Deforestation isn’t associated with higher malaria prevalence in children in 17 African countries. Nor is it associated with higher fever in children in 41 countries across Africa, Asia, and Latin America. That’s the surprising conclusion of our new CGD working paper.

This means that, at least in Africa where 88 percent of malaria cases occur, public health efforts to reduce malaria should continue to focus on proven anti-malarial interventions. These include insecticide-treated bed nets, indoor spraying, housing improvements, and prompt clinical treatment, which along with other interventions have reduced the incidence of this killer disease by 41 percent between 2000-2015.

For advocates of forest conservation in Africa, there are many good reasons to keep forests standing. These include carbon storage, biodiversity habitat, and clean water provision, alongside other goods and services, as elaborated in my (Jonah’s) book, Why Forests? Why Now? However, forest conservation might not have anti-malarial benefits, at least not in Africa.

A surprising finding

These results may come as a surprise to people following the literature on deforestation and malaria. Certainly, it is well established that deforestation can increase malaria risk factors in some settings. Relative to forests, deforested lands have been found to have higher temperatures, more sunlight, and more standing water, which favor some types of malaria-transmitting mosquitoes. And relative to forests, cleared lands have also been found to have fewer insectivores, more species competing for ecological niche, and arguably fewer “dead-end hosts” to dilute malaria. Furthermore, “frontier malaria” can result from the unstable socio-economic conditions associated with deforestation in many parts of the world, including rapid in-migration, new human exposure and low immunity, poor housing quality, and sparse availability of health services.

But increased malaria risk might not necessarily translate to higher rates of malaria in humans (i.e., “prevalence”). That’s because there’s considerably nuance in the effects listed above. For example, deforested areas may be favored by some mosquito species but not others; deforestation is generally considered to increase the density of malaria-transmitting mosquitoes in Africa and Latin America but decrease their density in Asia. In addition, many other factors besides deforestation also affect malaria prevalence in humans, including climate, community demographics, access to health facilities, and people’s behaviors to avoid malaria.

Nine previous studies have compared deforestation to malaria prevalence in humans (see table below). These studies generally analyzed small amounts of data from a handful of countries—four from Brazil, two from Indonesia, and one each from Malaysia and Paraguay, as well as one study that compared national-level statistics across 67 countries. Most, though not all, found that more deforestation is associated with more malaria. So, it was a surprise to find no association between deforestation and malaria in our study.

So then, why might studies find that deforestation leads to higher malaria rates in South America and Southeast Asia but not in Africa? The explanation, we speculate in our paper, may have something to do with the difference between how deforestation happens in Africa versus elsewhere. Deforestation in Africa is largely driven by the slow expansion of rotational agriculture for domestic use by long-time smallholder farmers in stable socio-economic settings rather than by rapid clearing for market-driven agricultural exports by new frontier migrants as in Latin America and Asia. We hope that this hypothesis can be supported or refuted by future work.

How we got there

We came to our conclusions by assembling massive data sets on deforestation and malaria. Our data set on deforestation included annual tree-cover loss between 2001-2015 in 1.5 million ~5.5-kilometer grid-cells across the tropics, compiled from Global Forest Watch as part of a previous CGD working paper. We also obtained data from malaria tests of around 60,000 children in rural Africa and fever recall surveys of around 470,000 children across the rural Tropics conducted under the auspices of USAID’s Demographic and Health Surveys. We combined these two data sets in a multivariate regression analysis that also considered temperature, precipitation, housing quality, water source, access to health services, child age, and bed-net usage.

In addition to our main comparison of deforestation and malaria, we also tested hypotheses generated in advance from previous studies. Did smaller cuts lead to more malaria on a per-hectare basis than larger cuts? Did deforestation have a bigger effect in places with more forest? Did deforestation have a bigger effect on fever in African and Latin America than Asia? The answer to all three questions is a resounding “no.”

We’d originally also planned to compare the cost-effectiveness of preventing malaria through forest conservation to the cost-effectiveness of common interventions such as bed nets and spraying, as measured in disability-adjusted life years (DALY) per dollar. But since deforestation wasn’t found to affect malaria rates, the DALY-per-dollar benefit was essentially zero.

Bolstering credibility with a pre-analysis plan

We expected our findings were bound to be controversial, no matter what we found. A previous study of deforestation and malaria in the Brazilian Amazon generated some heated back-and-forth. So to bolster the integrity and credibility of our research we used a pre-analysis plan. That is, we wrote down and time-stamped all our hypotheses, methods, models, and variables in advance. Then we stuck with them.

Pre-analysis plans are common and even required for some types of clinical research. But they are still new to social sciences, including economics, where common research practice often involves testing many possible combinations of variables and model specifications. If the authors of such a study only report tests showing favorable results while relegating the results of other tests to the digital trash bin (“data mining” or “p-hacking”), they can inadvertently or deliberately place a thumb on the scale to achieve desired results. This is what we wanted to avoid by writing and following a pre-analysis plan. Since prominent repositories for pre-analysis plans hosted by the American Economic Association and the International Initiative for Impact Evaluation compile pre-analysis plans for randomized controlled trials (RCTs) but not other types of studies, we published our pre-analysis plan on the CGD website (available in two parts, here and here).

It has also been claimed that the use of pre-analysis plans can make null findings less likely to be rejected for publication. We certainly hope this is the case—research on important topics ought to be equally likely to be submitted, published, and reported on no matter what the finding. At stake in a full and accurate understanding of deforestation and malaria are the lives and health of millions of people and the conservation of millions of hectares of forest.

Comparing deforestation to malaria prevalence in humans

Study Methods Explanatory variables Positive association between deforestation or forest cover reduction and malaria?
Wayant et al., Geospatial Health, 2010 Univariate correlation between NDVI-forest cover change interaction and malaria case rates over 260 months in two departments in Paraguay Forest cover change yes
Pattanayak et al., ERID working paper, 2010 Conditional correlation in cross-sectional regressions of primary and secondary forest area and 500 household surveys in Flores, Indonesia Forest cover, family size, number of children, gender, native born, child age, caregiver age, caregiver health, caregiver education, household wealth, housing quality, village public health facility, village population, village area, village elevation yes
Olson et al., Emerging Infectious Diseases, 2010 Conditional correlation in cross-sectional regressions of deforestation and malaria incidence across 54 health districts in Mancio Lima County in Acre, Brazil Deforested land area, deforestation, access to care, area yes
Hahn et al., PLoS ONE, 2014a Cross-sectional regression of deforestation and incidence in 602 municipalities of the Brazilian Amazon Deforested land area, deforestation, Paved road density, unpaved road density, area affected by fire, no
Valle and Clark, PLoS ONE, 2013 (see also Hahn et al., 2014b, Valle 2014) Association between forest cover and malaria incidence across 401 20km radii around towns in the Brazilian Amazon Forest cover, deforestation, population, lagged precipitation, lagged drought index no
Garg, job market paper, 2014 Panel regression between occurrence of village-level outbreak and MODIS monthly hectares of district-level deforestation across four islands of Indonesia Deforestation, village poverty, village health, access to hospital, population density, rice field area, proximity to river, elevation, rainfall yes
Terrazas et al., Malaria Journal, 2015 Correlation between incidence of malaria and average annual deforestation rate across 62 municipalities of the state of Amazonas, Brazil Forest cover, deforestation human development, education, income, poverty, unemployment, health surveillance, watercourses yes
Fornace et al., Emerging Infectious Diseases, 2016 Association between incidence of P. knowlesi and historical forest loss within a 1-5 km radius of 405 villages in Sabah, Malaysia Forest cover, deforestation, elevation yes
Austin et al., AIMS Environmental Science, 2017 Structural equation model of malaria prevalence rate in 2013 vs self reported changes in forest cover (FAO FRA) 2012-2013 across 67 countries Forest cover change, latitude, GDP per capita, Sub-Saharan Africa, agriculture as % of GDP, rural population growth, public health conditions yes
Bauhoff and Busch, CGD Working Paper, 2017 Conditional correlation in cross-sectional regression of deforestation and malaria prevalence in 60,305 children in 17 African countries; fever in 469,539 children in 41 countries Forest cover, deforestation, temperature, precipitation, child age, floor type, water source no