Epidemiology and Causation Quiz

Epidemiology and Causation

• Causation in epidemiology is the relationship between a cause and its effect on a population.
• Association is a relationship between two variables, but doesn't mean one causes the other.
• Causal inference uses statistical methods and study designs to determine if observed associations are causal.
• Randomized controlled trials (RCTs) are considered the gold standard for establishing causation by randomly assigning participants to exposure and control groups.
• Cohort studies follow a group of people over time to assess how exposure affects disease incidence, they can be prospective or retrospective.
• Case-control studies compare people with disease (cases) to those without (controls) to identify prior exposures, useful for studying rare diseases.
• Cross-sectional studies assess exposure and disease status at one point in time, they identify associations but cannot establish causation.
• Ecological studies analyze data at the population level, identifying trends but limited by ecological fallacy.
• Causal diagrams, like directed acyclic graphs (DAGs), help visualize and understand potential causal relationships and confounding factors.
• Bradford Hill's criteria are used for evaluating whether an observed association is causal.
• Temporality is critical: the exposure must occur before the outcome for a causal link.
• Experimentation (RCTs) provides the strongest evidence for causation as they can control confounding variables.
• Biological plausibility makes a causal relationship more credible if a known biological mechanism exists.

Association vs. Causation

• Association is a statistical relationship between two variables, change in one is related to change in another.
• Causation means one event directly affects another, a change in the exposure leads to a change in the outcome.
• Directional causality is clear, the cause precedes the effect in time.
• Confounding factors can influence associations and need to be controlled for, often in study design or by statistical adjustments.

Public Health Interventions

• Public health interventions are based on identifying associations and establishing causation.
• Observational studies identify associations between exposures and health outcomes.
• Bradford Hill's criteria, RCTs, and causal inference methods are used to establish causation.
• Interventions need to target true causes, not just correlated factors.
• Ethical considerations, resource availability, and cultural contexts are important for intervention success.

Examples of Public Health Interventions

• Smoking and lung cancer: Association led to interventions like anti-smoking campaigns, warning labels, and bans, decreasing smoking rates and health issues.
• Polio vaccination: Strong evidence of causation led to global vaccination campaigns, greatly reducing polio cases and nearly eradicating the disease.
• Lead exposure and cognitive development: Studies established a causal relationship and resulted in policies to reduce lead exposure.
• HIV/AIDS prevention: Association between behaviors and transmission led to interventions promoting condom use, needle exchange programs, and antiretroviral treatments.
• Diet and cardiovascular disease: Research identified associations and led to public health campaigns promoting healthier eating habits, improving heart health outcomes.

Establishing a Case for a Disease

• Case definition consistently identifies cases with clinical criteria and laboratory criteria.
• Case identification involves finding and counting cases using health records, surveys, and reports.
• Diagnosis verification confirms that the identified cases are truly instances of the disease in question.
• Descriptive epidemiology analyzes data on time, place, and person characteristics of the cases.
• Hypotheses are generated and tested about the source and mode of transmission through analytical studies like case-control studies.
• Control measures are implemented based on findings to prevent further spread.

Diagnosis of Disease

• Patient history, physical examination, differential diagnosis, diagnostic testing, clinical reasoning, and provisional/definitive diagnosis are all steps in disease diagnosis.
• Treatment and follow-up are important after diagnosis and monitor the patient's response to treatment.
• This process is iterative, with steps revisited based on new information and the patient's condition.