Literature DB >> 30708234

ExpoQual: Evaluating measured and modeled human exposure data.

Judy S LaKind1, Cian O'Mahony2, Thomas Armstrong3, Rosalie Tibaldi4, Benjamin C Blount5, Daniel Q Naiman6.   

Abstract

Recent rapid technological advances are producing exposure data sets for which there are no available data quality assessment tools. At the same time, regulatory agencies are moving in the direction of data quality assessment for environmental risk assessment and decision-making. A transparent and systematic approach to evaluating exposure data will aid in those efforts. Any approach to assessing data quality must consider the level of quality needed for the ultimate use of the data. While various fields have developed approaches to assess data quality, there is as yet no general, user-friendly approach to assess both measured and modeled data in the context of a fit-for-purpose risk assessment. Here we describe ExpoQual, an instrument developed for this purpose which applies recognized parameters and exposure data quality elements from existing approaches for assessing exposure data quality. Broad data streams such as quantitative measured and modeled human exposure data as well as newer and developing approaches can be evaluated. The key strength of ExpoQual is that it facilitates a structured, reproducible and transparent approach to exposure data quality evaluation and provides for an explicit fit-for-purpose determination. ExpoQual was designed to minimize subjectivity and to include transparency in aspects based on professional judgment. ExpoQual is freely available on-line for testing and user feedback (exposurequality.com).
Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  BEES-C; Biomonitoring; ExpoQual; Exposure; Fit-for-purpose; Human; Instrument; Model uncertainty; Quality

Mesh:

Year:  2019        PMID: 30708234     DOI: 10.1016/j.envres.2019.01.039

Source DB:  PubMed          Journal:  Environ Res        ISSN: 0013-9351            Impact factor:   6.498


  3 in total

1.  GRADE Guidelines 30: the GRADE approach to assessing the certainty of modeled evidence-An overview in the context of health decision-making.

Authors:  Jan L Brozek; Carlos Canelo-Aybar; Elie A Akl; James M Bowen; John Bucher; Weihsueh A Chiu; Mark Cronin; Benjamin Djulbegovic; Maicon Falavigna; Gordon H Guyatt; Ami A Gordon; Michele Hilton Boon; Raymond C W Hutubessy; Manuela A Joore; Vittal Katikireddi; Judy LaKind; Miranda Langendam; Veena Manja; Kristen Magnuson; Alexander G Mathioudakis; Joerg Meerpohl; Dominik Mertz; Roman Mezencev; Rebecca Morgan; Gian Paolo Morgano; Reem Mustafa; Martin O'Flaherty; Grace Patlewicz; John J Riva; Margarita Posso; Andrew Rooney; Paul M Schlosser; Lisa Schwartz; Ian Shemilt; Jean-Eric Tarride; Kristina A Thayer; Katya Tsaioun; Luke Vale; John Wambaugh; Jessica Wignall; Ashley Williams; Feng Xie; Yuan Zhang; Holger J Schünemann
Journal:  J Clin Epidemiol       Date:  2020-09-24       Impact factor: 6.437

2.  Translation of Exposure and Epidemiology for Risk Assessment: A Shifting Paradigm.

Authors:  Judy S LaKind; Joshua Naiman; Carol J Burns
Journal:  Int J Environ Res Public Health       Date:  2020-06-12       Impact factor: 3.390

3.  Wrangling environmental exposure data: guidance for getting the best information from your laboratory measurements.

Authors:  Julia O Udesky; Robin E Dodson; Laura J Perovich; Ruthann A Rudel
Journal:  Environ Health       Date:  2019-11-21       Impact factor: 5.984

  3 in total

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