Epid Final Review 2011-1

7/28/2019 Epid Final Review 2011-1

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Epidemiology

Review2011

This slideset is a modification of theone used to review for the former

MPH exit exam


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Distribution

Who, What, When, Where and Howmuch.

Who and Where define the population e.g. 40 to 60 year old men in Georgia

What refers to the condition of interest e.g. 40 to 60 year old men in Georgia who develop

prostate cancer

When - specifying a time period helps

determine the public health impact e.g. 40 to 60 year old men in Georgia who develop

prostate cancer over the next 5 years


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Distribution

How much - this is the part that is

measured

Different ways to study and state how muchdisease there is, or how much is developing

Measures of Disease Frequency

Rates, Risks, and proportions of the population

that exist or develop


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DistributionMeasures of Disease Frequency

Count disease - not very helpful withoutknowing the size of the population at risk

Prevalence - count the number of peoplewith disease, divide by the size of thepopulation

P = # with disease / # in the population

No time period is specified, its just thepercentage of the population that has diseaseright now


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Risk aka Cumulative Incidence (CI)

Also a proportion

Proportion of a population that develops disease over

some time period

CI = # cases during time period

# of people at risk at the beginning of the time period

At the beginning of the time period, there are no casesof disease

the population is considered at risk for developing the disease

the cases that occur over the time period are incident cases,

or new cases that develop during the study period


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Risk, CI

If 8% of 40 to 60 year-old men in Georgia are

diagnosed with prostate cancer over 5 years, we

would say that: the 5-year risk (or CI) of developing prostate cancer among

men in Georgia aged 40 to 60 is 0.08 or 8%

Important that the time period is specified

Who

,What

,When

,Where

and

How much

is now defined


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Disadvantage to Cumulative Incidence

The denominator is the population at risk at the

beginningof the follow-up period What about people who leave, die of a competing

risk, or get the disease of interest?

Individuals are at risk for different amounts of time, yet

they are all counted equally in the denominator

Individuals who leave the population might have become

cases but we dont get a chance to count them in the

numerator


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Incidence Rate (IR) sometimes called Incidence

Density

Solves the issue with Risk by counting the amount of

time contributed by each person at risk, and makingthat the denominator

People who leave, die of competing risks, or get the

disease will only contribute the amount of time they

were at risk Numerator is the same as Risk, its the number of

incident cases


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Incidence Rate

among 1,000 40 to 60 year old men in Georgia, 80

are diagnosed with prostate cancer over a 5-year

period. Cumultaive Incidence (Risk )= 80/1000 = 8% However, 100 people either die, or leave our

population making it impossible to determine if they

get disease

Assume that on average the 80 cases and the 100people lost left, died, or became cases at 2.5 years


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Calculating person-time and IR 180 people contribute 2.5 years each at

risk before becoming cases, leaving, or

dieing. 180 people x 2.5 years = 450 person-years at

risk

Remaining 820 people at risk go 5-years

820 people x 5 years = 4,100 person-years atrisk

Total person-time = 4,550 person-years


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IR = 80 cases/ 4,550 person-years

= 0.0176 cases / person-year

Easier to understand as 17.6 cases per 1,000person-years

Tells us how fast cases are appearing in

the population


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Cumulative Incidence

Pros

Easy to understand a

proportion

Easy to understand at anindividual level

Cons

Underestimates the true

incidence as the study

time increases

Underestimate when there

is a lot of loss to follow-up

Incidence Rate

Pros

More accurate than the CI,

especially as follow-up time

increases Tells us the instantaneous

amount of disease that is

occurring in the population

Cons

Difficult to understand,especially at the individual

level


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An additional point about prevalence

Prevalence is determined by the incidence

rate of disease and the duration of disease

Prevalence = IR x Duration

Factors that increase duration may be

harmful, e.g. continued exposure to some

toxin, but they can also be helpful, e.g. insulintreatment for diabetes


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Epidemiology is the study of the

distribution and determinantsof

health and disease in a population for

the purpose of disease control and

health promotion

Distributions were the measures offrequency of disease

Determinants are the exposures that

increase or decrease the incidence ofdisease


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DeterminantsMeasures of Effect

Measuring the effect of some exposure

Compare an exposed population with an

unexposed population

Estimate the effect, or magnitude of the

association between an exposure and

disease

An association does not mean causality


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Two basic types of measures

Ratios and Difference

Ratios are relative measures How strong is the effect?

Difference are absolute measures

How much of an effect is there?


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Ratios Incidence Rate Ratios (IRR), Risk Ratios (RR), Odds Ratios (OR)

IRR= IRe/IRu

RR = CIe/CIu

Compare IR in 40 to 60 year old men in Georgia who have a highfat diet (the exposure) with the IR in this population without theexposure

(Pretend data) - IR in the high fat population is 13.7 cases /1,000 person years. IRu is 5 cases / 1,000 person-years

IRR=IRe/IRu = 13.7/5 = 2.74

Interpretation: Incidence Rate of prostate cancer in thoseexposed to high fat diets is 2.74 times as high as the IR in theunexposed over 5 years.

Or: Men exposed to a high fat diet are 2.74 times as likely asunexposed men to develop prostate cancer over 5 years

Or: Exposed men are 174% more likely to develop prostate

cancer over 5 years as unexposed men


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Differences

IRe=13.7 cases/1,000 py, IRu= 5 cases/1,000

py.

13.7 - 5 = 8.7 cases/1,000 py.

Interpretation: Exposure causes an additional

8.7 cases per 1,000 people each year


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Risk Difference

Re= 0.069, Ru=0.025, RR=2.74, RD=0.044

Interpretation: An additional 4.4% of theexposed population will develop prostate

cancer over 5 years compared with the

unexposed population


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Attributable Fraction in the exposed

How much of the disease in the exposed is due to

exposure?

Assume that some of the cases in the exposed group occurfor the same reason they occurred in the unexposed

Ratio: effect in unexposed is 1, effect in exposed is the ratio,

IRR-1/IRR = attributable fraction

2.74 - 1/2.74 = 0.64

64% of the cases in the exposed group is due to the exposure

Difference: IRe - IRu / IRe = (13.7 - 5) / 13.7 = 64%


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Epidemiology is the study of the distributionand determinants of health and disease in a

population for the purpose of disease controland health promotion

Studies are performed to measure incidence ofdisease, and the effect of an exposure

Two main types Experimental

The investigator assigns the exposure to a group,compares with unexposed group

Observational

The investigator is a passive observer of theincidence in an exposed group and unexposedgroup; or an observer of the amount of exposure in adiseased group with the amount of exposure in anunexposed group


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Study Design

Experimental

Randomized Control Trials

e.g. a clinical trial looking at a new drug

Interventional studies

e.g. the effect of providing fruits and vegetables in

a school instead of snack machines, and

comparing the two groups for obesity after 1 year


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Study Design

Experimental - Main characteristics of a gooddesign Randomization: investigator randomly assigns

exposure Double-Blinded: neither the investigator nor the

participants know which group is exposed

Placebo Controlled: unexposed group is given aplacebo (or the currently accepted treatment) to

ensure they do not know they are unexposed


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Study Design

Experimental

Randomization causes both groups to benearly identical in terms of every variablebesides the exposure

Only study design that can prove causalrelationships

Only preventative interventions or

therapeutic treatments can be studied unethical to expose people to something

harmful


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Study Design

Observational

Types: cohort, case-control, cross-sectional,ecological Each has advantages and disadvantages

exposed and unexposed populations may havedifferences besides their exposure status

these differences may affect the outcome

these differences (aka confounders) make itimpossible to prove causality in observational studies

In randomized studies, known and unknownconfounders are eliminated due to randomization


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Study DesignObservational

Cohort

Separate the population at risk into two

populations based on exposure status

At the end of the study, compare theincidence in the exposed to the incidence in

the unexposed

Remember, if the follow-up period is long, the

IR is preferable to cumulative incidence


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Cohort

Example study the incidence of schizophrenia among 15-year

old boys over 10 years

compare boys exposed to the parasite Toxomplasmagondiito unexposed boys

enroll 15 year old boys without current evidence ofmental illness. blood test separates exposed fromunexposed

check every 6 months for cases of disease, orunexposed boys becoming exposed thus, subjects can contribute unexposed and exposed person

time (another advantage of IR over risk)


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Cohort

Retrospective cohort study rather than following populations over time, look for

groups with evidence of past exposure, then checkmedical or other records for incident cases

Advantage: Less time than prospective; lessexpensive

Disadvantage: someone else measure exposure anddisease status, worry about mistakes in the recording;

also have no control over the measurement ofpossible confounders.


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Case-Control

Identify a diseased population (cases) and anon-diseased population (controls).

Measure the odds of exposure in each group

A more efficient way to calculate an effectmeasure than a cohort study controls are sampled in such a way that the ratio of

exposed to unexposed controls equals the ratio of

exposed to unexposed person time in the largerpopulation

The sample of controls is like a window into theexposure status of a larger population


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Case-Control

Since controls are a sample, incidence

rates and cumulative incidence cannot be

calculated

But a ratio measure, the odds ratio can be

calculated

The odds ratio is an unbiased estimate of

the incidence rate ratio if the controls areselected properly


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Case-Control

Control selection

Controls must be sampled independently of

their exposure status

Control population must be a population that if

they had the disease, they would have been

counted as cases

In other words, controls must come from thesame source population as the cases


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Case-Control

Control selection

Study to examine if drinking coffee isassociated with stomach cancer

Cases are identified at several participatingreferral hospitals

During the study period, all incident casesdiagnosed at these hospitals get counted, andinvestigators ask them about coffeeconsumption


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Case-Control

Control selection

What is the source population that gave riseto cases? It is the population of people who would go to one

of these specialty hospitals if they were diagnosedwith stomach cancer

For people without stomach cancer, how do we

know if they would go to these hospitals if they didhave it?

They probably dont know themselves


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Case-Control

Control selection

What is the source population that gave rise to

cases?

People in these hospitals for other conditions wouldprobably go to these hospitals for stomach cancer

This population would be identified as cases by the

investigators

These controls must be in the hospital for conditions

unrelated to stomach cancer, and unrelated to coffee

consumption


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Case-Control

Calculating the odds ratio It is the odds of exposure in cases divided by the

odds of exposure in controls

An easier way, the cross product ratio (exposed cases * unexposed controls)/ (exposed controls *

unexposed cases)

(40 * 120) / (60 * 180) = 1

Interpretation: odds of exposure in cases is the same as theodds of exposure in the control

Easier interpretation: Coffee drinkers are just as likely to getstomach cancer as non-coffee drinkers

Cases Controls

Exposed 40 80

Unexposed 60 120

St d D i


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Cohort vs. Case-Control

Cohort Good when the exposure is rare (since we select

based on exposure)

Allows direct calculation of Incidence Rates andRisks, as well as difference measure

Case-Control Good design when the disease is rare

More efficient Odds ratio can be an unbiased estimate of the IRR

Cant measure actual rates, risks, or differences -only determine relative effect, not absolute effects

St d D i


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Cross-Sectional

Snapshot of the population

Measure both disease prevalence and exposure

prevalence

Prevalence ratio = prevalence in exposed /prevalence in unexposed

Good for getting an idea about the burden of disease

in the population, but doesnt tell us about the effect

of the exposure However, it might suggest a study that should be

done

Study Design


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Ecologic Compare rates of disease in an entire population that has

some characteristic to rates in a population without thatcharacteristic

Looks at population, not individuals

No knowledge of individual exposure

Example: Miscarriage rate in Norwegian cities with fluoridein the water compared to cities without fluoride

difference in rates suggests an effect of fluoride, but noknowledge if women who had miscarriages drank thatwater

Ecologic fallacy - assign a population levelcharacteristic to individuals in that population

Documents

Epid Final Review 2011-1