I got it from Agnes- Tom Lehrer

Transcription

1 Epidemiology

2 I got it from Agnes- Tom Lehrer I love my friends and they love me We're just as close as we can be And just because we really care Whatever we get, we share! I got it from Agnes She got it from Jim We all agree it must have been him Louise who gave it to him Now she got it from Harry Who got it from Marie And ev'rybody knows that Marie Got it from me Giles got it from Daphne She got it from Joan Who picked it up in County Cork A-kissin' the Blarney Stone Pierre gave it to Sheila Who must have brought it there He got it from Francois and Jacques Aha, lucky Pierre! Max got it from Edith Who gets it ev'ry spring She got it from her Daddy Who just gives her ev'rything She then gave it to Daniel Whose spaniel has it now Our dentist even got it And we're still wondering how But I got it from Agnes Or maybe it was Sue Or Millie or Billie or Gillie or Willie It doesn't matter who It might have been at the pub or at the club, or in the loo And if you will be my friend, then I might... (Mind you, I said "might"...) Give it to you!

3 Key Terms and Concepts Morbidity and Mortality Incidence and Prevalence Validity Reliability Sensitivity (TP/(TP+FN)) Specificity (TN/(TN+FP)) Positive Predictive Value (TP/(TP+FP)) Negative Predictive Value (TN/(TN+FN))

4 FIGURE 1 - Scenario:1 Scenario 2 Scenario 3 Disease Prevalence: 10% 5% 1% 90,000(TP) 180,000(FP) 45,000(TP) 190,000(FP) 9,000(TP) 198,000(FP) 10,000(FN) 720,000(TN) 5,000(FN) 760,000(TN) 1,000(FN) 792,000(TN) 100, ,000 50, ,000 10, ,000 Sensitivity (TP ) (TP + FN) = 90% 90% 90% Specificity (TN ) (TN + FP) = 80% 80% 80% PPV (TP ) (TP + FP) = NPV (TN ) TN + FN) = TP = True Positive TN = True Negative PPV = Positive Predictive Value FP = False Positive FN = False Negative NPV = Negative Predictive Value

5 Example of How to Use Epidemiological Information Suppose you developed a new test to screen for colon cancer. Your R&D Department tells you that the test s s sensitivity is 91% and the specificity Is 100%! Question #1: Is there a market for such a product? Question #2: If there is a market, how much would you charge for this test?

6 Before you answer these questions, what information do you need to know? How good are the current tests at detection? Sensitivity of current test is 26-92%...Assume 68% Specificity of current test is 90-99%...Assume 99%...Assume 96% If Sensitivity=TP/TP+FN and Specificity=TN/TN+FP Then: FP=.04 FN=.32 TN=.96 TP=.68

7 ml

8 How do these numbers translate to real people? Prevalence= Incidence= K/yr 160K/yr Assume a population of 100,000 persons: Given that the prevalence is.00047: 32(TP) 3998 (FP) 15(FN) 95,955(TN) 47 99,953

9 So how does this new test compare? Recall: Sensitivity=TP/TP+FN=0.91 Specificity=TN/TN+FP=1.00 Therefore: FP=0 TN=1.00 FN=0.09 TP=0.91

10 What does this test mean to real people? 43 (TP) 0 (FP) 4 (FN) 99,953 (TN) 47 99,953 NOTE: For every 100,000 people tested, the new test eliminates 3998 False Positives and 11 False Negatives.

11 So How much can you charge for this new and improved test? A couple thoughts: If you are eliminating about 4000 colonoscopies a year by reducing false positives to 0 and each costs $1000: (4000 tests saved)x($1000/test) 100,000 people tested =$40 per person

12 Also If the test reduces False Negatives by 11/100,000, and if all of those people would have lived 20 more years, and the value of a quality adjusted life year (QALY) is $100,000 Then: (11cases/yr)x(20yrs/case)x($100k/case/yr)x 100,000 cases/yr screened= $220

13 Study Designs Case Control Cohort Study Randomized Control Meta-analysis analysis

14 Figure 2. CASE CONTROL STUDIES PAST PRESENT Exposed Unexposed Exposed Unexposed Cases (Affected) Controls (Unaffected)

15 Figure 3. Odds Ratio (Proxy for Relative Risk) Exposed CASES a CONTROLS c Unexposed b d

16 Figure 4. Evidence that an Association is Cause and Effect Criterion - Temporality - Strength - Dose-response - Reversibility - Consistency - Plausibility Source: Philip Greenland, MD Comments - Cause precedes effect - Large relative risk - Higher attack rates with higher exposure - Reducing exposure lowers risk of disease - Similar results in different studies in different places, time, etc. - Makes sense

17 Figure 5.1 PROSPECTIVE COHORT STUDY PRESENT Exposed (Cohort 1) Unexposed (Cohort 2) FUTURE OUTCOME(S) OUTCOME(S)

18 Figure 5.2 RETROSPECTIVE COHORT STUDY PAST Exposed (Cohort 1) Unexposed (Cohort 2) PRESENT OUTCOME(S) OUTCOME(S)

19 Figure 6 OUTCOME Exposed YES a NO c Unexposed b d Incidence of outcome in exposed cohort = a (a + c) Incidence of outcome in unexposed cohort = b (b+ d) Relative risk = incidence in exposed cohort incidence in unexposed cohort.

20 Figure 7 HEART ATTACK (OUTCOME) Medication (Exposed) No Medication (Unexposed) YES NO Incidence on medication = 50/ ( ) =.05 Incidence without medication = 200/( ) =.10 Relative Risk =.05/.10 = 0.5 Attributable risk = = -.05 Attributable fraction =.50 (.50-1) x 100 = 33% (where ƒ =.50).50 (.50-1) + 1

21 Figure 8 TARGET POPULATION RANDOMIZE INTERVENTION CONTROL ENDPOINT ENDPOINT

22 Meta-analysis analysis