ADDIN EN.CITE <EndNote><Author>Turner</Author><Year>2016</Year><RecNum>544</RecNum><DisplayText>Turner et al., 2016</DisplayText><record><rec-number>544</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732084">544</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Turner, M. C.</author><author>Jerrett, M.</author><author>Pope, A., III</author><author>Krewski, D.</author><author>Gapstur, S. M.</author><author>Diver, W. R.</author><author>Beckerman, B. S.</author><author>Marshall, J. D.</author><author>Su, J.</author><author>Crouse, D. L.</author><author>Burnett, R. T.</author></authors></contributors><titles><title>Long-term ozone exposure and mortality in a large prospective study</title><secondary-title>American Journal of Respiratory and Critical Care Medicine</secondary-title><alt-title>Am J Respir Crit Care Med</alt-title></titles><periodical><full-title>American Journal of Respiratory and Critical Care Medicine</full-title>Am. J. Respir. Crit. Care Med.Am J Respir Crit Care Med</periodical><alt-periodical><full-title>American Journal of Respiratory and Critical Care Medicine</full-title>Am. J. Respir. Crit. Care Med.Am J Respir Crit Care Med</alt-periodical><pages>1134-1142</pages><volume>193</volume><number>10</number><dates><year>2016</year></dates><isbn>ISSN 1073-449X
EISSN 1535-4970</isbn><accession-num>26680605</accession-num>3060878<urls><related-urls><url>http://dx.doi.org/10.1164/rccm.201508-1633OC</url></related-urls></urls><electronic-resource-num>10.1164/rccm.201508-1633OC</electronic-resource-num><language>English</language></record></EndNote> ADDIN EN.CITE <EndNote><Author>Pope</Author><Year>2015</Year><RecNum>542</RecNum><DisplayText>Pope et al., 2015</DisplayText><record><rec-number>542</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732084">542</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Pope, C. A.</author><author>Turner, M. C.</author><author>Burnett, R.</author><author>Jerrett, M.</author><author>Gapstur, S. M.</author><author>Diver, W. R.</author><author>Krewski, D.</author><author>Brook, R. D.</author></authors></contributors><titles><title>Relationships between fine particulate air pollution, cardiometabolic disorders and cardiovascular mortality</title><secondary-title>Circulation Research</secondary-title><alt-title>Circ Res</alt-title></titles><periodical><full-title>Circulation Research</full-title>Circul. Res.Circul Res</periodical><pages>108-U258</pages><volume>116</volume><number>1</number><dates><year>2015</year></dates><isbn>ISSN 0009-7330
EISSN 1524-4571</isbn><accession-num>25348167</accession-num>2534118<urls><related-urls><url>http://dx.doi.org/10.1161/CIRCRESAHA.116.305060</url></related-urls></urls><electronic-resource-num>10.1161/CIRCRESAHA.116.305060</electronic-resource-num><language>English</language></record></EndNote> ADDIN EN.CITE <EndNote><Author>Turner</Author><Year>2016</Year><RecNum>544</RecNum><DisplayText>Turner et al., 2016</DisplayText><record><rec-number>544</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732084">544</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Turner, M. C.</author><author>Jerrett, M.</author><author>Pope, A., III</author><author>Krewski, D.</author><author>Gapstur, S. M.</author><author>Diver, W. R.</author><author>Beckerman, B. S.</author><author>Marshall, J. D.</author><author>Su, J.</author><author>Crouse, D. L.</author><author>Burnett, R. T.</author></authors></contributors><titles><title>Long-term ozone exposure and mortality in a large prospective study</title><secondary-title>American Journal of Respiratory and Critical Care Medicine</secondary-title><alt-title>Am J Respir Crit Care Med</alt-title></titles><periodical><full-title>American Journal of Respiratory and Critical Care Medicine</full-title>Am. J. Respir. Crit. Care Med.Am J Respir Crit Care Med</periodical><alt-periodical><full-title>American Journal of Respiratory and Critical Care Medicine</full-title>Am. J. Respir. Crit. Care Med.Am J Respir Crit Care Med</alt-periodical><pages>1134-1142</pages><volume>193</volume><number>10</number><dates><year>2016</year></dates><isbn>ISSN 1073-449X
EISSN 1535-4970</isbn><accession-num>26680605</accession-num>3060878<urls><related-urls><url>http://dx.doi.org/10.1164/rccm.201508-1633OC</url></related-urls></urls><electronic-resource-num>10.1164/rccm.201508-1633OC</electronic-resource-num><language>English</language></record></EndNote> ADDIN EN.CITE <EndNote><Author>Pope</Author><Year>2015</Year><RecNum>542</RecNum><DisplayText>Pope et al., 2015</DisplayText><record><rec-number>542</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732084">542</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Pope, C. A.</author><author>Turner, M. C.</author><author>Burnett, R.</author><author>Jerrett, M.</author><author>Gapstur, S. M.</author><author>Diver, W. R.</author><author>Krewski, D.</author><author>Brook, R. D.</author></authors></contributors><titles><title>Relationships between fine particulate air pollution, cardiometabolic disorders and cardiovascular mortality</title><secondary-title>Circulation Research</secondary-title><alt-title>Circ Res</alt-title></titles><periodical><full-title>Circulation Research</full-title>Circul. Res.Circul Res</periodical><pages>108-U258</pages><volume>116</volume><number>1</number><dates><year>2015</year></dates><isbn>ISSN 0009-7330
EISSN 1524-4571</isbn><accession-num>25348167</accession-num>2534118<urls><related-urls><url>http://dx.doi.org/10.1161/CIRCRESAHA.116.305060</url></related-urls></urls><electronic-resource-num>10.1161/CIRCRESAHA.116.305060</electronic-resource-num><language>English</language></record></EndNote> REF _Ref60065982 \h
SEQ Table \* ARABIC PM2.5-Attributable ACS CSP-II Mortality Risk Estimates per 10 ug/m[3] from Different Exposure Estimation Techniques
Exposure Technique
Risk Estimate
HBM
1.06 (1.04-1.08)
LURBME
1.07 (1.06 - 1.09)

Confounding by O3
When considering the relationship between pollutant exposure and health effects, it can be informative to consider whether risk estimates are subjected to confounding when including other pollutants in copollutant models, especially when health impacts of more than two highly correlated pollutants are being estimated concurrently. Regarding long-term exposures, the 2019 PM ISA concluded that "positive associations observed between long-term PM2.5 exposure and total mortality remain relatively unchanged after adjustment for O3, NO2, and PM10−2.5" (U.S. EPA, 2019b).
Both Turner et al., 2016 and Di et al., 2017a provided single-pollutant and two-pollutant (including O3 as a copollutant) PM2.5-attributable mortality risk estimates. Although the 2019 PM ISA found that, in general, PM2.5 risk estimates were relatively unchanged to the inclusion of O3 in copollutant models, a comparison of risk estimates that either do or do not include O3 as a copollutant is included to clarify this potential sensitivity with respect to all-cause PM2.5-attributable mortality (U.S. EPA, 2019b). Differences in the magnitude of risk estimates including or excluding O3 as a copollutant are provided in Table 35. Italicized risk estimates were identified for use in the main benefits assessment.
Table 35. ADDIN EN.CITE <EndNote><Author>Turner</Author><Year>2016</Year><RecNum>544</RecNum><DisplayText>Turner et al., 2016</DisplayText><record><rec-number>544</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732084">544</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Turner, M. C.</author><author>Jerrett, M.</author><author>Pope, A., III</author><author>Krewski, D.</author><author>Gapstur, S. M.</author><author>Diver, W. R.</author><author>Beckerman, B. S.</author><author>Marshall, J. D.</author><author>Su, J.</author><author>Crouse, D. L.</author><author>Burnett, R. T.</author></authors></contributors><titles><title>Long-term ozone exposure and mortality in a large prospective study</title><secondary-title>American Journal of Respiratory and Critical Care Medicine</secondary-title><alt-title>Am J Respir Crit Care Med</alt-title></titles><periodical><full-title>American Journal of Respiratory and Critical Care Medicine</full-title>Am. J. Respir. Crit. Care Med.Am J Respir Crit Care Med</periodical><alt-periodical><full-title>American Journal of Respiratory and Critical Care Medicine</full-title>Am. J. Respir. Crit. Care Med.Am J Respir Crit Care Med</alt-periodical><pages>1134-1142</pages><volume>193</volume><number>10</number><dates><year>2016</year></dates><isbn>ISSN 1073-449X
EISSN 1535-4970</isbn><accession-num>26680605</accession-num>3060878<urls><related-urls><url>http://dx.doi.org/10.1164/rccm.201508-1633OC</url></related-urls></urls><electronic-resource-num>10.1164/rccm.201508-1633OC</electronic-resource-num><language>English</language></record></EndNote> ADDIN EN.CITE <EndNote><Author>Di</Author><Year>2017</Year><RecNum>1169</RecNum><DisplayText>Di et al., 2017a</DisplayText><record><rec-number>1169</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603987569">1169</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Di, Q.</author><author>Dai, L.</author><author>Wang, Y.</author><author>Zanobetti, A.</author><author>Choirat, C.</author><author>Schwartz, J. D.</author><author>Dominici, F.</author></authors></contributors><titles><title>Association of short-term exposure to air pollution with mortality in older adults</title><secondary-title>JAMA: Journal of the American Medical Association</secondary-title><alt-title>JAMA</alt-title></titles><periodical><full-title>JAMA: Journal of the American Medical Association</full-title>J. Am. Med. Assoc.J Am Med Assoc</periodical><alt-periodical><full-title>JAMA</full-title></alt-periodical><pages>2446-2456</pages><volume>318</volume><number>24</number><edition>December 26, 2017</edition><dates><year>2017</year><pub-dates><date>12</date></pub-dates></dates><isbn>ISSN 0098-7484
EISSN 1538-3598</isbn><accession-num>29279932</accession-num>4166831<urls><related-urls><url>http://dx.doi.org/10.1001/jama.2017.17923</url></related-urls></urls><electronic-resource-num>10.1001/jama.2017.17923</electronic-resource-num><language>English</language></record></EndNote> REF _Ref60063588 \h  \* MERGEFORMAT
SEQ Table \* ARABIC Single- and Two-Pollutant (Including O3 as a Copollutant) PM2.5-Attributable Mortality Risk Estimates per 10 ug/m[3]

Turner et al., 2016 ADDIN EN.CITE <EndNote><Author>Turner</Author><Year>2016</Year><RecNum>544</RecNum><DisplayText>Turner et al., 2016</DisplayText><record><rec-number>544</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732084">544</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Turner, M. C.</author><author>Jerrett, M.</author><author>Pope, A., III</author><author>Krewski, D.</author><author>Gapstur, S. M.</author><author>Diver, W. R.</author><author>Beckerman, B. S.</author><author>Marshall, J. D.</author><author>Su, J.</author><author>Crouse, D. L.</author><author>Burnett, R. T.</author></authors></contributors><titles><title>Long-term ozone exposure and mortality in a large prospective study</title><secondary-title>American Journal of Respiratory and Critical Care Medicine</secondary-title><alt-title>Am J Respir Crit Care Med</alt-title></titles><periodical><full-title>American Journal of Respiratory and Critical Care Medicine</full-title>Am. J. Respir. Crit. Care Med.Am J Respir Crit Care Med</periodical><alt-periodical><full-title>American Journal of Respiratory and Critical Care Medicine</full-title>Am. J. Respir. Crit. Care Med.Am J Respir Crit Care Med</alt-periodical><pages>1134-1142</pages><volume>193</volume><number>10</number><dates><year>2016</year></dates><isbn>ISSN 1073-449X
EISSN 1535-4970</isbn><accession-num>26680605</accession-num>3060878<urls><related-urls><url>http://dx.doi.org/10.1164/rccm.201508-1633OC</url></related-urls></urls><electronic-resource-num>10.1164/rccm.201508-1633OC</electronic-resource-num><language>English</language></record></EndNote>
Di et al., 2017a ADDIN EN.CITE <EndNote><Author>Di</Author><Year>2017</Year><RecNum>1169</RecNum><DisplayText>Di et al., 2017a</DisplayText><record><rec-number>1169</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603987569">1169</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Di, Q.</author><author>Dai, L.</author><author>Wang, Y.</author><author>Zanobetti, A.</author><author>Choirat, C.</author><author>Schwartz, J. D.</author><author>Dominici, F.</author></authors></contributors><titles><title>Association of short-term exposure to air pollution with mortality in older adults</title><secondary-title>JAMA: Journal of the American Medical Association</secondary-title><alt-title>JAMA</alt-title></titles><periodical><full-title>JAMA: Journal of the American Medical Association</full-title>J. Am. Med. Assoc.J Am Med Assoc</periodical><alt-periodical><full-title>JAMA</full-title></alt-periodical><pages>2446-2456</pages><volume>318</volume><number>24</number><edition>December 26, 2017</edition><dates><year>2017</year><pub-dates><date>12</date></pub-dates></dates><isbn>ISSN 0098-7484
EISSN 1538-3598</isbn><accession-num>29279932</accession-num>4166831<urls><related-urls><url>http://dx.doi.org/10.1001/jama.2017.17923</url></related-urls></urls><electronic-resource-num>10.1001/jama.2017.17923</electronic-resource-num><language>English</language></record></EndNote>
Two-Pollutant
1.06 (1.04-1.08)
1.073 (1.071, 1.075)
Single-Pollutant
1.06 (1.04-1.08)
1.084 (1.081, 1.086)
Statistical Technique
 ADDIN EN.CITE <EndNote><Author>Di</Author><Year>2017</Year><RecNum>1169</RecNum><DisplayText>Di et al., 2017a</DisplayText><record><rec-number>1169</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603987569">1169</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Di, Q.</author><author>Dai, L.</author><author>Wang, Y.</author><author>Zanobetti, A.</author><author>Choirat, C.</author><author>Schwartz, J. D.</author><author>Dominici, F.</author></authors></contributors><titles><title>Association of short-term exposure to air pollution with mortality in older adults</title><secondary-title>JAMA: Journal of the American Medical Association</secondary-title><alt-title>JAMA</alt-title></titles><periodical><full-title>JAMA: Journal of the American Medical Association</full-title>J. Am. Med. Assoc.J Am Med Assoc</periodical><alt-periodical><full-title>JAMA</full-title></alt-periodical><pages>2446-2456</pages><volume>318</volume><number>24</number><edition>December 26, 2017</edition><dates><year>2017</year><pub-dates><date>12</date></pub-dates></dates><isbn>ISSN 0098-7484
EISSN 1538-3598</isbn><accession-num>29279932</accession-num>4166831<urls><related-urls><url>http://dx.doi.org/10.1001/jama.2017.17923</url></related-urls></urls><electronic-resource-num>10.1001/jama.2017.17923</electronic-resource-num><language>English</language></record></EndNote> REF _Ref60064143 \h
Table 37. Wu, 2020 reported hazard ratios estimated using causal inference approaches, wherein the authors "design the study creating a pseudo-population which mimics a randomized experiment..." Wu and co-authors applied three variants of the Generalized Propensity Score (GPS) technique: (1) matching by GPS; (2) weighting by GPS; and (3) adjustment by GPS. Each of the three techniques provide similar Hazard Ratios, providing evidence that the results were robust to confounding (Table 36)
Table 36. Hazard Ratios per 10 ug/m[3] Estimated Using Three Causal Inference Approaches in Wu et al., 2020
 ADDIN EN.CITE <EndNote><Author>Di</Author><Year>2017</Year><RecNum>1169</RecNum><DisplayText>Di et al., 2017a</DisplayText><record><rec-number>1169</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603987569">1169</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Di, Q.</author><author>Dai, L.</author><author>Wang, Y.</author><author>Zanobetti, A.</author><author>Choirat, C.</author><author>Schwartz, J. D.</author><author>Dominici, F.</author></authors></contributors><titles><title>Association of short-term exposure to air pollution with mortality in older adults</title><secondary-title>JAMA: Journal of the American Medical Association</secondary-title><alt-title>JAMA</alt-title></titles><periodical><full-title>JAMA: Journal of the American Medical Association</full-title>J. Am. Med. Assoc.J Am Med Assoc</periodical><alt-periodical><full-title>JAMA</full-title></alt-periodical><pages>2446-2456</pages><volume>318</volume><number>24</number><edition>December 26, 2017</edition><dates><year>2017</year><pub-dates><date>12</date></pub-dates></dates><isbn>ISSN 0098-7484
EISSN 1538-3598</isbn><accession-num>29279932</accession-num>4166831<urls><related-urls><url>http://dx.doi.org/10.1001/jama.2017.17923</url></related-urls></urls><electronic-resource-num>10.1001/jama.2017.17923</electronic-resource-num><language>English</language></record></EndNote>
Causal Inference Technique
                                 Hazard Ratio
                          and 95% Confidence Interval
Weighting
                             1.076 (1.065 -- 1.088)
Matching
                             1.068 (1.054 -- 1.083)
Adjustment
                             1.072 (1.061 -- 1.082)

Pope III et al., 2019 also generated Cox proportional hazard estimates using both complex and basic models, of which the basic model did not account for complex survey design (using PHREG procedure in SAS version 9.3). The two estimates for all-cause mortality from long-term PM2.5 exposures were nearly identical (Table 37).
Table 37. Complex and Basic Cox Proportional Hazard Model Estimates of PM2.5-Attributable Mortality per 10 ug/m[3]

Pope III et al., 2019
Complex
1.13 (1.11-1.16)
Basic
1.13 (1.11-1.15)

Asthma Onset in Children
For a number of health endpoints, we identified plausible alternative risk estimates to characterize the sensitivity of the main risk estimate to alternative assumptions and/or input parameters. Below we detail: 1) the endpoints for which we considered alternative risk estimates; and 2) the studies from which we drew the alternative risk estimates. This type of sensitivity assessment is also performed for other PM2.5 and O3 health endpoints in sections 6.1.4, 0, and 6.2.4.
The study identified as best characterizing risk for this health endpoint took place in Canada (Tetreault et al., 2016). Even though comparatively Tetreault et al., 2016 was preferred in all identification criteria to other available studies (e.g., study size, exposure estimation technique, study period, etc.) other than location, we thought it useful to include the available U.S.-based risk estimates as uncertainty analyses. An overall comparison of the main risk estimate and 95% confidence interval from Tetreault et al., 2016 and the alternative risk estimates and confidence intervals from  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>McConnell et al., 2010 and Nishimura et al., 2013 can be found in Table 38. Details about the two studies providing alternate risk estimates is below.
Table 38. Potential Sensitivity of Estimated Instances of Asthma Onset
                        Potential Source of Uncertainty
      Potential Insights Gained from Quantitative Uncertainty Analyzes[2]
Application of Risk Estimates to Other Locations and Populations
Tetreault et al., 2016 included only Canadians whereas  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>Nishimura et al., 2013 included five U.S. urban areas and McConnell et al., 2010 was restricted to southern CA
Study Size
Tetreault et al., 2016 included the largest study size, approximately twenty-five times the size of either  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>Nishimura et al., 2013 or McConnell et al., 2010
Study Period
Tetreault et al., 2016 evaluated the most recent health study period (1996-2011) compared to 2002-2006 for  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>McConnell et al., 2010 and 1986-2005 for Nishimura et al., 2013
Exposure Estimate
Tetreault et al., 2016 used hybrid exposure estimates whereas sever states, whereas  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>Nishimura et al., 2013 and McConnell et al., 2010 used monitor-based estimates
Statistical Technique
Tetreault et al., 2016 and  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>McConnell et al., 2010 use time-varying and multilevel Cox proportional hazard models, respectively, whereas Nishimura et al., 2013 uses logistical regression models

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

Cardiovascular Hospital Admissions
Bell et al., 2015 was identified as best characterizing risk across the U.S. for benefits assessment purposes as it included the largest study size, most recent time period, and a nationally representative geographic area. However, it was restricted to ages >64 and based exposure estimates solely on monitoring data. There was also another large study of PM2.5-attributable cardiovascular hospital admission impacts that included all ages and incorporated hybrid exposure estimation techniques (Talbott et al., 2014 ADDIN EN.CITE <EndNote><Author>Talbott</Author><Year>2014</Year><RecNum>1190</RecNum><DisplayText>Talbott et al., 2014</DisplayText><record><rec-number>1190</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1604002257">1190</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Talbott, E. O.</author><author>Rager, J. R.</author><author>Benson, S.</author><author>Brink, L. A.</author><author>Bilonick, R. A.</author><author>Wu, C.</author></authors></contributors><auth-address>University of Pittsburgh, Graduate School of Public Health, Department of Epidemiology, Pittsburgh, PA 15261, United States.</auth-address><titles><title>A case-crossover analysis of the impact of PM(2.5) on cardiovascular disease hospitalizations for selected CDC tracking states</title><secondary-title>Environ Res</secondary-title></titles><periodical><full-title>Environmental Research</full-title>Environ. Res.Environ Res</periodical><pages>455-65</pages><volume>134</volume><edition>2014/10/04</edition><keywords><keyword>Cardiovascular Diseases/*chemically induced</keyword><keyword>Centers for Disease Control and Prevention, U.S.</keyword><keyword>Cross-Over Studies</keyword><keyword>*Hospitalization</keyword><keyword>Humans</keyword><keyword>Particulate Matter/*toxicity</keyword><keyword>United States</keyword><keyword>Air pollution</keyword><keyword>Cardiovascular disease</keyword><keyword>Case-crossover design</keyword><keyword>Environmental public health tracking</keyword><keyword>Pm(2.5)</keyword></keywords><dates><year>2014</year><pub-dates><date>Oct</date></pub-dates></dates><isbn>1096-0953 (Electronic)
0013-9351 (Linking)</isbn><accession-num>25277761</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/25277761</url></related-urls></urls><electronic-resource-num>10.1016/j.envres.2014.06.018</electronic-resource-num></record></EndNote>). Differences in the age ranges and ICD-9 codes prevented pooling of the two estimates but comparing the two estimates could provide insights into uncertainties associated with epidemiologic estimates of this health endpoint (Table 40). Therefore, we include a risk estimate of cardiovascular hospital admission impacts from long-term PM2.5 exposure from Talbott et al., 2014 ADDIN EN.CITE <EndNote><Author>Talbott</Author><Year>2014</Year><RecNum>1190</RecNum><DisplayText>Talbott et al., 2014</DisplayText><record><rec-number>1190</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1604002257">1190</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Talbott, E. O.</author><author>Rager, J. R.</author><author>Benson, S.</author><author>Brink, L. A.</author><author>Bilonick, R. A.</author><author>Wu, C.</author></authors></contributors><auth-address>University of Pittsburgh, Graduate School of Public Health, Department of Epidemiology, Pittsburgh, PA 15261, United States.</auth-address><titles><title>A case-crossover analysis of the impact of PM(2.5) on cardiovascular disease hospitalizations for selected CDC tracking states</title><secondary-title>Environ Res</secondary-title></titles><periodical><full-title>Environmental Research</full-title>Environ. Res.Environ Res</periodical><pages>455-65</pages><volume>134</volume><edition>2014/10/04</edition><keywords><keyword>Cardiovascular Diseases/*chemically induced</keyword><keyword>Centers for Disease Control and Prevention, U.S.</keyword><keyword>Cross-Over Studies</keyword><keyword>*Hospitalization</keyword><keyword>Humans</keyword><keyword>Particulate Matter/*toxicity</keyword><keyword>United States</keyword><keyword>Air pollution</keyword><keyword>Cardiovascular disease</keyword><keyword>Case-crossover design</keyword><keyword>Environmental public health tracking</keyword><keyword>Pm(2.5)</keyword></keywords><dates><year>2014</year><pub-dates><date>Oct</date></pub-dates></dates><isbn>1096-0953 (Electronic)
0013-9351 (Linking)</isbn><accession-num>25277761</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/25277761</url></related-urls></urls><electronic-resource-num>10.1016/j.envres.2014.06.018</electronic-resource-num></record></EndNote> as a sensitivity analysis of this health endpoint (Table 41). Please note that Talbott et al., 2014 ADDIN EN.CITE <EndNote><Author>Talbott</Author><Year>2014</Year><RecNum>1190</RecNum><DisplayText>Talbott et al., 2014</DisplayText><record><rec-number>1190</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1604002257">1190</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Talbott, E. O.</author><author>Rager, J. R.</author><author>Benson, S.</author><author>Brink, L. A.</author><author>Bilonick, R. A.</author><author>Wu, C.</author></authors></contributors><auth-address>University of Pittsburgh, Graduate School of Public Health, Department of Epidemiology, Pittsburgh, PA 15261, United States.</auth-address><titles><title>A case-crossover analysis of the impact of PM(2.5) on cardiovascular disease hospitalizations for selected CDC tracking states</title><secondary-title>Environ Res</secondary-title></titles><periodical><full-title>Environmental Research</full-title>Environ. Res.Environ Res</periodical><pages>455-65</pages><volume>134</volume><edition>2014/10/04</edition><keywords><keyword>Cardiovascular Diseases/*chemically induced</keyword><keyword>Centers for Disease Control and Prevention, U.S.</keyword><keyword>Cross-Over Studies</keyword><keyword>*Hospitalization</keyword><keyword>Humans</keyword><keyword>Particulate Matter/*toxicity</keyword><keyword>United States</keyword><keyword>Air pollution</keyword><keyword>Cardiovascular disease</keyword><keyword>Case-crossover design</keyword><keyword>Environmental public health tracking</keyword><keyword>Pm(2.5)</keyword></keywords><dates><year>2014</year><pub-dates><date>Oct</date></pub-dates></dates><isbn>1096-0953 (Electronic)
0013-9351 (Linking)</isbn><accession-num>25277761</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/25277761</url></related-urls></urls><electronic-resource-num>10.1016/j.envres.2014.06.018</electronic-resource-num></record></EndNote> provides individual risk estimates for each state, which will be pooled into a single estimate to compare with Bell et al., 2015.
Talbott et al., 2014 ADDIN EN.CITE <EndNote><Author>Talbott</Author><Year>2014</Year><RecNum>1190</RecNum><DisplayText>Talbott et al., 2014</DisplayText><record><rec-number>1190</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1604002257">1190</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Talbott, E. O.</author><author>Rager, J. R.</author><author>Benson, S.</author><author>Brink, L. A.</author><author>Bilonick, R. A.</author><author>Wu, C.</author></authors></contributors><auth-address>University of Pittsburgh, Graduate School of Public Health, Department of Epidemiology, Pittsburgh, PA 15261, United States.</auth-address><titles><title>A case-crossover analysis of the impact of PM(2.5) on cardiovascular disease hospitalizations for selected CDC tracking states</title><secondary-title>Environ Res</secondary-title></titles><periodical><full-title>Environmental Research</full-title>Environ. Res.Environ Res</periodical><pages>455-65</pages><volume>134</volume><edition>2014/10/04</edition><keywords><keyword>Cardiovascular Diseases/*chemically induced</keyword><keyword>Centers for Disease Control and Prevention, U.S.</keyword><keyword>Cross-Over Studies</keyword><keyword>*Hospitalization</keyword><keyword>Humans</keyword><keyword>Particulate Matter/*toxicity</keyword><keyword>United States</keyword><keyword>Air pollution</keyword><keyword>Cardiovascular disease</keyword><keyword>Case-crossover design</keyword><keyword>Environmental public health tracking</keyword><keyword>Pm(2.5)</keyword></keywords><dates><year>2014</year><pub-dates><date>Oct</date></pub-dates></dates><isbn>1096-0953 (Electronic)
0013-9351 (Linking)</isbn><accession-num>25277761</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/25277761</url></related-urls></urls><electronic-resource-num>10.1016/j.envres.2014.06.018</electronic-resource-num></record></EndNote> assessed daily PM2.5 concentrations and hospitalizations for cardiovascular disease in Florida, Massachusetts, New Hampshire, New Jersey, New Mexico, New York, and Washington from 2001 to 2008. The authors gathered hospital discharge data from each state's respective data stewards. Talbott et al., 2014 ADDIN EN.CITE <EndNote><Author>Talbott</Author><Year>2014</Year><RecNum>1190</RecNum><DisplayText>Talbott et al., 2014</DisplayText><record><rec-number>1190</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1604002257">1190</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Talbott, E. O.</author><author>Rager, J. R.</author><author>Benson, S.</author><author>Brink, L. A.</author><author>Bilonick, R. A.</author><author>Wu, C.</author></authors></contributors><auth-address>University of Pittsburgh, Graduate School of Public Health, Department of Epidemiology, Pittsburgh, PA 15261, United States.</auth-address><titles><title>A case-crossover analysis of the impact of PM(2.5) on cardiovascular disease hospitalizations for selected CDC tracking states</title><secondary-title>Environ Res</secondary-title></titles><periodical><full-title>Environmental Research</full-title>Environ. Res.Environ Res</periodical><pages>455-65</pages><volume>134</volume><edition>2014/10/04</edition><keywords><keyword>Cardiovascular Diseases/*chemically induced</keyword><keyword>Centers for Disease Control and Prevention, U.S.</keyword><keyword>Cross-Over Studies</keyword><keyword>*Hospitalization</keyword><keyword>Humans</keyword><keyword>Particulate Matter/*toxicity</keyword><keyword>United States</keyword><keyword>Air pollution</keyword><keyword>Cardiovascular disease</keyword><keyword>Case-crossover design</keyword><keyword>Environmental public health tracking</keyword><keyword>Pm(2.5)</keyword></keywords><dates><year>2014</year><pub-dates><date>Oct</date></pub-dates></dates><isbn>1096-0953 (Electronic)
0013-9351 (Linking)</isbn><accession-num>25277761</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/25277761</url></related-urls></urls><electronic-resource-num>10.1016/j.envres.2014.06.018</electronic-resource-num></record></EndNote> conducted a time-stratified case crossover study using hospitalization data for all cardiovascular diseases (ICD-9 390-459) and for several specific cardiovascular diseases within the ICD-9 390-459 range. Authors used a downscaling Bayesian space-time modeling approach to combine air monitoring data and air gridded numerical outputs from CMAQ to predict daily PM2.5 concentrations. The authors gathered meteorological data from the Centers for Disease Control (CDC) Wonder North America Land Data Assimilation System Daily Air Temperatures and Heat Index. Risk estimates were presented for a 10 ug/m[3] increase in PM2.5 for each state and season across three single-day lags (0, 1, and 2) and a three-day lag average (0-2) by diagnosis. All-year risk estimates were identified over season-specific estimates and estimates of multiday average lag period were identified over single-day lag estimates. The seven state-specific risk estimates identified as sensitivity analyses were pooled using the random or fixed effects algorithm in BenMAP-CE. The seven risk estimates reflect a mix of positive and negative values. State-specific risk estimates identified from Talbott et al., 2014 ADDIN EN.CITE <EndNote><Author>Talbott</Author><Year>2014</Year><RecNum>1190</RecNum><DisplayText>Talbott et al., 2014</DisplayText><record><rec-number>1190</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1604002257">1190</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Talbott, E. O.</author><author>Rager, J. R.</author><author>Benson, S.</author><author>Brink, L. A.</author><author>Bilonick, R. A.</author><author>Wu, C.</author></authors></contributors><auth-address>University of Pittsburgh, Graduate School of Public Health, Department of Epidemiology, Pittsburgh, PA 15261, United States.</auth-address><titles><title>A case-crossover analysis of the impact of PM(2.5) on cardiovascular disease hospitalizations for selected CDC tracking states</title><secondary-title>Environ Res</secondary-title></titles><periodical><full-title>Environmental Research</full-title>Environ. Res.Environ Res</periodical><pages>455-65</pages><volume>134</volume><edition>2014/10/04</edition><keywords><keyword>Cardiovascular Diseases/*chemically induced</keyword><keyword>Centers for Disease Control and Prevention, U.S.</keyword><keyword>Cross-Over Studies</keyword><keyword>*Hospitalization</keyword><keyword>Humans</keyword><keyword>Particulate Matter/*toxicity</keyword><keyword>United States</keyword><keyword>Air pollution</keyword><keyword>Cardiovascular disease</keyword><keyword>Case-crossover design</keyword><keyword>Environmental public health tracking</keyword><keyword>Pm(2.5)</keyword></keywords><dates><year>2014</year><pub-dates><date>Oct</date></pub-dates></dates><isbn>1096-0953 (Electronic)
0013-9351 (Linking)</isbn><accession-num>25277761</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/25277761</url></related-urls></urls><electronic-resource-num>10.1016/j.envres.2014.06.018</electronic-resource-num></record></EndNote> come from a two-pollutant multivariable model including O3 of ICD-9 codes 390-459: 1.005 (95% CI: 0.998-1.012) for Massachusetts; 1.011 (95% CI: 1.007-1.016) for New Jersey; 1.011 (95% CI: 0.973-1.050) for New Mexico; and 1.011 (95% CI: 1.008-1.014) for New York. Each odds ratio is for a 10 ug/m[3] increase in the averaged daily mean PM2.5 concentration 0-, 1-, and 2-day lags (Talbott et al., 2014 ADDIN EN.CITE <EndNote><Author>Talbott</Author><Year>2014</Year><RecNum>1190</RecNum><DisplayText>Talbott et al., 2014</DisplayText><record><rec-number>1190</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1604002257">1190</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Talbott, E. O.</author><author>Rager, J. R.</author><author>Benson, S.</author><author>Brink, L. A.</author><author>Bilonick, R. A.</author><author>Wu, C.</author></authors></contributors><auth-address>University of Pittsburgh, Graduate School of Public Health, Department of Epidemiology, Pittsburgh, PA 15261, United States.</auth-address><titles><title>A case-crossover analysis of the impact of PM(2.5) on cardiovascular disease hospitalizations for selected CDC tracking states</title><secondary-title>Environ Res</secondary-title></titles><periodical><full-title>Environmental Research</full-title>Environ. Res.Environ Res</periodical><pages>455-65</pages><volume>134</volume><edition>2014/10/04</edition><keywords><keyword>Cardiovascular Diseases/*chemically induced</keyword><keyword>Centers for Disease Control and Prevention, U.S.</keyword><keyword>Cross-Over Studies</keyword><keyword>*Hospitalization</keyword><keyword>Humans</keyword><keyword>Particulate Matter/*toxicity</keyword><keyword>United States</keyword><keyword>Air pollution</keyword><keyword>Cardiovascular disease</keyword><keyword>Case-crossover design</keyword><keyword>Environmental public health tracking</keyword><keyword>Pm(2.5)</keyword></keywords><dates><year>2014</year><pub-dates><date>Oct</date></pub-dates></dates><isbn>1096-0953 (Electronic)
0013-9351 (Linking)</isbn><accession-num>25277761</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/25277761</url></related-urls></urls><electronic-resource-num>10.1016/j.envres.2014.06.018</electronic-resource-num></record></EndNote>, Table 3). Beta effect coefficients from the main (italicized) and sensitivity analyses are available in Table 41.
Table 40. Potential Sensitivity of Estimated Cardiovascular Hospital Admissions
                        Potential Source of Uncertainty
       Potential Insights Gained from Quantitative Uncertainty Analyzes
Application of Risk Estimates to Other Locations and Populations
Talbott et al., 2014 included all ages whereas  ADDIN EN.CITE <EndNote><Author>Talbott</Author><Year>2014</Year><RecNum>1190</RecNum><DisplayText>Talbott et al., 2014</DisplayText><record><rec-number>1190</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1604002257">1190</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Talbott, E. O.</author><author>Rager, J. R.</author><author>Benson, S.</author><author>Brink, L. A.</author><author>Bilonick, R. A.</author><author>Wu, C.</author></authors></contributors><auth-address>University of Pittsburgh, Graduate School of Public Health, Department of Epidemiology, Pittsburgh, PA 15261, United States.</auth-address><titles><title>A case-crossover analysis of the impact of PM(2.5) on cardiovascular disease hospitalizations for selected CDC tracking states</title><secondary-title>Environ Res</secondary-title></titles><periodical><full-title>Environmental Research</full-title>Environ. Res.Environ Res</periodical><pages>455-65</pages><volume>134</volume><edition>2014/10/04</edition><keywords><keyword>Cardiovascular Diseases/*chemically induced</keyword><keyword>Centers for Disease Control and Prevention, U.S.</keyword><keyword>Cross-Over Studies</keyword><keyword>*Hospitalization</keyword><keyword>Humans</keyword><keyword>Particulate Matter/*toxicity</keyword><keyword>United States</keyword><keyword>Air pollution</keyword><keyword>Cardiovascular disease</keyword><keyword>Case-crossover design</keyword><keyword>Environmental public health tracking</keyword><keyword>Pm(2.5)</keyword></keywords><dates><year>2014</year><pub-dates><date>Oct</date></pub-dates></dates><isbn>1096-0953 (Electronic)
0013-9351 (Linking)</isbn><accession-num>25277761</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/25277761</url></related-urls></urls><electronic-resource-num>10.1016/j.envres.2014.06.018</electronic-resource-num></record></EndNote>Bell et al., 2015 was restricted to ages >64
Confounding by Individual Risk Factors (Location)
Talbott et al., 2014 was restricted to seven states,  ADDIN EN.CITE <EndNote><Author>Talbott</Author><Year>2014</Year><RecNum>1190</RecNum><DisplayText>Talbott et al., 2014</DisplayText><record><rec-number>1190</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1604002257">1190</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Talbott, E. O.</author><author>Rager, J. R.</author><author>Benson, S.</author><author>Brink, L. A.</author><author>Bilonick, R. A.</author><author>Wu, C.</author></authors></contributors><auth-address>University of Pittsburgh, Graduate School of Public Health, Department of Epidemiology, Pittsburgh, PA 15261, United States.</auth-address><titles><title>A case-crossover analysis of the impact of PM(2.5) on cardiovascular disease hospitalizations for selected CDC tracking states</title><secondary-title>Environ Res</secondary-title></titles><periodical><full-title>Environmental Research</full-title>Environ. Res.Environ Res</periodical><pages>455-65</pages><volume>134</volume><edition>2014/10/04</edition><keywords><keyword>Cardiovascular Diseases/*chemically induced</keyword><keyword>Centers for Disease Control and Prevention, U.S.</keyword><keyword>Cross-Over Studies</keyword><keyword>*Hospitalization</keyword><keyword>Humans</keyword><keyword>Particulate Matter/*toxicity</keyword><keyword>United States</keyword><keyword>Air pollution</keyword><keyword>Cardiovascular disease</keyword><keyword>Case-crossover design</keyword><keyword>Environmental public health tracking</keyword><keyword>Pm(2.5)</keyword></keywords><dates><year>2014</year><pub-dates><date>Oct</date></pub-dates></dates><isbn>1096-0953 (Electronic)
0013-9351 (Linking)</isbn><accession-num>25277761</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/25277761</url></related-urls></urls><electronic-resource-num>10.1016/j.envres.2014.06.018</electronic-resource-num></record></EndNote>Bell et al., 2015 included all states
Confounding by Other Pollutants
Talbott et al., 2014 included the copollutant O3 ADDIN EN.CITE <EndNote><Author>Talbott</Author><Year>2014</Year><RecNum>1190</RecNum><DisplayText>Talbott et al., 2014</DisplayText><record><rec-number>1190</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1604002257">1190</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Talbott, E. O.</author><author>Rager, J. R.</author><author>Benson, S.</author><author>Brink, L. A.</author><author>Bilonick, R. A.</author><author>Wu, C.</author></authors></contributors><auth-address>University of Pittsburgh, Graduate School of Public Health, Department of Epidemiology, Pittsburgh, PA 15261, United States.</auth-address><titles><title>A case-crossover analysis of the impact of PM(2.5) on cardiovascular disease hospitalizations for selected CDC tracking states</title><secondary-title>Environ Res</secondary-title></titles><periodical><full-title>Environmental Research</full-title>Environ. Res.Environ Res</periodical><pages>455-65</pages><volume>134</volume><edition>2014/10/04</edition><keywords><keyword>Cardiovascular Diseases/*chemically induced</keyword><keyword>Centers for Disease Control and Prevention, U.S.</keyword><keyword>Cross-Over Studies</keyword><keyword>*Hospitalization</keyword><keyword>Humans</keyword><keyword>Particulate Matter/*toxicity</keyword><keyword>United States</keyword><keyword>Air pollution</keyword><keyword>Cardiovascular disease</keyword><keyword>Case-crossover design</keyword><keyword>Environmental public health tracking</keyword><keyword>Pm(2.5)</keyword></keywords><dates><year>2014</year><pub-dates><date>Oct</date></pub-dates></dates><isbn>1096-0953 (Electronic)
0013-9351 (Linking)</isbn><accession-num>25277761</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/25277761</url></related-urls></urls><electronic-resource-num>10.1016/j.envres.2014.06.018</electronic-resource-num></record></EndNote>
Exposure Estimate
Talbott et al., 2014 used hybrid exposure estimates whereas sever states,  ADDIN EN.CITE <EndNote><Author>Talbott</Author><Year>2014</Year><RecNum>1190</RecNum><DisplayText>Talbott et al., 2014</DisplayText><record><rec-number>1190</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1604002257">1190</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Talbott, E. O.</author><author>Rager, J. R.</author><author>Benson, S.</author><author>Brink, L. A.</author><author>Bilonick, R. A.</author><author>Wu, C.</author></authors></contributors><auth-address>University of Pittsburgh, Graduate School of Public Health, Department of Epidemiology, Pittsburgh, PA 15261, United States.</auth-address><titles><title>A case-crossover analysis of the impact of PM(2.5) on cardiovascular disease hospitalizations for selected CDC tracking states</title><secondary-title>Environ Res</secondary-title></titles><periodical><full-title>Environmental Research</full-title>Environ. Res.Environ Res</periodical><pages>455-65</pages><volume>134</volume><edition>2014/10/04</edition><keywords><keyword>Cardiovascular Diseases/*chemically induced</keyword><keyword>Centers for Disease Control and Prevention, U.S.</keyword><keyword>Cross-Over Studies</keyword><keyword>*Hospitalization</keyword><keyword>Humans</keyword><keyword>Particulate Matter/*toxicity</keyword><keyword>United States</keyword><keyword>Air pollution</keyword><keyword>Cardiovascular disease</keyword><keyword>Case-crossover design</keyword><keyword>Environmental public health tracking</keyword><keyword>Pm(2.5)</keyword></keywords><dates><year>2014</year><pub-dates><date>Oct</date></pub-dates></dates><isbn>1096-0953 (Electronic)
0013-9351 (Linking)</isbn><accession-num>25277761</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/25277761</url></related-urls></urls><electronic-resource-num>10.1016/j.envres.2014.06.018</electronic-resource-num></record></EndNote>Bell et al., 2015 used monitor-based estimates

Table 41. PM2.5-Attributable Cardiovascular Hospital Admissions Beta Estimates
Study
Beta Coefficient (SE)
Bell et al., 2015
0.00065 (0.00009)
Talbott et al., 2014, MA ADDIN EN.CITE <EndNote><Author>Talbott</Author><Year>2014</Year><RecNum>1190</RecNum><DisplayText>Talbott et al., 2014</DisplayText><record><rec-number>1190</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1604002257">1190</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Talbott, E. O.</author><author>Rager, J. R.</author><author>Benson, S.</author><author>Brink, L. A.</author><author>Bilonick, R. A.</author><author>Wu, C.</author></authors></contributors><auth-address>University of Pittsburgh, Graduate School of Public Health, Department of Epidemiology, Pittsburgh, PA 15261, United States.</auth-address><titles><title>A case-crossover analysis of the impact of PM(2.5) on cardiovascular disease hospitalizations for selected CDC tracking states</title><secondary-title>Environ Res</secondary-title></titles><periodical><full-title>Environmental Research</full-title>Environ. Res.Environ Res</periodical><pages>455-65</pages><volume>134</volume><edition>2014/10/04</edition><keywords><keyword>Cardiovascular Diseases/*chemically induced</keyword><keyword>Centers for Disease Control and Prevention, U.S.</keyword><keyword>Cross-Over Studies</keyword><keyword>*Hospitalization</keyword><keyword>Humans</keyword><keyword>Particulate Matter/*toxicity</keyword><keyword>United States</keyword><keyword>Air pollution</keyword><keyword>Cardiovascular disease</keyword><keyword>Case-crossover design</keyword><keyword>Environmental public health tracking</keyword><keyword>Pm(2.5)</keyword></keywords><dates><year>2014</year><pub-dates><date>Oct</date></pub-dates></dates><isbn>1096-0953 (Electronic)
0013-9351 (Linking)</isbn><accession-num>25277761</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/25277761</url></related-urls></urls><electronic-resource-num>10.1016/j.envres.2014.06.018</electronic-resource-num></record></EndNote>
MA: 0.00050 (0.00035), NJ: 0.00109 (0.0002), NM: 0.00109 (0.0019), NY: 0.00109 (0.00015), FL: -0.00040 (0.0003), NH: -0.00121 (0.0012) , WA: -0.00090 (0.0005)

A study from the 2020 PM ISA Supplement also evaluated cardiovascular hospital admissions. Danesh Yazdi et al., 2021 examined the relationship between long-term ambient air pollution (NO2, O3, PM2.5) exposure and hospital admissions for four cardiovascular and respiratory outcomes among 63,006,793 Medicare beneficiaries over the age of 64 in the contiguous U.S. The authors retrieved data from the Medicare denominator file and the Medicare Provider Analysis and review file, including all hospital admissions from 2000 through 2016 with primary discharge codes for myocardial infarction, ischemic stroke, atrial fibrillation and flutter, and pneumonia. They modeled PM2.5 exposure at a 1-km[2] scale using high-resolution spatiotemporal models that combined three machine learning algorithms using land use terms, chemical transport model predictors, meteorologic variables, and satellite measurements and validating against monitor data. Danesh Yazdi et al., 2021 employed doubly robust additive models (DRAM), which account for confounding through inverse probability weights of exposure and adjustment in the outcome regression model. They examined the relationship between hospital admissions for myocardial infarction, ischemic stroke, atrial fibrillation and flutter, or pneumonia and each of three ambient air pollutants, adjusting each model for individual, socioeconomic, and behavioral covariates as well as the other two pollutants. Because we were unable to obtain validated Hazard Ratios from this study, we elected not to include it in our primary analysis.

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

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

One study analyzed the effects of long-term PM2.5 exposure on cardiovascular hospital admissions with PM2.5 exposures. Kloog et al., 2012 collected medical data from 67,678 adults aged 65 to 99 in the U.S. Medicare program database from 2000 to 2006. They defined all respiratory, cardiovascular disease, stroke, and diabetes based on emergency department visits and primary discharge diagnosis records. Authors used a hybrid exposure technique comprised of daily PM2.5 concentration data from aerosol optical depth (AOD) measurements and ambient air monitors from the U.S. EPA and Interagency Monitoring of Protected Visual Improvements (IMPROVE). Authors also obtained land use regressions, meteorological data (National Climatic Data Center), and socioeconomic data (U.S. Census Bureau) matched to zip codes. Utilizing land use Poisson regression single-pollutant models, the authors found an 3.49% (95% CI: 0.09-5.18) increase in stroke incidence for a 10 ug/m[3] increase in the 7-year mean PM2.5 concentrations. However, as the study area was restricted to New England (Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, and Vermont) from 2000 to 2006, this study serves only as an exposure duration sensitivity analysis for the main benefits assessment endpoints.

Emergency Hospital Admissions (EHAs)
Interestingly, a substantial subset of the ISA-identified recent epidemiologic literature evaluating respiratory hospitalizations restricted analyses to emergency hospital admissions (EHAs), defined as hospitalizations admitted from the emergency department (section 3.2). Due to time and resource requirements, we were unable to develop county-level baseline incidence data for EHAs, in addition to total hospital admissions. However, as we were interested in how estimates of EHAs compared to total, we include a risk estimate of respiratory EHAs from Zanobetti et al., 2009 ADDIN EN.CITE <EndNote><Author>Zanobetti</Author><Year>2009</Year><RecNum>1140</RecNum><DisplayText>Zanobetti et al., 2009</DisplayText><record><rec-number>1140</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732560">1140</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Zanobetti, Antonella</author><author>Franklin, Meredith</author><author>Koutrakis, Petros</author><author>Schwartz, Joel</author></authors></contributors><titles><title>Fine particulate air pollution and its components in association with cause-specific emergency admissions</title><secondary-title>Environmental Health</secondary-title></titles><periodical><full-title>Environmental Health</full-title>Environ. HealthEnviron Health</periodical><pages>58</pages><volume>8</volume><number>1</number><dates><year>2009</year></dates><isbn>1476-069X</isbn><urls></urls></record></EndNote> using national baseline incidence data. Though the EHA estimate came from a smaller and older study then the main analysis respiratory hospital admission study, the EHA estimate is nearly an order of magnitude larger than the risk estimate included in the main estimate (italicized).
Table 44. Comparison of the PM2.5-Attributable Respiratory Hospital Admissions Beta Risk Estimate to the EHA Respiratory Estimate
Study
Beta Coefficient (SE)
Bell et al., 2015
0.00025 (0.0001)
Zanobetti et al., 2009 ADDIN EN.CITE <EndNote><Author>Zanobetti</Author><Year>2009</Year><RecNum>1140</RecNum><DisplayText>Zanobetti et al., 2009</DisplayText><record><rec-number>1140</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732560">1140</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Zanobetti, Antonella</author><author>Franklin, Meredith</author><author>Koutrakis, Petros</author><author>Schwartz, Joel</author></authors></contributors><titles><title>Fine particulate air pollution and its components in association with cause-specific emergency admissions</title><secondary-title>Environmental Health</secondary-title></titles><periodical><full-title>Environmental Health</full-title>Environ. HealthEnviron Health</periodical><pages>58</pages><volume>8</volume><number>1</number><dates><year>2009</year></dates><isbn>1476-069X</isbn><urls></urls></record></EndNote>
0.00204 (0.0004)
Hypertension
The 2022 PM ISA Supplement states that "[r]ecent studies add to the evidence providing support for positive associations among post-menopausal women and referenced four epidemiologic studies meeting the minimum criteria (section 2.1.1) of PM2.5-attributable hypertension (U.S. EPA, 2022b). Additionally, we identified a recent publication estimating the lifetime COI for incidence hypertension (section <IP_ADDRESS>). However, we include hypertension only as a sensitivity analysis here due to the limited evidence available in the 2019 PM ISA and 2022 Supplement to the ISA (U.S. EPA, 2019b, U.S. EPA, 2022b).
 Specifically:
      The literature assessed in the 2019 PM ISA provided evidence of associations between long-term PM2.5 exposure and hypertension. Recent studies are generally consistent with this assessment, reporting positive associations among post-menopausal women enrolled in the WHI study and in cardiac catheterization patients. However, no association between long-term PM2.5 exposure and hypertension was observed among Black women enrolled in the JHS.
Identification of Epidemiologic Studies and Risk Estimates
Available Epidemiologic Literature
The 2022 supplement to the PM ISA identified three epidemiologic studies of hypertension that met the minimum identified criteria above (U.S. EPA, 2022b, section 2.1.1).
Identifying Suitable Studies for Use in Benefits Assessments
Of the three available studies, one was more representative of the U.S. with respect to the population observed and the geographic scope (Honda et al., 2017).
Studies and Risk Estimates Identified as Most Suitable for Use in Benefits Assessments
Honda et al., 2017 studied the association between long-term PM2.5 exposure and incident hypertension in a cohort of 44,255 post-menopausal (aged 50-79 years at baseline) women enrolled in the Women's Health Initiative (WHI) clinical trials nationwide who were free of hypertension at baseline. Authors defined incident hypertension as systolic blood pressure > 140 mmHg, diastolic blood pressure > 90 mmHg, or self-reported anti-hypertensive medication use, and participants were followed from enrollment until 2010 for a total of 298,383 person-years of follow-up. Honda et al., 2017 employed a hybrid modeling technique using daily PM2.5 measurements and geographic covariates to calculate annual moving average estimates of PM2.5, PM10, and PM10-2.5 exposure for each participant. They modeled the association between hypertension incidence and particulate matter exposure using single-pollutant and two-pollutant (PM2.5 and PM10-2.5) Cox proportional hazards models that adjusted for numerous individual-level covariates, clinical trial treatment arm, and WHI study clinical site.
Identification of Valuation Estimates
The lifetime cost-of-illness (COI) for incidence hypertension is derived from Kirkland et al., 2018, who estimate the annual out-of-pocket expenditures associated with high blood pressure among adults ages 45 and older in the United States. The authors utilize data from the Medical Expenditures Panel Survey (MEPS) from 2003-2014 and identify patients with hypertension using the ICD-9 code 401.xx. Cost elements include inpatient, outpatient, and emergency room services as well as prescription medicine expenses. The authors adjust annual expenses such that they reflect the incremental expenditures for individuals with hypertension versus without hypertension. They calculate an annual cost of $1,850 ($2015) associated with hypertension. To calculate the lifetime costs of hypertension we calculate the costs incurred over 20 years, reflective of the rounded age-weighted life-expectancy of women diagnosed with hypertension in Canada (Loukine et al., 2011). The continuing annual costs are discounted by three or seven percent. Over 20 years, the total medical costs are calculated as $28,348 using a 3 percent discount rate and $20,970 using a 7 percent discount rate.
Effect Modification of Health Effects in At-Risk Populations
ISAs typically include an assessment of the weight of evidence demonstrating that certain subpopulations experience increased mortality or morbidity risks from air pollutant exposure compared to other groups. This is also known as effect modification and occurs when the measure of an effect changes across levels of a variable other than PM2.5 exposure. The 2019 PM ISA examined toxicological, controlled human exposures, and epidemiologic literature considering whether certain populations and lifestages might be at increased risk of air pollutant-related health effects (3202019c3200xd5s9009txfw1eafstv220i5ds2wvtpwf5d1573071075">320Washington, DCNational U.S. EPA, 2019b).
The ISAs categorize relationships between exposure and effect modification for various population and lifestages into the following four groups:
 Adequate evidence: There is substantial, consistent evidence within a discipline to conclude that a factor results in a population or lifestage being at increased risk of air pollutant-related health effect(s) relative to some reference population or lifestage. Where applicable, this evidence includes coherence across disciplines. Evidence includes multiple high-quality studies.
 Suggestive evidence: The collective evidence suggests that a factor results in a population or lifestage being at increased risk of air pollutant-related health effect(s) relative to some reference population or lifestage, but the evidence is limited due to some inconsistency within a discipline or, where applicable, a lack of coherence across disciplines.
 Inadequate evidence: The collective evidence is inadequate to conclude whether a factor results in a population or lifestage being at increased risk of air pollutant-related health effect(s) relative to some reference population or lifestage. The available studies are of insufficient quantity, quality, consistency, and/or statistical power to permit a conclusion to be drawn.
 Evidence of no effect: There is substantial, consistent evidence within a discipline to conclude that a factor does not result in a population or lifestage being at increased risk of air pollutant-related health effect(s) relative to some reference population or lifestage. Where applicable, the evidence includes coherence across disciplines. Evidence includes multiple high-quality studies.
Presenting PM2.5-attributable benefit estimates stratified by the value of another covariate can provide insight into risk within various population subgroups. To accomplish this, we reviewed relevant chapters from the 2019 PM ISA in order to compile and assess studies cited in support of the Agency's determinations, focusing on studies referenced in Table 12-3 for population characteristics with either "adequate evidence" (i.e., substantial, consistent evidence) or "suggestive evidence" (i.e., limited evidence due to inconsistency or a lack of coherence in research) of increased risk (sections <IP_ADDRESS> and <IP_ADDRESS>) (U.S. EPA, 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> The factors with "adequate evidence" for PM2.5 are lifestage (children) and race (nonwhite populations), while the factors with "suggestive evidence" are pre-existing disease (cardiovascular disease, respiratory disease, and obesity), genetic factors (variant genotypes), low socioeconomic status, and smoking.
Study and Risk Estimate Identification Criteria for Populations At-Risk for PM2.5 Exposures
We identified all studies in the related section of PM ISA Chapter 12 for each at-risk factor listed above, resulting in a set of 123 studies for at-risk populations (U.S. EPA, 2019b). This collection includes the following number of studies, with some studies duplicated for multiple endpoints.
 8 studies for lifestage (children),
 25 studies for race (nonwhite populations),
 67 studies for pre-existing disease across disease types,
 18 studies for genetic factors,
 25 studies for socioeconomic status, and
 14 studies for smoking.
We then focused our review on the risk factors with "adequate evidence", due to stronger supporting evidence as well as because they could be evaluated using currently available data. We extracted study information from all studies with "adequate evidence" and applied initial screening criteria to identify peer-reviewed, epidemiological studies focused on PM2.5 conducted in the US or Canada. We also documented the mortality and/or morbidity health endpoints included in each study, focusing on all-cause mortality and respiratory morbidity endpoints. We then evaluated the group of remaining studies based on additional identification criteria built off the criteria for the primary analysis, described in Table 45.
Table 45. PM2.5 At-Risk Study Identification Criteria
Criteria
Description
Peer-Review
Peer-reviewed research exclusively
Study design
Epidemiological study
PM2.5 Study
PM2.5 exposure rather than other PM10 or other sizes
Study Location
US or Canada
Study Duration
Long-term studies preferred
Population Attributes
Presents risk estimates for clearly defined at-risk groups for which data currently exist in BenMAP-CE
Causal or Likely Causal Health Effects
Adequate evidence for at-risk groups in ISA
Economically Valuable Health Effects
Health endpoints for which economic values have been or could reasonably be developed
Baseline Incidence Data
Must be able to identify baseline incidence data for subpopulations

All-Cause Mortality
For the mortality endpoint, seven all-cause mortality studies for the nonwhite population met our criteria. No mortality studies for the child at-risk group met the initial screening criteria. Of the seven studies of nonwhite populations, three were short-term exposure studies relating daily PM2.5 exposure and daily deaths and four were long-term exposure studies relating annual PM2.5 exposure and annual mortality. Consistent with the main benefits assessment, we focused on the following four long-term studies as long-term exposure studies may include some effects of short-term exposures (section <IP_ADDRESS>): Di et al., 2017b ADDIN EN.CITE <EndNote><Author>Di</Author><Year>2017</Year><RecNum>536</RecNum><DisplayText>Di et al., 2017b</DisplayText><record><rec-number>536</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732083">536</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Di, Q.</author><author>Wang, Y.</author><author>Zanobetti, A.</author><author>Wang, Y.</author><author>Koutrakis, P.</author><author>Choirat, C.</author><author>Dominici, F.</author><author>Schwartz, J. D.</author></authors></contributors><titles><title>Air pollution and mortality in the Medicare population</title><secondary-title>New England Journal of Medicine</secondary-title><alt-title>N Engl J Med</alt-title></titles><periodical><full-title>New England Journal of Medicine</full-title>New Engl. J. Med.New Engl J Med</periodical><alt-periodical><full-title>N Engl J Med</full-title></alt-periodical><pages>2513-2522</pages><volume>376</volume><number>26</number><edition>June 29, 2017</edition><dates><year>2017</year><pub-dates><date>06</date></pub-dates></dates><isbn>ISSN 0028-4793
EISSN 1533-4406</isbn><accession-num>28657878</accession-num>3841341<urls><related-urls><url>http://dx.doi.org/10.1056/NEJMoa1702747</url></related-urls></urls><electronic-resource-num>10.1056/NEJMoa1702747</electronic-resource-num><language>English</language></record></EndNote>, Kioumourtzoglou et al., 2016 ADDIN EN.CITE <EndNote><Author>Kioumourtzoglou</Author><Year>2016</Year><RecNum>1104</RecNum><DisplayText>Kioumourtzoglou et al., 2016</DisplayText><record><rec-number>1104</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732551">1104</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Kioumourtzoglou, M. A.</author><author>Schwartz, J.</author><author>James, P.</author><author>Dominici, F.</author><author>Zanobetti, A.</author></authors></contributors><titles><title></title><secondary-title>Epidemiology</secondary-title><alt-title>Epidemiology</alt-title></titles><periodical><full-title>Epidemiology</full-title>EpidemiologyEpidemiology</periodical><alt-periodical><full-title>Epidemiology</full-title>EpidemiologyEpidemiology</alt-periodical><pages>221-227</pages><volume>27</volume><number>2</number><dates><year>2016</year></dates><isbn>ISSN 1044-3983
EISSN 1531-5487</isbn><accession-num>26600257</accession-num>3079253<urls><related-urls><url>http://dx.doi.org/10.1097/EDE.0000000000000422</url></related-urls></urls><electronic-resource-num>10.1097/EDE.0000000000000422</electronic-resource-num><language>English</language></record></EndNote>, Parker et al., 2018 ADDIN EN.CITE <EndNote><Author>Parker</Author><Year>2018</Year><RecNum>1411</RecNum><DisplayText>Parker et al., 2018</DisplayText><record><rec-number>1411</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1611254656">1411</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Parker, Jennifer D</author><author>Kravets, Nataliya</author><author>Vaidyanathan, Ambarish</author></authors></contributors><titles><title>Particulate matter air pollution exposure and heart disease mortality risks by race and ethnicity in the United States: 1997 to 2009 National Health Interview Survey with mortality follow-up through 2011</title><secondary-title>Circulation</secondary-title></titles><periodical><full-title>Circulation</full-title></periodical><pages>1688-1697</pages><volume>137</volume><number>16</number><dates><year>2018</year></dates><isbn>0009-7322</isbn><urls></urls></record></EndNote>, and 111111110xd5s9009txfw1eafstv220i5ds2wvtpwf5d1603732553">1111>EpidemiologyEpidemiology</abbr-2Wang et al., 2017.
We evaluated specific details of risk estimates provided by each study to determine if sufficient information exists for use in a quantitative sensitivity analysis. Of the studies, only Di et al., 2017b ADDIN EN.CITE <EndNote><Author>Di</Author><Year>2017</Year><RecNum>536</RecNum><DisplayText>Di et al., 2017b</DisplayText><record><rec-number>536</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732083">536</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Di, Q.</author><author>Wang, Y.</author><author>Zanobetti, A.</author><author>Wang, Y.</author><author>Koutrakis, P.</author><author>Choirat, C.</author><author>Dominici, F.</author><author>Schwartz, J. D.</author></authors></contributors><titles><title>Air pollution and mortality in the Medicare population</title><secondary-title>New England Journal of Medicine</secondary-title><alt-title>N Engl J Med</alt-title></titles><periodical><full-title>New England Journal of Medicine</full-title>New Engl. J. Med.New Engl J Med</periodical><alt-periodical><full-title>N Engl J Med</full-title></alt-periodical><pages>2513-2522</pages><volume>376</volume><number>26</number><edition>June 29, 2017</edition><dates><year>2017</year><pub-dates><date>06</date></pub-dates></dates><isbn>ISSN 0028-4793
EISSN 1533-4406</isbn><accession-num>28657878</accession-num>3841341<urls><related-urls><url>http://dx.doi.org/10.1056/NEJMoa1702747</url></related-urls></urls><electronic-resource-num>10.1056/NEJMoa1702747</electronic-resource-num><language>English</language></record></EndNote> provided sufficient information to apply risk models quantifying increased risks to nonwhite groups, including non-Hispanic white, Black, Asian, American Indian, and Hispanic-white populations. Additional detail on the study can be found in section <IP_ADDRESS>.3.1.2 or in the associated Study Information Table.
We applied similar criteria to morbidity endpoints for the child and nonwhite at-risk groups. No studies cited for the child subgroup met our criteria for inclusion, and one endpoint, emergency room visits for asthma, was chosen for quantification for the nonwhite populations at-risk factor group. The study we chose to evaluate was 141214120xd5s9009txfw1eafstv220i5ds2wvtpwf5d1611255412">1412Exposure ScienceEnvironmental EpidemiologyAlhanti et al., 2016, which presents risk information for white and pooled nonwhite populations disaggregated into five age groups.
We developed BenMAP-ready Health Impact Functions for each at-risk group described by Di et al., 2017b and Alhanti et al., 2016, summarized in  ADDIN EN.CITE <EndNote><Author>Di</Author><Year>2017</Year><RecNum>536</RecNum><DisplayText>Di et al., 2017b</DisplayText><record><rec-number>536</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732083">536</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Di, Q.</author><author>Wang, Y.</author><author>Zanobetti, A.</author><author>Wang, Y.</author><author>Koutrakis, P.</author><author>Choirat, C.</author><author>Dominici, F.</author><author>Schwartz, J. D.</author></authors></contributors><titles><title>Air pollution and mortality in the Medicare population</title><secondary-title>New England Journal of Medicine</secondary-title><alt-title>N Engl J Med</alt-title></titles><periodical><full-title>New England Journal of Medicine</full-title>New Engl. J. Med.New Engl J Med</periodical><alt-periodical><full-title>N Engl J Med</full-title></alt-periodical><pages>2513-2522</pages><volume>376</volume><number>26</number><edition>June 29, 2017</edition><dates><year>2017</year><pub-dates><date>06</date></pub-dates></dates><isbn>ISSN 0028-4793
EISSN 1533-4406</isbn><accession-num>28657878</accession-num>3841341<urls><related-urls><url>http://dx.doi.org/10.1056/NEJMoa1702747</url></related-urls></urls><electronic-resource-num>10.1056/NEJMoa1702747</electronic-resource-num><language>English</language></record></EndNote> ADDIN EN.CITE <EndNote><Author>Alhanti</Author><Year>2016</Year><RecNum>1412</RecNum><DisplayText>Alhanti et al., 2016</DisplayText><record><rec-number>1412</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1611255412">1412</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Alhanti, Brooke A</author><author>Chang, Howard H</author><author>Winquist, Andrea</author><author>Mulholland, James A</author><author>Darrow, Lyndsey A</author><author>Sarnat, Stefanie Ebelt</author></authors></contributors><titles><title>Ambient air pollution and emergency department visits for asthma: a multi-city assessment of effect modification by age</title><secondary-title>Journal of Exposure Science & Environmental Epidemiology</secondary-title></titles><periodical><full-title>Journal of exposure science & environmental epidemiology</full-title></periodical><pages>180-188</pages><volume>26</volume><number>2</number><dates><year>2016</year></dates><isbn>1559-064X</isbn><urls></urls></record></EndNote>Table 46.
Table 46. Identified PM2.5 At-Risk Beta Coefficients and Standard Errors
At-Risk Factor
Endpoint
Study
Population Demographic
Beta Coefficient (SE)
Race, nonwhite populations
Morality, All Cause
Di et al., 2017b ADDIN EN.CITE <EndNote><Author>Di</Author><Year>2017</Year><RecNum>536</RecNum><DisplayText>Di et al., 2017b</DisplayText><record><rec-number>536</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732083">536</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Di, Q.</author><author>Wang, Y.</author><author>Zanobetti, A.</author><author>Wang, Y.</author><author>Koutrakis, P.</author><author>Choirat, C.</author><author>Dominici, F.</author><author>Schwartz, J. D.</author></authors></contributors><titles><title>Air pollution and mortality in the Medicare population</title><secondary-title>New England Journal of Medicine</secondary-title><alt-title>N Engl J Med</alt-title></titles><periodical><full-title>New England Journal of Medicine</full-title>New Engl. J. Med.New Engl J Med</periodical><alt-periodical><full-title>N Engl J Med</full-title></alt-periodical><pages>2513-2522</pages><volume>376</volume><number>26</number><edition>June 29, 2017</edition><dates><year>2017</year><pub-dates><date>06</date></pub-dates></dates><isbn>ISSN 0028-4793
EISSN 1533-4406</isbn><accession-num>28657878</accession-num>3841341<urls><related-urls><url>http://dx.doi.org/10.1056/NEJMoa1702747</url></related-urls></urls><electronic-resource-num>10.1056/NEJMoa1702747</electronic-resource-num><language>English</language></record></EndNote>
White
0.0061 (0.0001)

Hispanic
0.0110 (0.0008)

Black
0.0189 (0.0004)

Asian
0.0092 (0.0010)

American Indian
0.0095 (0.0019)
Race, nonwhite populations
Morality, All Cause
Pope III et al., 2019
Non-Hispanic White
0.001806 (0.00184)

Hispanic
0.004084 (0.00403)

Non-Hispanic Black
0.005064 (0.00485)

Other/unknown
0.007732 (0.00788)
Race, nonwhite populations
Emergency Room Visits, Asthma
Alhanti et al., 2016 ADDIN EN.CITE <EndNote><Author>Alhanti</Author><Year>2016</Year><RecNum>1412</RecNum><DisplayText>Alhanti et al., 2016</DisplayText><record><rec-number>1412</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1611255412">1412</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Alhanti, Brooke A</author><author>Chang, Howard H</author><author>Winquist, Andrea</author><author>Mulholland, James A</author><author>Darrow, Lyndsey A</author><author>Sarnat, Stefanie Ebelt</author></authors></contributors><titles><title>Ambient air pollution and emergency department visits for asthma: a multi-city assessment of effect modification by age</title><secondary-title>Journal of Exposure Science & Environmental Epidemiology</secondary-title></titles><periodical><full-title>Journal of exposure science & environmental epidemiology</full-title></periodical><pages>180-188</pages><volume>26</volume><number>2</number><dates><year>2016</year></dates><isbn>1559-064X</isbn><urls></urls></record></EndNote>
White, age 0-4
0.0025 (0.0019)

Nonwhite, age 0-4
0.0037 (0.0012)

White, age 5-18
0.0025 (0.0016)

Nonwhite, age 5-18
0.0049 (0.0012)

Quantitative Characterization of O3 Uncertainties and Evaluating Sensitivity to Alternate O3 Assumptions
Statistical Uncertainty Around the Risk Estimate (Monte-Carlo Assessment)
For all endpoints analyzed, we use a Monte Carlo simulation in which we sample from the standard error associated with each risk estimate and present the resulting 2.5[th] and 97.5[th] percentile values from this distribution as a 95[th] percentile confidence interval around the estimated health impact and monetized health benefits. Monte Carlo methods are a well-established means of characterizing random sampling error associated with the risk estimates from epidemiologic studies. This approach randomly samples from a distribution of incidence and valuation estimates to characterize the effects of uncertainty on output variables. The reported standard errors in the epidemiologic studies determined the distributions for individual effect estimates for endpoints estimated using a single study. For endpoints estimated using a pooled estimate of multiple studies, the confidence intervals reflect both the standard errors and the variance across studies. The confidence intervals around the monetized benefits incorporate the standard errors from the epidemiologic risk estimate as well as the distribution of the valuation function. These confidence intervals do not reflect other sources of uncertainty inherent within the estimates, such as baseline incidence rates, populations exposed, and transferability of the effect estimate to diverse locations. As a result, the reported confidence intervals and range of estimates give an incomplete picture about the overall uncertainty in the benefits estimates.
Respiratory Mortality
Confounding by PM2.5
When considering the relationship between pollutant exposure and health impacts, it can be informative to consider whether risk estimates are changed when other pollutants are included in copollutant models, especially when health impacts of multiple pollutants are being estimated concurrently. While no conclusions were formed regarding the impact of copollutant confounding on long-term exposure-related respiratory mortality, the 2020 O3 ISA found that "positive associations observed between long term O3 exposure and total mortality remain relatively unchanged after adjustment for PM2.5 and NO2" (U.S. EPA, 2020b).
Turner et al., 2016 ADDIN EN.CITE <EndNote><Author>Turner</Author><Year>2016</Year><RecNum>544</RecNum><DisplayText>Turner et al., 2016</DisplayText><record><rec-number>544</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732084">544</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Turner, M. C.</author><author>Jerrett, M.</author><author>Pope, A., III</author><author>Krewski, D.</author><author>Gapstur, S. M.</author><author>Diver, W. R.</author><author>Beckerman, B. S.</author><author>Marshall, J. D.</author><author>Su, J.</author><author>Crouse, D. L.</author><author>Burnett, R. T.</author></authors></contributors><titles><title>Long-term ozone exposure and mortality in a large prospective study</title><secondary-title>American Journal of Respiratory and Critical Care Medicine</secondary-title><alt-title>Am J Respir Crit Care Med</alt-title></titles><periodical><full-title>American Journal of Respiratory and Critical Care Medicine</full-title>Am. J. Respir. Crit. Care Med.Am J Respir Crit Care Med</periodical><alt-periodical><full-title>American Journal of Respiratory and Critical Care Medicine</full-title>Am. J. Respir. Crit. Care Med.Am J Respir Crit Care Med</alt-periodical><pages>1134-1142</pages><volume>193</volume><number>10</number><dates><year>2016</year></dates><isbn>ISSN 1073-449X
EISSN 1535-4970</isbn><accession-num>26680605</accession-num>3060878<urls><related-urls><url>http://dx.doi.org/10.1164/rccm.201508-1633OC</url></related-urls></urls><electronic-resource-num>10.1164/rccm.201508-1633OC</electronic-resource-num><language>English</language></record></EndNote> provided single- and multipollutant (including PM2.5 as a copollutant) O3-attributable respiratory mortality risk estimates. A comparison of risk estimates that either do or do not include PM2.5 as a copollutant is included to clarify this potential sensitivity with respect to O3-attributable respiratory mortality. Differences in the magnitude of risk estimates including or excluding PM2.5 as a copollutant are provided in Table 47.
Table 47. Single- and Two-Pollutant (Including PM2.5 as a Copollutant) Long-Term O3-Attributable Respiratory Mortality Risk Estimates per 10 ppb
Study
Single-Pollutant
Multipollutant
Turner et al., 2016 ADDIN EN.CITE <EndNote><Author>Turner</Author><Year>2016</Year><RecNum>544</RecNum><DisplayText>Turner et al., 2016</DisplayText><record><rec-number>544</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732084">544</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Turner, M. C.</author><author>Jerrett, M.</author><author>Pope, A., III</author><author>Krewski, D.</author><author>Gapstur, S. M.</author><author>Diver, W. R.</author><author>Beckerman, B. S.</author><author>Marshall, J. D.</author><author>Su, J.</author><author>Crouse, D. L.</author><author>Burnett, R. T.</author></authors></contributors><titles><title>Long-term ozone exposure and mortality in a large prospective study</title><secondary-title>American Journal of Respiratory and Critical Care Medicine</secondary-title><alt-title>Am J Respir Crit Care Med</alt-title></titles><periodical><full-title>American Journal of Respiratory and Critical Care Medicine</full-title>Am. J. Respir. Crit. Care Med.Am J Respir Crit Care Med</periodical><alt-periodical><full-title>American Journal of Respiratory and Critical Care Medicine</full-title>Am. J. Respir. Crit. Care Med.Am J Respir Crit Care Med</alt-periodical><pages>1134-1142</pages><volume>193</volume><number>10</number><dates><year>2016</year></dates><isbn>ISSN 1073-449X
EISSN 1535-4970</isbn><accession-num>26680605</accession-num>3060878<urls><related-urls><url>http://dx.doi.org/10.1164/rccm.201508-1633OC</url></related-urls></urls><electronic-resource-num>10.1164/rccm.201508-1633OC</electronic-resource-num><language>English</language></record></EndNote>
1.14 (1.10-1.18)
1.12 (1.08-1.16)

Short-Term O3 Exposures
Potential Threshold Analysis
The 2020 final O3 ISA evaluated a number of studies examining the shape of the concentration-response relationship for short-term O3 exposure and total/nonaccidental mortality, which we use to inform the long-term O3-attributable respiratory mortality relationship (U.S. EPA, 2020b). The O3 ISA found that "studies that used different statistical approaches and ozone averaging times (i.e., 24 hour avg and 8 hour max) provide evidence of a linear concentration-response relationship, with less certainty in the shape of the curve at lower concentrations [i.e., ...30 ppb for 8 hour max], [although] an examination of whether a threshold exists in the ozone mortality concentration-response relationship provided no evidence of a concentration below which mortality effects do not occur when examining 5 μg/m[3] (~2.55 ppb) increments across the range of 1 hour max concentrations reported in the U.S. and Canadian cities included in [a large cohort]." As the Zanobetti and Schwartz, 2008 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> risk estimate uses the MDA8 metric, it can be used to quantitatively assess the effect of an O3 threshold at 30 ppb would have on benefits estimates. For context, approximately 3.7% of the contiguous U.S. population is projected to reside in areas where MDA8 O3 concentrations are annually below 30 ppb in 2024 (U.S. EPA, 2020c). ADDIN EN.CITE <EndNote><Author>U.S. EPA</Author><Year>2020</Year><RecNum>1389</RecNum><DisplayText>U.S. EPA, 2020b</DisplayText><record><rec-number>1389</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1610138929">1389</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 for the Proposed Revised Cross-State Air Pollution Rule (CSAPR) Update for the 2008 Ozone NAAQS</title></titles><edition>October 2020</edition><dates><year>2020</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/P-20-003</isbn><urls><related-urls><url>Available at: https://www.epa.gov/sites/production/files/2020-10/documents/revised_csapr_update_ria_proposal.pdf</url></related-urls></urls></record></EndNote> Clinical evidence provides little indication that adverse effects occur at extremely low levels in most individuals. Epidemiologic evidence is qualitatively discussed further in section <IP_ADDRESS>.
Confounding by PM
Regarding short-term exposures, the 2020 O3 ISA found that "the few recent multicity studies that examined potential copollutant confounding provide evidence supporting that O3 mortality risk estimates are relatively unchanged or slightly attenuated, but remain positive, in copollutant models with PM2.5, PM10, and NO2."
Katsouyanni et al., 2009 provided single- and two-pollutant (including PM10 as a copollutant) short-term O3-attributable respiratory mortality risk estimates for a subset of 15 of the 86 cities analyzed. A comparison of risk estimates that either do or do not include PM10 as a copollutant is included to clarify this potential sensitivity with respect to O3-attributable respiratory mortality. Differences in the magnitude of risk estimates including or excluding PM2.5 as a copollutant are provided in Table 48. Please note, as distributed lag risk estimates were not provided for the two-pollutant analyses, in additional to the inclusion of PM2.5 as a copollutant and the number of cities analyzed, there is a difference in the lag duration between the estimates in Table 48.
Table 48. Single- and Two-Pollutant (Including PM1o as a Copollutant) Short-Term O3 Exposure O3-Attributable Excess Premature Respiratory Mortality Risk Estimates per 10 ppb
Study
Single-Pollutant
Two-Pollutant
Katsouyanni et al., 2009
0.77% (0.17%, 1.37%)
0.99% (-0.33%, 2.31%)

All-Cause Mortality
When estimating air pollutant-attributable health impacts, EPA focuses on endpoints for which the underlying scientific evidence is strongest. That is, when evaluating evidence across scientific disciplines (i.e., clinical, animal toxicological, and epidemiologic) there is often consistency of effects within a discipline, coherence of effects across disciplines, and evidence of biological plausibility. Such an approach gives us greater confidence in the relationship between exposure and health outcome. For criteria pollutants, EPA typically relies upon the causality determinations in the latest ISA or equivalent, which are made using a weight-of evidence approach. Generally, to estimate the pollutant-attributable human health benefits in which we are most confident, we at least assess health effects identified as having a `causal' or `likely to be causal' relationship with the pollutant of interest in the most recently published ISA. This is not to imply there may not be benefits associated with endpoints having a "suggestive of, but not sufficient to infer, a causal relationship," but rather that there is greater uncertainty in these potential benefits.
Because of the significance of the endpoint, we include a limited quantitative sensitivity analysis of total mortality associated with long-term O3 exposure. While the 2020 O3 ISA concluded that evidence was "suggestive of, but not sufficient to infer," a causal relationship between long-term exposures and total mortality, the reduction of this risk is likely still valuable to society (U.S. EPA, 2020b). As such, for this sensitivity discussion, we include risk estimates of long-term, all-cause O3-attributable total mortality from the two studies used to estimate PM2.5-attributable mortality risk (Table 49). Please note these long-term, all-cause risk estimates include respiratory mortality estimates and should not be added to the respiratory mortality estimates.
Table 49. Long-Term O3-Attributable Total Mortality Risk Estimates per 10 ppb
Study
Risk Estimate (per 10 ppb increase in O3)
Risk Estimate Details
Turner et al., 2016 ADDIN EN.CITE <EndNote><Author>Turner</Author><Year>2016</Year><RecNum>544</RecNum><DisplayText>Turner et al., 2016</DisplayText><record><rec-number>544</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732084">544</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Turner, M. C.</author><author>Jerrett, M.</author><author>Pope, A., III</author><author>Krewski, D.</author><author>Gapstur, S. M.</author><author>Diver, W. R.</author><author>Beckerman, B. S.</author><author>Marshall, J. D.</author><author>Su, J.</author><author>Crouse, D. L.</author><author>Burnett, R. T.</author></authors></contributors><titles><title>Long-term ozone exposure and mortality in a large prospective study</title><secondary-title>American Journal of Respiratory and Critical Care Medicine</secondary-title><alt-title>Am J Respir Crit Care Med</alt-title></titles><periodical><full-title>American Journal of Respiratory and Critical Care Medicine</full-title>Am. J. Respir. Crit. Care Med.Am J Respir Crit Care Med</periodical><alt-periodical><full-title>American Journal of Respiratory and Critical Care Medicine</full-title>Am. J. Respir. Crit. Care Med.Am J Respir Crit Care Med</alt-periodical><pages>1134-1142</pages><volume>193</volume><number>10</number><dates><year>2016</year></dates><isbn>ISSN 1073-449X
EISSN 1535-4970</isbn><accession-num>26680605</accession-num>3060878<urls><related-urls><url>http://dx.doi.org/10.1164/rccm.201508-1633OC</url></related-urls></urls><electronic-resource-num>10.1164/rccm.201508-1633OC</electronic-resource-num><language>English</language></record></EndNote>
1.02 (1.01-1.03)
Fully adjusted HBM multipollutant estimate from Table E9, ages 35-99
Di et al., 2017b ADDIN EN.CITE <EndNote><Author>Di</Author><Year>2017</Year><RecNum>536</RecNum><DisplayText>Di et al., 2017b</DisplayText><record><rec-number>536</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732083">536</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Di, Q.</author><author>Wang, Y.</author><author>Zanobetti, A.</author><author>Wang, Y.</author><author>Koutrakis, P.</author><author>Choirat, C.</author><author>Dominici, F.</author><author>Schwartz, J. D.</author></authors></contributors><titles><title>Air pollution and mortality in the Medicare population</title><secondary-title>New England Journal of Medicine</secondary-title><alt-title>N Engl J Med</alt-title></titles><periodical><full-title>New England Journal of Medicine</full-title>New Engl. J. Med.New Engl J Med</periodical><alt-periodical><full-title>N Engl J Med</full-title></alt-periodical><pages>2513-2522</pages><volume>376</volume><number>26</number><edition>June 29, 2017</edition><dates><year>2017</year><pub-dates><date>06</date></pub-dates></dates><isbn>ISSN 0028-4793
EISSN 1533-4406</isbn><accession-num>28657878</accession-num>3841341<urls><related-urls><url>http://dx.doi.org/10.1056/NEJMoa1702747</url></related-urls></urls><electronic-resource-num>10.1056/NEJMoa1702747</electronic-resource-num><language>English</language></record></EndNote>
1.011 (1.010, 1.012)
GEE two-pollutant main analysis estimate from Table 2, ages 65-99

Asthma Onset in Children
The study identified as best characterizing risk for asthma onset in children was conducted in Canada (Tetreault et al., 2016). Even though comparatively Tetreault et al., 2016 was preferred in identification criteria to other available studies (e.g., study size, exposure estimation technique, etc.) other than location and study period, we thought it useful to include the largest and most recent U.S.-based risk estimates as a sensitivity analysis. An overall comparison of the main risk estimate from Tetreault et al., 2016 and the alternative risk estimates from Garcia et al., 2019 can be found in