Academic journal article Environmental Health Perspectives

Progression from Beryllium Exposure to Chronic Beryllium Disease: An Analytic Model

Academic journal article Environmental Health Perspectives

Progression from Beryllium Exposure to Chronic Beryllium Disease: An Analytic Model

Article excerpt

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.

KEY WORDS: beryllium, beryllium sensitization, chronic beryllium disease, cost-effectiveness, screening. Environ Health Perspect 117:970-974 (2009). doi:10.1289/ehp.0800440 available via http://dx.doi.org/ [Online 27 February 2009]

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Chronic beryllium disease (CBD) is an increasingly recognized occupational health problem (Newman et al. 2001, 2005). Three categories of health status with respect to CBD have been identified: a) beryllium exposed without sensitization (BeE), b) beryllium-sensitized without disease (BeS)--presence of blood lymphocytes with in vitro proliferation in response to beryllium, and c) CBD--a chronic granulomatous disease that involves a beryllium-specific cell-mediated immune response and that is similar in clinical presentation to sarcoidosis (Rossman 1996; Williams 1996). CBD predominantly affects the lungs and may lead to severe disability or death (Rossman 2008). Currently, a two-stage screening process is used. The first stage of screening for this immunologic disorder (Rossman 1996; Saltini et al. 1998) is applied to exposed individuals and is based upon testing for lymphocyte proliferation to beryllium stimulation. Those with positive results undergo detailed clinical assessment with more extensive testing such as pulmonary function testing, high-resolution computed tomography scans, and fiber optic bronchoscopy with transbronchial biopsy (Maier 2002; Rossman 1996).

The large population of workers and community members (Maier et al. 2008) with potential exposure makes it important to understand the frequency and time course of development of both sensitization and CBD among exposed individuals. The available clinical and epidemiologic data are not adequate to fully characterize the processes of sensitization and development of lung disease. Although the literature concerning treatment is limited (Marchand-Adam et al. 2008; Preuss 1985; Rossman 2008; Sood et al. 2004), it appears likely that there is benefit of screening and early treatment in many cases. Therefore, we have developed an analytic approach to model progression from BeE to BeS and from BeS to CBD with the goal of optimizing screening among exposed populations (Table 1).

Table 1. Examples of studies reporting prevalence.

                         BeS + CBD
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