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Objective: Learning Health Systems (LHSs) have not directly informed evidence‐based policymaking. The Translation‐to‐Policy (T2P) Learning Cycle aligns scientists, end‐users, and policymakers to support a repeatable roadmap of innovation and quality improvement to optimize effective policies toward a common public health goal. We describe T2P learning cycle components and provide examples of their application. Methods: The T2P Learning Cycle is based on the U.S. Department of Veterans Affairs (VA) Office of Research and Development and Quality Enhancement Research Initiative (QUERI), which supports research and quality improvement addressing national public health priorities to inform policy and ensure programs are evidence‐based and work for end‐users. Incorporating LHS infrastructure, the T2P Learning Cycle is responsive to the Foundations for Evidence‐based Policymaking Act, which requires U.S. government agencies to justify budgets using evidence. Results: The learning community (patients, providers, clinical/policy leaders, and investigators) drives the T2P Learning Cycle, working toward one or more specific, shared priority goals, and supports a repeatable cycle of evidence‐building and evaluation. Core T2P Learning Cycle functions observed in the examples from housing/economic security, precision oncology, and aging include governance and standard operating procedures to promote effective priority‐setting; complementary research and quality improvement initiatives, which inform ongoing data curation at the learning system level; and sustainment of continuous improvement and evidence‐based policymaking. Conclusions: The T2P Learning Cycle integrates research translation with evidence‐based policymaking, ensuring that scientific innovations address public health priorities and serve end‐users through a repeatable process of research and quality improvement that ensures policies are scientifically based, effective, and sustainable. [ABSTRACT FROM AUTHOR]

The article discusses the Quality Enhancement Research Initiative Rapid Response Teams (RRTs) as a learning health system approach to addressing emerging health system challenges. The RRT process aims to align research and health system priorities by deploying implementation, evaluation, and quality improvement expertise to address national and regional developments. The RRTs work closely with operations partners to complete projects and disseminate findings to enhance the uptake of effective programs and policies. The article highlights the practical and flexible nature of the RRT model and its efforts to bridge the gap between research and clinical practice. [Extracted from the article]

Gliomas account for approximately 80 % of all primary malignant brain tumors and, despite improvements in clinical care over the last 20 years, remain among the most lethal tumors, underscoring the need for gaining new insights that could translate into clinical advances. Recent genome-wide association studies (GWAS) have identified seven new susceptibility regions. We conducted a new independent GWAS of glioma using 1,856 cases and 4,955 controls (from 14 cohort studies, 3 case-control studies, and 1 population-based case-only study) and found evidence of strong replication for three of the seven previously reported associations at 20q13.33 (RTEL), 5p15.33 (TERT), and 9p21.3 (CDKN2BAS), and consistent association signals for the remaining four at 7p11.2 (EGFR both loci), 8q24.21 (CCDC26) and 11q23.3 (PHLDB1). The direction and magnitude of the signal were consistent for samples from cohort and case-control studies, but the strength of the association was more pronounced for loci rs6010620 (20q,13.33; RTEL) and rs2736100 (5p15.33, TERT) in cohort studies despite the smaller number of cases in this group, likely due to relatively more higher grade tumors being captured in the cohort studies. We further examined the 85 most promising single nucleotide polymorphism (SNP) markers identified in our study in three replication sets (5,015 cases and 11,601 controls), but no new markers reached genome-wide significance. Our findings suggest that larger studies focusing on novel approaches as well as specific tumor subtypes or subgroups will be required to identify additional common susceptibility loci for glioma risk.

Several single-nucleotide polymorphisms (SNPs) have been associated with papillary and follicular thyroid cancer (PTC and FTC, respectively) risk, but few have replicated. After analyzing 17 525 tag SNPs in 1129 candidate genes, we found associations with PTC risk in SERPINA5, FTO, HEMGN (near FOXE1) and other genes. Here, we report results from a replication effort in a large independent PTC/FTC case-control study conducted in Germany. We evaluated the best tagging SNPs from our previous PTC study and additionally included SNPs in or near FOXE1 and NKX2-1 genes, known susceptibility loci for thyroid cancer. We genotyped 422 PTC and 130 FTC cases and 752 controls recruited from three German clinical centers. We used polytomous logistic regression to simultaneously estimate PTC and FTC associations for 79 SNPs based on log-additive models. We assessed effect modification by body mass index (BMI), gender and age for all SNPs, and selected SNP by SNP interactions. We confirmed associations with PTC and SNPs in FOXE1/HEMGN, SERPINA5 (rs2069974), FTO (rs8047395), EVPL (rs2071194), TICAM1 (rs8120) and SCARB1 (rs11057820) genes. We found associations with SNPs in FOXE1, SERPINA5, FTO, TICAM1 and HSPA6 and FTC. We found two significant interactions between FTO (rs8047395) and BMI (P = 0.0321) and between TICAM1 (rs8120) and FOXE1 (rs10984377) (P = 0.0006). Besides the known associations with FOXE1 SNPs, we confirmed additional PTC SNP associations reported previously. We also found several new associations with FTC risk and noteworthy interactions. We conclude that multiple variants and host factors might interact in complex ways to increase risk of PTC and FTC. [ABSTRACT FROM AUTHOR]

Background: Statistical training across the continuum of medical education may not have advanced at the pace of statistical reporting in the medical literature, yet a comprehensive understanding of statistical concepts most commonly presented in current research is critical to the effective practice of Evidence Based Medicine. The objective of this content analysis was to describe statistical techniques used in a leading medical journal, JAMA, across a 20-year period, with a focus on implications for medical education. Methods and Findings: Two issues of JAMA published each month in 1990, 2000, and 2010 were randomly selected; from these, 361 articles were reviewed. Primary focus, study design, and statistical components were abstracted and examined by year of publication. The number of published RCTs and cohort studies differed significantly across years of interest, with an increasing trend of publication. The most commonly reported statistics over the 20-year period of interest included measures of morbidity and mortality, descriptive statistics, and epidemiologic outcomes. However, between 1990 and 2010, there was an increase in reporting of more advanced methods, such as multivariable regression, multilevel modeling, survival analysis, and sensitivity analysis. While this study is limited by a focus on one specific journal, a strength is that the journal examined is widely read by a range of clinical specialties and is considered a leading journal in the medical field, setting standards for published research. Conclusions: The increases in frequency and complexity of statistical reporting in the literature over the past two decades may suggest that moving beyond basic statistical concepts to a more comprehensive understanding of statistical methods is an important component of clinicians' ability to effectively read and use the medical research. These findings provide information to consider as medical schools and graduate medical education training programs review and revise their statistical training components. [ABSTRACT FROM AUTHOR]

This article explores how the Quality Enhancement Research Initiative (QUERI) priority-setting process is used to identify and integrate research priorities for chronic pain and opioid use disorder (OUD) within the Veterans Affairs (VA) health system. The process involves engaging leaders, conducting surveys and live polling, and refining priorities through discussions. The final priorities are then incorporated into funding opportunities for research. This approach helps align research efforts with clinical and organizational needs, promotes a common understanding of health system needs, and encourages the integration and alignment of research with health system operations. [Extracted from the article]

This article explores the concept of learning health systems (LHS) and the need for funding to support their development and sustainability. LHSs are defined as systems that align science, informatics, incentives, and culture to continuously improve and integrate knowledge into research and care delivery. The article proposes an updated scientific agenda for funding agencies to support the development of LHSs, which includes methods such as implementation science, data science, engagement science, systems science, and policy analysis. The text also discusses the benefits of these methods for end-users, provides examples of healthcare organizations implementing LHSs, and emphasizes the importance of stakeholder engagement, evidence-based policy development, and addressing health disparities within LHS. [Extracted from the article]

Background: We previously reported evidence of a dose-response relationship between ionising-radiation exposure from paediatric computed tomography (CT) scans and the risk of leukaemia and brain tumours in a large UK cohort. Underlying unreported conditions could have introduced bias into these findings., Methods: We collected and reviewed additional clinical information from radiology information systems (RIS) databases, underlying cause of death and pathology reports. We conducted sensitivity analyses excluding participants with cancer-predisposing conditions or previous unreported cancers and compared the dose-response analyses with our original results., Results: We obtained information from the RIS and death certificates for about 40% of the cohort (n∼180 000) and found cancer-predisposing conditions in 4 out of 74 leukaemia/myelodysplastic syndrome (MDS) cases and 13 out of 135 brain tumour cases. As these conditions were unrelated to CT exposure, exclusion of these participants did not alter the dose-response relationships. We found evidence of previous unreported cancers in 2 leukaemia/MDS cases, 7 brain tumour cases and 232 in non-cases. These previous cancers were related to increased number of CTs. Exclusion of these cancers reduced the excess relative risk per mGy by 15% from 0.036 to 0.033 for leukaemia/MDS (P-trend=0.02) and by 30% from 0.023 to 0.016 (P-trend<0.0001) for brain tumours. When we included pathology reports we had additional clinical information for 90% of the cases. Additional exclusions from these reports further reduced the risk estimates, but this sensitivity analysis may have underestimated risks as reports were only available for cases., Conclusions: Although there was evidence of some bias in our original risk estimates, re-analysis of the cohort with additional clinical data still showed an increased cancer risk after low-dose radiation exposure from CT scans in young patients.