2017
DOI: 10.1007/s10653-017-9919-4
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Seeking evidence of multidisciplinarity in environmental geochemistry and health: an analysis of arsenic in drinking water research

Abstract: A multidisciplinary approach to research affords the opportunity of objectivity, creation of new knowledge and potentially a more generally acceptable solution to problems that informed the research in the first place. It increasingly features in national programmes supporting basic and applied research, but for over 40 years, has been the arena for many research teams in environmental geochemistry and health. This study explores the nature of multidisciplinary research in the earth and health sciences using a… Show more

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Cited by 13 publications
(8 citation statements)
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References 51 publications
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“…Linking human-health outcomes to environmental exposure can be challenging when the data are available on different geographic scales or no exposure data are available. However, methods are increasingly being developed to make these linkages more achievable. , The previously developed LR model was used as a covariate in studies of national scale human health data. , The machine learning models developed here are an improvement upon the previous model and will provide additional information for future environmental epidemiology studies. Until now, spatially continuous geographic data on arsenic occurrence at varying concentrations in private-supply drinking water has been lacking within the U.S. Our arsenic models provide a new and coherent resource to evaluate associations of private well arsenic occurrence at various concentrations with human health outcomes at the CONUS scale.…”
Section: Results and Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…Linking human-health outcomes to environmental exposure can be challenging when the data are available on different geographic scales or no exposure data are available. However, methods are increasingly being developed to make these linkages more achievable. , The previously developed LR model was used as a covariate in studies of national scale human health data. , The machine learning models developed here are an improvement upon the previous model and will provide additional information for future environmental epidemiology studies. Until now, spatially continuous geographic data on arsenic occurrence at varying concentrations in private-supply drinking water has been lacking within the U.S. Our arsenic models provide a new and coherent resource to evaluate associations of private well arsenic occurrence at various concentrations with human health outcomes at the CONUS scale.…”
Section: Results and Discussionmentioning
confidence: 99%
“…However, methods are increasingly being developed to make these linkages more achievable. 107,108 The previously developed LR model was used as a covariate in studies of national scale human health data. 109,110 The machine learning models developed here are an improvement upon the previous model and will provide additional information for future environmental epidemiology studies.…”
Section: ■ Introductionmentioning
confidence: 99%
“…Academic publications in scientific journals are considered the most important resources for this type of bibliometric analysis [ 19 ]. Bibliometric methods have been used to measure scientific progress in many disciplines of science and engineering and are a common research instrument for systematic analysis of research trends [ 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ].…”
Section: Introductionmentioning
confidence: 99%
“…Public Health professionals can thus make an important contribution, both from their understanding of health and disease, as well as through facilitation of the integration of hard and soft evidence from different stakeholders. The skills needed in our professional circles include the necessary high quality subject specialism as well as the ability to integrate across disciplines [107]. This is an effective and powerful way to address complex humanenvironment relationships but is fraught with many challenges, none the least in learning the language of contributing disciplines.…”
Section: Putting It All Together: Preventing Adverse Health Outcomesmentioning
confidence: 99%