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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
24
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
25
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