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Background. Large epidemiological studies evaluating the etiologies, management decisions, and outcomes of adults with meningitis or encephalitis in the United States (US) are lacking. Methods. Adult patients (≥18 years) with meningitis or encephalitis by International Classification of Diseases, Ninth Revision codes available in the Premier Healthcare Database during 2011–2014 were analyzed. Results. A total of 26 429 patients with meningitis or encephalitis were identified. The median age was 43 years; 53% were female. The most common etiology was enterovirus (13 463 [51.6%]), followed by unknown (4944 [21.4%]), bacterial meningitis (3692 [14.1%]), herpes simplex virus (2184 [8.3%]), noninfectious (921 [3.5%]), fungal (720 [2.7%]), arboviruses (291 [1.1%]), and other viruses (214 [0.8%]). Empiric antibiotics, antivirals, and antifungals were administered in 85.8%, 53.4%, and 7.8%, respectively, and varied by etiologies. Adjunctive steroids were utilized in 15.9% of all patients and in 39.3% of patients with pneumococcal meningitis, with an associated decrease in mortality (6.67% vs 12.5%, P = .0245). The median length of stay was 4 days, with the longest duration in those with fungal (13), arboviral (10), and bacterial meningitis (7). Overall inpatient mortality was 2.9% and was higher in those with bacterial (8.2%), fungal (8.2%), or arboviral (8.9%) disease. Overall readmission rate at 30 days was 3.2%; patients with arboviral (12.7%), bacterial (6.7%), and fungal (5.4%) etiologies had higher rates. Conclusions. Viruses are the most common cause of meningitis and encephalitis in the United States and are treated with antibiotic therapy in the majority of cases. Adjunctive steroid treatment is underutilized in pneumococcal meningitis, where it has shown to decrease mortality. [ABSTRACT FROM AUTHOR]

Background: Engaging patients across the research trajectory supports research that is generalizable, high quality, timely and actionable. However, this approach comes with challenges and opportunities as investigators and engaged patient stakeholders encounter institutional policies around patient engagement, privacy and research participant protection.Objective: To describe the resolution and impact of quandaries arising when patient stakeholders' values and preferences conflicted with institutional policies.Methods: Case study from a Patient-Centered Outcomes Research Institute-funded trial.Results: The first example focuses on the tension between the health care organization's requirements for background checks for all patient advisors and the funders' requirement to engage hard-to-reach populations. To create an environment of mutual trust and respect with patient stakeholders, the research team decided against imposing background checks. All 53 patient and parent advisors have served continuously for 2 years and meeting attendance exceeds 95%. The second example describes parent stakeholders' role in revising a letter informing patients about a privacy violation. Among 49 families affected by and informed about this violation, 35 (71%) agreed to participate. The third example focuses on how patient stakeholder preferences about study reminders conflict with the 1996 Health Insurance Portability and Accountability Act rules. While patient stakeholders strongly endorsed text message reminders, regulations and technology do not permit reminders with enough detail to ensure clarity. Although retention rates exceeded 90%, attendance at study appointments was below 75% and below 60% for minority and low socio-economic status families.Conclusion: Patient engagement positively impacts research. Resolving conflicts between patient-engaged research and existing institutional policies and regulations would allow this impact to become fully realized. [ABSTRACT FROM AUTHOR]

Background Bioinformatics software tools are often created ad hoc , frequently by people without extensive training in software development. In particular, for beginners, the barrier to entry in bioinformatics software development is high, especially if they want to adopt good programming practices. Even experienced developers do not always follow best practices. This results in the proliferation of poorer-quality bioinformatics software, leading to limited scalability and inefficient use of resources; lack of reproducibility, usability, adaptability, and interoperability; and erroneous or inaccurate results. Findings We have developed Bionitio, a tool that automates the process of starting new bioinformatics software projects following recommended best practices. With a single command, the user can create a new well-structured project in 1 of 12 programming languages. The resulting software is functional, carrying out a prototypical bioinformatics task, and thus serves as both a working example and a template for building new tools. Key features include command-line argument parsing, error handling, progress logging, defined exit status values, a test suite, a version number, standardized building and packaging, user documentation, code documentation, a standard open source software license, software revision control, and containerization. Conclusions Bionitio serves as a learning aid for beginner-to-intermediate bioinformatics programmers and provides an excellent starting point for new projects. This helps developers adopt good programming practices from the beginning of a project and encourages high-quality tools to be developed more rapidly. This also benefits users because tools are more easily installed and consistent in their usage. Bionitio is released as open source software under the MIT License and is available at https://github.com/bionitio-team/bionitio. [ABSTRACT FROM AUTHOR]