Local data commons: the sleeping beauty in the community of data commons.

BACKGROUND: Public Data Commons (PDC) have been highlighted in the scientific literature for their capacity to collect and harmonize big data. On the other hand, local data commons (LDC), located within an institution or organization, have been underrepresented in the scientific literature, even though they are a critical part of research infrastructure. Being closest to the sources of data, LDCs provide the ability to collect and maintain the most up-to-date, high-quality data within an organization, closest to the sources of the data. As a data provider, LDCs have many challenges in both collecting and standardizing data, moreover, as a consumer of PDC, they face problems of data harmonization stemming from the monolithic harmonization pipeline designs commonly adapted by many PDCs. Unfortunately, existing guidelines and resources for building and maintaining data commons exclusively focus on PDC and provide very little information on LDC. RESULTS: This article focuses on four important observations. First, there are three different types of LDC service models that are defined based on their roles and requirements. These can be used as guidelines for building new LDC or enhancing the services of existing LDC. Second, the seven core services of LDC are discussed, including cohort identification and facilitation of genomic sequencing, the management of molecular reports and associated infrastructure, quality control, data harmonization, data integration, data sharing, and data access control. Third, instead of commonly developed monolithic systems, we propose a new data sharing method for data harmonization that combines both divide-and-conquer and bottom-up approaches. Finally, an end-to-end LDC implementation is introduced with real-world examples. CONCLUSIONS: Although LDCs are an optimal place to identify and address data quality issues, they have traditionally been relegated to the role of passive data provider for much larger PDC. Indeed, many LDCs limit their functions to only conducting routine data storage and transmission tasks due to a lack of information on how to design, develop, and improve their services using limited resources. We hope that this work will be the first small step in raising awareness among the LDCs of their expanded utility and to publicize to a wider audience the importance of LDC.

Evaluation of partial and total splenectomy in children with sickle cell disease using an Internet-based registry.

BACKGROUND: Clinical outcomes of children with sickle cell disease (SCD) who undergo total or partial splenectomy (PS) are poorly defined. The purpose of this retrospective study was to initiate an Internet-based registry to facilitate analysis of clinical outcomes for these children. We hypothesized that both surgical procedures would be well tolerated and would eliminate risk of splenic sequestration. METHODS: We developed a web-based registry using the Research Electronic Data Capture (REDCap) platform. Children were included if they had SCD and underwent total splenectomy (TS) or PS between 2003 and 2010. Clinical outcomes were compared between cohorts, with follow-up to 1 year. RESULTS: Twenty-four children were included, TS (n = 15) and PS (n = 9). There were no differences in surgical time or intraoperative blood loss. The length of stay was longer after PS (4.1 ± 1.7 days) compared to TS, (2.4 ± 1.2 days, P = 0.02). Within 30 days of surgery, 2 (20%) patients had acute chest syndrome (ACS) following TS and 2 (15%) patients had ACS after PS. During 1-year follow-up, no patient in either cohort had recurrent splenic sequestration, venous thrombosis or overwhelming postsplenectomy sepsis. All children who were transfused preoperatively to prevent recurrent splenic sequestration successfully discontinued transfusions. CONCLUSIONS: Both TS and PS result in favorable hematologic outcomes and low risk of adverse events for children with SCD. A REDCap-based registry may facilitate data entry and analysis of clinical outcomes to allow for comparison between different types of splenectomy.

Prevalence of SARS-CoV-2 IgG antibodies in health care workers at a tertiary care academic medical center - An assessment of occupational infection risk.

BACKGROUND: The purpose of this study was to assess the occupational SARS-CoV-2 infection risk among health care workers (HCW) at University of Kentucky HealthCare (UKHC) by evaluating the prevalence of SARS-CoV-2 antibodies. METHODS: This is a prospective cohort study of HCW at UKHC. SARS-CoV-2 IgG antibody seropositivity was measured in a CLIA-certified laboratory utilizing the Abbott Architect SARS-CoV-2 IgG antibody assay. Demographics and work type were self-reported by study participants via an emailed survey. RESULTS: The overall antibody positivity rate of HCW was 1.55% (5/322; 95% confidence interval: 0.65%-3.71%) at cohort entry. There were no differences in antibody positivity between those that worked directly with SARS-CoV-2 infected patients and those that did not. The antibody rate of positivity of patients during the same time period was similar, 1.8% (9/499; 95% confidence interval 0.94%-3.45%). CONCLUSIONS: Antibody positivity was low and similar between HCW and patients tested during a similar time period. HCW positivity rates did not appear to be impacted by caring for known SARS-CoV-2 infected patients suggesting that appropriate use of personal protective equipment is effective in protecting individuals from transmission.

Assessing Research Collaboration through Co-authorship Network Analysis.

Interdisciplinary research collaboration is needed to perform transformative science and accelerate innovation. The Science of Team Science strives to investigate, evaluate, and foster team science, including institutional policies that may promote or hinder collaborative interdisciplinary research and the resources and infrastructure needed to promote team science within and across institutions. Social network analysis (SNA) has emerged as a useful method to measure interdisciplinary science through the evaluation of several types of collaboration networks, including co-authorship networks. Likewise, research administrators are responsible for conducting rigorous evaluation of policies and initiatives. Within this paper, we present a case study using SNA to evaluate inter-programmatic collaboration (evidenced by co-authoring scientific papers) from 2007-2014 among scientists who are members of four formal research programs at an NCI-designated Cancer Center, the Markey Cancer Center (MCC) at the University of Kentucky. We evaluate change in network descriptives over time and implement separable temporal exponential-family random graph models (STERGMs) to estimate the effect of author and network variables on the tendency to form a co-authorship tie. We measure the diversity of the articles published over time (Blau's Index) to understand whether the changes in the co-authorship network are reflected in the diversity of articles published by research members. Over the 8-year period, we found increased inter-programmatic collaboration among research members as evidenced by co-authorship of published scientific papers. Over time, MCC Members collaborated more with others outside of their research program and outside their initial dense co-authorship groups, however tie formation continues to be driven by co-authoring with individuals of the same research program and academic department. Papers increased in diversity over time on all measures with the exception of author gender. This inter-programmatic research was fostered by policy changes in cancer center administration encouraging interdisciplinary research through both informal (e.g., annual retreats, seminar series) and formal (e.g., requiring investigators from more than two research programs on applications for pilot funding) means. Within this cancer center, interdisciplinary co-authorship increased over time as policies encouraging this collaboration were implemented. Yet, there is room for improvement in creating more interdisciplinary and diverse ties between research program members.

Research management team (RMT): a model for research support services at Duke University.

Collecting and managing data for clinical and translational research presents significant challenges for clinical and translational researchers, many of whom lack needed access to data management expertise, methods, and tools. At many institutions, funding constraints result in differential levels of research informatics support among investigators. In addition, the lack of widely shared models and ontologies for clinical research informatics and health information technology hampers the accurate assessment of investigators' needs and complicates the efficient allocation of crucial resources for research projects, ultimately affecting the quality and reliability of research. In this paper, we present a model for providing flexible, cost-efficient institutional support for clinical and translational research data management and informatics, the research management team, and describe our initial experiences with deploying this model at our institution.