The eligibility of COVID-19 vaccines has been expanded to children aged 12 and above in several countries including Japan, and there is a plan to further lower the age. This study aimed to assess factors related to parental COVID-19 vaccine hesitancy. A nationwide internet-based cross-sectional study was conducted between May 25 and June 3, 2021 in Japan. The target population was parents of children aged 3–14 years who resided in Japan, and agreed to answer the online questionnaire. Parental COVID-19 vaccine hesitancy (their intention to vaccinate their child) and related factors were analyzed using logistic regression models. Interaction effects of gender of parents and their level of social relationship satisfaction related to parental vaccine hesitancy was tested using log likelihood ratio test (LRT). Social media as the most trusted information source increased parental vaccine hesitancy compared to those who trusted official information (Adjusted Odds Ratio: aOR 2.80, 95% CI 1.53–5.12). Being a mother and low perceived risk of infection also increased parental vaccine hesitancy compared to father (aOR 2.43, 95% CI 1.57–3.74) and those with higher perceived risk of infection (aOR 1.55, 95% CI 1.04–2.32) respectively. People with lower satisfaction to social relationships tended to be more hesitant to vaccinate their child among mothers in contrast to fathers who showed constant intention to vaccinate their child regardless of the level of satisfaction to social relationship (LRT p = 0.021). Our findings suggest that dissemination of targeted information about COVID-19 vaccine by considering means of communication, gender and people who are isolated during measures of social distancing may help to increase parental vaccine acceptance.

Japan Environment and Children's Study Group IMPORTANCE It is unclear to what extent the duration of screen time in infancy is associated with the subsequent diagnosis of autism spectrum disorder. OBJECTIVE To examine the association between screen time in infancy and the development of autism spectrum disorder at 3 years of age. DESIGN, SETTING, AND PARTICIPANTSThis cohort study analyzed data from mother-child dyads in a large birth cohort in Japan. This study included children born to women recruited between January 2011 and March 2014, and data were analyzed in December 2020. The study was conducted by the Japan Environment and Children's Study Group in collaboration with 15 regional centers across Japan.EXPOSURES Screen time at 1 year of age. MAIN OUTCOMES AND MEASURESThe outcome variable, children diagnosed with autism spectrum disorder at 3 years of age, was assessed using a questionnaire administered to mothers of the participating children.RESULTS A total of 84 030 mother-child dyads were analyzed. The prevalence of children with autism spectrum disorder at 3 years of age was 392 per 100 000 (0.4%), and boys were 3 times more likely to have been diagnosed with autism spectrum disorder than were girls. Logistic regression analysis showed that among boys, when "no screen" was the reference, the adjusted odds ratios were as follows: less than 1 hour, odds ratio, 1.38 (95 % CI, 0.71-2.69; P = .35), 1 hour to less than 2 hours, odds ratio, 2.16 (95 % CI, 1.13-4.14; P = .02), 2 hours to less than 4 hours, odds ratio, 3.48 (95% CI, 1.83-6.65; P < .001), and more than 4 hours, odds ratio, 3.02 (95% CI, 1.44-6.34; P = .04). Among girls, however, there was no association between autism spectrum disorder and screen time.CONCLUSIONS AND RELEVANCE Among boys, longer screen time at 1 year of age was significantly associated with autism spectrum disorder at 3 years of age. With the rapid increase in device usage, it is necessary to review the health effects of screen time on infants and to control excessive screen time.

To clarify the physical and mental conditions of children during the coronavirus disease 2019 pandemic and consequent social distancing in relation to the mental condition of their caregivers. This internet-based nationwide cross-sectional study was conducted between April 30 and May 13, 2020. The participants were 1,200 caregivers of children aged 3–14 years. Child health issues were categorized into “at least one” or “none” according to caregivers’ perception. Caregivers’ mental status was assessed using the Japanese version of the Kessler Psychological Distress Scale-6. The association between caregivers’ mental status and child health issues was analyzed using logistic regression models. Among the participants, 289 (24.1%) had moderate and 352 (29.3%) had severe mental distress and 69.8% of children in their care had health issues. The number of caregivers with mental distress was more than double that reported during the 2016 national survey. After adjusting for covariates, child health issues increased among caregivers with moderate mental distress (odds ratio 2.24, 95% confidence interval 1.59–3.16) and severe mental distress (odds ratio 3.05, 95% confidence interval 2.17–4.29) compared with caregivers with no mental distress. The results highlight parents’ psychological stress during the pandemic, suggesting the need for adequate parenting support. However, our study did not consider risk factors of caregivers’ mental distress such as socioeconomic background. There is an urgent need for further research to identify vulnerable populations and children’s needs to develop sustainable social support programs for those affected by the outbreak.

IntroductionEarly intervention in type 2 diabetes can prevent exacerbation of insulin resistance. More effective interventions can be implemented by early and precise prediction of the change in glycated haemoglobin A1c (HbA1c). Artificial intelligence (AI), which has been introduced into various medical fields, may be useful in predicting changes in HbA1c. However, the inability to explain the predictive factors has been a problem in the use of deep learning, the leading AI technology. Therefore, we applied a highly interpretable AI method, random forest (RF), to large-scale health check-up data and examined whether there was an advantage over a conventional prediction model.Research design and methodsThis study included a cumulative total of 42 908 subjects not receiving treatment for diabetes with an HbA1c <6.5%. The objective variable was the change in HbA1c in the next year. Each prediction model was created with 51 health-check items and part of their change values from the previous year. We used two analytical methods to compare the predictive powers: RF as a new model and multivariate logistic regression (MLR) as a conventional model. We also created models excluding the change values to determine whether it positively affected the predictions. In addition, variable importance was calculated in the RF analysis, and standard regression coefficients were calculated in the MLR analysis to identify the predictors.ResultsThe RF model showed a higher predictive power for the change in HbA1c than MLR in all models. The RF model including change values showed the highest predictive power. In the RF prediction model, HbA1c, fasting blood glucose, body weight, alkaline phosphatase and platelet count were factors with high predictive power.ConclusionsCorrect use of the RF method may enable highly accurate risk prediction for the change in HbA1c and may allow the identification of new diabetes risk predictors.

Background Bronchiolitis is the leading cause of hospitalization in U.S. infants and a major risk factor for childhood asthma. Growing evidence supports clinical heterogeneity within bronchiolitis. We aimed to identify endotypes of infant bronchiolitis by integrating clinical, virus, and serum proteome data, and examine their relationships with asthma development. Methods This is a multicenter prospective cohort study of infants hospitalized for physician‐diagnosis of bronchiolitis. We identified bronchiolitis endotypes by applying unsupervised machine learning (clustering) approaches to integrated clinical, virus (respiratory syncytial virus [RSV], rhinovirus [RV]), and serum proteome data measured at hospitalization. We then examined their longitudinal association with the risk for developing asthma by age 6 years. Results In 140 infants hospitalized with bronchiolitis, we identified three endotypes: (1) clinicalatopicvirusRVproteomeNFκB‐dysregulated, (2) clinicalnon‐atopicvirusRSV/RVproteomeTNF‐dysregulated, and (3) clinicalclassicvirusRSVproteomeNFκB/TNF‐regulated endotypes. Endotype 1 infants were characterized by high proportion of IgE sensitization and RV infection. These endotype 1 infants also had dysregulated NFκB pathways (FDR < 0.001) and significantly higher risks for developing asthma (53% vs. 22%; adjOR 4.04; 95% CI, 1.49–11.0; p = 0.006), compared with endotype 3 (clinically resembling “classic” bronchiolitis). Likewise, endotype 2 infants were characterized by low proportion of IgE sensitization and high proportion of RSV or RV infection. These endotype 2 infants had dysregulated tumor necrosis factor (TNF)‐mediated signaling pathway (FDR <0.001) and significantly higher risks for developing asthma (44% vs. 22%; adjOR 2.71; 95% CI, 1.03–7.11, p = 0.04). Conclusion In this multicenter cohort, integrated clustering of clinical, virus, and proteome data identified biologically distinct endotypes of bronchiolitis that have differential risks of asthma development.