Opioid Prescriptions for US Patients Undergoing Long-Term Dialysis or with Kidney Transplant from 2011 to 2020.

BACKGROUND: Pain is important for patients with kidney failure, but opioid medication prescriptions are associated with morbidity and mortality. The Centers for Disease Control and Prevention issued opioid prescription guidelines in 2016 and 2022, associated with dramatically decreased prescription rates in the United States. It is critical to know if nationwide opioid prescription rates for patients with kidney failure have decreased. METHODS: We analyzed the USRDS database from 2011 to 2020 to describe trends in the proportion of ESKD patients who received one or more, or long-term opioid prescriptions, examined factors associated with long-term opioid prescriptions, and evaluated associations of all-cause death with short-term or long-term opioid prescriptions. RESULTS: From 2011-2022, the percentage of patients with kidney failure (dialysis and kidney transplant) who received at least one or more, or who had received long-term opioid medication prescriptions decreased steadily, from 60% to 42%, and from 23% to 13%, respectively (both P for trend <0.001). The largest reductions in prescription rates were for hydrocodone and oxycodone. Similar trends existed for dialysis and kidney transplant patients. Women, the poor and those in rural settings were more likely to receive long-term opioid prescriptions. Prescription rates were highest in White patients and those 45 to 64 years old. Short-term and long-term opioid medication prescriptions were associated with higher mortality in both dialysis and kidney transplant patients. CONCLUSIONS: ESKD patients' opioid prescription rates decreased between 2011 and 2020. Higher mortality risk was associated with both short-term and long-term opioid prescriptions. Mortality risk was monotonically associated with morphine milligram equivalents in patients with kidney failure who received long-term opioid prescriptions.

The Intersections of COVID-19, HIV, and Race/Ethnicity: Machine Learning Methods to Identify and Model Risk Factors for Severe COVID-19 in a Large U.S. National Dataset.

We investigate risk factors for severe COVID-19 in persons living with HIV (PWH), including among racialized PWH, using the U.S. population-sampled National COVID Cohort Collaborative (N3C) data released from January 1, 2020 to October 10, 2022. We defined severe COVID-19 as hospitalized with invasive mechanical ventilation, extracorporeal membrane oxygenation, discharge to hospice or death. We used machine learning methods to identify highly ranked, uncorrelated factors predicting severe COVID-19, and used multivariable logistic regression models to assess the associations of these variables with severe COVID-19 in several models, including race-stratified models. There were 3 241 627 individuals with incident COVID-19 cases and 81 549 (2.5%) with severe COVID-19, of which 17 445 incident COVID-19 and 1 020 (5.8%) severe cases were among PWH. The top highly ranked factors of severe COVID-19 were age, congestive heart failure (CHF), dementia, renal disease, sodium concentration, smoking status, and sex. Among PWH, age and sodium concentration were important predictors of COVID-19 severity, and the effect of sodium concentration was more pronounced in Hispanics (aOR 4.11 compared to aOR range: 1.47-1.88 for Black, White, and Other non-Hispanics). Dementia, CHF, and renal disease was associated with higher odds of severe COVID-19 among Black, Hispanic, and Other non-Hispanics PWH, respectively. Our findings suggest that the impact of factors, especially clinical comorbidities, predictive of severe COVID-19 among PWH varies by racialized groups, highlighting a need to account for race and comorbidity burden when assessing the risk of PWH developing severe COVID-19.

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.

Vitamin C Urinary Loss and Deficiency in Human Immunodeficiency Virus (HIV): Cross-sectional Study of Vitamin C Renal Leak in Women With HIV.

BACKGROUND: Reduced plasma vitamin C (vitC) concentrations in human immunodeficiency virus (HIV) may result from abnormal urinary excretion: a renal leak. VitC renal leak indicates underlying nutritional dysregulation independent of diet. We hypothesized that increased renal leak prevalence in HIV would be associated with deficient vitC concentrations. METHODS: We conducted an outpatient cross-sectional study of 96 women (40 HIV [PWH] and 56 without HIV [PWOH]) at the National Institutes of Health and Georgetown University. Renal leak was defined as abnormal urinary vitC excretion at fasting plasma concentrations <43.2µM, 2 SDs below vitC renal threshold in healthy women. To determine the primary outcome of renal leak prevalence, matched urine and plasma samples were collected the morning after overnight fast. Secondary outcomes assessed group differences in mean plasma vitC concentrations and prevalence of vitC deficiency. Exploratory outcomes assessed clinical parameters associated with renal leak. VitC was measured by high-performance liquid chromatography with coulometric electrochemical detection. RESULTS: PWH had significantly higher renal leak prevalence (73%vs14%; OR (odds ratio):16; P<.001), lower mean plasma vitC concentrations (14µMvs50µM; P<.001), and higher prevalence of vitC deficiency (43%vs7%; OR:10; P<.001) compared with PWOH, unchanged by adjustments for confounding factors. Significant predictors of renal leak included antiretroviral therapy (ART), Black race, older age, and metabolic comorbidities but not viral load or CD4 count. When compared with other chronic disease cohorts, PWH had the highest prevalence of renal leak and vitC deficiency (P<.001). CONCLUSIONS: High prevalence of vitC renal leak in HIV was associated with vitC deficiency, ART use, and race/ethnicity differences.

Readmission and Mortality After Hospitalization With Acute Kidney Injury.

RATIONALE & OBJECTIVE: Acute kidney injury (AKI) carries high rates of morbidity and mortality. This study quantified various short- and long-term outcomes after hospitalization with AKI. STUDY DESIGN: Retrospective propensity score (PS)-matched cohort study. SETTING & PARTICIPANTS: Optum Clinformatics, a national claims database, was used to identify patients hospitalized with and without an AKI discharge diagnosis between January 2007 and September 2020. EXPOSURE: Among patients with prior continuous enrollment for at least 2years without AKI hospitalization, 471,176 patients hospitalized with AKI were identified and PS-matched to 471,176 patients hospitalized without AKI. OUTCOME(S): All-cause and selected-cause rehospitalizations and mortality 90 and 365 days after index hospitalization. ANALYTICAL APPROACH: After PS matching, rehospitalization and death incidences were estimated using the cumulative incidence function method and compared using Gray's test. The association of AKI hospitalization with each outcome was tested using Cox models for all-cause mortality and, with mortality as competing risk, cause-specific hazard modeling for all-cause and selected-cause rehospitalization. Overall and stratified analyses were performed to evaluate for interaction between an AKI hospitalization and preexisting chronic kidney disease (CKD). RESULTS: After PS matching, AKI was associated with higher rates of rehospitalization for any cause (hazard ratio [HR], 1.62; 95% CI, 1.60-1.65), end-stage renal disease (HR, 6.21; 95% CI, 1.04-36.92), heart failure (HR, 2.81; 95% CI, 2.66, 2.97), sepsis (HR, 2.62; 95% CI, 2.49-2.75), pneumonia (HR, 1.47; 95% CI, 1.37-1.57), myocardial infarction (HR, 1.48; 95% CI, 1.33-1.65), and volume depletion (HR, 1.64; 95% CI, 1.37-1.96) at 90 days after discharge compared with the group without AKI, with similar findings at 365 days. Mortality rate was higher in the group with AKI than in the group without AKI at 90 (HR, 2.66; 95% CI, 2.61-2.72) and 365 days (HR, 2.11; 95% CI, 2.08-2.14). The higher risk of outcomes persisted when participants were stratified by CKD status (P<0.01). LIMITATIONS: Causal associations between AKI and the reported outcomes cannot be inferred. CONCLUSIONS: AKI during hospitalization in patients with and without CKD is associated with increased risk of 90- and 365-day all-cause/selected-cause rehospitalization and death.

Vitamin C Urinary Loss in Fabry Disease: Clinical and Genomic Characteristics of Vitamin C Renal Leak.

BACKGROUND: Reduced plasma vitamin C concentrations in chronic diseases may result from abnormal urinary excretion of vitamin C: a renal leak. We hypothesized that vitamin C renal leak may be associated with disease-mediated renal dysregulation, resulting in aberrant vitamin C renal reabsorption and increased urinary loss. OBJECTIVES: We investigated the prevalence, clinical characteristics, and genomic associations of vitamin C renal leak in Fabry disease, an X-linked lysosomal disease associated with renal tubular dysfunction and low plasma vitamin C concentrations. METHODS: We conducted a non-randomized cross-sectional cohort study of men aged 24-42 y, with Fabry disease (n = 34) and controls without acute or chronic disease (n = 33). To match anticipated plasma vitamin C concentrations, controls were placed on a low-vitamin C diet 3 wk before inpatient admission. To determine the primary outcome of vitamin C renal leak prevalence, subjects were fasted overnight, and matched urine and fasting plasma vitamin C measurements were obtained the following morning. Vitamin C renal leak was defined as presence of urinary vitamin C at plasma concentrations below 38 µM. Exploratory outcomes assessed the association between renal leak and clinical parameters, and genomic associations with renal leak using single nucleotide polymorphisms (SNPs) in the vitamin C transporter SLC23A1. RESULTS: Compared with controls, the Fabry cohort had 16-fold higher odds of renal leak (6% vs. 52%; OR: 16; 95% CI: 3.30, 162; P < 0.001). Renal leak was associated with higher protein creatinine ratio (P < 0.01) and lower hemoglobin (P = 0.002), but not estimated glomerular filtration rate (P = 0.54). Renal leak, but not plasma vitamin C, was associated with a nonsynonymous single nucleotide polymorphism in vitamin C transporter SLC23A1 (OR: 15; 95% CI: 1.6, 777; P = 0.01). CONCLUSIONS: Increased prevalence of renal leak in adult men with Fabry disease may result from dysregulated vitamin C renal physiology and is associated with abnormal clinical outcomes and genomic variation.

Does COVID-19 Infection Increase the Risk of Diabetes? Current Evidence.

PURPOSE OF REVIEW: Multiple studies report an increased incidence of diabetes following SARS-CoV-2 infection. Given the potential increased global burden of diabetes, understanding the effect of SARS-CoV-2 in the epidemiology of diabetes is important. Our aim was to review the evidence pertaining to the risk of incident diabetes after COVID-19 infection. RECENT FINDINGS: Incident diabetes risk increased by approximately 60% compared to patients without SARS-CoV-2 infection. Risk also increased compared to non-COVID-19 respiratory infections, suggesting SARS-CoV-2-mediated mechanisms rather than general morbidity after respiratory illness. Evidence is mixed regarding the association between SARS-CoV-2 infection and T1D. SARS-CoV-2 infection is associated with an elevated risk of T2D, but it is unclear whether the incident diabetes is persistent over time or differs in severity over time. SARS-CoV-2 infection is associated with an increased risk of incident diabetes. Future studies should evaluate vaccination, viral variant, and patient- and treatment-related factors that influence risk.

Data quality considerations for evaluating COVID-19 treatments using real world data: learnings from the National COVID Cohort Collaborative (N3C).

BACKGROUND: Multi-institution electronic health records (EHR) are a rich source of real world data (RWD) for generating real world evidence (RWE) regarding the utilization, benefits and harms of medical interventions. They provide access to clinical data from large pooled patient populations in addition to laboratory measurements unavailable in insurance claims-based data. However, secondary use of these data for research requires specialized knowledge and careful evaluation of data quality and completeness. We discuss data quality assessments undertaken during the conduct of prep-to-research, focusing on the investigation of treatment safety and effectiveness. METHODS: Using the National COVID Cohort Collaborative (N3C) enclave, we defined a patient population using criteria typical in non-interventional inpatient drug effectiveness studies. We present the challenges encountered when constructing this dataset, beginning with an examination of data quality across data partners. We then discuss the methods and best practices used to operationalize several important study elements: exposure to treatment, baseline health comorbidities, and key outcomes of interest. RESULTS: We share our experiences and lessons learned when working with heterogeneous EHR data from over 65 healthcare institutions and 4 common data models. We discuss six key areas of data variability and quality. (1) The specific EHR data elements captured from a site can vary depending on source data model and practice. (2) Data missingness remains a significant issue. (3) Drug exposures can be recorded at different levels and may not contain route of administration or dosage information. (4) Reconstruction of continuous drug exposure intervals may not always be possible. (5) EHR discontinuity is a major concern for capturing history of prior treatment and comorbidities. Lastly, (6) access to EHR data alone limits the potential outcomes which can be used in studies. CONCLUSIONS: The creation of large scale centralized multi-site EHR databases such as N3C enables a wide range of research aimed at better understanding treatments and health impacts of many conditions including COVID-19. As with all observational research, it is important that research teams engage with appropriate domain experts to understand the data in order to define research questions that are both clinically important and feasible to address using these real world data.

A method for comparing multiple imputation techniques: A case study on the U.S. national COVID cohort collaborative.

Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful for assessing associations between patients' predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases, whose removal may introduce severe bias. Several multiple imputation algorithms have been proposed to attempt to recover the missing information under an assumed missingness mechanism. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithm works best in a given scenario. Furthermore, the selection of each algorithm's parameters and data-related modeling choices are also both crucial and challenging. In this paper we propose a novel framework to numerically evaluate strategies for handling missing data in the context of statistical analysis, with a particular focus on multiple imputation techniques. We demonstrate the feasibility of our approach on a large cohort of type-2 diabetes patients provided by the National COVID Cohort Collaborative (N3C) Enclave, where we explored the influence of various patient characteristics on outcomes related to COVID-19. Our analysis included classic multiple imputation techniques as well as simple complete-case Inverse Probability Weighted models. Extensive experiments show that our approach can effectively highlight the most promising and performant missing-data handling strategy for our case study. Moreover, our methodology allowed a better understanding of the behavior of the different models and of how it changed as we modified their parameters. Our method is general and can be applied to different research fields and on datasets containing heterogeneous types.

MutLγ promotes repeat expansion in a Fragile X mouse model while EXO1 is protective.

The Fragile X-related disorders (FXDs) are Repeat Expansion Diseases resulting from an expansion of a CGG-repeat tract at the 5' end of the FMR1 gene. The mechanism responsible for this unusual mutation is not fully understood. We have previously shown that mismatch repair (MMR) complexes, MSH2/MSH3 (MutSß) and MSH2/MSH6 (MutSα), together with Polß, a DNA polymerase important for base excision repair (BER), are important for expansions in a mouse model of these disorders. Here we show that MLH1/MLH3 (MutLγ), a protein complex that can act downstream of MutSß in MMR, is also required for all germ line and somatic expansions. However, exonuclease I (EXO1), which acts downstream of MutL proteins in MMR, is not required. In fact, a null mutation in Exo1 results in more extensive germ line and somatic expansions than is seen in Exo1+/+ animals. Furthermore, mice homozygous for a point mutation (D173A) in Exo1 that eliminates its nuclease activity but retains its native conformation, shows a level of expansion that is intermediate between Exo1+/+ and Exo1-/- animals. Thus, our data suggests that expansion of the FX repeat in this mouse model occurs via a MutLγ-dependent, EXO1-independent pathway, with EXO1 protecting against expansion both in a nuclease-dependent and a nuclease-independent manner. Our data thus have implications for the expansion mechanism and add to our understanding of the genetic factors that may be modifiers of expansion risk in humans.

Statistical methods for building better biomarkers of chronic kidney disease.

The last two decades have witnessed an explosion in research focused on the development and assessment of novel biomarkers for improved prognosis of diseases. As a result, best practice standards guiding biomarker research have undergone extensive development. Currently, there is great interest in the promise of biomarkers to enhance research efforts and clinical practice in the setting of chronic kidney disease, acute kidney injury, and glomerular disease. However, some have questioned whether biomarkers currently add value to the clinical practice of nephrology. The current state of the art pertaining to statistical analyses regarding the use of such measures is critical. In December 2014, the National Institute of Diabetes and Digestive and Kidney Diseases convened a meeting, "Toward Building Better Biomarker Statistical Methodology," with the goals of summarizing the current best practice recommendations and articulating new directions for methodological research. This report summarizes its conclusions and describes areas that need attention. Suggestions are made regarding metrics that should be commonly reported. We outline the methodological issues related to traditional metrics and considerations in prognostic modeling, including discrimination and case mix, calibration, validation, and cost-benefit analysis. We highlight the approach to improved risk communication and the value of graphical displays. Finally, we address some "new frontiers" in prognostic biomarker research, including the competing risk framework, the use of longitudinal biomarkers, and analyses in distributed research networks.

Antagonism of scavenger receptor CD36 by 5A peptide prevents chronic kidney disease progression in mice independent of blood pressure regulation.

Scavenger receptor CD36 participates in lipid metabolism and inflammatory pathways important for cardiovascular disease and chronic kidney disease (CKD). Few pharmacological agents are available to slow the progression of CKD. However, apolipoprotein A-I-mimetic peptide 5A antagonizes CD36 in vitro. To test the efficacy of 5A, and to test the role of CD36 during CKD, we compared wild-type to CD36 knockout mice and wild-type mice treated with 5A, in a progressive CKD model that resembles human disease. Knockout and 5A-treated wild-type mice were protected from CKD progression without changes in blood pressure and had reductions in cardiovascular risk surrogate markers that are associated with CKD. Treatment with 5A did not further protect CD36 knockout mice from CKD progression, implicating CD36 as its main site of action. In a separate model of kidney fibrosis, 5A-treated wild-type mice had less macrophage infiltration and interstitial fibrosis. Peptide 5A exerted anti-inflammatory effects in the kidney and decreased renal expression of inflammasome genes. Thus, CD36 is a new therapeutic target for CKD and its associated cardiovascular risk factors. Peptide 5A may be a promising new agent to slow CKD progression.

Hygiene strategies to prevent methicillin-resistant Staphylococcus aureus skin and soft tissue infections: a cluster-randomized controlled trial among high-risk military trainees.

BACKGROUND: Effective measures are needed to prevent methicillin-resistant Staphylococcus aureus (MRSA) skin and soft tissue infections (SSTIs) in high-risk community settings. The study objective was to evaluate the effect of personal hygiene-based strategies on rates of overall SSTI and MRSA SSTI. METHODS: We conducted a prospective, field-based, cluster-randomized trial in US Army Infantry trainees from May 2010 through January 2012. There were 3 study groups with incrementally increased education and hygiene-based interventions: standard (S), enhanced standard (ES), and chlorhexidine (CHG). The primary endpoints were incidence of overall SSTI and MRSA SSTI. RESULTS: The study included 30 209 trainees constituting 540 platoons (168 S, 192 ES, and 180 CHG). A total of 1203 (4%) participants developed SSTI, 316 (26%) due to MRSA. The overall SSTI rate was 4.15 (95% confidence interval [CI], 3.77-4.58) per 100 person-cycles. SSTI rates by study group were 3.48 (95% CI, 2.87-4.22) for S, 4.18 (95% CI, 3.56-4.90) for ES, and 4.71 (95% CI, 4.03-5.50) for CHG. The MRSA SSTI rate per 100 person-cycles for all groups was 1.10 (95% CI, .91-1.32). MRSA SSTI rates by study group were 1.0 (95% CI, .70-1.42) for S, 1.29 (95% CI, .98-1.71) for ES, and 0.97 (95% CI, .70-1.36) for CHG. CONCLUSIONS: Personal hygiene and education measures, including once-weekly use of chlorhexidine body wash, did not prevent overall SSTI or MRSA SSTI in a high-risk population of military trainees. CLINICAL TRIALS REGISTRATION: NCT01105767.

Epidemiology of Infantile Ureteropelvic Junction Obstruction in the US.

OBJECTIVE: To describe sex- and diagnosis-specific comorbidities, outcomes, and secular trends associated with ureteropelvic junction obstruction (UPJO) in a large, real-world population diagnosed with hydronephrosis in infancy. MATERIALS AND METHODS: We identified all infants ≤1 year old with ≥1 claim in the Optum Clinformatics 2007-2020 nationwide population database and used univariable and multivariable Cox regression analyses to estimate associations of demographic and clinical characteristics of infants with a UPJO diagnosis with surgical status. RESULTS: Of 22,349 infants with hydronephrosis (1.1% of infants; males-1.4%, females-0.7%), 1722 (7.7%; 7.9%-males, 7.2%-females) had UPJO. Follow-up was ≥1 year in 1198 (70%) and ≥3 years in 555 (32%) cases, and UPJO repair was performed in 542 children (31.5%; 32.3%-males, 29.5%-females); 77.7% within 1 year and 97.3% within 3 years. UPJO repair was associated with prior urinary tract infection (UTI) (hazard ratio (HR) 1.41, 95% confidence interval (CI) 1.12-1.76) and South (HR 1.42, 95% CI 1.14-1.78) or Midwest (HR 1.60, 95% CI 1.26-2.04) geographic region but did not change over time. CONCLUSION: This population-based study provides a real-world view of postnatally diagnosed hydronephrosis, focusing on UPJO, for which 522 cases (∼1/3) had ≥3 years continuous coverage. UPJO-associated comorbidities were more common in females, and the frequencies of UPJO-associated surgery and comorbidities were higher than in other studies. Other than UTI, no other associated kidney or urinary tract diagnoses were associated with UPJO repair. We identified unique sex- and diagnosis-specific differences in associated comorbidities and interventions in children diagnosed with UPJO in the first year of life.

Pregnancy Outcomes among Pregnant Persons after COVID-19 Vaccination: Assessing Vaccine Safety in Retrospective Cohort Analysis of U.S. National COVID Cohort Collaborative (N3C).

COVID-19 vaccines have been shown to be effective in preventing severe illness, including among pregnant persons. The vaccines appear to be safe in pregnancy, supporting a continuously favorable overall risk/benefit profile, though supportive data for the U.S. over different periods of variant predominance are lacking. We sought to analyze the association of adverse pregnancy outcomes with COVID-19 vaccinations in the pre-Delta, Delta, and Omicron SARS-CoV-2 variants' dominant periods (constituting 50% or more of each pregnancy) for pregnant persons in a large, nationally sampled electronic health record repository in the U.S. Our overall analysis included 311,057 pregnant persons from December 2020 to October 2023 at a time when there were approximately 3.6 million births per year. We compared rates of preterm births and stillbirths among pregnant persons who were vaccinated before or during pregnancy to persons vaccinated after pregnancy or those who were not vaccinated. We performed a multivariable Poisson regression with generalized estimated equations to address data site heterogeneity for preterm births and unadjusted exact models for stillbirths, stratified by the dominant variant period. We found lower rates of preterm birth in the majority of modeled periods (adjusted incidence rate ratio [aIRR] range: 0.42 to 0.85; p-value range: <0.001 to 0.06) and lower rates of stillbirth (IRR range: 0.53 to 1.82; p-value range: <0.001 to 0.976) in most periods among those who were vaccinated before or during pregnancy compared to those who were vaccinated after pregnancy or not vaccinated. We largely found no adverse associations between COVID-19 vaccination and preterm birth or stillbirth; these findings reinforce the safety of COVID-19 vaccination during pregnancy and bolster confidence for pregnant persons, providers, and policymakers in the importance of COVID-19 vaccination for this group despite the end of the public health emergency.

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

OBJECTIVE: 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. RESEARCH DESIGN AND METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Evaluating COVID-19 vaccine effectiveness during pre-Delta, Delta and Omicron dominant periods among pregnant people in the U.S.: Retrospective cohort analysis from a nationally sampled cohort in National COVID Collaborative Cohort (N3C).

Objectives: To evaluate the effectiveness of COVID-19 vaccinations (initial and booster) during pre-Delta, Delta, and Omicron dominant periods among pregnant people via (1) COVID-19 incident and severe infections among pregnant people who were vaccinated vs. unvaccinated and (2) post-COVID-19 vaccination breakthrough infections and severe infections among vaccinated females who were pregnant vs. non-pregnant. Design: Retrospective cohort study using nationally sampled electronic health records data from the National COVID Cohort Collaborative (N3C), December 10, 2020, to June 07, 2022. Participants: Cohort 1 included pregnant people (15-55 years), and Cohort 2 included vaccinated females of reproductive age (15-55 years). Exposures: (1) COVID-19 vaccination and (2) pregnancy. Main outcome measures: Adjusted hazard ratios (aHRs) for COVID-19 incident or breakthrough infections and severe infections (i.e., COVID-19 infections with related hospitalizations). Results: In Cohort 1, 301,107 pregnant people were included. Compared to unvaccinated pregnant people, the aHRs for pregnant people with initial vaccinations during pregnancy of incident COVID-19 were 0.77 (95% CI: 0.62, 0.96) and 0.88 (95%CI: 0.73, 1.07) and aHRs of severe COVID-19 infections were 0.65 (95% CI: 0.47, 0.90) and 0.79 (95% CI: 0.51, 1.21) during the Delta and Omicron periods, respectively. Compared to pregnant people with full initial vaccinations, the aHR of incident COVID-19 for pregnant people with booster vaccinations was 0.64 (95% CI: 0.58, 0.71) during the Omicron period. In Cohort 2, 934,337 vaccinated people were included. Compared to vaccinated non-pregnant females, the aHRs of severe COVID-19 infections for people with initial vaccinations during pregnancy was 2.71 (95% CI: 1.31, 5.60) during the Omicron periods. Conclusions: Pregnant people with initial and booster vaccinations during pregnancy had a lower risk of incident and severe COVID-19 infections compared to unvaccinated pregnant people across the pandemic stages. However, vaccinated pregnant people still had a higher risk of severe infections compared to non-pregnant females.

Association of post-COVID phenotypic manifestations with new-onset psychiatric disease.

Acute COVID-19 infection can be followed by diverse clinical manifestations referred to as Post Acute Sequelae of SARS-CoV2 Infection (PASC). Studies have shown an increased risk of being diagnosed with new-onset psychiatric disease following a diagnosis of acute COVID-19. However, it was unclear whether non-psychiatric PASC-associated manifestations (PASC-AMs) are associated with an increased risk of new-onset psychiatric disease following COVID-19. A retrospective electronic health record (EHR) cohort study of 2,391,006 individuals with acute COVID-19 was performed to evaluate whether non-psychiatric PASC-AMs are associated with new-onset psychiatric disease. Data were obtained from the National COVID Cohort Collaborative (N3C), which has EHR data from 76 clinical organizations. EHR codes were mapped to 151 non-psychiatric PASC-AMs recorded 28-120 days following SARS-CoV-2 diagnosis and before diagnosis of new-onset psychiatric disease. Association of newly diagnosed psychiatric disease with age, sex, race, pre-existing comorbidities, and PASC-AMs in seven categories was assessed by logistic regression. There were significant associations between a diagnosis of any psychiatric disease and five categories of PASC-AMs with odds ratios highest for neurological, cardiovascular, and constitutional PASC-AMs with odds ratios of 1.31, 1.29, and 1.23 respectively. Secondary analysis revealed that the proportions of 50 individual clinical features significantly differed between patients diagnosed with different psychiatric diseases. Our study provides evidence for association between non-psychiatric PASC-AMs and the incidence of newly diagnosed psychiatric disease. Significant associations were found for features related to multiple organ systems. This information could prove useful in understanding risk stratification for new-onset psychiatric disease following COVID-19. Prospective studies are needed to corroborate these findings.

Assessing associations between individual-level social determinants of health and COVID-19 hospitalizations: Investigating racial/ethnic disparities among people living with human immunodeficiency virus (HIV) in the U.S. National COVID Cohort Collaborative (N3C).

Background: Leveraging the National COVID-19 Cohort Collaborative (N3C), a nationally sampled electronic health records repository, we explored associations between individual-level social determinants of health (SDoH) and COVID-19-related hospitalizations among racialized minority people with human immunodeficiency virus (HIV) (PWH), who have been historically adversely affected by SDoH. Methods: We retrospectively studied PWH and people without HIV (PWoH) using N3C data from January 2020 to November 2023. We evaluated SDoH variables across three domains in the Healthy People 2030 framework: (1) healthcare access, (2) economic stability, and (3) social cohesion with our primary outcome, COVID-19-related hospitalization. We conducted hierarchically nested additive and adjusted mixed-effects logistic regression models, stratifying by HIV status and race/ethnicity groups, accounting for age, sex, comorbidities, and data partners. Results: Our analytic sample included 280,441 individuals from 24 data partner sites, where 3,291 (1.17%) were PWH, with racialized minority PWH having higher proportions of adverse SDoH exposures than racialized minority PWoH. COVID-19-related hospitalizations occurred in 11.23% of all individuals (9.17% among PWH, 11.26% among PWoH). In our initial additive modeling, we observed that all three SDoH domains were significantly associated with hospitalizations, even with progressive adjustments (adjusted odds ratios [aOR] range 1.36-1.97). Subsequently, our HIV-stratified analyses indicated economic instability was associated with hospitalization in both PWH and PWoH (aOR range 1.35-1.48). Lastly, our fully adjusted, race/ethnicity-stratified analysis, indicated access to healthcare issues was associated with hospitalization across various racialized groups (aOR range 1.36-2.00). Conclusion: Our study underscores the importance of assessing individual-level SDoH variables to unravel the complex interplay of these factors for racialized minority groups.

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.