0
                                    $47,623

Allergic Rhinitis (Hay Fever)
Two potential valuation sources for allergic rhinitis were reviewed: Soni, 2008 and Bhattacharyya, 2011. Both studies utilize data from the Medical Expenditure Panel Survey (MEPS) and identify allergic rhinitis (also referred to as hay fever) using ICD-9 code 477. Each study analyzes medical expenditures for differing years, Soni, 2008 for the years 2000 and 2005, and Bhattacharyya, 2011 for the year 2007. Soni, 2008 calculates the cost-of-illness for allergic rhinitis as the mean expenditures for ambulatory care, in-patient services, and prescription medications per person. Bhattacharyya, 2011 calculates the incremental difference in annual healthcare expenditures for individuals with and without allergic rhinitis. Although Bhattacharyya, 2011 uses more recent data, the estimates are not specific to children. Therefore, we derived our COI estimates from the 2005 data presented by Soni, 2008, which are stratified by age group. The resulting COI for allergic rhinitis is $600 for ages zero to seventeen (2015$; Table 21). These COI estimates represent mean annual medical costs for patients with hay fever. Given that the health impact function for this endpoint relates to allergic rhinitis prevalence, these estimates are more applicable than values representing only first-year costs.
Asthma Onset
Belova et al., 2020 estimated the lifetime cost of asthma using data from the 2002 to 2010 Medical Expenditure Panel Survey (MEPS). The authors identify all individuals with current asthma (9,409 out of 158,867 respondents) using the ICD-9 code 493 in the MEPS Medical Conditions Files. Additionally, they identify the date of asthma onset for these individuals. Using the MEPS Medical Events files, which capture most types of medical expenditures (e.g., hospitalizations, emergency room visits, outpatient visits, prescriptions), Belova et al., 2020 estimated annual expenditures by asthma duration and age at onset. The annual healthcare costs for asthma -- as measured by healthcare expenditures by all paying parties -- vary from $700 to $1,800 for children and $800 to $2,200 for adults (2010$). They extrapolate these values to a lifetime cost stream for an incident chronic asthma case to generate present value estimates by onset age using discount rates of 3% and 7%. Additionally, the authors consider productivity impacts that capture 1) the probability of not being able to work due to health reasons, 2) the impact of asthma on occupational choice, and 3) impact of asthma on weekly earnings.
We adapt the Belova et al., 2020 estimates to align with the age groups 0 to 17, 4 to 21, and 35 to 99. This calculation entails weighting the Belova et al., 2020 age groups by their relative prevalence and propagating the standard errors to derive new uncertainty bounds. The results are summarized in Table 27. Confidence intervals are not provided for productivity losses to mirror the valuation functions in BenMAP-CE, which at present are only capable of reflecting uncertainty in one parameter (in this case, medical costs) (Table 21).
Table 27. Age-adjusted Belova et al., 2020 Estimates of Lifetime Asthma Costs
                              Age of asthma onset
                                 Discount rate
                           Healthcare costs (2015$)
                           Productivity Loss (2015$)
                                   0  -  17
                                      3%
                                    $17,232
                              ($16,366, $18,097)
                                    $27,426
                                   0  -  17
                                      7%
                                    $10,187
                               ($9,643, $10,730)
                                    $17,502

Asthma Symptoms/Exacerbation
Albuterol Use
As albuterol use is a new measure of PM2.5-attributable asthma symptoms, we developed a method for valuing this health endpoint. We estimate the economic value for albuterol use associated with asthma symptoms using prescription prices for albuterol inhalers. Epocrates and GoodRx provide cost and actuation information for four common types of albuterol inhalers in 2020 dollars.[,] Both online resources utilize published price lists, purchases, claim records, and pharmaceutical data to provide clinical statistics. Epocrates and the FDA provide cost and actuation information for one additional, less common, albuterol inhaler. We divide the cost of inhalers by the actuations per inhaler to calculate an average cost per actuation across all inhaler types. We then adjust the values to 2015$ using the Consumer Price Index (CPI) for medical care. Since medical cost index data were unavailable for 2020 at the time of these calculations, we used the most recently available index (2019). The resulting value for asthma symptoms, albuterol use is $0.35 per actuation (2015$) (Table 21).
Cough, Wheeze, Chest Tightness, and Shortness of Breath
While the risk estimates for both PM2.5- and O3-attributable asthma symptoms were updated, the valuation estimates for cough, wheeze, chest tightness, and shortness of breath are still based on the previous method, using the Dickie and Messman, 2004 analysis of parents' WTP to relieve asthma symptoms in children and adults. The authors derive the WTP estimates from an attribute-based, stated-choice question assessing preferences to avoid acute illness as part of a survey performed in Hattiesburg, Mississippi in 2000. Survey respondents are asked to identify whether they or their child have experienced the following asthma symptoms in the past year: cough with phlegm, shortness of breath with wheezing, chest pain on deep inspiration, and fever with muscle pain and fatigue. Respondents were then assigned one of sixteen illness profiles varying by symptom, symptom duration, in days, as well as discomfort level. Dickie and Messman, 2004 calculate the WTP for children ages zero to seventeen as $219, for one avoided mild symptom-day (2015$). The authors also provide WTP estimates by symptom, however, they represent six avoided symptom-days. Therefore, we apply the same WTP value, for one avoided mild symptom-day, to each asthma symptom endpoint (Table 21).
Cardiac Arrest
The COI for cardiac arrests occurring outside of the hospital is derived from O'Sullivan et al., 2011, who estimate three-year medical costs associated with cardiovascular disease events among adults ages 35 and older in the U.S. The authors rely on administrative claims data from a large U.S. health plan and develop econometric models to predict medical costs for 15 different cardiovascular events, including cardiac arrest, referred to as resuscitated cardiac arrest. The dataset includes over 20 million commercial and Medical Advantage members between 2002 and 2006. Cardiac arrests are identified using the ICD-9 code 427.5. The authors use propensity score matching to develop a control group with which to compare costs versus individuals that suffered cardiac arrest. Medical costs occurring within the month of the event were excluded to avoid double counting hospitalization costs, which are separately captured by the hospitalization valuation functions. Over three years, the total medical costs, excluding hospitalization, are $36,142 (undiscounted, inflated to 2015$), or $35,753 using a 3% discount rate and $35,282 for a 7% discount rate (Table 28 and Table 21).
Table 28. Valuation Estimate for Cardiac Arrests (2015$)
                                     Costs
                               Cumulative Costs
                                 Annual Costs

                                 Undiscounted
                               3% Discount Rate
                               7% Discount Rate
Month of Event*
$43,904
$43,904
$43,904
$43,904
Year 1
$71,901
$27,997
$27,997
$27,997
Year 2
$74,701
$2,800
$2,718
$2,617
Year 3
$80,046
$5,345
$5,038
$4,668
Years 1-3
$80,046
$36,142
$35,753
$35,282

Lung Cancer
The unit value for non-fatal lung cancer incidence is derived from the direct medical costs of lung cancer treatment estimated by Kaye et al., 2018. This COI value incorporates only direct medical costs and not lost earnings associated with lung cancer incidence because the average age of lung cancer diagnosis is approximately 70 and it is assumed that those aged 65 and older are retired and thus have exited the labor market. Lung cancer treatment costs depend to a large extent on the phase of care, with costs in the initial year of treatment (e.g., $17,422 for males) far exceeding the continuing costs of treatment in subsequent years (e.g., $3,269 for males). We calculate costs over a five-year span, beginning with the initial onset which is occurs with a delay after exposure.  The specific lag periods between exposure and onset are discussed in Section 6.4.2. The initial year's treatment cost is summed with four years of continuing annual costs discounted by 3% and 7%.
Furthermore, Kaye et al., 2018 provides separate treatment cost estimates for men and women. The distribution of new lung cancer cases by sex in the United States from Siegel et al., 2019 is approximately 51% male and 49% female. This distribution of new lung cancer cases was used to weight the sex-specific cost estimates from Kaye et al., 2018 to obtain a combined five-year cost estimate for both sexes. In order to adjust the cost estimate to 2015$ using a medical cost index, we assume that costs presented by Kaye et al., 2018 are in 2010$ as an approximate midpoint of the data years 2007-2012. Altogether, the cost of non-fatal lung cancer incidence over a five-year period is estimated to be $33,809 using a 3% discount rate or $32,548 using a 7% discount rate (Table 21).
For an outcome such as lung cancer, there is an expected time lag between changes in pollutant exposure in a given year and the total realization of health effect benefits, commonly referred to in regulatory analyses as the "cessation lag." The time between exposure and diagnosis can be quite long, on the order of years to decades, to realize the full benefits of the air quality improvements. This latency period is important in order to properly discount the economic value of these health benefits.
To estimate the latency period, we performed a literature search using the keywords "non-fatal lung cancer," "lung cancer," "PM2.5," "latency," and "incidence." Five papers that estimate the risk of lung cancer incidence from PM2.5 exposure using a latency period were identified. The latency period length and country of the identified papers are summarized in Table 29. Based on estimates of lung cancer latency from the literature, 10 years was the most common latency period estimate found in the literature (i.e., the mode).
Table 29. Latency Periods Used in Lung Cancer Risk Assessment Papers
Study
Latency Period (years)
Location
Gogna et al., 2019
                                       5
                                    Canada
Bai et al., 2020
                                     4; 10
                                    Canada
Kulhanova et al., 2018
                                      10
                                    France
Coleman et al., 2020
                                    10; 15
                                      US
Harrison et al., 2004
                                      20
                                      US

To account for the latency period between air pollution reductions and avoided lung cancer diagnoses in our economic valuation estimates, we developed an age-at-diagnosis cessation lag distribution method based on an approach previously used to estimate avoided cases of kidney cancer in analyses of water quality rules (U.S. EPA, 2017). The method uses lung and bronchus cancer diagnosis age-distribution from the Surveillance, Epidemiology, and End Results Program (SEER). For this model, we assumed that the case reduction distribution would follow the age-pattern of cancer diagnosis between the age at which the exposure change occurs and 99 years. Table 30 shows an example case reduction distribution calculation for an exposure change experienced at 55. SEER estimates 92.2% of lung and bronchus cancer cases occur in individuals 55 years and older. Dividing the percentages in the remaining age bins by 92.2% (the percent of lung and bronchus cancer diagnoses between the age of exposure change and end of lifetime), we find that there is a 24% chance that the risk reductions for a 55-year-old occur between ages 55 and 64, a 37% chance that the case reductions occurs between ages 65 and 74, etc. For distributing avoided cases within an age bin, we assume an equal incidence distribution across years within each bin.

Table 30. Percent Lung and Bronchus Cancer Incidence by Age and Distribution of Risk Reduction by Age for an Exposure Change at 55
                                   Age Group
                      Percent New Cases per Year by Age*
             Percent of New Cases Occurring at or After Age 55[1]
0-20
                                       0
                                      NA
20-34
                                      0.2
                                      NA
35-44
                                      0.9
                                      NA
45-54
                                      6.6
                                      NA
55-64
                                     21.8
                                      24
65-74
                                     34.1
                                      37
75-84
                                     26.6
                                      29
85-99
                                      9.7
                                      11
55-99
                                     92.2
                                      100
*May not sum to 100% due to rounding
[1]Calulcated as the percentage in column 2 divided by 92.2%, where 92.2% is the percentage of lung and bronchus incidence between age 55 and 99.
This and other potential cessation lag distribution models for lung cancer onset are described and compared in section 6.4.2.
Minor Restricted Activity Days (MRADs)
Due to their definition, for the purposes of benefits estimation minor respiratory-restricted activity days (MRRAD) are assumed to constitute all MRADs (Ostro and Rothschild, 1989). While no peer-reviewed studies estimating WTP to avoid a MRRAD are available, a central estimate and upper and lower bounds of WTP to avoid a MRRAD were developed by IEc (IEc, 1993). When estimating benefits associated with an MRAD, we use a triangular distribution centered at the estimate.
Any estimate of mean WTP to avoid a MRRAD (or any other type of restricted activity day other than Work Loss Day (WLD)) will be somewhat arbitrary because the endpoint itself is not precisely defined. Many different combinations of symptoms could presumably result in some minor or less minor restriction in activity. Krupnick and Cropper, 1992 argued that mild symptoms will not be sufficient to result in a MRRAD, so that WTP to avoid a MRRAD should exceed WTP to avoid any single mild symptom. A single severe symptom or a combination of symptoms could, however, be sufficient to restrict activity. Therefore, WTP to avoid a MRRAD should, these authors argue, not necessarily exceed WTP to avoid a single severe symptom or a combination of symptoms. The "severity" of a symptom, however, is similarly not precisely defined; moreover, one level of severity of a symptom could induce restriction of activity for one individual while not doing so for another. The same is true for any combination of symptoms.
Given that there is inherently a substantial degree of arbitrariness in any point estimate of WTP to avoid a MRRAD (or other kinds of restricted activity days), the reasonable bounds on such an estimate must be considered. By definition, a MRRAD does not result in loss of work. WTP to avoid a MRRAD should therefore be less than WTP to avoid a WLD. At the other extreme, WTP to avoid a MRRAD should exceed WTP to avoid a single mild symptom. The highest IEc midrange estimate of WTP to avoid a single symptom is $28.51 (2015$), for eye irritation. The point estimate of WTP to avoid a WLD in the benefit analysis is $110.62 (2015$). If all the single symptoms evaluated by the studies are not severe, then the estimate of WTP to avoid a MRRAD should be somewhere between $28.51 and $110.62. Because the IEc estimate of $69.58 (2015$) falls within this range (and acknowledging the degree of arbitrariness associated with any estimate within this range), we use the IEc estimate of $69.58 (2015$) (Table 21).
School Loss Days
There is currently one unit value available in BenMAP for school loss days, based on (1) the probability that, if a school child stays home from school, a parent will have to stay home from work to care for the child, and (2) the value of the parent's lost productivity. We first estimated the proportion of families with school-age children in which both parents work, and then valued a school loss day as the probability of a work loss day resulting from a school loss day (i.e., the proportion of households with school-age children in which both parents work) times a measure of lost wages.
From the U.S. Bureau of Labor Statistics (2015) we obtained the rate of participation in the workforce of women with children under 18 years of age. We multiplied this rate (69.9%) by the estimated daily lost wage (if a mother must stay at home with a sick child), based on the median full-time weekly wage among women 25 and older in 2015. This median weekly wage is $759 (2015$). Dividing by five work days per week gives an estimated median daily wage of $152. The expected loss in wages due to a day of school absence in which the mother would have to stay home with her child is estimated as the probability that the mother is in the workforce times the daily wage she would lose if she missed a day = 69.9% of $152, or $106. We currently have insufficient information to characterize the uncertainty surrounding this estimate.
A unit value based on the approach described above is likely to understate the value of a school loss day in four ways. First, it omits WTP to avoid the symptoms/illness which resulted in the school absence. Second, it effectively gives zero value to school absences which do not result in a work loss day. Third, the approach may use a wage rate that is too low by assuming that men do not stay at home with sick children. Fourth, does not account for deleterious effects on student learning and subsequent utility or productivity. The unit value of $106 is therefore considered an "interim" value until such time as alternative means of estimating this unit value become available (Table 22).
Stroke
Mu et al., 2017 estimates COI of non-fatal stroke incidence using direct medical costs incurred during initial hospitalization and the 360 days following hospital discharge. The study identifies individuals experiencing a first-time stroke using ICD-9 codes 434 and 436. The authors analyze medical claims from January 2006 to March 2015 utilizing the retrospective IMS LifeLink PharMetrics Plus database for individuals ages 18 to 65, and Medicare Advantage and Medicare Supplemental Claims for individuals above the age of 65. The authors present acute care and long-term care costs stratified by three discharge classifications: dead at discharge, discharged with disability, and discharged without disability. We estimate the average costs for non-fatal cases by weighting the costs for individuals discharged with disability and without disability by their prevalence (23 and 77 percent, respectively). The resulting COI for non-fatal stroke incidence is $33,962 (2015$) (Table 21). This value reflects one-year medical costs following stroke and does not include hospitalization costs, as these costs are separately captured by hospitalization valuation functions. We reviewed several studies that estimate longer-term medical costs (Goodwin et al., 2011, Lee et al., 2007, Luengo-Fernandez et al., 2012, Nicholson et al., 2016) and concluded that roughly three quarters of costs are incurred in first year after stroke occurrence.
Work Loss Days (WLDs)
Work loss days are valued at a day's wage. BenMAP calculates county-specific median daily wages from county-specific annual wages by dividing by (52*5), on the theory that a worker's vacation days are valued at the same daily rate as workdays. This estimate does not include benefits rate for lost work time. The resulting COI for work loss days varies by county, but has a median value of $150 (2015$) (IEc, 1993).(Table 21).
Developing Income Growth Adjustment Factors for Health Endpoint Onset/Occurrence
Chapter 4 of the BenMAP-CE User Manual provides instructions for formatting and adding income growth data (U.S. EPA, 2018). These values are used to adjust WTP estimates for growth in real income. As discussed in that chapter, evidence and theory suggest that WTP should increase as real income increases.  When reviewing the economic literature to develop income growth adjustment factors, it is important to have an economist assist. For an overview of valuation, see Chapter 7 of the BenMAP-CE User Manual, "Aggregating, Pooling, and Valuing".
Adjusting WTP to reflect growth in real income requires three steps:
 Identify relevant income elasticity estimates from the peer-reviewed literature.
 Calculate changes in future income.
 Calculate adjustments to WTP based on changes in future income and income elasticity estimates.
1. Identifying income elasticity estimates
Income elasticity estimates relate changes in demand for goods to changes in income. Positive income elasticity suggests that as income rises, demand for the good also rises. Negative income elasticity suggests that as income rises, demand for the good falls. We do not adjust COI estimates according to changes in income elasticity due to the fact that COI estimates the direct cost of a health outcome; instead we adjust this metric using inflation factors described above. We include income elasticity estimates specific to the type of health endpoint associated with the WTP estimate for three types of health effects: minor, severe and mortality. Minor health effects are those of short duration. Severe, or chronic, health effects are of longer duration. Consistent with economic theory, the peer reviewed literature indicates that income elasticity varies according to the severity of the health effect. A review of the literature revealed a range of income elasticity estimates that varied across the studies and according to the severity of health effect. Table 31 summarizes the income elasticity estimates for minor health effect, severe health effect and mortality. Here we have provided a lower, upper, and central elasticity estimate for each type of health endpoint.
Table 31. Income Elasticity Estimates for Minor Health Effects, Severe Health Effects, and Mortality
Health Endpoint
Lower Bound
Central Estimate
Upper Bound
Minor Health Effect
0.04
0.15
0.30
Severe and Chronic Health Effects
0.25
0.45
0.60
Mortality
0.08
0.40
1.00

2. Calculating changes in future income
The next input to the WTP adjustment is annual changes in future income. The Congressional Budget Office's (2016) ten-year projections of US Gross Domestic Product (GDP) are used to estimate changes in future income. Historical GDP data came from the U.S. Bureau of Commerce's Bureau of Economic Analysis. GDP values were adjusted for inflation as needed using the Implicit Price Deflator annual index, published by the Economic Research Division of the Federal Reserve Bank of St. Louis. We divided the projected change in GDP by the Woods & Poole (2015) projected change in total US population to produce an estimate of the future GDP per capita.
3. Calculating changes in WTP
The income elasticity estimates from Table 31 and the estimated changes in future income may then be used to estimate changes in future WTP for each health endpoint. The adjustment formula follows four steps:
1)
ε=∆WTPWTP∆II=WTP2-WTP1x(I2+I1)(I2-I1)x(WTP2+WTP1)

2)
εI2WTP2+ εI2WTP1- εI1WTP2-εI1WTP1=I2WTP2+I1WTP2- I2WTP1-I1WTP1

3)
WTP2x εI2- εI1- I2- I1=WTP1x εI1- εI2- I1- I2

4)
WTP2=WTP1x εI1- εI2- I1- I2 εI2- εI1- I2- I1

Table 32 summarizes the income-based WTP adjustments used within BenMAP-CE for minor health endpoints, severe health endpoints, and premature mortality. BenMAP-CE applies the "mid" income growth adjustment to the WTP for each corresponding health endpoint. The "low" and "upper" are provided for bounding the "mid" estimate. More information on the uncertainties associated with the choice of income elasticity is provided in section 6.4.3.
Table 32. Income-Based WTP Adjustments by Health Effect and Year

Characterizing Uncertainty and Evaluating Sensitivity to Alternate Assumptions
Complex analyses such as the one presented in the final RIA for the Revised CSAPR Update rule use many estimated parameters and inputs. The approach for estimating PM2.5 and O3 benefits includes health effect risk estimates from epidemiologic studies, population data, population growth estimates, economic data for monetizing benefits, and assumptions regarding the future state of the world (i.e., on-the-books regulations). When the uncertainties from each stage of the analysis are compounded, even small uncertainties can have large effects on the total quantified benefits.
After reviewing the EPA's approach to quantifying benefits, the National Research Council (NRC) (2002, 2008) highlighted the need to conduct rigorous quantitative analyses of uncertainty and to present benefits estimates to decision makers in ways that foster an appropriate appreciation of their inherent uncertainty. Since the publication of these reports, the EPA has continued improving its techniques for characterizing uncertainty in the estimated air pollution-attributable benefits.
In light of these recommendations, we incorporate new quantitative and qualitative characterizations of uncertainty. Where possible, we quantitatively assess uncertainty in each input parameter (for example, we characterize statistical uncertainty by performing Monte Carlo simulations). We invest the time and resources in performing the most comprehensive uncertainty analyses for those input parameters that most greatly influence on the size of the estimated health impacts.
In some cases, this type of quantitative analysis is not possible due to lack of data, so we instead characterize the sensitivity of the results to alternative plausible input parameters. And, for some inputs into the benefits analysis, such as the air quality data, we lack the data to perform either a quantitative uncertainty analysis or sensitivity analysis.
Sections 6.1 and 6.2 quantitatively describe the uncertainty associated with estimated PM2.5 and O3-attributable incidence. Section 6.3 provides information on the sensitivity to more granular baseline incidence rates. Section 6.4 quantitatively discusses the influence of uncertainty in the economic valuation functions. Lastly, section 6.5 qualitatively discusses the various potential sources of uncertainty, sometimes including sources of uncertainty touched upon quantitatively.
Quantitative Characterization of PM2.5 Uncertainty and Evaluating Sensitivity to Alternate PM2.5 Assumptions
Below we describe our approach for characterizing uncertainty in the estimated PM2.5-related effects. We start first with the role of Monte Carlo assessment in generating a quantitative distribution of results. We next describe how alternative risk estimates can be useful for assessing the sensitivity of the estimated PM2.5-related mortality and morbidity to plausible alternative input parameters; this gives insight to the influence of the functional form of the model or alternative epidemiologic approaches. Quantitative sensitivity analyses using alternative or additional risk estimates are included for the following PM2.5-attributable health endpoints: mortality in adults (section 6.1.1), asthma onset in children (section 6.1.3), cardiovascular hospital admissions (section 6.1.4), and respiratory hospital admissions (section 6.1.5).
Statistical Uncertainty Around the Risk Estimate (Monte-Carlo Assessment)
For all endpoints analyzed, we use a Monte Carlo simulation in which we sample from the standard error associated with each risk estimate and present the resulting 2.5[th] and 97.5[th] percentile values from this distribution as a 95[th] percentile confidence interval around the estimated health impact and monetized health benefits. Monte Carlo methods are a well-established means of characterizing random sampling error associated with the risk estimates from epidemiological studies.  This approach randomly samples from a distribution of incidence and valuation estimates to characterize the effects of uncertainty in those inputs on output variables. The reported standard errors in the epidemiological studies determined the distributions for individual effect estimates for endpoints estimated using a single study. The confidence intervals around the monetized benefits incorporate the epidemiology standard errors as well as the distribution of the valuation function. These confidence intervals do not reflect other sources of uncertainty inherent within the estimates, such as baseline incidence rates, populations exposed, and transferability of the effect estimate to diverse locations. As a result, the reported confidence intervals and range of estimates give an incomplete picture about the overall uncertainty in the benefits estimates.
Adult All-Cause Mortality
Two studies of all-cause, long-term PM2.5 exposure and mortality were identified as best characterizing U.S. risk in adults, Di et al., 2017b and Turner et al., 2016. Additional information regarding the cohort concentration exposure distributions (section <IP_ADDRESS>) and additional risk estimates potentially providing insight into the effect of various potential sources of uncertainty, such as different exposure estimation techniques (section <IP_ADDRESS>), confounding by O3 (section <IP_ADDRESS>), statistical regression techniques and methods to control for confounders (section <IP_ADDRESS>), and effect modification by individual risk factors (section 6.1.6).
Low Concentration Exposures
Each epidemiological risk estimate is based on a distribution of air quality concentrations experienced by the original cohort population. As such, it is important to consider the relationship between the concentrations from which the mortality estimates are derived and the concentrations at which the estimates are subsequently applied in future policy scenarios in which concentrations are likely to be lower due to decreasing air pollution trends. When estimating health impacts, we are most confident in results estimated using projected air quality concentrations that closely align with those observed in the epidemiological study from which the risk estimate was obtained (i.e., we are less confident applying risk estimates to pollutant concentrations that do not match the original cohort due to changes in air pollutant concentrations over time). To address the potential mismatch between projected air quality levels and those in the epidemiologic study, we include air quality information from the original epidemiologic studies where feasible.
Additional information was requested from mortality study authors regarding the ambient concentrations used to estimate exposure of the original cohort. Study authors provided cohort specific PM2.5 concentration data at varying levels of detail. PM2.5 concentrations for the two long-term exposure epidemiologic cohort studies examining mortality, ACS CSP-II and Medicare are presented in Figure 13 (Di et al., 2017b, Turner et al., 2016). We also included the distribution of PM2.5 concentrations from a recent analysis of the CanCHEC cohort in order to compare to some of the lowest reported concentrations in North America (Crouse et al., 2015). Points reflect cohort specific PM2.5 concentration data, with connecting lines estimating missing data.

Figure 13. Cumulative Percentile of PM2.5 Cohort Exposure from the ACS CSP-II, Medicare, and CanCHEC Cohorts
As air pollution concentrations continue to decline an increasing fraction of the population will be exposed to PM2.5 concentrations at the lower end of the air quality distribution experienced by the study cohort. The distribution of PM2.5 concentrations for each of three large, long-term exposure cohorts are provided in Table 33. For comparison, the lowest reported PM2.5 concentrations from previous studies (Krewski et al., 2009, Lepeule et al., 2012) in which risk estimates were used to estimate all-cause mortality attributed to long-term PM2.5 exposure were 5.8 and 8.0 ug/m[3], whereas the recent studies identified as best characterizing long-term PM2.5 exposure and the risk of all-cause mortality include PM2.5 concentrations below 3 ug/m[3] (Di et al., 2017b, Turner et al., 2016).
Table 33. Low Concentration PM2.5 Exposures from the ACS CSP-II, Medicare, and CanCHEC Cohorts
                                    Cohort
                   Percentile of Cohort Exposure (ug/m[3])

                                     0.0%
                                     1.0%
                                     1.5%
                                     2.0%
                                     2.5%
                                     3.0%
                                     3.5%
                                     4.0%
                                     4.5%
                                     5.0%
                                     10.0%
ACS CSP-II
                                      2.8
                                      5.8

                                      6.3
                                      6.5
                                      6.6

                                      7.0

                                      7.4

Medicare
                                      0.0
                                      5.5
                                      5.9
                                      6.1
                                      6.3
                                      6.5
                                      6.7
                                      6.8
                                      6.9
                                      7.1
                                      7.9
CanCHEC
                                      0.0
                                      3.2

                                      3.5
                                      3.6

                                      4.0
                                      4.7

We note that PM2.5 concentrations reported in cohort studies are not equivalent to NAAQS design values (DVs). Information relating PM2.5 concentrations from cohort studies discussed within this section to PM2.5 DVs can be found in section 3.2 of the 2020 PM PA (U.S. EPA, 2020c).
Estimating and Assigning Exposures in Epidemiology Studies
New developments in exposure assessment, including hybrid spatiotemporal models that incorporate satellite observations of AOD, land use variables, surface monitoring data from monitors, and chemical transport models, have led to improvements in the spatial resolution and extent of pollutant concentration surfaces. After reviewing the current state of exposure science, the 2019 PM ISA stated that "a number of studies demonstrate that the positive associations observed between long-term PM2.5 exposure and mortality are robust to different methods of assigning exposure" and the 2020 O3 ISA articulated that "hybrid methods have produced lower error predictions of ozone concentration compared with spatiotemporal models using land use and other geospatial data alone but may be subject to overfitting given the many different sources of data incorporated into the hybrid framework."
Although these advancements may reduce bias and uncertainty in risk estimates, the accuracy of hybrid exposure estimates can be difficult to confirm in areas lacking monitors. On the other hand, studies using monitor data as the only exposure information have increasing exposure uncertainty the farther people live from the monitor site.
Di et al., 2017a provided PM2.5-attributable mortality risk estimates based on either a hybrid exposure estimation approach combining photochemical air quality modeling with ground-level monitoring data or only on monitoring data. Comparing these two estimates aids in understanding how sensitive long-term, all-cause estimates of PM2.5-attributable mortality are to exposure estimation method.  A comparison of the risk estimates using either the hybrid or monitor-based exposure estimates is available in Table 34. The italicized risk estimate was identified for use in the main PM2.5 benefits assessment.
Table 34. Di et al., 2017a PM2.5-Attributable Mortality Risk Estimates per 10 ug/m[3] from Different Exposure Estimation Techniques
Exposure Technique
Risk Estimate
Hybrid Exposure Estimate
1.073 (1.071, 1.075)
Monitor-Based Exposure Estimate
1.061 (1.059, 1.063)

Turner et al., 2016 and Pope et al., 2015 analyzed the same ACS CSP-II population over the same time period but used different hybrid exposure estimation techniques. Turner et al., 2016 used the hierarchical Bayesian space - time model (HBM) approach, which combines ambient measurement data with gridded estimates from the CMAQ photochemical model. Pope et al., 2015 used a land use regression model with Bayesian Maximum Entropy kriging of residuals (LURBME). Sensitivity of the risk estimate to the exposure estimation technique is available in Table 35, including the estimate identified for the main benefits assessment in italics.
Table 35. PM2.5-Attributable ACS CSP-II Mortality Risk Estimates per 10 ug/m[3] from Different Exposure Estimation Techniques
Exposure Technique
Risk Estimate
HBM
1.06 (1.04-1.08)
LURBME
1.07 (1.06 - 1.09)

Confounding by O3
When considering the relationship between pollutant exposure and health effects, it can be informative to consider whether risk estimates are subjected to confounding when including other pollutants in copollutant models, especially when health impacts of more than two highly correlated pollutants are being estimated concurrently. Regarding long-term exposures, the 2019 PM ISA concluded that "positive associations observed between long-term PM2.5 exposure and total mortality remain relatively unchanged after adjustment for O3, NO2, and PM10−2.5"
Both Turner et al., 2016 and Di et al., 2017a provided single-pollutant and two-pollutant (including O3 as a copollutant) PM2.5-attributable mortality risk estimates. Although the 2019 PM ISA found that, in general, PM2.5 risk estimates were relatively unchanged to the inclusion of O3 in copollutant models, a comparison of risk estimates that either do or do not include O3 as a copollutant is included to clarify this potential sensitivity with respect to all-cause PM2.5-attributable mortality. Differences in the magnitude of risk estimates including or excluding O3 as a copollutant are provided in Table 36. Italicized risk estimates were identified for use in the main benefits assessment.
Table 36. Single- and Two-Pollutant (Including O3 as a Copollutant) PM2.5-Attributable Mortality Risk Estimates per 10 ug/m[3]

Turner et al., 2016
Di et al., 2017a
Two-Pollutant
1.06 (1.04-1.08)
1.073 (1.071, 1.075)
Single-Pollutant
1.06 (1.04-1.08)
1.084 (1.081, 1.086)
Statistical Technique
Di et al., 2017a provided mortality risk estimates using two different statistical methods to adjust for covariates, potentially providing insight into model uncertainties associated with statistical regression techniques. A comparison of the risk estimates using either the generalized estimating equation (GEE) approach, which the authors identified as the main analysis, or the mixed-effects model (COXME) can be found in Table 37.
Table 37. Di et al., 2017a PM2.5-Attributable Mortality Risk Estimates per 10 ug/m[3] from Different Statistical Techniques
Statistical Technique
Risk Estimate
GEE
1.073 (1.071, 1.075)
COXME
1.081 (1.078, 1.083)

Asthma Onset in Children
For a number of health endpoints we identified plausible alternative risk estimates to characterize the sensitivity of the main risk estimate to alternative assumptions and/or input parameters. Below we detail: 1) the endpoints for which we considered alternative risk estimates; and 2) the studies from which we drew the alternative risk estimates. This type of sensitivity assessment is also performed for other PM2.5 and O3 health endpoints in sections 6.1.4, 6.1.5, and 6.2.4.
The study identified as best characterizing risk for this health endpoint took place in Canada (Tetreault et al., 2016). Even though comparatively Tetreault et al., 2016 was preferred in all identification criteria to other available studies (e.g., study size, exposure estimation technique, study period, etc.) other than location, we thought it useful to include the available U.S.-based risk estimates as uncertainty analyses. An overall comparison of the main risk estimate and 95% confidence interval from Tetreault et al., 2016 and the alternative risk estimates and confidence intervals from McConnell et al., 2010 and Nishimura et al., 2013 can be found in Table 38. Details about the two studies providing alternate risk estimates is below.
Table 38. Potential Sensitivity of Estimated Instances of Asthma Onset
                        Potential Source of Uncertainty
      Potential Insights Gained from Quantitative Uncertainty Analyzes[2]
Application of Risk Estimates to Other Locations and Populations
Tetreault et al., 2016 included only Canadians whereas Nishimura et al., 2013 included five U.S. urban areas and McConnell et al., 2010 was restricted to southern CA
Study Size
Tetreault et al., 2016 included the largest study size, approximately twenty-five times the size of either Nishimura et al., 2013 or McConnell et al., 2010
Study Period
Tetreault et al., 2016 evaluated the most recent health study period (1996-2011) compared to 2002-2006 for McConnell et al., 2010 and 1986-2005 for Nishimura et al., 2013
Exposure Estimate
Tetreault et al., 2016 used hybrid exposure estimates whereas sever states, whereas Nishimura et al., 2013 and McConnell et al., 2010 used monitor-based estimates
Statistical Technique
Tetreault et al., 2016 and McConnell et al., 2010 use time-varying and multilevel Cox proportional hazard models, respectively, whereas Nishimura et al., 2013 uses logistical regression models

Two of the five ISA-identified studies of asthma onset took place in the U.S. (McConnell et al., 2010, Nishimura et al., 2013). McConnell et al., 2010 examined the association between long-term traffic-related air pollution (PM2.5, PM10, O3, and NO2) exposure and incident asthma in children. The authors collected data for three years from a cohort of 2,497 kindergarten and first-grade children aged 4-9 who entered the Southern California Children's Health Study without asthma or wheeze. McConnell et al., 2010 defined new-onset asthma as physician-diagnosed asthma reported by parents on a yearly questionnaire. While the primary focus of the study was traffic-related air pollution from local vehicle emissions, the authors also utilized ambient air pollution exposure data from central site monitors in each of the 13 communities in the Southern California Children's Health Study. The authors used a multilevel Cox proportional hazards model to estimate the association between ambient air pollution exposure and new-onset asthma, controlling for race/ethnicity, secondhand smoke exposure, and pets in the home. The identified hazard ratio of 1.66 (95% CI: 0.91-3.05) for a 17.4 ug/m[3] (range of exposure in the 13 communities) increase in annual average PM2.5 exposure came from a single pollutant model.
The other study, Nishimura et al., 2013, investigated the relationship between long-term early-life pollution exposure (PM2.5, PM10, O3, NO2 , and SO2) and asthma onset in 3,343 Latino and African American children in five urban areas (Chicago, IL; Bronx, NY; Houston, TX; San Francisco, CA; Puerto Rico). The authors obtained data from the Genes - environments and Admixture in Latino Americans (GALA II) Study and the Study of African Americans, Asthma, Genes and Environments (SAGE II). GALA II and SAGE II are case-control studies that enrolled children with and without asthma. The studies defined case subjects as children with physician-diagnosed asthma plus two or more symptoms of coughing, wheezing, or shortness of breath in the two years before study enrollment while control subjects were children with no reported history of asthma, lung disease, or chronic illness, and no reported symptoms of coughing, wheezing, or shortness of breath in the two years before study enrollment. The authors estimated annual average pollution exposures during the first year of life as well as the first three years of life from self-reported residential histories by calculating inverse distance-squared weighted averages from the four closest U.S. EPA Air Quality System monitoring stations within 50 km. The authors first used regional- and study-specific logistic regression models to estimate the association between asthma diagnosis and pollution exposure, controlling for demographics and socioeconomic status and subsequently combined the regression coefficients into a multi-region estimate using a random-effects meta-analysis. The identified odds ratio of 1.03 (95% CI: 0.90-1.18) for a 1 ug/m[3] increase in average annual PM2.5 levels at the residential address during the first year of life came from a single pollutant model. Beta effect coefficients from the main (italicized) and sensitivity analyses are available in Table 39.
Table 39. Beta Coefficients and Standard Errors (SE) from Studies of Examining Long-term PM2.5 Exposure and New Onset Asthma in Children
Study
Age Range
Beta Coefficient (SE)
Tetreault et al., 2016
0-17
0.044 (0.0009)
McConnell et al., 2010
4-17
0.029 (0.017)
Nishimura et al., 2013
7-21
0.030 (0.069)

Cardiovascular Hospital Admissions
Bell et al., 2015 was identified as best characterizing risk across the U.S. for benefits assessment purposes as it included the largest study size, most recent time period, and a nationally representative geographic area. However, it was restricted to ages >64 and based exposure estimates solely on monitoring data. There was also another large study of PM2.5-attributable cardiovascular hospital admission impacts that included all ages and incorporated hybrid exposure estimation techniques (Talbott et al., 2014). Differences in the age ranges and ICD-9 codes prevented pooling of the two estimates but comparing the two estimates could provide insights into uncertainties associated with epidemiologic estimates of this health endpoint (Table 40). Therefore, we include a risk estimate of cardiovascular hospital admission impacts from long-term PM2.5 exposure from Talbott et al., 2014 as a sensitivity analysis of this health endpoint (Table 41). Please note that Talbott et al., 2014 provides individual risk estimates for each state, which will be pooled into a single estimate to compare with Bell et al., 2015.
Talbott et al., 2014 assessed daily PM2.5 concentrations and hospitalizations for cardiovascular disease in Florida, Massachusetts, New Hampshire, New Jersey, New Mexico, New York, and Washington from 2001 to 2008. The authors gathered hospital discharge data from each state's respective data stewards. Talbott et al., 2014 conducted a time-stratified case crossover study using hospitalization data for all cardiovascular diseases (ICD-9 390-459) and for several specific cardiovascular diseases within the ICD-9 390-459 range. Authors used a downscaling Bayesian space-time modeling approach to combine air monitoring data and air gridded numerical outputs from CMAQ to predict daily PM2.5 concentrations. The authors gathered meteorological data from the Centers for Disease Control (CDC) Wonder North America Land Data Assimilation System Daily Air Temperatures and Heat Index. Risk estimates were presented for a 10 ug/m[3] increase in PM2.5 for each state and season across three single-day lags (0, 1, and 2) and a three-day lag average (0-2) by diagnosis. All-year risk estimates were identified over season-specific estimates and estimates of multiday average lag period were identified over single-day lag estimates. The seven state-specific risk estimates identified as sensitivity analyses were pooled using the random or fixed effects algorithm in BenMAP-CE. The seven risk estimates reflect a mix of positive and negative values. State-specific risk estimates identified from Talbott et al., 2014  come from a two-pollutant multivariable model including O3 of ICD-9 codes 390-459: 1.005 (95% CI: 0.998-1.012) for Massachusetts; 1.011 (95% CI: 1.007-1.016) for New Jersey; 1.011 (95% CI: 0.973-1.050) for New Mexico; and 1.011 (95% CI: 1.008-1.014) for New York. Each odds ratio is for a 10 ug/m[3] increase in the averaged daily mean PM2.5 concentration 0-, 1-, and 2-day lags (Talbott et al., 2014, Table 3). Beta effect coefficients from the main (italicized) and sensitivity analyses are available in Table 41.
Table 40. Potential Sensitivity of Estimated Cardiovascular Hospital Admissions
                        Potential Source of Uncertainty
       Potential Insights Gained from Quantitative Uncertainty Analyzes
Application of Risk Estimates to Other Locations and Populations
Talbott et al., 2014 included all ages whereas Bell et al., 2015 was restricted to ages >64
Confounding by Individual Risk Factors (Location)
Talbott et al., 2014 was restricted to seven states, Bell et al., 2015 included all states
Confounding by Other Pollutants
Talbott et al., 2014 included the copollutant O3
Exposure Estimate
Talbott et al., 2014 used hybrid exposure estimates whereas sever states, Bell et al., 2015 used monitor-based estimates

Table 41. PM2.5-Attributable Cardiovascular Hospital Admissions Beta Estimates
Study
Beta Coefficient (SE)
Bell et al., 2015
0.00065 (0.00009)
Talbott et al., 2014, MA
MA: 0.00050 (0.00035), NJ: 0.00109 (0.0002), NM: 0.00109 (0.0019), NY: 0.00109 (0.00015), FL: -0.00040 (0.0003), NH: -0.00121 (0.0012) , WA: -0.00090 (0.0005)

Respiratory Hospital Admissions
Similar to cardiovascular hospital admissions, there was an estimate of PM2.5-attributable respiratory hospital admissions that included all ages and utilized a hybrid exposure estimation approach, but was geographically limited, in this case to a single state. However, we thought it useful to include this estimate as a sensitivity analysis due to the contrasts between it and the italicized main benefits estimates (Table 42 and Table 43). As compared to PM2.5-attributable mortality and cardiovascular hospital admission impact estimates, there may be greater uncertainty associated with estimates of PM2.5-attributable respiratory hospital admissions (Table 43).
Jones et al., 2015 encompassed all ages, races, and ethnicities with a case-crossover analysis in New York state, using 24-hour average PM2.5 concentrations from CMAQ and meteorological data from the National Climactic Data Center. The authors assessed hospital discharge data from the New York State Department of Health State Planning and Research Cooperative System through principle diagnosis categorized by ICD-9 code (chronic bronchitis (ICD-9 491), emphysema (ICD-9 492), asthma (ICD-9 493), and chronic airway obstruction (ICD-9 496)). Authors used a single pollutant conditional logistic regression model to analyze the respiratory hospital admission and PM2.5 chemical constituent data over time and by season. The authors calculated hazard ratios using SAS (version 9.2) with 95% confidence intervals from the regression models. The estimate best characterizing U.S. risk comes from the 4-day lag all-year PM2.5 estimate in Figure 2a: 1.006 (1.004-1.009). Please note, this risk estimate was derived from the figure, as the exact numbers were not provided in the paper and the authors did not respond to our request.
Table 42. Potential Respiratory Hospital Admission Sensitivity Insights
                        Potential Source of Uncertainty
      Potential Insights Gained from Quantitative Uncertainty Analyzes[2]
Application of Risk Estimates to Other Locations and Populations
Jones et al., 2015 included all ages whereas Bell et al., 2015 was restricted to ages >64
Confounding by Individual Risk Factors (Location)
Jones et al., 2015 was restricted to a single state, Bell et al., 2015 included all states
Exposure Estimate
Jones et al., 2015 used hybrid exposure estimates, whereas Bell et al., 2015 used monitor-based estimates

Table 43. PM2.5-Attributable Respiratory Hospital Admissions Beta Risk Estimates
Study
Age Range
Beta Coefficient (SE)
Bell et al., 2015
65-99
0.00025 (0.0001)
Ostro et al., 2016
0-18
0.00275 (0.0008)
Jones et al., 2015
0-99
0.00080 (0.0002)

Emergency Hospital Admissions (EHAs)
Interestingly, a substantial subset of the ISA-identified recent epidemiologic literature evaluating respiratory hospitalizations restricted analyses to emergency hospital admissions (EHAs), defined as hospitalizations admitted from the emergency department (section 3.2). Due to time and resource requirements, we were unable to develop county-level baseline incidence data for EHAs, in addition to total hospital admissions. However, as we were interested in how estimates of EHAs compared to total, we include a risk estimate of respiratory EHAs from Zanobetti et al., 2009 using national baseline incidence data. Though the EHA estimate came from a smaller and older study then the main analysis respiratory hospital admission study, the EHA estimate is nearly an order of magnitude larger than the risk estimate included in the main estimate (italicized).
Table 44. Comparison of the PM2.5-Attributable Respiratory Hospital Admissions Beta Risk Estimate to the EHA Respiratory Estimate
Study
Beta Coefficient (SE)
Bell et al., 2015
0.00025 (0.0001)
Zanobetti et al., 2009
0.00204 (0.0004)

Effect Modification of Health Impacts in At-Risk Populations
ISAs typically include an assessment of the weight of evidence demonstrating that certain subpopulations experience increased mortality or morbidity risks from air pollutant exposure compared to other groups. This is also known as effect modification and occurs when the measure of an effect changes across levels of a variable other than PM2.5 exposure. The 2019 PM ISA examined toxicological, controlled human exposures, and epidemiologic literature considering whether certain populations and lifestages might be at increased risk of air pollutant-related health effects (U.S. EPA, 2019c).
The ISAs categorize relationships between exposure and effect modification for various population and lifestages into the following four groups:
 Adequate evidence: There is substantial, consistent evidence within a discipline to conclude that a factor results in a population or lifestage being at increased risk of air pollutant-related health effect(s) relative to some reference population or lifestage. Where applicable, this evidence includes coherence across disciplines. Evidence includes multiple high-quality studies.
 Suggestive evidence: The collective evidence suggests that a factor results in a population or lifestage being at increased risk of air pollutant-related health effect(s) relative to some reference population or lifestage, but the evidence is limited due to some inconsistency within a discipline or, where applicable, a lack of coherence across disciplines.
 Inadequate evidence: The collective evidence is inadequate to conclude whether a factor results in a population or lifestage being at increased risk of air pollutant-related health effect(s) relative to some reference population or lifestage. The available studies are of insufficient quantity, quality, consistency, and/or statistical power to permit a conclusion to be drawn.
 Evidence of no effect: There is substantial, consistent evidence within a discipline to conclude that a factor does not result in a population or lifestage being at increased risk of air pollutant-related health effect(s) relative to some reference population or lifestage. Where applicable, the evidence includes coherence across disciplines. Evidence includes multiple high-quality studies.
Presenting PM2.5-attributable benefit estimates striated by the value of another covariate can provide insight into risk within various population subgroups. To accomplish this, we reviewed relevant chapters from the 2019 PM ISA in order to compile and assess studies cited in support of the Agency's determinations, focusing on studies referenced in Table 12-3 for population characteristics with either "adequate evidence" (i.e., substantial, consistent evidence) or "suggestive evidence" (i.e., limited evidence due to inconsistency or a lack of coherence in research) of increased risk (sections <IP_ADDRESS> and <IP_ADDRESS>) (U.S. EPA, 2019c). The factors with "adequate evidence" for PM2.5 are lifestage (children) and race (nonwhite populations), while the factors with "suggestive evidence" are pre-existing disease (cardiovascular disease, respiratory disease, and obesity), genetic factors (variant genotypes), low socioeconomic status, and smoking.
Study and Risk Estimate Identification Criteria for Populations At-Risk for PM2.5 Exposures
We identified all studies in the related section of ISA Chapter 12 for each at-risk factor listed above, resulting in a set of 123 studies for at-risk populations. This collection includes the following number of studies, with some studies duplicated for multiple endpoints.
 8 studies for lifestage (children),
 25 studies for race (nonwhite populations),
 67 studies for pre-existing disease across disease types,
 18 studies for genetic factors, 25 studies for socioeconomic status, and
 14 studies for smoking.
We then focused our review on the risk factors with "adequate evidence", due to stronger supporting evidence as well as because they could be evaluated using currently available data. We extracted study information from all studies with "adequate evidence" and applied initial screening criteria to identify peer-reviewed, epidemiological studies focused on PM2.5 conducted in the US or Canada. We also documented the mortality and/or morbidity health endpoints included in each study, focusing on all-cause mortality and respiratory morbidity endpoints. We then evaluated the group of remaining studies based on additional identification criteria built off the criteria for the primary analysis, described in Table 45.
Table 45. PM2.5 At-Risk Study Identification Criteria
Criterion
Notes
Peer-Review
Peer-reviewed research exclusively
Study design
Epidemiological study
PM2.5 Study
PM2.5 exposure rather than other PM10 or other sizes
Study Location
US or Canada
Study Duration
Long-term studies preferred
Population Attributes
Presents risk estimates for clearly defined at-risk groups for which data currently exist in BenMAP-CE
Causal or Likely Causal Health Effects
Adequate evidence for at-risk groups in ISA
Economically Valuable Health Effects
Health endpoints for which economic values have been or could reasonably be developed
Baseline Incidence Data
Must be able to identify baseline incidence data for subpopulations

All-Cause Mortality
For the mortality endpoint, seven all-cause mortality studies for the nonwhite population met our criteria. No mortality studies for the child at-risk group met the initial screening criteria. Of the seven studies of nonwhite populations, three were short-term exposure studies relating daily PM2.5 exposure and daily deaths and four were long-term exposure studies relating annual PM2.5 exposure and annual mortality. Consistent with the main benefits assessment, we focused on the following four long-term studies as long-term exposure studies may include some effects of short-term exposures (section <IP_ADDRESS>): Di et al., 2017b, Kioumourtzoglou et al., 2016, Parker et al., 2018, and Wang et al., 2017.
We evaluated specific details of risk estimates provided by each study to determine if sufficient information exists for use in a quantitative sensitivity analysis. Of the studies, only Di et al., 2017b provided sufficient information to apply risk models quantifying increased risks to nonwhite groups, including non-Hispanic white, Black, Asian, Native American, and Hispanic-white populations. Additional detail on the study can be found in section <IP_ADDRESS>.3.1.2 or in the associated Study Information Table.
We applied similar criteria to morbidity endpoints for the child and nonwhite at-risk groups. No studies cited for the child subgroup met our criteria for inclusion, and one endpoint, emergency room visits for asthma, was chosen for quantification for the nonwhite populations at-risk factor group. The study we chose to evaluate was Alhanti et al., 2016, which presents risk information for white and pooled nonwhite populations disaggregated into five age groups.
We developed BenMAP-ready Health Impact Functions for each at-risk group described by Di et al., 2017b and Alhanti et al., 2016, summarized in Table 46.
Table 46. Identified PM2.5 At-Risk Beta Coefficients and Standard Errors
At-Risk Factor
Endpoint
Study
Subgroup
Beta Coefficient (SE)
Race, nonwhite populations
Morality, All Cause
Di et al., 2017b
Non-Hispanic White
0.0061 (0.0001)

Hispanic White
0.0110 (0.0008)

Black
0.0189 (0.0004)

Asian
0.0092 (0.0010)

Native American
0.0095 (0.0019)
Race, nonwhite populations
Emergency Room Visits, Asthma
Alhanti et al., 2016
White, age 0-4
0.0025 (0.0019)

Nonwhite, age 0-4
0.0037 (0.0012)

White, age 5-18
0.0025 (0.0016)

Nonwhite, age 5-18
0.0049 (0.0012)

Quantitative Characterization of O3 Uncertainties and Evaluating Sensitivity to Alternate O3 Assumptions
Statistical Uncertainty Around the Risk Estimate (Monte-Carlo Assessment)
For all endpoints analyzed, we use a Monte Carlo simulation in which we sample from the standard error associated with each risk estimate and present the resulting 2.5[th] and 97.5[th] percentile values from this distribution as a 95[th] percentile confidence interval around the estimated health impact and monetized health benefits. Monte Carlo methods are a well-established means of characterizing random sampling error associated with the risk estimates from epidemiologic studies. This approach randomly samples from a distribution of incidence and valuation estimates to characterize the effects of uncertainty on output variables. The reported standard errors in the epidemiologic studies determined the distributions for individual effect estimates for endpoints estimated using a single study. For endpoints estimated using a pooled estimate of multiple studies, the confidence intervals reflect both the standard errors and the variance across studies. The confidence intervals around the monetized benefits incorporate the standard errors from the epidemiologic risk estimate as well as the distribution of the valuation function. These confidence intervals do not reflect other sources of uncertainty inherent within the estimates, such as baseline incidence rates, populations exposed and transferability of the effect estimate to diverse locations. As a result, the reported confidence intervals and range of estimates give an incomplete picture about the overall uncertainty in the benefits estimates.
Respiratory Mortality
Confounding by PM2.5
When considering the relationship between pollutant exposure and health impacts, it can be informative to consider whether risk estimates are changed when other pollutants are included in copollutant models, especially when health impacts of multiple pollutants are being estimated concurrently. While no conclusions were formed regarding the impact of copollutant confounding on long-term exposure-related respiratory mortality, the 2020 O3 ISA found that "positive associations observed between long term O3 exposure and total mortality remain relatively unchanged after adjustment for PM2.5 and NO2."
Turner et al., 2016 provided single- and multipollutant (including PM2.5 as a copollutant) O3-attributable respiratory mortality risk estimates. A comparison of risk estimates that either do or do not include PM2.5 as a copollutant is included to clarify this potential sensitivity with respect to O3-attributable respiratory mortality. Differences in the magnitude of risk estimates including or excluding PM2.5 as a copollutant are provided in Table 47.
Table 47. Single- and Two-Pollutant (Including PM2.5 as a Copollutant) Long-Term O3-Attributable Respiratory Mortality Risk Estimates per 10 ppb
Study
Single-Pollutant
Multipollutant
Turner et al., 2016
1.14 (1.10-1.18)
1.12 (1.08-1.16)

Short-Term O3 Exposures
Potential Threshold Analysis
The 2020 final O3 ISA evaluated a number of studies examining the shape of the concentration-response relationship for short-term O3 exposure and total/nonaccidental mortality, which we use to inform the long-term O3-attributable respiratory mortality relationship. The ozone ISA found that "studies that used different statistical approaches and ozone averaging times (i.e., 24 hour avg and 8 hour max) provide evidence of a linear concentration-response relationship, with less certainty in the shape of the curve at lower concentrations [i.e., ...30 ppb for 8 hour max], [although] an examination of whether a threshold exists in the ozone mortality concentration-response relationship provided no evidence of a concentration below which mortality effects do not occur when examining 5 μg/m[3] (~2.55 ppb) increments across the range of 1 hour max concentrations reported in the U.S. and Canadian cities included in [a large cohort]." As the Zanobetti and Schwartz, 2008 risk estimate uses the MDA8 metric, it can be used to quantitatively assess the effect of an O3 threshold at 30 ppb would have on benefits estimates. For context, approximately 3.7% of the contiguous U.S. population is projected to reside in areas where MDA8 O3 concentrations are annually below 30 ppb in 2024 (U.S. EPA, 2020b). Clinical evidence provides little indication that adverse effects occur at extremely low levels in most individuals. Epidemiologic evidence is qualitatively discussed further in section <IP_ADDRESS>.
Confounding by PM
Regarding short-term exposures, the 2020 O3 ISA found that "the few recent multicity studies that examined potential copollutant confounding provide evidence supporting that O3 mortality risk estimates are relatively unchanged or slightly attenuated, but remain positive, in copollutant models with PM2.5, PM10, and NO2."
Katsouyanni et al., 2009 provided single- and two-pollutant (including PM10 as a copollutant) short-term O3-attributable respiratory mortality risk estimates for a subset of 15 of the 86 cities analyzed. A comparison of risk estimates that either do or do not include PM10 as a copollutant is included to clarify this potential sensitivity with respect to O3-attributable respiratory mortality. Differences in the magnitude of risk estimates including or excluding PM2.5 as a copollutant are provided in Table 48. Please note, as distributed lag risk estimates were not provided for the two-pollutant analyses, in additional to the inclusion of PM2.5 as a copollutant and the number of cities analyzed, there is a difference in the lag duration between the estimates in Table 48.
Table 48. Single- and Two-Pollutant (Including PM1o as a Copollutant) Short-Term O3 Exposure O3-Attributable Excess Premature Respiratory Mortality Risk Estimates per 10 ppb
Study
Single-Pollutant
Two-Pollutant
Katsouyanni et al., 2009
0.77% (0.17%, 1.37%)
0.99% (-0.33%, 2.31%)

All-Cause Mortality
When estimating air pollutant-attributable health impacts, EPA focuses on endpoints for which the underlying scientific evidence is strongest. That is, when evaluating evidence across scientific disciplines (i.e., clinical, animal toxicological, and epidemiologic) there is often consistency of effects within a discipline, coherence of effects across disciplines, and evidence of biological plausibility. Such an approach gives us greater confidence in the relationship between exposure and health outcome. For criteria pollutants, EPA typically relies upon the causality determinations in the latest ISA or equivalent, which are made using a weight-of evidence approach. Generally, to estimate the pollutant-attributable human health benefits in which we are most confident, we at least assess health effects identified as having a `causal' or `likely to be causal' relationship with the pollutant of interest in the most recently published ISA. This is not to imply there may not be benefits associated with endpoints having a "suggestive of, but not sufficient to infer, a causal relationship," but rather that there is greater uncertainty in these potential benefits.
Because of the significance of the endpoint, we include a limited quantitative sensitivity analysis of total mortality associated with long-term O3 exposure. While the 2020 O3 ISA concluded that evidence was "suggestive of, but not sufficient to infer," a causal relationship between long-term exposures and total mortality, the reduction of this risk is likely still valuable to society. As such, for this sensitivity discussion, we include risk estimates of long-term, all-cause O3-attributable total mortality from the two studies used to estimate PM2.5-attributable mortality risk (Table 49). Please note these long-term, all-cause risk estimates include respiratory mortality estimates and should not be added to the respiratory mortality estimates.
Table 49. Long-Term O3-Attributable Total Mortality Risk Estimates per 10 ppb
Study
Risk Estimate (per 10 ppb increase in O3)
Risk Estimate Details
Turner et al., 2016
1.02 (1.01-1.03)
Fully adjusted HBM multipollutant estimate from Table E9, ages 35-99
Di et al., 2017b
1.011 (1.010, 1.012)
GEE two-pollutant main analysis estimate from Table 2, ages 65-99

Asthma Onset in Children
The study identified as best characterizing risk for asthma onset in children was conducted in Canada (Tetreault et al., 2016). Even though comparatively Tetreault et al., 2016 was preferred in identification criteria to other available studies (e.g., study size, exposure estimation technique, etc.) other than location and study period, we thought it useful to include the largest and most recent U.S.-based risk estimates as a sensitivity analysis. An overall comparison of the main risk estimate from Tetreault et al., 2016 and the alternative risk estimates from Garcia et al., 2019 can be found in Table 50. Details about the study providing an alternate risk estimate is below.
Table 50. Potential Sensitivity of Estimated Instances of Asthma Onset
                        Potential Source of Uncertainty
      Potential Insights Gained from Quantitative Uncertainty Analyses[2]
Application of Risk Estimates to Other Locations and Populations
Tetreault et al., 2016 included only Canadians whereas Garcia et al., 2019 was restricted to southern CA
Study Size
Tetreault et al., 2016 included the largest study size, approximately twenty-five times the size of Garcia et al., 2019
Study Period
Garcia et al., 2019 evaluated a more recent and longer health study period (1993-2014) compared to 2002-2011 for Tetreault et al., 2016
Exposure Estimate
Tetreault et al., 2016 used hybrid exposure estimates whereas sever states, whereas Garcia et al., 2019 used monitor-based estimates
Statistical Technique
Tetreault et al., 2016 used time-varying Cox proportional hazard models, whereas Garcia et al., 2019 uses Poisson log-linear regression models

Three of the four ISA-identified studies of long-term O3-attributable asthma onset took place in the U.S., although only one included a study period more recent than 2005 (Garcia et al., 2019). Garcia et al., 2019 examined the associations between long-term ozone exposure and asthma onset in children (aged nine-18 years) with no history of asthma in Southern California. The authors followed three waves of participants from the Children's Health Study for eight years between 1993 and 2014. Garcia et al., 2019 obtained health and demographic data from parents, guardians, or participants, who completed questionnaires annually. In order to calculate annual mean, community-level ozone exposure, the authors acquired daily eight-hour mean O3 concentrations through ambient air pollution monitors. Multi-level Poisson regression models with one-year lag showed no statistically significant associations between long-term O3 exposure and asthma onset in children. Models adjusted for demographic variables as well as factors pertaining to family medical history, environmental factors, and near-roadway pollution.
The magnitudes of main and alternate risk estimates of long-term O3 exposure and asthma onset in children provided in Table 51.
Table 51. Long-Term O3-Attributable Asthma Beta Coefficients
Study
Beta Coefficient
Age Range
Tetreault et al., 2016
0.020754
0-17
Garcia et al., 2019
0.01695
9-18

Understanding the Effect Modification of Health Impacts in At-Risk Populations
Effect modification was also investigated with regard to O3 exposures. We reviewed relevant chapters from 2020 O3 ISA and used a similar methods to that described for PM2.5 to compile and assess studies cited in support of the Agency's determinations (section 6.1.6). As the 2020 O3 ISA only presents an evaluation of at-risk groups in summary form and extensively references the findings from the 2013 O3 ISA, we focused on the detailed chapter from that previous document in identifying the at-risk factors and studies to review (U.S. EPA, 2013).Factors with "adequate evidence" are genetic factors, asthma, children, older adults, diet, and outdoor workers. Factors with "suggestive evidence" are sex, SES, and obesity. Considering feasibility and our review criteria, we focused on studies addressing increased risks based on age in the adequate evidence group and note that some health functions already applied in the primary analysis focus on asthmatic subpopulations. We also elected to include illustrative calculations for some risk factors with "suggestive evidence", specifically those for sex.
Study and Risk Estimate Identification Criteria for Populations At-Risk for O3 Exposures
We compiled epidemiologic studies from the related section of Chapter 8 of the 2013 O3 ISA for the following at-risk factors, excluding all other study types (e.g. toxicological studies), for a total of 28 studies. This collection includes the following number of studies, with some studies duplicated for multiple endpoints.
 9 studies for children,
 10 studies for older adults, and
 18 studies for sex
We excluded the genetic factors population from our analysis, as we do not currently have the capability to estimate health impacts among variant genotypes. We excluded diet, outdoor workers, and obesity for similar reasons, as we have no representative dataset for use in analysis with these risk factors. Effects on asthmatics were not included in this analysis because we currently lack highly resolved spatial data on asthma prevalence, in part because effects on asthmatic populations are included in the main analysis. We also excluded the SES group as the studies associated with the SES group for O3 were associated with other methodological challenges. We coded the identified studies into a spreadsheet and applied the initial screening criteria described previously. We collected information on mortality and/or morbidity endpoints assessed in each study and focused on all-cause mortality and respiratory morbidity endpoints. The remaining studies were evaluated based on the additional identification criteria described in Table 52.
Table 52. O3 At-Risk Study Identification Criteria
Criterion
Notes
Peer-Review
Peer-reviewed research exclusively
Study Design
Epidemiologic study
Ozone Study
Research on ozone exposure is used
Study Location
U.S. or Canada
Population Attributes
Presents risk estimates for clearly defined at-risk groups for which data currently exist in BenMAP-CE
Exposure Duration
Both short- and long-term exposure studies
Causal or Likely Causal Health Effects
Adequate or suggestive evidence for at-risk groups in ISA
Economically Valuable Health Effects
Health endpoints for which economic values have been or could reasonably be developed
Baseline Incidence Data
Must be able to identify baseline incidence data for subpopulations
Season
All year exposure or warm season exposure
Ozone Exposure Metrics
MDA8, or able to be converted to MDA8
Lag Structure
Choose model that most clearly represents the relationship between ozone exposure and the physiologic changes for the health endpoint

Total Mortality
Regarding the health endpoint of mortality, three studies for older adults met our criteria: Medina-Ramon and Schwartz, 2008, Zanobetti and Schwartz, 2008, and Katsouyanni et al., 2009. All three studies provided sufficient details to apply risk model information for short-term all-cause mortality among adult age groups.  There were no mortality studies for the child at-risk group in either the 2013 or 2020 O3 ISAs (U.S. EPA, 2013, U.S. EPA, 2020a). For the at-risk population stratified by sex, two studies met our initial criteria: Medina-Ramon and Schwartz, 2008, which evaluated short-term O3 exposure and all-cause mortality, and Jerrett et al., 2009, which evaluated long-term O3 exposure and respiratory mortality. Both studies provided sufficient data to apply risk model information to male and female subpopulations. We developed health impact functions for these studies.
The O3-mortality risk estimates for at-risk subpopulations reported in Medina-Ramon and Schwartz, 2008 required additional modification in order to use those results to develop health impact functions.  The authors presented excess risk estimates for each subpopulation as the additional percent change in mortality for persons who have the condition, compared to persons without the condition. For our populations of interest, these subgroups were persons age 65 or older compared to those younger than 65, and females relative to males. However, they did not report the risk estimate for these comparison groups, so in order to estimate the total excess risk for each exposed at-risk group, we needed to first back-calculate the excess risk for the comparison group without the factor of interest. We accomplished this by assuming that the authors' overall reported excess risk for the full sample of 0.65% (95% confidence interval = 0.38% to 0.93%) could be expressed as a weighted average of the unreported excess risk ("x") and the full excess risk for the at-risk group, which would be expressed as the sum of x and the reported excess risk from Medina-Ramon and Schwartz, 2008 Table 2, where the weights are calculated using the total and at-risk group sample sizes in Table 1 of that paper. For example, to calculate the total excess risks for the females in the sample, we used the following equation:

             ERTotal= ERMale(PopMale)+ERFemale(PopFemale)PopTotal
where ERTotal is the full sample excess risk of 0.65%; ERFemale is the excess risk of ozone exposures for females; ERMale is the excess risk of ozone exposures for males; PopTotal is the total sample population; and PopFemale and PopMale are the size of the female and male subsets of the sample population, respectively. We also know from Table 2 of that paper that:
                            ERFemale= ERMale+0.58 %
Substituting and using the available information from Medina-Ramon and Schwartz, 2008 Tables 1 and 2, we can solve for ERMale and then ERFemale:

             0.65%= ERMale1,365,937+0.58%+ERMale1,363,7032,729,640

                                 ERMale=0.36 %
                                      and
                          ERFemale= 0.36%+0.58%=0.94%

We then used the full excess risk value for the female subpopulation to derive a health impact function for ozone-related mortality for females.
Respiratory Morbidity
We applied the same identification criteria described in section <IP_ADDRESS> to respiratory morbidity endpoints for the child and sex at-risk groups. Three studies for children met our criteria for inclusion: Mar and Koenig, 2009, Paulu and Smith, 2008, and Villeneuve et al., 2007. Each study evaluated emergency room visits for asthma and provided sufficient risk model information stratified by age. No studies cited for the older adult population met our criteria for inclusion. Three studies for sex met our identification criteria: Paulu and Smith, 2008, Cakmak et al., 2006, and Lin et al., 2005. These studies evaluated emergency room visits for asthma, all respiratory hospital admissions, and hospital admissions for lower respiratory infection, respectively. Each study provided sufficient risk model information for male and female subpopulations. We developed health impact functions for all studies identified above. All the at-risk studies we identified are summarized in Table 53.
Table 53. Identified O3 At-Risk Beta Coefficients and Standard Errors
At-Risk Factor
Endpoint
Study
Subgroup
Beta Coefficient (SE)[1]
Lifestage, older adults
Mortality, All Cause
Medina-Ramon and Schwartz, 2008 and Zanobetti and Schwartz, 2008
Age 0-64
-0.0001 (0.0001)

Age 65+
0.0010 (0.0002)
Lifestage, older adults
Mortality, All Cause
Katsouyanni et al., 2009
Age 0-74
0.0008 (0.0002)

Age 75+
0.0007 (0.0003)
Lifestage, older adults
Mortality, All Cause
Zanobetti and Schwartz, 2008
Age 0-20
0.0001 (0.0003)

Age 21-30
0.0001 (0.0004)

Age 31-40
0.0001 (0.0002)

Age 41-50
0.0001 (0.0002)

Age 51-60
0.0005 (0.0002)

Age 61-70
0.0004 (0.0001)

Age 71-80
0.0005 (0.0001)

Age 81+
0.0003 (0.0001)
Sex
Mortality, All Cause
Medina-Ramon and Schwartz, 2008 and Zanobetti and Schwartz, 2008
Female
0.0009 (0.0002)

Male
0.0004 (0.00004)
Sex
Mortality, Respiratory
Jerrett et al., 2009
Female
0.0044 (0.0011)

Male
0.0011 (0.0014)
Lifestage, children
Emergency Room Visits, Asthma
Mar and Koenig, 2009
Age 0-17
0.0104 (0.0044)

Age 18+
0.0039 (0.0027)
Lifestage, children
Emergency Room Visits, Asthma
Paulu and Smith, 2008
Age 2-14
0.0104 (0.0050)

Age 15-34
0.0148 (0.0035)
Lifestage, children
Emergency Room Visits, Asthma
Villeneuve et al., 2007
Age 2-4
0.0032 (0.0033)

Age 5-14
0.0073 (0.0024)

Age 15-44
0.0058 (0.0018)

Age 45-64
0.0063 (0.0033)

Age 65-74
0.0073 (0.0055)

Age 75+
-0.0006 (0.0067)
Sex
Emergency Room Visits, Asthma
Paulu and Smith, 2008
Female
0.0113 (0.0027)

Male
0.0104 (0.0032)
Sex
Hospital Admissions, All Respiratory
Cakmak et al., 2006
Female
0.0013 (0.0004)

Male
0.0017 (0.0003)
Sex
Hospital Admissions, Lower Respiratory Infection
Lin et al., 2005
Female
0.0087 (0.0060)

Male
0.0040 (0.0052)
[1] Beta coefficients and SEs in this table have been converted to MDA8 for comparability
Quantitative Characterization of Baseline Incidence Rate Uncertainties
When available from HCUP, we incorporate county-level hospital admissions and emergency department visit baseline incidence data. Comparisons of the county-level data (box and whisker plot) to the national-level data (red circles) are available in Figure 14.
Figure 14. Example County-Level and National-Level Emergency Department Visit and Hospital Admission Baseline Incidence Data

We performed several quality assurance checks to ensure the incidence rates accurately reflect observed health outcomes in the underlying counties. These checks included:
 Examining data inputs to ensure the endpoints reflect the specified set of ICD codes from the epidemiological studies;
 Reviewing data processing scripts to ensure all calculations implement the intended procedures, as documented in the BenMAP-CE User Manual (U.S. EPA, 2018);
 Re-processing existing incidence rates in BenMAP-CE's "Other Incidence (2014)" to confirm that changes to data processing to incorporate new endpoints have no or minimal impact on incidence rate data for existing endpoints from BenMAP-CE's 2017 data update;
 Comparing the relative magnitude of related endpoints to ensure that incidence rates for broader endpoints (e.g., HA, All Respiratory) are greater than incidence rates for endpoints with a narrower set of ICD codes (e.g., HA, Asthma);
 Comparing national baseline incidence counts when using county-level incidence rates and nation-level incidence rates to ensure that, in aggregate, the two datasets produce similar results; and
 Examining the geographic distribution of incidence rates to ensure no counties, states, or regions, are characterized by anomalously low or high incidence.
We identified no systematic errors or bias in the raw data or data processing steps. The main source of uncertainty in these data is related to imputation of rates where county data for specific endpoints were suppressed due to statistical reliability or privacy concerns. The state or regional rates used to substitute for these suppressed values may under- or over-estimate individual county rates.
Quantitative Characterization of Economic Valuation Estimate Uncertainties
Mortality Cessation Lag
Following advice from the Health Effects Subcommittee of EPA's independent Science Advisory Board (SAB-HES), the agency typically assumes that some amount of time lapses between when air pollution is reduced and when PM-attributable mortality is reduced fully. Within the context of benefits analyses, this term is often referred to as "cessation lag." The duration of this lag affects how changes in PM-attributable mortality associated with long-term (i.e., years-long) exposure are valued. Economic theory suggests that the value of these future impacts should be discounted. The primary analysis included in recent RIAs assumes that this lag is distributed over a 20-year period, with 30% of deaths reduced in year 0, 50% occur in years 1-5, and the remaining 20% occur in years 6-20. This approach is generally support by SAB recommendations (Cameron, 2001, Cameron, 2004, Hammitt and Bailar, 2010).
Based on SAB requests and recommendations, we previously performed several quantitative uncertainty analyses with the goal of better understanding potential impacts of different cessation lag distribution assumptions (U.S. EPA, 2012b). Although it was determined that certain extreme lag structure assumptions may substantially impact monetized benefits, potentially increasing or decreasing monetized impacts by up to 25%, for most reasonable distributed lag model structures, differences in the specific shape of the lag function had relatively small impacts on overall PM2.5 benefits estimates.
We do not know how long-term O3 exposure-related respiratory deaths are distributed over time. Hence, when discounting the value of O3-attributable deaths we use two lag structures originally developed for PM2.5 (the 20-year segmented lag used for PM2.5 and an assumption of zero lag) as sensitivity analyses.
Lung Cancer Cessation Lag
For a given health effect attributable to air pollution exposure, EPA reports the number of avoided cases associated with the estimated pollutant reduction in a specified year. However, for some health effects, there is an expected time lag between changes in pollutant exposure in a given year and the total realization of health effect benefits, commonly referred to in regulatory analyses as the "cessation lag" (section 6.4.1). For an outcome such as lung cancer, where the time between exposure and diagnosis can be quite long, it may take decades to realize the full benefits of the air quality improvements. Properly estimating the time course over which lung cancer health benefits are realized is critical for proper discounting of the economic value of these health benefits.
Following guidance from EPA's Science Advisory Board (Ostro, 2004, EPA RIAs have applied a 20-year distributed cessation lag model to estimate the temporal distribution of reductions in mortality risks, including fatal lung cancer cases. In the 20-year distributed lag model, 30 percent of the total mortality risk reductions occur in the first year following the exposure reduction, 50 percent are distributed evenly among years two through five, and the remaining 20 percent are distributed evenly among years six through 20. This structure reflects mortality risks from a variety of causes, with the mortality risk reductions occurring later representing mortality risks from lung cancer.
For non-fatal cancer incidence, we considered a similar cessation lag approach based on estimates of the lung cancer "latency period," or the time that passes between exposure and diagnosis, when diseases processes may be occurring undetected and not yet resulting in observable symptoms. Based on findings in the 2019 PM ISA, EPA has recently developed a health impact function based on Gharibvand et al., 2017 for non-fatal lung cancer incidence. To support the new non-fatal lung cancer risk estimate, we applied an age-at-diagnosis cessation lag distribution for the main analysis as it accounts for age-specific latency period, instead of assuming a single latency duration. However, other potentially applicable distribution models are available that also take into account the latency between exposure and lung cancer diagnosis, such as the adapted 20-year distribution (section <IP_ADDRESS>) and the latency-based triangular distribution (section <IP_ADDRESS>). All potential lag cessation distributions, including the traditional 20-year lag distribution, are compared in section <IP_ADDRESS>.
Adapted 20-Year Distribution
We adapted the 20-year distributed lag model applied to VSL estimates in previous EPA RIAs using the estimated 10-year latency period. Following the latency period, the adapted 20-year model has zero cancer case reductions in years one through five and an even distribution of case reductions in years six through 20, resulting in scaling factors of 0.71 for a 3% discount rate or 0.46 for a 7% discount rate.
Latency-Based Triangular Distribution
A continuous probability distribution shaped like a triangle may better assess lung cancer lag cessation. Triangular distribution based on a search of lung cancer latency periods from the peer-reviewed literature. Using the most common latency period of 10-years observed in the literature (Table 29), we estimated a triangular distribution that spans from five to 20 years, with the peak of the distribution at ten years, the most common latency period estimate found in the literature (i.e., the mode).  We identified a triangular distribution to reflect the uncertainty of latency period duration found in the literature, given the limited amount of information available to establish the shape and form of an uncertainty distribution. We used the cumulative probability function for this distribution to estimate the incremental annual number of cases likely to be diagnosed year to year by subtracting the cumulative probability from the previous year from the cumulative probability of the current year. We then used the resulting percentages to create a cessation lag model, allocating cases avoided in the years following an exposure change according to the corresponding yearly percentages.
Comparison of Lung Cancer Lag Cessation Distribution Models
The effect of each potential cessation lag distribution model was converted into scaling factors (Table 54). The scaling factors of the adjusted 20-year lag distribution estimate falls between that of the traditional cessation lag and the triangular distribution lag estimates. Also, the adjusted cessation lag distribution underestimates as compared to the age-at-diagnosis distribution.
Table 54. Scaling Factors for Various Lung Cancer Lag Cessation Distribution Models
Discount Rate
Age Range
Scaling Factor
Lag Cessation Distribution Model
3%
65-99
0.668980939
Traditional VSL cessation lag, 3% DR
7%
65-99
0.398456232
Traditional VSL cessation lag, 7% DR
3%
30-99
0.70711338
Adjusted 20-Year Distributed Lag Adjustment Factor
7%
30-99
0.463225599
Adjusted 20-Year Distributed Lag Adjustment Factor
3%
30-99
0.72192703
Triangular Adjustment Factor
7%
30-99
0.480176715
Triangular Adjustment Factor
3%
30-34
0.350285148
SEER Age-Distribution Adjustment Factor
3%
35-44
0.427591186
SEER Age-Distribution Adjustment Factor
3%
45-54
0.553445022
SEER Age-Distribution Adjustment Factor
3%
55-64
0.675599356
SEER Age-Distribution Adjustment Factor
3%
65-74
0.775053763
SEER Age-Distribution Adjustment Factor
3%
75-84
0.843760064
SEER Age-Distribution Adjustment Factor
3%
85-99
0.916741635
SEER Age-Distribution Adjustment Factor
7%
30-34
0.108397669
SEER Age-Distribution Adjustment Factor
7%
35-44
0.168901798
SEER Age-Distribution Adjustment Factor
7%
45-54
0.294643444
SEER Age-Distribution Adjustment Factor
7%
55-64
0.445786107
SEER Age-Distribution Adjustment Factor
7%
65-74
0.590393871
SEER Age-Distribution Adjustment Factor
7%
75-84
0.702750875
SEER Age-Distribution Adjustment Factor
7%
85-99
0.82379138
SEER Age-Distribution Adjustment Factor

Using the lung cancer incidence risk estimates and a hypothetical scenario, we compared the three potential lung cancer cessation lag models. The annual reduction in cancer cases was estimated from zero to 100 years after the exposure change. For the triangular and adjusted 20-year distributed lag, all annual reductions occur within 20 years after exposure change and for the age of diagnosis distribution, all annual reductions fall within 67 years after exposure change (Figure 15) with 90% occurring by year 26.
Figure 15. Lung Cancer Cases Cessation Lag Distribution by Model
A POTENTIAL LIMITATION OF THE TRIANGULAR DISTRIBUTION AND ADJUSTED 20-YEAR DISTRIBUTED LAG IS THAT THE SAME LATENCY PERIOD IS USED FOR ALL AGES. FOR AN EXPOSURE CHANGE EXPERIENCED AT 30, BOTH THE TRIANGULAR DISTRIBUTION AND ADJUSTED 20-YEAR DISTRIBUTED LAG ESTIMATE THAT REDUCTIONS OCCUR BETWEEN AGES 35 AND 50. HOWEVER, SEER DATA INDICATES THAT LESS THAN 5% OF LUNG AND BRONCHUS CANCER DIAGNOSES OCCUR DURING THIS PERIOD. CONVERSELY, FOR AN EXPOSURE CHANGE EXPERIENCED AT 90, THE REDUCTIONS ARE REALIZED FROM AGES 95 TO 110 (GREATER THAN LIFE EXPECTANCY).
A limitation of the age-of-diagnosis distribution methods is that the highest reductions occur in the first five years for the age-of-diagnosis distribution and not all ages display latency periods (Figure 15). The factor used in this method estimates the time pattern of benefits based on the percentage of cancer incidence remaining in the life and results in older age bins without latency periods (Figure 16). While an age-dependent latency period may more accurately reflect the diagnosis data, the age-of-diagnosis distribution method may overestimate case reductions in earlier years by assuming all reduced cases for a change in exposure at later ages are realized by the end of life (age 99). At the same time, some cases are delayed by two to five decades, beyond latency values reported in the literature for lung cancer.
FIGURE 16. Lung Cancer Cases Reduction Distribution

Income Elasticity of Willingness to Pay
The degree to which one's WTP to reduce the risk of adverse effects changes in proportion to future changes in income is uncertain. We previously evaluated the potential impact of this factor on the monetized benefits in a sensitivity analysis (U.S. EPA, 2012b). Results are available below.
Our estimates of monetized benefits account for growth in real gross domestic product per capita by adjusting the WTP for individual endpoints based on the central estimate of the adjustment factor for each of the categories (minor health effects, severe and chronic health effects, mortality, and visibility). We previously examined how sensitive the estimate of total benefits is to alternative estimates of the income elasticities. Table 55 lists the ranges of elasticity values used to calculate the income adjustment factors, while Table 56 lists the ranges of corresponding adjustment factors. The results of this sensitivity analysis, giving the monetized benefit subtotals for the four benefit categories, are presented in Table 57.
Table 55. Ranges of Elasticity Values Used to Account for Projected Real Income Growth[a]
Benefit Category
Lower Sensitivity Bound
Upper Sensitivity Bound
Minor Health Effect
0.04
0.30
Mortality
0.08
1.00
[a]Derivation of these ranges can be found in Kleckner and Neumann, 1999. COI estimates are assigned an adjustment factor of 1.0.
Table 56. Ranges of Adjustment Factors Used to Account for Projected Real Income Growth[a]
Benefit Category
Lower Sensitivity Bound
Upper Sensitivity Bound
Minor Health Effect
1.018
1.147
Mortality
1.037
1.591
[a]Based on elasticity values reported in Table 55, U.S. Census population projections, and projections of real GDP per capita.
Table 57. Sensitivity of Monetized Benefits to Alternative Income Elasticities[a]
Benefit Category
Benefits Incremental to Baseline (Millions of 2006$)

Lower Sensitivity Bound
Upper Sensitivity Bound
Minor Health Effect
$30
$31
Mortality[b]
$3,600
$3,800
[a]All estimates rounded to two significant digits.
[b]Using mortality effect estimate from Krewski et al., 2000 and 3% discount rate. Results using Laden et al. (2006) or a 7% discount rate would show the same proportional range.
Consistent with the impact of mortality on total benefits, the adjustment factor for mortality has the largest impact on total benefits. The value of mortality in 2020 ranges from 96% to 108% of the main estimate based on the lower and upper sensitivity bounds on the income adjustment factor. The effect on the value of minor health effects is much less pronounced, ranging from 86% to 133% of the main estimate for minor effects.
Statistical Estimates of VSL
EPA relies on published peer-reviewed studies to provide statistical estimates of the value of avoided statistical mortality risk (VSL).  These studies provide a range of differ estimates due to varying study design and different statistical samples.  EPA uses a distribution of values fit to the studies' estimates as described in Section 5.1.1 and Table 22.
Alzheimer's Disease and Parkinson's Disease Onset Lifetime Costs
The epidemiologic study from with the risk estimates for Alzheimer's and Parkinson's disease were identified used time to first hospital admission as the health endpoint readout. As the authors note that this is not necessarily indicative of disease onset, we only include valuation estimates of associated hospital admissions costs in the main benefits assessment. However, we include information here regarding how the benefits estimates would increase if the first hospital admission were used as a surrogate for disease onset.
Alzheimer's Disease
Potential valuation sources of Alzheimer's disease lifetime medical costs were available from the Alzheimer's Association, 2020 report and Jutkowitz et al., 2017. Using Alzheimer's Association, 2020, we first developed an estimate of incremental annual medical expenses for Medicare beneficiaries living with Alzheimer's Disease (Table 58). Then, using the estimated life expectancy duration of 5 year from Jutkowitz et al., 2017, 3% and 7% discounted costs were extrapolated (Table 59). We note that the average/median age of Alzheimer's disease diagnosis/onset is after the age of 65, at which we assume retirement, so any potential lost wages are not included in this valuation estimate. Lifetime medical costs, excluding hospitalization, are estimated at $156,920 using a 3% discount rate or $145,946 using a 7% discount rate in 2015$ Alzheimer's Association, 2020.
Table 58. Annual Alzheimer's Disease Valuation Estimate Calculation
Service
Beneficiaries with Alzheimer's Disease or Other Dementia
Beneficiaries without Alzheimer's Disease or Other Dementia
Inpatient hospital
$11,465
$3,703
Medical provider
$5,762
$3,589
Skilled nursing facility
$7,213
$493
Nursing home
$16,523
$800
Hospice
$2,126
$161
Home health care
$2,661
$386
Prescription Medications
$3,481
$2,986
Annual Medical Expenses ($2019)
$49,231
$12,118
Annual Medical Expenses ($2015)
$44,128
$10,862
Incremental Annual Medical Expenses for Medicare beneficiaries with AD ($2015)
$33,266

Table 59. Lifetime Alzheimer's Disease Valuation Estimate Calculation (2015$)
Year
3% Discount Rate
7% Discount Rate
0
$33,266
$33,266
1
$32,297
$31,090
2
$31,357
$29,056
3
$30,443
$27,155
4
$29,557
$25,379
Total Lifetime Costs
$156,920
$145,946
Jutkowitz et al., 2017 provided information needed to separately develop a lifetime Alzheimer's Disease cost estimate with a 3% discount rate, but not with a 7% discount rate (Table 60. Additional Lifetime Alzheimer's Disease Valuation Estimate Calculation with a 3% Discount Rate (2015$)). As the 3% discount rate estimate of $156,920 from Alzheimer's Association, 2020 is fairly similar to the lifetime 3% discount rate estimate of $184,500 from Jutkowitz et al., 2017, we have additional confidence in the validity of the Alzheimer's Association, 2020 estimates (Table 21).
Table 60. Additional Lifetime Alzheimer's Disease Valuation Estimate Calculation with a 3% Discount Rate (2015$)
Service
Base-Case (83-year-old incident dementia case)
Counterfactual Dementia Free
Incremental Increase in Lifetime Costs
Value of informal caregiving
$135,300
$2,460
$132,840
Out-of-pocket expenditures
$89,840
$64,720
$25,120
Medicaid expenditures
$44,090
$37,450
$6,640
Medicare expenditures
$52,540
$32,650
$19,890
Total value
$321,780
$137,280
$184,500

Parkinson's Disease
Yang et al., 2020 provided estimates of lifetime costs, including direct, indirect, and non-medical costs. Using Yang et al., 2020, we first developed an annual estimate of excess costs associated with living with Parkinson's Disease for one year (Table 61). Then, using the estimated life expectancy duration of 14.6 years from De Pablo-Fernandez et al., 2017, the 3% and 7% discounted costs were extrapolated (Table 62). Lifetime medical costs are estimated at $567,285 using a 3% discount rate or $445,792 using a 7% discount rate in 2015$ (Table 21).
Table 61. Annual Parkinson's Disease Valuation Estimate Calculation
Service
Excess Cost per Person with Parkinson's Disease
Description
Direct Medical Costs
  Non-acute institutional care
$6,888
Quantify excess healthcare cost of each person with Parkinson's Disease compared with 10 matched individuals without Parkinson's Disease
  Hospital inpatient
$6,932

  Outpatient
$5,308

  Physician office
$1,182

  Durable medical equipment
$140

  Prescription medication
$3,988

Direct Medical Costs Subtotal
$24,438

Indirect Medical Costs
    Paid daily non-medical care
$3,709
Home caretakes/long-term care facilities
    Home modification
$2,151

    Motor vehicle modification
$897

    Other expenses
$508

Indirect Medical Costs Subtotal
$7,265

    Non-Medical Costs
    Reduced employment
$2,579
Reduced labor market participation due to early retirement
    Absenteeism
$4,869
Lost work days
    Presenteeism
$2,841
Lost work productivity at work
    Social productivity loss in volunteer
    work
$997

    Supplemental security income (SSI)
$541
SS disability supplemental income
    Social security disability insurance
    (SSDI)
$1,617

    Other disability income
$2,431
Includes other disability income sources such as VA disability, gov't employee disability, & state disability insurance or personal disability insurance payments
Non-Medical Costs Subtotal
$18,293

Annual Medical Expenses ($2017)
$47,578

Annual Medical Expenses ($2015)
$44,718

Table 62. Lifetime Parkinson's Disease Valuation Estimate Calculation
Year
3% Discount Rate
7% Discount Rate
0
$44,718
$44,718
1
$43,416
$41,793
2
$42,151
$39,059
3
$40,924
$36,503
4
$39,732
$34,115
5
$38,574
$31,883
6
$37,451
$29,798
7
$36,360
$27,848
8
$35,301
$26,026
9
$34,273
$24,324
10
$33,275
$22,732
11
$32,305
$21,245
12
$31,364
$19,855
13
$30,451
$18,556
14
$29,564
$17,343
14.6
$17,427
$9,992
Total Lifetime Costs (14.6 yr survival)
$567,285
$445,792
Qualitative Characterization of Uncertainties
There are several uncertainties we are unable to fully or partially quantitatively assess, but qualitatively discuss below, in alphabetical order.
Applying Risk Estimates to Locations and Populations not Specified in the Epidemiologic Study
EPA regulatory actions often affect portions of the country and populations that differ from those considered in the epidemiologic studies providing the risk estimates. EPA commonly transfers risk estimates from one location or population to another, following a procedure called benefits transfer, a potential source of uncertainty. When available, risk estimates based on nationwide studies reflecting the overall population demographics of U.S. residents will be used when estimating health benefits. Epidemiologic studies exploring the relationship between air pollution and the risk of mortality often consider populations whose characteristics are broadly representative of the U.S. (e.g., Medicare-based estimates will be applied to those >64). By contrast, epidemiologic studies examining morbidity outcomes may focus on population subsets, such as those residing in specific geographic regions, a single sex, or selected races/ethnicities. In this context, two or more epidemiologic studies may report risk estimates that, when pooled, can better characterize risks experienced by U.S. populations. However, in some cases it may be scientifically inappropriate to pool risk estimates -- for example, a nationwide analysis of populations ages 65-99 and a less-geographically diverse analysis of populations ages 0-99. In a situation such as this, the estimate best characterizing risk in the U.S. will be included in the main benefits assessment and the others will be included in quantitative sensitivity analyses (sections 6.1 and 6.1.6).
Causality Determination
When estimating air pollutant-attributable health impacts, EPA focuses on endpoints for which the underlying scientific evidence is strongest. This approach is based on evaluating evidence across scientific disciplines (i.e., clinical, animal toxicological, and epidemiologic) with regard to consistency of effects within a discipline, coherence of effects across disciplines, and evidence of biological plausibility. Such an approach gives us greater confidence in the relationship between exposure and health outcome. For criteria pollutants, EPA typically relies upon the causality determinations in the latest ISA or equivalent, which are made using a weight-of evidence approach. These causality determinations are, however, made for categories of health effects and not for specific endpoints. Thus, the extent to which the relationship exists for the specific endpoint and the exposure circumstances of interest in a benefits assessment is a source of uncertainty.
An expert elicitation sponsored by EPA to characterize the uncertainty in the relationship between PM2.5 and mortality, including causal uncertainty, was released in 2006 and reviewed by the Advisory Council on Clean Air Compliance (Hammitt, 2008, IEc, 2006). Although the 12 expert-defined concentration-response functions provide useful information on the sensitivity of the health benefits estimates, additional epidemiology literature which addresses some of the weaknesses identified by the expert elicitation has since become available, such as improved exposure estimation techniques and the use of cohorts more representative of the U.S population. For these reasons we do not include the expert-derived results as a sensitivity analysis here but consider it as qualitative support for the relationship between long-term PM2.5 exposures and all-cause mortality impacts.
Causal inference is another method of establishing a causal connection that evaluates associations under changing conditions. The 2019 PM ISA stated that "overall, the results of these causal inference studies contribute to the body of epidemiologic evidence that informs the causal relationship between long-term (one month to years) PM2.5 exposure and total mortality (U.S. EPA, 2019c). Observing consistent results for this relationship across studies using different analytic techniques (i.e., difference-in-difference approach) increases our confidence in the relationship."
Estimating and Assigning Exposures in Epidemiology Studies
New developments in exposure assessment, including hybrid spatiotemporal models that incorporate satellite observations of aerial optical density, land use variables, surface monitoring data from monitors, and chemical transport models, have led to improvements in the spatial resolution extent of pollutant concentration surfaces. After reviewing the current state of exposure science, the 2019 PM ISA stated that "a number of studies demonstrate that the positive associations observed between long-term PM2.5 exposure and mortality are robust to different methods of assigning exposure" and the 2020 O3 ISA articulated that "hybrid methods have produced lower error predictions of ozone concentration compared with spatiotemporal models using land use and other geospatial data alone but may be subject to overfitting given the many different sources of data incorporated into the hybrid framework."
Although these advancements may reduce bias and uncertainty in risk estimates, the accuracy of hybrid exposure estimates is difficult to confirm in areas lacking monitors. On the other hand, studies using monitor data as the only exposure information have increasing exposure uncertainty the farther people live from the monitor site.
Uncertainties related to PM2.5 and O3 exposure estimation vary. For example, the PM2.5 HBM method had Pearson R's ranging from 0.91 to 0.94 when applied across the U.S. at a 36-km resolution, depending on the geoimputation approach of the CMAQ data (U.S. EPA, 2019c). The evaluation of the O3 HBM method has been relatively limited. However, overall conclusions regarding long-term O3 exposure estimates that include fixed-site monitor measurements are that "the true effect of long-term exposure to ambient ozone may be underestimated or overestimated by the model" and  that it "is much more common for the effect estimate to be underestimated, and the bias is typically small in magnitude" (U.S. EPA, 2020a).
Differential Toxicity of PM2.5 According to Chemical Composition
PM2.5 is a heterogenous mixture of solid and liquid particles suspended in air and can vary with regards to size, composition, and source. The 2020 PM ISA found that "across exposure durations and health effects categories...many PM2.5 components and sources are associated with many health effects, and the evidence does not indicate that any one source or component is consistently more strongly related to health effects than PM2.5 mass;" although, it was also noted that "most studies that examine PM sources and components focused on PM2.5" (U.S. EPA, 2019c).
Since the 2019 PM ISA concluded that "recent studies continue to demonstrate that no individual PM2.5 component or source is a better predictor of mortality than PM2.5 mass" and "many PM2.5 components and sources are associated with many health effects and that the evidence does not indicate that any one source or component is consistently more strongly related with health effects than PM2.5 mass" we continue to assume that all fine particles, regardless of their chemical composition, are equally potent in causing mortality and do not quantitatively assess uncertainties related to potential differences in PM2.5 toxicity or composition. A qualitative discussion of this uncertainty as it relates to respiratory effects, cardiovascular effects, and mortality can be found in section 1.5.4 of the 2019 PM ISA (U.S. EPA, 2019c).
Different Long-Term Exposure Windows
The delay between changes in exposure and changes in health is an empirical challenge in estimating potential health effects associated with air pollution exposure. For example, if health impacts of high pollutant exposures have a long latency, risk estimates attributing to lower pollutant concentrations experienced more recently may be biased away from the null. However, the 2019 PM ISA states that "new evidence from recent studies continues to support the previous conclusion that health benefits from reducing air pollution could be expected with a few years of intervention" (U.S. EPA, 2019c). This issue is likely less relevant to O3 exposure-attributable mortality, as those studies often have very similar, if not overlapping, health and air quality data.
Discounting Future Benefit Estimates
Discounting reflects that people prefer benefits presently more than in the future. When appropriately applied, discounting can allow for the direct comparison of future benefits to costs. However, there are potential uncertainties associated with discounting future benefit estimates. EPA bounds discounted benefits and costs using an estimate of the consumption rate of interest and a rate of return on private capital given that the share of capital that is displaced by a regulation is unknown. OMB currently recommends, and EPA uses, a real consumption rate of discount of 3% and a real rate of 7% for the opportunity cost of private capital based on prior empirical estimates (OMB, 2003). These values are estimates and therefore introduce uncertainty. Additional detail on discounting can be found in the EPA Guidelines for Preparing Economic Analyses (U.S. EPA, 2014).
Statistical Estimates of WTP
EPA relies on published peer-reviewed studies to provide statistical estimates of the value of avoided pain and suffering (WTP).  While most of these studies provide estimates of the uncertainty due to statistical sampling, there are other important sources of error.  First, the statistical models used to produce these estimates may be incorrect, termed modeling error.  Second, the statistical samples used to produce these estimates may be selectively chosen and unlike the population of interest, leading to selection error.  Third, WTP values are unavailable for many health endpoints of interest. Assigning a value of zero is clearly incorrect, but the EPA has no basis on which to assign other values.
Confounding by Individual Risk Factors
Interindividual variability in both physiological responses and exposures to ambient air pollution can affect the size of reported risk estimates in epidemiologic studies. Well-designed epidemiology studies account for individual risk factors as covariates in their models, and so all else being equal we identify risk estimates adjusted to control for the most covariates that could reasonably impact the risk estimate. However, confounding by individual risk factors remains a potential source of uncertainty as additional relevant covariates may exist that are not included as covariates in epidemiological risk estimates used for health benefits assessment. Unfortunately, we are currently unable to quantitively assess this area of uncertainty but will include qualitative discussions when possible.
Confounding by Other Pollutants
When considering the relationship between pollutant exposure and health impacts, it can be informative to consider whether risk estimates are changed when other pollutants are included in copollutant models, especially when health impacts of multiple pollutants are being estimated concurrently. Regarding long-term exposures, the 2019 PM ISA concluded that "positive associations observed between long-term PM2.5 exposure and total mortality remain relatively unchanged after adjustment for...NO2 and PM10−2.5" and the 2020 O3 ISA found that "positive associations observed between long term O3 exposure and total mortality remain relatively unchanged after adjustment for...NO2." However, confounding due to the effects of copollutants other than O3 and PM2.5 are a potential source of uncertainty.
Risk Attributable to Long-Term and Short-Term Exposures
Long- and short-term exposures may follow similar or divergent biological pathways. When pathways are similar, estimates of impacts from long-term exposures may include short-term impacts and vice-versa. However, if pathways diverge, long- and short-term impacts may be the sum, or even greater than the sum, of the two exposure duration impacts. As there is little research directly comparing long- and short-term effects, we are currently unable to quantitatively assess this area of uncertainty for either PM2.5- or O3-attributable health effects.
Heterogeneity of Risk Estimates
Epidemiologic studies often differ according to study design, geographic locations, age groups, population attributes, study size, methods for estimating exposure, range of pollutant concentrations, time periods, study sizes, and follow-up durations. These differences in turn influence the magnitude and standard error of study-reported risk estimates. The diversity of identified risk estimates could reflect either the variability across the populations studied or uncertainty around the risk estimates. Importantly, heterogeneous risk estimates are not necessarily indicative of bias, but could also result from variability of the underlying input parameters.
O3 Metrics
O3 exposure metrics used to develop risk estimates can take many forms, though the most widely used metrics are the maximum daily 8-hour average (MDA8), daily average 24-hour (DA24), daily average 8-hr from 10AM to 6PM (DA8), and maximum daily 1-hour average (MDA1) metrics.
Historically, if epidemiologic studies developed risk estimates based on O3 metrics other than MDA8, EPA would adapt the risk estimates based on average conversion ratios to be appropriate for use with air quality surfaces projected in the MDA8 metric (Anderson and Bell, 2010). This approach brings with it uncertainties associated with the simplifying assumptions used to develop the conversion ratios. In most cases, the day to day variation in different metrics (e.g., DA24 vs MDA8) is highly correlated. As such, the relationships between health impacts and different ozone metrics will also be highly correlated. However, when we apply risk estimates derived from time series results to evaluate the impacts of a specific policy scenario, we focus most on the shift in the overall distribution of O3 concentrations over an entire season, instead of on the day to day variation in O3 levels. Because specific policy scenarios might result in different temporal distributions of ozone concentrations than was observed in the monitored ozone data used in the studies, it is important to choose an O3 metric that is best suited to capturing changes in O3 that are likely to occur during hours where populations are likely to be exposed.
Converting O3 Risk Estimate Exposure Metrics
When epidemiologic risk estimates are developed using non-MDA8 O3 exposure metrics, EPA has typically converted the beta risk estimates into MDA8 metrics, which brings in a potential source of uncertainty (Anderson and Bell, 2010). We discuss uncertainties associated with converting various common O3 exposure metrics into the MDA8 metric below.
DA24 to MDA8
Currently, air quality projections using the MDA24 metric are unavailable, so a conversion factor from Anderson and Bell, 2010 is used in order to apply these risk estimates to MDA8 air quality surface projections. We multiply the beta risk estimate by the inverse of the median summer ratio of MDA8 to DA24 mean O3 concentrations (i.e., 1 / 1.53 = 0.6536) for studies assessing summer O3 exposure or by the inverse of the fixed effects average ratio of MDA8 to DA24 mean O3 concentrations (i.e., 1 / 1.53 = 0.6536) for studies assessing all-year O3 exposure. We note that Anderson and Bell, 2010 included a range of ratios from 1.23-1.83.
DA8 to MDA8
A comparison of the MDA8 and DA8 metrics using 20 years of O3 monitoring data (2000-2019) for the entire contiguous U.S. resulted in a very high rate of correlation (Figure 17). The correlation was based on a simple linear regression with zero intercept, meaning that if the MDA8 is 0, then the DA8 mean must also be zero. The green line is the regression line and the light gray line represents a 1:1 relationship. Please note, the MDA8 cannot exceed the DA8 and the high density of the ~7 million points shown in the graph cluster near the 1:1 line. In fact, the MDA8 and DA8 metrics are identical approximately 30% of the time and differ by 2 ppb or less about 80% of the time. Based on this comparison, the conversion factor from DA8 to MDA8 is 0.97.
Figure 17. Correlation of MDA8 and DA8 O3 Exposures Between 2000-2019 (R=0.986)

MDA1 to MDA8
Due to time and resource limitations, air quality projections using the MDA1 metric are also unavailable for the Revised CSAPR Update final rulemaking, so a conversion factor from Anderson and Bell, 2010 is used in order to apply these risk estimates to MDA8 air quality surface projections. We multiply the beta risk estimate by the median summer ratio of MDA1 to MDA8 O3 concentrations (i.e., 1.13) for studies assessing summer O3 exposure or by the fixed effects average ratio of MDA1 to MDA8 O3 concentrations (i.e., 1.14) for studies assessing all-year O3 exposure. We note that Anderson and Bell, 2010 included a range of ratios from 1.08-1.26.
O3 Season
Studies of O3 vary with regards to O3 season, limiting analyses to various definitions of summer (e.g., April-September, May-September or June-August) and exposures over the full calendar year. These differences can reflect state-specific, EPA-defined O3 seasons or another seasonal definition chosen by the study author.  O3 exposure estimates are arguably more accurate during the summer when concentrations are typically higher and more monitors are operational. In addition, respiratory effects associated with short-term exposures are commonly limited to the warm season and therefore reflect the incidence that occurs during the 5- or 6-month O3 season (U.S. EPA, 2020d). Recently, there are an increased number of long-term analyses of O3-attributable health impacts over the full calendar year using hybrid modeling techniques and where O3 monitoring data is collected for the entire year. These studies likely represent a more complete estimate of O3-attributable health impacts.
While epidemiologic studies assessing all-year O3 exposures would likely present more comprehensive estimates of health impacts, hybrid O3 surface projections for baseline and policy rulemaking scenarios are not currently available. As such, we identified epidemiologic studies and associated risk estimates that evaluated associations between exposures and warm season effects when available. There was some variability amongst the warm season definitions within the list of studies identified in this update (e.g., April-September and June-August), although only the respiratory emergency department and asthma symptom risk estimate was based on full year O3 exposures (Barry et al., 2019, Lewis et al., 2013). It should be noted that the exposures for asthma symptoms among the identified studies were not evenly distributed across all the seasons (I.e. three in Spring, two in Summer, two in Fall, and one in Winter).
There is some variability regarding the definition of the warm season amongst epidemiologic studies included in the ISAs and the main risk estimates identified here for O3 benefits estimates. When there is a substantial difference, such as the June-August warm season assessed by Zanobetti and Schwartz, 2008, we develop season-specific air quality projections, when feasible. However, many studies begin the 5-7 month warm season in either April or May and conclude the season in September or October. Since projected full year hybrid O3 surfaces are currently not available, epidemiologic risk estimates will be applied to the air quality projection most closely matching the exposure season in the study (e.g., April-September exposures will be applied to May-September air quality projections). We expect this seasonal mismatch will only have a limited effect on the magnitude of related health incidence.
Shape of the Concentration-Response Relationship
PM2.5
An important consideration when characterizing uncertainty is whether the concentration-response relationship is linear across the full concentration range that is encountered, or if there are concentration ranges where there are departures from linearity. Overall, evidence from the 2019 PM ISA continues to "support a linear, no-threshold concentration-response relationship for long-term exposure to PM2.5 and total (nonaccidental) mortality, especially at lower ambient PM2.5 concentrations, with confidence in some studies in the range of 5−8 μg/m[3]" and "there is less certainty in the shape of the concentration-response curve at mean annual PM2.5 concentrations generally below 8 μg/m[3], although some studies characterize the concentration-response relationship with certainty down to 4 μg/m[3]" (U.S. EPA, 2019c).
Although ten large cohort studies of long-term PM2.5-attributable mortality observed linear, no-threshold concentration-response relationships, three Canadian studies presented evidence of deviations from linearity down to the lowest concentration evaluated. Two studies found evidence of a supralinear relationship at lower concentrations, although only one was statistically significant. And a single study found that the best fit for the long-term PM2.5 mortality relationship was in a threshold model with a threshold at 11 μg/m[3].
There are several potential explanations for these results, one of which is that studies may be unable to adequately evaluate the relationship at low levels without sufficient population exposure at those levels. Consistent with that hypothesis, the single statistically significant study finding evidence of supralinearity did have one of the lowest mean PM2.5 concentrations, at 6.3 μg/m[3]. Another possible explanation with support from the 2019 ISA is that the shape of the concentration-response relationship could differ by health outcome.