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Abstract Data sharing is necessary to maximize the actionable knowledge generated from research data. Data challenges can encourage secondary analyses of datasets. Data challenges in biomedicine often rely on advanced cloud-based computing infrastructure and expensive industry partnerships. Examples include challenges that use Google Cloud virtual machines and the Sage Bionetworks Dream Challenges platform. Such robust infrastructures can be financially prohibitive for investigators without substantial resources. Given the potential to develop scientific and clinical knowledge and the NIH emphasis on data sharing and reuse, there is a need for inexpensive and computationally lightweight methods for data sharing and hosting data challenges. To fill that gap, we developed a workflow that allows for reproducible model training, testing, and evaluation. We leveraged public GitHub repositories, open-source computational languages, and Docker technology. In addition, we conducted a data challenge using the infrastructure we developed. In this manuscript, we report on the infrastructure, workflow, and data challenge results. The infrastructure and workflow are likely to be useful for data challenges and education.

Abstract Background More than one-third of individuals experience post-acute sequelae of SARS-CoV-2 infection (PASC, which includes long-COVID). The objective is to identify risk factors associated with PASC/long-COVID diagnosis. Methods This was a retrospective case–control study including 31 health systems in the United States from the National COVID Cohort Collaborative (N3C). 8,325 individuals with PASC (defined by the presence of the International Classification of Diseases, version 10 code U09.9 or a long-COVID clinic visit) matched to 41,625 controls within the same health system and COVID index date within ± 45 days of the corresponding case's earliest COVID index date. Measurements of risk factors included demographics, comorbidities, treatment and acute characteristics related to COVID-19. Multivariable logistic regression, random forest, and XGBoost were used to determine the associations between risk factors and PASC. Results Among 8,325 individuals with PASC, the majority were > 50 years of age (56.6%), female (62.8%), and non-Hispanic White (68.6%). In logistic regression, middle-age categories (40 to 69 years; OR ranging from 2.32 to 2.58), female sex (OR 1.4, 95% CI 1.33–1.48), hospitalization associated with COVID-19 (OR 3.8, 95% CI 3.05–4.73), long (8–30 days, OR 1.69, 95% CI 1.31–2.17) or extended hospital stay (30 + days, OR 3.38, 95% CI 2.45–4.67), receipt of mechanical ventilation (OR 1.44, 95% CI 1.18–1.74), and several comorbidities including depression (OR 1.50, 95% CI 1.40–1.60), chronic lung disease (OR 1.63, 95% CI 1.53–1.74), and obesity (OR 1.23, 95% CI 1.16–1.3) were associated with increased likelihood of PASC diagnosis or care at a long-COVID clinic. Characteristics associated with a lower likelihood of PASC diagnosis or care at a long-COVID clinic included younger age (18 to 29 years), male sex, non-Hispanic Black race, and comorbidities such as substance abuse, cardiomyopathy, psychosis, and dementia. More doctors per capita in the county of residence was associated with an increased likelihood of PASC diagnosis or care at a long-COVID clinic. Our findings were consistent in sensitivity analyses using a variety of analytic techniques and approaches to select controls. Conclusions This national study identified important risk factors for PASC diagnosis such as middle age, severe COVID-19 disease, and specific comorbidities. Further clinical and epidemiological research is needed to better understand underlying mechanisms and the potential role of vaccines and therapeutics in altering PASC course.

ABSTRACT Objectives Trauma and, in particular, traumatic brain injury (TBI) cause enormous morbidity and mortality worldwide. Children who survive trauma have many health burdens, but how often those burdens lead to emergency care or hospital readmission is unknown. Readmissions occur frequently in adult TBI survivors: approximately 21-28% within one year. No single existing U.S. database contains the necessary variables to study readmissions of children hospitalized after trauma. We previously demonstrated that probabilistic linkage with Markov chain Monte Carlo parameter refinement could accurately link records of children with severe TBI without using protected health information. That work was presented at the IHDLN Conference in 2014. The objectives of this study are to 1) expand the previous linkage to include children with any type of trauma and 2) to determine the 1-year rates of Emergency Department (ED) visit and hospital readmission in children with TBI versus other trauma. Approach Using the algorithm we reported previously, we linked the records of children hospitalized after trauma during 2007-2012 in the U.S. National Trauma Data Bank (NTDB) with the Pediatric Health Information System (PHIS) database. The NTDB contains the Glasgow Coma Scale (GCS), necessary to categorize TBI. The PHIS database contains a unique identifier for each patient at each hospital, making longitudinal studies possible. Probabilistic linkage was performed using LinkSolv (Strategic Matching, Inc., Morrisonville, NY). We identified ED visits and hospital readmissions of the index cohort in the PHIS database through March 31, 2015. Results Among children hospitalized after acute trauma for >= 2 days, we found that 83% of NTDB records linked accurately. Of the children hospitalized at 28 U.S. hospitals in 19 states, 49,477 were discharged alive; 15,438 had TBI. This represents approximately 10% of U.S. pediatric TBI hospitalizations during that period. The median age was 8 years and 64% were male. The 1-year hospital readmission rate was higher (12.2%, 283/2,306) in those with both TBI and other injuries than those with isolated TBI (8.5%, 1,120/13,132) or non-TBI injuries (8.1%, 2,790/34,039, p < 0.01 across groups). The 1-year ED visit rate differed only modestly between those three groups (14.1% versus 15.4% versus 14.2%, p < 0.01). Conclusions This study leverages two linked national datasets to investigate emergency care and hospital readmission in children who survive trauma. Multivariable analyses are ongoing and will be completed by August 2016. We will generate hypotheses about potentially modifiable causes of emergency care and readmission.