"Listen First! responds to four questions often heard: what is listening to social media, how is it used, how is it done, and where is it headed? Currently there isn't an authoritative source that organizes and synthesizes what is known about social media listening and presents it in a way that was useful for making marketing and branding decisions. This book is the response to fill that void. The author explains the state of the art in social media listening, selecting cases based on solid research that stand up to careful scrutiny and whose methods supported doing excellent listening work. This book will start the conversation on online listening in all companies who want to run more efficiently"--
This paper evaluates the decision by the Occupational Safety and Health Administration (OSHA) to base its Air Contaminants Standard on the threshold limit values (TLVs) of the American Conference of Governmental Industrial Hygienists. Contrary to the claim made by OSHA in promulgating the standard, the TLV list was not the sole available basis for a generic standard covering toxic air contaminants. The National Institute for Occupational Safety and Health (NIOSH) presented data indicating that the TLVs were insufficiently protective for 98 substances. NIOSH Recommended Exposure Limits (RELs) were available for 59 of these substances. The ratio of PEL to REL ranged up to 1,000, with a median of 2.5 and a mean of 71.4. OSHA excluded 42 substances from the standard altogether despite the availability of NIOSH RELs, solely because no TLV had been established.
Assessments of occupational exposures to chemicals are generally based upon the practice of compliance testing in which the probability of compliance is related to the exceedance [γ, the likelihood that any measurement would exceed an occupational exposure limit (OEL)] and the number of measurements obtained. On the other hand, workers' chronic health risks generally depend upon cumulative lifetime exposures which are not directly related to the probability of compliance. In this paper we define the probability of "overexposure" (θ) as the likelihood that individual risk (a function of cumulative exposure) exceeds the risk inherent in the OEL (a function of the OEL and duration of exposure). We regard θ as a relevant measure of individual risk for chemicals, such as carcinogens, which produce chronic effects after long‐term exposures but not necessarily for acutely‐toxic substances which can produce effects relatively quickly. We apply a random‐effects model to data from 179 groups of workers, exposed to a variety of chemical agents, and obtain parameter estimates for the group mean exposure and the within‐ and between‐worker components of variance. These estimates are then combined with OELs to generate estimates of γ and θ. We show that compliance testing can significantly underestimate the health risk when sample sizes are small. That is, there can be large probabilities of compliance with typical sample sizes, despite the fact that large proportions of the working population have individual risks greater than the risk inherent in the OEL. We demonstrate further that, because the relationship between θ and γ depends upon the within‐ and between‐worker components of variance, it cannot be assumed a priori that exceedance is a conservative surrogate for overexposure. Thus, we conclude that assessment practices which focus upon either compliance or exceedance are problematic and recommend that employers evaluate exposures relative to the probabilities of overexposure.
Benzene is myelotoxic and leukemogenic in humans exposed at high doses (>1 ppm, more definitely above 10 ppm) for extended periods. However, leukemia risks at lower exposures are uncertain. Benzene occurs widely in the work environment and also indoor air, but mostly below 1 ppm, so assessing the leukemia risks at these low concentrations is important. Here, we describe a human physiologically‐based pharmacokinetic (PBPK) model that quantifies tissue doses of benzene and its key metabolites, benzene oxide, phenol, and hydroquinone after inhalation and oral exposures. The model was integrated into a statistical framework that acknowledges sources of variation due to inherent intra‐ and interindividual variation, measurement error, and other data collection issues. A primary contribution of this work is the estimation of population distributions of key PBPK model parameters. We hypothesized that observed interindividual variability in the dosimetry of benzene and its metabolites resulted primarily from known or estimated variability in key metabolic parameters and that a statistical PBPK model that explicitly included variability in only those metabolic parameters would sufficiently describe the observed variability. We then identified parameter distributions for the PBPK model to characterize observed variability through the use of Markov chain Monte Carlo analysis applied to two data sets. The identified parameter distributions described most of the observed variability, but variability in physiological parameters such as organ weights may also be helpful to faithfully predict the observed human‐population variability in benzene dosimetry.
