Healthcare workers’ willingness to respond following a disaster: a novel statistical approach toward data analysis - Stav Shapira; Michael Friger; Yaron Bar-Dayan; Limor Aharonson-Daniel (BMC Medical Education, 2019)
Abstract 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.