1. Epidemiology: Principles and Methods
Prof. dr. Bhisma Murti, MPH, MSc, PhD
Department of Public Health,
Faculty of Medicine, Universitas Sebelas Maret
Epidemiology is the basic science of public health and preventive medicine.
Biostatistics is the quantitative foundation of epidemiology.
It is important to present a comprehensive view of the fields of epidemiology, biostatistics, preventive medicine, and public health by showing their interrelationships and by emphasizing their relevance to clinical practice, research, and public health policy.
Our goal is to combine the theory and application of epidemiology, with a smattering of biostatistics, in a manner that allows students to critically appraise and interpret the scientific literature with increased understanding.

2. Definitions in Epidemiology
Definition and aims of epidemiology
Study designs used in epidemiology
Measures of Disease Frequency
Incidence (Cumulative Incidence and Incidence Density)
Prevalence
Measures of Association
Bias
Confounding
Chance
Causal Inference

3. Epidemiology
A study of the distribution of disease frequency in human population and the determinants of that distribution
Epidemiologists are not concerned with an individual’s 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

4. Definition of Epidemiology
The study of the distribution and determinants of health-related states or events in specified population, and the application of this study to control 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 as disability, residual dysfunction, complication, recurrence, but also causes of death, behavior, provision and use of health services.
Epidemiology has been defined in many ways - the simplest is the distribution and determinants of disease in a population.
Epidemiology is one of the ways in which disease is studied.
Others you are very familiar with include:
1. Sub-molecular or molecular level -
cell biology, biochemistry, and immunology
2. Tissue or organ level -
anatomy and pathology
3. Personal level -
the individual patient (clinical medicine)
4. Population level -
health of populations (epidemiology)

5. Aims of Epidemiologic Research
Describe the health status of a population
To assess the public health importance of diseases
To describe the natural history of disease,
Explain the etiology of disease
Predict the disease occurrence
To evaluate the prevention and control of disease
Control the disease distribution
Descriptive epidemiology Analytic epidemiology Applied epidemiology

6. Descriptive and Analytical Epidemiology
Descriptive epidemiology
Describes the occurrence of disease (cross-sectional)
Analytic epidemiology:
Observational (cohort, case control, cross-sectional, ecologic study) – researcher observes association between exposure and disease, estimates and tests it
Experimental (RCT, quasi experiment) – researcher assigns intervention (treatment), and estimates and tests its effect on health outcome

7. Epidemiologic Study Designs

8. Epidemiologic Study Designs

9. Study Design and Its Strength of Evidence
Systematic review, meta-analysis: secondary data analysis
Randomized Controlled Trials (RCT)
Cohort: prospective or retrospective
Quasi experiment
Case control: prospective or retrospective
Cross sectional
Case Reports / Case Series
Strongest evidence
Weakest evidence

10. Which Disease if More Important to Public Health
Which Disease if More Important to Public Health? Measure of Disease Occurence
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 is a Cumulative Incidence; it shows the risk (probability) of disease to occur in a population
In regard to risk, measles is the most important disease to public health while rubella being the least
Attack rate =
Number of Ill persons (new cases)
Population at risk exposed

11. 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

12. Natural History of Disease

13. Transmission 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 T
Susceptible
Immune
Sub-clinical
Clinical

14. Timeline of Infectiousness
Infection
Susceptible
Dynamics of
infectiousness
Dynamics of disease
Incubation period
Symptomatic
period
Non-diseased
Latent
Infectious
Non-infectious
Time

15. Measure of Disease Frequency
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 time period. Value from 0 to 1.
Incidence Density (Incidence Rate, ID, IR)=
Person time at risk
Indicates the velocity (speed) of the disease to occur in population over a time period. Value from 0 to infinity
Prevalence (Point Prevalence):
Number of new and old cases at a point of time
Population
Indicates burden of disease. Value from 0 to 1.

16. Endemic vs. Epidemic Number of Cases of a Disease Epidemic Endemic
Time

17. Levels of Disease Occurence
Sporadic level: occasional cases occurring at irregular intervals
Endemic level: persistent occurrence with a low to moderate level
Hyperendemic level: persistently high level of occurrence
Epidemic or outbreak: occurrence clearly in excess of the expected level for a given time period
Pandemic: epidemic spread over several countries or continents, affecting a large number of people

18. Factors Influencing Disease Transmission
Agent
Environment
Infectivity
Pathogenicity
Virulence
Immunogenicity
Antigenic stability
Survival
Weather
Housing
Geography
Occupational setting
Air quality
Food
Host
Age
Sex
Genotype
Behaviour
Nutritional status
Health status

19. Measures of Infectivity, Pathogenecity, Mortality
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 factors

20. 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
With attention on the application to control,
The BEINGS acronym for remembering categories of preventable causes of disease:
Biological factors: infectious agents, allergens, vaccines
Behavioral factors: smoking, drinking, exercise, diet, health-seeking behavior
Environmental factors: polutants and contamination, as well as ticks, air conditioning systems
Immunologic factors: natural immunity, acquired immunity
Nutritional factors: obesity, malnutrition, dis-equilibrium in diet
Genetic factors: genetic susceptability
Services: access to care
Social factors: family support
Spiritual factors: belief system

21. 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
Intermediate cause
Distal cause

22. To Study Disease Etiology
Kuartil asupan buah dan sayur

23. To Study Prognosis (Survival)

24. Validity of Estimated Association and Causation
Smoking Lung Cancer
OR = 7.3
True association
causal
non-causal
Bias? Confounding?
Chance?

25. The Role of Bias, Confounding, and Chance in The Estimated Association
Selection Bias and
Information Bias?
Confounding ?
Chance ?
True association
present
absent
likely
unlikely
False
association

26. 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)

27. Confounding 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
This third factor is so called confounding factor

29. Confounding Down’s syndrome Birth Order Maternal age
Observed (but spurious) association, presumed causation
Down’s syndrome
Birth Order
Unobserved association
True association
Maternal age

30. Apakah Ada Hubungan antara Urutan Kelahiran dan Risiko Sindroma Down?

31. Confounding [Biomedical Bestiary: Michael, Boyce & Wilcox, Little Brown. 1984]
Observed (but spurious) association, presumed causation
Gambling
Cancer
Smoking, Alcohol, other Factors
Unobserved association
True association

32. Hill’s Criteria for Causation
Strength of association
Specificity
Temporal sequence
Biologic gradient (dose-response relationship)
Biologic plausibility
Consistency
Coherence
Experimental study
Analogy