Association of Water Arsenic With Incident Diabetes in U.S. Adults: The Multi-Ethnic Study of Atherosclerosis and the Strong Heart Study.

OBJECTIVE: We examined the association of arsenic in federally regulated community water systems (CWSs) and unregulated private wells with type 2 diabetes (T2D) incidence in the Strong Heart Family Study (SHFS), a prospective study of American Indian communities, and the Multi-Ethnic Study of Atherosclerosis (MESA), a prospective study of racially and ethnically diverse urban U.S. communities. RESEARCH DESIGN AND METHODS: We evaluated 1,791 participants from SHFS and 5,777 participants from MESA who had water arsenic estimates available and were free of T2D at baseline (2001-2003 and 2000-2002, respectively). Participants were followed for incident T2D until 2010 (SHFS cohort) or 2019 (MESA cohort). We used Cox proportional hazards mixed-effects models to account for clustering by family and residential zip code, with adjustment for sex, baseline age, BMI, smoking status, and education. RESULTS: T2D incidence was 24.4 cases per 1,000 person-years (mean follow-up, 5.6 years) in SHFS and 11.2 per 1,000 person-years (mean follow-up, 14.0 years) in MESA. In a meta-analysis across the SHFS and MESA cohorts, the hazard ratio (95% CI) per doubling in CWS arsenic was 1.10 (1.02, 1.18). The corresponding hazard ratio was 1.09 (0.95, 1.26) in the SHFS group and 1.10 (1.01, 1.20) in the MESA group. The corresponding hazard ratio (95% CI) for arsenic in private wells and incident T2D in SHFS was 1.05 (0.95, 1.16). We observed statistical interaction and larger magnitude hazard ratios for participants with BMI <25 kg/m2 and female participants. CONCLUSIONS: Low to moderate water arsenic levels (<10 µg/L) were associated with T2D incidence in the SHFS and MESA cohorts.

Exploring Chemical Reaction Space with Machine Learning Models: Representation and Feature Perspective.

Chemical reactions serve as foundational building blocks for organic chemistry and drug design. In the era of large AI models, data-driven approaches have emerged to innovate the design of novel reactions, optimize existing ones for higher yields, and discover new pathways for synthesizing chemical structures comprehensively. To effectively address these challenges with machine learning models, it is imperative to derive robust and informative representations or engage in feature engineering using extensive data sets of reactions. This work aims to provide a comprehensive review of established reaction featurization approaches, offering insights into the selection of representations and the design of features for a wide array of tasks. The advantages and limitations of employing SMILES, molecular fingerprints, molecular graphs, and physics-based properties are meticulously elaborated. Solutions to bridge the gap between different representations will also be critically evaluated. Additionally, we introduce a new frontier in chemical reaction pretraining, holding promise as an innovative yet unexplored avenue.

Rapid single-particle chemical imaging of nanoplastics by SRS microscopy.

Plastics are now omnipresent in our daily lives. The existence of microplastics (1 µm to 5 mm in length) and possibly even nanoplastics (<1 µm) has recently raised health concerns. In particular, nanoplastics are believed to be more toxic since their smaller size renders them much more amenable, compared to microplastics, to enter the human body. However, detecting nanoplastics imposes tremendous analytical challenges on both the nano-level sensitivity and the plastic-identifying specificity, leading to a knowledge gap in this mysterious nanoworld surrounding us. To address these challenges, we developed a hyperspectral stimulated Raman scattering (SRS) imaging platform with an automated plastic identification algorithm that allows micro-nano plastic analysis at the single-particle level with high chemical specificity and throughput. We first validated the sensitivity enhancement of the narrow band of SRS to enable high-speed single nanoplastic detection below 100 nm. We then devised a data-driven spectral matching algorithm to address spectral identification challenges imposed by sensitive narrow-band hyperspectral imaging and achieve robust determination of common plastic polymers. With the established technique, we studied the micro-nano plastics from bottled water as a model system. We successfully detected and identified nanoplastics from major plastic types. Micro-nano plastics concentrations were estimated to be about 2.4 ± 1.3 × 105 particles per liter of bottled water, about 90% of which are nanoplastics. This is orders of magnitude more than the microplastic abundance reported previously in bottled water. High-throughput single-particle counting revealed extraordinary particle heterogeneity and nonorthogonality between plastic composition and morphologies; the resulting multidimensional profiling sheds light on the science of nanoplastics.

Changing adolescent activity patterns and the correlation of self-esteem and externalizing mental health symptoms across time: results from the USA from 1991 through 2020.

BACKGROUND: Common adolescent psychiatric symptoms cluster into two dominant domains: internalizing and externalizing. Both domains are linked to self-esteem, which serves as a protective factor against a wide range of internalizing and externalizing problems. This study examined trends in US adolescents' self-esteem and externalizing symptoms, and their correlation, by sex and patterns of time use. METHODS: Using Monitoring the Future data (N = 338 896 adolescents, grades:8/10/12, years:1991-2020), we generated six patterns of time use using latent profile analysis with 17 behavior items (e.g. sports participation, parties, paid work). Groups were differentiated by high/low engagement in sports and either paid work or high/low peer socialization. Within each group, we mapped annual, sex-stratified means of (and correlation between) self-esteem and externalizing factors. We also examined past-decade rates of change for factor means using linear regression and mapped proportions with top-quartile levels of poor self-esteem, externalizing symptoms, or both. RESULTS: We found consistent increases in poor self-esteem, decreases in externalizing symptoms, and a positive correlation between the two across nearly all activity groups. We also identified a relatively constant proportion of those with high levels of both in every group. Increases in poor self-esteem were most pronounced for female adolescents with low levels of socializing, among whom externalizing symptoms also increased. CONCLUSIONS: Rising trends in poor self-esteem are consistent across time use groups, as is the existence of a group facing poor self-esteem and externalizing symptoms. Effective interventions for adolescents' poor self-esteem/co-occurring symptoms are needed broadly, but especially among female adolescents with low peer socialization.

Multiple imputation of more than one environmental exposure with nondifferential measurement error.

Measurement error is common in environmental epidemiologic studies, but methods for correcting measurement error in regression models with multiple environmental exposures as covariates have not been well investigated. We consider a multiple imputation approach, combining external or internal calibration samples that contain information on both true and error-prone exposures with the main study data of multiple exposures measured with error. We propose a constrained chained equations multiple imputation (CEMI) algorithm that places constraints on the imputation model parameters in the chained equations imputation based on the assumptions of strong nondifferential measurement error. We also extend the constrained CEMI method to accommodate nondetects in the error-prone exposures in the main study data. We estimate the variance of the regression coefficients using the bootstrap with two imputations of each bootstrapped sample. The constrained CEMI method is shown by simulations to outperform existing methods, namely the method that ignores measurement error, classical calibration, and regression prediction, yielding estimated regression coefficients with smaller bias and confidence intervals with coverage close to the nominal level. We apply the proposed method to the Neighborhood Asthma and Allergy Study to investigate the associations between the concentrations of multiple indoor allergens and the fractional exhaled nitric oxide level among asthmatic children in New York City. The constrained CEMI method can be implemented by imposing constraints on the imputation matrix using the mice and bootImpute packages in R.

Contribution of arsenic and uranium in private wells and community water systems to urinary biomarkers in US adults: The Strong Heart Study and the Multi-Ethnic Study of Atherosclerosis.

BACKGROUND: Chronic exposure to inorganic arsenic (As) and uranium (U) in the United States (US) occurs from unregulated private wells and federally regulated community water systems (CWSs). The contribution of water to total exposure is assumed to be low when water As and U concentrations are low. OBJECTIVE: We examined the contribution of water As and U to urinary biomarkers in the Strong Heart Family Study (SHFS), a prospective study of American Indian communities, and the Multi-Ethnic Study of Atherosclerosis (MESA), a prospective study of racially/ethnically diverse urban U.S. communities. METHODS: We assigned residential zip code-level estimates in CWSs (µg/L) and private wells (90th percentile probability of As >10 µg/L) to up to 1485 and 6722 participants with dietary information and urinary biomarkers in the SHFS (2001-2003) and MESA (2000-2002; 2010-2011), respectively. Urine As was estimated as the sum of inorganic and methylated species, and urine U was total uranium. We used linear mixed-effects models to account for participant clustering and removed the effect of dietary sources via regression adjustment. RESULTS: The median (interquartile range) urine As was 5.32 (3.29, 8.53) and 6.32 (3.34, 12.48) µg/L for SHFS and MESA, respectively, and urine U was 0.037 (0.014, 0.071) and 0.007 (0.003, 0.018) µg/L. In a meta-analysis across both studies, urine As was 11% (95% CI: 3, 20%) higher and urine U was 35% (5, 73%) higher per twofold higher CWS As and U, respectively. In the SHFS, zip-code level factors such as private well and CWS As contributed 46% of variation in urine As, while in MESA, zip-code level factors, e.g., CWS As and U, contribute 30 and 49% of variation in urine As and U, respectively. IMPACT STATEMENT: We found that water from unregulated private wells and regulated CWSs is a major contributor to urinary As and U (an estimated measure of internal dose) in both rural, American Indian populations and urban, racially/ethnically diverse populations nationwide, even at levels below the current regulatory standard. Our findings indicate that additional drinking water interventions, regulations, and policies can have a major impact on reducing total exposures to As and U, which are linked to adverse health effects even at low levels.

Developing radiology diagnostic tools for pulmonary fibrosis using machine learning methods.

BACKGROUND: Accurate and prompt diagnosis of the different patterns for pulmonary fibrosis is essential for patient management. However, accurate diagnosis of the specific pattern is challenging due to overlapping radiographic characteristics. MATERIALS AND METHODS: We conducted a retrospective chart review utilizing two machine learning methods, classification and regression tree and Bayesian additive regression tree, to select the most important radiographic features for diagnosing the three most common fibrosis patterns and created an online diagnostic app for convenient implementation. RESULTS: Four hundred patients (median age of 67 with inter quartile range 58-73; 200 males) were included in the study. Peripheral distribution, homogeneity, lower lobe predominance and mosaic attenuation of fibrosis are the four most important features identified. Bayesian additive regression tree demonstrates better performance than classification and regression tree in diagnosis prediction and provides the predicted probability of each diagnosis with uncertainty intervals for each combination of features. CONCLUSION: The model and app built with Bayesian additive regression tree can be used as an effective tool in assisting radiologists in the diagnostic process of pulmonary fibrosis pattern recognition.

Using leave-one-out cross validation (LOO) in a multilevel regression and poststratification (MRP) workflow: A cautionary tale.

In recent decades, multilevel regression and poststratification (MRP) has surged in popularity for population inference. However, the validity of the estimates can depend on details of the model, and there is currently little research on validation. We explore how leave-one-out cross validation (LOO) can be used to compare Bayesian models for MRP. We investigate two approximate calculations of LOO: Pareto smoothed importance sampling (PSIS-LOO) and a survey-weighted alternative (WTD-PSIS-LOO). Using two simulation designs, we examine how accurately these two criteria recover the correct ordering of model goodness at predicting population and small-area estimands. Focusing first on variable selection, we find that neither PSIS-LOO nor WTD-PSIS-LOO correctly recovers the models' order for an MRP population estimand, although both criteria correctly identify the best and worst model. When considering small-area estimation, the best model differs for different small areas, highlighting the complexity of MRP validation. When considering different priors, the models' order seems slightly better at smaller-area levels. These findings suggest that, while not terrible, PSIS-LOO-based ranking techniques may not be suitable to evaluate MRP as a method. We suggest this is due to the aggregation stage of MRP, where individual-level prediction errors average out. We validate these results by applying to the real world National Health and Nutrition Examination Survey (NHANES) data in the United States. Altogether, these results show that PSIS-LOO-based model validation tools need to be used with caution and might not convey the full story when validating MRP as a method.

Trajectory analysis of rhinitis in a birth cohort from lower-income New York City neighborhoods.

BACKGROUND: Rhinitis is a prevalent, chronic nasal condition associated with asthma. However, its developmental trajectories remain poorly characterized. OBJECTIVE: We sought to describe the course of rhinitis from infancy to adolescence and the association between identified phenotypes, asthma-related symptoms, and physician-diagnosed asthma. METHODS: We collected rhinitis data from questionnaires repeated across 22 time points among 688 children from infancy to age 11 years and used latent class mixed modeling (LCMM) to identify phenotypes. Once children were between ages 5 and 12, a study physician determined asthma diagnosis. We collected information on the following asthma symptoms: any wheeze, exercise-induced wheeze, nighttime coughing, and emergency department visits. For each, we used LCMM to identify symptom phenotypes. Using logistic regression, we described the association between rhinitis phenotype and asthma diagnosis and each symptom overall and stratified by atopic predisposition and sex. RESULTS: LCMM identified 5 rhinitis trajectory groups: never/infrequent; transient; late onset, infrequent; late onset, frequent; and persistent. LCMM identified 2 trajectories for each symptom, classified as frequent and never/infrequent. Participants with persistent and late onset, frequent phenotypes were more likely to be diagnosed with asthma and to have the frequent phenotype for all symptoms (P < .01). We identified interaction between seroatopy and rhinitis phenotype for physician-diagnosed asthma (P = .04) and exercise-induced wheeze (P = .08). Severe seroatopy was more common among children with late onset, frequent and persistent rhinitis, with nearly 25% of these 2 groups exhibiting sensitivity to 4 or 5 of the 5 allergens tested. CONCLUSIONS: In this prospective, population-based birth cohort, persistent and late onset, frequent rhinitis phenotypes were associated with increased risk of asthma diagnosis and symptoms during adolescence.

Comparison of Influenza-Like Illness (ILI) incidence data from the novel LeCellPHIA participatory surveillance system with COVID-19 case count data, Lesotho, July 2020 - July 2021.

BACKGROUND: While laboratory testing for infectious diseases such as COVID-19 is the surveillance gold standard, it is not always feasible, particularly in settings where resources are scarce. In the small country of Lesotho, located in sub-Saharan Africa, COVID-19 testing has been limited, thus surveillance data available to local authorities are limited. The goal of this study was to compare a participatory influenza-like illness (ILI) surveillance system in Lesotho with COVID-19 case count data, and ultimately to determine whether the participatory surveillance system adequately estimates the case count data. METHODS: A nationally-representative sample was called on their mobile phones weekly to create an estimate of incidence of ILI between July 2020 and July 2021. Case counts from the website Our World in Data (OWID) were used as the gold standard to which our participatory surveillance data were compared. We calculated Spearman's and Pearson's correlation coefficients to compare the weekly incidence of ILI reports to COVID-19 case count data. RESULTS: Over course of the study period, an ILI symptom was reported 1,085 times via participatory surveillance for an average annual cumulative incidence of 45.7 per 100 people (95% Confidence Interval [CI]: 40.7 - 51.4). The cumulative incidence of reports of ILI symptoms was similar among males (46.5, 95% CI: 39.6 - 54.4) and females (45.1, 95% CI: 39.8 - 51.1). There was a slightly higher annual cumulative incidence of ILI among persons living in peri-urban (49.5, 95% CI: 31.7 - 77.3) and urban settings compared to rural areas. The January peak of the participatory surveillance system ILI estimates correlated significantly with the January peak of the COVID-19 case count data (Spearman's correlation coefficient = 0.49; P < 0.001) (Pearson's correlation coefficient = 0.67; P < 0.0001). CONCLUSIONS: The ILI trends captured by the participatory surveillance system in Lesotho mirrored trends of the COVID-19 case count data from Our World in Data. Public health practitioners in geographies that lack the resources to conduct direct surveillance of infectious diseases may be able to use cell phone-based data collection to monitor trends.

Adolescent simultaneous use of alcohol and marijuana by trends in cigarette and nicotine vaping from 2000 to 2020.

BACKGROUND: Simultaneous alcohol and marijuana (SAM) use is associated with adverse consequences for youth. While SAM use is overall declining among youth, prior studies indicate increasing marijuana use among US adolescents who ever used cigarettes, suggesting possible moderation of the alcohol-marijuana relationship by cigarette use. METHODS: We included 43,845 12-th grade students participating in Monitoring the Future data (2000-2020). A 5-level alcohol/marijuana measure was used, including past-year SAM, alcohol-only, marijuana-only, non-simultaneous alcohol and marijuana, or no use. Multinomial logistic regressions estimated associations between time periods (categorized based on sample size: 2000-2005, 2006-2009, 2010-2014, 2015-2020) and the 5-level alcohol/marijuana measure. Models adjusted for sex, race, parental education and survey mode and included interactions of time periods and lifetime cigarette or vaped nicotine use. RESULTS: While overall SAM among 12th graders decreased from 23.65% to 18.31% between 2000 and 2020, SAM increased among students who never used cigarettes or vaped nicotine (from 5.42% to 7.03%). Among students who ever used cigarettes or vaped nicotine, SAM increased from 39.2% in 2000-2005-44.1% in 2010-2014 then declined to 37.8% in 2015-2020. Adjusted models controlling for demographics indicated that among students with no lifetime cigarette or vaped nicotine use, students in 2015-2020 had 1.40 (95% C.I. 1.15-1.71) times the odds of SAM, and 5.43 (95% C.I. 3.63-8.12) times the odds of marijuana-only (i.e., no alcohol use) compared to students who used neither in 2000-2005. Alcohol-only declined over time in both students who ever and never used cigarettes or nicotine vape products. CONCLUSION: Paradoxically, while SAM declined in the overall adolescent US population, the prevalence of SAM increased among students who have never smoked cigarettes or vaped nicotine. This effect arises because of a substantial decline in the prevalence of cigarette smoking; smoking is a risk factor for SAM, and fewer students smoke. Increases in vaping are offsetting these changes, however. Preventing adolescent use of cigarettes and nicotine vaped products could have extended benefits for other substance use, including SAM.

Nicotine Vaping and Co-occurring Substance Use Among Adolescents in the United States from 2017-2019.

Background: The use of electronic cigarettes (or "vaping") among adolescents remains a public health concern given exposure to harmful substances, plus potential association with cannabis and alcohol. Understanding vaping as it intersects with combustible cigarette use and other substance use can inform nicotine prevention efforts. Methods: Data were drawn from 51,872 US adolescents (grades 8, 10, 12, years: 2017-2019) from Monitoring the Future. Multinomial logistic regression analyses assessed links of past 30-day nicotine use (none, smoking-only, vaping-only, and any smoking plus vaping) with both past 30-day cannabis use and past two-week binge drinking. Results: Nicotine use patterns were strongly associated with greater likelihood of cannabis use and binge drinking, particularly for the highest levels of each. For instance, those who smoked and vaped nicotine had 36.53 [95% CI:16.16, 82.60] times higher odds of having 10+ past 2-week binge drinking instances compared to non-users of nicotine. Discussion: Given the strong associations between nicotine use and both cannabis use and binge drinking, there is a need for sustained interventions, advertising and promotion restrictions, and national public education efforts to reduce adolescent nicotine vaping, efforts that acknowledge co-occurring use.

Self-Report Survey Measures of Alcohol-Impaired Driving: A Systematic Review.

OBJECTIVE: Alcohol-impaired driving is a major contributor to motor vehicle crash deaths and injury. Many survey studies include self-report measures of alcohol-impaired driving, but no guidance is available to help researchers select from among available measures. The aims of this systematic review were to compile a list of measures that researchers have used previously, to compare performance between measures, and to identify the measures with highest validity and reliability. METHOD: Literature searches of PubMed, Scopus, and Web of Science identified studies that assessed alcohol-impaired driving behavior through self-report. The measures from each study and, if available, indices of reliability or validity were extracted. Using the measures' text, we developed 10 codes to group similar measures and compare them. For example, the "alcohol effects" code refers to driving while feeling dizzy or lightheaded after drinking, and the "drink count" code pertains to the number of drinks someone consumed before driving. For measures with multiple items, each item was categorized separately. RESULTS: After screening according to the eligibility criteria, 41 articles were included in the review. Thirteen articles reported on reliability. No articles reported on validity. The self-report measures with the highest reliability coefficients contained items from multiple codes, namely alcohol effects and drink count. CONCLUSIONS: Self-report alcohol-impaired driving measures with multiple items evaluating distinct aspects of alcohol-impaired driving show better reliability than measures using a single item. Future work investigating the validity of these measures is needed to determine the best approach for conducting self-report research in this area.

Inference from Nonrandom Samples Using Bayesian Machine Learning.

We consider inference from nonrandom samples in data-rich settings where high-dimensional auxiliary information is available both in the sample and the target population, with survey inference being a special case. We propose a regularized prediction approach that predicts the outcomes in the population using a large number of auxiliary variables such that the ignorability assumption is reasonable and the Bayesian framework is straightforward for quantification of uncertainty. Besides the auxiliary variables, we also extend the approach by estimating the propensity score for a unit to be included in the sample and also including it as a predictor in the machine learning models. We find in simulation studies that the regularized predictions using soft Bayesian additive regression trees yield valid inference for the population means and coverage rates close to the nominal levels. We demonstrate the application of the proposed methods using two different real data applications, one in a survey and one in an epidemiologic study.

Trends in alcohol-impaired crashes in California, 2016 to 2021: A time series analysis for alcohol involvement and crash distribution among demographic subgroups.

BACKGROUND: In 2020, the COVID-19 pandemic and control measures changed alcohol consumption in the United States (US) and globally. Before the pandemic, alcohol-impaired crashes contributed to approximately one-third of all road traffic crash injuries and fatalities nationally. We examined the impact of the COVID-19 pandemic on crashes and examined differences in alcohol-involved crashes across various subgroups. METHODS: The University of California Berkeley Transportation Injury Mapping Systems provided information on all crashes reported to the California Highway Patrol from January 1, 2016 through December 31, 2021. Using autoregressive integrated moving average (ARIMA) models applied to weekly time series data, we estimated the effect of California's first mandatory statewide shelter-in-place order (March 19, 2020) on crashes per 100,000 population. We also examined crash subgroups according to crash severity, sex, race/ethnicity, age, and alcohol involvement. RESULTS: In California, the mean crash rate per week before the pandemic (January 1, 2016-March 18, 2020) was 9.5 crashes per 100,000 population, and 10.3% of those were alcohol-involved. After the initiation of the COVID-19 stay-at-home order, the percentage of crashes that were alcohol-involved rose to 12.7%. Overall, the crash rate across California decreased significantly (-4.6 crashes per 100,000; 95% CI: -5.3, -3.9), including across all examined subgroups, with the greatest decrease among the least severe crashes. However, there was a 2.3% absolute increase in the proportion of crashes that were alcohol-involved (0.02 crashes per 100,000; 95% CI: 0.02, 0.03). CONCLUSIONS: The initiation of a COVID-19 stay-at-home ordinance in California was associated with a substantial decrease in overall crash rates. While crashes have returned to pre-pandemic levels, alcohol-involved crashes remain elevated. The initiation of the stay-at-home order significantly increased alcohol-impaired driving, which has remained elevated.

Optimizing sobriety checkpoints to maximize public health benefits and minimize operational costs.

BACKGROUND: Sobriety checkpoints are a highly effective strategy to reduce alcohol-impaired driving, but they are used infrequently in the USA. Recent evidence from observational studies suggests that using optimized sobriety checkpoints-operating for shorter duration with fewer officers-can minimize operational costs without reducing public health benefits. The aim of this research was to conduct a pilot study to test whether police can feasibly implement optimized sobriety checkpoints and whether researchers can examine optimized sobriety checkpoints compared to usual practice within a non-randomized controlled trial study design. METHODS: The study site was the Town of Apex, NC. We worked with Apex Police Department to develop a schedule of sobriety checkpoints during calendar year 2021 that comprised 2 control checkpoints (conducted according to routine practice) and 4 optimized checkpoints staffed by fewer officers. Our primary operations aim was to test whether police can feasibly implement optimized sobriety checkpoints. Our primary research aim was to identify barriers and facilitators for conducting an intervention study of optimized sobriety checkpoints compared to usual practice. A secondary aim was to assess motorist support for sobriety checkpoints and momentary stress while passing through checkpoints. RESULTS: Apex PD conducted 5 of the 6 checkpoints and reported similar operational capabilities and results during the optimized checkpoints compared to control checkpoints. For example, a mean of 4 drivers were investigated for possibly driving while impaired at the optimized checkpoints, compared to 2 drivers at control checkpoints. The field team conducted intercept surveys among 112 motorists at 4 of the 6 checkpoints in the trial schedule. The survey response rate was 11% from among 1,045 motorists who passed through these checkpoints. Over 90% of respondents supported sobriety checkpoints, and momentary stress during checkpoints was greater for motorists who reported consuming any alcohol in the last 90 days compared to nondrinkers (OR = 6.7, 95%CI: 1.6, 27.1). CONCLUSIONS: Results of this study indicate the sobriety checkpoints can feasibly be optimized by municipal police departments, but it will be very difficult to assess the impacts of optimized checkpoints compared to usual practice using an experimental study design.

Parenting Practices and Adolescent Internalizing Symptoms in the United States, 1991-2019.

PURPOSE: Adolescent internalizing symptoms are increasing in the United States. Changes in parenting practices, including monitoring and communication, have been hypothesized to contribute to these increases. We aimed to estimate trends in parenting practices and understand whether shifts in such practices explain increases in internalizing symptoms. METHODS: Using 1991-2019 Monitoring the Future data (N = 933,645), we examined trends in five parental practices (i.e., knowledge [three combined indicators], monitoring [four combined indicators], communication, weekend curfew, social permission) with ordinal regressions. We tested associations between parental practices and indicators of being in the top decile of depressive affect, low self-esteem, and self-derogation using survey-weighted logistic regressions, adjusted for gender, race/ethnicity, grade, and parental education. RESULTS: The prevalences of parental practices have not changed over time, with the exception of increases in parental knowledge, specifically parents knowing where an adolescent is after school (1999-2019 mean increase: 4.34 to 4.61 out of 5) and knowing an adolescent's location (4.16-4.49) and company at night (4.26-4.51). Higher levels of each practice were associated with lower internalizing symptoms (e.g., adjusted odds ratio for a high depressive affect based on a one-unit increase in parental knowledge: 0.89, 95% confidence interval: 0.88, 0.90). Patterns were consistent across internalizing outcomes and decade. DISCUSSION: Parental knowledge, monitoring, and other practices are stable protective factors for adolescent mental health. These factors are not changing in a manner that would plausibly underlie increases in internalizing symptoms. Future interventions should provide resources that support these parental practices which are tied to adolescent internalizing symptoms.

Rectified Bloch oscillations in dynamically modulated waveguide arrays.

We study the dynamics of excitations in dynamically modulated waveguide arrays with an external spatial linear potential. Longitudinally periodic modulation may cause a significant change in the width of the quasi-energy band and leads to the dynamical band suppression with a linear dispersion relation. This substantially affects the Bloch oscillation dynamics. Novel dynamical phenomena with no analogue in ordinary discrete waveguides, named rectified Bloch oscillations, are highlighted. Due to the interplay between directional coupling between adjacent waveguides and diffraction suppression by the introduced onsite energy difference, at odd times of half Bloch oscillations period, the new submodes are continuously excited along two opposite rectification directions and experience same oscillation evolution, and eventually lead to the formation of a diamondlike intensity network. Both the amplitude and direction of the rectified Bloch oscillations strongly depend on the coupling strength. When coupling strength passes the critical value at which dynamical band suppression with a linear dispersion relation occurs, the direction of Bloch oscillations is inverted.

Adolescents' Use of Free Time and Associations with Substance Use from 1991 to 2019.

Background: Understanding time trends in risk factors for substance use may contextualize and explain differing time trends in substance use. Methods: We examined data (N = 536,291; grades 8/10/12) from Monitoring the Future, years 1991-2019. Using Latent Profile Analyses, we identified six time use patterns: one for those working at a paid job and the other five defined by levels of socialization (low/high) and engagement in structured activities like sports (engaged/disengaged), with the high social/engaged group split further by levels of unsupervised social activities. We tested associations between time use profiles and past two-week binge drinking as well as past-month alcohol use, cigarette use, cannabis use, other substance use, and vaping. We examined trends and group differences overall and by decade (or for vaping outcomes, year). Results: Prevalence of most substance use outcomes decreased over time among all groups. Cannabis use increased, with the largest increase in the group engaged in paid employment. Vaping substantially increased, with the highest nicotine vaping increase in the high social/engaged group with less supervision and the highest cannabis vaping increase in the highly social but otherwise disengaged group. Substance use was lowest in the low social groups, highest in the high social and employed groups. Conclusions: While alcohol, cigarette, and other substance use have declined for all groups, use remained elevated given high levels of social time, especially with low engagement in structured activities or low supervision, or paid employment. Cannabis use and vaping are increasing across groups, suggesting the need for enhanced public health measures.

Temporal trends in alcohol, cannabis, and simultaneous use among 12th-grade U.S. adolescents from 2000 to 2020: Differences by sex, parental education, and race and ethnicity.

BACKGROUND: Simultaneous use of alcohol and cannabis to enhance each other's effect can cause potential harm. Time trends are diverging in adolescent use of alcohol, which is declining, and cannabis, which is increasing among certain subgroups. However, little is known about trends in their simultaneous and non-simultaneous use. Racial and socioeconomic disparities are emerging in cannabis use, which may portend consequences to public health. METHODS: The 2000 to 2020 Monitoring the Future surveys included approximately 38,000 U.S. 12th-grade students with information on simultaneous use and pertinent demographic factors. A 5-level alcohol/cannabis measure included past-year simultaneous use (i.e., alcohol and cannabis use at the same time), non-simultaneous alcohol and cannabis use, alcohol-use-only, cannabis-use-only, and no use. Multinomial logistic regressions estimated associations (adjusted relative risk ratios; aRRR) with time period (2000 to 2004, 2005 to 2009, 2010 to 2014, 2015 to 2020). Models were adjusted and included interactions with sex, race/ethnicity, and parental education. RESULTS: Between 2000 and 2020, simultaneous alcohol/cannabis use among 12th graders decreased from 24.4% to 18.7%. From 2015 to 2020 compared to 2000 to 2004, the odds of simultaneous use (adjusted relative risk ratio (aRRR) vs. no use = 0.57, 95% CI [0.50, 0.66]) and alcohol-use-only (aRRR = 0.55, 95% CI [0.49, 0.61]) decreased, while cannabis-use-only odds increased (aRRR = 2.59, 95% CI [1.87, 3.59]). Notably, the prevalence of cannabis-use-only more than doubled from 2011 to 2019. The odds of simultaneous use, alcohol-use-only, and non-simultaneous use of alcohol and cannabis declined more rapidly among males than females, whereas the odds for cannabis-use-only increased faster for females than males. Increases in cannabis-use-only were faster for non-white adolescents. CONCLUSION: Simultaneous use of alcohol and cannabis is declining among U.S. adolescents, but the decline is slower among females than males. Declines in simultaneous use are largely concomitant with historical declines in alcohol use, indicating that a continued focus on reducing alcohol use among adolescents and young adults has extended benefits to other adolescent substance use. However, cannabis use without any reported past-year alcohol use more than doubled in the last decade, a concerning trend.