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Presentation by Prof. George Gray, Director of the Centre for Risk Science and Public Health, George Washington University, at the Workshop on Risk Assessment in Regulatory Policy Analysis (RIA), Session 12, Mexico, 9-11 June 2014. Further information is available at http://www.oecd.org/gov/regulatory-policy/
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Center for Risk Science and Public Health
Exposure Assessment
George Gray
Center for Risk Science and Public Health Department of Environmental and Occupational Health
Milken Institute School of Public Health
Center for Risk Science and Public Health
Exposure and Risk
• “Exposure” is used to describe the extent of contact with the proposed causative agent (or its proxy) in a risk relationship
• Examples
Risk Exposure Measure Lung cancer from cigarette smoking
Pack-years of exposure
Automobile road fatalities Vehicle Miles Traveled (VMT)
Workplace injuries Working days
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Critical Point – Exposure Measure Units
• Exposure needs to be measured or modeled in units that match with risk relationship
• Good: Exposure = Tcp = annual minutes on phone while driving
• Bad: Incremental Cancer Risk = Cancer Slope Factor (mg/kg/day)-1 x Ingestion (mg/kg/day)
Exposure = ppm benzene in air
Cohen, JT and Graham, JD (2003) A Revised Economic Analysis of Restrictions on the Use of Cell Phones While Driving. Risk Analysis 23: 5-17
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The Risk Assessment Paradigm
Exposure Assessment asks the questions:
• To how much of the substance of concern are people exposed?
• What are the sources of exposure?
• What are the pathways of exposure?
• What is the appropriate measure of dose?
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Exposed Populations
• Individuals • Populations • Sensitive Subpopulations
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Exposure Assessment
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Exposure Media and Routes
Air
Soil
Water
Diet
Ingestion
Inhalation
Dermal Contact
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Exposure Pathways
• Examples (not exhaustive!): • Ingestion of soil, water, food, or particles • Inhalation of air or particles • Dermal contact with soil, sediment, water or air
Air
Soil
Water
Diet
Ingestion
Inhalation
Dermal Contact
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Making Equivalent Doses
X
X X X X
X
500 mg Drug X
Mouse = 0.07 kg Human = 70 kg
500 mg/0.07 kg = 7,143 mg/kg 500 mg/70 kg = 7.143 mg/kg
7,143 mg/kg Drug X
mg/kg is common way to make doses equivalent across species
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ADD: average daily dose C: concentration IR: intake rate T: time (duration of exposure) AF: absorption (bioavailability) factor BW: body weight AT: averaging time Units: typically mg pollutant /kgbody weight /d
Generic Average Daily Dose Equation
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Aggregate Exposure
• Sometimes there may be several sources of exposure to a single compound
• Ex/ Benzene in air • Workplace • Automobile refueling and driving (gasoline) • Personal habits (cigarette smoke)
• Which belong in (or out) depend on the scope of RIA and options considered
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Exposure Assessment
Exposures can be either measured or modeled (or combination of the two)
• Measured • more precise (sampling!) • more expensive, not always methods • can’t be used to predict future risk
• Modeled • estimate concentrations, exposures or doses • require many assumptions • models imprecise and rarely validated • allow better incorporation of time in exposure
estimates
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Estimating Exposure: Pesticide Residues on Food
• Goal is understanding of amount of pesticides “on the dinner plate”
• 3 Ways used to estimate pesticide residues on food • theoretical maximum residue contribution • farm gate data • residue monitoring
• Residue estimates combined with consumption data to estimate exposure
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Theoretical Maximum Residue Contribution
• Assume every acre of a crop has pesticide at maximum allowable level (tolerance level)
• Assume level does not decrease with time, processing, storage, or cooking
• Considered upper bound on true level of consumer exposure to pesticide residue on food
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Farm Gate Data
• Derived from field trials of pesticide use • Measure level of pesticide on crop at
“farm gate” - after treatment at highest allowable level with minimum preharvest interval
• May be adjusted with experimentally determined processing, washing, or cooking factors and estimates of percentage of crop treated
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Residue Monitoring
• Based on measurements of pesticide residues on food as purchased at grocery store
• Reflects normal preparation (washing, cooking, etc.)
• Reflects actual agricultural practices such as pesticide application rates, different preharvest intervals, and the effects of time and storage
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Does The Choice Matter?
Example: Chlorothalonil (Bravo®) on Celery
chlorothalonil (ppm) % of tolerance • TMRC 15.0 100.0
• Field Data 4.1 27.1
• Residue Monitoring 0.8 0.12
Source: Gary L. Eilrich (1991) Tracking the Fate of Residues from the Farm Gate to the Table, in Pesticides and Food Safety (Tweedy, B.G., Dishburger, H.J., Ballantine, L.G., and McCarthy, J. eds.) American Chemical Society, Washington, D.C.
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Quantitative Approaches
• Point Estimates (for either individuals or populations)
• Central Tendency
• Extrema (“Reasonable Maximally Exposed”)
• Bounding Point Estimates
• Interval Estimates
• Distributional Estimates
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Point Estimation
• Example: Inhalation of fine particulate matter (PM) in ambient air for a city of 1M • Ambient concentration: 50 µg/m3
• Average inhalation rate: 15 m3/d • Average body weight: 70 kg
• Average daily dose (ADD): 10.7 µg/kg/d
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Interval Estimation
• Same Example:
• Ambient concentration: 50 µg/m3
• Inhalation rate range: 2 m3/d – 44 m3/d • Body weight range: 1 kg - 650 kg
• Minimum Possible Dose: 0.2 µg/kg/d • Maximum Possible Dose: 2,200 µg/kg/d • Dose based on midpoints: 3.5 µg/kg/d
• Now What?
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Uncertainty & Variability
• Variability • “…represents heterogeneity or diversity in a well-
characterized population which is usually not reducible through further measurement or study.”
• Uncertainty • “…represents ignorance about a poorly characterized
phenomenon which is sometimes reducible through further measurement or study.”
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Distribution of Body Weight
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Distribution of Inhalation Rate
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Distributional Approach
• Same Example:
• Treat BW, InhR as variable • BW ~ LogNormal(mean = 70kg, GSD = 1.29) • InhR ~ LogNormal(mean = 15m3/d, GSD = 1.9)
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Distributional Result (n=1000)
Median = 7.8
Mean = 10.0
95%ile = 26.2
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Standardized Exposure Factors
• Examine a regulatory risk assessment and you will find a large set of standardized assumptions for estimating exposure. • Body weight: 70 kg • Lifetime: 75 yrs • Daily breathing rate: 20 m3/d • Water intake: 2 L/d
• Why do these exist? • Is the use of these factors good or bad?
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Final Thoughts
• To be useful, an estimate of exposure or any other element of a risk assessment requires a description of uncertainty • “Usually, exposure assessments will contain less
uncertainty than other steps in a risk assessment, especially the dose response portion” -- Dennis Paustenbach, Human and Ecological Risk Assessment
• Precision required of exposure estimates depends on precision required in output (risk estimate) and the precision available in other inputs.
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