October 2012

Prepared for

Office of Air Quality Planning and Standards

U.S. Environmental Protection Agency

Research Triangle Park, NC

Neal Fann, Project Manager

Prepared by

Abt Associates Inc.

User's Manual

Appendices
November 2012

2

BenMAP User's Manual Appendices

Table of Contents

Appendix A: Monitor Rollback Algorithms 9

A.1 Percen..t.a..g..e...R..o..l.l.b..a..c..k............................................................................................................. 9

A.2 Incre.m...e..n..t.a..l..R..o..l.l.b..a..c..k.......................................................................................................... 10

A.3 Rollb.a..c..k..t.o...a...S...t.a..n..d..a..r.d........................................................................................................ 10

A.3.1 Interday Ro..l.l.b...a..c..k...-..G...e..n...e..r..a..t.i.n..g....T..a..r..g..e..t...M...e..t..r.i.c...V...a..l.u..e...s................................................................................ 11

Interday Ro..l.lb..a..c..k...-..P..e..r..c..e..n..t.a..g..e.......................................................................................................................... 12

Interday Ro..l.lb..a..c..k...-..I.n..c..r.e..m...e..n..t.a..l......................................................................................................................... 13

Interday Ro..l.lb..a..c..k...-..P..e..a..k...S..h..a..v..i.n..g....................................................................................................................... 14

A.3.2 Intraday Ro..l.l.b..a..c..k....-..A...d..j.u..s...t.i.n..g....H..o..u...r.l.y...O...b..s...e..r..v..a..t.i.o..n...s.................................................................................... 15

Intraday Ro..l.lb..a..c..k...-..P..e..r..c..e..n..t.a..g..e.......................................................................................................................... 16

A.3.3 Intraday Ro..l.l.b..a..c..k....-..I.n..c..r..e..m....e..n...t.a..l..................................................................................................................... 20

A.3.4 Interday an..d...I.n...t.r..a..d..a..y...R..o...l.l.b..a..c..k...-...Q..u...a..d..r..a..t.i.c................................................................................................... 22

Quadratic R...o..ll.b..a..c..k...-..I.n..t.e..r.d..a..y............................................................................................................................. 23

Quadratic R...o..ll.b..a..c..k...-..I.n..t.r.a..d..a..y............................................................................................................................. 24

Appendix B: Air Pollution Exposure Estimation Algorithms 25

B.1 Direc.t..M...o..d..e..l.i.n..g................................................................................................................... 26

B.2 Close.s..t..M...o..n..i.t.o..r................................................................................................................... 26

B.2.1 Closest Mo...n..i.t.o...r..–...T...e..m....p..o..r..a..l..S..c..a..l.i.n...g............................................................................................................. 27

B.2.2 Closest Mo...n..i.t.o...r..–...S...p..a..t.i.a..l..S...c..a..l.i.n..g................................................................................................................... 28

B.2.3 Closest Mo...n..i.t.o...r..–...T...e..m....p..o..r..a..l..a..n..d....S..p..a..t..i.a..l..S..c..a..l.i.n..g......................................................................................... 29

B.3 Voron..o..i..N..e..i.g..h..b..o..r...A..v..e..r.a..g..i.n..g...(.V..N..A...).................................................................................... 30

B.3.1 VNA | Tem.p..o..r..a..l..S..c..a..l.i.n...g.................................................................................................................................... 34

B.3.2 Voronoi Ne..i.g..h...b..o..r...A...v..e..r..a..g..i.n..g....(.V...N..A...)..–...S..p...a..t.i.a..l..S..c..a..l.i.n...g................................................................................. 38

B.3.3 Voronoi Ne..i.g..h...b..o..r...A...v..e..r..a..g..i.n..g....(.V...N..A...)..–...T..e...m...p...o..r..a..l..&...S...p..a..t.i.a..l..S...c..a..l.i.n..g........................................................... 39

B.4 Fixed...R..a..d..i.u..s....................................................................................................................... 39

B.5 Temp..o..r.a..l..a..n..d...S..p..a..t.i.a..l..S..c..a..l.i.n..g...A..d..j.u..s..t.m...e..n..t..F..a..c..t.o..r.s............................................................. 39

B.5.1 Calculation...o..f..S...c..a..l.i.n..g...F..a..c..t..o..r..s......................................................................................................................... 40

Example: P.M...2...5...S..c..a..l.in..g...F..a..c..t.o..r..s...in...U....S.....S..e..t.u..p................................................................................................... 40

Example: O..z..o..n..e...S..c..a..l.in..g...i.n...U....S.....S..e..t.u..p............................................................................................................... 42

B.6 Binne..d...M...e..t.r.i.c..s.................................................................................................................... 43

Appendix C: Deriving Health Impact Functions 44

C.1 Overv..i.e..w.............................................................................................................................. 44

C.2 Revie..w...R...e..l.a..t.i.v.e...R...i.s.k...a..n..d...O..d..d..s...R..a..t.i.o.................................................................................. 45

C.3 Linea..r..M...o..d..e..l...................................................................................................................... 46
Contents

November 2012

3

BenMAP User's Manual Appendices

C.4 Log-l.i.n..e..a..r..M...o..d..e..l................................................................................................................ 47

C.5 Logis.t.i.c...M...o..d..e..l.................................................................................................................... 49

C.6 Cox p..r.o..p..o..r.t.i.o..n..a..l..H...a..z.a..r..d..s..M...o..d..e..l........................................................................................ 55

Appendix D: Health Incidence & Prevalence Data in U.S.

Setup 57

D.1 Morta..l.i.t.y.............................................................................................................................. 57

D.1.1 Mortality D.a..t.a...f..o..r...2..0..0..4..-.2..0..0..6............................................................................................................................. 57

D.1.2 Mortality R.a..t.e....P..r..o..j.e..c..t..i.o..n..s....2..0..1..0..-.2..0..5..0............................................................................................................. 59

D.2 Hospi.t.a..l.i.z..a..t.i.o..n..s................................................................................................................... 60

D.3 Nonfa..t.a..l..H..e..a..r.t..A...t.t.a..c.k..s......................................................................................................... 65

D.4 Emer.g..e..n..c..y..D...e..p..a..r.t.m...e..n..t..V..i.s..i.t.s............................................................................................. 66

D.5 Scho.o..l..L..o..s.s...D..a..y..s................................................................................................................ 68

D.6 Other..A...c.u..t.e...a..n..d...C...h..r.o..n..i.c...E..f.f.e..c.t.s.......................................................................................... 69

D.6.1 Acute Bron..c..h..i.t..i.s................................................................................................................................................ 70

D.6.2 Chronic Br.o..n...c..h..i.t.i.s....I.n..c..i.d..e...n..c..e...R...a..t.e................................................................................................................. 70

D.6.3 Chronic Br.o..n...c..h..i.t.i.s....P..r..e..v..a..l.e..n...c..e...R..a..t..e.............................................................................................................. 70

D.6.4 Lower Res..p..i.r..a..t.o..r..y...S..y..m....p..t..o..m....s....................................................................................................................... 70

D.6.5 Minor Rest..r.i.c..t..e..d...A...c..t.i.v..i.t..y...D..a..y..s...(..M...R..A...D..)......................................................................................................... 71

D.6.6 Work Loss...D..a..y..s................................................................................................................................................. 71

D.7 Asthm..a..-.R...e..l.a..t.e..d...H..e..a..l.t.h...E..f.f.e..c..t.s............................................................................................ 71

D.7.1 Shortness..o..f...B..r..e..a..t.h.......................................................................................................................................... 71

D.7.2 Wheeze .......................................................................................................................................................... 72

D.7.3 Cough .......................................................................................................................................................... 72

D.7.4 Upper Res.p..i.r..a..t.o..r..y...S..y..m....p..t..o..m....s........................................................................................................................ 72

D.7.5 Asthma Po..p..u..l.a..t..i.o..n...E..s...t.i.m....a..t.e..s......................................................................................................................... 72

Appendix E: Particulate Matter Health Impact Functions in

U.S. Setup 74

E.1 Long.-.t.e..r.m....M...o..r.t.a..l.i.t.y............................................................................................................. 74

E.1.1 Expert Fun.c..t.i.o...n..s................................................................................................................................................ 75

Parametric..D..i.s..t.r.i.b..u..t.io..n..s...................................................................................................................................... 76

Non-Param.e..t.r..ic...D...is..t.r..ib..u..t.i.o..n..s.............................................................................................................................. 79

Using Expe..r.t..F..u..n..c..t.i.o..n..s...in...B...e..n..M..A...P.................................................................................................................... 81

Distribution.a..l..D..e..t.a..i.l.s...b..y...E..x..p..e..r.t.......................................................................................................................... 83

E.1.2 Laden et a.l...(.2..0..0..6..)............................................................................................................................................. 98

E.1.3 Pope et al...(.2..0..0..2..)............................................................................................................................................... 98

E.1.4 Woodruff e..t..a..l....(..1..9..9..7..)....................................................................................................................................... 99

E.1.5 Woodruff..e..t..a..l....(..2..0..0..6..)..................................................................................................................................... 100

E.1.6 Krewski e..t...a..l...(.2..0..0..9..)....................................................................................................................................... 100

E.2 Chro..n..i.c.../..S..e..v..e..r.e...I.l.l.n..e..s.s..................................................................................................... 101

E.2.1 Abbey et .a..l....(.1..9..9..5..b...)........................................................................................................................................ 102
November 2012

4

BenMAP User's Manual Appendices

E.2.2 Peters et..a..l...(.2..0..0..1..).......................................................................................................................................... 103

E.2.3 Pope et a.l...(.2..0..0..6..)............................................................................................................................................. 104

E.2.4 Sullivan e.t..a..l....(..2..0..0..5..)........................................................................................................................................ 104

E.2.5 Zanobetti..a..n..d....S..c..h..w....a..r.t..z...(.2..0..0..6..)..................................................................................................................... 105

E.2.6 Zanobetti..e..t...a..l...(.2..0..0..9..)..................................................................................................................................... 105

E.3 Hosp..i.t.a..l.i.z..a..t.i.o..n..s................................................................................................................. 106

E.3.1 Babin et a.l....(..2..0..0..7..)............................................................................................................................................ 107

E.3.2 Bell et al. .(.2..0..0..8..)................................................................................................................................................ 107

E.3.3 Ito (2003) .......................................................................................................................................................... 108

E.3.4 Moolgavk.a..r...(.2..0..0..0..a..)..,..C..h...r.o...n..i.c...L..u...n..g............................................................................................................... 109

E.3.5 Moolgavk.a..r...(.2..0..0..0..b...).,..C...a..r.d...i.o..v..a..s..c..u...l.a..r........................................................................................................... 110

E.3.6 Moolgavk.a..r...(.2..0..0..3..)........................................................................................................................................... 111

E.3.7 Peng et a.l...(.2..0..0..8..)............................................................................................................................................. 112

E.3.8 Peng et a.l...(.2..0..0..9..)............................................................................................................................................. 112

E.3.9 Sheppard..(..2..0..0..3..).............................................................................................................................................. 113

E.3.10 Zanobetti..e..t...a..l...(.2..0..0..9..)..................................................................................................................................... 114

E.4 Eme.r..g..e..n..c.y...R..o..o..m....V...i.s.i.t.s..................................................................................................... 115

E.4.1 Mar et al..2..0..1..0................................................................................................................................................... 115

E.4.2 Norris et .a..l...(.1..9..9..9..)........................................................................................................................................... 115

E.4.3 Slaughter..e...t..a..l....(.2..0..0..5..)..................................................................................................................................... 116

E.5 Mino..r..E..f.f.e..c..t.s...................................................................................................................... 116

E.5.1 Dockery e..t..a..l....(..1..9..9..6..)....................................................................................................................................... 117

E.5.2 Ostro (198..7..)...................................................................................................................................................... 118

E.5.3 Ostro and...R..o..t..h..s..c..h...i.l.d...(.1..9..8..9..).......................................................................................................................... 119

E.5.4 Schwartz..a..n..d...N...e..a..s...(..2..0..0..0..)............................................................................................................................. 120

E.6 Asth.m...a..-.R..e..l.a..t.e..d...E..f.f.e..c..t.s...................................................................................................... 121

E.6.1 Mar et al..(..2..0..0..4..)............................................................................................................................................... 121

E.6.2 Ostro et a..l...(.2..0..0..1..)............................................................................................................................................ 122

E.6.3 Pope et a.l...(.1..9..9..1..)............................................................................................................................................. 124

Appendix F: Ozone Health Impact Functions in U.S. Setup 125

F.1 Sho.r.t.-.t.e..r.m....M...o..r.t.a..l.i.t.y........................................................................................................... 125

F.1.1 Bell et al. .(.2..0..0..4..)................................................................................................................................................ 126

F.1.2 Bell et al. .(.2..0..0..5..)................................................................................................................................................ 127

F.1.3 Huang et .a..l...(.2..0..0..5..)........................................................................................................................................... 128

F.1.4 Levy et al....(.2..0..0..5..).............................................................................................................................................. 128

F.1.5 Ito and Th..u..r..s..t.o...n...(.1..9..9..6..).................................................................................................................................. 129

F.1.6 Ito et al. (2..0..0..5..).................................................................................................................................................. 129

F.1.7 Moolgavk.a..r...e..t...a..l...(.1..9..9..5..)................................................................................................................................. 130

F.1.8 Samet et .a..l....(.1..9..9..7..)........................................................................................................................................... 130

F.1.9 Schwartz..(.2..0..0..5..)............................................................................................................................................... 131

F.2 Long..-.T..e..r..m....M..o..r..t.a..l.i.t.y.......................................................................................................... 131

F.2.1 Jerret et .a..l...(.2..0..0..9..)........................................................................................................................................... 132

F.3 Hosp..i.t.a..l..A..d..m...i.s..s.i.o..n..s........................................................................................................... 133

F.3.1 Burnett e.t..a..l....(.2..0..0..1..)......................................................................................................................................... 134
Contents

November 2012

5

BenMAP User's Manual Appendices

F.3.2 Moolgavk.a..r...e..t...a..l...(.1..9..9..7..)................................................................................................................................. 135

F.3.3 Schwartz..(.1..9..9..4..a..)............................................................................................................................................. 135

F.3.4 Schwartz..(.1..9..9..4..b..)............................................................................................................................................. 136

F.3.5 Schwartz..(.1..9..9..5..)............................................................................................................................................... 136

F.4 Eme.r..g..e..n..c.y...R..o..o..m....V...i.s.i.t.s..................................................................................................... 137

F.4.1 Peel et al...(..2..0..0..5..).............................................................................................................................................. 138

F.4.2 Wilson et..a..l...(.2..0..0..5..).......................................................................................................................................... 138

F.5 Mino..r..E..f.f.e..c..t.s...................................................................................................................... 139

F.5.1 Chen et a.l....(.2..0..0..0..)............................................................................................................................................. 139

F.5.2 Gilliland e.t...a..l...(.2..0..0..1..)........................................................................................................................................ 141

F.5.3 Ostro and...R..o..t..h..s..c..h...i.l.d...(.1..9..8..9..).......................................................................................................................... 143

F.6 Con.v.e..r..t.i.n..g...F..u..n..c.t.i.o..n..s...t.o...8..-.H..o..u..r..D..a..i.l.y...M...a..x..i.m...u..m....M...e..t.r.i.c..................................................... 144

Appendix G: Nitrogen Dioxide Health Impact Functions in U.S.

Setup 148

G.1 Hosp..i.t.a..l..A..d..m...i.s..s.i.o..n..s........................................................................................................... 148

G.1.1 Fung et al....(..2..0..0..6..)............................................................................................................................................. 149

G.1.2 Linn et al...(..2..0..0..0..).............................................................................................................................................. 149

G.1.3 Luginaah.e...t..a..l....(.2..0..0..5..)...................................................................................................................................... 150

G.1.4 Moolgavk.a..r...(.2..0..0..3..)........................................................................................................................................... 150

G.1.5 Yang et al....(..2..0..0..3..)............................................................................................................................................. 151

G.1.6 Yang et al....(..2..0..0..5..)............................................................................................................................................. 151

G.2 Eme.r..g..e..n..c.y...R..o..o..m....V...i.s.i.t.s..................................................................................................... 152

G.2.1 Ito et al. (2..0..0..7..).................................................................................................................................................. 152

G.2.2 NYDOH (20..0..6..).................................................................................................................................................... 153

G.2.3 Peel et al...(..2..0..0..5..).............................................................................................................................................. 153

G.2.4 Villeneuv.e...e..t...a..l...(.2..0..0..7..)................................................................................................................................... 153

G.3 Asth.m...a...E..x..a..c..e..r.b..a..t.i.o..n......................................................................................................... 155

G.3.1 Delfino et..a..l....(.2..0..0..2..).......................................................................................................................................... 156

G.3.2 Delfino et..a..l....(.2..0..0..3..).......................................................................................................................................... 156

G.3.3 Mortimer..e..t...a..l...(.2..0..0..2..)..................................................................................................................................... 157

G.3.4 O'Connor..e..t...a..l...(.2..0..0..8..)..................................................................................................................................... 158

G.3.5 Ostro et a..l...(.2..0..0..1..)............................................................................................................................................ 159

G.3.6 Schildcro.u..t..e...t..a..l....(.2..0..0..6..).................................................................................................................................. 161

G.4 Mino..r..E..f.f.e..c..t.s...................................................................................................................... 161

G.4.1 Schwartz..e..t..a..l....(..1..9..9..4..)..................................................................................................................................... 161

Appendix H: Sulfur Dioxide Health Impact Functions in U.S.

Setup 163

H.1 Hosp..i.t.a..l..A..d..m...i.s..s.i.o..n..s........................................................................................................... 163

H.1.1 Fung et al....(..2..0..0..6..)............................................................................................................................................. 164

H.1.2 Luginaah.e...t..a..l....(.2..0..0..5..)...................................................................................................................................... 164
November 2012

6

BenMAP User's Manual Appendices

H.1.3 Schwartz..e..t..a..l....(..1..9..9..6..)..................................................................................................................................... 165

H.1.4 Sheppard..(..2..0..0..3..).............................................................................................................................................. 165

H.1.5 Yang et al....(..2..0..0..3..)............................................................................................................................................. 165

H.1.6 Yang et al....(..2..0..0..5..)............................................................................................................................................. 166

H.2 Eme.r..g..e..n..c.y...R..o..o..m....V...i.s.i.t.s..................................................................................................... 166

H.2.1 Ito et al. (2..0..0..7..).................................................................................................................................................. 168

H.2.2 Michaud e..t...a..l...(.2..0..0..4..)....................................................................................................................................... 168

H.2.3 NYDOH (20..0..6..).................................................................................................................................................... 168

H.2.4 Peel et al...(..2..0..0..5..).............................................................................................................................................. 170

H.2.5 Villeneuv.e...e..t...a..l...(.2..0..0..7..)................................................................................................................................... 170

H.2.6 Wilson et..a..l...(.2..0..0..5..).......................................................................................................................................... 171

H.3 Asth.m...a...E..x..a..c..e..r.b..a..t.i.o..n......................................................................................................... 171

H.3.1 Delfino et..a..l....(.2..0..0..3..).......................................................................................................................................... 172

H.3.2 Mortimer..e..t...a..l...(.2..0..0..2..)..................................................................................................................................... 172

H.3.3 O'Connor..e..t...a..l...(.2..0..0..8..)..................................................................................................................................... 173

H.3.4 Schildcro.u..t..e...t..a..l....(.2..0..0..6..).................................................................................................................................. 174

H.4 Mino..r..E..f.f.e..c..t.s...................................................................................................................... 175

H.4.1 Schwartz..e..t..a..l....(..1..9..9..4..)..................................................................................................................................... 175

Appendix I: Health Valuation Functions in U.S. Setup 177

I.1 Mort.a..l.i.t.y............................................................................................................................. 177

I.1.1 Value of a...S..t.a..t..i.s..t.i.c..a..l..L...i.f.e...B...a..s..e..d....o..n...2..6...S...t.u..d...i.e..s.......................................................................................... 178

I.1.2 Value of a...S..t.a..t..i.s..t.i.c..a..l..L...i.f.e...B...a..s..e..d....o..n...S...e..l.e..c..t..e..d...S...t.u..d...i.e..s.............................................................................. 178

I.2 Chro..n..i.c...I.l.l.n..e..s.s................................................................................................................... 179

I.2.1 Chronic B.r..o..n...c..h..i.t.i.s........................................................................................................................................... 179

Unit Value...B..a..s..e..d...o..n...T..w....o...S..t.u..d..i.e..s...o..f..W...T..P....................................................................................................... 179

Alternativ.e...C..o..s..t..o..f...Il.l.n..e..s..s...E..s..t.im...a..t.e..s................................................................................................................ 181

I.2.2 Chronic B.r..o..n...c..h..i.t.i.s....R..e..v..e...r.s...a..l.s....................................................................................................................... 182

I.2.3 Chronic A..s..t.h...m...a............................................................................................................................................... 182

I.2.4 Non-Fatal.M....y..o..c..a..r..d..i.a..l..I.n..f..a..r.c..t..i.o..n..s....(.H..e...a..r.t...A..t..t.a..c..k..s...)..................................................................................... 183

I.3 Hosp..i.t.a..l..A..d..m...i.s..s.i.o..n..s...&...E..m...e..r.g..e..n..c..y...R..o..o..m....V..i.s..i.t.s................................................................. 185

I.3.1 Hospital A..d..m....i.s..s...i.o..n..s....................................................................................................................................... 185

I.3.2 Emergenc..y...R...o..o..m.....V...i.s..i.t.s....f.o..r...A...s..t.h...m...a........................................................................................................... 187

I.4 Acut.e...S...y.m...p..t.o..m...s...a..n..d...I.l.l.n..e..s.s...N..o..t..R..e..q..u..i.r..i.n..g...H..o..s.p..i.t.a..l.i.z..a..t.i.o..n............................................... 188

I.4.1 Acute Bro..n..c..h..i.t..i.s...i.n...C...h..i.l.d...r.e...n......................................................................................................................... 190

I.4.2 Upper Res...p..i.r..a..t.o..r..y...S..y..m....p..t..o..m....s...(.U...R..S..)...i.n...C...h..i.l.d..r..e..n....................................................................................... 191

I.4.3 Lower Re.s...p..i.r..a..t.o..r..y...S..y..m....p..t..o..m....s...(.L...R..S..)...i.n...C...h..i.l.d..r..e..n...................................................................................... 192

I.4.4 Any of 19.R...e..s..p...i.r.a..t..o..r..y...S..y..m....p..t.o...m...s................................................................................................................. 193

I.4.5 Work Los.s....D..a..y..s...(..W...L..D...s..)................................................................................................................................ 193

I.4.6 Minor Res..t..r.i.c..t..e..d...A...c..t.i.v..i.t..y...D..a..y..s...(..M...R..A...D..s...).................................................................................................... 193

I.4.7 Asthma E.x..a..c..e..r..b..a..t..i.o..n...................................................................................................................................... 194

I.4.8 School Lo..s..s...D...a..y..s............................................................................................................................................ 195
Contents

November 2012

7

BenMAP User's Manual Appendices

Appendix J: Population & Other Data in U.S. Setup 197

J.1 Pop.u..l.a..t.i.o..n...D..a..t.a...i.n...U....S.....S...e..t.u..p........................................................................................... 197

J.1.1 How BenM...A...P...F..o..r..e..c..a..s...t.s...P...o..p..u...l.a..t.i.o..n............................................................................................................. 198

J.1.2 Data Need..e..d...f..o..r...F..o..r..e..c..a..s..t..i.n..g......................................................................................................................... 199

Block-Lev..e..l.C...e..n..s..u..s...2..0..1..0................................................................................................................................ 200

County-Le..v..e..l..F..o..r.e..c..a..s..t.s................................................................................................................................... 203

J.1.3 PopGrid .......................................................................................................................................................... 203

How to U.s..e...P..o..p..G..r..id......................................................................................................................................... 204

PopGrid O..u..t.p..u..t................................................................................................................................................ 208

J.2 Mon.i.t.o..r..D...a..t.a...i.n...U....S.....S..e..t.u..p................................................................................................. 211

J.2.1 Data Proc.e..s..s...i.n..g............................................................................................................................................... 212

J.2.2 Output Fil.e..s....................................................................................................................................................... 213

Appendix K: Uncertainty & Pooling 214

K.1 Unce..r..t.a..i.n..t.y........................................................................................................................ 214

K.1.1 Character..i.z..a..t.i.o..n....o..f..U...n..c..e..r..t.a..i.n...t.y...S..u...r.r..o..u...n..d..i.n...g...I.n..c..i.d...e..n..c..e....C..h...a..n..g..e...s........................................................ 214

K.1.2 Character..i.z..a..t.i.o..n....o..f..U...n..c..e..r..t.a..i.n...t.y...S..u...r.r..o..u...n..d..i.n...g...D..o...l.l.a..r..B...e..n..e...f.i.t.s................................................................. 215

K.1.3 Character..i.z..a..t.i.o..n....o..f..U...n..c..e..r..t.a..i.n...t.y...S..u...r.r..o..u...n..d..i.n...g...Q...A..L...Y...E..s..t..i.m...a..t..e..s.............................................................. 216

K.2 Poo.l.i.n..g.............................................................................................................................. 216

K.2.1 Weights U..s..e...d...f.o...r..P...o..o..l.i.n...g.............................................................................................................................. 217

Subjective...W....e..ig..h..t.s........................................................................................................................................... 217

Automatic..a..ll.y...G...e..n..e..r.a..t.e..d...W...e..i.g..h..t.s.................................................................................................................... 218

Fixed-Effe..c..t..W....e..ig..h..t.s........................................................................................................................................ 218

Random- ./..F..i.x..e..d..-.E..f.f..e..c..t..W...e..i.g..h..t.s...................................................................................................................... 219

K.2.2 Mechanic.s...o...f..P..o...o..l.i.n..g....i.n...B..e...n..M...A...P................................................................................................................. 222

K.2.3 Summing..D...i.s..t.r..i.b..u...t.i.o..n...s.................................................................................................................................. 223

K.2.4 Subtractin..g....D..i.s..t..r.i.b...u..t.i.o...n..s............................................................................................................................... 224

Appendix L: Command Line BenMAP 226

L.1 Ove.r.v..i.e..w............................................................................................................................ 226

L.2 Vari.a..b..l.e..s........................................................................................................................... 226

L.3 Com..m...a..n..d..s........................................................................................................................ 227

L.3.1 Set Active...S..e...t.u..p............................................................................................................................................... 227

L.3.2 Create AQ..G........................................................................................................................................................ 227

Model Dire..c..t..................................................................................................................................................... 228

Monitor D.ir..e..c..t.................................................................................................................................................. 229

Monitor M.o..d..e..l..R..e..l.a..t.iv..e...................................................................................................................................... 230

Monitor R.o..ll.b..a..c..k............................................................................................................................................... 231

L.3.3 Run CFG .......................................................................................................................................................... 232

L.3.4 Run APV .......................................................................................................................................................... 232

L.3.5 Generate..R..e..p...o..r..t............................................................................................................................................. 233

Audit Trai.l........................................................................................................................................................ 234
November 2012

8

BenMAP User's Manual Appendices

CFGR Rep..o..r..t................................................................................................................................................... 234

APVR Re.p..o..r.t................................................................................................................................................... 234

L.4 Exam...p..l.e...1.......................................................................................................................... 235

L.5 Exam...p..l.e...2.......................................................................................................................... 237

Appendix M: Function Editor 239

M.1 User..D...e..f.i.n..e..d...V..a..r.i.a..b..l.e..s...................................................................................................... 239

M.2 The.S...c.r..i.p..t..L..a..n..g..u..a..g..e......................................................................................................... 240

M.3 Ope.r.a..n..d..s........................................................................................................................... 241

M.4 Ope.r.a..t.i.o..n..s......................................................................................................................... 241

M.5 Arith..m...e..t.i.c...F..u..n..c.t.i.o..n..s.......................................................................................................... 242

M.6 Agg.r.e..g..a..t.e...F..u..n..c..t.i.o..n..s......................................................................................................... 243

References 244
Appendix A: Monitor Rollback Algorithms

November 2012

9

BenMAP User's Manual Appendices

Appendix A: Monitor Rollback Algorithms

This Appendix details the rollback procedures that you can perform on monitor data. The

rollback procedure is a quick way to determine the monitor levels that would exist under various

kinds of changes that you can specify. This includes three basic types of rollbacks: Percentage,

Increment, and Rollback to Standard.

Once a set of monitors has been selected, the user may define one or more non-overlapping

rollback regions. A region is simply a set ofstates with an associated set of rollback parameter

values. Three rollback types are available:

Percentage Rollback. Monitor values are reduced the same percentage.

IncrementalRollback. Monitor values are reduced by the same fixed increment.

Rollback to a Standard. Monitor values are reduced so that attainment of a specified standard

is reached.

Each of these rollback types has different rollback parameters associated with it.

A.1 Percentage Rollback

Percentage Rollback involves setting only two parameters - a percentage and a background

level. The rollback procedure is similarly straightforward - each observation at each monitor in

the region has the portion of its value which is above background level reduced by

percentage.

Example: Background Level: 35; Percentage: 25

Initial Observations at a monitor in rollback region:

20 20 25 59 35 51 83 35 30 67 87 79 63 35 35

If we select the background level of 35, we first calculate the portion of each observation that is

above background level, that is, we subtract the background levelfrom the initial observation

level. Observations below background level are given a value of 0.

Observation portions above background level:

0 0 0 24 0 16 48 0 0 32 52 44 28 0 0

When we apply the rollback percentage, each observation portion gets reduced by 25%.

Reduced portions above background level:
Appendix A: Monitor Rollback Algorithms

November 2012

10

BenMAP User's Manual Appendices

0 0 0 18 0 12 36 0 0 24 39 33 21 0 0

Then, each reduced portion is added to the background level of 35. Zero values are replaced by

the initial observations.

Reduced Observations:

20 20 25 53 35 47 71 35 30 59 74 68 56 35 35

A.2 Incremental Rollback

Incremental Rollback similarly involves setting only two parameters - an increment and a

background level. The rollback procedure is quite similar to the percentage rollback procedure

- each observation at each monitor in the region has the portion of its value which is above

background level reduced by increment. The reduced values are not allowed to become

negative, however - that is, they are truncated at zero.

Example: Background Level: 35; Increment: 25

Initial Observations:

20 20 25 59 35 51 83 35 30 67 87 79 63 35 35

Observation portions above background level:

0 0 0 24 0 16 48 0 0 32 52 44 28 0 0

Reduced portions above background level:

0 0 0 0 0 0 23 0 0 7 27 19 3 0 0

Reduced Observations:

20 20 25 35 35 35 58 35 30 42 62 54 38 35 35

A.3 Rollback to a Standard

Rollback to a Standard has two groups of parameters - those associated with the Attainment

Test, which determines whether a monitor is in attainment (meets the standard), and those

associated with the Rollback Methods, which are used to bring out of attainment monitors into
Appendix A: Monitor Rollback Algorithms

November 2012

11

BenMAP User's Manual Appendices

attainment.

The Attainment Test parameters are Metric, Ordinality, and Standard. A monitor is

considered in attainment if the n

th highest value of the metric specified by Metric is at or below

the value specified by Standard, where n is the value specified by Ordinality. For example, if

Metric is TwentyFourHourDailyAverage, Ordinality is two, and Standard is eighty five, a

monitor will be considered in attainment if the second highest value of

TwentyFourHourDailyAverage is at or below eighty five.

Supported metrics for pollutants with hourly observations (Ozone) include

FiveHourDailyAverage, EightHourDailyAverage, TwelveHourDailyAverage,

TwentyFourHourDailyAverage, OneHourDailyMax, and EightHourDailyMax. Supported

metrics for pollutants with daily observations (PM10, PM2.5) include

TwentyFourHourDailyAverage and AnnualAverage. For Annual Average, Ordinality does not

apply, since there is only a single metric value to work with.

The Rollback Method parameters are Interday Rollback Method, Interday Background

Level, Intraday Rollback Method, and Interday Background Level. These four

parameters determine the rollback procedures used to bring out of attainment monitors into

attainment. The Interday Rollback Method and Background Level are used to generate

target values for the metric specified by the Attainment Test. The Intraday Rollback

Method and Background Level are used to adjust hourly observations to meet the target

metric values generated in the previous step. As such, the Intraday Rollback Method and

Background Level are used only for pollutants with hourly observations (ozone).

A.3.1 Interday Rollback - Generating Target Metric Values

Because standards are defined on metrics, not directly on observations, the first step in rolling

back out of attainment monitors is generating target metric values. There are four supported

rollback methods for Interday Rollbacks - Percentage, Incremental, Peak Shaving, and

Quadratic. Each of these rollback methods requires some preprocessing of the initial monitor

metric values. We will discuss this preprocessing first, and then go through Percentage,

Incremental, and Peak Shaving rollbacks in turn. Quadratic rollback is more complicated than

these first three, and has its own section.

The Interday Background Levelspecifies the portion of each metric value which cannot be

affected by human intervention - we call this portion the non-anthropogenic portion. Whatever

portion is left over after subtracting out the background level is referred to as the anthropogenic

portion. The anthropogenic portion of the initial monitor metric values is the only part which will

be affected by the Interday Rollback Method.

BenMAP calculates an out of attainment value by determining the particular monitor metric value

which caused the monitor to be out of attainment - this value is the nth highest value of the metric

specified by the Attainment Test metric, where n is the Attainment Test ordinality. BenMAP then

calculates an anthropogenic out of attainment value by subtracting the Interday Background Level

from the out of attainment value. BenMAP also calculates an anthropogenic standard by
Appendix A: Monitor Rollback Algorithms

November 2012

12

BenMAP User's Manual Appendices

subtracting the Interday Background Levelfrom the Attainment Test standard. Finally, BenMAP

calculates a set of anthropogenic metric values and a set of non-anthropogenic metric values using

the following procedure on each initial monitor metric value:

IF the metric value is less than or equal to the Interday Background Level,

non-anthropogenic metric value = metric value

anthropogenic metric value = 0

ELSE

non-anthropogenic metric value = Interday Background Level

anthropogenic metric value = metric value - Interday Background Level

A.3.1.1 Interday Rollback - Percentage

To generate target metric values using Percentage rollback, BenMAP calculates the percentage

required to reduce the anthropogenic out of attainment value to exactly the anthropogenic

standard. This percentage reduction is then applied to all of the anthropogenic metric values.

Finally, these reduced anthropogenic metric values are added to the non-anthropogenic metric

values to give the final target metric values.

Example:

Initial Metric Values:

30 35 50 10

0

80 44 67 88 90 70 50 30 55 90 80 85

Attainment Test: Highest value of metric <= 70

Interday Background Level: 40

Out of Attainment Value: 100

Anthropogenic Out of Attainment Value: 60 (= 100 - 40)

Anthropogenic Standard: 30 (= 70 - 40)

Percentage Reduction Required: 50% (=(60-30)/60)

Non-Anthropogenic Metric Values:

30 35 40 40 40 40 40 40 40 40 40 30 40 40 40 40
Appendix A: Monitor Rollback Algorithms

November 2012

13

BenMAP User's Manual Appendices

Anthropogenic Metric Values:

0 0 10 60 40 4 27 48 50 30 10 0 15 50 40 45

Reduced Anthropogenic Metric Values:

0 0 5 30 20 2 14 24 25 15 5 0 8 25 20 23

Target Metric Values:

30 35 45 70 60 42 54 64 65 55 45 30 48 65 60 63

A.3.1.2 Interday Rollback - Incremental

To generate target metric values using IncrementalRollback, BenMAP calculates the increment

required to reduce the anthropogenic out of attainment value to exactly the anthropogenic

standard. This incrementalreduction is then applied to all of the anthropogenic metric values (but

they are not allowed to fall below zero). Finally, these reduced anthropogenic metric values are

added to the non-anthropogenic metric values to give the final target metric values.

Example:

Initial Metric Values:

30 35 50 10

0

80 44 67 88 90 70 50 30 55 90 80 85

Attainment Test: Highest value of metric <= 70

Interday Background Level: 40

Interday Rollback Method:Incremental

Out of Attainment Value: 100

Anthropogenic Out of Attainment Value: 60

Anthropogenic Standard: 30 (=70 - 30)

IncrementalReduction Required: 30

Non-Anthropogenic Metric Values:

30 35 40 40 40 40 40 40 40 40 40 30 40 40 40 40
Appendix A: Monitor Rollback Algorithms

November 2012

14

BenMAP User's Manual Appendices

Anthropogenic Metric Values:

0 0 10 60 40 4 27 48 50 30 10 0 15 50 40 45

Reduced Anthropogenic Metric Values:

0 0 5 30 20 2 14 24 25 15 5 0 8 25 20 23

Target Metric Values:

30 35 45 70 60 42 54 64 65 55 45 30 48 65 60 63

A.3.1.3 Interday Rollback - Peak Shaving

To generate target metric values using Peak Shaving rollback, BenMAP simply truncates all

anthropogenic metric values at the anthropogenic standard. These reduced anthropogenic metric

values are added to the non-anthropogenic metric values to give the final target metric values.

Example:

Initial Metric Values:

30 35 50 100 80 44 67 88 90 70 50 30 55 90 80 85

Attainment Test: Highest value of metric <= 70

Interday Background Level: 40

Interday Rollback Method: Peak Shaving

Anthropogenic Standard: 30

Non-Anthropogenic Metric Values:

30 35 40 40 40 40 40 40 40 40 40 30 40 40 40 40

Anthropogenic Metric Values:

0 0 10 60 40 4 27 48 50 30 10 0 15 50 40 45

Reduced Anthropogenic Metric Values:

0 0 10 30 30 4 27 30 30 30 10 0 15 30 30 30
Appendix A: Monitor Rollback Algorithms

November 2012

15

BenMAP User's Manual Appendices

Target Metric Values:

30 35 50 70 70 44 67 70 70 70 50 30 55 70 70 70

A.3.2 Intraday Rollback - Adjusting Hourly Observations

Once a set of target metric values has been calculated for a pollutant with hourly observations (e.

g., Ozone), BenMAP must adjust the hourly observations so that they produce the target metric

values. There are three supported rollback methods for Intraday Rollback - Percentage,

Incremental, and Quadratic. Each of these rollback methods requires some preprocessing of the

initial monitor observations, and each can require multiple iterations to hit the target metric values.

We will discuss this preprocessing and iteration first, and then go through Percentage and

Incrementalrollbacks in turn. Quadratic rollback is more complicated than these first two, and

has its own section.

For various reasons, each of the Intraday Rollback methods can fail to hit the target metric values

during a single pass through the rollback procedure (these will be discussed in detail below). As

such, each of the rollback methods uses an iterative approach to get within a threshold of each of

the target metric values - currently this threshold is 0.05. The iterative approach works as

follows:

For each target metric value, BenMAP calculates the current value of the Attainment Test metric.

For the first iteration, the metric value will be calculated using unadjusted hourly observations.

For subsequent iterations, the metric value will be calculated using the current values of the

adjusted hourly observations.

If the difference between the metric value and the target metric value is less than or equal to 0.05,

the rollback procedure is finished. Otherwise, another iteration is required.

The Intraday Background Levelspecifies the portion of each observation which cannot be

affected by human intervention - we call this portion the non-anthropogenic portion. Whatever

portion is left over after subtracting out the background level is referred to as the anthropogenic

portion. The anthropogenic portion of the initial monitor observations is the only part which will

be affected by the Intraday Rollback Method.

In a way analogous to the Interday Rollback procedure, BenMAP calculates the twenty-four

hourly anthropogenic observations and the twenty-four hourly non-anthropogenic observations

using the following procedure for each hourly observation:

IF the current value of the observation is less than or equal to the Intraday Background Level,

non-anthropogenic observation = observation

anthropogenic observation = 0

ELSE
Appendix A: Monitor Rollback Algorithms

November 2012

16

BenMAP User's Manual Appendices

non-anthropogenic observation = Intraday Background Level

anthropogenic observation = observation - Intraday Background Level

Given (i) an Attainment Test Metric (e.g., EightHourDailyMax), (ii) an Intraday Background

Level, and (iii) a target metric value for the day, BenMAP proceeds to adjust hourly observations

in the following steps:

1. Calculate the Attainment Test metric (e.g., the 8-hour daily maximum);

2. Identify the “window” – i.e., the set of hours used to calculate the metric (e.g., if the 8-hour

daily maximum is achieved in the first 8 hours, then the window is comprised of the first 8 hours);

3. Calculate the non-anthropogenic hourly observations (=min(hourly observation, Intraday

Background Level));

4. Calculate the anthropogenic hourly observations (=hourly observation - Intraday

Background Level);

5. Calculate the non-anthropogenic metric value (= the metric using the non-anthropogenic

hourly observations in the “window”);

6. Calculate the anthropogenic metric value (= the metric using the anthropogenic hourly

observations in the “window”);

7. Calculate the anthropogenic target metric value (= the target metric value minus the nonanthropogenic metric value);

8. Calculate the reduction required to get the anthropogenic metric value down to the

anthropogenic target metric value;

9. Adjust all anthropogenic hourly observations by the reduction calculated on the previous

step;

10. Calculate the adjusted hourly observations (= the adjusted anthropogenic hourly observation

+ the non-anthropogenic hourly observation).

A.3.2.1 Intraday Rollback - Percentage

Below, we present two examples of a percentage-based Intraday Rollback. In one example, a

single iteration is needed, and in the second example, two iterations are required because a

number of the monitor values fall below the assumed background level.

A.3.2.1.1 Example: All Hourly Observations Exceed the Intraday Background (Single Iteration)

If all of the hourly observations in a day are greater than the Intraday Background Level, then the
Appendix A: Monitor Rollback Algorithms

November 2012

17

BenMAP User's Manual Appendices

above procedure is straightforward and can be accomplished in a single iteration. We illustrate

with the following example. Suppose that:

Metric = EightHourDailyMax,

Target metric value for a given day = 85

Intraday Background Level = 40.

And that the hourly observations on that day are:

530 45 50 60 45 45 45 60 70 100 100 100 100

100 100 100 100 60 45 50 45 45 47 47

Based on these observations, we see that the 8-hour daily maximum = 110.

Assuming a background level of 40, then the Anthropogenic hourly observations are:

490 5 10 20 5 5 5 20 30 60 60 60 60 60

60 60 60 20 5 10 5 5 7 7

Then, we know:

Anthropogenic metric value = 70.

Non-anthropogenic metric value = 40.

Anthropogenic target metric value = 45.

Percentage reduction required = ((70-45)/70) = 35.7%

All of the hourly anthropogenic observations are reduced by 35.7%. The average of the first 8

values (the window on which the Test metric is based) will be exactly 45, the anthropogenic

target metric value. Finally, the adjusted hourly observations are calculated by adding the nonanthropogenic hourly observation to the adjusted hourly anthropogenic observations.

A.3.2.1.2 Example: Some Hourly Observations are Below the Intraday Background (Multiple

Iterations Required)

In the above example, the anthropogenic target metric value was met on a single iteration because

all of the hourly observations were greater than the Intraday Background Level. In this case, a

simple percent reduction of all hourly values will produce an average in the window that is equal

to the anthropogenic target metric value. Ifsome of the hourly observations in a day are less than

or equal to the Intraday Background Level, however, then BenMAP uses an iterative procedure.
Appendix A: Monitor Rollback Algorithms

November 2012

18

BenMAP User's Manual Appendices

On each iteration, it adjusts hourly observations using the 10-step method given above. It then

compares the new metric value to the target metric value. If the difference is less than or equal to

0.05 ppb, the rollback procedure is finished. Otherwise, another iteration is required. The

iterative procedure is illustrated in the following example.

Suppose that:

Metric = EightHourDailyMax,

Target metric value for a given day = 85

Intraday Background Level = 40.

Suppose also that the hourly observations on that day are:

530 20 25 60 35 35 40 60 70 100 100 100 100

100 100 100 100 60 33 40 30 30 25 20

Then, we know that the 8-hour daily maximum = 100.6.

Non-Anthropogenic Hourly Observations, Iteration One:

40 20 25 40 35 35 40 40 40 40 40

40 40 40 40 40 40 40 33 40 30 30 25 20

Anthropogenic Hourly Observations, Iteration One:

490 0 0 20 0 0 0 20 30 60 60 60 60 60

60 60 60 20 0 0 0 0 0 0

Non-Anthropogenic Metric Value: 34.4 (EightHourDailyMax - calculated over the same

eight hour window as the initial metric value was calculated over)

Anthropogenic Metric Value: 66.3

Anthropogenic Target Metric Value: 50.6

Percentage Reduction Required: 23.6%

Reduced Anthropogenic Hourly Observations, Iteration One:

374 0 0 15 0 0 0 15 23 46 46 46

46 46 46 46 46 15 0 0 0 0 0 0
Appendix A: Monitor Rollback Algorithms

November 2012

19

BenMAP User's Manual Appendices

Reduced Hourly Observations, Iteration One:

414 20 25 55 35 35 40 55 63 86 86 86 86

86 86 86 86 55 33 40 30 30 25 20

Reduced Metric Value (EightHourDailyMax): 85.8

Target Metric Value (EightHourDailyMax): 85

Non-Anthropogenic Hourly Observations, Iteration Two:

40 20 25 40 35 35 40 40 40 40 40 40 40

40 40 40 40 40 33 40 30 30 25 20

Anthropogenic Hourly Observations, Iteration Two:

374 0 0 15 0 0 0 15 23 46 46 46 46

46 46 46 46 15 0 0 0 0 0 0

Non-Anthropogenic Metric Value: 40 (EightHourDailyMax - calculated over the same

eight hour window the initial metric value was calculated over)

Anthropogenic Metric Value: 45.8

Anthropogenic Target Metric Value: 45

Percentage Reduction Required: 1.9%

Reduced Anthropogenic Hourly Observations, Iteration Two:

368 0 0 15 0 0 0 15 23 45 45 45 45 45

45 45 45 15 0 0 0 0 0 0

Reduced Hourly Observations, Iteration Two:

408 20 25 55 35 35 40 55 63 85 85 85 85 85

85 85 85 55 33 40 30 30 25 20
Appendix A: Monitor Rollback Algorithms

November 2012

20

BenMAP User's Manual Appendices

Reduced Metric Value (EightHourDailyMax): 85

The above example, in addition to illustrating the Intraday Percentage Rollback, also illustrates

one reason why the iterative procedure can be necessary. When using the EightHourDailyMax

metric in the Attainment Test, it is possible for the window over which the maximum eight hour

average occurs to move after a single pass through the rollback procedure. When this happens,

it becomes necessary to go through additional iterations to hit the target metric value.

A.3.3 Intraday Rollback - Incremental

To adjust hourly observations using Incrementalrollback, BenMAP calculates the increment

required to reduce the anthropogenic metric value to exactly the anthropogenic target metric

value. This incrementalreduction is then applied to all of the anthropogenic observations (but -

they are not allowed to fall below zero). Finally, these reduced anthropogenic observations are

added to the non-anthropogenic observations to give the finalreduced observations.

Example:

Initial Hourly Observations:

20 20 25 60 35 35 40 70 35 30 65 90 76

65 35 35 54 60 33 40 30 30 25 20

Initial Metric Value (EightHourDailyMax): 60

Target Metric Value (EightHourDailyMax): 55

Intraday Background Level: 40

Intraday Rollback Method:Incremental

Non-Anthropogenic Hourly Observations, Iteration One:

20 20 25 40 35 35 40 40 35 30 40 40 40

40 35 35 40 40 33 40 30 30 25 20

Anthropogenic Hourly Observations, Iteration One:

0 0 0 20 0 0 0 30 0 0 25 50 36

25 0 0 14 20 0 0 0 0 0 0

Non-Anthropogenic Metric Value (EightHourDailyMax): 38.8
Appendix A: Monitor Rollback Algorithms

November 2012

21

BenMAP User's Manual Appendices

Anthropogenic Metric Value (EightHourDailyMax): 21.3

Anthropogenic Target Metric Value (EightHourDailyMax): 16.3

IncrementalReduction Required: 5.0

Reduced Anthropogenic Hourly Observations, Iteration One:

0 0 0 15 0 0 0 25 0 0 20 45 31

20 0 0 9 15 0 0 0 0 0 0

Reduced Hourly Observations, Iteration One:

20 20 25 55 35 35 40 65 35 30 60 85 71

60 35 35 49 55 33 40 30 30 25 20

Reduced Metric Value (EightHourDailyMax): 56.25

Target Metric Value (EightHourDailyMax): 55

Non-Anthropogenic Hourly Observations, Iteration Two:

20 20 25 40 35 35 40 40 35 30 40 40 40

40 35 35 40 40 33 40 30 30 25 20

Anthropogenic Hourly Observations, Iteration Two:

0 0 0 15 0 0 0 25 0 0 20 45 31

20 0 0 9 15 0 0 0 0 0 0

Non-Anthropogenic Metric Value (EightHourDailyMax): 38.8

Anthropogenic Metric Value (EightHourDailyMax): 17.5

Anthropogenic Target Metric Value (EightHourDailyMax): 16.3

IncrementalReduction Required: 1.25
Appendix A: Monitor Rollback Algorithms

November 2012

22

BenMAP User's Manual Appendices

Reduced Anthropogenic Hourly Observations, Iteration Two:

0 0 0 14 0 0 0 24 0 0 19 44 30

19 0 0 8 14 0 0 0 0 0 0

Reduced Hourly Observations, Iteration Two:

20 20 25 54 35 35 40 64 35 30 59 84 70

59 35 35 48 54 33 40 30 30 25 20

Reduced Metric Value (EightHourDailyMax): 55.3

Target Metric Value (EightHourDailyMax): 55

This example should actually continue for one further iteration, with a new IncrementalReduction

of 0.3. This illustrates another reason why the iterative procedure can be necessary - for

incrementalreductions, the prohibition against values becoming negative can cause target metric

values to not be met. Incrementalreductions thus very often require multiple iterations.

A.3.4 Interday and Intraday Rollback - Quadratic

Quadratic rollback is based on an algorithm developed by Horst and Duff . The idea behind

quadratic rollback is to reduce large values proportionally more than small values while just

achieving the standard - that is, the out-of-attainment value should be more or less at the standard

after the rollback (some small amount of error is involved).

The original quadratic rollback algorithm is designed to roll back hourly observations given a

desired peak value. That is, it assumes that the Attainment Test metric is the one-hour average

and the Attainment Test ordinality is one. As such, the algorithm was modified slightly to allow

for ordinalities other than one to be used.

The basic formula for quadratic rollback is:

Reduced Observation = [ 1 - ( A + B * Initial Observation ) ] * Initial Observation

where:

iranges over the days being reduced.

A = 1 - V
Appendix A: Monitor Rollback Algorithms

November 2012

23

BenMAP User's Manual Appendices

V = Min( 1, Vi)

Vi = ( 2 * Maximum Observation Value * Standard ) / Xi

Xi = ( 2 * Maximum Observation Value * Metricsi) - Metricsi2

B = Max( 0, [( V * Out of Attainment Value - Standard ) / Out of Attainment Value2] )

A.3.4.1 Quadratic Rollback - Interday

Because Quadratic Rollback was originally designed to adjust hourly observations to meet a daily

metric standard, it is slightly complicated to use it to generate target metric values.

First, Quadratic Rollback calculates the anthropogenic out of attainment value by subtracting the

Intraday Background Levelfrom the out of attainment value. Note that this differs from the other

interday rollback methods, which subtract the Interday Background Levelfrom the out of

attainment value. Similarly, the anthropogenic standard is calculated by subtracting the Intraday

Background Levelfrom the standard.

The anthropogenic observations and non-anthropogenic observations are then calculated. For

pollutants which have daily observations (PM10, PM2.5) the anthropogenic metric values are used

(see above for their calculation). For pollutants which have hourly observations (Ozone),

Quadratic Rollback loops through each metric value and calculates the twenty four corresponding

anthropogenic observations and non-anthropogenic observations as follows:

IF the metric value is at or below the Interday Background Level,

For each observation,

non-anthropogenic observation = observation

anthropogenic observation = 0

ELSE

For each observation,

IF the observation is at or below the Intraday Background Level

non-anthropogenic observation = observation

anthropogenic observation = 0

ELSE

non-anthropogenic observation = Intraday Background Level

anthropogenic observation = observation - Intraday Background

Level

A new set of anthropogenic metric values is then calculated by generating the Attainment Test

metric from the anthropogenic observations. The Quadratic Rollback algorithm is then called,

passing in the anthropogenic metric values as Metrics, anthropogenic observations as

Observations, anthropogenic standard as Standard, and anthropogenic out of attainment value as
Appendix A: Monitor Rollback Algorithms

November 2012

24

BenMAP User's Manual Appendices

Out of Attainment Value. The result is a set of reduced anthropogenic observations. These are

then added together with the non-anthropogenic observations to give a finalset of reduced

observations.

Then, if Quadratic Rollback was also selected as the Intraday Rollback method, these

observations are used as the finalreduced observations for the monitor. Otherwise, metric

targets are generated from these hourly observations, and the observations themselves are

discarded.

A.3.4.2 Quadratic Rollback - Intraday

Quadratic Rollback can also be used to adjust hourly observations to meet metric targets

generated via a different rollback method. In this case, the algorithm is used to adjust each set of

twenty four hourly observations to meet the corresponding metric target. Intraday Quadratic

Rollback uses the normalset of anthropogenic observations as Observations, a single normal

anthropogenic metric value as Metrics, and the normal anthropogenic metric target as Standard.

Intraday Quadratic Rollback tends to always slightly miss its metric target, so it is not run in an

iterative fashion as the other Intraday Rollback Methods are (doing so would sometimes result in

an infinite loop).
Appendix B: Air Pollution Exposure Estimation Algorithms

November 2012

25

BenMAP User's Manual Appendices

Appendix B: Air Pollution Exposure Estimation

Algorithms

BenMAP has grouped individuals into what we refer to as “population grid-cells,” where the

grid-cells typically conform to some type of grid used in an air quality model, such as the

REMSAD air quality model, or just the counties of the United States. For each type of grid, the

population is built in each grid-cell by aggregating census block data. In the next step, BenMAP

estimates the air pollution exposure for each grid-cell, with the assumption that people living

within a particular grid-cell experience the same air pollution levels.

You have a variety of approaches to estimate the exposure to air pollution for the people living

within a given population grid-cell. Perhaps the simplest approach is to use model data directly,

and to assume that the people living within a particular model grid-cell experience the level

estimated by the model. An alternative approach is to use air pollution monitoring data, where

you may choose the closest monitor data to the center of a grid-cell or take an average of nearby

monitors. In a third general approach, you may combine both modeling and monitoring data to

estimate exposure.

When combining modeling and monitoring data, BenMAP scales or adjusts the monitoring data

with modeling data. The advantage of modeling data is that they can provide predictions for

years in which monitoring data are not available, as well as to provide predictions in areas of the

country for which monitoring data are not available. And the advantage of monitor data is that

they are based on actual observations. Combining both sources of information, allows BenMAP

to make more informed predictions.

The goal of estimating exposure is to provide the necessary input for concentration-response

functions, so that BenMAP can estimate the impact of air pollution on adverse health effects.

Table B-1 lists the types of metrics commonly used in concentration-response functions. In the

case of air pollution metrics calculated on a daily basis, such as the one-hour maximum and the

24-hour average, it is often the case that there are missing days of data. Air quality modeling is

often conducted on a subset of the days in the year, and air quality monitors often miss a number

of observations through out the year. To account for missing days, BenMAP represents the

distribution of daily metrics with a certain number points or “bins,” where each bin represents a

certain range of the distribution, with the underlying assumption that missing days have the same

distribution as the available data. For example, for analyses of the United States the

Environmental Protection Agency has typically used 153 bins to represent the ozone season from

May through September, and for particulate matter they have used 365 bins to represent the

year. In addition to being able to account for incomplete or missing data, and using bins to

represent the distribution provides a uniform approach that allows for easy comparison of

different monitors.
Appendix B: Air Pollution Exposure Estimation Algorithms

November 2012

26

BenMAP User's Manual Appendices

Table B-1. Metrics Typically Used in Concentration-Response Functions for Criteria

Air Pollutants

Measurement

Frequency

Metric Name Metric Description

Daily

(e.g., PM2.5,

PM10)

Daily Average Daily average

Annual Average Average of four quarterly averages. The four quarters are

defined as:Jan-Mar, April-June, Jul-Sep, Oct-Dec.

Annual Median Median of values through out the year.

Hourly

(e.g., Ozone)

1-hour Daily Max Highest hourly value from12:00 A.M. through 11:59 P.M.

8-hour Daily

Average

Average of hourly values from9:00 A.M. through 4:59 P.M.

12-hour Daily

Average

Average of hourly values from8:00 A.M. through 7:59 P.M.

24-hour Daily

Average

Average of hours from12:00 A.M. through 11:59 P.M.

B.1 Direct Modeling

When using direct modeling data to estimate exposure, BenMAP assumes that the people living

within a particular air pollution model grid-cell experience the same air pollution levels. BenMAP

then estimates the air pollution metrics of interest, as defined for each pollutant. (See the section

on defining pollutants in the Loading Data chapter.)

Generally modeling data providing hourly observations are complete for any given day.

However, it is common to have missing days of modeling data during the course of a year. Given

the estimated metrics from the available data, BenMAP then represents the distribution of daily

metrics with the number of days specified for each pollutant. By calculating bins with the

available days, BenMAP assumes that the distribution of missing days is similar to the distribution

of available data.

B.2 Closest Monitor

When using the closet monitor to represent air pollution levels at a population grid-cell, BenMAP

identifies the center of the population grid-cell, and then chooses the monitor that is closest to the

center. In the simplest case, BenMAP assigns the closest monitor to a population grid-cell, uses

the monitoring data to calculate the annual and daily air pollution metrics, and then calculates the

bins that represent the distribution of the daily metrics. The annual metrics and the binned daily

metrics are then used in the calculation of health effects.

The figure below presents nine population grid-cells and three monitors, with the focus on
Appendix B: Air Pollution Exposure Estimation Algorithms

November 2012

27

BenMAP User's Manual Appendices

identifying the monitor closest to grid-cell “E.” In this example, the closest monitor happens to be

10 miles away from the center of grid-cell E, and the data from this monitor would be used to

estimate air pollution levels for the population in this grid-cell. An analogous procedure would be

used to estimate air pollution levels in the other grid-cells (A, B, C, D, F, G, H, and I).

To capture some of the information generated by air pollution models, BenMAP can also scale

the data from the closest monitor with air pollution modeling data. BenMAP includes two types

ofscaling – “temporal” and “spatial” scaling. We discuss each below.

B.2.1 Closest Monitor – Temporal Scaling

With temporalscaling, BenMAP scales monitoring data with the ratio of the future-year to baseyear modeling data, where the modeling data is from the modeling grid-cell containing the

monitor. In the case of pollutants typically measured hourly, such as ozone, BenMAP scales the

hourly monitor values, calculates the annual and daily metrics of interest, and then bins the daily

metrics. In the case of pollutants typically measured daily, BenMAP scales the daily values,

calculates the annual metrics of interest, and then bins the daily metric.

Consider the case in the figure below. To forecast air pollution levels for 2030, BenMAP would

multiply the 1995 monitor value (80 ppb) by the ratio of the 2030 model value (70 ppb) to the

1995 model value (95 ppb):

Forecast2030 = Monitor Value 1995 * (Model Value D, 2030 / Model Value D, 1995)

Forecast2030 = 80 ppb * (70 ppb / 95 ppb) = 58.9 ppb.
Appendix B: Air Pollution Exposure Estimation Algorithms

November 2012

28

BenMAP User's Manual Appendices

In this example, we have examined the adjustment of a single monitor value with the ratio ofsingle

model values. The approach is essentially the same when there are multiple monitor values and

multiple model values.

B.2.2 Closest Monitor – Spatial Scaling

With spatialscaling, we are estimating a monitor value for the center of each population grid-cell.

We start by choosing the closest monitor to the center of each population grid-cell, and then we

scale this closest monitor with modeling data. In particular, BenMAP multiplies the monitoring

data with the ratio of the base-year modeling data for the destination grid-cell to the base-year

modeling data for grid-cell containing the monitor. The spatialscaling occurs in the same fashion

as with temporalscaling. In the case of pollutants typically measured hourly, such as ozone,

BenMAP scales the hourly monitor values, calculates the annual and daily metrics of interest, and

then bins the daily metrics. In the case of pollutants typically measured daily, BenMAP scales the

daily values, calculates the annual metrics of interest, and then bins the daily metric.

To estimate air pollution levels for 1995 in grid-cell “E” below, BenMAP would multiply the

1995 closest monitor value (80 ppb) by the ratio of the 1995 model value for grid-cell “E” (70

ppb) to the 1995 model value for grid-cell “D” (95 ppb):

Forecast1995 = Monitor Value1995 * (Model Value E, 1995 / Model Value D, 1995)

Forecast1995 = 80 ppb * (85 ppb / 95 ppb) = 71.6 ppb.
Appendix B: Air Pollution Exposure Estimation Algorithms

November 2012

29

BenMAP User's Manual Appendices

B.2.3 Closest Monitor – Temporal and Spatial Scaling

Combining both temporal and spatialscaling, BenMAP first multiplies monitoring data with both

the ratio of the future-year to base-year modeling data, where the modeling data is from the

modeling grid-cell containing the monitor. This gives a temporary forecast for 2030. BenMAP

then multiplies this temporary forecast with the ratio of the future-year modeling data for the

destination grid-cell to the future-year modeling data for grid-cell containing the monitor. As seen

below, this simplifies to multiplying monitoring data with both the ratio of future-year modeling

data from the destination grid-cell to the base-year modeling data from the grid-cell containing the

monitor. Again, as described for temporal and spatialscaling, BenMAP first scales the hourly and

daily values, generates the metrics of interest and then bins the daily metrics.

To forecast air pollution levels for 2030 in the figure below, BenMAP would multiply the 1995

monitor value (80 ppb) by the ratio of the 2030 model value (70 ppb) to the 1995 model value

(95 ppb):

Temporary Forecast 2030 = Monitor Value 1995 * (Model Value D, 2030 / Model

Value D, 1995)

Temporary Forecast 2030 = 80 ppb * (70 ppb / 95 ppb) = 58.9 ppb.

Forecast 2030 = Temporary Forecast 2030 * (Model Value E, 2030 / Model Value D,

2030)
Appendix B: Air Pollution Exposure Estimation Algorithms

November 2012

30

BenMAP User's Manual Appendices

Forecast 2030 = 58.9 ppb * (60 ppb / 70 ppb) = 50.5 ppb.

Note that through cancellation, this equation simplifies to:

Forecast 2030 = Monitor Value 1995 * (Model Value E, 2030 / Model Value D, 1995)

B.3 Voronoi Neighbor Averaging (VNA)

Like the closest monitor approach, the Voronoi Neighbor Averaging (VNA) algorithm uses

monitor data directly or in combination with modeling data. However, instead of using the single

closest monitor to estimate exposure at a population grid-cell, the VNA algorithm interpolates air

quality at every population grid cell by first identifying the set of monitors that best “surround” the

center of the population grid-cell.
Appendix B: Air Pollution Exposure Estimation Algorithms

November 2012

31

BenMAP User's Manual Appendices

In particular, BenMAP identifies the nearest monitors, or “neighbors,” by drawing a polygon, or

“Voronoi” cell, around the center of each BenMAP grid cell. The polygons have the special

property that the boundaries are the same distance from the two closest points.
Appendix B: Air Pollution Exposure Estimation Algorithms

November 2012

32

BenMAP User's Manual Appendices

BenMAP then chooses those monitors that share a boundary with the center of grid-cell “E.”

These are the nearest neighbors, BenMAP uses these monitors to estimate the air pollution level

for this grid-cell.
Appendix B: Air Pollution Exposure Estimation Algorithms

November 2012

33

BenMAP User's Manual Appendices

To estimate the air pollution level in each grid-cell, BenMAP calculates the metrics for each of the

neighboring monitors, and then calculates an inverse-distance weighted average of the metrics.

The further the monitor is from the BenMAP grid-cell, the smaller the weight.

In the figure below, the weight for the monitor 10 miles from the center of grid-cell E is calculated

as follows:

The weights for the other monitors would be calculated in a similar fashion. BenMAP would then

calculate an inverse-distance weighted average for 1995 air pollution levels in grid-cell E as

follows:

Forecast 1995 = 0.35*80 ppb + 0.24*90 ppb+ 0.24*60 ppb + 0.18*100 ppb = 81.2 ppb .
Appendix B: Air Pollution Exposure Estimation Algorithms

November 2012

34

BenMAP User's Manual Appendices

B.3.1 VNA | Temporal Scaling

Like the closest monitor approach, the Voronoi Neighbor Averaging (VNA) algorithm uses

monitor data directly or in combination with modeling data. However, instead of using the single

closest monitor to estimate exposure at a population grid-cell, the VNA algorithm interpolates air

quality at every population grid cell by first identifying the set of monitors that best “surround” the

center of the population grid-cell.
Appendix B: Air Pollution Exposure Estimation Algorithms

November 2012

35

BenMAP User's Manual Appendices

In particular, BenMAP identifies the nearest monitors, or “neighbors,” by drawing a polygon, or

“Voronoi” cell, around the center of each BenMAP grid cell. The polygons have the special

property that the boundaries are the same distance from the two closest points.
Appendix B: Air Pollution Exposure Estimation Algorithms

November 2012

36

BenMAP User's Manual Appendices

We then choose those monitors that share a boundary with the center of grid-cell “E.” These are

the nearest neighbors, we use these monitors to estimate the air pollution levelfor this grid-cell.

To estimate the air pollution level in each grid-cell, BenMAP calculates the annual and the binned
Appendix B: Air Pollution Exposure Estimation Algorithms

November 2012

37

BenMAP User's Manual Appendices

daily metrics for each of the neighboring monitors, and then calculates an inverse-distance

weighted average of the metrics. The further the monitor is from the BenMAP grid-cell, the

smaller the weight.

In the figure below, the weight for the monitor 10 miles from the center of grid-cell E is calculated

as follows:

The weights for the other monitors would be calculated in a similar fashion. BenMAP would then

calculate an inverse-distance weighted average for 1995 air pollution levels in grid-cell E as

follows:

Forecast 1995 = 0.35*80 ppb + 0.24*90 ppb+ 0.24*60 ppb + 0.18*100 ppb = 81.2 ppb .

Note that BenMAP is calculating an inverse-distance weighted average of the annual metrics and

the binned daily metrics. Alternatively, BenMAP could calculate an inverse-distance weighted

average of the hourly and daily observations, calculated the annual and daily metrics, and then

binned the daily metrics.
Appendix B: Air Pollution Exposure Estimation Algorithms

November 2012

38

BenMAP User's Manual Appendices

B.3.2 Voronoi Neighbor Averaging (VNA) – Spatial Scaling

BenMAP can also combine VNA with spatialscaling. For each of the neighbor monitors,

BenMAP multiplies the monitoring data with the ratio of the base-year modeling data for the

destination grid-cell to the base-year modeling data for grid-cell containing the monitor. The

spatialscaling occurs in the same fashion as with temporalscaling. In the case of pollutants

typically measured hourly, such as ozone, BenMAP scales the hourly monitor values, calculates

the annual and daily metrics of interest, and then bins the daily metrics. In the case of pollutants

typically measured daily, BenMAP scales the daily values, calculates the annual metrics of

interest, and then bins the daily metric.

Consider the example in the figure below. To forecast air pollution levels for 1995, BenMAP

would multiply the 1995 monitor value by the ratio of the 1995 model value to the 1995 model

value:
Appendix B: Air Pollution Exposure Estimation Algorithms

November 2012

39

BenMAP User's Manual Appendices

B.3.3 Voronoi Neighbor Averaging (VNA) – Temporal & Spatial Scaling

Combining both temporal and spatialscaling, BenMAP multiplies monitoring data with the ratio

of the future-year to base-year modeling data, where the future-year modeling data are from the

destination grid-cell and the base-year modeling data are from the grid-cell containing the

monitor. One the hourly and daily monitoring data are scaled, BenMAP generates the metrics of

interest, bins the daily metrics, and then uses the metrics to estimate adverse health effects in the

population grid-cell.

The figure below gives an example of combining temporal and spatialscaling.

B.4 Fixed Radius

When using the fixed radius option to represent air pollution levels at a population grid-cell,

BenMAP identifies all monitors within a specified distance of the center of the population gridcell, calculates the metrics at each monitor, and then calculates a weighted average of the metrics

using the algorithms described for VNA. When no monitors are within the specified distance,

BenMAP assigns the closest monitor to a population grid-cell, and calculates the metrics using

the algorithms described for the closest monitor approach.

B.5 Temporal and Spatial Scaling Adjustment Factors

As presented in the preceding examples of temporal and spatialscaling, the closest monitor,

VNA, and fixed radius approaches can use model data to scale monitor observations. In the

examples above, we scaled single monitor values with the ratio ofsingle model values. In fact,

however, the scaling involves multiple monitor values and multiple model values.

To proceed with the scaling, BenMAP takes the modeling values and splits them into groups,

depending on how the pollutant is defined. (See the section on defining pollutants in the Loading

Data chapter.) The United States setup has defined ozone to have a default of 10 adjustment

factors for the ozone season, where the first group represents the first 10 percent of the model

observations; the second group represents the observations between the 10th and 20th

percentile; and so on through the tenth group, which represents the observations between the

90th and 100th percentiles. BenMAP then averages the values in each group. The United States

setup has defined particulate matter to have five adjustment factors for each of the four seasons in

the year, where the first group in each season represents the first 20 percent of the model

observations; the second group represents the observations between the 20th and 40th
Appendix B: Air Pollution Exposure Estimation Algorithms

November 2012

40

BenMAP User's Manual Appendices

percentiles; and so on. Then, as for ozone model values, BenMAP averages the particulate

matter model values in each group.

BenMAP treats the monitor values in a similar way. It sorts the monitor values from low to high,

and divides them into the same number groups as there are scaling factors.

B.5.1 Calculation of Scaling Factors

In developing scaling factors for the standard United States setup, BenMAP sorts the modeling

data into either 10 groups or 20 groups, depending on the pollutant (e.g., 10 for ozone and 20

for particulate matter, 5 for each of the 4 seasons). Given the number of groups, BenMAP then

determines how to assign the model values. In determining to which group a value belongs,

BenMAP assigns a two-digit "percentile" to each value. With values in a given grid-cellsorted

from low to high, the percentile for each value will equal:(the observation rank number minus

0.5) divided by (the total number of values) multiplied by (100). If there are 250 hourly values,

the first hourly value will have a percentile = (1-0.5)/(250)*(100) = 0.20%; the 27th value will

have a percentile = (27-0.5)/(250)*(100) = 10.60%; and so on.

Each data group is represented by "group-lo" and "group-hi" values. These are the minimum and

the maximum percentiles in each group, where group-lo equals:(group rank minus 1) multiplied

by (100) divided by ( the number of groups); and group-hi equals:(group rank) multiplied by

(100) divided by ( the number of groups) minus 0.001. If there are ten groups: the first group will

have: group-lo = (1-1)/100*10 = 0.000%, and group-hi = (1/100*10)-0.001 = 9.999% ; the

second group will have: group-lo = (2-1)/100*10 = 10.000%, and group-hi = (2/100*10)-

0.001 = 19.999% ; and so on to the tenth group, which will have: group-lo = (10-1)/100*10 =

90.000%, and group-hi = (10/100*10)-0.001 = 99.999%. BenMAP assigns each observation

to a particular group with the following algorithm: if "group-lo" <"percentile" < "group-hi", then

assign the observation to that data group.

Below we give some examples of the calculations that BenMAP performs when scaling.

B.5.1.1 Example: PM2.5 Scaling Factors in U.S. Setup

After preparing the PM2.5 model and monitor data, BenMAP calculates the following:

where:

adjusted monitor = predicted daily PM2.5level, after

adjustment by model data (μg/m3)

monitor = observed daily PM2.5 monitor level

(μg/m3)
Appendix B: Air Pollution Exposure Estimation Algorithms

November 2012

41

BenMAP User's Manual Appendices

i = day identifier

j = modelseason/quintile group (1 to

20)

k = grid cell identifier for population grid

cell

l = grid cell identifier for grid cell

containing monitor

base = base-year (e.g., 2000)

future = future-year (e.g., 2020)

REMSAD = representative modelseason/quintile

value (μg/m3)

After adjusting the monitor values to reflect air quality modeling, BenMAP calculates for each

monitor thePM2.5 metrics needed to estimate adverse health effects. In the case of VNA,

BenMAP then calculates a weighted average (e.g., inverse-distance weighted average) of the

neighbors identified for each population grid cell:

where:

population grid cell = inverse distance-weighted PM2.5

metric at population grid cell (μg/m3)

adjusted monitor = predicted PM2.5 metric, after

adjustment by model data (μg/m3)

m = monitor identifier

base = base-year (e.g., 2000)

future = future-year (e.g., 2020)

weight = inverse-distance weight for monitor

After generating the bins for both the baseline and controlscenarios, BenMAP uses these to

calculate the change in air quality needed in most health impact functions to calculate the change

in adverse health effects. To calculate the change in air quality, BenMAP subtracts the baseline

value in the first bin from the control value in the first bin, and so on for each of the bins created

for the daily PM2.5average.
Appendix B: Air Pollution Exposure Estimation Algorithms

November 2012

42

BenMAP User's Manual Appendices

B.5.1.2 Example: Ozone Scaling in U.S. Setup

After preparing the ozone model and monitor data, BenMAP calculates the following:

where:

adjusted monitor = predicted hourly ozone level, after

adjustment by model data (ppb)

monitor = observed hourly ozone monitor

level(ppb)

i = hour identifier

j = model decile group (1 to 10)

k = grid cell identifier for population

grid cell

l = grid cell identifier for grid cell

containing monitor

base = base-year (e.g., 1996)

future = future-year (e.g., 2030)

CAMX = representative model decile value

(ppb)

After adjusting the monitor values to reflect air quality modeling, BenMAP calculates for each

monitor the ozone metrics needed to estimate adverse health effects. In the case of VNA,

BenMAP then calculates a weighted average (e.g., inverse-distance weighted average) of the

neighbors identified for each population grid cell:

where:

population grid cell = inverse distance-weighted ozone

metric at population grid cell (ppb)
Appendix B: Air Pollution Exposure Estimation Algorithms

November 2012

43

BenMAP User's Manual Appendices

adjusted monitor = predicted ozone metric, after

adjustment by model data (ppb)

m = monitor identifier

future = future-year (2020, 2030)

weight = inverse-distance weight for

monitor

After generating the bins for both the baseline and controlscenarios, BenMAP can use these to

calculate the change in air quality needed in most C-R functions to calculate the change in adverse

health effects. To calculate the change in air quality, BenMAP subtracts the baseline value in the

first bin from the control value in the first bin, and so on for each of the bins created for the daily

ozone metrics.

B.6 Binned Metrics

When estimating air pollution exposure, it will often happen that metrics are often not available for

each day in the year. To remedy this, BenMAP calculates representative values or bins with the

available daily metrics, under the assumption that the missing days have a similar distribution.

Each bin represents a day. In the case where there are 365 bins, the set of bins represents the

entire year.

When combining air pollution metrics from multiple monitors, BenMAP first calculates the bins

for the daily metrics, and then combines the bins, such as with some form of VNA. Once

BenMAP has calculated binned exposure measures for both a baseline and a controlscenario,

BenMAP then takes the difference between the two scenarios for each bin – taking the difference

between the baseline value in the first bin and the control value in the first bin, and so on for each

of the bins.
Appendix C: Deriving Health Impact Functions

November 2012

44

BenMAP User's Manual Appendices

Appendix C: Deriving Health Impact Functions

This Appendix provides of an overview regarding the health impact functions that BenMAP uses

to estimate the impact of a change in air pollution on adverse health effects. It provides a

description of the particular types of health impact functions that BenMAP uses.

The functionalform of the relationship between the change in pollutant concentration, x, and the

change in population health response (usually an incidence rate), y depends on the functional

form of the C-R function from which it is derived, and this depends on the underlying relationship

assumed in the epidemiologicalstudy chosen to estimate a given effect. For expository simplicity,

the following subsections refer simply to a generic adverse health effect, “y” and uses particulate

matter (PM) as the pollutant – that is, x = PM – to illustrate how the relationship between x

and y is derived from each ofseveral different C-R functions.

Estimating the relationship between PM and y can be thought of as consisting of three steps:

(1) choosing a functional form of the relationship between PM and y (the C-R function),

(2) estimating the values of the parameters in the C-R function assumed, and

(3) deriving the relationship between PM and y (the health impact function) from the

relationship between PM and y (the C-R function).

Epidemiologicalstudies have used a variety of functionalforms for C-R functions. Some studies

have assumed that the relationship between adverse health and pollution is best described by a

linear form, where the relationship between y and PM is estimated by a linear regression in which

y is the dependent variable and PM is one ofseveral independent variables. Log-linear

regression and logistic regression are other common forms.

Note that the the log-linear form used in the epidemiological literature is often referred to as

“Poisson regression” because the underlying dependent variable is a count (e.g., number of

deaths), believed to be Poisson distributed. The model may be estimated by regression

techniques but is often estimated by maximum likelihood techniques. The form of the model,

however, is still log-linear.

C.1 Overview

The relationship between the concentration of a pollutant, x, and the population response, y, is

called the concentration-response (C-R) function. For example, the concentration of the

pollutant may be fine particulate matter (PM2.5) in μg/m3 per day, and the population response

may be the number of premature deaths per 100,000 population per day. C-R functions are

estimated in epidemiologicalstudies. A functionalform is chosen by the researcher, and the

parameters of the function are estimated using data on the pollutant (e.g., daily levels of PM2.5)

and the health response (e.g., daily mortality counts). There are several different functionalforms,
Appendix C: Deriving Health Impact Functions

November 2012

45

BenMAP User's Manual Appendices

discussed below, that have been used for C-R functions. The one most commonly used is the

log-linear form, in which the natural logarithm of the health response is a linear function of the

pollutant concentration.

For the purposes of estimating benefits, we are not interested in the C-R function itself, however,

but the relationship between the change in concentration of the pollutant, x, and the

corresponding change in the population health response, y. We want to know, for example, if

the concentration of PM2.5is reduced by 10 μg/m3, how many premature deaths will be

avoided? The relationship between x and y can be derived from the C-R function, as

described below, and we refer to this relationship as a health impact function.

Many epidemiologicalstudies, however, do not report the C-R function, but instead report some

measure of the change in the population health response associated with a specific change in the

pollutant concentration. The most common measure reported is the relative risk associated with

a given change in the pollutant concentration. A generalrelationship between x and y can,

however, be derived from the relative risk. The relative risk and similar measures reported in

epidemiologicalstudies are discussed in the sections below. The derivation of the relationship of

interest for BenMAP – the relationship between x and y – is discussed in the subsequent

sections.

C.2 Review Relative Risk and Odds Ratio

The terms relative risk and odds ratio are related but distinct. Table C-1 provides the basis for

demonstrating their relationship.

Table C-1. Relative Risk and Odds Ratio Notation

Exposure Fraction of Population Adverse Effect Measure

Affected Not Affected Relative Risk Odds

Baseline Pollutant Exposure y0 1-y0

y0/yc

y0/(1-yc)

Control Pollutant Exposure yc 1-yc yc/(1-yc)

The “risk” that people with baseline pollutant exposure will be adversely affected (e.g., develop

chronic bronchitis) is equal to y

0

, while people with control pollutant exposure face a risk, y

c

, of

being adversely affected. The relative risk (RR) is simply:
Appendix C: Deriving Health Impact Functions

November 2012

46

BenMAP User's Manual Appendices

The odds that an individualfacing high exposure will be adversely affected is:

The odds ratio is then:

This can be rearranged as follows:

As the risk associated with the specified change in pollutant exposure gets small(i.e., both y

0

and

y

c

approach zero), the ratio of (1-y

c

) to (1-y

0

) approaches one, and the odds ratio approaches

the relative risk. This relationship can be used to calculate the pollutant coefficient in the C-R

function from which the reported odds ratio or relative risk is derived, as described below.

C.3 Linear Model

A linear relationship between the rate of adverse health effects (incidence rate) and various

explanatory variables is of the form:

where α incorporates all the other independent variables in the regression (evaluated at their mean

values, for example) times their respective coefficients. The relationship between the change in the

rate of the adverse health effect from the baseline rate (y0) to the rate after control(yc) associated

with a change from PM0to PMcis then:
Appendix C: Deriving Health Impact Functions

November 2012

47

BenMAP User's Manual Appendices

For example, Ostro et al. (1991, Table 5) reported a PM2.5coefficient of 0.0006 (with a

standard error of 0.0003) for a linear relationship between asthma and PM2.5exposure.

The lower and upper bound estimates for the PM2.5coefficient are calculated as follows:

It is then straightforward to calculate lower and upper bound estimates of the change in asthma.

C.4 Log-linear Model

The log-linear relationship defines the incidence rate (y) as:

or, equivalently,

where the parameter B is the incidence rate of y when the concentration of PM is zero, the

parameter β is the coefficient of PM, ln(y) is the natural logarithm of y, and α = ln(B). Other

covariates besides pollution clearly affect mortality. The parameter B might be thought of as

containing these other covariates, for example, evaluated at their means. That is,

where Bo is the incidence of y when all covariates in the model are zero, and x1, ... , xn are the

other covariates evaluated at their mean values. The parameter B drops out of the model,

however, when changes in y are calculated, and is therefore not important.

The relationship between ∆PM and ∆y is:

This may be rewritten as:
Appendix C: Deriving Health Impact Functions

November 2012

48

BenMAP User's Manual Appendices

where y0is the baseline incidence rate of the health effect (i.e., the incidence rate before the

change in PM).

The change in the incidence of adverse health effects can then be calculated by multiplying the

change in the incidence rate, ∆y, by the relevant population (e.g., if the rate is number per

100,000 population, then the relevant population is the number of 100,000s in the population).

When the PM coefficient (β) and its standard error (σβ) are published (e.g., Ostro et al., 1989),

then the coefficient estimates associated with the lower and upper bound may be calculated easily

as follows:

Epidemiologicalstudies often report a relative risk for a given PM, rather than the coefficient, β

(e.g., Schwartz et al., 1995, Table 4). Recall that the relative risk (RR) is simply the ratio of two

risks:

Taking the natural log of both sides, the coefficient in the C-R function underlying the relative risk

can be derived as:

The coefficients associated with the lower and upper bounds (e.g., the 2.5 and 97.5 percentiles)

can be calculated by using a published confidence intervalfor relative risk, and then calculating

the associated coefficients.

Because of rounding of the published RR and its confidence interval, the standard error for the

coefficient implied by the lower bound of the RR will not exactly equal that implied by the upper

bound, so an average of the two estimates is used. The underlying standard error for the

coefficient (σβ) can be approximated by:
Appendix C: Deriving Health Impact Functions

November 2012

49

BenMAP User's Manual Appendices

C.5 Logistic Model

In some epidemiologicalstudies, a logistic model is used to estimate the probability of an

occurrence of an adverse health effect. Given a vector of explanatory variables, X, the logistic

model assumes the probability of an occurrence is:

where β is a vector of coefficients. Greene (1997, p. 874) presents models with discrete

dependent variables, such as the logit model. See also Judge et al. (1985, p. 763). This may be

rewritten as:

The odds of an occurrence is:
Appendix C: Deriving Health Impact Functions

November 2012

50

BenMAP User's Manual Appendices

The odds ratio for the baseline (odds

0

) versus the controlscenario (odds

c

) is then:

The change in the probability of an occurrence from the baseline to the control( y), assuming

that all the other covariates remain constant, may be derived from this odds ratio:
Appendix C: Deriving Health Impact Functions

November 2012

51

BenMAP User's Manual Appendices

When the coefficient (β) and its standard error (σβ) are published (e.g., Pope et al., 1991, Table

5), then the coefficient estimates associated with the lower and upper bound may be calculated

easily as follows:

Often the logistic regression coefficients are not published, and only the odds ratio corresponding

to a specified change in PM is presented (e.g., Schwartz et al., 1994). It is easy to calculate the

underlying coefficient as follows:

The coefficients associated with the lower and upper bound estimates of the odds ratios are
Appendix C: Deriving Health Impact Functions

November 2012

52

BenMAP User's Manual Appendices

calculated analogously.

The underlying standard error for the coefficient (σβ) can be approximated by:

Sometimes, however, the relative risk is presented. The relative risk does not equal the odds

ratio, and a different procedure should be used to estimate the underlying coefficient. Note that

ESEERCO (1994, p. V-21) calculated (incorrectly) the underlying regression coefficient for

Abbey et al. (1993, Table 5) by taking the logarithm of the relative risk and dividing by the

change in TSP.

The relative risk (RR) is simply:

where y0is the risk (i.e., probability of an occurrence) at the baseline PM exposure and yc is the

risk at the control PM exposure.

When the baseline incidence rate (y0) is given, then it is easy to solve for the control incidence

rate (yc):

The odds ratio, may then be calculated:
Appendix C: Deriving Health Impact Functions

November 2012

53

BenMAP User's Manual Appendices

Given the odds ratio, the underlying coefficient (β) may be calculated as before:

The odds ratio and the coefficient calculated from it are dependent on the baseline and control

incidence rates. Unfortunately, it is not always clear what the baseline and control incidence rates

should be. Abbey et al. (1995b, Table 2) reported that there are 117 new cases of chronic

bronchitis out of a sample of 1,631, or a 7.17 percent rate. In addition, they reported the relative

risk (RR = 1.81) for a new case of chronic bronchitis associated with an annual mean

concentration “increment” of 45 μg/m3 of PM2.5exposure.

Assuming that the baseline rate for chronic bronchitis (y0) should be 7.17 percent, the question

becomes whether the “increment” of 45 μg/m3 should be added to or subtracted from the

existing PM2.5concentration. If added the control incidence rate (yc) would be greater than the

baseline rate (y0), while subtraction would give a controlrate less than the incidence rate. In

effect, one might reasonably derive two estimates of the odds ratio:
Appendix C: Deriving Health Impact Functions

November 2012

54

BenMAP User's Manual Appendices

An alternative is to simply assume that the relative risk (1.81) is reasonably close to the odds

ratio and calculate the underlying coefficient. It is easy to show that the relative risk equals:

Assuming that:

It is then possible to calculate the underlying coefficient:
Appendix C: Deriving Health Impact Functions

November 2012

55

BenMAP User's Manual Appendices

Since this coefficient estimate is based on the assumption that

it should be used in a C-R function that maintains this assumption. In effect, it should be applied

to a log-linear C-R function:

Using the formula for the change in the incidence rate and assuming a 10 μg/m3 decline in PM2.5,

it is shown that this results in changes within the bounds suggested by the two estimates based on

using the estimated odds ratios:

In this instance, it seems that simply using the relative risk to estimate the underlying coefficient

results in a good approximation of the change in incidence. Since it is unclear which of the two

other coefficients (β1or β2) should be used – as the published work was not explicit – the

coefficient based on the relative risk and the log-linear functionalform seems like a reasonable

approach.

C.6 Cox proportional Hazards Model

Use of a Cox proportional hazards model in an epidemiologicalstudy results in a C-R function
Appendix C: Deriving Health Impact Functions

November 2012

56

BenMAP User's Manual Appendices

that is log-linear in form. It is often used to modelsurvival times, and as a result, this discussion

focuses on mortality impacts.

The Cox proportional hazards model is based on a hazard function, defined as the probability

that an individual dies at time t, conditional on having survived up to time t (Collet, 1994, p. 10).

More formally, the hazard function equals the probability density function for the risk of dying

divided by one minus the cumulative probability density function:

The proportional hazards model takes the form:

where X is a vector of explanatory variables, β is a vector of coefficients, and h0(t) is the socalled “baseline hazard” rate. This terminology differs from that used in most of this discussion:

this “baseline hazard” is the risk when all of the covariates (X) are set to zero; this is not the risk

in the baseline scenario.

The Cox proportional hazards model is sometimes termed a “semi-parametric” model, because

the baseline hazard rate is calculated using a non-parametric method, while the impact of

explanatory variables is parameterized. Collet (1994) details the estimation of Cox proportional

hazards models; in particular, see Collet’s discussion (pp. 95-97) of nonparametric estimation of

the baseline hazard.

Taking the ratio of the hazard functions for the baseline and controlscenarios gives the relative

risk:

where it is assumed that the only difference between the baseline and control is the level of PM

pollution.

The relative risk is often presented rather than the coefficient β, so it is necessary to estimate β in

order to develop functionalrelationship between ∆PM and ∆y, as described previously for loglinear C-R functions.
Appendix D: Health Incidence & Prevalence Data in U.S. Setup

November 2012

57

BenMAP User's Manual Appendices

Appendix D: Health Incidence & Prevalence Data

in U.S. Setup

Health impact functions developed from log-linear or logistic models estimate the percent change

in an adverse health effect associated with a given pollutant change. In order to estimate the

absolute change in incidence using these functions, we need the baseline incidence rate of the

adverse health effect. And for certain health effects, such as asthma exacerbation, we need a

prevalence rate, which estimates the percentage of the general population with a given ailment like

asthma. This appendix describes the data used to estimate baseline incidence and prevalence

rates for the health effects considered in this analysis.

D.1 Mortality

This section describes the development of county mortality rates for years 2010 through 2050 for

use in BenMAP. First, we describe the source of 2004-2006 individual-level mortality data and

the calculation of county-level mortality rates. Then we describe how we use national-level

Census mortality rate projections to develop county-level mortality rate projections for years

2010-2050.

D.1.1 Mortality Data for 2004-2006

We obtained individual-level mortality data from 2004-2006 for the whole United States from

the Centers for Disease Control(CDC), NationalCenter for Health Statistics (NCHS). The

data were compressed into a CD-ROM, which contains death information for each decedent,

including residence county FIPS, age at death, month of death, and underlying causes (ICD-10

codes).

Since the detailed mortality data obtained from CDC do not include population, we combined

them with U.S. Census Bureau postcensal population estimates exported from BenMAP. We

then generated age-, cause-, and county-specific mortality rates using the following formula:

where R

i,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.

Following CDC Wonder (http://wonder.cdc.gov), we treated mortality rates as “unreliable” when

the death count is less than 20. Among all the calculated age-, cause-, and county-specific

mortality rates, there were about 67% “unreliable” rates. For each combination of age group and
Appendix D: Health Incidence & Prevalence Data in U.S. Setup

November 2012

58

BenMAP User's Manual Appendices

mortality cause, we used the following procedure to deal with the problem of “unreliable” rates:

For a given state, we grouped the counties where the death count (i.e., the numerator on the

right-hand side of the above equation) was less than 20 and summed those death counts across

those counties. If the sum of deaths was greater than or equal to 20, we then summed the

populations in those counties, and calculated a single rate for the “state collection of counties”

by dividing the sum of deaths by the sum of populations in those counties. After this

adjustment, there were 17% unreliable rates left. This rate was then applied to each of those

counties.

If the sum of deaths calculated in the above step was still less than 20, the counties in the “state

collection of counties” were not assigned the single rate from the above step. Instead, we

proceeded to the regional level, according to the regional definitions shown below in Table D1. In each region, we identified all counties whose death counts were less than 20 (excluding

any such counties that were assigned a rate in the previous step). We summed the death

counts in those counties. If the sum of deaths was greater than or equal to 20, we then

summed the populations in those counties, and calculated a single rate for the “regional

collection of counties” by dividing the sum of deaths by the sum of populations in those

counties. This rate was then applied to each of those counties in the “regional collection of

counties.”

Table D-1. Regional Definitions from U.S. Census

Region States Included

Northeast Maine, New Hampshire, Vermont, Massachusetts, Rhode Island, Connecticut, New York, New

Jersey, Pennsylvania

Midwest Ohio, Indiana, Illinois, Michigan, Wisconsin, Minnesota, Iowa, Missouri, North Dakota, South

Dakota, Nebraska, Kansas

South Delaware, Maryland, District of Columbia, Virginia, West Virginia, North Carolina, South

Carolina, Georgia, Florida, Kentucky, Tennessee, Alabama, Mississippi, Arkansas, Louisiana,

Oklahoma, Texas

West Montana, Idaho, Wyoming, Colorado, New Mexico, Arizona, Utah, Nevada, Washington,

Oregon, California, Alaska, Hawaii

If the sum of deaths calculated in the previous (regional) step was still less than 20, the counties

in the “regional collection of counties” were not assigned the single rate from the above step.

Instead, we proceeded to the national level, identifying all counties in the nation whose death

counts were less than 20 (excluding any such counties that were assigned a rate in the previous

steps). We summed the death counts in those counties and divided by the sum of the

populations in those counties to derive a single rate for the “national collection of counties.”

This rate was then applied to each of those counties in the “national collection of counties.”
Appendix D: Health Incidence & Prevalence Data in U.S. Setup

November 2012

59

BenMAP User's Manual Appendices

Even after this national adjustment, there were about 1% unreliable rates left. In these cases,

we simply calculated a single rate for the “national collection of counties, even though it was

“unreliable,” and assigned it to those counties in the “national collection of counties.”

Table D-2. National Mortality Rates (per 100 people per year) by Health Endpoint and

Age Group

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 0.22981 0.02813 0.08869 0.10429 0.19248 0.43195 0.90832 2.12421 5.26957 13.97875

Mortality, NonAccidentalA00-

R99

0.19705 0.01138 0.02118 0.04298 0.12831 0.36278 0.85309 2.06079 5.13808 13.66477

Mortality,

Respiratory

J00-J98 0.01570 0.00100 0.00126 0.00211 0.00560 0.01817 0.06366 0.22866 0.63404 1.51364

Mortality,

Chronic Lung

J40-J47,

J67

0.00076 0.00029 0.00040 0.00064 0.00200 0.00906 0.04086 0.15507 0.37626 0.62631

Mortality, Lung

Cancer

C34 0.00001 0.00001 0.00006 0.00036 0.00516 0.02980 0.10372 0.25966 0.37795 0.30788

Mortality,

Ischemic Heart

Disease

I20-I25 0.00019 0.00006 0.00041 0.00262 0.01574 0.05952 0.15438 0.37559 1.03204 3.19986

Mortality,

CardioPulmonary

I00-I78,

J10-J18,

J40-J47,

J67

0.01685 0.00193 0.00468 0.01171 0.04044 0.12568 0.31814 0.86408 2.52456 7.83928

* We estimate post-neonatal mortality (deaths after the first month) for infants because the health impact function (see

Appendix E) estimates post-neonatal mortality.

D.1.2 Mortality Rate Projections 2010-2050

To estimate age- and county-specific mortality rates in years 2010 through 2050, we calculated

adjustment factors, based on a series of Census Bureau projected national mortality rates (for allcause mortality), to adjust the age- and county-specific mortality rates calculated using 2004-

2006 data as described above. We used the following procedure:

For each age group, we obtained the series of projected national mortality rates from 2005 to

2050 (see the 2005 rate in Table D-3) 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 = 2010, 2011, ..., 2050) to the 2005 rate. These ratios are shown

for selected years in Table D-4.

Finally, to estimate mortality rates in year Y (Y = 2010, 2010, ..., 2050) that are both age

group-specific and county-specific, we multiplied the county- and age-group-specific mortality
Appendix D: Health Incidence & Prevalence Data in U.S. Setup

November 2012

60

BenMAP User's Manual Appendices

rates for 2004-2006 by the appropriate ratio calculated in the previous step. For example, to

estimate the projected mortality rate in 2010 among ages 18-24 in Wayne County, MI, we

multiplied the mortality rate for ages 18-24 in Wayne County in 2004-2006 by the ratio of

Census Bureau projected national mortality rate in 2010 for ages 18-24 to Census Bureau

national mortality rate in 2005 for ages 18-24.

Table D-3. All-Cause Mortality Rate (per 100 people per year), by Source, Year, and

Age Group

Source & Year Infant 1-17 18-24 25-34 35-44 45-54 55-64 65-74 75-84 85+

Calculated CDC

2004-2006

0.230* 0.028 0.089 0.104 0.192 0.432 0.908 2.124 5.270 13.979

Census Bureau

2005**

0.654 0.029 0.088 0.102 0.183 0.387 0.930 2.292 5.409 13.091

* The Census Bureau estimate is for all deaths in the first year of life. BenMAP 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 postneonatal mortality. For comparison purpose, we also calculated the rate for all deaths in the first year, which is 0.684 per

100 people).

**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 Population Projections of the United States: 1999 to 2100,

Working Paper #38.", which is available on http://www.census.gov/population/www/documentation/twps0038/

twps0038.html.

Table D-4. Ratio of Future Year All-Cause Mortality Rate to 2005 Estimated AllCause Mortality Rate, by Age Group

Year Infant 1--17 18--24 25--34 35--44 45--54 55--64 65--74 75--84 85+

2010 0.95 0.93 0.95 0.96 0.94 0.95 0.96 0.93 0.95 0.96

2015 0.90 0.86 0.90 0.93 0.89 0.90 0.91 0.88 0.89 0.94

2020 0.85 0.81 0.86 0.90 0.83 0.85 0.87 0.85 0.83 0.91

2025 0.78 0.76 0.81 0.85 0.79 0.79 0.82 0.81 0.78 0.87

2030 0.71 0.70 0.76 0.79 0.74 0.74 0.77 0.78 0.76 0.82

2035 0.66 0.65 0.71 0.74 0.70 0.70 0.72 0.74 0.72 0.77

2040 0.60 0.60 0.67 0.70 0.65 0.66 0.67 0.70 0.70 0.74

2045 0.55 0.56 0.63 0.66 0.61 0.62 0.64 0.66 0.67 0.73

2050 0.51 0.52 0.59 0.62 0.57 0.57 0.60 0.63 0.63 0.71

D.2 Hospitalizations

Hospitalization rates were calculated using data from the Healthcare Cost and Utilization Project
Appendix D: Health Incidence & Prevalence Data in U.S. Setup

November 2012

61

BenMAP User's Manual Appendices

(HCUP). HCUP is a family of health care databases developed through a Federal-StateIndustry 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). HCUP databases can be obtained from the following

data services:

The HCUP Central Distributor: Many of the HCUP databases are available for purchase through

the HCUP Central Distributor. The databases include detailed information for individual

discharges, such as primary diagnosis (in ICD-9 codes), patient’s age and residence county.

HCUP State Partners: Some HCUP participating states do not release their data to the Central

Distributor; however, the data may be obtained through contacting the State Partners. Some

State Partners (e.g., CA, TX, PA, MO, and NY) provided discharge-level data; others (e.g., OH)

provided summarized data.

HCUPnet: This is a free, on-line query system based on data from HCUP. It provides access to

summary statistics at the state, regional and national levels.

Figure D-1 shows the level of hospitalization data (e.g, discharge-level or state-level) for each

state. Note that for some states neither discharge-level nor state-level data were available. In

such cases we used regionalstatistics from HCUPnet to estimate hospitalization rates for those

states. The data year for most states is 2007; the exception is MA, for which the data year is

2006. We assume hospitalization rates are reasonably constant from 2006-2007 and consider all

as 2007 rates.
Appendix D: Health Incidence & Prevalence Data in U.S. Setup

November 2012

62

BenMAP User's Manual Appendices

Figure D-1. Hospitalization Data from HCUP

More information about HCUP can be found at http://www.hcup-us.ahrq.gov/.

The procedures for calculating hospitalization rates are summarized as follows:

For states with discharge-level data:

We calculated age-, health endpoint-, and county-specific hospitalization counts. Ohio was

the only state that, while not providing discharge-level data, did provide county-level data

for each age group-endpoint combination.

The above calculation excluded hospitalizations with missing patient age or county FIPS,

which may lead to underestimation of rates. Therefore we scaled up the previously

calculated age-, endpoint-, and county-specific counts using an adjustment factor obtained

as follows:

We first counted the number of discharges for a specific endpoint in the state including

those discharges with missing age or county FIPS.

We then counted the number of discharges for the endpoint in the state excluding those

records with missing age or county FIPS.

The adjustment factor is the ratio of the two counts.
Appendix D: Health Incidence & Prevalence Data in U.S. Setup

November 2012

63

BenMAP User's Manual Appendices

We calculated hospitalization rates for each county by dividing the adjusted county-level

hospitalization counts by the Census estimated county-level population for the

corresponding year (2006 or 2007). Following CDC Wonder, we treated rates as

“unreliable” when the hospitalization count was less than 20, using the same procedure we

used for mortality rates (see Section D.1.1).

For states with summarized state statistics (from HCUPnet) we calculated the state-, age-,

endpoint- specific hospitalization rates and applied them to each county in the state. We used

the previously described procedure to adjust the “unreliable” rates.

For states without discharge-level or state-level data:

We obtained the endpoint-specific hospitalization counts in each region from HCUPnet/NIS

(we refer to this count for the ith endpoint in the jth region as “TOTALij”)

For those states in the jth region that do have discharge-level or state-level data, we

summed the hospital admissions by endpoint (we refer to this count for the ith endpoint in

the jth region as “SUB ij”).

We then estimated the hospitalization count for states without discharge or state data for the

ith endpoint in the jth region as TOTALij – SUB ij. Note that while this count is endpointand region- specific, it is not age-specific. We obtained the distribution of hospital admission

counts across age groups based on the National Hospital Discharge Survey (NHDS) and

assumed the same distribution for the HCUP hospitalizations. We then applied this

distribution to the estimated hospital counts (i.e., TOTALij – SUB ij) to obtain endpoint-,

region-, and age-specific counts.

Using the corresponding age- and region-specific populations, we calculated age-specific

hospitalization rates for the ith endpoint in the jth region and applied them to those counties

in the region that didn’t have discharge-level or state-level data.

The endpoints in hospitalization studies are defined using different combinations of ICD codes.

Rather than generating a unique baseline incidence rate for each ICD code combination, for the

purposes of this analysis, we identified a core group of hospitalization rates from the studies and

applied the appropriate combinations of these rates in the health impact functions:

congestive heart failure (ICD-9 428)

dysrhythmia (ICD-9 427)

heart rhythm disturbances (ICD-9 426-427)

acute myocardial infarction (ICD-9 410)

ischemic heart disease – 1 (ICD-9 410-414)

ischemic heart disease – 2 (ICD-9410-414, 429)

ischemic heart disease (less myocardial infarction) (ICD-9 411-414)
Appendix D: Health Incidence & Prevalence Data in U.S. Setup

November 2012

64

BenMAP User's Manual Appendices

all cardiovascular (ICD-9 390-429)

all cardiovascular (less myocardial infarctions) (ICD-9 390-409, 411-429)

cardiovascular, cerebrovascular and peripheral vascular diseases (ICD-9 410–414, 429, 426–

427, 428, 430–438, 440–449)

all cardiac outcomes (ICD-9 390-459)

cerebrovascular events (ICD-9 430-438)

stroke (ICD-9 431-437)

peripheral vascular disease -1 (ICD-9 440-448)

peripheral vascular disease -2 (ICD-9 440-449)

allrespiratory (ICD-9 460-519)

respiratory illness -1 (ICD-9 466, 480-486, 490-493)

respiratory illness -2 (ICD-9 464–466, 480–487, 490–492)

chronic lung disease (ICD-9 490-496)

chronic lung disease (less asthma) (ICD-9 490-492, 494-496)

chronic lung disease (less asthma) -2 (ICD-9 490-492, 494, 496)

chronic lung disease (less asthma) -3 (ICD-9 490-492)

chronic lung disease (less asthma) -4 (ICD-9 491,492, 494, 496)

pneumonia (ICD-9 480-486)

asthma (ICD-9 493)

lower respiratory infection (ICD-9 466.1, 466.0, 480-487, 490, 510-511)

For each C-R function, we selected the baseline rate or combination of rates that most closely

matches to the study endpoint definition. For studies that define chronic lung disease as ICD 490-

492, 494-496, we subtracted the incidence rate for asthma (ICD 493) from the chronic lung

disease rate (ICD 490-496). In some cases, the baseline rate will not match exactly to the

endpoint definition in the study. For example, Burnett et al. (2001) studied the following

respiratory conditions in infants <2 years of age:ICD 464.4, 466, 480-486, 493. For this C-R

function we apply an aggregate of the following rates:ICD 464, 466, 480-487, 493. Although

they do not match exactly, we assume that relationship observed between the pollutant and

study-defined endpoint is applicable for the additional codes. Table D-5 presents a summary of

the national hospitalization rates for 2007 from HCUP.
Appendix D: Health Incidence & Prevalence Data in U.S. Setup

November 2012

65

BenMAP User's Manual Appendices

Table D-5. Hospitalization Rates (per 100 people per year), by Health Endpoint and

Age

Hospitalization

Category

ICD-9

Code

Age

0-1

2-17 18-24 25-34 35-44 45-55 55-64 65-74 75-84 85+

Respiratory

All Respiratory 460-

519

2.872 0.426 0.205 0.231 0.408 0.791 1.313 2.972 4.970 8.045

Pneumonia 480-

486

0.695 0.144 0.057 0.078 0.132 0.250 0.412 1.046 1.972 3.822

Chronic Lung

Disease

490-

496

0.350 0.158 0.057 0.064 0.135 0.299 0.503 1.028 1.502 1.506

Asthma 493 0.330 0.155 0.051 0.055 0.094 0.136 0.160 0.198 0.240 0.302

Cardiovascular

All

Cardiovascular

390-

429

0.056 0.017 0.107 0.164 0.481 1.221 2.272 4.681 7.749 11.583

Acute Myocardial

Infarction,

Nonfatal

410 0.000 0.000 0.010 0.017 0.071 0.215 0.379 0.673 1.096 1.858

Ischemic Heart

Disease

410-

414

0.001 0.000 0.019 0.032 0.177 0.597 1.144 2.001 2.754 3.078

Dysrhythmia 427 0.017 0.006 0.022 0.031 0.071 0.166 0.345 0.870 1.608 2.216

Congestive Heart

Failure

428 0.015 0.001 0.014 0.025 0.075 0.198 0.399 1.080 2.183 4.512

Stroke 431-

437

0.007 0.003 0.014 0.020 0.071 0.204 0.415 1.043 1.860 2.953

D.3 Nonfatal Heart Attacks

The relationship between short-term particulate matter exposure and heart attacks was quantified

in a case-crossover analysis by Peters et al. (2001). The study population was selected from

heart attack survivors in a medical clinic. Therefore, the applicable population to apply to the CR function 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
Appendix D: Health Incidence & Prevalence Data in U.S. Setup

November 2012

66

BenMAP User's Manual Appendices

willresult in a hospitalization. Details about HCUP data are described in Section D.2.

According to the 2007 HCUP data there were approximately 624,936 hospitalizations due to

heart attacks (acute myocardial infarction:ICD-9 410, primary diagnosis). We used rates based

on HCUP data over estimates extrapolated from cohort studies because the former is a national

database with a larger sample size, which is intended to provide reliable national estimates. The

incidence rate calculation is also described in Section D.2. and the incidence rates for AMI

hospitalization are presented in Table D-5.

Rosamond et al. (1999) reported that approximately six percent of male and eight percent of

female hospitalized heart attack patients die within 28 days (either in or outside of the hospital).

We, therefore, applied a factor of 0.93 to the estimated number of PM-related acute myocardial

infarctions 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

epidemiologicalstudies are designed. Those studies consider total admissions for AMIs, which

includes individuals living at the time the studies were conducted. Therefore, we use the definition

of AMI that matches the definition in the epidemiologicalstudies.

D.4 Emergency Department Visits

The data source for emergency department/room (ED or ER) visits is also HCUP, i.e., SID,

SEDD, and NEDS. And the types of data providers are also the same as those described in

Section D.2. Figure D-2 shows the emergency department data in each state.
Appendix D: Health Incidence & Prevalence Data in U.S. Setup

November 2012

67

BenMAP User's Manual Appendices

Figure D-2. Emergency Department Data from HCUP

The calculation of ER visit rates is also similar to the calculation of hospitalization rates, except for

the following differences:

The SEDD databases include only those ER visits that ended with discharge. To identify the

ER visits that ended in hospitalization, we used a variable called “admission source” in the SID

databases. Admission source identified as “emergency room” indicates that the hospital

admission came from the ER – i.e., the ER visit ended in hospitalization. For each combination

of age group, endpoint and county, we summed the ER visits that ended with discharge and

those that resulted in hospitalization.

The data year varies across the states from 2005 to 2007; we assumed that ER visit rates are

reasonably constant across these three years and consider them as 2007 rates.

Instead of using HCUPnet/NIS and NHDS in the last step as described in Section D.2., we

used HCUPnet/NEDS and the National Ambulatory MedicalCare Survey (NAMCS) to

calculate ER visit rates for states without discharge level or state level data. Table D-6

presents the estimated asthma emergency room rates by health endpoint and age group.
Appendix D: Health Incidence & Prevalence Data in U.S. Setup

November 2012

68

BenMAP User's Manual Appendices

Table D-6. Emergency Department Visit Rates (per 100 people per year)

by Health Endpoint and Age Group

Emergency Department

Category

ICD-9 Codes Age 0-17 18-44 45-64 65-84 85+

Asthma 493 0.865 0.557 0.441 0.381 0.368

Respiratory 491-493, 460-466, 477.0-

477.9, 480-486, 496,

786.07, 786.09

5.772 2.889 2.273 4.275 6.817

Cardiovascular 410-414, 427-428, 433-

437, 440.0-440.9, 443-

445, 451-453

0.029 0.302 1.695 5.950 12.512

D.5 School Loss Days

Epidemiologicalstudies have examined the relationship between air pollution and a variety of

measures ofschool absence. These measures include:school loss days for all causes, illnessrelated, and respiratory illness-related. We have two sources of information. The first is the

NationalCenter for Education Statistics, which provided an estimate of all-cause school loss

days, and the other is the National Health Interview Survey (Adams et al., 1999, Table 47),

which has data on different categories of acute school loss days. Table D-7 presents the

estimated school loss day rates. Further detail is provided below on these rates.

Table D-7. 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

* We based illness-related school loss day rates on data fromthe 1996 NHIS and an estimate of 180

school days per year. This excludes school loss days due to injuries. We based the all-cause school

loss day rate on data fromthe National Center for Education Statistics.

All-Cause School Loss Day Rate

Based on data from the U.S. Department of Education (1996, Table 42-1), the NationalCenter

for Education Statistics estimates that for the 1993-1994 school year, 5.5 percent ofstudents are

absent from school on a given day. This estimate is comparable to study-specific estimates from

Chen et al. (2000) and Ransom and Pope (1992), which ranged from 4.5 to 5.1 percent.

Illness-Related School Loss Day Rate

The National Health Interview Survey (NHIS) has regional estimates ofschool loss days due to a
Appendix D: Health Incidence & Prevalence Data in U.S. Setup

November 2012

69

BenMAP User's Manual Appendices

variety of acute conditions (Adams et al., 1999). NHIS is a nationwide sample-based survey of

the health of the noninstitutionalized, civilian population, conducted by NCHS. The survey

collects data on acute conditions, prevalence of chronic conditions, episodes of injury, activity

limitations, and self-reported health status. However, it does not provide an estimate of all-cause

school loss days.

In estimating illness-related school loss days, we started with school loss days due to acute

problems (Adams et al., 1999, Table 47) and subtracted lost days due to injuries, in order to

match the definition of the study used in the C-R function to estimate illness-related school

absences (Gilliland et al., 2001). We then divided by 180 school days per to estimate illnessrelated school absence rates per school day. Similarly, when estimating respiratory illness-related

school loss days, we use data from Adams et al. (1999, Table 47). Note that we estimated 180

school days in a year to calculate respiratory illness-related school absence rates per year.

D.6 Other Acute and Chronic Effects

For many of the minor effect studies, baseline rates from a single study are often the only source

of information, and we assume that these rates hold for locations in the U.S. The use ofstudyspecific estimates are likely to increase the uncertainty around the estimate because they are often

estimated from a single location using a relatively smallsample. These endpoints include: acute

bronchitis, chronic bronchitis, upper respiratory symptoms, lower respiratory symptoms. Table

D-8 presents a summary of these baseline rates.

Table D-8. Selected Acute and Chronic Incidence (Cases / Person-Year)

& Prevalence (Percentage Population)

Endpoint Age Parameter Rate Source

Acute Bronchitis 8-12 Incidence 0.043

American Lung Association (2002b,

Table 11)

Chronic Bronchitis 27+ Incidence 0.00378 Abbey et al. (1993, Table 3)

Chronic Bronchitis

18+

Prevalence

4.37% American Lung Association (2010a,

Table 4). The rate numbers may be

slightly different from those in Table

4 because we received more current

estimates from ALA.

18-44 3.15%

45-64 5.49%

65+ 5.63%

Lower Respiratory

Symptoms (LRS)

7-14 Incidence 0.438 Schwartz et al. (1994, Table 2)

Minor Restricted Activity

Days (MRAD)

18-64 Incidence 7.8 Ostro and Rothschild (1989, p. 243)

Work Loss Day (WLD)

18-64

Incidence

2.172

Adams et al. (1999, Table )

U.S. Bureau of the Census (1997,

No. 22)

18-24 1.971

25-44 2.475

45-64 1.796
Appendix D: Health Incidence & Prevalence Data in U.S. Setup

November 2012

70

BenMAP User's Manual Appendices

NOTE: The incidence rate is the number of cases per person per year. Prevalence refers to the fraction

of people that have a particular illness during a particular time period.

D.6.1 Acute Bronchitis

The annualrate of acute bronchitis for children ages 5 to 17 was obtained from the American

Lung Association (2002b, Table 11). The authors reported an annual incidence rate per person

of 0.043, derived from the 1996 National Health Interview Survey.

D.6.2 Chronic Bronchitis Incidence Rate

The annual incidence rate for chronic bronchitis is estimated from data reported by Abbey et al.

(1993, Table 3). The rate is calculated by taking the number of new cases (234), dividing by the

number of individuals in the sample (3,310), dividing by the ten years covered in the sample, and

then multiplying by one minus the reversalrate (estimated to be 46.6% based on Abbey et al.

(1995a, Table 1).

Age-specific incidence rates are not available. Abbey et al. (1995a, Table 1) did report the

incidences by three age groups (25-54, 55-74, and 75+) for “cough type” and “sputum type”

bronchitis. However, they did not report an overall incidence rate for bronchitis by age-group.

Since, the cough and sputum types of bronchitis overlap to an unknown extent, we did not

attempt to generate age-specific incidence rates for the over-allrate of bronchitis.

D.6.3 Chronic Bronchitis Prevalence Rate

We obtained the annual prevalence rate for chronic bronchitis from the American Lung

Association (2010a, Table 4). Based on an analysis of 2008 National Health Interview Survey

data, they estimated a rate of 0.0437 for persons 18 and older; they also reported the following

prevalence rates for people in the age groups 18-44, 45-64, and 65+: 0.0315, 0.0549, and

0.0563, respectively.

D.6.4 Lower Respiratory Symptoms

Lower respiratory symptoms (LRS) are defined as two or more of the following: cough, chest

pain, phlegm, wheeze. The proposed yearly incidence rate for 100 people, 43.8, is based on the

percentiles in Schwartz et al. (Schwartz et al., 1994, Table 2). The authors did not report the

mean incidence rate, but rather reported various percentiles from the incidence rate distribution.

The percentiles and associated per person per day values are 10

th = 0 percent, 25th = 0 percent,

50

th = 0 percent, 75th = 0.29 percent, and 90th = 0.34 percent. The most conservative estimate

consistent with the data are to assume the incidence per person per day is zero up to the 75

th

percentile, a constant 0.29 percent between the 75

th and 90th percentiles, and a constant 0.34

percent between the 90

th and 100th percentiles. Alternatively, assuming a linear slope between

the 50

th and 75th

, 75

th and 90th

, and 90

th

to 100

th percentiles, the estimated mean incidence

rate per person per day is 0.12 percent. (For example, the 62.5th percentile would have an

estimated incidence rate per person per day of 0.145 percent.) We used the latter approach in

this analysis.
Appendix D: Health Incidence & Prevalence Data in U.S. Setup

November 2012

71

BenMAP User's Manual Appendices

D.6.5 Minor Restricted Activity Days (MRAD)

Ostro and Rothschild (1989, p. 243) provide an estimate of the annual incidence rate of MRADs

per person of 7.8.

D.6.6 Work Loss Days

The yearly work-loss-day incidence rate per 100 people is based on estimates from the 1996

National Health Interview Survey (Adams et al., 1999, Table 41). They reported a total annual

work loss days of 352 million for individuals ages 18 to 65. The total population of individuals of

this age group in 1996 (162 million) was obtained from (U.S. Bureau of the Census, 1997, No.

22). The average annualrate 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.

D.7 Asthma-Related Health Effects

Severalstudies have examined the impact of air pollution on asthma development or

exacerbation. Many of the baseline incidence rates used in the health impact functions are based

on study-specific estimates. The baseline rates for the various endpoints are described below

and summarized in Table D-9. The prevalence of asthma is summarized in Table D-10.

Table D-9. Asthma-Related Health Effects Incidence Rates

Endpoint Age Parameter Rate Source

Asthma Exacerbation, Shortness

of Breath, African American

8-13 Incidence 13.51

Ostro et al. (2001, p. 202)

8-13 Prevalence 7.40%

Asthma Exacerbation, Wheeze,

African American

8-13 Incidence 27.74

Ostro et al. (2001, p. 202)

8-13 Prevalence 17.30%

Asthma Exacerbation, Cough,

African American

8-13 Incidence 24.46

Ostro et al. (2001, p. 202)

8-13 Prevalence 14.50%

Upper Respiratory Symptoms

(URS)

9-11 Incidence 124.79 Pope et al. (1991, Table 2)

NOTE: The incidence rate is the number of asthma attacks per person per year.

Prevalence refers to the fraction of people that have a particular illness during a

particular time period.

D.7.1 Shortness of Breath

To estimate the annualrate of new shortness of breath episodes among African-American

asthmatics, ages 8-13, we used the rate reported by Ostro et al. (2001, p.202).
Appendix D: Health Incidence & Prevalence Data in U.S. Setup

November 2012

72

BenMAP User's Manual Appendices

D.7.2 Wheeze

The daily rate of new wheeze episodes among African-American asthmatics, ages 8-13, is

reported by Ostro et al. (2001, p.202) as 0.076. We multiplied this value by 100 and by 365 to

get the annual incidence rate per 100 people.

D.7.3 Cough

The daily rate of new cough episodes among African-American asthmatics, ages 8-13, is

reported by Ostro et al. (2001, p.202) as 0.067. We multiplied this value by 100 and by 365 to

get the annual incidence rate per 100 people.

D.7.4 Upper Respiratory Symptoms

Upper Respiratory Symptoms are defined as one or more of the following:runny or stuffy nose;

wet cough; burning, aching, or red eyes. Using the incidence rates for upper respiratory

symptoms among asthmatics, published in Pope et al. (1991, Table 2), we calculated a sample

size-weighted average incidence rate.

D.7.5 Asthma Population Estimates

In studies examining the association between air pollution and the development or exacerbation of

asthma, often times an estimate of the percent of the population with asthma is required. Asthma

percentages were obtained from an American Lung Association (2010b) report summarizing data

from NHIS. Table D-10 presents asthma prevalence rates used to define asthmatic populations

in the health impact functions.

Table D-10. Asthma Prevalence Rates Used to Estimate Asthmatic Populations

Population Group Asthma

Prevalence

Source

All Ages 7.80%

American Lung Association (2010b, Table 7)

<5 6.14%

<18 9.41%

5-17 10.70%

18-44 7.19%

45-64 7.45%

65+ 7.16%

African-American, <5 9.98% American Lung Association (2010b, Table 9)
Appendix D: Health Incidence & Prevalence Data in U.S. Setup

November 2012

73

BenMAP User's Manual Appendices

Population Group Asthma

Prevalence

Source

African-American, 5 to 17 17.76%

African-American, <18 15.53% American Lung Association*

* Calculated by ALA for U.S. EPA, based on NHIS data (CDC, 2008).
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

74

BenMAP User's Manual Appendices

Appendix E: Particulate Matter Health Impact

Functions in U.S. Setup

In this Appendix, we present the PM-related health impact functions in BenMAP. Each subsection has a table with a brief description of the health impact function and the underlying

parameters. Following each table, we present a briefsummary of each of the studies and any

items that are unique to the study.

Note that Appendix C mathematically derives the standard types of health impact functions

encountered in the epidemiological literature, such as, log-linear, logistic and linear, so we simply

note here the type of functionalform. And Appendix D presents a description of the sources for

the incidence and prevalence data used in the health impact functions.

E.1 Long-term Mortality

There are two types of exposure to PM that may result in premature mortality. Short-term

exposure may result in excess mortality on the same day or within a few days of exposure.

Long-term exposure over, say, a year or more, may result in mortality in excess of what it would

be if PM levels were generally lower, although the excess mortality that occurs will not

necessarily be associated with any particular episode of elevated air pollution levels. In other

words, long-term exposure may capture a facet of the association between PM and mortality that

is not captured by short-term exposure. Table E-1 lists the long-term mortality health impact

functions.

Table E-1. Health Impact Functions for Particulate Matter and Long-Term Mortality

Eff ect Author Year Location Age Metric Beta Std Err Form Notes

Mortality, All

Cause

Expert A 2006 30-99 Annual 0.015180 Log-linear

Mortality, All

Cause

Expert B 2006 30-99 Annual 0.012620 Log-linear Range >10 to 30 μg. Unconditional

dist. 2% no causality included.

Mortality, All

Cause

Expert B 2006 30-99 Annual 0.011950 Log-linear Range 4 to 10 μg. Unconditional dist.

2% no causality included.

Mortality, All

Cause

Expert C 2006 30-99 Annual 0.011930 Log-linear

Mortality, All

Cause

Expert D 2006 30-99 Annual 0.008380 Log-linear Unconditional dist. 5% no causality

included.

Mortality, All

Cause

Expert E 2006 30-99 Annual 0.019670 Log-linear Unconditional dist. 1% no causality

included.

Mortality, All

Cause

Expert F 2006 30-99 Annual 0.011440 Log-linear Range >7 to 30 μg

Mortality, All

Cause

Expert F 2006 30-99 Annual 0.009370 Log-linear Range 4 to 7 μg

Mortality, All

Cause

Expert G 2006 30-99 Annual 0.006970 Log-linear Unconditional dist. 30% no causality

included.

Mortality, All

Cause

Expert H 2006 30-99 Annual 0.008700 Log-linear

Mortality, All

Cause

Expert I 2006 30-99 Annual 0.011810 Log-linear Unconditional dist. 5% no causality

included.
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

75

BenMAP User's Manual Appendices

Eff ect Author Year Location Age Metric Beta Std Err Form Notes

Mortality, All

Cause

Expert J 2006 30-99 Annual 0.009620 Log-linear

Mortality, All

Cause

Expert K 2006 30-99 Annual 0.006890 Log-linear Range >16 to 30. No threshold.

Conditional dist.

Mortality, All

Cause

Expert K 2006 30-99 Annual 0.003940 Log-linear Range 4 to 16 μg. No threshold.

Conditional dist.

Mortality, All

Cause

Expert K 2006 30-99 Annual 0.003940 Log-linear Range 4 to 16 μg. Threshold 0 to 5 μg.

Conditional dist.

Mortality, All

Cause

Expert K 2006 30-99 Annual 0.003940 Log-linear Range 4 to 16 ug. Threshold 5 to 10 μg.

Conditional dist.

Mortality, All

Cause

Expert L 2006 30-99 Annual 0.009340 Log-linear Range >10 to 30 μg. Unconditional

dist. 1% no causality included.

Mortality, All

Cause

Expert L 2006 30-99 Annual 0.007390 Log-linear Range 4 to 10 μg. Unconditional dist.

25% no causality included.

Mortality, All

Cause

Laden et al. 2006 6 cities 25-99 Annual 0.014842 0.004170 Log-linear

Mortality, All

Cause

Pope et al. 2002 51 cities 30-99 Annual 0.005827 0.002157 Log-linear

Mortality, All

Cause

Woodruff et al. 1997 86 cities Infant Annual 0.003922 0.001221 Logistic

Mortality, All

Cause

Woodruff et al. 2006 204 counties Infant Annual 0.006766 0.007339 Logistic

Mortality, All

Cause

Krewski et al. 2009 116 U.S. cities 30-99 Annual 0.005827 0.000963 Log-linear

Mortality,

Lung Camcer

Krewski et al. 2009 116 U.S. cities 30-99 Annual 0.013103 0.003795 Log-linear

Mortality, IHD Krewski et al. 2009 116 U.S. cities 30-99 Annual 0.021511 0.002058 Log-linear

E.1.1 Expert Functions

In this section, we describe the approach taken to incorporate into BenMAP concentrationresponse (C-R) functions that were obtained through expert elicitation for EPA (IEc, 2006).

We have specified expert distributions for the PM2.5effect either as truncated parametric

distributions or as non-parametric distributions. Therefore they can only be included in BenMAP

in the form of custom distribution tables containing 15,000 random draws (with replacement)

from an underlying distribution. We first describe the way these custom distribution tables were

created. Then we explain how these custom distribution tables should be handled in a

configuration file to represent the expert-specified distribution as closely as possible.

Note that the table on page 3-30 of the expert elicitation report (IEc, 2006) refers to the nonparametric distributions as “custom” distributions. However, BenMAP refers to distribution

tables that are supplied in the form of a simulated draw as “custom distribution tables”. In order

to avoid confusion in terminology, we will call the expert-specified distributions, which did not

have a parametric shape, “non-parametric” expert distributions.

We divided the experts into two groups – those who specified a parametric distribution and those

who specified a non-parametric distribution. This division was necessary because the two groups

required different methods for generating the custom distribution tables. We describe the

respective algorithms below and then provide an assessment of the results for each expert.
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

76

BenMAP User's Manual Appendices

E.1.1.1 Parametric Distributions

Experts A, C, D, E, G, I, J, and K chose parametric distribution functions to represent their

subjective beliefs about the percent change in risk associated with an increase in PM2.5. In

particular, they specified the following characteristics of the distribution:

The shape (e.g., Normal, Triangular, Weibull)

The truncation points (i.e., minimum and/or maximum)

Two or three percentile points

The likelihood that the association is causal and whether the function includes that (i.e., whether

the function is conditional on the association being causal or unconditional).

There were two types of inconsistencies encountered in these specifications:

(1) The experts who chose Normal or Weibullshapes for their distributions also specified

minimum and/or maximum values at which there could be an effect. The Normal distribution has

an unlimited support from -8 to +8 . The Weibull distribution has support (l,+8 ), where l is a

location parameter that can be any value on the real line. The specification of a minimum or a

maximum value for the effect is therefore inconsistent with specifying these distributions.

Therefore, we interpreted these experts’ distributions as truncated Normal or truncated Weibull

distributions. In other words, we assumed that the shape of the distribution is Normal or Weibull

between the truncation points.

(2) Experts A, C, and J indicated that they included the likelihood of causality in their subjective

distributions. However, the continuous parametric distributions specified were inconsistent with

the causality likelihoods provided by these experts. Because there was no way to reconcile this,

we chose to interpret the distributions of these experts as unconditional and ignore the additional

information on the likelihood of causality. For example, Expert A specified a truncated Normal

distribution with a minimum 0 and a maximum 4. The expert also indicated that the likelihood of

causality is 95 percent and it is included in the distribution. This implies that the 5th percentile of

the truncated Normal distribution should be zero. The minimum and 5th percentile of the

distribution both being zero imply a density with a large (discrete) mass at zero. This, however, is

not consistent with specifying a continuous Normal density. (In the case of Expert A, in addition,

he specified a 5th percentile value of 0.29, whereas a 5 percent chance of non-causality would

imply a 5th percentile value of 0.)

In order to create a random draw from a parametric distribution it is not sufficient to know its

shape and truncation points. In addition, one needs to know the values of parameters that

distinguish this particular distribution from a class ofsimilarly shaped distributions with identical

truncation points. Experts D and I reported parameter values of their subjective distributions (see

details in Table 1). Therefore, we simply drew 15,000 times from each of their distributions.

However, the only information, in addition to the shape and truncation points, which the other

experts provided was the percentile points. To derive the parameter values of interest, we used
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

77

BenMAP User's Manual Appendices

this information as follows:

Let F(x;θ,min,max) be a truncated continuous parametric (cumulative) distribution function with

(vector of) parameters θ and truncation points min and max. The nth percentile point is defined

as the value xn such that F(xn;θ,min,max)=n/100. Thus, if we know that the expert

distribution’s nth percentile point is xn and mth percentile point is xm then the following has to

hold:

F(xn;θ,min,max)=n/100

F(xm;θ,min,max)=m/100

This is a system of non-linear equations that can be solved for the unknown distribution

parameters θ. We used the Nelder and Mead (1965) numeric optimization algorithm, available

in R, to find the best-fitting estimates of parameters θ for the truncated distributions specified by

the experts. Once estimates of θ were obtained, the distributions were specified fully and we had

enough information to make 15,000 draws from each.

Table E-2 below summarizes the results for each expert who specified a parametric distribution.

In each case, we provide an “input” line that has all the information that was provided by the

expert. We also show the “output” line that contains the inferred parameters and five percentile

points of the distribution from which draws were made.

Highlighted in yellow are the percentiles specified by the expert and used to create the equation

system for the optimization. After finding the best-fitting parameters, we calculated the

associated percentiles and confirmed that they are close to the input values.

Table E-2. Description of the Parametric Expert Functions

Expert Information Distribution Min P5 P25 P50 P75 P95 Max Parameters

A

input Normal 0 0.290 2.900 4

mean=?

sd=?

output 0.290 0.929 1.481 2.059 2.900

mean=1.42

sd=0.895

C

input Normal 0 1.200 2.000 +8

mean=?

sd=?

output 0.423 0.875 1.200 1.528 2.000

mean=1.196

sd=0.488

D

input Triangular 0.100 1.600 mode=0.95

output 0.350 0.662 0.897 1.107 1.382

E

input Normal 0 2.000 3.000 +8

mean=?

sd=?

output 1.002 1.590 2.000 2.410 3.000

mean=2

sd=0.608
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

78

BenMAP User's Manual Appendices

Expert Information Distribution Min P5 P25 P50 P75 P95 Max Parameters

G

input Normal -8 1.000 1.300 1.500

mean=?

sd=?

output 0.695 0.875 1.000 1.124 1.300

mean=1.001

sd=0.185

I

input Normal 0.200 2.300

mean=1.25

sd=0.53

output 0.473 0.912 1.250 1.588 2.027

J

input Weibull 0 0.150 0.900 2.000 3.000

shape=?

scale=?

location=?

output 0.150 0.525 0.900 1.331 2.000

shape=2.21

scale=1.413

location=-

0.326

K1

4-16

μg/m3

input Normal -8 0.100 0.400 0.800

mean=?

sd=?

output 0.100 0.277 0.400 0.521 0.682

mean=0.404

sd=0.184

K2

>16-30

μg/m3

input Normal -8 0.100 0.700 1.500

mean=?

sd=?

output 0.100 0.455 0.700 0.942 1.264

mean=0.707

sd=0.367

For example, Expert A indicated that the distribution of the effect is Normal, with minimum 0 and

maximum 4. Under the assumption that this is actually a truncated Normal distribution, we looked

for the corresponding mean and standard deviation for it. The 5th and the 95th percentile values

(0.29 and 2.90, respectively) were used to specify the following equations:

N(0.29;mean=?,sd=?,min=0,max=4)=0.05

N(2.90; mean=?,sd=?,min=0,max=4)=0.95

The solution to this system was a mean of 1.42 and a standard deviation of 0.89. We also verified

that these parameters produced percentile values consistent with the ones supplied by the expert.

We similarly solved for the parameters of the other experts who specified parametric distributions,

with the exception of experts D and I, who specified their distributions fully.

The experts were asked to describe uncertainty distributions for the percent change in mortality

risk associated with a 1 μg/m3 change in PM2.5. All of the experts assumed log-linear (or

piecewise log-linear) C-R functions. If Z denotes the percent change elicited from an expert, the

relative risk associated with a 1 μg/m3 change in PM2.5is (1+Z/100), and the PM2.5coefficient in

the log-linear C-R function is ln(1+(Z/100)). We applied this transformation to the values drawn
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

79

BenMAP User's Manual Appendices

from each distribution.

Finally, some experts stated that their distribution does not incorporate the likelihood of causality

– i.e., they specified conditional distributions. We made 15,000 draws from an expert’s

conditional distribution. BenMAP contains a function that is zero. If an expert specified, for

example, a five percent chance that there is not a causal association, BenMAP will draw from this

zero function with five percent probability and draw from the 15,000-draw custom distribution (of

positive values) with 95 percent probability. Table E-3 below shows summary statistics for the

draws from the parametric distributions that became BenMAP “custom” distribution tables.

Additional details on the form of the distributions are below and in Belova et al. (2007).

Table E-3. Descriptive Statistics of the Random Draws from the Parametric Expert

Distributions

Expert Mean Standard

Deviation

Min P25 P50 P75 Max

A 0.01518 0.00773 0.00000 0.00944 0.01483 0.02051 0.03917

C 0.01193 0.00466 0.00001 0.00870 0.01189 0.01509 0.02848

D (cond) 0.00884 0.00305 0.00105 0.00671 0.00899 0.01108 0.01577

D 0.00838 0.00354 0.00000 0.00623 0.00875 0.01092 0.01577

E (cond) 0.01975 0.00591 0.00026 0.01577 0.01986 0.02376 0.04534

E 0.01967 0.00619 0.00000 0.01575 0.01989 0.02381 0.04534

G(cond) 0.00996 0.00181 0.00256 0.00873 0.00996 0.01123 0.01489

G 0.00697 0.00480 0.00000 0.00000 0.00892 0.01062 0.01489

I (cond) 0.01240 0.00458 0.00200 0.00905 0.01244 0.01575 0.02273

I 0.01181 0.00523 0.00000 0.00845 0.01214 0.01559 0.02273

J 0.00962 0.00567 0.00000 0.00525 0.00902 0.01329 0.02936

K1 (cond) 0.00394 0.00175 -0.00262 0.00278 0.00398 0.00520 0.00797

K1 0.00139 0.00215 -0.00262 0.00000 0.00000 0.00298 0.00796

K2 (cond) 0.00689 0.00350 -0.00766 0.00452 0.00698 0.00937 0.01489

K2 0.00237 0.00382 -0.00402 0.00000 0.00000 0.00489 0.01488

E.1.1.2 Non-Parametric Distributions

Experts B, F, H, and L chose a non-parametric distribution function to represent their subjective

beliefs about the percent change in risk associated with 1 μg/m3increase in PM2.5. They specified

the following characteristics of the distribution:

The truncation points (i.e., minimum and/or maximum)
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

80

BenMAP User's Manual Appendices

Five percentile points

The likelihood that the association is causal and whether the function includes that (i.e., whether

the function is conditional on the association being causal or unconditional)

The only information that we had about these distributions was the minimum, the maximum, and

the five percentiles. The shape of the distribution was unknown. Therefore, we made an

assumption that the cumulative distribution function (cdf) is piece-wise linear. In other words, we

assumed that all values between the percentiles are equally likely. Following this assumption, we

used linear interpolation between the percentile points to derive the cdf for each expert. We then

made 15,000 draws from each cdf.

Table E-4 shows the inputs and the outputs of this process for each expert. The inputs are the

minimum, the maximum, and the percentiles. The outputs are the percentiles that we calculated

from the draws from the respective linearly interpolated cdfs.

Table E-4. Description of the Non-Parametric Expert Functions

Expert Information Min P5 P10 P25 P50 P75 P95 Max

B1

4-10

μg/m3

input 0.010 0.100 0.200 1.200 2.100 2.600 2.800

output 0.099 0.203 1.213 2.092 2.599

B2

>10-30

μg/m3

input 0.100 0.200 0.500 1.200 2.100 2.600 2.800

output 0.198 0.501 1.191 2.096 2.597

F1

4-7

μg/m3

input 0.370 0.580 0.730 0.930 1.100 1.400 1.700

output 0.581 0.732 0.928 1.097 1.407

F2

>7-30

μg/m3

input 0.290 0.770 0.960 1.100 1.400 1.600 1.800

output 0.771 0.958 1.100 1.398 1.606

H

input 0 0 0 0.400 0.700 1.300 2.000 3.000

output 0 0 0.407 0.710 1.320 2.010

L1

4-10

μg/m3

input 0 0.200 0.570 1.000 1.400 1.600 2.700

output 0.201 0.570 0.996 1.400 1.619

L2

>10-30

μg/m3

input 0.020 0.200 0.570 1.000 1.400 1.600 2.700

0.018 0.568 1.003 1.396 1.634

Table E-5 below shows summary statistics for the draws from the non-parametric distributions

that became BenMAP “custom” distribution tables. The section below on distributional details

contains histograms for all the experts’ distributions.
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

81

BenMAP User's Manual Appendices

Table E-5. Descriptive Statistics of the Random Draws from the Non-Parametric

Expert Distributions

Expert Mean Standard

Deviation

Min P25 P50 P75 Max

B1 (cond) 0.01217 0.00897 0.00010 0.00200 0.01195 0.02090 0.02761

B1 0.01195 0.00901 0.00000 0.00195 0.01167 0.02075 0.02761

B2 (cond) 0.01290 0.00813 0.00100 0.00489 0.01187 0.02068 0.02761

B2 0.01262 0.00827 0.00000 0.00464 0.01159 0.02042 0.02761

F1 0.00937 0.00268 0.00370 0.00727 0.00924 0.01092 0.01686

F2 0.01144 0.00292 0.00290 0.00951 0.01091 0.01387 0.01784

H 0.00870 0.00662 0.00000 0.00406 0.00702 0.01302 0.02954

L1 (cond) 0.00985 0.00511 0.00001 0.00582 0.00999 0.01391 0.02662

L1 0.00739 0.00613 0.00000 0.00001 0.00727 0.01250 0.02659

L2 (cond)c 0.00953 0.00544 0.00000 0.00567 0.00991 0.01389 0.02661

L2 0.00934 0.00549 0.00000 0.00531 0.00964 0.01371 0.02661

E.1.1.3 Using Expert Functions in BenMAP

When an expert has specified certain functionalspecifics with certain probabilities, the resulting

“C-R function” becomes a set of possible functions, each with an associated probability. For

example, expert K specified a piecewise log-linear function (i.e., two different log-linear functions

on two different parts of the range of PM2.5); this expert also specified a threshold within different

ranges with different probabilities (and no threshold with a specified probability). BenMAP

incorporates such a set of possible functions specified by an expert function by assigning

appropriate weights to each specification. We illustrate this using expert K’s specification.

Expert K specified one log-linear function if the baseline PM2.5value falls within the range from 4

μg/m3to 16 μg/m3 and another log-linear function if the baseline value falls within the range from

>16 μg/m3to 30 μg/m3. BenMAP thus incorporates two sets of functions – one set for each of

these two PM2.5ranges – and selects from the set appropriate for a given PM2.5baseline value.

Expert K also specified a 64% probability that there is no causalrelationship; an 18% probability

that there is a causalrelationship with no threshold, a 4% probability that there is a causal

relationship with a threshold somewhere between 5 μg/m3to 10 μg/m3, and a 14% probability

that there is a causalrelationship with a threshold somewhere between 0 μg/m3to 5 μg/m3. Thus,

the set of log-linear functions in BenMAP for expert K on the range from 4 μg/m3to 16 μg/m3

contains:

a function with PM2.5coefficient = 0 (no causality), which BenMAP selects with 65%
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

82

BenMAP User's Manual Appendices

probability;

a function with the PM2.5coefficient expert K specified for the log-linear function on that range

and no threshold, which BenMAP selects with 18% probability;

a function with the PM2.5coefficient expert K specified for the log-linear function on that range

and a threshold (with uniform probability) between 0 μg/m3to 5 μg/m3, which BenMAP selects

with 14% probability; and

a function with the PM2.5coefficient expert K specified for the log-linear function on that range

and a threshold (with uniform probability) between 5 μg/m3to 10 μg/m3, which BenMAP

selects with 4% probability.

If the PM2.5baseline value is greater than 16 μg/m3, BenMAP goes through an analogous

procedure to select a function from among the two functions in that set.
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

83

BenMAP User's Manual Appendices

E.1.1.4 Distributional Details by Expert

Distributional details on each expert distribution are presented below. The derivation of the

distributions is described above with additional details provided by Belova et al. (2007).

E.1.1.4.1 Expert A

Figure E-1. Histogram of the Random Draw from the Distribution of the PM2.5 Effect

Specified by Expert A

Notes:

- Expert A specified a truncated Normal Distribution. We inferred the following values for the

parameters of this distribution: mean=1.42 and standard deviation=0.89.

- The experts specified distributions for the percent changes in the relative risk. The distribution

of the corresponding PM2.5effects was the following transformation of the percent change in

relative risk Z – log(1+(Z/100)).
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

84

BenMAP User's Manual Appendices

E.1.1.4.2 Expert B

Figure E-2. Characteristics of the Random Draw from the Approximated Distribution of

the PM2.5 Effect Specified by Expert B

(1) Results for the range 4-10 μg/m3

Notes:

- Expert B specified a non-parametric distribution using five percentile points. We linearly

interpolated the cdf between them. Panel(a) shows q-q plot of the expert percentiles and

empirical percentiles for the draw. Panel(b) shows empirical cdf associated with the draw, the

red “X” marks indicate corresponding expert percentiles. The distribution was conditional on

causality. We created a corresponding unconditional distribution by adding extra 2 percent

zeros to the draw. Panels (c) and (d) show the respective distributions.
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

85

BenMAP User's Manual Appendices

- The experts specified distributions for the percent changes in the relative risk. The distribution

of the corresponding PM2.5effects was the following transformation of the percent change in

relative risk Z – log(1+(Z/100)).

Figure E-2. Characteristics of the Random Draw from the Approximated Distribution of

the PM2.5 Effect Specified by Expert B (continued)

(2) Results for the range >10-30 μg/m3

Notes:

- Expert B specified a non-parametric distribution using five percentile points. We linearly

interpolated the cdf between them. Panel(a) shows q-q plot of the expert percentiles and

empirical percentiles for the draw. Panel(b) shows empirical cdf associated with the draw, the

red “X” marks indicate corresponding expert percentiles. The distribution was conditional on

causality. We created a corresponding unconditional distribution by adding extra 2 percent
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

86

BenMAP User's Manual Appendices

zeros to the draw. Panels (c) and (d) show the respective distributions.

- The experts specified distributions for the percent changes in the relative risk. The distribution

of the corresponding PM2.5effects was the following transformation of the percent change in

relative risk Z – log(1+(Z/100)).

E.1.1.4.3 Expert C

Figure E-3. Histogram of the Random Draw from the Distribution of the PM2.5 Effect

Specified by Expert C

Notes:

- Expert C specified a truncated Normal Distribution. We inferred the following values for the

parameters of this distribution: mean=1.20 and standard deviation=0.49.

- The experts specified distributions for the percent changes in the relative risk. The distribution

of the corresponding PM2.5effects was the following transformation of the percent change in

relative risk Z – log(1+(Z/100)).
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

87

BenMAP User's Manual Appendices

E.1.1.4.4 Expert D

Figure E-4. Histogram of the Random Draw from the Distribution of the PM2.5 Effect

Specified by Expert D

Notes:

- Expert D specified a Triangular Distribution with minimum=0.1, maximum=1.6, and

mode=0.95. The distribution was conditional on causality. We created a corresponding

unconditional distribution by adding extra 5 percent zeros to the draw.

- The experts specified distributions for the percent changes in the relative risk. The distribution

of the corresponding PM2.5effects was the following transformation of the percent change in

relative risk Z – log(1+(Z/100)).
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

88

BenMAP User's Manual Appendices

E.1.1.4.5 Expert E

Figure E-5. Histogram of the Random Draw from the Distribution of the PM2.5 Effect

Specified by Expert E

Notes:

- Expert E specified a truncated Normal Distribution. We inferred the following parameters for

this distribution: mean=2.00 and standard deviation=0.61. The distribution was conditional on

causality. We created a corresponding unconditional distribution by adding extra 1 percent

zeros to the draw.

- The experts specified distributions for the percent changes in the relative risk. The distribution

of the corresponding PM2.5effects was the following transformation of the percent change in

relative risk Z – log(1+(Z/100)).
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

89

BenMAP User's Manual Appendices

E.1.1.4.6 Expert F

Figure E-6. Characteristics of the Random Draw from the Approximated Distribution of

the PM2.5 Effect Specified by Expert F

(1) Results for the range 4-7 μg/m3

Notes:

- Expert F specified a non-parametric distribution using five percentile points. We linearly

interpolated the cdf between them. Panel(a) shows q-q plot of the expert percentiles and

empirical percentiles for the draw. Panel(b) shows empirical cdf associated with the draw, the

red “X” marks indicate corresponding expert percentiles. Panel(c) shows the histogram of the

distribution.

- The experts specified distributions for the percent changes in the relative risk. The distribution

of the corresponding PM2.5effects was the following transformation of the percent change in

relative risk Z – log(1+(Z/100)).
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

90

BenMAP User's Manual Appendices

Figure E-6. Characteristics of the Random Draw from the Approximated Distribution of

the PM2.5 Effect Specified by Expert F (continued)

(2) Results for the range >7-30 μg/m3

Notes:

- Expert F specified a non-parametric distribution using five percentile points. We linearly

interpolated the cdf between them. Panel(a) shows q-q plot of the expert percentiles and

empirical percentiles for the draw. Panel(b) shows empirical cdf associated with the draw, the

red “X” marks indicate corresponding expert percentiles. Panel(c) shows the histogram of the

distribution.

- The experts specified distributions for the percent changes in the relative risk. The distribution

of the corresponding PM2.5effects was the following transformation of the percent change in

relative risk Z – log(1+(Z/100)).
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

91

BenMAP User's Manual Appendices

E.1.1.4.7 Expert G

Figure E-7. Histogram of the Random Draw from the Distribution of the PM2.5 Effect

Specified by Expert G

Notes:

- Expert G specified a truncated Normal Distribution. We inferred the following parameters for

this distribution: mean=1.00 and standard deviation=0.19. The distribution was conditional on

causality. We created a corresponding unconditional distribution by adding extra 30 percent

zeros to the draw.

- The experts specified distributions for the percent changes in the relative risk. The distribution

of the corresponding PM2.5effects was the following transformation of the percent change in

relative risk Z – log(1+(Z/100)).
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

92

BenMAP User's Manual Appendices

E.1.1.4.8 Expert H

Figure E-8. Characteristics of the Random Draw from the Approximated Distribution of

the PM2.5 Effect Specified by Expert H

Notes:

- Expert H specified a non-parametric distribution using six percentile points. We linearly

interpolated the cdf between them. Panel(a) shows q-q plot of the expert percentiles and

empirical percentiles for the draw. Panel(b) shows empirical cdf associated with the draw, the

red “X” marks indicate corresponding expert percentiles. Panel(c) shows the histogram of the

distribution.

- The experts specified distributions for the percent changes in the relative risk. The distribution

of the corresponding PM2.5effects was the following transformation of the percent change in

relative risk Z – log(1+(Z/100)).
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

93

BenMAP User's Manual Appendices

E.1.1.4.9 Expert I

Figure E-9. Histogram of the Random Draw from the Distribution of the PM2.5 Effect

Specified by Expert I

Notes:

- Expert I specified a truncated Normal Distribution with mean=1.25 and standard

deviation=0.53. The distribution was conditional on causality. We created a corresponding

unconditional distribution by adding extra 5 percent zeros to the draw.

- The experts specified distributions for the percent changes in the relative risk. The distribution

of the corresponding PM2.5effects was the following transformation of the percent change in

relative risk Z – log(1+(Z/100)).
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

94

BenMAP User's Manual Appendices

E.1.1.4.10 Expert J

Figure E-10. Histogram of the Random Draw from the Distribution of the PM2.5 Effect

Specified by Expert J

Notes:

- Expert J specified a truncated Weibull Distribution. We inferred the following values for the

parameters of this distribution:shape=2.21, scale=1.41, and location=-0.33.

- The experts specified distributions for the percent changes in the relative risk. The distribution

of the corresponding PM2.5effects was the following transformation of the percent change in

relative risk Z – log(1+(Z/100)).
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

95

BenMAP User's Manual Appendices

E.1.1.4.11 Expert K

Figure E-11. Histogram of the Random Draw from the Distribution of the PM2.5 Effect

Specified by Expert K

Notes:

- Expert K specified a truncated Normal Distribution for two ranges separately (4-16 μg/m3 and

>16-30 μg/m3). We inferred the following parameters for this distribution: mean=0.40 and

standard deviation=0.18 in the lower range and mean=0.71 and standard deviation=0.37 in the

upper range. The distribution was conditional on causality. We created a corresponding

unconditional distribution by adding extra 65 percent zeros to the draws in each range.

- The experts specified distributions for the percent changes in the relative risk. The distribution

of the corresponding PM2.5effects was the following transformation of the percent change in

relative risk Z – log(1+(Z/100)).
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

96

BenMAP User's Manual Appendices

E.1.1.4.12 Expert L

Figure E-12. Characteristics of the Random Draw from the Approximated Distribution

of the PM2.5 Effect Specified by Expert L

(1) Results for the range 4-10 μg/m3

Notes:

- Expert L specified a non-parametric distribution using five percentile points. We linearly

interpolated the cdf between them. Panel(a) shows q-q plot of the expert percentiles and

empirical percentiles for the draw. Panel(b) shows empirical cdf associated with the draw, the

red “X” marks indicate corresponding expert percentiles. The distribution was conditional on

causality. We created a corresponding unconditional distribution by adding extra 25 percent

zeros to the draw. Panels (c) and (d) show the respective distributions.

- The experts specified distributions for the percent changes in the relative risk. The distribution

of the corresponding PM2.5effects was the following transformation of the percent change in

relative risk Z – log(1+(Z/100)).
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

97

BenMAP User's Manual Appendices

Figure E-12. Characteristics of the Random Draw from the Approximated Distribution

of the PM2.5 Effect Specified by Expert L (continued)

(2) Results for the range >10-30 μg/m3

Notes:

- Expert L specified a non-parametric distribution using five percentile points. We linearly

interpolated the cdf between them. Panel(a) shows q-q plot of the expert percentiles and

empirical percentiles for the draw. Panel(b) shows empirical cdf associated with the draw, the

red “X” marks indicate corresponding expert percentiles. The distribution was conditional on

causality. We created a corresponding unconditional distribution by adding extra 1 percent

zeros to the draw. Panels (c) and (d) show the respective distributions.

- The experts specified distributions for the percent changes in the relative risk. The distribution

of the corresponding PM2.5effects was the following transformation of the percent change in

relative risk Z – log(1+(Z/100)).
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

98

BenMAP User's Manual Appendices

E.1.2 Laden et al. (2006)

A large body of epidemiologic literature has found an association of increased fine particulate air

pollution (PM2.5) with acute and chronic mortality. The effect of improvements in particle

exposure is less clear. Earlier analysis of the Harvard Six Cities adult cohort study showed an

association between long-term ambient PM2.5and mortality between enrollment in the mid-1970's

and follow-up until 1990. The authors extended mortality follow-up for eight years in a period of

reduced air pollution concentrations. Annual city-specific PM2.5concentrations were measured

between 1979-1988, and estimated for later years from publicly available data. E xposure was

defined as (1) city-specific mean PM2.5 during the two follow-up periods, (2) mean PM2.5in the

first period and change between these periods, (3) overall mean PM2.5across the entire followup, and (4) year-specific mean PM2.5

. Mortality rate ratios were estimated with Cox

proportional hazards regression controlling for individualrisk factors. The authors found an

increase in overall mortality associated with each 10 μg/m3increase in PM2.5 modeled either as

the overall mean (RR=1.16, 95%CI=1.07-1.26) or as exposure in the year of death (RR=1.14,

95%CI=1.06-1.22). PM2.5exposure was associated with lung cancer (RR=1.27, 95%CI=0.96-

1.69) and cardiovascular deaths (RR=1.28, 95%CI=1.13-1.44). Improved overall mortality

was associated with decreased mean PM2.5(10 microg/m(3)) between periods (RR=0.73, 95%

CI=0.57-0.95). Total, cardiovascular, and lung cancer mortality were each positively associated

with ambient PM2.5concentrations. Reduced PM2.5concentrations were associated with reduced

mortality risk.

All-Cause Mortality

The coefficient and standard error for PM2.5are estimated from the relative risk (1.16) and 95%

confidence interval of (1.07-1.26) associated with a change in annual mean exposure of 10.0

μg/m3(Laden et al., 2006, p. 667).

E.1.3 Pope et al. (2002)

The Pope et al. (2002) analysis is a longitudinal cohort tracking study that uses the same

American Cancer Society (ACS) cohort as the original Pope et al. (1995) study, and the

Krewski et al. (2000) reanalysis. Pope et al. (2002) analyzed survival data for the cohort from

1982 through 1998, 9 years longer than the original Pope study. Pope et al. (2002) also

obtained PM2.5data in 116 metropolitan areas collected in 1999, and the first three quarters of

2000. This is more metropolitan areas with PM2.5data than was available in the Krewski

reanalysis (61 areas), or the original Pope study (50 areas), providing a larger size cohort.

They used a Cox proportional hazard model to estimate the impact of long-term PM exposure

using three alternative measures of PM2.5exposure; metropolitan area-wide annual mean PM

levels from the beginning of tracking period (1979-1983 PM data, conducted for 61

metropolitan areas with 359,000 individuals), annual mean PM from the end of the tracking

period (1999-2000, for 116 areas with 500,000 individuals), and the average annual mean PM
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

99

BenMAP User's Manual Appendices

levels of the two periods (for 51 metropolitan areas, with 319,000 individuals). PM levels were

lower in 1999-2000 than in 1979-1983 in most cities, with the largest improvements occurring in

cities with the highest original levels.

Pope et al. (2002) followed Krewski et al. (2000) and Pope et al. (1995, Table 2) and reported

results for all-cause deaths, lung cancer (ICD-9 code: 162), cardiopulmonary deaths (ICD-9

codes: 401-440 and 460-519), and “all other” deaths. All-cause mortality includes accidents,

suicides, homicides and legal interventions. The category “all other” deaths is all-cause mortality

less lung cancer and cardiopulmonary deaths. Like the earlier studies, Pope et al. (2002) found

that mean PM2.5is significantly related to all-cause and cardiopulmonary mortality. In addition,

Pope et al. (2002) found a significant relationship with lung cancer mortality, which was not found

in the earlier studies. None of the three studies found a significant relationship with “all other”

deaths.

Pope et al. (2002) obtained ambient data on gaseous pollutants routinely monitored by EPA

during the 1982-1998 observation period, including SO2, NO2, CO, and ozone. They did not

find significant relationships between NO2, CO, and ozone and premature mortality, but there

were significant relationships between SO4(as well as SO2), and all-cause, cardiopulmonary,

lung cancer and “all other” mortality.

All-Cause Mortality, 1979-1983 Exposure

The coefficient and standard error for PM2.5using the 1979-1983 PM data are estimated from

the relative risk (1.04) and 95% confidence interval(1.01-1.08) associated with a change in

annual mean exposure of 10.0 μg/m3(Pope et al., 2002, Table 2).

All-Cause Mortality, Average of 1979-1983 and 1999-2000 Exposure

The coefficient and standard error for PM2.5using the average of 1979-1983 and 1999-2000

PM data are estimated from the relative risk (1.06) and 95% confidence interval(1.02-1.11)

associated with a change in annual mean exposure of 10.0 μg/m3(Pope et al., 2002, Table 2).

E.1.4 Woodruff et al. (1997)

In a study of four million infants in 86 U.S. metropolitan areas conducted from 1989 to 1991,

Woodruff et al. (1997) found a significant link between PM10exposure in the first two months of

an infant’s life with the probability of dying between the ages of 28 days and 364 days. PM10

exposure was significant for all-cause mortality. PM10 was also significant for respiratory

mortality in average birth-weight infants, but not low birth-weight infants.

Post-Neonatal Mortality

The coefficient and standard error are based on the odds ratio (1.04) and the 95% confidence
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

100

BenMAP User's Manual Appendices

interval(1.02-1.07) associated with a 10 μg/m3 change in PM10(Woodruff et al., 1997, Table

3).

E.1.5 Woodruff et al. (2006)

Studies suggest that airborne particulate matter (PM) may be associated with postneonatal infant

mortality, particularly with respiratory causes and sudden infant death syndrome (SIDS). To

further explore this issue, the authors examined the relationship between long-term exposure to

fine PM air pollution and postneonatal infant mortality in California. They linked monitoring data

for PM2.5to infants born in California in 1999 and 2000 using maternal addresses for mothers

who lived within 5 miles of a PM2.5 monitor. They matched each postneonatal infant death to four

infants surviving to 1 year of age, by birth weight category and date of birth (within 2 weeks). For

each matched set, they calculated exposure as the average PM2.5concentration over the period

of life for the infant who died. They used conditional logistic regression to estimate the odds of

postneonatal all-cause, respiratory-related, SIDS, and external-cause (a control category)

mortality by exposure to PM2.5, controlling for the matched sets and maternal demographic

factors. They matched 788 postneonatal infant deaths to 3,089 infant survivors, with 51 and 120

postneonatal deaths due to respiratory causes and SIDS, respectively. They found an adjusted

odds ratio for a 10-microg/m3increase in PM2.5 of 1.07 [95% confidence interval(CI), 0.93-

1.24] for overall postneonatal mortality, 2.13 (95% CI, 1.12-4.05) for respiratory-related

postneonatal mortality, 0.82 (95% CI, 0.55-1.23) for SIDS, and 0.83 (95% CI, 0.50-1.39) for

external causes.

Post-Neonatal Mortality

The coefficient and standard error for PM2.5are estimated from the relative risk (1.07) and the

95% confidence interval(0.93-1.24) associated with a change in annual mean exposure of 10.0

μg/m3(Woodruff et al., 2006, p. 786).

E.1.6 Krewski et al. (2009)

This cohort study consists of approximately 360,000 participants residing in areas of the country

that have adequate monitoring information on levels of PM2.5for 1980 and about 500,000

participants in areas with adequate information for 2000. The causes of death that were analyzed

included all causes, cardiopulmonary disease (CPD), ischemic heart disease (IHD), lung cancer,

and allremaining causes. Data for 44 personal, individual-level covariates, based on participants’

answers to a 1982 enrollment questionnaire, were also used for the analyses. The authors also

collected data for seven ecologic (neighborhood-level) covariates, each of which represents local

factors known or suspected to influence mortality, such as poverty level, level of education, and

unemployment (at both Zip Code and city levels). Long-term average exposure variables were

constructed for PM2.5from monitoring data for two periods: 1979–1983 and 1999–2000.

Similar variables were constructed for long-term exposure to other pollutants of interest from

single-year (1980) averages, including totalsuspended particles, ozone, nitrogen dioxide, and
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

101

BenMAP User's Manual Appendices

sulfur dioxide. Exposure was averaged for all monitors within a metropolitan statistical area

(MSA) and assigned to participants according to their Zip Code area (ZCA) of residence. The

authors chose the standard Cox proportional-hazards model(and a variation to allow for random

effects) to calculate hazard ratios for various cause-of-death categories associated with the levels

of air pollution exposure in the cohort. They extended the random effects Cox model to

accommodate two levels of information for clustering and for ecologic covariates. Three main

analyses were conducted: a Nationwide Analysis, Intra-Urban Analyses in the New York City

(NYC) and Los Angeles (LA) regions, and an analysis designed to investigate whether critical

time windows of exposure to pollutants might have affected mortality in the cohort.

Mortality, All-Cause

In a random effects Cox model, the coefficient and standard error are estimated from the relative

risks (1.06) and 95% confidence intervals (95% CI: 1.04-1.08) for a 10 μg/m3increase in the

average of PM2.5exposure levelfor 1999-2000 (Krewski, et al., 2009, Commentary Table 4).

The results were adjusted for the 44 individual-level covariates and the 7 ecologic covariates at

the MSA & DIFF levels.

Mortality, Lung Cancer (ICD-10 code C30-C39)

In a random effects Cox model, the coefficient and standard error are estimated from the relative

risks (1.14) and 95% confidence intervals (95% CI: 1.06-1.23) for a 10 μg/m3increase in the

average of PM2.5exposure levelfor 1999-2000 (Krewski, et al., 2009, Commentary Table 4).

The results were adjusted for the 44 individual-level covariates and the 7 ecologic covariates at

the MSA & DIFF levels.

Mortality, Ischemic Heart Disease (ICD-10 code I20-I25)

In a random effects Cox model, the coefficient and standard error are estimated from the relative

risks (1.24) and 95% confidence intervals (95% CI: 1.19-1.29) for a 10 μg/m3increase in the

average of PM2.5exposure levelfor 1999-2000 (Krewski, et al., 2009, Commentary Table 4).

The results were adjusted for the 44 individual-level covariates and the 7 ecologic covariates at

the MSA & DIFF levels.

E.2 Chronic / Severe Illness

Table E-6 below summarizes the health impacts functions used to estimate the relationship

between PM2.5and chronic / severe health effects. We present a briefsummary of each of the

studies and any items that are unique to the study.
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

102

BenMAP User's Manual Appendices

Table E-6. Health Impact Functions for Particulate Matter and Chronic Illness

Eff ect Author Year Location Age Co-Poll Metric Beta Std Err Form Notes

Chronic Bronchitis Abbey et

al.

1995 SF, SD, South

Coast Air Basin

27-99 Annual 0.013185 0.006796 Logistic

Acute Myocardial

Infarction, Nonfatal

Peters et

al.

2001 Boston, MA 18-99 D24HourMean 0.024121 0.009285 Logistic

Acute Myocardial

Infarction, Nonfatal

Pope et al. 2006 Greater Salt

Lake City, UT

0-99 D24HourMean 0.0048 0.0019 Logistic Index MI and

unstable

angina

Acute Myocardial

Infarction, Nonfatal

Sullivan

et al.

2005 King County,

WA

0-99 D24HourMean 0.0019 0.0022 Logistic

Acute Myocardial

Infarction, Nonfatal

Zanobetti

and

Schwartz

2006 Greater Boston,

MA

0-99 D24HourMean 0.0053 0.0022 Logistic Age range

adjusted.

Admissions

through ER

visits only.

Acute Myocardial

Infarction, Nonfatal

Zanobetti

et al.

2009 26 U.S.

Communities

0-99 D24HourMean 0.0022 0.0006 LoglinearAge range

adjusted. All

Seasons.

E.2.1 Abbey et al. (1995b)

Abbey et al. (1995b) examined the relationship between estimated PM2.5(annual mean from

1966 to 1977), PM10(annual mean from 1973 to 1977) and TSP (annual mean from 1973 to

1977) and the same chronic respiratory symptoms in a sample population of 1,868 Californian

Seventh Day Adventists. The initialsurvey was conducted in 1977 and the finalsurvey in 1987.

To ensure a better estimate of exposure, the study participants had to have been living in the

same area for an extended period of time. In single-pollutant models, there was a statistically

significant PM2.5relationship with development of chronic bronchitis, but not for AOD or asthma;

PM10 was significantly associated with chronic bronchitis and AOD; and TSP was significantly

associated with all cases of all three chronic symptoms. Other pollutants were not examined.

Chronic Bronchitis

The estimated coefficient (0.0137) is presented for a one μg/m3 change in PM2.5(Abbey et al.,

1995b, Table 2). The standard error is calculated from the reported relative risk (1.81) and

95% confidence interval(0.98-3.25) for a 45 μg/m3 change in PM2.5.

Incidence Rate: annual bronchitis incidence rate per person (Abbey et al., 1993, Table 3) =

0.00378

Population: population of ages 27 and older without chronic bronchitis = 95.57% of population 27+.

Using the same data set, Abbey et al. (1995a, p. 140) reported that the respondents in 1977

ranged in age from 27 to 95. The American Lung Association (2010a, Table 4) reports a chronic
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

103

BenMAP User's Manual Appendices

bronchitis prevalence rate for ages 18 and over of 4.37%.

E.2.2 Peters et al. (2001)

Peters et al. (2001) studied the relationship between increased particulate air pollution and onset

of heart attacks in the Boston area from 1995 to 1996. The authors used air quality data for

PM10, PM10-2.5, PM2.5,“black carbon”, O3, CO, NO2, and SO2in a case-crossover analysis.

For each subject, the case period was matched to three control periods, each 24 hours apart. In

univariate analyses, the authors observed a positive association between heart attack occurrence

and PM2.5levels hours before and days before onset. The authors estimated multivariate

conditional logistic models including two-hour and twenty-four hour pollutant concentrations for

each pollutant. They found significant and independent associations between heart attack

occurrence and both two-hour and twenty-four hour PM2.5concentrations before onset.

Significant associations were observed for PM10as well. None of the other particle measures or

gaseous pollutants were significantly associated with acute myocardial infarction for the two hour

or twenty-four hour period before onset.

The patient population for this study was selected from health centers across the United States.

The mean age of participants was 62 years old, with 21% of the study population under the age

of 50. In order to capture the full magnitude of heart attack occurrence potentially associated

with air pollution and because age was not listed as an inclusion criteria for sample selection, we

apply an age range of 18 and over in the C-R function. According to the National Hospital

Discharge Survey, there were no hospitalizations for heart attacks among children <15 years of

age in 1999 and only 5.5% of all hospitalizations occurred in 15-44 year olds (Popovic, 2001,

Table 10).

Acute Myocardial Infarction, Nonfatal

The coefficient and standard error are calculated from an odds ratio of 1.62 (95% CI 1.13-2.34)

for a 20 μg/m3increase in twenty-four hour average PM2.5(Peters et al., 2001, Table 4, p.

2813).

Incidence Rate: We use the county-specific daily AMI hospitalization rate (ICD-9 code 410) for

the population of individuals aged 18 years and older as the estimate for the incidence rate of

nonfatal heart attack, assuming all heart attacks that are not instantly fatal will result in a

hospitalization. We did not adjust for fatal AMIs in the incidence rate estimation, due to the way

that the epidemiologicalstudies are designed. Those studies consider total admissions for AMIs,

which includes individuals living at the time the studies were conducted. Therefore, we use the

definition of AMI that matches the definition in the epidemiologicalstudies.

Population: Population of ages 18 and older

Adjustment: As some fraction of the admitted individuals die in the hospital, we apply a survival

rate of 93% in calculating the avoided cases of AMI in order to avoid double counting (once in the

calculation of AMI cases and once in the calculation of PM-related mortality).
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

104

BenMAP User's Manual Appendices

E.2.3 Pope et al. (2006)

Pope et al. (2006) evaluated the association between short-term exposure to PM2.5and acute

ischemic heart disease events, including acute nonfatal myocardial infarction, all acute coronary

events, and subsequent myocardial infarctions in individuals living in greater Salt Lake City, Utah.

In a case-crossover study, these ischemic events were assessed in relation to a 10 μg/m3

increase in PM2.5. The researchers determined that a 10 μg/m3increase in PM2.5resulted in a

4.5% increase (95% CI: 1.1-8.0) in unstable angina and myocardial infarction.

Acute Myocardial Infarction, Nonfatal

Index MI and Unstable Angina

In a single-pollutant model the coefficient and standard error were estimated from the percent

increase (4.81%) and 95% confidence interval(95% CI: 0.98-8.79) for a 10 μg/m3increase in

daily 24-hour mean PM2.5(Pope et al., 2006, Table 3).

Incidence Rate: AMI hospital admission rate for all ages. See the incidence rate discussion under

Peters et al. (2001) in Section E.2.2.

Population: All ages

Adjustment: See the adjustment description in Section E.2.2.

E.2.4 Sullivan et al. (2005)

Sullivan et al. (2005) studied the relationship between onset time of acute myocardial infarction

and the preceding hourly PM2.5concentrations in 5,793 confirmed cased of myocardial infarction

through King County, Washington. In this case-crossover study from 1988-1994, air pollution

exposure levels averaged 1 hour, 2 hours, 4 hours, and 24 hours before onset of myocardial

infarction were compared to a set of time-stratified referent exposures from the same day of the

week in the month of the case event. The authors estimated that an associated risk of 1.01 (95%

CI: 0.98-1.05) for myocardial infarction onset could be attributed to a 10 μg/m3increase in PM

2.5

the hour before the MI onset. No increased risk was found in all cases with preexisting

cardiac diseases with an odds ratio of 1.05 (95% CI: 0.95-1.16). Furthermore, stratification for

hypertension, diabetes, and smoking status did not modify the association between PM2.5and

onset of myocardial infarction.

Acute Myocardial Infarction, Nonfatal

In a single-pollutant model the coefficient and standard error were estimated from the odds ratio

(1.02) and 95% confidence interval(95% CI: 0.98-1.07) for a 10 μg/m3increase in daily 24-

hour mean PM2.5lagged 1 day (Sullivan et al., 2005, Table 3).

Incidence Rate: AMI hospital admission rate for all ages. See the incidence rate discussion under

Peters et al. (2001) in Section E.2.2.
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

105

BenMAP User's Manual Appendices

Population: All ages

Adjustment: See the adjustment description in Section E.2.2.

E.2.5 Zanobetti and Schwartz (2006)

Zanobetti and Schwartz (2006) analyzed hospital admissions through emergency department for

myocardial infarction (ICD-9 code 410) and pneumonia (ICD-9 codes 480-487) for

associations with fine particulate air pollution, ozone, black carbon, nitrogen dioxide, PM not

from traffic, and CO in the greater Boston area from 1995-1999. The authors used a casecrossover analysis with control days matched on temperature. Significant associations were

detected for NO2 with a 12.7% increase 95% CI: 5.8-18.0), PM2.5 with an 8.6% increase

(95% CI: 1.2-15.4), and black carbon with an 8.3% increase (95% CI: 0.2-15.8) in emergency

myocardial infarction hospitalizations. Similarly, significant associations were identified for PM2.5

with a 6.5% increase (95% CI: 1.1-11.4) and CO with a 5.5% increase (95% CI: 1.1-9.5) in

pneumonia hospitalizations.

Acute Myocardial Infarction, Nonfatal

The study looked at hospital admissions of AMI through ER. Under the assumption that all heart

attacks will end in hospitalization, we consider the endpoint as heart attack events to be

consistent with other studies. In a single-pollutant model, the coefficient and standard error are

estimated from the percent change in risk (8.65%) and 95% confidence interval(95% CI: 1.22-

15.38%) for a 16.32 μg/m3 increase in daily 24-hour mean PM2.5for an average of the 0- and

1-day lag (Zanobetti A. and Schwartz, 2006, Table 4).

Incidence Rate: AMI hospital admission rate for all ages. See the incidence rate discussion under

Peters et al. (2001) in Section E.2.2.

Population: All ages. Note that although Zanobetti and Schwartz (2006) reports results for the 65-

99 year old age range, for comparability to other studies, we apply the results to all ages. Since the

vast majority of AMIs occur among population 65-99, over-counting may not be an issue when

applying the risk coefficient to all ages.

Adjustment: See the adjustment description in Section E.2.2.

E.2.6 Zanobetti et al. (2009)

Zanobetti et al. (2009) examined the relationship between daily PM2.5levels and emergency

hospital admissions for cardiovascular causes, myocardial infarction, congestive heart failure,

respiratory disease, and diabetes among 26 U.S. communities from 2000-2003. The authors

used meta-regression to examine how this association was modified by season- and communityspecific PM2.5

composition while controlling for seasonal temperature as a substitute for

ventilation. Overall, the authors found that PM2.5 mass higher in Ni, As, and Cr as well as Br

and organic carbon significantly increased its effects on hospital admissions. For a 10 μg/m3

increase in 2-day averaged PM2.5, a 1.89% (95% CI: 1.34-2.45) increase in cardiovascular
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

106

BenMAP User's Manual Appendices

disease admissions, a 2.25% (95% CI: 1.10-3.42) increase in myocardial infarction admissions,

a 1.85% (95% CI: 1.19-2.51) increase in congestive heart failure admissions, a 2.74% (95% CI:

1.30-4.20) increase in diabetes admissions, and a 2.07% (95% CI: 1.20-2.95) increase in

respiratory admissions were observed. The relationship between PM2.5and cardiovascular

admissions was significantly modified when the mass of PM2.5 was high in Br, Cr, Ni, and sodium

ions, while mass high in As, Cr, Mn, organic carbon, Ni and sodium ions modified the myocardial

infarction relationship and mass high in As, orgarnic carbon, and sulfate ions modified the

diabetes admission rates.

Acute Myocardial Infarction, Nonfatal

The study looked at hospital admissions of AMI through ER. Under the assumption that all heart

attacks will end in hospitalization, we consider the endpoint as heart attack events to be

consistent with other studies. In a single-pollutant model the coefficient and standard error are

estimated from the percent change in risk (2.25%) and 95% confidence interval(95% CI: 1.10-

3.42) for a 10 μg/m3increase in 2-day averaged PM2.5 (Zanobetti et al., 2009, Table 3).

Incidence Rate: AMI hospital admission rate for all ages. See the incidence rate discussion under

Peters et al. (2001) in Section E.2.2.

Population: All ages. Note that although Zanobetti et al. (2009) reports results for the 65-99 year

old age range, for comparability to other studies, we apply the results to all ages. Since the vast

majority of AMIs occur among population 65-99, over-counting may not be an issue when applying

the risk coefficient to all ages.

Adjustment: See the adjustment description in Section E.2.2.

E.3 Hospitalizations

Table E-7 summarizes the health impacts functions used to estimate the relationship between

PM2.5and hospital admissions. Below, we present a briefsummary of each of the studies and

any items that are unique to the study.

Table E-7. Health Impact Functions for Particulate Matter and Hospital Admissions

Eff ect Author Year Location Age Co-Poll Metric Beta Std Err Form Notes

Asthma Babin et al. 2007 Washington,

D.C.

0-17 D24HourMean 0.0020 0.0043 Log-linear Age range adjusted.

Admission from

emergency

department only.

All Cardiovascular (less

Myocardial Infarctions)

Bell et al. 2008 202 U.S.

Counties

65-99 D24HourMean 0.0008 0.0001 Log-linear Urgent admissions

only. Yearly

national estimates.

Congestive Heart Failure Ito 2003 Detroit, MI 65-99 D24HourMean 0.003074 0.001292 Log-linear

Dysrhythmia Ito 2003 Detroit, MI 65-99 D24HourMean 0.001249 0.002033 Log-linear

Ischemic Heart (less

Myocardial Infarctions)

Ito 2003 Detroit, MI 65-99 D24HourMean 0.001435 0.001156 Log-linear
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

107

BenMAP User's Manual Appendices

Eff ect Author Year Location Age Co-Poll Metric Beta Std Err Form Notes

Chronic Lung Ito 2003 Detroit, MI 65-99 D24HourMean 0.001169 0.002064 Log-linear

Pneumonia Ito 2003 Detroit, MI 65-99 D24HourMean 0.003979 0.001659 Log-linear

All Cardiovascular (less

Myocardial Infarctions)

Moolgavkar 2000 Los Angeles,

CA

18-64 D24HourMean 0.001400 0.000341 Log-linear

Chronic Lung (less

Asthma)

Moolgavkar 2000 Los Angeles,

CA

18-64 D24HourMean 0.002200 0.000733 Log-linear

All Cardiovascular (less

Myocardial Infarctions)

Moolgavkar 2003 Los Angeles,

CA

65-99 D24HourMean 0.001580 0.000344 Log-linear

Chronic Lung Moolgavkar 2003 Los Angeles,

CA

65-99 D24HourMean 0.001850 0.000524 Log-linear

All Cardiovascular (less

Myocardial Infarctions)

Peng et al. 2008 108 U.S.

Counties

65-99 D24HourMean 0.0007 0.0001 Log-linear Emergency HA only

All Cardiovascular (less

Myocardial Infarctions)

Peng et al. 2009 119 U.S.

Counties

65-99 D24HourMean 0.0007 0.0002 Log-linear Urgent or

emergency HA

Asthma Sheppard 2003 Seattle, WA 0-64 D24HourMean 0.003324 0.001045 Log-linear

All Cardiovascular (less

Myocardial Infarctions)

Zanobetti et

al.

2009 26 U.S.

Communities

65-99 D24HourMean 0.00019 0.0003 Log-linear All seasons

All Respiratory Zanobetti et

al.

2009 26 U.S.

Communities

65-99 D24HourMean 0.0021 0.0004 Log-linear All seasons

E.3.1 Babin et al. (2007)

Babin et al. (2007) examined pediatric asthma-related emergency room (ER) visits and hospital

admissions (ICD-9 code 493) in Washington, D.C. from 2001-2004 and their short-term

associations with ozone, particulate matter, socioeconomic status, and age group. The

association between PM2.5and asthma hospitalization was found statistically insignificant.

Hospital Admissions, Asthma (ICD-9 code 493)

In a single-pollutant model, the coefficient and standard error are estimated from the average

percent increase in risk (0.2%) and 95% confidence interval(95% CI:-0.6% - 1.1%) for a 1

μg/m3increase in same-day daily 24-hour mean PM2.5(Babin et al., 2007, Table 2).

Note that although Babin et al. (2007) reports results for the 1-17 year old age range, for

comparability to other studies, we apply the results to the population of ages 0 to 17.

E.3.2 Bell et al. (2008)

Bell et al. (2008) evaluated the association between short-term exposure to PM2.5and the risk of

cardiovascular (ICD-9 codes 410-414, 26-427, 428, 429, 430-438, and 440-449) and

respiratory (ICD-9 codes 464-466, 480-487, and 490-492) hospital admissions among

Medicare enrollees =65 years old varied by season and geographic region in 202 U.S. counties

with populations greater than 200,000 from 1999-2005. Three time-series models were used to

provide three key variables: consistent PM effects across the year, different PM effects by

season, and smoothly varying PM effects throughout the year. A two-stage Bayesian hierarchical
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

108

BenMAP User's Manual Appendices

model was used to estimate the association between PM2.5and hospitalization rates, with the first

stage estimating the association within a single county and the second stage combining countyspecific estimates. The authors found statistically significant evidence ofseasonal and regional

variation. Respiratory hospitalizations were highest in winter with a 1.05% increase (95%PI:

0.29-1.82) in hospitalizations per 10 μg/m3increase in same-day PM2.5. A 1.49% increase

(95% PI: 1.09-1.89) in cardiovascular hospital admissions were also found for the winter season,

and associations were observed in other seasons as well. The strongest association was for the

northeast for both respiratory and cardiovascular admissions.

Hospital Admissions, Cardio-, Cerebro- and Peripheral Vascular Disease (ICD-9 codes

426-427, 428, 430-438, 410-414, 429; 440-449)

For different seasons (i.e., autumn, spring, summer, winter, and all-year) and regions (i.e.,

southwest, northwest, southeast, southwest, and nationwide), the coefficient and standard error

are estimated from the average percent increase in risk and 95% confidence intervalfor a 10 μg/

m3increase in same-day (lag 0) daily 24-hour mean PM2.5(Bell et al., 2008, Table 2).

Note that Bell et al. (2008) considered a broader range of ICD-9 codes and estimated the risk

of both cardiovascular events and cerebro- and peripheral vascular disease. For comparability to

other studies, EPA decided to apply a baseline hospitalization rate for ICD-9 codes 390-409

and 411-429 when using this C-R function in quantifying impacts.

E.3.3 Ito (2003)

Lippmann et al. (2000) studied the association between particulate matter and daily mortality and

hospitalizations among the elderly in Detroit, MI. Data were analyzed for two separate study

periods, 1985-1990 and 1992-1994. The 1992-1994 study period had a greater variety of data

on PM size and was the main focus of the report. The authors collected hospitalization data for a

variety of cardiovascular and respiratory endpoints. They used daily air quality data for PM10,

PM2.5, and PM10-2.5in a Poisson regression model with generalized additive models (GAM) to

adjust for nonlinear relationships and temporal trends. In single pollutant models, all PM metrics

were statistically significant for pneumonia (ICD codes 480-486), PM10-2.5and PM10 were

significant for ischemic heart disease (ICD code 410-414), and PM2.5and PM10 were significant

for heart failure (ICD code 428). There were positive, but not statistically significant

associations, between the PM metrics and COPD (ICD codes 490-496) and dysrhythmia (ICD

code 427). In separate co-pollutant models with PM and either ozone, SO2, NO2, or CO, the

results were generally comparable.

In response to concerns with the Splus issue, Ito (2003) reanalyzed the study by Lippmann et al.

(2000). The reanalysis by Ito reported that more generalized additive models with stringent

convergence criteria and generalized linear models resulted in smaller relative risk estimates.
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

109

BenMAP User's Manual Appendices

Chronic Lung Disease (ICD-9 codes 490-496)

The coefficient and standard error are based on the relative risk (1.043) and 95% confidence

interval(0.902-1.207) for a 36 μg/m3increase in PM2.5in the 3-day lag GAM stringent model

(Ito, 2003, Table 8).

Pneumonia (ICD-9 codes 480-487)

The estimated PM2.5coefficient and standard error are based on a relative risk of 1.154 (95%

CI -1.027, 1.298) due to a PM2.5change of 36 μg/m3in the 1-day lag GAM stringent model

(Ito, 2003, Table 7).

Dysrhythmia (ICD-9 code 427)

The co-pollutant coefficient and standard error are calculated from a relative risk of 1.046 (95%

CI 0.906-1.207) for a 36 μg/m3increase in PM2.5in the 1-day lag GAM stringent model(Ito,

2003, Table 10).

Congestive Heart Failure (ICD-9 code 428)

The co-pollutant coefficient and standard error are calculated from a relative risk of 1.117 (95%

CI 1.020-1.224) for a 36 μg/m3increase in PM2.5in the 1-day lag GAM stringent model(Ito,

2003, Table 11).

Ischemic Heart Disease (ICD-9 codes 411-414)

The co-pollutant coefficient and standard error are calculated from a relative risk of 1.053 (95%

CI 0.971-1.143) for a 36 μg/m3increase in PM2.5in the 1-day lag GAM stringent model(Ito,

2003, Table 9)

Note that Lippmann et al. (2000) report results for ICD codes 410-414. In the benefit analysis,

avoided nonfatal heart attacks are estimated using the results reported by Peters et al. (2001).

The baseline rate in the Peters et al. function is a modified heart attack hospitalization rate (ICD

code 410), since most, if not all, nonfatal heart attacks willrequire hospitalization. In order to

avoid double counting heart attack hospitalizations, we have excluded ICD code 410 from the

baseline incidence rate used in this function.

E.3.4 Moolgavkar (2000a), Chronic Lung

Moolgavkar (2000a) examined the association between air pollution and COPD hospital

admissions (ICD 490-496) in the Chicago, Los Angeles, and Phoenix metropolitan areas. He

collected daily air pollution data for ozone, SO2, NO2, CO, and PM10in all three areas. PM2.5

data was available only in Los Angeles. The data were analyzed using a Poisson regression

model with generalized additive models to adjust for temporal trends. Separate models were run
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

110

BenMAP User's Manual Appendices

for 0 to 5 day lags in each location. Among the 65+ age group in Chicago and Phoenix, weak

associations were observed between the gaseous pollutants and admissions. No consistent

associations were observed for PM10. In Los Angeles, marginally significant associations were

observed for PM2.5, which were generally lower than for the gases. In co-pollutant models with

CO, the PM2.5effect was reduced. Similar results were observed in the 0-19 and 20-64 year

old age groups.

The PM2.5 C-R functions are based on the single and co-pollutant models (PM2.5and CO)

reported for the 20-64 and 65+ age groups. Since the true PM effect is most likely best

represented by a distributed lag model, then any single lag modelshould underestimate the total

PM effect. As a result, we selected the lag models with the greatest effect estimates for use in the

C-R functions.

Hospital Admissions, Chronic Lung Disease Less Asthma (ICD-9 codes 490-492, 494-

496)

In a model with CO, the coefficient and standard error are calculated from an estimated percent

change of 2.0 and t-statistic of 2.2 for a 10 μg/m3increase in PM2.5in the two-day lag model

(Moolgavkar, 2000a, Table 4, p. 81). In a log-linear model, the percent change is equal to (RR -

1) * 100.

In this study, Moolgavkar defines and reports the “estimated” percent change as (log RR * 100).

Because the relative risk is close to 1, RR-1 and log RR are essentially the same. For example, a

true percent change of 2.0 would result in a relative risk of 1.020 and coefficient of 0.001980.

The “estimated” percent change, as reported by Moolgavkar, of 2.0 results in a relative risk of

1.020201 and coefficient of 0.002.

Note that although Moolgavkar (2000a) reports results for the 20-64 year old age range, for

comparability to other studies, we apply the results to the population of ages 18 to 64. Note also

that in order to avoid double counting non-elderly asthma hospitalizations (ICD code 493), which

are typically estimated separately in EPA benefit analyses, we have excluded ICD code 493

from the baseline incidence rate used in this function.

E.3.5 Moolgavkar (2000b), Cardiovascular

Moolgavkar (2000b) examined the association between air pollution and cardiovascular hospital

admissions (ICD 390-429) in the Chicago, Los Angeles, and Phoenix metropolitan areas. He

collected daily air pollution data for ozone, SO2, NO2, CO, and PM10in all three areas. PM2.5

data was available only in Los Angeles. The data were analyzed using a Poisson regression

model with generalized additive models to adjust for temporal trends. Separate models were run

for 0 to 5 day lags in each location. Among the 65+ age group, the gaseous pollutants generally

exhibited stronger effects than PM10or PM2.5. The strongest overall effects were observed for

SO2and CO. In a single pollutant model, PM2.5 was statistically significant for lag 0 and lag 1.

In co-pollutant models with CO, the PM2.5effect dropped out and CO remained significant. For
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

111

BenMAP User's Manual Appendices

ages 20-64, SO2and CO exhibited the strongest effect and any PM2.5effect dropped out in copollutant models with CO.

Hospital Admissions, All Cardiovascular (ICD codes 390-409, 411-429)

The single pollutant coefficient and standard error are calculated from an estimated percent

change of 1.4 and t-statistic of 4.1 for a 10 μg/m3increase in PM2.5in the zero lag model

(Moolgavkar, 2000b, Table 4, p. 1203).

Note that (Moolgavkar (2000b) report results that include ICD code 410 (heart attack). In a

benefit analysis, avoided nonfatal heart attacks are typically estimated separately. The baseline

rate in the Peters et al. function is a modified heart attack hospitalization rate (ICD code 410),

since most, if not all, nonfatal heart attacks willrequire hospitalization. In order to avoid double

counting heart attack hospitalizations, we have excluded ICD code 410 from the baseline

incidence rate used in this function.

E.3.6 Moolgavkar (2003)

Moolgavkar (2000a) examined the association between air pollution and COPD hospital

admissions (ICD 490-496) in the Chicago, Los Angeles, and Phoenix metropolitan areas. In

response to concerns with Splus issue, Moolgavkar (2003) reanalyzed his earlier studies. In the

reanalysis, he reported that more generalized additive models with stringent convergence criteria

and generalized linear models resulted in smaller relative risk estimates.

Hospital Admissions, Chronic Lung (ICD-9 codes 490-496)

The coefficient and standard error are calculated from an estimated percentage change of 1.85

and a t-statistic of 3.53 for a 10 μg/m3increase in PM2.5in the 2-day lag GAM-30dfstringent

(10

-8

) model(Moolgavkar, 2003, Table 17). In a log-linear model, the percent change is equal

to (RR - 1) * 100.

The PM2.5 C-R functions for the 65+ age group are based on the reanalysis in Moolgavkar

(2003) of the single and co-pollutant models (PM2.5and CO). The true PM effect is most likely

best represented by a distributed lag model, then any single lag modelshould underestimate the

total PM effect. As a result, we selected the lag models with the greatest effect estimates for use

in the C-R functions.

Hospital Admissions, All Cardiovascular (ICD-9 codes 390-429)

The single pollutant coefficient and standard error are calculated from an estimated percent

change of 1.58 and t-statistic of 4.59 for a 10 μg/m3increase in PM2.5in the 0-day lag GAM30dfstringent (10-8) model(Moolgavkar, 2003, Table 12). In a log-linear model, the percent

change is equal to (RR - 1) * 100.
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

112

BenMAP User's Manual Appendices

E.3.7 Peng et al. (2008)

Peng et al. (2008) examined the risk of hospital admissions for cardiovascular and respiratory

diseases in relation to particulate matter (PM10-2.5and PM2.5). To accomplish this, the authors

utilized a database of 108 U.S. counties with daily emergency hospital admission rates for

cardiovascular and respiratory diseases among Medicare enrollees living 9 miles from air

monitors, temperature, and dew-point temperature. PM10-2.5and PM2.5concentrations were

calculated by using monitoring data from January 1, 1999 through December 31, 2005. Overall,

there were 3.7 million cardiovascular disease and 1.4 million respiratory disease-related hospital

admissions for the time period assessed. The authors found that a 10 μg/m3increase in PM10-2.5

was associated with a 0.36% increase (95% PI: 0.05-0.68%) in cardiovascular disease

admissions on the same day, and a 0.25% increase (95% PI:-0.11-0.60%) after adjusting for

PM2.5. For respiratory disease admissions, a 10 μg/m3increase in PM10-2.5 was found to be

associated with an unadjusted 0.33% increase in respiratory disease admissions (95% PI:-0.21-

0.86%) and an adjusted 0.26% increase (95% PI:-0.32-0.84%) in emergency admissions.

Also, unadjusted associations of PM2.5 with cardiovascular and respiratory disease admissions

were 0.71% (95% PI: 0.45-0.96%) for same-day exposure and 0.44% (95% PI: 0.06-0.82%)

for exposure lagged by 2 days prior to hospital admission.

Hospital Admissions, Cardio-, Cerebro-, and Peripheral Vascular Disease (ICD-9 codes

426-427, 428, 430-438, 410-414, 429, 440-448)

In a single-pollutant model, the coefficient and standard error are estimated from the percent

change in daily admission (0.44%) and 95% posterior interval(95% PI: 0.06-0.82%) for a 10

μg/m3increase in daily 24-hour mean PM2.5concentrations for the same day (Peng et al., 2008,

page 2175).

Note that Peng et al. (2008) considered a broader range of ICD-9 codes and estimated the risk

of both cardiovascular events and cerebro- and peripheral vascular disease. For comparability to

other studies, EPA decided to apply a baseline hospitalization rate for ICD-9 codes 390-409

and 411-429 when using this C-R function in quantifying impacts.

E.3.8 Peng et al. (2009)

Peng et al. (2009) investigated the relationship between hospital admissions for cardiovascular

and respiratory disease and the chemical components of PM2.5across 119 U.S. urban

communities for 12 million Medicare enrollees using log-linear Poisson regression models. This

was achieved using a national database with daily data from 2000-2006 on emergency hospital

admissions of cardiovascular and respiratory outcomes, ambient levels of PM2.5components and

weather variables. Bayesian hierarchicalstatistical models were used to estimate the

associations. Three scenarios for PM2.5exposure were assessed which were as follows: 1) for

the period 2000-2006 and including only days with available measurements for all 7 PM2.5
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

113

BenMAP User's Manual Appendices

components from the Speciation Trends network (STN); 2) PM2.5 measured by the STN for the

period 2000-2006 and including only days with available measurements for all 7 PM2.5

components from the STN and 3) PM2.5estimated as the sum of the 7 largest components of

PM2.5 mass for the period 2000-2006. Results of percent increases in emergency admissions

associated with PM2.5at lag 0 under these scenarios were showed in Figure 2 and the results for

the components of PM2.5from both single and multi-pollutant models were showed in Figure 3.

In multi-pollutant models that adjusted for the levels of other pollutants, the authors found that an

interquartile range increase in elemental carbon was associated with a 0.80% increase (95% PI:

0.34-1.27%) in risk ofsame-day cardiovascular admissions. Similarly, an interquartile range

increase in organic carbon matter was associated with a 1.01% increase (95% PI: 0.04-1.98%)

risk of respiratory admissions on the same day.

Hospital Admissions, Cardio-, Cerebro-, and Peripheral Vascular Disease (ICD-9 codes

426-427, 428, 430-438, 410-414, 429, 440-448)

In a single-pollutant model, the coefficient and standard error are estimated from the percent

change in daily admission (0.68%) and 95% posterior interval(95% PI: 0.26-1.10%) for a 10

μg/m3increase in daily 24-hour mean PM2.5concentrations for the same day (Peng et al., 2009,

page 960).

Note that Peng et al. (2009) considered a broader range of ICD-9 codes and estimated the risk

of both cardiovascular events and cerebro- and peripheral vascular disease. For comparability to

other studies, EPA decided to apply a baseline hospitalization rate for ICD-9 codes 390-409

and 411-429 when using this C-R function in quantifying impacts.

E.3.9 Sheppard (2003)

Sheppard et al. (1999) studied the relation between air pollution in Seattle and nonelderly (<65)

hospital admissions for asthma from 1987 to 1994. They used air quality data for PM10, PM2.5,

coarse PM10-2.5, SO2, ozone, and CO in a Poisson regression model with controlfor time

trends, seasonal variations, and temperature-related weather effects. PM2.5levels were estimated

from light scattering data. They found asthma hospital admissions associated with PM10, PM2.5,

PM10-2.5, CO, and ozone. They did not observe an association for SO2. They found PM and

CO to be jointly associated with asthma admissions. The best fitting co-pollutant models were

found using ozone. However, ozone data was only available April through October, so they did

not consider ozone further. For the remaining pollutants, the best fitting models included PM2.5

and CO. Results for other co-pollutant models were not reported.

In response to concerns that the work by Sheppard et al. (1999) may be biased because of the

Splus issue, Sheppard (2003) reanalyzed some of this work, in particular Sheppard reanalyzed

the originalstudy’s PM2.5single pollutant model.
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

114

BenMAP User's Manual Appendices

Hospital Admissions, Asthma (ICD-9 code 493)

The coefficient and standard error are based on the relative risk (1.04) and 95% confidence

interval(1.01-1.06) for a 11.8 μg/m3increase in PM2.5in the 1-day lag GAM stringent model

(Sheppard, 2003, pp. 228-229).

E.3.10 Zanobetti et al. (2009)

Zanobetti et al. (2009) examined the relationship between daily PM2.5levels and emergency

hospital admissions for cardiovascular causes, myocardial infarction, congestive heart failure,

respiratory disease and diabetes among 26 U.S. communities from 2000-2003. The authors

used meta-regression to examine how this association was modified by season- and communityspecific PM2.5

composition while controlling for seasonal temperature as a substitute for

ventilation. Overall, the authors found that PM2.5 mass higher in Ni, As, and Cr as well as Br

and organic carbon significantly increased its effects on hospital admissions. For a 10 μg/m3

increase in 2-day averaged PM2.5, the authors found a 1.89% (95% CI: 1.34-2.45) increase in

cardiovascular disease admissions, a 2.25% (95% CI: 1.10-3.42) increase in myocardial

infarction admissions, a 1.85% (95% CI: 1.19-2.51) increase in congestive heart failure

admissions, a 2.74% (95% CI: 1.30-4.20) increase in diabetes admissions, and a 2.07% (95%

CI: 1.20-2.95) increase in respiratory admissions. The relationship between PM2.5and

cardiovascular admissions was significantly modified when the mass of PM2.5 was high in Br, Cr,

Ni, and sodium ions, while mass high in As, Cr, Mn, organic carbon, Ni and sodium ions

modified the myocardial infarction relationship and mass high in As, organic carbon, and sulfate

ions modified the diabetes admission rates.

Hospital Admissions, All Cardiovascular (ICD-9 codes 390-429)

In a single-pollutant model, the coefficient and standard error are estimated from the percent

change in risk (1.89%) and 95% confidence interval(1.34%-2.45%) for a 10 μg/m3increase in

2-day averaged PM2.5(Zanobetti et al., 2009, Table 3).

Note that Zanobetti et al. (2009) report results for ICD codes 390-429. In the benefit analysis,

avoided nonfatal heart attacks are estimated separately. In order to avoid double counting heart

attack hospitalizations, we have excluded ICD code 410 from the baseline incidence rate used in

this function.

Hospital Admissions, All Respiratory (ICD-9 codes 460-519)

In a single-pollutant model, the coefficient and standard error are estimated from the percent

change in risk (2.07%) and 95% confidence interval(1.2% - 2.95%) for a 10 μg/m3increase in

2-day averaged PM2.5(Zanobetti et al., 2009, Table 3).
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

115

BenMAP User's Manual Appendices

E.4 Emergency Room Visits

Table E-8 summarizes the health impacts functions used to estimate the relationship between

PM2.5and emergency room visits. Below, we present a briefsummary of each of the studies and

any items that are unique to the study.

Table E-8. Health Impact Functions for Particulate Matter and Emergency Room Visits

Effect Author Year Lcoation Age Co-Poll Metric Beta Std Err Form

Asthma Mar et al. 2010

Greater

Tacoma, WA 0-99 D24HourMean 0.0056 0.0021 Log-linear

Asthma Norris et al. 1999 Seattle, WA 0-17

NO2,

SO2 D24HourMean 0.016527 0.004139 Log-linear

Asthma

Slaughter et

al. 2005 Spokane, WA 0-99 D24HourMean 0.0029 0.0027 Log-linear

E.4.1 Mar et al. 2010

Mar et al. (2010) assessed the effect of particulate matter air pollution, including emissions from

diesel generators, on emergency room visits for asthma in the greater Tacoma, Washington area

from January 3, 1998, to May 30, 2002, using Poisson regression models. Health data were

collected for individuals of all ages from 6 Tacoma hospitals. The authors also assessed the

impacts of diesel generator use on emergency room visits for asthma from January 24, 2001, to

June 2, 2001. Overall, the researchers found an association between daily PM2.5levels and

emergency room visits for asthma at lag days 2 and 3, with a relative risk for lag day 2 of 1.04

(95% CI: 1.01-1.07) and a relative risk for lag day 3 of 1.03 (95% CI: 1.0-1.06). No significant

association between emergency room visits for asthma and increased use of the diesel generators

was observed.

Emergency Room Visits, Asthma (ICD-9 code not reported)

In a single-pollutant model, the coefficient and standard error are estimated from the relative risk

(1.04) and 95% confidence interval(95% CI: 1.01-1.07) for a 7 μg/m3increase in daily 24-hour

mean PM2.5at lag day 2 (Mar et al., 2010, Table 4).

E.4.2 Norris et al. (1999)

Norris et al. (1999) examined the relation between air pollution in Seattle and childhood (<18)

hospital admissions for asthma from 1995 to 1996. The authors used air quality data for PM10,
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

116

BenMAP User's Manual Appendices

light scattering (used to estimate fine PM), CO, SO2, NO2, and O3in a Poisson regression

model with adjustments for day of the week, time trends, temperature, and dew point. They

found significant associations between asthma ER visits and light scattering (converted to PM2.5),

PM10, and CO. No association was found between O3, NO2, or SO2and asthma ER visits,

although O3had a significant amount of missing data. In multipollutant models with either PM

metric (light scattering or PM10) and NO2and SO2, the PM coefficients remained significant

while the gaseous pollutants were not associated with increased asthma ER visits. The PM C-R

functions are based on results of the single and multipollutant models reported.

Emergency Room Visits, Asthma

In a model with NO2and SO2, the PM2.5coefficient and standard error are calculated from a

relative risk of 1.17 (95% CI 1.08-1.26) for a 9.5 μg/m3increase in PM2.5(Norris et al., 1999,

p. 491).

E.4.3 Slaughter et al. (2005)

Slaughter et al. (2005) examined the short-term association of particulate matter (PM1, PM2.5,

PM10, and PM10-2.5) and carbon monoxide with hospital admissions and emergency room visits

for respiratory and cardiac outcomes and mortality in Spokane, Washington, from January 1995

to June 2001 using a log-linear generalized linear model. The authors found no association

between respiratory emergency room visits and any size fraction of PM, but there was a

suggestive relationship between fine PM and respiratory effects when compared to coarse PM.

No association between cardiac hospital admissions or mortality and any size fraction of PM or

CO was observed at the 0- to 3-day lag. CO, on the other hand, was found to be associated

with allrespiratory emergency room visits and visits for asthma at the 3-day lag.

Emergency Room Visits, Asthma (ICD-9 code 493)

In a single-pollutant model, the coefficient and standard error are estimated from the relative risk

(1.03) and 95% confidence interval(95% CI: 0.98-1.09) for a 10 μg/m3increase in daily 24-

hour mean PM2.5at 1-day lag (Slaughter et al., 2005, Table 4).

E.5 Minor Effects

Table E-9 summarizes the health impacts functions used to estimate the relationship between

PM2.5and minor effects. Below, we present a briefsummary of each of the studies and any

items that are unique to the study.
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

117

BenMAP User's Manual Appendices

Table E-9. Health Impact Functions for Particulate Matter and Minor Effects

Effect Author Year Location Age Co-Poll Metric Beta Std Err Form

Acute Bronchitis Dockery et al. 1996 24

communities

8-12 Annual 0.027212 0.017096 Logistic

Work Loss Days Ostro 1987 Nationwide 18-64 D24HourMean 0.004600 0.000360 Log-linear

Minor Restricted

Activity Days

Ostro and

Rothschild

1989 Nationwide 18-64 Ozone D24HourMean 0.007410 0.000700 Log-linear

Lower

Respiratory

Symptoms

Schwartz and

Neas

2000 6 U.S. cities 7-14 D24HourMean 0.019012 0.006005 Logistic

E.5.1 Dockery et al. (1996)

Dockery et al. (1996) examined the relationship between PM and other pollutants on the

reported rates of asthma, persistent wheeze, chronic cough, and bronchitis, in a study of 13,369

children ages 8-12 living in 24 communities in U.S. and Canada. Health data were collected in

1988-1991, and single-pollutant models were used in the analysis to test a number of measures

of particulate air pollution. Dockery et al. found that annual level ofsulfates and particle acidity

were significantly related to bronchitis, and PM2.1and PM10 were marginally significantly related

to bronchitis. The originalstudy measured PM2.1, however when using the study's results we use

PM2.5. This makes only a negligible difference, assuming that the adverse effects of PM2.1and

PM2.5are comparable. They also found nitrates were linked to asthma, and sulfates linked to

chronic phlegm. It is important to note that the study examined annual pollution exposures, and

the authors did not rule out that acute (daily) exposures could be related to asthma attacks and

other acute episodes. Earlier work, by Dockery et al. (1989), based on six U.S. cities, found

acute bronchitis and chronic cough significantly related to PM15. Because it is based on a larger

sample, the Dockery et al. (1996) study is the better study to develop a C-R function linking

PM2.5 with bronchitis.

Bronchitis was counted in the study only if there were “reports ofsymptoms in the past 12

months” (Dockery et al., 1996, p. 501). It is unclear, however, if the cases of bronchitis are

acute and temporary, or if the bronchitis is a chronic condition. Dockery et al. found no

relationship between PM and chronic cough and chronic phlegm, which are important indicators

of chronic bronchitis. For this analysis, we assumed that the C-R function based on Dockery et

al. is measuring acute bronchitis. The C-R function is based on results of the single pollutant

modelreported in Table 1.

Acute Bronchitis

The estimated logistic coefficient and standard error are based on the odds ratio (1.50) and 95%

confidence interval(0.91-2.47) associated with being in the most polluted city (PM2.1 = 20.7

μg/m3) versus the least polluted city (PM2.1 = 5.8 μg/m3) (Dockery et al., 1996, Tables 1 and 4).
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

118

BenMAP User's Manual Appendices

The originalstudy used PM2.1, however, we use the PM2.1coefficient and apply it to PM2.5

data.

Incidence Rate: annual bronchitis incidence rate per person = 0.043 (American Lung Association,

2002a, Table 11)

Population: population of ages 8-12.

E.5.2 Ostro (1987)

Ostro (1987) estimated the impact of PM2.5on the incidence of work-loss days (WLDs),

restricted activity days (RADs), and respiratory-related RADs (RRADs) in a nationalsample of

the adult working population, ages 18 to 65, living in metropolitan areas. The study population is

based on the Health Interview Survey (HIS), conducted by the NationalCenter for Health

Statistics. The annual nationalsurvey results used in this analysis were conducted in 1976-1981.

Ostro reported that two-week average PM2.5levels were significantly linked to work-loss days,

RADs, and RRADs, however there was some year-to-year variability in the results. Separate

coefficients were developed for each year in the analysis (1976-1981); these coefficients were

pooled. The coefficient used in the concentration-response function presented here is a weighted

average of the coefficients in Ostro (1987, Table III) using the inverse of the variance as the

weight.

Work Loss Days

The coefficient used in the C-R function is a weighted average of the coefficients in Ostro (1987,

Table III) using the inverse of the variance as the weight:

The standard error of the coefficient is calculated as follows, assuming that the estimated yearspecific coefficients are independent:

This eventually reduces down to:
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

119

BenMAP User's Manual Appendices

Incidence Rate: daily work-loss-day incidence rate per person ages 18 to 64 = 0.00595 (U.S.

Bureau of the Census, 1997, No. 22; Adams et al., 1999, Table 41)

Population: adult population ages 18 to 64

E.5.3 Ostro and Rothschild (1989)

Ostro and Rothschild (1989) estimated the impact of PM2.5and ozone on the incidence of minor

restricted activity days (MRADs) and respiratory-related restricted activity days (RRADs) in a

nationalsample of the adult working population, ages 18 to 65, living in metropolitan areas. The

study population is based on the Health Interview Survey (HIS), conducted by the National

Center for Health Statistics. In publications from this ongoing survey, non-elderly adult

populations are generally reported as ages 18-64. From the study, it is not clear if the age range

stops at 65 or includes 65 year olds. We apply the C-R function to individuals ages 18-64 for

consistency with other studies estimating impacts to non-elderly adult populations. The annual

nationalsurvey results used in this analysis were conducted in the period 1976-1981. Controlling

for PM2.5, two-week average ozone has highly variable association with RRADs and MRADs.

Controlling for ozone, two-week average PM2.5 was significantly linked to both health endpoints

in most years.

Minor Restricted Activity Days

Using the results of the two-pollutant model, we developed separate coefficients for each year in

the analysis, which were then combined for use in this analysis. The coefficient is a weighted

average of the coefficients in Ostro and Rothschild (1989, Table 4) using the inverse of the

variance as the weight:

The standard error of the coefficient is calculated as follows, assuming that the estimated yearspecific coefficients are independent:
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

120

BenMAP User's Manual Appendices

This reduces down to:

Incidence Rate: daily incidence rate for minor restricted activity days (MRAD) = 0.02137 (Ostro

and Rothschild, 1989, p. 243)

Population: adult population ages 18 to 64

E.5.4 Schwartz and Neas (2000)

Schwartz et al. (2000) replicated a previous analysis (Schwartz et al., 1994) linking PM levels to

lower respiratory symptoms in children in six cities in the U.S. The originalstudy enrolled 1,844

children into a year-long study that was conducted in different years (1984 to 1988) in six cities.

The students were in grades two through five at the time of enrollment in 1984. By the

completion of the finalstudy, the cohort would then be in the eighth grade (ages 13-14); this

suggests an age range of 7 to 14. The previous study focused on PM10, acid aerosols, and

gaseous pollutants, although single-pollutant PM2.5results were reported. Schwartz et al. (2000)

focused more on the associations between PM2.5and PM10-2.5and lower respiratory symptoms.

In single and co-pollutant models, PM2.5 was significantly associated with lower respiratory

symptoms, while PM10-2.5 was not. PM10-2.5exhibited a stronger association with cough than

did PM2.5. The PM2.5 C-R functions for lower respiratory symptoms are based on the results of

the reported single pollutant and co-pollutant model(PM2.5and PM10-2.5).

Lower Respiratory Symptoms

The coefficient and standard error are calculated from the reported odds ratio (1.33) and 95%

confidence interval(1.11-1.58) associated with a 15 μg/m3 change in PM2.5(Schwartz and

Neas, 2000, Table 2).

Incidence Rate: daily lower respiratory symptom incidence rate per person = 0.0012 (Schwartz et

al., 1994, Table 2)

Population: population of ages 7 to 14
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

121

BenMAP User's Manual Appendices

E.6 Asthma-Related Effects

Table E-10 summarizes the health impacts functions used to estimate the relationship between

PM2.5and asthma exacerbation. Below, we present a briefsummary of each of the studies and

any items that are unique to the study.

Table E-10. Health Impact Functions for Particulate Matter and Asthma-Related

Effects

Effect Author Year Location Age CoPollMetric Beta Std Err Form Notes

Cough Mar et al. 2004 Spokane, WA 6-18 D24HourMean 0.0191 0.0098 Logistic Uses

incidence

rate from

Ostro et al.

(2001). Age

range

adjusted.

Shortness

of Breath

Mar et al. 2004 Spokane, WA 6-18 D24HourMean 0.0122 0.0138 Logistic Uses

incidence

rate from

Ostro et al.

(2001). Age

range

adjusted.

Cough Ostro et

al.

2001 Los Angeles,

CA

6-18 D24HourMean 0.00098

5

0.00074

7

Logistic

Shortness

of Breath

Ostro et

al.

2001 Los Angeles,

CA

6-18 D24HourMean 0.00256

5

0.00133

5

Logistic

Wheeze Ostro et

al.

2001 Los Angeles,

CA

6-18 D24HourMean 0.00194

2

0.00080

3

Logistic

Upper

Respiratory

Symptoms

Pope et

al.

1991 Utah Valley 9-11 D24HourMean 0.00360

0

0.00150

0

Logistic

E.6.1 Mar et al. (2004)

Mar et al. (2004) studied the effects of various size fractions of particulate matter on respiratory

symptoms of adults and children with asthma, monitored over many months. The study was

conduced in Spokane, Washington, a semiarid city with diverse sources of particulate matter.

Data on respiratory symptoms and medication use were recorded daily by the study’s subjects,

while air pollution data was collected by the local air agency and Washington State University.

Subjects in the study consisted of 16 adults—the majority of whom participated for over a year

—and nine children, all of whom were studied for over eight months. Among the children, the

authors found a strong association between cough symptoms and several metrics of particulate
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

122

BenMAP User's Manual Appendices

matter, including PM2.5. However, the authors found no association between respiratory

symptoms and PM of any metric in adults. Mar et al. therefore concluded that the discrepancy in

results between children and adults was due either to the way in which air quality was monitored,

or a greater sensitivity of children than adults to increased levels of PM air pollution.

Asthma Exacerbation, Cough

In a single-pollutant model, the coefficient and standard error are estimated from the odds ratio

(1.21) and 95% confidence interval(1.00-1.47) for a 10.0 μg/m3increase in 1-day lagged

concentration of PM2.5(Mar et al., 2004, Table 7).

Incidence Rate: Daily cough rate per person = 14.5%. Mar et al. (2004) did not report the

incidence rate for each type of asthma exacerbation. The daily cough rate from Ostro et al. (2001,

p.202) is applied here.

Population: The study reported results for population ages 7-12. For comparability to other studies,

we apply the results to the population of ages 6 to 18. We treat this as two groups based on the

available information from American Lung Association (2010b, Table 7). Asthmatic population ages

6 to 17 = 10.70% of population ages 6 to 17 and asthmatic population age 18 = 7.19% of population

age 18. The American Lung Association (2010b, Table 7) estimates asthma prevalence for children

5- 17 and adults 18-44 at 10.70% and 7.19% respectively (based on data from the 2008 National

Health Interview Survey).

Asthma Exacerbation, Shortness of Breath

In a single-pollutant model, the coefficient and standard error are estimated from the odds ratio

(1.13) and 95% confidence interval(0.86-1.48) for a 10.0 μg/m3increase in current-day

concentration of PM2.5(Mar et al., 2004, Table 7).

Incidence Rate: Daily shortness of breath rate per person = 7.4%. Mar et al. (2004) did not report

the incidence rate for each type of asthma exacerbation. The daily rate of shortness of breath from

Ostro et al. (2001, p.202) is applied here.

Population: See the population description for “Asthma Exacerbation, Cough” from Mar et al.

(2004).

E.6.2 Ostro et al. (2001)

Ostro et al. (2001) studied the relation between air pollution in Los Angeles and asthma

exacerbation in African-American children (8 to 13 years old) from August to November 1993.

They used air quality data for PM10, PM2.5, NO2, and O3in a logistic regression model with

controlfor age, income, time trends, and temperature-related weather effects. The authors note

that there were 26 days in which PM2.5concentrations were reported higher than PM10

concentrations. The majority of results the authors reported were based on the full dataset.

These results were used for the basis for the C-R functions. Asthma symptom endpoints were

defined in two ways: “probability of a day with symptoms” and “onset ofsymptom episodes”.
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

123

BenMAP User's Manual Appendices

New onset of a symptom episode was defined as a day with symptoms followed by a symptomfree day.

The authors found cough prevalence associated with PM10and PM2.5and cough incidence

associated with PM2.5, PM10, and NO2. Ozone was not significantly associated with cough

among asthmatics. The authors found that both the prevalent and incident episodes ofshortness

of breath were associated with PM2.5and PM10. Neither ozone nor NO2 were significantly

associated with shortness of breath among asthmatics. The authors found both the prevalence

and incidence of wheeze associated with PM2.5, PM10, and NO2. Ozone was not significantly

associated with wheeze among asthmatics.

The derived health impact functions are based on the results ofsingle pollutant models looking at

the probability ofsymptoms.

Asthma Exacerbation, Cough

The coefficient and standard error are based on an odds ratio of 1.03 (95% CI 0.98-1.07) for a

30 μg/m3increase in 12-hour average PM2.5concentration (Ostro et al., 2001, Table 4, p.204).

Incidence Rate: daily cough rate per person (Ostro et al., 2001, p.202) = 0.145

Population: The study reported results for African-American population ages 8-13. For

comparability to other studies, we apply the results to the African-American population of ages 6 to

18. We treat this as two groups based on the available information from American Lung

Association (2010b, Table 9). Asthmatic African-American population ages 6 to 17 = 17.76% of

African-American population ages 6 to 17 and asthmatic African-American population age 18 =

7.52% of African-American population age 18. The American Lung Association (2010b, Table 9)

estimates asthma prevalence for children 5- 17 and adults 18-44 at 17.76% and 7.52% respectively

(based on data from the 2008 National Health Interview Survey).

Asthma Exacerbation, Shortness of Breath

The coefficient and standard error are based on an odds ratio of 1.08 (95% CI 1.00-1.17) for a

30 μg/m3increase in 12-hour average PM2.5concentration (Ostro et al., 2001, Table 4, p.204).

Incidence Rate: daily shortness of breath rate per person (Ostro et al., 2001, p.202) = 0.074

Population: The study reported results for African-American population ages 8-13. For

comparability to other studies, we apply the results to the African-American population of ages 6 to

18. We treat this as two groups based on the available information from American Lung

Association (2010b, Table 9). Asthmatic African-American population ages 6 to 17 = 17.76% of

African-American population ages 6 to 17 and asthmatic African-American population age 18 =

7.52% of African-American population age 18. The American Lung Association (2010b, Table 9)

estimates asthma prevalence for children 5- 17 and adults 18-44 at 17.76% and 7.52% respectively

(based on data from the 2008 National Health Interview Survey).
Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

November 2012

124

BenMAP User's Manual Appendices

Asthma Exacerbation, Wheeze

The coefficient and standard error are based on an odds ratio of 1.06 (95% CI 1.01-1.11) for a

30 μg/m3increase in 12-hour average PM2.5concentration (Ostro et al., 2001, Table 4, p.204).

Incidence Rate: daily wheeze rate per person (Ostro et al., 2001, p.202) = 0.173

Population: asthmatic African-American population ages 8 to 13 = 17.76% of African-American

population ages 8 to 13. (Described above.)

E.6.3 Pope et al. (1991)

Using logistic regression, Pope et al. (1991) estimated the impact of PM10on the incidence of a

variety of minor symptoms in 55 subjects (34 “school-based” and 21 “patient-based”) living in

the Utah Valley from December 1989 through March 1990. The children in the Pope et al. study

were asked to record respiratory symptoms in a daily diary. With this information, the daily

occurrences of upper respiratory symptoms (URS) and lower respiratory symptoms (LRS) were

related to daily PM10concentrations. Pope et al. describe URS as consisting of one or more of

the following symptoms: runny or stuffy nose; wet cough; and burning, aching, or red eyes.

Levels of ozone, NO2, and SO2 were reported low during this period, and were not included in

the analysis. The sample in this study is relatively small and is most representative of the

asthmatic population, rather than the general population. The school-based subjects (ranging in

age from 9 to 11) were chosen based on “a positive response to one or more of three questions:

ever wheezed without a cold, wheezed for 3 days or more out of the week for a month or longer,

and/or had a doctor say the ‘child has asthma’ (Pope et al., 1991, p. 669).” The patient-based

subjects (ranging in age from 8 to 72) were receiving treatment for asthma and were referred by

local physicians. Regression results for the school-based sample (Pope et al., 1991, Table 5)

show PM10significantly associated with both upper and lower respiratory symptoms. The

patient-based sample did not find a significant PM10effect. The results from the school-based

sample are used here.

Upper Respiratory Symptoms

The coefficient and standard error for a one μg/m3 change in PM10is reported in Table 5.

Incidence Rate: daily upper respiratory symptom incidence rate per person = 0.3419 (Pope et al.,

1991, Table 2)

Population: asthmatic population ages 9 to 11 = 10.70% of population ages 9 to 11. (The

American Lung Association (2010b, Table 7) estimates asthma prevalence for children ages 5 to 17

at 10.70%, based on data from the 2008 National Health Interview Survey.)
Appendix F: Ozone Health Impact Functions in U.S. Setup

November 2012

125

BenMAP User's Manual Appendices

Appendix F: Ozone Health Impact Functions in

U.S. Setup

In this Appendix, we present the health impact functions used to estimate ozone-related adverse

health effects. Each sub-section has a table with a brief description of each health impact function

and the underlying parameters. Following each table, we present a briefsummary of each of the

studies and any items that are unique to the study.

Note that Appendix C mathematically derives the standard types of health impact functions

encountered in the epidemiological literature, such as, log-linear, logistic and linear, so we simply

note here the type of functionalform. And Appendix D presents a description of the sources for

the incidence and prevalence data used in the health impact functions.

F.1 Short-term Mortality

Table F-1 summarizes the health impacts functions used to estimate the relationship between

ozone and short-term mortality. Below, we present a briefsummary of each of the studies and

any items that are unique to the study.

Table F-1. Health Impact Functions for Ozone and Short-Term Mortality

Effect Author Year Location Age CoPollMetric Beta Std Err Form Notes

NonAccidentalBell et al. 2004 95 US

cities

0-99 D24Hour

Mean

0.000390 0.000133 Log-linear Warm season.

NonAccidentalBell et al. 2004 95 US

cities

0-99 D24Hour

Mean

0.000520 0.000128 Log-linear All year.

NonAccidentalBell et al. 2004 95 US

cities

0-99 D8Hour

Max

0.000261 0.000089 Log-linear Warm season.

8-hour max

from 24-hour

mean.

All Cause Bell et al. 2005 US & nonUS0-99 D24Hour

Mean

0.001500 0.000401 Log-linear Warm season.

All Cause Bell et al. 2005 US & nonUS0-99 D8Hour

Max

0.000795 0.000212 Log-linear Warm season.

8-hour max

from 24-hour

mean.

CardiopulmonaryHuang et al. 2005 19 US

cities

0-99 D24Hour

Mean

0.001250 0.000398 Log-linear Warm season.

CardiopulmonaryHuang et al. 2005 19 US

cities

0-99 D8Hour

Max

0.000813 0.000259 Log-linear Warm season.

8-hour max

from 24-hour

mean.
Appendix F: Ozone Health Impact Functions in U.S. Setup

November 2012

126

BenMAP User's Manual Appendices

Effect Author Year Location Age CoPollMetric Beta Std Err Form Notes

NonAccidentalIto and

Thurston

1996 Chicago,

IL

18-99 PM

10

D1Hour

Max

0.000634 0.000251 Log-linear

NonAccidentalIto et al. 2005 0-99 D1Hour

Max

0.000400 0.000066 Log-linear 1-hour max.

NonAccidentalIto et al. 2005 0-99 D24Hour

Mean

0.001750 0.000357 Log-linear Warm season.

24-hour

mean.

NonAccidentalIto et al. 2005 0-99 D8Hour

Max

0.001173 0.000239 Log-linear Warm season.

8-hour max

from 24-hour

mean.

NonAccidentalIto et al. 2005 0-99 D8Hour

Max

0.000532 0.000088 Log-linear 8-hour max

from 1-hour

max.

All Cause Levy et al. 2005 US and

non-US

0-99 D1Hour

Max

0.000841 0.000134 Log-linear Warm season.

All Cause Levy et al. 2005 US and

non-US

0-99 D8Hou

rMax

0.001119 0.000179 Log-linear Warm season.

8-hour max

from 1-hour

max.

NonAccidentalMoolgavkar

et al.

1995 Phila., PA 0-99 D24Hour

Mean

0.001398 0.000266 Log-linear Warm season.

NonAccidentalMoolgavkar

et al.

1995 Phila., PA 0-99 TSP,

SO2

D24Hour

Mean

0.001389 0.000373 Log-linear Warm season.

NonAccidentalMoolgavkar

et al.

1995 Phila., PA 18-99 TSP,

SO2

D24Hour

Mean

0.000611 0.000216 Log-linear

NonAccidentalSamet et al. 1997 Phila., PA 18-99 CO,

NO2,

SO2,

TSP

D24Hour

Mean

0.000936 0.000312 Log-linear

NonAccidentalSchwartz 2005 14 US

cities

0-99 D1Hour

Max

0.000370 0.000130 Logistic Warm season.

NonAccidentalSchwartz 2005 14 US

cities

0-99 D8Hour

Max

0.000426 0.000150 Logistic Warm season.

8-hour max

from 1-hour

max.

F.1.1 Bell et al. (2004)

Ozone has been associated with various adverse health effects, including increased rates of

hospital admissions and exacerbation of respiratory illnesses. Although numerous time-series

studies have estimated associations between day-to-day variation in ozone levels and mortality

counts, results have been inconclusive. The authors investigated whether short-term (daily and

weekly) exposure to ambient ozone is associated with mortality in the United States. Using

analytical methods and databases developed for the National Morbidity, Mortality, and Air

Pollution Study, they estimated a national average relative rate of mortality associated with short-
Appendix F: Ozone Health Impact Functions in U.S. Setup

November 2012

127

BenMAP User's Manual Appendices

term exposure to ambient ozone for 95 large US urban communities from 1987-2000. The

authors used distributed-lag models for estimating community-specific relative rates of mortality

adjusted for time-varying confounders (particulate matter, weather, seasonality, and long-term

trends) and hierarchical models for combining relative rates across communities to estimate a

national average relative rate, taking into account spatial heterogeneity. A 10-ppb increase in the

previous week's ozone was associated with a 0.52% increase in daily mortality (95% posterior

interval[PI], 0.27%-0.77%) and a 0.64% increase in cardiovascular and respiratory mortality

(95% PI, 0.31%-0.98%). Effect estimates for aggregate ozone during the previous week were

larger than for models considering only a single day's exposure. Results were robust to

adjustment for particulate matter, weather, seasonality, and long-term trends. These results

indicate a statistically significant association between short-term changes in ozone and mortality

on average for 95 large US urban communities, which include about 40% of the total US

population.

Non-Accidental Mortality

The coefficient and standard error are based on the relative risk (1.003908) and 95% confidence

interval(1.0013-1.0065) associated with a 10 ppb increase in daily average ozone (Bell et al.,

2004, p. 2376).

F.1.2 Bell et al. (2005)

Although many time-series studies of ozone and mortality have identified positive associations,

others have yielded null or inconclusive results, making the results of these studies difficult to

interpret. The authors performed a meta-analysis of 144 effect estimates from 39 time-series

studies, and estimated pooled effects by lags, age groups, cause-specific mortality, and

concentration metrics. They compared results with pooled estimates from the National

Morbidity, Mortality, and Air Pollution Study (NMMAPS), a time-series study of 95 large U.S.

urban centers from 1987 to 2000. Both meta-analysis and NMMAPS results provided strong

evidence of a short-term association between ozone and mortality, with larger effects for

cardiovascular and respiratory mortality, the elderly, and current-day ozone exposure. In both

analyses, results were insensitive to adjustment for particulate matter and modelspecifications. In

the meta-analysis, a 10-ppb increase in daily ozone at single-day or 2-day average of lags 0, 1,

or 2 days was associated with an 0.87% increase in total mortality (95% posterior interval =

0.55% to 1.18%), whereas the lag 0 NMMAPS estimate is 0.25% (0.12% to 0.39%). Several

findings indicate possible publication bias: meta-analysis results were consistently larger than

those from NMMAPS; meta-analysis pooled estimates at lags 0 or 1 were larger when only a

single lag was reported than when estimates for multiple lags were reported; and heterogeneity of

city-specific estimates in the meta-analysis were larger than with NMMAPS.

All-Cause Mortality

The coefficient and standard error are based on the relative risk (1.008738) and 95% confidence

interval(1.0055-1.0119) associated with a 10 ppb increase in daily average ozone (Bell et al.,

2005, Table 6).
Appendix F: Ozone Health Impact Functions in U.S. Setup

November 2012

128

BenMAP User's Manual Appendices

F.1.3 Huang et al. (2005)

The authors developed Bayesian hierarchical distributed lag models for estimating associations

between daily variations in summer ozone levels and daily variations in cardiovascular and

respiratory (CVDRESP) mortality counts for 19 large U.S. cities included in the National

Morbidity, Mortality and Air Pollution Study (NMMAPS) for the summers of 1987–1994. In

the first stage, they defined a semi-parametric distributed lag Poisson regression model to

estimate city-specific relative rates of CVDRESP mortality associated with short-term exposure

to summer ozone. In the second stage, they specified a class of distributions for the true cityspecific relative rates to estimate an overall effect by taking into account the variability within and

across cities. They performed the calculations with respect to severalrandom effects

distributions (normal, t-student, and mixture of normal), thus relaxing the common assumption of

a two-stage normal–normal hierarchical model. They assessed the sensitivity of the results to:(i)

lag structure for ozone exposure; (ii) degree of adjustment for long-term trends; (iii) inclusion of

other pollutants in the model; (iv) heat waves; (v) random effects distributions; and (vi) prior

hyperparameters. On average across cities, the authors found that a 10 ppb increase in summer

ozone level over the previous week is associated with a 1.25 per cent increase in CVDRESP

mortality (95 per cent posterior regions: 0.47, 2.03). The relative rate estimates are also positive

and statistically significant at lags 0, 1 and 2. They found that associations between summer ozone

and CVDRESP mortality are sensitive to the confounding adjustment for PM10, but are robust to:

(i) the adjustment for long-term trends, other gaseous pollutants (NO2, SO2and CO); (ii) the

distributional assumptions at the second stage of the hierarchical model; and (iii) the prior

distributions on all unknown parameters.

Cardiopulmonary Mortality

Assuming a 10 ppb change in ozone, Huang et al. (2005, Table 1) reported a 1.25% change in

CVDRESP mortality with a 95% confidence interval of 0.47% to 2.03%.

Note that Huang et al. (2005, p. 549) define CVDRESP as including ICD-9 codes: 390-448,

480-487, 490-496, and 507. This differs somewhat from the the definition of "cardiopulmonary"

mortality in BenMAP -- defined as ICD-9 codes 401-440 and 460-519.

F.1.4 Levy et al. (2005)

The authors conducted an empiric Bayes metaregression to estimate the ozone effect on

mortality, and to assess whether this effect varies as a function of hypothesized confounders or

effect modifiers. They gathered 71 time-series studies relating ozone to all-cause mortality, and

tjey selected 48 estimates from 28 studies for the metaregression. Metaregression covariates

included the relationship between ozone concentrations and concentrations of other air pollutants,

proxies for personal exposure-ambient concentration relationships, and the statistical methods

used in the studies. For the metaregression, they applied a hierarchical linear model with known

level-1 variances. The authors estimated a grand mean of a 0.21% increase (95% confidence

interval = 0.16-0.26%) in mortality per 10-microg/m increase of 1-hour maximum ozone (0.41%

increase per 10 ppb) without controlling for other air pollutants. In the metaregression, air-
Appendix F: Ozone Health Impact Functions in U.S. Setup

November 2012

129

BenMAP User's Manual Appendices

conditioning prevalence and lag time were the strongest predictors of between-study variability.

Air pollution covariates yielded inconsistent findings in regression models, although correlation

analyses indicated a potential influence ofsummertime PM2.5.

All-Cause Mortality

Levy et al. (2005, Table 1) reported a 0.43% change in all-cause mortality with a 95%

confidence interval of 0.29% to 0.56% associated with a 10 μg/m3 change in ozone. We

converted μg/m3to ppb with an assumed relationship of 1.96 μg/m3 per 1.0 ppb.

F.1.5 Ito and Thurston (1996)

In this study, race, gender, and cause-specific counts of daily mortality in Cook County, Illinois

(which encompasses the city of Chicago) during 1985-1990 were analyzed to determine if there

was any heterogeneity in air pollution/weather/mortality associations across these various

population subcategories. Seasonal cross-correlations between mortality and environmental

variables first were examined to identify appropriate lag structures. Of the pollution variables

considered -- PM10, ozone, CO, SO2, and visualrange-derived extinction coefficient -- both

PM10and ozone showed significant associations with same-day and next-day mortality. The

Poisson regression models employed included seasonal cycles (sine/cosine series), square and

linear terms of lagged temperature, trend line, day-of-week dummy variables, and the average of

the same day's and previous day's PM10or ozone.

The authors reported a significant relationship for ozone and PM10 with both pollutants in the

model; no significant effects were found for SO2and CO. In single pollutant models the effects

were slightly larger. The health impact function for ozone is based on results from the co-pollutant

models.

Non-Accidental Mortality

For a co-pollutant model with PM10, the ozone coefficient (0.000634) and standard error

(0.000251) were obtained directly from the author because the published paper reported

incorrect information.

F.1.6 Ito et al. (2005)

The authors conducted a review and meta-analysis ofshort-term ozone mortality studies,

identified unresolved issues, and conducted an additional time-series analysis for 7 U.S. cities

(Chicago, Detroit, Houston, Minneapolis-St. Paul, New York City, Philadelphia, and St. Louis).

They found a combined estimate of 0.39% (95% confidence interval = 0.26-0.51%) per 10-ppb

increase in 1-hour daily maximum ozone for the all-age nonaccidental cause/single pollutant

model(43 studies). Adjusting for the funnel plot asymmetry resulted in a slightly reduced

estimate (0.35%; 0.23-0.47%). In a subset for which particulate matter (PM) data were

available (15 studies), the corresponding estimates were 0.40% (0.27-0.53%) for ozone alone
Appendix F: Ozone Health Impact Functions in U.S. Setup

November 2012

130

BenMAP User's Manual Appendices

and 0.37% (0.20-0.54%) with PM in model. The estimates for warm seasons were generally

larger than those for cold seasons. The additional time-series analysis found that including PM in

the model did not substantially reduce the ozone risk estimates. However, the difference in the

weather adjustment model could result in a 2-fold difference in risk estimates (eg, 0.24% to

0.49% in multicity combined estimates across alternative weather models for the ozone-only allyear case). The authors concluded that the results suggest short-term associations between ozone

and daily mortality in the majority of the cities, although the estimates appear to be heterogeneous

across cities.

Non-Accidental Mortality

Ito et al. (2005) reported results for functions with both 1-hour daily maximum and 24-hour daily

average metrics. We present both below.

One-hour Max Function

Assuming a 10 ppb change in the daily 1-hour maximum, Ito et al. (2005, p. 446) reported a

0.40% change in non-accidental mortality with a 95% confidence interval of 0.27% to 0.53%.

Daily Average Function

Assuming a 20 ppb change in the daily 24-hour average, Ito et al. (2005, p. 448) reported a

3.5% change in non-accidental mortality with a 95% confidence interval of 2.1% to 4.9%.

F.1.7 Moolgavkar et al. (1995)

Moolgavkar et al. (1995) examined the relationship between daily non-accidental mortality and

air pollution levels in Philadelphia, Pennsylvania from 1973 to 1988. They examined ozone, TSP,

and SO2in a three-pollutant model, and found a significant relationship for ozone and SO2; TSP

was not significant. In season-specific models, ozone was significantly associated with mortality

only in the summer months.

Mortality, Non-Accidental

The health impact function for ozone is based on the full-year three-pollutant modelreported in

Table 5 (Moolgavkar et al., 1995, p. 482). The coefficient and standard error are based on the

relative risk (1.063) and 95% confidence interval(1.018-1.108) associated with a 100 ppb

increase in daily average ozone.

F.1.8 Samet et al. (1997)

Samet et al. (1997) examined the relationship between daily non-accidental mortality and air

pollution levels in Philadelphia, Pennsylvania from 1974 to 1988. They examined ozone, TSP,

SO2, NO2, and CO in a Poisson regression model. In single pollutant models, ozone, SO2,

TSP, and CO were significantly associated with mortality. In a five-pollutant model, they found a

positive statistically significant relationship for each pollutant except NO2.
Appendix F: Ozone Health Impact Functions in U.S. Setup

November 2012

131

BenMAP User's Manual Appendices

Mortality, Non-Accidental

The health impact function for ozone is based on the five-pollutant model(ozone, CO, NO2, SO2

and TSP) reported in Table 9 (Samet et al., 1997, p. 20). The ozone coefficient and standard

error are based on the percent increase (1.91) and t-statistic (3) associated with a 20.219 ppb

increase in two-day average ozone.

F.1.9 Schwartz (2005)

The author used the case-crossover approach, where the controlfor each person is the same

person on a day near in time, when he or she did not die. This method controls for season and

individualrisk factors by matching. One can also choose the control day to have the same

temperature as the event day. The author applied this approach to a study of more than 1 million

deaths in 14 U.S. cities. He found that, with matching on temperature, a 10-ppb increase in

maximum hourly ozone concentrations was associated with a 0.23% (95% confidence interval

[CI] 0.01%, 0.44%) increase in the risk of dying. This finding was indistinguishable from the risk

when only matching on season and controlling for temperature with regression splines (0.19%;

95% CI 03%, 0.35%). Controlfor suspended particulate matter with an aerodynamic diameter

of 10 mum or less (PM(10)) did not change this risk. However, the association was restricted to

the warm months (0.37% increase; 95% CI 0.11%, 0.62%), with no effect in the cold months.

The author concluded that the association between ozone and mortality risk is unlikely to be

caused by confounding by temperature.

Non-Accidental Mortality

Assuming a 10 ppb change in the daily 1-hour maximum, Schwartz (2005, Table 2) reported a

0.37% change in non-accidental mortality with a 95% confidence interval of 0.11% to 0.62%.

F.2 Long-Term Mortality

Table F-2 summarizes the health impacts functions used to estimate the relationship between

ozone and long-term mortality. Below, we present a briefsummary of each of the studies and

any items that are unique to the study.
Appendix F: Ozone Health Impact Functions in U.S. Setup

November 2012

132

BenMAP User's Manual Appendices

Table F-2. Health Impact Functions for Ozone and Long-Term Mortality

Effect Author Year Location Age CoPollMetric Beta Std. Err. Fun. Form

Mortality,

Respiratory

Jerrett et al. 2009 86 urban

areas

30-

99

PM2.5Annual 0.003922 0.001325 Log-linear

Mortality,

Respiratory

Jerrett et al. 2009 Northeast 30-

99

Annual -0.001005 0.003853 Log-linear

Mortality,

Respiratory

Jerrett et al. 2009 Industrial

Midwest

30-

99

Annual 0.000000 0.004604 Log-linear

Mortality,

Respiratory

Jerrett et al. 2009 Southeast 30-

99

Annual 0.011333 0.003193 Log-linear

Mortality,

Respiratory

Jerrett et al. 2009 Upper

Midwest

30-

99

Annual 0.013103 0.026212 Log-linear

Mortality,

Respiratory

Jerrett et al. 2009 Northwest 30-

99

Annual 0.005827 0.003118 Log-linear

Mortality,

Respiratory

Jerrett et al. 2009 Southwest 30-

99

Annual 0.019062 0.007583 Log-linear

Mortality,

Respiratory

Jerrett et al. 2009 Southern

California

30-

99

Annual 0.000995 0.002767 Log-linear

Mortality,

Respiratory

Jerrett et al. 2009 86 urban

areas

30-

99

PM2.5Annual 0.004471 0.001510 Log-linear

F.2.1 Jerret et al. (2009)

Jerrett et al. (2009) examined the potential contribution of long-term ozone exposure to the risk

of death from cardiopulmonary causes and specifically to death from respiratory causes. Data

from the study cohort of the American Cancer Society Cancer Prevention Study II were

correlated with air-pollution data from 96 metropolitan statistical areas in the United States.

Associations between ozone concentrations and the risk of death were evaluated with the use of

standard and multilevelCox regression models. In single-pollutant models, increased

concentrations of either PM2.5or ozone were significantly associated with an increased risk of

death from cardiopulmonary causes. In two-pollutant models, PM2.5 was associated with the risk

of death from cardiovascular causes, whereas ozone was associated with the risk of death from

respiratory causes. The estimated relative risk of death from respiratory causes that was

associated with an increment in ozone concentration of 10 ppb was 1.040 (95% confidence

interval, 1.010 to 1.067). The association of ozone with the risk of death from respiratory causes

was insensitive to adjustment for confounders and to the type ofstatistical model used. The

authors concluded that they were not able to detect an effect of ozone on the risk of death from

cardiovascular causes when the concentration of PM2.5 was taken into account. But they did

demonstrate a significant increase in the risk of death from respiratory causes in association with

an increase in ozone concentration.
Appendix F: Ozone Health Impact Functions in U.S. Setup

November 2012

133

BenMAP User's Manual Appendices

Mortality, Respiratory (ICD-9 code 460-519) --- 86 U.S. urban areas

In a two-pollutant model the coefficient and standard error are estimated from the relative risk

(1.040) and 95% confidence interval(95% CI: 1.013-1.067) for a 10 ppb increase in ambient

ozone concentration measured from April to September during the years from 1977 to 2000 in

86 MSAs (Jerrett, et al., 2009, Table 3).

Mortality, Respiratory (ICD-9 code 460-519) --- by region

In single-pollutant models the coefficient and standard error for different regions are estimated

from the relative risks and 95% confidence intervals for a 10 ppb increase in ambient ozone

concentration measured from April to September during the years from 1977 to 2000 (Jerrett, et

al., 2009, Table 4).

Mortality, Respiratory (ICD-9 code 460-519) --- adjusted daily metric

Based on the coefficients estimated from the two-pollutant model in the 86 urban areas using

daily 1-hour max metric, the coefficients were adjusted for daily 8-hour max metric using a ratio

of 1.14 (Anderson & Bell table 2).

F.3 Hospital Admissions

Table F-3 summarizes the health impacts functions used to estimate the relationship between

ozone and hospital admissions. Below, we present a briefsummary of each of the studies and

any items that are unique to the study.

Table F-3. Health Impact Functions for Ozone and Hospital Admissions

Effect Author Year Location Age Co-Poll Metric Beta Std Err Form Notes

All

Respiratory

Burnett et al. 2001 Toronto,

CAN

0-1 PM2.5 D1Hour

Max

0.007301 0.002122 LoglinearWarm

season.

All

Respiratory

Burnett et al. 2001 Toronto,

CAN

0-1 PM2.5 D8Hour

Max

0.008177 0.002377 LoglinearWarm

season. 8-

hour max

from 1-hour

max.

Chronic

Lung

Moolgavkar

et al.

1997 Minneapolis,

MN

65-

99

PM10,

CO

D24Hour

Mean

0.002800 0.001769 Loglinear

Chronic

Lung

Moolgavkar

et al.

1997 Minneapolis,

MN

65-

99

PM10,

CO

D8Hour

Max

0.001960 0.001238 LoglinearAll year. 8-

hour max

from 24-

hour mean.

Pneumonia Moolgavkar

et al.

1997 Minneapolis,

MN

65-

99

PM10,

SO2,

NO2

D24Hour

Mean

0.003800 0.001088 Loglinear

Pneumonia Moolgavkar

et al.

1997 Minneapolis,

MN

65-

99

PM10,

SO2,

D8Hour

Max

0.002660 0.000762 LoglinearAll year. 8-

hour max
Appendix F: Ozone Health Impact Functions in U.S. Setup

November 2012

134

BenMAP User's Manual Appendices

Effect Author Year Location Age Co-Poll Metric Beta Std Err Form Notes

NO2 from 24-

hour mean.

Chronic

Lung (less

Asthma)

Schwartz 1994 Detroit, MI 65-

99

PM10 D24Hour

Mean

0.005523 0.002085 LoglinearAll year.

Chronic

Lung (less

Asthma)

Schwartz 1994 Detroit, MI 65-

99

PM10 D8Hour

Max

0.003424 0.001293 LoglinearAll year. 8-

hour max

from 24-

hour mean.

Pneumonia Schwartz 1994 Detroit, MI 65-

99

PM10 D24Hour

Mean

0.005210 0.001300 LoglinearAll year.

Pneumonia Schwartz 1994 Minneapolis,

MN

65-

99

PM10 D24Hour

Mean

0.003977 0.001865 LoglinearAll year.

Pneumonia Schwartz 1994 Detroit, MI 65-

99

PM10 D8Hour

Max

0.003230 0.000806 LoglinearAll year. 8-

hour max

from 24-

hour mean.

Pneumonia Schwartz 1994 Minneapolis,

MN

65-

99

PM10 D8Hour

Max

0.002784 0.001305 LoglinearAll year. 8-

hour max

from 24-

hour mean.

All

Respiratory

Schwartz 1995 New Haven,

CT

65-

99

PM10 D24Hour

Mean

0.002652 0.001398 LoglinearWarm

season.

All

Respiratory

Schwartz 1995 Tacoma,

WA

65-

99

PM10 D24Hour

Mean

0.007147 0.002565 LoglinearWarm

season.

All

Respiratory

Schwartz 1995 New Haven,

CT

65-

99

PM10 D8Hour

Max

0.001777 0.000936 LoglinearWarm

season. 8-

hour max

from 24-

hour mean.

All

Respiratory

Schwartz 1995 Tacoma,

WA

65-

99

PM10 D8Hour

Max

0.004931 0.001770 LoglinearWarm

season. 8-

hour max

from 24-

hour mean.

F.3.1 Burnett et al. (2001)

Burnett et al. (2001) studied the association between air pollution and acute respiratory hospital

admissions (ICD codes 493, 466, 464.4, 480-486) in Toronto from 1980-1994, among children

less than 2 years of age. They collected hourly concentrations of the gaseous pollutants, CO,

NO2, SO2, and ozone. Daily measures of particulate matter were estimated for the May to

August period of 1992-1994 using TSP, sulfates, and coefficient of haze data. The authors

report a positive association between ozone in the May through August months and respiratory

hospital admissions, for severalsingle days after elevated ozone levels.

The strongest association was found using a five-day moving average of ozone. No association

was found in the September through April months. In co-pollutant models with a particulate

matter or another gaseous pollutant, the ozone effect was only slightly diminished. The effects for
Appendix F: Ozone Health Impact Functions in U.S. Setup

November 2012

135

BenMAP User's Manual Appendices

PM and gaseous pollutants were generally significant in single pollutant models but diminished in

co-pollutant models with ozone, with the exception of CO. The C-R functions for ozone are

based on a single pollutant and two co-pollutant models, using the five-day moving average of

one-hour max ozone.

Hospital Admissions, All Respiratory (ICD-9 codes 464, 466, 480-487, 493)

In a model with PM2.5, the coefficient and standard error are based on the percent increase

(33.0) and t-statistic (3.44) associated with a 45.2 ppb increase in the five-day moving average

of one-hour max ozone (Burnett et al., 2001, Table 3).

F.3.2 Moolgavkar et al. (1997)

Moolgavkar et al. (1997) examined the relationship between air pollution and hospital admissions

(ICD codes 490-496) for individuals 65 and older in Minneapolis-St. Paul, Minnesota, from

January 1986 to December 1991. In a Poisson regression, they found no significant effect for

any of the pollutants (PM10, ozone, or CO). The effect for ozone was marginally significant. The

model with a 100 dfsmoother was reported to be optimal(p. 368). The health impact function

for chronic lung disease is based on the results from a three-pollutant model(ozone, CO, PM10)

using the 100 dfsmoother; the function for Pneumonia uses the 130 dfsmoother.

Hospital Admissions, Chronic Lung Disease (ICD-9 codes 490-496)

In a model with CO and PM10, the estimated coefficient and standard error are based on the

percent increase (4.2) and 95% confidence interval of the percent increase (-1.0-9.4) associated

with a change in daily average ozone levels of 15 ppb (Moolgavkar et al., 1997, Table 4).

Hospital Admissions, Pneumonia (ICD-9 codes 480-487)

In a model with NO2, PM10,and SO2, the estimated coefficient and standard error are based on

the percent increase (5.7) and 95% confidence interval of the percent increase (2.5-8.9)

associated with an increase in daily average ozone levels of 15 ppb (Moolgavkar et al., 1997,

Table 4).

F.3.3 Schwartz (1994a)

Schwartz (1994a) examined the relationship between air pollution and hospital admissions for

individuals 65 and older in Minneapolis-St. Paul, Minnesota, from January 1986 to December

1989. In single-pollutant Poisson regression models, both ozone and PM10 were significantly

associated with pneumonia admissions. In a two-pollutant model, Schwartz found PM10

significantly related to pneumonia; ozone was weakly linked to pneumonia. The results were not

sensitive to the methods used to controlfor seasonal patterns and weather. The ozone C-R

functions are based on the results of the single pollutant model and the two-pollutant model

(PM10and ozone) with spline smoothing for temporal patterns and weather.
Appendix F: Ozone Health Impact Functions in U.S. Setup

November 2012

136

BenMAP User's Manual Appendices

Hospital Admissions, Pneumonia (ICD-9 codes 480-487)

In a model with PM10and spline functions to adjust for time and weather, the coefficient and

standard error are based on the relative risk (1.22) and 95% confidence interval(1.02, 1.47) for

a 50 ppb increase in daily average ozone levels (Schwartz, 1994a, Table 4).

F.3.4 Schwartz (1994b)

Schwartz (1994b) examined the relationship between air pollution and hospital admissions (ICD

codes 491-492, 494-496) for individuals 65 and older in Detroit, Michigan, from January 1986

to December 1989. In a two-pollutant Poisson regression model, Schwartz found both PM10

and ozone significantly linked to pneumonia and COPD. The authors state that effect estimates

were relatively unchanged compared to the unreported single pollutant models. No significant

associations were found between either pollutant and asthma admissions. The C-R function for

chronic lung disease incidence is based on the results of the “basic” co-pollutant model(ozone

and PM10) presented in Table 4 (p. 651). The study also reports results using generalized

additive models to fit time and temperature variables, however no standard error or confidence

intervals were reported.

Hospital Admissions, Chronic Lung Disease less Asthma (ICD-9 codes 490-492, 494-

496)

The coefficient and standard error for the “basic” model are reported in Table 4 (Schwartz,

1994b, p.651) for a one ppb change in daily average ozone.

Hospital Admissions, Pneumonia (ICD-9 codes 480-487)

The ozone C-R function for pneumonia incidence is based on the coefficient and standard error

for the “basic” co-pollutant model presented in Table 4 (Schwartz, 1994b, p. 651).

F.3.5 Schwartz (1995)

Studies have reported associations between short term changes in air pollution and respiratory

hospital admissions. This relationship was examined in two cities with substantially different levels

ofsulphur dioxide (SO2) but similar levels of airborne particles in an attempt to separate the

effects of the two pollutants. Significant differences in weather between the two cities allowed the

evaluation of that potential confounder also. Daily counts of admissions to all hospitals for

respiratory disease (ICD 9 460-519) were constructed for persons aged 65 years and older in

two cities - New Haven, Connecticut and Tacoma, Washington.

Each city was analysed separately. Average daily concentrations of SO2, inhalable particles

(PM10), and ozone were computed from all monitors in each city, and daily average temperature

and humidity were obtained from the US weather service. Daily respiratory admission counts

were regressed on temperature, humidity, day of the week indicators, and air pollution. A 19

day weighted moving regression filter was used to remove allseasonal and subseasonal patterns
Appendix F: Ozone Health Impact Functions in U.S. Setup

November 2012

137

BenMAP User's Manual Appendices

from the data. Possible U- shaped dependence of admissions on temperature was dealt with

using indicator variables for eight categories each of temperature and humidity.

Each pollutant was first examined individually and then multiple pollutant models were fitted. All

three pollutants were associated with respiratory hospital admissions of the elderly. The PM10

associations were little changed by controlfor either ozone or SO2. The ozone association was

likewise independent of the other pollutants. The SO2association was substantially attenuated by

controlfor ozone in both cities, and by controlfor PM10in Tacoma. The magnitude of the effect

was small(relative risk 1.06 in New Haven and 1.10 in Tacoma for a 50 micrograms/m3

increase in PM10, for example) but, given the ubiquitous exposure, this has some public health

significance. The authors concluded that air pollution concentrations within current guidelines

were associated with increased respiratory hospital admissions of the elderly. The strongest

evidence for an independent association was for PM10, followed by ozone.

Hospital Admissions, All Respiratory (ICD-9 codes 460-519) -- Tacoma

In a model with PM10, the coefficient and standard error are estimated from the relative risk

(1.20) and 95% confidence interval(1.06-1.37) for a 50 μg/m3increase in average daily ozone

levels (Schwartz, 1995, Table 6, p. 535).To calculate the coefficient, a conversion of 1.96 μg/m3

per ppb was used, based on a density of ozone of 1.96 grams per liter (at 25 degrees Celsius).

Hospital Admissions, All Respiratory (ICD-9 codes 460-519) -- NewHaven

In a model with PM10, the coefficient and standard error are estimated from the relative risk

(1.07) and 95% confidence interval(1.00-1.15) for a 50 μg/m3increase in average daily ozone

levels (Schwartz, 1995, Table 3, p. 534).To calculate the coefficient, a conversion of 1.96 μg/m3

per ppb was used, based on a density of ozone of 1.96 grams per liter (at 25 degrees Celsius).

F.4 Emergency Room Visits

Table F-4 summarizes the health impacts functions used to estimate the relationship between

ozone and emergency room (ER) visits. Below, we present a briefsummary of each of the

studies and any items that are unique to the study.
Appendix F: Ozone Health Impact Functions in U.S. Setup

November 2012

138

BenMAP User's Manual Appendices

Table F-4. Health Impact Functions for Ozone and Emergency Room Visits

Effect Author Year Location Age Co-Poll Metric Beta Std Err Form

Asthma Peel et al. 2005 Atlanta, GA 0-99 D8HourMax 0.000870 0.000529 Log-linear

Asthma Wilson et

al.

2005 Portland, ME 0-99 D8HourMax 0.003000 0.001000 Log-linear

Asthma Wilson et

al.

2005 Manchester, NH 0-99 D8HourMax -0.001000 0.002000 Log-linear

F.4.1 Peel et al. (2005)

A number of emergency department studies have corroborated findings from mortality and

hospital admission studies regarding an association of ambient air pollution and respiratory

outcomes. More refined assessment has been limited by study size and available air quality data.

Measurements of 5 pollutants PM10, ozone, NO2, CO, and SO2 were available for the entire

study period (1 January 1993 to 31 August 2000); detailed measurements of particulate matter

were available for 25 months. The authors obtained data on 4 million emergency department

visits from 31 hospitals in Atlanta. Visits for asthma, chronic obstructive pulmonary disease,

upper respiratory infection, and pneumonia were assessed in relation to air pollutants using

Poisson generalized estimating equations. In single-pollutant models examining 3-day moving

averages of pollutants (lags 0, 1, and 2):standard deviation increases of ozone, NO2, CO, and

PM10 were associated with 1-3% increases in URI visits; a 2 microg/m increase of PM2.5

organic carbon was associated with a 3% increase in pneumonia visits; and standard deviation

increases of NO2and CO were associated with 2-3% increases in chronic obstructive

pulmonary disease visits. Positive associations persisted beyond 3 days for several of the

outcomes, and over a week for asthma. The results of this study contribute to the evidence of an

association ofseveral correlated gaseous and particulate pollutants, including ozone, NO2, CO,

PM, and organic carbon, with specific respiratory conditions.

Emergency Room Visits, Asthma

The ozone coefficient and standard error are reported per 25 ppb increment of the maximum

daily 8-hour average ozone level(Peel et al., 2003, Table 4). We used the results from the three

cities combined. The relative risk is 1.022, with a 95 percent confidence interval of 0.996 to

1.049.

F.4.2 Wilson et al. (2005)

Daily emergency room (ER) visits for allrespiratory (ICD-9 460-519) and asthma (ICD-9 493)

were compared with daily SO2, ozone, and weather variables over the period 1998-2000 in

Portland, Maine (population 248,000), and 1996-2000 in Manchester, New Hampshire

(population 176,000). Seasonal variability was removed from all variables using nonparametric

smoothed function (LOESS) of day ofstudy. Generalized additive models were used to estimate
Appendix F: Ozone Health Impact Functions in U.S. Setup

November 2012

139

BenMAP User's Manual Appendices

the effect of elevated levels of pollutants on ER visits. Relative risks of pollutants were reported

over their interquartile range (IQR, the 75th -25th percentile pollutant values). I n Portland, an

IQR increase in SO2 was associated with a 5% (95% CI 2-7%) increase in allrespiratory ER

visits and a 6% (95% CI 1-12%) increase in asthma visits. An IQR increase in O3 was

associated with a 5% (95% CI 1-10%) increase in Portland asthmatic ER visits. No significant

associations were found in Manchester, New Hampshire, possibly due to statistical limitations of

analyzing a smaller population. The absence ofstatistical evidence for a relationship should not

be used as evidence of no relationship. This analysis reveals that, on a daily basis, elevated SO2

and O3have a significant impact on public health in Portland, Maine.

Emergency Room Visits, Asthma

The coefficient and standard error are taken from Wilson et al. (2005, Table 5).

F.5 Minor Effects

Table F-5 summarizes the health impacts functions used to estimate the relationship between

ozone and minor effects. Below, we present a briefsummary of each of the studies and any

items that are unique to the study.

Table F-5. Health Impact Functions for Ozone and Minor Effects

Effect Author Year Location Age CoPollMetric Beta Std Err Form Notes

School Loss

Days, All

Cause

Chen et al. 2000 Washoe Co,

NV

5-17 PM10,

CO

D1Hour

Max

0.013247 0.004985 Linear

School Loss

Days, All

Cause

Chen et al. 2000 Washoe Co,

NV

5-17 PM10,

CO

D8Hour

Max

0.015763 0.004985 Linear All year. 8-hour

max from 1-hour

max.

School Loss

Days, All

Cause

Gilliland et

al.

2001 Southern

California

5-17 D8Hour

Max

0.007824 0.004445 LoglinearAll year. 8-hour

max from 8-hour

mean.

School Loss

Days, All

Cause

Gilliland et

al.

2001 Southern

California

5-17 D8Hour

Mean

0.008150 0.004630 Loglinear

Minor

Restricted

Activity Days

Ostro and

Rothschild

1989 Nationwide 18-64 PM2.5 D1Hour

Max

0.002200 0.000658 Loglinear

Minor

Restricted

Activity Days

Ostro and

Rothschild

1989 Nationwide 18-64 PM2.5 D8Hour

Max

0.002596 0.000776 Loglinear8-hour max from

1-hour max.

F.5.1 Chen et al. (2000)

Chen et al. (2000) studied the association between air pollution and elementary school
Appendix F: Ozone Health Impact Functions in U.S. Setup

November 2012

140

BenMAP User's Manual Appendices

absenteeism (grades 1-6) in Washoe County, Nevada. Assuming that most children start

kindergarten at age 5, the corresponding ages for grades 1 through 6 would be 6 through 11.

Daily absence data were available for all elementary schools in the Washoe Country School

District. The authors regressed daily total absence rate on the three air pollutants, meteorological

variables, and indicators for day of the week, month, and holidays. They reported statistically

significant associations between both ozone and CO and daily total absence rate for grades one

through six. PM10 was negatively associated with absence rate, after adjustment for ozone, CO,

and meteorological and temporal variables. The C-R function for ozone is based on the results

from a multiple linear regression model with CO, ozone, and PM10.

School Loss Days, All Cause

The coefficient and standard error are presented in Table 3 (Chen et al., 2000, p. 1008) for a

unit ppm increase in the two-week average of daily one-hour maximum ozone concentration.

This is converted to unit ppb increase by dividing by 1,000.

The reported coefficient represents an absolute increase in absenteeism rate for a unit increase in

ozone. If we apply this study to other locations, we assume that the same absolute increase will

occur for a unit increase in ozone, regardless of the baseline rate. If the study location has a

particularly high baseline rate, we may be overestimating decreases in absenteeism nationally, and

vice-versa. As an example, consider if the baseline absenteeism rate were 10% in the study and

5% nationally. An absolute increase in absence rate of 2% associated with a given increase in

ozone reflects a relative increase in absence rate of 20% for the study population. However, in

the national estimate, we would assume the same absolute increase of 2%, but this would reflect

a relative increase in the absenteeism rate of 40%.

An alternative approach is to estimate apply the relative increase in absenteeism rate in the C-R

function by adjusting the results by the ratio of the national absenteeism rate to the study-specific

rate. As a result, the percent increase in absenteeism rate associated with an increase in ozone is

extrapolated nationally rather than the absolute increase in absenteeism rate. The incidence

derivation section above describes the data used to estimate national and study-specific absence

rates.

In addition to this scaling factor, there are two other scaling factors which are applied to the

function. A scaling factor of 0.01 is used to convert the beta from a percentage (x 100) per unit

increase of ozone to a proportion per unit increase of ozone. As a result it can be applied

directly to the national population ofschool children ages 6 through 11 to estimate the number of

absences avoided.

The finalscaling factor adjusts for the number ofschool days in the ozone season. In the

modeling program, the function is applied to every day in the ozone season (May 1 - September

30), however, in reality, school absences will be avoided only on school days. We assume that

children are in school during weekdays for all of May, two weeks in June, one week in August,

and all of September. This corresponds to approximately 2.75 months out of the 5 month

season, resulting in an estimate of 39.3% of days (2.75/5*5/7). The C-R function parameters
Appendix F: Ozone Health Impact Functions in U.S. Setup

November 2012

141

BenMAP User's Manual Appendices

are shown below.

Population: population of children ages 6-11

Scaling Factor 1: Ratio of nationalschool absence rate to study-specific school absence rate =

1.081. (Nationalschool absence rate of 5.5% obtained from the U.S. Department of Education

(1996, Table 42-1). Study-specific school absence rate of 5.09% obtained from Chen et al. (2000,

Table 1).)

Scaling Factor 2: Convert beta in percentage terms to a proportion = 0.01

Scaling Factor 3: Proportion of days that are school days in the ozone season = 0.393. (Ozone is

modeled for the 5 months from May 1 through September 30. We assume that children are in

school during weekdays for all of May, 2 weeks in June, 1 week in August, and all of September.

This corresponds to approximately 2.75 months out of the 5 month season, resulting in an estimate

of 39.3% of days (2.75/5*5/7).)

F.5.2 Gilliland et al. (2001)

Gilliland et al. (2001) examined the association between air pollution and school absenteeism

among 4th grade school children (ages 9-10) in 12 southern Californian communities. The study

was conducted from January through June 1996. The authors used schoolrecords to collect

daily absence data and parental telephone interviews to identify causes. They defined illnessrelated absences as respiratory or non-respiratory. A respiratory illness was defined as an illness

that included at least one of the following:runny nose/sneezing, sore throat, cough, earache,

wheezing, or asthma attack. The authors used 15 and 30 day distributed lag models to quantify

the association between ozone, PM10, and NO2and incident school absences. Ozone levels

were positively associated with allschool absence measures and significantly associated with all

illness-related school absences (non-respiratory illness, respiratory illness, URI and LRI).

Neither PM10nor NO2 was significantly associated with illness-related school absences, but

PM10 was associated with non-illness related absences. The health impact function for ozone is

based on the results of the single pollutant model.

School Loss Days

Gilliland et al. (2001) defines an incident absence as an absence that followed attendance on the

previous day and the incidence rate as the number of incident absences on a given day over the

population at risk for an absence on a given day (i.e. those children who were not absent on the

previous day). Since school absences due to air pollution may last longer than one day, an

estimate of the average duration ofschool absences could be used to calculated the total avoided

school loss days from an estimate of avoided new absences. A simple ratio of the total absence

rate divided by the new absence rate would provide an estimate of the average duration ofschool

absences, which could be applied to the estimate of avoided new absences as follows:
Appendix F: Ozone Health Impact Functions in U.S. Setup

November 2012

142

BenMAP User's Manual Appendices

Since the function is log-linear, the baseline incidence rate (in this case, the rate of new absences)

is multiplied by duration, which reduces to the totalschool absence rate. Therefore, the same

result would be obtained by using a single estimate of the totalschool absence rate in the C-R

function. Using this approach, we assume that the same relationship observed between pollutant

and new school absences in the study would be observed for total absences on a given day. As

a result, the totalschool absence rate is used in the function below. The derivation of this rate is

described in the section on baseline incidence rate estimation.

For all absences, the coefficient and standard error are based on a percent increase of 16.3

percent (95% CI -2.6 percent, 38.9 percent) associated with a 20 ppb increase in 8-hour

average ozone concentration (2001, Table 6, p. 52).

A scaling factor is used to adjust for the number ofschool days in the ozone season. In the

modeling program, the function is applied to every day in the ozone season (May 1 - September

30), however, in reality, school absences will be avoided only on school days. We assume that

children are in school during weekdays for all of May, two weeks in June, one week in August,

and all of September. This corresponds to approximately 2.75 months out of the 5 month

season, resulting in an estimate of 39.3% of days (2.75/5*5/7).

In addition, not all children are at-risk for a new school absence, as defined by the study. On

average, 5.5% ofschool children are absent from school on a given day (U.S. Department of

Education, 1996, Table 42-1). Only those who are in school on the previous day are at risk for

a new absence (1-0.055 = 94.5%). As a result, a factor of 94.5% is used in the function to

estimate the population ofschool children at-risk for a new absence.

Incidence Rate: daily school absence rate = 0.055 (U.S. Department of Education, 1996, Table

42-1)

Population: population of children ages 9-10 not absent from school on a given day = 94.5% of

children ages 9-10 (The proportion of children not absent from school on a given day (5.5%) is

based on 1996 data from the U.S. Department of Education (1996, Table 42-1).)

Scaling Factor: Proportion of days that are school days in the ozone season = 0.393.

(Ozone is modeled for the 5 months from May 1 through September 30. We assume that children

are in school during weekdays for all of May, 2 weeks in June, 1 week in August, and all of

September. This corresponds to approximately 2.75 months out of the 5 month season, resulting in

an estimate of 39.3% of days (2.75/5*5/7). )
Appendix F: Ozone Health Impact Functions in U.S. Setup

November 2012

143

BenMAP User's Manual Appendices

F.5.3 Ostro and Rothschild (1989)

Ostro and Rothschild (1989) estimated the impact of PM2.5and ozone on the incidence of minor

restricted activity days (MRADs) and respiratory-related restricted activity days (RRADs) in a

nationalsample of the adult working population, ages 18 to 65, living in metropolitan areas. The

study population is based on the Health Interview Survey (HIS), conducted by the National

Center for Health Statistics. In publications from this ongoing survey, non-elderly adult

populations are generally reported as ages 18-64. From the study, it is not clear if the age range

stops at 65 or includes 65 year olds. We apply the C-R function to individuals ages 18-64 for

consistency with other studies estimating impacts to non-elderly adult populations. The annual

nationalsurvey results used in this analysis were conducted in 1976-1981. Controlling for PM2.5,

two-week average ozone had a highly variable association with RRADs and MRADs.

Controlling for ozone, two-week average PM2.5 was significantly linked to both health endpoints

in most years. The C-R function for ozone is based on the co-pollutant model with PM2.5.

The study is based on a “convenience” sample of non-elderly individuals. Applying the C-R

function to this age group is likely a slight underestimate, as it seems likely that elderly are at least

as susceptible to ozone as individuals under 65. A number ofstudies have found that hospital

admissions for the elderly are related to ozone exposures (e.g., Schwartz, 1994b; Schwartz,

1995).

Minor Restricted Activity Days

The coefficient and standard error used in the C-R function are based on a weighted average of

the coefficients in Ostro and Rothschild (1989, Table 4). The derivation of these estimates is

described below.

Incidence Rate: daily incidence rate for minor restricted activity days (MRAD) = 0.02137 (Ostro

and Rothschild, 1989, p. 243)

Population: adult population ages 18 to 64

The coefficient used in the C-R function is a weighted average of the coefficients in Ostro and

Rothschild (1989, Table 4) using the inverse of the variance as the weight. The calculation of the

MRAD coefficient and its standard error is exactly analogous to the calculation done for the

work-loss days coefficient based on Ostro (1987).

The standard error of the coefficient is calculated as follows, assuming that the estimated year-
Appendix F: Ozone Health Impact Functions in U.S. Setup

November 2012

144

BenMAP User's Manual Appendices

specific coefficients are independent:

This reduces down to:

F.6 Converting Functions to 8-Hour Daily Maximum Metric

A number of health impact functions were converted from 1-hour maximum, 24-hour average,

and 8-hour average to the 8-hour maximum metric. To convert, say, a 1-hour maximum

function, we multiplied the 1-hour maximum coefficient with the ratio of the typical 1-hour

maximum value to the typical 8-hour maximum value. We calculated ozone metric ratios for each

quarter and year in the period 2000-2007. We calculated ratios by monitor, and by county, core

business statistical area (CBSA), state, and nation.

For each monitor, a day was considered valid if it had at least 18 hourly values out of 24. A

quarter was considered valid if it had at least 85 percent valid days. Ratios are calculated for the

year, only if that year had four quarterly values. The CBSA codes, which were defined by OMB

on 6-6-03, were obtained from: http://www.census.gov/population/estimates/metro-city/03msa.

txt.

We chose the time period for the ratio calculation (e.g., spring and summer quarters) and the

locations based on the data used in each epidemiologicalstudy. Table F-6 presents the 8-hour

adjustment used for each study. Tables G-7 through G-9 present supporting documentation for

some of the multi-city 8-hour adjustments.

Table F-6. Eight-Hour Adjustments by Study

Eff ect Author Year Location Adjustment Factor Location Quarters Metric 8-Hour Adj Notes

Mortality, NonAccidentalBell et al. 2004 95 US cities Nation 2-3 24HourMean 0.67

Mortality, All

Cause

Bell et al. 2005 Meta-analysis Fromstudy. See comment. -- 24HourMean 0.53

HA, All

Respiratory

Burnett et

al.

2001 Toronto, CAN Buffalo-Cheektowaga-Tonawanda,

NY MSA

2-3 1HourMax 1.12
Appendix F: Ozone Health Impact Functions in U.S. Setup

November 2012

145

BenMAP User's Manual Appendices

Eff ect Author Year Location Adjustment Factor Location Quarters Metric 8-Hour Adj Notes

School Loss

Days, All Cause

Chen et al. 2000 Washoe Co, NV Washoe County 1-4 1HourMax 1.19

School Loss

Days, All Cause

Gilliland et

al.

2001 Southern

California

Los Angeles-Long Beach-Santa

Ana, CA MSA

1-4 8HourMean 0.96 The statewide avg is

0.96.

Mortality,

Cardiopulmonary

Huang et al. 2005 19 US cities See below See below 24HourMean 0.66

Mortality, NonAccidentalIto et al. 2005 6 US cities See below See below 24HourMean 0.65

Mortality, NonAccidentalIto et al. 2005 Meta-analysis Fromstudy. See comment. -- 24HourMean 0.67

Mortality, NonAccidentalIto et al. 2006 Meta-analysis Fromstudy. See comment. -- 1HourMax 1.33

Mortality, All

Cause

Levy et al. 2005 Meta-analysis Fromstudy. See comment. -- 1HourMax 1.33

HA, Chronic

Lung Disease

Moolgavkar

et al.

1997 Minneapolis,

MN

Minneapolis-St. PaulBloomington, MN-WI MSA1-4 24HourMean 0.70 Data 2004-2007

only.

HA, Pneumonia Moolgavkar

et al.

1997 Minneapolis,

MN

Minneapolis-St. PaulBloomington, MN-WI MSA1-4 24HourMean 0.70 Data 2004-2007

only.

Minor Restricted

Activity Days

Ostro and

Rothschild

1989 Nationwide Nation 1-4 1HourMax 1.18

HA, Chronic

Lung Disease

(less Asthma)

Schwartz 1994 Detroit, MI Detroit-Warren-Livonia, MI MSA 1-4 24HourMean 0.62 Data 2006 only.

HA, Pneumonia Schwartz 1994 Detroit, MI Detroit-Warren-Livonia, MI MSA 1-4 24HourMean 0.62 Data 2006 only.

HA, Pneumonia Schwartz 1994 Minneapolis,

MN

Minneapolis-St. PaulBloomington, MN-WI MSA1-4 24HourMean 0.70 Data 2004-2007

only.

HA, All

Respiratory

Schwartz 1995 New Haven, CT New Haven-Milford, CT MSA 2-3 24HourMean 0.67

HA, All

Respiratory

Schwartz 1995 Tacoma, WA Seattle-Tacoma-Bellevue, WA

MSA

2-3 24HourMean 0.69

Mortality, NonAccidentalSchwartz 2004 14 US cities See below See below 1HourMax 0.67

Table F-7. Eight-Hour Adjustment Details -- 6-City Study

City/County CBSAs or Counties Used in Ratio Average

Quarters

Used Study Metric 8-Hour Adj

Detroit Detroit-Warren-Livonia, MI MSA 2-3 24HourMean 0.63

Cook County Cook County 2-3 24HourMean 0.65

Houston Houston-Baytown-Sugar Land, TX MSA 2-3 24HourMean 0.59

Minneapolis

Minneapolis-St. Paul-Bloomington, MN-WI

MSA 2-3 24HourMean 0.70

Philadelphia

Philadelphia-Camden-Wilmington, PA-NJ-DEMD MSA 2-3 24HourMean 0.65

St. Louis St. Louis, MO-IL MSA 2-3 24HourMean 0.64

Average 0.64
Appendix F: Ozone Health Impact Functions in U.S. Setup

November 2012

146

BenMAP User's Manual Appendices

Table F-8. Eight-Hour Adjustment Details -- 14-City Study

City/County

CBSAs or Counties Used in

Ratio Average

Quarters

Used

Study

Metric

8-Hour

Adj Notes

Birmingham Birmingham-Hoover, AL MSA 2-3 1HourMax 1.15

Boulder Boulder, CO MSA 2-3 1HourMax 1.15

Canton Canton-Massillon, OH MSA 2-3 1HourMax 1.12

Chicago

Chicago-Naperville-Joliet, IL-INWI MSA 2-3 1HourMax 1.16

Cincinnati

Cincinnati-Middletown, OH-KYIN MSA 2-3 1HourMax 1.15

Colorado

Springs Colorado Springs, CO MSA 2-3 1HourMax 1.11

Columbus Columbus, OH MSA 2-3 1HourMax 1.13

Detroit Detroit-Warren-Livonia, MI MSA 2-3 1HourMax 1.17

Houston

Houston-Baytown-Sugar Land, TX

MSA 2-3 1HourMax 1.22

New Haven New Haven-Milford, CT MSA 2-3 1HourMax 1.17

Pittsburgh Pittsburgh, PA MSA 2-3 1HourMax 1.15

Provo Provo-Orem, UT MSA 3 1HourMax 1.13

Only quarter 3

available

Seattle

Seattle-Tacoma-Bellevue, WA

MSA 2-3 1HourMax 1.15

Spokane Spokane, WA MSA 3 1HourMax 1.08

Only quarter 3

available

Average 1.15

Table F-9. Eight-Hour Adjustment Details -- 19-City Study

City/County

CBSAs or Counties Used in

Ratio Average

Quarters

Used

Study

Metric

8-Hour

Adj Notes

Atlanta

Atlanta-Sandy Springs-Marietta,

GA MSA 2-3 24HourMean 0.59

Chicago

Chicago-Naperville-Joliet, IL-INWI MSA 2-3 24HourMean 0.65

Cleveland

Cleveland-Elyria-Mentor, OH

MSA 2-3 24HourMean 0.69

Dallas/Ft

Worth

Dallas-Fort Worth-Arlington, TX

MSA 2-3 24HourMean 0.67

Detroit Detroit-Warren-Livonia, MI MSA 2-3 24HourMean 0.63
Appendix F: Ozone Health Impact Functions in U.S. Setup

November 2012

147

BenMAP User's Manual Appendices

City/County

CBSAs or Counties Used in

Ratio Average

Quarters

Used

Study

Metric

8-Hour

Adj Notes

Houston

Houston-Baytown-Sugar Land, TX

MSA 2-3 24HourMean 0.59

Los Angeles

Los Angeles-LongBeach-Santa

Ana, CA MSA 2-3 24HourMean 0.59

Los Angeles and Santa

Ana/Anaheim have

same CBSA.

Santa Ana/

Anaheim

Los Angeles-LongBeach-Santa

Ana, CA MSA 2-3 24HourMean 0.59

Miami

Miami-Fort Lauderdale-Miami

Beach, FL MSA 2-3 24HourMean 0.71

New York

New York-Newark-Edison, NYNJ-PA MSA 2-3 24HourMean 0.66

Philadelphia

Philadelphia-Camden-Wilmington,

PA-NJ-DE-MD MSA 2-3 24HourMean 0.65

Phoenix

Phoenix-Mesa-Scottsdale, AZ

MSA 2-3 24HourMean 0.66

Pittsburgh Pittsburgh, PA MSA 2-3 24HourMean 0.61

San

Bernardino

Riverside-San Bernardino-Ontario,

CA MSA 2-3 24HourMean 0.65

San Antonio San Antonio, TX MSA 2-3 24HourMean 0.66

San Diego

San Diego-Carlsbad-San Marcos,

CA MSA 2-3 24HourMean 0.70

Oakland

San Francisco-Oakland-Fremont,

CA MSA 2-3 24HourMean 0.70

San Jose

San Jose-Sunnyvale-Santa Clara,

CA MSA 2-3 24HourMean 0.64

Seattle

Seattle-Tacoma-Bellevue, WA

MSA 2-3 24HourMean 0.69

Average 0.65

Keeping 1 Los

Angeles keeps 8-

hour adj at 0.65.
Appendix G: Nitrogen Dioxide Health Impact Functions in U.S. Setup

November 2012

148

BenMAP User's Manual Appendices

Appendix G: Nitrogen Dioxide Health Impact

Functions in U.S. Setup

In this Appendix, we present the health impact functions used to estimate NO2-related adverse

health effects. Each sub-section has a table with a brief description of each health impact function

and the underlying parameters. Following each table, we present a briefsummary of each of the

studies and any items that are unique to the study.

Note that Appendix C mathematically derives the standard types of health impact functions

encountered in the epidemiological literature, such as, log-linear, logistic and linear, so we simply

note here the type of functionalform. And Appendix D presents a description of the sources for

the incidence and prevalence data used in the health impact functions.

G.1 Hospital Admissions

Table G-1 summarizes the health impacts functions used to estimate the relationship between

nitrogen dioxide and hospital admissions. Below, we present a briefsummary of each of the

studies and any items that are unique to the study.

Table G-1. Health Impact Functions for Nitrogen Dioxide and Hospital Admissions

Effect Author Year Location Age CoPollMetric Beta Std. Err. Form Notes

All

Respiratory

Fung et al. 2006 Vancouver,

Canada

65-99 D24Hour

Mean

0.003285 0.001707 Log-linear

Asthma Linn et al. 2000 Metropolitan

Los Angeles

0-29 D24Hour

Mean

0.002400 0.000800 Log-linear

Asthma Linn et al. 2000 Metropolitan

Los Angeles

30-99 D24Hour

Mean

0.001400 0.000500 Log-linear

All

Respiratory

Luginaah et

al.

2005 Windsor,

Ontario

0-14 D1Hour

Max

0.006747 0.003628 Log-linear Female

All

Respiratory

Luginaah et

al.

2005 Windsor,

Ontario

0-14 D1Hour

Max

-0.002878 0.003132 Log-linear Male

All

Respiratory

Luginaah et

al.

2005 Windsor,

Ontario

15-64 D1Hour

Max

0.007139 0.004353 Log-linear Female

All

Respiratory

Luginaah et

al.

2005 Windsor,

Ontario

15-64 D1Hour

Max

0.000746 0.005879 Log-linear Male

All

Respiratory

Luginaah et

al.

2005 Windsor,

Ontario

65-99 D1Hour

Max

0.001238 0.002961 Log-linear Female

All

Respiratory

Luginaah et

al.

2005 Windsor,

Ontario

65-99 D1Hour

Max

-0.001435 0.004295 Log-linear Male

Chronic Lung

Disease

Moolgavkar

S.H.

2003 Los Angeles

County, CA

65-99 D24Hour

Mean

0.001800 0.000188 Log-linear

Chronic Lung

Disease

Moolgavkar

S.H.

2003 Cook

County, CA

65-99 D24Hour

Mean

0.002400 0.000803 Log-linear

All

Respiratory

Yang et al. 2003 Vancouver,

Canada

65-99 SO

2

, D24Hour

Mean

0.008759 0.003069 Logistic
Appendix G: Nitrogen Dioxide Health Impact Functions in U.S. Setup

November 2012

149

BenMAP User's Manual Appendices

Effect Author Year Location Age CoPollMetric Beta Std. Err. Form Notes

O

3

,

CO,

COH

Chronic Lung

Disease (less

Asthma)

Yang et al. 2005 Vancouver,

Canada

65-99 O

3

D24Hour

Mean

0.020605 0.006637 Log-linear

G.1.1 Fung et al. (2006)

Fung et al. (2006) assessed the impact of ambient gaseous pollutants (SO2, NO2, CO, and O3)

and particulate matters (PM10, PM2.5, and PM10-2.5) as well as the coefficient of haze (COH) on

recurrent respiratory hospital admissions (ICD-9 codes 460-519) among the elderly in

Vancouver, Canada, for the period ofJune 1, 1995, to March 31, 1999, using a new method

proposed by Dewanji and Moolgavkar(2000; 2002). The associations were conducted at

current day, 3-day, 5-day, and 7-day moving averages. The strongest association between NO2

and hospital admissions was observed at 3-day lag (RR = 1.018, 95% CI: 1.000-1.037). For

SO2, significant associations were found between admissions and 3-day, 5-day, and 7-day

moving averages of the ambient SO2 concentrations, with the strongest association observed at

the 7-day lag (RR = 1.044, 95% CI: 1.018-1.070). The authors found PM10-2.5for 3-day and 5-

day lag to be significant, with the strongest association at 5-day lag (RR = 1.020, 95% CI:

1.001-1.039). No significant associations with admission were found with current day exposure.

Hospital Admissions, All Respiratory (ICD-9 codes 460-519)

In a single-pollutant model the coefficient and standard error are estimated from the relative risk

(1.018) and 95% confidence interval(95% CI: 1.000-1.037) for a 5.431 ppb increase in 3-day

moving average of NO2(Fung, et al., 2006, Table 4).

G.1.2 Linn et al. (2000)

Linn et al. (2000) evaluated associations between air pollution and hospital admissions for

cardiopulmonary illnesses in metropolitan Los Angeles during 1992-1995. In a single-pollutant

Poisson regression model, daily average of NO2(year-round) was found significantly associated

with same-day asthma hospital admissions for both age groups (i.e., 0-29 and 30-99). The

results for winter and autumn were also reported but insignificant.

Hospital Admissions, Asthma (ICD-9 code 493)

Linn et al. (2000, Table 9) reported the coefficients and standard error for patients = 30 years of

age and Linn et al. (2000, p.432) reported the coefficients and standard error for patients <30

years of age.
Appendix G: Nitrogen Dioxide Health Impact Functions in U.S. Setup

November 2012

150

BenMAP User's Manual Appendices

G.1.3 Luginaah et al. (2005)

Luginaah et al. (2005) assessed the association between air pollution and daily respiratory

hospitalization (ICD-9 codes 460-519) for different age and sex groups from 1995 to 2000. The

pollutants included were NO2, SO2, CO, O3, PM10, coefficient of haze (COH), and total

reduced sulfur (TRS). The authors estimated relative risks (RR) using both time-series and casecrossover methods after controlling for appropriate confounders (temperature, humidity, and

change in barometric pressure). The results of both analyses were consistent. They found

associations between NO2, SO2, CO, COH, or PM10and daily hospital admission of

respiratory diseases especially among females. For females 0-14 years of age, there was 1-day

delayed effect of NO2(RR = 1.19, case-crossover method), a current-day SO2(RR = 1.11,

time series), and current-day and 1- and 2-day delayed effects for CO by case crossover (RR =

1.15, 1.19, 1.22, respectively). Time-series analysis showed that 1-day delayed effect of PM10

on respiratory admissions of adult males (15-64 years of age), with an RR of 1.18. COH had

significant effects on female respiratory hospitalization, especially for 2-day delayed effects on

adult females, with RRs of 1.15 and 1.29 using time-series and case-crossover analysis,

respectively. There were no significant associations between O3and TRS with respiratory

admissions.

Hospital Admissions, All Respiratory (ICD-9 codes 460-519)

For different age and sex groups, the coefficient and standard error are estimated from the

relative risks and 95% confidence intervalreported for single-pollutant models using time-series

method for a 16 ppb increase in daily 1-hour max NO2 levels (Luginaah, et al., 2005, Table 3).

The time-series method is chosen based on the discussion in Fung et al.(2003).

G.1.4 Moolgavkar (2003)

Moolgavkar (2003) presented re-analyses of Moolgavkar (2000c; 2000a; 2000b) about the

associations between air pollution and daily deaths and hospital admissions in Los Angeles and

Cook counties in the United States. The principalreason for conducting these re-analyses was to

assess the impact of using convergence criteria that are more stringent than the default criteria

used in the S-Plus software package. The author also reported the results of generalized linear

model(GLM) analyses using naturalsplines with the same degree of freedom as the smoothing

splines he used in the generalized additive model(GAM) analyses. In single-pollutant Poisson

regression models, hospital admissions for COPD (ICD-9 code 490-496) were associated with

daily average of NO2levels at lags of 0, 1, 2, 3, 4 and 5 days for individuals 65 and older. The

association was strongest at lag 0 using both GAM (stringent convergence) and GLM.

Hospital Admissions, Chronic Lung Disease (ICD-9 code 490-496) --- Los Angeles

County

Moolgavkar (2003, Table 18 ) shows the estimated percentage change (log RR x100) in daily

COPD admissions associated with increases of 10 ppb NO2from single-pollutant models in Los
Appendix G: Nitrogen Dioxide Health Impact Functions in U.S. Setup

November 2012

151

BenMAP User's Manual Appendices

Angeles County. The coefficients can be calculated based on the reported percentage changes,

that is, βNO2 = log(RR)/10ppb. Along with the t-statistics reported in the same table, standard

errors (SE) are calculated. For Los Angeles County βNO2 = 0.0018 and SE = 0.00019.

Hospital Admissions, Chronic Lung Disease (ICD-9 code 490-496) --- Cook County

Moolgavkar (2003, Table 20) shows the estimated percentage change (log RR x100) in daily

COPD admissions associated with increases of 10 ppb NO2from single-pollutant models in

Cook County. The coefficient and standard error are calculated in the same way as described

above. For Cook County βNO2 = 0.0024 and SE = 0.00080.

G.1.5 Yang et al. (2003)

Yang et al.(2003) examined the impact of ozone, nitrogen dioxide, sulfur dioxide, carbon

monoxide, and coefficient of haze on daily respiratory admissions (ICD-9 codes 460-519) in

both young children (<3 years of age) and the elderly (65-99 years of age) in greater Vancouver,

British Columbia during the 13-yr period 1986-1998. Bidirectional case-crossover analysis was

used to investigate associations and odds ratios were reported for single-pollutant, two-pollutants

and multiple pollutants models. NO2 was found significantly associated with respiratory

admissions in all models for elderly. For children NO2effect was found significant in singlepollutant model and two pollutants model with O3

.

Hospital Admissions, All Respiratory (ICD-9 codes 460-519)

In a multi-pollutant model with SO2, O3, CO, and COH, the coefficient for NO2and standard

error are estimated for age 65-99 from the relative risks (1.05) and 95% confidence interval

(1.01-1.08) for a 5.57 ppb increase in daily average of NO2at lag 1 day (Yang, et al., 2003,

Table 3).

G.1.6 Yang et al. (2005)

Yang et al. (2005) examined the associations between gaseous pollutants and hospitalization for

chronic lung disease less asthma (ICD-9 codes 490-492, 494, and 496) among elderly people

living in Vancouver, Canada. The authors regressed the logarithm of daily counts of acute COPD

hospitalization during the 5-year period from 1994 to 1998 on the daily mean levels of each

pollutant, after accounting for seasonal and subseasonalfluctuations, non-Poisson dispersion, and

weather variables. They found that nitrogen dioxide and carbon monoxide were significantly

associated with hospitalization for COPD, and the magnitude of effects was increased slightly

with increasing days of exposure averaging, with the relative risk for a 7-day average being 1.12

(95% CI: 1.04, 1.20) for nitrogen dioxide in a two-pollutant model with O3. There was no

significant association between either sulfur dioxide or ozone and COPD hospitalization. The

combined relative risk for allfour gaseous pollutants (CO, O3, NO2, and SO2) on COPD

hospitalization was 1.21. The effects of gaseous pollutants on COPD hospitalization were not
Appendix G: Nitrogen Dioxide Health Impact Functions in U.S. Setup

November 2012

152

BenMAP User's Manual Appendices

significant after adjustment for PM10, although its inclusion did not have a marked effect on the

point estimates for relative risks.

Hospital Admissions, Chronic Lung Disease less Asthma (ICD-9 codes 490-492, 494,

and 496)

In a two-pollutant model with O3, the coefficient and standard error are estimated from the

relative risk (1.12) and 95% confidence interval(1.04-1.20) for a 5.5 ppb increase in 7-day

average NO2levels (Yang, et al., 2005, Table 5).

G.2 Emergency Room Visits

Table G-2 summarizes the health impacts functions used to estimate the relationship between

nitrogen dioxide and emergency room visits. Below, we present a briefsummary of each of the

studies and any items that are unique to the study.

Table G-2. Health Impact Functions for Nitrogen Dioxide and Emergency Room Visits

Effect Author Year Location Age CoPollMetric Beta Std. Err. Functional

Form

Asthma Ito et al. 2007 NYC 0-99 D24HourMean 0.005460 0.000933 Log-linear

Asthma NYDOH 2006 Bronx, NYC 0-99 O

3

D24HourMean 0.002264 0.001178 Log-linear

Asthma Peel et al. 2005 Atlanta, GA 0-99 D1HourMax 0.002296 0.000901 Log-linear

Asthma Villeneuve

et al.

2007 Edmonton,

Canada

75-99 D24HourMean 0.013505 0.005345 Logistic

G.2.1 Ito et al. (2007)

Ito et al.(2007) assessed associations between air pollution and asthma emergency department

visits in New York City for all ages. Specifically they examined the temporalrelationships among

air pollution and weather variables in the context of air pollution health effects models. The

authors compiled daily data for PM2.5, O3, NO2, SO2, CO, temperature, dew point, relative

humidity, wind speed, and barometric pressure for New York City for the years 1999-2002.The

authors evaluated the relationship between the various pollutants' risk estimates and their

respective concurvities, and discuss the limitations that the results imply about the interpretability

of multi-pollutant health effects models.

Emergency Room Visits, Asthma (ICD-9 code 493)

In a single-pollutant model, the coefficient and standard error are estimated from the relative risk

(1.14) and 95% confidence interval(1.09-1.19) for a 24 ppb increase in the average of 0- and

1-day lag of NO2(Ito, et al., 2007, p. S52).
Appendix G: Nitrogen Dioxide Health Impact Functions in U.S. Setup

November 2012

153

BenMAP User's Manual Appendices

G.2.2 NYDOH (2006)

New York State Department of Health (NYDOH) investigated whether day-to-day variations in

air pollution were associated with asthma emergency department (ED) visits in Manhattan and

Bronx, NYC and compared the magnitude of the air pollution effect between the two

communities. NYDOH(2006) used Poisson regression to test for effects of 14 key air

contaminants on daily ED visits, with controlfor temporal cycles, temperature, and day-of-week

effects. The core analysis utilized the average exposure for the zero- to four-day lags. Mean daily

NO2 was found significantly associated with asthma ED visits in Bronx but not Manhattan. Their

findings of more significant air pollution effects in the Bronx are likely to relate in part to greater

statistical power for identifying effects in the Bronx where baseline ED visits were greater, but

they may also reflect greater sensitivity to air pollution effects in the Bronx.

Emergency Room Visits, Asthma (ICD-9 code 493)

In a two-pollutant model with O3, the coefficient and standard error are estimated from the

relative risk (1.08) and 95% confidence interval(1.00-1.17) for a 34 ppb increase in the average

of 0- to 4-day lags of NO2(NYDOH, 2006, Table 9).

G.2.3 Peel et al. (2005)

Peel et al. (2005) examined the associations between air pollution and respiratory emergency

department visits (i.e., asthma (ICD-9 code 493, 786.09), COPD (491,492,496), URI (460-

466, 477), pneumonia (480-486), and an allrespiratory-disease group) in Atlanta, GA from 1

January 1993 to 31 August 2000. They used 3-Day Moving Average (Lags of 0, 1, and 2 Days)

and unconstrained distributed lag (Lags of 0 to 13 Days) in the Poisson regression analyses. In

single-pollutant models, the authors found that positive associations persisted beyond 3 days for

several outcomes, and over a week for asthma. Standard deviation increases of O3, NO2, CO,

and PM10 were associated with 1-3% increases in URI visits; a 2 μg/m3 increase of PM2.5 organic

carbon was associated with a 3% increase in pneumonia visits; and standard deviation increases

of NO2 and CO were associated with 2-3% increases in chronic obstructive pulmonary disease

visits.

Emergency Room Visits, Asthma (ICD-9 code 493)

In the single-pollutant model using unconstrained distributed lag, the coefficient and standard

error are estimated from the relative risk (1.047) and 95% confidence interval(1.011-1.085) for

a 20 ppb increase in NO2(Peel, et al., 2005, Table 4).

G.2.4 Villeneuve et al. (2007)

Villeneuve et al.(2007) examined the associations between air pollution and emergency

department (ED) visits for asthma among individuals two years of age and older in the census

metropolitan area of Edmonton, Canada between April 1, 1992 and March 31, 2002 using a

time stratified case-crossover design. Daily air pollution levels for the entire region were estimated
Appendix G: Nitrogen Dioxide Health Impact Functions in U.S. Setup

November 2012

154

BenMAP User's Manual Appendices

from three fixed-site monitoring stations. Odds ratios and their corresponding 95% confidence

intervals were estimated using conditional logistic regression with adjustment for temperature,

relative humidity and seasonal epidemic of viralrelated respiratory disease. Villeneuve et al.

(2007) found positive associations for asthma ED visits with outdoor air pollution levels between

April and September, but such associations were absent during the remainder of the year. Effects

were strongest among young children (2-4 years of age) and elderly (>75 years of age). Air

pollution risk estimates were largely unchanged after adjustment for aeroallergen levels.

Emergency Room Visits, Asthma (ICD-9 code 493)

In a single-pollutant modelfor allseasons, the coefficient and standard error are estimated from

the relative risk (1.20) and 95% confidence interval(1.04-1.38) for a 13.5 ppb increase in 5-day

average of NO2(Villeneuve, et al., 2007, Table 9).
Appendix G: Nitrogen Dioxide Health Impact Functions in U.S. Setup

November 2012

155

BenMAP User's Manual Appendices

G.3 Asthma Exacerbation

Table G-3 summarizes the health impacts functions used to estimate the relationship between

nitrogen dioxide and asthma exacerbation. Below, we present a briefsummary of each of the

studies and any items that are unique to the study.

Table G-3. Health Impact Functions for Nitrogen Dioxide and Asthma Exacerbation

Eff ect Author Year Location Age Co-Poll Metric Beta Std. Err. Functional

Form

Notes

One or More

Symptoms

Delfino et

al.

2002 Southern

Califormia

9-18 D8Hour

Max

0.019939 0.011443 Logistic

One or More

Symptoms

Delfino et

al.

2003 Los Angeles 10-16 D8Hour

Max

0.240337 0.115256 Logistic Hispanic

asthmatics

One or More

Symptoms

Mortimer

et al.

2002 7 urban areas

(US)

4-12 O

3

,

SO

2

D4Hour

Mean

0.013501 0.011179 Logistic Warmseason;

Adjusted age

One or More

Symptoms

O'Connor

et al.

2008 7 inner cities 4-12 PM2.5,

O

3

D24Hour

Mean

0.010545 0.004988 Log-linear* Adjusted age

Nighttime

asthma

O'Connor

et al.

2008 7 inner cities 4-12 PM2.5,

O

3

D24Hour

Mean

0.012482 0.006488 Log-linear* Adjusted age

Slow play O'Connor

et al.

2008 7 inner cities 4-12 PM2.5,

O

3

D24Hour

Mean

0.013979 0.005609 Log-linear* Adjusted age

Missed school

days

O'Connor

et al.

2008 7 inner cities 4-12 PM2.5,

O

3

D24Hour

Mean

0.013979 0.010534 Logistic Adjusted age

Cough Ostro et

al.

2001 Central Los

Angeles, CA

4-12 D1Hour

Max

0.000591 0.000595 Logistic AfricanAmericans;

Adjusted age

Cough (New

Cases)

Ostro et

al.

2001 Central Los

Angeles, CA

4-12 D1Hour

Max

0.002267 0.001098 Logistic AfricanAmericans;

Adjusted age

Shortness of

Breath

Ostro et

al.

2001 Central Los

Angeles, CA

4-12 D1Hour

Max

0.001539 0.000896 Logistic AfricanAmericans;

Adjusted age

Shortness of

Breath (New

Cases)

Ostro et

al.

2001 Central Los

Angeles, CA

4-12 D1Hour

Max

0.002621 0.001429 Logistic AfricanAmericans;

Adjusted age

Wheeze Ostro et

al.

2001 Central Los

Angeles, CA

4-12 D1Hour

Max

0.001539 0.000612 Logistic AfricanAmericans;

Adjusted age

Wheeze (New

Cases)

Ostro et

al.

2001 Central Los

Angeles, CA

4-12 D1Hour

Max

0.002444 0.000897 Logistic AfricanAmericans;

Adjusted age

One or More

Symptoms

Schildcro

ut et al.

2006 Eight U.S. cities 4-12 D24Hour

Mean

0.004309 0.001406 Logistic Adjusted age

* O'Connor et al. (2008) did not specify the functional form. Log-linear assumption is made based on the reported results

(Table IV on page 1137).
Appendix G: Nitrogen Dioxide Health Impact Functions in U.S. Setup

November 2012

156

BenMAP User's Manual Appendices

G.3.1 Delfino et al. (2002)

Delfino et al.(2002) examined the association between air pollution and asthma symptoms among

22 asthmatic children (9-19 years of age) followed March through April 1996 (1,248 persondays) in Southern California. Air quality data for PM10

, NO2, O3, fungi and pollen were used in a

logistic model with controlfor temperature, relative humidity, day-of-week trends and linear time

trends. The odds ratio (95% confidence interval) for asthma episodes in relation to lag0 20 ppb

changes in 8-hr max NO2is 1.49 (0.95-2.33). The authors also considered subgroups of

asthmatic children who were on versus not on regularly scheduled anti-inflammatory medications

and found that pollutant associations were stronger during respiratory infections in subjects not on

anti-inflammatory medications.

Asthma Exacerbation, One or More Symptoms

In a single-pollutant model, the coefficient and standard error are estimated from the odds ratio

(1.49) and the 95% confidence interval(0.95-2.33) for a 20 ppb increase in 8-hr max NO2

(Delfino, et al., 2002, Table 4).

Incidence Rate: Delfino et al.(2002, Table 1) reported asthma episodes in the 22 asthmatic

children. Asthma episodes are defined as having asthma symptoms that interfered with daily

activities (symptom sore >2). The incidence rate is calculated as the ratio of number of person-days

that symptom sore >2 and the total number of person-days, i.e., daily asthma episodes incidence

rate = 196/1248=0.157

Population: The study population includes asthmatics from 9 to 18 years of age. We treat these as

two groups based on the available information from American Lung Association (2010b, Table 7).

Asthmatic population ages 9 to 17 = 10.70% of population ages 9 to 17 and asthmatic population

age 18 = 7.19% of population age 18. The American Lung Association (2010b, Table 7) estimates

asthma prevalence for children 5- 17 and adults 18-44 at 10.70% and 7.19% respectively (based on

data from the 2008 National Health Interview Survey).

G.3.2 Delfino et al. (2003)

Delfino et al.(2003) conducted a panelstudy of 22 Hispanic children with asthma who were 10-

16 years old and living in a Los Angeles community with high traffic density. Subjects filled out

symptom diaries daily for up to 3 months (November 1999 through January 2000). Pollutants

included ambient hourly values of ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), and

carbon monoxide (CO) and 24-hr values of volatile organic compounds (VOCs), particulate

matter with aerodynamic diameter < 10 microm (PM10), and elemental carbon (EC) and organic

carbon (OC) PM10fractions. Asthma symptom severity was regressed on pollutants using logistic

models. The authors found positive associations ofsymptoms with criteria air pollutants (NO2,

SO2, O3, and PM10). Selected adjusted odds ratio for more severe asthma symptoms from

interquartile range increases in pollutants was, for 1.4 ppb 8-hr max NO2, 1.40 [95% confidence

interval(CI), 1.02-1.92]. Their findings support the view that air toxins in the pollutant mix from

traffic and industrialsources may have adverse effects on asthma in children.
Appendix G: Nitrogen Dioxide Health Impact Functions in U.S. Setup

November 2012

157

BenMAP User's Manual Appendices

Asthma Exacerbation, One or More Symptoms

In a single-pollutant model the coefficient and standard error are based on the odds ratio (95%

CI) for more severe asthma symptoms, i.e., odds ratio of 1.40 [95% confidence interval(CI),

1.02-1.92] for a 1.4 ppb increase in 8-hr max NO2(Delfino, et al., 2003, Table 4).

Incidence Rate: Daily asthma symptoms incidence rate = 2.3% (Delfino, et al., 2003, Table 1)

Population: Delfino et al.(2003) examined Hispanic asthmatic population, whose prevalence is not

available. We use the prevalence of asthmatic population from the American Lung Association

(2010b, Table 7) as a proxy. Asthmatic population ages 10 to 16 = 10.70% of population ages 10 to

16. The American Lung Association (2010b, Table 7) estimates asthma prevalence for children 5-

17 at 10.70% (based on data from the 2008 National Health Interview Survey).

G.3.3 Mortimer et al. (2002)

Mortimer et al.(2002) examined the effect of daily ambient air pollution within a cohort of 846

asthmatic children residing in eight urban areas of the USA between June 1 to August 31, 1993,

using data from the NationalCooperative Inner-City Asthma Study. Daily air pollution

concentrations were extracted from the Aerometric Information Retrieval System database from

the Environment Protection Agency in the USA. Logistic models were used to evaluate the

effects ofseveral air pollutants (O3, NO2, SO2 and PM10) on peak expiratory flow rate (PEFR)

and symptoms in 846 children (ages 4-9 yrs) with a history of asthma. In single pollutant models,

each pollutant was associated with an increased incidence of morning symptoms:(odds ratio(OR)

=1.16 (95% CI 1.02-1.30) per IQR increase in 4-day average O3, OR=1.32 (95% CI 1.03-

1.70) per IQR increase in 2-day average SO2, OR=1.48 (95% CI 1.02-2.16) per IQR increase

in 6-day average NO2 and OR=1.26 (95% CI 1.0-1.59) per IQR increase in two-day average

PM10. This longitudinal analysis supports previous time-series findings that at levels below current

USA air-quality standards, summer-air pollution is significantly related to symptoms and

decreased pulmonary function among children with asthma.

Asthma Exacerbation, One or More Symptoms

In a three-pollutant model with O3 and SO2, the coefficient for NO2 and standard error are based

on the odds ratio (95% CI) for morning asthma symptoms, i.e., odds ratio of 1.31 [95%

confidence interval(CI), 0.87-2.09] for a 20 ppb increase in 1-6 day average of NO2(Delfino,

et al., 2003, Table 4).

Incidence Rate: Daily incidence rate for "any morning symptom" is 11.6% (Mortimer, et al., 2002,

Table 1).

Population: Asthmatic population ages 4 to 12 = 10.70% of population ages 4 to 12. The American

Lung Association (2010b, Table 7) estimates asthma prevalence for children 5- 17 at 10.70%

(based on data from the 2008 National Health Interview Survey). Based on the American Lung

Association (2010b, Table 7), the estimated asthma prevalence for children under 5 years old at

6.14% (based on data from the 2008 National Health Interview Survey), which is close to the rate
Appendix G: Nitrogen Dioxide Health Impact Functions in U.S. Setup

November 2012

158

BenMAP User's Manual Appendices

of ages 5-17. Therefore we use the rate of population 5-17 as an estimate for asthma prevalence in

population ages 4-9 for simplicity.

G.3.4 O'Connor et al. (2008)

O'Connor et al.(2008) investigated the association between fluctuations in outdoor air pollution

and asthma exacerbation among 861 inner-city children (5-12 years of age) with asthma in 7 US

urban communities. Asthma symptom data were collected every 2 months during the 2-year

study period. Daily pollution measurements were obtained from the Aerometric Information

Retrieval System between August 1998 and July 2001. The relationship ofsymptoms to

fluctuations in pollutant concentrations was examined by using logistic models. In single-pollutant

models, significant or nearly significant positive associations were observed between higher NO2

concentrations and each of the health outcomes. Significant positive associations with symptoms

but not school absence were observed in the single-pollutant modelfor CO. The O3, PM2.5, and

SO2 concentrations did not appear significantly associated with symptoms or school absence

except for a significant association between PM2.5 and school absence. The authors concluded

that the associations with NO2 suggest that motor vehicle emissions may be causing excess

morbidity in this population.

Asthma Exacerbation, Missed school

In a three-pollutant model with O3 and PM2.5, the coefficient and standard error are estimated

from the odds ratio (1.33) and 95% confidence interval(0.87-2.02) for a 20.4 ppb increase in

19-day average of NO2(O'Connor, et al., 2008, Table IV). The independent pollution variable

was the mean concentration during the 19 days preceding the interview, that is, the 14 days of the

symptom recall period plus a 5-day lag period preceding the symptom recall period.

Incidence Rate: Daily incidence rate for missed school = 5.7% (O'Connor, et al., 2008, Table I).

Population: Same as population used in Mortimer et al. (2002). See description above.

Asthma Exacerbation, One or More Symptoms

O'Connor et al. (2008, Table IV) reported a 24% increase in symptom frequency with a 95%

confidence interval of 2% to 52% associated with a 20.4 ppb increase in the 19-day average of

NO2.

Incidence Rate: Daily incidence rate for one or more symptoms = 20.7% (O'Connor, et al., 2008,

Table I).

Population: See population description above.

Asthma Exacerbation, Nighttime Asthma

O'Connor et al. (2008, Table IV) reported a 29% increase in symptom frequency with a 95%

confidence interval of 0% to 68% associated with a 20.4 ppb increase in the 19-day average of

NO2.
Appendix G: Nitrogen Dioxide Health Impact Functions in U.S. Setup

November 2012

159

BenMAP User's Manual Appendices

Incidence Rate: Daily incidence rate for nighttime asthma = 12.1% (O'Connor, et al., 2008, Table

I).

Population: See population description above.

Asthma Exacerbation, Slow Play

O'Connor et al. (2008, Table IV) reported a 33% increase in symptom frequency with a 95%

confidence interval of 6% to 66% associated with a 20.4 ppb increase in the 19-day average of

NO2.

Incidence Rate: Daily incidence rate for slow play = 15.7% (O'Connor, et al., 2008, Table I).

Population: See population description above.

G.3.5 Ostro et al. (2001)

Ostro et al.(2001) examined relations between several air pollutants and asthma exacerbation in

African-Americans children (8 to 13 years old) in central Los Angeles from August to November

1993. Air quality data for PM10, PM2.5, NO2, and O3 were used in a logistic regression model

with controlfor age, income, time trends, and temperature-related weather effects. Asthma

symptom endpoints were defined in two ways: “probability of a day with symptoms” and “onset

ofsymptom episodes”. New onset of a symptom episode was defined as a day with symptoms

followed by a symptom-free day. The authors found cough prevalence associated with PM10 and

PM2.5 and cough incidence associated with PM2.5, PM10, and NO2. Ozone was not significantly

associated with cough among asthmatics. The authors found that both the prevalent and incident

episodes ofshortness of breath were associated with PM2.5 and PM10. Neither ozone nor NO2

were significantly associated with shortness of breath among asthmatics. The authors found both

the prevalence and incidence of wheeze associated with PM2.5, PM10, and NO2. Ozone was not

significantly associated with wheeze among asthmatics.

Asthma Exacerbation, Cough

The coefficient and standard error are based on an odds ratio of 1.03 (95% CI 0.97-1.09) for a

50 ppb increase in 1-h max NO2 concentration (Ostro, et al., 2001, Table 4, p.204).

Incidence Rate: Use daily prevalence of cough reported on p.202 of Ostro et al. (2001) and then

take weighted average with weight being the population in the two study locations, i.e., daily cough

rate per person = (0.151*115+0.115*23)/ (115+23) = 0.145

Population: Asthmatic African-American population ages 4 to 12 = 17.76% of African-American

population ages 4 to 12. The American Lung Association (2010b, Table 9) estimates asthma

prevalence for African-American children ages 5 to 17 at 17.76% (based on data from the 2008

National Health Interview Survey). Based on the American Lung Association (2010b, Table 9), the

estimated asthma prevalence for African children under 5 years old is 9.98% (based on data from

the 2008 National Health Interview Survey), which is close to the rate of ages 5-17. Therefore we

use the rate of population 5-17 as an estimate for asthma prevalence in population ages 4-9 for

simplicity.
Appendix G: Nitrogen Dioxide Health Impact Functions in U.S. Setup

November 2012

160

BenMAP User's Manual Appendices

Asthma Exacerbation, Cough (New Cases)

The coefficient and standard error are based on an odds ratio of 1.12 (95% CI 1.00-1.24) for a

50 ppb increase in 1-h max NO2 concentration (Ostro, et al., 2001, Table 5, p.204).

Incidence Rate: daily cough incidence rate = 0.067 (Ostro, et al., 2001, p.202).

Population: Asthmatic African-American population ages 4 to 12 = 7.26% of African-American

population ages 4 to 12 (described above).

Asthma Exacerbation, Shortness of Breath

The coefficient and standard error are based on an odds ratio of 1.08 (95% CI 0.99-1.18) for a

50 ppb increase in 1-h max NO2 concentration (Ostro, et al., 2001, Table 4, p.204).

Incidence Rate: Use daily prevalence of shortness of breath reported on p.202 of Ostro et al.

(2001) and then take weighted average with weight being the population in the two study locations,

i.e., daily shortness of breath rate per person = (0.075*115+0.081*23)/ (115+23) = 0.074

Population: Asthmatic African-American population ages 4 to 12 = 7.26% of African-American

population ages 4 to 12 (described above).

Asthma Exacerbation, Shortness of Breath (New Cases)

The coefficient and standard error are based on an odds ratio of 1.14 (95% CI 0.99-1.31) for a

50 ppb increase in 1-h max NO2 concentration (Ostro, et al., 2001, Table 5, p.204).

Incidence Rate: daily shortness of breath incidence rate = 0.037 (Ostro, et al., 2001, p.202)

Population: Asthmatic African-American population ages 4 to 12 = 7.26% of African-American

population ages 4 to 12 (described above).

Asthma Exacerbation, Wheeze

The coefficient and standard error are based on an odds ratio of 1.08 (95% CI 1.02-1.15) for a

50 ppb increase in 1-h max NO2 concentration (Ostro, et al., 2001, Table 4, p.204).

Incidence Rate: Use daily prevalence of wheeze reported on p.202 of Ostro et al. (2001) and

then take weighted average with weight being the population in the two study locations, i.e., daily

wheeze rate per person = (0.173*115+0.172*23)/ (115+23) = 0.173

Population: Asthmatic African-American population ages 4 to 12 = 7.26% of African-American

population ages 4 to 12 (described above).

Asthma Exacerbation, Wheeze (New Cases)

The coefficient and standard error are based on an odds ratio of 1.13 (95% CI 1.04-1.24) for a

50 ppb increase in 1-h max NO2 concentration (Ostro, et al., 2001, Table 5, p.204).

Incidence Rate: daily wheeze incidence rate = 0.076 (Ostro, et al., 2001, p.202)

Population: Asthmatic African-American population ages 4 to 12 = 7.26% of African-American

population ages 4 to 12 (described above).
Appendix G: Nitrogen Dioxide Health Impact Functions in U.S. Setup

November 2012

161

BenMAP User's Manual Appendices

G.3.6 Schildcrout et al. (2006)

Schildcrout et al.(2006) investigated the relation between ambient concentrations of the five

criteria pollutants (PM10, O3, NO2, SO2, and CO) and asthma exacerbations (daily symptoms and

use of rescue inhalers) among 990 children in eight North American cities during the 22-month

prerandomization phase (November 1993-September 1995) of the Childhood Asthma

Management Program. Short-term effects of CO, NO2, PM10, SO2, and warm-season O3 were

examined in both one-pollutant and two-pollutant models, using lags of up to 2 days in logistic

and Poisson regressions. Lags in CO and NO2 were positively associated with both measures of

asthma exacerbation, and the 3-day moving sum of SO2levels was marginally related to asthma

symptoms. PM10 and O3were unrelated to exacerbations. The strongest effects tended to be seen

with 2-day lags, where a 1-parts-per-million change in CO and a 20-parts-per-billion change in

NO2 were associated with symptom odds ratios of 1.08 (95% confidence interval(CI): 1.02,

1.15) and 1.09 (95% CI: 1.03, 1.15), respectively.

Asthma Excerbation, One or More Symptoms

In a single-pollutant model, Schildcrout et al.(2006, Figure 1) reported an odds ratio of 1.09

(95% CI: 1.03, 1.15) for daily asthma symptoms associated with 20 ppb change in 24-hr mean

of NO2 at lag 2.

Incidence Rate: Daily incidence rate for one or more symptoms (symptom score>0) = 52%

(Schildcrout, et al., 2006, Table 1)

Population: Asthmatic population ages 4 to 12 = 10.70% of population ages 4 to 12. The American

Lung Association (2010b, Table 7) estimates asthma prevalence for children 5- 17 at 10.70%

(based on data from the 2008 National Health Interview Survey).

G.4 Minor Effects

Table G-4 summarizes the health impacts function used to estimate the relationship between

nitrogen dioxide and minor effects. Below, we present a briefsummary of the study and any

items that are unique to the study.

Table G-4. Health Impact Functions for Nitrogen Dioxide and Minor Effects

Effect Author Year Location Age CoPollMetric Beta Std. Err. Function

al Form

Notes

Cough Schwartz

et al.

1994 Six U.S.

cities

7-14 PM10D24Hour

Mean

0.015700 0.011232 Logistic Warm

season

G.4.1 Schwartz et al. (1994)

Schwartz et al.(1994) studied the association between ambient air pollution exposures and

respiratory illness among 1,844 school children (7-14 years of age) in six U.S. cities during five

warm seasons months between April and August. Daily measurements of ambient sulfur dioxide

(SO2), nitrogen dioxide (NO2), ozone (O3), inhalable particles (PM10), respirable particles (PM2.5
Appendix G: Nitrogen Dioxide Health Impact Functions in U.S. Setup

November 2012

162

BenMAP User's Manual Appendices

), light scattering, and sulfate particles were made, along with integrated 24-h measures of aerosol

strong acidity. Significant associations in single pollutant models were found between SO2, NO2,

or PM2.5 and incidence of cough, and between sulfur dioxide and incidence of lower respiratory

symptoms. Significant associations were also found between incidence of coughing symptoms

and incidence of lower respiratory symptoms and PM10, and a marginally significant association

between upper respiratory symptoms and PM10.

Acute Respiratory Symptoms, Cough

In a two-pollutant model with PM10, the coefficient and standard error are based on an odds ratio

of 1.17 (95% CI 0.94-1.46) for a 10 ppb increase in the lag 1-4 days average of NO2

concentration (Schwartz, et al., 1994, Table 4).

Incidence Rate: The proposed incidence rate, 0.416 percent, is based on the percentiles in

Schwartz et al. (1994, Table 2). The authors did not report the mean incidence rate, but rather

reported various percentiles from the incidence rate distribution. The percentiles and associated per

person per day values are 10th = 0 percent, 25th = 0 percent, 50th = 0.31 percent, 75th = 0.58

percent, and 90th = 0.86 percent. The most conservative estimate consistent with the data is to

assume the incidence is zero up to the 50th percentile, a constant 0.31 percent between the 50th

and 75th percentiles, and a constant 0.58 percent between the 75th and 90th percentiles, a constant

0.86 percent between the 90th and 100th percentiles. Alternatively, assuming a linear slope

between the 50th and 75th, 75th and 90th, and 90th to 100th percentiles, the estimated mean

incidence rate is 0.461 percent, which is used in this analysis.

Population: Population of ages 7 to 14
Appendix H: Sulfur Dioxide Health Impact Functions in U.S. Setup

November 2012

163

BenMAP User's Manual Appendices

Appendix H: Sulfur Dioxide Health Impact

Functions in U.S. Setup

In this Appendix, we present the health impact functions used to estimate SO2-related adverse

health effects. Each sub-section has a table with a brief description of each health impact function

and the underlying parameters. Following each table, we present a briefsummary of each of the

studies and any items that are unique to the study.

Note that Appendix C mathematically derives the standard types of health impact functions

encountered in the epidemiological literature, such as, log-linear, logistic and linear, so we simply

note here the type of functionalform. And Appendix D presents a description of the sources for

the incidence and prevalence data used in the health impact functions.

H.1 Hospital Admissions

Table H-1 summarizes the health impacts functions used to estimate the relationship between

sulfur dioxide and hospital admissions. Below, we present a briefsummary of each of the studies

and any items that are unique to the study.

Table H-1. Health Impact Functions for Sulfur Dioxide and Hospital Admissions

Eff ect Author Year Location Age Co-Poll Metric Beta Std. Err. Functional

Form

Notes

All

Respiratory

Fung et al. 2006 Vancouver,

Canada

65-99 D24Hour

Mean

0.017224 0.005084 Log-linear

All

Respiratory

Luginaah et

al.

2005 Windsor,

Ontario

0-99 D1Hour

Max

0.002336 0.001671 Log-linear Female

All

Respiratory

Luginaah et

al.

2005 Windsor,

Ontario

0-99 D1Hour

Max

-0.000670 0.001718 Log-linear Male

All

Respiratory

Luginaah et

al.

2005 Windsor,

Ontario

0-14 D1Hour

Max

0.005468 0.002501 Log-linear Female

All

Respiratory

Luginaah et

al.

2005 Windsor,

Ontario

0-14 D1Hour

Max

-0.000260 0.002552 Log-linear Male

All

Respiratory

Luginaah et

al.

2005 Windsor,

Ontario

15-64 D1Hour

Max

0.003418 0.003118 Log-linear Female

All

Respiratory

Luginaah et

al.

2005 Windsor,

Ontario

15-64 D1Hour

Max

0.002336 0.004039 Log-linear Male

All

Respiratory

Luginaah et

al.

2005 Windsor,

Ontario

65-99 D1Hour

Max

0.003709 0.003274 Log-linear Female

All

Respiratory

Luginaah et

al.

2005 Windsor,

Ontario

65-99 D1Hour

Max

-0.002332 0.003620 Log-linear Male

All

Respiratory

Schwartz et

al.

1996 Cleveland,

Ohio

65-99 D24Hour

Mean

0.000769 0.000453 Log-linear

Asthma Sheppard, L. 2003 Seattle, WA 0-64 D24Hour

Mean

0.002031 0.002591 Log-linear

All

Respiratory

Yang et al. 2003 Vancouver,

Canada

65-99 NO

2

, O

3

, D24Hour

Mean

0.002843 0.003627 Logistic
Appendix H: Sulfur Dioxide Health Impact Functions in U.S. Setup

November 2012

164

BenMAP User's Manual Appendices

Eff ect Author Year Location Age Co-Poll Metric Beta Std. Err. Functional

Form

Notes

CO, COH

Chronic Lung

Disease (less

Asthma)

Yang et al. 2005 Vancouver,

Canada

65-99 O

3

D24Hour

Mean

0.024164 0.011938 Log-linear

H.1.1 Fung et al. (2006)

Fung et al. (2006) assessed the impact of ambient gaseous pollutants (SO2, NO2, CO, and O3)

and particulate matters (PM10, PM2.5, and PM10-2.5) as well as the coefficient of haze (COH) on

recurrent respiratory hospital admissions (ICD-9 codes 460-519) among the elderly in

Vancouver, Canada, for the period ofJune 1, 1995, to March 31, 1999, using a new method

proposed by Dewanji and Moolgavkar(2000; 2002). The authors found significant associations

between respiratory hospital admissions and 3-day, 5-day, and 7-day moving averages of the

ambient SO2 concentrations, with the strongest association observed at the 7-day lag (RR =

1.044, 95% CI: 1.018-1.070). The authors also found PM10-2.5for 3-day and 5-day lag to be

significant, with the strongest association at 5-day lag (RR = 1.020, 95% CI: 1.001-1.039). No

significant associations with admission were found with current day exposure.

Hospital Admissions, All Respiratory (ICD-9 codes 460-519)

In a single-pollutant model the coefficient and standard error are estimated from the relative risk

(1.044) and 95% confidence interval(95% CI: 1.018-1.070) for a 2.5 ppb increase in 7-day

moving average of SO2(Fung, et al., 2006, Table 4).

H.1.2 Luginaah et al. (2005)

Luginaah et al. (2005) assessed the association between air pollution and daily respiratory

hospitalization (ICD-9 codes 460-519) for different age and sex groups from 1995 to 2000. The

pollutants included were NO2, SO2, CO, O3, PM10, coefficient of haze (COH), and totalreduced

sulfur (TRS). The authors estimated relative risks (RR) using both time-series and case-crossover

methods after controlling for appropriate confounders (temperature, humidity, and change in

barometric pressure). The results of both analyses were consistent. They found associations

between NO2, SO2, CO, COH, or PM10 and daily hospital admission of respiratory diseases

especially among females. For females 0-14 years of age, there was 1-day delayed effect of NO2

(RR = 1.19, case-crossover method), a current-day SO2(RR = 1.11, time series), and currentday and 1- and 2-day delayed effects for CO by case crossover (RR = 1.15, 1.19, 1.22,

respectively). Time-series analysis showed that 1-day delayed effect of PM10 on respiratory

admissions of adult males (15-64 years of age), with an RR of 1.18. COH had significant effects

on female respiratory hospitalization, especially for 2-day delayed effects on adult females, with

RRs of 1.15 and 1.29 using time-series and case-crossover analysis, respectively. There were no

significant associations between O3 and TRS with respiratory admissions.
Appendix H: Sulfur Dioxide Health Impact Functions in U.S. Setup

November 2012

165

BenMAP User's Manual Appendices

Hospital Admissions, All Respiratory (ICD-9 codes 460-519)

For different age and sex groups, the coefficient and standard error are estimated from the

relative risks and 95% confidence intervalreported for single-pollutant models using time-series

method for a 19.25 ppb increase in daily 1-hour max SO2levels (Luginaah, et al., 2005, Table

3). The time-series method is chosen based on the discussion in Fung et al.(2003)

H.1.3 Schwartz et al. (1996)

Schwartz et al.(1996) is a review paper with an example drawn from hospital admissions of the

elderly in Cleveland, Ohio from 1988-1990. The authors argued that the central issue is control

for seasonality. They illustrated the use of categorical variables for weather and sinusoidal terms

for filtering season in the Cleveland example. After controlling for season, weather, and day of the

week effects, hospital admissions of persons aged 65 and older in Cleveland for respiratory

illness was associated with ozone (RR = 1.09, 95% CI 1.02, 1.16) and PM10(RR = 1.12, 95%

CI 1.01, 1.24), and marginally associated with SO2(RR = 1.03, 95% CI = 0.99, 1.06). All of

the relative risks are for a 100 micrograms/m3 increase in the pollutant.

Hospital Admissions, All Respiratory (ICD-9 codes 460-519)

Schwartz et al.(1996, Table 2) reported the relative risk (1.03) with the 95% confidence interval

(0.99-1.06) from Poisson regression models for a 100μg/ m3 increase in average of lag0-1 day of

SO2. In order to obtain the coefficient and standard error, a conversion of 0.3846 ppb per μg/m3

was used, based on http://www.epa.gov/air/criteria.html.

H.1.4 Sheppard (2003)

Sheppard (2003) reanalyzed data from Sheppard et al.(1999) on nonelderly (0-64 years of age)

hospital admissions for asthma in Seattle, Washington, to evaluate the effect of the fitting

procedure (GAM/Splus issue). The author found that the effect estimates were slightly lower

when more stringent convergence criteria were used in the GAM and an additionalsmall

reduction in the estimates was found when generalized linear models (GLMs) with naturalsplines

were used instead.

Hospital Admissions, Asthma (ICD-9 code 493)

Sheppard (2003, p. 229) reported the re-calculated relative risk (1.01) and 95% confidence

interval(0.98-1.03) for a 4.9 ppb increase in the daily average ofsame day SO2(lag 0).

H.1.5 Yang et al. (2003)

Yang et al. (2003) examined the impact of ozone, nitrogen dioxide, sulfur dioxide, carbon

monoxide, and coefficient of haze on daily respiratory admissions (ICD-9 codes 460-519) in

both young children (<3 years of age) and the elderly (65-99 years of age) in greater Vancouver,

British Columbia during the 13-yr period 1986-1998. Bidirectional case-crossover analysis was
Appendix H: Sulfur Dioxide Health Impact Functions in U.S. Setup

November 2012

166

BenMAP User's Manual Appendices

used to investigate associations and odds ratios were reported for single-pollutant, two-pollutant

and multiple-pollutant models. Sulfur dioxide was found marginally significant in all models for

elderly.

Hospital Admissions, All Respiratory (ICD-9 codes 460-519)

In a multi-pollutant model with NO2, O3, CO, and COH, the coefficient for SO2 and standard

error are estimated for age 65-99 from the relative risk (1.01) and 95% confidence interval

(0.98-1.03) for a 5.57 ppb increase in daily average ofsame day SO2(Yang, et al., 2003, Table

3).

H.1.6 Yang et al. (2005)

Yang et al. (2005) examined the associations between gaseous pollutants and hospitalization for

chronic lung disease less asthma (ICD-9 codes 490-492, 494, and 496) among elderly people

living in Vancouver, Canada. The authors regressed the logarithm of daily counts of acute COPD

hospitalization during the 5-year period from 1994 to 1998 on the daily mean levels of each

pollutant, after accounting for seasonal and subseasonalfluctuations, non-Poisson dispersion, and

weather variables. They found that nitrogen dioxide and carbon monoxide were significantly

associated with hospitalization for COPD, and the magnitude of effects was increased slightly

with increasing days of exposure averaging, with the relative risk for a 7-day average being 1.12

(95%CI: 1.04, 1.20) for nitrogen dioxide in a two-pollutant model with O3. Sulfur dioxide was

found marginally significant in a two-pollutant model with ozone. The combined relative risk for all

four gaseous pollutants (CO, O3, NO2, and SO2) on COPD hospitalization was 1.21. The effects

of gaseous pollutants on COPD hospitalization were not significant after adjustment for PM10,

although its inclusion did not have a marked effect on the point estimates for relative risks.

Hospital Admissions, Chronic Lung Disease less Asthma (ICD-9 codes 490-492, 494,

and 496)

In a two-pollutant model with O3, the coefficient and standard error are estimated from the

relative risk (1.07) and 95% confidence interval(1.00-1.14) for a 2.8 ppb increase in 7-day

average of SO2(Yang, et al., 2005, Table 5).

H.2 Emergency Room Visits

Table H-2 summarizes the health impacts functions used to estimate the relationship between

sulfur dioxide and emergency room visits. Below, we present a briefsummary of each of the

studies and any items that are unique to the study.
Appendix H: Sulfur Dioxide Health Impact Functions in U.S. Setup

November 2012

167

BenMAP User's Manual Appendices

Table H-2. Health Impact Functions for Sulfur Dioxide and Emergency Room Visits

Eff ect Author Year Location Age Co-Poll Metric Beta Std. Err. Functional

Form

Notes

Asthma Ito et al. 2007 NYC 0-99 D24Hour

Mean

0.030692 0.005439 Log-linear Warmseason

Asthma Ito et al. 2007 NYC 0-99 D24Hour

Mean

0.004372 0.001702 Log-linear All year

Asthma Michaud et

al.

2004 Hilo, Hawai'i 0-99 D24Hour

Mean

0.002956 0.002386 Log-linear

Asthma NYDOH 2006 Bronx, NYC 0-4 D24Hour

Mean

0.011111 0.005129 Log-linear

Asthma NYDOH 2006 Bronx, NYC 0-99 D24Hour

Mean

0.006996 0.003641 Log-linear Male

Asthma NYDOH 2006 Bronx, NYC 0-99 D24Hour

Mean

0.011912 0.003450 Log-linear Female

Asthma NYDOH 2006 Bronx, NYC 0-99 PM2.5D24Hour

Mean

0.009487 0.002929 Log-linear

Asthma NYDOH 2006 Bronx, NYC 0-99 NO

2

D24Hour

Mean

0.009487 0.002732 Log-linear

Asthma NYDOH 2006 Bronx, NYC 0-99 O

3

D24Hour

Mean

0.009487 0.002510 Log-linear

Asthma NYDOH 2006 Bronx, NYC 35-64 D24Hour

Mean

0.015047 0.004515 Log-linear

Asthma Peel et al. 2005 Atlanta, GA 0-99 D1HourMax 0.000744 0.001030 Log-linear

Asthma Villeneuve

et al.

2007 Edmonton,

Canada

2-99 D24Hour

Mean

-0.006734 0.002617 Logistic 1-day lag

Asthma Villeneuve

et al.

2007 Edmonton,

Canada

2-4 D24Hour

Mean

0.000000 0.008510 Logistic 0-day lag

Asthma Villeneuve

et al.

2007 Edmonton,

Canada

5-14 D24Hour

Mean

-0.006734 0.006945 Logistic 1-day lag

Asthma Villeneuve

et al.

2007 Edmonton,

Canada

15-44 D24Hour

Mean

-0.006734 0.004317 Logistic 1-day lag

Asthma Villeneuve

et al.

2007 Edmonton,

Canada

45-64 D24Hour

Mean

-0.003350 0.007697 Logistic 1-day lag

Asthma Villeneuve

et al.

2007 Edmonton,

Canada

65-74 D24Hour

Mean

0.003317 0.012590 Logistic 0-day lag

Asthma Villeneuve

et al.

2007 Edmonton,

Canada

75-99 D24Hour

Mean

0.019423 0.027251 Logistic 5-day

average

Asthma Wilson et

al.

2005 Porland ME 0-14 D24Hour

Mean

0.005000 0.009000 Log-linear

Asthma Wilson et

al.

2005 Manchester, NH 0-14 D24Hour

Mean

0.018000 0.011000 Log-linear

Asthma Wilson et

al.

2005 Porland ME 15-64 D24Hour

Mean

0.011000 0.005000 Log-linear

Asthma Wilson et

al.

2005 Manchester, NH 15-64 D24Hour

Mean

0.003000 0.006000 Log-linear

Asthma Wilson et

al.

2005 Porland ME 65-99 D24Hour

Mean

0.011000 0.014000 Log-linear

Asthma Wilson et

al.

2005 Manchester, NH 65-99 D24Hour

Mean

0.011000 0.023000 Log-linear
Appendix H: Sulfur Dioxide Health Impact Functions in U.S. Setup

November 2012

168

BenMAP User's Manual Appendices

Eff ect Author Year Location Age Co-Poll Metric Beta Std. Err. Functional

Form

Notes

Asthma Wilson et

al.

2005 Porland ME 0-99 D24Hour

Mean

0.010000 0.009000 Log-linear

Asthma Wilson et

al.

2005 Manchester, NH 0-99 D24Hour

Mean

0.006000 0.011000 Log-linear

H.2.1 Ito et al. (2007)

Ito et al.(2007) assessed associations between air pollution and asthma emergency department

visits in New York City for all ages. Specifically they examined the temporalrelationships among

air pollution and weather variables in the context of air pollution health effects models. The

authors compiled daily data for PM2.5, O3, NO2, SO2, CO, temperature, dew point, relative

humidity, wind speed, and barometric pressure for New York City for the years 1999-2002.The

authors evaluated the relationship between the various pollutants' risk estimates and their

respective concurvities, and discuss the limitations that the results imply about the interpretability

of multi-pollutant health effects models.

Emergency Room Visits, Asthma (ICD-9 code 493)

In a single-pollutant modelfor warm season, the coefficient and standard error are estimated

from the relative risk (1.20) and 95% confidence interval(1.13-1.28) for a 6 ppb increase in the

average of 0- and 1-day lag of SO2(Ito, et al., 2007, through contacting the author).

H.2.2 Michaud et al. (2004)

Michaud et al.(2004) examined the association of emergency department (ED) visits in Hilo,

Hawai'i, from January 1997 to May 2001 with volcanic fog, or "vog", measured as sulfur dioxide

(SO2) and submicrometer particulate matter (PM1). Log-linear regression models were used with

robust standard errors. The authors studied four diagnostic groups: asthma/COPD; cardiac; flu,

cold, and pneumonia; and gastroenteritis. Before adjustments, highly significant associations with

vog-related air quality were seen for all diagnostic groups except gastroenteritis. After adjusting

for month, year, and day of the week, only asthma/COPD had consistently positive associations

with air quality. They found that the strongest associations were for SO2 with a 3-day lag (6.8%

per 10 ppb; P=0.001) and PM1, with a 1-day lag (13.8% per 10 μg/m3; P=0.011).

Emergency Room Visits, Asthma (ICD-9 codes 493 and 495)

In a single-pollutant model adjusted for month, year and day of the week, the coefficient and

standard error are estimated from the relative risks (1.03) and 95% confidence interval(1.02-

1.12) for a 10 ppb increase in daily average of SO2 at lag 3-day (Michaud, et al., 2004, Table

3).

H.2.3 NYDOH (2006)

New York State Department of Health (NYDOH) investigated whether day-to-day variations in
Appendix H: Sulfur Dioxide Health Impact Functions in U.S. Setup

November 2012

169

BenMAP User's Manual Appendices

air pollution were associated with asthma emergency department (ED) visits in Manhattan and

Bronx, NYC and compared the magnitude of the air pollution effect between the two

communities. NYDOH(2006) used Poisson regression to test for effects of 14 key air

contaminants on daily ED visits, with controlfor temporal cycles, temperature, and day-of-week

effects. The core analysis utilized the average exposure for the 0- to 4-day lags. Mean daily SO2

was found significantly associated with asthma ED visits in Bronx but not Manhattan. Their

findings of more significant air pollution effects in the Bronx are likely to relate in part to greater

statistical power for identifying effects in the Bronx where baseline ED visits were greater, but

they may also reflect greater sensitivity to air pollution effects in the Bronx.

Emergency Room Visits, Asthma (ICD-9 code 493) ---Bronx

Function for Children (0-4 years of age)

In a single pollutant model, the coefficient and standard error are estimated from the relative risk

(1.13) and 95% confidence interval(1.01-1.26) for a 11 ppb increase in the average of 0- to 4-

day lags of SO2(NYDOH, 2006, Table 13).

Function for Adults (35-64 years of age)

In a single pollutant model, the coefficient and standard error are estimated from the relative risk

(1.18) and 95% confidence interval(1.07-1.30) for a 11 ppb increase in the average of 0- to 4-

day lags of SO2(NYDOH, 2006, Table 13).

Function for Females (All ages)

In a single pollutant model, the coefficient and standard error are estimated from the relative risk

(1.14) and 95% confidence interval(1.06-1.23) for a 11 ppb increase in the average of 0- to 4-

day lags of SO2(NYDOH, 2006, Table 12).

Function for Males (All ages)

In a single pollutant model, the coefficient and standard error are estimated from the relative risk

(1.08) and 95% confidence interval(1.00-1.17) for a 11 ppb increase in the average of 0- to 4-

day lags of SO2(NYDOH, 2006, Table 12).

Functions for All Population

We use results from three different two-pollutant models for all population.

In a two-pollutant model with O3, the coefficient and standard error are estimated from the

relative risk (1.11) and 95% confidence interval(1.05-1.17) for a 11 ppb increase in the average

of 0- to 4-day lags of SO2(NYDOH, 2006, Table 9).

In a two-pollutant model with PM2.5, the coefficient and standard error are estimated from the

relative risk (1.11) and 95% confidence interval(1.04-1.18) for a 11 ppb increase in the average
Appendix H: Sulfur Dioxide Health Impact Functions in U.S. Setup

November 2012

170

BenMAP User's Manual Appendices

of 0- to 4-day lags of SO2 (NYDOH, 2006, Table 9).

In a two-pollutant model with NO2, the coefficient and standard error are estimated from the

relative risk (1.11) and 95% confidence interval(1.04-1.17) for a 11 ppb increase in the average

of 0- to 4-day lags of SO2 (NYDOH, 2006, Table 9).

H.2.4 Peel et al. (2005)

Peel et al. (2005) examined the associations between air pollution and respiratory emergency

department visits (i.e., asthma (ICD-9 code 493, 786.09), COPD (491,492,496), URI (460-

466, 477), pneumonia (480-486), and an allrespiratory-disease group) in Atlanta, GA from 1

January 1993 to 31 August 2000. They used 3-Day Moving Average (Lags of 0, 1, and 2 Days)

and unconstrained distributed lag (Lags of 0 to 13 Days) in the Poisson regression analyses. In

single-pollutant models, positive associations persisted beyond 3 days for several outcomes, and

over a week for asthma. The effects of NO2, CO or PM10 on asthma ED visits were found

significant but SO2 or O3 were not significantly associated with asthma ED visits.

Emergency Room Visits, Asthma (ICD-9 code 493)

In the single-pollutant model using unconstrained distributed lag, the coefficient and standard

error are estimated from the relative risk (1.015) and 95% confidence interval(0.975-1.057) for

a 20 ppb increase in 1-hour max SO2(Peel, et al., 2005, Table 4).

H.2.5 Villeneuve et al. (2007)

Villeneuve et al.(2007) examined the associations between air pollution and emergency

department (ED) visits for asthma among individuals two years of age and older in the census

metropolitan area of Edmonton, Canada between April 1, 1992 and March 31, 2002 using a

time stratified case-crossover design. Daily air pollution levels for the entire region were estimated

from three fixed-site monitoring stations. Odds ratios and their corresponding 95% confidence

intervals were estimated using conditional logistic regression with adjustment for temperature,

relative humidity and seasonal epidemic of viralrelated respiratory disease. Villeneuve et al.

(2007) found positive associations for asthma ED visits with outdoor air pollution levels between

April and September, but such associations were absent during the remainder of the year. Effects

were strongest among young children (2-4 years of age) and elderly (>75 years of age). Air

pollution risk estimates were largely unchanged after adjustment for aeroallergen levels.

Emergency Room Visits, Asthma (ICD-9 code 493)

In single-pollutant models for allseasons, the coefficients and standard errors for different age

groups are estimated from the relative risks and 95% confidence intervals for a 3 ppb increase in

daily average of SO2(Villeneuve, et al., 2007, Table3-9).
Appendix H: Sulfur Dioxide Health Impact Functions in U.S. Setup

November 2012

171

BenMAP User's Manual Appendices

H.2.6 Wilson et al. (2005)

Daily emergency room (ER) visits for allrespiratory (ICD-9 codes 460-519) and asthma (ICD-9

code 493) were compared with daily SO2, O3, and weather variables over the period 1998-

2000 in Portland, Maine and 1996-2000 in Manchester, New Hampshire. Seasonal variability

was removed from all variables using nonparametric smoothed function (LOESS). Wilson et al.

(2005) used generalized additive models to estimate the effect of elevated levels of pollutants on

ER visits. Relative risks of pollutants were reported over their inter-quartile range (IQR, the 75th -

25th percentile pollutant values). In Portland, an IQR increase in SO2 was associated with a 5%

(95% CI 2-7%) increase in allrespiratory ER visits and a 6% (95% CI 1-12%) increase in

asthma visits. An IQR increase in O3 was associated with a 5% (95% CI 1-10%) increase in

Portland asthmatic ER visits. No significant associations were found in Manchester, New

Hampshire, possibly due to statistical limitations of analyzing a smaller population. The absence of

statistical evidence for a relationship should not be used as evidence of no relationship. This

analysis reveals that, on a daily basis, elevated SO2 and O3 have a significant impact on public

health in Portland, Maine.

Emergency Room Visits, Asthma

The coefficients and standard errors for different age groups in the two cities are taken from

Wilson et al.(2005, Table 5).

H.3 Asthma Exacerbation

Table H-3 summarizes the health impacts functions used to estimate the relationship between

sulfur dioxide and asthma exacerbation. Below, we present a briefsummary of each of the

studies and any items that are unique to the study.

Table H-3. Health Impact Functions for Sulfur Dioxide and Asthma Exacerbation

Eff ect Author Year Location Age CoPollMetric Beta Std. Err. Functional

Form

Notes

One or More

Symptoms

Delfino et al. 2003 Los Angeles 10-15 D8HourMax 0.12299

4

0.046891 Logistic Hispanic

children

One or More

Symptoms

Mortimer et al. 2002 7 urban areas

(US)

4-12 O

3

,

NO

2

D3HourMean 0.00869

8

0.003515 Logistic Warmseason;

Adjusted age

Missed

school days

O'Connor et

al.

2008 7 inner cities 4-12 D24HourMea

n

0.00985

6

0.015289 Logistic Adjusted age

One or More

Symptoms

O'Connor et

al.

2008 7 inner cities 4-12 D24HourMea

n

0.00469

9

0.008263 Log-linear* Adjusted age

Nighttime

asthma

O'Connor et

al.

2008 7 inner cities 4-12 D24HourMea

n

0.01056

7

0.010042 Log-linear* Adjusted age

Slow play O'Connor et

al.

2008 7 inner cities 4-12 D24HourMea

n

0.00545

6

0.009517 Log-linear* Adjusted age

One or More

Symptoms

Schildcrout et

al.

2006 Eight U.S.

cities

4-12 D24HourMea

n

0.00392

2

0.001963 Logistic Adjusted age
Appendix H: Sulfur Dioxide Health Impact Functions in U.S. Setup

November 2012

172

BenMAP User's Manual Appendices

H.3.1 Delfino et al. (2003)

Delfino et al.(2003) conducted a panelstudy of 22 Hispanic children with asthma who were 10-

16 years old and living in a Los Angeles community with high traffic density. Subjects filled out

symptom diaries daily for up to 3 months (November 1999 through January 2000). Pollutants

included ambient hourly values of ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), and

carbon monoxide (CO) and 24-hr values of volatile organic compounds (VOCs), particulate

matter with aerodynamic diameter < 10 microm (PM10), and elemental carbon (EC) and organic

carbon (OC) PM10fractions. Asthma symptom severity was regressed on pollutants using logistic

models. The authors found positive associations ofsymptoms with criteria air pollutants (NO2,

SO2, O3,and PM10). Selected adjusted odds ratio for more severe asthma symptoms from

interquartile range increases in pollutants was, for 2.5 ppb 8-hr max SO2, 1.36 [95% confidence

interval(CI), 1.08-1.71]. Their findings support the view that air toxins in the pollutant mix from

traffic and industrialsources may have adverse effects on asthma in children.

Asthma Exacerbation, One or More Symptoms

In a single-pollutant model, the coefficient and standard error are based on the odds ratio (95%

CI) for more severe asthma symptoms, i.e., odds ratio of 1.36 [95% confidence interval(CI),

1.08-1.71] for a 2.5 ppb increase in 8-hr max SO2(Delfino, et al., 2003, Table 4).

Incidence Rate: Daily asthma symptoms incidence rate = 2.3% (Delfino, et al., 2003, Table 1)

Population: Delfino et al.(2003) examined Hispanic asthmatic population, whose prevalence is not

available. We use the prevalence of asthmatic population from the American Lung Association

(2010b, Table 7) as a proxy. Asthmatic population ages 10 to 16 = 10.70% of population ages 10 to

16. The American Lung Association (2010b, Table 7) estimates asthma prevalence for children 5-

17 at 10.70% (based on data from the 2008 National Health Interview Survey).

H.3.2 Mortimer et al. (2002)

Mortimer et al.(2002) examined the effect of daily ambient air pollution within a cohort of 846

asthmatic children residing in eight urban areas of the USA between June 1 to August 31, 1993,

using data from the NationalCooperative Inner-City Asthma Study. Daily air pollution

concentrations were extracted from the Aerometric Information Retrieval System database from

the Environment Protection Agency in the USA. Logistic models were used to evaluate the

effects ofseveral air pollutants (O3, NO2, SO2 and PM10) on peak expiratory flow rate (PEFR)

and symptoms in 846 children (ages 4-9 yrs) with a history of asthma. In single pollutant models,

each pollutant was associated with an increased incidence of morning symptoms:(odds ratio(OR)

=1.16 (95% CI 1.02-1.30) per IQR increase in 4-day average O3, OR=1.32 (95% CI 1.03-

1.70) per IQR increase in 2-day average SO2, OR=1.48 (95% CI 1.02-2.16) per IQR increase

in 6-day average NO2 and OR=1.26 (95% CI 1.0-1.59) per IQR increase in two-day average

PM10. This longitudinal analysis supports previous time-series findings that at levels below current

USA air-quality standards, summer-air pollution is significantly related to symptoms and

decreased pulmonary function among children with asthma.
Appendix H: Sulfur Dioxide Health Impact Functions in U.S. Setup

November 2012

173

BenMAP User's Manual Appendices

Asthma Exacerbation, One or More Symptoms

In a three-pollutant model with O3 and NO2, the coefficient for SO2 and standard error are based

on the odds ratio (95% CI) for morning asthma symptoms, i.e., odds ratio of 1.19 [95%

confidence interval(CI), 1.04-1.37] for a 20 ppb increase in the average of lag 1-2 day SO2

(Delfino, et al., 2003, Table 4).

Incidence Rate: Daily incidence rate for "any morning symptom" is 11.6% (Mortimer, et al., 2002,

Table 1).

Population: Asthmatic population ages 4 to 12 = 10.70% of population ages 4 to 12. The American

Lung Association (2010b, Table 7) estimates asthma prevalence for children 5- 17 at 10.70%

(based on data from the 2008 National Health Interview Survey). Based on the American Lung

Association (2010b, Table 7), the estimated asthma prevalence for children under 5 years old at

6.14% (based on data from the 2008 National Health Interview Survey), which is close to the rate

of ages 5-17. Therefore we use the rate of population 5-17 as an estimate for asthma prevalence in

population ages 4-9 for simplicity.

H.3.3 O'Connor et al. (2008)

O'Connor et al.(2008) investigated the association between fluctuations in outdoor air pollution

and asthma exacerbation (wheeze-cough, nighttime asthma, slow play and school absence)

among 861 inner-city children (5-12 years of age) with asthma in 7 US urban communities.

Asthma symptom data were collected every 2 months during the 2-year study period. Daily

pollution measurements were obtained from the Aerometric Information Retrieval System

between August 1998 and July 2001. The relationship ofsymptoms to fluctuations in pollutant

concentrations was examined by using logistic models. In single-pollutant models, significant or

nearly significant positive associations were observed between higher NO2 concentrations and

each of the health outcomes. Significant positive associations with symptoms but not school

absence were observed in the single-pollutant modelfor CO. The O3, PM2.5, and SO2

concentrations did not appear significantly associated with symptoms or school absence except

for a significant association between PM2.5 and school absence. The authors concluded that the

associations with NO2 suggest that motor vehicle emissions may be causing excess morbidity in

this population.

Asthma Exacerbation, Missed school

In a single-pollutant model, the coefficient and standard error are estimated from the odds ratio

(1.13) and 95% confidence interval(0.78-1.64) for a 12.4 ppb increase in 19-day average of

SO2(O'Connor, et al., 2008, Table IV). The independent pollution variable was the mean

concentration during the 19 days preceding the interview, that is, the 14 days of the symptom

recall period plus a 5-day lag period preceding the symptom recall period.

Incidence Rate: Daily incidence rate for missed school = 5.7% (O'Connor, et al., 2008, Table I).

Population: Same as population used in Mortimer et al. (2002). See description above.
Appendix H: Sulfur Dioxide Health Impact Functions in U.S. Setup

November 2012

174

BenMAP User's Manual Appendices

Asthma Exacerbation, One or More Symptoms (Wheeze-cough)

O'Connor et al. (2008, Table IV) reported a 6% increase in symptom frequency with a 95%

confidence interval of -13% to 30% associated with a 12.4 ppb increase in the 19-day average

of SO2.

Incidence Rate: Daily incidence rate for one or more symptoms = 20.7% (O'Connor, et al., 2008,

Table I).

Population: See population description above.

Asthma Exacerbation, Nighttime Asthma

O'Connor et al. (2008, Table IV) reported a 14% increase in symptom frequency with a 95%

confidence interval of -11% to 45% associated with a 12.4 ppb increase in the 19-day average

of SO2.

Incidence Rate: Daily incidence rate for nighttime asthma = 12.1% (O'Connor, et al., 2008, Table

I).

Population: See population description above.

Asthma Exacerbation, Slow Play

O'Connor et al. (2008, Table IV) reported a 7% increase in symptom frequency with a 95%

confidence interval of -15% to 35% associated with a 12.4 ppb increase in the 19-day average

of SO2.

Incidence Rate: Daily incidence rate for slow play = 15.7% (O'Connor, et al., 2008, Table I).

Population: See population description above.

H.3.4 Schildcrout et al. (2006)

Schildcrout et al.(2006) investigated the relation between ambient concentrations of the five

criteria pollutants (PM10, O3, NO2, SO2, and CO) and asthma exacerbations (daily symptoms and

use of rescue inhalers) among 990 children in eight North American cities during the 22-month

prerandomization phase (November 1993-September 1995) of the Childhood Asthma

Management Program. Short-term effects of CO, NO2, PM10, SO2, and warm-season O3 were

examined in both one-pollutant and two-pollutant models, using lags of up to 2 days in logistic

and Poisson regressions. Lags in CO and NO2 were positively associated with both measures of

asthma exacerbation, and the 3-day moving sum of SO2levels was marginally related to asthma

symptoms. PM10 and O3were unrelated to exacerbations. The strongest effects tended to be seen

with 2-day lags, where a 1-parts-per-million change in CO and a 20-parts-per-billion change in

NO2 were associated with symptom odds ratios of 1.08 (95% confidence interval(CI): 1.02,

1.15) and 1.09 (95% CI: 1.03, 1.15), respectively.
Appendix H: Sulfur Dioxide Health Impact Functions in U.S. Setup

November 2012

175

BenMAP User's Manual Appendices

Asthma Exacerbation, One or More Symptoms

In a single-pollutant model, Schildcrout et al.(2006, Figure 1) reported an odds ratio of 1.04

(95% CI: 1.00, 1.08) for daily asthma symptoms associated with 10 ppb change in 24-hr mean

of SO2.

Incidence Rate: Daily incidence rate for one or more symptoms (symptom score>0) = 52%

(Schildcrout, et al., 2006, Table 1)

Population: Asthmatic population ages 4 to 12 = 10.70% of population ages 4 to 12. The American

Lung Association (2010b, Table 7) estimates asthma prevalence for children 5- 17 at 10.70%

(based on data from the 2008 National Health Interview Survey).

H.4 Minor Effects

Table H-4 summarizes the health impacts function used to estimate the relationship between sulfur

dioxide and minor effects. Below, we present a briefsummary of the study and any items that

are unique to the study.

Table H-4. Health Impact Functions for Sulfur Dioxide and Minor Effects

Effect Author Year Location Age CoPollMetric Beta Std. Err. Functional

Form

Notes

Cough Schwartz

et al.

1994 Six U.S.

cities

7-14 PM10D24Hour

Mean

0.008618 0.007429 Logistic Warm

season

H.4.1 Schwartz et al. (1994)

Schwartz et al.(1994) studied the association between ambient air pollution exposures and

respiratory illness among 1,844 school children (7-14 years of age) in six U.S. cities during five

warm season months between April and August. Daily measurements of ambient sulfur dioxide

(SO2), nitrogen dioxide (NO2), ozone (O3), respirable particles (PM2.5), inhalable particles (PM

10), light scattering, and sulfate particles were made, along with integrated 24-hour measures of

aerosolstrong acidity.Significant associations in single pollutant models were found between SO2,

NO2, or PM2.5 and incidence of cough, and between sulfur dioxide and incidence of lower

respiratory symptoms. Significant associations were also found between incidence of coughing

symptoms and incidence of lower respiratory symptoms and PM10, and a marginally significant

association between upper respiratory symptoms and PM10.

Acute Respiratory Symptoms, Cough

In a two-pollutant model with PM10, the coefficient and standard error are based on an odds ratio

of 1.09 (95% CI 0.94-1.3) for a 10 ppb increase in the lag 1-4 days average of SO2

concentration (Schwartz, et al., 1994, Table 4).
Appendix H: Sulfur Dioxide Health Impact Functions in U.S. Setup

November 2012

176

BenMAP User's Manual Appendices

Incidence Rate: The proposed incidence rate, 0.416 percent, is based on the percentiles in

Schwartz et al. (1994, Table 2). The authors did not report the mean incidence rate, but rather

reported various percentiles from the incidence rate distribution. The percentiles and associated per

person per day values are 10th = 0 percent, 25th = 0 percent, 50th = 0.31 percent, 75th = 0.58

percent, and 90th = 0.86 percent. The most conservative estimate consistent with the data is to

assume the incidence is zero up to the 50th percentile, a constant 0.31 percent between the 50th

and 75th percentiles, and a constant 0.58 percent between the 75th and 90th percentiles, a constant

0.86 percent between the 90th and 100th percentiles. Alternatively, assuming a linear slope

between the 50th and 75th, 75th and 90th, and 90th to 100th percentiles, the estimated mean

incidence rate is 0.461 percent, which is used in this analysis.

Population: Population of ages 7 to 14
Appendix I: Health Valuation Functions in U.S. Setup

November 2012

177

BenMAP User's Manual Appendices

Appendix I: Health Valuation Functions in U.S.

Setup

This appendix presents the unit values that are available in BenMAP for each of the health

endpoints included in the current suite of health impact functions. Wherever possible, we present

a distribution of the unit value, characterizing the uncertainty surrounding any point estimate. The

mean of the distribution is taken as the point estimate of the unit value, and the distribution itself is

used to characterize the uncertainty surrounding the unit value, which feeds into the uncertainty

surrounding the monetary benefits associated with reducing the incidence of the health endpoint.

Below we give detailed descriptions of the derivations of unit values and their distributions, as

well as tables listing the unit values and their distributions, available for each health endpoint. The

definitions of the distributions and their parameters is given in Table I-1.

Table I-1. Unit Value Uncertainty Distributions and Their Parameters

Distribution * Parameter 1 (P1) Parameter 2 (P2)

Normal standard deviation –

Triangular minimum value maximum value

Lognormal ** mean of corresponding normal

distribution

standard deviation of corresponding

normal distribution

Uniform minimum value maximum vaue

Weibull *** α β

* In all cases, BenMAP calculates the mean of the distribution, which is used as the “point estimate” of the unit

value.

** If Y is a normal random variable, and Y = logeX, then X is lognormally distributed. Equivalently, X is

lognormally distributed if X = eY, where Y is normally distributed.

*** The Weibull distribution has the following probability density function:

I.1 Mortality

The economics literature concerning the appropriate method for valuing reductions in premature

mortality risk is still developing. The adoption of a value for the projected reduction in the risk of

premature mortality is the subject of continuing discussion within the economics and public policy

analysis communities. Issues such as the appropriate discount rate and whether there are factors,

such as age or the quality of life, that should be taken into consideration when estimating the value

of avoided premature mortality are still under discussion. BenMAP currently offers a variety of

options reflecting the uncertainty surrounding the unit value for premature mortality.
Appendix I: Health Valuation Functions in U.S. Setup

November 2012

178

BenMAP User's Manual Appendices

I.1.1 Value of a Statistical Life Based on 26 Studies

One unit value available in BenMAP is $6.3 million. This estimate is the mean of a distribution

fitted to 26 “value ofstatistical life” (VSL) estimates that appear in the economics literature and

that have been identified in the Section 812 Reports to Congress as “applicable to policy

analysis.” This represents an intermediate value from a variety of estimates, and it is a value EPA

has frequently used in Regulatory Impact Analyses (RIAs) as well as in the Section 812

Retrospective and Prospective Analyses of the Clean Air Act.

The VSL approach and the set ofselected studies mirrors that of Viscusi(1992) (with the

addition of two studies), and uses the same criteria as Viscusi in his review of value-of-life

studies. The $6.3 million estimate is consistent with Viscusi’s conclusion (updated to 2000$) that

“most of the reasonable estimates of the value of life are clustered in the $3.8 to $8.9 million

range.” Five of the 26 studies are contingent valuation (CV) studies, which directly solicit WTP

information from subjects; the rest are wage-risk studies, which base WTP estimates on

estimates of the additional compensation demanded in the labor market for riskier jobs. Because

this VSL-based unit value does not distinguish among people based on the age at their death or

the quality of their lives, it can be applied to all premature deaths.

I.1.2 Value of a Statistical Life Based on Selected Studies

In addition to the value of a statistical life based on the results of 26 studies, we have included

three alternatives based loosely on the results of recent work by Mrozek and Taylor (2002) and

Viscusi and Aldy (2003). Each of the four alternatives has a mean value of $5.5 million (2000$),

but with a different distributions: normal, uniform, triangular, and beta. Table I-2 presents the

distribution parameters for the suite of mortality valuations currently available in BenMAP.

Table I-2. Unit Values Available for Mortality

Basis for Estimate * Age Range at

Death

Unit Value

(VSL)

(2000$)

Distribution

of Unit

Value

Parameters of Distribution

min. max. P1 P2

VSL, based on 26 value-of-life studies. 0 99 $6,324,101 Weibull 5.32E-6 1.509588

VSL based on range from $1 million to $10

million – 95% CI of assumed normal

distribution.

0 99 $5,500,000 Normal 2,295,960.54 --

VSL based on range from $1 million to $10

million – assumed uniform distribution.

0 99 $5,500,000 Uniform 1,000,000 10,000,000

VSL based on range from $1 million to $10

million – assumed triangular distribution.

0 99 $5,500,000 Triangular 1,000,000 10,000,000

*The original value of a statistical life was calculated in 1990 $. We have used a factor of 1.3175, based on the

All-Items CPI-U.
Appendix I: Health Valuation Functions in U.S. Setup

November 2012

179

BenMAP User's Manual Appendices

I.2 Chronic Illness

This sub-section presents the unit values developed for chronic bronchitis, chronic asthma, and

non-fatal myocardial infarctions.

I.2.1 Chronic Bronchitis

PM-related chronic bronchitis is expected to last from the initial onset of the illness throughout the

rest of the individual’s life. WTP to avoid chronic bronchitis would therefore be expected to

incorporate the present discounted value of a potentially long stream of costs (e.g., medical

expenditures and lost earnings) as well as WTP to avoid the pain and suffering associated with

the illness. Both WTP and COI estimates are currently available in BenMAP.

I.2.1.1 Unit Value Based on Two Studies of WTP

Two contingent valuation studies, Viscusi et al. (1991) and Krupnick and Cropper (1992),

provide estimates of WTP to avoid a case of chronic bronchitis. Viscusi et al. (1991) and

Krupnick and Cropper (1992) were experimentalstudies intended to examine new

methodologies for eliciting values for morbidity endpoints. Although these studies were not

specifically designed for policy analysis, they can be used to provide reasonable estimates of

WTP to avoid a case of chronic bronchitis. As with other contingent valuation studies, the

reliability of the WTP estimates depends on the methods used to obtain the WTP values. The

Viscusi et al. and the Krupnick and Cropper studies are broadly consistent with current

contingent valuation practices, although specific attributes of the studies may not be.

The study by Viscusi et al. (1991) uses a sample that is larger and more representative of the

general population than the study by Krupnick and Cropper (1992), which selects people who

have a relative with the disease. However, the chronic bronchitis described to study subjects in

the Viscusistudy is severe, whereas a pollution-related case may be less severe.

The relationship between the severity of a case of chronic bronchitis and WTP to avoid it was

estimated by Krupnick and Cropper (1992). We used that estimated relationship to derive a

relationship between WTP to avoid a severe case of chronic bronchitis, as described in the

Viscusis study, and WTP to avoid a less severe case. The estimated relationship (see Table 4 in

Krupnick and Cropper) can be written as:

where a denotes all the other variables in the regression model and their coefficients, ß is the

coefficient ofsev, estimated to be 0.18, and sev denotes the severity level(a number from 1 to

13). Let x (< 13) denote the severity level of a pollution-related case of chronic bronchitis, and

13 denote the highest severity level(as described in Viscusi et al., 1991). Then
Appendix I: Health Valuation Functions in U.S. Setup

November 2012

180

BenMAP User's Manual Appendices

and

Subtracting one equation from the other,

or

.

Exponentiating and rearranging terms,

There is uncertainty surrounding the exact values of WTP13; x, and ß, and this uncertainty can be

incorporated in the equation, if you request that the analysis be carried out in “uncertainty mode.”

The distribution of WTP to avoid a severe case of chronic bronchitis, WTP13, is based on the

distribution of WTP responses in the Viscusi et al. (1991) study. The distribution of x, the

severity level of an average case of pollution-related chronic bronchitis, is modeled as a triangular

distribution centered at 6.5, with endpoints at 1.0 and 12.0. And the distribution of ß is normal

with mean = 0.18 and std. dev.= 0.0669 (the estimate of b and standard error reported in

Krupnick and Cropper, 1992).

In uncertainty mode, BenMAP uses a Monte Carlo approach. On each Monte Carlo iteration,

random draws for these three variables are made, and the resulting WTPx is calculated from the

equation above. Because this function is non-linear, the expected value of WTP for a pollutionrelated case of CB cannot be obtained by using the expected values of the three uncertain inputs

in the function (doing that willsubstantially understate mean WTP). A Monte Carlo analysis

suggests, however, that the mean WTP to avoid a case of pollution-related chronic bronchitis is

about $340,000 (in $2000, but not adjusted for the growth of income). Therefore, if you request

that the analysis be carried out in “point estimate” mode, that is the unit value that is used.
Appendix I: Health Valuation Functions in U.S. Setup

November 2012

181

BenMAP User's Manual Appendices

I.2.1.2 Alternative Cost of Illness Estimates

Cost of illness estimates for chronic bronchitis were derived from estimates of annual medical

costs and annual lost earnings by Cropper and Krupnick (1999). This study estimated annual

lost earnings resulting from chronic bronchitis as a function of age at onset of the illness, for the

following age categories: 25-43, 35-44, 45-54, and 55-65 (see Cropper and Krupnick, Table

8). Annual medical expenses were estimated for 10-years age groups (0-9, 10-19, 20-29, ...,

80-89). We derived estimates of the present discounted value of the stream of medical and

opportunity costs for people whose age of onset is 30, 40, 50, 60, 70, and 80. Medical costs

(which are in 1977$ in the Cropper and Krupnick study) were inflated to 2000$ using the CPI-U

for medical care; lost earnings (opportunity costs) were inflated to 2000$ using the Employment

Cost Index for Wages and Salaries. Life expectancies were assumed to be unaffected by the

illness. For example, an individual at age 70 has a life expectancy of 14.3 more years, and we

assumed that someone whose age of onset of chronic bronchitis is 70 will also live for 14.3 more

years. ( Source of life expectancies: NationalCenter for Health Statistics, 1999, Table 5.) We

also assumed that opportunity costs at ages 66 and over were zero. Present discounted values

were calculated using three and seven percent discount rates.

For each of the two discount rates, there are three cost of illness unit values for chronic bronchitis

available in BenMAP, for the following age categories: 27-44, 45-64, and 65+. These are the

age categories that were used in the epidemiologicalstudy that estimated a concentrationresponse function for chronic bronchitis (Abbey et al., 1995b). The estimate for the 27-44 age

group is an average of the present discounted values calculated for ages 30 and 40; the estimate

for the 45-64 age category is an average of the present discounted values calculated for ages 50

and 60; and the estimate for the 65+ age category is an average of the present discounted values

calculated for ages 70 and 80. The suite of unit values available for use in BenMAP are shown in

Table I-3 below.

Table I-3. Unit Values Available for Chronic Bronchitis

Basis for Estimate Age of Onset Present

Discounted

Value of

Medical

Costs

Present

Discounted

Value of

Opportunity

Costs

Unit

Value

Distribution

min. max.

WTP: average severity 30 99 N/A N/A $340,482 custom

COI: med costs + wage loss, 3% DR 27 44 $18,960 $135,463 $154,422 none

45 64 $23,759 $76,029 $99,788 none

65 99 $11,088 $0 $11,088 none

COI: med costs + wage loss, 7% DR 27 44 $7,886 $80,444 $88,331 none

45 64 $14,390 $59,577 $73,967 none

65 99 $9,030 $0 $9,030 none
Appendix I: Health Valuation Functions in U.S. Setup

November 2012

182

BenMAP User's Manual Appendices

I.2.2 Chronic Bronchitis Reversals

The unit value for chronic bronchitis reversals assumes that this is chronic bronchitis with a

severity level of 1. The method for generating a distribution of unit values in BenMAP is

therefore the same as the WTP-based unit value method for chronic bronchitis (see above), with

x =1. The mean of this distribution is $150,221.

I.2.3 Chronic Asthma

Two studies have estimated WTP to avoid chronic asthma in adults. Blumenschein and

Johannesson (1998) used two different contingent valuation (CV) methods, the dichotomous

choice method and a bidding game, to estimate mean willingness to pay for a cure for asthma.

The mean WTP elicited from the bidding game was $189 per month, or $2,268 per year (in

1996$). The mean WTP elicited from the dichotomous choice approach was $343 per month,

or $4,116 per year (in 1996$). Using $2,268 per year, a three percent discount rate, and 1997

life expectancies for males in the United States (NationalCenter for Health Statistics, 1999,

Table 5), the present discounted value of the stream of annual WTPs is $47,637 (in 2000$).

O’Conor and Blomquist (1997) estimated WTP to avoid chronic asthma from estimates of riskrisk tradeoffs. Combining the risk-risk tradeoffs with a statistical value of life, the annual value of

avoiding asthma can be derived. Assuming a value of a statistical life of $6 million, they derived

an annual WTP to avoid asthma of $1500 (O'Connor and Blomquist, 1997, p. 677). For a

value of a statistical life of $5,894,400 (in 1997 $), the corresponding implied annual value of

avoiding chronic asthma, based on O’Conor and Blomquist would be $1,474. Assuming a three

percent discount rate and 1997 life expectancies for males in the United States, the present

discounted value of the stream of annual WTPs would be $30,257 (in 2000$). A unit value,

based on a three percent discount rate, is the average of the two estimates, or $38,947.

Following the method used for the §812 Prospective analysis, the uncertainty surrounding the

WTP to avoid a case of chronic asthma among adult males was characterized by a triangular

distribution on the range determined by the two study-specific WTP estimates.

A second unit value, using a seven percent discount rate, is also available for use in BenMAP.

The method used to derive this unit value is the same as that described above for the three

percent discount rate unit value. The unit values available for use in BenMAP are summarized in

Table I-4 below.

Table I-4. Unit Values Available for Chronic Asthma

Basis for Estimate Age Range Unit Value Distribution of

Unit Value

Parameters of

Distribution

min. max. P1 P2

WTP: 3% DR (Discount

Rate)

27 99 $38,947 triangular $30,257 $47,637

WTP: 7% DR 27 99 $25,357 triangular $19,699 $31,015
Appendix I: Health Valuation Functions in U.S. Setup

November 2012

183

BenMAP User's Manual Appendices

I.2.4 Non-Fatal Myocardial Infarctions (Heart Attacks)

In the absence of a suitable WTP value for reductions in the risk of non-fatal heart attacks, there

are a variety of cost-of-illness unit values available for use in BenMAP. These cost-of-illness unit

values incorporate two components: the direct medical costs and the opportunity cost (lost

earnings) associated with the illness event. Because the costs associated with a heart attack

extend beyond the initial event itself, the unit values include costs incurred over five years. Using

age-specific annual lost earnings estimated by Cropper and Krupnick (1999), and a three

percent discount rate, we estimated the following present discounted values in lost earnings over

5 years due to a heart attack: $8,774 for someone between the ages of 25 and 44, $12,932 for

someone between the ages of 45 and 54, and $74,746 for someone between the ages of 55 and

65. The corresponding age-specific estimates of lost earnings using a seven percent discount rate

are $7,855, $11,578, and $66,920, respectively. Cropper and Krupnick do not provide lost

earnings estimates for populations under 25 or over 65. As such we do not include lost earnings

in the cost estimates for these age groups.

We have found three possible sources of estimates of the direct medical costs of a myocardial

infarction (MI) in the literature:

Wittels et al. (1990) estimated expected total medical costs of MI over 5 years to be $51,211

(in 1986$) for people who were admitted to the hospital and survived hospitalization. (There

does not appear to be any discounting used.) Wittels et al. was used to value coronary heart

disease in the 812 Retrospective Analysis of the Clean Air Act. Using the CPI-U for medical

care, the Wittels estimate is $109,474 in year 2000$. This estimated cost is based on a

medical cost model, which incorporated therapeutic options, projected outcomes and prices

(using “knowledgeable cardiologists” as consultants). The model used medical data and

medical decision algorithms to estimate the probabilities of certain events and/or medical

procedures being used. The authors note that the average length of hospitalization for acute MI

has decreased over time (from an average of 12.9 days in 1980 to an average of 11 days in

1983). Wittels et al. used 10 days as the average in their study. It is unclear how much further

the length ofstay (LOS) for MI may have decreased from 1983 to the present. The average

LOS for ICD code 410 (MI) in the year-2000 AHQR HCUP database is 5.5 days.

However, this may include patients who died in the hospital(not included among our non-fatal

MI cases), whose LOS was therefore substantially shorter than it would be if they hadn’t died.

Eisenstein et al. (2001) estimated 10-year costs of $44,663, in 1997$ (using a three percent

discount rate), or $49,651 in 2000$ for MI patients, using statistical prediction (regression)

models to estimate inpatient costs. Only inpatient costs (physician fees and hospital costs)

were included.

Russell et al. (1998) estimated first-year direct medical costs of treating nonfatal MI of

$15,540 (in 1995$), and $1,051 annually thereafter. Converting to year 2000$, that would be

$18,880 for a 5-year period, using a three percent discount rate, or $17,850, using a seven

percent discount rate.

The age group-specific estimates of opportunity cost over a five-year period are combined with
Appendix I: Health Valuation Functions in U.S. Setup

November 2012

184

BenMAP User's Manual Appendices

the medical cost estimates from each of the three studies listed above. Because opportunity costs

are derived for each of five age groups, there are 3 x 5 = 15 unit values for each of 2 discount

rates, or 30 unit values available for use in BenMAP. These are given in Table I-5 below.

Note that we were unable to achieve complete consistency, unfortunately, because of limitations

in the input studies. For example, although we calculated opportunity costs over a five-year

period using a 3 percent and a 7 percent discount rate, we were not able to do the same for

medical costs, except for the medical costs estimated by Russell et al. (in which they estimate an

annual cost). Wittels et al. appear to have used no discounting in their estimate; Eisenstein et al.

used a 3 percent discount rate. Similarly, although almost all cost estimates (opportunity costs

and medical costs) are for a 5-year period, the medical cost estimate reported by Eisenstein et al.

is for a 10-year period. There was no reasonable method for inferring from that study what costs

over a 5-year period would be.

Table I-5. Unit Values Available for Myocardial Infarction

Basis of Estimate Age Range Medical Cost * Opportunity

Cost **

Total Cost

Min Max

COI: 5 yrs med, 5 yrs wages, 3%

DR, Wittels (1990)

0 24 $109,474 $0 $109,474

25 44 $109,474 $9,033 $118,507

45 54 $109,474 $13,313 $122,787

55 65 $109,474 $76,951 $186,425

66 99 $109,474 $0 $109,474

COI: 10 yrs med, 5 yrs wages, 3%

DR, Eisenstein (2001)

0 24 $49,651 $0 $49,651

25 44 $49,651 $9,033 $58,683

45 54 $49,651 $13,313 $62,964

55 65 $49,651 $76,951 $126,602

66 99 $49,651 $0 $49,651

COI: 5 yrs med, 5 yrs wages, 3%

DR, Russell (1998)

0 24 $22,331 $0 $22,331

25 44 $22,331 $9,033 $31,363

45 54 $22,331 $13,313 $35,644

55 65 $22,331 $76,951 $99,281

66 99 $22,331 $0 $22,331

COI: 5 yrs med, 5 yrs wages, 7%

DR, Wittels (1990)

0 24 $109,474 $0 $109,474

25 44 $109,474 $8,087 $117,561

45 54 $109,474 $11,919 $121,393

55 65 $109,474 $68,894 $178,368

66 99 $109,474 $0 $109,474
Appendix I: Health Valuation Functions in U.S. Setup

November 2012

185

BenMAP User's Manual Appendices

Basis of Estimate Age Range Medical Cost * Opportunity

Cost **

Total Cost

Min Max

COI: 10 yrs med, 5 yrs wages, 7%

DR, Eisenstein (2001)

0 24 $49,651 $0 $49,651

25 44 $49,651 $8,087 $57,738

45 54 $49,651 $11,919 $61,570

55 65 $49,651 $68,894 $118,545

66 99 $49,651 $0 $49,651

COI: 5 yrs med, 5 yrs wages, 7%

DR, Russell (1998)

0 24 $21,113 $0 $21,113

25 44 $21,113 $8,087 $29,200

45 54 $21,113 $11,919 $33,032

55 65 $21,113 $68,894 $90,007

66 99 $21,113 $0 $21,113

* From Cropper and Krupnick (1999). Present discounted value of 5 yrs of lost earnings, at 3% and 7% discount

rate, adjusted from 1977$ to 2000$ usingCPI-U “all items”.

** An average of the 5-year costs estimated by Wittels et al. (1990) and Russell et al. (1998). Note that Wittels

et al. appears not to have used discounting in deriving a 5-year cost of $109,474; Russell et al. estimated firstyear direct medical costs and annual costs thereafter. The resulting 5-year cost is $22,331, using a 3% discount

rate, and $21,113, using a 7% discount rate. Medical costs were inflated to 2000$ usingCPI-U for medical care.

I.3 Hospital Admissions & Emergency Room Visits

This section presents the values for avoided hospital admissions, as well as avoided emergency

room visits. We assume that hospital admissions due to acute exposure to air pollution pass

through the emergency room. However, the value of hospital admissions that we have calculated

here does not account for the cost incurred in the emergency room visit.

I.3.1 Hospital Admissions

As suggested above, the total value to society of an individual’s avoidance of a hospital admission

can be thought of as having two components: (1) the cost of illness (COI) to society, including

the total medical costs plus the value of the lost productivity, as well as (2) the WTP of the

individual, as well as that of others, to avoid the pain and suffering resulting from the illness.

In the absence of estimates ofsocial WTP to avoid hospital admissions for specific illnesses

(components 1 plus 2 above), estimates of totalCOI (component 1) are available for use in

BenMAP as conservative (lower bound) estimates. Because these estimates do not include the

value of avoiding the pain and suffering resulting from the illness (component 2), they are biased

downward. Some analyses adjust COI estimates upward by multiplying by an estimate of the

ratio of WTP to COI, to better approximate total WTP. Other analyses have avoided making

this adjustment because of the possibility of over-adjusting -- that is, possibly replacing a known

downward bias with an upward bias. Based on Science Advisory Board (SAB) advice, the COI

values currently available for use in BenMAP are not adjusted.

Unit values are based on ICD-code-specific estimated hospital charges and opportunity cost of
Appendix I: Health Valuation Functions in U.S. Setup

November 2012

186

BenMAP User's Manual Appendices

time spent in the hospital(based on the average length of a hospitalstay for the illness). The

opportunity cost of a day spent in the hospital is estimated as the value of the lost daily wage,

regardless of whether or not the individual is in the workforce.

For all hospital admissions endpoints available in BenMAP, estimates of hospital charges and

lengths of hospitalstays were based on discharge statistics provided by the Agency for

Healthcare Research and Quality’s Healthcare Utilization Project NationalInpatient Sample

(NIS) database (2007). The NIS is the largest inpatient care database in the United States, and it

is the only national hospital database containing charge information on all patients. It contains data

from a very large nationally representative sample of about eight million hospital discharges, and

therefore provides the best estimates of mean hospital charges and mean lengths ofstay available,

with negligible standard errors. The sampling frame for the 2007 NIS is a sample of hospitals that

comprises approximately 90 percent of all hospital discharges in the United States. Since the NIS

is based on discharge samples, the discharge-level weight was used to weight discharges in order

to produce national estimates. The principle diagnoses were used to define the health endpoints.

Since most pollution-related hospital admissions are likely unscheduled, the unit values of avoided

hospital admissions used in BenMAP are based solely on unscheduled hospitalizations. The total

COI for an ICD-code-specific hospitalstay lasting n days is estimated as the mean hospital

charge plus n times the daily lost wage.

County-specific median annual income divided by (52*5) was used to estimate county-specific

median daily wage. The data source for median annual income is the 2010 American Community

Survey (ACS). ACS provided data for median annual earnings of full-time males and females at

least 16 years old in 800 counties. For these 800 counties, the average of full-time male and

female earnings was used to estimate the county-specific median annual income. For all other

counties, the ratio ofstate-specific median annual income in 2010 to 2000 was calculated. This

ratio was then applied to the 2000 county-specific median annual income to obtain the estimate

for 2010 county-specific income (the source for 2000 county-specific income is Geolytics,

2001). Because wage data used in BenMAP are county-specific, the unit value for a hospital

admission varies from one county to another.

Although the data for hospital charge are from year 2007 and median income data are from

2010, the default hospital admission unit values in BenMAP are in year 2000 dollars to be

consistent with the unit values of other health endpoints in BenMAP. This was done by deflating

the medical costs (2007 dollars) and median income (2010 dollars) to 2000 dollars using

BenMAP's inflation index. Then when BenMAP estimates year 2007 dollars, the answer will be

the same as if 2007 NIS and 2010 ACS data were used.

The hospital admission outcomes for which unit values are available in BenMAP are given in

Table I-6. Although unit values available for use in BenMAP are county-specific, the national

median daily wage was used to calculate opportunity costs and total costs for the table below, to

give a general idea of the cost of illness estimates for the different hospital admissions endpoints.
Appendix I: Health Valuation Functions in U.S. Setup

November 2012

187

BenMAP User's Manual Appendices

Table I-6. Unit Values Available for Hospital Admissions

Endpoint ICD Codes Age Range Mean Hospital

Charge

(2000 $)

Mean Length

of Stay

(days)

Total Cost of

Illiness

(Unit Value in

2000 $)*

min max

HA, All Cardiovascular 390-429 18 64 $ 26,654 4.12 $ 27,119

HA, All Cardiovascular 390-429 65 99 $ 24,893 4.88 $ 25,444

HA, All Cardiovascular 390-429 0 99 $ 25,605 4.59 $ 26,123

HA, Congestive Heart

Failure

428 65 99 $ 19,693 5.32 $ 20,293

HA, Dysrhythmia 427 65 99 $ 19,560 3.98 $ 20,008

HA, Dysrhythmia 427 0 99 $ 19,301 3.72 $ 19,720

HA, Ischemic Heart Disease 410-414 65 99 $ 32,452 4.61 $ 32,972

HA, All Respiratory 460-519 0 1 $ 9,883 3.19 $ 10,242

HA, All Respiratory 460-519 65 99 $ 20,667 6.07 $ 21,351

HA, All Respiratory 460-519 0 99 $ 19,009 5.35 $ 19,612

HA, Asthma 493 0 64 $ 9,723 3.00 $ 10,061

HA, Asthma 493 65 99 $ 15,267 4.79 $ 15,808

HA, Asthma 493 0 99 $ 10,852 3.37 $ 11,232

HA, Chronic Lung Disease 490-496 18 64 $ 12,836 3.90 $ 13,276

HA, Chronic Lung Disease 490-496 65 99 $ 14,835 4.79 $ 15,375

HA, Chronic Lung Disease 490-496 0 99 $ 13,025 4.10 $ 13,487

HA, Chronic Lung Disease

(less Asthma)

490-492,494-496 18 64 $ 13,999 4.23 $ 14,475

HA, Chronic Lung Disease

(less Asthma)

490-492,494-496 65 99 $ 14,742 4.79 $ 15,282

HA, Chronic Lung Disease

(less Asthma)

490-492,494-496 0 99 $ 14,497 4.59 $ 15,015

HA, Pneumonia 480-487 65 99 $ 17,647 5.77 $ 18,297

HA, Pneumonia 480-487 0 99 $ 16,956 5.25 $ 17,548

* The opportunity cost of a day spent in the hospital was estimated, for the above exhibit, at the median daily wage of all

workers, regardless of age.The median daily wage was calculated by dividing the median weekly wage ($695 in 2007 dollar or

$564.3 in 2000 dollar) by 5. The median weekly wages for 2007 was obtained from the U.S. Census Bureau, Statistical

Abstract of the United States: 2009, Section 12, Table 626: “Full-Time Wage and Salary Workers – Numbers and Earnings:

2000 to 2007".

I.3.2 Emergency Room Visits for Asthma

Two unit values are currently available for use in BenMAP for asthma emergency room (ER)

visits. One is $311.55, from Smith et al., 1997, who reported that there were approximately 1.2

million asthma-related ER visits made in 1987, at a total cost of $186.5 million, in 1987$. The
Appendix I: Health Valuation Functions in U.S. Setup

November 2012

188

BenMAP User's Manual Appendices

average cost per visit was therefore $155 in 1987$, or $311.55 in 2000 $ (using the CPI-U for

medical care to adjust to 2000$). The uncertainty surrounding this estimate, based on the

uncertainty surrounding the number of ER visits and the total cost of all visits reported by Smith et

al. is characterized by a triangular distribution centered at $311.55, on the interval[$230.67,

$430.93].

A second unit value is $260.67 from Stanford et al. (1999). This study considered asthmatics in

1996-1997, in comparison to the Smith et al. (1997) study, which used 1987 National Medical

Expenditure Survey (NMES) data). In comparing their study, the authors note that the 1987

NMES, used by Smith et al., “may not reflect changes in treatment patterns during the 1990s.” In

addition, its costs are the costs to the hospital(or ER) for treating asthma rather than charges or

payments by the patient and/or third party payer. Costs to the ER are probably a better measure

of the value of the medicalresources used up on an asthma ER visit (see above for a discussion

of costs versus charges).

The unit values and the corresponding distributions available in BenMAP for asthma-related ER

visits are summarized in Table I-7.

Table I-7. Unit Values Available for Asthma-Related ER Visits

Basis for Estimate Age Range Unit

Value

Distribution of

Unit Value

Parameters of Distribution

min. max. P1 P2

COI: Smith et al. (1997) 0 99 $312 triangular $231 $431

COI: Standford et al. (1999) 0 99 $261 normal 5.22 –

I.4 Acute Symptoms and Illness Not Requiring Hospitalization

Several acute symptoms and illnesses have been associated with air pollution, including acute

bronchitis in children, upper and lower respiratory symptoms, and exacerbation of asthma (as

indicated by one ofseveralsymptoms whose occurrence in an asthmatic generally suggests the

onset of an asthma episode). In addition, several more general health endpoints which are

associated with one or more of these acute symptoms and illnesses, such as minor restricted

activity days, school loss days, and work loss days, have also been associated with air pollution.

We briefly discuss the derivation of the unit values for each of these acute symptoms and

illnesses. Tables J-8 and J-9 summarize the values.
Appendix I: Health Valuation Functions in U.S. Setup

November 2012

189

BenMAP User's Manual Appendices

Table I-8. Unit Values Available for Acute Symptoms and Illnesses (in 2000 $)

Health

Endpoint

Basis for Estimate * Age Range Unit

Value

Distribution

of Unit

Value

Parameters of

Distribution

min. max. P1 P2

Acute

Bronchitis

WTP: 1 day illness, CV studies 0 17 $59 uniform 17.51 101.11

WTP: 6 day illness, CV studies 0 17 $356 uniform 105.06 606.64

WTP: 28 symptom-days, Dickie

and Ulery

0 17 $374 lognormal 5.947 0.0907

Any of 19

Respiratory

Symptoms

WTP: 1 day illness, CV studies 18 65 $24 uniform 0 48.25

Minor

Restricted

Activity Days

WTP: 1 day, CV studies 18 99 $51 triangular 20.71 80.37

WTP: 3 symptoms 1 day,

Dickie and Ulery (2002).

18 99 $98 lognormal 4.6088 0.0649

Lower

Respiratory

Symptoms

WTP: 1 day, CV studies 0 17 $16 uniform 6.94 24.47

WTP: 2 symptoms 1 day,

Dickie and Ulery (2002).

0 17 $187 lognormal 5.2556 0.07048

WTP: 2 x 1 day, CV studies 0 17 $31 uniform 13.89 48.93

School Loss

Days

Described in text. 0 17 $75 none N/A N/A

Upper

Respiratory

Symptoms

WTP: 1 day, CV studies 0 17 $25 uniform 9.22 43.11

WTP: 2 symptoms 1 day,

Dickie and Ulery (2002)

0 17 $187 lognormal 5.2556 0.07048

WTP: 2 x 1 day, CV studies 0 17 $49 uniform 18.45 86.22

Work Loss

Days **

Median daily wage, countyspecific18 65 $115 none N/A N/A

* All unit values pulled from a lognormal distribution from Model 1, Table III in Dickie and Ulery are multiplied

by 0.973811 to adjust for a difference in mean household income between the study participants and the general

population. The unit values shown here have already been adjusted.

** Unit values for work loss days are county-specific, based on county-specific median wages. The unit value

shown here is the national median daily wage, given for illustrative purposes only.
Appendix I: Health Valuation Functions in U.S. Setup

November 2012

190

BenMAP User's Manual Appendices

Table I-9. Unit Values Available for Asthma-related Acute Symptoms and Illnesses

Health

Endpoint

Basis for WTP Estimate* Age Range Unit

Value

Unit Value

Distributio

n

Parameters of

Distribution

min. max. P1 P2

Asthma

Attacks;

Cough;

Moderate or

Worse; One

or more

symptoms;

Shortness of

Breath;

Wheeze

Bad asthma day, Rowe and Chestnut

(1986)

18 99 $43 uniform 15.56 70.88

1 symptom-day, Dickie and Ulery (2002) 18 99 $74 lognormal 4.321 0.0957

Bbad asthma day, Rowe and Chestnut

(1986)

0 17 $43 uniform 15.56 70.88

2 x bad asthma day, Rowe and Chestnut

(1986)

0 17 $86 uniform 31.12 141.77

1 symptom-day, Dickie and Ulery (2002) 0 17 $156 lognormal 5.074 0.0925

*All unit values pulled from a lognormal distribution from Model 1, Table III in Dickie and Ulery, 2002, are

multiplied by 0.973811 to adjust for a difference in mean household income between the study participants and

the general population. The unit values shown here have already been adjusted.

I.4.1 Acute Bronchitis in Children

Estimating WTP to avoid a case of acute bronchitis is difficult for severalreasons. First, WTP to

avoid acute bronchitis itself has not been estimated. Estimation of WTP to avoid this health

endpoint therefore must be based on estimates of WTP to avoid symptoms that occur with this

illness. Second, a case of acute bronchitis may last more than one day, whereas it is a day of

avoided symptoms that is typically valued. Finally, the C-R function used in the benefit analysis

for acute bronchitis was estimated for children, whereas WTP estimates for those symptoms

associated with acute bronchitis were obtained from adults.

Three unit values are available in BenMAP for acute bronchitis in children. In previous benefit

analyses, EPA used a unit value of $59.31. This is the midpoint between a low estimate and a

high estimate. The low estimate is the sum of the midrange values recommended by IEc (1994)

for two symptoms believed to be associated with acute bronchitis: coughing and chest tightness.

The high estimate was taken to be twice the value of a minor respiratory restricted activity day.

For a more complete description of the derivation of this estimate, see Abt Associates (2000, p.

4-30).

The above unit value assumes that an episode of acute bronchitis lasts only one day. However,

this is generally not the case. More typically, it can last for 6 or 7 days. A simple adjustment,

then, would be to multiply the original unit value of $59.31 by 6 or 7. A second unit value of

$356 (=$59.31 x 6) was therefore derived.

Finally, as noted above, the epidemiologicalstudy relating air pollution to the incidence of acute

bronchitis referred to children specifically. The value of an avoided case should therefore be

WTP to avoid a case in a child, which may be different from WTP to avoid a case in an adult.

Recent work by Dickie and Ulery (2002) suggests, in fact, that parents are generally willing to

pay about twice as much to avoid sickness in their children as in themselves. In one ofseveral
Appendix I: Health Valuation Functions in U.S. Setup

November 2012

191

BenMAP User's Manual Appendices

models they estimated, the natural logarithm of parents’ WTP was related both to the number of

symptom-days avoided and to whether it was their child or themselves at issue. Dickie and Ulery

noted that “experiencing all of the symptoms [considered in their study – cough and phlegm,

shortness of breath/wheezing, chest pain, and fever] for 7 days, or 28 symptom-days altogether,

is roughly equivalent to a case of acute bronchitis ...” Using this model, and assuming that a case

of acute bronchitis can be reasonably modeled as consisting of 28 symptom-days, we estimated

parents’ WTP to avoid a case of acute bronchitis in a child to be $374. This is the third unit value

available in BenMAP.

The mean household income among participants in the Dickie and Ulery CV survey was slightly

higher than the national average. We therefore adjusted all WTP estimates that resulted from

their models downward slightly, using an income elasticity of WTP of 0.147, the average of the

income elasticities estimated in the four models in the study. The adjustment factor thus derived

was 0.9738.

I.4.2 Upper Respiratory Symptoms (URS) in Children

In past benefit analyses, EPA based willingness to pay to avoid a day of URS on symptomspecific WTPs to avoid those symptoms identified as part of the URS complex ofsymptoms.

Pope et al. (1991) defined a day of URS as consisting of one or more of the following symptoms:

runny or stuffy nose; wet cough; and burning, aching, or red eyes. The three contingent valuation

(CV) studies shown in Table I-10 have estimated WTP to avoid various morbidity symptoms

that are either within the URS symptom complex defined by Pope et al., or are similar to those

symptoms.

Table I-10. Median WTP Estimates and Derived Midrange Estimates (in 1999 $)

Symptom * Dickie et al. Tolley et al.

(1986)

Loehman et al.

(1979)

Mid-Range

Estimate

Throat congestion 4.81 20.84 - 12.75

Head/sinus congestion 5.61 22.45 10.45 12.75

Coughing 1.61 17.65 6.35 8.93

Eye irritation - 20.03 - 20.03

Headache 1.61 32.07 - 12.75

Shortness of breath 0.00 - 13.47 6.37

Pain upon deep inhalation

(PDI)

5.63 - - 5.63

Wheeze 3.21 - - 3.21

Coughing up phlegm 3.51 ** - - 3.51

Chest tightness 8.03 - - 8.03

* All estimates are WTP to avoid one day of symptom. Midrange estimates were derived by IEc (1993).

** 10% trimmed mean.
Appendix I: Health Valuation Functions in U.S. Setup

November 2012

192

BenMAP User's Manual Appendices

The three individualsymptoms that were identified as most closely matching those listed by Pope

et al. for URS are cough, head/sinus congestion, and eye irritation, corresponding to “wet

cough,” “runny or stuffy nose,” and “burning, aching or red eyes,” respectively. A day of URS

could consist of any one of the seven possible “symptom complexes” consisting of at least one of

these three symptoms. The original unit value for URS was based on the assumption that each of

these seven URS complexes is equally likely. This unit value for URS, $24.64, is just an average

of the seven estimates of mean WTP for the different URS complexes.

The WTP estimates on which the first unit value is based were elicited from adults, whereas the

health endpoint associated with air pollution in the epidemiologicalstudy is in children. As noted

above, recent research by Dickie and Ulery (2002) suggests that parental WTP to avoid

symptoms and illnesses in their children is about twice what it is to avoid those symptoms and

illnesses in themselves. We therefore derived a second unit value of $49.28 (=2 x $24.64) from

the first unit value.

A third unit value was derived by using Model 1, Table III in Dickie and Ulery (2002) (the same

model used for acute bronchitis), assuming that a day of URS consists of 2 symptoms. As noted

above, this modelrelates parental WTP to the number ofsymptom-days avoided and to whether

it is the parent or the child at issue. The unit value derived from this model is $187.

A WTP estimate elicited from parents concerning their WTP to avoid symptoms in their children

may well include some calculation of lost earnings resulting from having to lose a day of work.

Estimates from the Dickie and Ulery model therefore (appropriately) probably include not only

their WTP to have their children avoid the pain and suffering associated with their illness, but also

the opportunity cost of a parent having to stay home with a sick child.

I.4.3 Lower Respiratory Symptoms (LRS) in Children

The three unit values for LRS in children currently available in BenMAP follow the same pattern

as those for URS in children. In past benefit analyses, EPA based willingness to pay to avoid a

day of LRS on symptom-specific WTPs to avoid those symptoms identified as part of the LRS

complex ofsymptoms. Schwartz et al. (1994) defined a day of LRS as consisting of at least two

of the following symptoms: cough, chest tightness, coughing up phlegm, and wheeze. Of the

symptoms for which WTP estimates are available (listed in Table I-10), those that most closely

match the symptoms listed by Schwartz et al. are coughing, chest tightness, coughing up phlegm,

and wheeze. A day of LRS, as defined by Schwartz et al., could consist of any one of 11

possible combinations of at least two of these four symptoms. In the absence of any further

information, each of the 11 possible “symptom clusters” was considered equally likely. The

original unit value for LRS, $15.57, is just an average of the eleven estimates of mean WTP for

the different LRS symptom clusters.

A second unit value is twice the original unit value, or $31.15, based on the evidence from Dickie

and Ulery (2002) that parents are willing to pay about twice as much to avoid symptoms and

illness in their children as in themselves. The third unit value is based on Model 1, Table III in

Dickie and Ulery, assuming that, as for URS, a day of LRS consists of 2 symptoms. As noted
Appendix I: Health Valuation Functions in U.S. Setup

November 2012

193

BenMAP User's Manual Appendices

above, this modelrelates parental WTP to the number ofsymptom-days avoided and to whether

it is the parent or the child at issue. The unit value derived from this model is $187.

I.4.4 Any of 19 Respiratory Symptoms

The presence of “any of 19 acute respiratory symptoms” is a somewhat subjective health effect

used by Krupnick et al. (1990). Moreover, not all 19 symptoms are listed in the Krupnick et al.

study. It is therefore not clear exactly what symptoms were included in the study. Even if all 19

symptoms were known, it is unlikely that WTP estimates could be obtained for all of the

symptoms. Finally, even if all 19 symptoms were known and WTP estimates could be obtained

for all 19 symptoms, the assumption of additivity of WTPs becomes tenuous with such a large

number ofsymptoms. The likelihood that all 19 symptoms would occur simultaneously,

moreover, is very small.

Acute respiratory symptoms must be either upper respiratory symptoms or lower respiratory

symptoms. In the absence of further knowledge about which of the two types ofsymptoms is

more likely to occur among the “any of 19 acute respiratory symptoms,” we assumed that they

occur with equal probability. Because this health endpoint may also consist of combinations of

symptoms, it was also assumed that there is some (smaller) probability that upper and lower

respiratory symptoms occur together. To value avoidance of a day of “the presence of any of 19

acute respiratory symptoms” we therefore assumed that this health endpoint consists either of

URS, or LRS, or both. We also assumed that it is as likely to be URS as LRS and that it is half

as likely to be both together. That is, it was assumed that “the presence of any of 19 acute

respiratory symptoms” is a day of URS with 40 percent probability, a day of LRS with 40

percent probability, and a day of both URS and LRS with 20 percent probability. Using the

point estimates of WTP to avoid a day of URS and LRS derived above, the point estimate of

WTP to avoid a day of “the presence of any of 19 acute respiratory symptoms” is:

(0.40)($24.64) + (0.40)($15.57) + (0.20)($24.64 + $15.57) = $24.12.

Because this health endpoint is only vaguely defined, and because of the lack of information on

the relative frequencies of the different combinations of acute respiratory symptoms that might

qualify as “any of 19 acute respiratory symptoms,” the unit dollar value derived for this health

endpoint must be considered only a rough approximation.

I.4.5 Work Loss Days (WLDs)

Work loss days are valued at a day’s wage. BenMAP calculates county-specific median daily

wages from county-specific annual wages by dividing by (52*5), on the theory that a worker’s

vacation days are valued at the same daily rate as work days.

I.4.6 Minor Restricted Activity Days (MRADs)

Two unit values are currently available in BenMAP for MRADs. No studies are reported to

have estimated WTP to avoid a minor restricted activity day (MRAD). However, IEc (1993)
Appendix I: Health Valuation Functions in U.S. Setup

November 2012

194

BenMAP User's Manual Appendices

derived an estimate of WTP to avoid a minor respiratory restricted activity day (MRRAD), using

WTP estimates from Tolley et al. (1986) for avoiding a three-symptom combination of coughing,

throat congestion, and sinusitis. This estimate of WTP to avoid a MRRAD, so defined, is $38.37

(1990 $). Although Ostro and Rothschild (1989) estimated the relationship between PM2.5and

MRADs, rather than MRRADs (a component of MRADs), it is likely that most of the MRADs

associated with exposure to PM2.5are in fact MRRADs. The original unit value, then, assumes

that MRADs associated with PM exposure may be more specifically defined as MRRADs, and

uses the estimate of mean WTP to avoid a MRRAD.

Any estimate of mean WTP to avoid a MRRAD (or any other type of restricted activity day

other than WLD) will be somewhat arbitrary because the endpoint itself is not precisely defined.

Many different combinations ofsymptoms could presumably result in some minor or less minor

restriction in activity. Krupnick and Kopp (1988) argued that mild symptoms will not be

sufficient to result in a MRRAD, so that WTP to avoid a MRRAD should exceed WTP to avoid

any single mild symptom. A single severe symptom or a combination ofsymptoms could,

however, be sufficient to restrict activity. Therefore WTP to avoid a MRRAD should, these

authors argue, not necessarily exceed WTP to avoid a single severe symptom or a combination

ofsymptoms. The “severity” of a symptom, however, is similarly not precisely defined;

moreover, one level ofseverity of a symptom could induce restriction of activity for one individual

while not doing so for another. The same is true for any particular combination ofsymptoms.

Given that there is inherently a substantial degree of arbitrariness in any point estimate of WTP to

avoid a MRRAD (or other kinds of restricted activity days), the reasonable bounds on such an

estimate must be considered. By definition, a MRRAD does not result in loss of work. WTP to

avoid a MRRAD should therefore be less than WTP to avoid a WLD. At the other extreme,

WTP to avoid a MRRAD should exceed WTP to avoid a single mild symptom. The highest IEc

midrange estimate of WTP to avoid a single symptom is $20.03 (1999 $), for eye irritation. The

point estimate of WTP to avoid a WLD in the benefit analysis is $83 (1990 $). If all the single

symptoms evaluated by the studies are not severe, then the estimate of WTP to avoid a MRRAD

should be somewhere between $16 and $83. Because the IEc estimate of $38 falls within this

range (and acknowledging the degree of arbitrariness associated with any estimate within this

range), the IEc estimate is used as the mean of a triangular distribution centered at $38, ranging

from $16 to $61. Adjusting to 2000 $, this is a triangular distribution centered at $50.55, ranging

from $21 to $80.

A second unit value is based on Model 1, Table III in Dickie and Ulery (2002). This model

estimates the natural logarithm of parents’ WTP to avoid symptoms as a linear function of the

natural logarithm of the number ofsymptom-days avoided and whether or not the person

avoiding the symptoms is the parent or the child. The unit value derived from this model,

assuming that an MRAD consists of one day of 3 symptoms in an adult, is $98.

I.4.7 Asthma Exacerbation

Severalrespiratory symptoms in asthmatics or characterizations of an asthma episode have been

associated with exposure to air pollutants. All of these can generally be taken as indications of an
Appendix I: Health Valuation Functions in U.S. Setup

November 2012

195

BenMAP User's Manual Appendices

asthma exacerbation (“asthma attack”) when they occur in an asthmatic. BenMAP therefore

uses the same set of unit values for all of the variations of “asthma exacerbation” that appear in

the epidemiological literature.

Two unit values are currently available in BenMAP for asthma exacerbation in adults, and three

are currently available for asthma exacerbation in children. In past benefit analyses, EPA based

willingness to pay to avoid an asthma exacerbation on four WTP estimates from Rowe and

Chestnut (1986) for avoiding a “bad asthma day.” The mean of the four average WTPs is $32

(1990 $), or $43 in 2000$. The uncertainty surrounding this estimate was characterized by a

continuous uniform distribution on the range defined by the lowest and highest of the four average

WTP estimates from Rowe and Chestnut, [$12, $54] in 1990$, or [$16, $71] in 2000 $. This

unit value is available for both adults and children.

A second unit value for adults was derived by using Model 1, Table III in Dickie and Ulery

(2002) -- the same model used for acute bronchitis, LRS, and URS -- assuming that an asthma

exacerbation consists of 1 symptom-day. As noted above, this modelrelates parental WTP to

the number ofsymptom-days avoided and to whether it is the parent or the child at issue. The

unit value derived from this modelfor adults is $74.

Two additional unit values are available for children. One of these is twice the original unit value,

or $86, based on the evidence from Dickie and Ulery (2002) that parents are willing to pay

about twice as much to avoid symptoms and illness in their children as in themselves. The third

unit value is based on Model 1, Table III in Dickie and Ulery (the same model used for asthma

exacerbation in adults, only now with the “adult or child” variable set to 1 rather than 0). The unit

value derived from this model is $156.

I.4.8 School Loss Days

There is currently one unit value available in BenMAP for school loss days, based on (1) the

probability that, if a school child stays home from school, a parent will have to stay home from

work to care for the child, and (2) the value of the parent’s lost productivity. We first estimated

the proportion of families with school-age children in which both parents work, and then valued a

school loss day as the probability of a work loss day resulting from a school loss day (i.e., the

proportion of households with school-age children in which both parents work) times a measure

of lost wages.

From the U.S. Bureau of the Census (2002) we obtained (1) the numbers ofsingle, married, and

“other” (i.e., widowed, divorced, or separated) women with children in the workforce, and (2)

the rates of participation in the workforce ofsingle, married, and “other” women with children.

From these two sets ofstatistics, we calculated a weighted average participation rate of 72.85

percent, as shown in Table I-11.

Our estimated daily lost wage (if a mother must stay at home with a sick child) is based on the

median weekly wage among women age 25 and older in 2000 . This median weekly wage is

$551. Dividing by 5 gives an estimated median daily wage of $103. The expected loss in wages

due to a day ofschool absence in which the mother would have to stay home with her child is
Appendix I: Health Valuation Functions in U.S. Setup

November 2012

196

BenMAP User's Manual Appendices

estimated as the probability that the mother is in the workforce times the daily wage she would

lose ifshe missed a day = 72.85% of $103, or $75. We currently have insufficient information to

characterize the uncertainty surrounding this estimate.

Table I-11. Women with Children: Number and Percent in the Labor Force, 2000, and

Weighted Average Participation Rate

Category

Women in

Labor Force

(millions) *

Participation

Rate

(%) *

ImpliedTotal

Number in

Population (in

millions)

ImpliedPercent

in Population

PopulationWeighted

Average

Participation

Rate

(1) (2) (3) = (1)/(2) (4) [=sum (2)*(4)

over rows]

Single 3.1 73.9% 4.19 11.84% --

Married 18.2 70.6% 25.78 72.79% --

Other ** 4.5 82.7% 5.44 15.36% --

Total -- -- 35.42 -- 72.85%

* Source: U.S. Bureau of the Census (2002).

** Widowed, divorced, or separated.

A unit value based on the approach described above is likely to understate the value of a school

loss day in two ways. First, it omits WTP to avoid the symptoms/illness which resulted in the

school absence. Second, it effectively gives zero value to school absences which do not result in

a work loss day. The unit value of $75 is therefore considered an “interim” value untilsuch time

as alternative means of estimating this unit value become available.
Appendix J: Population & Other Data in U.S. Setup

November 2012

197

BenMAP User's Manual Appendices

Appendix J: Population & Other Data in U.S.

Setup

This section describes the population and monitor data in the United States setup.

Population Data. This describes how BenMAP forecasts population; the block-level and

county-level data underlying the forecasts; and the PopGrid software application, which

aggregates block-level population data to whatever grid definition might be needed.

Monitor Data. The default United States setup has ozone, PM2.5, PM10, lead, NO2, and SO2

monitor data for the years 2000-2007. Data for CO are available at the BenMAP website:

http://www.epa.gov/air/benmap/.

J.1 Population Data in U.S. Setup

The U.S. setup in BenMAP calculates health impacts for any desired grid definition, so long as

you have a shapefile for that grid definition and population data for that grid definition. In this

description, we use the term “population grid cell” to refer to a cell(e.g., county) within a grid

definition. The foundation for calculating the population level in the population grid-cells is 2010

Census block data. A separate application developed by Abt Associates, called “PopGrid,”

described below, combines the Census block data with any user-specified set of population gridcells, so long as they are defined by a GIS shape file. Unfortunately, PopGrid relies on extremely

large census files that are too large to include with BenMAP -- hence the need for the separate

application. If you are interested in PopGrid, please email: benmap@epa.gov.

Within any given population grid-cell, BenMAP has 304 unique race-ethnicity-gender-age

groups: 19 age groups by 2 ethnic groups by gender by 4 racial groups (19*2*2*4=304). Table

J-1 presents the 304 population variables available in BenMAP. As discussed below, these

variables are available for use in developing age estimates in whatever grouping desired by you.

Table J-1. Demographic Groups and Variables Available in BenMAP

Racial Group Ethnicity Age Gender

White, African American,

Asian, American Indian

Hispanic,

Non-Hispanic

<1, 1-4, 5-9, 10-14, 15-19, 20-24, 25-29, 30-34, 35-39,

40-44, 45-49, 50-54, 55-59, 60-64, 65-69, 70-74, 75-

79, 80-84, 85+

Male,

Female

In this section on population data in the U.S. setup, we describe:

Forecasting Population. This describes how BenMAP forecasts population.

Data Needed. This section describes the block-level and county-level data underlying the
Appendix J: Population & Other Data in U.S. Setup

November 2012

198

BenMAP User's Manual Appendices

forecasts.

PopGrid. This section reviews the PopGrid software application, which aggregates block-level

population data to whatever grid definition might be needed.

J.1.1 How BenMAP Forecasts Population

In calculating the population in age groups that may include a portion of one of the pre-specified

demographic groups in Table J-1, BenMAP assumes the population is uniformly distributed in the

age group. For example, to calculate the number of children ages 3 through 12, BenMAP

calculates:

age age age age 3 12 1 4 5 9 10 14

1

2

3

5

.

To estimate population levels for the years after the last Census in 2010, BenMAP scales the 2010

Census-based estimate with the ratio of the county-levelforecast for the future year of interest

over the 2010 county-level population level. Woods & Poole (2012) provides the county-level

population forecasts used to calculate the scaling ratios; these data are discussed in detail below.

In the simplest case, where one is forecasting a single population variable, say, children ages 4 to

9 in the year 2020, BenMAP calculates:

where the gth population grid-cell is wholly located within a given county.

In the case, where the gth grid-cell includes “n” counties in its boundary, the situation is somewhat

more complicated. BenMAP first estimates the fraction of individuals in a given age group (e.g.,

ages 4 to 9) that reside in the part of each county within the gth grid-cell. BenMAP calculates

this fraction by simply dividing the population all ages of a given county within the gth grid-cell by

the total population in the gth grid-cell:

fraction of age

age

age

g in county all g in county all g

c

c 4 9 ,

,

,

Multiplying this fraction with the number of individuals ages 4 to 9 in the year 2010 gives an

estimate of the number of individuals ages 4 to 9 that reside in the fraction of the county within the

gth grid-cell in the year 2010:
Appendix J: Population & Other Data in U.S. Setup

November 2012

199

BenMAP User's Manual Appendices

To then forecast the population in 2020, we scale the 2010 estimate with the ratio of the county

projection for 2020 to the county projection for 2010:

Combining all these steps for “n” counties within the gth grid-cell, we forecast the population of

persons ages 4 to 9 in the year 2020 as follows:

In the case where there are multiple age groups and multiple counties, BenMAP first calculates

the forecasted population levelfor individual age groups, and then combines the forecasted age

groups. In calculating the number of children ages 4 to 12, BenMAP calculates:

J.1.2 Data Needed for Forecasting

Underlying the population forecasts in BenMAP there are block-level databases used to provide

year 2010 population estimates and a county-level database of forecast ratios. Both files have

the same set of 304 race-ethnicity-gender-age population groups.
Appendix J: Population & Other Data in U.S. Setup

November 2012

200

BenMAP User's Manual Appendices

The block-level data is typically not used directly in BenMAP, and instead is used with the

PopGrid software (described below) to provide year 2010 estimates for a grid definition of

interest (e.g., 12 kilometer CMAQ grid). The output from PopGrid with the year 2010

population estimates can then be loaded into BenMAP.

The county-level data comes pre-installed in the U.S. setup, and is not something that the user

needs to load herself. These data are simply county-levelratios of a year (2009, 2011-2040)

and year 2010 population data for each county and each of the 304 race-ethnicity-gender-age

population groups.

We describe the development of each databases below.

J.1.2.1 Block-Level Census 2010

There are about five million "blocks" in the United States, and for each block we have 304 raceethnicity-gender-age groups. The block-level population database is created separately for each

state, in order to make the data more manageable. (A single nationalfile of block data would be

about six gigabytes.)

The initial block file from the U.S. Census Bureau is not in the form needed. The block data has

7 racial categories and 23 age groups, as opposed to the 4 and 19 used in BenMAP. Table J-2

summarizes the initialset of variables and the final desired set of variables.

Table J-2. Race, Ethnicity and Age Variables in 2010 Census Block Data

Type Race Ethnicity Gender Age

Initial

Variables

(SF1 file)

White Alone, Black Alone, Native

American Alone, Asian Alone,

Pacific Islander / Hawaiian Alone,

Other Alone, Two or More Alone

-- Male,

Female

0-4, 5-9, 10-14, 15-17, 18-19, 20,

21, 22-24, 25-29, 30-34, 35-39, 40-

44, 45-49, 50-54, 55-59, 60-61, 62-

64, 65-66, 67-69, 70-74, 75-79, 80-

84, 85+

Final

Desired

Variables

White, African-American, AsianAmerican, Native-AmericanHispanic,

NonHispanic

Female,

Male

<1, 1-4, 5-9, 10-14, 15-19, 20-24,

25-29, 30-34, 35-39, 40-44, 45-49,

50-54, 55-59, 60-64, 65-69, 70-74,

75-79, 80-84, 85+

The initialset of input files are as follows.

Census 2010 block-level and tract-levelfiles (Summary File 1)

Data: http://www2.census.gov/census_2010/04-Summary_File_1/

Docs: http://www.census.gov/prod/cen2010/doc/sf1.pdf

Census 2000 MARS national-levelsummary

Docs: http://www.census.gov/popest/archives/files/MRSF-01-US1.pdf
Appendix J: Population & Other Data in U.S. Setup

November 2012

201

BenMAP User's Manual Appendices

The SF1 tract-level and MARS data, as described below, are needed to reorganize the variables

that come initially in the block-levelSF1 file. (For the sake of completeness, we note that there

exists a county-levelCensus 2000 MARS file; however, due to major population count

discrepancies between the county-level MARS file and block-level SF1 file, we used only the

nation-levelsummary table. Tables in MARS documentation file did not have the discrepancies

that the county-levelfile had. We were unable to get an adequate explanation of this from the U.

S. Census.)

The steps in preparing the data are as follows:

1. Adjust Age-classifications:

We combined some age groups in the block-level SF1 data to match the age groups wanted for

BenMAP. For example, we combined age groups 15-17 and 18-19 to create the 15-19 age

group used in BenMAP. Then, in the case of the 0-4 age group, we split it into <1 and 1-4 using

the tract-level SF1 data, which gave us the fraction of 0-4 year-olds who are <1.

2. Fill in Missing Racial-Ethnic Interactions:

We used the tract-level SF1 data to calculate the fraction of Hispanics in each ethnicallyaggregated subpopulation from the block-level data, by age and sex. We used these fractions to

distribute each age-sex-race-block-level datum into Hispanics and non-Hispanics.

3. Assign "Other" and "Multi-Racial" to the Remaining Four Racial Categories:

We assign the “Other" race category in two steps. First, based on the national MARS data, we

estimated how many people in the “multi-racial” category checked off “some other race” as one

of their races, for Hispanics and non-Hispanics separately. In each age-sex-race-block-level

datum, we added those people to “other race” category to create the re-distribution pool,

analogously to the method implemented by Census while creating MARS data (see U.S. Census

Bureau, 2002a, Table 1, below). Second, based on the nationalre-allocation fractions for

Hispanics and non-Hispanics (derived from the MARS data), we assigned the “Other” race into

the four races of interest and “multi-race”.

After the assignment of the "Other" race category, we then assigned “multi-racial” category to the

four racial categories categories, using state fractions of these races in each age-sex-race-blocklevel datum.
Appendix J: Population & Other Data in U.S. Setup

November 2012

202

BenMAP User's Manual Appendices
Appendix J: Population & Other Data in U.S. Setup

November 2012

203

BenMAP User's Manual Appendices

J.1.2.2 County-Level Forecasts

Woods & Poole (2012) developed county-levelforecasts for each year from 2000 through

2040, by age and gender for non-Hispanic White, African-American, Asian-American, and

Native-American and for all Hispanics. The detailed documentation can be found at http://www.

woodsandpoole.com/pdfs/CED12.pdf. As discussed below, the adjustments necessary to

prepare the data for use in BenMAP are relatively straightforward.

For each non-Hispanic subset of the population and each year from 2000-2040, we divided the

Woods and Poole population for that year by the Woods and Poole population for that subset in

2010. These serve as the growth coefficients for the non-Hispanic subsets of each race. We

used a similar calculation to determine the growth rates for the Hispanic population. We assume

that each Hispanic race grows at the same rate, and use these growth rates for the Hispanic

subsets of each race.

Matching Age Groups Used in BenMAP

There are 86 age groups, so it is a simple matter of aggregating age groups to match the 19 used

in BenMAP.

Matching Counties Used in U.S. Census

The county geographic boundaries used by Woods & Poole are somewhat more aggregated than

the county definitions used in the 2010 Census and those in BenMAP, and the FIPS codes used

by Woods and Poole are not always the standard codes used in the Census. To make the

Woods and Poole data consistent with the county definitions in BenMAP, we disaggregated the

Woods and Poole data and changed some of the FIPS codes to match the U.S. Census.

Calculating Growth Ratios with Zero Population in 2000

There are a small number of cases were the 2010 county population for a specific demographic

group is zero, so the ratio of any future year to the year 2010 data is undefined. In these

relatively rare cases, we prepared statewide and national totals and used ratios at the higher

levels of geographic aggregation when the more localratios caused divide-by-zero errors.

J.1.3 PopGrid

If the geographic center of a Census block falls within a population grid-cell, PopGrid assigns the

block population to this particular population grid-cell. Note that the grid-cells in an air quality

model, such as CMAQ, may cross multiple county boundaries. PopGrid keeps track of the total

number of people in each race-ethnic group by county within a particular population grid-cell. Of

course, when the population grid-cell is for U.S. counties, then there is only a single county

associated with the population grid-cell. However, with air quality models, there can clearly be

multiple counties in a population grid-cell.

Keeping track of the total number of people in a county is necessary when forecasting

population, as the population forecast for a given grid cell is equal to the year 2010 population
Appendix J: Population & Other Data in U.S. Setup

November 2012

204

BenMAP User's Manual Appendices

estimate from the Census Bureau multiplied by the ratio of future-year to year 2010 county

population estimates from Woods & Poole. BenMAP assumes that all age-gender groups within

a given race-ethnic group have the same geographic distribution.

J.1.3.1 How to Use PopGrid

After installing PopGrid, double-click on the PopGrid executable "PopGrid4.exe." The following

screen will appear:

The Census Data Files Directory box points PopGrid to where the block data are located that

PopGrid uses. Make sure that the files in this directory are unzipped. This data folder should

look something like the following:
Appendix J: Population & Other Data in U.S. Setup

November 2012

205

BenMAP User's Manual Appendices

The Result Population File box provides the path and the name of the file that you want to

create. In the example above, PopGrid is being used to estimate population for the intersection

of air basins and counties in California (CA_AirBasin_by_County).

Click on the Step 2: Shape File tab. Choose the shapefile that you want to use. The example

for air basins and counties in California looks as follows:
Appendix J: Population & Other Data in U.S. Setup

November 2012

206

BenMAP User's Manual Appendices

After choosing your shapefile, go to the Step 3: Run tab, which should look as follows:
Appendix J: Population & Other Data in U.S. Setup

November 2012

207

BenMAP User's Manual Appendices

Click Run. PopGrid will now begin processing. It can take a very long time to run. When

PopGrid has finished running, check the log file. The log file notes the start time, the files that

PopGrid used, and the end time. Also, at the very end of the log file, PopGrid notes the number

of people that PopGrid assigned to your grid definition ("Population covered by grid") and the

number of people that PopGrid determined are outside of your grid definition ("Population

outside grid").
Appendix J: Population & Other Data in U.S. Setup

November 2012

208

BenMAP User's Manual Appendices

J.1.3.2 PopGrid Output

PopGrid generates two files. One file has the number of people in each grid cellfor each of the

304 race-ethnicity-gender-age demographic groups available in PopGrid. Table J-4 presents an

example of what the population file looks like from PopGrid. The Row and Column uniquely

identify each grid cell. Note that the Race, Ethnicity, Gender and AgeRange variables are

precisely defined (see section on loading population data LoadData_Setups_Population).

Table J-4. Population File Fragment from PopGrid

Row Column Year Population Race Ethnicity Gender AgeRange

58 81 2000 1.54 WHITE HISPANIC MALE 0TO0

58 81 2000 0.03 BLACK HISPANIC MALE 0TO0

58 81 2000 0.01 NATAMER HISPANIC MALE 0TO0

58 81 2000 0.01 ASIAN HISPANIC MALE 0TO0

58 81 2000 4.86 WHITE HISPANIC MALE 1TO4

58 81 2000 0.12 BLACK HISPANIC MALE 1TO4
Appendix J: Population & Other Data in U.S. Setup

November 2012

209

BenMAP User's Manual Appendices

Row Column Year Population Race Ethnicity Gender AgeRange

58 81 2000 0.03 NATAMER HISPANIC MALE 1TO4

58 81 2000 0.03 ASIAN HISPANIC MALE 1TO4

58 81 2000 6.79 WHITE HISPANIC MALE 5TO9

58 81 2000 0.21 BLACK HISPANIC MALE 5TO9

58 81 2000 0.05 NATAMER HISPANIC MALE 5TO9

58 81 2000 0.05 ASIAN HISPANIC MALE 5TO9

58 81 2000 0.90 WHITE HISPANIC MALE 10TO14

58 81 2000 0.04 BLACK HISPANIC MALE 10TO14

58 81 2000 0.01 NATAMER HISPANIC MALE 10TO14

58 81 2000 0.01 ASIAN HISPANIC MALE 10TO14

58 81 2000 3.44 WHITE HISPANIC MALE 15TO19

58 81 2000 0.15 BLACK HISPANIC MALE 15TO19

58 81 2000 0.04 NATAMER HISPANIC MALE 15TO19

58 81 2000 0.04 ASIAN HISPANIC MALE 15TO19

58 81 2000 1.49 WHITE HISPANIC MALE 20TO24

58 81 2000 0.06 BLACK HISPANIC MALE 20TO24

58 81 2000 0.02 NATAMER HISPANIC MALE 20TO24

58 81 2000 0.03 ASIAN HISPANIC MALE 20TO24

58 81 2000 1.93 WHITE HISPANIC MALE 25TO29

58 81 2000 0.04 BLACK HISPANIC MALE 25TO29

58 81 2000 0.01 NATAMER HISPANIC MALE 25TO29

58 81 2000 0.01 ASIAN HISPANIC MALE 25TO29

58 81 2000 1.87 WHITE HISPANIC MALE 30TO34

58 81 2000 0.08 BLACK HISPANIC MALE 30TO34

PopGrid generates a second file that keeps track of the fraction of the total population in each of

the eight race-ethnic groups that comes from each county in the United States. Table J-2

presents a sample. The SourceCol and SourceRow uniquely identify each county, and the

TargetCol and TargetRow uniquely identify each grid cell. The Value variable gives the fraction

of the total population in the grid cellfor a given race-ethnic group that comes from the "source"

county.

When a grid cell lies completely within a county, then the fraction will be 1. When a grid cell is in

more than county, then the sum of the fractions across the counties for a given race-ethnic group
Appendix J: Population & Other Data in U.S. Setup

November 2012

210

BenMAP User's Manual Appendices

must sum to one. In Table J-5, you can see that for grid cell(TargetCol=123, TargetRow=18)

that the fraction of Asian Non-Hispanic coming from county (SourceCol=16, SourceRow=71) is

0.49 and for county (SourceCol=49, SourceRow=3) the fraction is 0.51. In this case, about half

the population of Asian Non-Hispanics comes from each of the two counties. In the case of

Black Hispanics, the fraction from county (SourceCol=16, SourceRow=71) is only 0.12, with

most Black Hispanics in this grid cell coming from county (SourceCol=49, SourceRow=3).

Table J-5. Population-Weight File Fragment from PopGrid

SourceCol SourceRow TargetCol TargetRow Race Ethnicity Value Year

16 71 123 18 ASIAN NON-HISPANIC 0.49 2000

16 71 123 18 ASIAN HISPANIC 0.21 2000

16 71 123 18 BLACK NON-HISPANIC 0.49 2000

16 71 123 18 BLACK HISPANIC 0.12 2000

16 71 123 18 NATAMER NON-HISPANIC 0.98 2000

16 71 123 18 NATAMER HISPANIC 0.43 2000

16 71 123 18 WHITE NON-HISPANIC 0.23 2000

16 71 123 18 WHITE HISPANIC 0.06 2000

49 3 123 18 ASIAN NON-HISPANIC 0.51 2000

49 3 123 18 ASIAN HISPANIC 0.79 2000

49 3 123 18 BLACK NON-HISPANIC 0.51 2000

49 3 123 18 BLACK HISPANIC 0.88 2000

49 3 123 18 NATAMER NON-HISPANIC 0.02 2000

49 3 123 18 NATAMER HISPANIC 0.57 2000

49 3 123 18 WHITE NON-HISPANIC 0.77 2000

49 3 123 18 WHITE HISPANIC 0.94 2000

6 23 45 1 ASIAN NON-HISPANIC 0.00 2000

6 23 45 1 ASIAN HISPANIC 0.00 2000

6 23 45 1 BLACK NON-HISPANIC 0.00 2000

6 23 45 1 BLACK HISPANIC 0.00 2000

6 23 45 1 NATAMER NON-HISPANIC 1.00 2000

6 23 45 1 NATAMER HISPANIC 1.00 2000

6 23 45 1 WHITE NON-HISPANIC 1.00 2000

6 23 45 1 WHITE HISPANIC 1.00 2000
Appendix J: Population & Other Data in U.S. Setup

November 2012

211

BenMAP User's Manual Appendices

SourceCol SourceRow TargetCol TargetRow Race Ethnicity Value Year

6 23 45 2 ASIAN NON-HISPANIC 1.00 2000

6 23 45 2 ASIAN HISPANIC 1.00 2000

6 23 45 2 BLACK NON-HISPANIC 1.00 2000

6 23 45 2 BLACK HISPANIC 1.00 2000

6 23 45 2 NATAMER NON-HISPANIC 1.00 2000

6 23 45 2 NATAMER HISPANIC 1.00 2000

6 23 45 2 WHITE NON-HISPANIC 1.00 2000

6 23 45 2 WHITE HISPANIC 1.00 2000

J.2 Monitor Data in U.S. Setup

BenMAP-ready data files were created from 2000 through 2007 data, as reported to the U.S.

Environmental Protection Agency’s (EPA) Air Quality System (AQS), for PM2.5, PM10STP and

LC, lead TSP, ozone, NO2, SO2, and CO. Table J-6 summarizes the data sources and vintage

of the processed data.

Table J-6. Underlying Data Sources for BenMAP Air Quality Data Files
Appendix J: Population & Other Data in U.S. Setup

November 2012

212

BenMAP User's Manual Appendices

The AQS data were uploaded to the STI Air Quality Archive (AQA) Oracle database. The

AQA database performs additional quality control(QC) checks against the AQS data, such as

uniqueness by AQS site, method, parameter occurrence code (POC), and duration codes;

checks of minimum and maximum values; and maximum rate of change between consecutive data

values (where appropriate). The specific QC checks imposed on the BenMAP data are outlined

in Table J-7. No maximum value filters were applied to the concentration data. High aerosol

concentration values caused by dust storms or other exceptional events are included in the

BenMAP-ready data files.

Table J-7. Pollutant-Specific QC Checks Performed in AQA

J.2.1 Data Processing

STI developed data processing procedures consistent with those used in the past by Abt

Associates to create air quality data for files for use in the BenMAP model. Critical data

processing rules implemented in the deliverable data are listed below:

Data delivered by STI are reported with consistent units: μg/m3for aerosols; ppb for ozone,

NO2, and SO2; and ppm for CO.

The “monitor name” field is populated by concatenating the AQS site, parameter, and POC

codes.

The “monitor description” field is populated with the following metadata: method code, land

use, location setting, POC, and AQS parameter code. The AQS probe location and

monitoring objective code fields are left blank in STI-processed data.
Appendix J: Population & Other Data in U.S. Setup

November 2012

213

BenMAP User's Manual Appendices

The data were formatted with one record per site, pollutant, POC, and year for use in the

BenMAP program. Data for 365 days, or 8,760 hourly values, are expected per record. This

format is satisfied regardless of leap years; an average of February 28 and 29 data are

reported.

The monitoring method is allowed to change over the course of a year. To provide a more

complete record, data with multiple method codes for a given site, parameter, POC, and year

were combined and the first reported method code was reported in the BenMAP-ready data

files.

Aerosol data collected with 24-hr sample durations were used before data collected with

underlying 1-hr sample durations.One-hour sampling duration data are used for ozone, NO2,

SO2, and CO.

J.2.2 Output Files

Table J-8 lists the number of monitors by pollutant and year, represented in the resulting

BenMAP-ready data files.

Table J-8. Number of Monitors by Pollutant, AQS Parameter Code, and Year Included

in the BenMAP-Ready Data Files
Appendix K: Uncertainty & Pooling

November 2012

214

BenMAP User's Manual Appendices

Appendix K: Uncertainty & Pooling

This Appendix discusses the treatment of uncertainty in BenMAP, both for incidence changes and

associated dollar benefits. Some background is then given on pooling methodology. Finally, the

mechanics of the various Pooling Methods available in BenMAP are discussed in detail, including

Subjective Weight based pooling, Fixed Effects pooling, Random / Fixed Effects pooling, and

independent and dependent Sum and Subtraction.

K.1 Uncertainty

Although there are severalsources of uncertainty affecting estimates of incidence changes and

associated benefits, the sources of uncertainty that are most readily quantifiable in benefit analyses

are uncertainty surrounding the health impact functions and uncertainty surrounding unit dollar

values. The total dollar benefit associated with a given endpoint group depends on how much the

endpoint group will change in the controlscenario (e.g., how many premature deaths will be

avoided) and how much each unit of change is worth (e.g., how much a statistical death avoided

is worth).

Both the uncertainty about the incidence changes and uncertainty about unit dollar values can be

characterized by distributions. Each “uncertainty distribution” characterizes our beliefs about

what the true value of an unknown (e.g., the true change in incidence of a given health effect) is

likely to be, based on the available information from relevant studies. Although such an

“uncertainty distribution” is not formally a Bayesian posterior distribution, it is very similar in

concept and function (see, for example, the discussion of the Bayesian approach in Kennedy

1990, pp. 168-172). Unlike a sampling distribution (which describes the possible values that an

estimator of an unknown value might take on), this uncertainty distribution describes our beliefs

about what values the unknown value itself might be.

Such uncertainty distributions can be constructed for each underlying unknown (such as a

particular pollutant coefficient for a particular location) or for a function ofseveral underlying

unknowns (such as the total dollar benefit of a regulation). In either case, an uncertainty

distribution is a characterization of our beliefs about what the unknown (or the function of

unknowns) is likely to be, based on all the available relevant information. Uncertainty statements

based on such distributions are typically expressed as 90 percent credible intervals. This is the

intervalfrom the fifth percentile point of the uncertainty distribution to the ninety-fifth percentile

point. The 90 percent credible interval is a “credible range” within which, according to the

available information (embodied in the uncertainty distribution of possible values), we believe the

true value to lie with 90 percent probability. The uncertainty surrounding both incidence

estimates and dollar benefits estimates can be characterized quantitatively in BenMAP. Each is

described separately below.

K.1.1 Characterization of Uncertainty Surrounding Incidence Changes

To calculate point estimates of the changes in incidence of a given adverse health effect

associated with a given set of air quality changes, BenMAP performs a series of calculations at
Appendix K: Uncertainty & Pooling

November 2012

215

BenMAP User's Manual Appendices

each grid-cell. First, it accesses the health impact functions needed for the analysis, and then it

accesses any data needed by the health impact functions. Typically, these include the grid-cell

population, the change in population exposure at the grid-cell, and the appropriate baseline

incidence rate. BenMAP then calculates the change in incidence of adverse health effects for

each selected health impact function. The resulting incidence change is stored, and BenMAP

proceeds to the next grid-cell, where the above process is repeated.

In Latin Hypercube mode, BenMAP reflects the uncertainty surrounding estimated incidence

changes (resulting from the sampling uncertainty surrounding the pollutant coefficients in the health

impact functions used) by producing a distribution of possible incidence changes rather than a

single point estimate. To do this, it uses the distribution (Dist Beta) associated with the pollutant

coefficient (Beta, or β), and potentially the point estimate (Beta) and two parameters (P1Beta,

P2Beta). Typically, pollutant coefficients are normally distributed, with mean Beta and standard

deviation P1Beta.

BenMAP uses an N-point Latin Hypercube to represent the underlying distribution of β and to

create a corresponding distribution of incidence changes in each population grid cell, where N is

specified by you. The Latin Hypercube method represents an underlying distribution by N

percentile points of the distribution, where the n

th percentile point is equal to:

The Latin Hypercube method is used to enhance computer processing efficiency. It is a sampling

method that divides a probability distribution into intervals of equal probability, with an

assumption value for each interval assigned according to the interval’s probability distribution.

Compared with conventional Monte Carlo sampling, the Latin Hypercube approach is more

precise over a fewer number of trials because the distribution is sampled in a more even,

consistent manner (Decisioneering, 1996, pp. 104-105).

Suppose, for example, that you elect to use a 20-point Latin Hypercube. BenMAP would then

represent the distribution of β by 20 percentile points, specifically the 2.5

th

, 7.5

th

, ..., 97.5

th

. To

do this, the inverse cumulative distribution function specified by the distribution of β is called with

the input probability equal to each the 20 percentile points. BenMAP then generates an estimate

of the incidence change in a grid-cellfor each of these values of β, resulting in a distribution of N

incidence changes. This distribution is stored, and BenMAP proceeds to the next population

grid-cell, where the process is repeated.

K.1.2 Characterization of Uncertainty Surrounding Dollar Benefits

The uncertainty distribution of the dollar benefits associated with a given health or welfare effect is

derived from the two underlying uncertainty distributions – the distribution of the change in

incidence of the effect (number of cases avoided) and the distribution of the value of a case

avoided (the “unit value”). The derivation of the uncertainty distribution for incidence change is
Appendix K: Uncertainty & Pooling

November 2012

216

BenMAP User's Manual Appendices

described above. The distributions used to characterize the uncertainty surrounding unit values

are described in detail in the appendix on the Economic Value of Health Effects. As noted in that

Appendix, a variety of distributions have been used to characterize the uncertainty of unit values,

including uniform, triangular, normal, and Weibull.

To represent the underlying distribution of uncertainty surrounding unit values, a 100-point Latin

Hypercube is generated in the same way described in the previous section for the distribution of β

. That is, the unit value distribution is represented using the 0.5

th

, 1.5

th

, ..., and 99.5

th percentile

values of its distribution.

A distribution of the uncertainty surrounding the dollar benefits associated with a given endpoint is

then derived from Latin Hypercube values generated to represent the change in incidence and the

Latin Hypercube values generated to represent the unit value distribution. To derive this new

distribution, each of the 100 unit values is multiplied by each of the N incidence change values,

yielding a set of 100 * N dollar benefits. These values are sorted low to high and binned down

to a final distribution of N dollar benefit values.

K.1.3 Characterization of Uncertainty Surrounding QALY Estimates

The uncertainty distribution of the QALY estimates associated with a given health effect is similar

to that for dollar benefits. That is, it is derived from the two underlying uncertainty distributions –

the distribution of the change in incidence of the effect (number of cases avoided) and the

distribution of the QALYs per case avoided. The derivation of the uncertainty distribution for

incidence change is described above. The distributions used to characterize the uncertainty

surrounding QALYs are described in detail in the appendix on the Economic Value of Health

Effects. As noted in that Appendix, a variety of distributions have been used to characterize the

uncertainty of unit values, including uniform, triangular, normal, and Weibull.

To represent the underlying distribution of uncertainty surrounding unit values, a 100-point Latin

Hypercube is generated in the same way that was described in the previous section for the

distribution of β. That is, the unit value distribution is represented using the 0.5

th

, 1.5

th

, ..., and

99.5

th percentile values of its distribution.

A distribution of the uncertainty surrounding the QALYs associated with a given endpoint is then

derived from Latin Hypercube values generated to represent the change in incidence and the

Latin Hypercube values generated to represent the QALY distribution. To derive this new

distribution, each of the 100 QALY weights is multiplied by each of the N incidence change

values. These values are sorted low to high and binned down to a final distribution of QALY

values.

K.2 Pooling

There is often more than one study that has estimated a health impact function for a given

pollutant-health endpoint combination. Each study provides an estimate of the pollutant

coefficient, β, along with a measure of the uncertainty of the estimate. Because uncertainty
Appendix K: Uncertainty & Pooling

November 2012

217

BenMAP User's Manual Appendices

decreases as sample size increases, combining data sets is expected to yield more reliable

estimates of β, and therefore more reliable estimates of the incidence change predicted using β.

Combining data from several comparable studies in order to analyze them together is often

referred to as meta-analysis.

For a number of reasons, including data confidentiality, it is often impractical or impossible to

combine the original data sets. Combining the results ofstudies in order to produce better

estimates of β provides a second-best but still valuable way to synthesize information . This is

referred to as pooling. Pooling β’s requires that all of the studies contributing estimates of β use

the same functionalform for the health impact function. That is, the β’s must be measuring the

same thing.

It is also possible to pool the study-specific estimates of incidence change derived from the health

impact functions, instead of pooling the underlying β’s themselves. For a variety of reasons, this

is often possible when it is not feasible to pool the underlying β’s. For example, if one study is

log-linear and another is linear, we could not pool the β’s because they are not different estimates

of a coefficient in the same health impact function, but are instead estimates of coefficients in

different health impact functions. We can, however, calculate the incidence change predicted by

each health impact function (for a given change in pollutant concentration and, for the log-linear

function, a given baseline incidence rate), and pool these incidence changes. BenMAP allows the

pooling of incidence changes predicted by severalstudies for the same pollutant-health endpoint

group combination. It also allows the pooling of the corresponding study-specific estimates of

monetary benefits.

As with estimates based on only a single study, BenMAP allows you to characterize the

uncertainty surrounding pooled estimates of incidence change and/or monetary benefit. To do

this, BenMAP pools the study-specific distributions of incidence changes (or monetary benefit or

QALYs) to derive a pooled distribution. This pooled distribution incorporates information from

all the studies used in the pooling procedure.

K.2.1 Weights Used for Pooling

The relative contribution of any one study in the pooling process depends on the weight assigned

to that study. A key component of the pooling process, then, is the determination of the weight

given to each study. There are various methods that can be used to assign weights to studies.

Below we discuss the possible weighting schemes that are available in BenMAP.

K.2.1.1 Subjective Weights

BenMAP allows you the option ofspecifying the weights to be used. Suppose, for example, you

want to simply average allstudy-specific results. You would then assign a weight of 1/N to each

of the N study-specific distributions that are to be pooled. Note that subjective weights are

limited to two decimal places, and are normalized to sum to one, if they do not already sum to

one.
Appendix K: Uncertainty & Pooling

November 2012

218

BenMAP User's Manual Appendices

K.2.1.2 Automatically Generated Weights

A simple average has the advantage ofsimplicity but the disadvantage of not taking into account

the uncertainty of each of the estimates. Estimates with great uncertainty surrounding them are

given the same weight as estimates with very little uncertainty. A common method for weighting

estimates involves using their variances. Variance takes into account both the consistency of data

and the sample size used to obtain the estimate, two key factors that influence the reliability of

results. BenMAP has two methods of automatically generating pooling weights using the

variances of the input distributions - Fixed Ef ects Pooling and Random / Fixed Ef ects

Pooling.

The discussion of these two weighting schemes is first presented in terms of pooling the pollutant

coefficients (the β’s), because that most closely matches the discussion of the method for pooling

study results as it was originally presented by DerSimonian and Laird. We then give an overview

of the analogous weighting process used within BenMAP to generate weights for incidence

changes rather than β’s.

K.2.1.3 Fixed-Effect Weights

The fixed effects model assumes that there is a single true concentration-response relationship

and therefore a single true value for the parameter β that applies everywhere. Differences among

β’s reported by different studies are therefore simply the result ofsampling error. That is, each

reported β is an estimate of the same underlying parameter. The certainty of an estimate is

reflected in its variance (the larger the variance, the less certain the estimate). Fixed effects

pooling therefore weights each estimate under consideration in proportion to the inverse of its

variance.

Suppose there are n studies, with the ith study providing an estimate βi with variance vi(I = 1, ...,

n). Let

denote the sum of the inverse variances. Then the weight, wi, given to the ith estimate, βi, is:

This means that estimates with small variances (i.e., estimates with relatively little uncertainty

surrounding them) receive large weights, and those with large variances receive small weights.

The estimate produced by pooling based on a fixed effects model, then, is just a weighted

average of the estimates from the studies being considered, with the weights as defined above.
Appendix K: Uncertainty & Pooling

November 2012

219

BenMAP User's Manual Appendices

That is:

The variance associated with this pooled estimate is the inverse of the sum of the inverse

variances:

Table K-1 shows the relevant calculations for this pooling for three sample studies.

Table K-1. Example of Fixed Effects Model Calculations

Study βi vi 1/vi wi wi*βi

1 0.75 0.1225 8.16 0.016 0.012

2 1.25 0.0025 400 0.787 0.984

3 1.00 0.0100 100 0.197 0.197

Sum ? = 508.16 ? = 1.000 ? = 1.193

The sum of weighted contributions in the last column is the pooled estimate of β based on the

fixed effects model. This estimate (1.193) is considerably closer to the estimate from study 2

(1.25) than is the estimate (1.0) that simply averages the study estimates. This reflects the fact

that the estimate from study 2 has a much smaller variance than the estimates from the other two

studies and is therefore more heavily weighted in the pooling.

The variance of the pooled estimate, v

fe

, is the inverse of the sum of the variances, or 0.00197.

(The sums of the βi and vi are not shown, since they are of no importance. The sum of the 1/v

i

is S, used to calculate the weights. The sum of the weights, wi, i=1, ..., n, is 1.0, as expected.)

K.2.1.4 Random- / Fixed-Effect Weights

An alternative to the fixed effects model is the random effects model, which allows the possibility

that the estimates βifrom the different studies may in fact be estimates of different parameters,

rather than just different estimates of a single underlying parameter. In studies of the effects of

PM10 on mortality, for example, if the composition of PM10 varies among study locations the

underlying relationship between mortality and PM10 may be different from one study location to
Appendix K: Uncertainty & Pooling

November 2012

220

BenMAP User's Manual Appendices

another. For example, fine particles make up a greater fraction of PM10in Philadelphia than in El

Paso. If fine particles are disproportionately responsible for mortality relative to coarse particles,

then one would expect the true value of β in Philadelphia to be greater than the true value of β in El

Paso. This would violate the assumption of the fixed effects model.

The following procedure can test whether it is appropriate to base the pooling on the random

effects model (vs. the fixed effects model):

A test statistic, Qw, the weighted sum of squared differences of the separate study estimates from

the pooled estimate based on the fixed effects model, is calculated as:

Under the null hypothesis that there is a single underlying parameter, β, of which all the βi’s are

estimates, Qw has a chi-squared distribution with n-1 degrees of freedom. (Recall that n is the

number of studies in the meta-analysis.) If Qwis greater than the critical value corresponding to

the desired confidence level, the null hypothesis is rejected. That is, in this case the evidence does

not support the fixed effects model, and the random effects model is assumed, allowing the

possibility that each study is estimating a different β. (BenMAP uses a five percent one-tailed

test).

The weights used in a pooling based on the random effects model must take into account not only

the within-study variances (used in a meta-analysis based on the fixed effects model) but the

between-study variance as well. These weights are calculated as follows:

Using Qw, the between-study variance, η

2

, is:

It can be shown that the denominator is always positive. Therefore, if the numerator is negative (i.

e., if Qw < n-1), then η

2

is a negative number, and it is not possible to calculate a random effects

estimate. In this case, however, the small value of Qw would presumably have led to accepting the

null hypothesis described above, and the meta-analysis would be based on the fixed effects model.

The remaining discussion therefore assumes that η

2

is positive.

Given a value for η

2

, the random effects estimate is calculated in almost the same way as the fixed

effects estimate. However, the weights now incorporate both the within-study variance (vi) and

the between-study variance ( η

2

). Whereas the weights implied by the fixed effects model used

only vi, the within-study variance, the weights implied by the random effects model use vi + η

2

.
Appendix K: Uncertainty & Pooling

November 2012

221

BenMAP User's Manual Appendices

Let vi* = vi + η

2

. Then:

The estimate produced by pooling based on the random effects model, then, is just a weighted

average of the estimates from the studies being considered, with the weights as defined above.

That is:

The variance associated with this random effects pooled estimate is, as it was for the fixed effects

pooled estimate, the inverse of the sum of the inverse variances:

The weighting scheme used in a pooling based on the random effects model is basically the same as

that used if a fixed effects model is assumed, but the variances used in the calculations are

different. This is because a fixed effects model assumes that the variability among the estimates

from different studies is due only to sampling error (i.e., each study is thought of as representing just

another sample from the same underlying population), while the random effects model assumes that

there is not only sampling error associated with each study, but that there is also between-study

variability -- each study is estimating a different underlying β. Therefore, the sum of the withinstudy variance and the between-study variance yields an overall variance estimate.

Fixed Effects and Random / Fixed Effects Weighting to Pool Incidence Change

Distributions and Dollar Benefit Distributions

Weights can be derived for pooling incidence changes predicted by different studies, using either

the fixed effects or the fixed / random effects model, in a way that is analogous to the derivation

of weights for pooling the β’s in the C-R functions. As described above, BenMAP generates a

Latin Hypercube representation of the distribution of incidence change corresponding to each

health impact function selected. The means of those study-specific Latin Hypercube distributions
Appendix K: Uncertainty & Pooling

November 2012

222

BenMAP User's Manual Appendices

of incidence change are used in exactly the same way as the reported β’s are used in the

calculation of fixed effects and random effects weights described above. The variances of

incidence change are used in the same way as the variances of the β’s. The formulas above for

calculating fixed effects weights, for testing the fixed effects hypothesis, and for calculating

random effects weights can all be used by substituting the mean incidence change for the ith

health impact function for βiand the variance of incidence change for the ith health impact

function for v

i

.

Similarly, weights can be derived for dollar benefit distributions. As described above, BenMAP

generates a Latin Hypercube representation of the distribution of dollar benefits. The means of

those Latin Hypercube distributions are used in exactly the same way as the reported β’s are

used in the calculation of fixed effects and random effects weights described above. The

variances of dollar benefits are used in the same way as the variances of the β’s. The formulas

above for calculating fixed effects weights, for testing the fixed effects hypothesis, and for

calculating random effects weights can all be used by substituting the mean dollar benefit change

for the ith valuation for βiand the variance of dollar benefits for the ith valuation for v

i

.

BenMAP always derives Fixed Effects and Random / Fixed Effects weights using nationally

aggregated results, and uses those weights for pooling at each grid cell(or county, etc. if you

choose to aggregate results prior to pooling). This is done because BenMAP does not include

any regionally based uncertainty – that is, all uncertainty is at the national level in BenMAP, and

allregional differences (population, for example) are treated as certain.

K.2.2 Mechanics of Pooling in BenMAP

Once weights are generated for each input distribution, BenMAP has three options for using

these weights to combine the input distributions into a single new distribution. These options are

referred to as Advanced Pooling Methods.

Round Weights to Two Digits

This is BenMAP’s default Advanced Pooling Method, and is always the method used when

Subjective Weights are used. The first step is converting the weights to two digit integers by

multiplying them by 100 and rounding to the nearest integer. If all the integral weights thus

generated are divisible by the smallest weight, they are each divided by that smallest weight. For

example, if the original weights were 0.1, 0.2, 0.3, and 0.4, the resulting integral weights would

be 10/10, 20/10, 30/10, and 40/10 (or 1, 2, 3, and 4).

BenMAP then creates a new distribution by sampling each entire input distribution according to

its weight. That is, in the above example the first distribution would be sampled once, the second

distribution twice, and so forth. The advantage ofsampling whole distributions is that it preserves

the characteristics (i.e., the moments - the mean, the variance, etc.) of the underlying distributions.

Assuming n latin hypercube points, the resulting distribution will contain a maximum of 100 * n

values, which are then sorted low to high and binned down to n values, which willrepresent the

new, pooled distribution.
Appendix K: Uncertainty & Pooling

November 2012

223

BenMAP User's Manual Appendices

Round Weights to Three Digits

This Advanced Pooling Method is essentially the same as rounding weights to two digits, except

that the weights are converted to three digit integers, and so forth. That is, the weights are

multiplied by 1000 and rounded to the nearest integer. Again, if all the integral weights thus

generated are divisible by the smallest weight, they are each divided by that smallest weight.

Assuming n Latin Hypercube points, the resulting distribution with this Advanced Pooling

Method can contain a maximum of 1000 * n values, which are sorted low to high and binned

down to n values, which represent the new, pooled distribution.

Exact Weights for Monte Carlo

This Advanced Pooling Method uses a Monte Carlo method to combine the input distributions.

Using this method, on each of many iterations, (1) an input distribution is selected (with the

probability ofselection equal to the weight assigned to the distribution), and (2) a value is

randomly drawn from that distribution. Values chosen in this way are placed into a temporary

pooled distribution, which will have one point per iteration of the Monte Carlo method. The

number of iterations is specified by the user, and defaults to 5,000. After the temporary

distribution is fully generated, it is sorted low to high and binned down to n values (where n is the

number of Latin Hypercube Points chosen for the analysis).

K.2.3 Summing Distributions

Sometimes rather than pooling distributions we want to add them. For example, some studies

have estimated a health impact function for hospital admissions for COPD and another health

impact function for hospital admissions for pneumonia. From each of these health impact

functions, BenMAP can derive the corresponding distributions for incidence change. Hospital

admissions for COPD and pneumonia are two of the most important components of respiratory

hospital admissions, and we may want to estimate the number of cases of “respiratory hospital

admissions,” as characterized by being either COPD or pneumonia. To do this we would add

the two distributions.

Summing across distributions can be done in one of two ways: We can assume the two

distributions are independent of each other or dependent. Which is the more reasonable

assumption depends on the particulars of the distributions being summed.

Assuming Independence

This is the Sum (Independent) Pooling Method. To sum two distributions that are independent,

on each of many iterations of a Monte Carlo procedure, BenMAP (1) randomly selects a value

from the first input distribution, (2) randomly selects a value from the second input distribution,

and (3) adds the two values together. To sum N distributions that are independent, BenMAP

follows an analogous procedure in which, on each iteration it makes a random selection from

each of the input distributions and then adds the results together. When the Monte Carlo

procedure is completed, allsuch generated results are sorted low to high and binned down to the
Appendix K: Uncertainty & Pooling

November 2012

224

BenMAP User's Manual Appendices

appropriate number of latin hypercube points. The number of iterations is determined by the

Monte Carlo Iterations setting.

Assuming Dependence

This is the Sum (Dependent) Pooling Method. Recall that the uncertainty distributions in

BenMAP are latin hypercube representations, consisting of N percentile points. To sum two

distributions assumed to be dependent, BenMAP simply generates a new N point latin hypercube

where each point is the sum of the corresponding points from the input latin hypercubes. That is,

the first point in the new latin hypercube is the sum of the first points in the two input latin

hypercubes, and so forth. To sum n distributions that are assumed to be dependent, BenMAP

follows an analogous procedure in which each point in the new latin hypercube is the sum of the

corresponding points from each of the input latin hypercubes.

K.2.4 Subtracting Distributions

In some cases, you may want to subtract one or more distribution(s) from another. For example,

one study may have estimated a health impact function for minor restricted activity days

(MRADs), and another study may have estimated a health impact function for asthma “episodes.”

You may want to subtract the change in incidence of asthma episodes from the change in

incidence from MRADs before estimating the monetary value of the MRADs, so that the

monetary value of asthma episodes avoided will not be included.

Subtracting across distributions can be done in one of two ways: we can assume the two

distributions are independent of each other or dependent. Which is the more reasonable

assumption depends on the particulars of the distributions being subtracted.

Assuming Independence

This is the Subtraction (Independent) Pooling Method. To subtract one distribution from

another, assuming independence, on each of many iterations of a Monte Carlo procedure,

BenMAP (1) randomly selects a value from the first input distribution, (2) randomly selects a

value from the second input distribution, and (3) subtracts the second value from the first. To

subtract N distributions from another distribution, assuming independence, BenMAP follows an

analogous procedure in which, on each iteration it makes a random selection from each of the

input distributions and then subtracts the second through the Nth from the first. When the Monte

Carlo procedure is completed, allsuch generated results are sorted low to high and binned down

to the appropriate number of Latin Hypercube points. The number of iterations is determined by

the Monte Carlo Iterations setting.

Assuming Dependence

This is the Subtraction (Dependent) Pooling Method (see Chapter 6 for details). Recall that the

uncertainty distributions in BenMAP are Latin Hypercube representations, consisting of N

percentile points. To subtract one distribution from another, assuming them to be dependent,

BenMAP simply generates a new N point Latin Hypercube where each point is the result of
Appendix K: Uncertainty & Pooling

November 2012

225

BenMAP User's Manual Appendices

subtracting the corresponding point of the second input Latin Hypercube from the corresponding

point of the first input Latin Hypercube. That is, the first point in the new Latin Hypercube is the

result ofsubtracting the first point in the second Latin Hypercube from the first point of the first

Latin Hypercube, and so forth. To subtract n distributions from another distribution, assuming

dependence, BenMAP follows an analogous procedure in which each point in the new Latin

Hypercube is the result ofsubtracting the corresponding points of the second through the Nth

input Latin Hypercubes from the corresponding point of the first.
Appendix L: Command Line BenMAP

November 2012

226

BenMAP User's Manual Appendices

Appendix L: Command Line BenMAP

The command line version of BenMAP is capable of performing all of the functions of the GUIbased version. It is most usefulfor large, complex analyses that require generation of a substantial

number of files. This appendix describes the syntax and use of the command line version.

L.1 Overview

The overallformat of the file is a variable definitions section followed by a commands section.

Comment statements are supported at any point in the file. Lines beginning with a pound

character (#) are considered comment lines and will be ignored during file parsing.

Additionally, LOAD <filename> statements are supported at any point in the file. These work as

string replacements – the contents of the file specified by <filename> are simply inserted into the

main file. Multi-level LOAD statements are supported, but no attempt is made to detect cycles

(two files referencing each other with LOAD statements, for example).

The controlfile is, in general, not case sensitive. In the case of user-defined strings, (variable

values, etc.), it is preserved.

L.2 Variables

The variable definitions section is optional, and if present will consist of a single line with the word

“Variables” on it, followed by one or more lines that define variables. A variable definition

consists of a variable name and a variable value. When parsing lines in the commands section of

the controlfile, all occurrences of the variable name will be replaced by the variable value.

All variable names must begin and end with the percent character (%).

Variable Name/Value replacement will be done in multiple passes (until no variable names

remain), so variable values may contain other variable names. No attempt will be made to detect

cycles, however, so be careful not to introduce them. For example, avoid variable definitions like

the following:

%BENMAPDIR% %AQGDIR%\

%AQGDIR% %BENMAPDIR%\Air Quality Grids

Variable values must be contained in a single line, and will consist of the first non-whitespace

character after the variable name through the newline character. Watch out for undesired trailing

whitespaces!
Appendix L: Command Line BenMAP

November 2012

227

BenMAP User's Manual Appendices

L.3 Commands

The commands section is required, and will consist of one or more command sections. There are

five types of command sections:

SETACTIVESETUP

CREATE AQG

RUN CFG

RUN APV

GENERATE REPORT

This section will discuss each one in turn.

In general, in command sections, there must be at least one white space between each token

(where a token is either a command, a parameter name, or a parameter value). Additional white

space is ignored, including newline characters. To include white space in a parameter value, you

must enclose the parameter in double quotes. The double quotes will not be included in the

parameter value in this case (If you wish to include beginning and trailing double quotes in a

parameter value, put two in a row at the beginning and end – e.g. ““Look at all those double

quotes.””).

L.3.1 Set Active Setup

For the US version of the BenMAP command line executable the only valid value is United

States. The SETACTIVESETUP section is required.

Example

-ActiveSetup “United States”

L.3.2 Create AQG

This section initiates the creation of one or more air quality grids (normally one, potentially two in

the case of monitor rollback grid creation – see below). It always starts with the words

CREATE AQG. It must then include the following options, in any order:

-Filename <filename>

-Gridtype <gridtype>

-Pollutant <pollutant>
Appendix L: Command Line BenMAP

November 2012

228

BenMAP User's Manual Appendices

The Filename value is the name of the air quality grid that will be created.

The GridType value must be one found in the BenMAP database. The actual values for this

parameter are found on the Modify Setup screen in the Grid Definitions list box.

Supported Pollutant values are:

-Ozone

-PM10

-PM2.5

These values are also found on the Modify Setup screen in the Pollutants list box.

After these required options, the type of grid creation must be identified, and then the parameters

for that grid creation type must be specified. There are four air quality grid creation types:

-ModelDirect

-MonitorDirect

-MonitorModelRelative

-MonitorRollback

L.3.2.1 Model Direct

This section initiates the creation of a model direct air quality grid.

This creation type has two required parameters:

-ModelFilename <filename>

-DSNName <ODBC DSN name>

and one optional parameter:

-TableName <tablename>

Supported DSNName values are:

“Excel Files” Excel Spreadsheet (.xls)

“Text Files” Comma-delimited (.csv) files

“MS Access Database” Access Database (.mdb)

If the DSNName is “Excel Files” and there is more than one worksheet in the workbook or “MS

Access Database” and there is more than one table in the database then the TableName

parameter must indicate the worksheet or table name.
Appendix L: Command Line BenMAP

November 2012

229

BenMAP User's Manual Appendices

L.3.2.2 Monitor Direct

This section initiates the creation of a monitor direct air quality grid.

The required parameters are:

-MonitorDataType <DataSource descriptor>

-InterpolationMethod <Interpolation Method>

Valid values for MonitorDataType are:

-Library

-DatabaseRows

-DatabaseColumns

-TextFile

Valid values for Interpolation method are:

-ClosestMonitor

-VNA

If MonitorDataType is Library then the following parameters are required:

-MonitorDataSet <Monitor Dataset Name>

MonitorDataSet is the Dataset name of Monitor data stored in the BenMAP

database. These values can be found on the Modify Setup screen in Monitor

Datasets list box.

-MonitorYear <Year>

MonitorYear specifies the year of interest in the monitor library.

If MonitorDataType is DatabaseRows then the following parameters are required:

-MonitorFile <filename>

-DSNName <ODBC DSN name>

and one optional parameter:

-TableName <tablename>

Supported DSNName values are:

-“Excel Files” Excel Spreadsheet (.xls)

-“Text Files” Comma-delimited (.csv) files

-“MS Access Database” Access Database (.mdb)
Appendix L: Command Line BenMAP

November 2012

230

BenMAP User's Manual Appendices

If the DSNName is “Excel Files” and there is more than one worksheet in the workbook or “MS

Access Database” and there is more than one table in the database then the TableName

parameter must indicate the worksheet or table name.

If MonitorDataType is DatabaseColumns then the same parameters for MonitorDataType

DatabaseRows are required along with the following:

-MonitorDefFilename

-DefDSNName

-DefTableName

These parameters behave the same as the corresponding DatabaseRows parameters.

If MonitorDataType is TextFile the following parameter is required:

-MonitorFile <filename>

MonitorFile specifies a comma separated values (*.csv, generally) file containing

monitor data.

Optional Parameters:

-MaxDistance <real>

Specifies the maximum distance (in kilometers) to be used in ClosestMonitor

interpolation or VNA interpolation. Monitors outside this distance will not be

considered in the interpolation procedure.

-MaxRelativeDistance <real>

Specifies the maximum relative distance to be used in VNA interpolation, where

relative distance is the multiple of the distance to the closest monitor used in the

interpolation procedure.

-WeightingMethod <method>

Specifies the weighting procedure used for monitors in VNA interpolation.

Supported values are InverseDistance and InverseDistanceSquared. If this

parameter is not specified, InverseDistance weighting is used.

L.3.2.3 Monitor Model Relative

This section initiates the creation of a monitor modelrelative air quality grid. This creation type

has all the same required and optional parameters as the MonitorDirect creation type. In

addition, it has two/three new required parameters.

Required Parameters:
Appendix L: Command Line BenMAP

November 2012

231

BenMAP User's Manual Appendices

-ScalingMethod <scaling method>

Supported scaling methods are Spatial, Temporal, and Both.

-BaseYearFilename <filename>

Specifies the base year adjustment file to use in monitor scaling.

-BaseYearDSNName <ODBC DSN Name>

Supported –BaseYearDSNName values are

“Excel Files” Excel Spreadsheet (.xls)

“Text Files” Comma-delimited (.csv) files

“MS Access Database” Access Database (.mdb)

When the ScalingMethod is Temporal or Both, the FutureYearFileName and

FutureYearDSNName parameters are required. These specify the future year adjustment file to

use in monitor scaling.

L.3.2.4 Monitor Rollback

// MonitorRollback

SpatialScaling = '-SpatialScaling';

BaselineFilename = '-BaselineFilename';

// RollbackOptions

Percentage = '-Percentage';

Increment = '-Increment';

// RollbackToStandardOptions

Standard = '-Standard';

Metric = '-Metric';

Ordinality = '-Ordinality';

InterdayRollbackMethod = '-InterdayRollbackMethod';

IntradayRollbackMethod = '-IntradayRollbackMethod';
Appendix L: Command Line BenMAP

November 2012

232

BenMAP User's Manual Appendices

L.3.3 Run CFG

The command line version of BenMAP does not support creation of new .cfg files, both because

this would be quite cumbersome to do in plain text, and because it probably is not needed. Slight

modifications of existing .cfg files are supported, and it is thought that at this point this should be

enough.

As such, the only required parameter to run a configuration is the configuration filename.

Optional parameters allow the slight modifications mentioned above.

Required Parameters

-CFGFilename <filename>

Specifies the .cfg file to run.

-ResultsFilename <filename>

Specifies the .cfgr file to save the results in.

Optional Parameters

-BaselineAQG <filename>

Specifies the baseline air quality grid file to use when running the configuration –

overrides whatever value is present in the .cfg file.

-ControlAQG <filename>

Specifies the control air quality grid file to use when running the configuration –

overrides whatever value is present in the .cfg file.

-Year <Integer>

Year in which to run the configuration (this will affect the population numbers used) –

overrides whatever value is present in the .cfg file. Supported values are 1990 and up.

-LatinHypercubePoints <integer>

Number of latin hypercube points to generate when running the configuration (zero

means run in point mode), overrides whatever value is present in the .cfg file.

-Threshold <real>

Threshold to use when running the configuration – overrides whatever value is present

in the .cfg file.

L.3.4 Run APV

The command line version of BenMAP does not support creation of new .apv files, both because

this would be quite cumbersome to do in plain text, and because it probably is not needed. Slight

modifications of existing .apv files are supported, and it is thought that at this point this should be

enough.

As such, the only required parameter to run an APV configuration is the APV configuration

filename. Optional parameters allow the slight modifications mentioned above.
Appendix L: Command Line BenMAP

November 2012

233

BenMAP User's Manual Appendices

Required Parameters

-APVFilename <filename>

Specifies the .apv file to run.

-ResultsFilename <filename>

Specifies the .apvr file to save the results in.

Optional Parameters

-CFGRFilename <filename>

Specifies the .cfgr file to use when running the APV configuration – note that this file

must contain the same set of results which the .cfgr file originally used to

generate the .apv file contained. Overrides whatever value is present in the .apv

file.

-IncidenceAggregation <aggregation level>

Level to aggregate incidence results to before pooling them. Supported values are

None, County, State, and Nation. Overrides whatever value is present in the .apv file.

-ValuationAggregation <aggregation level>

Level to aggregate valuation results to before pooling them. Supported values are

None, County, State, and Nation (though the value must be greater than or equal to

IncidenceAggregation). Overrides whatever value is present in the .apv file.

-RandomSeed <integer>

Random seed to use for all procedures requiring pseudo-random numbers (e.g. monte

carlo procedures). Overrides the default behavior, which is to generate a new random

seed each time the APV configuration is run.

-DollarYear <integer>

Year in which dollar figures should be reported. Supported values are 1980 – 2001.

Overrides whatever value is present in the .apv file.

L.3.5 Generate Report

Reports come in three main varieties – Audit TrailReports, which can be generated from any

BenMAP file; Configuration Results Reports, which can be generated from .cfgr files; and APV

Configuration Results Reports, which can be generated from .apvr files. All these report types

need an input filename and an output filename. CFGR reports and APVR reports additionally

take many optional parameters.

The format for each report type is:

GENERATE REPORT <ReportType>
Appendix L: Command Line BenMAP

November 2012

234

BenMAP User's Manual Appendices

-InputFile <filename>

-ReportFile <filename>

<optional parameters>

Supported ReportType values are: AuditTrail, CFGR, and APVR.

L.3.5.1 Audit Trail

Audit trailreports require only the parameters described in the "Generate Report" section.

L.3.5.2 CFGR Report

A CFGR report may be generating using only the parameters described in the "Generate Report"

section. However, there are also a number of additional options, described below.

Optional Parameters

-GridFields <comma separated field names>

Specifies the set of grid fields to include in the report. Grid fields include Column and

Row. If this parameter is not present, all fields will be included in the report.

-CustomFields <comma separated field names>

Specifies the set of custom fields (C-R Function identifiers, in this case) to include in

the report. If this parameter is not present, all fields will be included in the report.

-ResultFields <comma separated field names>

Specifies the set of result fields to include in the report. Result fields include Point

Estimate, Population, Delta, Mean, Standard Deviation, Variance, and Latin

Hypercube Points. If this parameter is not present, all fields will be included in the

report.

-Grouping <grouping method>

Specifies the grouping for the results – Gridcell, then C-R Function, or C-R Function,

then Gridcell. Supported values are GridcellFirst, GridcellLast. The default value is

GridcellFirst.

-DecimalDigits <integer>

Specifies the number of digits after the decimal point to include in the report.

Supported values are zero to eight. The default value is four.

L.3.5.3 APVR Report

Required Parameters

APVR Reports require one additional parameter beyond those required for Audit Trail or CFGR

Reports.
Appendix L: Command Line BenMAP

November 2012

235

BenMAP User's Manual Appendices

-ResultType <result type>

Specifies the result type for which a report should be created. Supported result

types are: IncidenceResults, AggregatedIncidence, PooledIncidence,Valuation,

AggregatedValuation, PooledValuation, QALYValuation,

AggregatedQALYValuation and PooledQALYValuation.

Optional Parameters

All of the CFGR report parameters are supported for APVR reports as well, except that

Population and Delta are not supported ResultField elements.

-Totals <total type>

Specifies the type of totals which should be included in the report. Supported types

are Dependent and Independent. Totals can only be generated for valuation results

(Valuation, AggregatedValuation, and PooledValuation result types).

L.4 Example 1

VARIABLES

%CFG% C:\BenMAP\CommandLine\Configurations\PM25 Wizard.cfg

%APV% C:\BenMAP\CommandLine\Configurations\PM25 Wizard.apv

%RESULTSDIR% C:\BenMAP\Temp

%REPORTDIR% C:\BenMAP\Temp

%AQG% C:\BenMAP\CommandLine\Air Quality Grids

COMMANDS

SETACTIVESETUP

-ActiveSetup "United States"

CREATE AQG

-Filename %AQG%\PM25_2002Baseline_50km.aqg

-GridType "CMAQ 12km"

-Pollutant PM2.5

MonitorDirect

-InterpolationMethod VNA_Alt

-MonitorDataType Library

-MonitorDataSet "EPA Standard Monitors"

-MonitorYear 2002

-MaxDistance 50

CREATE AQG
Appendix L: Command Line BenMAP

November 2012

236

BenMAP User's Manual Appendices

-Filename %AQG%\PM25_2002Control_50km.aqg

-GridType "CMAQ 12km"

-Pollutant PM2.5

MonitorRollback

-InterpolationMethod VNA_Alt

-MonitorDataType Library

-MonitorDataSet "EPA Standard Monitors"

-MonitorYear 2002

-RollbackGridType State

-MaxDistance 50

RollbackToStandardOptions

-Standard 65

-Metric D24HourMean

-InterdayRollbackMethodQuadratic

RUN CFG

-CFGFilename %CFG%

-ResultsFilename %RESULTSDIR%\PM25_2002_50km.cfgr

-BaselineAQG %AQG%\PM25_2002Baseline_50km.aqg

-ControlAQG %AQG%\PM25_2002Control_50km.aqg

RUN APV

-APVFilename %APV%

-ResultsFilename %RESULTSDIR%\PM25_2002_50km.apvr

-CFGRFilename %RESULTSDIR%\PM25_2002_50km.cfgr

-IncidenceAggregation Nation

-ValuationAggregation Nation

GENERATE REPORT APVR

-InputFile %RESULTSDIR%\PM25_2002_50km.apvr

-ReportFile %REPORTDIR%\PM25_2002_50km_IncidenceNation.csv

-ResultType PooledIncidence

-CustomFields "Endpoint Group,Author,Start Age,Endpoint,Qualifier,Pooling Window"

-ResultFields "Mean,Standard Deviation,Latin Hypercube Points"

-DecimalDigits 0

GENERATE REPORT APVR

-InputFile %RESULTSDIR%\PM25_2002_50km.apvr

-ReportFile %REPORTDIR%\PM25_2002_50km_ValuationNation.csv

-ResultType PooledValuation

-CustomFields "Endpoint Group,Author,Start Age,Endpoint,Qualifier,Pooling Window"

-ResultFields "Mean,Standard Deviation,Latin Hypercube Points"

-DecimalDigits 0
Appendix L: Command Line BenMAP

November 2012

237

BenMAP User's Manual Appendices

L.5 Example 2

VARIABLES

%CFG% C:\BenMAP\CommandLine\Configurations\PM25 Wizard.cfg

%APV% C:\BenMAP\CommandLine\Configurations\PM25 Wizard.apv

%RESULTSDIR% C:\BenMAP\Temp

%REPORTDIR% C:\BenMAP\Temp

%AQG% C:\BenMAP\CommandLine\Air Quality Grids

COMMANDS

SETACTIVESETUP

-ActiveSetup "United States"

CREATE AQG

-Filename %AQG%\PM25_2004Baseline.aqg

-GridType "County"

-Pollutant PM2.5

MonitorDirect

-InterpolationMethod VNA_Alt

-MonitorDataType Library

-MonitorDataSet "EPA Standard Monitors"

-MonitorYear 2004

CREATE AQG

-Filename %AQG%\PM25_2004_Control.aqg

-GridType "County"

-Pollutant PM2.5

MonitorRollback

-InterpolationMethod VNA_Alt

-MonitorDataType Library

-MonitorDataSet "EPA Standard Monitors"

-MonitorYear 2004

-RollbackGridType State

-MaxDistance 50

RollbackToStandardOptions

-Standard 35

-Metric D24HourMean

-InterdayRollbackMethodQuadratic
Appendix L: Command Line BenMAP

November 2012

238

BenMAP User's Manual Appendices

RUN CFG

-CFGFilename %CFG%

-ResultsFilename %RESULTSDIR%\PM25_2004.cfgr

-BaselineAQG %AQG%\PM25_2004Baseline.aqg

-ControlAQG %AQG%\PM25_2004Control.aqg

RUN APV

-APVFilename %APV%

-ResultsFilename %RESULTSDIR%\PM25_2004.apvr

-CFGRFilename %RESULTSDIR%\PM25_2004.cfgr

-IncidenceAggregation Nation

-ValuationAggregation Nation

GENERATE REPORT APVR

-InputFile %RESULTSDIR%\PM25_2004.apvr

-ReportFile %REPORTDIR%\PM25_2004_IncidenceNation.csv

-ResultType PooledIncidence

-CustomFields "Endpoint Group,Author,Start Age,Endpoint,Qualifier,Pooling Window"

-ResultFields "Mean,Standard Deviation,Latin Hypercube Points"

-DecimalDigits 0

GENERATE REPORT APVR

-InputFile %RESULTSDIR%\PM25_2004.apvr

-ReportFile %REPORTDIR%\PM25_2004_ValuationNation.csv

-ResultType PooledValuation

-CustomFields "Endpoint Group,Author,Start Age,Endpoint,Qualifier,Pooling Window"

-ResultFields "Mean,Standard Deviation,Latin Hypercube Points"

-DecimalDigits 0
Appendix M: Function Editor

November 2012

239

BenMAP User's Manual Appendices

Appendix M: Function Editor

The function editor is used to develop both health impact functions and valuation functions. This

appendix describes the syntax of this editor.

M.1 User Defined Variables

In addition to pre-defined variables that you can select from the Available Variables list, you

can create your own variables in the C-R Function Editor.

A variable is an identifier whose value can change at runtime. Put differently, a variable is a name

for a location in memory; you can use the name to read or write to the memory location.

Variables are like containers for data, and, because they are typed, they tell the compiler how to

interpret the data they hold.

The basic syntax for a variable declaration is

var identifierList: type;

where identifierList is a comma-delimited list of valid identifiers and type is any valid type. For

example,

var I: Integer;

declares a variable I of type Integer, while

var X, Y: Real;

declares two variables--X and Y--of type Real.

Consecutive variable declarations do not have to repeat the reserved word var:

var

X, Y, Z: Double;

I, J, K: Integer;

Digit: 0..9;

IndicatorName: String;

Okay: Boolean;

Variables can be initialized at the same time they are declared, using the syntax

var identifier: type = constantExpression;

where constantExpression is any constant expression representing a value of type type. Thus the

declaration

var I: Integer = 7;
Appendix M: Function Editor

November 2012

240

BenMAP User's Manual Appendices

is equivalent to the declaration and statement

var I: Integer;

...

I := 7;

Multiple variable declarations (such as var X, Y, Z: Real;) cannot include initializations, nor can

declarations of variant and file-type variables.

M.2 The Script Language

In the C-R Function Editor, you can evaluate complex block ofstatements.

You can use constructions like:

If...then...else;

for I:= ... to .. do ;

while... do ;

repeat .... until...;

break;

assignment (...:=....;)

try...finally...end;

try...except...end;

Each function you create can be a single statement or a block ofstatements.

When you specify it as a block ofstatements, your script must conform to the rules of the script

language, as follows:

1. Each single statement must end with a semicolon (;)

2. You can use the following statements:

variable := expression;

If logical expression then statement(s) [else statement(s)];

for variable := from_expression to/downto to_expression do statement(s);

while logical_expression do statement(s);

repeat statement(s) until logical_expression;

try statement(s) finally statement(s) end;

try statement(s) except statement(s) end;

inline comments: // comment... until the end of the line
Appendix M: Function Editor

November 2012

241

BenMAP User's Manual Appendices

nested comments: { nested comment }

Statement(s) in the above declarations states that you can specify either a single statement or a

block ofstatements. The block ofstatements must be enclosed in begin ... end keywords. It is

not necessary to enclose the body of the function in begin .. end. Cycle statements can use

break keyword to break the cycle (break must also end with semicolon.)

M.3 Operands

Expressions may contain the following constant and variable types:

Integer numbers;

Floating point numbers;

Scientific numbers;

Decimalseparator for allfloating point and scientific-format numbers in expressions, is

independent of the Regional Settings of Windows and always is a decimal point ('.').

Boolean values - TRUE or FALSE;

Date type values - values of that type must be put in quotes ( ' ' ), and also date separator

character is independent of the Regional Settings of Windows and always is a slash - /, i.e. -

'01/01/2005'

String values - values of that type must be put in double quotes (" "); If a string contains double

quotes, you should double them(i.e., "this is a ""string"" ");

M.4 Operations

Arithmetical

+, -, *, /;

div - integer division;

mod - modulo;

^ - power of;

- - negate;

Logical

<, <=, >=, >, <>, =;

and, or, xor, not;

Bitwise
Appendix M: Function Editor

November 2012

242

BenMAP User's Manual Appendices

and, or, xor;

~ - negate;

M.5 Arithmetic Functions

ABS(X) absolute value

SQR(X) square = X^2 = X*X

SQRT(X) square root

SIGN(X) sign of X; =1 for X>0, =0 for X=0,

=-1 for X<0

ZERO(X) =0 for X=0, =1 for X<>0

TRUNC(X)=INT(X) integer part

FRAC(X) fractional part

ROUND(X) rounds X to the nearest integer value

CEIL(X) always returns "ceil" integer value

FLOOR(X) always returns "floor" integer value

DEC(X) decrements a value X by 1 and

returns a new value

INC(X) increments a value X by 1 and

returns a new value

ARG(X,Y) argument(phase) of X and Y

RADIUS(X,Y) = sqrt(sqr(X)+sqr(Y))

POWER(X,Y) raises X to a power of Y (Y is a

floating point value)

IPOWER(X,Y) raises X to a power of Y (Y is a

integer value)

X ^ Y raises X to a power of Y (same as

above two functions)

EXP(X) exponent

LN(X) natural logarithm
Appendix M: Function Editor

November 2012

243

BenMAP User's Manual Appendices

LG(X) decimal logarithm

LOG(X) base 2 logarithm

SIN(X) sine

COS(X) cosine

TAN(X) tangent

COTAN(X) cotangent

ASIN(X) arcsine

ACOS(X) arccosine

ATAN(X) arctangent

SINH(X) hyperbolic sine

COSH(X) hyperbolic cosine

TANH(X) hyperbolic tangent

M.6 Aggregate Functions

AVG(X1,X2,...) returns average value of (unlimited number of) arguments.

MAX(X1,X2,...) maximum of (unlimited number of) arguments.

MIN(X1,X2,...) minimum of (unlimited number of) arguments.

SUM(X1,X2,...) sum of (unlimited number of) arguments.

PROD(X1,X2,..) product of (unlimited number of) arguments.
References

November 2012

244

BenMAP User's Manual Appendices

References

Abbey, D. E., B. L. Hwang, R. J. Burchette, T. Vancuren and P. K. Mills. 1995a. Estimated

long-term ambient concentrations of PM10 and development of respiratory symptoms in a

nonsmoking population. Arch Environ Health. Vol. 50 (2): 139-52.

Abbey, D. E., B. E. Ostro, F. Petersen and R. J. Burchette. 1995b. Chronic Respiratory

Symptoms Associated with Estimated Long-Term Ambient Concentrations of Fine

Particulates Less Than 2.5 Microns in Aerodynamic Diameter (PM2.5) and Other Air

Pollutants. J Expo Anal Environ Epidemiol. Vol. 5 (2): 137-159.

Abbey, D. E., F. Petersen, P. K. Mills and W. L. Beeson. 1993. Long-Term Ambient

Concentrations of Total Suspended Particulates, Ozone, and Sulfur Dioxide and Respiratory

Symptoms in a Nonsmoking Population. Archives of Environmental Health. Vol. 48 (1): 33-

46.

Abt Associates Inc. 2000. Final Heavy Duty Engine/Diesel FuelRule: Air Quality Estimation,

Selected Health and Welfare Benefits Methods, and Benefit Analysis Results. Prepared for U.

S. EPA, Office of Air Quality Planning and Standards, Research Triangle Park, NC.

Bethesda, MD. December.

Adams, P. F., G. E. Hendershot and M. A. Marano. 1999. Current Estimates from the National

Health Interview Survey, 1996. Vital Health Stat. Vol. 10 (200): 1-212.

Agency for Healthcare Research and Quality (AHRQ). 2007. Healthcare Cost and Utilization

Project. NationalInpatient Sample (NIS) Rockville, Maryland. Available at: http://www.ahrq.

gov/data/hcup/ [accessed February, 2012].

American Lung Association. 2002a. Trends in Asthma Morbidity and Mortality. American Lung

Association, Best Practices and Program Services, Epidemiology and Statistics Unit.http://

www.lungusa.org/data/asthma/ASTHMAdt.pdf.

American Lung Association. 2002b. Trends in Chronic Bronchitis and Emphysema: Morbidity

and Mortality. American Lung Association, Best Practices and Program Services,

Epidemiology and Statistics Unit.http://www.lungusa.org/data/copd/COPD1.pdf.

American Lung Association. 2002c. Trends in Morbidity and Mortality: Pneumonia, Influenza,

and Acute Respiratory Conditions. American Lung Association, Best Practices and Program

Services, Epidemiology and Statistics Unit.http://www.lungusa.org/data/pi/PI_1.pdf.

American Lung Association. 2010a. Trends in COPD (Chronic Bronchitis and Emphysema):

Morbidity and Mortality. American Lung Association Epidemiology and Statistics Unit

Research and Program Services Division. February. Table 4. Based on NHIS data (CDC,

2008). Available at http://www.lungusa.org/finding-cures/our-research/trend-reports/copdtrend-report.pdf.
References

November 2012

245

BenMAP User's Manual Appendices

American Lung Association. 2010b. Trends in Asthma Morbidity and Mortality. American Lung

Association Epidemiology and Statistics Unit Research and Program Services Division.

February. Table 7. Based on NHIS data (CDC, 2008). Available at http://www.lungusa.org/

finding-cures/our-research/trend-reports/asthma-trend-report.pdf.

Babin, S. M., H. S. Burkom, et al. (2007). "Pediatric patient asthma-related emergency

department visits and admissions in Washington, DC, from 2001-2004, and associations with

air quality, socio-economic status and age group." Environ Health 6: 9.

Bell, M. L., K. Ebisu, et al. (2008). "Seasonal and Regional Short-term Effects of Fine Particles

on Hospital Admissions in 202 US Counties, 1999–2005." American Journal of Epidemiology

168 (11): 1301-1310.

Bell, M. L., F. Dominici and J. M. Samet. 2005. A meta-analysis of time-series studies of ozone

and mortality with comparison to the national morbidity, mortality, and air pollution study.

Epidemiology. Vol. 16 (4): 436-45.

Bell, M. L., A. McDermott, S. L. Zeger, J. M. Samet and F. Dominici. 2004. Ozone and shortterm mortality in 95 US urban communities, 1987-2000. Jama. Vol. 292 (19): 2372-8.

Belova, A., E. Post and D. McCubbin. 2007. Using Expert Distributions in BenMAP.

Memorandum prepared for Bryan Hubbell at U.S. EPA, Office of Air Quality Planning and

Standards. Prepared by Abt Associates, Bethesda, MD. March 7.

Blumenschein, K. and M. Johannesson. 1998. Relationship between quality of life instruments,

health state utilities, and willingness to pay in patients with asthma. Ann Allergy Asthma

Immunol. Vol. 80 (2): 189-94.

Burnett, R. T., M. Smith-Doiron, D. Stieb, M. E. Raizenne, J. R. Brook, R. E. Dales, J. A.

Leech, S. Cakmak and D. Krewski. 2001. Association between ozone and hospitalization for

acute respiratory diseases in children less than 2 years of age. Am J Epidemiol. Vol. 153 (5):

444-52.

Centers for Disease Control and Prevention (CDC). 2008. NationalCenter for Health Statistics.

National Health Interview Survey, 1999-2008.

Chen, L., B. L. Jennison, W. Yang and S. T. Omaye. 2000. Elementary school absenteeism and

air pollution. Inhal Toxicol. Vol. 12 (11): 997-1016.

Chiang, P. L. 1967. Variance and Covariance of Life Table Functions Estimated from a Sample

of Deaths. NationalCenter for Health Statistics. Washington, DC. March.

Collet, D. 1994. Modelling Survival Data in MedicalResearch. Chapman & Hall: New York.
References

November 2012

246

BenMAP User's Manual Appendices

Cropper, Maureen R. and Alan J. Krupnick. 1999. The social costs of chronic heart and lung

disease, in Maureen Cropper, Valuing EnvironmentalBenefits: Selected Essays of Maureen

Cropper, Edward Elgar: Northampton, MA.

Decisioneering. 1996. CrystalBall: Forecasting and Risk Analysis for Spreadsheet Users: User

Manual. Version 4.0. Denver, CO. www.decisioneering .com.

Delfino, R. J., H. Gong, Jr., W. S. Linn, E. D. Pellizzari and Y. Hu. 2003. Asthma symptoms in

Hispanic children and daily ambient exposures to toxic and criteria air pollutants. Environ

Health Perspect. Vol. 111 (4): 647-56.

Delfino, R. J., R. S. Zeiger, J. M. Seltzer, D. H. Street and C. E. McLaren. 2002. Association of

asthma symptoms with peak particulate air pollution and effect modification by antiinflammatory medication use. Environ Health Perspect. Vol. 110 (10): A607-17.

DerSimonian, R. and N. Laird. 1986. Meta-Analysis in Clinical Trials. Controlled Clinical Trials.

Vol. 7: 177-188.

Dewanji, A. and S. H. Moolgavkar. 2000. A Poisson process approach for recurrent event data

with environmental covariates. Environmetrics. Vol. 11: 665-673.

Dewanji, A. and S. H. Moolgavkar. 2002. Choices ofstratification in Poisson process analysis of

recurrent event data with environmental covariates. Stat. Med. Vol. 21: 3383-3393.

Dickie, M. and S. Gerking. 1987. Reconciling Averting Behavior and Contingent Valuation

Benefit Estimates of Reducing Symptoms of Ozone Exposure (draft), as cited in Neumann, J.

E., M. Dickie, and R.E. Unsworth. 1994. Prepared by Industrial Economics. Prepared for

Jim DeMocker, U.S. EPA, Office of Air and Radiation. March 31.

Dickie, M. and V. L. Ulery. 2002. Parental Altruism and the Value of Avoiding Acute Illness:

Are Kids Worth More Than Parents? (Paper to be submitted for publication. Presented at

Association of Environmental and Resource Economists 2001 Workshop, "Assessing and

Managing Environmental and Public Health Risks."). December.

Dockery, D. W., J. Cunningham, A. I. Damokosh, L. M. Neas, J. D. Spengler, P. Koutrakis, J.

H. Ware, M. Raizenne and F. E. Speizer. 1996. Health Effects of Acid Aerosols On North

American Children - Respiratory Symptoms. Environmental Health Perspectives. Vol. 104

(5): 500-505.

Dockery, D. W., F. E. Speizer, D. O. Stram, J. H. Ware, J. D. Spengler and B. G. Ferris, Jr.

1989. Effects of Inhalable Particles on Respiratory Health of Children. Am Rev Respir Dis.

Vol. 139: 587-594.

Eisenstein, E. L., L. K. Shaw, K. J. Anstrom, C. L. Nelson, Z. Hakim, V. Hasselblad and D. B.

Mark. 2001. Assessing the clinical and economic burden of coronary artery disease: 1986-

1998. Med Care. Vol. 39 (8): 824-35.
References

November 2012

247

BenMAP User's Manual Appendices

ESEERCO. 1994. New York State Environmental Externalities Cost Study. Report 2:

Methodology. Empire State Electric Energy Research Corporation.

Fung, K. Y., S. Khan, D. Krewski and Y. Chen. 2006. Association between air pollution and

multiple respiratory hospitalizations among the elderly in Vancouver, Canada. Inhal Toxicol.

Vol. 18 (13): 1005-11.

Fung, K. Y. K., Daniel; Chen, Yue; Burnett, Rick; Cakmak, Sabit. 2003. Comparison of time

series and case-crossover analyses of air pollution and hospital admission data. International

Journal of Epidemiology. Vol. 32: 1064–1070.

GeoLytics Inc. 2001. CensusCD® 1990 + Maps, Release 4.1. CD-ROM. GeoLytics, Inc.

Gilliland, F. D., K. Berhane, E. B. Rappaport, D. C. Thomas, E. Avol, W. J. Gauderman, S. J.

London, H. G. Margolis, R. McConnell, K. T. Islam and J. M. Peters. 2001. The effects of

ambient air pollution on school absenteeism due to respiratory illnesses. Epidemiology. Vol.

12 (1): 43-54.

Greene, W. H. 1997. Econometric Analysis. Prentice Hall: Upper Saddle River, NJ.

Haase, N. 2002. Phone conversation. American Heart Association, October.

Huang, Y., F. Dominici and M. L. Bell. 2005. Bayesian hierarchical distributed lag models for

summer ozone exposure and cardio-respiratory mortality. Environmetrics. Vol. 16: 547–562.

Industrial Economics Incorporated (IEc). 1993. Memorandum to Jim DeMocker, U.S.

Environmental Protection Agency, Office of Air and Radiation, Office of Policy Analysis and

Review. September 30.

Industrial Economics Incorporated (IEc). 1994. Linkage Between Health Effects Estimation and

Morbidity Valuation in the Section 812 Analysis -- Draft Valuation Document. Memorandum

to Jim DeMocker, U.S. Environmental Protection Agency, Office of Air and Radiation, Office

of Policy Analysis and Review. Prepared by J.E. Neumann, M.T. Dickie, and R.E.

Unsworth. March 31.

Industrial Economics Incorporated (IEc). 2006. Expanded Expert Judgment Assessment of the

Concentration-Response Relationship Between PM2.5 Exposure and Mortality, FinalReport.

Prepared for U.S. Environmental Protection Agency, Office of Air and Radiation, Office of

Policy Analysis and Review. Prepared by IEc. Cambridge, MA. September 21.

Ito, K. 2003. Associations of Particulate Matter Components with Daily Mortality and Morbidity

in Detroit, Michigan. In:Revised Analyses of Time-Series Studies of Air Pollution and Health.

Health Effects Institute. Boston, MA. May.

Ito, K., S. F. De Leon and M. Lippmann. 2005. Associations between ozone and daily mortality:

analysis and meta-analysis. Epidemiology. Vol. 16 (4): 446-57.
References

November 2012

248

BenMAP User's Manual Appendices

Ito, K. and G. D. Thurston. 1996. Daily PM10/mortality associations: an investigations of at-risk

subpopulations. Journal of Exposure Analysis and Environmental Epidemiology. Vol. 6 (1):

79-95.

Ito, K., G. D. Thurston and R. A. Silverman. 2007. Characterization of PM2.5, gaseous

pollutants, and meteorological interactions in the context of time-series health effects models. J

Expo Sci Environ Epidemiol. Vol. 17 Suppl 2: S45-60.

Jerrett, M., R. Burnett, A. I. Pope, K. Ito, G. Thurston, D. Krewski, Y. Shi, E. Calle and M.

Thun. 2009. Long-Term Ozone Exposure and Mortality. The New England Journal of

Medicine. Vol. 360 (11): 1085-95.

Judge, G. G., W. E. Griffiths, R. C. Hill, H. Lutkepohl and T.-C. Lee. 1985. The Theory and

Practice of Econometrics. John Wiley and Sons: New York.

Kennedy, P. 1990. A Guide to Econometrics. MIT Press:Cambridge, MA.

Krewski, D., M. Jerrett, R. Burnett, R. Ma, E. Hughes, Y. Shi, M. C. Turner, C. A. I. Pope, G.

Thurston, E. Calle and M. J. Thun. 2009. Extended follow-up and spatial analysis of the

American Cancer Society study linking particulate air pollution and mortality. HEI Research

Report, 140, Health Effects Institute, Boston, MA.

Krupnick, A. J. and M. L. Cropper. 1992. The Effect of Information On Health Risk Valuations.

Journal of Risk and Uncertainty. Vol. 5 (1): 29-48.

Laden, F., J. Schwartz, F. E. Speizer and D. W. Dockery. 2006. Reduction in Fine Particulate

Air Pollution and Mortality: Extended follow-up of the Harvard Six Cities Study. Am J Respir

Crit Care Med. Vol. 173 (6): 667-72.

Levy, J. I., S. M. Chemerynski and J. A. Sarnat. 2005. Ozone exposure and mortality: an

empiric bayes metaregression analysis. Epidemiology. Vol. 16 (4): 458-68.

Linn, W. S., Y. Szlachcic, H. Gong, Jr., P. L. Kinney and K. T. Berhane. 2000. Air pollution

and daily hospital admissions in metropolitan Los Angeles. Environ Health Perspect. Vol. 108

(5): 427-34.

Lippmann, M., K. Ito, A. Nádas and R. Burnett. 2000. Association of Particulate Matter

Components with Daily Mortality and Morbidity in Urban Populations. Health Effects

Institute. Number 95. August.

Loehman, E. T., S. V. Berg, A. A. Arroyo, R. A. Hedinger, J. M. Schwartz, M. E. Shaw, R. W.

Fahien, V. H. De, R. P. Fishe, D. E. Rio, W. F. Rossley and A. E. S. Green. 1979.

Distributional Analysis of RegionalBenefits and Cost of Air Quality Control. Journal of

Environmental Economics and Management. Vol. 6: 222-243.
References

November 2012

249

BenMAP User's Manual Appendices

Luginaah, I. N., K. Y. Fung, K. M. Gorey, G. Webster and C. Wills. 2005. Association of

ambient air pollution with respiratory hospitalization in a government-designated "area of

concern": the case of Windsor, Ontario. Environ Health Perspect. Vol. 113 (3): 290-6.

Mar, T. F., J. Q. Koenig, et al. (2010). "Associations between asthma emergency visits and

particulate matter sources, including diesel emissions from stationary generators in Tacoma,

Washington." Inhal Toxicol 22 (6): 445-8.

Mar, T. F., T. V. Larson, et al. (2004). "An analysis of the association between respiratory

symptoms in subjects with asthma and daily air pollution in Spokane, Washington." Inhal

Toxicol 16 (13): 809-15.

McConnell, R., K. Berhane, F. Gilliland, S. J. London, H. Vora, E. Avol, W. J. Gauderman, H.

G. Margolis, F. Lurmann, D. C. Thomas and J. M. Peters. 1999. Air pollution and bronchitic

symptoms in Southern California children with asthma. Environ Health Perspect. Vol. 107 (9):

757-60.

Michaud, J. P., J. S. Grove and D. Krupitsky. 2004. Emergency department visits and "vog"-

related air quality in Hilo, Hawai'i. Environ Res. Vol. 95 (1): 11-9.

Moolgavkar, S. H. 2000a. Air Pollution and Hospital Admissions for Chronic Obstructive

Pulmonary Disease in Three Metropolitan Areas in the United States. Inhalation Toxicology.

Vol. 12 (Supplement 4): 75-90.

Moolgavkar, S. H. 2000b. Air pollution and hospital admissions for diseases of the circulatory

system in three U.S. metropolitan areas. J Air Waste Manag Assoc. Vol. 50 (7): 1199-206.

Moolgavkar, S. H. 2003. Air Pollution and Daily Deaths and Hospital Admissions in Los

Angeles and Cook Counties. In:Revised Analyses of Time-Series Studies of Air Pollution

and Health. Health Effects Institute. Boston, MA. May.

Moolgavkar, S. H., E. G. Luebeck and E. L. Anderson. 1997. Air pollution and hospital

admissions for respiratory causes in Minneapolis St. Paul and Birmingham. Epidemiology. Vol.

8 (4): 364-370.

Moolgavkar, S. H., E. G. Luebeck, T. A. Hall and E. L. Anderson. 1995. Air Pollution and

Daily Mortality in Philadelphia. Epidemiology. Vol. 6 (5): 476-484.

Mortimer, K. M., L. M. Neas, D. W. Dockery, S. Redline and I. B. Tager. 2002. The effect of

air pollution on inner-city children with asthma. Eur Respir J. Vol. 19 (4): 699-705.

Mrozek, J. R. and L. O. Taylor. 2002. What Determines the Value of Life? A Meta-Analysis.

Journal of Policy Analysis and Management. Vol. 21: 253-270.
References

November 2012

250

BenMAP User's Manual Appendices

NationalCenter for Health Statistics. 1999. National Vital Statistics Reports. U.S. Department of

Health and Human Services, Centers for Disease Control and Prevention, NationalCenter for

Health Statistics. Washington, DC. Volume 47, Number 19. June 30.

Nelder, J. A. and R. Mead. 1965. A simplex algorithm for function minimization. Computer

Journal. Vol. 7: 308-313.

New York Department of Health (NYDOH). 2006. A Study of Ambient Air Contaminants and

Asthma in New York City. New York State Department of Health Center for Environmental

Health.

Norris, G., S. N. YoungPong, J. Q. Koenig, T. V. Larson, L. Sheppard and J. W. Stout. 1999.

An association between fine particles and asthma emergency department visits for children in

Seattle. Environ Health Perspect. Vol. 107 (6): 489-93.

NYDOH. 2006. A Study of Ambient Air Contaminants and Asthma in New York City. New

York State Department of Health Center for Environmental Health.

O'Connor, R. M. and G. C. Blomquist. 1997. Measurement of Consumer-Patient Preferences

Using a Hybrid Contingent Valuation Method. Journal of Health Economics. Vol. 16: 667-

683.

Ostro, B., M. Lipsett, J. Mann, H. Braxton-Owens and M. White. 2001. Air pollution and

exacerbation of asthma in African-American children in Los Angeles. Epidemiology. Vol. 12

(2): 200-8.

Ostro, B. D. 1987. Air Pollution and Morbidity Revisited: A Specification Test. Journal of

Environmental Economics and Management. Vol. 14: 87-98.

Ostro, B. D., M. J. Lipsett, M. B. Wiener and J. C. Selner. 1991. Asthmatic Responses to

Airborne Acid Aerosols. Am J Public Health. Vol. 81 (6): 694-702.

Ostro, B. D. and S. Rothschild. 1989. Air Pollution and Acute Respiratory Morbidity - an

Observational Study of Multiple Pollutants. Environ Res. Vol. 50 (2): 238-247.

Owings, M. F. and L. Lawrence. 1999. Detailed Diagnoses and Procedures, National Hospital

Discharge Survey, 1997. NationalCenter for Health Statistics. Hyattsville, MD. Vital Health

Statistics, Series 13, No. 145. December.

Peel, J. L., P. E. Tolbert, M. Klein, K. B. Metzger, W. D. Flanders, K. Todd, J. A. Mulholland,

P. B. Ryan and H. Frumkin. 2005. Ambient air pollution and respiratory emergency

department visits. Epidemiology. Vol. 16 (2): 164-74.

Peng, R. D., M. L. Bell, et al. (2009). "Emergency admissions for cardiovascular and respiratory

diseases and the chemical composition of fine particle air pollution." Environ Health Perspect

117 (6): 957-63.
References

November 2012

251

BenMAP User's Manual Appendices

Peng, R. D., H. H. Chang, et al. (2008). "Coarse particulate matter air pollution and hospital

admissions for cardiovascular and respiratory diseases among Medicare patients." Jama 299

(18): 2172-9.

Peters, A., D. W. Dockery, J. E. Muller and M. A. Mittleman. 2001. Increased particulate air

pollution and the triggering of myocardial infarction. Circulation. Vol. 103 (23): 2810-5. http://

www.circulationaha.org/cgi/content/full/103/23/2810.

Pope, C. A., 3rd, J. B. Muhlestein, et al. (2006). "Ischemic heart disease events triggered by

short-term exposure to fine particulate air pollution."Circulation 114 (23): 2443-8.

Pope, C. A., 3rd, R. T. Burnett, M. J. Thun, E. E. Calle, D. Krewski, K. Ito and G. D.

Thurston. 2002. Lung cancer, cardiopulmonary mortality, and long-term exposure to fine

particulate air pollution. Jama. Vol. 287 (9): 1132-41.

Pope, C. A., D. W. Dockery, J. D. Spengler and M. E. Raizenne. 1991. Respiratory Health and

PM10 Pollution - a Daily Time Series Analysis. American Review of Respiratory Disease.

Vol. 144 (3): 668-674.

Popovic, J. R. 2001. 1999 National Hospital Discharge Survey: annualsummary with detailed

diagnosis and procedure data. Vital Health Stat 13. (151): i-v, 1-206.

Ransom, M. R. and C. A. Pope. 1992. Elementary school absences and PM10 pollution in Utah

Valley. Environ Res. Vol. 58 (2): 204-19.

Roger V.L., A.S. Go, D.M. Lloyd-Jones, E.J. Benjamin, J.D. Berry, W.B. Borden, D.M.

Bravata, S. Dai, E.S. Ford, C.S. Fox, H.J. Fullerton, C. Gillespie, S.M. Hailpern, J.A. Heit,

V.J. Howard, B.M. Kissel, S.J. Kittner, D.T. Lackland, J.H. Lichtman, L.D. Lisabeth, D.M.

Makuc, G.M. Marcus, A. Marelli, D.B. Matchar, C.S. Moy, D. Mozaffarian, M.E.

Mussolino, G. Nichol, N.P. Paynter, E.Z. Soliman, P.D. Sorlie, N. Sotoodehnia, T.N. Turan,

S.S. Virani, N.D. Wong, D. Woo and M.B. Turner; on behalf of the American Heart

Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and

stroke statistics—2012 update: a report from the American Heart Association. Circulation.

2012;125:e2– e220.

Rosamond, W., G. Broda, E. Kawalec, S. Rywik, A. Pajak, L. Cooper and L. Chambless.

1999. Comparison of medical care and survival of hospitalized patients with acute myocardial

infarction in Poland and the United States. Am J Cardiol. Vol. 83 (8): 1180-5.

Rowe, R. D. and L. G. Chestnust. 1986. Oxidants and Asthmatics in Los Angeles: A Benefits

Analysis -- Executive Summary. Prepared for U.S. Environmental Protection Agency, Office

of Policy Analysis. Prepared by Energy and Resource Consultants, Inc. Washington, DC.

EPA-230-09-86-018. March.

Russell, M. W., D. M. Huse, S. Drowns, E. C. Hamel and S. C. Hartz. 1998. Direct medical

costs of coronary artery disease in the United States. Am J Cardiol. Vol. 81 (9): 1110-5.
References

November 2012

252

BenMAP User's Manual Appendices

Samet, J. M., S. L. Zeger, J. E. Kelsall, J. Xu and L. S. Kalkstein. 1997. Air Pollution, Weather,

and Mortality in Philadelphia 1973-1988. Health Effects Institute. Cambridge, MA. March.

Schildcrout, J. S., L. Sheppard, T. Lumley, J. C. Slaughter, J. Q. Koenig and G. G. Shapiro.

2006. Ambient air pollution and asthma exacerbations in children: an eight-city analysis. Am J

Epidemiol. Vol. 164 (6): 505-17.

Schwartz, J. 1994a. Air Pollution and Hospital Admissions For the Elderly in Detroit, Michigan.

American Journal of Respiratory and CriticalCare Medicine. Vol. 150 (3): 648-655.

Schwartz, J. 1994b. PM(10) Ozone, and Hospital Admissions For the Elderly in Minneapolis St

Paul, Minnesota. Archives of Environmental Health. Vol. 49 (5): 366-374.

Schwartz, J. 1995. Short term fluctuations in air pollution and hospital admissions of the elderly

for respiratory disease. Thorax. Vol. 50 (5): 531-538.

Schwartz, J. 2005. How sensitive is the association between ozone and daily deaths to control

for temperature? Am J Respir Crit Care Med. Vol. 171 (6): 627-31.

Schwartz, J., C. Spix, G. Touloumi, L. Bacharova, T. Barumamdzadeh, A. le Tertre, T.

Piekarksi, A. Ponce de Leon, A. Ponka, G. Rossi, M. Saez and J.P. Schouten. 1996.

Methodological issues in studies of air pollution and daily counts of deaths or hospital

admissions. J. Epidemiol. Community Health. Vol. 50 Suppl. 1: S3-11.

Schwartz, J., D. W. Dockery, L. M. Neas, D. Wypij, J. H. Ware, J. D. Spengler, P. Koutrakis,

F. E. Speizer and B. G. Ferris. 1994. Acute Effects of Summer Air Pollution On Respiratory

Symptom Reporting in Children. Am J Respir Crit Care Med. Vol. 150 (5): 1234-1242.

Schwartz, J. and L. M. Neas. 2000. Fine particles are more strongly associated than coarse

particles with acute respiratory health effects in schoolchildren. Epidemiology. Vol. 11 (1): 6-

10.

Sheppard, L. 2003. Ambient Air Pollution and Nonelderly Asthma Hospital Admissions in

Seattle, Washington, 1987-1994. In:Revised Analyses of Time-Series Studies of Air

Pollution and Health. Health Effects Institute. Boston, MA. May.

Sheppard, L., D. Levy, G. Norris, T. V. Larson and J. Q. Koenig. 1999. Effects of ambient air

pollution on nonelderly asthma hospital admissions in Seattle, Washington, 1987-1994.

Epidemiology. Vol. 10 (1): 23-30.

Slaughter, J. C., E. Kim, et al. (2005). "Association between particulate matter and emergency

room visits, hospital admissions and mortality in Spokane, Washington." J Expo Anal Environ

Epidemiol 15(2): 153-9.
References

November 2012

253

BenMAP User's Manual Appendices

Smith, D. H., D. C. Malone, K. A. Lawson, L. J. Okamoto, C. Battista and W. B. Saunders.

1997. A national estimate of the economic costs of asthma. Am J Respir Crit Care Med. Vol.

156 (3 Pt 1): 787-93.

Stanford, R., T. McLaughlin and L. J. Okamoto. 1999. The cost of asthma in the emergency

department and hospital. Am J Respir Crit Care Med. Vol. 160 (1): 211-5.

Sullivan, J., L. Sheppard, et al. (2005). "Relation between short-term fine-particulate matter

exposure and onset of myocardial infarction." Epidemiology 16 (1): 41-8.

Tolley, G. S. and et al. 1986. Valuation of Reductions in Human Health Symptoms and Risks.

Prepared for U.S. Environmental Protection Agency. January.

U.S. Bureau of the Census. 1997. Statistical Abstract of the United States: 1997. Washington,

DC.

U.S. Bureau of the Census. 2002a. Modified Race Data Summary File 2000 Technical

Documentation. Census of Population and Housing: Washington DC. http://www.census.gov/

popest/archives/files/MRSF-01-US1.pdf.

U.S. Bureau of the Census. 2002b. Statistical Abstract of the United States: 2001. Washington

DC.

U.S. Department of Education. 1996. The Condition of Education 1996, Indicator 42: Student

Absenteeism and Tardiness. NationalCenter for Education Statistics. Washington DC.

U.S. EPA. 1997. The Benefits and Costs of the Clean Air Act: 1970 to 1990. U.S. EPA, Office

of Air and Radiation, Office of Policy, Planning and Evaluation. Washington, DC. EPA 410-

R-97-002. October. http://www.epa.gov/airprogm/oar/sect812/index.html.

U.S. EPA. 1999a. The Benefits and Costs of the Clean Air Act: 1990 to 2010: EPA Report to

Congress. U.S. EPA, Office of Air and Radiation, Office of Policy. Washington, DC. EPA

410-R-99-001. November. http://www.epa.gov/airprogm/oar/sect812/index.html.

U.S. EPA. 1999b. OAQPS Economic Analysis Resource Document. Prepared by U.S. EPA,

Office of Air Quality Planning and Standards, Innovative Strategies and Economics Group.

Durham, NC. April. http://www.epa.gov/ttn/ecas/analguid.html.

U.S. EPA. 2000. Guidelines for Preparing Economic Analyses. Office of the Administrator.

Washington, DC. EPA #: 240-R-00-003. September. http://yosemite.epa.gov/ee/epa/eed.nsf/

webpages/Guidelines.html/$file/Guidelines.pdf.

U.S. EPA. 2004. FinalRegulatory Analysis:Control of Emissions from Nonroad Diesel Engine.

U.S. EPA, Office of Transportation and Air Quality. Washington, DC. EPA 420-R-04-007.

April.
References

November 2012

254

BenMAP User's Manual Appendices

U.S. EPA. 2006. FinalRegulatory Impact Analysis: PM2.5 NAAQS. Office of Air and

Radiation, Office of Air Quality Planning and Standards. Research Triangle Park, NC. http://

www.epa.gov/ttn/ecas/ria.html.

U.S. EPA. 2008a. Final Ozone NAAQS Regulatory Impact Analysis. Prepared by Office of Air

and Radiation, Office of Air Quality Planning and Standards. Research Triangle Park, NC.

http://www.epa.gov/ttn/ecas/ria.html.

U.S. EPA. 2008b. Regulatory Impact Analysis:Control of Emissions of Air Pollution from

Locomotive Engines and Marine Compression Ignition Engines Less than 30 Liters Per

Cylinder. Office of Transportation and Air Quality, Assesssment and Standards Division.

Washington, DC. EPA420-R-08-001. March. http://www.epa.gov/otaq/locomotv.htm.

Villeneuve, P. J., L. Chen, B. H. Rowe and F. Coates. 2007. Outdoor air pollution and

emergency department visits for asthma among children and adults: a case-crossover study in

northern Alberta, Canada. Environ Health. Vol. 6 (1): 40.

Viscusi, W. K. 1992. Fatal Tradeoffs: Public and Private Responsibilities for Risk. Oxford

University Press: New York.

Viscusi, W. K. and J. E. Aldy. 2003. The Value of a Statistical LIfe: A CriticalReview of

Market Estimates throughout the World. AEI-Brookings Joint Center for Regulatory Studies.

Washington, DC. January.

Viscusi, W. K., W. A. Magat and J. Huber. 1991. Pricing Environmental Health Risks - Survey

Assessments of Risk - Risk and Risk - Dollar Trade-Offs For Chronic Bronchitis. Journal of

Environmental Economics and Management. Vol. 21 (1): 32-51.

Wilson, A. M., C. P. Wake, T. Kelly and J. C. Salloway. 2005. Air pollution, weather, and

respiratory emergency room visits in two northern New England cities: an ecological timeseries study. Environ Res. Vol. 97 (3): 312-21.

Wittels, E. H., J. W. Hay and A. M. Gotto, Jr. 1990. Medical costs of coronary artery disease in

the United States. Am J Cardiol. Vol. 65 (7): 432-40.

Woodruff, T. J., J. Grillo and K. C. Schoendorf. 1997. The relationship between selected causes

of postneonatal infant mortality and particulate air pollution in the United States. Environmental

Health Perspectives. Vol. 105 (6): 608-612.

Woodruff, T. J., J. D. Parker and K. C. Schoendorf. 2006. Fine particulate matter (PM2.5) air

pollution and selected causes of postneonatal infant mortality in California. Environmental

Health Perspectives. Vol. 114: 786–790.

Woods & Poole Economics Inc. 2012. Complete Demographic Database. Washington, DC.

http://www.woodsandpoole.com/index.php.
References

November 2012

255

BenMAP User's Manual Appendices

Yang, Q., Y. Chen, D. Krewski, R. T. Burnett, Y. Shi and K. M. McGrail. 2005. Effect of

short-term exposure to low levels of gaseous pollutants on chronic obstructive pulmonary

disease hospitalizations. Environ Res. Vol. 99 (1): 99-105.

Yang, Q., Y. Chen, Y. Shi, R. T. Burnett, K. M. McGrail and D. Krewski. 2003. Association

between ozone and respiratory admissions among children and the elderly in Vancouver,

Canada. Inhal Toxicol. Vol. 15 (13): 1297-308.

Zanobetti, A., M. Franklin, et al. (2009). "Fine particulate air pollution and its components in

association with cause-specific emergency admissions." Environmental Health 8: 58-60.

Zanobetti A. and Schwartz, J. (2006). "Air pollution and emergency admissions in Boston, MA."

J EpidemiolCommunity Health 60 (10): 890-5.