A clinical and translational scientist (CTS) often seeks to increase their knowledge of statistical topics to effectively conduct biomedical research studies. A common method for obtaining this knowledge is through existing online educational materials that are suggested by a biostatistical collaborator or identified by the CTS. However, the volume of available educational materials on diverse statistical topics makes the task of identifying high-quality educational resources at an appropriate level challenging and time consuming for CTSs and collaborative biostatisticians. In response to these challenges, the Biostats4You website was created, where existing online educational materials for a variety of statistical topics are vetted to identify those most appropriate for CTSs. In this manuscript, we describe the resource review process, provide information about statistical topics and resources currently available, and make recommendations for how CTSs and collaborative biostatisticians can utilize the Biostats4You website to improve training, mentoring, and collaborative research practices.

OBJECTIVES/GOALS: Quantitative Staff are an essential workforce for biomedical research. While faculty can engage with peers locally and through national organizations, similar opportunities are limited for staff and often do not meet their unique needs and interests. Creating a professional community is valuable for supporting and developing this workforce. METHODS/STUDY POPULATION: We established the Quantitative Scientific Staff National Network (QS2N2) with the mission to provide professional development and networking opportunities, and to serve as an information resource and advocate through the fostering of community among staff quantitative analysts at any career stage. The initial membership outreach was to all Biostatistics, Epidemiology, and Research Design (BERD) programs through members of ACTS BERD Special Interest Group (SIG). We created a Leadership Team and an Advisory Board consisting of staff and faculty biostatisticians with experience working as or managing staff to govern the network. A Core Planning Committee consisting of 15 members guides planning, implementation, and execution of network activities as operationalized through subcommittees. RESULTS/ANTICIPATED RESULTS: The network currently has 131 members from over 30 health science institutions. Subcommittees focused on Education and Training, Membership, Communication and Web Development, and Mentoring were created and are developing events, programs and infrastructure to further the network’s mission. Network events such as webinars will be offered quarterly; with our first event planned for Nov 3rd. Expansion and maturation of QS2N2 will be done through regular remote meetings where members can connect with peers at other institutions, engage in career development activities, and attend technical seminars. Additional membership outreach will seek to connect with staff in government and private sectors. DISCUSSION/SIGNIFICANCE: Knowledgeable, highly skilled collaborative analysts (e.g., biostatisticians, data scientists) are an essential workforce in clinical and translational science and health research centers.The QS2N2 will support professional development, engagement and growth of this critical workforce which is necessary to advance quality research.

Ongoing professional development is important for collaborative biostatisticians, as it enables them to remain current with the latest advances in statistical methodology and software, refine their analytical skills, and expand their domain knowledge, thereby facilitating their ability to contribute effectively to biomedical research. Although external opportunities for professional development, such as attending conferences and workshops, are widely recognized and valued in the field of biostatistics, there has been comparatively little attention given to internal opportunities for enhancing the skills and knowledge of biostatisticians which can be implemented with lower financial and time investment than external offerings. The purpose of this paper is to offer guidance for ongoing internal professional development activities that can be employed by collaborative biostatistics units in universities and academic medical centers to complement structured curricula and initial training. Specific examples of activities are provided so that collaborative biostatisticians and/or managers of biostatistical units can flexibly combine components to create an appropriately scaled, customized program that meets the needs of themselves or of the unit.

OBJECTIVES/GOALS: The goal was to create and deploy an intuitive, easy-to-use tool that clinical investigators can apply to their data to identify erroneous or inconsistent data entries. Investigators can then correct any errors prior to sharing the data with their statistician for analysis. METHODS/STUDY POPULATION: We developed an interactive shiny app, the Data Loofah, using R Studio that researchers or data analysts can use to examine data. After an investigator uploads data, the app reports which variables are numeric or categorical. Means, standard deviation, median, 25th and 75th quantiles, range and number of missing values are reported for numeric variables. Counts and percentages of categorical variables are summarized. Graphical displays further enhance identification of errors. Access to the Data Loofah is through a secure, university-maintained website with access restricted to university personnel. Supporting materials consisting of instructional step-by-step handouts and videos were developed to assist investigators in the use of the app. RESULTS/ANTICIPATED RESULTS: We will integrate use of the Data Loofah into our Clinical and Translational Science Program’s biostatistics consultative practice. Investigators will use the Data Loofah to pre-screen their data prior to sending it to a statistician, identify errors and inconsistencies, and facilitate making necessary corrections. Statisticians will also use the Data Loofah to review data with investigators prior to starting analyses. Through use of this app, investigators are expected to develop a better understanding of their data specifically and more generally about requirements for preparing data for statistical analysis. Most significantly, regular use of the Data Loofah is expected to result in higher quality data and more efficient use of statistician resources due to reduced effort for data cleaning. DISCUSSION/SIGNIFICANCE: Data cleaning is a time-consuming task and finding data errors can be difficult for data analysts not familiar with clinical variables under study. Further, failure to identify data errors can lead to erroneous results. By facilitating identification of data errors by clinical investigators, the Data Loofah will improve and enhance research output.

An adequate intake of PUFA plays a vital role in human health. Therefore, it is important to assess PUFA intakes in different populations and validate them with biomarkers, but only a few small studies are in paediatric populations. We calculated the dietary intake of PUFA and their main food sources in children and assessed associations between PUFA intakes and plasma proportions. Dietary intakes of 7-year-old children (n 8242) enrolled in the Avon Longitudinal Study of Parents and Children were calculated from the parental-completed FFQ. Plasma PUFA were measured in 5571 children 8 months later, and 4380 children had complete dietary and plasma data. The association between dietary and plasma PUFA proportions was estimated using Spearman’s correlation coefficients, quintile cross-classification and Cohen’s κ coefficients. Mean total PUFA intake was 13·2 g/d (sd 4·2), contributing 6·5 % of total energy intake; n-6 PUFA contributed 5·2 % and n-3 PUFA 0·7 %. The n-6:n-3 ratio was 7·9:1. Mean intakes of EPA and DHA were 35·7 mg/d and 49·7 mg/d, respectively. Most n-3 and n-6 PUFA intakes were weakly correlated with their respective plasma lipids (0·07 ≤ r ≤ 0·16, P < 0·001). The correlation between dietary and plasma DHA was stronger though (r = 0·34, P < 0·001), supported by a modest level of agreement between quintiles (k = 0·32). The results indicate that the FFQ was able to reasonably rank the long-chain (LC) PUFA, DHA, in this paediatric population. Public health initiatives need to address the suboptimal ratio of n-6:n-3 PUFA and very low n-3 LC-PUFA intakes in school-age children in the UK.

This research note describes the Canadian Municipal Elections Database (CMED), a new publicly available and actively maintained dataset of more than 24,000 municipal elections in Canada. We describe the need for high-quality election results data for municipal politics research and describe the content, sources and construction of the CMED. To illustrate the value of the CMED, we estimate gender differences in municipal electoral performance for the first time, finding that women are, on average, more likely than men to win municipal elections in Canada.

OBJECTIVES/GOALS: Morehouse School of Medicine (MSM), TxTM is a scientific philosophy promoting interdisciplinary approaches towards exponential advances in community and population health. Objectives are to detail the model, pilot funding mechanism, early research findings and infrastructure investments. METHODS/STUDY POPULATION: The health research system has widely acknowledged challenges that can delay research translation to systems that advance health for chronically disadvantaged health disparity population groups. MSM’s vision is to lead the creation and advancement of health equity. The vision-aligned strategic plan prioritized formalization of a TX TM implementation priority. The study population was the institution’s research faculty and leaders, research administration, and communication arm. Through a cross-institutional working group, a plan was deployed to 1) assess the institutional landscape, 2) review the grey and peer reviewed literature on translational research and 3) invest in a pilot research funding mechanism. RESULTS/ANTICIPATED RESULTS: Over $700K has been invested in TX TM implementation. Over half of research faculty completed an institutional landscape assessment to identify translational research expertise, interests and points of interest in new collaboration. The most frequently cited collaborative research interests were clinical research with human subjects, patient-centered outcomes and laboratory-based research with human subjects/specimens. Funded multidisciplinary and/or community-engaged pilot studies investigate the role for circadian rhythms and shift work, cultural variables influencing mental health among Haitians living in the US and integrating prescription reconciliation telehealth in primary care. DISCUSSION/SIGNIFICANCE OF IMPACT: TX TM requires interdisciplinary collaboration across translational research spheres and beyond the academy. Institutional investment, infrastructure support and senior-level champions are central to awareness and rewarding such scholarship towards scaling approaches that advance health equity. CONFLICT OF INTEREST DESCRIPTION: Coined at Morehouse School of Medicine (MSM), Tx TM symbolizes an approach and scientific philosophy designed to intentionally promote and support convergence of interdisciplinary approaches and scientists to stimulate exponential advances for the health of diverse communities.

OBJECTIVES/GOALS: Access to biostatistics expertise is essential for a successful clinical and translational research program. However, demand for statistical support at academic research centers can strain the capacity of biostatistics units. Our objective was to efficiently increase access to statistical expertise. METHODS/STUDY POPULATION: In cooperation with the Cancer Center Biostatistics Shared Resource, we replaced an informal 1-hour drop-in consultation program with structured office hours to provide statistical support to clinical and translational researchers at the University of California, Davis Medical Center. We doubled office hours to 2 hours per week and established six 20-minute appointments. Two Ph.D. level statisticians staff office hours. Researchers schedule appointments through Acuity Scheduling, a free on-line resource. Availability of the service is advertised monthly by sending an informational flyer to various university listservs. RESULTS/ANTICIPATED RESULTS: Prior to implementing the program in 2014, we averaged 91 office hour consults per year. Subsequently, consultations jumped to 171 in 2014 and have averaged 150 per year since then. Office hours attract students, residents, staff and faculty from a wide range of disciplines including the Schools of Medicine, Nursing, Veterinary Medicine and basic science departments. Project types span the clinical and translational spectrum covering lab, animal, clinical and population-level studies. Most consults related to data analysis and interpretation (57%) followed by sample size calculations/study design (29%) and response to reviewers (4%), with general statistical advice as the remainder. DISCUSSION/SIGNIFICANCE OF IMPACT: With 6 micro-consults per week, we can meet with many investigators and triage their statistical support needs. This program has proved very popular and was highly rated in a recent user survey, with several investigators noting that the consults facilitated successful publications and proposals.