A web-based surveillance model of eosinophilic meningitis: future prediction and distribution patterns

Background: web-based surveillance is a useful tool for predicting future cases of various emerging infectious diseases. There are limited data available on web-based surveillance and patterns of distribution of eosinophilic meningitis (EOM), which is an emerging infectious disease in various countries around the world.  Methods: this study applied web-based surveillance to the prediction of EOM incidence and the analysis of its distribution pattern by using a national database, which may be used for future prevention and control. The number cases of EOM in each month over a period of 12 years (between 2006 to 2017) from Loei province were retrieved from the National Disease Surveillance (Report 506) website, operated by Thailand's Public Health Center.  Results: we developed autoregressive integrated moving average (ARIMA) models and seasonal ARIMA (SARIMA) models. The best model was used for predicting numbers of future cases. The forecast values from the SARIMA (1, 1, 2)(0,1,1)6 model were close to actual values and were the most valid, as they had the lowest RMSE and AIC. The predictive model for future cases of EOM was related to previous numbers of EOM cases over the past eight months. The disease exhibited a seasonal pattern during the study period.  Conclusions: web-based surveillance can be used for future prediction of EOM, that the predictive model applied here was valid, and that EOM exhibits a seasonal pattern

Additional risk factors associated with symptomatic hydrochlorothiazide-induced hyponatremia in hypertensive patients

Background. Hydrochlorothiazide is a cheap and effective antihypertensive agent but may cause hyponatremia. Even though several risk factors for hydrochlorothiazide-induced hyponatremia have been reported, this study aimed to evaluate additional risk factors for hydrochlorothiazide-induced hyponatremia in hypertensive patients. Material and methods. The inclusion criteria were: adult patients, diagnosed with hypertension and receiving hydrochlorothiazide treatment. Eligible patients were divided into two groups: with and without hyponatremia. Those with hyponatremia were identified by using the ICD-10 code E871, while those without hyponatremia were patients who did not have any reported hyponatremia until the last visit. The ratio between hyponatremia and non-hyponatremia group was 1:2. Predictors for hyponatremia were analyzed by using logistic regression analysis. Results. During the study period, there were 68 patients admitted due to symptomatic hyponatremia from hydrochlorothiazide. There were four independent factors in the model predictive of occurrence of symptomatic hydrochlorothiazide-induced hyponatremia in hypertensive patients: sex, body mass index, plasma glucose, and serum albumin. Male sex, body mass index, and serum albumin were negatively associated with occurrence of symptomatic hydrochlorothiazide-induced hyponatremia in hypertensive patients with adjusted OR of 0.099, 0.683, and 0.122, respectively. The plasma glucose had adjusted OR of 1.030 [95% CI of (1.009, 1.051)]. Conclusions. Factors associated with hydrochlorothiazide-induced symptomatic hyponatremia in hypertensive patients were sex, body mass index, plasma glucose level, and serum albumin level. The latter two risk factors have never been reported as risk factors for hydrochlorothiazide-induced symptomatic hyponatremia in hypertensive patients