Cross-sectional Studies

Question 1

Define cross-sectional studies and their role in measuring disease prevalence:

Answer 1

A cross-sectional study measures exposure and disease status at a single point in time within a population. It is also called a prevalence study

Role in measuring disease prevalence :

• provides a snapshot of disease burden in a population
• useful for public health planning and resource allocation
• Estimates of population prevalence
• cross sectional associations between variables

Question 2

Describe the steps in conducting a cross-sectional study:

Answer 2

Define the population ensuring a representative sample is taken

Collect data on exposure and outcome - measure both at the same time

Analyse associations - compare disease prevalence between exposed and unexposed groups using measures like prevalence ratios

Interpret findings - Determine if exposure and outcome appear related, while acknowledging limitations

Question 3

What are ecological studies and discuss their advantages and disadvantages ?

Answer 3

An ecological study examines associations between exposure and disease at the population level rather than individual level

Uses aggregate data e.g air pollution + asthma rates across the city

Advantages:

• inexpensive and quick to undertake
• may be the only appropriate design for some research questions, e.g.,
  when within-population variation insufficient
• makes use of routine/existing data

Disadvantages:

• ecological fallacy -> group level associations may not apply to individuals
• can’t establish causation
• reliant on constraints of existing data

Question 4

Define the key strengths and weaknesses of cross-sectional studies:

Answer 4

Strengths:

• quick and cost effective
• measures prevalence accurately
• useful for public health monitoring
• can analyse multiple exposures and outcomes

Weaknesses:

• can’t establish causality
• prone to reverse causation bias
• not useful for studying rare diseases
• Subject to selection bias

Question 5

What are the key biases of cross-sectional studies ?

Answer 5

Reverse causation - unclear if exposure caused disease

Selection bias - some groups may be underrepresented in the sample

Survivorship bias - those with severe disease may have died, skewing prevalence

Question 6

Describe analytical epidemiology and its goals:

Answer 6

Identifies associations between exposures and health outcomes to understand disease causation

• quantifies exposure-outcome relationship
• determines risk factors and their impact on health outcomes
• helps guide public health interventions and policies
• uses statistical analysis to assess the strength of associations

Question 7

What are the key study designs used in analytical epidemiology ?

Answer 7

Observational studies:

Cross-sectional studies - measure prevalence at a single time point

Case-control studies - compare past exposures between disease cases and healthy controls

Cohort studies - follow exposed and unexposed individuals over time to measure incidence

Experimental Studies (intervention applied):

Randomised Controlled Trials - gold standard for testing causation

Question 8

How do you determine the weight of evidence and assessing causality ?

Answer 8

To determine whether an exposure truly causes an outcome, epidemiologists use causal inference frameworks

Bradford Hill Criteria for Causation:

Strength of Association -stronger associations suggest causality

Consistency – findings must be replicated across different studies

Specificity – the exposure leads to a specific disease

Temporality – exposure must precede the disease

Biological Gradient (Dose-Response Relationship) – Higher exposure levels should increase disease risk

Plausibility – findings must be biologically possible

Coherence – consistency with existing knowledge

Experiment – Randomised trials strengthen causal evidence

Analogy – Similar exposures cause similar outcomes