Epidemiology, aetiology and seasonality of infectious diarrhoea in adult outpatients through active surveillance in Shanghai, China, 2012-2016: a cross-sectional study.

OBJECTIVES: This study aimed to identify the epidemiology, clinical characteristics, aetiology and seasonality of sporadic infectious diarrhoea in adults in Shanghai. SETTING: This study was based on a city-wide, active continuous hospital-based diarrhoea surveillance network established by Shanghai Municipal Center for Disease Control and Prevention. There were 22 sentinel hospitals in all 16 districts (9 primary-level hospitals, 6 secondary-level hospitals and 7 tertiary-level hospitals) which were selected using probability proportionate to size sampling method. PARTICIPANTS: From 1 May 2012 to 31 May 2016, 90 713 patients were included in this study. Among 8797 patients whose stool samples were collected and detected, 4392 patients were male. RESULTS: The positive rate was 47.96%. Bacterial and viral infections accounted for 27.19% and 69.07% separately. Norovirus was the most common pathogen (43.10%), followed by rotavirus, Vibrio parahaemolyticus, diarrhoeagenic Escherichia coli (DEC) and Salmonella spp. Patients between 30-44 and 45-59 years were more likely to have infectious diarrhoea and viral diarrhoea. Those aged 30-44 years were the most likely to get infected with V. parahaemolyticus (adjusted OR, aOR vs 60+ years: 2.04, 95% CI 1.47 to 2.78) and norovirus (aOR vs 60+ years: 1.32, 95% CI 1.12 to 1.56). Bacterial (except V. parahaemolyticus) diarrhoea was characterised by fever, abdominal pain and loose stool; while viral diarrhoea was characterised by nausea, vomiting and watery stool. A seasonal distribution of infectious diarrhoea was observed with larger peaks in winter and smaller peaks in summer. Winter peaks were mainly due to norovirus and rotavirus, and summer peaks were due to bacterial infections. An emerging spring peak of norovirus around March was observed in the past 3 years. CONCLUSION: Viral infections were predominant, and norovirus played a leading role. A seasonal distribution was observed and an emerging spring peak of norovirus was noted. Our findings highlight the necessity for conducting an active, comprehensive surveillance in adults, to monitor changing dynamics in the epidemiology and aetiology of infectious diarrhoea.

[Preliminary application of Back-Propagation artificial neural network model on the prediction of infectious diarrhea incidence in Shanghai].

OBJECTIVE: To establish BP artificial neural network predicting model regarding the daily cases of infectious diarrhea in Shanghai. METHODS: Data regarding both the incidence of infectious diarrhea from 2005 to 2008 in Shanghai and meteorological factors including temperature, relative humidity, rainfall, atmospheric pressure, duration of sunshine and wind speed within the same periods were collected and analyzed with the MatLab R2012b software. Meteorological factors that were correlated with infectious diarrhea were screened by Spearman correlation analysis. Principal component analysis (PCA) was used to remove the multi-colinearities between meteorological factors. Back-Propagation (BP) neural network was employed to establish related prediction models regarding the daily infectious diarrhea incidence, using artificial neural networks toolbox. The established models were evaluated through the fitting, predicting and forecasting processes. RESULTS: Data from Spearman correlation analysis indicated that the incidence of infectious diarrhea had a highly positive correlation with factors as daily maximum temperature, minimum temperature, average temperature, minimum relative humidity and average relative humidity in the previous two days (P < 0.01), and a relatively high negative correlation with the daily average air pressure in the previous two days (P < 0.01). Factors as mean absolute error, root mean square error, correlation coefficient(r), and the coefficient of determination (r(2)) of BP neural network model were established under the input of 4 meteorological principal components, extracted by PCA and used for training and prediction. Then appeared to be 4.7811, 6.8921,0.7918,0.8418 and 5.8163, 7.8062,0.7202,0.8180, respectively. The rate on mean error regarding the predictive value to actual incidence in 2008 was 5.30% and the forecasting precision reached 95.63% . CONCLUSION: Temperature and air pressure showed important impact on the incidence of infectious diarrhea. The BP neural network model had the advantages of low simulation forecasting errors and high forecasting hit rate that could ideally predict and forecast the effects on the incidence of infectious diarrhea.

[Study on the epidemiological characteristics and molecular typing of Salmonella enterica subsp. enterica serovar Senftenberg in Shanghai].

OBJECTIVE: To study the molecular epidemiological characteristics of Salmonella enterica subsp. enterica serovar Senftenberg (Salmonella senftenberg) in Shanghai, from 2006 to 2007. METHODS: A retrospective analysis in 2006 and 2007 was performed to explore the source of food-borne Salmonella senftenberg. The isolates from diarrhea patients between 2006 and 2007 were identified, including biochemical test, hilA and invA gene phenotyping, drug susceptibility, Riboprinter((R)) (RP) and pulsed-field gel electrophoresis (PFGE). RESULTS: Of the diarrhea patients isolates in the monitoring program on non-typhi Salmonella infection in the year of 2006 in Shanghai, number of patients caused by Salmonella senftenberg ranked the third. The stock of Salmonella senftenberg food-born isolates were derived from swine and beef products between 2003 and 2005. All of the strains from diarrhea patients were susceptible to antibiotics except tetracylina (75.6%). With RP and PFGE molecular typing, the two groups (with hydrogen sulfide and hilA, invA gene or without) could be divided into two different independent clone cluster in genetics. 34 strains of diarrhea were divided into 16 PFGE typing-pattern, and among them 12 strains including type 4 (4 strains), type 5 (1 strains), type 6 (6 strains), type 7 (1 strains) and 13 strains including type 11 (3 strains), type 17 (5 strains), type 23 (5 strains) were two different dominant clone cluster. CONCLUSION: The epidemic of Salmonella senftenberg within 2006 might have been the result of a long period of case occurrence in Shanghai. This rare outbreak belonged to a cluster of outbreaks caused by two different PFGE clone clusters. Data suggested that the genetic clone of Salmonella senftenberg might have been unstable and the source of contamination were complicated, with the characteristics as the obvious decreasing number of patients, with no food-borne isolates in 2007.