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Long weekly working hours and ischaemic heart disease: a follow-up study among 145 861 randomly selected workers in Denmark

Objectives The aim of the present study was to test if incidences of ischaemic heart disease (IHD) and usage of antihypertensive drugs are independent of weekly working hours (WWH) among full-time employees in Denmark. Design and participants Data on WWH from participants of the Danish labour force surveys, 1999–2013, were linked on an individual level to national registers with data on socioeconomic status (SES), industry, emigrations, redeemed prescriptions, hospital contacts and deaths. Participants were followed until the end of 2014 (on average 7.7 years). Poisson regression was used to model incidence rates as a function of WWH. The analyses were controlled for calendar time, time passed since start of follow-up, employment in the healthcare industry, age, sex, SES and night work. Results In total, we found 3635 cases of IHD and 20 648 cases of antihypertensive drug usage. The rate ratio of IHD was 0.95 (95% CI 0.85 to 1.06) for 41–48 compared with 32–40 WWH and 1.07 (0.94 to 1.21) for >48 compared with 32–40 WWH. The corresponding rate ratios for antihypertensive drug usage were 0.99 (0.95 to 1.04) and 1.02 (0.97 to 1.08). No statistically significant interactions between WWH and sex, SES and night work, respectively, were found. Conclusion In this Danish sample, we did not find any statistically significant association between WWH and IHD or antihypertensive drug usage.

1) The study addresses an important occupational and public health topic given the increasing prevalence of long working hours in some large economy such as the US.
2) The large sample size and the virtually complete follow-up with national registers are important strengths of the study. Disregard of repeat exposure measures and the sole reliance on self-reported "usual work hours" instead of "actual work hours" are weaknesses that probably led to conservative misclassification bias, i.e. an underestimation of effect sizes. 3) The authors rely mostly on statistical significance testing in their overall and interaction analyses and interpretation of results. Unfortunately, for one of the outcomes, IHD incidence, that has misled the authors to several conclusions that are not supported by the observed effect estimates and confidence intervals and stratified analyses presented. In fact, in contrast to the author's interpretation, data are more supportive of cited previous meta-analyses showing positive associations between work hours and CVD incidence, especially in low SES jobs.

Detailed Comments:
1) Abstract: The conclusion that results "do not support the hypothesis that long work hours are associated with IHD" is not supported by the data. The opposite is conclusion is more in line with the actual data and should be stated. (see detailed comments below) 2) Abstract: A conclusion regarding antihypertensive treatment should be added. 3) Page 3, line 26: The cited rate ratios in the literature are actually neither clear nor convincing evidence against an association between long work hours and CHD. The cited effect measures and confidence intervals favor the opposite conclusion. (see more detailed comment below) 4) Page 3, line 29: Although a desirable feature, especially for meta-analyses, the lack of a pre-published protocol is not a strong indicator of the validity of any individual study results; instead the methodological quality and validity of individual research studies depends mostly on the appropriateness of the actual methods being used rather than their independent and early appearance in the literature. 5) Page 3, line 35: Add: "Research on working time and progression of atherosclerosis, a precursor and mechanism in the development of IHD, has also shown interactions with pre-existing IHD (add reference: Krause N, Brand RJ, Kauhanen J, Kaplan GA, Syme SL, Wong CC, Salonen JT, Work time and 11-year progression of carotid atherosclerosis. Prev Chronic Dis 2009;6(1). http://www.cdc.gov/pcd/issues/2009/jan/07_0270.htm.) 6) Ibid: Because of the strong interactions between working time and pre-existing IHD as observed in the study cited above and because of the high prevalence of CVD in aging worker populations, the authors should consider to add a sub-analysis stratified by baseline IHD. This is important for the protection of this vulnerable working population given that IHD is a severely disabling and often fatal disease. 7) Page 3, line 55: I would strongly recommend to add preexisting IHD to this list of potential effect modifiers to be investigated in this large dataset (see comment above). 8) The "multiple testing problem" is not universally recognized as a valid issue and respective adjustment of significance levels is not universally accepted as appropriate. The authors should review the respective literature and justify and possibly modify their approach in light of this literature. 9) With regard to p-values for statistical significance testing of interactions, several biostatisticians and epidemiologists have suggested to (a) use higher p-levels (e.g. 0.1, 0.15, or 0.2) for multiplicative interaction (departure from multiplicative effects), (b)investigate departure from additive effects (that may be less than multiplicative but biological substantial), or to evaluate the existence of interaction based on the observed magnitude of the difference of effect sizes in respective stratified analyses. This magnitude should be evaluated with respect to potential biologically, clinically, or epidemiologically (in terms of impact on population health) relevance regardless of any statistical significance test results. (See for example standard texts such as K. Rossman, Textbook of modern epidemiology and S.Selvin, Statistical analyses of epidemiological data). 10) Page 4, line 52: No mention of anti-hypertensive therapy in the study protocol but in abstract and results? See also inconsistent use in abstract. 11) Page 5, line 56. The treatment of weekly work hours as a categorical variable needs to be justified here. One rationale may be to provide data that match other studies or worktime regulations or a facilitation of communication of research results to a wider non-scientific audience. However, it should be acknowledged that categorization of a wide range of continuous data into three categories introduces an enormous amount of misclassification likely to result in a conservative bias towards the null. While it is ok. To present analyses with categorical data, these analyses should at least be complemented by an analyses of continuous exposure data. 12) Page 7, line 57: Sensitivity analyses with additional exclusion of workers with hospitalization IHD in previous years will yield more reliable results for the subgroup of workers without IHD. However, because of their higher a priory risk of repeat IHD incidence or even fatal IHD events, a supplemental subgroup analysis among those with hospitalization for IHD or outpatient IHD diagnosis should be performed as well. (see comment above re interaction between worktime and CVD) 13) Page 8, line 35-37: The sentence " We did not find any statistical significant associations between work hours and IHD incidence" is not consistent with the data. First, at least for workers of low SES, table 2 reports statistically significant results: Rate Ration 1.27 (95%CI 1.05-1.53). Further, an inspection of overall effects and 95% CIs in table 1 support the opposite conclusion: Since overall effect measures are greater than 1 for all workers (1.07) and 95%CIs cover a wider range above one than below 1, the data are actually more consistent with the hypothesis that work hours are associated with IHD incidence. See Rothman and Greenland papers and textbook of modern epidemiology for use and abuse of p-values and statistical testing in the interpretation of epidemiological data. A good summary of the critique of traditional use of p-values and an instruction for appropriate understanding and interpretation of confidence intervals, see the (relative easy to understand without mathematical formulas) recent 2016 presentation by

19)
Page 11, line 50 ff Implications. These blanket recommendations for practice and non-regulation of work hours are neither backed up by the data of this study nor do they take into consideration the strong evidence of previously published work cited and not cited in this article. There is actually an urgent need to supplement current broad work hour regulations with specific recommendations and regulations to protect the most vulnerable subgroups of workers, those with multiple low-wage jobs, performing heavy manual labor, and the increasing number of older workers with pre-existing CVD that seems to substantially increase the risks associated with long work hours. Also, the discussion and conclusions needs to take into consideration several forms of the so-called healthy worker effect that is prone to bias observed risks downward towards the null: people with symptomatic IHD are less likely to enter or stay in physically demanding jobs or jobs with long work hours leading to a conservative health-based selection bias that needs to be considered in the evaluation of these and other study results.
In conclusion, this study has a great potential to further our knowledge about the health effects of long working hours. I strongly encourage the authors to supplement analyses of workers with per-existing CVD conditions, and to reevaluate results in the light of the comments made above and not to disregard their own findings based on inappropriate and outdated use of p-values and confidence intervals.
This reviewer is fully aware that most researchers have received training in statistical methods that emphases statistical testing as performed in this study. However, given the multi-decade long critique of this approach by an increasing number of research methodologists it seems time to change this practice. The high quality of the data and the high qualifications of the author team bode well for an exceptional contribution to the literature of work hours and IHD. This critique was provided in as encouragement and assistance for a successful revision of this manuscript.

GENERAL COMMENTS
This study is well-performed with a large sample and pre-published study protocol, and shows substantial effort. The manuscript is clearly written in a concise manner and authors draw reasonable conclusions. But there are several major and minor issues before the manuscript is acceptable for publication. Below, please find my comments and suggestions. I hope authors find them contributing to their research.
1) First of all, I can't agree with the title of this study. The authors declared that the aim of study was "to test if incidences of ischaemic heart disease (IHD) and usage of antihypertensive drugs", but it is not clear in title which only mentioned "ischaemic heart disease". In my opinion, usage of antihypertensive drugs is quite different from ischaemic heart disease in terms of disease nature. Therefore, authors need to consider addressing them each, or to change the title of the study.
2) In this study SES was based on the participant's occupation, but income level would have been more relevant to study about working hours. As authors mentioned, long working hours may increase income level and thereby reduce the risk or intensity of financial strain, which is a known risk factor for hypertension as well as IHD.
In that sense, we need to know how income level modifies the relationship between working hours and risk of IHD or hypertension.
3) Reference group of weekly working hours was 32-40 hours in analytic models. But authors may also consider 32-48 hours as reference because 48 hours per week is generally considered long working hours in Denmark. Additionally, I guess the majority of study population work just 40 hours per week, so just 40 hours can be set as reference group for further analysis.
Minor 1) In table 1, I wonder that the p-values of second row are for what hypothesis? It was described "The incidence is prospectively independent of weekly working hours as well as interaction between weekly working hours and socioeconomic status, sex and night work, respectively", but I am confusing whether p-value is for "weekly working hours" or "interactions"

VERSION 1 -AUTHOR RESPONSE
Reviewer: 1 General Comments: 1) The study addresses an important occupational and public health topic given the increasing prevalence of long working hours in some large economy such as the US.
2) The large sample size and the virtually complete follow-up with national registers are important strengths of the study. Disregard of repeat exposure measures and the sole reliance on self-reported "usual work hours" instead of "actual work hours" are weaknesses that probably led to conservative misclassification bias, i.e. an underestimation of effect sizes.
3) The authors rely mostly on statistical significance testing in their overall and interaction analyses and interpretation of results. Unfortunately, for one of the outcomes, IHD incidence, that has misled the authors to several conclusions that are not supported by the observed effect estimates and confidence intervals and stratified analyses presented. In fact, in contrast to the author's interpretation, data are more supportive of cited previous meta-analyses showing positive associations between work hours and CVD incidence, especially in low SES jobs.
Detailed Comments: 1) Abstract: The conclusion that results "do not support the hypothesis that long work hours are associated with IHD" is not supported by the data. The opposite is conclusion is more in line with the actual data and should be stated. (see detailed comments below) Authors' response: You are right; a statistical test may provide evidence against a clinically important effect but it can never prove that an effect is exactly equal to zero. We apologise for this shortcoming. To rectify we have changed the conclusion from "Our analyses in a Danish sample do not support the hypothesis that long weekly working hours are associated with IHD." to "Our analyses in a Danish sample do not support the hypothesis of a general important association between long working hours and IHD or antihypertensive drug usage." 2) Abstract: A conclusion regarding antihypertensive treatment should be added.
Authors' response: We apologise for this shortcoming. The conclusion has been changed. See above.
3) Page 3, line 26: The cited rate ratios in the literature are actually neither clear nor convincing evidence against an association between long work hours and CHD. The cited effect measures and confidence intervals favor the opposite conclusion. (see more detailed comment below) Authors' response: As mentioned above, a statistical test can never prove that an effect is exactly equal to zero. It may however provide evidence against a clinically important effect, and from this viewpoint, we would like to point out that we are not talking about evidence against "an association". We are talking about evidence against "a general important association". 4) Page 3, line 29: Although a desirable feature, especially for meta-analyses, the lack of a prepublished protocol is not a strong indicator of the validity of any individual study results; instead the methodological quality and validity of individual research studies depends mostly on the appropriateness of the actual methods being used rather than their independent and early appearance in the literature.
Authors' response: Most researchers agree that a trial with blinded data collectors and outcome adjudicators is more reliable than a trial with non-blinded data collectors and outcome adjudicators. A pre-published study protocol, in which all statistical methods and hypotheses are completely defined before the analysts are allowed to look at the data, will (given that the protocol is followed) preclude the possibility of within-study selection bias, and thereby increase the validity of the study.
Anyway, your comment made us realise that we had failed to mention our study protocol in the discussion section of our manuscript. We apologise for this shortcoming. To rectify, we have added the following text to the discussion about strengths and weaknesses: "The study was further strengthened by its pre-published study protocol, in which all statistical methods and hypotheses were completely defined and peer-reviewed before the analysts were allowed to link the exposure data to the outcome data. Since the study protocol was followed, it guarantees that the study is free from within-study selection bias." Authors' response: Thank you for directing our attention to this interesting paper. After reading it, we realised that we had forgotten to address the issue of vulnerable patient groups in the limitation section of the discussion. We apologise for this shortcoming. To rectify, we changed the subtitle "Strength and weaknesses" to "Strength, weaknesses and limitations" and we added the following text to that section of the manuscript: "Finally, it should be noted that the present study concerns tendencies in the general working population of Denmark, and that the results therefore not can be generalised to patient populations; that something is safe for an average worker does not mean that it also is safe for workers who are treated for hypertension or other types of circulatory disease [cf. 6) Ibid: Because of the strong interactions between working time and pre-existing IHD as observed in the study cited above and because of the high prevalence of CVD in aging worker populations, the authors should consider to add a sub-analysis stratified by baseline IHD. This is important for the protection of this vulnerable working population given that IHD is a severely disabling and often fatal disease.
Authors' response: We agree that this is an important issue. An examination of the association between working hours and relapse rates among former hospital-patients is however beyond the scope of the present study. Moreover, we do not feel that our design and data material are suitable for such an endeavour. An ischaemic heart disease can be more or less severe, the more severe cases are unlikely to work long hours, and our data material does not allow us to control for severity of the illness. We apologise for this shortcoming, and have included the following statement regarding vulnerable subpopulations in the section in implications for practice: "However, this general effect does not preclude the possibility of a detrimental effect among potentially vulnerable subpopulations e.g. previous IHD patients". 7) Page 3, line 55: I would strongly recommend to add pre-existing IHD to this list of potential effect modifiers to be investigated in this large dataset (see comment above).
Authors' response: See response to comment #6.
8) The "multiple testing problem" is not universally recognized as a valid issue and respective adjustment of significance levels is not universally accepted as appropriate. The authors should review the respective literature and justify and possibly modify their approach in light of this literature.
Authors' response: The appropriateness of an adjustment for multiple comparisons depends on the situation.
If we are dealing with a purely descriptive/exploratory analysis, where no significances are to be checked for or declared, then there is obviously no need to adjust for multiple testing.
If we, on the other hand, are dealing with a confirmatory analysis where we want to test an association for statistical significance at a predefined significance level, then we would need a testing procedure which ensures that the probability of a type 1 error is less than or equal to that significance level. Without proper adjustment for multiple comparisons, we might wind up with a test in which the probability of a type 1 error is much higher than its predefined significance level.
Yes, all researchers do not recognise that adjustment for multiple comparisons is appropriate in all situations (neither do we). Most researches would, however, recognise that the following is a logically flawed statement: "We shall test if the association is statistically significant at a significance level of 0.05 and to accomplish this we will use a testing procedure which ensures that the probability of a type 1 error is less than or equal to 0.94." The purpose of the present study was to perform a confirmatory statistical analysis with a predefined significance level in order to reduce the probability of type 1 error. Therefore we have applied a more conservative statistical approach. 9) With regard to p-values for statistical significance testing of interactions, several biostatisticians and epidemiologists have suggested to (a) use higher p-levels (e.g. 0.1, 0.15, or 0.2) for multiplicative interaction (departure from multiplicative effects), (b)investigate departure from additive effects (that may be less than multiplicative but biological substantial), or to evaluate the existence of interaction based on the observed magnitude of the difference of effect sizes in respective stratified analyses. This magnitude should be evaluated with respect to potential biologically, clinically, or epidemiologically (in terms of impact on population health) relevance regardless of any statistical significance test results. (See for example standard texts such as K. Rossman, Textbook of modern epidemiology and S. Selvin, Statistical analyses of epidemiological data).
Authors' response: Yes, we recognise that some readers may want to use a less conservative statistical approach and interpret the results from a significance level of 0.1, 0.15 or 0.2. Therefore, we have provided all the necessary information to do so together with all information on effect sizes in the stratified analyses. Thus, readers who want to use other approaches can do this. We have also included an evaluation of the effect observed in the analyses stratified by SES in the discussion and the implication for practice sections.
10) Page 4, line 52: No mention of anti-hypertensive therapy in the study protocol but in abstract and results? See also inconsistent use in abstract.
Authors' response: We apologise for these mistakes. To rectify we have corrected the abstract (see response to comment #1) and we have changed the faulty sentences in the method section from "One study concerned the association between weekly working hours and risk of IHD (reported here) while the other concerned the association between night-time work and risk of IHD (results to be reported elsewhere)." to "One study concerned the association between weekly working hours and risk of IHD or antihypertensive drug usage (reported here) while the other concerned the association between night-time work and risk of IHD or antihypertensive drug usage (results to be reported elsewhere)." 11) Page 5, line 56. The treatment of weekly work hours as a categorical variable needs to be justified here. One rationale may be to provide data that match other studies or worktime regulations or a facilitation of communication of research results to a wider non-scientific audience. However, it should be acknowledged that categorization of a wide range of continuous data into three categories introduces an enormous amount of misclassification likely to result in a conservative bias towards the null. While it is ok. To present analyses with categorical data, these analyses should at least be complemented by an analyses of continuous exposure data. The categorisation would facilitate interpretation of the results in relation to the EU Working Time Directive in a Danish context, in accordance with the following arguments: 1. If a rate ratio is statistically significantly high among employees with 41 -48 working hours a week then it might be of practical importance, since it suggests that the 48-hour threshold of the EU Working Time Directive may need to be lowered to protect against IHD from long working hours. 2. If a rate ratio is statistically significantly low among employees who work more than 48 hours a week then it might be of practical importance, since it suggests that the 48-hour threshold of the EU Working Time Directive either is unnecessary or unnecessarily low (when it comes to protecting employees against IHD from long working hours). 3. If a rate ratio is statistically significantly high among employees who work more than 48 hours a week but not among employees who work 41 -48 hours a week, then the results do not indicate any need to change the threshold of the Working Time Directive. The elevated rate ratio may, however, be of practical importance from a public health perspective, since it identifies a group of people who might be in need of health promotion.
Another advantage of treating the working hours as a categorical variable rather than a continuous variable in a log-linear regression is that it allows the association to be u-shaped." Re. misclassifications: Yes, misclassifications of working hours may result in a conservative bias towards the null. It is however also possible that they result in a bias away from null. E.g.: If workers perceive their work as more arduous when they are in a poor cardiovascular health than they do when they are in a good physical health then it is possible that they also perceive their work days as longer when they are in a poor cardiovascular health than they do when they are in good physical health, and if this is the case it would bias the results towards the hypothesis of an increased risk among workers with long working hours. 12) Page 7, line 57: Sensitivity analyses with additional exclusion of workers with hospitalization IHD in previous years will yield more reliable results for the subgroup of workers without IHD. However, because of their higher a priory risk of repeat IHD incidence or even fatal IHD events, a supplemental subgroup analysis among those with hospitalization for IHD or outpatient IHD diagnosis should be performed as well. (see comment above re interaction between worktime and CVD)

ADDED REFERENCES
Authors' response: See response to comment #6.
13) Page 8, line 35-37: The sentence "We did not find any statistical significant associations between work hours and IHD incidence" is not consistent with the data. First, at least for workers of low SES, Authors' response: The probability of an event depends on the context in which it occurred.
Consider a population in which there is no association between weekly working hours and IHD or antihypertensive drug usage (the null hypothesis). If we test the association on a randomly chosen subset of this population by use of a single rate ratio (long vs standard working hours) and the 95% confidence interval around the estimate does not contain unity, then the result is significant at the level 0.05. This is, however, not a sufficient criterion for statistical significance at the level 0.05 if several rate ratios of the concerned association are estimated on several subsets of the same population. If the null-hypothesis is true then the probability that at least one out of two independent rate ratios would come with a confidence interval that does not contain unity equals 1 -(0.95 x 0.95) = 0.0975 > 0.05, and the probability that at least one out of n such rate ratios comes with a confidence interval that does not contain unity = 1 -(0.95 to the power of n).
If we had adopted the convention that an estimated rate ratio is statistically significant whenever its 95% CI does not contain unity regardless of the context in which it was obtained, then, due to the number of "tests" (46 CIs and 10 P-values), the a priori overall significance level of our study would have been set to 1 -(0.95 to the power of 56) = 0.94 instead of 0.05, and such a significance level is obviously unacceptable not only to us but also to many other researchers in this field.
Re. p-values vs. confidence intervals: We would like to point out that there is a one to one relation between p-values and confidence intervals; if the estimate is known then the central limit theorem can be used to convert a p-value to a confidence interval and vice versa. Hence, confidence levels may be used and abused in exactly the same way as p-values.
14) Page 8, line 47: It would be important to report some of the stronger effects found in subgroup analyses here, e.g. in male workers and in low SES groups. There seems to be a substantial gender and SES interaction and whenever this is observed, stratified analyses and results are the least biased results and should be preferred and emphasized over results from analyses of the entire sample.
Authors' response: We recognise that an association may be viewed as substantial in some situations while negligible in others. We also recognise that an association may be viewed as substantial to some people while negligible to others. The reviewer apparently views some of the estimated associations as substantial. We do not share this viewpoint.
Hereby an alternative approach, which we feel will give a more nuanced interpretation of the strength of the observed associations: In observational cohort studies, Monson (1990) recommends epidemiologists to interpret rate ratios in the open interval 0.9 to 1.2 as 'no association', to allow for possible effects of selection bias, misclassifications and uncontrolled confounding. According to his guide to strength of association, a rate ratio in the interval 0.9 -1.2 = no association (too small to be detected by epidemiologic methods), a rate ratio in the interval 0.7 -0.9 or 1.2 -1.5 is a weak association, a rate ratio in the interval 0.4 -0.7 or 1.5 -3.0 is a moderate association and a rate ratio < 0.4 or > 3.0 is a strong association.
Monson's guide may not be applicable in all situations but he definitely has a point when he states that we need to allow for possible effects of selection bias, misclassifications and uncontrolled confounding, when we evaluate rate ratios obtained in observational studies.
We note that, out of the 46 rate ratios that were estimated in the present study, 41 fell into the category "no association", 2 fell into the category "a weak positive association" and 3 fell into the category "a weak negative association", according to Monson. We also note that none of the associations were statistically significant after adjustment for multiple testing at our predefined significance level 0.05.
15) Page 10, line 31: This critique of the Virtanen Review is not convincing: Given that pre-published study protocols are not available for most studies, exclusion of studies based on this criterion would actually lead to a major bias due to ignoring a major proportion of the published high quality evidence about this subject. Failure to include non-published studies is bad enough, not including published studies on top of this publication bias would increase and not decrease it.
Authors' response: A non-blinded observational study can seldom generate high quality evidence. We acknowledge, however, that a study may be blinded even if it does not have a study-protocol attached to it, and that it is the blinding, not the protocol, which is important. On top of this, we found that the concerned sentence contained a grammar error. We apologise for these mistakes. To rectify we have changed the faulty sentence from "Some drawbacks with the meta-analysis by Virtanen et al. [40] is that it only included published studies and that it did not require the studies to have a pre-published study protocol, which means that the results may have been influenced by publication bias and within study selection bias." to "A drawback with the meta-analysis by Virtanen et al. [40] is that it only included published studies and that it did not require that the statistical analyses of the studies were blinded, which means that the results of the meta-analysis may have been influenced by publication bias and within study selection bias." 16) Page 11, line 13: This bias towards unity means that the true association between long working hours and IHD incidence would actually be even stronger than reported by these authors.
Authors' response: Yes, this bias towards unity in the study by O'Reilly and Rosato (2013) suggests that the true associations between long working hours and IHD incidence may have been stronger than reported by the authors.
17) Page 11, line 20: The statement that interaction effects with SES are not supported by this study is false given the actual strong effect modification by SES reported in the tables. See comments above about on how to evaluate interaction effects and why not to rely on p-values for that purpose.
Authors' response: See response to comment #14.
18) Page 11, line 28: The sedentary work of drivers may or may not be a risk factor for IHD, Danish researchers have shown that the higher rates among drivers are probably due to job stress rather than sedentary work as originally postulated by Morris in his landmark study on London bus drivers. A much more plausible explanation for higher IHD rates seen in low SES groups who work long hours are the excessive physical job demands and insufficient recovery time for many of these often manual workers who stand, walk, and lift objects much of their workday, all known risk factors for IHD. Authors' response: We apologise for this shortcoming. To rectify, we have changed the concerned sentence from "A possible explanation for an increased risk in this group is that drivers are included and their work is characterized by long hours with sedentary work [41], an unhealthy life-style and an elevated risk of IHD [42]." to "A possible explanation for an increased risk in this group is that drivers are included and their work has been associated with long working hours [41], an unhealthy life-style and an elevated risk of IHD [42]." PS. In Denmark, it has been shown that blue-collar workers in occupations associated with heavy lifting are less likely to work long hours than blue-collar workers in other occupations [Hannerz and Holtermann, 2014], and the rate ratio for IHD among blue-collar workers in occupations associated with heavy lifting versus other blue-collar workers has been estimated at 0.97 (95% CI: 0.94 -1.00) [Hannerz and Holtermann, 2016].
strong evidence of previously published work cited and not cited in this article. There is actually an urgent need to supplement current broad work hour regulations with specific recommendations and regulations to protect the most vulnerable subgroups of workers, those with multiple low-wage jobs, performing heavy manual labor, and the increasing number of older workers with pre-existing CVD that seems to substantially increase the risks associated with long work hours.
Authors' response: We have updated the implications and the text has been changed from "In the present study, working long hours was overall not associated with an increased risk of IHD. Thus with the current level of working hours duration there appears to be no increased risk of IHD. Duration of working hours is in Denmark regulated by collective agreements and the working hour law which takes EU's Working Time Directive (2003/88/EC) into account. The directive requires that the weekly working hours of workers in EU must not exceed 48 hours on average including any overtime. Thus duration of working hours has been regulated for a long time and more than 48 hours per week is generally considered long working hours in Denmark. Since overtime work which lies within the limits of the directive (41 -48 h a week) was not associated with an increased risk of IHD in any of the examined sub-groups, the present study does not indicate any need for further regulations." to "In the present study, working long hours was overall not associated with an increased risk of IHD in a random sample of the general workforce of Denmark. Thus with the current level of working hours duration there appears to be no increased risk of IHD in the general, healthy population. However, this general effect does not preclude the possibility of a detrimental effect among potentially vulnerable subpopulations e.g. previous IHD patients. Further, secondary analyses indicate that there may be an increased risk of IHD among workers with a low SES and very long working hours.
Although not statistically significant according to the conservative approach of the present study, this warrants further attention and from a precautionary principle special attention should be given to this group. Duration of working hours is in Denmark regulated by collective agreements and the working hour law which takes EU's Working Time Directive (2003/88/EC) into account. The directive requires that the weekly working hours of workers in EU must not exceed 48 hours on average including any overtime. Thus duration of working hours has been regulated for a long time and more than 48 hours per week is generally considered long working hours in Denmark. Since overtime work which lies within the limits of the directive (41 -48 h a week) was not associated with an increased risk of IHD in any of the examined sub-groups, the present study does not indicate any need for further regulations. Furthermore, there may be different reasons for working long hours in different countries. In Denmark, those working long hours may be more likely to be healthy since most employees can support a family based on a 40 hours per week salary." 19b) Also, the discussion and conclusions needs to take into consideration several forms of the socalled healthy worker effect that is prone to bias observed risks downward towards the null: people with symptomatic IHD are less likely to enter or stay in physically demanding jobs or jobs with long work hours leading to a conservative health-based selection bias that needs to be considered in the evaluation of these and other study results.
Authors' response: We apologise for this shortcoming. To rectify we added the following text to the limitation section of the manuscript: "It should also be noted that workers with cardiovascular health problems may be less likely to enter or stay in jobs with long working hours. Hence, the so-called healthy worker effect may have biased rate ratios downward. The rate ratios for IHD were, however, very close to unity also in the sensitivity analysis which only included workers who were free from hospital treated IHD during a five year period prior to baseline." 19c) In conclusion, this study has a great potential to further our knowledge about the health effects of long working hours.
19d) I strongly encourage the authors to supplement analyses of workers with per-existing CVD conditions.
Authors' response: See response to comment #6.
19e) I strongly encourage the authors to re-evaluate results in the light of the comments made above and not to disregard their own findings based on inappropriate and outdated use of p-values and confidence intervals. This reviewer is fully aware that most researchers have received training in statistical methods that emphases statistical testing as performed in this study. However, given the multi-decade long critique of this approach by an increasing number of research methodologists it seems time to change this practice.
Authors' response: We apologise for being conservative, but we feel that a proper confirmatory statistical analysis should be based on the laws of probability theory and mathematical statistics. This is also in line with the analysis described in the protocol for the present study. In order for others to apply others approaches we have provided the necessary information in terms of p-values and results from stratified analysis.
19f) The high quality of the data and the high qualifications of the author team bode well for an exceptional contribution to the literature of work hours and IHD.
19g) This critique was provided in as encouragement and assistance for a successful revision of this manuscript.
Authors' response: We are thankful for this very thorough review, which has been of great help in the revision of the manuscript. We find that it has given us the possibility to clarify some issues and thereby improve the manuscript. Thank you.

Reviewer: 2
This study is well-performed with a large sample and pre-published study protocol, and shows substantial effort. The manuscript is clearly written in a concise manner and authors draw reasonable conclusions. But there are several major and minor issues before the manuscript is acceptable for publication. Below, please find my comments and suggestions. I hope authors find them contributing to their research.
1) First of all, I can't agree with the title of this study. The authors declared that the aim of study was "to test if incidences of ischaemic heart disease (IHD) and usage of antihypertensive drugs", but it is not clear in title which only mentioned "ischaemic heart disease". In my opinion, usage of antihypertensive drugs is quite different from ischaemic heart disease in terms of disease nature. Therefore, authors need to consider addressing them each, or to change the title of the study.
Authors' response: Thank you for bringing this problem to our attention. Your comment made us realise that important information about the status of the two outcomes was missing in our manuscript. The status of the outcomes was declared in the study protocol, by the following text: "Hospital treatment or death due to IHD is the primary outcome of the study, and a statically significant association with this outcome would afford direct statistical evidence of an association with IHD.
Hypertension plays an important role in the aetiology of IHD [Law et al., 2009], and relative rates of antihypertensive drug usage have been shown to be highly correlated with relative rates of IHD among occupational groups in Denmark [Hannerz et al., 2014;Latza et al., 2015]. We will therefore regard results obtained for antihypertensive drug usage as indirect statistical evidence of an association with IHD if they are statistically significant and show a similar pattern to the results obtained for hospital treatment or death due to IHD." We apologise for our failure to mention this important piece of information in our manuscript. To rectify this shortcoming we added the following text to the section entitled 'Aims and Hypothesis': "The statuses of the outcomes were declared in our study protocol [Hannerz et al., 2016] as follows: 'Hospital treatment or death due to IHD is the primary outcome of the study, and a statically significant association with this outcome would afford direct statistical evidence of an association with IHD.
Hypertension plays an important role in the aetiology of IHD [Law et al., 2009], and relative rates of antihypertensive drug usage have been shown to be highly correlated with relative rates of IHD among occupational groups in Denmark [Hannerz et al., 2014;Latza et al., 2015]. We will therefore regard results obtained for antihypertensive drug usage as indirect statistical evidence of an association with IHD if they are statistically significant and show a similar pattern to the results obtained for hospital treatment or death due to IHD.'" Since IHD was declared as the primary outcome while usage of antihypertensive drugs was declared as a subservient auxiliary outcome, we would like to keep the title as it is (but will let the decision be up to the editor). 2) In this study SES was based on the participant's occupation, but income level would have been more relevant to study about working hours. As authors mentioned, long working hours may increase income level and thereby reduce the risk or intensity of financial strain, which is a known risk factor for hypertension as well as IHD. In that sense, we need to know how income level modifies the relationship between working hours and risk of IHD or hypertension.

ADDED REFERENCES
Authors' response: Unfortunately, we did not have access to income data in the present project. We apologise for this shortcoming. We also apologise for our failure to mention this shortcoming in the manuscript. To rectify we added the following text to the limitation section of the paper: "Another limitation of the study is that it lacks information on sleeping habits and income. As mentioned in the introduction, one of the main theoretical reasons for a detrimental effect of a long work week is its association with short sleep, while one of the main theoretical reasons for a beneficial effect is its association with an increased income. Hence, it would have been of interest to study effect modification by income and sleeping habits." 3) Reference group of weekly working hours was 32-40 hours in analytic models. But authors may also consider 32-48 hours as reference because 48 hours per week is generally considered long working hours in Denmark. Additionally, I guess the majority of study population work just 40 hours per week, so just 40 hours can be set as reference group for further analysis.
Authors' response: We apologise for our failure to explain the rationale behind the working hour categorisation. To rectify, we have changed the part of the method section in which the categories are defined (see response to reviewer 1, comment #11).
In Denmark, fulltime work is normally 37 hours.
Minor 1) In table 1, I wonder that the p-values of second row are for what hypothesis? It was described "The incidence is prospectively independent of weekly working hours as well as interaction between weekly working hours and socioeconomic status, sex and night work, respectively", but I am confusing whether p-value is for "weekly working hours" or "interactions" Authors' response: We apologise for this lack of clarity. To rectify, we have replaced the word 'main' in the legend of table 1 with 'overall' and we have changed the following text of the method section from "The parameters were estimated by use of the maximum likelihood method and the P-values of the various hypotheses were based on likelihood ratio tests. Rate ratios with 95% confidence intervals were calculated as a function of weekly working hours both with and without stratification by sex, socioeconomic status and night work, respectively. Further details about the statistical models are given by Hannerz et al. (2016) [14]." to "The first regression model contained the covariates sex, age, night work, calendar time, time passed since start of follow-up, employment in the health care industry and SES. The second model contained the covariates of model 1 plus weekly working hours. The third model contained the covariates of model 2 plus interaction between SES and weekly working hours. The fourth model contained the covariates of model 2 plus interaction between sex and weekly working hours. The fifth model contained the covariates of model 2 plus interaction between night work and weekly working hours. The sixth model contained the covariates of model 2 plus interaction between weekly working hours and SES, sex and night work, respectively.
The parameters were estimated by use of the maximum likelihood method and the P-values of the various hypotheses were based on likelihood ratio tests. The overall hypotheses (which state that the incidence is prospectively independent of weekly working hours as well as interaction between weekly working hours and SES, sex and night work, respectively) were tested by comparing model 6 to model 1. The hypotheses which state that the incidence is prospectively independent of weekly working hours when we disregard interaction effects were tested by comparing model 2 to model 1.The test for interaction between SES and weekly working hours compared model 3 to model 2. The test for interaction between sex and weekly working hours compared model 4 to model 2. The test for interaction between night work and weekly working hours compared model 5 to model 2.
Parameter estimates were used to obtain rate ratios and 95% confidence as a function of weekly working hours, with and without stratification by SES, sex and night work. Rate ratios by SES were based on the parameter estimates of model 3, rate ratios by sex were based on the parameter estimates of model 4, rate ratios by night work were based on the parameter estimates of model 5 and rate ratios, without stratification by SES, sex or night work, were based on the parameter estimates of model 2."

GENERAL COMMENTS
Authors have provided very comprehensive comments that adequately address the majority of the comments and questions raised by the reviews of the first submitted version of the paper. Now I think it is acceptable.

Niklas Krause
University of California los Angeles, USA REVIEW RETURNED 28-Dec-2017

GENERAL COMMENTS
Reviewer 1 to the Editor regarding his review of the revised manuscript: Dear Editor, This is an important study and deserves publication by the journal, however, in its current form that seems not advisable. I would like to summarize for you the main reasons for my recommendation of a further major revision: 1) The main study conclusion is in my opinion not supported by the data. In fact, an opposite conclusion appears to be at least equally if not more compatible with the data. This is a serious concern given the potential impact of this study on work time policies.
2) The authors draw strong conclusions from this study without giving adequate consideration to potentially strong biases including several forms of selection bias, exposure misclassification, and lack of control for traditional CVD risk factors and other potential confounders.
3) The conclusion in the abstract now includes an ill-defined term ("generally important" association between work hours and IHD).
4) The authors clarified in their response that the study was designed to evaluate if the current EU Work Time Directive is sufficient to protect workers from detrimental cardiovascular health effects. The study design is not appropriate to address this question because the study excluded workers with pre-existing IHD from the study population although previous research has demonstrated that they are especially vulnerable to detrimental cardiovascular health effects of long work hours. The EU Directive on worktime covers all workers regardless of any chronic disease they may have. Therefore I think the exclusion of workers with existing IHD is an inappropriate design. I believe that the authors can easily correct the design and include these workers. In addition I would recommend that they provide also stratified analyses by IHD status at baseline.

5) Statistics:
The authors rely exclusively on statistical significance testing in their interpretation of data and mostly ignore actual effect estimates and confidence intervals in their interpretation of results. P-value adjustment for multiple comparisons and the significance levels chosen to evaluate interactions are also problematic in light of modern epidemiological and biostatistical reasoning and introduce additional conservative bias.
While the authors have responded diligently to all comments, they seem to have misunderstood some of them or the underlying concepts. Although I am a PhD trained epidemiologist with extensive training in biostatistics and have tried to explain my concerns to the authors, it may be necessary to consider an additional specialist statistical-epidemiological review to arbitrate any remaining differences in perspective that may persist beyond this review.
I recommend to provide the authors an additional opportunity to revise their manuscript and I am interested and open to review their paper again.

REVIEWER
Christian Torp-Pedersen Department of Epidemiology and Biostatistics<br>Aalborg University Hospital<br>Denmark REVIEW RETURNED 10-Jan-2018

GENERAL COMMENTS
I enter the group of reviewers at a time when two reviewers have provided detailed comments and the authors have responded in detailand provided a revised manuscript. I will therefore without further introduction address the methodological questions I was requested to address and add additional comments: The use of Poisson regression for the current study is natural and the method appears to have been used correctlybut somewhat insufficiently described: 1. I assume that data were split by the selected time dependent variables, but this should be written. 2. The basic assumption when using Poisson regression for time dependent survival type analysis is that the event rate in intervals is constant. This is usually tested by documenting similar results using split by smaller time intervals. Was this done? 3. It is customary to writhe which program package was used.
There is an intense debate between reviewer 1 and the authors concerning interpretations of the estimates, confidence intervals and p-values. I agree completely with the interpretations of the authors.
The drugs listed for treatment of hypertension are used for many other indications. Since the endpoint was predefined it should not be changed, but this limitation needs to be addressed. We have previously validated that the use of TWO antihypertensive drugs has a useful predictive accuracy (Olesen, BMJ, Jan 31;342:d124. doi: 10.1136/bmj.d124). The drugs listed are mostly for various cardiovascular diseases including angina, heart failure and renal failure, but migraine is one of the indications for a beta blocker.
The authors very correctly list the knowledge of validity of ischemic heart disease diagnoses. It would be a strength if a specific analysis of myocardial infarction were included given that this diagnosis has a particular high specificity as well as sensitivity.

VERSION 2 -AUTHOR RESPONSE
Response to comments by reviewer 1 After careful consideration we have decided to give a detailed response to a part of the reviewer's response to our response of comment 8. As regards the remaining comments, we propose that the problems related to the differences of opinion between the reviewer and authors might be solved by the following strategy: · We agree to disagree on the interpretation of the methods and results of the study.

·
We oblige the reviewer's request "to provide the reader with the possible alternative interpretation of the data based on the arguments made in this review" by adding the following sentences towards the end of the discussion section: "Finally, it should be noted that the authors' interpretation of the presented methods and results are not shared by all researchers in the field. We therefore recommend the reader to also read the article's supplemental material, in which one of our reviewers provides an alternative interpretation of the methods and results of the study." · To underline that our research methods were approved and published by an international peer-reviewed journal that is completely independent of any Danish government agency, we have added the following text at the end of the abstract: "Trial registration: A peer-reviewed study protocol is available at http://www.researchprotocols.org/2016/2/e130/." · We have performed one of the requested post hoc analyses and we have added the following text to the discussion section:"As requested by a reviewer, we performed a post hoc analysis, which estimated the rate ratio for IHD without exclusion of prevalent cases at 0.95 (95% CI: 0.86 -1.06) for 41 -48 and 1.06 (0.94 -1.20) for > 48 vs. 32 -40 working hours a week. The analysis was controlled for the same covariates and used the same follow-up period as the primary analysis. Since the rate ratios obtained in this post hoc analysis were very close to the ones obtained in the primary analysis, we do not believe that the null finding of the present study was due to the decision to exclude workers who were treated for IHD sometime during the calendar year preceding the start of the follow-up." · We have changed the conclusion in the abstract from "Our analyses in a Danish sample do not support the hypothesis of a general important association between long working hours and IHD or antihypertensive drug usage" to "In this Danish sample, we did not find any statistically significant association between long working hours and IHD or antihypertensive drug usage." Detailed response to comment 8 Reviewer 1 comment 8: The "multiple testing problem" is not universally recognized as a valid issue and respective adjustment of significance levels is not universally accepted as appropriate. The authors should review the respective literature and justify and possibly modify their approach in light of this literature.
Authors' response: The appropriateness of an adjustment for multiple comparisons depends on the situation. If we are dealing with a purely descriptive/exploratory analysis, where no significances are to be checked for or declared, then there is obviously no need to adjust for multiple testing.
If we, on the other hand, are dealing with a confirmatory analysis where we want to test an association for statistical significance at a predefined significance level, then we would need a testing procedure which ensures that the probability of a type 1 error is less than or equal to that significance level. Without proper adjustment for multiple comparisons, we might wind up with a test in which the probability of a type 1 error is much higher than its predefined significance level.
Yes, all researchers do not recognise that adjustment for multiple comparisons is appropriate in all situations (neither do we). Most researches would, however, recognise that the following is a logically flawed statement: "We shall test if the association is statistically significant at a significance level of 0.05 and to accomplish this we will use a testing procedure which ensures that the probability of a type 1 error is less than or equal to 0.94." The purpose of the present study was to perform a confirmatory statistical analysis with a predefined significance level in order to reduce the probability of type 1 error. Therefore we have applied a more conservative statistical approach.
Reviewer 1 response: … If this study is about finding out if the UE directive on work time is able to protect workers from possible adverse health effects, independent and government funded research charged with the protection of population health should follow the precautionary principle and abandon the extremely conservative technique of adjustment for multiple comparisons. This reviewer maintains that such an adjustment is ultimately not compatible with medical ethics and should not be accepted for publication in medical, public health, or occupational health science journals anymore.
If the authors should continue to disagree on this issue after reconsideration of these arguments and the respective literature on this topic and still wish to insist on presentation of multiple adjusted results, the editor needs to decide on the acceptability of this approach. However, even if the editor should not object to multiple adjustment, the authors should then at least provide the reader with the possible alternative interpretation of their data based on the arguments made in this review and provide a convincing rationale for rejecting the arguments made by Rothman and others against multiple adjustment. … Authors' response to reviewer's response: First of all we would like to stress that the study is not 'about finding out if the UE directive on work time is able to protect workers from possible adverse health effects'. The aim was to study 'if incidences of ischaemic heart disease (IHD) and usage of antihypertensive drugs among employees in Denmark are independent of weekly working hours.' For this purpose we (and others) find that it is necessary to take multiple testing into account when interpreting our results.
Let's say that Mr X suspects that the three dice that Mr Y is using are loaded to favour the outcome six. To check this he borrows the dice and rolls them repeatedly, looking for a triple six. It takes him 200 attempts to obtain a triple 6. He wants to check if this is statistically significant at a significance level of 0.01. If he chooses to abide by the rules set out by reviewer 1 (do not take the multiplicity of trials into account) then he will arrive at conclusion A (see below). If he, on the other hand, chooses to base his test on logics and the laws of probability theory he will arrive at conclusion B.
A. He did observe a triple 6 and the probability of such an event in a single trial with three fair dice equals 1/216 < 0.01. Hence, the result of his experiment supports the hypothesis that the dice are loaded to favour the outcome six.
B. It took him 200 trials to obtain a triple 6, and the probability of observing at least one triple 6 in 200 trials with three fair dice equals 0.6. Hence, the result of his experiment does not support the hypothesis that the dice are loaded to favour the outcome six.
According to the rules set out by reviewer 1, A is correct and B is incorrect. We do not agree with this notion. The reason for our disagreement lies in the logical contradiction, we do not feel that it would be right to say that the probability of observing a triple 6 in the context of the experiment was less than 0.01 when the laws of probability theory dictates that it was equal to 0.6. We hold that probability theoretic operations should be based on the rules and definitions of probability theory in the same way as we hold that arithmetic operations should be based on the rules and definitions of arithmetic. Hence, we reject the notion that A is correct and B is incorrect.
Of less importance, but still of interest is that the STROBE explanatory article [Vandenbroucke et al., 2007] states that authors should take multiplicity of analyses into account when they interpret their results. Comments by reviewer 2

REFERENCE
Authors have provided very comprehensive comments that adequately address the majority of the comments and questions raised by the reviews of the first submitted version of the paper. Now I think it is acceptable.
/Authors' response: Thank you. / Comments by reviewer 3 I enter the group of reviewers at a time when two reviewers have provided detailed comments and the authors have responded in detailand provided a revised manuscript. I will therefore without further introduction address the methodological questions I was requested to address and add additional comments: The use of Poisson regression for the current study is natural and the method appears to have been used correctlybut somewhat insufficiently described: 1. I assume that data were split by the selected time dependent variables, but this should be written.
/Authors' response: We apologise for this shortcoming. The following text has been added to the method section: "The data were split by calendar year. Time dependent variables were updated first of January each year and were thereafter held constant throughout the rest of the calendar year."/ 2. The basic assumption when using Poisson regression for time dependent survival type analysis is that the event rate in intervals is constant. This is usually tested by documenting similar results using split by smaller time intervals. Was this done?
/Authors' response: No, we did not test for constant event rates in smaller time intervals. It is only necessary to control for risk variations within a calendar year if the time distribution of the person years at risk depends on the exposure, which is not the case in the present study (see figure below). Example: Even if the risk would increase with 10% each month, the rate ratios between the groups would not increase by more than a factor of 1.003. Figure S1. Distribution of person years at risk by month (1 = January … 12 = December) for each of the three working hour categories / 3. It is customary to writhe which program package was used.
Authors' response: We apologise for this shortcoming. The following text has been added to the method section: "The analyses were implemented in the GENMOD procedure of SAS version 9.4." / There is an intense debate between reviewer 1 and the authors concerning interpretations of the estimates, confidence intervals and p-values. I agree completely with the interpretations of the authors.
/Authors' response: Thank you. / The drugs listed for treatment of hypertension are used for many other indications. Since the endpoint was predefined it should not be changed, but this limitation needs to be addressed. We have previously validated that the use of TWO antihypertensive drugs has a useful predictive accuracy (Olesen, BMJ, Jan 31;342:d124. doi: 10.1136/bmj.d124). The drugs listed are mostly for various cardiovascular diseases including angina, heart failure and renal failure, but migraine is one of the indications for a beta blocker.
/Authors' response: We apologise for this shortcoming. To rectify, we have added the following text to the discussion section of the manuscript: "In the analysis of antihypertensive drug usage, we included all types of antihypertensive drugs in the case definition. Here, it needs to be mentioned that diuretics may be prescribed not only for treatment of hypertension but also for chronic kidney disease [Agarwal and Sinha, 2012] and that beta blockers and calcium channel blockers may be prescribed for migraine [Jacob and Kostev, 2017]. It is therefore possible that the estimations of rate ratios for antihypertensive drug usage were slightly biased either towards or away from unity." /Authors' response: Good idea. We have performed the analysis (see below table) and we have added the following text to the discussion: "When we analyse hospital discharge data there is always a possibility of referral or detection bias. Mild forms of IHD might go undetected by the workers as well as by their general practitioners. It is, moreover, possible that the probability of becoming aware of as well as the inclination to seek treatment for a minor IHD depends on the person's weekly working hours. As suggested by one of the reviewers, we addressed this issue in a post hoc analysis in which the case definition was restricted to acute myocardial infarction (ICD-10: I21). The rate ratios for acute myocardial infarction were estimated at 0.96 (95% CI: 0.80 -1.14) for 41 -48 and 0.98 (0.80 -1.20) for > 48 vs. 32 -40 working hours a week. The statistical model was otherwise the same as the one used in the primary analysis. We note that the rate ratios of the post hoc analysis were closer to unity than the ones obtained in the primary analysis. It is therefore unlikely that the null-finding of the present study was due to referral or detection bias."