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Results provide qualitative insights for use in VHSV surveillance and risk-management planning, and quantitative estimates of contextual risk for use in a Bayesian model combining multiple evidence streams for joint probability assessment of disease freedom status.
It is also leading to the development of methods to handle the assessment and integration of data within and across multiple evidence streams.
Also, there is a need to integrate data from multiple evidence streams (human, animal, and "other relevant data" including mechanistic or in vitro studies) in order to reach conclusions regarding potential health effects from exposure to substances in our environment.
Here we explain the framework developed by OHAT that uses procedures to integrate multiple evidence streams including observational human study findings, experimental animal toxicology results, and other relevant data in developing hazard identification conclusions or state-of-the-science evaluations regarding health effects from exposure to environmental substances.
However, we also recognized the necessity to extend existing systematic review methods to accommodate our need in environmental health to integrate data from multiple evidence streams (human, animal, in vitro) and focus on observational human studies rather than on the randomized clinical trials more commonly encountered in the field of health-care intervention (NTP 2012a, 2012b).
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Evidence streams of this kind contain no possibly falsifying outcomes.
That is, it applies to all evidence streams for which hj is fully outcome-compatible with hi on each ck in the evidence stream.
Integrating the evidence across evidence streams.
2. Diverse evidence streams are combined.
We used this rating process to evaluate the human and nonhuman evidence streams separately.
Steps 1 3 are briefly summarized below for the human and nonhuman evidence streams.
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