I started RachelTalksTox here on WordPress more than ten years ago. Some of you have been with me since the start (thanks Mom & Dad!), and some have joined more recently. Thank you to everyone who has been following my journey and explorations in environmental health. And, thank you to one of my readers – a mentor and role model in this field – for encouraging me to launch on this new platform.
Going forward, I will maintain both sites with the same content. So, if you prefer to keep reading here, you won’t miss anything. But if you prefer to read on Substack (along with many of your other subscriptions, I assume), feel free to hop over to that site to subscribe.
When the air quality index (AQI) is, say, 64 (“yellow”), what does that really mean? Even I, who did my PhD on air pollution, cannot grasp in a tangible way how exactly that number impacts my health.
To make things more concrete, some have translated daily air pollution levels (specifically, fine particulate matter (PM2.5)) into cigarette equivalents (i.e., breathing polluted air = smoking a certain number of cigarettes). (I also found a calculation based on passive cigarettes, though this one has gotten less traction in the media.)
You can get these PM2.5-cigarette comparisons in various easy-to-use forms, from an online calculator to an app. Most of these tools are based on a Berkeley Earth paper, nicely summarized here and here, but there is also a previous conversion derived from work by Arden Pope et al.
These calculations are compelling, because the dangers of smoking have been drilled into us. Plus, we all want better ways to understand the health risks from air pollution (especially for wildfire smoke, which will likely be ramping up soon in many parts of the US).
But, what’s behind these numbers? And, is this an accurate comparison to make?
There are two main approaches to expressing PM2.5 in terms of cigarette smoking. (If these math-y explanations will make your eyes glaze over, skip ahead to the next section.)
(1) Based on mortality
In 2015, Muller et al. developed a conversion by linking deaths from cigarettes and deaths from PM2.5. Briefly, here’s what they did:
How many deaths are there per year for every cigarette sold? They calculated this rate by taking the total number of people who die from cigarettes each year in the US (~480,000) and dividing it by the average number of cigarettes sold in the US per year (~350 billion) to get an estimate of 1.37 x10-6 deaths per cigarette.
How many people die in China each year from exposure to PM2.5? Based on their 2015 analysis, 1.6 million people die every year from PM2.5 levels averaging 52 μg/m3.
How many cigarettes would it take to kill the same number of people as PM2.5? At a rate of 1.37 x10-6 deaths per cigarette (step 1), it would take 1.1 trillion cigarettes to kill 1.6 million people. Based on China’s population of 1.35 billion at the time, they calculated that it would take about 864 cigarettes per person per year – or 2.4 cigarettes per day – to kill 1.6 million people.
Final conversion to scale down to one cigarette: If breathing an average PM2.5 level of 52 µg/m3 is equivalent to (i.e., kills the same number of people as) 2.4 cigarettes per day, then breathing in an average PM2.5 level of 22 µg/m3 is equivalent to smoking one cigarette.
(Yes, there are uncertainties introduced every step of the way here – including by linking data on air-pollution related deaths from China with cigarette-related deaths from the US. So, take this number with a big grain of salt.)
(2) Based on inhaled PM2.5 dose
In 2009, Arden Pope, one of the pioneers in air pollution research, and his team evaluated the risks of mortality from cardiovascular disease in relation to average daily dose of PM2.5 from cigarette smoke, secondhand cigarette smoke, and air pollution. In these analyses, they developed a conversion linking cigarette smoke and air pollution based on inhaled PM2.5 dose in the following way:
What is the average person’s daily breathing rate? Based on data available at the time, they estimated an average breathing rate to be 18 m3/day.
How much PM2.5 is inhaled per cigarette? Of course, this number depends on cigarette composition and smoking patterns. Measurements suggest a range of 7-17.5 mg per cigarette, and the authors selected an average of 12 mg per cigarette.
How much PM2.5 is inhaled from ambient air? They calculated this dose based on ambient PM2.5 levels combined with average breathing rate (step 1). For example, an ambient PM2.5 level of 10 µg/m3 was determined to result in an actual inhaled dose of 0.18 mg PM2.5.
And then, an additional step to get the number in terms of a single cigarette:
Final conversion to equate inhaled PM2.5 from cigarettes with inhaled PM2.5 from ambient air: If one cigarette results in an average inhaled dose of 12 mg PM2.5 and an ambient PM2.5 level of 10 µg/m3 results in an inhaled dose of 0.18 mg PM2.5, then breathing ambient air when PM2.5 levels are 66 µg/m3 is equivalent to smoking one cigarette.
Comparing cigarette calculations
The numbers derived above are pretty different:
In approach #1 (based on mortality), 22 µg/m3ofambient PM2.5 = smoking one cigarette.
In approach #2 (based on inhaled PM2.5 dose), 66 µg/m3ofambient PM2.5 (3x as much as in approach #1!) = smoking one cigarette.
Which one is “right”?
It’s hard to answer that question. Both of these approaches are approximations, each with their own uncertainties baked in. One is based on mortality – but PM2.5 does more than “just” kill you (more on this below). And the other is based on inhaled dose – but not all doses of PM2.5 are the same (more on this below as well).
Overall, they are both inexact in different ways and should just be viewed as communication tools to help make abstract numbers a bit more tangible.
Caution with these comparisons
I’m generally supportive of efforts to describe the impacts of pollution in terms that the wider public can understand. Yet, the detail-oriented scientist in me wants to highlight a few ways that these catchy conversions are oversimplified and not entirely accurate.
What doesn’t kill you makes you stronger wreaks havoc across your body
As I noted above, the cigarette conversion that has gotten the most attention is based on linking mortality from cigarettes with mortality from PM2.5 (approach #1).
However, PM2.5 is associated with much more than mortality. In fact, PM2.5 affects the health and functioning of nearly your entire bodyacross your whole life, from early years (e.g., preterm birth and neurodevelopment) to later life (e.g., neurodegeneration). So, while it is certainly convenient and relatively simple to express PM2.5 deaths in terms of cigarette deaths, this conversion underestimates the actual risks from breathing PM2.5: it does not account for all of the other ways that exposure can damage your health and impact your daily functioning over your lifespan.
Differential toxicity of PM2.5
Different types of PM2.5 are differentially toxic. The source matters: particles from traffic exhaust seem to be more toxic than other types of particles (for example, road dust particles). And, even certain components of traffic-related particles may be more harmful than others (for example, elemental carbon more so than sulfate).
When comparing ambient PM2.5 to tobacco PM2.5, specifically, Arden Pope commented that: “although the potential differential toxicity of fine particulate matter air pollution from various sources is not fully understood, fine PM from the burning of coal, diesel, and other fossil fuels as well as high temperature industrial processes may be more toxic than particles from the burning of tobacco.”
So, for both of these approaches, simply equating PM2.5 and cigarettes via death or inhaled dose minimizes the fact that not all PM2.5 is created equal.
Different effects at different doses
A “dose-response” curve describes how health effects change with increasing or decreasing exposure.
Research shows that PM2.5 does not have a simple linear dose-response curve (blue line below). Instead, we actually see something called a “supralinear” curve (red line below), where the curve is steeper (i.e., effect is stronger per dose) at lower levels of exposure and then flattens out (still increasing, just less steep) at higher levels of exposure. (You can read more about this supralinear curve in the context of air pollution here).
Figure adapted from: Marshall JD, Apte JS, Coggins JS, Goodkind AL. Blue Skies Bluer? Environ Sci Technol. 2015 Dec 15;49(24):13929-36.
This supralinear curve partially explains why fewer people die from cigarette-related mortality than would be predicted if we simply extrapolated up based on the risk of mortality from ambient PM2.5. In other words: if the relationship were linear, based on what we know about PM2.5 and mortality, there would be a lot more deaths from smoking! There are several possible reasons for this observation ranging from biological (e.g., saturation of key mechanisms) to behavioral (e.g., sensitive people avoid smoking; heavy smokers inhale less).
If we expect the shape of the curve to differ at different levels of exposure, then we need to take these approaches with even more of a grain of salt. These comparisons suggest a linear relationship between PM2.5 and death. Instead, a supralinear curve would suggest that breathing in lower levels of air pollution might be relatively more harmful than higher levels of air pollution (e.g., if one cigarette is considered equivalent to 22 µg/m3 ambient PM2.5, then five cigarettes would not be similar to 110 µg/m3 but instead some value less than that).
And not only do effects differ at different doses, but they also may differ based on the nature of the dose. Smoking delivers high concentrations episodically while air pollution is (usually, depending on where you live) a more continuous low level exposure. These distinct patterns impact how the body responds.
All models are wrong, but some are useful
Writing this post gave me a chance to explore and explain the cigarette-PM2.5 analogies that get thrown around in the media and about which I am often asked.
My takeaway: they are not particularly accurate.
However, I think it is crucial for environmental health scientists to develop accessible approaches for talking about the harms of everyday exposures. Our work is far too important to remain lost in translation.
So, while we should be aware of the limitations and uncertainties of this communication tool specifically, it is also important to acknowledge that it has been very powerful and useful for the public. When the cigarette comparison is used, people pay attention.
In the end, maybe it is ok to have something approximate yet compelling to help us convey the dangers of air pollution? In fact, maybe the unexpected lesson here (and for other parts of my life as well, to be honest) is … don’t let perfect be the enemy of the good?
Thanks to Annie Doubleday and one other (anonymous) colleague for helpful comments on an earlier version of this post.
The following is adapted from a short tribute that I posted on LinkedIn. The University of Washington (UW) Seattle Department of Environmental and Occupational Health Sciences (DEOHS) has also posted a blog incorporating reflections from many of us who worked with Steve over the years.
I first met Steve when he gave a talk on “The Ethics of Epigenetics” at the Environmental Protection Agency (EPA)’s North Carolina campus in 2015, a few months before I started graduate school at UW Seattle. His presentation inspired me to write my first blog post on RachelTalksTox, kicking off a fun and fulfilling journey in science communication (which we both saw as an important complement to traditional scientific work).
During my 5 years in Seattle, Steve and I met regularly for conversations and dreaming/scheming about what a better world could look like. These big ideas energized and nourished me, especially during the more challenging years of my PhD program. We took some of these ideas (especially on Pb, one of his primary concerns) to action not only through a publication but also through state policy advocacy and op-eds. Steve was not afraid to speak out, modeling for me the kind of scientist I wanted to be.
Steve had already been diagnosed with Parkinson’s when we met, but he faced his disease with courage and – true to his dedication to teaching and science communication – sought out opportunities to share his story and treatment experiences so that others could learn how to navigate similar challenges.
I’m grateful to have been able to call Steve a mentor and friend during such a formative time in my life. My formal PhD program provided me with key skills for my career, of course, but my conversations and side projects with Steve during those years were just as important – fueling me to think big, be bold, and speak up.
Thank you, Steve.
Presenting a poster with Steve at the 2017 Society of Toxicology (SOT) meeting
I have long been interested in how research is applied in action, especially for assessments and evaluations of chemicals. And since joining the U.S. Environmental Protection Agency (EPA) almost five years ago, I have jumped right into the action incorporating epidemiological research into the risk assessment process.
Risk assessment is the way that scientific research is translated into policy, for better or for worse. Yet, historically, epidemiological research has played a limited role in risk assessment, and epidemiologists have had a limited understanding of the process.
That dual disconnect is problematic.
The role of epidemiology in the risk assessment process
While animal toxicology studies can provide important information for risk assessment, human epidemiological studies are generally preferred because they are conducted in the species of interest (humans) for questions of public health. Air pollution assessments (to support the National Ambient Air Quality Standards (NAAQS), for example) have always relied primarily on epidemiology studies. But other EPA assessments (such as from the Integrated Risk Information System (IRIS) program, where I work) have relied mostly on animal toxicology studies. This dynamic was likely due to a lack of epidemiological expertise within the EPA and limited robust environmental epidemiological studies outside of an occupational context.
But the times are changing!
While only 8% of IRIS assessments finalized between 1983-2007 conducted dose-response modeling (the second step of the risk assessment process) with epidemiological data, that number increased to 23% for assessments finalized between 2007-2025. This trend tracks with increased hiring of epidemiologists within EPA as well as growth in the field of environmental epidemiology (with innovations in both technology and statistical methods that allow for more accurate evaluation of the effects of low-level exposures that are common across the population).
While 23% is good progress, I think we can do better…
A gap remains
One barrier to more widespread incorporation of epidemiological studies into the risk assessment process is that, sometimes, key information is not provided in published studies. Because many research epidemiologists do not fully understand how these assessments are conducted, they are not necessarily aware of how to make their work most amenable to being utilized.
To be clear, I don’t blame researchers: they are trained and incentivized to focus on other topics. Risk assessment coursework is not part of standard graduate epidemiology programs.
But, if epidemiologists want their research to be impactful in the policy process, risk assessment is the ticket. A better understanding of risk assessment could allow them to tailor their work to this process.
The first part of our commentary describes how the EPA IRIS program conducts dose-response modeling. This information (summarized in this table) can serve as an educational resource for the epidemiology community. It could also be useful for any scientists serving on committees that review EPA assessment products, since these reviews necessitate some familiarity with the risk assessment process.
The second part of our commentary provides a set of recommendations targeted to researchers to facilitate the use of their work in dose-response assessment. These recommendations – which cover both modeling and reporting of data – are not particularly challenging to implement, but they would substantially increase the usability of epidemiological research in the assessment process.
Most research epidemiologists and risk assessors have the same ultimate goal: to improve public health. I hope this new paper gives epidemiologists a window into the inputs necessary for risk assessment so they can publish research that is more easily integrated into this process, which serves as the foundation for population-level impact.
A few months ago, a friend (who is an environmental lawyer) sent me a newly published article from the Harvard Environmental Law Review: De-Risking Environmental Law. Since then, I’ve shared it with colleagues near and far, and I’ve thought about it almost daily.
For those of you who don’t have the patience to read through 100 pages of a law review article, the author (William Boyd) summarizes his points nicely in this Legal Planet blog post (in particular, starting at the section “The Failures of Risk Assessment”).
Or even more succinctly, from the abstract: Boyd’s “central claim is that quantitative risk assessment has operated first and foremost as a political technology intended to discipline agencies and constrain their ability to solve complex problems rather than as a tool to generate useful information about the world.”
Reading this was somewhat shattering, as risk assessment is the process that we use to translate scientific research into policy. My day job involves contributing to (and believing in) this process, and much of my recent work is related to advancing aspects of risk assessment (e.g., here, here, and here).
Boyd suggests that the practice of risk assessment overly elevates scientific and technical questions (especially the seemingly endless road to reducing uncertainties in our calculations) while diminishing the inherent ethical questions in a system that allows for continued exposures (and harm). We spend years trying to obtain the best data and the best models to craft tenable policies, yet the population suffers health harms from continued exposures on the long road to regulation.
In the end, the author argues for an expert-judgment driven, precautionary approach that can move quickly to protect health based on hazard and drive innovation towards more sustainable chemistries.
(I, too, have had seemingly long-shot ideas about how to improve our current chemical assessment approaches. Yet, sometimes, we can see progress in those new directions, even if it is just incremental.)
I’m fired-up by Boyd’s provocative article. How many more late lessons from early warnings do we need before taking steps towards a system that can better – and more rapidly – protect public health?
As environmental epidemiologists with expertise in air pollution, we have both thought about this question in relation to our research as well as on a personal level from living through multiple difficult wildfire seasons in the Pacific Northwest.
Introduction to the AQI
The Air Quality Index (AQI) is, just as the name implies, an index to report air quality and is used as a public health tool for communicating risk due to air pollution.
Below is the basic version of the AQI table, with color coded categories and levels of concern corresponding to each numerical range of AQI values.
EPA actually develops a specific AQI table for five of the “criteria pollutants” regulated under the Clean Air Act: ozone, particulate matter (PM2.5 & PM10), carbon monoxide, sulfur dioxide, and nitrogen dioxide (see pp. 4-5 in this technical assistance document for a master AQI table for all of these pollutants). The overall AQI for a given day is determined by the pollutant with the highest concentration (usually PM2.5 or ozone).
The Meaning Behind the AQI
But how are these generic index values (e.g., 0-50, 51-100) linked to air pollutant levels?
Let’s explore this question in the context of the AQI for PM2.5, the most relevant index to consider for wildfire smoke.
“…the health effects evidence indicates that the level of 55 µg/m3 …is appropriate to use” (2013 Federal Register Notice)
Unhealthy
151-200
55.5-150.4
Linear extrapolation approach, assuming that increased PM2.5 concentrations are associated with greater proportions of the population affected
Very Unhealthy
201-300
150.5-250.4
Hazardous
>300
>250.4
The AQI cut-point of 50 (defining the upper end of the “Good” category) corresponds to an average PM2.5 concentration of 12.0 µg/m3 over a 24-hr period, the current National Ambient Air Quality Standard (NAAQS) for annual average exposure to PM2.5. (EPA has a structured process to revisit the NAAQS (approximately every 5 years), and these associated AQI cut-points get updated each time the standards are updated.)
The AQI cut-point of 100 (defining the upper end of the “Moderate” category) corresponds to 35 µg/m3, the current 24-hr average NAAQS for PM2.5.
And what about the cut-points for the higher categories? These are also not linked to any specific health effects evidence but instead determined by a linear extrapolation approach, with the assumption that increased PM2.5 concentrations are associated with greater proportions of the population affected.
The bottom line: the lower levels of the AQI are closely linked to national air quality standards and associated scientific research, but there is more uncertainty about the evidence used to set the upper levels.
Be cautious with the AQI
The AQI is the best approach that we currently have for quickly communicating information about air pollution risk to the general public. However, to be an informed “user” of this information (in essence, to calibrate your individual actions in relation to your own risk), it is important to understand the uncertainties and limitations in the index.
Differing composition of wildfire smoke PM2.5 vs. ambient PM2.5
The research used to set the NAAQS for PM2.5 – and correspondingly the AQI, as described above – is primarily based on ambient PM2.5 (usually from traffic, urban, or industrial sources), which has been the focus of extensive research for the past several decades. However, there is growing evidence that wildfire-associated PM2.5 is different from industrial/traffic-related PM2.5. For example, research to date indicates that wildfire-associated PM2.5 contains more toxic metals and is overall potentially more toxic to human health. This suggests that using an AQI based on ambient PM2.5 to understand risk from wildfire PM2.5 could lead to some uncertainties in estimating risks.
Uncertainties about the relevant exposure period
An important consideration in thinking about how an environmental exposure, like wildfire smoke, affects human health is to figure out which “exposure metric” is most relevant. For example, is it:
peak exposure (e.g., a one-time, very high exposure that pushes your body over a threshold to initiate a cascade of adversity)?
average exposure?
cumulative exposure (i.e., over a lifetime)?
The AQI is based on 24-hr average pollutant concentrations, so the implicit assumption is that average daily exposure is a good metric for understanding/predicting health impacts. Most studies on wildfire smoke exposure have focused on 24-hour exposure or cumulative exposure over 2-7 days. We don’t yet have a complete picture on how peak exposure or cumulative exposure over a lifetime might affect health outcomes. So, if you are making decisions based on the AQI, remember that this index is focused on short term exposures and short term outcomes.
A single pollutant index in a multi-pollutant world
Each individual pollutant’s AQI only considers potential health effects linked to that single pollutant. And each day’s overall AQI is only based on the highest overall single pollutant AQI. For example, if the AQIs for ozone, PM2.5, and carbon monoxide are 126, 102, and 90, respectively, the overall AQI for the day will be 126. In the case of wildfire smoke, the AQI is based on PM2.5 alone rather than the associated toxic gasses. This ignores the potential additive or synergistic effects of exposure to multiple air pollutants at high levels over a single 24-hr period.
No clear “safe” level of exposure
The more we learn about PM2.5, the more we realize that there might not be a clear “safe” level of exposure. This idea was described in the 2013 FR notice: “the epidemiological evidence upon which these [divisions] are based provides no evidence of discernible thresholds, below which effects do not occur in either sensitive groups or in the general population.”
The AQI paints a slightly different picture. For example, the index indicates that this 24-hr average PM2.5 of 35-55 µg/m3 is only concerning for “sensitive groups.” Based on this guidance, the general population is not likely to make behavior changes in this range. However, evidenceindicates population-wide impacts at and below these exposure ranges. In fact, an analysis of New York hospitalization data indicated that most excess hospital admissions occurred when the AQI was <100 (35-55 µg/m3).
Of course, getting PM2.5 down to zero is an infeasible goal from a regulatory perspective, and public health guidance is based on a combination of science and practicality. But with the growing understanding of PM2.5’s acute or chronic effects on almost every organ system at even very low levels of exposure, we recommend taking steps to reduce exposure even at lower levels of the AQI.
So, what does this all mean?
The AQI, like any other public health index or set of recommendations, has to distill complex information down to relatively simple forms and be able to make general recommendations despite real uncertainties and nuances (we saw this with public health messaging around COVID-19, also).
No index will be complete or perfect. But to be able to use them effectively/appropriately, it is important to understand some of their details and uncertainties (as we’ve described above).
In the case of wildfire smoke pollution, the AQI provides a good starting point to guide individual actions. However, it is important to listen to your body, pay attention to any symptoms, and consider reducing personal exposure to wildfire smoke even if the AQI category doesn’t officially suggest modifying behavior. Similarly, given the uncertainties regarding particulate composition, potential chronic effects from repeated exposure, mixture effects, and effects from low level exposure, we suggest interpreting the index cautiously. In other words, taking steps to reduce exposure to wildfire smoke is always better!
In the coming years, we hope that emerging research on wildfire smoke can inform updates to the index to better guide appropriate individual actions. Given the growing risk of fires in a changing climate, we will all need to learn how to live with – and protect ourselves – from wildfire smoke.
The goal of most environmental health research is to understand how a specific exposure impacts human health. In essence, the focus is on discovery.
The goal of risk assessment, however, is to use existing data to quantify population risk from exposures, with the aim of informing policy and regulations. Here, the focus is on evaluation andsynthesis of the published research.
But, because systematic review is focused on a specific research question, it is actually too narrow to inform the early stages of chemical assessments and evaluations – which often need to start by broadly scoping the entire evidence base before prioritizing topics for in-depth analyses.
A (relatively) new approach: Systematic Evidence Maps
A more useful tool for this context is a systematic evidence map (SEM). SEMs utilize the same systematic and transparent structure as systematic reviews, but they have a broader (and more neutral) goal: to characterize the entire evidence base for a certain topic and map the features of the data through visualization tools (e.g., Tableau). In contrast to systematic reviews, SEMs do not draw any conclusions.
When I first joined the EPA’s Integrated Risk Information System (IRIS) program, everyone seemed to be working on SEMs (since they are now often part of the IRIS assessment development process). Coming from my training in academia, I was not particularly excited about these “descriptive” products. The focus on tallying features of the evidence base (“X” number of epidemiological studies on developmental outcomes, “Y” number of experimental studies on developmental outcomes, etc) seemed pretty dull, to be honest.
But after almost two years into my job here, I’ve definitely come to see their value.
SEMs, like many other steps in the chemical assessment process (e.g., study evaluation, data extraction, etc), are not at all sexy. But they can substantially increase the transparency, efficiency, and credibility of assessments. (And that’s the goal, right – more trustworthy assessments? Yes, indeed.)
The benefits of SEMs
You can think about SEMs as very detailed scoping outlines developed from structured literature searches. By looking at an (often) interactive SEM (here’s one recently published for naphthalene), we can quickly identify data gaps, decide whether a new or updated assessment is needed, and/or refine the assessment priorities. This process ultimately saves us time and ensures that our assessments focus on topics with enough evidence to draw conclusions. (Of course, lack of data does not mean lack of risk, but we can’t develop robust assessments on topics with minimal data.)
The National Academy of Sciences, Engineering, and Medicine (NASEM) has also highlighted that SEMs could be valuable in themselves as publicly accessible databases of all of the research on a certain topic. (Check out this great one on PFAS from a few years ago!). Community groups, NGOs, and local, state, or federal agencies could use a published SEM as a starting point for their own particular goals and needs.
Advancing coordination
The idea that one group’s work to develop an SEM could be used as a starting point for another group’s projects is very exciting to me. It sounds so simple, but there is just not enough data sharing in the environmental health field. Sharing SEM content, which is broad enough to support downstream work in a variety of different contexts, could save a lot of time and resources.
This type of coordination is something that I’ve thought a lot about since publishing an infographic depicting the existing – and very complex – landscape for chemical evaluations and assessments. There’s no need to re-create the wheel for each assessment if we can start from a common and trusted SEM. Then, each program could take that body of work and use it for their particular statutory needs.
To further support this type of collaboration and coordination, the IRIS program has just published our SEM template (and an associated introductory article with relevant context). It’s not the most thrilling publication (especially to those outside the world of chemical assessments). But trust me – it is exciting! Using unified methods and reporting approaches can advance interoperability of SEMs and promote harmonization across the environmental health field.
Thayer, Kristina A., et al. “Use of Systematic Evidence Maps within the US Environmental Protection Agency (EPA) Integrated Risk Information System (IRIS) program: Advancements to date and looking ahead.” Environment International (2022): 107363. https://doi.org/10.1016/j.envint.2022.107363
Thayer, Kristina A., et al. “Systematic Evidence Map (SEM) Template: Report Format and Methods Used for the US EPA Integrated Risk Information System (IRIS) Program, Provisional Peer Reviewed Toxicity Value (PPRTV) Program, and Other “Fit for Purpose” Literature-Based Human Health Analyses.” Environment International (2022): 107468. https://doi.org/10.1016/j.envint.2022.107468
Today, I’m excited to share a video about my journey in the environmental health field. You can watch it below as well as on my newly launched website: https://rachelshaffer.com/.
Some of you have been following this blog for several years; others might be relatively new. Either way, I’m hoping this video gives you more of a sense of who I am and what draws me to the work that I do.
It’s been just over a year since I completed my PhD, and my final dissertation paper has now been published.
Although you may not have guessed it from the diversity of topics that I’ve covered on this blog, my dissertation research focused on the association between fine particulate matter (PM2.5) and dementia. To explore this link, my advisor (Lianne Sheppard, a biostatistician) and I worked together to craft a series of interrelated epidemiological analyses that took into account my background in toxicology.
Cerebrospinal fluid (CSF) is the fluid that bathes the brain. We can evaluate the CSF to get a sense of what is happening inside the brain and identify signs of disease. In this project, we looked at the association between PM2.5 and biomarkers of vascular injury in the CSF.
Why vascular injury, when our overarching research question was focused on dementia?
There is a growing understanding of the vascular contributions to cognitive decline and dementia, and cardiovascular disease itself is a major risk factor for dementia. Previous studies had looked at PM2.5 and vascular injury biomarkers in the blood, but no one had looked at the CSF – which directly reflects the condition of the brain. Understanding whether PM2.5 is associated with vascular dysfunction in the brain could shed light on the potential mechanisms linking PM2.5 and dementia.
The challenge with using CSF is that it is fairly invasive to collect, so we had a limited sample size for this project. Yet, we observed that short-term (7-day) and long-term (1-year) average PM2.5 exposures were associated with elevated levels of certain markers of vascular injury.
Our findings provide the first evidence of an association between PM2.5 exposure and vascular injury biomarkers in CSF. Of course, future studies are needed to confirm these conclusions. Nevertheless, these results are aligned with previous work linking PM2.5 to other measures of vascular injury and suggest a possible role of vascular dysfunction in the association between PM2.5 and dementia.
While the CSF project above probed signs of dementia-relevant injury during life, my next aim used autopsy data to evaluate markers of dementia-relevant neuropathology at death. For this project, we had access to more than 800 brains collected since 1994 as part of the Adult Changes in Thought (ACT) study.
Since Alzheimer’s Disease (AD) develops over a period of years – even decades – we focused our analyses on average exposures to PM2.5 over a 10-year period and severity of AD pathology (based on both beta-amyloid plaques and neurofibrillary tau tangles). As with the CSF analysis, we were the first cohort study to evaluate the link between PM2.5 exposure and AD neuropathology at autopsy in a cohort of older adults.
Going into this project, we were prepared to address the challenge of selection bias, a scenario where our autopsy sample might not be representative of the general population. It is plausible, for example, that people who agree to donate their brains after death might be systematically different than those who do not. To ensure that our results could still be generalizable to a broader population, we incorporated a statistical technique called inverse probability weighting (IPW) into our analyses.
Solving this problem requires the development of new statistical methods, and we’re in the middle of working on this issue with University of Washington (UW) biostatisticians.
Our current publication on this topic suggests inconclusive associations between PM2.5 and AD neuropathology, but we anticipate updating this analysis once the new methods have been developed. So, stay tuned…
The first two aims of my PhD research looked at dementia-relevant biological changes linked to air pollution. My final aim asked the “big picture” question: does elevated exposure to PM2.5 increase the risk of developing clinical dementia?
We were not the first group to investigate this question, but we thought it would be important to pursue in the Puget Sound-based ACT cohort for several reasons:
Many studies had used administrative data to obtain information on dementia. With this approach, there are potential problems of “misclassification” (resulting from mistakes, misdiagnoses, etc). The ACT cohort uses standardized, high quality protocols to diagnosis dementia, and all cases are confirmed through a consensus conference of clinicians. This gives us high confidence in our outcome data.
Because of pioneering efforts by UW professors, we have data on air pollution in the Puget Sound region dating back to the 1970s. Most areas only have air pollution monitoring data starting in the 1990s, which is when EPA began nationwide collection. So, we were able to estimate exposures to air pollution 40 years back in time – almost double the length of time any previous study could cover.
Even though dementia develops over a period of years/decades, all prior studies of PM2.5 and dementia had evaluated exposure periods of 5 years or less (and most just looked at 1-3 years of time). These recent exposures are not necessarily representative of exposures in earlier years, especially since there have been strong declines in air pollution in many areas of the world. With our extensive air pollution monitoring data (see #2 above) and a newly developed spatiotemporal model (see this blog post for background), we were able to focus our analysis on a 10-year average exposure period (and we even looked at a 20-year period as a sensitivity analysis!). We think this gives us better coverage of the window of time that would be relevant to triggering dementia-related processes. (However, the reality is that the scientific community still doesn’t fully understand the “critical window” of susceptibility that is most important to dementia development…)
Our study found that elevated 10-year exposure to PM2.5 was associated with an increased risk of dementia diagnosis (specifically, a 1 µg/m3 increase in PM2.5 was associated with a 16% increased risk of dementia). (See this blog post for some background on interpreting risk estimates).
Of course, our study (like all studies) had limitations. We used a spatiotemporal model to estimate exposures for our participants; see this blog post for my discussions on this and other approaches to estimating air pollution exposures in epidemiological studies. Additionally, we focused only on exposures to PM2.5. However, in reality, air pollution is a complex mixture of many particles and gases. The evidence does seem to be most compelling for the role of PM2.5 compared to other pollutants. But, we do need more research on the effects of co-pollutant mixtures as well as on particles that are even smaller than PM2.5: ultrafine particles (UFPs).
Because there is no successful treatment for dementia, we must focus on prevention to reduce the burden of this terrible disease. In August 2020 (just one month after I defended my PhD), the Lancet Commission on Dementia Prevention, Intervention, and Care identified air pollution as a potentially modifiable risk factor for dementia. Our newly published work strengthens a growing evidence base suggesting that reducing exposure to air pollution could contribute to reducing dementia incidence.
From Research to Action
There are some things that you can do to reduce your own exposure to air pollution, including wearing N95 masks (which we are all too familiar with now, due to COVID), using air purifiers in the home (which many people already have because of wildfire smoke, allergies, or other concerns), and altering outdoor activity patterns when air pollution is high.
The first part of the article introduces the infographic and discusses how it might be useful to the environmental health community (i.e., in risk assessment courses, or to identify public engagement opportunities).
The second part of the article suggests that this infographic (which looks daunting, even to me!) could motivate improved coordination and collaboration – both between and within agencies. The existing decentralized system allows each agency to develop specialized assessments focused on particular exposure scenarios, but it neglects the fact that we are exposed to the same chemicals from multiple sources that cross agency boundaries (for example, from food (the domain of FDA) and water (the domain of EPA)). To address this reality, we need more consistent consideration of aggregate risk (risk from multiple sources and pathways).
While the article is behind a paywall, the interactive infographic is available for free download as a ZIP file in the “supporting information” as noted above. If you’d like a copy of the full article but don’t have access, feel free to reach out to me.
rachel talks tox 