An ecological study of eosinophilic meningitis caused by the nematode, Angiostrongylus cantonensis (Chen, 1935) (Nematoda: Metastrongylidae). (October 2019)

• Record Type: Journal Article
• Authors: Aekphachaisawat, Noppadol; Sawanyawisuth, Kittisak; Khamsai, Sittichai; Chattakul, Paiboon; Takahashi, Ken; Chotmongkol, Verajit; Tiamkao, Somsak; Limpawattana, Panita; Senthong, Vichai; Chindaprasirt, Jarin; Theeranut, Ampornpan; Ngamjarus, Chetta
• Is Part Of: Parasitology international. Volume 72(2019)
• Publication Date: 2019-10
• Subjects: Climate change -- Predictive model -- Snails -- Slugs -- Weather; Parasitology -- Periodicals; Parasites -- Periodicals; Parasitic Diseases -- Periodicals; Parasitology -- Periodicals; Parasitologie -- Périodiques; 571.99905
• DOI: 10.1016/j.parint.2019.101944
• Languages: English
• ISSNs: 1383-5769

Abstract

Abstract: Climate change and other weather factors are associated with several infectious diseases, but are rarely reported as being associated with nematode infection. Eosinophilic meningitis (EOM) is an emerging disease worldwide caused by the nematode, Angiostrongylus cantonensis . It is transmitted through various agents such as snails and slugs. Temperature and rainfall are associated with snail population. There have been no previous studies on the relationship between weather and EOM. This was an ecological study. Numbers of EOM patients and weather data in Thailand's Loei province from 2006 to 2017 were obtained using a national database. A Spearman correlation was used to explore the relationship between EOM and weather variables. We developed a Poisson time series model combined with a distributed lag model (DLM) for estimating the effects of weather on EOM. We also created an autoregressive integrated moving average with exogeneous variable (ARIMAX) model for predicting future EOM cases over the following 12 months. There were 1126 EOM patients in the study. Among several weather factors, wind was significantly negatively correlated with the number of EOM patients (rs : −0.204, 95% CI: −0.361 to −0.058; p value: 0.014). The ARIMAX(3, 0, 0) model with wind speed as a variable was appropriate for predicting the number of EOM patients. The predicted and actual numbers of EOM patients in 2018 were highly concordant. In conclusion, wind speed is significantly negatively correlated with the number of EOM patients.