HC 13: applied biostatistics and epidemiology (04/10/22)
-Learning objectives:
To understand the basics of Epidemiology and Applied Biostatistics
To understand its use/application in Biomedical sciences
To understand infectious disease progression and diagnostics
To understand and interpret epidemiology of infectious disease outbreaks
To select and interpret study designs and analytical methods and techniques commonly applied in
the fields of immunology, neurobiology, infectious diseases, and science and society studies
The essentials of statistics used in biomedical sciences
Epidemiology
-Definition -> Study what is upon the people -> Study about what is actually going on with the people.
About different things and health conditions connected to people.
-Real definition -> The study of the distribution and determinants of health- related states or events in
specified populations, and the application of this study to the control of health problems
Three important nuances:
o Distribution -> How much is prevalent in a certain population and in what location.
o Determinants -> What are the different factors/predictors/exposures what relates to the
disease.
o Application -> How are we going to apply it, how is it going to help?
-Uses of epidemiology -> When is epidemiology needed?
Public health surveillance -> keep a check on how the population is doing.
o Via questionaries for example
Field investigation -> See what is happening in the community
Analytic studies -> Work in laboratory
Evaluation public health services
Policy development
-History – origins of epidemiology
Began in London with John Snow in 1850 -> Severe disease and a lot of people died.
o People thought it was because of the bad air.
o Doctor John Snow thought it was something else -> started to see where the most people
died (location) -> found out: along the areas along the fountains or water spots, people
were having the disease.
o Disease was Cholera, so he was quite correct
o He further looked at the water companies, only one company did not have too many
deaths. Lambeth company had there pumps upstream, so where the toilet water wasn’t
being dumped.
Basics of study design
-Study designs:
Two main types:
o Observational -> You observe and measure a set of characteristics in a defined sample
population, you don’t make any changes
Like case studies
Retrospective studies
Cohort studies
You can divide it in:
Analytical -> Cohort, case control and cross-sectional -> Have a very
important role in medical research. For example, in understanding risk
factors.
, Descriptive -> Case report, case series and cross sectional -> Have several
important roles in medical research. They are often the first foray into a new
disease or area of inquiry -> the first scientific “toe in the water”.
Differences between analytical and descriptive:
Analytical:
o Study the determinants of the disease (what is causing it/relations).
o Question: Why? -> It is all about comparing persons, groups etc.
o Example research question -> “Are epidemiology students more likely
to have symptoms of “anxiety” than theology students?”
o Differences between analytical studies -> What is the temporal
direction?
o Cohort -> Two groups and you know what they are exposed to
(exposed and non-exposed), you follow them over a defined period
and see who of them develop an outcome/interest.
A cohort -> A group of persons that share at least one
characteristic, eg: students who completed IBMS course in
2022.
You give something or measure a difference and follow them
for a certain time.
You follow and see what the outcome is.
Is prospective (can also be retrospective).
Example: Two groups based on exposure to Saccharine (those
who consume it VS who don’t) →outcome Pancreatic Cance
When to use this design, advantages: (+)
Sequence between cause and outcome is usually clear.
You can study multiple outcomes that might arise after
a single exposure.
o e.g., cigarette smoking (the exposure) and
stroke, oral cancer, and heart disease (the
outcomes)
Usefull with rare exposures!
Cohort studies allow calculation of incidence rates,
relative risks, confidence intervals and other measures.
o By contrast, case-control studies cannot provide
incidence rates; at best, odds ratios that
approximate relative risks , but only when the
outcome is uncommon.
, Limited change of informative bias
Disadvantages: (-)
Selection bias can happen!
o As both groups should be exactly the same, but
this seldom occurs.
Not optimum for rare diseases or those that take a long
time to develop (e.g., cancer).
o However, several large (and thus expensive)
cohort studies have made landmark
contributions to our knowledge of uncommon
diseases.
Loss to follow-up can be a difficulty.
o Differential losses to follow-up between those
exposed and unexposed can bias results.
Over time, the exposure status of study participants
can change.
Expensive -> To follow takes a lot of time, which results
in more money
o Case control -> Look at the outcome and search back for differences
of exposure (look back in time).
Is thus retrospective
Contribute greatly to the research toolbox of an
epidemiologist
You already have two groups of people, one who has the
disease and one who doesn’t have the disease and then you
compare their historical exposure.
So, study groups are defined by outcome, and you look
back retrospectively to identify the exposures.
You know the outcomes, but you search for the exposures.
Example: influentialin understanding risk groups (e.g., men
who have sex with men, blood transfusion recipients) and risk
factors (e.g., multiple sex partners) for HIV/AIDS.
Advantages: (+)
Inexpensive
Can yield important findings in short time
Efficient if when incidence of outcome is low / for
diseases with a long latency period
Valuable for rare disease
Disadvantages: (-)
Multiple methodological issues that affect the validity
of the study:
o Hard to go back and remember certain things ->
Recall bias (information bias)
o Selection bias (improper selection of controls)
-Learning objectives:
To understand the basics of Epidemiology and Applied Biostatistics
To understand its use/application in Biomedical sciences
To understand infectious disease progression and diagnostics
To understand and interpret epidemiology of infectious disease outbreaks
To select and interpret study designs and analytical methods and techniques commonly applied in
the fields of immunology, neurobiology, infectious diseases, and science and society studies
The essentials of statistics used in biomedical sciences
Epidemiology
-Definition -> Study what is upon the people -> Study about what is actually going on with the people.
About different things and health conditions connected to people.
-Real definition -> The study of the distribution and determinants of health- related states or events in
specified populations, and the application of this study to the control of health problems
Three important nuances:
o Distribution -> How much is prevalent in a certain population and in what location.
o Determinants -> What are the different factors/predictors/exposures what relates to the
disease.
o Application -> How are we going to apply it, how is it going to help?
-Uses of epidemiology -> When is epidemiology needed?
Public health surveillance -> keep a check on how the population is doing.
o Via questionaries for example
Field investigation -> See what is happening in the community
Analytic studies -> Work in laboratory
Evaluation public health services
Policy development
-History – origins of epidemiology
Began in London with John Snow in 1850 -> Severe disease and a lot of people died.
o People thought it was because of the bad air.
o Doctor John Snow thought it was something else -> started to see where the most people
died (location) -> found out: along the areas along the fountains or water spots, people
were having the disease.
o Disease was Cholera, so he was quite correct
o He further looked at the water companies, only one company did not have too many
deaths. Lambeth company had there pumps upstream, so where the toilet water wasn’t
being dumped.
Basics of study design
-Study designs:
Two main types:
o Observational -> You observe and measure a set of characteristics in a defined sample
population, you don’t make any changes
Like case studies
Retrospective studies
Cohort studies
You can divide it in:
Analytical -> Cohort, case control and cross-sectional -> Have a very
important role in medical research. For example, in understanding risk
factors.
, Descriptive -> Case report, case series and cross sectional -> Have several
important roles in medical research. They are often the first foray into a new
disease or area of inquiry -> the first scientific “toe in the water”.
Differences between analytical and descriptive:
Analytical:
o Study the determinants of the disease (what is causing it/relations).
o Question: Why? -> It is all about comparing persons, groups etc.
o Example research question -> “Are epidemiology students more likely
to have symptoms of “anxiety” than theology students?”
o Differences between analytical studies -> What is the temporal
direction?
o Cohort -> Two groups and you know what they are exposed to
(exposed and non-exposed), you follow them over a defined period
and see who of them develop an outcome/interest.
A cohort -> A group of persons that share at least one
characteristic, eg: students who completed IBMS course in
2022.
You give something or measure a difference and follow them
for a certain time.
You follow and see what the outcome is.
Is prospective (can also be retrospective).
Example: Two groups based on exposure to Saccharine (those
who consume it VS who don’t) →outcome Pancreatic Cance
When to use this design, advantages: (+)
Sequence between cause and outcome is usually clear.
You can study multiple outcomes that might arise after
a single exposure.
o e.g., cigarette smoking (the exposure) and
stroke, oral cancer, and heart disease (the
outcomes)
Usefull with rare exposures!
Cohort studies allow calculation of incidence rates,
relative risks, confidence intervals and other measures.
o By contrast, case-control studies cannot provide
incidence rates; at best, odds ratios that
approximate relative risks , but only when the
outcome is uncommon.
, Limited change of informative bias
Disadvantages: (-)
Selection bias can happen!
o As both groups should be exactly the same, but
this seldom occurs.
Not optimum for rare diseases or those that take a long
time to develop (e.g., cancer).
o However, several large (and thus expensive)
cohort studies have made landmark
contributions to our knowledge of uncommon
diseases.
Loss to follow-up can be a difficulty.
o Differential losses to follow-up between those
exposed and unexposed can bias results.
Over time, the exposure status of study participants
can change.
Expensive -> To follow takes a lot of time, which results
in more money
o Case control -> Look at the outcome and search back for differences
of exposure (look back in time).
Is thus retrospective
Contribute greatly to the research toolbox of an
epidemiologist
You already have two groups of people, one who has the
disease and one who doesn’t have the disease and then you
compare their historical exposure.
So, study groups are defined by outcome, and you look
back retrospectively to identify the exposures.
You know the outcomes, but you search for the exposures.
Example: influentialin understanding risk groups (e.g., men
who have sex with men, blood transfusion recipients) and risk
factors (e.g., multiple sex partners) for HIV/AIDS.
Advantages: (+)
Inexpensive
Can yield important findings in short time
Efficient if when incidence of outcome is low / for
diseases with a long latency period
Valuable for rare disease
Disadvantages: (-)
Multiple methodological issues that affect the validity
of the study:
o Hard to go back and remember certain things ->
Recall bias (information bias)
o Selection bias (improper selection of controls)
