How primary care providers (PCPs) respond to genomic secondary findings (SFs) of varying clinical significance (pathogenic, uncertain significance VUS, or benign) is unknown.
We randomized 148 ...American Academy of Family Physicians members to review three reports with varying significance for Lynch syndrome. Participants provided open-ended responses about the follow-up they would address and organized the SF reports and five other topics in the order they would prioritize responding to them (1 = highest priority, 6 = lowest priority).
PCPs suggested referrals more often for pathogenic variants or VUS than benign variants (72% vs. 16%, p < 0.001). PCPs were also more likely to address further workup, like a colonoscopy or esophagogastroduodenoscopy, in response to pathogenic variants or VUS than benign variants (43% vs. 4%, p < 0.001). The likelihoods of addressing referrals or further workup were similar when PCPs reviewed pathogenic variants and VUS (both p > 0.46). SF reports were prioritized highest for pathogenic variants (2.7 for pathogenic variants, 3.6 for VUS, 4.3 for benign variants, all p ≤ 0.014).
Results suggest that while PCPs appreciated the differences in clinical significance, disclosure of VUS as SFs would substantially increase downstream health-care utilization.
Background
Obtaining comprehensive family health history (FHH) to inform colorectal cancer (CRC) risk management in primary care settings is challenging.
Objective
To examine the effectiveness of a ...patient-facing FHH platform to identify and manage patients at increased CRC risk.
Design
Two-site, two-arm, cluster-randomized, implementation-effectiveness trial with primary care providers (PCPs) randomized to immediate intervention versus wait-list control.
Participants
PCPs treating patients at least one half-day per week; patients aged 40–64 with no medical conditions that increased CRC risk.
Interventions
Immediate-arm patients entered their FHH into a web-based platform that provided risk assessment and guideline-driven decision support; wait-list control patients did so 12 months later.
Main Measures
McNemar’s test examined differences between the platform and electronic medical record (EMR) in rates of increased risk documentation. General estimating equations using logistic regression models compared arms in risk-concordant provider actions and patient screening test completion. Referral for genetic consultation was analyzed descriptively.
Key Results
Seventeen PCPs were randomized to each arm. Patients (
n
= 252 immediate,
n
= 253 control) averaged 51.4 (SD = 7.2) years, with 83% assigned male at birth, 58% White persons, and 33% Black persons. The percentage of patients identified as increased risk for CRC was greater with the platform (9.9%) versus EMR (5.2%), difference = 4.8% (95% CI: 2.6%, 6.9%),
p
< .0001. There was no difference in PCP risk-concordant action odds ratio (OR) = 0.7, 95% CI (0.4, 1.2;
p
= 0.16). Among 177 patients with a risk-concordant screening test ordered, there was no difference in test completion, OR = 0.8 0.5,1.3;
p
= 0.36. Of 50 patients identified by the platform as increased risk, 78.6% immediate and 68.2% control patients received a recommendation for genetic consultation, of which only one in each arm had a referral placed.
Conclusions
FHH tools could accurately assess and document the clinical needs of patients at increased risk for CRC. Barriers to acting on those recommendations warrant further exploration.
Trial Registration Number
ClinicalTrials.gov
NCT02247336
https://clinicaltrials.gov/ct2/show/NCT02247336
To assess the value of genetic testing from the perspective of the Department of Veterans Affairs (VA) clinical leadership.
We administered an Internet-based survey to VA clinical leaders nationwide. ...Respondents rated the value (on a 5-point scale) of each of six possible reasons for genetic testing. Bivariate and linear regressions identified associations between value ratings and environmental, organizational, provider, patient, and encounter characteristics.
Respondents (n = 353; 63% response rate) represented 92% of VA medical centers. Tests that inform clinical management had the highest value rating (58.6%), followed by tests that inform disease prevention (56.4%), reproductive options (50.1%), life planning (43.9%), and a suspected (39.9%) or established (32.3%) diagnosis. Factors positively associated with high value included a culture that fosters adoption of genomics, specialist versus primary care provider, genetic tests available on laboratory menus, availability of genetic testing guidelines, clinicians knowing when to request genetics referrals, and availability of genetics professionals.
Our results demonstrate the varied value of genetic testing from the perspective of clinical leadership within a health-care system. Engaging organizational leadership in understanding the various reasons for genetic testing and its value beyond clinical utility may increase adoption of genetic tests to support patient-centered care.Genet Med advance online publication 15 December 2016.
Purpose: To evaluate the use of self-reported family medical history as a potential screening tool to identify people at-risk for diabetes.
Methods: The HealthStyles 2004 mail survey comprises 4345 ...US adults who completed a questionnaire to ascertain personal and family history of diabetes, perceived risk of diabetes, and practice of risk-reducing behaviors. Using number and type of affected relatives, respondents were ranked into three familial risk levels. Adjusted odds ratios (AORs) were obtained to evaluate associations between familial risk and prevalent diabetes, perceived risk of disease, and risk-reducing behaviors. Validity of family history as a screening tool was examined by calculating sensitivity, specificity, and positive and negative predictive values.
Results: Compared to those of average risk, people with moderate and high familial risk of diabetes were more likely to report a diagnosis of diabetes (AOR: 3.6, 95% CI: 2.8, 4.7; OR: 7.6, 95% CI: 5.9, 9.8, respectively), a higher perceived risk of diabetes (AOR: 4.6, 95% CI: 3.7, 5.7; OR: 8.5, 95% CI: 6.6, 17.7, respectively), and making lifestyle changes to prevent diabetes (AOR: 2.2, 95% CI: 1.8, 2.7; OR: 4.5, 95% CI: 3.6, 5.6, respectively). A positive familial risk of diabetes identified 73% of all respondents with diabetes and correctly predicted prevalent diabetes in 21.5% of respondents.
Conclusion: Family history of diabetes is not only a risk factor for the disease but is also positively associated with risk awareness and risk-reducing behaviors. It may provide a useful screening tool for detection and prevention of diabetes.
Precision medicine promises to improve patient outcomes, but much is unknown about its adoption within health-care systems. A comprehensive implementation plan is needed to realize its benefits.
We ...convened 80 stakeholders for agenda setting to inform precision medicine policy, delivery, and research. Conference proceedings were audio-recorded, transcribed, and thematically analyzed. We mapped themes representing opportunities, challenges, and implementation strategies to a logic model, and two implementation science frameworks provided context.
The logic model components included inputs: precision medicine infrastructure (clinical, research, and information technology), big data (from data sources to analytics), and resources (e.g., workforce and funding); activities: precision medicine research, practice, and education; outputs: precision medicine diagnosis; outcomes: personal utility, clinical utility, and health-care utilization; and impacts: precision medicine value, equity and access, and economic indicators. Precision medicine implementation challenges include evidence gaps demonstrating precision medicine utility, an unprepared workforce, the need to improve precision medicine access and reduce variation, and uncertain impacts on health-care utilization. Opportunities include integrated health-care systems, partnerships, and data analytics to support clinical decisions. Examples of implementation strategies to promote precision medicine are: changing record systems, data warehousing techniques, centralized technical assistance, and engaging consumers.
We developed a theory-based, context-specific logic model that can be used by health-care organizations to facilitate precision medicine implementation.
Family history guides cancer prevention and genetic testing. We sought to estimate the population prevalence of increased familial risk for breast, ovarian, endometrial, prostate, and colorectal ...cancers and hereditary cancer syndromes that include these cancers.
Using the 2005 California Health Interview Survey data, a weak, moderate, or strong familial cancer risk was assigned to 33,187 respondents. Guidelines were applied to identify individuals with hereditary breast-ovarian cancer and hereditary nonpolyposis colon cancer.
Among respondents without a personal history of cancer, familial breast cancer was most prevalent; 7% had a moderate and 5% a strong familial risk. Older individuals and women were more likely to report family history of cancer. Generally, whites had the highest prevalence, and Asians and Latinos had the lowest prevalence. Among women without a personal history of breast or ovarian cancer, 2.5% met criteria for hereditary breast-ovarian cancer, and among individuals without a personal history of colorectal, endometrial or ovarian cancer, 1.1% met criteria for hereditary nonpolyposis colon cancer.
We provide population-based prevalence estimates for moderate and strong familial risk for five common cancers and hereditary breast-ovarian cancer and hereditary nonpolyposis colon cancer. Such estimates are helpful in planning and evaluation of genetic services and prevention programs, and assessment of cancer surveillance and prevention strategies.
We sought to identify characteristics of genetic services that facilitate or hinder adoption.
We conducted semi-structured key informant interviews in five clinical specialties (primary care, medical ...oncology, neurology, cardiology, pathology/laboratory medicine) within 13 Veterans Administration facilities.
Genetic services (defined as genetic testing and consultation) were not typically characterized by informants (n = 64) as advantageous for their facilities or their patients; compatible with organizational norms of low cost and high clinical impact; or applicable to patient populations or norms of clinical care. Furthermore, genetic services had not been systematically adopted in most facilities because of their complexity: knowledge of and expertise on genetic testing was limited, and organizational barriers to utilization of genetic services were formidable. The few facilities that had some success with implementation of genetic services had knowledgeable clinicians interested in developing services and organizational-level facilitators such as accessible genetic test-ordering processes.
Adoption and implementation of genetic services will require a multilevel effort that includes education of providers and administrators, opportunities for observing the benefits of genetic medicine, strategies for reducing the complexity of genomic medicine, expanded strategies for accessing genetics expertise and streamlining utilization, and resources dedicated to assessing the value of genetic information for the outcomes that matter to health-care organizations.
Family health history reflects the effects of genetic, environmental, and behavioral factors and is an important risk factor for a variety of disorders including coronary heart disease, cancer, and ...diabetes. In 2004, the Centers for Disease Control and Prevention developed Family Healthware, a new interactive, Web-based tool that assesses familial risk for 6 diseases (coronary heart disease, stroke, diabetes, and colorectal, breast, and ovarian cancer) and provides a "prevention plan" with personalized recommendations for lifestyle changes and screening. The tool collects data on health behaviors, screening tests, and disease history of a person's first- and second-degree relatives. Algorithms in the software analyze the family history data and assess familial risk based on the number of relatives affected, their age at disease onset, their sex, how closely related the relatives are to each other and to the user, and the combinations of diseases in the family. A second set of algorithms uses the data on familial risk level, health behaviors, and screening to generate personalized prevention messages. Qualitative and quantitative formative research on lay understanding of family history and genetics helped shape the tool's content, labels, and messages. Lab-based usability testing helped refine messages and tool navigation. The tool is being evaluated by 3 academic centers by using a network of primary care practices to determine whether personalized prevention messages tailored to familial risk will motivate people at risk to change their lifestyles or screening behaviors.
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