Technical Support Document (TSD)
for the Final Revised Cross-State Air Pollution Rule Update for the 2008 Ozone Season NAAQS

                      Docket ID No. EPA-HQ-OAR-2020-0272

           Estimating PM2.5- and Ozone-Attributable Health Benefits

                      U.S Environmental Protection Agency
                          Office of Air and Radiation
                    Research Triangle Park, North Carolina
                                  March 2021

     Contents
1	Introduction	1
1.1	Benefits Analysis Background	1
1.2	The Relationship Between Identifying Health Endpoints for Valuation and WTP	2
1.3	Document Purpose and Overview	3
2	Approach to Identifying Studies and Risk Estimates	5
2.1	Study and Risk Estimate Identification Criteria	5
2.1.1	Minimum Criteria	5
2.1.2	Preferred Criteria Categories	6
2.2	Available Epidemiologic Literature	10
2.2.1	Identification of Exposure-Attributable Health Outcomes	10
2.2.2	Identification of Quantifiable Health Outcomes	21
2.2.3	Study Information Table	22
2.2.4	Methods for Presenting Health Benefits Estimates Using Multiple Risk Estimates for a Single Endpoint	24
2.2.5	PM2.5	25
2.2.6	O3	47
2.3	Identified Study and Risk Estimates for Benefits Assessments	57
2.3.1	Health Endpoints	57
2.3.2	Risk Estimates	59
3	Baseline Incidence and Prevalence Estimates	63
3.1	Mortality	65
3.1.1	Mortality Data for 2012-2014	66
3.1.2	Mortality Rate Projections 2015-2060	68
3.1.3	Race-Stratified Incidence Rates	69
3.2	Hospitalizations	69
3.3	Emergency Department Visits	71
3.4	Health Endpoint Onset/Occurrence	71
3.4.1	Acute Myocardial Infarctions (AMIs)	71
3.4.2	Asthma Onset and Symptoms	72
3.4.3	Allergic Rhinitis	74
3.4.4	Lung Cancer	74
3.4.5	Minor Restricted Activity Days (MRAD)	74
3.4.6	School Loss Days	75
3.4.7	Work Loss Days	75
4	Demographic Information	77
5	Health Endpoint Valuation	78
5.1	Mortality	81
5.1.1	Value of a Statistical Life (VSL)	81
5.2	Hospitalizations and Emergency Department Visits	82
5.3	Health Endpoint Onset/Occurrence	85
5.3.1	Acute Myocardial Infarctions (AMIs)	85
5.3.2	Allergic Rhinitis (Hay Fever)	86
5.3.3	Asthma Onset	86
5.3.4	Asthma Symptoms/Exacerbation	87
5.3.5	Cardiac Arrest	88
5.3.6	Lung Cancer	88
5.3.7	Minor Restricted Activity Days (MRADs)	90
5.3.8	School Loss Days	91
5.3.9	Stroke	91
5.3.10	Work Loss Days (WLDs)	92
5.4	Developing Income Growth Adjustment Factors for Health Endpoint Onset/Occurrence	92
6	Characterizing Uncertainty and Evaluating Sensitivity to Alternate Assumptions	96
6.1	Quantitative Characterization of PM2.5 Uncertainty and Evaluating Sensitivity to Alternate PM2.5 Assumptions	96
6.1.1	Statistical Uncertainty Around the Risk Estimate (Monte-Carlo Assessment)	97
6.1.2	Adult All-Cause Mortality	97
6.1.3	Asthma Onset in Children	101
6.1.4	Cardiovascular Hospital Admissions	103
6.1.5	Respiratory Hospital Admissions	104
6.1.6	Effect Modification of Health Impacts in At-Risk Populations	106
6.2	Quantitative Characterization of O3 Uncertainties and Evaluating Sensitivity to Alternate O3 Assumptions	109
6.2.1	Statistical Uncertainty Around the Risk Estimate (Monte-Carlo Assessment)	109
6.2.2	Respiratory Mortality	109
6.2.3	All-Cause Mortality	110
6.2.4	Asthma Onset in Children	111
6.2.5	Understanding the Effect Modification of Health Impacts in At-Risk Populations	113
6.3	Quantitative Characterization of Baseline Incidence Rate Uncertainties	117
6.4	Quantitative Characterization of Economic Valuation Estimate Uncertainties	118
6.4.1	Mortality Cessation Lag	118
6.4.2	Lung Cancer Cessation Lag	118
6.4.3	Income Elasticity of Willingness to Pay	122
6.4.4	Statistical Estimates of VSL	123
6.4.5	Alzheimer's Disease and Parkinson's Disease Onset Lifetime Costs	123
6.5	Qualitative Characterization of Uncertainties	127
6.5.1	Applying Risk Estimates to Locations and Populations not Specified in the Epidemiologic Study	.......................................................................................................................................................127
6.5.2	Causality Determination	128
6.5.3	Estimating and Assigning Exposures in Epidemiology Studies	128
6.5.4	Differential Toxicity of PM2.5 According to Chemical Composition	129
6.5.5	Different Long-Term Exposure Windows	129
6.5.6	Discounting Future Benefit Estimates	129
6.5.7	Statistical Estimates of WTP	130
6.5.8	Confounding by Individual Risk Factors	130
6.5.9	Confounding by Other Pollutants	130
6.5.10	Risk Attributable to Long-Term and Short-Term Exposures	130
6.5.11	Heterogeneity of Risk Estimates	131
6.5.12	O3 Metrics	131
6.5.13	O3 Season	133
6.5.14	Shape of the Concentration-Response Relationship	133
6.5.15	Short-Term Lag Structure	136
6.5.16	Statistical Technique/Model Used to Quantify Risks in Epidemiologic Study	136
6.5.17	Temperature and Weather	137
6.5.18	Unquantified Impacts	137
7	References	138

     List of Tables
Table 1. Criteria for Identifying Studies and Risk Estimates for Application in Benefits Assessment	6
Table 2. Study and Risk Estimate Criteria Prioritization Order	8
Table 3. Causality Determinations for PM2.5-Related Health Effects	12
Table 4. Causality Determinations for O3-Related Health Effects	13
Table 5. Study Information Tables	22
Table 6. PM2.5 Study and Risk Estimate Identification Diagram*	27
Table 7. O3 Study and Risk Estimate Identification Diagram	48
Table 8. Set of Health Endpoints for Main PM2.5 Benefits Assessments	58
Table 9. Set of Health Endpoints for Main O3 Benefits Assessments	59
Table 10. Set of Risk Estimates for Main PM2.5 Benefits Assessments	59
Table 11. Set of Risk Estimates for Main O3 Benefits Assessments	61
Table 12. Baseline Incidence Rates for Use in Impact Functions	64
Table 13.  National Mortality Rates (per 100 people per year) by Health Endpoint and Age Group, 2012-2014	67
Table 14. All-Cause Mortality Rate (per 100 people per year), by Source, Year, and Age Group	68
Table 15. Ratio of Future Year All-Cause Mortality Rate to 2013 Estimated All-Cause Mortality Rate, by Age Group	69
Table 16. Asthma Prevalence Rates	73
Table 17. Weighted Average Asthma Prevalence by Study	73
Table 18. Lung Cancer Incidence Rates	74
Table 19. School Loss Day Rates (per student per year)	75
Table 20. Cost of Illness Economic Study Identification Consideration Factors	79
Table 21. Unit Values for Economic Valuation of Health Endpoints (2015$)[1]	80
Table 22. Central Unit Value for VSL based on 26-value-of-life studies	82
Table 23. Hospitalization and Emergency Department Cost Elements by Endpoint	82
Table 24. Medical Costs and Hospital Stay Data from the HCUP Database	84
Table 25. Medical Costs for AMIs (2015$)	85
Table 26. Total Valuation Estimates for AMIs (2015$)	86
Table 27. Age-adjusted Belova et al., 2020 Estimates of Lifetime Asthma Costs	87
Table 28. Valuation Estimate for Cardiac Arrests (2015$)	88
Table 29. Latency Periods Used in Lung Cancer Risk Assessment Papers	89
Table 30. Percent Lung and Bronchus Cancer Incidence by Age and Distribution of Risk Reduction by Age for an Exposure Change at 55	90
Table 31. Income Elasticity Estimates for Minor Health Effects, Severe Health Effects, and Mortality	93
Table 32. Income-Based WTP Adjustments by Health Effect and Year	95
Table 33. Low Concentration PM2.5 Exposures from the ACS CSP-II, Medicare, and CanCHEC Cohorts	99
Table 34. Di et al., 2017a PM2.5-Attributable Mortality Risk Estimates per 10 ug/m[3] from Different Exposure Estimation Techniques	100
Table 35. PM2.5-Attributable ACS CSP-II Mortality Risk Estimates per 10 ug/m[3] from Different Exposure Estimation Techniques	100
Table 36. Single- and Two-Pollutant (Including O3 as a Copollutant) PM2.5-Attributable Mortality Risk Estimates per 10 ug/m[3]	100
Table 37. Di et al., 2017a PM2.5-Attributable Mortality Risk Estimates per 10 ug/m[3] from Different Statistical Techniques	101
Table 38. Potential Sensitivity of Estimated Instances of Asthma Onset	102
Table 39. Beta Coefficients and Standard Errors (SE) from Studies of Examining Long-term PM2.5 Exposure and New Onset Asthma in Children	103
Table 40. Potential Sensitivity of Estimated Cardiovascular Hospital Admissions	104
Table 41. PM2.5-Attributable Cardiovascular Hospital Admissions Beta Estimates	104
Table 42. Potential Respiratory Hospital Admission Sensitivity Insights	105
Table 43. PM2.5-Attributable Respiratory Hospital Admissions Beta Risk Estimates	105
Table 44. Comparison of the PM2.5-Attributable Respiratory Hospital Admissions Beta Risk Estimate to the EHA Respiratory Estimate	106
Table 45. PM2.5 At-Risk Study Identification Criteria	107
Table 46. Identified PM2.5 At-Risk Beta Coefficients and Standard Errors	108
Table 47. Single- and Two-Pollutant (Including PM2.5 as a Copollutant) Long-Term O3-Attributable Respiratory Mortality Risk Estimates per 10 ppb	109
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	110
Table 49. Long-Term O3-Attributable Total Mortality Risk Estimates per 10 ppb	111
Table 50. Potential Sensitivity of Estimated Instances of Asthma Onset	112
Table 51. Long-Term O3-Attributable Asthma Beta Coefficients	112
Table 52. O3 At-Risk Study Identification Criteria	114
Table 53. Identified O3 At-Risk Beta Coefficients and Standard Errors	116
Table 54. Scaling Factors for Various Lung Cancer Lag Cessation Distribution Models	120
Table 55. Ranges of Elasticity Values Used to Account for Projected Real Income Growth[a]	123
Table 56. Ranges of Adjustment Factors Used to Account for Projected Real Income Growth[a]	123
Table 57. Sensitivity of Monetized Benefits to Alternative Income Elasticities[a]	123
Table 58. Annual Alzheimer's Disease Valuation Estimate Calculation	124
Table 59. Lifetime Alzheimer's Disease Valuation Estimate Calculation (2015$)	124
Table 60. Additional Lifetime Alzheimer's Disease Valuation Estimate Calculation with a 3% Discount Rate (2015$)	125
Table 61. Annual Parkinson's Disease Valuation Estimate Calculation	126
Table 62. Lifetime Parkinson's Disease Valuation Estimate Calculation	127

     List of Figures
Figure 1. Illustrative Diagram of Potential Biological Pathways of Health Effects Following Pollutant Exposure.	15
Figure 2. Potential Biological Pathways for Cardiovascular Effects Following Short-Term PM2.5 Exposure	16
Figure 3. Potential Biological Pathways for Cardiovascular Effects Following Long-Term PM2.5 Exposure	17
Figure 4. Potential Biological Pathways for Respiratory Effects Following Short-Term PM2.5 Exposure	17
Figure 5. Potential Biological Pathways for Respiratory Effects Following Long-Term PM2.5 Exposure	18
Figure 6. Potential Biological Pathways for Cancer Effects Following Long-Term PM2.5 Exposure	18
Figure 7. Potential Biological Pathways for Nervous System Effects Following Long-Term PM2.5 Exposure	19
Figure 8. Potential Biological Pathways for Respiratory Effects Following Short-Term O3 Exposure	20
Figure 9. Potential Biological Pathways for Respiratory Effects Following Long-Term O3 Exposure	21
Figure 10. Potential Biological Pathways for Metabolic Effects Following Short-Term O3 Exposure	21
Figure 11. Functional Form of the Identified ACS CSP-II Risk Estimate	30
Figure 12. Functional Form of the Identified Medicare Risk Estimate	31
Figure 13. Cumulative Percentile of PM2.5 Cohort Exposure from the ACS CSP-II, Medicare, and CanCHEC Cohorts	98
Figure 14. Example County-Level and National-Level Emergency Department Visit and Hospital Admission Baseline Incidence Data	117
Figure 15. Lung Cancer Cases Cessation Lag Distribution by Model	121
Figure 16. Lung Cancer Cases Reduction Distribution	122
Figure 17. Correlation of MDA8 and DA8 O3 Exposures Between 2000-2019 (R=0.986)	132

Introduction
Benefits Analysis Background
The EPA's Guidelines for Preparing Economic Analyses describe the purpose of benefit-cost analysis (BCA), related economic analyses, and the best-practices for conducting them (U.S. EPA, 2014). As described in the Guidelines, the fundamental objective of a BCA is to determine whether those who experience a net gain from a regulatory action can potentially compensate those who experience a net loss and remain no worse off. These gains and losses are measured by an individual's willingness to pay (WTP) for, or willingness to accept, changes attributable to the regulatory action. Consistent with economic theory, the WTP for reductions in exposure to an environmental hazard, like PM2.5 or O3, depends on the expected effect of those reductions on human health. BCA is the primary tool used for regulatory analysis and is used to inform the decision of whether the benefits of an action are likely to justify the costs (EO 12886, 1993, OMB, 2003).
Estimating the health benefits of reductions in PM2.5 and O3 exposure in a BCA begins with estimating the change in exposure for each individual and then estimating the change in each individual's risks for those health outcomes affected by exposure. The benefit of the reduction in each health risk is based on the exposed individual's WTP for the risk change. The greater the magnitude of the risk reduction from a given change in concentration, the greater the individual's WTP, all else equal. The social benefit of the change in health risks equals the sum of the individual WTP estimates across all of the affected individuals. There are various sources of uncertainty inherent in each of these steps, many of which are discussed in section 6.
There are three key information collection and assessment steps for implementing this framework for evaluating the health benefits of changes in exposure:
      (1) Identifying health endpoints affected by exposure by assessing the strength of evidence,
      (2) Identifying suitable empirical estimates of the magnitude of the relationship between exposure and these health endpoints, and
      (3) Estimating the WTP for reductions in the risk of these health endpoints.
This document describes all three steps for the purposes of estimating health benefits from changes in ambient PM2.5 and O3 exposure.
The Relationship Between Identifying Health Endpoints for Valuation and WTP
The first step requires collecting and integrating scientific evidence from different types of studies and scientific fields (e.g., epidemiologic, controlled human exposure, and animal toxicological studies), as well as evaluating the quality of evidence and the consistency in the pattern of effects. Determining the strengths, limitations, and uncertainties in the overall evidence are key components, all of which could affect WTP, as this information is the basis of the desire to avoid or reduce PM2.5 and O3 exposure.
While the first and third step are presented as independent, they are related for an individual. All else equal, WTP is expected to be higher when there is stronger evidence of a causal relationship between exposure to the contaminant and changes in a health outcome (McGartland et al., 2017).[,] For example, in the case where there is no evidence of a potential relationship the WTP would be expected to be zero and the effect should be excluded from the analysis. Alternatively, when there is some evidence of a relationship between exposure and the health outcome, but that evidence is insufficient to definitively conclude that there is a causal relationship, individuals may have a positive WTP for a reduction in exposure to that hazard (Honeycutt, 2020, Kivi and Shogren, 2010). Lastly, the WTP for reductions in exposure to pollutants with strong evidence of a relationship between exposure and effect are likely positive and larger than for endpoints where evidence is weak, all else equal. Unfortunately, the economic literature currently lacks a settled approach for accounting for how WTP may vary with uncertainty about causal relationships.
Given these challenges, for step 1 the EPA draws its assessment of the strength of evidence on the relationship between exposure to PM2.5 or O3 and potential health endpoints from the Integrated Science Assessments (ISAs) that are developed for the NAAQS process. Specifically, in the PM2.5 and O3 benefits analysis for the final Revised Cross-State Air Pollution Rule (CSAPR) Update RIA, the EPA quantifies and monetizes all health effects that the ISA determines are "causal" or "likely to be causal," using scientific assessment methods described in the ISAs. The focus on categories identified as having a "causal" or "likely to be causal" relationship with the pollutant of interest is to estimate the pollutant-attributable human health benefits in which we are most confident. All else equal, this approach may underestimate the benefits of PM2.5 and O3 exposure reductions as individuals may be WTP to avoid specific risks where the evidence is insufficient to conclude they are "likely to be caus[ed]" by exposure to these pollutants. At the same time, WTP may be lower for those health outcomes for which causality has not been definitively established. This approach treats relationships with ISA causality determinations of "likely to be causal" as if they are known to be causal, and therefore benefits could be overestimated (section 6.5.2). This approach may be revisited in the future with scientific advancements and development of a theoretically consistent framework that jointly accounts for causal uncertainty and individuals' WTP for reducing uncertain health impacts.
Document Purpose and Overview
This is a technical support document (TSD) to the Final Revised CSAPR Update for the 2008 Ozone (O3) Season NAAQS rulemaking. Sections relate to the three key information collection and assessment steps presented in section 1.1 and detail the methodological approaches used for identifying new benefits assessment data inputs:
 Establish criteria for identifying studies and risk estimates most appropriate to inform a PM2.5 and O3 benefit analysis for an RIA (section 2.1). Study criteria, such as study design, location, population characteristics, and other attributes, were used to identify the most suitable estimates. This step precedes health endpoint identification to ensure impartial health endpoint identification and prevent identification of non-quantifiable endpoints.
 Identify pollutant-attributable health effects for which the ISA reports strong evidence and that may be quantified in a benefits assessment (section 2.2). EPA considered new evidence reported in the recent ISAs (U.S. EPA, 2019c, U.S. EPA, 2020a) and clinically significant outcomes (e.g. premature mortality and hospital admissions) for which endpoint-specific baseline incidence data is available.
 Collect baseline incidence and prevalence estimates (section 3) and demographic information (section 4). EPA develops either daily or annual baseline incidence and prevalence rates at the most geographically- and age-specific levels feasible for each health endpoint assessed.  EPA uses population projections based on economic forecasting models developed by Woods and Poole, Inc. (Woods & Poole, 2015). The Woods and Poole (WP) database contains county-level projections of population by age, sex, and race out to 2050, relative to a baseline using the 2010 Census data.
 Develop economic unit values (section 5). To directly compare benefits estimates associated with a rulemaking to cost estimates, the number of instances of each air pollution-attributable health impact must be converted to a monetary value. This requires a valuation estimate for each unique health endpoint, and potentially also discounting if the benefits are expected to accrue over more than a single year. EPA develops valuation estimates at the most age-refined level feasible for each health endpoint assessed.
 Characterize uncertainty associated with quantified benefits estimates (section 6). Building on EPA's current methods for characterizing uncertainty, these approaches include, among others, reporting confidence intervals calculated from risk estimates, separate quantification using multiple studies and risk estimates for particularly influential endpoints (e.g., mortality risk), and approaches for aggregating and representing the results of multiple studies evaluating a particular health endpoint.

 Approach to Identifying Studies and Risk Estimates
This section describes the criteria EPA applies to available fine particulate matter (PM2.5) and ozone (O3) epidemiologic studies and risk estimates to quantify air pollution-attributable health impacts for regulatory purposes, such as Regulatory Impact Analyses (RIAs). We specify the criteria used to identify the available body of epidemiologic literature potentially suitable for supporting a benefits analysis (section 2.2); apply the identification criteria to the body of available literature (section <IP_ADDRESS>); and, finally, present the identified health endpoints and risk estimates (section 2.3) that best characterize risk to the U.S. population for health impact benefits assessments. The identification criteria precede the health endpoint identification because epidemiologic studies must meet certain minimum criteria (section 2.1.1).
Study and Risk Estimate Identification Criteria
We follow a systematic approach to identify the studies and risk estimates most appropriate to inform a PM2.5 and O3 benefit analysis for an RIA. Epidemiologic studies report estimated risks of population exposure to one or more pollutants across a variety of geographic locations, age groups, population attributes, methods for estimating exposure, PM2.5 and O3 concentrations, time periods, study sizes, follow-up durations, and other attributes. Identification criteria, specified below, provide transparency into the scientific judgements used for identifying benefit assessment input parameters. These criteria are similar to those applied in previous EPA RIAs (Table 1) with the primary goal of identifying risk estimates that best characterize risk from PM2.5 and O3 exposure among the total population located throughout the U.S.
Minimum Criteria
All studies must meet the following minimum required criteria to be considered for use in PM2.5 and O3 benefits assessments. These minimum criteria ensure that the subset of studies evaluated include the information necessary to justifiably quantify health effects when estimating benefits across the U.S.
 The study must be referenced in the latest externally reviewed ISA or equivalent assessment (e.g., provisional assessment or supplement) to ensure the literature search and screening process were performed in a transparent and systematic manner and only included peer-reviewed research.
 The study must have been conducted in either the U.S. or Canada and represent air quality conditions, affected populations, and other underlying characteristics of the U.S.
 The study must have been epidemiologic in nature, assess either PM2.5 or O3, and report numerical risks/hazards expressed as per a unit change in pollutant concentration to provide necessary information for health effect quantification.
Preferred Criteria Categories
Studies meeting the minimum criteria are then evaluated based on various factors, which we call preferred criteria, in order to identify risk estimates that best characterize risk across the U.S. These preferred criteria define other important study design features or attributes and are considered collectively (Table 1). Most criteria described below can be applied to both the studies and risk estimates, though criteria applicable only to risk estimates are noted.
Importantly, preferred criteria are established prior to study and risk estimate evaluation and these choices are based on study quality and suitability. Conversely, factors such as the magnitude of the risk estimates, are not considered when identifying studies and risk estimates. Considering these factors might inadvertently bias our choice of studies or risk estimates.
Preferred criteria (Table 1) are considered simultaneously when identifying studies and risk estimates best for use in benefits assessment. Table 2 identifies specific attributes within each preferred criterion that make a particular study more (or less) suitable for identification. In practice, an identified study or risk estimate identified may not have the ideal attributes for all criteria, thus there needs to be a simultaneous assessment of the collective merits of any study or risk estimate. This means that the risk estimates ultimately identified for application in benefits assessment may not be the highest ranked in each individual preferred study criterion category, but that they rise to the top when all criteria are considered simultaneously.
Table 1. Criteria for Identifying Studies and Risk Estimates for Application in Benefits Assessment
Criteria[1]
                                  Description
Study Period
Studies examining a relatively longer period of time (and therefore having more temporal coverage) are preferred because they have greater statistical power to detect effects (e.g., all else being equal, a study over a five year duration would be preferred over a study duration of one year). Studies that are more recent are also preferred because of possible changes in pollution mixes, medical care, and lifestyle over time. When identifying risk estimates, models with the broadest time coverage and overlapping air quality and health data are preferred.
Exposure Estimate
Studies estimating air quality/exposure using a combination of approaches (e.g., remote sensing techniques ground-truthed by monitoring data) are preferred over those that use a single method (e.g., monitor data), because multiple measurement methods can reduce exposure estimate bias and generate higher-resolution of estimates than exposure data from a single source. When available, studies of long-term/chronic exposure are preferred over short-term exposure (i.e., hours up to 1 month), considering the limitations of each exposure duration, as risk estimates based on long-term exposures may include some short-term exposure effects and provide a more comprehensive estimates of health impacts.
Study Type
Among epidemiologic studies that consider long-term exposure (e.g., one month to years), cohort studies are preferred over case-control studies when estimating benefits across the U.S., as they are more representative of the overall population, and both are preferred over cross-sectional or ecological studies because they control for important individual-level confounding. An exception to the preference for cohort studies is for rare disease, when case-control studies may have more power and less selection bias. For short-term exposure studies, case-crossover and time series studies are preferred over cross-sectional or prevalence studies also because they are better able to control for potential confounders.
Population Attributes
Study populations representative (in terms of age, sex, race/ethnicity, etc.) of the population in which health effects are supported are preferred. The most technically appropriate measures of benefits would be based on health impact functions that cover the entire sensitive population but allow for effect modification by age, sex, race/ethnicity, or other relevant demographic factors (e.g., educational status). In the absence of effect estimates specific to age, sex, preexisting condition status, or other relevant factors, it may be appropriate to identify effect estimates that cover the broadest population to match with the desired outcome of the analysis, which for most EPA benefit-cost analyses is total national-level health impacts. Where both are available, both age-stratified and overall risk estimates should be considered for inclusion.
Study Location
U.S. or Canadian studies are used exclusively because studies conducted elsewhere may exhibit influences of potential differences in pollution characteristics, exposure patterns, medical care system, population behavior, and lifestyle. National estimates are most appropriate when benefits are nationally distributed; the impact of regional differences may be important when benefits only accrue to a single area. City-specific risk estimates from multi-city studies of hospital admissions or emergency department visits for non-fatal morbidities may be evaluated for site-specific application to the corresponding city. Canadian studies are considered when U.S study options are limited or less informative. Risk estimates with the broadest geographic coverage are preferred (e.g., multi-city studies preferred to single-city studies) because they provide a more generalizable representation of the health impacts.
Health Endpoint
To comprehensively capture the suite of attributable public health impacts and increase the power to detect effects, when estimating hospital admissions and emergency department visits, broad health endpoints are preferred over narrower, more specific endpoints. For example, more-inclusive respiratory hospital admissions endpoint would be selected over combining hospital admissions for various individual respiratory endpoints, such as asthma, long-term obstructive pulmonary disease, and respiratory infection. Please note, broad endpoint categories do not overlap (e.g., nervous system effects and respiratory effects), so there is no potential for double counting impacts.
Study Size
Studies examining a relatively large sample are preferred because they generally have more power to detect small magnitude effects. A large sample can be obtained in several ways, including through a large study population, through repeated observations on a smaller population (e.g., through a symptom diary recorded for a panel of asthmatic children) or through a case crossover study design. In general, studies of larger numbers of participants and/or events are preferred.
Pollutant Concentrations
Studies evaluating air pollutant exposures closer to or below current conditions are preferred, as the risk associated with exposure may change at different pollutant concentrations and air pollution concentrations may decrease in the future,
Hazard/Risk Estimate
Studies evaluating multiple well-established statistical models adjusted for the most relevant covariates are preferred.
Inclusion of Other Pollutants
When estimating the effects of O3 and PM (or other pollutant combinations) jointly, it is preferable to use properly specified health impact functions that include both pollutants. Using single-pollutant estimates in cases where both pollutants are expected to affect a health outcome can lead to double-counting of benefits when pollutants are correlated.
Lag Period
Lag durations were identified according to the hierarchy described in Table A-1 of the PM and O3 ISAs. Briefly, the strongest multi-day/distributed lag periods that are more biologically plausible are preferred.
O3 Season
Studies and risk estimates of O3 exposure for the full year are preferred over those estimating O3 exposures in the summer or warm season only, as O3 concentrations can remain relatively high outside of the standard warm season in many parts of the country. As such, year-round time coverage can provide a more complete estimate of O3 exposure health impacts.
O3 Metric
Risk estimates based on changes in the maximum daily 8-hour average (MDA8) O3 concentration are preferred. As discussed in the 2020 PM Policy Assessment (PA), there is considerable support from human chamber and epidemiologic studies, as well as advice from EPA's Clean Air Scientific Advisory Committee (CASAC) to support relationships between an 8-hour exposure period and short- and long-term health impacts of O3 (U.S. EPA, 2020c).
[1] Although preferred criteria categories are not hierarchical, not all criteria are weighted equally, and expert judgement is involved.
Prioritizing Preferred Identification Criteria
Where Table 1 provides general information on how we determine which studies and risk estimates best characterize U.S. risk, Table 2 describes how the attributes for each of the 13 criteria are prioritized within each criteria category. Again, we use the overall study information, and studies ultimately identified generally performed better across all categories. Importantly, the order of prioritization presented in Table 2 are relative. For example, the most preferred option may be considered only slightly more preferable than the other alternative.
Table 2. Study and Risk Estimate Criteria Prioritization Order
                                   Criteria
          Prioritization Detail (In order of most to least preferred)
Study Period
1. Most recent years with overlapping air quality and health data
2. Less recent years with partially overlapping air quality and health data
3. Studies with air quality monitoring conducted prior to 2000
Exposure Estimate
1. Studies estimating exposure using a combination of approaches (e.g., chemical transport modeling, monitoring data, land use regression techniques, and satellite data)
2. Studies estimating exposure using some, but not all, of the above approaches (prioritized if using monitoring data and/or chemical transport modeling)
3. Studies estimating exposure using monitoring data only (prefer data from federal reference [FRM] monitors
Study Type
Long-Term Exposure (i.e., one month to years) Studies
1. Cohort studies[2]
2. Case-control studies
Short-Term Exposure (i.e., hours up to one month) Studies
1. Case-crossover (each subject serves as own control)/Time series studies[1]
2. Cross-sectional/prevalence (population-level) studies

Population Attributes
Prefer studies that include broad population attributes with diverse race/ethnicities, both sexes, and broader age groups (e.g., 0-99 as opposed to only ages 0-17 or only ages 65-99)
Study Location
1. Nationwide coverage (most or all states represented), including rural areas
2. Nationwide coverage, including only urban areas
3. Multi-city and multi-state coverage
4. Multi-city or multi-state coverage
5. Single-city or -state coverage
Health Endpoints
1. Broad hospital admissions and emergency department visit endpoint categories (e.g., hospital admissions and emergency department visits for cardiovascular and respiratory effects as opposed to admissions or emergency department visits by individual ICD codes) and broad age groups (e.g., 0-99 as opposed to only 0-17 or only 65-99)
2. Broad hospital admissions and emergency department visit health endpoint categories and specific age groups
3.Specific hospital admissions and emergency department visit health endpoint categories and broad age groups
4. Specific hospital admissions and emergency department visit health endpoints and specific age groups
Note: The first two options are highly preferred over the second two options
Study Size
Larger study size preferred
Pollutant Concentrations
Pollutant exposures concentrations closest to current conditions preferred.
Hazard/Risk Estimate
1. Risk estimates including the most relevant covariates (e.g., age, sex, race, education, smoking status, etc.)
2. Risk estimates including some relevant covariates
3. Risk estimates that do not include relevant covariates
Inclusion of Other Pollutants
1. Multipollutant risk estimates including other pollutants and not likely to be affected by collinearity among pollutant covariates.
2. Copollutant risk estimates including either PM2.5 or O3.
3. Single-pollutant risk estimates.
Lag Period
1. Distributed lag models
2. Average of multiple days (e.g., 0-2)
3. A priori lag days
4. Individual lag days, using expert judgment to identify the appropriate result to focus on considering the time course for physiologic changes for the health effect or outcome being evaluated.
O3 Season
Annual/full-year exposures are preferred over summer/warm season-only O3 exposures for long-term exposure-related health endpoints. Summer/warm season-only exposures are preferred over annual/full-year exposures for short-term O3 exposure-related health endpoints.
O3 Metric
1. 8-hour maximum O3
2. 1-hour maximum O3
3. 24-hour average O3
4. Other metrics
ICD- International Statistical Classification of Diseases and Related Health Problems
[1]If a study presents both case crossover and time series results, case crossover will be identified
[2]An exception to the preference for cohort studies is for rare disease, when case control studies may have more power and less selection bias.
Available Epidemiologic Literature
We follow a structured and transparent process, documented below, to identifying epidemiologic literature from the body of available epidemiologic literature (section 2.1). This involves the identification of health endpoints that are both attributable to exposure (section 2.2.1) and for which we can quantify counts of cases (section 2.2.2). This literature is then reviewed using the criteria identified in Table 1 and Table 2.
Identification of Exposure-Attributable Health Outcomes
Our process for identifying exposure-attributable health endpoints is informed by the findings of the Integrated Scientific Assessment (ISA), which identifies broad endpoint categories causally related to pollutant exposure (section <IP_ADDRESS>); these findings are in turn supported by plausible biological pathways of disease (section <IP_ADDRESS>).
Each potential health endpoint must satisfy the below conditions prior to inclusion in the main benefits assessment:
 The broad endpoint category is sufficiently causally related to exposure (section <IP_ADDRESS>)
 The specific health endpoint is a biologically plausible health effect of exposure (section <IP_ADDRESS>)
The air quality criteria used to support the review of the National Ambient Air Quality Standards (NAAQS) undergo a structured and transparent review process for evaluating scientific information and reaching conclusions about causal determinations that are supported by the scientific information for air pollution exposures and health effects, as presented in the ISAs. To inform the NAAQS, ISAs draw upon the existing body of evidence to comprehensively evaluate and synthesize policy-relevant air pollution science. ISAs transparently identify, critically evaluate, and synthesize the current scientific literature, including epidemiology studies, making them a suitable source of 1) the causal relationships between exposure and health outcomes (section <IP_ADDRESS>), and 2) available epidemiologic literature from which to identify studies and risk estimates for consideration in benefits assessments (section 2.2.3).
A 2002 National Academy of Science review supported the use of ISAs as the basis for determining which health endpoints to include in benefits assessment, stating "the goal of health benefits analysis is to consider all relevant health outcomes" and "a comprehensive discussion of causality is not necessary for a benefits analysis" if the information is "provided in the scientific documentation for the rule-making, such as the criteria document and other related reports, and in guidance provided by EPA's Science Advisory Board" (NRC, 2002). For background, we provide a "brief review of the evidence for causality" from the most recent ISAs to "provide justification for inclusion and exclusion of specific health outcomes considered" and to acknowledge "uncertaint[ies] associated with this assumption" (section 2.2.1). This section of the TSD also provides background information with regard to potential biological plausibility pathways presented in the ISAs (section <IP_ADDRESS>) that support the causality determinations.
In addition to the causality determinations, ISAs can also serve as a curated source of pollutant-, exposure-, and endpoint-specific available epidemiologic literature. Each ISA begins with a broad, thoroughly documented literature search, the results of which undergo several screening stages to ensure included studies are within the clearly defined scope of each ISA, in order to identify the most policy-relevant science (U.S. EPA, 2015b, U.S. EPA, 2019c). For example, with regard to the PM2.5-related health effects, the 2019 PM ISA focused on epidemiologic exposures reflecting current PM2.5 levels for health effect categories where the 2009 PM ISA concluded a "causal" or "likely to be causal" relationship (U.S. EPA, 2009). As such, EPA relies on the systematic and externally reviewed ISAs for criteria pollutant health endpoints and began the process of identifying epidemiologic risk estimates for PM2.5- and O3-attributable benefits assessment with the literature sets identified in the 2019 PM and 2020 O3 ISAs (U.S. EPA, 2019c, U.S. EPA, 2020a). All epidemiologic studies newly considered for use in benefits estimation are available in a separate Study Information Table, described in section 2.2.3.
ISA Causality Determinations
ISAs characterize the strength and consistency of underlying human clinical, animal toxicological, and epidemiologic evidence in making causality determinations. Generally, to estimate the pollutant-attributable human health benefits in which we are most confident, we estimate benefits resulting from health effects that we have high confidence are attributable to pollutant exposure, so we focus on categories identified as having a "causal" or "likely to be causal" relationship with the pollutant of interest in the most recently published ISA. These causality determinations are applied to broad health endpoint categories (e.g., mortality, cardiovascular effects, respiratory effects, nervous system effects, metabolic effects, etc.) using a weight-of evidence approach (U.S. EPA, 2015b, U.S. EPA, 2019c), according to the rationale described below:
 Causal relationship- Evidence is sufficient to conclude that there is a causal relationship with relevant pollutant exposures (e.g., doses or exposures generally within one to two orders of magnitude of recent concentrations). That is, the pollutant has been shown to result in health effects in studies in which chance, confounding, and other biases could be ruled out with reasonable confidence. For example: (1) controlled human exposure studies that demonstrate consistent effects, or (2) observational studies that cannot be explained by plausible alternatives or that are supported by other lines of evidence (e.g., animal studies or mode-of-action information). Generally, the determination is based on multiple high-quality studies conducted by multiple research groups.
 Likely to be causal relationship- Evidence is sufficient to conclude that a causal relationship is likely to exist with relevant pollutant exposures. That is, the pollutant has been shown to result in health effects in studies where results are not explained by chance, confounding, and other biases, but uncertainties remain in the evidence overall. For example: (1) observational studies show an association, but copollutant exposures are difficult to address and/or other lines of evidence (controlled human exposure, animal, or mode of action information) are limited or inconsistent or (2) animal toxicological evidence from multiple studies from different laboratories demonstrate effects but limited or no human data are available. Generally, the determination is based on multiple high-quality studies.
Conclusions made in the 2019 PM and 2020 O3 ISAs regarding the relationships between exposure and various broad health endpoints, as well as previous determinations from the 2009 PM and 2013 O3 ISAs, are provided below, with "causal" and "likely to be causal" judgements highlighted (Table 3 and Table 4) (U.S. EPA, 2009, U.S. EPA, 2013, U.S. EPA, 2019c, U.S. EPA, 2020a). There were no "causal" or "likely to be causal" relationships for PM10-2.5 or ultrafine particles in the 2019 PM ISA, so Table 3 focuses on PM2.5 determinations. Table 3 also highlights how the causal determinations in the 2019 PM ISA are similar to, or different from, the determinations from the 2009 PM ISA. Table 4 highlights how the new causal determinations in the 2020 O3 ISA are similar to, or different from, the determinations from the 2013 O3 ISA. Sections of the 2019 PM and 2020 O3 ISAs related to "causal" and "likely to be causal" determinations were used as the basis for identifying the set of available epidemiologic literature best suited for consideration in benefit estimation (U.S. EPA, 2009, U.S. EPA, 2013, U.S. EPA, 2019c, U.S. EPA, 2020a), as discussed in more detail on section <IP_ADDRESS>.
 Table 3. Causality Determinations for PM2.5-Related Health Effects
                                   Exposure
                                Health Outcome
                              2009 ISA Conclusion
                              2019 ISA Conclusion
                                   Long-term
                                 Mortality[1]
                                    Causal
                                    Causal

                            Cardiovascular Effects
                                    Causal
                                    Causal

                              Respiratory Effects
                              Likely to be causal
                              Likely to be causal

                            Nervous System Effects
                                     None
                              Likely to be causal

                                    Cancer
                  Suggestive of, but not sufficient to infer
                              Likely to be causal

                               Metabolic Effects
                                     None
                                  Suggestive
                        of, but not sufficient to infer

                  Male and Female Reproduction and Fertility
                  Suggestive of, but not sufficient to infer
                  Suggestive of, but not sufficient to infer

                         Pregnancy and Birth Outcomes
                  Suggestive of, but not sufficient to infer
                                  Suggestive
                        of, but not sufficient to infer
                                  Short-term
                                 Mortality[1]
                                    Causal
                                    Causal

                            Cardiovascular Effects
                                    Causal
                                    Causal

                              Respiratory Effects
                              Likely to be causal
                              Likely to be causal

                               Metabolic Effects
                                     None
                                  Suggestive
                        of, but not sufficient to infer

                            Nervous System Effects
                              Inadequate to infer
                                  Suggestive
                        of, but not sufficient to infer
[1]Total mortality includes all nonaccidental causes of mortality and is informed by findings for the spectrum of morbidity effects (e.g., respiratory, cardiovascular) that can lead to mortality. Many studies contributing to the total mortality determination assess all causes of mortality. The proportion of cause-specific deaths differs by analysis.
Table 4. Causality Determinations for O3-Related Health Effects
                                   Exposure
                                Health Outcome
                              2013 ISA Conclusion
                              2020 ISA Conclusion
                                   Long-term
                              Respiratory Effects
                              Likely to be causal
                              Likely to be causal

                            Cardiovascular Effects
                      Suggestive of a causal relationship
       Suggestive of, but not sufficient to infer, a causal relationship

                               Metabolic Effects
                                     None
       Suggestive of, but not sufficient to infer, a causal relationship

                              Total Mortality[1]
                     Suggestive of a causal relationship
       Suggestive of, but not sufficient to infer, a causal relationship

                             Reproductive Effects
                      Suggestive of a causal relationship
Effects on fertility and reproduction: suggestive of, but not sufficient to infer, a causal relationship

Effects on pregnancy and birth outcomes: suggestive of, but not sufficient to infer, a causal relationship

                        Central Nervous System Effects
                      Suggestive of a causal relationship
       Suggestive of, but not sufficient to infer, a causal relationship
                                  Short-term
                              Respiratory Effects
                                    Causal
                                    Causal

                              Total Mortality[1]
                              Likely to be causal
       Suggestive of, but not sufficient to infer, a causal relationship

                            Cardiovascular Effects
                              Likely to be causal
       Suggestive of, but not sufficient to infer, a causal relationship

                               Metabolic Effects
                                     None
                              Likely to be causal

                        Central Nervous System Effects
                      Suggestive of a causal relationship
       Suggestive of, but not sufficient to infer, a causal relationship
[1]Total mortality includes all nonaccidental causes of mortality and is informed by findings for the spectrum of morbidity effects (e.g., respiratory, cardiovascular) that can lead to mortality. Many studies contributing to the total mortality determination assess all causes of mortality. The proportion of cause-specific deaths differs by analysis.
Biological Plausibility
ISAs establish causality determinations for broad health effect categories (e.g., cardiovascular effects) and provide information on the strength and consistency of the evidence supporting more specific endpoints (e.g., heart failure) within each section. Both types of information are utilized in benefits assessments. Broad causality determinations can support the use of more comprehensive endpoints found in epidemiologic studies of hospital admissions and emergency department visits, whereas the support of specific endpoints resulting from pollutant exposure may be more relevant to incidence endpoints such as cardiac arrest.
In forming the key science judgments for each of the health effects categories evaluated, the recent ISAs draw conclusions about relationships between PM and O3 exposure and health effects by integrating information across scientific disciplines and related health outcomes and synthesizing evidence from previous and recent studies. An advancement in these most recent ISAs is the inclusion of biological plausibility sections that are specific for each exposure duration and broad health outcome category for which causality determinations are formed. These discussions outline potential pathways along the exposure-to-outcome continuum and provide plausible links between pollutant inhalation and health outcomes at the population level. We include unedited diagrams from the biological plausibility sections of the 2019 PM and 2020 O3 ISAs here to provide information regarding the plausibility of individual health endpoints resulting from PM and/or O3 exposures.
Biological plausibility can strengthen the basis for causal inference (U.S. EPA, 2015b). In the recent ISAs, biological plausibility is part of the weight-of-evidence analysis that considers the totality of the health effects evidence, including consistency and coherence of effects described in experimental and observational studies. Although there is some overlap in the potential pathways between the ISA health effects chapters, each biological plausibility section is tailored to the health outcome category, pollutant, and exposure duration being evaluated within the respective section of each ISA health effects chapter. Diagrams illustrate possible pathways relating exposure to evidence evaluated in current and previous assessments, considering physiology and pathophysiology (Figure 1). These diagrams portray the available evidence that supports the biological plausibility of exposures leading to specific health outcomes, but does not provide information on the weight of evidence supporting each biological pathway (section <IP_ADDRESS>). Gaps and limitations in the evidence base, shown by the absence of a connecting arrow, correspond to gaps in the figure.
Each box represents evidence that has been demonstrated in a study or group of studies for a particular effect related to exposure. While most of the studies used to develop the figures are experimental studies (i.e., animal toxicological and controlled human exposure studies), some observational epidemiologic studies also contribute to the pathways. These epidemiologic studies are generally: 1) panel studies that measure the same or similar effects as the experimental studies (and thus provide supportive evidence) or 2) emergency department and hospital admission studies or studies of mortality, which are effects observed at the population level. The boxes are arranged horizontally, with boxes on the left side representing initial effects that reflect early biological responses and boxes to the right representing potential intermediate (i.e., subclinical or clinical) effects and potential effects at the population level. The boxes are color coded according to their position in the exposure-to-outcome continuum.
The arrows that connect the boxes indicate a potential progression of effects resulting from exposure. In most cases, arrows are dotted (Figure 1, Arrow 1), denoting a possible relationship between the effects. While most arrows point from left to right, some arrows point from right to left, reflecting progression of effects in the opposite direction or a feedback loop (Figure 1, Arrow 2). In a few cases, the arrows are solid (Figure 1, Arrow 2), indicating that progression from the upstream to downstream effect occurs as a direct result of exposure. This relationship between the boxes, where the upstream effect is necessary for progression to the downstream effect, is termed "essentiality" (OECD, 2016). Evidence supporting essentiality is generally provided by experimental studies using pharmacologic agents (i.e., inhibitors) or animal models in which the molecular pathway is obstructed. The use of solid lines, as opposed to dotted lines, reflects the availability of specific experimental evidence that exposure results in an upstream effect which is necessary for progression to a downstream effect, for example, by a genetically deficient model or a chemical inhibitor used in an experimental study involving pollutant exposure.
In the diagrams, upstream effects are sometimes linked to multiple potential downstream effects. Boxes represent the effects for which there is experimental or epidemiologic evidence related to air pollutant exposure, and the arrows indicate a proposed relationship between those effects. To illustrate the proposed relationship using a minimum number of arrows, downstream boxes are grouped together within a larger shaded box and a single arrow (Figure 1, Arrow 3) connects the upstream single box to the outside of the downstream shaded box containing multiple green boxes. Multiple upstream effects may similarly be linked to a single downstream effect using an arrow (Figure 1, Arrow 4) that connects the outside of a shaded box which contains multiple boxes, to an individual box. In addition, arrows sometimes connect one individual box to another individual box that is contained within a larger shaded box (Figure 1, Arrow 2) or two individual boxes both contained within larger shaded boxes (Figure 1, Arrow 5). Thus, arrows may connect individual boxes, groupings of boxes, and individual boxes within groupings of boxes depending on the proposed relationships between effects represented by the boxes. Population level effects generally reflect results of epidemiologic studies. When there are gaps in the evidence base, there are complementary gaps in the figure and the accompanying text below.

Note: For additional information, please refer to the original biological plausibility diagrams in the ISAs (U.S. EPA, 2019c, U.S. EPA, 2020a).
Figure 1. Illustrative Diagram of Potential Biological Pathways of Health Effects Following Pollutant Exposure.
PM2.5-Attributable Endpoints and Biological Plausibility
Below are the ISA biological plausibility diagrams for PM2.5 and O3 endpoints judged to have either a "causal" and "likely to be causal" relationship with pollutant exposure in the 2019 PM and 2020 O3 ISAs, as well as information on which of the endpoints identified in the diagrams have or have not been previously included in benefits assessments. These diagrams have been reproduced verbatim from the ISAs, for the convenience of the reader, and no new independent judgements are rendered regarding biological plausibility in this TSD. Although it is not possible to develop a biological plausibility diagram for total mortality, taken together, the individual endpoint-specific biological plausibility diagrams each provide potential pathways by which PM2.5 exposures could result in mortality.
Cardiovascular Effects
The 2019 PM ISA diagram of biological pathways for cardiovascular effects following short-term PM2.5 exposure includes emergency department visits and hospital admissions as population level effects, for which EPA has historically presented benefits impact estimates (Figure 2). The diagram also includes mortality as a key endpoint, which EPA has not included in benefits estimates due to the possibility of overlap with all-cause mortality impacts from long-term exposure resulting in double counting.

Figure 2. Potential Biological Pathways for Cardiovascular Effects Following Short-Term PM2.5 Exposure
The 2019 PM ISA diagram of biological pathways for cardiovascular effects following long-term PM2.5 exposure includes acute myocardial infarctions (AMI; heart attacks) and mortality, which EPA has historically presented benefits impact estimates for, and conductance abnormalities/arrhythmia, heart failure, stroke, and thromboembolic disease, which have not been included in previous benefits estimates (Figure 3).

Figure 3. Potential Biological Pathways for Cardiovascular Effects Following Long-Term PM2.5 Exposure
Respiratory Effects
The 2019 PM ISA diagram of biological pathways for respiratory effects following short-term PM2.5 exposure includes emergency department visits and hospital admissions for asthma exacerbation/symptoms, chronic obstructive pulmonary disease (COPD), and respiratory infections as key population level health endpoints, for which EPA has historically presented benefits impact estimates (Figure 4).

Figure 4. Potential Biological Pathways for Respiratory Effects Following Short-Term PM2.5 Exposure
The 2019 PM ISA diagram of biological pathways for respiratory effects following long-term PM2.5 exposure includes asthma development/onset and impaired lung function, for which we have not previously presented benefits impact estimates (Figure 5).

Figure 5. Potential Biological Pathways for Respiratory Effects Following Long-Term PM2.5 Exposure
Cancer
The diagram of biological pathways of cancer following long-term PM2.5 exposure is provided (Figure 6)., This relationship was  "suggestive" in the 2009 PM ISA and "likely to be causal" in the 2019 ISA (U.S. EPA, 2009, U.S. EPA, 2019c).
As cancer is a long-term disease, the 2019 PM ISA did not provide a diagram of biological pathways for cancer following short-term PM2.5 exposure.

Figure 6. Potential Biological Pathways for Cancer Effects Following Long-Term PM2.5 Exposure
Nervous System Effects
The 2019 PM ISA diagram of biological pathways for nervous system effects following long-term PM2.5 exposure includes neurodevelopmental disorders, Parkinson's and Alzheimer's disease hospital admissions and emergency department visits, cognitive decrements/behavioral effects, and cognitive issues (Figure 7). Please note, the weight of evidence supporting each potential is not equivalent and additional information can be found in the 2019 PM ISA (U.S. EPA, 2019c). As the previous nervous system effect ISA determination did not rise to the "causal" or "likely to be causal" level, EPA has not previously included any nervous system endpoints in benefits impact estimates.

Figure 7. Potential Biological Pathways for Nervous System Effects Following Long-Term PM2.5 Exposure
O3-Attributable Endpoints and Biological Plausibility
Respiratory Effects
The 2020 O3 ISA diagram of biological pathways for respiratory effects following short-term O3 exposure includes emergency department visits and hospital admissions for asthma exacerbation/symptoms and respiratory infections, for which we have historically presented benefits impact estimates, and lung function decrements, which EPA has not previously estimates associated benefits (Figure 8). Although respiratory mortality is supported as a key clinical effect of short-term ozone exposure in the ISA text and should be included in this diagram, it was mistakenly left out due to the expedited timeline of the 2020 O3 ISA.

Figure 8. Potential Biological Pathways for Respiratory Effects Following Short-Term O3 Exposure
The 2020 O3 ISA diagram of biological pathways for respiratory effects following long-term O3 exposure includes mortality, which EPA has included in prior benefits assessments, and asthma development/onset, fibrotic- or emphysema-like disease/COPD, and altered lung development, which EPA has not previously included in benefits impact estimates (Figure 9).

Figure 9. Potential Biological Pathways for Respiratory Effects Following Long-Term O3 Exposure
Metabolic Effects
The 2020 O3 ISA concluded that short-term exposure was likely to cause metabolic effects and long-term exposure was suggestive of a causal relationship. Neither short- nor long-term causal determinations were made for metabolic effects in the 2013 O3 ISA. The ISA diagram of biological pathways for metabolic effects indicates that long-term O3 exposure leads to complications related to diabetes and changes or contributors to metabolic syndrome, which EPA has not previously included in benefits assessments.

Figure 10. Potential Biological Pathways for Metabolic Effects Following Short-Term O3 Exposure
Identification of Quantifiable Health Outcomes
Health endpoints referenced in the latest externally reviewed ISA or equivalent assessment include both subclinical and clinically relevant endpoints. However, health impacts assessments tend to focus on quantifying the number of instances of clinically relevant endpoints (e.g., mortality, hospital admissions, and disease onset/development) and not subclinical endpoints (e.g., inflammation, oxidative stress, changes in circulation biomarkers, or changes in heart or lung function) for several reasons.
 Specific baseline event incidence, or the amount of a particular health endpoint present within the population, is required when using epidemiologic risk estimates to project health impacts of changes in air quality. Baseline incidence data is more likely to be available for clinically relevant health endpoints (e.g., hospital admissions for cardiovascular ICD codes 390-459 or the prevalence of asthma in children) then for subclinical endpoints (e.g., forced expiratory volume or hypertension).
 Quantifying subclinical endpoints involve additional uncertainties when relating upstream subclinical effects with clinically relevant downstream health impacts. This does not mean that subclinical health impacts of PM2.5 and O3 do not exist. In fact, considerably more instances of pollutant-attributable subclinical effects than clinically relevant effects would be expected.
Although causal determinations are made for broad health endpoint categories (e.g., cardiovascular effects), the ISAs do review support for specific endpoints (e.g., acute myocardial infarctions) (section <IP_ADDRESS>). Evidence associating specific health endpoints falling under broad health endpoints with "likely to be causal" or "causal" relationships with air pollution exposures are used to identify comprehensive, but not overlapping, health endpoints, when suitable studies for quantification based on the criteria identified above are available.
Study Information Table
Extensive and comprehensive study information is provided for transparency regarding study comparisons and identification for benefits assessment. Specific study information, corresponding to the preferred criteria in section 2.1.2, is available for all endpoint-specific, ISA-derived epidemiologic studies newly considered for use in the main and sensitivity benefits estimation in a separate Excel file titled Study Information Table for the Estimating PM2.5- and Ozone-Attributable Health Benefits TSD.[,] Specific studies are listed once per pollutant health endpoint.
Descriptions of the specific types of information extracted from the studies and included in the Study Information Table are available in Table 5. Studies differed in the type and level of detail of information provided. Additionally, sometimes information was not reported (NR) by the study and is therefore not available in the Study Information Table. For example, not all studies provided information on the race, sex, age range of the study population. Please note, individual studies may be listed multiple times in the Study Information Table if they report results for multiple endpoints, but they are listed only once per pollutant endpoint.
Table 5. Study Information Tables
                                  Column Name
                                  Description
Endpoint Group
"Causal" or "likely to be causal" health endpoint included in benefits assessment.
Endpoint
Specific health endpoint included in benefits assessment.
HERO ID
Identifier used by the Health and Environmental Research Online (HERO) database. This database is a repository for studies and other references and is used for various peer-reviewed documents, such as ISAs and research projects.
First Author
First study author listed.
Publication Year
Year the article was published, according to PubMed.
Pollutant
PM2.5 or O3.
Study Type
Epidemiologic study design (e.g., cohort, case control, case crossover, etc.).
Meta-Analysis
Identifies meta-analyses for use when considering pooling city- or regional-specific estimates to generate an overall risk estimate.
Exposure Duration
Long-term (one month to years) or short-term (hours to less than one month) exposures.
Study Population
Cohort name or description if unnamed.
Study Size (number of participants or events)
This can take various forms, such as the number of participants, person-years, number of hospitalizations/admissions/discharges, or number of cases and controls. All information provided by the study is included.
Demographics
Demographics of the study participants, such as race/ethnicity, education level, income level, and socioeconomic status.
Ages
Ages of study participants, with maximum age of 99 reported when maximum participant age 99 and older.
Exposure Method
Summary of the type of exposure estimate technique. Monitor studies denote monitor-based studies, and often include land use regression (LUR) techniques. Hybrid studies include photochemical model and/or satellite data.
Country
Location of study population (U.S. or Canada).
Study Location
Brief description of the locations included in the study.
Health Years
Years of health data included.
Air Quality Years
Years of air quality data included. Many studies used a specific common time frame for entire sample, but some used other criteria, such as exposure over the first year of life.
Pollutant Concentrations (author-reported)
Typically, the overall mean and/or median concentrations across study areas, but sometimes provided information was at a different geographic and temporal scales (e.g., by state and over multiple years).
LRL/Minimum Exposure Concentration
The lowest reported pollutant level/concentration.
Pollutant Concentration Notes
Author-reported exposure estimation method. If multiple types of exposure estimation techniques were used for an individual pollutant, all are included.
Outcome Measure
Specific health outcome. Examples include the ICD codes used for hospital admission and emergency department visits or the criteria used to identify disease onset.
Lag Periods
For short-term studies, the time period of exposure prior to health effect.
Copollutants
Adjustments for copollutants in the risk estimates.
Covariates/Confounders
Author-reported covariates/confounders included in the risk estimate.
Statistical Technique
Analytical methods used to generate the risk estimate.
Qualitative Limitations
Summary comparison of each study to others investigating the same pollutant and specific health endpoint.
Relative Determination
Denotes which studies were identified as best characterizing risk as compared to other available studies for each pollutant endpoint.

Methods for Presenting Health Benefits Estimates Using Multiple Risk Estimates for a Single Endpoint
Pooling
If more than one study or risk estimate is suitable for characterizing risk across the U.S. for an individual health endpoint, we prefer to use multiple risk coefficients to the extent technically feasible. In such instances, we combine the risk estimates using pooling methods in order to avoid a loss of information from multiple suitable studies; this approach is consistent with advice received from the National Academies of Sciences (NRC, 2002). Pooling yields a summary mean value estimate and confidence intervals reflecting variability across the pooled risk estimates. These pooled estimates take into account both the within-study variances and the between-study variance when weighting.
Unfortunately, the heterogeneous nature of epidemiological studies often make them difficult to pool or otherwise aggregate. For example, it would be inadvisable to combine results from a long-term exposure study with a short-term exposure study. Other types of study heterogeneity that would prevent one from aggregating across studies include exposure duration (i.e., short- and long-term), some population attributes (e.g., age or race/ethnicity), health endpoint outcome measure (e.g., specific international classification of disease [ICD] codes), and study type (e.g., cohort vs case control).
Combining studies that differ in other aspects can be less straightforward, as there can be both advantages and disadvantages. For example, recent advancements in exposure estimation methods allow newer hybrid techniques to estimate pollutant concentrations at more detailed temporal and spatial scales. As the uncertainties associated with hybrid- and monitor-based exposure estimates vary and we consider the quality of the exposure estimate during study and risk estimate identification, we expect pooling risk estimates that vary by exposure technique will increase the confidence in the overall benefits estimate. We also consider pooling studies that differ in the study period, North American country, geographic area, pollutant concentrations, included covariates, or regression technique.
Conversely, while consistency between studies is generally desirable when pooling, there are some instances when it could introduce uncertainty and/or bias. For example, we would not pool multiple studies of the same cohort over different time periods, as these are not independent results -- but rather different results from the same cohort. Hence, we would instead identify the most recent analysis or the analysis considering the longest time series of air quality data, so that the study population is not over-represented when estimating health impacts.
Individual Alternate Risk Estimates
In situations where multiple scientifically robust risk estimates should not or cannot be pooled, we instead estimate incidence using each risk estimate independently. Where pooling synthesizes the results of multiple risk estimates into a single value, presenting multiple estimates from various key epidemiological studies identified by the latest ISA or equivalent could provide readers with insight to the plausible range of air pollution-attributable impacts. Therefore, if pooling is infeasible due to the issues mentioned above, we report individual results from each risk estimate suitable for characterizing risk across the U.S. for an individual health endpoint separately. Reporting a large number of individual estimates may characterize the heterogeneity associated with risk but may also make the resulting risk estimates more difficult to interpret. To keep results manageable, we may report additional estimates as a quantitative sensitivity analysis.
Systematic Identification of Epidemiologic Studies and Risk Estimates for Benefits Assessment
This section describes the systematic application of the identification criteria (section 2.1) to the body of available epidemiologic studies and risk estimates (section 2.2). Summary information on the number of available and included studies and risk estimates is presented in Table 6 and Table 7. Descriptions of endpoint-specific ISA support and available epidemiologic literature are available for each pollutant-attributable and quantifiable health endpoint.
PM2.5
The following sections of the PM ISA correspond to health endpoints judged as having a "causal" or "likely to be causal" relationship with PM2.5 exposure:
 5.1 Short-Term PM2.5 Exposure and Respiratory Effects,
 5.2 Long-Term PM2.5 Exposure and Respiratory Effects,
 6.1 Short-Term PM2.5 Exposure and Cardiovascular Effects,
 6.2 Long-Term PM2.5 Exposure and Cardiovascular Effects,
 8.2 Long-Term PM2.5 Exposure and Nervous System Effects,
 10.2 [Long-Term] PM2.5 Exposure and Cancer,
 11.2 Long-Term PM2.5 Exposure and Total Mortality
Following the approach to identifying available epidemiologic literature (section 2.2), we began with the 2,656 studies cited by the 2019 PM ISA. Of these, 491 studies evaluated mortality or morbidity health endpoints that the 2019 PM ISA determined as having a "causal" or "likely causal" relationship with PM2.5 exposure and are clinically relevant (sections 2.2.1 and 2.2.2).  Of these, 82 studies met the minimum required criteria (section 2.1.1).
As studies that evaluated broad and more inclusive hospital admissions and emergency department visit health endpoints (e.g., hospital admissions including a variety of respiratory endpoints) were preferred over studies that focused on hospital admissions or emergency department visits for specific health endpoints (e.g., hospital admissions for asthma only), we began by focusing on epidemiologic studies including broad hospital admissions and emergency department visits ICD-9 codes. This reflects strong support for these broad endpoints in the ISA, the desire to avoid double-counting of health benefits across categories, and recommendations from advisory board recommendations. The remaining studies were sorted by health endpoint and PM2.5 exposure relationship (i.e., short-term or long-term exposure to PM2.5). These studies included 16 unique health endpoints (Table 6). In Table 6, the number of available studies refers to the number of North American studies meeting the minimum required criteria within each health endpoint, and a single study may be relevant to multiple endpoints. The risk estimates from the different studies for each endpoint can either be pooled (section <IP_ADDRESS>) or kept as separate estimates (section <IP_ADDRESS>), the latter of which is more common for mortality endpoints. The two columns to the far left provide the number of available risk estimates from each included study, as well as the number of risk estimates to be pooled or kept separate for each endpoint.
Once the studies were grouped by health endpoint, we applied the preferred criteria to obtain the final set of studies to inform each health endpoint. For each of the 16 endpoints, we performed a study ranking process based on these criteria that emphasized characteristics in Table 2. When no new epidemiologic studies of health endpoints previously supported by the ISAs were available, the risk estimates used previously were brought forward (e.g., work loss days).
Table 6. PM2.5 Study and Risk Estimate Identification Diagram*
                       PM Endpoint and Exposure Duration
                               Studies Available
                               Studies Included
                                     Ages
                           Risk Estimates Available
                            Risk Estimates Included
Mortality (LT)
                                       2
                                       1
                                    Infants
                                       1
                                       1

                                      19
                                       2
                            Adults and older adults
                                      19
                                       1

                                 Older adults
                                      64
                                       1
Cardiovascular Hospital Admissions (ST)
                                      10
                                       1
                      Children, adults, and older adults
                                      28
                                       7
Cardiovascular Emergency Department Visits (ST)
                                       1
                                       1
                      Children, adults, and older adults
                                       3
                                       1
AMI (ST)
                                      NA
                                       1
                            Adults and older adults
                                      NA
                                       1
Stroke (LT)
                                       3
                                       1
                                 Older adults
                                       1
                                       1
Cardiac Arrest (ST)
                                       3
                                       3
                            Adults and older adults
                                      12
                                       1

                                      12
                                       1

                                       7
                                       1
Respiratory Hospital Admissions (ST)
                                      13
                                       2
                                   Children
                                       3
                                       1

                                 Older adults
                                       4
                                       1
Respiratory Emergency Department Visits (ST)
                                      10
                                       1
                                   Children
                                      16
                                       4
Asthma Onset (LT)
                                       5
                                       1
                                   Children
                                       7
                                       1
Asthma Symptoms (ST)
                                       8
                                       1
                                   Children
                                       8
                                       1
Allergic Rhinitis
                                       1
                                       1
                                   Children
                                       5
                                       1
Minor Restricted Activity Days
                                      NA
                                       1
                            Adults and older adults
                                      NA
                                       1
Work Loss Days
                                      NA
                                       1
                            Adults and older adults
                                      NA
                                       1
Lung Cancer (LT)
                                       4
                                       1
                            Adults and older adults
                                      24
                                       1
Alzheimer's Disease (LT)
                                       1
                                       1
                                 Older adults
                                      53
                                       1
Parkinson's Disease (LT)
                                       3
                                       1
                                 Older adults
                                      53
                                       1
ST- short-term exposure; LT- long-term exposure; NA- not applicable due to the absence of recent available epidemiologic studies in the ISA; Risk estimates identified in the 2012 PM NAAQS RIA will continue to be utilized.
*See associated Study Information Table for specific study details.

All-Cause Mortality
The 2019 PM ISA concluded that a "causal" relationship exists between both long- and short-term PM2.5 exposure and all-cause mortality. Specifically, the 2019 ISA states that:
Recent U.S. and Canadian cohort studies demonstrate consistent, positive associations between long-term PM2.5 exposure and mortality across various spatial extents, exposure assessment metrics, and statistical techniques, and locations, where mean annual average concentrations are <=12 μg/m[3]. Additionally, the evidence from recent studies reduce uncertainties related to potential copollutant confounding and continues to provide strong support for a linear, no-threshold concentration-response relationship. The body of evidence for total mortality is supported by generally consistent positive associations with cardiovascular and respiratory mortality. There is coherence of effects across the scientific disciplines (i.e., animal toxicological, controlled human exposure, and epidemiologic studies) and biological plausibility for PM2.5-related cardiovascular, respiratory, and metabolic disease, which supports the PM2.5-mortality relationship. (U.S. EPA, 2019c, section 11.2.7)
As the biological pathways by which short- and long term PM2.5 exposures are understood to lead to health effects are quite similar (section <IP_ADDRESS>.1), we assume that effects found in studies of long-term exposures may include some effects of short-term exposures. Therefore, only mortality impacts from long-term PM2.5 exposure will be quantified, so as not to overestimate impacts. This may potentially bias long-term, all-cause PM2.5-attributable mortality impact estimates toward the null in the main benefit estimate.
Additional support for including estimates of all-cause PM2.5 mortality, as opposed to cause-specific, comes from the recommendations of advisory boards and review panels. For example, in response to suggestions made in a 2002 National Resources Council (NRC) report, expert judgement studies of mortality impacts were conducted (NRC, 2002). The report recommended that "the main quantitative question should focus on all-cause mortality as the outcome, rather than eliciting separate concentration-response functions for specific causes of death" (IEc, 2006).  Similarly, a more recent Health Effects Subcommittee (HES) of the Advisory Council on Clean Air Compliance Analysis (Council) affirmed the inclusion of all-cause long-term PM2.5 estimate with no threshold was "sound" (Hammitt and Bailar, 2010).
Available Epidemiologic Literature
Whereas for all other health endpoints we began by identifying North American epidemiologic studies from the relevant ISA (U.S. EPA, 2019c, Figures 11-17 and 11-18), available literature for this health endpoint had been further reviewed by EPA in the 2020 PM Policy Assessment (PA) (U.S. EPA, 2020c section 3.2 and Figure 3-3). As part of this review, the PA identified multi-city studies and more recent reanalysis or extensions of some of the commonly used cohorts. As such, for this health endpoint we began with the 19 epidemiologic multi-city cohort studies identified in the 2020 PM PA, which all met the minimum criteria (section 2.1.1).
We separately evaluated the more limited literature available regarding PM2.5-attibutable infant mortality (ages 0-12 months) cited in the 2009 ISA, as no more recent studies of PM2.5-attributable all-cause infant mortality were available in the 2019 ISA. Full study information can be found in the Study Information Table.
Identifying Suitable Studies for Use in Benefits Assessments
The systematic identification criteria (section 1) was applied to the 19 studies of PM2.5-attributable long-term all-cause mortality in adults, which prioritized particularly germane attributes including geographic coverage, population representativeness, and method of exposure estimation. As it is not relevant to study identification, we did not consider the risk effect magnitude as a criterion for study identification.
The 19 studies varied considerably with regards to all criteria considered. For example, study sizes ranged from the thousands to the tens of millions. Ultimately, all preferred criteria relevant to PM2.5 factored into the identification of the studies best characterizing risk across the country, although geographic diversity, exposure estimation technique, population attributes, PM2.5 concentrations, and inclusion of the copollutant O3 were particularly germane. Specific information can be found in the corresponding Study Information Table.
Studies and Risk Estimates Identified as Most Suitable for Use in Benefits Assessments
As mortality impacts constitute such a large portion of health impacts and every study has inherent strengths and limitations, we expect to present mortality estimates using multiple cohort risk estimates. This approach is also consistent with previous RIAs (e.g., U.S. EPA, 2011b, U.S. EPA, 2011c, U.S. EPA, 2011d, U.S. EPA, 2012a, U.S. EPA, 2012b, U.S. EPA, 2015a, U.S. EPA, 2019a).
The systematic approach led to the initial identification of three studies best characterizing risk across the U.S. (Di et al., 2017b, Pope et al., 2015, Turner et al., 2016).[,] These three studies used data from two cohorts, a retrospective analysis of Medicare beneficiaries (Medicare) and the American Cancer Society Cancer Prevention II study (ACS CPS-II). The identification of these studies is consistent with the 2019 PM ISA, which concluded that the ACS CSP-II and Medicare cohorts provide strong evidence of the association between long-term PM2.5 exposure and mortality with support from several additional cohort studies (U.S. EPA, 2019c). We discuss uncertainty and sensitivity considerations related to the identified mortality risk estimates in sections 6.1.2 and 6.5.
Adult Mortality
ACS CSP-II
Two independent studies evaluated the same years of data from the large, nationwide ACS CSP-II cohort of those > 29 years old (Pope et al., 2015, Turner et al., 2016). These studies extended the follow-up period of the ACS CSP-II to 22 years (1982-2004), evaluating 669,046 participants over 12,662,562 person-years of follow up and 237,201 observed deaths. These two studies applied a more advanced exposure estimation approach than had previously been used when analyzing the ACS cohort, combining the geostatistical Bayesian Maximum Entropy framework with national-level land use regression models.
In addition to adjusting for individual-level and ecological covariates, Turner et al., 2016 also controlled for occupational PM2.5 exposure and adjusted for the potential copollutants O3 and nitrogen dioxide. Although the copollutant adjustment did not significantly change the hazard ratio, similar to the risk assessment performed as part of the PM PA (U.S. EPA, 2020c), we identified it as the most suitable hazard ratio when estimating health benefit impacts. Thus, the total mortality risk estimate is based on the random-effects Cox proportional hazard model that incorporates multiple individual and ecological covariates (relative risk  =1.06, 95% confidence intervals 1.04 - 1.08 per 10ug/m[3] increase in PM2.5). The relative risk estimate is identical to a risk estimate drawn from earlier ACS analysis of all-cause long-term exposure PM2.5-attributable mortality (Krewski et al., 2009). Of note, the ACS cohort participants were recruited by approximately 77,000 ACS volunteers and may not precisely represent the overall U.S. population demographics.
A depiction of the slope and standard error of the hazard ratio associated with the identified risk estimate from the minimum to maximum PM2.5 concentrations evaluated is provided (Figure 11). The static standard error is reflected in the proportionally constant 95% confidence intervals shown with red dashed lines, depicted as relative to the lowest reported level.

Figure 11. Functional Form of the Identified ACS CSP-II Risk Estimate
Medicare
The recent Di et al., 2017b analysis evaluated nearly 61 million U.S. Medicare enrollees through 460 million person-years of follow-up and roughly 22 million observed deaths. This cohort comprised approximately 15% of the total U.S. population, included people living in rural areas, and is one of the largest cohort studies published to date. The authors modeled PM2.5 exposure across the contiguous U.S. using a sophisticated hybrid methodology that included land use regression, satellite data, and monitor data, and resolved estimations to 1 x 1-kilometer areas. Adjustment for potential confounding by the copollutant O3 was performed, which slightly attenuated the relationship between PM2.5 and mortality. The authors also performed statistical testing for the potential of non-linear effects and concluded that the data supported a nearly linear concentration-response relationship with no signal of a threshold down to at least 5 ug/m[3].  This study is restricted to adults over the age of 64, and thus will only be applied to that age group in benefits assessments.
In addition to the main hazard ratio, Di et al., 2017b presented three additional hazard ratios: one that excluded the copollutant O3, one that estimated exposure using only monitor data, and one that evaluated a subset of the population experiencing lower exposures. Of these hazard ratios, only the low-exposure analysis differed substantially from the others and was considerably higher (HR = 1.136 [1.131-1.141] per 10 μg/m[3] PM2.5). However, this analysis was restricted to person-years with both PM2.5 exposures lower than 12 μg/m[3] and O3 exposures lower than 50 parts per billion (ppb). Restricting the analysis in this way reduces the sample size and restricts the air quality concentrations, making estimates of risk less applicable to the entire U.S. Similarly, we prefer methods for assigning exposure that leverage both monitor and modeling techniques and models that account for potential copollutant confounding. Hence, we identified a hazard ratio from the main analysis to be the most appropriate for use (HR=1.073 [1.071-1.075] per 10 μg/m[3] PM2.5).
A depiction of the slope and 95% confidence intervals of the hazard ratio associated with the identified risk estimate from the minimum to maximum PM2.5 concentrations evaluated is provided (Figure 12).

Figure 12. Functional Form of the Identified Medicare Risk Estimate
Summary
EPA previously used the two best estimates of mortality available, one from the ACS cohort and one from the Harvard Six Cities study. While two estimates were again identified as best characterizing risk across the U.S., their relative magnitude will depend on the populations included in the analysis (e.g., analyses of older or younger populations experiencing higher concentrations will lead to the Medicare estimate or the ACS CSP-II generating the "higher" estimate, respectively). Qualitatively, the two risk estimates identified here are both very similar to the previously used Krewski et al., 2009 estimate of mortality derived from the ACS cohort.
Infant Mortality
In addition to the adult mortality studies described above, several studies show an association between PM exposure and mortality in children under 5 years of age (U.S. EPA, 2009). The 2019 PM ISA concluded that evidence exists for a stronger effect at the post neonatal period and for respiratory-related mortality, although this trend is not consistent across all studies. In addition, compared to avoided deaths estimated for adult mortality, avoided deaths for infants are significantly smaller because the number of infants in the population is much smaller than the number of adults and the epidemiology studies on infant mortality provide smaller risk estimates associated with exposure to PM. EPA has included estimates of post neonatal infant mortality from Woodruff et al., 1997 (U.S. EPA, 2012a, U.S. EPA, 2019a).
In a more recent study including a larger and more nationally-representative study size by the same group, authors examined the relationship between long-term exposure to fine PM2.5 air pollution and post neonatal infant mortality in 3,583,495 births from 96 counties containing >249,999 residents across the U.S. between 1999-2002 using data from the National Center for Health Statistics (Woodruff et al., 2008). They linked average PM2.5 monitoring data over the first two months of life with 6,639 post neonatal deaths, using logistic regression that incorporated generalized estimating equations (GEE) to estimate the odds ratios for all-cause and cause-specific post neonatal mortality by exposure to air pollution. The study population experienced a median PM2.5 concentration of 14.8 ug/m[3], with 25% of the population experiencing concentrations below 12 ug/m[3] and above 18.8 ug/m[3]. The study included an evaluation of the appropriateness of a linear form from analysis based on quartiles of exposure and determined the linear form as a reasonable assumption. Study results included a single risk estimate of PM2.5-attributable all-cause mortality, 1.04 (0.98-1.11) per 7 ug/m[3] (interquartile range) increase in PM2.5.
Cardiovascular Hospital Admissions
The ISA found "generally consistent, positive associations observed in numerous epidemiologic studies of emergency department visits and hospital admissions for ischemic heart disease, heart failure, and combined cardiovascular-related endpoints" (U.S. EPA, 2019c section 6.1.16). Also, the ISA calls out cardiovascular hospital admissions as a population level health endpoint related to short-term PM2.5 exposure (section <IP_ADDRESS>.1.1).
Available Epidemiologic Literature
Ten North American epidemiologic studies of cardiovascular hospital admissions were identified in section 6.1 of the PM ISA (U.S. EPA, 2019c). Relevant information related to the identification criteria, including study location, population attributes, and study period, were extracted from the studies and is available in the associated Study Information Table. The hospital admissions endpoint reports the number of events, as opposed to the number of individuals who experienced the event.
Identifying Suitable Studies for Use in Benefits Assessments
Relevant study information was used to identify the most nationally representative study or studies available. Study details can be found in the associated Study Information Table. The available cardiovascular hospital admissions studies predominantly included locations across the contiguous U.S. and evaluated the Medicare cohort, although two studies evaluated all ages. Few studies included health or air quality data post-2006, used hybrid exposure estimation techniques, or included O3 as a copollutant in the risk estimates. Importantly, while all studies assessed a broad range of cardiovascular effects, the specific ICD codes included varied widely. Of the available studies, Bell et al., 2015 evaluated the most recent study period and included the most nationally representative study locations.
Studies and Risk Estimates Identified as Most Suitable for Use in Benefits Assessments
Bell et al., 2015 investigated the effects of short-term fine particulate matter (PM2.5) exposure on cardiovascular health (ICD-9 410, omitting 410.x2; 410-414; 426-427; 428; 429; 430-438; and 440-448). Authors acquired data for 213 U.S. counties (1999-2010) from the Medicare Claims Inpatient Files for U.S. residents >65 years of age. Authors chose variables including sex, age, county of residence, and cause of hospital admission, as determined by ICD-9 codes. Authors collected PM2.5 exposure data from county population-based ambient monitors from the US EPA Air Quality System and averaged for county and day. Data were present for 56.5% of study days. Bell et al. (2015) utilized Bayesian hierarchal modeling to examine the links between PM2.5 and hospital admissions, running separate models to generate risk models for time lags (0-2 days) and season for any estimated variation in health effects. The percent increase in risk of 0.65% (95% CI: 0.48-0.83) for an increase of 10 ug/m[3] in same-day daily mean PM2.5 concentrations came from a single pollutant model.
Cardiovascular Emergency Department Visits
The ISA found that "generally consistent, positive associations observed in numerous epidemiologic studies of emergency department visits and hospital admissions for ischemic heart disease, heart failure, and combined cardiovascular-related endpoints" (U.S. EPA, 2019c section 6.1.16). The ISA also calls out cardiovascular emergency department visits as a population level health endpoint related to short-term PM2.5 exposure (section <IP_ADDRESS>.1.1).
Available Epidemiologic Literature
Although there were several studies of both emergency department visits and hospital admissions, there was only one short-term exposure epidemiologic study specific to cardiovascular emergency department visits available in the 2019 PM ISA (U.S. EPA, 2019c).
Study and Risk Estimates Identified as Most Suitable for Use in Benefits Assessments
Ostro et al., 2016 investigated the association between short-term, source-specific (vehicular emissions, biomass burning, soil, and secondary NO[-]3 and SO4 sources) PM2.5 concentrations and emergency department visits for respiratory and cardiovascular diseases in eight cities in California from 2005 to 2008. Authors obtained medical and demographic data from the Office of Statewide Health Planning and Development in California, and diagnosis was defined with ICD-9 codes: all cardiovascular (390-459), ischemic heart disease (410 - 414), AMI (410), cardiac dysrhythmia (427), and heart failure (428). Ostro et al., 2016 conducted a case cross-over analysis, stratified by year and month, controlling for weather and day of the week covariates. Authors used a county-level logistic regression and random-effects meta-analysis to examine the association between source-specific PM2.5 and emergency department visits for respiratory and cardiovascular diseases. Results indicate a positive association between vehicle PM2.5 emissions and emergency department visits for all cardiovascular diseases. The identified excess risk estimate of 0.7% (95% CI: -0.2-1.7) per 11.4 ug/m[3] (interquartile range) daily mean PM2.5 concentration increase came from a single pollutant model lagged by 2 days.
Cardiac Arrest (Out-of-Hospital)
The 2019 PM ISA stated that "in contrast to the studies from the previous review, recent studies have reported generally positive associations between short-term PM2.5 exposure and out-of-hospital cardiac arrest" (U.S. EPA, 2019c, section 6.1). The ISA also called out conductance abnormalities as a key clinical effects associated with both short-and long-term PM2.5 exposures (section <IP_ADDRESS>.1.1).
This endpoint, like several others (e.g., lung cancer incidence, section <IP_ADDRESS>) has a very high rate of fatality. As mortality due to any cause is captured separately (section <IP_ADDRESS>), we focus on impacts following cardiac arrest,  in the population that survive the initial event when considering this health endpoint.
Available Epidemiologic Literature
The 2019 PM ISA included three epidemiologic studies of out-of-hospital cardiac arrest that met our minimum identification criteria (section 2.1.1).
Identifying Suitable Studies for Use in Benefits Assessments
All three studies each evaluated separate locations and were similar with regards to study aspects such as age range (Ensor et al., 2013, Rosenthal et al., 2008, Silverman et al., 2010). Due to differences only in the study period and locations, the three studies are pooled using the random or fixed effects pooling method for benefits assessment purposes.
Studies and Risk Estimates Identified as Most Suitable for Use in Benefits Assessments
Ensor et al., 2013 studied the association between short-term ambient air pollution (PM2.5 and O3) exposure and out-of-hospital cardiac arrest. Ensor et al., 2013 gathered medical and demographic data from an Emergency Medical Services database in Houston, Texas between 2004 and 2011. Authors assessed the medical data and defined out-of-hospital cardiac arrest as emergency medical services performing chest compressions. Authors collected ambient air pollution and weather data from Texas Commission of Environmental Quality monitors and calculated hourly and daily averages for PM2.5 and O3. The authors used a time-stratified case crossover analysis and conditional logistic regression to interpret the data and found that with a daily increase of 6 ug/m[3] in PM2.5, averaged from a 0- and 1-day lag, there was an increased risk of out-of-hospital cardiac arrest of 3.9% (95% CI: 0.5-7.4).
Silverman et al., 2010 investigated the link between short-term ambient air pollution exposure (PM2.5, NO2, SO2, O3, and CO) and out-of-hospital cardiac arrest in New York City between 2002 and 2006. Authors obtained medical data from the Emergency Medical Services of the New York City Fire Department for 8,216 subjects aged 0 to 99, average age 65.6 with slightly more men than women. Authors collected air pollution and weather data from the US EPA's Air Quality System monitors within a 20-mile radius of New York City and averaged over 24-hour periods. Authors conducted time series and case crossover analyses with 0- and 1-day lagged air pollution levels and by season. Silverman et al., 2010 found that in a single-pollutant case crossover model, each 10 ug/m[3] increase in ambient PM2.5 resulted in a relative risk of 1.04 (95% CI: 0.99-1.08) in out-of-hospital cardiac arrest incidences 0- and 1-day prior to onset.
Rosenthal et al., 2008 examined the effects of short-term PM2.5 exposure on out-of-hospital cardiac arrest incidence and whether these effects were connected to demographic data or presence of heart rhythm. Additionally, Rosenthal et al., 2008 compared exposure time and measurement method on the effects of short-term PM2.5 exposure and out-of-hospital cardiac arrest incidence. Authors obtained medical data from the Wishard Ambulance Service, a local emergency medical service in Indianapolis, Indiana, from July 2, 2002 to July 7, 2006. The study defined out-of-hospital cardiac arrest using the same criteria as Ensor et al., 2013 and Silverman et al., 2010. Authors collected daily and hourly PM2.5 concentrations from two City of Indianapolis monitoring sites and using two separate methods: the Federal Reference Method (FRM) for 24-hour filter samples, and a Federal Equivalence Method (FEM). The authors used a case crossover analysis with conditional logistic regressions in order to study the effects of short-term PM2.5 exposure on out-of-hospital cardiac arrest incidence. Rosenthal et al., 2008 found a positive but statistically insignificant association between non-dead on arrival (non-DOA) out-of-hospital cardiac arrest cases and ambient PM2.5 concentrations. Although they also noted a statistically significant positive association when restricted to witnessed, non-DOA out-of-hospital cardiac arrest cases, that subgroup is less applicable to the available baseline incidence rate of non-DOA out-of-hospital cardiac arrest cases. The identified risk estimate of 1.02 (95% CI: 0.92-1.12) for each 10 ug/m[3] increase in daily mean PM2.5 concentrations lagged by 0-1 days, came from a single-pollutant model of all non-DOA out-of-hospital cardiac arrest cases.
Acute Myocardial Infarctions (AMI)
The 2019 PM ISA found that "evidence from the current review strengthens the epidemiologic results reported in the 2009 PM ISA" with respect to AMI. Specifically:
Several new epidemiologic studies conducted in the U.S. and Europe provide additional evidence of positive associations between short-term PM2.5 exposure and [ischemic heart disease emergency department] visits and hospital admissions. Uncertainties noted in the last review with respect to exposure measurement error for those not living near a PM2.5 monitor were reduced in the current review by considering recent studies that applied hybrid exposure assessment techniques that combine land use regression data with satellite measurements and PM2.5 concentrations measured at fixed-site monitors to estimate PM2.5 concentrations. In addition to these [emergency department] visit and hospital admissions studies, there is also evidence for ST segment depression from epidemiologic panel studies. (U.S. EPA, 2019c).
The 2019 PM ISA also stated that "associations between long-term exposure to PM2.5 and cardiovascular morbidity outcomes (i.e., ischemic heart disease, stroke) were observed in some studies with the most consistent results in people with pre-existing diseases" (U.S. EPA, 2019c, section 6.2). The ISA also found that "although the results are not entirely consistent across studies or stroke subtype, some recent well-conducted studies also support a positive association between long-term exposure to PM2.5 and stroke." Additionally, conductance abnormalities, which can lead to cardiac arrest, were called out as a key clinically relevant outcome associated with both short-and long-term PM2.5 exposures (section <IP_ADDRESS>.1.1).
Available Epidemiologic Literature
While the 2019 PM ISA did identify epidemiological studies associating AMIs with PM2.5 exposures, the studies that passed the initial screening stage were not more suitable than those currently used for benefits estimation. One (Zhang et al., 2009) involved only postmenopausal women and the other (Delfino et al., 2011) studied a population with a history of coronary artery disease. Hence, we retained all five studies used in the 2012 PM NAAQS RIA. The 2019 PM ISA did not identify any newer studies of this type so current risk estimates will continue to be used in benefit assessments (U.S. EPA, 2019c).
Studies and Risk Estimates Identified as Most Suitable for Use in Benefits Assessments
As no studies new to the 2019 PM ISA provided better estimates of PM2.5 -attributable AMI risk, EPA continues to rely upon a study by Peters et al., 2001 as the basis for the impact function estimating the relationship between PM2.5 and nonfatal heart attacks. Peters et al., 2001 exhibits a number of strengths. In particular, it includes a robust characterization of populations experiencing AMIs. The researchers interviewed patients within four days of their AMI events and, for inclusion in the study, patients were required to meet a series of criteria including minimum kinase levels, an identifiable onset of pain or other symptoms and the ability to indicate the time, place and other characteristics of their AMI pain in an interview.
Since the publication of Peters et al., 2001, a number of other single and multi-city studies have appeared in the literature. These studies include Sullivan et al., 2005, which considered the risk of PM2.5-related hospitalization for AMIs in King County, WA; Pope III et al., 2006, based in Wasatch Range, UT; Zanobetti and Schwartz, 2006, based in Boston; and, Zanobetti et al., 2009, a multi-city study of 26 U.S. communities. Each of these single and multi-city studies, except for Pope III et al., 2006, measure AMIs using hospital discharge rates. Conversely, the Pope III et al., 2006 study is based on a large registry with angiographically characterized patients -- arguably a more precise indicator of AMI. Because the Pope III et al., 2006 study reflected both myocardial infarctions and unstable angina, this produces a more comprehensive estimate of acute ischemic heart disease events than the other studies. However, unlike the Peters et al., 2001, Pope III et al., 2006 did not measure the time of symptom onset, and PM2.5 data were not measured on an hourly basis.
As a means of recognizing the strengths of Peters et al., 2001 while also incorporating the more recent evidence found in the four single and multi-city studies, we present a range of AMI estimates. The upper end of the range is calculated using Peters et al., 2001 while the lower end of the range is the result of an equal-weights pooling of the four newer studies (Pope III et al., 2006, Sullivan et al., 2005, Zanobetti et al., 2009, Zanobetti and Schwartz, 2006).
Peters et al., 2001 studied the relationship between increased particulate air pollution and onset of heart attacks in the Boston area from 1995 to 1996. The authors used air quality data for PM10, PM10-2.5, PM2.5, "black carbon", O3, CO, NO2, and SO2 in a case crossover analysis. For each subject, the case period was matched to three control periods, each 24 hours apart. In univariate analyses, the authors observed a positive association between heart attack occurrence and PM2.5 levels hours before and days before onset. The authors estimated multivariate conditional logistic models including 2-hour and 24-hour pollutant concentrations for each pollutant. They found significant and independent associations between heart attack occurrence and 24-hour PM2.5 concentrations before onset. Significant associations were observed for PM10 as well. None of the other particle measures or gaseous pollutants were significantly associated with AMI for the 2-hour or 24-hour period before onset. The mean age of participants was 62 years old, with 21% of the study population under the age of 50. In order to capture the full magnitude of heart attack occurrence potentially associated with air pollution and because age was not listed as an inclusion criterion for sample selection, we apply an age range of 18 and over in the risk estimate. According to the National Hospital Discharge Survey, there were no hospitalizations for heart attacks among children <15 years of age in 1999 and only 5.5% of all hospitalizations occurred in those aged 15-44 years. The odds ratio is 1.62 (95% CI: 1.13-2.34) for a 20 ug/m[3] increase in 24-hour average PM2.5.
Pope III et al., 2006 evaluated the association between short-term exposure to PM2.5 and acute ischemic heart disease events, including nonfatal AMI, all acute coronary events, and subsequent myocardial infarctions in individuals living in greater Salt Lake City, Utah. In a case crossover study, these ischemic events were assessed in relation to a 10 ug/m[3] increase in PM2.5. The researchers determined that a 10 ug/m[3] increase in PM2.5 resulted in a 4.5% increase (95% CI: 1.1-8.0) in unstable angina and myocardial infarction.
Sullivan et al., 2005 studied the relationship between onset time of acute myocardial infarction and the preceding hourly PM2.5 concentrations in 5,793 confirmed cased of myocardial infarction through King County, Washington. In this case crossover study from 1988-1994, air pollution exposure levels averaged before onset of myocardial infarction were compared to a set of time-stratified referent exposures from the same day of the week in the month of the case event. The authors estimated that an associated risk of 1.01 (95% CI: 0.98-1.05) for myocardial infarction onset could be attributed to a 10 ug/m[3] increase in PM 2.5 the hour before the MI onset. No increased risk was found in all cases with preexisting cardiac diseases with an odds ratio of 1.05 (95% CI: 0.95-1.16). Furthermore, stratification for hypertension, diabetes, and smoking status did not modify the association between PM2.5 and onset of myocardial infarction.
Zanobetti et al., 2009 examined the relationship between daily PM2.5 levels and emergency hospital admissions for cardiovascular causes, myocardial infarction, congestive heart failure, respiratory disease, and diabetes among 26 U.S. communities from 2000-2003. The authors used meta-regression to examine how this association was modified by season- and community-specific PM2.5 composition while controlling for seasonal temperature as a substitute for ventilation. For a 10 ug/m[3] increase in 2-day averaged PM2.5, a 1.89% (95% CI: 1.34-2.45) increase in cardiovascular disease admissions, a 2.25% (95% CI: 1.10-3.42) increase in myocardial infarction admissions, a 1.85% (95% CI: 1.19-2.51) increase in congestive heart failure admissions, a 2.74% (95% CI: 1.30-4.20) increase in diabetes admissions, and a 2.07% (95% CI: 1.20-2.95) increase in respiratory admissions were observed. The relationship between PM2.5 and cardiovascular admissions was significantly modified when the mass of PM2.5 was high in Br, Cr, Ni, and sodium ions, while mass high in As, Cr, Mn, organic carbon, Ni and sodium ions modified the myocardial infarction relationship and mass high in As, organic carbon, and sulfate ions modified the diabetes admission rates.
Zanobetti and Schwartz, 2006 analyzed hospital admissions through emergency department for MI (ICD-9 code 410) and pneumonia (ICD-9 codes 480-487) for associations with fine particulate air pollution in the greater Boston area from 1995- 1999. The authors used a case-crossover analysis with control days matched on temperature. Significant associations were detected for PM2.5 with an 8.6% increase (95% CI: 1.2-15.4) in emergency myocardial infarction hospitalizations. The study looked at hospital admissions of AMI through the ER. Under the assumption that all heart attacks will end in hospitalization, we consider the endpoint as heart attack events to be consistent with other studies. In a single-pollutant model, the coefficient and standard error are estimated from the percent change in risk (8.65%) and 95% confidence interval (95% CI: 1.22-15.38%) for a 16.32 ug/m[3] increase in daily 24-hour mean PM2.5 for an average of the 0- and 1-day lag (Zanobetti and Schwartz, 2006, Table 4).
Stroke
Available Epidemiologic Literature
The 2019 PM ISA included three epidemiologic studies of stroke that met the minimum identification criteria (section 2.1.1).
Identifying Suitable Studies for Use in Benefits Assessments
One of the available epidemiologic studies was more recent, included a larger population, evaluated long-term exposure effects, and was more representative of the U.S. with regards to both geography and population attributes than the other two studies (Kloog et al., 2012).
Studies and Risk Estimates Identified as Most Suitable for Use in Benefits Assessments
Kloog et al., 2012 analyzed the effects of long- and short-term PM2.5 exposure on hospital admissions  due to strokes with a new PM2.5 exposure model in New England (Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, and Vermont) from 2000 to 2006. We use this endpoint as a surrogate for PM2.5-attributable stroke incidence. Authors collected medical data from 67,678 adults aged 65 to 99 in the U.S. Medicare program database from 2000 to 2006. They defined all respiratory, cardiovascular disease, stroke, and diabetes based on emergency department visits and primary discharge diagnosis records. Authors used a hybrid exposure technique comprised of daily PM2.5 concentration data from aerosol optical depth (AOD) measurements and ambient air monitors from the U.S. EPA and Interagency Monitoring of Protected Visual Improvements (IMPROVE). Authors also obtained land use regressions, meteorological data (National Climatic Data Center), and socioeconomic data (U.S. Census Bureau) matched to zip codes. Utilizing land use Poisson regression single-pollutant models, the authors found an 3.49% (95% CI: 0.09-5.18) increase in stroke incidence for a 10 ug/m[3] increase in the 7-year mean PM2.5 concentrations.
Respiratory Hospital Admissions
After considering the relationships between specific and broad respiratory hospital admissions endpoints, the 2019 PM ISA stated that "recent studies further expand analyses with older adults, with multicity studies conducted in the U.S. providing evidence of consistent, positive associations between short-term PM2.5 exposure and respiratory-related diseases" ((U.S. EPA, 2019c, section 5.1.6).
The ISA noted that "it is often difficult to determine whether the associations observed indicate that PM2.5 may affect the spectrum of respiratory diseases or reflects the evidence supporting associations with specific respiratory diseases, such as asthma." Taking this into consideration, hospital admissions for asthma exacerbation/symptoms, COPD, and respiratory infections were specifically called out in the short-term PM2.5 exposure biological plausibility diagram (section <IP_ADDRESS>.1.2).
Available Epidemiologic Literature
Like the cardiovascular hospital admission/emergency department visit endpoints, several respiratory studies identified by the ISA combined the hospital admissions and emergency department endpoints. There was also a subset of studies that only considered emergency hospital admission, defined as hospital admissions that originated in the emergency department. As using either studies of emergency hospital admissions or combined emergency department and hospital admission studies would result in increased uncertainty around the economic estimate and/or with the baseline incidence data, we limited our pool of available studies to those specifically evaluating unplanned respiratory hospital admissions, of which there were 12 available studies.
Identifying Suitable Studies for Use in Benefits Assessments
Studies for this endpoint tended to focus on specific age groups, with approximately half focusing on older adults (>64) and none specifying ages 19-64. Importantly, studies varied widely by ICD codes, making pooling of two or more studies difficult. Considering the preferred criteria, two studies were identified, one of children and one of older adults, primarily due to the inclusion of diverse and large study locations. The single study of older adults is more informative than pooling it with other studies of the same population as it is more recent and includes exposure to lower PM2.5 concentration levels.
Studies and Risk Estimates Identified as Most Suitable for Use in Benefits Assessments
Bell et al., 2015 investigated the effects of short-term fine particulate matter (PM2.5) exposure on respiratory health (ICD-9 464-466, 480-487, 490-492, 493) in older adults (>64 years). Authors acquired data for 213 U.S. counties (1999-2010) from the Medicare Claims Inpatient Files for U.S. residents >65 years of age. Authors chose variables including sex, age, county of residence, and cause of hospital admission, as determined by ICD-9 codes. Authors collected PM2.5 exposure data from county population-based ambient monitors from the US EPA Air Quality System and averaged for county and day. Data were present for 56.5% of study days due to the sampling schedule of the monitors. Bell et al., 2015 utilized Bayesian hierarchal modeling to examine the links between PM2.5 and hospital admissions. They ran separate models for time lags (0-2 days) and season to determine if there were any estimated variation in health effects. The identified percent increase in risk of 0.25% (95% CI: 0.01-0.48) for an increase of 10 ug/m[3] in same-day daily mean PM2.5 concentrations came from a single-pollutant model.
Ostro et al., 2009 estimated the association between ambient PM2.5, EC, organic carbon (OC), NO3, and SO4 on hospital admissions for respiratory diseases in children ages 5 to 19. The study used the California Office of Statewide Health Planning and Development, Healthcare Quality and Analysis Division hospitalization data from six California counties for the 2000 to 2003 study period. Ostro et al., 2009 classified hospital admissions into: all respiratory disease (ICD-9 codes 460-519), asthma (ICD-9 code 493), acute bronchitis (ICD-9 code 466), and pneumonia (ICD-9 codes 480-486). They aggregated the hospital admission data to the county level to create a daily time series of admissions for each county. Authors took air quality measurements from the California Air Resources Board, which captured speciated 24-hour average pollutant measurements using a filter-based Met One Speciation Air Sampling System. Meteorological measurements for average daily temperature and relative humidity came from the California Air Resources Board or the California Irrigation Management Information System. Authors analyzed data using a Poisson regression with time, day of the week, temperature, relative humidity, and pollutant as explanatory variables. Ostro et al., 2009 controlled for seasonality and time dependent effects by including a natural spline smoother for the daily time trend and meteorology. The identified percent increase in risk of excess risk of 4.1% (95% CI: 1.8-6.4) for a 14.6 ug/m[3] increase in daily mean PM2.5 concentrations, lagged by 3 days, came from a single-pollutant model.
Respiratory Emergency Department Visits
After considering the relationships between specific and broad respiratory emergency department visit endpoints, the 2019 PM ISA stated that "recent studies further expand analyses with older adults, with multicity studies conducted in the U.S. providing evidence of consistent, positive associations between short-term PM2.5 exposure and respiratory-related diseases" (U.S. EPA, 2019c, section 5.1.6).
The ISA noted that "it is often difficult to determine whether the associations observed indicate that PM2.5 may affect the spectrum of respiratory diseases or reflects the evidence supporting associations with specific respiratory diseases, such as asthma." Emergency department visits for asthma exacerbation/symptoms, COPD, and respiratory infections were specifically called out in the short-term PM2.5 exposure biological plausibility diagram (section <IP_ADDRESS>.1.2).
Available Epidemiologic Literature
Like the cardiovascular hospital admission/emergency department visit endpoints, several respiratory studies identified by the ISA combined the hospital admissions and emergency department endpoints. As using the combined study endpoint would result in increased uncertainty around the economic estimate, we limited our pool of available studies to those specifically looking at respiratory emergency department visits for the main benefits assessment, of which there were 10 studies.
Identifying Suitable Studies for Use in Benefits Assessments
One study out of the 10 was more recent, provided greater geographic representation, and included all ages.
Studies and Risk Estimates Identified as Most Suitable for Use in Benefits Assessments
Krall et al., 2013 investigated the associations between short-term, source-specific (traffic and coal combustion) ambient PM2.5 exposure and emergency department visits for respiratory diseases in U.S. cities (Atlanta, GA, Birmingham, AL, St. Louis, MO, and Dallas, TX). Authors obtained medical data from hospital electronic billings for emergency department visits due to respiratory disease, identified using ICD-9 codes (460-465, 466, 477, 480-486, 491, 492, 493, 496, 786.07). Authors collected PM2.5 concentrations from one ambient air monitor in each of the four cities and gathered meteorological data from the National Climactic Data Center. Krall et al., 2013 estimated source-specific PM2.5 using apportionment models, which separate PM2.5 sources based on chemical composition. This model also included data on gaseous pollutant concentrations from the Community Multiscale Air Quality (CMAQ) with Tracers model. Krall et al., 2013 used Poisson time series regression models to analyze associations between short-term PM2.5 exposure and emergency department visits for respiratory diseases. They then compared source-specific PM2.5 exposures across cities to estimate associations with the emergency department visit data. To limit confounders, the authors adjusted models for indicator variables, meteorological variables, and long-term trends in emergency department visits. The identified relative risk estimates of 1.005 (95% CI: 1.000-1.010) for Atlanta, GA; 1.009 (95% CI: 1.003-1.015) for Birmingham, AL; 1.008 (95% CI: 1.002-1.014) for St. Louis, MO; and 1.012 (95% CI: 1.002-1.023) for Dallas, TX were calculated from a single-pollutant model for a 9.16 ug/m[3] increase in daily mean PM2.5 concentrations, lagged by 0 days.
Asthma Onset