Background: The transmission of influenza virus in households, especially by children, is a major route of infection. Prior studies suggest that timely antiviral treatment of ill cases may reduce infection in household contacts. The aim of the study was to compare the effects of oseltamivir (OTV) and baloxavir marboxil (BXM) treatment of index cases on the secondary attack rate (SAR) of influenza within household.Methods: A post hoc analysis was done in BLOCKSTONE trial—a placebo‐controlled, double‐blinded post‐exposure prophylaxis of BXM. Data were derived from the laboratory‐confirmed index cases' household contacts who received placebo in the trial and also from household members who did not participate in the trial but completed illness questionnaires. To assess the SAR of household members, multivariate analyses adjusted for factors including age, vaccination status, and household size were performed and compared between contacts of index cases treated with BXM or OTV.Results: In total, 185 index cases (116 treated with BXM and 69 treated with OTV) and 410 household contacts (201 from trial, 209 by questionnaire) were included. The Poisson regression modeling showed that the SAR in household contacts of index cases treated with BXM and OTV was 10.8% and 18.5%, respectively; the adjusted relative reduction in SAR was 41.8% (95% confidence interval: 1.0%–65.7%, p = 0.0456) greater with BXM than OTV. Similar reductions were found in contacts from the trial and those included by questionnaire.Conclusion: BXM treatment of index cases appeared to result in a greater reduction in secondary household transmission than OTV treatment.

Background: Work characteristics, such as teleworking rate, have been studied in relation to stress. However, the use of work-related data to improve a high-performance stress prediction model that suits an individual’s lifestyle has not been evaluated. Objective: This study aims to develop a novel, high-performance algorithm to predict an employee’s stress among a group of employees with similar working characteristics. Methods: This prospective observational study evaluated participants’ responses to web‑based questionnaires, including attendance records and data collected using a wearable device. Data spanning 12 weeks (between January 17, 2022, and April 10, 2022) were collected from 194 Shionogi Group employees. Participants wore the Fitbit Charge 4 wearable device, which collected data on daily sleep, activity, and heart rate. Daily work shift data included details of working hours. Weekly questionnaire responses included the K6 questionnaire for depression/anxiety, a behavioral questionnaire, and the number of days lunch was missed. The proposed prediction model used a neighborhood cluster (N=20) with working-style characteristics similar to those of the prediction target person. Data from the previous week predicted stress levels the following week. Three models were compared by selecting appropriate training data: (1) single model, (2) proposed method 1, and (3) proposed method 2. Shapley Additive Explanations (SHAP) were calculated for the top 10 extracted features from the Extreme Gradient Boosting (XGBoost) model to evaluate the amount and contribution direction categorized by teleworking rates (mean): low: <0.2 (more than 4 days/week in office), middle: 0.2 to <0.6 (2 to 4 days/week in office), and high: ≥0.6 (less than 2 days/week in office). Results: Data from 190 participants were used, with a teleworking rate ranging from 0% to 79%. The area under the curve (AUC) of the proposed method 2 was 0.84 (true positive vs false positive: 0.77 vs 0.26). Among participants with low teleworking rates, most features extracted were related to sleep, followed by activity and work. Among participants with high teleworking rates, most features were related to activity, followed by sleep and work. SHAP analysis showed that for participants with high teleworking rates, skipping lunch, working more/less than scheduled, higher fluctuations in heart rate, and lower mean sleep duration contributed to stress. In participants with low teleworking rates, coming too early or late to work (before/after 9 AM), a higher/lower than mean heart rate, lower fluctuations in heart rate, and burning more/fewer calories than normal contributed to stress. Conclusions: Forming a neighborhood cluster with similar working styles based on teleworking rates and using it as training data improved the prediction performance. The validity of the neighborhood cluster approach is indicated by differences in the contributing features and their contribution directions among teleworking levels. Trial Registration: UMIN UMIN000046394; https://www.umin.ac.jp/ctr/index.htm

A single oral dose of baloxavir marboxil, a cap-dependent endonuclease inhibitor, is approved for patients with influenza A or B infection; however, real-world evidence is limited. We evaluated the effectiveness of baloxavir vs neuraminidase inhibitors in reducing the incidence of severe illness in influenza outpatients aged 5-11 years. In this retrospective cohort study, we analyzed individual-level data from patients treated with these antivirals, using a large, Japanese health insurance claims database (JMDC). Patients were included at the first date of diagnosis of the influenza virus infection (Day 1) in two influenza seasons. The primary outcome was the incidence of hospitalization from Day 2-14. Of 196,749 included patients (Season 2018/2019, n = 103,709; Season 2019/2020, n = 93,040), 20.9 % received baloxavir, 38.8 % received oseltamivir, 28.7 % received laninamivir, and 11.6 % received zanamivir. In each treatment group, 61-70 % patients had influenza A. The incidence of hospitalization from Day 2-14 was significantly lower for baloxavir than for oseltamivir, laninamivir, and zanamivir. The adjusted risk ratio (95 % CI) for oseltamivir, laninamivir, and zanamivir were 1.86 (1.30-2.68), 2.11 (1.37-3.25), and 1.90 (1.31-2.77), respectively, compared with baloxavir. Comparative incidence of hospitalizations between agents were unaffected by season or virus type. Using a large, Japanese health insurance claims database, a lower rate of hospitalization was demonstrated in children aged 5-11 years with an influenza virus infection when treated with baloxavir vs neuraminidase inhibitors. Thus, single dose, oral baloxavir may reduce the incidence of severe illness in these patients.

Recurrent outbreaks of coronavirus disease 2019 (COVID-19) in Japan highlight the continued need for effective prevention, particularly household transmission. This study clarifies the epidemiological profile of household transmission of COVID-19 and influenza by quantifying transmission rates according to demographic and relational characteristics. An anonymized health insurance claims database was used to identify index patients diagnosed with COVID-19 or influenza between November 2021 and August 2023. Transmission events were defined as cases in which household members were diagnosed with the same infection in the week following the index patient’s diagnosis (Day 1). Analyses were stratified by the age of the index patients and age-based transmission patterns between index patients and household members. A total of 1,001,509 index patients with COVID-19 and 207,090 with influenza met the inclusion criteria. Transmission rates were highest among index patients aged <12 years (COVID-19, 40.17%; influenza, 33.51%). Transmission from children was disproportionately high, while age-combination analysis demonstrated elevated transmission both from and to children for both diseases. COVID-19 exhibited increased transmission among adults. As the primary drivers of household transmission for both COVID-19 and influenza, preventive strategies should be tailored to children. COVID-19 interventions must also address transmission among adults to strengthen household-level infection control.

BACKGROUND Work characteristics, such as teleworking rate, have been studied in relation to stress. However, the use of work-related data to improve a high-performance stress prediction model that suits an individual’s lifestyle has not been evaluated. OBJECTIVE This study aims to develop a novel, high-performance algorithm to predict an employee’s stress among a group of employees with similar working characteristics. METHODS This prospective observational study evaluated participants’ responses to web‑based questionnaires, including attendance records and data collected using a wearable device. Data spanning 12 weeks (between January 17, 2022, and April 10, 2022) were collected from 194 Shionogi Group employees. Participants wore the Fitbit Charge 4 wearable device, which collected data on daily sleep, activity, and heart rate. Daily work shift data included details of working hours. Weekly questionnaire responses included the K6 questionnaire for depression/anxiety, a behavioral questionnaire, and the number of days lunch was missed. The proposed prediction model used a neighborhood cluster (N=20) with working-style characteristics similar to those of the prediction target person. Data from the previous week predicted stress levels the following week. Three models were compared by selecting appropriate training data: (1) single model, (2) proposed method 1, and (3) proposed method 2. Shapley Additive Explanations (SHAP) were calculated for the top 10 extracted features from the Extreme Gradient Boosting (XGBoost) model to evaluate the amount and contribution direction categorized by teleworking rates (mean): low: <0.2 (more than 4 days/week in office), middle: 0.2 to <0.6 (2 to 4 days/week in office), and high: ≥0.6 (less than 2 days/week in office). RESULTS Data from 190 participants were used, with a teleworking rate ranging from 0% to 79%. The area under the curve (AUC) of the proposed method 2 was 0.84 (true positive vs false positive: 0.77 vs 0.26). Among participants with low teleworking rates, most features extracted were related to sleep, followed by activity and work. Among participants with high teleworking rates, most features were related to activity, followed by sleep and work. SHAP analysis showed that for participants with high teleworking rates, skipping lunch, working more/less than scheduled, higher fluctuations in heart rate, and lower mean sleep duration contributed to stress. In participants with low teleworking rates, coming too early or late to work (before/after 9 AM), a higher/lower than mean heart rate, lower fluctuations in heart rate, and burning more/fewer calories than normal contributed to stress. CONCLUSIONS Forming a neighborhood cluster with similar working styles based on teleworking rates and using it as training data improved the prediction performance. The validity of the neighborhood cluster approach is indicated by differences in the contributing features and their contribution directions among teleworking levels. CLINICALTRIAL UMIN UMIN000046394; https://www.umin.ac.jp/ctr/index.htm