PennCIL

5/5📊 The R package and Shiny web app include example datasets for easy exploration and demonstration of the tool's capabilities. Feedback and suggestions are welcome—we’d love to hear your thoughts!

4/5 Explore the details and try it out: 📄 Manuscript: arxiv.org/abs/2411.12674 📦 R package: cran.r-project.org/web/packages/O… 🌐 Shiny web app: origamiplot.shinyapps.io/origami_shinya…

1/5 We’re excited to share our manuscript introducing OrigamiPlot, a new tool for enhancing multivariate data visualization.

6/6 These findings suggest that overweight and obesity are important risk factors for pediatric PASC, highlighting the need for targeted care to prevent chronic conditions in at-risk children and young adults who are infected with SARS-CoV-2.

5/6 compared with peers with a healthy BMI, there was a 25.4% increased risk of PASC among those with obesity or severe obesity had a 25.4%, or and a 42.1% increased incidence risk of PASC among those with severe obesity.

1/6 Our new JAMA Network Open, Body Mass Index and Postacute Sequelae of SARS-CoV-2 Infection in Children and Young Adults, is online on October 28, 2024! Read more at: [jamanetwork.com/journals/jaman…]

14/n We pre-specified 40 negative control outcomes that are not causally related to the vaccines. By applying the same method to each of these outcomes, we estimated the distribution of the potential residual biases, which was used to adjust our primary results.

1/n Our new #AnnalsofInternalMedicine paper, Efficacy of BNT162b2 Bivalent Vaccine Against Delta and Omicron Variants in Pediatrics, is online yesterday! Read more: [acpjournals.org/doi/full/10.73…] #PediatricVaccineStudy #COVID19Research #PublicHealth #AnnalsofInternalMedicine

13/n In addition to adjusting for a rich set of confounding variables and under-reported vaccination status in our proposed method, we performed negative control experiments to further mitigate potential residual bias.

12/n Our study was the first to rigorously address the under-reporting issue in vaccination status by health systems in the USA.

11/n Thus, the “vaccinated” status in the data was likely to be true, whereas “unvaccinated” status in the data could be truly vaccinated. To address the potential bias from the under-reporting issue, we developed and employed a novel comparative effectiveness research method.

10/n A key issue in EHR data is the under-reported vaccination status that can lead to biased vaccine effectiveness estimates. Children or adolescents could get vaccinated from various places, whereas only some of these places share the vaccination data with the health system.

9/n The variables include vaccination status, COVID-19 outcomes (documented infection, severe illness, and ICU admission), cardiac complications, healthcare utilization (i.e., negative COVID-19 tests), pediatric comorbidity indicator, and preexisting chronic conditions.

8/n The study covered a diverse and representative pediatric population from primary care, specialty care, emergency department (ED), testing center, and inpatient settings. By using electronic health records data from PEDSnet, we defined a rich set of variables for the analysis.

7/n Our study involves large USA pediatric cohorts totaling 250,000 children and adolescents from PEDSnet, a national network of U.S. pediatric health systems.

6/n 📊Key findings: e. During the Delta wave, there was no evidence of significant waning in vaccine’s effectiveness over the subsequent 4 months. f. During the Omicron wave, the data showed some reduction in effectiveness near the end of 4 months following vaccination.