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It has long been suspected that a worker’s risk of developing an ischemic heart disease (IHD) may be influenced by his or her working time arrangements. A multitude of studies have been performed, and special attention has been given to long working hours and nighttime work. The statistical powers of the individual studies have, however, generally been too low to either dismiss or confirm an actual relationship, and meta-analyses of underpowered studies are generally associated with publication bias. Hence, uncertainty remains and whether these factors indeed are related to IHD has yet to be settled.
This project will test whether the incidences of IHD and usage of antihypertensive drugs among employees in Denmark are independent of weekly working hours and nighttime work. The objective of this paper is to present the intended analyses.
We will link individual participant data from the Danish labor force survey, 1999–2013, to data on socioeconomic status, industry, emigrations, redeemed prescriptions, hospitalizations, and deaths from registers covering the entire population of Denmark. The study will include approximately 160,000 participants, who will be followed through the registers, from the time of the interview until the end of 2014, for first occurrence of IHD and for antihypertensive drug treatment. We will use Poisson regression to analyze incidence rates as a function of nighttime work and of weekly working hours.
We expect results to be ready in mid-2017.
To our knowledge, this will be the largest study ever of its kind. It will, moreover, be free from hindsight bias, since the hypotheses, inclusion criteria, significance levels, and statistical models will be completely defined and published before we are allowed to link the exposure data to the outcome data.
Our project will look at rates of ischemic heart disease (IHD) among Danish employees as a function of weekly working hours and nighttime work.
From the viewpoint of cardiovascular risk factors, there appear to be both advantages and disadvantages of long working hours and nighttime work.
One of the advantages of nighttime work is that it usually eliminates exposure to rush hour commuting stress, both to and from work; such stress has been associated with psychological strain [
Another potential advantage of nighttime work and long working hours is that they can generate extra income, compared with ordinary daytime work, and thereby reduce the risk or intensity of financial strain. An increased income has been associated with a decreased risk of IHD [
The disadvantage of long working hours and nighttime or shift work is that they usually are associated with short sleep duration, mismatch of circadian rhythm, social disruption, and behavioral changes [
The evidence of an association between night or shift work and IHD has been reviewed by Frost et al [
Vyas et al [
Virtanen et al [
The reviews by Vyas et al [
Kivimaki et al [
Kivimaki et al [
We want to know whether the incidence of antihypertensive drug usage and the incidence of hospital treatment or death due to IHD are independent of weekly working hours and nighttime work among full-time employees in Denmark, and will address these research questions in a series of nested hypothesis tests (
1. The incidence of antihypertensive drug usage and the incidence of hospital treatment or death due to ischemic heart disease (IHD) among full-time employees in Denmark are prospectively independent of weekly working hours, as well as interaction between weekly working hours and each of the following variables: socioeconomic status, sex, and nighttime work.
1.1. The incidence of hospital treatment or death due to IHD is prospectively independent of weekly working hours, as well as interaction between weekly working hours and each of the following variables: socioeconomic status, sex, and nighttime work.
1.1.1. The prospective association between weekly working hours and incidence of hospital treatment or death due to IHD is independent of socioeconomic status.
1.1.2. The prospective association between weekly working hours and incidence of hospital treatment or death due to IHD is independent of sex.
1.1.3. The prospective association between weekly working hours and incidence of hospital treatment or death due to IHD is independent of nighttime work.
1.1.4. The incidence of hospital treatment or death due to IHD is prospectively independent of weekly working hours when we disregard interaction effects.
1.2. The incidence of antihypertensive drug usage is prospectively independent of weekly working hours, as well as interaction between weekly working hours and each of the following variables: socioeconomic status, sex, and nighttime work.
1.2.1. The prospective association between weekly working hours and incidence of antihypertensive drug usage is independent of socioeconomic status.
1.2.2. The prospective association between weekly working hours and incidence of antihypertensive drug usage is independent of sex.
1.2.3. The prospective association between weekly working hours and incidence of antihypertensive drug usage is independent of nighttime work.
1.2.4. The incidence of antihypertensive drug usage is prospectively independent of weekly working hours when we disregard interaction effects.
2. The incidence of antihypertensive drug usage and the incidence of hospital treatment or death due to IHD among full-time employees in Denmark is prospectively independent of nighttime work, as well as interaction between nighttime work and each of the variables socioeconomic status and sex.
2.1. The incidence of hospital treatment or death due to IHD is prospectively independent of nighttime work, as well as interaction between nighttime work and each of the variables socioeconomic status and sex.
2.1.1. The prospective association between nighttime work and incidence of hospital treatment or death due to IHD is independent of socioeconomic status.
2.1.2. The prospective association between nighttime work and incidence of hospital treatment or death due to IHD is independent of sex.
2.1.3. The incidence of hospital treatment or death due to IHD is prospectively independent of nighttime work when we disregard interaction effects.
2.2. The incidence of antihypertensive drug usage is prospectively independent of nighttime work, as well as interaction between nighttime work and each of the variables socioeconomic status and sex.
2.2.1. The prospective association between nighttime work and incidence of antihypertensive drug usage is independent of socioeconomic status.
2.2.2. The prospective association between nighttime work and incidence of antihypertensive drug usage is independent of sex.
2.2.3. The incidence of antihypertensive drug usage is prospectively independent of nighttime work when we disregard interaction effects.
We will set the overall significance level for the effect of weekly working hours at .05 and we will set the overall significance level for the effect of nighttime work at .05. We will solve the multiple testing problems by the following strategy:
A null hypothesis at the first level will be rejected if either of its two second-level null hypotheses is rejected.
A null hypothesis at the second level will be rejected if the
A null hypothesis at the third level will be rejected if (1) its associated second-level null hypothesis is rejected and (2) the
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 etiology of IHD [
The study will comply with The Act on Processing of Personal Data, Denmark (Act No. 429 of May 31, 2000), which implements the European Union (EU) Directive 95/46/EC on the protection of individuals. The data usage is approved by the Danish Data Protection Agency, file number 2001-54-0180. The ethical aspect of the project was examined and approved by Statistics Denmark, account number 704291.
The data base of the project will consist of interview data from the Danish Labour Force Survey 1999–2013, which are linked to data from the central person register [
The Danish Labour Force Survey has been conducted since 1994, in accordance with EU directives, which apply to all member states of the EU. It is based on random samples of 15- to 74-year-old people in the Danish population. The samples are drawn quarterly and the participants are invited to be interviewed 4 times over a period of one and a half years. The structured interviews, which are done by telephone, cover various aspects of labor market participation, including specifications on working hours and work schedules [
The central person register contains information on sex, addresses, and dates of birth, death, and migrations for every person who is or has been an inhabitant of Denmark sometime between 1968 and the present. A person’s socioeconomic status (SES), occupation, and industry have been registered annually in the employment classification module since 1975. The national hospital register has existed since 1977 and contains data from all public hospitals in Denmark (>99% of all admissions). From 1977 to 1994, the register only included inpatients, but from 1995 it has also covered outpatients and emergency ward visits. Since 1994, the diagnoses have been coded according to
The labor force surveys gather person-based information on weekly working hours, calculated by adding the hours worked in secondary jobs to the ones worked in a primary job. The participants are asked first how many hours they usually work and then how many hours they worked during the reference week (a predetermined work week, which occurred 1–4 weeks prior to the interview). They are also asked whether and to what extent they work at night. The questions used to gather this information have changed slightly with time. Before 2001, there was no mention of whether meal breaks should be counted as working hours. During 2001–2006, all participants were instructed to exclude meal breaks when they counted their work hours. As of 2007, the time used for meal breaks is to be counted if the person was paid while eating and is to be excluded otherwise. Another peculiarity that was introduced in 2007 is that the participants are asked whether the weekly working hours vary a lot or there are other reasons that make it difficult to provide a meaningful estimate of usual weekly working hours. If they answer “yes" to any of these questions, then “average working hours" is to be used as a proxy for “usual working hours."
Before 2001, the participants were simply asked whether they worked at night, but from 2001 onward the question has been whether they worked at night during the last 4 weeks. Until 2006 the response categories were “yes, regularly," “yes, occasionally," and “no, never". From 2007 onward the response categories were expanded to “yes, regularly" (ie, more than half of the working days in the last 4 weeks), “yes occasionally" (ie, at least once within the last 4 weeks, but less than half of the working days), and “no, not within the last 4 weeks."
We will disregard the changes in the data collection routines in the primary analyses of this project. We will define the exposure variables as follows.
In keeping with Kleppa et al [
Participants who responded with either “yes, regularly" or “yes, occasionally" to the question about nighttime work will be defined as being exposed and those who responded with “no..." will be defined as being unexposed to nighttime work.
The primary end point is hospital treatment or death, with IHD as the principal diagnosis or cause of death, respectively. The case definition includes the following ICD-10 codes: I20 angina pectoris, I21 acute myocardial infarction, I22 subsequent myocardial infarction, I23 certain current complications following acute myocardial infarction, I24 other acute IHDs, I25 chronic IHD. The secondary end point is redemption of a prescription for antihypertensive drugs. The following ATC codes are included: C02 antihypertensives, C03 diuretics, C07 alpha- and beta-blockers, C08 calcium channel blockers, and C09 angiotensin-converting enzyme inhibitors and angiotensin-II antagonists.
The participants will be followed from the beginning of the calendar year that succeeds that of their baseline interview. The follow-up will end at the time the participant is diagnosed with IHD, emigrates, or dies, or the end of the study period (December 31, 2014), whichever comes first. To be eligible for inclusion, they should be between 21 and 59 years old at the start of the follow-up period and employed with ≥32 weekly working hours at the time of the interview. People who received hospital treatment for IHD during the calendar year of the interview will be excluded from the IHD analysis. People who redeemed a prescription for antihypertensive drugs during the calendar year of the interview will be excluded from the antihypertensive drug analysis.
We will use Poisson regression to analyze incidence rates of hospital treatment or death due to IHD as a function of weekly working hours (32–40, 41–48, or >48 hours/week), nighttime work (yes vs no), sex, age (10-year classes), calendar time (2000–2004, 2005–2009, or 2010–2014), time passed since start of follow-up (0–4, 5–9, or ≥10 years), employment in the health care industry (yes vs no), and SES (low, medium, high, or unknown). Age, calendar time, and time passed since start of follow-up will be treated as dynamic (time-varying) variables. The remaining variables will be fixed at baseline (the calendar year of the interview). The logarithm of person-years at risk will be used as offset. People who participated in more than one interview will be classified in accordance with the responses given in their first interview. Later interviews will be disregarded.
We will retrieve information on occupation and industry from the employment classification module, and refer it to the status during the calendar year of the baseline interview. Industries were coded in accordance with the Statistics Denmark classification DB93 [
We will code the variable “employment in the health care industry" as “yes" if the 3-digit industrial code of DB93 or DB03 equals 851 or the 2-digit code of DB07 equals 86.
We will base SES on the participant’s occupation and will code it as “high," “medium," or “low" in accordance with the 3-class version of the European Socio-economic Classification (ESeC). The coding will be performed in accordance with the SAS (SAS Institute) programming statements shown in
/* SES classification (ESeC three class version) of employees by use of DISCO-88 */
if '1' le substr(DISCO_88, 1, 1) le '2' then SES = "High";
if '3' le substr(DISCO_88, 1, 1) le '4' then SES = "Medium";
if '5' le substr(DISCO_88, 1, 1) le '9' then SES = "Low";
if '31' le substr(DISCO_88, 1, 2) le '32' or substr(DISCO_88, 1, 3) in ('334', '342', '344', '345', '348', '521') then SES = "High";
if substr(DISCO_88, 1, 3) = '731' then SES = "Medium";
if substr(DISCO_88, 1, 3) in ('413', '414', '421', '422') then SES = "Low";
/* SES classification (ESeC three class version) of employees by use of DISCO-08 */
if '1' le substr(DISCO_08, 1, 1) le '2' then SES = "High";
if '3' le substr(DISCO_08, 1, 1) le '4' then SES = "Medium";
if '5' le substr(DISCO_08, 1, 1) le '9' then SES = "Low";
if substr(DISCO_08, 1, 3) in ('311', '312', '314', '315', '321', '322' '323') then SES = "High";
if substr(DISCO_08, 1, 3) in ('224', '742') then SES = "Medium";
if substr(DISCO_08, 1, 2) = '42' or substr(DISCO_08, 1, 3) = '432' then SES = "Low";
The full model will include the following covariates: calendar time, time passed since start of follow-up, employment in the health care industry, age, sex, SES, nighttime work, working hours, working hours × sex, working hours × SES, and working hours × nighttime work. We will use the parameter estimates obtained with the full model to calculate RRs for incident use of antihypertensive drugs and for hospitalization or death due to IHD as a function of weekly working hours, by sex, SES, and nighttime work. We will consider the following contrasts: 41–48 versus 32–40 working hours/week, and >48 versus 32–40 working hours/week. The results will be presented as outlined in
Dummy table for reporting the RRa with 95% CI for incident use of antihypertensive drugs and hospitalization or death due to IHDb as a function of weekly working hours among Danish employees during 2000–2014, stratified by sex, socioeconomic status, and night shift status.
Worker subgroups | Weekly working hours | Antihypertensive drugs | Hospitalization or death due to IHD | ||||||
Cases | RR | 95% CI | Cases | RR | 95% CI | ||||
Male | >48 | ||||||||
41–48 | |||||||||
32–40 | 1.00 | – | 1.00 | – | |||||
Female | >48 | ||||||||
41–48 | |||||||||
32–40 | 1.00 | – | 1.00 | – | |||||
Low | >48 | ||||||||
41–48 | |||||||||
32–40 | 1.00 | – | 1.00 | – | |||||
Medium | >48 | ||||||||
41–48 | |||||||||
32–40 | 1.00 | – | 1.00 | – | |||||
High | >48 | ||||||||
41–48 | |||||||||
32–40 | 1.00 | – | 1.00 | – | |||||
Unknown | >48 | ||||||||
41–48 | |||||||||
32–40 | 1.00 | – | 1.00 | – | |||||
Yes | >48 | ||||||||
41–48 | |||||||||
32–40 | 1.00 | – | 1.00 | – | |||||
No | >48 | ||||||||
41–48 | |||||||||
32–40 | 1.00 | – | 1.00 | – |
aRR: rate ratio.
bIHD: ischemic heart disease.
We will test hypotheses 1.1 and 1.2 by use of likelihood ratios comparing the full model with a model containing only the following covariates: calendar time, time passed since start of follow-up, employment in the health care industry, age, sex, SES, and nighttime work.
We will test hypotheses 1.1.1 and 1.2.1 by use of likelihood ratios comparing the full model with a model containing only the following covariates: calendar time, time passed since start of follow-up, employment in the health care industry, age, sex, SES, nighttime work, working hours, working hours × sex, and working hours × nighttime work.
We will test hypotheses 1.1.2 and 1.2.2 by use of likelihood ratios comparing the full model with a model containing only the following covariates: calendar time, time passed since start of follow-up, employment in the health care industry, age, sex, SES, nighttime work, working hours, working hours × SES, and working hours × nighttime work.
We will test hypotheses 1.1.3 and 1.2.3 by use of likelihood ratios comparing the full model with a model containing only the following covariates: calendar time, time passed since start of follow-up, employment in the health care industry, age, sex, SES, nighttime work, working hours, working hours × sex, and working hours × SES.
We will test hypotheses 1.1.4 and 1.2.4 by use of likelihood ratios comparing a model containing only the covariates calendar time, time passed since start of follow-up, employment in the health care industry, age, sex, SES, nighttime work, and working hours with a model containing only the covariates calendar time, time passed since start of follow-up, employment in the health care industry, age, sex, SES, and nighttime work.
The full model will include the following covariates: calendar time, time passed since start of follow-up, employment in the health care industry, age, sex, SES, working hours, nighttime work, nighttime work × sex, and nighttime work × SES. We will use the parameter estimates obtained with the full model to calculate RRs for incident use of antihypertensive drugs and hospitalization or death due to IHD as a function of nighttime work, by sex and SES. The results will be presented as outlined in
Dummy table for reporting the RRa with 95% CI for incident use of antihypertensive drugs and hospitalization or death due to IHDb as a function of nighttime work among Danish employees during 2000–2014, stratified by sex and socioeconomic status.
Worker subgroups | Nighttime work | Antihypertensive drugs | Hospitalization or death due to IHD | |||||
Cases | RR | 95% CI | Cases | RR | 95% CI | |||
Male | Yes | |||||||
No | 1.00 | – | 1.00 | – | ||||
Female | Yes | |||||||
No | 1.00 | – | 1.00 | – | ||||
Low | Yes | |||||||
No | 1.00 | – | 1.00 | – | ||||
Medium | Yes | |||||||
No | 1.00 | – | 1.00 | – | ||||
High | Yes | |||||||
No | 1.00 | – | 1.00 | – | ||||
Unknown | Yes | |||||||
No | 1.00 | – | 1.00 | – |
aRR: rate ratio.
bIHD: ischemic heart disease.
We will test hypotheses 2.1 and 2.2 by use of likelihood ratios comparing the full model with a model containing only the following covariates: calendar time, time passed since start of follow-up, employment in the health care industry, age, sex, SES, and working hours.
We will test hypotheses 2.1.1 and 2.2.1 by use of likelihood ratios comparing the full model with a model containing only the following covariates: calendar time, time passed since start of follow-up, employment in the health care industry, age, sex, SES, nighttime work, working hours, and working hours × sex.
We will test hypotheses 2.1.2 and 2.2.2 by use of likelihood ratios comparing the full model with a model containing only the following covariates: calendar time, time passed since start of follow-up, employment in the health care industry, age, sex, SES, nighttime work, working hours, and working hours × SES.
We will test hypotheses 2.1.3 and 2.2.3 by use of likelihood ratios comparing a model containing the covariates calendar time, time passed since start of follow-up, employment in the health care industry, age, sex, SES, nighttime work, and working hours with a model containing only the covariates calendar time, time passed since start of follow-up, employment in the health care industry, age, sex, SES, and working hours.
To estimate the statistical power of the hypothesis tests, we first needed to estimate the expected number of cases in the various exposure categories.
To obtain such estimates, we followed the entire population of 21- to 59-year-old employees in Denmark from January 1, 2000 and onward in the same registers that we will use to follow up the samples of this project. While discounting prevalent cases (those who had experienced the clinical end point of the study during the calendar year 1999), we noted all new cases that occurred and tabulated those against the time that had passed since the start of the follow-up.
Cumulative percentage of new cases among employees in Denmark aged 21–59 years at baseline, as a function of time passed since start of follow-up (January 1, 2000).
Years of follow-up | Hospitalization or death due to IHDa | Antihypertensive drugs | ||
Men | Women | Men | Women | |
1 | 0.34 | 0.14 | 1.41 | 2.38 |
2 | 0.65 | 0.26 | 2.89 | 4.63 |
3 | 0.98 | 0.41 | 4.44 | 6.76 |
4 | 1.32 | 0.55 | 6.15 | 8.98 |
5 | 1.67 | 0.70 | 8.07 | 11.23 |
6 | 2.03 | 0.86 | 10.01 | 13.38 |
7 | 2.39 | 1.04 | 12.14 | 15.62 |
8 | 2.76 | 1.22 | 14.37 | 17.89 |
9 | 3.14 | 1.41 | 16.55 | 20.05 |
10 | 3.53 | 1.63 | 18.63 | 22.02 |
11 | 3.98 | 1.88 | 20.70 | 23.92 |
12 | 4.42 | 2.13 | 22.69 | 25.75 |
13 | 4.83 | 2.36 | 24.64 | 27.54 |
14 | 5.21 | 2.58 | 26.56 | 29.32 |
15 | 5.58 | 2.80 | 28.48 | 31.09 |
aIHD: ischemic heart disease.
Since the sample of participants who were interviewed in calendar year
By relating the frequency distribution of the participants stratified by calendar year of interview, sex, and exposure category to the percentages given in
Expected number of new cases under the null hypothesis.
Type of exposure | Level | No. of participants | Expected no. of IHDa cases | Expected no. of antihypertensive drug cases |
Night shifts | Yes | 20,337 | 439 | 2924 |
No | 137,521 | 2786 | 21,068 | |
Weekly working hours | >48 | 9734 | 210 | 1304 |
41–48 | 15,872 | 297 | 2082 | |
32–40 | 132,252 | 2718 | 20,606 |
aIHD: ischemic heart disease.
Since our hypotheses will be evaluated by use of chi-square distributed likelihood ratio tests, we have chosen to depict the statistical power as a function of Cohen w, which is an effect size defined by equation (1) (
Equation (1): calculation of Cohen effect size w, where
Power to detect that the examined incidences depend on weekly working hours either as a general effect or as an effect of interaction with sex, socioeconomic status, or nighttime work, as a function of Cohen w. IHD: ischemic heart disease.
Power to detect that the examined incidences depend on nighttime work either as a general effect or as an effect of interaction with sex or socioeconomic status, as a function of Cohen w. IHD: ischemic heart disease.
Since the questions used to obtain information about nighttime work and weekly working hours were revised in 2001 and then again in 2007, we will perform a sensitivity analysis with the results stratified by calendar period of interview (1999–2000, 2001–2006, and 2007–2013). The end point, covariates, and statistical model of the sensitivity analysis will be the same as the ones used to test hypotheses 1.1.4 and 2.1.3.
To ascertain that an observed instance of hospital treatment during the follow-up is a new episode rather than a revisit in a course of treatment that was initiated before baseline, the primary analysis will exclude all workers who were treated for IHD sometime during the calendar year preceding baseline. It will, however, not exclude all former cases of IHD, and it is possible that the estimates of the primary analysis will be affected by nonexcluded workers who were treated for IHD more than 1 year earlier than baseline. We will address this issue with a sensitivity analysis, which will exclude all workers who received hospital treatment for IHD one or more times during a 5-year period prior to baseline. The analysis will include only those who were at least 20 years old and lived in Denmark throughout the 5-year period of interest. In all other respects, the design will be the same as the one used to test hypotheses 1.1.4 and 2.1.3.
The actual working hours, that is, the hours worked during the reference week, constitute a well-defined quantity with minimal recall bias. The usual working hours are less well defined, and the way they are understood and remembered might vary between individuals. In spite of this drawback, we chose to base our analysis on the workers’ usual rather than their actual working hours. We did so because some of the participants, by chance, would have worked less than usual during the reference week due to, for example, holidays, vacation, or sickness absence, while others would have worked more than usual due to, for example, a deadline or a temporary staff shortage. Since the usual working hours might be associated with recall bias, we will perform a sensitivity analysis in which we include only participants who belong to the same category according to their actual working hours as they do according to their usual working hours. In all other respects, the design will be the same as the one used to test hypotheses 1.1.4 and 2.1.3.
Number of economically active 21- to 59-year-old participants, stratified by combinations of actual and usual weekly working hours.
Actual weekly working hours | Usual weekly working hours | ||||
0–31 | 32–40 | 41–48 | >48 | Total | |
Missing | 187 | 56 | 16 | 19 | 278 |
0–31 | 31,094 | 32,748 | 2888 | 1545 | 68,275 |
32–40 | 2603 | 82,781 | 2762 | 788 | 88,934 |
41–48 | 462 | 10,981 | 8250 | 869 | 20,562 |
>48 | 413 | 5686 | 1956 | 6513 | 14,568 |
Total | 34,759 | 132,252 | 15,872 | 9734 | 192,617 |
It is recognized that the risk of IHD depends on a person’s body mass index (BMI) and smoking habits. Among Danish employees, the RR for IHD has been estimated at 1.54 for current versus never smokers [
Unfortunately, the Danish Labour Force Survey does not contain any information about the worker’s weight and smoking habits, which makes us unable to control for these factors in our analyses. We therefore wanted to know in what direction and to what extent we can expect the estimates of the project to be influenced by differences in smoking habits and BMI. To shed some light on this issue, we compiled some descriptive statistics on the prevalence of smoking and high BMI in relation to long working hours and nighttime work among a random sample of employees in Denmark (
Crude percentages of current smokers, persons with moderate overweight (25≤BMIa<30 kg/m2), and persons with obesity (BMI≥30 kg/m2), by working time arrangement, in a random sample of 20- to 59-year-old employees in Denmark, 2010.
Working time arrangements | Current smoker | 25≤BMI<30 | BMI≥30 |
% (n/N) | % (n/N) | % (n/N) | |
32–40 working hours/week | 22.4 (1205/5383) | 33.8 (1821/5383) | 12.9 (695/5383) |
41–48 working hours/week | 20.7 (255/1231) | 36.1 (445/1231) | 12.8 (157/1231) |
>48 working hours/week | 21.0 (141/671) | 39.6 (266/671) | 12.1 (81/671) |
Without nighttime work | 21.7 (1465/6766) | 34.5 (2335/6766) | 12.7 (858/6766) |
With nighttime work | 26.2 (136/519) | 38.0 (197/519) | 14.5 (75/519) |
aBMI: body mass index.
Age (10-year classes) and sex standardized percentages of current smokers, persons with moderate overweight (25≤BMIa<30 kg/m2), and persons with obesity (BMI≥30 kg/m2), by working time arrangement, in a random sample of 20- to 59-year-old employees in Denmark, 2010.
Working time arrangement | Current smoker | 25≤BMI<30 | BMI≥30 | ||||
% | 95% CI | % | 95% CI | % | 95% CI | ||
32–40 working hours/week | 22.6 | 21.5–23.7 | 34.7 | 33.4–35.9 | 12.9 | 12.1–13.9 | |
41–48 working hours/week | 21.0 | 18.8–23.5 | 34.8 | 32.2–37.5 | 12.5 | 10.7–14.5 | |
>48 working hours/week | 21.3 | 18.0–25.2 | 35.3 | 31.6–39.5 | 10.8 | 8.5–13.7 | |
Without nighttime work | 21.6 | 20.7–22.6 | 34.5 | 33.4–35.6 | 12.7 | 11.9–13.5 | |
With nighttime work | 25.8 | 22.3–30.0 | 38.4 | 34.5–42.8 | 15.4 | 12.5–19.0 |
aBMI: body mass index.
Our calculations imply that a failure to control for smoking, overweight, and obesity (in a study population in which the prevalences are equal to those given in
We expect results to be ready in mid-2017.
This study protocol provides a detailed description of the hypotheses, inclusion criteria, significance levels, and statistical models of a project designed to estimate prospective associations between different types of work time arrangements and IHD in the general working population of Denmark.
Statistics Denmark randomly sampled the participants in the study from the target population and we have strengthened the prospective design by the strategy to exclude prevalent cases.
For nighttime work and long working hours, the statistical power is sufficient (compare [
Since the design of the project is being peer reviewed and published before the exposure data are linked to health data, we have eliminated hindsight and within-study selection bias. We have also eliminated bias from missing follow-up data, since the clinical end points are ascertained through registers that cover the entire target population.
It has previously been shown that the incidence of IHD is highly dependent on age [
Another drawback is that the definition of “night worker" that we use in this project differs from the legal definition that is used in the EU Working Time Directive. According to the Danish implementation of the directive, “nighttime" means any period of at least 7 hours, which includes the period between midnight and 5:00 AM, while “night worker" means “any worker, who, during nighttime, work at least three hours of his daily working time as a normal course" or “any worker who is likely to work at nighttime at least 300 hours during a period of twelve months" [
In conclusion, this is an observational study, which cannot be used to confirm etiological hypotheses. It may, however, confirm that long working hours or nighttime work, or both, are predictors for IHD and thereby lend support to the hypothesis of a causal relationship.
Anatomical Therapeutic Chemical Classification System
body mass index
Danish version of the International Standard Classification of Occupations
expected rate ratio
European Socio-economic Classification
European Union
International Classification of Diseases, Tenth Revision
ischemic heart disease
International Standard Classification of Occupations
rate ratio
socioeconomic status
The project was initiated by Otto Melchior Poulsen, former research director at the National Research Centre for the Working Environment (NRCWE). It is funded by the Danish taxpayers, through the Danish Work Environment Research Foundation, grant number 20130069288. The data of the project were and will be supplied by Statistics Denmark. Jesper Møller Pedersen and Simone Visbjerg Møller, NRCWE, helped us with project administration and data management.
We would like to thank the members of our advisory committee: Hermann Burr, Bundesanstalt für Arbeitsschutz und Arbeitsmedizin (BAuA); Karen Albertsen, Team Working Life, Denmark; Martin Lindhart Nielsen, Bispebjerg Hospital, Denmark; Jan Hyld Pejtersen, Danish National Centre for Social Research; and Hans Bay, NRCWE.
We would also like to thank Mette Andersen Nexø, Elsa Bach, Elizabeth Bengtsen, Rikke Nilsson, Pia Gøtterup, Tina Norlén Thomsen, and Bodil Holst, NRCWE, for valuable advice and discussions.
None declared.