Literature DB >> 18668405

Professional judgment and the interpretation of viable mold air sampling data.

David Johnson1, David Thompson, Rodney Clinkenbeard, Jason Redus.   

Abstract

Although mold air sampling is technically straightforward, interpreting the results to decide if there is an indoor source is not. Applying formal statistical tests to mold sampling data is an error-prone practice due to the extreme data variability. With neither established exposure limits nor useful statistical techniques, indoor air quality investigators often must rely on their professional judgment, but the lack of a consensus "decision strategy" incorporating explicit decision criteria requires professionals to establish their own personal set of criteria when interpreting air sampling data. This study examined the level of agreement among indoor air quality practitioners in their evaluation of airborne mold sampling data and explored differences in inter-evaluator assessments. Eighteen investigators independently judged 30 sets of viable mold air sampling results to indicate: "definite indoor mold source," "likely indoor mold source," "not enough information to decide," "likely no indoor mold source," or "definitely no indoor mold source." Kappa coefficient analysis indicated weak inter-observer reliability, and comparison of evaluator mean scores showed clear inter-evaluator differences in their overall scoring patterns. The responses were modeled on indicator "traits" of the data sets using a generalized, linear mixed model approach and showed several traits to be associated with respondents' ratings, but they also demonstrated distinct and divergent inter-evaluator response patterns. Conclusions were that there was only weak overall agreement in evaluation of the mold sampling data, that particular traits of the data were associated with the conclusions reached, and that there were substantial inter-evaluator differences that were likely due to differences in the personal decision criteria employed by the individual evaluators. The overall conclusion was that there is a need for additional work to rigorously explore the constellation of decision criteria, the weightings employed by individual practitioners, and the rationale under which criteria are adopted as first steps toward the larger goal of developing a consensus mold decision strategy.

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Year:  2008        PMID: 18668405     DOI: 10.1080/15459620802310796

Source DB:  PubMed          Journal:  J Occup Environ Hyg        ISSN: 1545-9624            Impact factor:   2.155


  4 in total

1.  Correlation between ERMI values and other moisture and mold assessments of homes in the American Healthy Homes Survey.

Authors:  Stephen Vesper; Craig McKinstry; David Cox; Gary Dewalt
Journal:  J Urban Health       Date:  2009-11       Impact factor: 3.671

2.  Field sampling of indoor bioaerosols.

Authors:  Jennie Cox; Hamza Mbareche; William G Lindsley; Caroline Duchaine
Journal:  Aerosol Sci Technol       Date:  2019-11-21       Impact factor: 2.908

3.  Permutation/randomization-based inference for environmental data.

Authors:  R Christopher Spicer; Harry J Gangloff
Journal:  Environ Monit Assess       Date:  2016-02-06       Impact factor: 2.513

4.  Applicability of the environmental relative moldiness index for quantification of residential mold contamination in an air pollution health effects study.

Authors:  Ali Kamal; Janet Burke; Stephen Vesper; Stuart Batterman; Alan Vette; Christopher Godwin; Marina Chavez-Camarena; Gary Norris
Journal:  J Environ Public Health       Date:  2014-11-09
  4 in total

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