We are pleased to share the published results from the MIREC studies. We thank the MIREC participants for making this research possible. Before you look at these papers, we thought it would be helpful to provide some background on research studies and how to interpret them. We welcome your comments on how these results are presented.
MIREC researchers and their students, as well as other scientists who have analysed the data in the MIREC Biobank, have published their results in the following papers. These papers have been peer reviewed by other scientists who are experts in the area being studied.
Biomonitoring is the measurement of chemicals (or their metabolites – see definition below) in a person’s body fluids or tissues, such as blood or urine. This measurement gives us an estimate of the amount of the chemical that actually gets into people from all sources (for example, from air, soil, water, dust, and food) combined.
Because of new technology, laboratories now have highly sensitive equipment and tests capable of detecting tiny amounts of many chemicals. For example, chemical levels in urine or blood are often reported in micrograms per liter (µg/L) or parts per billion (ppb).
Whether someone will have an adverse health effect depends on many factors including:
Biomonitoring data for a population, such as the participants in the MIREC Study, can tell us which chemicals are in their bodies and at what level (concentration). These data can then be used to identify which groups of women have levels higher than other groups (e.g., by age, race, household income) and to look at associations between the levels of chemicals and health conditions.
When the body metabolizes a chemical, it breaks it down or transforms it into another substance called a metabolite, which may make it less toxic or more easily eliminated from the body.
In general, one study by itself is not able to say at what levels a chemical causes health effects. Instead, studies often report a statistical association between levels of the chemical and the health of people in the study.
A single research study cannot establish a clear cause and effect relationship. The five main criteria for establishing a likely causal relationship are:
Several studies in different populations observing similar associations and estimates of risk are required before the weight of the evidence would support a causal relationship between levels of the chemical and the health outcome observed.
For some chemicals, such as those from smoking cigarettes, many research studies have been done which together give us a good understanding of the health risks associated with smoking. However, for most of the environmental chemicals measured in MIREC, more research is needed to determine whether exposure at the levels reported is a reason to be concerned.
One of the methods for measuring potential associations between exposure to a chemical and an adverse health effect is to calculate the relative risk. The risk of an adverse health effect after being exposed to a chemical is only a probability (or odds) for a group of people and cannot be interpreted for an individual. Studies often report a relative risk (RR), which is the probability of an adverse health effect in an exposed group divided by the probability of this effect in a similar non-exposed (control) group. A relative risk of 1.0 indicates that the exposure had no effect – the risk of the adverse effect was the same in both groups (50:50 chance of getting the health effect in both groups). A RR of 1.8 means that the risk of an adverse health effect is almost double in the group exposed to the chemical compared to the group not exposed. The same way, a RR of 3.5 would mean that the exposed group is 3 and a half times more likely to have an adverse health effect than the non-exposed group.
Researchers also calculate a 95% confidence interval (CI) for each relative risk (RR) to indicate whether the result is statistically significant or likely occurred by chance. For example, a RR = 1.8; 95% CI 1.2 – 3.5 is statistically significant because the confidence interval does not include 1.0. If the confidence interval (CI) included 1.0, such as a RR = 1.8 with a 95% CI ranging from 0.79 – 4.3, then this result is said to be “not statistically significant” and suggests that there was no association between the exposure and the adverse health effect.