SummaryAims: This systematic review and meta-analysis evaluated the associations between shift work patterns and risks of specific types of obesity.Methods: PubMed was searched until March 2017 for observational studies that examined the relationships between shift work patterns and obesity. Odds ratio for obesity was extracted using a fixed-effects or random-effects model. Subgroup meta-analyses were carried out for study design, specific obesity types and characteristics of shift work pattern.Results: A total of 28 studies were included in this meta-analysis. The overall odds ratio of night shift work was 1.23 (95% confidence interval = 1.17-1.29) for risk of obesity/overweight. Cross-sectional studies showed a higher risk of 1.26 than those with the cohort design (risk ratio = 1.10). Shift workers had a higher frequency of developing abdominal obesity (odds ratio = 1.35) than other obesity types. Permanent night workers demonstrated a 29% higher risk than rotating shift workers (odds ratio 1.43 vs. 1.14). Conclusion:This meta-analysis confirmed the risks of night shift work for the development of overweight and obesity with a potential gradient association suggested, especially for abdominal obesity. Modification of working schedules is recommended, particularly for prolonged permanent night work. More accurate and detailed measurements on shift work patterns should be conducted in future research.

This study aims to quantitatively summarize the association between night shift work and the risk of metabolic syndrome (MetS), with special reference to the dose-response relationship with years of night shift work. We systematically searched all observational studies published in English on PubMed and Embase from 1971 to 2013. We extracted effect measures (relative risk, RR; or odd ratio, OR) with 95% confidence interval (CI) from individual studies to generate pooled results using meta-analysis approach. Pooled RR was calculated using random- or fixed-effect model. Downs and Black scale was applied to assess the methodological quality of included studies. A total of 13 studies were included. The pooled RR for the association between 'ever exposed to night shift work' and MetS risk was 1.57 (95% CI = 1.24-1.98, pheterogeneity  = 0.001), while a higher risk was indicated in workers with longer exposure to night shifts (RR = 1.77, 95% CI = 1.32-2.36, pheterogeneity  = 0.936). Further stratification analysis demonstrated a higher pooled effect of 1.84 (95% CI = 1.45-2.34) for studies using the NCEP-ATPIII criteria, among female workers (RR = 1.61, 95% CI = 1.10-2.34) and the countries other than Asia (RR = 1.65, 95% CI = 1.39-1.95). Sensitivity analysis confirmed the robustness of the results. No evidence of publication bias was detected. The present meta-analysis suggested that night shift work is significantly associated with the risk of MetS, and a positive dose-response relationship with duration of exposure was indicated.

This study aimed to conduct a systematic review to sum up evidence of the associations between different aspects of night shift work and female breast cancer using a dose-response meta-analysis approach. We systematicly searched all cohort and case-control studies published in English on MEDLINE, Embase, PSYCInfo, APC Journal Club and Global Health, from January 1971 to May 2013. We extracted effect measures (relative risk, RR; odd ratio, OR; or hazard ratio, HR) from individual studies to generate pooled results using meta-analysis approaches. A log-linear dose-response regression model was used to evaluate the relationship between various indicators of exposure to night shift work and breast cancer risk. Downs and Black scale was applied to assess the methodological quality of included studies. Ten studies were included in the meta-analysis. A pooled adjusted relative risk for the association between 'ever exposed to night shift work' and breast cancer was 1.19 [95% confidence interval (CI) 1.05-1.35]. Further meta-analyses on dose-response relationship showed that every 5-year increase of exposure to night shift work would correspondingly enhance the risk of breast cancer of the female by 3% (pooled RR = 1.03, 95% CI 1.01-1.05; Pheterogeneity < 0.001). Our meta-analysis also suggested that an increase in 500-night shifts would result in a 13% (RR = 1.13, 95% CI 1.07-1.21; Pheterogeneity = 0.06) increase in breast cancer risk. This systematic review updated the evidence that a positive dose-response relationship is likely to present for breast cancer with increasing years of employment and cumulative shifts involved in the work.

Study Objectives: There is limited information on the relationship between risk of cardiovascular disease and the joint effects of sleep quality and sleep duration, especially from large, prospective, cohort studies. This study is to prospectively investigate the joint effects of sleep quality and sleep duration on the development of coronary heart disease. Methods: This study examined 60,586 adults aged 40 years or older. A self-administered questionnaire was used to collect information on sleep quality and sleep duration as well as a wide range of potential confounders. Events of coronary heart disease were self-reported in subsequent medical examinations. Two types of Sleep Score (multiplicative and additive) were constructed to reflect the participants' sleep profiles, considering both sleep quality and sleep duration. The Cox regression model was used to estimate the hazard ratio (HR) and the 95% confidence interval (CI). Results: A total of 2,740 participants (4.5%) reported new events of coronary heart disease at follow-up. For sleep duration, participants in the group of < 6 h/d was significantly associated with an increased risk of coronary heart disease (HR: 1.13, 95% CI: 1.04-1.23). However, the association in the participants with long sleep duration (> 8 h/d) did not reach statistical significance (HR: 1.11, 95% CI: 0.98-1.26). For sleep quality, both dreamy sleep (HR: 1.21, 95% CI: 1.10-1.32) and difficult to fall asleep/use of sleeping pills or drugs (HR: 1.40, 95% CI: 1.25-1.56) were associated with an increased risk of the disease. Participants in the lowest quartile of multiplicative Sleep Score (HR: 1.31, 95% CI: 1.16-1.47) and of additive sleep score (HR: 1.31, 95% CI: 1.16-1.47) were associated with increased risk of coronary heart disease compared with those in the highest quartile. Conclusions: Both short sleep duration and poor sleep quality are associated with the risk of coronary heart disease. The association for long sleep duration does not reach statistical significance. Lower Sleep Score (poorer sleep profile) increases the risk of coronary heart disease, suggesting the importance of considering sleep duration and sleep quality together when developing strategies to improve sleep for cardiovascular disease prevention. Keywords: cohort study, coronary heart disease, sleep duration, sleep quality, sleep score Citation: Lao XQ, Liu X, Deng HB, Chan TC, Ho KF, Wang F, Vermeulen R, Tam T, Wong MC, Tse LA, Chang LY, Yeoh EK. Sleep quality, sleep duration, and the risk of coronary heart disease: a prospective cohort study with 60,586 adults.

BackgroundTuberculosis (TB) is a serious public health issue in developing countries. Early prediction of TB epidemic is very important for its control and intervention. We aimed to develop an appropriate model for predicting TB epidemics and analyze its seasonality in China.MethodsData of monthly TB incidence cases from January 2005 to December 2011 were obtained from the Ministry of Health, China. A seasonal autoregressive integrated moving average (SARIMA) model and a hybrid model which combined the SARIMA model and a generalized regression neural network model were used to fit the data from 2005 to 2010. Simulation performance parameters of mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to compare the goodness-of-fit between these two models. Data from 2011 TB incidence data was used to validate the chosen model.ResultsAlthough both two models could reasonably forecast the incidence of TB, the hybrid model demonstrated better goodness-of-fit than the SARIMA model. For the hybrid model, the MSE, MAE and MAPE were 38969150, 3406.593 and 0.030, respectively. For the SARIMA model, the corresponding figures were 161835310, 8781.971 and 0.076, respectively. The seasonal trend of TB incidence is predicted to have lower monthly incidence in January and February and higher incidence from March to June.ConclusionsThe hybrid model showed better TB incidence forecasting than the SARIMA model. There is an obvious seasonal trend of TB incidence in China that differed from other countries.