The ultimate goal of value-based laboratory medicine is maximizing the effectiveness of laboratory tests in improving patient outcomes, optimizing resources and minimizing unnecessary costs. This approach abandons the oversimplified notion of test volume and cost, in favor of emphasizing the clinical utility and quality of diagnostic tests in the clinical decision-making. Several key elements characterize value-based laboratory medicine, which can be summarized in some basic concepts, such as organization of in vitro diagnostics (including appropriateness, integrated diagnostics, networking, remote patient monitoring, disruptive innovations), translation of laboratory data into clinical information and measurable outcomes, sustainability, reimbursement, ethics (e.g., patient empowerment and safety, data protection, analysis of big data, scientific publishing). Education and training are also crucial, along with considerations for the future of the profession, which will be largely influenced by advances in automation, information technology, artificial intelligence, and regulations concerning in vitro diagnostics. This collective opinion paper, composed of summaries from presentations given at the two-day European Federation of Laboratory Medicine (EFLM) Strategic Conference "A vision to the future: value-based laboratory medicine" (Padova, Italy; September 23-24, 2024), aims to provide a comprehensive overview of value-based laboratory medicine, projecting the profession into a more clinically effective and sustainable future.

Newborn screening (NBS), a comprehensive system that includes testing, diagnosis, follow-up, treatment, education, and evaluation, was recently named one of the Top 10 Great Public Health Achievements by the Centers for Disease Control and Prevention (CDC).1 Each year, approximately 10,000 infants are identified with NBS conditions, which frequently go unnoticed at birth.2 NBS is administered by state public health programs across the country and provides for early identification of newborns with certain congenital, metabolic, endocrine, hematologic, and other genetic conditions. Early identification of these conditions in newborns facilitates timely interventions that result in significant decreases in morbidity, mortality, and disability.1 | | Screening begins by pricking a newborn's heel to get enough blood to fill a few circles on a filter paper card. The specimen, referred to as a dried blood spot, is collected by a health-care provider—typically at the birthing facility—during the first 24–48 hours of life. Some states are required to collect two specimens, in which case the second specimen is collected between seven and 15 days of life. The specimens are then sent to a state-designated NBS laboratory for analysis. When a test result is out of normal range, laboratory or follow-up personnel contact the birthing facility and the newborn's physician to ensure the child receives the appropriate diagnostic work-up and treatment. NBS goes beyond blood-spot screening to include point-of-care testing for hearing and, in some states, critical congenital heart disease. These tests are performed at the hospital shortly after birth, and the state NBS program performs follow-up testing. Although there is some variability in protocols among states, most NBS programs have similar components, including specimen collection, laboratory testing, follow-up, education of providers and the public, verification of a diagnosis, treatment, and ongoing program evaluation.3

PURPOSE: We evaluated a template for molecular genetic test reports that was developed as a strategy to reduce communication errors between the laboratory and ordering clinician. METHODS: We surveyed 1,600 primary care physicians to assess satisfaction, ease of use, and effectiveness of genetic test reports developed using our template and reports developed by clinical laboratories. Mean score differences of responses between the reports were compared using t-tests. Two-way analysis of variance evaluated the effect of template versus standard reports and the influence of physician characteristics. RESULTS: There were 396 (24%) respondents. Template reports had higher scores than the standard reports for each survey item. The gender and specialty of the physician did not influence scores; however, younger physicians gave higher scores regardless of report type. There was significant interaction between report type and whether physicians ordered or reviewed any genetic tests (none versus at least one) in the past year, P = 0.005. CONCLUSION: For each survey item assessing satisfaction, ease of use, and effectiveness, physicians gave higher ratings to genetic test reports developed with the template than standard reports used by clinical laboratories. Physicians least familiar with genetic test reports, and possibly having the greatest need for better communication, were best served by the template reports.

The goal of this article is to describe an integrated parallel process for the co-development of written and computable clinical practice guidelines (CPGs) to accelerate adoption and increase the impact of guideline recommendations in clinical practice. From February 2018 through December 2021, interdisciplinary work groups were formed after an initial Kaizen event and using expert consensus and available literature, produced a 12-phase integrated process (IP). The IP includes activities, resources, and iterative feedback loops for developing, implementing, disseminating, communicating, and evaluating CPGs. The IP incorporates guideline standards and informatics practices and clarifies how informaticians, implementers, health communicators, evaluators, and clinicians can help guideline developers throughout the development and implementation cycle to effectively co-develop written and computable guidelines. More efficient processes are essential to create actionable CPGs, disseminate and communicate recommendations to clinical end users, and evaluate CPG performance. Pilot testing is underway to determine how this IP expedites the implementation of CPGs into clinical practice and improves guideline uptake and health outcomes.

Clinical practice guidelines (CPGs) support individual and population health by translating new, evidence-based knowledge into recommendations for health practice. CPGs can be provided as computable, machine-readable guidelines that support the translation of recommendations into shareable, interoperable clinical decision support and other digital tools (eg, quality measures, case reports, care plans). Interdisciplinary collaboration among guideline developers and health information technology experts can facilitate the translation of written guidelines into computable ones. The benefits of interdisciplinary work include a focus on the needs of end-users who apply guidelines in practice through clinic decision support systems as part of the Centers for Disease Control and Prevention's (CDC's) Adapting Clinical Guidelines for the Digital Age (ACG) initiative, a group of interdisciplinary experts proposed a process to facilitate the codevelopment of written and computable CPGs, referred to as the "integrated process (IP)."1 This paper presents a framework for evaluating the IP based on a combination of vetted evaluation models and expert opinions. This framework combines 3 types of evaluations: process, product, and outcomes. These evaluations assess the value of interdisciplinary expert collaboration in carrying out the IP, the quality, usefulness, timeliness, and acceptance of the guideline, and the guideline's health impact, respectively. A case study is presented that illustrates application of the framework.

BACKGROUND: The Diesel Exhaust in Miners Study (DEMS) was an important contributor to the International Agency for Research on Cancer reclassification of diesel exhaust as a Group I carcinogen and subsequent risk assessment. We extended the DEMS cohort follow-up by 18 y and the nested case-control study to include all newly identified lung cancer deaths and matched controls (DEMS II), nearly doubling the number of lung cancer deaths. OBJECTIVE: Our purpose was to characterize the exposure-response relationship with a focus on the effects of timing of exposure and exposure cessation. METHODS: We conducted a case-control study of lung cancer nested in a cohort of 12,315 workers in eight nonmetal mines (376 lung cancer deaths, 718 controls). Controls were selected from workers who were alive when the case died, individually matched on mine, sex, race/ethnicity, and birth year (within 5 y). Based on an extensive historical exposure assessment, we estimated respirable elemental carbon (REC), an index of diesel exposure, for each cohort member. Odds ratios (ORs) were estimated by conditional regression analyses controlling for smoking and other confounders. To evaluate time windows of exposure, we evaluated the joint OR patterns for cumulative REC within each of four preselected exposure time windows,  < 5, 5-9, 10-19, and  ≥ 20 y prior to death/reference date, and we evaluated the interaction of cumulative exposure across time windows under additive and multiplicative forms for the joint association. RESULTS: ORs increased with increasing 15-y lagged cumulative exposure, peaking with a tripling of risk for exposures of  ∼ 950 to < 1,700 μg/m3-y [OR = 3.23; 95% confidence interval (CI): 1.47, 7.10], followed by a plateau/decline among the heavily exposed (OR = 1.85; 95% CI: 0.85, 4.04). Patterns of risk by cumulative REC exposure varied across four exposure time windows (phomogeneity < 0.001), with ORs increasing for exposures accrued primarily 10-19 y prior to death (ptrend < 0.001). Results provided little support for a waning of risk among workers whose exposures ceased for  ≥ 20 y. CONCLUSION: DEMS II findings provide insight into the exposure-response relationship between diesel exhaust and lung cancer mortality. The pronounced effect of exposures occurring in the window 10-19 y prior to death, the sustained risk 20 or more years after exposure ceases, and the plateau/decline in risk among the most heavily exposed provide direction for future research on the mechanism of diesel-induced carcinogenesis in addition to having important implications for the assessment of risk from diesel exhaust by regulatory agencies. https://doi.org/10.1289/EHP11980.

Modern genomic sequencing tests often interrogate large numbers of genes. Identification of appropriate reference materials for development, validation studies, and quality assurance of these tests poses a significant challenge for laboratories. It is difficult to develop and maintain expert knowledge to identify all variants that must be validated to assure analytic and clinical validity. Additionally, it is usually not possible to procure appropriate and characterized genomic DNA reference materials containing the number and scope of variants required. To address these challenges, the Centers for Disease Control and Prevention’s Genetic Testing Reference Material Program (GeT-RM) has partnered with the Clinical Genome Resource (ClinGen) to develop a publicly available list of expert curated, clinically important variants. ClinGen Variant Curation Expert Panels nominated 546 variants found in 84 disease associated genes, including common pathogenic and difficult to detect variants. Variant types nominated included 346 SNVs, 104 deletions, 37 CNVs, 25 duplications, 18 deletion-insertions, 5 inversions, 4 insertions, 2 complex rearrangements, 3 in difficult to sequence regions, and 2 fusions. This expert-curated variant list is a resource that provides a foundation for designing comprehensive validation studies and for creating in silico reference materials for clinical genomic test development and validation.Competing Interest StatementThe authors have declared no competing interest.Funding StatementClinGen is primarily funded by the National Human Genome Research Institute (NHGRI), through the following three grants: U41HG006834, U41HG009649, U41HG009650. ClinGen also receives support for content curation from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), through the following three grants: U24HD093483, U24HD093486, U24HD093487.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:This study did not involve human subjects and therefore no IRB was required.All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesAll data is included in the figures, tables and supplement included in the manuscript.

OBJECTIVES: Developing an expanded representation of the total testing process that includes contemporary elements of laboratory practice can be useful to understanding and optimizing testing workflows across clinical laboratory and patient care settings. METHODS: Published literature and meeting reports were used by the coauthors to inform the development of the expanded representation of the total testing process and relevant examples describing its uses. RESULTS: A visual representation of the total testing process was developed and contextualized to patient care scenarios using a number of examples covering the detection of blood culture contamination, use of next-generation sequencing, and pharmacogenetic testing. CONCLUSIONS: The expanded representation of the total testing process can serve as a model and framework to document and improve the use of clinical testing within the broader context of health care delivery. This representation recognizes increased engagement among clinical laboratory professionals with patients and other health care providers as essential to making informed decisions. The increasing use of data is highlighted as important to ensuring quality, appropriate test utilization, and sustaining an efficient workflow across clinical laboratory and patient care settings. Maintaining a properly resourced and competent workforce is also featured as an essential component to the testing process.

BACKGROUND: The glycated hemoglobin (A1c) test is not recommended for sickle cell disease (SCD) patients. We examine ordering patterns of diabetes-related tests for SCD patients to explore misutilization of tests among this underserved population. METHODS: We used de-identified electronic health record (EHR) data in the Cerner Health Facts™ (HF) data warehouse to evaluate the frequency of A1c and fructosamine tests during 2010 to 2016, for 37,151 SCD patients from 393 healthcare facilities across the United States. After excluding facilities with no A1c data, we defined three groups of facilities based on the prevalence of SCD patients with A1c test(s): adherent facilities (no SCD patients with A1c test(s)), minor non-adherent facilities, major non-adherent facilities. RESULTS: We determined that 11% of SCD patients (3927 patients) treated at 393 facilities in the US received orders for at least one A1c test. Of the 3927 SCD patients with an A1c test, only 89 patients (2.3%) received an order for a fructosamine test. At the minor non-adherent facilities, 5% of the SCD patients received an A1c test while 58% of the SCD patients at the least adherent facilities had at least one A1c test. Overall, the percent of A1c tests ordered for SCD patients between 2010 and 2016 remained similar. CONCLUSIONS: Inappropriate A1c test orders among a sickle cell population is a significant quality gap. Interventions to advance adoption of professional recommendations that advocate for alternate tests, such as fructosamine, can guide clinicians in test selection to reduce this quality gap are discussed. The informatics strategy used in this work can inform other largescale analyses of lab test utilization using de-identified EHR data.

Modern genomic sequencing tests often interrogate large numbers of genes. Identification of appropriate reference materials for development, validation studies, and quality assurance of these tests poses a significant challenge for laboratories. It is difficult to develop and maintain expert knowledge to identify all variants that must be validated to assure analytic and clinical validity. Additionally, it is usually not possible to procure appropriate and characterized genomic DNA reference materials containing the number and scope of variants required. To address these challenges, the Centers for Disease Control and Prevention's Genetic Testing Reference Material Program (GeT-RM) has partnered with the Clinical Genome Resource (ClinGen) to develop a publicly available list of expert curated, clinically important variants. ClinGen Variant Curation Expert Panels nominated 546 variants found in 84 disease associated genes, including common pathogenic and difficult to detect variants. Variant types nominated included 346 SNVs, 104 deletions, 37 CNVs, 25 duplications, 18 deletion-insertions, 5 inversions, 4 insertions, 2 complex rearrangements, 3 in difficult to sequence regions, and 2 fusions. This expert-curated variant list is a resource that provides a foundation for designing comprehensive validation studies and for creating in silico reference materials for clinical genomic test development and validation.

OBJECTIVES: Clinical laboratory testing provides essential data for making medical diagnoses. Generating accurate and timely test results clearly communicated to the treating clinician, and ultimately the patient, is a critical component that supports diagnostic excellence. On the other hand, failure to achieve this can lead to diagnostic errors that manifest in missed, delayed and wrong diagnoses. CONTENT: Innovations that support diagnostic excellence address: 1) test utilization, 2) leveraging clinical and laboratory data, 3) promoting the use of credible information resources, 4) enhancing communication among laboratory professionals, health care providers and the patient, and 5) advancing the use of diagnostic management teams. Integrating evidence-based laboratory and patient-care quality management approaches may provide a strategy to support diagnostic excellence. Professional societies, government agencies, and healthcare systems are actively engaged in efforts to advance diagnostic excellence. Leveraging clinical laboratory capabilities within a healthcare system can measurably improve the diagnostic process and reduce diagnostic errors. SUMMARY: An expanded quality management approach that builds on existing processes and measures can promote diagnostic excellence and provide a pathway to transition innovative concepts to practice. OUTLOOK: There are increasing opportunities for clinical laboratory professionals and organizations to be part of a strategy to improve diagnoses.

BACKGROUND: Ionizing radiation is an established carcinogen, but risks from low-dose exposures are controversial. Since the Biological Effects of Ionizing Radiation VII review of the epidemiological data in 2006, many subsequent publications have reported excess cancer risks from low-dose exposures. Our aim was to systematically review these studies to assess the magnitude of the risk and whether the positive findings could be explained by biases. METHODS: Eligible studies had mean cumulative doses of less than 100 mGy, individualized dose estimates, risk estimates, and confidence intervals (CI) for the dose-response and were published in 2006-2017. We summarized the evidence for bias (dose error, confounding, outcome ascertainment) and its likely direction for each study. We tested whether the median excess relative risk (ERR) per unit dose equals zero and assessed the impact of excluding positive studies with potential bias away from the null. We performed a meta-analysis to quantify the ERR and assess consistency across studies for all solid cancers and leukemia. RESULTS: Of the 26 eligible studies, 8 concerned environmental, 4 medical, and 14 occupational exposure. For solid cancers, 16 of 22 studies reported positive ERRs per unit dose, and we rejected the hypothesis that the median ERR equals zero (P = .03). After exclusion of 4 positive studies with potential positive bias, 12 of 18 studies reported positive ERRs per unit dose (P  = .12). For leukemia, 17 of 20 studies were positive, and we rejected the hypothesis that the median ERR per unit dose equals zero (P  = .001), also after exclusion of 5 positive studies with potential positive bias (P  = .02). For adulthood exposure, the meta-ERR at 100 mGy was 0.029 (95% CI = 0.011 to 0.047) for solid cancers and 0.16 (95% CI = 0.07 to 0.25) for leukemia. For childhood exposure, the meta-ERR at 100 mGy for leukemia was 2.84 (95% CI = 0.37 to 5.32); there were only two eligible studies of all solid cancers. CONCLUSIONS: Our systematic assessments in this monograph showed that these new epidemiological studies are characterized by several limitations, but only a few positive studies were potentially biased away from the null. After exclusion of these studies, the majority of studies still reported positive risk estimates. We therefore conclude that these new epidemiological studies directly support excess cancer risks from low-dose ionizing radiation. Furthermore, the magnitude of the cancer risks from these low-dose radiation exposures was statistically compatible with the radiation dose-related cancer risks of the atomic bomb survivors.

Whether low-dose ionizing radiation can cause cancer is a critical and long-debated question in radiation protection. Since the Biological Effects of Ionizing Radiation report by the National Academies in 2006, new publications from large, well-powered epidemiological studies of low doses have reported positive dose-response relationships. It has been suggested, however, that biases could explain these findings. We conducted a systematic review of epidemiological studies with mean doses less than 100 mGy published 2006-2017. We required individualized doses and dose-response estimates with confidence intervals. We identified 26 eligible studies (eight environmental, four medical, and 14 occupational), including 91 000 solid cancers and 13 000 leukemias. Mean doses ranged from 0.1 to 82 mGy. The excess relative risk at 100 mGy was positive for 16 of 22 solid cancer studies and 17 of 20 leukemia studies. The aim of this monograph was to systematically review the potential biases in these studies (including dose uncertainty, confounding, and outcome misclassification) and to assess whether the subset of minimally biased studies provides evidence for cancer risks from low-dose radiation. Here, we describe the framework for the systematic bias review and provide an overview of the eligible studies.

Background An increasing number of diagnostic evaluations incorporate genetic testing to facilitate accurate and timely diagnoses. The increasing number and complexity of genetic tests continue to pose challenges in deciding when to test, selecting the correct test(s), and using results to inform medical diagnoses, especially for medical professionals lacking genetic expertise. Careful consideration of a diagnostic workflow can be helpful in understanding the appropriate uses of genetic testing within a broader diagnostic workup. Content The diagnosis of long QT syndrome (LQTS), a life-threatening cardiac arrhythmia, provides an example for this approach. Electrocardiography is the preferred means for diagnosing LQTS but can be uninformative for some patients due to the variable presentation of the condition. Family history and genetic testing can augment physiological testing to inform a diagnosis and subsequent therapy. Clinical and laboratory professionals informed by peer- reviewed literature and professional recommendations constructed a generalized LQTS diagnostic workflow. This workflow served to explore decisions regarding the use of genetic testing for diagnosing LQTS. Summary and outlook Understanding the complexities and approaches to integrating genetic testing into a broader diagnostic evaluation is anticipated to support appropriate test utilization, optimize diagnostic evaluation, and facilitate a multidisciplinary approach essential for achieving accurate and timely diagnoses.

Clinical laboratory quality improvement (QI) efforts can include population test utilization. The authors used a health care organization's Medical Data Warehouse (MDW) to characterize a gap in guideline-concordant laboratory testing recommended for safe use of antirheumatic agents, then tested the effectiveness of laboratory-led, technology-enabled outreach to patients at reducing this gap. Data linkages available through the Kaiser Permanente Colorado MDW and electronic health record were used to identify ambulatory adults taking antirheumatic agents who were due/overdue for alanine aminotransferase (ALT), aspartate aminotransferase (AST), complete blood count (CBC), or serum creatinine (SCr) testing. Outreach was implemented using an interactive voice response system to send patients text or phone call reminders. Interrupted time series analysis was used to estimate reminder effectiveness. Rates of guideline-concordant testing and testing timeliness in baseline vs. intervention periods were determined using generalized linear models for repeated measures. Results revealed a decrease in percentage of 3763 patients taking antirheumatic agents due/overdue for testing at any given time: baseline 24.3% vs. intervention 17.5% (P < 0.001). Among 3205 patients taking conventional antirheumatic agents, concordance for all ALT testing was baseline 52.8% vs. intervention 65.4% (P < 0.001) among patients chronically using these agents and baseline 20.6% vs. intervention 26.1% (P < 0.001) among patients newly starting these agents. The 95(th) percentiles for days to ALT testing were baseline 149 vs. intervention 117 among chronic users and baseline 134 vs. intervention 92 among new starts. AST, CBC, and SCr findings were similar. Technology-enabled outreach reminding patients to obtain laboratory testing improves health care system outcomes.

CONTEXT.-: The laboratory total testing process includes preanalytic, analytic, and postanalytic phases, but most laboratory quality improvement efforts address the analytic phase. Expanding quality improvement to preanalytic and postanalytic phases via use of medical data warehouses, repositories that include clinical, utilization, and administrative data, can improve patient care by ensuring appropriate test utilization. Cross-department, multidisciplinary collaboration to address gaps and improve patient and system outcomes is beneficial. OBJECTIVE.-: To demonstrate medical data warehouse utility for characterizing laboratory-associated quality gaps amenable to preanalytic or postanalytic interventions. DESIGN.-: A multidisciplinary team identified quality gaps. Medical data warehouse data were queried to characterize gaps. Organizational leaders were interviewed about quality improvement priorities. A decision aid with elements including national guidelines, local and national importance, and measurable outcomes was completed for each gap. RESULTS.-: Gaps identified included (1) test ordering; (2) diagnosis, detection, and documentation, and (3) high-risk medication monitoring. After examination of medical data warehouse data including enrollment, diagnoses, laboratory, pharmacy, and procedures for baseline performance, high-risk medication monitoring was selected, specifically alanine aminotransferase, aspartate aminotransferase, complete blood count, and creatinine testing among patients receiving disease-modifying antirheumatic drugs. The test utilization gap was in monitoring timeliness (eg, >60% of patients had a monitoring gap exceeding the guideline recommended frequency). Other contributors to selecting this gap were organizational enthusiasm, regulatory labeling, and feasibility of a significant laboratory role in addressing the gap. CONCLUSIONS.-: A multidisciplinary process facilitated identification and selection of a laboratory medicine quality gap. Medical data warehouse data were instrumental in characterizing gaps.

BACKGROUND: Although general population studies of air pollution suggest that particulate matter - diesel exhaust emissions in particular - is a potential risk factor for cardiovascular disease, direct evidence from occupational cohorts using quantitative metrics of exposure is limited. In this study, we assess counterfactual risk of ischemic heart disease (IHD) mortality under hypothetical scenarios limiting exposure levels of diesel exhaust and of respirable mine/ore dust in the Diesel Exhaust in Miners Study (DEMS) cohort. METHODS: We analyzed data on 10,779 male miners from 8 non-metal, non-coal mines - hired after diesel equipment was introduced in the respective facilities - and followed from 1948 to 1997, with 297 observed IHD deaths in this sample. We applied the parametric g-formula to assess risk under hypothetical scenarios with various limits for respirable elemental carbon (a surrogate for diesel exhaust), and respirable dust, separately and jointly. RESULTS: The risk ratio comparing the observed risk to cumulative IHD mortality risk at age 80 under a hypothetical scenario where exposures to elemental carbon and respirable dust are eliminated was 0.79 (95% confidence interval (CI): 0.64, 0.97). The corresponding risk difference was -3.0% (95% CI: -5.7, -0.3). CONCLUSION: Our findings, based on data from a cohort of non-metal miners, are consistent with the hypothesis that interventions to eliminate exposures to diesel exhaust and respirable dust would reduce IHD mortality risk.

Residents of agricultural areas experience pesticide exposures from sources other than direct agricultural work. We developed a quantitative, active ingredient-specific algorithm for cumulative (adult, married lifetime) non-occupational pesticide exposure intensity for spouses of farmers who applied pesticides in the Agricultural Health Study (AHS). The algorithm addressed three exposure pathways: take-home, agricultural drift, and residential pesticide use. Pathway-specific equations combined (i) weights derived from previous meta-analyses of published pesticide exposure data and (ii) information from the questionnaire on frequency and duration of pesticide use by applicators, home proximity to treated fields, residential pesticide usage (e.g., termite treatments), and spouse's off-farm employment (proxy for time at home). The residential use equation also incorporated a published probability matrix that documented the likelihood active ingredients were used in home pest treatment products. We illustrate use of these equations by calculating exposure intensities for the insecticide chlorpyrifos and herbicide atrazine for 19,959 spouses. Non-zero estimates for >/=1 pathway were found for 78% and 77% of spouses for chlorpyrifos and atrazine, respectively. Variability in exposed spouses' intensity estimates was observed for both pesticides, with 75th to 25th percentile ratios ranging from 7.1 to 7.3 for take-home, 6.5 to 8.5 for drift, 2.4 to 2.8 for residential use, and 3.8 to 7.0 for the summed pathways. Take-home and drift estimates were highly correlated (>/=0.98), but were not correlated with residential use (0.010.02). This algorithm represents an important advancement in quantifying non-occupational pesticide relative exposure differences and will facilitate improved etiologic analyses in the AHS spouses. The algorithm could be adapted to studies with similar information.

Next-generation sequencing (NGS) methods for cancer testing have been rapidly adopted by clinical laboratories. To establish analytical validation best practice guidelines for NGS gene panel testing of somatic variants, a working group was convened by the Association of Molecular Pathology with liaison representation from the College of American Pathologists. These joint consensus recommendations address NGS test development, optimization, and validation, including recommendations on panel content selection and rationale for optimization and familiarization phase conducted before test validation; utilization of reference cell lines and reference materials for evaluation of assay performance; determining of positive percentage agreement and positive predictive value for each variant type; and requirements for minimal depth of coverage and minimum number of samples that should be used to establish test performance characteristics. The recommendations emphasize the role of laboratory director in using an error-based approach that identifies potential sources of errors that may occur throughout the analytical process and addressing these potential errors through test design, method validation, or quality controls so that no harm comes to the patient. The recommendations contained herein are intended to assist clinical laboratories with the validation and ongoing monitoring of NGS testing for detection of somatic variants and to ensure high quality of sequencing results.

A national workgroup convened by the Centers for Disease Control and Prevention identified principles and made recommendations for standardizing the description of sequence data contained within the variant file generated during the course of clinical next-generation sequence analysis for diagnosing human heritable conditions. The specifications for variant files were initially developed to be flexible with regard to content representation to support a variety of research applications. This flexibility permits variation with regard to how sequence findings are described and this depends, in part, on the conventions used. For clinical laboratory testing, this poses a problem because these differences can compromise the capability to compare sequence findings among laboratories to confirm results and to query databases to identify clinically relevant variants. To provide for a more consistent representation of sequence findings described within variant files, the workgroup made several recommendations that considered alignment to a common reference sequence, variant caller settings, use of genomic coordinates, and gene and variant naming conventions. These recommendations were considered with regard to the existing variant file specifications presently used in the clinical setting. Adoption of these recommendations is anticipated to reduce the potential for ambiguity in describing sequence findings and facilitate the sharing of genomic data among clinical laboratories and other entities.

Clinical microbiology and public health laboratories are beginning to utilize next-generation sequencing (NGS) for a range of applications. This technology has the potential to transform the field by providing approaches that will complement, or even replace, many conventional laboratory tests. While the benefits of NGS are significant, the complexities of these assays require an evolving set of standards to assure testing quality. Regulatory and accreditation requirements, professional guidelines, and best practices that help to assure the quality of NGS-based tests are emerging. This review will highlight currently available standards and guidelines for the implementation of NGS in the clinical and public health laboratory setting, and includes considerations for NGS test validation, quality control procedures, proficiency testing, and reference materials.

BACKGROUND: Increased pesticide concentrations in house dust in agricultural areas have been attributed to several exposure pathways, including agricultural drift, para-occupational, and residential use. OBJECTIVE: To guide future exposure assessment efforts, we quantified relative contributions of these pathways using meta-regression models of published data on dust pesticide concentrations. METHODS: From studies in North American agricultural areas published from 1995-2015, we abstracted dust pesticide concentrations reported as summary statistics (e.g., geometric means (GM)). We analyzed these data using mixed-effects meta-regression models that weighted each summary statistic by its inverse variance. Dependent variables were either the log-transformed GM (drift) or the log-transformed ratio of GMs from two groups (para-occupational, residential use). RESULTS: For the drift pathway, predicted GMs decreased sharply and nonlinearly, with GMs 64% lower in homes 250 m versus 23 m from fields (inter-quartile range of published data) based on 52 statistics from 7 studies. For the para-occupational pathway, GMs were 2.3 times higher (95% confidence interval [CI]: 1.5-3.3; 15 statistics, 5 studies) in homes of farmers who applied pesticides more versus less recently or frequently. For the residential use pathway, GMs were 1.3 (95%CI: 1.1-1.4) and 1.5 (95%CI: 1.2-1.9) times higher in treated versus untreated homes, when the probability that a pesticide was used for the pest treatment was 1-19% and ≥20%, respectively (88 statistics, 5 studies). CONCLUSION: Our quantification of the relative contributions of pesticide exposure pathways in agricultural populations could improve exposure assessments in epidemiologic studies. The meta-regression models can be updated when additional data become available.

OBJECTIVE: To determine whether electronic health record (EHR) tools improve documentation of pre- and postanalytic care processes for genetic tests ordered by nongeneticists. METHODS: We conducted a nonrandomized, controlled, pre-/postintervention study of EHR point-of-care tools (informational messages and template report) for three genetic tests. Chart review assessed documentation of genetic testing processes of care, with points assigned for each documented item. Multiple linear and logistic regressions assessed factors associated with documentation. RESULTS: Preimplementation, there were no significant site differences (P > 0.05). Postimplementation, mean documentation scores increased (5.9 (2.1) vs. 5.0 (2.2); P = 0.0001) and records with clinically meaningful documentation increased (score >5: 59 vs. 47%; P = 0.02) at the intervention versus the control site. Pre- and postimplementation, a score >5 was positively associated with abnormal test results (OR = 4.0; 95% CI: 1.8-9.2) and trainee provider (OR = 2.3; 95% CI: 1.2-4.6). Postimplementation, a score >5 was also positively associated with intervention site (OR = 2.3; 95% CI: 1.1-5.1) and specialty clinic (OR = 2.0; 95% CI: 1.1-3.6). There were also significantly fewer tests ordered after implementation (264/100,000 vs. 204/100,000; P = 0.03), with no significant change at the control site (280/100,000 vs. 257/100,000; P = 0.50). CONCLUSIONS: EHR point-of-care tools improved documentation of genetic testing processes and decreased utilization of genetic tests commonly ordered by nongeneticists.Genet Med advance online publication 30 June 2016Genetics in Medicine (2016); doi:10.1038/gim.2016.73.

Clinician attitudes toward multiplexed genomic testing may be vital to the success of translational programs. We surveyed clinicians at an academic medical center about their views on a large pharmacogenomics implementation, the PREDICT (Pharmacogenomic Resource for Enhanced Decisions in Care and Treatment) program. Participants were asked about test ordering, major factors influencing use of results, expectations of efficacy and responsibility for applying results to patient care. Virtually all respondents (99%) agreed that pharmacogenomics variants influence patients' response to drug therapy. The majority (92%) favored immediate, active notification when a clinically significant drug-genome interaction was present. However, clinicians were divided on which providers were responsible for acting on a result when a prescription change was indicated and whether patients should be directly notified of a significant result. We concluded genotype results were valued for tailoring prescriptions, but clinicians do not agree on how to appropriately assign clinical responsibility for actionable results from a multiplexed panel.

We report principles and guidelines (Supplementary Note) that were developed by the Next-Generation Sequencing: Standardization of Clinical Testing II (Nex-StoCT II) informatics workgroup, which was first convened on October 11–12, 2012, in Atlanta, Georgia, by the US Centers for Disease Control and Prevention (CDC; Atlanta, GA). We present here recommendations for the design, optimization and implementation of an informatics pipeline for clinical next-generation sequencing (NGS) to detect germline sequence variants in compliance with existing regulatory and professional quality standards1. The workgroup, which included informatics experts, clinical and research laboratory professionals, physicians with experience in interpreting NGS results, NGS test platform and software developers and participants from US government agencies and professional organizations, also discussed the use of NGS in testing for cancer and infectious disease. A typical NGS analytical process and selected workgroup recommendations are summarized in Table 1, and detailed in the guidelines presented in the Supplementary Note.

Metolachlor, a widely used herbicide, is classified as a Group C carcinogen by the U.S. Environmental Protection Agency based on increased liver neoplasms in female rats. Epidemiologic studies of the health effects of metolachlor have been limited. The Agricultural Health Study (AHS) is a prospective cohort study including licensed private and commercial pesticide applicators in Iowa and North Carolina enrolled 1993-7. We evaluated cancer incidence through 2010/2011 (NC/IA) for 49,616 applicators, 53% of whom reported ever using metolachlor. We used Poisson regression to evaluate relations between two metrics of metolachlor use (lifetime days, intensity-weighted lifetime days) and cancer incidence. We saw no association between metolachlor use and incidence of all cancers combined (n=5701 with a 5-year lag) or most site-specific cancers. For liver cancer, in analyses restricted to exposed workers, elevations observed at higher categories of use were not statistically significant. However, trends for both lifetime and intensity-weighted lifetime days of metolachor use were positive and statistically significant with an unexposed reference group. A similar pattern was observed for follicular-cell lymphoma, but no other lymphoma subtypes. An earlier suggestion of increased lung cancer risk at high levels of metolachlor use in this cohort was not confirmed in this update. This suggestion of an association between metolachlor and liver cancer among pesticide applicators is a novel finding and echoes observation of increased liver neoplasms in some animal studies. However, our findings for both liver cancer and follicular-cell lymphoma warrant follow-up to better differentiate effects of metolachlor use from other factors.

Advances in sequencing technologies have facilitated concurrent testing for many disorders, and the results generated may provide information about a patient's health that is unrelated to the clinical indication, commonly referred to as incidental findings. This is a paradigm shift from traditional genetic testing in which testing and reporting are tailored to a patient's specific clinical condition. Clinical laboratories and physicians are wrestling with this increased complexity in genomic testing and reporting of the incidental findings to patients. An enormous amount of discussion has taken place since the release of a set of recommendations from the American College of Medical Genetics and Genomics. This discussion has largely focused on the content of the incidental findings, but the laboratory perspective and patient autonomy have been overlooked. This report by the Association of Molecular Pathology workgroup discusses the pros and cons of next-generation sequencing technology, potential benefits, and harms for reporting of incidental findings, including the effect on both the laboratory and the patient, and compares those with other areas of medicine. The importance of genetic counseling to preserve patient autonomy is also reviewed. The discussion and recommendations presented by the workgroup underline the need for continued research and discussion among all stakeholders to improve our understanding of the effect of different policies on patients, providers, and laboratories.

Since its registration in 1994 acetochlor has become a commonly used herbicide in the US, yet no epidemiologic study has evaluated its carcinogenicity in humans. We evaluated the use of acetochlor and cancer incidence among licensed pesticide applicators in the Agricultural Health Study. In telephone interviews administered during 1999-2005, participants provided information on acetochlor use, use of other pesticides and additional potential confounders. We used Poisson regression to estimate relative risks (RR) and 95% confidence intervals (95% CI) for cancers that occurred from the time of interview through 2011 in Iowa and 2010 in North Carolina. Among 33,484 men, there were 4,026 applicators who used acetochlor and 3,234 incident cancers, with 304 acetochlor-exposed cases. Increased risk of lung cancer was observed among acetochlor users (RR = 1.74; 95% CI: 1.07-2.84) compared to nonusers, and among individuals who reported using acetochlor/atrazine product mixtures (RR = 2.33; 95% CI: 1.30-4.17), compared to nonusers of acetochlor. Colorectal cancer risk was significantly elevated among the highest category of acetochlor users (RR = 1.75; 95% CI: 1.08-2.83) compared to never users. Additionally, borderline significantly increased risk of melanoma (RR = 1.61; 95% CI: 0.98-2.66) and pancreatic cancer (RR = 2.36; 95% CI: 0.98-5.65) were observed among acetochlor users. The associations between acetochlor use and lung cancer, colorectal cancer, melanoma and pancreatic cancer are suggestive, however the lack of exposure-response trends, small number of exposed cases and relatively short time between acetochlor use and cancer development prohibit definitive conclusions.

Prospective cohorts have played a major role in understanding the contribution of diet, physical activity, medical conditions, and genes to the development of many diseases, but have not been widely used for occupational exposures. Studies in agriculture are an exception. We draw upon our experience using this design to study agricultural workers to identify conditions that might foster use of prospective cohorts to study other occupational settings. Prospective cohort studies are perceived by many as the strongest epidemiologic design. It allows updating of information on exposure and other factors, collection of biologic samples before disease diagnosis for biomarker studies, assessment of effect modification by genes, lifestyle, and other occupational exposures, and evaluation of a wide range of health outcomes. Increased use of prospective cohorts would be beneficial in identifying hazardous exposures in the workplace. Occupational epidemiologists should seek opportunities to initiate prospective cohorts to investigate high priority, occupational exposures.