1-s2.0-S0048969714016027-main.pdf

7232019 1-s20-S0048969714016027-mainpdf

httpslidepdfcomreaderfull1-s20-s0048969714016027-mainpdf 113

Health impact assessment of traf 1047297c-related air pollution at the urbanproject scale In1047298uence of variability and uncertainty

Chidsanuphong Chart-asa a Jacqueline MacDonald Gibson b

a Institute for the Study of Natural Resources and Environmental Management Mae Fah Luang University Chiang Rai Thailandb Department of Environmental Sciences and Engineering Gillings School of Global Public Health University of NC Chapel Hill USA

a b s t r a c ta r t i c l e i n f o

Article history

Received 2 December 2013Received in revised form 30 October 2014

Accepted 5 November 2014

Available online 26 November 2014

Editor Lidia Morawska

Keywords

PM25

Traf 1047297c

Health impact assessment

Variability

Uncertainty

Thispaper developsand then demonstratesa new approach for quantifyinghealth impacts of traf 1047297c-related par-

ticulate matter airpollution at the urban project scale that includesvariability and uncertainty in the analysis We

focus on primary particulate matter having a diameter less than 25 μ m (PM25) The new approach accounts for

variability in vehicle emissions due to temperature roadgrade and traf 1047297c behavior variability seasonal variabil-

ity in concentrationndashresponse coef 1047297cients demographicvariability at a 1047297ne spatial scale uncertaintyin air qual-

ity model accuracy and uncertainty in concentrationndashresponse coef 1047297cients We demonstrate the approach for a

case study roadway corridor with a population of 16000 where a new extension of the University of North

Carolina (UNC) at Chapel Hill campus is slated for construction The results indicate that at this case study site

health impact estimates increased by factors of 4ndash9 depending on the health impact considered compared to

using a conventional health impact assessment approach that overlooks these variability and uncertainty

sources In addition we demonstrate how the method can be used to assess health disparities For example in

the case study corridor our method demonstrates the existence of statistically signi1047297cant racial disparities in ex-

posure to traf 1047297c-related PM25under present-day traf 1047297c conditions thecorrelationbetween percentblackand an-

nual attributable deaths in each census block is 037 (t (114) = 42 p b 00001) Overall our results show that the

proposed newcampuswill cause only a small incremental increase in healthrisks (annual risk 6 times 10minus10 lifetime

risk 4 times 10minus8) compared to if the campus is not built Nonetheless the approach we illustrate could be useful for

improving the quality of information to support decision-making for other urban development projectscopy 2014 Elsevier BV All rights reserved

1 Introduction

In the United States nonpro1047297t organizations and public health prac-

titioners increasingly advocate for formal health impact assessments

(HIAs) to inform regional and local land-use and transportation plan-

ning decisions (Wernham 2011 Bhatia and Corburn 2011) Signaling

the heightened interest in HIAs the US National Academy of Sciences

in 2011 published a report Improving Health in the United States The

Role of Health Impact Assessment concluding that ldquoHIA is a particularly

promising approach for integrating health implications into decision-

makingrdquo (National Research Council 2011) The report offered the fol-

lowing formal de1047297nition of HIA

HIA is a systematic process that usesan arrayof data sources and an-

alytic methods and considers input from stakeholders to determine

the potential effects of a proposed policy plan program or project

on the health of a population and the distribution of those effects

within the population HIA provides recommendations on monitor-

ing and managing those effects

As the National Academies report explainsthe increasing demand for

HIAs in the United States is driven by thegrowing recognition that reduc-

ing obesity and chronic disease rates will require substantial changes to

decision-making processes in arenas outside the traditional healthcare

sector For example decisions by transportation and municipal planning

organizations can promote or limit opportunities for physical activity

and can exacerbate or decrease exposure to ambient air pollution

While HIAs of local and regional decisions have been used in Europe

Australia Canada and Thailand for decades the 1047297rst US HIA of a local

project was completed in 1999 by the San FranciscoDepartment of Public

Health (Bhatia and Corburn 2011 National Research Council 2011) By

the end of 2012 however at least 115 HIAs of local or regional US pro-

jects had been completed and another 64 were under way (Singleton-

Baldrey 2012) Of the completed HIAs 70 (more than 60) focused on

proposed local or regional changes to the built environment andor trans-

portation networks (Singleton-Baldrey 2012 Dannenberg et al 2008)

Science of the Total Environment 506ndash507 (2015) 409ndash421

Corresponding author at Department of Environmental Sciences and Engineering

Gillings School of Global Public Health University of North Carolina Campus Box 7431

Chapel Hill NC 27599-7431 USA Tel +1 919 966 7892

E-mail addresses chidsanuphonggmailcom (C Chart-asa) jackiemacdonaldunc

edu (JM Gibson)

httpdxdoiorg101016jscitotenv201411020

0048-9697copy 2014 Elsevier BV All rights reserved

Contents lists available at ScienceDirect

Science of the Total Environment

j o u r n a l h o m e p a g e w w w e l s e v i e r c o m l o c a t e s c i t o t e n v

7232019 1-s20-S0048969714016027-mainpdf


To facilitate comparison of alternatives and guide decision-making

HIAs ideally would provide quantitative estimates of the health out-

comes of the decision options under consideration That is they would

estimate the number of deaths and illnesses prevented or caused by

each alternative This information could be used to quantify the health

costs (positive or negative) of each alternative Quanti1047297cation can pres-

ent health impacts more concisely (as numerical summaries) than

lengthy qualitative discussions In addition (rightly or wrongly) quanti-

tative assessment can lend legitimacy to the analysis Furthermoresome federal and state regulations require quantitative costndashbene1047297t

analyses (Federal Highway Administration 2003) However only 5 of

the 70 US HIAs focusing on local or regional transportation projects

carried out prior to 2013 quanti1047297ed the expected health impacts

(Singleton-Baldrey 2012 Bhatia and Seto 2011) Table 1 summarizes

these HIAs The remaining HIAs expressed qualitative conclusions

The Aerotropolis Atlanta Brown 1047297eld Redevelopment HIA (Ross et al

2011) illustrates the qualitative approach used by most previous local

and regional US HIAs This HIA evaluated a plan to convert a former

Ford assembly plant near Atlanta Georgia to a new community called

ldquoAerotropolis Atlantardquo The HIAs analysis of air quality impacts was

based on a review of previous studies (not associated with this project)

of traf 1047297c impacts on air quality and health It concluded ldquoAerotropolis

may lead to a change in traf 1047297c volume around the site hellip potentially

impacting people who live work or visit within the air-shed of the af-

fected streetsrdquo The HIA recommended several mitigation measures in-

cluding congestion pricing increased public transit zoning of sensitive

uses away from roadways and vegetation buffers around roadways

However the HIA did not quantify the air quality or health impacts of

the proposed new development or these mitigation alternatives

While the above-mentioned 1047297ve previous quantitative HIAs esti-

mate the magnitude of air quality and related health impacts none con-

siders the potential variability and uncertainty of the estimates Rather

these HIAs each provide a single deterministic prediction of health im-

pacts for each decision option (see Table 1) In so doing these HIAs not

only convey a potentially misleading degree of certainty but also ne-

glect to provide decision-makers with information about the plausible

range of impacts US Environmental Protection Agency guidance

documents indicate that health risk assessments of national and statepolicies should include sensitivity and uncertainty analyses (US

EnvironmentalProtection Agency 2001) Indeed sensitivity and uncer-

tainty analyses are cornerstones of health impact estimates the agency

prepares to inform national policy decisions such as changes to air pol-

lution standards (US Environmental Protection Agency 2010) None-

theless current US local-level HIAs do not report variability and

uncertainty in their health impact estimates

The reliance of local HIA practitioners on deterministic estimates is a

major limitation for several reasons First it fails to consider the full

range of potential risksmdashthat is the potential for risks at the tails of

the risk distribution For example vulnerable populations are often at

the upper tails not the centers of the exposure and effect distributions

(Fann et al 2011) Second risk estimates relying only on central ten-

dencies of each input variable may differ from those considering the

full distributions of each input variable Except in special cases the ex-

pected value of a function of random variables is not the same as the

function applied to the expected values of each variable Third deter-

ministic approaches ignore the potential dependencies among model

inputvariables (for example dependencies in meteorological character-

istics used to estimate pollutant dispersion) Fourth deterministic

Table 1

Previous quantitative transportation-related HIAs in the United States

Title Project scenario

analyzed

Traf 1047297c-related

air pollutants

considered

Study area

population

Estimated annual health impactsa

Pittsburg Railroad Avenue speci1047297c plan

HIA (Human Impact Partners 2008)

Construction of new Bay Area Rapid

Transit (BART) station and mixed-usevillage in Pittsburg CA including 1600

housing units and 450000 sq ft of retail

commercial and public service spaces

PM25 4770 bull 6 deaths (age ge 30) from long-term exposures

β = 00046 (00034 00058)bull 5 hospital admissions for asthma (age b 65)

from short-term exposures β = 00025

(00015 00036)

bull 12 lower respiratory symptom days (ages 7ndash14)


(00124 00241)

Evaluating the healthfulness of

affordable housing opportunity

sites along the San

Pablo Avenue Corridor using


Construction of affordable housing sites

in El Cerrito and Richmond CA

PM25 1000000 bull 33ndash41 deaths (all ages) from long-term exposures

RR = 1014 (no report of 95 con1047297dence interval)

Oak to Ninth Avenue HIA (UC Berkeley

Health Impact Group 2006)

Development of new waterfront community

in Oakland CA including 3100 housing units

and 200000 sq ft of retail commercial

and public service spaces


β = 00046 (00034 00058)

bull 04 chronic bronchitis cases (age ge 27) from

long-term exposures β = 00132 (00064 00200)

bull 106 emergency room visits for asthma (age b 65)


(00024 00049)

MacArthur BART Transit Village HIA

(UC Berkeley Health Impact Group

2007)

Redevelopment of parking lot into a

mixed-use village in Oakland CA

including 625 housing units and

30000 sq ft of retail commercial



β = 00046 (00034 00058)



bull 342 acute bronchitis cases (ages 8-12) from

short-term exposures β = 00272 (00101 00443)

bull 01 hospital admissions for asthma (age b 65)


(00015 00036)


from short-term exposures β = 00182 (00124

00241)

Health impact assessment of the Port of

Oakland (UC Berkeley Health Impact

Group 2010)

Ongoing growth of port operations in West

Oakland CA


β = 00046 (00034 00058)

a

β = concentrationndashresponse coef 1047297cient used to estimate health impacts RR = relative risk used to estimate health impacts

410 C Chart-asa JM Gibson Science of the Total Environment 506 ndash507 (2015) 409ndash421

7232019 1-s20-S0048969714016027-mainpdf


estimates fail to provide information to decision-makers about the de-

gree of certainty in the estimated risks For example decision-makers

may be more concerned about a risk factor with a relatively low central

riskestimate (for example 1 in10000) ifthere isa good chancethatthe

risk could be much higher than thecentral estimate (for example a 10

chance of the risk exceeding 1 in 100) than they would be if presented

only with the central estimate of risk

Variability and uncertainty in estimated risks of traf 1047297c-related air

pollution can arise from multiple sources Variability arises naturallydue to differences in members of a population weather patterns traf 1047297c

geographic features and so on it is a property of nature ldquousually not re-

ducible through further measurement or studyrdquo (Frey and Burmaster

1999) On theother hand uncertainty arises due to thelack of informa-

tion or knowledge including limited data on a population partial igno-

rance of phenomena in1047298uencing a particular risk and disagreements

between models and the reality they are intended to represent (Frey

and Burmaster 1999) Example sources of uncertainty include the

mathematical form used to predict the effects of changes in pollution

exposure on public health the parameters in such mathematical equa-

tions and the accuracy of models predicting air pollution levels under

different traf 1047297c scenarios Theoretically uncertainty can be decreased

through further studies

This paper aims to strengthen the knowledge base and toolset avail-

able to HIA practitioners wishing to incorporate variability and uncer-

tainty in quantitative transportation-related HIAs Like the HIAs listed

in Table 1 this analysis focuses on a potential new land development

expected to increase future traf 1047297c on a major municipal road corridor

(see the ldquoCase study siterdquo section) The potential for increased traf 1047297c

has raised concerns about increases in air pollution and its associated

adverse health effects including increased risks of cardiovascular and

respiratory diseases Like four of the1047297ve HIAs in Table 1 the analysis fo-

cuses on airborne particulate matter having a diameter less than 25μ m

(denoted as PM25) as an indicator of traf 1047297c-related air pollution Like

the other HIAs this analysis is restricted to primary PM 25 (that is

PM25 emitted directly by vehicle operations rather than that formed

by chemical reactions in the atmosphere) This study considers the ef-

fects of short-term exposure to traf 1047297c-related PM25 on cardiovascular

and respiratory mortality (all ages) and unscheduled hospital admis-sions (age 65 and over) These health outcomes were previously select-

ed for the core analysis in the US Environmental Protection Agencys

(EPAs) quantitative health risk assessment for supporting the review

of the US National Ambient Air Quality Standards for PM (US

Environmental Protection Agency 2010)

We use the case study road corridor to explore the effects on health

impact estimates of PM25 from roadway traf 1047297c when including or ex-

cluding various sources of variability and uncertainty We 1047297rst use a

portion of the road corridor to explore the question ldquoWhich variability

and uncertainty sources have the greatest effects on the mean values

and upper con1047297dence limits of estimated health risksrdquo Then we dem-

onstrate a method for incorporating the key variability and uncertainty

sources in a comprehensive assessment of potential air pollution-

related health risks for the entire case study roadway corridor undercurrent conditions and future conditions with and without the pro-

posed new development

2 Case study site

We demonstrate the suggested new assessment process to explore

some of the potential health impacts arising from a planned new cam-

pus extension for the University of North Carolina (UNC) at Chapel

Hill The new campus called ldquoCarolina Northrdquo is intended to increase

the universitys capability to translate research into applications It will

be located about 3 km (2 miles) north of the existing campus ( Fig 1)

If constructed it is expected to increase the number of trips to the

area by 10000 per day by 2015 with most of the increases expected

to occur along MartinLuther King Jr Blvd themain link to the existing

campus and the major northndashsouth road corridor in Chapel Hill (Vanasse

Hangen Brustlin Inc 2009) By 2025 the number of additional daily trips

to the campus is expected to increase by as many as 40000 We consider

the potential impacts of the expected additional traf 1047297c-related air pollu-

tion among residents living in census blocks within 500 m of Martin Lu-

ther King Jr Blvd In all this area encompasses 160 US census blocks

(see Fig 1) and has a total population of about 16000mdashmore than one-

quarter of Chapel Hills total population of 57000

We analyze the effects of primary emissions from traf 1047297

c along Mar-tin Luther King Jr Blvd on ambient PM25 concentrations and popula-

tion health under three different scenarios (1) the year 2009

(2) 2025 assuming the new campus is not built and (3) 2025 with

the new campus The baseline comparison year is 2009 because the

most comprehensive transportation analysis of the study corridor was

conducted using 2009 data (Vanasse Hangen Brustlin Inc 2009)

Table 2 provides summary information about the population size and

traf 1047297c volumes under these three scenarios

3 Methods and data sources

This analysis has two main parts

1) Analyze the effects of including variability and uncertainty in the HIA

First we investigate in the effects on health impact estimates of in-cludingseveral different uncertainty and variability sources as com-

pared to results obtained using the conventional deterministic

approach For computational ef 1047297ciency we focus on the 12 census

blocks highlighted in Fig 1B which our prior air quality modeling in-

dicated are more vulnerable to traf 1047297c-related PM25 than most other

census blocks in the corridor (Chart-asa et al 2013) The total pop-

ulation in the 12 blocks is 1117 (about 7 of the total population in

the study corridor)

2) Quantify the health impacts of traf 1047297c from the proposed new campus in

the study corridor Second we quantify traf 1047297c-related air quality and

health outcomes along the entire study corridor for the three devel-

opment scenarios in Table 2 This analysis includes the variability

and uncertainty sources identi1047297ed in part 1 as having an in1047298uence

on the central estimates or upper con1047297

dence estimates of themodeled risks

Both analyses use the same modeling framework describedin detail

in the followingsections However the1047297rst analysis introducesvariabil-

ity and uncertainty sources one at a time in order to explore their po-

tential in1047298uence on the computed health risks while the second

analysis includes all key variability and uncertainty sources

31 Modeling framework overview

Quantifying the health impacts of traf 1047297c-related air pollution re-

quires three categories of information (1) estimates of the excess

PM25 concentrations to which the population is exposed as a result of

primary emissions from traf 1047297c (2) concentrationndashresponse functions

relating exposure concentrations to probabilities of adverse health out-comes and (3) incidence rates of the health outcomes of concern (from

all causes) in the exposed population (Ostro 2004 Ostro and Chestnut

1998 Cohen et al 2005 Li et al 2010 ) Fig 2 summarizes how this

analysis combines these three information categories (shows as shaded

boxes)to estimate health impacts Theunshaded boxes show variability

and uncertainty sources considered in this study The subscript notation

indicatesthat theanalysisis conductedat thecensus block scalewhere i

represents an individual block That is health risks are characterized

separately for each census block considering variability in traf 1047297c-

related PM25 exposure concentrations and population demographic

characteristics within each block The subscripts j k and l indicate dif-

ferences in baseline health status by age ( j) gender (k) and race (l)

In addition this analysis considers seasonal (subscript m) variability

because epidemiologic evidence suggests seasonal differences in

411C Chart-asa JM Gibson Science of the Total Environment 506 ndash507 (2015) 409ndash421

7232019 1-s20-S0048969714016027-mainpdf


dosendashresponse functions (Zanobetti and Schwartz 2009 Bell et al

2008) The following sections provide details on our methods for esti-

mating PM25 exposure concentrations (left-most shaded diamond in

Fig 2) selecting concentrationndashresponse functions (central diamond)

estimating baseline incidence rates of adverse health outcomes in the

study population (right-most diamond) and incorporating variability

and uncertainty (white rectangles) into the analysis

32 PM 25 concentrations attributable to primary emissions from traf 1047297c

The 24-hour exposures to PM25 arising from primary emissions

from traf 1047297c along the case study roadway corridor were estimated

using an integrated air quality modeling approach described in Chart-

asa et al (2013) In brief the approach employs standard traf 1047297c emis-

sions and air quality dispersion modeling tools but it adds a novel ap-

proach for modeling variability in vehicle emissions due to variability

in hourly temperature roadgrade and traf 1047297c behavior (including cruis-

ing speed and percent time spent idling decelerating and accelerating)

The exposure modeling approach links a novel application of MOVES2010b commonly used in the United States to estimate vehicle emis-

sions factors (gvehicle-mile) and CAL3QHCR which characterizes

PM25 dispersion away from roadways By linking these models and

employing a new approach for characterizing variabilityin emission fac-

tors we simulated probability distributions of the average 24-hour

Fig 1 (A)The study corridorbetween the intersection of Martin LutherKing JrBlvdand Whit1047297eld Rdand theintersection of South ColumbiaSt andMt Carmel Church Rd Chapel Hill

NC and the census blocks located within 500 m from the study corridor (B) The road segment and census blocks for simulations to demonstrate differences in health burden estimates

when including variability and the uncertainty in the modeling approach Dots represent census block centroids

Table 2

Population size and traf 1047297c volumes under three scenarios considered

Scenario Traf 1047297c volumes of road segments on study corridor (vehh) a Total population of 160 census blocks located within 500 m from study corridor

2009 4ndash1758 16042

2025 without the new campus 5ndash2443 19140b

2025 with the new campus 5ndash2832 19140b

a Ranges indicate variability in traf 1047297c 1047298ow by road segment day of week and time of dayb

Computed from growth rates forecasted by the North Carolina Capital Area Metropolitan Planning Organization (2005)


7232019 1-s20-S0048969714016027-mainpdf


PM25 concentration in each season (winter spring summer and fall) at

the centroid of each of the 160 census blocks in the study corridorOur analysis considers variability in vehicle emission factors by di-

viding the 82-km roadway corridor into 1200 links and estimating sep-

arate emission factors for each link for each hour of each simulation day

(Chart-asa et al 2013) Unlike previous studies linking MOVES and

CAL3QHCR our analysis considers hourly variability in temperature

and link-speci1047297c variability in road grade and vehicle behavior Hourly

meteorological pro1047297les for 2006ndash2012 were obtained from the national

weather stations in ChapelHill andGreensboroNorthCarolinaand me-

teorological pro1047297les for input to CAL3QHCR were generated from EPAs

Meteorological Processor for Regulatory Models (NCDC 2013 NOAA

2013) The meteorological pro1047297les contained a total of 2100 days with

complete required data (525 days for winter 560 days for spring

532 days for summer and 483 days for fall) For each census block we

used CAL3QHCR to estimate the PM25 concentration (averaged over24 h) attributable to primary traf 1047297c emissions from each of the 1200

roadway links for each of the 2100 days for which meteorological data

were available Separate estimates were prepared for 2009 and 2025

using emission factors from MOVES modeling and simulated traf 1047297c

data for 2009 and 2025 scenarios with or without Carolina North from

the Transportation Impact Analysis (TIA) for the Carolina North Devel-

opment (Vanasse Hangen Brustlin Inc 2009) Then for each develop-

ment scenario seven separate mean estimatesmdashone for each of the

seven years for which meteorological data were availablemdashof the sea-

sonal mean value of the24-hour average PM25exposure concentrations

were computed for each season

For each scenario (year 2009 and year 2025 with and without con-

structingCarolina North) andeach season we then computed bootstrap

estimatesof the mean value andstandarddeviation of theseasonal daily

average PM25 exposure concentrations by randomly selecting one of

the seven years assigning the associated seasonal mean concentrationsas computed using that years data to each census block and then re-

peating the process 1999 times For each of the 160 census blocks the

result was an estimated mean value and standard deviation of the sea-

sonal 24-hour-average concentration of PM25 attributable to primary

emissions from traf 1047297c along the roadway corridor under each scenario

Within each census block and for each scenario the seasonal average

traf 1047297c-related 24-hour PM25 concentration then was represented as a

normaldistribution(left-truncatedat zero) with themean andstandard

deviation estimated from the corresponding 2000 bootstrap simula-

tions The TIA estimated hourly traf 1047297c counts for each scenario along

each roadway link only for weekdays we assumed traf 1047297c counts on

weekends would be the same and hence may have slightly over-

estimated exposure concentrations

In addition to considering variability in PM25exposures arising fromprimary traf 1047297c emissions we assessed the effects of uncertainty in the

accuracy of the air quality model predictions Our previous research

on the integrated air quality modeling approach as well as previous

work by others suggests that the combined MOVESndashCAL3QHCR

model generally predicts PM25 concentration within a factor of two of

measured concentrations (although accuracy varies with local condi-

tions and the quality of data available to support the model) ( Chart-

asa etal2013 Yura etal2007) FollowingMorganand Henrionsguid-

ance (Morgan et al 1990) we represented model uncertainty with an

uncertainty factor (UF ) parameterized by a triangular probability distri-

bution with lower limit = 05 upper limit = 20 and mode = 10

(spanning the expected factor-of-two uncertainty in the model) Ac-

cording to Morgan and Henrion the triangular distribution is especially

appropriate for situations in which ldquothe distributions of variables in a

Fig 2 Overview of framework for incorporating variability and uncertainty into assessment of the health impacts of traf 1047297c-related PM25 The rectangles show sources of variability and

uncertainty The shaded diamonds show the three major information categories needed for quantitative health impact assessment


7232019 1-s20-S0048969714016027-mainpdf


model arenot preciselyknownrdquo but in which ldquovaluestowardthe middle

of the range of possible values are considered more likely to occur than

values near either extremerdquo Based on previous evaluations of the per-

formance of near-roadway air pollutant dispersion models the exact

form of the distribution representing model uncertainty is not known

making the triangular distribution an appropriate choice for character-

izing model uncertainty Correspondingly in each census block the ex-

cess PM25 24-hour average exposure concentration attributable to

primary emissions from traf 1047297

c on the case study roadway was estimat-ed for each season as

PM exposureimfrac14 U F PM modelim

eth1THORN

where PM exposureimrepresents the average 24-hour PM25 concentration

in census block i (i = 1ndash160) and season m (m = winter spring sum-

mer fall) attributable to primary traf 1047297c emissions on the case study

roadway UF is the model uncertainty factor and PM modelimis thecorre-

sponding model-predicted seasonal daily average PM25 concentration

arising from primary emissions from traf 1047297c

321 Concentrationndashresponse functions

As recommended by the World Health Organization and others

(Ostro and Chestnut 1998 Li et al 2010 Aunan 1996) we use

the following relationship to describe the link between seasonal

daily average PM25 concentrations and the relative risk of cardio-

vascular and respiratory health outcomes

RRimn frac14 e β mn P M exposureim eth2THORN

where β mn is the concentrationndashresponse coef 1047297cient describing the

effects of PM on health outcome n during season m and RRimn is the

relative risk of health outcome n during season m in census block i

The number of adverse health cases in the population attributable

to traf 1047297c-related PM25 then can be determined from the following

relationship

Δ yi jklmn frac14 y0i jklmn A F i jklmn 3a

frac14 y0i jklmn

RRimnminus1

RRimn

3b

frac14 y0i jklmn

e β mn P M exposureimminus1

e β mn P M exposureim

3c

frac14 y0i jklmn 1minuse

minus β mn P M exposureim

3d

where AF i jklmn and Δ yi jklmn are the fraction and number of casesof adverse health event n attributable to traf 1047297c-related PM25 in sea-

son m in census block i for age group j gender k and race l and

where yi jklmn0 is the observed total number of cases in the same lo-

cation and among the same population group Eqs (2) (3a) (3b)

(3c) and (3d) are the standard equations used in analyses by the

WHO and other organizations to attribute observed cases of ad-

verse health events to speci1047297c risk factors ( Ostro and Chestnut

1998 Murray et al 2003 Mathers et al 2001 Pruumlss-uumlstuumln et al

2003)

The β values in Eqs (2) (3c) and (3d) (known as dosendashresponse co-

ef 1047297cients) were drawn from the US Environmental Protection Agency

guidance document Quantitative Health Risk Assessment for Particulate

Matter (US Environmental Protection Agency 2010 Zanobetti and

Schwartz 2009 Bell et al 2008) Table 3 shows the coef 1047297cient values

used in this analysis EPA retrieved these coef 1047297cients from peer-

reviewed epidemiologic studies that met certain quality-assurance

criteria including for example the estimation of exposure from mea-

sured rather than modeled PM25 data For mortality effects the coef 1047297-

cients are speci1047297c to 15 US metropolitan areas For morbidity effects

coef 1047297cients are speci1047297c to region (Northeast Southeast Northwest

and Southwest) This study employed mortality coef 1047297cients developed

from studies in Atlanta since Atlanta is climatologically the most similar

to Chapel Hill among the 15 cities studied We used morbiditycoef 1047297cients for the Southeast region in which Chapel Hill is located

All concentrationndashresponse coef 1047297cients were represented as normal

distributions with all negative valuestruncated at zero (to avoid associ-

ating PM exposure with positive health effects) Standard deviationsfor

each season and health outcome were estimated from the con1047297dence

intervals in Table 3

33 Baseline incidence rates of adverse health outcomes

Data on baseline incidence rates of health outcomes were obtained

from North Carolina public health databases Annual mortality rates

for each age group (Table 4) were calculated by dividing thetotal num-

ber of deaths in Orange County (where Chapel Hill is located) in 2010

(North Carolina State Center for Health Statistics 2012) bythe 2010Or-

ange County census population (Minnesota Population Center 2011)

Annual unscheduled hospital admission rates (Table 5) were obtained

from 2009 emergency department visit data reported by the North Car-

olina Disease Event Tracking and Epidemiologic Collection Tool (NC DE-

TECT) (University of North Carolina at Chapel Hill 2011) We were

unable to obtain data on incidence rates by gender and race so we as-

sume that incidence rates are the same for both genders and all races

(which is a limitation of this analysis) It should be noted as well that

the ICD codes speci1047297c to the concentrationndashresponse coef 1047297cients

might not be entirely matched to the ICD codes speci1047297c to the incidence

rates used in this study depending on reported data Moreover emer-

gency department visits may not result in hospital admissions and

some hospital admissions may occur without 1047297rst visiting the emergen-

cy department

Tore1047298ect seasonal variation we adjusted the incidence rates for car-diovascular and respiratory mortality and unscheduled hospital admis-

sions using data on temporal variability in cardiovascular and

respiratory deaths in Orange County during 1999ndash2010 from the CDC

WONDER database (Centers for Disease Control and Prevention

2013) The fractions for cardiovascular events are 025 031 020 and

024 for winter spring summer and fall respectively while the frac-

tions for respiratory events are 030 026 021 and 023 for winter

spring summer and fall respectively

To determine the total number of cases in any given season (ie

yi jklmn0 in Eqs (3a) (3b) (3c) and (3d)) we multiplied the given inci-

dence rateby the corresponding size of each demographic group in each

census block

34 Testing the effects of variability and uncertainty on health impact estimates

Five simulations of 2000 iterations each were run using Analytica

version 45 (Lumina Decision Systems Los Gatos California) to demon-

strate differences in health burden estimates when including variability

and uncertainty Table 6 lists the1047297ve simulations and the variability and

uncertainty considered in each The1047297rst simulation (1a) follows the de-

terministic approach of previous HIAs usingaverage traf 1047297c volumes and

a constant traf 1047297c emission factor corresponding to traf 1047297c cruising at

35 mph on a 1047298at roadway under a constant ambient temperature of

70 degF Like previous HIAs simulation 1a accounts for neither uncertainty

in the concentrationndashresponse coef 1047297cient (usingthe meanvalue asa de-

terministic estimate) nor seasonal variability Simulation 1b is identical

to simulation 1a except that it uses seasonal concentrationndashresponse


7232019 1-s20-S0048969714016027-mainpdf


coef 1047297cients (also deterministic) Simulations 2ndash4 systematically include

(one at a time) variability in vehicle emissions rates (simulations 2ndash4)

uncertainty in concentrationndashresponse coef 1047297cients (simulations 3ndash4)

and air quality model prediction error (simulation 4)

35 Comparing health impacts under alternative scenarios

As noted previously we simulated health impacts for the full study

corridor (160 census blocks) for three different scenarios (Table 2)

year 2009 and year 2025 with and without the new campus For each

scenario 2000 simulations were run in Analytica Traf 1047297c patterns (traf-

1047297c volumes along each roadway link in the corridor) for each scenario

were taken from a previous traf 1047297c impact analysis conducted for theTownof Chapel Hill(Vanasse Hangen Brustlin Inc2009)The 2025cen-

sus block populations were obtained from forecasts by the North

Carolina Capital Area Metropolitan Planning Organization (2005)

These growth rates account for demographic changes expected to

occur if the Carolina North campus is built

4 Results and discussion

This analysis explored the effects of variability and uncertainty on

health impact estimates of near-roadway air pollution arising from traf-1047297c attracted by newsuburban development projects Most previous US

HIAs of such projects have provided qualitative rather than quantitative

assessments of health impacts the few quantitative HIAs have not

systematically represented variability and uncertainty in the variables

used to estimate health impacts or in the resulting health outcome pre-

dictions We explored whether including variability and uncertainty

makes a difference in centralestimates of health impacts and we exam-

ined the magnitude of uncertainty in the resulting estimates We then

employed an approach that accounts for variability and uncertainty to

model the expected health impacts in the year 2025 of new traf 1047297c gen-

erated by a new research campus development along a busy roadway

corridor in Chapel Hill North Carolina

41 Effect of including variability and uncertainty

Our results suggest that the conventional deterministic HIA ap-

proach may systematically under-estimate potential health impacts of

traf 1047297c-related PM25 exposure (Fig 3)

Incorporating traf 1047297c emission variability into the analysis (as in sim-

ulation 2) caused the mean value of estimated health impacts to in-

crease by more than a factor of two compared to estimates that

exclude such variability (simulation 1b) This increase occurred because

neglecting the effects on vehicle emissions of variability in temperature

road grade vehicle speed and traf 1047297c behavior (idling accelerating de-

celerating or cruising) resulted in under-estimates of PM25 exposure

Table 3

Concentrationndashresponse coef 1047297cients used in this study

Health outcome Disease category ICD-9 or ICD-10 codea Age group Season Mean concentration-response

coef 1047297cient (95 CI) per 10 μ gm3b

Mortality Cardiovascular I01ndashI59 All ages All-yearc 066 (minus066 198)

Winter 135 (minus193 462)

Spring 076 (minus273 425)

Summer 062 (minus222 347)

Fall minus018 (minus293 257)

Respiratory J00ndash J99 All ages All-yearc

121 (minus048 290)Winter 093 (minus144 329)



Fall 096 (minus134 325)

Unscheduled hospital admissions Cardiovascular 410ndash414 426ndash429 430ndash438 and 440ndash4 49 6 5 a nd over A ll-yea rc 029 (minus019 077)



Summer minus067 (minus161 026)

Fall 017 (minus072 106)

Respiratory 464ndash466 480ndash487 and 490ndash492 65 and over All-yearc 035 (minus044 113)




Fall 014 (minus130 158)

a ICD-10 for mortality and ICD-9 for unscheduled hospital admissionsb Coef 1047297cients were originally from Zanobetti and Schwartz (2009) and Bell et al (2008) respectively

c Used only in simulation 1

Table 4Annual mortality rates by race gender and age group for Orange County (per 1000 people)

Cause of death ICD-10 code Age group Race and gender

White male Black male Other male White female Black female Other female

Cardiovascular disease I05ndashI09 I10ndashI15 I20ndashI25 I26ndashI28 and I30ndashI52 0 to 34 000 000 000 000 000 000

35 to 44 017 000 000 000 095 000

45 to 54 029 272 000 013 153 000

55 to 64 179 235 257 091 105 000

65 to 74 343 725 000 244 000 000

75 to 84 1647 1724 000 770 2065 000

85+ 5251 2632 12500 3034 2299 000

Respiratory disease J00ndash J99 0 to 54 000 000 000 000 000 000

55 to 64 049 000 000 045 105 000

65 to 74 206 483 000 183 345 000

75 to 84 524 575 116 495 590 000

85+ 3580 1316 000 787 1149 000


7232019 1-s20-S0048969714016027-mainpdf


concentrations on average For example vehicle emission rates nearly

tripled when the road grade changed from 0 (as assumed in theconventional modeling approach) to 10 (Chart-asa et al 2013) Simi-

larly emissions doubled when the temperature decreased from 70 degF

(the default assumption under the conventional assessment approach)

to10degF (Chart-asa et al 2013) For 1047298at roadways with traf 1047297c moving at

constant speeds in climates with minimal temperature 1047298uctuation var-

iability in emissions factors is expected to be small but for most cases it

is clear that emissions factor variability is an important consideration

when predicting health impacts

When additionally including the uncertainty in concentrationndashre-

sponse coef 1047297cients into the modeling approach (simulation 3) the

mean estimate of health impacts increased still further the estimated

number of attributable CVD deaths and respiratory hospital admissions

more than doubled while respiratory deaths and CVD hospital admis-

sions increased by 69 and 11 respectively This result occurred be-

cause we represented concentrationndashresponse coef 1047297cients as right-

skewed probability distributions (normal distributions left-truncated

at zero) This representation is appropriate because of the constraint

that the coef 1047297cients must be non-negative (since PM25 exposure does

not bene1047297t public health) The result is that the mean value of the con-

centrationndashresponse coef 1047297cients is greater than the median value

which in turn increased the mean estimated health impacts compared

to when such uncertainty was excluded

When additionally including the uncertainty in model prediction

error (simulation 4) the mean estimates increased by another 16ndash17

compared to simulation 3 This result occurred because of the right

skew in the triangular distribution used to represent model uncertainty

and the interactions of this distribution with that used to represent the

concentrationndashresponse coef 1047297cient As previously explained the trian-

gular distribution re1047298ects previous research on the performance of theCAL3QHCR model (Chart-asa et al 2013)

In summary incorporating variability and uncertainty into the

model predictions increased the mean value of estimated health im-

pacts compared to predictions that excluded variability and uncertain-

ty The health impact estimates increased by factors of 7 8 4 and 9 for

CVD deaths CVD hospital admissions respiratory deaths and respirato-

ry hospital admissions respectively The estimates that excluded vari-

ability and uncertainty are biased so low that they are outside the 95

con1047297dence intervals of estimates including variability and uncertainty

These biased predictions could have important implications fordecision-making For example it is possible that excluding variability

and uncertainty and hence producing unrealistically low estimates of

health impacts could result in a decision not to pursue mitigation mea-

sures that would have been determined cost-effective had the full im-

pacts of variability and uncertainty been considered

42 Overall population health impacts at the case study site

This analysis predicted that by 2025 the total number of adverse

health cases attributable to traf 1047297c-related PM25 on the case study road-

way will decreaserelative to 2009 with or without theCarolina North De-

velopment (although the decrease is lower with the development)

(Table 7) This decrease in the number of adverse health outcomes is

predicted to occur despite an expected 20 increase in the population

by 2025 Overall the numberof cases of CVD mortality CVD hospital ad-

missions respiratory mortality and respiratory hospital admissions are

expected to decrease by 42 38 47 and 42 respectively The de-

creased risks arise from the built-in assumptions of MOVES that future

vehicles will be cleaner than todays 1047298eet resulting in traf 1047297c emissions

that decline by about 50 on average compared to todays vehicles

However the increased traf 1047297c associated with the new campus will off-

set even greater decreases in near-roadway PM25 expected to occur in

2025 in the absence of the new campus the number of adverse health

outcomes is expected to be about 30 lower if the new campus is not

built compared to if it is built (results not shown)

The health risks of primary PM25 from traf 1047297c on the case study

roadway vary considerably by season and location (Fig 4) For CVD

mortality effects arehighest in winterdue to the in1047298uencesof high con-

centrationndashresponse coef 1047297cients seasonal incidence variations andtraf 1047297c emission factors during low temperatures The spatial variability

in risk is especially pronounced in winter as illustrated by the grada-

tions by censusblock illustrated in Fig 4 Similar seasonal and spatialef-

fects are observed for the other three health outcomes (not shown)

To investigate the potential factors explaining the spatial distribu-

tion of risk we calculated correlations between several potential ex-

planatory variables and the total excess mortality and morbidity

attributable to PM25 from the roadway in each census block for the

year 2009 Variables included distance from the roadway to the census

block centroid total census block population population over age 64

percent of the population identifying as black and mean PM25 concen-

trationattributable to theroadway acrossall seasons Forexcess mortal-

itythe correlations are largest for mean PM25 concentration (r = 042 t

(158) = 58 p = 14 times 10minus8

) and percentage of the population identi-fying as black (r = 037 t (114) = 42 p =25times10minus5) The correlations

are smaller fordistance to theroadway (r =minus022 t (158) =minus28 p =

00028) total population (r = 015 t (158) = 20 p = 0025) and pop-

ulation over age 64 (r = 016 t (158) = 20 p = 0025) The results are

similar for excess morbidity The spatial distribution in risk arises from

complex interactionsamong a variety of factors including factors affect-

ing population susceptibility (potentially including age and race) and

factors affecting exposure concentration Factors that affect the spatial

distribution of exposure concentrations include not only distance from

the roadway but also roadgrade vehicle typesvehicle speedtraf 1047297c vol-

ume the presence of intersections and wind speed and direction The

effects of such factors are described in detail in Chart-asa et al (2013)

The above-noted correlation between mortality risk associated with

traf 1047297c-related PM25 exposures and the percentage of the census block

Table 5

Annual emergency department visits rates for North Carolina

Cause of Visit ICD 9 code Age group Annual rate

Cardiovascular disease 4275 428 and 5184 (excluding failure due to fumes and vapors) 430ndash435 and 4370ndash4371 65 and over 00856

Respiratory diseas e 466 and 480ndash486 65 and over 00355

Table 6

Sources of uncertainty and variability included in the 1047297ve simulations

Uncertainty and variability sources Simulation

number

1a 1 b 2 3 4

Sources of uncertainty

PM25 exposure concentration

bull Air quality model prediction accuracy x

Dosendashresponse function

bull Dosendashresponse coef 1047297cient x x

Sources of variability


bull Vehicle emissions variability on each roadway link arising

from the following sources temperature road grade

cruising speed and percent time spent decelerating idling

accelerating and cruising

x x x


bull Seasonal variability x x x x

Demographic characteristics of exposed population

bull Age race and gender (by census block) x x x x x


7232019 1-s20-S0048969714016027-mainpdf


population identifying as black suggests the possible presence of racial

disparities in exposure risks The census block having the highest num-

berof total deathsattributable to traf 1047297c on the study corridor under cur-

rent conditions (block number 371350118002002 with a population of

201) also has a very high percentage of black residents at 47 com-

pared to 9 in the study area as a whole This census block is the

home of a public housing community Airport Gardens intended for

low-income families The block has the second-highest PM25 exposure

concentration among all blocks in the study area Of the 10 census

blocks with the highest number of attributable deaths seven have

higher percentage black populations (17ndash47) than the average for

the study area Nonetheless even in the highest-risk of these census

blocks the annual per-person risk of premature mortality due to

traf 1047297c-related PM25 exposure is vanishingly small 58 times 10minus8

(obtain-ed by dividing the annual attributable deaths by the total population

of the census block) Over a 70-year lifetime this equates to a risk of

41 times 10minus6 Along other busier roadways however the health signi1047297-

cance of such disparities could be much greater

Overall we predict that future risks of primary PM25 from increased

traf 1047297c associatedwith theCarolina North campus will be extremelylow

If the new campus is built then 9 times 10minus6 excess CVD deaths and 2 times

10minus6 excess respiratory deathsare expected compared to if thecampus

isnot built (Table 7) Summingthese two estimates and dividing by the

future study corridor population of 19140 yields a per-person annual

risk of about 6 times 10minus10 These risks are low even if one assumes a resi-

dent is exposed to such a risk level for a lifetime For a 70-year lifetime

the per-person lifetime risk is 4 times 10minus8 Even in the most-exposed cen-

sus block lifetime risks attributable exclusively to the new campus arerelatively low (about 1 times 10minus8 per year or less than one-in-one-

million over a lifetime)

43 Sensitivity and uncertainty analysis

The 95 con1047297dence interval values of the risk estimates in Table 7

range over a factor of about 6ndash7 For example theupper 95 con1047297dence

interval estimate of annual CVD deaths attributable to roadway traf 1047297c

10times 10minus4 isabout 7 times largerthan the lower 95 con1047297dence inter-

val estimate 15 times 10minus5 While from a policy standpoint the risks at

both ends of this con1047297dence interval are relatively low at other sites

the optimal policy decision might change if the actual risk were close

to the upper or lower 95 con1047297dence interval value rather than the cen-

tral estimate Hence in future applications of the HIA analysis approach

demonstrated in this article identifying the variables with the biggest

in1047298uence on the mean value of and uncertainty in the risk estimates

may be important in order to guide additional data collection prior tomaking a risk-informed decision

In a future application a decision-maker may wish to know the ef-

fects of changing each random variable in an HIA model to plausible

high and low values Three key random variables underlie this analysis

the PM25 concentration in each census block as predicted by the com-

bined MOVESCAL3QHCR model the model uncertainty factor

(representing the departure of this combined model from actual PM25

concentrations) and the dosendashresponse coef 1047297cient Fig 5 shows the ef-

fects on thepredicted number of CVD deaths of 1047297xing each of these var-

iables at its lower and upper 95 con1047297dence interval value while

keeping all other variables the same The effects vary by census block

and hence are presented as cumulative distribution functions (CDFs)

For example the dosendashresponse coef 1047297cient relating PM25 exposure

concentration to the risk of CVD mortality in winter is represented inthe base model as a truncated normal distribution with mean 135 times

10minus3 and standard deviation 17 times 10minus3 the lower 95 CI of this

Fig 3 Effect on health impact estimates of including the variability and uncertainty sources shown in Table 6 Error bars represent 95 con1047297dence intervals

Table 7

Comparison of HIA results by development scenario

Scenario Number of census

blocks affecteda

Range of mean exposure

concentrations in affected

blocks (μ gm3)b

Total cases times 106

CVD

mortality

CVD hospital

admissions

Respiratory

mortality

Respiratory hospital

admissions

2009 118ndash148 00002ndash016 48 (15ndash100) 140 (47ndash280) 15 (5ndash30) 73 (21ndash160)

2025 without Carolina North 75ndash122 00002ndash010 19 (56ndash42) 61 (19ndash120) 55 (17ndash12) 30 (8ndash66)

2025 with Carolina North 84ndash137 00002ndash013 28 (79ndash61) 87 (27ndash170) 79 (24ndash17) 42 (12ndash93)

a Number of census blocks with exposure concentrations greater than zero (varies by season)b

Lowest and highest mean seasonal exposure concentration in affected census blocks (also varies by season)


7232019 1-s20-S0048969714016027-mainpdf


distribution is 12 times 10minus4 and the upper 95 CI is 47 times 10minus3 The

ldquoDosendashResponse Coef 1047297cient Highrdquo curve in Fig 5 shows a CDF of the

risk estimates for census blocks when this coef 1047297cient and those for the

other three seasons are 1047297xed at their upper 95 CI values (in the case

of winter 47 times 10minus3) rather than varying randomly while leaving

the other model variables unchanged Fig 5 shows that for all census

blocks the risk estimates are more sensitive to the concentrationndash

response coef 1047297cient than to the other random variables in the risk

model (air quality model uncertainty factor and predicted PM25 expo-

sure concentration) When the effects of 1047297xing each seasonal dosendash

response coef 1047297cient for CVD mortality at lower or upper 95 CI valuesare summed across all census blocks then the estimated number of

CVD deaths changes from the mean estimate of 47 times 10minus6 to 25 times

10minus6 and 120 times 10minus6 respectively (Fig 6) These results illustrate the

potential importance for futureHIAs of strengthening the epidemiologic

basis for predicting the health effects of PM25 exposures in order to de-

crease the potential for producing risk estimates that are either too high

or too low (Note that results for other health outcomes not shown

here as similar to those illustrated in Figs 5ndash6)

A second question that decision-makers might ask is why the 95

con1047297dence intervals in estimated risks are so wide One approach to an-

swering this question is to examine the rank-order correlation between

the estimated risks and each random variable in the model A high rank-

order correlation between an input variable and the risk estimate indi-

cates that high values of the input variable drive the risk estimate

toward comparably high values For this analysis the rank-order corre-

lations differ by census block season and health outcome Fig 7 shows

CDFs of the rank-order correlations between each random input vari-

able andCVD mortality risks among thecensus blocksby season In win-

ter the season in which PM25 exposure concentrations are highest

uncertainty in the dosendashresponse coef 1047297cient drives uncertainty in the

risk estimates in all census blocks In spring and summer the air quality

model uncertainty factor drives the uncertainty in the risk estimates In

fall the model uncertainty factor drives uncertainty except for in about

20 of census blocks where the dosendashresponse coef 1047297cient contributes

the most uncertainty Hence overall to decrease uncertainty in therisk predictions both the strength of the epidemiologic evidence and

the performance of near-roadway air pollutant dispersion models

must be improved

In summary Figs 5ndash7 illustrate the importance for future

transportation-related HIAs of decreasing uncertainty in epidemiologic

estimates of the concentrationndashresponse coef 1047297cient and improving the

ability to model near-roadway concentrations of PM25 from traf 1047297c

5 Limitations

Key limitations in this analysis arise from de1047297ciencies in the avail-

able epidemiologic evidence the capabilities of the air quality model

and future population data In addition the attributable fraction ap-

proach considers effects of PM25 exposure on the incidence of

Fig 4 Spatial distribution of cardiovascular deaths (times 106) attributable to PM25 before and after Carolina North development


7232019 1-s20-S0048969714016027-mainpdf


cardiovascular and respiratory deaths but may overlook effects on the

population prevalence of CHD and respiratory diseases

One limitation arises from the assumption that all PM25mdashincluding

that generated by traf 1047297cmdashhas thesame health effects asPM25measured

at population-oriented central site monitors used as the basis for expo-

sure estimates in the epidemiologic studies from which the concentra-

tionndashresponse coef 1047297cients are drawn This assumption is common to

nearly all air quality risk assessments to date (eg Cohen et al 2005

Liet al2010Fann et al 2012) because the understanding of differen-

tial health effects of PM25 from different sources is still emerging Ac-

cording to a meta-analysis by Janssen et al traf 1047297c-associated PM25

may have greater health risks than PM25 from non-combustion sources

( Janssen et al 2011) Janssen et al found that theoretically risk esti-mates using black carbon particles which are associated with combus-

tion from motor vehicle engines and other sources as an indicator of

traf 1047297c-related pollution yielded risk estimates 4ndash9 times higherthan es-

timates using overall PM25 as an indicator However our analysis re-

quired use of PM25 since MOVES and CAL3QHCR do not provide the

capability to estimate black carbon particle concentrations Further-

more the available epidemiologic evidence on the association between

black carbon particlesand health risks is not nearlyas extensiveor thor-

oughly reviewed as that for PM25 ( Janssen et al 2011) Updating near-

roadway dispersion models to predict black carbon particle

concentrations and conducting further epidemiologic studies examin-

ing the effects of vehicle emissions on health are important areas of re-

search Nonetheless forthe case study site theestimated risks would be

very low even assuming the risks are under-estimated by a factor of 9

(the upper bound of Janssen et als predicted under-estimation when

using PM25 rather than black carbon particles as an air pollution indica-

tor) In the baseline scenario (year 2009) the annual average CVD or re-

spiratory mortality risk to an individual from traf 1047297c-related air pollution

predicted by our model is 36 times 10minus9 (=45 times 10minus6 CVD deaths plus

13 times 10minus6 respiratory deaths divided by a population of 16000) As-

suming a 70-year lifetime exposure period the resulting lifetime risk

is 25 times 10minus7 Increasing these risks by a factor of 9 results in an annual

risk of 33 times 10minus8 and a lifetime risks of 23 times 10minus6mdashrisks that are con-sidered very low accordingto US EPA guidelines which in general have

long designated as acceptable risks of less than 10minus4 to 10minus6 (EPA

1989)

A second limitation is that the concentrationndashresponse coef 1047297cients

assume that the exposure histories of current and future residents of

the case study area will be similar to those in the areas from which

the epidemiologic studies were drawn (Atlanta and the southeastern

United States) Once again this limitation is inherent in current airqual-

ity risk assessments due to the costs of conducting epidemiologic stud-

ies and theresulting lack of studies for each US metropolitan area This

limitation may bias the absolute results of the risk estimates but it does

not affect the estimates of risks of one scenario relative to another

Hence the conclusion that the development of the Carolina North cam-

pusis unlikely to lead to substantial traf 1047297c-related air quality health im-pacts is valid even if exposure histories of the Chapel Hill population

differ from those of the populations from which relative risk estimates

were derived

A third limitation is that Eqs (3a) (3b) (3c) and (3d) which have

been used as the basis for assessing health impacts of air pollution

exposure by nearly all researchers to date may neglect the effects of

airpollutionexposureon thedisease progression leading up to hospital-

izations for respiratory illnessesand CVD (Perez et al 2013) Perez et al

recently found that including such effects in analyzing health impacts of

traf 1047297c-related road pollution increased estimated health impacts on av-

erage by a factor of about 10 in a study of 10 major European cities

(Perez et al 2013) However implementing the approach of Perez

et al is not possible when attempting to predict changes in health effect

estimates in the distant future because Perezs calculation relies on

Fig 5 Effects of changingrisk model input variables to their upper andlower95 con1047297dence interval valuesThe cumulativedistribution functions illustrate thevariability in these effects

by census block in the case study roadway corridor

Fig 6 Overall effect (across all census blocks) of changing random variables in the risk

modelto theupperand lowerendsof their95con1047297dence intervals Thechart is centered

on the mean value of theriskestimate 48times 10minus6 Theendsof each barcorrespond tothe

new risk estimate if the variable is changed to its low (left side) or high (right side) 95

con1047297

dence interval value


7232019 1-s20-S0048969714016027-mainpdf


epidemiologic studies that useproximityto a busy roadwayas theexpo-

sure metric For estimatingthe effect of roadway emissions on coronary

heart disease (CHD) prevalence for example Perez relies on an epide-

miologic study in Germany showing that living within 150 m of a

busy roadway (de1047297ned as an autobahn or federal highway) increased

therelative riskof CHD by 85 compared to not living near such a road-

way Because per-vehicle emissions are expected to decrease substan-

tially in the future such studies cannot be used as the basis for

predicting the effects of road traf 1047297c pollution on populations in the dis-

tant future We expect that future health impacts of living near busy

roads will decrease as vehicle emissions controls improve so including

the effects on disease prevalence also would not change the conclusion

that thefuture risks will be less than todays risks even if thenew cam-pus is built

6 Conclusions

This study developed an improved modeling approach for estimat-

ing the health impacts of traf 1047297c-related PM25 air pollution under alter-

native future urban development scenarios We then demonstrated the

approach by quantifying health impacts in a case study roadway corri-

dor that could be affected by a new UNC campus extension in Chapel

Hill The new approach accounts for the effects of variability in traf 1047297c

emissions factors and for seasonal variabilityin concentrationndashresponse

coef 1047297cients It also accounts for uncertainty in concentrationndashresponse

coef 1047297

cients and air quality model prediction error The approach could

serve as a model for future health impact assessments considering

traf 1047297c-related PM25

Comparisons to the conventional modeling approach used in other

quantitative HIAs revealed that those HIAs could under-estimate poten-

tial health burdens by failing to consider variability and uncertainty in

input variables used to generate the health impact estimates Our anal-

ysis showed that in the case study corridor the conventional approach

under-predicted health impacts by a factor of 4 to 9 depending on the

health endpoint As such the conventional HIA approach could in

some circumstances lead to decisions that are not cost effective andor

are not suf 1047297ciently protective of public health

This analysis also showed that a 1047297ne-scale approach that quanti1047297es

impacts over a small grid (in this case US census blocks) accountingfor demographic variability in each grid cell along with the previously

mentioned variability and uncertainty in model inputs can be useful

for identifying health disparities For example this analysis reveals

that the neighborhood in the study area with the highest health burden

also has a very high minority population compared to that in the rest of

the study area In addition this method of accounting for demographic

variability can be used to analyze differences in risks among age and

gender groups It could be extended to analyze impacts among popula-

tions with pre-existinghealth conditions andoramong groups with dif-

ferent income levels and educational attainment levels as suggested in

a recent analysis of distributional effects of air quality policies by Fann

et al (Fann et al 2011) An analysis of distributional effects would re-

quire data on health outcomes educational attainment and income at

the census block level such data were not available for this analysis

Fig 7 Cumulative distribution functions of rank-order correlations betweenmodelinputvariablesand thepredictedrisk of CVDmortalityby season forthe census blocks in thecase study

roadway corridor A highrank-ordercorrelation indicates that thevariable has a strong in1047298uence on theuncertainty in the estimatedrisk so reducinguncertainty in thevariable will sub-

stantially reduce uncertainty in the estimated risk The cumulative distribution functions show the variability in these effects by spatial location (ie by census block) and season


7232019 1-s20-S0048969714016027-mainpdf


but potentially could be obtained through special requests to thecounty

health department and US Census Bureau

Overall the annual mortality risks of traf 1047297c-related PM25 from the

proposed new campus are very low (less than 1 times 10minus8) even for the

most-exposed populations Nonetheless it is important to recognize

that we consider only onetypeof traf 1047297c-related pollutant and one road-

way corridor Risks would be higher if including all roadways affected

by traf 1047297c from the new campus and all traf 1047297c-related pollutants Fur-

thermore it is important to keep in mindthe manyother sources of am-bient air pollution exposure in the study area and the cumulative effects

of multiple exposures Taking steps to reduce traf 1047297c from the new cam-

pus (eg increasing the frequency of public transit service encouraging

carpooling charging for parking and other steps) will reduce air pollu-

tion exposures and produce bene1047297ts beyond those along the single

roadway considered in this case study

Overall this work highlights the sensitivity of traf 1047297c-related health

impact assessments to uncertainty and variability in concentrationndashre-

sponse coef 1047297cients air quality model prediction accuracy and traf 1047297c

emissions factors Future HIAs should account for these in1047298uential vari-

ability and uncertainty sources

References

Aunan K Exposurendashresponse functions for health effects of air pollutants based on epide-miological 1047297ndings Risk Anal Oct 199616(5)693ndash709

Bell ML Ebisu K Peng RDWalkerJ Samet JM ZegerSL Dominici F Seasonaland regionalshort-term effects of 1047297ne particles on hospital admissions in 202 US counties 1999ndash2005 Am J Epidemiol Dec 2008168(11)1301ndash10

Bhatia R Corburn J Lessons from San Francisco health impact assessments have ad-vanced political conditions for improving population health Health Aff (Millwood)Dec 201130(12)2410ndash8

Bhatia R Seto E Quantitative estimation in health impact assessment opportunities andchallenges Environ Impact Assess Rev Apr 201131(3)301ndash9

Centers for Disease Control and P revention CDC WONDER 2013Chart-asa C Sexton KG Macdonald Gibson J Traf 1047297c impacts on 1047297ne particulate matter air

pollution at the urban project scale a quantitative assessment J Environ Prot (IrvineCalif) December 20134

Cohen AJRoss AndersonH Ostro B Pandey KD Krzyzanowski M Kuumlnzli N GutschmidtKPope A Romieu I Samet JM Smith K The global burden of disease due to outdoor airpollution J Toxicol Environ Health A 200568(13ndash14)1301ndash7

Dannenberg AL Bhatia R Cole BL Heaton SK Feldman JD Rutt CD Use of health impactassessment in the US 27 case studies 1999ndash2007 Am J Prev Med Mar 200834

(3)241ndash56EPA Risk Assessment Guidance for Superfund (part A) 1989 [Washington DC]FannN Lamson AD Anenberg SC Wesson K Risley D HubbellBJ Estimating the national

public health burden associated with exposure to ambientPM25 and ozone RiskAnal Jan 201232(1)81ndash95

Fann N Roman HA Fulcher CM Gentile MA Hubbell BJ Wesson K Levy JI Maximizinghealth bene1047297ts and minimizing inequality incorporating local-scale data in the de-sign and evaluation of air quality policies Risk Anal Jun 201131(6)908ndash22

Federal Highway Administration Economic analysis primer bene1047297tndashcost analysis 2003[Washington DC]

Frey HC Burmaster DE Methods for characterizing variability and uncertainty compari-son of bootstrap simulation and likelihood-based approaches Risk Anal Feb 199919(1)109ndash30

Human Impact Partners Pittsburg Railroad Avenue speci1047297c plan health impact assess-ment 2008 [Oakland CA]

Human Impact Partners Pathways to community health evaluating the healthfulness of affordable housing opportunity sites along the San Pablo Avenue Corridor usinghealth impact assessment 2009 [Oakland CA]

Janssen NAH Hoek G Simic-lawson M Fischer P Van Bree L Brink H Keuken M AtkinsonRW Anderson HR Cassee FR Van Bree L Black carbon as an additional indicator of

the adverse health effects of airborne particles Environ Health Perspect 201119(12)1691ndash9

Li Y Gibson JM Jat P Puggioni G Hasan M West JJ Vizuete W Sexton K Serre M Burdenof disease attributed to anthropogenic air pollution in the United Arab Emirates es-timates based on observed air quality data Sci Total Environ Nov 2010408(23)5784ndash93

Mathers C Vos T Lopez A Salomon JEzzati M National burdenof diseasestudies a prac-tical guide Edition 20 Global program on evidence for health policy 2001 [GenevaSwitzerland]

Minnesota Population Center National Historical Geographic Information System ver-sion 20 Minneapolis MN University of Minnesota 2011

Morgan MG Henrion M Small M Uncertainty a guide to dealing with uncertainty inquantitative risk and policy analysis Cambridge New York Cambridge UniversityPress 1990 p 332

Murray CJ Ezzati M Lopez AD Rodgers A Vander Hoorn S Comparative quanti1047297cation of health risks conceptual framework and methodological issues Popul Health Metr20031(1)1

National Research Council Improving health in the United States the role of health im-pact assessment Washington DC National Academy Press 2011

NCDC Quality Controlled Local Climatological Data (QCLCD) 2013NOAA NOAAESRL Radiosonde Database 2013North Carolina Capital Area Metropolitan Planning Organization Socio-economic demo-

graphic forecasts shape1047297le 2030 Long Range Transportation Plan 2005North Carolina State Center for Health Statistics Detailed mortality statistics 2010 2012Ostro B Outdoor air pollution assessing the environmental burden of disease at national

and local levels no 5 Geneva WHO 2004Ostro B ChestnutL Assessing the health bene1047297ts of reducingparticulate matter air pollu-

tion in the United States Environ Res 199876(2)94ndash106Perez L Declercq C Intildeiguez C Aguilera I Badaloni C Ballester F Bouland C Chanel O

Cirarda FB Forastiere F Forsberg B Haluza D Hedlund B Cambra K Lacasantildea M

Moshammer H Otorepec P Rodriacuteguez-Barranco M Medina S Kuumlnzli N Chronic bur-den of near-roadway traf 1047297c pollution in 10 Europeancities(APHEKOM network) EurRespir J Sep 201342(3)594ndash605

Pruumlss-uumlstuumln A Mathers CD Corvalan C Woodward A Introduction and methodsassessing the environmental burden of disease at national and local levels 2003[Geneva]

Ross CL Elliott ML Rushing MM Barringer J Cox S Frackelton A Kent J Rao AAerotropolis Atlanta Brown1047297eld redevelopment health impact assessment Vol IAtlanta Center for Quality Growth and Regional Development at the GeorgiaInstitute of Technology 2011 p 169

Singleton-Baldrey L The impacts of health impact assessment a review of 54 health im-pact assessments 2007ndash2012 University of North Carolina at Chapel Hill 2012

University of North Carolina at Chapel Hill The UNC Department of Emergency MedicineCarolina Center for Health Informatics report overview and analysis of NC DETECTemergency department data 2009 2011 [Chapel Hill NC]

UC Berkeley Health Impact Group Oak to Ninth Avenue health impact assessment 2006[Berkeley CA]

UC Berkeley Health Impact Group MacArthur BART health impact assessment 2007[Berkeley CA]

UC BerkeleyHealthImpactGroup Health impact assessment of the Portof Oakland 2010[Berkeley CA]

US Environmental Protection Agency Risk Assessment Guidance for Superfund (RAGS)volume III mdash part A process for conducting probabilistic risk assessment 2001[Washington DC]

US Environmental Protection Agency Quantitative health risk assessment for particulatematter EPA-452R-10-005 NC Research Triangle Park 2010


Vanasse Hangen BrustlinInc Transportation impact analysis forthe CarolinaNorth devel-opment 2009 [Watertown MA]

Wernham A Health impact assessments are needed in decision making about environ-mental and land-use policy Health Aff (Millwood) May 201130(5)947ndash56

YuraEA Kear T Niemeier D Using CALINE dispersion to assess vehicular PM25 emissionsAtmos Environ Dec 200741(38)8747ndash57

Zanobetti A Schwartz J Theeffectof 1047297ne andcoarseparticulate airpollution on mortalitya national analysis Environ Health Perspect 2009117(6)898ndash903


7232019 1-s20-S0048969714016027-mainpdf


To facilitate comparison of alternatives and guide decision-making

HIAs ideally would provide quantitative estimates of the health out-

comes of the decision options under consideration That is they would

estimate the number of deaths and illnesses prevented or caused by

each alternative This information could be used to quantify the health

costs (positive or negative) of each alternative Quanti1047297cation can pres-

ent health impacts more concisely (as numerical summaries) than

lengthy qualitative discussions In addition (rightly or wrongly) quanti-

tative assessment can lend legitimacy to the analysis Furthermoresome federal and state regulations require quantitative costndashbene1047297t

analyses (Federal Highway Administration 2003) However only 5 of

the 70 US HIAs focusing on local or regional transportation projects

carried out prior to 2013 quanti1047297ed the expected health impacts

(Singleton-Baldrey 2012 Bhatia and Seto 2011) Table 1 summarizes

these HIAs The remaining HIAs expressed qualitative conclusions

The Aerotropolis Atlanta Brown 1047297eld Redevelopment HIA (Ross et al

2011) illustrates the qualitative approach used by most previous local

and regional US HIAs This HIA evaluated a plan to convert a former

Ford assembly plant near Atlanta Georgia to a new community called

ldquoAerotropolis Atlantardquo The HIAs analysis of air quality impacts was

based on a review of previous studies (not associated with this project)

of traf 1047297c impacts on air quality and health It concluded ldquoAerotropolis

may lead to a change in traf 1047297c volume around the site hellip potentially

impacting people who live work or visit within the air-shed of the af-

fected streetsrdquo The HIA recommended several mitigation measures in-

cluding congestion pricing increased public transit zoning of sensitive

uses away from roadways and vegetation buffers around roadways

However the HIA did not quantify the air quality or health impacts of

the proposed new development or these mitigation alternatives

While the above-mentioned 1047297ve previous quantitative HIAs esti-

mate the magnitude of air quality and related health impacts none con-

siders the potential variability and uncertainty of the estimates Rather

these HIAs each provide a single deterministic prediction of health im-

pacts for each decision option (see Table 1) In so doing these HIAs not

only convey a potentially misleading degree of certainty but also ne-

glect to provide decision-makers with information about the plausible

range of impacts US Environmental Protection Agency guidance

documents indicate that health risk assessments of national and statepolicies should include sensitivity and uncertainty analyses (US

EnvironmentalProtection Agency 2001) Indeed sensitivity and uncer-

tainty analyses are cornerstones of health impact estimates the agency

prepares to inform national policy decisions such as changes to air pol-

lution standards (US Environmental Protection Agency 2010) None-

theless current US local-level HIAs do not report variability and

uncertainty in their health impact estimates

The reliance of local HIA practitioners on deterministic estimates is a

major limitation for several reasons First it fails to consider the full

range of potential risksmdashthat is the potential for risks at the tails of

the risk distribution For example vulnerable populations are often at

the upper tails not the centers of the exposure and effect distributions

(Fann et al 2011) Second risk estimates relying only on central ten-

dencies of each input variable may differ from those considering the

full distributions of each input variable Except in special cases the ex-

pected value of a function of random variables is not the same as the

function applied to the expected values of each variable Third deter-

ministic approaches ignore the potential dependencies among model

inputvariables (for example dependencies in meteorological character-

istics used to estimate pollutant dispersion) Fourth deterministic

Table 1

Previous quantitative transportation-related HIAs in the United States

Title Project scenario

analyzed

Traf 1047297c-related

air pollutants

considered

Study area

population

Estimated annual health impactsa

Pittsburg Railroad Avenue speci1047297c plan


Construction of new Bay Area Rapid

Transit (BART) station and mixed-usevillage in Pittsburg CA including 1600

housing units and 450000 sq ft of retail

commercial and public service spaces


β = 00046 (00034 00058)bull 5 hospital admissions for asthma (age b 65)


(00015 00036)



(00124 00241)

Evaluating the healthfulness of

affordable housing opportunity

sites along the San

Pablo Avenue Corridor using


Construction of affordable housing sites

in El Cerrito and Richmond CA

PM25 1000000 bull 33ndash41 deaths (all ages) from long-term exposures

RR = 1014 (no report of 95 con1047297dence interval)

Oak to Ninth Avenue HIA (UC Berkeley

Health Impact Group 2006)

Development of new waterfront community

in Oakland CA including 3100 housing units

and 200000 sq ft of retail commercial



β = 00046 (00034 00058)



bull 106 emergency room visits for asthma (age b 65)


(00024 00049)

MacArthur BART Transit Village HIA

(UC Berkeley Health Impact Group

2007)

Redevelopment of parking lot into a

mixed-use village in Oakland CA

including 625 housing units and

30000 sq ft of retail commercial



β = 00046 (00034 00058)



bull 342 acute bronchitis cases (ages 8-12) from

short-term exposures β = 00272 (00101 00443)

bull 01 hospital admissions for asthma (age b 65)


(00015 00036)


from short-term exposures β = 00182 (00124

00241)

Health impact assessment of the Port of

Oakland (UC Berkeley Health Impact

Group 2010)

Ongoing growth of port operations in West

Oakland CA


β = 00046 (00034 00058)

a

β = concentrationndashresponse coef 1047297cient used to estimate health impacts RR = relative risk used to estimate health impacts


7232019 1-s20-S0048969714016027-mainpdf





















































2 Case study site





































the study corridor)

































7232019 1-s20-S0048969714016027-mainpdf


























Table 2



2009 4ndash1758 16042






7232019 1-s20-S0048969714016027-mainpdf





























































7232019 1-s20-S0048969714016027-mainpdf













eth1THORN



















relationship


frac14 y0i jklmn

RRimnminus1

RRimn

3b

frac14 y0i jklmn



3c



3d









2003)







































cy department












census block















7232019 1-s20-S0048969714016027-mainpdf










































Table 3













Fall 096 (minus134 325)





Fall 017 (minus072 106)





Fall 014 (minus130 158)







35 to 44 017 000 000 000 095 000

45 to 54 029 272 000 013 153 000

55 to 64 179 235 257 091 105 000

65 to 74 343 725 000 244 000 000

75 to 84 1647 1724 000 770 2065 000

85+ 5251 2632 12500 3034 2299 000


55 to 64 049 000 000 045 105 000

65 to 74 206 483 000 183 345 000

75 to 84 524 575 116 495 590 000

85+ 3580 1316 000 787 1149 000


7232019 1-s20-S0048969714016027-mainpdf















































































(158) = 58 p = 14 times 10minus8















Table 5





Table 6



number

1a 1 b 2 3 4












x x x






7232019 1-s20-S0048969714016027-mainpdf



























































Table 7



blocks affecteda



blocks (μ gm3)b


CVD

mortality

CVD hospital

admissions

Respiratory

mortality


admissions







7232019 1-s20-S0048969714016027-mainpdf






































must be improved





5 Limitations







7232019 1-s20-S0048969714016027-mainpdf






































1989)













were derived


















con1047297



7232019 1-s20-S0048969714016027-mainpdf















6 Conclusions









coef 1047297
































7232019 1-s20-S0048969714016027-mainpdf





















References













































7232019 1-s20-S0048969714016027-mainpdf





















































2 Case study site





































the study corridor)

































7232019 1-s20-S0048969714016027-mainpdf


























Table 2



2009 4ndash1758 16042






7232019 1-s20-S0048969714016027-mainpdf





























































7232019 1-s20-S0048969714016027-mainpdf













eth1THORN



















relationship


frac14 y0i jklmn

RRimnminus1

RRimn

3b

frac14 y0i jklmn



3c



3d









2003)







































cy department












census block















7232019 1-s20-S0048969714016027-mainpdf










































Table 3













Fall 096 (minus134 325)





Fall 017 (minus072 106)





Fall 014 (minus130 158)







35 to 44 017 000 000 000 095 000

45 to 54 029 272 000 013 153 000

55 to 64 179 235 257 091 105 000

65 to 74 343 725 000 244 000 000

75 to 84 1647 1724 000 770 2065 000

85+ 5251 2632 12500 3034 2299 000


55 to 64 049 000 000 045 105 000

65 to 74 206 483 000 183 345 000

75 to 84 524 575 116 495 590 000

85+ 3580 1316 000 787 1149 000


7232019 1-s20-S0048969714016027-mainpdf















































































(158) = 58 p = 14 times 10minus8















Table 5





Table 6



number

1a 1 b 2 3 4












x x x






7232019 1-s20-S0048969714016027-mainpdf



























































Table 7



blocks affecteda



blocks (μ gm3)b


CVD

mortality

CVD hospital

admissions

Respiratory

mortality


admissions







7232019 1-s20-S0048969714016027-mainpdf






































must be improved





5 Limitations







7232019 1-s20-S0048969714016027-mainpdf






































1989)













were derived


















con1047297



7232019 1-s20-S0048969714016027-mainpdf















6 Conclusions









coef 1047297
































7232019 1-s20-S0048969714016027-mainpdf





















References













































7232019 1-s20-S0048969714016027-mainpdf


























Table 2



2009 4ndash1758 16042






7232019 1-s20-S0048969714016027-mainpdf





























































7232019 1-s20-S0048969714016027-mainpdf













eth1THORN



















relationship


frac14 y0i jklmn

RRimnminus1

RRimn

3b

frac14 y0i jklmn



3c



3d









2003)







































cy department












census block















7232019 1-s20-S0048969714016027-mainpdf










































Table 3













Fall 096 (minus134 325)





Fall 017 (minus072 106)





Fall 014 (minus130 158)







35 to 44 017 000 000 000 095 000

45 to 54 029 272 000 013 153 000

55 to 64 179 235 257 091 105 000

65 to 74 343 725 000 244 000 000

75 to 84 1647 1724 000 770 2065 000

85+ 5251 2632 12500 3034 2299 000


55 to 64 049 000 000 045 105 000

65 to 74 206 483 000 183 345 000

75 to 84 524 575 116 495 590 000

85+ 3580 1316 000 787 1149 000


7232019 1-s20-S0048969714016027-mainpdf















































































(158) = 58 p = 14 times 10minus8















Table 5





Table 6



number

1a 1 b 2 3 4












x x x






7232019 1-s20-S0048969714016027-mainpdf



























































Table 7



blocks affecteda



blocks (μ gm3)b


CVD

mortality

CVD hospital

admissions

Respiratory

mortality


admissions







7232019 1-s20-S0048969714016027-mainpdf






































must be improved





5 Limitations







7232019 1-s20-S0048969714016027-mainpdf






































1989)













were derived


















con1047297



7232019 1-s20-S0048969714016027-mainpdf















6 Conclusions









coef 1047297
































7232019 1-s20-S0048969714016027-mainpdf





















References













































7232019 1-s20-S0048969714016027-mainpdf





























































7232019 1-s20-S0048969714016027-mainpdf













eth1THORN



















relationship


frac14 y0i jklmn

RRimnminus1

RRimn

3b

frac14 y0i jklmn



3c



3d









2003)







































cy department












census block















7232019 1-s20-S0048969714016027-mainpdf










































Table 3













Fall 096 (minus134 325)





Fall 017 (minus072 106)





Fall 014 (minus130 158)







35 to 44 017 000 000 000 095 000

45 to 54 029 272 000 013 153 000

55 to 64 179 235 257 091 105 000

65 to 74 343 725 000 244 000 000

75 to 84 1647 1724 000 770 2065 000

85+ 5251 2632 12500 3034 2299 000


55 to 64 049 000 000 045 105 000

65 to 74 206 483 000 183 345 000

75 to 84 524 575 116 495 590 000

85+ 3580 1316 000 787 1149 000


7232019 1-s20-S0048969714016027-mainpdf















































































(158) = 58 p = 14 times 10minus8















Table 5





Table 6



number

1a 1 b 2 3 4












x x x






7232019 1-s20-S0048969714016027-mainpdf



























































Table 7



blocks affecteda



blocks (μ gm3)b


CVD

mortality

CVD hospital

admissions

Respiratory

mortality


admissions







7232019 1-s20-S0048969714016027-mainpdf






































must be improved





5 Limitations







7232019 1-s20-S0048969714016027-mainpdf






































1989)













were derived


















con1047297



7232019 1-s20-S0048969714016027-mainpdf















6 Conclusions









coef 1047297
































7232019 1-s20-S0048969714016027-mainpdf





















References













































7232019 1-s20-S0048969714016027-mainpdf













eth1THORN



















relationship


frac14 y0i jklmn

RRimnminus1

RRimn

3b

frac14 y0i jklmn



3c



3d









2003)







































cy department












census block















7232019 1-s20-S0048969714016027-mainpdf










































Table 3













Fall 096 (minus134 325)





Fall 017 (minus072 106)





Fall 014 (minus130 158)







35 to 44 017 000 000 000 095 000

45 to 54 029 272 000 013 153 000

55 to 64 179 235 257 091 105 000

65 to 74 343 725 000 244 000 000

75 to 84 1647 1724 000 770 2065 000

85+ 5251 2632 12500 3034 2299 000


55 to 64 049 000 000 045 105 000

65 to 74 206 483 000 183 345 000

75 to 84 524 575 116 495 590 000

85+ 3580 1316 000 787 1149 000


7232019 1-s20-S0048969714016027-mainpdf















































































(158) = 58 p = 14 times 10minus8















Table 5





Table 6



number

1a 1 b 2 3 4












x x x






7232019 1-s20-S0048969714016027-mainpdf



























































Table 7



blocks affecteda



blocks (μ gm3)b


CVD

mortality

CVD hospital

admissions

Respiratory

mortality


admissions







7232019 1-s20-S0048969714016027-mainpdf






































must be improved





5 Limitations







7232019 1-s20-S0048969714016027-mainpdf






































1989)













were derived


















con1047297



7232019 1-s20-S0048969714016027-mainpdf















6 Conclusions









coef 1047297
































7232019 1-s20-S0048969714016027-mainpdf





















References













































7232019 1-s20-S0048969714016027-mainpdf










































Table 3













Fall 096 (minus134 325)





Fall 017 (minus072 106)





Fall 014 (minus130 158)







35 to 44 017 000 000 000 095 000

45 to 54 029 272 000 013 153 000

55 to 64 179 235 257 091 105 000

65 to 74 343 725 000 244 000 000

75 to 84 1647 1724 000 770 2065 000

85+ 5251 2632 12500 3034 2299 000


55 to 64 049 000 000 045 105 000

65 to 74 206 483 000 183 345 000

75 to 84 524 575 116 495 590 000

85+ 3580 1316 000 787 1149 000


7232019 1-s20-S0048969714016027-mainpdf















































































(158) = 58 p = 14 times 10minus8















Table 5





Table 6



number

1a 1 b 2 3 4












x x x






7232019 1-s20-S0048969714016027-mainpdf



























































Table 7



blocks affecteda



blocks (μ gm3)b


CVD

mortality

CVD hospital

admissions

Respiratory

mortality


admissions







7232019 1-s20-S0048969714016027-mainpdf






































must be improved





5 Limitations







7232019 1-s20-S0048969714016027-mainpdf






































1989)













were derived


















con1047297



7232019 1-s20-S0048969714016027-mainpdf















6 Conclusions









coef 1047297
































7232019 1-s20-S0048969714016027-mainpdf





















References













































7232019 1-s20-S0048969714016027-mainpdf















































































(158) = 58 p = 14 times 10minus8















Table 5





Table 6



number

1a 1 b 2 3 4












x x x






7232019 1-s20-S0048969714016027-mainpdf



























































Table 7



blocks affecteda



blocks (μ gm3)b


CVD

mortality

CVD hospital

admissions

Respiratory

mortality


admissions







7232019 1-s20-S0048969714016027-mainpdf






































must be improved





5 Limitations







7232019 1-s20-S0048969714016027-mainpdf






































1989)













were derived


















con1047297



7232019 1-s20-S0048969714016027-mainpdf















6 Conclusions









coef 1047297
































7232019 1-s20-S0048969714016027-mainpdf





















References













































7232019 1-s20-S0048969714016027-mainpdf



























































Table 7



blocks affecteda



blocks (μ gm3)b


CVD

mortality

CVD hospital

admissions

Respiratory

mortality


admissions







7232019 1-s20-S0048969714016027-mainpdf






































must be improved





5 Limitations







7232019 1-s20-S0048969714016027-mainpdf






































1989)













were derived


















con1047297



7232019 1-s20-S0048969714016027-mainpdf















6 Conclusions









coef 1047297
































7232019 1-s20-S0048969714016027-mainpdf





















References













































7232019 1-s20-S0048969714016027-mainpdf






































must be improved





5 Limitations







7232019 1-s20-S0048969714016027-mainpdf






































1989)













were derived


















con1047297



7232019 1-s20-S0048969714016027-mainpdf















6 Conclusions









coef 1047297
































7232019 1-s20-S0048969714016027-mainpdf





















References













































7232019 1-s20-S0048969714016027-mainpdf






































1989)













were derived


















con1047297



7232019 1-s20-S0048969714016027-mainpdf















6 Conclusions









coef 1047297
































7232019 1-s20-S0048969714016027-mainpdf





















References













































7232019 1-s20-S0048969714016027-mainpdf















6 Conclusions









coef 1047297
































7232019 1-s20-S0048969714016027-mainpdf





















References













































7232019 1-s20-S0048969714016027-mainpdf





















References













































1-s2.0-S0048969714016027-main.pdf

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Transcript of 1-s2.0-S0048969714016027-main.pdf