Fuzhong Xue

Detecting Spatial-Temporal Clusters of HFMD from 2007 to 2011 in Shandong Province, China

Background Hand, foot, and mouth disease (HFMD) has caused major public health concerns worldwide, and has become one of the leading causes of children death. China is the most serious epidemic area with a total of 3,419,149 reported cases just from 2008 to 2010, and its different geographic areas might have different spatial epidemiology characteristics at different spatial-temporal scale levels. We conducted spatial and spatial-temporal epidemiology analysis to HFMD at county level in Shandong Province, China. Methods Based on the China National Disease Surveillance Reporting and Management System, the spatial-temporal database of HFMD from 2007 to 2011 was built. The global autocorrelation statistic (Moran’s I) was first used to detect the spatial autocorrelation of HFMD cases in each year. Purely Spatial scan statistics combined with Space-time scan statistic were used to detect epidemic clusters. Results The annual average incidence rate was 93.70 per 100,000 in Shandong Province. Most HFMD cases (93.94%) were aged within 0–5 years old with an average male-to-female sex ratio 1.71, and the incidence seasonal peak was between April and July. The dominant pathogen was EV71 (47.35%), and CoxA16 (26.59%). HFMD had positive spatial autocorrelation at medium spatial scale level (county level) with higher Moran’s I from 0.31 to 0.62 (P<0.001). Seven spatial-temporal clusters were detected from 2007 to 2011 in the landscape of the whole Shandong, with EV71 or CoxA16 as the dominant pathogen for most hotspots areas. Conclusions The spatial-temporal clusters of HFMD wandered around the whole Shandong Province during 2007 to 2011, with EV71 or CoxA16 as the dominant pathogen. These findings suggested that a real-time spatial-temporal surveillance system should be established for identifying high incidence region and conducting prevention to HFMD timely.

A longitudinal cohort based association study between uric acid level and metabolic syndrome in Chinese Han urban male population

BackgroundIt has been recently demonstrated that serum uric acid (UA) is associated with metabolic syndrome (MetS) or its related clinical indications based on cross-sectional or prospective cohort studies. Nonetheless, due to the fact that UA level constantly fluctuates from time to time even for the person, using a single measure of UA level at baseline of those studies may not be sufficient for estimating the UA-Mets association.MethodsTo further estimate this time-dependent association, we fitted a generalized estimating equation (GEE) regression model with data from a large-scale 6-year longitudinal study, which included 2222 participants aged > =25 years with an average of 3.5 repeated measures of UA per person in the Health Management Center of Shandong Provincial Hospital, Shandong, China.ResultsAfter adjusting for other potential confounding factors (i.e., total cholesterol, low-density lipoprotein), it was verified that time-dependent UA level was an independent risk factor for MetS (OR = 1.6920, p < 0.0001). It was found that UA level was positively associated with obesity, hypertension, and dyslipidemia, but was inversely associated with hyperglycemia.ConclusionsSerum UA level may serve as an important risk factor of MetS. Additionally, our study suggested that UA level be an independent risk factor to obesity, hypertension and dyslipidemia, but a protective factor to hyperglycemia. These findings are concordant with results from other studies on Asian populations, and jointly provide a basis to further develop a risk assessment model for predicting MetS using UA levels and other factors in China.

The spatial epidemiology of tuberculosis in Linyi City, China, 2005-2010.

BackgroundTuberculosis (TB) remains a major public health burden in many developing countries. China alone accounted for an estimated 12% of all incident TB cases worldwide in 2010. Several studies showed that the spatial distribution of TB was nonrandom and clustered. Thus, a spatial analysis was conducted with the aim to explore the spatial epidemiology of TB in Linyi City, which can provide guidance for formulating regional prevention and control strategies.MethodsThe study was based on the reported cases of TB, between 2005 and 2010. 35,308 TB cases were geo-coded at the town level (n = 180). The spatial empirical Bayes smoothing, spatial autocorrelation and space-time scan statistic were used in this analysis.ResultsSpatial distribution of TB in Linyi City from 2005 to 2010 was mapped at town level in the aspects of crude incidence, excess hazard and spatial smoothed incidence. The spatial distribution of TB was nonrandom and clustered with the significant Moran’s I for each year. Local Gi* detected five significant spatial clusters for high incidence of TB. The space-time analysis identified one most likely cluster and nine secondary clusters for high incidence of TB.ConclusionsThere is evidence for the existence of statistically significant TB clusters in Linyi City, China. The result of this study may assist health departments to develop a better preventive strategy and increase the public health intervention’s effectiveness.

A spatial analysis of genetic structure of human populations in China reveals distinct difference between maternal and paternal lineages

Analyses of archeological, anatomical, linguistic, and genetic data suggested consistently the presence of a significant boundary between the populations of north and south in China. However, the exact location and the strength of this boundary have remained controversial. In this study, we systematically explored the spatial genetic structure and the boundary of north–south division of human populations using mtDNA data in 91 populations and Y-chromosome data in 143 populations. Our results highlight a distinct difference between spatial genetic structures of maternal and paternal lineages. A substantial genetic differentiation between northern and southern populations is the characteristic of maternal structure, with a significant uninterrupted genetic boundary extending approximately along the Huai River and Qin Mountains north to Yangtze River. On the paternal side, however, no obvious genetic differentiation between northern and southern populations is revealed.

Spatio-temporal analysis of the relationship between climate and hand, foot, and mouth disease in Shandong province, China, 2008–2012

BackgroundHand, foot, and mouth disease (HFMD) is the most common communicable disease in China. Shandong Province is one of the most seriously affected areas. The distribution of HFMD had spatial heterogeneity and seasonal characteristic in this setting. The aim of this study was to explore the associations between climate and HFMD by a Bayesian approach from spatio-temporal interactions perspective.MethodsThe HFMD data of Shandong Province during 2008–2012 were derived from the China National Disease Surveillance Reporting and Management System. And six climatic indicators were obtained from the Meteorological Bureau of Shandong Province. The global spatial autocorrelation statistic (Moran’s I) was used to detect the spatial autocorrelation of HFMD cases in each year. The optimal one among four Bayesian models was further adopted to estimate the relative risk of the occurrence of HFMD via Markov chain Monte Carlo.ResultsThe annual average incidence rate of HFMD was 104.40 per 100,000 in Shandong Province. Positive spatial autocorrelation appeared at county level (Moran’s I ≥0.30, P < 0.001). The best fitting Spatio-temporal interactive model showed that annual average temperature, annual average pressure, annual average relative humidity, annual average wind speed and annual sunshine hours were significantly positive related to the occurrence of HFMD. The estimated relative risk of 36, 87, 91, 79, 65 out of 140 counties for 2008–2012 respectively were significantly more than 1.ConclusionsThere were obvious spatio-temporal heterogeneity of HFMD in Shandong Province, and the climatic indicators were associated with the epidemic of HFMD. Bayesian approach should be recommended to capture the spatial-temporal pattern of HFMD.

Metabolic syndrome and its components as predictors of nonalcoholic fatty liver disease in a northern urban Han Chinese population: A prospective cohort study

OBJECTIVES To explore the longitudinal effect of metabolic syndrome (MetS) and its components on the development of non-alcoholic fatty liver disease (NAFLD) and to evaluate the significance of MetS and its components as early markers of NAFLD risk in a northern urban Han Chinese population. MATERIALS AND METHODS A total of 15,791 cohort members without NAFLD at baseline were included in the current study between 2005 and 2011. The baseline characteristics of the cohort were compared by MetS status at baseline and NAFLD status after follow-up. Cox proportional hazards models were used to estimate the unadjusted or adjusted hazard ratios (HRs) for development of NAFLD among individuals with MetS compared with individuals without MetS at baseline. RESULTS During 51,652 person-years of follow-up, 3913 (24.78%) new cases of NAFLD occurred between 2005 and 2011. In the unadjusted model, the HR (95% confidence interval [CI]) for NAFLD was 2.51 (2.30, 2.73). After adjusting for gender, age, diet, smoking status, and regular exercise, the HR was 1.94 (1.78, 2.13). Gender differences were observed, with adjusted HRs (95% CIs) of 1.89 (1.71, 2.09) and 1.72 (1.43, 2.07) among males and females, respectively. Compared with individuals without MetS components, the HRs were 1.92 (1.76, 2.09), 2.64 (2.40, 2.90) and 3.51 (3.15, 3.91) for individuals with one, two, or three or more MetS components, respectively. Moreover, participants with obesity or hyperlipidemia had a higher risk of NAFLD than patients with hypertension or hyperglycemia, with HRs of 2.03 (1.83, 2.25) for obesity, 1.94 (1.72, 2.19) for hyperlipidemia, and 3.01 (2.68, 3.37) for these factors in combination. CONCLUSION The present study indicates that MetS and its components independently predict the risk of NAFLD in a northern urban Han Chinese population and suggests that people with MetS or its component should initiate lifestyle changes to prevent the development of NAFLD.

A gene-based method for detecting gene-gene co-association in a case-control association study.

Association study (especially the genome-wide association study) now has a key function in identification and characterization of disease-predisposing genetic variant(s), which customarily involve multiple single nucleotide polymorphisms (SNPs) in a candidate region or across the genome. Case–control association design remains the most popular and a challenging issue in the statistical analysis is the optimal use of all information contained in these SNPs. Previous approaches often treated gene–gene interaction as deviation from additive genetic effects or replaced it with SNP–SNP interaction. However, these approaches are limited for their failure of consideration of gene–gene interaction or gene–gene co-association at gene level. Although the co-association of the SNPs within a candidate gene can be detected by principal component analysis-based logistic regression model, the detection of co-association between genes in genome remains uncertain. Here, we proposed a canonical correlation-based U statistic (CCU) for detecting gene-based gene–gene co-association in the case–control design. We explored its type I error rates and power through simulation and analyzed two real data sets. By treating gene as a functional unit in analysis, we found that CCU was a strong alternative to previous approaches. We discussed the performance of CCU as a gene-based gene–gene co-association statistic and the prospect of further improvement.

Detecting the association between meteorological factors and hand, foot, and mouth disease using spatial panel data models

OBJECTIVES The aim of this study was to quantify the relationship between meteorological factors and the occurrence of hand, foot, and mouth disease (HFMD) among children in Shandong Province, China, at a county level, using spatial panel data models. METHODS Descriptive analysis was applied to describe the epidemic characteristics of HFMD from January 2008 to December 2012, and then a global autocorrelation statistic (Morans I) was used to detect the spatial autocorrelation of HFMD in each year. Finally, spatial panel data models were performed to explore the association between the incidence of HFMD and meteorological factors. RESULTS Morans I at the county level were high, from 0.30 to 0.45 (p < 0.001), indicating the existence of a high spatial autocorrelation on HFMD. Spatial panel data models are more appropriate to describe the data. Results showed that the incidences of HFMD in Shandong Province, China were significantly associated with average temperature, relative humidity, vapor pressure, and wind speed. CONCLUSIONS Spatial panel data models are useful when longitudinal data with multiple units are available and spatial autocorrelation exists. The association found between HFMD and meteorological factors makes a contribution towards advancing knowledge with respect to the causality of HFMD and has policy implications for HFMD prevention and control.

Identification of reciprocal causality between non-alcoholic fatty liver disease and metabolic syndrome by a simplified Bayesian network in a Chinese population

Objectives It remains unclear whether non-alcoholic fatty liver disease (NAFLD) is a cause or a consequence of metabolic syndrome (MetS). We proposed a simplified Bayesian network (BN) and attempted to confirm their reciprocal causality. Setting Bidirectional longitudinal cohorts (subcohorts A and B) were designed and followed up from 2005 to 2011 based on a large-scale health check-up in a Chinese population. Participants Subcohort A (from NAFLD to MetS, n=8426) included the participants with or without NAFLD at baseline to follow-up the incidence of MetS, while subcohort B (from MetS to NAFLD, n=16 110) included the participants with or without MetS at baseline to follow-up the incidence of NAFLD. Results Incidence densities were 2.47 and 17.39 per 100 person-years in subcohorts A and B, respectively. Generalised estimating equation analyses demonstrated that NAFLD was a potential causal factor for MetS (relative risk, RR, 95% CI 5.23, 3.50 to 7.81), while MetS was also a factor for NAFLD (2.55, 2.23 to 2.92). A BN with 5 simplification strategies was used for the reciprocal causal inference. The BNs causal inference illustrated that the total effect of NAFLD on MetS (attributable risks, AR%) was 2.49%, while it was 19.92% for MetS on NAFLD. The total effect of NAFLD on MetS components was different, with dyslipidemia having the greatest (AR%, 10.15%), followed by obesity (7.63%), diabetes (3.90%) and hypertension (3.51%). Similar patterns were inferred for MetS components on NAFLD, with obesity having the greatest (16.37%) effect, followed by diabetes (10.85%), dyslipidemia (10.74%) and hypertension (7.36%). Furthermore, the most important causal pathway from NAFLD to MetS was that NAFLD led to elevated GGT, then to MetS components, while the dominant causal pathway from MetS to NAFLD began with dyslipidaemia. Conclusions The findings suggest a reciprocal causality between NAFLD and MetS, and the effect of MetS on NAFLD is significantly greater than that of NAFLD on MetS.

Ecological niche modeling for predicting the potential risk areas of severe fever with thrombocytopenia syndrome.

BACKGROUND Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease caused by a novel bunyavirus. The spatial distribution has continued to expand, while the areas at potential high risk of SFTS have, to date, remained unclear. METHODS Using ecological factors as predictors, the MaxEnt model was first trained based on the locations of human SFTS occurrence in Shandong Province. The risk prediction map of China was then created by projecting the training model onto the whole country. The performance of the model was assessed using the known locations of disease occurrence in China. RESULTS The key environmental factors affecting SFTS occurrence were temperature, precipitation, land cover, normalized difference vegetation index (NDVI), and duration of sunshine. The risk prediction maps suggested that central, southwestern, northeastern, and the eastern coast of China are potential areas at high risk of SFTS. CONCLUSIONS The potential high risk SFTS areas are distributed widely in China. The epidemiological surveillance system should be enhanced in these high risk regions.