measures of impact 18 th epiet/euphem introductory course september-october 2012 lazareto, menorca,...

Measures of Impact

18th EPIET/EUPHEM Introductory Course

September-October 2012Lazareto, Menorca, Spain

Ioannis Karagiannis

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Objectives

• To define measures of impact

• To calculate the attributable risk- among the exposed

- in the population

• Eventually, make sense of stuff

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Scenario

• You are in charge of health promotion “Preventing automobile-related deaths”

• Limited budget best reduction of deaths

• Evidence: retrospective cohort study: “causes of automobile-related deaths”

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Relative Risks

• Best reduction of deaths?

• Prevent drink & drive?

• Prevent speeding?

Relative Risk

Driving too fast 5

Driving while drunk 11

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Relative Risks

0.0000050.000001

0.50.1

Risk (exposed) Risk (unexposed)

RR = 5.0

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Measures of Impact

• Provide information about the public health impact of an exposure

• Contribution of an exposure to the frequency of disease

• Several concepts- Attributable risk (AR)

- Attributable risk among exposed (AR%)

- Attributable risk in the population (PAR)

- Preventable fraction among exposed (PF)

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Attributable Risk (AR)(synonyms: Risk Difference)

• Quantifies disease burden in exposed group attributable to exposure in absolute terms

• AR = Re - Ru

• Answers:- what is the risk attributed to the exposure?

- what is the excess risk due to the exposure?

• Only use if causality “exposure outcome”

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Attributable Risk (AR)

• AR = Re - Ru

Outcome

a

c d

yes no

exposed

not exposed

b

Attributable Risk =

a

a+b

c

c+d

a+b

c+d

a

a+b

c

c+d-

Attributable Risk = Re – background risk

= Re

= Ru

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Attributable Risk (AR)

Risk

0.01

0.05

Risk of death by speeding

Risk of death by driving below the speed limit

How high is the added risk of dying caused by the exposure “speeding“?

Added risk ?

exposure: speeding0.00

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AR Speeding

AR (speeding) = 0.05 - 0.01 = 0.04 “speeding increases the risk of dying by 0.04. Four out of 100 speeding drivers will die in addition to normal (=background) because they drove too fast“.

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AR Drunk driving

AR (drunk driving) = 0.15 - 0.01 = 0.14

“drunk driving increases the risk of dying by 0.14. Fourteen out of 100 drunk drivers die in addition to normal (background) death by driving because they were drunk while driving."

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Summary so far

Measure Speeding Drunk driving

Relative Risk 5 11

Attributable Risk 0.04 0.14

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Attributable Risk Percent (AR%)(synonyms: Attributable Fraction)

• Attributable risk expressed as a percentage of risk in the exposed population

• Proportion of disease among the exposed which:

- can be attributed to the exposure

- could be prevented by eliminating the exposure

• AR% looks at exposed population,not the total population

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Attributable Risk Percent (AR%)

• Example speeding: What proportion of all speeding deaths (denominator) died because they drove too fast (numerator)?

deaths caused by speeding

deaths of all who drove too fastAR% = x 100

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Attributable Risk Percent (AR%)

Risk (exposed) - Risk (unexposed)

Risk (exposed)x 100

RR > 1

AR% =

Risk (exposed) Risk (unexposed)

Risk (exposed) Risk (exposed)= - x 100

1

Relative Risk= 1 - x 100

RR - 1

RR= x 100

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AR% Speeding drivers

AR% (speeding) = 80%“80% of all people who died while driving too fast, died because they drove too fast“.

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AR% Drunk drivers

AR% (drunk driving) = 93%“93% of all people who died while being drunk, died because they were drunk“.

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Summary so far

Measure Speeding Drunk driving

Relative Risk 5 11

Attributable Risk 0.04 0.14

Attributable Risk% 80% 93%

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AR & AR% in Case-Control Studies

• No direct risk estimates in case-control study- AR (risk difference) and AR% calculation

IMPOSSIBLE!

Relative Risk - 1

Relative RiskAR% = x 100

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AR & AR% in Case-Control Studies

• No direct risk estimates in case-control study- AR (risk difference) and AR% calculation

IMPOSSIBLE?

• If odds ratio approximates relative risk, then

Relative Risk - 1

Relative RiskAR% = x 100

Odds Ratio - 1

Odds RatioAR% = x 100

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Population Attributable Risk (PAR%)

• Proportion of cases in the total population attributable to the exposure

• Proportion of disease in the total population that could be prevented if we could eliminate the risk factor

• Determines exposures relevant to public health in community

• Only use if causality “exposure outcome”

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Population Attributable Risk (PAR%)

• Example speeding: What proportion of all people who died (denominator) died because they drove too fast (numerator)?

deaths caused by speeding

total deaths in the populationPAR% = x 100

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Population Attributable Risk (PAR%)

Risk (total pop) - Risk (unexposed)

Risk (total pop)x 100PAR% =

p (RR - 1)

p (RR - 1) +1x 100PAR% =

p = proportion of population exposed

PAR% = p(cases) x AR%

p(cases) = proportion of cases exposed

PAR(%) according to the relative riskfor various level of exposure frequency

among cases

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

100.0%

1 2 3 4 5 6 7 8 9 10

Relative risks

Pop

ulat

ion

attr

ibut

able

fra

ctio

n

Pe 10%Pe 25%Pe 50%Pe 75%Pe 100% (AFe)

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PAR% Speeding

Risk (total) - Risk (not exposed)

Risk(total)PAR% = = = 0.44

0.018 - 0.01

0.018

= 44%

risk in total population

risk in unexposed

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PAR% Speeding

100

80 7920

2000

8000

10000

dead alive

180 9820

speeding

notspeeding

1900

Risk

100/2000 = 0.05

80/8000 = 0.01

Attributable Risk (AR) = 0.05 - 0.01 = 0.04

AR

Risk(exposed)AR% = x 100 = (0.04/0.05) x 100 = 80%

p(cases) = % cases exposed = 100/180 = 0.55

PAR% = pc x AR% = 0.55 x 80% = 44%

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PAR% Drunk driving

Risk (total) - Risk (unexposed)

Risk(total)PAR% = = = 0.22

0.018 - 0.014

0.018

= 22%

risk in total population

risk in unexposed

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Summary

Measure Speeding Drunk driving

Relative Risk 5 11

Attributable Risk 0.04 0.14

Attributable Risk% 80% 93%Pop. attributable risk% 44% 22%% drivers with risk factor in population

20% 3%

• Best reduction of deaths?

• Prevent drinking or speeding?

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PAR% in Case-Control Studies

• proportion of controls exposed ≈ proportion of population exposed

PAR% =Pcontrols – (OR – 1)

x 100Pcontrols (OR – 1) + 1Pcontrols = Proportion of controls exposed

PAR% = Pcases ( OR – 1 ) x 100OR

Where Pcases = proportion cases exposed

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Summary

Measure Meaning Question answered

RR, OR Strength of association (between exposure and outcome)

Is the exposure associated with the risk of getting ill/ the outcome?

AR Excess risk of exposed (in absolute terms)

What is the difference in risk between exposed and not exposed?

AR% Proportion of risk of exposed attributed to exposure, potential prevention of exposed

What proportion of those who are exposed and ill is likely due to the exposure?

PAR% Proportion of risk of population attributed to exposure, potential prevention of population,

Public Health relevance

What proportion of those who are ill in the population is likely due to the exposure?

Take-home message

• There is more death and disability from frequent exposure to lower risks than to rare exposures to higher risks

• Examples: More people die from marginally elevated blood

pressure (common) than from seriously elevated blood pressure (uncommon)

More people acquire HCV from unsafe injection (common exposure, lower risk) than from unsafe blood products (rare exposure, high risk)

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Preventable fraction (PF)

• Exposure associated with decreased risk

• Where RR < 1, exposure is protective

• Proportion of cases that would have occurred if exposure hadn’t been present

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• RR < 1 protective exposure (protective factor)

• Proportion of cases that were prevented because of the exposure

Risk (unexposed) - Risk (exposed)

Risk (unexposed)

Preventable fraction (PF)

PF =

Risk (unexposed) Risk (exposed)

Risk (unexposed) Risk (unexposed)

PF = -

PF = 1 - Relative Risk

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Preventable Fraction (PF)Vaccine efficacy

  Pop. CasesCases

/100,000

Vaccinated 200,000 100 50

Unvaccinated 300,000 600 200

Total 500,000

Risk (unexposed) - Risk (exposed)

Risk (unexposed)PF =

PF = 600/300,000 - 100/200,000

600/300,000= 0.75

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Preventable Fraction (PF)Vaccine efficacy

• How many people would have been ill without the vaccine?

• 200/100,000 cases of unvaccinated

• In population of 200,000 we expect 400 cases

• Only 100 cases occurred; 300 cases were prevented (by vaccine)

• 300/400 = 75% of hypothetical cases were prevented

True or false?

• The relative risk of lung cancer and smoking is 9

• Therefore, if nobody smoked, the incidence of lung cancer would be nine times lower than it currently is

FalseMeasures of association are not measures of impact.The prevalence of smoking in the population also

matters!

True or false?

• 90% of patients with lung cancer are smokers

• Therefore, if nobody smoked, the incidence of lung cancer would be reduced by 90%

False The proportion of a disease that may be explained by a specific exposure does not depend on the proportion of cases exposed. It also depends on the strength of the association (90% of patients with lung cancer also eat fresh salad for lunch every day)

Thank you