Communicating Scientific Uncertainty About the COVID-19 Pandemic: Online Experimental Study of an Uncertainty-Normalizing Strategy.

BACKGROUND: Communicating scientific uncertainty about public health threats such as COVID-19 is an ethically desirable task endorsed by expert guidelines on crisis communication. However, the communication of scientific uncertainty is challenging because of its potential to promote ambiguity aversion-a well-described syndrome of negative psychological responses consisting of heightened risk perceptions, emotional distress, and decision avoidance. Communication strategies that can inform the public about scientific uncertainty while mitigating ambiguity aversion are a critical unmet need. OBJECTIVE: This study aimed to evaluate whether an "uncertainty-normalizing" communication strategy-aimed at reinforcing the expected nature of scientific uncertainty about the COVID-19 pandemic-can reduce ambiguity aversion, and to compare its effectiveness to conventional public communication strategies aimed at promoting hope and prosocial values. METHODS: In an online factorial experiment conducted from May to June 2020, a national sample of 1497 US adults read one of five versions of an informational message describing the nature, transmission, prevention, and treatment of COVID-19; the versions varied in level of expressed scientific uncertainty and supplemental focus (ie, uncertainty-normalizing, hope-promoting, and prosocial). Participants then completed measures of cognitive, emotional, and behavioral manifestations of ambiguity aversion (ie, perceived likelihood of getting COVID-19, COVID-19 worry, and intentions for COVID-19 risk-reducing behaviors and vaccination). Analyses assessed (1) the extent to which communicating uncertainty produced ambiguity-averse psychological responses; (2) the comparative effectiveness of uncertainty-normalizing, hope-promoting, and prosocial communication strategies in reducing ambiguity-averse responses; and (3) potential moderators of the effects of alternative uncertainty communication strategies. RESULTS: The communication of scientific uncertainty about the COVID-19 pandemic increased perceived likelihood of getting COVID-19 and worry about COVID-19, consistent with ambiguity aversion. However, it did not affect intentions for risk-reducing behaviors or vaccination. The uncertainty-normalizing strategy reduced these aversive effects of communicating scientific uncertainty, resulting in levels of both perceived likelihood of getting COVID-19 and worry about COVID-19 that did not differ from the control message that did not communicate uncertainty. In contrast, the hope-promoting and prosocial strategies did not decrease ambiguity-averse responses to scientific uncertainty. Age and political affiliation, respectively, moderated the effects of uncertainty communication strategies on intentions for COVID-19 risk-reducing behaviors and worry about COVID-19. CONCLUSIONS: Communicating scientific uncertainty about the COVID-19 pandemic produces ambiguity-averse cognitive and emotional, but not behavioral, responses among the general public, and an uncertainty-normalizing communication strategy reduces these responses. Normalizing uncertainty may be an effective strategy for mitigating ambiguity aversion in crisis communication efforts. More research is needed to test uncertainty-normalizing communication strategies and to elucidate the factors that moderate their effectiveness.

A taxonomy of medical uncertainties in clinical genome sequencing.

PURPOSE: Clinical next-generation sequencing (CNGS) is introducing new opportunities and challenges into the practice of medicine. Simultaneously, these technologies are generating uncertainties of an unprecedented scale that laboratories, clinicians, and patients are required to address and manage. We describe in this report the conceptual design of a new taxonomy of uncertainties around the use of CNGS in health care. METHODS: Interviews to delineate the dimensions of uncertainty in CNGS were conducted with genomics experts and themes were extracted in order to expand on a previously published three-dimensional taxonomy of medical uncertainty. In parallel, we developed an interactive website to disseminate the CNGS taxonomy to researchers and engage them in its continued refinement. RESULTS: The proposed taxonomy divides uncertainty along three axes-source, issue, and locus-and further discriminates the uncertainties into five layers with multiple domains. Using a hypothetical clinical example, we illustrate how the taxonomy can be applied to findings from CNGS and used to guide stakeholders through interpretation and implementation of variant results. CONCLUSION: The utility of the proposed taxonomy lies in promoting consistency in describing dimensions of uncertainty in publications and presentations, to facilitate research design and management of the uncertainties inherent in the implementation of CNGS.Genet Med advance online publication 19 January 2017.

Calibrating longitudinal cognition in Alzheimer's disease across diverse test batteries and datasets.

BACKGROUND: We sought to identify optimal approaches by calibrating longitudinal cognitive performance across studies with different neuropsychological batteries. METHODS: We examined four approaches to calibrate cognitive performance in nine longitudinal studies of Alzheimer's disease (AD) (n = 10,875): (1) common test, (2) standardize and average available tests, (3) confirmatory factor analysis (CFA) with continuous indicators, and (4) CFA with categorical indicators. To compare precision, we determined the minimum sample sizes needed to detect 25% cognitive decline with 80% power. To compare criterion validity, we correlated cognitive change from each approach with 6-year changes in average cortical thickness and hippocampal volume using available MRI data from the AD Neuroimaging Initiative. RESULTS: CFA with categorical indicators required the smallest sample size to detect 25% cognitive decline with 80% power (n = 232) compared to common test (n = 277), standardize-and-average (n = 291), and CFA with continuous indicators (n = 315) approaches. Associations with changes in biomarkers changes were the strongest for CFA with categorical indicators. CONCLUSIONS: CFA with categorical indicators demonstrated greater power to detect change and superior criterion validity compared to other approaches. It has wide applicability to directly compare cognitive performance across studies, making it a good way to obtain operational phenotypes for genetic analyses of cognitive decline among people with AD.

Engaging At-Risk Rural Residents in Secondary Lung Cancer Prevention.

INTRODUCTION: Rural residents are at elevated risk for lung cancer and related mortality, yet limited research has explored their perspectives on cancer risk or prevention options, including tobacco treatment and lung cancer screening with low-dose computed tomography (LDCT). This qualitative study examined attitudes and beliefs among rural adults who reported either current or former tobacco use, as well as disengagement from the health care system. METHODS: We conducted 6 focus groups with rural Maine residents at risk for lung cancer based on age and smoking history (n = 50). Semistructured interviews explored participants' knowledge, perceptions, and attitudes regarding lung cancer risk, LDCT screening, and patient provider relationships. Inductive qualitative analysis of interview transcripts was conducted to identify key themes. RESULTS: Participants were cognizant of their elevated lung cancer risk, yet few were aware of LDCT screening. When informed about LDCT, most participants indicated a willingness to undergo screening, although a substantial minority indicated reluctance related to fear and fatalism. Participants generally expressed the belief that relationships with a primary care provider could support their health and identified several provider factors that influence these relationships, including attention and time for patient concerns; respect and non-judgmental, nonstigmatizing attitudes; treating patients as individuals; and provider empathy and emotional support. CONCLUSIONS: Rural residents at risk for lung cancer report limited knowledge and substantial ambivalence regarding LDCT screening, but identify provider behaviors that may promote patient-provider relationships and greater engagement with their health. More research is needed to confirm these findings and understand how to help rural residents and healthcare providers work together to reduce lung cancer risk.

Area Deprivation Index and Rurality in Relation to Lung Cancer Prevalence and Mortality in a Rural State.

BACKGROUND: We sought to describe lung cancer prevalence and mortality in relation to socioeconomic deprivation and rurality. METHODS: We conducted a population-based cross-sectional analysis of prevalent lung cancers from a statewide all-payer claims dataset from 2012 to 2016, lung cancer deaths in Maine from the state death registry from 2012 to 2016, rurality, and area deprivation index (ADI), a geographic area-based measure of socioeconomic deprivation. Analyses examined rate ratios for lung cancer prevalence and mortality according to rurality (small and isolated rural, large rural, or urban) and ADI (quintiles, with highest reflecting the most deprivation) and after adjusting for age, sex, and area-level smoking rates as determined by the Behavioral Risk Factor Surveillance System. RESULTS: Among 1 223 006 adults aged 20 years and older during the 5-year observation period, 8297 received lung cancer care, and 4616 died. Lung cancer prevalence and mortality were positively associated with increasing rurality, but these associations did not persist after adjusting for age, sex, and smoking rates. Lung cancer prevalence and mortality were positively associated with increasing ADI in models adjusted for age, sex, and smoking rates (prevalence rate ratio for ADI quintile 5 compared with quintile 1 = 1.41, 95% confidence interval [CI] =1.30 to 1.54) and mortality rate ratio = 1.59, 95% CI = 1.41 to 1.79). CONCLUSION: Socioeconomic deprivation, but not rurality, was associated with higher lung cancer prevalence and mortality. Interventions should target populations with socioeconomic deprivation, rather than rurality per se, and aim to reduce lung cancer risk via tobacco treatment and control interventions and to improve patient access to lung cancer prevention, screening, and treatment services.

Disclosing genetic risk for Alzheimer's dementia to individuals with mild cognitive impairment.

INTRODUCTION: The safety of predicting conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) dementia using apolipoprotein E (APOE) genotyping is unknown. METHODS: We randomized 114 individuals with MCI to receive estimates of 3-year risk of conversion to AD dementia informed by APOE genotyping (disclosure arm) or not (non-disclosure arm) in a non-inferiority clinical trial. Primary outcomes were anxiety and depression scores. Secondary outcomes included other psychological measures. RESULTS: Upper confidence limits for randomization arm differences were 2.3 on the State Trait Anxiety Index and 0.5 on the Geriatric Depression Scale, below non-inferiority margins of 3.3 and 1.0. Moreover, mean scores were lower in the disclosure arm than non-disclosure arm for test-related positive impact (difference: -1.9, indicating more positive feelings) and AD concern (difference: -0.3). DISCUSSION: Providing genetic information to individuals with MCI about imminent risk for AD does not increase risks of anxiety or depression and may provide psychological benefits.

Association Between Rurality and Lung Cancer Treatment Characteristics and Timeliness.

BACKGROUND: Lung cancer is the leading cause of cancer-related mortality in the United States, and rural states bear a greater burden of disease. METHODS: We analyzed tumor registry data to examine relationships between rurality and lung cancer stage at diagnosis and treatment. Cases were from the Maine Cancer Registry from 2012 to 2015, and rurality was defined using rural-urban commuting areas. Multivariable models were used to examine the relationships between rurality and treatment, adjusting for age, sex, poverty, education, insurance status, and cancer stage. RESULTS: We identified 5,338 adults with incident lung cancer; 3,429 (64.2%) were diagnosed at a late stage (III or IV). Rurality was not associated with stage at diagnosis. For patients with early-stage disease (I or II), rurality was not associated with receipt of treatment. However, for patients with late-stage disease, residents of large rural areas received more surgery (10%) compared with metropolitan (9%) or small/isolated rural areas (6%), P = .01. In multivariable analyses, patients in large rural areas received more chemotherapy (OR 1.48; 95% CI: 1.08-2.02) than those in metropolitan areas. Patients with early-stage disease residing in small/ isolated rural areas had delays in treatment (median time to first treatment = 43 days, interquartile range [IQR] 22-68) compared with large rural (34 days, IQR 17-55) and metropolitan areas (35 days, IQR 17-60), P = .0009. CONCLUSION: Rurality is associated with differences in receipt of specific lung cancer treatments and in timeliness of treatment.

Timeliness of access to lung cancer diagnosis and treatment: A scoping literature review.

The Institute of Medicine recently called for increased understanding of and commitment to timely care. Lung cancer can be difficult to diagnose, resulting in delays that may adversely affect survival; rapid diagnosis and treatment therefore is critical for enabling improved patient outcomes. This scoping review provides an update on timeliness of lung cancer care over the past decade. We searched PubMed for English-language articles published from 2007 to 2016 that report wait time intervals related to diagnosis and treatment of lung cancer. Two authors independently reviewed titles and abstracts for inclusion. Abstracted data included sample size, patient population, study type, dates of study, wait times, and information on disparities, survival, costs, healthcare utilization, and interventions. The final review included 65 studies from 21 different countries. A total of 96 unique variations of wait intervals were reported (e.g., time to diagnosis from first pulmonologist visit, imaging, or initial evaluation), making comparisons across studies difficult. The most common interval was diagnosis to treatment initiation, with reported medians ranging from 6 to 45 days. Fourteen articles reported information on survival, 14 on healthcare utilization, 18 on disparities, and 14 on interventions; results varied by study. Significant variation exists in how access to care time delays are reported. Many patients across different facilities and countries appear to be facing substantial waits to receive lung cancer diagnosis and care. Further research, using common wait-interval metrics, is needed to evaluate and improve timeliness of lung cancer diagnosis and treatment.

Social Media Use in Research: Engaging Communities in Cohort Studies to Support Recruitment and Retention.

BACKGROUND: This paper presents the first formal evaluation of social media (SM) use in the National Children's Study (NCS). The NCS is a prospective, longitudinal study of the effects of environment and genetics on children's health, growth and development. The Study employed a multifaceted community outreach campaign in combination with a SM campaign to educate participants and their communities about the Study. SM essentially erases geographic differences between people due to its omnipresence, which was an important consideration in this multi-site national study. Using SM in the research setting requires an understanding of potential threats to confidentiality and privacy and the role that posted content plays as an extension of the informed consent process. OBJECTIVE: This pilot demonstrates the feasibility of creating linkages and databases to measure and compare SM with new content and engagement metrics. METHODS: Metrics presented include basic use metrics for Facebook as well as newly created metrics to assist with Facebook content and engagement analyses. RESULTS: Increasing Likes per month demonstrates that online communities can be quickly generated. Content and Engagement analyses describe what content of posts NCS Study Centers were using, what content they were posting about, and what the online NCS communities found most engaging. CONCLUSIONS: These metrics highlight opportunities to optimize time and effort while determining the content of future posts. Further research about content analysis, optimal metrics to describe engagement in research, the role of localized content and stakeholders, and social media use in participant recruitment is warranted.