Adolescent use and co-use of tobacco and cannabis in California: The roles of local policy and density of tobacco, vape, and cannabis retailers around schools

Adolescent tobacco use (particularly vaping) and co-use of cannabis and tobacco have increased, leading some jurisdictions to implement policies intended to reduce youth access to these products; however, their impacts remain unclear. We examine associations between local policy, density of tobacco, vape, and cannabis retailers around schools, and adolescent use and co-use of tobacco/vape and cannabis. We combined 2018 statewide California (US) data on: (a) jurisdiction-level policies related to tobacco and cannabis retail environments, (b) jurisdiction-level sociodemographic composition, (c) retailer locations (tobacco, vape, and cannabis shops), and (d) survey data on 534,176 middle and high school students (California Healthy Kids Survey). Structural equation models examined how local policies and retailer density near schools are associated with frequency of past 30-day cigarette smoking or vaping, cannabis use, and co-use of tobacco/vape and cannabis, controlling for jurisdiction-, school-, and individual-level confounders. Stricter retail environment policies were associated with lower odds of past-month use of tobacco/vape, cannabis, and co-use of tobacco/vape and cannabis. Stronger tobacco/vape policies were associated with higher tobacco/vape retailer density near schools, while stronger cannabis policies and overall policy strength (tobacco/vape and cannabis combined) were associated with lower cannabis and combined retailer densities (summed tobacco/vape and cannabis), respectively. Tobacco/vape shop density near schools was positively associated with tobacco/vape use odds, as was summed retailer density near schools and co-use of tobacco, cannabis. Considering jurisdiction-level tobacco and cannabis control policies are associated with adolescent use of these substances, policymakers may proactively leverage such policies to curb youth tobacco and cannabis use.

Examining the utility of nonlinear machine learning approaches versus linear regression for predicting body image outcomes: The U.S. Body Project I

Most body image studies assess only linear relations between predictors and outcome variables, relying on techniques such as multiple Linear Regression. These predictor variables are often validated multi-item measures that aggregate individual items into a single scale. The advent of machine learning has made it possible to apply Nonlinear Regression algorithms-such as Random Forest and Deep Neural Networks-to identify potentially complex linear and nonlinear connections between a multitude of predictors (e.g., all individual items from a scale) and outcome (output) variables. Using a national dataset, we tested the extent to which these techniques allowed us to explain a greater share of the variance in body-image outcomes (adjusted R²) than possible with Linear Regression. We examined how well the connections between body dissatisfaction and dieting behavior could be predicted from demographic factors and measures derived from objectification theory and the tripartite-influence model. In this particular case, although Random Forest analyses sometimes provided greater predictive power than Linear Regression models, the advantages were small. More generally, however, this paper demonstrates how body image researchers might harness the power of machine learning techniques to identify previously undiscovered relations among body image variables.

Unsupervised Machine Learning for Identifying Challenging Behavior Profiles to Explore Cluster-Based Treatment Efficacy in Children With Autism Spectrum Disorder: Retrospective Data Analysis Study

Background

Challenging behaviors are prevalent among individuals with autism spectrum disorder; however, research exploring the impact of challenging behaviors on treatment response is lacking.

Objective

The purpose of this study was to identify types of autism spectrum disorder based on engagement in different challenging behaviors and evaluate differences in treatment response between groups.

Methods

Retrospective data on challenging behaviors and treatment progress for 854 children with autism spectrum disorder were analyzed. Participants were clustered based on 8 observed challenging behaviors using k means, and multiple linear regression was performed to test interactions between skill mastery and treatment hours, cluster assignment, and gender.

Results

Seven clusters were identified, which demonstrated a single dominant challenging behavior. For some clusters, significant differences in treatment response were found. Specifically, a cluster characterized by low levels of stereotypy was found to have significantly higher levels of skill mastery than clusters characterized by self-injurious behavior and aggression (P<.003).

Conclusions

These findings have implications on the treatment of individuals with autism spectrum disorder. Self-injurious behavior and aggression were prevalent among participants with the worst treatment response, thus interventions targeting these challenging behaviors may be worth prioritizing. Furthermore, the use of unsupervised machine learning models to identify types of autism spectrum disorder shows promise.

Association Between Coffee Intake and Incident Heart Failure Risk: A Machine Learning Analysis of the FHS, the ARIC Study, and the CHS

BACKGROUND

Coronary heart disease, heart failure (HF), and stroke are complex diseases with multiple phenotypes. While many risk factors for these diseases are well known, investigation of as-yet unidentified risk factors may improve risk assessment and patient adherence to prevention guidelines. We investigated the diet domain in FHS (Framingham Heart Study), CHS (Cardiovascular Heart Study), and the ARIC study (Atherosclerosis Risk in Communities) to identify potential lifestyle and behavioral factors associated with coronary heart disease, HF, and stroke.

METHODS

We used machine learning feature selection based on random forest analysis to identify potential risk factors associated with coronary heart disease, stroke, and HF in FHS. We evaluated the significance of selected variables using univariable and multivariable Cox proportional hazards analysis adjusted for known cardiovascular risks. Findings from FHS were then validated using CHS and ARIC.

RESULTS

We identified multiple dietary and behavioral risk factors for cardiovascular disease outcomes including marital status, red meat consumption, whole milk consumption, and coffee consumption. Among these dietary variables, increasing coffee consumption was associated with decreasing long-term risk of HF congruently in FHS, ARIC, and CHS.

CONCLUSIONS

Higher coffee intake was found to be associated with reduced risk of HF in all three studies. Further study is warranted to better define the role, possible causality, and potential mechanism of coffee consumption as a potential modifiable risk factor for HF.

Developing Employment Environments Where Individuals with ASD Thrive: Using Machine Learning to Explore Employer Policies and Practices

An online survey instrument was developed to assess employers' perspectives on hiring job candidates with Autism Spectrum Disorder (ASD). The investigators used K-means clustering to categorize companies in clusters based on their hiring practices related to individuals with ASD. This methodology allowed the investigators to assess and compare the various factors of businesses that successfully hire employees with ASD versus those that do not. The cluster analysis indicated that company structures, policies and practices, and perceptions, as well as the needs of employers and employees, were important in determining who would successfully hire individuals with ASD. Key areas that require focused policies and practices include recruitment and hiring, training, accessibility and accommodations, and retention and advancement.

ML-MEDIC: A Preliminary Study of an Interactive Visual Analysis Tool Facilitating Clinical Applications of Machine Learning for Precision Medicine

Accessible interactive tools that integrate machine learning methods with clinical research and reduce the programming experience required are needed to move science forward. Here, we present Machine Learning for Medical Exploration and Data-Inspired Care (ML-MEDIC), a point-and-click, interactive tool with a visual interface for facilitating machine learning and statistical analyses in clinical research. We deployed ML-MEDIC in the American Heart Association (AHA) Precision Medicine Platform to provide secure internet access and facilitate collaboration. ML-MEDIC's efficacy for facilitating the adoption of machine learning was evaluated through two case studies in collaboration with clinical domain experts. A domain expert review was also conducted to obtain an impression of the usability and potential limitations.

Exploring Age-Related Metamemory Differences using Modified Brier Scores and Hierarchical Clustering

Older adults (OAs) typically experience memory failures as they age. However, with some exceptions, studies of OAs’ ability to assess their own memory functions—Metamemory (MM) — find little evidence that this function is susceptible to age-related decline. Our study examines OAs’ and young adults’ (YAs) MM performance and strategy use. Groups of YAs (N = 138) and OAs (N = 79) performed a MM task that required participants to place bets on how likely they were to remember words in a list. Our analytical approach includes hierarchical clustering, and we introduce a new measure of MM—the modified Brier—in order to adjust for differences in scale usage between participants. Our data indicate that OAs and YAs differ in the strategies they use to assess their memory and in how well their MM matches with memory performance. However, there was no evidence that the chosen strategies were associated with differences in MM match, indicating that there are multiple strategies that might be effective (i.e. lead to similar match) in this MM task.

Trends in Opioid Use in Pediatric Patients in US Emergency Departments From 2006 to 2015

IMPORTANCE

The use of opioids to treat pain in pediatric patients has been viewed as necessary; however, this practice has raised concerns regarding opioid abuse and the effects of opioid use. To effectively adjust policy regarding opioids in the pediatric population, prescribing patterns must be better understood.

OBJECTIVE

To evaluate opioid prescribing patterns in US pediatric patients and factors associated with opioid prescribing.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study used publicly available data from the National Hospital Ambulatory Medical Care Survey from January 1, 2006, to December 31, 2015. Analysis included the use of bivariate and multivariate models to evaluate factors associated with opioid prescribing. Practitioners from emergency departments throughout the United States were surveyed, and data were collected using a representative sample of visits to hospital emergency departments. The study analyzed all emergency department visits included in the National Hospital Ambulatory Medical Care Survey for patients younger than 18 years. All statistical analysis was completed in June of 2018 and updated upon receiving reviewer feedback in October of 2018.

EXPOSURES

Information regarding participants' medications was collected at time of visit. Participants who reported taking 1 or more opioids were identified.

MAIN OUTCOMES AND MEASURES

Evaluation of opioid prescribing patterns across demographic factors and pain diagnoses.

RESULTS

A total of 69 152 visits with patients younger than 18 years (32 727 female) were included, which were extrapolated by the National Hospital Ambulatory Medical Care Survey to represent 293 528 632 visits nationwide, with opioid use representing 21 276 831 (7.25%) of the extrapolated visits. Factors including geographic region, race, age, and payment method were associated with statistically significant differences in opioid prescribing. The Northeast reported an opioid prescribing rate of 4.69% (95% CI, 3.69%-5.70%) vs 8.84% (95% CI, 6.82%-10.86%) in the West (P = .004). White individuals were prescribed an opioid at 8.11% (95% CI, 7.23%-8.99%) of visits vs 5.31% (95% CI, 4.31%-6.32%) for nonwhite individuals (P < .001). Those aged 13 to 17 years were significantly more likely to receive opioid prescriptions (16.20%; 95% CI, 14.29%-18.12%) than those aged 3 to 12 years (6.59%; 95% CI, 5.75%-7.43%) or 0 to 2 years (1.70%; 95% CI, 1.42%-1.98%). Patients using Medicaid for payment were less likely to receive an opioid than those using private insurance (5.47%; 95% CI, 4.79%-6.15% vs 9.73%; 95% CI, 8.56%-10.90%). There was no significant difference in opioid prescription across sexes. Opioid prescribing rates decreased when comparing 2006 to 2010 with 2011 to 2015 (8.23% [95% CI, 6.75%-9.70%] vs 6.30% [95% CI, 5.44%-7.17%]; P < .001); however, opioid prescribing rates remained unchanged in specific pain diagnoses, including pelvic and back pain.

CONCLUSIONS AND RELEVANCE

This research demonstrated an overall reduction in opioid use among pediatric patients from 2011 to 2015 compared with the previous 5 years; however, there appear to be variations in factors associated with opioid prescribing. The association of location, race, payment method, and pain diagnoses with rates of prescribing of opioids suggests areas of potential quality improvement and further research.

Remote sensing observation of annual dust cycles and possible causality of Kawasaki disease outbreaks in Japan

Kawasaki disease (KD) is a rare vascular disease that, if left untreated, can result in irreparable cardiac damage in children. While the symptoms of KD are well-known, as are best practices for treatment, the etiology of the disease and the factors contributing to KD outbreaks remain puzzling to both medical practitioners and scientists alike. Recently, a fungus known as Candida, originating in the farmlands of China, has been blamed for outbreaks in China and Japan, with the hypothesis that it can be transported over long ranges via different wind mechanisms. This paper provides evidence to understand the transport mechanisms of dust at different geographic locations and the cause of the annual spike of KD in Japan. Candida is carried along with many other dusts, particles or aerosols, of various sizes in major seasonal wind currents. The evidence is based upon particle categorization using the Moderate Resolution Imaging Spectrometer (MODIS) Aerosol Optical Depth (AOD), Fine Mode Fraction (FMF) and Ångström Exponent (AE), the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) attenuated backscatter and aerosol subtype, and the Aerosol Robotic Network's (AERONET) derived volume concentration. We found that seasonality associated with aerosol size distribution at different geographic locations plays a role in identifying dominant abundance at each location. Knowing the typical size of the Candida fungus, and analyzing aerosol characteristics using AERONET data reveals possible particle transport association with KD events at different locations. Thus, understanding transport mechanisms and accurate identification of aerosol sources is important in order to understand possible triggers to outbreaks of KD. This work provides future opportunities to leverage machine learning, including state-of-the-art deep architectures, to build predictive models of KD outbreaks, with the ultimate goal of early forecasting and intervention within a nascent global health early-warning system.

An evaluation of the effects of intensity and duration on outcomes across treatment domains for children with autism spectrum disorder

Applied behavior analysis (ABA) is considered an effective treatment for individuals with autism spectrum disorder (ASD), and many researchers have further investigated factors associated with treatment outcomes. However, few studies have focused on whether treatment intensity and duration have differential influences on separate skills. The aim of the current study was to investigate how treatment intensity and duration impact learning across different treatment domains, including academic, adaptive, cognitive, executive function, language, motor, play, and social. Separate multiple linear regression analyses were used to evaluate these relationships. Participants included 1468 children with ASD, ages 18 months to 12 years old, M=7.57 years, s.d.=2.37, who were receiving individualized ABA services. The results indicated that treatment intensity and duration were both significant predictors of mastered learning objectives across all eight treatment domains. The academic and language domains showed the strongest response, with effect sizes of 1.68 and 1.85 for treatment intensity and 4.70 and 9.02 for treatment duration, respectively. These findings are consistent with previous research that total dosage of treatment positively influences outcomes. The current study also expands on extant literature by providing a better understanding of the differential impact that these treatment variables have across various treatment domains.

T-Time: A data repository of T cell and calcium release-activated calcium channel activation imagery

Background

A fundamental understanding of live-cell dynamics is necessary in order to advance scientific techniques and personalized medicine. For this understanding to be possible, image processing techniques, probes, tracking algorithms and many other methodologies must be improved. Currently there are no large open-source datasets containing live-cell imaging to act as a standard for the community. As a result, researchers cannot evaluate their methodologies on an independent benchmark or leverage such a dataset to formulate scientific questions.

Findings

Here we present T-Time, the largest free and publicly available data set of T cell phase contrast imagery designed with the intention of furthering live-cell dynamics research. T-Time consists of over 40 GB of imagery data, and includes annotations derived from these images using a custom T cell identification and tracking algorithm. The data set contains 71 time-lapse sequences containing T cell movement and calcium release activated calcium channel activation, along with 50 time-lapse sequences of T cell activation and T reg interactions. The database includes a user-friendly web interface, summary information on the time-lapse images, and a mechanism for users to download tailored image datasets for their own research. T-Time is freely available on the web at http://ttime.mlatlab.org.

Conclusions

T-Time is a novel data set of T cell images and associated metadata. It allows users to study T cell interaction and activation.

A Program Evaluation of Home and Center-Based Treatment for Autism Spectrum Disorder

The present study aimed to retrospectively compare the relative rates of mastery of exemplars for individuals with ASD (N = 313) who received home-based and center-based services. A between-group analysis found that participants mastered significantly more exemplars per hour when receiving center-based services than home-based services. Likewise, a paired-sample analysis found that participants who received both home and center-based services had mastered 100 % more per hour while at the center than at home. These analyses indicated that participants demonstrated higher rates of learning during treatment that was provided in a center setting than in the participant's home.

AMP: A platform for managing and mining data in the treatment of Autism Spectrum Disorder

We introduce AMP (Autism Management Platform), an integrated health care information system for capturing, analyzing, and managing data associated with the diagnosis and treatment of Autism Spectrum Disorder in children. AMP's mobile application simplifies the means by which parents, guardians, and clinicians can collect and share multimedia data with one another, facilitating communication and reducing data redundancy, while simplifying retrieval. Additionally, AMP provides an intelligent web interface and analytics platform which allow physicians and specialists to aggregate and mine patient data in real-time, as well as give relevant feedback to automatically learn data filtering preferences over time. Together AMP's mobile app, web client, and analytics engine implement a rich set of features that streamline the data collection and analysis process in the context of a secure and easy-to-use system so that data may be more effectively leveraged to guide treatment.

An Evaluation of the Impact of Supervision Intensity, Supervisor Qualifications, and Caseload on Outcomes in the Treatment of Autism Spectrum Disorder

Ample research has shown the benefits of intensive applied behavior analysis (ABA) treatment for autism spectrum disorder (ASD); research that investigates the role of treatment supervision, however, is limited. The present study examined the relationship between mastery of learning objectives and supervision hours, supervisor credentials, years of experience, and caseload in a large sample of children with ASD (N = 638). These data were retrieved from a large archival database of children with ASD receiving community-based ABA services. When analyzed together via a multiple linear regression, supervision hours and treatment hours accounted for only slightly more of the observed variance (r² = 0.34) than treatment hours alone (r² = 0.32), indicating that increased supervision hours do not dramatically increase the number of mastered learning objectives. In additional regression analyses, supervisor credentials were found to have a significant impact on the number of mastered learning objectives, wherein those receiving supervision from a Board Certified Behavior Analyst (BCBA) mastered significantly more learning objectives. Likewise, the years of experience as a clinical supervisor showed a small but significant impact on the mastery of learning objectives. A supervisor's caseload, however, was not a significant predictor of the number of learning objectives mastered. These findings provide guidance for best practice recommendations.

ChemDB update--full-text search and virtual chemical space

ChemDB is a chemical database containing nearly 5M commercially available small molecules, important for use as synthetic building blocks, probes in systems biology and as leads for the discovery of drugs and other useful compounds. The data is publicly available over the web for download and for targeted searches using a variety of powerful methods. The chemical data includes predicted or experimentally determined physicochemical properties, such as 3D structure, melting temperature and solubility. Recent developments include optimization of chemical structure (and substructure) retrieval algorithms, enabling full database searches in less than a second. A text-based search engine allows efficient searching of compounds based on over 65M annotations from over 150 vendors. When searching for chemicals by name, fuzzy text matching capabilities yield productive results even when the correct spelling of a chemical name is unknown, taking advantage of both systematic and common names. Finally, built in reaction models enable searches through virtual chemical space, consisting of hypothetical products readily synthesizable from the building blocks in ChemDB.

AVAILABILITY

ChemDB and Supplementary Materials are available at http://cdb.ics.uci.edu.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.