Week 1
- Introduction to the field 1 Epidemiology is ... 1-26
- Measures of frequency 2 How long is a piece of string? Measuring disease frequency:27-41 –
(No need to study Box 2.6 on p.40)
- Haveman_Chapter 1 PDF document Chapter 1: Read pages 14-26. This material belongs to
week 1 (What is epidemiology?)
1. Epidemiology is …
Definitions epidemiology:
- Concise Oxford Dictionary (1964) defined it as ‘the science of epidemics’.
- MacMahon and Pugh (1970) ‘the study of the distribution and determinants of disease’.
Their definition succinctly identifies the two core strands of traditional epidemiology: who is
developing disease (and where and when), and why are they developing it?
- Dictionary of Epidemiology (Porta, 2008) by broadening the scope to include health in
general, not just disease, as well as highlighting the direct role of epidemiology in disease
control.
Epidemiology is therefore about measuring health, identifying the causes of ill-health and intervening
to improve health. It is to provide a logic and structure for the analysis of health problems.
In general there is almost always some interaction between genetic and environmental factors in the
causation of disease.
Environmental factors: use this term to include all non-genetic factors, including psychological,
behavioural, social and cultural traits, as well as obvious environmental exposures such as air
pollution.
Health: a state of physical, mental, and social well-being. (WHO 1948)
Instead of measuring life expectancy for better international comparisons:
- HALE: health-adjusted life expectancy
- DALYs: disability-adjusted life expectancy
Outbreak investigations can best be compared by percentages of people. (for example:
156÷343=0.45=45%) So 45% of people who ate hot chicken became sick. This is known as the
Attack rate for hot chicken, i.e. 45% of hot-chicken eaters were ‘attacked’ by food poisoning
Relative risk: For example, 45% of people who ate hot chicken became ill, compared with 32% (people
who became ill) of people who did not eat hot chicken. Hot-chicken eaters were therefore 1.4 times
(45% ÷32%=1.4) more likely to become ill than people who did not eat hot chicken. This measure gives
us the risk of sickness in hot chicken eaters relative to non-eaters, hence its name– relative risk
Epidemiology: the ‘study (of what is) up on the people’. Such study suggests a simple set of questions
that have long lain at the heart of epidemiology.
- What disease/condition is present in excess?
- Who is ill?
- Where do they live?
- When did they become ill?
, - Why did they become ill?
The term shoe-leather epidemiology is sometimes used when the epidemiologist travels around to
interview people
The following questions are designed to help you identify key features of the data.
1. What is distinctive about this isolated population with regard to the numbers of men and
women(sex distribution), the numbers of adults and children (age distribution) and the
numbers in each socioeconomic group(socioeconomic distribution)?
2. What strikes you about the percentage of people who died (the ‘deathrate’)? Is this different
for (a) adults and children, (b) men and women, (c) high and low socioeconomic status (SES)
and (d) any particular combinations of the above?
3. How many times more likely were men to die than women and those of low SES to die than
those of high SES?
4. To what historical event might these data refer?
The beginnings:
- Hippocrates of Cos recognised that both environmental and behavioural factors could affect
health (460-375 BC)
- Causal reasoning development during the Dark and Middle Ages (AD 500-1500)
- John Graunt (1620-1674) contributed to the introduction of more quantitative methods into
epidemiology and biology and medicine in general. (semi-influence by his friend William
Petty). 1662 Graunt Publisher his: Natural and Political Observations Mentioned in a
Following Index and Made Upon the Bills of Mortality. He provided a numerical account of
the plague in London and an attempt to estimate the size of population and constructed the
first life-table; summarised the health of a population in terms of the chance of an individual
surviving to a particular age.
- Charles-Alexandre Louis (1787–1872), conducted some of the first epidemiological studies of
treatment effectiveness, when he suggested that bloodletting (aderlaten) did not help
recovery from illness.
- Farr (1807–1883), physician, statistician, and director of the Registrar-General's Office for
England and Wales. Farr studied mortality rates in various occupations and institutions, and
in married and single individuals, as well as other aspects of disease spread. He published
these and other findings in the Registrar-General's Annual Reports, and the current UK
system of vital statistics derives directly from his work.
- John Snow is now remembered for his pioneering work in elucidating the mode of
transmission of cholera (Snow, 1855). This remains a classic and exciting example of
epidemiological detection. His initial observations were based on a series of reports of
individual cholera cases, and in each case he was able to link the case to contact with another
infected person (or their goods), thus demonstrating that the disease could spread from
person to person. Also through contaminated water(linking a terrible cholera outbreak,
London, in 1854)
- Joseph Goldberger (the early 1900), a physician working for the U.S. Public Health Service,
demonstrated that pellagra was not contagious but had a dietary source, and Wade Hampton
Frost, another pioneer in the field, articulated the value of non-experimental epidemiology in
discovering disease locations.
- Doll and Hill, 1950 & Wynder and Graham, 1950, 2 case-control studies showed that patients
with lung cancer (cases) were significantly more likely to smoke than those without lung
cancer (controls). Doll and Hill then went on to confirm their findings using a different,
prospective design (a cohort study).
, 2. How long is a piece of string? Measuring disease
frequency
The goal of public health is to improve the overall health of a population by reducing the burden of
disease and premature death.
The diagnosis of disease is based on a combination of, subjective indications of disease reported by
the person symptoms themselves; signs, objective indications of disease apparent to the physician;
and additional tests.
Prevalence = the number of individuals in a specific population who have a disease or health
condition at a defined time. It includes all existing cases (e.g., at the end of 2012, how many people
have the disease in total).
- Increases when: incidence rises (more new cases) or disease duration lengthens (people live
longer with the disease).
- Decreases when: incidence falls (fewer new cases) or disease duration shortens (faster
recovery or higher mortality).
Incidence = the number of new cases of a disease in a population during a defined time period (e.g.,
all new cases in 2012). It is used to study risk factors and causes of disease.
Relation: Prevalence is usually higher than incidence because it counts both new and existing cases,
while incidence only counts new cases.
Both measures are important: incidence shows the underlying forces driving disease occurrence,
while prevalence is useful for long-term conditions (e.g., type 2 diabetes, osteoarthritis) and
congenital conditions. Together, they help describe disease burden and guide healthcare planning.
Period prevalence: which measures the proportion of the population that had the disease at any
time during a specified period. This is a complex measure that combines the prevalence (everybody
who had the disease at the start of the period) and incidence (all of the new cases of disease during
the period).
Prevalence is measured through cross-sectional studies, where a random sample of the population is
assessed for a condition at a specific time. Incidence is measured with cohort studies, starting with
disease-free individuals at risk, who are followed over time to see who develops the disease.
Incidence Proportion (Cumulative Incidence, Risk)
Definition: New cases ÷ population at risk at the start.
Interpretation: The probability (risk) that someone in the population develops the disease
during the period.
When to use:
o Closed cohorts (fixed population, no new entries).
o When follow-up time is the same for everyone.
, o Short, well-defined study periods (e.g., an outbreak in a school).
Example: In a flu outbreak in a school of 1,000 children, 50 develop flu over 2 weeks → Risk =
5%.
Incidence Rate (Incidence Density)
Definition: New cases ÷ total person-time at risk.
Interpretation: The speed (rate) at which new cases appear in the population.
When to use:
o Open/dynamic cohorts (people enter/leave during follow-up).
o When individuals are followed for different lengths of time (loss to follow-up, death,
migration).
o Long-term studies with varying follow-up.
Example: In a city, 50 new HIV cases are observed over 2,500 person-years → 20 per 1,000
person-years.
Person-years = de optelsom van de tijd dat alle deelnemers gevolgd worden.
Voorbeeld: 10 mensen gevolgd 1 jaar = 10 PY; 1 persoon 10 jaar = ook 10 PY. Of 100 people for 5
years= 500 person years.
Incidence Proportion = Risk → best for fixed groups, tells you chance of getting sick.
Incidence Rate = Speed → best for changing groups or variable follow-up, tells you pace at which
cases occur.
Haveman Chapter 1 Evolution of epidemiology in
society
Epi (upon, among), demos (people, district) and logos (study, word, discourse): it applies only to
human populations.
- (Morabia, 2004) Epidemiology is characterised by the combination of population thinking and
group comparisons aiming to discover the determinants of human health.
- In the fifth century BC, Hippocrates was the first to suggest that the development of human
disease might be related to the external as well as the personal environment of an individual.
Endemics: for diseases usually found in some places but not in others.
Epidemics: for diseases that are seen at some times but not others.
4 phases in evolution of epidemiology
1. Pre-formal Epidemiology (18th–19th century)
Key figures:
- James Lind (1716–1794): First clinical trial (1747) → proved citrus fruits cured scurvy.
- Adolphe Vorderman (1857–1902): Prison study in Java → beriberi linked to polished rice,
later explained as vitamin B1 deficiency.
- Ignaz Semmelweis (1818–1865): Introduced handwashing (chloride of lime) to prevent
puerperal fever → mortality dropped.
- Pierre Charles Alexandre Louis (1787–1872): Showed need for group comparisons in
medicine → bloodletting ineffective.
Main features:
- First systematic experiments and observations.
- Introduction to the field 1 Epidemiology is ... 1-26
- Measures of frequency 2 How long is a piece of string? Measuring disease frequency:27-41 –
(No need to study Box 2.6 on p.40)
- Haveman_Chapter 1 PDF document Chapter 1: Read pages 14-26. This material belongs to
week 1 (What is epidemiology?)
1. Epidemiology is …
Definitions epidemiology:
- Concise Oxford Dictionary (1964) defined it as ‘the science of epidemics’.
- MacMahon and Pugh (1970) ‘the study of the distribution and determinants of disease’.
Their definition succinctly identifies the two core strands of traditional epidemiology: who is
developing disease (and where and when), and why are they developing it?
- Dictionary of Epidemiology (Porta, 2008) by broadening the scope to include health in
general, not just disease, as well as highlighting the direct role of epidemiology in disease
control.
Epidemiology is therefore about measuring health, identifying the causes of ill-health and intervening
to improve health. It is to provide a logic and structure for the analysis of health problems.
In general there is almost always some interaction between genetic and environmental factors in the
causation of disease.
Environmental factors: use this term to include all non-genetic factors, including psychological,
behavioural, social and cultural traits, as well as obvious environmental exposures such as air
pollution.
Health: a state of physical, mental, and social well-being. (WHO 1948)
Instead of measuring life expectancy for better international comparisons:
- HALE: health-adjusted life expectancy
- DALYs: disability-adjusted life expectancy
Outbreak investigations can best be compared by percentages of people. (for example:
156÷343=0.45=45%) So 45% of people who ate hot chicken became sick. This is known as the
Attack rate for hot chicken, i.e. 45% of hot-chicken eaters were ‘attacked’ by food poisoning
Relative risk: For example, 45% of people who ate hot chicken became ill, compared with 32% (people
who became ill) of people who did not eat hot chicken. Hot-chicken eaters were therefore 1.4 times
(45% ÷32%=1.4) more likely to become ill than people who did not eat hot chicken. This measure gives
us the risk of sickness in hot chicken eaters relative to non-eaters, hence its name– relative risk
Epidemiology: the ‘study (of what is) up on the people’. Such study suggests a simple set of questions
that have long lain at the heart of epidemiology.
- What disease/condition is present in excess?
- Who is ill?
- Where do they live?
- When did they become ill?
, - Why did they become ill?
The term shoe-leather epidemiology is sometimes used when the epidemiologist travels around to
interview people
The following questions are designed to help you identify key features of the data.
1. What is distinctive about this isolated population with regard to the numbers of men and
women(sex distribution), the numbers of adults and children (age distribution) and the
numbers in each socioeconomic group(socioeconomic distribution)?
2. What strikes you about the percentage of people who died (the ‘deathrate’)? Is this different
for (a) adults and children, (b) men and women, (c) high and low socioeconomic status (SES)
and (d) any particular combinations of the above?
3. How many times more likely were men to die than women and those of low SES to die than
those of high SES?
4. To what historical event might these data refer?
The beginnings:
- Hippocrates of Cos recognised that both environmental and behavioural factors could affect
health (460-375 BC)
- Causal reasoning development during the Dark and Middle Ages (AD 500-1500)
- John Graunt (1620-1674) contributed to the introduction of more quantitative methods into
epidemiology and biology and medicine in general. (semi-influence by his friend William
Petty). 1662 Graunt Publisher his: Natural and Political Observations Mentioned in a
Following Index and Made Upon the Bills of Mortality. He provided a numerical account of
the plague in London and an attempt to estimate the size of population and constructed the
first life-table; summarised the health of a population in terms of the chance of an individual
surviving to a particular age.
- Charles-Alexandre Louis (1787–1872), conducted some of the first epidemiological studies of
treatment effectiveness, when he suggested that bloodletting (aderlaten) did not help
recovery from illness.
- Farr (1807–1883), physician, statistician, and director of the Registrar-General's Office for
England and Wales. Farr studied mortality rates in various occupations and institutions, and
in married and single individuals, as well as other aspects of disease spread. He published
these and other findings in the Registrar-General's Annual Reports, and the current UK
system of vital statistics derives directly from his work.
- John Snow is now remembered for his pioneering work in elucidating the mode of
transmission of cholera (Snow, 1855). This remains a classic and exciting example of
epidemiological detection. His initial observations were based on a series of reports of
individual cholera cases, and in each case he was able to link the case to contact with another
infected person (or their goods), thus demonstrating that the disease could spread from
person to person. Also through contaminated water(linking a terrible cholera outbreak,
London, in 1854)
- Joseph Goldberger (the early 1900), a physician working for the U.S. Public Health Service,
demonstrated that pellagra was not contagious but had a dietary source, and Wade Hampton
Frost, another pioneer in the field, articulated the value of non-experimental epidemiology in
discovering disease locations.
- Doll and Hill, 1950 & Wynder and Graham, 1950, 2 case-control studies showed that patients
with lung cancer (cases) were significantly more likely to smoke than those without lung
cancer (controls). Doll and Hill then went on to confirm their findings using a different,
prospective design (a cohort study).
, 2. How long is a piece of string? Measuring disease
frequency
The goal of public health is to improve the overall health of a population by reducing the burden of
disease and premature death.
The diagnosis of disease is based on a combination of, subjective indications of disease reported by
the person symptoms themselves; signs, objective indications of disease apparent to the physician;
and additional tests.
Prevalence = the number of individuals in a specific population who have a disease or health
condition at a defined time. It includes all existing cases (e.g., at the end of 2012, how many people
have the disease in total).
- Increases when: incidence rises (more new cases) or disease duration lengthens (people live
longer with the disease).
- Decreases when: incidence falls (fewer new cases) or disease duration shortens (faster
recovery or higher mortality).
Incidence = the number of new cases of a disease in a population during a defined time period (e.g.,
all new cases in 2012). It is used to study risk factors and causes of disease.
Relation: Prevalence is usually higher than incidence because it counts both new and existing cases,
while incidence only counts new cases.
Both measures are important: incidence shows the underlying forces driving disease occurrence,
while prevalence is useful for long-term conditions (e.g., type 2 diabetes, osteoarthritis) and
congenital conditions. Together, they help describe disease burden and guide healthcare planning.
Period prevalence: which measures the proportion of the population that had the disease at any
time during a specified period. This is a complex measure that combines the prevalence (everybody
who had the disease at the start of the period) and incidence (all of the new cases of disease during
the period).
Prevalence is measured through cross-sectional studies, where a random sample of the population is
assessed for a condition at a specific time. Incidence is measured with cohort studies, starting with
disease-free individuals at risk, who are followed over time to see who develops the disease.
Incidence Proportion (Cumulative Incidence, Risk)
Definition: New cases ÷ population at risk at the start.
Interpretation: The probability (risk) that someone in the population develops the disease
during the period.
When to use:
o Closed cohorts (fixed population, no new entries).
o When follow-up time is the same for everyone.
, o Short, well-defined study periods (e.g., an outbreak in a school).
Example: In a flu outbreak in a school of 1,000 children, 50 develop flu over 2 weeks → Risk =
5%.
Incidence Rate (Incidence Density)
Definition: New cases ÷ total person-time at risk.
Interpretation: The speed (rate) at which new cases appear in the population.
When to use:
o Open/dynamic cohorts (people enter/leave during follow-up).
o When individuals are followed for different lengths of time (loss to follow-up, death,
migration).
o Long-term studies with varying follow-up.
Example: In a city, 50 new HIV cases are observed over 2,500 person-years → 20 per 1,000
person-years.
Person-years = de optelsom van de tijd dat alle deelnemers gevolgd worden.
Voorbeeld: 10 mensen gevolgd 1 jaar = 10 PY; 1 persoon 10 jaar = ook 10 PY. Of 100 people for 5
years= 500 person years.
Incidence Proportion = Risk → best for fixed groups, tells you chance of getting sick.
Incidence Rate = Speed → best for changing groups or variable follow-up, tells you pace at which
cases occur.
Haveman Chapter 1 Evolution of epidemiology in
society
Epi (upon, among), demos (people, district) and logos (study, word, discourse): it applies only to
human populations.
- (Morabia, 2004) Epidemiology is characterised by the combination of population thinking and
group comparisons aiming to discover the determinants of human health.
- In the fifth century BC, Hippocrates was the first to suggest that the development of human
disease might be related to the external as well as the personal environment of an individual.
Endemics: for diseases usually found in some places but not in others.
Epidemics: for diseases that are seen at some times but not others.
4 phases in evolution of epidemiology
1. Pre-formal Epidemiology (18th–19th century)
Key figures:
- James Lind (1716–1794): First clinical trial (1747) → proved citrus fruits cured scurvy.
- Adolphe Vorderman (1857–1902): Prison study in Java → beriberi linked to polished rice,
later explained as vitamin B1 deficiency.
- Ignaz Semmelweis (1818–1865): Introduced handwashing (chloride of lime) to prevent
puerperal fever → mortality dropped.
- Pierre Charles Alexandre Louis (1787–1872): Showed need for group comparisons in
medicine → bloodletting ineffective.
Main features:
- First systematic experiments and observations.
