Chapter 6: Incidence & Prevalence Chapter 3 Epidemiologic Measures 4/19/2017 Epi Kept Simple Chapter 3 Epidemiologic Measures (c) B. Gerstman Epidemiology 1

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

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

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

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

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

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

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

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

Example: Incidence Proportion (Average Risk) 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 10-year average risk is .011 or 1.1%. (c) B. Gerstman Chapter 3 10

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

Rate is .00111 per year or 11.1 per 10,000 years 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

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

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

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

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

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

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

3.2 Measures of Assocation Chapter 8: Association & Impact 4/19/2017 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 In epidemiology it is common to use the term exposure to denote any explanatory variable i.E we may speak of smoking as an exposure that causes lung cancer or advanced maternal age at pregnancy as an exposure that causes Down Syndrome (Prof. G. could you please help me understand the inactive lifestyle E+??)){We arbitrary define the risk factor as “exposure” to an inactive lifestyle. Then we compare the mortality rate in the exposed (inactive lifestyle) and nonexposed (active lifestyle) groups. I’ve added a third bullet to this effect.} Gerstman 19 Epi Kept Simple 19

Arithmetic (αριθμός) Comparisons Chapter 8: Association & Impact 4/19/2017 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 We compare the weight of a man of 100 kg to the weight of a woman of 50 kg. -Absolute comparisons are derived by subtraction and using (original units of measure kg) -{Relative comparisons are derived by division (the division cancels out units, making a unit-free comparison} "For the things of this world cannot be made known without a knowledge of mathematics."- Roger Bacon Gerstman Chapter 8 20 Epi Kept Simple 20

Chapter 8: Association & Impact 4/19/2017 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… We compare the weight of a man of 100 kg to the weight of a woman of 50 kg. -Absolute comparisons are derived by subtraction and using (original units of measure kg) -{Relative comparisons are derived by division (the division cancels out units, making a unit-free comparison} Gerstman Chapter 8 21 Epi Kept Simple 21

Relative Comparison Recall that I have $2 and you have $1. Chapter 8: Association & Impact 4/19/2017 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 We compare the weight of a man of 100 kg to the weight of a woman of 50 kg. -Absolute comparisons are derived by subtraction and using (original units of measure kg) -{Relative comparisons are derived by division (the division cancels out units, making a unit-free comparison} Gerstman Chapter 8 22 Epi Kept Simple 22

Absolutes Comparisons Applied to Risks Chapter 8: Association & Impact 4/19/2017 Absolutes Comparisons Applied to Risks 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. In absolute terms, I have 2% MINUS 1% = 1% greater risk of the disease This is the risk difference We compare the weight of a man of 100 kg to the weight of a woman of 50 kg. -Absolute comparisons are derived by subtraction and using (original units of measure kg) -{Relative comparisons are derived by division (the division cancels out units, making a unit-free comparison} Gerstman Chapter 8 23 Epi Kept Simple 23

Relative Comparisons Applied to Risks Chapter 8: Association & Impact 4/19/2017 Relative Comparisons Applied to Risks In relative terms I have 2% ÷ 1% = 2  twice your risk This is the relative risk associated with the exposure We compare the weight of a man of 100 kg to the weight of a woman of 50 kg. -Absolute comparisons are derived by subtraction and using (original units of measure kg) -{Relative comparisons are derived by division (the division cancels out units, making a unit-free comparison} Gerstman Chapter 8 24 Epi Kept Simple 24

Chapter 8: Association & Impact 4/19/2017 Terminology For simplicity sake, the terms “risk” and “rate” will be applied to all incidence and prevalence measures. Let’s apply arithmetic to risks. These are the formulas. {The formulas are simple: RD is a subtraction and RR is a division. The key is to understand how we interpret the RR and the RD. They both quantify the relation between E and D, but they tell you something different about the association. Going through lots of examples in the book will help understand subtleties.} Gerstman Chapter 8 25 Epi Kept Simple 25

Rate or Risk Difference Chapter 8: Association & Impact 4/19/2017 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 Let’s apply arithmetic to risks. These are the formulas. {The formulas are simple: RD is a subtraction and RR is a division. The key is to understand how we interpret the RR and the RD. They both quantify the relation between E and D, but they tell you something different about the association. Going through lots of examples in the book will help understand subtleties.} Interpretation: Excess risk associated with the exposure in absolute terms Gerstman Chapter 8 26 Epi Kept Simple 26

Rate or Risk Ratio Let RR represent the rate or risk ratio Chapter 8: Association & Impact 4/19/2017 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 Let’s apply arithmetic to risks. These are the formulas. {The formulas are simple: RD is a subtraction and RR is a division. The key is to understand how we interpret the RR and the RD. They both quantify the relation between E and D, but they tell you something different about the association. Going through lots of examples in the book will help understand subtleties.} Interpretation: excess risk associated with the exposure in relative terms. Gerstman Chapter 8 27 Epi Kept Simple 27

Example Fitness & Mortality (Blair et al., 1995) Chapter 8: Association & Impact 4/19/2017 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 {See p. 159 for details.} Gerstman 28 Epi Kept Simple 28

Fitness and Mortality: RD Chapter 8: Association & Impact 4/19/2017 Fitness and Mortality: RD What is the effect of improved fitness on mortality in absolute terms? {See p. 159 for details.} The effect of improved fitness was to decrease mortality by 54.4 per 100,000 person-years Gerstman 29 Epi Kept Simple 29

Chapter 8: Association & Impact 4/19/2017 Example Relative Risk What is the effect of improved fitness on mortality in relative terms? {See p. 159 for details.} The effect of the improved fitness was to almost cut the rate of death in half. Gerstman 30 Epi Kept Simple 30

Designation of Exposure Chapter 8: Association & Impact 4/19/2017 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”) {See p. 159 for details.} Gerstman Chapter 8 31 Epi Kept Simple 31

2-by-2 Table Format Disease + Disease − Total Exposure + A1 B1 N1 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

Fitness Data, table format Fitness Improved? Died Person-years Yes 25 -- 4054 No 32 2937 Rates per 10,000 person-years Gerstman Chapter 8 33

Food borne Outbreak Example Exposure ≡ eating a particular dish Disease ≡ gastroenteritis Disease + Disease − Total Exposure + 63 25 88 Exposure – 1 6 7 64 31 95 Gerstman Chapter 8 34

Food borne Outbreak Data Disease + Disease − Total Exposure + 63 25 88 Exposure – 1 6 7 64 31 95 Exposed group had 5 times the risk Gerstman Chapter 8 35

Comparison of RR and RD RR  strength of effect Chapter 8: Association & Impact 4/19/2017 Comparison of RR and RD RR  strength of effect RD  effect in absolute terms Rates (per 100000) of Lung CA & CHD assoc. w/smoking Smoker Nonsmoke RR RD LungCA 104 10 10.40 94 CHD 565 413 1.37 152 Smoking causes more heart disease even though the association between smoking a heart disease is weaker than the association between smoking an lung cancer. This is because heart disease is more common in the population. Smoking  Stronger effect for LungCA Smoking  Causes more CHD Gerstman Chapter 8 36 Epi Kept Simple 36

What do you do when you have multiple levels of exposure? Compare rates to least exposed “reference” group LungCA Rate (per 100,000 person-years) RR Non-smoker (0) 10 1.0 (ref.) Light smoker (1) 52 5.2 Mod. smoker (2) 106 10.6 Heavy sm. (3) 224 22.4 Gerstman Chapter 8 37

The Odds Ratio Similar to a RR, but based on odds rather than risks D+ Total E+ A1 B1 N1 E− A0 B0 N0 M1 M0 N 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 “Cross-product ratio” Gerstman Chapter 8 38

Odds Ratio, Example Milunsky et al, 1989, Table 4 NTD = Neural Tube Defect Folic Acid+ 10 10,703 Folic Acid− 39 11,905 Exposed group had 0.29 times (about a quarter) the risk of the nonexposed group Gerstman Chapter 8 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

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

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

AFp equivalent formulas Gerstman Chapter 8 44

AFp for Cancer Mortality, Selected Exposures Doll & Peto, 1981 Miller, 1992 Tobacco 30% 29% Dietary 35% 20% Occupational 4% 9% Repro/Sexual 7% Sun/Radiation 3% 1% Alcohol 6% Pollution 2% - Medication Infection 10% Gerstman Chapter 8 45