Objective: To clarify the relationship between betamimetic administration duration and maternal adverse events (MAEs) Design: Retrospective cohort study Setting: A nationwide, acute-care hospital setting in Japan, using the Diagnosis Procedure Combination (DPC) database. Population: Pregnant women who were administered the betamimetic ritodrine hydrochloride (ritodrine) between April 2012 and March 2023 (n=96,991) Methods: We utilized the DPC database. Logistic regression analysis, adjusted for potential confounders, assessed the association between ritodrine duration (acute tocolysis [AT] ≤ 48 hours vs. maintenance tocolysis [MT] >48 hours) and MAEs. Main Outcome Measures: Comprehensive ritodrine-related MAEs: pulmonary oedema, heart failure, liver dysfunction, neutropenia, rhabdomyolysis, arrhythmia, foetal arrhythmia, hypokalaemia, hyperglycaemia, gestational diabetes mellitus (GDM), and venous thromboembolism (VTE). Results: Among 96,991 patients, 21.45% received AT and 78.55% MT. The incidence of MAEs, including VTE, GDM, liver dysfunction, hypokalaemia, rhabdomyolysis, and neutropenia, was higher in the MT than in the AT group. Logistic regression analysis showed that MT was associated with higher odds of VTE (odds ratio [OR] 1.61, p < 0.001), GDM (OR 3.23, p < 0.001), and liver dysfunction (OR 2.94, p < 0.001) than was AT. Meanwhile, AT was associated with heart failure and pulmonary oedema (OR 1.30, p < 0.042; OR 1.41, p < 0.043). Conclusions: These findings emphasize the need for careful maternal monitoring during ritodrine use, regardless of duration, to prevent severe side effects.

This article reviews the essential role of mathematical models in understanding and combatting the pandemic of novel coronaviruses, in particular focusing the advance in the use of mathematical models in disease control in Japan. Highlighting the integral role of mathematical models in public health, the article introduces a model that factors in the heterogeneity of infectious contacts, concentrating on the effectiveness of testing and isolation, alongside a model that involves economic losses. The models exhibit how, given such heterogeneity, milder behavioral restrictions can still achieve suppression, rigorous testing and isolation can effectively curb the spread, and containment measures can mitigate economic losses. These models aid in grasping the complicated dynamics of disease transmission and optimizing interventions. The knowledge of population ecology is also considered effective for public health in statistical analysis, organizing concepts using dynamic mathematical models, which lead to policy proposals and deepen understanding. Evolution theory may help the understanding of virulence subject to change. However, effective prevention necessitates not only models but also the practical implementation of efficacious measures. The cooperation of various disciplines is particularly crucial in achieving a balance between health measures, economic interests, and human rights. Moreover, the article acknowledges the limitations of models and underscores the significance of real-world execution. Overall, the article advocates for a broader outlook to tackle future pandemics and related challenges, underscoring the importance of ongoing academic cooperation and global governance to effectively address emerging infectious diseases and their far-reaching implications.