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      Systems Thinking for Effective Interventions in Global Environmental Health

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          Abstract

          Environmental health risks such as household air pollution due to burning solid fuels, inadequate water, sanitation, and hygiene, and chemical pollution disproportionately affect the poorest and most marginalized populations. While billions of dollars and countless hours of research have been applied toward addressing these issues in both development and humanitarian contexts, many interventions fail to achieve or sustain desired outcomes over time. This pattern points to the perpetuation of linear thinking, despite the complex nature of environmental health within these contexts. There is a need and an opportunity to engage in critical reflection of the dominant paradigms in the global environmental health community, including how they affect decision-making and collective learning. These paradigms should be adapted as needed toward the integration of diverse perspectives and the uptake of systems thinking. Participatory modeling, complexity-aware monitoring, and virtual simulation modeling can help achieve this. Additionally, virtual simulation modeling is relatively inexpensive and can provide a low-stakes environment for testing interventions before implementation.

          Abstract

          This Perspective describes three broadly applicable opportunities that may improve health outcomes for people disproportionately affected by environmental risks.

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          Most cited references71

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          The Lancet Commission on pollution and health

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            Towards a common definition of global health

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              Learning from evidence in a complex world.

              Policies to promote public health and welfare often fail or worsen the problems they are intended to solve. Evidence-based learning should prevent such policy resistance, but learning in complex systems is often weak and slow. Complexity hinders our ability to discover the delayed and distal impacts of interventions, generating unintended "side effects." Yet learning often fails even when strong evidence is available: common mental models lead to erroneous but self-confirming inferences, allowing harmful beliefs and behaviors to persist and undermining implementation of beneficial policies. Here I show how systems thinking and simulation modeling can help expand the boundaries of our mental models, enhance our ability to generate and learn from evidence, and catalyze effective change in public health and beyond.
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                Author and article information

                Journal
                Environ Sci Technol
                Environ Sci Technol
                es
                esthag
                Environmental Science & Technology
                American Chemical Society
                0013-936X
                1520-5851
                04 January 2022
                18 January 2022
                : 56
                : 2
                : 732-738
                Affiliations
                []Department of Civil & Environmental Engineering, University of South Florida , 4202 E Fowler Ave, Tampa, Florida 33620, United States
                []USAID Uganda Sanitation for Health Activity, Tetra Tech , Plot 12A, Farady Road, Bugolobi, Kampala, Uganda
                [§ ]Independent , 349 West Parkwood Road, Decatur, Georgia 30030, United States
                []Latin America and Caribbean Regional Office, UNICEF , PO Box 0843-03045, Panama City, 07144, Panama
                Author notes
                Author information
                https://orcid.org/0000-0002-5939-6340
                https://orcid.org/0000-0002-1736-9264
                Article
                10.1021/acs.est.1c04110
                8969763
                34982546
                d4c992d1-fadb-4cea-b5c1-d6e70e0c5c8e
                © 2022 American Chemical Society

                Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works ( https://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 22 June 2021
                Funding
                Funded by: National Science Foundation, doi 10.13039/100000001;
                Award ID: 1735320
                Categories
                Perspective
                Custom metadata
                es1c04110
                es1c04110

                General environmental science
                sustainable development goals,wash,household air pollution,chemical pollution,participatory modeling,complexity-aware monitoring,virtual simulation modeling

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