Jane E Ferrie

Sleep epidemiology—a rapidly growing field

The human body has adapted to daily changes in dark and light such that it anticipates periods of sleep and activity. Deviations from this circadian rhythm come with functional consequences. Thus, 17 hours of sustained wakefulness in adults leads to a decrease in performance equivalent to a blood alcohol-level of 0.05%;[1] the legal level for drink driving in many countries.[2] Rats deprived of sleep die after 32 days,[3] and, with longer periods of sleep deprivation, this would also be the case in human beings. Indeed, sleep deprivation is a common form of torture.[4] n nGiven the readily observable effects of sleep in everyday life, it is not surprising that there has been scholarly interest in sleep since the beginning of recorded history.[5] Sleep epidemiology as a subject in its own right has a recognisable history of just over 30 years,[6] with the first modern epidemiological studies of sleep disturbances appearing around 1980.[7;8] Nevertheless, a PubMed search for terms “sleep/insomnia” and “epidemiology” shows that the cumulative number of papers on the subject over the past 10 years is already about 10,000. Although this is less than for standard risk factors, such as obesity (>60,000) and smoking (50,000)(Figure 1), the annual number of papers on sleep epidemiology is rising rapidly (Figure 2). This issue of IJE includes a review[9] of the first comprehensive textbook of Sleep Epidemiology,[10] and the purpose of our Editorial is further to highlight recent developments to give the reader an idea why the coming years are likely to see an increasing interest in sleep studies. n n n nFigure 1 n nExposures AND Epidemiology 2000–2010 n n n n n nFigure 2 n nSleep/insomnia AND Epidemiology by year n n n n nWhy the upsurge in interest? nThere are several reasons for an increase in interest in sleep from an epidemiological perspective. First, sleep problems are associated with accidents and human errors. It has been estimated that 10–15% of fatal motor-vehicle crashes are due to sleepiness or driver fatigue. Furthermore, by 2020 the number of people killed in motor-vehicle crashes is expected to double to 2.3 million deaths worldwide, of which approximately 230,000–345,000 will be due to sleepiness or fatigue.[11] It has been estimated that nearly 100,000 deaths occur each year in US hospitals due to medical errors and sleep deprivation have been shown to make a significant contribution.[12] Similarly, in a national sample in Sweden of over 50,000 people interviewed over 20 years disturbed sleep almost doubled the risk of a fatal accident at work.[13] n nSecond, sleep problems are common. Population studies show that sleep deprivation and disorders affect many more people worldwide than had been previously thought. A recent study found 20% of 25–45 year-olds slept “90 minutes less than they needed to be in good shape”.[14] Insomnia is the most common specific sleep disorder, with ‘some insomnia problems over the past year’ reported by approximately 30% of adults and chronic insomnia by approximately 10%.[15] Prevalence of obstructive sleep apnoea, characterized by respiratory difficulties during sleep, is also very high with estimates of 9–21% in women and 24–31% in men.[16;17] n nThird, sleep problems are likely to increase. The rapid advent of the 24/7 society involving round-the-clock activities and increasing night time use of TV, internet and mobile phones mean that adequate sleep durations may become increasingly compromised. Some data suggest a decline in sleep duration of up to 18 minutes per night over the past 30 years.[18;19] Complaints of sleeping problems have increased substantially over the same period, with short sleep (<6 hours/night) in full-time workers becoming more prevalent.[19;20] As more shift work is required to service 24/7 societies the proportion of workers exposed to circadian rhythm disorders, such as shift work sleep disorder, and their effects on health and performance is likely to rise. Sleep architecture is known to change with age; slow-wave (or deep) sleep decreases and lighter sleep increases. Other changes include increases in nocturnal sleep disruption and daytime sleepiness. As the proportion of elderly people in populations across the world increases, these changing sleep patterns will raise the prevalence of sleep disorders. Similarly, the increasing worldwide obesity epidemic and the prevalence of obstructive sleep apnoea, which is over double among the obese, ensure sleep disorders will be of increasing public health importance in lower as well as high income countries.[16;21] n nFourth, sleep problems are associated with short and long-term effects on health and well-being. Immediate effects at the individual level relate to well-being, performance, daytime sleepiness and fatigue. Longer term, evidence has accumulated of associations between sleep deprivation and sleep disorders and numerous health outcomes including premature mortality, cardiovascular disease, hypertension, inflammation, obesity, diabetes and impaired glucose tolerance, and psychiatric disorders, such as anxiety and depression. As this evidence represents the core of Sleep Epidemiology, we provide below a snapshot on key findings.

Effect of retirement on alcohol consumption: longitudinal evidence from the French gazel cohort study

Background Little is known about the effect of retirement on alcohol consumption. The objectives were to examine changes in alcohol consumption following retirement, and whether these patterns differ by gender and socioeconomic status. Methods and Findings We assessed alcohol consumption annually from 5 years before to 5 years after retirement among 10,023 men and 2,361 women of the French Gazel study. Data were analyzed separately for men and women, using repeated-measures logistic regression analysis with generalized estimating equations. Five years prior to retirement, the prevalence of heavy drinking was about 16% among men, and not patterned by socioeconomic status. Among women, this prevalence was 19.5% in managers, 14.7% in intermediate occupations, and 12.8% in clerical workers. Around retirement, the estimated prevalence of heavy drinking increased in both sexes. In men, this increase was 3.1 percentage points for managers, 3.2 in intermediate occupations, 4.6 in clerical workers, and 1.3 in manual workers. In women, this increase was 6.6 percentage points among managers, 4.3 in intermediate occupations, and 3.3 among clerical workers. In men the increase around retirement was followed by a decrease over the following four years, not significant among manual workers; among women such a decrease was also observed in the non-managerial occupations. It is difficult to assess the extent to which the results observed in this cohort would hold for other working populations, other conditions of employment, or in other cultural settings. A plausible explanation for the increase in heavy drinking around retirement could be that increased leisure time after retirement provides more opportunities for drinking, and not having to work during the day after may decrease constraints on drinking. Conclusions Our findings of increased consumption around retirement suggest that information about negative effects of alcohol consumption should be included in pre-retirement planning programs.

Does adding information on job strain improve risk prediction for coronary heart disease beyond the standard Framingham risk score? The Whitehall II study

Background Guidelines for coronary heart disease (CHD) prevention recommend using multifactorial risk prediction algorithms, particularly the Framingham risk score. We sought to examine whether adding information on job strain to the Framingham model improves its predictive power in a low-risk working population. Methods Our analyses are based on data from the prospective Whitehall II cohort study, UK. Job strain among 5533 adults (mean age 48.9 years, 1666 women) was ascertained in Phases 1 (1985–88), 2 (1989–90) and 3 (1991–93). Variables comprising the Framingham score (blood lipids, blood pressure, diabetes and smoking) were measured at Phase 3. In men and women who were CHD free at baseline, CHD mortality and non-fatal myocardial infarction (MI) were ascertained from 5-yearly screenings and linkage to mortality and hospital records until Phase 7 (2002–04). Results A total of 160 coronary deaths and non-fatal MIs occurred during the mean follow-up period of 11.3 years. The addition of indicators of job strain to the Framingham score increased the C-statistics from 0.725 [95% confidence intervals (95% CIs): 0.575–0.854] to only 0.726 (0.577–0.855), corresponding to a net reclassification improvement of 0.7% (95% CIs: −4.2 to 5.6%). The findings were similar after inclusion of definite angina in the CHD outcome (352 total cases) and when using alternative operational definitions for job strain. Conclusion In this middle-aged low-risk working population, job strain was associated with an increased risk of CHD. However, when compared with the Framingham algorithm, adding job strain did not improve the models predictive performance.

Socioeconomic and Psychosocial Adversity in Midlife and Depressive Symptoms Post Retirement: A 21-year Follow-up of the Whitehall II Study

Objective We examined whether socioeconomic and psychosocial adversity in midlife predicts post-retirement depressive symptoms. Design and Setting A prospective cohort study of British civil servants who responded to a self-administered questionnaire in middle-age and at older ages, 21 years later. Participants The study sample consisted of 3,939 Whitehall II Study participants (2,789 men, 1,150 women; mean age 67.6 years at follow-up) who were employed at baseline and retired at follow-up. Measurements Midlife adversity was assessed by self-reported socioeconomic adversity (low occupational position; poor standard of living) and psychosocial adversity (high job strain; few close relationships). Symptoms of depression post-retirement were measured by the Center for Epidemiologic Studies Depression scale. Results After adjustment for sociodemographic and health-related covariates at baseline and follow-up, there were strong associations between midlife adversities and post-retirement depressive symptoms: low occupational position (odds ratio [OR]: 1.70, 95% confidence interval [CI]: 1.15–2.51), poor standard of living (OR: 2.37, 95% CI: 1.66–3.39), high job strain (OR: 1.52, 95% CI: 1.09–2.14), and few close relationships (OR: 1.51, 95% CI: 1.12–2.03). The strength of the associations between socioeconomic, psychosocial, work-related, or non-work related exposures and depressive symptoms was similar. Conclusions Robust associations from observational data suggest that several socioeconomic and psychosocial risk factors for symptoms of depression post-retirement can be detected already in midlife.

Social class inequalities in health among occupational cohorts from Finland, Britain and Japan: A follow up study

We examined whether relative occupational social class inequalities in physical health functioning widen, narrow or remain stable among white collar employees from three affluent countries. Health functioning was assessed twice in occupational cohorts from Britain (1997-1999 and 2003-2004), Finland (2000-2002 and 2007) and Japan (1998-1999 and 2003). Widening inequalities were seen for British and Finnish men, whereas inequalities among British and Finnish women remained relatively stable. Japanese women showed reverse inequalities at follow up, but no health inequalities were seen among Japanese men. Health behaviours and social relations explained 4-37% of the magnitude in health inequalities, but not their widening.

Are sickness absence frequencies in the study of EU countries underestimates

The paper by Gimeno et al provides a comparison of sickness absence between 15 European Union (EU) countries.1 According to this study, 14.5% of employees were absent at least one day in the past 12 months by an accident at work, by health problems caused by the work, or by other health problems. For Finnish employees, for instance, this percentage was 24%, the highest among the 15 EU countries; in the UK it was …