Abstract
Objectives
Shift work is associated with myocardial infarction and stroke. We studied if shift work is also associated with incident atrial fibrillation (AF) and if this association differs, depending on sex and age.
Methods
We studied 22 339 participants (age 37.0±9.8 years, 49% women) with paid work from the third (1986–1987), fourth (1994–1995), fifth (2001) and sixth (2007–2008) surveys of the population-based Tromsø Study, Norway. Participants were followed up for ECG-confirmed AF through 2016. Shift work was assessed by questionnaire at each survey. We used unadjusted and multivariable-adjusted Cox regression models to study the association of shift work with 10-year incident AF and incident AF during extensive follow-up up to 31 years. Interactions with sex and age were tested in the multivariable model.
Results
Shift work was reported by 21% of participants at the first attended survey. There was an interaction between shift work and age for 10-year incident AF (p=0.069). When adjusted for AF risk factors, shift work was significantly associated with 10-year incident AF in participants <40 years (HR 2.90, 95% CI 1.12 to 7.49) but not≥40 years of age (HR 0.90, 95% CI 0.53 to 1.51). Shift work was not associated with incident AF during extensive follow-up (HR 1.03, 95% CI 0.89 to 1.20). There was no interaction between shift work and sex.
Conclusions
Shift work was associated with 10-year incident AF in individuals <40 years but not ≥40 years of age. Shift work was not associated with incident AF during extensive follow-up up to 31 years, and there were no sex differences.
Keywords: atrial fibrillation, epidemiology, risk factors
WHAT IS ALREADY KNOWN ON THIS TOPIC
Shift work is associated with coronary artery disease and stroke. However, comparatively little is known about the association between shift work and atrial fibrillation (AF).
One previous study found a significant association between shift work and AF but did not include younger individuals.
WHAT THIS STUDY ADDS
In a large population-based cohort, we found that shift work was associated with 10-year incident AF in individuals <40 years but not ≥40 years of age.
HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY
Our findings demonstrate that future studies into shift work and AF, as well as potential preventive measures in shift workers, should also focus on younger individuals.
Introduction
Atrial fibrillation (AF) is a major cause of morbidity, mortality and reduced quality of life.1 2 The lifetime risk of AF is estimated to be up to one in three, and its prevalence will likely increase even further due to increases in life expectancy and rising prevalence of risk factors for AF.1 3 In order to improve prevention and early detection of AF, it is essential to identify people at risk of AF.
Shift work, which is generally defined as any type of work that is done outside of conventional daytime working schedules,4 is becoming increasingly common due to increasing demands from the so-called ‘24-hour society’ that has emerged in recent years.5 6 In 2015, approximately 21% of workers in the European Union reported doing shift work, compared with 17% in 2005 and 2010.7 Previous studies have demonstrated that shift work is associated with adverse cardiovascular outcomes, in particular coronary artery disease and stroke.4 6 8 9 Since AF and other types of cardiovascular disease (CVD) share similar risk factors and increase the risk of each other,10 11 it seems likely that shift work could also be associated with AF. Furthermore, shift work is associated with reduced heart rate variability and autonomic dysfunction, which are in turn associated with AF.12–16 Indeed, previous studies have demonstrated that unhealthy sleep patterns and long working hours, both of which may be a consequence of shift work, are associated with a higher risk of AF.17 18
Nevertheless, only one study has previously studied the association of shift work with AF.19 This study demonstrated that current shift work was associated with increased risk of incident AF in individuals 40–69 years of age.19 Furthermore, the study reported a significant association between lifetime exposure to shift work and incident AF in women but not in men.19 Still, additional studies are needed in order to corroborate these findings. Furthermore, it remains unknown if shift work is associated with an increased risk of AF in younger individuals.
Therefore, we studied the association between shift work and incident AF as well as potential interactions with age and sex.
Methods
Study population and procedures
The study was performed using data from the Tromsø Study, which has been described in detail previously.20 21 In short, the Tromsø Study is an ongoing population-based cohort study conducted in the municipality of Tromsø, Norway. The study includes participants from the general population, who are invited based on birth cohorts and random cohorts. Starting in 1974, seven surveys (Tromsø1–Tromsø7) have been performed thus far, all of which have included extensive questionnaires, clinical examinations and blood sampling. At each survey, participants from previous surveys as well as new participants are included.
For the present study, we used data from the Tromsø3 (1986–1987), Tromsø4 (1994–1995), Tromsø5 (2001) and Tromsø6 (2007–2008) surveys. We included all participants with paid work who attended at least one of these four surveys (n=24 535). If participants attended multiple surveys, the first attended survey was considered the baseline visit. Tromsø3 was the first attended survey for 69% of all included participants. Tromsø4, Tromsø5 and Tromsø6 were the first attended survey for 22%, 2% and 7% of participants, respectively. We excluded participants with insufficient data on AF or in whom it was unclear whether they developed AF or not (n=1066), participants with prevalent AF at baseline (n=43) and participants without available data on shift work (n=1062), leaving a total of 22 339 participants.
Ascertainment of AF
The method of AF ascertainment has been described in detail previously.22 23 In summary, participants were linked to the hospital diagnosis registry of the University Hospital of North Norway, which is the only hospital in the Tromsø municipality. This registry contains diagnoses from inpatient and outpatient visits, which are coded according to the 9th and/or 10th revision of the International Classification of Diseases (ICD-9 and ICD-10). In order to identify potential cases of AF, the registry was searched for ICD-9 code 427 (cardiac dysrhythmias) and ICD-10 codes I47 (paroxysmal tachycardia) and I48 (AF and flutter). In addition, the medical records of all participants with other cardiovascular or cerebrovascular events (based on ICD-9 codes 410–414, 428 and 430–438 and ICD-10 codes I20-I25, I46, I50 and I60–I69) were searched for notes mentioning AF. All events were adjudicated by an independent endpoint committee. The diagnosis of AF was confirmed only if AF was documented by ECG. AF that occurred within 28 days of acute myocardial infarction, acute heart failure or cardiac surgery, as well as AF occurring within the 7 days prior to death, was not classified as AF. For the present study, follow-up data for AF were available through 2016.
Exposure
Shift work was defined as self-reported shift or night work, as assessed by a single yes/no questionnaire item at each survey.
Covariates
Systolic and diastolic blood pressures, resting heart rate, body mass index (BMI) and total cholesterol were measured according to previously published methods.23 Information regarding diabetes, use of antihypertensive drugs, history of myocardial infarction and stroke, smoking, physical activity, education level and (paid) work status was determined by questionnaire. Hypertension was defined as systolic blood pressure of ≥140 mm Hg and/or diastolic blood pressure of ≥90 mm Hg and/or use of antihypertensive drugs. Overweight and obesity were defined as BMI of 25–30 kg/m2 and BMI of ≥30 kg/m2, respectively. Education level was classified as <10 years (primary education only), 10–12 years (high school education) or >12 years (higher education).
Follow-up
The follow-up duration was calculated as the time from the first attended survey to the date of incident AF, censoring due to migration or death or the end of the follow-up period (31 December 2016), whichever came first. Data regarding death and migration were retrieved from the National Population Register of Norway.
Statistical analyses
Clinical characteristics were compared between participants with and without shift work. For continuous data, characteristics were compared using the independent samples t-test. For binary data, Pearson’s χ2 test was used.
We used Kaplan-Meier analyses and Cox regression models to study the association of shift work at baseline with (1) 10-year incident AF and (2) incident AF during extensive follow-up through 2016. For both outcomes, we built four Cox regression models: (1) an unadjusted model including shift work only; (2) model 1, additionally adjusted for age and sex; (3) model 2, additionally adjusted for components of the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) AF risk model (weight, height, systolic blood pressure, diastolic blood pressure, use of antihypertensive drugs, diabetes, smoking and history of myocardial infarction)24; and (4) model 3, additionally adjusted for education level. Next, we tested for interactions with age and sex in the full multivariable model. In case of a significant interaction, the analyses were stratified by the interaction variable. For age, a cut-off of 40 years was chosen since participants <40 years of age were not included in previous studies on shift work and AF.
We performed several sensitivity analyses. For 10-year incident AF, due to the modest number of AF events in participants <40 years and the consequent risk of overfitting for models 3 and 4, we built additional Cox regression models in which we used propensity score adjustment rather than adjustment for all separate covariates. The propensity score for the presence of shift work was calculated using relevant clinical characteristics as included in table 1. Furthermore, we performed sensitivity analyses in which the follow-up duration was restricted to 62, 67 or 75 years of age (the minimum, usual and maximum retirement age in Norway, respectively). For incident AF during extensive follow-up, we performed additional Cox regression analyses in which shift work status was allowed to change over time (ie, shift work was included as a time-varying covariate). In these analyses, we additionally adjusted for paid work status at each visit in order to account for changes in employment status after baseline. Finally, we additionally performed Cox regression analyses in which we classified participants according to shift work status at the first and second attended surveys: (1) no shift work at either survey (reference category), (2) shift work at the first but not the second survey, (3) shift work at the second survey only and (4) shift work at both surveys. For these additional analyses, we included all participants with available shift work data at two or more surveys. Here, the second attended survey was considered to be the baseline, and we excluded participants in whom AF was diagnosed prior to this date.
Table 1.
Clinical characteristics of participants with and without shift work: the Tromsø Study
| Characteristics | Shift work | P value | |
| No (n=17 623) | Yes (n=4716) | ||
| Age (years) | 37.4±9.9 | 35.6±9.4 | <0.001 |
| Female sex | 8544 (48%) | 2336 (50%) | 0.199 |
| BMI (kg/m2) | 24.1±3.4 | 24.3±3.7 | 0.001 |
| Overweight | 5053 (29%) | 1378 (29%) | 0.471 |
| Obesity | 1023 (5.8%) | 346 (7.3%) | <0.001 |
| SBP (mmHg) | 127.3±14.8 | 125.8±14.4 | <0.001 |
| DBP (mmHg) | 75.6±10.6 | 74.1±10.4 | <0.001 |
| Antihypertensive drug use | 389 (2.2%) | 76 (1.6%) | 0.011 |
| Hypertension | 3766 (21%) | 830 (18%) | <0.001 |
| Resting heart rate (beats/min) | 71.5±13.2 | 71.5±12.7 | 0.970 |
| Total cholesterol (mmol/L) | 5.6±1.2 | 5.6±1.2 | 0.601 |
| Diabetes | 96 (0.5%) | 21 (0.4%) | 0.398 |
| History of myocardial infarction | 80 (0.5%) | 21 (0.4%) | 0.934 |
| History of stroke | 32 (0.2%) | 5 (0.1%) | 0.256 |
| Current smoking | 6860 (39%) | 2220 (47%) | <0.001 |
| Sedentary lifestyle | 4234 (24%) | 1255 (27%) | <0.001 |
| Highly active lifestyle | 3509 (20%) | 853 (18%) | 0.005 |
| Primary education only | 4326 (25%) | 1314 (28%) | <0.001 |
| Higher education | 7509 (43%) | 1655 (35%) | <0.001 |
Data presented as mean±SD or count (%). P values represent the difference between participants with and without shift work at baseline. Overweight was defined as BMI of 25–30 kg/m2, obesity as BMI of ≥30 kg/m2 and hypertension as SBP of ≥140 mm Hg and/or DBP of ≥90 mm Hg and/or use of antihypertensive drugs.
BMI, body mass index; DBP, diastolic blood pressure; SBP, systolic blood pressure.
The Cox proportional hazards assumption was assessed graphically using Kaplan-Meier curves,25 which revealed no violations of the proportional hazards assumption. Analyses were performed using IBM SPSS V.25. P values of <0.05 or interaction p values of <0.10 were considered statistically significant.
Results
Participant characteristics
Mean age was 37.0±9.8 years, and 49% were women. Current shift work was reported by 4716 (21%) participants at the first attended survey. Shift workers were younger (35.6±9.4 vs 37.4±9.9) and less physically active (27% vs 24% with sedentary lifestyle and 18% vs 20% with highly active lifestyle), and had lower education levels (35% vs 43% higher education), lower prevalence of hypertension (18% vs 21%), higher BMI (24.3±3.7 vs 24.1±3.4) and higher prevalence of smoking (47% vs 39%) compared with those without shift work (table 1).
Shift work and 10-year incident AF
During 10 years of follow-up, 129 participants (0.6%) developed incident AF. Of all 10-year incident AF cases, 25 occurred in participants with shift work (incidence rate 0.59 per 1000 person-years) and 104 in participants without shift work (incidence rate 0.65 per 1000 person-years).
In the total study population, shift work was not significantly associated with 10-year incident AF (online supplemental table S1). However, there was a significant interaction between shift work and age (p for interaction=0.069) for 10-year incident AF. Age-stratified Kaplan-Meier plots demonstrated that shift work was significantly associated with 10-year incident AF in participants <40 years (p=0.033) but not ≥40 years of age (p=0.601) (figure 1A, B, and online supplemental figure S1A, B). Age-stratified participant characteristics are shown in online supplemental table S2A, B. Further stratification of the older age group revealed no significant associations between shift work and incident AF in participants 40–49 years, 50–59 years or 60–69 years of age (online supplemental table S3).
Figure 1.
(A) Kaplan-Meier cumulative incidence plot for incident AF in participants <40 years with and without shift work: the Tromsø Study. (B) Kaplan-Meier cumulative incidence plot for incident AF in participants ≥40 years with and without shift work:The Tromsø Study. AF, atrial fibrillation.
openhrt-2022-002086supp001.pdf (202.4KB, pdf)
Age-stratified Cox regression showed that, after adjustment for age, sex and risk factors, shift work was significantly associated with 10-year incident AF in participants <40 years (HR 2.90, 95% CI 1.12 to 7.49, p=0.028) but not ≥40 years of age (HR 0.90, 95% CI 0.53 to 1.51, p=0.677) (table 2). The association of shift work with 10-year incident AF in participants <40 years of age was slightly attenuated after additional adjustment for education level and physical activity (HR 2.64, 95% CI 0.99 to 7.00, p=0.051) (table 2). Propensity score-adjusted Cox regression models produced virtually identical results (online supplemental table S4). Classifying participants according to shift work status at the first and second attended surveys also produced largely similar results (online supplemental table S5). There was no significant interaction between shift work and sex (p for interaction=0.743).
Table 2.
HRs for the association between shift work and 10-year incidence of AF, stratified by age: the Tromsø Study
| Determinant and age group | HR (95% CI) | P value |
| Model 1: unadjusted | ||
| Shift work (age <40 years) | 2.65 (1.05 to 6.71) | 0.040 |
| Shift work (age ≥40 years) | 0.87 (0.52 to 1.46) | 0.601 |
| Model 2: adjusted for age and sex | ||
| Shift work (age <40 years) | 2.90 (1.14 to 7.34) | 0.025 |
| Shift work (age ≥40 years) | 0.93 (0.55 to 1.56) | 0.778 |
| Model 3: adjusted for age, sex and CHARGE-AF risk model | ||
| Shift work (age <40 years) | 2.90 (1.12 to 7.49) | 0.028 |
| Shift work (age ≥40 years) | 0.90 (0.53 to 1.51) | 0.677 |
| Model 4: adjusted for age, sex, CHARGE-AF risk model, education level and physical activity | ||
| Shift work (age <40 years) | 2.64 (0.99 to 7.00) | 0.051 |
| Shift work (age ≥40 years) | 0.92 (0.54 to 1.55) | 0.744 |
HRs and p values represent the association between shift work and 10-year AF in participants aged <40 years (n=13 711, number of AF cases=18, crude incidence=0.1%) or participants aged ≥40 years (n=8628, number of AF cases=111, crude incidence=1.3%). Components of the CHARGE-AF risk model were weight, height, systolic and diastolic blood pressures, antihypertensive drug use, diabetes, smoking and history of myocardial infarction.
AF, atrial fibrillation.
Shift work and incident AF during extensive follow-up of up to 31 years
During extensive follow-up with a median duration of 24 (15–30) years, 1244 participants (5.6%) developed AF. Of all incident AF cases, 221 occurred in participants with shift work (incidence rate 2.3 per 1000 person-years) and 1023 in participants without shift work (incidence rate 2.7 per 1000 person-years).
During the extensive follow-up, shift work was not significantly associated with incident AF in any of the models (HR 0.88, 95% CI 0.76 to 1.02, p=0.083 for model 1; HR 1.02, 95% CI 0.88 to 1.19, p=0.773 for model 4) (table 3), and none of the interaction terms with age (p for interaction=0.170) or sex (p for interaction=0.511) were significant. Sensitivity analyses in which the follow-up was restricted to 62, 67 or 75 years of age (results not shown), in which shift work was included as a time-varying covariate (online supplemental table S6), or in which participants were classified according to shift work status at the first and second attended surveys (online supplemental table S7), did not yield materially different results.
Table 3.
HRs for the association between shift work and incident AF during extensive follow-up of up to 31 years: the Tromsø Study
| HR (95% CI) | P value |
| Model 1: unadjusted | |
| 0.88 (0.76 to 1.02) | 0.083 |
| Model 2: adjusted for age and sex | |
| 1.07 (0.92 to 1.23) | 0.385 |
| Model 3: adjusted for age, sex and CHARGE-AF risk model | |
| 1.03 (0.89 to 1.20) | 0.675 |
| Model 4: adjusted for age, sex, CHARGE-AF risk model, education level and physical activity | |
| 1.02 (0.88 to 1.19) | 0.773 |
HRs and p values represent the association between shift work and incident AF during extensive follow-up (number of AF cases=1244) in the total study population (n=22 339). Components of the CHARGE-AF risk model were weight, height, systolic and diastolic blood pressures, antihypertensive drug use, diabetes, smoking and history of myocardial infarction.
AF, atrial fibrillation.
Discussion
In this study, we demonstrated that shift work was associated with 10-year incident AF in working participants <40 years but not ≥40 years of age from the population-based Tromsø Study. Shift work was not associated with incident AF during extensive follow-up of up to 31 years, and we did not find sex differences in the association between shift work and AF.
Only one study has previously studied the relationship between shift work and AF: in working individuals from the UK Biobank, during a median follow-up of 10.4 years, current night shift work and lifetime exposure to night shift work were associated with incident AF.19 Our finding that shift work was associated with 10-year incident AF in participants aged 40 years is seemingly in line with these previous results. However, in contrast to the Tromsø Study, only participants 40–69 years of age were included in the UK Biobank. Furthermore, the study on UK Biobank participants did not report on possible interactions between shift work and age. Therefore, our study provides novel and important insights regarding the association between shift work and AF in different age groups.
In contrast to the results from the UK Biobank, we did not find a significant association between shift work and 10-year incident AF in participants aged ≥40 years, nor did we find a significant interaction between shift work and sex. Potential explanations for these contrasting findings may be regional differences (Northern Norway vs UK) or differences in the time period during which participants were included (1986–2008 for the Tromsø Study vs 2006–2010 for the UK Biobank). Such contrasts in time and region may in turn be associated with differences in lifestyle and the type of (shift) work, as well as healthcare differences. Furthermore, different definitions were used between the UK Biobank (night shift work) and the Tromsø Study (shift work in general).
Our finding that shift work was associated with 10-year incident AF in younger, but not older, participants seems consistent with previous studies which have demonstrated that risk factors generally confer a larger relative risk of CVD in younger age groups, despite lower absolute risk.26–28 This may be explained by the fact that multiple risk factors (including age itself) accumulate with advancing age.29 As a consequence of these competing risk factors, the role of shift work may become comparatively small in older individuals. In general, estimates of relative risk are dependent on baseline risk.30 Although the relative risk of AF associated with shift work was higher in younger compared with older participants, the absolute risk of AF was substantially lower in younger participants.
Another factor that may have affected our results is the so-called ‘healthy worker effect’: individuals with existing health issues may opt for jobs with conventional working schedules, and shift workers who develop new health issues (potentially as a consequence of shift work) may stop doing shift work as a result.5 8 Because shift workers are less at risk to begin with, the risk associated with shift work may be underestimated.29 In our study, shift workers less often had hypertension (despite higher prevalence of obesity and smoking) compared with non-shift workers, which could be indicative of the healthy worker effect. Furthermore, in participants ≥40 years of age, the risk of AF in shift work was lower during the first years of follow-up (possibly due to the healthy worker effect), but caught up with non-shift workers after roughly 8 years of follow-up (potentially due to competing risk factors).
Finally, during extensive follow-up of up to 31 years, we found no significant association between shift work and incident AF, and the interaction with age became non-significant. This could be due to competing risk factors, retirement of older participants, changes in employment or changes in shift work status, all of which may have diluted the long-term results. In order to account for retirement, we performed sensitivity analyses in which we limited the follow-up duration to the minimum, usual and maximum retirement ages in Norway. In order to account for changes in employment and shift work status, we additionally performed sensitivity analyses with time-varying covariates for shift work and paid work status. None of the sensitivity analyses materially altered the results. Still, since not all participants attended multiple surveys, unknown changes in employment and shift work status may have influenced the long-term results.
Strengths and limitations
Our study was performed in a large population-based cohort with prospective data collection, long-term follow-up and solid validation of incident AF.
Limitations include the lack of information regarding the frequency or lifetime exposure to shift work, both of which were previously shown to be associated with incident AF.19 Furthermore, no distinction was made between different types of shift work (eg, evening or night work, rotational shifts), which may have different effects on the risk of AF and should be taken into account in future studies. The incidence of AF in our study was fairly low, which may be explained by the inclusion of mainly young and healthy participants (healthy volunteer selection bias), a known issue to population-based cohort studies.19 Given the modest number of incident AF events, particularly in younger participants, our findings should be interpreted with some caution. Despite the thorough adjudication of hospital records for possible AF events, cases of unrecognised AF as well as cases treated exclusively in primary care may have been missed. Although we extensively adjusted for components of the CHARGE-AF risk model, residual confounding by unknown factors may play a role in the association between shift work and AF. Since the majority of participants were first included in Tromsø3 (1986–1987), results for 10-year incident AF might be different in a more contemporary setting due to changes in (the type of) shift work and lifestyle, as well as advances in risk factor management and AF screening. Finally, the population from Tromsø is predominantly Caucasian, which may limit the generalisability of our results.
Conclusions
In a large, population-based cohort, shift work at baseline was associated with 10-year risk of AF in individuals <40 years but not ≥40 years of age. During extensive follow-up of up to 31 years, shift work was not associated with incident AF. We did not find sex differences in the association between shift work and AF. Additional studies are needed to further clarify the relationship between shift work and AF, and such studies should take into account frequency, lifetime exposure and type of shift work. Our findings highlight that future studies, as well as potential preventive measures in shift workers, should also focus on younger individuals.
Footnotes
Presented at: Part of this work was presented during the EHRA 2022 Congress and published as a conference abstract (EP Europace, Volume 24, Issue Supplement_1, May 2022, euac053.156, https://doi.org/10.1093/europace/euac053.156).
Contributors: VWZ, ES, MLL and MR contributed to the conception, design and acquisition of the work, data analysis and interpretation. VWZ and MR drafted the manuscript and are responsible for the overall content of the work as guarantors. All authors contributed to the critical revision of the manuscript, gave final approval of the version to be published and agreed to be accountable for all aspects of the work.
Funding: This work was supported by the Dutch Heart Foundation (CVON RED-CVD, grant 2017-11).
Competing interests: Outside of the submitted work, the authors disclosed the following financial support: RAdB reports grants from the Dutch Heart Foundation (CVON SHE-PREDICTS-HF, grant 2017-21; CVON RED-CVD, grant 2017-11; CVON PREDICT2, grant 2018-30; and CVON DOUBLE DOSE, grant 2020B005), leDucq Foundation (Cure PhosphoLambaN induced Cardiomyopathy) and the European Research Council (SECRETE-HF, ERC CoG 818715). The UMCG, which employs RAdB, received research grants and/or fees from AstraZeneca, Abbott, Boehringer Ingelheim, Cardior Pharmaceuticals Gmbh, Ionis Pharmaceuticals, Novo Nordisk and Roche. RAdB received speaker fees from Abbott, AstraZeneca, Bayer, Novartis and Roche. MR reports grants from the Dutch Heart Foundation (CVON RACE V, grant 2014-09; CVON RED-CVD, grant 2017-11; CVON-AI, grant 2018B017; DECISION, grant 2018B024). The UMCG, which employs MR, received grants from SJM/Abbott (VIP-HF study) and Medtronic (Cryoballoon AF registry/biobank study).
Provenance and peer review: Not commissioned; externally peer reviewed.
Supplemental material: This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.
Data availability statement
Data are available upon reasonable request. Data may be obtained by submitting an application to the Tromsø Study. For more information, please visit: https://uit.no/research/tromsostudy.
Ethics statements
Patient consent for publication
Not applicable.
Ethics approval
The Tromsø Study as well as the present study were approved by the Regional Committee for Medical and Health Research Ethics, North Norway (reference number for the present study: 138884). The Tromsø Study was performed in accordance with the Declaration of Helsinki. From Tromsø4 and onwards, participants provided written informed consent, and living participants from prior surveys were provided the opportunity to withdraw from the study.
References
- 1.Hindricks G, Potpara T, Dagres N, et al. 2020 ESC guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European association for Cardio-Thoracic surgery (EACTS). Eur Heart J 2021;42:373–498. 10.1093/eurheartj/ehaa612 [DOI] [PubMed] [Google Scholar]
- 2.Thrall G, Lane D, Carroll D, et al. Quality of life in patients with atrial fibrillation: a systematic review. Am J Med 2006;119:448.e1–448.e19. 10.1016/j.amjmed.2005.10.057 [DOI] [PubMed] [Google Scholar]
- 3.Krijthe BP, Kunst A, Benjamin EJ, et al. Projections on the number of individuals with atrial fibrillation in the European Union, from 2000 to 2060. Eur Heart J 2013;34:2746–51. 10.1093/eurheartj/eht280 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Vyas MV, Garg AX, Iansavichus AV, et al. Shift work and vascular events: systematic review and meta-analysis. BMJ 2012;345:1–11. 10.1136/bmj.e4800 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Christensen JO, Nilsen KB, Hopstock LA, et al. Shift work, low-grade inflammation, and chronic pain: a 7-year prospective study. Int Arch Occup Environ Health 2021;94:1013–22. 10.1007/s00420-020-01626-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Torquati L, Mielke GI, Brown WJ, et al. Shift work and the risk of cardiovascular disease. A systematic review and meta-analysis including dose-response relationship. Scand J Work Environ Health 2018;44:229–38. 10.5271/sjweh.3700 [DOI] [PubMed] [Google Scholar]
- 7.Eurofound . Sixth European Working Conditions Survey - Overview report (2017 update). Publications Office of the European Union, Luxembourg, 2017. https://www.eurofound.europa.eu/sites/default/files/ef_publication/field_ef_document/ef1634en.pdf%0A%0A [Google Scholar]
- 8.Bigert C, Kader M, Andersson T, et al. Night and shift work and incidence of cerebrovascular disease - a prospective cohort study of healthcare employees in Stockholm. Scand J Work Environ Health 2022;48:31–40. 10.5271/sjweh.3986 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Brown DL, Feskanich D, Sánchez BN, et al. Rotating night shift work and the risk of ischemic stroke. Am J Epidemiol 2009;169:1370–7. 10.1093/aje/kwp056 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Börschel CS, Schnabel RB. The imminent epidemic of atrial fibrillation and its concomitant diseases - Myocardial infarction and heart failure - A cause for concern. Int J Cardiol 2019;287:162–73. 10.1016/j.ijcard.2018.11.123 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Ball J, Carrington MJ, McMurray JJV, et al. Atrial fibrillation: profile and burden of an evolving epidemic in the 21st century. Int J Cardiol 2013;167:1807–24. 10.1016/j.ijcard.2012.12.093 [DOI] [PubMed] [Google Scholar]
- 12.van Amelsvoort LG, Schouten EG, Maan AC, et al. Occupational determinants of heart rate variability. Int Arch Occup Environ Health 2000;73:255–62. 10.1007/s004200050425 [DOI] [PubMed] [Google Scholar]
- 13.Murata K, Yano E, Hashimoto H, et al. Effects of shift work on QTc interval and blood pressure in relation to heart rate variability. Int Arch Occup Environ Health 2005;78:287–92. 10.1007/s00420-004-0592-4 [DOI] [PubMed] [Google Scholar]
- 14.Hulsegge G, Gupta N, Proper KI, et al. Shift work is associated with reduced heart rate variability among men but not women. Int J Cardiol 2018;258:109–14. 10.1016/j.ijcard.2018.01.089 [DOI] [PubMed] [Google Scholar]
- 15.Habibi M, Chahal H, Greenland P, et al. Resting heart rate, short-term heart rate variability and incident atrial fibrillation (from the multi-ethnic study of atherosclerosis (MESA)). Am J Cardiol 2019;124:1684–9. 10.1016/j.amjcard.2019.08.025 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Agarwal SK, Norby FL, Whitsel EA, et al. Cardiac autonomic dysfunction and incidence of atrial fibrillation: results from 20 years follow-up. J Am Coll Cardiol 2017;69:291–9. 10.1016/j.jacc.2016.10.059 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Li X, Zhou T, Ma H, et al. Healthy sleep patterns and risk of incident arrhythmias. J Am Coll Cardiol 2021;78:1197–207. 10.1016/j.jacc.2021.07.023 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Kivimäki M, Nyberg ST, Batty GD, et al. Long working hours as a risk factor for atrial fibrillation: a multi-cohort study. Eur Heart J 2017;38:2621–8. 10.1093/eurheartj/ehx324 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Wang N, Sun Y, Zhang H, et al. Long-term night shift work is associated with the risk of atrial fibrillation and coronary heart disease. Eur Heart J 2021;42:4180–8. 10.1093/eurheartj/ehab505 [DOI] [PubMed] [Google Scholar]
- 20.Jacobsen BK, Eggen AE, Mathiesen EB, et al. Cohort profile: the Tromso study. Int J Epidemiol 2012;41:961–7. 10.1093/ije/dyr049 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Njølstad I, Mathiesen EB, Schirmer H, et al. The Tromsø study 1974-2016: 40 years of cardiovascular research. Scand Cardiovasc J 2016;50:276–81. 10.1080/14017431.2016.1239837 [DOI] [PubMed] [Google Scholar]
- 22.Nyrnes A, Mathiesen EB, Njølstad I, et al. Palpitations are predictive of future atrial fibrillation. An 11-year follow-up of 22,815 men and women: the Tromsø study. Eur J Prev Cardiol 2013;20:729–36. 10.1177/2047487312446562 [DOI] [PubMed] [Google Scholar]
- 23.Sharashova E, Wilsgaard T, Ball J, et al. Long-Term blood pressure trajectories and incident atrial fibrillation in women and men: the Tromsø study. Eur Heart J 2020;41:1554–62. 10.1093/eurheartj/ehz234 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Alonso A, Krijthe BP, Aspelund T, et al. Simple risk model predicts incidence of atrial fibrillation in a racially and geographically diverse population: the CHARGE-AF Consortium. J Am Heart Assoc 2013;2:e000102. 10.1161/JAHA.112.000102 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Bellera CA, MacGrogan G, Debled M, et al. Variables with time-varying effects and the COX model: some statistical concepts illustrated with a prognostic factor study in breast cancer. BMC Med Res Methodol 2010;10:20. 10.1186/1471-2288-10-20 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Rapsomaniki E, Timmis A, George J, et al. Blood pressure and incidence of twelve cardiovascular diseases: lifetime risks, healthy life-years lost, and age-specific associations in 1·25 million people. Lancet 2014;383:1899–911. 10.1016/S0140-6736(14)60685-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Tromp J, Paniagua SMA, Lau ES, et al. Age dependent associations of risk factors with heart failure: pooled population based cohort study. BMJ 2021;372:n461. 10.1136/bmj.n461 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Morseth B, Geelhoed B, Linneberg A, et al. Age-specific atrial fibrillation incidence, attributable risk factors and risk of stroke and mortality: results from the MORGAM Consortium. Open Heart 2021;8:e001624–7. 10.1136/openhrt-2021-001624 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Knutsson A. Mortality of shift workers. Scand J Work Environ Health 2017;43:97–8. 10.5271/sjweh.3622 [DOI] [PubMed] [Google Scholar]
- 30.Dronkers J, Meems LMG, van Veldhuisen DJ, et al. Sex differences in associations of comorbidities with incident cardiovascular disease: focus on absolute risk. Eur Heart J Open 2022;2:1–10. 10.1093/ehjopen/oeac017 [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
openhrt-2022-002086supp001.pdf (202.4KB, pdf)
Data Availability Statement
Data are available upon reasonable request. Data may be obtained by submitting an application to the Tromsø Study. For more information, please visit: https://uit.no/research/tromsostudy.

