Association of Depression and Antidepressant Use With Driving Behaviors in Older Adults: A LongROAD Study.

Older adults aged 70 and older who drive have higher crash death rates per mile driven compared to middle aged (35-54 years) adults who drive in the US. Prior studies have found that depression and or antidepressant medication use in older adults are associated with an increase in the vehicular crash rate. Using data from the prospective multi-site AAA Longitudinal Research on Aging Drivers Study, this analysis examined the independent and interdependent associations of self-reported depression and antidepressant use with driving behaviors that can increase motor vehicle crash risk such as hard braking, speeding, and night-time driving in adults over age 65. Of the 2951 participants, 6.4% reported having depression and 21.9% were on an antidepressant medication. Correcting for age, race, gender, and education level, participants on an antidepressant had increased hard braking events (1.22 [1.10-1.34]) but self-reported depression alone was not associated with changes in driving behaviors.

Impact of driving cessation on health-related quality of life trajectories.

BACKGROUND: Trajectories of health-related quality of life (HRQoL) after driving cessation (DC) are thought to decline steeply, but for some, HRQoL may improve after DC. Our objective is to examine trajectories of HRQoL for individuals before and after DC. We hypothesize that for urban drivers, volunteers and those who access alternative transportation participants' health may remain unchanged or improve. METHODS: This study uses data from the AAA Longitudinal Research on Aging Drivers (LongROAD) study, a prospective cohort of 2,990 older drivers (ages 65-79 at enrollment). The LongROAD study is a five-year multisite study and data collection ended October 31, 2022. Participants were recruited using a convenience sample from the health centers roster. The number of participants approached were 40,806 with 7.3% enrolling in the study. Sixty-one participants stopped driving permanently by year five and had data before and after DC. The PROMIS®-29 Adult Profile was utilized and includes: 1) Depression, 2) Anxiety, 3) Ability to Participate in Social Roles and Activities, 4) Physical Function, 5) Fatigue, 6) Pain Interference, 7) Sleep Disturbance, and 8) Numeric Pain Rating Scale. Adjusted (age, education and gender) individual growth models with 2989 participants with up to six observations from baseline to year 5 in the models (ranging from n = 15,041 to 15,300) were utilized. RESULTS: Ability to participate in social roles and activities after DC improved overall. For those who volunteered, social roles and activities declined not supporting our hypothesis. For those who accessed alternative transportation, fatigue had an initial large increase immediately following DC thus not supporting our hypothesis. Urban residents had worse function and more symptoms after DC compared to rural residents (not supporting our hypothesis) except for social roles and activities that declined steeply (supporting our hypothesis). CONCLUSIONS: Educating older adults that utilizing alternative transportation may cause initial fatigue after DC is recommended. Accessing alternative transportation to maintain social roles and activities is paramount for rural older adults after DC especially for older adults who like to volunteer.

A border health crisis at the United States-Mexico border: an urgent call to action.

In this Viewpoint, we provide an overview of the worsening trend of traumatic injuries across the United States-Mexico border after its recent fortification and height extension to 30-feet. We further characterize the international factors driving migration and the current U.S. policies and political climate that will allow this public health crisis to progress. Finally, we provide recommendations involving prevention efforts, effective resource allocation, and advocacy that will start addressing the humanitarian and economic consequences of current U.S. border policies and infrastructure.

Relationship of Decisional Conflict About Driving Habits Between Older Adult Drivers and Their Family Members and Close Friends.

This study examines the relationship of decisional conflict about driving habits between older adult drivers (≥70 years old) and their family members and close friends. This secondary analysis utilizes data originating from a multi-site randomized controlled trial assessing the effect of a driving decision aid (DDA) intervention. Decisional conflict about stopping or changing driving habits for drivers was measured with the Decisional Conflict Scale (DCS). Dyadic associations between drivers' and study partners' (SPs') DCS scores were analyzed using an actor-partner interdependence model. Among 228 driver-SP dyads, Dyadic DCS was correlated at baseline (r = .18, p < .01), and pre-intervention DCS was associated with post-intervention DCS (p < .001 for SPs [ß = .73] and drivers [ß = .73]). Drivers' baseline DCS and SPs' post-intervention DCS were slighly correlated (ß = .10; p = .036). Higher decisional conflict about driving among older drivers is frequently shared by their SPs. Shared decisional conflict may persist beyond intervening to support decision-making about driving cessation.

Migraine headaches are associated with motor vehicle crashes and driving habits among older drivers: Prospective cohort study.

BACKGROUND: Migraine headache is common in older adults, often causing symptoms that may affect driving safety. This study examined associations of migraine with motor vehicle crashes (MVCs) and driving habits in older drivers and assessed modification of associations by medication use. METHODS: In a multi-site, prospective cohort study of active drivers aged 65-79 (53% female), we assessed prevalent migraine (i.e., ever had migraine, reported at enrollment), incident migraine (diagnosis first reported at a follow-up visit), and medications typically used for migraine prophylaxis and treatment. During 2-year follow-up, we recorded self-reported MVCs and measured driving habits using in-vehicle GPS devices. Associations of prevalent migraine with driving outcomes were estimated in multivariable mixed models. Using a matched design, associations of incident migraine with MVCs in the subsequent year were estimated with conditional logistic regression. Interactions between migraine and medications were tested in all models. RESULTS: Of 2589 drivers, 324 (12.5%) reported prevalent migraine and 34 (1.3%) incident migraine. Interactions between migraine and medications were not statistically significant in any models. Prevalent migraine was not associated with MVCs in the subsequent 2 years (adjusted OR [aOR] = 0.98; 95% CI: 0.72, 1.35), whereas incident migraine significantly increased the odds of having an MVC within 1 year (aOR = 3.27; 1.21, 8.82). Prevalent migraine was associated with small reductions in driving days and trips per month and increases in hard braking events in adjusted models. CONCLUSION: Our results suggest substantially increased likelihood of MVCs in the year after newly diagnosed migraine, indicating a potential need for driving safety interventions in these patients. We found little evidence for MVC risk or substantial changes in driving habits associated with prevalent migraine. Future research should examine timing, frequency, and severity of migraine diagnosis and symptoms, and use of medications specifically prescribed for migraine, in relation to driving outcomes.

Cognitive Preference and Clinical Experience: The Relationship Between Student Nurse Anesthetists' and Certified Registered Nurse Anesthetists' Thinking Styles.

This study examined the relationship between cognitive preference and clinical experience in student registered nurse anesthetists (SRNAs) and certified registered nurse anesthetists (CRNAs). Survey data was collected from enrolled SRNAs and practicing CRNAs via an email link distributive through a network sampling technique. Participants completed the Rational Experiential Inventory (REI-40), which assesses individuals' preference, ability, and engagement with rational and experiential cognitive styles. Data analysis revealed that SRNAs and CRNAs have the ability and engagement preference for rational decision-making. Furthermore, there was no statistical significance in years of clinical experience to cognitive preference, nor was there a statistically significant difference between SRNA and CRNA REI-40 Inventory results. Based on these findings, the dominant cognitive preference is rational cognition and experiential thinking preference remains constant with increased experience. This knowledge contributes to our understanding of CRNAs' decision-making related to cognitive processes and provides insight into SRNA clinical education and CRNA continuing development.

Older Adults and Unintentional Injury.

Clinicians play an important role in the prevention of unintentional injuries. Falls and motor vehicle crashes (MVC) have predictable and overlapping antecedents. Systematic screening for and management of vision impairment, frailty, cognitive impairment, polypharmacy, and inappropriate medications will reduce both falls and MVC risks. Fall-prevention measures, such as strength training, need to be more widely prescribed by physicians and implemented by older adults. Technologically tailored approaches are needed to leverage fall-reduction programs at home, as well as education of older adults regarding home hazards.

Motor Vehicle Crash Risk in Older Adult Drivers With Attention-Deficit/Hyperactivity Disorder.

Importance: Symptoms of attention-deficit/hyperactivity disorder (ADHD), such as inattentiveness and impulsivity, could affect daily functioning and driving performance throughout the life span. Previous research on ADHD and driving safety is largely limited to adolescents and young adults. Objective: To examine the prevalence of ADHD and the association between ADHD and crash risk among older adult drivers. Design, Setting, and Participants: This prospective cohort study collected data from primary care clinics and residential communities in 5 US sites (Ann Arbor, Michigan; Baltimore, Maryland; Cooperstown, New York; Denver, Colorado; and San Diego, California) between July 6, 2015, and March 31, 2019. Participants were active drivers aged 65 to 79 years at baseline enrolled in the Longitudinal Research on Aging Drivers project who were studied for up to 44 months through in-vehicle data recording devices and annual assessments. The data analysis was performed between July 15, 2022, and August 14, 2023. Exposure: Lifetime ADHD based on an affirmative response to the question of whether the participant had ever had ADHD or had ever been told by a physician or other health professional that he or she had ADHD. Main Outcomes and Measures: The main outcomes were hard-braking events defined as maneuvers with deceleration rates of 0.4g or greater, self-reported traffic ticket events, and self-reported vehicular crashes. Multivariable negative binomial modeling was used to estimate adjusted incidence rate ratios (aIRRs) and 95% CIs of outcomes according to exposure status. Results: Of the 2832 drivers studied, 1500 (53.0%) were women and 1332 (47.0%) were men with a mean (SD) age of 71 (4) years. The lifetime prevalence of ADHD in the study sample was 2.6%. Older adult drivers with ADHD had significantly higher incidence rates of hard-braking events per 1000 miles than those without ADHD (1.35 [95% CI, 1.30-1.41] vs 1.15 [95% CI, 1.14-1.16]), as well as self-reported traffic ticket events per 1 million miles (22.47 [95% CI, 16.06-31.45] vs 9.74 [95% CI, 8.99-10.55]) and self-reported vehicular crashes per 1 million miles (27.10 [95% CI, 19.95-36.80] vs 13.50 [95% CI, 12.61-14.46]). With adjustment for baseline characteristics, ADHD was associated with a significant 7% increased risk of hard-braking events (aIRR, 1.07; 95% CI, 1.02-1.12), a 102% increased risk of self-reported traffic ticket events (aIRR, 2.02; 95% CI, 1.42-2.88), and a 74% increased risk of self-reported vehicular crashes (aIRR, 1.74; 95% CI, 1.26-2.40). Conclusions and Relevance: As observed in this prospective cohort study, older adult drivers with ADHD may be at a significantly elevated crash risk compared with their counterparts without ADHD. These findings suggest that effective interventions to improve the diagnosis and clinical management of ADHD among older adults are warranted to promote safe mobility and healthy aging.

Interaction between benzodiazepines and prescription opioids on incidence of hard braking events in older drivers.

BACKGROUND: Polypharmacy use among older adults is of increasing concern for driving safety. This study assesses the individual and joint effects of benzodiazepines and prescription opioids on the incidence of hard braking events in older drivers. METHODS: Data for this study came from the Longitudinal Research on Aging Drivers project-a multisite, prospective cohort study of 2990 drivers aged 65-79 years at enrollment (2015-2017). Adjusted incidence rate ratios (aIRRs) and 95% confidence intervals (CIs) of hard braking events (defined as maneuvers with deceleration rates ≥0.4 g and commonly known as near-crashes) were estimated through multivariable negative binominal modeling. RESULTS: Of the 2929 drivers studied, 167 (5.7%) were taking benzodiazepines,  163 (5.6%) prescription opioids, and 23 (0.8%) both drugs at baseline. The incidence rates of hard braking events per 1000 miles driven were 1.14 (95% CI 1.10-1.18) for drivers using neither benzodiazepines nor prescription opioids, 1.25 (95% CI 1.07-1.43) for those using benzodiazepines only, 1.55 (95% CI 1.35-1.76) for those using prescription opioids only, and 1.63 (95% CI 1.11-2.16) for those using both medications. Multivariable modeling revealed that the use of prescription opioids was associated with a 19% increased risk of hard braking events (aIRR 1.19, 95% CI 1.03-1.36). There existed a positive interaction between the two drugs on the additive scale but not on the multiplicative scale. CONCLUSION: Concurrent use of benzodiazepines and prescription opioids by older drivers appears to affect driving safety through increased incidence of hard braking events.

Mild Cognitive Impairment is Associated with Poorer Everyday Decision Making.

BACKGROUND: Older adults are faced with many unique and highly consequential decisions such as those related to finances, healthcare, and everyday functioning (e.g., driving cessation). Given the significant impact of these decisions on independence, wellbeing, and safety, an understanding of how cognitive impairment may impact decision making in older age is important. OBJECTIVE: To examine the impact of mild cognitive impairment (MCI) on responses to a modified version of the Short Portable Assessment of Capacity for Everyday Decision making (SPACED). METHODS: Participants were community-dwelling, actively driving older adults (N = 301; M age = 77.1 years, SD = 5.1; 69.4% with a college degree or higher; 51.2% female; 95.3% White) enrolled in the Advancing Understanding of Transportation Options (AUTO) study. A generalized linear model adjusted for age, education, sex, randomization group, cognitive assessment method, and study site was used to examine the relationship between MCI status and decision making. RESULTS: MCI status was associated with poorer decision making; participants with MCI missed an average of 2.17 times more points on the SPACED than those without MCI (adjusted mean ratio: 2.17, 95% CI: 1.02, 4.61, p = 0.044). CONCLUSION: This finding supports the idea that older adults with MCI exhibit poorer decision-making abilities than cognitively normal older adults. It also suggests that older adults with MCI may exhibit poorer decision making across a wide range of decision contexts.

Detecting mild cognitive impairment and dementia in older adults using naturalistic driving data and interaction-based classification from influence score.

Several recent studies indicate that atypical changes in driving behaviors appear to be early signs of mild cognitive impairment (MCI) and dementia. These studies, however, are limited by small sample sizes and short follow-up duration. This study aims to develop an interaction-based classification method building on a statistic named Influence Score (i.e., I-score) for prediction of MCI and dementia using naturalistic driving data collected from the Longitudinal Research on Aging Drivers (LongROAD) project. Naturalistic driving trajectories were collected through in-vehicle recording devices for up to 44 months from 2977 participants who were cognitively intact at the time of enrollment. These data were further processed and aggregated to generate 31 time-series driving variables. Because of high dimensional time-series features for driving variables, we used I-score for variable selection. I-score is a measure to evaluate variables' ability to predict and is proven to be effective in differentiating between noisy and predictive variables in big data. It is introduced here to select influential variable modules or groups that account for compound interactions among explanatory variables. It is explainable regarding to what extent variables and their interactions contribute to the predictiveness of a classifier. In addition, I-score boosts the performance of classifiers over imbalanced datasets due to its association with the F1 score. Using predictive variables selected by I-score, interaction-based residual blocks are constructed over top I-score modules to generate predictors and ensemble learning aggregates these predictors to boost the prediction of the overall classifier. Experiments using naturalistic driving data show that our proposed classification method achieves the best accuracy (96%) for predicting MCI and dementia, followed by random forest (93%) and logistic regression (88%). In terms of F1 score and AUC, our proposed classifier achieves 98% and 87%, respectively, followed by random forest (with an F1 score of 96% and an AUC of 79%) and logistic regression (with an F1 score of 92% and an AUC of 77%). The results indicate that incorporating I-score into machine learning algorithms could considerably improve the model performance for predicting MCI and dementia in older drivers. We also performed the feature importance analysis and found that the right to left turn ratio and the number of hard braking events are the most important driving variables to predict MCI and dementia.

Associations of cannabis use with motor vehicle crashes and traffic stops among older drivers: AAA LongROAD study.

BACKGROUND: Acute cannabis use is associated with a higher risk of motor vehicle crashes (MVC). This study aimed to determine if self-reported past-year cannabis use is associated with MVC or traffic stops among older drivers. METHODS: This cross-sectional analysis used data from a multi-center study enrolling active drivers aged 65-79 years. Data regarding cannabis use, MVC, and traffic stops (i.e., being pulled over by police, whether ticketed or not) within the previous 12 months were collected through participant interviews. Log-binomial regression models examined associations of past-year cannabis use with MVC and traffic stops, adjusting for site and sociodemographic and mental health characteristics. RESULTS: Of 2,095 participating older drivers, 186 (8.88%) used cannabis in the past year but only 10 (<0.5%) within an hour before driving in the last 30 days; 11.41% reported an MVC and 9.45% reported a traffic stop. Past-year cannabis users had a higher prevalence of MVC (adjusted prevalence ratio [aPR] = 1.38; 95%CI: 0.96, 2.00; p = 0.086) and traffic stops (aPR = 1.58; 1.06, 2.35; p = 0.024). CONCLUSIONS: Past-year cannabis use was associated with increased traffic stops, which are correlated modestly with increased MVC in past studies and may indicate impaired driving performance. We did not find a statistically significant association of past-year cannabis use with MVC, which may indicate limited sustained effects on driving performance from periodic use among older adults, who report rarely driving immediately after use.

Neighborhood Factors Associated with COVID-19 Cases in California.

BACKGROUND: There is a need to assess neighborhood-level factors driving COVID-19 disparities across racial and ethnic groups. OBJECTIVE: To use census tract-level data to investigate neighborhood-level factors contributing to racial and ethnic group-specific COVID-19 case rates in California. DESIGN: Quasi-Poisson generalized linear models were used to identify neighborhood-level factors associated with COVID-19 cases. In separate sequential models for Hispanic, Black, and Asian, we characterized the associations between neighborhood factors on neighborhood COVID-19 cases. Subanalyses were conducted on neighborhoods with majority Hispanic, Black, and Asian residents to identify factors that might be unique to these neighborhoods. Geographically weighted regression using a quasi-Poisson model was conducted to identify regional differences. MAIN MEASURES: All COVID-19 cases and tests reported through January 31, 2021, to the California Department of Public Health. Neighborhood-level data from census tracts were obtained from American Community Survey 5-year estimates (2015-2019), United States Census (2010), and United States Department of Housing and Urban Development. KEY RESULTS: The neighborhood factors associated with COVID-19 case rate were racial and ethnic composition, age, limited English proficiency (LEP), income, household size, and population density. LEP had the largest influence on the positive association between proportion of Hispanic residents and COVID-19 cases (- 2.1% change). This was also true for proportion of Asian residents (- 1.8% change), but not for the proportion of Black residents (- 0.1% change). The influence of LEP was strongest in areas of the Bay Area, Los Angeles, and San Diego. CONCLUSION: Neighborhood-level contextual drivers of COVID-19 burden differ across racial and ethnic groups.

Distraction "Hangover": Characterization of the Delayed Return to Baseline Driving Risk After Distracting Behaviors.

OBJECTIVE: We measured how long distraction by a smartphone affects simulated driving behaviors after the tasks are completed (i.e., the distraction hangover). BACKGROUND: Most drivers know that smartphones distract. Trying to limit distraction, drivers can use hands-free devices, where they only briefly glance at the smartphone. However, the cognitive cost of switching tasks from driving to communicating and back to driving adds an underappreciated, potentially long period to the total distraction time. METHOD: Ninety-seven 21- to 78-year-old individuals who self-identified as active drivers and smartphone users engaged in a simulated driving scenario that included smartphone distractions. Peripheral-cue and car-following tasks were used to assess driving behavior, along with synchronized eye tracking. RESULTS: The participants' lateral speed was larger than baseline for 15 s after the end of a voice distraction and for up to 25 s after a text distraction. Correct identification of peripheral cues dropped about 5% per decade of age, and participants from the 71+ age group missed seeing about 50% of peripheral cues within 4 s of the distraction. During distraction, coherence with the lead car in a following task dropped from 0.54 to 0.045, and seven participants rear-ended the lead car. Breadth of scanning contracted by 50% after distraction. CONCLUSION: Simulated driving performance drops dramatically after smartphone distraction for all ages and for both voice and texting. APPLICATION: Public education should include the dangers of any smartphone use during driving, including hands-free.

Association of falls and fear of falling with objectively-measured driving habits among older drivers: LongROAD study.

OBJECTIVE: Falls in older adults are associated with increased motor vehicle crash risk, possibly mediated by driving behavior. We examined the relationship of falls and fear of falling (FOF) with subsequent objectively measured driving habits. METHODS: This multi-site, prospective cohort study enrolled 2990 active drivers aged 65-79 (53% female). At enrollment, we assessed falls in the past year and FOF (Short Falls Efficacy Scale-International). Driving outcomes included exposure, avoidance of difficult conditions, and unsafe driving during one-year follow-up, using in-vehicle Global Positioning System devices. RESULTS: Past-year falls were associated with more hard braking events (HBE). High FOF was associated with driving fewer days, miles, and trips, driving nearer home and more HBE. Differences were attenuated and not significant after accounting for health, function, medications and sociodemographics. DISCUSSION: Differences in objectively measured driving habits according to past-year fall history and FOF were largely accounted for by differences in health and medications. Rather than directly affecting driving, falls and FOF may serve as markers for crash risk and reduced community mobility due to age-related changes and poor health.

Equipping Small Businesses to Navigate Health Orders and Occupational Safety During the COVID-19 Pandemic.

From July 2020 to June 2021, the UC San Diego COVID-19 Small Business Outreach Project conducted COVID-19-related educational outreach to small businesses in high-risk communities of San Diego County and distributed over 1,200 toolkits containing COVID-19-related safety tips, best practices, and a summary of pertinent guidelines and COVID-19 vaccine information.

Impact of the COVID-19 Pandemic on Older Adult Driving in the United States.

OBJECTIVES: To examine how the COVID-19 pandemic affected driving and health outcomes in older adults. METHODS: We compared Advancing Understanding of Transportation Options (AUTO) study participants enrolled before (December 2019 to March 2020) versus during the pandemic (May 2020 to June 2021). Participants were English-speaking, licensed drivers (≥70 years) who drove weekly and had a primary care provider at a study site and ≥1 medical condition potentially associated with driving cessation. We used baseline self-reported measures on mobility and health. RESULTS: Compared to those enrolled pre-COVID-19 (n = 61), more participants enrolled during COVID-19 (n = 240) reported driving reductions (26% vs. 70%, p < .001) and more often for personal preference (vs. medical/emotional reasons). While mean social isolation was higher during than pre-COVID-19, self-reported depression, stress, and overall health PROMIS scores did not differ significantly. DISCUSSION: Our findings highlight the resiliency of some older adults and have implications for mitigating the negative effects of driving cessation.

"Is it time to stop driving?": A randomized clinical trial of an online decision aid for older drivers.

BACKGROUND: Many older adults face the difficult decision of when to stop driving. We sought to test whether an online driving decision aid (DDA) would improve decision quality. METHODS: This prospective two-arm randomized trial enrolled English-speaking licensed drivers (age ≥70 years) without significant cognitive impairment but with ≥1 diagnosis associated with increased likelihood of driving cessation; all participants received primary care in clinics associated with study sites in three states. The intervention was the online Healthwise® DDA for older adults addressing "Is it time to stop driving?"; control was web-based information for older drivers only. The primary outcome was decision conflict as estimated by the Decisional Conflict Scale (DCS; lower scores indicate higher quality). Secondary outcomes were knowledge and decision self-efficacy about driving decisions. We examined postrandomization differences in primary and secondary outcomes by study arm using generalized linear mixed-effects models with adjustment for site and prerandomization scores. RESULTS: Among 301 participants (mean age: 77.1 years), 51.2% identified as female and the majority as non-Hispanic (99.0%) and white (95.3%); 98.0% lived in an urban area. Participant characteristics were similar by study arm but differed across sites. Intervention participants had a lower mean DCS score (12.3 DDA vs 15.2 control; adjusted mean ratio [AMR] 0.76, 95%CI 0.61-0.95; p = 0.017). Intervention participants had higher mean knowledge scores (88.9 DDA vs. 79.9 control; OR 1.13, 95%CI 1.01-1.27, p = 0.038); there was no difference between groups in self-efficacy scores. The DDA had high acceptability; 86.9% of those who viewed it said they would recommend it to others in similar situations. CONCLUSIONS: The online Healthwise® DDA decreased decision conflict and increased knowledge in this sample of English-speaking, older adults without significant cognitive impairment, although most chose to continue driving. Use of such resources in clinical or community settings may support older adults as they transition from driving to other forms of mobility. TRIAL REGISTRATION: ClinicalTrials.gov identifier "Advancing Understanding of Transportation Options (AUTO)" NCT04141891.