Epidemiology 101: basic concepts

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Epidemiology 101 basic concepts

• Dr Ike Anya
• Specialist Registrar in Public Health Medicine,
• Bristol Joint Directorate of Public Health UK and
  Visiting Lecturer London School of Hygiene and
  Tropical Medicine
• ikeanya_at_doctors.org.uk

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Learning objectives

• To explore the definition of epidemiology
• To introduce key concepts in epidemiology
• To introduce the concepts of risk, risk
  measurement and standardization in epidemiology

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What is epidemiology?

• The study of epidemics?
• The study of diseases?
• The study of diseases of the skin?
• Something scientists and academics use to confuse
  other people?

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Definition of epidemiology

• The study of the distribution and
• determinants of health related states or events
  in specified populations and the application of
  this study to control health problems
• - James Last
• A Dictionary of
  Epidemiology

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Unpacking that definition

• Study Observing,recording,experimenting
• Distribution Who, where, when
• Determinants Why?
• Health related states
• Specified populations
• Application

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Epidemiology asks or uses

• Person- Who?
• Place- Where?
• Time- When?
• Helps us to understand Why?

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Specified populations

• How many people in this room are infected with
  the HIV virus?
• How many people in Toronto are infected ?
• How many people in Canada are infected?

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Why is it important to specify the population

• In order to be able to compare between two
  populations, we need to know what the defined
  population is
• For example,if we say 50 people in this room have
  an infection compared with 100 people in the next
  room, does it mean that infections are less
  common in this room?

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Numerators and denominators

• (N/d)
• Numerator the top half of the fraction
• Denominator- the bottom number in the fraction

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Numerators and denominators 2

• There may be fewer people in this room than in
  the next room
• Lets assume that there are 100 people in this
  room and 1000 people in the next room
• So 50 people with infections out of 100 people in
  this room means half (50/100) of the people in
  this room have infections
• 100 people with infections out of a 1000 in the
  next room means only a tenth (100/1000) of the
  next room have colds

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Measuring disease frequency

• There are 2 main measures used
• Prevalence
• Incidence

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Prevalence and incidence

• Prevalence - the number of people with a
  particular condition, habit at a specified time
  within a defined population eg prevalence of
  colds,smoking
• Incidence - the number of NEW cases of a
  condition/habit in a defined population over a
  specified period of time

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Distinguishing between incidence and prevalence

• Prevalence includes both old and new cases and is
  usually expressed as a percentage
• Incidence includes only NEW cases and is
  expressed as the number of cases per population
  per year
• Time period and population must be specified

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Prevalence

• Prevalence of colds in this class
• Number of cases (people with colds) 3
• Population of class 30
• Prevalence 3/30
• Expressed as a percentage 3/30 X 100
• 10

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Incidence

• Number of cases of newly diagnosed HIV
  infection in a city in 2003 is 900
• Population of the city is 100 000
• Incidence of HIV is 900 per 100 000 in 2003

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Defining risk

• Probability that an event will occur
• Different from causation
• Chance that if exposed to certain risk factors
  will develop condition

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Risk and risk factors

• Risk factors are factors that increase the
  probability that a disease will occur
• Risk factors could be
• environmental
• behavioural/lifestyle
• genetic

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Differentiating between risk and causation

• Risk is about probability or likelihood
• Causation is about certainty
• Identifying a risk may be the first step to
  understanding causation eg smoking and lung
  cancer

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Types of risk

• Absolute risk
• Relative risk
• Attributable risk

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Measures of risk absolute risk

• Number of cases in a defined population
• Similar to incidence
• If 100 people are infected with HIV in a town of
  1000 people, the absolute risk of HIV in the town
  is 100 per 1000
• But the people in the town have different
  lifestyles, genes,living conditions which
  absolute risk does not take note of

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Measures of risk relative risk

• Going back to our example, we could divide the
  population of the town into injecting drug users
  (IDUs) and non-injecting drug users non-IDUs)
• Count the number of cases of HIV in IDUs and
  count the number in non-IDUs
• Relative risk (risk ratio) is the ratio between
  the two
• I.e.Risk in the exposed /risk in the unexposed

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Relative risk

• In our example, there were 400 IDUs in the town,
  and 80 of them were diagnosed with HIV in the
  year of our study. The risk of HIV in IDUs was
  therefore 80/400 0.2
• There were 20 diagnoses of HIV in the non-IDU
  population of 600, so the risk of HIV in non-IDUs
  was 20/600 0.033
• The relative risk is therefore 0.2 divided by
  0.0336.06

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What does the relative risk mean?

• From the example, we obtained a relative risk of
  6.06
• In simple terms it means that IDUs in the town in
  that year were 6.06 times more likely to be
  diagnosed with HIV than non-IDUs

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Attributable risk

• Difference between risk in the exposed and risk
  in the unexposed
• Risk in exposed minus risk in unexposed
• From our example the attributable risk for
  smokers in the town was
• 0.2-0.0330.167

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Rates

• Rates are another means of expressing measurement
• 3 broad types of rates commonly used in
  epidemiology
• Crude rates
• Specific rates
• Standardized rates

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Crude rates

• Looking at the death records in Newtown which has
  a population of 100 000 we find that 500 people
  died in 2005
• In neighbouring OldTown with the same population
  of 100 000, there were 800 deaths in 2005

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Comparing crude rates

• Newtown had a crude death rate of 500 per 100 000
• Oldtown had a crude death rate of 800 per 100 000
• Oldtown appears to have a higher death rate than
  Newtown, but do the crude rates tell the whole
  story?

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Delving deeper specific rates

• Looking at the number of deaths in different age
  groups we get a different picture
• The majority of deaths in Oldtown occurred in
  people over the age of 60
• The majority of deaths in Newtown occurred in
  people under the age of 40

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Specific rates

• Specific rates give us more detail by looking at
  the occurrence of events in a subgroup of the
  population
• In the example, we used age groups, but could
  have used gender, ethnicity,occupation,etc

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Comparing rates - standardisation

• Going back to the example, we know that there
  were different patterns in the deaths recorded in
  the two towns
• But we may find it difficult to compare rates
  between the two towns
• Why?

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Why standardize ?

• Perhaps Oldtown is a retirement town with many
  old people and few young people?
• Perhaps Newtown has very few old people and is a
  barracks town consisting largely of soldiers
  going to Iraq?
• To enable valid comparison, we need to be
  comparing like with like hence standardization

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What are standardized rates?

• Standardized rates are rates that take into
  account the structure of the population and
  adjust for differences in population structure
• Rates can be age-standardized, sex-standardized,
  etc

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Summary

• Epidemiology uses person, time and place to study
  how illness and health are distributed in
  populations
• In epidemiology, specifying populations and time
  periods is important
• When interpreting epidemiology, always check that
  like is being compared with like