For short-term chemical inhalation exposures to hazardous chemicals, the incidence of a health effect in biological testing usually conforms to a general linear model with a probit link function dependent on inhalant concentration C and the duration of exposure t. The National Academy's Acute Exposure Guideline Levels (AEGLs) Committee relies on these models when establishing AEGLs. Threshold concentrations at AEGL durations are established by the toxic load equation Cn x t = constant, which toxic load exponent n (TLE or n-value) directly follows from the bivariate probit model. When multiple probit datasets are available, the AEGL Committee routinely pools studies' incidence data. Such meta-analytical models are valid only when the pooled data are homogeneous, with similar sensitivities and equivalent responses to exposure concentrations and durations. In the present study, the homogeneity of datasets meta-analyzed by the AEGL Committee was examined, finding that 70% of datasets pooled by the AEGL Committee are heterogeneous. In these instances, data pooling leads to a statistically invalid model and TLE estimate, potentially resulting in under- or over-estimated inhalation guidance levels. When data pooling is inappropriate, other meta-analysis options include categorical regression, fixed-effect and random-effects models, or even designation of a key study based on scientific judgement. In the present work, options of TLE meta-analysis are summarized in a decision tree contingent on statistical testing.
Keywords: Acute exposure guideline levels (AEGLs); Categorical regression; General linear model; Meta-analysis; Probit; Risk assessment; Short-term inhalation exposure; Ten Berge equation; Toxic load; Toxic load exponent (TLE); n-value.
Published by Elsevier Inc.