Seasonal autoregressive integrated moving average (SARIMA) has been used to model nationwide tuberculosis (TB) incidence in other countries. This study aimed to characterise monthly TB notification rate in China. Monthly TB notification rate from 2005 to 2017 was used. Time-series analysis was based on a SARIMA model and a hybrid model of SARIMA-generalised regression neural network (GRNN) model. A decreasing trend (3.17% per years, P < 0.01) and seasonal variation of TB notification rate were found from 2005 to 2016 in China, with a predominant peak in spring. A SARIMA model of ARIMA (0,1,1) (0,1,1)12 was identified. The mean error rate of the single SARIMA model and the SARIMA-GRNN combination model was 6.07% and 2.56%, and the determination coefficient was 0.73 and 0.94, respectively. The better performance of the SARIMA-GRNN combination model was further confirmed with the forecasting dataset (2017). TB is a seasonal disease in China, with a predominant peak in spring, and the trend of TB decreased by 3.17% per year. The SARIMA-GRNN model was more effective than the widely used SARIMA model at predicting TB incidence.

Background More than 200 articles have been published in the past 20 years on associations between genetic variants and risk of cervical cancer but the results have generally been inconsistent.Objective To provide a synopsis of the current understanding of the genetic architecture of the risk of cervical cancer by conducting a systematic review and meta-analysis.Search strategy We conducted a systematic literature search by a two-stage strategy using PubMed and other databases on or before 31 March 2012.Selection criteria Cross-sectional, case-control or cohort studies about the relationship between genetic variants and cervical cancer were included.Data collection and analysis Study outcomes were presented as odds ratios (ORs) with a 95% confidence interval.We did the meta-analysis for genetic variants which had at least three data sources and for which the significant associations were assessed using the Venice criteria.Main results A total of 5605 publications were screened, of which 286 were eligible. Meta-analysis was conducted for 58 variants in 25 genes or loci. Fourteen variants in 11 genes or loci could increase the risk of cervical cancer and five variants in three genes or loci could decrease the risk. The epidemiological evidence of the association was graded as strong for four variants in CTLA4 and HLA DQB1, moderate for five variants in IL-1B, IL-10, XRCC3 and HLA DQA1, and weak for 10 variants.Conclusions Many genetic variants were associated with the risk of cervical cancer as supported by the epidemiological evidence in this meta-analysis.

Aims/hypothesis A meta-analysis was performed to assess the association of C47T (rs4880) (also called Val16Ala) polymorphism in SOD2 gene with reduced risk of diabetes mellitus, including type 1 and type 2 diabetes, and diabetic microvascular complications (DMI) including diabetic nephropathy, diabetic retinopathy and diabetic polyneuropathy. Methods A comprehensive search was conducted to identify all case-control or cohort design studies of the abovementioned associations. The fixed or random effect pooled measure was selected on the basis of homogeneity test among studies. Heterogeneity among studies was evaluated using the I 2 . Meta-regression and the 'leave one out' sensitive analysis of Patsopoulos et al. were used to explore potential sources of between-study heterogeneity. Publication bias was estimated using modified Egger's linear regression test as proposed by Harbord et al. Results Seventeen articles were included. After excluding articles that deviated from Hardy-Weinberg equilibrium in cases and/or in controls, and were also the key contributors to between-study heterogeneity, the meta-analysis showed a significant association of the C allele with reduced risk of DMI in dominant (OR 0.788, 95% CI 0.680-0.914), recessive (OR 0.808, 95% CI 0.685-0.953) and codominant (OR 0.828, 95% CI 0.751-0.913) models. It also showed a significant association with reduced risk of diabetic nephropathy in the dominant model (OR 0.801,, and reduced risk of diabetic retinopathy in the dominant (OR 0.601, 95% CI 0.423-0.855), recessive (OR 0.548, 95% CI 0.369-0.814) and codominant (OR 0.651, 95% CI 0.517-0.820) models. Conclusions/interpretation The meta-analysis suggested that C allele of C47T polymorphism in SOD2 gene has protective effects on risk of DMI, diabetic nephropathy and diabetic retinopathy. This risk needs to be confirmed by further studies.

Results from observational studies on the association of fish and n-3 fatty acid consumption with type 2 diabetes mellitus (T2DM) risk are conflicting. Hence, a meta-analysis was performed to investigate this association from cohort studies. A comprehensive search was then conducted to identify cohort studies on the association of fish and/or n-3 fatty acid intake with T2DM risk. In the highest v. lowest categorical analyses, the fixed or random-effect model was selected based on the homogeneity test among studies. Linear and nonlinear dose -response relationships were also assessed by univariate and bivariate random-effect meta-regression with restricted maximum likelihood estimation. In the highest v. lowest categorical analyses, the pooled relative risk (RR) of T2DM for intake of fish and n-3 fatty acid was 1·146 (95 % CI 0·975, 1·346) and 1·076 (95 % CI 0·955, 1·213), respectively. In the linear dose -response relationship, the pooled RR for an increment of one time (about 105 g)/week of fish intake (four times/month) and of 0·1 g/d of n-3 fatty acid intake was 1·042 (95 % CI 1·026, 1·058) and 1·057 (95 % CI 1·042, 1·073), respectively. The significant non-linear dose -response associations of fish and n-3 fatty acid intake with T2DM risk were not observed. The present evidence from observational studies suggests that the intake of both fish and n-3 fatty acids might be weakly positively associated with the T2DM risk. Further studies are needed to confirm these results.

Seasonal autoregressive-integrated moving average (SARIMA) has been widely used to model and forecast incidence of infectious diseases in time-series analysis. This study aimed to model and forecast monthly cases of hand, foot and mouth disease (HFMD) in China. Monthly incidence HFMD cases in China from May 2008 to August 2018 were analysed with the SARIMA model. A seasonal variation of HFMD incidence was found from May 2008 to August 2018 in China, with a predominant peak from April to July and a trough from January to March. In addition, the annual peak occurred periodically with a large annual peak followed by a relatively small annual peak. A SARIMA model of SARIMA (1, 1, 2) (0, 1, 1)12 was identified, and the mean error rate and determination coefficient were 16.86% and 94.27%, respectively. There was an annual periodicity and seasonal variation of HFMD incidence in China, which could be predicted well by a SARIMA (1, 1, 2) (0, 1, 1)12 model.