‘Forest malaria’ in Myanmar? Tracking transmission landscapes in a diversity of environments
Issued Date
2023-12-01
Resource Type
eISSN
17563305
Scopus ID
2-s2.0-85170708962
Pubmed ID
37700295
Journal Title
Parasites and Vectors
Volume
16
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Parasites and Vectors Vol.16 No.1 (2023)
Suggested Citation
Legendre E., Girond F., Herbreteau V., Hoeun S., Rebaudet S., Thu A.M., Rae J.D., Lehot L., Dieng S., Delmas G., Nosten F., Gaudart J., Landier J. ‘Forest malaria’ in Myanmar? Tracking transmission landscapes in a diversity of environments. Parasites and Vectors Vol.16 No.1 (2023). doi:10.1186/s13071-023-05915-w Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/90087
Title
‘Forest malaria’ in Myanmar? Tracking transmission landscapes in a diversity of environments
Other Contributor(s)
Abstract
Background: In the Greater Mekong Subregion, case–control studies and national-level analyses have shown an association between malaria transmission and forest activities. The term ‘forest malaria’ hides the diversity of ecosystems in the GMS, which likely do not share a uniform malaria risk. To reach malaria elimination goals, it is crucial to document accurately (both spatially and temporally) the influence of environmental factors on malaria to improve resource allocation and policy planning within given areas. The aim of this ecological study is to characterize the association between malaria dynamics and detailed ecological environments determined at village level over a period of several years in Kayin State, Myanmar. Methods: We characterized malaria incidence profiles at village scale based on intra- and inter-annual variations in amplitude, seasonality, and trend over 4 years (2016–2020). Environment was described independently of village localization by overlaying a 2-km hexagonal grid over the region. Specifically, hierarchical classification on principal components, using remote sensing data of high spatial resolution, was used to assign a landscape and a climate type to each grid cell. We used conditional inference trees and random forests to study the association between the malaria incidence profile of each village, climate and landscape. Finally, we constructed eco-epidemiological zones to stratify and map malaria risk in the region by summarizing incidence and environment association information. Results: We identified a high diversity of landscapes (n = 19) corresponding to a gradient from pristine to highly anthropogenically modified landscapes. Within this diversity of landscapes, only three were associated with malaria-affected profiles. These landscapes were composed of a mosaic of dense and sparse forest fragmented by small agricultural patches. A single climate with moderate rainfall and a temperature range suitable for mosquito presence was also associated with malaria-affected profiles. Based on these environmental associations, we identified three eco-epidemiological zones marked by later persistence of Plasmodium falciparum, high Plasmodium vivax incidence after 2018, or a seasonality pattern in the rainy season. Conclusions: The term forest malaria covers a multitude of contexts of malaria persistence, dynamics and populations at risk. Intervention planning and surveillance could benefit from consideration of the diversity of landscapes to focus on those specifically associated with malaria transmission. Graphical Abstract: [Figure not available: see fulltext.].