Academic journal article Scandinavian Journal of Work, Environment & Health

Sensitization and Chronic Beryllium Disease at a Primary Manufacturing Facility, Part 2: Validation of Historical Exposures

Academic journal article Scandinavian Journal of Work, Environment & Health

Sensitization and Chronic Beryllium Disease at a Primary Manufacturing Facility, Part 2: Validation of Historical Exposures

Article excerpt

Objective The aim of this study was to evaluate the validity of a job exposure matrix (JEM) constructed for the period 1994-1999. Historical exposure estimates (HEE) for the JEM were constructed for all job and year combinations by applying temporal factors reflecting annual change in area air measurements (1994-1998) to the personal baseline exposure estimates (BEE) collected in 1999. The JEM was generated for an epidemiologie study to examine quantitative exposure-response relationships with Sensitization and chronic beryllium disease.

Methods The validity of the BEE and HEE was evaluated by comparing them with a validation dataset of independently collected personal beryllium exposure measurements from 1999 and 1994-1998, respectively. Agreement between the JEM and validation data was assessed using relative bias and concordance correlation coefficients (CCC).

Results The BEE and HEE overestimated the measured exposures in their respective validation dataseis by 8% and 6%, respectively. The CCC reflecting the deviation of the fitted line from the concordance line, showed good agreement for both BEE (CCC=0.80) and HEE (CCC=0.72). Proportional difference did not change with exposure levels or by process area and year. Overall, the agreement between the JEM and validation estimates (from combined HEE and BEE) was high (CCC=0.77).

Conclusions This study demonstrated that the reconstructed beryllium exposures at a manufacturing facility were reliable and can be used in epidemiologie studies.

Key terms agreement; exposure reconstruction; job exposure matrix; total exposure; validation.

(ProQuest: ... denotes formulae omitted.)

Exposure to beryllium has been known to lead to beryllium Sensitization (BeS) and cause chronic beryllium disease (CBD) (1). However, exposure-response relationships for BeS and CBD have been inconsistent (2, 3). Possible reasons for this inconsistency include lack of accurate and precise estimates of historical exposure leading to exposure misclassification, lack of biologically relevant exposure indices and summary measures, different bioavailability among the various forms of beryllium, exclusion of the skin as a route of exposure for sensitization, and lack of consideration of the impact of dose rate and genetic susceptibility (4). Few epidemiologie studies of BeS and CBD have utilized quantitative exposure data (3, 5-10). In these studies, exposure measurements were collected using different approaches including shortduration breathing zone samples, general area air samples, daily-weighted average measurements (a combination of the breathing zone and general area samples with activity-time data), 37 mm closed-face cassettes "total" samples, size-fractionated impactor samples, and fixed air-head area samples. The daily-weighted average has also been used in other studies of beryllium exposures or for evaluating health outcomes such as cancer or changes in pulmonary function (11-14). In all these studies, estimates of past exposures have been based on historical daily-weighted average, area air sampling data, or on the assumption that past exposures are not different from current exposures. Furthermore, none of the epidemiologie studies that have utilized historical exposure data have validated their exposure estimates.

Validation of reconstructed exposures as part of epidemiologie studies is an important process that ensures the reliability of the exposure estimates and provides a degree of confidence in the resulting exposure-response relationships. Hornung et al (15) used internal validation to verify the reliability of estimates reconstructed from an exposure model by splitting the data into model building and validation dataseis. However, internal validation is rarely conducted often due to lack of adequate sample size. Many exposure reconstruction studies have used external dataseis such as published literatee (16-18) or addilional exposure dala from lhe same or differenl workplaces (19-21) for validation. …

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