Response to the Critique by Hahn and Others Entitled “Conservation and Malaria in the Brazilian Amazon”

Abstract

Abstract. Hahn and others have recently criticized our study, "Conservation efforts may increase malaria burden in the Brazilian Amazon," suggesting that results were flawed because of methodological limitations. Here, we briefly comment on some of their claims, showing that (1) several of their criticisms are misleading and others are incorrect, (2) they heavily criticize methods that they themselves have previously used, and (3) they selectively highlight some findings while ignoring others. We end this rebuttal by suggesting a way forward in this debate.Hahn and others 1 have recently written a perspective piece, which was published in The American Journal of Tropical Medicine and Hygiene, criticizing our study published in 2013.2 Here, we respond to their critique, commenting and clarifying some of the points raised. Our response is organized in the same order as the issues were raised.Hahn and others 1 provide literature that supports their view that intact forests can help eliminate local malaria transmission. They 1 place special emphasis on a study that was based on a theoretical model parameterized to a different vector and applied to a completely different ecosystem (~1,000 km away from our study region) on a region that has not had any reported malaria cases for the past 30 years. Unfortunately, Hahn and others 1 fail to acknowledge the large literature that support the opposite view regarding the role of forests, and most of those studies were conducted in the Brazilian Amazon. -10Hahn and others 1 claim that it is problematic to assume a constant population given that the Brazilian Amazon population increased from 2000 to 2010 by 23%. First, this statement is misleading, because the length of our study corresponds to less than one-half of this time interval. Second, population data arise from the Brazilian Census, which was conducted in 2000, 2007, and 2010. To account for fluctuation in population size, one would have to interpolate between three data points for each county, and it is not clear if this method is a better solution than adopting the 2007 population count for the 2004-2008 study period. Nevertheless, we performed our analysis again (this time using only 2007 malaria data) and found that our original conclusions hold (results available on request).Hahn and others 1 then criticize the fact that we excluded rural health facilities and the two easternmost states in the Brazilian Amazon (Maranhao and Tocantins). First, we did not have data from Maranhao and Tocantins, and therefore, these data were not excluded. Second, as explicitly mentioned in ref. 2, we excluded the rural health facilities because we did not have their spatial coordinates, thus precluding the assessment of the effect of proximity to forests. Third, the remark that we only accounted for 4.8% of the Brazilian Amazon region is misleading, because it ignores the fact that the human population in this region is highly clustered in the vicinities of established cities.11,12 Even if we had the geographical location of all hea...

Comparison of malaria incidence rates and socioeconomic-environmental factors between the states of Acre and Rondônia: a spatio-temporal modelling study

“…The generality of the relationship between deforestation and malaria emergence was challenged [35] because the authors found higher malaria incidence in human settlements near priority areas for nature conservation. The controversy between the deforestation-malaria hypothesis [35] stimulated intensive debates [45, 46]. An alternative was proposed: deforestation may benefit or be harmful to the malarial vector population, depending on the pattern and proportion of forest cover [24].…”

Does Deforestation Increase Malaria Prevalence? Evidence from Satellite Data and Health Surveys

“…In principle, we could have tested many permutations and chosen to present a combination of model specifications and included variables that yielded a result that was statistically significant, or had a particular effect direction, while not reporting on many other tests conducted ("data mining"; "p-hacking") (Olken, 2015). Ensuring the credibility of the analysis is especially relevant when testing a hypothesis prone to controversy, as in the case of deforestation and malaria (see for example Valle and Clark, 2013, Hahn et al, 2014b, Valle, 2014.…”

“…A recent strand of empirical literature has tested the hypothesis that deforestation increases malaria prevalence in humans using multivariate econometric tests (Wayant et al, 2010;Pattanayak et al, 2010;Olson et al, 2010;Valle and Clark, 2013;Hahn et al, 2014a;Garg, 2014;Terrazas et al, 2015;Fornace et al, 2016;Austin et al, 2017). Seven of these studies (including two studies not yet published in peer-reviewed literature) found a positive relationship, while one found no relationship (Hahn et al, 2014a) and one found a negative relationship (Valle and Clark, 2013, but see also Hahn et al (2014b) and Valle (2014)) (Table 1). However, these previous empirical studies of deforestation and malaria prevalence had several limitations.…”

Malaria transmission in landscapes with varying deforestation levels and timelines in the Amazon: a longitudinal spatiotemporal study

“…darlingi , benefits from recently deforested landscapes intermixed with human-modified habitats 33 – 39 . This process of forest fragmentation creates the forest fringe effect in which malaria incidence increases due to expanded host-vector contact rates when human dwellings encroach on or are very near the forest edge 40 – 43 . The pattern of malaria incidence and accumulated deforestation at a fine scale is represented by a unimodal curve (Fig.…”