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Background: The Trial Innovation Network (TIN) is a collaborative initiative within the National Center for Advancing Translational Science (NCATS) Clinical and Translational Science Awards (CTSA) Program. To improve and innovate the conduct of clinical trials, it is exploring the uses of gamification to better engage the trial workforce and improve the efficiencies of trial activities. The gamification structures described in this article are part of a TIN website gamification toolkit, available online to the clinical trial scientific community. Methods: The game designers used existing electronic trial platforms to gamify the tasks required to meet trial start-up timelines to create friendly competitions. Key indicators and familiar metrics were mapped to scoreboards. Webinars were organized to share and applaud trial and game performance. Results: Game scores were significantly associated with an increase in achieving start-up milestones in activation, institutional review board (IRB) submission, and IRB approval times, indicating the probability of completing site activation faster by using games. Overall game enjoyment and feelings that the game did not apply too much pressure appeared to be an important moderator of performance in one trial but had little effect on performance in a second. Conclusion: This retrospective examination of available data from gaming experiences may be a first-of-kind use in clinical trials. There are signals that gaming may accelerate performance and increase enjoyment during the start-up phase of a trial. Isolating the effect of gamification on trial outcomes will depend on a larger sampling from future trials, using well-defined, hypothesis-driven statistical analysis plans. [ABSTRACT FROM AUTHOR]

Background: Neuropsychiatric symptoms are common after traumatic brain injury (TBI) and often resolve within 3 months post-injury. However, the degree to which individual patients follow this course is unknown. We characterized trajectories of neuropsychiatric symptoms over 12 months post-TBI. We hypothesized that a substantial proportion of individuals would display trajectories distinct from the group-average course, with some exhibiting less favorable courses., Methods: Participants were level 1 trauma center patients with TBI ( n = 1943), orthopedic trauma controls ( n = 257), and non-injured friend controls ( n = 300). Trajectories of six symptom dimensions (Depression, Anxiety, Fear, Sleep, Physical, and Pain) were identified using growth mixture modeling from 2 weeks to 12 months post-injury., Results: Depression, Anxiety, Fear, and Physical symptoms displayed three trajectories: Stable-Low (86.2-88.6%), Worsening (5.6-10.9%), and Improving (2.6-6.4%). Among symptomatic trajectories (Worsening, Improving), lower-severity TBI was associated with higher prevalence of elevated symptoms at 2 weeks that steadily resolved over 12 months compared to all other groups, whereas higher-severity TBI was associated with higher prevalence of symptoms that gradually worsened from 3-12 months. Sleep and Pain displayed more variable recovery courses, and the most common trajectory entailed an average level of problems that remained stable over time (Stable-Average; 46.7-82.6%). Symptomatic Sleep and Pain trajectories (Stable-Average, Improving) were more common in traumatically injured groups., Conclusions: Findings illustrate the nature and rates of distinct neuropsychiatric symptom trajectories and their relationship to traumatic injuries. Providers may use these results as a referent for gauging typical v. atypical recovery in the first 12 months post-injury.

Background: Anxiety and depression are frequently comorbid yet phenotypically distinct. This study identifies differences in the clinically observable phenome across a wide variety of physical and mental disorders comparing patients with diagnoses of depression without anxiety, anxiety without depression, or both depression and anxiety., Methods: Using electronic health records for 14 994 participants with depression and/or anxiety in the Mayo Clinic Biobank, a phenotype-based phenome-wide association study (Phe 2 WAS) was performed to test for differences between these groups across a broad range of clinical diagnoses observed in the electronic health record. Additional analyses were performed to determine the temporal sequencing of diagnoses., Results: Compared to patients diagnosed only with anxiety, those diagnosed only with depression were more likely to have diagnoses of obesity (OR 1.75; p = 1 × 10 -27 ), sleep apnea (OR 1.71; p = 1 × 10 -22 ), and type II diabetes (OR 1.74; p = 9 × 10 -18 ). Compared to those diagnosed only with depression, those diagnosed only with anxiety were more likely to have diagnoses of palpitations (OR 1.91; p = 2 × 10 -25 ), benign skin neoplasms (OR 1.61; p = 2 × 10 -17 ), and cardiac dysrhythmias (OR 1.45; p = 2 × 10 -12 ). Patients with comorbid depression and anxiety were more likely to have diagnoses of other mental health disorders, substance use disorders, sleep problems, and gastroesophageal reflux relative to isolated depression., Conclusions: While depression and anxiety are closely related, this study suggests that phenotypic distinctions exist between depression and anxiety. Improving phenotypic characterization within the broad categories of depression and anxiety could improve the clinical assessment of depression and anxiety.

Background: Depression and anxiety are common and highly comorbid, and their comorbidity is associated with poorer outcomes posing clinical and public health concerns. We evaluated the polygenic contribution to comorbid depression and anxiety, and to each in isolation., Methods: Diagnostic codes were extracted from electronic health records for four biobanks [ N = 177 865 including 138 632 European (77.9%), 25 612 African (14.4%), and 13 621 Hispanic (7.7%) ancestry participants]. The outcome was a four-level variable representing the depression/anxiety diagnosis group: neither, depression-only, anxiety-only, and comorbid. Multinomial regression was used to test for association of depression and anxiety polygenic risk scores (PRSs) with the outcome while adjusting for principal components of ancestry., Results: In total, 132 960 patients had neither diagnosis (74.8%), 16 092 depression-only (9.0%), 13 098 anxiety-only (7.4%), and 16 584 comorbid (9.3%). In the European meta-analysis across biobanks, both PRSs were higher in each diagnosis group compared to controls. Notably, depression-PRS (OR 1.20 per s.d. increase in PRS; 95% CI 1.18-1.23) and anxiety-PRS (OR 1.07; 95% CI 1.05-1.09) had the largest effect when the comorbid group was compared with controls. Furthermore, the depression-PRS was significantly higher in the comorbid group than the depression-only group (OR 1.09; 95% CI 1.06-1.12) and the anxiety-only group (OR 1.15; 95% CI 1.11-1.19) and was significantly higher in the depression-only group than the anxiety-only group (OR 1.06; 95% CI 1.02-1.09), showing a genetic risk gradient across the conditions and the comorbidity., Conclusions: This study suggests that depression and anxiety have partially independent genetic liabilities and the genetic vulnerabilities to depression and anxiety make distinct contributions to comorbid depression and anxiety.

Background: Several social determinants of health (SDoH) have been associated with the onset of major depressive disorder (MDD). However, prior studies largely focused on individual SDoH and thus less is known about the relative importance (RI) of SDoH variables, especially in older adults. Given that risk factors for MDD may differ across the lifespan, we aimed to identify the SDoH that was most strongly related to newly diagnosed MDD in a cohort of older adults., Methods: We used self-reported health-related survey data from 41 174 older adults (50-89 years, median age = 67 years) who participated in the Mayo Clinic Biobank, and linked ICD codes for MDD in the participants' electronic health records. Participants with a history of clinically documented or self-reported MDD prior to survey completion were excluded from analysis ( N = 10 938, 27%). We used Cox proportional hazards models with a gradient boosting machine approach to quantify the RI of 30 pre-selected SDoH variables on the risk of future MDD diagnosis., Results: Following biobank enrollment, 2073 older participants were diagnosed with MDD during the follow-up period (median duration = 6.7 years). The most influential SDoH was perceived level of social activity (RI = 0.17). Lower level of social activity was associated with a higher risk of MDD [hazard ratio = 2.27 (95% CI 2.00-2.50) for highest v. lowest level]., Conclusion: Across a range of SDoH variables, perceived level of social activity is most strongly related to MDD in older adults. Monitoring changes in the level of social activity may help identify older adults at an increased risk of MDD.

Background: Studies examining the role of geographic factors in coronavirus disease-2019 (COVID-19) epidemiology among rural populations are lacking., Methods: Our study is a population-based longitudinal study based on rural residents in four southeast Minnesota counties from March through October 2020. We used a kernel density estimation approach to identify hotspots for COVID-19 cases. Temporal trends of cases and testing were examined by generating a series of hotspot maps during the study period. Household/individual-level socioeconomic status (SES) was measured using the HOUSES index and examined for association between identified hotspots and SES., Results: During the study period, 24,243 of 90,975 residents (26.6%) were tested for COVID-19 at least once; 1498 (6.2%) of these tested positive. Compared to other rural residents, hotspot residents were overall younger (median age: 40.5 vs 43.2), more likely to be minorities (10.7% vs 9.7%), and of higher SES (lowest HOUSES [SES] quadrant: 14.6% vs 18.7%). Hotspots accounted for 30.1% of cases (14.5% of population) for rural cities and 60.8% of cases (27.1% of population) for townships. Lower SES and minority households were primarily affected early in the pandemic and higher SES and non-minority households affected later., Conclusion: In rural areas of these four counties in Minnesota, geographic factors (hotspots) play a significant role in the overall burden of COVID-19 with associated racial/ethnic and SES disparities, of which pattern differed by the timing of the pandemic (earlier in pandemic vs later). The study results could more precisely guide community outreach efforts (e.g., public health education, testing/tracing, and vaccine roll out) to those residing in hotspots., Competing Interests: The authors have nothing to disclose that poses a conflict of interest., (© The Author(s) 2021.)

Background: Given the significant health effects, we assessed geospatial patterns of adverse events (AEs), defined as physical or sexual abuse and accidents or poisonings at home, among children in a mixed rural-urban community., Methods: We conducted a population-based cohort study of children (<18 years) living in Olmsted County, Minnesota, to assess geographic patterns of AEs between April 2004 and March 2009 using International Classification of Diseases, Ninth Revision codes. We identified hotspots by calculating the relative difference between observed and expected case densities accounting for population characteristics (; hotspot ≥ 0.33) using kernel density methods. A Bayesian geospatial logistic regression model was used to test for association of subject characteristics (including residential features) with AEs, adjusting for age, sex, and socioeconomic status (SES)., Results: Of the 30,227 eligible children (<18 years), 974 (3.2%) experienced at least one AE. Of the nine total hotspots identified, five were mobile home communities (MHCs). Among non-Hispanic White children (85% of total children), those living in MHCs had higher AE prevalence compared to those outside MHCs, independent of SES (mean posterior odds ratio: 1.80; 95% credible interval: 1.22-2.54). MHC residency in minority children was not associated with higher prevalence of AEs. Of addresses requiring manual correction, 85.5% belonged to mobile homes., Conclusions: MHC residence is a significant unrecognized risk factor for AEs among non-Hispanic, White children in a mixed rural-urban community. Given plausible outreach difficulty due to address discrepancies, MHC residents might be a geographically underserved population for clinical care and research., Competing Interests: The authors have no conflicts of interest to disclose., (© The Association for Clinical and Translational Science 2020.)

Introduction: Prior studies indicate greater disease burden for obesity among rural compared with urban residents but no differences for mood disorder based on geographic location. Recent attention has focused on the need to examine regional rural-urban disparities in disease burden. We focused on mood disorders and obesity prevalence within three southeastern Minnesota counties served by the Mayo Clinic Center for Translational Science Award, in Rochester, Minnesota, as these were top priorities identified in community health needs assessments., Methods: Cross-sectional study to assess the association of rural-urban locality on 5-year (2009-2014) prevalence of mood disorder and obesity obtained using the Rochester Epidemiological Project medical records linkage system, among subjects residing in three mixed rural-urban counties on April 1, 2014. Multivariable analyses adjusted for demographics, socioeconomic status using an individual housing-based measure, and counties., Results: The study cohort (percent rural location) included 91,202 (15%) for Olmsted, 10,197 (51%) in Dodge, and 10,184 (57%) in Wabasha counties. On multivariate analysis, 5-year prevalence of mood disorders and obesity was significantly greater for urban compared with rural residents, after adjusting for confounders; odds ratios (95% confidence intervals): 1.21 (1.17-1.26), P < 0.001, and 1.05 (1.01-1.10), P = 0.016, respectively. Observed effects were not modified in additional models adjusted for health care utilization (HCU; ≥1 general medical examination visit and flu vaccination)., Conclusions: Rural-urban health disparities for burden of mood disorders and obesity are independent of socioeconomic status and HCU in a Midwestern community. It is important to assess potential regional heterogeneity of rural-urban disparities on health outcomes., Competing Interests: The authors have no conflicts of interest to declare., (© The Association for Clinical and Translational Science 2020.)