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A Model for Optimization of Biomarker Testing Frequency to Minimize Disease and Cost: Example of Beryllium Sensitization Testing
Author(s) -
Judd Nancy L.,
Griffith William C.,
Takaro Tim,
Faustman Elaine M.
Publication year - 2003
Publication title -
risk analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.972
H-Index - 130
eISSN - 1539-6924
pISSN - 0272-4332
DOI - 10.1111/j.0272-4332.2003.00396.x
Subject(s) - beryllium , sensitization , medicine , biomarker , disease , test strategy , reliability engineering , computer science , immunology , engineering , pathology , chemistry , software , biochemistry , organic chemistry , programming language
A common problem with medical surveillance programs using biomarkers is determining the optimal frequency of testing to minimize adverse health effects and cost. In the case of beryllium‐exposed workers, frequency of testing for beryllium sensitization may be especially important. Recent studies indicate a lack of dose response for beryllium sensitization, but do support a dose response for the development of chronic beryllium disease (CBD). Though unproven, this implies that early identification of sensitization and immediate removal from exposure may reduce development of CBD. A model is proposed to project the optimal frequency of sensitization testing using the current beryllium lymphocyte proliferation test (BeLPT) to minimize disease‐related costs, assuming that a positive BeLPT will precede CBD. Conversion rates for cumulative exposure to disease development were adapted from the literature and used with testing costs and cost of disease estimates in the model. The model was run assuming several test frequency regimes. Results support the use of periodic testing in line with the annual schedule proposed in the Final Chronic Beryllium Disease Prevention Program Rule (1999) following initial testing within three months of first beryllium exposure. The financial and health benefits of reducing the time from exposure to detection of early disease was also explored with the model and demonstrated as a highly desirable characteristic for an alternative test or improved BeLPT. Limitations of the approach are discussed as well as options for adapting this biomarker optimization methodology to consider biomarkers of other exposure‐associated diseases.