In a cohort of nearly 7100 men and women apparently free of coronary heart disease, we show long working hours to predict incident hard endpoint coronary heart disease and contribute to coronary heart disease risk prediction, over and above the Framingham score. The net reclassification improvement was 4.7%. This was achieved by the more accurate classification of individuals who experienced coronary heart disease to a higher risk group (sensitivity gain) rather than by improving detection of those unlikely to develop the disease. Our findings show the potential predictive utility of long working hours in identifying individuals at increased 10-year risk of coronary heart disease in a low-risk employed population.
We searched the MEDLINE database (accessed November 2010) and identified 5 case-control studies (7
) and 4 cohort studies (12
) that have previously examined the association between long working hours and cardiovascular endpoints. Six studies reported a statistically significant positive association in that a higher risk of acute myocardial infarctions or coronary deaths was observed among those doing overtime in diverse working populations in Sweden, the Netherlands, the United Kingdom and Japan (7
). Conversely, two Japanese studies provided no firm evidence of an association (36
), and a 30-year follow-up of Danish men found employees working long hours to be at increased risk of death from ischemic heart disease, but only if they additionally had poor physical fitness (13
). Our study from a British cohort adds to the existing evidence by showing that information on long working hours may have the potential to help clinicians more accurately to determine CHD risk for patients.
In this low-risk working population, a C-statistic of 0.71 for risk prediction based on the Framingham score plus working hours is comparable to those found in other studies attempting to improve risk prediction. Examples are the Women's Health Initiative that added 18 biomarkers to the Framingham score (C=0.73)(38
), the Atherosclerosis Risk in Communities (ARIC) study that added ultrasound scans of carotid intima-media thickness and plaques (C=0.76)(22
) and the Multi-Ethnic Study of Atherosclerosis that added coronary artery calcium scores (C=0.81)(39
) to the Framingham risk score. Overall these statistics indicate moderate discrimination; thus a clinician estimating the 10-year coronary heart disease risk of a given patient may prefer to take into account further information not included in these scores (40
Cost-effectiveness is an additional aspect of the evaluation of potential new risk markers (20
). A potential advantage of working hours as a risk marker is that its ascertainment in a clinical interview is simple, quick and virtually cost-free (20
). Furthermore, no safety or acceptability issues are attached to the assessment of working hours.
There are a few caveats to the results reported here. First, our study was not sufficiently powered to allow the partition of data into estimation and validation datasets. Thus, the predictive utility of working hours could not be validated in a dataset independent of the derivation dataset. However, the bootstrapped estimate of the net reclassification index suggests that our estimate is not overoptimistic. Second, we did not account for changes in the risk factors or medications during the follow-up – an approach that is standard in attempts to create or improve risk prediction algorithms. Third, our cohort was comprised primarily of low risk individuals and did not include blue-collar workers. Thus, the findings may not be generalizable to higher-risk groups in the general population.
Given that working long hours are common and have increased in many developed countries in recent years (42
), our study potentially has important implications. However, further testing is needed to confirm the added value of information on long working hours for clinical decision making. First, additional studies need to examine whether the improvement in coronary heart disease prediction is limited to specific populations or is observable across different cohorts, particularly in groups with a risk ≥20% risk. Second, future studies should assess whether incorporating information on working hours in the risk prediction algorithm improves the management of patients compared with current standard care. Ideally, this would be undertaken by a clinical trial comparing the two models. Third, it is important to clarify whether long working hours are a marker of coronary heart disease risk or are also a causal risk factor. In the first case, information on working hours could contribute to risk prediction but not preventive treatment. In the second case, clinical benefits avoiding long working hours would need to be shown.