Citations

Abstract Background The recognition of autoimmune causes of encephalitis has led to epidemiological shifts in the worldwide characteristics of encephalitis. N-methyl-D-aspartate receptor (NMDAR) antibody encephalitis leads to well-established complex neuropsychiatric manifestations. In low- and middle-income countries, including Vietnam, its relative incidence, especially in children, is unknown and most neurologists currently consider infectious encephalitis prior to autoimmune etiologies. Methods The study was prospectively conducted at Children's Hospital 1 in Ho Chi Minh City between March 2020 and December 2022. Any child admitted to the Department of Infectious Diseases and Neurology fulfilling the case definition of encephalitis was eligible to participate. Cerebrospinal fluid samples were collected alongside meta-clinical data for analysis. Results We recruited 164 children with a clinical diagnosis of encephalitis. Etiologies were determined as NMDAR antibody encephalitis in 23 of 164 cases (14.0%), Japanese encephalitis virus in 14 of 164 (8.5%), and herpes simplex virus in 4 of 164 (2.4%). Clinical categorizations suggested idiopathic viral encephalitis in another 71 (43.3%), and autoimmune encephalitis of unknown origin in the remaining 52. Factors including demographics, specific clinical features, cerebrospinal fluid and electroencephalogram findings, and length of hospital stay were significantly different between NMDAR antibody encephalitis and Japanese encephalitis. Conclusions At a tertiary children's hospital in Vietnam, the prevalence of NMDAR antibody encephalitis exceeds that of Japanese encephalitis, the most common infectious encephalitis cause in Southeast Asia. NMDAR antibody encephalitis is associated with long hospital stay and poor outcomes. These findings should change pediatric diagnostics, to earlier consider autoimmune treatments in this clinical setting.

ABSTRACT. Encephalitis is a significant global health problem, especially in children. Knowledge of its economic burden is essential for policymakers in prioritizing the development and implementation of interventions but remains limited. An observational study was prospectively conducted at a major children’s hospital in Ho Chi Minh City, Vietnam from 2020 to 2022. Data on direct medical costs, direct nonmedical costs, and productivity costs were collected alongside demographic information, clinical features, diagnosis, severity, and outcomes of study participants. This was used to undertake a cost of illness analysis from a societal perspective. Data were collected from a total of 164 pediatric patients. The median cost of illness was estimated at US $1,859 (interquartile range [IQR]: US $1,273–$3,128). The direct costs were the main cost driver, accounting for 83.9% of the total cost of illness (US $1,560; IQR: US $975–$2,460). The productivity costs accounted for a median of US $275 (IQR: US $154–$474). The cost of illness was higher in more severe patients, patients with sequelae, patients with morbidities, and ventilated patients. Most direct medical costs were attributed to hospitalization and resulted in out-of-pocket payments from the patient’s family (30.2%; US $316). The results showed that the cost of illness of encephalitis in children is considerable and will be useful for policymakers in prioritizing resources for the development and implementation of intervention strategies to reduce the burden of pediatric encephalitis.

Abstract Background: Accuracy of microbial community detection in 16S rRNA marker-gene and metagenomic studies suffers from contamination and sequencing errors that lead to either falsely identifying microbial taxa that were not in the sample or misclassifying the taxa of DNA fragment reads. Filtering is defined as removing taxa that are present in a small number of samples and have small counts in the samples where they are observed. This approach reduces extreme sparsity of microbiome data and has been shown to correctly remove contaminant taxa in cultured "mock" datasets, where the true taxa compositions are known. Although filtering is frequently used, careful evaluation of its effect on the data analysis and scientific conclusions remains unreported. Here, we assess the effect of filtering on the alpha and beta diversity estimation, as well as its impact on identifying taxa that discriminate between disease states. Results: The effect of filtering on microbiome data analysis is illustrated on four datasets: two mock quality control datasets where same cultured samples with known microbial composition are processed at different labs and two disease study datasets. Results show that in microbiome quality control datasets, filtering reduces the magnitude of differences in alpha diversity and alleviates technical variability between labs, while preserving between samples similarity (beta diversity). In the disease study datasets, DESeq2 and linear discriminant analysis Effect Size (LEfSe) methods were used to identify taxa that are differentially expressed across groups of samples, and random forest models to rank features with largest contribution towards disease classiffcation. Results reveal that filtering retains significant taxa and preserves the model classification ability measured by the area under the receiver operating characteristic curve (AUC). The comparison between filtering and contaminant removal method shows that they have complementary effects and are advised to be used in conjunction. Conclusions: Filtering reduces the complexity of microbiome data, while preserving their integrity in downstream analysis. This leads to mitigation of the classification methods' sensitivity and reduction of technical variability, allowing researchers to generate more reproducible and comparable results in microbiome data analysis.