Dickie and Messman, 2004 ADDIN EN.CITE <EndNote><Author>Dickie</Author><Year>2004</Year><RecNum>1251</RecNum><DisplayText>Dickie and Messman, 2004</DisplayText><record><rec-number>1251</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608158913">1251</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Dickie, Mark</author><author>Messman, Victoria L.</author></authors></contributors><titles><title>Parental altruism and the value of avoiding acute illness: are kids worth more than parents?</title><secondary-title>Journal of Environmental Economics and Management</secondary-title></titles><periodical><full-title>Journal of Environmental Economics and Management</full-title>J. Environ. Econ. Manage.J Environ Econ Manage</periodical><pages>1146-1174</pages><volume>48</volume><number>3</number><keywords><keyword>Family economics</keyword><keyword>Children's health</keyword><keyword>Health valuation</keyword><keyword>Altruism</keyword><keyword>Benefit - cost analysis</keyword><keyword>Environmental justice</keyword><keyword>Environmental policy</keyword><keyword>Morbidity valuation</keyword><keyword>Willingness to pay</keyword></keywords><dates><year>2004</year><pub-dates><date>2004/11/01/</date></pub-dates></dates><isbn>0095-0696</isbn><urls><related-urls><url>http://www.sciencedirect.com/science/article/pii/S0095069604000154</url></related-urls></urls><electronic-resource-num>https://doi.org/10.1016/j.jeem.2003.12.005</electronic-resource-num></record></EndNote>
Asthma Onset[c]
Lifetime medical costs and lost productivity
3%: $17,000
7%: $10,000
Belova et al., 2020 ADDIN EN.CITE <EndNote><Author>Belova</Author><Year>2020</Year><RecNum>1248</RecNum><DisplayText>Belova et al., 2020</DisplayText><record><rec-number>1248</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608158195">1248</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Belova, A.</author><author>Fann, N.</author><author>Haskell, J.</author><author>Hubbell, B.</author><author>Narayan, T.</author></authors></contributors><auth-address>Abt Associates, Inc., Rockville, Maryland; and.
U.S. Environmental Protection Agency, Research Triangle Park, North Carolina.</auth-address><titles><title>Estimating Lifetime Cost of Illness. An Application to Asthma</title><secondary-title>Ann Am Thorac Soc</secondary-title></titles><periodical><full-title>Ann Am Thorac Soc</full-title></periodical><pages>1558-1569</pages><volume>17</volume><number>12</number><edition>2020/09/16</edition><keywords><keyword>asthma</keyword><keyword>foregone income</keyword><keyword>health care</keyword><keyword>labor productivity</keyword><keyword>occupational choice</keyword></keywords><dates><year>2020</year><pub-dates><date>Dec</date></pub-dates></dates><isbn>2325-6621 (Electronic)
2325-6621 (Linking)</isbn><accession-num>32931705</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/32931705</url></related-urls></urls><electronic-resource-num>10.1513/AnnalsATS.201910-729OC</electronic-resource-num></record></EndNote>
Allergic Rhinitis[c]
1-year medical costs
$600
Soni, 2008 ADDIN EN.CITE <EndNote><Author>Soni</Author><Year>2008</Year><RecNum>1293</RecNum><DisplayText>Soni, 2008</DisplayText><record><rec-number>1293</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608216073">1293</key></foreign-keys><ref-type name="Book">6</ref-type><contributors><authors><author>Soni, Anita</author></authors></contributors><titles><title>Allergic Rhinitis: Trends in use and expenditures, 2000 and 2005</title></titles><dates><year>2008</year></dates><publisher>Medical Expenditure Panel Survey, Agency for Healthcare Research and Quality</publisher><urls></urls></record></EndNote>
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 ADDIN EN.CITE <EndNote><Author>Kaye</Author><Year>2018</Year><RecNum>1252</RecNum><DisplayText>Kaye et al., 2018</DisplayText><record><rec-number>1252</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608159123">1252</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Kaye, D. R.</author><author>Min, H. S.</author><author>Herrel, L. A.</author><author>Dupree, J. M.</author><author>Ellimoottil, C.</author><author>Miller, D. C.</author></authors></contributors><auth-address>Univ Michigan, Dept Urol, Dow Div Hlth Serv Res, Ann Arbor, MI 48109 USA</auth-address><titles><title>Costs of Cancer Care Across the Disease Continuum</title><secondary-title>Oncologist</secondary-title><alt-title>Oncologist</alt-title></titles><periodical><full-title>Oncologist</full-title>OncologistOncologist</periodical><alt-periodical><full-title>Oncologist</full-title>OncologistOncologist</alt-periodical><pages>798-805</pages><volume>23</volume><number>7</number><keywords><keyword>cancer</keyword><keyword>costs</keyword><keyword>disease continuum</keyword><keyword>united-states</keyword><keyword>lung-cancer</keyword><keyword>breast-cancer</keyword><keyword>disparities</keyword><keyword>chemotherapy</keyword><keyword>life</keyword><keyword>end</keyword></keywords><dates><year>2018</year><pub-dates><date>Jul</date></pub-dates></dates><isbn>1083-7159</isbn><accession-num>WOS:000439877700014</accession-num><urls><related-urls><url><Go to ISI>://WOS:000439877700014</url></related-urls></urls><electronic-resource-num>10.1634/theoncologist.2017-0481</electronic-resource-num><language>English</language></record></EndNote>
Stroke[c]
1-year medical costs[a]
$34,000
Mu et al., 2017 ADDIN EN.CITE <EndNote><Author>Mu</Author><Year>2017</Year><RecNum>1294</RecNum><DisplayText>Mu et al., 2017</DisplayText><record><rec-number>1294</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608217657">1294</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Mu, F</author><author>Hurley, D</author><author>Betts, KA</author><author>Messali, AJ</author><author>Paschoalin, M</author><author>Kelley, C</author><author>Wu, EQ</author></authors></contributors><titles><title>Real-world costs of ischemic stroke by discharge status</title><secondary-title>Current medical research and opinion</secondary-title></titles><periodical><full-title>Current medical research and opinion</full-title></periodical><pages>371-378</pages><volume>33</volume><number>2</number><dates><year>2017</year></dates><isbn>0300-7995</isbn><urls></urls></record></EndNote>
Work Loss Days
Median daily wage
U.S. median: $150
IEc, 1993 ADDIN EN.CITE <EndNote><Author>IEc</Author><Year>1993</Year><RecNum>1385</RecNum><DisplayText>IEc, 1993</DisplayText><record><rec-number>1385</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1609785896">1385</key></foreign-keys><ref-type name="Personal Communication">26</ref-type><contributors><authors><author>IEc,</author></authors></contributors><titles><title>Memorandum to Jim DeMocker, U.S. Environmental Protection Agency, Office of Air and Radiation, Office of Policy Analysis and Review. September 30. </title></titles><dates><year>1993</year></dates><publisher>U.S. EPA</publisher><urls></urls></record></EndNote>
School Loss Days
Lost productivity of parent
3%: $1000
7%: $610
Liu et al., 2021, US Bureau of Labor Statistics, 2021b, US Bureau of Labor Statistics, 2021a, Chetty et al., 2014, U.S. Census Bureau, 2010, U.S. EPA, 2020a, U.S. Census Bureau, 2021 ADDIN EN.CITE <EndNote><Author>US Bureau of Labor Statistics</Author><Year>2015</Year><RecNum>1424</RecNum><DisplayText>US Bureau of Labor Statistics, 2015</DisplayText><record><rec-number>1424</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1613688201">1424</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>US Bureau of Labor Statistics,</author></authors></contributors><titles><title>Current Population Survey</title></titles><dates><year>2015</year></dates><publisher>2015 Annual Social and Economic Supplement</publisher><urls><related-urls><url>Available at: https://www.bls.gov/opub/reports/womens-databook/2016/home.htm</url></related-urls></urls></record></EndNote>
Minor Restricted-Activity Days
Median WTP
$70
IEc, 1993 ADDIN EN.CITE <EndNote><Author>IEc</Author><Year>1993</Year><RecNum>1385</RecNum><DisplayText>IEc, 1993</DisplayText><record><rec-number>1385</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1609785896">1385</key></foreign-keys><ref-type name="Personal Communication">26</ref-type><contributors><authors><author>IEc,</author></authors></contributors><titles><title>Memorandum to Jim DeMocker, U.S. Environmental Protection Agency, Office of Air and Radiation, Office of Policy Analysis and Review. September 30. </title></titles><dates><year>1993</year></dates><publisher>U.S. EPA</publisher><urls></urls></record></EndNote>
3%- three percent real discount rate; 7%- seven percent real discount rate (OMB, 2003 ADDIN EN.CITE <EndNote><Author>OMB</Author><Year>2003</Year><RecNum>1398</RecNum><DisplayText>OMB, 2003</DisplayText><record><rec-number>1398</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1611076276">1398</key></foreign-keys><ref-type name="Government Document">46</ref-type><contributors><authors><author>OMB, </author></authors></contributors><titles><title>Circular A-4</title></titles><pages>58366</pages><edition>October 2003</edition><dates><year>2003</year></dates><pub-location>Washinton, DC</pub-location><publisher>Office of Management and Budget</publisher><isbn>68 FR 58366</isbn><urls><related-urls><url>Available at: https://www.federalregister.gov/documents/2003/10/09/03-25606/circular-a-4-regulatory-analysis</url></related-urls></urls><custom1>Office of Management and Budget</custom1><electronic-resource-num>03-25606</electronic-resource-num></record></EndNote>); 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 ADDIN EN.CITE <EndNote><Author>Stavins</Author><Year>2000</Year><RecNum>1150</RecNum><DisplayText>Stavins, 2000</DisplayText><record><rec-number>1150</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732563">1150</key></foreign-keys><ref-type name="Personal Communication">26</ref-type><contributors><authors><author>Stavins, Robert</author></authors></contributors><titles><title>Letter from Robert Stevens, Chair, Science Advisory Board, and the Chair, Environmental Economics Advisory Committee, to EPA Administrator Carol M. Browner, Re: EPA's White Paper Valuing the Benefits of Fatal Cancer Risk Reduction</title></titles><num-vols>EPA-SAB-EEAC-00-013</num-vols><dates><year>2000</year></dates><pub-location>U.S. EPA HQ, Washington DC</pub-location><publisher>Office of the Administrator, Science Advisory Board</publisher><urls></urls></record></EndNote>). 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). ADDIN EN.CITE <EndNote><Author>U.S. EPA</Author><Year>2014</Year><RecNum>1300</RecNum><DisplayText>U.S. EPA, 2014</DisplayText><record><rec-number>1300</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608221942">1300</key></foreign-keys><ref-type name="Generic">13</ref-type><contributors><authors><author>U.S. EPA, </author></authors></contributors><titles><title>Guidelines for Preparing Economic Analyses</title></titles><dates><year>2014</year></dates><publisher>National Center for Environmental Economics. US Environmental Protection Agency Washington, DC</publisher><urls><related-urls><url>https://www.epa.gov/environmental-economics/guidelines-preparing-economic-analyses</url></related-urls></urls></record></EndNote> 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). ADDIN EN.CITE <EndNote><Author>U.S. EPA</Author><Year>2011</Year><RecNum>1121</RecNum><DisplayText>U.S. EPA, 2011a</DisplayText><record><rec-number>1121</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732554">1121</key></foreign-keys><ref-type name="Government Document">46</ref-type><contributors><authors><author>U.S. EPA,</author></authors><secondary-authors><author>U.S. EPA</author></secondary-authors></contributors><titles><title>The Benefits and Costs of the Clean Air Act 1990 to 2020: Final Report</title></titles><dates><year>2011</year></dates><pub-location>Washington, DC</pub-location><publisher>Office of Air and Radiation</publisher><urls><related-urls><url>Available at: https://www.epa.gov/sites/production/files/2015-07/documents/fullreport_rev_a.pdf</url></related-urls></urls></record></EndNote> 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 ADDIN EN.CITE <EndNote><Author>U.S. EPA</Author><Year>2011</Year><RecNum>1121</RecNum><DisplayText>U.S. EPA, 2011a</DisplayText><record><rec-number>1121</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732554">1121</key></foreign-keys><ref-type name="Government Document">46</ref-type><contributors><authors><author>U.S. EPA,</author></authors><secondary-authors><author>U.S. EPA</author></secondary-authors></contributors><titles><title>The Benefits and Costs of the Clean Air Act 1990 to 2020: Final Report</title></titles><dates><year>2011</year></dates><pub-location>Washington, DC</pub-location><publisher>Office of Air and Radiation</publisher><urls><related-urls><url>Available at: https://www.epa.gov/sites/production/files/2015-07/documents/fullreport_rev_a.pdf</url></related-urls></urls></record></EndNote>).
The VSL approach mirrors that of Viscusi, 1992 ADDIN EN.CITE <EndNote><Author>Viscusi</Author><Year>1992</Year><RecNum>1380</RecNum><DisplayText>Viscusi, 1992</DisplayText><record><rec-number>1380</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1609353857">1380</key></foreign-keys><ref-type name="Book">6</ref-type><contributors><authors><author>Viscusi, W. Kip</author></authors></contributors><titles><title>Fatal tradeoffs : public and private responsibilities for risk</title></titles><pages>xi, 306 p.</pages><keywords><keyword>Risk.</keyword><keyword>Decision making.</keyword><keyword>Life Economic aspects.</keyword><keyword>Safety regulations Economic aspects.</keyword></keywords><dates><year>1992</year></dates><pub-location>New York</pub-location><publisher>Oxford University Press</publisher><isbn>0195072782 (acid-free paper)</isbn><accession-num>2488370</accession-num><call-num>HB615 .V57 1992</call-num><urls><related-urls><url>Publisher description http://www.loc.gov/catdir/enhancements/fy0635/91036121-d.html</url></related-urls></urls></record></EndNote> and uses the same criteria as in his review of value of statistical life studies. The $8.7 million estimate is consistent with the conclusions of Viscusi, 1992 ADDIN EN.CITE <EndNote><Author>Viscusi</Author><Year>1992</Year><RecNum>1380</RecNum><DisplayText>Viscusi, 1992</DisplayText><record><rec-number>1380</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1609353857">1380</key></foreign-keys><ref-type name="Book">6</ref-type><contributors><authors><author>Viscusi, W. Kip</author></authors></contributors><titles><title>Fatal tradeoffs : public and private responsibilities for risk</title></titles><pages>xi, 306 p.</pages><keywords><keyword>Risk.</keyword><keyword>Decision making.</keyword><keyword>Life Economic aspects.</keyword><keyword>Safety regulations Economic aspects.</keyword></keywords><dates><year>1992</year></dates><pub-location>New York</pub-location><publisher>Oxford University Press</publisher><isbn>0195072782 (acid-free paper)</isbn><accession-num>2488370</accession-num><call-num>HB615 .V57 1992</call-num><urls><related-urls><url>Publisher description http://www.loc.gov/catdir/enhancements/fy0635/91036121-d.html</url></related-urls></urls></record></EndNote> (updated to 2015$) 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
                                   Endpoint
                                    Studies

                                   ICD Codes
Age Range
Mean Hospital Charge (2015$)
                          Mean Length of Stay (days)
                 Total Cost of Illness (Unit Value in 2015$)*

                                     Start
                                      End

HA, All Cardiac Outcomes
                             Talbott et al., 2014
                                    390-459
                                       0
                                      99
                           Jones et al., 2015$16,045
                                     5.05
                                    $16,918

 ADDIN EN.CITE  ADDIN EN.CITE.DATA

HA, All Respiratory

                              Ostro et al., 2009
                                    460-519
                                       0
                                      18
                                    $9,075
                                     3.49
                                    $9,678

 ADDIN EN.CITE <EndNote><Author>Talbott</Author><Year>2014</Year><RecNum>1190</RecNum><DisplayText>Talbott et al., 2014</DisplayText><record><rec-number>1190</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1604002257">1190</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Talbott, E. O.</author><author>Rager, J. R.</author><author>Benson, S.</author><author>Brink, L. A.</author><author>Bilonick, R. A.</author><author>Wu, C.</author></authors></contributors><auth-address>University of Pittsburgh, Graduate School of Public Health, Department of Epidemiology, Pittsburgh, PA 15261, United States.</auth-address><titles><title>A case-crossover analysis of the impact of PM(2.5) on cardiovascular disease hospitalizations for selected CDC tracking states</title><secondary-title>Environ Res</secondary-title></titles><periodical><full-title>Environmental Research</full-title>Environ. Res.Environ Res</periodical><pages>455-65</pages><volume>134</volume><edition>2014/10/04</edition><keywords><keyword>Cardiovascular Diseases/*chemically induced</keyword><keyword>Centers for Disease Control and Prevention, U.S.</keyword><keyword>Cross-Over Studies</keyword><keyword>*Hospitalization</keyword><keyword>Humans</keyword><keyword>Particulate Matter/*toxicity</keyword><keyword>United States</keyword><keyword>Air pollution</keyword><keyword>Cardiovascular disease</keyword><keyword>Case-crossover design</keyword><keyword>Environmental public health tracking</keyword><keyword>Pm(2.5)</keyword></keywords><dates><year>2014</year><pub-dates><date>Oct</date></pub-dates></dates><isbn>1096-0953 (Electronic)
0013-9351 (Linking)</isbn><accession-num>25277761</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/25277761</url></related-urls></urls><electronic-resource-num>10.1016/j.envres.2014.06.018</electronic-resource-num></record></EndNote>

HA, Alzheimer's Disease
                         Kioumourtzoglou et al., 2016
 ADDIN EN.CITE <EndNote><Author>Kioumourtzoglou</Author><Year>2016</Year><RecNum>1104</RecNum><DisplayText>Kioumourtzoglou et al., 2016</DisplayText><record><rec-number>1104</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732551">1104</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Kioumourtzoglou, M. A.</author><author>Schwartz, J.</author><author>James, P.</author><author>Dominici, F.</author><author>Zanobetti, A.</author></authors></contributors><titles><title></title><secondary-title>Epidemiology</secondary-title><alt-title>Epidemiology</alt-title></titles><periodical><full-title>Epidemiology</full-title>EpidemiologyEpidemiology</periodical><alt-periodical><full-title>Epidemiology</full-title>EpidemiologyEpidemiology</alt-periodical><pages>221-227</pages><volume>27</volume><number>2</number><dates><year>2016</year></dates><isbn>ISSN 1044-3983
EISSN 1531-5487</isbn><accession-num>26600257</accession-num>3079253<urls><related-urls><url>http://dx.doi.org/10.1097/EDE.0000000000000422</url></related-urls></urls><electronic-resource-num>10.1097/EDE.0000000000000422</electronic-resource-num><language>English</language></record></EndNote>
                                    331.0**
                                      65
                                      99
                                    $10,696
                                     7.95
                                    $12,070
HA, Cardio-, Cerebro- and Peripheral Vascular Disease
                               Bell et al., 2015
 ADDIN EN.CITE  ADDIN EN.CITE.DATA
                410- 414, 429, 426- 427, 428, 430-438, 440-449
                                      65
                                      99
                                    $14,665
                                     4.82
                                    $15,498
HA, Respiratory-1
                              Jones et al., 2015
 ADDIN EN.CITE <EndNote><Author>Krall</Author><Year>2013</Year><RecNum>1197</RecNum><DisplayText>Krall et al., 2013</DisplayText><record><rec-number>1197</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1604066229">1197</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Krall, J. R.</author><author>Anderson, G. B.</author><author>Dominici, F.</author><author>Bell, M. L.</author><author>Peng, R. D.</author></authors></contributors><auth-address>Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.</auth-address><titles><title>Short-term exposure to particulate matter constituents and mortality in a national study of U.S. urban communities</title><secondary-title>Environ Health Perspect</secondary-title></titles><periodical><full-title>Environmental Health Perspectives</full-title>Environ. Health Perspect.Environ Health Perspect</periodical><pages>1148-53</pages><volume>121</volume><number>10</number><edition>2013/08/06</edition><keywords><keyword>Air Pollutants/*analysis/chemistry</keyword><keyword>*Environmental Monitoring</keyword><keyword>Humans</keyword><keyword>*Mortality</keyword><keyword>Particulate Matter/*analysis/chemistry</keyword><keyword>Regression Analysis</keyword><keyword>Seasons</keyword><keyword>United States</keyword><keyword>Urban Population</keyword></keywords><dates><year>2013</year><pub-dates><date>Oct</date></pub-dates></dates><isbn>1552-9924 (Electronic)
0091-6765 (Linking)</isbn><accession-num>23912641</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/23912641</url></related-urls></urls><custom2>PMC3801200</custom2><electronic-resource-num>10.1289/ehp.1206185</electronic-resource-num></record></EndNote>
                              491, 492, 493, 496
                                       0
                                      99
                                    $7,676
                                     3.86
                                    $8,343

 ADDIN EN.CITE  ADDIN EN.CITE.DATA

HA, Respiratory-2
                               Bell et al., 2015
 ADDIN EN.CITE  ADDIN EN.CITE.DATA
                        464-466, 480-487, 490-492, 493
                                      65
                                      99
                                    $9,003
                                     4.66
                                    $9,808
HA, Ischemic Stroke (ICD9 433.X1, 434.X1, 436)
                           Danesh Yazdi et al., 2021
                             433.X1, 434.X1, 436**
                                      65
                                      99
                                    $12,212
                                     4.29
                                    $12,954
HA, Atrial Fibrillation and Flutter (ICD9 427.3)
                           Danesh Yazdi et al., 2021
                                    427.3**
                                      65
                                      99
                                    $10,656
                                     3.66
                                    $11,288
HA, Parkinson's Disease
                         Kioumourtzoglou et al., 2016
                                      332
                                      18
                                      99
                                    $12,190
                                     3.83
                                    $12,852
* The opportunity cost of a day spent in the hospital was estimated, for the above exhibit, at the median daily wage of all workers, regardless of age. The median daily wage was calculated by dividing the median weekly wage ($864 in 2015$) by 5. The median weekly wages for 2015 were obtained from the U.S. Census Bureau's 2015 American Community Survey, "Selected Economic Characteristics: 2015 American Community Survey 1-Year Estimates."
**Although the health impact function and incidence data were developed using the 331.0 ICD-9 code for Alzheimer's Disease, the COI was derived using the three-digit ICD-9 code of 331. Therefore, the COI is representative of all health endpoints captured by the 331 ICD-9 code.

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 ADDIN EN.CITE <EndNote><Author>Cropper</Author><Year>1990</Year><RecNum>1396</RecNum><DisplayText>Cropper and Krupnick, 1990</DisplayText><record><rec-number>1396</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1610649447">1396</key></foreign-keys><ref-type name="Book">6</ref-type><contributors><authors><author>Cropper, Maureen L</author><author>Krupnick, Alan J</author></authors></contributors><titles><title>Social costs of chronic heart and lung disease</title></titles><dates><year>1990</year></dates><publisher>Quality of the Environment Division, Resources for the Future</publisher><urls></urls></record></EndNote>. 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 ADDIN EN.CITE <EndNote><Author>Cropper</Author><Year>1990</Year><RecNum>1396</RecNum><DisplayText>Cropper and Krupnick, 1990</DisplayText><record><rec-number>1396</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1610649447">1396</key></foreign-keys><ref-type name="Book">6</ref-type><contributors><authors><author>Cropper, Maureen L</author><author>Krupnick, Alan J</author></authors></contributors><titles><title>Social costs of chronic heart and lung disease</title></titles><dates><year>1990</year></dates><publisher>Quality of the Environment Division, Resources for the Future</publisher><urls></urls></record></EndNote> 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
                                       0
                                    $47,623

Allergic Rhinitis (Hay Fever)
Two potential valuation sources for allergic rhinitis were reviewed: Soni, 2008 ADDIN EN.CITE <EndNote><Author>Soni</Author><Year>2008</Year><RecNum>1293</RecNum><DisplayText>Soni, 2008</DisplayText><record><rec-number>1293</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608216073">1293</key></foreign-keys><ref-type name="Book">6</ref-type><contributors><authors><author>Soni, Anita</author></authors></contributors><titles><title>Allergic Rhinitis: Trends in use and expenditures, 2000 and 2005</title></titles><dates><year>2008</year></dates><publisher>Medical Expenditure Panel Survey, Agency for Healthcare Research and Quality</publisher><urls></urls></record></EndNote> and Bhattacharyya, 2011. ADDIN EN.CITE <EndNote><Author>Bhattacharyya</Author><Year>2011</Year><RecNum>1292</RecNum><DisplayText>Bhattacharyya, 2011</DisplayText><record><rec-number>1292</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608216019">1292</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Bhattacharyya, Neil</author></authors></contributors><titles><title>Incremental healthcare utilization and expenditures for allergic rhinitis in the United States</title><secondary-title>The Laryngoscope</secondary-title></titles><periodical><full-title>The Laryngoscope</full-title></periodical><pages>1830-1833</pages><volume>121</volume><number>9</number><dates><year>2011</year></dates><isbn>0023-852X</isbn><urls></urls></record></EndNote> Both studies utilize data from the Medical Expenditure Panel Survey (MEPS) and identify allergic rhinitis (also referred to as hay fever) using ICD-9 code 477. Each study analyzes medical expenditures for differing years, Soni, 2008 ADDIN EN.CITE <EndNote><Author>Soni</Author><Year>2008</Year><RecNum>1293</RecNum><DisplayText>Soni, 2008</DisplayText><record><rec-number>1293</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608216073">1293</key></foreign-keys><ref-type name="Book">6</ref-type><contributors><authors><author>Soni, Anita</author></authors></contributors><titles><title>Allergic Rhinitis: Trends in use and expenditures, 2000 and 2005</title></titles><dates><year>2008</year></dates><publisher>Medical Expenditure Panel Survey, Agency for Healthcare Research and Quality</publisher><urls></urls></record></EndNote> for the years 2000 and 2005, and Bhattacharyya, 2011 ADDIN EN.CITE <EndNote><Author>Bhattacharyya</Author><Year>2011</Year><RecNum>1292</RecNum><DisplayText>Bhattacharyya, 2011</DisplayText><record><rec-number>1292</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608216019">1292</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Bhattacharyya, Neil</author></authors></contributors><titles><title>Incremental healthcare utilization and expenditures for allergic rhinitis in the United States</title><secondary-title>The Laryngoscope</secondary-title></titles><periodical><full-title>The Laryngoscope</full-title></periodical><pages>1830-1833</pages><volume>121</volume><number>9</number><dates><year>2011</year></dates><isbn>0023-852X</isbn><urls></urls></record></EndNote> for the year 2007. Soni, 2008 ADDIN EN.CITE <EndNote><Author>Soni</Author><Year>2008</Year><RecNum>1293</RecNum><DisplayText>Soni, 2008</DisplayText><record><rec-number>1293</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608216073">1293</key></foreign-keys><ref-type name="Book">6</ref-type><contributors><authors><author>Soni, Anita</author></authors></contributors><titles><title>Allergic Rhinitis: Trends in use and expenditures, 2000 and 2005</title></titles><dates><year>2008</year></dates><publisher>Medical Expenditure Panel Survey, Agency for Healthcare Research and Quality</publisher><urls></urls></record></EndNote> calculates the cost-of-illness for allergic rhinitis as the mean expenditures for ambulatory care, in-patient services, and prescription medications per person. Bhattacharyya, 2011 ADDIN EN.CITE <EndNote><Author>Bhattacharyya</Author><Year>2011</Year><RecNum>1292</RecNum><DisplayText>Bhattacharyya, 2011</DisplayText><record><rec-number>1292</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608216019">1292</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Bhattacharyya, Neil</author></authors></contributors><titles><title>Incremental healthcare utilization and expenditures for allergic rhinitis in the United States</title><secondary-title>The Laryngoscope</secondary-title></titles><periodical><full-title>The Laryngoscope</full-title></periodical><pages>1830-1833</pages><volume>121</volume><number>9</number><dates><year>2011</year></dates><isbn>0023-852X</isbn><urls></urls></record></EndNote> calculates the incremental difference in annual healthcare expenditures for individuals with and without allergic rhinitis. Although Bhattacharyya, 2011 ADDIN EN.CITE <EndNote><Author>Bhattacharyya</Author><Year>2011</Year><RecNum>1292</RecNum><DisplayText>Bhattacharyya, 2011</DisplayText><record><rec-number>1292</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608216019">1292</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Bhattacharyya, Neil</author></authors></contributors><titles><title>Incremental healthcare utilization and expenditures for allergic rhinitis in the United States</title><secondary-title>The Laryngoscope</secondary-title></titles><periodical><full-title>The Laryngoscope</full-title></periodical><pages>1830-1833</pages><volume>121</volume><number>9</number><dates><year>2011</year></dates><isbn>0023-852X</isbn><urls></urls></record></EndNote> uses more recent data, the estimates are not specific to children. Therefore, we derived our COI estimates from the 2005 data presented by Soni, 2008 ADDIN EN.CITE <EndNote><Author>Soni</Author><Year>2008</Year><RecNum>1293</RecNum><DisplayText>Soni, 2008</DisplayText><record><rec-number>1293</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608216073">1293</key></foreign-keys><ref-type name="Book">6</ref-type><contributors><authors><author>Soni, Anita</author></authors></contributors><titles><title>Allergic Rhinitis: Trends in use and expenditures, 2000 and 2005</title></titles><dates><year>2008</year></dates><publisher>Medical Expenditure Panel Survey, Agency for Healthcare Research and Quality</publisher><urls></urls></record></EndNote>, which are stratified by age group. The resulting COI for allergic rhinitis is $600 for ages zero to seventeen (2015$; Table 21). These COI estimates represent mean annual medical costs for patients with hay fever. Given that the health impact function for this endpoint relates to allergic rhinitis prevalence, these estimates are more applicable than values representing only first-year costs.
Asthma Onset
Belova et al., 2020 ADDIN EN.CITE <EndNote><Author>Belova</Author><Year>2020</Year><RecNum>1248</RecNum><DisplayText>Belova et al., 2020</DisplayText><record><rec-number>1248</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608158195">1248</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Belova, A.</author><author>Fann, N.</author><author>Haskell, J.</author><author>Hubbell, B.</author><author>Narayan, T.</author></authors></contributors><auth-address>Abt Associates, Inc., Rockville, Maryland; and.
U.S. Environmental Protection Agency, Research Triangle Park, North Carolina.</auth-address><titles><title>Estimating Lifetime Cost of Illness. An Application to Asthma</title><secondary-title>Ann Am Thorac Soc</secondary-title></titles><periodical><full-title>Ann Am Thorac Soc</full-title></periodical><pages>1558-1569</pages><volume>17</volume><number>12</number><edition>2020/09/16</edition><keywords><keyword>asthma</keyword><keyword>foregone income</keyword><keyword>health care</keyword><keyword>labor productivity</keyword><keyword>occupational choice</keyword></keywords><dates><year>2020</year><pub-dates><date>Dec</date></pub-dates></dates><isbn>2325-6621 (Electronic)
2325-6621 (Linking)</isbn><accession-num>32931705</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/32931705</url></related-urls></urls><electronic-resource-num>10.1513/AnnalsATS.201910-729OC</electronic-resource-num></record></EndNote> estimated the lifetime cost of asthma using data from the 2002 to 2010 Medical Expenditure Panel Survey (MEPS). The authors identify all individuals with current asthma (9,409 out of 158,867 respondents) using the ICD-9 code 493 in the MEPS Medical Conditions Files. Additionally, they identify the date of asthma onset for these individuals. Using the MEPS Medical Events files, which capture most types of medical expenditures (e.g., hospitalizations, emergency room visits, outpatient visits, prescriptions), Belova et al., 2020 ADDIN EN.CITE <EndNote><Author>Belova</Author><Year>2020</Year><RecNum>1248</RecNum><DisplayText>Belova et al., 2020</DisplayText><record><rec-number>1248</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608158195">1248</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Belova, A.</author><author>Fann, N.</author><author>Haskell, J.</author><author>Hubbell, B.</author><author>Narayan, T.</author></authors></contributors><auth-address>Abt Associates, Inc., Rockville, Maryland; and.
U.S. Environmental Protection Agency, Research Triangle Park, North Carolina.</auth-address><titles><title>Estimating Lifetime Cost of Illness. An Application to Asthma</title><secondary-title>Ann Am Thorac Soc</secondary-title></titles><periodical><full-title>Ann Am Thorac Soc</full-title></periodical><pages>1558-1569</pages><volume>17</volume><number>12</number><edition>2020/09/16</edition><keywords><keyword>asthma</keyword><keyword>foregone income</keyword><keyword>health care</keyword><keyword>labor productivity</keyword><keyword>occupational choice</keyword></keywords><dates><year>2020</year><pub-dates><date>Dec</date></pub-dates></dates><isbn>2325-6621 (Electronic)
2325-6621 (Linking)</isbn><accession-num>32931705</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/32931705</url></related-urls></urls><electronic-resource-num>10.1513/AnnalsATS.201910-729OC</electronic-resource-num></record></EndNote> estimated annual expenditures by asthma duration and age at onset. The annual healthcare costs for asthma -- as measured by healthcare expenditures by all paying parties -- vary from $700 to $1,800 for children and $800 to $2,200 for adults (2010$). They extrapolate these values to a lifetime cost stream for an incident chronic asthma case to generate present value estimates by onset age using discount rates of 3% and 7%. Additionally, the authors consider productivity impacts that capture 1) the probability of not being able to work due to health reasons, 2) the impact of asthma on occupational choice, and 3) impact of asthma on weekly earnings.
We adapt the Belova et al., 2020 ADDIN EN.CITE <EndNote><Author>Belova</Author><Year>2020</Year><RecNum>1248</RecNum><DisplayText>Belova et al., 2020</DisplayText><record><rec-number>1248</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608158195">1248</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Belova, A.</author><author>Fann, N.</author><author>Haskell, J.</author><author>Hubbell, B.</author><author>Narayan, T.</author></authors></contributors><auth-address>Abt Associates, Inc., Rockville, Maryland; and.
U.S. Environmental Protection Agency, Research Triangle Park, North Carolina.</auth-address><titles><title>Estimating Lifetime Cost of Illness. An Application to Asthma</title><secondary-title>Ann Am Thorac Soc</secondary-title></titles><periodical><full-title>Ann Am Thorac Soc</full-title></periodical><pages>1558-1569</pages><volume>17</volume><number>12</number><edition>2020/09/16</edition><keywords><keyword>asthma</keyword><keyword>foregone income</keyword><keyword>health care</keyword><keyword>labor productivity</keyword><keyword>occupational choice</keyword></keywords><dates><year>2020</year><pub-dates><date>Dec</date></pub-dates></dates><isbn>2325-6621 (Electronic)
2325-6621 (Linking)</isbn><accession-num>32931705</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/32931705</url></related-urls></urls><electronic-resource-num>10.1513/AnnalsATS.201910-729OC</electronic-resource-num></record></EndNote> estimates to align with the age groups 0 to 17, 4 to 21, and 35 to 99. This calculation entails weighting the Belova et al., 2020 ADDIN EN.CITE <EndNote><Author>Belova</Author><Year>2020</Year><RecNum>1248</RecNum><DisplayText>Belova et al., 2020</DisplayText><record><rec-number>1248</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608158195">1248</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Belova, A.</author><author>Fann, N.</author><author>Haskell, J.</author><author>Hubbell, B.</author><author>Narayan, T.</author></authors></contributors><auth-address>Abt Associates, Inc., Rockville, Maryland; and.
U.S. Environmental Protection Agency, Research Triangle Park, North Carolina.</auth-address><titles><title>Estimating Lifetime Cost of Illness. An Application to Asthma</title><secondary-title>Ann Am Thorac Soc</secondary-title></titles><periodical><full-title>Ann Am Thorac Soc</full-title></periodical><pages>1558-1569</pages><volume>17</volume><number>12</number><edition>2020/09/16</edition><keywords><keyword>asthma</keyword><keyword>foregone income</keyword><keyword>health care</keyword><keyword>labor productivity</keyword><keyword>occupational choice</keyword></keywords><dates><year>2020</year><pub-dates><date>Dec</date></pub-dates></dates><isbn>2325-6621 (Electronic)
2325-6621 (Linking)</isbn><accession-num>32931705</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/32931705</url></related-urls></urls><electronic-resource-num>10.1513/AnnalsATS.201910-729OC</electronic-resource-num></record></EndNote> age groups by their relative prevalence and propagating the standard errors to derive new uncertainty bounds. The results are summarized in Table 27. Confidence intervals are not provided for productivity losses to mirror the valuation functions in BenMAP-CE, which at present are only capable of reflecting uncertainty in one parameter (in this case, medical costs) (Table 21).
Table 27. Age-adjusted Belova et al., 2020 ADDIN EN.CITE <EndNote><Author>Belova</Author><Year>2020</Year><RecNum>1248</RecNum><DisplayText>Belova et al., 2020</DisplayText><record><rec-number>1248</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608158195">1248</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Belova, A.</author><author>Fann, N.</author><author>Haskell, J.</author><author>Hubbell, B.</author><author>Narayan, T.</author></authors></contributors><auth-address>Abt Associates, Inc., Rockville, Maryland; and.
U.S. Environmental Protection Agency, Research Triangle Park, North Carolina.</auth-address><titles><title>Estimating Lifetime Cost of Illness. An Application to Asthma</title><secondary-title>Ann Am Thorac Soc</secondary-title></titles><periodical><full-title>Ann Am Thorac Soc</full-title></periodical><pages>1558-1569</pages><volume>17</volume><number>12</number><edition>2020/09/16</edition><keywords><keyword>asthma</keyword><keyword>foregone income</keyword><keyword>health care</keyword><keyword>labor productivity</keyword><keyword>occupational choice</keyword></keywords><dates><year>2020</year><pub-dates><date>Dec</date></pub-dates></dates><isbn>2325-6621 (Electronic)
2325-6621 (Linking)</isbn><accession-num>32931705</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/32931705</url></related-urls></urls><electronic-resource-num>10.1513/AnnalsATS.201910-729OC</electronic-resource-num></record></EndNote> Estimates of Lifetime Asthma Costs
                              Age of asthma onset
                                 Discount rate
                           Healthcare costs (2015$)
                           Productivity Loss (2015$)
                                   0  -  17
                                      3%
                                    $17,232
                              ($16,366, $18,097)
                                    $27,426
                                   0  -  17
                                      7%
                                    $10,187
                               ($9,643, $10,730)
                                    $17,502

Asthma Symptoms/Exacerbation
Albuterol Use
As albuterol use is a new measure of PM2.5-attributable asthma symptoms, we developed a method for valuing this health endpoint. We estimate the economic value for albuterol use associated with asthma symptoms using prescription prices for albuterol inhalers. Epocrates and GoodRx provide cost and actuation information for four common types of albuterol inhalers in 2020 dollars.[,] Both online resources utilize published price lists, purchases, claim records, and pharmaceutical data to provide clinical statistics. Epocrates and the FDA provide cost and actuation information for one additional, less common, albuterol inhaler. We divide the cost of inhalers by the actuations per inhaler to calculate an average cost per actuation across all inhaler types. We then adjust the values to 2015$ using the Consumer Price Index (CPI) for medical care. Since medical cost index data were unavailable for 2020 at the time of these calculations, we used the most recently available index (2019). The resulting value for asthma symptoms, albuterol use is $0.35 per actuation (2015$) (Table 21).
Cough, Wheeze, Chest Tightness, and Shortness of Breath
While the risk estimates for both PM₂ꓸ₅- and O3-attributable asthma symptoms were updated, the valuation estimates for cough, wheeze, chest tightness, and shortness of breath are still based on the previous method, using the Dickie and Messman, 2004 ADDIN EN.CITE <EndNote><Author>Dickie</Author><Year>2004</Year><RecNum>1251</RecNum><DisplayText>Dickie and Messman, 2004</DisplayText><record><rec-number>1251</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608158913">1251</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Dickie, Mark</author><author>Messman, Victoria L.</author></authors></contributors><titles><title>Parental altruism and the value of avoiding acute illness: are kids worth more than parents?</title><secondary-title>Journal of Environmental Economics and Management</secondary-title></titles><periodical><full-title>Journal of Environmental Economics and Management</full-title>J. Environ. Econ. Manage.J Environ Econ Manage</periodical><pages>1146-1174</pages><volume>48</volume><number>3</number><keywords><keyword>Family economics</keyword><keyword>Children's health</keyword><keyword>Health valuation</keyword><keyword>Altruism</keyword><keyword>Benefit - cost analysis</keyword><keyword>Environmental justice</keyword><keyword>Environmental policy</keyword><keyword>Morbidity valuation</keyword><keyword>Willingness to pay</keyword></keywords><dates><year>2004</year><pub-dates><date>2004/11/01/</date></pub-dates></dates><isbn>0095-0696</isbn><urls><related-urls><url>http://www.sciencedirect.com/science/article/pii/S0095069604000154</url></related-urls></urls><electronic-resource-num>https://doi.org/10.1016/j.jeem.2003.12.005</electronic-resource-num></record></EndNote> analysis of parents' WTP to relieve asthma symptoms in children and adults. The authors derive the WTP estimates from an attribute-based, stated-choice question assessing preferences to avoid acute illness as part of a survey performed in Hattiesburg, Mississippi in 2000. Survey respondents are asked to identify whether they or their child have experienced the following asthma symptoms in the past year: cough with phlegm, shortness of breath with wheezing, chest pain on deep inspiration, and fever with muscle pain and fatigue. Respondents were then assigned one of sixteen illness profiles varying by symptom, symptom duration, in days, as well as discomfort level. Dickie and Messman, 2004 ADDIN EN.CITE <EndNote><Author>Dickie</Author><Year>2004</Year><RecNum>1251</RecNum><DisplayText>Dickie and Messman, 2004</DisplayText><record><rec-number>1251</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608158913">1251</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Dickie, Mark</author><author>Messman, Victoria L.</author></authors></contributors><titles><title>Parental altruism and the value of avoiding acute illness: are kids worth more than parents?</title><secondary-title>Journal of Environmental Economics and Management</secondary-title></titles><periodical><full-title>Journal of Environmental Economics and Management</full-title>J. Environ. Econ. Manage.J Environ Econ Manage</periodical><pages>1146-1174</pages><volume>48</volume><number>3</number><keywords><keyword>Family economics</keyword><keyword>Children's health</keyword><keyword>Health valuation</keyword><keyword>Altruism</keyword><keyword>Benefit - cost analysis</keyword><keyword>Environmental justice</keyword><keyword>Environmental policy</keyword><keyword>Morbidity valuation</keyword><keyword>Willingness to pay</keyword></keywords><dates><year>2004</year><pub-dates><date>2004/11/01/</date></pub-dates></dates><isbn>0095-0696</isbn><urls><related-urls><url>http://www.sciencedirect.com/science/article/pii/S0095069604000154</url></related-urls></urls><electronic-resource-num>https://doi.org/10.1016/j.jeem.2003.12.005</electronic-resource-num></record></EndNote> calculate the WTP for children ages zero to seventeen as $219, for one avoided mild symptom-day (2015$). The authors also provide WTP estimates by symptom, however, they represent six avoided symptom-days. Therefore, we apply the same WTP value, for one avoided mild symptom-day, to each asthma symptom endpoint (Table 21).
Cardiac Arrest
The COI for cardiac arrests occurring outside of the hospital is derived from O'Sullivan et al., 2011, who estimate three-year medical costs associated with cardiovascular disease events among adults ages 35 and older in the U.S. The authors rely on administrative claims data from a large U.S. health plan and develop econometric models to predict medical costs for 15 different cardiovascular events, including cardiac arrest, referred to as resuscitated cardiac arrest. The dataset includes over 20 million commercial and Medical Advantage members between 2002 and 2006. Cardiac arrests are identified using the ICD-9 code 427.5. The authors use propensity score matching to develop a control group with which to compare costs versus individuals that suffered cardiac arrest. Medical costs occurring within the month of the event were excluded to avoid double counting hospitalization costs, which are separately captured by the hospitalization valuation functions. Over three years, the total medical costs, excluding hospitalization, are $36,142 (undiscounted, inflated to 2015$), or $35,753 using a 3% discount rate and $35,282 for a 7% discount rate (Table 28 and Table 21).
Table 28. Valuation Estimate for Cardiac Arrests (2015$)
                                     Costs
                               Cumulative Costs
                                 Annual Costs

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

Lung Cancer
The unit value for non-fatal lung cancer incidence is derived from the direct medical costs of lung cancer treatment estimated by Kaye et al., 2018 ADDIN EN.CITE <EndNote><Author>Kaye</Author><Year>2018</Year><RecNum>1252</RecNum><DisplayText>Kaye et al., 2018</DisplayText><record><rec-number>1252</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608159123">1252</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Kaye, D. R.</author><author>Min, H. S.</author><author>Herrel, L. A.</author><author>Dupree, J. M.</author><author>Ellimoottil, C.</author><author>Miller, D. C.</author></authors></contributors><auth-address>Univ Michigan, Dept Urol, Dow Div Hlth Serv Res, Ann Arbor, MI 48109 USA</auth-address><titles><title>Costs of Cancer Care Across the Disease Continuum</title><secondary-title>Oncologist</secondary-title><alt-title>Oncologist</alt-title></titles><periodical><full-title>Oncologist</full-title>OncologistOncologist</periodical><alt-periodical><full-title>Oncologist</full-title>OncologistOncologist</alt-periodical><pages>798-805</pages><volume>23</volume><number>7</number><keywords><keyword>cancer</keyword><keyword>costs</keyword><keyword>disease continuum</keyword><keyword>united-states</keyword><keyword>lung-cancer</keyword><keyword>breast-cancer</keyword><keyword>disparities</keyword><keyword>chemotherapy</keyword><keyword>life</keyword><keyword>end</keyword></keywords><dates><year>2018</year><pub-dates><date>Jul</date></pub-dates></dates><isbn>1083-7159</isbn><accession-num>WOS:000439877700014</accession-num><urls><related-urls><url><Go to ISI>://WOS:000439877700014</url></related-urls></urls><electronic-resource-num>10.1634/theoncologist.2017-0481</electronic-resource-num><language>English</language></record></EndNote>. This COI value incorporates only direct medical costs and not lost earnings associated with lung cancer incidence because the average age of lung cancer diagnosis is approximately 70 and it is assumed that those aged 65 and older are retired and thus have exited the labor market. Lung cancer treatment costs depend to a large extent on the phase of care, with costs in the initial year of treatment (e.g., $17,422 for males) far exceeding the continuing costs of treatment in subsequent years (e.g., $3,269 for males). We calculate costs over a five-year span, beginning with the initial onset which is occurs with a delay after exposure. The specific lag periods between exposure and onset are discussed in Section 6.4.2. The initial year's treatment cost is summed with four years of continuing annual costs discounted by 3% and 7%.
Furthermore, Kaye et al., 2018 ADDIN EN.CITE <EndNote><Author>Kaye</Author><Year>2018</Year><RecNum>1252</RecNum><DisplayText>Kaye et al., 2018</DisplayText><record><rec-number>1252</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608159123">1252</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Kaye, D. R.</author><author>Min, H. S.</author><author>Herrel, L. A.</author><author>Dupree, J. M.</author><author>Ellimoottil, C.</author><author>Miller, D. C.</author></authors></contributors><auth-address>Univ Michigan, Dept Urol, Dow Div Hlth Serv Res, Ann Arbor, MI 48109 USA</auth-address><titles><title>Costs of Cancer Care Across the Disease Continuum</title><secondary-title>Oncologist</secondary-title><alt-title>Oncologist</alt-title></titles><periodical><full-title>Oncologist</full-title>OncologistOncologist</periodical><alt-periodical><full-title>Oncologist</full-title>OncologistOncologist</alt-periodical><pages>798-805</pages><volume>23</volume><number>7</number><keywords><keyword>cancer</keyword><keyword>costs</keyword><keyword>disease continuum</keyword><keyword>united-states</keyword><keyword>lung-cancer</keyword><keyword>breast-cancer</keyword><keyword>disparities</keyword><keyword>chemotherapy</keyword><keyword>life</keyword><keyword>end</keyword></keywords><dates><year>2018</year><pub-dates><date>Jul</date></pub-dates></dates><isbn>1083-7159</isbn><accession-num>WOS:000439877700014</accession-num><urls><related-urls><url><Go to ISI>://WOS:000439877700014</url></related-urls></urls><electronic-resource-num>10.1634/theoncologist.2017-0481</electronic-resource-num><language>English</language></record></EndNote> provides separate treatment cost estimates for men and women. The distribution of new lung cancer cases by sex in the United States from Siegel et al., 2019 ADDIN EN.CITE <EndNote><Author>Siegel</Author><Year>2019</Year><RecNum>1262</RecNum><DisplayText>Siegel et al., 2019</DisplayText><record><rec-number>1262</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608159433">1262</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Siegel, R. L.</author><author>Miller, K. D.</author><author>Jemal, A.</author></authors></contributors><auth-address>Scientific Director, Surveillance Research, American Cancer Society, Atlanta, GA.
Senior Associate Scientist, Surveillance Research, American Cancer Society, Atlanta, GA.
Scientific Vice President, Surveillance and Health Services Research, American Cancer Society, Atlanta, GA.</auth-address><titles><title>Cancer statistics, 2019</title><secondary-title>CA Cancer J Clin</secondary-title></titles><periodical><full-title>CA Cancer J Clin</full-title></periodical><pages>7-34</pages><volume>69</volume><number>1</number><edition>2019/01/09</edition><keywords><keyword>American Cancer Society</keyword><keyword>Ethnic Groups/statistics & numerical data</keyword><keyword>Female</keyword><keyword>Health Status Disparities</keyword><keyword>Healthcare Disparities/trends</keyword><keyword>Humans</keyword><keyword>Incidence</keyword><keyword>Male</keyword><keyword>Neoplasms/diagnosis/*epidemiology/therapy</keyword><keyword>Socioeconomic Factors</keyword><keyword>Survival Rate</keyword><keyword>United States/epidemiology</keyword><keyword>*cancer cases</keyword><keyword>*cancer statistics</keyword><keyword>*death rates</keyword><keyword>*incidence</keyword><keyword>*mortality</keyword></keywords><dates><year>2019</year><pub-dates><date>Jan</date></pub-dates></dates><isbn>1542-4863 (Electronic)
0007-9235 (Linking)</isbn><accession-num>30620402</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/30620402</url></related-urls></urls><electronic-resource-num>10.3322/caac.21551</electronic-resource-num></record></EndNote> is approximately 51% male and 49% female. This distribution of new lung cancer cases was used to weight the sex-specific cost estimates from Kaye et al., 2018 ADDIN EN.CITE <EndNote><Author>Kaye</Author><Year>2018</Year><RecNum>1252</RecNum><DisplayText>Kaye et al., 2018</DisplayText><record><rec-number>1252</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608159123">1252</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Kaye, D. R.</author><author>Min, H. S.</author><author>Herrel, L. A.</author><author>Dupree, J. M.</author><author>Ellimoottil, C.</author><author>Miller, D. C.</author></authors></contributors><auth-address>Univ Michigan, Dept Urol, Dow Div Hlth Serv Res, Ann Arbor, MI 48109 USA</auth-address><titles><title>Costs of Cancer Care Across the Disease Continuum</title><secondary-title>Oncologist</secondary-title><alt-title>Oncologist</alt-title></titles><periodical><full-title>Oncologist</full-title>OncologistOncologist</periodical><alt-periodical><full-title>Oncologist</full-title>OncologistOncologist</alt-periodical><pages>798-805</pages><volume>23</volume><number>7</number><keywords><keyword>cancer</keyword><keyword>costs</keyword><keyword>disease continuum</keyword><keyword>united-states</keyword><keyword>lung-cancer</keyword><keyword>breast-cancer</keyword><keyword>disparities</keyword><keyword>chemotherapy</keyword><keyword>life</keyword><keyword>end</keyword></keywords><dates><year>2018</year><pub-dates><date>Jul</date></pub-dates></dates><isbn>1083-7159</isbn><accession-num>WOS:000439877700014</accession-num><urls><related-urls><url><Go to ISI>://WOS:000439877700014</url></related-urls></urls><electronic-resource-num>10.1634/theoncologist.2017-0481</electronic-resource-num><language>English</language></record></EndNote> to obtain a combined five-year cost estimate for both sexes. In order to adjust the cost estimate to 2015$ using a medical cost index, we assume that costs presented by Kaye et al., 2018 ADDIN EN.CITE <EndNote><Author>Kaye</Author><Year>2018</Year><RecNum>1252</RecNum><DisplayText>Kaye et al., 2018</DisplayText><record><rec-number>1252</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608159123">1252</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Kaye, D. R.</author><author>Min, H. S.</author><author>Herrel, L. A.</author><author>Dupree, J. M.</author><author>Ellimoottil, C.</author><author>Miller, D. C.</author></authors></contributors><auth-address>Univ Michigan, Dept Urol, Dow Div Hlth Serv Res, Ann Arbor, MI 48109 USA</auth-address><titles><title>Costs of Cancer Care Across the Disease Continuum</title><secondary-title>Oncologist</secondary-title><alt-title>Oncologist</alt-title></titles><periodical><full-title>Oncologist</full-title>OncologistOncologist</periodical><alt-periodical><full-title>Oncologist</full-title>OncologistOncologist</alt-periodical><pages>798-805</pages><volume>23</volume><number>7</number><keywords><keyword>cancer</keyword><keyword>costs</keyword><keyword>disease continuum</keyword><keyword>united-states</keyword><keyword>lung-cancer</keyword><keyword>breast-cancer</keyword><keyword>disparities</keyword><keyword>chemotherapy</keyword><keyword>life</keyword><keyword>end</keyword></keywords><dates><year>2018</year><pub-dates><date>Jul</date></pub-dates></dates><isbn>1083-7159</isbn><accession-num>WOS:000439877700014</accession-num><urls><related-urls><url><Go to ISI>://WOS:000439877700014</url></related-urls></urls><electronic-resource-num>10.1634/theoncologist.2017-0481</electronic-resource-num><language>English</language></record></EndNote> are in 2010$ as an approximate midpoint of the data years 2007-2012. Altogether, the cost of non-fatal lung cancer incidence over a five-year period is estimated to be $33,809 using a 3% discount rate or $32,548 using a 7% discount rate (Table 21).
For an outcome such as lung cancer, there is an expected time lag between changes in pollutant exposure in a given year and the total realization of health effect benefits, commonly referred to in regulatory analyses as the "cessation lag." The time between exposure and diagnosis can be quite long, on the order of years to decades, to realize the full benefits of the air quality improvements. This latency period is important in order to properly discount the economic value of these health benefits.
To estimate the latency period, we performed a literature search using the keywords "non-fatal lung cancer," "lung cancer," "PM2.5," "latency," and "incidence." Five papers that estimate the risk of lung cancer incidence from PM2.5 exposure using a latency period were identified. The latency period length and country of the identified papers are summarized in Table 29. Based on estimates of lung cancer latency from the literature, 10 years was the most common latency period estimate found in the literature (i.e., the mode).
Table 29. Latency Periods Used in Lung Cancer Risk Assessment Papers
Study
Latency Period (years)
Location
Gogna et al., 2019
                                       5
                                    Canada
Bai et al., 2020
                                     4; 10
                                    Canada
Kulhanova et al., 2018
                                      10
                                    France
Coleman et al., 2020
                                    10; 15
                                      US
Harrison et al., 2004 ADDIN EN.CITE <EndNote><Author>Harrison</Author><Year>2004</Year><RecNum>1403</RecNum><DisplayText>Harrison et al., 2004</DisplayText><record><rec-number>1403</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1611099753">1403</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Harrison, Roy M</author><author>Smith, DJT</author><author>Kibble, AJ</author></authors></contributors><titles><title>What is responsible for the carcinogenicity of PM2. 5?</title><secondary-title>Occupational and Environmental Medicine</secondary-title></titles><periodical><full-title>Occupational and Environmental Medicine</full-title>Occup. Environ. Med.Occup Environ Med</periodical><pages>799-805</pages><volume>61</volume><number>10</number><dates><year>2004</year></dates><isbn>1351-0711</isbn><urls></urls></record></EndNote>
                                      20
                                      US

To account for the latency period between air pollution reductions and avoided lung cancer diagnoses in our economic valuation estimates, we developed an age-at-diagnosis cessation lag distribution method based on an approach previously used to estimate avoided cases of kidney cancer in analyses of water quality rules (U.S. EPA, 2017 ADDIN EN.CITE <EndNote><Author>U.S. EPA</Author><Year>2017</Year><RecNum>1404</RecNum><DisplayText>U.S. EPA, 2017</DisplayText><record><rec-number>1404</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1611099928">1404</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>U.S. EPA,</author></authors></contributors><titles><title>Trichloroethylene (TCE); Regulation of Use in Vapor Degreasing Under TSCA Section 6(a)</title></titles><dates><year>2017</year><pub-dates><date>January 19, 2017</date></pub-dates></dates><pub-location>Washington, DC</pub-location><isbn>40 CFR 751</isbn><work-type>EPA Report</work-type><urls><related-urls><url>Available at: https://www.federalregister.gov/documents/2017/01/19/2017-01229/trichloroethylene-tce-regulation-of-use-in-vapor-degreasing-under-tsca-section-6a</url></related-urls></urls><language>English</language></record></EndNote>). The method uses lung and bronchus cancer diagnosis age-distribution from the Surveillance, Epidemiology, and End Results Program (SEER). For this model, we assumed that the case reduction distribution would follow the age-pattern of cancer diagnosis between the age at which the exposure change occurs and 99 years. Table 30 shows an example case reduction distribution calculation for an exposure change experienced at 55. SEER estimates 92.2% of lung and bronchus cancer cases occur in individuals 55 years and older. Dividing the percentages in the remaining age bins by 92.2% (the percent of lung and bronchus cancer diagnoses between the age of exposure change and end of lifetime), we find that there is a 24% chance that the risk reductions for a 55-year-old occur between ages 55 and 64, a 37% chance that the case reductions occurs between ages 65 and 74, etc. For distributing avoided cases within an age bin, we assume an equal incidence distribution across years within each bin.

Table 30. Percent Lung and Bronchus Cancer Incidence by Age and Distribution of Risk Reduction by Age for an Exposure Change at 55
                                   Age Group
                      Percent New Cases per Year by Age*
             Percent of New Cases Occurring at or After Age 55[1]
0-20
                                       0
                                      NA
20-34
                                      0.2
                                      NA
35-44
                                      0.9
                                      NA
45-54
                                      6.6
                                      NA
55-64
                                     21.8
                                      24
65-74
                                     34.1
                                      37
75-84
                                     26.6
                                      29
85-99
                                      9.7
                                      11
55-99
                                     92.2
                                      100
*May not sum to 100% due to rounding
[1]Calulcated as the percentage in column 2 divided by 92.2%, where 92.2% is the percentage of lung and bronchus incidence between age 55 and 99.
This and other potential cessation lag distribution models for lung cancer onset are described and compared in section 6.4.2.
Minor Restricted Activity Days (MRADs)
Due to their definition, for the purposes of benefits estimation minor respiratory-restricted activity days (MRRAD) are assumed to constitute all MRADs (Ostro and Rothschild, 1989). While no peer-reviewed studies estimating WTP to avoid a MRRAD are available, a central estimate and upper and lower bounds of WTP to avoid a MRRAD were developed by IEc (IEc, 1993). When estimating benefits associated with an MRAD, we use a triangular distribution centered at the estimate.
 ADDIN EN.CITE <EndNote><Author>Ostro</Author><Year>1989</Year><RecNum>1372</RecNum><DisplayText>Ostro and Rothschild, 1989</DisplayText><record><rec-number>1372</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608588120">1372</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Ostro, B. D.</author><author>Rothschild, S.</author></authors></contributors><auth-address>California Public Health Foundation, Berkeley 94704.</auth-address><titles><title>Air pollution and acute respiratory morbidity: an observational study of multiple pollutants</title><secondary-title>Environ Res</secondary-title></titles><periodical><full-title>Environmental Research</full-title>Environ. Res.Environ Res</periodical><pages>238-47</pages><volume>50</volume><number>2</number><edition>1989/12/01</edition><keywords><keyword>Adolescent</keyword><keyword>Adult</keyword><keyword>Aged</keyword><keyword>Air Pollutants/adverse effects</keyword><keyword>Air Pollution/*adverse effects/statistics & numerical data</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Information Systems</keyword><keyword>Male</keyword><keyword>Middle Aged</keyword><keyword>Ozone/adverse effects</keyword><keyword>Regression Analysis</keyword><keyword>Respiratory Tract Diseases/epidemiology/*mortality</keyword><keyword>United States</keyword></keywords><dates><year>1989</year><pub-dates><date>Dec</date></pub-dates></dates><isbn>0013-9351 (Print)
0013-9351 (Linking)</isbn><accession-num>2583069</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/2583069</url></related-urls></urls><electronic-resource-num>10.1016/s0013-9351(89)80004-0</electronic-resource-num></record></EndNote> ADDIN EN.CITE <EndNote><Author>IEc</Author><Year>1993</Year><RecNum>1385</RecNum><DisplayText>IEc, 1993</DisplayText><record><rec-number>1385</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1609785896">1385</key></foreign-keys><ref-type name="Personal Communication">26</ref-type><contributors><authors><author>IEc,</author></authors></contributors><titles><title>Memorandum to Jim DeMocker, U.S. Environmental Protection Agency, Office of Air and Radiation, Office of Policy Analysis and Review. September 30. </title></titles><dates><year>1993</year></dates><publisher>U.S. EPA</publisher><urls></urls></record></EndNote>
Any estimate of mean WTP to avoid a MRRAD (or any other type of restricted activity day other than Work Loss Day (WLD) will be somewhat arbitrary because the endpoint itself is not precisely defined. Many different combinations of symptoms could presumably result in some minor or less minor restriction in activity. Krupnick and Cropper, 1992 ADDIN EN.CITE <EndNote><Author>Krupnick</Author><Year>1992</Year><RecNum>1384</RecNum><DisplayText>Krupnick and Cropper, 1992</DisplayText><record><rec-number>1384</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1609785812">1384</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Krupnick, Alan J</author><author>Cropper, Maureen L</author></authors></contributors><titles><title>The effect of information on health risk valuations</title><secondary-title>Journal of Risk and Uncertainty</secondary-title></titles><periodical><full-title>Journal of Risk and Uncertainty</full-title></periodical><pages>29-48</pages><volume>5</volume><number>1</number><dates><year>1992</year></dates><isbn>0895-5646</isbn><urls></urls></record></EndNote> argued that mild symptoms will not be sufficient to result in a MRRAD, so that WTP to avoid a MRRAD should exceed WTP to avoid any single mild symptom. A single severe symptom or a combination of symptoms could, however, be sufficient to restrict activity. Therefore, WTP to avoid a MRRAD should, these authors argue, not necessarily exceed WTP to avoid a single severe symptom or a combination of symptoms. The "severity" of a symptom, however, is similarly not precisely defined; moreover, one level of severity of a symptom could induce restriction of activity for one individual while not doing so for another. The same is true for any combination of symptoms.
Given that there is inherently a substantial degree of arbitrariness in any point estimate of WTP to avoid a MRRAD (or other kinds of restricted activity days), the reasonable bounds on such an estimate must be considered. By definition, a MRRAD does not result in loss of work. WTP to avoid a MRRAD should therefore be less than WTP to avoid a WLD. At the other extreme, WTP to avoid a MRRAD should exceed WTP to avoid a single mild symptom. The highest IEc midrange estimate of WTP to avoid a single symptom is $28.51 (2015$), for eye irritation. The point estimate of WTP to avoid a WLD in the benefit analysis is $110.62 (2015$). If all the single symptoms evaluated by the studies are not severe, then the estimate of WTP to avoid a MRRAD should be somewhere between $28.51 and $110.62. Because the IEc estimate of $69.58 (2015$) falls within this range (and acknowledging the degree of arbitrariness associated with any estimate within this range), we use the IEc estimate of $69.58 (2015$) (Table 21).
School Loss Days
We include two costs of school loss days: caregiver costs and loss of learning. We calculate each separately and then sum them. Caregiver costs are valued at their employers' average cost for employed caregivers. For unemployed caregivers, the opportunity cost of their time is calculated as the average take-home pay. The loss of learning is calculated based on the impact of absences on learning multiplied by the impact of school learning on adult earnings. The loss of learning estimate is currently only available for middle and high school students. The two costs are summed.
The caregiver costs assumes that an adult caregiver stays home with the child and loses any wage income they would have earned that day. For working caregivers, we follow EPA guidance and value their time at the average wage including fringe benefits and overhead costs (U.S. EPA, 2020a). The average daily wage in 2021 was $195 (2015$, assumed to be the average weekly wage divided by 5, US Bureau of Labor Statistics, 2021a), which yields an average daily labor cost of $340 for employed parents applying average multiplier of 1.46 for fringe benefits and 1.2 for overhead. For nonworking caregivers, we assume that the opportunity cost of time is the average after tax earnings. We estimate the income tax rate for a median household to be 7%, yielding net earnings of $195*0.93 or $181 ($2015). The income tax rate of 7% is the percentage difference in median post-tax income and median income from (U.S. Census Bureau, 2021).
The probability that a parent is working is measured with the employment population ratio among people with their own children under 18 and is 77.2% (US Bureau of Labor Statistics, 2021b). Combining the cost of working and nonworking caregivers yields a caregiver cost of $305 per school loss day.
To measure the loss of learning, we update the Liu et al., 2021 estimate. Liu et al., 2021 estimated the impact of a school absence on learnings as measured by an end-of-course test score.  We multiply by an estimate of the impact of learning as measured by end-of-course test scores on adult income from Chetty et al., 2014. This approach yields an estimated learning loss of $2,230 per school absence (discounted at 3%) or $975 per school absence (discounted at 7%).
We updated the Chetty et al., 2014 estimate to use 2010 income and to estimate lifetime incomes discounted at 3% and 7%.  Liu et al., 2021 provide an estimate that a school absence leads to a $1,200 reduction in lifetime earnings, based on the Chetty et al., 2014 estimate that lifetime earnings are $522,000 (2010$).  We use 2010 ACS data from IPUMS to calculate expected lifetime earnings of $892,579 (discounting at 3%) and $390,393 (discounting at 7%).   We then multiply the Liu et al., 2021 estimate of $1200 by (892,579/522,000) and (390,393/522,000) and convert from 2010 dollars to 2015 dollars based on the Consumer Price Index for All Urban Consumers.
We use caregiver costs for preschool and elementary school children and the sum of caregiver costs and loss of learning for middle school and high school students. We calculate that 31% of children under 18 are middle school and high school ages 13-18, assuming each bin distributed equally), so the combined average effect is ($305 + $2,230*0.31) or $1000 with 3% discounting, or ($305 + $975*0.31) or $610 with 7% discounting (U.S. Census Bureau, 2010).

A unit value based on the approach described above is likely to understate the value of a school loss day in at least four ways:
 It omits WTP to avoid the symptoms/illness which resulted in the school absence
 It omits the opportunity cost of time for non-working caregivers' day
 The approach omits other aspects of school attendance such as social and emotional development or meals
 It does not account for deleterious effects on student learning in other subjects.
Stroke
Mu et al., 2017 ADDIN EN.CITE <EndNote><Author>Mu</Author><Year>2017</Year><RecNum>1294</RecNum><DisplayText>Mu et al., 2017</DisplayText><record><rec-number>1294</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608217657">1294</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Mu, F</author><author>Hurley, D</author><author>Betts, KA</author><author>Messali, AJ</author><author>Paschoalin, M</author><author>Kelley, C</author><author>Wu, EQ</author></authors></contributors><titles><title>Real-world costs of ischemic stroke by discharge status</title><secondary-title>Current medical research and opinion</secondary-title></titles><periodical><full-title>Current medical research and opinion</full-title></periodical><pages>371-378</pages><volume>33</volume><number>2</number><dates><year>2017</year></dates><isbn>0300-7995</isbn><urls></urls></record></EndNote> estimates COI of non-fatal stroke incidence using direct medical costs incurred during initial hospitalization and the 360 days following hospital discharge. The study identifies individuals experiencing a first-time stroke using ICD-9 codes 434 and 436. The authors analyze medical claims from January 2006 to March 2015 utilizing the retrospective IMS LifeLink PharMetrics Plus database for individuals ages 18 to 65, and Medicare Advantage and Medicare Supplemental Claims for individuals above the age of 65. The authors present acute care and long-term care costs stratified by three discharge classifications: dead at discharge, discharged with disability, and discharged without disability. We estimate the average costs for non-fatal cases by weighting the costs for individuals discharged with disability and without disability by their prevalence (23 and 77 percent, respectively). The resulting COI for non-fatal stroke incidence is $33,962 (2015$) (Table 21). This value reflects one-year medical costs following stroke and does not include hospitalization costs, as these costs are separately captured by hospitalization valuation functions. We reviewed several studies that estimate longer-term medical costs (Goodwin et al., 2011, Lee et al., 2007, Luengo-Fernandez et al., 2012, Nicholson et al., 2016) and concluded that roughly three quarters of costs are incurred in first year after stroke occurrence.
Work Loss Days (WLDs)
Work loss days are valued at a day's wage. BenMAP calculates county-specific median daily wages from county-specific annual wages by dividing by (52*5), on the theory that a worker's vacation days are valued at the same daily rate as workdays. This estimate does not include benefits rate for lost work time. The resulting COI for work loss days varies by county but has a median value of $150 (2015$) (IEc, 1993 ADDIN EN.CITE <EndNote><Author>IEc</Author><Year>1993</Year><RecNum>1385</RecNum><DisplayText>IEc, 1993</DisplayText><record><rec-number>1385</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1609785896">1385</key></foreign-keys><ref-type name="Personal Communication">26</ref-type><contributors><authors><author>IEc,</author></authors></contributors><titles><title>Memorandum to Jim DeMocker, U.S. Environmental Protection Agency, Office of Air and Radiation, Office of Policy Analysis and Review. September 30. </title></titles><dates><year>1993</year></dates><publisher>U.S. EPA</publisher><urls></urls></record></EndNote>) (Table 21 REF _Ref59095919 \h ).
Developing Income Growth Adjustment Factors for Health Endpoint Onset/Occurrence
Chapter 4 of the BenMAP-CE User Manual provides instructions for formatting and adding income growth data (U.S. EPA, 2018 ADDIN EN.CITE <EndNote><Author>U.S. EPA</Author><Year>2018</Year><RecNum>629</RecNum><DisplayText>U.S. EPA, 2018</DisplayText><record><rec-number>629</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732120">629</key></foreign-keys><ref-type name="Government Document">46</ref-type><contributors><authors><author>U.S. EPA, </author></authors><secondary-authors><author>U.S. EPA </author></secondary-authors></contributors><titles><title>Environmental Benefits Mapping and Analysis Program  -  Community Edition User's Manual</title></titles><volume>Updated for BenMAP-CE Version 1.4.8</volume><dates><year>2018</year></dates><pub-location>Research Triangle Park, NC</pub-location><publisher>Office of Air Quality Planning and Standards</publisher><urls><related-urls><url>Available at: https://www.epa.gov/sites/production/files/2015-04/documents/benmap-ce_user_manual_march_2015.pdf</url></related-urls></urls></record></EndNote>). These values are used to adjust WTP estimates for growth in real income. As discussed in that chapter, evidence and theory suggest that WTP should increase as real income increases. When reviewing the economic literature to develop income growth adjustment factors, it is important to have an economist assist. For an overview of valuation, see Chapter 7 of the BenMAP-CE User Manual, "Aggregating, Pooling, and Valuing".
Adjusting WTP to reflect growth in real income requires three steps:
           Identify relevant income elasticity estimates from the peer-reviewed literature.
           Calculate changes in future income.
           Calculate adjustments to WTP based on changes in future income and income elasticity estimates.
1. Identifying income elasticity estimates
Income elasticity estimates relate changes in demand for goods to changes in income. Positive income elasticity suggests that as income rises, demand for the good also rises. Negative income elasticity suggests that as income rises, demand for the good falls. We do not adjust COI estimates according to changes in income elasticity due to the fact that COI estimates the direct cost of a health outcome; instead, we adjust this metric using inflation factors described above. We include income elasticity estimates specific to the type of health endpoint associated with the WTP estimate for three types of health effects: minor, severe and mortality. Minor health effects are those of short duration. Severe, or chronic, health effects are of longer duration. Consistent with economic theory, the peer reviewed literature indicates that income elasticity varies according to the severity of the health effect. A review of the literature revealed a range of income elasticity estimates that varied across the studies and according to the severity of health effect. Table 31 summarizes the income elasticity estimates for minor health effect, severe health effect and mortality. Here we have provided a lower, upper, and central elasticity estimate for each type of health endpoint.
Table 31. Income Elasticity Estimates for Minor Health Effects, Severe Health Effects, and Mortality
Health Endpoint
Lower Bound
Central Estimate
Upper Bound
Minor Health Effect
                                     0.04
                                     0.15
                                     0.30
Severe and Chronic Health Effects
                                     0.25
                                     0.45
                                     0.60
Mortality
                                     0.08
                                     0.40
                                     1.00

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

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

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

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

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

Characterizing Uncertainty and Evaluating Sensitivity to Alternate Assumptions
Complex analyses such as the one presented in the final RIA for the Revised CSAPR Update rule use many estimated parameters and inputs. The approach for estimating PM2.5 and O3 benefits includes health effect risk estimates from epidemiologic studies, population data, population growth estimates, economic data for monetizing benefits, and assumptions regarding the future state of the world (i.e., on-the-books regulations). When the uncertainties from each stage of the analysis are compounded, even small uncertainties can have large effects on the total quantified benefits.
After reviewing the EPA's approach to quantifying benefits, the National Research Council (National Resources Council, 2004) highlighted the need to conduct rigorous quantitative analyses of uncertainty and to present benefits estimates to decision makers in ways that foster an appropriate appreciation of their inherent uncertainty. Since the publication of these reports, the EPA has continued improving its techniques for characterizing uncertainty in the estimated air pollution-attributable benefits.
In light of these recommendations, we incorporate new quantitative and qualitative characterizations of uncertainty. Where possible, we quantitatively assess uncertainty in each input parameter (for example, we characterize statistical uncertainty by performing Monte Carlo simulations). We invest the time and resources in performing the most comprehensive uncertainty analyses for those input parameters that most greatly influence on the size of the estimated health impacts.
In some cases, this type of quantitative analysis is not possible due to lack of data, so we instead characterize the sensitivity of the results to alternative plausible input parameters. And, for some inputs into the benefits analysis, such as the air quality data, we lack the data to perform either a quantitative uncertainty analysis or sensitivity analysis.
Sections 6.1 and 6.2 quantitatively describe the uncertainty associated with estimated PM2.5 and O3-attributable incidence. Section 6.3 provides information on the sensitivity to more granular baseline incidence rates. Section 6.4 quantitatively discusses the influence of uncertainty in the economic valuation functions. Lastly, section 6.5 qualitatively discusses the various potential sources of uncertainty, sometimes including sources of uncertainty touched upon quantitatively.
Quantitative Characterization of PM2.5 Uncertainty and Evaluating Sensitivity to Alternate PM2.5 Assumptions
Below we describe our approach for characterizing uncertainty in the estimated PM2.5-related effects. We start first with the role of Monte Carlo assessment in generating a quantitative distribution of results. We next describe how alternative risk estimates can be useful for assessing the sensitivity of the estimated PM2.5-related mortality and morbidity to plausible alternative input parameters; this gives insight to the influence of the functional form of the model or alternative epidemiologic approaches. Quantitative sensitivity analyses using alternative or additional risk estimates are included for the following PM2.5-attributable health endpoints: mortality in adults (section 6.1.1), asthma onset in children (section 6.1.3), cardiovascular hospital admissions (section 6.1.4), and respiratory hospital admissions (section 0).
Statistical Uncertainty Around the Risk Estimate (Monte-Carlo Assessment)
For all endpoints analyzed, we use a Monte Carlo simulation in which we sample from the standard error associated with each risk estimate and present the resulting 2.5[th] and 97.5[th] percentile values from this distribution as a 95[th] percentile confidence interval around the estimated health impact and monetized health benefits. Monte Carlo methods are a well-established means of characterizing random sampling error associated with the risk estimates from epidemiological studies. This approach randomly samples from a distribution of incidence and valuation estimates to characterize the effects of uncertainty in those inputs on output variables. The reported standard errors in the epidemiological studies determined the distributions for individual effect estimates for endpoints estimated using a single study. The confidence intervals around the monetized benefits incorporate the epidemiology standard errors as well as the distribution of the valuation function. These confidence intervals do not reflect other sources of uncertainty inherent within the estimates, such as baseline incidence rates, populations exposed, and transferability of the effect estimate to diverse locations. As a result, the reported confidence intervals and range of estimates give an incomplete picture about the overall uncertainty in the benefits estimates.
Adult All-Cause Mortality
Three studies of all-cause, long-term PM2.5 exposure and mortality were identified as best characterizing U.S. risk in adults, Pope III et al., 2019, Turner et al., 2016, Wu et al., 2020. ADDIN EN.CITE <EndNote><Author>Di</Author><Year>2017</Year><RecNum>536</RecNum><DisplayText>Di et al., 2017b</DisplayText><record><rec-number>536</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732083">536</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Di, Q.</author><author>Wang, Y.</author><author>Zanobetti, A.</author><author>Wang, Y.</author><author>Koutrakis, P.</author><author>Choirat, C.</author><author>Dominici, F.</author><author>Schwartz, J. D.</author></authors></contributors><titles><title>Air pollution and mortality in the Medicare population</title><secondary-title>New England Journal of Medicine</secondary-title><alt-title>N Engl J Med</alt-title></titles><periodical><full-title>New England Journal of Medicine</full-title>New Engl. J. Med.New Engl J Med</periodical><alt-periodical><full-title>N Engl J Med</full-title></alt-periodical><pages>2513-2522</pages><volume>376</volume><number>26</number><edition>June 29, 2017</edition><dates><year>2017</year><pub-dates><date>06</date></pub-dates></dates><isbn>ISSN 0028-4793
EISSN 1533-4406</isbn><accession-num>28657878</accession-num>3841341<urls><related-urls><url>http://dx.doi.org/10.1056/NEJMoa1702747</url></related-urls></urls><electronic-resource-num>10.1056/NEJMoa1702747</electronic-resource-num><language>English</language></record></EndNote> ADDIN EN.CITE <EndNote><Author>Turner</Author><Year>2016</Year><RecNum>544</RecNum><DisplayText>Turner et al., 2016</DisplayText><record><rec-number>544</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732084">544</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Turner, M. C.</author><author>Jerrett, M.</author><author>Pope, A., III</author><author>Krewski, D.</author><author>Gapstur, S. M.</author><author>Diver, W. R.</author><author>Beckerman, B. S.</author><author>Marshall, J. D.</author><author>Su, J.</author><author>Crouse, D. L.</author><author>Burnett, R. T.</author></authors></contributors><titles><title>Long-term ozone exposure and mortality in a large prospective study</title><secondary-title>American Journal of Respiratory and Critical Care Medicine</secondary-title><alt-title>Am J Respir Crit Care Med</alt-title></titles><periodical><full-title>American Journal of Respiratory and Critical Care Medicine</full-title>Am. J. Respir. Crit. Care Med.Am J Respir Crit Care Med</periodical><alt-periodical><full-title>American Journal of Respiratory and Critical Care Medicine</full-title>Am. J. Respir. Crit. Care Med.Am J Respir Crit Care Med</alt-periodical><pages>1134-1142</pages><volume>193</volume><number>10</number><dates><year>2016</year></dates><isbn>ISSN 1073-449X
EISSN 1535-4970</isbn><accession-num>26680605</accession-num>3060878<urls><related-urls><url>http://dx.doi.org/10.1164/rccm.201508-1633OC</url></related-urls></urls><electronic-resource-num>10.1164/rccm.201508-1633OC</electronic-resource-num><language>English</language></record></EndNote> Additional information regarding the cohort concentration exposure distributions (section <IP_ADDRESS>) and additional risk estimates potentially providing insight into the effect of various potential sources of uncertainty, such as different exposure estimation techniques (section <IP_ADDRESS>), confounding by O3 (section <IP_ADDRESS>), statistical regression techniques and methods to control for confounders (section <IP_ADDRESS>), and effect modification by individual risk factors (section 6.1.6).
Low Concentration Exposures
Each epidemiological risk estimate is based on a distribution of air quality concentrations experienced by the original cohort population. As such, it is important to consider the relationship between the concentrations from which the mortality estimates are derived and the concentrations at which the estimates are subsequently applied in future policy scenarios in which concentrations are likely to be lower due to decreasing air pollution trends. When estimating health impacts, we are most confident in results estimated using projected air quality concentrations that closely align with those observed in the epidemiological study from which the risk estimate was obtained (i.e., we are less confident applying risk estimates to pollutant concentrations that do not match the original cohort due to changes in air pollutant concentrations over time). To address the potential mismatch between projected air quality levels and those in the epidemiologic study, we include air quality information from the original epidemiologic studies where feasible.
Additional information was requested from mortality study authors regarding the ambient concentrations used to estimate exposure of the original cohort. Study authors provided cohort specific PM2.5 concentration data at varying levels of detail. PM2.5 concentrations for the three long-term exposure epidemiologic cohort studies examining mortality, ACS CSP-II and Medicare are presented in Figure 12 (Di et al., 2017b, Turner et al., 2016, Wu et al., 2020); the distribution of PM exposure incorporated in the Pope III et al., 2019 study is reflected in the histogram reported below (Figure 13). We also included the distribution of PM2.5 concentrations from a recent analysis of the CanCHEC cohort in order to compare to some of the lowest reported concentrations in North America (110111010xd5s9009txfw1eafstv220i5ds2wvtpwf5d1603732551">1101..</>Environ Health Perspect</abbr-2. Health Perspect.Environ2Crouse et al., 2015). Points reflect cohort specific PM2.5 concentration data, with connecting lines estimating missing data.

Figure 12. Cumulative Percentile of PM2.5 Cohort Exposure from the ACS CSP-II, Medicare and CanCHEC Cohorts

As air pollution concentrations continue to decline an increasing fraction of the population will be exposed to PM2.5 concentrations at the lower end of the air quality distribution experienced by the study cohort. The distribution of PM2.5 concentrations for each of three large, long-term exposure cohorts are provided in Table 33. For comparison, the lowest reported PM2.5 concentrations from previous studies (Krewski et al., 2009, Lepeule et al., 2012 ADDIN EN.CITE <EndNote><Author>Krewski</Author><Year>2009</Year><RecNum>554</RecNum><DisplayText>Krewski et al., 2009, Lepeule et al., 2012</DisplayText><record><rec-number>554</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732085">554</key></foreign-keys><ref-type name="Book">6</ref-type><contributors><authors><author>Krewski, Daniel</author><author>Jerrett, Michael</author><author>Burnett, Richard T</author><author>Ma, Renjun</author><author>Hughes, Edward</author><author>Shi, Yuanli</author><author>Turner, Michelle C</author><author>Pope III, C Arden</author><author>Thurston, George</author><author>Calle, Eugenia E</author></authors></contributors><titles><title>Extended follow-up and spatial analysis of the American Cancer Society study linking particulate air pollution and mortality</title></titles><number>140</number><dates><year>2009</year></dates><publisher>Health Effects Institute Boston, MA</publisher><urls></urls></record><Author>Lepeule</Author><Year>2012</Year><RecNum>614</RecNum><record><rec-number>614</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732117">614</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Lepeule, Johanna</author><author>Laden, Francine</author><author>Dockery, Douglas</author><author>Schwartz, Joel %J Environmental health perspectives</author></authors></contributors><titles><title>Chronic exposure to fine particles and mortality: an extended follow-up of the Harvard Six Cities study from 1974 to 2009</title><secondary-title>Environmental Health Perspectives</secondary-title></titles><periodical><full-title>Environmental Health Perspectives</full-title>Environ. Health Perspect.Environ Health Perspect</periodical><pages>965-970</pages><volume>120</volume><number>7</number><dates><year>2012</year></dates><isbn>0091-6765</isbn><urls></urls></record></EndNote>) in which risk estimates were used to estimate all-cause mortality attributed to long-term PM2.5 exposure were 5.8 and 8.0 ug/m[3], whereas the recent studies identified as best characterizing long-term PM2.5 exposure and the risk of all-cause mortality include PM2.5 concentrations below 3 ug/m[3] (Di et al., 2017b, Pope III et al., 2019, Turner et al., 2016, Wu et al., 2020). Pope and co-authors separately reported the exposure distribution (Higbee et al., 2020); we report the histogram from this manuscript below.
Table 33. Low Concentration PM2.5 Exposures from the ACS CSP-II, Medicare and CanCHEC Cohorts
                                    Cohort
                   Percentile of Cohort Exposure (ug/m[3])

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

                                      6.3
                                      6.5
                                      6.6

                                      7.0

                                      7.4

Medicare
                                    0.0[A]
                                      3.2
                                      3.5
                                      3.8
                                      4.1
                                      4.3
                                      4.5
                                      4.7
                                      4.9
                                      5.0
                                      6.1
CanCHEC
                                      0.0
                                      3.2

                                      3.5
                                      3.6

                                      4.0
                                      4.7
[A] Lowest modeled value is 0.0078 ug/m[3]

Figure 13. Modeled PM2.5 Exposure Distribution for NHIS Study Population, with Select Fitted Probability Density Functions (from Higbee et al., 2020, publisher permission pending)

PM2.5 concentrations reported in cohort studies are not equivalent to NAAQS design values (DVs). Information relating PM2.5 concentrations from cohort studies discussed within this section to PM2.5 DVs can be found in section 3.2 of the 2022 PM PA<PHONE_NUMBER>0c3220xd5s9009txfw1eafstv220i5ds2wvtpwf5d1573071274">322Review</January<PHONE_NUMBER>-002https://www.epa.gov/naaqs/particulate-matter-pm-standards-policy-assessments-current-review-0U.S. EPA, 2022a).
Estimating and Assigning Exposures in Epidemiology Studies
New developments in exposure assessment, including hybrid spatiotemporal models that incorporate satellite observations of AOD, land use variables, surface monitoring data from monitors, and chemical transport models, have led to improvements in the spatial resolution and extent of pollutant concentration surfaces. After reviewing the current state of exposure science, the 2019 PM ISA stated that "a number of studies demonstrate that the positive associations observed between long-term PM2.5 exposure and mortality are robust to different methods of assigning exposure" and the 2020 O3 ISA articulated that "hybrid methods have produced lower error predictions of ozone concentration compared with spatiotemporal models using land use and other geospatial data alone but may be subject to overfitting given the many different sources of data incorporated into the hybrid framework" (U.S. EPA, 2019b).
Although these advancements may reduce bias and uncertainty in risk estimates, the accuracy of hybrid exposure estimates can be difficult to confirm in areas lacking monitors. On the other hand, studies using monitor data as the only exposure information have increasing exposure uncertainty the farther people live from the monitor site.
Turner et al., 2016 and Pope et al., 2015 analyzed the same ACS CSP-II population over the same time period but used different hybrid exposure estimation techniques. Turner et al., 2016 used the hierarchical Bayesian space - time model (HBM) approach, which combines ambient measurement data with gridded estimates from the CMAQ photochemical model. Pope et al., 2015 used a land use regression model with Bayesian Maximum Entropy kriging of residuals (LURBME). Sensitivity of the risk estimate to the exposure estimation technique is available in Table 34, including the estimate identified for the main benefits assessment in italics.
Table 34. ADDIN EN.CITE <EndNote><Author>Di</Author><Year>2017</Year><RecNum>1169</RecNum><DisplayText>Di et al., 2017a</DisplayText><record><rec-number>1169</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603987569">1169</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Di, Q.</author><author>Dai, L.</author><author>Wang, Y.</author><author>Zanobetti, A.</author><author>Choirat, C.</author><author>Schwartz, J. D.</author><author>Dominici, F.</author></authors></contributors><titles><title>Association of short-term exposure to air pollution with mortality in older adults</title><secondary-title>JAMA: Journal of the American Medical Association</secondary-title><alt-title>JAMA</alt-title></titles><periodical><full-title>JAMA: Journal of the American Medical Association</full-title>J. Am. Med. Assoc.J Am Med Assoc</periodical><alt-periodical><full-title>JAMA</full-title></alt-periodical><pages>2446-2456</pages><volume>318</volume><number>24</number><edition>December 26, 2017</edition><dates><year>2017</year><pub-dates><date>12</date></pub-dates></dates><isbn>ISSN 0098-7484
EISSN 1538-3598</isbn><accession-num>29279932</accession-num>4166831<urls><related-urls><url>http://dx.doi.org/10.1001/jama.2017.17923</url></related-urls></urls><electronic-resource-num>10.1001/jama.2017.17923</electronic-resource-num><language>English</language></record></EndNote> REF _Ref60064903 \h
SEQ Table \* ARABIC ADDIN EN.CITE <EndNote><Author>Di</Author><Year>2017</Year><RecNum>1169</RecNum><DisplayText>Di et al., 2017a</DisplayText><record><rec-number>1169</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603987569">1169</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Di, Q.</author><author>Dai, L.</author><author>Wang, Y.</author><author>Zanobetti, A.</author><author>Choirat, C.</author><author>Schwartz, J. D.</author><author>Dominici, F.</author></authors></contributors><titles><title>Association of short-term exposure to air pollution with mortality in older adults</title><secondary-title>JAMA: Journal of the American Medical Association</secondary-title><alt-title>JAMA</alt-title></titles><periodical><full-title>JAMA: Journal of the American Medical Association</full-title>J. Am. Med. Assoc.J Am Med Assoc</periodical><alt-periodical><full-title>JAMA</full-title></alt-periodical><pages>2446-2456</pages><volume>318</volume><number>24</number><edition>December 26, 2017</edition><dates><year>2017</year><pub-dates><date>12</date></pub-dates></dates><isbn>ISSN 0098-7484
EISSN 1538-3598</isbn><accession-num>29279932</accession-num>4166831<urls><related-urls><url>http://dx.doi.org/10.1001/jama.2017.17923</url></related-urls></urls><electronic-resource-num>10.1001/jama.2017.17923</electronic-resource-num><language>English</language></record></EndNote>