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Public health impacts of ecosystem change in the Brazilian Amazon.


ABSTRACT: The claim that nature delivers health benefits rests on a thin empirical evidence base. Even less evidence exists on how specific conservation policies affect multiple health outcomes. We address these gaps in knowledge by combining municipal-level panel data on diseases, public health services, climatic factors, demographics, conservation policies, and other drivers of land-use change in the Brazilian Amazon. To fully exploit this dataset, we estimate random-effects and quantile regression models of disease incidence. We find that malaria, acute respiratory infection (ARI), and diarrhea incidence are significantly and negatively correlated with the area under strict environmental protection. Results vary by disease for other types of protected areas (PAs), roads, and mining. The relationships between diseases and land-use change drivers also vary by quantile of the disease distribution. Conservation scenarios based on estimated regression results suggest that malaria, ARI, and diarrhea incidence would be reduced by expanding strict PAs, and malaria could be further reduced by restricting roads and mining. Although these relationships are complex, we conclude that interventions to preserve natural capital can deliver cobenefits by also increasing human (health) capital.

SUBMITTER: Bauch SC 

PROVIDER: S-EPMC4475939 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature

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Public health impacts of ecosystem change in the Brazilian Amazon.

Bauch Simone C SC   Birkenbach Anna M AM   Pattanayak Subhrendu K SK   Sills Erin O EO  

Proceedings of the National Academy of Sciences of the United States of America 20150601 24


The claim that nature delivers health benefits rests on a thin empirical evidence base. Even less evidence exists on how specific conservation policies affect multiple health outcomes. We address these gaps in knowledge by combining municipal-level panel data on diseases, public health services, climatic factors, demographics, conservation policies, and other drivers of land-use change in the Brazilian Amazon. To fully exploit this dataset, we estimate random-effects and quantile regression mode  ...[more]

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