1. Hss4303b – intro to epidemiology April 8, 2010 – epidemiology and health policy

2. Exam Review April 22, 2pm-3:30pm, SCS E218 April 26, 2pm-3:30pm, SCS E218

3. Poster Sh*t For those printing their posters with the Geography dept or Merriam Print, the deadline is TODAY in order to assure pick-up by Saturday

4. Important Poster Sh*t The agenda and list of presenters is now posted on the website Your presentation time is also listed If you are scheduled to present in the afternoon, you are still encouraged (dare I say, required?) to register in the morning – If you are not there to present to the judges when they come around, you will receive zero

5. What is this?

6. OTTAWA - Health Canada is advising Canadians about important safety information for CRESTOR® (rosuvastatin). A recent US study has found that Asian patients may be at greater risk of developing muscle-related adverse events with this drug. CRESTOR® is a cholesterol-lowering drug in the "statin" family. "Statins" are a specific type of cholesterol-lowering medication. In Canada, and internationally, CRESTOR® has been associated with reports of a serious condition called rhabdomyolysis. Rhabdomyolysis results in muscle breakdown and the release of muscle cell contents into the bloodstream. Symptoms of rhabdomyolysis include muscle pain, weakness, tenderness, fever, dark urine, nausea, and vomiting. In severe cases, rhabdomyolysis can lead to kidney failure and be life-threatening. For some patients, there may be pre-existing conditions or other personal factors that could cause a greater risk of developing muscle-related problems, including rhabdomyolysis, if they are using "statin" medications.

7. RISK The techniques of epidemiology are used to collect data and create information to quantify risk in order to allow more informed policy.

8. What is health policy?

9. Dark blue slides are from Dr Spasoff, supercourse

10. Light blue slides by Dr Akram, supercourse

12. Policy is like sausage: it may taste good, but it’s best that you don’t know what went into it

13. Epidemiology contributes at each step

17. “What if” questions “What if” questions like “What would be the effect on the overall health of the population if we reduced smoking by 20%? Sort of like program evaluation

20. Clinical Decision Making In a clinical medical environment, sometimes we need to use evidence to quantify our decision-making process Eg, to choose one therapy over another

21. Decision Tree Also called “chance node” Also called “choice node”

22. Data for Decision Tree Epidemiology – Probabilities of outcomes – Meta-analyses – Systematic reviews – Analytical studies – Pilot studies

23. Motivating Case: Ms. Brooks is a 50 year old woman with an incidental cerebral aneurysm. She presented with new vertigo 3 weeks ago and her primary MD ordered a head MRI. Her vertigo has subsequently resolved and has been attributed to labyrinthitis. Her MRI suggested a left posterior communicating artery aneurysm, and a catheter angiogram confirmed a 6 mm berry aneurysm. Example Slides by Dr James Kahn, UCSF, 2010 “Decision Analysis”

24. Case Presentation (cont’d) Past medical history is remarkable only for 35 pack- years of cigarette smoking. Exam is normal. Ms. Brooks: “I don’t want to die before my time.” Question is: Do we recommend surgical clipping of the aneurysm or no treatment?

25. Overview of DA Steps 1. Formulate an explicit question 2. Make a decision tree. (squares = decision nodes, circles = chance nodes) a) Alternative actions = branches of the decision node. b) Possible outcomes of each = branches of chance nodes. 3. Estimate probabilities of outcomes at each chance node. 4. Estimate utilities = numerical preference for outcomes. 5. Compute the expected utility of each possible action 6. Perform sensitivity analysis

26. 1. FORMULATE AN EXPLICIT QUESTION - Formulate explicit, answerable question. - May require modification as analysis progresses. - The simpler the question, without losing important detail, the easier and better the decision analysis. In the aneurysm example, our interest is in determining what’s best for Ms. Brooks so we'll take her perspective. We will begin with the following question: Which treatment strategy, surgical clipping or no treatment, is better for Ms. Brooks considering her primary concern about living a normal life span?

27. 2. MAKE A DECISION TREE Creating a decision tree = structuring the problem Provide a reasonably complete depiction of the problem. Best is one decision node (on the left, at the beginning of the tree). Branches of each chance node -- exhaustive and mutually exclusive. Proceed incrementally. Begin simple.

28. Simple Tree

29. More complicated tree

30. Crazy complicated

31. 3. Fill in the Probabilities Use info from the literature – Case fatality rates – Population mortality rates – etc

33. Expected Utility The average or expected outcome if one follows a given branch of the tree Sum of desirable outcomes within a given branch

34. Example of Expected utility Disease = cardiac valve failure Intervention (decision) = surgery vs no surgery If surgery, possible outcomes are: complications vs no complications – Further possible outcomes are death or survival If no surgery, the only possible outcomes are death or survival

35. Example of Expected utility Let’s follow surgery node: – 90% chance of no complications 90% survive – 10% chance of complications 50% survive What is expected utility at the surgery node?

36. Example of Expected utility Let’s follow surgery node: – 90% chance of no complications 90% survive – 10% chance of complications 50% survive EU = (P of no complications)(survival) + (P of complications)(survival) = 0.90 x 0.90 + 0.10 x 0.50 = 0.81 + 0.05 = 0.86

37. COMPUTE THE EXPECTED UTILITY OF EACH BRANCH Called "folding back" the tree. Expected utility of action = each possible outcome weighted by its probability. Simple arithmetic calculations

38. Back to Ms Brooks (Using a fairly complex system that I won’t expect you to duplicate)

39. 5. Compute expected utility of each branch =0 =.55

40. 5. Compute expected utility of each branch =1.0 =.55 =.9825 =0 =.9921 =.977

41. Ms. Brooks “Thanks… But I meant I wanted to live the most years possible. Dying at age 80 isn’t as bad as dying tomorrow…” We recommend NO surgery.

42. Improve the Analysis Add layers of complexity to produce a more realistic analysis.

43. Eg: Add Another Outcome Three outcomes Determine utility as a portion of expected life span -Normal survival 1.0 -Early death 0.5 -Immediate death 0

44. Summary of Formal Decision Analysis Explicit question. Decision tree. Probabilities of each outcome. Utilities for each outcome. Expected utility of each course of action. Sensitivity analysis.