Spatio-temporal associations between deforestation and malaria incidence in Lao PDR

As countries in the Greater Mekong Sub-region (GMS) increasingly focus their malaria control and elimination efforts on reducing forest-related transmission, greater understanding of the relationship between deforestation and malaria incidence will be essential for programs to assess and meet their 2030 elimination goals. Leveraging village-level health facility surveillance data and forest cover data in a spatio-temporal modeling framework, we found evidence that deforestation is associated with short-term increases, but long-term decreases confirmed malaria case incidence in Lao People’s Democratic Republic (Lao PDR). We identified strong associations with deforestation measured within 30 km of villages but not with deforestation in the near (10 km) and immediate (1 km) vicinity. Results appear driven by deforestation in densely forested areas and were more pronounced for infections with Plasmodium falciparum (P. falciparum) than for Plasmodium vivax (P. vivax). These findings highlight the influence of forest activities on malaria transmission in the GMS.


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Malaria incidence data was abstracted from the surveillance system in all the malaria registries available collected at health facilities in the study area over the study period.
(See subsection "Malaria case data" of the "Materials and Methods" section) Treatment-seeking data, used to adjust malaria incidence data, was abstracted from two cross-sectional surveys. The sample-size of those two cross-sectional surveys have been decided to answer research questions outside the scope of this study and their estimation do not pertain to this study. (See subsection "Study site and population" and subsection "Treatment-seeking data" of the "Materials and Methods" section)

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Instead, data from different sources were combined to assess how much of the variation in outcome Y (monthly village-level malaria incidence) could be explained by the variation in exposure X (percent area around villages that experienced deforestation). (See figure 8 in Methods section) No outliers were encountered.
12% of malaria registries data were excluded from the analysis because of missing GPS coordinates (and therefore no exposure data). An additional 0.3% were excluded because of missing date (and therefore no outcome data). (See subsection "Georeferencing" of the "Results" section)

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Please see subsection "Spatio-temporal analysis" of the "Results" section and supplemental material "S2: Travel times and treatment-seeking results" for statistical tests used.
Note that we purposely stayed away from any p-value. Not only can they be misleading, they do not provide any more information that the 95% confidence intervals, which have been reported for all estimates. In addition and importantly, our main results stress on the patterns of associations rather than on the statistical significance of any single association.

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