Cardiovascular risk profile in shift workers : cardiac control, biological and lifestyle risk factors

Background: Evidence available so far indicates a 40% excess cardiovascular disease risk among shift workers. As, in the Netherlands alone, about one million people are working in shifts, this might have a considerable public health impact. Factors responsible for this elevated risk have not yet been elucidated. Both changes in biological and lifestyle risk factors and disturbance of the cardiac control, as reflected by an increased frequency of premature ventricular complexes and decreased heart rate variability, might be involved in this excess risk. The purpose of this study was to investigate whether shift work related changes occur in these factors that might explain the elevated CVD risk among shift workers.Methods: A cohort study was carried out in 1997 to 1999 among 227 shift workers and 150 controls working in daytime, all nurses and industrial workers. One-year changes in cardiac control (premature ventricular complexes and heart rate variability), biological risk factors (blood pressure, body mass index, waist to hip ratio and blood cholesterol) and lifestyle risk factors (dietary habits, smoking, and decreased physical activity) were investigated.Results: We observed a significantly greater one-year increase in the frequency of premature ventricular complexes in the shift workers compared with the day workers. The frequency of ventricular extrasystoles went up in 48.9 % of the shift workers, and in 27.3 % of the day workers. The Spearman correlation coefficient between the number of nights worked and the change in frequency of PVC's was 0.33; P = 0.004. The one-year change in the HRV parameters measured (SDNNi, Low and High frequency power and %LF) was similar between the shift and day workers. However, among the shift workers the low frequency power component of the total heart rate variability (%LF) was stronger during sleep after a night shift than after a day shift (%LF + 3.04, P &lt; 0.01). This suggests an increased sympathetic activity during a sleep after night shift. The magnitude of the reported effects was related to the shift schedule. Backward rotating schedules (three to five shifts of night work, evening work, day work, respectively) appeared to be the most unfavourable. Smoking was the only variable among the other biological and lifestyle risk factors that showed an unfavourable one-year change in the shift workers compared with the day workers (difference in change: 2.5 cigarettes per day; p &lt; 0.05).</p

Changes in frequency of premature complexes and heart rate variability related to shift work

OBJECTIVES To investigate whether an increased risk of cardiovascular disease might be caused by increased arrhythmogeneity and by unfavourable changes in autonomic cardiac control the changes in the occurrence of premature complexes (PVCs) and in heart rate variability (HRV) were studied in subjects who started to work in shifts. METHODS1 Year changes in frequency of PVCs and HRV were measured in 49 shift workers and 22 control subjects working in daytime. All respondents were starting in a new job in integrated circuit or waste incinerator plants. RESULTSThe incidence of PVC increased significantly in shift workers over the 1 year follow up, compared with daytime workers. The frequency of ventricular extrasystoles increased in 48.9 f the shift workers, and in 27.3 f the daytime workers. The Spearman correlation coefficient between the number of nights worked and the change in PVCs was 0.33 (p=0.004). A small non-significant unfavourable change in HRV was found in both the shift and daytime workers. CONCLUSIONSA change in arrhythmogeneity, but not in cardiac autonomic control, might explain the increased risk of cardiovascular disease in shift worker

Impact of One Year of Shift Work on Cardiovascular Disease Risk Factors

The purpose of the study was to investigate whether the reported increased cardiovascular disease risk in shift workers could be explained by changes in cardiovascular risk factors. In a cohort of 239 shift and 157 daytime workers, 1-year changes in biological and lifestyle cardiovascular risk factors were monitored between the start Of a new job and I year later. Both body mass index and low-density lipoprotein/high-density lipoprotein cholesterol ratio decreased significantly in shift workers compared with daytime workers (body mass index change: -0.31 and +0.13 kg/m(2) low-density lipoprotein/high-density lipoprotein ratio change: -0.33 and -0.13 respectively). Cigarettes smoked per day increased significantly in shift compared with daytime workers (+1.42 and -1.03, respectively). Therefore, only for smoking, an unfavorable change was observed. This may explain, at most, only a part of the excess cardiovascular disease risk reported in shaft workers

Approaches for predicting long-term sickness absence. Re: Schouten et al. "Screening manual and office workers for risk of long-term sickness absence: cut-off points for the Work Ability Index"

We read with much interest the article of Schouten et al (1) on identifying workers with a high risk for future long-term sickness absence using the Work Ability Index (WAI). The ability to identify high-risk workers might facilitate targeted interventions for such workers and, consequently, can reduce sickness absence levels and improve workers' health. Earlier studies by both Tamela et al (2), Kant et al (3), and Lexis et al (4) have demonstrated that such an approach, based on the identification of high-risk workers and a subsequent intervention, can be effectively applied in practice to reduce sickness absence significantly. The reason for our letter on Schouten et al's article is twofold. First, by including workers already on sick leave in a study predicting long-term sick leave will result in an overestimation of the predictive properties of the instrument and biased predictors, especially when also the outcome of interest is included as a factor in the prediction model. Second, we object to the use of the term "screening" when subjects with the condition screened for are included in the study. Reinforced by the inclusion of sickness absence in the prediction model, including workers already on sick leave will shift the focus of the study findings towards the prediction of (re)current sickness absence and workers with a below-average return-to-work rate, rather than the identification of workers at high risk for the onset of future long-term sickness absence. The possibilities for prevention will shift from pure secondary prevention to a mix of secondary and tertiary prevention. As a consequence, the predictors of the model presented in the Schouten et al article can be used as a basis for tailoring neither preventive measures nor interventions. Moreover, including the outcome (sickness absence) as a predictor in the model, especially in a mixed population including workers with and without the condition (on sick leave), will result in biased predictors and an overestimation of the predictive value. A methodological approach of related issues is provided in the works of Glymour et al (5) and Hamilton et al (6). This phenomenon is even more clearly illustrated by the predictive properties of the workability index, as described by Alavinia et al (7, page 328), which reported that "when adjusted for individual characteristics, lifestyle factors, and work characteristics, two dimensions of the WAI were significant predictors for both moderate and long durations of sickness absence: (i) the presence of sickness absence in the past 12 months prior to the medical examination and (ii) experienced limitations due to health problems." So, when applied to the study by Schouten et al (1), this means that most of the predictive value would be related to the factors "sickness absence in the past 12 months". In addition, we object to the use of the term "screening" in the Schouten et al study as it includes workers with the intended outcome (long-term sickness absence). One can identify three separate aims to study the longitudinal association between risk factors and subsequent long-term sickness absence: (i) to establish causal risk factors for long-term sickness absence, often to find clues for primary preventive strategies (beyond the scope here); (ii) to identify high-risk workers who are still at work and might benefit from an intervention before sickness absence occurs (secondary prevention); and (iii) to identify workers on sick leave who might suffer a below-average return-to-work rate or have a high risk for the recurrence of (long-term) sickness absence and might benefit from intensification or optimization of the return-to-work process (tertiary prevention). In this light, one needs to separate screening instruments from predictive instruments and reserve the term "screening" for the situation as defined by Wilson and Junger (8, page 7): "The object of screening for disease is to discover those among the apparently well who are in fact suffering from disease" (ie, situations of secondary prevention). This means that, when applying this definition on long-term sickness absence under the precondition that the individuals are still at work, screening enables the identification of high-risk individuals in the early "stages" of a "disease" that can progress into long-term sickness absence. In the case of the Schouten et al study, the population at risk, as derived from their predictive instrument, consists of workers with and without sickness absence, and as such excludes the use of the term "screening" in this case. To conclude, we have substantiated that, in addition to correct usage of the term "screening", careful selection of the study population, predictors and most importantly the aim of the predic

Vital working hour schemes: The dynamic balance between various interests

"Balancing Interests", the theme of the 17th International Symposium on Shift Work and Working Time held in Hoofddorp, The Netherlands (September 2005), refers to the ambition to reach an optimal balance between the various aspects of shift work. Economic, ergonomic, physical, and psychosocial factors all interact in determining the impact of shift work at the individual, organizational, and societal level. It is the challenge of this multidisciplinary field of research to model all relevant factors in such a way that it will allow us to optimize the dynamic trade-off between the yield and the risk of shift work. The organizers of the 17th International Symposium and the co-editors of these proceedings are convinced that the high quality of the contributions will bring us closer to this ultimate goal