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Latent health factor index: a statistical modeling approach for ecological health assessment

2010; Wiley; Volume: 22; Issue: 3 Linguagem: Inglês

10.1002/env.1055

1180-4009

Grace S. Chiu, Peter Guttorp, Anton H. Westveld, Shahedul A. Khan, Jun Liang,

Economic and Environmental Valuation

EnvironmetricsVolume 22, Issue 3 p. 243-255 Research Article Latent health factor index: a statistical modeling approach for ecological health assessment Grace S. Chiu, Corresponding Author Grace S. Chiu [email protected] CSIRO Mathematics, Informatics and Statistics, GPO Box 664, Canberra, ACT 2601, Australia Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario N2L 3G1, CanadaCSIRO Mathematics, Informatics and Statistics, GPO Box 664, Canberra, ACT 2601, Australia.Search for more papers by this authorPeter Guttorp, Peter Guttorp Department of Statistics, University of Washington, Seattle, WA 98195, U.S.A.Search for more papers by this authorAnton H. Westveld, Anton H. Westveld Department of Mathematical Sciences, University of Nevada Las Vegas, NV 89154, U.S.A.Search for more papers by this authorShahedul A. Khan, Shahedul A. Khan Department of Mathematics and Statistics, University of Saskatchewan, Saskatoon, Saskatchewan S7N 5E6, CanadaSearch for more papers by this authorJun Liang, Jun Liang Canadian Institute for Health Information, 90 Eglinton Avenue East, Suite 300, Toronto, Ontario M4P 2Y3, CanadaSearch for more papers by this author Grace S. Chiu, Corresponding Author Grace S. Chiu [email protected] CSIRO Mathematics, Informatics and Statistics, GPO Box 664, Canberra, ACT 2601, Australia Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario N2L 3G1, CanadaCSIRO Mathematics, Informatics and Statistics, GPO Box 664, Canberra, ACT 2601, Australia.Search for more papers by this authorPeter Guttorp, Peter Guttorp Department of Statistics, University of Washington, Seattle, WA 98195, U.S.A.Search for more papers by this authorAnton H. Westveld, Anton H. Westveld Department of Mathematical Sciences, University of Nevada Las Vegas, NV 89154, U.S.A.Search for more papers by this authorShahedul A. Khan, Shahedul A. Khan Department of Mathematics and Statistics, University of Saskatchewan, Saskatoon, Saskatchewan S7N 5E6, CanadaSearch for more papers by this authorJun Liang, Jun Liang Canadian Institute for Health Information, 90 Eglinton Avenue East, Suite 300, Toronto, Ontario M4P 2Y3, CanadaSearch for more papers by this author First published: 18 November 2010 https://doi.org/10.1002/env.1055Citations: 11Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Abstract Multimetric indices (MMIs) are appealing scalar-valued tools for policy makers when rating ecosystems with respect to biological conditions that are not directly measurable. For conventional assessment of ecological health using MMIs, the quantitative calibration of health qualities can be specific to the investigator, and to the geographical region and time frame of interest. We propose a statistical-model-based approach that provides a systematic mechanism to construct MMIs; our approach aims to address some common issues of conventional practices, including the loss of information from data, spatio-temporal restrictions, and concerns over arbitrariness and costs. Our latent health factor index (LHFI) is obtained via statistical inference for an unobservable health factor term in a mixed-effects analysis-of-covariance regression that directly models the relationship among metrics, a very general notion of health, and factors that can influence health. We illustrate the approach by constructing an LHFI for a freshwater system using benthic taxonomic data in various Bayesian hierarchical formulations of generalized linear mixed models, implemented by Markov chain Monte Carlo techniques. The concept of the LHFI is also applicable to medical and other contexts. Copyright © 2010 John Wiley & Sons, Ltd. Citing Literature Volume22, Issue3May 2011Pages 243-255 RelatedInformation