“Nothing is so frightening as what’s behind the closed door… The audience holds its breath along with the protagonist as she/he (more often she) approaches that door. The protagonist throws it open, and there is a ten-foot-tall bug. The audience screams, but this particular scream has an oddly relieved sound to it. ‘A bug ten feet tall is pretty horrible’, the audience thinks, ‘but I can deal with a ten-foot-tall bug. I was afraid it might be a hundred feet tall’.”
– Stephen King, Danse Macabre
Right now, we are in the COVID-19 corridor, holding our breath as we approach Stephen King’s closed door. Every day brings news of more cases, more deaths, more restrictions, and more fear.
Using science as their guide, legislators from both parties came together to improve air quality around the country. A major breakthrough came with the Clean Air Act, signed into law by President Nixon in 1970 and strengthened under President Bush in 1990.
In February, just as coronavirus fears were escalating, the Trump Administration released its 2021 budget. It calls for cutting U.S. Environmental Protection Agency funding by 26 percent, a reduction that will severely hamper the agency’s ability to conduct cutting-edge research and implement programs that will save lives and improve the health of millions of Americans.
When the coronavirus threat is defused—as it surely will be—we will still face airborne killers. Our only effective protection against the familiar foe of air pollution, the monster we’ve seen, is prevention, reducing harmful emissions from sources like power plants and vehicles.
Just as with our rush to contain the coronavirus, our lives depend on it.
Acknowledgement: Thank you to my adviser and my lab group for their helpful feedback and discussion related to this post.
Even though I’m pursuing a PhD in environmental toxicology, my dissertation uses methods from environmental epidemiology to answer the question of whether long-term air pollution exposure (specifically, fine particulate matter (PM2.5)) is associated with dementia or dementia pathology in a cohort based in the Seattle area.
When I tell people about my project, they always ask: “how are you measuring exposure to air pollution?”
Different pollutants require different methods of measurement (for example, some are measured internally, in urine or blood), but here I’ll focus on common methods to assess long term exposure to ambient (outdoor) air pollution for use in cohort studies evaluating chronic health effects. Other methods can be used for time-series studies, which are focused on short-term exposures and acute health effects. All of this information exists in the academic literature, but as per the title of this post, my goal is to present a summary so that non-exposure science experts (like me!) can understand it.
A brief review of selected air pollution exposure assessment methods for cohort studies
(1) Monitoring & Simple Interpolation Models
TLDR: We can use information from ground-level monitors, or a weighted average from those monitors, as a basic way to estimate air pollution for a given population. This is easy and cheap, but overly simplistic.
The most consistent, long-term measurements of air pollution come from stationary monitors, such as the Environmental Protection Agency’s (EPA’s) Air Quality System (AQS). A highly simplistic (and inexpensive) approach, then, would be to represent each person’s exposure by the nearest monitor. But, there are several problems with this method. First, as you can see if you click on this map (and choose pollutant monitors under the “select layer” function), the existing network of monitors is quite sparse. Here’s a map of the three active PM2.5 monitors in Seattle:
I live in northwest Seattle (see the blue triangle on the map), so it is highly unlikely that those monitors in south Seattle adequately represent my long-term air pollution exposures – particularly for pollutants linked to specific local sources, like roadways or factories. Second, these monitors usually only take measurements every third or sixth day, so there are a lot of missing data over time. (However, long term averages in the region tend to be fairly stable from day-to-day, so this probably isn’t a huge problem for cohort studies looking at the effects of long-term exposures). And third, since we need some amount of population variability to estimate a meaningful association between an exposure and an outcome, assigning the same monitoring information to everyone in a certain area essentially eliminates (spatial) contrasts and decreases our chances of detecting any effects.
To overcome the lack of spatial coverage from federal regulatory monitors, researchers can set up their own monitors. But, developing a new monitoring network is expensive, and you can only get exposure data for the specific period of time that your monitors are set up (as opposed to being able to look at the cumulative effects of exposures over years in the past, which is what most cohort studies want to do).
We can improve on this with “interpolation,” which is a basic model that assigns more weight (influence) to the monitors that are closer to the individual (such as through inverse distance weighting or kriging). You can think about it like a weighted average. While this is an improvement, it often still does not really provide the type of detailed (spatial) resolution that we need.
Overall, these are fairly basic approaches that were more commonly used in the early days of air pollution epidemiology (ex: the seminal study by Dockery et al.), especially when the research focus was on between-city rather than within-city differences in health outcomes. We can definitely do better now.
(2) Spatial Regression Models
TLDR: We can build regression models based on ground monitors, geography, and population features to predict air pollution at various locations. These models are commonly used in cohort studies, but each one varies in quality.
Land use regression (LUR) and related spatial regression models (e.g., universal kriging) use monitoring data as their foundation, but they also integrate information such as traffic density, population, land use, geographic features, and proximity to known emissions sources. All of these inputs are incorporated into a complex regression model to predict concentrations at sites where no monitoring data exist.
LUR model quality is judged through one or more validation processes, to see how well they can predict concentrations at sites where the true concentration is known. Researchers also look at how much of the total variability in the exposure can be explained by the model (the technical term for this is “R2”).
While LUR models are better than relying on monitors alone, they are only as good as their inputs. So, if the model does not include some crucial feature that substantially affects air pollution concentration (like a local factory) or if the input data are not detailed enough, the final model will be inaccurate. There are also concerns with how measurement/prediction errors affect the health effect analyses, a complicated topic that deserves a post of its own (hopefully forthcoming!).
LUR is one of the most common methods used to estimate exposure in air pollution cohort studies. However, model quality varies greatly. And in reality, there’s no single number or assessment that can truly capture whether a model is “good enough;” it’s complicated and depends more on the model inputs and methods as well as the decisions that the modelers make along the way. In other words, you have to read the “methods” sections of the publication very carefully. (I know that is not very satisfying, especially since the quality of these models can affect how much we can trust the results of the associated health analysis…)
TLDR: We can use physical-chemical models to predict air pollution concentrations. These models don’t use ground-level data, which is good if you want to predict in places where those data don’t exist, but it’s not ideal in the broader sense that we generally trust ground-level information more than theoretical models alone.
As with LUR, these models need to be validated, and model performance seems to vary by pollutant. A strength of these models is that they can provide information in places where we don’t have any monitoring data. But, because they are often very expensive to develop, provide limited spatial detail, and are not anchored to any ground-level data (which we tend to trust more than pure models), they are less commonly used in epidemiologic cohort studies than LUR.
TLDR: We have advanced satellite technology that can estimate air pollution from space, based on light scattering! Cool! But, right now, these models provide limited spatial resolution, so they are best when supplemented with ground-level data.
At a global scale, particularly when there are no ground-level monitors or detailed information on local emissions sources (such as in many low income countries), satellite measures of aerosol optical depth (AOD) can provide good estimates of certain air pollutants.
How does this work? A satellite measures the absorption and scattering of specific wavelengths of light at the same time in the same location each day, and these measures are then converted to ground level concentrations using chemical models such as GEOS-Chem.
Incredibly enough, these models can provide fairly accurate predictions of some ground-level pollutants, such as nitrogen dioxide and PM2.5 (but, modeling based on ground-level monitors is usually still more accurate). Remote sensing works less well for ozone, however, since the high levels of ozone in the stratosphere make it more complicated to accurately extrapolate to the earth’s surface. Other issues include potential interference from clouds, limited temporal characterization (usually just at a single time per day), and limited spatial detail (though spatial resolution continues to improve).
That last point – limited spatial detail – is the main downside of remote sensing right now. One way to mitigate this issue, though, is to incorporate data from LUR or ground level monitors, which can substantially improve accuracy.
Application to my work
I’ve skimmed over many details about each of these methods in the interest of making this post semi-accessible (but even so, I know it’s long!). There are also several other assessment methods as well as some interesting hybrid approaches. (For more details, I recommend Chapter 3 of EPA’s recent Integrated Science Assessment for Particulate Matter).
For my study, we’re relying on a sophisticated version of a LUR model with kriging that incorporates both spatial and temporal variability, similar to what has been done previously for other regions of the country. The inputs come from five federal agency monitoring sources and three different monitoring campaigns that have been implemented by the UW Dept. of Environmental and Occupational Health Sciences over the years, and – like all LURs – the final regression model incorporates different geographic features of the area that influence local pollutant levels. In the end, we will have a model that can estimate air pollution exposure at any location in the Puget Sound area all the way back to the early 1980’s!
The elephant in the room (or, the person in the house)
TLDR: Why is it relevant and meaningful to use measures of outdoor air pollution in our health effect studies? 1) Outdoor air enters the home; 2) Measurement error likely underestimates health effects (so we are not exaggerating our results!); 3) Health effects based on outdoor aircan influence policy.
All of the methods I’ve described so far provide exposure estimates based on a given geographic area or point. Some new, small studies use air pollution sensors to get measurements for each person, but that is too expensive for studies with thousands of people – and completely infeasible for those that were started years ago (like the one I’m using). So, instead, I will get estimates of outdoor air pollution based on each person’s residential address and then look at associations with dementia and related pathology.
There are several reasons why a measure of outdoor air pollution based on a home address might not accurately represent someone’s actual exposure. First, we spend most (apparently, 90%!) of our time inside. Second, most of us spend a majority of our waking hours away from our homes, including commuting (in traffic) and working. (Although, for my study of mostly retired individuals, this should be less of an issue). Additionally, from a toxicology perspective, it’s important to consider that a measure of external exposure is very different from internal dose; what actually gets inside your body depends on your breathing rate, among other factors.
So, how can we trust results from epidemiological studies that use these measures of outdoor air pollution from someone’s home address as the “exposure?” When I first began my research in this area, I was quite bothered by this question and talked extensively with my adviser. Here are a few responses and associated thoughts (some more satisfying than others, I admit):
Outdoor pollutants infiltrate into our homes
While we like to believe that we are protected from outdoor air when inside, there is actually measurable and meaningful infiltration of outdoor pollutants into our homes. I experienced this firsthand during the wildfires in Seattle two summers ago, when I could smell smoke inside my room even though all the windows were closed.
When we measure pollution at people’s homes, we are not capturing the full picture of their exposures. For someone like me, who commutes by bike, a measure of outdoor air pollution at my home is likely an underestimate of my daily exposure.
This concept – when the measured/predicted exposure does not represent the actual exposure – is a technical term referred to as “measurement error.” As I mentioned above, this is a complicated topic that I plan to return to in a future post. For now, a highly simplistic summary is that in most cases, exposure measurement error either 1) attenuates (decreases) the observed association, or 2) has no impact on the observed association (but it could affect the margin of error). (See here and here, for example.) So, in this simplified framework, we assume that the consequence of using these imperfect estimates of ambient air pollution is that our results are underestimated.
[Note: to reiterate…this is very simplified. Research by my own adviser suggests that it could also have the opposite effect.]
[Another note: this simplified summary assumes that the measurement error is “non-differential” (similar for all groups). When measurement error is “differential” (differs between groups), the impact on the health effect estimates could be different than what I described. See, I told you this was complicated!]
Effects linked to outdoor air pollution are policy-relevant
Some people consider personal monitors to be the ideal method for exposure assessment. However, a feature of this approach is that they also capture air pollutants from indoor sources, since they measure everything a person is exposed to throughout the day.
Yet, the Clean Air Act regulates outdoor air pollution, not personal or indoor exposures. Therefore, a study demonstrating that exposure to a certain amount of outdoor air pollution is associated with a specific health outcome provides meaningful information that could inform updates to air pollution standards.
Yes, I’m the first to admit that our methods to assess exposure to air pollution are imperfect. But, they allow us to efficiently quantify exposure for large segments of the population. (We really do need these data on large populations to be able to see evidence of the relatively small increased risks for various health effects.) And, while these exposure estimates might not capture the whole story, they are close enough to allow us to consistently see that air pollution is associated with premature mortality, cardiovascular disease, diabetes, asthma….and more.
In the future, I think we will see exciting refinements to these approaches, such as incorporating infiltration rates (to describe how outdoor pollutants enter indoor environments) or more personal behavior information. We might also be able to assess the specific type of particle (ie: metal vs. carbon-based) rather than just evaluating by size (as is done now, in distinguishing between PM2.5 and. PM10); this is important because different particle types may be associated with different health effects. These additional details will increase the accuracy of our exposure assessment methods.
One final note to try to assuage any remaining doubts about epidemiological studies based on these methods. Take the case of PM2.5 and mortality, for example…. Sure, we might not trust one-off results from a study using a single exposure assessment method on a single population. But science moves forward incrementally, based on the results of many studies rather than a single finding. When we see similar results from many studies, even across different populations and based on different exposure assessment methods, we can have strong confidence that PM2.5 is associated with increased risk of death (for example).
In this way, I think that air pollution epidemiology is strengthened by the use of these various methods, each with their own pros and cons. It’s certainly good to be skeptical, though. I think our work (like all science) could benefit from some healthy questioning of the accepted approaches, which could prompt us to improve our methods.
Last week, The New York Times published an article in their Food section highlighting meal ideas based on canned food. In response, Dr. Leonardo Trasande (NYU) and I wrote a letter to the editor with some of our concerns. This letter did not get published, so I’m posting here instead.
As avid cooks, we love reading columns from Melissa Clark. But as environmental health researchers, we were concerned that her recent piece, “A Love Letter to Canned Food,” fails to discuss potential health concerns associated with metal cans. Their linings contain bisphenols, such as bisphenol-A (BPA), or the wide array of “regrettable substitutes,” which can interfere with our body’s hormones and disrupt our developmental, reproductive, neurological, and immune systems. All of this is described in our American Academy of Pediatrics technical report and policy statement on “Food Additives and Child Health.” For canned food to continue to be a convenient, affordable and nutritional option for feeding our families, we need systemic policy changes that ensure that any additives are fully tested for safety prior to use in the marketplace. Our own work suggests that replacing BPA in cans with safer alternatives may produce economic benefits to society greater than the costs.
Rachel M. Shaffer, MPH PhD Candidate, Environmental Toxicology Department of Environmental and Occupational Health Sciences University of Washington School of Public Health
Leonardo Trasande, MD, MPP Jim G. Hendrick, MD Professor and Vice Chair, Department of Pediatrics Chief, Division of Environmental Pediatrics Professor of Environmental Medicine & Population Health NYU School of Medicine
[For a refresher on how this effort fits into the bigger
picture of chemical assessments, you can review my infographic.]
So far, the agency has released draft risk evaluations for four chemicals: PV-29, HBCD, 1,4-dioxane, and 1-bromopropane. I’ve been working with my former colleagues at EDF Health to carefully review the drafts for the latter two chemicals. Unfortunately, as expected, these drafts put out by the Trump EPA have a number of problems, which we’ve detailed in public comments.
Today, I’m excited to share an infographic that I made, depicting all of the different chemical evaluations and assessments that various federal agencies (in the U.S.) conduct.
If you want to hear about the backstory & process for creating this, read on below.
Otherwise, here’s a link to a PDF version of the graphic. There are hyperlinks throughout, if you want to explore any of the information further. Yes, I know this is very detailed; it is meant to be digested by zooming around to different sections of the graphic.
I’ve tried to be as accurate as possible. But if you catch something that doesn’t look right, please let me know.
I hope this helps the environmental health community (and others who might be interested) better understand the review processes that are intended to keep us safe (unless/until politics get in the way…).
[What are public comments, you might ask? Public comments are a way for the public to provide feedback during the federal rulemaking process. Under the Administrative Procedure Act (1946), whenever a federal agency develops a new regulation, they are required to solicit input from the public. (For more on the public comment process and how you can get involved, check out the Public Comment Project!)]
As I was reviewing ATSDR’s ToxProfile, I realized that I did not fully understand how this effort was distinct from EPA’s assessment of glyphosate. ATSDR and EPA are two separate federal agencies with different missions, so clearly these assessments served different purposes.
I soon realized that elucidating this distinction was just one part of a larger story. So, I decided to create a master chart to better understand all of the different types of reviews, evaluations, and assessments that different federal agencies conduct, the main purposes of these evaluations, and what other processes or regulations they might relate to.
I started collecting this information in an excel chart, but this format is not very conducive to easy online reading or sharing. So, I decided to challenge myself to make an infographic, which I had never done before. I experimented with various online tools before settling on draw.io, which I also used to make the timeline in the glyphosate meta-analysis. I’ll spare you the details, but let’s just say, this took me a LONG time (sorry, dissertation, I’ll get back to you soon).
I imagine that I’ll continue to refine this over the next few months/years. If you see anything that looks wrong or have suggestions for improvement, let me know.
One of the complicated parts of assessing the hazards and risks of glyphosate is that the product that everyone uses (for example, Round-Up) is not just glyphosate. The active ingredient is glyphosate, but the final formulation sold in stores is a combination of glyphosate and other “inert” ingredients.
[Note: I’m going to stubbornly use quotation marks around the words “inert” throughout this article, to emphasize my point that this is not an accurate characterization. “Inert” implies inactive, which is not true. Read on for more.]
These “inert” ingredients are subject to essentially no testing, disclosure, and regulatory requirements, even though they almost always make up a larger percentage of the final product than active ingredients. And, evidence indicates that combinations of the “inert” and active ingredients can actually be more toxic than the pure active compound (for example, see here, here, and here).
A new publication by Mesnage et al. in Food and Chemical Toxicology reviews the problems with the status quo and the implications for health effects research. Given the relevance of this topic to my previous blog posts on glyphosate (see here and here) and pesticides in general, I’ll summarize some of the authors’ key points below.
But first, some terminology: what is the difference between active and “inert” pesticide ingredients?
Under the U.S. Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA), an active ingredient is one that is intended to be toxic to the target species. For example, glyphosate, the active ingredient used in glyphosate-based herbicides (GBHs), blocks an essential enzyme pathway in plants. All other ingredients in a pesticide product, often added to improve effectiveness, are classified as “inert.”
But, it’s challenging to get extensive and accurate information about these chemicals because:
Neither the “inert” ingredients nor the final formulations (the combination of active + “inert” ingredients) are subject to most standard regulatory toxicity tests, such as evaluation of cancer and reproductive effects. As a result, pesticide approvals are based on the unrealistic scenario of exposure to the active ingredient alone.
Companies can routinely claim the identity and concentration of these “inert” ingredients as confidential business information (CBI). That is why you’ll often see labels like the extremely vague one below. As a result, it’s difficult – actually, essentially impossible – for scientists to independently evaluate possible toxicity. We are kept blind to these final products.
Because we don’t know the identity of these “inert” ingredients, there are essentially no monitoring data on environmental or human exposure.
So, in summary, we don’t know how toxic the “inert” ingredients or final formulations are; the identity of these “inert” ingredients is kept secret from the public; and we aren’t monitoring any of these chemicals for their presence in our bodies or the environment.
All of this makes it challenging for the EPA to conduct accurate pesticide risk assessments, which require information on both hazard (ie: toxicity) and exposure.
How are we supposed to evaluate the health effects of such a moving target? Robust epidemiological studies require precise definitions of exposure (referred to as the “consistency” principle) to prove causality. In essence, the exposure under investigation should be defined very specifically, such that it is not possible for variations in versions of the exposure to have different effects, which could muddy the overall conclusion of the study.
(As a concrete example, think about investigating the impact of “exercise” on health. Exercise is very broad, so it wouldn’t be helpful or informative to evaluate the effect of general “exercise,” which could span everything from a 30-minute walk once per month to a 2-hour run each day. The effects of these different types of exercise could have very different impacts on health. So, a better study question would be focused on a more specific type of exercise.)
For pesticide epidemiology, all of these changing formulations make it very challenging to draw conclusions on health effects across time and space. It’s quite likely that one study based in multiple locations could be evaluating the effects of different products at the same time. A study looking at one region over a period of several years also faces the same problem. As the authors of the recent publication stated, “formulated GBHs with the same product name from different countries may not be the same mixture of chemicals, nor the same as the brand-name product bought previously, or in the future.”
This is one possible reason for differing conclusions about hazard, and it makes reproducibility nearly impossible.
The authors put forth a few suggestions to improve this murky situation. Some can be acted on by researchers now, such as including detailed product information (ex: trade name, dates of manufacture, product ID number) in the methods sections of their papers, to facilitate reproducibility and comparison across studies.
Other proposals will need to wait until there is political will for policy change. Most important is the need for full public disclosure of pesticide product composition. (By the way, back in 1997, the American Medical Association urged Congress to “support all efforts to list both active and inert ingredients on pesticide container labels.”) The authors also suggest monitoring of food and feed for concentrations of the “inert” ingredients (that is, if we can get access to information about their identities!), so we can understand patterns of exposure.
Additionally, it is essential to revise the pesticide approval processes to include full testing of “inert” ingredients as well as the final formulated products. We urgently need a regulatory system that accounts for these real-world exposures.
It’s high time for transparency on these formulations and their effects on human health and the environment.
Last week, we published a meta-analysis that found that high exposure to glyphosate-based herbicides was associated with an increased risk of non-Hodgkin Lymphoma (NHL). There was a lot of discussion about this paper in the news, on social media, and across internet forums (as expected, given the ongoing controversy and high stakes of this conclusion). Most articles focused on the specific risk estimate that we reported, with headlines such as:
A common critique of these headlines (and our article) was that they (and we) were being misleading, because we reported the 41% increased relative risk of NHL – which sounds very scary!—rather than a 0.8% increased absolute risk of NHL – which sounds less scary.
At the risk of more undue attention on the 41% number (which as I said in my previous post, is less important than the finding of a significant association itself), let me explain a few things about (1) how we report results in epidemiological research, (2) why small increases in risk matter, and (3) how agencies like the Environmental Protection Agency (EPA) regulate on risk.
Relative risks vs. absolute risks
In epidemiology, we are trying to understand whether an exposure is associated with a disease. To do this, we compare the disease rate in the exposed group with the disease rate in the unexposed group. This ratio gives us the relative risk of disease between the two groups.
[Side note: this is why it is crucial for researchers to select an appropriate comparison group! The relative risk depends entirely on this decision! If your comparison group has an unusually high rate of cancer, you will get a very skewed (and wrong) answer about the effects of the exposure.]
This relative risk, however, does not give us any information on the absolute risk of the disease at the individual level. It only tells us whether the exposed group has a higher or lower chance of developing the disease than the comparison group. In our paper, we report that individuals with high exposure to glyphosate-based herbicides (for example, people who spray it daily for many years) have a 41% increased risk of developing NHL over their lifetimes, compared to those who were not highly exposed (infrequent or no history of use).
The absolute risk, by contrast, tells us the actual risk of the disease for a given level of exposure. This is much more intuitive. For example, on average in the US, approximately 2 out of every 100 people develop NHL during their lifetime. So, the absolute risk of NHL over a lifetime is 2%. Therefore, when our study reports a 41% increased risk for those who are highly exposed, that is equivalent to saying that these individuals now have an absolute risk of 2.8% risk of NHL.
These statistics are communicating the same basic information, but they sound very different. In our epidemiology courses, we learn that absolute risk is better for communicating to the public because it is easier to understand. But, because of the way that epidemiological studies are designed (comparing disease rates in one group vs. the other), our default is to report relative risks. And because we are used to thinking about these ratios, we don’t always realize that this information can be misinterpreted, misunderstood, and confusing. Maybe we should report both metrics in our abstracts.
Nevertheless, both ways of talking about risk give us the same answer to the central question of carcinogenicity: evidence suggests that glyphosate exposure is associated with an increased risk of cancer.
Why seemingly low risks are still important
Some environmental exposures have very high relative risks. Individuals exposed to high levels of asbestos in their homes, for example, have an 800% increased risk of developing mesothelioma, a very rare type of lung cancer.
Most common environmental exposures, however, are associated with relatively small increased relative risks. Let’s take a look at air pollution, a very common exposure. And more specifically, fine particulate matter (PM2.5), very tiny particles emitted from vehicles, industrial facilities, and fires. While exact estimates vary based on the population studied, an increased concentration (of 10 ug/m3, to be exact) in 24-hour average PM2.5 has been associated with a 0.4%-1.0% increased risk of death (mostly from cardiovascular disease). An increase (again, of 10 ug/m3) in long term average PM2.5 has been associated with an overall 10% increased risk of death.
Those seem like small changes in risk. So, can we stop worrying about air pollution?
No, definitely not.
Low relative risks applied to large populations can be extremely consequential. We are all exposed to air pollution. Everyday. And all of those exposures add up. In fact, PM2.5 was ranked as the 5th most important cause of death around the world in 2015, accounting for approximately 4.2 million deaths.
Glyphosate-based herbicides are the most heavily used herbicides in the world, with an estimated 1.8 billion pounds applied in 2014. Most of this usage is on commercial agricultural farms by workers with potentially high cumulative exposures over their lifetimes. Given the large number of people possibly exposed, any significant increase in risk – especially the 41% estimate that we report – is meaningful to consider at the population level.
Finally, I want to bring up a point about cancer risk in relation to regulations. The US EPA and Food and Drug Administration (FDA), among other agencies, have to manage and regulate risks for the population. For most scenarios, they have decided that an “acceptable risk” for a carcinogen in the general population is between 1 in a million and 1 in 10,000 (over a lifetime). In other words, EPA and FDA are supposed to take action to prevent exposure to carcinogens that would result in risks higher than those rates (the specific threshold depends on the scenario and, sometimes, technologic feasibility).
Our findings suggest that the absolute risk of NHL over a lifetime might shift from approximately 2% to 2.8% with high exposure to glyphosate-based herbicides. This difference represents an increase of 8/1000 – certainly above EPA’s threshold of concern for the general population.
Note, however, that some of the studies in our meta-analysis were focused on people using glyphosate in commercial agricultural settings. EPA usually allows a higher risk of cancer in occupational scenarios, approximately 1 in 1000. Even with that standard, however, our results would suggest a need for action.
I’m just using these comparisons to put our results in context, because many people seemed to discount this work because of the small absolute risk estimates. Before any actual regulatory action, EPA would need to consider extensive evidence on hazard and exposure in a formal risk assessment.
In closing, I hope that I’ve clarified a few points about risk that were raised in the aftermath of the glyphosate publication. But once again, let me emphasize that you should not focus too much on the specific numerical estimates above but rather use them to better understand that:
Relative risks are different than absolute risks. Epidemiologists usually use relative risks, so that is what you will see in published papers (and, likely, the headlines as well).
Exposures with low relative risks can still have huge impacts at the population level.
Regulatory agencies set certain benchmarks for acceptable lifetime cancer risk in the population. You might not agree with the thresholds, but those are the standards. Keep that in mind when you are reading about risks from environmental exposures.
Apologies for the long blog absence. I’ve been busy PhD-ing (including preparing for and passing my oral general exam!) and working on various side projects.
One of those side projects has been focused on glyphosate. Glyphosate, the active ingredient in Monsanto’s (now owned by Bayer) Roundup, is the most widely used herbicide in the world. First marketed in 1974, its usage skyrocketed after the introduction of “Roundup-ready” (i.e.: Roundup resistant) crops in 1996 and the practice of “green-burndown” (i.e.: using the chemical as a desiccant shortly before harvest) in the mid-2000s. In 2014, global usage was estimated to be 1.8 billion pounds.
But these staggering statistics are not the only claim to fame for glyphosate. It has also been the subject of intense international regulatory and scientific scrutiny in recent years, for its possible link to cancer. The stakes are high (billions of dollars for Monsanto, related to sales of both the herbicide itself and its line of herbicide-resistant crops), and the conclusions are controversial.
Carcinogenic or not, that is the question.
In 2015, the International Agency on Cancer (IARC) declared that glyphosate was a “probable human carcinogen” (relevant links: explanation of IARC classifications; official summary for glyphosate; IARC webpage with follow-up links). However, that same year, the European Food Safety Authority (EFSA) concluded that “glyphosate is unlikely to pose a carcinogenic hazard to humans, and the evidence does not support classification with regard to its carcinogenic potential.” In 2016, the US Environmental Protection Agency (EPA) determined that glyphosate was “not likely to be carcinogenic to humans at doses relevant for human health risk assessment.”
Ok, so that’s confusing. How did these agencies, all of which are supposed to conduct unbiased reviews of all of the evidence come to such different conclusions? There have been several recent publications that explain these inconsistencies (for example, see here and here). In essence, it boils down to: 1) differences in how the agencies weighed peer-reviewed, publicly available studies (most show adverse health effects) versus unpublished regulatory studies submitted by manufacturers (most do not show adverse health effects); 2) whether the agencies focused on studies of pure glyphosate or the final formulated glyphosate-based product that is used in agricultural applications (which is known to be more toxic); and 3) whether the agencies considered dietary exposures to the general population only or also took into account elevated exposures in occupational scenarios (i.e. individuals who apply glyphosate-based herbicides in agricultural settings).
Meanwhile, as the debate continues… 27 countries (as of November 2018) have decided to move forward with implementing their own bans or restrictions. And, Monsanto/Bayer faces more than 9,000 lawsuits in the US from individuals who link their cancer to the herbicide. (The courts ruled the first case in favor of the plaintiff, though Monsanto is appealing the decision).
This highly contentious area is outside the topic of my dissertation research, but I got involved because my advisor was a member of the EPA scientific advisory panel that reviewed the agency’s draft assessment of glyphosate in 2016. The panel’s final report raised a number of concerns with EPA’s process and conclusions, including that the agency did not follow its own cancer guidelines and made some inappropriate statistical decisions in the analysis.
Because of their dissatisfaction with EPA’s report, my advisor and two other panel members decided to pursue related research to dig further into the issues. I enthusiastically accepted the invitation to join.
Our collaborative group recently published two review papers on glyphosate. I’ll provide brief highlights of both below.
Reviewing our reviews, part 1: exposure to glyphosate
In January 2019, we published a review of the evidence of worldwide exposure to glyphosate. Even though glyphosate-based products are the most heavily used herbicides in the world, we were surprised (and dismayed) to find less than twenty published studies documenting exposure in only 3721 individuals.
So, our paper mostly serves to highlight the limitations of the existing data:
These studies sampled small numbers of individuals from certain geographic regions, mostly in the US and Europe, and therefore are not representative of the full scope of global exposures
Most studies relied on a single urine spot sample, which does not represent exposure over the long term and/or in different agricultural seasons
The occupational studies only covered 403 workers in total, a serious deficiency given its widespread agricultural use. Few assessed exposure before and after spraying; and no studies evaluated patterns related to seasonality, crop use, etc.
Only two small studies evaluated how population exposure has changed over time. So, we definitely don’t know enough about whether the dramatic increases in global usage have resulted in similarly dramatic increased concentrations in our bodies. (Presumably, yes).
In addition to highlighting the need to address the points above, we specifically recommended incorporating glyphosate into the National Health and Nutrition Examination Survey (NHANES), a national survey that monitors exposure to many chemicals – including other common pesticides. This is an obvious and fairly straightforward suggestion; in reality, it’s quite bizarre that it has not already been incorporated into NHANES. Testing for glyphosate would allow us to better understand exposure across the US – which is not reflective of global levels, of course, but an important start.
Reviewing our reviews, part 2: glyphosate & non-Hodgkin Lymphoma (NHL)
Our second paper, published earlier this week, was a meta-analysis of the link between glyphosate exposure and non-Hodgkin Lymphoma (NHL). Yes, diving right in to the controversy.
There had already been several prior meta-analyses that showed an association between glyphosate and NHL, but ours incorporates new research and applies a method that would be more sensitive to detecting an association.
A meta-analysis combines results from separate studies to better understand the overall association. While they technically do not generate any “new” data, meta-analyses are essential in the field of public health. A single study may have certain weaknesses, focus only on selected populations, or reflect a chance finding. In drawing conclusions about hazards (especially in this scenario, affecting millions of people and billions of dollars), we want to look across the collection of data from many studies so we can be confident in our assessment.
We were able to include a newly published follow-up study of over 54,000 licensed pesticide applicators (part of the Agricultural Health Study (AHS)). Compared to an earlier paper of the same cohort, this updated AHS study reports on data for an additional 11-12 years. This extension is important to consider, given that cancer develops over a long period of time, and shorter studies may not have followed individuals long enough for the disease to arise.
We conducted this meta-analysis with a specific and somewhat unusual approach. We decided to focus on the highly exposed groups in order to most directly address the question of carcinogenicity. In other words, we would expect the dangers (or, proof of safety: is it safe enough to drink?) to be most obvious in those who are highly exposed. Combining people who have low exposure with those who have high exposure would dilute the association. IMPORTANT NOTE: this approach of picking out the high exposure groups is only appropriate because we are simply looking for the presence or absence of a link. If you were interested in the specific dose-response relationship (i.e.: how a certain level of exposure relates to a certain level of hazard), this would not be ok.
Our results indicate that individuals who are highly exposed to glyphosate have an increased risk of NHL, compared to the control/comparison groups. This finding itself is not entirely earth-shattering: the results from prior meta-analyses were similar. But, it adds more support to the carcinogenic classification.
More specifically, we report a 41% increased risk. For comparison, the average lifetime risk of NHL is about 2%. However, I want to emphasize that because our analytical method prioritized the high exposure groups, the precise numerical estimate is less important than the significant positive correlation. Basically, the purpose of this and other related assessments (like IARC’s) is to understand whether glyphosate is carcinogenic or not: this is a yes/no question. It is up to regulatory agencies to judge the scale of this effect and decide how to act on this information.
As with any scientific project, there are several limitations. In particular, we combined estimates from studies that differed in important ways, including their design (cohort vs. case-control), how they controlled for confounding by exposure to other pesticides, and which reference group they chose for the comparison (unexposed vs. lowest exposed). When studies are very different, we need to be cautious about combining them. This is another reason to focus more on the direction of the effect rather than the exact numerical estimate.
Beyond the headlines
The news coverage of this work has focused on the overarching results (especially the 41% statistic), as expected. But I want to highlight a few other aspects that have been overlooked.
To better understand the timing of these studies in relation to glyphosate usage, we put together a timeline of market milestones and epidemiological study events.
Of note is that all of the studies conducted to date evaluated cancers that developed prior to 2012-2013, at the latest. Most were much earlier (80s, 90s, early 00s). As illustrated in the timeline, we’ve seen a huge increase in glyphosate usage since green burndown started in the mid-2000s. Yet none of these studies would have captured the effects of these exposures, which means the correlation should be easier to see in newer studies if/when they are conducted.
Also, as I mentioned above, we included the newly published AHS cohort study in our meta-analysis. One might expect the old and new AHS studies to be directly comparable, given that they were conducted by the same research group. However, our deep dive into both papers elucidated important differences; consequently, they are not directly comparable (see Table 8 of our paper). An in-depth discussion of these issues (and some of their potential implications) is a topic for a separate post, but there’s a clear lesson here about how important it is to carefully understand study design and exposure assessment methods when interpreting results.
Finally, two brief points on the animal toxicology studies, which we also reviewed in our paper because they provide complementary evidence for assessing hazard in humans. We discuss these data but did not conduct a formal pooled analysis (to combine results from separate but similarly designed animal studies), which would allow us to better understand overarching results from the animal studies. Anyone ready for a project?
Additionally, in future animal toxicology studies, researchers should use the formulated glyphosate product that is actually used around the world rather than the pure glyphosate chemical that has been the focus of prior testing. There is growing evidence to suggest that the final formulated product is more toxic, perhaps due to the added adjuvants and surfactants. And this would allow for better comparisons to the human epidemiological studies, which assess effects of exposure to the formulated product.
Reflecting on the process
I had followed the evolving story on glyphosate with great interest for several years, so it was exciting to be part of these projects. Contributing to research with a real-world public health impact has always been a priority for me, and this high-profile research (affecting millions of people, billions of dollars) certainly fits the bill.
That being said, it was not an easy process. These two papers represent years of work by our group, which we did on top of our regular commitments. Collaborating with three researchers whom I had never met also proved challenging, since we did not have established rapport or an understanding of each other’s work and communication styles. So, in addition to gaining skills in conducting literature reviews and meta-analyses, I learned valuable lessons in group dynamics. 🙂
Given the high-stakes and high-profile nature of this work, we were extra meticulous about the details of this project. We knew that it would be scrutinized carefully, and any error could damage our credibility (especially worrisome for me, since I’m just establishing myself in my career). It took many, many rounds of review and editing to get everything right. A good lesson in patience.
Speaking of patience, I know that scientific research and related policy decisions take time. But I hope that these two projects can contribute to moving forward in a direction that protects public health.
Or, should she use infant formula, which avoids the problem of breast milk contaminants but does not offer the same benefits to her newborn and may also contain toxic chemicals (because of lax food safety regulations or if contaminated water is used to reconstitute the formula, for example).
Last month, two papers (from the same group of collaborators) published in Environmental Health Perspectives attempted to address these issues by reviewing decades of relevant research. These papers are both quite extensive and represent impressive work by the authors – but it’s unlikely that non-scientists will wade through the details. So, I’ll do my best to help you out.
Breast milk vs. infant formula: What chemicals are in each?
The first paper starts by documenting all of the chemicals detected in either breast milk or infant formula, based on studies published between the years 2000-2014 (mostly in the United States). Below is a highly simplified table, with just the chemicals rather than other details (refer to the paper if you’re interested in more).
What can we learn from these data, other than that it looks like complicated alphabet soup?
Well, toxic chemicals have been detected in both breast milk and infant formula, but there are some differences in the types of chemicals found in each. Breast milk is more likely to contain lipophilic (fat-loving/stored in fat) and long-lasting chemicals, such as dioxins and certain pesticides. By contrast, breast milk and formula both have some common short-lived chemicals, such as bisphenol-A (BPA) and parabens.
While the paper also provides information about the average and range of concentrations of chemicals in each medium (and how they compare to acceptable levels of exposure for infants), it’s hard to draw general conclusions because there are such limited data available. It is complicated, expensive and invasive to get samples of breast milk across wide segments of the population, and relatively few studies have looked at chemicals found in infant formula. We need more information before we can accurately understand the patterns of exposure across the population.
Nevertheless, the presence of all of these chemicals seems concerning. No one wants to deliver toxic milk to children during their early months of life, when they are more vulnerable because their organ systems and defense mechanisms are still developing.
But, what do the data indicate about the health consequences of these exposures?
Early dietary exposures and child health outcomes
That’s where the second paper comes in. Here, the same group of authors reviewed the literature on the association between chemicals in breast milk and adverse health outcomes in children. (Note: they had planned to ask the same question for infant formula, but there were not enough published studies). They looked at many chemicals (such as dioxins, PCBs, organochlorine pesticides, PBDEs) and many outcomes (including neurological development, growth & maturation, immune system, respiratory illness, infection, thyroid hormone levels).
Overall, when looking across various chemicals and health outcomes, the current literature is actually… inconclusive. Many studies reported no associations, and studies asking similar questions often reported conflicting results. Furthermore, studies that reported significant effects often evaluated health outcomes at only one or two periods in early life, and we don’t know if those changes really persist over time.
A glass half full…of challenges
In the end, the authors ended up with more questions than answers – and a long list of challenges that prevent us from understanding the effects of breast milk-related chemical exposures on children’s health. For example:
Chemicals in breast milk are often also present in the mother during pregnancy. How can we disentangle the effects of exposures during the prenatal period from exposures due only to breast milk in early postnatal life?
Many of these studies represent a classic case of “looking for your keys under the lamppost.” We can only study chemicals and outcomes that we choose to focus on, so we could be missing other important associations that exist.
On a related note, most studies focused on exposure to only one or a small group of chemicals, rather than the real-world scenario of the complex mixtures in breast milk.
There was little study replication (ie: more than one study looking at the same question). Generally, we feel more confident drawing conclusions based on a larger pool of studies.
The few studies that did ask the same questions often used different experimental designs. These distinctions also pose challenges for interpretation, since differences in how researchers measure exposures and outcomes could affect their results.
Most studies evaluated levels of chemicals in breast milk using one or two samples only. How accurate are these exposure assessments, given that levels in the milk may change over time?
Measuring chemicals in breast milk is just one aspect of exposure, but it doesn’t tell us how much the infant actually received. Mothers breastfeed for different amounts of time, which affects how much is delivered to the infant. These person-to-person differences within a study could make it challenging to see clear results in an analysis.
Filling in the gaps
Perhaps the only certain conclusion from these publications is that much work remains. Not only do we need more studies that document the levels of chemicals in breast milk and infant formula (as the first paper highlighted), but we also need more data on the links between these exposures and health outcomes – including targeted research to address the challenges and key gaps noted above.
Importantly, because breastfeeding is associated with many key health benefits (such as improved neurodevelopment and reduced risk of obesity, diabetes, infections, and more), any study that looks at the impact of chemical exposures in breast milk should also ask a similar question in a comparison group of formula-fed infants. It is likely that the positive effects of breast milk far outweigh any potential negative impacts from the chemicals in the milk, and that the infants would actually be worse off if they were fed formula that had the same level of chemicals (but did not receive the benefits of breast milk).
I’ll be the first to admit: it is scary to think about all of these chemicals in breast milk. But, all decisions have trade-offs, and here, when weighing the risks and benefits, the balance still seems to favor breastfeeding in most situations.
My friends think that I’m slightly obsessed. I prefer to think of myself as extremely passionate.
I’m getting my PhD in toxicology, but my interests don’t stop when I leave campus. I live and breathe this stuff: I love reading, learning, and writing about all things environmental health.
So, naturally, I’m worried about my own exposures to the overwhelming number of pollutants that we’re surrounded by. Though much is out of my control, I do what I can to minimize my exposures by buying organic (even on a student budget, and sometimes to an extreme that annoys my friends and family), avoiding processed and packaged foods, minimizing my use of plastics, choosing fragrance-free products, obsessively searching for flame–retardant free furniture, etc. The list goes on. My environmental health knowledge and concern for my health (as well as the health of my potential future children?) drive my lifestyle and purchasing decisions.
Yet, there’s one lifestyle choice that I’m not willing to give up, especially while living in Seattle: biking. I bike commute to school almost every day of the year. (I only missed 2 days this winter, when the roads were icy). I love riding to campus; it’s my morning and evening meditation/reflection time and my exercise.
While I do ride on the Burke–Gilman trail for most of the way, with gorgeous views of the water, mountains and city skyline, there are also several segments on roads. Obviously, biking on busy, car-filled streets presents immediate physical dangers, like car-bike collisions (my housemate has been hit twice in 6 months) and getting doored (ouch! Looks so painful). But, there’s also all that disgusting air pollution I inhale as take deep breaths alongside the ever-steady stream of cars. Luckily, at least for now, I’m not affected by asthma or other respiratory conditions. Yet, air pollution has been linked to many health problems, including cardiovascular disease and dementia (the latter is the subject of my PhD dissertation); I can’t help but think about those dangers on my daily rides.
Am I causing more harm than good to myself by commuting by bike? Why am I willing to impose such strict controls on other parts of my life (ie: purchasing decisions), but I allow myself to take deep breaths of noxious miasma every single day? Sure, exercise is good for me – for both my physical health and my brain health. But, do the negatives (pollution, collisions) outweigh the positives (physical and emotional health)?
This report (prepared for the National Institute for Transportation and Communities) has a good summary of the research (as of 2014) about traffic-related air pollution exposure to bicyclists. Since then, more studies (for example, in Salt Lake City, Utah; Minneapolis, Minnesota; and Montreal, Canada (here and here)) have also quantified exposures to city cyclists; others (like this one in New York City) are in process now. All of these assessments are specific to each city, season, time, and route. It will take much more research to develop a body of information that reflects the average and range of possible exposures to cyclists.
Such research with individual level measurements is crucial. We routinely track ambient air pollution across the country with a surprisingly few number of monitors (check out this interactive map to explore your area). These devices, which are often located away from major roadways or pollution sources, would definitely underestimate my own exposure, especially when I’m biking right behind a bus.
Exact quantification pending, what am I breathing in on my morning and evening commutes?
Sulfur dioxide (SO2): Sulfur dioxide is released from industrial facilities and vehicles burning fuel with high sulfur content. It is linked to respiratory problems (like asthma attacks and airway irritation) and can contribute to formation of PM.
Of course, we are all exposed to these air pollutants when we walk outside (and while driving in cars). But, during vigorous exercise, like biking up Seattle’s killer hills, we breathe in at 2-5x higher rates, and also more deeply, than at rest. So, I’m inhaling much more of all this bad stuff when I bike – in traffic – each day.
What are the consequences?
Research on the effects of air pollution exposure during exercise and active transportation (ie: walking and cycling) is beginning to emerge. According to one recent study, walking along busy streets reduced the short term cardiovascular benefits of the exercise compared to walking in a park. In studies of cyclists, researchers have found that biking in traffic is associated with various physiological changes, such as increases in certain inflammatory blood cells, alterations in heart rate variability (see here, here, and here) and other cardiovascular measures, and decreases in lung function. The implications of these changes are still unclear, however. As usual, we need more research on the short- and long-term health effects of cycling in traffic.
Several studies (see selected examples from 2017, 2016, 2015, 2014, and 2010) have tried to examine the overall health trade-offs of cycling in cities. The general conclusion is that the long-term benefits of active transportation (ie: namely, physical activity) outweigh the potential risks from traffic accidents and air pollution. However, I think these assessments are limited in several ways:
Most focus on the impact on mortality only, rather than the other myriad of health effects from air pollution that could lead to decreased quality of life and then, indirectly, mortality.
Most only consider the effects of a single pollutant (usually PM2.5) rather than the effects of combined exposures to multiple traffic-related air pollutants (ie: what happens in the real world).
On a more technical level, they assume a linear dose-response (solid line, below), where the relationship between exposure and outcome is the same across all levels of exposure. However, some evidence suggests that this may not actually be the case for PM2.5. Instead, the curve might be supralinear (dashed line, below), where the risk increases more steeply at lower levels of exposure. In this scenario, there might be greater benefits to health per unit decrease in exposure at lower ends of the spectrum, which would alter the modeling calculations.
The alternative scenario (in epidemiology speak, the “counterfactual”) used in these cost-benefit assessments is decreased physical exercise. In other words, they are roughly comparing: [exercise + pollution] vs. [no exercise + pollution]. Because the benefits of physical activity are so enormous, this equation tips towards the [exercise + pollution] side. However, if I didn’t commute by bike, I would replace this exercise with alternative activities (with less exposure to air pollution, presumably). If my equation is instead [exercise + pollution] vs. [exercise + less pollution], it would likely tip in the other direction.
So, in summary, we don’t fully understand all of the physiological impacts of biking in traffic-related air pollution, and I think that the current cost-benefit analyses may actually underestimate the long-term costs to my health.
Hmmm…. Should I re-evaluate my decision?
Last summer, I bought myself an air pollution mask to wear while biking. But, while I hate to admit it, I don’t use it every day. (It often causes my sunglasses to fog up!). I really should use it – assuming it is as effective as it claims?
Even though Seattle has the reputation of having fairly good air quality, the 2018 American Lung Association State of the Air Report indicates that there is still enormous room for improvement. (And, as I noted above, this city-level ranking, based on ambient air monitors, likely definitely underestimates my exposure while biking).
Everyone makes risk-related choices differently, based on their own calculations and priorities; risk perception and decision-making is complex and not entirely rational. But, in general, people are more likely to accept risks that they perceive as controllable, familiar and natural compared to those they perceive as imposed by others, uncontrollable, and unfamiliar (see image, below).
I’m still trying to understand the calculations that led me to my decision to expose myself to substantial pollution every day. Maybe it is related to the fact that I have control in this situation, since it is my choice to bike? Maybe it is because Seattle appears to have relatively clean air, compared to other places I’ve lived, like Atlanta and Bangkok, where the pollution is more directly visible?
Maybe… maybe… I just love biking too much, and this is where I draw my personal line. While it is definitely important to me to minimize harmful exposures and prioritize my health, I cannot and do not want to live in a complete bubble (though sometimes it seems to others that I already do). Life involves risk, and I’ve somehow decided that this is one I’m willing to take. Biking every day brings me too much happiness to give up (at least for now). Plus, cars are no safe haven; there’s plenty of dirty air inside from both internal and external sources.
While I take this risk, which is perhaps ironic given my PhD research on air pollution and dementia, there are some things I can do to mitigate my exposures. In addition to wearing my mask more consistently, I can check local air quality (like through this pollution app) and avoid riding on particularly bad days (like last summer, when Seattle was choked by horrific wildfire smoke). When bike paths are not available, I can do a better job of altering my route to prioritize low traffic roads, where I will be less exposed than on busier routes.
However, like for all pollutants, individuals only have limited abilities to control their own exposures. In the end, we need systemic, societal changes to make cities safer and healthier for people: stricter controls of vehicle emissions, increased utilization of electric cars and buses, improved public transportation, better bicycling infrastructure (eg: off-street bike paths), more greenspace, etc. The intersection of urban planning and public health definitely intrigues me (my next PhD? No, just kidding).
Ultimately, I hope that my own research can demonstrate the importance of strengthening air quality regulations and help motivate policies to reduce exposures across the population.