Conservation efforts may increase malaria burden in the Brazilian Amazon

Abstract

Background: Large-scale forest conservation projects are underway in the Brazilian Amazon but little is known regarding their public health impact. Current literature emphasizes how land clearing increases malaria incidence, leading to the conclusion that forest conservation decreases malaria burden. Yet, there is also evidence that proximity to forest fringes increases malaria incidence, which implies the opposite relationship between forest conservation and malaria. We compare the effect of these environmental factors on malaria and explore its implications.

Methods and findings: Using a large malaria dataset (~1,300,000 positive malaria tests collected over ~4.5 million km(2)), satellite imagery, permutation tests, and hierarchical Bayesian regressions, we show that greater forest cover (as a proxy for proximity to forest fringes) tends to be associated with higher malaria incidence, and that forest cover effect was 25 times greater than the land clearing effect, the often cited culprit of malaria in the region. These findings have important implications for land use/land cover (LULC) policies in the region. We find that cities close to protected areas (PA's) tend to have higher malaria incidence than cities far from PA's. Using future LULC scenarios, we show that avoiding 10% of deforestation through better governance might result in an average 2-fold increase in malaria incidence by 2050 in urban health posts.

Conclusions: Our results suggest that cost analysis of reduced carbon emissions from conservation efforts in the region should account for increased malaria morbidity, and that conservation initiatives should consider adopting malaria mitigation strategies. Coordinated actions from disparate science fields, government ministries, and global initiatives (e.g., Reduced Emissions from Deforestation and Degradation; Millenium Development Goals; Roll Back Malaria; and Global Fund to Fight AIDS, Tuberculosis and Malaria), will be required to decrease malaria toll in the region while preserving these important ecosystems.

Figure 1. Malaria incidence is higher in areas with more forest cover whereas no clear pattern arises regarding deforestation rates.

Upper panels: Data were stratified into 10 percentile population size classes and average number of malaria cases per month for each year and city was depicted. Within each size class, we compare cities with high (green box-plots) vs. low forest cover (white box-plots) (upper left panel); and cities with high (grey box-plots ) vs. low deforestation rate (white box-plots) (upper right panel). Cities with high forest cover (or high deforestation rates) are cities that have forest cover (or deforestation rate) higher than the median for that size class. ‘n.s’, ‘*’, ‘**’, and ‘***’ are non-significant (p>0.05), significant (0.01

Figure 2. Malaria incidence tends to be higher for cities close to protected areas (PA’s).

Data were stratified into 10 percentile population size classes and average number of malaria cases per month for each city was depicted. Within each size class, we compare cities close (green box-plots) vs. distant from PA’s (white box-plots). Cities close to PA’s (i.e., indigenous lands, state and federal parks) are those whose catchment area intersected one or more PA’s. ‘n.s’, ‘*’, ‘**’, and ‘***’ are non-significant (p>0.05), significant (0.01

Figure 3. Predicted malaria incidence in urban health posts is higher in the governance scenario than in the business-as-usual scenario.

Maps depict the ratio of the expected number of malaria cases per month for each year and city under the governance (GOV) and the business-as-usual (BAU) future LULC scenarios (i.e., ), where values >1 indicate that the GOV scenario results in more malaria cases than the BAU scenario. Areas that were deforested in the BAU scenario but not in the GOV scenario (i.e., prevented deforestation) are depicted in the background for reference. Circles represent the cities in our original malaria dataset.

Figure 4. Malaria incidence increase at urban health posts in the governance scenario is predicted to be a direct consequence of prevented deforestation.

We depict the relationship between future prevented deforestation under the governance scenario (green line), and the ratio of the expected malaria incidence for each year and city under the governance (GOV) and business-as-usual (BAU) future LULC scenarios (red line) (i.e., ), averaged across all cities. The red polygon represents the 95% credible interval of the average ratio.