*Epidemiology studies risk factors, and determines Relative Risk (RR). A Relative risk of 1.0 indicates no effect. A RR of 1.25 means the risk is increased by 25%; a RR of .75 means the risk is decreased by 25%, and indicates a protective effect.
Epidemology deals with probabilities, and is an imprecise science. The Confidence Interval (CI) can be thought of as the margin of error – the real RR could be anywhere within the CI. For example, in the WHO Boffetta study (# 98 on this chart) spouses were assigned an RR of 1.16, with a CI of .93-1.44. That means the real RR could be anywhere between .93 (a 7% decrease in risk) or 1.44, (a 44% increase). It could even be 1.0 – no effect at all. When the CI straddles 1.0, as it does in this case, the RR is not statistically significant. Note that in nearly all SHS studies the RR is not statistically significant.
Studies of behavior are difficult because people's habits and lifestyles vary so greatly. These variations are called confounders, and must be considered when analyzing the numbers. In studies of SHS, confounders include age, gender, allergies, nationality, race, medications, compliance with medications, education, gas heating and cooking, gender, socioeconomic status, exposure to other chemicals, occupation, use of alcohol, use of marijuana, consumption of saturated fat and other dietary considerations, family history of cancer and domestic radon exposure, to name a few.
Because it is easy to overlook an important confounder, the rule of thumb is that an RR of less than 2.0 is suspect, even if it is statistically significant, and an RR of 3.0 or more is preferred. Marcia Angell, the former editor of the prestigious New England Journal of Medicine says, “As a general rule of thumb we are looking for a relative risk of 3 or more before accepting a paper for publication." Robert Temple of the Food and Drug Administration said, “My basic rule is if the relative risk isn’t at least 3 or 4, forget it.” Even further, The National Cancer Institute explains, “Relative risks of less than 2 are considered small and are usually difficult to interpret. Such increases may be due to chance, statistical bias, or the effect of confounding factors that are sometimes not evident.”
For more information on how to interpret these numbers, visit Epidemology 101 and Epidemology 102 at The Facts.