The 2019 PM ISA stated that "longitudinal studies provide evidence of associations with asthma incidence in children" and found "evidence for a relationship between PM2.5 exposure and asthma prevalence in children and childhood wheeze" (U.S. EPA, 2019c). Additionally, asthma onset was called out as a key clinically relevant health endpoint in the biological plausibility pathways included in the ISA (section <IP_ADDRESS>.1.2) (U.S. EPA, 2019c).
Available Epidemiologic Literature
The final 2019 PM ISA found that "recent studies of asthma in children supplement the limited number of studies reviewed in the 2009 PM ISA and provide evidence of an association between long-term PM2.5 exposure and asthma development in children" (U.S. EPA, 2019c). There was also evidence of PM2.5-attributable asthma onset in adults, but results were inconsistent across studies. As a result, asthma onset in adults is not included in our main benefits assessment.
Five North American epidemiologic studies of asthma onset in children were identified in section 5.2 of the 2019 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 corresponding Excel file.
Identifying Suitable Studies for Use in Benefits Assessments
Although the available asthma onset studies vary widely in all criteria considered, relevant study information was again used to identify the most nationally representative study or studies (see Excel file). Interestingly, three of the five studies were conducted in Canada. One study conducted in Canada evaluated a recent and extensive time series of air quality and health data; assigned exposures to populations using a combination of monitor and remote sensing data; validated the outcome measure; observed effects with relatively low (~10 ug/m[3]) PM2.5 concentrations, and included over 30-fold the number of participants as any other study. Other available literature was also more limited with regards to population demographic and geographic diversity.
Study Identified as Most Suitable for Use in Benefits Assessments
Tetreault et al., 2016 investigated the relationship between childhood asthma onset and long-term pollution exposure (PM2.5, NO2, O3) in Quebec, Canada. The authors obtained data from four medical-administrative databases collectively known as Quebec Integrated Chronic Disease Surveillance System (QICDSS) between April 1, 1996 and March 31, 2011. The study defined the onset of asthma as a hospital discharged diagnosis of asthma or two reports of asthma from two separate physicians within a two-year period. The authors used Cox proportional hazard models to estimate the association between asthma onset and pollution exposure, controlling for demographics and socioeconomic status. Time-varying exposure models assessed time-varying exposures to the three pollutants in question. Tetreault et al., 2016 showed that childhood asthma onset may be associated with exposure to PM2.5, NO2, and O3.
The identified study presented 24 hazard ratios using various adjustment methods and included multiple sensitivity analyses, evaluating the effects by sex, urbanicity, and those who moved during the study period. More-adjusted risk estimates using a time-varying estimate of PM2.5 exposure and including the full cohort were identified over less-adjusted or striated estimates using exposure estimates at birth.  The study identified as best characterizing risk across the U.S. was Tetreault et al., 2016, although the two older and demographic-limited U.S. studies were also identified as potentially informative.
The risk estimate identified from Tetreault et al., 2016 for use in the main benefits estimates was a single-pollutant time-varying hazard ratio model of 1.33 (95% CI: 1.31-1.34) for a 6.53 ug/m[3] (interquartile range) increase in annual PM2.5 concentration at the residential address.
As the physiology and etiology of lung development in children is similar in children 6-17, we apply the 4-12 year age-striated effect estimate from Tetreault et al., 2016 to children ages 4-17 (Baena-Cagnani et al., 2007, Guerra et al., 2004, Ochs et al., 2004, Sparrow et al., 1991, Trivedi and Denton, 2019).
Asthma Symptoms/Exacerbation
The 2019 PM ISA stated that "evidence for effects on asthma exacerbation are generally more consistent than associations for other respiratory outcomes." The ISA went on to note that "recent studies strengthen the relationship between asthma exacerbation in children and short-term PM2.5 exposure, while, in adults, the relationship continues to be inconsistent."
Available Epidemiologic Literature
Based on evidence provided by the 2019 PM ISA, available studies of asthma symptoms were limited to children, of which there were eight.
Identifying Suitable Studies for Use in Benefits Assessments
Due to the specificity required when evaluating this health endpoint, Individual studies of asthma exacerbation/symptoms tended to focus on relatively small cohorts of children of discrete ages in distinct locations, making pooling difficult. One study evaluated a directly monetizable outcome of albuterol inhaler use. Albuterol inhalers are separated from long-term asthma control medications and is considered a "rescue medication" by the Mayo Clinic, making it an informative endpoint when considering asthma symptoms.
Studies and Risk Estimates Identified as Most Suitable for Use in Benefits Assessments
Rabinovitch et al., 2006 analyzed the relationship between short-term PM2.5 exposure and asthma exacerbation in children. The study followed children, ages 6 to 13 attending the Kunsberg School at the National Jewish Medical Research Center with diagnosed asthma for two consecutive winters from 2001-2003. Authors gave an electronic bronchodilator (albuterol) to the children to capture the frequency of use within a 24-hour period. The children also responded to three questions to determine if they may have an upper respiratory infection (URI), and urine samples were taken to measure urinary leukotriene E4 levels on select days. The authors collected hourly ambient PM2.5 levels from the Colorado Department of Health Air Pollution Control Division's Tapered Element Oscillating Microbalance (TEOM) monitor, located 2.7 miles west of the school. Additionally, a Federal Reference Monitor (FRM) located next to the TEOM measured 24-hour PM2.5 levels. The authors obtained meteorological data from the Colorado Department of Health Air Pollution Control Division and the National Climatic Data Center. A Poisson regression modeled albuterol use as a function of the morning (12:00am to 11:00 am) maximum hourly PM2.5 level or the morning mean hourly PM2.5 level. The model used both the TEOM and FRM data, individually, incorporated four lag periods (0 to 2 days and 0- to 2-day average), and included several covariates: temperature, pressure, humidity, time trend, Friday indicator, and URI indicator. Rabinovitch et al., 2006 found that, although the PM2.5 pollution levels were well below the National Ambient Air Quality Standards, there is a consistent association between peak ambient PM2.5 levels and increased albuterol use in asthmatic children. The identified percent of use increase estimate of 1.2% (95% CI: -0.6-2.9) for a 6 ug/m[3] increase in averaged daily mean PM2.5 concentration lagged by 0-, 1-, and 2-days came from a single-pollutant model.
Allergic Rhinitis (Hay Fever/Respiratory Allergies))
The 2019 PM2.5 ISA stated that "recent studies evaluated associations between long-term exposure to PM2.5 and various allergic outcomes in a mix of large representative cohort and cross-sectional survey studies" finding "generally consistent evidence of an association between long-term PM2.5 exposure and allergic sensitization in single pollutant models" ((U.S. EPA, 2020a, section 5.2.4). Additionally, the ISA called out "allergic responses" in the biological plausibility diagram for long-term PM2.5-attributable respiratory effects (U.S. EPA, 2020a, section <IP_ADDRESS>.1.2). Although cross sectional analyses do not establish a temporal sequence, they can be used to estimate benefits associated with changes in air quality.
Available Epidemiologic Literature
The 2019 PM ISA identified one epidemiologic study of long-term 2019 PM2.5 exposure and allergic rhinitis.
Studies and Risk Estimates Identified as Most Suitable for Use in Benefits Assessments
Parker et al., 2009 investigated the associations between long-term PM2.5 exposure and respiratory allergies in an unrestricted population of children (aged 3-17 years) sampled from the United States National Health Interview Survey. Authors obtained symptom data from participant parents, who reported respiratory allergies on annual surveys. Parker et al., 2009 placed all study participants reporting symptoms of respiratory allergies or hay fever into a combined rhinitis group. Parker et al., 2009 then linked annual averages of SO2, NO2, PM2.5, and PM2.5-10 and warm season (May to September) O3 averages to participant's addresses through ambient air pollution and meteorological data (O3, SO2, NO2, PM2.5, and PM10-2.5) collected from US EPA Air Quality System monitors. The authors adjusted models for survey year, poverty-level, race/ethnicity, age, family structure, insurance coverage, usual source of care, education of adult, urban-rural status, region, and median county-level income. Through multi-pollutant, logistic regression models, an odds ratio of 1.29 (95% CI: 1.07-1.56) for a 10 ug/m[3] increase in PM2.5 concentrations and respiratory allergies was identified.
Minor Restricted Activity Days
No new epidemiologic studies of minor restricted activity days (MRADs) were identified in the 2019 PM ISA (U.S. EPA, 2019c).
Studies and Risk Estimates Identified as Most Suitable for Use in Benefits Assessments
Ostro and Rothschild, 1989 estimated the impact of PM2.5 and O3 on the incidence of minor restricted activity days (MRADs) and respiratory-related restricted activity days (RRADs) in a national sample of the adult working population, ages 18 to 65, living in metropolitan areas. The study population is based on the Health Interview Survey (HIS), conducted by the National Center for Health Statistics. In publications from this ongoing survey, non-elderly adult populations are generally reported as ages 18-64. From the study, it is not clear if the age range stops at or includes those aged 65. We apply the risk estimate function to individuals ages 18-64 for consistency with other studies estimating impacts to non-elderly adult populations. The annual national survey results used in this analysis were conducted in the period 1976-1981, controlling for PM2.5, two-week average O3.
Work Loss Days
No new studies of work loss days (WLDs) were identified in the 2019 PM ISA (U.S. EPA, 2019c).
Studies and Risk Estimates Identified as Most Suitable for Use in Benefits Assessments
Ostro, 1987 estimated the impact of PM2.5 on the incidence of work-loss days (WLDs), restricted activity days (RADs), and respiratory-related RADs (RRADs) in a national sample of the adult working population, ages 18 to 65, living in metropolitan areas. The study population is based on the Health Interview Survey (HIS), conducted by the National Center for Health Statistics. The annual national survey results used in this analysis were conducted in 1976-1981. Ostro, 1987 reported that two-week average PM2.5 levels were significantly linked to work-loss days, RADs, and RRADs, however there was some year-to-year variability in the results. Separate coefficients were developed for each year in the analysis (1976-1981); these coefficients were pooled. The coefficient used in the concentration-response function presented here is a weighted average of the coefficients in Ostro, 1987, Table III, using the inverse of the variance as the weight.
Lung Cancer
The 2019 PM ISA determined that a "likely to be causal" relationship exists between long-term PM2.5 exposure and cancer (U.S. EPA, 2019c), a change in the causality determination from the 2009 ISA (U.S. EPA, 2009). Specifically, the ISA found evidence of generally consistent positive associations between long-term PM2.5 exposure and lung cancer incidence. Additional details regarding potential pathways of disease development are summarized in the biological plausibility diagram provided by the ISA (section <IP_ADDRESS>.1.3).
For an outcome such as lung cancer, there is an expected time lag between changes in pollutant exposure in a given year and the reduction in lung cancer incidence, known as the latency period. The time between exposure and diagnosis can be quite long, on the order of years to decades. We discuss methods used to account for the latency period and other economic considerations relevant to this health endpoint in section 5.3.6. Importantly, we include this health endpoint to assess impacts of living with a lung cancer diagnosis, prior to disease resolution or death.
Available Epidemiologic Literature
We limited our pool of available studies to those of lung cancer incidence, excluding those assessing lung cancer mortality as that endpoint is included in the long-term exposure-attributable all-cause mortality endpoint (section <IP_ADDRESS>.3.1). This resulted in four study options.
Identifying Suitable Studies for Use in Benefits Assessments
The four available studies varied in terms of population demographics included and country. Two of the four studies took place entirely in Canada. Of the two U.S.-based studies, one included all ages, sexes, and demographics and was restricted to non- and never-smokers, although it included some participants living in Canada. The identified study was most suitable as it took place in the U.S., included both males and females, and was restricted to non- and never-smokers (Gharibvand et al., 2017).
Studies and Risk Estimates Identified as Most Suitable for Use in Benefits Assessments
Gharibvand et al., 2017 evaluated whether positive associations exist between PM2.5 exposure and incidence of lung cancer in non-smokers among the Adventist Health and Smog Study-2 (AHSMOG-2), a group of health-conscious individuals of which 81% are never smokers. Authors collected ambient air pollution data (PM2.5 and O3) from the US EPA Air Quality system over two years (January 2000-December 2001). Three a priori factors were added to the models as covariates: time spent outdoors, residence length, and moving distance during follow-up. Authors modeled the association between PM2.5 exposure and incidence of lung cancer using a Cox proportional hazards regression, with attained age as the time variable. The authors conducted both a single and a two-pollutant (PM2.5 and O3) analyses. The study concluded that each 10 ug/m[3] increase in ambient PM2.5 concentrations was positively associated with increased lung cancer risks within the single-pollutant and two-pollutant multivariable models with O3. The identified hazard ratio of 1.46 (95% CI: 1.13-1.89) for each 10 ug/m[3] increase in mean monthly ambient PM2.5 concentrations came from a two-pollutant multivariable model with O3 (including a priori covariates).
Alzheimer's Disease
Evidence connecting long-term PM2.5 exposure to nervous system effects led to the 2019 ISA concluding a "likely to be causal" relationship exists (U.S. EPA, 2019c) and various clinically relevant nervous system endpoints were called out in the biological plausibility section, including Alzheimer's disease, Parkinson's disease, autism spectral disorder, cognitive decline, and dementia (section <IP_ADDRESS>.1.3). Regarding biological plausibility, the ISA stated that "neuroinflammation and neurodegeneration provide biological plausibility for epidemiologic results of increased hospital admissions or emergency department visits for Alzheimer's and Parkinson's disease."
There were over a dozen epidemiologic studies in the 2019 PM ISA evaluating cognitive-related outcomes (U.S. EPA, 2019c, sections 8.2.5-8.2.7). However, due to the nature of the endpoint, many of the outcomes were defined using scales and scores from cognitive tests. As we are currently unable to transfer that type of result into a clinically relevant population level outcome, we focused on the more clearly defined outcomes of Alzheimer's disease and Parkinson's Disease. These endpoints were also specifically called out in the ISA, as "epidemiologic studies also provide evidence of cognitive impairment and Alzheimer's and Parkinson's disease in association with exposure to PM2.5" ((U.S. EPA, 2019c, section 8.2.6).
Available Epidemiologic Literature
One epidemiologic study of Alzheimer's disease met our minimum required identification criteria (section 2.1.1).
Studies and Risk Estimates Identified as Most Suitable for Use in Benefits Assessments
Kioumourtzoglou et al., 2016 evaluated the potential impact of long-term PM2.5 exposure on first hospital admission for dementia, Alzheimer's, or Parkinson's diseases among Medicare beneficiaries (>= 65 years old) in 50 cities in the northeastern U.S. (Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont, and Washington, D.C.). Authors retrieved medical data from the Center for Medicaid and Medicare from the years 1999-2010. The study followed enrollees as a cohort, which included annual follow-up records identifying the first hospital admissions for dementia (ICD-9 290), Alzheimer's (ICD-9 331.0), Parkinson's (ICD-9 332), and other cardiovascular comorbidities. With respect to Alzheimer's disease, the study evaluated 9,817,806 Medicare enrollees and included 266,725 cause-specific hospital admissions indicating disease onset. Annual average PM2.5 concentrations were estimated for each city using data from the U.S. EPA Air Quality System database. Kioumourtzoglou et al., 2016 fit a time-varying Cox proportional hazards model for each city, using the city-wide annual PM2.5 concentrations as the time-varying exposure of interest and a linear term for the calendar year. This eliminated the impact of PM2.5 variation by city and any PM2.5 trends within cities. The model adjusted for cardiovascular comorbidities, and incorporated a counting process extension which created an observation for each year of follow-up per person. The results were then pooled across individuals and cities. A single-pollutant model was used to develop the identified hazard ratio of 1.15 (95% CI: 1.11-1.19) for a 1 ug/m[3] increase in the average annual PM2.5 concentrations.
Parkinson's Disease
Evidence connecting long-term PM2.5 exposure to nervous system effects led to the 2019 ISA concluding a "likely to be causal" relation exists (U.S. EPA, 2019c) and various clinically relevant nervous system endpoints were called out in the biological plausibility section, including Alzheimer's disease, Parkinson's disease, autism spectral disorder, cognitive decline, and dementia (section <IP_ADDRESS>.1.3). Regarding biological plausibility, the ISA stated that "neuroinflammation and neurodegeneration provide biological plausibility for epidemiologic results of increased hospital admissions or emergency department visits for Alzheimer's and Parkinson's disease."
There were over a dozen epidemiologic studies in the 2019 PM ISA evaluating cognitive-related outcomes (U.S. EPA, 2019c, sections 8.2.5-8.2.7). However, due to the nature of the endpoint, many of the outcomes were defined using scales and scores from cognitive tests. As we are currently unable to transfer that type of result into a clinically relevant disease incidence, we focused on the more clearly defined outcomes of Alzheimer's disease and Parkinson's Disease. These endpoints were also specifically called out in the ISA, as "epidemiologic studies also provide evidence of cognitive impairment and Alzheimer's and Parkinson's disease in association with exposure to PM2.5" ((U.S. EPA, 2019c, section 8.2.6).
Available Epidemiologic Literature
Three epidemiologic studies of Parkinson's disease were identified in the 2019 PM ISA. All evaluated relatively low PM2.5 concentrations and included participants from multiple states, however there were differences with respect to the ages and sexes evaluated, number of overall participants, and exposure estimation technique.
Identifying Suitable Studies for Use in Benefits Assessments
The prospective study with the lowest mean PM2.5 concentrations and most recent timespan included over 14 times the number of participants as the other two studies combined. It was also the only study to include participants over the age of 71, which is relevant as Parkinson's disease prevalence rises with age (Pringsheim et al., 2014).
Studies and Risk Estimates Identified as Most Suitable for Use in Benefits Assessments
Kioumourtzoglou et al., 2016 evaluated the potential impact of long-term PM2.5 exposure on first hospital admission for dementia, Alzheimer's, or Parkinson's diseases among Medicare beneficiaries (>= 65 years old) in 50 cities in the northeastern U.S. (Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont, and Washington, D.C.). Authors retrieved medical data from the Center for Medicaid and Medicare from the years 1999-2010. The study followed enrollees as a cohort, which included annual follow-up records identifying the first hospital admissions for dementia (ICD-9 290), Alzheimer's (ICD-9 331.0), Parkinson's (ICD-9 332), and other cardiovascular comorbidities. With respect to Parkinson's disease, the study evaluated 9,817,806 Medicare enrollees and included 119,425 cause-specific hospital admissions indicating disease onset. Annual average PM2.5 concentrations were estimated for each city using data from the US EPA Air Quality System database. Kioumourtzoglou et al., 2016 fit a time-varying Cox proportional hazards model for each city, using the city-wide annual PM2.5 concentrations as the time-varying exposure of interest and a linear term for the calendar year. This eliminated the impact of PM2.5 variation by city and any PM2.5 trends within cities. The model adjusted for cardiovascular comorbidities, and incorporated a counting process extension which created an observation for each year of follow-up per person. The results were then pooled across individuals and cities. A single-pollutant model was used to develop the identified hazard ratio of 1.08 (1.04  -  1.12) for a 1 ug/m[3] increase in the average annual PM2.5 concentrations.
O3
The following sections of the 2020 O3 ISA correspond to health endpoints judged as having a "causal" or "likely causal" relationship with O3 exposure:
 Appendix 3: Health Effects  -  Respiratory, 3.1 Short-Term Ozone Exposure;
 Appendix 3: Health Effects  -  Respiratory, 3.2 Long-Term Ozone Exposure;
 Appendix 5: Health Effects  -  Metabolic Effects, 5.1 Short-Term Ozone Exposure;
 Appendix 6: Health Effects  -  Mortality, 6.1 Short-Term Ozone Exposure and Mortality; and
 Appendix 6: Health Effects  -  Mortality, 6.2 Long-Term Ozone Exposure and Mortality.
Following the approach to identifying available epidemiologic literature (section 2.2), we began with the 1,678 studies cited by the 2020 O3 ISA. Of these, 130 morbidity studies evaluate health endpoints the 2020 O3 ISA determined as having a "causal" or "likely causal" relationship with O3 exposure (sections 2.2.1 and 2.2.2). 77 studies remained after the required minimum criteria were applied, and that number decreased to 27 when broad hospital admissions and emergency department endpoints were identified. No studies of short-term O3 exposure metabolic effects meeting the minimum required criteria (section 2.1.1) were identified in the ISA.
Table 7. O3 Study and Risk Estimate Identification Diagram
                       O3 Endpoint and Exposure Duration
                               Studies Available
                               Studies Included
                                     Ages
                           Risk Estimates Available
                            Risk Estimates Included
Respiratory Mortality (LT)
                                       4
                                       1
                            Adults and older adults
                                       2
                                       1
Respiratory Mortality (ST)
                                       6
                                       2
                      Children, adults, and older adults
                                      16
                                       1

                                      120
                                       1
Respiratory Hospital Admissions (ST)
                                       3
                                       1
                                 Older adults
                                       7
                                       1
Respiratory Emergency Department Visits (ST)
                                       7
                                       1
                      Children, adults, and older adults
                                      45
                                       5
Asthma Onset (LT)
                                       4
                                       1
                                   Children
                                       8
                                       1
Asthma Symptoms (ST)
                                       4
                                       1
                                   Children
                                      160
                                       1
Minor Restricted Activity Days
                                      NA
                                       1
                                    Adults
                                      NA
                                       1
Allergic Rhinitis
                                       1
                                       1
                                   Children
                                       5
                                       1
School Loss Days
                                      NA
                                       2
                                   Children
                                      NA
                                       1

                                      NA
                                       1
NA- not applicable due to the absence of additional ISA evidence. Risk estimates identified in the 2015 Ozone NAAQS RIA will continue to be utilized.
Respiratory Mortality
We separate respiratory mortality impacts resulting from short- and long-term exposures for several reasons. Firstly, the biological pathways of short- and long-term O3-attributable health effects may differ in ways that affect the manner in which this endpoint is quantified (section <IP_ADDRESS>.2.1). For example, some impacts of long-term exposure to O3 may be incremental to impacts attributable to short-term exposure. Conversely, some impacts associated with long-term exposure to O3 may include impacts attributable to short-term exposure. However, we lack the evidence to determine the extent to which these risks are mutually exclusive or overlapping. Secondly, the level of support for respiratory mortality effects of short- and long-term O3 exposures may differ. Therefore, we continue to include risk estimates of respiratory mortality from both short- and long-term exposures to present a range of health impact estimates.
Respiratory Mortality Attributable to Short-Term Exposures
The 2020 O3 ISA determined that there exists a "causal" relationship between short-term O3 exposure and respiratory outcomes (U.S. EPA, 2020a). The short-term exposure causality determination "was made on the basis of a strong body of evidence integrated across controlled human exposure, animal toxicological, and epidemiologic studies, in addition to established findings from previous [Air Quality Criteria Documents], demonstrating respiratory effects due to short-term exposure to ozone." While the ISA found that "recent epidemiological evidence for respiratory mortality is limited, but there remains evidence of consistent, positive associations, specifically in the summer months" and "when  recent evidence is considered in the context of the larger number of studies evaluated in the 2013 Ozone ISA, there remains consistent evidence of an association between short-term ozone exposure and respiratory mortality." Due to the strength of the ISA evidence relating short-term exposures to respiratory mortality, estimates of respiratory mortality impacts are included in the main benefits assessment of O3-attributable health impacts.
Separately, 2020 ISA determined that the relationship between short-term O3 exposure and total mortality is "suggestive of, but not sufficient to infer, a causal relationship." By comparison, the 2013 ISA identified this endpoint as "likely to be causal." Evidence supporting a relationship between short-term O3 exposure and total mortality included consistent epidemiologic evidence from multiple high-quality studies at relevant ozone concentrations, some support for an independent O3 association, and biological plausibility from studies of respiratory morbidity. In contrast, uncertainties remain regarding geographic heterogeneity in O3 mortality associations and there is limited biological plausibility from studies of cardiovascular morbidity. Regarding the biological plausibility of cardiovascular effects, while animal toxicological studies provide evidence of cardiovascular effects, recent controlled human exposure studies do not provide evidence to support potential biological pathways. Additionally, there is a lack of coherence with epidemiologic studies of cardiovascular morbidity, specifically, cardiovascular-related emergency department visits and hospital admissions, to support cardiovascular mortality. Due to limitations in ISA evidence relating short-term exposures to total mortality, estimates of all-cause mortality impacts will not be calculated when estimating benefits attributable to changes in O3 exposure.
Available Epidemiologic Literature
There were six North American studies of short-term O3-attributable respiratory mortality identified in the 2020 O3 ISA, one of which was new to this review but took place entirely in Canada. Of the U.S.-based studies, two were single-city. The other three studies were fairly equally geographically and demographically representative, although one was a meta-analysis.
Identifying Suitable Studies for Use in Benefits Assessments
Of the six studies of short-term O3-attributable respiratory mortality, all but one study period ended during or before the year 2000 and the individual study that extended into the 2000s was geographically limited to a single city. This list also included a meta-analysis and a study that took place entirely in Canada. Two U.S.-based, nationally representative studies were identified as best characterizing risk across the U.S. for this endpoint.
Studies and Risk Estimates Identified as Most Suitable for Use in Benefits Assessments
As Zanobetti and Schwartz, 2008 investigated the effects of short-term O3 exposure on mortality (all-cause, cardiovascular, stroke, and respiratory) in an unrestricted population of children, adults, and older adults (aged 0-99 years), it remained the superior analysis of short-term O3-attributable respiratory mortality. Between 1998 and 2000, the authors collected mortality data from the National Center for Health Statistic in 48 cities across the United States. Along with eight-hour ozone concentrations and meteorological data obtained from US EPA's Air Quality System Technology Transfer Network, the authors utilized a generalized linear model with quasi Poisson link functions to estimate the effects of short-term ozone on respiratory mortality. The model adjusted for season, day of the week, and temperature. Since ozone concentrations vary between seasons, the authors decided to restrict their analysis to ozone warm season (June - August). The identified single-pollutant, warm season excess risk estimate of 0.83% (95% CI: 0.38-1.28%) for an increase of 10 ppb in DA8 O3 concentrations over a summed lag structure of zero to three days.
Katsouyanni et al., 2009 also used time series methods to examine the relationship between short-term O3 exposures and mortality across the U.S for all ages. The study utilized mortality data from the National Center for Health Statistics (www.cdc.gov/nchs) for years 1987 through 1996, excluding accidental deaths (i.e., International Classification of Diseases (ICD]-9 800). 90 U.S. cities with population sizes varying from about 250,000 to above 9 million with the largest populations were included. Daily number of deaths ranged from 5 to 198. All 90 cities had daily summer O3 measurements. Investigators conducted extensive simulation studies to test 1) the choice of the smoothing method and basic functions used to estimate the smooth function of time in the city-specific models, and 2) the number of degrees of freedom to be used in the smooth function of time. The investigators also evaluated whether each city should be assigned the same model specification or whether each city-specific model should depend on city-specific characteristics. For the former, the same degrees of freedom (ranging from 1 to 20 df/year of data) were assigned to the smooth function of time for every city. The range was determined by choosing the minimum possible degrees of freedom per year up to a maximum degrees of freedom per year that essentially removed all variation in the data beyond time scales of one week. Also, the collective experience of the investigators indicated that using more than 20 df/year does not substantially affect the risk estimates. For the latter approach, the degrees of freedom for the smooth function of time were chosen separately for each city using a fit criterion, such as the Akaike Information Criterion (AIC), or by minimizing the partial autocorrelation function (PACF) of the residuals. Nonparametric methods underestimated the standard error of the air pollution regression coefficient, penalized splines gave relatively small bias, and PACF in combination with penalized splines performed relatively well in terms of bias. Therefore, the identified risk estimate was a summer-only penalized spline estimate of respiratory mortality of 0.73 (-0.39, 1.85) per 10 ug/m[3] increase in O3 from distributed lag days was identified.
The two risk estimates identified are not directly comparable to previous estimates of short-term exposure-related mortality as previous estimates were of nonaccidental mortality and current estimates are of respiratory mortality.
Respiratory Mortality Attributable to Long-Term Exposures
The 2020 O3 ISA determined that there exists a "likely to be causal" relationship between long-term O3 exposure and respiratory outcomes ({U.S. EPA, 2020 #343. The overall "likely to be causal" determination for long-term exposures "was based on epidemiologic evidence of associations between long-term ozone exposure and asthma development, respiratory symptoms in children with asthma, and respiratory mortality." More specifically, the ISA found that "there is strong coherence between animal toxicological studies of changes in lung morphology and epidemiologic studies reporting positive associations between long-term ozone exposure and new onset asthma, respiratory symptoms in children with asthma, and respiratory mortality" and the "several multicity studies and a multi-continent study reported associations between short-term increases in ozone concentrations and increases in respiratory mortality." Overall, the 2020 O3 ISA concluded there was "some evidence that long-term ozone exposure is associated with respiratory mortality, but the evidence is not consistent across studies." Due to the strength of the ISA evidence relating long-term exposures to respiratory mortality, estimates of respiratory mortality impacts are included when estimating benefits attributable to changes in O3 exposure.
Available Epidemiologic Literature
There were four North American studies of long-term O3-attributable respiratory mortality identified in the 2020 O3 ISA, three of which were new to this review. All four studies evaluated either the ACS CSP-II or the Canadian Census Health and Environment Cohort (CanCHEC) prospective cohorts, differing in study size, timespan, exposure estimation technique, and specific risk models analyzed.
Identifying Suitable Studies for Use in Benefits Assessments
Three of the four studies evaluating long-term O3-attributable respiratory mortality assessed the ACS CSP-II prospective cohort and the fourth evaluated the Canadian Census Health and Environment Cohort (CanCHEC) prospective cohort. Two of the three ACS CSP-II analyses were nationwide, with the third focusing on California. One of the two nationwide ACS CSP-II analyses included a longer and more recent study period, utilized hybrid exposure estimates, and included a larger number of participants.
Studies and Risk Estimates Identified as Most Suitable for Use in Benefits Assessments
{Turner, 2016 #544} examined the relationship between long-term O3 exposure (1982-2004) and mortality (all-cause, cause-specific) in American Cancer Society Cancer Prevention Study-II participants (aged 30-99 years). A hierarchal Bayesian space-time model based on National Air Monitoring Stations, State and Local Air Monitoring Stations, and Community Multi-Scale Air Quality model data estimated daily eight-hour maximum ozone concentrations at the participant's address. The models considered meteorological data and levels of other ambient pollutants (PM2.5, both regional and near-source, and NO2). Turner et al., 2016 utilized Cox proportional hazard models adjusted a priori for individual, socio-demographic, and ecological variables. The hazard ratio of 1.12 (1.08  -  1.16) from a multi-pollutant, all-year model of respiratory mortality for a 10 ppb increase in the annual average of daily 8-hour maximum ozone concentrations was likely the most comprehensive risk estimate. This study also provided a warm-season specific hazard ratio of 1.08 (1.06-1.11) per 10 ppb increase in seasonal average of daily 8-hour maximum O3 concentrations, which will be used when air quality surfaces are only available for the summer season. Notably, the study compared annual mortality with warm-season O3 exposures, so full-year baseline incidence rates will be used with risk estimates from this study.
The identified risk estimate of long-term exposure associated mortality is larger than the risk estimate used in previous benefits assessments (Jerrett et al., 2009), but differs in many aspects including study size, included study locations, and exposure estimation technique.
Respiratory Hospital Admissions
After considering the relationships between specific and broad respiratory hospital admission endpoints, the 2020 O3 ISA stated that "studies conducted in diverse locations with a variety of exposure assignment techniques continue to provide evidence of an association between ozone and both hospital admissions and emergency department visits for combined respiratory diseases" (U.S. EPA, 2020a, section 3.1.8).
Available Epidemiologic Literature
Three epidemiologic studies of respiratory hospital admission were identified in the 2020 O3 ISA, which varied considerably with respect to the timespans evaluated and study population locations (U.S. EPA, 2020a).
Identifying Suitable Studies for Use in Benefits Assessments
The two older studies either included only Canadian participants or included U.S., Canadian, and some European participants. Therefore, we identified the most recent and only entirely U.S.-based study as best characterizing risk across the U.S.
Studies and Risk Estimates Identified as Most Suitable for Use in Benefits Assessments
Katsouyanni et al., 2009 used time series methods to examine the relationship between daily O3 concentrations and hospital admissions in North America. For U.S. benefits estimation purposes, we focus on analyses performed using the U.S hospital admission datasets. These datasets included 14 cities with populations between 291,000 and 5,377,000 between 1987-1996 with city-wide MDA1 O3 concentrations ranging from ~34-60 ug/m[3]. The authors used a first stage analysis protocol that used generalized linear models with either penalized or natural splines to adjust for seasonality, with varying degrees of freedom. The number of degrees of freedom were also chosen by minimizing the partial autocorrelation function of the model's residuals. Model specification approach accounted for seasonal patterns, weekend and vacation effects, and epistemics of respiratory disease. Data were also analyzed to detect potential thresholds in the concentration-response relationships. The second stage analysis used pooling approaches and assessed potential effect modification by sociodemographic characteristic and indicators of the pollution mixture across study regions. The identified percent change in respiratory disease admission for those aged >64 was from a copollutant model including PM10 is 0.28 (-0.07, 0.62) per 10 ug/m[3] increase in O3.
Respiratory Emergency Department Visits
After considering the relationships between specific and broad respiratory emergency department visit endpoints, the 2020 O3 ISA stated that "studies conducted in diverse locations with a variety of exposure assignment techniques continue to provide evidence of an association between ozone and both hospital admissions and emergency department visits for combined respiratory diseases" and "there is some evidence, previously characterized in the 2013 O3 ISA, that daily 8 hour max, 1 hour max, and daytime average O3 concentrations may be most strongly associated with respiratory emergency department visits" (U.S. EPA, 2020a, section 3.1.8).
Available Epidemiologic Literature
Seven U.S.-based studies of respiratory emergency department visits were identified in the 2020 O3 ISA (U.S. EPA, 2020a). As is common with hospital admission and emergency department health endpoints, the specific ICD codes varied across all studies, making pooling difficult. Most studies evaluated only a single city or state and took place in a similar time period, including the early 2000s.
Identifying Suitable Studies for Use in Benefits Assessments
Available studies varied most widely by geographic area, exposure estimation method, population age range, and O3 season. While most studies focused on a specific city, state or region, one study included five different multi-county areas. In addition, it included a recent time period, all ages, current O3 concentrations, and was one of only two studies based on hybrid exposure techniques.
Studies and Risk Estimates Identified as Most Suitable for Use in Benefits Assessments
Barry et al. (2018) investigated the effects of short-term ozone exposure on emergency department visits for respiratory disease (ICD-9 493, 786.07, 460-466, 477, 491, 492, 496, 480 - 486, 466.1, 466.11, 466.19) in an unrestricted population of children, adults, and older adults (aged zero-99 years) within five cities (Atlanta, GA, Birmingham, AL, Dallas, TX, Pittsburgh, PA, and St. Louis, MO-IL) across the United States. Authors obtained individual-level health data from hospitals and hospital associations in each of the five cities. Models fusing air quality monitor data with Community Multi-Scale Air Quality modeled data at 12 x 12-km grids were used to estimate ozone exposure. Barry et al. (2018) assessed associations with short-term ozone exposure with daily eight-hour maximum ozone concentrations. The authors implemented Poisson log-linear models to estimate risk values with three day moving averages. They identified single-pollutant rate ratios of 1.03 (95% CI: 1.01-1.05) in Atlanta, GA, 1.03 (95% CI: 1.00-1.06) in Birmingham, AL, 1.05 (95% CI: 1.02-1.07) in Dallas TX, 1.03 (95% CI: 1.01-1.05) in Pittsburgh, PA, and 1.02 (95% CI: 1.01-1.04) in St. Louis, MO-IL for an increase of 25 ppb in full-year  MDA8 O3 concentrations (three day moving average). Results from individual cities are pooled.
Asthma Onset
The 2020 O3 ISA concluded that "recent epidemiologic studies provide generally consistent evidence for associations of long-term ozone exposure with the development of asthma in children" (U.S. EPA, 2020a, section IS.4.3). The ISA also found that "recent animal toxicological studies demonstrate effects on airway development in rodents...and build on and expand the evidence for long-term ozone exposure-induced effects that may lead to asthma development" and asthma onset was called out as a key population level clinically relevant health endpoint in the biological plausibility pathways (section <IP_ADDRESS>.2.1, Figure 9). More specifically, the O3 ISA stated that a "limited number of recent epidemiologic studies provide generally consistent evidence that long-term ozone exposure is associated with the development of asthma in children" (U.S. EPA, 2020a, section 3.2.6).
Available Epidemiologic Literature
The 2020 O3 ISA identified children as the population in which this health effect was observed, so we began with the four ISA-identified studies of people <21 (U.S. EPA, 2020a).
Identifying Suitable Studies for Use in Benefits Assessments
Three studies evaluated prospective cohorts, two of which included more recent timespans and likely lower O3 concentrations. One of those studies took place entirely in Canada but included a substantially larger study size (>200 times larger) than the other. As the asthma onset endpoint is consistent between studies, pooling may be appropriate.
Studies and Risk Estimates Identified as Most Suitable for Use in Benefits Assessments
Tetreault et al., 2016 investigated the effects of long-term O3 exposure on asthma onset in children (aged zero-12 years) from Québec, Canada. The study followed participants from the Québec Integrated Chronic Disease Surveillance System open birth cohort between 1999 and 2011. The authors defined new cases of asthma based on hospital discharge reports and physician diagnoses (two diagnoses within a two-year span). Monitor data (Canadian National Air Pollution Surveillance network) and land-use mixed effect models estimated warm season (June to August) O3 exposures. Authors assessed associations with asthma onset with both time of birth and time-varying exposure models and adjusted for year of birth, sex, and indices of social and material deprivation. Tetreault et al., 2016 used Cox proportional hazard models to observe associations between long-term O3 exposure and asthma onset in children. The identified single-pollutant, warm-season hazard ratio was 1.07 (95% CI: 1.06-1.08) for a 3.26 ppb (interquartile range) increase in annual O3 concentrations.
As the physiology and etiology of lung development in children is similar in children 6-17 (Baena-Cagnani et al., 2007, Guerra et al., 2004, Ochs et al., 2004, Sparrow et al., 1991, Trivedi and Denton, 2019), we apply the 4-12 year age-stratified effect estimate from Tetreault et al., 2016 to children ages 4-17.
Asthma Symptoms
Asthma symptoms/exacerbation is identified as a health effect of short-term O3 exposure (section <IP_ADDRESS>.2.1). Overall, the ISA found that "evidence from recent epidemiologic and experimental studies continues to support an association between ozone and asthma exacerbation" with "associations...observed across a range of ozone concentrations, and...consistent in models with measured or modeled concentrations" (U.S. EPA, 2020a, section <IP_ADDRESS>).
Available Epidemiologic Literature
Four epidemiologic studies of asthma symptoms meeting our minimum criteria (section 2.1.1) were identified in the 2020 O3 ISA (U.S. EPA, 2020a). Most studies took place in the late nineties and very early 2000s, and although no study included >1000 participants, there was appreciable geographic representation. There were also differences regarding the ozone season. Only the oldest study specifically evaluated a warm season, although a more recent study did skew slightly toward warmer seasons, evaluating eight seasons over a four-year timespan (two Summers, three Springs, two Falls, and one Winter).
Identifying Suitable Studies for Use in Benefits Assessments
Two of the studies evaluated much higher O3 concentrations (~50 ppb vs 30 ppb). Of the two studies evaluating lower pollutant concentrations, one employed a prospective study design and clearly defined the specific asthma symptoms evaluated (e.g., wheeze).
Studies and Risk Estimates Identified as Most Suitable for Use in Benefits Assessments
Lewis et al., 2013 studied the effects of short-term O3 exposure on frequency of asthma symptoms in an asthmatic population of primarily lower-income, African American and Latino children (aged five-12 years) in East and Southwest Detroit, MI. Authors obtained health and demographic data through questionnaires filled out by parents or guardians for 14 consecutive days in each studied season. Questionnaires highlighted participant's asthma symptoms (cough, wheeze, shortness of breath, chest tightness), demographic information, medication use, and presence of second-hand smoke. The authors acquired maximum one-hour and maximum 8-hour O3 concentrations and meteorological data from two community-level monitors placed on East and Southwest Detroit, MI school rooftops. Lewis et al., 2013 implemented a combination of generalized estimating equations and alternative logistic regression models to estimate the associations between short-term O3 exposure and rate of asthma symptoms. Models adjusted for age, sex, location (Eastside or Southwest), race, household income, smoker in the home, season, and variables for companion home intervention study (control or intervention), time (pre- or post-intervention), and the interaction between intervention group status and time. Lewis et al., 2013 observed positive associations between short-term O3 exposure and asthma symptoms, including the identified single-pollutant, all year odds ratios of 1.12 (95% CI: 0.99-1.25) for cough, 1.13 (95% CI: 0.99-1.28) for wheeze, 1.20 (95% CI: 1.02-1.40) for chest tightness, and 1.07 (95% CI: 0.95-1.21) for shortness of breath, all for a 16 ppb (interquartile range) increase in 8-hour maximum O3 concentrations (five-day average lag).
Minor Restricted Activity Days
No new epidemiologic studies of minor restricted activity days (MRADs) were identified in the 2020 O3 ISA (U.S. EPA, 2020a).
Studies and Risk Estimates Identified as Most Suitable for Use in Benefits Assessments
Ostro and Rothschild, 1989 estimated the impact of PM2.5 and O3 on the incidence of minor restricted activity days (MRADs) and respiratory-related restricted activity days (RRADs) in a national sample of the adult working population, ages 18 to 65, living in metropolitan areas. The study population is based on the Health Interview Survey (HIS), conducted by the National Center for Health Statistics. In publications from this ongoing survey, non-elderly adult populations are generally reported as ages 18-64. From the study, it is not clear if the age range stops at or includes those aged 65. We apply the risk estimate function to individuals ages 18-64 for consistency with other studies estimating impacts to non-elderly adult populations. The annual national survey results used in this analysis were conducted in the period 1976-1981, controlling for PM2.5, two-week average O3.
Allergic Rhinitis (Hay Fever/Respiratory Allergies))
The 2020 O3 ISA stated that "cross-sectional epidemiologic studies provide generally consistent evidence that ozone concentrations are associated with hay fever/rhinitis" and included "allergic responses" in the biological plausibility diagram for long-term O3-attributal respiratory effects (U.S. EPA, 2020a, section <IP_ADDRESS>.2.1). Although cross sectional analyses do not establish a temporal sequence, they can be used to estimate benefits associated with changes in air quality.
Available Epidemiologic Literature
The 2020 O3 ISA identified one epidemiologic study of long-term O3 exposure and allergic rhinitis.
Studies and Risk Estimates Identified as Most Suitable for Use in Benefits Assessments
Parker et al., 2009 investigated the associations between long-term O3 exposure and respiratory allergies in an unrestricted population of children (aged 3-17 years) sampled from the United States National Health Interview Survey. Authors obtained symptom data from participant parents, who reported respiratory allergies on annual surveys. Parker et al., 2009 placed all study participants reporting symptoms of respiratory allergies or hay fever into a combined rhinitis group. Parker et al., 2009 linked annual averages of SO2, NO2, PM2.5, and PM2.5-10 and warm season (May to September) O3 averages to participant's addresses through ambient air pollution and meteorological data collected from US EPA Air Quality System monitors. The authors adjusted models for survey year, poverty-level, race/ethnicity, age, family structure, insurance coverage, usual source of care, education of adult, urban-rural status, region, and median county-level income. Through multi-pollutant, logistic regression models, the odds ratio of 1.18 (95% CI: 1.09-1.27) for a 10 ppb increase in 24-hour mean, warm season O3 and respiratory allergies was identified.
School Loss Days
No new studies of work loss days (WLDs) were identified in the 2020 O3 ISA (U.S. EPA, 2020a).
Studies and Risk Estimates Identified as Most Suitable for Use in Benefits Assessments
Gilliland et al., 2001 examined the association between air pollution and school absenteeism among fourth grade school children (aged nine to 10) in 12 southern Californian communities. The study was conducted from January through June 1996. The authors used school records to collect daily absence data and parental telephone interviews to identify causes. They defined illness- related absences as respiratory or non-respiratory. A respiratory illness was defined as an illness that included at least one of the following: runny nose/sneezing, sore throat, cough, earache, wheezing, or asthma attack. The authors used 15- and 30-day distributed lag models to quantify the association between O3 and incident school absences. O3 levels were positively associated with all school absence measures and significantly associated with all illness-related school absences (non-respiratory illness, respiratory illness, URI and LRI). The health impact function for ozone is based on the results of the single pollutant model.
Gilliland et al., 2001 defines an incident absence as an absence that followed attendance on the previous day and the incidence rate as the number of incident absences on a given day over the population at risk for an absence on a given day (i.e. those children who were not absent on the previous day). Since school absences due to air pollution may last longer than one day, an estimate of the average duration of school absences could be used to calculate the total avoided school loss days from an estimate of avoided new absences. A simple ratio of the total absence rate divided by the new absence rate would provide an estimate of the average duration of school absences, which could be applied to the estimate of avoided new absences as follows:

Duration=TotalAbsencesNewAbsences

∆TotalAbsences= -incidences xe-βxO3-1xdurationxpop

Since the function is log-linear, the baseline incidence rate (in this case, the rate of new absences) is multiplied by duration, which reduces to the total school absence rate. Therefore, the same result would be obtained by using a single estimate of the total school absence rate in the risk estimate. Using this approach, we assume that the same relationship observed between pollutant and new school absences in the study would be observed for total absences on a given day. As a result, the total school absence rate is used in the function below. The derivation of this rate is described in the section on baseline incidence rate estimation.
For all absences, the coefficient and standard error are based on a percent increase of 16.3 percent (95% CI -2.6 percent, 38.9 percent) associated with a 20 ppb increase in eight-hour average ozone concentration (2001, Table 6, p. 52).
A scaling factor is used to adjust for the number of school days in the ozone season. In the modeling program, the function is applied to every day in the ozone season (May 1 - September 30), however, in reality, school absences will be avoided only on school days. We assume that children are in school during weekdays for all of May, two weeks in June, one week in August, and all of September. This corresponds to approximately 2.75 months out of the five-month season, resulting in an estimate of 39.3% of days (2.75/5*5/7).
In addition, not all children are at-risk for a new school absence, as defined by the study. On average, 5.5% of school children are absent from school on a given day (NCES, 1996, Table 42-1). Only those who are in school on the previous day are at risk for a new absence (1-0.055 = 94.5%). As a result, a factor of 94.5% is used in the function to estimate the population of school children at-risk for a new absence.
Identified Study and Risk Estimates for Benefits Assessments
While we begin with studies identified in ISAs, the goals of an ISA differ greatly from those of benefits assessments. ISAs evaluate the overall state of the science and develop overarching conclusions relating exposure to health effects. This includes analyses of specific subgroups, such as people with pre-existing conditions, that may not be transferrable to the entire U.S. population.
In an effort to make our study and risk estimate identification process as transparent and reproducible as possible, we have explicitly stated the criteria used in our approach (section 1) as well as the available epidemiologic studies evaluated (section 2.2). However, even with such detailed information, expert judgment can be required if multiple estimates meet the required criteria, satisfy a similar number of preferred criteria, and are unable to be statistically aggregated into a single risk estimate (i.e., pooling).
The two tables in this section provide information on the health endpoints and risk estimates identified for use in PM2.5 and O3 benefits estimation (Table 10 and Table 11) using the systematic approach described above (sections 2.1 and 2.2).
Health Endpoints
These summary tables provided an overview of the PM2.5 and O3 health endpoints included in the main benefits analysis. They are the outcome of the systematic approach described above, which involved consideration of recent ISA conclusions along with the availability of clinically relevant epidemiologic risk estimates (Table 8 and Table 9).
PM2.5
Table 8. Set of Health Endpoints for Main PM2.5 Benefits Assessments
                                Endpoint Group
                                 Endpoint Type
                               Specific Endpoint
                                   Exposure
                                     Ages
Mortality
Mortality
All cause
                                      LT
Adults and older adults (30-99 and 65-99 years)

                                      ST
Infants (1-12 months)
Cardiovascular Effects
Hospital Admissions
Cardiovascular Outcomes
                                      ST
Older adults (65-99 years)

Emergency Department Visits
Cardiovascular Outcomes
                                      ST
Children, adults, and older adults (0-99 years)

Incidence
Acute Myocardial Infarction
                                      ST
Adults and older adults (18-99 years)

Stroke[a]
                                      LT
Older adults (65-99 years)

Cardiac Arrest[a]
                                      ST
Adults and older adults (0-99 years)
Respiratory Effects
Hospital Admissions
Respiratory Outcomes
                                      ST
Children and older adults (65-99 years)

Emergency Department Visits
Respiratory Outcomes
                                      ST
Children, adults, and older adults (0-99 years)

Incidence
Asthma Onset[a]
                                      LT
Children (0-17 years)

Asthma Symptoms
                                      ST
Children (6-17 years)

Allergic Rhinitis[a]
                                      LT
Children (3-17 years)

Minor Restricted Activity Days
                                      NA
Adults and older adults (18-64 years)

Work Loss Days
                                      NA
Adults and older adults (18-64 years)
Cancer
Incidence
Lung Cancer[a]
                                      LT
Adults and older adults (30-99 years)
Nervous System Effects
Hospital Admissions
Alzheimer's Disease[a]
                                      LT
Older adults (65-99 years)

Parkinson's Disease[a]
                                      LT
Older adults (65-99 years)
[a]New health endpoint.

O3
Table 9. Set of Health Endpoints for Main O3 Benefits Assessments
                                Endpoint Group
                                 Endpoint Type
                               Specific Endpoint
                                   Exposure
                                     Ages
Mortality
Mortality
Respiratory[a]
                                      ST
Children, adults, and older adults (0-99 years)

                                      LT
Adults and older adults (30-99 years)
Respiratory Effects
Hospital Admissions
Respiratory Outcomes
                                      ST
Older adults (65-99 years)

Emergency Department Visits
Respiratory Outcomes
                                      ST
Children, adults, and older adults (0-99 years)

Incidence
Asthma Onset[a]
                                      LT
Children (0-17 years)

Asthma Symptoms
                                      ST
Children (5-17 years)

Allergic Rhinitis[a]
                                      LT
Children (3-17 years)

Minor Restricted Activity Days
                                      ST
Adults and older adults (18-64 years)

School Loss Days
                                      ST
Children (5-12 years)

                                      ST
Children (9-10 years)
[a]New or updated health endpoint.
Risk Estimates
This section presents the risk estimates identified for the main PM2.5 (section <IP_ADDRESS>) and O3 (section <IP_ADDRESS>) benefits assessments. These lists reflect the application of the available epidemiologic literature (section 2.2) to the identification criteria (section 2.1).
PM2.5
Table 10. Set of Risk Estimates for Main PM2.5 Benefits Assessments
Endpoint
Study Information
Ages
Exposure Duration
Beta Coefficient
(SE)[1]
Mortality
Di et al., 2017b
Older adults (65-99 years)
LT
β = 0.0070 (0.0001)

Turner et al., 2016
Adults (30-99 years)
LT
β = 0.0058 (0.00096)

Woodruff et al., 2008
Infants (1-12 months)
LT
β = 0.0056 (0.00454)
Hospital Admissions, Cardiovascular
Bell et al., 2015  -- ICD 410, omitting 410.x2; 410-414; 426-427; 428; 429; 430-438; and 440-448
Older adults (65-99 years)
ST
β = 0.00065 (0.00009)
Emergency Department Visits, Cardiovascular
Ostro et al., 2016 -- ICD 390-459
Children older adults (0-99 years)
ST
β = 0.00061 (0.00042)
Acute Myocardial Infarction
Peters et al., 2001
Adults and older adults (18-99 years)
ST
β = 0.02412 (0.00928)

Pope III et al., 2006
Sullivan et al., 2005
Zanobetti et al., 2009
Zanobetti and Schwartz, 2006
Adults and older adults (18-99 years)
ST
β = 0.00481 (0.00199)
β = 0.00198 (0.00224)
β = 0.00225 (0.00059)
β = 0.0053 (0.00221)
Cardiac Arrest
Ensor et al., 2013
Rosenthal et al., 2008
Silverman et al., 2010
Adults and older adults (0-99 years)
ST
β = 0.00638 (0.00282)
β = 0.00198 (0.00502)
β = 0.00392 (0.00222)
Stroke
Kloog et al., 2012 -- ICD 430-436
Older adults (65-99 years)
LT
β = 0.00343 (0.00127)
Hospital Admissions, Respiratory
Bell et al., 2015 -- ICD 490-492, 464-466, 480-487, 493
Older adults (65-99 years)
ST
β = 0.00025 (0.00012)

Ostro et al., 2009 -- ICD 460-519
Children (0-18 years)
ST
β = 0.00275 (0.00077)
Emergency Department Visits, Respiratory
Krall et al., 2013 -- ICD 480-486, 491, 492, 496, 460-465, 466, 477, 493, 786.07
Children, adults, and older adults (0-99 years)
ST
β = 0.00055 (0.00027) (GA)
β = 0.00097 (0.00035) (AL)
β = 0.00083 (0.00033) (MO)
β = 0.00135 (0.00059) (TX)
Asthma Onset
Tetreault et al., 2016
Children (0-17 years)
LT
β = 0.04367 (0.00088)
Allergic Rhinitis
Parker et al., 2009
Children (3-17)
LT
β = 0.02546 (0.00962)
Lung Cancer
Gharibvand et al., 2017
Adults and older adults (>29 years)
LT
β = 0.03784 (0.01312)
Alzheimer's Disease
Kioumourtzoglou et al., 2016 -- ICD 331.0
Older adults (>64 years)
LT
β = 0.13976 (0.01775)
Parkinson's Disease
Kioumourtzoglou et al., 2016 -- ICD 332
Older adults (>64 years)
LT
β = 0.07696 (0.01891)
Asthma Symptoms
Rabinovitch et al., 2006
Children (6-17 years)
ST
β = 0.00200 (0.00148)
Minor Restricted Activity Days
Ostro and Rothschild, 1989
Adults and older adults (18-64 years)
N/A
β = 0.00741 (0.0007)
Work Loss Days
Ostro, 1987
Adults and older adults (18-64 years)
N/A
β = 0.0046 (0.00036)
ST- short-term; LT- long-term, β- beta risk estimate; ICD- International Statistical Classification of Diseases
Notes: Horizontal lines separating studies within an endpoint indicates that the studies are not intended to be pooled.
[1] Risk estimates have been mathematically converted to beta coefficients, which include the increment of pollutant change and allow for more direct comparisons of risk estimates within health endpoints.
O3
Table 11. Set of Risk Estimates for Main O3 Benefits Assessments
Endpoint
Study Information
Ages
Exposure (Duration; Season; Metric)
Beta Coefficient
(SE)[1]
Respiratory Mortality
Zanobetti and Schwartz, 2008
Children, adults, and older adults (0-99 years)
ST; June-August; DA8
β = 0.00083 (0.00023) (warm season)

Katsouyanni et al., 2009
Children, adults, and older adults (0-99 years)
ST; April-September; MDA1
β = 0.00073 (0.00057) (warm season)

Turner et al., 2016 -- ICD 460-519
Adults and older adults (30-99 years)
LT; April-September; MDA8
β = 0.007696 (0.00118) (warm season)
Hospital Admissions, Respiratory
Katsouyanni et al., 2009 -- ICD 460 - 519
Older adults (65-99 years)
ST; April-September; MDA1

β = 0.00028 (0.00018) (warm season)
Emergency Department Visits, Respiratory
Barry et al., 2019 -- ICD 493, 786.07, 460-466, 477, 491, 492, 496, 480 - 486, 466.1, 466.11, 466.19
Children, adults, and older adults (0-99 years)
ST; January-December; MDA8
β = 0.00118 (0.00040) (Atlanta, GA)
β = 0.00118 (0.00059) (Birmingham, AL)
β = 0.00195 (0.00049) (Dallas, TX)
β = 0.00118 (0.00040) (Pittsburgh, PA)
β = 0.00079 (0.00030) (St. Louis, MO-IL)
Asthma Onset
Tetreault et al., 2016

Children (0-17 years)
LT; June-August; MDA8
β = 0.02075 (0.00146) (warm season)
Asthma Symptoms
Lewis et al., 2013
Children (5-17 years)
ST; January-December; MDA8
β = 0.00708 (0.00372) (Cough)
β = 0.00764 (0.00410) (Wheeze)
β = 0.01140 (0.00505) (Chest tightness)
β = 0.00423 (0.00386) (Shortness of breath)
Allergic Rhinitis
Parker et al., 2009
Children (3-17 years)
LT; May-September; DA24
β = 0.01655 (0.00390) (warm season)
Minor Restricted Activity Days
Ostro and Rothschild, 1989 (MRADs)
Adults and older adults (18-64 years)
ST; April-September; MDA1
β = 0.0022 (0.000658)
School Loss Days
Gilliland et al., 2001
Children (5-17 years)
ST; January-June; DA8
β = 0.0078 (0.0044)
ST- short-term; LT- long-term, β- risk estimate (beta); ICD- International Statistical Classification of Diseases; DA8- daily 8-hour average; MDA8- maximum daily 8-hour average; MDA1- maximum daily 1-hour average; DA24- daily 24-hour average
Notes: Horizontal lines separating studies within an endpoint indicates that the studies are not intended to be pooled
[1] Risk estimates have been mathematically converted to beta coefficients, which include the increment of pollutant change and allow for more direct comparisons risk estimates within health endpoints.

Baseline Incidence and Prevalence Estimates
A baseline incidence rate is an estimate of the number of new cases in the assessment location over a specific timespan, typically one year. For example, in 2018 the mortality rate was 868 deaths per 100,000 people in the U.S. The baseline incidence of the health effect is necessary to convert the relative risk of a health effect provided by epidemiologic studies into an estimated number of cases. To derive the total baseline incidence per year, this rate must be multiplied by the corresponding population. Continuing with the above example, there were 327 million people in the U.S. in 2018, leading to a total baseline incidence of 2.8 million deaths in that year.
Prevalence rates are the proportion of the population experiencing a health endpoint at a point in time. This rate is important when estimating impacts of chronic illnesses, such as asthma, in order to exclude those already diagnosed from the population at risk. For example, if the prevalence of asthmatic children is 8%, only the remaining 92% are at risk of developing asthma.
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. For many locations within the U.S., these data are available resolved at the county- or state-level, providing a better characterization of the geographic distribution of hospital and emergency department visits than the national rates. For this update, we focused on developing baseline incidence rates for new health endpoints. Detailed information on baseline incidence data developed previously can be found in Appendix D of the BenMAP-CE User Manual (U.S. EPA, 2018). Importantly, when applying either the daily or annual baseline incidence rates to a health impact estimate, the temporal scale over which the health endpoint was assessed within each study is taken into account. For example, if a long-term O3 exposure study associated annual deaths with warm-season exposures, full-year baseline incidence rates will be used when estimating benefits.
Table 12 summarizes the sources of baseline incidence rates and provides national average (where used) incidence rates for the endpoints included in the analysis. For both baseline incidence and prevalence data, we used age-stratified rates where available. We applied risk estimates to individual age groups and then sum them over the relevant age range to estimate total population benefits. In some cases we used a single national incidence rate, due to a lack of more spatially disaggregated data, time, or resources. In these cases, whenever possible we used national average rates, because these data are most applicable to a national assessment of benefits. For some studies, however, the only available incidence information comes from the studies themselves; in these cases, incidence in the study population is assumed to represent typical incidence at the national level.
Table 12. Baseline Incidence Rates for Use in Impact Functions
                                   Endpoint
                                   Parameter
                                     Rates

                                     Value
                                    Source
Mortality[1]
Daily or annual projected incidence to 2060 in 5-year increments (0--99)
Age-, cause-, race-, and county-stratified rates
CDC WONDER (2012 - 2014)
U.S. Census Bureau, 2012
Hospitalizations[2]
Daily incidence rates for all ages
Age-, region/state/county-, and cause- stratified rates
2011-2014 HCUP data files and data requested from and supplied by individual states
Emergency Department Visits[2]
Daily emergency department visit incidence rates for all ages
Age-, region-, state-, county-, and cause- stratified rates
2011-2014 HCUP data files and data requested from and supplied by individual states
Nonfatal Acute Myocardial Infarction
Daily nonfatal AMI incidence rate per person aged 18-99
Age-, region-, state-, and county- stratified rates
AHRQ, 2016
Asthma Symptoms
Daily incidence among asthmatic children

Wheeze (ages 5-12)
Cough (ages 5-12)
Shortness of breath (ages 5-12)
Albuterol use (ages 6-13)

Age- and race- stratified rates

2.2 puffs per day

Ostro et al., 2001

Rabinovitch et al., 2006
Asthma Onset
Annual incidence
0 - 4
5 - 11
12 - 17

0.0234
0.0111
0.0044
Winer et al., 2012
Alzheimer's Disease
Daily incidence rates for all ages
Age-, region-, state-, and county- stratified rates

2011-2014 HCUP data files
Parkinson's Disease
Annual incidence
18 - 44
45 - 64
65 - 84
85 - 99

0.0000011
0.0000366
0.0002001
0.0002483
HCUPnet
Allergic Rhinitis
Respondents aged 3-17 experiencing allergic rhinitis/hay fever symptoms within the year prior to the survey
0.192
Parker et al., 2009
Cardiac Arrest
Daily nonfatal incidence rates
0 - 17
18 - 39
40 - 64
65 - 99

0.00000002
0.00000009
0.00000056
0.00000133
Ensor et al., 2013, Rosenthal et al., 2008, Silverman et al., 2010
Lung Cancer
Annual nonfatal incidence
25 - 34
35 - 44
45 - 54
55 - 64
65 - 74
75 - 84
95 - 99

0.000001746
0.000014919
0.000067463
0.000208053
0.000052370
0.000576950
0.000557130
NCI, 2015 and Gharibvand et al., 2017
Stroke
Annual nonfatal incidence in ages 65-99
0.00446
Kloog et al., 2012
Work Loss Days
Daily incidence rate per person (18 - 64)
Aged 18 - 24
Aged 25 - 44
Aged 45 - 64

0.00540
0.00678
0.00492
Adams et al., 1999, Table 41; U.S. Census Bureau (2000)
School Loss Days
Rate per person per year, assuming 180 school days per year
9.9
Adams et al., 1999, Table 47
Minor Restricted-Activity Days
Daily MRAD incidence rate per person (18-64)
0.02137
Ostro and Rothschild, 1989, p. 243
CDC-Centers for Disease Control; NHS-National Health Interview Survey
[1]Mortality rates are only available in 5-year increments. The Healthcare Cost and Utilization Program (HCUP) database contains individual level, state and regional-level hospital and emergency department discharges for a variety of International Classification of Diseases (ICD) codes (AHRQ, 2016).
[2]Baseline incidence rates now include corrections from the states of Indiana and Montana.
Mortality
Baseline incidence rate estimates for mortality remain the same as they were for previous benefits assessments (U.S. EPA, 2018). However, information is provided below for reference. Notably, the Turner et al., 2016 analysis of long-term O3-attributable health impacts compares warm-season exposures to full-year baseline incidence rates. As such our baseline incidence rate estimates also reflect the full year.
Mortality Data for 2012-2014
We obtained county-level mortality and population data from 2012-2014 for 11 causes for the contiguous United States by downloading the data from the Centers for Disease Control (CDC) WONDER database.
Since the detailed mortality data obtained from CDC do not include population, we combined them with U.S. Census Bureau population estimates exported from BenMAP. We then generated age-, cause-, and county-specific mortality rates using the following formula:
       Ri,j,k=Di,j,k2012+Di,j,k2013+Di,j,k2014Pi,k2012+Pi,k2013+Pi,k2014
where Ri,j,k is the mortality rate for age group i, cause j, and county k; D is the death count; and P is the population. Additional details about the translation of the CDC WONDER data to age-, cause-, and county-specific mortality rates are provided in the BenMAP-CE User's Manual (U.S. EPA, 2018).

Table 13.  National Mortality Rates (per 100 people per year) by Health Endpoint and Age Group, 2012-2014
Mortality Category
ICD-10 Codes
Infant*
1-17
18-24
25-34
35-44
45-54
55-64
65-74
75-84
85+
Mortality, All Cause
All
0.59396
0.01951
0.07804
0.10665
0.17264
0.40542
0.86162
1.79670
4.62837
13.58034
Mortality, Non-Accidental
A00-R99
0.55495
0.00949
0.01874
0.04112
0.10876
0.33084
0.79395
1.73208
4.49595
13.20867
Mortality, Respiratory
J00-J98
0.01297
0.00102
0.00127
0.00253
0.00570
0.02013
0.06560
0.20585
0.57827
1.42362
Mortality, Chronic Lung
J40-J47, J67
0.00053
0.00032
0.00040
0.00074
0.00186
0.01033
0.04045
0.13873
0.36008
0.68593
Mortality, Lung Cancer
C34
0.00002
0.00001
0.00007
0.00033
0.00282
0.02378
0.07992
0.19701
0.32952
0.31820
Mortality, Ischemic Heart Disease
I20-I25
0.00033
0.00004
0.00039
0.00234
0.01242
0.04854
0.12174
0.25698
0.68000
2.27271
Mortality, Cardio-Pulmonary
I00-I78, J10-J18, J40-J47, J67
0.00539
0.00069
0.00099
0.00214
0.00502
0.01794
0.05877
0.18453
0.51055
1.26213
Mortality, NCD + LRI
**
0.18459
0.00618
0.01168
0.02751
0.08129
0.26214
0.63767
1.37694
3.44731
9.47467
Mortality, Lower Respiratory Infection
A48.1, A70, B97.4-B97.6, J09-J15.8, J16, J20-J21, P23.0-P23.4, U04
0.00269
0.00618
0.01168
0.00030
0.00062
0.00112
0.00196
0.00300
0.00758
0.02693
Mortality, Cerebro-vascular
G45-G46.8, I60-I63.9, I65-I66.9, I67.0-I67.3, I67.5-I67.6, I68.1-I68.2, I69.0-I69.3
0.00116
0.00012
0.00034
0.00096
0.00314
0.00809
0.01455
0.02892
0.08553
0.20863
Mortality, COPD
J40-J44, J47
0.00048
0.00005
0.00004
0.00015
0.00102
0.00904
0.03888
0.13689
0.35661
0.67457
*We estimate post-neonatal mortality (deaths after the first month) for infants because the health impact function (see Appendix E) estimates post-neonatal mortality.
**For a full list of codes for non-communicable diseases (NCD) and lower respiratory infections (LRI), see the IHME GBD Code mapping: http://ghdx.healthdata.org/record/ihme-data/gbd-2017-cause-icd-code-mappings.

Mortality Rate Projections 2015-2060
To estimate age- and county-specific mortality rates in years 2015 through 2060, we calculated annual adjustment factors, based on a series of Census Bureau projected national mortality rates (for all- cause mortality), to adjust the age-, county-, and cause-specific mortality rates calculated using 2012-2014 data as described above.  We used the following procedure:
For each age group, we obtained the series of projected national mortality rates from 2013 to 2050 (see the 2013 rate in Table 14) based on Census Bureau projected life tables.
We then calculated, separately for each age group, the ratio of Census Bureau national mortality rate in year Y (Y = 2014, 2015, ..., 2060) to the 2013 rate, which is assumed to be representative of the 2012-2014 data and used for the base "year." These ratios are shown for selected years in Table 15.
Finally, to estimate mortality rates in year Y (Y = 2015, 2020, ..., 2060) that are both age-group-specific and county-specific, we multiplied the county- and age-group-specific mortality rates for 2012-2014 by the appropriate ratio calculated in the previous step. For example, to estimate the projected mortality rate in 2015 among ages 18-24 in Wayne County, MI, we multiplied the mortality rate for ages 18-24 in Wayne County in 2012-2014 by the ratio of Census Bureau projected national mortality rate in 2015 for ages 18-24 to Census Bureau national mortality rate in 2013 for ages 18-24.
Table 14. All-Cause Mortality Rate (per 100 people per year), by Source, Year, and Age Group
                                Source and Year
                                    Infant
                                     1-17
                                     18-24
                                     25-34
                                     35-44
                                     45-54
                                     55-64
                                     65-74
                                     75-84
                                      85+
Calculated CDC 2012-2014
0.594[1]
0.020
0.078
0.107
0.173
0.405
0.862
1.797
4.628
13.580
Census Bureau 2013[2]
0.654
0.029
0.088
0.102
0.183
0.387
0.930
2.292
5.409
13.091
[1]The Census Bureau estimate is for all deaths in the first year of life. EPA benefits assessments uses post-neonatal mortality (deaths after the first month, i.e., 0.23 per 100 people) because the health impact function (see Appendix E) estimates post- neonatal mortality. For comparison purpose, we also calculated the rate for all deaths in the first year, which is 0.684 per 100 people.
[2]For a detailed description of the model, the assumptions, and the data used to create Census Bureau projections, see the working paper, "Methodology and Assumptions for the 2012 National Projections," which is available on http://www.census.gov/population/projections/files/methodology/methodstatement12.pdf

Table 15. Ratio of Future Year All-Cause Mortality Rate to 2013 Estimated All-Cause Mortality Rate, by Age Group
Year
Infant
1-17
18-24
25-34
35-44
45-54
55-64
65-74
75-84
85+
2015
0.93
0.93
0.96
1.02
0.96
0.96
1.01
1.02
1.03
1.00
2020
0.94
0.94
0.98
1.04
0.97
0.98
1.02
1.03
1.03
1.00
2025
0.85
0.81
0.74
0.80
0.75
0.77
0.85
0.91
0.93
0.97
2030
0.81
0.75
0.66
0.70
0.67
0.69
0.78
0.86
0.89
0.92
2035
0.76
0.70
0.58
0.62
0.60
0.62
0.71
0.81
0.87
0.87
2040
0.73
0.65
0.51
0.53
0.53
0.56
0.64
0.76
0.84
0.86
2045
0.70
0.60
0.45
0.46
0.46
0.50
0.58
0.71
0.80
0.86
2050
0.67
0.56
0.39
0.40
0.40
0.44
0.53
0.66
0.77
0.87
2055
0.64
0.52
0.34
0.35
0.35
0.39
0.48
0.62
0.73
0.88
2060
0.61
0.48
0.30
0.30
0.31
0.34
0.43
0.58
0.70
0.87
Race-Stratified Incidence Rates
To estimate race-stratified and age-stratified incidence rates at the county level, we downloaded all-cause mortality data from 2007 to 2016 from the CDC WONDER mortality database. Race-stratified incidence rates were calculated for the following age groups: < 1 year, 1- 4 years, 5-14 years, 15-24 years, 25-34 years, 35-44 years, 45-54 years, 55-64 years, 65-74 years, 75-84 years, and 85+ years. We stratified the data into two race categories, White and Non-White, and follow all methods outlined in section D.1.1 of the BenMAP-CE User Manual (U.S. EPA, 2018). To properly impute incidence rates for suppressed and unreliable counties, we downloaded data at the state, regional, and national scales.
Hospitalizations
The approach for estimating hospitalization baseline incidence rates for new health endpoints is based on HCUP data, developed to match the granularity and timeframe of other hospitalization endpoints used in benefits assessments. New hospitalization endpoints are comprised of new sets of ICD-9 codes that correspond to newer studies evaluating air pollution-attributable hospitalizations. Detailed information is provided below and available in the BenMAP-CE User Manual (U.S. EPA, 2018).
Hospitalization rates were calculated using data from the Healthcare Cost and Utilization Project (HCUP). HCUP is a family of health care databases developed through a Federal-State-Industry partnership and sponsored by the Agency for Healthcare Research and Quality (AHRQ). HCUP products include the State Inpatient Databases (SID), the State Emergency Department Databases (SEDD), the Nationwide Inpatient Sample (NIS), and the Nationwide Emergency Department Sample (NEDS).
The level of hospitalization data available differs by state. While many states provide granular discharge-level data, others may only provide county- or state level-data. Also, 14 states, mostly in the southeast, do not provide data to HCUP. For these states, regional statistics from HCUPnet were used to estimate baseline hospitalization rates.
HCUP categorizes hospital admissions in various ways. Hospitalization admission types used when reporting data to HCUP include emergency (admitted from the emergency department), urgent (admitted from another hospital), elective (admitted from another health facility, including long-term care), newborn (admitted for delivery), trauma (not used by all states), and other/missing/invalid. As PM2.5 and O3 exposure predominantly leads to cardiovascular and respiratory health effects, we provide some information on the proportion of these types of hospitalizations, based on an analysis of hospitalizations from the state of Florida in 2014. Florida was selected for this analysis as it was the most populated state providing details regarding hospital admission type.
 Emergency hospital admissions comprise approximately 80% of cardiovascular and 85% of respiratory admissions
 Urgent hospital admissions comprise approximately 10% of cardiovascular and 8% of respiratory admissions
 Elective hospital admissions comprise approximately 10% of cardiovascular and 7% of respiratory admissions
 Newborn hospital admissions comprise no cardiovascular and respiratory admissions
 Trauma hospital admissions comprise approximately 0.1% of cardiovascular and respiratory admissions
 Other/missing/invalid hospital admissions comprise no cardiovascular or respiratory admissions
All hospital admission baseline incidence data used in this analysis (and input into BenMAP-CE) reflects total hospital admissions, due to time constraints limiting the ability to separate types (e.g., emergency, urgent, elective, etc) within HCUP data by various states and regions. However, the breakdown of hospital admission types generally reflects the types of health endpoints associated with air pollution exposures, with the majority of effects falling into the emergency and urgent types (e.g., heart or asthma attack) with a small subset potentially leading to elective hospital admissions (e.g., exacerbation of heart failure).
Health endpoints in hospitalization studies are defined using different combinations of ICD codes corresponding to specific diagnoses. Some span large categories of diagnoses, such as all cardiovascular or all respiratory admissions, while others reflect specific conditions, including Alzheimer's disease and Parkinson's disease. For each ICD code combination, unique baseline incidence rates are developed.
Emergency Department Visits
As new studies evaluating air pollution-attributable emergency department utilizing new sets of ICD-9 codes were identified for use in benefits assessment here, we developed corresponding new emergency department baseline incidence rates. Similar to hospitalization baseline incidence rates, the approach for estimating emergency department visit baseline incidence rates also utilizes HCUP data and remains the same as in previous benefits assessments, details for which can be found in the BenMAP-CE User Manual (U.S. EPA, 2018). Information is provided below for reference.
Similar to hospitalization rates, the data source for emergency department/room visits is also HCUP, (i.e., SID, SEDD, and NEDS), states vary by level of data provided (i.e., discharge-, county-, state, and regional-level), and unique baseline incidence rates are generated for each health endpoint ICD code combination.
Health Endpoint Onset/Occurrence
Baseline incidence estimates for health endpoint onset or occurrences are described below, listed in alphabetical order. Onset indicates the development of a health endpoint (e.g., asthma diagnosis), whereas occurrence refers to an instance of that health endpoint (e.g., asthma attack).
Acute Myocardial Infarctions (AMIs)
Baseline incidence rate estimates for AMIs remain the same as they were for previous benefits assessments. However, detailed information is provided below for reference.
The relationship between short-term particulate matter exposure and heart attacks was originally quantified in a case-crossover analysis by Peters et al., 2001 and supplemented with evidence found in more recent single and multi-city studies (Pope III et al., 2006, Sullivan et al., 2005). The population in the original study was identified from heart attack survivors in a medical clinic. Therefore, the applicable population to apply to the risk estimate is all individuals surviving a heart attack in a given year. Several data sources are available to estimate the number of heart attacks per year. For example, several cohort studies have reported estimates of heart attack incidence rates in the specific populations under study. However, these rates depend on the specific characteristics of the populations under study and may not be the best data to extrapolate nationally. The American Heart Association reports approximately 785,000 new heart attacks per year (Roger et al., 2012). Exclusion of heart attack deaths reported by CDC Wonder yields approximately 575,000 nonfatal cases per year.
An alternative approach to the estimation of heart attack rates is to use data from the Healthcare Cost and Utilization Project (HCUP), assuming that all heart attacks that are not instantly fatal will result in a hospitalization. Details about HCUP data are described in Section D.2 of the BenMAP-CE User Manual (U.S. EPA, 2018).  According to the 2014 HCUP data there were approximately 608,795 hospitalizations due to heart attacks (acute myocardial infarction: ICD-9 410, primary diagnosis). We estimated baseline rates based on HCUP data rather than extrapolating from cohort studies because HCUP is a national database with a larger sample size intended to provide reliable national estimates. The incidence rate calculation is also described in Section D.2 of the BenMAP-CE User Manual and the incidence rates for AMI hospitalization are presented in Table D-5. An alternative approach to the estimation of AMI rates is to use data from HCUP and assume that all AMIs that are not instantly fatal will result in a hospitalization.
It is important to note that when calculating the incidence of nonfatal AMIs, the fraction of fatal heart attacks is subtracted to ensure that there is no double-counting with mortality estimates. Specifically, we apply an adjustment factor in the risk estimate to reflect the probability of surviving a heart attack. The adjustment factor comes from Rosamond et al., 1999, which reported that approximately 6% of male and 8% of female hospitalized AMI patients die within 28 days (either in or outside of the hospital). Therefore, we applied a factor of 0.93 to the estimated number of PM-related AMIs to exclude the number of cases that result in death within the first month. Note that we did not adjust for fatal AMIs in the incidence rate estimation, due to the way that the epidemiological studies are designed. Those studies consider total admissions for AMIs, which includes individuals living at the time the studies were conducted. We use the definition of AMI that matches the definition in the epidemiological studies. Age-specific baseline incidence rates are  based on data from the Agency for Healthcare Research and Quality's HCUP NIS database (AHRQ, 2016). We identified death rates for adults hospitalized with AMI stratified by age (e.g., 1.852% for ages 18-44, 2.8188% for ages 45-64, and 7.4339% for ages 65+). These rates show a clear downward trend over time between 1994 and 2009 for the average adult and thus replace the 93% survival rate previously applied across all age groups from Rosamond et al., 1999.
Asthma Onset and Symptoms
Asthma Onset
Baseline incidence rates for new asthma onset are estimated from Winer et al., 2012. Winer et al., 2012 identify newly diagnosed asthma from the 2006-2008 Asthma Call-Back Survey (ACBS) and Behavioral Risk Factor Surveillance System (BRFSS) as individuals diagnosed by a doctor, or other health professional, within the 12 months prior to the surveys. Table 12 details the breakdown, by age, of the annual national incidence rates for asthma onset.
For the set of endpoints affecting the asthmatic population, in addition to baseline incidence rates, prevalence rates of asthma in the population are needed to define the applicable population. We derive asthma prevalence data from the National Health Interview Survey (NHIS). For functions with age ranges that do not align with the ranges reported in the NHIS data table, we develop a weighted-average prevalence rate for the age range, where the weights are the number of years that overlap with each NHIS age group. Table 16 provides the breakdown of the 2018 NHIS rates used to calculate the weighted averages. Table 17 details the resulting weighted averages by study and age group. Note that these reflect recent asthma prevalence and assume no change in prevalence rates in future years.
Table 16. Asthma Prevalence Rates
                                NHIS Age Group
                            Asthma Prevalence Rate
                                     0 - 4
                                     0.038
                                    5 - 11
                                     0.081
                                    5 - 14
                                     0.086
                                    12 - 17
                                     0.099
                                    15 - 19
                                     0.110
                                    20 - 24
                                     0.081
                                    0 - 17
                                     0.075

Table 17. Weighted Average Asthma Prevalence by Study
Endpoint
Ages
Author[1]
Pollutant
Weighted Prevalence
Asthma Onset
0  -  4
Tetreault et al., 2016
PM2.5
0.0380

5 - 17
Tetreault et al., 2016
PM2.5
0.0893

0 - 17
Tetreault et al., 2016
O3
0.0750
Asthma symptoms, albuterol use
6 - 13
Rabinovitch et al., 2006
PM2.5
0.0860
[1]Prevalence rate derived for albuterol use must be loaded into BenMAP-CE as part of a separate incidence or prevalence dataset, unlike the remainder of the rates, which are embedded within the health impact functions.

Albuterol Use
We develop incidence rates for albuterol use from the rates presented in Rabinovitch et al., 2006, the same study from which the risk estimate was developed. As described in the `Recommended Set of Health Endpoints and Health Impact Functions' section, Rabinovitch et al., 2006 analyzed the relationship between short-term PM2.5 exposure and asthma exacerbation in children ages 6 to 13 years old. The authors use an electronic inhaler to record the number of actuations (`puffs') for each 24-hour period and calculate an average albuterol use rate of 2.2 `puffs' per child per day.
As described in section <IP_ADDRESS>, in addition to the baseline incidence rates, we apply a weighted-average asthma prevalence rate of 0.086, based on the 5-14 age group, using the NHIS prevalence data to identify the applicable population.
Asthma Symptoms
We develop incidence rates for asthma symptoms using the estimates presented in Lewis et al., 2013, the same study from which the concentration-response function was developed. As described in the `Recommended Set of Health Endpoints and Health Impact Functions' section, Lewis et al., 2013 studied the effects of short-term O3 exposure on frequency of asthma symptoms in an asthmatic population of children ages 5 to 12 years old. The authors estimate the incidence of each asthma symptom using the number of person-days where children reported experiencing the symptom divided by the total number of person-days monitored for that symptom. The percent of days monitored during which children experienced each symptom are calculated as 30.1% for cough, 19.4% for wheeze, 18.5% for shortness of breath, and 12.7% for chest tightness. Therefore, the national incidence rates of asthma symptoms are 0.301 for cough, 0.194 for wheeze, 0.185 for shortness of breath, and 0.127 for chest tightness. `Prevalence rates for asthma symptoms remains the same as in previous benefits assessments (previously referred to as asthma exacerbation) (Table 16).
Allergic Rhinitis
We develop prevalence rates for hay fever/rhinitis using the estimates presented in Parker et al., 2009, the same study from which the concentration-response function was developed. As described in the `Recommended set of Health Endpoints and Health Impact Functions' section, Parker et al., 2009 investigates the associations between long-term ozone exposure and respiratory allergies in children ages 3 to 17 years old. The authors use prevalence data from the NHIS household interview survey and define allergic rhinitis as children with reported hay fever, respiratory allergy, or both within the 12 months prior to the survey. Of the eligible population (72,279), 19.2% of respondents experience allergic rhinitis symptoms within the year prior to the survey, therefore, the national prevalence rate of hay allergic rhinitis is 0.192.
Lung Cancer
We use the existent baseline incidence rate for lung cancer mortality in combination with the five-year lung cancer survival rate from NCI, 2015 to develop baseline incidence rates for non-fatal lung cancer. We first use the five-year lung cancer survival rate to calculate the total incidence of lung cancer (both fatal and non-fatal) from the baseline mortality rate using the following formula: baseline mortality rate / (1  -  five-year survival rate). We then calculate the incidence of non-fatal lung cancer as the difference between total lung cancer incidence and fatal lung cancer incidence (NCI, 2015). presents the baseline incidence of lung cancer mortality, the SEER five-year survival rate, the estimated total lung cancer incidence, and the estimated non-fatal lung cancer incidence rate by age group.
Table 18. Lung Cancer Incidence Rates
                                   Age Group
                    Annual Lung Cancer Mortality Incidence
                                      [A]
                            Five-Year Survival Rate
                                      [B]
                          Total Lung Cancer Incidence
                                    [C] =
                                [A] / (1 - [B])
                        Non-fatal Lung Cancer Incidence
                                     [D] =
                                  [C]  -  [A]
25-34
0.0000033
34.6%
0.0000050
0.00000175
35-44
0.0000282
34.6%
0.0000431
0.00001492
45-54
0.0002378
22.1%
0.0003053
0.00006746
55-64
0.0007922
20.8%
0.0010003
0.00020805
65-74
0.00019701
21.0%
0.0002494
0.00005237
75-84
0.0032952
14.9%
0.0038722
0.00057695
85+
0.0031820
14.9%
0.0037391
0.00055713

Minor Restricted Activity Days (MRAD)
The incidence estimate for this health endpoint remains the same as in previous benefits assessments. Ostro and Rothschild, 1989 (p. 243) provide an estimate of the annual incidence rate of MRADs per person of 7.8.
School Loss Days
Baseline incidence rate estimates for school loss days remain the same as they were for previous benefits assessments. However, detailed information is provided below for reference.
We have two sources of information to use when estimating the baseline incidence rates of missed school days: the National Center for Education Statistics (NCES), which provided an estimate of all-cause school loss days, and the National Health Interview Survey (NHIS) (Adams et al., 1999, NCES, 1996, Table 47), which has data on different categories of acute school loss days. Table 19 presents the estimated school loss day rates. Further detail is provided below on these rates.
Table 19. School Loss Day Rates (per student per year)
Type
Northeast
Midwest
South
West
Respiratory illness-related absences
1.3
1.7
1.1
2.2
Illness-related absences
2.4
2.6
2.6
3.7
All-cause
9.9
9.9
9.9
9.9
*Illness-related school loss day rates were based on data from the 1996 NHIS and an estimate of 180 school days per year, excluding school loss days due to injuries. All-cause school loss day rates were based on data from the NCES.
All-Cause School Loss Day Rate
Based on data from the U.S. Department of Education (1996, Table 42-1), the National Center for Education Statistics estimates that for the 1993-1994 school year, 5.5% of students are absent from school on a given day. This estimate is comparable to study-specific estimates from Chen et al., 2000 and Ransom and Pope, 1992, which ranged from 4.5% to 5.1%.
Illness-Related School Loss Day Rate
The National Health Interview Survey (NHIS) has regional estimates of school loss days due to a variety of acute conditions (Adams et al., 1999). NHIS is a nationwide sample-based survey of the health of the noninstitutionalized, civilian population, conducted by NCHS. The survey collects data on acute conditions, prevalence of chronic conditions, episodes of injury, activity limitations, and self-reported health status. However, it does not provide an estimate of all-cause school loss days.
In estimating illness-related school loss days, we started with school loss days due to acute problems (Adams et al., 1999, Table 47) and subtracted lost days due to injuries, in order to match the definition of the study used in the risk estimate to estimate illness-related school absences (Gilliland et al., 2001). We then divided by 180 school days per to estimate illness-related school absence rates per school day. Similarly, when estimating respiratory illness-related school loss days, we use data from Adams et al., 1999, Table 47. Note that we estimated 180 school days in a year to calculate respiratory illness-related school absence rates per year.
Work Loss Days
The incidence estimate for this health endpoint remains the same as in previous benefits assessments. The yearly work-loss-day incidence rate per 100 people is based on estimates from the 1996 National Health Interview Survey (Adams et al., 1999, Table 41). They reported a total annual work loss days of 352 million for individuals ages 18 to 65. The total population of individuals of this age group in 1996 (162 million) was obtained from (U.S. Census Bureau, 1998). The average annual rate of work loss days per individual is 2.17. Using a similar approach, we calculated work-loss-day rates for ages 18-24, 25-44, and 45-64, respectively.

Demographic Information
Quantified and monetized human health impacts are calculated using information regarding the demographic characteristics of the population exposed to air pollution; these data include the age, sex, race/ethnicity and location of the population. We use 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. Projections in each county are determined simultaneously with every other county in the U.S to take into account patterns of economic growth and migration.
   The sum of growth in county-level populations is constrained to equal a previously determined national population growth, based on Bureau of Census estimates (Hollmann et al., 2000). According to WP, linking county-level growth projections together and constraining to a national-level total growth avoids potential errors introduced by forecasting each county independently. County projections are developed in a four-stage process:
 National-level variables such as income, employment, and populations are forecasted.
 Employment projections are made for 179 economic areas defined by the Bureau of Economic Analysis (U.S. BEA, 2004), using an "export-base" approach, which relies on linking industrial-sector production of non-locally consumed production items, such as outputs from mining, agriculture, and manufacturing with the national economy. The export-based approach requires estimation of demand equations or calculation of historical growth rates for output and employment by sector.
 Population is projected for each economic area based on net migration rates derived from employment opportunities and following a cohort-component method based on fertility and mortality in each area.
 Employment and population projections are repeated for counties, using the economic region totals as bounds. The age, sex, and race distributions for each region or county are determined by aging the population by single year of age by sex and race for each year through 2050 based on historical rates of mortality, fertility, and migration.

Health Endpoint Valuation
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, as recommended by the U.S. EPA, 2014.
As reductions in ambient concentrations of air pollution generally lower the risk of future adverse health effects by a small amount for a large population, the most appropriate economic measure is the ex ante (before the effect has occurred) willingness-to-pay (WTP) for changes in risk. WTP values are calculated by dividing the monetary value an individual is willing to pay for a specific risk reduction  by that change in risk. Using this approach, the size of the affected population is automatically taken into account by the number of incidences predicted by epidemiological studies applied to the relevant population.  There are three primary components of the value to society of an individual's avoidance of a non-fatal illness: 1) medical costs, 2) lost productivity, and 3) impacts on quality of life (i.e., "pain and suffering"). Estimates of individual WTP are conventionally thought to reflect all three of these components and are the preferred welfare valuation measure.  However, WTP values are available for a very limited subset of health endpoints, such as mortality.
For health endpoints where WTP estimates are not available, such as hospital admissions, we instead use the cost of treating or mitigating the effect to estimate the economic value. Cost-of-illness (COI) estimates are generally considered to be a lower bound estimate of the true value of reducing the risk of a health effect because they reflect the direct expenditures related to treatment and in some cases costs such as associated productivity losses, but not the value of avoided pain and suffering (Berger et al., 1987, Harrington and Portney, 1987, U.S. EPA, 2014). Additionally, COI estimates require additional parsing of individual health endpoints. For example, a stroke may initially involve an emergency department visit and hospitalization, but will also likely include additional follow-up medical costs, such as doctor visits and medications.
To prevent double counting of health impacts, when estimating monetary valuations, health endpoints are separated into the following non-overlapping categories: mortality (section 5.1), hospital admissions, emergency department visits (section 5.2), and health endpoint onset/occurrence (section 5.3).
EPA develops valuation estimates at the most age-refined level feasible for each health endpoint assessed. While we focused on identifying valuation estimates from peer-reviewed and published literature for new health endpoints, we were also able to update several valuation estimates for endpoints evaluated in previous benefits analyses, such as stroke, cardiac arrest, and AMIs. New hospitalizations and emergency department visits health endpoint valuations reflect specific ICD-9 codes evaluated by the epidemiologic study. New onset or follow-up/management health endpoints reflect WTP or COI valuation estimates that exclude death and initial emergency department and hospitalization costs.
These COI measures represent an update to EPA's previous method to producing COI estimates in three important respects (U.S. EPA, 2018):
 Estimates are of the costs of medical treatment, rather than charges by medical providers.
 Sampling parameters are used in survey data to express statistical uncertainty in mean cost estimates.
 More recent data is being used to reflect current treatment and healthcare costs.
When multiple valuation studies are available, the strengths and limitations of each study are considered, in a manner similar to that described for epidemiologic studies (section 2.1). The criteria for evaluation of these studies are listed in Table 20. In some cases, judgment is required to identify studies for valuation estimates when a similar number of preferred attributes are satisfied by multiple studies.
Table 20. Cost of Illness Economic Study Identification Consideration Factors
Criteria[a]
Prioritization Detail (In order of most to least preferred)
Peer-Reviewed Research
Peer-reviewed and published literature only
Endpoint Definition
1. ICD codes align with the epidemiological study
2. ICD codes overlap with the epidemiological study
Population Attributes
Prefer studies that match epidemiological study's population (specifically by age)
Study Period
More recent data are preferred
Measure of Costs[b]
1. Total payments
2. Allowable charges
3. Cost-adjusted charges
4. Unadjusted charges
Study Location
1. Nationwide coverage
2. Multi-city and/or multi-state coverage
3. Local study population
Coverage of cost elements
Studies that account for more cost elements (e.g., treatment settings) and longer time horizons are preferred
Study Size
Larger study size preferred
[a] This table focuses on COI because WTP measures are not currently available for the health endpoints of interest. Had WTP estimates been available, additional criteria would be relevant. It also excludes valuation estimates of hospitalizations and emergency department visits, which are developed by EPA and described in the appendices to U.S. EPA, 2018.
[b]Onukwugha et al., 2016 provides more information on these methods.

We provide unit values for health endpoints (along with information on the distribution of the unit value) in Table 21. All values are in constant year 2015$, adjusted for growth in real income for WTP estimates out to 2024 using projections provided by Standard and Poor's, which is discussed in further detail below.  Additional detail regarding the development of each health endpoint valuation is also provided below.
Table 21. Unit Values for Economic Valuation of Health Endpoints (2015$)[1]
Health Endpoint
Type
Central Estimate of Value Per Statistical Incidence (2015$)
Source
Mortality
Value of Statistical Life (VSL)
3%: $7,800,000
7%: $7,100,000
Weibull distribution fitted to 26 published VSL estimates (5 contingent valuation and 21 labor market studies). Underlying studies, distribution parameters, and other information are available in Appendix B of the EPA's Guidelines for Preparing Economic Analyses (U.S. EPA, 2014). Adjusted for income growth appropriate to the year of analysis.
Hospitalizations
Medical costs and opportunity cost of time
Varies by ICD codes, ranging between $7,700 and $16,000
HCUP data (details available in section 3.2)
Emergency Department Visits
Medical costs
Varies by ICD codes, ranging between $600 and $1,200
HCUP data (details available in section 3.3)
Nonfatal Myocardial Infarction (AMI)[a]
3-year medical costs[b]
3%: $49,000
7%: $48,000
O'Sullivan et al., 2011
Asthma Symptom- Albuterol Use[c]
Medical costs
$0.35 per albuterol inhaler puff
Average prescription costs derived from Epocrates.com and Goodrx.com accessed March 19, 2020
Asthma Symptom- Chest Tightness, Cough, Shortness of Breath, or Wheeze
WTP for 1 symptom day
$219
Dickie and Messman, 2004
Asthma Onset[c]
Lifetime medical costs and lost productivity
3%: $17,000
7%: $10,000
Belova et al., 2020
Allergic Rhinitis[c]
1-year medical costs
$600
Soni, 2008
Cardiac Arrest[c]
3-year medical costs
3%: $36,000
7%: $35,000
O'Sullivan et al., 2011
Lung Cancer[c]
5-year medical costs
3%: $34,000
7%: $33,000
Kaye et al., 2018
Stroke[c]
1-year medical costs[a]
$34,000
Mu et al., 2017
Work Loss Days
Median daily wage
U.S. median: $150
IEc, 1993
School Loss Days
Lost productivity of parent
$106
US Bureau of Labor Statistics, 2015
Minor Restricted-Activity Days
Median WTP
$70
IEc, 1993
3%- three percent real discount rate; 7%- seven percent real discount rate (OMB, 2003); All estimates rounded to two significant figures.
[a]Valuation estimate has been updated to reflect recent literature.
[b]Excludes initial emergency department and hospitalization costs, which are captured separately.
[c]Valuation estimate is for a new health endpoint.
Mortality
Following the advice of the SAB's Environmental Economics Advisory Committee (SAB-EEAC), the EPA currently uses the value of statistical life (VSL) approach in calculating the core estimate of mortality benefits, because we believe this calculation provides the most reasonable single estimate of an individual's willingness to trade money for reductions in mortality risk (Stavins, 2000). The VSL approach is a summary measure for the value of small changes in mortality risk experienced by a large number of people.
Value of a Statistical Life (VSL)
The current undiscounted VSL used by EPA is $8.7 million (2015$), or $7.8 million (2015$) using a 3% discount rate and $7.1 million (2015$) using a 7% discount rate (U.S. EPA, 2014). This estimate is the mean of a distribution fitted to 26 VSL estimates that appear in the economics literature and that have been identified in the Section 812 Reports to Congress as "applicable to policy analysis" (U.S. EPA, 2011a). It is a value EPA uses in RIAs as well as in the Section 812 Retrospective and Prospective Analyses of the Clean Air Act (U.S. EPA, 2011a).
The VSL approach mirrors that of Viscusi, 1992 and uses the same criteria as in his review of value of statistical life studies. The $8.7 million estimate is consistent with the conclusions of Viscusi, 1992 (updated to 2015$) that "most of the reasonable estimates of the value of life are clustered in the $5.2 to $12.3 million range." Five of the 26 studies are contingent valuation studies, which directly solicit WTP information from subjects; the rest are wage-risk studies, which base WTP estimates on estimates of the additional compensation demanded in the labor market for riskier jobs. Because this VSL-based unit value does not distinguish among people based on the age at their death or the quality of their lives, it can be applied to all deaths. Table 22 presents the central unit value from the 26 value of statistical life studies and their underlying distribution.
Table 22. Central Unit Value for VSL based on 26-value-of-life studies
                              Basis for Estimate
                              Age Range at Death
                           Unit Value (VSL) (2015$)
                          Distribution of Unit Value
                          Parameters of Distribution

                                      Min
                                      Max

                                      P1
                                      P2
VSL, based on 26 value of statistical life studies
0
99
8,705,114
Weibull
9,648,168
1.509588
Hospitalizations and Emergency Department Visits
To value hospitalizations, emergency room visits we develop primary COI estimates using data from the Healthcare Cost and Utilization Project (HCUP). The 2016 National Inpatient Sample (NIS) and Nationwide Emergency Department Sample (NEDS) provide recent, nationally representative information on medical treatment in hospitals and emergency departments. In the case of hospital admissions, valuation estimates are calculated as a combination of medical costs and the opportunity cost of time spent at the hospital, measured by lost wages during the hospital stay. In the case of emergency department visits, valuation estimates include only the medical costs. These cost components are summarized in Table 23.
Table 23. Hospitalization and Emergency Department Cost Elements by Endpoint
                                   Endpoint
                        Medical Costs (Emergency Room)
                           Medical Costs (Hospital)
                               Lost Productivity
Hospitalizations

                                      
                                      
Emergency department visits
                                      

Emergency hospitalizations
                                      
                                      
                                      

The NIS and NEDS datasets include discharge-level observations. That is, each data point represents one individual being discharged from the hospital (NIS) or emergency department (NEDS). Because individuals are treated in these settings for a variety of reasons, we use medical billing codes to extract observations related to each health endpoint. The epidemiological studies described above provide ICD-9 codes for each illness; however, recent HCUP datasets (including NIS and NEDS) use ICD-10 codes. Thus, we first crosswalk the relevant ICD-9 codes to associated ICD-10 codes using a mapping provided by the U.S. Centers for Disease Control. We then identify all discharges in the HCUP datasets with ICD-10 codes that match to a study's ICD-9 code(s). Because HCUP datasets often include multiple ICD-10 codes for each discharge, we focus on the principal diagnosis (i.e., the first-listed ICD-10 code). Other key variables used from HCUP include total charges, cost-to-charge ratio (NIS), and length of stay (NIS).
In the NIS dataset, we convert total charges (i.e., the amount billed to patients, employers, or insurance providers) into estimates of total costs (i.e., the final reimbursements for medical treatment). Unadjusted charges are not suitable for use in regulatory analysis because posted prices generally do not reflect actual medical costs due, in part, to negotiation between medical providers and payers (e.g., insurance companies). We assume that adjusted charges reflect the actual revenue the hospital receives and thus the actual cost of providing care. This conversion is completed using hospital-specific cost-to-charge (CCR) ratios provided with NIS. Because CCRs are not available for NEDS, we apply average CCRs for each endpoint in NIS to the same set of ICD-10 codes in NEDS.
For each health endpoint, mean estimates are calculated using estimation commands for survey data to account for the sampling design and sample discharge weights of the HCUP data. This results in estimates of mean costs and a 95% confidence interval, which represents uncertainty in our valuation estimates of medical costs. The resulting estimates are presented in Table 24. Confidence intervals for length of stay cannot be accounted for in the valuation methodology because the EPA's current tool is only capable of reflecting uncertainty in one parameter.
Table 24. Medical Costs and Hospital Stay Data from the HCUP Database
                                   Endpoint
                                     Ages
                          Epidemiologic Study Author
                                  ICD-9 Codes
                      Length of Stay in Days (95% CI)[1]
                       Medical Costs (95% CI) (2015$)[2]
HA, Respiratory-1
0 - 99
Jones et al., 2015
491, 492, 493, 496
3.86 (3.82, 3.90)
$7,676 ($7,574, $7,778)
HA, Respiratory-2
65 - 99
Bell et al., 2015
490-492, 464-466, 480-487, 493
4.66 (4.62, 4.69)
$9,004 ($8,894, $9,113)
HA, All Respiratory
0 - 18
Ostro et al., 2009
460-519
3.50 (3.37, 3.62)
$9,075 ($8,282, $9,868)
HA, All Cardiac Outcomes
0 - 99
Talbott et al., 2014
390-459
5.05 (5.00, 5.11)
$16,045 ($15,721, $16,368)
HA, Alzheimer's Disease
65 - 99
Kioumourtzoglou et al., 2016
331.0
7.95 (7.70, 8.21)
$10,696 ($10,400, $10,992)
HA, Cardio-, Cerebro- and Peripheral Vascular Disease
65 - 99
Bell et al., 2015
426-427,428,430-438, 410-414, 429, 440-448
4.82 (4.78, 4.87)
$14,665 ($14,434, $14,896)
ED, Respiratory
0 - 99
Krall et al., 2013
480-486, 491, 492, 496, 460-465, 466, 477, 493, 786.07
-
$875 ($826, $923)
ED, All Cardiac Outcomes
0 - 99
Ostro et al., 2016
390-459
-
$1,161 ($1,112, $1,210)
ED, Respiratory
0 - 99
Barry et al., 2019
480-486, 491, 492, 496, 460-465, 466, 477, 493, 786.07
-
$875 ($826, $923)
[1]Confidence intervals (CIs) associated with the length of hospital stay are presented for information only and are not used in analyses due to technical limitations. Importantly, the length of stay is a factor in the overall COI estimate.
[2]Medical costs reflect the expenditures per treatment episode/event (e.g., per hospitalization) and confidence intervals (CIs) reflect the 95% CI around the population mean value and not that 95% of patients observe costs within these bounds. Does not include productivity losses.
Health Endpoint Onset/Occurrence
Monetary valuation estimates for health endpoint onset or occurrences are described below, listed in alphabetical order. Onset indicates the development of a health endpoint (e.g., asthma diagnosis), whereas occurrence refers to an instance of that health endpoint (e.g., asthma attack).
Acute Myocardial Infarctions (AMIs)
Economic values for acute myocardial infarctions (AMIs, also known as heart attacks) have been updated to be derived from O'Sullivan et al., 2011, which estimate three-year medical costs associated with cardiovascular disease events among adults ages 35 and older in the U.S. The authors rely on administrative claims data from a large U.S. health plan and develop econometric models to estimate medical costs for 15 different cardiovascular events, including AMIs. The dataset includes over 20 million commercial and Medical Advantage members between 2002 and 2006. AMIs are identified using the ICD-9 code 410. The authors use propensity score matching to develop a control group with which to compare costs versus individuals that suffered AMIs. We exclude medical costs within the month of the event in an attempt avoid double counting hospitalization costs, which are captured separately in the hospitalization valuation endpoints. Over three years, the total medical costs, excluding hospitalization, are $49,758 (undiscounted, inflated to 2015$), or $48,796 using a 3% discount rate and $47,623 for a 7% discount rate (Table 25). Although this study analyzed costs associated with individuals ages 35 and older, we apply the total medical costs to all ages from zero to 99 since only a small portion (<10%) of annual AMI incidence occurs in the age range below 35.
Table 25. Medical Costs for AMIs (2015$)
                                     Costs
                               Cumulative Costs
                                 Annual Costs

                                 Undiscounted
                              3% Discount Rate[1]
                              7% Discount Rate[1]
Month of Event*
$43,523
$43,523
$43,523
$43,523
Year 1
$70,629
$27,106
$27,106
$27,106
Year 2
$82,591
$11,962
$11,614
$11,180
Year 3
$93,281
$10,690
$10,076
$9,337
Years 1-3
$93,281
$49,758
$48,796
$47,623
[1]Uses end-of-year discounting.
We supplement AMI medical costs with estimates of lost earnings using age-specific estimates from Cropper and Krupnick, 1990. Using a 3% discount rate, we estimated the following present discounted values in lost earnings over 5 years due to a heart attack: 0.219 times annual earnings for someone between the ages of 25 and 44, 3.534 times annual earnings for someone between the ages of 45 and 54, and 1.245 times annual earnings for someone between the ages of 55 and 65. The corresponding age-specific estimates of lost earnings using a 7% discount rate are 0.203, 3.287, and 1.158 times annual earnings, respectively. Cropper and Krupnick, 1990 does not provide lost earnings estimates for populations under 25 or over 65. As such we do not include lost earnings in the cost estimates for these age groups. These costs, along with the total valuation estimates for AMIs, are presented in Table 26.
Table 26. Total Valuation Estimates for AMIs (2015$)
                                 Discount Rate
                                   Age Range
                                 Medical Cost
                           Lost Earnings Multiplier
                                  Total Cost

                                      Min
                                      Max

                                      3%
                                                                              0
                                                                             24
                                    $48,796
                                       0
                                    $48,796

                                                                             25
                                                                             44
                                    $48,796
                                     0.219
                           $48,796 + 0.219*earnings

                                                                             45
                                                                             54
                                    $48,796
                                     3.534
                            $48,796+ 3.534*earnings

                                                                             55
                                                                             65
                                    $48,796
                                     1.245
                            $48,796+ 1.245*earnings

                                                                             66
                                                                             99
                                    $48,796
                                       0
                                    $48,796
                                      7%
                                                                              0
                                                                             24
                                    $47,623
                                       0
                                    $47,623

                                                                             25
                                                                             44
                                    $47,623
                                     0.203
                            $47,623+ 0.203*earnings

                                                                             45
                                                                             54
                                    $47,623
                                     3.287
                            $47,623+ 3.287*earnings

                                                                             55
                                                                             65
                                    $47,623
                                     1.158
                            $47,623+ 1.158*earnings

                                                                             66
                                                                             99
                                    $47,623