Meteorological Factors Affecting Infectious Diarrhea in Different Climate Zones of China

The effect of temperature on infectious diarrhea disease: A systematic review

This study aimed to ascertain the delayed effects of various exposure temperatures on infectious diarrhea. We performed a Bayesian random-effects network meta-analysis to calculate relative risks (RR) with 95 % confidence intervals (95 % CI). The heterogeneity was analyzed by subgroup analysis. There were 25 cross-sectional studies totaling 6858735 patients included in this analysis, with 12 articles each investigating the effects of both hyperthermia and hypothermia. Results revealed that both high temperature (RRsingle = 1.22, 95%CI:1.04-1.44, RRcum = 2.96, 95%CI:1.60-5.48, P < 0.05) and low temperature (RRsingle = 1.17, 95%CI:1.02-1.37, RRcum = 2.19, 95%CI:1.33-3.64, P < 0.05) significantly increased the risk of infectious diarrhea, while high temperature caused greater. As-sociations with strengthening in bacillary dysentery were found for high temperatures (RRcum = 2.03, 95%CI:1.41-3.01, P < 0.05; RRsingle = 1.17, 95%CI:0.90-1.62, P > 0.05), while the statistical significance of low temperatures in lowering bacterial dysentery had vanished. This investigation examined that high temperature and low temperature were the conditions that posed the greatest risk for infectious diarrhea. This research offers fresh perspectives on preventing infectious diarrhea and will hopefully enlighten future studies on the impact of temperature management on infectious diarrhea.

Predicting the incidence of infectious diarrhea with symptom surveillance data using a stacking-based ensembled model

BACKGROUND

Infectious diarrhea remains a major public health problem worldwide. This study used stacking ensemble to developed a predictive model for the incidence of infectious diarrhea, aiming to achieve better prediction performance.

METHODS

Based on the surveillance data of infectious diarrhea cases, relevant symptoms and meteorological factors of Guangzhou from 2016 to 2021, we developed four base prediction models using artificial neural networks (ANN), Long Short-Term Memory networks (LSTM), support vector regression (SVR) and extreme gradient boosting regression trees (XGBoost), which were then ensembled using stacking to obtain the final prediction model. All the models were evaluated with three metrics: mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE).

RESULTS

Base models that incorporated symptom surveillance data and weekly number of infectious diarrhea cases were able to achieve lower RMSEs, MAEs, and MAPEs than models that added meteorological data and weekly number of infectious diarrhea cases. The LSTM had the best prediction performance among the four base models, and its RMSE, MAE, and MAPE were: 84.85, 57.50 and 15.92%, respectively. The stacking ensembled model outperformed the four base models, whose RMSE, MAE, and MAPE were 75.82, 55.93, and 15.70%, respectively.

CONCLUSIONS

The incorporation of symptom surveillance data could improve the predictive accuracy of infectious diarrhea prediction models, and symptom surveillance data was more effective than meteorological data in enhancing model performance. Using stacking to combine multiple prediction models were able to alleviate the difficulty in selecting the optimal model, and could obtain a model with better performance than base models.

A microfluidic chip-based multiplex PCR-reverse dot blot hybridization technique for rapid detection of enteropathogenic bacteria

Diarrhea caused by enteropathogenic bacteria is a major public health issue worldwide, especially in developing countries. In this study, a microfluidic chip-based multiplex polymerase chain reaction (PCR)-reverse dot blot hybridization technology for the rapid and simultaneous detection of 11 enteropathogenic bacteria was developed and the entire process was completed within 3-4 h. The specificity of this method was analyzed using 11 types of pure target bacterial colonies and another 7 types of pure bacterial colonies, and its sensitivity was evaluated with the serial 10-fold dilution of 11 types of pure target bacterial colonies. The detection limit of this method was as low as 10³-10² CFU/mL, and it exhibited high specificity for enteropathogenic bacteria. A total of 60 clinical diarrheal fecal samples were detected using this method, the results of which were compared with those of the conventional reference method, which resulted in a positive coincident rate of 100% and a negative coincident rate of 93.75%. Based on the findings, it could be concluded that multiplex PCR-reverse dot blot hybridization based on the microfluidic chip is a rapid, economical, sensitive, specific, and high-throughput method for detecting enteropathogenic bacteria.

Exploring the Association between Ambient Temperature and Daily Hospital Admissions for Diarrhea in Mopani District, Limpopo Province, South Africa

Diarrhea contributes significantly to global morbidity and mortality. There is evidence that diarrhea prevalence is associated with ambient temperature. This study aimed to determine if there was an association between ambient temperature and diarrhea at a rural site in South Africa. Daily diarrheal hospital admissions (2007 to 2016) at two large district hospitals in Mopani district, Limpopo province were compared to average daily temperature and apparent temperature (Tapp, 'real-feel' temperature that combined temperature, relative humidity, and wind speed). Linear regression and threshold regression, age-stratified to participants ≤5 years and >5 years old, considered changes in daily admissions by unit °C increase in Tapp. Daily ranges in ambient temperature and Tapp were 2-42 °C and -5-34 °C, respectively. For every 1 °C increase in average daily temperature, there was a 6% increase in hospital admissions for diarrhea for individuals of all ages (95% CI: 0.04-0.08; p < 0.001) and a 4% increase in admissions for individuals older than 5 years (95% CI: 0.02-0.05; p < 0.001). A positive linear relationship between average daily Tapp and all daily diarrheal admissions for children ≤5 years old was not statistically significant (95% CI: -0.00-0.03; p = 0.107). Diarrhea is common in children ≤5 years old, however, is more likely triggered by factors other than temperature/Tapp, while it is likely associated with increased temperature in individuals >5 years old. We are limited by lack of data on confounders and effect modifiers, thus, our findings are exploratory. To fully quantify how temperature affects hospital admission counts for diarrhea, future studies should include socio-economic-demographic factors as well as WASH-related data such as personal hygiene practices and access to clean water.

Clinical characteristics and influencing factors of infectious diarrhea in preschool children: An observational study

Infectious diarrhea is a common disease in preschool children, but the pathogenic species, origins, and influencing factors remain debatable. Therefore, more studies are required to solve these debatable topics. A number of 260 eligible preschool children diagnosed with infectious diarrhea in our hospital were enrolled in the infection group. Meanwhile, a number of 260 matched healthy children from the health center were enrolled in the control group. The pathogenic species and origins, the time of onset of infectious diarrhea in the infection group, demographic data, exposure history, hygiene habits, dietary habits, and other variables in both groups were initially collected from medical documents. In addition, a questionnaire was used to complete and confirm study variables through face-to-face or telephone interviews. Then, the univariate and multivariate regression analyses were used to screen the influencing factors of infectious diarrhea. Among 260 infected children, salmonella (15.77%), rotavirus (13.85%), shigella (11.54%), vibrio (10.38%), and norovirus (8.85%) were the top 5 common pathogens; January (13.85%), December (12.69%), August (12.31%), February (11.92%), and July (8.46%) were the top 5 frequent times of infectious diarrhea. The distribution of onset time for infectious diarrhea was commonly found in winter and summer, and the pathogens always originated from foods. The results of multivariate regression analysis showed that recent exposure to diarrhea, flies, and/or cockroaches indoors were the 2 risk factors for infectious diarrhea; Meanwhile, rotavirus vaccination, regular hand-washing, tableware disinfection, separate preparation of cooked and raw foods, and regular intake of lactobacillus products were the 5 protective factors for infectious diarrhea in preschool children. Infectious diarrhea has a diversity of pathogenic species, origins, and influencing factors in preschool children. Activities focusing on these influencing factors such as rotavirus vaccination, consumption of lactobacillus products, and other conventional factors would be beneficial to preschool children's health.