Comparison of Fan-Traps and Gravitraps for Aedes Mosquito Surveillance in Taiwan.

A key component of integrated vector management strategies is the efficient implementation of mosquito traps for surveillance and control. Numerous trap types have been created with distinct designs and capture mechanisms, but identification of the most effective trap type is critical for effective implementation. For dengue vector surveillance, previous studies have demonstrated that active traps utilizing CO2 attractant are more effective than passive traps for capturing Aedes mosquitoes. However, maintaining CO2 supply in traps is so labor intensive as to be likely unfeasible in crowded residential areas, and it is unclear how much more effective active traps lacking attractants are than purely passive traps. In this study, we analyzed Aedes capture data collected in 2019 from six urban areas in Kaohsiung City to compare Aedes mosquito catch rates between (passive) gravitraps and (active) fan-traps. The average gravitrap index (GI) and fan-trap index (FI) values were 0.68 and 3.39 respectively at peak catch times from June to August 2019, with consistently higher FI values calculated in all areas studied. We compared trap indices to reported cases of dengue fever and correlated them with weekly fluctuations in temperature and rainfall. We found that FI trends aligned more closely with case numbers and rainfall than GI values, supporting the use of fan-traps for Aedes mosquito surveillance and control as part of broader vector management strategies. Furthermore, combining fan-trap catch data with rapid testing for dengue infections may improve the early identification and prevention of future disease outbreaks.

Predictors for Depression, Sleep Disturbance, and Subjective Pain among Inpatients with Depressive Disorders during the COVID-19 Pandemic: A Cross-Sectional Study.

The coronavirus disease 2019 (COVID-19) pandemic can have a negative impact on patients with mood disorders. The aim of this study is to explore the societal influence of COVID-19 and associated impacts on levels of depression, sleep disturbance, and subjective pain among patients with mood disorders. This cross-sectional study recruited inpatients with depression and bipolar disorder. Levels of depression, sleep disturbance, subjective pain, and related demographic variables were collected through self-reported questionnaires. Potential factors associated with levels of depression, sleep disturbance, and subjective pain were identified using univariate linear regression and further entered into a stepwise multivariate linear regression model to identify the independent predictors. A total of 119 participants were included in the analysis, of whom 50.42% had bipolar disorder and 49.58% had unipolar depression. Multivariate analysis showed that a higher level of depression was associated with female subjects, subjects with partners, present history of psychological trauma, and drinking alcohol. Sleep disturbance was associated with subjects with partners and drinking alcohol. A higher level of subjective pain was associated with a higher level of social anxiety and a history of psychological trauma. The current study identified several predictors of psychological burden and subjective pain among inpatients with depression during the COVID-19 pandemic. Further investigations are warranted to extend the application and generalizability of our results.