Ostro and Rothschild, 1989 ADDIN EN.CITE <EndNote><Author>Ostro</Author><Year>1989</Year><RecNum>1372</RecNum><DisplayText>Ostro and Rothschild, 1989</DisplayText><record><rec-number>1372</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608588120">1372</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Ostro, B. D.</author><author>Rothschild, S.</author></authors></contributors><auth-address>California Public Health Foundation, Berkeley 94704.</auth-address><titles><title>Air pollution and acute respiratory morbidity: an observational study of multiple pollutants</title><secondary-title>Environ Res</secondary-title></titles><periodical><full-title>Environmental Research</full-title>Environ. Res.Environ Res</periodical><pages>238-47</pages><volume>50</volume><number>2</number><edition>1989/12/01</edition><keywords><keyword>Adolescent</keyword><keyword>Adult</keyword><keyword>Aged</keyword><keyword>Air Pollutants/adverse effects</keyword><keyword>Air Pollution/*adverse effects/statistics & numerical data</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Information Systems</keyword><keyword>Male</keyword><keyword>Middle Aged</keyword><keyword>Ozone/adverse effects</keyword><keyword>Regression Analysis</keyword><keyword>Respiratory Tract Diseases/epidemiology/*mortality</keyword><keyword>United States</keyword></keywords><dates><year>1989</year><pub-dates><date>Dec</date></pub-dates></dates><isbn>0013-9351 (Print)
0013-9351 (Linking)</isbn><accession-num>2583069</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/2583069</url></related-urls></urls><electronic-resource-num>10.1016/s0013-9351(89)80004-0</electronic-resource-num></record></EndNote>
Adults and older adults (18-64 years)
N/A
β = 0.00741 (0.0007)
Work Loss Days
Ostro, 1987 ADDIN EN.CITE <EndNote><Author>Ostro</Author><Year>1987</Year><RecNum>1201</RecNum><DisplayText>Ostro, 1987</DisplayText><record><rec-number>1201</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1604071574">1201</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Ostro, BD</author></authors></contributors><titles><title>Air pollution and morbidity revisited: A specification test</title><secondary-title>Journal of Environmental Economics and Management</secondary-title></titles><periodical><full-title>Journal of Environmental Economics and Management</full-title>J. Environ. Econ. Manage.J Environ Econ Manage</periodical><pages>87-98</pages><volume>14</volume><number>1</number><dates><year>1987</year><pub-dates><date>March 1987</date></pub-dates></dates><urls></urls><electronic-resource-num>10.1016/0095-0696(87)90008-8</electronic-resource-num></record></EndNote>
Adults and older adults (18-64 years)
N/A
β = 0.0046 (0.00036)
ST- short-term; LT- long-term, β- beta risk estimate; ICD- International Statistical Classification of Diseases
Notes: Horizontal lines separating studies within an endpoint indicates that the studies are not intended to be pooled.
[1] Risk estimates have been mathematically converted to beta coefficients, which include the increment of pollutant change and allow for more direct comparisons of risk estimates within health endpoints.
O3
Table 11. Set of Risk Estimates for Main O3 Benefits Assessments
Endpoint
Study Information
Ages
Exposure (Duration; Season; Metric)
Beta Coefficient
(SE)[1]
Respiratory Mortality
Zanobetti and Schwartz, 2008 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>
Children, adults, and older adults (0-99 years)
ST; June-August; DA8
β = 0.00083 (0.00023) (warm season)

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

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

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

Children (0-17 years)
LT; June-August; MDA8
β = 0.02075 (0.00146) (warm season)
Asthma Symptoms
Lewis et al., 2013
Children (5-17 years)
ST; January-December; MDA8
β = 0.00708 (0.00372) (Cough)
β = 0.00764 (0.00410) (Wheeze)
β = 0.01140 (0.00505) (Chest tightness)
β = 0.00423 (0.00386) (Shortness of breath)
Allergic Rhinitis
Parker et al., 2009 ADDIN EN.CITE <EndNote><Author>Parker</Author><Year>2009</Year><RecNum>1247</RecNum><DisplayText>Parker et al., 2009</DisplayText><record><rec-number>1247</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608146211">1247</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Parker, J. D.</author><author>Akinbami, L. J.</author><author>Woodruff, T. J.</author></authors></contributors><auth-address>National Center for Health Statistics, Centers for Disease Control and Prevention, Hyattsville, Maryland 20782, USA<EMAIL_ADDRESS>pollution and childhood respiratory allergies in the United States</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>140-7</pages><volume>117</volume><number>1</number><edition>2009/01/24</edition><keywords><keyword>Adolescent</keyword><keyword>*Air Pollution</keyword><keyword>Child</keyword><keyword>Child, Preschool</keyword><keyword>Humans</keyword><keyword>Hypersensitivity/*epidemiology</keyword><keyword>United States/epidemiology</keyword><keyword>United States Environmental Protection Agency</keyword><keyword>allergy</keyword><keyword>children</keyword><keyword>hay fever</keyword><keyword>ozone</keyword><keyword>particulate matter</keyword></keywords><dates><year>2009</year><pub-dates><date>Jan</date></pub-dates></dates><isbn>0091-6765 (Print)
0091-6765 (Linking)</isbn><accession-num>19165401</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/19165401</url></related-urls></urls><custom2>PMC2627858</custom2><electronic-resource-num>10.1289/ehp.11497</electronic-resource-num></record></EndNote>
Children (3-17 years)
LT; May-September; DA24
β = 0.01655 (0.00390) (warm season)
Minor Restricted Activity Days
Ostro and Rothschild, 1989 (MRADs) ADDIN EN.CITE <EndNote><Author>Ostro</Author><Year>1989</Year><RecNum>1372</RecNum><DisplayText>Ostro and Rothschild, 1989</DisplayText><record><rec-number>1372</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608588120">1372</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Ostro, B. D.</author><author>Rothschild, S.</author></authors></contributors><auth-address>California Public Health Foundation, Berkeley 94704.</auth-address><titles><title>Air pollution and acute respiratory morbidity: an observational study of multiple pollutants</title><secondary-title>Environ Res</secondary-title></titles><periodical><full-title>Environmental Research</full-title>Environ. Res.Environ Res</periodical><pages>238-47</pages><volume>50</volume><number>2</number><edition>1989/12/01</edition><keywords><keyword>Adolescent</keyword><keyword>Adult</keyword><keyword>Aged</keyword><keyword>Air Pollutants/adverse effects</keyword><keyword>Air Pollution/*adverse effects/statistics & numerical data</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Information Systems</keyword><keyword>Male</keyword><keyword>Middle Aged</keyword><keyword>Ozone/adverse effects</keyword><keyword>Regression Analysis</keyword><keyword>Respiratory Tract Diseases/epidemiology/*mortality</keyword><keyword>United States</keyword></keywords><dates><year>1989</year><pub-dates><date>Dec</date></pub-dates></dates><isbn>0013-9351 (Print)
0013-9351 (Linking)</isbn><accession-num>2583069</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/2583069</url></related-urls></urls><electronic-resource-num>10.1016/s0013-9351(89)80004-0</electronic-resource-num></record></EndNote>
Adults and older adults (18-64 years)
ST; April-September; MDA1
β = 0.0022 (0.000658)
School Loss Days
Gilliland et al., 2001 ADDIN EN.CITE <EndNote><Author>Gilliland</Author><Year>2001</Year><RecNum>1377</RecNum><DisplayText>Gilliland et al., 2001</DisplayText><record><rec-number>1377</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1609171575">1377</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Gilliland, Frank D</author><author>Berhane, Kiros</author><author>Rappaport, Edward B</author><author>Thomas, Duncan C</author><author>Avol, Edward</author><author>Gauderman, W James</author><author>London, Stephanie J</author><author>Margolis, Helene G</author><author>McConnell, Rob</author><author>Islam, K Talat</author></authors></contributors><titles><title>The effects of ambient air pollution on school absenteeism due to respiratory illnesses</title><secondary-title>Epidemiology</secondary-title></titles><periodical><full-title>Epidemiology</full-title>EpidemiologyEpidemiology</periodical><pages>43-54</pages><dates><year>2001</year></dates><isbn>1044-3983</isbn><urls></urls></record></EndNote>
Children (5-17 years)
ST; January-June; DA8
β = 0.0078 (0.0044)
ST- short-term; LT- long-term, β- risk estimate (beta); ICD- International Statistical Classification of Diseases; DA8- daily 8-hour average; MDA8- maximum daily 8-hour average; MDA1- maximum daily 1-hour average; DA24- daily 24-hour average
Notes: Horizontal lines separating studies within an endpoint indicates that the studies are not intended to be pooled
[1] Risk estimates have been mathematically converted to beta coefficients, which include the increment of pollutant change and allow for more direct comparisons risk estimates within health endpoints.

Baseline Incidence and Prevalence Estimates
A baseline incidence rate is an estimate of the number of new cases in the assessment location over a specific timespan, typically one year. For example, in 2018 the mortality rate was 868 deaths per 100,000 people in the U.S. The baseline incidence of the health effect is necessary to convert the relative risk of a health effect provided by epidemiologic studies into an estimated number of cases. To derive the total baseline incidence per year, this rate must be multiplied by the corresponding population. Continuing with the above example, there were 327 million people in the U.S. in 2018, leading to a total baseline incidence of 2.8 million deaths in that year.
Prevalence rates are the proportion of the population experiencing a health endpoint at a point in time. This rate is important when estimating impacts of chronic illnesses, such as asthma, in order to exclude those already diagnosed from the population at risk. For example, if the prevalence of asthmatic children is 8%, only the remaining 92% are at risk of developing asthma.
EPA develops either daily or annual baseline incidence and prevalence rates at the most geographically- and age-specific levels feasible for each health endpoint assessed. For many locations within the U.S., these data are available resolved at the county- or state-level, providing a better characterization of the geographic distribution of hospital and emergency department visits than the national rates. For this update, we focused on developing baseline incidence rates for new health endpoints. Detailed information on baseline incidence data developed previously can be found in Appendix D of the BenMAP-CE User Manual (U.S. EPA, 2018 ADDIN EN.CITE <EndNote><Author>U.S. EPA</Author><Year>2018</Year><RecNum>629</RecNum><DisplayText>U.S. EPA, 2018</DisplayText><record><rec-number>629</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732120">629</key></foreign-keys><ref-type name="Government Document">46</ref-type><contributors><authors><author>U.S. EPA, </author></authors><secondary-authors><author>U.S. EPA </author></secondary-authors></contributors><titles><title>Environmental Benefits Mapping and Analysis Program  -  Community Edition User's Manual</title></titles><volume>Updated for BenMAP-CE Version 1.4.8</volume><dates><year>2018</year></dates><pub-location>Research Triangle Park, NC</pub-location><publisher>Office of Air Quality Planning and Standards</publisher><urls><related-urls><url>Available at: https://www.epa.gov/sites/production/files/2015-04/documents/benmap-ce_user_manual_march_2015.pdf</url></related-urls></urls></record></EndNote>). Importantly, when applying either the daily or annual baseline incidence rates to a health impact estimate, the temporal scale over which the health endpoint was assessed within each study is taken into account. For example, if a long-term O3 exposure study associated annual deaths with warm-season exposures, full-year baseline incidence rates will be used when estimating benefits.
Table 12 summarizes the sources of baseline incidence rates and provides national average (where used) incidence rates for the endpoints included in the analysis. For both baseline incidence and prevalence data, we used age-stratified rates where available. We applied risk estimates to individual age groups and then sum them over the relevant age range to estimate total population benefits. In some cases, we used a single national incidence rate, due to a lack of more spatially disaggregated data, time, or resources. In these cases, whenever possible we used national average rates, because these data are most applicable to a national assessment of benefits. For some studies, however, the only available incidence information comes from the studies themselves; in these cases, incidence in the study population is assumed to represent typical incidence at the national level.
Table 12. Baseline Incidence Rates for Use in Impact Functions
                                   Endpoint
                                   Parameter
                                     Rates

                                     Value
                                    Source
Mortality[1]
Daily or annual projected incidence to 2060 in 5-year increments (0--99)
Age-, cause-, race-, and county-stratified rates
CDC WONDER (2012 - 2014)
U.S. Census Bureau, 2012 ADDIN EN.CITE <EndNote><Author>U.S. Census Bureau</Author><Year>2012</Year><RecNum>293</RecNum><DisplayText>U.S. Census Bureau, 2012</DisplayText><record><rec-number>293</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1569355923">293</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>U.S. Census Bureau,</author></authors></contributors><titles><title>Technical documentation - 2010 Census Summary File 1 -- Technical Documentation/prepared by the U.S. Census Bureau, Revised 2012. Available at: http://www.census.gov/prod/cen2010/doc/sf1.pdf. Employment Status from the 5-year American Community Survey (ACS) data, 2010 U.S. Census American FactFinder. Available at: http://factfinder2.census.gov/. Commuting times file from U.S. Census data portal (http://dataferrett.census.gov/), Table P31, variables P031001-P031015.</title></titles><dates><year>2012</year></dates><urls></urls></record></EndNote>
Hospitalizations[2]
Daily incidence rates for all ages
Age-, region/state/county-, and cause- stratified rates
2011-2014 HCUP data files and data requested from and supplied by individual states
Emergency Department Visits[2]
Daily emergency department visit incidence rates for all ages
Age-, region-, state-, county-, and cause- stratified rates
2011-2014 HCUP data files and data requested from and supplied by individual states
Nonfatal Acute Myocardial Infarction
Daily nonfatal AMI incidence rate per person aged 18-99
Age-, region-, state-, and county- stratified rates
AHRQ, 2016  ADDIN EN.CITE <EndNote><Author>AHRQ</Author><Year>2016</Year><RecNum>633</RecNum><DisplayText>AHRQ, 2016</DisplayText><record><rec-number>633</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732120">633</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>AHRQ</author></authors><tertiary-authors><author>Agency for Healthcare Research and Quality</author></tertiary-authors><subsidiary-authors><author>Agency for Healthcare Research and Quality</author></subsidiary-authors></contributors><titles><title>HCUPnet, Healthcare Cost and Utilization Project</title></titles><dates><year>2016</year></dates><publisher>Agency for Healthcare Research and Quality</publisher><urls><related-urls><url>Available at: https://hcupnet.ahrq.gov/</url></related-urls></urls></record></EndNote>
Asthma Symptoms
Daily incidence among asthmatic children

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

Age- and race- stratified rates

2.2 puffs per day

Ostro et al., 2001 ADDIN EN.CITE <EndNote><Author>Ostro</Author><Year>2001</Year><RecNum>628</RecNum><DisplayText>Ostro et al., 2001</DisplayText><record><rec-number>628</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732120">628</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Ostro, Bart</author><author>Lipsett, Michael</author><author>Mann, Jennifer</author><author>Braxton-Owens, Hazel</author><author>White, Mary</author></authors></contributors><titles><title>Air pollution and exacerbation of asthma in African-American children in Los Angeles</title><secondary-title>Journal of Epidemiology</secondary-title></titles><periodical><full-title>Journal of Epidemiology</full-title></periodical><pages>200-208</pages><volume>12</volume><number>2</number><dates><year>2001</year></dates><isbn>1044-3983</isbn><urls></urls></record></EndNote>

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

0.0234
0.0111
0.0044
Winer et al., 2012 ADDIN EN.CITE <EndNote><Author>Winer</Author><Year>2012</Year><RecNum>1395</RecNum><DisplayText>Winer et al., 2012</DisplayText><record><rec-number>1395</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1610559576">1395</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Winer, Rachel A</author><author>Qin, Xiaoting</author><author>Harrington, Theresa</author><author>Moorman, Jeanne</author><author>Zahran, Hatice</author></authors></contributors><titles><title>Asthma incidence among children and adults: findings from the Behavioral Risk Factor Surveillance system asthma call-back survey -- United States, 2006 - 2008</title><secondary-title>Journal of Asthma</secondary-title></titles><periodical><full-title>Journal of Asthma</full-title>J. AsthmaJ Asthma</periodical><pages>16-22</pages><volume>49</volume><number>1</number><dates><year>2012</year></dates><isbn>0277-0903</isbn><urls></urls></record></EndNote>
Alzheimer's Disease
Daily incidence rates for all ages
Age-, region-, state-, and county- stratified rates

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

0.0000011
0.0000366
0.0002001
0.0002483
HCUPnet
Allergic Rhinitis
Respondents aged 3-17 experiencing allergic rhinitis/hay fever symptoms within the year prior to the survey
0.192
Parker et al., 2009 ADDIN EN.CITE <EndNote><Author>Parker</Author><Year>2009</Year><RecNum>1247</RecNum><DisplayText>Parker et al., 2009</DisplayText><record><rec-number>1247</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608146211">1247</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Parker, J. D.</author><author>Akinbami, L. J.</author><author>Woodruff, T. J.</author></authors></contributors><auth-address>National Center for Health Statistics, Centers for Disease Control and Prevention, Hyattsville, Maryland 20782, USA<EMAIL_ADDRESS>pollution and childhood respiratory allergies in the United States</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>140-7</pages><volume>117</volume><number>1</number><edition>2009/01/24</edition><keywords><keyword>Adolescent</keyword><keyword>*Air Pollution</keyword><keyword>Child</keyword><keyword>Child, Preschool</keyword><keyword>Humans</keyword><keyword>Hypersensitivity/*epidemiology</keyword><keyword>United States/epidemiology</keyword><keyword>United States Environmental Protection Agency</keyword><keyword>allergy</keyword><keyword>children</keyword><keyword>hay fever</keyword><keyword>ozone</keyword><keyword>particulate matter</keyword></keywords><dates><year>2009</year><pub-dates><date>Jan</date></pub-dates></dates><isbn>0091-6765 (Print)
0091-6765 (Linking)</isbn><accession-num>19165401</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/19165401</url></related-urls></urls><custom2>PMC2627858</custom2><electronic-resource-num>10.1289/ehp.11497</electronic-resource-num></record></EndNote>
Cardiac Arrest
Daily nonfatal incidence rates
0 - 17
18 - 39
40 - 64
65 - 99

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

0.000001746
0.000014919
0.000067463
0.000208053
0.000052370
0.000576950
0.000557130
SEER, 2015 and Gharibvand et al., 2017 ADDIN EN.CITE <EndNote><Author>NCI</Author><Year>2015</Year><RecNum>1371</RecNum><DisplayText>NCI, 2015</DisplayText><record><rec-number>1371</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608586710">1371</key></foreign-keys><ref-type name="Book">6</ref-type><contributors><authors><author>NCI,</author></authors></contributors><titles><title>SEER, Surveillance, Epidemiology, and End Results Program</title><secondary-title>NIH Publication</secondary-title></titles><keywords><keyword>SEER Program (National Cancer Institute (U.S.))</keyword><keyword>Cancer Reporting United States.</keyword><keyword>Cancer Research United States.</keyword></keywords><dates><year>2015</year></dates><pub-location>Bethesda, MD</pub-location><publisher>National Institutes of Health, National Cancer Institute (NCI)</publisher><urls></urls></record></EndNote> ADDIN EN.CITE <EndNote><Author>Gharibvand</Author><Year>2017</Year><RecNum>1198</RecNum><DisplayText>Gharibvand et al., 2017</DisplayText><record><rec-number>1198</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1604071026">1198</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Gharibvand, L.</author><author>Shavlik, D.</author><author>Ghamsary, M.</author><author>Beeson, W. L.</author><author>Soret, S.</author><author>Knutsen, R.</author><author>Knutsen, S. F.</author></authors></contributors><auth-address>Adventist Health Study-2, School of Public Health, Loma Linda University, Loma Linda, California, USA.</auth-address><titles><title>The Association between Ambient Fine Particulate Air Pollution and Lung Cancer Incidence: Results from the AHSMOG-2 Study</title><secondary-title>Environ Health Perspect</secondary-title></titles><periodical><full-title>Environmental Health Perspectives</full-title>Environ. Health Perspect.Environ Health Perspect</periodical><pages>378-384</pages><volume>125</volume><number>3</number><edition>2016/08/16</edition><keywords><keyword>Air Pollutants/*analysis</keyword><keyword>Air Pollution/*statistics & numerical data</keyword><keyword>Environmental Exposure/*statistics & numerical data</keyword><keyword>Humans</keyword><keyword>Incidence</keyword><keyword>Lung Neoplasms/*epidemiology</keyword><keyword>Particulate Matter/*analysis</keyword></keywords><dates><year>2017</year><pub-dates><date>Mar</date></pub-dates></dates><isbn>1552-9924 (Electronic)
0091-6765 (Linking)</isbn><accession-num>27519054</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/27519054</url></related-urls></urls><custom2>PMC5332173</custom2><electronic-resource-num>10.1289/EHP124</electronic-resource-num></record></EndNote>
Stroke
Annual nonfatal incidence in ages 65-99
0.00446
Kloog et al., 2012 ADDIN EN.CITE <EndNote><Author>Kloog</Author><Year>2012</Year><RecNum>1170</RecNum><DisplayText>Kloog et al., 2012</DisplayText><record><rec-number>1170</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603987570">1170</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Kloog, I.</author><author>Coull, B. A.</author><author>Zanobetti, A.</author><author>Koutrakis, P.</author><author>Schwartz, J. D.</author></authors></contributors><titles><title>Acute and chronic effects of particles on hospital admissions in New-England</title><secondary-title>PLoS ONE</secondary-title><alt-title>PLoS ONE</alt-title></titles><periodical><full-title>PLoS One</full-title></periodical><alt-periodical><full-title>PLoS One</full-title></alt-periodical><pages>e34664</pages><volume>7</volume><number>4</number><dates><year>2012</year></dates><isbn>EISSN 1932-6203</isbn><accession-num>22529923</accession-num>1255201<urls><related-urls><url>http://dx.doi.org/10.1371/journal.pone.0034664</url></related-urls></urls><electronic-resource-num>10.1371/journal.pone.0034664</electronic-resource-num><language>English</language></record></EndNote>
Work Loss Days
Daily incidence rate per person (18 - 64)
Aged 18 - 24
Aged 25 - 44
Aged 45 - 64

0.00540
0.00678
0.00492
Adams et al., 1999, Table 41; U.S. Census Bureau, 2000 ADDIN EN.CITE <EndNote><Author>Adams</Author><Year>1999</Year><RecNum>627</RecNum><DisplayText>Adams et al., 1999</DisplayText><record><rec-number>627</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732120">627</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Adams, Patricia F</author><author>Hendershot, Gerry E</author><author>Marano, Marie A</author></authors></contributors><titles><title>Current estimates from the national health interview survey, 1996</title><secondary-title>Vital and Health Statistics</secondary-title></titles><periodical><full-title>Vital and Health statistics</full-title></periodical><pages>1-203</pages><number>Series 10</number><dates><year>1999</year></dates><urls></urls></record></EndNote>
School Loss Days
Rate per person per year, assuming 180 school days per year
9.9
Adams et al., 1999, Table 47 ADDIN EN.CITE <EndNote><Author>Adams</Author><Year>1999</Year><RecNum>627</RecNum><DisplayText>Adams et al., 1999</DisplayText><record><rec-number>627</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732120">627</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Adams, Patricia F</author><author>Hendershot, Gerry E</author><author>Marano, Marie A</author></authors></contributors><titles><title>Current estimates from the national health interview survey, 1996</title><secondary-title>Vital and Health Statistics</secondary-title></titles><periodical><full-title>Vital and Health statistics</full-title></periodical><pages>1-203</pages><number>Series 10</number><dates><year>1999</year></dates><urls></urls></record></EndNote>
Minor Restricted-Activity Days
Daily MRAD incidence rate per person (18-64)
0.02137
Ostro and Rothschild, 1989, p. 243 ADDIN EN.CITE <EndNote><Author>Ostro</Author><Year>1989</Year><RecNum>1372</RecNum><DisplayText>Ostro and Rothschild, 1989</DisplayText><record><rec-number>1372</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608588120">1372</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Ostro, B. D.</author><author>Rothschild, S.</author></authors></contributors><auth-address>California Public Health Foundation, Berkeley 94704.</auth-address><titles><title>Air pollution and acute respiratory morbidity: an observational study of multiple pollutants</title><secondary-title>Environ Res</secondary-title></titles><periodical><full-title>Environmental Research</full-title>Environ. Res.Environ Res</periodical><pages>238-47</pages><volume>50</volume><number>2</number><edition>1989/12/01</edition><keywords><keyword>Adolescent</keyword><keyword>Adult</keyword><keyword>Aged</keyword><keyword>Air Pollutants/adverse effects</keyword><keyword>Air Pollution/*adverse effects/statistics & numerical data</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Information Systems</keyword><keyword>Male</keyword><keyword>Middle Aged</keyword><keyword>Ozone/adverse effects</keyword><keyword>Regression Analysis</keyword><keyword>Respiratory Tract Diseases/epidemiology/*mortality</keyword><keyword>United States</keyword></keywords><dates><year>1989</year><pub-dates><date>Dec</date></pub-dates></dates><isbn>0013-9351 (Print)
0013-9351 (Linking)</isbn><accession-num>2583069</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/2583069</url></related-urls></urls><electronic-resource-num>10.1016/s0013-9351(89)80004-0</electronic-resource-num></record></EndNote>
CDC-Centers for Disease Control; NHS-National Health Interview Survey
[1]Mortality rates are only available in 5-year increments. The Healthcare Cost and Utilization Program (HCUP) database contains individual level, state and regional-level hospital and emergency department discharges for a variety of International Classification of Diseases (ICD) codes (AHRQ, 2016 ADDIN EN.CITE <EndNote><Author>AHRQ</Author><Year>2016</Year><RecNum>633</RecNum><DisplayText>AHRQ, 2016</DisplayText><record><rec-number>633</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732120">633</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>AHRQ</author></authors><tertiary-authors><author>Agency for Healthcare Research and Quality</author></tertiary-authors><subsidiary-authors><author>Agency for Healthcare Research and Quality</author></subsidiary-authors></contributors><titles><title>HCUPnet, Healthcare Cost and Utilization Project</title></titles><dates><year>2016</year></dates><publisher>Agency for Healthcare Research and Quality</publisher><urls><related-urls><url>Available at: https://hcupnet.ahrq.gov/</url></related-urls></urls></record></EndNote>).
[2]Baseline incidence rates now include corrections from the states of Indiana and Montana.
Mortality
Baseline incidence rate estimates for mortality remain the same as they were for previous benefits assessments (U.S. EPA, 2018 ADDIN EN.CITE <EndNote><Author>U.S. EPA</Author><Year>2018</Year><RecNum>629</RecNum><DisplayText>U.S. EPA, 2018</DisplayText><record><rec-number>629</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732120">629</key></foreign-keys><ref-type name="Government Document">46</ref-type><contributors><authors><author>U.S. EPA, </author></authors><secondary-authors><author>U.S. EPA </author></secondary-authors></contributors><titles><title>Environmental Benefits Mapping and Analysis Program  -  Community Edition User's Manual</title></titles><volume>Updated for BenMAP-CE Version 1.4.8</volume><dates><year>2018</year></dates><pub-location>Research Triangle Park, NC</pub-location><publisher>Office of Air Quality Planning and Standards</publisher><urls><related-urls><url>Available at: https://www.epa.gov/sites/production/files/2015-04/documents/benmap-ce_user_manual_march_2015.pdf</url></related-urls></urls></record></EndNote>). However, information is provided below for reference. Notably, the 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> analysis of long-term O3-attributable health impacts compares warm-season exposures to full-year baseline incidence rates. As such our baseline incidence rate estimates also reflect the full year.
Mortality Data for 2012-2014
We obtained county-level mortality and population data from 2012-2014 for 11 causes for the contiguous United States by downloading the data from the Centers for Disease Control (CDC) WONDER database.
Since the detailed mortality data obtained from CDC do not include population, we combined them with U.S. Census Bureau population estimates exported from BenMAP. We then generated age-, cause-, and county-specific mortality rates using the following formula:
       Ri,j,k=Di,j,k2012+Di,j,k2013+Di,j,k2014Pi,k2012+Pi,k2013+Pi,k2014
where Ri,j,k is the mortality rate for age group i, cause j, and county k; D is the death count; and P is the population. Additional details about the translation of the CDC WONDER data to age-, cause-, and county-specific mortality rates are provided in the BenMAP-CE User's Manual (U.S. EPA, 2018 ADDIN EN.CITE <EndNote><Author>U.S. EPA</Author><Year>2018</Year><RecNum>629</RecNum><DisplayText>U.S. EPA, 2018</DisplayText><record><rec-number>629</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732120">629</key></foreign-keys><ref-type name="Government Document">46</ref-type><contributors><authors><author>U.S. EPA, </author></authors><secondary-authors><author>U.S. EPA </author></secondary-authors></contributors><titles><title>Environmental Benefits Mapping and Analysis Program  -  Community Edition User's Manual</title></titles><volume>Updated for BenMAP-CE Version 1.4.8</volume><dates><year>2018</year></dates><pub-location>Research Triangle Park, NC</pub-location><publisher>Office of Air Quality Planning and Standards</publisher><urls><related-urls><url>Available at: https://www.epa.gov/sites/production/files/2015-04/documents/benmap-ce_user_manual_march_2015.pdf</url></related-urls></urls></record></EndNote>).

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

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

Table 15. Ratio of Future Year All-Cause Mortality Rate to 2013 Estimated All-Cause Mortality Rate, by Age Group
Year
Infant
1-17
18-24
25-34
35-44
45-54
55-64
65-74
75-84
85+
2015
0.93
0.93
0.96
1.02
0.96
0.96
1.01
1.02
1.03
1.00
2020
0.94
0.94
0.98
1.04
0.97
0.98
1.02
1.03
1.03
1.00
2025
0.85
0.81
0.74
0.80
0.75
0.77
0.85
0.91
0.93
0.97
2030
0.81
0.75
0.66
0.70
0.67
0.69
0.78
0.86
0.89
0.92
2035
0.76
0.70
0.58
0.62
0.60
0.62
0.71
0.81
0.87
0.87
2040
0.73
0.65
0.51
0.53
0.53
0.56
0.64
0.76
0.84
0.86
2045
0.70
0.60
0.45
0.46
0.46
0.50
0.58
0.71
0.80
0.86
2050
0.67
0.56
0.39
0.40
0.40
0.44
0.53
0.66
0.77
0.87
2055
0.64
0.52
0.34
0.35
0.35
0.39
0.48
0.62
0.73
0.88
2060
0.61
0.48
0.30
0.30
0.31
0.34
0.43
0.58
0.70
0.87
Race- and Ethnicity-Stratified Incidence Rates
To estimate race-stratified and age-stratified all-cause and respiratory mortality incidence rates at the county level, data from 2007 to 2016 was downloaded from the CDC WONDER mortality database with and without the `Hispanic Origin' disaggregation for two age groups: >25 and <25. The average race rates were calculated for the 10-year timespan before the death/incidence rate for race combinations were back calculated. Correction ratios were calculated for the Hispanic and non-Hispanic rates of each region/race (e.g., (Black - Hispanic) /(Black - All) = Hispanic correction factor and (Black - non-Hispanic) / (Black - All) = non-Hispanic correction factor). The age-group Hispanic and Non-Hispanic correction factors were then applied to the existing, county-level race-stratified baseline incidence rates for all-cause mortality in BenMAP-CE.
Race-Stratified Incidence Rates
To estimate race-stratified and age-stratified all-cause and respiratory mortality incidence rates at the county level, we downloaded data from 2007 to 2016 from the CDC WONDER mortality database. Race-stratified incidence rates were calculated for the following age groups: < 1 year, 1- 4 years, 5-14 years, 15-24 years, 25-34 years, 35-44 years, 45-54 years, 55-64 years, 65-74 years, 75-84 years, and 85+ years. To address the frequent county-level data suppression for race-specific death counts, we stratified the county-level data into two broad race categories, White and Non-White populations. In a later step, we stratified the non-White incidence rates by race (Black, Asian, American Indian) using the relative magnitudes of incidence values by race at the regional level, described in more detail below.  ADDIN EN.CITE <EndNote><Author>U.S. EPA</Author><Year>2018</Year><RecNum>629</RecNum><DisplayText>U.S. EPA, 2018</DisplayText><record><rec-number>629</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732120">629</key></foreign-keys><ref-type name="Government Document">46</ref-type><contributors><authors><author>U.S. EPA, </author></authors><secondary-authors><author>U.S. EPA </author></secondary-authors></contributors><titles><title>Environmental Benefits Mapping and Analysis Program  -  Community Edition User's Manual</title></titles><volume>Updated for BenMAP-CE Version 1.4.8</volume><dates><year>2018</year></dates><pub-location>Research Triangle Park, NC</pub-location><publisher>Office of Air Quality Planning and Standards</publisher><urls><related-urls><url>Available at: https://www.epa.gov/sites/production/files/2015-04/documents/benmap-ce_user_manual_march_2015.pdf</url></related-urls></urls></record></EndNote>
We followed the methods outlined in Section D.1.1 of the BenMAP User Manual with one notable difference in methodology; we included an intermediate spatial scale between county and state for imputation purposes (U.S. EPA, 2018). We designated urban and rural counties within each state using CDC WONDER and, where possible, imputed missing data using the state-urban and state-rural classifications before relying on broader statewide data. We followed methods for dealing with suppressed and unreliable data at each spatial scale as described in Section D.1.1.
A pooled non-White incidence rate masks important differences in mortality risks by race. To estimate county-level mortality rates by individual race (Black, Asian, American Indian), we applied regional race-specific incidence relationships to the county-level pooled non-White incidence rates. We calculated a weighted average of race-specific incidence rates using regional incidence rates for each region/age/race group normalized to one reference population (the Asian race group) and county population proportions based on race-specific county populations from CDC WONDER where available. In cases of population suppression across two or more races per county, we replaced all three race-specific population proportions derived from CDC WONDER with population proportions derived from 2010 Census data in BenMAP-CE (e.g., 50 percent Black, 30 percent Asian, 20 percent American Indian).
Ethnicity-Stratified Incidence Rates
To estimate ethnicity-stratified and age-stratified all-cause and respiratory mortality incidence rates at the county level, we downloaded data from 2007 to 2016 from the CDC WONDER mortality database. Ethnicity-stratified incidence rates were calculated for the following age groups: < 1 year, 1 - 4 years, 5 - 14 years, 15 - 24 years, 25 - 34 years, 35 - 44 years, 45 - 54 years, 55 - 64 years, 65 - 74 years, 75 - 84 years, and 85+ years. We stratified county-level data by Hispanic origin (Hispanic and non-Hispanic). We followed the methods outlined in Section D.1.1 to deal with suppressed and unreliable data (U.S. EPA, 2018). We also included an intermediate spatial scale between county and state designating urban and rural counties for imputation purposes, described in detail in Section D.1.3 of the BenMAP User Manual.
Hospitalizations
The approach for estimating hospitalization baseline incidence rates for new health endpoints is based on HCUP data, developed to match the granularity and timeframe of other hospitalization endpoints used in benefits assessments. New hospitalization endpoints are comprised of new sets of ICD-9 codes that correspond to newer studies evaluating air pollution-attributable hospitalizations. Detailed information is provided below and available in the BenMAP-CE User Manual (U.S. EPA, 2018 ADDIN EN.CITE <EndNote><Author>U.S. EPA</Author><Year>2018</Year><RecNum>629</RecNum><DisplayText>U.S. EPA, 2018</DisplayText><record><rec-number>629</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732120">629</key></foreign-keys><ref-type name="Government Document">46</ref-type><contributors><authors><author>U.S. EPA, </author></authors><secondary-authors><author>U.S. EPA </author></secondary-authors></contributors><titles><title>Environmental Benefits Mapping and Analysis Program  -  Community Edition User's Manual</title></titles><volume>Updated for BenMAP-CE Version 1.4.8</volume><dates><year>2018</year></dates><pub-location>Research Triangle Park, NC</pub-location><publisher>Office of Air Quality Planning and Standards</publisher><urls><related-urls><url>Available at: https://www.epa.gov/sites/production/files/2015-04/documents/benmap-ce_user_manual_march_2015.pdf</url></related-urls></urls></record></EndNote>).
Hospitalization rates were calculated using data from the Healthcare Cost and Utilization Project (HCUP). HCUP is a family of health care databases developed through a Federal-State-Industry partnership and sponsored by the Agency for Healthcare Research and Quality (AHRQ). HCUP products include the State Inpatient Databases (SID), the State Emergency Department Databases (SEDD), the Nationwide Inpatient Sample (NIS), and the Nationwide Emergency Department Sample (NEDS).
The level of hospitalization data available differs by state. While many states provide granular discharge-level data, others may only provide county- or state level-data. Also, 14 states, mostly in the southeast, do not provide data to HCUP. For these states, regional statistics from HCUPnet were used to estimate baseline hospitalization rates.
HCUP categorizes hospital admissions in various ways. Hospitalization admission types used when reporting data to HCUP include emergency (admitted from the emergency department), urgent (admitted from another hospital), elective (admitted from another health facility, including long-term care), newborn (admitted for delivery), trauma (not used by all states), and other/missing/invalid. As PM2.5 and O3 exposure predominantly leads to cardiovascular and respiratory health effects, we provide some information on the proportion of these types of hospitalizations, based on an analysis of hospitalizations from the state of Florida in 2014. Florida was selected for this analysis as it was the most populated state providing details regarding hospital admission type.
 Emergency hospital admissions comprise approximately 80% of cardiovascular and 85% of respiratory admissions
 Urgent hospital admissions comprise approximately 10% of cardiovascular and 8% of respiratory admissions
 Elective hospital admissions comprise approximately 10% of cardiovascular and 7% of respiratory admissions
 Newborn hospital admissions comprise no cardiovascular and respiratory admissions
 Trauma hospital admissions comprise approximately 0.1% of cardiovascular and respiratory admissions
 Other/missing/invalid hospital admissions comprise no cardiovascular or respiratory admissions
All hospital admission baseline incidence data used in this analysis (and input into BenMAP-CE) reflects total hospital admissions, due to time constraints limiting the ability to separate types (e.g., emergency, urgent, elective, etc) within HCUP data by various states and regions. However, the breakdown of hospital admission types generally reflects the types of health endpoints associated with air pollution exposures, with the majority of effects falling into the emergency and urgent types (e.g., heart or asthma attack) with a small subset potentially leading to elective hospital admissions (e.g., exacerbation of heart failure).
Health endpoints in hospitalization studies are defined using different combinations of ICD codes corresponding to specific diagnoses. Some span large categories of diagnoses, such as all cardiovascular or all respiratory admissions, while others reflect specific conditions, including Alzheimer's disease and Parkinson's disease. For each ICD code combination, unique baseline incidence rates are developed.
In this TSD update, adjustments were made to three existing hospital admission sensitivity endpoints:
      Myocardial Infarction (ICD Codes 410.X0 and 410.X1)
      Using data from the Healthcare Cost and Utilization Project (HCUPnet), Hospital Inpatient National Statistics, we downloaded the 2014 national estimates of discharge rates per 100,000 people for the set of ICD-9 codes associated with myocardial infarction in BenMAP (410) and the set of ICD-9 codes associated with myocardial infarction in Danesh Yazdi et al., 2021 (410.X0, 410.X1). We applied the ratio of (national discharge ratesICD-9 = 410.X0, 410.X1 : national discharge ratesICD-9 = 410), 0.995, to the existing, county-level baseline incidence rates for Acute Myocardial Infarction, Nonfatal in BenMAP.
      Ischemic Stroke (ICD Codes 433.X1, 434.X1, and 436)
      Using data from the Healthcare Cost and Utilization Project (HCUPnet), Hospital Inpatient National Statistics, we downloaded the 2014 national estimates of discharge rates per 100,000 people for the set of ICD-9 codes associated with stroke in BenMAP (431-437) and the set of ICD-9 codes associated with ischemic stroke in Danesh Yazdi et al., 2021 (433.X1, 434.X1, 436). We applied the ratio of (national discharge ratesICD-9 = 433.X1, 434.X1, 436 : national discharge ratesICD-9 = 431-437), 0.564, to the existing, county-level baseline incidence rates for HA, Stroke in BenMAP.
      Atrial Fibrillation and Flutter (ICD Codes 427.3)
      Using data from the Healthcare Cost and Utilization Project (HCUPnet), Hospital Inpatient National Statistics, we downloaded the 2014 national estimates of discharge rates per 100,000 people for the set of ICD-9 codes associated with dysrhythmia in BenMAP (427) and the set of ICD-9 codes associated with atrial fibrillation and flutter in Danesh Yazdi et al., 2021 (427.3). We applied the ratio of (national discharge ratesICD-9 = 427.3 : national discharge ratesICD-9 = 427), 0.666, to the existing, county-level baseline incidence rates for HA, Dysrhythmia in BenMAP.
Emergency Department Visits
As new studies evaluating air pollution-attributable emergency department utilizing new sets of ICD-9 codes were identified for use in benefits assessment here, we developed corresponding new emergency department baseline incidence rates. Similar to hospitalization baseline incidence rates, the approach for estimating emergency department visit baseline incidence rates also utilizes HCUP data and remains the same as in previous benefits assessments, details for which can be found in the BenMAP-CE User Manual (U.S. EPA, 2018 ADDIN EN.CITE <EndNote><Author>U.S. EPA</Author><Year>2018</Year><RecNum>629</RecNum><DisplayText>U.S. EPA, 2018</DisplayText><record><rec-number>629</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732120">629</key></foreign-keys><ref-type name="Government Document">46</ref-type><contributors><authors><author>U.S. EPA, </author></authors><secondary-authors><author>U.S. EPA </author></secondary-authors></contributors><titles><title>Environmental Benefits Mapping and Analysis Program  -  Community Edition User's Manual</title></titles><volume>Updated for BenMAP-CE Version 1.4.8</volume><dates><year>2018</year></dates><pub-location>Research Triangle Park, NC</pub-location><publisher>Office of Air Quality Planning and Standards</publisher><urls><related-urls><url>Available at: https://www.epa.gov/sites/production/files/2015-04/documents/benmap-ce_user_manual_march_2015.pdf</url></related-urls></urls></record></EndNote>). Information is provided below for reference.
Similar to hospitalization rates, the data source for emergency department/room visits is also HCUP, (i.e., SID, SEDD, and NEDS), states vary by level of data provided (i.e., discharge-, county-, state, and regional-level), and unique baseline incidence rates are generated for each health endpoint ICD code combination.
Health Endpoint Onset/Occurrence
Baseline incidence estimates for health endpoint onset or occurrences are described below, listed in alphabetical order. Onset indicates the development of a health endpoint (e.g., asthma diagnosis), whereas occurrence refers to an instance of that health endpoint (e.g., asthma attack).
Acute Myocardial Infarctions (AMIs)
Baseline incidence rate estimates for AMIs remain the same as they were for previous benefits assessments. However, detailed information is provided below for reference.
The relationship between short-term particulate matter exposure and heart attacks was originally quantified in a case-crossover analysis by Peters et al., 2001 ADDIN EN.CITE <EndNote><Author>Peters</Author><Year>2001</Year><RecNum>1137</RecNum><DisplayText>Peters et al., 2001</DisplayText><record><rec-number>1137</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732560">1137</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Peters, Annette</author><author>Dockery, Douglas W</author><author>Muller, James E</author><author>Mittleman, Murray A</author></authors></contributors><titles><title>Increased particulate air pollution and the triggering of myocardial infarction</title><secondary-title>Circulation</secondary-title></titles><periodical><full-title>Circulation</full-title></periodical><pages>2810-2815</pages><volume>103</volume><number>23</number><dates><year>2001</year></dates><isbn>0009-7322</isbn><urls></urls></record></EndNote> and supplemented with evidence found in more recent single and multi-city studies (Pope III et al., 2006, Sullivan et al., 2005 ADDIN EN.CITE <EndNote><Author>Pope III</Author><Year>2006</Year><RecNum>1138</RecNum><DisplayText>Pope III et al., 2006, Sullivan et al., 2005</DisplayText><record><rec-number>1138</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732560">1138</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Pope III, CArden</author><author>Muhlestein, Joseph B</author><author>May, Heidi T</author><author>Renlund, Dale G</author><author>Anderson, Jeffrey L</author><author>Horne, Benjamin D</author></authors></contributors><titles><title>Ischemic heart disease events triggered by short-term exposure to fine particulate air pollution</title><secondary-title>Circulation</secondary-title></titles><periodical><full-title>Circulation</full-title></periodical><pages>2443-2448</pages><volume>114</volume><number>23</number><dates><year>2006</year></dates><isbn>0009-7322</isbn><urls></urls></record><Author>Sullivan</Author><Year>2005</Year><RecNum>1136</RecNum><record><rec-number>1136</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732559">1136</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Sullivan, Jeffrey</author><author>Sheppard, Lianne</author><author>Schreuder, Astrid</author><author>Ishikawa, Naomi</author><author>Siscovick, David</author><author>Kaufman, Joel</author></authors></contributors><titles><title>Relation between short-term fine-particulate matter exposure and onset of myocardial infarction</title><secondary-title>Epidemiology</secondary-title></titles><periodical><full-title>Epidemiology</full-title>EpidemiologyEpidemiology</periodical><pages>41-48</pages><dates><year>2005</year></dates><isbn>1044-3983</isbn><urls></urls></record></EndNote>). The population in the original study was identified from heart attack survivors in a medical clinic. Therefore, the applicable population to apply to the risk estimate is all individuals surviving a heart attack in a given year. Several data sources are available to estimate the number of heart attacks per year. For example, several cohort studies have reported estimates of heart attack incidence rates in the specific populations under study. However, these rates depend on the specific characteristics of the populations under study and may not be the best data to extrapolate nationally. The American Heart Association reports approximately 785,000 new heart attacks per year (Roger et al., 2012). Exclusion of heart attack deaths reported by CDC Wonder yields approximately 575,000 nonfatal cases per year.
An alternative approach to the estimation of heart attack rates is to use data from the Healthcare Cost and Utilization Project (HCUP), assuming that all heart attacks that are not instantly fatal will result in a hospitalization. Details about HCUP data are described in Section D.2 of the BenMAP-CE User Manual (U.S. EPA, 2018 ADDIN EN.CITE <EndNote><Author>U.S. EPA</Author><Year>2018</Year><RecNum>629</RecNum><DisplayText>U.S. EPA, 2018</DisplayText><record><rec-number>629</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732120">629</key></foreign-keys><ref-type name="Government Document">46</ref-type><contributors><authors><author>U.S. EPA, </author></authors><secondary-authors><author>U.S. EPA </author></secondary-authors></contributors><titles><title>Environmental Benefits Mapping and Analysis Program  -  Community Edition User's Manual</title></titles><volume>Updated for BenMAP-CE Version 1.4.8</volume><dates><year>2018</year></dates><pub-location>Research Triangle Park, NC</pub-location><publisher>Office of Air Quality Planning and Standards</publisher><urls><related-urls><url>Available at: https://www.epa.gov/sites/production/files/2015-04/documents/benmap-ce_user_manual_march_2015.pdf</url></related-urls></urls></record></EndNote>).  According to the 2014 HCUP data there were approximately 608,795 hospitalizations due to heart attacks (acute myocardial infarction: ICD-9 410, primary diagnosis). We estimated baseline rates based on HCUP data rather than extrapolating from cohort studies because HCUP is a national database with a larger sample size intended to provide reliable national estimates. The incidence rate calculation is also described in Section D.2 of the BenMAP-CE User Manual and the incidence rates for AMI hospitalization are presented in Table D-5. An alternative approach to the estimation of AMI rates is to use data from HCUP and assume that all AMIs that are not instantly fatal will result in a hospitalization.
It is important to note that when calculating the incidence of nonfatal AMIs, the fraction of fatal heart attacks is subtracted to ensure that there is no double-counting with mortality estimates. Specifically, we apply an adjustment factor in the risk estimate to reflect the probability of surviving a heart attack. The adjustment factor comes from Rosamond et al., 1999, which reported that approximately 6% of male and 8% of female hospitalized AMI patients die within 28 days (either in or outside of the hospital). Therefore, we applied a factor of 0.93 to the estimated number of PM-related AMIs to exclude the number of cases that result in death within the first month. Note that we did not adjust for fatal AMIs in the incidence rate estimation, due to the way that the epidemiological studies are designed. Those studies consider total admissions for AMIs, which includes individuals living at the time the studies were conducted. We use the definition of AMI that matches the definition in the epidemiological studies. Age-specific baseline incidence rates are  based on data from the Agency for Healthcare Research and Quality's HCUP NIS database (AHRQ, 2016 ADDIN EN.CITE <EndNote><Author>AHRQ</Author><Year>2016</Year><RecNum>633</RecNum><DisplayText>AHRQ, 2016</DisplayText><record><rec-number>633</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732120">633</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>AHRQ</author></authors><tertiary-authors><author>Agency for Healthcare Research and Quality</author></tertiary-authors><subsidiary-authors><author>Agency for Healthcare Research and Quality</author></subsidiary-authors></contributors><titles><title>HCUPnet, Healthcare Cost and Utilization Project</title></titles><dates><year>2016</year></dates><publisher>Agency for Healthcare Research and Quality</publisher><urls><related-urls><url>Available at: https://hcupnet.ahrq.gov/</url></related-urls></urls></record></EndNote>). We identified death rates for adults hospitalized with AMI stratified by age (e.g., 1.852% for ages 18-44, 2.8188% for ages 45-64, and 7.4339% for ages 65+). These rates show a clear downward trend over time between 1994 and 2009 for the average adult and thus replace the 93% survival rate previously applied across all age groups from Rosamond et al., 1999 ADDIN EN.CITE <EndNote><Author>Rosamond</Author><Year>1999</Year><RecNum>622</RecNum><DisplayText>Rosamond et al., 1999</DisplayText><record><rec-number>622</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732119">622</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Rosamond, Wayne</author><author>Broda, Grazyna</author><author>Kawalec, Ewa</author><author>Rywik, Stefan</author><author>Pajak, Andrzej</author><author>Cooper, Lawton</author><author>Chambless, Lloyd</author></authors></contributors><titles><title>Comparison of medical care and survival of hospitalized patients with acute myocardial infarction in Poland and the United States</title><secondary-title>The American Journal of Cardiology</secondary-title></titles><periodical><full-title>The American Journal of Cardiology</full-title></periodical><pages>1180-1185</pages><volume>83</volume><number>8</number><dates><year>1999</year></dates><isbn>0002-9149</isbn><urls></urls></record></EndNote>.
Asthma Onset and Symptoms
Asthma Onset
Baseline incidence rates for new asthma onset are estimated from Winer et al., 2012 ADDIN EN.CITE <EndNote><Author>Winer</Author><Year>2012</Year><RecNum>1395</RecNum><DisplayText>Winer et al., 2012</DisplayText><record><rec-number>1395</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1610559576">1395</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Winer, Rachel A</author><author>Qin, Xiaoting</author><author>Harrington, Theresa</author><author>Moorman, Jeanne</author><author>Zahran, Hatice</author></authors></contributors><titles><title>Asthma incidence among children and adults: findings from the Behavioral Risk Factor Surveillance system asthma call-back survey -- United States, 2006 - 2008</title><secondary-title>Journal of Asthma</secondary-title></titles><periodical><full-title>Journal of Asthma</full-title>J. AsthmaJ Asthma</periodical><pages>16-22</pages><volume>49</volume><number>1</number><dates><year>2012</year></dates><isbn>0277-0903</isbn><urls></urls></record></EndNote>. Winer et al., 2012 ADDIN EN.CITE <EndNote><Author>Winer</Author><Year>2012</Year><RecNum>1395</RecNum><DisplayText>Winer et al., 2012</DisplayText><record><rec-number>1395</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1610559576">1395</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Winer, Rachel A</author><author>Qin, Xiaoting</author><author>Harrington, Theresa</author><author>Moorman, Jeanne</author><author>Zahran, Hatice</author></authors></contributors><titles><title>Asthma incidence among children and adults: findings from the Behavioral Risk Factor Surveillance system asthma call-back survey -- United States, 2006 - 2008</title><secondary-title>Journal of Asthma</secondary-title></titles><periodical><full-title>Journal of Asthma</full-title>J. AsthmaJ Asthma</periodical><pages>16-22</pages><volume>49</volume><number>1</number><dates><year>2012</year></dates><isbn>0277-0903</isbn><urls></urls></record></EndNote> identify newly diagnosed asthma from the 2006-2008 Asthma Call-Back Survey (ACBS) and Behavioral Risk Factor Surveillance System (BRFSS) as individuals diagnosed by a doctor, or other health professional, within the 12 months prior to the surveys. Table 12 details the breakdown, by age, of the annual national incidence rates for asthma onset.
For the set of endpoints affecting the asthmatic population, in addition to baseline incidence rates, prevalence rates of asthma in the population are needed to define the applicable population. We derive asthma prevalence data from the National Health Interview Survey (NHIS). For functions with age ranges that do not align with the ranges reported in the NHIS data table, we develop a weighted-average prevalence rate for the age range, where the weights are the number of years that overlap with each NHIS age group. Table 16 provides the breakdown of the 2018 NHIS rates used to calculate the weighted averages. Table 17 details the resulting weighted averages by study and age group. Note that these reflect recent asthma prevalence and assume no change in prevalence rates in future years.
Table 16. Asthma Prevalence Rates
                                NHIS Age Group
                            Asthma Prevalence Rate
                                     0 - 4
                                     0.038
                                    5 - 11
                                     0.081
                                    5 - 14
                                     0.086
                                    12 - 17
                                     0.099
                                    15 - 19
                                     0.110
                                    20 - 24
                                     0.081
                                    0 - 17
                                     0.075

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

5 - 17
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>
PM2.5
0.0893

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

Albuterol Use
We develop incidence rates for albuterol use from the rates presented in Rabinovitch et al., 2006, the same study from which the risk estimate was developed. As described in the `Recommended Set of Health Endpoints and Health Impact Functions' section, Rabinovitch et al., 2006 analyzed the relationship between short-term PM2.5 exposure and asthma exacerbation in children ages 6 to 13 years old. The authors use an electronic inhaler to record the number of actuations (`puffs') for each 24-hour period and calculate an average albuterol use rate of 2.2 `puffs' per child per day.
As described in section <IP_ADDRESS>, in addition to the baseline incidence rates, we apply a weighted-average asthma prevalence rate of 0.086, based on the 5-14 age group, using the NHIS prevalence data to identify the applicable population.
Asthma Symptoms
We develop incidence rates for asthma symptoms using the estimates presented in Lewis et al., 2013, the same study from which the concentration-response function was developed. As described in the `Recommended Set of Health Endpoints and Health Impact Functions' section, Lewis et al., 2013 studied the effects of short-term O3 exposure on frequency of asthma symptoms in an asthmatic population of children ages 5 to 12 years old. The authors estimate the incidence of each asthma symptom using the number of person-days where children reported experiencing the symptom divided by the total number of person-days monitored for that symptom. The percent of days monitored during which children experienced each symptom are calculated as 30.1% for cough, 19.4% for wheeze, 18.5% for shortness of breath, and 12.7% for chest tightness. Therefore, the national incidence rates of asthma symptoms are 0.301 for cough, 0.194 for wheeze, 0.185 for shortness of breath, and 0.127 for chest tightness. `Prevalence rates for asthma symptoms remains the same as in previous benefits assessments (previously referred to as asthma exacerbation) (Table 16).
Allergic Rhinitis
We develop prevalence rates for hay fever/rhinitis using the estimates presented in Parker et al., 2009 ADDIN EN.CITE <EndNote><Author>Parker</Author><Year>2009</Year><RecNum>1247</RecNum><DisplayText>Parker et al., 2009</DisplayText><record><rec-number>1247</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608146211">1247</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Parker, J. D.</author><author>Akinbami, L. J.</author><author>Woodruff, T. J.</author></authors></contributors><auth-address>National Center for Health Statistics, Centers for Disease Control and Prevention, Hyattsville, Maryland 20782, USA<EMAIL_ADDRESS>pollution and childhood respiratory allergies in the United States</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>140-7</pages><volume>117</volume><number>1</number><edition>2009/01/24</edition><keywords><keyword>Adolescent</keyword><keyword>*Air Pollution</keyword><keyword>Child</keyword><keyword>Child, Preschool</keyword><keyword>Humans</keyword><keyword>Hypersensitivity/*epidemiology</keyword><keyword>United States/epidemiology</keyword><keyword>United States Environmental Protection Agency</keyword><keyword>allergy</keyword><keyword>children</keyword><keyword>hay fever</keyword><keyword>ozone</keyword><keyword>particulate matter</keyword></keywords><dates><year>2009</year><pub-dates><date>Jan</date></pub-dates></dates><isbn>0091-6765 (Print)
0091-6765 (Linking)</isbn><accession-num>19165401</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/19165401</url></related-urls></urls><custom2>PMC2627858</custom2><electronic-resource-num>10.1289/ehp.11497</electronic-resource-num></record></EndNote>, the same study from which the concentration-response function was developed. As described in the `Recommended set of Health Endpoints and Health Impact Functions' section, Parker et al., 2009 ADDIN EN.CITE <EndNote><Author>Parker</Author><Year>2009</Year><RecNum>1247</RecNum><DisplayText>Parker et al., 2009</DisplayText><record><rec-number>1247</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608146211">1247</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Parker, J. D.</author><author>Akinbami, L. J.</author><author>Woodruff, T. J.</author></authors></contributors><auth-address>National Center for Health Statistics, Centers for Disease Control and Prevention, Hyattsville, Maryland 20782, USA<EMAIL_ADDRESS>pollution and childhood respiratory allergies in the United States</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>140-7</pages><volume>117</volume><number>1</number><edition>2009/01/24</edition><keywords><keyword>Adolescent</keyword><keyword>*Air Pollution</keyword><keyword>Child</keyword><keyword>Child, Preschool</keyword><keyword>Humans</keyword><keyword>Hypersensitivity/*epidemiology</keyword><keyword>United States/epidemiology</keyword><keyword>United States Environmental Protection Agency</keyword><keyword>allergy</keyword><keyword>children</keyword><keyword>hay fever</keyword><keyword>ozone</keyword><keyword>particulate matter</keyword></keywords><dates><year>2009</year><pub-dates><date>Jan</date></pub-dates></dates><isbn>0091-6765 (Print)
0091-6765 (Linking)</isbn><accession-num>19165401</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/19165401</url></related-urls></urls><custom2>PMC2627858</custom2><electronic-resource-num>10.1289/ehp.11497</electronic-resource-num></record></EndNote> investigates the associations between long-term ozone exposure and respiratory allergies in children ages 3 to 17 years old. The authors use prevalence data from the NHIS household interview survey and define allergic rhinitis as children with reported hay fever, respiratory allergy, or both within the 12 months prior to the survey. Of the eligible population (72,279), 19.2% of respondents experience allergic rhinitis symptoms within the year prior to the survey, therefore, the national prevalence rate of hay allergic rhinitis is 0.192.
Lung Cancer
We use the existent baseline incidence rate for lung cancer mortality in combination with the five-year lung cancer survival rate from SEER, 2015 ADDIN EN.CITE <EndNote><Author>NCI</Author><Year>2015</Year><RecNum>1371</RecNum><DisplayText>NCI, 2015</DisplayText><record><rec-number>1371</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608586710">1371</key></foreign-keys><ref-type name="Book">6</ref-type><contributors><authors><author>NCI,</author></authors></contributors><titles><title>SEER, Surveillance, Epidemiology, and End Results Program</title><secondary-title>NIH Publication</secondary-title></titles><keywords><keyword>SEER Program (National Cancer Institute (U.S.))</keyword><keyword>Cancer Reporting United States.</keyword><keyword>Cancer Research United States.</keyword></keywords><dates><year>2015</year></dates><pub-location>Bethesda, MD</pub-location><publisher>National Institutes of Health, National Cancer Institute (NCI)</publisher><urls></urls></record></EndNote> to develop baseline incidence rates for non-fatal lung cancer. We first use the five-year lung cancer survival rate to calculate the total incidence of lung cancer (both fatal and non-fatal) from the baseline mortality rate using the following formula: baseline mortality rate / (1  -  five-year survival rate). We then calculate the incidence of non-fatal lung cancer as the difference between total lung cancer incidence and fatal lung cancer incidence (SEER, 2015). Table 18 ADDIN EN.CITE <EndNote><Author>NCI</Author><Year>2015</Year><RecNum>1371</RecNum><DisplayText>NCI, 2015</DisplayText><record><rec-number>1371</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608586710">1371</key></foreign-keys><ref-type name="Book">6</ref-type><contributors><authors><author>NCI,</author></authors></contributors><titles><title>SEER, Surveillance, Epidemiology, and End Results Program</title><secondary-title>NIH Publication</secondary-title></titles><keywords><keyword>SEER Program (National Cancer Institute (U.S.))</keyword><keyword>Cancer Reporting United States.</keyword><keyword>Cancer Research United States.</keyword></keywords><dates><year>2015</year></dates><pub-location>Bethesda, MD</pub-location><publisher>National Institutes of Health, National Cancer Institute (NCI)</publisher><urls></urls></record></EndNote> presents the baseline incidence of lung cancer mortality, the SEER five-year survival rate, the estimated total lung cancer incidence, and the estimated non-fatal lung cancer incidence rate by age group.
Table 18. Lung Cancer Incidence Rates
                                   Age Group
                    Annual Lung Cancer Mortality Incidence
                                      [A]
                            Five-Year Survival Rate
                                      [B]
                          Total Lung Cancer Incidence
                                    [C] =
                                [A] / (1 - [B])
                        Non-fatal Lung Cancer Incidence
                                     [D] =
                                  [C]  -  [A]
25-34
0.0000033
34.6%
0.0000050
0.00000175
35-44
0.0000282
34.6%
0.0000431
0.00001492
45-54
0.0002378
22.1%
0.0003053
0.00006746
55-64
0.0007922
20.8%
0.0010003
0.00020805
65-74
0.00019701
21.0%
0.0002494
0.00005237
75-84
0.0032952
14.9%
0.0038722
0.00057695
85+
0.0031820
14.9%
0.0037391
0.00055713

Minor Restricted Activity Days (MRAD)
The incidence estimate for this health endpoint remains the same as in previous benefits assessments. Ostro and Rothschild, 1989 (p. ADDIN EN.CITE <EndNote><Author>Ostro</Author><Year>1989</Year><RecNum>1372</RecNum><DisplayText>Ostro and Rothschild, 1989</DisplayText><record><rec-number>1372</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608588120">1372</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Ostro, B. D.</author><author>Rothschild, S.</author></authors></contributors><auth-address>California Public Health Foundation, Berkeley 94704.</auth-address><titles><title>Air pollution and acute respiratory morbidity: an observational study of multiple pollutants</title><secondary-title>Environ Res</secondary-title></titles><periodical><full-title>Environmental Research</full-title>Environ. Res.Environ Res</periodical><pages>238-47</pages><volume>50</volume><number>2</number><edition>1989/12/01</edition><keywords><keyword>Adolescent</keyword><keyword>Adult</keyword><keyword>Aged</keyword><keyword>Air Pollutants/adverse effects</keyword><keyword>Air Pollution/*adverse effects/statistics & numerical data</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Information Systems</keyword><keyword>Male</keyword><keyword>Middle Aged</keyword><keyword>Ozone/adverse effects</keyword><keyword>Regression Analysis</keyword><keyword>Respiratory Tract Diseases/epidemiology/*mortality</keyword><keyword>United States</keyword></keywords><dates><year>1989</year><pub-dates><date>Dec</date></pub-dates></dates><isbn>0013-9351 (Print)
0013-9351 (Linking)</isbn><accession-num>2583069</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/2583069</url></related-urls></urls><electronic-resource-num>10.1016/s0013-9351(89)80004-0</electronic-resource-num></record></EndNote> 243) provide an estimate of the annual incidence rate of MRADs per person of 7.8.
School Loss Days
Baseline incidence rate estimates for school loss days remain the same as they were for previous benefits assessments. However, detailed information is provided below for reference.
We have two sources of information to use when estimating the baseline incidence rates of missed school days: the National Center for Education Statistics (NCES), which provided an estimate of all-cause school loss days, and the National Health Interview Survey (NHIS) (Adams et al., 1999, NCES, 1996 ADDIN EN.CITE <EndNote><Author>NCES</Author><Year>1996</Year><RecNum>1375</RecNum><DisplayText>Adams et al., 1999, NCES, 1996</DisplayText><record><rec-number>1375</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1609171139">1375</key></foreign-keys><ref-type name="Government Document">46</ref-type><contributors><authors><author>NCES, </author></authors><secondary-authors><author>U.S. Department of Education</author></secondary-authors></contributors><titles><title>The Condition of Education 1996, Indicator 42: Student Absenteeism and Tardiness</title></titles><dates><year>1996</year></dates><pub-location>Washington, DC</pub-location><publisher>Office of Educational Research and Improvement</publisher><isbn>NCES 96-304</isbn><urls><related-urls><url>Available at: https://nces.ed.gov/pubs96/96304.pdf</url></related-urls></urls></record><Author>Adams</Author><Year>1999</Year><RecNum>627</RecNum><record><rec-number>627</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732120">627</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Adams, Patricia F</author><author>Hendershot, Gerry E</author><author>Marano, Marie A</author></authors></contributors><titles><title>Current estimates from the national health interview survey, 1996</title><secondary-title>Vital and Health Statistics</secondary-title></titles><periodical><full-title>Vital and Health statistics</full-title></periodical><pages>1-203</pages><number>Series 10</number><dates><year>1999</year></dates><urls></urls></record></EndNote>, Table 47), which has data on different categories of acute school loss days. Table 19 presents the estimated school loss day rates. Further detail is provided below on these rates.
Table 19. School Loss Day Rates (per student per year)
Type
Northeast
Midwest
South
West
Respiratory illness-related absences
1.3
1.7
1.1
2.2
Illness-related absences
2.4
2.6
2.6
3.7
All-cause
9.9
9.9
9.9
9.9
*Illness-related school loss day rates were based on data from the 1996 NHIS and an estimate of 180 school days per year, excluding school loss days due to injuries. All-cause school loss day rates were based on data from the NCES.
All-Cause School Loss Day Rate
Based on data from the U.S. Department of Education (1996, Table 42-1), the National Center for Education Statistics estimates that for the 1993-1994 school year, 5.5% of students are absent from school on a given day. This estimate is comparable to study-specific estimates from Chen et al., 2000 ADDIN EN.CITE <EndNote><Author>Chen</Author><Year>2000</Year><RecNum>1229</RecNum><DisplayText>Chen et al., 2000</DisplayText><record><rec-number>1229</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1604525309">1229</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Chen, L.</author><author>Jennison, B. L.</author><author>Yang, W.</author><author>Omaye, S. T.</author></authors></contributors><auth-address>Department of Nutrition, Mail Stop 142, University of Nevada, Reno, NV 89557-0132, USA.</auth-address><titles><title>Elementary school absenteeism and air pollution</title><secondary-title>Inhal Toxicol</secondary-title></titles><periodical><full-title>Inhalation Toxicology</full-title>Inhal. Toxicol.Inhal Toxicol</periodical><pages>997-1016</pages><volume>12</volume><number>11</number><edition>2000/10/03</edition><keywords><keyword>*Absenteeism</keyword><keyword>Air Pollution/*adverse effects</keyword><keyword>Carbon Monoxide/adverse effects</keyword><keyword>Child</keyword><keyword>Humans</keyword><keyword>Ozone/adverse effects</keyword><keyword>Regression Analysis</keyword><keyword>Schools</keyword></keywords><dates><year>2000</year><pub-dates><date>Nov</date></pub-dates></dates><isbn>0895-8378 (Print)
0895-8378 (Linking)</isbn><accession-num>11015140</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/11015140</url></related-urls></urls><electronic-resource-num>10.1080/08958370050164626</electronic-resource-num></record></EndNote> and 137613760xd5s9009txfw1eafstv220i5ds2wvtpwf5d1609171384">1376..</>Environ Res</abbr-2Ransom and Pope, 1992, which ranged from 4.5% to 5.1%.
Illness-Related School Loss Day Rate
The National Health Interview Survey (NHIS) has regional estimates of school loss days due to a variety of acute conditions (Adams et al., 1999 ADDIN EN.CITE <EndNote><Author>Adams</Author><Year>1999</Year><RecNum>627</RecNum><DisplayText>Adams et al., 1999</DisplayText><record><rec-number>627</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732120">627</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Adams, Patricia F</author><author>Hendershot, Gerry E</author><author>Marano, Marie A</author></authors></contributors><titles><title>Current estimates from the national health interview survey, 1996</title><secondary-title>Vital and Health Statistics</secondary-title></titles><periodical><full-title>Vital and Health statistics</full-title></periodical><pages>1-203</pages><number>Series 10</number><dates><year>1999</year></dates><urls></urls></record></EndNote>). NHIS is a nationwide sample-based survey of the health of the noninstitutionalized, civilian population, conducted by NCHS. The survey collects data on acute conditions, prevalence of chronic conditions, episodes of injury, activity limitations, and self-reported health status. However, it does not provide an estimate of all-cause school loss days.
In estimating illness-related school loss days, we started with school loss days due to acute problems (Adams et al., 1999 ADDIN EN.CITE <EndNote><Author>Adams</Author><Year>1999</Year><RecNum>627</RecNum><DisplayText>Adams et al., 1999</DisplayText><record><rec-number>627</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732120">627</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Adams, Patricia F</author><author>Hendershot, Gerry E</author><author>Marano, Marie A</author></authors></contributors><titles><title>Current estimates from the national health interview survey, 1996</title><secondary-title>Vital and Health Statistics</secondary-title></titles><periodical><full-title>Vital and Health statistics</full-title></periodical><pages>1-203</pages><number>Series 10</number><dates><year>1999</year></dates><urls></urls></record></EndNote>, Table 47) and subtracted lost days due to injuries, in order to match the definition of the study used in the risk estimate to estimate illness-related school absences (Gilliland et al., 2001 ADDIN EN.CITE <EndNote><Author>Gilliland</Author><Year>2001</Year><RecNum>1377</RecNum><DisplayText>Gilliland et al., 2001</DisplayText><record><rec-number>1377</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1609171575">1377</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Gilliland, Frank D</author><author>Berhane, Kiros</author><author>Rappaport, Edward B</author><author>Thomas, Duncan C</author><author>Avol, Edward</author><author>Gauderman, W James</author><author>London, Stephanie J</author><author>Margolis, Helene G</author><author>McConnell, Rob</author><author>Islam, K Talat</author></authors></contributors><titles><title>The effects of ambient air pollution on school absenteeism due to respiratory illnesses</title><secondary-title>Epidemiology</secondary-title></titles><periodical><full-title>Epidemiology</full-title>EpidemiologyEpidemiology</periodical><pages>43-54</pages><dates><year>2001</year></dates><isbn>1044-3983</isbn><urls></urls></record></EndNote>). We then divided by 180 school days per to estimate illness-related school absence rates per school day. Similarly, when estimating respiratory illness-related school loss days, we use data from Adams et al., 1999 ADDIN EN.CITE <EndNote><Author>Adams</Author><Year>1999</Year><RecNum>627</RecNum><DisplayText>Adams et al., 1999</DisplayText><record><rec-number>627</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732120">627</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Adams, Patricia F</author><author>Hendershot, Gerry E</author><author>Marano, Marie A</author></authors></contributors><titles><title>Current estimates from the national health interview survey, 1996</title><secondary-title>Vital and Health Statistics</secondary-title></titles><periodical><full-title>Vital and Health statistics</full-title></periodical><pages>1-203</pages><number>Series 10</number><dates><year>1999</year></dates><urls></urls></record></EndNote>, Table 47. Note that we estimated 180 school days in a year to calculate respiratory illness-related school absence rates per year.
Work Loss Days
The incidence estimate for this health endpoint remains the same as in previous benefits assessments. The yearly work-loss-day incidence rate per 100 people is based on estimates from the 1996 National Health Interview Survey (Adams et al., 1999 ADDIN EN.CITE <EndNote><Author>Adams</Author><Year>1999</Year><RecNum>627</RecNum><DisplayText>Adams et al., 1999</DisplayText><record><rec-number>627</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732120">627</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Adams, Patricia F</author><author>Hendershot, Gerry E</author><author>Marano, Marie A</author></authors></contributors><titles><title>Current estimates from the national health interview survey, 1996</title><secondary-title>Vital and Health Statistics</secondary-title></titles><periodical><full-title>Vital and Health statistics</full-title></periodical><pages>1-203</pages><number>Series 10</number><dates><year>1999</year></dates><urls></urls></record></EndNote>, Table 41). They reported total annual work loss days of 352 million for individuals ages 18 to 65. The total population of individuals of this age group in 1996 (162 million) was obtained from (141614160xd5s9009txfw1eafstv220i5ds2wvtpwf5d1611332244">1416U.S. Census Bureau, 1998). The average annual rate of work loss days per individual is 2.17. Using a similar approach, we calculated work-loss-day rates for ages 18-24, 25-44, and 45-64, respectively.
Hypertension
The state-level baseline incidence rates for hypertension among women ages 50-99 were calculated using the following relationship between incidence and prevalence:
     Prevalence1 -Prevalence=Incidence *Average Duration of Disease.
This relationship assumes that hypertension prevalence is at a steady state in the population of interest. Data from the National Health and Nutrition Examination Survey supports this assumption, finding no significant change in age-adjusted hypertension prevalence among U.S. women from 1999-2000 to 2017-2018.

Figure 11. Age-Adjusted Trend in Hypertension Prevalence Among Adults Aged 18 and Over by Sex in the U.S. Between 1999-2018
We calculated state-level hypertension prevalence rates using data from the Behavioral Risk Factor Surveillance System (BRFSS), a system of health-related telephone surveys administered by the CDC that collects data from all fifty states. We calculated national-level duration of disease using data from the CDC, the Canadian Chronic Disease Surveillance System (CCDSS), and Loukine et al. (2010). In this calculation, we assumed that hypertension, once diagnosed, lasts until death (even if controlled by medication or diet). The definition of hypertension employed by BRFSS (see below) supports this assumption.
The procedure for calculating state-level hypertension prevalence rates is summarized as follows:
 We obtained individual-level records from the 2019 BRFSS Data file for all states except New Jersey, which was not included in the 2019 data file.
 For consistency with the relevant C-R function, we filtered the individual-level 2019 BRFSS dataset to include responses from female participants aged 50 and older.
 We aggregated the resulting sample by state, using raking-derived weights assigned to each respondent by BRFSS. State-level prevalence was calculated as the sum of weights associated with respondents who responded "Yes" to the question, "Have you ever been told by a doctor, nurse, or other health professional that you have high blood pressure?" divided by the sum of weights of all respondents in the state.
 For New Jersey, we repeated the above steps using the 2017 BRFSS Data File, the most recent year available for the state.
The procedure for calculating the national-level duration of disease is summarized as follows:
 For each five-year age group (50-54 years, 55-59 years, 60-64 years, 65-69 years, 70-74 years, 75-79 years, and 80+ years), we estimated duration of disease using the following equation:
Duration of disease = LE All U.S. females  -  (LE All Canadian females  -  LE Canadian females with hypertension)
 We obtained the life expectancy (LE) values from the following sources:
 We obtained LE All U.S. females from CDC Vital Statistics Rapid Release and applied the life expectancy of the lower bound of each age group (i.e., we applied the life expectancy at age 50 to the 50-54 years age group).
 We calculated the LE All Canadian females by taking the weighted average of LE Canadian females with hypertension and LE Canadian females without hypertension from Loukine et al. (2010).
 Weights were derived from hypertension prevalence estimates among Canadian females (aged 50-64, 65-79, and 80+) from CCDSS.
 We are not aware of any U.S. studies that estimate the change in life expectancy associated with hypertension at 5-year intervals.
 We obtained LE Canadian females with hypertension from Loukine et al. (2010).
 In all three instances, LE for the 80+ age group was calculated as the average of LE for the 80-84 and 85+ age groups for consistency with age groups provided by BRFSS.
 We calculated the overall duration of disease for females ages 50-99 by taking the weighted average of the duration of disease values for the five-year age groups.
 Weights were calculated from the raking-derived weights provided by BRFSS as the fraction of the female hypertensive population aged 50-99 that falls within the given age class.

Demographic Information
Quantified and monetized human health impacts are calculated using information regarding the demographic characteristics of the population exposed to air pollution; these data include the age, sex, race/ethnicity and location of the population. We use population projections based on economic forecasting models developed by Woods and Poole, Inc. (Woods & Poole, 2015 ADDIN EN.CITE <EndNote><Author>Woods & Poole</Author><Year>2015</Year><RecNum>605</RecNum><DisplayText>Woods & Poole, 2015</DisplayText><record><rec-number>605</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732115">605</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Woods & Poole,</author></authors></contributors><titles><title>Complete Demographic Database</title></titles><dates><year>2015</year></dates><urls><related-urls><url>Available at: https://www.woodsandpoole.com/</url></related-urls></urls></record></EndNote>). The Woods and Poole (WP) database contains county-level projections of population by age, sex, and race out to 2050, relative to a baseline using the 2010 Census data. Projections in each county are determined simultaneously with every other county in the U.S to take into account patterns of economic growth and migration.
   The sum of growth in county-level populations is constrained to equal a previously determined national population growth, based on Bureau of Census estimates (Hollmann et al., 2000 ADDIN EN.CITE <EndNote><Author>Hollmann</Author><Year>2000</Year><RecNum>604</RecNum><DisplayText>Hollmann et al., 2000</DisplayText><record><rec-number>604</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732115">604</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Hollmann, FW</author><author>Mulder, TJ</author><author>Kallan, JE %J Washington, DC: US Bureau of the Census</author></authors></contributors><titles><title>Methodology and assumptions for the population projections of the United States: 1999 to 2100 (Population Division Working Paper No. 38)</title></titles><volume>338</volume><dates><year>2000</year></dates><urls></urls></record></EndNote>). According to WP, linking county-level growth projections together and constraining to a national-level total growth avoids potential errors introduced by forecasting each county independently. County- and tract-level projections are developed in a four-stage process:
 National-level variables such as income, employment, and populations are forecasted.
 Employment projections are made for 179 economic areas defined by the Bureau of Economic Analysis (U.S. BEA, 2004), using an "export-base" approach, which relies on linking industrial-sector production of non-locally consumed production items, such as outputs from mining, agriculture, and manufacturing with the national economy. The export-based approach requires estimation of demand equations or calculation of historical growth rates for output and employment by sector.
 Population is projected for each economic area based on net migration rates derived from employment opportunities and following a cohort-component method based on fertility and mortality in each area.
 Employment and population projections are repeated for counties, using the economic region totals as bounds. The age, sex, and race distributions for each region or county are determined by aging the population by single year of age by sex and race for each year through 2050 based on historical rates of mortality, fertility, and migration.
Population projections stratified by race/ethnicity, age, and sex are based on economic forecasting models developed by Woods and Poole (Woods & Poole, 2015). The Woods and Poole database contains county-level projections of population by age, sex, and race out to 2050, relative to a baseline using the 2010 Census data. Population projections for each county are determined simultaneously with every other county in the U.S to consider patterns of economic growth and migration. County-level estimates of population percentages within the poverty status and educational attainment groups were derived from 2015-2019 5-year average ACS estimates. Additional information can be found in Appendix J of the BenMAP-CE User's Manual (https://www.epa.gov/benmap/benmap-ce-manual-and-appendices).
Health Endpoint Valuation
To directly compare benefits estimates associated with a rulemaking to cost estimates, the number of instances of each air pollution-attributable health impact must be converted to a monetary value. This requires a valuation estimate for each unique health endpoint, and potentially also discounting if the benefits are expected to accrue over more than a single year, as recommended by the U.S. EPA, 2014 ADDIN EN.CITE <EndNote><Author>U.S. EPA</Author><Year>2014</Year><RecNum>1300</RecNum><DisplayText>U.S. EPA, 2014</DisplayText><record><rec-number>1300</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608221942">1300</key></foreign-keys><ref-type name="Generic">13</ref-type><contributors><authors><author>U.S. EPA, </author></authors></contributors><titles><title>Guidelines for Preparing Economic Analyses</title></titles><dates><year>2014</year></dates><publisher>National Center for Environmental Economics. US Environmental Protection Agency Washington, DC</publisher><urls><related-urls><url>https://www.epa.gov/environmental-economics/guidelines-preparing-economic-analyses</url></related-urls></urls></record></EndNote>.
As reductions in ambient concentrations of air pollution generally lower the risk of future adverse health effects by a small amount for a large population, the most appropriate economic measure is the ex ante (before the effect has occurred) willingness-to-pay (WTP) for changes in risk. WTP values are calculated by dividing the monetary value an individual is willing to pay for a specific risk reduction by that change in risk. Using this approach, the size of the affected population is automatically taken into account by the number of incidences predicted by epidemiological studies applied to the relevant population.  There are three primary components of the value to society of an individual's avoidance of a non-fatal illness: 1) medical costs, 2) lost productivity, and 3) impacts on quality of life (i.e., "pain and suffering"). Estimates of individual WTP are conventionally thought to reflect all three of these components and are the preferred welfare valuation measure.  However, WTP values are available for a very limited subset of health endpoints, such as mortality.
For health endpoints where WTP estimates are not available, such as hospital admissions, we instead use the cost of treating or mitigating the effect to estimate the economic value. Cost-of-illness (COI) estimates are generally considered to be a lower bound estimate of the true value of reducing the risk of a health effect because they reflect the direct expenditures related to treatment and in some cases costs such as associated productivity losses, but not the value of avoided pain and suffering (Berger et al., 1987, Harrington and Portney, 1987, U.S. EPA, 2014). Additionally, COI estimates require additional parsing of individual health endpoints. For example, a stroke may initially involve an emergency department visit and hospitalization, but will also likely include additional follow-up medical costs, such as doctor visits and medications.
To prevent double counting of health impacts, when estimating monetary valuations, health endpoints are separated into the following non-overlapping categories: mortality (section 5.1), hospital admissions, emergency department visits (section 5.2), and health endpoint onset/occurrence (section 5.3).
EPA develops valuation estimates at the most age-refined level feasible for each health endpoint assessed. While we focused on identifying valuation estimates from peer-reviewed and published literature for new health endpoints, we were also able to update several valuation estimates for endpoints evaluated in previous benefits analyses, such as stroke, cardiac arrest, and AMIs. New hospitalizations and emergency department visits health endpoint valuations reflect specific ICD-9 codes evaluated by the epidemiologic study. New onset or follow-up/management health endpoints reflect WTP or COI valuation estimates that exclude death and initial emergency department and hospitalization costs.
These COI measures represent an update to EPA's previous method to producing COI estimates in three important respects (U.S. EPA, 2018 ADDIN EN.CITE <EndNote><Author>U.S. EPA</Author><Year>2018</Year><RecNum>629</RecNum><DisplayText>U.S. EPA, 2018</DisplayText><record><rec-number>629</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732120">629</key></foreign-keys><ref-type name="Government Document">46</ref-type><contributors><authors><author>U.S. EPA, </author></authors><secondary-authors><author>U.S. EPA </author></secondary-authors></contributors><titles><title>Environmental Benefits Mapping and Analysis Program  -  Community Edition User's Manual</title></titles><volume>Updated for BenMAP-CE Version 1.4.8</volume><dates><year>2018</year></dates><pub-location>Research Triangle Park, NC</pub-location><publisher>Office of Air Quality Planning and Standards</publisher><urls><related-urls><url>Available at: https://www.epa.gov/sites/production/files/2015-04/documents/benmap-ce_user_manual_march_2015.pdf</url></related-urls></urls></record></EndNote>):
 Estimates are of the costs of medical treatment, rather than charges by medical providers.
 Sampling parameters are used in survey data to express statistical uncertainty in mean cost estimates.
 More recent data is being used to reflect current treatment and healthcare costs.
When multiple valuation studies are available, the strengths and limitations of each study are considered, in a manner similar to that described for epidemiologic studies (section 2.1). The criteria for evaluation of these studies are listed in Table 20. In some cases, judgment is required to identify studies for valuation estimates when a similar number of preferred attributes are satisfied by multiple studies.
Table 20. Cost of Illness Economic Study Identification Consideration Factors
Criteria[a]
Prioritization Detail (In order of most to least preferred)
Peer-Reviewed Research
Peer-reviewed and published literature only
Endpoint Definition
1. ICD codes align with the epidemiological study
2. ICD codes overlap with the epidemiological study
Population Attributes
Prefer studies that match epidemiological study's population (specifically by age)
Study Period
More recent data are preferred
Measure of Costs[b]
1. Total payments
2. Allowable charges
3. Cost-adjusted charges
4. Unadjusted charges
Study Location
1. Nationwide coverage
2. Multi-city and/or multi-state coverage
3. Local study population
Coverage of cost elements
Studies that account for more cost elements (e.g., treatment settings) and longer time horizons are preferred
Study Size
Larger study size preferred
[a] This table focuses on COI because WTP measures are not currently available for the health endpoints of interest. Had WTP estimates been available, additional criteria would be relevant. It also excludes valuation estimates of hospitalizations and emergency department visits, which are developed by EPA and described in the appendices to 6296290xd5s9009txfw1eafstv220i5ds2wvtpwf5d1603732120">629U.S. EPA, 2018.
[b]Onukwugha et al., 2016 ADDIN EN.CITE <EndNote><Author>Onukwugha</Author><Year>2016</Year><RecNum>1374</RecNum><DisplayText>Onukwugha et al., 2016</DisplayText><record><rec-number>1374</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608741147">1374</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Onukwugha, E.</author><author>McRae, J.</author><author>Kravetz, A.</author><author>Varga, S.</author><author>Khairnar, R.</author><author>Mullins, C. D.</author></authors></contributors><auth-address>Department of Pharmaceutical Health Services Research, School of Pharmacy, University of Maryland, 220 Arch Street, Baltimore, MD, 21201, USA. eonukwug@rx.umaryland.edu.
College of Pharmacy, Mercer University, Atlanta, GA, USA.
Department of Pharmaceutical Health Services Research, School of Pharmacy, University of Maryland, 220 Arch Street, Baltimore, MD, 21201, USA.
School of Pharmacy, Notre Dame of Maryland University, Baltimore, MD, USA.</auth-address><titles><title>Cost-of-Illness Studies: An Updated Review of Current Methods</title><secondary-title>Pharmacoeconomics</secondary-title></titles><periodical><full-title>Pharmacoeconomics</full-title></periodical><pages>43-58</pages><volume>34</volume><number>1</number><edition>2015/09/20</edition><keywords><keyword>Health Care Costs/*statistics & numerical data</keyword><keyword>Humans</keyword></keywords><dates><year>2016</year><pub-dates><date>Jan</date></pub-dates></dates><isbn>1179-2027 (Electronic)
1170-7690 (Linking)</isbn><accession-num>26385101</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/26385101</url></related-urls></urls><electronic-resource-num>10.1007/s40273-015-0325-4</electronic-resource-num></record></EndNote> provides more information on these methods.

We provide unit values for health endpoints (along with information on the distribution of the unit value) in Table 21. All values are in constant year 2015$, adjusted for growth in real income for WTP estimates out to 2024 using projections provided by Standard and Poor's, which is discussed in further detail below.  Additional detail regarding the development of each health endpoint valuation is also provided below.
Table 21. Unit Values for Economic Valuation of Health Endpoints (2015$)[1]
Health Endpoint
Type
Central Estimate of Value Per Statistical Incidence (2015$)
Source
Mortality
Value of Statistical Life (VSL)
3%: $7,800,000
7%: $7,100,000
Weibull distribution fitted to 26 published VSL estimates (5 contingent valuation and 21 labor market studies). Underlying studies, distribution parameters, and other information are available in Appendix B of the EPA's Guidelines for Preparing Economic Analyses (U.S. EPA, 2014 ADDIN EN.CITE <EndNote><Author>U.S. EPA</Author><Year>2014</Year><RecNum>1300</RecNum><DisplayText>U.S. EPA, 2014</DisplayText><record><rec-number>1300</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608221942">1300</key></foreign-keys><ref-type name="Generic">13</ref-type><contributors><authors><author>U.S. EPA, </author></authors></contributors><titles><title>Guidelines for Preparing Economic Analyses</title></titles><dates><year>2014</year></dates><publisher>National Center for Environmental Economics. US Environmental Protection Agency Washington, DC</publisher><urls><related-urls><url>https://www.epa.gov/environmental-economics/guidelines-preparing-economic-analyses</url></related-urls></urls></record></EndNote>). Adjusted for income growth appropriate to the year of analysis.
Hospitalizations
Medical costs and opportunity cost of time
Varies by ICD codes, ranging between $7,700 and $16,000
HCUP data (details available in section 3.2 REF _Ref60052235 \r \h )
Emergency Department Visits
Medical costs
Varies by ICD codes, ranging between $600 and $1,200
HCUP data (details available in section 3.3 REF _Ref62136561 \r \h )
Nonfatal Myocardial Infarction (AMI)[a]
3-year medical costs[b]
3%: $49,000
7%: $48,000
O'Sullivan et al., 2011
Asthma Symptom- Albuterol Use[c]
Medical costs
$0.35 per albuterol inhaler puff
Average prescription costs derived from Epocrates.com and Goodrx.com accessed March 19, 2020
Asthma Symptom- Chest Tightness, Cough, Shortness of Breath, or Wheeze
WTP for 1 symptom day
$219