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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. Definitions in Epidemiology. Definition and aims of epidemiology Study designs used in epidemiology M easures of Disease Frequency

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Epidemiology principles and methods

Epidemiology:Principles and Methods

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

Department of Public Health,

Faculty of Medicine, Universitas Sebelas Maret

Definitions in epidemiology
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


  • 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

Definition of epidemiology
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.

Aims of epidemiologic research
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

Descriptive and analytical epidemiology
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

  • Study design and its strength of evidence
    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

    Which disease if more important to public health measure of disease occurence

    Number of Ill persons (new cases)

    Population at risk exposed

    Attack rate =

    Which Disease if More Important to Public Health? Measure of Disease Occurence

    • 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

    Description of disease distribution in the population
    Description of Disease Distribution in the Population

    Disease reaches its peak in frequency in Week 6

    Disease affects mostly people under five years of age

    Disease affects people living alongside the river













    • 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

    Timeline of infectiousness

    Dynamics of











    Dynamics of disease

    Incubation period






    Timeline of Infectiousness

    Measure of disease frequency
    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)=

      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 time period. Value from 0 to infinity

    • Prevalence (Point Prevalence):

      Number of new and old cases at a point of time


      Indicates burden of disease. Value from 0 to 1.

    Endemic vs epidemic

    Number of Cases of a Disease




    Endemic vs. Epidemic

    Levels of disease occurence
    Levels of Disease Occurence

    Sporadiclevel: occasional cases occurring at irregular intervals

    Endemiclevel: 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

    Factors influencing disease transmission
    Factors Influencing Disease Transmission



    • Infectivity

    • Pathogenicity

    • Virulence

    • Immunogenicity

    • Antigenic stability

    • Survival

    • Weather

    • Housing

    • Geography

    • Occupational setting

    • Air quality

    • Food


    • Age

    • Sex

    • Genotype

    • Behaviour

    • Nutritional status

    • Health status

    Measures of infectivity pathogenecity mortality
    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

    Preventable causes of disease
    Preventable Causes of Disease


    • 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

    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)

    Causal Model of Risk Factors for CVD


    Proximate cause

    Intermediate cause

    Distal cause

    To study disease etiology

    Kuartil asupan buah dan sayur

    Kuartil asupan buah dan sayur

    To Study Disease Etiology

    Validity of estimated association and causation

    True association



    Bias? Confounding?


    Validity of Estimated Association and Causation

    Smoking Lung Cancer

    OR = 7.3

    The role of bias confounding and chance in the estimated association


    Association ?



    Selection Bias and

    Information Bias?





    Confounding ?



    Chance ?


    True association

    The Role of Bias, Confounding, and Chance in The Estimated Association


    • 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)


    • 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

  • Confounding1

    Observed (but spurious) association, presumed causation

    Down’s syndrome

    Birth Order

    Unobserved association

    True association

    Maternal age

    Confounding biomedical bestiary michael boyce wilcox little brown 1984
    Confounding Sindroma Down?[Biomedical Bestiary: Michael, Boyce & Wilcox, Little Brown. 1984]

    Observed (but spurious) association, presumed causation



    Smoking, Alcohol, other Factors

    Unobserved association

    True association

    Hill s criteria for causation
    Hill’s Criteria for Causation Sindroma Down?

    • Strength of association

    • Specificity

    • Temporal sequence

    • Biologic gradient (dose-response relationship)

    • Biologic plausibility

    • Consistency

    • Coherence

    • Experimental study

    • Analogy