Epi Kept Simple

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Epi Kept Simple

• Chapter 3
• Epidemiologic Measures

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Outline

• 3.1 Measures of disease frequency
• 3.2 Measures of association
• 3.3 Measures of potential impact
• 3.4 Rate adjustment

measure noun \'me-zh?r,
'ma-\ Definition of MEASURE 1b the dimensions,
capacity, or amount of something ascertained by
measuring
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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
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Types of Populations

• We measure disease frequency in
• Closed populations ? cohorts
• Open populations

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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
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Open Populations

• Inflow (immigration, births)
• Outflow (emigration, death)
• An open population in steady state (constant
  size and age) is said to be stationary

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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

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Denominators
Denominators ? a measure of population size or
person-time Person-time (no. of people)
(time of observation)
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Incidence Proportion (IP)
Can be calculated in cohorts only Requires
follow-up of individuals

• Synonyms risk, cumulative incidence, attack rate
• Interpretation average risk

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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.
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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

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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
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Individual follow-up in a Cohort
PY person-year
25 PYs
50 PYs
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Incidence Rate, Open Population
Example 2,391,630 deaths in 1999 (one
year) Population size 272,705,815
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Prevalence

• Interpretation A proportion with condition
• Interpretation B probability a person selected
  at random will have the condition

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Example Prevalence of hysterectomy

• Recruit 1000 women
• Ascertain 100 had hysterectomies

Prevalence is 10
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Dynamics of PrevalenceCistern Analogy
Ways to increase prevalence
Increase incidence ? increase inflow
Increase average duration of disease ? decreased
outflow
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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

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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

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Arithmetic (a???µ??) 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
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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
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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

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Absolutes ComparisonsApplied 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

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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

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Terminology
For simplicity sake, the terms risk and rate
will be applied to all incidence and prevalence
measures.
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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
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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.
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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 1R1 67.7 per 100,000
  PYs
• Mortality rate, group 0R0 122.0 per 100,000
  PYs

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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
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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.
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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)

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2-by-2 Table Format
Disease Disease - Total
Exposure A1 B1 N1
Exposure A0 B0 N0
Total M1 M0 N
For person-time data let N1 person-time in
group 1 and N0 person-time in group 0, and
ignore cells B1 and B0
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Fitness Data, table format
Fitness Improved? Died Person-years
Yes 25 -- 4054
No 32 -- 2937
Rates per 10,000 person-years
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Food borne Outbreak Example
Exposure eating a particular dish Disease
gastroenteritis
Disease Disease - Total
Exposure 63 25 88
Exposure 1 6 7
Total 64 31 95
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Food borne Outbreak Data
Disease Disease - Total
Exposure 63 25 88
Exposure 1 6 7
Total 64 31 95
Exposed group had 5 times the risk
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Comparison of RR and RD
RR ? strength of effect RD ? effect in absolute
terms
Rates (per 100000) of Lung CA CHD assoc. w/smoking Rates (per 100000) of Lung CA CHD assoc. w/smoking Rates (per 100000) of Lung CA CHD assoc. w/smoking Rates (per 100000) of Lung CA CHD assoc. w/smoking 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 CHD
Smoking ? Stronger effect for LungCA
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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
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The Odds Ratio
Similar to a RR, but based on odds rather than
risks
D D- Total
E A1 B1 N1
E- A0 B0 N0
Total 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
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Odds Ratio, ExampleMilunsky et al, 1989, Table
4NTD Neural Tube Defect
NTD NTD-
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
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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

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Attributable Fraction Exposed Cases (AFe)
Proportion of exposed cases averted with
elimination of the exposure
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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.
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Attributable Fraction, Population (AFp)
Proportion of all cases averted with elimination
of exposure from the population
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AFp equivalent formulas
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AFp for Cancer Mortality, Selected Exposures
Exposure Doll Peto, 1981 Miller, 1992
Tobacco 30 29
Dietary 35 20
Occupational 4 9
Repro/Sexual 7 7
Sun/Radiation 3 1
Alcohol 3 6
Pollution 2 -
Medication 1 2
Infection 10 -
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