Epid 600 Class 5 Cohort Studies

EPID 600; Class 5 Cohort studies

University of Michigan School of Public Health

Drug Abuse: A workshop on behavioral and economic research

October 18-20, 2004

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Three key dimensions to epidemiologic studies

Measures of association Relative measures (relative risks, rates, and odds) Absolute measures (risk and rate differences) Study design Observational Cohort Case-control Cross-sectional Experimental Randomized trial Field trials Group randomized trials Units of analysis Individual Group

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Three key dimensions to epidemiologic studies

Measures of association Relative measures (relative risks, rates, and odds) Absolute measures (risk and rate differences) Study design Observational Cohort Case-control Cross-sectional Experimental Randomized trial Field trials Group randomized trials Units of analysis Individual Group

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The world

persons “exposed” persons “unexposed”

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The cohort study

persons “exposed” persons “unexposed”

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The cohort study

persons “exposed” with disease persons “unexposed” with disease

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What is a cohort?

1. A place to play tennis 2. The tenth part of a Roman legion 3. A population that is surveyed at a given moment in time 4. People born a hundred years apart 5. Equivalent to a trohoc

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Why epidemiologic studies

Why epidemiologic studies? To determine whether there is an association between “exposure” and “outcome”

Example Does aspirin prevent myocardial infarctions? Is eating carrots associated with increased risk of skin cancer?

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Concurrent Mixed Non-concurrent Prospective Retrospective

It is where the INVESTIGATOR sits that determines the type of cohort

Types of cohort studies

time

i i i

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Concurrent Mixed Non-concurrent Prospective Retrospective

It is where the INVESTIGATOR sits that determines the type of cohort

time

i i i

Types of cohort studies

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Prospective vs. retrospective

In prospective cohort studies, exposure and non-exposure ascertainment happens in present then study groups followed over time to measure disease In retrospective cohort studies, exposure and non-exposure are ascertained in the past In “mixed” cohort studies, we have a bit of each approach

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Retrospective vs. prospective

Advantages of retrospective studies Less expensive Less time consuming Efficient for study of diseases with long latency periods

Disadvantages of retrospective studies Introduces possible error in the form of recall of past information, challenges in collecting data retrospectively, primarily information bias (to be covered in a future lecture)

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Fixed vs. open cohorts

Fixed cohort Has fixed membership Once group is defined and follow-up begins, no-one is added

Open cohort Also called a “dynamic cohort”, can take on new members during study period

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Fixed cohort

Fixed cohort

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Fixed cohort

Fixed cohort

cohort is set; no new participants

size of cohort gets smaller over time

there may be withdrawals from cohort

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Closed fixed cohort

Fixed cohort

cohort is set; no new participants

there are no withdrawals, all persons are followed until end of follow-up period of until they get disease

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Open cohort

Open cohort

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Open

Open (dynamic) cohort

new cohort members

cohort may be replenished; size of cohort does not necessarily shrink

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Rothman KJ. Epidemiology: An Introduction. Oxford, 2002. 19

Exposed

Unexposed

D+

D-

D+

D-

Cohort studies

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Exposed

Unexposed Disease No

disease

Exposed a b

Not Exposed c d

D+

D-

D+

D-

Cohort studies

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Exposed


disease

Exposed a b

Not Exposed c d

D+

D-

D+

D-

Cohort studies

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Exposed


disease

Exposed a b Not Exposed c d

D+

D-

D+

D-

Cohort studies

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Exposed


disease

Exposed a b

Not Exposed c d

D+

D-

D+

D-

Cohort studies

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Exposed


disease

Exposed a b

Not Exposed c d

D+

D-

D+

D-

Cohort studies

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Advantages of cohort studies

Maintains temporal sequence, i.e., assesses exposure before outcome Good for assessing rare exposures and rapidly fatal diseases Can study multiple diseases/outcomes from a given exposure Can calculate incidence among exposed and unexposed Minimizes error in ascertainment of exposure (at least if prospective) Provides complete description of experience subsequent to exposure, including rate of progression and natural history of disease

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Disadvantages of cohort studies

Expensive Inefficient for rare diseases Potentially long duration for follow-up Secular trends in technology, behaviors, and changes that may influence behavior and study characteristics over time

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An example

Population: A cadmium factory in South Dakota

Exposure: Exposure to cadmium production (which involves the gaseous decomposition of cadmium compounds); exposure assessed by information on jobs at high risk of exposure between 1950 and 1970

Outcome: respiratory cancers, mostly lung and nasal cancer

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What kind of study is this?

Exposure interval, 1950-1970

You (the investigator) are here

End of follow-up, 2000

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Therefore...

This study aims to identify association between cadmium exposure and subsequent development of respiratory cancer

Who is at risk of respiratory cancer? Persons with lungs and noses Persons who do not already have respiratory cancer at baseline

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Exposed

Unexposed

D+

D-

D+

D-

What did we find?

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Exposed

Unexposed

D+

D-

D+

D-

250

450

100

90

150

360

What did we find?

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2x2 table

Cancer No cancer Total

Exposed 100 150 250

Not Exposed 90 360 450

Total 190 510 700

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How many of you eat breakfast?

A cross-sectional association between skipping breakfast and obesity has been shown in adults. Is it that people with low SES skip breakfast so tend to be obese, or is it that skipping breakfast in itself makes people obese?

A research team in Britain decided to investigate the association between proportion of calories consumed at breakfast and weight gain.

Purslow et al. Energy intake at breakfast and weight change: prospective study of 6,764 middle-aged men and women. Am J Epid. 2007; 167(2):188-192.

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Breakfast study: cohort mechanics

STEP 1: Indentify a group of people of interest.

Participants recruited for the European Prospective Investigation into Cancer and Nutrition (Norfolk cohort); first (baseline) measurement on nutrition and weight

STEP 2: Follow them through time and monitor outcome of interest

During 1998-2000, a second measurement on nutrition and weight; include only those who did not report stroke, cancer, or heart attack at baseline

STEP 3: Classify participants according to outcome across exposure categories.

Classify people as breakfast eaters (exposed cohort) or non-eaters (unexposed cohort); assess weight change in both cohorts


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Breakfast study: Find people and follow them through time

2) 1998-2000: 2nd measurement on nutrition and weight; include only those who did not report stroke, cancer, or heart attack at baseline

1) 1993-1997: 1st (baseline) measurement on nutrition and weight

3) 2007: Analysis; Assess weight change in both cohorts


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Breakfast study: set up

What is the “exposure”? > Breakfast consumption How do the investigators measure the exposure? > They determine the percentage of total energy intake consumed at breakfast What is the outcome of interest? > Weight gain How do the investigators measure the outcome? > Change in weight in kg from time 1 to time 2


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Breakfast study: findings

Calorie Distribution Avg weight gain in the follow up

Big breakfast (22-50% of total intake)

1.23 kg

Small Breakfast (0-11% of total intake)

0.79 kg

Everyone gained weight over time….

But larger breakfasts are associated with lesser weight gain among the middle aged participants of this study

This association is independent of the quantity of calories consumed in the day, social class, physical activity level, fruit and vegetable intake, fat/carb/protein intake, smoking, BMI

10 yrs x 1 lb/year = 10 lbs of potentially avoidable weight

gain!


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So, what measures of disease occurrence and what measures of association can we calculate?

Risk (incidence proportion) Odds Prevalence at any point during the cohort (but making some assumptions about duration of disease) Incidence rate?

And...

Risk ratio Risk difference Odds ratio (relative odds) Incidence rate difference?

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Relative risk (risk ratio)

The ratio of risks for two populations

exp

exp

osed

un osed

RRR

R= exp

exp

10025090450

100100*450250 2.090 250*90

450

osed

un osed

R

R

RR

=

=

= = =

So, the risk of developing respiratory cancer among South Dakota miners exposed to cadmium was 2.0 higher than among miners not exposed to cadmium in a cohort study with a follow-up period of 30-50 years 40

Risk difference

exp exposed un osedRD R R= −

The additional risk among those exposed when compared to those unexposed

100 90 (100*450) (90*250) 0.2250 450 (250*450)

RD −= − = =

So, the difference in the risk of developing respiratory cancer among South Dakota miners exposed to cadmium compared to miners not exposed to cadmium was 0.2 in a cohort study with a follow-up period of 30-50 years 41

Can we calculate incidence rate ratio from this information?

We could, if we made some assumptions Length of follow-up same for everyone No competing risks No loss to follow-up Assume then that average follow-up was 40 years; i.e., we are assuming that everyone was followed for 40 years

exp

exp

250*40 10000

450*40 18000osed

un osed

PYOPYO

= =

= =42

Relative rate (rate ratio)

The ratio of rates for two populations

exp

exp

osed

un osed

IRIRR

IR=

So, the rate of developing respiratory cancer among South Dakota miners exposed to cadmium was 2.0 higher than among miners not exposed to cadmium in a cohort study with a follow-up period of 30-50 years And, the relative rate and relative risk are the same assuming that time of follow-up is identical; of course this assumption is only valid if there is nothing else is competing as a risk, that follow-up is complete, and that this is a closed cohort

100100*18,00010,000 2.0 90 10,000*90

18,000

IRR = = =

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Relative odds (odds ratio)

exp

exp

100250 250

150 100 250 100* 100 360250 250 150 150 * 2.6790 90 450 90 150 90*450 450 450 360 3601

360450

exp

osed exp

unexpunexposed

un

100

p 1001-Odds 1- p 250 = p 90Oddsp

901-450

= = = = = =

−

Cancer No cancer

Total

Exposed 100 150 250

Not Exposed

90 360 450

Total 190 510 700

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exp

exp

100250 250

150 100 250 100* 100 360250 250 150 150 * 2.6790 90 450 90 150 90*450 450 450 360 3601

360450

exp

osed exp

unexpunexposed

un

100

p 1001-Odds 1- p 250 = p 90Oddsp

901-450

= = = = = =

−

Cancer No cancer

Total

Exposed 100 150 250

Not Exposed

90 360 450

Total 190 510 700

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exp

exp

100250 250

150 100 250 100* 100 360250 250 150 150 * 2.6790 90 450 90 150 90*450 450 450 360 3601

360450

exp

osed exp

unexpunexposed

un

100

p 1001-Odds 1- p 250 = p 90Oddsp

901-450

= = = = = =

−

Cancer No cancer

Total

Exposed 100 150 250

Not Exposed

90 360 450

Total 190 510 700

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exp

exp

100250 250

150 100 250 100* 100 360250 250 150 150 * 2.6790 90 450 90 150 90*450 450 450 360 3601

360450

exp

osed exp

unexpunexposed

un

100

p 1001-Odds 1- p 250 = p 90Oddsp

901-450

= = = = = =

−

**

a dtherefore Odds Ratio b c

=

Cancer No cancer

Total

Exposed 100 150 250

Not Exposed

90 360 450

Total 190 510 700

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Interpretation of odds ratio

The odds of respiratory cancer is 2.67 times greater in those exposed to cadmium compared to unexposed

Note that RR < OR...remember why?

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Reality...

Exposure interval, 1950-1970 End of follow-up, 2000

p1

p2 p3 p4

etc 49

So we can take into account PYO

Cancer PYO

Exposed 100 20,000

Not Exposed 90 30,000

Total 190 50,000

That is, by taking into consideration, the actual time of follow-up

100100*30,00020,000Re 1.6790 90*20,000

30,000

lative rate = = =

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But of course we can do better

Cancer PYO

Exposed 100 20,000


Total 190 50,000


Without knowing

actual PYO we cannot know how

RR is estimating

IRR

100100*30,00020,000Re 1.6790 90*20,000

30,000

lative rate = = =

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But of course we can do better

Cancer PYO

Exposed 100 20,000


Total 190 50,000


If PYOs were different, would it be possible

that cadmium is actually

protective?

100100*30,00020,000Re 1.6790 90*20,000

30,000

lative rate = = =

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Rate difference

The additional incidence rate comparing those exposed vs. those unexposed

exp exposed un osedIRD IR IR= −

100 90 0.00220,000 30,000

2IRD or 1000 person years

= − =

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Attributable fraction among exposed

AFexp osed =

Rexp osed − Run exp osed

Rexp osed

Proportion of the disease burden among exposed people that is due to the exposure

AFexp osed =

100250

− 90450

100250

=0.20.4

= 0.5

So, we say, that 50% of disease among exposed is attributable to exposure; or, if we removed all the exposure, we might expect to reduce disease by 50% among exposed 54

Attributable fraction in population

AFpopulation =

Rpopulation − Run exp osed

Rpopulation

Proportion of the disease burden among the whole population that is due to the exposure

AFpopulation =

190700

− 90450

190700

=0.0720.271

= 0.26

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Or...

AFpopulation =

p * (RR −1)p * (RR −1) +1

So, if exposure is removed, we would expect that disease would be reduced by ~26% in the whole population Attributable fraction in population is always lower than attributable fraction among exposed, as long as exposure is associated with more disease. Why?

prevalence of exposure

AFpopulation ≅

250700

* (2.0 −1)

250700

* (2.0 −1) +1=

0.361.36

= 0.26

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Summary

Cohort studies follow participants forward in time Cohort studies allow us to calculate all cardinal measures of association Key limitations to cohort studies are unsuitability for rare disease and the fact that they take a long time (and are expensive!) to implement

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Prevalence studies “Snapshot” look at exposure and outcome at a point in time Only provide relative odds; do not allow us to calculate either risk or rates, so no relative risks or relative rates Potential over-representation of diseases with long duration (P=ID)

Aside...cross-sectional studies

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Epid 600 Class 5 Cohort Studies

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