Intro to Epidemiology Flashcards
Epidemiology
The study of the distribution and determinants of health-related states or events (including disease), and the application of this study to the control of diseases and other health problems (WHO, 2024).
The study of the distribution and determinants of disease in occurrence in human populations.
- outcome of interest
-exposure of interest
-biostatistical methods
Study Base
-Reference population
-Source of the study population
-Population giving rise to the cases
-Defined before cases appear by a geographical area or some other entity like a cohort study
Person time
-Estimate of the actual time-at-risk in years, months, or days that all persons contributed to a study
-Time will differ by people depending on endpoint: disease, death, end of follow-up
Exposure
An exposure, risk factor, or other characteristic being observed or measured that is hypothesised to influence an event or manifestation.
Outcome
-Disease
-Disease progression
-Death
-Comorbidity
-Questionnaire data
-Biological endpoints – expression levels
- The result, or effect, of an action, situation, or event.
- Typical outcomes investigated in medical research include:
1.Mortality (how long did the patient survive after the onset of disease.)*
2.Progression (did the disease progress, and how long after onset did this progression occur).*
3.Morbidity (did the disease result in the occurrence of further illness or disease).*
Prevalence
Proportion of a population found to have a condition at a specific point in time.
Incidence
There were about 357,000 new cases of cancer in the UK on average each year.
Risk
Probability of disease developing in an individual in a specified time interval.
Measurement of effect
Relative: exposed versus unexposed
Absolute: i.e. incidence, prevalence
Thing to consider when designing a research study
- research question (what is the research question?)
-study design (how will you undertake the study?)
-subjects (who are the subjects? how will they be selected?)
-data (what data needs to be collected? what kind of measures?)
-analysis: what analyses do you need to undertake?
-interpretation: what do the results mean? how valid are they?
Quantitative study
:Uses numbers; exposures and outcomes are measurable
-How many?
-Who is at risk?
-What causes this disease?
-Is there an improvement?
Qualitative
Uses words; stories, experiences, observations
-Why do people do ……?
-How do they feel about…?
-What is their experience of…?
Observational vs Experimental
Does the researcher control the exposure?
yes: experimental
no: observational
Do you want to determine if something is “causal”?
yes: experimental
no: observational
Types of observational studies
Descriptive: used to formulate a certain hypothesis.
Examples: case-studies; cross-sectional studies, ecological studies
Analytical: used to test hypotheses:
Examples: case-control, cohort
How do you choose between observational methods?
Depends on:
- how rare the outcome is
- what data exists for the population of interest
- whether the temporal relationship is important
i.e. Exposure –> Outcome (cause –> effect)
- how quickly you want the answer
money / resources
Ecological studies
Studies that investigate risk factors of health outcomes in which the unit of analysis is at the group level rather than the individual.
Group measures (exposure and or outcome) can include:
- summary measures of a group (mean, average rate)
- environmental factors (air pollution, hours of sun-light, fast-food shops)
i.e. something that is not measured at the individual level
Examples:
Time trends, geographic comparisons
What are the advantages and disadvantages of ecological studies?
Advantages:
- Easy to do
- No individual data necessary
- Good to generate ideas about potential associations
Disadvantages:
- No information on the individual level
- Not able to account for other factors that might explain the association –> ecological fallacy
Ecological fallacy
The ecological fallacy occurs where an analysis of group data is used to draw conclusions about the individual.
Example:
The average salary is higher in countries that sell more reading glasses.
Therefore if you wear reading glasses you are likely to have a higher salary.
Likely to be due to other factors that are not taken into account (confounders)
Cross-sectional study design process and example
Select a sample (representing the population of interest)
Measure exposure and outcome variables at the same time
Determine prevalence
Data is collected at a single point in time from a population or a representative sample. It provides a snapshot of the prevalence of certain outcomes and exposures at a specific moment.
Participants are selected based on certain characteristics (e.g., age, gender, location) and data is collected through surveys, interviews, or examinations at one specific time point.
What is the prevalence of smoking among college students?
Study Design: Researchers survey 500 college students from various universities about their smoking habits during a single time period. They collect data on whether the students smoke, how many cigarettes they smoke per day, and other relevant factors.
Outcome: The researchers analyse the survey responses to determine the proportion of college students who smoke and explore any associations between smoking and other variables, such as gender, age, or academic performance. However, since this is a cross-sectional study, they cannot determine whether smoking causes certain outcomes or vice versa; it only provides a snapshot of smoking prevalence among college students at that particular time.
Cross-sectional studies strengths and weaknesses
Strengths
- fast and inexpensive
- immediate answers – no follow-up time
- no loss to follow-up (but can have non-responders)
Weaknesses
- can’t determine temporal relationship
- not good for rare exposures or outcomes
- bias can be a problem - measurement bias, survivor bias
Cohort study design process and example
- Start with the POPULATION of interest
- Identify or assemble a cohort
- Measure risk factor(s) and potential confounders
- Measure the outcome over the follow-up period
A group of individuals, known as a cohort, is followed over a period of time to observe and analyse outcomes related to exposure to certain factors.
Participants are initially identified as either exposed or unexposed to a particular factor of interest. They are then followed prospectively to observe the development of outcomes.
Does regular exercise reduce the risk of heart disease?
Study Design: Researchers recruit a group of 1000 middle-aged adults who are free of heart disease. They divide them into two cohorts: one that engages in regular exercise (at least 30 minutes of moderate-intensity exercise five times a week) and one that does not. The participants are followed for 10 years, during which their incidence of heart disease is monitored.
Outcome: Researchers compare the incidence of heart disease between the two cohorts to determine if regular exercise is associated with a lower risk of heart disease.
Cohort study design can be:
Prospective: start with assembling a cohort, measure risk factors then follow over time to measure outcomes –> where data is collected forward in time.
OR
Retrospective (historical): identify a suitable cohort (from the past), collect risk factor data measured in the past, collect subsequent outcome data –> where data is collected from past records.
Cohort studies strengths (in general, then for each)
Cohort studies (in general):
- Can establish sequence of events
- Can assess risk of multiple outcomes at the same time
- Can estimate incidence (how many new events within a certain time)
- Able to directly calculate absolute and relative risk
Prospective cohort studies:
- Can control who is in the cohort
- Lower risk of bias (exposures measured before outcomes)
Retrospective cohort studies:
- More efficient: less time, less costly
Cohort studies weaknesses (in general, then for each)
Cohort studies (in general):
- not a controlled experiment so can’t claim ‘causation’
- difficult to control for all other confounding factors
- expensive – often require large sample
not good choice for rare outcomes
Prospective:
- not timely, long follow-up
- potential loss to follow-up (can lead to bias)
Retrospective:
- knowing the outcome might lead to bias
- limited to data already collected
- little control over who is in the cohort