Individual and structural determinants of COVID-19 vaccine uptake in a marginalized community in the United States.

Socially and medically vulnerable groups (e.g., people 65 years or older, minoritized racial groups, non-telework essential workers, and people with comorbid conditions) experience barriers to COVID-19 prevention and treatment, increased burden of disease, and increased risk of death from COVID-19. Researchers are paying increased attention to social determinants of health (SDH) in explaining inequities in COVID-19-related health outcomes and rates of vaccine uptake. The purpose of the present manuscript is to identify clinically significant predictors of COVID-19 vaccine uptake among people who were socially and medically vulnerable to SARs-CoV-2 infection. Analysis was informed by the SDH framework and included a sample of 641 baseline surveys from participants in a clinical trial designed to increase COVID-19 testing. All participants were at high risk of developing COVID-19-related complications or dying from COVID-19. Following community-based participatory research principles, a well-established community collaborative board conducted every aspect of the study. Multiple logistic regressions were conducted to examine the relationships between individual and structural factors and COVID-19 vaccine uptake. In the final time adjusted model, we found that vaccine uptake was only predicted by specific individual-level factors: being 65 years and older, living with HIV/AIDS, and having previously received a flu vaccine or a COVID-19 test. Those reporting to believe in COVID-19-conspiracy theories were less likely to get the COVID-19 vaccine. More research is needed to identify predictors of vaccine uptake among people with comorbidities that make them more vulnerable to COVID-19 complications or death.

Use of research electronic data capture (REDCap) in a sequential multiple assignment randomized trial (SMART): a practical example of automating double randomization.

BACKGROUND: Adaptive interventions are often used in individualized health care to meet the unique needs of clients. Recently, more researchers have adopted the Sequential Multiple Assignment Randomized Trial (SMART), a type of research design, to build optimal adaptive interventions. SMART requires research participants to be randomized multiple times over time, depending upon their response to earlier interventions. Despite the increasing popularity of SMART designs, conducting a successful SMART study poses unique technological and logistical challenges (e.g., effectively concealing and masking allocation sequence to investigators, involved health care providers, and subjects) in addition to other challenges common to all study designs (e.g., study invitations, eligibility screening, consenting procedures, and data confidentiality protocols). Research Electronic Data Capture (REDCap) is a secure, browser-based web application widely used by researchers for data collection. REDCap offers unique features that support researchers' ability to conduct rigorous SMARTs. This manuscript provides an effective strategy for performing automatic double randomization for SMARTs using REDCap. METHODS: Between January and March 2022, we conducted a SMART using a sample of adult (age 18 and older) New Jersey residents to optimize an adaptive intervention to increase COVID-19 testing uptake. In the current report, we discuss how we used REDCap for our SMART, which required double randomization. Further, we share our REDCap project XML file for future investigators to use when designing and conducting SMARTs. RESULTS: We report on the randomization feature that REDCap offers and describe how the study team automated an additional randomization that was required for our SMART. An application programming interface was used to automate the double randomizations in conjunction with the randomization feature provided by REDCap. CONCLUSIONS: REDCap offers powerful tools to facilitate the implementation of longitudinal data collection and SMARTs. Investigators can make use of this electronic data capturing system to reduce errors and bias in the implementation of their SMARTs by automating double randomization. TRIAL REGISTRATION: The SMART study was prospectively registered at Clinicaltrials.gov; registration number: NCT04757298, date of registration: 17/02/2021.

Use of research electronic data capture (REDCap) in a sequential multiple assignment randomized trial (SMART): A practical example of automating double randomization.

Background: Adaptive interventions are often used in individualized health care to meet the unique needs of clients. Recently, more researchers have adopted the Sequential Multiple Assignment Randomized Trial (SMART), a type of research design, to build optimal adaptive interventions. SMART requires research participants to be randomized multiple times over time, depending upon their response to earlier interventions. Despite the increasing popularity of SMART designs, conducting a successful SMART study poses unique technological and logistical challenges (e.g., effectively concealing and masking allocation sequence to investigators, involved health care providers, and subjects) in addition to other challenges common to all study designs (e.g., study invitations, eligibility screening, consenting procedures, and data confidentiality protocols). Research Electronic Data Capture (REDCap) is a secure, browser-based web application widely used by researchers for data collection. REDCap offers unique features that support researchers’ ability to conduct rigorous SMARTs. This manuscript provides an effective strategy for performing automatic double randomization for SMARTs using REDCap. Methods: Between January and March 2022, we conducted a SMART using a sample of adult (age 18 and older) New Jersey residents to optimize an adaptive intervention to increase COVID-19 testing uptake. In the current report, we discuss how we used REDCap for our SMART, which required double randomization. Further, we share our REDCap project XML file for future investigators to use when designing and conducting SMARTs. Results: We report on the randomization feature that REDCap offers and describe how the study team automated an additional randomization that was required for our SMART. An application programming interface was used to automate the double randomizations in conjunction with the randomization feature provided by REDCap. Conclusions: REDCap offers powerful tools to facilitate the implementation of longitudinal data collection and SMARTs. Investigators can make use of this electronic data capturing system to reduce errors and bias in the implementation of their SMARTs by automating double randomization. Trial registration: The SMART study was prospectively registered at Clinicaltrials.gov; registration number: NCT04757298, date of registration: 17/02/2021. Keywords: Research Electronic Data Capture (REDCap), randomized controlled trials (RCT), adaptive interventions, Sequential Multiple Assignment Randomized Trial (SMART), randomization, experimental design, reducing human errors, automation.

Sex Differences in the Phenotype of Transthyretin Cardiac Amyloidosis Due to Val122Ile Mutation: Insights from Noninvasive Pressure-Volume Analysis.

BACKGROUND: Transthyretin cardiac amyloidosis (ATTR-CA) is an under-recognized cause of heart failure with preserved ejection fraction. In the United States, the valine-to-isoleucine substitution (Val122Ile) is the most common inherited variant. Data on sex differences in presentation and outcomes of Val122Ile associated ATTR-CA are lacking. METHODS AND RESULTS: In a retrospective, single-center study of 73 patients diagnosed with Val122Ile associated ATTR-CA between 2001 and 2018, sex differences in clinical and echocardiographic data at the time of diagnosis were evaluated. Pressure-volume analysis using noninvasive single beat techniques was used to compare chamber performance. Compared with men (n = 46), women (n = 27) were significantly older at diagnosis, 76 years vs 69 years; P < .001. The end-systolic pressure-volume relationship, 5.1 mm Hg*m2/mL vs 4.3 mm Hg*m2/mL; P = .27, arterial elastance, 5.5 mm Hg*m2/mL vs 5.7 mm Hg*m2/mL; P = .62, and left ventricular capacitance were similar between sexes as was pressure-volume areas indexed to a left ventricular end-diastolic pressure of 30 mm Hg, a measure of overall pump function. The 3-year mortality rates were also similar, 34% vs 43%; P = .64. CONCLUSIONS: Despite being significantly older at time of diagnosis with Val122Ile associated ATTR-CA, women have similar overall cardiac chamber function and rates of mortality to men, suggesting a less aggressive disease trajectory. These findings should be confirmed with longitudinal studies.