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High levels of PPE knowledge were significantly correlated to HCWs' confidence in PPE and may help increase PPE usage and reduce absenteeism. (Diaster Med Public Health Preparedness. 2014;0:1-8).

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Background The willingness of healthcare workers (HCW) to respond is an important factor in the health system’s response capacity during emergencies. Although much research has been devoted to exploring this issue, the statistical methods employed have been predominantly traditional and have not enabled in-depth analysis focused on absenteeism-prone employees during emergencies. The present study employs an innovative statistical approach for modeling HCWs’ willingness to respond (WTR) following an earthquake. Methods A validated questionnaire measuring knowledge, perceptions, and attitudes toward an earthquake scenario was distributed among Israeli HCWs in a hospital setting. Two regression models were employed for data analysis – a traditional linear model, and a quantile regression model that makes it possible to examine associations between explanatory variables across different levels of a dependent variable. A supplementary analysis was performed for selected variables using broken line spline regression. Results Females under the age of forty, and nurses were the most absenteeism-prone sub-groups of employees (showed low WTR) in earthquake events. Professional commitment to care and perception of efficacy were the most powerful predictors associated with WTR across all quantiles. Both marital status (married) and concern for family wellbeing, designated as statistically significant in the linear model, were found to be statistically significant in only one of the WTR quantiles (the former in Q10 and the latter in Q50). Gender and number of children, which were not significantly associated with WTR in the linear model, were found to be statistically significant in the 25th quantile of WTR. Conclusions This study contributes to both methodological and practical aspects. Quantile regression provides a more comprehensive view of associations between variables than is afforded by linear regression alone. Adopting an advanced statistical approach in WTR modeling can facilitate effective implementation of research findings in the field. Electronic supplementary material The online version of this article (10.1186/s12909-019-1561-7) contains supplementary material, which is available to authorized users.

The outbreak of the COVID-19 pandemic has led to an acceleration in the development of web-based interventions to alleviate related mental health impacts. The current study explored the effects of a short-term digital group intervention aimed at providing cognitive behavioral and mindfulness tools and skills to reduce loneliness and depression and to increase social support among older adults in Israel. This pilot randomized controlled trial included community-dwelling older adults (n = 82; aged between 65–90 years; 80% female) who were randomized either to an intervention group (n = 64) or a wait-list control group (n = 18). The intervention included seven online sessions, over 3.5 weeks. Depression, loneliness, and social support measures were administered at baseline, immediately post-intervention, and at 1-month follow-up. Repeated measures ANOVA revealed statistically and clinically significant reductions in depression in the intervention group, with results maintained at one-month follow-up. Loneliness levels also significantly decreased post-intervention; however, this benefit was not maintained at one-month follow-up. Social support slightly increased both post-intervention and 1-month follow-up—but these changes were not statistically significant. There were no overall changes for the wait-list control group. Our intervention provided promising evidence regarding the effectiveness of an online group intervention to alleviate mental health effects and to promote the coping of older adults during the COVID-19 pandemic. This relatively simple model can be effectively utilized by communities globally to help connect lonely and isolated older inhabitants, both during the pandemic and in more routine times.

Earthquakes pose substantial risks of human health. Preparedness and mitigation strategies can reduce earthquake-related injuries and deaths and information from casualty models that predict earthquake outcomes can help communities prepare. This study identifies epidemiologic and medical risk factors for earthquake casualties, and compares them with engineering casualty models for the purpose of providing evidence that integrates these approaches. It aims to improve earthquake casualty modeling and to offer better accurate estimations. Epidemiological studies that used analytical designs and reported risk factors related to earthquake-induced casualties and studies that examined the association between medical preparedness and earthquake-induced casualties were reviewed. Engineering casualty estimation models were reviewed to identify which risk factors were considered in the models. Epidemiological studies identified the following risk factors: gender, age, socioeconomic status, physical disability and human behavior. Medical preparedness factors were also related to earthquake-induced injury and death. Global casualty estimation models do not currently consider these factors. This study provides evidence that integrating demographic and socioeconomic characteristics of the population and levels of medical preparedness into the existing casualty estimation models may improve their accuracy.

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