Comparison of deforestation and forest land use factors for malaria elimination in Myanmar.

Objectives: Within the remote region of Ann Township in Myanmar's Rakhine State, malaria prevalence has remained steady at ∼10% of the population from 2016-2019. Previous studies have linked areas of higher malaria prevalence in the region to heavily forested areas, however, little is known about how people live, work, and move through these areas. This work aims to disentangle landscape from land use in regard to malaria exposure. Methods: We investigated the roles of forest cover, forest loss, and land use activities with malaria prevalence through the combined use of land use surveys, malaria surveillance, and satellite earth observations. Results: Our results confirm previous research that linked areas of high forest cover with high malaria prevalence. However, areas experiencing high levels of deforestation were not associated with malaria prevalence. The land use factors that contribute most significantly to increased malaria risk remained those which put people in direct contact with forests, including conducting forest chores, having an outdoor job, and having a primary occupation in the logging and/or plantation industry. Conclusion: Malaria prevention methods in Myanmar should focus on anyone who lives near forests or engages in land use activities that bring them within proximity of forested landscapes, whether through occupation or chores.

Malaria Exposure in Ann Township, Myanmar, as a Function of Land Cover and Land Use: Combining Satellite Earth Observations and Field Surveys.

Despite progress toward malaria elimination in the Greater Mekong Subregion, challenges remain owing to the emergence of drug resistance and the persistence of focal transmission reservoirs. Malaria transmission foci in Myanmar are heterogeneous and complex, and many remaining infections are clinically silent, rendering them invisible to routine monitoring. The goal of this research is to define criteria for easy-to-implement methodologies, not reliant on routine monitoring, that can increase the efficiency of targeted malaria elimination strategies. Studies have shown relationships between malaria risk and land cover and land use (LCLU), which can be mapped using remote sensing methodologies. Here we aim to explain malaria risk as a function of LCLU for five rural villages in Myanmar's Rakhine State. Malaria prevalence and incidence data were analyzed through logistic regression with a land use survey of ~1,000 participants and a 30-m land cover map. Malaria prevalence per village ranged from 5% to 20% with the overwhelming majority of cases being subclinical. Villages with high forest cover were associated with increased risk of malaria, even for villagers who did not report visits to forests. Villagers living near croplands experienced decreased malaria risk unless they were directly engaged in farm work. Finally, land cover change (specifically, natural forest loss) appeared to be a substantial contributor to malaria risk in the region, although this was not confirmed through sensitivity analyses. Overall, this study demonstrates that remotely sensed data contextualized with field survey data can be used to inform critical targeting strategies in support of malaria elimination.