Influence of anti-coronavirus disease 2019 policies on 10 pediatric infectious diseases.

BACKGROUND: To combat the coronavirus disease 2019 pandemic, many countries, including Japan, implemented policies limiting social activities and encouraging preventive behaviors. This study examines the influence of such policies on the trends of 10 infectious pediatric diseases: pharyngoconjunctival fever; group A streptococcal pharyngitis; infectious gastroenteritis; chickenpox; erythema infectiosum; hand, foot, and mouth disease; herpangina; respiratory syncytial virus; exanthem subitum; and mumps. METHODS: The research adopted a retrospective cohort study design. We collected data from Japan's National Epidemiological Surveillance Program detailing the incidences of the 10 diseases per pediatric sentinel site for a period beginning at 9 weeks before government-ordered school closures and ending at 9 weeks after the end of the state of emergency. We obtained corresponding data for the equivalent weeks in 2015-2019. We estimated the influence of the policies using a difference-in-differences regression model. RESULTS: For seven diseases (pharyngoconjunctival fever; group A streptococcal pharyngitis; infectious gastroenteritis; chickenpox; erythema infectiosum; hand, foot, and mouth disease; and herpangina), the incidence in 2020 decreased significantly during and after the school closures. Sensitivity analysis, in which the focus area was limited to the policy-implementation period or existing trend patterns, replicated these significant decreases for one of the above mentioned seven diseases - infectious gastroenteritis. CONCLUSIONS: Policies such as school closures and encouragement of preventive behaviors were associated with significant decreases in the incidences of most of the 10 diseases, which sensitivity analysis replicated in infectious gastroenteritis. To determine the long-term effects of these policies, prospective cohort studies are needed.

Development and Initial Psychometric Validation of the COVID-19 Pandemic Burden Index for Healthcare Workers.

BACKGROUND: The burden of COVID-19 on healthcare workers (HCWs) is reported to be increasing, yet the psychometric scales now in use evaluate only single aspects; few measure the pandemic-specific burden on HCWs comprehensively. OBJECTIVE: To develop a scale to quantify the physical, mental, and socioeconomic burden of the COVID-19 pandemic on HCWs. DESIGN: Scale development and cross-sectional survey. PARTICIPANTS: Consenting HCWs aged ≥20. MAIN MEASURES: Development of an item-list based on literature reviews and HCW panel input, evaluation of content validity and item selection using the Delphi method, psychometric testing conducted on HCWs, validity assessment by factor analyses and hypothesis verification, internal consistency evaluation by Cronbach's alpha, test-retest analysis, and interpretability assessment. KEY RESULTS: Through the Delphi process, a 29-item pilot scale was generated. In psychometric testing, data from 863 HCWs contributed to the development of the final version of this scale, called Pandemic Burden Index twenty for HCWs (PBI-20), a 20-item scale to measure six domains: fatigue, fear of infection, inadequacy as a medical professional, mental health concerns, prejudice or discrimination, and anxiety about one's livelihood and daily life. Factor analysis showed each factor corresponded to the six domains of this scale. Hypothesis verification showed the PBI-20 total score to be moderately to highly correlated with the Short Form 36 vitality score and mental health score and with intention of turnover. The PBI-20 had good internal consistency (Cronbach's alpha 0.92). Test-retest analysis showed the intraclass correlation coefficient to be 0.70 and the minimal important change to be -7.0. CONCLUSIONS: The psychometrically sound questionnaire we developed to measure pandemic-specific burdens for HCWs provides an understanding of comprehensive burdens on HCWs and may serve to evaluate interventions to reduce the burdens.

Retracted randomized controlled trials were cited and not corrected in systematic reviews and clinical practice guidelines.

BACKGROUND AND OBJECTIVES: To investigate whether and when the correction is done in Systematic Reviews (SRs) and Clinical Practice Guidelines (CPGs) when included Randomized Controlled Trials (RCTs) have been retracted. METHODS: In this meta-epidemiological study, we included SRs and CPGs citing the retracted RCTs from the Retraction Watch Database. We investigated how often the retracted RCTs were cited in SRs and CPGs. We also investigated whether and when such SRs and CPGs corrected themselves. RESULTS: We identified 587 articles (525 SRs and 62 CPGs) citing retracted RCTs. Among the 587 articles, 252 (43%) were published after retraction, and 335 (57%) were published before retraction. Among 127 articles published citing already retracted RCTs in their evidence synthesis without caution, none corrected themselves after publication. Of 335 articles published before retraction, 239 included RCTs that were later retracted in their evidence synthesis. Among them, only 5% of SRs (9/196) and 5% of CPGs (2/43) corrected or retracted their results. CONCLUSION: Many SRs and CPGs included already or later retracted RCTs without caution. Most of them were never corrected. The scientific community, including publishers and researchers, should make systematic and concerted efforts to remove the impact of retracted RCTs.

Integrated model for COVID-19 diagnosis based on computed tomography artificial intelligence, and clinical features: a multicenter cohort study.

Background: We developed and validated a machine learning diagnostic model for the novel coronavirus (COVID-19) disease, integrating artificial-intelligence-based computed tomography (CT) imaging and clinical features. Methods: We conducted a retrospective cohort study in 11 Japanese tertiary care facilities that treated COVID-19 patients. Participants were tested using both real-time reverse transcription polymerase chain reaction (RT-PCR) and chest CTs between January 1 and May 30, 2020. We chronologically split the dataset in each hospital into training and test sets, containing patients in a 7:3 ratio. A Light Gradient Boosting Machine model was used for the analysis. Results: A total of 703 patients were included, and two models-the full model and the A-blood model-were developed for their diagnosis. The A-blood model included eight variables (the Ali-M3 confidence, along with seven clinical features of blood counts and biochemistry markers). The areas under the receiver-operator curve of both models [0.91, 95% confidence interval (CI): 0.86 to 0.95 for the full model and 0.90, 95% CI: 0.86 to 0.94 for the A-blood model] were better than that of the Ali-M3 confidence (0.78, 95% CI: 0.71 to 0.83) in the test set. Conclusions: The A-blood model, a COVID-19 diagnostic model developed in this study, combines machine-learning and CT evaluation with blood test data and performs better than the Ali-M3 framework existing for this purpose. This would significantly aid physicians in making a quicker diagnosis of COVID-19.

Gender Differences in Anxiety Among COVID-19 Inpatients Under Isolation: A Questionnaire Survey During the First and Second Waves of the COVID-19 Pandemic in Japan.

This study assesses the gender differences in health and anxiety, especially pertaining to mental health problems and time-course effects. We surveyed 121 patients admitted to a hospital with a COVID-19 diagnosis between March 1 and August 31, 2020. Their mental status was evaluated on admission using the Japanese General Health Questionnaire-28 (GHQ-28) and State-Trait Anxiety Inventory-Form JYZ (STAI). The patients were divided into two groups depending on the period of prevalence, that is, the first and second waves of the pandemic in Japan (from the beginning of March to the end of May 2020, Time 1 = T1; and from the beginning of June to the end of August 2020, Time 2 = T2). A multivariate analysis of covariance revealed significant differences in gender by time interactions in the GHQ-28 subscale "Insomnia and anxiety" and STAI subscale "State-Anxiety." Post-hoc t-tests revealed that the scores of "Insomnia and Anxiety" and "State-Anxiety" were higher in women than in men at T1. However, no difference was observed at T2. Further, "Insomnia and Anxiety" and "State-Anxiety" were significantly higher at T1 than at T2 in female patients. There was no significant difference in males. Thus, female patients were more anxious and depressed in the early phase of the pandemic, whereas male patients had difficulties in coping with anxiety. We suggest more gender-specific mental care, particularly for women at the early stages of infection.

Accuracy of deep learning-based computed tomography diagnostic system for COVID-19: A consecutive sampling external validation cohort study.

Ali-M3, an artificial intelligence program, analyzes chest computed tomography (CT) and detects the likelihood of coronavirus disease (COVID-19) based on scores ranging from 0 to 1. However, Ali-M3 has not been externally validated. Our aim was to evaluate the accuracy of Ali-M3 for detecting COVID-19 and discuss its clinical value. We evaluated the external validity of Ali-M3 using sequential Japanese sampling data. In this retrospective cohort study, COVID-19 infection probabilities for 617 symptomatic patients were determined using Ali-M3. In 11 Japanese tertiary care facilities, these patients underwent reverse transcription-polymerase chain reaction (RT-PCR) testing. They also underwent chest CT to confirm a diagnosis of COVID-19. Of the 617 patients, 289 (46.8%) were RT-PCR-positive. The area under the curve (AUC) of Ali-M3 for predicting a COVID-19 diagnosis was 0.797 (95% confidence interval: 0.762‒0.833) and the goodness-of-fit was P = 0.156. With a cut-off probability of a diagnosis of COVID-19 by Ali-M3 set at 0.5, the sensitivity and specificity were 80.6% and 68.3%, respectively. A cut-off of 0.2 yielded a sensitivity and specificity of 89.2% and 43.2%, respectively. Among the 223 patients who required oxygen, the AUC was 0.825. Sensitivity at a cut-off of 0.5% and 0.2% was 88.7% and 97.9%, respectively. Although the sensitivity was lower when the days from symptom onset were fewer, the sensitivity increased for both cut-off values after 5 days. We evaluated Ali-M3 using external validation with symptomatic patient data from Japanese tertiary care facilities. As Ali-M3 showed sufficient sensitivity performance, despite a lower specificity performance, Ali-M3 could be useful in excluding a diagnosis of COVID-19.

Telerehabilitation in Subjects With Respiratory Disease: A Scoping Review.

Considering the current coronavirus disease (COVID-19) pandemic, telerehabilitation may be a viable first-line option for patients with respiratory tract disease. To date, there has been no systematic review on telerehabilitation for respiratory tract diseases, including COVID-19. Therefore, this scoping review aimed to determine what telerehabilitation for patients with respiratory tract diseases consists of, how safe telerehabilitation is for patients with respiratory tract diseases, and how feasible telerehabilitation is for hospitalized patients with COVID-19. In May 2020, we conducted a search of the following publication databases on the use of telerehabilitation in the treatment of respiratory tract diseases: Medical Literature Analysis and Retrieval System Online, Embase, Cochrane Central Register of Controlled Trials, Cumulative Index to Nursing and Allied Literature, and Physiotherapy Evidence Database. Of the 208 articles identified, 23 studies were subsequently included in this scoping review. In 22 of the included studies, subjects had stable COPD and underwent telerehabilitation at home. The final included study was a case series of subjects with severe acute respiratory syndrome coronavirus 2 infection who underwent telerehabilitation in-hospital. Most telerehabilitation programs consisted of aerobic exercises using a cycle ergometer or a treadmill, walking, and muscle-strengthening exercises. The reported number of adverse events was low, and most studies reported that the average session adherence rate was > 70%. The majority of the telerehabilitation programs included a face-to-face rehabilitation assessment. Our findings indicate that, in its current state, telerehabilitation may be safe and feasible and may lead to reduced face-to-face rehabilitation therapy; in addition, remote rehabilitation assessment should be considered during the COVID-19 pandemic. Further research that targets a more diverse range of respiratory tract diseases and considers telerehabilitation in a hospital setting is required.