ADDIN EN.CITE <EndNote><Author>Tetreault</Author><Year>2016</Year><RecNum>1183</RecNum><DisplayText>Tetreault et al., 2016</DisplayText><record><rec-number>1183</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603988120">1183</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Tetreault, L. F.</author><author>Doucet, M.</author><author>Gamache, P.</author><author>Fournier, M.</author><author>Brand, A.</author><author>Kosatsky, T.</author><author>Smargiassi, A.</author></authors></contributors><auth-address>Department of Environmental and Occupational Health, School of Public Health University of Montreal, Montreal, Quebec, Canada.</auth-address><titles><title>Childhood Exposure to Ambient Air Pollutants and the Onset of Asthma: An Administrative Cohort Study in Quebec</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>1276-82</pages><volume>124</volume><number>8</number><edition>2016/01/06</edition><keywords><keyword>Air Pollutants/analysis</keyword><keyword>Asthma/*epidemiology</keyword><keyword>Environmental Exposure/analysis/*statistics & numerical data</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Male</keyword><keyword>Nitrogen Dioxide/analysis</keyword><keyword>Particulate Matter/analysis</keyword><keyword>Proportional Hazards Models</keyword><keyword>Quebec/epidemiology</keyword></keywords><dates><year>2016</year><pub-dates><date>Aug</date></pub-dates></dates><isbn>1552-9924 (Electronic)
0091-6765 (Linking)</isbn><accession-num>26731790</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/26731790</url></related-urls></urls><custom2>PMC4977042</custom2><electronic-resource-num>10.1289/ehp.1509838</electronic-resource-num></record></EndNote> ADDIN EN.CITE <EndNote><Author>Tetreault</Author><Year>2016</Year><RecNum>1183</RecNum><DisplayText>Tetreault et al., 2016</DisplayText><record><rec-number>1183</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603988120">1183</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Tetreault, L. F.</author><author>Doucet, M.</author><author>Gamache, P.</author><author>Fournier, M.</author><author>Brand, A.</author><author>Kosatsky, T.</author><author>Smargiassi, A.</author></authors></contributors><auth-address>Department of Environmental and Occupational Health, School of Public Health University of Montreal, Montreal, Quebec, Canada.</auth-address><titles><title>Childhood Exposure to Ambient Air Pollutants and the Onset of Asthma: An Administrative Cohort Study in Quebec</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>1276-82</pages><volume>124</volume><number>8</number><edition>2016/01/06</edition><keywords><keyword>Air Pollutants/analysis</keyword><keyword>Asthma/*epidemiology</keyword><keyword>Environmental Exposure/analysis/*statistics & numerical data</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Male</keyword><keyword>Nitrogen Dioxide/analysis</keyword><keyword>Particulate Matter/analysis</keyword><keyword>Proportional Hazards Models</keyword><keyword>Quebec/epidemiology</keyword></keywords><dates><year>2016</year><pub-dates><date>Aug</date></pub-dates></dates><isbn>1552-9924 (Electronic)
0091-6765 (Linking)</isbn><accession-num>26731790</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/26731790</url></related-urls></urls><custom2>PMC4977042</custom2><electronic-resource-num>10.1289/ehp.1509838</electronic-resource-num></record></EndNote> ADDIN EN.CITE <EndNote><Author>Tetreault</Author><Year>2016</Year><RecNum>1183</RecNum><DisplayText>Tetreault et al., 2016</DisplayText><record><rec-number>1183</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603988120">1183</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Tetreault, L. F.</author><author>Doucet, M.</author><author>Gamache, P.</author><author>Fournier, M.</author><author>Brand, A.</author><author>Kosatsky, T.</author><author>Smargiassi, A.</author></authors></contributors><auth-address>Department of Environmental and Occupational Health, School of Public Health University of Montreal, Montreal, Quebec, Canada.</auth-address><titles><title>Childhood Exposure to Ambient Air Pollutants and the Onset of Asthma: An Administrative Cohort Study in Quebec</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>1276-82</pages><volume>124</volume><number>8</number><edition>2016/01/06</edition><keywords><keyword>Air Pollutants/analysis</keyword><keyword>Asthma/*epidemiology</keyword><keyword>Environmental Exposure/analysis/*statistics & numerical data</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Male</keyword><keyword>Nitrogen Dioxide/analysis</keyword><keyword>Particulate Matter/analysis</keyword><keyword>Proportional Hazards Models</keyword><keyword>Quebec/epidemiology</keyword></keywords><dates><year>2016</year><pub-dates><date>Aug</date></pub-dates></dates><isbn>1552-9924 (Electronic)
0091-6765 (Linking)</isbn><accession-num>26731790</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/26731790</url></related-urls></urls><custom2>PMC4977042</custom2><electronic-resource-num>10.1289/ehp.1509838</electronic-resource-num></record></EndNote> ADDIN EN.CITE <EndNote><Author>Garcia</Author><Year>2019</Year><RecNum>1207</RecNum><DisplayText>Garcia et al., 2019</DisplayText><record><rec-number>1207</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1604357019">1207</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Garcia, E.</author><author>Berhane, K. T.</author><author>Islam, T.</author><author>McConnell, R.</author><author>Urman, R.</author><author>Chen, Z.</author><author>Gilliland, F. D.</author></authors></contributors><auth-address>Department of Preventive Medicine, University of Southern California, Los Angeles.</auth-address><titles><title>Association of Changes in Air Quality With Incident Asthma in Children in California, 1993-2014</title><secondary-title>JAMA</secondary-title></titles><periodical><full-title>JAMA</full-title></periodical><pages>1906-1915</pages><volume>321</volume><number>19</number><edition>2019/05/22</edition><keywords><keyword>Air Pollutants/*adverse effects/analysis</keyword><keyword>Air Pollution/*adverse effects/analysis</keyword><keyword>Asthma/*epidemiology/etiology</keyword><keyword>California/epidemiology</keyword><keyword>Child</keyword><keyword>Environmental Exposure/adverse effects/analysis</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Incidence</keyword><keyword>Longitudinal Studies</keyword><keyword>Male</keyword><keyword>Nitrogen Dioxide/*adverse effects/analysis</keyword><keyword>Ozone/adverse effects/analysis</keyword><keyword>Particulate Matter/*adverse effects/analysis</keyword></keywords><dates><year>2019</year><pub-dates><date>May 21</date></pub-dates></dates><isbn>1538-3598 (Electronic)
0098-7484 (Linking)</isbn><accession-num>31112259</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/31112259</url></related-urls></urls><custom2>PMC6537847</custom2><electronic-resource-num>10.1001/jama.2019.5357</electronic-resource-num></record></EndNote>Table 50. Details about the study providing an alternate risk estimate is below.
Table 50. Potential Sensitivity of Estimated Instances of Asthma Onset
                        Potential Source of Uncertainty
      Potential Insights Gained from Quantitative Uncertainty Analyses[2]
Application of Risk Estimates to Other Locations and Populations
Tetreault et al., 2016 included only Canadians whereas Garcia et al., 2019 was restricted to southern CA ADDIN EN.CITE <EndNote><Author>Tetreault</Author><Year>2016</Year><RecNum>1183</RecNum><DisplayText>Tetreault et al., 2016</DisplayText><record><rec-number>1183</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603988120">1183</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Tetreault, L. F.</author><author>Doucet, M.</author><author>Gamache, P.</author><author>Fournier, M.</author><author>Brand, A.</author><author>Kosatsky, T.</author><author>Smargiassi, A.</author></authors></contributors><auth-address>Department of Environmental and Occupational Health, School of Public Health University of Montreal, Montreal, Quebec, Canada.</auth-address><titles><title>Childhood Exposure to Ambient Air Pollutants and the Onset of Asthma: An Administrative Cohort Study in Quebec</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>1276-82</pages><volume>124</volume><number>8</number><edition>2016/01/06</edition><keywords><keyword>Air Pollutants/analysis</keyword><keyword>Asthma/*epidemiology</keyword><keyword>Environmental Exposure/analysis/*statistics & numerical data</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Male</keyword><keyword>Nitrogen Dioxide/analysis</keyword><keyword>Particulate Matter/analysis</keyword><keyword>Proportional Hazards Models</keyword><keyword>Quebec/epidemiology</keyword></keywords><dates><year>2016</year><pub-dates><date>Aug</date></pub-dates></dates><isbn>1552-9924 (Electronic)
0091-6765 (Linking)</isbn><accession-num>26731790</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/26731790</url></related-urls></urls><custom2>PMC4977042</custom2><electronic-resource-num>10.1289/ehp.1509838</electronic-resource-num></record></EndNote> ADDIN EN.CITE <EndNote><Author>Garcia</Author><Year>2019</Year><RecNum>1207</RecNum><DisplayText>Garcia et al., 2019</DisplayText><record><rec-number>1207</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1604357019">1207</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Garcia, E.</author><author>Berhane, K. T.</author><author>Islam, T.</author><author>McConnell, R.</author><author>Urman, R.</author><author>Chen, Z.</author><author>Gilliland, F. D.</author></authors></contributors><auth-address>Department of Preventive Medicine, University of Southern California, Los Angeles.</auth-address><titles><title>Association of Changes in Air Quality With Incident Asthma in Children in California, 1993-2014</title><secondary-title>JAMA</secondary-title></titles><periodical><full-title>JAMA</full-title></periodical><pages>1906-1915</pages><volume>321</volume><number>19</number><edition>2019/05/22</edition><keywords><keyword>Air Pollutants/*adverse effects/analysis</keyword><keyword>Air Pollution/*adverse effects/analysis</keyword><keyword>Asthma/*epidemiology/etiology</keyword><keyword>California/epidemiology</keyword><keyword>Child</keyword><keyword>Environmental Exposure/adverse effects/analysis</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Incidence</keyword><keyword>Longitudinal Studies</keyword><keyword>Male</keyword><keyword>Nitrogen Dioxide/*adverse effects/analysis</keyword><keyword>Ozone/adverse effects/analysis</keyword><keyword>Particulate Matter/*adverse effects/analysis</keyword></keywords><dates><year>2019</year><pub-dates><date>May 21</date></pub-dates></dates><isbn>1538-3598 (Electronic)
0098-7484 (Linking)</isbn><accession-num>31112259</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/31112259</url></related-urls></urls><custom2>PMC6537847</custom2><electronic-resource-num>10.1001/jama.2019.5357</electronic-resource-num></record></EndNote>
Study Size
Tetreault et al., 2016 included the largest study size, approximately twenty-five times the size of Garcia et al., 2019 ADDIN EN.CITE <EndNote><Author>Tetreault</Author><Year>2016</Year><RecNum>1183</RecNum><DisplayText>Tetreault et al., 2016</DisplayText><record><rec-number>1183</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603988120">1183</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Tetreault, L. F.</author><author>Doucet, M.</author><author>Gamache, P.</author><author>Fournier, M.</author><author>Brand, A.</author><author>Kosatsky, T.</author><author>Smargiassi, A.</author></authors></contributors><auth-address>Department of Environmental and Occupational Health, School of Public Health University of Montreal, Montreal, Quebec, Canada.</auth-address><titles><title>Childhood Exposure to Ambient Air Pollutants and the Onset of Asthma: An Administrative Cohort Study in Quebec</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>1276-82</pages><volume>124</volume><number>8</number><edition>2016/01/06</edition><keywords><keyword>Air Pollutants/analysis</keyword><keyword>Asthma/*epidemiology</keyword><keyword>Environmental Exposure/analysis/*statistics & numerical data</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Male</keyword><keyword>Nitrogen Dioxide/analysis</keyword><keyword>Particulate Matter/analysis</keyword><keyword>Proportional Hazards Models</keyword><keyword>Quebec/epidemiology</keyword></keywords><dates><year>2016</year><pub-dates><date>Aug</date></pub-dates></dates><isbn>1552-9924 (Electronic)
0091-6765 (Linking)</isbn><accession-num>26731790</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/26731790</url></related-urls></urls><custom2>PMC4977042</custom2><electronic-resource-num>10.1289/ehp.1509838</electronic-resource-num></record></EndNote> ADDIN EN.CITE <EndNote><Author>Garcia</Author><Year>2019</Year><RecNum>1207</RecNum><DisplayText>Garcia et al., 2019</DisplayText><record><rec-number>1207</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1604357019">1207</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Garcia, E.</author><author>Berhane, K. T.</author><author>Islam, T.</author><author>McConnell, R.</author><author>Urman, R.</author><author>Chen, Z.</author><author>Gilliland, F. D.</author></authors></contributors><auth-address>Department of Preventive Medicine, University of Southern California, Los Angeles.</auth-address><titles><title>Association of Changes in Air Quality With Incident Asthma in Children in California, 1993-2014</title><secondary-title>JAMA</secondary-title></titles><periodical><full-title>JAMA</full-title></periodical><pages>1906-1915</pages><volume>321</volume><number>19</number><edition>2019/05/22</edition><keywords><keyword>Air Pollutants/*adverse effects/analysis</keyword><keyword>Air Pollution/*adverse effects/analysis</keyword><keyword>Asthma/*epidemiology/etiology</keyword><keyword>California/epidemiology</keyword><keyword>Child</keyword><keyword>Environmental Exposure/adverse effects/analysis</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Incidence</keyword><keyword>Longitudinal Studies</keyword><keyword>Male</keyword><keyword>Nitrogen Dioxide/*adverse effects/analysis</keyword><keyword>Ozone/adverse effects/analysis</keyword><keyword>Particulate Matter/*adverse effects/analysis</keyword></keywords><dates><year>2019</year><pub-dates><date>May 21</date></pub-dates></dates><isbn>1538-3598 (Electronic)
0098-7484 (Linking)</isbn><accession-num>31112259</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/31112259</url></related-urls></urls><custom2>PMC6537847</custom2><electronic-resource-num>10.1001/jama.2019.5357</electronic-resource-num></record></EndNote>
Study Period
Garcia et al., 2019 evaluated a more recent and longer health study period (1993-2014) compared to 2002-2011 for Tetreault et al., 2016 ADDIN EN.CITE <EndNote><Author>Garcia</Author><Year>2019</Year><RecNum>1207</RecNum><DisplayText>Garcia et al., 2019</DisplayText><record><rec-number>1207</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1604357019">1207</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Garcia, E.</author><author>Berhane, K. T.</author><author>Islam, T.</author><author>McConnell, R.</author><author>Urman, R.</author><author>Chen, Z.</author><author>Gilliland, F. D.</author></authors></contributors><auth-address>Department of Preventive Medicine, University of Southern California, Los Angeles.</auth-address><titles><title>Association of Changes in Air Quality With Incident Asthma in Children in California, 1993-2014</title><secondary-title>JAMA</secondary-title></titles><periodical><full-title>JAMA</full-title></periodical><pages>1906-1915</pages><volume>321</volume><number>19</number><edition>2019/05/22</edition><keywords><keyword>Air Pollutants/*adverse effects/analysis</keyword><keyword>Air Pollution/*adverse effects/analysis</keyword><keyword>Asthma/*epidemiology/etiology</keyword><keyword>California/epidemiology</keyword><keyword>Child</keyword><keyword>Environmental Exposure/adverse effects/analysis</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Incidence</keyword><keyword>Longitudinal Studies</keyword><keyword>Male</keyword><keyword>Nitrogen Dioxide/*adverse effects/analysis</keyword><keyword>Ozone/adverse effects/analysis</keyword><keyword>Particulate Matter/*adverse effects/analysis</keyword></keywords><dates><year>2019</year><pub-dates><date>May 21</date></pub-dates></dates><isbn>1538-3598 (Electronic)
0098-7484 (Linking)</isbn><accession-num>31112259</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/31112259</url></related-urls></urls><custom2>PMC6537847</custom2><electronic-resource-num>10.1001/jama.2019.5357</electronic-resource-num></record></EndNote> ADDIN EN.CITE <EndNote><Author>Tetreault</Author><Year>2016</Year><RecNum>1183</RecNum><DisplayText>Tetreault et al., 2016</DisplayText><record><rec-number>1183</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603988120">1183</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Tetreault, L. F.</author><author>Doucet, M.</author><author>Gamache, P.</author><author>Fournier, M.</author><author>Brand, A.</author><author>Kosatsky, T.</author><author>Smargiassi, A.</author></authors></contributors><auth-address>Department of Environmental and Occupational Health, School of Public Health University of Montreal, Montreal, Quebec, Canada.</auth-address><titles><title>Childhood Exposure to Ambient Air Pollutants and the Onset of Asthma: An Administrative Cohort Study in Quebec</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>1276-82</pages><volume>124</volume><number>8</number><edition>2016/01/06</edition><keywords><keyword>Air Pollutants/analysis</keyword><keyword>Asthma/*epidemiology</keyword><keyword>Environmental Exposure/analysis/*statistics & numerical data</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Male</keyword><keyword>Nitrogen Dioxide/analysis</keyword><keyword>Particulate Matter/analysis</keyword><keyword>Proportional Hazards Models</keyword><keyword>Quebec/epidemiology</keyword></keywords><dates><year>2016</year><pub-dates><date>Aug</date></pub-dates></dates><isbn>1552-9924 (Electronic)
0091-6765 (Linking)</isbn><accession-num>26731790</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/26731790</url></related-urls></urls><custom2>PMC4977042</custom2><electronic-resource-num>10.1289/ehp.1509838</electronic-resource-num></record></EndNote>
Exposure Estimate
Tetreault et al., 2016 used hybrid exposure estimates whereas sever states, whereas Garcia et al., 2019 used monitor-based estimates ADDIN EN.CITE <EndNote><Author>Tetreault</Author><Year>2016</Year><RecNum>1183</RecNum><DisplayText>Tetreault et al., 2016</DisplayText><record><rec-number>1183</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603988120">1183</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Tetreault, L. F.</author><author>Doucet, M.</author><author>Gamache, P.</author><author>Fournier, M.</author><author>Brand, A.</author><author>Kosatsky, T.</author><author>Smargiassi, A.</author></authors></contributors><auth-address>Department of Environmental and Occupational Health, School of Public Health University of Montreal, Montreal, Quebec, Canada.</auth-address><titles><title>Childhood Exposure to Ambient Air Pollutants and the Onset of Asthma: An Administrative Cohort Study in Quebec</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>1276-82</pages><volume>124</volume><number>8</number><edition>2016/01/06</edition><keywords><keyword>Air Pollutants/analysis</keyword><keyword>Asthma/*epidemiology</keyword><keyword>Environmental Exposure/analysis/*statistics & numerical data</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Male</keyword><keyword>Nitrogen Dioxide/analysis</keyword><keyword>Particulate Matter/analysis</keyword><keyword>Proportional Hazards Models</keyword><keyword>Quebec/epidemiology</keyword></keywords><dates><year>2016</year><pub-dates><date>Aug</date></pub-dates></dates><isbn>1552-9924 (Electronic)
0091-6765 (Linking)</isbn><accession-num>26731790</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/26731790</url></related-urls></urls><custom2>PMC4977042</custom2><electronic-resource-num>10.1289/ehp.1509838</electronic-resource-num></record></EndNote> ADDIN EN.CITE <EndNote><Author>Garcia</Author><Year>2019</Year><RecNum>1207</RecNum><DisplayText>Garcia et al., 2019</DisplayText><record><rec-number>1207</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1604357019">1207</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Garcia, E.</author><author>Berhane, K. T.</author><author>Islam, T.</author><author>McConnell, R.</author><author>Urman, R.</author><author>Chen, Z.</author><author>Gilliland, F. D.</author></authors></contributors><auth-address>Department of Preventive Medicine, University of Southern California, Los Angeles.</auth-address><titles><title>Association of Changes in Air Quality With Incident Asthma in Children in California, 1993-2014</title><secondary-title>JAMA</secondary-title></titles><periodical><full-title>JAMA</full-title></periodical><pages>1906-1915</pages><volume>321</volume><number>19</number><edition>2019/05/22</edition><keywords><keyword>Air Pollutants/*adverse effects/analysis</keyword><keyword>Air Pollution/*adverse effects/analysis</keyword><keyword>Asthma/*epidemiology/etiology</keyword><keyword>California/epidemiology</keyword><keyword>Child</keyword><keyword>Environmental Exposure/adverse effects/analysis</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Incidence</keyword><keyword>Longitudinal Studies</keyword><keyword>Male</keyword><keyword>Nitrogen Dioxide/*adverse effects/analysis</keyword><keyword>Ozone/adverse effects/analysis</keyword><keyword>Particulate Matter/*adverse effects/analysis</keyword></keywords><dates><year>2019</year><pub-dates><date>May 21</date></pub-dates></dates><isbn>1538-3598 (Electronic)
0098-7484 (Linking)</isbn><accession-num>31112259</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/31112259</url></related-urls></urls><custom2>PMC6537847</custom2><electronic-resource-num>10.1001/jama.2019.5357</electronic-resource-num></record></EndNote>
Statistical Technique
Tetreault et al., 2016 used time-varying Cox proportional hazard models, whereas Garcia et al., 2019 uses Poisson log-linear regression models ADDIN EN.CITE <EndNote><Author>Tetreault</Author><Year>2016</Year><RecNum>1183</RecNum><DisplayText>Tetreault et al., 2016</DisplayText><record><rec-number>1183</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603988120">1183</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Tetreault, L. F.</author><author>Doucet, M.</author><author>Gamache, P.</author><author>Fournier, M.</author><author>Brand, A.</author><author>Kosatsky, T.</author><author>Smargiassi, A.</author></authors></contributors><auth-address>Department of Environmental and Occupational Health, School of Public Health University of Montreal, Montreal, Quebec, Canada.</auth-address><titles><title>Childhood Exposure to Ambient Air Pollutants and the Onset of Asthma: An Administrative Cohort Study in Quebec</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>1276-82</pages><volume>124</volume><number>8</number><edition>2016/01/06</edition><keywords><keyword>Air Pollutants/analysis</keyword><keyword>Asthma/*epidemiology</keyword><keyword>Environmental Exposure/analysis/*statistics & numerical data</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Male</keyword><keyword>Nitrogen Dioxide/analysis</keyword><keyword>Particulate Matter/analysis</keyword><keyword>Proportional Hazards Models</keyword><keyword>Quebec/epidemiology</keyword></keywords><dates><year>2016</year><pub-dates><date>Aug</date></pub-dates></dates><isbn>1552-9924 (Electronic)
0091-6765 (Linking)</isbn><accession-num>26731790</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/26731790</url></related-urls></urls><custom2>PMC4977042</custom2><electronic-resource-num>10.1289/ehp.1509838</electronic-resource-num></record></EndNote> ADDIN EN.CITE <EndNote><Author>Garcia</Author><Year>2019</Year><RecNum>1207</RecNum><DisplayText>Garcia et al., 2019</DisplayText><record><rec-number>1207</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1604357019">1207</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Garcia, E.</author><author>Berhane, K. T.</author><author>Islam, T.</author><author>McConnell, R.</author><author>Urman, R.</author><author>Chen, Z.</author><author>Gilliland, F. D.</author></authors></contributors><auth-address>Department of Preventive Medicine, University of Southern California, Los Angeles.</auth-address><titles><title>Association of Changes in Air Quality With Incident Asthma in Children in California, 1993-2014</title><secondary-title>JAMA</secondary-title></titles><periodical><full-title>JAMA</full-title></periodical><pages>1906-1915</pages><volume>321</volume><number>19</number><edition>2019/05/22</edition><keywords><keyword>Air Pollutants/*adverse effects/analysis</keyword><keyword>Air Pollution/*adverse effects/analysis</keyword><keyword>Asthma/*epidemiology/etiology</keyword><keyword>California/epidemiology</keyword><keyword>Child</keyword><keyword>Environmental Exposure/adverse effects/analysis</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Incidence</keyword><keyword>Longitudinal Studies</keyword><keyword>Male</keyword><keyword>Nitrogen Dioxide/*adverse effects/analysis</keyword><keyword>Ozone/adverse effects/analysis</keyword><keyword>Particulate Matter/*adverse effects/analysis</keyword></keywords><dates><year>2019</year><pub-dates><date>May 21</date></pub-dates></dates><isbn>1538-3598 (Electronic)
0098-7484 (Linking)</isbn><accession-num>31112259</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/31112259</url></related-urls></urls><custom2>PMC6537847</custom2><electronic-resource-num>10.1001/jama.2019.5357</electronic-resource-num></record></EndNote>

Three of the four ISA-identified studies of long-term O3-attributable asthma onset took place in the U.S., although only one included a study period more recent than 2005 (Garcia et al., 2019 ADDIN EN.CITE <EndNote><Author>Garcia</Author><Year>2019</Year><RecNum>1207</RecNum><DisplayText>Garcia et al., 2019</DisplayText><record><rec-number>1207</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1604357019">1207</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Garcia, E.</author><author>Berhane, K. T.</author><author>Islam, T.</author><author>McConnell, R.</author><author>Urman, R.</author><author>Chen, Z.</author><author>Gilliland, F. D.</author></authors></contributors><auth-address>Department of Preventive Medicine, University of Southern California, Los Angeles.</auth-address><titles><title>Association of Changes in Air Quality With Incident Asthma in Children in California, 1993-2014</title><secondary-title>JAMA</secondary-title></titles><periodical><full-title>JAMA</full-title></periodical><pages>1906-1915</pages><volume>321</volume><number>19</number><edition>2019/05/22</edition><keywords><keyword>Air Pollutants/*adverse effects/analysis</keyword><keyword>Air Pollution/*adverse effects/analysis</keyword><keyword>Asthma/*epidemiology/etiology</keyword><keyword>California/epidemiology</keyword><keyword>Child</keyword><keyword>Environmental Exposure/adverse effects/analysis</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Incidence</keyword><keyword>Longitudinal Studies</keyword><keyword>Male</keyword><keyword>Nitrogen Dioxide/*adverse effects/analysis</keyword><keyword>Ozone/adverse effects/analysis</keyword><keyword>Particulate Matter/*adverse effects/analysis</keyword></keywords><dates><year>2019</year><pub-dates><date>May 21</date></pub-dates></dates><isbn>1538-3598 (Electronic)
0098-7484 (Linking)</isbn><accession-num>31112259</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/31112259</url></related-urls></urls><custom2>PMC6537847</custom2><electronic-resource-num>10.1001/jama.2019.5357</electronic-resource-num></record></EndNote>). Garcia et al., 2019 ADDIN EN.CITE <EndNote><Author>Garcia</Author><Year>2019</Year><RecNum>1207</RecNum><DisplayText>Garcia et al., 2019</DisplayText><record><rec-number>1207</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1604357019">1207</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Garcia, E.</author><author>Berhane, K. T.</author><author>Islam, T.</author><author>McConnell, R.</author><author>Urman, R.</author><author>Chen, Z.</author><author>Gilliland, F. D.</author></authors></contributors><auth-address>Department of Preventive Medicine, University of Southern California, Los Angeles.</auth-address><titles><title>Association of Changes in Air Quality With Incident Asthma in Children in California, 1993-2014</title><secondary-title>JAMA</secondary-title></titles><periodical><full-title>JAMA</full-title></periodical><pages>1906-1915</pages><volume>321</volume><number>19</number><edition>2019/05/22</edition><keywords><keyword>Air Pollutants/*adverse effects/analysis</keyword><keyword>Air Pollution/*adverse effects/analysis</keyword><keyword>Asthma/*epidemiology/etiology</keyword><keyword>California/epidemiology</keyword><keyword>Child</keyword><keyword>Environmental Exposure/adverse effects/analysis</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Incidence</keyword><keyword>Longitudinal Studies</keyword><keyword>Male</keyword><keyword>Nitrogen Dioxide/*adverse effects/analysis</keyword><keyword>Ozone/adverse effects/analysis</keyword><keyword>Particulate Matter/*adverse effects/analysis</keyword></keywords><dates><year>2019</year><pub-dates><date>May 21</date></pub-dates></dates><isbn>1538-3598 (Electronic)
0098-7484 (Linking)</isbn><accession-num>31112259</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/31112259</url></related-urls></urls><custom2>PMC6537847</custom2><electronic-resource-num>10.1001/jama.2019.5357</electronic-resource-num></record></EndNote> examined the associations between long-term ozone exposure and asthma onset in children (aged nine-18 years) with no history of asthma in Southern California. The authors followed three waves of participants from the Children's Health Study for eight years between 1993 and 2014. Garcia et al., 2019 ADDIN EN.CITE <EndNote><Author>Garcia</Author><Year>2019</Year><RecNum>1207</RecNum><DisplayText>Garcia et al., 2019</DisplayText><record><rec-number>1207</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1604357019">1207</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Garcia, E.</author><author>Berhane, K. T.</author><author>Islam, T.</author><author>McConnell, R.</author><author>Urman, R.</author><author>Chen, Z.</author><author>Gilliland, F. D.</author></authors></contributors><auth-address>Department of Preventive Medicine, University of Southern California, Los Angeles.</auth-address><titles><title>Association of Changes in Air Quality With Incident Asthma in Children in California, 1993-2014</title><secondary-title>JAMA</secondary-title></titles><periodical><full-title>JAMA</full-title></periodical><pages>1906-1915</pages><volume>321</volume><number>19</number><edition>2019/05/22</edition><keywords><keyword>Air Pollutants/*adverse effects/analysis</keyword><keyword>Air Pollution/*adverse effects/analysis</keyword><keyword>Asthma/*epidemiology/etiology</keyword><keyword>California/epidemiology</keyword><keyword>Child</keyword><keyword>Environmental Exposure/adverse effects/analysis</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Incidence</keyword><keyword>Longitudinal Studies</keyword><keyword>Male</keyword><keyword>Nitrogen Dioxide/*adverse effects/analysis</keyword><keyword>Ozone/adverse effects/analysis</keyword><keyword>Particulate Matter/*adverse effects/analysis</keyword></keywords><dates><year>2019</year><pub-dates><date>May 21</date></pub-dates></dates><isbn>1538-3598 (Electronic)
0098-7484 (Linking)</isbn><accession-num>31112259</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/31112259</url></related-urls></urls><custom2>PMC6537847</custom2><electronic-resource-num>10.1001/jama.2019.5357</electronic-resource-num></record></EndNote> obtained health and demographic data from parents, guardians, or participants, who completed questionnaires annually. In order to calculate annual mean, community-level ozone exposure, the authors acquired daily eight-hour mean O3 concentrations through ambient air pollution monitors. Multi-level Poisson regression models with one-year lag showed no statistically significant associations between long-term O3 exposure and asthma onset in children. Models adjusted for demographic variables as well as factors pertaining to family medical history, environmental factors, and near-roadway pollution.
The magnitudes of main and alternate risk estimates of long-term O3 exposure and asthma onset in children provided in Table 51.
Table 51. Long-Term O3-Attributable Asthma Beta Coefficients
Study
Beta Coefficient
Age Range
Tetreault et al., 2016 ADDIN EN.CITE <EndNote><Author>Tetreault</Author><Year>2016</Year><RecNum>1183</RecNum><DisplayText>Tetreault et al., 2016</DisplayText><record><rec-number>1183</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603988120">1183</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Tetreault, L. F.</author><author>Doucet, M.</author><author>Gamache, P.</author><author>Fournier, M.</author><author>Brand, A.</author><author>Kosatsky, T.</author><author>Smargiassi, A.</author></authors></contributors><auth-address>Department of Environmental and Occupational Health, School of Public Health University of Montreal, Montreal, Quebec, Canada.</auth-address><titles><title>Childhood Exposure to Ambient Air Pollutants and the Onset of Asthma: An Administrative Cohort Study in Quebec</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>1276-82</pages><volume>124</volume><number>8</number><edition>2016/01/06</edition><keywords><keyword>Air Pollutants/analysis</keyword><keyword>Asthma/*epidemiology</keyword><keyword>Environmental Exposure/analysis/*statistics & numerical data</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Male</keyword><keyword>Nitrogen Dioxide/analysis</keyword><keyword>Particulate Matter/analysis</keyword><keyword>Proportional Hazards Models</keyword><keyword>Quebec/epidemiology</keyword></keywords><dates><year>2016</year><pub-dates><date>Aug</date></pub-dates></dates><isbn>1552-9924 (Electronic)
0091-6765 (Linking)</isbn><accession-num>26731790</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/26731790</url></related-urls></urls><custom2>PMC4977042</custom2><electronic-resource-num>10.1289/ehp.1509838</electronic-resource-num></record></EndNote>
0.020754
0-17
Garcia et al., 2019 ADDIN EN.CITE <EndNote><Author>Garcia</Author><Year>2019</Year><RecNum>1207</RecNum><DisplayText>Garcia et al., 2019</DisplayText><record><rec-number>1207</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1604357019">1207</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Garcia, E.</author><author>Berhane, K. T.</author><author>Islam, T.</author><author>McConnell, R.</author><author>Urman, R.</author><author>Chen, Z.</author><author>Gilliland, F. D.</author></authors></contributors><auth-address>Department of Preventive Medicine, University of Southern California, Los Angeles.</auth-address><titles><title>Association of Changes in Air Quality With Incident Asthma in Children in California, 1993-2014</title><secondary-title>JAMA</secondary-title></titles><periodical><full-title>JAMA</full-title></periodical><pages>1906-1915</pages><volume>321</volume><number>19</number><edition>2019/05/22</edition><keywords><keyword>Air Pollutants/*adverse effects/analysis</keyword><keyword>Air Pollution/*adverse effects/analysis</keyword><keyword>Asthma/*epidemiology/etiology</keyword><keyword>California/epidemiology</keyword><keyword>Child</keyword><keyword>Environmental Exposure/adverse effects/analysis</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Incidence</keyword><keyword>Longitudinal Studies</keyword><keyword>Male</keyword><keyword>Nitrogen Dioxide/*adverse effects/analysis</keyword><keyword>Ozone/adverse effects/analysis</keyword><keyword>Particulate Matter/*adverse effects/analysis</keyword></keywords><dates><year>2019</year><pub-dates><date>May 21</date></pub-dates></dates><isbn>1538-3598 (Electronic)
0098-7484 (Linking)</isbn><accession-num>31112259</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/31112259</url></related-urls></urls><custom2>PMC6537847</custom2><electronic-resource-num>10.1001/jama.2019.5357</electronic-resource-num></record></EndNote>
0.01695
9-18

Effect Modification of Health Effects in At-Risk Populations
Effect modification was also investigated with regard to O3 exposures. We reviewed relevant chapters from 2020 O3 ISA and used a similar method to that described for PM2.5 to compile and assess studies cited in support of the Agency's determinations (U.S. EPA, 2020b, section 6.1.6). As the 2020 O3 ISA only presents an evaluation of at-risk groups in summary form and extensively references the findings from the 2013 O3 ISA, we focused on the detailed chapter from that previous document in identifying the at-risk factors and studies to review (U.S. EPA, 2013 ADDIN EN.CITE <EndNote><Author>U.S. EPA</Author><Year>2013</Year><RecNum>118</RecNum><DisplayText>U.S. EPA, 2013</DisplayText><record><rec-number>118</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="0">118</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>Integrated Science Assessment of Ozone and Related Photochemical Oxidants (Final Report).</title></titles><edition>February 2013</edition><keywords><keyword>ISA</keyword></keywords><dates><year>2013</year></dates><pub-location>Research Triangle Park, NC</pub-location><publisher>Office of Research and Development, National Center for Environmental Assessment</publisher><isbn>EPA-600/R-10-076F</isbn><urls><related-urls><url>Available at: https://nepis.epa.gov/Exe/ZyPURL.cgi?Dockey=P100KETF.txt</url></related-urls></urls></record></EndNote>). Factors with "adequate evidence" are genetic factors, asthma, children, older adults, diet, and outdoor workers. Factors with "suggestive evidence" are sex, SES, and obesity. Considering feasibility and our review criteria, we focused on studies addressing increased risks based on age in the adequate evidence group and note that some health functions already applied in the primary analysis focus on asthmatic subpopulations. We also elected to include illustrative calculations for some risk factors with "suggestive evidence", specifically those for sex.
Study and Risk Estimate Identification Criteria for Populations At-Risk for O3 Exposures
We compiled epidemiologic studies from the related section of Chapter 8 of the 2013 O3 ISA for the following at-risk factors, excluding all other study types (e.g., toxicological studies), for a total of 28 studies (U.S. EPA, 2020b). This collection includes the following number of studies, with some studies duplicated for multiple endpoints.
 9 studies for children,
 10 studies for older adults, and
 18 studies for sex
We excluded the genetic factors population from our analysis, as we do not currently have the capability to estimate health impacts among variant genotypes. We excluded diet, outdoor workers, and obesity for similar reasons, as we have no representative dataset for use in analysis with these risk factors. Effects on asthmatics were not included in this analysis because we currently lack highly resolved spatial data on asthma prevalence, in part because effects on asthmatic populations are included in the main analysis. We also excluded the SES group as the studies associated with the SES group for O3 were associated with other methodological challenges. We coded the identified studies into a spreadsheet and applied the initial screening criteria described previously. We collected information on mortality and/or morbidity endpoints assessed in each study and focused on all-cause mortality and respiratory morbidity endpoints. The remaining studies were evaluated based on the additional identification criteria described in Table 52.
Table 52. O3 At-Risk Study Identification Criteria
Criteria
Description
Peer-Review
Peer-reviewed research exclusively
Study Design
Epidemiologic study
Ozone Study
Research on ozone exposure is used
Study Location
U.S. or Canada
Population Attributes
Presents risk estimates for clearly defined at-risk groups for which data currently exist in BenMAP-CE
Exposure Duration
Both short- and long-term exposure studies
Causal or Likely Causal Health Effects
Adequate or suggestive evidence for at-risk groups in ISA
Economically Valuable Health Effects
Health endpoints for which economic values have been or could reasonably be developed
Baseline Incidence Data
Must be able to identify baseline incidence data for subpopulations
Season
All year exposure or warm season exposure
Ozone Exposure Metrics
MDA8, or able to be converted to MDA8
Lag Structure
Choose model that most clearly represents the relationship between ozone exposure and the physiologic changes for the health endpoint

Total Mortality
Regarding the health endpoint of mortality, three studies for older adults met our criteria: Medina-Ramon and Schwartz, 2008, Zanobetti and Schwartz, 2008, and  ADDIN EN.CITE <EndNote><Author>Medina-Ramon</Author><Year>2008</Year><RecNum>1405</RecNum><DisplayText>Medina-Ramon and Schwartz, 2008</DisplayText><record><rec-number>1405</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1611247265">1405</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Medina-Ramon, Mercedes</author><author>Schwartz, Joel</author></authors></contributors><titles><title>Who is more vulnerable to die from ozone air pollution?</title><secondary-title>Epidemiology</secondary-title></titles><periodical><full-title>Epidemiology</full-title>EpidemiologyEpidemiology</periodical><pages>672-679</pages><dates><year>2008</year></dates><isbn>1044-3983</isbn><urls></urls></record></EndNote> ADDIN EN.CITE <EndNote><Author>Zanobetti</Author><Year>2008</Year><RecNum>1184</RecNum><DisplayText>Zanobetti and Schwartz, 2008</DisplayText><record><rec-number>1184</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603988156">1184</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Zanobetti, A.</author><author>Schwartz, J.</author></authors></contributors><auth-address>Department of Environmental Health, Exposure Epidemiology and Risk Program, Harvard School of Public Health, 401 Park Drive, Landmark Center, Suite 415, P.O. Box 15698, Boston, MA 02215, USA<EMAIL_ADDRESS>displacement in the association of ozone with mortality: an analysis of 48 cities in the United States</title><secondary-title>Am J Respir Crit Care Med</secondary-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><pages>184-9</pages><volume>177</volume><number>2</number><edition>2007/10/13</edition><keywords><keyword>Cause of Death</keyword><keyword>Cities</keyword><keyword>Confidence Intervals</keyword><keyword>Environmental Exposure/*adverse effects</keyword><keyword>Humans</keyword><keyword>*Mortality</keyword><keyword>Oxidants, Photochemical/*adverse effects</keyword><keyword>Ozone/*adverse effects</keyword><keyword>Seasons</keyword><keyword>United States/epidemiology</keyword></keywords><dates><year>2008</year><pub-dates><date>Jan 15</date></pub-dates></dates><isbn>1535-4970 (Electronic)
1073-449X (Linking)</isbn><accession-num>17932375</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/17932375</url></related-urls></urls><electronic-resource-num>10.1164/rccm.200706-823OC</electronic-resource-num></record></EndNote>Katsouyanni et al., 2009. All three studies provided sufficient details to apply risk model information for short-term all-cause mortality among adult age groups.  There were no mortality studies for the child at-risk group in either the 2013 or 2020 O3 ISAs (U.S. EPA, 2013, U.S. EPA, 2020b). For the at-risk population stratified by sex, two studies met our initial criteria: Medina-Ramon and Schwartz, 2008, which evaluated short-term O3 exposure and all-cause mortality, and  ADDIN EN.CITE <EndNote><Author>U.S. EPA</Author><Year>2013</Year><RecNum>118</RecNum><DisplayText>U.S. EPA, 2013, U.S. EPA, 2020a</DisplayText><record><rec-number>118</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="0">118</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>Integrated Science Assessment of Ozone and Related Photochemical Oxidants (Final Report).</title></titles><edition>February 2013</edition><keywords><keyword>ISA</keyword></keywords><dates><year>2013</year></dates><pub-location>Research Triangle Park, NC</pub-location><publisher>Office of Research and Development, National Center for Environmental Assessment</publisher><isbn>EPA-600/R-10-076F</isbn><urls><related-urls><url>Available at: https://nepis.epa.gov/Exe/ZyPURL.cgi?Dockey=P100KETF.txt</url></related-urls></urls></record><Author>U.S. EPA</Author><Year>2020</Year><RecNum>343</RecNum><record><rec-number>343</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1581631219">343</key></foreign-keys><ref-type name="Government Document">46</ref-type><contributors><authors><author>U.S. EPA,</author></authors><secondary-authors><author>Office of Research and Development</author></secondary-authors></contributors><titles><title>Integrated Science Assessment for Ozone and Related Photochemical Oxidants</title></titles><dates><year>2020</year></dates><pub-location>Washington, DC</pub-location><publisher>U.S. Environmental Protection Agency</publisher><isbn>EPA/600/R-20/012</isbn><urls><related-urls><url>Available at: https://www.epa.gov/isa/integrated-science-assessment-isa-ozone-and-related-photochemical-oxidants</url></related-urls></urls></record></EndNote> ADDIN EN.CITE <EndNote><Author>Medina-Ramon</Author><Year>2008</Year><RecNum>1405</RecNum><DisplayText>Medina-Ramon and Schwartz, 2008</DisplayText><record><rec-number>1405</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1611247265">1405</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Medina-Ramon, Mercedes</author><author>Schwartz, Joel</author></authors></contributors><titles><title>Who is more vulnerable to die from ozone air pollution?</title><secondary-title>Epidemiology</secondary-title></titles><periodical><full-title>Epidemiology</full-title>EpidemiologyEpidemiology</periodical><pages>672-679</pages><dates><year>2008</year></dates><isbn>1044-3983</isbn><urls></urls></record></EndNote>Jerrett et al., 2009, which evaluated long-term O3 exposure and respiratory mortality. Both studies provided sufficient data to apply risk model information to male and female subpopulations. We developed health impact functions for these studies.
The O3-mortality risk estimates for at-risk subpopulations reported in Medina-Ramon and Schwartz, 2008 ADDIN EN.CITE <EndNote><Author>Medina-Ramon</Author><Year>2008</Year><RecNum>1405</RecNum><DisplayText>Medina-Ramon and Schwartz, 2008</DisplayText><record><rec-number>1405</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1611247265">1405</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Medina-Ramon, Mercedes</author><author>Schwartz, Joel</author></authors></contributors><titles><title>Who is more vulnerable to die from ozone air pollution?</title><secondary-title>Epidemiology</secondary-title></titles><periodical><full-title>Epidemiology</full-title>EpidemiologyEpidemiology</periodical><pages>672-679</pages><dates><year>2008</year></dates><isbn>1044-3983</isbn><urls></urls></record></EndNote> required additional modification in order to use those results to develop health impact functions.  The authors presented excess risk estimates for each subpopulation as the additional percent change in mortality for persons who have the condition, compared to persons without the condition. For our populations of interest, these subgroups were persons aged 65 or older compared to those younger than 65, and females relative to males. However, they did not report the risk estimate for these comparison groups, so in order to estimate the total excess risk for each exposed at-risk group, we needed to first back-calculate the excess risk for the comparison group without the factor of interest. We accomplished this by assuming that the authors' overall reported excess risk for the full sample of 0.65% (95% confidence interval = 0.38% to 0.93%) could be expressed as a weighted average of the unreported excess risk ("x") and the full excess risk for the at-risk group, which would be expressed as the sum of x and the reported excess risk from Medina-Ramon and Schwartz, 2008 ADDIN EN.CITE <EndNote><Author>Medina-Ramon</Author><Year>2008</Year><RecNum>1405</RecNum><DisplayText>Medina-Ramon and Schwartz, 2008</DisplayText><record><rec-number>1405</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1611247265">1405</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Medina-Ramon, Mercedes</author><author>Schwartz, Joel</author></authors></contributors><titles><title>Who is more vulnerable to die from ozone air pollution?</title><secondary-title>Epidemiology</secondary-title></titles><periodical><full-title>Epidemiology</full-title>EpidemiologyEpidemiology</periodical><pages>672-679</pages><dates><year>2008</year></dates><isbn>1044-3983</isbn><urls></urls></record></EndNote> Table 2, where the weights are calculated using the total and at-risk group sample sizes in Table 1 of that paper. For example, to calculate the total excess risks for the females in the sample, we used the following equation:

             ERTotal= ERMale(PopMale)+ERFemale(PopFemale)PopTotal
where ERTotal is the full sample excess risk of 0.65%; ERFemale is the excess risk of ozone exposures for females; ERMale is the excess risk of ozone exposures for males; PopTotal is the total sample population; and PopFemale and PopMale are the size of the female and male subsets of the sample population, respectively. We also know from Table 2 of that paper that:
                            ERFemale= ERMale+0.58 %
Substituting and using the available information from Medina-Ramon and Schwartz, 2008 Tables 1 and 2, we can solve for ERMale and then ERFemale:
 ADDIN EN.CITE <EndNote><Author>Medina-Ramon</Author><Year>2008</Year><RecNum>1405</RecNum><DisplayText>Medina-Ramon and Schwartz, 2008</DisplayText><record><rec-number>1405</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1611247265">1405</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Medina-Ramon, Mercedes</author><author>Schwartz, Joel</author></authors></contributors><titles><title>Who is more vulnerable to die from ozone air pollution?</title><secondary-title>Epidemiology</secondary-title></titles><periodical><full-title>Epidemiology</full-title>EpidemiologyEpidemiology</periodical><pages>672-679</pages><dates><year>2008</year></dates><isbn>1044-3983</isbn><urls></urls></record></EndNote>

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

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

We then used the full excess risk value for the female subpopulation to derive a health impact function for ozone-related mortality for females.
Respiratory Morbidity
We applied the same identification criteria described in section <IP_ADDRESS> to respiratory morbidity endpoints for the child and sex at-risk groups. Three studies for children met our criteria for inclusion: Mar and Koenig, 2009 ADDIN EN.CITE <EndNote><Author>Mar</Author><Year>2009</Year><RecNum>1406</RecNum><DisplayText>Mar and Koenig, 2009</DisplayText><record><rec-number>1406</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1611247928">1406</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Mar, Therese F</author><author>Koenig, Jane Q</author></authors></contributors><titles><title>Relationship between visits to emergency departments for asthma and ozone exposure in greater Seattle, Washington</title><secondary-title>Annals of Allergy, Asthma & Immunology</secondary-title></titles><periodical><full-title>Annals of Allergy, Asthma & Immunology</full-title>Ann. Allergy, Asthma Immunol.Ann Allergy, Asthma Immunol</periodical><pages>474-479</pages><volume>103</volume><number>6</number><dates><year>2009</year></dates><isbn>1081-1206</isbn><urls></urls></record></EndNote>, Paulu and Smith, 2008 ADDIN EN.CITE <EndNote><Author>Paulu</Author><Year>2008</Year><RecNum>1407</RecNum><DisplayText>Paulu and Smith, 2008</DisplayText><record><rec-number>1407</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1611247994">1407</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Paulu, Chris</author><author>Smith, Andy E</author></authors></contributors><titles><title>Tracking associations between ambient ozone and asthma-related emergency department visits using case-crossover analysis</title><secondary-title>Journal of Public Health Management and Practice</secondary-title></titles><periodical><full-title>Journal of Public Health Management and Practice</full-title></periodical><pages>581-591</pages><volume>14</volume><number>6</number><dates><year>2008</year></dates><isbn>1078-4659</isbn><urls></urls></record></EndNote>, and Villeneuve et al., 2007 ADDIN EN.CITE <EndNote><Author>Villeneuve</Author><Year>2007</Year><RecNum>1408</RecNum><DisplayText>Villeneuve et al., 2007</DisplayText><record><rec-number>1408</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1611248062">1408</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Villeneuve, Paul J</author><author>Chen, Li</author><author>Rowe, Brian H</author><author>Coates, Frances</author></authors></contributors><titles><title>Outdoor air pollution and emergency department visits for asthma among children and adults: a case-crossover study in northern Alberta, Canada</title><secondary-title>Environmental Health</secondary-title></titles><periodical><full-title>Environmental Health</full-title>Environ. HealthEnviron Health</periodical><pages>40</pages><volume>6</volume><number>1</number><dates><year>2007</year></dates><isbn>1476-069X</isbn><urls></urls></record></EndNote>. Each study evaluated emergency room visits for asthma and provided sufficient risk model information stratified by age. No studies cited for the older adult population met our criteria for inclusion. Three studies for sex met our identification criteria: Paulu and Smith, 2008 ADDIN EN.CITE <EndNote><Author>Paulu</Author><Year>2008</Year><RecNum>1407</RecNum><DisplayText>Paulu and Smith, 2008</DisplayText><record><rec-number>1407</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1611247994">1407</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Paulu, Chris</author><author>Smith, Andy E</author></authors></contributors><titles><title>Tracking associations between ambient ozone and asthma-related emergency department visits using case-crossover analysis</title><secondary-title>Journal of Public Health Management and Practice</secondary-title></titles><periodical><full-title>Journal of Public Health Management and Practice</full-title></periodical><pages>581-591</pages><volume>14</volume><number>6</number><dates><year>2008</year></dates><isbn>1078-4659</isbn><urls></urls></record></EndNote>, Cakmak et al., 2006 ADDIN EN.CITE <EndNote><Author>Cakmak</Author><Year>2006</Year><RecNum>1409</RecNum><DisplayText>Cakmak et al., 2006</DisplayText><record><rec-number>1409</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1611248147">1409</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Cakmak, Sabit</author><author>Dales, Robert E</author><author>Judek, Stan</author></authors></contributors><titles><title>Respiratory health effects of air pollution gases: modification by education and income</title><secondary-title>Archives of Environmental & Occupational Health</secondary-title></titles><periodical><full-title>Archives of environmental & occupational health</full-title></periodical><pages>5-10</pages><volume>61</volume><number>1</number><dates><year>2006</year></dates><isbn>1933-8244</isbn><urls></urls></record></EndNote>, and 141014100xd5s9009txfw1eafstv220i5ds2wvtpwf5d1611248221">1410>PediatricsPediatrics</abbr-2Lin et al., 2005. These studies evaluated emergency room visits for asthma, all respiratory hospital admissions, and hospital admissions for lower respiratory infection, respectively. Each study provided sufficient risk model information for male and female subpopulations. We developed health impact functions for all studies identified above. All the at-risk studies we identified are summarized in Table 53.
Table 53. Identified O3 At-Risk Beta Coefficients and Standard Errors
At-Risk Factor
Endpoint
Study
Subgroup
Beta Coefficient (SE)[1]
Lifestage, older adults
Mortality, All Cause
Medina-Ramon and Schwartz, 2008 and Zanobetti and Schwartz, 2008 ADDIN EN.CITE <EndNote><Author>Medina-Ramon</Author><Year>2008</Year><RecNum>1405</RecNum><DisplayText>Medina-Ramon and Schwartz, 2008</DisplayText><record><rec-number>1405</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1611247265">1405</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Medina-Ramon, Mercedes</author><author>Schwartz, Joel</author></authors></contributors><titles><title>Who is more vulnerable to die from ozone air pollution?</title><secondary-title>Epidemiology</secondary-title></titles><periodical><full-title>Epidemiology</full-title>EpidemiologyEpidemiology</periodical><pages>672-679</pages><dates><year>2008</year></dates><isbn>1044-3983</isbn><urls></urls></record></EndNote> ADDIN EN.CITE <EndNote><Author>Zanobetti</Author><Year>2008</Year><RecNum>1184</RecNum><DisplayText>Zanobetti and Schwartz, 2008</DisplayText><record><rec-number>1184</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603988156">1184</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Zanobetti, A.</author><author>Schwartz, J.</author></authors></contributors><auth-address>Department of Environmental Health, Exposure Epidemiology and Risk Program, Harvard School of Public Health, 401 Park Drive, Landmark Center, Suite 415, P.O. Box 15698, Boston, MA 02215, USA<EMAIL_ADDRESS>displacement in the association of ozone with mortality: an analysis of 48 cities in the United States</title><secondary-title>Am J Respir Crit Care Med</secondary-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><pages>184-9</pages><volume>177</volume><number>2</number><edition>2007/10/13</edition><keywords><keyword>Cause of Death</keyword><keyword>Cities</keyword><keyword>Confidence Intervals</keyword><keyword>Environmental Exposure/*adverse effects</keyword><keyword>Humans</keyword><keyword>*Mortality</keyword><keyword>Oxidants, Photochemical/*adverse effects</keyword><keyword>Ozone/*adverse effects</keyword><keyword>Seasons</keyword><keyword>United States/epidemiology</keyword></keywords><dates><year>2008</year><pub-dates><date>Jan 15</date></pub-dates></dates><isbn>1535-4970 (Electronic)
1073-449X (Linking)</isbn><accession-num>17932375</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/17932375</url></related-urls></urls><electronic-resource-num>10.1164/rccm.200706-823OC</electronic-resource-num></record></EndNote>
Age 0-64
-0.0001 (0.0001)

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

Age 75+
0.0007 (0.0003)
Lifestage, older adults
Mortality, All Cause
Zanobetti and Schwartz, 2008 ADDIN EN.CITE <EndNote><Author>Zanobetti</Author><Year>2008</Year><RecNum>1184</RecNum><DisplayText>Zanobetti and Schwartz, 2008</DisplayText><record><rec-number>1184</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603988156">1184</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Zanobetti, A.</author><author>Schwartz, J.</author></authors></contributors><auth-address>Department of Environmental Health, Exposure Epidemiology and Risk Program, Harvard School of Public Health, 401 Park Drive, Landmark Center, Suite 415, P.O. Box 15698, Boston, MA 02215, USA<EMAIL_ADDRESS>displacement in the association of ozone with mortality: an analysis of 48 cities in the United States</title><secondary-title>Am J Respir Crit Care Med</secondary-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><pages>184-9</pages><volume>177</volume><number>2</number><edition>2007/10/13</edition><keywords><keyword>Cause of Death</keyword><keyword>Cities</keyword><keyword>Confidence Intervals</keyword><keyword>Environmental Exposure/*adverse effects</keyword><keyword>Humans</keyword><keyword>*Mortality</keyword><keyword>Oxidants, Photochemical/*adverse effects</keyword><keyword>Ozone/*adverse effects</keyword><keyword>Seasons</keyword><keyword>United States/epidemiology</keyword></keywords><dates><year>2008</year><pub-dates><date>Jan 15</date></pub-dates></dates><isbn>1535-4970 (Electronic)
1073-449X (Linking)</isbn><accession-num>17932375</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/17932375</url></related-urls></urls><electronic-resource-num>10.1164/rccm.200706-823OC</electronic-resource-num></record></EndNote>
Age 0-20
0.0001 (0.0003)

Age 21-30
0.0001 (0.0004)

Age 31-40
0.0001 (0.0002)

Age 41-50
0.0001 (0.0002)

Age 51-60
0.0005 (0.0002)

Age 61-70
0.0004 (0.0001)

Age 71-80
0.0005 (0.0001)

Age 81+
0.0003 (0.0001)
Sex
Mortality, All Cause
Medina-Ramon and Schwartz, 2008 and Zanobetti and Schwartz, 2008 ADDIN EN.CITE <EndNote><Author>Medina-Ramon</Author><Year>2008</Year><RecNum>1405</RecNum><DisplayText>Medina-Ramon and Schwartz, 2008</DisplayText><record><rec-number>1405</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1611247265">1405</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Medina-Ramon, Mercedes</author><author>Schwartz, Joel</author></authors></contributors><titles><title>Who is more vulnerable to die from ozone air pollution?</title><secondary-title>Epidemiology</secondary-title></titles><periodical><full-title>Epidemiology</full-title>EpidemiologyEpidemiology</periodical><pages>672-679</pages><dates><year>2008</year></dates><isbn>1044-3983</isbn><urls></urls></record></EndNote> ADDIN EN.CITE <EndNote><Author>Zanobetti</Author><Year>2008</Year><RecNum>1184</RecNum><DisplayText>Zanobetti and Schwartz, 2008</DisplayText><record><rec-number>1184</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603988156">1184</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Zanobetti, A.</author><author>Schwartz, J.</author></authors></contributors><auth-address>Department of Environmental Health, Exposure Epidemiology and Risk Program, Harvard School of Public Health, 401 Park Drive, Landmark Center, Suite 415, P.O. Box 15698, Boston, MA 02215, USA<EMAIL_ADDRESS>displacement in the association of ozone with mortality: an analysis of 48 cities in the United States</title><secondary-title>Am J Respir Crit Care Med</secondary-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><pages>184-9</pages><volume>177</volume><number>2</number><edition>2007/10/13</edition><keywords><keyword>Cause of Death</keyword><keyword>Cities</keyword><keyword>Confidence Intervals</keyword><keyword>Environmental Exposure/*adverse effects</keyword><keyword>Humans</keyword><keyword>*Mortality</keyword><keyword>Oxidants, Photochemical/*adverse effects</keyword><keyword>Ozone/*adverse effects</keyword><keyword>Seasons</keyword><keyword>United States/epidemiology</keyword></keywords><dates><year>2008</year><pub-dates><date>Jan 15</date></pub-dates></dates><isbn>1535-4970 (Electronic)
1073-449X (Linking)</isbn><accession-num>17932375</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/17932375</url></related-urls></urls><electronic-resource-num>10.1164/rccm.200706-823OC</electronic-resource-num></record></EndNote>
Female
0.0009 (0.0002)

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

Male
0.0011 (0.0014)
Lifestage, children
Emergency Room Visits, Asthma
Mar and Koenig, 2009 ADDIN EN.CITE <EndNote><Author>Mar</Author><Year>2009</Year><RecNum>1406</RecNum><DisplayText>Mar and Koenig, 2009</DisplayText><record><rec-number>1406</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1611247928">1406</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Mar, Therese F</author><author>Koenig, Jane Q</author></authors></contributors><titles><title>Relationship between visits to emergency departments for asthma and ozone exposure in greater Seattle, Washington</title><secondary-title>Annals of Allergy, Asthma & Immunology</secondary-title></titles><periodical><full-title>Annals of Allergy, Asthma & Immunology</full-title>Ann. Allergy, Asthma Immunol.Ann Allergy, Asthma Immunol</periodical><pages>474-479</pages><volume>103</volume><number>6</number><dates><year>2009</year></dates><isbn>1081-1206</isbn><urls></urls></record></EndNote>
Age 0-17
0.0104 (0.0044)

Age 18+
0.0039 (0.0027)
Lifestage, children
Emergency Room Visits, Asthma
Paulu and Smith, 2008 ADDIN EN.CITE <EndNote><Author>Paulu</Author><Year>2008</Year><RecNum>1407</RecNum><DisplayText>Paulu and Smith, 2008</DisplayText><record><rec-number>1407</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1611247994">1407</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Paulu, Chris</author><author>Smith, Andy E</author></authors></contributors><titles><title>Tracking associations between ambient ozone and asthma-related emergency department visits using case-crossover analysis</title><secondary-title>Journal of Public Health Management and Practice</secondary-title></titles><periodical><full-title>Journal of Public Health Management and Practice</full-title></periodical><pages>581-591</pages><volume>14</volume><number>6</number><dates><year>2008</year></dates><isbn>1078-4659</isbn><urls></urls></record></EndNote>
Age 2-14
0.0104 (0.0050)

Age 15-34
0.0148 (0.0035)
Lifestage, children
Emergency Room Visits, Asthma
Villeneuve et al., 2007 ADDIN EN.CITE <EndNote><Author>Villeneuve</Author><Year>2007</Year><RecNum>1408</RecNum><DisplayText>Villeneuve et al., 2007</DisplayText><record><rec-number>1408</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1611248062">1408</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Villeneuve, Paul J</author><author>Chen, Li</author><author>Rowe, Brian H</author><author>Coates, Frances</author></authors></contributors><titles><title>Outdoor air pollution and emergency department visits for asthma among children and adults: a case-crossover study in northern Alberta, Canada</title><secondary-title>Environmental Health</secondary-title></titles><periodical><full-title>Environmental Health</full-title>Environ. HealthEnviron Health</periodical><pages>40</pages><volume>6</volume><number>1</number><dates><year>2007</year></dates><isbn>1476-069X</isbn><urls></urls></record></EndNote>
Age 2-4
0.0032 (0.0033)

Age 5-14
0.0073 (0.0024)

Age 15-44
0.0058 (0.0018)

Age 45-64
0.0063 (0.0033)

Age 65-74
0.0073 (0.0055)

Age 75+
-0.0006 (0.0067)
Sex
Emergency Room Visits, Asthma
Paulu and Smith, 2008 ADDIN EN.CITE <EndNote><Author>Paulu</Author><Year>2008</Year><RecNum>1407</RecNum><DisplayText>Paulu and Smith, 2008</DisplayText><record><rec-number>1407</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1611247994">1407</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Paulu, Chris</author><author>Smith, Andy E</author></authors></contributors><titles><title>Tracking associations between ambient ozone and asthma-related emergency department visits using case-crossover analysis</title><secondary-title>Journal of Public Health Management and Practice</secondary-title></titles><periodical><full-title>Journal of Public Health Management and Practice</full-title></periodical><pages>581-591</pages><volume>14</volume><number>6</number><dates><year>2008</year></dates><isbn>1078-4659</isbn><urls></urls></record></EndNote>
Female
0.0113 (0.0027)

Male
0.0104 (0.0032)
Sex
Hospital Admissions, All Respiratory
Cakmak et al., 2006 ADDIN EN.CITE <EndNote><Author>Cakmak</Author><Year>2006</Year><RecNum>1409</RecNum><DisplayText>Cakmak et al., 2006</DisplayText><record><rec-number>1409</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1611248147">1409</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Cakmak, Sabit</author><author>Dales, Robert E</author><author>Judek, Stan</author></authors></contributors><titles><title>Respiratory health effects of air pollution gases: modification by education and income</title><secondary-title>Archives of Environmental & Occupational Health</secondary-title></titles><periodical><full-title>Archives of environmental & occupational health</full-title></periodical><pages>5-10</pages><volume>61</volume><number>1</number><dates><year>2006</year></dates><isbn>1933-8244</isbn><urls></urls></record></EndNote>
Female
0.0013 (0.0004)

Male
0.0017 (0.0003)
Sex
Hospital Admissions, Lower Respiratory Infection
Lin et al., 2005 ADDIN EN.CITE <EndNote><Author>Lin</Author><Year>2005</Year><RecNum>1410</RecNum><DisplayText>Lin et al., 2005</DisplayText><record><rec-number>1410</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1611248221">1410</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Lin, Mei</author><author>Stieb, David M</author><author>Chen, Yue</author></authors></contributors><titles><title>Coarse particulate matter and hospitalization for respiratory infections in children younger than 15 years in Toronto: A case-crossover analysis</title><secondary-title>Pediatrics</secondary-title></titles><periodical><full-title>Pediatrics</full-title>PediatricsPediatrics</periodical><pages>e235-e240</pages><volume>116</volume><number>2</number><dates><year>2005</year></dates><isbn>0031-4005</isbn><urls></urls></record></EndNote>
Female
0.0087 (0.0060)

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

We performed several quality assurance checks to ensure the incidence rates accurately reflect observed health outcomes in the underlying counties. These checks included:
 Examining data inputs to ensure the endpoints reflect the specified set of ICD codes from the epidemiological studies;
 Reviewing data processing scripts to ensure all calculations implement the intended procedures, as documented in the BenMAP-CE User Manual (U.S. EPA, 2018 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>);
 Re-processing existing incidence rates in BenMAP-CE's "Other Incidence (2014)" to confirm that changes to data processing to incorporate new endpoints have no or minimal impact on incidence rate data for existing endpoints from BenMAP-CE's 2017 data update;
 Comparing the relative magnitude of related endpoints to ensure that incidence rates for broader endpoints (e.g., HA, All Respiratory) are greater than incidence rates for endpoints with a narrower set of ICD codes (e.g., HA, Asthma);
 Comparing national baseline incidence counts when using county-level incidence rates and nation-level incidence rates to ensure that, in aggregate, the two datasets produce similar results; and
 Examining the geographic distribution of incidence rates to ensure no counties, states, or regions, are characterized by anomalously low or high incidence.
We identified no systematic errors or bias in the raw data or data processing steps. The main source of uncertainty in these data is related to imputation of rates where county data for specific endpoints were suppressed due to statistical reliability or privacy concerns. The state or regional rates used to substitute for these suppressed values may under- or over-estimate individual county rates.
Quantitative Characterization of Economic Valuation Estimate Uncertainties
Mortality Cessation Lag
Following advice from the Health Effects Subcommittee of EPA's independent Science Advisory Board (SAB-HES), the agency typically assumes that some amount of time lapses between when air pollution is reduced and when PM-attributable mortality is reduced fully. Within the context of benefits analyses, this term is often referred to as "cessation lag." The duration of this lag affects how changes in PM-attributable mortality associated with long-term (i.e., years-long) exposure are valued. Economic theory suggests that the value of these future impacts should be discounted. The primary analysis included in recent RIAs assumes that this lag is distributed over a 20-year period, with 30% of deaths reduced in year 0, 50% occur in years 1-5, and the remaining 20% occur in years 6-20. This approach is generally support by SAB recommendations (Cameron, 2001, Cameron, 2004, Hammitt and Bailar, 2010).
Based on SAB requests and recommendations, we previously performed several quantitative uncertainty analyses with the goal of better understanding potential impacts of different cessation lag distribution assumptions (U.S. EPA, 2012b ADDIN EN.CITE <EndNote><Author>U.S. EPA</Author><Year>2012</Year><RecNum>553</RecNum><DisplayText>U.S. EPA, 2012b</DisplayText><record><rec-number>553</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732085">553</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>Regulatory Impact Analysis for the Final Revisions to the National Ambient Air Quality Standards for Particulate Matter</title></titles><edition>December 2012</edition><dates><year>2012</year></dates><pub-location>Research Triangle Park, NC</pub-location><publisher>U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Health and Environmental Impact Division</publisher><isbn>EPA-452/R-12-005</isbn><urls><related-urls><url>Available at: https://www3.epa.gov/ttnecas1/regdata/RIAs/finalria.pdf</url></related-urls></urls></record></EndNote>). Although it was determined that certain extreme lag structure assumptions may substantially impact monetized benefits, potentially increasing or decreasing monetized impacts by up to 25%, for most reasonable distributed lag model structures, differences in the specific shape of the lag function had relatively small impacts on overall PM2.5 benefits estimates.
Newer evidence suggests the lag between exposure and the change in the risk of PM-attributable mortality may be shorter than the 20 years EPA now assumes. A long-term exposure cohort study performed by Crouse and colleagues of the CanCHEC evaluated the estimated hazard of non-accidental mortality according to three temporal moving averages (1, 3 and 8 years) Crouse et al., 2020. The authors found that "...longer moving averages resulted in stronger associations between PM2.5 and mortality." An analysis of the Harvard Six Cities cohort observed that PM2.5 concentrations observed in the year prior to mortality "...were the best fit exposure window for all-cause mortality."Lepeule et al., 2012
We do not know how long-term O3 exposure-related respiratory deaths are distributed over time. Hence, when discounting the value of O3-attributable deaths we use two lag structures originally developed for PM2.5 (the 20-year segmented lag used for PM2.5 and an assumption of zero lag) as sensitivity analyses.
Lung Cancer Cessation Lag
For a given health effect attributable to air pollution exposure, EPA reports the number of avoided cases associated with the estimated pollutant reduction in a specified year. However, for some health effects, there is an expected time lag between changes in pollutant exposure in a given year and the total realization of health effect benefits, commonly referred to in regulatory analyses as the "cessation lag" (section 6.4.1). For an outcome such as lung cancer, where the time between exposure and diagnosis can be quite long, it may take decades to realize the full benefits of the air quality improvements. Properly estimating the time course over which lung cancer health benefits are realized is critical for proper discounting of the economic value of these health benefits.
Following guidance from EPA's Science Advisory Board (Ostro, 2004), ADDIN EN.CITE <EndNote><Author>Ostro</Author><Year>2004</Year><RecNum>1120</RecNum><DisplayText>Ostro, 2004</DisplayText><record><rec-number>1120</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732554">1120</key></foreign-keys><ref-type name="Personal Communication">26</ref-type><contributors><authors><author>Ostro, B.; Cameron, T. A.</author></authors><secondary-authors><author>Leavitt, Micheal</author></secondary-authors></contributors><titles><title>Letter from Dr. Bart Ostro, Chair, Health Effects Subcommittee and Dr. Trudy Ann Cameron, Chair, Advisory Council on Clean Air Compliance Analysis to Honorable Michael O. Leavitt, Administrator, US EPA. Re: Advisory on Plans for Health Effects Analysis in the Analytical Plan for EPA's Second Prospective Analysis - Benefits and Costs of the Clean Air Act, 1990-2020.</title></titles><num-vols>EPA-SAB-COUNCIL-ADV-04-002</num-vols><dates><year>2004</year><pub-dates><date>March 2004</date></pub-dates></dates><pub-location>U.S. EPA HQ, Washington DC</pub-location><publisher>Office of the Administrator, Science Advisory Board</publisher><urls><related-urls><url>Available at: https://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/08E1155AD24F871C85256E5400433D5D/$File/council_adv_04002.pdf</url></related-urls></urls></record></EndNote> EPA RIAs have applied a 20-year distributed cessation lag model to estimate the temporal distribution of reductions in mortality risks, including fatal lung cancer cases. In the 20-year distributed lag model, 30 percent of the total mortality risk reductions occur in the first year following the exposure reduction, 50 percent are distributed evenly among years two through five, and the remaining 20 percent are distributed evenly among years six through 20. This structure reflects mortality risks from a variety of causes, with the mortality risk reductions occurring later representing mortality risks from lung cancer.
For non-fatal cancer incidence, we considered a similar cessation lag approach based on estimates of the lung cancer "latency period," or the time that passes between exposure and diagnosis, when diseases processes may be occurring undetected and not yet resulting in observable symptoms. Based on findings in the 2019 PM ISA, EPA has recently developed a health impact function based on Gharibvand et al., 2017 for non-fatal lung cancer incidence (U.S. EPA, 2019b). ADDIN EN.CITE <EndNote><Author>Gharibvand</Author><Year>2017</Year><RecNum>1198</RecNum><DisplayText>Gharibvand et al., 2017</DisplayText><record><rec-number>1198</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1604071026">1198</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Gharibvand, L.</author><author>Shavlik, D.</author><author>Ghamsary, M.</author><author>Beeson, W. L.</author><author>Soret, S.</author><author>Knutsen, R.</author><author>Knutsen, S. F.</author></authors></contributors><auth-address>Adventist Health Study-2, School of Public Health, Loma Linda University, Loma Linda, California, USA.</auth-address><titles><title>The Association between Ambient Fine Particulate Air Pollution and Lung Cancer Incidence: Results from the AHSMOG-2 Study</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>378-384</pages><volume>125</volume><number>3</number><edition>2016/08/16</edition><keywords><keyword>Air Pollutants/*analysis</keyword><keyword>Air Pollution/*statistics & numerical data</keyword><keyword>Environmental Exposure/*statistics & numerical data</keyword><keyword>Humans</keyword><keyword>Incidence</keyword><keyword>Lung Neoplasms/*epidemiology</keyword><keyword>Particulate Matter/*analysis</keyword></keywords><dates><year>2017</year><pub-dates><date>Mar</date></pub-dates></dates><isbn>1552-9924 (Electronic)
0091-6765 (Linking)</isbn><accession-num>27519054</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/27519054</url></related-urls></urls><custom2>PMC5332173</custom2><electronic-resource-num>10.1289/EHP124</electronic-resource-num></record></EndNote> To support the new non-fatal lung cancer risk estimate, we applied an age-at-diagnosis cessation lag distribution for the main analysis as it accounts for age-specific latency period, instead of assuming a single latency duration. However, other potentially applicable distribution models are available that also take into account the latency between exposure and lung cancer diagnosis, such as the adapted 20-year distribution (section <IP_ADDRESS>) and the latency-based triangular distribution (section <IP_ADDRESS>). All potential lag cessation distributions, including the traditional 20-year lag distribution, are compared in section <IP_ADDRESS>.
Adapted 20-Year Distribution
We adapted the 20-year distributed lag model applied to VSL estimates in previous EPA RIAs using the estimated 10-year latency period. Following the latency period, the adapted 20-year model has zero cancer case reductions in years one through five and an even distribution of case reductions in years six through 20, resulting in scaling factors of 0.71 for a 3% discount rate or 0.46 for a 7% discount rate.
Latency-Based Triangular Distribution
A continuous probability distribution shaped like a triangle may better assess lung cancer lag cessation. Triangular distribution based on a search of lung cancer latency periods from the peer-reviewed literature. Using the most common latency period of 10-years observed in the literature (Table 29), we estimated a triangular distribution that spans from five to 20 years, with the peak of the distribution at ten years, the most common latency period estimate found in the literature (i.e., the mode).  We identified a triangular distribution to reflect the uncertainty of latency period duration found in the literature, given the limited amount of information available to establish the shape and form of an uncertainty distribution. We used the cumulative probability function for this distribution to estimate the incremental annual number of cases likely to be diagnosed year to year by subtracting the cumulative probability from the previous year from the cumulative probability of the current year. We then used the resulting percentages to create a cessation lag model, allocating cases avoided in the years following an exposure change according to the corresponding yearly percentages.
Comparison of Lung Cancer Lag Cessation Distribution Models
The effect of each potential cessation lag distribution model was converted into scaling factors (Table 54). The scaling factors of the adjusted 20-year lag distribution estimate falls between that of the traditional cessation lag and the triangular distribution lag estimates. Also, the adjusted cessation lag distribution underestimates as compared to the age-at-diagnosis distribution.
Table 54. Scaling Factors for Various Lung Cancer Lag Cessation Distribution Models
Discount Rate
Age Range
Scaling Factor
Lag Cessation Distribution Model
3%
65-99
0.668980939
Traditional VSL cessation lag, 3% DR
7%
65-99
0.398456232
Traditional VSL cessation lag, 7% DR
3%
30-99
0.70711338
Adjusted 20-Year Distributed Lag Adjustment Factor
7%
30-99
0.463225599
Adjusted 20-Year Distributed Lag Adjustment Factor
3%
30-99
0.72192703
Triangular Adjustment Factor
7%
30-99
0.480176715
Triangular Adjustment Factor
3%
30-34
0.350285148
SEER Age-Distribution Adjustment Factor
3%
35-44
0.427591186
SEER Age-Distribution Adjustment Factor
3%
45-54
0.553445022
SEER Age-Distribution Adjustment Factor
3%
55-64
0.675599356
SEER Age-Distribution Adjustment Factor
3%
65-74
0.775053763
SEER Age-Distribution Adjustment Factor
3%
75-84
0.843760064
SEER Age-Distribution Adjustment Factor
3%
85-99
0.916741635
SEER Age-Distribution Adjustment Factor
7%
30-34
0.108397669
SEER Age-Distribution Adjustment Factor
7%
35-44
0.168901798
SEER Age-Distribution Adjustment Factor
7%
45-54
0.294643444
SEER Age-Distribution Adjustment Factor
7%
55-64
0.445786107
SEER Age-Distribution Adjustment Factor
7%
65-74
0.590393871
SEER Age-Distribution Adjustment Factor
7%
75-84
0.702750875
SEER Age-Distribution Adjustment Factor
7%
85-99
0.82379138
SEER Age-Distribution Adjustment Factor

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

Income Elasticity of Willingness to Pay
The degree to which one's WTP to reduce the risk of adverse effects changes in proportion to future changes in income is uncertain. We previously evaluated the potential impact of this factor on the monetized benefits in a sensitivity analysis (U.S. EPA, 2012b ADDIN EN.CITE <EndNote><Author>U.S. EPA</Author><Year>2012</Year><RecNum>553</RecNum><DisplayText>U.S. EPA, 2012b</DisplayText><record><rec-number>553</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732085">553</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>Regulatory Impact Analysis for the Final Revisions to the National Ambient Air Quality Standards for Particulate Matter</title></titles><edition>December 2012</edition><dates><year>2012</year></dates><pub-location>Research Triangle Park, NC</pub-location><publisher>U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Health and Environmental Impact Division</publisher><isbn>EPA-452/R-12-005</isbn><urls><related-urls><url>Available at: https://www3.epa.gov/ttnecas1/regdata/RIAs/finalria.pdf</url></related-urls></urls></record></EndNote>). Results are available below.
Our estimates of monetized benefits account for growth in real gross domestic product per capita by adjusting the WTP for individual endpoints based on the central estimate of the adjustment factor for each of the categories (minor health effects, severe and chronic health effects, mortality, and visibility). We previously examined how sensitive the estimate of total benefits is to alternative estimates of the income elasticities. Table 55 lists the ranges of elasticity values used to calculate the income adjustment factors, while Table 56 lists the ranges of corresponding adjustment factors. The results of this sensitivity analysis, giving the monetized benefit subtotals for the four benefit categories, are presented in Table 57.
Table 55. Ranges of Elasticity Values Used to Account for Projected Real Income Growth[a]
Benefit Category
Lower Sensitivity Bound
Upper Sensitivity Bound
Minor Health Effect
0.04
0.30
Mortality
0.08
1.00
[a]Derivation of these ranges can be found in Kleckner and Neumann, 1999 ADDIN EN.CITE <EndNote><Author>Kleckner</Author><Year>1999</Year><RecNum>1391</RecNum><DisplayText>Kleckner and Neumann, 1999</DisplayText><record><rec-number>1391</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1610549877">1391</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Kleckner, N</author><author>Neumann, J</author></authors></contributors><titles><title>Recommended Approach to Adjusting WTP Estimates to Reflect Changes in Real Income</title><secondary-title>Memorandum to Jim Democker, US EPA/OPAR, June</secondary-title></titles><periodical><full-title>Memorandum to Jim Democker, US EPA/OPAR, June</full-title></periodical><volume>3</volume><dates><year>1999</year></dates><urls></urls></record></EndNote>. COI estimates are assigned an adjustment factor of 1.0.
Table 56. Ranges of Adjustment Factors Used to Account for Projected Real Income Growth[a]
Benefit Category
Lower Sensitivity Bound
Upper Sensitivity Bound
Minor Health Effect
1.018
1.147
Mortality
1.037
1.591
[a]Based on elasticity values reported in Table 55, U.S. Census population projections, and projections of real GDP per capita.
Table 57. Sensitivity of Monetized Benefits to Alternative Income Elasticities[a]
Benefit Category
Benefits Incremental to Baseline (Millions of 2006$)

Lower Sensitivity Bound
Upper Sensitivity Bound
Minor Health Effect
$30
$31
Mortality[b]
$3,600
$3,800
[a]All estimates rounded to two significant digits.
[b]Using mortality effect estimate from Krewski et al., 2000 and 3% discount rate. Results using Laden et al., 2006 ADDIN EN.CITE <EndNote><Author>Krewski</Author><Year>2000</Year><RecNum>1097</RecNum><DisplayText>Krewski et al., 2000</DisplayText><record><rec-number>1097</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732550">1097</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Krewski, D</author><author>Burnett, RT</author><author>Goldberg, M</author><author>Hoover, K</author><author>Siemiatycki, J</author><author>Jerrett, M</author><author>Abrahamowich, M</author><author>White, W</author></authors></contributors><titles><title>Reanalysis of the Harvard six-cities study and the American Cancer Society study of air pollution and mortality, phase II: sensitivity analysis</title><secondary-title>Health Effects Institute</secondary-title></titles><periodical><full-title>Health Effects Institute</full-title></periodical><volume>295</volume><dates><year>2000</year></dates><urls></urls></record></EndNote> or a 7% discount rate would show the same proportional range.
Consistent with the impact of mortality on total benefits, the adjustment factor for mortality has the largest impact on total benefits. The value of mortality in 2020 ranges from 96% to 108% of the main estimate based on the lower and upper sensitivity bounds on the income adjustment factor. The effect on the value of minor health effects is much less pronounced, ranging from 86% to 133% of the main estimate for minor effects.
Statistical Estimates of VSL
EPA relies on published peer-reviewed studies to provide statistical estimates of the value of avoided statistical mortality risk (VSL).  These studies provide a range of differ estimates due to varying study design and different statistical samples.  EPA uses a distribution of values fit to the studies' estimates as described in Section 5.1.1 and Table 22.
Alzheimer's Disease and Parkinson's Disease Onset Lifetime Costs
The epidemiologic study from with the risk estimates for Alzheimer's and Parkinson's disease were identified used time to first hospital admission as the health endpoint readout. As the authors note that this is not necessarily indicative of disease onset, we only include valuation estimates of associated hospital admissions costs in the main benefits assessment. However, we include information here regarding how the benefits estimates would increase if the first hospital admission were used as a surrogate for disease onset.
Alzheimer's Disease
Potential valuation sources of Alzheimer's disease lifetime medical costs were available from the Alzheimer's, 2020 ADDIN EN.CITE <EndNote><Author>Alzheimer's Association</Author><Year>2020</Year><RecNum>1302</RecNum><DisplayText>Alzheimer's Association, 2020</DisplayText><record><rec-number>1302</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608234385">1302</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>Alzheimer's Association, </author></authors></contributors><titles><title>2020 Alzheimer's Disease Facts and Figures</title></titles><edition>2020/03/12</edition><keywords><keyword>Alzheimer's dementia</keyword><keyword>Alzheimer's disease</keyword><keyword>Alzheimer's disease continuum</keyword><keyword>Biomarkers</keyword><keyword>Caregivers</keyword><keyword>Dementia</keyword><keyword>Dementia care training</keyword><keyword>Family caregiver</keyword><keyword>Geriatrician</keyword><keyword>Health care costs</keyword><keyword>Health care expenditures</keyword><keyword>Health care professional</keyword><keyword>Incidence</keyword><keyword>Long-term care costs</keyword><keyword>Medicaid spending</keyword><keyword>Medicare spending</keyword><keyword>Morbidity</keyword><keyword>Mortality</keyword><keyword>Prevalence</keyword><keyword>Primary care physician</keyword><keyword>Risk factors</keyword><keyword>Spouse caregiver</keyword></keywords><dates><year>2020</year><pub-dates><date>Mar 10</date></pub-dates></dates><accession-num>32157811</accession-num><urls><related-urls><url>Available at: https://www.alz.org/media/Documents/alzheimers-facts-and-figures.pdf</url></related-urls></urls><electronic-resource-num>10.1002/alz.12068</electronic-resource-num></record></EndNote> report and Jutkowitz et al., 2017. Using Alzheimer's, 2020 ADDIN EN.CITE <EndNote><Author>Alzheimer's Association</Author><Year>2020</Year><RecNum>1302</RecNum><DisplayText>Alzheimer's Association, 2020</DisplayText><record><rec-number>1302</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608234385">1302</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>Alzheimer's Association, </author></authors></contributors><titles><title>2020 Alzheimer's Disease Facts and Figures</title></titles><edition>2020/03/12</edition><keywords><keyword>Alzheimer's dementia</keyword><keyword>Alzheimer's disease</keyword><keyword>Alzheimer's disease continuum</keyword><keyword>Biomarkers</keyword><keyword>Caregivers</keyword><keyword>Dementia</keyword><keyword>Dementia care training</keyword><keyword>Family caregiver</keyword><keyword>Geriatrician</keyword><keyword>Health care costs</keyword><keyword>Health care expenditures</keyword><keyword>Health care professional</keyword><keyword>Incidence</keyword><keyword>Long-term care costs</keyword><keyword>Medicaid spending</keyword><keyword>Medicare spending</keyword><keyword>Morbidity</keyword><keyword>Mortality</keyword><keyword>Prevalence</keyword><keyword>Primary care physician</keyword><keyword>Risk factors</keyword><keyword>Spouse caregiver</keyword></keywords><dates><year>2020</year><pub-dates><date>Mar 10</date></pub-dates></dates><accession-num>32157811</accession-num><urls><related-urls><url>Available at: https://www.alz.org/media/Documents/alzheimers-facts-and-figures.pdf</url></related-urls></urls><electronic-resource-num>10.1002/alz.12068</electronic-resource-num></record></EndNote>, we first developed an estimate of incremental annual medical expenses for Medicare beneficiaries living with Alzheimer's Disease (Table 58). Then, using the estimated life expectancy duration of 5 year from Jutkowitz et al., 2017, 3% and 7% discounted costs were extrapolated (Table 59). We note that the average/median age of Alzheimer's disease diagnosis/onset is after the age of 65, at which we assume retirement, so any potential lost wages are not included in this valuation estimate. Lifetime medical costs, excluding hospitalization, are estimated at $156,920 using a 3% discount rate or $145,946 using a 7% discount rate in 2015$ Alzheimer's, 2020. ADDIN EN.CITE <EndNote><Author>Alzheimer's Association</Author><Year>2020</Year><RecNum>1302</RecNum><DisplayText>Alzheimer's Association, 2020</DisplayText><record><rec-number>1302</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608234385">1302</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>Alzheimer's Association, </author></authors></contributors><titles><title>2020 Alzheimer's Disease Facts and Figures</title></titles><edition>2020/03/12</edition><keywords><keyword>Alzheimer's dementia</keyword><keyword>Alzheimer's disease</keyword><keyword>Alzheimer's disease continuum</keyword><keyword>Biomarkers</keyword><keyword>Caregivers</keyword><keyword>Dementia</keyword><keyword>Dementia care training</keyword><keyword>Family caregiver</keyword><keyword>Geriatrician</keyword><keyword>Health care costs</keyword><keyword>Health care expenditures</keyword><keyword>Health care professional</keyword><keyword>Incidence</keyword><keyword>Long-term care costs</keyword><keyword>Medicaid spending</keyword><keyword>Medicare spending</keyword><keyword>Morbidity</keyword><keyword>Mortality</keyword><keyword>Prevalence</keyword><keyword>Primary care physician</keyword><keyword>Risk factors</keyword><keyword>Spouse caregiver</keyword></keywords><dates><year>2020</year><pub-dates><date>Mar 10</date></pub-dates></dates><accession-num>32157811</accession-num><urls><related-urls><url>Available at: https://www.alz.org/media/Documents/alzheimers-facts-and-figures.pdf</url></related-urls></urls><electronic-resource-num>10.1002/alz.12068</electronic-resource-num></record></EndNote>
Table 58. Annual Alzheimer's Disease Valuation Estimate Calculation
Service
Beneficiaries with Alzheimer's Disease or Other Dementia
Beneficiaries without Alzheimer's Disease or Other Dementia
Inpatient hospital
$11,465
$3,703
Medical provider
$5,762
$3,589
Skilled nursing facility
$7,213
$493
Nursing home
$16,523
$800
Hospice
$2,126
$161
Home health care
$2,661
$386
Prescription Medications
$3,481
$2,986
Annual Medical Expenses ($2019)
$49,231
$12,118
Annual Medical Expenses ($2015)
$44,128
$10,862
Incremental Annual Medical Expenses for Medicare beneficiaries with AD ($2015)
$33,266

Table 59. Lifetime Alzheimer's Disease Valuation Estimate Calculation (2015$)
Year
3% Discount Rate
7% Discount Rate
0
$33,266
$33,266
1
$32,297
$31,090
2
$31,357
$29,056
3
$30,443
$27,155
4
$29,557
$25,379
Total Lifetime Costs
$156,920
$145,946
Jutkowitz et al., 2017 provided information needed to separately develop a lifetime Alzheimer's Disease cost estimate with a 3% discount rate, but not with a 7% discount rate (Table 60. Additional Lifetime Alzheimer's Disease Valuation Estimate Calculation with a 3% Discount Rate (2015$)). As the 3% discount rate estimate of $156,920 from Alzheimer's, 2020 ADDIN EN.CITE <EndNote><Author>Alzheimer's Association</Author><Year>2020</Year><RecNum>1302</RecNum><DisplayText>Alzheimer's Association, 2020</DisplayText><record><rec-number>1302</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608234385">1302</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>Alzheimer's Association, </author></authors></contributors><titles><title>2020 Alzheimer's Disease Facts and Figures</title></titles><edition>2020/03/12</edition><keywords><keyword>Alzheimer's dementia</keyword><keyword>Alzheimer's disease</keyword><keyword>Alzheimer's disease continuum</keyword><keyword>Biomarkers</keyword><keyword>Caregivers</keyword><keyword>Dementia</keyword><keyword>Dementia care training</keyword><keyword>Family caregiver</keyword><keyword>Geriatrician</keyword><keyword>Health care costs</keyword><keyword>Health care expenditures</keyword><keyword>Health care professional</keyword><keyword>Incidence</keyword><keyword>Long-term care costs</keyword><keyword>Medicaid spending</keyword><keyword>Medicare spending</keyword><keyword>Morbidity</keyword><keyword>Mortality</keyword><keyword>Prevalence</keyword><keyword>Primary care physician</keyword><keyword>Risk factors</keyword><keyword>Spouse caregiver</keyword></keywords><dates><year>2020</year><pub-dates><date>Mar 10</date></pub-dates></dates><accession-num>32157811</accession-num><urls><related-urls><url>Available at: https://www.alz.org/media/Documents/alzheimers-facts-and-figures.pdf</url></related-urls></urls><electronic-resource-num>10.1002/alz.12068</electronic-resource-num></record></EndNote> is fairly similar to the lifetime 3% discount rate estimate of $184,500 from Jutkowitz et al., 2017, we have additional confidence in the validity of the  Association1302 Association13020xd5s9009txfw1eafstv220i5ds2wvtpwf5d1608234385">1302Report">27, Disease Facts and Figures</title></titlesAvailable at: alz.org/media/Documents/alzheimers-facts-and-figures.pdfAlzheimer's, 2020 estimates (Table 21).
Table 60. Additional Lifetime Alzheimer's Disease Valuation Estimate Calculation with a 3% Discount Rate (2015$)
Service
Base-Case (83-year-old incident dementia case)
Counterfactual Dementia Free
Incremental Increase in Lifetime Costs
Value of informal caregiving
$135,300
$2,460
$132,840
Out-of-pocket expenditures
$89,840
$64,720
$25,120
Medicaid expenditures
$44,090
$37,450
$6,640
Medicare expenditures
$52,540
$32,650
$19,890
Total value
$321,780
$137,280
$184,500

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

  Outpatient
$5,308

  Physician office
$1,182

  Durable medical equipment
$140

  Prescription medication
$3,988

Direct Medical Costs Subtotal
$24,438

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

    Motor vehicle modification
$897

    Other expenses
$508

Indirect Medical Costs Subtotal
$7,265

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

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

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

Annual Medical Expenses ($2017)
$47,578

Annual Medical Expenses ($2015)
$44,718

Table 62. Lifetime Parkinson's Disease Valuation Estimate Calculation
Year
3% Discount Rate
7% Discount Rate
0
$44,718
$44,718
1
$43,416
$41,793
2
$42,151
$39,059
3
$40,924
$36,503
4
$39,732
$34,115
5
$38,574
$31,883
6
$37,451
$29,798
7
$36,360
$27,848
8
$35,301
$26,026
9
$34,273
$24,324
10
$33,275
$22,732
11
$32,305
$21,245
12
$31,364
$19,855
13
$30,451
$18,556
14
$29,564
$17,343
14.6
$17,427
$9,992
Total Lifetime Costs (14.6 yr survival)
$567,285
$445,792
Qualitative Characterization of Uncertainties
There are several uncertainties we are unable to fully or partially quantitatively assess, but qualitatively discuss below, in alphabetical order.
Applying Risk Estimates to Locations and Populations not Specified in the Epidemiologic Study
EPA regulatory actions often affect portions of the country and populations that differ from those considered in the epidemiologic studies providing the risk estimates. EPA commonly transfers risk estimates from one location or population to another, following a procedure called benefits transfer, a potential source of uncertainty. When available, risk estimates based on nationwide studies reflecting the overall population demographics of U.S. residents will be used when estimating health benefits. Epidemiologic studies exploring the relationship between air pollution and the risk of mortality often consider populations whose characteristics are broadly representative of the U.S. (e.g., Medicare-based estimates will be applied to those >64). By contrast, epidemiologic studies examining morbidity outcomes may focus on population subsets, such as those residing in specific geographic regions, a single sex, or selected races/ethnicities. In this context, two or more epidemiologic studies may report risk estimates that, when pooled, can better characterize risks experienced by U.S. populations. However, in some cases it may be scientifically inappropriate to pool risk estimates -- for example, a nationwide analysis of populations ages 65-99 and a less-geographically diverse analysis of populations ages 0-99. In a situation such as this, the estimate best characterizing risk in the U.S. will be included in the main benefits assessment and the others will be included in quantitative sensitivity analyses (sections 6.1 and 6.1.6).
Causality Determination
When estimating air pollutant-attributable health impacts, EPA focuses on endpoints for which the underlying scientific evidence is strongest. This approach is based on evaluating evidence across scientific disciplines (i.e., clinical, animal toxicological, and epidemiologic) with regard to consistency of effects within a discipline, coherence of effects across disciplines, and evidence of biological plausibility. Such an approach gives us greater confidence in the relationship between exposure and health outcome. For criteria pollutants, EPA typically relies upon the causality determinations in the latest ISA or equivalent, which are made using a weight-of evidence approach. These causality determinations are, however, made for categories of health effects and not for specific endpoints. Thus, the extent to which the relationship exists for the specific endpoint and the exposure circumstances of interest in a benefits assessment is a source of uncertainty.
An expert elicitation sponsored by EPA to characterize the uncertainty in the relationship between PM2.5 and mortality, including causal uncertainty, was released in 2006 and reviewed by the Advisory Council on Clean Air Compliance (Hammitt, 2008, IEc, 2006). Although the 12 expert-defined concentration-response functions provide useful information on the sensitivity of the health benefits estimates, additional epidemiology literature which addresses some of the weaknesses identified by the expert elicitation has since become available, such as improved exposure estimation techniques and the use of cohorts more representative of the U.S population. For these reasons we do not include the expert-derived results as a sensitivity analysis here but consider it as qualitative support for the relationship between long-term PM2.5 exposures and all-cause mortality impacts.
Causal inference is another method of establishing a causal connection that evaluates associations under changing conditions. The 2019 PM ISA stated that "overall, the results of these causal inference studies contribute to the body of epidemiologic evidence that informs the causal relationship between long-term (one month to years) PM2.5 exposure and total mortality (U.S. EPA, 2019b ADDIN EN.CITE <EndNote><Author>U.S. EPA</Author><Year>2019</Year><RecNum>320</RecNum><DisplayText>U.S. EPA, 2019c</DisplayText><record><rec-number>320</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1573071075">320</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>Integrated Science Assessment (ISA) for Particulate Matter (Final Report)</title></titles><edition>December 2019</edition><dates><year>2019</year></dates><pub-location>Washington, DC</pub-location><publisher>U.S. Environmental Protection Agency, Office of Research and Development, National Center for Environmental Assessment</publisher><isbn>EPA/600/R-19/188</isbn><urls><related-urls><url>Available at: https://www.epa.gov/naaqs/particulate-matter-pm-standards-integrated-science-assessments-current-review</url></related-urls></urls></record></EndNote>). Observing consistent results for this relationship across studies using different analytic techniques (i.e., difference-in-difference approach) increases our confidence in the relationship."
Estimating and Assigning Exposures in Epidemiology Studies
New developments in exposure assessment, including hybrid spatiotemporal models that incorporate satellite observations of aerial optical density, land use variables, surface monitoring data from monitors, and chemical transport models, have led to improvements in pollutant concentration estimates. 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, 2013, U.S. EPA, 2019b).
Although these advancements may reduce bias and uncertainty in risk estimates, the accuracy of hybrid exposure estimates is difficult to confirm in areas lacking monitors. On the other hand, studies using monitor data as the only exposure information have increasing exposure uncertainty the farther people live from the monitor site.
Uncertainties related to PM2.5 and O3 exposure estimation vary. For example, the PM2.5 HBM method had Pearson R's ranging from 0.91 to 0.94 when applied across the U.S. at a 36-km resolution, depending on the geoimputation approach of the CMAQ data (U.S. EPA, 2019b). Evaluation ADDIN EN.CITE <EndNote><Author>U.S. EPA</Author><Year>2019</Year><RecNum>320</RecNum><DisplayText>U.S. EPA, 2019c</DisplayText><record><rec-number>320</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1573071075">320</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>Integrated Science Assessment (ISA) for Particulate Matter (Final Report)</title></titles><edition>December 2019</edition><dates><year>2019</year></dates><pub-location>Washington, DC</pub-location><publisher>U.S. Environmental Protection Agency, Office of Research and Development, National Center for Environmental Assessment</publisher><isbn>EPA/600/R-19/188</isbn><urls><related-urls><url>Available at: https://www.epa.gov/naaqs/particulate-matter-pm-standards-integrated-science-assessments-current-review</url></related-urls></urls></record></EndNote> of the O3 HBM method has been relatively limited. However, overall conclusions regarding long-term O3 exposure estimates that include fixed-site monitor measurements are that "the true effect of long-term exposure to ambient ozone may be underestimated or overestimated by the model" and that it "is much more common for the effect estimate to be underestimated, and the bias is typically small in magnitude" (3432020a3430xd5s9009txfw1eafstv220i5ds2wvtpwf5d1581631219">343</U.S. EPA, 2020b).
Modeling the Influence of Air Pollution on the Risk of Mortality Over Time
Air pollution benefits assessments commonly use a "pulse" approach, wherein counts of premature deaths and illnesses are attributed to air quality changes in a single year. To the extent that simulated changes in air quality persist over time -- that is, concentrations are reduced over a multi-year period -- then the pulse method may under-estimate the cumulative impact of air pollution on health (Roman et al., 2022). Partly for this reason, researchers have employed a "dynamic" approach to estimating multi-year changes in air pollution risk using a life table Miller and Hurley, 2003. A life table estimates the air pollution-attributable risk for each individual in a specified cohort on a year-to-year basis. When estimating the risk of premature death, the probability of dying in each year is conditional upon having lived to that year. Noting the advantages of this approach compared to the pulse method, the U.S. EPA Science Advisory Board noted that life tables "...provide the most realistic available modeling how, over time, changes in population risk led to changes in the size and age distribution of the population, with consequent implications for estimated mortality impacts" (Hammitt and Bailar, 2010).
EPA's PopSim tool, which builds upon the World Health Organization (WHO) LIFET tool, estimates changes in life expectancy at birth, life years and counts of attributable deaths (Miller and Hurley, 2003). In contrast with the BenMAP-CE tool, PopSim quantifies these effects at a national scale and thus (in its current form) is unable to estimate spatially resolved effects; it is also not designed to quantify morbidity effects and does not yet include baseline death rates stratified by race or ethnicity. Hence, the tool is a complement, rather than a substitute, for BenMAP-CE.
Differential Toxicity of PM2.5 According to Chemical Composition
PM2.5 is a heterogenous mixture of solid and liquid particles suspended in air and can vary with regards to size, composition, and source. The 2019 PM ISA found that "across exposure durations and health effects categories...many PM2.5 components and sources are associated with many health effects, and the evidence does not indicate that any one source or component is consistently more strongly related to health effects than PM2.5 mass;" although, it was also noted that "most studies that examine PM sources and components focused on PM2.5" (3202019c3200xd5s9009txfw1eafstv220i5ds2wvtpwf5d1573071075">320Washington, DCNational U.S. EPA, 2019b).
Since the 2019 PM ISA concluded that "recent studies continue to demonstrate that no individual PM2.5 component or source is a better predictor of mortality than PM2.5 mass" and "many PM2.5 components and sources are associated with many health effects and that the evidence does not indicate that any one source or component is consistently more strongly related with health effects than PM2.5 mass" we continue to assume that all fine particles, regardless of their chemical composition, are equally potent in causing mortality and do not quantitatively assess uncertainties related to potential differences in PM2.5 toxicity or composition. A qualitative discussion of this uncertainty as it relates to respiratory effects, cardiovascular effects, and mortality can be found in section 1.5.4 of the 2019 PM ISA (3202019c3200xd5s9009txfw1eafstv220i5ds2wvtpwf5d1573071075">320Washington, DCNational U.S. EPA, 2019b).
Different Long-Term Exposure Windows