Evaluation analytics for occupational health: how well do laboratories assess workplace concentrations of respirable crystalline silica?

Abstract

Chapters 8 and 10 have introduced important themes of evaluation analytics: discovering through independent replication of previous work (Chap. 8) and by applying new methods such as modern predictive and causal analytics algorithms to previously collected observational data (Chap. 10) whether published claims are reproducible and whether predicted effects caused by changes in exposures have actually occurred. This chapter provides an example of evaluation analytics in a context where experimentation is possible. It illustrates how a designed experiment with samples having known properties can be used to evaluate how consistently and accurately the laboratory system used to assess compliance of workplaces with occupational safety standards for respirable crystalline silica (RCS) performs in correctly classifying exposure concentrations as above or below a desired level. In this context, causation appears to be clear: concentrations of RCS in airlead to concentrations on air filters sent to laboratories. However, as we shall see, there is enough unexplained noise or random variation in the process so that even control samples with no RCS are sometimes mistakenly identified as carrying significant positive loads of RCS (Cox et al. 2015). Thus, the causes of laboratory-reported values include substantial contributions from measurement errors.