ObjectivesIn a 24/7 society, the negative metabolic effects of rotating night shift work have been increasingly explored. This study aimed to examine the association between rotating night shift work and non-alcoholic fatty liver disease (NAFLD) in steelworkers.MethodsA total of 6881 subjects was included in this study. Different exposure metrics of night shift work including current shift status, duration of night shifts (years), cumulative number of night shifts (nights), cumulative length of night shifts (hours), average frequency of night shifts (nights/month) and average length of night shifts (hours/night) were used to examine the relationship between night shift work and NAFLD.ResultsCurrent night shift workers had elevated odds of NAFLD (OR, 1.23, 95% CI 1.02 to 1.48) compared with those who never worked night shifts after adjustment for potential confounders. Duration of night shifts, cumulative number of night shifts and cumulative length of night shifts were positively associated with NAFLD. Both the average frequency of night shifts (>7 nights/month vs ≤7 nights/month: OR, 1.24, 95% CI 1.06 to 1.45) and average length of night shifts (>8 hours/night vs ≤8 hours/night: OR, 1.27, 95% CI 1.08 to 1.51) were independently associated with overall NAFLD after mutually adjusting for the duration of night shifts and other potential confounders among night shift workers. No significant association was found in female workers between different exposure metrics of night shift work and NAFLD.ConclusionsRotating night shift work is associated with elevated odds of NAFLD in male steelworkers.

ObjectiveTuberculosis (TB) remains a major deadly threat in mainland China. Early warning and advanced response systems play a central role in addressing such a wide-ranging threat. The purpose of this study is to establish a new hybrid model combining a seasonal autoregressive integrated moving average (SARIMA) model and a non-linear autoregressive neural network with exogenous input (NARNNX) model to understand the future epidemiological patterns of TB morbidity.MethodsWe develop a SARIMA-NARNNX hybrid model for forecasting future levels of TB incidence based on data containing 255 observations from January 1997 to March 2018 in mainland China, and the ultimate simulating and forecasting performances were compared with the basic SARIMA, non-linear autoregressive neural network (NARNN) and error-trend-seasonal (ETS) approaches, as well as the SARIMA-generalised regression neural network (GRNN) and SARIMA-NARNN hybrid techniques.ResultsIn terms of the root mean square error, mean absolute error, mean error rate and mean absolute percentage error, the identified best-fitting SARIMA-NARNNX combined model with 17 hidden neurons and 4 feedback delays had smaller values in both in-sample simulating scheme and the out-of-sample forecasting scheme than the preferred single SARIMA(2,1,3)(0,1,1)12model, a NARNN with 19 hidden neurons and 6 feedback delays and ETS(M,A,A), and the best-performing SARIMA-GRNN and SARIMA-NARNN models with 32 hidden neurons and 6 feedback delays. Every year, there was an obvious high-risk season for the notified TB cases in March and April. Importantly, the epidemic levels of TB from 2006 to 2017 trended slightly downward. According to the projection results from 2018 to 2025, TB incidence will continue to drop by 3.002% annually but will remain high.ConclusionsThe new SARIMA-NARNNX combined model visibly outperforms the other methods. This hybrid model should be used for forecasting the long-term epidemic patterns of TB, and it may serve as a beneficial and effective tool for controlling this disease.

BackgroundGenetic polymorphism (rs762551A>C) in gene encoding cytochrome P450 1A2 (CYP1A2) has been shown to influence the inducibility of CYP1A2 expression and thus might be associated with risk of several types of human cancer. However, the results of previous studies on the associations of this polymorphism with risk of cancer are not all consistent. To clarify the potential contribution of CYP1A2 rs762551 to cancer risk, we performed a meta-analysis of the published case–control studies.MethodsWe used PubMed, Embase, OVID, ScienceDirect, and Chinese National Knowledge Infrastructure databases to identify the related publications for this meta-analysis. The pooled odds ratio (OR) and 95% confidence interval (CI) were calculated using random effect model to evaluate the association of rs762551 with cancer risk. A χ2-based Q-test was used to examine the heterogeneity assumption and the funnel plot and Egger’s test were used to examine the potential publication bias. The leave-one-out sensitivity analysis was conducted to determine whether our assumptions or decisions have a major effect on the results of the review.ResultsOur analysis of 19 eligible case–control studies showed a significant association between rs762551C variant with risk of cancer in the genetic model of CC versus AA (OR = 1.30, 95% CI = 1.02-1.64) and the dominant model (OR = 1.19, 95% CI = 1.04-1.36). In subgroup analysis based on ethnicity, the rs762551CC genotype was associated with increased cancer risk (OR = 1.29, 95% CI = 1.27-1.63 in co-dominate model and OR = 1.17, 95% CI = 1.02-1.34 in dominant model in Caucasians, but not in Asians and the mixed population.ConclusionThese results suggested that CYP1A2 rs762551 polymorphism is likely to be associated with susceptibility to cancer in Caucasians.

BackgroundPrior to 1970, coal mining technology and prevention measures in China were poor. Mechanized coal mining equipment and advanced protection measures were continuously installed in the mines after 1970. All these improvements may have resulted in a change in the incidence of coal workers’ pneumoconiosis (CWP). Therefore, it is important to identify the characteristics of CWP today and trends for the incidence of CWP in the future.Methodology/Principal FindingsA total of 17,023 coal workers from the Kailuan Colliery Group were studied. A life-table method was used to calculate the cumulative incidence rate of CWP and predict the number of new CWP patients in the future. The probability of developing CWP was estimated by a multilayer perceptron artificial neural network for each coal worker without CWP. The results showed that the cumulative incidence rates of CWP for tunneling, mining, combining, and helping workers were 31.8%, 27.5%, 24.2%, and 2.6%, respectively, during the same observation period of 40 years. It was estimated that there would be 844 new CWP cases among 16,185 coal workers without CWP within their life expectancy. There would be 273.1, 273.1, 227.6, and 69.9 new CWP patients in the next <10, 10-, 20-, and 30- years respectively in the study cohort within their life expectancy. It was identified that coal workers whose risk probabilities were over 0.2 were at high risk for CWP, and whose risk probabilities were under 0.1 were at low risk.Conclusion/SignificanceThe present and future incidence trends of CWP remain high among coal workers. We suggest that coal workers at high risk of CWP undergo a physical examination for pneumoconiosis every year, and the coal workers at low risk of CWP be examined every 5 years.

BackgroundScarlet fever is recognized as being a major public health issue owing to its increase in notifications in mainland China, and an advanced response based on forecasting techniques is being adopted to tackle this. Here, we construct a new hybrid method incorporating seasonal autoregressive integrated moving average (SARIMA) with a nonlinear autoregressive with external input(NARX) to analyze its seasonality and trend in order to efficiently prevent and control this re-emerging disease.MethodsFour statistical models, including a basic SARIMA, basic nonlinear autoregressive (NAR) method, traditional SARIMA-NAR and new SARIMA-NARX hybrid approaches, were developed based on scarlet fever incidence data between January 2004 and July 2018 to evaluate its temporal patterns, and their mimic and predictive capacities were compared to discover the optimal using the mean absolute percentage error, root mean square error, mean error rate, and root mean square percentage error.ResultsThe four preferred models identified were comprised of the SARIMA(0,1,0)(0,1,1)12, NAR with 14 hidden neurons and five delays, SARIMA-NAR with 33 hidden neurons and five delays, and SARIMA-NARX with 16 hidden neurons and 4 delays. Among which presenting the lowest values of the aforementioned indices in both simulation and prediction horizons is the SARIMA-NARX method. Analyses from the data suggested that scarlet fever was a seasonal disease with predominant peaks of summer and winter and a substantial rising trend in the scarlet fever notifications was observed with an acceleration of 9.641% annually, particularly since 2011 with 12.869%, and moreover such a trend will be projected to continue in the coming year.ConclusionsThe SARIMA-NARX technique has the promising ability to better consider both linearity and non-linearity behind scarlet fever data than the others, which significantly facilitates its prevention and intervention of scarlet fever. Besides, under current trend of ongoing resurgence, specific strategies and countermeasures should be formulated to target scarlet fever.