Abstract
When pandemics lead to a higher workload in the healthcare sector, the attitude of healthcare staff and, more importantly, the ability to predict the rate of absence due to sickness are crucial factors in emergency preparedness and resource allocation. The aim of this study was to design and validate a questionnaire to measure the attitude of hospital staff toward work attendance during an influenza pandemic. An online questionnaire was designed and electronically distributed to the staff of a teaching medical institution in the United Kingdom. The questionnaire was designed de novo following discussions with colleagues at Imperial College and with reference to the literature on the severe acute respiratory syndrome (SARS) epidemic. The questionnaire included 15 independent fact variables and 33 dependent measure variables. A total of 367 responses were received in this survey. The data from the measurement variables were not normally distributed. Three different methods (standardized residuals, Mahalanobis distance and Cook's distance) were used to identify the outliers. In all, 19 respondents (5.17%) were identified as outliers and were excluded. The responses to this questionnaire had a wide range of missing data, from 1 to 74 cases in the measured variables. To improve the quality of the data, missing value analysis, using Expectation Maximization Algorithm (EMA) with a non-normal distribution model, was applied to the responses. The collected data were checked for homoscedasticity and multicollinearity of the variables. These tests suggested that some of the questions should be merged. In the last step, the reliability of the questionnaire was evaluated. This process showed that three questions reduced the reliability of the questionnaire. Removing those questions helped to achieve the desired level of reliability. With the changes proposed in this article, the questionnaire for measuring staff attitudes concerning pandemic influenza can be converted to a standardized and validated questionnaire to properly measure the expectations and attendance of healthcare staff in the event of pandemic flu.
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