This report details the 10 leading causes for the 20,360 deaths of children and adolescents in the United States in 2016. The analysis also includes trends over time and comparisons among countries.
It is critical to characterize submarine landslide hazards near dense coastal populations, especially in areas with active faults, which can trigger slope failure, subsequent tsunamis, and damage ...seabed infrastructure during earthquake shaking. Offshore southern California, numerous marine geophysical surveys have been conducted over the past decade, and high‐resolution bathymetric and subsurface data now cover about 60 percent of the total region between Point Conception and the United States‐Mexico border from the California coast out to the base of Patton Escarpment ∼200 km offshore. In a comprehensive compilation and interpretive mapping effort, we find evidence of seafloor failure throughout offshore southern California with nearly 1,500 submarine landslide‐related features, including 63 discrete slide deposits with debris and >1,400 slide‐related scarps. In our analysis, we highlight new mapping of submarine landslides in Catalina Basin, the Del Mar slide, the San Gabriel slide complex, and the 232 km2 San Nicolas slide, the largest area of any known submarine landslide mass offshore southern California. Analysis of the spatial distribution of submarine landslide features suggests that most mapped slide features are located relatively near coastal sediment sources, particularly during sea‐level lowstand conditions, which underscores the importance of sediment supply and sediment accumulation on low‐gradient slopes as failure preconditioning processes. Tectonically driven uplift at shelf edges and along basin flanks is another key preconditioning factor, and our results also suggest that earthquakes along active faults trigger mass wasting, especially for repeated, small‐scale failures on tectonically steepened slopes.
Plain Language Summary
Submarine landslides can damage seabed infrastructure such as cables and moorings, cause tsunamis, and be triggered by shaking from earthquakes. It is important to understand the risk of submarine landslides near dense coastal populations, particularly where earthquakes also pose hazards. Offshore southern California, we have new high‐resolution seafloor and subsurface imaging data that help us to identify submarine landslide deposits in the marine environment. In our study, we map and compile evidence for submarine landslides and find nearly 1,500 slide‐related features, 63 of which feature significant debris deposits. We describe some of the larger slides in this study for the first time, including submarine landslides in Catalina Basin, the Del Mar slide, the San Gabriel slide complex, and the 232 square kilometer San Nicolas slide, which is one of the largest known submarine landslide masses offshore southern California. Our work suggests that submarine landslide failure processes offshore southern California require a combination of (a) significant sediment supply, which is enhanced during low sea‐level conditions, (b) uplift and steepening along faults, and (c) earthquake shaking to trigger slide events.
Key Points
Comprehensive analysis of submarine landslides in southern California provides new metrics on their size, distribution, timing, and geology
Submarine landslide failure processes are controlled by a combination of sediment deposition, tectonic uplift, and earthquake triggering
Small‐scale failures dominate steep areas near Quaternary faults; large slides tend to occur on lower slopes farther from faults
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Glacial retreat in recent decades has exposed unstable slopes and allowed deep water to extend beneath some of those slopes. Slope failure at the terminus of Tyndall Glacier on 17 October 2015 sent ...180 million tons of rock into Taan Fiord, Alaska. The resulting tsunami reached elevations as high as 193 m, one of the highest tsunami runups ever documented worldwide. Precursory deformation began decades before failure, and the event left a distinct sedimentary record, showing that geologic evidence can help understand past occurrences of similar events, and might provide forewarning. The event was detected within hours through automated seismological techniques, which also estimated the mass and direction of the slide - all of which were later confirmed by remote sensing. Our field observations provide a benchmark for modeling landslide and tsunami hazards. Inverse and forward modeling can provide the framework of a detailed understanding of the geologic and hazards implications of similar events. Our results call attention to an indirect effect of climate change that is increasing the frequency and magnitude of natural hazards near glaciated mountains.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Just-in-time adaptive interventions (JITAIs) are time-varying adaptive interventions that use frequent opportunities for the intervention to be adapted-weekly, daily, or even many times a day. The ...microrandomized trial (MRT) has emerged for use in informing the construction of JITAIs. MRTs can be used to address research questions about whether and under what circumstances JITAI components are effective, with the ultimate objective of developing effective and efficient JITAI. The purpose of this article is to clarify why, when, and how to use MRTs; to highlight elements that must be considered when designing and implementing an MRT; and to review primary and secondary analyses methods for MRTs. We briefly review key elements of JITAIs and discuss a variety of considerations that go into planning and designing an MRT. We provide a definition of causal excursion effects suitable for use in primary and secondary analyses of MRT data to inform JITAI development. We review the weighted and centered least-squares (WCLS) estimator which provides consistent causal excursion effect estimators from MRT data. We describe how the WCLS estimator along with associated test statistics can be obtained using standard statistical software such as R (R Core Team, 2019). Throughout we illustrate the MRT design and analyses using the HeartSteps MRT, for developing a JITAI to increase physical activity among sedentary individuals. We supplement the HeartSteps MRT with two other MRTs, SARA and BariFit, each of which highlights different research questions that can be addressed using the MRT and experimental design considerations that might arise.
Translational AbstractWith the development of smartphone and wearable sensors, we have unprecedented opportunity to use mobile devices to facilitate healthy behavior change. Mobile health interventions, such as push notifications containing helpful suggestions have the potential to make an impact as people go about their day-to-day lives. However, delivering too many push notifications or delivering these notifications at the wrong time could be irritating and burdensome, making the intervention less effective. Therefore, it is crucial to find out when, in what context, and what intervention content to deliver to each person to make the intervention the most effective. In this paper we review the microrandomized trial (MRT), a study design that can be used to improve mobile health interventions by answering the above questions. In an MRT, each person is repeatedly randomized to receive or not receive an intervention, often hundreds of thousands of times throughout the trial. We review the key elements of MRTs and provide three case studies of real-world MRTs in various application realms including physical activity and substance abuse. We also provide an accessible review of data analysis methods for MRTs.
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CEKLJ, FFLJ, NUK, ODKLJ, PEFLJ, UPUK
Individuals in stressful work environments often experience mental health issues, such as depression. Reducing depression rates is difficult because of persistently stressful work environments and ...inadequate time or resources to access traditional mental health care services. Mobile health (mHealth) interventions provide an opportunity to deliver real-time interventions in the real world. In addition, the delivery times of interventions can be based on real-time data collected with a mobile device. To date, data and analyses informing the timing of delivery of mHealth interventions are generally lacking.
This study aimed to investigate when to provide mHealth interventions to individuals in stressful work environments to improve their behavior and mental health. The mHealth interventions targeted 3 categories of behavior: mood, activity, and sleep. The interventions aimed to improve 3 different outcomes: weekly mood (assessed through a daily survey), weekly step count, and weekly sleep time. We explored when these interventions were most effective, based on previous mood, step, and sleep scores.
We conducted a 6-month micro-randomized trial on 1565 medical interns. Medical internship, during the first year of physician residency training, is highly stressful, resulting in depression rates several folds higher than those of the general population. Every week, interns were randomly assigned to receive push notifications related to a particular category (mood, activity, sleep, or no notifications). Every day, we collected interns' daily mood valence, sleep, and step data. We assessed the causal effect moderation by the previous week's mood, steps, and sleep. Specifically, we examined changes in the effect of notifications containing mood, activity, and sleep messages based on the previous week's mood, step, and sleep scores. Moderation was assessed with a weighted and centered least-squares estimator.
We found that the previous week's mood negatively moderated the effect of notifications on the current week's mood with an estimated moderation of -0.052 (P=.001). That is, notifications had a better impact on mood when the studied interns had a low mood in the previous week. Similarly, we found that the previous week's step count negatively moderated the effect of activity notifications on the current week's step count, with an estimated moderation of -0.039 (P=.01) and that the previous week's sleep negatively moderated the effect of sleep notifications on the current week's sleep with an estimated moderation of -0.075 (P<.001). For all three of these moderators, we estimated that the treatment effect was positive (beneficial) when the moderator was low, and negative (harmful) when the moderator was high.
These findings suggest that an individual's current state meaningfully influences their receptivity to mHealth interventions for mental health. Timing interventions to match an individual's state may be critical to maximizing the efficacy of interventions.
ClinicalTrials.gov NCT03972293; http://clinicaltrials.gov/ct2/show/NCT03972293.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Precipitants of alcohol use transitions can differ from generalized risk factors. We extend prior research by predicting transitions in alcohol use disorder (AUD) during adolescence and emerging ...adulthood.
From 12/2009-9/2011, research assistants recruited 599 drug-using youth age 14-24 from Level-1 Emergency Department in Flint, Michigan. Youth were assessed at baseline and four biannual follow-ups, including a MINI Neuropsychiatric interview to diagnose AUD (abuse/dependence). We modeled AUD transitions using continuous time Markov Chains with transition probabilities modulated by validated measures of demographics, anxiety/depression symptoms, cannabis use, peer drinking, parental drinking, and violence exposure. Separate models were fit for underage (<21) and those of legal drinking age.
We observed 2,024 pairs of consecutive AUD states, including 264 transitions (119 No-AUD→AUD; 145 AUD→No-AUD); 194 (32.4%) individuals were diagnosed with AUD at ≥1 assessment. Among age 14-20, peer drinking increased AUD onset (No-AUD→AUD transition) rates (Hazard ratio-HR = 1.70; 95%CI: 1.13,2.54), parental drinking lowered AUD remission (AUD→No-AUD transition) rates (HR = 0.53; 95%CI: 0.29,0.97), and cannabis use severity both hastened AUD onset (HR = 1.18; 95%CI: 1.06,1.32) and slowed AUD remission (HR = 0.85; 95%CI: 0.76,0.95). Among age 21-24, anxiety/depression symptoms both increased AUD onset rates (HR = 1.35; 95%CI: 1.13,1.60) and decreased AUD remission rates (HR = 0.74; 95%CI: 0.63,0.88). Friend drinking hastened AUD onset (HR = 1.18, 95%CI: 1.05,1.33), and slowed AUD remission (HR = 0.84; 95%CI: 0.75,0.95). Community violence exposure slowed AUD remission (HR = 0.69, 95%CI: 0.48,0.99). In both age groups, males had >2x the AUD onset rate of females, but there were no sex differences in AUD remission rates. Limitations, most notably that this study occurred at a single site, are discussed.
Social influences broadly predicted AUD transitions in both age groups. Transitions among younger youth were predicted by cannabis use, while those among older youth were predicted more by internalizing symptoms and stress exposure (e.g., community violence). Our results suggest age-specific AUD etiology, and contrasts between prevention and treatment strategies.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
To characterize youth seeking care for assault injuries, the context of violence, and previous emergency department (ED) service utilization to inform ED-based injury prevention.
A consecutive sample ...of youth (14-24) presenting to an urban ED with an assault injury completed a survey of partner violence, gun/knife victimization, gang membership, and context of the fight.
A total of 925 youth entered the ED with an assault injury; 718 completed the survey (15.4% refused); 730 comparison youth were sampled. The fights leading to the ED visit occurred at home (37.6%) or on streets (30.4%), and were commonly with a known person (68.3%). Fights were caused by issues of territory (23.3%) and retaliation (8.9%); 20.8% of youth reported substance use before the fight. The assault-injured group reported more peer/partner violence and more gun experiences. Assault-injured youth reported higher past ED utilization for assault (odds ratio OR: 2.16) or mental health reasons (OR: 7.98). Regression analysis found the assault-injured youth had more frequent weapon use (OR: 1.25) and substance misuse (OR: 1.41).
Assault-injured youth seeking ED care report higher levels of previous violence, weapon experience, and substance use compared with a comparison group seeking care for other complaints. Almost 10% of assault-injured youth had another fight-related ED visit in the previous year, and ~5% had an ED visit for mental health. Most fights were with people known to them and for well-defined reasons, and were therefore likely preventable. The ED is a critical time to interact with youth to prevent future morbidity.
Objective Preventing sexual violence among college students is a public health priority. This paper was catalyzed by a summit convened in 2018 to review the state of the science on campus sexual ...violence prevention. We summarize key risk and vulnerability factors and campus-based interventions, and provide directions for future research pertaining to campus sexual violence. Results and Conclusions: Although studies have identified risk factors for campus sexual violence, longitudinal research is needed to examine time-varying risk factors across social ecological levels (individual, relationship, campus context/broader community and culture) and data are particularly needed to identify protective factors. In terms of prevention, promising individual and relational level interventions exist, including active bystander, resistance, and gender transformative approaches; however, further evidence-based interventions are needed, particularly at the community-level, with attention to vulnerability factors and inclusion for marginalized students.
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DOBA, FSPLJ, IJS, IZUM, KILJ, NUK, OILJ, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK, VSZLJ
Emerging adults’ (EAs; ages 18–25) perceived risk of cannabis-related harms has decreased in recent decades, potentially contributing to their high prevalence of cannabis consumption. With the ...changing cannabis policy and product landscape, it is critical to understand perceived risk related to different consumption methods (e.g., smoking, dabbing). We examined differences in cannabis risk perceptions by method and consumption patterns.
EAs recruited from an emergency department (N=359, 71.3% female, 53.5% Black) completed assessments on individual characteristics, cannabis/other substance use, and perceived risk of cannabis-related harm for four different methods (smoking, vaping, dabbing, ingestion) and two use frequencies (occasional, regular). Analyses examined associations between variables of interest and three mutually exclusive groups: no cannabis use, smoking-only, and multiple/other methods.
Forty-two percent of EAs reported no past 3-month cannabis use, 22.8% reported smoking only, and 35.1% reported consumption via multiple/other methods. Among all participants, the methods and frequency with the largest number of EAs endorsing any perceived risk from cannabis were dabbing and vaping cannabis regularly; smoking occasionally had the smallest number of EAs endorsing perceived risk. A greater proportion of EAs in the no use group viewed vaping cannabis regularly as having the most risk (63.6%), whereas the largest proportion of EAs in the smoking-only (64.6%) and multiple/other methods (47.2%) groups perceived dabbing regularly as having the most risk.
This work shows that EAs vary in perceptions of risk across methods of cannabis use and can inform potential directions for public health and policy efforts.
•Cannabis risk perceptions vary by cannabis consumption patterns and method.•Emerging adults (EAs) perceived smoking cannabis occasionally with less risk.•Dabbing and vaping cannabis regularly were perceived as having greater risk.•EAs who consumed cannabis via multiple/other methods reported less perceived cannabis risk.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The risk for firearm violence among high-risk youth after treatment for an assault is unknown.
In this 2-year prospective cohort study, data were analyzed from a consecutive sample of 14- to ...24-year-olds with drug use in the past 6 months seeking assault-injury care (AIG) at an urban level 1 emergency department (ED) compared with a proportionally sampled comparison group (CG) of drug-using nonassaulted youth. Validated measures were administered at baseline and follow-up (6, 12, 18, 24 months).
A total of 349 AIG and 250 CG youth were followed for 24 months. During the follow-up period, 59% of the AIG reported firearm violence, a 40% higher risk than was observed among the CG (59.0% vs. 42.5%; relative risk RR = 1.39). Among those reporting firearm violence, 31.7% reported aggression, and 96.4% reported victimization, including 19 firearm injuries requiring medical care and 2 homicides. The majority with firearm violence (63.5%) reported at least 1 event within the first 6 months. Poisson regression identified baseline predictors of firearm violence, including male gender (RR = 1.51), African American race (RR = 1.26), assault-injury (RR = 1.35), firearm possession (RR = 1.23), attitudes favoring retaliation (RR = 1.03), posttraumatic stress disorder (RR = 1.39), and a drug use disorder (RR = 1.22).
High-risk youth presenting to urban EDs for assault have elevated rates of subsequent firearm violence. Interventions at an index visit addressing substance use, mental health needs, retaliatory attitudes, and firearm possession may help decrease firearm violence among urban youth.
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