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Cited by 250Malaria Research and ControlConservation, Biodiversity, and Resource ManagementMosquito-borne diseases and control

Amazon deforestation drives malaria transmission, and malaria burden reduces forest clearing

Open access
MacDonald, Andrew J., Mordecai, Erin A.·2019·Proceedings of the National Academy of Sciences
DOI 10.1073/pnas.1905315116PMC PMC6825316
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Abstract

Significance: Widespread human impacts on the environment are expected to harm human health, which may in turn alter our interactions with the environment. However, evidence for impacts of environmental changes on health, and for feedbacks between environmental change and health, remains locally specific and context dependent. Using a large, geospatial dataset encompassing the Brazilian Amazon rainforest across 13 y, we identify strong evidence for a feedback between deforestation and malaria: Deforestation significantly increases malaria transmission, while high malaria burden simultaneously reduces forest clearing. Our results put into broader context the contradictory effects of deforestation on malaria found in earlier studies and provide evidence useful to land use policy and public health interventions that provide win–win solutions for conservation and health.

AI snapshot

Written from the paper’s full text.

TL;DR

Using 2003–2015 data from 795 Brazilian Amazon municipalities and instrumental-variable econometrics, the paper finds deforestation increases malaria incidence while malaria burden reduces forest clearing. These bidirectional effects are strongest in interior/frontier areas and attenuate where forest has already been cleared.

Key findings

  • Deforestation significantly increased malaria incidence (β = 0.327, SE = 0.145, P = 0.024); the IV model predicted a 10% increase in deforestation (∼1,567 additional km² cleared Amazon-wide in 2008) would cause a 3.27% increase in malaria incidence (∼9,980 additional cases in 2008).
  • The effect was larger in the interior and absent in outer Amazonian states: for *P. falciparum* in the interior, β = 0.716, SE = 0.323, P = 0.027; in outer states, β = 0.020, SE = 0.014, P = 0.136.
  • Malaria burden significantly reduced forest clearing (β = −1.410, SE = 0.654, P = 0.031); a 1% increase in malaria incidence (∼3,024 additional cases in 2008) was expected to reduce deforestation by ∼1.41% (∼219 fewer km² lost in 2008).
  • The negative effect of malaria on deforestation was consistent in the interior but disappeared in outer Amazonian states; malaria incidence also increased with precipitation and optimal dry-season temperature and decreased with population density and per capita GDP.

Methods

  • Observational geospatial panel of 795 municipalities across the Brazilian Amazon from 2003 to 2015, using annual malaria incidence from SIVEP-Malaria, annual forest loss/cover from the Hansen Global Forest Change dataset, and covariates including population density, per capita GDP, temperature, and precipitation.
  • Econometric analysis used OLS, least-squares dummy variable models with municipality and year fixed effects, and instrumental-variable/2SLS regression to address omitted-variable and simultaneity bias; deforestation was instrumented with dry-season aerosol pollution, and malaria was instrumented with optimal dry-season transmission temperature.
  • Robustness checks included total, *P. vivax*, and *P. falciparum* malaria models; interior versus outer Amazonian state subsets; total forest loss versus loss near settlements; alternative temperature windows, precipitation measures, aerosol timing, and comparison with PRODES deforestation data.

Limitations

  • The paper states that municipality-year aggregate data may mask local-scale and intraannual heterogeneity in environmental conditions and malaria transmission, and that the broad spatial-temporal scale trades off with resolution.
  • The paper states that, as with all observational studies, it cannot be certain all possible confounding factors are captured, and the results do not directly identify the underlying mechanisms; local mechanistic studies are needed.
  • Evident from the text: causal interpretation depends on the instrument exogeneity assumptions—that dry-season aerosol pollution affects malaria only through deforestation and that optimal transmission temperature is not otherwise related to deforestation—which the paper argues for but cannot fully prove.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1073/pnas.1905315116

Cites

67 of 67 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 17 cited sources checked · 0 supported · 0 flagged

  • Findingown data

    Deforestation, measured as annual area of forest lost within a municipality, significantly increases malaria incidence in the Brazilian Amazon.

    “We found that deforestation, specifically measured as annual area of forest lost within a given municipality, significantly increased malaria incidence (β = 0.327, SE = 0.145, P = 0.024* [*indicates statistical significance at the P < 0.05 level]; Fig. 2 and SI Appendix, Table S1 and Fig. S1).”
    • Also stated in: § Discussion · boosted
      “Our work provides clear large-scale evidence that deforestation increases malaria, by using econometric techniques that approximate the gold standard of randomized controlled trials with observational data where controlled experiments are impossible.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Aerosol pollution is a valid instrument for deforestation (correlated with deforestation but not independently correlated with malaria).
  • Findingown data

    Deforestation significantly increases malaria incidence in the interior where forest cover remains high, particularly for P. falciparum (β = 0.716, SE = 0.323, P = 0.027).

    “Additionally, parsing the Amazonian states into”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [35]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Findingown data

    A 1% increase in malaria incidence (approximately 3,024 additional cases in 2008) is expected to reduce deforestation by approximately 1.41% (approximately 219 fewer km2 lost in 2008).

    “We estimate a 1% increase in malaria incidence results in a 1.4% decrease in forest area cleared (∼219 fewer km 2 cleared associated with 3,024 additional cases in 2008).”
    • Also stated in: § Results · hedged
      “A 1% increase in malaria incidence (∼3,024 ad- ditional cases in 2008, for example) is expected to reduce de- forestation by ∼1.41% (∼219 fewer km 2 lost in 2008).”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Optimal transmission temperature is plausibly exogenous to the larger economic and policy context that determines deforestation, making it a valid instrument for malaria.
  • Findingreasoning

    P. falciparum malaria had a stronger relationship with deforestation than P. vivax malaria.

    “P. falciparum malaria had a stronger relationship with deforestation, while P. vivax malaria exhibited attenuated relationships with deforestation and other predictors, likely because this parasite can relapse months to years after initial infection, decoupling transmission from the onset of disease.”
    Quote in the paperNot a substantial claim
  • Findingown data

    A 10% increase in deforestation leads to a 3.3% increase in malaria incidence (approximately 9,980 additional cases associated with 1,567 additional km2 lost in 2008, Amazon-wide).

    “Our results suggest a 10% increase in deforestation leads to a 3.3% increase in malaria incidence (∼9,980 additional cases associated with 1,567 additional km 2 lost in 2008, the study midpoint, Amazon-wide).”
    • Also stated in: § Results · hedged
      “The model that used aerosol pollution as an instrument for deforestation predicted that, on average, a 10% increase in deforestation (∼1,567 addi- tional km 2 cleared across the Amazon in 2008, the midpoint of the study, for example) would result in a 3.27% increase in malaria incidence (∼9,980 additional cases overall in 2008) (SI Appendix, Table S1).”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Aerosol pollution is a valid instrument for deforestation (correlated with deforestation but not independently correlated with malaria).
  • May combine two claims. 12
  • Classified as probably not a substantial claim. 9

  • May combine two claims.

    Findingown data

    Malaria incidence increased with precipitation and optimal temperature for transmission in the dry season, and decreased with population density and per capita GDP.

    “Malaria incidence also increased with precipitation and optimal temperature for transmission in the dry season, when most cases of malaria are transmitted, and decreased with population density and per capita gross domestic product (GDP) (SI Appendix, Table S1).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    The effect of deforestation on malaria is larger in the interior of the Amazon and absent in outer Amazonian states where little forest remains.

    “The effect is larger in the interior and absent in outer Amazonian states where little forest remains.”
    • Also stated in: § Results · plain
      “In contrast, we find that the slope of the relationship is not significantly different from zero in the outer Amazonian states where most forest has already been cleared (β = 0.020, SE = 0.014, P = 0.136).”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingown data

    The quadratic term for deforestation was not significant and did not substantially influence model results.

    “however, the quadratic term was not significant and did not substantially influence model results, so this specification was dropped.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Malaria and forest cover are highest in the interior of the Brazilian Amazon, while active deforestation and aerosol pollution peak at the interface between the forest interior and outer Amazonian states.

    “Malaria and forest cover are highest in the interior of the Brazilian Amazon, while active deforestation and aerosol pollution peak at the interface between the forest interior and outer Amazonian states.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    Forest clearing has a direct impact on human health, in addition to loss of other ecosystem services, that is quantifiable and predictable at regional and decadal scales.

    “The key implication is that forest clearing has a direct impact on human health, in addition to the loss of other ecosystem services such as species diversity, water quality, and carbon storage, that is quantifiable and predictable at regional and decadal scales.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationcitation

    The effects of deforestation on malaria are largest in the early stages of deforestation in the interior of the Amazon as forest edge habitat increases, promoting mosquito vector breeding habitat, survival, and human biting rate, but the effects attenuate as forest loss progresses, forest edge area declines, and human settlements become larger and further removed from forest.

    “The effects of deforestation on malaria are largest in the early stages of deforestation in the interior of the Amazon as forest edge habitat increases, promoting mosquito vector breeding habitat, survival, and human biting rate (5, 6, 15), but the effects attenuate as forest loss progresses, forest edge area declines, and human settlements become larger and further removed from forest (10,21).”
    Quote in the paperSubstantial claim0 of 5 sources checked
    • cites [5]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [6]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [15]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [10]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [21]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • May combine two claims.

    Interpretationreasoning

    P. vivax malaria exhibited attenuated relationships with deforestation and other predictors, likely because this parasite can relapse months to years after initial infection, decoupling transmission from the onset of disease.

    “P. falciparum malaria had a stronger relationship with deforestation, while P. vivax malaria exhibited attenuated relationships with deforestation and other predictors, likely because this parasite can relapse months to years after initial infection, decoupling transmission from the onset of disease.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    The study uses aggregate data at the scale of municipalities at an annual time step, which may mask local-scale and intraannual heterogeneity in environmental conditions and malaria transmission.

    “For example, we use aggregate data at the scale of municipalities at an annual time step, which may mask local-scale and intraannual heterogeneity in environmental conditions and malaria transmission.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    Elevated malaria burden may also reduce rates of deforestation, although numerous other economic, political, social, and environmental factors play a much more substantial role in determining where and how much forest is cleared.

    “Thus, while numerous other economic, political, social, and environmental factors play a much more substantial role in determining where and how much forest is cleared, here we find evidence that elevated malaria burden may also reduce rates of deforestation.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Study design and conductnone given

    The study's statistical approach controls for many possible confounding factors unique to municipalities, regions, and years, and is robust to a variety of different specifications and data subsets.

    “Our statistical approach controls for many possible confounding factors unique to municipalities, regions, and years, and is robust to a variety of different specifications and data subsets.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductreasoning

    The study cannot say with certainty that it captures all possible confounding factors that would otherwise preclude a causal interpretation of the results.

    “However, as with all observational data studies, we cannot say with certainty that we capture all possible confounding factors that would otherwise preclude a causal interpretation of the results.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    Ignoring the statistically well-supported feedback leads to substantial underestimates of the true effect of deforestation on malaria transmission, which may have confounded the results of earlier studies.

    “Importantly, ignoring this statistically wellsupported feedback leads to substantial underestimates of the true effect of deforestation on malaria transmission, which may have confounded the results of earlier studies.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    All control variables were retained in the first model because VIF < 10.

    “however, all control variables were retained in this first model (VIF < 10).”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    Year dummy variables were included to control for year shocks shared by the Amazon basin.

    “We also incorporate year dummy variables to control for year shocks shared by the Amazon basin (e.g., the global recession, changes in international commodity prices for beef or soy, changes in health or forest conservation policy) that may directly or indirectly influence malaria transmission from year to year across the study region.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    Municipality dummy variables were included to control for time-invariant municipality characteristics.

    “we use municipality dummy variables that control for roughly time-invariant characteristics of municipalities (e.g., perennial water bodies, protected areas, elevation) for which we do not include or have data.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductreasoning

    Control variables were included to reduce bias from omitted variables.

    “Control variables are included to reduce bias from omitted variables, although these variables can exhibit high collinearity, which may produce unreliable coefficient estimates.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Study design and conductcitation

    Microregion clusters were used to account for spatial and temporal autocorrelation between municipalities within microregions.

    “We use microregion clusters (Fig. 1A) in the OLS (Eq.1), LSDV (Eq.2), and IV (Eq.4) models to account for spatial and temporal autocorrelation between municipalities within microregions, which were defined by”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites IBGE, https://www.ibge.gov.br/

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • May combine two claims.

    Premisecitation

    The parameter estimates are insensitive to the cluster specification with CRSEs, and thus remain consistent and unbiased even if the clusters are incorrectly specified.

    “In particular, the parameter estimates are insensitive to the cluster specification with CRSEs, and thus remain consistent and unbiased even if the clusters are incorrectly specified (63).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [63]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • May combine two claims.

    Interpretationown data

    The analysis provides evidence of endogeneity between deforestation and malaria, suggesting a possible effect of malaria on deforestation.

    “following evidence for endogeneity between deforestation and malaria obtained above, which suggests a possible effect of malaria on deforestation, we specified a series of models in the opposite direction to directly investigate this relationship.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Study design and conductcitation

    Cluster-robust SEs were used, which are robust to arbitrary within-cluster correlation and heteroscedasticity.

    “We use cluster-robust SEs (CRSEs), which are robust to arbitrary within-cluster correlation in the errors as well as heteroscedasticity (62).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [62]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

20 of 20

  • The paper relies on this without support.

    Aerosol pollution is a valid instrument for deforestation (correlated with deforestation but not independently correlated with malaria).

    “The model that used aerosol pollution as an instrument for deforestation predicted that, on average, a 10% increase in deforestation (∼1,567 addi- tional km 2 cleared across the Amazon in 2008, the midpoint of the study, for example) would result in a 3.27% increase in malaria incidence (∼9,980 additional cases overall in 2008) (SI Appendix, Table S1).”

  • The paper relies on this without support.

    Optimal transmission temperature is plausibly exogenous to the larger economic and policy context that determines deforestation, making it a valid instrument for malaria.

    “Our instrument for malaria, optimal transmission temperature, is plausibly exogenous to the larger economic and policy context that determines deforestation.”

  • The paper relies on this without support.

    Aerosol pollution in September does not affect annual malaria incidence except through deforestation.

    “unlikely to independently drive malaria transmission”

  • The paper relies on this without support.

    Optimal temperature for transmission in the dry season is not otherwise related to deforestation.

    “but not otherwise related to deforestation.”
    “which is highly correlated with annual malaria incidence, as an instrument for malaria (see diagnostics in SI Appendix, Tables S15, S22-S24, and S27), but not otherwise related to deforestation.”
4 of 4

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1 of the 68 references couldn’t be matched to a known paper.

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Related

Authors
MacDonald, A. J., & Mordecai, E. A.
Affiliations
  • University of California, Santa Barbara (US)
  • Stanford University (US)
ORCID
Venue
Proceedings of the National Academy of Sciences
Publisher
National Academy of Sciences
Published
2019-10-14
Volume
116
Issue
44
Pages
22212-22218
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
DOI
10.1073/pnas.1905315116
PMC
PMC6825316
OpenAlex
W2980466780
Semantic Scholar
459f1dce5682568561b0aba7bf92d1de0e752a11
Cited by
250
References
68
Citations by year
2026: 30 · 2025: 35 · 2024: 38 · 2023: 38 · 2022: 35 · 2021: 48 · 2020: 25 · 2019: 1
FWCI
25.09
Type
journal article
Language
en
Update notices
correction (2020-08-10)
Open access
Licence
www.pnas.org/site/aboutpnas/licenses.xhtml (vor, 183 d)
Locations
  • Proceedings of the National Academy of Sciences (publishedVersion)
  • Europe PMC (PubMed Central) (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
Funders
National Science Foundation (1611767, DEB-1518681)
Topics
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Malaria Research and Control
  • Physical Sciences › Environmental Science › Global and Planetary Change › Conservation, Biodiversity, and Resource Management
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Mosquito-borne diseases and control
Keywords
deforestation, malaria transmission, malaria burden, forest clearing, environmental health, environmental change and health, Amazon rainforest, land use policy
MeSH terms
Brazil, Conservation of Natural Resources, Humans, Malaria, Rainforest

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