The Quality Enhancement Research Initiative (QUERI) Impact Framework: Measuring the Real-world Impact of Implementation Science.

BACKGROUND: Measuring the health, economic, and cultural gains generated by scientific investments is crucial to reducing waste and improving quality of care. To date, there is no comprehensive framework for assessing the multi-faceted contributions of implementation and quality improvement sciences towards quality, cost, and patient and provider experiences in health systems. OBJECTIVE: We describe the Quality Enhancement Research Initiative (QUERI) Impact Framework and its application to QUERI investments. METHODS: The QUERI Impact Framework adapts and expands on metrics from the National Academy of Medicine, incorporating lessons learned from QUERI initiatives. The cross-cutting impact metrics reflect QUERI's strategic methodology across five domains of impact (Alignment, Commitment, Tailoring, Informing the field, Observing healthcare changes and generating New questions/projects or ACTION). Key impact metrics, including the number of implementation facilities, number of staff trained, and number of patients served, were derived directly from health system performance plan goals. QUERI applied the Framework by conducting iterative rapid assessments of impacts for QUERI Program centers, which are implementation laboratories that support 3-7 initiatives aligned with a cross-disciplinary goal addressing a national priority. KEY RESULTS: From October 2015 to September 2019, QUERI Programs supported implementation of 49 evidence-based practices and promising innovations across 465 facilities, including 15 facilities that are experiencing quality gaps. As part of these implementation efforts, the programs worked with 71 operations partners to develop 71 tools/toolkits/manuals and support training of 5147 VA staff, serving 250,159 Veterans. CONCLUSIONS: The QUERI Impact Framework aligns multiple stakeholders at different levels of a health system around common metrics, which cross implementation science and quality improvement boundaries. The Framework supports a comprehensive assessment of the short-term and distal impacts of implementation efforts in a health system, allowing both research and operations leadership to understand the value of implementation and quality improvement investments to inform program and policy decisions.

Quality Enhancement Research Initiative Implementation Roadmap: Toward Sustainability of Evidence-based Practices in a Learning Health System.

BACKGROUND: Learning Health Systems strive to continuously integrate innovations and evidence-based practices (EBPs) into routine care settings. Few models provide a specified pathway to accelerate adoption and spread of EBPs across diverse settings. OBJECTIVE: The US Department of Veterans Affairs Quality Enhancement Research Initiative (QUERI) Implementation Roadmap facilitates uptake of EBPs in routine practice by aligning research and health system priorities. METHODS: The Roadmap is based on earlier iterations of the QUERI translational research pipeline, incorporating recent advancements in quality improvement and implementation science. Progressive, dynamic phases were operationalized to form an implementation process that promoted a participatory approach which enables stakeholders (health care consumers, clinicians, administrators, and leaders) to systematically plan, deploy, evaluate, and sustain EBPs using implementation strategies within a Learning Health System framework. RESULTS: The Roadmap consists of Preimplementation, Implementation, and Sustainment phases. Preimplementation identifies a high-priority need, selects EBPs to address the need, engages stakeholders to build implementation capacity, specifies needed EBP adaptions and evaluation goals, and activates leadership support. During Implementation, clinical and research leaders use implementation strategies to promote EBP technical competency and adaptive skills to motivate providers to own and sustain EBPs. Sustainment includes evaluation analyses that establish the EBP business case, and hand-off to system leadership to own EBP implementation maintenance over time. CONCLUSIONS: The QUERI Implementation Roadmap systematically guides identification, implementation, and sustainment of EBPs, demystifying implementation science for stakeholders in a Learning Health System to ensure that EBPs are more rapidly implemented into practice to improve overall consumer health.

Research Lifecycle to Increase the Substantial Real-world Impact of Research: Accelerating Innovations to Application.

BACKGROUND: 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. OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Selected single-nucleotide polymorphisms in FOXE1, SERPINA5, FTO, EVPL, TICAM1 and SCARB1 are associated with papillary and follicular thyroid cancer risk: replication study in a German population.

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 17525 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.

Relationship between paediatric CT scans and subsequent risk of leukaemia and brain tumours: assessment of the impact of underlying conditions.

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.

Adolescent and mid-life diet and subsequent risk of thyroid cancer in the NIH-AARP diet and health study.

Although thyroid cancer is suspected to have a nutritional etiology, prospective studies examining the relationship between diet and thyroid cancer are lacking. During 1996-1997, NIH-AARP Diet and Health Study participants, ages 51-72 years, completed a 37-item food frequency questionnaire about diet at ages 12-13 years (adolescence) and 10 years before baseline (mid-life). Over a median 10 years of follow-up, 325 individuals (143 men and 182 women) were diagnosed with thyroid cancer. Multivariable-adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated for intakes of foods and food groups comparing the highest to the lowest quartiles. Adolescent intakes of chicken/turkey (HR = 1.59, 95% CI: 0.97-2.60; ptrend < 0.01) and sweet baked goods (HR = 1.59, 95% CI: 1.09-2.34; ptrend = 0.04) were positively associated with thyroid cancer risk, while intake of butter/margarine was inversely associated with risk (HR = 0.64, 95% CI: 0.44-0.91; ptrend < 0.02). Similar to adolescent diet, mid-life intake of sweet baked goods was nonsignificantly associated with an increased risk of thyroid cancer (HR = 1.39, 95% CI: 0.96-2.00; ptrend = 0.11), but intake of butter/margarine was inversely associated with risk (HR = 0.66, 95% CI: 0.46-0.95; ptrend = 0.03). Among men, higher adolescent consumption of canned tuna was positively associated with risk of thyroid cancer (HR = 1.69, 95% CI: 1.01-2.83; ptrend = 0.03), and greater mid-life intake of broccoli was associated with a twofold increased risk (HR = 2.13, 95% CI: 1.13-3.99; ptrend < 0.01). This large prospective study suggests that several components of the adolescent and mid-life diet, including iodine-rich foods and goitrogens, may influence thyroid cancer risk.

The translation-to-policy learning cycle to improve public health.

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.

Genome-wide association study of glioma and meta-analysis.

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.

Aligning quality improvement efforts and policy goals in a national integrated health system.

OBJECTIVE: To describe the design and impact of a systematic, enterprise-wide process for engaging US Department of Veterans Affairs (VA) leadership in prioritizing scarce implementation and evaluation resources. DATA SOURCES: From 2017 to 2021, the VA Quality Enhancement Research Initiative (QUERI) identified priorities from local, regional, and national leaders through qualitative discussions and a national survey and tracked impacts via reports generated from competitively funded initiatives addressing these priorities. STUDY DESIGN: Guided by the Learning Health System framework and QUERI Implementation Roadmap, QUERI engaged stakeholders to nominate and rank-order priorities, peer-reviewed and funded initiatives to scale up and spread evidence-based practices (EBPs) using theory-based implementation strategies, and evaluated the impact of these initiatives using the QUERI Impact Framework. DATA COLLECTION/EXTRACTION METHODS: QUERI collected priority nominations through qualitative discussions and a web-based survey, and live voting was used to rank-order priorities. QUERI-funded teams regularly submitted progress reports describing the key activities, findings, and impacts of the quality improvement (QI) initiatives using a standardized form created in the VA Research Electronic Data Capture (REDCap). PRINCIPAL FINDINGS: QUERI launched five QI initiatives to address priorities selected by VA leadership. In partnership with 28 health system leaders, these initiatives are implementing 10 EBPs across 53 sites, supporting 1055 VA employees in delivering evidence-based care. The success of these initiatives led to an expansion of QUERI's process to address 2021 VA leadership priorities: virtual care, health disparities, delayed or suppressed care due to COVID-19, employee burnout, long-term and home care options, and quality and cost of community care. CONCLUSIONS: QUERI, a unique program embedded in a national integrated health system, deployed a novel approach to inform policy making and enhance the real-world impact of research through prioritization of limited resources, rigorous peer-review, and assessment of impacts on the health system, employees, and Veterans.

Benign breast and gynecologic conditions, reproductive and hormonal factors, and risk of thyroid cancer.

The higher incidence of thyroid cancer in women compared with men suggests an influence of sex steroid hormones in the etiology of this malignancy. We investigated a comprehensive set of potential indicators of lifetime sex steroid hormone exposure in relation to thyroid cancer risk. Using data from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial, which enrolled 70,047 women, 50 to 78 years old, we prospectively examined associations of self-reported history of benign breast and gynecologic conditions, reproductive factors, and exogenous sex hormone use with thyroid cancer risk. Multivariable-adjusted HRs and 95% confidence intervals (CI) were calculated in models using age as the time metric. During follow-up (median, 11 years), 127 women were diagnosed with first primary thyroid cancer. Older age at natural menopause (≥55 vs. <50 years; HR, 2.24; 95% CI, 1.20-4.18), greater estimated lifetime number of ovulatory cycles (≥490 vs. <415 cycles; HR, 2.40; 95% CI, 1.33-4.30), greater number of live births (≥5 vs. 1-2; HR, 1.72; 95% CI, 1.05-2.82), and history of uterine fibroids (HR, 1.72; 95% CI, 1.18-2.50) were associated with an increased risk of thyroid cancer. Earlier age at menarche, greater number of reproductive years, history of a tubal ligation, and history of ovarian cysts were nonsignificantly associated with increased thyroid cancer risk. No associations were observed for oral contraceptive use, menopausal hormone therapy, or history of benign breast disease or endometriosis. In general, we found that factors reflecting a greater length of exposure to endogenous hormones, particularly during the reproductive years, were associated with risk of postmenopausal thyroid cancer.

Personal history of diabetes, genetic susceptibility to diabetes, and risk of brain glioma: a pooled analysis of observational studies.

BACKGROUND: Brain glioma is a relatively rare and fatal malignancy in adulthood with few known risk factors. Some observational studies have reported inverse associations between diabetes and subsequent glioma risk, but possible mechanisms are unclear. METHODS: We conducted a pooled analysis of original data from five nested case-control studies and two case-control studies from the United States and China that included 962 glioma cases and 2,195 controls. We examined self-reported diabetes history in relation to glioma risk, as well as effect modification by seven glioma risk-associated single-nucleotide polymorphisms (SNP). We also examined the associations between 13 diabetes risk-associated SNPs, identified from genome-wide association studies, and glioma risk. Odds ratios (OR) and 95% confidence intervals (CI) were calculated using multivariable-adjusted logistic regression models. RESULTS: We observed a 42% reduced risk of glioma for individuals with a history of diabetes (OR = 0.58; 95% CI, 0.40-0.84). The association did not differ by sex, study design, or after restricting to glioblastoma, the most common histological subtype. We did not observe any significant per-allele trends among the 13 diabetes-related SNPs examined in relation to glioma risk. CONCLUSION: These results support an inverse association between diabetes history and glioma risk. The role of genetic susceptibility to diabetes cannot be excluded, and should be pursued in future studies together with other factors that might be responsible for the diabetes-glioma association. IMPACT: These data suggest the need for studies that can evaluate, separately, the association between type 1 and type 2 diabetes and subsequent risk of adult glioma.

Statistical trends in the Journal of the American Medical Association and implications for training across the continuum of medical education.

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.

Ionizing radiation and the risk of brain and central nervous system tumors: a systematic review.

Although exposure to moderate-to-high doses of ionizing radiation is the only established environmental risk factor for brain and CNS tumors, it is not clear whether this relationship differs across tumor subtypes, by sex or age at exposure, or at the low-to-moderate range of exposure. This systematic review summarizes the epidemiologic evidence on the association between ionizing radiation exposure and risk of brain/CNS tumors. Articles included in this review estimated radiation exposure doses to the brain and reported excess relative risk (ERR) estimates for brain/CNS tumors. Eight cohorts were eligible for inclusion in the analysis. Average age at exposure ranged from 8 months to 26 years. Mean dose to the brain ranged from 0.07 to 10 Gy. Elevated risks for brain/CNS tumors were consistently observed in relation to ionizing radiation exposure, but the strength of this association varied across cohorts. Generally, ionizing radiation was more strongly associated with risk for meningioma compared with glioma. The positive association between ionizing radiation exposure and risk for glioma was stronger for younger vs older ages at exposure. We did not observe an effect modification on the risk for meningioma by sex, age at exposure, time since exposure, or attained age. The etiologic role of ionizing radiation in the development of brain/CNS tumors needs to be clarified further through additional studies that quantify the association between ionizing radiation and risk for brain/CNS tumors at low-to-moderate doses, examine risks across tumor subtypes, and account for potential effect modifiers.

Association between adult height, genetic susceptibility and risk of glioma.

BACKGROUND: Some, but not all, observational studies have suggested that taller stature is associated with a significant increased risk of glioma. In a pooled analysis of observational studies, we investigated the strength and consistency of this association, overall and for major sub-types, and investigated effect modification by genetic susceptibility to the disease. METHODS: We standardized and combined individual-level data on 1354 cases and 4734 control subjects from 13 prospective and 2 case-control studies. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) for glioma and glioma sub-types were estimated using logistic regression models stratified by sex and adjusted for birth cohort and study. Pooled ORs were additionally estimated after stratifying the models according to seven recently identified glioma-related genetic variants. RESULTS: Among men, we found a positive association between height and glioma risk (≥ 190 vs 170-174 cm, pooled OR = 1.70, 95% CI: 1.11-2.61; P-trend = 0.01), which was slightly stronger after restricting to cases with glioblastoma (pooled OR = 1.99, 95% CI: 1.17-3.38; P-trend = 0.02). Among women, these associations were less clear (≥ 175 vs 160-164 cm, pooled OR for glioma = 1.06, 95% CI: 0.70-1.62; P-trend = 0.22; pooled OR for glioblastoma = 1.36, 95% CI: 0.77-2.39; P-trend = 0.04). In general, we did not observe evidence of effect modification by glioma-related genotypes on the association between height and glioma risk. CONCLUSION: An association of taller adult stature with glioma, particularly for men and stronger for glioblastoma, should be investigated further to clarify the role of environmental and genetic determinants of height in the etiology of this disease.