Relative Excess Risk due to Interaction (RERI)

Relative Excess Risk due to Interaction (RERI) is an epidemiology measure of whether two exposures together create more risk than you would expect from adding their separate effects. It is used to study effect modification in population health data.

What is Relative Excess Risk due to Interaction (RERI)?

Relative Excess Risk due to Interaction, or RERI, is a way to check whether two exposures combine to produce more risk than you would predict from their separate effects in Intro to Epidemiology. It is one of the main ways epidemiologists describe interaction on an additive scale.

The basic idea is simple: if exposure A raises risk and exposure B raises risk, do they just add up, or do they amplify each other? RERI answers that by comparing the observed combined relative risk with the risks for each exposure alone. A common formula is RERI = RRAB - RRA - RRB + 1, where RRAB is the relative risk for people exposed to both factors, and RRA and RRB are the risks for each factor alone.

If RERI is greater than 0, the joint effect is more than additive. That suggests positive interaction or synergy, meaning the two exposures together create extra excess risk. If RERI is less than 0, the joint effect is less than expected from simple addition, which can mean the exposures weaken each other’s effect or that the combined pattern is not synergistic.

RERI is tied to effect modification, which asks whether the size of an exposure-outcome relationship changes across levels of another variable. A common mistake is to treat interaction as just “two things happening at once.” In epidemiology, interaction has a specific meaning, and RERI gives you a numeric way to describe it on the risk scale.

This comes up when you compare four groups in a study, such as no exposure, A only, B only, and both A and B. That setup makes it easier to see whether the combined risk is just the sum of the parts or something stronger. RERI is especially useful when you want to identify subgroups facing a higher-than-expected burden of disease.

Why Relative Excess Risk due to Interaction (RERI) matters in Intro to Epidemiology

RERI matters because Intro to Epidemiology is not just about whether an exposure is linked to an outcome. It is also about whether exposures work differently together, which changes how you interpret risk patterns in real populations.

If a study finds a positive RERI, that tells you the combined exposure group may deserve special attention in prevention planning. For example, if smoking and another exposure together create more excess risk than expected, public health messaging and screening can focus on the paired risk rather than treating each factor separately.

RERI also helps you separate a true interaction from a simple main effect. Two exposures can both be harmful without interacting on the additive scale, so the number keeps you from overreading the data. In class problems, that means you are often comparing group risks, identifying whether the joint category stands out, and deciding whether the pattern looks synergistic or not.

It connects directly to effect modification, attributable risk, and other ways epidemiologists talk about how disease burden is distributed across groups. Once you can read RERI, you can explain why certain populations experience more cases than you would expect from either exposure alone.

How Relative Excess Risk due to Interaction (RERI) connects across the course

Effect Modification

RERI is one way to measure effect modification. If the effect of one exposure changes depending on another exposure, RERI helps show whether that change creates extra risk beyond simple addition. In class, you often use it after noticing that one subgroup does not follow the same risk pattern as another.

Interaction

Interaction is the broader idea, and RERI is a numeric measure of it on the additive scale. The term tells you whether two exposures work together in a way that is more or less than expected. If you see interaction in a data table, RERI gives you a clearer way to summarize it.

Attributable Risk

Attributable risk looks at excess risk caused by an exposure, and RERI extends that thinking to two exposures together. Instead of asking only how much risk one factor adds, you ask whether the joint exposure adds even more than the parts alone. That makes the comparison useful for prevention and burden estimates.

synergy index (s)

The synergy index is another way to describe whether two exposures act together more strongly than expected. RERI and the synergy index are related, but they do not summarize interaction in exactly the same way. If your instructor gives both, RERI usually focuses on excess risk, while the synergy index compares the strength of joint effects.

Is Relative Excess Risk due to Interaction (RERI) on the Intro to Epidemiology exam?

A quiz or problem set may give you four relative risks for the no-exposure group, each single-exposure group, and the double-exposure group, then ask whether there is positive interaction. Your job is to plug the numbers into the RERI formula and interpret the sign, not just calculate the value. If RERI is above 0, say the combined effect is greater than additive. If it is below 0, explain that the joint effect is less than expected from adding the separate effects. In short-answer or discussion questions, you may also need to connect RERI to effect modification and name which population subgroup is carrying the extra risk.

Relative Excess Risk due to Interaction (RERI) vs Attributable Risk

Attributable risk measures excess risk from one exposure compared with a nonexposed group. RERI looks at the extra risk created when two exposures occur together beyond what you would expect from their separate effects. The overlap makes them easy to mix up, but RERI is specifically about interaction.

Key things to remember about Relative Excess Risk due to Interaction (RERI)

• RERI tells you whether two exposures create more excess risk together than you would expect from adding their separate effects.

• A RERI greater than 0 suggests positive interaction, which is also called synergy on the additive scale.

• A RERI less than 0 suggests the combined effect is smaller than expected from simple addition.

• You usually interpret RERI by comparing four groups: no exposure, exposure A only, exposure B only, and both exposures.

• In Intro to Epidemiology, RERI is most useful when you are analyzing effect modification and identifying high-risk subgroups.

Frequently asked questions about Relative Excess Risk due to Interaction (RERI)

What is Relative Excess Risk due to Interaction (RERI) in Intro to Epidemiology?

RERI is a measure of whether two exposures together cause more risk than you would expect from their individual effects. It is used to describe interaction on an additive scale, which makes it useful for effect modification questions in epidemiology. A positive value means the combined risk is bigger than expected.

How do you calculate RERI?

A common formula is RERI = RRAB - RRA - RRB + 1. RRAB is the relative risk for people exposed to both factors, while RRA and RRB are the relative risks for each factor alone. After you calculate it, the sign tells you whether the joint effect is more or less than additive.

What does a positive RERI mean?

A positive RERI means the two exposures together produce more excess risk than expected if you just added their separate risks. In epidemiology, that points to positive interaction or synergy. This can signal a subgroup that may need targeted prevention or closer monitoring.

Is RERI the same as confounding?

No. Confounding is a bias problem that can distort the exposure-outcome relationship, while RERI is a way to measure interaction. If you are using RERI, you are asking whether the combined effect of two exposures differs from what addition would predict. Confounding and interaction are separate ideas.