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The supply / demand issue in behavioral health care is a well-established fact, and the mental health toll of the COVID-19 pandemic continues to add challenges to an already taxed system. Existing healthcare models are not set up to adequately address the increasing mental health related needs. As such, innovative models are needed to provide patients with access to appropriate, evidence-based behavioral health care within routine clinical care. This paper introduces Precision Behavioral Health (PBH) as an example of such a model. PBH is an innovative, digital first care delivery model that provides an ecosystem of evidence-based digital mental health interventions to patients as a frontline behavioral health treatment within routine care in a large multispecialty group medical center in the United States. This paper describes the implementation of PBH within a practice research network set-up as part of an integrated behavioral health department. We will present how our team leveraged the RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance; "What is RE-AIM?," n.d.) implementation science framework, which emphasizes the design, dissemination, and implementation processes at the individual, staff, and organizational levels, to prioritize key implementation constructs to enhance the successful integration of PBH within routine care. We describe how each of these constructs were operationalized to aid data gathering for rapid evaluation and lessons learned. We discuss the benefits of these types of initiatives across multiple stakeholders including patients, providers, organizations, payers, and digital intervention vendors. [ABSTRACT FROM AUTHOR]

Influenza viruses undergo rapid evolutionary changes, which requires continuous surveillance to monitor for genetic and potential antigenic changes in circulating viruses that can guide control and prevention decision making. We sequenced and phylogenetically analyzed A(H1N1)pdm09 virus genome sequences obtained from specimens collected from hospitalized patients of all ages with or without pneumonia between 2009 and 2018 from seven sentinel surveillance sites across Kenya. We compared these sequences with recommended vaccine strains during the study period to infer genetic and potential antigenic changes in circulating viruses and associations of clinical outcome. We generated and analyzed a total of 383 A(H1N1)pdm09 virus genome sequences. Phylogenetic analyses of HA protein revealed that multiple genetic groups (clades, subclades, and subgroups) of A(H1N1)pdm09 virus circulated in Kenya over the study period; these evolved away from their vaccine strain, forming clades 7 and 6, subclades 6C, 6B, and 6B.1, and subgroups 6B.1A and 6B.1A1 through acquisition of additional substitutions. Several amino acid substitutions among circulating viruses were associated with continued evolution of the viruses, especially in antigenic epitopes and receptor binding sites (RBS) of circulating viruses. Disease severity declined with an increase in age among children aged < 5 years. Our study highlights the necessity of timely genomic surveillance to monitor the evolutionary changes of influenza viruses. Routine influenza surveillance with broad geographic representation and whole genome sequencing capacity to inform on prioritization of antigenic analysis and the severity of circulating strains are critical to improved selection of influenza strains for inclusion in vaccines. [ABSTRACT FROM AUTHOR]

The great majority of globally circulating pathogens go undetected, undermining patient care and hindering outbreak preparedness and response. To enable routine surveillance and comprehensive diagnostic applications, there is a need for detection technologies that can scale to test many samples1–3 while simultaneously testing for many pathogens4–6. Here, we develop Combinatorial Arrayed Reactions for Multiplexed Evaluation of Nucleic acids (CARMEN), a platform for scalable, multiplexed pathogen detection. In the CARMEN platform, nanolitre droplets containing CRISPR-based nucleic acid detection reagents7 self-organize in a microwell array8 to pair with droplets of amplified samples, testing each sample against each CRISPR RNA (crRNA) in replicate. The combination of CARMEN and Cas13 detection (CARMEN–Cas13) enables robust testing of more than 4,500 crRNA–target pairs on a single array. Using CARMEN–Cas13, we developed a multiplexed assay that simultaneously differentiates all 169 human-associated viruses with at least 10 published genome sequences and rapidly incorporated an additional crRNA to detect the causative agent of the 2020 COVID-19 pandemic. CARMEN–Cas13 further enables comprehensive subtyping of influenza A strains and multiplexed identification of dozens of HIV drug-resistance mutations. The intrinsic multiplexing and throughput capabilities of CARMEN make it practical to scale, as miniaturization decreases reagent cost per test by more than 300-fold. Scalable, highly multiplexed CRISPR-based nucleic acid detection shifts diagnostic and surveillance efforts from targeted testing of high-priority samples to comprehensive testing of large sample sets, greatly benefiting patients and public health9–11. CRISPR-based nucleic acid detection is used in a platform that can simultaneously detect 169 human-associated viruses in multiple samples, providing scalable, multiplexed pathogen detection aimed at routine surveillance for public health. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented recommending how to design, optimize and implement informatics pipelines in light of the guidelines developed by the Next-Generation Sequencing: Standardization of Clinical Testing II (Nex-StoCT II) informatics workgroup.