Samenvatting
Summary of Epidemiology
- Instelling
- Vrije Universiteit Amsterdam (VU)
A Summary of all Epidemiology lectures
[Meer zien]Voorbeeld 4 van de 38 pagina's
Voorbeeld 4 van de 38 pagina's
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Door: elenatyaneva • 1 jaar geleden
Voorbeeld van de inhoud
Lecture 1 – Introduction and Study DesignsCourse objectives:
- Describe and select an appropriate study design
- Understand, calculate and apply measures of occurrence and association
- Understand and assess possible bias and effect modification
- Understand and apply principles of accuracy
- Understand screening and calculate related measures
- Critically interpret and evaluate epidemiological studies
What is Epidemiology?
Epi = upon
Demos = people
Logos = study
“Study what is upon the people”
CDC defines epidemiology as: Epidemiology is 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.
Unpacking the definition:
Distribution:
Frequency: refers not only to the number of health events in a population, but also to the
relationship of that number to the size of the population.
Pattern: refers to the occurrence of health-related events by time (e.g. annual), place (e.g.
urban/rural), and person (e.g. age, sex).
Determinants: causes and other factors that influence the occurrence of disease and other
health-related events.
Health related states or events (not only disease, but also injuries, disabilities)
Application (practice, implications, recommendations)
John Snow – origins of epidemiology
1850s
Cholera outbreak in London
Miasma theory (bad air)
,The 5 Ws of epidemiology
What is the disease or condition being studied?
Who has the disease in question? (=person)
When is the disease common or rare? (=time)
Where does the disease arise? (=place)
Why did the disease or condition arise?
Uses of epidemiology
- Public health surveillance
- Field investigations
- Analytic studies
- Evaluation public health services
- Policy development
Key types of epidemiological studies
,Observational – you observe and measure a set of characteristics in a defined sample
population
Experimental – you manipulate a characteristic within a defined sample population with the
purpose of studying the effect this has
Observational studies: descriptive vs analytical epidemiology
Descriptive cover who, when and where? (clues for the why)
Analytical cover why?
, Descriptive studies 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.
Types: individual (case report, case series report, cross sectional prevalence studies) or
aggregate (ecological studies).
Examples:
Case report: Novel coronavirus in a 15-day-old neonate with clinical signs of sepsis, a case
report
Case series: COVID 2019 among pregnant Chinese women: case series data on the safety of
vaginal birth and breastfeeding.
Cross sectional study: Clinical characteristics of COVID 2019 patients with digestive
symptoms in Hubei, China: a descriptive, cross-sectional, multicenter study.
Ecological studies: correlational studies that look for associations between exposures and
outcomes in populations rather than in individuals. Looks into aggregate and not individual
data. Example: COVID-19 and BCG vaccination/Vit-D/Smoking; Death rates from coronary
artery disease correlate with per capita sales of cigarettes.
But important limitations; ecological fallacy (data at country level cannot be used to tell
stories about individuals), the inability to link exposure to outcome in individuals, the
inability to control for confounding.
The ecological fallacy consists in thinking that relationships observed for groups necessarily
hold for individuals: if countries with more Protestants tend to have higher suicide rates,
then Protestants must be more likely to commit suicide; if countries with more fat in the diet
have higher rates of breast cancer, than women who eat fatty foods must be more likely to
get breast cancer. These inferences may be correct but are only weakly supported by the
aggregate data.
Analytical studies have a very important role in medical research. For example, understand
risk factors.
Types: cross sectional, cohort, case control.
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