The WHO/ILO report on long working hours and ischaemic heart disease - Conclusions are not supported by the evidence

Working hours is a ubiquitous exposure given that most adults are employed, and one that is modifiable via legislative change if not always through individual-level choice. According to a recent report from the World Health Organization (WHO) and International Labour Organization (ILO), there is currently sufficient evidence to conclude that long working hours (i.e., ≥55 h per week) elevate the risk of fatal and non-fatal ischaemic heart disease to a clinically meaningful extent. After assessing the data used by the ILO/WHO, we feel that the expert group has not correctly applied their own framework for assessing the strength of the evidence. In the meta-analysis of observational studies in the report, the association between long working hours and incident heart disease appeared stronger in lower quality cohort studies with a high risk of bias (minimally-adjusted hazard ratio 1.20, 95% CI 1.01-1.41, compared to standard 35-40 weekly hours) than in the superior-quality studies with a lower risk of bias for which the estimate was not significantly different from the null (1.08, 95% CI 0.93-1.25). There was also marked effect modification, such that there was no increase in ischaemic heart disease for those working long hours in high socioeconomic status occupations, a finding also reported in analyses of a recent census-based cohort study which was not included in the report. Our meta-analysis of all these studies confirm that the findings are not consistent but differ between subgroups and that the summary age- and sex-adjusted hazard ratio for long working hours in high socioeconomic status occupations does not support excess risk: 0.85, 95% CI 0.63-1.13 (Pinteraction = 0.005, total N = 451,982). For these and other reasons detailed in this commentary, we advance a more cautious interpretation of the existing evidence. The conclusions should be restricted to low socioeconomic status occupations only and more research is still needed to confirm or refute harmfulness and determine clinical relevance

Job strain and risk of obesity: systematic review and meta-analysis of cohort studies

Job strain, the most widely used indicator of work stress, is a risk factor for obesity-related disorders such as cardiovascular disease and type 2 diabetes. However, the extent to which job strain is related to the development of obesity itself has not been systematically evaluated. We carried out a systematic review (PubMed and Embase until May 2014) and meta-analysis of cohort studies to address this issue. Eight studies that fulfilled inclusion criteria showed no overall association between job strain and the risk of weight gain (pooled odds ratio for job strain compared with no job strain 1.04, 95% confidence interval (CI) 0.99-1.09, NTotal=18 240) or becoming obese (1.00, 95% CI 0.89-1.13, NTotal=42 222). In addition, a reduction in job strain over time was not associated with lower obesity risk (1.13, 95% CI 0.90-1.41, NTotal=6507). These longitudinal findings do not support the hypothesis that job strain is an important risk factor for obesity or a promising target for obesity prevention.International Journal of Obesity advance online publication, 30 June 2015; doi:10.1038/ijo.2015.103

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

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

Modifications to residential neighbourhood characteristics and risk of 79 common health conditions: a prospective cohort study.

BACKGROUND: Observational studies have identified a link between unfavourable neighbourhood characteristics and increased risk of morbidity, but it is unclear whether changes in neighbourhoods affect future disease risk. We used a data-driven approach to assess the impact of neighbourhood modification on 79 health outcomes. METHODS: In this prospective cohort study, we used pooled, individual-level data from two Finnish cohort studies: the Health and Social Support study and the Finnish Public Sector study. Neighbourhood characteristics (mean educational level, median income, and employment rate of residents, and neighbourhood green space) and individual lifestyle factors of community-dwelling individuals were assessed at baseline (at different waves starting between 1998 and 2013). We repeated assessment of neighbourhood characteristics and lifestyle factors approximately 5 years from each baseline assessment, after which follow-up began for health conditions diagnosed according to the WHO International Classification of Diseases for 79 common health conditions using linkage to electronic health records. We used Cox proportional hazard regression models to compute adjusted hazard ratios (HRs) of incident disease associated with neighbourhood characteristics and changes in neighbourhood characteristics over time and logistic regression analysis to compute adjusted odds of association between changes in neighbourhood characteristics and individual lifestyle factors. FINDINGS: 114 786 individuals (87 012 [75·8%] women; mean age 44·4 years [SD 11·1]) had complete data and were included in this cohort study. During 1·17 million person-years at risk, we recorded 164 368 new-onset health conditions and 3438 deaths. Favourable changes in neighbourhood characteristics were associated with reduced risk of all-cause mortality and incidence of 19 specific health conditions. Unfavourable changes were correspondingly associated with increased risk of mortality and 27 specific health conditions. Among participants who did not move residence during the observation period, relative to individuals who continually lived in disadvantaged neighbourhoods, those who experienced favourable modifications in neighbourhood characteristics had a lower risk of future diabetes (HR 0·84, 95% CI 0·75-0·93), stroke (0·49, 0·29-0·83), skin disease (0·72, 0·53-0·97), and osteoarthritis (0·87, 0·77-0·99). Living in a neighbourhood with improving characteristics was also associated with improvements in individual-level health-related lifestyle factors. Among participants who lived in advantaged residential environments at baseline, unfavourable changes in neighbourhood characteristics were associated with an increased risk of diabetes, stroke, skin disease, and osteoarthritis compared with individuals who lived in advantaged neighbourhoods throughout the study period. INTERPRETATION: Favourable modifications to residential neighbourhoods showed robust, longitudinal associations with a range of improvements in health outcomes, including improved health behaviours and reduced risk of cardiometabolic, infectious, and orthopaedic conditions. FUNDING: UK Medical Research Council, US National Institute on Aging, NordForsk, and Academy of Finland

Predicting long-term sickness absence with employee questionnaires and administrative records: a prospective cohort study of hospital employees

Objective: This study aimed to compare the utility of risk estimation derived from questionnaires and administrative records in predicting long-term sickness absence among shift workers. Methods This prospective cohort study comprised 3197 shift-working hospital employees (mean age 44.5 years, 88.0% women) who responded to a brief 8-item questionnaire on work disability risk factors and were linked to 28 variables on their working hour and workplace characteristics obtained from administrative registries at study baseline. The primary outcome was the first sickness absence lasting ≥90 days during a 4-year follow-up. Results The C-index of 0.73 [95% confidence interval (CI) 0.70–0.77] for a questionnaire-only based prediction model, 0.71 (95% CI 0.67–0.75) for an administrative records-only model, and 0.79 (95% CI 0.76–0.82) for a model combining variables from both data sources indicated good discriminatory ability. For a 5%-estimated risk as a threshold for positive test results, the detection rates were 76%, 74%, and 75% and the false positive rates were 40%, 45% and 34% for the three models. For a 20%-risk threshold, the corresponding detection rates were 14%, 8%, and 27% and the false positive rates were 2%, 2%, and 4%. To detect one true positive case with these models, the number of false positive cases accompanied varied between 7 and 10 using the 5%-estimated risk, and between 2 and 3 using the 20%-estimated risk cut-off. The pattern of results was similar using 30-day sickness absence as the outcome. Conclusions The best predictive performance was reached with a model including both questionnaire responses and administrative records. Prediction was almost as accurate with models using only variables from one of these data sources. Further research is needed to examine the generalizability of these findings

Contribution of income and job strain to the association between education and cardiovascular disease in 1.6 million Danish employees

AIMS: We examined the extent to which associations between education and cardiovascular disease (CVD) morbidity and mortality are attributable to income and work stress. METHODS AND RESULTS: We included all employed Danish residents aged 30-59 years in 2000. Cardiovascular disease morbidity analyses included 1 638 270 individuals, free of cardiometabolic disease (CVD or diabetes). Mortality analyses included 41 944 individuals with cardiometabolic disease. We assessed education and income annually from population registers and work stress, defined as job strain, with a job-exposure matrix. Outcomes were ascertained until 2014 from health registers and risk was estimated using Cox regression. During 10 957 399 (men) and 10 776 516 person-years (women), we identified 51 585 and 24 075 incident CVD cases, respectively. For men with low education, risk of CVD was 1.62 [95% confidence interval (CI) 1.58-1.66] before and 1.46 (95% CI 1.42-1.50) after adjustment for income and job strain (25% reduction). In women, estimates were 1.66 (95% CI 1.61-1.72) and 1.53 (95% CI 1.47-1.58) (21% reduction). Of individuals with cardiometabolic disease, 1736 men (362 234 person-years) and 341 women (179 402 person-years) died from CVD. Education predicted CVD mortality in both sexes. Estimates were reduced with 54% (men) and 33% (women) after adjustment for income and job strain. CONCLUSION: Low education predicted incident CVD in initially healthy individuals and CVD mortality in individuals with prevalent cardiometabolic disease. In men with cardiometabolic disease, income and job strain explained half of the higher CVD mortality in the low education group. In healthy men and in women regardless of cardiometabolic disease, these factors explained 21-33% of the higher CVD morbidity and mortality

Association between change in cardiovascular risk scores and future cardiovascular disease: analyses of data from the Whitehall II longitudinal, prospective cohort study

BACKGROUND: Evaluation of cardiovascular disease risk in primary care, which is recommended every 5 years in middle-aged and older adults (typical age range 40-75 years), is based on risk scores, such as the European Society of Cardiology Systematic Coronary Risk Evaluation (SCORE) and American College of Cardiology/American Heart Association Atherosclerotic Cardiovascular Disease (ASCVD) algorithms. This evaluation currently uses only the most recent risk factor assessment. We aimed to examine whether 5-year changes in SCORE and ASCVD risk scores are associated with future cardiovascular disease risk. METHODS: We analysed data from the Whitehall II longitudinal, prospective cohort study for individuals with no history of stroke, myocardial infarction, coronary artery bypass graft, percutaneous coronary intervention, definite angina, heart failure, or peripheral artery disease. Participants underwent clinical examinations in 5-year intervals between Aug 7, 1991, and Dec 6, 2016, and were followed up for incident cardiovascular disease until Oct 2, 2019. Levels of, and 5-year changes in, cardiovascular disease risk were assessed using the SCORE and ASCVD risk scores and were analysed as predictors of cardiovascular disease. Harrell's C index, continuous net reclassification improvement, the Akaike information criterion, and calibration analysis were used to assess whether incorporating change in risk scores into a model including only a single risk score assessment improved the predictive performance. We assessed the levels of, and 5-year changes in, SCORE and ASCVD risk scores as predictors of cardiovascular disease and disease-free life-years using Cox proportional hazards and flexible parametric survival models. FINDINGS: 7574 participants (5233 [69·1%] men, 2341 [30·9%] women) aged 40-75 years were included in analyses of risk score change between April 24, 1997, and Oct 2, 2019. During a mean follow-up of 18·7 years (SD 5·5), 1441 (19·0%; 1042 [72·3%] men and 399 [27·7%] women) participants developed cardiovascular disease. Adding 5-year change in risk score to a model that included only a single risk score assessment improved model performance according to Harrell's C index (from 0·685 to 0·690, change 0·004 [95% CI 0·000 to 0·008] for SCORE; from 0·699 to 0·700, change 0·001 [0·000 to 0·003] for ASCVD), the Akaike information criterion (from 17 255 to 17 200, change -57 [95% CI -97 to -13] for SCORE; from 14 739 to 14 729, change -10 [-28 to 7] for ASCVD), and the continuous net reclassification index (0·353 [95% CI 0·234 to 0·447] for SCORE; 0·232 [0·030 to 0·344] for ASCVD). Both favourable and unfavourable changes in SCORE and ASCVD were associated with cardiovascular disease risk and disease-free life-years. The associations were seen in both sexes and all age groups up to the age of 75 years. At the age of 45 years, each 2-unit improvement in risk scores was associated with an additional 1·3 life-years (95% CI 0·4 to 2·2) free of cardiovascular disease for SCORE and an additional 0·9 life-years (95% CI 0·5 to 1·3) for ASCVD. At age 65 years, this same improvement was associated with an additional 0·4 life-years (95% CI 0·0 to 0·7) free of cardiovascular disease for SCORE and 0·3 life-years (95% CI 0·1 to 0·5) for ASCVD. These models were developed into an interactive calculator, which enables estimation of the number of cardiovascular disease-free life-years for an individual as a function of two risk score measurements. INTERPRETATION: Changes in the SCORE and ASCVD risk scores over time inform cardiovascular disease risk prediction beyond a single risk score assessment. Repeat data might allow more accurate cardiovascular risk stratification and strengthen the evidence base for decisions on preventive interventions. FUNDING: UK Medical Research Council, British Heart Foundation, Wellcome Trust, and US National Institute on Aging

Supporting older and younger adults’ memory for recent everyday events: a prospective sampling study using SenseCam

This study measured the effect of a wearable camera, SenseCam, on older and younger adults’ memories of recently experienced everyday events. Participants used SenseCam to prospectively sample events from a typical week, which they recalled two weeks later. Recall was cued by a self-generated title only (control condition), by the title and forward-order SenseCam images, or by the title and random-order SenseCam images. In the control condition, older and younger adults’ memories were comparably episodic, but older adults recalled more semantic details. Both forward- and random-order SenseCam images were associated with increased episodic and semantic recall in both groups, and there was a small but significant effect of temporal order favouring the forward-order condition. These findings suggest that SenseCam is effective in supporting retrieval of memory for recent events, and the results of the temporal order manipulation also shed light on the mechanism of SenseCam’s effect