Recently a study titled “Prenatal and infant exposure to ambient pesticides and autism spectrum disorder in children: population based case-control study” was published in The British Medical Journal (BMJ) with the following conclusion:
“Findings suggest that an offspring’s risk of autism spectrum disorder (ASD) increases following prenatal exposure to ambient pesticides within 2000 m of their mother’s residence during pregnancy, compared with offspring of women from the same agricultural region without such exposure. Infant exposure could further increase risks for autism spectrum disorder with comorbid intellectual disability.”
The conclusion that was drawn was much more definitive than the data supports, especially in light of the many assumptions that had to be made and significant limitations of the study. Of course, people who were already on the anti-glyphosate train are grabbing ahold of the conclusion and using it as “evidence” that glyphosate and other pesticides cause autism, but that is a completely incorrect interpretation of the study. Make sure to correct anyone who has misinterpreted the study in this way, because it absolutely does not show anything more than weak correlations.
The study was conducted by looking at data from California state mandated Pesticide Use Reporting, which was integrated into a geographic information system tool to estimate prenatal and infant exposures to pesticides as measured as pounds of pesticides applied per acre/month within 2000 meters from the maternal residence. Eleven high-use pesticides were selected for examination, including glyphosate. They defined exposures as any versus none to a specific substance during three specific developmental periods. Are you thinking what I’m thinking? That seems like a lot of variables to account for. How could they have possibly come to the conclusion that they did when there were this many degrees of freedom? One of the first responses to the study in The BMJ was by John Tucker, Ph.D., who brought up this exact point:
“In a textbook example of multiple hypothesis testing , the authors examined the effects of estimated exposure to 11 different pesticides during 3 different developmental periods against two different adverse developmental outcomes. From among the 66 evaluated endpoints, they conclude that prenatal exposure to 6 of these pesticides is associated with 10-20% increases in risk of autism disorder, and that prenatal exposure to a partially overlapping list (3 of 6) is associated with autism disorder with intellectual disability.”
It’s not necessarily a bad thing to test multiple hypotheses in a preliminary study so that one can confirm the results with further studies, but to use a preliminary study like this to make such a definite sounding conclusion is absolutely irresponsible.
Not only that, but there were some very significant assumptions that had to be made and some very large limitations as well. First, they had to assume that individuals were present at their residences around the application dates and that these applications resulted in exposures in the targeted periods only and did not get trapped in or around homes over extended periods of time. Another limitation was that they only had birth addresses available and that 9 percent to 30 percent of families could have moved during pregnancy. Finally, they lacked information about passive and active smoking. Those seem like some pretty significant assumptions and limitations to me. In addition, since this is a population-based observational study, there are many confounding factors, which Tucker went on to explain:
“The study measures the effect of these exposures by comparing outcomes among exposed children to those among randomly selected unexposed children from the same county. However, each of the counties included in the study contain large urban populations. The test group differs from the control group not only in having documented pesticide exposure, but by all the demographic, lifestyle, and ascertainment differences that distinguish urban populations from agricultural ones.”
Here are the details of the study’s results:
Risk of autism spectrum disorder was associated with prenatal exposure to glyphosate (odds ratio 1.16, 95% confidence interval 1.06 to 1.27), chlorpyrifos (1.13, 1.05 to 1.23), diazinon (1.11, 1.01 to 1.21), malathion (1.11, 1.01 to 1.22), avermectin (1.12, 1.04 to 1.22), and permethrin (1.10, 1.01 to 1.20). For autism spectrum disorder with intellectual disability, estimated odds ratios were higher (by about 30%) for prenatal exposure to glyphosate (1.33, 1.05 to 1.69), chlorpyrifos (1.27, 1.04 to 1.56), diazinon (1.41, 1.15 to 1.73), permethrin (1.46, 1.20 to 1.78), methyl bromide (1.33, 1.07 to 1.64), and myclobutanil (1.32, 1.09 to 1.60); exposure in the first year of life increased the odds for the disorder with comorbid intellectual disability by up to 50% for some pesticide substances.
The chemicals included in the study don’t have any obvious common characteristics besides the fact that they are all pesticides, but they all supposedly produce the same adverse effects with very similar relative risks, which makes the results even more suspect.
The only thing this study shows are some very weak correlations, but they could very well just be false positives due to the various methodological flaws. It does not show any casual links between any of the pesticides in the study and autism. Further research needs to be done in order to determine whether these findings can a) be replicated and b) show a casual relationship. Unfortunately, the damage has already been done. The conclusion highly overstates the findings to make it seem as though there is a link between prenatal and infant exposure to ambient pesticides and autism. I’ve already seen people using it on social media to further push their anti-glyphosate, anti-Monsanto, anti-GMO agendas. Perhaps publications such as The BMJ should more closely review the content that they publish before unnecessarily adding more fuel to the anti-science fire.
Food Science Babe is the pseudonym of an agvocate and writer who focuses specifically on the science behind our food. She has a degree in chemical engineering and has worked in the food industry for more than decade, both in the conventional and in the natural/organic sectors.