Factors Associated with COVID-19 Vaccination Promptness after Eligibility in a North Carolina Longitudinal Cohort Study.

Many studies identified factors associated with vaccination intention and hesitancy, but factors associated with vaccination promptness and the effect of vaccination intention on vaccination promptness are unknown. This study identified factors associated with COVID-19 vaccination promptness and evaluated the role of vaccination intention on vaccination promptness in 1223 participants in a community-based longitudinal cohort study (June 2020 to December 2021). Participants answered questions regarding COVID-19 vaccination intention, vaccination status, and reasons for not receiving a vaccine. The association of baseline vaccine hesitancy with vaccination was assessed by the Kaplan-Meier survival analysis. Follow-up analyses tested the importance of other variables predicting vaccination using the Cox proportional hazards model. Older age was associated with shorter time to vaccination (HR = 1.76 [1.37-2.25] 85-year-old versus 65-year-old). Lower education levels (HR = 0.80 [0.69-0.92]), household incomes (HR = 0.84 [0.72-0.98]), and baseline vaccination intention of 'No' (HR = 0.16 [0.11-0.23]) were associated with longer times to vaccination. The most common reasons for not being vaccinated (N = 58) were vaccine safety concerns (n = 33), side effects (n = 28), and vaccine effectiveness (n = 25). Vaccination campaigns that target populations prone to hesitancy and address vaccine safety and effectiveness could be helpful in future vaccination rollouts.

COVID-19 Infection Risk Among Previously Uninfected Adults: Development of a Prognostic Model.

Background: Few models exist that incorporate measures from an array of individual characteristics to predict the risk of COVID-19 infection in the general population. The aim was to develop a prognostic model for COVID-19 using readily obtainable clinical variables. Methods: Over 74 weeks surveys were periodically administered to a cohort of 1381 participants previously uninfected with COVID-19 (June 2020 to December 2021). Candidate predictors of incident infection during follow-up included demographics, living situation, financial status, physical activity, health conditions, flu vaccination history, COVID-19 vaccine intention, work/employment status, and use of COVID-19 mitigation behaviors. The final logistic regression model was created using a penalized regression method known as the least absolute shrinkage and selection operator. Model performance was assessed by discrimination and calibration. Internal validation was performed via bootstrapping, and results were adjusted for overoptimism. Results: Of the 1381 participants, 154 (11.2%) had an incident COVID-19 infection during the follow-up period. The final model included six variables: health insurance, race, household size, and the frequency of practicing three mitigation behavior (working at home, avoiding high-risk situations, and using facemasks). The c-statistic of the final model was 0.631 (0.617 after bootstrapped optimism-correction). A calibration plot suggested that with this sample the model shows modest concordance with incident infection at the lowest risk. Conclusion: This prognostic model can help identify which community-dwelling older adults are at the highest risk for incident COVID-19 infection and may inform medical provider counseling of their patients about the risk of incident COVID-19 infection.

Mitigation behavior prior to COVID-19 vaccination availability is associated with COVID-19 infection and time to vaccination.

BACKGROUND: Mitigation behaviors reduce the incidence of COVID-19 infection. Determining characteristics of groups defined by mitigation behaviors compliance may be useful to inform targeted public health policies and interventions. This study aimed to identify groups of individuals according to self-reported compliance with COVID-19 mitigation behaviors, define compliance class characteristics, and explore associations between compliance classes and important study and public health outcomes. METHODS AND FINDINGS: We studied 1,410 participants in the Cabarrus County COVID-19 Prevalence and Immunity longitudinal cohort study (June 2020 to December 2021) who were asked 10 questions regarding compliance with recommended COVID-19 mitigation behaviors. By Latent Class Analysis, 1,381 participants were categorized into 3 classes (most [49.4%], moderately [45.0%], and least [5.6%] compliant). Compared with the most compliant class, the least and moderately compliant classes were younger (mean = 61.9 v. 59.0 v. 53.8 years), had fewer medical conditions per individual (1.37 v. 1.08 v. 0.77), and differed in Hispanic ethnicity (6.2% v. 2.8% v. 9.1%) and COVID-19 vaccine intention (65.8% v. 59.8% v. 35.1%). Compared to the most compliant class, the least compliant class had fewer women (54.6% v. 76.3%), fewer insured individuals (92.2% v. 97.4%), and more withdrew from study participation early (28.6% v. 16.0%). Relative to the most compliant class, the least compliant class had a higher likelihood of COVID-19 infection (OR = 2.08 [95% CI 1.13, 3.85]), lower rate of COVID-19 vaccination (72.6% v. 95.1%), and longer time to 50% COVID-19 vaccination following eligibility (8-9 vs 16 days). CONCLUSIONS: Classes defined by mitigation behaviors compliance had distinct characteristics, including age, sex, medical history, and ethnicity, and were associated with important study and public health outcomes. Targeted public health policies and interventions according to the compliance group characteristics may be of value in current and future pandemic responses to increase compliance.

The Cabarrus County COVID-19 Prevalence and Immunity (C3PI) Study: design, methods, and baseline characteristics.

OBJECTIVES: Coronavirus Disease 2019 (COVID-19) is a viral illness with public health importance. The Cabarrus County COVID-19 Prevalence and Immunity (C3PI) Study is a prospective, longitudinal cohort study designed to contribute valuable information on community prevalence of active COVID-19 infection and SARS-CoV-2 antibodies as the pandemic and responses to it have and continue to evolve. We present the rationale, study design, and baseline characteristics of the C3PI Study. METHODS: We recruited 1,426 participants between June 2020 and August 2020 from the Measurement to Understand the Reclassification of Disease of Cabarrus/Kannapolis (MURDOCK) Study Community Registry and Biorepository, a previously established, community-based, longitudinal cohort. Participants completed a baseline survey and follow-up surveys every two weeks. A nested weighted, random sub-cohort (n=300) was recruited to measure the incidence and prevalence of active COVID-19 infection and SARS-CoV-2 IgG antibodies. RESULTS: The sub-cohort was younger (56 vs 61 years), had more men (39.0% vs 30.9%), and a higher proportion of Hispanic (11.0% vs 5.1%) and Black participants (17.0% vs 8.2%) compared with the overall cohort. They had similar anthropometrics and medical histories, but a greater proportion of the sub-cohort had a higher educational degree (36.1% vs 31.3%) and reported a pre-pandemic annual household income of >$90,000 (57.1% vs 47.9%). CONCLUSION: This study is part of a multisite consortium that will provide critical data on the epidemiology of COVID-19 and community perspectives about the pandemic, behaviors and mitigation strategies, and individual and community burden in North Carolina.

Comparison of a Blood Self-Collection System with Routine Phlebotomy for SARS-CoV-2 Antibody Testing.

The Coronavirus Disease 2019 (COVID-19) pandemic forced researchers to reconsider in-person assessments due to transmission risk. We conducted a pilot study to evaluate the feasibility of using the Tasso-SST (Tasso, Inc, Seattle, Washington) device for blood self-collection for use in SARS-CoV-2 antibody testing in an ongoing COVID-19 prevalence and immunity research study. 100 participants were recruited between January and March 2021 from a previously identified sub-cohort of the Cabarrus County COVID-19 Prevalence and Immunity (C3PI) Study who were under-going bimonthly COVID-19 antibody testing. Participants were given a Tasso-SST kit and asked to self-collect blood during a scheduled visit where trained laboratory personnel performed routine phlebotomy. All participants completed an after-visit survey about their experience. Overall, 70.0% of participants were able to collect an adequate sample for testing using the device. Among those with an adequate sample, there was a high concordance in results between the Tasso-SST and phlebotomy blood collection methods (Cohen's kappa coefficient = 0.88, Interclass correlation coefficient 0.98 [0.97, 0.99], p < 0.0001). The device received a high-level (90.0%) of acceptance among all participants. Overall, the Tasso-SST could prove to be a valuable tool for seroprevalence testing. However, future studies in larger, diverse populations over longer periods may provide a better understanding of device usability and acceptance among older participants and those with comorbidities in various use scenarios.

Lipid changes in the metabolome of a single case study with maple syrup urine disease (MSUD) after five days of improved diet adherence of controlled branched-chain amino acids (BCAA).

BACKGROUND: Distinguishing systemic metabolic disruptions in maple syrup urine disease (MSUD) beyond amino acid pathways is under-investigated, yet important to understanding disease pathology and treatment options. METHODS: An adolescent female (15 years) with MSUD without liver transplant, attended 2 study visits, 5 days apart. Medical diet adherence was determined based on her 3-day diet records and plasma branched-chain amino acid (BCAA) concentrations at both study visits. Plasma from a single age- and sex-matched control (MURDOCK Study, Duke University) and the case patient were analyzed with UPLC/MS/MS for intensity (m/z), annotated, and normalized against a median of 1 (Metabolon, Morrisville NC). Differences between case/control and 5-day comparisons were defined as ≥ Ç€ 0.5 ǀ. RESULTS: 434 lipid metabolites were identified across samples; 90 (20.7%) were higher and 120 (27.6%) lower in the MSUD case at baseline compared with control. By study visit 2, plasma BCAA had declined, while 48 (53%) of elevated lipids and 14 (11.7%) of lower lipid values had moved to within ǀ 0.5 ǀ of control. Most shifts towards control by day 5 were seen in long-chain fatty acid intermediates (42%) and acylcarnitines (32%). Although androgenic (28%) and bile acid (23%) metabolites increased towards control, neither reached control level by day 5. DISCUSSION: This comparative metabolomics study in a single MSUD case and healthy control suggests intrinsic differences in MSUD lipid metabolism potentially influenced by therapeutic diet. Findings suggest influences on hormone regulation, fatty acid oxidation, and bile acid synthesis, but further studies are needed to confirm an association between MSUD and lipid dysregulation. SYNOPSIS: Within 5 days of improved dietary adherence, a single MSUD case experienced substantial changes in lipid markers potentially related to changes in plasma branched-chain amino acids.

A comparison of phenotype definitions for diabetes mellitus.

OBJECTIVE: This study compares the yield and characteristics of diabetes cohorts identified using heterogeneous phenotype definitions. MATERIALS AND METHODS: Inclusion criteria from seven diabetes phenotype definitions were translated into query algorithms and applied to a population (n=173 503) of adult patients from Duke University Health System. The numbers of patients meeting criteria for each definition and component (diagnosis, diabetes-associated medications, and laboratory results) were compared. RESULTS: Three phenotype definitions based heavily on ICD-9-CM codes identified 9-11% of the patient population. A broad definition for the Durham Diabetes Coalition included additional criteria and identified 13%. The electronic medical records and genomics, NYC A1c Registry, and diabetes-associated medications definitions, which have restricted or no ICD-9-CM criteria, identified the smallest proportions of patients (7%). The demographic characteristics for all seven phenotype definitions were similar (56-57% women, mean age range 56-57 years).The NYC A1c Registry definition had higher average patient encounters (54) than the other definitions (range 44-48) and the reference population (20) over the 5-year observation period. The concordance between populations returned by different phenotype definitions ranged from 50 to 86%. Overall, more patients met ICD-9-CM and laboratory criteria than medication criteria, but the number of patients that met abnormal laboratory criteria exclusively was greater than the numbers meeting diagnostic or medication data exclusively. DISCUSSION: Differences across phenotype definitions can potentially affect their application in healthcare organizations and the subsequent interpretation of data. CONCLUSIONS: Further research focused on defining the clinical characteristics of standard diabetes cohorts is important to identify appropriate phenotype definitions for health, policy, and research.

Electronic health records based phenotyping in next-generation clinical trials: a perspective from the NIH Health Care Systems Collaboratory.

Widespread sharing of data from electronic health records and patient-reported outcomes can strengthen the national capacity for conducting cost-effective clinical trials and allow research to be embedded within routine care delivery. While pragmatic clinical trials (PCTs) have been performed for decades, they now can draw on rich sources of clinical and operational data that are continuously fed back to inform research and practice. The Health Care Systems Collaboratory program, initiated by the NIH Common Fund in 2012, engages healthcare systems as partners in discussing and promoting activities, tools, and strategies for supporting active participation in PCTs. The NIH Collaboratory consists of seven demonstration projects, and seven problem-specific working group 'Cores', aimed at leveraging the data captured in heterogeneous 'real-world' environments for research, thereby improving the efficiency, relevance, and generalizability of trials. Here, we introduce the Collaboratory, focusing on its Phenotype, Data Standards, and Data Quality Core, and present early observations from researchers implementing PCTs within large healthcare systems. We also identify gaps in knowledge and present an informatics research agenda that includes identifying methods for the definition and appropriate application of phenotypes in diverse healthcare settings, and methods for validating both the definition and execution of electronic health records based phenotypes.