Long working hours, socioeconomic status, and the risk of incident type 2 diabetes: a meta-analysis of published and unpublished data from 222 120 individuals

Background Working long hours might have adverse health effects, but whether this is true for all socioeconomic status groups is unclear. In this meta-analysis stratified by socioeconomic status, we investigated the role of long working hours as a risk factor for type 2 diabetes. Methods We identified four published studies through a systematic literature search of PubMed and Embase up to April 30, 2014. Study inclusion criteria were English-language publication; prospective design (cohort study); investigation of the effect of working hours or overtime work; incident diabetes as an outcome; and relative risks, odds ratios, or hazard ratios (HRs) with 95% CIs, or sufficient information to calculate these estimates. Additionally, we used unpublished individual-level data from 19 cohort studies from the Individual-Participant-Data Meta-analysis in Working-Populations Consortium and international open-access data archives. Effect estimates from published and unpublished data from 222 120 men and women from the USA, Europe, Japan, and Australia were pooled with random-effects meta-analysis. Findings During 1·7 million person-years at risk, 4963 individuals developed diabetes (incidence 29 per 10 000 personyears). The minimally adjusted summary risk ratio for long (≥55 h per week) compared with standard working hours (35–40 h) was 1·07 (95% CI 0·89–1·27, difference in incidence three cases per 10 000 person-years) with signifi cant heterogeneity in study-specific estimates (I²=53%, p=0·0016). In an analysis stratified by socioeconomic status, the association between long working hours and diabetes was evident in the low socioeconomic status group (risk ratio 1·29, 95% CI 1·06–1·57, diff erence in incidence 13 per 10 000 person-years, I²=0%, p=0·4662), but was null in the high socioeconomic status group (1·00, 95% CI 0·80–1·25, incidence diff erence zero per 10 000 person-years, I²=15%, p=0·2464). The association in the low socioeconomic status group was robust to adjustment for age, sex, obesity, and physical activity, and remained after exclusion of shift workers. Interpretation In this meta-analysis, the link between longer working hours and type 2 diabetes was apparent only in individuals in the low socioeconomic status groups

Overweight, obesity and risk of cardiometabolic multimorbidity: pooled analysis of individual-level data for 120,813 adults from 16 cohort studies from the USA and Europe

Background Although overweight and obesity have been studied in relation to individual cardiometabolic diseases, their association with risk of cardiometabolic multimorbidity is poorly understood. Here we aimed to establish the risk of incident cardiometabolic multimorbidity (ie, at least two from: type 2 diabetes, coronary heart disease, and stroke) in adults who are overweight and obese compared with those who are a healthy weight. Methods We pooled individual-participant data for BMI and incident cardiometabolic multimorbidity from 16 prospective cohort studies from the USA and Europe. Participants included in the analyses were 35 years or older and had data available for BMI at baseline and for type 2 diabetes, coronary heart disease, and stroke at baseline and follow-up. We excluded participants with a diagnosis of diabetes, coronary heart disease, or stroke at or before study baseline. According to WHO recommendations, we classified BMI into categories of healthy (20·0–24·9 kg/m²), overweight (25·0–29·9 kg/m²), class I (mild) obesity (30·0–34·9 kg/m²), and class II and III (severe) obesity (≥35·0 kg/m²). We used an inclusive definition of underweight (<20 kg/m²) to achieve sufficient case numbers for analysis. The main outcome was cardiometabolic multimorbidity (ie, developing at least two from: type 2 diabetes, coronary heart disease, and stroke). Incident cardiometabolic multimorbidity was ascertained via resurvey or linkage to electronic medical records (including hospital admissions and death). We analysed data from each cohort separately using logistic regression and then pooled cohort-specific estimates using random-effects meta-analysis. Findings Participants were 120 813 adults (mean age 51·4 years, range 35–103; 71445 women) who did not have diabetes, coronary heart disease, or stroke at study baseline (1973–2012). During a mean follow-up of 10·7 years (1995–2014), we identified 1627 cases of multimorbidity. After adjustment for sociodemographic and lifestyle factors, compared with individuals with a healthy weight, the risk of developing cardiometabolic multimorbidity in overweight individuals was twice as high (odds ratio [OR] 2·0, 95% CI 1·7–2·4; p<0·0001), almost five times higher for individuals with class I obesity (4·5, 3·5–5·8; p<0·0001), and almost 15 times higher for individuals with classes II and III obesity combined (14·5, 10·1–21·0; p<0·0001). This association was noted in men and women, young and old, and white and non-white participants, and was not dependent on the method of exposure assessment or outcome ascertainment. In analyses of different combinations of cardiometabolic conditions, odds ratios associated with classes II and III obesity were 2·2 (95% CI 1·9–2·6) for vascular disease only (coronary heart disease or stroke), 12·0 (8·1–17·9) for vascular disease followed by diabetes, 18·6 (16·6–20·9) for diabetes only, and 29·8 (21·7–40·8) for diabetes followed by vascular disease. Interpretation The risk of cardiometabolic multimorbidity increases as BMI increases; from double in overweight people to more than ten times in severely obese people compared with individuals with a healthy BMI. Our findings highlight the need for clinicians to actively screen for diabetes in overweight and obese patients with vascular disease, and pay increased attention to prevention of vascular disease in obese individuals with diabetes

Long working hours as a risk factor for atrial fibrillation: A multi-cohort study

Aims Studies suggest that people who work long hours are at increased risk of stroke, but the association of long working hours with atrial fibrillation, the most common cardiac arrhythmia and a risk factor for stroke, is unknown. We examined the risk of atrial fibrillation in individuals working long hours (>55 per week) and those working standard 35-40 hours per week. Methods In this prospective multi-cohort study from the Individual-Participant-Data Meta-analysis in and results Working Populations (IPD-Work) Consortium, the study population was 85,494 working men and women (mean age 43.4 years) with no recorded atrial fibrillation. Working hours were assessed at study baseline (1991-2004). Mean follow-up for incident atrial fibrillation was 10 years and cases were defined using data on electrocardiograms, hospital records, drug reimbursement registers, and death certificates. We identified 1061 new cases of atrial fibrillation (10-year cumulative incidence 12.4 per 1000). After adjustment for age, sex and socioeconomic status, individuals working long hours had a 1.4-fold increased risk of atrial fibrillation compared to those working standard hours (hazard ratio=1.42, 95%CI=1.13-1.80, P=0.003). There was no significant heterogeneity between the cohortspecific effect estimates (I2=0%, P=0.66) and the finding remained after excluding participants with coronary heart disease or stroke at baseline or during the follow-up (N=2006, hazard ratio=1.36, 95%CI=1.05-1.76, P=0. 0180). Adjustment for potential confounding factors, such as obesity, risky alcohol use and high blood pressure, had little impact on this association. Conclusion Individuals who worked long hours were more likely to develop atrial fibrillation than those working standard hours

Job strain as a risk factor for clinical depression: systematic review and meta-analysis with additional individual participant data

Background Adverse psychosocial working environments characterized by job strain (the combination of high demands and low control at work) are associated with an increased risk of depressive symptoms among employees, but evidence on clinically diagnosed depression is scarce. We examined job strain as a risk factor for clinical depression. Methods We identified published cohort studies from a systematic literature search in PubMed and PsycNET and obtained 14 cohort studies with unpublished individuallevel data from the Individual-Participant-Data Meta-analysis in Working Populations (IPD-Work) consortium. Summary estimates of the association were obtained using random effects models. Individual-level data analyses were based on a pre-published study protocol (F1000Res 2013;2:233). Results We included 6 published studies with a total of 27 461 individuals and 914 incident cases of clinical depression. From unpublished datasets we included 120 221 individuals and 982 first episodes of hospital-treated clinical depression. Job strain was associated with an increased risk of clinical depression in both published (Relative Risk [RR]= 1.77, 95% confidence interval [CI] 1.47-2.13) and unpublished datasets (RR=1.27, 95% CI 1.04-1.55). Further individual participant analyses showed a similar association across sociodemographic subgroups and after excluding individuals with baseline somatic disease. The association was unchanged when excluding individuals with baseline depressive symptoms (RR=1.25, 95% CI: 0.94-1.65), but attenuated on adjustment for a continuous depressive symptoms score (RR=1.03, 95% CI: 0.81- 1.32). Conclusion Job strain may precipitate clinical depression among employees. Future intervention studies

Long working hours and alcohol use: systematic review and meta-analysis of published studies and unpublished individual participant data

Objective To quantify the association between long working hours and alcohol use. Design Systematic review and meta-analysis of published studies and unpublished individual participant data. Data sources A systematic search of PubMed and Embase databases in April 2014 for published studies, supplemented with manual searches. Unpublished individual participant data were obtained from 27 additional studies. Review methods The search strategy was designed to retrieve cross sectional and prospective studies of the association between long working hours and alcohol use. Summary estimates were obtained with random effects meta-analysis. Sources of heterogeneity were examined with meta-regression. Results Cross sectional analysis was based on 61 studies representing 333 693 participants from 14 countries. Prospective analysis was based on 20 studies representing 100 602 participants from nine countries. The pooled maximum adjusted odds ratio for the association between long working hours and alcohol use was 1.11 (95% confidence interval 1.05 to 1.18) in the cross sectional analysis of published and unpublished data. Odds ratio of new onset risky alcohol use was 1.12 (1.04 to 1.20) in the analysis of prospective published and unpublished data. In the 18 studies with individual participant data it was possible to assess the European Union Working Time Directive, which recommends an upper limit of 48 hours a week. Odds ratios of new onset risky alcohol use for those working 49-54 hours and ≥55 hours a week were 1.13 (1.02 to 1.26; adjusted difference in incidence 0.8 percentage points) and 1.12 (1.01 to 1.25; adjusted difference in incidence 0.7 percentage points), respectively, compared with working standard 35-40 hours (incidence of new onset risky alcohol use 6.2%). There was no difference in these associations between men and women or by age or socioeconomic groups, geographical regions, sample type (population based v occupational cohort), prevalence of risky alcohol use in the cohort, or sample attrition rate. Conclusions Individuals whose working hours exceed standard recommendations are more likely to increase their alcohol use to levels that pose a health risk

Association between working hours at baseline and a major depressive episode at follow-up, the Whitehall II study, 1991-9.

<p>*Unadjusted.</p>†<p>Adjusted for age and sex.</p>‡<p>As previous model but additionally adjusted for occupational grade and marital status.</p>§<p>As previous model but additionally adjusted for chronic physical disease, smoking, and alcohol use.</p>¶<p>As previous model but additionally adjusted for job strain and social support at work.</p><p>CI = Confidence interval.</p

Associations between indicators of socioeconomic status (SES) and length of work disability due to depression.

a<p>Separate models (SES indicators entered separately) adjusted for age, sex, and year (when the disability begun).</p>b<p>Model 1 adjusted for age, sex, and for all other SES indicators.</p>c<p>Model 2 adjusted as Model 1 and for presence of chronic somatic disease as well as work disability due to mental or behavioral disorder (ICD-10 codes F00–F99) in the previous year.</p

Associations between indicators of socioeconomic status (SES) work disability days due to depression in 2005–2011.

a<p>Cumulative disability days due to depressive disorders per cumulative person-years in 2005–2011.</p>b<p>Separate models (SES indicators entered separately) adjusted for age and sex.</p>c<p>Model 1 adjusted for age and sex, and for all other SES indicators.</p>d<p>Model 2 adjusted as model 1 and for presence of chronic somatic disease as well as work disability due to mental or behavioral disorder (ICD-10 codes F00–F99) in the previous year.</p

Associations between indicators of socioeconomic status and recurrence of work disability due to depression.

a<p>Separate models (SES indicators entered separately) adjusted for age and sex.</p>b<p>Model 1 adjusted for age, sex, and for all other SES indicators.</p>c<p>Model 2 adjusted as Model 1 and for presence of chronic somatic disease as well as work disability due to mental or behavioral disorder (ICD-10 codes F00–F99) in the previous year.</p

Associations between BMI status and different types and anatomical sites of occupational injury: the Finnish Public Sector Study (<i>N</i> = 69,515).

<p>BMI, body mass index; HR, hazard ratio; CI, confidence interval. Adjusted for age, sex, marital status, education, socio-economic status, type of job contract, and night/shift work, smoking, heavy drinking, insufficient physical activity, psychological distress, self-rated health, insomnia symptoms, and sleep duration.</p