31_Dose_Response

31_Dose_Response - CEE597Lecture31 DoseResponseModels...

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 9 April 2008  Jery Stedinger Lecture 31 1 CEE 597 - Lecture 31 Dose-Response Models
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 9 April 2008  Jery Stedinger Lecture 31 2 Bush restores water purity standard Ithaca Journal 11/1/2001
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 9 April 2008  Jery Stedinger Lecture 31 3 Readings for Health Risk Analysis Packet Molak - Toxic Chemicals Non-cancer Risk Analysis, 1997 Packet NRC 1983 – basic ideas and steps Packet McClellan A Risk Assessment Primer, updates NRC 1993 Packet Anderson et al.   – basic ideas; dose-response analysis, scaling Packet Crump, “Methods for Carcinogenic Risk Assessment” Packet Hattis and Kennedy – the goal, issues in analysis Readings p. 156 Calabrese, “Hormesis: Changing view…” Packet
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 9 April 2008  Jery Stedinger Lecture 31 4 Readings for Health Risk Analysis - 2 Readings p.43 Ames et al.  – what are the risks? Readings p. 76 Epstein/ Swartz’s comment –not really? Readings p. 96 Abelson in Science  – Health risk issues recapped Packet Colborn et al. Our Stolen Future  – Beyond cancer… Packet Rhomberg , Are chemicals…? Packet Steingraber,  Having Faith Packet
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 9 April 2008  Jery Stedinger Lecture 31 5 comic
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 9 April 2008  Jery Stedinger Lecture 31 6 Difficult extrapolations introduce uncertainty  into risk assessment
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 9 April 2008  Jery Stedinger Lecture 31 7 Dose Response Models Environmental Contaminants How do we relate dose d to probability A(d) that the dose causes cancer? We need a reasonable functional relationship! This is a modeling exercise: We seek to fit a probability model to a data set to describe the  relationships between the probability of a response and the application  of an agent
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 9 April 2008  Jery Stedinger Lecture 31 8 One-hit Model: Important Very simple;   Linear response;   No threshold;   No repair d = dose k = potency υ  = “arrival rate” of hits = k d  A(d)  = Pr{ at least one hit}  = 1 – Pr{ no hits}   = 1 –  υ 0 e - υ /0!       <<Poisson distribution; P(0) = 1 – exp[ – k d ]        k d      for  k d << 1   <<first-order approximation
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 9 April 2008  Jery Stedinger Lecture 31 9 Multi-hit Dose Response Model Results from assuming cancer occurs whenever cell “hit” m times: A(d) = Pr[ >  m hits in a  Poisson Process  with  υ  = kd ] υ m e - υ /m! +  υ m+1 e - υ /(m+1)! + … υ e - υ /m!  {  1 +  υ /(m+1) +  υ 2 /[(m+1)(m+2)] + … } where for  υ  = k d << 1, e - υ  = e -kd  is essentially 1 so that   A(d)     (kd) m /m!        <<This is low-dose cancer risk model.
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31_Dose_Response - CEE597Lecture31 DoseResponseModels...

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