Demonstrating a systems approach for integrating disparate data streams to inform decisions on children's environmental health.

Autor: Hubal EAC; Center for Public Health and Environmental Assessment, US EPA, Research Triangle Park, NC, USA. hubal.elaine@epa.gov., DeLuca NM; Center for Public Health and Environmental Assessment, US EPA, Research Triangle Park, NC, USA., Mullikin A; Center for Public Health and Environmental Assessment, US EPA, Research Triangle Park, NC, USA., Slover R; Center for Public Health and Environmental Assessment, US EPA, Research Triangle Park, NC, USA., Little JC; Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, VA, USA., Reif DM; Department of Biological Sciences, North Carolina State University, Raleigh, NC, USA.
Jazyk: angličtina
Zdroj: BMC public health [BMC Public Health] 2022 Feb 15; Vol. 22 (1), pp. 313. Date of Electronic Publication: 2022 Feb 15.
DOI: 10.1186/s12889-022-12682-3
Abstrakt: Background: The use of systems science methodologies to understand complex environmental and human health relationships is increasing. Requirements for advanced datasets, models, and expertise limit current application of these approaches by many environmental and public health practitioners.
Methods: A conceptual system-of-systems model was applied for children in North Carolina counties that includes example indicators of children's physical environment (home age, Brownfield sites, Superfund sites), social environment (caregiver's income, education, insurance), and health (low birthweight, asthma, blood lead levels). The web-based Toxicological Prioritization Index (ToxPi) tool was used to normalize the data, rank the resulting vulnerability index, and visualize impacts from each indicator in a county. Hierarchical clustering was used to sort the 100 North Carolina counties into groups based on similar ToxPi model results. The ToxPi charts for each county were also superimposed over a map of percentage county population under age 5 to visualize spatial distribution of vulnerability clusters across the state.
Results: Data driven clustering for this systems model suggests 5 groups of counties. One group includes 6 counties with the highest vulnerability scores showing strong influences from all three categories of indicators (social environment, physical environment, and health). A second group contains 15 counties with high vulnerability scores driven by strong influences from home age in the physical environment and poverty in the social environment. A third group is driven by data on Superfund sites in the physical environment.
Conclusions: This analysis demonstrated how systems science principles can be used to synthesize holistic insights for decision making using publicly available data and computational tools, focusing on a children's environmental health example. Where more traditional reductionist approaches can elucidate individual relationships between environmental variables and health, the study of collective, system-wide interactions can enable insights into the factors that contribute to regional vulnerabilities and interventions that better address complex real-world conditions.
(© 2022. The Author(s).)
Databáze: MEDLINE
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