Malaria, a disease caused by Plasmodium parasites, persists despite global incidence decreases. Social vulnerability, poverty, and landscape changes for economic development and population growth fuel malaria transmission, particularly in developing countries. In 2018, there were an estimated 228 million cases globally, resulting in 405,000 deaths, primarily affecting children under five in Sub-Saharan Africa. Despite advancements in vaccines, treatments, and diagnostics, controlling malaria remains challenging and expensive. The WHO emphasizes the need for integrated global vector control. Over 90% of malaria cases occur in the Amazon, Congo, and Mekong basins, where deforestation, resource exploitation, migration, and land-use changes are linked to increased malaria incidence and vector abundance (Anopheles species and Nyssorhynchus darlingi). Studies in Indonesia and Nigeria demonstrate a correlation between forest cover decline and malaria outbreaks. A cross-national study revealed connections between rural population growth, agricultural specialization, forest loss, and malaria prevalence. Research in the Amazon shows a bidirectional relationship between deforestation and malaria, with forest clearing increasing malaria incidence and vice versa. Deforestation alters vector ecology, increasing larval habitat suitability, reducing development time, increasing adult survival, and decreasing biodiversity, thereby reducing predation on Anopheles species. Natural resource exploitation, driven by global demand for commodities like timber, soy, beef, palm oil, tobacco, cocoa, coffee, and cotton, accelerates deforestation in these regions. This study aims to understand how export-oriented production leads to deforestation and malaria risk in developing countries, reveal the connections between consumers and producers through international trade, and identify countries facing significant malaria risk due to global consumption and trade. This research goes beyond simple correlation by linking malaria incidence in specific locations to deforestation and its connection to global consumption via supply chains.

Numerous studies have established a link between deforestation and malaria risk. Research across various regions, including East Africa, Southeast Asia, and the Americas, has demonstrated the impact of deforestation on mosquito populations and malaria transmission. Deforestation alters microclimates, creating more favorable conditions for mosquito breeding and survival. Furthermore, the loss of biodiversity reduces natural predation on mosquito larvae and adults, leading to increased vector abundance. The existing literature primarily focuses on the ecological impacts of deforestation on malaria transmission. This study expands upon this by investigating the role of international trade and global consumption patterns in driving deforestation and thus increasing malaria risk in developing countries. Existing studies have shown correlations between deforestation and malaria incidence, but this study aims to quantify the contribution of international trade to this risk.

To connect malaria risk with global consumption, the study utilizes Leontief's method within a global multi-regional input-output (MRIO) analysis. This approach traces commodities from their origin in deforestation-linked production in tropical countries through the global supply chain to their final consumption in developed countries. This methodology has been previously used to assess the impact of international trade on biodiversity loss, air pollution, and other environmental and social indicators. The study calculates a malaria footprint tensor (F^r_st) that represents the malaria risk transferred from producing country *r* to consuming country *t* via processing country *s*. This tensor accounts for the complex interplay of various commodities and supply chains. The tensor can be simplified into bilateral malaria footprints (F^r_t) and net malaria risk trade (F^r₊ - F^r₋). The methodology defines malaria risk as the number of malaria cases that would occur in the presence of deforestation but in the absence of any public health interventions. To estimate this, a multiple regression model is used, regressing actual malaria cases against cumulative tree cover loss, the proportion of the population using insecticide-treated mosquito nets (ITN), and the proportion of the population using artemisinin-based combination therapies (ACT). The model is used to estimate the counterfactual scenario of malaria risk with deforestation but without the interventions of ITN and ACT. This risk is then allocated across economic sectors using FAOSTAT data on deforestation-linked commodities (soybeans, oil palm fruit, cattle, sheep, buffalo, timber, wood products, tobacco, cocoa, coffee, and cotton). The resulting malaria risk matrix is incorporated into the MRIO model to quantify the contribution of international trade to malaria risk. The Eora MRIO database provides data on global final consumption, total output, and the Leontief inverse, which represents the structure of the global supply chain network.

The study finds that approximately 20% of malaria risk in deforestation hotspots is associated with international trade in deforestation-implicated commodities. This represents around 110 million people at risk in 2015. Ten countries are identified as significant net importers of these commodities, whose demand contributes to malaria risk in exporting, primarily African, countries. Nigeria experiences the highest risk (5.98 million people), linked to timber exports to China, cocoa exports to Europe, and charcoal exports to Europe. Tanzania faces high risk (5.66 million) due to exports of tobacco and cotton, and Uganda (5.49 million) due to coffee exports. Cameroon (5.49 million) is affected by cocoa exports to Europe, wood exports to China, and timber exports to several countries. The analysis also identifies complex supply chains, illustrating how commodities are processed and added value in various countries before reaching final consumers. High-income countries, including the Netherlands, Belgium, Germany, and the UK, exhibit high malaria footprints per capita, driven by consumption of processed goods from deforestation-linked commodities. Analysis of 2000-2015 data shows increasing trends in international trade of deforestation-implicated products and associated deforestation, particularly in tropical Africa and Southeast Asia. While developing countries experience deforestation and increased malaria risk, many developed countries importing these products simultaneously increased their forest cover. The study’s multiple regression model, with a high R² (0.91), links malaria incidence to deforestation even when controlling for ITN and ACT use, further highlighting the significance of deforestation.

The study's findings demonstrate that global demand for commodities contributes significantly to malaria risk in developing countries. The unequal value chain, where low-income producers bear the environmental and health costs while high-income countries capture most of the economic benefits, highlights a critical injustice. While high-income countries provide some financial support for malaria control, the funding is insufficient, underscoring the need for complementary strategies. The study argues that demand-side measures can effectively complement existing malaria control interventions. Targeting the demand for deforestation-implicated products reduces the need for constant malaria control, aligns with existing initiatives aimed at reducing deforestation, and offers a broader, more sustainable approach to malaria control.

This study highlights the significant contribution of international trade in deforestation-implicated commodities to malaria risk in developing countries. The unequal value chain necessitates complementary demand-side measures alongside existing malaria control interventions. Future research could focus on refining the quantification of malaria risk associated with specific commodities and supply chains, exploring the effectiveness of different demand-side policies, and investigating the social and economic dimensions of implementing such policies in diverse contexts.

The study's reliance on correlation does not establish direct causation between deforestation, trade, and malaria. Data limitations, including the accuracy of deforestation data and the challenge of precisely attributing malaria cases to specific commodities, might affect the precision of estimates. The methodology assumes a linear relationship between deforestation, interventions, and malaria cases; however, this relationship might be more complex. Despite these limitations, the study offers valuable insights into the connections between global consumption patterns and malaria risk.