During 2012, global detection of a new norovirus (NoV) strain, GII.4 Sydney, raised concerns about its potential effect in the United States. We analyzed data from NoV outbreaks in 5 states and ...emergency department visits for gastrointestinal illness in 1 state during the 2012-13 season and compared the data with those of previous seasons. During August 2012-April 2013, a total of 637 NoV outbreaks were reported compared with 536 and 432 in 2011-2012 and 2010-2011 during the same period. The proportion of outbreaks attributed to GII.4 Sydney increased from 8% in September 2012 to 82% in March 2013. The increase in emergency department visits for gastrointestinal illness during the 2012-13 season was similar to that of previous seasons. GII.4 Sydney has become the predominant US NoV outbreak strain during the 2012-13 season, but its emergence did not cause outbreak activity to substantially increase from that of previous seasons.
US health care systems face a growing demand to incorporate innovations that improve patient outcomes at a lower cost. Funding agencies increasingly must demonstrate the impact of research ...investments on public health. The Learning Health System promotes continuous institutional innovation, yet specific processes to develop innovations for further research and implementation into real-world health care settings to maximize health impacts have not been specified.
We describe the Research Lifecycle and how it leverages institutional priorities to support the translation of research discoveries to clinical application, serving as a broader operational approach to enhance the Learning Health System.
Developed by the US Department of Veterans Affairs Office of Research and Development Research-to-Real-World Workgroup, the Research Lifecycle incorporates frameworks from product development, translational science, and implementation science methods. The Lifecycle is based on Workgroup recommendations to overcome barriers to more direct translation of innovations to clinical application and support practice implementation and sustainability.
The Research Lifecycle posits 5 phases which support a seamless pathway from discovery to implementation: prioritization (leadership priority alignment), discovery (innovation development), validation (clinical, operational feasibility), scale-up and spread (implementation strategies, performance monitoring), and sustainability (business case, workforce training). An example of how the Research Lifecycle has been applied within a health system is provided.
The Research Lifecycle aligns research and health system investments to maximize real-world practice impact via a feasible pathway, where priority-driven innovations are adapted for effective clinical use and supported through implementation strategies, leading to continuous improvement in real-world health care.
The Veterans Health Administration (VHA) Diffusion of Excellence (DoE) program developed and manages a framework for identification, replication, and diffusion of promising practices throughout the ...nation's largest integrated health care system. DoE identifies promising practices through a "Shark Tank" competition with winning bidders receiving external implementation facilitation. DoE further supports diffusion of successful practices across the VHA.
This article presents results of a mixed methods implementation evaluation of DoE, focusing on program reach, program participation and decisions to adopt innovative practices, implementation processes, and practice sustainment. Data sources include practice adoption metrics, focus groups with bidders (two focus groups), observations of DoE events (seven events), surveys of stakeholders (five separate surveys), and semistructured interviews of facility directors, practice developers, implementation teams, and facilitators (133 participants).
In the first four Shark Tank cohorts (2016-2018), 1,676 practices were submitted; 47 were designated Gold Status Practices (practices with facilitated implementation). Motivation for participation varied. Generally, staff led projects targeting problems they felt passionate about, facility directors focused on big-picture quality metrics and getting middle manager support, and frontline staff displayed variable motivation to implement new projects. Approximately half of facilitated implementation efforts were successful; barriers included insufficient infrastructure, staff, and resources. At the facility level, 73.3% of facilities originating or receiving facilitated implementation support have maintained the practice. VHA-wide, 834 decisions to adopt these practices were made.
DoE has resulted in the identification of many candidate practices, promoted adoption of promising practices by facility directors, and supported practice implementation and diffusion across the VHA.
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•Acute radiation-induced toxicities (RITS) are heritable phenotypes and ∼29% of their variances are explained by common genetic variants.•Three potential novel mechanisms for RITs are ...exoribonuclease activity, inositol phosphate-mediated signaling, and drug catabolic process.•Using genetic markers is effective to understand the mechanism of acute RITs, and to predict acute RITs by employing polygenic risk scores.•Genetically enriched prediction model for RITs followed by radiation-dose adjustment contributes to attaining personalized radiotherapy.
We aimed to the genetic components and susceptibility variants associated with acute radiation-induced toxicities (RITs) in patients with head and neck cancer (HNC).
We performed the largest meta-GWAS of seven European cohorts (n = 4,042). Patients were scored weekly during radiotherapy for acute RITs including dysphagia, mucositis, and xerostomia. We analyzed the effect of variants on the average burden (measured as area under curve, AUC) per each RIT, and standardized total average acute toxicity (STATacute) score using a multivariate linear regression. We tested suggestive variants (p < 1.0x10-5) in discovery set (three cohorts; n = 2,640) in a replication set (four cohorts; n = 1,402). We meta-analysed all cohorts to calculate RITs specific SNP-based heritability, and effect of polygenic risk scores (PRSs), and genetic correlations among RITS.
From 393 suggestive SNPs identified in discovery set; 37 were nominally significant (preplication < 0.05) in replication set, but none reached genome-wide significance (pcombined < 5 × 10-8). In-silico functional analyses identified “3′-5'-exoribonuclease activity” (FDR = 1.6e-10) for dysphagia, “inositol phosphate-mediated signalling” for mucositis (FDR = 2.20e-09), and “drug catabolic process” for STATacute (FDR = 3.57e-12) as the most enriched pathways by the RIT specific suggestive genes. The SNP-based heritability (±standard error) was 29 ± 0.08 % for dysphagia, 9 ± 0.12 % (mucositis) and 27 ± 0.09 % (STATacute). Positive genetic correlation was rg = 0.65 (p = 0.048) between dysphagia and STATacute. PRSs explained limited variation of dysphagia (3 %), mucositis (2.5 %), and STATacute (0.4 %).
In HNC patients, acute RITs are modestly heritable, sharing 10 % genetic susceptibility, when PRS explains < 3 % of their variance. We identified numerus suggestive SNPs, which remain to be replicated in larger studies.
Understanding how the electronic structures of electron donor−bridge−acceptor (D−B−A) molecules influence the lifetimes of radical ion pairs (RPs) photogenerated within them (D+•−B−A-•) is critical ...to designing and developing molecular systems for solar energy conversion. A general question that often arises is whether the HOMOs or LUMOs of D, B, and A within D+•−B−A-• are primarily involved in charge recombination. We have developed a new series of D−B−A molecules consisting of a 3,5-dimethyl-4-(9-anthracenyl)julolidine (DMJ-An) electron donor linked to a naphthalene-1,8:4,5-bis(dicarboximide) (NI) acceptor via a series of Ph n oligomers, where n = 1−4, to give DMJ-An-Ph n -NI. The photoexcited charge transfer state of DMJ-An acts as a high-potential photoreductant to rapidly and nearly quantitatively transfer an electron across the Ph n bridge to produce a spin-coherent singlet RP 1(DMJ+•-An-Ph n -NI-•). Subsequent radical pair intersystem crossing yields 3(DMJ+•-An-Ph n -NI-•). Charge recombination within the triplet RP then gives the neutral triplet state. Time-resolved EPR spectroscopy shows directly that charge recombination of the RP initially produces a spin-polarized triplet state, DMJ-An-Ph n -3*NI, that can only be produced by hole transfer involving the HOMOs of D, B, and A within the D−B−A system. After the initial formation of DMJ-An-Ph n -3*NI, triplet−triplet energy transfer occurs to produce DMJ-3*An-Ph n -NI with rate constants that show a distance dependence consistent with those determined for charge separation and recombination.
A t-butylphenylnitroxide (BPNO•) stable radical is attached to an electron donor−bridge−acceptor (D−B−A) system having well-defined distances between the components: MeOAn−6ANI−Ph(BPNO•)−NI, where ...MeOAn = p-methoxyaniline, 6ANI = 4-(N-piperidinyl)naphthalene-1,8-dicarboximide, Ph = phenyl, and NI = naphthalene-1,8:4,5-bis(dicarboximide). MeOAn−6ANI, BPNO•, and NI are attached to the 1, 3, and 5 positions of the Ph bridge, respectively. Time-resolved optical and EPR spectroscopy show that BPNO• influences the spin dynamics of the photogenerated triradical states 2,4(MeOAn+•−6ANI−Ph(BPNO•)−NI-•), resulting in slower charge recombination within the triradical, as compared to the corresponding biradical lacking BPNO•. The observed spin−spin exchange interaction between the photogenerated radicals MeOAn+• and NI-• is not altered by the presence of BPNO•. However, the increased spin density on the bridge greatly increases radical pair (RP) intersystem crossing from the photogenerated singlet RP to the triplet RP. Rapid formation of the triplet RP makes it possible to observe a biexponential decay of the total RP population with components of τ = 740 ps (0.75) and 104 ns (0.25). Kinetic modeling shows that the faster decay rate is due to rapid establishment of an equilibrium between the triplet RP and the neutral triplet state resulting from charge recombination, whereas the slower rate monitors recombination of the singlet RP to ground state.
The extent to which physical and social attributes of neighborhoods play a role in childhood asthma remains understudied.
To examine associations of neighborhood-level opportunity and social ...vulnerability measures with childhood asthma incidence.
This cohort study used data from children in 46 cohorts participating in the Environmental Influences on Child Health Outcomes (ECHO) Program between January 1, 1995, and August 31, 2022. Participant inclusion required at least 1 geocoded residential address from birth and parent or caregiver report of a physician's diagnosis of asthma. Participants were followed up to the date of asthma diagnosis, date of last visit or loss to follow-up, or age 20 years.
Census tract-level Child Opportunity Index (COI) and Social Vulnerability Index (SVI) at birth, infancy, or early childhood, grouped into very low (<20th percentile), low (20th to <40th percentile), moderate (40th to <60th percentile), high (60th to <80th percentile), or very high (≥80th percentile) COI or SVI.
The main outcome was parent or caregiver report of a physician's diagnosis of childhood asthma (yes or no). Poisson regression models estimated asthma incidence rate ratios (IRRs) associated with COI and SVI scores at each life stage.
The study included 10 516 children (median age at follow-up, 9.1 years IQR, 7.0-11.6 years; 52.2% male), of whom 20.6% lived in neighborhoods with very high COI and very low SVI. The overall asthma incidence rate was 23.3 cases per 1000 child-years (median age at asthma diagnosis, 6.6 years IQR, 4.1-9.9 years). High and very high (vs very low) COI at birth, infancy, or early childhood were associated with lower subsequent asthma incidence independent of sociodemographic characteristics, parental asthma history, and parity. For example, compared with very low COI, the adjusted IRR for asthma was 0.87 (95% CI, 0.75-1.00) for high COI at birth and 0.83 (95% CI, 0.71-0.98) for very high COI at birth. These associations appeared to be attributable to the health and environmental and the social and economic domains of the COI. The SVI during early life was not significantly associated with asthma incidence. For example, compared with a very high SVI, the adjusted IRR for asthma was 0.88 (95% CI, 0.75-1.02) for low SVI at birth and 0.89 (95% CI, 0.76-1.03) for very low SVI at birth.
In this cohort study, high and very high neighborhood opportunity during early life compared with very low neighborhood opportunity were associated with lower childhood asthma incidence. These findings suggest the need for future studies examining whether investing in health and environmental or social and economic resources in early life would promote health equity in pediatric asthma.
Objectives
The U.S. Department of Veterans Affairs (VA) has been a national leader in Learning Health System (LHS) implementation due to its combined mission of research, education, clinical care, ...and emergency preparedness. We describe the current VA LHS training ecosystem within the Veterans Health Administration's Office of Academic Affiliations (OAA), Office of Research and Development (ORD), ORD's Health Services Research and Development (HSR&D) program, and Innovation Ecosystem (IE), including lessons learned regarding their sustainment.
Methods
The VA LHS training ecosystem is based on the Learning Loop and HSR&D Quality Enhancement Research Initiative (QUERI) Roadmap, which describes VA learning opportunities, underlying infrastructures, and core competencies.
Results
VA‐focused LHS educational programs include data‐to‐knowledge initiatives in health sciences and analytics, for example, OAA/HSR&D health services and informatics research fellowships; knowledge‐to‐performance opportunities in implementation and quality improvement, for example, QUERI Learning Hubs and IEs' Diffusion of Excellence Initiative; and performance‐to‐data embedded opportunities, for example, IE's entrepreneur fellowship programs and QUERI's Advancing Diversity in Implementation Leadership. These training programs are supported by combined VA research and clinical operations investments in funding, informatics, governance, and processes. Lessons learned include ongoing alignment of research funding with operational priorities and capacity, relentless recruitment and retention of implementation, system, and information scientists especially from under‐represented groups, sustainment of data infrastructures suitable for research and quality improvement, and ensuring sustainable funding opportunities for researchers to work on system‐wide health care problems.
Conclusions
There is an urgent need to expand training opportunities in LHSs, especially as health care is increasingly driven by multiple interested parties, impacted by persistent health disparities exacerbated by emerging public health threats, and rapid technology growth. With ongoing alignment of research and clinical goals, foundational support through research funding, underlying clinical operations infrastructures, and active engagement interested parties, VA's LHS training ecosystem promotes a more LHS‐savvy, 21st century workforce.
Physical and social neighborhood attributes may have implications for children's growth and development patterns. The extent to which these attributes are associated with body mass index (BMI) ...trajectories and obesity risk from childhood to adolescence remains understudied.
To examine associations of neighborhood-level measures of opportunity and social vulnerability with trajectories of BMI and obesity risk from birth to adolescence.
This cohort study used data from 54 cohorts (20 677 children) participating in the Environmental Influences on Child Health Outcomes (ECHO) program from January 1, 1995, to January 1, 2022. Participant inclusion required at least 1 geocoded residential address and anthropometric measure (taken at the same time or after the address date) from birth through adolescence. Data were analyzed from February 1 to June 30, 2022.
Census tract-level Child Opportunity Index (COI) and Social Vulnerability Index (SVI) linked to geocoded residential addresses at birth and in infancy (age range, 0.5-1.5 years), early childhood (age range, 2.0-4.8 years), and mid-childhood (age range, 5.0-9.8 years).
BMI (calculated as weight in kilograms divided by length if aged <2 years or height in meters squared) and obesity (age- and sex-specific BMI ≥95th percentile). Based on nationwide distributions of the COI and SVI, Census tract rankings were grouped into 5 categories: very low (<20th percentile), low (20th percentile to <40th percentile), moderate (40th percentile to <60th percentile), high (60th percentile to <80th percentile), or very high (≥80th percentile) opportunity (COI) or vulnerability (SVI).
Among 20 677 children, 10 747 (52.0%) were male; 12 463 of 20 105 (62.0%) were White, and 16 036 of 20 333 (78.9%) were non-Hispanic. (Some data for race and ethnicity were missing.) Overall, 29.9% of children in the ECHO program resided in areas with the most advantageous characteristics. For example, at birth, 26.7% of children lived in areas with very high COI, and 25.3% lived in areas with very low SVI; in mid-childhood, 30.6% lived in areas with very high COI and 28.4% lived in areas with very low SVI. Linear mixed-effects models revealed that at every life stage, children who resided in areas with higher COI (vs very low COI) had lower mean BMI trajectories and lower risk of obesity from childhood to adolescence, independent of family sociodemographic and prenatal characteristics. For example, among children with obesity at age 10 years, the risk ratio was 0.21 (95% CI, 0.12-0.34) for very high COI at birth, 0.31 (95% CI, 0.20-0.51) for high COI at birth, 0.46 (95% CI, 0.28-0.74) for moderate COI at birth, and 0.53 (95% CI, 0.32-0.86) for low COI at birth. Similar patterns of findings were observed for children who resided in areas with lower SVI (vs very high SVI). For example, among children with obesity at age 10 years, the risk ratio was 0.17 (95% CI, 0.10-0.30) for very low SVI at birth, 0.20 (95% CI, 0.11-0.35) for low SVI at birth, 0.42 (95% CI, 0.24-0.75) for moderate SVI at birth, and 0.43 (95% CI, 0.24-0.76) for high SVI at birth. For both indices, effect estimates for mean BMI difference and obesity risk were larger at an older age of outcome measurement. In addition, exposure to COI or SVI at birth was associated with the most substantial difference in subsequent mean BMI and risk of obesity compared with exposure at later life stages.
In this cohort study, residing in higher-opportunity and lower-vulnerability neighborhoods in early life, especially at birth, was associated with a lower mean BMI trajectory and a lower risk of obesity from childhood to adolescence. Future research should clarify whether initiatives or policies that alter specific components of neighborhood environment would be beneficial in preventing excess weight in children.
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