Associations between media parenting practices and early adolescent screen use.

BACKGROUND: To assess the prevalence of various media parenting practices and identify their associations with early adolescent screen time and problematic social media, video game, and mobile phone use. METHODS: Cross-sectional data from Year 3 of the Adolescent Brain Cognitive Development (ABCD) Study (2019-2022) that included 10,048 adolescents (12-13 years, 48.3% female, 45.6% racial/ethnic minorities) in the US were analyzed using multiple linear regression analyses adjusting for potential confounders. RESULTS: Parent screen use, family mealtime screen use, and bedroom screen use were associated with greater adolescent screen time and problematic social media, video game, and mobile phone use. Parental use of screens to control behavior (e.g., as a reward or punishment) was associated with higher screen time and greater problematic video game use. Parental monitoring of screens was associated with lower screen time and less problematic social media and mobile phone use. Parental limit setting of screens was associated with lower screen time and less problematic social media, video game, and mobile phone use. DISCUSSION: Parent screen use, mealtime screen use, and bedroom screen use were associated with higher adolescent problematic screen use and could be limited in a family media use plan. Parental monitoring and limiting of screen time are associated with less problematic screen use. IMPACT STATEMENT: Although the American Academy of Pediatrics provides guidance for screen use for children 5-18 years, there is a paucity of evidence-based guidance for media parenting practices, specifically for early adolescents. In a diverse sample of 10,048 early adolescents across the US, we found cross-sectional associations between parent, mealtime, and bedroom screen use and higher adolescent problematic screen use. Parental monitoring and limiting of adolescent screen time were cross-sectionally associated with less problematic screen use in our analytic sample and may be incorporated into a family media use plan.

Automated Computational Detection of Interstitial Fibrosis, Tubular Atrophy, and Glomerulosclerosis.

BACKGROUND: Interstitial fibrosis, tubular atrophy (IFTA), and glomerulosclerosis are indicators of irrecoverable kidney injury. Modern machine learning (ML) tools have enabled robust, automated identification of image structures that can be comparable with analysis by human experts. ML algorithms were developed and tested for the ability to replicate the detection and quantification of IFTA and glomerulosclerosis that renal pathologists perform. METHODS: A renal pathologist annotated renal biopsy specimens from 116 whole-slide images (WSIs) for IFTA and glomerulosclerosis. A total of 79 WSIs were used for training different configurations of a convolutional neural network (CNN), and 17 and 20 WSIs were used as internal and external testing cases, respectively. The best model was compared against the input of four renal pathologists on 20 new testing slides. Further, for 87 testing biopsy specimens, IFTA and glomerulosclerosis measurements made by pathologists and the CNN were correlated to patient outcome using classic statistical tools. RESULTS: The best average performance across all image classes came from a DeepLab version 2 network trained at 40× magnification. IFTA and glomerulosclerosis percentages derived from this CNN achieved high levels of agreement with four renal pathologists. The pathologist- and CNN-based analyses of IFTA and glomerulosclerosis showed statistically significant and equivalent correlation with all patient-outcome variables. CONCLUSIONS: ML algorithms can be trained to replicate the IFTA and glomerulosclerosis assessment performed by renal pathologists. This suggests computational methods may be able to provide a standardized approach to evaluate the extent of chronic kidney injury in situations in which renal-pathologist time is restricted or unavailable.

Social epidemiology of online dating in U.S. early adolescents.

OBJECTIVE: To investigate the prevalence and sociodemographic associations of online dating in a demographically diverse U.S. national cohort of early adolescents. METHODS: We analyzed cross-sectional data from the Adolescent Brain Cognitive Development Study (Year 2, 2018-2020, ages 11-12; N = 10,157). Multivariable logistic regression analyses were employed to estimate associations between sociodemographic factors (e.g., age, sex, race/ethnicity, sexual orientation, household income, parental education) and early adolescent-reported online dating behaviors. RESULTS: Overall, 0.4% (n = 38) of participants reported ever using a dating app. Males (AOR 2.72, 95% CI 1.11-6.78) had higher odds of online dating compared to females, and sexual minority identification (e.g., lesbian, gay, or bisexual; AOR 12.97, 95% CI 4.32-38.96) was associated with greater odds of online dating compared to heterosexual identification. CONCLUSION: Given the occurrence of online dating among early adolescents despite age restrictions, interventions might address age misrepresentation. Adolescent sexual health education may consider incorporating anticipatory guidance on online dating, especially for males and sexual minorities. Future research could further investigate online dating patterns from early to late adolescence and associated health effects.

Mental health symptoms are comparable in patients hospitalized with acute illness and patients hospitalized with injury.

BACKGROUND: High rates of mental health symptoms such as depression, anxiety, and posttraumatic stress disorder (PTSD) have been found in patients hospitalized with traumatic injuries, but little is known about these problems in patients hospitalized with acute illnesses. A similarly high prevalence of mental health problems in patients hospitalized with acute illness would have significant public health implications because acute illness and injury are both common, and mental health problems of depression, anxiety, and PTSD are highly debilitating. METHODS AND FINDINGS: In patients admitted after emergency care for Acute Illness (N = 656) or Injury (N = 661) to three hospitals across the United States, symptoms of depression, anxiety, and posttraumatic stress were compared acutely (Acute Stress Disorder) and two months post-admission (PTSD). Patients were ethnically/racially diverse and 54% female. No differences were found between the Acute Illness and Injury groups in levels of any symptoms acutely or two months post-admission. At two months post-admission, at least one symptom type was elevated for 37% of the Acute Illness group and 39% of the Injury group. Within racial/ethnic groups, PTSD symptoms were higher in Black patients with injuries than for Black patients with acute illness. A disproportionate number of Black patients had been assaulted. CONCLUSIONS: This study found comparable levels of mental health sequelae in patients hospitalized after emergency care for acute illness as in patients hospitalized after emergency care for injury. Findings of significantly higher symptoms and interpersonal violence injuries in Black patients with injury suggest that there may be important and actionable differences in mental health sequelae across ethnic/racial identities and/or mechanisms of injury or illness. Routine screening for mental health risk for all patients admitted after emergency care could foster preventive care and reduce ethnic/racial disparities in mental health responses to acute illness or injury.

Automated En Masse Machine Learning Model Generation Shows Comparable Performance as Classic Regression Models for Predicting Delayed Graft Function in Renal Allografts.

BACKGROUND: Several groups have previously developed logistic regression models for predicting delayed graft function (DGF). In this study, we used an automated machine learning (ML) modeling pipeline to generate and optimize DGF prediction models en masse. METHODS: Deceased donor renal transplants at our institution from 2010 to 2018 were included. Input data consisted of 21 donor features from United Network for Organ Sharing. A training set composed of ~50%/50% split in DGF-positive and DGF-negative cases was used to generate 400 869 models. Each model was based on 1 of 7 ML algorithms (gradient boosting machine, k-nearest neighbor, logistic regression, neural network, naive Bayes, random forest, support vector machine) with various combinations of feature sets and hyperparameter values. Performance of each model was based on a separate secondary test dataset and assessed by common statistical metrics. RESULTS: The best performing models were based on neural network algorithms, with the highest area under the receiver operating characteristic curve of 0.7595. This model used 10 out of the original 21 donor features, including age, height, weight, ethnicity, serum creatinine, blood urea nitrogen, hypertension history, donation after cardiac death status, cause of death, and cold ischemia time. With the same donor data, the highest area under the receiver operating characteristic curve for logistic regression models was 0.7484, using all donor features. CONCLUSIONS: Our automated en masse ML modeling approach was able to rapidly generate ML models for DGF prediction. The performance of the ML models was comparable with classic logistic regression models.