Genome-wide association of multiple complex traits in outbred mice by ultra low-coverage sequencing

The authors wish to acknowledge excellent technical assistance from A. Kurioka, L. Swadling, C. de Lara, J. Ussher, R. Townsend, S. Lionikaite, A.S. Lionikiene, R. Wolswinkel and I. van der Made. We would like to thank T.M. Keane and A.G. Doran for their help in annotating variants and adding the FVB/NJ strain to the MGP. We thank the High-Throughput Genomics Group at the Wellcome Trust Centre for Human Genetics and the Wellcome Trust Sanger Institute for the generation of the sequencing data. This work was funded by Wellcome Trust grant 090532/Z/09/Z (J.F.). Primary phenotyping of the mice was supported by the Mary Lyon Centre and Mammalian Genetics Unit (Medical Research Council, UK Hub grant G0900747 91070 and Medical Research Council, UK grant MC U142684172). D.A.B. acknowledges support from NIH R01AR056280. The sleep work was supported by the state of Vaud (Switzerland) and the Swiss National Science Foundation (SNF 14694 and 136201 to P.F.). The ECG work was supported by the Netherlands CardioVascular Research Initiative (Dutch Heart Foundation, Dutch Federation of University Medical Centres, Netherlands Organization for Health Research and Development and the Royal Netherlands Academy of Sciences) PREDICT project, InterUniversity Cardiology Institute of the Netherlands (ICIN; 061.02; C.A.R. and C.R.B.). N.C. is supported by the Agency of Science, Technology and Research (A*STAR) Graduate Academy. R.W.D. is supported by a grant from the Wellcome Trust (097308/Z/11/Z).Peer reviewedPostprin

Two Models for Improving Colorectal Cancer Screening Rates in Health Plan Populations

Background: Screening decreases colorectal cancer (CRC) incidence and mortality by 30%–60%; however, CRC screening rates remain low among minorities and low-income individuals. No available data shows the effectiveness of a direct-mail program initiated by health insurance plans that serve these populations. The BeneFIT study supports two health plans implementing a program that mails fecal immunochemical tests (FIT) to patients’ homes. Methods: We present the implementation models and decision factors about participating in BeneFIT. BeneFIT involves two health plans: one in a single state with ~250,000 enrollees, another in multiple states with several million enrollees. These health plans are using two distinct models to implement BeneFIT. Results: One health plan is using a collaborative model. A vendor centrally mails the FIT kits and reminder letters; completed FITs are returned to the clinic, where labs are ordered. This model reduces staff burden while still enabling clinics to use their standard lab, follow-up and referral processes. Early implementation challenges have been logistical issues for smaller clinics, the need for lab vendors to provide free kits (claims pay for processing of completed FITs), and data issues with patient-clinic assignment lists. The other health plan is using a centralized model. A vendor orders and mails the FITs and conducts reminder calls; a central lab receives completed FITs and sends results to the vendor, which notifies the patient-assigned clinic. The plan uses its care coordinators to follow-up positive FITs. The model has economics of scale for administration and plan-based follow-up of FIT results. Challenges to implementation have been incomplete prior CRC screening data and possible redundancy of screening. Baseline qualitative interviews with the health plans identified motivations to participate including increasing patient education, the possibility to improve screening rates and health outcomes, and the opportunity to translate a promising approach to an underserved population and formally evaluate the results. Factors that could affect future health plan decisions to maintain the direct mail approach include return rates, staff and resource requirements, and provider/patient satisfaction with the BeneFIT program. Conclusion: Weighing the successes and challenges in these two plans will help decision makers choose between outreach strategies for CRC screening

Two Models for Improving Colorectal Cancer Screening Rates in Health Plan Populations

Background: Screening decreases colorectal cancer (CRC) incidence and mortality by 30%–60%; however, CRC screening rates remain low among minorities and low-income individuals. No available data shows the effectiveness of a direct-mail program initiated by health insurance plans that serve these populations. The BeneFIT study supports two health plans implementing a program that mails fecal immunochemical tests (FIT) to patients’ homes. Methods: We present the implementation models and decision factors about participating in BeneFIT. BeneFIT involves two health plans: one in a single state with ~250,000 enrollees, another in multiple states with several million enrollees. These health plans are using two distinct models to implement BeneFIT. Results: One health plan is using a collaborative model. A vendor centrally mails the FIT kits and reminder letters; completed FITs are returned to the clinic, where labs are ordered. This model reduces staff burden while still enabling clinics to use their standard lab, follow-up and referral processes. Early implementation challenges have been logistical issues for smaller clinics, the need for lab vendors to provide free kits (claims pay for processing of completed FITs), and data issues with patient-clinic assignment lists. The other health plan is using a centralized model. A vendor orders and mails the FITs and conducts reminder calls; a central lab receives completed FITs and sends results to the vendor, which notifies the patient-assigned clinic. The plan uses its care coordinators to follow-up positive FITs. The model has economics of scale for administration and plan-based follow-up of FIT results. Challenges to implementation have been incomplete prior CRC screening data and possible redundancy of screening. Baseline qualitative interviews with the health plans identified motivations to participate including increasing patient education, the possibility to improve screening rates and health outcomes, and the opportunity to translate a promising approach to an underserved population and formally evaluate the results. Factors that could affect future health plan decisions to maintain the direct mail approach include return rates, staff and resource requirements, and provider/patient satisfaction with the BeneFIT program. Conclusion: Weighing the successes and challenges in these two plans will help decision makers choose between outreach strategies for CRC screening

Applying the Plan-Do-Study-Act (PDSA) approach to a large pragmatic study involving safety net clinics

Abstract Background The Plan-Do-Study-Act (PDSA) cycle is a commonly used improvement process in health care settings, although its documented use in pragmatic clinical research is rare. A recent pragmatic clinical research study, called the Strategies and Opportunities to STOP Colon Cancer in Priority Populations (STOP CRC), used this process to optimize the research implementation of an automated colon cancer screening outreach program in intervention clinics. We describe the process of using this PDSA approach, the selection of PDSA topics by clinic leaders, and project leaders’ reactions to using PDSA in pragmatic research. Methods STOP CRC is a cluster-randomized pragmatic study that aims to test the effectiveness of a direct-mail fecal immunochemical testing (FIT) program involving eight Federally Qualified Health Centers in Oregon and California. We and a practice improvement specialist trained in the PDSA process delivered structured presentations to leaders of these centers; the presentations addressed how to apply the PDSA process to improve implementation of a mailed outreach program offering colorectal cancer screening through FIT tests. Center leaders submitted PDSA plans and delivered reports via webinar at quarterly meetings of the project’s advisory board. Project staff conducted one-on-one, 45-min interviews with project leads from each health center to assess the reaction to and value of the PDSA process in supporting the implementation of STOP CRC. Results Clinic-selected PDSA activities included refining the intervention staffing model, improving outreach materials, and changing workflow steps. Common benefits of using PDSA cycles in pragmatic research were that it provided a structure for staff to focus on improving the program and it allowed staff to test the change they wanted to see. A commonly reported challenge was measuring the success of the PDSA process with the available electronic medical record tools. Conclusion Understanding how the PDSA process can be applied to pragmatic trials and the reaction of clinic staff to their use may help clinics integrate evidence-based interventions into their everyday care processes. Trial registration Clinicaltrials.gov NCT01742065 . Registered October 31, 2013

Methods for scaling up an outreach intervention to increase colorectal cancer screening rates in rural areas

Abstract Background Mailed fecal immunochemical test (FIT) outreach and patient navigation are evidence-based practices shown to improve rates of colorectal cancer (CRC) and follow-up in various settings, yet these programs have not been broadly adopted by health systems and organizations that serve diverse populations. Reasons for low adoption rates are multifactorial, and little research explores approaches for scaling up a complex, multi-level CRC screening outreach intervention to advance equity in rural settings. Methods SMARTER CRC, a National Cancer Institute Cancer Moonshot project, is a cluster-randomized controlled trial of a mailed FIT and patient navigation program involving 3 Medicaid health plans and 28 rural primary care practices in Oregon and Idaho followed by a national scale-up trial. The SMARTER CRC intervention combines mailed FIT outreach supported by clinics, health plans, and vendors and patient navigation for colonoscopy following an abnormal FIT result. We applied the framework from Perez and colleagues to identify the intervention’s components (including functions and forms) and scale-up dissemination strategies and worked with a national advisory board to support scale-up to additional organizations. The team is recruiting health plans, primary care clinics, and regional and national organizations in the USA that serve a rural population. To teach organizations about the intervention, activities include Extension for Community Healthcare Outcomes (ECHO) tele-mentoring learning collaboratives, a facilitation guide and other materials, a patient navigation workshop, webinars, and individualized technical assistance. Our primary outcome is program adoption (by component), measured 6 months after participation in an ECHO learning collaborative. We also assess engagement and adaptations (implemented and desired) to learn how the multicomponent intervention might be modified to best support broad scale-up. Discussion Findings may inform approaches for adapting and scaling evidence-based approaches to promote CRC screening participation in underserved populations and settings. Trial registration Registered at ClinicalTrials.gov (NCT04890054) and at the NCI’s Clinical Trials Reporting Program (CTRP no.: NCI-2021–01032) on May 11, 2021

Evaluation of an artificial intelligence-based medical device for diagnosis of autism spectrum disorder.

Autism spectrum disorder (ASD) can be reliably diagnosed at 18 months, yet significant diagnostic delays persist in the United States. This double-blinded, multi-site, prospective, active comparator cohort study tested the accuracy of an artificial intelligence-based Software as a Medical Device designed to aid primary care healthcare providers (HCPs) in diagnosing ASD. The Device combines behavioral features from three distinct inputs (a caregiver questionnaire, analysis of two short home videos, and an HCP questionnaire) in a gradient boosted decision tree machine learning algorithm to produce either an ASD positive, ASD negative, or indeterminate output. This study compared Device outputs to diagnostic agreement by two or more independent specialists in a cohort of 18-72-month-olds with developmental delay concerns (425 study completers, 36% female, 29% ASD prevalence). Device output PPV for all study completers was 80.8% (95% confidence intervals (CI), 70.3%-88.8%) and NPV was 98.3% (90.6%-100%). For the 31.8% of participants who received a determinate output (ASD positive or negative) Device sensitivity was 98.4% (91.6%-100%) and specificity was 78.9% (67.6%-87.7%). The Device's indeterminate output acts as a risk control measure when inputs are insufficiently granular to make a determinate recommendation with confidence. If this risk control measure were removed, the sensitivity for all study completers would fall to 51.6% (63/122) (95% CI 42.4%, 60.8%), and specificity would fall to 18.5% (56/303) (95% CI 14.3%, 23.3%). Among participants for whom the Device abstained from providing a result, specialists identified that 91% had one or more complex neurodevelopmental disorders. No significant differences in Device performance were found across participants' sex, race/ethnicity, income, or education level. For nearly a third of this primary care sample, the Device enabled timely diagnostic evaluation with a high degree of accuracy. The Device shows promise to significantly increase the number of children able to be diagnosed with ASD in a primary care setting, potentially facilitating earlier intervention and more efficient use of specialist resources