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.

Society of pediatric liver transplantation: Current registry status 2011-2018

BACKGROUND

SPLIT was founded in 1995 in order to collect comprehensive prospective data on pediatric liver transplantation, including waiting list data, transplant, and early and late outcomes. Since 2011, data collection of the current registry has been refined to focus on prospective data and outcomes only after transplant to serve as a foundation for the future development of targeted clinical studies.

OBJECTIVE

To report the outcomes of the SPLIT registry from 2011 to 2018.

METHODS

This is a multicenter, cross-sectional analysis characterizing patients transplanted and enrolled in the SPLIT registry between 2011 and 2018. All patients, <18 years of age, received a first liver-only, a combined liver-kidney, or a combined liver-pancreas transplant during this study period.

RESULTS

A total of 1911 recipients from 39 participating centers in North America were registered. Indications included biliary atresia (38.5%), metabolic disease (19.1%), tumors (11.7%), and fulminant liver failure (11.5%). Greater than 50% of recipients were transplanted as either Status 1A/1B or with a MELD/PELD exception score. Incompatible transplants were performed in 4.1%. Kaplan-Meier estimates of 1-year patient and graft survival were 97.3% and 96.6%. First 30 days of surgical complications included reoperation (31.7%), hepatic artery thrombosis (6.3%), and portal vein thrombosis (3.2%). In the first 90 days, biliary tract complications were reported in 13.6%. Acute cellular rejection during first year was 34.7%. At 1 and 2 years of follow-up, 39.2% and 50.6% had normal liver tests on monotherapy (tacrolimus or sirolimus). Further surgical, survival, allograft function, and complications are detailed.