Confounding, Mediation, and Interaction

Introduction

Understanding the connections between exposures and results in epidemiological studies requires accounting for confounding, mediation, and effect modification. An external variable causes confounding when it acts on both the exposure and the result. “mediation” describes the variables’ role in the exposure-outcome causal pathway. Effect modification, however, occurs when there is a conditional relationship between exposure and result, where the strength of the relationship depends on the amounts of some other variable. This debate delves into these ideas and how they affect the analysis of research findings.

Confounding

When another variable is connected to both the exposure and the outcome, it can cloud the results of any analysis to determine whether there is a link between the two. A confounding variable is something like this: Confounding may cause exposure-outcome misinterpretation. Consider a caffeine-CVD study. Age complicates the coffee-cardiovascular disease relationship. Let us pretend the research ignores the effect of age (Szklo & Nieto, 2019). In that instance, the age-related confounding effect may lead people to conclude incorrectly that coffee drinking causes heart disease.

Mediation

Mediation occurs when one or more intervening variables moderate an exposure-outcome relationship. These mediating factors play a role in the chain of events that begins with exposure and ends with a result. A mediation analysis must first identify the mediator’s role in the relationship to determine how much a mediator moderates an exposure’s impact on a result. For instance, an investigation on the relationship between exercise and weight loss (Szklo & Nieto, 2019). Physical activity can mediate between the two components in this situation since it influences nutritional intake. Through mediation analysis, it is possible to learn more about whether the effect of exercise on weight loss is direct or whether alterations in eating habits mediate it.

Effect Modification

When the correlation between an exposure and its subsequent effect varies with the values of some other factor, this is known as an interaction. An effect modifier designates the variable that serves this function. Variation in the impact of exposure on the outcome across different levels of the effect modifier is a hallmark of effect modification. Take a research project into lowering blood pressure with a new drug as an illustration. If the medication’s impact on blood pressure varies with age, age may moderate the effect (Szklo & Nieto, 2019). This indicates that the drug’s impact on blood pressure varies with age, a phenomenon known as effect modification.

Criteria of a Confounder

To be considered a confounder, a variable must meet three criteria:

It needs to be connected to the potential risk.

It has to be connected to the relevant outcome.

It cannot be part of the chain of events that leads from exposure to the result.

Meaning of Additive and Multiplicative Interactions

Additive Interaction

When two or more exposures influence an outcome more significantly than the sum of their individual effects, this is known as an additive interaction. A second exposure might change how the first one works, a phenomenon known as additive interaction. The combined effect of the exposures is more or less significant than what would be predicted from the sum of their individual effects. The interaction contrast ratio (ICR) and the attributable proportion (AP) are common ways to evaluate this interaction (Szklo & Nieto, 2019).

Multiplicative Interaction

The existence of additional exposure on a multiplicative scale changes the effect of one exposure on a result, which is known as multiplicative interaction. When two exposures interact, their combined effect is multiplied by the first. This implies that the sum of the exposures is not merely additive but a result of the individual exposures. The statistical interaction term in regression models can be used to analyze multiplicative interaction (Szklo & Nieto, 2019). Alternatively, the ratio of the total effect to the effect expected under independence can be used.

Conclusion

Accurately interpreting study results requires awareness of and attention to potential sources of bias, such as mediation and effect modification. Associations may be misinterpreted if confounding factors are not discovered and adjusted for. Understanding how exposure affects an outcome can be determined through mediation analysis. The concept of impact modification highlights the significance of evaluating the variety of effects across different subgroups (Szklo & Nieto, 2019). By rigorously evaluating and accounting for these characteristics, epidemiological studies can contribute to evidence-based decision-making in public health by providing more accurate and nuanced insights into the links between exposures and outcomes.

References

Szklo, M., & Nieto, F. J. (2019). Defining and assessing heterogeneity of effects: Interaction. In

Epidemiology: Beyond the basics (4th ed., pp. 209–256). Jones & Bartlett Learning.

Szklo, M., & Nieto, F. J. (2019). Identifying noncausal associations: Confounding. In

Epidemiology: Beyond the basics (4th ed., pp. 175–208). Jones & Bartlett Learning.

Szklo, M., & Nieto, F. J. (2019). Epidemiologic issues in the interface with public health policy.

In Epidemiology: Beyond the basics (4th ed., pp. 437–482). Jones & Bartlett Learning.