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# • DEVELOPMENT O F DECISIONANALYSI S METHODOLOGY FOR HEALT H RIS K ASSESSMENT



# c

c 

# *(3£ ! A n Illustrativ e Applicatio n t o Alternativ e Ai r Qualit yStandard s for Carbo n Monoxid e

Fina l Repor t



### By : Ann e E . Smit h Pete r C . McNamee , Projec t Leade r Mile yW . Merkhofer , Projec t Superviso r
Projec t Officer : Thoma s McCurd y Contract : 68-02-357 5 Prepare d for : Environmenta l Protectio nAgenc y Researc hTriangl e Par k Nort hCarolin a2771 1
SR IProjec t

343 2

333 Ravenswoo d Ave
. . Mani cPark
. C A9^02 5 859-620 0
. TWX: 910-373-204 6 : 48 6P. 3

CONTENT S
LIS T O F ILLUSTRATION S vi i LIS T O F TABLE S x i PREFAC E xii i PAR T I—OVERVIE W O F RIS K ASSESSMENT PROCEDUR E 1 I INTRODUCTION , RESEARC H SUMMARY , AN D CONCLUSION S 3 MethodologyRequirement s 3 Overvie wo f the Decision-Analyti c Ris k Assessmen t Proces s . 4 Summaryan

d Result s o f the Illustrativ e Applicatio n o f the Ris k Assessmen t Proces s t o Carbo n Monoxid e 9 Methodologica l Conclusion s 1 4 Overal l Characteristic s o f the Progra m1 7 REFERENCE S 1 9 PART II—EMISSIONS , ATMOSPHERI C DISPERSION , AN D EXPOSUR E MODEL S . 2 1 I I EMISSION S AN

D ATMOSPHERI C DISPERSION2 3 Emissio n an

d Atmospheri c Dispersio n Submode l 2 3 Propose d Ambien t Ai r Qualit yStandard s Analyze d i n thi s Repor t 2 8 EP AMode l o f Implementatio n o f Standard s 2 9 Summary3 0 REFERENCE S 3 1 II I EXPOSUR E MODE L 3 3 Exposur e Mode l fo r C O 3 3 Dat a Supplie d fo r Thi s Stud yb yEP AMode l 3 7 Summary3 8 REFERENCE S 4 1 PAR T III—HEALT H DOSE-RESPONS E MODEL S 4 3 IV HEALT H EFFECT S DOSE-RESPONS E MODEL S 4 5 Th e Modelin g Approac h4 5 Choic e o f Sensitiv e Group s 4 7 Measure s o f Healt hRis k 5 0 V ARTERIOSCLEROTI C HEAR T DISEAS E 5 3 Background an

d Modelin g Issue s 5 3 Deterministi c Mode l 5 5


### ii iP. 4

V ARTERIOSCLEROTI C HEAR T DISEAS E (Concluded ) Probabilisti c Phas e 6 3 Discussio n 7 1 Othe r Manifestation s o f ASH D 7 7 Summary7 8 REFERENCE S 7 9 V I FETAL HEALT H EFFECT S 8 1 Physiologica l Background 8 1 Deterministi c Mode l . 8 2 Probabilisti c Phas e ' . . 8 8 Discussio n o f Result s 9 1 Summary9 3 REFERENCE S 9 5 VI I CHRONI C OBSTRUCTIVE PULMONARY DISEAS E 9 7 Background an

d Modelin g Issue s ... . 9 7 Deterministi c Mode l 9 8 Probabilisti c Mode l 10 5 Summary10 8 REFERENCE S 11 5 VII I . CENTRAL NERVOU S SYSTE M 11 7 Background an

d Modelin g Issue s 11 7 Deterministi c Mode l 11 9 Probabilisti c Mode l 13 0 Discussio n 13 3 Summary13 3 REFERENCE S 13 5 PART IV—-SIGNIFICANC E O F RESULT S 13 7 I X HEALT H RISK S FOR TH E NATION13 9 Relatio n o f the Healt hRis k an d Exposur e Model s 13 9 Estimate s o f Healt hRisk s fo r Entir e Natio n 14 3 Probabilisti c Phas e 14 8 Summary14 8 REFERENCE S 15 7 X SIGNIFICANC E WEIGHTING 15 9 Choic e o f Unit s 15 9 Assignmen t o f EDRA s 16 0 Result s 16 1 Conclusio n 16 4 X I UNCERTAINTIE S WIT H HIGH VALUE S O F RESOLUTION16 7 APPENDI X A17 1



### i vP. 5 r r - -IJT T

APPENDI X B 19 7 Pitfall s o f Encodin g 19 7 Th e Encodin g Session s 19 9 Us e o f Encode d Distribution s 20 2 Summaryo f Othe r Inverview s 20 7 REFERENCE S 21 3P. 6

ILLUSTRATION S
1. 1 Healt hRis k Assessmen t Cycl e 5 1. 2 Majo r Component s o f Healt hImpac t Outcom e Calculato r .. . 6 2. 1 Emission s an d Atmospheri c Dispersio n Mode l 2 4 3. 1 Exposur e Mode l 3 4 5. 1 Probabilit yDistributio n o n Maxima l Myocardia l Oxyge n Suppl y5 6 5. 2 Grou

p Mean s o f Aronow' s Result s fo r Doubl e Produc t a t Onse t o f Angin a 5 8 5. 3 Probabilit yDistributio n o n MO C 5 9 5. 4 Relationship s Betwee n Lowere d MO S an d Highe r Frequenc yo f Angina l Pai n 6 1 5. 5 Probabilit yTre e fo r Angin a Mode l 6 4 5. 6 Uncertaint yi n Measuremen t o f Slop e o f Doubl e Produc t a t Onse t o f Angin a 6 7 5. 7 Probabilit yDistribution s o f Incidenc e o f Angina l Pai n fo r Lo s Angele s 6 8 5. 8 Probabilit yDistribution s o n Fractiona l Chang e fro mAs-I s Situatio n fo r Ischemi c Episode s (Lo s Angeles ) 7 0 5. 9 Exampl e o f a Sigmoida l Dose-Respons e Curv e 7 4 5.1 0 Healt hDose-Respons e Curv e wit hSuperimpose d Threshol d . . 7 5 6. 1 Probabilit yDistributio n fo r Feta l Oxyge n Tensio n i n the Umbilica l Artery8 4 6. 2 Probabilit yo f Feta l Deat ha s a Functio n o f Oxyge n Tensio n i n Feta l Umbilica l Artery8 6 6. 3 Probabilit yTre e Use d wit hFeta l Deat hMode l 8 9 6. 4 Probabilit yDistributio n o n Feta l Death s (>2 0 Wee k Gestation ) pe r Yea r i n Lo s Angele s 9 0 7. 1 Probabilit ytha t P a 2 Fall s Belo wthe Valu e Indicate d During the Dayfo r a Sensitiv e COP D Patien t 10 1 7. 2 Probabilit ytha t Sa Q 2 Fall s belo wthe State d Valu e o n a Give n Dayfo r Sensitiv e COP D Patient s 10 2 7. 3 Probabilit yo f Acut e RespiratoryFailur e a s a Functio n o f

Sa 0 2 10 4 7. 4 Probabilit yo f Deat ha s a Functio n o f Sa 02 , Give n tha t Acut e RespiratoryFailur e ha s Occurre d 10 6 

### vi iP. 7 "I- * 1 lllf-fl l

7. 5 Probabilit yTre e Use d fo r COP D Mode l 10 7 7. 6 Probabilit yDistributio n fo r Hospitalizations/Yea r fo r the Sensitiv e COP D Populatio n i n Lo s Angele s 10 9 7. 7 Probabilit yDistributio n fo r the Lif e Expectanc yfo r a Sensitiv e COP D Patien t Il l 7. 8 Probabilit yDistribution s o n the Chang e i n Lif e Expectanc y fro mAs-I s fo r a COP D Patien t i n Lo s Angele s 11 2 8. 1 Effect s o f Alcoho l o n Driving-Skil l Test s . . . * 12 0 8. 2 Effect s o f COHbo n Driving-Relate d Skill s 12 1 8. 3 Compariso n o f Tes t Result s wit hCOHbRescale d t o Effectiv e Alcoho l Concentratio n 12 3 8. 4 Compariso n o f Thre e Basi c EBA C Function s 12 4 8. 5 Probabilit yo f Fata l Acciden t a s Functio n o f Blood-Alcoho l Conten t Relativ e t o Probabilit ya t Blood-Alcoho l Conten t o f Zer o 12 5 8. 6 Hypothetica l Relationshi p Betwee n Averag e COHbLevel s whil e Driving an

d Fractio n o f the Drivin g Tim e whe n Fata l Acciden t i s Possibl e 12 7 8. 7 Exampl e o f ho wDTF Ai s a Functio n o f the Standar d fo r Carbo n Monoxid e 12 8 8. 8 Derivatio n o f Ne t Acciden t Ris k Functio n fro mPhysica l an

d Huma n Element s 12 9 8. 9 Probabilit yTre e Use d fo r Traffi c Accident s Mode l 13 1 8.1 0 Probabilit yDistribution s fo r Traffi c Death s i n Lo s Angele s 13 2 9. 1 Angin a Attack s pe r Daype r Patien t a s a Functio n o f Averag e COHb14 0 9. 2 Angin a Attack s pe r Yea r pe r Millio n Adul t Populatio n a s a Functio n o f Averag e COHb14 1 9. 3 Feta l Death s pe r Thousand Pregnancie s a s a Functio n o f Averag e COHb14 4 9. 4 Numbe r o f Hospitalization s pe r Yea r o f a Patien t Sufferin g COP D a s a Functio n o f the Averag e Highes t Dail yValu e o f COHbExperience d 14 5 9. 5 Lif e Expectanc yo f a Patien t Suffering Sever e COP D a s a Functio n o f the Averag e Highes t Dail yValu e o f COHb Experience d 14 6 9. 6 Distribution s fo r Nationa l Urba n Death s du e t o ASH D Unde r Variou s Propose d Standard s 14 9 9. 7 Distribution s fo r Nationa l Urba n Feta l Death s Unde r Variou s Propose d Standard s 15 0 9. 8 Distribution s fo r Tota l Day s o f Lif e Los t fo r Nationa l Urba n COP D Sensitiv e Patient s whe n th e Standar d i s Change d fro mthe Strictes t Standar d 15 1 vii iP. 8

9. 9 Distribution s fo r Nationa l Urba n Death s i n Ca r Accident s Unde r Variou s Standard s 15 2 9.1 0 Distribution s o n Angin a Attack s fo r Nationa l Urba n ASH D Patient s ' 15 3 9.1 1 Distribution s fo r Hospitalization s o f Nationa l Urba n COP D Patient s 15 4 10. 1 Comparison s o f Range s o f EDRA s Attributabl e t o Eac hHealt h Effec t 16 2 10. 2 Probabilit yDistribution s fo r the Chang e i n EDRA s fro mMos t Stringen t Standar d fo r Si xLes s Stringen t Standard s ... . 16 5 11. 1 Distribution s fo r th e Nationa l Healt hImpac t pe r Yea r Unde r a Chang e fro

mthe Strictes t t o the Mos t Lenien t Propose d Standar d 16 8 A. l Distribution s o n Change s i n Ischemi c Episode s fro mAs-I s i n Chicag o 17 5 A. 2 Distribution s o n Change s i n Ischemi c Episode s fro mAs-I s i n Philadelphi a 17 7 A. 3 Distribution s o n Change s i n Ischemi c Episode s fro mAs-I s i n St . Loui s 17 9 A. 4 Probabilit yDistribution s o n Feta l Death s (>2 0 Week s Gestation ) pe r Yea r i n Chicag o 18 1 A. 5 Probabilit yDistribution s o n Feta l Death s (>2 0 Week s Gestation ) pe r Yea r i n Philadelphi a 18 3 A. 6 Probabilit yDistribution s o n Feta l Death s (>2 0 Week s Gestation ) pe r Yea r i n St . Loui s 18 5 A. 7 Probabilit yDistribution s o n Change s i n Lif e Expectanc yfo r Sensitiv e COP D Patient s i n Chicag o 19 0 A. 8 Probabilit yDistribution s o n Change s i n Lif e Expectanc yfo r Sensitiv e COP D Patient s i n Philadelphi a 19 2 A. 9 Probabilit yDistribution s o n Change s i n Lif e Expectanc yfo r Sensitiv e COP D Patient s i n St . Loui s 19 4 B. l Encode d Distributio n o n Pea k Hourl yHear t Rat e (Day , Subjec t Awake ) 20 3 B. 2 Encode d Distributio n o n Pea k Hourl yHear t Rat e (Night , Subjec t Asleep ) 20 4 B. 3 Encode d Distributio n o n Pea k Hourl yMea n Systoli c Bloo d Pressure , Give n Hear t Rat e o f 8 0 Beats/Minut e an d Subjec t Sleeping 20 5 B. 4 Encode d Distributio n o n Pea k Hourl yMea n Systoli c Bloo d Pressure , Give n Hear t Rat e o f 10 0 Beats/Minut e an d Subjec t Awak e 20 6 B. 5 Distributio n o n Doubl e Produc t Implie d b yEncode d Distribution s o n Hear t Rat e an d Bloo d Pressur e 20 8



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TABLE S
2. 1 Estimate d Coefficient s fo r Microenvironmen t Concentratio n . 2 7 2. 2 Standard s Analyze d i n Thi s Repor t 2 8 2. 3 Rollbac k Factor s fo r Fou r Citie s 2 9 2. 4 Estimate d Background Concentration s 3 0 3. 1 Age/Occupatio n Categorie s 3 5 3. 2 Parameter s Use d i n Cobur n Equatio n 3 8 3. 3 COHbLevels : Lo s Angeles , CA4 0 3. 4 COHbLevel s Unde r the Strictes t Propose d Standar d 4 0 4. 1 Sensitiv e Group s an d Associate d Healt hEffect s 4 8 4. 2 Rankin g o f Sensitiv e Group s 4 9 5. 1 Definitio n o f Cohort s Use d i n ASHD Mode l 6 2 5. 2 Frequencie s o f Doubl e Produc t fo r a Rang e o f Value s o f u . 6 5 5. 3 Mean s o f Tota l Annua l Incidenc e o f Angina l Pai n 6 9 5. 4 Compariso n o f Annua l Incidenc e o f Angina l Pai n 6 9 5. 5 Compariso n o f Predicte d Cardiovascula r Mortalit y7 1 5. 6 C O Monitor s Use d t o Tes t Implication s o f Hexte r Mode l .. . 7 6 5. 7 Compariso n o f Epidemiologica l Result s an d o f ASHD Mode l fo r Death s i n Lo s Angele s 7 6 6. 1 Distributio n o f Smoker s Amon g Pregnan t Wome n 8 7 6. 2 Annua l Pregnancie s b yCohor t 8 8 6. 3 Expecte d Change s i n Feta l Death s (>2 0 Week s Gestation ) Pe r Yea r 9 1 6. 4 Rati o o f Feta l Deat hRate s o f Smoker s t o Nonsmoker s ... . 9 2 6. 5 Extrapolatio n o f Feta l Deat hMode l t o Othe r Feta l Effect s . 9 3 7. 1 Descriptio n o f COP D Cohort s 10 0 7. 2 Compariso n o f Frequenc yo f Acut e RespiratoryFailur e Incidenc e i n Sensitiv e COP D Populatio n Acros s Standard s . . 11 0 7. 3 Compariso n o f Lif e Expectancie s fo r COP D Sensitiv e Populatio n Acros s Standard s 11 3 8. 1 Fata l Acciden t Level s an d Estimate d COHbExposur e Pattern s 13 0 9. 1 Possibl e Distribution s o f Urba n Populatio n b y Representativ e Cit yType s 14 7



### x iP.1 0

9. 2 Nationa l Extrapolatio n o f Predicte d Change s fro mStrictes t Standar d 15 5 9. 3 Nationa l Extrapolatio n o f Predicte d Percen t Change s from Strictes t Standar d 15 5 10. 1 Possibl e EDR

AAssignment s 16 0 10. 2 Change s i n EDR APe r Yea r a t th e Nationa l Leve l 16 1 10. 3 Percen t Contributio n o f Eac hHealt hEffec t t o Total , b y Standar d 16 3 A. l Compariso n o f Incidenc e o f Angina l Pain—Chicag o 17 2 A. 2 Compariso n o f Annua l Incidenc e o f Angina l Pain — Philadelphi a 17 3 A. 3 Compariso n o f Annua l Incidenc e o f Angina l Pain—St . Loui s . 17 4 A. 4 Compariso n o f Predicte d Cardiovascula r Mortality—Chicag o . 17 6 A. 5 Compariso n o f Predicte d Cardiovascula r Mortality — Philadelphi a 17 8 A. 6 Compariso n o f Predicte d Cardiovascula r Mortality — St . Loui s 18 0 A. 7 Expecte d Change s i n Feta l Death s (>2 0 Week s Gestation ) Pe r Year—Chicag o 18 2 A. 8 Expecte d Change s i n Feta l Death s (>2 0 Week s Gestation ) Pe r Year—Philadelphi a 18 4 A. 9 Expecte d Change s i n Feta l Death s (>2 0 Week s Gestation ) Pe r Year—St . Loui s 18 6 A.1 0 Compariso n o f Change s i n Acut e RespiratoryFailur e fo r COP D Sensitives—Chicag o 18 7 A . H Compariso n o f Change s i n Acut e RespiratoryFailur e fo r COP D Sensitives—Philadelphi a 18 8 A.1 2 Compariso n o f Change s i n Acut e RespiratoryFailur e fo r COP D Sensitives—St . Loui s 18 9 A.1 3 Compariso n o f Change s i n Lif e Expectanc yfo r Eac hCOP D Sensitiv e Patient—Chicag o 19 1 A.1 4 Compariso n o f Change s i n Lif e Expectanc yfo r Eac hCOP D Sensitiv e Patient—Philadelphi a 19 3 A.1 5 Compariso n o f Change s i n Lif e Expectanc yfo r Eac hCOP D Sensitiv e Patient—St . Loui s 19 5



### xi iP.1 1

PREFAC E
Th e purpos e o f the researc hdescribe d i n thi s repor t wa s t o furthe r develo p a methodologyt o asses s the healt hris k associate d wit halterna

tiv e ambien t ai r qualit ystandards . Thi s methodologywa s develope d i n th e contex t o f a n illustrativ e applicatio n t o ambien t ai r qualit ystan
dard s fo r carbo n monoxide , whic hwa s directe d toward two goals . Th e pri

mar ygoa l o f the analysi s wa s t o drawconclusion s concerning the useful

nes s an d applicabilit yo f the ris k assessmen t methodology . Th e secondary goa l o f the analysi s wa s t o creat e a structurall ysoun d an d functioning ris k model . Th e analysi s i s complet e i n the sens e tha t i t develop s o r link s al l the component s necessaryt o a n evaluation . I t i s preliminary i n tha t man

yassumption s an

d simplification s o f dat a requirement s hav e bee n made . Thes e simplification s ca n b e relaxe d readil yonc e the scien

tifi c communit yha s agree d o n the basi c structur e o f the models . I t shoul d b e emphasize d that , becaus e o f the preliminarynatur e o f thi s work , the resulting healt hris k assessment s fo r carbo n monoxid e shoul d no t b e referre d t o outsid e the contex t o f a preliminarytes t o f the model . Aful l scal e implementatio n o f th e mode l coul d verywel l giv e result s tha t ar e quit e different . Becaus e the wor k describe d i n thi s repor t represent s a lon g an d com ple xchai n o f reasoning an d calculation , i t ha s prove d convenien t t o di

vid e the repor t int o the following describe d four parts .
Par t I : Overvie wo f Ris k Assessmen t Procedure—Th e overal l struc
tur e o f the wor k i s specifie d an d the importan t step s o f analysi s ar e re viewed . Genera l conclusion s o n the methodologyar e presented . Par t II ; Emissions , Atmospheri c Dispersio n an d Exposur e Models—Th e majo r structura l element s o f thes e model s ar e discusse d a s the yappl yt o carbo n monoxide . Th e compute r mode l develope d b ythe Environmenta l Pro
tectio n Agenc yi s analyze d i n term s o f thi s genera l structure .
Par t III ; Healt hDose-Respons e Models—Detaile d healt hdose

respons e model s ar e develope d fo r the mos t importan t healt heffect s re sulting fro

mcarbo n monoxide . Importan t uncertaintie s i n the input s t o thes e model s ar e analyzed .
Par t IV : Significanc e o f Results— Ametho d i s develope d fo r extrap
olating healt heffect s fro mthe four citie s analyze d t o a nationa l scale . Asignificanc e weightin g i s performe d t o identif ythe mos t importan t healt heffect s an

d the area s i n whic hfutur e researc hwil l prov e mos t usefu l fo r the assessment . xii iP.1 2

PAR T I OVERVIE WO F RIS K ASSESSMENT PROCEDUR E
Th e proces s o f setting standard s t o protec t the healt ho f the gen era l populatio n i s difficul t becaus e i t involve s obtainin g reliabl e an swer s t o man

ydifferen t type s o f questions , an d thes e question s requir e inpu t fro

mman ydifferen t field s o f knowledge . I n thi s firs t par t o f th e report , a methodologyi s describe d fo r identifying , collecting , an d orga nizing relevan t informatio n int o a forma l mode l fo r assessing the healt h risk s o f alternativ e standards . Th e methodologyha s severa l majo r advan

tages : (1 ) i t analyze s eac hquestio n i n turn , allowing the judgment s o f expert s t o b e focuse d o n thos e part s o f the analysi s tha t fal l withi n the experts ' real mo f experience , (2 ) i t allow s fo r explici t consideratio n o f uncertaint ywher e ful l understandin g o f processe s ha s no t ye t bee n at
tained , an

d (3 ) i t present s the decisio n make r wit ha too l t o explor e the consequence s o f differen t course s o f action . Methodologica l conclusion s ar e draw n fro mthe experienc e gaine d i n performing the illustrativ e applicatio n o f the methodology . Thes e con clusion s shoul d ai d i n the planning an d executio n o f complet e futur e as sessment s o f healt hrisk s associate d wit halternativ e ambien t ai r qualit y standards .P.1 3

INTRODUCTION , RESEARC H SUMMARY , AN D CONCLUSION S
Th e Ambien t Standard s Branc ho f the U.S . Environmenta l Protectio n Agenc y(EPA ) Offic e o f Ai r Qualit yPlanning an d Standard s (OAQPS ) i s ex ploring the potentia l o f quantitativ e method s t o asses s the healt hrisk s associate d wit halternativ e ambien t ai r qualit ystandard s fo r non carcinogeni c pollutants . Th e Clea n Ai r Ac t require s th e EP AAdministra

to r t o se t nationa l primaryai r qualit ystandard s tha t i n the Administra

tor' s judgmen t "protec t the publi c health " wit ha n adequat e "margi n o f safety. " T o b e usefu l i n thi s decision-makin g process , the ris k assess men t methodologymus t estimat e the healt heffect s resulting fro meac h alternativ e standar d an d als o the uncertaint yi n thes e estimates . MethodologyRequirement s Estimatin g the healt hconsequence s o f alternativ e ai r qualit ystan
dard s i s extremel ydifficult . Awid e rang e o f healt heffect s ar e sus pecte d o f being associate d wit hexposure s t o criteri a pollutants , ranging fro mmild , temporary , an d reversibl e physiologica l effect s t o seriou s diseas e an

d discomfort . Th e severit yo f thes e effect s i s known t o b e a complicate d functio n o f exposur e conditions , individua l characteristics , an d environmenta l cofactors . Al l o f these , i n turn , ar e difficul t t o predic t becaus e o f variation s i n pollutan t concentration s ove r time an d space , becaus e o f the activit ypattern s o f people , an d becaus e o f inter

persona l difference s i n susceptibility . No t onl yi s the relationshi p betwee n ai r qualit yan d huma n healt h complex , i t i s als o highl yuncertai n an d controversial . Scientifi c evi

denc e fo r healt heffect s i s frequentl yambiguous , disputed , o r eve n con tradictory . Ofte n ther e i s onl yanima l stud yevidenc e o r theoretica l hy

pothese s o n a particula r healt heffect . Huma n informatio n tend s t o b e draw n fro mhealth

yindividual s o r populations . Synergistic , additive , subtractive , o r antagonisti c cofactor s ar e rarel yinvestigated . T o b e usefu l t o the EPA , healt hris k assessmen t methodologymus t b e capabl e o f dealing wit hcomplexit yan d the paucit yo f scientifi c data . I t shoul d b e ope n t o revie wb ythe publi c an

d othe r intereste d parties , an d it s assumption s shoul d b e clea r an d explicit . I t shoul d provid e an appropriat e us e o f logi c an d mathematic s an

d conside r al l relevan t infor

matio n an

d point s o f view . Furthermore , i t shoul d minimiz e arbitrari

ness , b e flexibl e enought o handl e differen t kind s o f healt heffects , an d b e capabl e o f incorporating methodologica l improvement s an d ne winforma

tion . Recen t application s o f decisio n analysi s t o ai r pollutio n contro l problem s sugges t the possibilit yo f designing a healt hris k assessmen t methodologybase d o n decisio n analysis . SR I Internationa l (SRI ) ha sP.1 4

therefor e conducte d researc ht o extend decisio n analysi s method s an d technique s an

d t o evaluat e thei r usefulnes s a s a basi s fo r healt hris k assessment . Apreviou s repor t propose d a conceptua l framewor k designe d t o serv e a s a basi s fo r a healt hris k assessmen t procedure . Thi s repor t describe s researc hperforme d b ySR I t o furthe r develo p tha t procedur e througha pilo t applicatio n t o the criteri a pollutan t carbo n monoxide . Overvie wo f the Decision-Analyti c Ris k Assessmen t Proces s Th e propose d methodologyi s a proces s fo r constructin g an d analyzing model s tha t describ e availabl e knowledg e an d uncertaint yi n the relation

shi p betwee n alternativ e standard s an d healt heffects . Definin g the methodologya s a proces s rathe r than a s a specifi c se t o f modelin g as sumption s an

d analysi s technique s i s appropriate , give n the realitie s o f the EP Astandards-settin g problem . Becaus e o f the lac k o f hard scien

tifi c data , the ris k assessmen t methodologymus t rel yheavil yo n the judgment s o f experts . Procedure s tha t enabl e expert s t o contribut e t o the assessmen t according t o thei r specifi c area s o f expertis e an d tha t enabl e uncertaintie s t o b e assesse d an d mad e explici t ar e centra l t o th e methodology . Figur e 1. 1 illustrate s the si xstep s o f th e propose d process , orga nize d int o thre e majo r phase s o f analysis— a deterministi c phase , a prob

abilisti c phase , an d a n informationa l phase . Th e objective s o f eac h phas e an

d a brie f descriptio n o f the componen t step s ar e presente d below . Deterministi c Phas e I n the deterministi c phase , a "healt heffect s outcom e calculator " i s create d an

d evaluated . Th e healt heffect s outcome'calculato r i s a se t o f equations , implemente d i n compute r code , representin g the bes t availabl e informatio n fo r the relationship s betwee n alternativ e ai r qualit ystan
dard s an

d a measur e o f the seriousnes s o f the resulting healt heffects . No attemp t i s mad e t o accoun t fo r judgmenta l uncertaint yi n thi s phase , bu t statistica l uncertainties , suc ha s change s i n ambien t concentration s becaus e o f the weather , ar e modeled . Step 1 ; Develo p an d Implemen t a Healt hEffect s Outcom e Calculator — Th e componen t model s neede d t o for ma n outcome calculato r ar e show n i n Figur e 1.2 : a n emission s an d atmospheri c dispersio n model , a n exposur e model , healt hdose-respons e models , an d a significanc e weightin g model . Th e purpos e o f the emission s an d atmospheri c dispersio n mode l i s t o estimat e the ambien t concentration s o f the pollutan t unde r alternativ e standards . A

n ambien t ai r qualit ystandar d usuall yspecifie s a concen tratio n level , averaging time , an d numbe r o f instance s i n whic hconcen tration s ar e permitte d t o excee d the leve l ove r a give n perio d o f time . Give n a specifi c standar d an d a se t o f inpu t assumptions , the emission s an d atmospheri c dispersio n mode l produce s a s outpu t a serie s o f frequenc y distribution s describin g concentration s fo r eac hgeographi c locatio n an d fo r variou s point s throughou t the time horizo n unde r consideration .Initia l Knowledg e Bas e DETERMINISTI C PHAS E

Step 1 Develo p an

d Implemen t a Healt hEffect s Outcom e Calculato r Step 2 Conduc t a Sensitivit y Analysi s o f Healt h Effect s Outcom e Calculato r

i i S i PROBABILISTI C PHAS E te p 3 Encod e Uncertaintie s

Ste p 4 Develo p a Probabilit y Distributio n o n th e Healt hImpac t Measur e

Informatio n Gathering an

d Mode l Updating INFORMATIONAL PHAS E Step 5 Identif yUncertaintie s wit hHig hValue s o f Resolutio n Step 6 Gathe r Furthe r Informatio n an

d Repea t Cycl e a s Necessar y Healt h Ris k Assessmen t Outpu t

FIGUR E 1. 1 HEALT H RIS K ASSESSMENT CYCL EP.1 6

Ambien t Ai r Qualit y" •** * Standard s Emission s an d Atmospheri c Dispersio n Mode l Ambien t Pollutan t Concentration s

Exposur e Mode l

Pollutan t Dosag e Condition s }

Healt h Dose-Respons e Mode l
Healt h Effect s ' Significanc e Weightin g Mode 

### il Measur e

o f Healt h Impac t 

# o

FIGUR E 1. 2 MAJO R COMPONENT S O F HEALT H

IMPAC T OUTCOM E CALCULATO RP.1 7

Th e exposur e mode l estimate s condition s o f exposur e t o the pollutan t fo r the populatio n a t risk . Th e mode l take s a s inpu t (1 ) ambien t concen tration s (o r modele d data ) an d (2 ) assumption s o n the numbe r o f individ

ual s i n variou s sensitiv e populatio n group s an d thei r activit ypatterns . Th e outpu t o f the exposur e mode l mus t b e i n a for msuitabl e fo r inpu t fo r the dose-respons e models . I n a detaile d case , thi s woul d consis t o f ex posur e profile s representing the frequenc yan d duratio n o f exposure s t o variou s pollutio n level s b yactivit y(fo r example , leve l o f exercise) , sensitiv e grou

p category , an

d environmenta l factor s tha t ma yinfluenc e healt hresponse . Healt hdose-respons e model s represen t curren t knowledg e concerning the healt heffect s o f exposur e t o the pollutant . Applicatio n o f the mod

el s convert s th e exposur e profile s int o number s o f healt heffect s o f var iou s types . Summing ove r time an d ove r geographi c location s provide s es timate s o f the tota l numbe r o f healt heffect s o f eac htyp e occurring unde r eac halternativ e regulatorystandard . Th e significanc e weightin g mode l i s designe d t o explor e alternativ e mean s fo r aggregating healt heffect s o f differen t type s t o produc e a n overal l summarystatisti c o f healt himpact . T o permi t aggregation , al l healt heffect s mus t b e expresse d i n some commo n uni t o f measuremen t (e.g. , "equivalen t day s o f restricte d activity") . Th e significanc e weight s ar e no t mean t t o defin e a precis e healt hequivalence , bu t rathe r t o provid e a mean s o f explicitl yincorporating valu e judgment s an d o f en abling the decisio n make r t o investigat e the sensitivit yo f the estimate d healt himpac t t o variou s uncertainties . Developmen t o f eac ho f the componen t model s i n the healt houtcom e calculato r i s accomplishe d througha n iterativ e proces s involving ana lyst s an

d experts . Expert s provid e basi c scientifi c evidenc e an d judg

men t concerning the importan t variable s an d thei r influences . Analyst s structur e the variable s int o mathematica l relationship s an d verif yth e structure s b

yreviewing the assumption s an d implication s o f the relation

ship s wit hthe experts . Fo r efficiency , al l model s shoul d b e implemente d i n compute r code . Wher e model s alread yexis t (fo r example , atmospheri c dispersio n models ) theycan b e use d fo r initia l Implementation s o f the outcom e calculato r provide d tha t thei r inpu t requirement s an d output s ca n b e mad e compatibl e wit hthe othe r component s o f the healt houtcom e calcu

lato r an

d provide d thei r operating characteristic s (e.g. , flexibility , operatin g costs ) ar e suitabl e t o the need s o f the ris k assessment .
Step 2 : Conduc t a Sensitivit yAnalysi s o f the Healt hEffect s Outcom e Calculator—Runnin g the compute r mode l representin g the healt h outcome calculato r fo r eac halternativ e ai r qualit ystandar d wit ha se t o f nomina l estimate s fo r the valu e o f al l inpu t variable s yield s a nomi

na l estimat e o f the healt himpac t unde r alternativ e standards . Th e out
come calculato r ca n als o b e use d t o identif ywhic huncertaintie s ar e mos t critica l t o the assessmen t an d mus t b e explicitl yaccounte d fo r i n the ris k estimate . Sensitivit yanalysi s i s performe d initiall yb yvaryin g eac hinpu t variabl e i n the outcome calculato r acros s a rang e o f value s selecte d t o approximat e the rang e o f uncertaint yo n tha t variable . Fo r thos e variable s whos e variatio n produce s the larges t chang e i n overal lP.1 8

healt himpact , the uncertaintie s shoul d b e explicitl yquantified . Join t sensitivitie s i n whic htwo o r mor e variable s ar e simultaneousl yvarie d acros s thei r respectiv e range s o f uncertaintie s ar e als o conducte d t o avoi d missin g variable s whos e sensitivitie s ar e relate d t o cros s depen

dencie s i n the models . Probabilisti c Phas e Th e objectiv e o f the probabilisti c phas e i s t o asses s uncertaint yo n the sensitiv e inpu t variable s an d t o measur e the impac t o f suc huncer taint yo n the estimate s o f healt hrisk . Ste p 3 : Encod e Uncertainties—The variable s fo r whic huncertaint y ha s bee n show

n t o b e importan t i n Step 2 ar e no wassigne d probabilit y distributions . Thes e probabilit ydistribution s shoul d b e elicite d from recognize d expert s using probabilit yencodin g techniques . Wher e differ

ence s i n scientifi c opinio n exist , expert s shoul d b e chose n t o encompas s eac hcredibl e poin t o f view . Becaus e mos t individual s hav e difficult yi n thinking abou t uncer tainty , the metho d o f extractin g the probabilit ydistribution s i s ex tremel yimportant . Th e probabilit ymetho d propose d fo r the healt hris k assessmen t mode l i s describe d i n Referenc e 2 . Difference s i n the distribution s assesse d fro mvariou s expert s can ofte n b e resolve d b ybringing the expert s togethe r an d allowing the

mt o shar e information . Nevertheless , i n man ysituation s i t i s impossibl e t o ge t tw o o r mor e expert s t o agre e o n a n appropriat e probabilit ydistri

bution . Rathe r tha n reconciling divergen t distribution s b yassigning weight s t o each , multiplying , an d adding , divergen t distribution s ar e groupe d Int o nominal , optimistic , an d pessimisti c sets . Whethe r the ex per t difference s ar e significan t an d mus t b e retaine d ca n the n b e deter

mine d throughsensitivit yanalysis . Ste p 4 : Develo p a Probabilit yDistributio n o n th e Healt hImpac t Measure—Havin g develope d an outcom e calculato r an d obtaine d probabilit y distribution s o n the crucia l inpu t variables , i t i s straightforwar d t o desig n a compute r progra mtha t wil l generat e the probabilit ydistributio n ove r the measur e o f overal l healt himpac t fo r variou s setting s o f the standard . Thi s probabilit ydistributio n summarize s uncertaint yi n th e healt himpac t resulting fro ma give n standard . Informationa l Phas e I n th e informationa l phase , additiona l sensitivit yanalyse s ar e con ducte d t o evaluat e the relativ e importanc e o f reducing uncertaint yi n eac hare a o f the outcom e calculator . Comparin g the relativ e importanc e o f eliminatin g eac huncertaint ywit hthe feasibilit yo f improving the correspondin g assessmen t permit s researc halternative s an d informatio n gatherin g option s t o b e prioritized . I t als o indicate s whic helement s o fP.1 9

the outcom e calculato r ca n provid e significan t improvement s i n th e as sessmen t i f theyar e expande d an d refined . Step 5 : Identif yUncertaintie s wit hHig hValue s o f Resolution—Th e firs t step i n the informationa l phas e i s a stochasti c sensitivit yanal

ysis . Eac hcrucia l uncertai n variabl e i s se t equa l t o a tria l valu e withi n it s rang e o f uncertainty . Al l othe r uncertai n variable s ar e as signe d the appropriat e conditiona l join t probabilit ydistributions . Run
ning the compute r progra mwil l the n produc e a probabilit ydistributio n o n the healt himpac t measure , an d the expecte d valu e o f thi s distributio n ca n b e calculated . Sweeping the tria l valu e fro mon e en

d o f it s rang e t o the othe r show s ho wmuc hthe distributio n an d it s expecte d valu e change , dependin g o n the actua l valu e o f the uncertai n variable . I f th e chang e i s small , ther e i s evidenc e tha t the analysi s mayb e simplifie d b yelimi

nating consideratio n o f uncertaint yo n tha t variable . Ste p 6 : Gathe r Furthe r Informatio n an d Repea t the Cycl e a s Necessary—Actua l information-gatherin g program s wil l seldomcompletel y resolv e the uncertaint yi n a give n variable . Selectio n o f specifi c step s t o b e take n t o improv e informatio n mus t therefor e b e base d no t onl yo n calculation s suc ha s thos e describe d i n Step 5 , bu t als o o n the antici

pate d effectivenes s o f the gathere d dat a a t reducing uncertainty , the costs , an

d the time require d t o obtai n the information . Severa l iteration s o f the ris k assessmen t cycl e mayb e necessaryt o complet e the assessment . Followin g the firs t applicatio n o f the thre e phases , the outcome calculato r wil l b e constructe d o f highl ysimplifie d o r readil yavailabl e models . Amajo r purpos e o f thi s firs t implementa

tion , the pilo t model , i s t o identif ynecessaryinformatio n an d mode l im

provements . A s additiona l informatio n i s gathered , the cycl e shoul d b e repeated , wit helement s o f the analysi s update d t o accoun t fo r the im

prove d information . Some variable s mayb e uncertai n primaril ybecaus e detaile d model s hav e no t bee n constructed . Using a combinatio n o f deter

ministi c an

d stochasti c sensitivit yanalysis , the analys t ca n direc t the developmen t o f the ris k assessmen t mode l i n a n efficien t way . Th e re sulting improve d implementatio n migh t b e calle d a prototyp e model . I t i s a mor e detaile d representatio n o f the problem , which , althoughi t wil l generall yhav e some aspect s tha t mus t b e improved , demonstrate s the over al l appearanc e an

d performanc e o f the fina l version . Th e fina l mode l re sult s whe n sensitivit yanalysi s show s tha t gathering additiona l readil y availabl e informatio n o r makin g mode l improvement s wil l no t significantl y reduc e uncertaint yi n healt himpacts . Thi s i s the mos t accurat e repre sentatio n o f the problemtha t shoul d b e produce d a s par t o f the healt h ris k assessment .
Summaryan

d Result s o f the Illustrativ e Applicatio n o f th e Ris k Assessmen t Proces s t o Carbo n Monoxid e Carbo n monoxid e (CO ) i s on e o f the seve n criteri a pollutant s identi

fied b yEP Afo r regulatio n unde r the Clea n Ai r Act . Th e primarysourc e o f CO a s a pollutan t i s the burning o f carbo n base d fuel s unde rP.2 0

fuel-rich , oxygen-poo r conditions ; i n urba n areas , mos t ambien t C O come s fro mthe emission s fro mtransportatio n vehicles . Becaus e o f the greate r affinit yo f bloo d fo r C O than fo r oxygen , C O tend s t o combin e wit hbloo d hemoglobi n an d henc e reduc e the oxyge n deliv

er yt o the tissues . Eve n a t curren t ambien t level s o f CO , ther e i s con cer n tha t certai n sensitiv e group s wit hcompromise d oxyge n suppl y(e.g. , cardiovascula r diseas e o r chroni c obstructiv e pulmonarydiseas e patients ) ma ysuffe r advers e effects . T o evaluat e an

d illustrat e th e healt hris k assessmen t proces s out
line d above , the proces s wa s applie d t o the analysi s o f healt hrisk s as sociate d wit heac ho f seve n alternativ e ambien t standard s fo r CO . Th e standard s selecte d fo r analysi s ar e show n i n Tabl e 2.2 . (Note : table s an d figure s appea r i n th e chapter s wher e theyar e discusse d i n detail. ) Healt heffect s unde r eac hstandar d wer e assesse d fo r four citie s (Chicago , IL ; Lo s Angeles , CA ; Philadelphia , PA ; St . Louis , MO) ; the analysi s wa s the n extrapolate d t o obtai n a preliminaryestimat e o f th e healt hrisk s fo r the entir e nation . An ystatemen t o f result s o f thi s ris k assessmen t mus t b e preface d wit ha strong disclaimer . Th e projec t wa s an exploratoryone , looking toward the problem s an d successe s o f the methodologyi n dealing wit ha ver ycomplicate d problem . I n term s o f a n actua l ris k assessment , i t i s a t bes t a pilo t model , a s describe d above .
Ste p 1 : Develo p an d Implemen t a Healt hEffect s Outcom e Calculato r A s note d above , implementatio n o f a n outcom e calculato r require s four majo r component s (Figur e 1.2) . Muc ho f the wor k describe d i n thi s repor t involve d developin g o r linking the componen t model s fo r thi s calculator . Emission s an

d Atmospheri c Dispersio n an d Exposur e Models—A n exist

in g compute r implementatio n develope d b yEPAwa s use d a s the basi s fo r two component s o f the outcom e calculator . Th e EP Aimplementatio n actu
all yprovide s a greate r leve l o f detai l than i s require d fo r thi s appli

cation . Th e mode l take s monitore d C O dat a fo r a give n city , populatio n densities , an

d huma n activit ypattern s fo r tha t cit yan d predict s th

e ex posure s t o C O unde r a give n standard . Becaus e the primaryeffec t o f C O o n human s i s though t t o b e the resul t o f the combinatio n o f CO wit hbloo d hemoglobin , importan t outpu t o f thi s mode l i s carboxyhemoglobi n (COHb ) level s fo r eac hhou r o f the yea r fo r a larg e numbe r o f subset s o f the populatio n (cohorts) . Fo r the purpose s o f the outcom e calculato r thi s outpu t wa s use d t o obtai n frequenc ydistribution s fo r the occurrenc e o f COHblevel s i n a muc hsmalle r numbe r o f cohorts . Table s 3. 3 an d 3. 4 illustrat e the natur e o f the outpu t produced . Healt hDose-Respons e Models—Table s 4. 1 an d 4. 2 sho wpopulatio n group s identifie d a s sensitiv e t o elevate d COHblevels . A n initia l ap plicatio n o f the ris k assessmen t cycl e reduce d the numbe r o f sensitiv e 

### 1 0P.2 1

group s selecte d fo r forma l analysi s t o four : arterioscleroti c hear t dis

eas e patient s (healt hrisk s ar e increase d incidenc e o f myocardia l pai n an d death s du e t o cardiovascula r disease) ; huma n fetuse s (healt hrisk s ar e feta l death , malformation , an d retardation) ; chroni c obstructiv e pul

monar ydiseas e patient s (healt hrisk s ar e increase d morbidit yan d de

crease d lif e expectancy) ; moto r vehicl e driver s (healt hris k i s increase d fata l accidents) • I n the initia l applicatio n o f the assessmen t cycle , i t wa s als o realize d tha t th e constructio n o f forma l model s o f healt hdos e respons e base d o n physiologywa s preferabl e t o direc t probabilisti c encoding o f dose-respons e curve s fro mexperts . Th e model s develope d ar e verysimple , preliminaryrepresentation s o f existing medica l knowledge ; nevertheless , the healt hmodel s hav e al l receive d some measur e o f verificatio n (an d interest ) fro

mthe healt hexpert s consulted . Fo r the arterioscleroti c hear t diseas e mode l an d the feta l effect s model , the physiologica l basi s o f the mode l i s the compariso n o f oxyge n need s (o f the hear t o r fetus ) an d oxyge n availabilit y(whic hi s lowere d b yCO exposure) . Th e model s tak e int o accoun t tha t bot ho f thes e factor s varyove r time i n a n unpredictabl e manner . Fo r chroni c obstructiv e pulmonarydisease , a differen t mode l logi c wa s used . Patient s suffering fro mthi s diseas e ca n suffe r acut e respira

tor yfailure ; on e o f the contributin g factor s t o thi s failur e i s reduce d bloo d oxygen , an

d C O exposur e reduce s bloo d oxygen . Result s o f acut e respiratoryfailur e ca n b e hospitalizatio n an d eve n death . Car e wa s take n i n definin g the structur e o f thi s mode l t o avoi d overestimatin g the effect s o f prolonge d period s o f hig hC O levels . Fo r moto r vehicl e accidents , stil l anothe r typ e o f mode l wa s used . Ther e i s littl e direc t evidenc e fo r the risk s o f CO exposur e a t ambien t concentration s i n moto r vehicl e driving . Th e mode l therefor e use s alco

ho l [fo r whic hthe risk s ar e wel l known (Figur e 8.7) ] a s a "referenc e substance. " I n addition , some car e wa s take n t o mode l the fac t tha t muc h drivin g a t highambien t C O concentration s take s plac e i n congeste d traf

fic , wher e the ris k o f fata l accident s i s reduce d (Figure s 8. 8 an d 8.10) ; thi s effec t ha s bee n see n i n a n epidemiologica l stud ydescribe d i n Chapte r VIII . Basi c inpu t t o al l o f the healt hdose-respons e model s wa s obtaine d fro mphysiologica l data , knowledg e concerning physiologica l processes , encode d distribution s representing scientifi c judgmen t o f uncertainties , an d availabl e demographica l an d epidemiologica l data . Significanc e Weightin g Model—Tw o calculation s wer e performe d b y thi s model . First , nationa l healt hrisk s wer e extrapolate d fro mthe analysi s o f four citie s studied . Ametho d fo r extrapolatio n using avail

abl e dat a wa s outline d base d o n genera l relationship s o f healt hrisk s t o the outpu t o f the exposur e mode l (Figure s 9.1-6) . Unfortunately , the dat a necessaryt o performthe extrapolatio n wer e no t readil yavailabl e a t the time thi s wor k wa s performed . Amuc hsimple r extrapolatio n wa s therefor e use d fo r illustratio n (Tabl e 9.1) . 

### 1 1P.2 2

Th e secon

d calculatio n i n the significanc e weightin g mode l wa s t o combin e the variou s healt heffect s b yusin g a n overal l measur e o f healt h impact . Th e measurin g scal e selecte d wa s "equivalen t day s o f restricte d acitivity. " Nomina l weight s an d the range s use d i n sensitivit yanalysi s ar e show

n i n Tabl e 10.1 . Step 2 : Conduc t a Sensitivit yAnalysi s o f th e Healt hEffect s Outcom e Calculato r I t wa s no t possibl e t o performa sensitivit yanalysi s wit hthe EP A compute r implementatio n selecte d t o serv e a s the Emission s an d Atmos
pheri c Dispersio n an d the Exposur e Models , becaus e onl ya limite d amoun t o f dat a wa s availabl e fro mEPA' s run s o f the models . Extensiv e sensitiv

it ytest s o n the othe r model s wer e performed , however , an d critica l vari

able s wer e identified . Step 3 . Encodin g Uncertaintie s I n th e preliminaryapplicatio n o f the assessmen t cycl e reporte d here , i t wa s discovere d tha t the critica l uncertaintie s identifie d throughsensitivit yanalysi s di d no t alway s fal l withi n the real mo f expertis e o f the healt hexpert s wh o ha d previousl ybee n selecte d t o pro vid e inpu t t o the analysis . Fo r example , critica l variable s concerne d the variatio n o f physiologica l parameter s i n dail ylife . Selecte d healt h experts , however , wer e ofte n known primaril yfo r researc hconducte d unde r clinica l o r laboratoryconditions . Amixtur e o f forma l encoding , infor

ma l encoding , physiologica l reasoning an d calibratio n o f the model s t o existing epidemiologica l dat a wa s use d t o arriv e a t simpl e preliminary probabilit ydistribution s fo r critica l uncertaintie s (Figure s 5.5 , 6.3 , 7.5 , 8.11) . Subsequen t iteration s o f the cycl e woul d develo p mor e re fine d model s an

d identif ythe expert s wh o coul d bes t provid e inpu t t o thes e models .
Step 4 : Develo p a Probabilit yDistributio n o n the Healt hImpac t Measur e Distribution s wer e develope d t o describ e uncertaintie s i n eac ho f the healt heffect s area s an d fo r eac hcit yanalyze d (e.g. , Figure s 5.7- 8 an d Table s 5.3-5) . Distribution s wer e the n extrapolate d t o estimat e the uncertaint yo n the magnitud e o f eac hhealt heffec t fo r the natio n a s a whol e (Figure s 9.6-1 1 an d Table s 9.2-3 ) an d fo r the nationa l healt him

pac t measur e (Figur e 10. 2 an d Table s 10.2-3) . Asynopsi s o f the conclusion s arrive d a t i n thi s repor t ar e pre sente d below ; the derivatio n o f thes e conclusion s ar e presente d i n subse

quen t chapters . I t shoul d b e emphasized , however , tha t thes e conclusion s ar e base d o n exposur e dat a provide d b yEPA . EP Aha s sinc e discovere d a significan t erro r i n th e dat a bas e use d i n calculatin g th e exposur e data ; thi s erro r ha s the effec t o f makin g the esimate s reporte d belo wto o large .


### 1 2P.2 3

Observing the magnitud e an d relativ e contribution s t o the measure d healt hris k unde r alternativ e standard s provide s importan t insights . Th e mos t importan t grou p sensitiv e t o change s i n C O standard s appear s t o b e person s wh o suffe r fro marterioscleroti c hear t disease . Thi s larg e grou p suffer s fro

mrestricte d oxyge n suppl yt o the heart , an d C O exposur e fur

the r reduce s thi s supply . Th e nationa l healt hris k incurre d i n going fro mthe strictes t propose d standar d ( 9 ppmCO , 8 hou r averaging , 1 ex pecte d exceedanc e pe r year ) t o the mos t lenien t propose d standar d (3 5 ppm , 1 hour , 1 expecte d exceedance ) i s estimate d t o b e 15,00 0 death s pe r year ; thi s i s abou t a 5 % increas e i n th e 330,00 0 death s pe r yea r fro m thi s diseas e i n urba n areas . Th e increas e i n angin a attack s pe r yea r i n thi s grou

p fo r the same chang e o f standar d i s estimate d t o b e 4% . Th e mos t sensitiv e grou p i n term s o f ris k pe r sensitiv e individua l appear s t o b e tha t o f patient s wit hadvance d chroni c obstructiv e pulmo

narydiseases . Fo r thi s group , the decreas e i n lif e expectanc yfro m lowes t t o highes t standar d i s abou t 7% . Althoughthe siz e o f thi s sensi

tiv e populatio n i s uncertain , i t i s fa r smalle r than tha t o f hear t dis

eas e patients . Roughl y50,00 0 peopl e i n the urba n Unite d State s ar e i n thi s group , a s compare d wit habou t 4 millio n urba n victim s o f arterio

scleroti c hear t disease . Anothe r significan t healt hris k exposur e occur s t o fetuses . Th e sensitiv e populatio n i s s o larg e tha t feta l effect s ma youtweig hlung diseas e effect s i n nationa l significance . Eac hyea r abou t 2 millio n pregnancie s advanc e beyond the secon d trimeste r i n the urba n population . Abou t 20,00 0 o f thes e fetuse s wil l di e befor e birth . Anothe r 100,00 0 wil l probabl yb e bor n wit hsom e physica l o r menta l defect . Change s i n C O wil l chang e ischemi a during gestation , whic hma yb e responsibl e fo r some portio n o f thes e defects . A n estimate d 13 0 fetuse s annuall yma ysurviv e unde r the strictes t standard , wh o woul d no t surviv e unde r the mos t leni

nen t standard . Nonfata l outcome s wer e no t modelled , bu t roughestimate s indicat e tha t the differenc e i n C O betwee n thes e tw o standard s ma yaffec t thousand s o f childre n bor n eac hyear . Step 5 : Identif yUncertaintie s wit hHig hValue s o f Resolutio n Uncertaintie s o n thre e parameter s o f the arterioscleroti c hear t dis

eas e mode l wer e identifie d a s having hig hvalue s o f resolutio n (Figur e 11.1) . Give n the curren t stat e o f knowledge , othe r uncertaintie s wer e relativel yunimportant . I t shoul d b e noted , however , tha t thi s i s predi

cate d o n verysimple , preliminaryversion s o f the healt heffect s models . Step 6 ; Gathe r Futhe r Informatio n an d Repea t Cycl e a s Necessar y Th e nex t iteratio n o f the cycl e shoul d concentrat e o n obtaining im

prove d Healt hRis k Models . I n particular , a n arterioscleroti c hear t dis

eas e mode l woul d requir e mor e differentiatio n betwee n patient s than i t ha s i n thi s work ; particula r emphasi s shoul d b e place d o n cardiovascula r mortalit yi n thi s model . I n addition , an elaboratio n o f the feta l ef

fect s mode l t o adequatel yinclud e malformatio n an d retardatio n (Tabl e 6.5 ) woul d b e important . 

### 1 3P.2 4

Methodologica l Conclusion s Th e primarypurpos e o f thi s projec t wa s t o furthe r develo p methodol

ogyfo r estimating the healt himpac t o f establishin g alternativ e ambien t ai r qualit ystandards . Anumbe r o f genera l methodologica l conclusion s ca n b e drawn fro

mthe wor k performe d i n the illustrativ e applicatio n t o carbo n monoxide . Thes e conclusion s fal l unde r four genera l headings : mode l building , probabilit yencoding , mode l output , an d overal l charac

teristic s o f the process . Mode l Buildin g Askin g healt hexpert s t o estimat e directl ya dose-respons e curv e re quire s a leap fromexposur e dat a t o healt hrisk s tha t mayplac e an unrea sonabl e demand o n thei r informatio n processin g capability . Estimatin g the numbe r o f healt heffect s t o b e expecte d unde r specifi c exposur e con dition s i s difficul t becaus e i t require s th e exper t simultaneousl yt o conside r a larg e numbe r o f uncertai n an d poorl yunderstoo d cause-effec t relationships . Decomposin g th e relationshi p int o it s logica l component s an d workin g wit hexpert s t o obtai n the bes t informatio n concernin g eac h o f thos e component s wa s foun d t o b e mor e effectiv e than asking expert s t o estimat e dose-respons e curve s directly . Expert s seeme d mor e satisfie d wit hthe qualit yo f the dat a theywer e supplying whe n the problemwa s de

composed . I n addition , i t wa s foun d i n severa l case s tha t some o f the consideration s necessaryfo r estimating healt heffect s wer e i n area s tha t wer e outsid e a give n expert' s are a o f expertise . Fo r example , an exper t o n cardiovascula r effect s o f COHbma yhav e difficult yi n estimatin g the frequenc ywit hwhic hphysiologica l parameter s reac hcertai n level s i n th e contex t o f dail ylife . Decomposin g the probleman d mor e accuratel yse lecting expert s t o mee t th e need s o f the assessmen t reduce s the likeli

hoo d tha t a n exper t wil l b e aske* d t o provid e informatio n tha t i s beyon d the bound s o f hi s o r he r expertise . Finally , direc t assessmen t o f healt h effect s ha s the disadvantag e tha t i t mayappea r t o the exper t tha t makin g suc ha n assessmen t i s tantamoun t t o recommending a standard . Fo r some experts , thi s i s a responsibilit ytheywoul d wis ht o avoid . T o reduc e the difficult yo f encoding healt hdose-respons e knowledge , model s wer e buil t t o bridg e the gap betwee n exposur e dat a an d healt him

pact . Although , i n accor d wit hthe purpos e an d resource s o f thi s study , the model s develope d ar e onl yprototypes—th e result s verif ytha t quanti

tativ e model s ca n b e buil t t o serv e thi s function . Constructin g healt hmodel s require s a strong grounding o f basi c physiology . I t i s recommende d tha t futur e use s o f thi s methodologyhav e a medica l exper t o n th e tea mt o facilitat e the incorporatio n o f thi s physiologica l understandin g int o the models . I t i s no t necessarytha t the medica l membe r o f the tea mb e a n exper t i n the specifi c healt hef

fect , the pollutant , o r i n modellin g techniques . Hi s o r he r rol e woul d b e t o provid e informatio n necessaryt o creat e simpl e model s o f the phys

ica l processe s involved . Thes e model s mus t ultimately , o f course , b e criticize d b

ythe expert s i n the healt heffect , pollutants , an d modelin g techniques .


### 1 4P.2 5

I n the developmen t o f the models , the variatio n o f huma n biologica l system s create s healt hrespons e uncertainty . Th e bod yi s no t a mechani

ca l systemwit ha give n vulnerabilit yt o a pollutant ; rathe r i t varie s rapidl yan

d randoml ywit htime i n it s susceptibilit yt o advers e healt h* effects . Becaus e pollutan t level s als o var yrapidl yan d randoml ywit h time , man

yo f the deterministi c model s hav e an importan t stochasti c ele

men t t o them . Fo r thi s reason , i t i s importan t tha t the ris k assessmen t tea mhav e a s a membe r a n analys t wh o i s capabl e o f dealing wit hprobabil

isti c analysis . Becaus e muc ho f the inpu t a t thi s leve l ma yb e judgmen

tal , the analys t shoul d b e wel l verse d i n interviewing , probabilit yen coding , an

d i n Bayesia n techniques . Existing healt hmodel s (othe r tha n physiologica l models ) wer e foun d (fo r CO , a t least ) t o b e insufficien t fo r the tas k a t hand . Epidemiolog

ica l studie s could , i n principle , serv e a s the basi s fo r a healt hris k model . Amode l base d o n epidemiologywoul d involv e the uncertaint yan d controvers yo f extrapolation s an d the questio n o f thresholds ; ther e i s als o the problemo f adequatel yseparating ou t the effec t o f environmenta l cofactors . Epidemiologica l studie s can , o f course , serv e a s importan t calibratin g point s fo r model s base d o n understandin g physica l processes . Model s base d o n anima l studie s wer e als o foun d t o b e insufficien t fo r the stud yo f C O healt heffects . Anima l dat a ca n b e precis e an d wel l con trolled . Fo r the C O effect s studied , however , huma n an d anima l physiol

og ywer e sufficientl ydifferen t tha t quantitativ e estimate s o f huma n healt heffect s had verylarg e (an d controversial ) uncertainties . Eac hhealt heffec t modele d i n thi s projec t wa s sufficientl ydiffer

en t tha t i t woul d appea r impossibl e t o creat e a genera l mode l applicabl e t o al l healt heffects . Th e creatio n o f specifi c model s fo r eac hhealt h effec t ca n maintai n the qualit yo f simplicit yi n mode l structure . Thi s i s importan t i n communication , criticis man d review . I t ma yb e desirabl e t o incorporat e specia l consideration s int o eac hmode l (suc ha s driving time a t ris k i n Chapte r VIII ) eve n i f thi s consideratio n i s no t importan t i n the ris k assessment . Th e idea l her e i s t o balanc e the complexit yo f the mode l agains t the nee d o f the mode l t o readil yanswe r al l question s an d objection s addresse d during the cours e o f the ris k assessmen t pro cess .Th e us e o f model s tha t hav e alread ybee n develope d fo r othe r pur pose s i s no t withou t problems . I n thi s study , the EP Aexposur e mode l wa s used . Althoughthe us e o f thi s existing mode l considerabl yreduce d mode l constructio n time an d expense , it s structur e wa s no t ideall ysuite d t o the analysis . Th e leve l o f detai l i n the mode l an d it s outpu t wa s far greate r than tha t require d fo r the analysis . On e fina l overal l commen t o n the healt heffect s model s i s important . A t the beginnin g o f interview s wit hman yexperts , a certai n reluctanc e an d hostilit ywa s evident . Whe n the healt heffect s model s wer e ex plained , the atmospher e rapidl ybecam e cordia l an d ofte n enthusiastic . Th e expert s evidentl yfel t tha t the models , despit e thei r exceedingl y simpl e an

d preliminaryform , represente d a logica l proces s tha t they though t reasonable . Th e model s no t onl yremov e the nee d fo r the mt o per for mmenta l gymnastic s t o arriv e a t a conclusion , bu t als o allo wthe mt o us e thei r expertis e i n area s wit hwhic htheywer e mos t familiar , an d 

### 1 5P.2 6

indicat e area s wher e othe r expert s coul d provid e bette r information . Uniformly , th e reactio n a t th e conclusio n o f th e intervie wwa s on e o f en couragement , an

d a feeling tha t thi s modelin g approach , i s an advancemen t ove r previou s attempt s a t healt heffect s quantification . Probabilit yEncodin g Th e majo r emphasi s o f thi s stud ywa s o n mode l building rathe r than direc t probabilit yencoding . I t wa s discovered , however , tha t muc ho f the uncertaint yi n the assessmen t o f healt hris k was , rathe r surpris

ingly , i n area s outsid e the experts ' specifi c real mo f expertise . Fo r instance , arterioscleroti c hear t diseas e effect s depend criticall yo n bloo d pressur e an

d hear t rate s experience d i n dail ylife ; expert s o n C O ten d t o b e familia r onl ywit hthe impac t o f thes e factor s i n controlle d environments . I n on e instance , whil e encoding the distributio n o n a n inpu t pa rameter , the exper t remarke d tha t h e had neve r realize d ho wcritica l thi s uncertai n variabl e wa s t o the C O question . Th e existenc e o f the mode l an d a simpl e encoding had suggeste d an importan t bu t neglecte d are a o f research . Car e shoul d b e take n i n encoding distribution s o n physiologica l pa rameter s i n dail ylife . Mos t experts ' experienc e wit hthes e parameter s i s unde r controlle d conditions , an d i t mayb e difficul t t o obtai n distri

bution s tha t reflec t peak value s experience d fo r shor t period s o f time becaus e o f some transien t emotiona l o r physica l effect . Mode l Outpu t Th e model s prove d flexibl e an d abl e t o answe r a larg e numbe r o f questions . I t wa s found , however , tha t differen t healt hrisk s len t them

selve s t o differen t type s o f output . Fo r mos t healt hrisks , fo r example , the numbe r o f instance s o f the healt heffec t pe r yea r wa s relevant . Fo r chroni c obstructiv e pulmonarydiseas e thi s measur e wa s no t relevant , bu t lif e expectanc ywa s (se e Chapte r VII) . I f a n overal l measur e o f healt h impac t (suc ha s EDRA ) i s no t used , meaningfu l measure s mus t b e develope d fo r eac hhealt hrisk . I n general , th e concept s o f "mos t sensitiv e groups " an d "margi n o f safety " mus t b e treate d wit hsome care . Th e huma n organis mi s no t a me chanica l systemwit ha fixe d susceptibilit yt o harma t some exposur e level ; i t i s a comple xan d unpredictabl e biologica l systemtha t varie s ove r time . Hence , al l member s o f a sensitiv e grou p may , a t some time s an d unde r some circumstances , suffe r advers e healt heffects . Similarly , ther e seemt o b e n o threshold s fo r man yhealt heffects , a t leas t i n the cas e o f the model s develope d fo r CO ; an yambien t leve l ha s the potentia l fo r provoking some advers e healt heffects .



### 1 6P.2 7

Overal l Characteristic s o f the Program Th e overal l healt hris k assessmen t process , a s see n i n thi s illus

trativ e example , doe s no t seemt o b e exorbitan t i n term s o f time o r ex pense . Muc ho f the effor t wa s exploratoryi n nature ; i t wa s difficul t t o determin e the typ e an d genera l structur e o f model s usefu l fo r the assess ment . I n a n iteratio n o f the assessmen t cycle , refinemen t an d elabora

tio n o f thes e model s (o r the developmen t o f ne wmodels ) woul d probabl y tak e significantl yles s time . Th e developmen t o f model s fo r othe r pollu

tant s woul d likel yb e considerabl ysimpler , give n the experienc e gaine d fro mthi s assessmen t o f alternativ e C O standards . I n term s o f actua l computing costs , the healt hris k model s ar e sim

pl e an d inexpensiv e t o run . I t i s har d t o se e ho wthes e models , eve n whe n refine d an

d elaborated , woul d entai l significan t expense . Th e ex isting EP AEmissions , Atmospheri c Dispersion , an d Exposur e Models , o n the othe r hand , coul d probabl yb e simplifie d withou t compromisin g the accu rac yo f the assessment . Th e difficult yo f th e probabilit yencoding proces s ha s bee n consid

erabl yreduce d b

ythe realizatio n that , i n part , the reluctanc e o f ex pert s t o provid e quantitativ e assessment s o f uncertaint yi s becaus e o f thei r perceptio n tha t some o f the critica l element s i n the assessmen t ar e no t i n thei r are a o f expertise . Probabilitie s ca n b e encode d fo r eac h elemen t o f the proces s b ypeopl e bes t abl e t o judg e tha t particula r vari

able . Th e proces s lend s itsel f t o decisio n makin g i n the publi c arena . Th e model s ar e explici t an d ope n t o criticism . Th e inpu t i s wel l de

fined , an

d difference s o f opinio n ca n b e tested . Publi c testimon ycan b e wel l focuse d an

d documented . Th e model s ar e prepare d t o respon d t o a wid e variet yo f question s an d objections . Th e implication s o f conflict

in g opinion s o n the outcom e ca n b e assesse d quickl yan d inexpensively . Thi s demonstratio n ha s provide d severa l provocativ e insight s an d lesson s fo r futur e work . Th e methodologyappear s t o b e well-founde d an d workabl e despit e the complexit yo f the standards-settin g problem . Fur
thermore , the model s develope d appea r t o hav e valu e a s a basi s upo n whic h a ful l quantificatio n o f the healt hrisk s o f CO ca n b e performed .



### 1 7P.2 8

REFERENCE S FOR CHAPTE R I
1 . Mile yW . Merkhofer , " AProcedur e fo r Assessin g Healt hRisk s Associ

ate d wit hAlternativ e Ambien t Ai r Qualit yStandards, " i n Conceptua l Approache s fo r Alternativ e NAAQS , U.S . E.P.A. , 1981 , pp . 4- 1 t o 4-42 . 2 . C-A . S . Stae l vo n Holstei n an d J . E . Matheson , " AManua l fo r Encod

in g Probabilit yDistributions, " SR I Fina l Report , Projec t 7078 , SR I International , Menl o Park , California , Septembe r 1978 .



### 1 9P.2 9

PART I I EMISSIONS , ATMOSPHERI C DISPERSION , AN D EXPOSUR E MODEL S
Th e model s use d t o estimat e concentration s o f carbo n monoxid e an d the exposur e o f the populatio n t o variou s level s o f concentratio n wer e no t develope d a s par t o f the wor k describe d i n thi s report . Amode l had bee n previousl ydevelope d b yEPA , an

d thi s mode l ha s bee n use d t o provid e dat a fo r Par t II I o f the report . Afina l implementatio n o f the ris k as sessmen t procedur e would , o f course , joi n al l part s o f the mode l int o a singl e piec e o f compute r code . I n thi s par t o f the report , a genera l methodologyi s described , an d the EP Amode l i s analyze d unde r thi s methodology . Unfortunately , becaus e o f the difficult yan d expens e o f dat a transmittal , th e EP Amode l wa s no t availabl e fo r sensitivit yanalysi s o r probabilisti c treatment ; fo r the same reason , estimate s fo r onl y2 month s o f the yea r (Januaryan d July ) wer e use d i n the analysi s describe d i n subsequen t part s o f thi s report . EP Aha s sinc e discovere d a n erro r i n the dat a bas e use d i n the model ; thi s erro r ha s the effec t o f makin g the estimate s derive d i n thi s repor t to o large .



### 2 1P.3 0

I I EMISSION S AN

D ATMOSPHERI C DISPERSION
Th e firs t step i n estimating the healt heffect s o f a n ai r qualit y standar d i s t o estimat e the effec t o f the standard o n the concentration s o f the pollutan t tha t ar e encountered . T o accomplis hthis , fou r differ

en t question s mus t b e answered :
o Ho wmuc ho f the pollutan t i s produced ? o Ho wmuc ho f thi s pollutan t can b e controlle d o r eliminate d befor e i t enter s the ambien t atmosphere ? o Onc e the pollutan t enter s the atmosphere , ho wdoe s i t spread geo graphically ? o Ho wwil l regulatio n an d enforcemen t policie s chang e the situa

tion ? Th e Emission s an

d Atmospheri c Dispersio n Mode l addresse s thes e question s i n a quantitativ e fashion . Th e discussio n belo wdescribe s the methodologytha t i s appropriat e t o ai r qualit ystandard s fo r carbo n monoxide . Th e discussio n als o de

scribe s the compute r implementatio n tha t wa s develope d b ythe Environmen

ta l Protectio n Agenc y(EPA ) fo r the Strategie s an d Ai r Standard s Divisio n (SASD) . Result s obtaine d fro mthi s mode l wer e provide d b yEP At o fur

nis hthe basi s fo r late r section s o f thi s report . Emissio n an

d Atmospheri c Dispersio n Submode l Th e Emission s an

d Atmospheri c Dispersio n Mode l consist s o f the fou r submodel s show

n i n Figur e 2.1 : Productio n Proces s Submodel ; Emissio n Containmen t an

d Contro l Submodel ; Atmospheri c Dispersio n Submodel ; Stat e an d Loca l Governmen t Regulatio n an d Enforcemen t Submodel . Eac ho f thes e submodel s i s discusse d below . Productio n Proces s Submode l Th e productio n proces s fo r C O ca n b e modele d rathe r simpl yfo r the following reason : i t i s estimate d tha t almos t al l o f ambien t carbo n mon oxid e i s cause d b

ymobil e sources . Nationwid e dat a fo r the Unite d State s indicat e tha t 75 % o f man-mad e C O emission s i s becaus e o f highway vehicles , 9 % becaus e o f nonhighwayvehicles , 9 % becaus e o f stationary fue l combustion , industria l processe s an d soli d waste , an

d 5 % becaus e o f othe r sources . Naturall yoccurring C O emission s ar e negligibl e i n com pariso n t o man-mad e sources , especiall yi n urba n areas . Becaus e the pre ponderanc e o f CO emission s i s cause d b yhighwayvehicles , mos t attentio n ha s bee n pai d t o reducing C O emission s fro mthi s source . 2 3

### INPUT ASSUMPTION S

r o 

### Spatial/Tempora l Pattern s o f Productio n

Activitie s • WindSpeed s an dDirection s • BackgroundConcentration s • Stat e Implementatio nPlan s # Ai r QualU y Qat a Bas e 

### • Emissio nContro l Technology Effectivenes s # Microenvironmen t 

### characteristic s

DECISION Ambien t Ai r Qualit y Standard s

UP


### STAT E ANDLOCAL GOVERNMEN T REGULATION ANDENFORCEMEN T SUBMODE L Monitorin g an dContro l
PRODUCTION PROCES S SUBMODE L Emission s Monitorin g

EMISSION CONTAINMEN T AN DCONTRO L SUBMODE L Emission s ATMOSPHERI C DISPERSION SUBMODE L Projection s o f Ambien t Concentration s ove r Spac e andTime

= >

FIGUR E 2. 1 EMISSION S AN D ATMOSPHERI C DISPERSIONMODE L

t oP.3 2

I n the EP Aimplementation , the mode l separate s C O concentration s int o tw o parts : emission s cause d b yloca l man-mad e source s an d "back

ground " concentration s cause d b yal l othe r nonloca l sources . Th e back

ground concentration s ar e estimate d fo r eac hurba n are a fro mrura l moni

tor s locate d upwind o f the urba n area . I n the presen t versio n o f thi s model , background concentration s d o no t varywithi n the variou s urba n area s an

d d o no t varywit htime . Nonbackground concentration s ar e ob

taine d b

ysubtracting th e estimate d background concentration s fro mactua l monitore d dat a o n C O concentrations . Bot hthes e component s o f C O concen trations , therefore , ar e give n fo r presen t level s o f C O emissio n con trols . Th e C O level s ar e disaggregate d spatiall yi n the following de

scribe d manner . Concentration s ar e obtaine d fro mmonitore d concentra
tion s fo r si xtype s o f neighborhoods : residential , commercial , an d industria l fo r bot hthe urba n an d the suburba n portion s o f eac hcit y analyzed . Th e C O dat a ar e disaggregate d temporall ya s well : concentra
tion s ar e give n fo r eac hhou r o f the yea r t o accoun t fo r diurna l an d sea sona l variations . Fo r eac hneighborhoo d i n eac hcity , dat a recorde d b y on e monito r during the cours e o f on e yea r hav e bee n used a s the inpu t data . A

n eve n mor e accurat e representatio n o f CO concentration s coul d b e obtaine d b

yusing severa l year s wort ho f observations , bu t thi s woul d rais e computationa l cost s considerably . Th e mode l doe s no t incorporat e the effect s o f long-ter mtrend s i n emission s becaus e o f suc hchange s a s the numbe r o f vehicl e mile s drive n i n eac hlocale . Suc hchange s migh t occu r becaus e o f changing populatio n densitie s o r change s i n vehicl e utilizatio n cause d b ychanging fue l price s o r traffi c congestion . Emissio n Containmen t an d Contro l Submode l Th e purpos e o f thi s mode l i s t o estimat e the effec t o f effort s t o contai n o r contro l emissions . Th e Productio n Proces s Mode l give s C O con centration s wit hthe presen t leve l o f containmen t an d control ; the Emis

sio n Containmen t an d Contro l Submodel , therefore , attempt s t o estimat e the effec t o f changes . Onl ychange s i n nonbackground C O concentration s ar e considered . Th e effect s o f change s i n contro l an d containmen t o n background concentration s ar e neglected ; thes e change s should , o f course , depend o n the sourc e o f the background concentratio n an d the exten t o f contro l used . Ther e ar e tw

o genera l cause s o f chang e i n nonbackground C O con centrations . First , emission s fro mhighwayvehicle s an d othe r simila r source s ca n b e controlle d b ythe us e o f technolog ysuc ha s improving the efficienc yo f the combustio n proces s o r b yapplying secondarycatalyti c combustio n reactor s i n wast e ga s streams . Othe r control s o f thi s typ e ar e als o possible , suc ha s the increase d us e o f alternativ e fuel s fo r vehicles . Second , emission s fro mhighwayvehicle s ca n b e controlle d b y suc hmean s a s changin g traffi c pattern s o r b ycurtailin g traffi c o n day s o n whic hcondition s ar e particularl ybad .



### 2 5P.3 3

I t shoul d b e note d that , eve n withou t change s i n presen t controls , ther e ar e lon

g ter mtrend s tha t shoul d b e reflecte d i n thi s model . Fo r instance , a s olde r car s ar e retire d fro mservice , th e presen t averag e level s o f contro l wil l probabl ygraduall yimprove . Thi s improvement , o f course , ma yb e offse t b ythe long-termtrend s i n vehicl e mile s drive n i n eac hlocal e (containe d i n Productio n Proces s Model) . Th e implementatio n o f thi s mode l b yEP Ai s discusse d below . Atmospheri c Dispersio n Submode l Th e genera l purpos e o f thi s submode l i s t o estimat e C O concentra
tion s throughou t a region , give n emission s fro ma particula r sourc e suc h a s a highway . I n general , thi s typ e o f mode l ca n becom e ver ycompli

cated , involving a n understandin g o f the physica l propertie s o f the pol

lutan t an

d o f the meteorologica l condition s o f the locale . I n th e cas e o f CO , thi s woul d b e especiall ydifficult . Th e EP Amode l fo r C O dispersio n follow s a simpler , alternativ e ap proach . Inpu t C O concentration s ar e take n fro mmonitore d dat a nea r high

way s i n eac ho f the si xneighborhood s fo r eac hcity . C O concentration s withi n variou s microenvironment s i n eac hneighborhoo d ar e the n estimated . Thes e microenvironment s are : indoors , wor k o r school ; indoors , hom e o r other ; insid e a transportatio n vehicle ; outdoor s along the roadside ; othe r outdoo r locations . Th e C O level s i n thes e microenvironment s ar e obtaine d b

ymultiplyin g the monitore d dat a b ya constan t t o reflec t con centratio n o r dilutio n o f the leve l i n the microenvironment . I n addi

tion , a quantit yi s adde d t o thi s t o reflec t C O generate d withi n the mi

croenvironment ; some source s o f CO withi n microenvironment s ar e smoking , ga s stoves , attache d garages , moto r vehicl e exhaus t systems , an d occupa tiona l sources . Th e factor s use d fo r thes e adjustment s hav e bee n esti

mate d b ymeasurin g C O concentration s i n a numbe r o f representativ e mi

croenvironments . Thu s the C O , the C O concentratio n i n microenvironmen t m , i s esti

mate d i n term s o r C O , the C O concentratio n estimate d fo r the centroi d o f c the district , a s

C O = A+ B xC O [2.1 ] mm mc Estimate s fo r the additiv e coefficient s Aan d the multiplicativ e coeffi

cient s B ar e presente d i n Tabl e 2.1 . Th e rang e o n Afo r hom e indoo r en vironment s represent s the possibilit yo f ga s stove s an d smoking i n the environment ; the mode l i s capabl e o f handlin g variou s type s o f home s an d differen t value s o f Afo r eac hhou r o f th e day . Th e coefficien t B fo r transportatio n vehicle s i s assume d t o incorporat e C O generate d withi n the vehicle . I t shoul d b e note d that , fo r the mode l run s use d fo r thi s report , the value s o f Awer e take n t o b e zero . Thi s mean s tha t exposur e esti

mate s d o no t includ e the effect s o f CO cause d b ynonambien t sources . 

### 2 6P.3 4

Tabl e 2. 1 ESTIMATE D COEFFICIENT S FO R MICROENVIRONMENT CONCENTRATION A (ppm ) 0.3-4. 0 3. 1 0 0 0 B

1. 0 1. 0 2 . 1 1. 2 1. 0 Indoor s (home ) Indoor s (other ) Transportatio n vehicl e Outdoor s (along roadway ) Outdoo r (other )
Finally , becaus e indoo r C O concentration s lag outdoo r concentration s an d becaus e C O concentration s decayslowl yi n enclose d spaces , i t mayb e appropriat e t o us e a lagged-tim e mode l fo r indoo r microenvironments ; thi s typ e o f mode l i s no t a t presen t incorporate d int o the EP Amodel . Stat e an

d Loca l Governmen t Regulatio n an d Enforcemen t Submode l Th e nationa l ambien t ai r qualit ystandar d (NAAQS ) doe s no t impl yan y particula r mechanis mfo r implementation . Eac hstat e propose s a Stat e Im
plementatio n Pla n (SIP ) which , i f approve d b yEPA , wil l provid e the regu latoryan

d enforcemen t mechanis muse d t o attemp t t o mee t the goal . Th e SIP s coul d influenc e C O concentration s throughth e Productio n Proces s Model . Suc hmeasure s a s encouraging publi c transportatio n an d car-poolin g ca n reduc e tota l emissions . I t i s thought , however , tha t complianc e wit hthe NAAQ S woul d b e attempte d thoughmeasure s tha t affec t the Emissio n Containmen t an d Contro l Model . Thes e measure s fal l int o two broa d categories . First , ther e ar e measure s tha t redirec t the flo wan d concentratio n o f traffic , including the possibilit yo f restricting traffi c o n day s whe n C O concentration s becom e particularl yhigh . Thu s condition s coul d b e ameliorate d a t thos e time s an d i n thos e location s a t whic hthe NAAQ S i s likel yt o b e violated . I t i s quit e possible , however , tha t thes e mea sure s woul d no t substantiall yreduc e averag e C O concentration s an d migh t eve n permi t highe r averag e C O concentrations . Asecon d typ e o f measur e woul d attemp t t o reduc e C O emission s pe r vehicl e mil e driven . Thes e measure s migh t includ e a specificatio n o f the exhaus t characteristic s require d o f ne wand/o r use d moto r vehicle s regis

tere d i n the state . The ymigh t als o includ e a mandator yperiodica l in

spectio n an

d maintenanc e progra mfo r moto r vehicles . Thi s typ e o f mea sur e woul d reduc e C O emission s o n al l days , reducing bot hthe pea k C O emission s an

d the averag e C O emissions .


### 2 7P.3 5

SIP s coul d varyfro mstat e t o state , o r locatio n t o location . Th e measure s take n wil l b e influence d b ymanyfactor s beside s the nee d fo r complianc e wit hthe C O NAAQS . Mos t o f thes e measure s wil l affec t the emission s o f othe r pollutant s an d complianc e wit hothe r standards . Mea
sure s coul d b e loca l o r statewide , dependin g o n the natur e o f the prob

lem s facing the state . Th e for mo f the implementatio n pla n i n some state s ma yals o b e affecte d b yprojecte d long-termtrend s i n populatio n density , vehicl e utilization , an d improvemen t i n emission s control . Severa l othe r question s mus t b e addresse d i n the model : Ar e the measure s effective ? D o theyameliorat e condition s onl yo n the wors t day s o r i n the wors t location , o r d o theyreduc e emission s uniformly ? Ar e the measure s applie d onl yt o certai n cities/localitie s o r ar e theystatewide ? Th e EP Aimplementatio n o f thi s mode l i s discusse d below . Propose d Ambien t Ai r Qualit yStandard s Analyze d i n thi s Repor t Th e propose d ambien t ai r qualit ystandard s fo r C O whic har e analyze d i n thi s repor t ar e statistica l i n nature : theyspecif yan averagin g time , a concentratio n level , an d the expecte d numbe r o f instance s i n whic hconcentration s ar e permitte d t o excee d the leve l ove r a give n perio d o f time . Th e seve n propose d standard s ar e presente d i n Tabl e 2.2 .

Tabl e 2. 2 STANDARD S ANALYZE D I NTHI S REPOR T

(Dail yMaximum )

Standar di-i 

### 2 3 4 5 6 7 C

O Concentratio n (ppm ) 

### 2 5 3 5 

### 9 1 2 

### 9 1 2 2 5 Averagin g

Tim e (hour ) 1 

### 1 8 8 8 8 1 Allowe d Numbe r o f Expecte d Exceedance s pe r Yea r 

### 1 1 1 1 5 5 5

I t shoul d b e note d tha t th e presen t standar d roughl ycorrespond s t o bot h 2 an d 3 above ; the strictes t par t o f the standar d i s 3 , i n tha t i t i s 

### 2 8P.3 6

mos t ofte n exceede d i n actuality . I t shoul d als o be note d tha t thes e standards , thoughverysimila r t o the one s presentl yi n force , ar e ex ceede d i n a larg e numbe r o f cities .
EP AMode l o f Implementatio n o f Standard s Fo r the purpose s o f thi s study , a simpl e situatio n i s addressed . Thi s situatio n assume s tha t measure s ar e take n tha t uniforml ychang e emission s (measure s suc ha s mandator yinspectio n an d maintenanc e o f ex haus t systems ) an

d tha t eac hcit yexactl ycomplie s wit hthe standar d (whic hma ymea n an improvemen t fro mpresen t condition s fo r some citie s an d a deterioratio n fo r othe r cities ) . Th e underlyin g structur e o f the Productio n Proces s Submode l i s ac tua l C O concentration s recorde d during 1 yea r fo r eac ho f si xneighbor

hoods . I n orde r t o asses s accuratel ythe effec t o n C O concentration s o f standard s whic hinvolv e onl ya fewexceedance s a year , analytica l distri

bution s (th e lognorma l o r the Weibul l distribution ) wer e fitte d t o the dat a t o mak e the predictio n o f thes e rar e event s computationall ytracta
ble . Amultiplicativ e facto r wa s the n determine d tha t reduce d (o r in

creased ) C O level s abov e background unti l the dat a jus t me t the standar d i n term s o f exceedances . Thi s procedur e wa s applie d t o the dat a fo r the neighborhoo d wit hthe highes t C O concentration s i n eac hcity ; the multi

plicativ e facto r (whic hi s calle d the "rollbac k factor" ) wa s the n applie d t o the dat a fo r al l the neighborhood s o f tha t city , reflecting the uni

for mreductio n i n C O emission s i n tha t city . Th e rollbac k factor s fo r the seve n propose d standard s (Tabl e 2.2 ) ar e presente d i n Tabl e 2. 3 fo r the four citie s analyzed .

Tabl e 2. 3 ROLLBAC K FACTOR S FOR FOU R CITIE S
Standar d Chicago , I L Lo s Angeles , CAPhiladelphia , PASt . Louis , M O 1.1 1 1.5 9 0.5 4 0.7 8 0.7 8 1.1 3 1.7 4 1 2 3 4 5 6 7 1.0 0

1.4 3 0.5 4 0.7 5 0.6 6 0.9 2 1.2 2 0.7 8

1.1 2 0.3 9 0.5 5 0.5 1 0.7 1 1.0 7 1.3 2

1.8 7 0.6 0 0.8 3 0.9 0 1.2 3 1.8 7

Th e background concentration s (t o whic hthes e rollbac k factor s d o no t apply ) ar e presente d i n Tabl e 2.4 . 

### 2 9P.3 7

Tabl e 2. 4 ESTIMATE D BACKGROUND CONCENTRATION S


### C O Concentratio n (ppm ) Chicago , I L 1.3 1 Lo s Angeles , CA1.7 5 Philadelphia , PA0.9 6 St . Louis , M O 2.2 7

Summary Th e Emission s an

d Atmospheri c Dispersio n Mode l provide s temporall y an d spatiall ydisaggregate d estimate s o f ambien t C O concentration s unde r variou s propose d standards . I n the EP Aimplementatio n o f thi s model , thes e dat a ar e divide d i n the following ways :
o Temporal : hourl yconcentration s throughou t the year . o Spatial : four citie s (Chicago , IL ; Lo s Angeles , CA ; Philadelphia , PA ; St . Louis , MO) . -

- Urba n an

d suburban .

- Residential , commercial , an d industria l neighborhoods .

- Indoor s (home) , indoor s (other) , transportatio n vehicle , out
door s (along roadway) , outdoor s (other) . Th e actua l dat a supplie d b yEP Afo r us e i n thi s repor t wer e slightl ysim

plifie d becaus e the primarypurpos e o f the repor t wa s t o develo p method

ology . Thu s the effec t o f C O generate d withi n a microenvironmen t (suc h a s smoker s an

d ga s stoves ) wa s omitted .



### 3 0P.3 8

REFERENCE S FOR CHAPTE R I I
1 . T . Johnso n an

d R . A . Paul , "Th e Neighborhoo d Versio n o f NAAQ S Expo
sur e Mode l (NEM ) Applie d t o Carbo n Dioxide, " PEDC o Environmental , Inc. , Durham , N

C 27701 . Prepare d fo r U.S . Environmenta l Protectio n Agency , Offic e o f Ai r Qualit yStandard s Division , Strategie s an d Ai r Standard s Division , Researc hTriangl e Park , N C 27711 , Septembe r 1981 . 2 . Federa l HighwayAdministration , "HighwayTrave l Forecasts. " U.S . Departmen t o f Transportation , Washington , D.C , Novembe r 1974 .



### 3 1P.3 9

II I EXPOSUR E MODE L
Th e Emission s an

d Atmospheri c Dispersio n Mode l predict s the C O con centration s tha t wil l b e encountere d a t variou s place s an d times . Th e Exposur e Mode l i s intende d t o answe r the questions :
o Wh o wil l b e presen t a t thes e variou s place s an d time s t o experi

enc e the C O concentrations ? o Wha t typ e o f activitie s wil l theyb e engaging in ? I n the subsequen t sections , the methodologyrequire d fo r an Exposur e Mode l fo r C O i s described , an d th e EP Amode l i s analyze d i n term s o f thi s methdology . Exposur e Mode l fo r C O Th e mode l develope d fo r C O ha s severa l propertie s tha t ar e differen t fro mexposur e model s fo r othe r type s o f standards . First , i n accordanc e wit hlega l requirements , the concer n o f ambien t ai r qualit yprimarystan
dard s i s huma n healt heffects . Henc e onl ythe exposur e o f huma n being s i s modeled ; the exposur e o f animals , vegetatio n an d nonlivin g object s i s neglecte d a s being irrelevan t t o the primarystandards . Second , becaus e C O i s though t t o affec t human s b ycombinin g wit hhemoglobi n i n the bloo d t o for mcarboxyhemoglobi n (COHb) , the outpu t o f the mode l o f interes t t o thi s researc hi s COHblevel s presen t i n the population ; thi s i s i n con tras t t o the outpu t relevan t fo r othe r standards , wher e measure s suc ha s the amoun t o f pollutan t inhale d o r ingeste d migh t b e appropriate . Fi

nally , becaus e COHblevel s buil d u p an d decreas e ove r a perio d o f severa l hours , a detaile d time historyo f C O exposur e mus t b e use d fo r eac hindi

vidual ; thi s i s i n contras t t o othe r pollutant s fo r whic ha cumulativ e exposur e o r pea k exposur e ma yb e sufficient . Th e Exposur e Mode l ha s thre e principa l components : the Populatio n Dynamic s Submodel ; th e Huma n Activitie s Submodel ; an d the CO-COHbConver
sio n Submode l (Figur e 3.1) . Populatio n Dynamic s Submode l Th e natur e o f CO demand s a particularl ydetaile d for mo f modeling . Th e effect s o f C O o n the huma n organis mar e no t instantaneous ; theyde

pen d o n the historyo f exposur e o f the individua l ove r a perio d o f ove r 8 hours . Fo r thi s reason , element s o f the populatio n mus t b e tracke d a s theymov e fro

mplac e t o place , expose d t o C O level s tha t varybot hb y time an d b ylocation . I n addition , differen t element s o f the populatio n hav e differen t senstivitie s t o CO . Fo r thi s reason , variou s group s o f the populatio n mus t b e tracke d separately . 3 3O J 

### Projection s o f Ambien t Concentration s Ove r Spac e an

dTime 

# [ 

# A

y Populatio n

Group s i

POPULATION DYNAMIC S SUBMODE L Spatial/Tempora l Distributio no f Populatio nGroup s

HUMA N ACTIVITIE S SUBMODE L Individua l Activitie s Profil e
CO-COH bCONVERSION

SUBMODE L 

# A) COH bEXPOSUR E PROFILE S BY : STANDAR D COHOR T HOU RO F YEA

R MICROENVIRONMEN T

FIGUR E 3. 1 EXPOSUR E MODE L

T 3P.4 1

I n the EP Aimplementatio n o f thi s model , 1 1 age/occupatio n group s ar e identified ; a n age/occupatio n grou p fo r far

mworker s wa s eliminate d a s being irrelevan t i n urban/suburba n areas . A t the reques t o f SRI , a twelft hgrou

p wa s define d t o describ e peopl e suffering fro msever e chroni c obstructiv e pulmonarydisease . Th e age/occupatio n group s ar e liste d i n Tabl e 3.1 .
Tabl e 3. 1 AGE/OCCUPATIONCATEGORIE S
Student s (ove r 1 7 year s o f age ) Professional s Sale s personne l Clerica l worker s Craftsme n Laborer s Servic e worker s Marrie d wome n No t working Childre n (unde r 5 year s o f age ) Childre n (betwee n 5 an d 1 5 year s o f age ) Specia l (chroni c obstructiv e pulmonarydisease )

Individual s fro

mthes e group s mov e fro mneighborhoo d t o neighbor

hood . Th e si xneighborhood s use d i n the Emission s an d Atmospheri c Dis

persio n Mode l are : residential , commercial , an d industria l neighborhood s fo r bot hurba n an

d suburba n areas . Fo r instance , a professiona l migh t liv e i n a suburba n residentia l are a an d wor k i n an urba n commercia l area . Censu s dat a (an

d origin-destinatio n dat a fro mtransportatio n planning agencie s i n the four cities ) wer e use d t o defin e a numbe r o f meaningfu l combination s o f home/wor k neighborhood s fo r eac ho f the age/occupatio n group s an

d t o determin e the numbe r o f peopl e i n eac hcategoryfo r eac h cit ymodeled .
Huma n Activitie s Submode l Give n the genera l locatio n o f an yindividua l a t a particula r hour , i t i s necessaryt o specif yhi s microenvironmen t an d exercis e level . Th e microenvironmen t h e find s himsel f i n i s importan t i n determinin g the leve l o f C O t o whic hh e i s exposed ; hi s exercis e leve l i s importan t i n determinin g the exten t t o whic hC O affect s him . I n the EP Aimplementatio n o f thi s model , us e i s mad e o f the result s o f a stud

yundertake n fo r EP Ab ySR I International . I n thi s study , es timate s ar e mad e a t eac hhou r o f the da yo f the distributio n the popula

tio n among microenvironment s an d o f the leve l o f exercis e undertake n i n thes e settings . 

### 3 5P.4 2

Using thes e results , thre e representativ e activit ypattern s wer e se t u p fo r eac ho f the age/occupatio n groups , an d the relevan t populatio n wa s divide d betwee n thes e activit ypatterns . Thes e activit ypattern s indi

cat e the microenvironmen t i n whic ha n individua l find s himsel f a t eac h hou r o f the day ; thes e microenvironmen t pattern s ar e differen t fo r week

day s an d Saturday s an

d Sundays . Fo r instance , the microenvironmen t com ponen t o f on e activit ypatter n fo r professional s o n weekday s is : a t home unti l 7 a.m. ; transportatio n vehicl e 7- 8 a.m. ; indoor s a t wor k 8 a.m. - 5 p.m. ; transportatio n vehicl e 5- 7 p.m. ; a t hom e afte r 7 p.m . Th e othe r componen t i n the thre e activit ypattern s i s the leve l o f exercis e the individua l engage s in . Thre e level s o f exercis e ar e de

fined : lo w(lying down , sitting , o r standing) , mediu m(walking o r othe r mil d exercising) , an d hig h(running an d othe r heav

yexercising) . Thu s the professiona l discusse d abov e i s a t a lo wleve l o f exercis e a t al l hour s o f weekdays ; o n weekends , however , ther e ar e hour s durin g whic hh e i s a t a mediu

mo r eve n hig hleve l o f exercise . I n all , the EP Amode l track s 9 8 cohort s (o r group s o f people ) througheac hhou r o f the year . Fo r eac hhou r o f the day , ther e i s a specifie d neighborhood , microenvironment , an d exercis e level ; thi s pat
tern i s differen t o n weekday s an d Saturday s an

d Sundays . Thu s ther e ar e 1 1 age/occupatio n group s eac hwit hthre e activitie s patterns ; the twelft h age/occupatio n grou p (chroni c obstructiv e pulmonarydisease ) ha s onl yon e activit ypattern . Thi s yield s 3 4 groups . Eac ho f thes e group s ca n hav e a home microenvironmen t tha t i s i n eithe r an urba n residentia l o r a sub

urba n residentia l neighborhood ; thi s bring s the tota l t o 6 8 groups . Fi

nally , the importan t combination s o f home neighborhood s an d wor k neigh

borhood s bring s the tota l t o 9 8 cohorts . CO-COHbConversio n Submode l Th e previou s model s enabl e on e t o matc hC O concentration s t o the populatio n fo r eac hhou r o f the year . Fo r the cas e o f CO , i t i s desira

bl e t o carrythe Exposur e Mode l on e step further . C O i s absorbe d b ythe hemoglobi n i n th e bloo d t o produc e carboxyhemoglobi n (COHb) . On e o f th e effect s o f elevate d COHblevel s i s t o reduc e the oxygen-carryin g capacit y o f the blood , a s wil l b e discusse d i n subsequen t chapters . Whethe r thi s i s the principa l effec t o f CO o n the huma n organis mo r not , i t i s gener all yaccepte d tha t COHblevel s ar e a goo d inde xo f ho wseverel ythe huma n organis mi s affecte d b yC O exposure . Th e COHblevel s a t an ymomen t ar e no t simpl ydetermine d b ythe C O concentration s experience d a t tha t moment . COHbbuild s u p i n the bloo d ove r time an

d i s eliminate d ove r time i n respons e t o changing C O concen tration s an

d t o changin g exercis e levels . T o estimat e the COHbleve l a t an ymoment , i t i s necessaryt o integrat e ove r abou t 8 hour s o f pas t his

tor yo f exposur e t o C O an d o f exercis e levels

- Thi s i s the reaso n tha t th e Populatio n Dynamic s Mode l an d th e Huma n Activitie s Mode l ar e s o de

tailed . Amode l ha s bee n develope d an d a t leas t partiall yverifie d tha t de

scribe s the physiologyrelating COHblevel s t o ambien t C O levels . Thi s 

### 3 6P.4 3

2 mode l i s embodie d i n the Cobur n equation . Thi s equatio n predict s th e rat e o f chang e o f COHba s a respons e t o change s i n ambien t C O levels . Th e equatio n take s int o accoun t pas t history(the leve l o f COHbpresen t i n the bloo d .before the chang e i n ambien t C O levels) , endogenou s produc

tio n o f CO , an

d the physiologyo f the individua l (including the leve l o f exercise). , .Thi s mode l ha s bee n clinicall yteste d wit ha smal l numbe r o f subjects . ' Th e smal l numbe r o f subject s o f eac hsexan d the relativel y lo wexercis e ventilatoryrate s i n thes e test s indicat e tha t furthe r veri

ficatio n o f thi s mode l shoul d b e sought . Th e EP Aimplementatio n o f the Exposur e Mode l use s the Cobur n equa tio n t o calculat e the COHblevel s presen t i n individual s o f eac ho f th e 9 8 cohort s fo r eac hhou r o f the year . Thi s i s the fina l step o f the model . Ther e no wexist s a mechanis m fo r estimatin g ho wthe populatio n i s affecte d b yC O exposur e unde r an y Nationa l Ambien t Ai r Qualit yStandar d (NAAQS ) fo r CO . I t i s the purpos e o f thi s repor t t o carrythi s proces s t o it s conclusio n b yanswerin g th e question : Wha t ar e the healt hconsequence s implie d b yeac hpropose d NAAQS ?
Dat a Supplie d Fo r Thi s Stud yb yEP AMode l A s wa s mentione d i n the Introduction , the purpos e o f thi s repor t i s primaril ymethodologica l i n nature . Th e outpu t dat a o f the combine d Emission s an

d Atmospheri c Dispersio n Mode l an d the Exposur e Mode l wer e no t i n the precis e for mneede d fo r the wor k describe d below . I n addi

tion , the rawoutpu t o f thes e model s wa s difficul t an d expensiv e t o transmi t t o SRI . Fo r thes e reasons , i t wa s decide d t o us e a simplifie d se t o f data . Th e inpu t dat a use d fo r the Emission s an d Atmospheri c Mode l wa s describe d i n Chapte r II . I n thi s section , the inpu t dat a use d fo r the Exposur e Mode l i s described . Th e Exposur e Mode l follow s 9 6 cohort s hourl ythroughthe year , spec ifying age/occupatio n group , neighborhoo d o f home an d neighborhoo d o f work , microenvironments , an d activit ylevels . -Fo r eac hcit yanalyzed , the populatio n i s divide d among thes e cohorts . Tw o additiona l cohort s wer e define d fo r the purpose s o f thi s study : urba n an d suburba n individ

ual s wh o suffe r fro msever e chroni c obstructiv e pulmonarydisease ; thes e cohort s follo wthe mos t sedentaryactivit ypatter n o f the "no t working " ag e occupatio n group . I n th e CO-COHbConversio n Model , variou s parameter s o f the Cobur n equatio n mus t b e specified . Th e parameter s use d b yEP Aare : Haldan e constant , 218 ; hemoglobi n concentration , 1 5 g r pe r 10 0 m l (1 8 g r pe r 10 0 m l fo r the chroni c obstructiv e pulmonarydiseas e cohorts) ; endogenou s C O productio n rate , 0.006 9 ml/min ; altitud e abov e se a level , 0 m . Param
eter s tha t diffe r fro mcohor t t o cohor t ar e specifie d i n Tabl e 3.2 . I t shoul d b e emphasize d tha t the effec t o f smoking i s no t include d i n the dat a supplie d fo r us e i n thi s study ; othe r loca l source s o f C O i n a microenvironmen t suc ha s ga s stoves , attache d garages , an d occupationa l source s ar e als o excluded . 

### 3 7P.4 4

Tabl e 3. 2 PARAMETER S USE D I NCOBUR NEQUATION

Age/Occupat d Student s (ove r 17 ) Professional s Sale s personne l Clerica l worker s Craftsme n Laborer s Servic e worker s Marrie d wome n No t workin g Childre n (unde r 5 ) Childre n ( 5 t o 17 ) Specia l (pulmonary 

### on

disease ) 

### C

O Diffusio n Rat e (ml/min / torr ) 

### 2 7 2 7 2 7 2 7 3 0 3 0 2 7 2 4 2 7 

### 6 2 1 2 0 Bloo d

Volum e (ml ) 4,80 0 4,80 0 4,80 0 4,80 0 5,50 0 5,50 0 4,80 0 4,00 0 4,80 0 

### 96 0 3,20 0 4,80 0 Ventilatio n

(ml/min )

Rat e Exercis e Leve l Lo w 5,50 0 5,50 0 5,50 0 5,50 0 6,00 0 6,00 0 5,50 0 5,00 0 5,50 0 1,10 0 3,60 0 3,10 0 Mediu

m 8,70 0 8,70 0 8,70 0 8,70 0 10,00 0 10,00 0 8,70 0 7,50 0 8,70 0 1,70 0 6,00 0 4,00 0 Hig h

17,40 0 17,40 0 17,40 0 17,40 0 20,00 0 20,00 0 17,40 0 15,00 0 17,40 0 3,40 0 12,00 0 4,00 0

Summary Th e Exposur e Model , couple d wit hth e Emission s an d Atmospheri c Dis

persio n Model , provide s estimate s o f COHblevel s i n the populatio n unde r variou s propose d standards . Th e dat a transmitte d t o SR I Internationa l wa s disaggregate d i n the following fashion :
o Standards : 7 propose d standard s an d the presen t as-i s situation . o Cities : Chicago , IL ; Lo s Angeles , CA ; Philadelphia , PA ; St . Louis , MO . o Cohorts : 9 8 cohort s define d b yage/occupatio n group , neighbor

hoo d o f home , neighborhoo d o f work , an d activit ypattern . o Time : hourl yexposures . Th e thre e activit ypattern s describe :
o Microenvironments : indoor s (a t home) , indoor s (other) , transpor
tatio n vehicle , outdoor s (along roadway) , outdoor s (other) . o Exercis e levels : low , medium , high .



### 3 8P.4 5

Source s o f loca l concentratio n o f CO wer e no t include d i n the mode l inpu t use d t o generat e the dat a supplie d t o SRI . I n particular , smoking i s a sourc e fo r loca l concentratio n o f C O (passiv e smoking ) an d a sourc e o f elevate d COHblevel s fo r the smoke r himsel f (activ e smoking) . Becaus e o f the verylarg e quantit yo f dat a involve d i n thi s disag

gregatio n an

d becaus e the purpos e o f thi s repor t i s methodological , i t wa s decide d t o furthe r limi t the amoun t o f dat a tha t wa s t o b e transmit

te d t o SRI . Thus , hourl yexposure s wer e supplie d onl yfo r the month s o f Januaryan

d July . Thes e month s wer e chose n t o represen t seasona l varia

tions ; winte r typicall yha s highe r C O concentration s than summer . SR I subsequentl yreduce d the leve l o f disaggregatio n i n accor d wit h the need s o f the model s develope d i n the following chapters . Th e dat a a s employe d b

ythes e model s i s disaggregate d i n the following fashion . o Standards : seve n propose d standard s an d the presen t as-i s situation . o Cities : Chicago , IL ; Lo s Angeles , CA ; Philadelphia , PA ; St . Louis , MO . o Cohorts : five composit e cohorts . o Microenvironments : al l microenvironments ; transportatio n vehicl e only . Th e dat a wer e reduce d t o a frequenc ydistributio n o n the numbe r o f time s individual s fro

mthe composit e cohort s woul d experienc e a leve l o f COHb i n the cours e o f a year . Some interesting characterization s o f the dat a ca n b e obtained . Tabl e 3. 3 show s dat a o n hourl yCOHblevel s fo r a cohor t tha t contain s professionals , craftsmen , an d laborers ; thi s cohor t contain s the bul k o f the populatio n tha t experience s highCOHblevels . Th e dat a ar e fo r Lo s Angeles , an

d thre e situation s ar e presented : the strictes t propose d standar d ( 9 ppm , 8 hou r averaging time , 1 expecte d exceedanc e pe r year) , th e mos t lenien t propose d standar d (3 5 ppm , 1 hou r averaging time , 1 ex pecte d exceedanc e pe r year) , an d the as-i s presen t situation . I t shoul d b e note d tha t bot hthe strictes t an d the mos t lenien t standard s ar e ver y simila r t o thos e presentl yi n force . Tabl e 3. 3 reflect s the fac t tha t Lo s Angele s i s no t i n complianc e wit hthe strictes t standard . COHbi n transportatio n vehicle s is , o n th e average , onl ysomewha t highe r than i n genera l life . Tabl e 3. 4 display s the COHblevel s (fo r the same cohor t a s above ) t o b e expecte d i n four citie s i f the strictes t standar d i s jus t met . Remarkabl e i n Tabl e 3. 4 i s Philadelphia , wher e ther e ar e occasiona l larg e C O concentrations . I n orde r t o compl ywit hthe standard , control s hav e t o b e suc htha t the averag e COHbleve l i s a littl e ove r hal f tha t permissibl e i n the othe r thre e cities ; a t the same time , the highes t COH b level s encountere d i n Philadelphi a woul d b e almos t twic e tha t o f th e othe r cities . Thi s patter n remain s tru e fo r the othe r standard s a s well . 

### 3 9P.4 6

"Tabl e 3. 3 COHbLEVELS : LO S ANGELES , CA (Professionals , Craftsmen , Laborers )

Strictes t proposa l standar d Presen t situatio n Mos t lenien t propose d standar d Averag e COHbi n Averag e Highes t Transportatio n COHbCOHbVehicle s 0.69 % 0.85 % 0.88 % 1.75 %

3.75 % 4.25 % 0.75 %

0.90 % 0.94 %

Tabl e 3. 4 COHbLEVEL S UNDE R TH E STRICTES T PROPOSE D STANDARD

(Professionals , Craftsmen , Laborers ) Averag e COH

bi n Averag e Highes t Transportatio n COHbCOHbVehicle s Chicago , I L Lo s Angeles , C A Philadelphia , P A St . Louis , M O 0.58 % 0.69 % 0.35 % 0.61 % 2.25 %

1.75 % 3.25 % 1.75 % 0.72 %

0.75 % 0.42 % 0.68 %

Th e genera l conclusio n t o b e draw n fro mTabl e 3. 3 i s tha t ther e i s a fairl yhig hdifferenc e i n COHblevel s fo r th e propose d standards . I t wil l b e the tas k o f the res t o f thi s repor t t o determin e i f thi s implie s a significan t differenc e i n healt heffects . Tabl e 3. 4 indicate s that , eve n i f standard s ar e exactl ymet , the patter n o f variatio n o f C O concen tration s i n differen t citie s ca n lead t o fa r differen t averag e level s o f COHb . Thi s effec t increase s the difficult yo f arriving a t nationa l esti

mate s fo r healt hrisks . I t shoul d b e noted , however , tha t a n erro r i n the EP Adat a bas e ha s recentl ybee n discovered . Correctio n o f thi s erro r wil l reduc e al l the estimate s i n thi s report .



### 4 0P.4 7

REFERENCE S FOR CHAPTE R II I
1 . Mar c F . Roddin , Haze l T . Ellis , an d Wahee d M . Siddiqee , "Background Dat a fo r Huma n Activit yPatterns, " Vols . 1 an d 2 . Draf t fina l re por t prepare d fo r Strategie s an d Ai r Standard s Division , Offic e o f Ai r Qualit yPlannin g an d Standards , U.S . EPA , Researc hTriangl e Park , N C 27711 , August , 1979 . 2 . R . F . Coburn , R . E . Forster , an d P . B . Kane , "Consideration s o f the Physiologica l Variable s tha t Determin e the Bloo d Carboxyhemoglobi n Concentratio n i n Man, " Journa l o f Clinica l Investigation , kk_, 1899

- 191 0 (1965) . 3 . J . E . Peterso n an d R . D . Stewart , "Absorptio n an d Eliminatio n o f Carbo n Monoxid e b

yInactiv e Young Men, " Arch . Environ . Health , 21 , 165-17 1 (1970) . 4 . J . E . Peterso n an d R . D . Stewart , "Predicting the Carboxyhemoglobi n Level s Resultin g fro mCarbo n Monoxid e Exposures, " J . Appl . Physiol. , 39_, 633-63 8 (1975) . 5 . T . Johnso n an

d R . A . Paul , "Th e Neighborhoo d Versio n o f NAAQ S Expo
sur e Mode l (NEM ) Applie d t o Carbo n Monoxide, " PEDC o Environmenta l Inc. , Durham , N

C 27701 . Prepare d fo r U.S . Environmenta l Protectio n Agency , Offic e o f Ai r Qualit yPlannin g an d Standards , • Researc hTri

angl e Park , N

C 27711 , Septembe r 1981 .



### 4 1P.4 8

PART II I HEALT H DOSE-RESPONS E MODEL S
On e o f the principa l task s describe d i n thi s repor t i s the develop

men t o f healt hdos e respons e model s tha t ar e capabl e o f linking exposur e dat a a t ambien t C O concentration s t o healt hrisks . Th e essentia l ele

ment s o f thes e model s wer e scattere d i n the medica l literatur e an d ex iste d i n the understandin g o f medica l an d othe r experts . Th e element s ha d not , however , bee n gathere d an d use d t o assis t i n the estimatio n o f risk .Becaus e thes e model s represen t a firs t step i n completing the uni

fied ris k assessmen t calculato r fo r CO , theymus t b e expose d t o publi c scrutin ybefor e the ris k assessmen t ca n b e used . I t i s anticipate d tha t thes e model s ma ybe improve d o r eve n substitute d b ybette r models ; they illustrat e a structur e an d provid e a challenge . I n addition , theyiden

tif ysevera l importan t variable s tha t ar e a t presen t no t wel l known , bu t whic happea r importan t fo r the ris k assessment . Fou r area s o f potentia l healt himpac t ar e studie d i n the following chapters . Fo r eac ho f thes e areas , a deterministi c mode l i s built . Pre
liminaryprobabilit ydistribution s ar e develope d fo r the mos t sensitiv e variable s i n thes e models , an d severa l measure s o f healt hris k ar e devel

oped . I t shoul d b e reemphasize d tha t thes e model s ar e preliminary . Fo r thi s reason , an

d becaus e the dat a supplie d fo r the stud yb yEP Awa s sim

plifie d an

d partiall yincorrec t (se e Chapter s I I an d III) , the healt h risk s estimate d her e shoul d no t b e use d fo r an ydecision-makin g purpose .



### 4 3P.4 9

IV HEALT H EFFECT S DOSE-RESPONS E MODEL S
Ther e ar e two majo r question s tha t mus t b e addresse d befor e an y analysi s an

d modelin g o f the healt heffect s i s undertaken : Wha t i s the approac ht o b e used ? Wha t specificall yi s t o b e modelled ? Thes e two question s ar e addresse d i n thi s chapter . Th e Modelin g Approac h Ther e ar e two distinc t approache s wit hwhic hon e ca n attemp t t o quantif yhealt heffects . Bot har e consisten t wit hthe decisio n analysi s methodologyfo r handling uncertainties . Th e choic e betwee n thes e alter

native s is , t o a certai n extent , determine d b ythe amoun t o f informatio n available , the complexit yo f the problem , an d the importanc e o f the quan tityt o b e modeled . I n addition , differen t analyst s hav e differen t phil

osophie s tha t mak e the mprefe r on e o r the other . On e metho d i s t o encod e the probabilitie s o f healt heffect s a s a di

rec t functio n o f the exposur e t o the pollutant . Atypica l encoding ses sio n woul d involv e question s suc ha s the following : "I f a grou p o f 1,00 0 pregnan t wome n wer e continuousl yexpose d t o COH blevel s o f 5% , ho wman y o f thes e wome n woul d suffe r los s o f the fetus? " Th e exper t mus t mak e severa l logica l connection s mentall yt o step fro mthe caus e t o the effec t i n thi s process . Th e philosoph ybehin d thi s approac hi s tha t expert s hav e informatio n o n the magnitud e o f thes e effects ; thi s typ e o f ques tion , however , mayno t b e i n a for mwit hwhic hthe exper t i s familiar , no r on e wit hwhic hh e i s comfortable . Th e advantag e o f thi s approac hi s tha t i t i s relativel yquic k an d inexpensive . Awid e variet yo f expert s ca n b e interviewe d an d a goo d pictur e o f curren t understandin g o f the effec t ca n b e obtained . Further

mor e , the outpu t i s easil ycomprehende d b ynontechnica l a s wel l a s tech

nica l reviewers . On e problemwit hthe abov e approac hi s tha t the result s canno t easil yb e critique d i f ther e i s disagreemen t among the experts . Thi s i s a drawbac k fo r decisio n makin g i n the publi c domain . Th e assumption s tha t ar e employe d t o suppor t a n ai r qualit ystandar d mus t b e clear , ex plicit , an

d pu t i n term s tha t manyca n understand . I f not , the n the analysi s i s vulnerabl e t o the attack s o f anyon e who wishe s t o disagre e wit hthe experts ' opinions . Ther e i s ofte n n o tes t possibl e t o distin

guis hbetwee n the conflictin g opinions . Arelate d drawbac k i s the lac k o f refutabilit yo f the encode d dis

tributions . Onl yepidemiologica l evidenc e can suppor t the experts ' opin

ions . Th e magnitud e o f the effects , however , i s ofte n to o smal l t o b e detecte d b

ysuc hstudies . B ywayo f contrast , a good healt heffect s



### 4 5P.5 0

estimatio n procedur e use s input s tha t are , i n principle , observabl e o r refutable . Wher e the uncertaint yo n a n inpu t i s important , encodin g o f exper t opinio n i s indicated ; if , o n the basi s o f the encode d distribu

tion , the valu e o f informatio n i s large , the n a goo d are a fo r futur e re searc hha s bee n identified . I f the analysi s ha s bee n wel l structured , the typ e o f researc hwhic hi s indicate d i s no t onl ywell-defined , bu t als o feasible . Athird criticis mi s tha t thi s approac hofte n fail s t o mak e explici t the logica l processe s b ywhic hthe exper t mus t drawhi s conclusions . Thi s result s i n two source s o f error . First , the exper t i s aske d t o as ses s variable s wit hwhic hh e i s leas t familiar , whil e hi s extensiv e knowledg e o f relate d factor s become s secondary . Second , the expert' s abilit yt o b e logicall yconsisten t o n a n intuitiona l leve l i s no t infal

lible , especiall yi f the issu e i s complex . No matte r ho wcarefu l the en coding process , ther e i s a strong possibilit yo f erro r becaus e o f logi c rathe r tha n simpl e lac k o f scientifi c knowledge . Asecon d approac ht o the quantificatio n o f healt heffect s trie s t o circumven t the weaknesse s o f the firs t b yrelying o n "extrapersonal " mod

el s whe n possible . Thes e mak e explici t the logica l processe s tha t the exper t mus t performimplicitl yi n the firs t approach . Th e mode l de

scribe s the physica l relationship s tha t ar e believe d t o connec t th e pol

lutan t exposur e wit hthe healt houtcome . Expert s ai d i n formulating an d refining the logica l structur e o f the model . Onc e the parameter s i n th e mode l ar e specified , th e mode l provide s a consisten t proces s b ywhic hon e can estimat e healt heffect s fo r differen t pollutan t exposures . Th e informatio n tha t expert s hav e i s separate d int o tw o categories : effect s o f the pollutan t o n physiologica l parameter s an d the effect s o f the physiologica l parameter s o n health . Th e connectio n betwee n the tw o i s mad e "extrapersonally " s o tha t logica l error s ar e reduced . B ydivid

in g the problemint o it s components , additiona l benefit s accrue . First , the expert s furnis hinformatio n concerning thos e variable s and relationship s wit hwhic htheyar e mos t familiar . Second , th e mode l an d the input s ca n b e critique d b yanyon e familia r wit hbasi c physiology . On e doe s no t nee d t o b e a n exper t o n the pollutan t t o b e abl e t o contrib

ut e usefu l informatio n t o the ris k assessmen t process . Third , anyon e dissatisfie d wit hthe estimate s tha t resul t fro mthe mode l mus t addres s eithe r the assumption s o n the physiologica l processe s o r thos e o n th e physiologica l variables . Thi s force s discussio n awayfromhighl ysubjec

tive an d largel yirrefutabl e statements . Instead , the publi c debat e ca n focu s o n relationship s tha t ca n b e teste d an d o n variable s tha t ca n b e measured . Th e outcom e o f a ris k assessmen t base d o n extrapersona l model s i s a clarificatio n o f the ground s fro mwhic hpubli c disagreemen t arises . Fur
thermore , direction s fo r valuabl e futur e researc hwil l b e wel l defined . Rathe r than indicat e tha t mor e researc hshoul d b e don e i n the are a o f cardiovascula r effects , the model s ca n indicat e specifi c avenue s o f thi s research , suc ha s the effect s o f the pollutan t o n bloo d pressure .



### 4 6P.5 1

Thi s ris k assessmen t ha s followe d the secon d approach , i n the belie f tha t i t i s the mos t fruitfu l directio n fo r a demonstratio n o f the publi c risk s o f carbo n monoxide . Th e complet e developmen t an d critiqu e o f suc h model s fo r eac hpossibl e healt heffec t i s a tas k tha t i s wel l beyond the purpos e an

d resource s o f thi s study . Fo r thi s reason , on e shoul d no t at
temp t t o us e the model s presente d fo r actua l policymaking a t thi s stag e i n thei r development . Rather , theycoul d b e use d a s a vehicl e t o provok e publi c commen t o n structur e an d assumptions . Suc hdialogu e wil l indicat e weaknesse s an

d error s tha t shoul d b e addresse d i n eac hmodel . Thi s for m o f publi c critiqu e shoul d b e a par t o f an yris k assessment . Withi n the limite d budge t o f the demonstration , the model s hav e receive d some revie w b yappropriat e "experts. " Th e exten t o f thi s revie wi s no t sufficien t fo r a finalizatio n o f the assessment . Whil e presentin g eac hmodel , the aspect s tha t shoul d clearl yb e refine d o r expande d wil l b e indicated . Other s coul d probabl yb e unearthe d i n the publi c revie wprocess . Despit e the preliminarynatur e o f the models , theyrepresen t an ad

vanc e i n the quantificatio n o f carbo n monoxid e healt heffects . Thes e pi

lo t model s ar e workable , an d can readil yb e elaborate d a s desired . A soli d basi s fo r a complet e ris k assessmen t no wexists . A s fa r a s ha s bee n possible , eac hmode l describe s the majo r mechanis mb ywhic hcarbo n monoxid e i s believe d t o affec t health . T o the exten t tha t the mechanis m i s correctl yidentified , a ful l assessmen t woul d onl yrequir e refinemen t an d elaboratio n o f the structure . I f the mechanis mi s incorrectl yiden

tified , the n inpu t fro mthe publi c ma yhel p identif ythe correc t one . A ne wmode l can b e develope d t o replac e the existing one , an d an advance

men t wil l hav e bee n mad e withou t a los s i n valu e o f the remaining ele

ment s o f the assessment . Choic e o f Sensitiv e Group s Th e rang e o f group s wh o ar e potentiall ysensitiv e t o C O i s broad . An yris k assessmen t o f C O mus t identif yal l o f thes e groups . Onc e iden

tified , the mor e importan t group s an d healt heffect s ca n b e filtere d ou t a s candidate s fo r detaile d healt heffect s modeling . Th e les s importan t effect s ar e no t quantifie d a t thi s stag e becaus e theyar e likel yt o b e insignifican t i n relatio n t o thos e healt heffect s tha t ar e chose n fo r modeling . On e shoul d b e careful , however , no t t o los e sigh t o f thes e re maining , unquantifie d effect s i n the fina l analysis . I n the cas e o f CO , the group s identifie d ar e liste d i n Tabl e 4.1 , followe d b

ythe specifi c healt heffect s associate d wit hthe groups . I t shoul d b e note d tha t effect s aggravate d b yhigh-altitud e hypoxi a wer e specificall yexclude d b yEP Afro

mthi s study . Evaluatio n o f relativ e importanc e fo r the firs t screening wa s base d o n thre e considerations . On e facto r i s the siz e o f the sensitiv e popula

tion . Eve n a smal l effect , o r on e o f relativel ymino r importanc e pe r event , ca n becom e a significan t publi c healt hconcer n i f i t occur s ver y commonl ywithi n the genera l population . Th e secon d consideratio n i s the potentia l fo r verygrav e outcome s suc ha s mortality . Thes e naturall yre ceiv e the mos t publi c attention , an d ar e particularl yimportan t whe n suc h risk s ar e couple d wit ha larg e sensitiv e population . Th e third 

### 4 7P.5 2

Tabl e 4. 1 SENSITIVE GROUP S AN D ASSOCIATE D HEALT H EFFECT S (1 ) Cardiovascula r diseas e

Myocardia l infarctio n Sudde n deat h(ventricula r fibrillation ) Aggravatio n o f acut e coronaryinsufficienc y Aggravatio n o f unstabl e angin a pectori s Aggravatio n o f stabl e angin a pectoris . (2 ) Chroni c obstructiv e pulmonarydisease s

Acut e exacerbation s o f pulmonaryfailur e Hear t failur e Asphyxiation . (3 ) Fetuse s

Feta l deat hdu e t o hypoxi a Nonfata l damag e t o nervou s system Malformation . (4 ) Anemic s

Aggravatio n o f hypoxia . (5 ) Hypertensiv e diseas e

Increase d ris k o f cerebrovascula r accident . (6 ) Peopl e engage d i n comple xan d hazardou s activitie s Increase d ris k o f accident . (7 ) Periphera l vascula r diseas e

Aggravatio n o f intermitten t claudication .
consideratio n i s the relativ e sensitivit yo f eac hgrou p t o the pollutant . I n some cases , smal l change s i n exposur e level s ca n resul t i n larg e change s i n the healt hstatu s o f on e grou p o f people . Tabl e 4. 2 present s a chec k lis t tha t indicate s whic hgroup s rank ver yhig hi n eac ho f the thre e categories . Thre e o f th e seve n origina l group s wer e eliminate d i n thi s screen ing . Anemic s wit hn o othe r complicatin g disease s wer e no t relativel yim

portan t o n an

yo f the thre e bases . Althoug hmor e sensitiv e t o C O than a perso n wit hnorma l hemoglobi n levels , the effect s o f C O o n thi s grou p ar e unlikel yt o b e a s larg e a s the effect s o n the res t o f the sensitiv e groups . Asimila r judgmen t ca n b e mad e wit hrespec t t o periphera l vascu la r disease . Her e the sensitiv e grou p i s large , bu t the likelihoo d o f majo r effect s i s small . Th e convers e i s tru e fo r hypertensiv e diseas e patients ; i f a n effec t occurs , i t i s o f majo r significance ; the grou p o f hypertensive s tha t i s likel yt o b e affecte d i s small , however , becaus e ambien t level s o f C O ar e no t known t o hav e larg e effect s o n bloo d pres sure s o r bloo d flow-rates . Cardiovascula r diseas e i s extremel yprevalent . Th e potentia l fo r increase d mortalit yrepresent s a majo r concern . Althoughthi s grou p i s 

### 4 8P.5 3

Tabl e 4. 2

. RANKIN

G O F SENSITIVE GROUP S

Larg e Grav e Relativ e Grou p Populatio n Outcome s Sensitivit y Cardiovascula r diseas e X X Chroni c obstructiv e pulmonarydiseas e X X Fetuse s XX X Anemic s Hypertensiv e diseas e X Moto r vehicl e driver s X X Periphera l vascula r diseas e X
probabl yno t extremel ysensitiv e t o C O o n average , smal l proportiona l ef

fect s ca n resul t i n significan t outcome s becaus e o f the firs t tw o fac

tors . Chroni c obstructiv e pulmonarydiseas e i s quit e prevalent , bu t thos e wh o woul d b e affecte d b yambien t level s o f C O ar e a smal l portio n o f the total . Th e morbidit yan d mortalit yo f thi s grou p i s greatl yaffecte d b y oxyge n availability . Becaus e eve n lo wconcentration s o f C O interfe r wit h oxygenation , it s potentia l effect s o n thi s grou p ha s bee n considere d mor e fully . Fetuse s rank highi n al l categories . Althoughperhap s no t a s sen sitiv e a s thos e wit hcompromise d pulmonaryfunction , theyar e unabl e t o eithe r indicat e stres s o f hypoxi a whe n i t occurs , o r t o escap e suc h event s b

ymodificatio n o f activity . Th e potentia l fo r accident s cause d b yCO ma yb e smal l fo r eac hindi

vidual . Virtuall ythe entir e population , however , i s subjecte d t o thi s ris k i n the for

mo f moto r vehicl e transportation . Asmal l ris k ma yre sul t i n majo r effect s whe n the prevalenc e o f thi s ris k an d the relativ e healt ho f thi s "sensitive " grou p i s accounte d for . I n the following four chapters , model s ar e develope d fo r four area s o f healt heffects : o Arterioscleroti c Hear t Diseas e o Feta l Healt hEffect s o Chroni c Obstructiv e PulmonaryDiseas e o Moto r Vehicl e Accidents .



### 4 9P.5 4

Onl ythe mos t importan t healt hrisk s i n eac ho f thes e categorie s wil l b e considered . I f i t shoul d prov e importan t o r desirable , i t woul d no t b e difficul t t o expan d th e model s t o includ e les s importan t healt hrisk s fo r thes e healt heffec t categories . I n addition , model s o f healt heffect s fo r othe r sensitiv e group s an d healt hrisk s shoul d no t b e difficul t t o develop , becaus e theywil l probabl yhav e a structur e simila r t o on e o f thos e develope d i n the following chapters .
Measure s o f Healt hRis k Model s suc ha s hav e bee n develope d i n thi s repor t ar e exceedingl y flexibl e an

d ca n answe r a larg e numbe r o f questions . EP Aha s requeste d tha t thre e type s o f measure s o f healt hris k b e obtained . Becaus e o f the specifi c healt heffect s chose n fo r evaluatio n b ySRI , no t al l o f thes e measure s yiel d usefu l result s fo r eac ho f the healt heffects . I t shoul d b e emphasize d tha t thi s i s becaus e o f the choic e o f healt heffect s an d the identificatio n o f the sensitiv e groups . Othe r healt heffect s ca n b e identifie d fo r whic hthes e measure s yiel d usefu l information . Fo r in

stance , the probabilit ytha t 5 % o f angin a patient s wil l suffe r a n angin a attac k unde r a definit e se t o f C O exposur e an d exercis e condition s ca n easil yb e calculated . Th e applicabilit yo f eac hmeasur e t o the effect s analyze d i n thi s researc har e discussee d below .
Measur e 1—Th e probabilit ytha t the numbe r o f person s experiencing , an d the expecte d numbe r o f incidence s of , eac hadvers e healt heffec t equal s o r exceed s a give n valu e i n a perio d o f 1 year :
o Fo r death s (an

d nonfata l effects ) o f fetuses , death s cause d b y arterioscleroti c hear t disease s an d death s cause d b

ymoto r vehi

cl e accidents , the numbe r o f person s experiencin g the effec t i s equa l t o the numbe r o f incidence s o f the effect . o Fo r hospitalization s cause d b ychroni c obstructiv e pulmonarydis

ease , the hospitalizatio n rat e o f the identifie d sensitiv e grou p i s suc htha t virtuall yal l individual s ar e hospitalize d a t leas t onc e during the year . Similarly , al l angin a patient s considere d i n thi s stud

yexperienc e manyattack s during a year . Henc e al l individual s i n thes e group s experienc e advers e healt heffect s durin g a year . o Fo r death s cause d b ychroni c obstructiv e pulmonarydisease , the numbe r o f death s pe r yea r i s no t a relevan t numbe r (se e Chapte r VII) . Th e measur e use d i s reductio n o f lif e expectancy . Fo r al l thes e effects , onl yuncertaintie s becaus e o f the Healt hRis k Model s ar e incorporated . Give n the limite d amoun t o f dat a obtaine d fro m EPA , i t wa s no t feasibl e t o includ e uncertaint yo n the Emissio n an d At
mospheri c Dispersio n Mode l an d the Exposur e Model . Uncertaint yo n the Cobur n equatio n an d it s parameters , fo r instance , i s probabl yimportant . Finally , wher e fairl yaccurat e base-cas e dat a are , i n principle , avail

abl e (e.g. , feta l death s an d moto r vehicl e acciden t deaths , give n today' s emissio n levels) , thes e dat a ar e take n a s certain ; thi s assumptio n fo

cuse s attentio n o n uncertaint yassociate d wit hchanging to a ne wstan
dard . 5 0P.5 5

Measur e 2—Th e expecte d numbe r o f person s experiencing , an d the ex pecte d numbe r o f incidence s of , eac hadvers e healt heffec t i n 1 year . Thi s measur e i s jus t the expecte d valu e o f the distribution s i n Measur e 1 , an

d henc e the same observation s apply . I n presentin g the re sults , i t wa s foun

d usefu l t o expres s the difference s i n standard s a s shift s fro

mthe presen t as-i s situatio n an d als o fro

mthe situatio n unde r the strictes t propose d standard .

• Measur e 3—Th e probabilit ytha t n percen t o f the mos t sensitiv e group , whe n exposed , wil l experienc e an advers e healt heffec t i n 1 yea r ( n = 1% , 5 % an

d 10%) . Thi s measur e prove d t o b e inapplicabl e fo r al l the healt heffect s analyze d i n thi s report . Becaus e the model s shown o threshol d fo r risk s becaus e o f C O exposure , al l person s i n the sensitiv e group s ar e contin

uousl yexposed . Ther e wa s n o natura l wayt o isolat e "mos t sensitive " group s becaus e o f the potentia l fo r circula r reasoning . Finally , eithe r the entir e identifie d sensitiv e grou p experience s healt heffect s durin g the yea r (angin a patients , chroni c obstructio n pulmonarydiseas e hospi

talizations , an

d lif e expectanc yreduction ) o r les s tha n 1 % o f the grou p experience s the healt heffec t during the yea r (feta l death s an d nonfata l effects , moto r vehicl e acciden t deaths) . Fo r cardiovascula r patients , curren t mortalit yi s abou t 10 % pe r year , an d variation s i n the C O stan
dard d o no t affec t thi s dramatically . Fo r thes e reasons , thi s measur e ha s no t bee n foun d t o b e directl y applicabl e t o the healt heffect s analyzed . A s a substitut e measure , the numbe r o f healt heffect s attributabl e t o ambien t C O i s estimated . I t is , o f course , recognize d tha t i t i s no t feasibl e t o completel yeliminat e ambien t CO . On e shoul d note , moreover , tha t althoug hthe model s shoul d predic t difference s betwee n standard s fairl ywell , the extrapolatio n t o the leve l o f zer o ambien t C O i s pron e t o greate r error .



### 5 1P.5 6

V ARTERIOSCLEROTI C HEAR T DISEAS E
Th e modelin g (o r direc t encoding ) o f arterioscleroti c hear t diseas e healt heffect s i s difficult . Physiologica l parameter s varywidel yamong individuals , an

d voluntaryaction s pla ya majo r rol e i n the effects . Th e following mode l avoid s the issue s o f interpersona l difference s b ylooking a t a singl e "composite " patien t fo r a demonstratio n o f mode l structure . Modelin g o f variation s betwee n person s i s require d o f a ful l ris k as sessment . However , thi s demonstratio n show s tha t a relativel ysimpl e quantitativ e mode l ca n b e develope d fo r thes e effects . Background an

d Modelin g Issue s Arterioscleroti c hear t diseas e (ASHD ) contribute s t o mor e death s i n the Unite d State s than an yothe r singl e cause . Approximatel y3 % o f th e adul t populatio n i n the Unite d State s suffer s fro msome manifestatio n o f thi s disease , suc ha s angin a o r myocardia l infarction . Anothe r 2 % o f adult s are2believe d t o suffe r fro mASH D withou t recognize d symptom s o r diagnosis . I n fact , fo r abou t 25 % o f thos e suffering-fromASHD , deat h wil l b e the firs t apparen t symptomo f thei r condition . Thi s grou p i s no t onl ylarge , bu t manyar e unawar e o f the risk s t o whic htheyar e sub

ject . ASH D i s characterize d -b ya reductio n i n the potentia l oxygenatio n o f the myocardia l tissue . Thi s i s cause d b ynarrowin g o f the coronaryar terie s tha t suppl yoxygenate d bloo d t o the heart . Th e severit yo f the diseas e depend s o n whic hcoronarie s ar e involve d a s wel l a s the degre e o f narrowing . Fo r a give n leve l o f severity , the patient' s ris k depend s o n hi s emotiona l an

d physica l activit ypatterns . Ahighl yactiv e o r emotiona l perso n place s grea t oxyge n demand s o n hi s heart . Ahealth yhear t ca n respon d t o the increase d oxyge n demand b y increase d bloo d flow . Thos e wit hASHD canno t respond a s well , an d the oxyge n demand s o f the ASH D hear t wil l sometime s excee d the oxyge n supply . Myocardia l ischemi a ca n hav e on e o f thre e outcomes : the patien t ma yfee l n o pain ; h e ma yfee l pai n tha t subside s wit hrest ; o r h e maysustai n a n infarction , wit hirreversibl e damag e t o the hear t muscle . I n al l thre e cases , however , the patien t i s subjecte d t o ris k o f deat hbecaus e o f fib

rillation . Th e increase d propensit yt o g o int o fibrillatio n during is

chemi a appear s t o b e becaus e o f (1 ) a prolonging o f the portio n o f the cardia c cycl e whe n the hear t i s mos t susceptibl e t o sustaine d tachycardi a an d (2 ) change s i n the electrolyti c balances-betwee n cells , whic hmak e a n initiating dysrhythmi a mor e likel yt o occur . Th e verynatur e o f coronaryhear t diseas e allow s on e t o suspec t tha t ur e t o carbo n mo i renc e o f ischemi c epi s exposur e t o carbo n monoxid e ma yb e a contributin g facto r i n the occu r : e o f ischemi c episodes . Arono w» » » » an d Anderso n hav e 

### 5 3P.5 7

"^^^ ^ ** • *

demonstrate d thi s i n clinica l studie s o n stabl e angin a patients . I n al l bu t on e o f thes e studies , angina l patient s followe d a standar d exercis e protoco l describe d b yRedwoo d ; thi s protoco l i s the mos t reliabl e i n measurin g the effect s o f drug s o r environmenta l factor s o n the suppl yan d demand o f oxyge n t o the myocardiu ma t the onse t o f angina . T o asses s the healt heffect s o f CO o n ASH D patient s on e shoul d mode l the relationshi p o f myocardia l oxyge n consumptio n (MOC ) an d maxima l oxy ge n suppl ycapacit y(MOS) . A t an ypoin t wher e MO C rise s abov e MOS , is

chemi a ca n b e sai d t o occur . Thi s wa s the consensu s o f the expert s con sulted . Al l o f the manifestation s o f ASH D occu r during episode s o f is

chemia . Therefore , on e ca n estimat e change s i n the frequenc yo f thes e healt heffect s b

ychange s i n the relativ e proportio n o f time a t whic hthe mode l predict s ischemi a t o occur . Th e bul k o f clinica l dat a look s a t patient s suffering fro mpainfu l ischemi a i n the for mo f stabl e angin a pectori s o f effort . Th e mode l pre sente d her e wil l estimat e frequenc yi n angina l pai n a s a n indicatio n o f frequenc yi n ischemi a i n general . Thi s approac hmake s optima l us e o f the dat a available . I t als o provide s prediction s tha t ca n b e use d t o tes t fo r mode l accuracy . Pai n occur s frequentl yenoughamong thos e sufferin g fro mangin a t o allo wfo r a statistica l analysi s o f it s relativ e frequenc y unde r differen t environmenta l conditions . Also , the occurrenc e o f pai n i s a mor e readil ydetecte d even t than the occurrenc e o f ischemia . On e shoul d b e awar e tha t the us e o f pai n a s the define d even t i s marre d b

yit s subjectivity . Peopl e hav e differen t resistance s t o pain . Also , becaus e manypeopl e experienc e painles s myocardia l ischemia , on e ca n reasonabl yassume tha t the degre e o f ischemi a require d t o induc e pai n ma yb e quit e differen t fro mcas e t o case . Probabl ythe risk s o f acut e infarctio n and/o r fibrillatio n ar e relate d t o the degre e o f ischemia . I n part , thi s problemi s reduce d becaus e the dat a allo wanalysi s o f ho wthe susceptibilit yt o pai n change s wit hCOHbfo r give n individuals . Th e rel

ativ e change s ar e mor e importan t her e than ar e the absolut e level s a t whic hth e pai n occurs . Thes e consideration s onl ypartiall yanswe r the problemo f subjectivity . Ideall yon e woul d defin e the even t o f ischemi a i n mor e measurable , physiologica l term s suc ha s coronarysinu s oxyge n tension , o r electrocardiographi c changes . I f dat a o n ho wthes e variable s ar e affecte d b

yC O becom e available , the n the mode l presente d her e shoul d b e modifie d t o incorporat e suc hinformation . Researcher s a t Genera l Motor s Researc hLaboratorie s ar e currentl ydevelopin g a n anima l mode l tha t relate s physiologica l measurement s o f ischemi a an d CO . Th e outcom e o f thei r effort s ma yb e directl yrelevan t t o suc ha modification . Th e consumptio n o f oxyge n b ythe myocardiu

mi s determine d b

ynumer ou s factor s suc ha s hear t rate , bloo d pressure , strok e volume , hear t size , an

d inotropi c state . Model s tha t estimat e MO C quickl ybecom e ver y complicate d i f on e trie s t o accoun t fo r al l o f the factor s i n the mos t realisti c way . On e commonl yuse d inde xo f MO C i s the systolic-tim e in

dex . Thi s i s approximate d b ythe "tripl e product " (hear t rat e xmea n systoli c bloo d pressur e xsystoli c ejectio n time) . Onl ythe third facto r i s difficul t t o observe . Fortunately , Redwoo d foun d tha t the "doubl e product " (hear t rat e xsystoli c bloo d pressure ) provide s a reasonabl e prox yfo r MO C unde r the circumstance s o f repeate d round s o f the standar d
5 4P.5 8

protocol . Eac hstabl e angin a patien t ha s a characteristi c valu e o f th e doubl e produc t (DP ) a t the onse t o f angin a tha t onl yfluctuate s b yabou t 10% . Thi s implie s tha t eac hstabl e angin a patien t ha s som e MO S beyond whic hincrease s i n MO C resul t i n ischemi c pain . Whe n presse d beyon d thi s threshold , angina l pai n commence s a s the metabolite s o f anaerobi c respi

ratio n buil d u

p i n the myocardia l tissue . If , whe n following the standar d protocol , stabl e angin a patient s ex perienc e a dro p i n the time unti l onse t o f pai n bu t no t a dro p i n the D P valu e a t onset , thi s i s evidenc e tha t something ha s occurre d t o rais e the MO C fo r a give n leve l o f exercise . If , o n the othe r hand , ther e i s a dro p i n bot hthe time unti l onse t o f pai n an d i n the D P a t onset , thi s indicate s a decreas e i n MOS . Arono wha s carefull ydocumente d the exis

tenc e o f a reductio n i n the MO S o f angina l patient s wh o hav e bee n expose d t o variou s level s o f carboxyhemoglobin . Hi s studie s indicat e tha t level s o f COHbtha t resul t fro mambien t exposure s d o no t affec t the demand fo r oxygen , bu t onl ythe supply . Deterministi c Mode l Amode l o f the magnitud e o f the possibl e aggravatio n o f angin a i n the genera l populatio n mus t accoun t fo r the hou r t o hou r change s i n bot h myocardia l oxyge n demand an d maxima l supply . Th e basi c assumptio n o f thi s mode l i s du e t o Aronow : fluctuation s i n COHbcaus e shift s i n the suppl ycapacity , bu t no t i n the oxyge n demand tha t the patien t wil l expe rience . Th e oxyge n demand o f the heart , however , i s assume d t o fluctuat e quit e widely , an

d i n a probabilisti c manner , ove r eac hhou r o f the day . Th e doubl e produc t (DP ) wil l b e use d a s a prox yvariabl e t o measur e MO C an d MOS . Th e mode l permit s MO S t o shif t becaus e o f change s i n COHb . MO S clearl ydoe s shif t fo r othe r reasons . Thi s i s partl yevidence d b yth e fluctuatio n i n D P a t onse t o f pain . I n genera l thes e shift s occu r o n a regula r diurna l basis . Fo r instance , man ypatient s ar e mos t susceptibl e t o attack s fo r give n stres s level s i n the mornings . Becaus e thes e non COHbinduce d shift s i n suppl ycapacit yar e largel ypredictabl e b ythe patient , h e i s assume d t o tr yt o compensat e fo r the mi n hi s activit y patterns . Effectively , thi s result s i n a fairl yconstan t relationshi p o f suppl ycapacit yt o MO C distributio n ove r al l hour s o f the dayfo r al l suppl yeffect s tha t ar e regula r i n nature . Th e element s o f suppl y change s tha t ar e unpredictable , o r fo r whic hthe patien t i s unabl e t o full ycompensate , ar e modele d b ya probabilit ydistributio n o n MOS . A s mentione d above , MO S ca n fluctuat e o n a regula r basi s b ya s muc ha s 10% . Th e distributio n use d i n thi s mode l assume s tha t MO S deviate s mor e than 10 % fro mthe mea n onl y20 % o f the time ; thi s i s i n accor d wit hstatement s b yArono wan

d others . COHbshift s the mean , a s describe d below . Th e distributio n use d i s depicte d i n Figur e 5.1 , fo r the cas e wher e the mea n valu e i s 20,000 : Pr(MOS|COHb ) i s the probabilit ydensit yfunctio n fo r MO S a t a give n leve l o f COHb . Th e result s fro

mthe Arono wstudie s cite d abov e wer e use d t o form

ulat e a n estimat e o f ho wmuc hthe mea n valu e o f MO S shift s a s a functio n o f COHb . Th e formul a wa s assume d t o b e linea r o n the rang e o f ambien t 

### 5 5

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### 16,00 0 20,00 0 24,00 0

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FIGUR E 5. 1 PROBABILIT Y THA T MAXIMA L MYOCARDIAL OXYGE NSUPPL Y (MOS )

I S LES S THA

NTH E VALU E INDICATED : Pr(MOS|COHb=0 ) I N CUMULATIVE FORMU l

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### g XI r t H O 3 o H i r t p r ro 0 o C L ro H h*- c o r t p r O s r t r t p r ro H ro H * O J r t *-• • O 3 C O p r h * XI C O o H i C O c T J T J H * a * *-• • ^ 3 r t O Q O H O C L C H T J H i •H i ro H ro 3 r t C O ro < ro i H r t •-<- i 0 Q • I O C T J C O < . O J 3 C L r t 3 * ro H H r t ro C O r t 1 ro C O c O S r t r t p r ro H 3 C L h*- < : H - C L c O s M *- • ro < ro H - • H p r ro T ) O S r t H ro 3 r t C O r t p r ro a . O J r t O i H i o 1 1 C O c Ci 3 * ro H O J c r o • 1 O J r t H O 3 c o « o c l- 1 C L H ro p . j a 3 ro o s n p r C O r t C C L l _ l < ^ r t C O £ O chC L a * ro O s 3 O S h- > • ^ N ro C u n o c *- • C u o " ro ohO J 0 1 C O H H i H ro C L c H H ro O J 3 O S 3 O s I- ' •*- < C O H C O O H i > H O 3 O £
_ C O a -ata 1- " : v O O 

## o i o x ) o O C O € H 3 * r t ro ro 3 : c ro i H * 3 C O 0 Q F O 3 O O S P C O * T ) O S ro r t XH i d ro o 3 c o r t C - i i ro < 3 * *-*- O C O 0 Q O J ro r t 3 ro r t H p rO J 3 o C A € p r ro 3 c r • I ro O S r t p r H 3 0 Q T J C n ro O J H H • o e H H ro 3 r t *- • ro i-"- < M r s *< j Q § * 8 ro O O S 3 O p r M ro ro c o < ro o s M 3 0 Q O H H i 3 O S i— 1 •O J O r t ;*-- « •O J o r t 3 * ro 0 o o . ro i- ' n o 3 c o H C L ro • 1 C O o 3 *- • H *-<J*-<*4 o o O H i c r r t O J O J c r p . o 3 c r t o 3 ro H i H ro X I c ro 3 o • ^ o • I ro XI n ro C O ro 3 r t H 3 H i O Q O J r t r t O J O ? r C O -, t r • ^ 3 i* i 0 c r ro i o H i o o H o 3 O J n H ro C O T J O s r t H ro 3 r t C O 

### H r t p r O s C L H H i H i ro n ro 3 r t w ro < ro H H r t p . v ^ 3 < o M < ro O H i O J 3 C L 0 Q « o n c r "- ^ M ro < ro

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### P 0 ro O J 3 C O H i H O 0 c ro O S

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• 1 ro O Q H •J^ D t . O J U1 0 * . ro - * L « . OJ U < • S C O r t T J p r ro C O ro O S r t c C L H ro 0 1 O s H ro T J H ro C O ro 3 r t ro C L 3 o O I- * ro C L c r ro o O J c C O ro r t p r ro T J • 1 o r t o Ct O i- * 0 1 0 Q 0 1 H 3 O S r t r t p r ro Ct o • i • i ro C O T J o 3 C L H 3 0 Q O O P C o O S C n ro 0 1 o s 3 C L € O S C O c r O s O S ro C L o 3 r t •s ro o *a ISl O S r t O 3 C O ro r t o H i ro X ro • I o H C O ro l p 3 C L c Ct ro C L 0 i 3 c ro o < P 1 H 3 O SU l 0 0 22,50 0

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## 1

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### 1 i 1 1

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6,00 0 10,00 0 14,00 0 18,00 0
MO C (m mHg—beats/min )
FIGUR E 5. 3 TH E PROBABILIT Y THAT MO C EXCEED S AGIVE NVALUE :

Pr(MOC ) I NCOMPLEMENTARY CUMULATIVE FORMcr*

. o O 0

o o P C H t rro

. O J C C O o ro

- d r t p . CTo - p . J U ro *- < 1 r t r r O O 1 r t a - p ro i ro *-* : ro H I XP - CTT J C p * ro r t O O J 3 O J r t r t O J O S V P - H i r t p r O J r t O J C ut o ro T J r t p - t r p - P* > F - r t *-<* ! 3 * O S 0 1 c r ro ro 3 F - 3 C L c o ro C L
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### g 3 ro H i 3 " 0 1 ro P - • i i i ro P 1 •-< : r t 3 " •" » 0 1 0 1 r t 3 C u ^ 2 ro 0 Ct OJ 0 J 3 3 C u 3 O P-> r t 0 J H ? rO Q 3 ro O P * « - ^ * •S • ^ 5 ro o 3 CT C O CTro ro H < p . p j C O I 0 s

C O p - H i r t p r ro •-< : « ro H ro 0 1 o r t c O S p * 0 1 r t r t 0 J O r t p r ro 3 c 0 c r ro H o H i O J o r t C O s h- 0 s r t r t O S O ! V 0 1

. H p r ro 0 o C L ro P * p r ro H ro r r H ro 0 J r t 0 1 0 J P " P * o r t p r ? rro 0 1

. w ro o 0 1 c C O ro H < F - O S ro c 3 3 ro Ct I o H i O i 3 0 i r t r t O J O 7? 0 J 3 C L O C i i r t O J F - P - r t r t O r t p r ro ro X r t ro 3 0 1 r t r t CT ro O i < ro n 0 1 r t 0 Q r t p r O J r t O J 3 O Q P - 3 O J p r" O ro F - H 0 J o r t p . < P - r t P - ro 0 1 « . r t p r ro * i ro
• S F - P - P* * CT ro O J n ro C L c o r t O J r t P - ro 3 r t C O 0 s • 1 ro O J CT P * ro r t O a . p - C O r > ro H 3 r t p r ro S O S H 3 P - 3 p . (J Q o 3 C O F - F-O Q 3 3 C O ro H i H ro X I e ro 3 o •^ r t p r 0 s r t F - O S

- d n o CT 0 s CT P - T J ro H p - o CT P - P * P - r t CL*-< J O H i 0 J *< ro 0 i H •* # o 3 ro < o c p * C L O S r t 0 s r t P - 0 1 f t O H i X ) 0 1 P - 3

• 1 P - C O ro a s w 0 J r t r t 0 i O 7? 0 1 O H i O J 3 F - 0 Q O 0 s P - P - P"-«* i p - C O r t ro X P - T J O O s P-> P * *- < T J • 1 ro C L F - O r t ro C u • o H i o o c 1 1 C O ro ro o r t r t p r ro O S ro 0 J r t r t O J o • V O J f t o o o n c 1 1 F - 3 O J CT ro o 9 CT p - P - p - r t

- < O H i

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. O < ro i r t CT ro e O J H C L O S •

* > O S r t p r ro 0 ro o s 3 T J n o c r <• ? I r t p r ro C L p - C O r t N P - CT C r t P - O 3 O 3 3 O CA C O CT F - H i r t t o p - r t • ^ r t p r 0 3 r t 3 p - r t T J P - P * f t O J *-< J p . v ^ C O O i O o O 0 o $. p - p * r t F - C O ro 0 s CT O < ro O S 3 P*--<* S ro H i r t t 0 1 1 1 C L 0 1 « . r t p r ro C L p - 0 1 o 3 ro o H i r t p r ro P « p - s v ro P r t V J H P - CT C r t P O 3 o 3 r t CT ro T J 1 1 o CT O J 1 < O S M c ro C O o H i 3 O C A

. £ CT ro 3 CT P - 3 O S r t o 3 o H i r t p r ro X I n o o i o

H i r t CTO S ro 3 0 Q C L p . P - 3 0 1 O S r t p - n P-X J CT0 s C P - r t 3 p . » O 3 0 s C O O 3 o s ro r t ro p r3 ro O Q CTX I H 0 1 CT P - P * P - O J r t F - n < 0 J P » c ro C O o H i 3 o H O S o * d CT CT 0 3 P - CTO p . d > p * p " p - p - r t *v < • ^ P - O 3 H i * i | O s P - 3 O Q O Q C P - H 3 ro 0 J P " U l

. CAX ) -P - *
. 0 J 3 C L
r t CT ro T J • 1 o CT O J CT 0 3 • p - 3 •^1 p - 0 Q o c 1 1 H o s ro T J r t

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. P 1

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J p - 1 - 1 H o CT O l CT 0 1 O Q P « • 0 ro 3 P * ro < ro P o H i 3 o C A 0 Q P . < ro 3 r t CT ro o o 3 3 CT P 1 • ? < ro P * 1 i 0 1 CT P - H i f t 0 1 r t O r t CT ro P ro H i r t O J Ct Ct o n C L F - 3 P - 0 Q P- * 1 > o s o o P C CT i l F - 0 1 ro CO *
. r t CT ro C L h * 0 1 r t H F - CT C r t P - o 3 o H i •T J n x - s 3 o CA —— n o P C CT N - ^ 1 1 r t CT ro x > H o CT 0 1 CT P - P 1 P - r t *-< J O H i OQ

P - < ro 3 M ro < ro p - O H i ClOHb • € CT ro H ro • n 1 •— » 3 o n V 3 O CA o o P C CT ^^. F - O J r t CT ro T J H O CT O J CT F - P - P - r t •-<• ; r t p r O J r t 3 o o ro X o ro ro C L C O 3 O CA H i O H 0 1 •- d • 1 /—\ 3 o o V 3 o C A O O P C CT N _ ^ u 3 o *_ ^ C A» m H y—\ 3 O C A • o o P C CT v ^ s t
_ o ^— » o V 3 o C A T J

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• - N 0 J ( X p - 0 J o H ro r t F - N ro C L < ro H 0 1 p - o 3 o H i r t p r ro cu p - 0 J r t H F - CT C r t P - o 3 p - 3 HI p - 0 Q C H ro U l • p • v , ^ •• p > 0 J 1 1 CT o C H s-\ 0 J 3 C L CT ro 3 o ro H i o H 0 J 0 Q P > < J ro 3 n o P C CT N - ^ CT ro o H i 3 o C A

. HI o 1 1 p - 3 c o r t O J 3 r s ro P • ro r t r t p r ro T J • 1 o CT 0 3 CT P - < : O s P * c ro CT ro o o 0 ro t o 0 1 3 ro X X ) ro o r t O J r t P - o 3 o < ro i r t CT ro P"- d P - r t C L ^4 ro t o o H F - CT ro C L CT • ^ r t p r ro H i o !-- • P - o i p - 3 0 Q C L p - 0 1 1 H i O r l 0 s 3 o C A P 1 ro < ro I - * H i o H 0 1 X J O s H r t F - O c 1 H o CT O J CT P - P 1 P - r t • ^ O H i 0 J r t r t O J o ? r O J r t ro O s n CT P - ro < ro P 3 o i o 3 w- < • o a ro Ct O S c 0 3 ro 3 O C A F - O S O S P- * O S O O J r t 0 J r t ro C L T J 1 o CT O J CT P - p r o c H r t p r O S r t P - 0 J ro X I c O J p " r t O r t CT ro X ) i l O CT O J CT P - P - P - r t P-*-<J P - 0 1 r t F - Cl O S P- * P 1 *- <
y - ^ Hi P - 0 Q c n ro r t CT O J r t 3 o O < P - P - P - CT ro UlO Q • p « N _ ^ • • r t p r ro H ro O J r t ro r i r t CT O S 3 0 J 3 H i P - v < 0 Q CT O c H • 

### E ? F - 0 J C L P - C O f t n F - CT C r t F - O 3 H ro 0 3 c M r t C O p - 3 O J T J n O CT O J CT P - P - P - r t ••<•! O H i O J 3 0 Q F - 3 O J O J r t r t 0 J O 7? 0 i r t C i i ro C L ro XI p - Ct r t C O r r CT ro T J 1 1 o CT 0 3 CT F - P " P - r t • ^ r t CT 0 3 r t 3 o O H P - 0 3 ro O S O J c r O < ro 0 3 o ro H r t 0 J p - 3 < 0 J P * c ro p - 3 • - N (JJ *> a "

- d o T J < ro ro 3 - Cu p . ( U X3 Cu D J v ^ p . •r t 0 1 T J C O 1 CT ^-\ p j S ^ o ro Ct *-— ' F - 0 3 p . c o o O r t 3 p ro s ro p - C O T J r t • i ro O 3 CTr t O J CT« ; p * p . P 1 r t P * CT r t •v ^ p 3 C U ro OJ 3 f l 0 3 r t P * C r t O s •-< ! P * H I ro C 3 3 O o o r t C U p* ro O C L 3 C L H i P - O C O 1 r t H 3 P - O CT n c •r t P - O H 3 CT F - 0 3

### 1. 0

Probabilit y

CT > 

### 0. 5

10,00 0 COHb= 0 

### 1. 0

30,00 0 

### 0. 5



### 0 C0Hb=12 %

10,00 0 30,00 0 Doubl e Produc t (m

mHg—beats/min )

FIGUR E 5. ARELATIONSHIP S BETWEE NLOWERE D MO S AND HIGHE R FREQUENC Y O F ANGINAL

PAIN , Pr(M0 C > MOS) . CURVE AI S Pr(MOS|COHb) ; CURVE B I S Pr(MO S > DOUBL E PRODUCT) ; CURVE C I S Pr(MO C > MOSjCOHb )P.6 5



### T o actuall ycalculat e th e frequenc yo f angina l pain , th e inhabitant s o f eac hcit ywer e divide d int o fiv e groups . Eac hgroup woul d hav e a dif

feren t patter n o f C O exposur e and o f incidenc e o f ASHD . Tabl e 5. 1 de

scribe s thes e groups . Th e range s presente d represen t difference s betwee n th e fou r cities ; "as-is " refer s t o th e exposure s estimate d o n th e actua l monitorin g data . Th e incidenc e o f angin a amon g th e cohort s i s no t wel l known , and illustrativ e number s wer e supplie d fo r us e i n th e analysis ; th e result s o f th e mode l ar e fortunatel yno t sensitiv e t o thi s assump tion . Th e mode l assume s th e sam e distribution s o n MO C and MO S fo r eac h group . Furthe r developmen t o f th e mode l woul d rela xthi s assumption , al

lowin g fo r th e cohort s t o hav e differen t distribution s o n th e severit yo f th e disease , and o n activit ypatterns .
Tabl e 5. 1 DEFINITIO NO F COHORT S USE D I NASH D MODE L

Cohor t 1 2 3 4 5 Descriptio n Student s and clerica l worker s Sale s personne l and servic e worker s Professionals , craftsme n and laborer s Marrie d wome n Retire d and unemploye d Rang e o f Avg . % COH b (As-Is ) .40-.8 1 .42-.8 2 .42-.8 5 .42-.6 5 .42-.6 0 Percen t

o f Cohor t tha t i s Mal e 34-3 8 49-5 7 7 7 0 39-4 2 Estimate d

Percen t Wit h Angin a Me n Wome n 0 0 2 .0 5 5 . 1 .0 5 1 0 2

Tota l attack s o f angin a pe r da yar e calculate d a s follows : 

# E ( # angin a patients ) xV ^ Pr(COHb ) xPr(M0 C > MO S | COHb ) [5.4 ] i n cohor t *-^ cohort s COHb Th e angin a mode l represent s ho wischemi a change s a s a functio n o f th e ambien t exposur e t o CO . Thi s mode l wa s easil ydevelope d becaus e o f th e clinica l dat a available . Th e mor e sever e effect s o f ASHD , suc ha s acut e coronar yinsufficiency , acut e myocardia l infarction , and sudde n hear t deat h(ventricula r fibrillation ) ar e no t eas yt o relat e clinicall y t o C O exposures . Controlle d test s ar e ou t o f th e question . Onl yepidem

iologica l and anima l dat a ca n b e collected .

6 2P.6 6

Despit e the problem s i n testing fo r thes e healt heffect s i n relatio n t o CO , a n estimat e o f the relativ e change s i n ris k i s possible . Th e an gin a mode l predict s relativ e frequencie s o f painfu l ischemia . Whethe r ischemi a i s painfu l o r painless , the patien t i s highl ysusceptibl e t o fibrillatio n a t tha t moment . Thu s on e woul d expec t tha t the incidence s o f fibrillatio n woul d chang e roughl yi n proportio n t o the change s i n fre

quenc yo f angina l pain . Drs . Kuller , Horvath , an d Arono wconcu r i n thi s (se e Appendi xB) . Incidenc e o f myocardia l infarctio n ca n b e estimate d i n a simila r manner . I f elevate d COHbi s causing additiona l morbidit yalong the en tir e spectru

mo f ASH D manifestations , the n on e mus t conclud e tha t the mos t sever e ischemi c episode s mus t als o increase . Thu s th e outpu t o f th e angin a mode l i s als o use d t o estimat e change s i n relativ e frequenc yo f acut e myocardia l infarction s (AMI) . Roughestimate s o f change s i n ASHD mortality , du e bot ht o AM I an d fibrillatio n ar e calculate d a s follows : (Mortalit yfo r give n standard ) = (Mortalit ya t curren t levels ) x( frequenc yo f angin a fo r standar d ) [5.5 ] (frequenc yo f angin a a t curren t levels )
Drs . Kuller , Horvath , an d Arono wagre e tha t thi s mode l i s a goo d prelimi

naryrepresentatio n o f the problem . Probabilisti c Phas e Thre e source s o f uncertaint yidentifie d i n a sensitivit yanalysi s wer e accounte d fo r explicitly . Tw o parameter s describ e the variabilit y o f MOC . Th e third refer s t o ho wmuc hMO S actuall yshift s fo r differen t concentration s o f COHb . Th e "composite " patien t experience s angina l pai n a t a D P betwee n 17,00 0 an

d 21,000 , whe n COHbi s no t elevated . Alognorma l distributio n fo r hi s MO C wa s assume d (Figur e 5.3) , wit ha minimu mvalu e o f 4,000 . Sensitivit ytest s indicate d tha t thi s minimu mvalu e i s no t an importan t sourc e o f uncertainty . Uncertaint yi n the two othe r parameter s o f the lognorma l distribution , u an d c r , di d resul t i n quit e a bi t o f uncer tainty . Withi n the rang e allowe d b yphysiologydiscretize d distribution s o f yan d c r ar e provide d i n Figur e 5.5 . Th e frequencie s o f differen t lev

el s o f MO C implie d b ythe thre e value s o f ya t the mea n valu e o f c r ar e liste d i n Tabl e 5.2 ; representativ e value s o f hear t rat e (HR ) an d sys toli c bloo d pressur e (SBP ) ar e liste d fo r eac hleve l o f MOC . Thes e prob

abilitie s shoul d b e closel yscrutinize d b yal l thos e familia r wit hactiv

it ypatterns . Theyshoul d b e representativ e o f wha t the typica l stabl e angin a patien t migh t b e experiencing . On e shoul d not e tha t the assump tio n o f lognormalit yi s a strong one . Th e relativ e result s o f th e mode l


### 6 3Paramete r y fo r MO C Paramete r a fo r MO C Slop e o f MO S a s functio n o f o f % COHb 4.5 4 

### .2 5 -50 0

CT > -77 0

-100 0

FIGUR E 5. 5 PROBABILIT Y TRE E FO R ANGIN AMODEL . TH E PARAMETER S yAN D o AR E TH E PARAMETER S FO R TH E LOGNORMAL DISTRIBUTIONP.6 8

Tabl e 5. 2 FREQUENCIE S O F DOUBL E PRODUC T FO R ARANG E O F VALUE S O F y



### DP (i n 100s ) 

### 4 0 6 0 8 0 

### 10 0 11 0 12 0 13 0 14 0 15 0 16 0 17 0 18 0 19 0 20 0 21 0 Represen
tativ e HR/SB P (45/90 ) (60/100 ) (70/115 ) (85/120 ) (90/125 ) (95/125 ) (100/130 ) (100/140 ) (110/140 ) (110/145 ) (115/150 ) (120/150 ) (120/160 ) (125/160 ) (130/160 ) Probabilit y

y= 4.5 4 

### 1 1 1 .9 3 .8 3 .7 0 (1 7 hr/day ) 

### .5 5 .4 1 .3 0 .2 0 (4- 5 hr/day ) 

### .1 4 .0 9 .0 6 .0 4 .0 2 ( 3 hr/week ) o f Exceedin g D P I n y= 4.6 9 

### 1 1 1 .9 8 .9 3 .8 5 (2 0 hr/day ) 

### .7 4 .6 1 .4 9 .3 7 ( 9 hr/day ) 

### .2 8 .2 0 .1 4 .1 0 .0 7 (1- 2 hr/day ) An

yHou r o f a Da y y= 4.8 3 

### 1 1 1 .9 9 .9 7 .9 3 (al l bu t 1 hr/day ) 

### .8 6 .7 7 .6 7 .5 6 (1 3 hr/day ) 

### .4 5 .3 5 .2 7 .2 0 .1 5 (3- 4 hr/day )

a = . 3 i n al l cases .

a t differen t standard s depend o n thi s assumption . Unti l bette r evidenc e i s available , thi s distributio n wa s fel t t o b e the mos t reasonabl e wayt o approximat e the activit ypatter n o f a sedentaryperson . Preliminaryin

formatio n an

d encoding (Appendi xB ) indicate d tha t thi s distributio n wa s no t unreasonable . Withi n time an d budget , however , SR I wa s no t abl e t o fin d a suitabl e exper t i n thi s area . Th e third uncertaint ylie s i n the slop e o f the functio n fo r the mea n MOS . Aronow' s evidenc e indicate s a slop e o f -77 0 (Equatio n 5.1) . Thi s slop e i s uncertai n becaus e o f the paucit yo f dat a point s fo r eac hindi

vidual , especiall ya t the highe r value s o f COHb . I n addition , i t i s b y n o mean s certai n tha t the subject s use d b yArono war e representativ e o f 

### 6 5P.6 9

the genera l populatio n o f angin a patients . Finally , eve n b yabstractin g t o a singl e "composite " type , uncertaint yremain s fo r tha t individual . Figur e 5. 6 demonstrate s ho wthe measure d slop e ma yvaryfo r a singl e in

dividual . Le t the tru e slop e b e -700 , a s indicate d b ythe soli d lines . MO S ca n fluctuat e b ya s muc ha s 10 % fro mtes t t o test . Durin g the con tro l test , the patien t ma yb e a t a relativel ylo wMOS , resultin g i n poin t A . Th e C O exposur e tes t mayoccu r a t a time whe n MO S i s higher , result

in g i n poin t B . Th e slop e measure d fo r thi s individua l (dotte d line ) woul d b e flatte r than the tru e valu e fo r tha t individual . Unfortunately , i t wa s no t possibl e t o encod e expert s o n thi s uncer tainty . Dr . Arono wwa s unwillin g t o commen t furthe r o n hi s data , an d othe r expert s wer e unwilling t o expres s thei r judgmen t withou t seeing hi s data . Thus , SR I wa s force d t o furnis ha preliminaryjudgmen t o f uncer tainty . T o captur e thes e uncertainties , a probabilit ydistributio n wa s place d o n the slop e o f -770 . Th e range s wer e approximate d b yan analysi s o f the variatio n among individual s i n Aronow' s studies . Th e discret e ap proximatio n use d i s illustrate d i n Figur e 5.5 . Th e probabilit ydistributio n o n averag e attack s pe r angin a patien t pe r da yi n Lo s Angele s ar e provide d i n Figur e 5. 7 fo r the strictes t an d mos t lenien t standards . Th e shap e o f the distribution s differ s verylit

tl e fro mstandard t o standar d an d cit yt o city . I t shoul d b e note d tha t the parameter s o f the distribution s coul d b e adjuste d s o tha t the "compo
site " perso n experience s some give n averag e numbe r o f attack s o f angin a pe r da yfo r the as-i s situation . Becaus e ther e i s littl e reliabl e infor

matio n o n the averag e numbe r o f angin a attack s pe r day , w e hav e preferre d t o maintai n the uncertaint yo n the parameter s o f the distribution .
* Thes e distribution s ar e base d o n a weighte d averag e acros s the fiv e cohorts . Therefore , t o calculat e the tota l numbe r o f attack s pe r year , on e shoul d multipl yb y(36 5 x120,000) , wher e 120,00 0 represent s the ap proximat e numbe r o f peopl e i n Lo s Angele s wh o suffe r fro mangina . Tabl e 5. 3 present s mea n value s fo r tota l angin a attack s i n Figur e 5.7 . Tabl e 5. 4 summarize s the expecte d change s i n attack s i f eac ho f the seve n standard s i s implemente d i n Lo s Angeles . T o obtai n a distributio n o n ASHD mortality , Equatio n 5. 5 an d distri

bution s o n relativ e change s i n angina l pai n fro mcurren t condition s wer e used . Figur e 5. 8 present s thes e relativ e change s i n predicte d angin a pai n fo r Lo s Angeles . Result s fo r the othe r citie s ar e i n Appendi xA . A s discussed , the mode l assume s tha t the relativ e frequenc yo f angina l pai n i s an indicato r o f relativ e frequenc yo f ischemi a i n general , an d henc e i n mortality . Avalu e o f 1.0 1 implie s a 1 % increas e i n ischemi c episode s ove r th e leve l currentl ybein g experienced . T o calculat e actua l mortality , on e multiplie s the relativ e factor s b ythe curren t numbe r o f ASH D death s i n the expose d population . Fo r Lo s Angele s County , approxi

matel y30,00 0 death s occu r eac hyea r becaus e o f AM I o r ventricula r fib

rillation . Th e secon d se t o f value s i n Figur e 5. 8 indicat e ho wman y death s woul d occu r a t the differen t standards , i n compariso n t o a n as sume d curren t leve l o f 30,000 . Tabl e 5. 5 compare s thes e result s t o th e 

### 6 622,50 0

20,00 0

cr* . -vi Doubl e Produc t 1 7 50 0 (m m Hg—beats/min )
15,00 0

12,50 0

FIGUR E 5. 6 UNCERTAINT Y I NMEASUREMENT O F SLOP E O F DOUBL E PRODUC T A T ONSE T O F ANGIN AP.7 1



### 1. 0 tx > rt X I o l- l C U 0. 5 

### 9 ppm , 8 hr , 1 exe x

3 5 ppm , 1 hr , 1 exe x

2 3 Attacks/day/patien t 

### 4 4 8 8 13 2 17 6 22 0

1 0 Attacks/yea r

FIGUR E 5. 7 DISTRIBUTION S O F INCIDENC E O F

ANGINA L PAI NFO R LO S ANGELE S



### 6 8P.7 2

Tabl e 5. 3 MEAN S O F TOTAL ANNUAL INCIDENC E

O F ANGINAL PAI N (Lo s Angeles )

Standar d 3 5 ppm1 hr 1 exe x 2 5 pp m1 hr 1 exe x 2 5 pp m1 hr 5 exe x 1 2 pp m8 hr 1 exe x 1 2 ppm8 h

r 5 exe x 9 pp m8 hr 1 exe x 9 pp m8 hr 5 exe x Mea n , (i n 10 b attacks ) 82. 3 80. 8 82. 1 79. 9 80. 5 78. 9 79. 7

Tabl e 5. 4 COMPARISONO F ANNUAL INCIDENC E O F ANGINAL PAI N

(Lo s Angeles )

Standar d 3 5 pp m1 hr 1 exe x 2 5 ppm1 hr 1 exe x 2 5 pp m1 h r 5 exe x 1 2 ppm8 hr 1 exe x 1 2 ppm8 hr 5 exe x 9 pp m8 hr 1 exe x 9 pp m8 h r 5 exe x Chang e F

Millio n Attack s 

### 0. 6 -0. 9 

### 0. 3 -1. 9 -1. 2 -2. 9 -2. 0 romAs-I s

Percen t o f As-I s 

### 0. 7 -1. 2 

### 0. 4 -2. 3 -1. 5 -3. 5 -2. 5 Chan g Strictes t Millio n Attack s 

### 3. 5 1. 9 3. 2 1. 0 1. 7

— 

### 0. 9 e From

Standar d Percen t o f Standar d 

### 4. 4 2. 5 4. 1 1. 3 2. 1

— 

### 1. 1

6 9P.7 3

l.OOr

Probabilit y (25,1 ) <35,1 )

1.0 0 1.0 5 chang e

Probabilit y 1.0 0

.75 .

.50 .

• 25 _ 5 exceedance s

pe r yea r (9,8 >

(12,8 ) 25,1 )



### .8 5 25,00 0 

### .9 0

27,00 0 

### .9 5

28,50 0 1.0 0

30,00 0 1.0 5

31,50 0 chang e

death s

FIGUR E 5. 8 PROBABILIT Y THAT FRACTIONAL CHANG E FRO M AS-I S

SITUATIONFO R ISCHEMI C EPISODE S I S LES S THA NTHAT INDICATE D (LO S ANGELES) . ESTIMAT E O F DEATH S ASSUME S CURRENT ASH D DEATH S I NLO S ANGELE S AR E 30,00 0 PE R YEAR . STANDARD S AR E INDICATE D B Y (CONCENTRATION [ppm] , AVERAGING TIM E [hr] )



### 7 0P.7 4

Tabl e 5. 5 COMPARISONO F PREDICTE D CARDIOVASCULA R MORTALIT Y (Lo s Angeles )

Standar d 3 5 ppm1 h r 1 exe x 2 5 ppm1 hr 1 exe x 2 5 pp m1 hr 5 exe x 1 2 pp m8 hr 1 exe x 1 2 pp m8 hr 5 exe x 9 pp m8 hr 1 exe x 9 pp m8 hr 5 exe x Chang e Death s 

### 23 1 -38 8 

### 13 1 -76 1 -50 4 -1,18 0 -82 2 * FromAs-I s Percen t o f As-I s 

### 0. 8 -1. 3 

### 0. 4 -2. 5 -1. 7 -3. 9 -2. 7 Chang e From

Strictes t Standar d

Percen t Death s o f Standar d 1,41 1 

### 79 2 1,31 0 

### 41 9 67 5

— 

### 35 8 4. 9

2. 7 4. 5 1. 5 2. 3

— 

### 1. 2

Tota l ASHD death s i n as-i s se t a t 30,000 . Tota l ASH D death s fo r strictes t standar d = 28,820 .
as-i s situatio n an d t o the expecte d valu e fo r the strictes t standar d o f 9 ppm , 8 hou r averag e ( 1 expecte d exceedanc e pe r year) . I t shoul d b e note d tha t ther e i s a larg e additiona l uncertaint yas sociate d wit hthe mode l fo r ASH D mortality . Th e deat hrat e ma yincreas e mor e slowl yo r mor e rapidl ythan the rat e o f ischemi c attacks . W e prefe r no t t o assign a probabilit ydistributio n t o thi s uncertaint yunti l the mode l ha s bee n expose d t o publi c comment . Fo r bot hangina l attack s an d cardiovascula r mortality , extrapolatio n o f the mode l t o zer o ambien t C O concentration s provide s a n estimat e tha t perhap s 10 % o f thes e effect s i n Lo s Angele s ca n b e attribute d t o ambien t C O concentrations . Discussio n Thes e estimate s o f effect s o n cardiovascula r diseas e patient s ar e significant , despit e larg e uncertainties . An expecte d 3.9 % o f the annua l ASH D death s i n Lo s Angele s migh t b e prevente d i f the strictes t standar d wer e met . Uncertaint yplace s a verylarg e rang e o n thi s value : fro m abou t 2 % t o abou t 8% , a s ca n b e see n i n Figur e 5.8 .



### 7 1On e shoul d kee p i n mind the crudenes s o f the input s tha t predicte d

thes e distributions . Thi s mode l ha s bee n base d o n a singl e "composite " patient , wit ha fairl yarbitrarydistributio n o n bot hMO C an d MOS . Wid e range s wer e place d o n the inpu t parameter s t o maintai n explici t recogni

tio n o f the majo r uncertaintie s involved . Despit e thes e larg e uncertain

ties , eve n the verylo wen d o f the rang e result s i n significan t change s i n mortalit y(2%) . Tw o percen t o f 30,00 0 death s implie s a decreas e o f 60 0 death s pe r yea r b yreaching the strictes t propose d standar d i n Lo s Angeles . Despit e the apparentl ylarg e absolut e effect s o n ASHD , the percen t change s fro

mcurren t condition s ar e quit e small . Epidemiologica l studie s woul d b e unlikel yt o detec t change s a s smal l a s 2% . Change s i n ASHD mor talit ydu e t o ambien t C O fluctuation s tha t occu r ove r a shor t time spa n woul d probabl yonl yappea r wit hborderlin e significanc e i n populatio n studies . I f the C O fluctuation s i n the sampl e ar e no t a s larg e a s thos e o f going fro

mon e standar d t o another , the n change s i n mortalit ymigh t no t appea r a t all . Afewepidemiologica l studie s o f C O an d ASHD hav e bee n performed . Thei r implication s ca n b e compare d t o the result s o f thi s model . Astud y b yKulle r e t al . o f myocardia l infarction s an d sudde n hear t death s i n a n are a wit hmoderat e ambien t C O level s foun d n o significan t change s i n th e sever e ASH D healt heffects , i n term s o f eithe r incidence , o r o f case fatalit yrate s (the proportio n o f hear t attac k victim s wh o d o no t surviv e th e attack) . Astud yb yCohe n e t al . an d on e b yHexte r an

d Goldsmit h i n a highe r exposur e area , however , d o fin d an apparen t effect . Th e Cohe n stud

ylook s onl ya t hospita l admission s an d case-fatalit y rate s fo r acut e myocardia l infarctio n (AMI) . I t neglect s the possibilit y o f a n increas e i n sudde n death s o n high-exposur e days , whic hneve r becom e hospita l admission s becaus e o f the highprehospita l mortalit yo f sudde n death . Th e dat a provid e n o evidenc e o f increase d AM I admission s a t all . Ther e i s some suggestio n i n Cohen' s result s o f a "threshold, " o r very fla t dose-respons e curv e i n low-C O period s fo r admission-fatalit yrate s (th e proportio n o f AM I hospita l admission s wh o die) . I n period s o f ex tende d highexposure s (weekl yaverag e C O > 8 ppm) , a n elevatio n i n AM I admission-fatalit yrate s di d appear . Th e Hexte r stud

yprovide s muc hcleare r evidenc e o f a correlatio n be

twee n dail yaverag e C O an d deaths . Th e exposure s i n thi s stud yar e ex tremel yhigh , i n the rang e o f 7. 3 ppmt o 20. 2 pp mdail ybasi n averages . Al l thes e value s ar e probabl ylocate d abov e the apparen t "threshold " o f 8 pp mweekl yaverag e i n the Cohe n study . Hexte r look s a t tota l death s rathe r than admission-fatalit yrates . Hi s paper , publishe d i n Science , provide s the coefficient s fro ma regressio n o f al l dail ydeath s i n Lo s Angele s Count yagains t the logarith mo f C O dail yaverage . Temperatur e an d time hav e als o bee n controlle d i n the regression . Th e t-statisti c o n the C O coefficien t i s significan t a t th e 1 % leve l ( t = 3.09 , degree s o f freedom>1000) . Simila r significanc e i s no t foun d whe n the same regres sio n i s performe d fo r tota l oxidants . Thi s fac t provide s evidenc e tha t the relationshi p t o C O i s no t spurious , o r cause d b yomitte d variables . Th e reaso n tha t oxidant s d o no t correlat e wit hdeath s i s probabl ybecaus e o f a lag i n it s healt heffects , wherea s physiologica l effect s o f CO ar e


### 7 2P.7 6

conduciv e t o immediat e respons e o f the organism , wit hlittl e i n the way o f longer-termeffects . Ther e ar e two problem s wit hthe us e o f the Hexte r results : (1 ) the regression s ar e fo r tota l mortality , no t ASH D alone ; an d (2 ) the us e o f a lo g transfor

mo f CO i n the regression . Followin g persona l communication , Dr . Hexte r provide d the unpublishe d result s fro madditiona l regressions . Thes e regression s divide d the tota l death s int o fiftee n categories , using the same model . Onl yfour o f thes e categorie s o f death s displaye d a sig

nifican t correlatio n (a t the 1 % level ) wit hCO : cardiovascula r diseases , ASH D i n particular , some respiratorydiseases , an d moto r vehicl e acci

dents . Th e coefficien t o f log, nC O fo r dail yASH D death s i s 15. 6 ( t = 3.85) . Hexter' s equatio n ca n b e translate d int o a dail ymortalit y rat e b ydividin g b

ythe populatio n i n Lo s Angele s Count ydurin g th e study , whic hwa s approximatel y6. 6 million . Th e following curv e (wher e al l cycli c an

d temperatur e effect s ar e assume d a t thei r means ) results :
ASH D dail ydeat hrat e = 3.96 5 + 2.3 7 (log 1Q C O dail yaverage ) [5.6 ] (pe r 1 0 pop )
Ther e remain s a problemwit hthi s curve . It s specificatio n canno t possibl yb e correc t o n a globa l basis . A s the dail yaverag e o f C O ap proache s zer o ppm , the predicte d dail ydeath s becom e negative . Extrapo
latio n o f regressio n result s beyond the rang e o f the origina l dat a i s a ver yspeciou s practic e i n an ycase . Probabl ya mor e reasonabl e specifi

catio n woul d hav e bee n a sigmoida l shape , a s i n Figur e 5.9 . Wit ha sigmoida l relationship , lowe r level s o f C O becom e insignifi

cant . A"threshold " effec t appears , a s i n Cohen' s study . At midrang e levels , the bul k o f the sensitiv e peopl e becom e affected . Th e apparen t effec t slow s a s highe r C O level s ar e reache d becaus e the sensitiv e peopl e hav e al l bee n affecte d b ytha t point . Hexte r apparentl ymad e hi s estima

tion s i n the middl e rang e (averag e dail yexposure s during the year s o f the stud

yhad a minimu

mvalu e o f 7 ppm) . Estimate s fro mEP Adat a fo r thi s ris k assessment , however , indicat e exposure s mostl yi n the lowe r range . Asimpl e extrapolatio n o f the Hexte r logarithmi c curv e woul d b e unreasonable . ( Ates t run doe s indee d resul t i n gros s underestimate s o f ASH D death s fo r th e as-i s case. ) On e possibl e approac ht o resolving thi s problemi s t o superimpos e a threshold . Crudely , thi s approximate s the sigmoida l curve , a s show n i n Figur e 5.10 . Th e functio n woul d b e
ASH D mortalit yrat e = = max[f(C 0 = threshold) , f(C0) ] [5.7 ]
Severa l threshol d value s ca n b e use d t o obtai n a rang e o n the implica

tion s o f Hexter' s results . T o estimat e the numbe r o f death s fromASH D predicte d b ythi s dose

respons e relationship , a mathematica l expectatio n i s take n ove r the 

### 7 3Respons e

Dos e

FIGUR E 5. 9 EXAMPL E O F ASIGMOIDAL DOSE-RESPONS E CURVEDail yASH D

Mortalit y

6 4 (death s /I O pop. )

U l

A6 C O Dail yAverag e (ppm )

FIGUR E 5.1 0 HEXTE R DOSE-RESPONS E CURVE WIT H

SUPERIMPOSE D THRESHOL D A T C O = 5 ppmP.7 9

curve . Th e probabilit ydensit yfunctio n use d fo r the expectatio n i s de

rive d fromthe EP Amonitorin g dat a use d i n the exposur e model . Fo r eac h city , a singl e monito r wa s chose n a s being "representative " o f the cit y a s a whole . Th e monitor s chose n ar e describe d i n Tabl e 5.6 .

Tabl e 5. 6 C O MONITOR S USE D T O TES T IMPLICATION S O F HEXTE R MODE L Cit y Chicag o Lo s Angele s Philadelphi a St . Loui s SAROA D Cod e 141220039F0 1 •05872000110 1 397140026H0 1 260200002G0 1 Statio n Typ e

Cit yCommercia l Suburba n Residentia l Cit yCommercia l Suburba n Residentia l

Tabl e 5. 7 compare s the result s o f the modifie d Hexte r mode l wit hthe prediction s o f the ASH D mode l fo r ASH D mortality . Th e range s o n the out
come s fo r the Hexte r mode l ar e base d o n a rang e o f CO "thresholds " fro m0 t o 5 pp mdail yaverage . Th e uncertaintie s ar e verylarge , an d no rea l conclusion s ca n b e drawn fro mthe results . However , the two model s ar e i n amazingl yclos e agreement .

Tabl e 5. 7 COMPARISONO F EPIDEMIOLOGICAL RESULT S AND O F ASH D MODE L FO R DEATH S I NLO S ANGELE S

Standar d 3 5 ppm1 h r 1 exe x 2 5 pp m1 hr 1 exe x 2 5 ppm1 hr 5 exe x 1 2 pp m8 hr 1 exe x 1 2 ppm8 hr 5 exe x 9 pp m8 hr 1 exe x 9 pp m8 hr 5 exe x Epidemiologica l Predictio n (Percen t o f As-Is ) 0. 3 t o 1. 1 -0. 4 t o -2. 0 0. 2 t o 0. 7 -0. 7 t o -4. 7 -0. 5 t o -2. 8 -0. 7 t o -6. 7 -0. 7 t o 5. 2 ASHD Mode l

Predictio n (Percen t o f As-Is ) 

### 0. 8 -1. 3 

### 0. 4 -2. 5 -1. 7 -3. 9 -2. 7



### 7 6P.8 0

Othe r Manifestation s o f ASH D Acut e coronaryinsufficienc yan d othe r unstabl e form s o f angin a ar e syndrome s tha t ar e intermediat e betwee n stabl e angin a (whic hdoe s no t re sul t i n irreversibl e damage ) an d infarctio n (whic hi s accompanie d b yir

reversibl e damage) . Th e mai n differenc e betwee n unstabl e an d stabl e an gin a pectori s i s tha t attack s o f unstabl e angina * ar e no t preceede d b y obviou s stress , o r d o no t subsid e whe n a t rest . Seru menzym e concentr
ation s ar e no t elevated , however , an d irreversibl e damag e i s no t appar ent . Thi s stud

yassume s tha t thes e attack s ar e simila r i n natur e t o thos e o f stabl e angina . Th e apparen t differenc e i s tha t the myocardia l oxyge n suppl ycapacity , MOS , ha s becom e s o lo wi n thes e patient s tha t eve n smal l increase s i n MO C canno t b e me t b ythe cardiovascula r system . Attack s occu r whe n n o stres s ha s bee n apparen t t o the patient . The yar e assumed , however , t o b e du e t o some smal l elevatio n i n MO C o r fal l i n MO S tha t occur s a t rest . Althoughunstabl e angin a patient s ar e mor e susceptibl e t o change s i n COHbbecaus e o f the steepnes s i n the MO C distributio n a t lowe r values , ther e i s assume d t o b e n o intrinsi c differenc e betwee n the mechanism s fo r stabl e an

d unstabl e angina l attacks . T o accoun t fo r unstabl e angin a pa tient s explicitly , on e woul d mode l thei r distributio n o f MO S an d thei r MO S response s t o COHbseparatel yfro mtha t fo r stabl e angina . Th e MO S functio n woul d b e mor e judgmenta l than tha t use d fo r stabl e angin a be

caus e ther e ar e n o clinica l dat a o n the response s o f thes e peopl e t o COHb . Th e curren t mode l assume s tha t the relativ e effect s o f COHbar e the same fo r bot hstabl e an d unstabl e angina , an d treat s thes e patient s a s i f theymatche d the characteristic s o f the composit e patien t describe d above . Angin a decubitu s als o seem s t o matc hthe structur e o f thi s model . Th e differenc e appear s t o b e du e t o myocardia l wal l stres s whil e reclin

ing . Thi s effectivel yincrease s the MO C during moment s whe n the patien t i s lying down , thereb yincreasing the probabilit yo f a n attac k fo r a give n MO S whe n reclining . Th e mode l her e doe s no t explicitl ytrea t thes e peopl e becaus e theyar e a smal l portio n o f the population . Prinzmetal s angin a i s no t handle d b ythi s mode l a t all . Firs t o f all , thi s i s a relativel yrar e form , wit hfewpeopl e i n the sensitiv e population . Thi s effec t I s believe d t o b e cause d b ycoronaryspasms , whic har e a suppl yeffect . Othe r form s o f angina l pai n cause d b yverylarg e an

d irregula r re duction s i n MO S ar e neglecte d b ythi s model . Include d i n thi s category ar e attack s cause d b yexposur e t o cold . Ther e i s n o obstacl e t o adding themvi a a greate r downsid e variatio n i n the MO S distribution . However , w e d o no t fee l tha t thi s elaboratio n woul d chang e the relativ e ris k o f ischemi a enought o validat e the effort . Anothe r painfu l manifestatio n o f arteriosclerosi s i s tha t o f inter

mitten t claudicatio n o f the extremitie s i n thos e wit hperiphera l vascula r disease . Th e pai n i s cause d agai n b ya defici t o f oxyge n supply , thi s tim e t o the skeleta l muscle . Similarl yt o angin a pectoris , the pai n seem s t o occu r afte r some give n leve l o r duratio n o f exercise , agai n 

### 7 7P.8 1

indicatin g the presenc e o f a relativel ystabl e threshol d o f oxyge n de

mand . Th e mode l fo r angin a thu s contain s al l o f the structur e tha t woul d b e require d t o identif ythe exten t o f aggravatio n o f intermitten t claudi

catio n cause d b

yCOHb . W e hav e no t explicitl ymodele d thi s effec t i n thi s ris k assessmen t fo r tw o reasons . Th e firs t i s tha t thi s particula r manifestatio n o f arteriosclerosi s i s no t a s commo n withi n the genera l populatio n a s the othe r effect s tha t ar e modeled . Th e secon d reaso n i s tha t i t i s no t considere d t o b e a s seriou s a n effec t a s the others . Be
caus e o f the larg e similaritie s i n structure , though , on e ca n obtai n a roughestimat e o f th e proportiona l change s i n the healt heffec t becaus e o f COHbfro

mthe proportiona l change s estimate d fo r angina .
Summary ASH D i s a n extremel ycommo n diseas e i n th e Unite d States . Eve n very smal l change s i n the risk s o f peopl e suffering fro mASH D ca n resul t i n significan t publi c healt heffects . Th e mode l look s specificall ya t tw o aspect s o f ASHD : mortalit yfro mAM I an

d sudde n deat han

d morbidit yi n th e for mo f painfu l ischemia . Despit e the larg e abstraction s an d the grea t uncertainties , on e ca n clearl yse e tha t C O migh t presen t a signifi

can t healt hris k t o thes e people . Abette r quantificatio n o f thes e ef

fect s i s necessaryfo r a prope r inpu t t o the standar d setting process . A s ca n b e see n b

ycompariso n wit hthe spars e epidemiologica l studies , however , the result s a t eve n thi s preliminarystag e mayno t b e far from the rea l situation .



### 7 8P.8 2

REFERENCE S FOR CHAPTE R V
1 . "Hear t Fact s Referenc e Sheet, " America n Hear t Association , 197 5 (PHE7-74-30M) . 2 . U.S . Departmen t o f Health , Educatio n an d Welfare , CoronaryHear t Disease s i n Adults , Unite d States : 1960-1962 . DHE WPublicatio n No . (PHS ) 79-1019 , Hyattsville , Md. , 1975 . 3 . Richar d S . Ross , "Ischemi c Hear t Disease, " Chapte r 240 , Harrison' s Principle s o f Interna l Medicine , Maxwel l W . Wintrobe , Georg e W . Thorn , e t al . (eds.) , 7t hedition , McGraw-Hil l Boo k Company , Ne w York , 1974 . 4 . Wilber t S . Aronow , Cliffor d N . Harris , Micahe l W . Isbell , e t al. , "Effec t o f FreewayTrave l o n Angin a Pectoris, " Annal s o f Interna l Medicine , Vol . 77 , 1972 , pp . 669-676 . 5 . Wilber t S . Arono wan d Michae l W . Isbell , "Carbo n Monoxid e Effec t o n Exercise-Induce d Angin a Pectoris, " Annal s o f Interna l Medicine , Vol . 79 , 1972 , pp . 669-676 . 6 . Wilber t S . Aronow , John Cassidy , Jac k Vangrow , e t al. , "Effec t o f Cigarett e Smoking an d Breathing Carbo n Monoxid e o n Cardiovascula r Hemodynamic s i n Angina l Patients, " Circulation , Vol . 50 , Augus t 1978 , pp . 340-347 . 7 . Wilber t S . Aronow , "Effec t o f Non-nicotin e Cigarette s an d Carbo n Monoxid e o n Angina, " Circulation , Vol . 61 , No . 2 , 1980 , pp . 262-265 . 8 . Wilber t S . Aronow , "Aggravatio n o f Angin a Pectori s b yTw o Percen t Carboxyhemoglobin, " America n Hear t Journal , Vol . 101 , No . 2 , Febrar y 1981 , pp . 154-157 . 9 . Eina r W . Anderson , Rober t J . Andelman , Josep hStrauch , e t al. , "Effec t o f Low-Leve l Carbo n Monoxid e Exposur e o n Onse t an d Duratio n o f Angin a Pectoris, " Annal s o f Interna l Medicine , Vol . 79 , 1973 , pp . 46-50 . 10 . Davi d R . Redwood , Dougla s R . Rosing , Rober t E . Goldstein , e t al. , "Importanc e o f th e Desig n o f a n Exercis e Protoco l i n the Evaluatio n o f Patient s wit hAngin a Pectoris, " Circulation , Vol . 43 , May1971 , pp . 618-628 . 11 . Naphtal i A . Britma n an d Herber t J . Levine , "Contractil e Elemen t Work : AMajo r Determinan t o f Myocardia l Oxyge n Demand, " Journa l o f Clinica l Investigation , Vol . 43 , No . 7 , 1964 , pp . 1397-1408 .


### 7 9P.8 3

12 . Dr . Steve n Horvath , persona l communication . 13 . Lewi s H . Kuller , Edwar d P . Radford , Davi d Swift , e t al. , "Carbo n Monoxid e an

d Hear t Attacks, " Archive s o f Environmenta l Health , Vol . 30 , Octobe r 1975 , pp . 477-482 . 14 . Seymou r J . Cohen , Margare t Deane , an d John R . Goldsmith , "Carbo n Monoxid e an

d Surviva l fromMyocardia l Infarction, " Archive s o f En
vironmenta l Health , Vol . 19 , Octobe r 1969 , pp . 510-517 . 15 . Alfre d C . Hexte r an d John R . Goldsmith , "Carbo n Monoxide : Associa

tio n o f Communit yAi r Pollutio n wit hMortality, " Science , Vol . 172 , Apri l 16 , 1971 , pp . 265-267 .



### 8 0P.8 4

V I FETAL HEALT H EFFECT S

Amode l ha s bee n constructe d t o estimat e feta l healt heffect s cause d b yambien t concentration s o f C O experience d b ythe mother . I t wa s judge d tha t the bes t rout e t o understandin g thes e effect s wa s t o mode l the phys

iologica l processe s involved . A t present , ther e i s grea t uncertaint yi n the inpu t dat a fo r suc ha model , bu t dat a ca n b e collecte d wit htime . Eve n wit hthe presen t uncertainties , the mode l matche s wel l wit hthe spars e epidemiologica l data . Becaus e o f the preliminarynatur e o f the model , onl yfeta l deat hrate s hav e bee n modele d i n detail ; roughesti

mate s ar e obtaine d fo r othe r feta l healt heffects . Physiologica l Background Fetuse s develo p i n a n environmen t tha t provide s sufficien t oxyge n fo r thei r needs . Feta l oxyge n mus t depend o n the oxyge n conten t i n the uterin e arteryo f the mothe r rathe r tha n directl yo n tha t o f the atmos
phere . T o some exten t thi s vulnerabilit yi s reduce d becaus e feta l hemo

globi n bind s mor e easil ywit hoxyge n than adul t hemoglobin . However , the oxyge n tensio n i n feta l tissu e i s inevitabl ylowe r than tha t i n adul t tissue . Thi s lowe r stat e o f oxygenatio n i s complicate d b ya lac k o f com pensatorymechanism s o f the feta l cardiovascula r system . Childre n an d adult s ar e abl e t o respond t o increase d oxyge n need s b yincrease d cardia c output , vasodilatio n an d large r oxyge n tensio n gradients . Suc hresponse s ar e limite d i n the fetus . Thus , a give n reductio n i n oxyge n availabilit y wil l b e mor e likel yt o affec t a fetu s than mos t adult s o r children . Al

thougha pregnan t woma n ma ydisplayn o sign s o f hypoxi a a t all , the chil d tha t she i s bearing ca n b e unde r sever e stres s du e t o oxyge n deprivation . Thes e fact s indicat e tha t fetuse s ma yb e on e o f the importan t group s sen sitiv e t o ambien t carbo n monoxide . 1 2 Studie s b

yHil l e t al . an

d b yGinsber g an

d Myer s indicat e tha t the presenc e o f COHbi n th e mothe r ha s two distinc t effect s o n feta l oxygena tion . Th e mos t immediat e effec t i s a decreas e i n the oxyge n availabl e fo r transfe r acros s the placent a t o the feta l circulatorysystem . T o compensate , eithe r feta l bloo d flo wmus t ris e o r oxyge n tension s mus t fall . Oxyge n tensio n (P 0--, ) i n the umbilica l arteryo f the fetu s ha s bee n observe d t o fal l a s materna l COHb(COH b) rise s i n anima l studies . Thi s i s consisten t wit hthe fac t tha t the feta l circulatorysystemha s limite d capacit yt o chang e it s bloo d flow . Asecon d effec t o f COHbexposur e i s the actua l transfe r o f C O acros s the placenta . Feta l COHb(COHb f ) rise s an d fall s mor e slowl ythan tha t o f the mother , bu t reache s a highe r steady-stat e value . Thi s effec t exacerbate s the feta l oxyge n tensio n problemb

yshifting the feta l oxyge n dissociatio n curv e leftwards . Th e dro p i n feta l arteria l P n 2 becaus e o f extremel ylarg e materna l COHblevel s i s significan t enought o caus e majo r damag e t o feta l tissu e 

### 8 1P.8 5

eve n whe n the-fetu s i s no t expose d long enought o hav e a n elevate d COHb level . Myer s ha s demonstrate d tha t the exten t o f brai n damage , change s i n cardiovascula r function , an d neonata l mortalit yi n ter mfeta l monkey s i s closel yrelate d t o the level s o f P~ 2 experience d i n the feta l abdomi

na l aorta . Th e materna l exposure s mus t b e extremel yhig ht o induc e suc h striking damage s t o ter mfetuse s afte r brie f exposures . However , two im

plication s o f a decrease d P-

_a t eve n lo wlevel s o f materna l COHbshoul d b e considered . Th e firs t implicatio n concern s the effect s o f lowe r exposure s t o COHb . Asampl e o f fetuse s o f an yspecie s wil l hav e a rang e o f suscepti

bilit yt o change s i n feta l P n 2 an d "t o change s i n COHb f . Fo r some portio n o f the sample , P _ ma yb e a t nea r critica l level s becaus e o f smoking o r diseas e a t the time o f materna l exposur e t o elevate d ambien t C O levels . A s the C O exposur e become s mor e extreme , a large r an d large r proportio n o f the sampl e wil l ente r the critica l range , i n orde r o f thei r initia l susceptibility . Therefore , exposure s tha t woul d no t endange r the averag e health yfetu s migh t stil l affec t a significan t numbe r o f pregnancies . Th e secon

d implicatio n concern s the potentia l effect s a t earlie r gestationa l ages . Myer s demonstrate d thes e effect s o n ter mfetuses . Th e biologica l system s ar e almos t completel ydevelope d a t thi s stag e o f ges tation . Ther e i s n o evidenc e o f the importanc e o f change s i n feta l P_ 2 earlie r i n gestation . Whe n the fetuses ' biologica l system s ar e veryru dimentaryan

d rapidl ydeveloping , a give n chang e i n oxygenatio n o f the tissu e ma yb e fa r mor e damaging . Th e potentia l fo r malformation , brai n damag e an

d feta l deat ha t thes e stage s ma yb e greate r a t eac hleve l o f materna l exposure . Thi s possibilit yshoul d b e considere d i n futur e re search . Deterministi c Mode l Tw o physiologica l effect s hav e bee n identifie d a s a sourc e o f poten
tia l har

mo f C O t o fetuses : decrease d oxyge n conten t o f the bloo d re sult s i n lowere d P „ i n the fetu s (Pf Q 2 ) ; increase d feta l COHbfurthe r reduce s th e Pf 02 * *** e fi r s t effec t i s immediate . Th e secon d partl yde

termine s the magnitud e o f th e firs t effect , an d depend s o n the lengt ho f the exposure . Hil l e t al . hav e develope d a mode l tha t predict s COHb

give n dat a o n the sequenc e o f COHb. Thi s mode l work s o n the same basi s a s th e Cobur n model , bu t include s ?h e blood-bloo d transfe r o f COHbbe

twee n the mothe r an d fetus . Th e mode l predict s bot hCOHb f an d P f . . A complet e developmen t o f the feta l healt heffect s mode l woul d involv e us e o f thi s mode l i n conjunctio n wit hthe EP Aexposur e model . Fo r thi s demonstration , a simple r mode l look s onl ya t approximat e change s i n Pf Q 2 a s COH bchanges . Ff 0

_

wil l b e predicte d o n the assump tio n tha t COHb, . i s a t approximatel ytne same leve l a s tha t o f the mother . Withi n a sampl e o f fetuses , on e wil l observ e a rang e o f Pf 0 2 values . Ove r th e cours e o f the pregnancy , everyfetu s wil l experienc e variatio n withi n some portio n o f thi s range . Althoug hthes e value s canno t pres entl yb e directl yobserve d i n human s fo r ethica l reasons , knowledg e o f the distribution s o n the determinant s o f Pf n 2 allow s a judgmenta l approx imation . Dr . Lawrenc e Long o ha s provide d preliminaryestimate s o f thi s 

### 8 2P.8 6



### distribution : Whe n COH bi s a t th e norma l endogeneou s leve l o f 'abou t 0.75 % fo r pregnan t women , th e umbilica l arter yPf 0 2 

### distributio n i s de

scribe d bya norma l distributio n wit hmea n o f 1 9 mmH g an d standar d devi

atio n o f 2 m

mHg . Thi s implie s tha t 95 % o f fetuse s wil l b e experiencin g Pf n 2 

### value s betwee n 1 5 m mand 2 3 m

mHg . COH baffect s thi s distributio n i n tw o ways . First , th e mea n Pf 0 2 

### wil l shif t downward s du e t o th e decrease d oxyge n conten t o f materna l blood . Asecond effec t o f th e decreasin g oxyge n conten t appear s i n th e decreas e o f th e varianc e o f th e distribution . A s Pf n 2 

### value s fall , on e move s int o steepe r portion s o f th e oxyge n dissociatio n curves : a give n reductio n i n oxyge n conten t occur s a t smalle r reduction s i n P f 2 

### . Th e bod yha s a built-i n mechanis mt o avoi d dangerousl ylo wlevel s o r oxyge n tension . Th e effect s o f COH bthu s ar e assume d t o b e describe d b y change s i n th e distributio n o r Pf Q 2 

### . Th e parameter s o f th e assume d nor ma l distribution , Pr(P f J COH b)—th e probabilit ytha t a valu e P f 2 

### oc curs , give n th e leve l o r COH b—follo wth e rule s below :

mea n = 1 9 - Ax( % COH b) [6.1 ] ml 

### ' standar d deviatio n = 2 - B x( % COH b) [6.2 ] m• Althoug hAan

d B canno t b e measure d wit hcurren t tools , discussio n wit hDr . Long o indicate d tha t theoretica l knowledg e o f th e adul t and feta l oxyge n dissociatio n curve s an d o f feta l oxyge n consumptio n allow s the mt o b e estimate d a t approximatel y. 5 and . 1 (m mHg)/( % COHb ) respec tively . Figur e 6. 1 indicate s ho wth e distributio n change s fro mCOH bo f 0 t o 10% . Not e tha t a s th e mea n o f Pr(Pf Q2 

### | COHb ) falls , th e varianc e de

creases . Damag e t o a fetu s i s assume d t o b e tie d t o th e level s o f Pf 0 2 

### expe rienced . Th e bloo d fromth e umbilica l vei n directl ysupplie s th e liver , heart , and brai n tissue . Bloo d tha t reache s othe r tissues , vi a th e de

scendin g aorta , i s les s saturate d wit hoxygen . Par t o f th e descendin g aort a bloo d i s shunte d t o th e umbilica l arter ywithou t feedin g thes e othe r tissues . Thu s th e oxyge n tensio n o f bloo d i n th e umbilica l arter y wil l b e highe r tha n tha t experience d i n muc ho f th e bod ytissue . APf n 2 

### o f 9 m mH g i n th e umbilica l arter yindicate s extrem e stres s t o th e fetus , wit htension s approachin g zer o i n som e part s o f th e body . Th e resistanc e o f th e fetu s t o lowere d oxyge n tension s depend s o n severa l effects . Th e exten t o f acidosi s and hypercarbi a ar e importan t variables . Hig hglycoge n level s i n th e tissu e ca n serv e a s buffer s by permittin g mor e anaerobi c respiration . Al l o f thes e factor s ar e i n par t determine d byth e lengt ho f th e hypoxia . I f on e wer e t o us e th e mor e elaborat e Hil l mode l , the n thes e effect s coul d b e approximate d fro mth e sequenc e o f COH b. I n thi s model , thes e effect s ar e assume d t o b e aver age d int o th e dose-respons e curv e used . Fo r obviou s ethica l reasons , ver ylittl e dat a exist s t o hel p esti

mat e th e risk s o f huma n fetuse s a t variou s umbilica l arter yPf r) 2 

### levels . Discussio n wit hDr . Long o resulte d i n a n approximatio n o f th e ris k o f
8 31.00.

-

Probabilit y

0 0 -t>

Pf Q 2 (m mHg )

FIGUR E 6. 1 PROBABILIT Y THAT FETAL OXYGE NTENSION S I NTH E UMBILICAL ARTER Y

AR E LES S THA

NTH E VALU E INDICATED : Pr(Pf Q2 |C0Hb ) I NCUMULATIV E FORMP.8 8

feta l death , t o b e use d unti l bette r dat a ar e available . Thi s dose

respons e curv e Pr(feta l death|Pf 02 )—th e probabilit yo f feta l deat hgive n a valu e o f Pf 0 2 fo r 1-hour exposure—i s picture d i n Figur e 6.2 . Curv e I I represent s the dose-respons e curv e encode d fromDr . Longo ; Curve s I an d II I represen t curve s betwee n whic hthe tru e curv e i s expecte d t o fall . Dr . Long o helpe d synthesiz e thes e dat a from,knowledg e o f feta l oxygena tion processe s an

d fro manima l tes t result s . Th e exposure s t o eac hPf n 2 leve l ar e assume d t o b e fo r 1 hour , becaus e the dat a o n COHbexposure s ar e provide d o n an hourl ybasis . Difference s i n acidosi s an d hypercarbi a ar e average d int o the probability . Futur e model s woul d wan t t o retai n informatio n o n the correlation s o f thes e Pf n 2 value s ove r time . I n tha t way , biase s i n assuming tha t highexposure s onl yoccu r fo r a singl e hou r ca n b e removed . Chroni c an d acut e exposure s ca n b e treate d separately . I t shoul d b e note d tha t the distributio n i n Figur e 6. 2 shoul d als o re flec t the probabilit yo f feta l deat htha t i s no t associate d wit hvaria

tio n i n Pf Q2 • Th e mode l calculate s the averag e probabilit yo f feta l deat ha s a :io n o f COHb. Thi s probabilit yi s derive d . r.hte d average ) take n ove r the dose-respons e i butio n o f Pf n 2 among fetuse s a t eac hCOHblevel : functio n o f COHb. Thi s probabilit yi s derive d a s an expectatio n (weighte d average ) take n ove r the dose-respons e curve , using the distri

Averag e Pro bFeta l Deat h(COH b) = m Pr(feta l death|pf n2 ) xPr(Pf Q2 | COHb ) [6.3 ]
Pf 0 2 range s Th e value s fo r Pr(feta l death | Pf Q2 ) ar e liste d i n Figur e 6.2 . Th e value s fo r Pr(Pf Q2 | COHb ) ar e take n fro mcurve s suc ha s the two presente d i n Fig

ur e 6.1 . The yar e derive d using Equation s 6. 1 an d 6.2 . Th e predicte d number s o f feta l death s ar e the n foun d b ytaking a weighte d averag e ove r thes e averag e probabilitie s o f feta l death . Th e weight s fo r thi s secon d averag e come fro

mthe distributio n o f COHbfo r eac hcohor t derive d b yth e exposur e model . Fiv e cohort s wer e aggregate d fro mthe 9 8 o f the exposur e model , a s describe d below . Eac hcohor t ha s a differen t exposur e experi

enc e a s wel l a s differen t pregnanc yrates . Withi n eac hcohort , the same distributio n o f smoking statu s wa s as sume d among the pregnan t women . Smoking wa s simulate d b yadding a con stan t valu e t o predicte d COHb. Values,fo r thes e constant s wer e derive d fro mvariou s source s i n the literature . ' I f on e believe s tha t ther e i s some for

mo f compensatorymechanis mtha t diminishe s the effect s o f C O fro mcigarettes , the n tha t ca n b e approximate d b yusing a n additiv e con stan t whic hi s les s than tha t actuall yobserved . Healt hsurvey s indi

cate d tha t 30 % o f young wome n smok e today . Th e distributio n i n Tabl e 6. 1 i s used .



### 8 5P.8 9



### "Umbilica l Probability Arter y 

### Pf 0 2 

### I

1. 0

.7 5

'robabilit

y, n



### .2 5

0 <9.5 0 

### 9-1 0 .6 8 10-1 1 .4 5 11-1 2 .2 5 12-1 3 .1 2
, . „ _I T 

### 1/ , n s \ V 14-1 5 .0 1 V 

### \ i 15-1 8 .00 9 \ \ >1 8 .00 9

N\ X \ \ 

# sW 1 1
\ x 

### „ 'II I \ \ \ \ \ \ \ \ \ ^ \ \ \ \



### 3 1 0 1 2 1 4 1 6 1 8

Pf Q 2 

### (m mHg )

FIGUR E 6. 2 PROBABILIT

Y OF FETA

L DEAT H AS AFUNCTIO

N O F OXYGENTENSIO

NINFETA

L UMBILICA

L ARTER Y (1-HOU R EXPOSURE) : Pr(FETA L DEATH|Pf Q2 

### )

8 6 o f Feta l II , 

### L.ao .8 0 .5 3 .3 8 .2 0 .0 5 .0 1 .00 9 .008 5 Death

II I 1.0. 0 .9 5 .7 2 .4 8 .3 0 .1 4 .0 3 .00 9 .00 6 .

-

-P.9 0

Tabl e 6. 1 DISTRIBUTIONO F SMOKER S AMON G PREGNANT WOME N

Smoking Statu s Nonsmoker , n o passiv e exposur e Nonsmoker , wit hdail ypassiv e exposur e Ligh t smoke r (< 1 pac k pe r day ) Heav ysmoke r (> 1 pac k pe r day ) Fractio n o f Pregnan t Wome n 

### .6 5 .0 5 .2 5 .0 5 Additiv e

Constan t Leve l ( % COHb ) 

### 0 1 t o 3 1 t o 3 2 t o 6

Th e tota l expecte d numbe r o f feta l death s i n a give n cit yi s approxi

matel ya s follows :
Feta l Death s - £ ( # pregnancies ) x] T (fractio n pregnant )

cohort s (i n cohor t ) smoking(wome n i n typ e ) type s

x2 J Pr(C0Hb) xA vPro bFeta l Death(C0Hb+additiv e constant ) [6.4 ] COHb mm m Th e fiv e cohort s ar e describe d i n Tabl e 6.2 . Tota l pregnancie s tha t las t beyond 2 0 week s o f gestatio n wer e estimate d fro mfertilit yrate s charac

teristi c o f eac hcity . The ywer e distribute d among the cohort s accordin g t o ag e an

d sexdistribution s fo r eac hcit yprovide d b yPEDC o Environmen

tal , Inc . The ywer e adjuste d s o tha t fertil e wome n i n differen t cohort s hav e differen t relativ e probabilitie s o f becomin g pregnant . Al l fertil e working wome n wer e assume d t o hav e equa l pregnanc yrate s regardles s o f profession . Students , marrie d nonworking women , an d unemploye d wome n (al l i n ag e rang e 15-4 4 years ) wer e estimate d t o b e 20 % mor e likel ythan thei r employe d counterpart s t o become pregnant ; thes e ar e illustrativ e numbers , bu t the result s ar e insensitiv e t o thi s distribution . Marrie d wome n fal l int o thre e categories : nonworking , working , an d unemployed . Onl ythe nonworkin g one s ar e counte d i n the "marrie d women " cohort . Also , the "marrie d women " cohor t include s thos e ove r 6 5 years . Thi s rep resent s a doubl e counting wit hthe "retired " category . Thos e wome n wer e roughl yapproximate d an d remove d befor e pregnanc yrate s wer e applie d t o the forme r cohort . Th e averag e COHbvalue s presente d i n Tabl e 6. 2 ar e indicativ e o f ho w exposure s diffe r among the cohorts . Th e range s represen t difference s i n thes e cohort s among the four cities . "As-is " represent s the currentl y experience d ambien t level s rathe r than thos e associate d wit ha particula r


### 8 7P.9 1

Cohor t 

### 1 2 Tabl e 

### 6. 2 ANNUAL PREGNANCIE S B Y COHOR T

, Descriptio n Student s an

d clerica l worker s Sale s personne l an d servic e Rang e o f

Avg . % COHb (As-Is ) .40-.8 1 .42-.8 2 Numbe r o f

Pregnancie s Pe r 1,00 0 I n Cohor t 25-3 3 16-2 1 Numbe r o f

Pregnancie s Pe r 1,00 0 Fertil e Wome n I n Cohor t 40-5 3 38-4 9

4 5 worker s Professionals , craftsme n an d .42-.8 5 9-1 2 laborer s Marrie d wome n .42-.6 5 36-4 4 Retire d an

d unemploye d .42-.6 0 15-1 6 

### 39-5 0 48-5 6 48-6 1

standard . Not e tha t the highes t exposure s ten d t o occu r i n the cohor t wit hthe lowes t birt hrate . B ykeepin g thes e cohort s separate , some bia s ha s bee n avoided . Probabilisti c Phas e Th e feta l healt heffect s mode l predict s expecte d feta l death s a t greate r than 2 0 week s gestation . Th e nationa l averag e fo r metropolita n countie s wa s 9.7/100 0 i n 1977 . Th e mode l i s calibrate d s o tha t thi s av erag e i s obtaine d fo r the as-i s case s i n the following manner . Ther e ar e four uncertai n parameter s tha t significantl yaffec t the model' s estimate s o f the feta l deat hrat e a t eac hCOHblevel . Thes e ar e (1 ) the additiv e factor s tha t simulat e the effect s o f cigarett e smoking ; (2 ) the curv e fo r the probabilit yo f deat ha t eac hPf Q 2 level , Pr(feta l death|Pf Q2 ) , an d (3 ) the coefficient s Aan d B i n Equation s 6. 1 an d 6.2 . Aan

d B wer e ad

juste d i n eac hcas e s o tha t whe n combine d wit hdifferen t specification s fo r the firs t two , the as-i s predictio n o f feta l death s ar e i n lin e wit h nationa l statistics . Th e probabilit ytre e use d fo r the feta l deat hpredictio n i s provide d i n Figur e 6.3 . Th e probabilitie s fo r the additiv e constan t fo r smoker s i s estimate d fro

mrange s foun

d i n the literature . ' Th e resultin g dis

tribution s fo r Lo s Angele s fo r eac ho f the standard s ar e give n i n Figur e 6.4 . Thes e result s ar e summarize d i n Tabl e 6.3 . Uncertaintie s becaus e o f the Emission s an d Atmospheri c Dispersio n Mode l an d the Exposur e Mode l ar e no t include d becaus e o f lac k o f data . Fo r clarity , i t i s assume d tha t feta l deat hrate s ar e exactl yknown unde r presen t conditions ; in

cludin g thi s uncertaint ywoul d onl yobscur e the difference s betwee n standards . 

### 8 8P.9 2
Additiv e constant s t o % COH b(light/passiv e smoker ; heav

ysmoker ) Curv e use d fo r P r (feta l deat hI Pf Q2 )



### I I



### II I

I I



### II I

I I Coefficient s

i n Eqs . 6.1 , 6. 2 AB .6 0 .1 2

,5 5 .1 1

,5 5 .1 1

.4 0 .0 8

.4 5 .0 9

.4 0 .0 8
,3 2 .0 6

,3 5 .0 7



### II I ,3 0 .0 6

FIGUR E 6. 3 PROBABILIT Y TRE E USE D WIT H FETAL DEAT H MODE L



### 8 9P.9 3
1.0 0 _

(35,1 )

Probabilit y

119 0 120 0 121 0 122 0

1.0 0



### ,7 5 5 Exceedance s

pe r yea r

Probabilit y 

### .5 0

.2 5

119 0 120 0 121 0 Feta l death s 122 0

FIGUR E 6. 4 PROBABILIT Y THAT FETAL DEATH S ( > 2 0 WEEK S GESTATION )

PE R YEAR I NLO S ANGELE S WIL L B E LES S THA NAMOUNT INDICATED ; I T I S ASSUME D THAT THER E AR E A T PRESENT 1,21 7 FETAL DEATH S PE R YEAR FRO M AL L CAUSE S I NLO S ANGELES . STANDARD S AR E INDICATE D B Y (CONCENTRATION [p p mm] , AVERAGING TIM E [hr] )



### 9 0P.9 4

Tabl e 6. 3 EXPECTE D CHANGE S I NFETAL DEATH S (>2 0 WEEK S GESTATION ) PE R YEA R (Lo s Angeles )



### S t 3 5 ppm 2 5 ppm 2 5 ppm 1 2 pp m 1 2 ppm 9 pp m 9 pp m andar d 

### 1 1 1 8 8 8 8 h r h r h r h r h r h r h r 

### 1 1 5 1 5 1 5 Ex Ex Ex E x Ex E x Ex Chang e Ove r As-I s Numbe r Percent ^ o f Death s o f As-I s 1.5 4 .1 3 -2.5 4 0.8 7 -4.8 3 -3.2 6 -8.1 1 -5.2 2 -.2 1



### .0 7 -.4 0 -.2 7 -.6 7 -.4 3 Chang e

Strictes t Numbe r o f Death s o 9.6 4 5.5 6 8.9 7 3.2 7 4.8 4

— 2.8 8 s Ove r

Standar d Percen t , f Standar d 

### .8 0 .4 6 .7 4 .2 7 .4 0

— 

### .2 4

Tota l feta l death s i n as-i s cas e = 1,217 . Expecte d tota l feta l death s fo r strictes t standar d = 1,209 .

Th e exten t t o whic hthe sensitiv e grou p i s affecte d i s verysmal l fo r thi s healt hrisk . Thi s i s tru e simpl ybecaus e the sensitiv e grou p i s larg e (al l fetuses ) an d the feta l deat hrate s smal l t o begi n with . Fo r Lo s Angeles , ther e ar e 125,00 0 pregnancie s pe r year ; 1 % o f fetuse s die , an d the differenc e i n death s betwee n the propose d standard s i s 0.01 % o f the numbe r o f pregnancies . Extrapolatio n o f the mode l t o zer o ambien t C O provide d a n estimat e tha t perhap s 2 % o f feta l death s ar e cause d b yambi

en t C O concentrations . Discussio n o f Result s On e are a i n whic ha fai r amoun t o f huma n dat a i s availabl e t o chec k th e implication s o f thi s mode l i s the relativ e spontaneou s abortio n rate s o f smoking an

d nonsmokin g mothers . Althoug hthe result s o f suc htest s vary , the rati o o f spontaneou s abortio n rate s o f smoker s t o nonsmoker s appear s t o b e betwee n 1. 1 t o 2.0 , an d appear s t o b e positivel yrelate d t o the numbe r o f cigarette s smoked . Socioeconomi c an d ag e factor s als o appea r significant . However , t o the exten t tha t ambien t exposure s ar e no t correlate d wit hthes e factors , thes e factor s ca n b e assume d t o aver ag e ou t i n an estimat e o f the effect s o f ambien t CO . Cigarett e smok e contain s man

yothe r element s suc ha s nicotine , whic hcoul d accoun t fo r some portio n o f thi s exces s mortality . Thes e ratio s can , however , b e


### 9 1P.9 5

considere d a s uppe r bound s o n the possibl e effect s o f COHba t level s at
taine d b

ysmoker s an

d nonsmokers . Eac hse t o f parameter s use d a s input s t o the mode l result s i n a dif

feren t predictio n o f difference s i n feta l mortalit yrate s betwee n smoker s an d nonsmokers . Using the probabilisti c input s o f Figur e 6.3 , on e ca n compar e the implie d distributio n o n exces s feta l death s du e t o the C O conten t o f cigarette s wit hthe dat a o n smoking mothers . Thi s distribu

tio n ha s a rang e o f 1.0 4 t o 1.31 , wit ha n expecte d valu e o f 1.16 . Thi s i s no t ou t o f lin e wit hthe epidemiologica l data . Discussio n wit hDr . Long o ha s permitte d u s t o loo k at* thes e ratio s mor e closely . Dr . Long o provide d estimate s o f ho wthi s rati o migh t var y accordin g t o the smoking experience s o f mothers . On e shoul d not e tha t no t al l o f the exces s feta l mortalit yfoun d i n suc hstudie s ca n b e at
tribute d t o CO . Thu s on e woul d expec t the epidemiologicall yderive d ra tio s t o b e highe r tha n thos e predicte d b ya mode l looking onl ya t the ef

fect s o f CO . Thes e judgmenta l range s ar e compare d wit hthe equivalen t one s fro

mthe mode l i n Tabl e 6.4 . I t i s interestin g ho wwel l the result s match .
Tabl e 6. 4

RATI O O F FETAL DEAT H RATE S O F SMOKER S TO NONSMOKER S

Smoking Mothe r Studie s Model' s Estimate s Ligh t smoker s (< 1 pack/day ) 1. 1 t o 1. 4 (mean 1.2 ) 1.0 3 t o 1.2 3 (mean 1.12 ) Heav ysmoker s (> 1 pack/day ) 1. 2 t o 1. 6 (mean 1.35 ) 1.0 9 t o 1.7 2 (mean 1.35 )

Thi s mode l attempt s t o quantif yonl yon e aspec t o f feta l healt hef

fects , a t onl yon e stag e o f feta l development . Fetuse s olde r than 2 0 week s o f gestatio n ar e quit e wel l developed , wit hal l o f th e biologica l system s nearl ycompletel yformed . Ther e i s littl e knowledg e abou t the relativ e susceptibilit yo f younge r fetuse s t o hypoxia . On e ca n argu e tha t theyar e likel yt o b e mor e sensitiv e becaus e thei r system s ar e going throughthe mos t importan t an d intricat e evolutions . A s a counter
argumen t on e ca n presen t evidenc e tha t at ' earlie r stages , ablate d tissu e ca n ofte n b e full yregenerated . Th e abilit yt o regenerat e tissu e de

crease s wit hgestationa l age . Thu s the damag e fro mhypoxi a ma yb e les s permanen t a t earlie r stage s o f gestation . Whateve r the truth , on e shoul d realiz e tha t the estimate s o f feta l death s presente d her e refe r onl yt o thos e sustaine d b

ya subgrou

p o f th e sensitiv e population . Furthe r wor k shoul d focu s o n ho wthi s mode l woul d appl yt o younge r fetuses . Amor e importan t concer n tha t ha s no t bee n addresse d b ythi s mode l i s the potentia l fo r nonfata l effects . Approximatel y5 % o f childre n ar e bor n wit hsome for mo f brai n damage , an d 2 % ar e bor n wit hsom e physica l


### 9 2P.9 6

abnormality . Hypoxi a ca n induc e bot heffects . I f on e believe s tha t C O ca n induc e fata l hypoxi a i n fetuses , the n on e canno t neglec t the"spectru m o f damage s tha t ma yb e suffere d b ythos e fetuse s tha t ar e severel y stresse d bu t surviv e the episod e o f hypoxia . No t onl yma ythes e effect s b e large , bu t societ yma yplac e greate r weigh t o n avoiding thes e form s o f damage . T o properl yestimat e thi s potentiall ylarg e se t o f risks , on e woul d nee d separat e dose-respons e curve s suc ha s tha t i n Figur e 6.2 , eac hspe cifi c t o a differen t effect . T o indicat e the magnitud e o f thi s risk , on e ca n mak e the assumptio n tha t change s i n ris k o f feta l deat har e propor tiona l t o change s i n ris k o f brai n damag e an d malformation . Thi s woul d translat e int o th e expecte d level s o f effect s fo r Lo s Angele s estimate d i n Tabl e 6.5 . Summary Despit e the data-availabilit yproblems , the outpu t o f the mode l i s veryencouraging . Compariso n wit hsmoking-mothe r studie s indicat e a ver y

Tabl e 6. 5 EXTRAPOLATIONO F FETAL DEAT H MODE L T O OTHE R FETAL EFFECT S (Lo s Angeles )

Effec t Menta l retardatio n (I Q <70 ) (a ) Mil d (b ) Moderat e (c ) Sever e (I Q <30 ) Cerebra l pals ywit hn o menta l retardatio n Seizur e disorder s Speec ho r languag e disorde r Learnin g disabilitie s Sever e physica l damag e Othe r congenita l anomalie s Curren t Percen t o f Birth s Affecte d
2. 6 0. 3 0. 1 

### 0. 2 0. 4 5. 0 5. 0 0. 5 1. 5 Approx
imat e Numbe r o f Birth s Affecte d Annuall y
325 0 37 5 12 5 

### 25 0 50 0 625 0 625 0 

### 62 5 187 5 Maximu

m Chang e i n Numbe r o f Birth s Affecte d Annuall y
2 6 3 1 

### 2 4 5 0 5 0 

### 5 1 5

Maximu mchang e i s tha t fro mmos t stringen t t o leas t stringen t propose d standard . 

### 9 3P.9 7

goo d correspondence . Thi s lead s on e t o believ e tha t the structur e an d input s ar e no t unreasonable . Clearl ythe mos t importan t focu s fo r futur e researc hwil l b e o n the potentia l effect s o f COHbo n rate s o f birt hdefect s an d brai n damage . A close r loo k a t ho whypoxi a ca n affec t fetuse s a t differen t stage s o f ges tatio n wil l ai d i n an extensio n o f thi s model .



### 9 4P.9 8

REFERENCE S FOR CHAPTE R V I
1 . Esthe r P . Hill , JayR . Hill , e t al. , "Carbo n Monoxid e Exchange s be

twee n the Huma n Fetu s an d Mother : a Mathematica l Model, " America n Journa l o f Physiology , Vol . 232 , 1977 , pp . H311-H323 . 2 . Myro n D . Ginsber g an d Ronal d E . Myers , "Feta l Brai n Damag e following Materna l Carbo n Monoxid e Intoxication : a n Experimenta l Study, " Act a Obst . Gynec . Scand• , Vo l 53 , 1974 , pp . 309-317 . 3 . Ronal d E . Myers , "Threshol d Value s o f Oxyge n Deficienc yLeadin g t o Cardiovascula r an

d Brai n Pathologica l Change s i n TermMonke y Fetuses, " i n Advance s i n Experimenta l Medicin e an d Biology , Vol . 37B , Duan e F . Birule yan d Hai mI . Biche r (eds.) , Plenu mPress , Ne w York : 1973 , pp . 1047-1053 . 4 . Gordo n G . Power , TomR . Bennet t an d Lawrenc e D . Longo , "Surviva l i n the Feta l Rabbi t Expose d t o Intermitten t Hypoxia. " America n Journa l o f Obstetric s an

d Gynecology , Vol . 127 , No . 4 , Feb . 15 , 1977 , pp . 428-433 . 5 . Wayn e Ott , APreliminaryAssessmen t o f the Nation' s Carboxy

hemoglobi n Dat a Base , repor t t o Offic e o f Researc han d Development , U.S.E.P.A. , Septembe r 1981 . 6 . Dr . Steve n Horvath , persona l communication . 7 . Healt hUnite d State s 1980 , Tabl e 27 , "Cigarett e Smoking Statu s o f person s 2 0 year s o f ag e an d over , according t o sex , race , an d age : Unite d States , 1965 , 1976 , 1979, " U.S . Departmen t o f Healt han d Huma n Services , Hyattsville , Md. , Dec . 1980 , p . 162 . 8 . RoyPaul , PEDC o Environmenta l Inc. , persona l communication . 9 . Vita l Statistic s o f the Unite d State s 1977 , Vol . II , Mortality . U.S . Departmen t o f Healt han d Huma n Services , (PHS ) 80-1102 , Hyattsville , Md. , 1980 . 10 . "Pregnanc yan

d Infan t Health, " Chapte r 8 i n Smoking an d Health : A_ Repor t o f the Surgeo n General , U.S . Departmen t o f Health , Educatio n an d Welfare , 1979 . 11 . Dr . Lawrenc e D . Longo , persona l communication .



### 9 5P.9 9

VI I CHRONI C OBSTRUCTIVE PULMONARY DISEAS E
Th e mode l presente d her e i s a simulatio n model . I t follow s the sen sitiv e grou

p ove r time , estimating the rate s o f hospitalizatio n an d mor talit ytha t woul d b e experience d a s the COHbexposure s fluctuate . Th e valu e o f thi s approac hi s tha t i t maintain s the correlation s o f COHbove r time . Becaus e the grou p considere d her e i s s o sensitiv e t o sligh t change s i n oxyge n availability , significan t bia s i n mode l prediction s woul d resul t i f thi s correlatio n wer e ignored . Background an

d Modelin g Issue s Peopl e wh o suffe r fro mchroni c obstructiv e pulmonarydisease , o r COP D (define d her e a s pulmonaryemphysema an d chroni c bronchitis) , ar e mor e sensitiv e t o carbo n monoxid e than thos e wit hnorma l lun g function

Ayer s an

d Arono whav e eac hfoun d clea r evidenc e o f thi s sensitivity . ' Thi s diseas e affect s a s muc ha s 4 % o f th e population , bu t the severit y o f the individua l case s lie s o n a broad continuum . Becaus e norma l lung s provid e a surplu s o f oxygen-deliverycapacity , quit e larg e deterioration s i n lung functio n ca n tak e plac e befor e a perso n suffering fro mCOP D i s likel yt o b e mor e affecte d than the genera l populatio n b ythe relativel y smal l change s i n COHbinduce d b ythe curren t ambien t level s o f CO . I n th e en d stage s o f thi s progressive , irreversibl e disease , the patien t become s chronicall yhypoxemic . A t thi s point , the lung s canno t provid e sufficien t oxyge n fo r eve n smal l exertions . Hi s activitie s ar e severel y restricted , an

d h e i s susceptibl e t o exacerbation s o f hi s chroni c respi

ratoryfailure . Excep t i n the cas e o f respiratoryinfection , the caus e o f thes e acut e attack s i s ofte n unknown . Th e symptom s o f acut e respiratoryfailur e ar e lowere d arteria l oxy ge n tensio n (Pa Q2 ) wit ha consequen t fal l i n oxyge n saturatio n o f the ar teria l bloo d (Sa Q 2 ) . Suc hbout s o f acut e respiratoryfailur e resul t i n hospitalizatio n to r anywher e fro ma wee k t o ove r a month . Furthermore , stabilizatio n o f the problemca n b e ver ydifficul t becaus e effort s t o rais e the oxyge n conten t o f th e bloo d throughenhance d oxyge n concentra
tio n o f inspire d ai r ca n caus e a suppressio n o f the impetu s t o breathe . A s a resul t o f the patient' s alread yweakene d stat e an d the delicac y neede d i n controlling bloo d gases , ther e i s a highfatalit yrat e associ

ate d wit hacut e respiratoryfailur e i n thos e wit hadvance d case s o f COPD . Amode l o f th

e effect s o f COHbo n COP D patient s mus t defin e the sen sitiv e grou

p veryprecisely . Th e majorit yo f C O healt heffect s wil l oc cu r withi n tha t portio n o f the populatio n tha t ha s becom e chronicall y hypoxemic . A t les s advance d stage s o f the diseas e the smal l incrementa l effect s o f COH

bo n oxygenatio n wil l rarel yhav e a n impact . A t the ver y fina l stage s o f the diseas e the patien t i s likel yt o b e o n continuou s oxyge n therapy , effectivel yshielde d fro msmal l exposure s t o CO . 

### 9 7P.10 0

Anothe r concer n i n modelin g the effect s o f C O o n COP D patient s i s t o accoun t properl yfo r the fac t tha t the sensitiv e grou p wil l b e hospital

ized quit e frequently . Whil e hospitalized , the patien t wil l b e remove d fro mthe risk s o f ambien t CO : bloo d gase s wil l b e closel ymonitored , an d oxyge n therapywil l b e immediatel yavailabl e i f an ydeterioratio n i n the patient' s conditio n occurs . I f highlevel s o f COHbcaus e increase d rate s o f hospitalization s o n on e day , the n th e sensitiv e grou p a t ris k wil l b e reduce d fo r severa l day s following . Thu s time-clusterin g o f highC O con centration s wil l no t hav e a s grea t o f a n effec t a s woul d b e predicte d b y a mode l tha t ignore s the sequentia l natur e o f the exposures . Thi s issu e lead s on e t o the us e o f a simulatio n model . Th e mode l assume s tha t a reductio n i n blood-oxyge n conten t du e t o elevate d COHbi s on e o f the factor s i n exacerbation s o f respirator yfail

ure . Th e borderlin e natur e o f the patient' s oxygenatio n render s hi mex tremel ysusceptibl e t o eve n smal l change s i n blood-oxyge n saturation . I t shoul d b e pointe d ou t tha t the exacerbation s tha t mayb e "attributable " t o C O ar e probabilisti c i n nature : theyresul t fro ma combinatio n o f factors , o f whic hCOHbi s onl yone . Becaus e mos t COP D patient s fluctuat e i n thei r hypoxemi c conditio n fromdayt o day , a relativel ylarg e dos e o f COHbo n on e dayma yb e tolerate d easily . Onl yday s later , a smalle r dos e o f COH bma yb e experience d concomitantl ywit ha lowere d Pa 0 o ' t* 1 -^* 3 dos s o f COHbmayb e enought o mak e the differenc e betwee n the patient' s retur n t o a les s hypoxemi c stat e an d a spiraling ou t o f contro l o f hi s respira

tor yfunction . Mortalit yestimate s i n thi s mode l ar e bes t interprete d i n term s o f lif e expectanc yrathe r tha n death s pe r year . COP D patient s i n the sensi

tiv e grou

p hav e a lif e expectanc yo n the orde r o f 5 years , an d usuall y di e o f cause s relate d t o o r precipitate d b ythe disease . T o the exten t tha t th e siz e o f the group i s stable , th e numbe r o f death s eac hyea r i s equa l t o the numbe r o f ne wpatient s enterin g the group . Change s i n C O condition s ar e assume d no t t o alte r the numbe r o f ne wpatients , bu t wil l chang e the lif e expectanc yo f member s o f the group . Afte r severa l years , the siz e o f the grou p wil l agai n stabiliz e wit hthe annua l numbe r o f death s equa l t o the annua l numbe r o f ne wpatients . Hence , fo r the healt h effect , change s i n CO condition s wil l hav e onl ya transitoryeffec t o n the annua l numbe r o f deaths . Th e effect s ar e bette r measure d i n term s o f change s i n lif e expectanc yfo r the group . Deterministi c Mode l Th e sensitiv e grou p i s define d a s tha t segmen t o f the COP D popula

tio n tha t i s chronicall yhypoxemic , bu t no t hospitalize d o r o n domicili

aryoxyge n therapy . Hypoxemi a occur s whe n Pa Q 2 fall s belo w7 0 m mHg . Whe n the patient' s stabl e leve l o f Pa~ 2 fall s t o 5 5 m mHg , he wil l almos t certainl yb e o n oxyge n therapy . Eve n a t 6 0 m mHg , doctor s ma yintroduc e supplementaryoxygen . Th e sensitiv e grou p i s thu s define d a s thos e peo pl e wit hstabl e value s o f Pa n 2 betwee n 6 0 an d 7 0 m mH g wh o ar e currentl y a t home , an

d whos e force d expiratoryvolum e i n 1 secon d (FEV ) i s les s tha n 1 liter . Th e patient s ar e assume d t o b e nearl ynormocapnic , o r pH
compensate d chronicall yhypercapni c whe n i n a stabl e condition .


### 9 8p. 1

Exposur e estimate s wer e calculate d withi n the EP Aexposur e model , bu t using differen t parameter s i n the Cobur n equation . Th e Cobur n equa tio n i s derive d fro massumption s o f norma l lung characteristic s suc ha s uniformratio s o f diffusio n t o alveola r ventilation . Thes e assumption s d o no t hol d fo r thos e wit hlung diseases . Thi s stud ywa s no t mean t t o tes t the reliabilit yo f the Cobur n equation , an d henc e it s estimate s ar e use d her e withou t furthe r question . However , the physiologica l parame ter s o f the Cobur n equatio n wer e adjuste d t o attemp t t o reflec t the ab

norma l characteristic s o f thi s sensitiv e cohort . Th e value s chose n wer e a s follows :
o CapillaryP n « = . 7 norma l (whic hi s base d o n atmospheri c pres sures ) • o Hemoglobi n = 1 8 gm/10 0 m l (secondarypolycythemia ) (norma l i s 15) . o Alveola r ventilatio n (resting ) = 310 0 ml/mi n (norma l i s 4000

- 5500) . o Alveola r ventilatio n (elevate d exercis e level ) = 400 0 ml/mi n (norma l i s 5000-20,000) . o Lung diffusio n rat e = > 2 0 ml/min/tor r (norma l i s 2 4 t o 30) . Th e sensitiv e type s wer e assume d t o b e home-bound . The ywer e therefor e divide d int o tw

o cohort s pe r city : suburba n an d urba n residents . Th e dail yactivit ypatter n followe d wa s tha t fo r the mos t sedentarygrou p o f retire d an

d unemploye d people . Thi s permitte d occasiona l automobil e trave l withi n the residentia l are a an d exercis e tha t raise d alveola r ventilatio n t o 400 0 ml/mi n abou t 5 hour s pe r week . Th e effect s fo r eac hcit yar e calculate d o n a per-perso n basi s be

caus e the acfua l siz e o f thi s cohor t i s s o uncertain . Tabl e 7. 1 de

scribe s the cohorts . Th e fractio n i n the urba n an d suburba n cohort s wa s determine d b

ythe fractio n o f the tota l populatio n i n eac hdistric t tha t i s implie d b

ythe EP Aexposur e model . COP D patient s hav e a heav yincidenc e o f smoking , whic hi s a causa l an d exacerbatoryfacto r i n the disease . Eve n a t thi s advance d stage , a larg e proportio n continu e t o smoke . T o simulat e the effect s o f smoking , a constan t o f 5 % COHbwas.adde d t o the maximu mdail yCOHbestimate s fo r tha t portio n o f eac hcohor t tha t i s assume d t o smoke . T o estimat e the effect s o f COHb , the mode l approximate s the patter n o f day-to-dayfluctuation s i n the dail yminimu mPa Q 2 • Fo r the demonstra

tio n here , the stabl e valu e wa s place d a t 6 5 m mHg . A

n invers e exponen tia l distributio n wit ha maximu mo f 6 5 m

mHg wa s employed . It s cumula

tive probabilit ydistributio n (th e probabilit ytha t P a _i s les s than x ) ha s the following formula :

Pr(Pa Q 2 < x ) = iexp[-X(6 5 - x) ] fo r x< 6 5 [7.1 ]

1 fo r x> 6 5



### 9 9P. 2

Tabl e 7. 1 DESCRIPTIONO F COP D COHORT S

Cit y Chicag o (urb ) Chicag o (sub ) Lo s Angele s (urb ) Lo s Angele s (sub ) Philadelphi a (urb ) Philadelphi a (sub ) St . Loui s (urb ) St . Loui s (sub ) .Approximat e Siz e o f Cohor t 

### 40 0 15 0 57 0 243 0 

### 28 0 72 0 22 5 22 5 Rang e o f

Maximu m Dail yCOHb Level s Estimate d (nonsmokers ) .52-1.0 9 .36-1.2 4 .36-1. 1 .56-1.9 1 .39-3.1 2 .27-1.3 4 .42-.7 2 .45-2.5 5

Th e reciproca l o f the paramete r X determine s the mea n valu e o f P a „ . Th e mode l wa s run her e wit hX = .19 . Thi s implie s a mea n Pa 0 2 ° f 6 0 m mHg . Th e distributio n i s illustrate d i n Figur e 7.1 . A n extensio n o f thi s mode l woul d investigat e the mos t likel yparameterizatio n fo r Pa 0 2 * Th e oxyge n conten t o f bloo d i s a functio n o f P a 02 ' COHb , an d tota l hemoglobin . A s COHbrises , the fractio n o f hemoglobi n availabl e fo r 0

- transfe r falls . Th e leftwar d shif t o f the oxyhemoglobin-dissociatio n curv e als o cause s the oxyge n remaining t o b e les s readil ytransferre d t o tissu e cells . Th e dange r o f chroni c hypoxemi a flaring int o an acut e ex acerbatio n o f respiratoryfailur e increase s a s hypoxemi a increases.

- Thus , the patient s wit hunusuall ylo wPa n « wil l b e th e mos t susceptibl e t o a n elevatio n o f COHb . 

### 0 2 Whe n COHblevel s ar e zero , on e ca n approximat e the oxyge n suppl yca pacit yo f the patien t b ythe us e o f S a Th e distributio n o n thi s vari

abl e i s calculate d b ya chang e o f variabl e techniqu e o n the distributio n o f Fa Q

_ . Th e resulting distributio n i s plotte d i n Figur e 7.2 . Th e us e o f Sa_ 2 i nthes e circumstance s i s a goo d approximatio n sinc e bloo d volum e an d hemoglobi n d o no t fluctuat e rapidly . Th e effec t o f increase d COHbi s t o reduc e the fractio n o f hemoglobi n availabl e fo r carrying oxygen , an d henc e t o reduc e the oxyge n suppl yca pacity . Althoughthe increas e o f COHbactuall yincrease s Sa 0 2 slightly ,



### 10 0P. 3



### ,7 5

J 3 C O mQ o U PL I 

### .5 0

.2 5

4 5 5 0 5 5 6 0 6 5 Pa Q 2 (m mHg )

FIGUR E 7. 1 PROBABILIT Y THAT Pa 0 2 FALL S BELO WTH E VALU E

INDICATE D DURING TH E DA Y FOR ASENSITIVE COP D PATIENT



### 10 1P. 4

cf l md o u P U

Sa.
- (percent )

FIGUR E 7. 2 PROBABILIT Y THAT Sa 0 2 FALL S BELO WTH E STATE D

VALU E ONAGIVE NDA Y FO R SENSITIVE COP D PATIENTS : Pr(Sa Q2 |COHb=0 )



### 10 2P. 5

the assumptio n o f thi s mode l i s tha t the reductio n i n oxyge n suppl yca pacit yfo r a give n leve l o f COH bca n b e modele d b ya n effectiv e Sa n2 : effectiv e Sa Q 2 - ( 1 - COHb ) x[Sa Q 2 fo r zer o COHb ] [7.2 ]
wher e COHbi s the fractio n o f hemoglobi n tha t i s carboxyhemoglobin . Th e morbidit yo f the patien t i s assume d t o b e a functio n o f hi s oxy genation . Th e ris k o f acut e respiratoryfailur e (ARF ) i s the n a functio n o f the effectiv e leve l o f Sa Q2 , whic hi s state d i n a probabilisti c for m tha t combine s the estimate d fluctuation s i n bot hPa 0 2 an d COHb . Th e ris k curve , Pr(ARF|Sa 02 ) , i s presente d i n Figur e 7.3 . Althoughbase d o n sketch yevidenc e i n th e literature , th e uncertaint yo n the curv e i s ac counte d fo r explicitly . T o estimat e expecte d incidenc e o f acut e respiratoryfailur e i n a give n city , the two COP D cohort s ar e followe d ove r a sequenc e o f 6 2 days . Th e exposur e dat a fo r thes e day s ar e the dail ymaximumlevel s o f COHbfo r the month s o f Januaryan d July . Asensitivit ytes t indicate d tha t us e o f al l 36 5 day s di d no t significantl yaffec t mode l estimates . O n eac hday , the predicte d maximu mCOHbresult s i n a particula r probabilit ydistributio n o n effectiv e Sa Q 2 fo r tha t day , Pr,(Sa 02 ) . Th e dail yaverag e ris k o f exacerbation , Pr.(ARF) , fo r the cohor t i s estimate d b ytaking a weighte d averag e o f ris k ove r thi s distribution .

Pr d (ARF ) = /pr(ARF|Sa 02 ) xPr d (Sa Q2 ) [7.3 ]

Sa 0 2 Th e numbe r o f ARF s fo r tha t dayan d tha t cohor t i s thi s averag e ris k mul

tiplie d b

ythe numbe r a t ris k i n the cohort :
AR F = Pr . (ARF ) x(/ / a t ris k i n cohor t o n dayd ) [7.4 ]
Th e uniqu e featur e o f thi s mode l i s tha t the numbe r a t ris k fo r eac h cohor t fluctuate s ove r time . Eac hpredicte d AR F i s assume d t o resul t i n a hospitalization . Thus , fo r the duratio n o f the hospitalization , the cohor t siz e wil l b e reduce d b ythe predicte d ARF . Th e lengt ho f hospi

talizatio n varie s widely , bu t i s estimate d t o b e 1 4 day s o n average . Thus , the siz e o f the cohor t a t ris k eac hdayi s a functio n o f the ARF s o n the previou s 1 4 days : 1 4 ( # i n cohor t a t ris k o n dayd ) = (tota l cohor t size ) - / ^ (ARF . ) [7.5 ] i= l



### 10 3Probabilit y5 Q



## o • P
S a 2 (percent )

FIGUR E 7. 3 PROBABILIT Y O F ACUT E RESPIRATORY FAILUR E A S FUNCTION

O F Sa Q2 —Pr(ARF|Sa Q2 )

0 >P. 7

Th e tota l AR F pe r dayfo r a give n cit yi s the n obtaine d b ysumming ove r cohorts . I f two equall yhighdail ymaximu mCOHbexposure s occu r consecu tively , the secon

d da ywil l resul t i n fewe r ARF s becaus e the numbe r o f patient s a t ris k wil l alread yb e reduce d b

yhospitalization s o n the pre viou s day . Thi s calculatio n avoid s the significan t doubl e countin g tha t woul d otherwis e resul t fo r suc ha highl ysensitiv e grou p o f people . Becaus e o f the seriousnes s o f ARF , increase d morbidit ymus t resul t i n decrease d lif e expectancy . Wit heac hAR F ther e i s some ris k o f death , an d thi s ris k ca n b e approximate d fro mhospita l case-fatalit yrates . I t increase s a s the effectiv e Sa n 2 a t the time o f exacerbatio n falls . Thi s neglect s the effec t o f uncontrolle d oxyge n therapytha t i s delivere d prio r t o admission . Whe n thi s occurs , ris k o f deat hi s actuall yhigher . Th e mode l use s the S a _valu e tha t woul d occu r withou t oxyge n therapy . Th e ris k curv e fo r death , Pr(death | ARF , Sa Q 2 ) , i s provide d i n Figur e 7.4 . Th e annua l numbe r o f death s fo r a give n cohor t i s calculate d a s follows :
Annua l death s i n on e cohor t = 36 5

^ (ARF d ) x/ Pr(death|Sa 02 , ARF ) xPr d (Sa Q2 |ARF d ) [7.6 ] 

## d- 1 4 0 2 Th e fina l quantit yi n the equatio n abov e ca n b e obtaine d fro mEquatio n 7.3 . A s discusse d above , change s i n thi s mortalit yfigur e d o no t ade

quatel yrepresen t the effec t o f differen t C O standards . I t onl yreflect s a chang e i n the deat hrat e fo r tha t siz e cohort . I f the numbe r o f ne w patient s pe r yea r remain s constant , an increas e i n lif e expectanc ywil l graduall yincreas e the siz e o f the cohor t unti l the numbe r o f death s eac h yea r agai n equal s the numbe r o f ne wpatients . Rathe r than mode l the pop ulatio n dynamics , i t wa s preferre d t o loo k a t change s i n lif e expectanc y a s the underlyin g effec t o f change s i n deat hrates . Th e averag e lif e ex pectanc yfo r a cohor t i s approximately :

(lif e expectancy ) = ( # i n cohort)/(annua l deaths ) [7.7 ]
Probabilisti c Mode l Th e uncertaintie s ar e larg e i n al l o f the parameter s o f thi s model . Wid e range s wer e place d o n thre e o f the mos t sensitiv e parameters : the ris k o f AR F curve ; the ris k o f deat hwhe n AR F occurs ; an d the proportio n o f the sensitiv e grou p whic hsmokes . Figur e 7. 5 provide s the probabilit y tre e employed . Not e tha t Pr(ARF|Sa 02 ) an d Pr(death|ARF , Sa Q2 ) hav e bee n approximate d b

ya discret e function . Th e distributio n o n the factio n tha t smoke s i s illustrative , bu t the result s ar e insensitiv e t o the exac t probabilities .


### 10 5

### 5 0

Probabilit y 

### .2 5

o ON

0 L _ 4 0 

### 5 0 6 0 7 0 8 0 9 0 S a (percent )

FIGUR E 7. 4 PROBABILIT Y O F DEAT H A S FUNCTIONO F Sa 0 2 , GIVE NTHA T

ACUT E RESPIRATORY FAILUR E HA S OCCURRED—Pr(DEATH|ARF , Sa Q2 )

0 0Pr(ARF|Sa Q2 )

(Sa 02 <.6;.6^Sa 02 ^.8;Sa 02 >8 ) Pr(death|ARF , S a „ ) 

### 02 ' (Sa 02 <.6;Sa 02 >.6 ) Fractio n o f COP D sensitiv e grou

p

(.5 ; .1 ; 0 ) (.25 ; .15 )

o

-• j (.25 ; .05 ; 0 ) (.1 ; .05 )

FIGUR E 7. 5 PROBABILITY TRE E USE D FO R COP D MODE LP.1 0

Th e probabilisti c phas e provide s distribution s o n hospitalization s an d o n lif e expectancy . Figur e 7. 6 indicate s the distributio n o n averag e hospitalization s fo r Lo s Angeles . Th e lowe r axi s indicate s the rat e pe r person , average d ove r cohorts , an d tota l estimate d ARF s pe r yea r fo r Lo s Angeles . Tabl e 7. 2 summarize s thi s data . Figur e 7. 7 indicate s the as-i s distributio n o n lif e expectancy . Thi s i s ver ysimila r fro mstandar d t o standard . Figur e 7. 8 provide s distribution s o n change s i n lif e expec tanc yfro

mthe as-i s estimat e fo r eac hstandar d fo r Lo s Angeles . Tabl e 7. 3 summarize s thi s output . Th e distribution s o n hospitalization s an d lif e expectanc yar e virtuall yidentica l fo r al l citie s sinc e the uncer taint yo n the as-i s level s i s s o large . Table s an d figure s tha t summa riz e the difference s betwee n standard s fo r the remainin g thre e citie s ar e provide d i n Appendi xA . Althoughth e range s o f th e outcome s ar e ver ywide , the expecte d val

ue s o f effect s ar e quit e similar . Expecte d ARF s pe r yea r chang e from 2.7 9 t o 3.0 4 pe r sensitiv e COP D patien t pe r yea r i n Lo s Angele s a s on e goe s fro

mthe strictes t t o th e mos t lenien t standard . Th e distributio n o n change s i n lif e expectanc yi s ver ylarge , wit ha spread a s grea t a s 18 0 day s (expecte d valu e = 7 7 days ) whe n on e compare s the strictes t stan
dard t o the as-i s situation . However , the siz e o f the populatio n tha t gain s thi s chang e i s smal l i n compariso n t o othe r healt heffect s suc ha s arterioscleroti c hear t disease . Thi s make s the overal l publi c healt h significanc e o f COP D an d C O small . Th e effect s o f al l ambien t C O ar e calculate d t o contribut e 8 % t o 9 % fo r Chicago , Philadelphia , an d St . Loui s an

d 13 % fo r Lo s Angele s t o the tota l morbidit yo f thi s group . As-i s lif e expectanc yi s decrease d b y equivalen t amount s fro mwha t i t woul d b e i f al l ambien t CO wer e removed . Thes e hig hvalue s illustrat e the sensitivit yo f thi s group . Summary Thi s mode l i s extremel ysensitiv e t o some o f the parameter s used . I n particular , the shap e o f the distributio n o n variatio n o f Pa n - ove r time (Figur e 7.1 ) i s importan t bu t no t wel l known . Give n the informatio n use d i n the . analysis , however , two conclusion s ca n b e drawn : (1 ) thi s grou p i s quit e sensitiv e t o change s i n ambien t C O standards , an d (2 ) be

caus e o f th e smal l siz e o f th e group , however , th e overal l magnitud e o f effect s i n probabl yles s significan t than the effect s fo r arterioscler

oti c hear t diseas e patients .



### 10 8

### 1. 0

.7 5 _

Probabilit y0. 5



### o .2 5 exe x

Averag e Annua l Hospital

ization/Patien t 18,00 0 Approximat e Tota l

Hospitalizations/yea r
FIGUR E 7. 6 PROBABILIT Y THAT HOSPITALIZATIONS/YEA R I S LES S THA

NTH E VALU E INDICATE D FO R TH E SENSITIVE COP D POPULATIONI NLO S ANGELE SP.1 2

Tabl e 7. 2 COMPARISONO F FREQUENC Y O F ACUT E RESPIRATORY FAILUR E INCIDENC E

I NSENSITIVE COP D POPULATIONACROS S STANDARD S

(Lo s Angeles ) Chang e fro m Standar d 3 5 ppm1 h r 1 exe x 2 5 ppm1 hr 1 exe x 2 5 pp m1 hr 5 exe x 1 2 ppm8 hr 1 exe x 1 2 ppm8 hr 5 exe x 9 pp m8 hr 1 exe x 9 ppm8 hr 5 exe x Chang e ARF/Yr / Patien t 

### .1 1 -.0 5 

### .0 2 -.1 1 -.0 7 -.1 4 -.1 2 FromAs-l s

Percen t o f As-l s 

### 3. 8 -1. 7 

### 0. 7 -3. 8 -2. 4 -4. 8 -4. 1 Strictes t

ARF/Yr / Patien t 

### .2 5 .0 9 .1 6 .0 3 .0 7

— 

### .0 2 0 . Standar d

Percen t f Standar d 

### 9. 0 .3. 2 

### 5. 7 1. 1 2. 5

— 

### 0. 7

11 0Probabilit y.5 0 —

Lif e Expectanc y(years )

FIGUR E 7. 7 PROBABILIT Y THA T LIF E EXPECTANC Y FO R ASENSITIVE COP D

PATIENT I S LES S THA NTH E VALU E INDICATE DP. M
1.00*

-

Probabilit y

Probabilit y 1.0 0

7 5 -



### .5 0

,2 5 5 exceedance s pe r yea r

2 5 ppm( 1 hr )

-18 0 

### -9 0 1 2 ppm( 8 hr )

9 ppm( 8 hr )



### 9 0 

### 18 0 Day s

FIGUR E 7. 8 PROBABILIT Y THAT TH E CHANG E I NLIF E EXPECTANC Y FRO M

AS-I S WIL L B E LES S THA NTH E VALU E INDICATE D FO R A COP D PATIENT I NLO S ANGELES . AS-I S LIF E EXPECTANC Y I S PREDICTE D T O B E ABOU T 4.4 8 YEAR S I NLO S ANGELE S FO R TH E SENSITIVE GROU P



### 11 2P.1 5

Tabl e 7. 3 COMPARISONO F LIF E EXPECTANCIE S FO R COP D SENSITIVE POPULATION ACROS S STANDARD S (Lo s Angeles )

Standar d 3 5 ppm1 hr 1 exe x 2 5 pp m1 hr 1 exe x 2 5 pp m1 h r 5 exe x 1 2 pp m8 hr 1 exe x 1 2 ppm8 hr 5 exe x 9 pp m8 hr 1 exe x 9 pp m8 hr 5 exe x Chang e Day s pe r Patien t -50. 3 28. 0 -8. 5 57. 4 36. 8 77. 5 63. 3 fro

mAs-l s Percen t o f As-l s -3. 1 

### 1. 7 -0. 5 

### 3. 5 2. 2 4. 7 3. 9 Chang e fro m Strictes t Day s pe r Patien t c -127. 8 -51. 1 -84. 0 -18. 3 -40. 2

— -14. 6 Standar d

Percen t i f Standar d -7. 5 -3. 0 -4. 9 -1. 1 -2. 3

— -0. 9



### 11 3P.1 6

REFERENCE S FOR CHAPTE R VI I
1 . Stephe n M . Ayres , Hiltrud S . Mueller , e t al. , "Systemi c an d Myocar
dia l Hemodynami c Response s t o Relativel ySmal l Concentration s o f Carboxyhemoglobi n (COHb), " Arc hEn vHealth , Vol . 18 , Apri l 1969 , pp . • 699-709 . 2 . Wilber t S . Aronow , Jac k Ferlinz , an d Frederic k Glauser , "Effec t o f Carbo n Monoxid e o n Exercis e Performanc e i n Chroni c Obstructiv e Pul

monar yDisease, " A mJ o f Medicine , Vol . 63 , Decembe r 1977 , pp . 904

- 908 . 3 . Prevalenc e o f Selecte d Chroni c Respirator yConditions , Unite d State s

- 1970 , Vita l Healt han d Statistics , Serie s 10 , No . 84 , Nationa l Cente r fo r Healt hStatistics , Rockville , Maryland , 1973 . 4 . R . F . Coburn , R . E . Forster , e t al. , "Consideration s o f th e Physio

logica l Variable s tha t Determin e the Bloo d Carboxyhemoglobi n Concen
tratio n i n Man, " J Cli n Invest , Vol . 44 , No . 11 , 1965 , p . 1903 .



### 11 5P.1 7

VII I CENTRAL NERVOU S SYSTE M
Th e effect s o f C O o n centra l nervou s systemfunctioning ar e diffi

cul t t o model . Clinica l studie s d o no t focu s o n the tru e publi c healt h risk s tha t ma yexist . Th e mode l develope d her e take s an approac horigi

nating i n the toxicologyliterature . Thi s approac hcenter s o n the us e o f "referenc e substances " t o interpre t the risk s implie d b yfunctiona l tests . Th e outcom e o f the mode l suggest s tha t the healt himpac t i n thi s are a ma yb e les s importan t tha n i n th e othe r area s considered , an d littl e furthe r developmen t o f the mode l i s indicate d a t thi s time . Background an

d Modelin g Issue s Th e centra l nervou s system(CNS ) i s among the firs t o f the physio

logica l systemt o b e impaire d whe n a n organis mi s deprive d o f oxygen . Th e possibilit yexist s tha t eve n smal l decrement s i n oxyge n availabilit y mayhav e noticeabl e effect s o n nervou s systemfunctions . Th e presenc e o f ambien t carbo n monoxid e ma yb e a contributin g facto r t o thes e effect s i f theyd o occur . Numerou s studie s hav e teste d the effect s o f variou s level s o f COHb o n differen t CN S functions . Some fin d verysignifican t changes , other s fin d a significan t change , whic hdisappear s a t highe r level s o f COHb . Manyfin

d no effec t a t all . Th e inconsistenc yo f result s ma yb e cause d b ythe difficult yo f formulating sensitiv e test s fo r mos t behaviora l functions . Anothe r problemma yb e tha t some aspect s o f CN S performanc e ar e mor e affecte d than others . Take n together , the evidenc e suggest s tha t some sor t o f effec t o f COHbo n CN S function s doe s indee d occur , perhap s wit ha threshold . Th e questio n tha t remain s i s t o identif ywha t seriou s healt hconsequence s ca n aris e fro

mthes e effects . Fe wwoul d argu e tha t the level s o f COHbasso ciate d wit hcurren t ambien t exposure s wil l induc e mor e than a mil d sen sor yand/o r intellectua l impairment . Suc himpairments , however , ca n con tribut e t o the ris k o f injuryo r deat hi n comple xsensorimoto r task s tha t requir e safet yprecautions . Amajo r sensorimoto r tas k tha t involve s a larg e portio n o f the populatio n i s moto r vehicl e driving . Th e level s o f COHbexperience d whil e driving ar e generall yhighe r than thos e foun d a t othe r times . Thus , i f ther e i s an ysignifican t healt heffec t o f C O du e t o CN S impairment , i t migh t b e foun d i n a correlatio n betwee n automobil e fatalitie s an

d COHblevel s i n drivers . I f thi s i s estimate d t o b e a par ticularl ylarg e effect , the n investigatio n o f othe r simila r acciden t risk s wil l b e indicated . Severa l test s hav e bee n mad e o f driving skill s an d othe r comple x task s unde r the influenc e o f COHb . A s wit hthe genera l vigilanc e tests , the result s ar e inconclusive . I n addition , attempt s t o fin d direc t 

### 11 7P.1 8

correlation s o f COHban d driver s i n fata l accident s hav e demonstrate d onl ysmal l effects . A s wil l b e discusse d below , i t i s eas yt o overesti

mat e the relevanc e o f thes e studie s whe n discussin g the effect s o f chang

in g standards . Issue s tha t mus t b e addresse d i n the mode l includ e the problemo f quantitativel yrelatin g th e result s o f functiona l test s o f th e CN S t o th e implie d risk s o f accident . An acciden t i s a rar e event . Test s o f acci

den t pronenes s afte r exposur e t o lo wlevel s o f COHbar e bot hdifficul t an d statisticall yinfeasible . Test s o f relate d CN S functions , althoug h difficul t t o desig n an d implement , ar e a n alternative . Adecremen t i n functiona l tes t scores , however , doe s no t directl yindicat e the magnitud e o f the associate d acciden t risk . Horvat han

d Franti k hav e propose d the us e o f "referenc e substances " i n estimatin g the risk s associate d wit hdecrement s i n functiona l tes t scores . ' Areferenc e substanc e i s on e tha t ha s a well-documente d dose

respons e relationshi p an d tha t ha s bee n use d i n numerou s functiona l tests . I n thi s method , on e determine s the dose s o f the substanc e o f con cer n tha t produc e the same effec t a s tha t o f give n dose s o f the referenc e substance . Thes e ar e calle d the "effectiv e levels, " an d theyma yb e de

termine d fo r a variet yo f functiona l tests . Fromthes e comparisons , on e ca n us e the dose-respons e curv e o f the referenc e substanc e fo r the effec

tive leve l o f the substanc e o f concern . Th e mai n problemi n thi s comparativ e toxicologyi s t o selec t the ap propriat e referenc e substance . Althoughpreferable , i t i s no t necessar y tha t a n identica l mechanis mo f toxicit yb e share d b ythe tw

o substances . Amor e importan t questio n i s ho wwel l the functiona l test s mode l the cap abilitie s whos e impairmen t coul d lea d t o the accident . Horvat han d Franti k poin t ou t tha t th e externa l similarit yo f the tes t t o th e drivin g situatio n i s no t ttt e onl yconcern . Test s o f .vigilance , sustaine d atten
tio n an d ris k taking ar e a s importan t t o conside r a s thos e o f drivin g skills . Th e referenc e substanc e selecte d fo r th e purpose s o f thi s stud yi s alcohol . Alcoho l ha s a n exceptionall ywell-documente d relationshi p t o driving risks . Furthermore , hypoxi a i s ofte n describe d a s having an ef

fec t simila r t o th e consumptio n o f alcohol . Thi s similarit yo f effec t ma yals o occu r a t lo wCOHban d lo wblood-alcoho l levels . T o investigat e the potentia l o f basing a mode l o n alcoho l a s a referenc e substance , th e literatur e o n the effect s o f alcoho l o n test s relate d t o driving skill s wa s reviewe d briefly . B ycomparing Figure s 8. 1 an d 8.2 , on e ca n se e tha t suc htest s resul t i n decrement s o f simila r magnitude s fo r blood-alcoho l an d COHbexposures . Thi s i s a n interesting compariso n i f the result s o n thes e functiona l test s hav e an yimplicatio n fo r change s i n the probabil

ityo f being involve d i n an automobil e accident . On e can us e the evidenc e fro mFigure s 8. 1 an

d 8. 2 t o approximat e the level s o f COHban

d bloo d alcoho l conten t (BAC ) whic hresul t i n comparabl e change s i n tes t scores . Thi s compariso n ca n b e state d mos t concisel ya s a mathematica l equatio n relating COHbt o a n "effectiv e bloo d alcoho l con tent " (EBAC) . O n the basi s o f the EBAC , on e ca n asses s risk s o f acci

dent s fro

mthe muc hmor e availabl e dat a o n alcoho l an d driving . 

### 11 8P.1 9

T o full yimplemen t the Horvat han d Franti k method , on e woul d wan t t o compar e COHban

d BAC fo r numerou s othe r functiona l tests . Dat a ar e availabl e fo r a mor e detaile d comparison . However , fo r thi s demonstra

tion , effectiv e level s fo r th e compariso n o f COHban d BA C wil l b e esti

mate d fro

mthi s smalle r sampl e o f dat a o n driving-relate d skills . Anothe r importan t issu e i n a mode l o f th e relationshi p o f COHban d traffi c accident s i s tha t o f "endogeneity. " Th e dose-respons e curv e an d the relationshi p o f COHbt o EBAC level s ar e no t sufficien t fo r a n esti

matio n o f the effect s o f C O o n expecte d automobil e death s an d injuries . A s traffi c densit ygrow s an d averag e vehicl e spee d slows , C O rises . A t th e same time tha t COH bi s becomin g hig henoug ht o mov e peopl e u p the dose-respons e curve , the kin d o f traffi c i n whic htheyar e driving sub

ject s themt o les s chanc e o f sever e accidents . Althoug hsmal l damag e ac cident s ma yincrease , th e chance s fo r accident s tha t resul t i n seriou s injurie s an

d death s wil l decrease . Fata l automobil e accident s occu r mainl yo n ope n road s wher e highspeed s ar e obtained . I n addition , bad weathe r i s negativel ycorrelate d wit hC O levels , s o tha t manyaccident s ar e mos t likel yt o occu r o n day s whe n C O i s low , despit e the lowe r chanc e o f acciden t o n thos e day s becaus e o f lowe r COHb . T o asses s properl ythe impac t o n driving accident s attributabl e t o COHb , on e mus t accoun t fo r thi s effec t o f changing driving conditions . Thi s ca n b e don e b yconstructin g a relationshi p betwee n the COHbleve l experience d whil e i n the ca r an d the averag e physica l driving condition s tha t coul d b e associate d wit htha t exposure . Th e physica l driving condi

tion s ca n the n b e combine d wit hthe elemen t o f the ris k contribute d b y impairmen t o f CN S functionin g t o asses s the averag e ne t ris k experience d a t eac hCOHblevel . Deterministi c Mode l Huma n Elemen t Figure s 8. 1 an

d 8. 2 indicat e tha t bot hCOHban d BAC ca n hav e simila r effect s o n driving skills . Th e approximatio n o f COHbi n term s o f effec

tiv e level s o f BA

C (EBAC ) require s judgment . Thre e ver ydifferen t func

tiona l form s ar e chose n t o examin e possibl e rescaling : logarithmic , lin

ear , an d exponential . Furthermore , ther e ma yb e some threshol d valu e o f COHbtha t mus t b e crosse d befor e thi s scaling i s applied . Azer o valu e o f BA C i s accompanie d b ysome averag e endogenou s leve l o f COHb . I f COHb ca n ris e befor e i t ha s an yaverag e effec t o n driving skills , the n the threshol d o f the EBAC functio n ma yb e highe r than the averag e endogenou s



### 11 9P.2 0

0 ) l- i 3 C O C O c u S C S c o o c 3 E n CD cn rt < u u o ( U o c c u c j C U 

## zu u

10 0 •

0 (.11 )

(9 ) (9 ) i (11 )



## (I D (7 ) (8 )

(8 ) (11 )

(10 )

(11 ) ( I D ( I D (7* 1 0 )

' .0 5 .1 0
Alcoho l Concentratio n (mg/lOOml )

FIGUR E 8. 1 EFFECT S O F ALCOHO L O NDRIVING-SKIL L TESTS .

NUMBER S INDICAT E REFERENC E FO R TH E STUDY FRO M WHIC H DATAPOINT WA S DERIVE D



### 12 0P.2 1

< U U 3 in C O C U S ci j c o
c j c 3 fa
c u c o c d c u u u c u o

• u c C U u u C U 0 4 20 0
_ _ (3 )
(3 )

10 0



# oL 0 

# tt (4)(6)(5 ) 

# J_ L 

# t (5 ) 1 0 1 5 2 0 COH b (percent )

FIGUR E 8. 2 EFFECT S O F COHbONDRIVING RELATE D SKILLS
NUMBER S INDICAT E REFERENC E FO R TH E STUD Y FRO M WHIC H DATAPOINT WA S DERIVED . ARROW S INDICAT E RANG E I NWHIC H RESUL T I S BELIEVE D T O FAL L WHE N EXAC T DATAI S NO T AVAILABL E



### 12 1P.2 2

level . Lettin g T b e the threshol d value , the thre e alternativ e relation

ship s migh t be : (a ) EBAC = max[(%COHb ) - T , 0 ] [8.1 ] 15 0 (b ) EBAC =0. 5 exp(%C0Hb- T ) - 1 [8.2 ] 10 0 (c ) EBAC =

» invers e of : (%C0Hb ) = T + 10 0 xEBAC exp(EBAC ) [8.3 ]
Th e coefficient s o f thes e function s hav e bee n chose n judgmentally . The y ar e the coefficient s tha t rescal e the COHbdat a o f Figur e 8. 2 t o mak e the mconformbes t wit hthe BAC dat a o f Figur e 8. 1 withi n the confine s o f eac hfunctiona l form . Figur e 8. 3 compare s the BA C dat a wit hthe rescale d COHbdat a t o indicat e the goodnes s o f fi t fo r eac hequation . Thes e graph s al l assume a threshol d valu e o f 2.5 % COHb , whic hi s consisten t wit hdata . Difference s i n threshol d woul d simpl yshif t the rescale d COHb dat a uniforml yt o the lef t o r th e right . Not e tha t the dat a suppor t a highe r threshol d than 2.5 % i n Figure s 8.3(a ) an d 8.3(b) . Thi s i s ac counte d fo r i n the probabilisti c phas e wher e the mea n threshol d use d i s 4% . Figur e 8. 4 compare s the thre e curves . Th e Departmen t o f Transportatio n ha s publishe d a curv e tha t repre sent s the probabilit yo f involvemen t i n a fata l automobil e acciden t a s a functio n o f bloo d alcoho l content . ' Thi s probabilit yi s relativ e t o the probabilit ywhe n the drive r ha s zero^BAC . I t i s thu s applicabl e t o driver s o f al l skil l levels . Th e curv e i s approximate d wel l b ythe following functio n i n th e region s o f interes t fo r C O exposur e (i.e. , fo r BA C betwee n 0 an

d .05%) : Relativ e Probabilit y= exp[30 0 x(BAC) 2 ] [8.4 ]
Thi s curv e i s illustrate d i n Figur e 8.5 . Whe n bloo d alcoho l i s zero , the relativ e probabilit yi s unity . A COHbvalu e o f 10 % i s equivalen t t o a bloo d alcoho l conten t o f .05% , usin g equatio n [8.2] . Thi s result s i n a relativ e acciden t probabilit ytwic e a s larg e a s whe n COHbi s 2.5 % o r less . T o approximat e the relativ e proba

bilit yo f a fata l acciden t a t eac hleve l o f COHb , RPFA(COHb) , relativ e t o a COHbo f zer o percent , the following formul a i s used :

RPFA(COHb ) = Relativ e Probability(EBAC )

= exp[30 0 x(EBAC) 2 ] [8.5 ]
wher e EBAC i s define d b yon e o f the function s 8. 1 through8.3 .



### 12 2CO C

U U 3 C O rt C U

o •r l U 

## Z 3 UA

c u C O rt a ) n o C U 20 0

10 0

0 X

•

• 

### 1 (a )

•

• • •• X •

•

• • * • • 

### 1 20 0

10 0

1 .0 5 1 0 » * x 

# ± (b ) 20 0

10 0



### J 0 .1 5 .0 5 .1 0 .1 5 (Effective ) Alcoho l Concentratio n (mg/10 0 ml ) 

### (c )

? * xx 

## Jl_ i L .0 5 ,1 0 

## J ,1 5

FIGUR E 8. 3 COMPARISONO F TES T RESULT S WIT H COHbRESCALE D T O EFFECTIVE ALCOHO L

CONCENTRATIONA S FOLLOWS : (a ) USING EQUATION8.2 , (b ) USING EQUATION 8.1 , (c ) USING EQUATION8.3 . THRESHOL D =2.5 % COHbI NEAC H CAS E

X = COHbdat a

• = Alcoho l dat aEq . 8. 3

EBAC (mg/10 0 ml )

N ) 4 >

COHb(percent )

FIGUR E 8. 4 COMPARISONO F THRE E EBA C FUNCTION S

FOR THRESHOL D =2.5 % COHbRelativ e

Probabilit y

L n

BAC (mg/10 0 ml )

FIGUR E 8. 5 PROBABILIT Y O F FATAL ACCIDENT A S FUNCTIONO F BLOOD-ALCOHO L

CONTENT RELATIVE T O PROBABILITY A T BLOOD-ALCOHO L CONTENT O F ZER OPhysica l Elemen t T o incorporat e the physica l elemen t associate d wit hdriving condi

tions , a relationshi p betwee n COHblevel s an d the amoun t o f driving time durin g whic htraffi c condition s permi t a chanc e o f a fata l acciden t i s proposed . Whil e sitting a t stop lights , o r drivin g i n slow , heav ytraf

fic , the chanc e o f a seriou s acciden t i s small . Thes e driving condi

tions , however , resul t i n the greates t increase s i n COHb . Figur e 8. 6 present s a curv e illustrating the assumptio n tha t the fractio n o f driving time during whic hther e i s a chanc e o f a sever e acciden t fall s a s averag e COHblevel s rise . Althoug hthi s curv e fo r drivin g time during whic ha fata l acciden t i s possible , DTFA(COHb) , i s hypothetical , i t i s poten
tiall ymeasurable . A s wil l b e describe d below , curren t uncertaint yo n thi s curv e i s handle d explicitl yi n the probabilisti c phase . Achang e i n the C O standar d result s i n a chang e i n the DTF Acurve . Unde r a mor e stingen t standard , a give n proportio n o f DFTAwoul d be asso ciate d wit ha lowe r leve l o f COHb . Thus , the rollbac k factor s ente r the mode l i n tw

o ways . Th e firs t i s vi a the COHbdistributio n estimate d b y the exposur e model . Second , the DTF Acurv e i s shifte d b ythe rollbac k facto r fo r eac hstandard . Thi s i s demonstrate d i n Figur e 8. 7 fo r a roll

bac k facto r o f 0.5 . Th e purpos e o f thi s adjustmen t i s t o mak e the aver ag e DTFAconstan t ove r differen t standards : on e woul d no t expec t an in

spectio n an

d maintenanc e pla n fo r C O emission s t o chang e the physica l condition s o f driving . Onl ythe huma n elemen t woul d b e affected .
Ne t Acciden t Risk s Th e effectiv e acciden t ris k account s fo r the opposin g effect s o n the huma n an

d physica l elements . Th e effectiv e curv e i s obtaine d b ymulti

plying the relativ e probabilit yo f fata l accident s b ythe fractio n o f drivin g time durin g whic hthe perso n wil l b e i n situation s wher e a sever e acciden t ca n occur . Thi s curv e i s depicte d i n Figur e 8.8 . Th e formul a fo r the expecte d ne t acciden t risk , NAR , is :
NA R =J ^ DTFA(C0Hb , Rollbac k factor) , xRPFA(COHb ) xPr(COHb ) [8.6 ] COHb
Th e difference s i n NA R fo r differen t standard s indicat e the relativ e change s i n the numbe r o f fata l accident s tha t woul d occu r bymovin g from on e standar d t o another . Curren t level s o f fata l accident s ar e known fro mpubli c statistics . B yapplyin g the proportiona l change s i n NA R from the as-i s cas e t o the curren t level s o f acciden t fatalities , on e ca n estimat e th e expecte d traffi c fatalitie s a t differen t standards . Th e exposur e pattern s fo r COHbwer e derive d fro mthe EP Aexposur e model . Aprobabilit ydistributio n o f COHbwa s calculate d fo r thos e hour s during whic hthe cohort s wer e sai d t o b e i n a n automobile . Al l adul t (driving age ) cohort s wer e aggregate d int o a singl e exposur e grou p be

caus e ther e i s nothing i n thi s simplifie d mode l t o discriminat e among cohort s i n term s o f sensitivitie s t o COHb . 12 6Fractio n

o f Driving Tim e

COHb(% )

FIGUR E 8. 6 HYPOTHETICAL RELATIONSHI P BETWEE NAVERAG E COK bLEVEL S WHIL E DRIVING AN D FRACTIONO F TH E DRIVING TIM E WHE NFATAL ACCIDEN T I S POSSIBL E (DTFA) : AAN D B AR E VALUE S O F COHbA T WHIC H SLOP E O F DTFACHANGE SDTF A(ROLLBAC K FACTO R =.5 )

Fractio n o f Drivin g Tim e
0 0 DTF A(ROLLBAC K FACTO R = 1 )

% COHb

FIGUR E 8. 7 EXAMPL E O F HO WDTF AI S AFUNCTIO NO F TH E

STANDARD FO R CARBONMONOXID ERelativ e Scal e

VO

COHb(percent )

FIGUR E 8. 8 DERIVATIONO F NE T ACCIDENT RIS K FUNCTIONFRO M

PHYSICAL AN D HUMA

NELEMENT SP.3 0

Asummaryo f averag e COHbexposure s attaine d b yindividual s i n car s i s provide d i n Tabl e 8. 1 fo r eac ho f the four cities .
Tabl e 8. 1 FATAL ACCIDENT LEVEL S AN D ESTIMATE D COHbEXPOSUR E PATTERN S (As-ls )

Cit y Chicag o Lo s Angele s Philadelphi a St . Loui s Approximat e

Annua l Traffi c Fatalitie s 24 0 

### 88 0 23 0 17 0 Averag e

% COHb i n Car s .9 0 

### .9 0 .4 8 .7 2 Averag e %COHb

i n Al l Microenvironment s .59-.7 2 .60-.8 5 .40-.4 2 .55-.6 7

Probabilisti c Mode l T o accoun t fo r uncertainties , illustrativ e probabilit ydistribution s wer e assigne d t o alternativ e form s fo r relating COHban d EBAC , t o possi

bl e threshol d value s i n the EBAC relationship , an d t o differen t value s fo r the paramete r Ai n DTFA . Despit e the larg e uncertaintie s assumed , the probabilit ydistributio n o n traffi c fatalitie s result s i n a very smal l rang e o n the distributions . Th e probabilit ytre e tha t wa s use d i s presente d i n Figur e 8.9 . Othe r uncertaintie s tha t ar e no t include d i n thi s probabilit ytre e wer e considere d i n the sensitivit yanalysis . Fo r instance , i f th e DTFA relationshi p i s remove d entirely , the predicte d effect s d o no t chang e significantly . Similarly , the relativ e probabilit ycurv e fo r BAC (equa

tio n 8.4 ) i s onl ya n approximation . Th e approximatio n use d her e repre sent s a n uppe r bound o n the actua l slope . An yprobabilit ydistributio n o n thi s relativ e ris k curv e woul d resul t i n smalle r estimate s o f change s i n the numbe r o f traffi c accidents . Th e distributio n o f traffi c accident s fo r the mos t stringen t an d leas t stringen t standard s ar e compare d fo r Lo s Angele s i n Figur e 8.10 . Th e predicte d effect s ar e verysmall , an d the distribution s hav e virtu

all yn o range . Eve n b ysetting the threshol d valu e a t .25 % COHban d re movin g the DTF Acurve , the estimate d accident s i n Lo s Angele s woul d ris e b ya maximu

mo f 1. 3 an

d fal l b

ya maximu

mo f 5 automobil e deaths .



### 13 0Functiona l for mrelatin

g COH ban d EBA C Threshol d i n EBA C Relationshi p Paramete r A i n DTFA Functio n
Exponentia l COH b2 % COH b1.5 %

• 5 0 COH

b2.5 %

COHb4 %

FIGURE 8. 9 PROBABILITY TRE E USE D FOR TRAFFI C ACCIDENT S MODE L1. 0

4 J •ri 

## 3 0. 5 ti x. o u

C o t o

0 -3 5 ppm , 1 hr , 1 exe x

9 ppm , 8 hr , 1 exe x

.999 8 899. 8 .999 9 899. 9 1.0 0 90 0 1.000 1 900. 1 Chang e fromas-i s death s
FIGURE 8.1 0 PROBABILITY THAT TRAFFI C DEATH S I NLO S ANGELE S AR E LES S THA NTH E VALU E INDICATED . TRAFFI C DEATH S I NPRESEN T AS-I S SITUATIO

NAR E ASSUME D T O B E 90 0 PE R YEA

RP.3 3

Discussio n Figur e 8. 8 show s tha t ou r mode l estimate s tha t th e correlatio n o f acciden t fatalitie s and drive r COH bma ybe. , negative . Hexte r i n fac t found a significan t negativ e relationship . Th e effec t o f changin g standard s ca n b e seriousl yoverestimate d i f th e change s describe d i n th e DTF Acurv e ar e no t addressed . Achang e i n standard s affect s no t onl y COH blevel s bu t als o th e DTF Acurve , and thes e effect s tend t o cance l eac hother .
Summar y Th e mode l fo r CN S effect s o f C O suggest s tha t thi s effec t produce s littl e contributio n t o publi c healt hrisks . Althoug hth e mode l i s inter

estin g i n it s us e o f data , ther e i s littl e reaso n t o develo p i t furthe r i n it s presen t form . On e questio n tha t ma yb e wort haddressing , however , concern s th e effec t o f ambien t C O o n worker s i n occupation s wher e safet y precaution s ar e necessary . Thi s woul d b e especiall yimportan t i f th e oc cupatio n involve d larg e occupationa l exposur e t o CO . Althoug hambien t C O migh t b e onl ya smal l additio n t o occupationa l CO , thes e worker s coul d b e o n a portio n o f th e dose-respons e curv e wher e eve n smal l increment s hav e importan t consequences .

13 3P.3 4

REFERENCE S FO R CHAPTE R VII I
1 . M . Horvat han

d E . Frantik , "Quantitativ e Interpretatio n o f Experi

menta l Toxicologica l Data : Th e Us e o f Referenc e Substances, " i n Advers e Effect s o f Environmenta l Chemical s an d Psychotropi c Drugs , Vol . 1 , Mila n Horvat h(ed.) . Elsevie r Scientifi c Publishin g Com
pany , Amsterdam , 1973 . 2 . M . Horvath , E. , Franti k an d K . Kopriva , "Alertnes s Impairing Effect s o f Chemica l Agent s i n Monotonou s Conditions : Laborator yAssess
ment, " i n Advers e Effect s o f Environmenta l Chemical s an d Psycho

tropi c Drugs , Vol . 2 , Mila n Horvat h(ed.) . Elsevie r Scientifi c Publishin g Company , Amsterdam , 1976 . 3 . A . M . Rayan

d T . H . Rockwell , "A n ExploratoryStud yo f Automobil e Drivin g Performanc e Unde r the Influenc e o f Lo wLevel s o f Carboxy

hemoglobin, " Annal s Ne wYor k Academ yo f Sciences , Vol . 174 , pp . 376

- 408 , 1970 . 4 . G . Wright , P . Randel l an d R . J . Shephard , "Carbo n Monoxid e an d Driv

in g Skills, " Arch . Environ . Health , Vol . 27 , pp . 349-354 , Decembe r 1973 . 5 . J . M . Ramsey , "Effect s o f Singl e Exposure s o f Carbo n Monoxid e o n Sensoryan

d Psychomoto r Response, " America n Industria l Hygien e Asso
ciatio n Journal , Vol . 34 , pp . 212-216 , 1973 . 6 . N . Rummo an

d K . Sarlanis , "Th e Effec t o f Carbo n Monoxid e o n Severa l Measure s o f Vigilanc e i n a Simulate d Drivin g Task, " J . Safet yRes. , Vol . 6 , pp . 126-130 , 1974 . 7 . M . Linnoila , C W . Erwin , D . Ram man d W . P . Cleveland , "Effect s o f Ag e an d Alcoho l o n Psychomoto r Performanc e o f Men, " Journa l o f Studie s o n Alcohol , Vol . 41 , pp . 488-495 , 1980 . 8 . F . MacCarth

yan d J . E . Tong , "Alcoho l an d Velocit yPerception : II . Stimulu s Discrimination, " Perceptua l an d Moto r Skills , Vol . 51 , pp . 968-970 , 1980 . 9 . G . J . Connor s an d S . A . Maisto , "Effect s o f Alcohol , Instruction s an d Consumptio n Rat e o n Moto r Performance, " Journa l o f Studie s o n Alcohol , Vol . 41 , No . 5 , pp . 509-517 , 1980 . 10 . C . W . Erwin , E . L . Wiener , M . I . Linnoila , T . R . Truscott , "Alcoho l Induce d Drowsines s an d Vigilanc e Performance, " J . Stud . Alcohol , Vol . 39 , p . 505 , 1978 .



### 13 5P.3 5

11 . M . Linnoila , "Effect s o f Alcoho l o n Psychomoto r Performanc e o f Me n an d Women, " Journa l o f Alcoho l Studies , Vol . 39 , p . 745 , 1978 . 12 . M . W . Perrine , J . A . Walle r an d L . S. Harris , "Alcoho l an d Highway Safety : Behaviora l an d Medica l Aspects, " DO T HS-800-599 , U.S . De
partmen t o f Transportation , NHTS ATechnica l Report , 1971 . 13 . R . E . Borkenstein , R . F . Growthe r e t al. , "Th e Rol e o f the Drinkin g Drive r i n Traffi c Accidents, " Indian a Univeresity , Bloomington , Indiana , 1964 . 14 . Dr . Alfre d C . Hexter , persona l communication .



### 13 6P.3 6

PART IV SIGNIFICANC E O F RESULT S
I n previou s section s o f thi s report , si xdifferen t healt hrisk s wer e analyze d fo r four differen t citie s unde r seve n propose d standards . Al

thougha complet e ris k analysi s woul d suppl ya wealt ho f informatio n a t thi s level , man

yo f the insight s usefu l t o a decisio n make r ar e bes t ob

taine d b

yobtaining a mor e syntheti c vie wo f the problem . I n thi s par t o f the report , a n extrapolatio n i s mad e t o an estimat e o f healt hrisk s fo r the nationa l urba n population . I t i s thi s populatio n tha t wil l b e mos t affecte d b ythe propose d standards . Th e healt hrisk s ar e the n combine d int o a singl e measur e o f nationa l healt himpact . Th e purpos e o f thi s healt himpac t measur e i s no t intende d t o establis hdefinitiv e trade-off s betwee n differen t healt hrisks , bu t rathe r t o identif ywhic hhealt hrisk s ar e mos t importan t fo r futur e de

velopmen t an

d evaluation . Finally , give n the curren t preliminaryanalysis , the uncertaintie s wit hthe highes t valu e o f resolutio n ar e identified . Thi s identificatio n ca n hel p se t prioritie s o n futur e area s o f research . I t shoul d b e reemphasize d tha t the estimate s derive d i n thi s stud y shoul d no t b e use d i n thei r presen t for mfo r decision-makin g purposes . No t onl yar e the model s preliminary , bu t als o the exposur e dat a furnishe d b yEP Awer e simplifie d an d partiall yi n erro r (se e Chapter s I I an d III) .



### 13 7P.3 7

I X HEALT H RISK S FOR TH E NATION
I n the previou s chapters , healt hrisk s hav e bee n estimate d fo r four cities . Extendin g thes e result s t o obtai n an estimat e o f the healt h risk s fo r the entir e natio n i s no t a trivia l task . I n thi s chapter , two question s wil l b e addressed : Ca n the result s o f the Healt hRis k Model s b e generalize d i n thei r relatio n t o the Exposur e Model ? Give n availabl e data , wha t estimate s ca n b e mad e o f the healt hrisk s fo r the nation ? Relatio n o f th e Healt hRis k an d Exposur e Model s I f the result s o f the Healt hRis k Model s ca n b e relate d i n a simpl e wayt o exposur e data , the n ther e exist s the possibilit yo f using rela

tivel yaccessibl e dat a fo r variou s citie s t o performthe health-ris k es timat e fo r eac hcit yi n the nation . Arterioscleroti c hear t diseas e effect s depend i n a relativel ysimpl e fashio n o n COHb . Fo r thi s reason , i t ma yb e possibl e t o obtai n a rela

tio n betwee n thes e effect s an d the averag e COHblevel s experience d i n eac hcity . I f a n averag e COHbleve l i s define d a s

a v% COHb= \ (fractio n o f person-occurrences ) x% COHb[9.1 ] a t COHbleve l COHb
the n a ver ygoo d fi t t o the expecte d valu e o f effect s foun d i n the four citie s fo r the seve n propose d standard s an d the presen t "as-is " situatio n ca n b e obtaine d (Figur e 9.1) : angin a attack s pe r daype r patien t = 1.5 4 + 0.4 3 xa v% COHb[9.2 ] Thi s relatio n i s no t especiall yconvenient , however , i n tha t the age/oc

cupuatio n distributio n differ s fro mcit yt o city . Aperhap s mor e usefu l (bu t les s accurate ) fi t i s (Figur e 9.2) : angin a attack s , pe r yea r pe r 1 0 adul t pop . = 13.2 7 + 3.0 4 xa v% COHb[9.3 ]



### 13 9Angin a Attack s

pe r Da y pe r Patien t 

### o . 8

. 7 

## 1

1 •^ ^ • 

### 1

•^^^



### 1 

## 1

1 1



## l 0. 4 0. 5 0. 6 Averag e % COHb 

### 0. 7 0. 8

FIGUR E 9. 1 ANGIN

AATTACK S PE R DA

Y PE R PATIENT A S AFUNCTIONO F AVERAG E COHbAngin a Attack s Pe r

Yea r Pe r IO 6 Populatio n I D

1 5

1 4 • 

## 1

•^' . 

# 1 • ix"^ -1 ^ 

## 1

•



## 1 • 

## 1

• •

• •

• •



## 1 1

• •

• •



## 1 • •

•



### 0. 4 0. 5 0. 6 0. 7 0. 8 Averag e % COH

b

FIGUR E 9. 2 ANGIN

AATTACK S PE R YEA

R PE R MILLIO NADUL T POPULATION A S AFUNCTIONO F AVERAG E COHb

C OP.4 0

Thes e rate s ca n als o b e use d t o estimat e cardiovascula r mortalit ythrough Equatio n 5.5 . Thes e fit s an

d the fit s obtaine d belo war e attempt s t o generaliz e the detaile d analysi s i n suc ha manne r tha t the result s can b e extrapo
late d t o citie s fo r whic ha detaile d analysi s i s impractica l withi n the resource s o f the ris k assessment . Al l the fit s ar e a least-square s fi t o f a straigh t lin e t o 2 8 point s ( 4 cities , 7 standards ) fo r expecte d healt heffect s a s a functio n o f averag e COHblevels . Amor e sophisti

cate d statistica l analysi s woul d b e appropriat e whe n mor e extensiv e dat a ar e available . T o us e Equatio n 9. 3 fo r a n extrapolatio n t o a nationa l scale , on e need s the adul t populatio n o f eac hcit yan d the averag e COHbfo r eac h cit yunde r eac hstandard . Th e problemwit hthi s approac hi s i n deter

minin g averag e COHbdat a i n term s o f C O data . Becaus e the EP AExposur e Mode l wa s no t readil yaccessible , i t wa s no t possibl e t o discove r a reli

abl e relationshi p betwee n averag e COHbvalue s an d averag e C O values . Th e structur e o f the exposur e mode l an d fit s t o availabl e dat a suggest , how

ever , tha t a simpl e relatio n probabl yexist s betwee n averag e COHban d th e populatio n weighte d averag e C O level s (and , i f necessary , the varianc e i n populatio n weighte d C O levels) . Th e C O level s mos t appropriat e fo r suc h a relationshi p shoul d probabl yb e thos e resulting fro ma 4 t o 8 hou r averaging , becaus e COHbbuild s u p slowl yi n the body . I f suc ha relatio n exists , the n four step s ar e indicated : (1 ) Determin e the relationshi p betwee n averag e COHban d averag e C O level s fo r four citie s unde r seve n standard s (an d the presen t as-i s situation ) using the Exposur e Model . (2 ) Determin e averag e C O level s (fo r some specifie d averaging time ) fo r variou s citie s fo r the as-i s situatio n fro mmonitore d data . (3 ) Determin e the rollbac k o f C O level s fo r thes e citie s fro mthe monitore d data . Fo r instance , i f a cit yexceed s 3 0 ppmC O onc e i n a yea r o n a n 8-hour averaging basis , the rollbac k t o a 9 ppm , 8 hr , 1 exe xstandar d i s 9/30 . Thi s rollbac k i s the n ap plie d t o the averag e C O levels ; some adjustmen t fo r background level s o f CO ma yb e made . (4 ) Determin e the adul t populatio n o f the city . Usin g thes e dat a an d Equation s 9. 3 an d 5.5 , roughapproximation s fo r ASHD healt heffect s ma yb e obtaine d fo r al l citie s fo r whic hmonitore d dat a ar e available . Th e basi c problemaddresse d abov e i s dat a managemen t an d avail

ability : ho wcan a ris k analysi s b e performe d economicall yfo r th e many citie s involve d i n a nationa l extrapolation ? Give n tha t detaile d analy

si s ca n b e performe d onl yfo r a smal l numbe r o f cities , thi s i s on e pos sibl e metho d t o link healt heffect s t o availabl e dat a fo r the remaining cities . Thre e crucia l question s mus t b e answere d befor e thi s metho d wil l b e useful : (1 ) I s ther e a relationshi p betwee n expecte d healt heffect s an d some aggregat e measur e o f dos e (suc ha s averag e COHb) ? (2 ) I s ther e a relationshi p betwee n the aggregat e measur e o f dos e an d some aggregat e


### 14 2P.4 1

measur e o f C O concentratio n tha t ca n b e obtaine d readil yfo r the citie s i n questio n (suc ha s averag e 4-hour C O concentration s an d perhap s th e varianc e o f the distribution) ? (3 ) Ca n the effec t o f a standar d (roll

bac k factor ) b e estimate d fo r the citie s fromavailabl e data ? Th e same procedur e ca n b e use d fo r the expecte d valu e o f feta l ef

fects . Figur e 9. 3 show s tha t feta l death s pe r pregnanc yar e closel yre late d t o averag e COHb :
feta l death s pe r 1,00 0 pregnancie s = 9.6 4 + 0.2 9 xa v% COHb[9.4 ] Give n th e dat a develope d fo r ASHD , i t i s onl ynecessaryt o determin e th e numbe r o f pregnancie s pe r yea r (o r the birt hrate ) fo r eac hcity . Fo r chroni c obstructiv e pulmonarydisease , the situatio n i s somewha t mor e complicated . Th e inpu t t o thi s mode l i s the dail yhighes t valu e o f COHb , an

d the mode l function s i n a rathe r comple xfashion . I n addition , th e urban/suburba n spli t o f thes e patient s differ s significantl yfrom cit yt o city . I n term s o f the averag e daily-highCOHbvalue s experience d i n hom e microenvironment s b ythes e patient s (weighte d b ythe urban/subur

ba n split) , a fairl ygoo d approximatio n ca n b e obtaine d (Figure s 9.4 , 9.5) : hospitalization s pe r patien t pe r yea r = 2.3 7 + 0.6 2 xa vdaily-hig h% COHb[9.5 ] lif e expectanc y(yr ) = 5.4 0 - 0.7 8 xa vdaily-hig h% COHb[9.6 ]
T o performextrapolation s using thi s model , i t woul d b e necessar yt o es timat e the siz e o f the sensitiv e populatio n (incidenc e rat e xpopulation ) an d the averag e daily-hig hCOHblevel s fo r thi s group . Some simpl e rela

tionshi p betwee n thi s an d some typ e o f C O averag e probabl yexists , bu t dat a wer e no t availabl e t o determin e it . Moto r vehicl e accident s presen t a slightl ydifferen t problem . Th e mode l use d i n Chapte r VII I ha s a COH bthreshol d belo wwhic hn o effect s occur . Fo r the model , the relevan t paramete r i s averag e COHbi n moto r vehicles , give n tha t the COHbi s abov e threshold . T o obtai n probabilisti c distribution s fo r the nation , equation s suc h a s the abov e ca n b e obtaine d fo r eac hsetting o f the uncertai n parameter s i n the models . Estimate s o f Healt hRisk s fo r Entir e Natio n Unfortunately , no t enoughdat a wer e availabl e t o performthe program describe d i n the previou s section . I n particular , a reliabl e relation

shi p betwee n averag e COHban d C O level s coul d no t b e determined . I n ad

dition , i t woul d b e a majo r tas k t o reconcil e C O monito r average s 

### 14 3

### 9. 9

Feta l Death s pe r 100 0 Pregancie s 

### 9. 8

•P
4 ^



### 9. 7 —

• 

## 1

1 • 

### 1

•



## 1 • 

## 1

• •



## 1 1

••

^^^^^ ••

••



### 1 • • •



### 0. 4 0. 5 0. 6 0. 7 0. 8 Averag e % COHb

FIGUR E 9. 3 FETAL DEATH S PE R THOUSAND PREGNANCIE S A S AFUNCTION

O F AVERAG E COHbHospitalization s

pe r Yea r

4>

- U l

Averag e Daily-High% COHb
FIGUR E 9. 4 NUMBE R O F HOSPITALIZATION S PE R YEAR O F APATIEN T SUFFERING

CHRONI C OBSTRUCTIVE PULMONARY DISEAS E A S AFUNCTIONO F TH E AVERAG E HIGHES T DAILY VALU E O F COH bEXPERIENCE D

### 5. 5

5. 0 _ Lif e Expectanc y (years )

•P
4. 5 ~



### 4. 0 —

- 

## 1

•



## 1 • • •• 

# 1

•• • •



## I 1

••• •

* ••

- •• •

•


### 1 1

* •

• • •



### 1 1

••



### 1 -

—



### 0. 4 0. 5 0. 6 0. 7 0. 8 0. 9 1. 0 Averag e Daily-High% COHb

FIGUR E 9. 5 LIF E EXPECTANC Y O F APATIENT SUFFERING SEVER E CHRONI C OBSTRUCTIV E

PULMONARY DISEASE S A S AFUNCTIONO F TH E AVERAG E HIGHES T DAIL Y VALU E O F COHbEXPERIENCE DP.4 5

published'fo r larg e citie s wit hthe particula r monitor s use d t o obtai n estimate s o f the rollbac k fo r thes e cities . Finally , give n the simplic

ityan d preliminarynatur e o f the healt hris k model s used , i t wa s fel t tha t suc ha n ambitiou s programmigh t len d a speciou s appearanc e o f valid

it yt o th e results . Asimple r mean s o f aggregatin g the dat a t o a nationa l scal e wa s therefor e used . Afte r examinatio n o f estimate d concentration s fo r vari

ou s larg e cities. , a t the 1 an d 5 expecte d exceedanc e level s ( 1 hou r an d 8 hou r averaging) , the adul t urba n populatio n o f th e Unite d State s wa s judgmentall ydivide d int o group s tha t live d i n four type s o f cities . Thes e cit ytype s correspond t o the fou r citie s analyze d i n thi s report . Thre e possibl e distribution s fo r the populatio n among thes e representa
tive cit ytype s i s define d i n Tabl e 9.1 . Distributio n I roughl ycorre spond s t o a classificatio n b yC O concentration s a t the 1 expecte d exceed

anc e leve l ( 1 hou r averaging ) experience d b ycitie s o f ove r 1 millio n inhabitants ; distributio n I I wa s obtaine d b yusing the 1 expecte d exceed

anc e leve l ( 8 hou r averaging ) concentrations ; distributio n II I wa s ob

taine d b

yconsidering thes e an d severa l othe r considerations .
Tabl e 9. 1 POSSIBL E DISTRIBUTION S O F URBA NPOPULATION B Y REPRESENTATIVE CITY TYPE S Distributio n Chicago , I L Lo s Angeles , CAPhiladelphia , P ASt . Louis , M O 

### I I I 

### II I 10 %

17 % 38 % 36 %

34 % 9 % 7 %



### 40 % 19 % 47 %

9 % 

### 34 %

I t shoul d b e note d tha t i n thi s typ e o f aggregation , al l informatio n o n the presen t as-i s situatio n I n eac hcit yi s lost . Th e nationa l healt h ris k estimate s wil l b e give n i n the following form :
o Assum e tha t al l citie s exactl ycompl ywit hthe strictes t standar d ( 9 ppm , 8 hr , 1 exex) . Fo r some citie s thi s wil l b e an improve

ment , fo r other s a deterioration . o Estimat e the healt hris k tha t wil l b e incurre d b ygoing t o some othe r standard , unde r the assumptio n tha t al l citie s wil l exactl y compl ywit hthe ne wstandard . Thi s i s clearl ya n oversimplificatio n o f the standard setting problem , a s ha s bee n discusse d previously . Fo r the remainde r o f thi s study , i t i s assume d tha t standard s appl y onl yt o citie s o f ove r 200,00 0 population ; smalle r citie s eithe r d o no t


### 14 7P.4 6

hav e a problem(an d henc e the standard s wil l no t apply ) o r ar e smal l enoughi n populatio n tha t theywoul d no t substantiall yaffec t the na tiona l estimates . Th e nationa l populatio n i n urbanize d area s o f ove r 200,00 0 populatio n wa s 11 5 millio n i n 1980 . Probabilisti c Phas e T o accoun t fo r uncertaint yi n the categorizatio n o f the populatio n b ycit ytype , eac ho f the thre e populatio n assignmen t distribution s i n Tabl e 9. 1 wer e assume d t o b e equall ylikely ; thus , eac hdistributio n ha s 1 chanc e i n 3 o f occurring . Th e distribution s o n healt hrisk s develope d i n previou s chapter s wer e converte d int o a pe r perso n basi s fo r eac hcit y an d the n multiplie d b yth e nationa l populatio n estimate d t o b e i n a cit y o f tha t type . Th e composit e distribution s fo r healt heffect s ar e pre sente d i n Figure s 9.6-11 . Th e expecte d valu e o f the healt hrisk s incurre d b ychanging fro

m the strictes t propose d standar d ar e presente d i n Tabl e 9.2 . T o pu t thes e figure s i n perspectiv e fo r the urbanize d area s considered , th e model s us e (o r predict ) estimate s o f abou t 330,00 0 death s pe r yea r fro mASHD , 1 bil

lio n angin a attack s pe r year , 100,00 0 attack s o f acut e respiratoryfail

ur e pe r year , 17,00 0 feta l death s pe r year , an d 25,00 0 traffi c death s pe r year . Lif e expectanc yfo r COP D patient s i s roughl y5 years ; fo r a sensi

tive grou

p o f roughl y50,000 , ther e ar e abou t 8 0 millio n day s o f lif e ex pectanc yfo r thi s group . Finally , the percen t chang e i n eac hhealt heffec t experience d i n going fro

mcomplianc e wit hthe strictes t standar d t o complianc e wit ha ne wstandar d i s presente d i n Tabl e 9.3 . Summary Th e mai n problemwit hestimatin g healt heffect s fo r the entir e na tio n i s on e o f efficien t us e o f resource s an d data . I t i s impractica l t o performa complete , detaile d analysi s fo r eac hcit yi n the nation . Th e problem , therefore , i s t o fin d a wayt o generaliz e the estimate s obtaine d fro mdetaile d analyse s and , using availabl e data , t o us e thes e generali

zation s t o estimat e th e healt heffect s fo r othe r cities . Tw o possibl e program s wer e outline d i n thi s chapter , bu t dat a wa s availabl e t o imple

men t onl yon e o f thes e programs .



### 14 8P.4 7 

### 1. 0

Cumulativ e Probabilit y

Cumulativ e Probabilit y 

### . 5 1 exceedanc e pe r



### 32 0 33 0 34 0

32 0 2 5 ppm( 1 hr )
3 5 ppm( 1 hr )



### 35 0 36 0

2 5 pp m( 1 hr )

33 0 34 0 35 0 Death s pe r yea r (thousands ) 

### 36 0

FIGUR E 9. 6 DISTRIBUTION S FO R NATIONAL UEBA NDEATH S DU E T O ASH D UNDE R VARIOU S PROPOSE D STANDARD S



### 14 9P.4 8 

### 1. 0

Cumulativ e . 5 Probabilit y 1 exceedanc e pe r yea r

17,20 0 l.Or

5 exceedance s pe r yea r 17,40 0

Cumulativ e Probabilit y 

### . 5 9 ppm( 8 hr )
1 2 ppm( 8 hr )

17,20 0 17,40 0 Death s pe r yea r 17,60 0



### hr )

17,60 0

FIGUR E 9. 7 DISTRIBUTION S FO R NATIONAL URBA NFETAL DEATH S UNDE R VARIOU S PROPOSE D STANDARD S



### 15 0P.4 9 1. 0 i —

Probabilit y 

### 0. 5 1 Exceedanc e pe r yea r 1 2 ppm

2 5 ppm( 1

Probabilit y

- 2 - 1 Day s pe r yea r (millions )

FIGUR E 9. 8 DISTRIBUTION S FO R TOTAL DAY S O F LIF E LOS T FO R

NATIONA L URBA

NCOP D SENSITIVE PATIENT S WHE NTH E STANDAR D I S CHANGE D FRO M TH E STRICTES T STANDAR D ( 9 ppm , 8 hr, l EXCEEDANCE )



### 15 1P.5 0 1. 0 i —

Probabilit y 

### 0. 5
- 9 ppm

24,99 5 

### 1. 0

Probabilit y 0. 5 -

24,99 5 . 2 ppm( 8 hr ) 1 Exceedanc e pe r yea r 25,00 5 25,01 5

-2 5 ppm( 1 hr )

9 ppm( 8 hr )
2 ppm( 8 hr ) 5 Exceedance s pe r yea r 

# J 25,00 5 Death s pe r yea r 25,01 5

FIGUR E 9. 9 DISTRIBUTION S FO R NATIONA L URBA NDEATH S I NCA R ACCIDENT S UNDE R VARIOU S STANDARD S



### 15 2Cumulativ e

Probabilit y

O i C O



### 20 0 60 0 1,00 0 1,40 0 Angin a attack s pe r yea r (millions ) 1,80 0 2,20 0

FIGUR E 9.1 0 DISTRIBUTIONONANGIN AATTACK S FO R NATIONAL URBA NASH D PATIENTS . AL L STANDARD S HAV E DISTRIBUTION S THAT AR E NO T DISTINGUISHABL E O NTHI S SCAL E

T l CJ lCumulativ e

Probabilit y

C n

5,00 0 

### 10,00 0 15,00 0 Hospitalization s pe r Yea r 20,00 0

FIGUR E 9.1 1 DISTRIBUTION S FOR HOSPITALIZATION S O F NATIONA L URBA NCOP D PATIENT S FO R TH E STRICTES T AN D MOS T LENIENT PROPOSE D STANDARDS . AL L OTHE R DISTRIBUTION S LI E BETWEE NTH E TW OP.5 3

Tabl e 9. 2 NATIONAL EXTRAPOLATIONO F PREDICTE D CHANGE S

FROM STRICTES T STANDARD

Million s Attack s



### 3 5 2 5 2 5 1 2 1 2 

### 9 Standar d 

### pp m pp m pp m pp m pp m pp m 1 h r 1 hr 1 h r 8 hr 8 h r 8 hr 

### 1 1 5 1 5 5 exe x exe x exe x exe x exe x exe x Death s from ASH D 15,23 0 8,35 0 14,23 0 3,71 0 7,33 5 3,23 0 Million s

Attack s o f Angin a 42.4 9 23.4 0 39.6 1 10.4 9 20.5 4 9.0 7 Day s

Lif e Expec
tanc y fo r COP D -1.0 4 -0.4 9 -0.9 4 -0.1 8 -0.4 3 -0.1 7 o f

Acut e Respi

ratory Failur e 8,85 4 3,90 1 7,80 0 1,41 8 3,43 0 1,32 0 Feta l

Death s (>2 0 weeks ) 13 0 

### 7 1 

### 12 2 3 2 6 3 2 8 Traffi c Death s 

### 2 0 2 0 0 0

Tabl e 9. 3 NATIONAL EXTRAPOLATIONO F PREDICTE D PERCENT CHANGE S

FROM STRICTES T STANDARD

Standar d 3 5 ppm1 hr 1 exe x 2 5 ppm1 hr 1 exe x 2 5 ppm1 hr 5 exe x 1 2 pp m8 hr 1 exe x 1 2 ppm8 h

r 5 exe x 9 pp m8 hr 5 exe x Death s from ASH D 

### 4. 6 2. 5 4. 3 1. 1 2. 2 1. 0 Angin a Attack s 

### 4. 1 2. 2 3. 8 1. 0 2. 0 0. 9 COP D

Lif e Expec
tanc y -5. 7 -2. 7 -5. 2 -1. 0 -2. 4 -0. 9 Acut e Respiratory Failur e 

### 7. 1 3. 1 6. 2 1. 1 2. 7 1. 1 Feta l

Death s (>2 0 weeks ) 

### 0. 7 0. 4 0. 7 0. 2 0. 4 0. 2 Traffi c Death s 

### 0 0 0 0 0 0

15 5P.5 4

REFERENCE S FOR CHAPTE R I X
1 . T . McCurdy , persona l communication .



### 15 7P.5 5

X SIGNIFICANC E WEIGHTING Decisio n analysi s suggest s tha t the outpu t fro mthe decisio n mode l b e state d i n a for mtha t permit s an immediat e ranking o f the decisio n alternatives . Thi s i s especiall yimportan t whe n the decisio n i s t o b e base d o n mor e than on e outcom e attribute . Th e evaluatio n o f alternativ e standard s fo r carbo n monoxid e present s a n exampl e o f multiattribut e outcomes . Some healt heffect s ar e mor e easil yreduce d throughcontrol s tha n ar e others . Thus , the relativ e con tributio n t o the publi c healt hproblemo f eac heffec t wil l diffe r fro m standar d t o standard . T o provid e insigh t an d a clea r statemen t o f the benefit s o f eac hleve l o f control , thes e variation s ca n b e compare d b ya significanc e weightin g mode l whic hstate s eac hhealt heffec t i n a commo n unit . Choic e o f Unit s Acommo n approac hi s t o translat e eac houtcom e int o a monetar y valu e s o tha t the tota l monetar yvalu e ove r al l o f the outcome s can b e directl ycompare d t o the cost s o f eac halternative . Ther e ar e numerou s reason s wh

ythi s approac hi s inappropriat e fo r the sepcifi c need s o f EP A i n setting Nationa l Ambien t Ai r Qualit yStandard s (NAAQS) . Th e firs t objectio n i s tha t a monetar yvalu e o f healt heffect s i s almos t impossibl e t o determin e a t the societa l level . Althoughnumerou s method s exis t fo r choosing suc hvalues , non e i s acceptabl e t o al l member s o f society . Th e us e o f suc hvalue s ma yobscur e the informatio n containe d i n the analysi s b

yintroducing the element s o f a verydifficul t an d emotion-lade n question . Asecon d argumen t agains t the us e o f monetar yvalue s i s tha t i t ha s n o us e i n the contex t o f NAAQS . Th e EP Acanno t conside r the cost s o f contro l i n it s choic e o f regulatorylevel . Rather , the healt hrisk s themselve s mus t b e the sol e basi s fo r the decision . A n outcom e variabl e tha t retain s a clea r healt hris k interpretatio n therefor e i s preferable . I n the ligh t o f thes e points , on e woul d prefe r a uni t tha t ha s n o explici t economi c valu e attache d t o i t an d whic hha s a direc t health-ris k connotation . I n thi s report , "equivalen t day s o f restricte d activity, " o r EDRAs , ar e used . Thi s metri c allow s on e t o obtai n a sens e o f the se verit yo f a n effec t withou t an ystatemen t o f it s absolut e societa l value . It s clos e relationshi p t o a measurabl e quantit yhelp s limi t the contro
vers yove r the numbe r o f unit s tha t shoul d b e assigne d t o eac hhealt hef

fect . Furthermore , i t retain s some relationshi p t o the actua l ris k tha t the publi c faces .


### 15 9P.5 6

Assignmen t o f EDRA s Lik e an yothe r for

mo f significanc e weighting , the assignmen t o f EDRA s t o eac hhealt heffec t doe s involv e valu e judgments . However , phys

ica l fac t permit s reasonabl e range s t o b e proposed . Fo r instance , a healt heffec t tha t result s i n a wee k o f hospitalizatio n wit hn o irrever

sibl e effect s woul d b e represente d b yseve n EDRAs . However , a hospitali

zatio n fo r on e wee k wit hsome irreversibl e damag e remainin g afterward s woul d probabl yb e give n a highe r valuation . I f the irreversibl e damag e implie s a decreas e i n lif e expectancy , the n the magnitud e o f thi s de

creas e ca n b e use d t o determin e the additiona l EDRAs . I f the damag e doe s no t affec t lif e expectancy , bu t impair s the qualit yo f lif e fo r the pa tient , the n the frequenc yo r qualit yo f thi s impairmen t an d the remainin g lengt ho f lif e ca n b e incorporate d int o the choic e o f weighting . Afina l commen t relate s t o the interpretatio n o f the EDR Aassign

ments . Th e absolut e leve l o f EDRA s i s no t a s importan t a s the relativ e weight s o f the healt heffect s i n thi s total . Th e usefulnes s o f a signif

icanc e weightin g scheme i s tha t i t allow s on e t o determin e whic hhealt h effec t pose s the mos t sever e problemt o the public . I t doe s s o b ycom bining the tw

o independen t factor s o f incidenc e an d severity . Unti l thi s i s done , coheren t summarystatement s o f the problemar e extremel ydiffi

cul t t o obtain , especiall ywhe n ther e ar e numerou s effect s t o consider . Possibl e EDR

Aassignment s fo r the effect s associate d wit hC O ar e provide d i n Tabl e 10.1 . Th e third colum n indicate s the rang e ove r whic h sensitivit yanalysi s i s performed . Th e value s hav e bee n drawn fro mesti

mate s o f expecte d day s o f restricte d activit y(ischemia , hospitalization ) o r expecte d day s o f lif e los t (death) , modifie d b yan estimat e o f the relativ e weigh t societ ymigh t appl yt o eac heffect . Th e range s hav e bee n mad e broad enought o encompas s man ypoint s o f view .

Tabl e 10. 1 POSSIBL E EDR

AASSIGNMENT S Rang e fo r Effec t EDRA s EDRA s Deat hfro

mmyocardia l infarctio n o r fibrillatio n 7. 5 year s 3-1 0 year s Painfu l episod e o f myocardia l ischemi a 0. 1 day0.05- 1 day Hospitalizatio n fo r acut e respiratoryfailur e 1 4 day s 3-2 1 day s On e daydecrease d lif e expectanc yfo r COP D patien t 1 day1- 3 day s Deat ho f fetu s ( > 2 0 week s gestation ) 7. 5 year s 1-7 5 year s Deat hbecaus e o f automobil e acciden t 3 0 year s 5-5 0 year s



### 16 0P.5 7

Result s Thes e weight s hav e bee n applie d t o the healt heffect s tha t hav e bee n estimate d a t the nationa l leve l i n Chapte r IX . Tabl e 10. 2 summarize s th e predicte d change s i n EDRA s tha t woul d b e implie d b ya decisio n t o reduc e the standar d closes t t o the curren t on e i n forc e -t o eac ho f the remain

ing , les s stringent , standard s considered. "

Tabl e 10. 2 CHANGE S I NEDR

APE R YEA

R A T TH E NATIONAL LEVE L

Standar d 3 5 pp m1 hr 1 exe x 2 5 ppm1 hr 1 exe x 2 5 ppm1 hr 5 exe x 1 2 pp m8 hr 1 exe x 1 2 ppm8 hr 5 exe x 9 ppm8 hr 5 exe x Chang e From Strictes t Standar d

EDRA/Y r (millions ) 47. 5 22. 9 44. 3 11. 5 22. 8 10. 0 . Approximat e

Percen t o f Strictes t Standar d 

### 3. 0 1. 6 2. 8 0. 7 1. 4 0. 6

Strictes t standar d estimate d t o hav e approxi

matel y160 0 x1 0 EDRA s associate d wit hthe si x healt heffect s include d here . C O i s responsibl e fo r onl ya smal l fractio n o f thes e EDRAs .

A s note d above , on e shoul d no t plac e emphasi s o n the absolut e level s indicate d i n Tabl e 10.2 . Amor e importan t featur e i s the relativ e dif

ference s among the standards . I t shoul d als o b e emphasize d tha t the change s i n EDRA s ar e fo r a hypothetica l situation . Veryfewcitie s in

clude d i n the sampl e compl ywit hthe strictes t standard . Th e figure s her e onl yrepresen t the chang e i n EDRA s tha t woul d occu r i f al l o f the citie s starte d i n complianc e wit hthe strictes t standard , an d the n C O level s wer e t o ris e s o tha t the citie s wer e exactl yi n complianc e wit h the les s stringen t standar d indicated . Figur e 10. 1 indicate s the contributio n o f eac ho f the si xhealt hef

fect s t o th e tota l change s i n EDRA s fo r 3 5 pp m( 1 hr , 1 exex ) a s compare d


### 16 1ASH D death s

Angina l pai n Chang e i n lif e expectanc yfo r COP D Feta l death s 

# H

• G
COP D hospitaliza

- • tio n

CTV t o Traffi c Accident s

1 5 3 0 EDRA s Pe r Yea r (millions ) 

### 4 5

FIGUR E 10. 1 COMPARISON S O F RANGE S O F EDRA S ATTRIBUTABL E T O EAC H HEALT H EFFECT .

VALUE S AR E CALCULATE D A S CHANGE S FRO M STRICTES T STANDAR D T O LEAS T STRINGENT STANDAR D A T NATIONA L LEVEL . CIRCLE S REPRESENT TH E MOS T LIKEL Y VALUES . DOTTE D LIN E INDICATE S POSSIBL E MAGNITUD E O NNONFATA L FETAL EFFECT SP.5 9

t o 9 pp m( 1 hr , 1 exex) . Th e larg e circle s represen t the mos t likel y value s fo r EDRAs , a s presente d i n Tabl e 10.1 . Th e range s indicat e ho w the contributio n fo r eac hhealt heffec t wil l shif t a s the relativ e valu

ation s ar e adjuste d acros s th e range s i n Tabl e 10.1 . Thi s sensitivit y analysi s i s simila r fo r eac hstandard . Th e dotte d lin e o n the feta l effect s i n Figur e 10. 1 indicate s a nom ina l estimat e o f ho wimportan t feta l effect s o f al l form s migh t be . T o calculat e the chang e i n EDRA s associate d wit hnonfata l effects , th e as sumptio n wa s mad e tha t a chang e i n feta l death s du e t o change s i n C O ex posure s woul d als o resul t i n a proportiona l chang e i n nonfata l effects . Th e nomina l EDRA s assigne d were : 6 5 year s fo r sever e menta l retardatio n (I Q <30) , cerebra l palsy , an d sever e physica l damag e a t birth ; 4 0 year s fo r moderat e menta l retardation ; 2 0 year s fo r brai n seizur e disorders ; 1 5 year s fo r mil d menta l retardatio n (I Q <70) , speec han d learning disor

ders ; an

d 5 year s fo r al l othe r congenita l anomalies . Th e proportio n o f birth s affecte d b

yeac ho f thes e i s provide d i n Tabl e 6.5 . Range s o f un certaint ywer e no t place d o n thi s estimat e becaus e i t i s veryapproximat e t o begi n with . Th e importan t featur e t o not e i s tha t manifestation s o f ASH D domi

nat e i n importanc e regardles s o f relativ e valuation . Change s i n lif e ex pectanc yfo r COP D patient s ar e o f secondaryrelativ e importanc e through

ou t th e entir e rang e o f possibl e valuations . Feta l effect s ar e smal l whe n onl yspontaneou s abortion s ar e considered . However , i f nonfata l effect s wer e included , thi s healt heffec t coul d ris e t o significan t lev

els . COP D morbidit yan d traffi c accident s ar e no t relativel yimportan t fo r an ylikel yvaluation . Tabl e 10. 3 indicate s the percen t contribution s o f eac hhealt heffec t t o the tota l chang e i n EDRA s fo r eac hstandard . On e ca n se e tha t ther e i s verylittl e chang e fro mstandar d t o standard . Th e mos t likel yvalue s fo r eac heffec t wer e use d t o generat e thes e per centages .

Tabl e 10. 3

PERCENT CONTRIBUTIONO F EAC H HEALT H EFFEC T T O TOTAL , B Y STANDARD

Standar d 3 5 pp m1 hr 1 exe x 2 5 ppm1 h r 1 exe x 2 5 pp m1 hr 5 exe x 1 2 pp m8 hr 1 exe x 1 2 pp m8 h r 5 exe x 9 ppm8 hr 5 exe x COP D Lif e ASH D Angin a Feta l Expec
- COP D Traffi c Death s Attack s Deat htancyMorbidit yAccident s 

### 87. 8 88. 1 88. 9 88. 3 88. 1 88. 3 8. 9

9. 0 8. 9 9. 1 9. 0 9. 1 0. 7

0. 7 0. 8 0. 8 0. 8 0. 8 2. 2

1. 9 2. 1 1. 6 1. 9 1. 7 0. 3

0. 2 0. 2 0. 2 0. 2 0. 2 0 . 1

0 0. 1 0 0 0 

### 16 3P.6 0

Th e dat a give n s o fa r hav e bee n base d o n the expecte d value s o f EfiRA s fo r eac hhealt heffec t an d standard . O f equa l importanc e i s the uncertaint yon.thes e values . Figur e 10. 2 present s probabilit ydistri

bution s o n the change s i n EDRA s fro mthe strictes t propose d standard . A s on e ca n see , the varianc e increase s a s the standar d become s les s strin

gent . Conclusio n Th e relativ e valuatio n o f differen t healt heffect s is , i n SRI' s view , a controversia l bu t integra l elemen t o f an yris k assessment , be

caus e i t ca n hel p determin e the leve l o f effor t applie d t o eac hpar t o f the assessment . Th e possibl e value s presente d her e ar e no t t o b e take n a s normative . However , sensitivit yanalysi s o n a broa d rang e fo r eac h valuatio n indicate s tha t the ranking o f relativ e importanc e i s invarian t Arterioscleroti c hear t diseas e patient s clearl yrepresen t the greates t componen t o f the publi c healt hrisk s o f CO , an d analysi s o f thi s healt h effec t shoul d b e don e wit hgrea t care .



### 16 41. 0 i —

Probabilit y

cr . C n 

### 0. 5

10 0 12 5 Chang e fromStrictes t Standar d

(1 0 EDR

Ape r year )
FIGUR E 10. 2 PROBABILIT Y THAT TH E CHANG E INEDRA S FRO M MOS T STRINGENT STANDAR D I S

LES S THA

NTH E VALU E INDICATE D FO R TH E SI X LES S STRINGENT STANDARDS , ONANATIONAL BASI S
KEY : (standard i n ppm ; averagin g time i n hours ; expecte d exceedance s pe r year )

T J c nP.6 2

X I UNCERTAINTIE S WIT H HIGH VALUE S O F RESOLUTION
On e o f the advantage s o f the approac huse d i n thi s repor t i s tha t i t ca n identif ythos e area s i n whic hresolutio n o f uncertaint ywoul d b e mos t usefu l fo r the decision-makin g process . A s ha s bee n emphasized , the primarygoa l o f the nex t cycl e o f analy

si s shoul d b e the elaboratio n o f the healt hris k models . Give n tha t th e model s retai n the same typ e o f uncertai n inpu t tha t theypresentl yhave , however , some estimat e ca n b e obtaine d o f the usefulnes s o f resolving un certaint yo n thes e parameters . Thi s ca n serv e a s a n indicatio n o f field s o f fruitfu l an

d usefu l research . Fo r eac ho f the variable s assume d t o b e uncertain , a sensitivit y analysi s ha s bee n performed . I n thi s analysis , a distributio n i s gener ate d unde r the assumptio n tha t eac hvariabl e i n turn take s it s highes t an d lowes t valu e (al l othe r variable s retai n the assigne d uncertainty) . Th e result s o f thi s analysi s indicat e tha t resolutio n o f uncertaint y o n the parameter s i n the arterioscleroti c hear t diseas e mode l i s mos t im

portant . Knowin g the tru e valu e o f an yon e o f the thre e parameter s i n Figur e 5. 5 coul d chang e the expecte d healt hris k i n going fro ma standar d simila r t o the curren t on e t o a ne won e b ya s muc ha s 25% . Resolutio n o f the uncertaint yo n ho wthe populatio n i s assigne d t o representativ e cit y type s coul d chang e the expecte d healt hris k becaus e o f a chang e o f stan
dar d b ya s muc ha s 15% . Resolutio n o f uncertaint yi n the variable s i n Figure s 6.3 , 7.5 , an d 8.1 1 woul d resul t i n les s than 5 % changes . Thi s i s illustrate d i n Figur e 11.1 , wher e the healt hris k i n going fromthe strictes t standar d ( 9 ppm , 8 hr , 1 exex ) t o the mos t lenien t (3 5 ppm , 1 hr , 1 exex ) i s presente d fo r thre e cases . I t i s assume d tha t the rat e o f chang e o f the doubl e produc t a t onse t o f angin a a s a functio n o f COHbca n b e determine d exactly ; the thre e curve s i n the figur e represen t th e cas e fo r eac hvalu e thi s slop e migh t take . Almos t identica l curve s woul d b e obtaine d i f uncertaint ywer e resolve d o n eithe r o f the remaining uncertai n variable s i n Figur e 5.5 . Th e utilit yo f thi s concep t i s apparen t fro mthe following purel y hypothetica l example . Assum e tha t relaxatio n o f the strictes t standar d i s permissibl e i f thi s relaxatio n ha s a 90 % probabilit yo f increasing the healt hris k b

yles s tha n 5 0 millio n EDRAs . Referenc e t o Figur e 10. 2 show s tha t two standard s (2 5 ppm , 8 hr , 5 exe xan d 3 5 ppm , 1 hr , 1 exex ) ar e inadmissable . Figur e 11.1 , however , show s tha t i f the actua l slop e o f the Arono wdat a i s around -500 , th e lenien t 3 5 ppm , 1 hr , 1 exe xstan
dar d i s indee d admissable ; the probabilitie s assume d i n Figur e 5. 5 indi

cat e tha t ther e I s 1 chanc e i n 4 tha t thi s coul d happen .



### 16 7

### Cumulativ e

Probabilit y

0 0

2 5 5 0 7 5 EDR Ape r yea r (millions ) 

### 10 0

FIGUR E 11. 1 DISTRIBUTION S FO R TH E NATIONAL HEALT H IMPAC T PE R YEAR UNDE R ACHANG E

FROM TH E STRICTES T T O TH E MOS T LENIENT PROPOSE D STANDARD . TH E CURVE S CORRESPOND T O SPECIFI C VALUE S FO R TH E SLOP E O F MO S A S AFUNCTIO NO F COHb : A,-500 ; B.-770 ; C.-100 0P.6 4

Thus , i n thi s hypothetica l example , th e decisio n make r know s tha t a progra mtha t coul d completel yresolv e thi s uncertaint yha s a 25 % chanc e o f makin g thi s mor e lenien t standar d admissable . Thi s procedur e ca n b e repeate d fo r othe r uncertai n variables , an d prioritie s ca n b e se t o n re searc hprograms , whic hcan substantiall yreduc e uncertainty .



### 16 9P.6 5

APPENDI X A
I n thi s appendix , the dat a fo r healt hrisk s ar e presente d fo r thre e cities : Chicago , IL ; Philadelphia , PA ; St . Louis , MO . Corresponding result s fo r Lo s Angeles , CA , ar e presente d i n the bod yo f the report .



### 17 1P.6 6

Tabl e A. l COMPARISONO F INCIDENC E O F ANGINAL PAI N

(Chicago )

Standar d 3 5 ppm1 h r 1 exe x 2 5 pp m1 hr 1 exe x 2 5 ppm1 hr 5 exe x 1 2 pp m8 h r 1 exe x 1 2 ppm8 hr 5 exe x 9 pp m8 hr 1 exe x 9 ppm8 hr 5 exe x * Chang e Ove r As-l s Millio n Percen t Attack s o f As-l s 

### .8 8 

### 0 .4 7 -.5 5 -.1 7 -1.0 3 -.7 3 

### 2. 8



### 0 1. 5 -1. 8 -0. 5 -3. 3 -2. 4 Chang e Ove r

Strictes t Standar d Millio n Percen t Attack s o f Standar d 1.9 1 1.0 4 1.4 9 

### .4 8 .8 6

— 

### .2 9 6. 4

3. 4 5. 0 1. 6 2. 7

— 

### 1. 0

As-i s predicte d incidenc e = 3 1 million/year . Strictes t standar d predicte d incidenc e = 2 0 million/year .



### 17 2P.6 7

Tabl e A. 2 COMPARISONO F ANNUAL INCIDENC E O F ANGINAL PAI N

(Philadelphia )

Standar d 3 5 ppm1 hr 1 exe x 2 5 pp m1 hr 1 exe x 2 5 ppm1 hr 5 exe x 1 2 pp m8 hr 1 exe x 1 2 ppm8 hr 5 exe x 9 pp m8 hr 1 exe x 9 pp m8 hr 5 exe x * Chang e Ove r As-l s Millio n Percen t Attack s o f As-l s 

### .7 9 .3 1 .8 0 -.1 7 

### .2 2 -.4 6 -.1 0 

### 2. 8

1. 1 2. 8 -0. 6 

### 0. 8 -1. 6 -0. 4 Chang e

Strictes t Millio n Attack s o 1.2 5 

### .7 6 1.2 5 

### .2 9 .6 8

— 

### .3 6 Ove r

Standar d Percen t f Standar d 

### 4. 4 2. 7 4. 5 1. 0 2. 4

— 

### 1. 3

As-i s predicte d incidenc e = 28. 6 million/year . Strictes t standar d predicte d incidenc e = 28. 2 million/year .



### 17 3P.6 8

Tabl e A. 3 COMPARISONO F ANNUAL INCIDENC E O F ANGINAL PAI N

(St . Louis )

Standard 3 5 ppm1 h r 1 exe x 2 5 ppm1 hr 1 exe x 2 5 ppm1 h r 5 exe x 1 2 pp m8 hr 1 exe x 1 2 pp m8 hr 5 exe x 9 ppm8 hr 1 exe x 9 ppm8 hr 5 exe x * Chang e Ove r As-l s Millio n Percen t Attack s o f As-l s 

### .1 5 .0 2 .2 0 -.0 5 

### .0 3 -.0 8 -.0 5 

### 1. 3

0. 2 1. 6 -0. 4 

### 0. 2 -0. 7 -0. 4 Chang e Ove r

Strictes t Standar d Millio n Percen t Attack s o f Standar d 

### .2 4 .1 1 .2 8 .0 4 .1 1

— 

### .3 7 1. 9

0. 9 2. 3 0. 3 0. 9

— 

### 0. 3

As-i s predicte d incidenc e = 12. 3 million/year . Strictes t standar d predicte d incidenc e = 12. 2 million/year ,



### 17 4P.6 9 

### 1. 0 r

Cumulativ

e Probability 2 5 pp m( 1 hr )
3 5 pp m( 1 hr )

. 5 -

0 1. 0

Cumulativ

e Probability

. 5 5 Exceedance s

pe r year 9 pp

m( 8 hr )



## 1 1 2 pp m( 8 hr )

•2 5 pp m( 1 hr )



## 1 .8 5 .9 0 .9 5 1. 0 1.0 5 1.1 0 1.1 5 chang e 5,95 0 6,30 0 6,65 0 7,00 0 7,35 0 7,70 0 8,05 0 death s

FIGURE A. l DISTRIBUTION S O NCHANGE S I NISCHEMI C EPISODE S FRO M AS-I S INCHICAGO . ESTIMATE S OF APPROXIMATE.DEATH S ARE ALS O PROVIDED . CURREN T ASH D DEATH S INCHICAG

O ARE ASSUME D TO B E 7000/YEA R

17 5P.7 0

Tabl e A. 4 COMPARISONO F PREDICTE D CARDIOVASCULA R MORTALIT Y (Chicago )

Standar d 3 5 ppm1 h r 1 exe x 2 5 pp m1 hr 1 exe x 2 5 pp m1 h r 5 exe x 1 2 pp m8 hr 1 exe x 1 2 ppm8 hr 5 exe x 9 pp m8 hr 1 exe x 9 ppm8 h r 5 exe x Chang e Ove r Numbe r o f Death s 

### 22 4 

### 2 11 8 -13 8 

### -4 3 -25 8 -18 5 *

As-l s Percen t o f As-l s 

### 3. 2 

### 0 1. 7 -2. 0 -0. 6 -3. 7 -2. 6 Chang e

Strictes t Numbe r o f Death s 

### 48 2 26 0 37 7 12 0 21 6

— 

### 7 3 Ove r

Standar d Percen t o f Standar d 

### 7. 2 3. 9 5. 6 1. 8 3. 2

— 

### 1. 1

As-i s ASH D death s assume d t o b e 7000 . ASH D death s predicte d fo r strictes t standar d = 6742 .



### 17 6P.7 1 1. 0 t
Cumulativ e Probabilit y 

### 0. 5

Cumulativ e Probabilit y 0

1. 0 9 ppm( 8 hr )



### 0. 5 9 ppm( 8 hr ) 1 Exceedanc e pe r yea r

5 Exceedance s pe r yea r

1 2 ppm( 8 hr )

•25 pp m( 1 hr )



### J .9 4 .9 7 1. 0 1.0 3 1.0 6 1.0 9 1.1 2 chang e 7,99 0 8,24 5 8,50 0 8,75 5 9,01 0 9,26 5 9,52 0 death s

FIGUR E A. 2 DISTRIBUTION S O NCHANGE S I NISCHEMI C EPISODE S

FRO M AS-I S I NPHILADELPHIA . ESTIMATE S O F APPROXIMAT E DEATH S AR E ALS O PROVIDED . CURRENT ASH D DEATH S I NPHILADELPHI AAR E ASSUME D T O B E 8,500/YEA

R



### 17 7P.7 2

Tabl e A. 5 COMPARISONO F PREDICTE D CARDIOVASCULA R MORTALIT Y (Philadelphia )

Standar d 3 5 ppm 2 5 ppm 2 5 pp m 1 2 pp m 1 2 pp m 9 ppm 9 ppm 

### 1 1 1 8 8 8 8 h r h r h r h r h r h r h r 

### 1 1 5 1 5 1 5 exe x exe x exe x exe x exe x exe x exe x Chang e Ove r

Numbe r o f Death s 26 9 

### 10 3 27 1 -5 7 7 5 -15 2 

### -3 4 As-l s

Percen t o f As-l s 3. 2 

### 1. 2 3. 2 -0. 7 

### 0. 9 -1. 8 -0. 4 Chang e

Strictes t Numbe r o f Death s 42 1 

### 25 6 42 3 9 6 

### 22 8

— 

### 11 9 . Ove r

Standar d Percen t o f Standar d 

### 5. 0 3. 1 5. 1 1. 1 2. 7

— 

### 1. 4

As-i s ASH D death s assume d t o b e 8500 . ASH D death s predicte d fo r strictes t standar d = 8348 .



### 17 8P.7 3 1. 0 r Cumulativ e Probabilit y

Cumulativ e Probabilit y 1 Exceedanc e pe r yea r

0.97 5 3,41 2 1.0 0

3,50 0 1.02 5

3,58 7 1.0 5

3,67 5 1.07 5

3,67 3 chang e

death s

FIGUR E A. 3 DISTRIBUTION S ONCHANGE S I NISCHEMI C EPISODE S FRO M

AS-I S I NST . LOUIS . TH E APPROXIMAT E DEATH S AR E ALS O PROVIDED . CURRENT ASH D DEATH S I NST . LOUI S AR E ASSUME D T O B E 3500/YEA R



### 17 9P.7 4

Tabl e A. 6 COMPARISONO F PREDICTE D CARDIOVASCULA R MORTALIT Y (St . Louis )

Standar d 3 5 ppm 2 5 pp m 2 5 pp m 1 2 ppm 1 2 ppm 9 ppm 9 ppm 

### 1 1 1 8 8 8 8 h r h r h r h r h r h r h r 

### 1 1 5 1 5 1 5 exe x exe x exe x exe x exe x exe x exe x Chang e Ove r

Numbe r o f Death s 5 0 

### 7 6 3 

### -1 5 

### 9 -2 6 -1 5 *

As-l s Percen t o f As-l s 1. 4 

### 0. 2 1. 8 -0. 4 

### 0. 3 -0. 8 -0. 4 Chang e

Strictes t Numbe r o f Death s 7 6 

### 3 4 9 0 1 2 3 6

— 

### 1 2 . Ove r

Standar d Percen t o f Standar d 

### 2. 2 1. 0 2. 3 0. 3 1. 0

— 

### 0. 3

As-i s ASHD death s assume d t o b e 3500 . ASH D death s predicte d fo r strictes t standar d = 3474 ,



### 18 0P.7 5

Cumulativ e Probabilit y 2 5 ppm( 1 hr )

Cumulativ e Probabilit y 

### 36 8

35 6 36 0 Feta l Death s pe r Yea r 

### 36 4 36 8

FIGUR E A. 4 PROBABILIT Y THAT FETAL DEATH S ( > 2 0 WEEK S

GESTATION ) PE R YEAR I NCHICAGO , IL , WIL L EXCEE D TH E GIVE NAMOUNT . THER E AR E 36 1 FETAL DEATH S PE R YEAR FRO M AL L CAUSE S I NCHICAG O UNDE R PRESENT CONDITION S



### 18 1P.7 6

Tabl e A. 7 EXPECTE D CHANGE S I NFETAL DEATH S (>2 0 WEEK S GESTATION ) PE R YEA R (Chicago )



### 3 5 2 5 2 5 1 2 1 2 

### 9 9 Standar d 

### pp m pp m pp m pp m pp m pp m pp m 

### 1 1 1 8 8 8 8 h r h r h r h r h r h r h r 

### 1 1 5 1 5 1 5 exe x exe x exe x exe x exe x exe x exe x Chang e Ove r

Numbe r o f Death s 1.8 0 0.0 2 0.9 4 -1.1 1 -0.3 4 -2.0 5 -1.4 7 *

As-l s Percen t o f As-l s 0.5 0 0.0 0 0.2 6 -0.3 1 -0.0 9 -0.5 7 -0.4 1 Change

Strictes t Numbe r o f Death s 3.8 6 2.0 7 2.9 9 0.9 5 1.7 1

— 0.5 8 iOve r

Standar d Percen t o f Standar d 1.0 8 0.5 8 0.8 4 0.2 6 0.4 8

— 0.1 6

Tota l feta l death s i n as-i s cas e = 361 . Expecte d tota l feta l death s fo r strictes t standar d = 359 .



### 18 2P.7 7

Cumulativ e Probabilit y

Cumulativ e Probabilit y



### 36 4 36 6 36 8 37 0 Feta l Death s pe r Yea r 

### 37 2 37 4

FIGUR E A. 5 PROBABILIT Y THAT FETAL DEATH S ( > 2 0 WEEK S

GESTATION ) PE R YEAR I NPHILADELPHIA , PA , WIL L EXCEE D GIVE NAMOUNT . THER E AR E 36 8 FETAL DEATH S PE R YEA R FRO M AL L CAUSE S I NPHILADELPHI AUNDE R PRESENT CONDITION S



### 18 3P.7 8

Tabl e A. 8 EXPECTE D CHANGE S I NFETAL DEATH S (>2 0 WEEK S GESTATION ) PE R YEA R (Philadelphia )

Standar d 3 5 ppm1 hr 1 exe x 2 5 ppm1 hr 1 exe x 2 5 ppm1 hr 5 exe x 1 2 pp m8 hr 1 exe x 1 2 ppm8 hr 5 exe x 9 pp m8 hr 1 exe x 9 pp m8 h r 5 exe x Chang e Ove r

Numbe r o f Death s 2.0 0 0.7 3 2.0 1 -0.3 9 0.5 3 -1.0 4 -0.2 2 *

As-l s Percen t o f As-l s 0.5 4 0.2 0 0.5 5 -0.1 1 0.1 4 -0.2 8 -0.0 6 Chang e Ove r

Strictes t Standar d Numbe r Percen t o f Death s o f Standar d 3.0 3 1.7 6 3.0 5 0.6 4 1.5 6

— 0.8 1 0.8 3

0.4 8 0.8 3 0.1 8 0.4 3

— 0.2 2

Tota l feta l death s i n as-i s cas e = 368 . Expecte d tota l feta l death s fo r strictes t standard = 36 7



### 18 4P.7 9
1. 0 | —

Cumulativ e Probabilit y

. 5 _ 1 Exceedanc e

pe r yea r

9 ppm( 8 hr )

1 2 pm( 8 2 5 ppm( 1 hr )

•3 5 ppm( 1 hr )



### 17 5 1. 0

Cumulativ e Probabilit y 17 6

5 Exceedance s pe r yea r 

### 17 7 17 8 17 9 18 0

9 ppm( 8 hr ) 1 2 ppm( 8 hr )



### 17 5 17 6 17 7 17 8 Feta l Death s pe r Yea r 

### 17 9 18 0

FIGUR E A. 6 PROBABILIT Y THAT FETAL DEATH S ( > 2 0 WEEK S

GESTATION ) PE R YEAR I NST . LOUIS , MO , WIL L EXCEE D GIVE NAMOUNT . THER E AR E 17 8 FETAL DEATH S PE R YEAR FRO M AL L CAUSE S I NST . LOUI S UNDE R PRESENT CONDITION S



### 18 5p^a o

Tabl e A. 9 EXPECTE D CHANGE S I NFETA L DEATH S (>2 0 WEEK S GESTATION ) PE R YEA R (St . Louis )



### 3 5 2 5 2 5 1 2 1 2 

### 9 9 S tandar d 

### pp m pp m pp m pp m pp m pp m pp m 

### 1 1 1 8 8 8 8 h r h r h r h r h r h r h r 

### 1 1 5 1 5 1 5 exe x exe x exe x exe x exe x exe x exe x Chang e Ove r

Numbe r o f Death s 0.4 7 0.0 7 0.6 1 -0.1 4 0.0 9 -0.2 4 -0.1 4 *

As-l s Percen t o f As-l s 0.2 6 0.0 4 0.3 4 -0.0 8 0.0 5 -0.1 4 -0.0 8 Chang e

Strictes t Numbe r o f Death s 0.7 1 0.3 2 0.8 5 0.1 0 0.3 3

— 0.1 0 iOve r

Standar d Percen t o f Standar d 0.4 0 0.1 8 0.4 8 0.0 6 0.1 9

— 0.0 6

Tota l feta l death s i n as-i s cas e = 178 . Expecte d tota l feta l death s fo r strictes t standar d = 177 .



### 18 6P.8 1

Tabl e A.1 0 COMPARISONO F CHANGE S I NACUT E RESPIRATORY FAILUR E

FO R COP D SENSITIVE S (Chicago )

Standar d 3 5 ppm1 h r 1 exe x 2 5 pp m1 h r 1 exe x 2 5 pp m1 hr 5 exe x 1 2 pp m8 hr 1 exe x 1 2 pp m8 hr 5 exe x 9 pp m8 hr 1 exe x 9 ppm8 hr 5 exe x Chang e Ove r ARF / Patient/Y r 

### .0 5 .00 1 

### .0 3 -.0 3 -.0 1 -.0 6 -.0 4 *

As-l s Percen t o f As-l s 

### 2. 0 0.0 4 

### 1. 0 -1. 1 -0. 3 -2. 0 -1. 5 Chang e Ove r

Strictes t Standar d ARF / Percen t Patient/Y r o f Standar d

' -11 . 

### .0 6 .0 8 .0 3 .0 5

— 

### .0 1 4. 1

2. 1 3. 1 0. 9 1. 7

— 

### 0. 6
ARF/yr/patien t predicte d as-i s = 2.73 . ARF/yr/patien t predicte d fo r strictes t standard = 2.68 .



### 18 7P.8 2

Tabl e A.1 1 COMPARISONO F CHANGE S I NACUT E RESPIRATORY FAILUR E

FOR COP D SENSITIVE S (Philadelphia )

Standar d 3 5 ppm1 hr 1 exe x 2 5 ppm1 hr 1 exe x 2 5 ppm1 hr 5 exe x 1 2 pp m8 hr 1 exe x 1 2 ppm8 hr 5 exe x 9 pp m8 hr 1 exe x 9 ppm8 hr 5 exe x Chang e Ove r ARF / Patient/Y r 

### .2 4 .0 7 .2 4 -.0 3 

### .0 5 -.0 8 -.0 2 *

As-l s Percen t o f As-l s 

### 7. 9 2. 3 8. 1 -1. 1 

### 1. 7 -2. 5 -.0 1 Chang e Ove r

Strictes t Standar d ARF / Percen t Patient/Y r o f Standar d 

### .3 1 .1 5 .3 2 .0 4 .1 3

— 

### .0 6 11. 7



### 5. 4 11. 8 

### 1. 5 4. 6

— 

### 2. 1
ARF/yr/patien t predicte d as-i s = 2.76 . ARF/yr/patien t predicte d fo r strictes t standar d = 2.6 9



### 18 8P.8 3

Tabl e A.1 2 COMPARISONO F CHANGE S I NACUT E RESPIRATORY FAILUR E

FOR COP D SENSITIVE S (St . Louis )

Standar d 3 5 ppm1 hr 1 exe x 2 5 pp m1 hr 1 exe x 2 5 ppm1 h r 5 exe x 1 2 pp m8 hr 1 exe x 1 2 ppm8 h r 5 exe x 9 pp m8 hr 1 exe x 9 pp m8 hr 5 exe x Chang e Ove r ARF / Patient/Y r 

### .0 7 .0 1 .0 9 -.0 2 

### .0 1 -.0 4 -.0 2 *

As-l s Percen t o f As-l s 

### 2. 5 0. 4 3. 3

. -0. 8 

### 0. 5 -1. 6 -0. 8 Chang e

Strictes t ARF / Patient/Y r 

### .1 1 .0 6 .1 4 .0 2 .0 6

— 

### .0 2 Ove r

Standar d Percen t o f Standar d 

### 4. 2 2. 1 5. 0 0. 9 2. 1

— 

### 0. 8
ARF/yr/patien t fo r as-i s = 2.78 . ARF/yr/patien t fo r strictes t standar d = 2.73 .



### 18 9P.8 4 

### 1. 0

Cumulativ e Probabilit y 

### 0. 5 1 Exceedance/yea r 3 5 ppm( 1 hr )
2 5 ppm( 1 hr )

Cumulativ e Probabilit y •1 2 ppm( 8 hr )

day s



### -8 0 day s

FIGUR E A. 7 PROBABILIT Y THAT CHANGE S I NLIF E EXPECTANC Y WIL L

B E LES S THA

NGIVE NAMOUNT FO R SENSITIVE COP D PATIENT S I NCHICAGO . CHANGE S AR E CALCULATE D FRO M AS-I S PREDICTION . AS-I S LIF E EXPECTANC Y I S PREDICTE D T O B E .47 8 YEAR S I NCHICAG O



### 19 0P.8 5

Tabl e A.1 3 COMPARISONO F CHANGE S I NLIF E EXPECTANC Y FO R EAC H COP D SENSITIVE PATIENT

(Chicago )

Standar d 3 5 ppm1 hr 1 exe x 2 5 pp m1 hr 1 exe x 2 5 ppm1 h r 5 exe x 1 2 pp m8 hr 1 exe x 1 2 ppm8 hr 5 exe x 9 pp m8 hr 1 exe x 9 ppm8 hr 5 exe x Chang e Days / Patien t 

### -3 1

- 1 

### -1 6 1 8 

### 6 3 3 2 4 * Ove r As-l s Percen t o f As-l s -1. 8 

### 0 -0. 9 

### 1. 0 0. 3 1. 9 1. 3 Chang e Ove r

Strictes t Standar d Days / Percen t Patien t o f Standar d 

### -6 4 -3 4 -4 9 -1 5 -2 8

—



### - 9 -3. 6

-1. 9 -2. 8 -0. 8 -1. 6

— -0. 5

Lif e expectanc yfo r as-i s i s predicte d t o b e 4.7 8 years . Lif e expectanc yfo r strictes t standard predicte d t o b e 4.8 7 years .



### 19 1P.8 6 

### 1. 0

Cumulativ e Probabilit y

Cumulativ e Probabilit y 

### 0. 5 1 Exceedanc e pe r yea r

3 5 ppm( 1 hr )

2 5 ppm( 1 hr ) 1 2 ppm( 8 hr )
9 ppm( 8 hr )

day s

9 ppm( 8 hr )

-30 0 -20 0 -10 0 day s

FIGUR E A. 8 PROBABILIT Y THAT CHANGE S I NLIF E EXPECTANC Y WIL L

B E LES S THA

NGIVE NAMOUNT FOR SENSITIVE COP D PATIENT S I NPHILADELPHIA . CHANGE S AR E CALCULATE D FRO M AS-I S PREDICTION . AS-I S LIF E EXPECTANC Y I S PREDICTE D T O B E 4.7 4 YEAR S I NPHILADELPHI A



### 19 2P.8 7

Tabl e A.1 4 COMPARISONO F CHANGE S I NLIF E EXPECTANC Y FOR EAC H COP D SENSITIVE PATIENT

(Philadelphia )

Standar d 3 5 ppm1 h r 1 exe x 2 5 ppm1 hr 1 exe x 2 5 ppm1 hr 5 exe x 1 2 pp m8 hr 1 exe x 1 2 ppm8 hr 5 exe x 9 pp m8 hr 1 exe x 9 ppm8 hr 5 exe x Chang e Days / Patien t -12 1 

### -3 8 -12 3 

### 2 0 

### -2 8 4 4 1 2 * Ove r As-l s Percen t o f As-l s -7. 0 -2. 2 -7. 1 

### 1. 1 -1. 6 

### 2. 5 0. 7 Chang e

Strictes t Days / Patien t o -16 5 

### -8 2 -16 7 

### -2 4 -7 2

— 

### -3 2 Ove r

Standar d Percen t f Standar d -9. 3 -4. 6 -9. 4 -1. 4 -4. 1

— -1. 8

* Lif e expectanc yfo r as-i s predicte d t o b e 4.7 4 years' . Lif e expectanc yfo r strictes t standard predicte d t o b e 4.8 6 year s



### 19 3P.8 8 1. 0 _

Cumulativ e Probabilit y 

### 0. 5 1 Exc e pe r yea r
1 2 ppm( 8 hr )

Cumulativ e Probabilit y day s



### 1. 0

9 ppm ( 8 hr )

-15 0 -10 0 

### -5 0 day s

FIGUR E A. 9 PROBABILIT Y THAT CHANGE S I NLIF E EXPECTANC Y WIL L

B E LES S THA

NGIVE NAMOUNT FOR SENSITIVE COP D PATIENT S I NST . LOUIS . CHANGE S AR E CALCULATE D FRO M AS-I S LIF E PREDICTION . AS-I S LIF E EXPECTANC Y I S PREDICTE D T O B E 4.7 0 YEAR S I NST . LOUI S



### 19 4P.8 9

Tabl e A.1 5 COMPARISONO F CHANGE S I NLIF E EXPECTANC Y FO R EAC H COP D SENSITIVE PATIENT

(St . Louis )

Standard 3 5 pp m1 h r 1 exe x 2 5 pp m1 hr 1 exe x 2 5 ppm1 h r 5 exe x 1 2 pp m8 hr 1 exe x 1 2 ppm8 hr 5 exe x 9 pp m8 hr 1 exe x 9 ppm8 hr 5 exe x Chang e Days / Patien t 

### -3 4

- 6 

### -4 2 1 1

- 7 2 4 1 1 * Ove r As-l s Percen t o f As-l s -1. 2 -0. 3 -2. 4 

### 0. 6 -0. 4 

### 1. 4 0. 6 Chang e Ove r

Strictes t Standar d Days / Percen t Patien t o f Standar d 

### -5 7 -3 0 -6 6 -1 3 -3 1

— 

### -1 3 -3. 3

-1. 7 -3. 8 -0. 7 -1. 8

— -0. 7

Lif e expectanc yfo r as-i s predicte d t o b e 4. 7 years . Lif e expectanc yfo r strictes t standar d predicte d t o b e 4.7 6 years .



### 19 5P.9 0

APPENDI X B
Aful l probabilit yencodin g o f an uncertai n variabl e ca n b e costl y a s wel l a s time-consuming . Fo r thi s reason , onl ythe mos t sensitiv e var iable s i n a n analysi s ar e treate d probabilistically . Th e deterministi c model s o f the carbo n monoxid e ris k assessmen t indicat e two importan t area s o f uncertainty . On e i s the slop e o f the functio n fo r maxima l myo cardia l oxyge n supply , whic hdepend s o n COHb . Th e othe r importan t uncer taint yi s the distributio n o n peak hourl yvalue s o f the doubl e produc t o f hear t rat e an

d mea n systoli c bloo d pressure . Th e uncertaint yo n the firs t wa s explicitl ystate d afte r revie wo f the individua l dat a i n the clinica l exercis e testing studie s an d discussio n wit hDr . Aronow ; unfor

tunately , the expert s wer e unwillin g t o participat e i n a forma l encodin g sessio n o n thi s variable . Encodin g session s wer e conducte d o n the othe r tw o variables , the hear t rat e an

d bloo d pressure . Carefu l conditionin g permitte d the re sult s t o b e combine d int o a singl e distributio n o n doubl e product . Thi s veryimportan t uncertaint yi s no t on e tha t i s particularl yfamilia r t o the expert s o n carbo n monoxid e healt heffects . T o asses s i t properly , exercis e physiologist s wh o hav e monitore d dail ypattern s i n hear t rate s an d bloo d pressure s woul d hav e t o b e identified . Becaus e suc hexpert s ar e currentl yunidentified , the uncertaint yo n the pea k hourl ydoubl e produc t wa s accounte d fo r b yus e o f ver ybroa d range s o n the parameter s o f a lognorma l distribution . Th e range s wer e chose n t o matc hwit hphys

iologica l facts . T o chec k the assumptio n o f lognormal!t yan d o f the choic e o f param eter s i n general , a distributio n o f peak hourl yhear t rat e an d o f pea k hourl ymea n systoli c bloo d pressur e wer e encode d fro ma physicia n famil

ia r wit hthi s demonstratio n project . Pitfall s o f Encodin g I n concept , the proces s o f probabilit yencoding i s deceptivel ysim

ple . I n practice , numerou s systemati c biase s ar e liabl e t o exis t i n the encodee' s mind . I f no t carefull ydone , the encoding proces s ma yretai n thes e biases . Spetzle r an d vo n Holstei n hav e outline d thes e biase s an d indicate d severa l technique s t o circumven t themi n the encoding process . Th e following sectio n enumerate s some o f thes e biases , an d indicate s way s i n whic htheyappeare d t o ente r the encoding o f hear t rat e an d bloo d pressure . Displacemen t bia s result s i n a distributio n tha t i s uniforml yto o lo wo r high . Th e shap e o f the distributio n remain s correct , however , un les s variabilit ybia s als o occurs . Althoug hi t i s difficul t t o determin e whethe r variabilit ybia s occurre d during eithe r encoding session , ther e 

### 19 7P.9 1

i s some evidenc e tha t a displacemen t bia s exist s i n the two distribution s tha t resulted . Th e sourc e o f thi s bia s i s though t t o b e a difficult yi n thinking abou t the pea k hourl yvalue s o f the tw o variable s a s theyoccu r i n dail ylife . Thi s i s a difficul t bia s t o remove , an d i n fac t wa s no t eve n suspecte d unti l afte r the encoding session . Furthe r exploratio n o f the questio n directl ywit hthe encode e woul d perhap s hel p determin e i f a displacemen t occurre d and , i f so , t o wha t extent . Motivationa l bia s occur s whe n the encode e feel s tha t th e outcom e o f the sessio n wil l b e use d t o evaluat e hi mo r hi s jobperformance . Fo r in

stance , a clinicia n wh o ha s performe d studie s o n the variabl e tha t i s be

in g encode d ma yfee l pressur e t o insis t o n repeatin g the result s o f hi s studie s rathe r tha n objectivel ythink abou t the inevitabl e uncertaintie s tha t exis t i n thes e results . H e ma yb e reluctan t t o plac e a prope r mar gi n o f erro r o n hi s assessment . Muc ho f thi s kind o f bia s ca n b e avoide d throughprope r conditionin g o f the subjec t befor e actua l encodin g begins . Motivationa l bia s wa s no t a significan t problemdurin g the two en coding session s t o b e describe d here . First , th e encode e had n o forma l connectio n t o the projec t o r t o EPA . Second , h e wa s furthe r assure d tha t the encoding wa s entirel yfo r exploratorypurposes , s o tha t the assess ment s h e provide d woul d no t b e considere d the fina l word o n the topic . Third , an

yfea r h e had o f being considere d professionall yunknowledgabl e wa s relieve d b

ya n agreemen t tha t h e remai n anonymous . Althoug hano nymit yi s undesirabl e fo r assessment s tha t woul d b e integra l t o a fina l model , i t wa s no t fel t t o b e a seriou s concer n fo r thi s exploratoryen coding . Cognitiv e biases , whic hresul t i n displacemen t an d variabilit y biases , aris e fro

ma numbe r o f differen t mode s o f judgmen t tha t caus e the subjec t t o adjus t hi s assessment s systematicall yi n on e direction . On e suc hmod e i s availability . I n the cas e o f physicians , the mos t easil y recalle d informatio n i s clinical . Effort s t o envisio n scenario s wher e a n extreme physiologica l valu e coul d occu r usuall yresul t i n envisionin g some pathologica l cause . Th e day-to-daycauses , whic har e perhap s mor e common , mayno t receiv e the appropriat e leve l o f emphasis . T o avoi d this , the subjec t shoul d b e encourage d t o conside r nonclinica l scenario s befor e the encodin g sessio n begins . Anchorin g o n a n initia l valu e i s alway s a problemi n encoding . T o avoi d thi s problema s muc ha s possible , the encoding sessio n shoul d al

way s begi n wit hemphasi s o n th e extrem e value s tha t ma yoccur . Thi s i s par t o f the conditionin g proces s befor e actua l probabilitie s ar e as signed . Anothe r commo n problemi s tha t ne winformatio n i s ofte n improperl y use d t o updat e prio r assessments . Some o f thi s problemca n b e remove d b y encodin g conditiona l variables . Thi s i n par t remove s the nee d fo r the subjec t t o performthe Bayesia n updatin g mentally . I n the cas e o f hear t rate , nighttim e an d daytim e distribution s wer e encode d separately . I n the cas e o f bloo d pressure , the distribution s wer e mad e dependen t o n hear t rate .



### 19 8P.9 2

Unstate d assumption s caus e simila r problems . Th e encode e maymak e hi s assessment s entirel yo n a n assumptio n o f "norma l conditions. " Fo r instance , the effect s o n hear t rat e o f a sudde n frigh t mayno t b e in

clude d i n the evaluatio n simpl ybecaus e the subjec t ha s implicitl yas sume d tha t h e i s onl yconsiderin g the typica l dail yevents . Thi s i s par ticularl ylikel yt o occu r i f th e subjec t i s drawing largel yo n clinicall y observe d data . O n the othe r hand , the effort s o f the encode r t o b e pre cis e i n definitio n ma yresul t i n to o muc hconditioning . I n the cas e o f hear t rat e an

d bloo d pressure , i t i s quit e possibl e tha t the "composit e patient " wa s to o carefull ydefined . Effort s t o clarif ythe value s o f in

teres t ma yhav e narrowe d the "patient " dow n t o the poin t wher e the en code e wa s induce d t o eliminat e man ypossibl e scenarios . Th e Encodin g Session s Ther e wer e thre e basi c stage s t o eac hencodin g session . First , sev era l minute s wer e devote d t o a definin g an d conditionin g o f the variabl e t o b e encoded , a s wel l a s motivatin g the necessit yt o encod e the vari

able . Followin g that , the actua l encoding too k place . Finally , the sub

jec t wa s permitte d t o revie wan d verif ythe distribution s tha t h e ha d as sessed . Th e following excerp t fro mthe firs t minute s o f the firs t encoding sessio n indicate s the approac htha t ca n b e take n t o structur e an d defin e the variabl e o f interest . Becaus e bot hencoding s referre d t o the same "composit e patient, " thi s extensiv e conditionin g step wa s onl ytake n once , prio r t o the encoding o f hear t rate . On e shoul d b e awar e als o that , afte r thi s step , th e encode e wa s permitte d t o choos e whic ho f th e two variable s h e woul d prefe r t o encod e first , leaving the secon d vari

abl e t o b e encode d conditionall yo n the first . On e wil l not e tha t littl e o f th e excerp t deal s wit hremoving o f motivationa l biases . A s explaine d above , motivationa l bia s wa s no t con sidere d a seriou s concer n wit hthi s subject . Also , the subjec t had some familiarit ywit hthe hear t diseas e mode l an d wit hthe goal s o f the ris k assessmen t i n general . Adiscussio n o f the probabilit yencoding tech

niqu e had als o precede d the interview , an d the subjec t had briefl yre viewe d the Spetzle r an d vo n Holstei n paper . Ofte n thi s initia l stag e wil l requir e quit e a bi t mor e time . I t ca n becom e the majo r par t o f th e session , bot hi n term s o f time an d importance . On e shoul d b e carefu l no t t o underemphasiz e it s valu e fo r providing a goo d distributio n o n the var iabl e a t hand .
Ann e Smit hWha t w e ar e intereste d i n i s estimating a n activit ypat
tern tha t describe s wha t i s happenin g t o a typica l angin a patient . W e ar e onl ylooking a t on e specifi c angin a pa tien t a t thi s moment . W e ar e no t trying t o combin e al l o f the possibilitie s fo r al l o f the differen t type s o f pa tients , becaus e ther e i s a tremendou s amoun t o f (interper

sonal ) variability . W e ar e using wha t i s calle d the "dou

bl e product " a s a prox yfo r a n activit ypatter n becaus e tha t i s proportiona l t o oxyge n deman d o f the heart... . W e 

### 19 9ar e intereste d i n a valu e o f the maximu mtha t wil l occu r durin g a n hou r fo r eac ho f the tw o [hear t rat e an d bloo d pressure] , an

d als o w e wil l hav e t o ge t the correlatio n betwee n the two .

Encode e

Ann e Smit h
Encode e Th e patien t tha t w e decide d t o loo k a t i s currentl ysuf

fering betwee n on e an d tw o attack s pe r day . H e i s no t o n medication , s o don' t tr yt o worr yabou t ho wthe hear t rat e an d bloo d pressur e ar e affecte d b ymedications . H e i s i n hi s mid-fiftie s o r earl ysixties . W e haven' t decide d whethe r h e ha s ha

d a myocardia l infarctio n o r not . Usuall yyo u ca n tel l b ylooking a t the electrocardiogram . Ther e i s usuall ya n evidenc e o f a n ol d infarc t i f h e ha s alread yhad one . Doe s tha t affec t th e wa ythe hear t rat e an d bloo d pressur e migh t chang e ove r time ? Well , tha t i s possible , was . I t depend s upo n ho wsever e i t
Pete r McName e
Encode e

Pete r McName e Enode e Wha t effec t woul d the severit yo f the infarc t hav e o n the

bloo d pressur e an

d the hear t rate ? I f i t wa s a reall ysever e infarct , yo u migh t no t hav e a verystrong lef t ventricula r response , an d yo u migh t no t b e abl e t o achiev e bloo d pressure s a s higha s someon e wh o had no t had a sever e hear t attack . The n ther e i s a certai n exten t t o whic hh e physiologicall y can' t achiev e a highbloo d pressure . Yes . Pete r McName e Ho wabou t behaviora l changes ? Encode e I s thi s al l par t o f the protocol ? Pete r McName e Yes . Encode e

Ann e Smit h I woul d gues s tha t someon e wh o ha s alread ysuffere d on e infarc t woul d b e les s likel yt o engag e i n mor e strenuou s kind s o f [activity] . Bu t someon e wh o ha s angin a i s going t o b e relativel y[sedentary] , anyway . I a mno t sur e i f i t wil l mak e a rea l difference , though . Tha t bring s u

p on e othe r caus e o f variabilit ytha t w e wan t t o loo k at . W e wan t t o tak e int o accoun t the fac t tha t ther e ar e stresse s othe r than purel yphysica l stresses . W e don' t wan t t o loo k a t wha t the hear t rat e woul d b e do

in g i f a perso n i s jus t sitting a t a table... . W e wan t t o captur e the variabilit ytha t migh t b e induce d b yquarrel s an d tension s a t wor k a s well .



### 20 0P.9 4

Encode e Tha t i s certainl yon e o f the bi g precipitator s o f angin a

attacks—ange r an

d represse d anger . Ann e Smit hW e ar e going o n tha t basis , becaus e w e realiz e tha t peopl e

ar e fairl ysedentary , an d i t i s unlikel ytha t mos t o f the variabilit yi n [the doubl e product ] wil l b e cause d b y physica l activities . W e hav e a prett yclea r pictur e o f the perso n w e ar e think

in g about... . I wan t t o isolat e some othe r detail s abou t the pathophys

iolog yo f the person' s physique . H e doesn' t have , say , lef t ventricula r hypertroph yor... . H e doesn' t hav e hypertension ? Ann e Smit hNo . Ho wmanyangin a patient s woul d b e likel yt o hav e

hypertension ? Abou t 50/5 0 or... ? Encode e I couldn' t tel l yo u that . Ann e Smit hAr e yo u getting a prett ygoo d pictur e o f who the patien t

is ? Pete r McName e Encode e

Encode e H e doesn' t hav e a sic k sinus , either ? Ann e Smit hI don' t kno wwha t tha t is . Encode e Tha t i s something tha t wil l affec t the hear t rate , wher e

someon e wh o ha s a lo t o f fibrosi s an d scar s i n the heart...the ygro wmino r infarct s tha t ma yno t b e picke d up , an d theyals o hav e problem s i n the sino-atria l nod e i n whic hcas e theywoul d hav e a muc hslowe r hear t rat e than normal . Eve n down t o the lowe r 40s . Ann e Smit hBu t that' s no t verycommon ? Encode e It' s commo n i n olde r people . I woul d guess , maybe , some on e wh o i s i n hi s fiftie s woul d hav e that . Ann e Smit hWell , let' s jus t ignor e that , fo r now . Encode e Al l right . I hav e a n ide a o f wh o yo u ar e looking a t then . Followin g the firs t phase , the encode e wa s introduce d t o the concep t o f the probabilit yencoding wheel , a n ai d frequentl yuse d i n encoding probabilit ydistribution s . Whe n h e fel t tha t h e understoo d th e purpos e o f thi s tool , the encoding itsel f began . First , h e wa s aske d t o indicat e whethe r h e wa s mor e comfortabl e conditionin g bloo d pressur e o n hear t rat e o r vic e versa . The n the unit s i n whic hh e though t o f thes e variable s wer e clarifie d a s beat s pe r minut e an d m mHg .



### 20 1Befor e actuall yusing the wheel , h e wa s aske d t o think abou t ver y

hig hvalue s an

d lo wvalue s o f th e hear t rate . Th e encode r the n aske d hi m t o provid e scenario s tha t coul d resul t i n eve n mor e extreme values . Th e purpos e o f thi s ste p i s t o avoi d anchorin g o n mid-rang e value s an d t o hel p hi mrecal l smal l probabilit yevents . Thi s help s counterac t the bia s cause d b

yavailability . Th e result s o f the encoding sessio n o n hear t rat e ar e provide d i n Figure s B. l an

d B.2 . On e wil l not e tha t t o avoi d forcing the subjec t t o performto o manymenta l steps , the variabl e wa s conditione d o n the time o f day . Th e encode r ca n late r combin e thes e throughthe calculu s o f probabilit yt o deriv e a singl e distribution . Th e number s nex t t o the encode d point s indicat e the orde r i n whic hthe point s wer e encoded ; th e firs t poin t encode d i n Figur e B. l reflect s the initia l unfamiliarit yo f the encode e wit hthe process . Afte r thes e distribution s had bee n encoded , the subjec t wa s aske d t o loo k a t the result s an d t o commen t o n them . Thi s verificatio n step i s ver yusefu l becaus e i t wil l indicat e ho wcomfortabl e the encode e i s wit h the outcome . I n thi s case , the subjec t actuall ymad e some change s t o the curve , whic happea r a s the dotte d lin e i n the figures . Afte r the modifi

cations , h e indicate d tha t h e wa s satisfie d wit hthe result . I n a mor e detaile d encoding , mor e attentio n woul d b e pai d t o the importan t regio n o f hig hhear t rates . Th e encoding o f the secon d variable , pea k hourl ymea n systoli c bloo d pressure , followe d verysimila r lines . Th e onl ydifferenc e wa s tha t thi s variabl e wa s conditione d o n give n value s o f pea k hear t rate . Figure s B. 3 an d B. 4 provid e the result s o f the encoding . During the encoding proces s fo r bloo d pressure , the encode e decide d tha t the time o f daywa s no t rel

evan t t o thi s variable . Severa l point s wer e note d afte r thi s encoding . First , the encode e wa s a t eas e wit hconcept s o f probability . Possibl yh e coul d hav e bee n permitte d t o dra wthe distribution s directly , rathe r than rel yo n the wheel . Also , the encode e appeare d t o hav e alread ygive n some though t t o th e value s prio r t o the session . Ther e i s the possibilit ytha t h e cam e int o the sessio n wit hth e distribution s almos t preconceived . Thi s ha s drawback s becaus e the conditionin g phas e woul d b e les s effectiv e o n suc h a subject . Anchorin g an d availabilit yma ythu s hav e bee n significan t problem s i n thes e sessions .
Us e o f Encode d Distribution s A s mentioned , thes e tw o variable s wer e necessarya s input s t o deriv e a distributio n o n the "doubl e product. " Fo r some subjects , on e migh t b e abl e t o encod e the doubl e produc t directly . Th e subjec t her e wa s bette r abl e t o conside r it s component s separately . I n vie wo f this , the encod

er s structure d the mode l s o tha t the individua l variable s coul d b e en code d separately , whil e probabilit ycalculu s coul d b e use d t o replac e th e menta l computation s o f combining them . Thi s approac havoid s logica l er ror s an d allow s the encode e t o concentrat e o n the element s wit hwhic hh e i s mos t familiar . 

### 20 2P.9 6

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Probabilit ycalculu s wa s use d t o deriv e the resultan t distributio n o n the doubl e produc t (Figur e B.5 ) fro mthe distribution s i n Figure s B.l-4 . Severa l comment s shoul d b e made . First , thi s distributio n i s wel l approximate d b ya lognorma l distribution . Second , thi s distributio n appear s t o b e quit e lo wcompare d t o thos e implie d b ythe parameteriza

tion s i n the demonstratio n model . Thi s ma yb e indicativ e o f a negativ e displacemen t bias . Thi s tentativ e conclusio n wa s drawn fo r two reasons . Th e extreme value s implie d b ythi s rang e ar e lowe r than thos e predicte d fo r the no t uncommo n event s suc ha s domesti c quarrels . Thi s distribu

tio n i s als o no t consisten t wit hthe statemen t tha t the patien t ha s on e t o tw o attack s o f angin a eac hday . Rather , i t indicate s approximatel y on e t o two pe r week . O f course , the assume d valu e fo r the doubl e produc t a t onse t o f angin a ma yinstead b e to o high . Thes e question s shoul d b e addresse d full yi n the revie wprocess . Thos e wh o ar e mos t familia r wit hangin a patient s shoul d hel p determin e whethe r the distributio n o n the peak hourl ydoubl e produc t encode d her e i s to o low , o r whethe r the doubl e produc t a t angin a use d i n the hear t diseas e mode l i s to o high . Furthermore , change s i n the distributio n fro m individua l t o individua l shoul d b e discusse d a t length , t o determin e range s o n the parameter s o f the lognorma l approximatio n o f the doubl e product . Sunn-nar yo f Othe r Interview s 12/11/8 1 Dr . Rodne yBear d

Stanfor d Medica l Cente r Dr . Bear d ha s performe d severa l o f the vigilanc e test s o f lowlevel s o f CO . (a ) H e believe s tha t ther e i s some kind o f effec t a t lo wlevel s (<5 % COHb ) althoughh e i s no t sur e o f the physiologica l expla

natio n . (b ) H e feel s tha t hear t diseas e effect s ar e the significan t publi c healt hproblem . (c ) H e had reservation s abou t usin g a n acciden t curv e fromalcoho l studie s t o extrapolat e t o CO-relate d accidents . However , h e fel t tha t the similaritie s i n effec t betwee n COHban d bloo d al

coho l wer e veryprovocativ e an d deservin g o f furthe r investiga

tion . 12/28/8 1 Dr . Victo r Latie s

Universit yo f Rocheste r Schoo l o f Publi c Healt h Th e potentia l centra l nervou s systemeffect s wer e discussed . Dr . Latie s i s veryfamilia r wit hal l o f the vigilanc e an d othe r CNS test s performe d o n CO . (a ) H e feel s tha t the significan t healt heffect s fro mambien t C O ar e the myocardia l an d respiratorycomplications . 

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## M 0. 0 0. 1 0. 2 0. 3 0. 4 0. 6 0. 8 0. 7 0. 8 0. 9 1. 0 5,00 0 8,00 0 11,00 0 14,00 0 17,00 0 Doubl e Produc t (m

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FIGUR E B. 5 DISTRIBUTIONONDOUBL E PRODUC T IMPLIE D B Y ENCODE D

DISTRIBUTION S ONHEAR T RAT E AND BLOO D PRESSUR EP. 3

(b ) H e indicate d a persona l belie f tha t CN S effect s d o no t occu r (hav e no t bee n demonstrate d sufficientl yt o b e believed ) a t level s belo wabou t 5 % COHb . (c ) Th e CN S effect s pe r s e ar e no t a publi c healt hproblem . Acci

den t potentia l i s the correc t are a fo r concern . (d ) Provide d strong suppor t fo r the approac ho f using the relation

shi p o f automobil e accident s an d alcohol . H e suggeste d some additiona l wor k tha t shoul d b e don e t o mak e the analysi s mor e thorough . H e provide d reference s fro mepidemiologyliteratur e tha t refe r t o the techniqu e explicitly . 1/4/8 2 Dr . Hanna hPeav y Nationa l Institut e o f Health , Bethesda , M D Dr . Peav

yi s a pulmonaryphysician . Althoug hno t an exper t o n ef

fect s o f CO , she wa s willin g t o discus s th e physiologica l assumption s an d relationship s i n the COP D model . (a ) Sh e helpe d pi n dow n the parameter s necessaryt o estimat e the siz e o f the sensitiv e group . (b ) Sh e discusse d the assumption s tha t wer e use d fo r the Cobur n equation . Som e actua l dat a fro mpatient s wh o wer e subjecte d t o exercis e wer e examined . (c ) Sh e note d tha t i n man ycases , a hear t attac k wil l b e the actua l mechanis mo f deat heve n thoughi t occur s during a respiratory exacerbation . Thu s ther e mayb e some doubl e counting vi a the hear t deat hmodel . (d ) Sh e provide d reference s fo r peopl e t o contac t concerning the assumption s specifi c t o CO . 1/6/8 2 Dr . Lewi s Kulle r

Universit yo f PittsburghSchoo l o f Publi c Healt h Dr . Kulle r ha s investigate d the hear t diseas e healt heffect s o f CO . H e i s himsel f intereste d i n developin g a n approac ht o quantifying thes e effects . (a ) Th e us e o f epidemiologica l studie s i n developing a dose

respons e curv e wa s no t discussed . H e feel s tha t suc hstudie s ca n completel yoverloo k veryrea l effect s becaus e the effect s o n the sensitiv e grou p becom e los t i n the larg e proportio n o f the populatio n tha t i s no t sensitive . Asigmoida l curv e i s ap propriate , bu t the curv e canno t b e estimate d whe n th e C O expo sure s ar e i n the lo wrange s commo n today . (b ) H e suggeste d a mor e structura l approach , whic hwoul d allo wthe angin a mode l t o b e tied mor e closel yt o tha t fo r hear t diseas e deaths . Th e angin a mode l ca n b e use d t o predic t relativ e change s i n the amoun t o f time i n whic hangin a patient s ar e sub

jecte d t o significan t ischemi a (define d i n tha t mode l a s 

### 20 9P. 4

frequenc yo f angina l pain) . Ther e i s n o known reaso n wh ymyo cardia l ischemi a sometime s result s i n irreparabl e tissu e damag e o r fibrillation . H e feel s tha t the relativ e frequenc yo f an gina l pai n i s proportiona l t o the relativ e ris k o f irreparabl e damage . On e ca n estimat e th e relativ e frequencie s fro mthe an gin a mode l an

d appl ythi s chang e t o the grou p o f peopl e suscep tibl e t o hear t attacks . (Thi s group i s large r tha n the popula

tio n o f angin a patients. ) (c ) Fo r thi s approac ht o b e realistic , th e relativ e frequencie s woul d hav e t o b e estimate d fo r population s a t severa l level s o f hear t attac k risk . Th e majorit yo f the incrementa l effect s woul d probabl yoccu r among the mos t sensitiv e group , whic hin

clude s thos e wit hCOP D an d co r pulmonale . Doubl e counting wit h the COP D mode l woul d hav e t o b e adjuste d for . 1/8/8 2 Dr . Jarosla wVosta l

Biomedica l Science s Department , Genera l Motor s Researc hLab s Warren , Michiga n Th e angin a mode l wa s presente d t o Dr . Vosta l an d othe r intereste d member s o f hi s department . Dr . Vosta l ha s bee n overseein g physiologica l investigation s o f the effect s o f CO o n myocardia l tissue . H e an d hi s staf f thu s ar e veryfamilia r wit hthe issue s surrounding the possibilit y o f a n effec t o f C O o n the healt ho f myocardia l patients . (a ) Th e structur e o f the angin a mode l wa s acceptabl e i n theory . Th e mai n criticis mo f the structur e wa s tha t i t appeare d t o attribut e al l angina l pai n t o shift s i n hear t rat e an d bloo d pressur e abov e thei r mea n values . Apparentl ysuppl yshift s ar e als o o f grea t importanc e (e.g. , vasoconstriction) . Th e mode l assume s tha t COHbshift s the suppl ydensit yfunctio n uniformly . Therefore , the bia s introduce d b yfixing suppl ya t the mea n valu e o f thi s densit yincrease s a s the loca l curvatur e o f the demand densit yfunctio n become s mor e pronounced . (Variation s i n suppl yhav e sinc e bee n incorporate d int o the mode l use d i n Chapte r V. ) (b ) Aronow' s studie s ar e weakene d becaus e "pain " i s subjective . I t woul d b e preferabl e t o bas e the suppl yshift s o n some physio

logica l determinatio n o f ischemia . Thi s i s apparentl yon e o f GM' s researc hgoals . Ther e ar e n o result s fro mthe researc h yet , s o us e o f Aronow' s wor k remain s th e onl yoptio n a t thi s point . (c ) The ywer e ver yconcerne d tha t the larg e uncertaintie s b e mad e explicit . 1/26/8 2 Dr . Wilber t Arono w

Long Beac hVeteran s Administratio n Hospita l Long Beach , CA Dr . Arono wha s performe d the majorit yo f clinica l studie s o f the ef

fect s o f C O o n humans . Th e discussio n wit hDr . Arono wcentere d aroun d 21 0P. 5

arterioscleroti c hear t diseas e an d focuse d closel yo n the assumption s o f the angin a mode l an d o n person-to-perso n differences . Th e applicatio n o f the angin a mode l t o al l cardiovascula r effect s wa s als o discussed . (a ) Person-to-perso n difference s ar e ver ylarge , an d h e doe s no t fee l tha t a "composit e patient " i s possibl e t o describe . H e feel s tha t the result s o f an ymode l base d o n a single , compo sit e patien t canno t b e use d t o extrapolat e t o the entir e popu lation . (b ) Th e mode l itsel f probabl ydescribe s the effect s o f CO well . • Th e problemwit hi t i s specifying the input s wel l enought o captur e the rea l situation . (c ) Cause s fo r variabilit yi n the outcom e o f hi s studie s wer e enu merated . Bound s wer e place d o n thi s variability . (d ) Arono wagree d wit hKuller' s suggestio n fo r applying the rela

tiv e change s i n angin a t o a n estimatio n o f the relativ e change s i n ris k o f infarctio n an d fibrillation . Epidemiologica l stud

ie s canno t captur e "small " effect s a t lowC O levels . H e feel s tha t the possibilit yo f a tru e threshol d i s nil . 1/29/8 2 Dr . Lawrenc e Long o

Loma Lind a Medica l School , Loma Linda , CA Dr . Long o i s a n exper t i n the fiel d o f feta l oxygenation . H e ha s become involve d i n th e effect s o f CO an d o f cigarette s o n fetuses . Many aspect s o f the feta l problem an d o f modelin g approache s wer e discussed . (a ) Adistributio n o n the effect s o f cigarett e smoking o n fetuse s wa s encoded . Hi s opinio n i s tha t C O ma ycontribut e abou t two
third s o f thi s effect . (b ) Anew , mor e structura l mode l o f the effect s o f CO o n fetuse s wa s develope d wit hhi s assistance . Estimate s o f the input s wer e als o provided . (c ) Dr . Long o believe s tha t feta l deat hi s "onl ythe ti p o f the iceberg. " H e feel s tha t nonfata l effect s ar e the majo r caus e fo r concer n fo r two reasons : (1 ) the numbe r o f effect s i s large r i n absolut e terms , an d (2 ) eac heffec t represent s a greate r problemt o societ yan d t o thos e affecte d tha n the los s o f a fetus . Thi s i s a valu e judgment , bu t on e tha t shoul d b e addresse d openly . (d ) H e feel s tha t thi s quantificatio n i s neede d an d expresse d interes t i n a mor e complet e developmen t o f i t i n the future . 2/8/8 2 Dr . Steve n Horvat h

Institut e o f Environmenta l Stres s Sant a Barbara , CA Dr . Horvat hi s intereste d i n al l healt hmanifestation s o f CO . Some discussio n wa s give n t o effect s o n thos e wit hpulmonarydiseas e an d o n 

### 21 1P. 6

fetuses. " Th e majorit yo f the discussio n centere d o n cardiovascula r effects . (a ) Pulmonar ydiseas e patient s an d fetuse s ar e judge d t o b e clearl y mor e sensitiv e t o CO , bu t thes e problem s ar e no t a s signfican t a s the cardiovascula r problembecaus e o f the differenc e i n pop ulatio n sizes . (b ) Dr . Horvat hagree d wit husing th e approac htha t Kulle r sug geste d fo r estimatin g hear t diseas e deaths . H e fel t that , i f anything , SRI' s preliminaryestimate s ar e low . (c ) Althougha ful l analysi s woul d requir e greate r breakdown o n the type s o f patients , h e di d no t fee l tha t SR I wa s wrong i n start
in g wit ha singl e composit e patient . (d ) Estimate s o f the variabilit yo f the doubl e produc t unde r dif

feren t circumstance s wer e provided . Aful l encoding di d no t occur , bu t range s wer e set . (e ) H e feel s tha t Aronow' s result s closel yapproximat e the effec t o f CO . Thi s bia s i n using the slop e base d o n grou p mean s di d no t appea r significan t t o him . H e di d feel , however , tha t the averag e patien t woul d b e mor e susceptible , becaus e medicatio n i s lighte r withi n the genera l populatio n than i n Aronow' s tes t group . AlthoughAronow' s subject s wer e no t medicate d durin g the tests , Horvat hfeel s tha t the effect s o f the medicatio n wer e stil l ther e residually .



### 21 2P. 7

REFERENCE S FO R APPENDI X B
1 . C . S . Spetzle r an d C-A . S . Stae l vo n Holstein , "Probabilit yEncodin g i n Decisio n Analysis, " Managemen t Science , Vol . 22 , pp . 340-358 , 1975 . 2 . Dr . Steve n Horvath , persona l communication .



### 21 3