[Social situation, health, and health behavior of children and adolescents in one-parent households at the end of the COVID-19 pandemic: Results of the KIDA study 2022-2023]. / Soziale Lage, Gesundheit und Gesundheitsverhalten von Kindern und Jugendlichen in Ein-Eltern-Haushalten zum Ende der COVID-19-Pandemie. Ergebnisse der KIDA-Studie 2022­2023.

INTRODUCTION: During the COVID-19 pandemic, single parents and their children were particularly exposed to stress due to the containment measures and to limited resources. We analyzed differences in the social and health situation of children and adolescents in one-parent households and two-parent households at the end of the pandemic. METHODS: The analysis is based on data from the KIDA study, in which parents of 3­ to 15-year-old children as well as 16- to 17-year-old adolescents were surveyed in 2022/2023 (telephone: n = 6992; online: n = 2896). Prevalences stratified by family type were calculated for the indicators psychosocial stress, social support, health, and health behavior. Poisson regressions were adjusted for gender, age, level of education, and household income. RESULTS: Children and adolescents from one-parent households are more likely to be burdened by financial restrictions, family conflicts, and poor living conditions and receive less school support than peers from two-parent households. They are more likely to have impairments in health as well as increased healthcare needs, and they use psychosocial services more frequently. Furthermore, they are less likely to be active in sports clubs, but they take part in sporting activities at schools as often as minors from two-parent households. The differences are also evident when controlling for income and education. DISCUSSION: Children and adolescents from one-parent households can be reached well through exercise programs in a school setting. Low-threshold offers in daycare centers, schools, and the community should therefore be further expanded. Furthermore, interventions are needed to improve the socioeconomic situation of single parents and their children.

Automatic Detection of Inadequate Pediatric Lateral Neck Radiographs of the Airway and Soft Tissues using Deep Learning.

PURPOSE: To develop and validate a deep learning (DL) algorithm to identify poor-quality lateral airway radiographs. MATERIALS AND METHODS: A total of 1200 lateral airway radiographs obtained in emergency department patients between January 1, 2000, and July 1, 2019, were retrospectively queried from the picture archiving and communication system. Two radiologists classified each radiograph as adequate or inadequate. Disagreements were adjudicated by a third radiologist. The radiographs were used to train and test the DL classifiers. Three technologists and three different radiologists classified the images in the test dataset, and their performance was compared with that of the DL classifiers. RESULTS: The training set had 961 radiographs and the test set had 239. The best DL classifier (ResNet-50) achieved sensitivity, specificity, and area under the receiver operating characteristic curve of 0.90 (95% confidence interval [CI]: 0.86, 0.94), 0.82 (95% CI: 0.76, 0.90), and 0.86 (95% CI: 0.81, 0.91), respectively. Interrater agreement for technologists was fair (Fleiss κ, 0.36 [95% CI: 0.29, 0.43]), while that for radiologists was moderate (Fleiss κ, 0.59 [95% CI: 0.52, 0.66]). Cohen κ value comparing the consensus rating of ResNet-50 iterations from fivefold cross-validation, consensus technologists' rating, and consensus radiologists' rating to the ground truth were 0.76 (95% CI: 0.63, 0.89), 0.49 (95% CI: 0.37, 0.61), and 0.66 (95% CI: 0.54, 0.78), respectively. CONCLUSION: The development and validation of DL classifiers to distinguish between adequate and inadequate lateral airway radiographs is reported. The classifiers performed significantly better than a group of technologists and as well as the radiologists.© RSNA, 2020.