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Overview of

Mata KuliahE P I D E M I L O G I

Overview of Epidemiology

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SESI 1Prof. dr. M. Hakimi, Sp.OG, PhD

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Epidemiologic Research AssumesEpidemiologic Research Assumes

Disease occ rrence is not random• Disease occurrence is not random

• Systematic investigation of different populations can identify causal and preventive factors

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• Making comparisons is the cornerstone of systematic investigations

Definition of EpidemiologyDefinition of Epidemiology

The study of the distribution and determinants of disease frequency in human populations and the application of this study to control

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application of this study to control health problems

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• Disease frequency - count cases, need system,

Key Words in DefinitionKey Words in Definition

q y yrecords

• Disease distribution - who, when, where

Frequency, distribution, other factors generate

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hypotheses about determinants

A determinant is a characteristic that influences whether or not disease occurs

History of Epidemiology• HIPPOCRATES (400 BC): “On Airs, Waters, and OC S ( 00 C) O s, ate s, a d

Places” -Hypothesized that disease might be associated with the physical environment, including seasonal variation in illness.

• JOHN GRAUNT (1662): “Nature and Political Observations Made Upon the Bills of Mortality”- First to employ quantitative methods in describing population

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p y q g p pvital statistics.

• JOHN SNOW (1850): Formulated natural epidemiological experiment to test the hypothesis that cholera was transmitted by contaminated water.

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Deaths from Cholera per 10,000 houses by source of water supply, London 1854

Number of Deaths from Deaths inHouses Cholera each

Water Supply 10,000 homes

Southwark & Vauxhall 40,046 1,263 315Company

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Lamberth Company 26,107 98 37

Rest of London 256,423 1,422 59

History of Epidemiology (cont.)

• DOLL & HILL (1950): Used a case-control design toDOLL & HILL (1950): Used a case control design to describe and test the association between smoking and lung cancer.

• FRANCES at al. (1950): Huge formal field trial of the Poliomyelitis vaccine In school children.

• DAWBER et al. (1955): Used the cohort design to study risk factors for cardiovascular disease in the

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study risk factors for cardiovascular disease in the Framingham Heart Study.

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Natural Progression in Natural Progression in Epidemiologic ReasoningEpidemiologic Reasoning

1st – Suspicion that a factor influences disease occurrence. Arises from clinical practice, lab research, examining disease patterns by person, place and time, prior epidemiologic studies

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epidemiologic studies

2nd – Formulation of a specific hypothesis

Natural Progression in Natural Progression in Epidemiologic ReasoningEpidemiologic Reasoning

3rd – Conduct epidemiologic study to determine3 – Conduct epidemiologic study to determine the relationship between the exposure and the disease. Need to consider chance, bias, confounding when interpreting the study results.

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4th – Judge whether association may be causal. Need to consider other research, strength of association, time directionality

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Hypothesis Formation and TestingHypothesis Formation and Testing

Cl f d i i i• Clues from many sources and imagination lead to hypothesis formation

• Conduct epidemiologic study to test hypothesis

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hypothesis

Descriptive EpidemiologyDescriptive Epidemiology

• Describe patterns of disease by person, place, and time

• Person: Who is getting the disease? (for l h t i th i li i

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example, what is their age, sex, religion, race, educational level etc?)

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Ex. Mortality rates per 100,000 from Ex. Mortality rates per 100,000 from diseases of the heart by age and sex (2000)diseases of the heart by age and sex (2000)What hypotheses can you generate from these data?What hypotheses can you generate from these data?

Age (in years) Men Women

25-34 10.3 5.535-44 41.6 17.245-54 142.7 50.355-64 378.6 160.465-74 909.2 479.975-84 2210.1 1501.585+ 6100.8 5740.1

PlacePlaceWhere are the rates of disease the highest and lowest? What hypotheses can you generate from this map?

Malignant Melanoma of SkinMalignant Melanoma of Skin

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PlacePlaceWhat hypotheses can you generate from this map?

Cancer of the Trachea, Bronchus and LungCancer of the Trachea, Bronchus and Lung

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Variation on Place: Migrant StudiesVariation on Place: Migrant StudiesMortality rates (per 100,000) due to stomach Mortality rates (per 100,000) due to stomach cancer. What hypotheses can you generate from cancer. What hypotheses can you generate from these data?these data?

Japanese in Japan 58.4

Japanese Immigrants to California 29.9

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Sons of Japanese Immigrants 11.7

Native Californians (Caucasians) 8.0

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Time:Time:• Is the present frequency of disease different from the

past?

• What hypotheses can you generate from these data?

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Solve the Mystery EpidemicSolve the Mystery EpidemicThe following data relate to an unusual

episode that actually occurred.p y

• Describe the epidemiologic features of this episode. For example, the overall mortality ‘rate’ was 68.2%

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• Based on the descriptive characteristics, formulate a hypothesis concerning the etiology of this episode.

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Solve the Mystery EpidemicSolve the Mystery EpidemicAdult Men Adult Women Children Total

Socio-economic Status

N % Died

N % Died

N % Died

N % Died

High 173 66.5 144 3.5 5 0.0 322 37.3

Middle 160 91.9 93 16.1 24 0.0 277 58.6

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Low 454 87.9 179 45.3 76 71.1 709 75.3

Unknown 875 78.4 23 8.7 0 --- 898 76.6

Total 1,662 81.0 439 23.5 105 51.4 2,206 68.2

Main Epidemiologic Study Main Epidemiologic Study Designs for Testing HypothesesDesigns for Testing Hypotheses

Cohort studyCohort studyExperimental study Case-control study

Each design represents a different way of harvesting information.

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harvesting information.Selection of one over another depends on the

particular research question, concerns of about data quality and efficiency, and practical and ethical considerations

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Which study design to choose?Which study design to choose?

In theory, it's possible to use eachdesign to test a hypothesis

Example: Suppose you want to study the relationship between dietary Vitamin A and

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relationship between dietary Vitamin A and lung cancer….

Cohort Study OptionCohort Study Option

Subjects are chosen on the basis of exposure status and followed to assess the occurrence of disease

• High Vitamin A consumption ---------------> lung cancer or not

Low Vitamin A Consumption > lung cancer

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• Low Vitamin A Consumption --------------> lung canceror not

What are the advantages and disadvantages of this option?

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Experimental Study OptionExperimental Study OptionSpecial type of cohort study in which investigator

assigns the exposure to individuals, preferably at drandom

Investigator assigns exposure to:

• High Vit A consumption ----------------> lung canceror not

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• Low Vit A consumption ----------------> lung canceror not

What are the advantages and disadvantages of this option?

CaseCase--Control Study OptionControl Study Option

Cases with the disease and controls who generally do not have the disease are chosen and past pexposure to a factor is determined

• Prior Vitamin A consumption <----------- lung cancer cases• Prior Vitamin A consumption <---------- controls

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What are the advantages and disadvantages of this option?

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In practice, choice of study design In practice, choice of study design depends on:depends on:

• State of knowledge• State of knowledge

• Frequency of exposure and disease

• Time, cost and other feasibility considerations

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• Each study design has unique and complementary advantages and disadvantages

The result of an epidemiologic study, regardless of type, is an association between the disease

Evaluating Validity and CausalityEvaluating Validity and Causality

of type, is an association between the disease under study and an exposure.

What we are trying to do is evaluate whether that relationship is causal.

How is this done?

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Two questions must be answered:• Is the observed association valid?• Is the association causal?

How is this done?

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Example: Lung Cancer and Smoking

Ex. Lung Cancer and Smoking

50,000 Smokers 50,000 Non-Smokers

100,000 People

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100 Lung Cancers 20 lung Cancers

Conclusion: The rate of lung cancer among smokers is five times that of nonsmokers. There is an association between smoking and lung cancer in these data.

Example: Lung Cancer and SmokingExample: Lung Cancer and Smoking

1st Question: Is the observed association valid?

Framework for assessing if observed association is valid:

• Random Error (chance)

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• Bias (systematic error)

• Confounding (mixing of effects)

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Random ErrorRandom Error

• Inference about experience of entire population based on study samplepopulation based on study sample

• Random variation from sample to sample

• Sample size determines degree to which chance affects findings

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• P values and confidence intervals quantify amount of random error

Random ErrorRandom ErrorPrior smoking and lung cancer study:

• Under null hypothesis (no association between yp (smoking and lung cancer) you would expect to see an equal number of lung cancers among smokers and nonsmokers (120/2 = 60).

• P value tells you probability of seeing 100 vs. 20 lung cancers given that you expected 60 in

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20 lung cancers given that you expected 60 in each group.

• P value tells you nothing about alternative explanations such as bias

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BiasBiasAny source of error in the determination of the

association between the disease and exposure

• Selection bias - bias in how subjects are selected

• Observation bias - bias in how information is bt i d

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obtained

Could bias explain the finding?

ConfoundingConfounding

• Not the investigator’s fault, just a fact of life

• Confounding is a mixing of effects between the association of the disease and third factor (the confounder)

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• What confounding factors could explain the finding?

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Let’s practice assessing validity.Let’s practice assessing validity.The Good News Survey was administered to

readers of the magazine Hippocrates.

Nearly 3,000 readers responded with information on their health, diets and habits.

The data were analyzed and many associations were seen Consider the following findings and

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were seen. Consider the following findings and state whether or not you think that they are valid. If not, give a possible alternative explanation for the association.

Frosted Flakes Prevent CancerTo avoid cancer, people should stay away from oatmeal and eat frosted Flakes. The Good News Survey (GNS) found that children who often ate Frosted Flakes went on to have half the rate of cancer of those who never ate cereal. Conversely, those who often ate oatmeal as children were four times more likely to develop cancer than

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times more likely to develop cancer than those who did not. The GNS research team speculates that the sugar coating on Frosted Flakes may be the long-sought “magic bullet” against cancer.

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The Routine Physical is a Routine Killer

Physical exams cause dangerous long-termPhysical exams cause dangerous long-term increases in blood pressure and cholesterol levels. GNS respondents who had been given a physical during the previous three years were twice as likely as other respondents to report problems with high

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respondents to report problems with high blood pressure and cholesterol.

Study Exonerates Over-sleepersFar from being a sign of laziness, sleeping late appears to be a genetic trait, according to a pp g glandmark study. The GNS found that children whose parents slept late on weekends were 60 percent more likely to sleep in past 11 a.m. on weekends. “Late sleepers shouldn’t be blamed for their actions,” said one researcher. “And it’s time

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we got rid of that old wives’ tale about early-to-rise making you healthy. Our study shows that late sleepers actually have lower rates of cancer and heart disease, not to mention more satisfactory sex lives.”

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Call Waiting Brings Headaches, Herpes

People with the Call Waiting feature on their telephones are more prone to splittingtelephones are more prone to splitting headaches, tennis elbow, and herpes. GNS researchers believe the headaches and tennis elbow may be due to rapid switching from one caller to another, but the herpes link remains puzzling. “It’s possible that Call Waiting is a sign of general promiscuity ” says one specialist

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of general promiscuity, says one specialist. “Although we always thought that anyone who put personal calls on hold every time an encyclopedia salesman called would quickly end up with no romantic life whatsoever.”

Valid statistical association does not Valid statistical association does not imply cause and effect.imply cause and effect.

You must use your judgment.

Remember: Causality is in the eyes of the beholder.

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Many epidemiologists think that Sir A.B. Hill's guidelines are useful for making the assessment.

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Time seq ence established There is

Some of Hill’s GuidelinesSome of Hill’s Guidelines

• Time sequence established: There is evidence that exposure preceded disease.

• Strength of association: Stronger

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Strength of association: Stronger associations are more likely to be causal.

• Consistency: If other investigations using different populations different study designs

Some of Hill’s GuidelinesSome of Hill’s Guidelines

different populations, different study designs show similar results, there is strong support for causality.

• Biologic credibility: Does association make sense biologically?

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sense biologically?

• Dose-response: Does disease risk increase as exposure level increases?