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33 Cards in this Set
- Front
- Back
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four risk ASSESSMENT paradigms |
1. hazard ID 2. dose response 3. exposure assessment 4. risk characterization |
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three risk ANALYSIS paradigms |
1. risk assessment 2. risk management 3. risk communication |
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How do we deal with uncertainty? |
fitting a curve reduces uncertainty |
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Bootstrapping |
takes data points from the simulation distribution/combined variables to plot a new curve less uncertainty as a result |
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IRIS(integrated Risk Information Systems) |
Identifies and characterizes health hazards of chemicals found in the environment |
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QMRA Wiki |
you get microbial data from this |
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time of exposure formula |
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four exposure routes |
1. inhilation 2. skin absorption 3. ingestion 4. injection |
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which step in risk assessment has the greatest uncertainty? |
Exposure assessment |
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Paloma and reynolds reading |
probability of infection from enteric and respiratory viruses in a workplace setting. uses monte carlo simulation Healthy workplace project (HWP) |
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Reynolds reading |
study on laundry and reduction/transfer of bacteria and viruses. drying kills most bacteria. uncertainties: -who was using the products -not knowing how people use them -uncertainty in experimental data and fit model -some people use a lot, some didn't |
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Stochiastic variables |
randomly distributed |
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deterministic variables |
uses mean values of the risk. gives a lot of uncertainty since you are overestimating the people with lower values mean value= worse value |
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exposure duration (child vs. adult) |
adult has a longer exposure duration than a child |
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why is body weight important with chemicals and not in microbial infections? |
body weight does not matter in microbial infections because a singular microbe can cause an infection less body weight= more concentrated chemicals adults have more space to "dilute" a chemical infection |
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chemical modeling |
utilizes RfD and slope factors |
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Microbial modeling |
utilizes poisson models and alpha/ beta |
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RfD |
non carcinogenic; estimation of daily intake over a lifetime =NOAEL/UF |
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slope factors |
carcinogens;represent potency of medications |
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exposure handbook |
statistical data on various factors use in assessing human exposure |
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Probabilistic |
based on or adapted to a theory of probability; subject to or involving chance variation |
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Point estimates |
single exposure risk EX: anthrax (1 spore inhaled) exponential dose response:1-exp(-dose x r) EX: cryptosporidium in water |
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Independence model |
each event is discrete risk of any other exposure is statistically independent |
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Dose accumulation model |
individual doses are additive interactions may be synergistic or antagonistic |
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when is it appropriate to using 99%? |
to be more conservative when you have ONE variable in regulatory climates stochastic variables result in 99% |
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risk characterization step |
microbe 1 in 1,000 risk of INFECTION Chemicals 1 in a MILLION |
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how can risk modeling assist in risk communications? |
inform public party of the correct dose response and different outcomes of a chemical or microbe |
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HQ (Hazard Quotient) |
ratio of estimated dose and reference dose =intake/ RfD <1 is acceptable |
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IELCR |
incremental excess lifetime cancer risk = cancer slope x lifetime average daily dose |
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interval estimates |
range of values, probability distribution consideres uncertainty ad variability |
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intrinsic heterogeneity |
differences in consumption cultural differences dose resonse sensitivity varies immune function |
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Monte carlo |
most widely used tool for risk distribution analysis; combines distributions -variability assesment -uncertainty assessment -combination of both uses random number generator |
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why use monte carlo? |
-combining distributions -draws randomly from two defined distributions -10,000 is recommended |
