Health Analytics Learning Goals 2024/2025
LG 2.1.
The triad of epidemiological questions: Time, place and person
- How does the pattern of the disease vary over time in this population?
- How does the place in which the population lives have an effect on the disease?
- How do personal characteristics have an effect on the disease in the population?
Variance: A measure of the variation.
Error: Mistake in finding the ‘real’ value, can be by measurement mistakes or processing.
Errors often apply to all groups close to equally.
Bias: Unevenly favoring one group over another. Bias also creates errors.
Confounder: A hidden association that causes the association that you observe.
Errors and biases are most often found in population selection, information collection, or
confounders.
LG 2.2.
Disease patterns: The natural history of how a disease progresses over time in a
population. The natural history looks at a population level.
Disease frequency: How does the frequency of the disease in the population progress over
time in a population.
Epidemiological association: An association between risk agents/exposure variables and
the risk of having a disease.
Variation: How the same disease can have different effects on people of the same
population, the spectrum of disease so to speak, which looks at an individual level.
People can alter in:
- genes
- behavior
- lifestyle
These factors can affect variation and disease patterns of a disease.
The environment can alter in:
- location
- quality of life
- healthcare
- major events
- culture shifts
- age shifts
These factors can affect disease frequency, but also disease patterns and variation.
, LG 2.4.
Epidemiological variables: Are important in depicting, analyzing and the interpreting of
differences of disease patterns within and between populations.
A good epidemiological variable should:
● Have an impact on health in individuals and the population.
● Be measurably accurate.
● Differentiate population in their experience of disease or health.
● Differentiate populations by underlying characteristics relevant to health.
● Help generate testable hypotheses
LG 2.5
Why can a different disease pattern be an artifact rather than a real difference?:
Causes of a disease pattern can either be attributed to the triad of epidemiological
questions. Personal factors, Environmental factors or Time.
Artifacts in disease can be found in a multitude of ways, just to name a few:
● Random chance: numbers fluctuate over time
● Errors of observation: biased way of classifying disease
● Changes in size or structure of a population: immigrants, baby-boom
● Do people go to the doctor?: A disease might not be worthwhile going
● Do people get diagnosed correctly?: Mistake in diagnostics
● Changes in clinical approach: A new instrument being used for diagnosing
● Changes in data collection: If more institutions start counting, frequency goes up
All of these artifacts and errors can be classified in either of three groups:
1. Being a measurement error in the population
2. Being a diagnostic error
3. Being a data-processing and presenting error
LG 3.1
Triangle of causation model:
The idea in an epidemiological cause-effect model is that there is always an interplay
between host, agent and environment.
Host: Age, Sex, Lifestyle, Genes
Agent: Virulence of organism, antibiotic resistance, serotype of organism
Environment: Weather, Workplace hygiene, Food contamination
LG 2.1.
The triad of epidemiological questions: Time, place and person
- How does the pattern of the disease vary over time in this population?
- How does the place in which the population lives have an effect on the disease?
- How do personal characteristics have an effect on the disease in the population?
Variance: A measure of the variation.
Error: Mistake in finding the ‘real’ value, can be by measurement mistakes or processing.
Errors often apply to all groups close to equally.
Bias: Unevenly favoring one group over another. Bias also creates errors.
Confounder: A hidden association that causes the association that you observe.
Errors and biases are most often found in population selection, information collection, or
confounders.
LG 2.2.
Disease patterns: The natural history of how a disease progresses over time in a
population. The natural history looks at a population level.
Disease frequency: How does the frequency of the disease in the population progress over
time in a population.
Epidemiological association: An association between risk agents/exposure variables and
the risk of having a disease.
Variation: How the same disease can have different effects on people of the same
population, the spectrum of disease so to speak, which looks at an individual level.
People can alter in:
- genes
- behavior
- lifestyle
These factors can affect variation and disease patterns of a disease.
The environment can alter in:
- location
- quality of life
- healthcare
- major events
- culture shifts
- age shifts
These factors can affect disease frequency, but also disease patterns and variation.
, LG 2.4.
Epidemiological variables: Are important in depicting, analyzing and the interpreting of
differences of disease patterns within and between populations.
A good epidemiological variable should:
● Have an impact on health in individuals and the population.
● Be measurably accurate.
● Differentiate population in their experience of disease or health.
● Differentiate populations by underlying characteristics relevant to health.
● Help generate testable hypotheses
LG 2.5
Why can a different disease pattern be an artifact rather than a real difference?:
Causes of a disease pattern can either be attributed to the triad of epidemiological
questions. Personal factors, Environmental factors or Time.
Artifacts in disease can be found in a multitude of ways, just to name a few:
● Random chance: numbers fluctuate over time
● Errors of observation: biased way of classifying disease
● Changes in size or structure of a population: immigrants, baby-boom
● Do people go to the doctor?: A disease might not be worthwhile going
● Do people get diagnosed correctly?: Mistake in diagnostics
● Changes in clinical approach: A new instrument being used for diagnosing
● Changes in data collection: If more institutions start counting, frequency goes up
All of these artifacts and errors can be classified in either of three groups:
1. Being a measurement error in the population
2. Being a diagnostic error
3. Being a data-processing and presenting error
LG 3.1
Triangle of causation model:
The idea in an epidemiological cause-effect model is that there is always an interplay
between host, agent and environment.
Host: Age, Sex, Lifestyle, Genes
Agent: Virulence of organism, antibiotic resistance, serotype of organism
Environment: Weather, Workplace hygiene, Food contamination
