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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
epidemiology
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
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

transmission
T

S

P

Susceptible

S

Immune

S

T

Sub-clinical

Clinical

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
timeline of infectiousness
Dynamics of

infectiousness

Latent

period

Infectious

period

Non-infectious

Susceptible

Time

Infection

Infection

Dynamics of disease

Incubation period

Symptomatic

period

Non-diseased

Susceptible

Time

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

Population

Indicates burden of disease. Value from 0 to 1.

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

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
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

“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
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

Disease

Proximate cause

Intermediate cause

Distal cause

validity of estimated association and causation
True association

causal

non-causal

Bias? Confounding?

Chance?

Validity of Estimated Association and Causation

Smoking Lung Cancer

OR = 7.3

the role of bias confounding and chance in the estimated association
absent

Association ?

present

present

Selection Bias and

Information Bias?

False

association

absent

likely

Confounding ?

unlikely

likely

Chance ?

unlikely

True association

The Role of Bias, Confounding, and Chance in The Estimated Association
slide26
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)
confounding
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
confounding1
Confounding

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

Observed (but spurious) association, presumed causation

Gambling

Cancer

Smoking, Alcohol, other Factors

Unobserved association

True association

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