture, mirroring natural clinician decision-making processes, contributed to its robustness and interpretability, which are crucial for clinical translatability. Further, the model's generalizability was evaluated over a new cross-sectional data as provided in the later stages of the L3C challenge. The study proposed and validated the effectiveness of age-stratified, tree-based classification models to diagnose PASC. Our approach highlights the potential of machine learning in addressing the diagnostic challenges posed by the heterogeneity of Long-COVID symptoms.

The Recent Increase in Invasive Bacterial Infections: A Report From the National COVID Cohort Collaborative

Background: When coronavirus disease 2019 (COVID-19) mitigation efforts waned, viral respiratory infections (VRIs) surged, potentially increasing the risk of postviral invasive bacterial infections (IBIs). We sought to evaluate the change in epidemiology and relationships between specific VRIs and IBIs [complicated pneumonia, complicated sinusitis and invasive group A streptococcus (iGAS)] over time using the National COVID Cohort Collaborative (N3C) dataset. Methods: We performed a secondary analysis of all prospectively collected pediatric (<19 years old) and adult encounters at 58 N3C institutions, stratified by era: pre-pandemic (January 1, 2018, to February 28, 2020) versus pandemic (March 1, 2020, to June 1, 2023). We compared the characteristics and outcomes of patients with prespecified VRIs and IBIs, including correlation between VRI cases and subsequent IBI cases. Results: We identified 965,777 pediatric and 9,336,737 adult hospitalizations. Compared with pre-pandemic, pandemic-era children demonstrated higher mean monthly cases of adenovirus (121 vs. 79.1), iGAS (5.8 vs. 3.3), complicated pneumonia (282 vs. 178) and complicated sinusitis (29.8 vs. 16.3), P < 0.005 for all. Among pandemic-era children, peak correlation between RSV cases and subsequent complicated sinusitis cases occurred with a 60-day lag (correlation coefficient 0.56, 95% confidence interval: 0.52–0.59, P < 0.001) while peak correlation between influenza and complicated sinusitis occurred with a 33-day lag (0.55, 0.51–0.58, P < 0.001). Correlation among other VRI–IBI pairs was modest during the pandemic and often lower than during the pre-pandemic era. Conclusions: Since COVID-19 emerged, mean monthly cases of iGAS, complicated pneumonia, and complicated sinusitis have been higher. Pandemic-era RSV and influenza cases were correlated with subsequent cases of complicated sinusitis in children. However, many other VRI–IBI correlations decreased during the pandemic.

SSRI use during acute COVID-19 and risk of long COVID among patients with depression

BackgroundLong COVID, also known as post-acute sequelae of COVID-19 (PASC), is a poorly understood condition with symptoms across a range of biological domains that often have debilitating consequences. Some have recently suggested that lingering SARS-CoV-2 virus particles in the gut may impede serotonin production and that low serotonin may drive many Long COVID symptoms across a range of biological systems. Therefore, selective serotonin reuptake inhibitors (SSRIs), which increase synaptic serotonin availability, may be used to prevent or treat Long COVID. SSRIs are commonly prescribed for depression, therefore restricting a study sample to only include patients with depression can reduce the concern of confounding by indication.MethodsIn an observational sample of electronic health records from patients in the National COVID Cohort Collaborative (N3C) with a COVID-19 diagnosis between September 1, 2021, and December 1, 2022, and a comorbid depressive disorder, the leading indication for SSRI use, we evaluated the relationship between SSRI use during acute COVID-19 and subsequent 12-month risk of Long COVID (defined by ICD-10 code U09.9). We defined SSRI use as a prescription for SSRI medication beginning at least 30 days before acute COVID-19 and not ending before SARS-CoV-2 infection. To minimize bias, we estimated relationships using nonparametric targeted maximum likelihood estimation to aggressively adjust for high-dimensional covariates.ResultsWe analyzed a sample (n = 302,626) of patients with a diagnosis of a depressive condition before COVID-19 diagnosis, where 100,803 (33%) were using an SSRI. We found that SSRI users had a significantly lower risk of Long COVID compared to nonusers (adjusted causal relative risk 0.92, 95% CI (0.86, 0.99)) and we found a similar relationship comparing new SSRI users (first SSRI prescription 1 to 4 months before acute COVID-19 with no prior history of SSRI use) to nonusers (adjusted causal relative risk 0.89, 95% CI (0.80, 0.98)).ConclusionsThese findings suggest that SSRI use during acute COVID-19 may be protective against Long COVID, supporting the hypothesis that serotonin may be a key mechanistic biomarker of Long COVID.

Influenza-Like Illness in Lesotho From July 2020 to July 2021: Population-Based Participatory Surveillance Results.

Participatory surveillance involves at-risk populations reporting their symptoms using technology. In Lesotho, a landlocked country of 2 million people in Southern Africa, laboratory and case-based COVID-19 surveillance systems were complemented by a participatory surveillance system called "LeCellPHIA" (Lesotho Cell Phone Population-Based HIV Impact Assessment Survey). This report describes the person, place, and time characteristics of influenza-like illness (ILI) in Lesotho from July 15, 2020, to July 15, 2021, and reports the risk ratio of ILI by key demographic variables. LeCellPHIA employed interviewers to call participants weekly to inquire about ILI. The average weekly incidence rate for the year-long period was created using a Quasi-Poisson model, which accounted for overdispersion. To identify factors associated with an increased risk of ILI, we conducted a weekly data analysis by fitting a multilevel Poisson regression model, which accounted for 3 levels of clustering. The overall response rate for the year of data collection was 75%, which resulted in 122,985 weekly reports from 1776 participants. ILI trends from LeCellPHIA mirrored COVID-19 testing data trends, with an epidemic peak in mid to late January 2021. Overall, any ILI symptoms (eg, fever, dry cough, and shortness of breath) were reported at an average weekly rate of 879 per 100,000 (95% CI 782-988) persons at risk. Compared to persons in the youngest age group (15-19 years), all older age groups had an elevated risk of ILI, with the highest risk of ILI in the oldest age group (≥60 years; risk ratio 2.6, 95% CI 1.7-3.8). Weekly data were shared in near real time with the National COVID-19 Secretariat and other stakeholders to monitor ILI trends, identify and respond to increases in reports of ILI, and inform policies and practices designed to reduce COVID-19 transmission in Lesotho. LeCellPHIA, an innovative and cost-effective system, could be replicated in countries where cell phone ownership is high but internet use is not yet high enough for a web- or app-based surveilance system.

Experiences of community engagement for SARS-COV-2 and COVID-19 community surveillance in a rural and urban population in South Western Uganda

Background: We describe lessons learnt from community engagement activities for the successful implementation of a COVID-19 community surveillance study in Kalungu district, south-western Uganda. Methods: One rural and one urban site were selected for COVID-19/SARS-CoV-2 community surveillance in selected households and three public health facilities. COVID-19 pandemic and national COVID-19 protection measures were described and explained in meetings with: district, sub-county/town, village council officials, religious leaders, health workers and Community Advisory Board members. The community surveillance study was introduced to the meeting attendees and minutes captured in English/Luganda (subsequently translated to English). All minutes were manually coded and analysed thematically. Results: The minutes revealed that community members were generally supportive of the COVID-19 surveillance study. Members requested wider communities’ sensitisation about COVID-19 and the survey beyond the selected households. Misinformation and mistrust of Government statements were widespread. People in the community did not understand the `random sample’ selection for the study. Providing appropriate medical care, face masks and honouring appointments for study participants and ensuring that COVID-19 prevention measures were followed during the study improved participation. Conclusion: Successful COVID-19 community surveillance required continuous, active community engagement between the research team, and community stakeholders while maximising previous gains and exploiting existing resources. Keywords: SARS-COV-2; COVID-19; community engagement; surveillance; acceptability; East Africa.

Comparative Effectiveness of Baricitinib Versus Tocilizumab in Hospitalized Patients With COVID-19: A Retrospective Cohort Study of the National Covid Collaborative.

COVID-19 treatment guidelines recommend baricitinib or tocilizumab for the management of hospitalized patients with COVID-19. We compared the effectiveness of baricitinib vs. tocilizumab on mortality and clinical outcomes among hospitalized patients with COVID-19. Multicenter, retrospective, propensity-weighted cohort study using a target trial emulation approach. The National COVID Cohort Collaborative (N3C), which is the largest electronic health records data on COVID-19 in the United States. The setting included 75 hospitals. Adults who were hospitalized for COVID-19. Newly initiated on baricitinib or tocilizumab. Our primary outcome was 28-day mortality. We used propensity scores with inverse probability of treatment weights (IPTWs) to control bias and confounding while comparing treatments. Among 10,661 individuals included in the study, 6,229 (58.4%) received baricitinib and 4,432 (41.6%) tocilizumab. Overall, the mean age of the cohort was 60.0 ± 15.1 years, 6429 (60.3%) were male, and 19.2% received invasive mechanical ventilation. After IPTW adjustment, baricitinib use was associated with lower 28-day mortality (odds ratio [OR], 0.91; 95% CI, 0.85-0.98) and hospital (OR, 0.88; 95% CI, 0.82-0.94) mortality compared with tocilizumab. Baricitinib was also associated with shorter hospital length of stay (incident rate ratio, 0.92; 95% CI, 0.90-0.94) and lower rates of hospital-acquired infections (OR, 0.86; 95% CI, 0.75-0.99), although no difference in ICU length of stay was noted between the two groups. In this large, diverse cohort of U.S. hospitalized adults with COVID-19, baricitinib was associated with significantly lower 28-day mortality, hospital mortality, shorter hospital length of stay, and less hospital-acquired infections compared with tocilizumab.

Measuring the Collective Community Capacity of a Network to Address Health Inequities during a Public Health Emergency: Findings from the National COVID-19 Resiliency Network.

This study assesses the collective community capacity of the National COVID-19 Resiliency Network (NCRN), a multisectoral network mitigating the disproportionate impact of COVID-19 on minoritized populations. From January to April 2022, we used two concurrent data collection methods: a Collective Community Capacity (C3) survey (n=65) and key informant interviews (KIIs) (n=26). The C3 assessed capacity for creation of a shared vision, engagement in community change, and distributive leadership. KIIs assessed perspectives on network formation and implementation. We used a convergent design and triangulation for interpretation. NCRN has growing collective community capacity. The C3 survey found high capacity for establishing a shared mission and evidence of mutual commitment, trust, and accountability. About three-quarters of respondents strongly agreed that partners addressed social, economic, and cultural barriers related to COVID-19. Interviewees valued NCRN leaders' openness, availability, and willingness to listen. Partners learned from one another, increased their health communication capacity, and supported sustainability. They sought greater opportunities to partner and support decision-making. NCRN developed a collaborative network with a shared vision of improving health equity during and beyond the COVID-19 pandemic, while identifying areas for improvement in distributive leadership. Findings can support other organizations seeking to build collective community capacity to address equity in public health emergencies.

Timing of total joint arthroplasty post-COVID-19: an evaluation of the optimal window to minimize perioperative risks

BackgroundTotal hip arthroplasty (THA) and total knee arthroplasty (TKA) are commonly performed orthopedic procedures. This study aimed to evaluate the impact of COVID-19 status on postoperative complications and mortality in patients undergoing THA and TKA.MethodsA total of 110,186 underwent either THA or TKA. Patients were grouped based on their COVID-19 status, gathered from the National COVID-19 Cohort Collaborative (N3C) in the 12 weeks preceding surgery and compared for various variables, including age, sex, BMI, and Charlson Comorbidity Index (CCI) scores. COVID-19 status was defined as a positive test result that was closest to the date of surgery regardless of testing positive previously. Postoperative complications such as venous thromboembolism (VTE), sepsis, surgical site infection, bleeding, acute kidney injury (AKI), 30-day, and 1-year all-cause mortality were examined. To compare the variables, an odds ratio with a 95% confidence interval was calculated with a significant level set at P < 0.05. Logistic regression using R programming was utilized for these calculations.ResultsUnivariate analysis was performed and rates of VTE (1.02% vs. 3.35%), 30-day mortality (0.25% vs. less than 5%), and 1-year mortality (1.42% vs. 5.43%) were higher in the COVID-19-positive group for THA patients (P < 0.001). For TKA patients, only 30-day mortality was significantly higher in the COVID-19-positive group (P = 0.034). Multivariate logistic regression revealed that a positive COVID-19 diagnosis within two weeks of surgery and a CCI score > 3 were significant predictors of postoperative complications and mortality for both TKA and THA.ConclusionsPatients with a positive COVID-19 diagnosis within 12 weeks of THA or TKA carried a significantly higher risk for postoperative complications and mortality. In addition, a CCI score > 3 is also a significant risk factor. These findings emphasize the importance of vigilant preoperative screening and risk stratification in the era of COVID-19.

Effect of SARS-CoV-2 Infection on Incident Diabetes by Viral Variant: Findings From the National COVID Cohort Collaborative (N3C).

The coronavirus 2019 (COVID-19) pandemic has evolved over time by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant, disease severity, treatment, and prevention. There is evidence of an elevated risk of incident diabetes after COVID-19; our objective was to evaluate whether this association is consistent across time and with contemporary viral variants. We conducted a retrospective cohort study using National COVID Cohort Collaborative (N3C) data to evaluate incident diabetes risk among COVID-positive adults compared with COVID-negative patients or control patients with acute respiratory illness (ARI). Cohorts were weighted on demographics, data site, and Charlson comorbidity index score. The primary outcome was the cumulative incidence ratio (CIR) of incident diabetes for each viral variant era. Risk of incident diabetes 1 year after COVID-19 was increased for patients with any viral variant compared with COVID-negative control patients (ancestral CIR 1.16 [95% CI 1.12-1.21]; Alpha CIR 1.14 [95% CI 1.11-1.17]; Delta CIR 1.17 [95% CI 1.13-1.21]; Omicron CIR 1.13 [95% CI 1.10-1.17]) and control patients with ARI (ancestral CIR 1.17 [95% CI 1.11-1.22]; Alpha CIR 1.14 [95% CI 1.09-1.19]; Delta CIR 1.18 [95% CI 1.11-1.26]; Omicron CIR 1.20 [95% CI 1.13-1.27]). There was latency in the timing of incident diabetes risk with the Omicron variant; in contrast with other variants, the risk presented after 180 days. Incident diabetes risk after COVID-19 was similar across different SARS-CoV-2 variants. However, there was greater latency in diabetes onset in the Omicron variant era.

Crowd-sourced machine learning prediction of long COVID using data from the National COVID Cohort Collaborative

BackgroundWhile many patients seem to recover from SARS-CoV-2 infections, many patients report experiencing SARS-CoV-2 symptoms for weeks or months after their acute COVID-19 ends, even developing new symptoms weeks after infection. These long-term effects are called post-acute sequelae of SARS-CoV-2 (PASC) or, more commonly, Long COVID. The overall prevalence of Long COVID is currently unknown, and tools are needed to help identify patients at risk for developing long COVID. MethodsA working group of the Rapid Acceleration of Diagnostics-radical (RADx-rad) program, comprised of individuals from various NIH institutes and centers, in collaboration with REsearching COVID to Enhance Recovery (RECOVER) developed and organized the Long COVID Computational Challenge (L3C), a community challenge aimed at incentivizing the broader scientific community to develop interpretable and accurate methods for identifying patients at risk of developing Long COVID. From August 2022 to December 2022, participants developed Long COVID risk prediction algorithms using the National COVID Cohort Collaborative (N3C) data enclave, a harmonized data repository from over 75 healthcare institutions from across the United States (U.S.). FindingsOver the course of the challenge, 74 teams designed and built 35 Long COVID prediction models using the N3C data enclave. The top 10 teams all scored above a 0.80 Area Under the Receiver Operator Curve (AUROC) with the highest scoring model achieving a mean AUROC of 0.895. Included in the top submission was a visualization dashboard that built timelines for each patient, updating the risk of a patient developing Long COVID in response to clinical events. InterpretationAs a result of L3C, federal reviewers identified multiple machine learning models that can be used to identify patients at risk for developing Long COVID. Many of the teams used approaches in their submissions which can be applied to future clinical prediction questions. FundingResearch reported in this RADx® Rad publication was supported by the National Institutes of Health. Timothy Bergquist, Johanna Loomba, and Emily Pfaff were supported by Axle Subcontract: NCATS-STSS-P00438.

Accuracy of COVID-19 Prediction Modeling Techniques

The unprecedented impact of the COVID-19 pandemic has revealed that forecasting capability is critically needed in making strategic decisions and formulating reasonable countermeasures. This study aimed to assess the predictive accuracy in forecasting the numbers of COVID-19 cases using Thailand’s national COVID-19 surveillance database from January 2020– June 2021 based on three analytical models: a susceptible-exposed-infectious-recovery compartmental model, an auto-regressive integrated moving average model, and a long short-term memory (LSTM) network model. All forecasting methods had model parameters adjusted weekly according to the most recent situation and predictive accuracy measures, including the mean absolute percent error (MAPE). We found that the MAPE values ranged from 19.65%–22.54%, 28.95%–32.35%, 47.78%–53.55%, and 75.03–84.91% for forecasting one, two, four, and eight weeks ahead, respectively. Among the three models, the LSTM model had slightly higher accuracy than the other two models within the same forecasting range. These prediction models can be used for short-range forecasts in other similar settings while long-range forecasting requires monitoring and updating periodically.

Navigating Ankle Fracture Surgery in the Shadow of COVID-19

Background This case control study aimed to evaluate the impact of preoperative COVID-19 diagnosis on postoperative complications in patients undergoing ankle fracture surgery using the National COVID-19 Cohort Collaborative (N3C) database. The investigation focused on the interplay between COVID-19 diagnosis timing, patient characteristics, and clinical outcomes, particularly considering the potential mechanisms by which COVID-19 may contribute to increased complications. Methods This case control study included patients who underwent ankle fracture surgery using the N3C database. The cohort was divided into two groups: patients without COVID-19 diagnosis within 12 weeks of surgery (n = 16 806) and those with a positive COVID-19 diagnosis within 12 weeks of surgery (n = 369). Demographic factors were analyzed. Clinical outcomes encompassed deep vein thrombosis (DVT), sepsis, surgical site infection, bleeding, acute kidney injury, 30-day mortality, and 365-day mortality. Multivariate logistic regression analyses were conducted. Results The COVID-19–positive cohort displayed a slightly higher mean age (52.95 ± 18.43 vs 51.62 ± 18.36, P = .169) and body mass index (34.88 ± 9.99 vs 33.86 ± 8.80, P = .028) compared to the negative cohort. Although some outcomes, such as DVT and sepsis, demonstrated slightly higher frequencies in the COVID-19–positive group, these differences were not statistically significant. Adjusted odds ratios (AORs) for various COVID-19 diagnosis periods were generally not significant, except for a heightened risk of 30-day all-cause mortality associated with COVID-19 positivity within 0 to 2 weeks of surgery (AOR = 6.29, P = .003). Conclusions Preoperative COVID-19 diagnosis within 12 weeks did not exhibit a significant association with most postoperative complications. While this study did not unveil substantial COVID-19–related effects, acknowledging the broader context of the pandemic remains essential in guiding comprehensive patient care strategies. Level of Evidence: Level III

Associations of County-Level Social Determinants of Health with COVID-19 Related Hospitalization Among People with HIV: A Retrospective Analysis of the U.S. National COVID Cohort Collaborative (N3C)

Individually, the COVID-19 and HIV pandemics have differentially impacted minoritized groups due to the role of social determinants of health (SDoH) in the U.S. Little is known how the collision of these two pandemics may have exacerbated adverse health outcomes. We evaluated county-level SDoH and associations with hospitalization after a COVID-19 diagnosis among people with (PWH) and without HIV (PWOH) by racial/ethnic groups. We used the U.S. National COVID Cohort Collaborative (January 2020-November 2023), a nationally-sampled electronic health record repository, to identify adults who were diagnosed with COVID-19 with HIV (n = 22,491) and without HIV (n = 2,220,660). We aggregated SDoH measures at the county-level and categorized racial/ethnic groups as Non-Hispanic (NH) White, NH-Black, Hispanic/Latinx, NH-Asian and Pacific Islander (AAPI), and NH-American Indian or Alaskan Native (AIAN). To estimate associations of county-level SDoH with hospitalization after a COVID-19 diagnosis, we used multilevel, multivariable logistic regressions, calculating adjusted relative risks (aRR) with 95% confidence intervals (95% CI). COVID-19 related hospitalization occurred among 11% of PWH and 7% of PWOH, with the highest proportion among NH-Black PWH (15%). In evaluating county-level SDoH among PWH, we found higher average household size was associated with lower risk of COVID-19 related hospitalization across racial/ethnic groups. Higher mean commute time (aRR: 1.76; 95% CI 1.10–2.62) and higher proportion of adults without health insurance (aRR: 1.40; 95% CI 1.04–1.84) was associated with a higher risk of COVID-19 hospitalization among NH-Black PWH, however, NH-Black PWOH did not demonstrate these associations. Differences by race and ethnicity exist in associations of adverse county-level SDoH with COVID-19 outcomes among people with and without HIV in the U.S.

Prevalent Metformin Use in Adults With Diabetes and the Incidence of Long COVID: An EHR-Based Cohort Study From the RECOVER Program.

Studies show metformin use before and during SARS-CoV-2 infection reduces severe COVID-19 and postacute sequelae of SARS-CoV-2 (PASC) in adults. Our objective was to describe the incidence of PASC and possible associations with prevalent metformin use in adults with type 2 diabetes mellitus (T2DM). This is a retrospective cohort analysis using the National COVID Cohort Collaborative (N3C) and Patient-Centered Clinical Research Network (PCORnet) electronic health record (EHR) databases with an active comparator design that examined metformin-exposed individuals versus nonmetformin-exposed individuals who were taking other diabetes medications. T2DM was defined by HbA1c ≥6.5 or T2DM EHR diagnosis code. The outcome was death or PASC within 6 months, defined by EHR code or computable phenotype. In the N3C, the hazard ratio (HR) for death or PASC with a U09.9 diagnosis code (PASC-U09.0) was 0.79 (95% CI 0.71-0.88; P < 0.001), and for death or N3C computable phenotype PASC (PASC-N3C) was 0.85 (95% CI 0.78-0.92; P < 0.001). In PCORnet, the HR for death or PASC-U09.9 was 0.87 (95% CI 0.66-1.14; P = 0.08), and for death or PCORnet computable phenotype PASC (PASC-PCORnet) was 1.04 (95% CI 0.97-1.11; P = 0.58). Incident PASC by diagnosis code was 1.6% metformin vs. 2.0% comparator in the N3C, and 2.1% metformin vs. 2.5% comparator in PCORnet. By computable phenotype, incidence was 4.8% metformin and 5.2% comparator in the N3C and 24.7% metformin vs. 26.1% comparator in PCORnet. Prevalent metformin use is associated with a slightly lower incidence of death or PASC after SARS-CoV-2 infection. PASC incidence by computable phenotype is higher than by EHR code, especially in PCORnet. These data are consistent with other observational analyses showing prevalent metformin is associated with favorable outcomes after SARS-CoV-2 infection in adults with T2DM.

COVID-19 Vaccine Safety in Pregnancy, A Nested Case-Control Study in Births From April 2021 to March 2022, England.

Assessment of COVID-19 vaccine safety in pregnancy using population-based data. Matched case-control study nested in a retrospective cohort. April 2021-March 2022, England. All pregnant individuals aged between 18 and 50 years with valid health records. Individuals identified from the national Maternity Services Data Set (MSDS) had their records linked to hospital admission, national COVID-19 vaccine and COVID-19 testing databases. Matching included participant's age and estimated week of conception. We compared outcomes across multiple COVID-19 vaccine exposures using conditional multivariable logistic regression, adjusting for demographic and health characteristics. Adverse pregnancy, maternal and neonatal outcomes. 514 013 individuals were included. We found lower odds of giving birth to a baby who was low birthweight (aOR = 0.86, 95% CI: 0.79-0.93), preterm (aOR = 0.89, 95% CI: 0.85-0.92) or who had an Apgar score < 7 at 5 min of age (aOR = 0.89, 95% CI: 0.80-0.98) for individuals who received at least one dose of COVID-19 vaccine during pregnancy. The odds of admission to intensive care unit during pregnancy were lower in those vaccinated (aOR = 0.85, 95% CI: 0.76-0.95). There was no association between vaccination in pregnancy and stillbirth, neonatal death, perinatal death and maternal venous thromboembolism in pregnancy. COVID-19 vaccines are safe to use in pregnancy. Our findings generated important information to communicate to pregnant individuals and health professionals to support COVID-19 maternal vaccination programmes.