Epidemiology Overview and Analytical Methods

Analytical & Experimental Epidemiology

• Epidemiology is the study of the distribution and determinants of health and disease in populations.
• Epidemiological studies are categorized as descriptive and analytical.
• Descriptive epidemiology describes disease patterns by time, place, and person.
• Analytical epidemiology measures associations between exposures and outcomes and studies causal relationships.
• Experimental epidemiology is a subset of analytical epidemiology, manipulating exposure or intervention in controlled settings.

Analytical Epidemiology

• Analytical epidemiology uses observational studies comparing groups with varying exposure levels.
• Its goal is to identify associations between exposures and outcomes and to evaluate causal relationships.
• Common types of analytical studies include cohort studies, case-control studies, and cross-sectional studies.

Cohort Studies

• Cohort studies follow a group of individuals over time, comparing outcomes with unexposed individuals.
• They can assess incidence and relative risk of disease, potentially prospectively or retrospectively.

Case-Control Studies

• Case-control studies compare individuals with a disease to those without, assessing exposure history.
• They are primarily retrospective and measure the odds ratio of exposure.

Cross-Sectional Studies

• Cross-sectional studies measure the prevalence of exposure and outcome in a population at a single point in time.
• They are also known as prevalence studies and assess the prevalence ratio of exposure.

Experimental Epidemiology

• Experimental epidemiology manipulates exposure or intervention in a controlled setting.
• The goal is to investigate causal relationships and assess intervention effectiveness/safety.
• A common example is a randomized controlled trial (RCT).
• Quasi-experimental studies are similar to RCTs but do not use randomization or control groups.

Bias in Epidemiological Studies

• Bias is a systematic error leading to inaccurate estimates of exposure-outcome associations.
• Bias can stem from various sources such as selecting participants, measuring exposure/outcome, data analysis, or result interpretation.
• Bias can affect study validity and reliability, potentially leading to false conclusions.

Selection Bias

• Selection bias occurs when researchers' choice of participants isn't random, leading to a sample differing from the whole population.
• Self-selection bias occurs when participants choose to join or leave a study based on personal preferences or characteristics.

Methods to Minimize Selection Bias

• Randomization ensures group similarity in exposure.
• Using appropriate control groups matched to cases enhances comparability.
• Representative sampling ensures the sample mirrors the population.

Information Bias

• Information bias arises from inaccuracies in data collection related to participant condition or researcher perception.
• Recall bias occurs when participants remember events inconsistently based on their condition.
• Observer bias occurs when researchers record data differently based on participant conditions or traits.
• Measurement bias results from inaccurate or inconsistent measurement tools across groups.

Methods to Minimize Information Bias

• Standardized methods of data collection ensure consistent techniques among participants.
• Data collectors' training and calibration ensure uniformity in data gathering.
• Validated measurement tools help ensure data quality.

Confounding Bias

• Confounding bias occurs when a factor, correlated with both the exposure and the outcome, influences the observed association.
• Age and socioeconomic status are examples of confounders.

Methods to Minimize Confounding Bias

• Study design incorporating strategies such as matching or stratification.
• Statistical techniques to adjust for confounder effects.
• Randomization in experimental studies helps prevent confounding.

Publication Bias

• Publication bias favours studies with significant results being published, while studies with negative results remain unpublished.
• Publication bias creates skew in literature representation, and it affects literature distortion, potentially leading to over/underestimations of the true effects.

Methods to Address Publication Bias

• Comprehensive literature searches, regardless of publication status, help identify unpublished data.
• Inclusion of unpublished data enhances representation of the full evidence.
• Registering study protocols promotes transparency and reduces selective reporting.

Recall Bias in Cohort Studies

• In cohort studies, recall bias occurs when participants have difficulty or inconsistently remember exposures related to the outcome that appears after a long time.

Recall Bias in Case-Control Studies

• In case-control studies, participants may recall prior exposures to risk factors differently.

Methods to Minimize Recall Bias

• Standardized questionnaires with clear questions and memory aids can minimize inconsistency.
• Objective measurements using biological or chemical markers help reduce variability.
• Collecting data as close as possible to events decreases memory inaccuracies.

Conclusion

• Analytical and experimental methodologies identify and quantify population exposure-outcome associations.
• Minimizing bias is critical for reliable and valid epidemiological conclusions.
• Numerous methods exist to mitigate bias at various stages.