RISK BENEFIT ANALYSIS Special Lectures University of Kuwait Richard Wilson Mallinckrodt Professor of Physics Harvard University January 13th, 14th and 15th 2002

January 13th 9 am to 2 pm What do we mean by Risk? Measures of Risk How do we Calculate Risk? (a) History (b) Animal analogy (c) Event Tree

Day 2. January 14th 2002 Uncertainties and Perception Types of Uncertainties Role of Perception. Kahneman’s 2002 economics Nobel prize We will try to show his effect in class List of interesting attributes Major differences between Public and Expert perceptions

Day 3 January 15th 2003 Formal Risk-benefit comparisons. Net Present Value Decision Tree Value of Information Probability of Causation Cases: Chernobyl, TMI Bhopal ALAR as a pesticide Research on particulates Sabotage and Terrorism

The Biggest Risk to Life is Birth. Birth always leads to death! We talk about premature death. Polls say Risk is Increasing (next slide) but history says the opposite. What do they mean?

WHAT IS LIFE EXPECTANCY ? An artificial construct assuming that the probability of dying as one ages is the same as the fraction of people dying at the same age at the date of one’s birth.

Both the specific death rate and the life expectancy at birth have a dip at 1919 world wide influenza epidemic. BUT anyone born in 1919 will not actually see this dip. Peculiarity of definition of life expectancy

Half the “Beijing men’ were teenagers. This puts life expectancy about 15 Roman writings imply a life expectancy of 25. Sweden started life expectancy statistics early. Russia has been going down since 1980

Risk is Calculated in Different Ways and that influences perception and decisions. (1) Historical data (2) Historical data where Causality is difficult (3) Analogy with Animals (4) Event tree if no Data exist

Risk is different for different measures of risk. Different decision makers will use different measures depending on their constituency

MEASURES of Risk Simple risk of Death (assuming no other causes) by age by cause Risk of Injury by cause by type by severity Per year lifetime unit operation event ton unit output

RISK MEASURES (continued) Loss of Life Expectancy (LOLE) Years of Life Lost (YOLL) Man Days Lost (MDL) Working Days Lost (WDL) Public Days Lost (PDL) Quality Adjusted Life Years (QALY) Disability Adjusted Life Years (DALY) Different decisions may demand different measures

LOLE from cigarette smoking In USA 600 billion cigarettes made (presumably smoked) 400,000 people have premature death (lung cancer, other cancers, heart) 1,500,000 cigarettes per death Each death takes about 17 years (8,935,200 minutes) off life or 6 minutes per cigarette ABOUT THE TIME IT TAKES TO SMOKE ONE (easy to remember)

Risks calculated from History seems simple. BUT The number of people dying and the number of persons in the risk pool often come from different data bases. Also units are often different

Add cartoon on units

Annual Occupation Fatality Rates (US)

Two problems in human diseases Effect is often delayed from the Cause then Causality is hard to prove. Proof of an effect is at high dose we want to know effect at low dose

Epidemiology Associate Death (or other Measure) to Postulated Cause Is it statistically significant? Are there alternative causes (confounders)? THINK. No case where cause is accepted unless there is a group where death rate has doubled. Risk Ratio (RR) > 2

We contrast two types of medical response to pollutants. ACUTE TOXIC EFECT A dose within a day causes death within a few days (causality easy to establish) CHRONIC EFFECT lower doses repeated give chronic effects (cancer, heart) within a lifetime. (Causality hard to establish)

Characteristics One dose or dose accumulated in a short time KILLS 1/10 the dose repeated 10 times DOES NOT KILL

Early Optimism Based on Poisons There is a threshold below which nothing happens __________ J.G. Crowther 1924 Probability of Ionizing a Cell is Linear with Dose

Typically an accumulated Chronic Dose equal to the Acute LD 50 gives CANCER to 10% of the population. Assumed to be proportional to dose E.g. LD 50 for radiation is about 350 Rems. At an accumulated exposure of 350 Rems about 10% of exposed get cancer. What does that say for Chernobyl? (more or less depending on rate of exposure)

CRITICAL ISSUES FOR LINEARITY at low doses THE POLLUTANT ACTS IN THE SAME WAY AS WHATEVER ELSE INFLUCENCES THE CHRONIC OUTCOME (CANCER) RATE CHRONIC OUTCOMES (CANCERS) CAUSED BY POLLUTANTS ARE INDISTINGUISHABLE FROM OTHER OUTCOMES implicit in Armitage and Doll (1954) explicit in Crump et al. (1976) extended to any outcome Crawford and Wilson (1996)

Note that the incremental Risk can actually be greater than the simple linearity assumption of a non-linear biological dose- response is assumed

We often have no human data and depend on analogy with animals Choose mammals Rats and mice

ANALOGY of animals and humans Start with Acute toxic effects data from paper of Rhomberg and Wolf

Assumptions for animal analogy with cancer: A man eating daily a fraction F of his body weight is as likely to get cancer (in his lifetime) as an animal eating daily the fraction f of his body weight.

Transparency of Allen et al.

Risks of New Technologies Old fashioned approach. Try it. If it gives trouble, fix it. E.g The first passenger railroad (Liverpool to Manchester) killed (a member of parliament) on the first day!

Risks of New technologies We now want more safety New technologies can kill more people at once. We do not want to have ANY history of accidents.

Design the system so that if a failure occurs there is a technology to fix it. (called DEFENSE IN DEPTH or Factorize the technology.) Draw an EVENT TREE following with time the possible consequences of an initiating event. Calculate the probability First done for Nuclear Power (Rasmussen et al. 1975)

Final Probability for an accident with serious consequencies P = P 1 X P 2 X P 3 X P 4 which can with care be 1/10,000,000 but without care can be 1/1,000

ASSUMPTIONS (1) We have drawn all possible trees with consequencies (2) The probabilities are independent (design to make them so; look very carefully about correlations (3) Consider carefully - with some confidentiality - actions that can artificially correlate the separate probabilities

The event tree analysis SHOULD have been used by NASA in the 1980s and it would have avoided the Challenger disaster

Example: Risk of a Space Probe major risk: Probe (powered by Plutonium) reenters the earth’s atmosphere burns up spreads its plutonium widely over everyone Causes an increase in lung cancer

2 Steps (1) What is the probability of reentry (2) What is the distribution of Plutonium Compare with what we know

We need to have a earth flyby to speed up by a slingshot approach. This is hundreds of miles up. But space is large so probability of mistake small. 1 in 1,000,000 If Probe burns up it double Pu in atmosphere; doubles our Pu absorption (already 1/(100,000)) of enough to give lung cancer rate equal to cigarette smoking Risk < 1/(1,000,000) These are independent so that risk of probe to an individual is <1/(1,000,000,000,000)