1. The confounder information is based on company-based job exposure matrix only with no published information on its validity.

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1 Author s response to reviews Title: Cumulative exposure to shift work and sickness absence: associations in a five-year historic cohort Authors: Alwin van Drongelen (a.vandrongelen@vumc.nl;) Cécile Boot (crl.boot@vumc.nl) Hynek Hlobil (hhlobil.health@klm.com) Allard van der Beek (a.vanderbeek@vumc.nl) Tjabe Smid (t.smid@vumc.nl) Version: 1 Date: 02 Sep 2016 Author s response to reviews: Amsterdam, september 1st, 2016 Dear Editor, Please find enclosed the revised manuscript Cumulative exposure to shift work and sickness absence: associations in a five-year historic cohort by Alwin van Drongelen, Cécile Boot, Hynek Hlobil, Allard van der Beek, and Tjabe Smid. The comments of the reviewers are addressed point-by-point below. Reviewer 1: Major comments 1. The confounder information is based on company-based job exposure matrix only with no published information on its validity. Although there is no published information available of the validity of the job classification system (ORBA) that we used, it is well-known in the Netherlands and developed by the largest Dutch Employers Organization (AWVN). It is the most widely used job evaluation and classification methodology in the Netherlands; the function of about 1.4 million employees, is described and evaluated using the ORBA methodology. (

2 We added the following statement in strengths and weaknesses section of the discussion: A part of the confounder information was based on a job classification system. Although this system is widely used to describe and evaluate functions in the Netherlands, there is no published information available about its validity. 2. The used statistical model seems to be overadjusted due to controlling for JEM instead of individual information on job characteristics. Unfortunately the outcome of the job classification system was the most specific information available. It is indeed possible that by using this, we overadjusted and underestimated the actual effect of shift work. We want to mention, however, that the job classification system described actual evaluated jobs, and was not based on job titles only (which is common for a JEM). We added the following statement in the discussion section: In addition, the job classification system is not able to take into account individual differences between jobs, for which it is possible we overadjusted our analyses. 3. In addition, information on the no of children and marital status has been obtained from somewhere - not explained in detail. We analysed data from the MORE cohort. The information that was available from the employees who were included in this cohort originated from sickness absence records (of the occupational health service of the airline company) and human resource records (of the airline company itself). We added the following statement in the method section: The human resource records included socio-demographic and work-related information and individual work schedules.. 4. Any other confounders (e.g. life-style, individual working conditions) are missing. Because we used only company data, we could not include other relevant confounding factors such as lifestyle or working conditions. We emphasize this in in the discussion section: Previous research showed that health conditions, lifestyle, psychosocial determinants, and coping mechanisms can also play an important role in both the development of sickness absence [17,20,21,23] and the tolerance to irregular working hours [49,61]. Because we only used company record data it was not possible to include all of these determinants in our analyses, which can have affected our results. 5. A weakness of the study design is that even having basically prospective data, it has been analyzed as a cross-sectional analysis comparing different subjects to each other but controlling still for previous sickness absence. Until now studies on the association between shift work and sickness absence have found conflicting results. One of the reasons for these contrary findings is that the actual exposure to

3 the specific type of shift work is unclear, or not included in the analysis. We therefore composed different shift work groups first, depending on their longitudinal exposure through the years, and analysed the differences between these groups in a cross-sectional way. In future studies, the same data could be used for more sophisticated types of analyses, taking into account changes over time. We included the following statement in the discussion section: It would for instance be possible to use similar cohorts, based on company data, for more sophisticated types of analyses, taking into account sickness absence changes over time. Specific comments 6. A strength of the MS is that selection bias is mostly controlled by analyzing separately the different shift type groups that differentiate groups with changes from one group to another. It is typical that shift workers who have problems with their shifts transfer to day work. Even the groups have been analyzed separately based on changes after 2005, it is probable that especially group 2 (day work ) included many earlier night shift workers. They should be excluded from the day worker group (to avoid reversed causality) or otherwise selection to day work will dilute the results. In any case, this should be discussed as a limitation of the study. We agree that it is very well possible that group 2 holds employees that used to be exposed to night shifts. Unfortunately, we do not have any information about the employees from before 2005, for which this group of previous night shift workers could not be excluded. We included the following statement in the discussion section: Because of the fact that we did not have information about the employees before 2005 it is possible that the group of day workers included a substantial number of previous night workers. Because it was not possible to exclude these employees, our results might have been diluted. 7. What does "the complete data on job classification" mean? What was non-complete and for which reasons there were lacking information? If you have objective working hour data you should be able to classify 100% of the shift schedules! This statement does not affect work schedule data. We found out that some jobs, mostly expert or high management jobs, were not evaluated by means of the job classification system. We included the following statement in the discussion section:.. it was decided to exclude the employees whose job was not evaluated by job classification system. Since these employees predominantly held an expert or high management function, the exclusion affected the external validity of the results. 8. Please clarify how the MORE job-exposure matrix has been created. It seems that this job exposure matrix has not been evaluated scientifically - has it been validated against questionnaire or other objective data?

4 Although there is no published information available of the validity of the job classification system (ORBA) that we used, it is well-known in the Netherlands and developed by the largest Dutch Employers Organization (AWVN). It is the most widely used job evaluation and classification methodology in the Netherlands; the function of about 1.4 million employees, is described and evaluated using the ORBA methodology. ( We added the following statement in strengths and weaknesses section of the discussion: A part of the confounder information was based on a job classification system. Although this system is widely used to describe and evaluate functions in the Netherlands, there is no published information available about its validity. 9. How is the information on the no of children and marital status has been obtained?- is it based on a questionnaire, during which year? What was the response rate? This information was derived from the human resource records of the company and retrieved from the database in We added the following sentence to the method section: The human resource records included socio-demographic and work-related information, and individual work schedules that were registered for each day of the cohort period. 10. The results are mostly significant in the crude and semi-crude models, but no more in the fully adjusted model that has been controlled additionally for the job title-based classification of the working conditions. This needs to be a critical point for acceptance, since the results have been declared mostly negative due to the results of the full-adjusted model. It is very possible that the full model is over-adjusted if there is a systematic association of shift type with the "operational requirements, knowledge requirements and aggravated working conditions". The scoring of "aggravated working conditions" is based on job titles, depending on the physiological and physical workload of the different occupations. Based on the earlier literature night workers have normally more heavy working conditions than day workers. The association of shift type groups) to the job classification variables should be shown in order to decide on the validity of the "full model". We fully agree that a systematic higher workload of shift workers would have led to overadjustment in the fully adjusted model. We therefore analyzed the association between the shift type groups and the aggravated working conditions ourselves, before submitting this manuscript. The cross tabulation did show that relatively more shift workers were categorized in the higher aggravated working conditions (as would be expected) but there is enough variation in the shift type groups to keep the variable in the fully adjusted model. Crosstab Aggravated working condition category / shift type 4 % of shift type >4 to 11 % of shift type >11 to 15 % of shift type >15 % of shift type Total

5 Day work % % 87 8% 14 1% 1049 Two-shift schedule % % % % 3228 Three-shift schedule % % % % 1614 Transfer out of three-shift % 83 20% 86 20% 24 6% 424 Transfer into three-shift % 36 6% % % 591 Other kind of transfer % 60 10% % 55 9% 606 Total In addition, we also included the following sentence in the discussion section: Overadjustment might also have occurred due to a systematic higher workload of the shift workers, which would have resulted in a systematic higher score on aggravated working conditions and a possible overadjustment in the fully adjusted model. However, a beforehand conducted crosstab analyses showed that there was sufficient variation of aggravated working conditions scores within the shift type groups. 11. I want to stress that it is correct to control for INDIVIDUAL work demands but not by using a job exposure matric where e.g. all night workers are systematically scored as workers with high load, unrelated to the individual true situation. The problems arise from the used crosssectional design (even controlling for earlier level of sickness absence) where the subjects are compared to each other instead of a longitudinal comparison to analyze e.g. the effects in shift types of shift characteristics in the SAME subjects. Although the job classification could not control for all individual job demands, the job classification system used did take into account as much variation as possible. To give an idea, the system identified and subsequently scored 1171 different jobs throughout the population that was used for this article. As a consequence we could see that there was enough variation between the job classification scores in order to use them for the analyses. 12. Similarly, the results on Table 3 does not make too much sense since the selection to different shift groups is no more controlled but all the groups are analyzed in one group depending on the no of night shifts. These results are thus strongly sensitive for possible selection bias. Table 3 could be a post-hoc analysis for each shift type group but it is not relevant to combine the groups. Please remove these results if there is not sufficient data for a post-hoc analysis according to shift type. For this analysis, we let go of the different shift type groups. Instead, we wanted to see if cumulative night shift exposure per se, independent of shift type, would impose sickness absence, due to for instance chronic disruption of the circadian rhythm. We therefore chose to keep the table in the manuscript. However, if you insist on removing the results we would be willing to do so.

6 Reviewer 2: Main comments 1. The study benefits from a large historic cohort with independent and detailed information on cumulative exposure (for a five year period), outcome and potential confounders. Unfortunately, the authors have concern that the main finding of no association was due to selection bias (line 308). No arguments against the likelihood or the likely size of selection bias are put forward. I think it is a major problem, if we cannot trust the main results concerning cumulative exposure, based on the otherwise good quality data, without challenging that the results were negatively biased due to selection. I suggest that the authors take a look into their data, if possible, or try to get further information that could help judge and challenge whether selection bias explained the main results. Otherwise, the 'negative' main results may be considered of limited scientific value, which then should be reflected in the conclusion. See also 5. below. As a result of your remark, and the first remark of the editor, we had a good look at our data and selection procedure. We agree that excluding 2877 (22%) of the total population due to previous sickness absence would lead to disproportional selection bias. We therefore reanalyzed the logbook of the study and found that unfortunately, two selection criteria were taken together as one. During the analyses, we found that for a certain amount of employees (n=2099), although they were employed, there were no working hour records available in As a consequence, we could not calculate their cumulative exposure, for which we decided to exclude them from the analyses. Post hoc analyses did not show any significant differences on other variables between these and the remaining employees. We adjusted the flow chart and the corresponding text in the method and the discussion section to clarify that we excluded employees with incomplete working hour data (n=2099) and employees with >365 days of sickness absence (n=778). Other comments 2. No specific a priori hypotheses were stated concerning the expected effects of exposure. Cumulative exposure to shift work did not predict long term absence and even seemed to protect against numbers of absence spells. Since the focus was on cumulative exposures, I believe that the conclusion should concentrate on these results and put less focus on the result concerning the relatively small group that transferred into three-shift schedule with expected lower level of cumulative exposure. We agree that the predominant focus of the article should be on the cumulative exposure groups. We therefore changed the emphasis of the conclusion in both the abstract and the main text. 3. The finding that the small subgroup of those, who transferred into three-shift schedule, had more long term absence is interesting, but there may be a number of alternative explanations

7 for the observed increase, besides problems to adapt. It would be interesting for readers to be able to compare the distributions of the socio-demographic and other factors for the shift type groups in general. From such information it would also be possible to judge whether persons who transferred in to three-shift schedule differed with respect to these factors. If so, they may differ also with respect to unmeasured variables, like alcohol consumption, smoking and other living habits/conditions, which may influence on taking sickness absence. A new layout of table 1, which tabulates the distributions of other factors and number of night shifts according to shift type could inform these issues, however supplemental results given in the text could also suffice together with a short discussion of the issue of alternative explanations of the results, concerning the group that transferred into three-shift schedule. It is indeed interesting to look at other factors that might have been involved in the increase in long-term sickness absence of this specific group as well. To our opinion, table one would become somewhat unreadable if we would add the information per shift type group, and therefore chose to provide the information in the main text of the discussion section: This lack of adaption might be one of the reasons for the finding that employees that changed into shift work that included night shifts showed an increased risk for long-term sickness absence. It is however also possible that these employees differed considerably from other shift workers in terms of lifestyle or household composition, that might have had an influence on sickness absence. Nevertheless, a post-hoc analysis showed that employees that started with night shifts were somewhat younger than other shift workers, but did not differ in terms of gender, household composition or commuting distance. 4. The reading of the method section would benefit from subheadings as e.g. 'Sources of information and study population', 'Outcome', 'Exposure', 'Other variables, and Analyses. Subheadings have been added. 5. In line 89, what is meant by a five-year historic cohort? When was the cohort identified? The definition of the study population (for this study) as a five-year cohort is a little hard to follow, since to be a member one also had to be employed at the end of follow up (January 1, 2010), i.e. only 'cohort survivors'. This means, that cohort members that fulfilled all inclusion criteria at start of follow up (January 1, 2009) were left out if not employed (for whatever reason) at the end of follow up. In a real historic cohort, such members should be allowed to contribute with information taking into account their length of follow up or at least some information should be provided on how many that were lost during follow up. The risk of selection bias due to the 'survivor criteria' is central and addressed in the discussion line 308 to 315 (c.f. 1.). If the number was very small a bias due to selection could be considered less likely. The cohort was identified at January 1, Subsequently, information about these employees was determined retrospectively up till All employees had to be employed from 2005 onwards because we wanted to make sure every employee had the same potential amount of exposure. So, in general, there were no employees in our initial MORE cohort of employees that dropped out between 2009 and The employees who were excluded (from this specific study) at January 1, 2009, had no job classification data. This was as non-random

8 selection however, because at the time of the data collection, the jobs with the highest salaries were not classified yet, and thus only employees with managerial jobs had to be excluded. We tried to better emphasize this procedure in the main text of the manuscript. 6. A little supplemental information in the method section is needed. Outcome was informed from the occupational health service of the company. How does this information come to the knowledge of the company health care and is it considered to be complete? Information on how sickness absence was reported and registered would be helpful to judge about this issue, although selective reporting probably is unlikely. We changed the part about the sickness absence registration into the following: Sickness absence was defined as registered absence from work due to health reasons. All employees of the company contacted the occupational health service when they called in sick, and when they wanted to return to work. The occupational health service digitally registered all sickness absence records. For this study, calendar days of the registered sickness absence episodes were counted as sickness absence days. Partial absence days were considered as full days of sickness absence [21]. Likewise, information on the job classification variables is needed. Which variables were comprised? What was the source of information? Was the numbers with missing information related to the main exposure variable (work schedule/numbers of night shifts). Who scored the job titles with regard to responsibility, knowledge requirements, etc.? What system was used? It is interesting if some kind of job exposure matrix was used. We did not use a job exposure matrix. The job classification system (ORBA) that we used however, is well-known in the Netherlands and developed by the largest Dutch Employers Organization (AWVN). It is the most widely used job evaluation and classification methodology in the Netherlands; the jobs of about 1.4 million employees, are described and evaluated using the ORBA methodology. ( In the method section, we modified the part about the job classification as follows: By means of the job classification system that was used within the company, all job titles (n=1171) were systematically scored by independent evaluators, on a continuous scale on the following characteristics: responsibility, knowledge requirements, social interaction, special operational requirements, and aggravated working conditions [36]. The job classification system was developed by the largest Dutch Employers Organization and is widely used throughout the country. In the discussion, we added the following sentence: Next, it was decided to exclude the employees whose job was not evaluated by job classification system. Since these employees predominantly held an expert or high management function, the exclusion affected the external validity of the results. 7. The number of persons with no night shift was 2837 (Table 1) while the total number in day or two-shift schedule (defined as rotating between morning/day and evening shifts) was

9 4292 ( ). As I understand, the definition of the shift-schedule types, the persons in day and two-shift schedules (n=4292) should not be able to accumulate exposure to night shifts since transferors have their own categories. May be, the definition of shift-schedules need further explanation, or else something may be wrong with the numbers. Thank you very much for this remark. We found out that due to a coding failure in SPSS we mixed up the two-shift (n=1615), with the three-shift schedule group (n=3232), and all of their analyses outcomes. We adjusted the relevant tables and rewrote the outcomes and the discussion where necessary. Some further explanations may also be needed to capture how the exposure variable 'Schifttype' accumulete (which?) exposure.` Unfortunately, it is not to clear to us what you mean with this remark. 8. Inconsistencies of the wordings and writing should be checked. I will only mention a couple. E.g. in the reference list, umlaut is sometimes used and sometimes not: Tuchsen in ref 28. and 42. but Tüchsen in ref 38. And in Table 2 and 4 different names are used for the same shift type exposure variable. Thank you for the remark. We checked the writing and changed the inconsistencies that we found. Editor's comments: employees were excluded because they were absent more than 365 days. This is a huge proportion of the sample. This point should be explained. This is crucial issue which was also raised by reviewer 2 (point 1). Please see our response there. 2. Occupation is an important variable and should be included in the study as an adjustment variable. We decided not to, because we did adjust for the characteristics of the job, which were scored using the job classification system. Inclusion of both occupation and job characteristics would most probably blow up the model due to multicollinearity. 3. The choice of the adjustment variables should be justified. Adjustment variables should be either well-known risk factors for the outcome, or variables that are found to be associated with the outcome in the study. Consequently, the authors should check whether the list of their adjustment variables is adequate. For example, the inclusion of the job classification variables may be questioned, indeed they are not the variables of interest, not associated with the outcome and not well-known risk factors, and their validity may also be questioned.

10 We did select the potential confounders by means of a pre-selection procedure. We therefore added the following sentence: A pre-selection procedure indicated that the selected potential confounders were all univariately (p<0.1) associated with the outcome measure. We also added the following sentence in the method section: Based on scientific literature, the following available variables were regarded as potential confounders and retrieved from the database at January 1, 2009 [19-23]. The point about the validity of the job classification system was raised by the other reviewers as well, we tried to answer their questions as good as possible at point 1,8, and 11 (reviewer 1), and 6 (reviewer 2). 4. Including previous sickness absence as an adjustment variable may lead to overadjustment, if people had previous sickness absence because of exposure to shift/night work. This point should be underlined and I suggest to include previous sickness absence in a final adjustment model only. We added an extra remark in the discussion section, stating the possible overadjustment due to adjusting for previous sickness absence: We might also have overadjusted by using previous sickness absence as a confounder since this absent might have been due to the specific shift work exposure. On the other hand, adjusting for previous sickness absence does take into account the reduced exposure to a certain schedule between 2005 and 2008, in relation to the outcome measure that was analysed during the year The statistical analysis for the number of sickness absence days is not adequate. This outcome has indeed two main features: overdispersion but also an inflated number of zeros, consequently Poisson regression model is not the adequate model. Please see our paper: Niedhammer et al. EJPH 2013 (negative binomial Hurdle model). One solution is to make two analyses: a logistic regression model to study the binary variable of sickness absence (yes/no) on the whole sample and a negative binomial regression model to study the number of sickness absence days on the subsample of those who had at least one day of sickness absence. Unfortunately, we were not aware of the possibility of the binomial Hurdle model to analyze sickness absence days. In addition, we did not choose to analyze the number of sickness absence days but the number of sickness absence spells. And although a different approach might be possible, the Poisson regression which takes into account overdispersion only is still widely used to analyze this specific count variable. In addition, we found that although many employees reported ill zero times (24%), this is not the majority of the population; almost just as many employees were sick one (26%) or two (23%) times. We therefore decided not to introduce a zero inflation model. 6. Important limitations of the study are neglected: no direct information and routine data, drastic selection of the study sample and potential selection bias, healthy worker effect, no study of the time between exposure and outcome, etc. We added the following sentence in order to address the matter of standard available company data: Because we only used indirect, standard available company data, it was not

11 possible to include all of these determinants in our analyses, which can have affected our results. The matter of the drastic selection and selection bias was addressed after the comments of reviewer 2 (point 1 and 5). We did already try to address the healthy worker effect in the discussion section: Consequently, only the employees who managed to keep working during the five-year exposure period were analysed, introducing the healthy worker effect. Therefore, analysing this selection of employees might have led to an underestimation of the association between shift work and sickness absence. The lack of longitudinal analysis was addressed after the comment of reviewer 1 (#5). 7. Line , this is true among men only. You are right, we added this information to the discussion. Editorial Requests 1. Please include the addresses for all authors at the title page of the manuscript. The addresses have been added 2. Please state within the Competing interests section specifically whether or not the authors have any competing interests The competing interest information has been added. 3. Please provide a reference justifying that as per Dutch law, this study does not require ethics approval A full ethics declaration was added. 4. Please format the Authors' Contributions section so that each author's contributions are specifically mentioned. Information about the authors contributions has been added. 5. Ethics: If your study involves humans, human data or animals, then your article should contain an ethics statement which includes the name of the committee that approved your study. If ethics was not required for your study, then this should be clearly stated and a rationale provided.

12 A full ethics declaration was added. 6. Consent: If your article is a prospective study involving human participants then your article should include a statement detailing consent for participation. If individual clinical data is presented in your article, then you must clarify whether consent for publication of these data was obtained. The following statement was added: because this study only used anonymised, standard available company data, an informed consent was not necessary 7. Availability of supporting data: BioMed Central strongly encourages all data sets on which the conclusions of the paper rely be either deposited in publicly available repositories (where available and appropriate) or presented in the main papers or additional supporting files, in machine-readable format whenever possible. Authors must include an Availability of Data and Materials section in their article detailing where the data supporting their findings can be found. The Accession Numbers of any nucleic acid sequences, protein sequences or atomic coordinates cited in the manuscript must be provided and include the corresponding database name. Unfortunately, the airline company involved in this study does not prefer to make their employee data publicly available. 8. Authors Contributions: Your 'Authors Contributions' section must detail the individual contribution for each individual author listed on your manuscript. Information about the authors contributions has been added. Looking forward to your response, On behalf of all authors, Yours sincerely, A. van Drongelen, Ph.D Department of Public and Occupational Health VU University Medical Center Van der Boechorststraat 7 NL-1081 BT Amsterdam a.vandrongelen@vumc.nl