Biting mosquitos spread the malaria parasite to humans. Along the Mekong River in Southeast Asia, spending time in the surrounding forest increases a person's risk of malaria. This has led to a debate about whether deforestation in this area, which is called the Greater Mekong Sub-region (GMS), will increase or decrease malaria transmission. The answer to the debate is not clear because some malaria-transmitting mosquitos thrive in heavily forested areas, in particular in the GMS, while others prefer less forested areas.

Scientists studying malaria in the Amazon in South America suspect that malaria transmission increases shortly after deforestation but decreases six to eight years later. Some studies have tested this ‘frontier malaria’ theory but the results have been conflicting. Fewer studies have tested this theory in Southeast Asia. But deforestation has been blamed for recent malaria outbreaks in the GMS.

Using data on malaria testing and forest cover in the GMS, Rerolle et al. show that deforestation around villages increases malaria transmission in the first two years and decreases malaria rates later. This trend was driven mostly by a type of malaria called Plasmodium falciparum and was less strong for Plasmodium vivax. The location of deforested areas also mattered. Deforestation within one to 10 kilometer of villages did not affect malaria rates. Deforestation further away in about a 30 kilometer radius did affect malaria transmission. Rerolle et al. suggest this may be because villagers have to spend longer times trekking through forests to hunt or harvest wood when the wider area is deforested.

Currently, National Malaria Control Programs in the GMS focus their efforts on reducing forest-related transmission. This study strengthens the evidence supporting this approach. The results also suggest that different malaria elimination strategies may be necessary for different types of malaria parasite. Using this new information could help malaria control programs better target resources or educate villagers on how to protect themselves. The innovative methods used by Rerolle et al. reveal a more complex role of deforestation in malaria transmission and may inspire other scientists to think more carefully about environmental drivers of malaria.

Engaging in forest activities, such as logging, hunting, or spending the night in the forest, is considered a primary risk factor for malaria infection in the Greater Mekong Sub-region (GMS) (Chaveepojnkamjorn and Pichainarong, 2004; Das et al., 2004; Lansang et al., 1997; Erhart et al., 2005; Trung et al., 2004) with recent outbreaks attributed to deforestation activities (Lyttleton, 2016). This is most likely the result of increased human exposure to the forest dwelling Anopheles dirus and Anopheles minimus, the most efficient and widespread malaria vectors in the GMS (Obsomer et al., 2007; Obsomer et al., 2013). However, deforestation may also alter this ‘forest malaria’ (Sharma and Kondrashin, 1991) ecosystem and eventually reduce malaria incidence, as is generally accepted to be the case in Southeast Asia (Guerra et al., 2006). Several previous studies have assessed the relationship between deforestation and malaria, but the majority focused outside of the GMS, most notably in the Amazonian forest (Wayant et al., 2010; Olson et al., 2010; Hahn et al., 2014a; Valle and Clark, 2013; Terrazas et al., 2015) where the evidence has been met with conflicting interpretations (Tucker Lima et al., 2017). As national malaria programs across the GMS target forest-going populations for prevention and treatment efforts (Guyant et al., 2015; WHO, 2016), improved understanding of the relationship between deforestation and malaria is critical for programs to assess and meet national 2030 malaria elimination goals (World Health Organization, 2016; WHO, 2018).

In the Amazon, the ‘frontier malaria’ hypothesis (Sawyer, 1988) posits that malaria temporarily increases following deforestation efforts to open a human settlement area in the forest. Subsequently, after approximately 6–8 years, settlements become more urbanized and isolated from the surrounding forest, and less suitable for malaria vectors, resulting in reduced malaria transmission (de Castro et al., 2006). Recent work has challenged this hypothesis, however, and found that some older settlements were also likely to have high malaria incidence (Ilacqua et al., 2018), highlighting the importance of assessing the relationship between deforestation and malaria at different spatio-temporal scales (Singer and de Castro, 2006).

A recent review of the literature on deforestation and malaria in the Amazon (Tucker Lima et al., 2017) recommended the integration of multiple socio-economic, demographic and ecological mechanisms to disentangle the relationship between deforestation and malaria. The complexity of land-use changes driving deforestation such as urbanization, agriculture, irrigation or resource mining can alter the environment in different ways. For example, deforestation in the Amazon has been shown to increase mosquitoes’ larval habitat (Vittor et al., 2009) through the creation of areas with abundant sunlight and pooling water, resulting in increased human biting activity (Vittor et al., 2006). Alternatively, immigration and rapid population movements, stirring human-vector interactions, are other mechanisms affecting malaria transmission in frontier areas (Moreno et al., 2007). A modeling study (Baeza et al., 2017) showed that the temporal pattern of increased incidence followed by a decrease can vary depending on ecological and socio-economic parameters in frontiers areas.

The importance of addressing complex confounding structures influencing the relationship between deforestation and malaria was also highlighted by Bauhoff and Busch, 2020. Variables such as temperature (Beck-Johnson et al., 2013; Mordecai et al., 2013), precipitation (Parham and Michael, 2010; Parham et al., 2012), or seasonality Hay et al., 1998 are known environmental predictors of malaria, although the spatio-temporal scale of those effects often varies across different areas (Teklehaimanot et al., 2004). Furthermore, remote areas may experience higher malaria rates because of poor access to public health services, but also have denser forest cover or lower deforestation rates (Busch and Ferretti-Gallon, 2017). Finally, forest-going populations in the GMS are also at higher risk for malaria (Parker et al., 2017) due to poor adherence to protective measures against mosquitoes such as insecticide-treated bed nets (ITNs) or long-lasting insecticidal hammocks (LLIHs) (Peeters Grietens et al., 2010; WHO, 2016) and inadequate access to treatment (Guyant et al., 2015).

Bauhoff and Busch, 2020 identified only 10 empirical studies that assessed the relationship between deforestation and malaria with appropriate adjustments for confounding. Of these, seven reported a positive association (Austin et al., 2017; Wayant et al., 2010; Olson et al., 2010; Terrazas et al., 2015; Pattanayak et al., 2010; Garg, 2015; Fornace et al., 2016), two did not find any associations (Bauhoff and Busch, 2020; Hahn et al., 2014a), and one disputed study found a negative association (Valle and Clark, 2013; Hahn et al., 2014b; Valle, 2014). Most recently, a study found deforestation to increase malaria risk and malaria to decrease deforestation activities in the Amazon, using an instrumental variable analysis to disentangle any reverse causality loop (MacDonald and Mordecai, 2019). However, only half of the above-mentioned studies used high-resolution forest data, with most studies using spatially aggregated data and exploring only a limited range of spatial and temporal scales. Only three of these studies were conducted in Southeast Asia (Pattanayak et al., 2010; Garg, 2015; Fornace et al., 2016), and none in the GMS. Importantly, all three found that malaria increases after deforestation, but all had limitations. The two studies in Indonesia Pattanayak et al., 2010; Garg, 2015 used coarsely aggregated forest data and potentially biased self-reported malaria data. The third study, in Malaysia (Fornace et al., 2016), focused on a specific and geographically confined malaria parasite, Plasmodium knowlesi, whose primary host is the long-tailed macaque and whose presence in the GMS, where P. falciparum and P. vivax dominate, is limited.

In this analysis, we examined the relationship between deforestation and malaria incidence by combining high-resolution forest coverage data (Hansen et al., 2013) and monthly malaria incidence data from 2013 to 2016 from two separate regions in the GMS: northern Lao People’s Democratic Republic (PDR) with very low malaria transmission and southern Lao PDR where P. falciparum and P. vivax are seasonal. By conducting the analysis at the village level, we were able to explore a wide range of spatial scales (1, 10, and 30 km around villages) that might be relevant in characterizing the relationship between deforestation and malaria. In addition, we leveraged the longitudinal nature of both the incidence data collected and the forest data produced from annual remote sensing imagery (Hansen et al., 2013) to explore the most relevant temporal scales. Finally, we considered alternative definitions of deforestation, restricted to areas with at least certain levels of forest cover, to investigate the type of deforestation driving the relationship with malaria.

To date, no prior studies have quantified the relationship between deforestation and malaria incidence in the GMS. Understanding this relationship is especially important in the GMS, where forest-going activities are a main source of income generation (Lao PDR, 2016) and malaria clusters in forest-going populations (World Health Organization, 2016; WHO, 2016). To assess the relationship between deforestation and malaria incidence, we modeled the monthly village-level malaria incidence in two regions of Lao PDR using health facility surveillance data and evaluated the most relevant spatio-temporal scale.

Based on a large dataset of health facility surveillance records in two regions of Lao PDR, we found evidence that deforestation around villages is associated with higher malaria incidence over the short-term but lower incidence over the long-term (e.g, in the south, within 30 km of villages: IRR = 1.16 [1.10; 1.22] for deforestation in the previous year and IRR = 0.93 [0.90; 0.97] for deforestation in the previous 3 years). Our evaluation of alternative spatial scales identified strong associations for deforestation within a 30 km radius around villages but not for deforestation in the near (10 km) and immediate (1 km) vicinity. Our results incorporated correction for the probability of seeking treatment, modeled as a function of distance to the closest health facility, as well as adjustment for several environmental covariates. Results appear driven by deforestation in densely forested areas and the patterns exhibited are clearer for infections with P. falciparum than for P. vivax.

The wide availability and longitudinal nature of malaria surveillance records collected routinely by the national program enabled exploration of the relationship between deforestation and malaria incidence over multiple spatio-temporal scales and across different levels of forest density. The spatio-temporal variability highlighted here provides insights into the causal mechanisms driving local-scale malaria incidence in the GMS. This approach not only quantified the deforestation-malaria incidence association in the GMS, but also strengthened the evidence for the key influence of forest-going populations on malaria transmission in the GMS.

This study’s results echo the frontier malaria hypothesis from the Amazon region, which posits an increase in malaria incidence in the first few years following deforestation and a decrease over the long term. However, we found an earlier inflexion point, 1–3 years after deforestation compared to 6–8 years in the Amazon (de Castro et al., 2006), most likely because of very different underlying human processes. Indeed, the frontier malaria hypothesis considers non-indigenous human settlements sprouting deeper and deeper in the forest whereas forest-going populations in the GMS are primarily members of established forest-fringe communities who regularly tour the forest overnight to hunt and collect wood (Dysoley et al., 2008). Industrial and agricultural projects or lucrative forest-based activities also attract mobile and migrant populations (MMPs) Guyant et al., 2015 in remote forested areas of the GMS but not on the same scale as the politically and economically driven unique colonization of the Amazon (de Castro et al., 2006).

Our results are also consistent with the three previous multivariable empirical studies (Garg, 2015; Pattanayak et al., 2010; Fornace et al., 2016) that assessed the effect of deforestation on malaria in Southeast Asia. Our study builds on these findings by using higher resolution forest data and exploring additional spatio-temporal scales. Using biennial village census data from Indonesia between 2003 and 2008 and district-aggregated remote sensing forest data, Garg, 2015 reported a 2–10.4% increase in the probability of a malaria outbreak in each village of districts that lost 1000 hectares of their forest cover in the same year. Using data from a 1996 cross-sectional household survey conducted in a quasi-experimental setting around a protected area in Indonesia, Pattanayak et al., 2010 found a positive association between disturbed forest (vs undisturbed) and malaria in children under 5, again using no temporal lag. Our analysis plan was largely inspired by Fornace et al., 2016, which used similar high-resolution forest data (Hansen et al., 2013) and 2008–2012 incidence data from Sabah, Malaysia. They reported a 2.22 (95% CI: [1.53; 2.93]) increase in the P. knowlesi incidence rate for villages where more than 14% (<8%, being the reference) of the surrounding area (within 2 km) experienced forest loss in the previous 5 years. On the other hand, our analysis explored wider spatial scales, bypassed any coarse categorization of forest and deforestation variables, corrected incidence for treatment-seeking probability, and most importantly focused on P. falciparum and P. vivax, the dominant malaria parasites in the GMS.

Engaging in forest activities, such as logging, hunting or spending the night in the forest, has been reported as a major risk factor by many studies in the region (Chaveepojnkamjorn and Pichainarong, 2004; Das et al., 2004; Lansang et al., 1997; Erhart et al., 2005; Trung et al., 2004). As countries of the GMS work toward malaria elimination, the literature stresses the key role of forest-going populations (Guyant et al., 2015; Nofal et al., 2019; Bannister-Tyrrell et al., 2019; Wen et al., 2016; Smith and Whittaker, 2014), although research programs highlight the challenges of accessing them (Bennett et al., 2021 ; Lover et al., 2019) as well as their diversity (Nofal et al., 2019; Bannister-Tyrrell et al., 2019). To our knowledge, no previous study has leveraged geo-spatial statistical analyses to characterize the importance of forest-going populations in the GMS. Our results suggest that deforestation in dense forests (Table 3) around villages, particularly areas further from the village (Table 1), is a driver of malaria in Lao PDR. We argue that this is indicative of the existence of a key high-risk group linking the deforestation patterns identified to malaria in the villages, namely a forest-going population. Deforestation captured by remote sensing in this setting likely reflects locations and times of heightened activity in the forest areas near villages, and therefore greater human-vector contact. We suspect longer and deeper trips into the forest result in increased exposure to mosquitoes, putting forest-goers at higher risk.

We conducted this study in northern and southern Lao PDR, where the malaria species composition differs, and assessed species-specific relationships in the south where P. falciparum and P. vivax are co-endemic. Our results highlight the challenges ahead of national programs with P. vivax elimination after successful P. falciparum elimination, as increasingly mentioned in the literature (Cotter et al., 2013; Kaehler et al., 2019). This study identified a clear pattern of spatio-temporal associations between P. falciparum and deforestation, but these were not apparent for P. vivax (Table 2). The increase in P. vivax incidence in the first 2 years following deforestation was identified as well but the associations were smaller than for P. falciparum. Importantly, deforestation was never associated with lower risks of P. vivax. A recent study in the Amazon MacDonald and Mordecai, 2019 reported a similar attenuation of the effects of deforestation on P. vivax compared to P. falciparum, most likely because of P. vivax parasites’ ability to relapse months or even years after infection, which decouples the association between transmission and incidence data. These species-specific differences may also explain why the pattern of spatio-temporal associations between malaria and deforestation were markedly clearer in the south than in the north where P. vivax dominates.

Our results did have some inherent limitations based upon routine health facility surveillance data. First, reliability of such records varies across and within countries of the GMS and may depend on malaria incidence level. This could lead to unmeasured residual confounding, further exacerbated by the lack of available data on malaria control activities in the region. Another challenge with these data is obtaining an accurate denominator for incidence, as not everyone attends a public health center when febrile. We addressed this issue by modeling the probability of seeking treatment as a function of travel time to the closest health facility using data from two cross-sectional surveys. Last, the village-level geo-referencing of malaria registries ignores the possibility that patients may become infected elsewhere. Unfortunately, these surveillance records did not include information about patients’ forest-going trips. Research to track and analyse micro-scale movements of forest-goers is needed to understand how they interact with the forest and where are the foci of infection.

The forest data we used has also been criticized, in particular for not distinguishing tropical forests from agroforestry (Tropek et al., 2014; Hansen et al., 2014) or man-made from natural causes of deforestation. The lack of temporal resolution for the forest gain variable (2000–2017 aggregate) as well as the assumption that forest loss happens all in 1 year are additional limitations of these data. Finally, our relative measure of deforestation, key to consistently compare the effects across different spatial scales, also implies that a 0.1% of the area that experienced forest loss within 30 km of a village is a much larger area (∼280 hectares) than within 1 km (∼0.3 hectare) and should be interpreted cautiously.

In conclusion, this study assessed the relationship between deforestation and malaria in Lao PDR. Our approach leveraged surveillance records collected by the national malaria program and high-resolution forest data and rigorously explored the spatio-temporal pattern of associations. As countries of the GMS work toward malaria elimination, our results highlight the challenges to transition from P. falciparum to P. vivax elimination, confirm and characterize the importance of high-risk populations engaging in forest activities and suggest malaria programs may benefit from monitoring areas of on-going deforestation using remotely sensed data.

All data generated or analysed during this study are included in the manuscript and supporting files. Source data files have been provided for Figures 2, 3, 4 and 5 and for Tables 1, 2 and 3.

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Author details

1. Francois Rerolle

   1. Malaria Elimination Initiative, The Global Health Group, University of California, San Francisco, San Francisco, United States
   2. Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   For correspondence

   francois.rerolle@ucsf.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3837-5700

2. Emily Dantzer

   Malaria Elimination Initiative, The Global Health Group, University of California, San Francisco, San Francisco, United States

   Contribution

   Data curation, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

3. Andrew A Lover

   Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst, United States

   Contribution

   Data curation, Funding acquisition, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2181-3559

4. John M Marshall

   Division of Epidemiology and Biostatistics, School of Public Health, University of California, Berkeley, Berkeley, United States

   Contribution

   Supervision, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0603-7341

5. Bouasy Hongvanthong

   Center for Malariology, Parasitology and Entomology, Ministry of Health, Vientiane, Lao People's Democratic Republic

   Contribution

   Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

6. Hugh JW Sturrock

   1. Malaria Elimination Initiative, The Global Health Group, University of California, San Francisco, San Francisco, United States
   2. Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, United States

   Contribution

   Supervision, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

7. Adam Bennett

   1. Malaria Elimination Initiative, The Global Health Group, University of California, San Francisco, San Francisco, United States
   2. Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, United States

   Contribution

   Conceptualization, Supervision, Funding acquisition, Methodology, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

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