Mental health conditions and unsafe driving behaviors: A naturalistic driving study on ADHD and depression.

INTRODUCTION: Road injuries remain a persistent public health concern across the world. The task of driving is complicated by mental health conditions, which may affect drivers' executive functioning and cognitive resource allocation. This study examines whether attention-deficit/hyperactivity disorder (ADHD) and depression are associated with unsafe driving behaviors. METHOD: Generalized linear mixed models were employed to estimate the association of self-reported ADHD and depression with 18 unsafe driving behavior types found prior to at-fault crashes and near-crashes using a large-scale naturalistic driving dataset. Driver demographics, cognitive traits, environmental factors, and driver random effects were included to reduce confounding and biases. RESULTS: Controlling for other covariates, people with self-reported ADHD were more likely to have performed improper braking or stopping (OR = 4.89, 95% CI 1.82-13.17) prior to an at-fault crash or near-crash, while those with self-reported depression did not have a significant association with any unsafe driving behavior. CONCLUSIONS: After accounting for demographic, cognitive, and environmental covariates, individuals with ADHD and depression were not prone to purposefully aggressive or reckless driving. Instead, drivers with self-reported ADHD may unintentionally execute unsafe driving behaviors in particular driving scenarios that require a high level of cognitive judgment. PRACTICAL APPLICATIONS: These findings can inform the curriculum design of driver's education programs that help learners with mental health conditions gain practice in certain road scenarios, for example, more practice on preemptively reducing speed instead of making sudden brakes and smooth turning on curved roads for students with ADHD. Furthermore, specific advanced driver assistance systems may prove particularly helpful for drivers with ADHD, such as detection of leading objects and curve speed warning.

How are different sources of distraction associated with at-fault crashes among drivers of different age gender groups?

INTRODUCTION: Distracted driving has been well researched, however the comparison between different age-gender groups on the impact of distracted driving has not been explored. Most crash analysis research does not distinguish driver responsibility, so the role that distractions has in at-fault crashes is unknown. Without distinguishing at-fault crashes from all-cause crashes, distracted driving's detrimental effects could be underestimated. OBJECTIVE: This study aims to systematically assess the risk of at-fault crashes associated with different sources of distraction among six groups by driver age (Teens 16-19, Adults 20-64, Seniors 65+) and gender. METHODS: Crashes where a study participant was deemed at fault were identified using human expert annotated variables from the Strategic Highway Research Program 2 (SHRP2) Naturalistic Driving Study dataset. Generalized linear mixed models were performed to assess the adjusted odds ratios of 10 distraction types associated with the at-fault crashes while controlling for environmental factors. RESULTS: The main findings are (1) The highest contributing distraction types in at-fault crashes were In-Cabin Objects, Mobile Device, External Scenes, and In-Vehicle Information Systems (IVIS) as indicated by their influence on multiple age-gender groups and the magnitude of odds ratios; (2) Teens and adults were more distraction-prone than seniors, although seniors had the greatest at-fault crash risks associated with In-Cabin Objects, Mobile Device, and IVIS; (3) Distractions impacted females and males similarly; (4) At-fault crashes were more likely to have the significant distraction types present than all-cause crashes. CONCLUSION: This study adds to the limited literature on at-fault crashes particularly as it explores the role of driver demographics and distracted driving. Analyzing the risks of distracted driving by age-gender group shows that specific activities can be riskier for a certain population. The effects of distractions may be overlooked without fault determination. Distractions by external scenes, in-vehicle technologies, and in-cabin objects should not be overlooked, in addition to mobile device use.

Determining the risk of driver-at-fault events associated with common distraction types using naturalistic driving data.

INTRODUCTION: Studies thus far have focused on automobile accidents that involve driver distraction. However, it is hard to discern whether distraction played a role if fault designation is missing because an accident could be caused by an unexpected external event over which the driver has no control. This study seeks to determine the effect of distraction in driver-at-fault events. METHOD: Two generalized linear mixed models, one with at-fault safety critical events (SCE) and the other with all-cause SCEs as the outcomes, were developed to compare the odds associated with common distraction types using data from the SHRP2 naturalistic driving study. RESULTS: Adjusting for environment and driver variation, 6 of 10 common distraction types significantly increased the risk of at-fault SCEs by 20-1330%. The three most hazardous sources of distraction were handling in-cabin objects (OR = 14.3), mobile device use (OR = 2.4), and external distraction (OR = 1.8). Mobile device use and external distraction were also among the most commonly occurring distraction types (10.1% and 11.0%, respectively). CONCLUSIONS: Focusing on at-fault events improves our understanding of the role of distraction in potentially avoidable automobile accidents. The in-cabin distraction that requires eye-hand coordination presents the most danger to drivers' ability in maintaining fault-free, safe driving. Practical Applications: The high risk of at-fault SCEs associated with in-cabin distraction should motivate the smart design of the interior and in-vehicle information system that requires less visual attention and manual effort.

The Exchange of Informational Support in Online Health Communities at the Onset of the COVID-19 Pandemic: Content Analysis.

BACKGROUND: Online health communities (OHCs) provide social support for ongoing health-related problems. COVID-19, the disease caused by SARS-CoV-2, has been an acute and substantial stressor worldwide. The disease and its impact, especially in the beginning phases, left many people with questions about the nature, treatment, and prevention of COVID-19. Unlike typical chronic ailments discussed on OHCs, which are more established, COVID-19, at least at the onset of the pandemic, is distinct in that it lacks a consensus of clinical diagnosis and an existing community foundation. OBJECTIVE: The study aims to investigate a newly formed OHC for COVID-19 to determine the topics and types of information exchange as well as the sources of information this community referenced during the early phases of the COVID-19 pandemic in the United States. METHODS: A total of 357 posts from a COVID-19 OHC on the MedHelp platform were annotated according to an open-coding process. Participants' engagement patterns, topics of posts, and sources of information were quantified. RESULTS: Participants who offered informational support had a significantly higher percentage of responding more than once than those seeking information (P<.001). Among the information-seeking topics, symptoms and public health practice and psychological impacts were the most frequently discussed, with 26% (17/65) and 15% (10/65) of posts, respectively. Most informational support was expressed through feedback/opinion (181/220, 82.3%). Additionally, the most frequently referenced source of information was news outlets/websites, at 55% (11/20). Governmental websites were referenced less frequently. CONCLUSIONS: The trends of this community could be useful in prioritizing public health responses to address the most common questions asked by the public during crisis communication and in identifying which venue of communication is most effective in reaching a public audience during such times.

Identifying Self-Management Support Needs for Pregnant Women With Opioid Misuse in Online Health Communities: Mixed Methods Analysis of Web Posts.

BACKGROUND: The current opioid crisis in the United States impacts broad population groups, including pregnant women. Opioid use during pregnancy can affect the health and wellness of both mothers and their infants. Understanding women's efforts to self-manage opioid use or misuse in pregnancy is needed to identify intervention points for improving maternal outcomes. OBJECTIVE: This study aims to identify the characteristics of women in an online health community (OHC) with opioid use or misuse during pregnancy and the self-management support needs of these mothers. METHODS: A total of 200 web posts by pregnant women with opioid use participating in an OHC were double coded. Concepts and their thematic connections were identified through an inductive process until theoretical saturation was reached. Statistical tests were performed to identify patterns. RESULTS: The majority of pregnant women (150/200, 75.0%) in the OHC exhibited signs of misuse, and 62.5% (125/200) of the participants were either contemplating or pursuing dosage reduction. Self-managed withdrawal was more common (P<.001) than professional treatment among the population. A total of 5 themes of self-management support needs were identified as women sought information about the potential adverse effects of gestational opioid use, protocols for self-managed withdrawal, pain management safety during pregnancy, hospital policies and legal procedures related to child protection, and strategies for navigating offline support systems. In addition, 58.5% (117/200) of the pregnant women expressed negative emotions, of whom only 10.2% (12/117) sought to address their emotional needs with the help of the OHC. CONCLUSIONS: OHCs provide vital self-management support for pregnant women with opioid use or misuse. Women pursuing self-managed dosage reduction are prone to misinformation and repeated relapses, which can result in extreme measures to avoid testing positive for drug use at labor. The study findings provide evidence for public policy considerations, including universal screening of substance use for pregnant women, emphasis on treatment rather than legal punishment, and further expansion of the Drug Addiction Treatment Act waiver training program. The improvement of web-based platforms that can organize geo-relevant information, dispense clinically validated withdrawal schedules, and offer structured peer support is envisioned for harm reduction among pregnant women who opt for self-management of opioid misuse.

Connectedness Based on Shared Engagement Predicts Remote Biochemically Verified Quit Status Within Smoking Cessation Treatment Groups on Facebook.

INTRODUCTION: Engagement with online content and online social network integration are associated with smoking behavior change, but less is known about social dynamics of shared engagement between participants in group-based social media interventions. METHODS: Participants were 251 young adult smokers aged 18 to 25 assigned to one of 29 secret Facebook groups tailored to their readiness to quit smoking ("pre-contemplation," "contemplation," and "preparation"). Groups varied in size and were randomly assigned to receive monetary incentives for engagement. All groups received daily posts for 90 days and were assessed for remote biochemically verified smoking abstinence at the end of the intervention. Across 29 groups, we examined associations between group features (group size, incentive condition, readiness to quit) with how connected members were within the group based on shared engagement with the same content (measured by density). At the individual level, we examined associations between 7-day biochemically verified smoking abstinence and how connected an individual was within the group (measured by degree centrality). RESULTS: After adjusting for comment volume, being in a contemplation group (vs. pre-contemplation group) was associated with a decrease in comment-based density. Individual degree centrality was significantly associated with biochemically verified smoking abstinence for both comments and likes. CONCLUSIONS: Future group-based social media interventions for smoking cessation may want to focus on promoting connected engagement between participants, rather than simply quantity of engagement. IMPLICATIONS: Participants in a smoking cessation intervention delivered through Facebook groups were more likely to have biochemically verified smoking abstinence if they were more connected to the rest of the group via shared engagement. Promoting shared engagement between participants may be more likely to promote behavior change than volume of engagement alone.

Imputation of Missing Data in Electronic Health Records Based on Patients' Similarities.

Using electronic health records (EHR) as the source of data for mining and analysis of different health conditions has become an increasingly common approach. However, due to irregular observation times and other uncertainties inherent in medical settings, the EHR data sets suffer from a large number of missing values. Most of the traditional data mining and machine learning approaches are designed to operate on complete data. In this paper, we propose a novel imputation method for missing data to facilitate using these approaches for the analysis of EHR data. The imputation is based on a set of interpatient, multivariate similarities among patients. For a missing data point in a patient's lab results during his/her intensive care unit stay, the method ranks other patients based on their similarities with the ego patient in terms of lab values, then the missing value is estimated as a weighted average of the known values of the same laboratory test from other patients, considering their similarities as weights. A comparison of the estimated values by the proposed method with values estimated by several common and state-of-the-are methods, such as MICE and 3D-MICE, shows that the proposed method outperforms them and produces promising results.

Detecting Patterns of Prescription Drug Use During Pregnancy and Lactation with Visualization Techniques.

BACKGROUND: Pharmacovigilance studies for the pregnancy population are an important tool to address the teratogenic and maternal effects of prescription drugs. OBJECTIVES: We sought to leverage existing data to provide empirical evidence that reflects current drug utilization by pregnant/lactating women for pharmacovigilance and effective study designs. METHODS: Temporal patterns of prescription drug use during pregnancy and lactation for four clinical conditions were visualized using a R package and an administrative claims dataset representing nationwide privately-insured women. RESULTS: During the years 2010 to 2014, there was a significant increase (p < 0.001) in overall use of opioid, antidepressant, antihypertensive, and antidiabetic prescriptions. Hydrocodone, sertraline, labetalol, and metformin were the most common prescriptions for perinatal use in its respective category. Trimester-specific prescription patterns were reported. CONCLUSIONS: Visualization of administrative claims data can quickly detect temporal patterns of prescription drug use during pregnancy and lactation.

Thoracic Oncology Multidisciplinary Clinic Reduces Unnecessary Health Care Expenditure Used in the Workup of Patients With Non-small-cell Lung Cancer.

BACKGROUND: National costs of lung cancer care exceed $12 billion. We investigate the resource-savings benefit of a single-day thoracic oncology multidisciplinary clinic (MDC) in the diagnostic period prior to non-small-cell lung cancer (NSCLC) treatment. MATERIALS AND METHODS: From July 2007 to January 2015, patients with NSCLC treated with multimodality therapy at a tertiary hospital-based cancer center in Maryland were identified. Patient and treatment details were collected. Health care resources utilized in the 90 days prior to receipt of first oncologic treatment were identified using billed activity codes. Associated total charges, including professional fees and hospital-based technical fees, were identified and inflated to 2014 dollars using the Consumer Price Index. Codes were categorized into provider visits, procedures, pathology/laboratory, radiology, and other tests. χ2, Student t, and Wilcoxon rank-sum tests compared charges of patients seen in and out of the MDC. RESULTS: Two-hundred ninety-seven (non-MDC = 161, 54%; MDC = 136, 46%) of 308 patients identified had total charges available. Patients seen through MDC had on average a 23% decrease in total charges per patient incurred ($5839 savings; range, $5213-$6464) compared with patients seen through non-MDC settings. Evaluation through MDC reduced the average number of provider visits per patient (non-MDC, 6.8 vs. MDC, 4.8; P < .01) prior to treatment start, which led to a 50% (average $3092; range, $2451-$3732) reduction in provider charges per patient (P < .01). CONCLUSIONS: Evaluation of patients with NSCLC through a coordinated single-day MDC reduced hospital charges per patient by 23% during the diagnostic period prior to treatment when compared with evaluation through traditional referral-based thoracic oncology clinics.