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We depicted the epidemiological characteristics of infectious diarrhoea in Jiangsu Province, China. Generalized additive models were employed to evaluate the age-specific effects of etiological and meteorological factors on prevalence. A long-term increasing prevalence with strong seasonality was observed. In those aged 0–5 years, disease risk increased rapidly with the positive rate of virus (rotavirus, norovirus, sapovirus, astrovirus) in the 20–50% range. In those aged > 20 years, disease risk increased with the positive rate of adenovirus and bacteria (Vibrio parahaemolyticus, Salmonella, Escherichia coli, Campylobacter jejuni) until reaching 5%, and thereafter stayed stable. The mean temperature, relative humidity, temperature range, and rainfall were all related to two-month lag morbidity in the group aged 0–5 years. Disease risk increased with relative humidity between 67–78%. Synchronous climate affected the incidence in those aged >20 years. Mean temperature and rainfall showed U-shape associations with disease risk (with threshold 15 °C and 100 mm per month, respectively). Meanwhile, disease risk increased gradually with sunshine duration over 150 hours per month. However, no associations were found in the group aged 6–19 years. In brief, etiological and meteorological factors had age-specific effects on the prevalence of infectious diarrhoea in Jiangsu. Surveillance efforts are needed to prevent its spread. [ABSTRACT FROM AUTHOR]

Objectives: To identify specific clinical and epidemiological parameters for clinical diagnosis of SFTSV infection with relatively higher accuracy. Methods: 231 suspected cases of SFTS were reported by various medical institutions from 2011 to 2013 in Jiangsu Province, China. They were followed with SFTSV diagnosis tests and interview-administered questionnaires about demographic characteristics, clinical symptoms and epidemiological exposure factors. Univariate and multivariable logistic regression analysis were used to examine the diagnostic value of these parameters. Results: SFTSV infection occurred only from April to October annually and usually in hilly areas of specific regions. Three prediction models of SFTSV infection were constructed. Model 3 with clinical and epidemiological parameters combined the benefits of both Model 1and Model 2, which was optimal and had an overall accuracy of 80.2%. Independent indicators for clinical diagnosis of SFTSV infection in Model 3 were as follows: lymphadenopathy (P = 0.01), leucopenia (P<0.01), age >50 years (P = 0.01), tick bites (P<0.01), raising domestic animals in the residential areas (P<0.01) and farming (P = 0.03). Conclusions: Our results show that using a combination of clinical and epidemiological parameters may be a feasible strategy to provide preliminary fast diagnosis as differentiating SFTSV infection from SFTS-like diseases, thus reducing the risk of misdiagnosis. [ABSTRACT FROM AUTHOR]

Having a thorough understanding of the infectivity of HIV, time of initiating treatment and emergence of drug resistant virus variants is crucial in mitigating HIV infection. There are many challenges to evaluating the long-term effect of the Highly Active Antiretroviral Therapy (HAART) on disease transmission at the population level. We proposed an individual based model by coupling within-host dynamics and between-host dynamics and conduct stochastic simulation in the group of men who have sex with men (MSM). The mean actual reproduction number is estimated to be 3.6320 (95% confidence interval: [3.46, 3.80]) for MSM group without treatment. Stochastic simulations show that given relatively high (low) level of drug efficacy after emergence of drug resistant variants, early initiation of treatment leads to a less (greater) actual reproduction number, lower (higher) prevalence and less (more) incidences, compared to late initiation of treatment. This implies early initiation of HAART may not always lower the actual reproduction number and prevalence of infection, depending on the level of treatment efficacy after emergence of drug resistant virus variants, frequency of high-risk behaviors and etc. This finding strongly suggests early initiation of HAART should be implemented with great care especially in the settings where the effective drugs are limited. Coupling within-host dynamics with between-host dynamics can provide critical information about impact of HAART on disease transmission and thus help to assist treatment strategy design and HIV/AIDS prevention and control. [ABSTRACT FROM AUTHOR]

Background: Many Burmese women have migrated to Yunnan Province and married local residents over the past few decades; however, limited information is available on their HIV prevalence and ability to cope with HIV. This study aims to assess the prevalence of HIV and knowledge related to AIDS, as well as to discover possible risk factors of HIV infection among foreign brides from Burma in Yunnan Province. Methods: A cross-sectional study was taken of all Burmese cross-border wives residing in Tengchong County using standardized questionnaires. HIV and syphilis testing was conducted at the same time. Results: Among 600 Burmese brides, the HIV prevalence was 2.17%. Those aged 21–30, those with higher education levels and those who had resided in China less than one year had higher infection rates. The AIDS awareness rate of 39.50% was very low in this population. Only 28.67% of participants had ever been involved in prevention services. The rate of condom use was low. Classification by age, education, occupation, prior HIV testing and prior use of HIV prevention services showed a statistically significant association with mean knowledge score (p<0.05). Residing in China less than one year (OR = 3.86, 95% CI = 1.09–13.70) and having casual sex in the last year (OR = 10.49, 95% CI = 1.20–91.59) were risk factors for HIV infection. Conclusions: Burmese brides in China are not only exposed to a high risk of HIV infection, but also seriously lack response capabilities. Educational interventions and control efforts are practical approaches that need to be strengthened among this population. [ABSTRACT FROM AUTHOR]

Background: Hemorrhagic fever with renal syndrome (HFRS) is endemic in mainland China, accounting for 90% of total reported cases worldwide, and Jiangsu is one of the most severely affected provinces. In this study, the authors conducted GIS-based spatial analyses in order to determine the spatial distribution of the HFRS cases, identify key areas and explore risk factors for public health planning and resource allocation. Methods: Interpolation maps by inverse distance weighting were produced to detect the spatial distribution of HFRS cases in Jiangsu from 2001 to 2011. Spatio-temporal clustering was applied to identify clusters at the county level. Spatial correlation analysis was conducted to detect influencing factors of HFRS in Jiangsu. Results: HFRS cases in Jiangsu from 2001 to 2011 were mapped and the results suggested that cases in Jiangsu were not distributed randomly. Cases were mainly distributed in northeastern and southwestern Jiangsu, especially in Dafeng and Sihong counties. It was notable that prior to this study, Sihong county had rarely been reported as a high-risk area of HFRS. With the maximum spatial size of 50% of the total population and the maximum temporal size of 50% of the total population, spatio-temporal clustering showed that there was one most likely cluster (LLR = 624.52, P<0.0001, RR = 8.19) and one second-most likely cluster (LLR = 553.97, P<0.0001, RR = 8.25), and both of these clusters appeared from 2001 to 2004. Spatial correlation analysis showed that the incidence of HFRS in Jiangsu was influenced by distances to highways, railways, rivers and lakes. Conclusion: The application of GIS together with spatial interpolation, spatio-temporal clustering and spatial correlation analysis can effectively identify high-risk areas and factors influencing HFRS incidence to lay a foundation for researching its pathogenesis. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to describe the spatial and temporal trends of Shigella incidence rates in Jiangsu Province, People's Republic of China. It also intended to explore complex risk modes facilitating Shigella transmission. Methods: County-level incidence rates were obtained for analysis using geographic information system (GIS) tools. Trend surface and incidence maps were established to describe geographic distributions. Spatio-temporal cluster analysis and autocorrelation analysis were used for detecting clusters. Based on the number of monthly Shigella cases, an autoregressive integrated moving average (ARIMA) model successfully established a time series model. A spatial correlation analysis and a case-control study were conducted to identify risk factors contributing to Shigella transmissions. Results: The far southwestern and northwestern areas of Jiangsu were the most infected. A cluster was detected in southwestern Jiangsu (LLR = 11674.74, P<0.001). The time series model was established as ARIMA (1, 12, 0), which predicted well for cases from August to December, 2011. Highways and water sources potentially caused spatial variation in Shigella development in Jiangsu. The case-control study confirmed not washing hands before dinner (OR = 3.64) and not having access to a safe water source (OR = 2.04) as the main causes of Shigella in Jiangsu Province. Conclusion: Improvement of sanitation and hygiene should be strengthened in economically developed counties, while access to a safe water supply in impoverished areas should be increased at the same time. [ABSTRACT FROM AUTHOR]

Hepatitis C virus infection is a perennial concern for hemodialysis units because the prevalence of hepatitis C is significantly higher there than in the general population. Through a systematic review and meta-analysis, we aim to assess the incidence rate of hepatitis C virus infection in hemodialysis units and explore its potential risk factors. Five electronic databases were used to search articles from 1990 to 2012, including PubMed, Embase, China National Knowledge Infrastructure, Chinese Biomedical Literature Database, and Wanfang. A random-effects analysis was used to estimate the overall incidence rate of hepatitis C virus infection. A subgroup analysis and meta-regression analysis were conducted to explore factors associated with heterogeneity between studies. Twenty-two eligible articles were found, including 23 incidence rate estimates. The overall incidence rate of hepatitis C virus infection was 1.47 per 100 patient-years (95% confidence interval [ CI] 1.14 to 1.80). In the subgroup analysis, the pooled incidence rate was 4.44 ( CI 2.65, 6.23) per 100 patient-years in the developing world and 0.99 ( CI 0.66, 1.29) per 100 patient-years in the developed world. [Correction added on 2 November 2012, after first online publication: Pooled incidence rate in the developed world has been changed.] In addition, in hemodialysis units with higher prevalence, the incidence rate of hepatitis C virus infection also tended to be higher. Meta-regression analysis showed that the country's development level and initial HCV prevalence combined could explain 67.91% of the observed heterogeneity. The incidence rate of hepatitis C virus infection among patients on hemodialysis was significantly high. Efforts should be taken to control hepatitis C virus infection in hemodialysis units, especially in developing countries. [ABSTRACT FROM AUTHOR]

Abstract: This paper first applies the sequential cluster method to set up the classification standard of infectious disease incidence state based on the fact that there are many uncertainty characteristics in the incidence course. Then the paper presents a weighted Markov chain, a method which is used to predict the future incidence state. This method assumes the standardized self-coefficients as weights based on the special characteristics of infectious disease incidence being a dependent stochastic variable. It also analyzes the characteristics of infectious diseases incidence via the Markov chain Monte Carlo method to make the long-term benefit of decision optimal. Our method is successfully validated using existing incidents data of infectious diseases in Jiangsu Province. In summation, this paper proposes ways to improve the accuracy of the weighted Markov chain, specifically in the field of infection epidemiology. [ABSTRACT FROM AUTHOR]

Family based association study (FBAS) has the advantages of controlling for population stratification and testing for linkage and association simultaneously. We propose a retrospective multilevel model (rMLM) approach to analyze sibship data by using genotypic information as the dependent variable. Simulated data sets were generated using the simulation of linkage and association (SIMLA) program. We compared rMLM to sib transmission/disequilibrium test (S-TDT), sibling disequilibrium test (SDT), conditional logistic regression (CLR) and generalized estimation equations (GEE) on the measures of power, type I error, estimation bias and standard error. The results indicated that rMLM was a valid test of association in the presence of linkage using sibship data. The advantages of rMLM became more evident when the data contained concordant sibships. Compared to GEE, rMLM had less underestimated odds ratio (OR). Our results support the application of rMLM to detect gene-disease associations using sibship data. However, the risk of increasing type I error rate should be cautioned when there is association without linkage between the disease locus and the genotyped marker.

Abstract In this paper, we propose an SIRS influenza model with general incidence rate to describe disease transmission in a heterogenous environment. The basic reproduction number R 0 is defined for the model, which can be used to govern the threshold dynamics of influenza disease: if R 0 < 1 , the unique disease-free equilibrium is globally asymptotic stable and there is no endemic equilibrium, while R 0 > 1 , there is at least one endemic equilibrium and the disease is uniformly persistent. Epidemiologically, we find that the spatial heterogeneity can enhance the infectious risk of the influenza and thus, in order to control the spread of the influenza, we must increase the recovery rate and the spatial heterogeneity in the transmission rate, or people should change their travelling plan and stay at home to reduce the value of the diffusion coefficient. [ABSTRACT FROM AUTHOR]