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Progression from Beryllium Exposure to Chronic Beryllium Disease: An Analytic Model
BACKGROUND: Understanding the progression from beryllium exposure (BeE) to chronic beryllium disease (CBD) is essential for optimizing screening and early intervention to prevent CBD. METHODS: We developed an analytic Markov model of progression to CBD that assigns annual probabilities for progressi...
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Formato: | Texto |
Lenguaje: | English |
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National Institute of Environmental Health Sciences
2009
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2702415/ https://www.ncbi.nlm.nih.gov/pubmed/19590692 http://dx.doi.org/10.1289/ehp.0800440 |
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author | Harber, Philip Bansal, Siddharth Balmes, John |
author_facet | Harber, Philip Bansal, Siddharth Balmes, John |
author_sort | Harber, Philip |
collection | PubMed |
description | BACKGROUND: Understanding the progression from beryllium exposure (BeE) to chronic beryllium disease (CBD) is essential for optimizing screening and early intervention to prevent CBD. METHODS: We developed an analytic Markov model of progression to CBD that assigns annual probabilities for progression through three states: from BeE to beryllium sensitization and then to CBD. We used calculations of the number in each state over time to assess which of several alternative progression models are most consistent with the limited available empirical data on prevalence and incidence. We estimated cost-effectiveness of screening considering both incremental (cost/case) and cumulative program costs. RESULTS: No combination of parameters for a simple model in which risk of progression remains constant over time can meet the empirical constraints of relatively frequent early cases and continuing development of new cases with long latencies. Modeling shows that the risk of progression is initially high and then declines over time. Also, it is likely that there are at least two populations that differ significantly in risk. The cost-effectiveness of repetitive screening declines over time, although new cases will still be found with long latencies. However, screening will be particularly cost-effective when applied to persons with long latencies who have not been previously screened. CONCLUSIONS: To optimize use of resources, the intensity of screening should decrease over time. Estimation of lifetime cumulative CBD risk should consider the declining risk of progression over time. |
format | Text |
id | pubmed-2702415 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2009 |
publisher | National Institute of Environmental Health Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-27024152009-07-09 Progression from Beryllium Exposure to Chronic Beryllium Disease: An Analytic Model Harber, Philip Bansal, Siddharth Balmes, John Environ Health Perspect Research BACKGROUND: Understanding the progression from beryllium exposure (BeE) to chronic beryllium disease (CBD) is essential for optimizing screening and early intervention to prevent CBD. METHODS: We developed an analytic Markov model of progression to CBD that assigns annual probabilities for progression through three states: from BeE to beryllium sensitization and then to CBD. We used calculations of the number in each state over time to assess which of several alternative progression models are most consistent with the limited available empirical data on prevalence and incidence. We estimated cost-effectiveness of screening considering both incremental (cost/case) and cumulative program costs. RESULTS: No combination of parameters for a simple model in which risk of progression remains constant over time can meet the empirical constraints of relatively frequent early cases and continuing development of new cases with long latencies. Modeling shows that the risk of progression is initially high and then declines over time. Also, it is likely that there are at least two populations that differ significantly in risk. The cost-effectiveness of repetitive screening declines over time, although new cases will still be found with long latencies. However, screening will be particularly cost-effective when applied to persons with long latencies who have not been previously screened. CONCLUSIONS: To optimize use of resources, the intensity of screening should decrease over time. Estimation of lifetime cumulative CBD risk should consider the declining risk of progression over time. National Institute of Environmental Health Sciences 2009-06 2009-02-27 /pmc/articles/PMC2702415/ /pubmed/19590692 http://dx.doi.org/10.1289/ehp.0800440 Text en http://creativecommons.org/publicdomain/mark/1.0/ Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. |
spellingShingle | Research Harber, Philip Bansal, Siddharth Balmes, John Progression from Beryllium Exposure to Chronic Beryllium Disease: An Analytic Model |
title | Progression from Beryllium Exposure to Chronic Beryllium Disease: An Analytic Model |
title_full | Progression from Beryllium Exposure to Chronic Beryllium Disease: An Analytic Model |
title_fullStr | Progression from Beryllium Exposure to Chronic Beryllium Disease: An Analytic Model |
title_full_unstemmed | Progression from Beryllium Exposure to Chronic Beryllium Disease: An Analytic Model |
title_short | Progression from Beryllium Exposure to Chronic Beryllium Disease: An Analytic Model |
title_sort | progression from beryllium exposure to chronic beryllium disease: an analytic model |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2702415/ https://www.ncbi.nlm.nih.gov/pubmed/19590692 http://dx.doi.org/10.1289/ehp.0800440 |
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