epidemiology principles and methods_prof bhisma murti

7/30/2019 Epidemiology Principles and Methods_Prof Bhisma Murti

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Epidemiology:

Principles and Methods

Prof. dr. Bhisma Murti, MPH, MSc, PhD

Department of Public Health,Faculty of Medicine, Universitas Sebelas Maret


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Definitions in Epidemiology

1. Definition and aims of epidemiology

2. Study designs used in epidemiology

3. Measures of Disease Frequency

Incidence (Cumulative Incidence and Incidence Density) Prevalence

4. Measures of Association

5. Bias

6. Confounding

7. Chance

8. Causal Inference


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Epidemiology

A study of the distribution of disease frequency in human

population and the determinants of that distribution

Epidemiologists are not concerned with an individuals

disease as clinicians do, but with a population distribution

of the disease

Distribution of disease by person, place, time

Assumption:

Disease does not occur randomly

Disease has identifiable causes

which can be altered and therefore

prevent disease from developing


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Definition of Epidemiology

The study of the distribution and determinants ofhealth-related states or events in specifiedpopulation, and the application of this study tocontrol of health problems.[source: Last (ed.) Dictionary of Epidemiology, 1995]

Determinants: physical, biological, social, cultural,and behavioral factors that influence health.

Health-related states or events: health status,diseases, death, other implications of disease such asdisability, residual dysfunction, complication,recurrence, but also causes of death, behavior,provision and use of health services.


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Aims of Epidemiologic Research

1. Describe the health status of apopulation

2. To assess the public healthimportance of diseases

3. To describe the natural history ofdisease,

4. Explain the etiology of disease

5. Predict the disease occurrence

6. To evaluate the prevention andcontrol of disease

7. Control the disease distribution

Descriptiveepidemiology

Analyticepidemiology

Appliedepidemiology


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Descriptive and Analytical

Epidemiology

1. Descriptive epidemiology Describes the occurrence of disease (cross-

sectional)

2. Analytic epidemiology: Observational (cohort, case control, cross-

sectional, ecologic study) researcher observesassociation between exposure and disease,

estimates and tests it Experimental(RCT, quasi experiment) researcher

assigns intervention (treatment), and estimatesand tests its effect on health outcome


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Natural History of Disease

Paparan pertama

kali dengan agen

penyebab (mis.

Asap rokok, M.

Tuberculosis)

Kasus baru

klinis

Kasus baru dan

lama klinis


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Epidemiologic Study Designs


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Epidemiologic Study Designs


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Study Design and Its Strength

of Evidence

1. Systematic review, meta-analysis:

secondary data analysis

2. Randomized Controlled Trials (RCT)

3. Cohort: prospective or retrospective

Quasi experiment

4. Case control: prospective or retrospective5. Cross sectional

6. Case Reports / Case Series

Strongest

evidence

Weakest

evidence


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Attack rate is a Cumulative Incidence; it shows the risk (probability) ofdisease to occur in a population

In regard to risk, measles is the most important disease to public healthwhile rubella being the least

Hypothetical Data

Measles Chickenpox Rubella

Children exposed

Children ill

Attack rate

251

201

0.80

238

172

0.72

218

82

0.38

Attack rate =Number of Ill persons (new cases)

Population at risk exposed

Which Disease if More Important to Public

Health? Measure of Disease Occurence


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Description of Disease Distribution

in the Population

Disease affects

mostly people under

five years of age

Disease affects

people living

alongside the river

Disease reaches its

peak in frequency in

Week 6


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Cases Index the first case identified

Primary the case that brings the infection into a population

Secondary infected by a primary case

Tertiary infected by a secondary case

P

S

S

T

Susceptible

Immune

Sub-clinical

Clinical

ST

Transmission


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Susceptible

Susceptible

Dynamics of

infectiousness

Dynamics of

disease

Incubation

period

Symptomatic

period

Non-diseased

Latent

period

Infectious

period

Non-infectious

Time

Time

Timeline of Infectiousness


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Measure of Disease Frequency

1. Cumulative Incidence (Incidence, Risk, I, R)=

Number of new case over a time period

Population at risk at the outset

- Indicates the risk for the disease to occur in population at risk over a timeperiod. Value from 0 to 1.

2. Incidence Density (Incidence Rate, ID, IR)=Number of new case over a time period

Person time at risk

Indicates the velocity (speed) of the disease to occur in population over a timeperiod. Value from 0 to infinity

3. Prevalence (Point Prevalence):Number of new and old cases at a point of time

Population

Indicates burden of disease. Value from 0 to 1.


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Endemic EpidemicNumb

erofCasesof

aDisease

Time

Endemic vs. Epidemic


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Sporadic level: occasional cases occurring at irregular

intervals

Endemic level: persistent occurrence with a low tomoderate level

Hyperendemic level: persistently high level of

occurrence

Epidemic or outbreak: occurrence clearly in excess ofthe expected level for a given time period

Pandemic: epidemic spread over several countries or

continents, affecting a large number of people

Levels of Disease Occurence


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Agent

Host

Environment

Age

Sex

Genotype

Behaviour

Nutritional status

Health status

Infectivity

Pathogenicity

Virulence

Immunogenicity

Antigenic stability

Survival

Weather

Housing

Geography

Occupational setting

Air quality

Food

Factors Influencing Disease

Transmission


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Infectivity (ability to infect)

(number infected / number susceptible) x 100

Pathogenicity (ability to cause disease)

(number with clinical disease / number infected) x 100

Virulence (ability to cause death)

(number of deaths / number with disease) x 100

All are dependent on host and environmental factors

Measures of Infectivity, Pathogenecity,

Mortality


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Preventable Causes of Disease

BEINGS Biological factors and Behavioral Factors

Environmental factors

Immunologic factors

Nutritional factors Genetic factors

Services, Social factors, and Spiritual factors[JF Jekel, Epidemiology, Biostatistics, and Preventive Medicine, 1996]

Types of Cause: Necessary cause: Mycobacterium tuberculosis

Sufficient cause: HIV

Contributory cause: Sufficient-Component Cause


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Causal Model of Risk Factors for CVD

Morbidity and Mortality

(Stroke, MI)

Biological Risk Factors

(Hypertension, Blood Lipids, Homocysteine)

Genetic Risk Factors

(Family History)

Behavioral Risk Factors

(Cigarette, Diet, Exercise)

Environmental Factors

(Socioeconomic Status, Work Environment)

Disease

Proximate

cause

Intermedi

ate cause

Distal

cause


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To Study Disease Etiology

Kuartil asupan buah dan sayurKuartil asupan buah dan sayur


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To Study Prognosis (Survival)


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Validity of Estimated

Association and Causation

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Smoking Lung Cancer

OR = 7.3

Bias?

Confounding?

Chance?

True association

causal

non-causal


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The Role of Bias, Confounding, and

Chance in The Estimated Association

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Association ?

Selection Bias and

Information Bias?

Confounding ?

Chance ?

True association

present

absent

likely

likely

unlikely

present

absent

unlikely

Falseassociation


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BIAS

Systematic errors in selection of study

subjects, collecting or interpreting data

such that there is deviation of results or

inferences from the truth. Selection bias: noncomparable procedure used to select

study subjects leading to noncamparable study groups in

their distribution of risk factors. Example: Healthy worker bias

Information bias: bias resulting from measurement error/

error in data collection (e.g. faulty instrument, differential or

non-differential misclassification of disease and/ or exposure

status. Example: interviewer bias,recall bias)


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Confounding

1. A mixing of effects

between the exposure, the disease, and a third

factor associated with both the exposure and the

disease

such that the effect of exposure on the disease is

distorted by the association between the exposure

and the third factor

2. This third factor is so called confoundingfactor


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Cases of Down syndroms by birth order

0

20

40

60

80

100

120

140

160

180

1 2 3 4 5

Birth order

Cases per 100 000

live births


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Confounding

Birth Order Downs

syndrome

Maternal age

Observed (but spurious) association,

presumed causation

Unobserved

association

True

association


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Apakah Ada Hubungan antara Urutan

Kelahiran dan Risiko Sindroma Down?


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Confounding[Biomedical Bestiary: Michael, Boyce & Wilcox, Little Brown. 1984]

Gambling Cancer

Smoking,

Alcohol,other

Factors

Observed (but spurious) association,

presumed causation

Unobserved

associationTrue

association


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Hills Criteria for Causation

1. Strength of association

2. Specificity

3. Temporal sequence

4. Biologic gradient (dose-response relationship)5. Biologic plausibility

6. Consistency

7. Coherence

8. Experimental study

9. Analogy

epidemiology principles and methods_prof bhisma murti

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