August 2010

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
August 2010

2

Table of Contents

Appendix A: Training Courses 10

A.1 Unite.d...S..t.a..t.e..s....................................................................................................................... 12

A.1.1 Section 1. D...a..t.a...F..i.l.e..s....N..e..e...d..e..d....f.o..r...T..r..a..i.n..i.n...g..................................................................................................... 12

A.1.2 Section 2. M....a..p..p..i.n...g...I.n..t..r.o...d..u..c..t..i.o..n..................................................................................................................... 12

Raw Monit.o..r..D..a..t.a............................................................................................................................................... 13

Example PM2.5 m.o..n..i.t.o..r..d..a..t.a...f..o..r..2..0..0..0.......................................................................................................... 13

Example O3 moni.t.o..r..d..a..t.a...f..o..r..2..0..0..0............................................................................................................... 20

Model Data......................................................................................................................................................... 22

Example Air Qual.i.t.y...G..r..id...F..i.l.e..:..B..a..s..e..l.in..e...P..M...2....5.............................................................................................. 23

Health Incid..e..n..c..e................................................................................................................................................. 25

Example Configur..a..t.io..n...R...e..s..u..lt.s...F..i.l.e..:..C..o..n..t.r.o..l..P..M...2...5...................................................................................... 25

Valuation M..a..p..................................................................................................................................................... 28

Example Valuatio.n...R..e..s..u..l.t.s...F..i.le..:..C...o..n..t.r.o..l..P..M..2....5............................................................................................. 28

Audit Trail ......................................................................................................................................................... 36

Example Health In..c..id..e..n..c..e..:..C...o..n..t.r.o..l..P..M..2....5..................................................................................................... 36

Report ......................................................................................................................................................... 38

Example Pooled In..c..i.d..e..n..c..e..:..C..o..n..t.r..o..l.P..M...2....5...R..I.A.............................................................................................. 38

Additional M...a..p..p..i.n..g...A..c..t.i.v..i.t.ie..s.............................................................................................................................. 41

A.1.3 Section 3. O...n..e...-.S..t..e..p...A...n..a..l.y..s...i.s.......................................................................................................................... 41

Example PM...2...5...C..o..n..t.r.o..l..2..0..2..0...1..5../.3..5...N..a..t.i.o..n..a..l..................................................................................................... 41

Example PM...2...5...C..o..n..t.r.o..l..2..0..2..0...1..4../.3..5...S..t.a..t.e........................................................................................................... 54

A.1.4 Section 4. C...r..e..a..t.i.n...g...G...r.i.d...s................................................................................................................................. 55

Air Quality .M...o..d..e..l..G..r..id..s....................................................................................................................................... 56

Example: PM2.5 C..o..n..t.r.o..l..2..0..2..0...1..5../.3..5...A...d..ju..s..t.e..d.............................................................................................. 56

Example: Control.P..M...2....5...R..I.A....2..0..2..0...1..4../.3..5...a..d..ju..s..t.e..d........................................................................................ 57

Monitor Gri.d..s...................................................................................................................................................... 57

Example: Baselin.e...P..M..2....5...2..0..0..4..................................................................................................................... 57

Monitor Ro.ll.b..a..c..k................................................................................................................................................. 61

Example: Control.P..M...2....5...2..0..0..4...P..e..r.c..e..n..t.a..g..e...R...o..ll.b..a..c..k..................................................................................... 61

Example: Control.P..M...2....5...2..0..0..4...M...u..lt.i.p..l.e...R..o..l.lb..a..c..k...T..e..c..h..n..i.q..u..e..s........................................................................ 66

A.1.5 Section 5. H...e..a..l.t.h....I.n..c..i.d..e...n..c..e.............................................................................................................................. 70

Example: P.M...2...5...C..o..n..t.r.o..l..2..0..2..0...1..4../.3..5................................................................................................................... 71

Example: P.M...2...5...C..o..n..t.r.o..l..2..0..2..0...1..4../.3..5...A...d..ju..s..t.e..d.................................................................................................... 88

A.1.6 Section 6. A...g..g...r.e...g..a..t.i.o...n..,..P..o...o..l.i.n..g...,..a..n..d...V...a..l.u..a..t..i.o..n........................................................................................... 92

Example: P.M...2...5...C..o..n..t.r.o..l..2..0..2..0...1..4../.3..5................................................................................................................... 92

Example: .P..M...2...5...C..o..n..t.r.o..l..2..0..2..0...1..4../.3..5...A...d..ju..s..t.e..d.................................................................................................. 123

Example: .M...o..d..if..y..in..g...O...n..e..-.S...t.e..p...A..n..a..l.y..s..i.s...P..a..r.a..m...e..t.e..r.s........................................................................................ 126

A.1.7 Section 7...A...d..d...i.n..g...N...e..w....D...a..t.a..s..e...t.s...&....I.n..d...e..p..e...n..d..e...n..t..S...t.u..d...y........................................................................... 128

Example: .A...d..d..in..g...D...a..t.a..s..e..t.s...f.o..r..D...e..t.r.o..i.t............................................................................................................. 129

Independe..n..t..S...t.u..d..y..:..D..e..t.r.o..i.t..B..e..n..e..f..it.s...A...n..a..l.y..s..is.................................................................................................. 145

A.1.8 Answers..t.o...T...r.a..i.n...i.n..g...E..x..e...r.c..i.s...e..s.................................................................................................................... 152

A.2 CityO...n..e.............................................................................................................................. 160

A.2.1 Step 1. Da.t..a...F..i.l.e..s...N...e..e..d...e..d...f..o..r...T..r..a..i.n..i.n..g......................................................................................................... 161

A.2.2 Step 2. Cr.e...a..t.e...A...i.r...Q..u...a..l.i.t.y...G...r.i.d...s...f.o...r..t..h..e...B...a..s..e..l.i.n...e...a..n..d....C..o...n..t.r..o..l..S...c..e..n..a..r..i.o..s............................................. 161

A.2.3 Step 3. Sp..e..c..i.f.y...C...o..n...f.i.g..u...r.a..t..i.o..n...S...e..t.t..i.n..g..s........................................................................................................ 167
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A.2.4 Step 4. Se..l.e..c..t..H...e..a..l.t.h....I.m...p...a..c..t..F..u..n...c..t.i.o..n...s....................................................................................................... 170

A.2.5 Step 5. Sp..e..c..i.f.y...A...g..g...r.e...g..a..t.i.o...n..,..P..o...o..l.i.n..g....a..n..d...V...a..l.u..a..t..i.o..n................................................................................. 172

A.2.6 Step 6. Ge..n..e...r.a..t..e...R..e...p..o..r..t.s............................................................................................................................... 180

A.2.7 Step 7. Vi.e..w....Y...o..u..r...R..e...p..o..r..t.s............................................................................................................................. 183

A.2.8 Step 8. Ma..p....Y..o..u...r..R...e..s..u...l.t.s............................................................................................................................... 184

Appendix B: Monitor Rollback Algorithms 191

B.1 Perc..e..n..t.a..g..e...R..o..l.l.b..a..c..k......................................................................................................... 191

B.2 Incre..m...e..n..t.a..l..R...o..l.l.b..a..c.k......................................................................................................... 192

B.3 Roll.b..a..c.k...t.o...a...S..t.a..n..d..a..r..d...................................................................................................... 192

B.3.1 Interday R..o..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.............................................................................. 193

Interday R..o..l.lb..a..c..k...-..P..e..r..c..e..n..t.a..g..e........................................................................................................................ 194

Interday R..o..l.lb..a..c..k...-..I.n..c..r.e..m...e..n..t.a..l....................................................................................................................... 195

Interday R..o..l.lb..a..c..k...-..P..e..a..k...S..h..a..v..i.n..g..................................................................................................................... 196

B.3.2 Intraday R..o..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.................................................................................. 197

Intraday R..o..l.lb..a..c..k...-..P..e..r..c..e..n..t.a..g..e........................................................................................................................ 198

......................................................................................................................................... 198

Example: All Hourly Observations Exceed the Intraday Background

(Single Iteration)

......................................................................................................................................... 199

Example: Some Hourly Observations are Below the Intraday

Background (Multiple Iterations Required)

B.3.3 Intraday R..o..l.l.b..a..c..k....-..I.n..c..r..e..m....e..n...t.a..l................................................................................................................... 202

B.3.4 Interday a..n..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................................................................................................. 204

Quadratic..R...o..ll.b..a..c..k...-..I.n..t.e..r.d..a..y........................................................................................................................... 205

Quadratic..R...o..ll.b..a..c..k...-..I.n..t.r.a..d..a..y........................................................................................................................... 206

Appendix C: Air Pollution Exposure Estimation Algorithms 207

C.1 Dire.c..t..M..o..d..e..l.i.n..g.................................................................................................................. 208

C.2 Clos.e..s..t..M..o..n..i.t.o..r.................................................................................................................. 208

C.2.1 Closest M..o...n..i.t.o...r..–...T...e..m....p..o..r..a..l..S..c..a..l.i.n...g........................................................................................................... 209

C.2.2 Closest M..o...n..i.t.o...r..–...S...p..a..t.i.a..l..S...c..a..l.i.n..g................................................................................................................. 210

C.2.3 Closest M..o...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....................................................................................... 211

C.3 Voro..n..o..i..N..e..i.g..h..b..o..r..A...v.e..r..a..g..i.n..g...(.V..N..A..)................................................................................... 212

C.3.1 VNA | Tem...p..o..r..a..l..S..c..a..l.i.n...g.................................................................................................................................. 216

C.3.2 Voronoi N..e..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............................................................................... 220

C.3.3 Voronoi N..e..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......................................................... 221

C.4 Fixe.d...R...a..d..i.u..s..................................................................................................................... 221

C.5 Tem.p..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........................................................... 221

C.5.1 Calculatio.n....o..f..S...c..a..l.i.n..g...F..a..c..t..o..r..s....................................................................................................................... 222

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................................................................................................. 222

Example: .O...z..o..n..e...S..c..a..l.in..g...i.n...U....S.....S..e..t.u..p............................................................................................................. 224

C.6 Binn..e..d...M..e..t.r..i.c.s................................................................................................................... 225
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Appendix D: Deriving Health Impact Functions 226

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

D.2 Revi.e..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................................................................................ 227

D.3 Line.a..r...M..o..d..e..l..................................................................................................................... 228

D.4 Log-.l.i.n..e..a..r...M..o..d..e..l............................................................................................................... 229

D.5 Logi.s..t.i.c...M..o..d..e..l................................................................................................................... 231

D.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...................................................................................... 237

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

Setup 239

E.1 Mort.a..l.i.t.y............................................................................................................................. 239

E.1.1 Mortality .D..a..t.a...f..o..r...2..0..0..4..-.2..0..0..6........................................................................................................................... 239

E.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........................................................................................................... 241

E.2 Hosp..i.t.a..l.i.z..a..t.i.o..n..s................................................................................................................. 242

................................................................................................................................... 247

E.3 Emergency Department Visit Data (2005, 2006 or 2007) and Rate

Calculation

E.4 Non.f.a..t.a..l..H..e..a..r.t..A..t.t.a..c..k..s....................................................................................................... 248

E.5 Scho..o..l..L..o..s..s..D...a..y.s............................................................................................................... 249

E.6 Othe..r..A..c..u..t.e...a..n..d...C..h..r..o..n..i.c..E..f.f.e..c..t.s........................................................................................ 250

E.6.1 Acute Bro..n..c..h..i.t..i.s.............................................................................................................................................. 251

E.6.2 Chronic B.r..o..n...c..h..i.t.i.s....I.n..c..i.d..e...n..c..e...R...a..t.e............................................................................................................... 251

E.6.3 Chronic B.r..o..n...c..h..i.t.i.s....P..r..e..v..a..l.e..n...c..e...R..a..t..e............................................................................................................ 252

E.6.4 Lower Re.s...p..i.r..a..t.o..r..y...S..y..m....p..t..o..m....s..................................................................................................................... 252

E.6.5 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..)....................................................................................................... 252

E.6.6 Work Los.s....D..a..y..s............................................................................................................................................... 252

E.7 Asth.m...a..-.R..e..l.a..t.e..d...H...e..a..l.t.h...E..f.f.e..c.t.s........................................................................................... 252

E.7.1 Shortnes.s...o..f...B..r..e..a..t.h........................................................................................................................................ 253

E.7.2 Wheeze .......................................................................................................................................................... 253

E.7.3 Cough .......................................................................................................................................................... 253

E.7.4 Upper Res...p..i.r..a..t.o..r..y...S..y..m....p..t..o..m....s...................................................................................................................... 253

E.7.5 Asthma P.o...p..u..l.a..t..i.o..n...E..s...t.i.m....a..t.e..s....................................................................................................................... 254

Appendix F: Particulate Matter Health Impact Functions in

U.S. Setup 255

F.1 Long..-.t.e..r..m....M..o..r..t.a..l.i.t.y........................................................................................................... 255

F.1.1 Expert Fu.n..c..t.i.o...n..s.............................................................................................................................................. 256

Parametri.c...D..i.s..t.r.i.b..u..t.io..n..s.................................................................................................................................... 257

Non-Para.m..e..t.r..ic...D...is..t.r..ib..u..t.i.o..n..s............................................................................................................................ 261
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Using Exp..e..r.t..F..u..n..c..t.i.o..n..s...in...B...e..n..M..A...P.................................................................................................................. 262

Distributio.n..a..l..D..e..t.a..i.l.s...b..y...E..x..p..e..r.t........................................................................................................................ 264

Expert A ......................................................................................................................................... 264

Expert B ......................................................................................................................................... 265

Expert C ......................................................................................................................................... 267

Expert D ......................................................................................................................................... 268

Expert E ......................................................................................................................................... 269

Expert F ......................................................................................................................................... 270

Expert G ......................................................................................................................................... 272

Expert H ......................................................................................................................................... 273

Expert I ......................................................................................................................................... 274

Expert J ......................................................................................................................................... 275

Expert K ......................................................................................................................................... 276

Expert L ......................................................................................................................................... 277

F.1.2 Laden et .a..l..(.2..0..0..6..)............................................................................................................................................ 279

F.1.3 Pope et a.l..(.2..0..0..2..).............................................................................................................................................. 279

F.1.4 Woodruff..e..t..a..l..(..1..9..9..7..)...................................................................................................................................... 280

F.1.5 Woodruff..e..t..a..l..(..2..0..0..6..)...................................................................................................................................... 281

F.1.6 Krewski e..t...a..l..(.2..0..0..9..)........................................................................................................................................ 281

F.2 Chro..n..i.c.../..S..e..v..e..r.e...I.l.l.n..e..s.s..................................................................................................... 283

F.2.1 Abbey et .a..l..(.1..9..9..5..b...)......................................................................................................................................... 283

F.2.2 Peters et..a..l..(.2..0..0..1..)........................................................................................................................................... 284

F.3 Hosp..i.t.a..l.i.z..a..t.i.o..n..s................................................................................................................. 285

F.3.1 Ito (2003) .......................................................................................................................................................... 286

F.3.2 Moolgavk.a..r...(.2..0..0..0..a..)..,..C..h...r.o...n..i.c...L..u...n..g............................................................................................................... 287

F.3.3 Moolgavk.a..r...(.2..0..0..0..b...).,..C...a..r.d...i.o..v..a..s..c..u...l.a..r........................................................................................................... 288

F.3.4 Moolgavk.a..r...(.2..0..0..3..)........................................................................................................................................... 289

F.3.5 Sheppard..(..2..0..0..3..).............................................................................................................................................. 289

F.4 Eme.r..g..e..n..c.y...R..o..o..m....V...i.s.i.t.s..................................................................................................... 290

F.4.1 Norris et .a..l..(.1..9..9..9..)............................................................................................................................................ 290

F.5 Mino..r..E..f.f.e..c..t.s...................................................................................................................... 291

F.5.1 Dockery e..t..a..l..(..1..9..9..6..)........................................................................................................................................ 292

F.5.2 Ostro (198..7..)...................................................................................................................................................... 292

F.5.3 Ostro and...R..o..t..h..s..c..h...i.l.d...(.1..9..8..9..).......................................................................................................................... 294

F.5.4 Schwartz..a..n..d...N...e..a..s...(..2..0..0..0..)............................................................................................................................. 295

F.6 Asth.m...a..-.R..e..l.a..t.e..d...E..f.f.e..c..t.s...................................................................................................... 295

F.6.1 Ostro et a..l..(.2..0..0..1..)............................................................................................................................................. 296

F.6.2 Pope et a.l..(.1..9..9..1..).............................................................................................................................................. 297

F.6.3 Vedal et a..l..(.1..9..9..8..)............................................................................................................................................. 298

F.7 Calc.u..l.a..t.i.n..g...T..h..r..e..s.h..o..l.d..-.A..d..j.u..s..t.e..d...F..u..n..c..t.i.o..n..s........................................................................ 299

Appendix G: Ozone Health Impact Functions in U.S. Setup 304

G.1 Sho.r.t.-.t.e..r.m....M...o..r.t.a..l.i.t.y........................................................................................................... 304

G.1.1 Bell et al (.2..0..0..4..)................................................................................................................................................. 305

G.1.2 Bell et al (.2..0..0..5..)................................................................................................................................................. 306

G.1.3 Huang et .a..l..(.2..0..0..5..)............................................................................................................................................ 306
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G.1.4 Levy et al.,..2..0..0..5................................................................................................................................................. 307

G.1.5 Ito and Th..u..r..s..t.o...n...(.1..9..9..6..).................................................................................................................................. 308

G.1.6 Ito et al (2.0..0..5..)................................................................................................................................................... 308

G.1.7 Moolgavk.a..r...e..t...a..l..(.1..9..9..5..).................................................................................................................................. 309

G.1.8 Samet et .a..l..(.1..9..9..7..)............................................................................................................................................ 309

G.1.9 Schwartz..(.2..0..0..5..)............................................................................................................................................... 310

G.2 Long..-.T..e..r..m....M..o..r..t.a..l.i.t.y.......................................................................................................... 310

G.2.1 Jerret et .a..l..(.2..0..0..9..)............................................................................................................................................ 311

G.3 Hosp..i.t.a..l..A..d..m...i.s..s.i.o..n..s........................................................................................................... 312

G.3.1 Burnett e.t..a..l..(.2..0..0..1..).......................................................................................................................................... 313

G.3.2 Moolgavk.a..r...e..t...a..l..(.1..9..9..7..).................................................................................................................................. 314

G.3.3 Schwartz..(.1..9..9..4..a..)............................................................................................................................................. 314

G.3.4 Schwartz..(.1..9..9..4..b..)............................................................................................................................................. 315

G.3.5 Schwartz..(.1..9..9..5..)............................................................................................................................................... 315

G.4 Eme.r..g..e..n..c.y...R..o..o..m....V...i.s.i.t.s..................................................................................................... 316

G.4.1 Jaffe et al..(..2..0..0..3..).............................................................................................................................................. 317

G.4.2 Peel et al.(..2..0..0..5..)............................................................................................................................................... 317

G.4.3 Stieb (199.6..)....................................................................................................................................................... 318

G.4.4 Wilson et..a..l..(.2..0..0..5..)........................................................................................................................................... 319

G.5 Mino..r..E..f.f.e..c..t.s...................................................................................................................... 319

G.5.1 Chen et a.l..(.2..0..0..0..).............................................................................................................................................. 320

G.5.2 Crocker a..n..d...H...o..r..s..t..(..1..9..8..1..).............................................................................................................................. 321

G.5.3 Gilliland e.t...a..l..(.2..0..0..1..)......................................................................................................................................... 322

G.5.4 Ostro and...R..o..t..h..s..c..h...i.l.d...(.1..9..8..9..).......................................................................................................................... 324

G.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..................................................... 325

Appendix H: Nitrogen Dioxide Health Impact Functions 329

H.1 Hosp..i.t.a..l..A..d..m...i.s..s.i.o..n..s........................................................................................................... 329

H.1.1 Fung et al..(..2..0..0..6..).............................................................................................................................................. 330

H.1.2 Linn et al.(..2..0..0..0..)............................................................................................................................................... 330

H.1.3 Luginaah.e...t..a..l....(.2..0..0..5..)...................................................................................................................................... 330

H.1.4 Moolgavk.a..r...(.2..0..0..3..)........................................................................................................................................... 331

H.1.5 Yang et al..(..2..0..0..3..).............................................................................................................................................. 332

H.1.6 Yang et al..(..2..0..0..5..).............................................................................................................................................. 332

H.2 Eme.r..g..e..n..c.y...R..o..o..m....V...i.s.i.t.s..................................................................................................... 333

H.2.1 Ito et al (2.0..0..7..)................................................................................................................................................... 333

H.2.2 Jaffe et al..(..2..0..0..3..).............................................................................................................................................. 334

H.2.3 NYDOH (20..0..6..).................................................................................................................................................... 334

H.2.4 Peel et al.(..2..0..0..5..)............................................................................................................................................... 335

H.2.5 Villeneuv.e...e..t...a..l..(.2..0..0..7..).................................................................................................................................... 335

H.3 Asth.m...a...E..x..a..c..e..r.b..a..t.i.o..n......................................................................................................... 336

H.3.1 Delfino et..a..l..(.2..0..0..2..)........................................................................................................................................... 337

H.3.2 Delfino et..a..l..(.2..0..0..3..)........................................................................................................................................... 337

H.3.3 Mortimer..e..t...a..l..(.2..0..0..2..)...................................................................................................................................... 338
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H.3.4 O'Connor..e..t...a..l..(.2..0..0..8..)...................................................................................................................................... 339

H.3.5 Ostro et a..l..(.2..0..0..1..)............................................................................................................................................. 340

H.3.6 Schildcro.u..t..e...t..a..l..(.2..0..0..6..)................................................................................................................................... 342

H.4 Mino..r..E..f.f.e..c..t.s...................................................................................................................... 343

H.4.1 Schwartz..e..t..a..l..(..1..9..9..4..)...................................................................................................................................... 343

Appendix I: Sulfur Dioxide Health Impact Functions 344

I.1 Hosp..i.t.a..l..A..d..m...i.s..s.i.o..n..s........................................................................................................... 344

I.1.1 Fung et al..(..2..0..0..6..).............................................................................................................................................. 345

I.1.2 Luginaah.e...t..a..l..(.2..0..0..5..)....................................................................................................................................... 345

I.1.3 Schwartz..e..t..a..l..(..1..9..9..6..)...................................................................................................................................... 346

I.1.4 Sheppard..(..2..0..0..3..).............................................................................................................................................. 346

I.1.5 Yang et al..(..2..0..0..3..).............................................................................................................................................. 346

I.1.6 Yang et al..(..2..0..0..5..).............................................................................................................................................. 347

I.2 Eme.r..g..e..n..c.y...R..o..o..m....V...i.s.i.t.s..................................................................................................... 347

I.2.1 Ito et al (2.0..0..7..)................................................................................................................................................... 349

I.2.2 Jaffe et al..(..2..0..0..3..).............................................................................................................................................. 349

I.2.3 Michaud e..t...a..l..(.2..0..0..4..)........................................................................................................................................ 350

I.2.4 NYDOH (20..0..6..).................................................................................................................................................... 350

I.2.5 Peel et al.(..2..0..0..5..)............................................................................................................................................... 351

I.2.6 Villeneuv.e...e..t...a..l..(.2..0..0..7..).................................................................................................................................... 352

I.2.7 Wilson et..a..l..(.2..0..0..5..)........................................................................................................................................... 352

I.3 Asth.m...a...E..x..a..c..e..r.b..a..t.i.o..n......................................................................................................... 353

I.3.1 Delfino et..a..l..(.2..0..0..3..)........................................................................................................................................... 353

I.3.2 Mortimer..e..t...a..l..(.2..0..0..2..)...................................................................................................................................... 354

I.3.3 O'Connor..e..t...a..l..(.2..0..0..8..)...................................................................................................................................... 355

I.3.4 Schildcro.u..t..e...t..a..l..(.2..0..0..6..)................................................................................................................................... 356

I.4 Mino..r..E..f.f.e..c..t.s...................................................................................................................... 357

I.4.1 Schwartz..e..t..a..l..(..1..9..9..4..)...................................................................................................................................... 357

Appendix J: Health Valuation Functions in U.S. Setup 359

J.1 Mort.a..l.i.t.y............................................................................................................................. 359

J.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.......................................................................................... 360

J.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.............................................................................. 360

J.2 Chro..n..i.c...I.l.l.n..e..s.s................................................................................................................... 361

J.2.1 Chronic B.r..o..n...c..h..i.t.i.s........................................................................................................................................... 361

Unit Value...B..a..s..e..d...o..n...T..w....o...S..t.u..d..i.e..s...o..f..W...T..P....................................................................................................... 361

Alternativ.e...C..o..s..t..o..f...Il.l.n..e..s..s...E..s..t.im...a..t.e..s................................................................................................................ 363

J.2.2 Chronic B.r..o..n...c..h..i.t.i.s....R..e..v..e...r.s...a..l.s....................................................................................................................... 364

J.2.3 Chronic A..s..t.h...m...a............................................................................................................................................... 364

J.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...)..................................................................................... 365

J.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................................................................. 367

J.3.1 Hospital A..d..m....i.s..s...i.o..n..s....................................................................................................................................... 367
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J.3.2 Emergenc..y...R...o..o..m.....V...i.s..i.t.s....f.o..r...A...s..t.h...m...a........................................................................................................... 369

J.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............................................... 370

J.4.1 Acute Bro..n..c..h..i.t..i.s...i.n...C...h..i.l.d...r.e...n......................................................................................................................... 372

J.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....................................................................................... 373

J.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...................................................................................... 374

J.4.4 Any of 19.R...e..s..p...i.r.a..t..o..r..y...S..y..m....p..t.o...m...s................................................................................................................. 375

J.4.5 Work Los.s....D..a..y..s...(..W...L..D...s..)................................................................................................................................ 375

J.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...).................................................................................................... 375

J.4.7 Asthma E.x..a..c..e..r..b..a..t..i.o..n...................................................................................................................................... 376

J.4.8 School Lo..s..s...D...a..y..s............................................................................................................................................ 377

Appendix K: Population & Other Data in U.S. Setup 379

K.1 Pop.u..l.a..t.i.o..n...D..a..t.a...i.n...U....S.....S...e..t.u..p........................................................................................... 379

K.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............................................................................................................. 380

K.1.2 Data Need..e..d...f..o..r...F..o..r..e..c..a..s..t..i.n..g......................................................................................................................... 382

Block-Lev..e..l.C...e..n..s..u..s...2..0..0..0................................................................................................................................ 382

County-Le..v..e..l..F..o..r.e..c..a..s..t.s................................................................................................................................... 386

K.1.3 PopGrid .......................................................................................................................................................... 387

How to U.s..e...P..o..p..G..r..id......................................................................................................................................... 387

PopGrid O..u..t.p..u..t................................................................................................................................................ 392

K.2 Mon.i.t.o..r..D...a..t.a...i.n...U....S.....S..e..t.u..p................................................................................................. 395

K.2.1 Data Proc.e..s..s...i.n..g............................................................................................................................................... 396

K.2.2 Output Fil.e..s....................................................................................................................................................... 397

Appendix L: Uncertainty & Pooling 398

L.1 Unce..r..t.a..i.n..t.y........................................................................................................................ 398

L.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........................................................ 398

L.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................................................................. 399

L.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.............................................................. 400

L.2 Poo.l.i.n..g.............................................................................................................................. 400

L.2.1 Weights U..s..e...d...f.o...r..P...o..o..l.i.n...g.............................................................................................................................. 401

Subjective...W....e..ig..h..t.s........................................................................................................................................... 401

Automatic..a..ll.y...G...e..n..e..r.a..t.e..d...W...e..i.g..h..t.s.................................................................................................................... 402

Fixed-Effe..c..t..W....e..ig..h..t.s........................................................................................................................................ 402

Random- ./..F..i.x..e..d..-.E..f.f..e..c..t..W...e..i.g..h..t.s...................................................................................................................... 403

L.2.2 Mechanic.s...o...f..P..o...o..l.i.n..g....i.n...B..e...n..M...A...P................................................................................................................. 406

L.2.3 Summing..D...i.s..t.r..i.b..u...t.i.o..n...s.................................................................................................................................. 407

L.2.4 Subtractin..g....D..i.s..t..r.i.b...u..t.i.o...n..s............................................................................................................................... 408

Appendix M: Command Line BenMAP 410

M.1 Ove.r.v..i.e..w............................................................................................................................ 410

M.2 Vari.a..b..l.e..s........................................................................................................................... 410
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M.3 Com..m...a..n..d..s........................................................................................................................ 411

M.3.1 Set Active...S..e...t.u..p............................................................................................................................................... 411

M.3.2 Create AQ..G........................................................................................................................................................ 411

Model Dire..c..t..................................................................................................................................................... 412

Monitor D.ir..e..c..t.................................................................................................................................................. 413

Monitor M.o..d..e..l..R..e..l.a..t.iv..e...................................................................................................................................... 415

Monitor R.o..ll.b..a..c..k............................................................................................................................................... 415

M.3.3 Run CFG .......................................................................................................................................................... 416

M.3.4 Run APV .......................................................................................................................................................... 417

M.3.5 Generate..R..e..p...o..r..t............................................................................................................................................. 418

Audit Trai.l........................................................................................................................................................ 418

CFGR Rep..o..r..t................................................................................................................................................... 418

APVR Re.p..o..r.t................................................................................................................................................... 419

M.4 Exam...p..l.e...1.......................................................................................................................... 419

M.5 Exam...p..l.e...2.......................................................................................................................... 421

Appendix N: Function Editor 423

N.1 User..D...e..f.i.n..e..d...V..a..r.i.a..b..l.e..s...................................................................................................... 423

N.2 The.S...c.r..i.p..t..L..a..n..g..u..a..g..e......................................................................................................... 424

N.3 Ope.r.a..n..d..s........................................................................................................................... 425

N.4 Ope.r.a..t.i.o..n..s......................................................................................................................... 425

N.5 Arith..m...e..t.i.c...F..u..n..c.t.i.o..n..s.......................................................................................................... 426

N.6 Agg.r.e..g..a..t.e...F..u..n..c..t.i.o..n..s......................................................................................................... 427

References 428
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Appendix A: Training Courses

BenMAP is intended primarily as a toolfor estimating the health impacts, and associated

economic values, associated with changes in ambient air pollution. It accomplishes this by running

health impact functions, which relate a change in the concentration of a pollutant to a change in

the incidence of a health endpoint. Inputs to health impact functions typically include:

(a) the change in ambient air pollution level,

(b) a health effect estimate,

(c) the background incidence rate of the health endpoint, and

(d) the exposed population.

For example, in the case of a premature mortality health impact function, we might have the

following:

Mortality Change = Air Pollution Change × Mortality Effect Estimate

× Mortality Incidence × Exposed Population

where each term on the right is defined as follows:

Air Pollution Change. The air quality change is calculated as the difference between the starting

air pollution level, also called the baseline, and the air pollution level after some change, such as

that caused by a regulation. In the case of particulate matter, this is typically estimated in

micrograms per cubic meter (μg/m3).

Mortality Effect Estimate. The mortality effect estimate is an estimate of the percentage change

in mortality due to a one unit change in ambient air pollution. Epidemiologicalstudies provide a

good source for effect estimates.

Mortality Incidence. The mortality incidence rate is an estimate of the average number of people

that die in a given population over a given period of time. For example, the mortality incidence

rate might be the probability that a person will die in a given year. Mortality incidence rates and

other health data are typically collected by each country’s government. In addition, the World

Health Organization is a good source for data.

Exposed Population. The exposed population is the number of people affected by the air pollution

reduction. The government census office is a good source for this information. In addition, private

companies may collect this information and offer it for sale.

BenMAP also calculates the economic value of health impacts. For example, after the calculation

of the mortality change, you can value the premature deaths avoided by multiplying the change in

mortality by an estimate of the value of a statistical life:

Value Mortality = Mortality Change × Value of Statistical Life

where the rightmost term is defined as follows:
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Value of Statistical Life. The value of a statistical life is the economic value placed on eliminating

the risk of one premature death.

Figure 1-1 provides the overallschematic of BenMAP, and the various major steps involved in

using it. This figure also highlights that BenMAP does not have air quality modeling capabilities,

and instead relies on modeling and monitoring inputs.

Figure 1-1. Overall schematic of BenMAP.

BenMAP also serves as a Geographic Information System (GIS), allowing users to create, utilize,

and visualize maps of air pollution, population, incidence rates, incidence rate changes, economic

valuations, and other types of data. BenMAP can thus be used for a variety of purposes,

including:

Generating population/community level ambient pollution exposure maps;

Comparing benefits associated with regulatory programs;

Estimating health impacts and costs of existing air pollution concentrations;

Estimating health benefits of alternative ambient air quality standards; and

Performing sensitivity analyses of health or valuation functions, or of other inputs.
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A wide range of people can use BenMAP, including scientists, policy analysts, and decision

makers. Advanced users can explore a broad array of options, such as using the map querying

features and exploring the impacts of different health impact and valuation functions.

A.1 United States

This training is intended for a beginning user of BenMAP that wants to use the U.S. setup that

comes with BenMAP. The training manual is organized into seven sections.

Section 1. Data Files Needed. This provides a list of the files that you will need for this training

and where you can get them.

Section 2. Mapping. This module is designed to help you become comfortable using BenMAP by

analyzing the various types of data that we will use throughout this course.

Section 3. One-step Analyses. You will create health incidence and valuation results. This onestep analysis uses pre-defined health incidence and economic valuation configurations, which you

can combine with your own baseline and control air quality grids.

Section 4. Creating Grids. In this section, you will create air quality grids (aqg) from both air

quality (AQ) model data and from monitor data. The overall goal will be to produce baseline and

control air quality grids for later estimations of health incidence and valuation.

Section 5. Health Incidence Estimation. In this section, you will modify an existing health

incidence configuration and use it to create new health incidence results. You will create two

separate sets of health incidence results based on the same configuration and two similar, but

distinct, controlstrategies.

Section 6. Aggregation, Valuation, and Pooling. In this section, you will create an aggregation,

pooling, and valuation (APV) configuration and use it to produce new valuation results. You will

create two separate sets of valuation results based on the same configuration and two similar, but

distinct, controlstrategies.

Section 7. Adding New Datasets and an Independent Case Study. In this section, you will add

new datasets to BenMAP and run a local-scale benefit analysis in Detroit, Michigan.

We have broken down each of the above sections into various modules, and each module

provides a context section, a module goal, and one or more example BenMAP applications. In

addition, there is a section with answers to embedded exercise questions for Sections 2 through 7.

A.1.1 Section 1. Data Files Needed for Training

A range of data files are needed for this training and can be accessed at the BenMAP website:

http://www.epa.gov/air/benmap/.

A.1.2 Section 2. Mapping Introduction

This module is designed to help you become comfortable using BenMAP by analyzing the various

types of data that you will use throughout this course. In this lesson, you willfocus on using

BenMAP's GIS tool to view maps of air quality data (file names ending in .aqg), health incidence

results (ending in .cfgr), and economic valuation results (ending in .apvr).
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A.1.2.1 RawMonitor Data

Several years of recent monitor data for Ozone, PM10and PM2.5 have been pre-loaded into the

BenMAP database. These data are derived from the extensive network of monitoring sites

throughout the U.S. For the PM (particulate matter) data, most of the monitor data is recorded

on a daily basis. The ozone measurements are recorded on an hourly basis. When displaying the

data for a pollutant, you can display different averaging techniques, typically referred to as air

quality metrics.

A.1.2.1.1 Example PM2.5 monitor data for 2000

Goal: To start learning about BenMAP and the GIS tool, and to explore PM2.5 monitor data for

the year 2000. You will be working with monitor values preloaded into BenMAP’s underlying

database

(a) Open BenMAP by clicking on the desktop icon or by choosing “Launch BenMAP 3” from the

Window's Start menu. This will bring up the main BenMAP window (Figure 2-1).
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Figure 2-1. Main BenMAP window.

(b) From the Tools menu, choose “GIS/Mapping” (Figure 2-2).
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Figure 2-2. Opening the GIS tool.

This will open the BenMAP GIS window (an example is shown later in Figure 2-4). At the top of

the GIS window, you willsee a series of buttons, described below. Note that in this terminology

a layer is a map, and “active layer” means the topmost map in the GIS window. You can have

multiple maps layered on top of each other in the GIS tool.

Open a File

Save active layer
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Zoom to full extent

Zoom in, Zoom out

Select a region to zoom into

Drag map

Display info - Displays information for all variables in active layer of that cell

Build queries to limit data displayed (e.g. values > 80 ppb)

Calculate layer statistics of the active layer

(c)Click on the Open a File button and choose “Monitors”. In the Select Monitors window

(Figure 2-3), set the pollutant to “PM2.5”, and under the library tab set the dataset to “EPA

Standard Monitors” and year to “2000”.

Although you will not use the Advanced button in this module, we could use it to further limit the

monitors that we would display based on location, state, or other monitor criteria.

Figure 2-3. Select Monitors window.
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(d) Click the OK button. This will bring up the monitor locations on the BenMAP GIS window

(Figure 2-4).

Figure 2-4. PM2.5 monitor locations.

(e) Double-click on the “PM2.5, 2000” layer. This will open the Display Options window

(Figure 2-5).

Set the variable to “QuarterlyMean”, which represents the average of the four quarterly means

(the annual average).

Set start and end sizes to “100”; this is the size of the monitor points.

Do not change the other seven fields in the window. The min and max values define your range.

You can edit this to narrow in on a specific range. The start and end color allow you to pick the

colors of the monitor points in your range. The default size and default color are for areas that

are outside your range. The decimal digits are the number of decimal digits displayed.
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Figure 2-5. Display Options window.

Background: The main PM metrics are D24HourMean (the daily value) and

QuarterlyMean (the mean of all days within an individual yearly quarter). The GIS

tool does not display the monitor values for particular day or quarter; rather, the GIS

tool willshow seasonal averages of these variables. For PM, the default "season" is

typically the full year. This will likely cause some confusion. When we display

D24HourMean, we are actually displaying the average of the 365 days of data, and

when we display QuarterlyMean we are showing the average of the 4 quarterly

means. In short, both these variables should be thought of as more akin to an annual

average. Note: the definition of metrics and seasons can be changed (discussed in

Lab 7).

(f)After clicking OK, you will get a map of the annually averaged PM2.5 values at all the monitor

locations. To gain a better sense of the monitor locations, use the

"-- Reference Layer --" drop down menu in the top right corner of the BenMAP GIS window

to select the "County" overlay (Figure 2-6). The reference layer overlays a specific grid type

(e.g. county, state, CMAQ) on top of your data layer. It provides geographic context to your

data layer.
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Figure 2-6. PM2.5 monitor locations overlaid with county boundaries.

(g)Now overlay a State reference layer. Experiment with zooming in and out of the map (using

the toolbar). Try out some of the other buttons, including Display info and Create layer

statistics. Note: the values are in micrograms per cubic meter (μg/m3).

(h)Exercise (2.1): What are the D24HourMean, QuarterlyMean, and lat/lon of the monitor at

the northern tip of Maine? Hint: Use the Display info and Zoom buttons.

Answer:

(i) Use the Build queries button to bring up the Build Query window (Figure 2-7). Use the

fields list and mathematical operator buttons or simply type in that window to create a query

that limits the monitors to those with QuarterlyMean less than 10 micrograms per cubic meter

(μg/m3). Click OK or Execute.
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Figure 2-7. Build Query window.

Tip: You construct a query in the query text field (indicated by red arrow above). You

can type in the field name (or double-click it, e.g. "QuarterlyMean"), the values, and the

operators (e.g. ">" or "=") in the query text field. To remove the query (i.e. see all your

monitors), delete the query text field and click OK (or Execute). Execute is the same

as OK except that it keeps the Build Query window open.

(j) Exercise (2.2): What states have monitors with QuarterlyMean values >20 μg/m3?

Answer:

(k)Remove the query so that you can see all the monitors. Do not close the BenMAP GIS

window, because it will be used again in the next example.

A.1.2.1.2 Example O3 monitor data for 2000

The goal of this task is to learn about layers in the GIS tool and to explore the O3 monitor data

for 2000.

(a) Using the same GIS window, open a second dataset by clicking on Open a File and selecting

"Monitors". This time, set the pollutant to “Ozone“. As before, set the year to “2000”. Click OK.

(b) Uncheck “PM2.5” in the Layers panel by clicking in the checkbox. The PM2.5 monitors should

disappear.

(c) Double-click on the “Ozone, 2000” layer to open up the Display Options window for that layer
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(Figure 2-8). For the variable, select “D8HourMax”, which is the average of the 8 hr maximum

window for each day in the ozone season.

Change the "Start Size" and "End Size" to “100” and colors from dark blue (Start Color) to light

blue (End Color). In other words, we are differentiating the ozone monitors from the PM2.5

monitors by having them range from dark to light blue. Click OK. Note: these values are the

concentrations of ozone in parts per billion (ppb).

Figure 2-8. Display Options window values described in step (c) above.

Background: There are a series of ozone metrics: D1HourMax (the maximum 1 hour value in a

day), D24HourMean (daily mean), D5HourMean (daily mean of hours 10am - 2pm),

D8HourMax (the greatest mean for any 8 hour window in a day), and D8HourMean (daily

mean of hours 9am - 4pm). Again, the GIS tool does not display the monitor values for any

particular day. It calculates and displays a seasonal average for each of the above metrics.

The default ozone season is from May 1st through September 30th. For example, the

D1HourMax is calculated by adding up the maximum value for that monitor for each day in the

season and then dividing by the number of days in the ozone season. Note: the definition of

metrics and seasons can be changed (discussed in Lab 7).
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(d)Recheck “PM2.5, 2000” in the Layers panel. You should now see both the O3and PM2.5 data.

In the Layers panel, switch the order of the layers by right-clicking on the "PM2.5, 2000" layer

and select "Move up" in the pop-up window. The active layer is always the topmost layer in the

Layers panel. Note: Only the active layer is used in getting information or performing queries.

(e)Exercise (2.3): What are the maximum and minimum QuarterlyMean values for the PM2.5

monitors? Hint: use the Layer Statistics button and PM2.5should be the active layer.

Answer:

(f)Exercise (2.4): What are the maximum and minimum D8HourMax values for the ozone

monitors? Which states have a D8HourMax greater than 60 parts per billion (ppb)? Hint:

ozone should be the active layer. When performing the query, you might want to uncheck the

PM2.5layer so that it is easier to see the ozone monitors.

Answer:

(g)After you are done, click Close at the bottom right of the GIS window. This completes the

“Raw Monitor Data” module for this lab.

A.1.2.2 Model Data

You will use CMAQ (an AQ model that simulates the chemistry and movement of various

pollutants) outputs from the PM2.5 Regulatory Impact Analysis (RIA) for this training module.

Unlike the monitor data visualized in the previous module, the model data has values that are on a

regular grid and cover the whole area of the map. The RIA model data is a forecast of the air

quality (AQ) for the year 2020. We willfocus on a baseline scenario (think of this as "businessas-usual") and two controlscenarios (in these cases additionalregulations have been applied to

emission source, resulting in generally lower pollution levels).

We recommend that you are detailed and consistent in naming your BenMAP files. In this lab,

the file name includes references to the annual PM2.5 NAAQS (National Ambient Air Quality

Standards) and a daily PM2.5 NAAQS. For example, the file

“Baseline_PM25_RIA_2020_cmaq_grid_15_Annual_65_Daily.aqg” refers to the baseline

modelrun on the CMAQ grid type in which most annual PM2.5 values are below 15 μg/m3 and

most daily values are below 65 μg/m3. For the rest of the modules, we will use the shorthand

“Baseline PM2.5 RIA 2020 15/65” to refer to the baseline scenario with 15 μg/m3 annual

NAAQS and 65 μg/m3 daily NAAQS. An equivalent shorthand will be used for the control
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scenarios.

A.1.2.2.1 Example Air Quality Grid File:Baseline PM2.5

The goal of this example is to look at model data using the BenMAP GIS tool and to learn about

the differences between political-type grids and CMAQ-type grids.

(a)From the main BenMAP window, open a new GIS window by choosing “GIS/Mapping” in the

Tools menu.

(b)Click on the Open a File button, then choose “Air Quality Grid (*.aqg)“. Under the “Air Quality

Grids” folder in the Open an Air Quality Grid window, navigate to the folder “PM25_RIA”,

select the file named “Baseline_PM25_RIA_2020_cmaq_grid_15_Annual_65_Daily.aqg”, and

click Open.

(c)In the GIS window that appears, double-click on the

“Baseline_PM25_RIA_2020_cmaq_grid_15_Annual_65_Daily.aqg” layer to open the Display

Options window.

In that window, set the variable to “QuarterlyMean”. This will cause the annual average data to be

displayed.

Also uncheck the grid outline; this is usually preferred, because the window often looks messy with

both the data and the grid outlines displayed. Click OK in the Display Options window.

Finally, use the drop-down menu near the upper right corner of the GIS window to overlay a "State"

reference layer (Figure 2-9).
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Figure 2-9. PM2.5 model output overlaid with state boundaries.

(d)Exercise (2.5): Use the Zoom in button to zoom into a state border region until you can see

the model grid cells. Do the grid cells align with the state boundaries? In other words, do the

model grid cells perfectly fit within the political boundaries?

Answer:
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(e)Exercise (2.6): What states are out of attainment in this baseline modelscenario? States that

are out of attainment are those that have at least one QuarterlyMean grid cell value above 15

μg/m3.

Answer:

(f)Try overlaying the "CMAQ 36km Nation Overlap” overlay reference layer. Zoom into a small

region. Note how the model values line-up with the reference layer.

(g)When you are done exploring, close the GIS window by clicking Close. This completes the

“Model Data” module.

A.1.2.3 Health Incidence

To produce health incidence results, the first step is to calculate the change in pollution

concentrations that would be produced by the application of a given set of emissions controls.

The concentration change in a pollutant (say, PM2.5) is the difference (the “delta”) between the

modeling results from a controlscenario and the modeling results from the baseline scenario.

These deltas and a gridded population dataset are then used in concentration-response (C-R)

functions to calculate the change in health incidence that would result from this change in pollution.

These C-R functions are based on epidemiologicalstudies and can be selected by the user (see

Lab 5). Typically, these health incidence results show the number of avoided health incidence (e.

g. the decrease in asthma, bronchitis, mortality, etc) due to a decrease in pollution.

In the rest of this module, we refer to the health incidence results via the shorthand versions of

their controlscenario names (as explained in Section 2.2.1). Also recall that the abbreviation

“cfgr” refers to health incidence results. Note: we don't actually go through the procedure of

creating these health incidence results in this lab (see Labs 3 and 5); rather, we are just looking at

the pre-computed results.

A.1.2.3.1 Example Configuration Results File:Control PM2.5

The goal of this exercise is to use the GIS tool to explore the reductions in health incidence that

would be due to the reductions in PM2.5caused by the RIA controlscenario. In particular, we

will look at reductions in mortality, acute respiratory symptoms, chronic bronchitis, and

emergency room (ER) visits.

(a)From the main BenMAP window, open a new GIS window.

(b)Click on the Open a File button, then select “Configuration Results (*.cfgr)”. Under the

"Configuration Results" folder, select the file named

“Control_PM25_RIA_2020_cmaq_grid_15_annual_35_daily.cfgr”. Click Open.
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(c)This will bring up an additional window Edit GIS Field Names. In the last column of this

window, change the names of the fields to more meaningful names, by highlighting the contents

of each of the four cells and typing in “Mortality”, “ChronBronc”, “ER”, and

“AcutResp” (Figure 2-10). Note: the GIS field names cannot exceed 10 characters in length.

Before clicking OK, do the next exercise.

Figure 2-10. Edit GIS Field Names window.

(d)Exercise (2.7): Look at the various columns in the Edit GIS Field Names window

(information and variables used in the C-R functions, e.g. "Pollutant" and "Author"). For what

age range are we calculating the change in mortality? When finished with this exercise, click

OK.

Answer:

(e)In the BenMAP GIS window, display mortality data, uncheck the CMAQ grid outline, and

overlay a state reference layer. Note: these values are number of deaths prevented by the

controlscenario.
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(f) Exercise (2.8): Under this controlstrategy, which states had more than 25 avoided deaths?

Answer:

(g)Display acute respiratory symptoms, "AcutResp".

(h)Exercise (2.9): How do the number of acute respiratory incidences avoided compare to the

number of premature deaths avoided? Hint: use the Display info or Calculate layer statistics

buttons.

Answer:

(i) Display and explore the GIS fields DELTAX and POPX. Here, "X" refers to a number.

Background: For each health incidence result, there is a corresponding population and

delta variable. For example, DELTA0 and POP0 are the PM2.5 delta and the population

that mortality was calculated from. The number comes from the initialresult names. For

example, Result0 (mortality) matches DELTA0 and POP0; Result1 (ChronBronc) matches

DELTA1 and POP1; etc. Note, the population of interest is determined by the C-R

function and is not necessarily equivalent between the various health incidence results (e.g.

different age ranges, gender, or ethnic groups).

(j) Exercise (2.10): Compare the delta and mortality values. Look at the spatial pattern of these

two variables. Why do the delta and the mortality values not exactly correlate?
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Answer:

(k)Close the GIS window. This completes the “Health Incidence” module.

A.1.2.4 Valuation Map

For the purposes of this course, “valuation results” are the economic benefits of avoiding the

previously calculated changes in health incidence. These are the monetized benefits of the avoided

premature mortalities and morbidities (i.e., non-fatal health effects), which are the two measures

we discuss in this module. These valuation results are calculated by taking the estimates of health

incidence avoided and applying valuation functions to express those incidences in dollar terms.

For example, the economic value of premature mortality avoided has a monetary value that is

expressed as a distribution. If you calculate a mean value of that distribution, you can then

multiply that mean value by the number of mortalities avoided (due to your controlstrategy),

resulting in an estimate of the economic benefit of avoiding those premature mortalities.

The valuation results (and the underlying health incidence) are typically aggregated from the

CMAQ or county grid to State or National totals. The results (health incidence and valuation)

are often pooled. Pooling are methods for combining similar health incidence or valuation results.

For example, if you have two different studies (valuation functions) for calculating the monetized

benefit of avoided ER visits, you would "pool" together these two results to create a single

valuation for ER (see Lab 6).

As in the “Health Incidence” module just completed, here we refer to the valuation results via the

shorthand versions of their controlscenario names. (Recall that the abbreviation “apvr” refers to

economic valuation results.)

A.1.2.4.1 Example Valuation Results File:Control PM2.5

The goal of this exercise is to use the GIS tool to explore the economic benefits of the above

reductions in health incidence due to this controlscenario. In particular, we will look at the cost

savings due to reductions in mortality and in morbidity. We will also compare the economic

benefits to the pooled and aggregated health incidence results.

(a)Open a new GIS window, click on the Open a File button, select “APV Configuration Results

(*.apvr)”, then select “Pooled Valuation Results” (Figure 2-11).
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Figure 2-11. Selecting the “Pooled Valuation Results” file.

Tip: There are a lot of options for what type of data to map with apvr files. Typically we use

"Pooled Incidence Results" (aggregated and pooled health incidence) or "Pooled Valuation

Results" (aggregated and pooled valuations).

(b)In the Open an APV Configuration Results File window, under the folder "Configuration

Results", select the file named

“Control_PM25_RIA_2020_cmaq_grid_15_annual_35_daily_state.apvr”. Click Open.

(c)In the Edit GIS Field Names window that appears, edit the GIS field names. Use the same

four field names as in the previous module (“Mortality”, “ChronBronc”, “ER”, and

“AcutResp”).

(d)After clicking OK, a Valuation Sums Layer window (Figure 2-12) will appear. This window is

the starting point for adding together various valuation results to get total values for mortality

and morbidity. We will view these summed results in the GIS window.
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Figure 2-12. Valuation Sums Layer window.

(1) To create a morbidity layer, click on the Add Sum button. An Add Valuation Sum

window will appear (Figure 2-13).

In the Include in Total column at the right, check all the health incidences that will be summed

together to make morbidity (chronic bronchitis, ER visits, and acute respiratory symptoms).

At the bottom left corner of this window, type “Morbidity” into the Valuation Sum Identifier field.

Leave the Summation Type field as “Dependent“.
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Figure 2-13. An Add Valuation Sum window showing the settings

for creating the morbidity layer.

(2)Click OK, which willreturn us to the Valuation Sums Layer window. In the GIS Field

Name column, highlight the cell and type in “Morbidity” (Figure 2-14).

Do not click OK in the Valuation Sums Layer window. Continue on to

step (3).
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Figure 2-14. Valuation Sums Layer window after the

morbidity sum identifier and GIS field name have been added.

(3)Next, add mortality to the Valuation Sums Layer window by clicking on the Add Sum

button again, then checking “Mortality” in the Add Valuation Sum window, typing

“Mortality” into the Valuation Sum Identifier field, and clicking OK.

Return to the Valuation Sums Layer window and enter “Mortality” into the

GIS Field Name cell in the mortality row that has been added to that window.

Finally, click OK to close the window.

(e)In the Layers area of the GIS window, you willsee that two layers have been added: “Pooled

Valuation Results Sums” and

“Control_PM25_RIA_2020_cmaq_grid_15_annual_35_daily_state.apvr”.

Double click on the "Pooled Valuation Results Sums" layer and set the display variable

to "Mortality" (Figure 2-15).
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Figure 2-15. Economic benefit due to the reduction in deaths.

(f) Display the morbidity valuation data. Use the Display info button to explore some of the

morbidity and mortality valuation values for specific states. Note: these benefits are in dollars.

(g)In the same GIS window, we will now overlay a pooled and aggregated health incidence layer

for the same controlscenario. We can use this layer to see the total number of prevented

health incidences in each State.

(1)Use the Open a File button, select “APV Configuration Results (*.apvr)”, then choose

“Pooled Incidence Results” (Figure 2-16).
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Figure 2-16. Selecting the “Pooled Incidence Results” file.

(2)Under the “Configuration Results” folder, open the same file:

“Control_PM25_RIA_2020_cmaq_grid_15_annual_35_daily_state.apvr”.

(3)In the Edit GIS Field Names window, edit the field names as before, then close that

window.

Double click the top layer,

"Control_PM25_RIA_2020_cmaq_grid_15_annual_35_daily_state.apvr (Pooled

Incidence Results)" and set the variable to "Mortality".

(4)We should now have the pooled and aggregated mortality incidence (number of deaths

avoided per state) overlaying the pooled and aggregated mortality valuation (economic

benefits per state) (Figure 2-17).
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Figure 2-17. State mortality totals overlaying state economic benefit totals.

(h)Exercise (2.11):Compare the number of incidences for mortality and acute respiratory

symptoms (one of the components of morbidity). What are the values for Washington State?

Now compare the economic valuations for mortality and morbidity in the same state. What

values did you get? What does contrasting the incidence numbers with the valuation numbers

tell us about the valuation function for mortality versus the one for morbidity? Hint: you may

need to switch the active layer by right clicking on the layer of interest in the Layers panel.

Answer:

(i) Close the GIS window. This completes the “Valuation Map” module.
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A.1.2.5 Audit Trail

The audit trail is a toolfor looking at the headers of files created through BenMAP. In other

words, it allows you to explore the metadata (the settings, inputs, and/or configurations) for a

BenMAP file. An audit trail is a usefulfeature to check your work and see which options you

selected in your analysis. You can use the audit trail to look at all of BenMAP's output files,

including air quality grids, configuration files, and results files.

A.1.2.5.1 Example Health Incidence:Control PM2.5

The goal of this exercise is to use the audit trail tool to explore the metadata of files created in

BenMAP.

(a)In the main BenMAP window, click on the Report graphic that is at the bottom of the righthand panel(under “Custom Analysis”). In the Select Report Type window that appears, select

“Audit TrailReports” (Figure 2-18). Click OK.

Figure 2-18. Select Report Type window.

(b)In the Open window, under the “Configuration Results” folder, open the file

“Control_PM25_RIA_2020_cmaq_grid_15_annual_35_daily.cfgr”.

(c)An Audit TrailReport window will open that contains a tree structure giving the audit trail

information (Figure 2-19). You can expand any section of the tree by clicking on the plus sign

next to the heading, or collapse a section by clicking on the minus sign.
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Figure 2-19. Audit Trail Report window.

(d)Exercise (2.12): What population year was used in this study? What is the name of the grid

type?

Answer:

(e)Exercise (2.13): What is the age range for the emergency room (ER) CR function, and who

was the author of the study?
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Answer:

(f) Export the audit trail. Click the Export button. In the Save as window, save the file under the

"Reports" folder as "Control_PM25_RIA_2020_cmaq_grid_15_annual_35_daily". This is a

".txt" file that can be easily opened in Microsoft Word or Notepad.

Answer:

(g)Close the Audit TrailReport window by clicking OK. This completes the “Audit Trail”

module.

A.1.2.6 Report

Reports are a good way to summarize your BenMAP results in a table (columns and rows) and

export them to be used in Excel or some other data analysis tool.

A.1.2.6.1 Example Pooled Incidence:Control PM2.5 RIA

The goal of this exercise is to create reports from BenMAP results. In particular, to look at the

pooled and aggregated health incidence results from the 2020 RIA controlscenario.

(a)In the main BenMAP window, click on the Report graphic under “Custom Analysis”.

In the Select Report Type window, select the “Incidence and Valuation Results” item.

In the Open window, under the “Configuration Results” folder, open the file

“Control_PM25_RIA_2020_cmaq_grid_15_annual_35_daily_state.apvr”.

(b) In the Choose a Result Type window that appears (Figure 2-20), select “Pooled Incidence

Results”. Click OK.
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Figure 2-20. Choose a Result Type window.

(c)In the Configuration Results Report window that appears (Figure 2-21), click on the

checkbox for “Endpoint Group” in the “Pooled C-R Function Fields” area, and uncheck the

checkboxes for "Variance" and "Latin Hypercube" in the “Result Fields” area. In other words,

we are selecting which of the many available columns to display in the results table.

Under the "Display Options", the "Elements in Preview" field determines the number of rows

included in this preview window (in our example, 25). When you save the report, you will get all

the rows.

Background: In the report window, there are two columns in the report that are grid fields

("Column" and "Row"). They are unique identifiers of each minimum spatial unit. For a

CMAQ grid, these are merely the column and row number for each cell in the grid. For

political grids, their meaning depends on your grid definition. In our example, the column is

the state code and the row is the FIPS code.
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Figure 2-21. Configuration Results Report window.

(d)Save the report to a file. To do this, you can either drop down the File menu at the upper lefthand corner of the window and choose “Save” or simply hit Ctrl-S on the keyboard.

In the Save As window, save the file under the "Reports" folder as

“Control_PM25_RIA_2020_15_annual_35_daily_incidence_state.csv“, a commaseparated-value file.

(e)Open the .csv file in Excel to review outputs. The full path to this folder is:

"C:\Program Files\Abt Associates Inc\BenMAP 2.4 US Version\Reports"

(f) Close the Configuration Results Report window by clicking Done. This completes the

“Report” module.
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This completes the "Mapping Introduction" lab. In the next lab, “One-Step Analysis” we willrun

the one-step analysis, creating both health incidence results (cfgr) and valuation results (apvr).

A.1.2.7 Additional Mapping Activities

1. Overlay the PM2.5 monitor data over the baseline model data. Look at the similarity and

differences in the pattern of monitor versus modeled data. Make sure that the monitors' layer

is topmost.

2. Overlay the baseline model data over the control 2020 RIA 14/35 data. Compare the regions

in the two layers that are out of attainment. Also, compare the regions that show PM2.5

concentrations greater than 10 μg/m3.

A.1.3 Section 3. One-Step Analysis

In this section, you will create health incidence and valuation results. This one-step analysis uses

pre-defined health incidence and economic valuation configurations, which you can combine with

your own baseline and control air quality grids. We willrun two separate one-step analyses for

two similar, but distinct controlstrategies.

The One-Step Analysis encapsulates all three stages of BenMAP: (1) Calculate the change in

pollution concentrations that would be produced by the application of a given set of emissions

controls. The concentration change in a pollutant (say, PM2.5) is the difference (the “delta”)

between the modeling results from an emissions controlscenario and the modeling results from

the baseline scenario. (2) These deltas, background incidence rates, and a gridded population

dataset are then used in concentration-response (C-R) functions to calculate the change in health

incidence that would result from this change in pollution. Typically, these health incidence results

show the decrease in health incidence (e.g. the decrease in asthma, bronchitis, mortality, etc) due

to a decrease in pollution. (3) These health incidence results are then used to calculate economic

benefits due to changes in the population's mortality and morbidity. For example, what is the

economic valuation of the avoided premature mortality due to the emissions control? The

valuation results (and the underlying health incidence) are typically then aggregated to county,

state, or national totals. Note: in One-Step Analysis, the health incidence and valuation functions

are standard EPA configurations.

A.1.3.1 Example PM2.5 Control 2020 15/35 National

The goal of this exercise is to re-create the controlRIA 2020 15 annual, 35 daily μg/m3 health

incidence and valuation study that we saw in the “Mapping Introduction” lab. We will produce

similar health incidence results (.cfgr file) and valuation results (.apvr file).

Procedures:

(a) In the main BenMAP window, open one-step analysis. Simply click on the left-hand

panel graphic under the label "One-Step Analysis". This will open the One Step
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Analysis window.

Figure 3-1. One-Step Analysis window.

(b) Set the "Run Name". This is the name that will be used for our cfgr and apvr files. A

recommended practice is to be specific and base it on the control. In the "Run Name"

field, type: “Control_PM25_RIA_2020_cmaq_grid_15_annual_35_daily_county”.

Note: we have added "county" to differentiate these results from our previous ControlRIA

2020 15/35 results that had been aggregated to State totals.

(c) Set the output directory. Use the Select button to select the “Configuration Results”

directory
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(d) In the panel "1. Air Quality Grids", set the baseline and control aqg. In many cases, the

control AQG will have lower pollution values than our baseline. This reduction in

modeled ambient PM2.5is the result of our policy scenario that reduced emissions from

industrial or other sources. Click the Open button next to the "Baseline File" field.

An Open window will appear. Navigate to the “PM25_RIA” folder under the “Air Quality

Grids” folder, select “Baseline_PM25_RIA_2020_cmaq_grid_15_Annual_65_Daily.aqg”,

and click Open.

Repeat these steps for the "Control File", this time selecting

“Control_PM25_RIA_2020_cmaq_grid_15_Annual_35_Daily.aqg”.

(e) Map the change in pollution (the deltas) between the baseline and the control. Click on

the Map Deltas button. A BenMAP GIS window will appear with 3 layers: "Delta" (the

change in PM2.5), "Control Grid" (the control modeled values), and "Baseline Grid" (the

baseline modeled values) (Figure 3-2):

Figure 3-2. GIS window for mapping deltas between the baseline and the control.

(1) Double click the “Delta” layer in the BenMAP GIS window. In the Display Option

window, set the variable to “QuarterlyMean” and turn off the grid outlines. Click OK.
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In the BenMAP GIS window, uncheck the "Control Grid" and "Baseline Grid" layers and

overlay a state reference layer.

Background: The "Delta" is a map of the change in AQ between your baseline and control

scenarios (i.e. baseline - control). Typically it is a good practice to check the delta. You can

use this map to see if the changes in air quality are in the right direction (typically that you are

getting positive values, i.e. reductions) and magnitude and that the changes are occurring in the

appropriate places. You can also use this window to look at the underlying control or baseline

scenarios: "Control Grid" and "Baseline Grid" in the Layers panel.

Figure 3-3. The resulting deltas between the baseline and the control.

(1) Exercise (3.1):In which states are the difference between the baseline

and the control greater than 0.2 μg/m3? Hint: use the Build queries button.

Answer:
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(2) Exercise (3.2): Off the coast of California and on the Northeastern edge

of the CMAQ grid, there are significant reductions in concentrations. Will

these areas change our health incidence and valuation values? Explain your

answer.

Answer:

(f) Click Close in the BenMAP GIS window. This willreturn you to the One Step

Analysis window.

(g) Aggregation. Aggregation refers to the summing of health incidence results and

valuation results to get more meaningful totals. In our case, the baseline and control

modeled AQ data are at the fine scale of CMAQ grid cells. We want to aggregate them

up to the county level.

(1) Exercise (3.3): Look at the aggregation levels for Incidence and

Valuation. What grids are available? If we set the Incidence grid to state,

what grids are now available for Valuation?

Answer:

(2) Set the aggregation levels to “County” under “2. Incidence” and “3.

Valuation”.

(h) Run one-step analysis. Click the Go button on the bottom of the One Step Analysis

window. A Progress window will appear. The run will have completed when the One
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Step Results window appears (Figure 3-4). Do not close the One Step Results

window when it appears.

Running a one-step analysis will take a few minutes. If we were running a larger domain,

the grid cells had finer resolution, or our configuration included more functions, the onestep analysis would take much longer. The majority of the computation time is taken up

in calculating the valuation results (apvr).

Analysis: The rest of the exercises in Section 3.2 focus on analyzing the results of our BenMAP

One-step Analysis run. Specifically, we will look at the newly created health incidence results (cfgr)

and valuation results (apvr) files.

(i) One Step Results are a series of customized reports and plots that were designed for

EPA’s apvr setup. Because we are using a simplified configuration, these reports

provide limited results.

Click the Audit Trail Report button in the One Step Results window.
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Figure 34. One Step Results window providing custom reports and plots.

Tip: By using the Load Apvr button (bottom of the One Step Analysis window), you can

select any results file (apvr). Note, the full capabilities of the One Step Results currently only

work for a full EPA RIA configuration.

(j) Exercise (3.4): In the audit trailreport and under "Configuration Results", open "CR

Function 0"? What is the function's endpoint? Who is the author of the underlying

study? Under "Advanced", what is the aggregation name for incidence and valuation

results?

Answer:
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(k) In the Audit Trail Report window, click OK to close the window. Click Close in the

One Step Results window. This willreturn you to the main BenMAP window.

(l) Open a new BenMAP GIS window, click on the Open a File button, then select

“Configuration Results (*.cfgr)”. Under the "Configuration Results" folder, select the

newly created file:

“Control_PM25_RIA_2020_cmaq_grid_15_annual_35_daily_county.cfgr”.

(m) In the Edit GIS Field Names window, change the field names to: “Mortality”,

“ChronBronc”, “ER”, and “AcutResp”. Click OK.

In the BenMAP GIS window, double click the layer

"Control_PM25_RIA_2020_cmaq_grid_15_annual_35_daily_county.cfgr” and display

the "AcutResp" variable.

(n) Exercise (3.5): Which grid is used in the health incidence results? Why is this different

than the aggregation level in the One-step analysis window? Note: you can use either

the audit trail or the GIS toolfor this exercise.

Answer:

(o) Exercise (3.6): Which states have a reduction of more than 4000 acute respiratory

incidences? Hint: use the Build queries button.

Answer:
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(p) Overlay the new .apvr (valuation results). Use the Open a File button, select “APV

Configuration Results (*.apvr)”, then choose “Pooled Incidence Results” (Figure 35).

Open the file “Control_PM25_RIA_2020_cmaq_grid_15_annual_35_daily_county.

apvr”. Edit the GIS field names to more meaningful names.

Figure 3-5. Opening pooled incidence results.

(q) Exercise (3.7): What grid is used in the pooled incidence results?

Answer:
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(r) Exercise (3.8): Select mortality for your pooled (and aggregated) incidence results.

What is the state (col) and FIPS (row) codes for the county with the highest number of

avoided mortalities? What is the value?

Answer:

(s) Exercise (3.9): What are the maximum value and sum for avoided mortalities in the

pooled incidence results? Make the incidence results

("Control_PM25_RIA_2020_cmaq_grid_15_annual_35_daily_county.cfgr ") the active

layer. What are the maximum value and sum for avoided mortalities in the incidence

results? Hint: Use the Calculate layer statistics button.

Answer:

(t) Overlay the pooled valuation results. As a reminder:

· Open a File, select “APV Configuration Results (*.apvr)”, then select “Pooled

Valuation Results”. Open

“Control_PM25_RIA_2020_cmaq_grid_15_annual_35_daily_county.apvr”.

· Edit the GIS field names. Use the same four field names (“Mortality”,

“ChronBronc”, “ER”, and “AcutResp”).

· In the Valuation Sums Layer window, click Add Sum to create a Mortality and

a Morbidity Sum (Figure 3-6). Click OK when you have added the two sums.

We will view these summed results in the GIS window.
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Figure 3-6. Creating valuation sums for the pooled valuation results.

· In the BenMAP GIS window, double click the “Pooled Valuation Result Sum”

layer and display the "Morbidity" variable.

(u) Exercise (3.10): What is the state (col) and FIPS (row) codes for the county with the

greatest benefit (valuation of avoided morbidity and mortality)? What are the morbidity

and mortality benefits for this county? What is the sum of the mortality and morbidity

benefits across the whole country? Note: you could also do this through Reports.

Answer:
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(v) Save the pooled valuation results to a shapefile. Click on the Save Active Layer icon

. The Save the active layer to file window will appear.

Under the "Reports" folder, save the shapefile as

"PM25_RIA_2020_15_annual_35_daily_county_pooled_valuation". Click Save. This

shapefile can be used as an input for another GIS software program. Click Close in the

GIS window returning you to the main BenMAP window.

(w) From the main BenMAP window, run a standard pooled valuation report on our apvr.

As a reminder:

Close the One-Step Analysis window. From the main BenMAP window, click the

Report graphic (bottom of the right-hand panel). Select a report type of “Incidence and

Valuation Results”.

After we have opened the newly created county apvr, select the “Pooled Valuation

Results” in the Choose a Results Type window.

In the report window, check the “Endpoint Group” in the “Pooled Valuation Method

Fields”, uncheck the “Latin Hypercube” from the “Result Fields”, and reduce the digits

after decimal point to 0 (Figure 3-7).
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Figure 3-7. Report of pooled valuation results.

(x) Exercise (3.11): For col 1 and row 9, which endpoint (i.e. ER visits, Mortality, etc) has

the greatest standard deviation? Which endpoint has the greatest coefficient of variation,

a.k.a. relative standard deviation (standard deviation/mean)?

Answer:
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(y) Close the APV Configuration Results Report window by clicking Done.

A.1.3.2 Example PM2.5 Control 2020 14/35 State

The goal of this exercise is to create a controlRIA 2020 14 annual, 35 daily μg/m3 health

incidence and valuation study. Unlike our previous example, we will aggregate to the state level.

Our intuition is that this will have greater benefits than the control 2020 RIA 15/35 μg/m3 analysis

because we have a lower annual NAAQS.

Procedures: We have abbreviated the instructions because they are very similar to the steps you

just completed in Section 3.2.

(a)Open the One Step Analysis window.

(b)Set the run name to: “Control_PM25_RIA_2020_cmaq_grid_14_annual_35_daily_state”.

(c)Set the output directory to "Configuration Results"

(d)Set the baseline and control to:

“Baseline_PM25_RIA_2020_cmaq_grid_15_Annual_65_Daily.aqg” and

“Control_PM25_RIA_2020_cmaq_grid_14_Annual_35_Daily.aqg”, respectively.

(e)Go through the process of mapping the deltas. See section 3.2(e) for explicit instructions.

(f) Exercise (3.12): How do the deltas for this analysis compare to our previous analysis. For

example, you could use the same query (QuarterlyMean > 0.2) to compare the deltas.

Answer:

(g)Set the health incidence and valuation aggregation to the “State” grid and click Go.

Note: the One-step Results will not work for State aggregation. The current One-step

Reports are designed only for National or Report region aggregation levels. They also

assume that we are using the full EPA configuration. Instead, we will use normalreports

and mapping to analyze this run.

Analysis: The rest of the exercises in Section 3.3 focus on analyzing the results of our BenMAP

One-step Analysis run. Specifically, we will look at the newly created health incidence c results

(cfgr) and valuation results (apvr) files.
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(h)Exercise (3.13): Map the pooled incidence results for

“Control_PM25_RIA_2020_cmaq_grid_15_annual_35_daily_state.apvr” and

“Control_PM25_RIA_2020_cmaq_grid_14_annual_35_daily_state.apvr”. Note:for detailed

instructions see Section 3.2(d) in the analysis section. How do the mortality values for IL

compare between the 2 controlscenarios? How does the mortality values for CA compare?

What are the overall differences between the 2 controlstrategies?

Answer:

(i) Exercise (3.14): Overlay the pooled valuation results for

“Control_PM25_RIA_2020_cmaq_grid_14_annual_35_daily_state”. Remember to create

“Mortality” and “Morbidity” variables in the Valuation Sums Layer window. What are the

mortality and morbidity values for CA? For IL? How does the sum of mortality and morbidity

benefits across the 48 states compare to our results from

“Control_PM25_RIA_2020_cmaq_grid_15_annual_35_daily_county” (see Exercise 3.10)?

Note:for detailed instructions see Section 3.2(l) in the analysis section. You could also use

reports to answer this question.

Answer:

(j) After finishing the exercise, close the BenMAP GIS window.

(k)This completes the "One-Step Analysis" lab. In the next lab, “Creating Grids” we will create

new air quality grids (aqg results) from both monitor and model data.

A.1.4 Section 4. Creating Grids

In this section, you will create air quality grids (aqg) from both air quality (AQ) model data and
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from monitor data. The overall goal will be to produce baseline and control air quality grids for

later estimations of health incidence and valuation.

A.1.4.1 Air Quality Model Grids

Think of AQ models as weather models for air pollution. They produce modelresults over the

whole map on a regular grid. Examples of AQ models are CMAQ and CAMx. These models

are post-processed to generate a csv file or a database table and their values are usually

averaged to daily or yearly statistics. These post-processed datasets can now be imported into

BenMAP.

A.1.4.1.1 Example: PM2.5 Control 2020 15/35 Adjusted

The goal of this exercise is to create a control AQ grid for PM2.5. The CMAQ model data input

is similar to the 15 annual, 35 daily μg/m3 controlscenario that we saw in the “Mapping

Introduction” and “One-Step Analysis” sections, except that it has been post-processed

(adjusted) to remove the extreme high values.

Procedures:

(a)Open Air Quality Grid Creation. Click on the graphic on the right-hand panel of BenMAP’s

main window

(b)Choose “Model Direct” in the Air Quality Grid Creation Method window (Figure 41).

Figure 4-1. Air Quality Grid Creation Method window.

(c)In the Model Direct Settings window, set the grid type to “CMAQ 36km Nation Overlap”

and the pollutant to "PM2.5". Leaving the tab as “Generic Model Databases”, click Browse.

Navigate to the "PM25_RIA" folder under the "Air Quality Grids" folder. In the Open

window, change the "Files of type" field to "Text files" and select the model data:

"Control_PM25_RIA_2020_cmaq_grid_15_Annual_35_Daily_adjusted".
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(d)Click OK to create a new model derived aqg. Save the output under the “PM25_RIA” folder

as: “Control_PM25_RIA_2020_cmaq_grid_15_Annual_35_Daily_adjusted”. Note: this may

take a few minutes to run.

A.1.4.1.2 Example:Control PM2.5 RIA 2020 14/35 adjusted

The goal of this exercise is to create a control AQ grid for PM2.5, similar to the 14 annual, 35

daily μg/m3 control that we saw in the Mapping Introduction and One-step Analysis sections.

Again, this model data has been post-processed to remove the extreme high values.

Procedures:

(a)Repeat the above steps, now using the 14 annual 35 daily adjusted model data

Analysis:

(b)Exercise (4.1): Open up either of the newly created adjusted model data using the GIS tool.

Using the Query button, select regions that have a PM2.5 QuarterlyMean greater than 10 μg/

m3. Now change the query to QuarterlyMean greater than 15 μg/m3(i.e. out of attainment).

How do these results compare to the non-adjusted aqg’s that we analyzed in the “Mapping

Introduction” section (see Exercise 2.6)?

Answer:

(c)Close the GIS window. This completes the “AQ Model Grids” module.

A.1.4.2 Monitor Grids

When we convert monitor data into an AQG, we need to interpolate from the monitor locations

to all the grid locations. There are two overarching interpolation techniques:Closest Monitor

(also known as nearest neighbor) and Voronoi Neighborhood Averaging (VNA). Closest

Monitor means that the grid cell will have the same value as the nearest monitor. VNA uses

distance to weight the average of monitors in calculating the grid cell’s value. There are multiple

advanced options to change the distance weighting functions and to apply maximum distance

thresholds to these calculations. Additional details are provided in the User’s Manual.

A.1.4.2.1 Example:Baseline PM2.5 2004

The goal of this exercise is to generate an AQ grid from PM2.5 monitoring data from 2004. In
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addition, we will compare closest monitor and VNA interpolation techniques.

Procedures:

(a)Click the Create Air Quality Grid button.

(b)Select “Monitor Direct” and click the Go! button (Figure 4-2).

Figure 4-2. Selecting Monitor Direct.

(c)In the Monitor Direct Settings window (Figure 4-3), select “CMAQ 36km Nation Overlap”

as the grid type.

Select “PM2.5” as the pollutant ,and “Closest Monitor” in the “Interpolation Method” list.

Make sure that the “Library” tab is selected, and select “EPA Standard Monitors” as the monitor

dataset and set the monitor library year to “2004”.
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Figure 4-3. Monitor Direct Settings window.

(d)Click the Map button. This will bring up the BenMAP GIS window (Figure 4-4).
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Figure 4-4. Map of monitors and closest monitor.

(e)Double click the “Air quality grid” in the Layers list on the left side of the window. Select

“QuarterlyMean” in the Variable list, and uncheck the “Grid Outline” box.

(f) In the GIS window, overlay the state reference layer

(g)Zoom into the California-Nevada border area and look at the pattern of the AQG.

(h)Click Close on the GIS window. This willreturn you to the Monitor Direct Settings window.

(i) Exercise (4.2):Change the interpolation technique to VNA. Remap the data and look at the

California-Nevada border. How does the VNA AQG compare to the Closest monitor AQG?

Note: this will likely take significantly longer to calculate.
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Answer:

(j) Return to the Monitor Direct Settings window (by closing the GIS window), and click Go!

Tip: If you click Cancel at this point, you will not have created an aqg. Only by

clicking Go! do you actually create a new aqg file. The Map button gives you a

preview of the interpolated aqg, it does not actually produce the file.

(k)In the Save as window, create a new folder, “PM25_Monitor” under the "Air Quality Grids"

folder. Save the aqg as: “Baseline_PM25_2004_VNA_CMAQ_grid”.

(l) This completes the “Monitor Grid” module.

A.1.4.3 Monitor Rollback

Monitor rollback is a method for creating controlscenarios. The rollback technique has three

main steps:(1) the pollutant and the monitoring data are chosen, (2) the chosen monitoring data is

reduced through one or more of the three rollback approaches, and (3) the rolled back monitors

are then interpolated to the chosen grid type. The three rollback approaches are: percentage,

incremental, and rollback to a standard. Note: in constructing the rollback, we differentiate

between the rolled back grid and the control grid. The rollback grid is the grid type under which

we want to perform the rollback. This typically is a political grid (e.g. State or county). In

contrast, the monitor data will be interpolated to our control grid type.

A.1.4.3.1 Example:Control PM2.5 2004 Percentage Rollback

The goal of this exercise is to create a control by performing a 10% rollback of monitors in the

West coast and in Pennsylvania (remember, these regions had the largest deltas in the One-step

analysis section). With this rollback approach, each daily value above the background level is

rolled back by 10 percent. We will produce a county grid aqg.

Procedures:

(a)Click the Create Air Quality Grid button. This will bring up the Air Quality Grid Creation

Method window.

(b)Select “Monitor Rollback” and click the Go! button (Figure 4-5).
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Figure 4-5. Selecting Monitor Rollback.

(c)This will bring up the Monitor Rollback Settings:(1) Select Monitors window (Figure 4-6).

Select “PM2.5” from the pollutant list.

Make sure that the “Library” tab is selected, and select “EPA Standard Monitors” from the

monitor dataset list. Set the monitor library year to “2004”. Select “State” from the rollback grid

type list.
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Figure 4-6. Monitor Rollback Settings: (1) Select Monitors window.

(d)Click Next, which will bring up the Monitor Rollback Settings: (2) Select Rollback Regions

& Settings window.

(e)In that window, click Add Region. A Select Region Rollback Type window will appear

(Figure 4-7). Throughout this region, we will apply one rollback technique. In this case, we

will be using a percentage rollback.

Select “Percentage Rollback” and click OK. You will be returned to the Monitor Rollback

Settings:(2) Select Rollback Regions & Settings window.
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Figure 4-7. Select Region Rollback Type window.

(f) In region 1, set the rollback to 10% and the background to 3 μg/m3

. In other words, all

monitor values that are greater than 3 will have a 10% reduction in value.

(g)Apply the controls to the West coast states and PA (four states in total). Use your mouse to

click on the map to select a state. For example, we have selected California in Figure 4-8.

Figure 4-8. Monitor Rollback Settings (2) Select Rollback Regions and Settings window.

Selecting a region for a 10% monitor rollback.
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(h)Click Next

(i) In the Monitor Rollback Settings (3) Additional Grid Settings window (Figure 4-9), we

want to use VNA interpolation and county grid type (i.e., the control grid). Uncheck “Make

Baseline Grid (in addition to Control Grid)” because we did this in a previous step.

Figure 4-9. Monitor Rollback Settings (3) Additional Grid Settings

window. Setting county grid type.

(j) Click Go! and save the aqg under “PM25_Monitor”:

“Control_PM25_2004_VNA_county_10pct_rollback_3_background”

Analysis:

(k)Exercise (4.3): Create an audit trail report on our new monitor rollback aqg. Under "Advanced",

what is the neighbor scaling type? Under "Monitor Rollback", what are the four states (names

and codes) that have been rolled back? What is the rollback method and value?
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Answer:

(l) Exercise (4.4): Map our new monitor rollback aqg. What states have a QuarterlyMean greater

than 15 μg/m3?

Answer:

A.1.4.3.2 Example:Control PM2.5 2004 Multiple Rollback Techniques

The goal of this exercise is to combine multiple rollback techniques into one controlscenario. We

willrollback the West coast incrementally. On the East coast, we willrollback to a standard using

peak shaving. In other words, on the West coast, we will decrease all monitors by a fixed

amount, while on the East coast we will define a standard and only reduce those monitors that

exceed that standard, for only those hours over the standard. The aqg will have a CMAQ grid

type.

Procedures:

(a)Go through the same steps as above to setup a monitor rollback for PM2.5

for 2004. Again

select “State” as the rollback grid type.

(b)Add the first region, then select “IncrementalRollback” in the Select Region Rollback Type

window (Figure 4-10) and click OK.
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Figure 4-10. Selecting incremental rollback.

(c)Set the rollback to 4 μg/m3 and the background to 3. Select all the West coast states.

(d)Add a second region. In the Select Region Rollback Type window, set the type to “Rollback

to a Standard”. Here we have many more options in defining what our standard is and how

we want to reduce the monitors so that they match that standard. In our case, we willset a

standard that no monitor should have a daily mean value greater than 35 μg/m3.

(1)Set daily metric to “D24HourMean”. Leave the seasonal metric blank and the annual

statistic type blank.

(2)Set the standard to 35 and leave the ordinality as 1.

Background: Ordinality refers to the number of monitor values in the season that can

exceed your standard. For example, if we had set the ordinality to 4, than a monitor

can have as many as three daily averages >35 μg/m3 without violating the standard (i.

e. it would not be rolled back). If it has more than 3 daily averages in an exceedance

of the standard, then the rollback technique will be applied to that monitor.

(3)Set the rollback method to “Peak Shaving”, and the background to 3 μg/m3

.

(4)Select all the East coast states. Figure 4-11 shows the selection of the first state, Maine.

Note: You may need to use the zoom button to get some of the smaller states.
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Figure 4-11. Monitor Rollback Settings (2) Select Rollback Regions and Settings window.

Adding a second rollback region, rollback to standard.

(e)Click Next to go to the Monitor Rollback Settings: (3) Additional Grid Settings window.

This time, set the grid type to “CMAQ 36km Nation Overlap” and the interpolation to

“VNA”.

Uncheck “Make Baseline Grid (in addition to Control Grid)” because we did this in a

previous step (Figure 4-12).
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Figure 4-12. Monitor Rollback Settings (3) Additional Grid Settings window.

Setting CMAQ grid type.

(f) Click Go!. Save the results under “PM25_Monitor” as:

“Control_PM25_2004_VNA_CMAQ_4_incremental_35_daily_3_background”

Analysis:

(g)Exercise (4.5): Create an audit trailreport on our new monitor rollback aqg. Under

"Monitor Rollback", describe the two rollback regions, focusing on their techniques?

Answer:
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(h)Exercise (4.6): Map the deltas between the Baseline PM2.5 VNA 2004 and this multiple

rollback control that we just created. How do the East Coast and West Coast deltas differ?

How do you explain the pattern on the eastern boundary of CA, WA, and OR? Hint: Use

the One-step analysis window to map the deltas. You don't need to perform a full One-step

analysis. Instead, in the "1. Air Quality Grids" panel, select your newly created

"Baseline_PM25_2004_VNA_CMAQ_grid.aqg" as the "Baseline File" and your newly

created control as the "Control File". Click Map Deltas.

Answer:

(i) Close any open BenMAP windows. This completes the “Monitor Rollback” module.

(j) This completes the "Creating Grids" lab. In the next lab, “Health Incidence” we will take our

aqg results and calculate the corresponding health incidence results due to the change in air

quality.

A.1.5 Section 5. Health Incidence

In this section, you will modify an existing health incidence configuration and use it to create new

health incidence results. You will create two separate sets of health incidence results based on the

same configuration and two similar, but distinct, controlstrategies.

Creating health incidence results has three main stages:

(1)Select baseline and control air quality grids (AQG) and other generalsettings. These

generalsettings include population dataset and analysis year, air quality threshold, and

statistical parameters. The delta between the baseline and control AQGs is combined

with the population data as a major input to the health impact functions.

(2)Select specific health impact functions and modify default parameters. Some of these

parameters include demographics (e.g., gender, race, or age ranges) and incidence and

prevalence rates.

(3)Save all the settings from the first two stages as a configuration (cfg), which can be reused later with other baseline/control pairs if desired. Finally, run the health incidence

configuration, which will create a new health incidence results file (cfgr).

Health impact functions relate the change in number of observed, adverse health effects in a given
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population to a given change in concentration for a given pollutant. These functions are usually

based upon epidemiologicalstudies with specific sub-populations, and have a baseline estimate of

the health effect. Having estimated changes in air quality levels from monitored or modeled data in

previous labs, in this section, we will learn how to use these changes in concentrations and health

impact functions to estimate changes in health incidence.

In this lab, we start from an existing configuration to help you develop a new configuration. After

working through this lab, however, you should also be able to create a new configuration from

scratch.

Note: For many of the health endpoints (e.g., mortality), there are many different health impact

functions that you could choose to include in your configuration. This lab has you select specific

functions and teaches you how to differentiate these functions and modify the input parameters.

This lab does not teach you how to determine which functions are the best ones for a particular

study. To determine the best choices for a particular analysis, we recommend that you read the

appendices accompanying BenMAP that describe the specific epidemiologicalstudies that

correspond to the specific health impact functions and/or that you discuss your choices with an

epidemiologist. If you want to use EPA’s standard set of functions, you can use the configurations

that are pre-loaded for the one-step analysis when you download BenMAP.

A.1.5.1 Example: PM2.5 Control 2020 14/35

The goal of this exercise is to create a new configuration by modifying the health incidence

configuration that was used in the One-step analysis (Section 3) and create health incidence

results for the controlscenario RIA 2020 14 annual, 35 daily μg/m3. We will add the following

health impact functions to the previous configuration: mortality for infants, hospital admissions due

to respiratory problems, and acute myocardial infarctions (AMIs), also known as heart attacks.

Procedures:

(a)In the main BenMAP window, begin a health incidence estimation by clicking on the graphic

titled "Incidence Estimation" located in the right-hand panel, under the “Step 2” heading. This

will open the Configuration Creation Method window (Figure 5-1).

Figure 5-1. Health incidence Configuration Creation Method window.
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(b)In this window, select "Open Existing Configuration". This means that we are starting from

some already selected health impact functions (i.e., we are not "starting from scratch"). Click

Go!. An Open window will appear. In the "Configurations" folder. Select the

"PM25_RIA_2020_course.cfg" file and click Open.

(c)The Configuration Settings window (Figure 5-2) is used to set the baseline and control

AQG files, and also to set some general parameters that will be used by all the health impact

functions. In this window, we will change only the baseline and control AQG files. The rest of

the settings (Latin Hypercube Points, Population DataSet and Year, Point Mode, and

Threshold) will be left unchanged.

(d)In the "Select Air Quality Grids" panel, next to the "Baseline File" field, click Open. The Open

window will appear. Navigate to the "PM25_RIA" folder under the "Air Quality Grids" folder,

select "Baseline_PM25_RIA_2020_cmaq_grid_15_Annual_65_Daily.aqg", and click Open.

(e)Repeat these steps for the "Control File", this time selecting

"Control_PM25_RIA_2020_cmaq_grid_14_Annual_35_Daily.aqg".
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Figure 5-2. Configuration Settings window - General parameters.

Background: The other settings in this window may be changed when you are doing your own

studies. The Point Mode versus the Latin Hypercube Points options allow you to generate

either an average incidence estimate or a range of results. With the Point Mode option,

BenMAP uses the mean values of the inputs to the health impact functions, and generates a

single “point estimate” of the change in adverse health effects; this is usefulfor quick analyses.

With the Latin Hypercube Points option, BenMAP estimates a distribution of incidence

results that expresses the variability in the incidence estimates; this option reports specific percentiles along the 

distribution. The greater the number of chosen points, the greater the number of estimates and hence the

greater the time needed by BenMAP to process the results. The Population DataSet and

Population Year specify the population data that will be used in the health impact functions.

The Population DataSet should match the grid definition of your baseline and controlfiles. The

Threshold indicates the air quality level below which the program will not calculate benefits.

That is, air quality metrics below the threshold will be replaced with the threshold value.
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(f) Before continuing on to select health impact functions, we should do some quality assurance

(QA) on the aqg files. Specifically, we want to look at the AQ deltas. In the Configuration

Settings window (Figure 5-2), click on Map Grids. This will open a new BenMAP GIS

window.

(g)In the BenMAP GIS window, uncheck the "Control Grid" and "Baseline Grid" in the "Layers"

panel(on the left side). Double-click on the "Delta" layer, which will open the Display

Options window.

In the Display Options window, uncheck "Grid Outline" and set the Variable to

"QuarterlyMean". Click OK.

Back in the BenMAP GIS window, overlay a State reference layer. Your GIS window

should now look similar to Figure 5-3.

Figure 5-3. Mapping delta AQ

(h)Exercise (5.1): Using the Create Layer Statistics button, what are the maximum and mean

for the QuarterlyMean of PM2.5? Using the Build query button, which states have a

QuarterlyMean greater than 1.0 μg/m3?
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Answer:

(i) After you have completed the above exercise, click Close in the BenMAP GIS window to

return to the Configuration Settings window. At this point we have set all the general

parameters for our configuration. Note: we did not modify the latin hypercube points,

population year, or threshold. Click Next.

(j) This will open a new Configuration Settings window (Figure 5-4). This window describes

the specific health impact functions that were selected in the configuration file. We will also use

this window to select new health impact functions for our new configuration. This window is

divided into two panels:

Available CR Functions: This describes all the available functions that are appropriate for

this type of AQ data. In our case, these are all the PM2.5functions. The left-hand panel is the

"Tree". It describes the hierarchy between endpoint groups (major groupings of adverse health

effects) and endpoints (specific adverse health effects). You can expand any section by

clicking on the plus sign next to the heading, or collapse a section by clicking on the minus sign

next to a heading. To see the specific health functions, you need to expand the endpoint

heading of choice. The right-hand panel is the "Data" panel. The data are all the details of the

specific health functions: the author of the study, location where the study was done, the

specific function that BenMAP uses to calculate that adverse health effect, whether there is a

qualifier to the health function, etc. A complete description of each column can be found in the

User's Manual. The scroll bar at the bottom of the “Available CR Functions” panel is for

panning across the "Data" columns.

Selected CR Functions: This describes the functions that have been chosen for this specific

configuration. The left-hand panel is "Function Identification", which contains all the columns

necessary to uniquely identify the function. The scroll bar at the bottom of the “Selected CR

Functions” panel is for panning across the "Function Identification" columns. We recommend

focusing on Endpoint, Author, Year and Qualifier. The right-hand panel is "Function

Parameters". These parameters are used by the health functions and some of them can be

edited by the user. For example, you might change the age range ("Start Age" and "End Age")

for a specific health impact function if you were interested in studying the impact on a certain

demographic.
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Figure 5-4. Configuration Settings window - Initial functions.

Tip: When looking at the CR functions, it is often useful to change the order and/or width of the

columns. To reorder the columns, click and hold your cursor on the variable name at the top of a

column, then drag the column either to the right or to the left. Release your cursor when you

have moved the column over the desired location – BenMAP will then rebuild the tree structure

using the newly specified variable order. To resize a column, either double click on the right edge

of the desired column (maximizes the column) or click on the edge of the column and drag it to

the right or left until it is the appropriate size.

(k)Before adding new functions to our configuration, we will look at the current set ofselected

health impact functions:

(1)In the "Tree" panel, expand the DataSet "Complete Version2". This should reveal the

available endpoint groups. You can expand any endpoint group to see the available
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endpoints within that endpoint group. You can also expand a specific endpoint so that

you can see the specific health functions for that particular endpoint. For example, expand

the endpoint group "Emergency Room Visits, Respiratory" and the revealed endpoint

"Emergency Room Visits, Asthma" to reveal the specific health impact functions.

(2)Exercise (5.2): How many health functions are there for the "Emergency Room Visits,

Asthma" endpoint? Who are the author(s) for these functions/studies? What are the

differences between these functions? (After you have completed this exercise, you might

want to collapse the "Emergency Room Visits, Respiratory" endpoint group. This will

reduce the clutter in the top panel.)

Answer:

(3)Exercise (5.3): What are the endpoints under the "Hospital Admissions, Respiratory"

endpoint group? How many functions are there under the "HA, Chronic Lung Disease"

endpoint and who are their authors? Note: You might have to expand the width of the

Endpoint column to make sure you have the right one.

Answer:
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(4)Exercise (5.4): Who is the author of the mortality function used in our current

configuration? Hint: Look at the "Function Identification" panel. If you look at the

available mortality functions, how many of them are by this author? Looking at the

Qualifier column, what differentiates this author's functions? Which of the functions are

we using in our current configuration? Hint: We recommend reorganizing the columns in

the "Data" panelso that you have Author, Start Age, End Age, Qualifier, and Function

next to each other (see previous Tip).

Answer:

(l) In the next series ofsteps, we will add new health functions to our configuration. We will add

an additional mortality function, some hospital admission functions, and some acute myocardial

infarction (AMI) functions. You will note that in many cases there will be multiple functions for

the same endpoint by the same author. In most cases these will be differentiated by the content

of the Qualifier column. In all of our cases, we willselect the function that does not have a

threshold or other qualifier.

(1)We willstart by adding a new "Mortality, AllCause" endpoint health function. Select the

function that has Woodruff as the author and no qualifier (no threshold). To add it to our

configuration, simply click on the specific row and drag it to the "Selected CR Functions"

panel(Figure 5-5).
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Figure 5-5. Adding a new health impact function.

Tip: If you mistakenly add the wrong health impact function, you can delete it by highlighting

the particular function in the "Selected CR Functions" panel and clicking Delete on your

keyboard.

(2)After moving a function into the configuration, you need to decide whether you want to

change any of the "Function Parameters", i.e., the inputs to the health function. In the

Woodruff case, we want to set the "Incidence DataSet" (i.e., the background incidence

rate for mortality). Click on the "Incidence DataSet" cellfor the Woodruffstudy. Use the

drop-down menu to select "2020 Mortality Incidence" (Figure 5-6). In other words, we

are using the incidence rate for mortality that has been projected to the year 2020.

Note:In this case there is an available incidence rate dataset for 2020. For other

endpoints (e.g. Chronic Bronchitis), the background incidence rates are only
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available for 2000 because they have not been projected to 2020.

Figure 5-6.Changing Function Parameters - the incidence rate.

(3)Exercise (5-5): Compare the Pope and Woodruff mortality studies in the “Available CR

Functions” panel. What is the age range for each study? Write down the functions for

each study. Note: compare the respective Pope and Woodruff functions that you

selected.

Answer:
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(4)Next we will add a health function for hospital admission due to pneumonia. Under the

"Hospital Admissions, Respiratory" endpoint group, expand the "HA, Pneumonia"

endpoint. Select the function by Ito without a threshold shown in the qualifier column.

Simply click on the appropriate function with your mouse and drag it to the "Selected CR

Functions" panel.

Note:In this case the incidence dataset has already been selected. If you try to

select another dataset you willsee that 2000 is the only available dataset. In other

words, the incidence and prevalence for hospital admissions due to pneumonia has

not been projected to 2020.

(5)Next we will add health functions for hospital admissions due to chronic lung disease.

Under the endpoint "HA, Chronic Lung Disease", add the functions by Ito and

Moolgavkar (without threshold). Note: Make sure you are not using the endpoint "HA,

Chronic Lung Disease (less Asthma)".

(6)Exercise (5-6): Compare the Ito and Moolgavkar studies. How do their specific

functions compare? What is the beta (regression coefficient) for each study? Bonus

question: Which function is more sensitive to changes in AQ?

Answer:

(7)Finally, we will add health functions for AMI. Under the "Acute MyocardialInfarction"

endpoint group and "Acute MyocardialInfarction, nonfatal" endpoint, select the Peters

study (no threshold). Here, we will do something slightly different. Drag four copies of

the Peters function to the "Selected CR Functions" panel. The age range for this function

is 18 to 99 years. We want to look at the effect on a more specific demographic—that is,

we want to break this into four age ranges.

Starting with the first Peters function, select the "End Age" cell in the "Function

Parameters" panel. Edit the cell to 44. The new age range for the first Peters

function is now 18 to 44, inclusive.

For the next Peters function, modify the Start Age to 45 and the End Age to 54.

For the remaining Peters functions, change the age range to 55-64 and 65-99,
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respectively. After modifying the age ranges, your Configuration Settings

window should look similar to Figure 5-7.

Figure 5-7. Four additional AMI functions with modified age ranges.

(8)You have completed adding all the new health functions to your configuration. You

should now have 12 functions in your "Selected CR Functions" panel(Figure 5-7).

Double check to be sure all of the selected functions have an empty qualifier cell(check

in the "Function Identification" panel by moving the bottom scroll bar until the Qualifier

column is visible, note that the Pope function willsay "no threshold").

(m)When you have set up all the health impact functions as instructed above, you are ready to

save the new configuration and generate the health incidence results. Click Run. This will bring
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up a Save Configuration window (Figure 5-8).

We want to save this new configuration (so that it can be re-used), so click Save. This

will bring up a Save As window. Under the "Configurations" folder, in the "File name"

field, type in the new configuration file name, "PM25_RIA_2020_course_modified", and

click Save. A Progress window will appear.

Figure 5-8. Save Configuration window.

(n)After the configuration is saved, you will be returned to the Save Configuration window

(Figure 5-8). Now run the configuration by clicking OK. This will bring up another Save As

window. Here we willsave the configuration results (cfgr). Under the "Configuration Results"

folder, save the results as

"Control_PM25_RIA_2020_modified_cmaq_grid_14_annual_35_daily".

The calculation of the results will begin and a Progress window will appear. The

calculation of the results may take a few minutes. When the calculations are finished, you

will be returned to the main BenMAP window.

Tip: If you are not ready to run this configuration, click Cancel. If you generated a

configuration, you can open the configuration and run it to generate valuation results

at a later time.

Analysis:

The rest of the exercises in Section 5.2 focus on analyzing the results of our BenMAP run.

Specifically, we will look at the newly created health incidence configuration file (cfg) and results

file (cfgr). A good habit to get into is to quality-assure both your configuration and your results.

Some things to check include whether you have the right functions and parameters selected and
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whether the results seem reasonable.

(o)First, we will look at the newly created configuration file (cfg) using the audit trail. From the

main BenMAP window, click on the "Report" graphic in the right-hand panel. Select the

"Audit TrailReports" in the Select Report Type window and click OK. Under the

"Configurations" folder, select the new configuration, "PM25_RIA_2020_course_modified.

cfg" and click Open.

(p)Exercise (5.7): What is the population year used in this configuration? How many health

functions (CR functions) were used? What are the location and the incidence rate dataset used

in the Woodruff mortality function? Hint: if you added the functions in the same order as the

lab, the Woodruffstudy should be "CR Function 4". When you are done with this exercise,

click OK to close the Audit Trail Report window.

Answer:

(q)Using the Tools menu in the main BenMAP window, open a BenMAP GIS window. Click

on Open a File, then select “Configuration Results (*.cfgr)”. Under the "Configuration

Results" folder, open our newly created file,

"Control_PM25_RIA_2020_modified_cmaq_grid_14_annual_35_daily.cfgr".

(r) In the Edit GIS Field Names window, provide more meaningful names for your health

incidence results, then click OK. Here are suggested names: MortPope, ChronBronc, ER,

AcutResp, MortWood, HAPneum, HAChrIto, HAChrMool, AMI18, AMI45, AMI55,

AMI65. Note:BenMAP GIS field names cannot exceed 10 characters in length and cannot

include commas.

(s)Exercise (5.8): Compare the Pope and Woodruff mortality results. How do the maximum

values compare? The Pope and Woodruff health impact functions are calculating avoided

mortalities for different subgroups within the population. What is the Pope result measuring

versus the Woodruff result? Do they have similar spatial patterns?
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Answer:

(t) Exercise (5.9): Compare the Ito and Moolgavkar Hospital Admissions due to Chronic Lung

Disease results. How do the maximum values compare? How do their respective populations

compare (by "population", we mean the population variable POP1, POP2, etc. that

correspond to the appropriate heath incidence result)? The spatial patterns of the two results

are identical while their magnitudes differ. How do you explain this? Hint: Look back at

Exercise 5.6.

Answer:

(u)Exercise (5.10): Compare the AMIs (heart attacks) for various age groups. What are the

maximum values for the AMI18 and AMI65 functions? What are the population maximum

values for these two functions? Why do you think that the value of AMI18 is less than the

value of AMI65? After you are done with the above exercises, close the BenMAP GIS

window by clicking Close.
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Answer:

(v)From the main BenMAP window, click on the "Report" graphic in the right-hand panel. Select

"Raw Incidence Results" in the Select Report Type window and click OK. Under the folder

"Configuration Results", open our results file,

"Control_PM25_RIA_2020_modified_cmaq_grid_14_annual_35_daily.cfgr".

In the “C-R Function Fields” panel within the “Column Selection” panel of the

Configuration Results Report window (Figure 5-9), select Endpoint, Author, Start

Age, and End Age. In the “Result Fields” panel, deselect Delta and Variance.

As you select or deselect items in the “Column Selection” panel, your choices are

reflected in the “Preview” panel in the bottom half of this window, which displays a

preview of the columns that will be included in the results report when you save the file.
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Figure 5-9. Report window for raw incidence results.

Background: Some of the key columns that can be included in results reports are the

following: the "mean" is the mean of the Latin Hypercube points for this result; the "point

estimate" is the single point estimate for this result; "percentiles" are the individual Latin

Hypercube points for this result; the "baseline" is the number of individuals experiencing this

adverse health effect due to all causes (typically incidence rate population); the "percent of

baseline" is the relative change in adverse health effects due to the controlscenario we are

considering (point estimate/baseline); the "population" is the population used in the particular

health function at this grid cell.

(w)Now that you have chosen all the columns to include in your results report, you can save the

report. From the Configuration Results Report window, type Ctrl-S. Under the "Reports"

folder, save the file as
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"Control_PM25_RIA_2020_modified_cmaq_grid_14_annual_35_daily". After the file has

been saved, close the window by clicking Done.

Use Windows Explorer to navigate to the "Reports" folder. Double-click on the newly

created csv file and it should open in Excel. Note: if you are using an earlier version of

Excel, it may give you an error message saying that it could not load the complete file.

Click OK in the warning window.

(x)Exercise (5.11): All of the following questions refer to the grid cell 14, 63 (column, row) for

the endpoint "Minor Restricted Activity Days" (within the endpoint group "Acute Respiratory

Symptoms"). What is the background incidence (the total number of minor restricted activity

days due to all causes) for this particular grid cell? Hint: the column is called "Baseline". What

is the change in adverse health effects (i.e., the number of minor restricted activity days

avoided) under the controlscenario? We know that the heath impact functions have an

underlying statisticalfunction that gives us a range of results. What is the estimate of the change

in adverse health effects at the 5th percentile? What is the estimate at the 95th percentile? When

you are finished with this exercise, close the Excel window.

Answer:

A.1.5.2 Example: PM2.5 Control 2020 14/35 Adjusted

The goal of this exercise is to re-use our newly modified health incidence configuration (from

Section 5.2) and create health incidence results for the adjusted controlscenario RIA 2020 14

annual, 35 daily μg/m3. Note: in the following instructions, we refer to "adjusted" and

"nonadjusted" results, AQ data, and/or configurations. The "nonadjusted" data and configuration

are from Section 5.2. The "adjusted" are from this Section (5.3).

Procedures: The following is a list of the necessary steps. We have abbreviated the instructions

because they are very similar to the steps you just completed in Section 5.2.

(a) Open "Incidence Estimation" from the main BenMAP window, Custom Analysis side.

(b) Select "Open Existing Configuration", and Click Go!. Choose the newly created

configuration "PM25_RIA_2020_course_modified.cfg" under the "Configurations"

folder and open it.
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(c) In the Configuration Settings window, change the baseline and control aqg files. Under

the "PM25_RIA" folder, select the

"Baseline_PM25_RIA_2020_cmaq_grid_15_Annual_65_Daily_adjusted.aqg" as the

baseline and the

"Control_PM25_RIA_2020_cmaq_grid_14_Annual_35_Daily_adjusted.aqg" as the

control. Leave the rest of the settings the same.

(d) Quality-assure the aqg files. Click on the Map Grids button. In the new GIS window

that opens, select only the "Delta" layer and in the Display Options window display the

QuarterlyMean.

(e) Exercise (5.12): What are the maximum and mean for the delta QuarterlyMean of

PM2.5? Which states have a delta QuarterlyMean greater than 1.0 μg/m3. Compare your

answers to Exercise 5.1. Which scenario (adjusted or nonadjusted) do you predict will

have greater number of avoided adverse health incidences? Explain your answer.

Answer:

(f) After returning to the Configuration Settings window, click Next.

(g) Review your selected health functions. There should be 12 of them. Make sure they are

the same functions you used in the last example then run the example by clicking Run.

(h) Do not save the configuration. There is no need to save it because the only changes

were to the baseline and controlfiles. Do create the health incidence results by clicking

OK. Under the "Configuration Results" folder, save the results as

"Control_PM25_RIA_2020_modified_cmaq_grid_14_annual_35_daily_adjusted". The

calculation of the results will begin and a Progress window will appear. The calculation

may take a few minutes.

Analysis: Now we will look at the newly created health incidence results file (cfgr). Because we

have already checked the cfg file in Section 5.2, we do not need to quality-assure this configuration

file again.
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(i) Click on the right-hand "Report" graphic in the main BenMAP window. Use the Select

Report Type window to open an audit trailfor the new health incidence results file,

"Control_PM25_RIA_2020_modified_cmaq_grid_14_annual_35_daily_adjusted.cfgr".

(j) Exercise (5.13): What is the population year used in this cfgr? How many health

functions (C-R functions) were used? How many Latin Hypercube points are used in

calculating the C-R functions’ statistical distribution?

Answer:

(k) Open a BenMAP GIS window then open the same health incidence results file under the

"Configuration Results" folder.

(l) Exercise (5.14): Compare the Pope and Woodruff mortality results. How do the

maximum values compare? How do these results compare to the nonadjusted results

(see Exercise 5.8)?

Answer:

(m) Exercise (5.15): Compare the AMIs for various demographics. What are the maximum

values for the AMI18 and AMI65 functions? What are the population maximum values

for the two functions? How do these results compare to the nonadjusted results (see

Exercise 5.10)?
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Answer:

(n) From the main BenMAP window, follow the steps needed to create a raw incidence

results report from the same health incidence results file. Select the same results columns

as before (see Figure 5-8).

Save the report under the "Reports" folder as

"Control_PM25_RIA_2020_modified_cmaq_grid_14_annual_35_daily_adjusted".

Then open the new csv file in Excel.

(o) Exercise (5.16): All of the following questions refer to the grid cell 14, 63 (column,

row) for the endpoint "Minor Restricted Activity Days" (within the endpoint group

"Acute Respiratory Symptoms"). What is the background incidence (the total number of

minor restricted activity days due to all causes) for this particular grid cell? What is the

change in adverse health effects (i.e. the number of minor restricted activity days

avoided) under the controlscenario? We know that the heath impact functions have an

underlying statisticalfunction that gives us a range of results. What is the estimate of the

change in adverse health effects at the 5th percentile? What is the estimate at the 95th

percentile? How do these results compare to the nonadjusted results (see Exercise

5.11)?

Answer:

(p) Close the Excelfile and any open BenMAP windows. This completes the "Health

Incidence" lab. In the next lab, “Aggregation, Pooling, and Valuation,” we will take our

health incidence results and calculate the corresponding monetized benefits due to these

health effect changes.
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A.1.6 Section 6. Aggregation, Pooling, and Valuation

In this section, you will create an aggregation, pooling, and valuation (APV) configuration and use

it to produce new valuation results. You will create two separate sets of valuation results based

on the same configuration and two similar, but distinct, controlstrategies.

Creating valuation results has four main stages:

(1)Select a health incidence results file (cfgr) and set up pooling for similar results (i.e.

combining similar results together into one result).

(2)Select specific valuation functions and poolsimilar valuations.

(3)Select additional parameters for the valuation functions, and decide on the aggregation

(e.g. summing results from county level to state level) for the health incidence results and

the valuation results.

(4)Save all the settings from the first three stages as a configuration file (apv), which can be

re-used later with other health incidence results if desired. Finally, run the aggregation,

pooling, and valuation configuration, which will create a valuation results file (apvr).

A reduction in air quality level is usually associated with lowering the risk for adverse health

effects for a population. This reduction in risk is usually not the same for all individuals, and there

is a need to translate the reduction in risk to a quantifiable economic value. BenMAP relies upon

published studies where the unit value ofsuch a reduction in risk has been calculated for various

health effects. Since multiple studies are sometimes available for a given health incidence, the

user needs to choose between them, or adopt techniques to pool(statistically combine) the

different functions in an appropriate manner. In this section, we will learn how to pool the results

and monetize the reduction in risk for adverse health effects due to changes in air quality levels.

Note: For many of the health endpoints (e.g., mortality), there are many different valuation

functions that you could choose to include in your configuration. In addition, there are multiple

ways to pool your health incidence and valuation results. This lab has you select specific functions

and teaches you how to differentiate these valuation functions and modify certain parameters.

This lab does not teach you how to determine which valuation functions and pooling options are

the best for a particular study. To determine the best choices for a particular analysis, we

recommend that you read the appendices accompanying BenMAP that describe the specific

studies that correspond to the specific valuation functions and/or that you discuss your choices

with an economist.

A.1.6.1 Example: PM2.5 Control 2020 14/35

The goal of this exercise is to create a new aggregation, pooling, and valuation configuration (apv)

and produce valuation results for the controlscenario RIA 2020 14 annual, 35 daily μg/m3.

Procedures:
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(a) In the main BenMAP window, begin the process of creating an apv configuration by

clicking on the graphic titled "Pooling, Aggregation, and Valuation" in the right-hand

panel, under the “Step 3” heading. This will open the APV Configuration Creation

Method window (Figure 6-1).

Figure 6-1. APV Configuration Creation Method window

(b) In the APV Configuration Creation Method window (above), select "Create New

Configuration for Aggregation, Pooling, and Valuation". Click Go!. An Open window

will appear.

We first have to select the health incidence results that this apv will be based on: the

results that we created in Section 5.2. In the "Configuration Results" folder, select

"Control_PM25_RIA_2020_modified_cmaq_grid_14_annual_35_daily.cfgr" file and

click Open. This will open the Incidence Pooling and Aggregation window (Figure 6-

2).
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Figure 6-2. APV Incidence Pooling and Aggregation window

(c) This window is where we will combine (pool) similar health incidence results together.

Before we begin pooling, let us look at the main features of this window. First, the

"Configuration Results File Name(s)" field near the bottom of the window shows the

health incidence results (cfgr) that you just selected for this apv. If you want to use a

different cfgr, you would use the Browse button to locate it. We will do this later, in the

second example for this lab.

The "Target Grid Type" is the grid definition used to generate the selected health

incidence results; it always matches the cfgr input's grid. The "Available Incidence

Results" panel has an expandable hierarchical tree that lists each of the health incidence

results in the cfgr file. The "Select Pooling Methods" panel has one or more pooling

windows. The pooling windows are where you willselect individual health incidence

results that you may want to pool. These selected results (whether or not they have been

pooled) will then be available for valuation (next window). The Add and Delete buttons

add new pooling windows or remove pooling windows that have already been created.

(d) First, change the name of the pooling window. (For our configuration, there will be only
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one pooling window.) Click in the "Pooling Window Name" field within the "Select

Pooling Methods" panel. Change the text "Pooling Window 1" to "Main Pooling

Window".

(e) In the "Available Incidence Results" panel, expand "PM2.5" by clicking on the plus [+]

sign. This willreveal the available endpoint groups. You can expand any endpoint group

to see the available endpoints within that endpoint group. In turn, you can expand a

specific endpoint so that you can see the specific health incidence results available for

that endpoint.

We will begin by expanding the "Chronic Bronchitis" endpoint group and endpoint.

Notice that the health incidence result is identified in the tree using the reference that

describes the study used to derive the underlying health incidence function. Click on the

chronic bronchitis result "Abbey D. E., B. E. Ostro, F. Pertersen and R. J. Burchette.

1995. ..." and drag it to the "Main Pooling Window" (Figure 6-3). That is all it takes—

you have added the first health incidence result.

Figure 6-3. Adding a health incidence result to a pooling window
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Tip: By hovering the cursor over the Available Incidence, you can see a more complete

description of these studies.

(f) For the "Emergency Room Visits, Respiratory" endpoint group, add the health incidence

result "Norris G., et al. ..." to the "Main Pooling Window". Again, simply click on the

specific result from the "Available Incidence Results" panel and drag it to the pooling

window.

(g) For the "Acute Respiratory Symptoms" endpoint group, add the health incidence result

"Ostro, B. D. and S. Rothschild ..." to the "Main Pooling Window". At this point you

should have three endpoints with their corresponding three health incidence results in the

pooling window (Figure 6-4).

Figure 6-4. Initial three health incidence results in the pooling window
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Tip: If you mistakenly add the wrong health incidence result, you can delete it by highlighting the

particular function in the "Select Pooling Methods" panel and clicking Delete on your keyboard.

The Delete button in the Select Pooling Methods panel willremove the whole pooling window.

Typically, you would not want to do this.

(h) For the rest of the endpoint groups, we will add multiple health incidence results per

group. In the "Available Incidence Results" panel, expand the "Mortality" endpoint

group. Drag the "Pope et al.,..." result into the "Main Pooling Window". Repeat for the

"Woodruff, T.J., J. Grillo..." result (Figure 6-5).

Figure 6-5. Addition of mortality health incidence results to the pooling window

(i) Now, please note a couple of points about the present state of the pooling window.

First, we have begun to create a hierarchy. Under the "Mortality" endpoint group we

have one endpoint, "Mortality, AllCause". Under this endpoint we have two health

incidence results, "Pope" and "Woodruff". You can use the scroll bar to pan across the

various columns of data describing the specific results (similar to what you did in the
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"Available CR Functions" panel of the health incidence “Configuration Settings” window

(Figure 5-7)).

Second, a "None" appeared in the Pooling Method column of the "Main Pooling

Window" in the "Mortality" row. When there are multiple health incidence results under a

single endpoint group, BenMAP allows you to pool the results. Unlike the other

endpoint groups, the "Mortality" group has two results; therefore, you have the option of

pooling them. By default, the pooling is set to "None"—that is, no pooling. We do want

to use pooling, but before setting up the pooling we are going to add the rest of the

health incidence results for the remaining endpoint groups.

Tips: (1) You can grab a whole group of endpoints and add all of their respective results to the

pooling window at once. (2) You do not need to add all of the available health incidence results

to your pooling window(s). If you are not interested in pooling or valuing a particular result, do

not add it to a pooling window.

(j) For the "Hospital Admissions, Respiratory" endpoint group, add these three items to the

pooling window:

the "Ito" results for the "HA, Pneumonia" endpoint

Both the "Ito" and the "Moolgavkar" results for the "HA, Chronic Lung Disease"

endpoint.

(k) For the "Acute MyocardialInfarction" endpoint group, add the four age-specific

"Peters" results to the pooling window (Figure 6-6). Note: although the four functions

seem to be missing in Figure 6-6, they are in the pooling window. You need to use the

scroll bar in the right hand panel to see the specific "Start Age" column.
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Figure 6-6. All health incidence results added to the pooling window, with no pooling

(l) The "Main Pooling Window" has four places we could potentially pool the results

(indicated by the red arrows in Figure 6-6).

(m) Starting with the "Mortality" endpoint group, click on the "None" in the corresponding

Pooling Method column. A drop-down menu will appear that lists all the possible

pooling methods. Select the "Sum (Dependent)"method (Figure 6-7). This will cause

the results from the Pope and Woodruffstudies to be summed together to create a single

mortality result.
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Figure 6-7. Adding a pooling method for the mortality results

Background: In the mortality case, we are using Sum because the results from the two studies

are distinct from each other. The number of avoided adult mortalities (Pope) does not overlap

with the number of avoided infant mortalities (Woodruff)—i.e., they are distinct age groups.

See "Pooling Approaches" in the appendices.

(n) Now we will add pooling to the “Hospital Admissions, Respiratory” results. There are

two levels of pooling that can be done in this endpoint group, because it contains

multiple endpoints (“HA, Pneumonia” and “HA, Chronic Lung Disease”), and one of

those endpoints contains multiple results (“Ito” and “Moolgavkar”). Starting with the

bottom of the hierarchy, we will combine the "HA, Chronic Lung Disease" endpoint's

results ("Ito" and "Moolgavkar"). After combining those results into a single result, we

will take this new, pooled result and combine it with the "HA, Pneumonia" endpoint to
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create a single, combined result for the whole endpoint group.

First, click the "None" corresponding to the "HA, Chronic Lung Disease" endpoint.

Select the "Random / Fixed Effects" pooling method from the drop-down menu. This

combines the two results into one pooled result for the "HA, Chronic Lung Disease"

endpoint.

Next, click the "None" next to "Hospital Admissions, Respiratory" endpoint group.

Select the "Sum (Dependent)" pooling method (Figure 6-8). With this step, we have

pooled the different endpoint results into one result for the endpoint group.

Figure 6-8. Adding pooling methods for the hospital admission results
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Background:In calculating the combined HA chronic lung disease results, we are looking at

overlapping populations (same endpoint, same age range); therefore, we want to use Random/

Fixed Effects to combine their distributions. In contrast, when we pool the HA endpoint

group, we are looking at nonoverlapping populations: pneumonia versus chronic lung disease (i.

e., different endpoints). Therefore, we want to pool the pneumonia and chronic lung disease

distributions by doing a Sum. See "Pooling Approaches" in the appendices.

(o) The "Random / Fixed Effects" pooling method has advanced settings. To access these

settings, double-click on the "Random / Fixed Effects" cell in the Pooling Method

column. This will bring up an Advanced Pooling Options window (Figure 6-9). There

are multiple options for customizing this type of pooling (See the user's guide for

specifics). We will not change the existing settings in this window. Click OK to close it.

Figure 6-9. Advanced Pooling Options window

(p) The "Acute MyocardialInfarction" endpoint group is the final group for which pooling

could be done. In this configuration, however, we will not pool the four age-specific

AMI results. Leaving the results unpooled will allow us to take advantage of age-specific

valuation functions in the next stage of the APV configuration setup. In other words, we

will have four separate AMI results to value instead of one single, combined result. To

not pool the results, leave the "Pooling Method" for the AMI endpoint group as "None".

We have now finished setting up the health incidence results pooling, which is the first of

the four stages in creating valuation functions (see the stages list at the beginning of

Section 6). Your pooling window should look like Figure 6-8.

(q) Exercise (6.1): If you were going to pool the four AMI results, which pooling method

would you use? Why?
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Answer:

(r) Now we move on to the second stage of creating valuation functions: choosing the

specific valuation functions and pooling similar valuations. Click Next in the Incidence

Pooling and Aggregation window. This will open the Select Valuation Methods,

Pooling, and Aggregation window (Figure 6-10).

Figure 6-10. Select Valuation Methods, Pooling, and Aggregation window

(s) This window is where we will apply valuation functions to our health incidence results

and combine (pool) similar valuation results together. Before beginning, let us look at the

main features of this window. The "Valuation Methods" panel has an expandable

hierarchical tree that lists the available valuation functions, based on the incidence results
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in this configuration. The right-hand panel contains the pooling windows that were

defined in the previous step. In our case, there is only one pooling window, "Main

Pooling Window". If there had been three pooling windows created in the previous

health incidence pooling step, then we would have three pooling windows in the Select

Valuation Methods, Pooling, and Aggregation window.

The "Variable DataSet" field at the top of the window defines specific variables used in

the valuation functions. The "Skip QALY Valuation" checkbox at the bottom of the

window determines whether or not we will configure and run QALY (Quality Adjusted

Life Years) functions. In our case we will not be calculating QALY since we are

interested in quantifying the benefits in dollars.

Background: In the context of air pollution benefit analysis, the QALY represents the

combined mortality and morbidity benefits ofsome air quality change. This combined metric is

calculated by counting:(1) the number of life years gained; (2) the number of life years lived

without some chronic condition. In step 2, the life years are weighted according to the severity

of the condition (the "quality" of that year), such that a year in near perfect health might be

counted as 0.9, but a year lived with chronic bronchitis might be counted as 0.5.

(t) In the "Valuation Methods" panel, expand "EPA Standard Valuation Functions". This

willreveal the available endpoint groups. You can expand any endpoint group to see the

available endpoints within that group. In turn, you can expand a specific endpoint so that

you can see the specific valuation functions available for that endpoint.

We will begin by expanding the "Chronic Bronchitis" endpoint group and endpoint. You

willsee six COI (Cost Of Illness) and one WTP (Willingness To Pay) functions. We will

add the WTP function for average bronchitis severity ages 30 to 99. Click on the "WTP:

average severity | 30-99" and drag it to the "Main Pooling Window" to the "Chronic

Bronchitis" endpoint group's row (Figure 6-11). That is all it takes—you have added the

first valuation function.
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Figure 6-11. Adding a valuation function for chronic bronchitis to the pooling window

Tip: By hovering your cursor over the valuation functions, you can read their full names.

If you drag and drop a function and nothing happens, then you probably tried to drop it

into the wrong endpoint. Retry with the correct endpoint.

(u) Next we will add a valuation function for acute respiratory symptoms. Under the "Acute

Respiratory Symptoms" endpoint group in the Valuation Methods column, expand the

"Minor Restricted Activity Days" endpoint. Add the WTP valuation function for one day

of lost work based on a contingent valuation (CV) study for ages 18 to 99 by clicking

on the "WTP: 1 day, CV studies| 18-99" function and dragging it to the corresponding

row in the "Main Pooling Window" (i.e. the "Acute Respiratory Symptoms" endpoint

group).

(v) Next we will add a valuation function for mortality. Under the "Mortality" endpoint

group, expand the "Mortality, AllCause" endpoint. Add the value of a statistical life
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(VSL) function that ranges from $1-10 million with a normal distribution by clicking and

dragging the function "VSL, based on range $1 to $10 million, normal distribution, | 0-

99" to the pooling window (Figure 6-12).

Figure 6-12. Initial three valuation functions in the pooling window

Background: Recall that in Section 6.2(m), we pooled the two underlying mortality results to

make this single, combined result to which we have just assigned a valuation function. Because

we pooled the health incidence results, we need only one valuation function to calculate the

value of mortality. If we had not pooled the results, we would need at least one valuation

function for each of the mortality results. The same holds true for the HA endpoint group (see

next step).

(w) Under the "Hospital Admissions, Respiratory" endpoint group, expand the "HA, All

Respiratory" endpoint. Add the valuation function for cost of illness (COI) medical costs

and wage loss ages 65 to 99. Recall that in Section 6.2(n) we combined the individual

HA chronic lung disease and pneumonia results to create a pooled result containing all

respiratory results for ages 65 to 99.
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Tip: You can click Previous to return to the Incidence Pooling and Aggregation window if

you want to inspect the underlying health incidence results or the incidence pooling. You will

not lose your present valuation configuration. When you are done exploring the incidence

results, click Next to return to the Select Valuation Methods, Pooling, and Aggregation

window.

(x) For the rest of the endpoint groups, we will be adding multiple valuation functions per

group. Expand the "Emergency Room Visits, Respiratory" endpoint group and then the

"Emergency Room Visits, Asthma" endpoint. Add the COI Smith valuation function.

Repeat for the COI Standford study (Figure 6-13).

Note: A "None" appeared in the Pooling Method column across from the endpoint

group "Emergency Room Visits, Respiratory". Similar to the incidence pooling window,

this indicates that we could potentially pool these two valuation functions. We will do this

pooling in a later step.
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Figure 6-13. Adding two emergency room visits valuation functions to the pooling window

(y) Next we will add valuation functions to the AMI health incidence results. We will add

age-specific valuation functions to each of the age-specific health incidence results. To

see the age-specific results, use the scroll bar for the pooling window to pan until you

can see the Start Age column.

In the “Valuation Methods” panel, expand "Acute MyocardialInfarction". We will add

two valuation functions per age range: one function by Russell and the other by Wittels.

We will use the COI function for 5 years of medical costs and 5 years of wages loss

with a 3% discount rate (DR). Add the valuation function "COI: 5 yrs med, 5 yrs

wages, 3% DR, Russell(1998) | 25-44" to the AMI health incidence result for ages 18 -

44 (i.e., start age = 18).

Repeat for "COI: 5 yrs med, 5 yrs wages, 3% DR, Wittels (1990) | 25-44" (Figure

614).
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Note: As happened with the endpoint group "Emergency Room Visits, Respiratory",

“None” appeared in the Pooling Method column next to the 18 to 44 result. Again, this

indicates that these two valuation functions could be pooled.

Figure 6-14. Adding two age specific AMI valuation functions to the pooling window

Tip: If you mistakenly add the wrong valuation function, you can delete it by highlighting the

particular function in the right hand pooling window panel and clicking Delete on your

keyboard.

(z) Continue to add one Russell and one Wittels function for each of the remaining age

ranges (i.e., 45-54, 55-64, and 65-99) (Figure 6-15).
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Figure 6-15. All valuation functions added to the pooling window

(aa) The "Main Pooling Window" now has six places we could potentially pool the results

(indicated by the red arrows in Figure 6-15). The pooling procedure is the same as the

one we used earlier in the Incidence Pooling and Aggregation window.

(ab) Starting with the "Emergency Room Visits, Respiratory" endpoint group, click on the

"None" in the corresponding Pooling Method column. A drop-down menu will appear

that lists all the possible pooling methods. Select the "Subjective Weights"method.

Background: You generally use subjective weights when you have overlapping populations

(same endpoint and same age range). Unlike the random/fixed effects method, you explicitly

determine the relative weights of the two (or more) distributions in calculating the combined

distribution. For example, if you want to emphasize one study over another, you would give it a

greater weight. We will assign these weights later, after all pooling methods have been selected.

(ac) For each of the AMI age ranges, we will pool together the Russell and Wittels age-
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specific valuation functions. Click on the "None" for the start age of 18 in the Pooling

Method column (make sure that you are not selecting the "None" in the row above

this one, which is for the entire the endpoint group), then select "Subjective Weights"

(Figure 6-16).

Repeat this step for each of the other three age ranges, choosing "Subjective Weights" in

every case.

Figure 6-16. Adding “Subjective Weights” pooling to the specific AMI valuation functions

(ad) At this point we have combined the two valuations for each age range. Now we want to

“move up a level” in the hierarchy and combine the four age ranges. Click the "None" in
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the same row as the AMI endpoint group, then select "Sum (Dependent)" (Figure 6-17).

This will create one valuation result for the entire endpoint group.

Figure 6-17. Adding “Sum (Dependent)” pooling to the AMI endpoint group

(ae) Finally, we need to set the specific weights to be used in the subjective weights pooling

methods. Double-click on any of the "Subjective Weight" pooling method cells. The

Select Subjective Weights window will open (Figure 6-18).

The default weight for each of the component functions is 0.5, i.e., the valuation

functions are being weighted equally. We will leave the default weights for AMI's four

poolings.
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For the emergency room (ER) pooling, we want to give more weight to the Smith

valuation function. Edit the Weights column so that the Smith function is 0.60 and the

Standford is 0.40. Click OK to set these weights and return to the Select Valuation

Methods, Pooling, and Aggregation window.

Figure 6-18. Changing the weights for “Subjective Weights” pooling of ER visits
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(af) Exercise (6.2): Why did we use “Sum (Dependent)” pooling for the AMI endpoint

group instead of “Random/fixed Effects” pooling?

Answer:

(ag) At this point we have set up the pooling for health incidence results (Stage 1 in creating

valuation functions), and have set up the specific functions and the pooling for similar

valuations (Stage 2). Moving on to Stage 3, we need to set some additional parameters

for the valuation functions, and decide on the aggregation levels for the health incidence

results and the valuation results. We will then save all these settings from the three stages

as a configuration (apv).

In the Select Valuation Methods, Pooling, and Aggregation window, click the

Advanced button. This will open the APV Configuration Advanced Settings window

(Figure 6-19).

Under the "Aggregation and Pooling" tab, use the drop-down menus to select "State" as

the aggregation levelfor the incidence and valuation results. In other words, the results

will be aggregated from CMAQ 36 km grid cells to states (minimum spatial unit).
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Figure 6-19. Advanced settings: Changing the aggregation level and the inflation dataset

(ah) Under the "Currency and Income" tab set the "Inflation DataSet" to "EPA Standard

Inflators" and make sure the "Currency Year" is set to 2000. Then, set the "Income

Growth Adjustment DataSet" to "Income Elasticity (3/21/2007). Change the "Year" to

2020, the same year used in our aqg files created earlier in the training. Then select all of

the "Endpoint Groups" by clicking on the first group, holding down the Shift key and

clicking on the last group (Figure 6-20).
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Figure 6-20. Advanced settings: Setting the income growth adjustment parameters

(ai) Since, as noted earlier, we are not doing anything in this training with QALY weights

(the third tab in the window), click OK. This willreturn you to the Select Valuation

Methods, Pooling, and Aggregation window.

The last parameter you need to set is the "Variable DataSet" at the top of the window.

Use the drop-down menu to select "EPA Standard Variables" (Figure 6-21). Note:If

you do not set the "Variable DataSet" you cannot save and run the configuration (next

step).
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Figure 6-21. Setting the variable dataset

Background: The variable dataset includes variables that are used in calculating the valuation—

for example, median income and average house size. Many of these variables are provided at

the county level.

(aj) When you have set up all the valuation functions and pooling as instructed above, you

are ready to move to Stage 4 of creating valuation results:saving the new configuration

file (apv) and generating the valuation results file (apvr).

Click Next. This will bring up a Save Aggregation, Pooling, and Valuation

Configuration window (Figure 6-22). We do want to save this new configuration (so

that it can be re-used), so click Save.
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This will bring up a Save As window. Under the "Configurations" folder, in the "File

name" field, type in the new configuration file name,

"PM25_RIA_2020_course_modified_state", and click Save.

Figure 6-22. Save Aggregation, Pooling, and Valuation Configuration window

(ak) After the APV configuration is saved, you will be returned to the Save Aggregation,

Pooling, and Valuation Configuration window (Figure 6-22). Now run the

configuration by clicking OK. This will bring up another Save As window. Here we will

save the valuation results (apvr). Under the "Configuration Results" folder, save the

results as

"Control_PM25_RIA_2020_modified_cmaq_grid_14_annual_35_daily_state".

The calculation of the results will begin and a Progress window will appear. The

calculation of the results may take a few minutes. When the calculations are finished, you

will be returned to the main BenMAP window.

Tip: If you are not ready to run this configuration, click Cancel. If you generated a

configuration, at a later time you can open the configuration and run it to generate valuation

results at a later time.

Background: The process of creating an aggregation, pooling, and valuation configuration

(apv) is more restrictive than the process of creating a health incidence configuration (cfg)

(discussed in Section 5). If you change the number or type of health incidence results used in an

APV process, you have to re-create the health incidence pooling. If you change the health

incidence pooling, you have to re-create the valuation functions and pooling.
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Analysis:

The rest of the exercises in Section 6.2 focus on analyzing the results of our BenMAP run.

Specifically, we will look at the newly created aggregation, pooling, and valuation configuration

file (apv) and results file (apvr). Recallfrom Section 5 that quality-assuring both your

configuration and your results is a good idea.

(al) First, we will use the audit trail to look at the newly created configuration file (apv).

From the main BenMAP window, click on the "Report" graphic in the right-hand panel.

Select the "Audit TrailReports" in the Select Report Type window and click OK. Under

the "Configurations" folder, select the new configuration, "

PM25_RIA_2020_course_modified_state.apv" and click Open.

(am)Exercise (6.3): What is the income growth adjustment year? For the incidence pooling,

what are the pooling methods for the mortality and HA endpoint groups? What is the

pooling method and advanced pooling method for the "HA, Chronic Lung Disease"

endpoint? For the valuation pooling, what is the pooling method for the ER and AMI

endpoint groups? What is the population year? When you are done with this exercise,

click OK to close the Audit TrailReport window.

Answer:

(an) Using the "Tools"menu in the main BenMAP window, open a BenMAP GIS window.

Open the "APV Configuration Results (*.apvr)" and select the "Pooled Incidence

Results". Under the "Configuration Results" folder, open our newly created file,

"Control_PM25_RIA_2020_modified_cmaq_grid_14_annual_35_daily_state.apvr".

(ao) In the Edit GIS Field Names window, provide more meaningful names for your health

incidence results then click OK. Here are some suggested names:ChronBronc, ER,

AcutResp, Mortality, HA, AMI18, AMI45, AMI55, AMI65.

Note: As you would expect, the individualresults have been pooled together (for

example, the Pope and Woodruff results have been pooled into one mortality result).
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(ap) Exercise (6.4): What are the numbers of avoided mortalities in California,

Pennsylvania, and Illinois? How many acute respiratory symptoms were avoided in the

same states?

Answer:

(aq) Exercise (6.5): Compare the AMI (heart attacks) for various age groups. What are the

maximum values for the AMI18 and AMI65 functions?

Answer:

(ar) Next we will overlay a pooled valuation map for the same apvr. From the same GIS

window, open "APV Configuration Results (*.apvr)" and select "Pooled Valuation

Results". Under the "Configuration Results" folder, open our newly created file,

"Control_PM25_RIA_2020_modified_cmaq_grid_14_annual_35_daily_state.apvr".

(as) In the Edit GIS Field Names window, provide more meaningful names for your

valuation results. Some suggested names are ChronBronc, ER, AcutResp, Mortality,

HA, AMI.

(at) In the Valuation Sums Layer window, add a sum for mortality and morbidity. For

morbidity, check the following endpoint groups: chronic bronchitis, ER, acute respiratory

symptoms, HA, AMI. Use "Dependent" as the summation type.

Remember to edit the "GIS Field Name" to "morbidity" and "mortality", respectively.

Make the "Control_PM25_RIA...(Pooled Valuation Results 0)" layer the active layer;
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simply right-click on that layer and select "Move Up".

(au) Exercise (6.6): What are the monetized benefits for the avoided acute respiratory

symptoms in California, Pennsylvania, and Illinois? Now make the "Pooled Valuation

Results Sums" layer active. What are the monetized benefits for the avoided morbidity

events in the same states? What are the monetized benefits for the avoided mortalities in

the same states? In comparing your answers to those for Exercise 6.5, what conclusion

can you draw about the mortality valuation function versus the acute respiratory

symptoms valuation function? When you are done with this exercise, close the GIS

window.

Answer:

(av) Create a new report for our valuation results. From the main BenMAP window, click on

"Report" in the right-hand panel and then select the "Incidence and Valuation Results:

Raw; Aggregated and Pooled" type. Under the "Configuration Results" folder, open our

newly created file,

"Control_PM25_RIA_2020_modified_cmaq_grid_14_annual_35_daily_state.apvr".

For the result type, select "Pooled Valuation Results".

In the APV Configuration Results Report window (Figure 6-23), select the "Endpoint

Group" within the "Pooled Valuation Method Fields" panel. In the "Results Fields" panel,

uncheck the "Variance". In the "Display Options" panel, reduce the "Digits After Decimal

Point" to 0. The “Preview” panel in the bottom half of this window willreflect your

choices.
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Figure 6-23. Pooled valuation report.

(aw)Now that you have chosen all the columns to include in your valuation results report, you

can save the report. From the APV Configuration Results Reports window, type CtrlS. Under the "Reports" folder, save the file as

"Control_PM25_RIA_2020_modified_cmaq_grid_14_annual_35_daily_state_value".

After the file has been saved, close the window by clicking Done. Use Windows

Explorer to navigate to the "Reports" folder. Double-click on the newly created csv file

and it should open in Excel.
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(ax) Exercise (6.7): All of the following questions refer to California, FIPS code 6 (i.e.,

column = 6, row = 1). What is the point estimate of the monetized benefit for the

number of avoided AMIs? What is the estimate of the monetized benefit for AMIs at the

0.5 percentile? What is the estimate at the 99.5 percentile? When you are finished with

this exercise, close the Excel window.

Answer:

A.1.6.2 Example: PM2.5 Control 2020 14/35 Adjusted

The goal of this exercise is to re-use our newly created aggregation, pooling, and valuation

configuration (apv) (Section 6.2) and produce valuation results for the adjusted controlscenario

RIA 2020 14 annual, 35 daily μg/m3.

Procedures:

(a)In the main BenMAP window, click on the graphic titled "Pooling, Aggregation, and

Valuation" in the right-hand panel. This will open the APV Configuration Creation Method

window

(b)In this window, select "Open Existing Configuration file for Aggregation, Pooling, and

Valuation". Under the "Configurations" folder, select the newly created configuration

"PM25_RIA_2020_course_modified_state.apv" and click Open.

(c)In the Incidence Pooling and Aggregation window that opens, we will change the input

health incidence file (cfgr). Click the Browse button next to the "Configuration Results File

Name(s)" field. Under the "Configuration Results" folder, select the health incidence file

“Control_PM25_RIA_2020_modified_cmaq_grid_14_annual_35_daily_adjusted.cfgr”.

(d)Review your health incidence pooling window, "Main Pooling Window". The pooling

configuration should be identical to our previous example (see Figure 6-8).

(e)Click Next. The Select Valuation Methods, Pooling, and Aggregation window will

appear. Review your valuation functions and pooling. The configuration should be identical to

our previous example (see Figure 6-17). Click on Advanced to confirm that these settings are

the same as before (see Figure 6-19 and Figure 6-20). When you are done reviewing the

configuration, click Next.
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(f) Do not save the configuration. There is no need to save since the only change was to the

health incidence results file (cfgr). Do create the valuation results (apvr) by clicking OK.

Under the "Configuration Results" folder, save the results as

"Control_PM25_RIA_2020_modified_cmaq_grid_14_annual_35_daily_state_adjusted".

The calculation of the results will begin and a Progress window will appear. The calculation

may take a few minutes.

Analysis: Now we will look at the newly created aggregation, pooling, and valuation results file

(apvr).

(g)Open an audit trailfor the new valuation results file,

"Control_PM25_RIA_2020_modified_cmaq_grid_14_annual_35_daily_state_adjusted.apvr".

(h)Exercise (6.8): What is the population year used in this apvr? What are the weights used for

pooling the two ER valuation functions?

Answer:

(i) Open a BenMAP GIS window, then open the same valuation results file (apvr). Map the

"Pooled Incidence Results".

(j) Exercise (6.9): What are the numbers of avoided mortalities in California, Pennsylvania, and

Illinois? How many acute respiratory symptoms were avoided in the same states? How do

these results compare to the nonadjusted results (see Exercise 6.4)?
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Answer:

(k)Overlay the "Pooled Valuation Results" for the same apvr. Edit the GIS field names. In the

Valuation Sums Layer window, create sums for morbidity and mortality the same way you

did above Exercise 6.6. Make the "Control_PM25_RIA...(Pooled Valuation Results 0)"

layer the active layer.

(l) Exercise (6.10): What are the monetized benefits for the avoided acute respiratory

symptoms in California, Pennsylvania, and Illinois? Now make the "Pooled Valuation Results

Sums" layer active. What are the monetized benefits for the avoided morbidity events in the

same states? What are the monetized benefits for the avoided mortalities in the same states?

How do these results compare to the nonadjusted results (see Exercise 6.6)? When you are

done with this exercise, close the GIS window.

Answer:

(m)Create a new report for the valuation results file. After selecting the report type and opening

the file, you should select the results type, "Pooled Valuation Results".

In the APV Configuration Results Report window, select the "Endpoint Group" within

the "Pooled Valuation Method Fields" panel. In the "Results Fields" panel, uncheck the

"Variance". In the "Display Options" panel, reduce the "Digits After Decimal Point" to

0.
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Under the "Reports" folder, save the file as "Control_PM25_RIA_2020_modified_cmaq_

grid_14_annual_35_daily_state_adjusted_value". Open the csv file in Excel.

(n)Exercise (6.11): All of the following questions refer to California, FIPS code 6 (i.e.,. column

= 6, row = 1). What is the point estimate of the monetized benefit for the number of avoided

AMIs? What is the estimate of the monetized benefit for AMIs at the 0.5 percentile? What is

the estimate at the 99.5 percentile? How do these results compare to the nonadjusted results

(see Exercise 6.7)? When you are finished with this exercise, close the Excel window.

Answer:

A.1.6.3 Example: Modifying One-Step Analysis Parameters

The goal of this exercise is to modify the One-Step Analysis to use our newly created health

incidence and aggregation, pooling, and valuation configurations.

Procedures:

(a)From the main BenMAP window, select "One Step Analysis" from the "Parameters"menu

(Figure 6-24).
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Figure 6-24. Parameters menu in the main BenMAP window.

(b)This will open the One Step Setup Parameters window (Figure 6-25). Here you can select

the health incidence (cfg) and valuation configuration (apv) files that will be used in the OneStep Analysis approach.
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Figure 6-25. Modifying configurations for One-Step Analysis

(c)Select "PM 2.5" from the Pollutant drop-down menu. Click the Browse button to the right of

the CFG File Name box. In the Open window, under the "Configurations" folder, select

"PM25_RIA_2020_course_modified.cfg" and click Open. This willreturn you to One Step

Setup Parameters window.

Click the Browse button to the right of the APV File Name box, so that you can select

the new aggregation, pooling, and valuation configuration. In the Open window, under

the "Configurations" folder, select "PM25_RIA_2020_course_modified_state.apv" and

click Open. This will again return us to One Step Setup Parameters window.

We have the option of changing the currency year, but in our case leave the "Currency

Year" as 2000. Finally, click Save. The next time you run One-Step Analysis, it will use

these new configurations.

(d)This completes the “Aggregation, Pooling, and Valuation” lab. In the next lab, "Adding New

Datasets & Independent Study", we will add the necessary data to do a metropolitan scale

analysis of Detroit. After adding the new datasets, we will use our new configurations (from

Sections 5 and 6) to create health incidence and valuation results for our new domain.

A.1.7 Section 7. Adding New Datasets & Independent Study

In this section, you will add new datasets to BenMAP and run a local-scale benefit analysis in

Detroit, Michigan.
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BenMAP contains pre-loaded data necessary to perform a health impact assessment that will

meet most users’ analytical needs. However, you can also import your own datasets into

BenMAP when the pre-loaded datasets are not adequate for your analysis. For example, you

can add new population datasets, new grid definitions, new health impact and valuation functions,

and new background incidence rates for specific health endpoints. If you decide to conduct either

a local-scale analysis or a non-U.S. analysis, you will likely need to add new datasets to model

the benefits and adequately reflect those localfactors. In other words, the U.S. national datasets

may not be the best available data or functions for your study.

In this section, you will add new datasets to conduct an entire benefit analysis for a change in air

quality in the Detroit metropolitan area. Below are a few key aspects of the analysis:

Our model area is the greater Detroit metropolitan area, partially covering three counties:

Wayne, Oakland, and Macomb.

The air quality grid cells are 1 km by 1 km.

The air quality model data are for 2020.

The controlscenario models a 14.5 μg/m3 annual PM2.5standard for the core of the

metropolitan region.

Unlike our previous nationalstudies, this lesson uses a finer-resolution grid, Detroit-specific

population data, and Detroit-specific background incidence rates for hospital admissions due to

asthma.

A.1.7.1 Example: Adding Datasets for Detroit

The goal of this exercise is to add the necessary datasets so that we can conduct a local-scale

analysis in Detroit. Specifically, we will add a new 1-km grid definition, a new population dataset

for Detroit, and new background incidence for asthma hospital admissions for Detroit.

Procedures:

(a)From the main BenMAP window, select "Modify Setup" from the Tools menu (Figure 7-1).
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Figure 7-1. Choosing “Modify Setup”

(b)This will open the Manage Setup window (Figure 7-2). Through this window, you can modify

many of the datasets, parameters, and functions used in BenMAP. Specifically, you can add

to or change any of the following 11 categories of data: grid definitions, pollutants, monitor

datasets, incidence/prevalence datasets, population datasets, C-R function datasets, variable

datasets, inflation datasets, valuation datasets, income growth adjustments, and QALY

distribution datasets. To modify any one of these, you can click the Edit button below the

appropriate list.
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Figure 7-2. Manage Setup window

(c)We willstart by adding a new grid definition. Click Edit below the Grid Definitions list. This

will open the Manage Grid Definitions window (Figure 7-3). Here you can add new grid

definitions or you can delete or edit existing grid definitions.
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Figure 7-3. Manage Grid Definitions window

Tip: You can use the Edit button to view other grid definitions. Simply highlight the particular

grid in the Available Grid Definitions list and click Edit. Make sure you do not accidentally

save any changes while you are viewing the definition. You can use this same technique to

view other datasets that have already been loaded (e.g., "Pollutants").

(d)We will add a new grid definition for Detroit. In the Manage Grid Definitions window, click

the Add button. This will open a Grid Definition window (Figure 7-4). Here we can define

our new grid.

Note: When adding a series of new datasets, you should generally load the new grid

definition first. Adding other datasets (e.g. adding population data) will typically depend

on the new grid definition.
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Figure 7-4. Grid Definition window

(e)We will define the new grid based on an ESRI shapefile. First, we willset the name of the new

grid. Edit the Grid ID field by changing the text to "Detroit CMAQ 1km".

Next we load our shapefile. Click the "Shapefile Grid" tab. Next to the Load Shapefile

field, click the browse button. Under the folder "Inputs" and the subfolder "Detroit",

select the file "Detroit_grid.shp" and click Open. This willreturn you to the Grid

Definition window.

Click Preview to view your new grid (Figure 7-5).
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Figure 7-5. Loading and previewing the Detroit CMAQ 1km grid

(f) Click OK. This willreturn you to the Manage Grid Definitions window. Scroll through the

list of available grid definitions to confirm that your new grid is included then click OK. This

willreturn you to the Manage Setup window. Note:It may take a minute or two to complete

the loading of the new grid (indicated by the Manage Setup window becoming active again).

Confirm that the new definition is in the Manage Setup window’s grid definitions list. If

it is, then you have successfully added a new grid definition.

(g)Next we will add a new background incidence rates dataset. This new dataset will have

specific rates for Detroit instead of national averages. However, for the purposes of this

lesson, the dataset will include only the "HA, Asthma" endpoint. Click Edit below the

Incidence/Prevalence DataSets list. This will open the Manage Incidence DataSets

window (Figure 7-6). Here you can add new background incidence rates datasets or you can

delete or edit existing background incidence rates datasets.
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Figure 7-6. Manage Incidence DataSets window

(h)Click the Add button. This will open an Incidence DataSet Definition window (Figure 7-7)

that we will use to define our new background incidence rates dataset.
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Figure 7-7. Incidence DataSet Definition window

(i) Edit the DataSet Name field by changing the text to "2004 HA Incidence Detroit". Click the

Load From Database button. This will open a Load Incidence/Prevalence DataSets

window. Using the Grid Definition drop-down menu, select the "Detroit CMAQ 1km" grid

definition (Figure 7-8).
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Figure 7-8. Load Incidence/Prevalence Database window

(j) Next to the Database field, click Browse. In the new Open window, set the Files of type to

"Excel Files". Under the folder "Inputs" and the subfolder "Detroit", select the file

"background_incidence_Detroit" and click Open.

(k)This will open a Select a Table window. Here you willselect the particular Excelsheet (within

the Excelfile) that contains the new background incidences rates data Using the drop-down

menu, select "Sheet1$" (Figure 7-9).
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Figure 7-9. Select a Table window

(l) Click OK. This willreturn you to the Load Incidence/Prevalence Database window. Click

OK in that window to return to the Incidence Dataset Definitions window, where fields have

now been filled in from the dataset you loaded. Explore some of the background incidence

rates by highlighting various age ranges in the "DataSets Incidence Rates" panel and using the

scrollbar in the right-hand panel(Figure 7-10).

Figure 7-10. Loading and viewing the Detroit background incidence rates
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(m)Click OK. This willreturn you to the Manage Incidence DataSets window. Scroll through

the list of available datasets to confirm that your new background incidence rates dataset is

included, then click OK. This willreturn you to the Manage Setup window. Note:It may take

a minute or two to complete the loading of the new dataset (indicated by the Manage Setup

window becoming active again).

Confirm that the new dataset is in the Manage Setup window’s incidence/prevalence datasets

list. If it is, then you have successfully added a new background incidence rates dataset.

(n)Next we will add a new population dataset. This new dataset will have more specific

population information for the Detroit area than the national datasets. Click Edit below the

Population DataSets list. This will open the Manage Population DataSets window (Figure

7-11). Here you can add new population datasets or you can delete (but not edit) existing

population datasets.

Note: The population datasets are relatively large, so it may take a few minutes to

display each of the windows discussed here.

Figure 7-11. Manage Population DataSets window

(o)Click the Add button. This will open a Load Population DataSet window (Figure 7-12).
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Figure 7-12. Load Population DataSet window

(p)Edit the Population DataSet Name field by changing the text to "Detroit CMAQ 1km". In

the Population Configuration field, use the drop-down menu to select "United States

Census". For the Grid Definition field, use the drop-down menu to select "Detroit CMAQ

1km".

Background: The “Population Configuration” defines the age range, race, and gender

variables in your population database. It is critical that the definitions in the population

configuration match those used in the development of the database.

(q)Next to the Database field, click the Browse button. In the new Open window, set the Files

of type to "Text Files". Under the folder "Inputs" and the subfolder "Detroit", select the file

"Detroit_Pop" and click Open. This willreturn you to the Load Population DataSet window

(Figure 7-13).
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Figure 7-13. Setting the population configuration and the grid definition

(r) Click OK. A Progress window will appear. It will take a few minutes to load the population

dataset. When it is loaded, you will be returned to the Manage Population DataSets

window, where the fields have now been filled in from the dataset you loaded. Explore your

new population dataset by highlighting "Detroit CMAQ 1km" in the Available DataSets list

and using the horizontal and verticalscrollbars on the right-hand panel(Figure 7-14).
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Figure 7-14. Loading and viewing the Detroit population dataset

(s)Click OK. This willreturn you to the Manage Setup window. Note:It may take a minute or

two to complete the loading of the new dataset (indicated by the Manage Setup window

becoming active again). Confirm that the new dataset is in the Manage Setup window's

population datasets list. If it is, then you have successfully added a new population dataset.

(t) You have now finished adding the necessary datasets for our Detroit study. From the Manage

Setup window, click OK. This willreturn you to the main BenMAP window.

Analysis:

The rest of Section 7.2 focuses on analyzing one of the new datasets for Detroit.

(u)We willfocus on the new population data for Detroit. Using the "Tools"menu in the main

BenMAP window, open a BenMAP GIS window. Open the "Population" dataset (Figure 7-

15).
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Figure 7-15. Mapping population data

(v)This will open a Select Population Data window. Using the drop-down menus, select

"Detroit CMAQ 1km" as the population dataset and 2020 as the population year (Figure 7-

16). Click OK. A Progress window will appear. It may take a few minutes for your

population data layer to appear in the "Layers" panel.

Figure 7-16. Select Population Data window
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(w)Display the population data for African-American females, ages 30 to 34 (B_F_30TO34).

Select the "Detroit CMAQ 1km" as the reference layer. Now, change the reference

layer to the county grid. This will automatically zoom out to the national domain. Zoom

back in to the Detroit area (Figure 7-17).

Figure 7-17. Detroit study area

Background: The following are example demographic codes in the Detroit dataset:

N_M_40TO44 = Native American male 40 to 44 years; B_F_50TO54 = AfricanAmerican female 50 to 54 years; A_M_60TO64 = Asian-American male 60 to 64 years;

W_F_50TO54 = white female 50 to 54 years; O_M_1TO4 = other male 1 to 4 years.

(x)Exercise (7.1): Compare the different demographics from the Detroit population dataset.

How does the spatial pattern of African-American females 30 to 34 differ from the spatial

pattern of white females 30 to 34?
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Answer:

A.1.7.2 Independent Study: Detroit Benefits Analysis

The goal of this exercise is to model the health effects and the subsequent monetized benefits of a

change in air pollution (PM2.5concentrations) for the Detroit region.

Procedures:

The following steps should be thought of as general guidance on how to complete the Detroit

benefit analysis. Unlike the earlier labs, this lab does not give detailed instructions on how to

complete each stage of the modelrun and analysis for the Detroit study. If needed, you may refer

to previous labs for more detailed instructions on specific steps.

(a) Create a baseline and control aqg from the corresponding model data. You willfind two

model datasets in the "Detroit" subfolder under the "Inputs" folder. Both datasets are for

the 2020 model year and are on the "Detroit CMAQ 1km" grid. The controlscenario

has some areas reduced to 14.5 μg/m3 annual average PM2.5 concentrations. Be sure to

clearly name your files so that you know which file is the control and which is the

baseline.

(b) Open and modify the health incidence configuration created in Section 5. Change the

population dataset to your new Detroit population dataset. Change the baseline and

control to your Detroit baseline and control.

Add a new function for the "HA, Asthma" endpoint (no threshold). Set the new "HA,

Asthma" function’s incidence rate to the Detroit-specific background incidence rates

dataset. Save a new health incidence configuration and create a results file.

(c) Re-create the apv configuration (from Section 6) using your new Detroit health incidence

results. Because we are adding a new incidence endpoint, you will have to start from

scratch (see Figures 6-8, 6-19, 6-20, and 6-21 for reference). Include the new "HA,

Asthma" incidence result in your "Hospital Admission, Respiratory" pooling.

In selecting the valuation function for "Hospital Admissions, Respiratory", take into

account your new age range for your pooled results (i.e., look at the age ranges of the

health impact functions that make up the pooled result). Aggregate your health incidence

and valuation results to the county level. Save the apv configuration and create a results
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file. This completes the modeling portion of the Detroit study.

Analysis:

We provide a series of exercises to guide your analysis of the results from the Detroit study.

(d) Exercise (7.2): What pooling type did you use to combine "HA, Pneumonia", "HA,

Asthma", and "HA, Chronic Lung Disease" together? Why?

Answer:

(e) Exercise (7.3): Compare the total adult population (30-99) from the mortality

incidence to the demographic population (Exercise 7.1). How does the “total”

population’s spatial pattern differ from the patterns for the two demographics (AfricanAmerican females ages 30 to 34 and white females ages 30 to 34)?

Answer:

(f) Exercise (7.4): Why are the aggregated incidence results a significant underestimate of

the total change in incidence for the three counties? Hint: look at the extent of the

nonaggregated domain.
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Answer:

(g) Exercise (7.5): What is the total(over the three counties) monetized value for avoided

premature mortalities? What is the total monetized value for avoided acute respiratory

symptoms? What are the total health incidence results for these two endpoints?

Answer:

(h) Exercise (7.6): Answer the following questions using the totals for each county: What

are number of "Hospital Admissions, Respiratory" avoided? What are the number of

"HA, Asthma" avoided? What is the background incidence for "HA, Asthma"?

Answer:

(i) Exercise (7.7): What is the total(summed over endpoints) monetized benefit for each

county? Give the mean and confidence intervals (0.5th and 99.5th percentiles).
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Answer:

Synthesis Questions:

The following questions are meant to help you synthesize what you have learned as you have

worked through the entire body of BenMAP course material. They draw from multiple labs and

course slides.

(j) Exercise (7.8): Would you expect the benefits to increase or decrease if you used a

later population year? Why?

Answer:

(k) Exercise (7.9): Would you expect the monetized benefits to be higher or lower if you

used a later currency year? Why?
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Answer:

(l) Exercise (7.10): Why might your results differ if you used a national incidence baseline

instead of a local incidence baseline for the "HA, Asthma" endpoint?

Answer:

(m) Exercise (7.11): Ifsome of the health incidence results extended beyond the analysis

year (in our case, 2020), should we discount these monetized benefits?

Answer:
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(n) Exercise (7.12): Unlike in our configuration, EPA generally uses more than one study

to model adult mortality. They also do not tend to pool their mortality results. Why might

you not want to pool adult mortality and instead report a range for your incidence

results?

Answer:

(o) Exercise (7.13): Is BenMAP better suited to perform national(large-scale) analyses or

local(urban-scale) analyses? Why?

Answer:

(p) Exercise (7.14): What are some of the benefits of reducing air pollution that BenMAP

does not currently quantify?
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Answer:

(q) Exercise (7.15): Do you think that valuation estimates would be higher if we used

willingness-to-pay (WTP) studies or cost-of-illness (COI) studies?

Answer:

(r) Exercise (7.16): What are some of the sources of uncertainty in a BenMAP analysis?

Answer:
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A.1.8 Answers to Training Exercises

Section 2 Answers

(2.1) D24HourMean = 10.56 μg/m3; QuarterlyMean =10.44 μg/m3; lat/lon = (47.35, -68.32).

(2.2) States of Alabama, California, Georgia, Illinois, Michigan, Pennsylvania, Ohio, Tennessee

(2.3) QuarterlyMean maximum = 28.32 μg/m3; minimum = 2.76 μg/m3

(2.4) D8HourMax maximum = 67.68 ppb; minimum = 17.19 ppb

States of Arizona, California, South Carolina, Tennessee, Utah.

(2.5) No, because the CMAQ grid is a regular grid, whereas the state and other political grids

have irregular borders.

(2.6) States of California, Montana, Oregon.

(2.7) The age range is 30 to 99 years.

(2.8) States of California, Oregon, Pennsylvania, Washington.

(2.9) The number of acute respiratory symptoms avoided is much greater than the number of

mortalities avoided.

(2.10) Because the health incidence values are a function of both delta and population. Therefore, a

high delta in a low population area willstill have a small health incidence change. In contrast,

a relatively low delta in a high population area may have a large health incidence change—in

other words, significantly fewer people having that health incidence.

(2.11) Incidence of mortality: 213; incidence of acute respiratory symptoms: 188,303.

Valuation of mortality: $1.4 billion; valuation of morbidity $66 million.

This means that even though there are far fewer mortalities, they are valued at a much higher

rate than the morbidity incidences (as one would expect).

(2.12) Population year 2020.

CMAQ 36 km

(2.13) 0-17 years.

Norris, G., et al.

Section 3 Answers

(3.1) States of Arizona, California, Idaho, Maryland, Montana, Nevada, Ohio, Oregon,

Pennsylvania, Utah, Washington, West Virginia, Wyoming.
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(3.2) No, because the C-R functions are based on deltas and population. The human population

in the ocean is 0, so both the incidence and valuation over the ocean will be 0.

(3.3) Initially:County, State, Report_regions, and Nation grids.

After setting Incidence to State, grids at the same scale or coarser are then available.

(3.4) endpoint = Mortality, AllCause

author = Pope et al.

incidence aggregation = valuation aggregation = County

(3.5) The CMAQ 36km Nation Overlap grid.

Because aggregation occurs in the valuation step, the aggregated health incidence data is in

the apvr file, not the cfgr file.

(3.6) States of California, Washington, Oregon, Utah, Michigan, Ohio, Pennsylvania, New Jersey,

New York, Maryland, and Virginia.

(3.7) The County grid.

(3.8) State code = 6, FIPS code = 37 (Los Angeles County); avoided mortalities = 132.76

(3.9) health incidence maximum = 66.68; sum = 1707.67

pooled incidence maximum = 132.76; sum = 1707.67

(3.10) State code = 6, FIPS code = 37 (Los Angeles County); mortality = $876 million; morbidity

= $38 million

National mortality: $11.27 billion; National morbidity: $403 million.

(3.11) For col 1, row 9, mortality has the greatest standard deviation = 247,031

Chronic bronchitis has the greatest coefficient of variation = 16.587/13,520 = 1.23

(3.12) The delta for the 14/35 analysis is significantly larger. For example, a larger portion of the

states have more than 0.2 μg/m3difference.

(3.13) Illinois: 227 mortality for 14/35 scenario, 3.75 mortality for 15/35 scenario.

California: 559 mortality for 14/35 scenario, 573 mortality for 15/35 scenario.

Overall, the 14/35 scenario has more states showing significant numbers of avoided

mortalities, especially in the East, whereas the 15/35 scenario has some western states with

slightly greater reductions (e.g., California, Oregon, Washington). Total number of

mortalities avoided for the lower 48 states is 4,787 for the 14/35 scenario and 1,707 for the

15/35 scenario.

(3.14) California: $3.69 billion saved in prevented mortality, $147 million saved in prevented

morbidity.

Illinois: $1.5 billion saved in prevented mortality, $50 million saved in prevented morbidity.

$31.6 billion mortality and $1.1 billion morbidity for the 14/35 scenario versus $11.2 billion
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mortality and $403 million morbidity for the 15/35 scenario. In other words, our intuition

was correct that the 14/35 scenario had higher benefits than the 15/35 scenario.

Section 4 Answers

(4.1) The adjusted aqg's have no values greater than 15 μg/m3. In comparison, the non-adjusted

aqg's do have regions with annual values greater than 15 μg/m3.

(4.2) The VNA AQG is much smoother, because it uses distance weighting to smooth the AQG

between monitor locations.

(4.3) Neighbor scaling type = inverse distance;

Rollback region = California (6), Oregon (41), Pennsylvania (42), and Washington (53);

Rollback method = percentage, the percentage of rollback = 0.1 (i.e. 10%)

(4.4) California, Ohio, West Virginia, Maryland, Georgia, and Alabama.

(4.5) Region 1: using peak shaving for inter and intraday rollback. Rolling back to a standard

(attainment test) of 35 μg/m3 on the D24HourMean metric and an ordinality of 1.

Region2: using incrementalrollback, reducing all monitors by an increment of 4 μg/m3.

(4.6) The Western states have a generally constant delta. This makes sense because we applied

an incremental change (a constant reduction) to all the monitors in the region. In contrast, the

East Coast has been reduced to a standard. Therefore, only areas that were over the

standard (35 μg/m3 daily mean) will be reduced. We see that the most significant changes are

in Pennsylvania and to a lesser degree in Georgia.

If we look at the eastern edge of the West coast states, we notice that the delta is not

constant across each state. Initially, we might expect the deltas to be constant across the

state, because we applied an incremental change to all the monitors. However, the AQG is

the result of interpolating from the monitors to the grid cells. Therefore, on the eastern edge

we are interpolating between monitors that did have a rollback and those areas that had no

change.

Section 5 Answers

(5.1) QuarterlyMean maximum = 1.67 μg/m3.

QuarterlyMean mean = 0.11 μg/m3 (misleading because includes model domain over the

ocean).

Washington, California, Oregon, Georgia, Pennsylvania, West Virginia.

(5.2) Two functions.

Both functions are by Norris.

Differences include a qualifier (10 μg/m3 threshold), Beta and P1Beta (standard deviation),

and C (C=10 for the threshold function).
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(5.3) Endpoint: "HA, Chronic Lung Disease (less Asthma)". "HA, Chronic Lung Disease", HA,

Pneumonia", "HA, Asthma".

Four functions: two by Ito and two by Moolgavkar.

(5.4) Pope.

Three functions are by Pope.

The three functions have different thresholds (0, 7.5, and 10 μg/m3).

The current configuration uses the 0 μg/m3 threshold Pope function.

(5.5) Pope age range 30-99 years, Woodruff age range 0-0 years.

Pope function = Inc Pop

e

Q

1

1

Woodruff function = Inc Pop

Inc e Inc

Q 1

1

1

(5.6) Ito and Moolgavkar have the same functionalform.

Ito's = 0.001169.

Moolgavkar's = 0.00183.

Bonus: Moolgavkar is more sensitive to changes in AQ. An equal change in AQ willresult in

a larger change in the health incidence from Moolgavkar's function compared to Ito's

function. Mathematically, the larger , the smaller the value of Q e

1

, and hence the larger

the value of the whole function.

(5.7) Population year = 2020.

12 Concentration-Response (C-R) functions.

Woodruff location is 86 cities and incidence dataset is 2020 Mortality Incidence Rates

(5.8) Pope maximum = 66.78 premature deaths (ages 30-99) avoided.

Woodruff maximum = 0.15 premature deaths (infant) avoided.

Similar but not the exact same spatial patterns.

(5.9) Ito maximum = 3.01.

Moolgavkar maximum = 4.73.

Moolgavkar population is the same as the Ito population.

They have identical inputs. The only difference between the two functions is the ; therefore,

the Moolgavkar study is more sensitive to changes in AQ.

(5.10) AMI18 maximum = 3.63, population maximum = 2,434,707.

AMI65 maximum = 65.36, population maximum = 988,819.

Although the population for the 18-44 group is much greater than the population for the 65-

99 group and the functions are the same, the incidence results are much smaller for the 18-

44 age group. This implies that the incidence rate for younger group is much smaller than the
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incidence rate for the older group. This result reflects our intuition.

(5.11) baseline = 5,452.946.

point estimate = 4.7457.

5th percentile = 4.0085.

95th percentile = 5.4827.

(5.12) QuarterlyMean maximum = 4.56 μg/m3.

QuarterlyMean mean = 0.32 μg/m3.

California, Washington, Oregon, Idaho, Wyoming, Montana, Utah, Pennsylvania, West

Virginia.

Compared to Exercise 5.1, the adjusted control has much larger AQ delta values and larger

geographic areas ofsignificant delta, especially in the West.

The adjusted scenario should have a significantly larger change in adverse health effects

because it has a significantly larger change in AQ.

(5.13) Population year = 2020.

12 CR functions.

Number of Latin Hypercube Points = 10.

(5.14) Pope maximum = 196.13.

Woodruff maximum = 0.52.

The adjusted results are greater than the nonadjusted results.

(5.15) AMI18 maximum = 11.13, population maximum = 2,434,707.

AMI65 maximum = 189.75, population maximum = 988,819.

The adjusted results are greater than the nonadjusted results. The adjusted population is

equal to the nonadjusted population. There was also a change in spatial pattern. For the

adjusted results, most of the significant results are in the West.

(5.16) baseline = 5452.946.

point estimate = 11.302.

5th percentile = 9.5474.

95th percentile = 13.0561.

The adjusted baseline equals the nonadjusted baseline. The adjusted mean is greater than the

nonadjusted mean. The adjusted statisticalspread and values are greater than the

nonadjusted statisticalspread and values.

Section 6 Answers

(6.1) Sum (Dependent) because the health incidence populations are nonoverlapping (distinct age

groups).

(6.2) We used Sum (Dependent) because the four AMI valuation results are for nonoverlapping
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populations (distinct age groups).

(6.3) Income growth adjustment year = 2020.

HA pooling and mortality pooling both use Sum (Dependent).

HA Chronic Lung Disease pooling uses Random / Fixed Effects, with advanced pooling

using round weight to two digits.

ER endpoint group valuation pooling = Subjective Weights

AMI endpoint group valuation pooling = Sum (Dependent)

Population year = 2020.

(6.4) California mortalities = 560, acute respiratory symptoms = 503,139.

Pennsylvania mortalities = 342, acute respiratory symptoms = 184,340.

Illinois mortalities = 227, acute respiratory symptoms = 163,566.

(6.5) AMI18 maximum = 30.

AMI65 maximum = 555.

6.6) California acute respiratory = $25.4 million, morbidity = $289.6 million,

mortality = $3.7 billion.

Pennsylvania acute respiratory = $9.3 million, morbidity = $141.6 million,

mortality = $2.3 billion.

Illinois acute respiratory = $8.3 million, morbidity = $106.1 million,

mortality = $1.5 billion.

The mortality incidence numbers are far smaller than the acute respiratory symptom

numbers, but the mortality valuation is far greater than the acute respiratory valuation.

Therefore, the mortality valuation function (VSL) must be much larger than the acute

respiratory symptoms function.

(6.7) AMI point estimate = $79.3 million.

0.5th percentile = $14.6 million.

99.5th percentile = $193.5 million.

(6.8) Population year = 2020.

Smith subjective weight = 0.60, Standford subjective weight = 0.40.

(6.9) California mortalities = 2,295, acute respiratory symptoms = 2.1 million.

Pennsylvania mortalities = 365, acute respiratory symptoms = 196,460.

Illinois mortalities = 231, acute respiratory symptoms = 166,252.

The adjusted health incidence results are greater than the nonadjusted results, especially in

the West.

6.10) California acute respiratory = $107.2 million, morbidity = $1.19 billion,

mortality = $15.15 billion.

Pennsylvania acute respiratory. = $9.9 million, morbidity = $151 million,

mortality = $2.41 billion.
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Illinois acute respiratory = $8.4 million, morbidity = $108 million,

mortality = $1.53 billion.

The adjusted valuation results are greater than the nonadjusted results, especially in the

West.

(6.11) AMI point estimate = $320 million.

0.5th percentile = $59.7 million.

99.5th percentile = $772 million.

The adjusted mean is greater than the nonadjusted mean. The adjusted statisticalspread and

values are greater than the nonadjusted statisticalspread and values.

Section 7 Answers

(7.1) African-American females age 30-34 are concentrated in the central and eastern band of

our domain (Northern Wayne Co.). White females age 30-34 are more concentrated in the

northern (southern Oakland and Macomb Co.) and southwestern (central Wayne Co.) parts

of our domain.

(7.2) We used sum (dependent) to combine together the various "HA" endpoints. We used

random/fixed effects to combine together the Ito and Moolgavkar "HA, Chronic Lung

Disease" results into one result. The sum (dependent) pooling makes sense for the three

endpoints because the populations are distinct, nonoverlapping. On the other hand, the two

"HA, Chronic Lung Disease" results are for an overlapping population; therefore, the

random/fixed effects pooling is appropriate.

(7.3) The spatial pattern of total adult population is more spatially homogeneous than the pattern

of the demographic data. It appears to be more similar to the combination of the AfricanAmerican female and white female spatial patterns than to either of the individual

demographic patterns.

(7.4) The aggregated incidence results are a significant underestimate of the total change in

incidence for the three counties because the "Detroit CMAQ 1km" grid does not cover the

entire spatial extent of the three counties. In other words, we are characterizing the whole

county's incidence change based on a subset of the county, i.e., based on a subset of the

population.

(7.5) Total monetized mortality benefit = $1.75 billion

Total monetized acute respiratory symptoms benefit = $8.64 million

Total mortality incidence results = 264

Total acute respiratory symptoms results = 170,868

(7.6) Macomb (FIPS 99):"HA, Respiratory " = 9.4, "HA, Asthma" = 0.85, "HA, Asthma"

baseline = 201.3

Oakland (FIPS 125): "HA, Respiratory " = 35.6, "HA, Asthma" = 3.77, "HA, Asthma"
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baseline = 608.3

Wayne (FIPS 163): "HA, Respiratory " = 115.0, "HA, Asthma" = 36.5, "HA, Asthma"

baseline = 3,954.3

(7.7) Macomb (FIPS 99) total monetized benefit: mean = $119 million, 0.5th = -$7.68 million,

99.5th = $347 million

Oakland (FIPS 125) total monetized benefit: mean = $464 million, 0.5th = -$29.2 million,

99.5th = $1.37 billion

Wayne (FIPS 163) total monetized benefit: mean = $1.28 billion, 0.5th = -$81.1 million,

99.5th = $3.75 billion

(7.8) For most areas, a later population year would mean a greater population. Because most of

the health impact functions are proportional to population, a greater population would mean

a greater benefit.

(7.9) A later currency year would generally mean that the monetized benefits would be higher. The

later the currency year, generally the greater the inflation, and hence the less the purchasing

power of an individual dollar. Therefore, the same benefit in a later currency year would

equal a greater number of dollars.

(7.10) A national background incidence rate would not reflect the local incidence rates for specific

health endpoints. A local background incidence rate would likely more closely reflect the

local population characteristics than the national average. The background incidence rates

are an important variable in the underlying health impact function.

(7.11) The AMI functions calculate a change in benefits beyond 2020. The AMI results are for five

years of medical costs and five years of opportunity costs. Because these costs span five

years (2020-2025), the benefits should be discounted back to 2020. We use a discount rate

to reflect our tendency to value future costs less than present costs. In other words, if the

combined costs were incurred only in 2020, we would value these costs more than if these

same costs were spread over five later years.

(7.12) By not pooling their adult mortality results, EPA is reporting a range of reasonable results.

You can think of the different mortality results as spanning the minimum to maximum

estimations of the health incidence results. In other words, a range provides a window

between the worst- and best-case scenarios.

(7.13) It depends. If you use only the national and regional-level health data pre-loaded in

BenMAP, it is better suited for national-scale analyses. Most of the input datasets have been

developed with a national or at least regional perspective. For example, many of the health

incidence functions were studied using large populations spread across multiple cities and

states. As one goes to a more localscale, the default health incidence functions, valuation

functions, and background incidence rates may become less and less representative of the

local population. In addition, if the scale becomes very small, then the populations become

very small. If you have a small population, then the sample size may become problematic,
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which undermines the statisticalfunctions used in BenMAP.

In contrast, imagine that you are able to find high quality local health incidence functions,

valuation functions, and background incidence rates for your particular study area. You

would expect that BenMAP would give more representative results from these specific

functions and datasets that have been "tuned" to your particular study area than it would for

a national analysis.

(7.14) BenMAP does not quantify the following benefits of reducing air pollution: improved

ecosystem health, climatological benefits, improved visibility and consequentially improved

aesthetics, and the reductions of pollution damages to infrastructure and buildings. However,

with the addition of appropriate impact functions, it would be possible to use BenMAP to

quantify these endpoints.

(7.15) The valuation estimates would probably be higher if we used WTP functions instead of COI

functions. COI functions do not include the cost of pain and suffering in the estimate of

monetized value. WTP functions attempt to capture both COI and the cost of pain and

suffering.

(7.16) There are many sources of uncertainty in a human health benefit analysis. EPA has attempted

to quantify some sources of uncertainty in BenMAP. For example, the uncertainty in the

regression coefficients for the health impact functions and the underlying distribution are

included in the valuation functions. Other uncertainties have not been quantified. For

example, there is significant uncertainty in the baseline and control AQG, in the geographic

variability of functions (i.e., which functions are really regional or local and do not translate to

other areas), in the differences between personal exposure and outdoor pollution

concentrations, and in the background incidence rates.

A.2 CityOne

Below is a very simple tutorial using the CityOne setup available at the BenMAP website (http://

www.epa.gov/air/benmap/). The tutorial is based on a hypotheticalscenario where ambient PM

2.5

concentrations are reduced by 25 percent in 2003. The steps in this analysis are as follows:

Step 1. Data Files Needed for Training

Step 2. Create Air Quality Grids for the Baseline & Control Scenario

Step 3. Specify Configuration Settings

Step 4. Select Health Impact Functions

Step 5. Specify Aggregation, Pooling and Valuation

Step 6. Generate Reports

Step 7. View Your Reports

Step 8. Map Your Results

Each step is explained in detail below.
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A.2.1 Step 1. Data Files Needed for Training

To do this training you need to download the CityOne files from the BenMAP website: http://

www.epa.gov/air/benmap/. The data are most easily accessible if loaded in using the Data

Import tool, which is discussed in the here in the chapter on loading data.

A.2.2 Step 2. Create Air Quality Grids for the Baseline and Control Scenarios

Click on the Create Air Quality Grids button to begin inputting the air quality data needed by

BenMAP. This will open up the window where you will input the air quality data. In general,

you need two air quality grids to conduct a benefit analysis, one for a baseline scenario and one

for the policy you are evaluating (the controlscenario). We will be creating our baseline and

controlscenarios together, through the Monitor Rollback air quality grid creation method.

Select Monitor Rollback from the list and click on Go!

This will take you to the Monitor Rolback Settings: (1) Select Monitors screen where you

will enter the information about the air quality monitoring data you want to use.

Choose PM2.5 from the Pollutant drop-down menu. On the Library tab, choose CityOne

Monitors from the Monitor DataSet drop-down menu, and choose 2003 from the Monitor

Library Year drop-down list. Finally, in the Rollback Grid Type choose Metropolitan Area.
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When your window looks like the window above, click Next.

This will take you to the Monitor Rolback Settings: (2) Select Rollback Regions and

Settings window where you will choose the type of rollback for the CityOne metropolitan area.
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Click Add Region. This will bring up the Select Region Rollback Type window.

In the Select Region Rollback Type window you may select from three rollback options.

Select the Percentage Rollback option as shown above. Click OK.
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In the box of Rollback Parameters for Region 1, type 25 in the Percent box. (This will

reduce each of the monitors in the CityOne area by 25 percent.) Then click on the Select All

box. When your window looks like the window above, click Next.

This will take you to the Monitor Rolback Settings: (3) Additional Grid Settings window,

the finalstep in creating rollback grids.

Choose the Voronoi Neighborhood Averaging interpolation method. Leave the scaling

method as None. From the Grid Type drop-down list choose County. Leave the box

checked next to Make Baseline Grid (in addition to Control Grid). This option will cause

BenMAP to create a baseline scenario air quality grid using the monitors selected in the previous

step, but without rolling their values back. BenMap will create a second grid with the rolled back

monitors, which willserve as our controlscenario air quality grid.
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When your window looks like the window above, click Go!.

BenMAP will now prompt you to save the baseline air quality grid. Make sure you are in the Air

Quality Grids subfolder in the BenMAP directory and then save the file as: PM2.5 CityOne

County Baseline 2003 VNA.aqg (you do not have to enter the “.aqg” extension).
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BenMAP will now prompt you to save the control air quality grid. Again, make sure you are in

the Air Quality Grids subfolder in the BenMAP directory and then save the file as: PM2.5

CityOne County 25 Pct Rollback 2003 VNA.aqg (you do not have to enter the “.aqg”

extension).
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BenMAP will now create baseline and control air quality grids that you can use in your benefit

analysis. When the progress bar is complete, BenMAP willreturn to the main BenMAP screen.

A.2.3 Step 3. Specify Configuration Settings

On the main BenMAP screen, click on the Create and Run Configuration button. In the

following box, select Create New Configuration and click Go!.

This will bring up the Configuration Settings form, where you will enter the basic information

about your analysis before selecting the health effects you wish to estimate.

In the Baseline File field, you can either enter the path for your baseline air quality grid, or click
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Open. For this example, click Open and browse to the Air Quality Grids folder. Select

PM2.5 CityOne County Baseline 2003 VNA and click Open.

Next, click on Open next to the Control File field and select PM2.5 CityOne County 25 Pct

Rollback 2003 VNA and click Open.

This specifies that you want to conduct a benefit analysis of the difference between the baseline

and controlscenarios for which we created air quality grids in Step 2.

In the Settings section of this window, there are severalfields which set the overallscope of the

analysis.

In the Population DataSet field, select CityOne Tract Population from the drop down menu.

This tells BenMAP that you want your analysis to use tract-level population data from this dataset

when calculating health impacts.

In the Population Year field, enter 2005 or select 2005 from the drop down menu. This tells

BenMAP that you want your analysis to use 2005 populations when calculating health impacts.

In the Latin Hypercube Points field, enter 10 or select 10 from the drop down menu. This tells

BenMAP that you want to estimate the percentiles of the distribution of health endpoint incidence

using Latin Hypercube Sampling with 10 percentiles of the distribution, representing the 5

th

, 15

th

,

25

th

, and so on up to the 95

th percentile.

Leave the Run in Point Mode box unchecked.

Leave the Threshold field blank. This tells BenMAP that you want to estimate benefits

associated with all changes in PM2.5, regardless of where those changes occur along the range of

PM2.5concentrations. Selecting a non-zero threshold means that you would only want to

calculate benefits for changes occurring above the threshold.
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When your window looks like the above, click Next.

This will bring up the next page of the Configuration Settings form, where you can select health

impact functions from a set of available health impact functions.
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(Note: This screen can be resized if you are having trouble seeing all of the information. Individual

columns can also be resized. Just click on the border of a column and drag to increase or

decrease its width.)

A.2.4 Step 4. Select Health Impact Functions

In this screen, you can select health impact functions to use in your analysis. For this example,

we are going to estimate the change in incidence of three health endpoints associated with PM2.5:

acute bronchitis, acute myocardial infarctions (heart attacks), and emergency room visits for

asthma. To select a health impact function, you must drag it from the upper box (Available C-R

Functions) in the window to the lower box (Selected C-R Functions). You can drag groups

of health impact functions over, or drill down and drag over individualfunctions.

For acute bronchitis, drill down until you see the function by the Author Dockery et al. Drag the

function into the lower panel of the window. You should see a new row with the Endpoint

Group Acute Bronchitis.
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For acute myocardial infarctions (AMI), drag the entire Endpoint Group titled Acute

Myocardial Infarction to the lower panel(do not drill down). This will include the fullset of

age-specific health impact functions for AMI.

For asthma emergency room visits, also drag over the entire the Endpoint Group titled

Emergency Room Visits, Respiratory.

You should now have seven health impact functions listed in the lower panel: one acute bronchitis

function, five AMI functions, and one ER visit function.
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BenMAP will then prompt you to save your file. Click Save. Browse to the Configurations

subfolder within the BenMAP directory and save the file as: PM25 Example Configuration.cfg

(you do not need to include the “.cfg” extension).

When you have saved the configuration file, click OK to run the configuration.

BenMAP will prompt you to “Save Configuration Results to File”. Browse to the Configuration

Results subfolder within the BenMAP directory and save the file as: PM2.5 CityOne County 25

Pct Rollback 2003 VNA Example.cfgr (you do not need to include the “.cfgr” extension)

Once you have entered the filename, BenMAP will begin calculating the change in incidence for

the set of health impact functions you have selected. The run may take a few minutes to finish; a

progress bar will let you know how it is proceeding. When BenMAP is finished running your

configuration, it willreturn to the main BenMAP screen.

A.2.5 Step 5. Specify Aggregation, Pooling and Valuation

This step allows you to take the incidence results that BenMAP just produced and place an
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economic valuation on them. Although not covered in this tutorial, this is also where you can

select the geographic level of aggregation and combine individual incidence results into pooling

groups.

From the main screen, click on the Aggregation, Pooling and Valuation button. This will bring

up a menu screen with two choices:Create New Configuration for Aggregation, Pooling and

Valuation, or Open Existing Configuration for Aggregation, Pooling and Valuation (*.apv

file).

Select Create New Configuration for Aggregation, Pooling and Valuation and click on Go!.

BenMAP will prompt you to open a Configuration Results File. Browse to the

Configuration Results subfolder and select PM2.5 CityOne County 25 Pct Rollback 2003

VNA Example.cfgr. Then click on Open.
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BenMAP will then open the Incidence Pooling and Aggregation window with the results from

running your configuration. You should see a window that looks like the following:
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Click on each of the results groups (acute bronchitis, acute myocardial infarction, and emergency

room visits) and drag them to the right panel.

For this example, we are not pooling any of the incidence results (although we will pool valuations

in the next window), so just click on Next at the bottom of the window.

This will take you to the Select Valuation Methods, Pooling, and Aggregation window.
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A) Select a value for acute bronchitis

To select a valuation method for acute bronchitis, drill down the Acute Bronchitis valuation

group until you see individual valuation methods. Click on the WTP: 6 day illness, CV studies |

0-17 method and drag it onto the Acute Bronchitis endpoint group in the right hand panel. You

should see the method appear under acute bronchitis in the Valuation Method column in the

right hand panel.

B) Select values for acute myocardial infarctions (heart attacks)

To select valuation methods for acute myocardial infarctions, drill down the AMI valuation group

until you see a (long) list of individual valuation methods. You might find it easier to expand the

column width of the Valuation Methods column (drag the right hand edge of the column to the

right to make it wider). We will be working with the valuation estimates from two studies,

Wittels and Russell. For each of the studies, there are a number of age specific valuations.

There are also two different discount rates (the discount rate is the rate at which future medical

costs are discounted to the present). Drag the age-specific valuation estimates from Wittels for

the 3 percent discount rate (COI, 5 yrs med, 5 yrs wages, 3% DR, Wittels (1990) | age) to each

matching age-specific line in the right hand panel(Pooling Window 1). You may have to scroll

over in the right hand panel to see the Low Age column.

Note that you will need to drag some age-specific valuation estimates to multiple lines in the

pooling window, since there is not a perfect match between the available age-specific valuation
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estimates and the age groups for which the incidence of heart attacks was estimated. For

example, you will have to drag the valuation estimate for the 25 to 44 age group to both the 25

to 35 age group and the 35 to 45 age group in the pooling window.

Now repeat this process using the Russell 3 percent discount rate valuation estimates. When you

are finished, you should have two valuation estimates for each AMI age group, and your pooling

window should look like the one below.

Now you can pool the valuation results for heart attacks in each age group using the unit values

from both Wittels and Rusell. In order to do so, you must select a pooling method.

BenMAP lets you select from several different pooling methods. For this example, you will be

using subjective weights. In other applications, you may wish to use fixed or random effects

weights.

To set the pooling method for each age group result, click on the Pooling Method field in the

row ABOVE each pair of valuation methods (where it says None) and use the drop down menu

to select Subjective Weights. You must repeat this for EACH age group in order for pooling to

take place over all age groups.

In addition to pooling the results over the two valuation methods, we also need to aggregate the

results into a total estimate across age groups. In order to do so, in the row with Endpoint
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Group (Endpoint Group = Acute Myocardial Infarction) click in the Pooling Method field

and select Sum (Dependent) from the drop down menu.

Your screen should look like the following:

This pooling configuration for acute myocardial infarctions will assign a starting set of equal

weights to each valuation method for the set of five age groups, and then create an overall

estimate of acute myocardial infarctions by summing the age-specific pooled estimates, treating

the distributions for each age group as dependent (i.e. a draw from the 5

th percentile of the 45 to

54 age group will be added to the draw from the 5

th percentile of the 55 to 64 age group and so

on).

C) Select values for asthma emergency room visits

To select values for asthma ER visits, drill down the Emergency Room Visits, Respiratory

heading to the Emergency Room Visits, Asthma, and then to the valuation approach Standford

et al, 1999 | 0 - Max. Drag this to the Emergency Room Visits entry in the right hand panel.

D) Choose variable dataset

In the Variable Dataset drop-down menu, choose CityOne Variables. (BenMAP requires

that there be a variable dataset be chosen before going to the next step.)

Your completed screen should look like the one below.
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E) Entering subjective weights

Once you have completed this step, click on Run. BenMAP will now bring up a window to

allow you to enter subjective weights.

BenMAP assigns a default equal weight to each selected valuation method. You can change

these weights by clicking in the weight cells. However, for this exercise, you should leave them at

0.5 for each study. Click on OK at the bottom of the screen. You should see a save dialog box.

Click on Save to save your APV configuration. Save the file as PM25 Direct example APV.
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Click on OK to start the pooling and aggregation. First you will be prompted to enter a filename

for the aggregation, pooling, and valuation configuration file that you just created. Enter PM25

Example Configuration.apv and click Save.

Then you will be prompted to enter a filename for the aggregation, pooling, and valuation results.

Enter PM2.5 CityOne County 25 Pct Rollback 2003 VNA Example.apvr and click Save.

When the progress bar disappears, you will be returned to the main BenMAP screen.

A.2.6 Step 6. Generate Reports

You may view your results within BenMAP, either in the preview window in the Create

Reports button or through the mapping functions. Alternatively, you may export the results to a

comma separated values file (*.csv), or a shapefile (*.shp) which can be viewed in a GIS

program such as ESRI’s ArcView product.

A) Generate a Pooled Incidence Results report.

A Pooled Incidence Results report contains the incidence results you previously generated,

using the aggregation level and pooling that you specified in the Incidence Pooling and

Aggregation window. Previously, in Step 5, we did not specify any pooling of incidence results

(although valuations were pooled), so in this case the Pooled Incidence Results report will look

just like the Aggregated Incidence Results report. Ifsome incidence results had been pooled,

the two reports would be different.
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Click on the Create Reports button from the main BenMAP screen. This will bring up the

Select Result Type window.

Select Incidence and Valuation Results: Raw, Aggregated and Pooled. Click OK. This will

bring up a window where you can select a results file. Chose the PM2.5 CityOne County 25

Pct Rollback 2003 VNA Example.apvr file, and click Open. This will bring up the Choose a

Result Type window.

In the Choose a Result Type window, choose Pooled Incidence Results. Then click OK.

This will bring up the APV Configuration Results Report, where you can customize your

report display and select the fields you want to see in the report. In the Pooled C-R Function

Fields box, check off Endpoint Group and Qualifier.
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When your window looks like the window above, then go to the File menu and choose Save. In

the Save As window that appears, type in the file name and browse to the location where you

want to store the exported file. The Reports subfolder is a good location to keep exported

reports. Type in the name, PM2.5 CityOne County 25 Pct Rollback 2003 VNA Example

Health.csv in the box and click Save. You can now open the report in another application, such

as a spreadsheet or database program.

B) Generate a Pooled Valuation Results report.

This report is similar to the Pooled Incidence Results report, and uses the valuation pooling

you previously specified. Click on the Create Reports button from the main BenMAP screen.

This will bring up the Select Report Type window. Select Incidence and Valuation Results:

Raw, Aggregated and Pooled. Click OK. This will bring up a window where you can select a

results file. Chose the PM2.5 CityOne County 25 Pct Rollback 2003 VNA Example.apvr file,

and click Open. This will bring up the Choose a Result Type window.

In the Choose a Result Type window, choose Pooled Valuation Results. Then click OK.
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This will bring up the APV Configuration Results Report window, where you can customize

your report display and select the fields you want to see in the report. In the Pooled Valuation

Methods Fields box, check off Endpoint Group.

When your window looks like the window above, then go to the File menu and choose Save.

In the Save As window that appears, type in the file name and browse to the location where you

want to store the exported file. The Reports subfolder is a good location to keep exported

reports. Type in the name, PM2.5 CityOne County 25 Pct Rollback 2003 VNA Example

Valuation.csv in the box and click Save. You can now open the report in another application,

such as a spreadsheet or database program.

A.2.7 Step 7. View Your Reports

BenMAP generates comma separated values files (*.csv) that can be read by various

spreadsheet and database applications, such as Microsoft Excel.
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A.2.8 Step 8. Map Your Results

You can also map any of the results that you have generated so far. This includes the air quality

grids, population data, incidence results, and valuation results. In this example, we will look at

the air quality grid for the base scenario, and view our incidence results. For more information on

these and other mapping functions, see Chapter 9.

To use the BenMAP mapping functionality, go to the Tools menu and choose GIS / Mapping.

The BenMAP GIS window will appear, with buttons at the top for managing files and navigating

the map.
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To see the name of each button, simply hold the cursor over it. Click on the Open a file button,

and select Air Quality Grid from the drop-down menu. Browse to the file PM2.5 CityOne

County 25 Pct Rollback 2003 VNA.aqg file and click Open.

The name of the file will appear in the left-hand panel under Layers. Double-click on the name

and a small box will appear with Display Options for viewing this layer. Here you can select the

variable contained in the layer (file) that you want to view. In the air quality grid, the variables

that are available are the Quarterly Mean and the Daily Mean (D24HourMean). Select

D24HourMean for the annual mean of the Daily Mean in the Variable. In this box, you can also

change the colors in the map display, and the maximum and minimum values displayed.
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When done choosing your display options, click OK. You should see a map like the one below.

To see tract outlines, select County from the Reference Layer drop-down menu at the top of

the screen. You may also use the other reference layers: Metropolitan Area and Tract.

However, since the results have been calculated at the county level in this example, the county

reference is generally most appropriate.
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Now you can look at a geographical display of the incidence results you created for cases of

bronchitis, acute myocardial infarctions, and emergency room visits. Click on the Open a file

button at the top of the screen and select APV Configuration Results, then Incidence Results.

In the next window, select PM2.5 CityOne County 25 Pct Rollback 2003 VNA Example.apvr

then click Open. BenMAP will load your incidence results and display them in a table. Because

GIS programs can typically only accommodate field names that are 10 characters or less, there is

a new column at the end of the table labeled GIS Field Name. Here you can name your

variables, as shown in the table below.
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When you are satisfied with the variable names, click OK. The new layer willshow in the

BenMAP GIS window on top of the first layer. If the previous layer is still checked, then it will

appear, but underneath the new layer. Uncheck the box next to the bottom (previous) layer to

hide it. Your screen should look like the one below.
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Like the previous layer, double click on the name to bring up the Display Options box. Under

Variable you willsee a list of the variable names you defined in the previous step. Select

Myo65up, uncheck the Grid Outline box, and click OK. The viewer will now display the

annual increase in the number of acute myocardial infarctions for people ages 65 and up, as

calculated between the base and controlscenarios. You can use the Display Options to select

other variables to view or change how the values are displayed.
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Appendix B: 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.

B.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:
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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

B.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

B.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
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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).

B.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
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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

B.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
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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

B.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
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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

B.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
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Target Metric Values:

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

B.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
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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).

B.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.

B.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
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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.

B.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.
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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
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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
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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.

B.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
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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
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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.

B.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
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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] )

B.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
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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.

B.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).
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Appendix C: 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 C-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.
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Table C-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.

C.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.

C.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
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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.

C.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.
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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.

C.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.
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C.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)
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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)

C.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.
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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.
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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.
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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 .
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C.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.
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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.
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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
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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.
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C.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:
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C.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.

C.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.

C.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
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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.

C.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.

C.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 (μ g/m3)

monitor = observed daily PM2.5 monitor level

(μ g/m3)
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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 (μ g/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 (μ g/m3)

adjusted monitor = predicted PM2.5 metric, after

adjustment by model data (μ g/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.
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C.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)
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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.

C.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.
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Appendix D: 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.

D.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 μ g/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,

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

D.2 Review Relative Risk and Odds Ratio

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

demonstrating their relationship.

Table D-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:

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

D.3 Linear Model

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

explanatory variables is of the form:

where 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:

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

D.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 is the coefficient of PM, ln(y) is the natural logarithm of y, and = 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:

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
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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 ( ) and its standard error ( ) 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,

(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 ( ) can be approximated by:
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D.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 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:
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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:
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When the coefficient ( ) and its standard error ( ) 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
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calculated analogously.

The underlying standard error for the coefficient ( ) 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:
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Given the odds ratio, the underlying coefficient ( ) 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 μ g/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 μ g/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:
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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:
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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 μ g/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 (

1

or

2

) 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.

D.6 Cox proportional Hazards Model

Use of a Cox proportional hazards model in an epidemiologicalstudy results in a C-R function
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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, 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 , so it is necessary to estimate in

order to develop functionalrelationship between PM and y, as described previously for loglinear C-R functions.
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Appendix E: 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.

E.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.

E.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
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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 E1. 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 E-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.”
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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 E-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 F) estimates post-neonatal mortality.

E.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 E-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 E-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
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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 E-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 F) 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 E-4. Ratio of Future Year All-Cause Mortality Rate to 2005 Estimated All-Cause

Mortality Rate, by Age Group

Year Infant 1--17 18--24 25--34 35--44 45--54 55--64 65--74 75--84 85+

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

E.2 Hospitalizations

Hospitalization rates were calculated using data from the Healthcare Cost and Utilization Project
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(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, 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 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.
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Figure E-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.

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 E.1.1).
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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)

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)
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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 E-5 presents a summary of

the national hospitalization rates for 2007 from HCUP.

Table E-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 490- 0.350 0.158 0.057 0.064 0.135 0.299 0.503 1.028 1.502 1.506
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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+

Disease 496

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

E.3 Emergency Department Visit Data (2005, 2006 or 2007) and Rate

Calculation

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 E.2. Figure 2 shows the emergency department data in each state.

Figure E-2. Emergency Department Data from HCUP
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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 2006 rates.

Instead of using HCUPnet/NIS and NHDS in the last step as described in Section E.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 E-6 presents

the estimated asthma emergency room rates by health endpoint and age group.

Table E-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

E.4 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 540,000 new heart attacks per year using data from a multi-center study (Haase,
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2002, to be published in the American Heart Association’s 2003 Statistical Handbook).

Exclusion of heart attack deaths reported by CDC Wonder yields approximately 330,000

nonfatal cases per year.

An alternative approach to the estimation of heart attack rates is to use data from the National

Hospital Discharge Survey, assuming that all heart attacks that are not instantly fatal willresult in a

hospitalization. According to the National Hospital Discharge Survey, in 1999 there were

approximately 829,000 hospitalizations due to heart attacks (acute myocardial infarction:ICD-9

410) (Popovic, 2001, Table 8). We used regional hospitalization rates over estimates

extrapolated from cohort studies because the former is part of a nationally representative survey

with a larger sample size, which is intended to provide reliable national estimates. As additional

information is provided regarding the American Heart Association methodology, we will evaluate

the usefulness of this estimate of heart attack incidence.

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 count of hospitalizations to estimate the number of

nonfatal heart attacks per year. To estimate the rate of nonfatal heart attack, we divided the

count by the population estimate for 2000 from the U.S. Census. Table E-7 presents the

regional nonfatal heart attack incidence rates.

Table E-7. Nonfatal Heart Attack Rates (per 100 people per year), by Region and Age

Group

Effect Region 0-18 18-64 65+

Nonfatal heart attacks (ICD-9

410)

Northeast 0.0000 0.2167 1.6359

Midwest 0.0003 0.1772 1.4898

South 0.0006 0.1620 1.1797

West 0.0000 0.1391 1.1971

* Rates are based on data fromthe 1999 National Hospital Discharge Survey (NHDS) and an estimate

fromRosamond et al. that approximately 7% of individuals hospitalized for a heart attack die within 28

days.

E.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 E-8 presents the

estimated school loss day rates. Further detail is provided below on these rates.
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Table E-8. 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

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.

E.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
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E-9 presents a summary of these baseline rates.

Table E-9. 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)

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

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.

E.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.

E.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.
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E.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.

E.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.

E.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.

E.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.

E.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 C-R functions are based on

study-specific estimates. The baseline rates for the various endpoints are described below and

summarized in Table E-10.
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253

Table E-10. Asthma-Related Health Effects Rates

Endpoint Age Parameter a 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%

Asthma Exacerbation, Cough 6-13 Incidence 31.39 Vedal et al (1998, Table 1)

Upper Respiratory Symptoms

(URS)

9-11 Incidence

124.79

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.

E.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). We estimated

the daily prevalence ofshortness of breath episodes among African-American asthmatics, ages

8-13, by taking a weighted average of the reported rates in Ostro et al. (2001, p.202).

E.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. The daily rate of prevalent wheeze episodes

(0.173) among African-American asthmatics, ages 8-13, is estimated by taking a weighted

average of the reported rates in Ostro et al. (2001, p.202)

E.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. The daily rate of prevalent cough episodes (0.145)

among African-American asthmatics, ages 8-13, is estimated by taking a weighted average of the

reported rates in Ostro et al. (2001, p.202).

E.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
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254

symptoms among asthmatics, published in Pope et al. (1991, Table 2), we calculated a sample

size-weighted average incidence rate.

E.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 E-11 presents asthma prevalence rates used to define asthmatic

populations in the health impact functions.

Table E-11. Asthma Prevalence Rates Used to Estimate Asthmatic Populations

Population Group 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)

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 F: Particulate Matter Health Impact Functions in U.S. Setup

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255

Appendix F: 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 D 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 E presents a description of the sources for

the incidence and prevalence data used in the health impact functions.

F.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 F-1 lists the long-term mortality health impact

functions.

Table F-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 ug. Unconditional

dist. 2% no causality included.

Mortality, All

Cause

Expert B 2006 30-99 Annual 0.011950 Log-linear Range 4 to 10 ug. 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 ug

Mortality, All

Cause

Expert F 2006 30-99 Annual 0.009370 Log-linear Range 4 to 7 ug

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 F: Particulate Matter Health Impact Functions in U.S. Setup

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256

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 ug. No threshold.

Conditional dist.

Mortality, All

Cause

Expert K 2006 30-99 Annual 0.003940 Log-linear Range 4 to 16 ug. Threshold 0 to 5 ug.

Conditional dist.

Mortality, All

Cause

Expert K 2006 30-99 Annual 0.003940 Log-linear Range 4 to 16 ug. Threshold 5 to 10 ug.

Conditional dist.

Mortality, All

Cause

Expert L 2006 30-99 Annual 0.009340 Log-linear Range >10 to 30 ug. Unconditional

dist. 1% no causality included.

Mortality, All

Cause

Expert L 2006 30-99 Annual 0.007390 Log-linear Range 4 to 10 ug. Unconditional dist.

25% no causality included.

Mortality, All

Cause

Laden et al. 2006 6 cities 25-99 Annual 0.014842 0.004170 Log-linear Adjusted coefficient with 10 ug/m3

threshold.

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 0.006555 0.002427 Log-linear Adjusted coefficient with 10 ug/m3

threshold.

Mortality, All

Cause

Pope et al. 2002 51 cities 30-99 Annual 0.007284 0.002696 Log-linear Adjusted coefficient with 12 ug/m3

threshold.

Mortality, All

Cause

Pope et al. 2002 51 cities 30-99 Annual 0.008740 0.003236 Log-linear Adjusted coefficient with 15 ug/m3

threshold.

Mortality, All

Cause

Pope et al. 2002 51 cities 30-99 Annual 0.005827 0.002157 Log-linear Adjusted coefficient with 7.5 ug/m3

threshold.

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 Adjusted coefficient with 10 ug/m3

threshold.

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 Adjusted coefficient with 10 ug/m3

threshold.

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

F.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.
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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.

F.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,
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258

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

this information as follows:

Let F(x; ,min,max) be a truncated continuous parametric (cumulative) distribution function with

(vector of) parameters and truncation points min and max. The nth percentile point is defined

as the value xn such that F(xn; ,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; ,min,max)=n/100

F(xm; ,min,max)=m/100

This is a system of non-linear equations that can be solved for the unknown distribution

parameters . We used the Nelder and Mead (1965) numeric optimization algorithm, available

in R, to find the best-fitting estimates of parameters for the truncated distributions specified by

the experts. Once estimates of were obtained, the distributions were specified fully and we had

enough information to make 15,000 draws from each.

Table F-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 F-2. Description of the Parametric Expert Functions

Expert Information Distributi

on

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
Appendix F: Particulate Matter Health Impact Functions in U.S. Setup

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Expert Information Distributi

on

Min P5 P25 P50 P75 P95 Max Parameters

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

0.10

0

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

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

0.20

0

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

ug/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

ug/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
Appendix F: Particulate Matter Health Impact Functions in U.S. Setup

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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 μ g/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 μ g/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

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 F-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 F-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
Appendix F: Particulate Matter Health Impact Functions in U.S. Setup

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Expert Mean Standard

Deviation

Min P25 P50 P75 Max

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

F.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 μ g/m3 increase in PM2.5. They specified

the following characteristics of the distribution:

The truncation points (i.e., minimum and/or maximum)

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 F-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 F-4. Description of the Non-Parametric Expert Functions

Expert Informatio

n

Min P5 P10 P25 P50 P75 P95 Max

B1

4-10

ug/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

ug/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

ug/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

ug/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
Appendix F: Particulate Matter Health Impact Functions in U.S. Setup

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Expert Informatio

n

Min P5 P10 P25 P50 P75 P95 Max

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

ug/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

ug/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 F-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.

Table F-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

F.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
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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

μ g/m3 to 16 μ g/m3 and another log-linear function if the baseline value falls within the range from

>16 μ g/m3 to 30 μ g/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 μ g/m3 to 10 μ g/m3, and a 14% probability

that there is a causalrelationship with a threshold somewhere between 0 μ g/m3 to 5 μ g/m3. Thus,

the set of log-linear functions in BenMAP for expert K on the range from 4 μ g/m3 to 16 μ g/m3

contains:

a function with PM2.5coefficient = 0 (no causality), which BenMAP selects with 65%

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 μ g/m3 to 5 μ g/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 μ g/m3 to 10 μ g/m3, which BenMAP

selects with 4% probability.

If the PM2.5baseline value is greater than 16 μ g/m3, BenMAP goes through an analogous

procedure to select a function from among the two functions in that set.
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F.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).

F.1.1.4.1 Expert A

Figure F-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)).
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F.1.1.4.2 Expert B

Figure F-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 ug/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.
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- 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 F-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 ug/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
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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)).

F.1.1.4.3 Expert C

Figure F-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)).
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F.1.1.4.4 Expert D

Figure F-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)).
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F.1.1.4.5 Expert E

Figure F-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)).
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F.1.1.4.6 Expert F

Figure F-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 ug/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)).
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Figure F-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 ug/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)).
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F.1.1.4.7 Expert G

Figure F-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)).
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F.1.1.4.8 Expert H

Figure F-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)).
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F.1.1.4.9 Expert I

Figure F-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)).
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F.1.1.4.10 Expert J

Figure F-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)).
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F.1.1.4.11 Expert K

Figure F-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 two ranges (4-16 ug/m3 and >16-30 ug/

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)).
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F.1.1.4.12 Expert L

Figure F-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 ug/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)).
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Figure F-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 ug/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)).
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F.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 ug/m3 increase 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).

F.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 (’79-’83 PM data, conducted for 61 metropolitan
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areas with 359,000 individuals), annual mean PM from the end of the tracking period (’99-’00,

for 116 areas with 500,000 individuals), and the average annual mean PM levels of the two

periods (for 51 metropolitan areas, with 319,000 individuals). PM levels were lower in ’99-00

than in ’79 - ’83 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, ’79-’83 Exposure

The coefficient and standard error for PM2.5using the ’79-’83 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 ’79-’83 and ’99-’00 Exposure

The coefficient and standard error for PM2.5using the average of ’79-’83 and ’99-’00 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).

F.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.
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Post-Neonatal Mortality

The coefficient and standard error are based on the odds ratio (1.04) and the 95% confidence

interval(1.02-1.07) associated with a 10 μg/m3 change in PM10(Woodruff et al., 1997, Table

3).

F.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/m3 increase 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).

F.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
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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

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/m3 increase 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/m3 increase 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/m3 increase 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.
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F.2 Chronic / Severe Illness

Table F-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.

Table F-6. Health Impact Functions for Particulate Matter and Chronic Illness

Eff ect Author Year Lcoation Age CoPollMetric 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 Adjusted

coefficient with 10

ug/m3 threshold.

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 0.033230 0.012791 Logistic Adjusted

coefficient with 10

ug/m3 threshold.

Acute Myocardial

Infarction,

Nonfatal

Peters et

al.

2001 Boston, MA 18-99 D24HourMean 0.024121 0.009285 Logistic

F.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.
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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

bronchitis prevalence rate for ages 18 and over of 4.37%.

F.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: region-specific daily nonfatal heart attack rate per person 18+ = 93% of regionspecific daily heart attack hospitalization rate (ICD code 410). This estimate assumes that all heart

attacks that are not instantly fatal will result in a hospitalization. In addition, Rosamond et al. (1999)

report 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 applied a factor of 0.93 to the

number of hospitalizations to estimate the number of nonfatal heart attacks per year.
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Population: population of ages 18 and older

F.3 Hospitalizations

Table F-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 F-7. Health Impact Functions for Particulate Matter and Hospital Admissions

Eff ect Author Year Lcoation Age Co-Poll Metric Beta Std Err Form Notes

Congestive Heart

Failure

Ito 2003 Detroit, MI 65-99 D24HourMean 0.003458 0.001453 Log-linearAdjusted coefficient

with 10 ug/m3

threshold.

Congestive Heart

Failure

Ito 2003 Detroit, MI 65-99 D24HourMean 0.003074 0.001292 Log-linear

HA, Dysrhythmia Ito 2003 Detroit, MI 65-99 D24HourMean 0.001405 0.002287 Log-linearAdjusted coefficient

with 10 ug/m3

threshold.

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 0.001614 0.001300 Log-linearAdjusted coefficient

with 10 ug/m3

threshold.

Ischemic Heart (less

Myocardial Infarctions)

Ito 2003 Detroit, MI 65-99 D24HourMean 0.001435 0.001156 Log-linear

Chronic Lung Ito 2003 Detroit, MI 65-99 D24HourMean 0.001316 0.002322 Log-linearAdjusted coefficient

with 10 ug/m3

threshold.

Chronic Lung Ito 2003 Detroit, MI 65-99 D24HourMean 0.001169 0.002064 Log-linear

Pneumonia Ito 2003 Detroit, MI 65-99 D24HourMean 0.004476 0.001867 Log-linearAdjusted coefficient

with 10 ug/m3

threshold.

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 0.001511 0.000368 Log-linearAdjusted coefficient

with 10 ug/m3

threshold.

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 0.002374 0.000791 Log-linearAdjusted coefficient

with 10 ug/m3

threshold.

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 0.001705 0.000371 Log-linearAdjusted coefficient

with 10 ug/m3

threshold.

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 0.001996 0.000565 Log-linearAdjusted coefficient

with 10 ug/m3

threshold.

Chronic Lung Moolgavkar 2003 Los Angeles,

CA

65-99 D24HourMean 0.001850 0.000524 Log-linear

Asthma Sheppard 2003 Seattle, WA 0-64 D24HourMean 0.003928 0.001235 Log-linearAdjusted coefficient

with 10 ug/m3
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Eff ect Author Year Lcoation Age Co-Poll Metric Beta Std Err Form Notes

threshold.

HA, Asthma Sheppard 2003 Seattle, WA 0-64 D24HourMean 0.003324 0.001045 Log-linear

F.3.1 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.

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).

Disrhythmia (ICD-9 code 429)
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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.

F.3.2 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

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.
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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.

F.3.3 Moolgavkar (2000b), Cardiovascular

Moolgavkar (2000b) examined the association between air pollution and cardiovascular hospital

admissions (ICD 390-448) 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

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, 411-459)

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
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289

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.

F.3.4 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 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.

F.3.5 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
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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.

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).

F.4 Emergency Room Visits

Table F-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 F-8. Health Impact Functions for Particulate Matter and Emergency Room Visits

Eff ect Author Year Lcoation Age Co-Poll Metric Beta Std Err Form Notes

Asthma Norris et al. 1999 Seattle, WA 0-17

NO2,

SO2 D24HourMean 0.018542 0.004644 Log-linear

Adjusted coefficient with

10 ug/m3 threshold.

Asthma Norris et al. 1999 Seattle, WA 0-17

NO2,

SO2 D24HourMean 0.016527 0.004139 Log-linear

F.4.1 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,

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
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291

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).

F.5 Minor Effects

Table F-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.

Table F-9. Health Impact Functions for Particulate Matter and Minor Effects

Eff ect Author Year Lcoation Age Co-Poll Metric Beta Std Err Form Notes

Acute Bronchitis Dockery et al. 1996 24

communities

8-12 Annual 0.037894 0.023806 Logistic Adjusted coefficient

with 10 ug/m3

threshold.

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 Adjusted coefficient

with 10 ug/m3

threshold.

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 Adjusted coefficient

with 10 ug/m3

threshold.

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 0.019712 0.006226 Logistic Adjusted coefficient

with 10 ug/m3

threshold.

Lower

Respiratory

Symptoms

Schwartz and

Neas

2000 6 U.S. cities 7-14 D24HourMean 0.019012 0.006005 Logistic
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F.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).

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.

F.5.2 Ostro (1987)

Ostro (1987) estimated the impact of PM2.5on the incidence of work-loss days (WLDs),
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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:

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
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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 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:

This reduces down to:
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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

F.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

F.6 Asthma-Related Effects

Table F-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.
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Table F-10. Health Impact Functions for Particulate Matter and Asthma-Related

Effects

Eff ect Author Year Lcoation Age CoPollMetric Beta Std Err Form Notes

Cough Ostro et al. 2001 Los Angeles,

CA

6-18 D24HourMean 0.001013 0.000768 Logistic Adjusted coefficient

with 10 ug/m3

threshold.

Cough Ostro et al. 2001 Los Angeles,

CA

6-18 D24HourMean 0.000985 0.000747 Logistic

Shortness of

Breath

Ostro et al. 2001 Los Angeles,

CA

6-18 D24HourMean 0.002636 0.001372 Logistic Adjusted coefficient

with 10 ug/m3

threshold.

Shortness of

Breath

Ostro et al. 2001 Los Angeles,

CA

6-18 D24HourMean 0.002565 0.001335 Logistic

Wheeze Ostro et al. 2001 Los Angeles,

CA

6-18 D24HourMean 0.001996 0.000825 Logistic Adjusted coefficient

with 10 ug/m3

threshold.

Wheeze Ostro et al. 2001 Los Angeles,

CA

6-18 D24HourMean 0.001942 0.000803 Logistic

Upper

Respiratory

Symptoms

Pope et al. 1991 Utah Valley 9-11 D24HourMean 0.003600 0.001500 Logistic Adjusted coefficient

with 10 ug/m3

threshold.

Upper

Respiratory

Symptoms

Pope et al. 1991 Utah Valley 9-11 D24HourMean 0.003600 0.001500 Logistic

Cough Vedal et al. 1998 Vancouver,

CAN

6-18 D24HourMean 0.008376 0.004273 Logistic Adjusted coefficient

with 10 ug/m3

threshold.

Cough Vedal et al. 1998 Vancouver,

CAN

6-18 D24HourMean 0.008000 0.004082 Logistic

F.6.1 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”.

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
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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: asthmatic African-American population ages 8 to 13 = 17.76% of African-American

population ages 8 to 13. 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).

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: asthmatic African-American population ages 8 to 13 = 7.26% of African-American

population ages 8 to 13. (Described above.)

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 = 7.26% of African-American

population ages 8 to 13. (Described above.)

F.6.2 Pope et al (1991)

Using logistic regression, Pope et al. (1991) estimated the impact of PM10on the incidence of a
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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.)

F.6.3 Vedal et al (1998)

Vedal et al. (1998) studied the relationship between air pollution and respiratory symptoms

among asthmatics and non-asthmatic children (ages 6 to 13) in Port Alberni, British Columbia,

Canada. Four groups of elementary school children were sampled from a prior cross-sectional

study:(1) all children with current asthma, (2) children without doctor diagnosed asthma who

experienced a drop in FEV after exercise, (3) children not in groups 1 or 2 who had evidence of

airway obstruction, and (4) a control group of children with matched by classroom. The authors

used logistic regression and generalized estimating equations to examine the association between

daily PM10levels and daily increases in various respiratory symptoms among these groups. In

the entire sample of children, PM10 was significantly associated with cough, phlegm, nose

symptoms, and throat soreness. Among children with diagnosed asthma, the authors report a

significant association between PM10and cough symptoms, while no consistent effects were

observed in the other groups. Since the study population has an over-representation of
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asthmatics, due to the sampling strategy, the results from the fullsample of children are not

generalizeable to the entire population. The C-R function presented below is based on results

among asthmatics only.

Asthma Exacerbation, Cough

The PM10coefficient and standard error are based on an increase in odds of 8% (95% CI 0-

16%) reported in the abstract for a 10 μg/m3increase in daily average PM10.

Incidence Rate: daily cough rate per person (Vedal et al., 1998, Table 1, p. 1038) = 0.086

Population: asthmatic population ages 6 to 13 = 10.70% of population ages 6 to 13. (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).)

F.7 Calculating Threshold-Adjusted Functions

Following the approach taken in OAQPS’ June 2005 particulate matter (PM) risk assessment,

we used a 10 ug/m3 cutpoint for short-term (daily) C-R functions and a 7.5 ug/m3 cutpoint for

long-term (annual metric) C-R functions from which PM2.5 health impact functions have been

derived. The risk assessment noted that while there are likely biological thresholds in individuals

for specific health responses, the available epidemiologicalstudies do not support or refute the

existence of thresholds at the population levelfor either long-term or short-term PM2.5exposures

within the range of air quality observed in the studies. It may therefore be appropriate to

consider health risks estimated not only with the reported log-linear or logistic C-R functions, but

also with modified functions that approximate non-linear, sigmoidal-shaped functions that would

better reflect possible population thresholds.

However, following the approach currently being taken in OAQPS’ ongoing O3risk assessment,

we did not derive threshold models for O3concentration-response functions. After debating the

merits of considering possible thresholds for O3for hospital admissions and mortality C-R

functions, OAQPS staff concluded that, because the studies report a relationship down to very

low ambient levels, at or below the estimated policy relevant background (PRB) concentrations

(roughly around 0.03 ppm), consideration of threshold models for O3is not warranted. In

addition, some of the O3studies reported effects for mortality and hospital admissions in

Canadian cities where the levels never exceeded the current 0.08 ppm standard.

We approximated hypotheticalsigmoidal PM2.5 C-R functions by “hockeystick” functions based

on the reported log-linear or logistic functions. This approximation consisted of (1) imposing a

cutpoint (i.e., an assumed threshold) on the originalC-R function, that is intended to reflect an

inflection point in a typicalsigmoidalshaped function, below which there is little or no population

response, and (2) adjusting the slope of the originalC-R function above the cutpoint.
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If the researchers in the originalstudy fit a log-linear, linear, or logistic model through data that

actually better support a sigmoidal or “hockeystick” form, the slope of the fitted curve would be

smaller than the slope of the upward-sloping portion of the “true” hockeystick relationship, as

shown in Exhibit 3. The horizontal portion of the data below the cutpoint would essentially cause

the estimated slope to be biased downward relative to the “true” slope of the upward-sloping

portion of the hockeystick. The slope of the upward-sloping portion of the hockeystick model

should therefore be adjusted upward (from the slope of the reported C-R function), as shown in

Figure F-13. If the data used in a study do not extend down below the cutpoint or extend only

slightly below it, then the extent of the downward bias of the reported PM2.5coefficient will be

minimal, as illustrated in Figure F-14.

Figure F-13. Relationship Between Estimated Log-Linear Concentration-Response

Function and Hockeystick Model With Threshold C -- General Case
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Figure F-14. Relationship Between Estimated Log-Linear Concentration-Response

Function and Hockeystick Model With Threshold C -- Lowest Measured Level (LML)

Close to Hypothetical Threshold

We used a simple slope adjustment method based on the idea discussed above – that, if the data in

the study were best described by a hockeystick model with a cutpoint at c, then the slope estimated

in the study using a log-linear or logistic model would be approximately a weighted average of the

two slopes of the hockeystick – namely, zero and the slope of the upward-sloping portion of the

hockeystick. If we let:
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That is, the “true” slope of the upward-sloping portion of the hockeystick would be the slope

estimated in the study (using a log-linear or logistic modelrather than a hockeystick model)

adjusted by the inverse of the proportion of the range of PM levels observed in the study that

was above the cutpoint. Note that if the LML was below the estimated PRB (or if it was not

available for the study), the estimated PRB was substituted for LML in the above equation.

Table F-11 presents the threshold adjustments that were used to multiply with both the mean

coefficient estimate and its standard error.

Table F-11. Threshold Adjustment Factors Based on Assumed Threshold of 10 ug/m3

Author Year Study Location Min Max Threshold Threshold

Adj

Notes

Abbey et al. 1995

SF, SD, South Coast

Air Basin 10 1.000

Dockery et al. 1996 24 communities 5.8 20.7 10 1.393

Ito 2003 Detroit, MI 6 42 10 1.125 Min and max based on 5% and 95%.

Laden et al. 2006 6 cities 10.8 25.5 10 1.000

Moolgavkar 2003 Los Angeles, CA 4 86 10 1.079

Norris et al. 1999 Seattle, WA 9 18.2 10 1.122

Ostro 1987 Nationwide 10 1.000

Study did not provide a mean, SD, or

pollutant range.
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Author Year Study Location Min Max Threshold Threshold

Adj

Notes

Ostro and

Rothschild 1989 Nationwide 10 1.000

Study gave mean and std dev. We

estimate min is above threshold of

10.

Ostro et al. 2001 Los Angeles, CA 4.5 208.7 10 1.028

Peters et al. 2001 Boston, MA 4.6 24.3 10 1.378 Min and max based on 5% and 95%.

Pope et al. 1991 Utah Valley 11 195 10 1.000

Pope et al. 2002 51 cities 7.5 30 7.5 1.000

Mean: 17.7 & SD: 3.7. PM risk

assessment assumed min = 7.5 and

max = 30

Pope et al. 2002 51 cities 7.5 30 10 1.125

Pope et al. 2002 51 cities 7.5 30 12 1.250

Pope et al. 2002 51 cities 7.5 30 15 1.500

Schwartz and

Neas 2000 6 U.S. cities 7.2 86 10 1.037

Sheppard 2003 Seattle, WA 6 32 10 1.182 Min and max based on 5% and 95%.

Vedal et al. 1998 Vancouver, CAN 3 159 10 1.047

Min = policy-relevant background.

Actual min was 0.2 in North and 0.5

in South.

Woodruff et al. 1997 86 cities 11.9 68.8 10 1.000

Woodruff et al. 2006 204 counties 10 1.000

Only presented interquartile PM2.5

range
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Appendix G: 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 D 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 E presents a description of the sources for

the incidence and prevalence data used in the health impact functions.

G.1 Short-term Mortality

Table G-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 G-1. Health Impact Functions for Ozone and Short-Term Mortality

Eff ect Author Year Lcoation Age Co-Poll Metric Beta Std Err Form Notes

Non-Accidental Bell et al. 2004 95 US cities 0-99 D24HourMean 0.000390 0.000133 LoglinearWarmseason.

Non-Accidental Bell et al. 2004 95 US cities 0-99 D24HourMean 0.000520 0.000128 LoglinearAll year.

Non-Accidental Bell et al. 2004 95 US cities 0-99 D8HourMax 0.000261 0.000089 LoglinearWarmseason. 8-

hour max from24-

hour mean.

All Cause Bell et al. 2005 US & non-US 0-99 D24HourMean 0.001500 0.000401 LoglinearWarmseason.

All Cause Bell et al. 2005 US & non-US 0-99 D8HourMax 0.000795 0.000212 LoglinearWarmseason. 8-

hour max from24-

hour mean.

Cardiopulmonary Huang et al. 2005 19 US cities 0-99 D24HourMean 0.001250 0.000398 LoglinearWarmseason.

Cardiopulmonary Huang et al. 2005 19 US cities 0-99 D8HourMax 0.000813 0.000259 LoglinearWarmseason. 8-

hour max from24-

hour mean.

Non-Accidental Ito and

Thurston

1996 Chicago, IL 18-

99

PM10 D1HourMax 0.000634 0.000251 Loglinear

Non-Accidental Ito et al. 2005 0-99 D1HourMax 0.000400 0.000066 Loglinear1-hour max.

Non-Accidental Ito et al. 2005 0-99 D24HourMean 0.001750 0.000357 LoglinearWarmseason. 24-

hour mean.
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Eff ect Author Year Lcoation Age Co-Poll Metric Beta Std Err Form Notes

Non-Accidental Ito et al. 2005 0-99 D8HourMax 0.001173 0.000239 LoglinearWarmseason. 8-

hour max from24-

hour mean.

Non-Accidental Ito et al. 2005 0-99 D8HourMax 0.000532 0.000088 Loglinear8-hour max from1-

hour max.

All Cause Levy et al. 2005 US and nonUS0-99 D1HourMax 0.000841 0.000134 LoglinearWarmseason.

All Cause Levy et al. 2005 US and nonUS0-99 D8HourMax 0.001119 0.000179 LoglinearWarmseason. 8-

hour max from1-

hour max.

Non-Accidental Moolgavkar et

al.

1995 Philadelphia,

PA

0-99 D24HourMean 0.001398 0.000266 LoglinearWarmseason.

Non-Accidental Moolgavkar et

al.

1995 Philadelphia,

PA

0-99 TSP,

SO2

D24HourMean 0.001389 0.000373 LoglinearWarmseason.

Non-Accidental Moolgavkar et

al.

1995 Philadelphia,

PA

18-

99

TSP,

SO2

D24HourMean 0.000611 0.000216 Loglinear

Non-Accidental Samet et al. 1997 Philadelphia,

PA

18-

99

CO,

NO2,

SO2,

TSP

D24HourMean 0.000936 0.000312 Loglinear

Non-Accidental Schwartz 2005 14 US cities 0-99 D1HourMax 0.000370 0.000130 Logistic Warmseason.

Non-Accidental Schwartz 2005 14 US cities 0-99 D8HourMax 0.000426 0.000150 Logistic Warmseason. 8-

hour max from1-

hour max.

G.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 shortterm 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.
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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).

G.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).

G.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
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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.

G.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, airconditioning 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%
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confidence interval of 0.29% to 0.56% associated with a 10 ug/m3 change in ozone. We

converted ug/m3 to ppb with an assumed relationship of 1.96 ug/m3 per 1.0 ppb.

G.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.

G.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

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
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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%.

G.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.

G.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,
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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.

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.

G.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%.

G.2 Long-Term Mortality

Table G-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.
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311

Table G-2. Health Impact Functions for Ozone and Long-Term Mortality

Effect Author Year Location Age CoPollMetric Beta Std. Err. Fun.

Form

Notes

Mortality,

Respiratory

Jerrett et al. 2009 86 urban

areas

30-

99

PM2.5 Annual 0.003922 0.001325 Loglinear

Mortality,

Respiratory

Jerrett et al. 2009 Northeast 30-

99

Annual -0.001005 0.003853 Loglinear

Mortality,

Respiratory

Jerrett et al. 2009 Industrial

Midwest

30-

99

Annual 0.000000 0.004604 Loglinear

Mortality,

Respiratory

Jerrett et al. 2009 Southeast 30-

99

Annual 0.011333 0.003193 Loglinear

Mortality,

Respiratory

Jerrett et al. 2009 Upper

Midwest

30-

99

Annual 0.013103 0.026212 Loglinear

Mortality,

Respiratory

Jerrett et al. 2009 Northwest 30-

99

Annual 0.005827 0.003118 Loglinear

Mortality,

Respiratory

Jerrett et al. 2009 Southwest 30-

99

Annual 0.019062 0.007583 Loglinear

Mortality,

Respiratory

Jerrett et al. 2009 Southern

California

30-

99

Annual 0.000995 0.002767 Loglinear

Mortality,

Respiratory

Jerrett et al. 2009 86 urban

areas

30-

99

PM2.5 Annual 0.00432 0.00121 LoglinearThreshold

adjusted

Mortality,

Respiratory

Jerrett et al. 2009 86 urban

areas

30-

99

PM2.5 Annual 0.004471 0.001510 Loglinear

G.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 PM2.5 or 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.

Mortality, Respiratory (ICD-9 code 460-519) --- 86 U.S. urban areas
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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 threshold

A threshold model with adjustment for ecological covariates was fit to the survival data in the 86

urban areas, with a threshold estimate of 56 ppb based on minimizing the log-likelihood. The

corresponding coefficient and standard error were reported in Jerrett et al. (2009, Table 3S).

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).

G.3 Hospital Admissions

Table G-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 G-3. Health Impact Functions for Ozone and Hospital Admissions

Eff ect Author Year Lcoation Age Co-Poll Metric Beta Std Err Form Notes

All

Respiratory

Burnett et al. 2001 Toronto, CAN 0-1 PM2.5 D1HourMax 0.007301 0.002122 Log-linear Warmseason.

All

Respiratory

Burnett et al. 2001 Toronto, CAN 0-1 PM2.5 D8HourMax 0.008177 0.002377 Log-linear Warmseason. 8-hour

max from1-hour max.

Chronic Lung Moolgavkar et

al.

1997 Minneapolis,

MN

65-99 PM10, CO D24HourMean 0.002800 0.001769 Log-linear

Chronic Lung Moolgavkar et

al.

1997 Minneapolis,

MN

65-99 PM10, CO D8HourMax 0.001960 0.001238 Log-linear All year. 8-hour max

from24-hour mean.

Pneumonia Moolgavkar et 1997 Minneapolis, 65-99 PM10, D24HourMean 0.003800 0.001088 Log-linear
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313

Eff ect Author Year Lcoation Age Co-Poll Metric Beta Std Err Form Notes

al. MN SO2, NO2

Pneumonia Moolgavkar et

al.

1997 Minneapolis,

MN

65-99 PM10,

SO2, NO2

D8HourMax 0.002660 0.000762 Log-linear All year. 8-hour max

from24-hour mean.

Chronic Lung

(less Asthma)

Schwartz 1994 Detroit, MI 65-99 PM10 D24HourMean 0.005523 0.002085 Log-linear All year.

Chronic Lung

(less Asthma)

Schwartz 1994 Detroit, MI 65-99 PM10 D8HourMax 0.003424 0.001293 Log-linear All year. 8-hour max

from24-hour mean.

Pneumonia Schwartz 1994 Detroit, MI 65-99 PM10 D24HourMean 0.005210 0.001300 Log-linear All year.

Pneumonia Schwartz 1994 Minneapolis,

MN

65-99 PM10 D24HourMean 0.003977 0.001865 Log-linear All year.

Pneumonia Schwartz 1994 Detroit, MI 65-99 PM10 D8HourMax 0.003230 0.000806 Log-linear All year. 8-hour max

from24-hour mean.

Pneumonia Schwartz 1994 Minneapolis,

MN

65-99 PM10 D8HourMax 0.002784 0.001305 Log-linear All year. 8-hour max

from24-hour mean.

All

Respiratory

Schwartz 1995 New Haven,

CT

65-99 PM10 D24HourMean 0.002652 0.001398 Log-linear Warmseason.

All

Respiratory

Schwartz 1995 Tacoma, WA 65-99 PM10 D24HourMean 0.007147 0.002565 Log-linear Warmseason.

All

Respiratory

Schwartz 1995 New Haven,

CT

65-99 PM10 D8HourMax 0.001777 0.000936 Log-linear Warmseason. 8-hour

max from24-hour

mean.

All

Respiratory

Schwartz 1995 Tacoma, WA 65-99 PM10 D8HourMax 0.004931 0.001770 Log-linear Warmseason. 8-hour

max from24-hour

mean.

G.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

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
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of one-hour max ozone (Burnett et al., 2001, Table 3).

G.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).

G.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.

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
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a 50 ppb increase in daily average ozone levels (Schwartz, 1994a, Table 4).

G.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).

G.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
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316

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 μ g/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 μ g/m3

per ppb was used, based on a density of ozone of 1.96 grams per liter (at 25 degrees Celsius).

G.4 Emergency Room Visits

Table G-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.
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Table G-4. Health Impact Functions for Ozone and Emergency Room Visits

Eff ect Author Year Lcoation Age Co-Poll Metric Beta Std Err Form Notes

Asthma Jaffe et al. 2003 Ohio cities 5-34 D8HourMax 0.003000 0.001531 Log-linear

Asthma Peel et al. 2005 Atlanta, GA 0-99 D8HourMax 0.000870 0.000529 Log-linear

Asthma Stieb et al. 1996 New Brunswick,

CAN

0-99 D1HourMax 0.000040 0.000020 Quadratic Warmseason.

Asthma Stieb et al. 1996 New Brunswick,

CAN

0-99 D24HourMean 0.000100 0.000040 Quadratic Warmseason.

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

G.4.1 Jaffe et al (2003)

Jaffe et al. (2003) examined the relationship between ER visits and air pollution for persons ages

5-34 in Cleveland, Columbus, and Cincinnati, Ohio, from 1991 through 1996. In single-pollutant

Poisson regression models, ozone and SO2 were linked to asthma visits, and no significant effect

was seen for NO2and PM10.

Emergency Room Visits, Asthma

Assuming a 10 ppb increase in the daily 8-hour maximum ozone level, Jaffe et al(2003, Table 3)

reported a 3.0% change in asthma ER visits with a 95% confidence interval of 0.0% to 6.0%.

G.4.2 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
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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.

G.4.3 Stieb (1996)

Stieb et al. (1996) examined the relationship between ER visits and air pollution for persons of all

ages in St. John, New Brunswick, Canada, from May through September in 1984-1992. Ozone

was significantly linked to ER visits, especially when ozone levels exceeded 75 ppb. The authors

reported results from a linear model, quadratic model, and linear-quadratic model using daily

average and 1-hour maximum ozone. In the linear model, ozone was borderline significant. In

the quadratic and linear-quadratic models, ozone was highly significant. This is consistent with

the author’s conclusion that “only ozone appeared to have a nonlinear relationship with visit rates”

(p. 1356) and that “quadratic, linear-quadratic, and indicator models consistently fit the data

better than the linear model ...” (p. 1358). The linear term in the linear-quadratic model is

negative, implying that at low ozone levels, increases in ozone are associated with decreases in

risk. Since this does not seem biologically plausible, the ozone health impact functions described

here are based on the results of the quadratic regression models presented in Table 2 (Stieb et

al., 1996, p. 1356).

Emergency Room Visits, Asthma

One-hour Max Function

The coefficient and standard error of the quadratic model are reported in Table 2 (Stieb et al.,

1996, p. 1356) for a 1 ppb increase in 1-hour daily maximum ozone levels. The C-R function to

estimate avoided emergency visits derived from a quadratic regression model is shown below:

Baseline Population: baseline population of St. John, New Brunswick (Stieb et al., 1996, p.

1354) = 125,000

Population: population of all ages
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Daily Average Function

The coefficient and standard error of the quadratic model are reported in Table 2 (p. 1356) for a

1 ppb increase in daily average ozone levels. The C-R function to estimate avoided emergency

visits derived from a quadratic regression model is shown below:

Baseline Population: baseline population of St. John, New Brunswick (Stieb et al., 1996, p. 1354)

= 125,000

Population: population of all ages

G.4.4 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

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).

G.5 Minor Effects

Table G-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.
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Table G-5. Health Impact Functions for Ozone and Minor Effects

Eff ect Author Year Lcoation Age Co-Poll Metric Beta Std Err Form Notes

School Loss

Days, All Cause

Chen et al. 2000 Washoe Co, NV 5-17 PM10,

CO

D1HourMax 0.013247 0.004985 Linear

School Loss

Days, All Cause

Chen et al. 2000 Washoe Co, NV 5-17 PM10,

CO

D8HourMax 0.015763 0.004985 Linear All year. 8-hour max

from1-hour max.

Worker

Productivity

Crocker and

Horst

1981 Nationwide 18-64 D24HourMean 0.142700 Linear All year.

Worker

Productivity

Crocker and

Horst

1981 Nationwide 18-64 D8HourMax 0.092755 Linear All year. 8-hour max

from24-hour mean.

School Loss

Days, All Cause

Gilliland et

al.

2001 Southern

California

5-17 D8HourMax 0.007824 0.004445 Log-linear All year. 8-hour max

from8-hour mean.

School Loss

Days, All Cause

Gilliland et

al.

2001 Southern

California

5-17 D8HourMean 0.008150 0.004630 Log-linear

Minor Restricted

Activity Days

Ostro and

Rothschild

1989 Nationwide 18-64 PM2.5 D1HourMax 0.002200 0.000658 Log-linear

Minor Restricted

Activity Days

Ostro and

Rothschild

1989 Nationwide 18-64 PM2.5 D8HourMax 0.002596 0.000776 Log-linear 8-hour max from1-

hour max.

G.5.1 Chen et al (2000)

Chen et al. (2000) studied the association between air pollution and elementary school

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
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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

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).)

G.5.2 Crocker and Horst (1981)

To monetize benefits associated with increased worker productivity resulting from improved

ozone air quality, we used information reported in Crocker and Horst (1981) and summarized in

EPA (1994). Crocker and Horst examined the impacts of ozone exposure on the productivity of

outdoor citrus workers. The study measured productivity impacts as the change in income

associated with a change in ozone exposure, given as the elasticity of income with respect to

ozone concentration (-0.1427). The relationship estimated by Crocker and Horst between wages

and ozone is a log-log relationship. Therefore the elasticity of wages with respect to ozone is a

constant, equal to the coefficient of the log of ozone in the model. The reported elasticity

translates a ten percent reduction in ozone to a 1.4 percent increase in income. Given the
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national median daily income for outdoor workers engaged in strenuous activity reported by the

U.S. Census Bureau (2002), $68 per day (2000$), a ten percent reduction in ozone yields about

$0.97 in increased daily wages. (The national median daily income for workers engaged in

“farming, forestry, and fishing” from the U.S. Census Bureau (2002, Table 621, p. 403) is used

as a surrogate for outdoor workers engaged in strenuous activity.) We adjust the national median

daily income estimate to reflect regional variations in income using a factor based on the ratio of

county median household income to national median household income. No information was

available for quantifying the uncertainty associated with the central valuation estimate. Therefore,

no uncertainty analysis was conducted for this endpoint.

Worker Productivity

The C-R function for estimating changes in worker productivity is shown below:

Daily Income: median daily income for outdoor workers. (The national median daily income for

workers engaged in “farming, forestry, and fishing” was obtained from the U.S. Census Bureau

(2002, Table 621, p. 403) and is used as a surrogate for outdoor workers engaged in strenuous

activity. This national median daily income ($68) is then scaled by the ratio of national median

income to county median income to estimate county median daily income for outdoor workers.)

Population: population of adults 18 to 64 employed as farm workers.

G.5.3 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.
Appendix G: Ozone Health Impact Functions in U.S. Setup

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323

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:

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
Appendix G: Ozone Health Impact Functions in U.S. Setup

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324

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). )

G.5.4 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
Appendix G: Ozone Health Impact Functions in U.S. Setup

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325

work-loss days coefficient based on Ostro (1987).

The standard error of the coefficient is calculated as follows, assuming that the estimated yearspecific coefficients are independent:

This reduces down to:

G.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 G-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.
Appendix G: Ozone Health Impact Functions in U.S. Setup

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326

Table G-6. Eight-Hour Adjustments by Study

Eff ect Author Year Lcoation 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

School Loss

Days, All Cause

Chen et al. 2000 Washoe Co, NV Washoe County 1-4 1HourMax 1.19

Worker

Productivity

Crocker and

Horst

1981 Florida FL 1-4 24HourMean 0.65

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 G-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
Appendix G: Ozone Health Impact Functions in U.S. Setup

August 2010

327

Minneapolis Minneapolis-St. Paul-Bloomington, MN-WI MSA 2-3 24HourMean 0.70

Philadelphia Philadelphia-Camden-Wilmington, PA-NJ-DE-MD MSA 2-3 24HourMean 0.65

St. Louis St. Louis, MO-IL MSA 2-3 24HourMean 0.64

Average 0.64

Table G-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-IN-WI MSA 2-3 1HourMax 1.16

Cincinnati Cincinnati-Middletown, OH-KY-IN 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 G-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-IN-WI 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

Houston Houston-Baytown-Sugar Land, TX MSA 2-3 24HourMean 0.59
Appendix G: Ozone Health Impact Functions in U.S. Setup

August 2010

328

City/County CBSAs or Counties Used in Ratio Average Quarters Used Study Metric 8-Hour Adj Notes

Los Angeles

Los Angeles-Long Beach-Santa Ana, CA

MSA 2-3 24HourMean 0.59

Los Angeles and Santa Ana/

Anaheimhave same CBSA.

Santa Ana/

Anaheim

Los Angeles-Long Beach-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, NY-NJ-PA MSA 2-3 24HourMean 0.66

Philadelphia

Philadelphia-Camden-Wilmington, PA-NJDE-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 H: Nitrogen Dioxide Health Impact Functions

August 2010

329

Appendix H: Nitrogen Dioxide Health Impact

Functions

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 D 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 E 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

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 H-1. Health Impact Functions for Nitrogen Dioxide and Hospital Admissions

Eff ect Author Year Location Age CoPollMetric Beta Std. Err. Form Notes

All Respiratory Fung et al. 2006 Vancouver,

Canada

65-99 D24HourMean 0.003285 0.001707 Log-linear

Asthma Linn et al. 2000 Metropolitan

Los Angeles

0-29 D24HourMean 0.002400 0.000800 Log-linear

Asthma Linn et al. 2000 Metropolitan

Los Angeles

30-99 D24HourMean 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 D1HourMax 0.001238 0.002961 Log-linear Female

All Respiratory Luginaah et al. 2005 Windsor,

Ontario

65-99 D1HourMax -0.001435 0.004295 Log-linear Male

Chronic Lung

Disease

Moolgavkar S.

H.

2003 Los Angeles

County, CA

65-99 D24HourMean 0.001800 0.000188 Log-linear

Chronic Lung

Disease

Moolgavkar S.

H.

2003 Cook County,

CA

65-99 D24HourMean 0.002400 0.000803 Log-linear

All Respiratory Yang et al. 2003 Vancouver,

Canada

65-99 SO

2

,

O

3

,

CO,

COH

D24HourMean 0.008759 0.003069 Logistic
Appendix H: Nitrogen Dioxide Health Impact Functions

August 2010

330

Eff ect Author Year Location Age CoPollMetric Beta Std. Err. Form Notes

Chronic Lung

Disease (less

Asthma)

Yang et al. 2005 Vancouver,

Canada

65-99 O

3

D24HourMean 0.020605 0.006637 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 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).

H.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.

H.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
Appendix H: Nitrogen Dioxide Health Impact Functions

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331

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).

H.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

Angeles County. The coefficients can be calculated based on the reported percentage changes,
Appendix H: Nitrogen Dioxide Health Impact Functions

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332

that is, NO2 = log(RR)/10ppb. Along with the t-statistics reported in the same table, standard

errors (SE) are calculated. For Los Angeles County NO2 = 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 NO2 = 0.0024 and SE = 0.00080.

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

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).

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. There was no

significant association between either sulfur dioxide or ozone and COPD hospitalization. The
Appendix H: Nitrogen Dioxide Health Impact Functions

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333

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

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).

H.2 Emergency Room Visits

Table H-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 H-2. Health Impact Functions for Nitrogen Dioxide and Emergency Room Visits

Eff ect Author Year Location Age CoPollMetric Beta Std. Err. Functional

Form

Notes

Asthma Ito et al. 2007 NYC 0-99 D24HourMean 0.005460 0.000933 Log-linear

Asthma Jaffe et al. 2003 Cincinnati and

Cleveland, Ohio

5-34 D1HourMax 0.003000 0.002041 Log-linear Asthmatics;

Warmseason

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

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)
Appendix H: Nitrogen Dioxide Health Impact Functions

August 2010

334

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).

H.2.2 Jaffe et al (2003)

Jaffe et al.(2003) examined the effects of NO2, O3, PM10, and SO2 on asthmatics ages 5-34

years enrolled in Medicaid in Cincinnati, Cleveland, and Columbus, OH during the summer

months, June-August, from July 1, 1991 to June 30, 1996. Poisson regression analyses were

performed for the number of daily emergency department (ED) visits for asthma in each city and

on the aggregate data controlling for time trends and minimum temperature. Jaffe et al.(2003)

found a 4% increased likelihood of an asthma ED visit per 10 ppb increase in NO2in Cleveland

[95% confidence interval(CI)= -1-8%] and a 6% increase per 10 ppb increase in NO2in

Cincinnati(95% CI= -1-13%). When data were analyzed for all three cities combined, the risk

of an ED visit increased for all pollutant increases and specifically by 3% (95% CI= -1-7%) per

10 ppb increase in NO2.

Emergency Room Visits, Asthma (ICD-9 code 493)

In a single-pollutant model, the coefficient ( ) and standard error (SE) are calculated from the

percentage change in risk (3%) and the confidence interval of the change (-1-7%) for a 10 ppb

increase in the 1-day lag of NO2(Jaffe, et al., 2003, Table 3), i.e., = 0.03 / 10 ppb = 0.003

and SE= (

97.5percentile

-

2.5percentile

)/(2*1.96) = 0.002.

H.2.3 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).
Appendix H: Nitrogen Dioxide Health Impact Functions

August 2010

335

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, 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).

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 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 H: Nitrogen Dioxide Health Impact Functions

August 2010

336

H.3 Asthma Exacerbation

Table H-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 H-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 H: Nitrogen Dioxide Health Impact Functions

August 2010

337

H.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 (symptomsore >2). The

incidence rate is calculated as the ratio of number of person-days that symptomsore >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 from9 to 18 years of age. We treat these as two

groups based on the available information fromAmerican 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 fromthe 2008 National Health

Interview Survey).

H.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 H: Nitrogen Dioxide Health Impact Functions

August 2010

338

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 fromthe 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.3.3 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
Appendix H: Nitrogen Dioxide Health Impact Functions

August 2010

339

estimated asthma prevalence for children under 5 years old at 6.14% (based on data fromthe 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.3.4 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
Appendix H: Nitrogen Dioxide Health Impact Functions

August 2010

340

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.

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.

H.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 =
Appendix H: Nitrogen Dioxide Health Impact Functions

August 2010

341

(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 AfricanAmerican children ages 5 to 17 at 17.76% (based on data fromthe 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 fromthe 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.

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
Appendix H: Nitrogen Dioxide Health Impact Functions

August 2010

342

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).

H.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 (symptomscore>0) = 52% (Schildcrout, et al.,

2006, Table 1)
Appendix H: Nitrogen Dioxide Health Impact Functions

August 2010

343

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 fromthe

2008 National Health Interview Survey).

H.4 Minor Effects

Table H-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 H-4. Health Impact Functions for Nitrogen Dioxide and Minor Effects

Eff ect Author Year Location Age Co-Poll Metric Beta Std. Err. Functional

Form

Notes

Cough Schwartz et

al.

1994 Six U.S.

cities

7-14 PM10D24HourMe

an

0.015700 0.011232 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 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

), 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

fromthe 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: Sulfur Dioxide Health Impact Functions

August 2010

344

Appendix I: Sulfur Dioxide Health Impact

Functions

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 D 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 E presents a description of the sources for

the incidence and prevalence data used in the health impact functions.

I.1 Hospital Admissions

Table I-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 I-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 D24HourMe

an

0.017224 0.005084 Log-linear

All

Respiratory

Luginaah et

al.

2005 Windsor,

Ontario

0-99 D1HourMax 0.002336 0.001671 Log-linear Female

All

Respiratory

Luginaah et

al.

2005 Windsor,

Ontario

0-99 D1HourMax -0.000670 0.001718 Log-linear Male

All

Respiratory

Luginaah et

al.

2005 Windsor,

Ontario

0-14 D1HourMax 0.005468 0.002501 Log-linear Female

All

Respiratory

Luginaah et

al.

2005 Windsor,

Ontario

0-14 D1HourMax -0.000260 0.002552 Log-linear Male

All

Respiratory

Luginaah et

al.

2005 Windsor,

Ontario

15-64 D1HourMax 0.003418 0.003118 Log-linear Female

All

Respiratory

Luginaah et

al.

2005 Windsor,

Ontario

15-64 D1HourMax 0.002336 0.004039 Log-linear Male

All

Respiratory

Luginaah et

al.

2005 Windsor,

Ontario

65-99 D1HourMax 0.003709 0.003274 Log-linear Female

All

Respiratory

Luginaah et

al.

2005 Windsor,

Ontario

65-99 D1HourMax -0.002332 0.003620 Log-linear Male

All

Respiratory

Schwartz et

al.

1996 Cleveland,

Ohio

65-99 D24HourMe

an

0.000769 0.000453 Log-linear

Asthma Sheppard, L. 2003 Seattle, WA 0-64 D24HourMe

an

0.002031 0.002591 Log-linear

All

Respiratory

Yang et al. 2003 Vancouver,

Canada

65-99 NO

2

, O

3

, D24HourMe

an

0.002843 0.003627 Logistic
Appendix I: Sulfur Dioxide Health Impact Functions

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345

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

D24HourMe

an

0.024164 0.011938 Log-linear

I.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).

I.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 I: Sulfur Dioxide Health Impact Functions

August 2010

346

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)

I.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 μ g/m3

was used, based on http://www.epa.gov/air/criteria.html.

I.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).

I.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,
Appendix I: Sulfur Dioxide Health Impact Functions

August 2010

347

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-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).

I.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).

I.2 Emergency Room Visits

Table I-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 I: Sulfur Dioxide Health Impact Functions

August 2010

348

Table I-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 Jaffe et al. 2003 Cincinnati,

Cleveland;

Columbus, Ohio

5-34 D1HourMax 0.006240 0.002918 Log-linear Asthmatics;

Warmseason

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
Appendix I: Sulfur Dioxide Health Impact Functions

August 2010

349

Eff ect Author Year Location Age Co-Poll Metric Beta Std. Err. Functional

Form

Notes

Asthma Wilson et

al.

2005 Manchester, NH 65-99 D24Hour

Mean

0.011000 0.023000 Log-linear

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

I.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).

I.2.2 Jaffe et al (2003)

Jaffe et al.(2003) examined the effects of NO2, O3, PM10, and SO2 on asthmatics ages 5-34

years enrolled in Medicaid in Cincinnati, Cleveland, and Columbus, OH during the summer

months, June-August, from July 1, 1991 to June 30, 1996. Poisson regression analyses were

performed for the number of daily emergency department (ED) visits for asthma in each city and

on the aggregate data controlling for time trends and minimum temperature. Jaffe et al.(2003)

found a 12% increased likelihood of an asthma ED visit per 50μg/m3 increase in SO2[95%

confidence interval(CI)= 1-23%] using the aggregate data for all three cities.

Emergency Room Visits, Asthma (ICD-9 code 493)

Jaffe et al.(2003, Table 3) reported a 12% increased likelihood of an ED visit for asthma per

50μg/m3 increase in 1-hour max SO2 at lag 2-day [95% confidence interval(CI)= 1-23%] from a

single-pollutant model. In order to obtain the coefficient and standard error, a conversion of

0.3846 ppb per μ g/m3 was used, based on http://www.epa.gov/air/criteria.html.
Appendix I: Sulfur Dioxide Health Impact Functions

August 2010

350

I.2.3 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 μ g/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).

I.2.4 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 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)
Appendix I: Sulfur Dioxide Health Impact Functions

August 2010

351

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

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).

I.2.5 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.
Appendix I: Sulfur Dioxide Health Impact Functions

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352

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).

I.2.6 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).

I.2.7 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.
Appendix I: Sulfur Dioxide Health Impact Functions

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353

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).

I.3 Asthma Exacerbation

Table I-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 I-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 D8HourM

ax

0.122994 0.046891 Logistic Hispanic

children

One or More

Symptoms

Mortimer et

al.

2002 7 urban areas

(US)

4-12 O

3

,

NO

2

D3HourM

ean

0.008698 0.003515 Logistic Warmseason;

Adjusted age

Missed

school days

O'Connor et

al.

2008 7 inner cities 4-12 D24Hour

Mean

0.009856 0.015289 Logistic Adjusted age

One or More

Symptoms

O'Connor et

al.

2008 7 inner cities 4-12 D24Hour

Mean

0.004699 0.008263 Log-linear* Adjusted age

Nighttime

asthma

O'Connor et

al.

2008 7 inner cities 4-12 D24Hour

Mean

0.010567 0.010042 Log-linear* Adjusted age

Slow play O'Connor et

al.

2008 7 inner cities 4-12 D24Hour

Mean

0.005456 0.009517 Log-linear* Adjusted age

One or More

Symptoms

Schildcrout et

al.

2006 Eight U.S.

cities

4-12 D24Hour

Mean

0.003922 0.001963 Logistic Adjusted age

I.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.
Appendix I: Sulfur Dioxide Health Impact Functions

August 2010

354

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 fromthe 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).

I.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.

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
Appendix I: Sulfur Dioxide Health Impact Functions

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355

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 fromthe 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.

I.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.

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.
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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.

I.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.

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.
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Incidence Rate: Daily incidence rate for one or more symptoms (symptomscore>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).

I.4 Minor Effects

Table I-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 I-4. Health Impact Functions for Sulfur Dioxide and Minor Effects

Eff ect Author Year Location Age Co-Poll Metric 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

I.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).

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

fromthe 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
Appendix I: Sulfur Dioxide Health Impact Functions

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358

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
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Appendix J: 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 J-1.

Table J-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 ***

* 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:

J.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.
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J.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.

J.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 J-2 presents the

distribution parameters for the suite of mortality valuations currently available in BenMAP.

Table J-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.
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J.2 Chronic Illness

This sub-section presents the unit values developed for chronic bronchitis, chronic asthma, and

non-fatal myocardial infarctions.

J.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.

J.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
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362

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.
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J.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 J-3 below.

Table J-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
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J.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.

J.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 J-4 below.
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Table J-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

J.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.
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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

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 J-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 J-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
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Basis of Estimate Age Range Medical Cost * Opportunity

Cost **

Total Cost

Min Max

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

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.

J.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.

J.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.
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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

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 (2000). The totalCOI for an

ICD-code-specific hospitalstay lasting n days is estimated as the mean hospital charge plus n

times the daily lost wage. Year 2000 county-specific median annual wages divided by (52*5)

were used to estimate county-specific median daily wages. (The source for median 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.

Most hospital admissions categories considered in epidemiologicalstudies consisted ofsets of

ICD codes. The unit value for the set of ICD codes was estimated as the weighted average of

the ICD-code-specific COI estimates. The weights were the relative frequencies of the ICD

codes among hospital discharges in the United States, as estimated by the National Hospital

Discharge Survey (Owings and Lawrence, 1999, Table 1). The hospital admissions for which

unit values are available in BenMAP are given in Table J-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.

The mean hospital charges and mean lengths ofstay provided by (AHRQ 2000) are based on a

very large nationally representative sample of about seven million hospital discharges, and are

therefore the best estimates of mean hospital charges and mean lengths ofstay available, with

negligible standard errors.
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Table J-6. Unit Values Available for Hospital Admissions

EndPoint ICD Codes Age Range Mean

Hospital

Charge *

Mean

Length of

Stay (days)

*

Total Cost

of Illness

(Unit

Value) **

min. max.

HA, All Cardiovascular 390-429 65 99 $20,607 5.07 $21,191

HA, All Cardiovascular 390-429 0 99 $20,873 4.71 $21,415

HA, All Cardiovascular 390-429 20 64 $22,300 4.15 $22,778

HA, Congestive Heart Failure 428 65 99 $14,573 5.60 $15,218

HA, Dysrhythmia 427 0 99 $14,811 3.70 $15,237

HA, Ischemic Heart Disease 410-414 65 99 $25,322 4.81 $25,876

HA, All Respiratory 460-519 65 99 $17,600 6.88 $18,393

HA, All Respiratory 460-519 0 99 $14,999 5.63 $15,647

HA, All Respiratory 460-519 0 2 $7,416 2.97 $7,759

HA, Asthma 493 0 64 $7,448 2.95 $7,788

HA, Asthma 493 65 99 $11,417 4.99 $11,991

HA, Asthma 493 0 99 $8,098 3.30 $8,478

HA, Chronic Lung Disease 490-496 65 99 $12,781 5.59 $13,425

HA, Chronic Lung Disease 490-496 0 99 $10,882 4.59 $11,412

HA, Chronic Lung Disease 490-496 20 64 $10,194 4.04 $10,660

HA, Chronic Lung Disease (less

Asthma)

490-492, 494-

496

65 99 $12,993 5.69 $13,648

HA, Chronic Lung Disease (less

Asthma)

490-492, 494-

496

0 99 $12,742 5.45 $13,370

HA, Chronic Lung Disease (less

Asthma)

490-492, 494-

496

20 64 $11,820 4.48 $11,820

HA, Pneumonia 480-487 65 99 $17,030 7.07 $17,844

HA, Pneumonia 480-487 0 99 $14,693 5.92 $15,375

* Source of hospital charges and lengths of stay: Agency for Healthcare Research and Quality. 2000. HCUPnet,

Healthcare Cost and Utilization Project. http://www.agrq.gov/data/hcup/hcupnet.htm .

** The opportunity cost of a day spent in the hospital was estimated, for this exhibit, at the median daily wage

of all workers, $115.20, regardless of age. The median daily wage was calculated by dividing the median weekly

wage ($576 in 2000$) by 5. The median weekly wage was obtained from U.S. Census Bureau, Statistical

Abstract of the United States: 2001, Section 12, Table 621: “Full-Time Wage and Salary Workers – Numbers and

Earnings: 1985 to 2000.” Actual unit values used in BenMAP are based on county-specific wages, and are

therefore county-specific.

J.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
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370

million asthma-related ER visits made in 1987, at a total cost of $186.5 million, in 1987$. The

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 J-7.

Table J-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 –

J.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.
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Table J-8. Unit Values Available for Acute Symptoms and Illnesses

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.

Table J-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

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
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Health

Endpoint

Basis for WTP Estimate* Age Range Unit

Value

Unit Value

Distributio

n

Parameters of

Distribution

min. max. P1 P2

Worse; One

or more

symptoms;

Shortness of

Breath;

Wheeze

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.

J.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

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
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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.

J.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 J-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 J-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.

The three individualsymptoms that were identified as most closely matching those listed by Pope
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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.

J.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 J-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

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.
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J.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.

J.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.

J.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)

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,
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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.

J.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

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
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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.

J.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 J-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

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
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characterize the uncertainty surrounding this estimate.

Table J-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

(%) *

Implied Total

Number in

Population (in

millions)

Implied Percent

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.
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Appendix K: 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, PM10and lead monitor data

for the years 2000-2007. Data for CO, NO2, and SO2are available at the BenMAP

website: http://www.epa.gov/air/benmap/.

K.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 2000

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

K-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 K-1. Demographic Groups and Variables Available in BenMAP

Racial/Ethnic Group Ethnicity Age Gender

White, African American,

Asian, American Indian, Other,

Hispanic

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
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380

forecasts.

PopGrid. This section reviews the PopGrid software application, which aggregates block-level

population data to whatever grid definition might be needed.

K.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 Exhibit K-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 2000, BenMAP scales the 2000

Census-based estimate with the ratio of the county-levelforecast for the future year of interest

over the 2000 county-level population level. Woods & Poole (2007) 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 2010, CAMPS calculates:

age age

age

age

g g

county

county 4 9 2010 4 9 2000

4 9 2010

4 9 2000

, , , ,

, ,

, ,

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 2000 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 2000:
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age age fraction age g in county g g in county c c 4 9 , , 2000 4 9 , , 2000 4 9 ,

To then forecast the population in 2010, we scale the 2000 estimate with the ratio of the county

projection for 2010 to the county projection for 2000:

age age

age

age

g in county g in county

county

county

c c

c

c 4 9 2010 4 9 2000

4 9 2010

4 9 2000

, , , ,

, ,

, , .

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 2010 as follows:

age age

total pop

total pop

age

age

g g

g in county

g

county

c county n

c c

c 4 9 2010 4 9 2000

4 9 2010

1 4 9 2000

, , , ,

, ,

, ,

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:

age age

total pop

total pop

age

age

g g

g in county

g

county

c county n

c c

c 4 9 2010 4 9 2000

4 9 2010

1 4 9 2000

, , , ,

, ,

, , age age

total pop

total pop

age

age

g g

g in county

g

county

c county n

c c

c 10 14 2010 10 14 2000

10 14 2010

1 10 14 2000

, , , ,

, ,

, , age age age 4 12 g 2010 4 9 g 2010 10 14 g 2010

3

5

, , , , , , .

Since the Woods and Poole (2007) projections only extend through 2030, we used the existing

projections and constant growth factors to provide additional projections. To estimate

population levels beyond 2030, CAPMS linearly extrapolates from the final two years of data.

For example, to forecast population in 2035, CAPMS calculates:
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age age age age 4 9, 2035 4 9, 2030 5 4 9, 2030 4 9, 2029.

K.1.2 Data Needed for Forecasting

Underlying the population forecasts in BenMAP there are block-level databases used to provide

year 2000 population estimates and a county-level database of forecast ratios. Both files have

the same set of 304 race-ethnicity-gender-age population groups.

The block-level data is typically not used directly in BenMAP, and instead is used with the

PopGrid software (described below) to provide year 2000 estimates for a grid definition of

interest (e.g., 12 kilometer CMAQ grid). The output from PopGrid with the year 2000

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 "future" year (2000-2030)

and year 2000 population data for each county and each of the 304 race-ethnicity-gender-age

population groups.

We describe the development of each databases below.

K.1.2.1 Block-Level Census 2000

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 K-2

summarizes the initialset of variables and the final desired set of variables.

Table K-2. Race, Ethnicity and Age Variables in 2000 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+
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The initialset of input files are as follows.

Census 2000 block-levelfile (Summary File 1)

Data:ftp.census.gov/census_2000/datasets/Summary_File_1

Docs: http://www.census.gov/prod/cen2000/doc/sf1.pdf

Census 2000 tract-levelfile (Summary File 4)

Data:ftp.census.gov/census_2000/datasets/Summary_File_4

Docs: http://www.census.gov/prod/cen2000/doc/sf4.pdf

Census 2000 MARS national-levelsummary

Docs: http://www.census.gov/popest/archives/files/MRSF-01-US1.pdf

The SF4 and MARS data, as described below, are needed to reorganize the variables that come

initially in the SF1 file. (For the sake of completeness, we note that there exists a county-level

Census 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 US Census.)

The steps in preparing the data are as follows:

1. Adjust Age-classifications:

We combined some age groups in the 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 countylevel SF4 data, which gave us the fraction of 0-4 year-olds who are <1.

2. Fill in Missing Racial-Ethnic Interactions:

We used the county-level SF4 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.

This process tended to underestimate the number of Hispanics. Therefore, we made a

correction. We calculated the ratio of total Hispanics in a state to the estimated Hispanics in a

state. In each age-sex-race-block-level datum, if the resulting total of Hispanics is not greater

than the total number of people in the datum, we increased the number of people according to

this ratio.
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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.
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Appendix K: Population & Other Data in U.S. Setup

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K.1.2.2 County-Level Forecasts

Woods & Poole (2006) developed county-levelforecasts for each year from 2000 through

2030, by age and gender for non-Hispanic White, African-American, Asian-American, and

Native-American and for all Hispanics. As discussed below, the adjustments necessary to

prepare the data for use in BenMAP are relatively straightforward. The starting data is the

following:

Woods & Poole county-levelfiles

Data: L:\project data\Benmap\Database Development\Population\Woods and Poole from

2007-2008\Data\

Docs: http://www.woodsandpoole.com/pdfs/CED07.pdf

For each non-Hispanic subset of the population and each year from 2000-2030, we divided the

Woods and Poole population for that year by the Woods and Poole population for that subset in

2000. 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 2000 Census (and 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 2000 county population for a specific demographic

group is zero, so the ratio of any future year to the year 2000 data is undefined. In these

relatively rare cases, we set the year 2000 ratio and allsubsequent ratios to 1, assuming no

growth.
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K.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 2000 population

estimate from the Census Bureau multiplied by the ratio of future-year to year 2000 county

population estimates from Woods & Poole. BenMAP assumes that all age-gender groups within

a given race-ethnic group have the same geographic distribution.

K.1.3.1 How to Use PopGrid

After installing PopGrid, double-click on the PopGrid executable "PopGrid4.exe." The following

screen will appear:
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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:
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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:
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After choosing your shapefile, go to the Step 3: Run tab, which should look as follows:
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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").
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K.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 K-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 K-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
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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 K-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
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must sum to one. In Table K-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 K-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
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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

K.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 K-6 summarizes the data sources and vintage

of the processed data.

Table K-6. Underlying data sources for BenMAP air quality data files.
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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 K-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 K-7. Pollutant-specific QC checks performed in AQA

K.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: μ g/m3 for 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.
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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.

K.2.2 Output Files

Table K-8 lists the number of monitors by pollutant and year, represented in the resulting

BenMAP-ready data files.

Table K-8. Number of Monitors by Pollutant, AQS Parameter Code, and Year Included

in the BenMAP-Ready Data Files
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Appendix L: 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.

L.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.

L.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
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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 ), 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 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 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 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 , 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.

L.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
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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

. 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.

L.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 described in the previous section for the distribution of

. 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.

L.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, , along with a measure of the uncertainty of the estimate. Because uncertainty
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decreases as sample size increases, combining data sets is expected to yield more reliable

estimates of , and therefore more reliable estimates of the incidence change predicted using .

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 provides a second-best but still valuable way to synthesize information . This is

referred to as pooling. Pooling ’s requires that all of the studies contributing estimates of use

the same functionalform for the health impact function. That is, the ’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 ’s themselves. For a variety of reasons, this

is often possible when it is not feasible to pool the underlying ’s. For example, if one study is

log-linear and another is linear, we could not pool the ’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.

L.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.

L.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.
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L.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 Effects Pooling and Random / Fixed Effects

Pooling.

The discussion of these two weighting schemes is first presented in terms of pooling the pollutant

coefficients (the ’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 ’s.

L.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 that applies everywhere. Differences among

’s reported by different studies are therefore simply the result ofsampling error. That is, each

reported 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 i with variance vi (I = 1, ...,

n). Let

denote the sum of the inverse variances. Then the weight, wi , given to the ith estimate, i , 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.
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That is:

The variance associated with this pooled estimate is the inverse of the sum of the inverse

variances:

Exhibit L-1 shows the relevant calculations for this pooling for three sample studies.

Exhibit L-1. Example of Fixed Effects Model Calculations

Study i vi 1/vi wi wi* i

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 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, vfe, is the inverse of the sum of the variances, or 0.00197.

(The sums of the i and vi are not shown, since they are of no importance. The sum of the 1/vi is

S, used to calculate the weights. The sum of the weights, wi , i=1, ..., n, is 1.0, as expected.)

L.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 i from 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

another. For example, fine particles make up a greater fraction of PM10 in Philadelphia than in El
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Paso. If fine particles are disproportionately responsible for mortality relative to coarse particles,

then one would expect the true value of in Philadelphia to be greater than the true value of 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, , of which all the i ’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 Qw is 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 . (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, 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 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 2 is positive.

Given a value for 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 ( 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 + 2.

Let vi* = vi + 2. Then:
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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 . 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 â’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

of incidence change are used in exactly the same way as the reported â’s are used in the

calculation of fixed effects and random effects weights described above. The variances of
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incidence change are used in the same way as the variances of the â’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 âi and the variance of incidence change for the ith health impact function for

vi.200

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 â’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 â’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 âi and the variance of dollar benefits for the ith valuation for vi.

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.

L.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.

Round Weights to Three Digits

This Advanced Pooling Method is essentially the same as rounding weights to two digits, except
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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).

L.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

appropriate number of latin hypercube points. The number of iterations is determined by the

Monte Carlo Iterations setting.
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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.

L.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

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
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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.
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Appendix M: 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.

M.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.

M.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!
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M.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.””).

M.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”

M.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>
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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

M.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)
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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.

M.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>
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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.

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
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values are InverseDistance and InverseDistanceSquared. If this parameter is not specified,

InverseDistance weighting is used.

M.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:

-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.

M.3.2.4 Monitor Rollback

// MonitorRollback

SpatialScaling = '-SpatialScaling';

BaselineFilename = '-BaselineFilename';

// RollbackOptions

Percentage = '-Percentage';

Increment = '-Increment';
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// RollbackToStandardOptions

Standard = '-Standard';

Metric = '-Metric';

Ordinality = '-Ordinality';

InterdayRollbackMethod = '-InterdayRollbackMethod';

IntradayRollbackMethod = '-IntradayRollbackMethod';

M.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
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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.

M.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.

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.
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Overrides whatever value is present in the .apv file.

M.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>

-InputFile <filename>

-ReportFile <filename>

<optional parameters>

Supported ReportType values are: AuditTrail, CFGR, and APVR.

M.3.5.1 Audit Trail

Audit trailreports require only the parameters described in the "Generate Report" section.

M.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>
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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.

M.3.5.3 APVR Report

Required Parameters

APVR Reports require one additional parameter beyond those required for Audit Trail or CFGR

Reports.

-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).

M.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
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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

-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
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-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

M.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
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-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

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 N: Function Editor

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Appendix N: Function Editor

The function editor is used to develop both health impact functions and valuation functions. This

appendix describes the syntax of this editor.

N.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
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declaration

var I: Integer = 7;

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.

N.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;
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try statement(s) except statement(s) end;

inline comments: // comment... until the end of the line

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.)

N.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"" ");

N.4 Operations

Arithmetical

+, -, *, /;

div - integer division;

mod - modulo;

^ - power of;

- - negate;

Logical

<, <=, >=, >, <>, =;

and, or, xor, not;
Appendix N: Function Editor

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Bitwise

and, or, xor;

~ - negate;

N.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
Appendix N: Function Editor

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427

LN(X) natural logarithm

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

N.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

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428

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