Spatial–temporal patterns and risk factors for human leptospirosis in Thailand, 2012–2018
Issued Date
2022-12-01
Resource Type
eISSN
20452322
Scopus ID
2-s2.0-85127028654
Pubmed ID
35332199
Journal Title
Scientific Reports
Volume
12
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Scientific Reports Vol.12 No.1 (2022)
Suggested Citation
Chadsuthi S., Chalvet-Monfray K., Geawduanglek S., Wongnak P., Cappelle J. Spatial–temporal patterns and risk factors for human leptospirosis in Thailand, 2012–2018. Scientific Reports Vol.12 No.1 (2022). doi:10.1038/s41598-022-09079-y Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/86435
Title
Spatial–temporal patterns and risk factors for human leptospirosis in Thailand, 2012–2018
Other Contributor(s)
Abstract
Leptospirosis is a globally important zoonotic disease. The disease is particularly important in tropical and subtropical countries. Infections in humans can be caused by exposure to infected animals or contaminated soil or water, which are suitable for Leptospira. To explore the cluster area, the Global Moran’s I index was calculated for incidences per 100,000 population at the province level during 2012–2018, using the monthly and annual data. The high-risk and low-risk provinces were identified using the local indicators of spatial association (LISA). The risk factors for leptospirosis were evaluated using a generalized linear mixed model (GLMM) with zero-inflation. We also added spatial and temporal correlation terms to take into account the spatial and temporal structures. The Global Moran’s I index showed significant positive values. It did not demonstrate a random distribution throughout the period of study. The high-risk provinces were almost all in the lower north-east and south parts of Thailand. For yearly reported cases, the significant risk factors from the final best-fitted model were population density, elevation, and primary rice crop arable areas. Interestingly, our study showed that leptospirosis cases were associated with large areas of rice production but were less prevalent in areas of high rice productivity. For monthly reported cases, the model using temperature range was found to be a better fit than using percentage of flooded area. The significant risk factors from the model using temperature range were temporal correlation, average soil moisture, normalized difference vegetation index, and temperature range. Temperature range, which has strongly negative correlation to percentage of flooded area was a significant risk factor for monthly data. Flood exposure controls should be used to reduce the risk of leptospirosis infection. These results could be used to develop a leptospirosis warning system to support public health organizations in Thailand.