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Epi Kept Simple

Epi Kept Simple. Chapter 3 Epidemiologic Measures. Outline. 3.1 Measures of disease frequency 3.2 Measures of association 3.3 Measures of potential impact 3.4 Rate adjustment. mea·sure noun ˈme-zhər, ˈmā- Definition of MEASURE

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Epi Kept Simple

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  1. Epi Kept Simple Chapter 3 Epidemiologic Measures

  2. Outline 3.1 Measures of disease frequency 3.2 Measures of association 3.3 Measures of potential impact 3.4 Rate adjustment mea·sure noun \ˈme-zhər, ˈmā-\ Definition of MEASURE 1b: the dimensions, capacity, or amount of something ascertained by measuring

  3. 3.1 Disease Frequency All are loosely called “rates,” but only the second is a true mathematical rate Incidence proportion (risk) Incidence rate (incidence density) Prevalence (c) B. Gerstman Chapter 3 3

  4. Types of Populations We measure disease frequency in: Closed populations  “cohorts” Open populations (c) B. Gerstman Chapter 3 4

  5. Closed Population ≡ Cohort Cohort (Latin cohors, meaning “enclosure”; also the basic tactical unit of a Roman legion Epidemiologic cohort ≡ a group of individuals followed over time (c) B. Gerstman Chapter 3 5

  6. Open Populations Inflow (immigration, births) Outflow (emigration, death) An open population in “steady state” (constant size and age) is said to be stationary Chapter 3 (c) B. Gerstman 6

  7. Numerators & Denominators Most measures of disease occurrence are ratios Ratios are composed of a numerator and denominator Numerator case count Incidence count  onsets only Prevalence count  all cases (c) B. Gerstman Chapter 3 7

  8. Denominators Denominators  a measure of population size or person-time* * Person-time ≈ (no. of people) × (time of observation) (c) B. Gerstman Chapter 3 8

  9. Incidence Proportion (IP) Can be calculated in cohorts only Requires follow-up of individuals Synonyms: risk, cumulative incidence, attack rate Interpretation: average risk Chapter 3 9 (c) B. Gerstman

  10. Example: Incidence Proportion (Average Risk) 10-year average risk is .011 or 1.1%. Objective: estimate the average risk of uterine cancer in a group Recruit 1000 women (cohort study) 100 had hysterectomies, leaving 900 at risk Follow the cohort for 10 years Observe 10 new uterine cancer cases (c) B. Gerstman 10 Chapter 3

  11. Incidence Rate (IR) Synonyms: incidence density, person-time rate Interpretation A: “Speed” at which events occur in a population Interpretation B: When disease is rare: rate per person-year ≈ one-year average risk Calculated differently in closed and open populations (c) B. Gerstman Chapter 3 11

  12. Example Objective: estimate rate of uterine cancer Recruit cohort of 1000 women 100 had hysterectomies, leaving 900 at risk Follow at risk individuals for 10 years Observe 10 onsets of uterine cancer Rate is .00111 per year or 11.1 per 10,000 years (c) B. Gerstman (c) B. Gerstman 12 12

  13. Individual follow-up in a Cohort PY = “person-year” 25 PYs 50 PYs (c) B. Gerstman (c) B. Gerstman 13 13

  14. Incidence Rate, Open Population Example: 2,391,630 deaths in 1999 (one year) Population size = 272,705,815 (c) B. Gerstman Chapter 3 14

  15. Prevalence Interpretation A: proportion with condition Interpretation B: probability a person selected at random will have the condition (c) B. Gerstman Chapter 3 15

  16. Example: Prevalence of hysterectomy Prevalence is 10% Recruit 1000 women Ascertain: 100 had hysterectomies (c) B. Gerstman Chapter 3 16

  17. Dynamics of PrevalenceCistern Analogy Ways to increase prevalence Increase incidence  increase inflow Increase average duration of disease  decreased outflow (c) B. Gerstman Chapter 3 17

  18. Relation Between Incidence and Prevalence Example: Incidence rate = 0.01 / year Average duration of the illness = 2 years. Prevalence ≈ 0.01 / year × 2 years = 0.02 When disease rare & population stationary (c) B. Gerstman Chapter 3 18

  19. 3.2 Measures of Assocation Exposure (E) an explanatory factor or potential health determinant; the independent variable Disease (D) the response or health-related outcome; the dependent variable Measure of association (syn. measure of effect)  any statistic that measures the effect on an exposure on the occurrence of an outcome Gerstman 19

  20. Arithmetic (αριθμός) Comparisons Measures of association are mathematical comparisons Mathematic comparisons can be done in absolute terms or relative terms Let us start with this ridiculously simple example: I have $2 You have $1 "For the things of this world cannot be made known without a knowledge of mathematics."- Roger Bacon Gerstman Chapter 8 20

  21. Absolute Comparison In absolute terms, I have $2 MINUS $1 = $1 more than you Note: the absolute comparison was made with subtraction It is as simple as that… Gerstman Chapter 8 21

  22. Relative Comparison Recall that I have $2 and you have $1. In relative terms, I have $2 ÷ $1 = 2 times as much as you Note: relative comparison was made by division Gerstman Chapter 8 22

  23. Suppose, I am exposed to a risk factor and have a 2% risk of disease. You are not exposed and you have a 1% risk of the disease. Absolutes ComparisonsApplied to Risks • In absolute terms, I have 2% MINUS 1% = 1% greater risk of the disease • This is the risk difference Gerstman Chapter 8 23

  24. In relative terms I have 2% ÷ 1% = 2  twice your risk This is the relative risk associated with the exposure Relative Comparisons Applied to Risks Gerstman Chapter 8 24

  25. Terminology For simplicity sake, the terms “risk” and “rate” will be applied to all incidence and prevalence measures. Gerstman Chapter 8 25

  26. Rate or Risk Difference Let RD represent the rate or risk difference where R1 ≡ the risk or rate in the exposed group R0≡ the risk or rate in the non-exposed group Interpretation: Excess risk associated with the exposure in absolute terms Gerstman Chapter 8 26

  27. Rate or Risk Ratio Let RR represent the rate or risk ratio where R1 ≡ the risk or rate in the exposed group R0≡ the risk or rate in the non-exposed group Interpretation: excess risk associated with the exposure in relative terms. Gerstman Chapter 8 27

  28. Example Fitness & Mortality (Blair et al., 1995) Is improved fitness associated with decreased mortality? Exposure ≡ improved fitness (1 = yes, 0 = no) Disease ≡ death(1 = yes, 0 = no) Mortality rate, group 1:R1 = 67.7 per 100,000 PYs Mortality rate, group 0:R0 = 122.0 per 100,000 PYs Gerstman 28

  29. Fitness and Mortality: RD What is the effect of improved fitness on mortality in absolute terms? The effect of improved fitness was to decrease mortality by 54.4 per 100,000 person-years Gerstman 29

  30. ExampleRelative Risk What is the effect of improved fitness on mortality in relative terms? The effect of the improved fitness was to almost cut the rate of death in half. Gerstman 30

  31. Designation of Exposure Switching the designation of “exposure” does not materially affect interpretations For example, if we had let “exposure” refer to failure to improve fitness RR = R1 / R0 = 122.0 / 67.7 = 1.80 (1.8 times or “almost twice the rate”) Gerstman Chapter 8 31

  32. 2-by-2 Table Format For person-time data: let N1 ≡ person-time in group 1 and N0 ≡ person-time in group 0, and ignore cells B1 and B0 Gerstman Chapter 8 32

  33. Fitness Data, table format Rates per 10,000 person-years Gerstman Chapter 8 33

  34. Food borne Outbreak Example Exposure ≡ eating a particular dish Disease ≡ gastroenteritis Gerstman Chapter 8 34

  35. Food borne Outbreak Data Exposed group had 5 times the risk Gerstman Chapter 8 35

  36. What do you do when you have multiple levels of exposure? Compare rates to least exposed “reference” group Gerstman Chapter 8 37

  37. The Odds Ratio When the disease is rare, interpret the same way you interpret a RR e.g. an OR of 1 means the risks are the same in the exposed and nonexposed groups Similar to a RR, but based on odds rather than risks “Cross-product ratio” Gerstman Chapter 8 38

  38. Odds Ratio, ExampleMilunsky et al, 1989, Table 4NTD = Neural Tube Defect Exposed group had 0.29 times (about a quarter) the risk of the nonexposed group Gerstman Chapter 8 39

  39. Measures of Potential Impact These measures predicted impact of removing a hazardous exposure from the population Two types Attributable fraction in exposed cases Attributable fraction in the population as a whole Gerstman Chapter 8 40

  40. Attributable Fraction Exposed Cases (AFe) Proportion of exposed cases averted with elimination of the exposure Gerstman Chapter 8 41

  41. Example: AFe RR of lung CA associated with moderate smoking is approx. 10.4. Therefore: Interpretation: 90.4% of lung cancer in moderate smokers would be averted if they had not smoked. Gerstman Chapter 8 42

  42. Attributable Fraction, Population (AFp) Proportion of all cases averted with elimination of exposure from the population Gerstman Chapter 8 43

  43. AFp equivalent formulas Gerstman Chapter 8 44

  44. AFp for Cancer Mortality, Selected Exposures Gerstman Chapter 8 45

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