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Design and validation of a questionnaire to measure the attitudes of hospital staff concerning pandemic influenza

Highlights ► Prediction of healthcare workers. ► Absence is crucial in emergency preparedness. ► We aimed to design and validate a questionnaire to measure the attitude of staff. ► This questionnaire is a validated tool to measure staff attitude about pandemic flu.

The responses to this questionnaire had a wide range of missing data, from 1 to 74

Introduction
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. Pandemic flu is one of the most important pathogens that cause outbreaks of disease, and it has a high risk of spreading rapidly because of the airborne nature of its dissemination. To identify the attitudes of healthcare professionals regarding work attendance during such an outbreak, a questionnaire was designed de novo following discussions with colleagues at Imperial College and with reference to the literature on severe acute respiratory syndrome (SARS) [1][2][3][4]. The choice of pandemic influenza was pragmatic because preparations were underway to prepare for an expected pandemic in the near future. To aid planning for business continuity, an understanding of staff attitudes toward pandemic flu is invaluable. Pandemic flu was compared with SARS, the most recent example of a global infectious disease that spread quickly around the world and affected patients and healthcare staff. Published literature was consulted to determine the lessons that had been learned. The questions for the questionnaire were empirically chosen based on the assumption that generic issues arising from the way the SARS outbreak was managed would be relevant to the management of an influenza pandemic. Subsequently, questions from a Department of Health (DH) questionnaire about pandemic flu that asked respondents about the distance of their residence from the hospital were combined with this questionnaire.
The individual questions were empirically formulated based on experience and consensus views. The questionnaire consists of 15 independent fact variables and 33 dependent measure variables. The fact variables focus on personal and work-related items and the respondents' contact history with infectious diseases. The measured dependent variables focus on the prospect of work attendance in the case of a pandemic flu, factors that may affect work attendance and respondents' expectations of the trust (i.e., public organization providing services on behalf of the National Health Service in England [5]) in the case of such events.
This study was conducted to ensure the reliability and validity of the questionnaire. The questionnaire was electronically advertised to the staff of Imperial College Healthcare hospitals in London. Senior managers were also asked to distribute the questionnaire to their staff. Because this study addresses a sensitive subject (attitudes about work absence), the responses were collected anonymously.

Material and methods
The questionnaire was evaluated through the four steps listed below: 1. Face validation: In this step, we ensured that the respondents' understanding of the questions was aligned with our goals. 2. Pilot data preparation: In this step, we ensured that response bias was minimized, and we investigated the possibility of predicting missing data.
3. Content validation: In this step, we verified that the question items targeted the aim of the study. 4. Content reliability: In this step, we investigated the relevance of the question items.
In the face validation step, the questionnaire was evaluated from the design point of view. None of the respondents reported problems understanding the content of the questionnaire. The options for scaling questions consisted of 6 items that were compatible with the recommendation by Fowler [6]. The items were labeled from negative (strongly disagree) to positive (strongly agree). Labeling the items on a scale using minimal descriptive words helped to eliminate confusion about the value of the scale levels [7]. The next step was data preparation. First, we analyzed the pilot data for outlier responses. The risk of extravagant respondents is a possibility in any survey, especially when the survey is anonymous. To identify the outliers, we used three methods: standardized residuals, Mahalanobis distance and Cook's distance. After removing the outlier responses, we scanned the data for missing values. Missing values are a major factor in reaching valid conclusions. There are methods for calculating these values that remove the effect of hidden bias in the data. We used Expectation Maximization Algorithm (EMA) to calculate the missing values [8].
The next step in the evaluation focused on content validity. There are two methods for measuring the validity of a questionnaire. The first method involves comparing the result with a highly valid measure, such as the work attendance of the participants, to exclude the effect of confounding factors. The second method uses the level of correlation between the dependent and independent variables [9]. The first method was not possible because of the anonymous nature of the study; thus, we applied the second method. To ensure content validity in the second method, the questionnaire should be checked for two issues. The first issue ensures that the independent variables have a minimum level of correlation with the measured dependent variables. This procedure was used to identify irrelevant questions. The second issue involves the identification of highly correlated independent variables. If there is a high degree of correlation between these variables, the questionnaire should be checked again. If possible, it is recommended that these questions be merged, resulting in a shorter questionnaire with the same level of validity.
The last step involved checking the reliability of the questionnaire. There are two methods to measure reliability: test-retest and internal consistency [9]. Because of the anonymous nature of the study, it was impossible to utilize the test-retest method; therefore, the internal consistency of the questionnaire was calculated. This test can identify variables that may reduce the inter-correlation between the question variables. If possible, omitting these variables improves the consistency of the questionnaire.

Results
This questionnaire was published online, and 367 responses were recorded in the system. The actual response rate could not be calculated because the questionnaire was electronically advertised, and all employees of the hospital may have been exposed to the study. The responders covered a wide range of hospital professions. The relative frequencies of the occupations are presented in Table 1. We received responses from different age groups and both genders, as shown below in Tables 2 and 3.
Regarding work and employment status, we received responses from a variety of groups, as presented in Tables 4 and 5.
Answers to three negatively worded questions were mirrored with their positive values. These  questions involved the adverse effect of dependency on public transportation and concerns about personal and family health on work attendance in the event of pandemic flu.
Regarding the outlier detection methods, the threshold level for the standardized residual was ±3.3. The degree of freedom for the Mahalanobis Distance test was 29, which resulted in a critical value of 58.3 for the alpha level of 0.001. Using these outlier detection methods, 19 cases (5.17%) were identified as outliers and were excluded from the final analysis.
Analysis of the responses showed a different range of missing data in the survey questions, as presented in Table 6.
By applying the EMA method to the data, the missing data were calculated. Because of the non-normal distribution of the data, Student's t distribution was used in this calculation instead of the normal distribution likelihood function. A maximum of 25 iterations was assigned for this algorithm. The missing values were predicted by this method.
The residual scatterplot shows a roughly rectangular shape, which supports the homoscedasticity of the results (Fig. 1).
The normal P-P plot of regression standardized residual (Fig. 2) shows a reasonably straight diagonal line from the bottom left to the top right, which supports the linearity of the pilot data obtained by this questionnaire. The above analysis shows the homogeneity of variance between the variables measured in the questionnaire. This homogeneity is required to measure the correlation between the variables. Evaluating the correlation between the independent (fact) question items and the dependent (measure) variables showed a correlation above 0.3. This correlation assures that there are no irrelevant independent variables in the questionnaire.
The effect of identifying flu cases in London or single or multiple cases in the trust on the probability of work absence showed a high multicollinearity. Furthermore, the items ''Expect rapid access to diagnosis'' and ''Expect rapid access to treatment'' were highly correlated. The other two correlated question items were ''Expectation of personal protection equipment for traveling to work on public transport'' and ''Dependency on   public transport for ability to come to work''. The existence of multicollinearity in the questionnaire variables can falsely overfit the regression models that are developed using the data collected with that questionnaire.
Cronbach's alpha test was used to measure the reliability of the questionnaire, and the initial result of this test was 0.507. This result is below the minimum acceptable value (0.7). Further evaluation of the questions showed that the items ''Years of working in the current job'', ''Years of working in National Health Service (NHS)'' and ''Working time if part-time'' caused this low reliability. The result of the same test without these question items increased the reliability to 0.712.

Discussion
The data collected through this survey represented a wide range of professions and age groups from both genders and employment states. The missing value analysis using the EMA method helped to improve the data and to handle missing values. We aimed to generate a validated questionnaire that provides the required information with the lowest number of questions. The questionnaire was validated using various processes and statistical methods, as discussed in the ''Results'' section. Based on these results, some changes were suggested to improve this questionnaire.
We merged three questions on the effect of flu infection in London or single and multiple cases in the trust on the probability of work absence because of the high correlation between these questions. A high correlation between the expectation for rapid diagnosis and the expectation for rapid treatment also supported merging these two items as ''Expectation for rapid diagnosis and treatment''.
To generalize this questionnaire, we recommended using the term ''your city'' instead of ''London'', which was the pilot city.
Although the dependency on public transportation and the expectation of protection equipment for this service were highly correlated, these two question items point to separate concepts. We retained both of the questions in the questionnaire for this reason and for the purpose of controlling the responses.
Repeating the Cronbach's alpha test without the ''Years of working in the current job'', ''Years of working in National Health Service (NHS)'' and ''Working time if part-time'' showed that the questionnaire had reached the required level of reliability. Although the removed items seemed to be related to the concept of the study in the design phase, the results from the pilot study showed that they should be omitted from the questionnaire. The final version of the questionnaire is included in Appendix 1.

Conclusion
This pilot study of a questionnaire designed to measure the attitude of healthcare staff about a flu outbreak resulted in some improvements in the questions used. The pilot study demonstrated that the items in the questionnaire are relevant to the subject, the questionnaire has an acceptable level of type I error and reliability and the questionnaire has been optimized to obtain the desired information with the fewest questions.
Overall, these measures show that the questionnaire can be used as a standardized and validated measurement of healthcare staff's expectations and attendance in the event of pandemic flu. However, there are limitations to this study. First, the questionnaire was electronically advertised, but it is not clear whether all employees had access to computers in the hospital, which could be a source of bias for the staff who completed the survey. Second, the actual response rate could not be calculated because the questionnaire was electronically advertised, and all of the hospital employees may have been exposed to the study. We are also unable to comment on the non-responders.
We recommend that this questionnaire be assessed in a multi-center study with a larger sample size to increase the reliability of the results and to establish a score model for the questionnaire. Furthermore, an investigation of other confounding factors using qualitative methods, such as soft system methodology (SSM) [9], could potentially improve this questionnaire.

Appendix A. Pandemic influenza: staff attitudes survey
This is an anonymous survey of attitudes and beliefs concerning the possible occurrence of pandemic influenza. It is being carried out by the occupational health service to help the trust prepare for this eventuality. It can be completed in no more than 5 min. Thank you for your assistance.
Please tick one of the following: Pand emic influenza: staff attitu des surve y This is an anonymous survey of attitudes and beliefs concernin g the poss ible occ urrence of pandemic influenza. It is being ca rried out by the occ upational h ealth service to help the trust prepare for this eventual ity. It can be comp leted in no mor e than 5 minutes. Th ank you for your assistance.
Please tick one of the following: Occupati onal gr oup