Residential Traffic Exposure and

Childhood Leukemia

A Systematic Review and Meta-analysis

Vickie L. Boothe, MPH, Tegan K. Boehmer, PhD, MPH, Arthur M. Wendel, MD, MPH,

Fuyuen Y. Yip, PhD, MPH

Context: Exposure to elevated concentrations of traffic-related air pollutants in the near-road

environment is associated with numerous adverse human health effects, including childhood cancer,

which has been increasing since 1975. Results of individual epidemiologic studies have been

inconsistent. Therefore, a meta-analysis was performed to examine the association between

residential traffic exposure and childhood cancer.

Evidence acquisition: Studies published between January 1980 and July 2011 were retrieved from

a systematic search of 18 bibliographic databases. Nine studies meeting the inclusion criteria were

identified. Weighted summary ORs were calculated using a random effects model for outcomes with

four or more studies. Subgroup and sensitivity analyses were performed.

Evidence synthesis: Childhood leukemia was positively associated (summary OR1⁄41.53, 95%

CI1⁄41.12, 2.10) with residential traffic exposure among seven studies using a postnatal exposure

window (e.g., childhood period or diagnosis address) and there was no association (summary

OR1⁄40.92, 95% CI1⁄40.78, 1.09) among four studies using a prenatal exposure window (e.g.,

pregnancy period or birth address). There were too few studies to analyze other childhood cancer

outcomes.

Conclusions: Current evidence suggests that childhood leukemia is associated with residential

traffic exposure during the postnatal period, but not during the prenatal period. Additional welldesigned epidemiologic studies that use complete residential history to estimate traffic exposure,

examine leukemia subtypes, and control for potential confounding factors are needed to confirm

these findings. As many people reside near busy roads, especially in urban areas, precautionary

public health messages and interventions designed to reduce population exposure to traffic might be

warranted.

(Am J Prev Med 2014;46(4):413–422) Published by Elsevier Inc. on behalf of American Journal of Preventive

Medicine

Introduction

I

n recent years, public health concern has grown

regarding population exposure to traffic-related air

pollutants and adverse human health effects.

Numerous studies have documented that concentrations

of traffic-related air pollutants are highest in the nearroad environment.1–4 In North America, an estimated

30%–45% of people in large urban areas live near major

roads,5 suggesting increased exposure to traffic-related

air pollution and risk of adverse health outcomes.

Epidemiologic studies of health effects associated with

residential traffic exposure have used varied exposure

assessment methods. One approach uses monitored or

modeled concentrations of specific traffic-related air

pollutants to investigate the potential causal agent(s)

associated with health outcomes.6 Exposure assessments

based on monitoring data do not reflect local variation in

pollutant concentrations and are limited to areas with air

pollution monitors.7 Assessments based on modeling can

capture local variations but are computationally complex,

From the Office of Public Health Scientific Services, Division of Epidemiology,

Analysis, and Library Services, Analytic Tools and Methods Branch (Boothe),

National Center for Environmental Health, Division of Environmental

Hazards and Health Effects, Air Pollution and Respiratory Health Branch

(Boehmer, Yip), and National Center for Environmental Health, Division of

Emergency and Environmental Health Services, Healthy Community Design

Initiative (Wendel), CDC, Atlanta, Georgia

Address correspondence to: Vickie L. Boothe, MPH, CDC, 1600 Clifton

Rd NE, MS E-33, Atlanta GA 30333. E-mail:veb6@cdc.gov.

0749-3797/$36.00

http://dx.doi.org/10.1016/j.amepre.2013.11.004

Published by Elsevier Inc. on behalf of American Journal of Preventive Medicine Am J Prev Med 2014;46(4):413–422 413
requiring detailed data that might not be readily available.7 A common alternative approach uses direct

measures of traffic, commonly referred to as “traffic

proximity measures,” which incorporate both distance to

roads and indicators of traffic density (e.g., distance to a

major road and traffic density within a buffer). Traffic

proximity measures capture the overall mixture of

tailpipe emissions; fugitive emissions from brake, tire,

and roadway wear; and other factors, including noise.

A large, growing body of scientific research has shown an

association between residential traffic proximity measures and adverse health outcomes, including asthma,

respiratory symptoms, and lung function8–12; cardiovascular disease13–15; adverse reproductive outcomes16,17

;

and premature mortality.18–20 Studies of traffic exposure

and childhood cancer have provided inconclusive

results.5

The incidence of childhood cancer in the U.S. has

been increasing since 1975.21 The most common form of

childhood cancer is leukemia, which represents approximately one third of all cancers among children aged

0–14 years.22 By subtype, acute lymphoblastic leukemia

(ALL) accounts for 79% of childhood leukemia cases,

followed by acute myeloblastic leukemia (AML) and

other rarer types.22 Although ALL can occur throughout

one’s life span, the median age of diagnosis is 14 years

with peak incidence occurring between age 2 and

14 years.23,24

The etiologic mechanism is unknown for approximately 90% of childhood leukemia cases.25 Much of this

uncertainty results from potential multifactorial etiologies and complex gene–environment interactions.26

Established risk factors for childhood leukemia include

age, gender, race/ethnicity, prenatal exposure to x-rays,

exposure to therapeutic radiation and chemotherapeutic

agents, and specific genetic syndromes.23,26 Other potential risk factors include exposure to benzene and polyaromatic hydrocarbons (PAHs), which are components

of traffic emissions.26–28 The role of SES as a risk factor

for childhood leukemia is controversial, and some

investigators recommend that SES be examined as a

potential confounder in future epidemiologic studies.26

Meta-analyses of observational studies can summarize

existing evidence and inform future epidemiologic and

mechanistic research by exploring the potential reasons

for heterogeneity among the included studies.29 This systematic review and meta-analysis is part of a larger effort

by the CDC to synthesize the growing body of evidence

on the association between residential traffic exposure

and numerous health outcomes, without any prior judgment of the specific pollutants or related factors. This

specific study examines the association between residential traffic exposure and childhood cancer.

Findings and Recommendations from Other

Reviews

No meta-analysis has been published to date of studies

assessing the association between residential traffic exposure and childhood cancer. In 2010, the Health Effects

Institute (HEI) issued a Special Report that summarized

and synthesized information on the health effects of trafficrelated air pollution.5 On the basis of a review of five

childhood cancer studies, the HEI report concluded that the

evidence was “inadequate and insufficient” to make inferences for causality between exposure to traffic pollution and

any childhood cancer, including childhood leukemia.5

Methods

Literature Search

A comprehensive literature search was performed to identify studies

examining the association between residential traffic exposure and

any health outcome, including childhood cancer. Electronic searches

were conducted in 18 bibliographic databases: MEDLINEþ,

Embase, PsycINFO, Cochrane, Eric, Sociological Abstracts, Social

Services Abstracts, Health and Safety Sciences Abstracts, CINAHL,

EconLit, Web of Science, Transportation Research Information

Services (TRIS), Global Health, Science Direct, LILACS, Enviroline,

Dissertation Abstracts, and Pollution Abstracts. A search strategy

was created for use in MEDLINEþ and was adapted to fit the other

databases. It included MeSH terms and key words representing

three constructs that were combined using the “AND” operator as

follows: 1) “health” (which included general terms such as disease,

illness, and mortality, in addition to outcome-specific terms such as

cancer, neoplasm, and leukemia), 2) “vehicle emissions” (which

included terms for pollution, emissions, and exhaust combined with

terms for traffic, vehicles, and roads), and 3) “exposure” (e.g.,

proximity, distance, density, and intensity). The electronic search

was limited to English-language articles published and indexed from

January 1980 through July 2011.

Inclusion Criteria

This systematic review included peer-reviewed journal articles,

abstracts, scientific reports, and dissertations. For inclusion in the

overall review, studies had to (1) be an original study; (2) use an

individual-level analytic design with a control group (i.e., crosssectional, case-control, or cohort design); (3) use a traffic exposure

measure based on the distance to roads and traffic density (i.e., not

measured or modeled concentrations of specific traffic-related

pollutants); (4) assess traffic exposure at the residential address

(i.e., not the postal code or census tract level); (5) provide or be able

to compute an effect size that estimates the association between

residential traffic exposure and a health outcome (e.g., OR, relative

risk [RR], standardized incidence ratio [SIR]); and (6) be conducted

in a “high-income economy” country, as designated by the World

Bank.30 This specific review is limited to studies of childhood cancer.

Study Selection and Data Extraction

All citations were independently screened for inclusion by two

coders in three sequential steps: title, abstract, and full text. For

414 Boothe et al / Am J Prev Med 2014;46(4):413–422

www.ajpmonline.org
each included citation, data extraction was performed independently by two coders, who were blinded to journal and authors’

names and affiliations, using a standard form that included

information on: study design, health outcome assessment, traffic

exposure assessment, participant characteristics, statistical analysis, and effect estimates and 95% CIs. This information was entered

into a Microsoft Access database and discrepancies were resolved

by the two coders.

Study quality was assessed using a subset of the extracted

information. A study quality scale was developed using elements of

existing scales31–33 and methodological factors specific to this

review (e.g., type of observational study design, quality of traffic

exposure assessment, and quality of health outcome assessment).

The scale included 17 items with a maximum possible score of 40.

Studies were categorized as either high or low quality for subgroup

analyses.

From each study, one effect estimate per health outcome was

selected based on the following independent considerations: it (1)

represented the longest exposure window (e.g., childhood period

preferred over pregnancy period or birth address); (2) best

characterized traffic exposure (e.g., multiple road metric preferred

over single road metric); (3) compared the highest to the lowest

exposure category; (4) addressed confounding by sociodemographic and behavioral factors (e.g., adjusted OR preferred over

crude OR); and (5) was not adjusted for measured or modeled

concentrations of traffic-related air pollutants (i.e., to avoid

possible overadjustment).

Statistical Analysis

A meta-analysis was conducted for childhood cancer outcomes

with four or more independent effect estimates. All but one of

the included studies quantified the association between traffic

exposure and childhood cancer using ORs. Weighted summary

ORs and 95% CIs were calculated using a random-effects model

to provide an overall estimate of the strength of the association

between residential traffic exposure and each childhood cancer

outcome. The random-effects model was chosen a priori, as

exposure metrics, populations, and contexts were expected to

vary substantially between studies. The analysis plan included

sensitivity and subgroup analyses and assessment of heterogeneity and publication bias. All statistical analyses were conducted using Comprehensive Meta-Analysis (version 2.2.055).34

The software will not calculate the SE if the 95% CIs are not

symmetric on a log scale, which can occur as a result of

rounding. In these situations, the single confidence limit (upper

or lower) that conservatively resulted in a larger estimated SE

was selected.

Sensitivity Analyses

A one-study-removed sensitivity analysis was conducted to

determine whether any individual study overly influenced the

summary OR.35 Sensitivity analyses were conducted to assess the

effect of potential outliers and inclusion of other effect estimates on

the findings.

Heterogeneity

Heterogeneity was assessed using the Q test36 and I

2 statistic.37 The

I

2 value, which can range from 0% to 100%, reflects the proportion

of variability in the summary estimate that can be attributed to

between-study heterogeneity; values of 25%, 50%, and 75% were

considered low, moderate, and high, respectively.38

Subgroup Analyses

To examine the role of potential effect modifiers and explore

heterogeneity sources, subgroup analyses were conducted on

variables with a minimum of two studies per subgroup. Subgroup

weighted summary ORs were calculated using a random-effects

model with a pooled estimate of tau-squared. A Q-test based on

ANOVA was used to compare summary effects between subgroups.

I

2 was calculated for each subgroup. The following study characteristics were considered for subgroup analyses: study location, study

time period, age of the study population, source of controls, type of

exposure metric, timing of exposure assessment, cancer type, control

for SES, and study quality. A study was considered to control for SES

if the results were adjusted for income, occupation, or education

level of either parent or the household, or if the authors reported that

adjustment for these factors had little effect on their findings.

Publication Bias

Publication bias was evaluated by visually inspecting a funnel plot39

and conducting the Begg and Mazumdar rank correlation test,40

Egger’s test of the intercept,41 and Orwin’s fail-safe N analysis.42

For the Orwin’s fail-safe N analysis, the trivial OR was set at 1.10

and the mean OR for the missing studies was assumed to be null

or 1.00.

Results

Evidence Synthesis

The literature search yielded more than 17,500 citations

that were screened for inclusion. Eleven citations that

met the inclusion criteria had childhood cancer as an

outcome.43–53 After the studies were extracted and

unblinded, two of the citations were found to have

examined the same, or a subset of the same, study

population as another included study. In the case of

Reynolds et al. 200148 and 2004,49 the later citation was

retained because it reported on the full study population;

the earlier citation reported on a pilot study among a

population subset. In the case of Savitz et al.50 and

Pearson et al.,46 the latter citation conducted an independent reanalysis of the Savitz et al. study population

using a slightly different exposure metric. Savitz et al. was

retained because it reported a smaller, more conservative

effect estimate and provided additional study details that

resulted in a higher quality score.

Among the nine unique childhood cancer studies,

eight case-control studies reported ORs and one

population-based study reported SIRs as the effect

measure. The population-based study by Visser et al.52

calculated SIRs for all cancer sites, all hematologic

malignancies, and ALL among children. All eight casecontrol studies examined childhood leukemia (i.e.,

Boothe et al / Am J Prev Med 2014;46(4):413–422 415

April 2014
leukemia, acute leukemia, or ALL) and three of eight

studies examined other childhood cancer outcomes (i.e.,

all cancer sites, lymphomas, central nervous system

tumors, brain tumors, and soft tissue tumors)

(Table 1). A meta-analysis of the eight childhood

leukemia case-control studies was conducted; inclusion

of the population-based study was examined in the

sensitivity analysis. There was an insufficient number

of studies (ofour) to conduct meta-analyses for the

other childhood cancer outcomes.

Descriptive Results

The characteristics of the eight case-control childhood

leukemia studies are summarized in Table 1. Seven

studies used population-based controls and Steffen

et al.51 used hospital-based controls. Four studies were

conducted in the U.S.45,49,50,53 and four were conducted

in Europe.43,44,47,51 The study time frame (i.e., year of

diagnosis) ranged from 1968 to 2004. Six studies included

children aged 0–14 years at diagnosis; Langholz et al.45

included children aged 0–10 years and Reynolds et al.49

included children aged 0–4 years.

A variety of traffic exposure measures were used

across the eight studies; however, each study used only

one traffic exposure measure that met our inclusion

criteria. Three studies45,49,53 used “multiple road measures” (e.g., cumulative traffic density within a 500-ft

radius or distance-weighted traffic density within a

1500-ft buffer) and five studies43,44,47,50,51 used “single

road” measures (e.g., distance to the nearest major road

or traffic density on the street of residence), of which

Steffen et al.51 was based on self-reports. In addition, the

studies assessed traffic exposure at various time points

and periods, representing different exposure windows.

Five of the studies assessed exposure using a single

residential location such as address at the time of

birth,49 at the time of diagnosis,43,44,50 and of the longest

duration between birth and diagnosis.45 Von Behren

et al.53 presented results for three exposure windows:

birth address, diagnosis address, and time-weighted

lifetime average (i.e., childhood period). RasschouNielsen et al.47 and Steffen et al.51 each examined two

period-based exposure windows, the pregnancy and

childhood periods. All eight studies addressed potential

confounding by known individual risk factors of age

and gender, either through matching or statistical adjustment. Three studies addressed potential confounding by SES through statistical adjustment for income,

occupation, or education.43,50,53 Savitz et al.50 presented

crude ORs and reported qualitatively that adjustment for a number of covariates, including gender,

age, and father’s education level, had little effect on their

findings.

Meta-analysis Results

The results of the initial meta-analysis of eight casecontrol studies indicated a positive association between

childhood leukemia and residential traffic exposure

(OR1⁄41.39, 95% CI1⁄41.03, 1.88) with a moderate to high

degree of heterogeneity (Q1⁄416.42, df1⁄47, p1⁄40.02,

I

2

1⁄457.4%). Examination of potential sources of heterogeneity determined that a meaningful difference in the

effect estimate existed by exposure window. Thus, separate

meta-analyses were conducted for two exposure windows.

The exposure windows were categorized as “prenatal” (i.e.,

pregnancy period or birth address) and “postnatal” (i.e.,

childhood period, diagnosis address, or address of longest

duration). The three studies that analyzed more than one

exposure window within the same study population were

included in both meta-analyses.

Postnatal exposure window. The weighted summary

OR was 1.53 (95% CI1⁄41.12, 2.10) for the seven studies

that used a postnatal exposure window to assess residential traffic exposure. Figure 1 shows a forest plot of the

OR and 95% CI from each study and the weighted

summary OR and 95% CI. In four instances in which

rounding by the study authors resulted in an asymmetric

95% CI, the lower43,45 or upper47,51 CI that conservatively

resulted in a larger estimated SE was selected. There was a

low-to-moderate degree of heterogeneity in the effect

estimates across the seven studies (Q=9.64, df=6, p=0.14,

I

2

=37.8%).

In the subgroup analyses, no statistically significant

differences were observed in the subgroup summary ORs

by study location, study time period, type of exposure

metric, cancer type, control for SES, or quality score

(Table 2). For each variable, the within-subgroup heterogeneity was moderate (I

2

1⁄442%–62%) for the subgroup

that contained Savitz et al.50 and low (I

2

1⁄40%–15%) for

the other subgroup. Too few studies per subgroup existed

to conduct subgroup analyses by other study characteristics (e.g., age of study population or source of controls).

In the one-study-removed sensitivity analysis, the weighted summary OR remained statistically significant in all

instances, ranging from 1.33 (95% CI1⁄41.05, 1.69) when

Savitz et al.50 was removed to 1.71 (95% CI1⁄41.20, 2.42)

when Steffen et al.51 was removed. In addition, removal of

Savitz et al. eliminated all evidence of heterogeneity among

the remaining six studies (I

2 decreased from 37.8% to 0%),

suggesting that this study’s large effect estimate (OR1⁄44.7)

could be an outlier. Thus, further sensitivity analyses with

this study were conducted. Using a smaller, more conservative OR from Savitz et al. (i.e., OR1⁄42.7 for exposure to

Z5000 vehicles per day vs o500 vehicles per day) slightly

reduced the summary OR from 1.53 to 1.48 (95% CI1⁄41.13,

1.92) and decreased heterogeneity among the seven studies

416 Boothe et al / Am J Prev Med 2014;46(4):413–422

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Table 1. Characteristics of included case-control studies examining the association between residential traffic exposure and child

Reference

Study

location and

time frame Cancer types

Number

of

leukemia

cases/

controls

Age of

subjects

(years)

Exposure

measure

type

Exposure

window

Traffic exposure

categories (high vs low)

Amigou

et al.,201141

France;

2003–2004

Acute leukemia

(ALL andANLLsubtypes)

763/1681 0–14 Single road

(proximityto majorroad)

Diagnosis

address

o500 m from both Class

1 and Class 2 roads vs4500 m from anyClass 1, 2, or 3 roadsb

Crosignani

et al.,200442

Italy; 1978–

1997

Leukemia 120/480 0–14 Single road

(proximityto majorroad)

Diagnosis

address

o20 m to road with

410,000 vpd vs4150 m to road witho10,000 vpd

Langholz

et al.,200243

U.S. (Los

AngelesCounty,California);1978–1984

Leukemia 212/202 0–10 Multiple

road(DWTDwithin1500-ftradius)

Residence

of longestduration

Approximately Z28,000

vpd vs o2,000 vpd

RaaschouNielsen

et al.,200145

Denmark;

1968–1991

Leukemia; CNS;

lymphoma; 3typescombined

986/1972 0–14 Single road

(traffic

density onstreet ofresidence)

Pregnancy

period;childhoodperiodd

Z10,000 vpd vs o500

vpd

Reynolds

et al.,200447

U.S.

(California);1988–1997

All cancer sites;

leukemia;CNS

1728/3456 0–4 Multiple

road(traffic

densitywithin500-ftradius)

Birth

address

Z236,000 VMT/mi2 vs

0 VMT/mi2 (no major

road within 500 ft)

Savitz

et al.,198948

U.S. (Denver

Colorado);1976–1983

All caner sites;

leukemia;lymphoma;brain; softtissue; other

98/262 0–14 Single road

(traffic

density onstreet ofresidence)

Diagnosis

address

Z10,000 vpd vs o500

vpd

Steffen

et al.,200449

France;

1995–1999

Acute leukemia 280/285 0–14 Single road

(selfreportproximityto majorroad)

Pregnancy

period;childhoodperiodf

o50 m from heavy

traffic road vs not

April 2014
(I

2

1⁄420.8%). These sensitivity analyses suggest that

no single study was overly influential in determining the weighted summary OR.

The impact of including the population-based

study by Visser et al.52 (SIR1⁄42.5, 95% CI1⁄40.8,

5.9) in the meta-analysis was assessed by entering

the SIR in the software as an OR (i.e., SE was

estimated on a log scale). The random effects

weighted summary OR increased slightly from

1.53 to 1.57 (95% CI1⁄41.17, 2.12) with the

inclusion of Visser et al.

Visual inspection of the funnel plot revealed

some evidence of publication bias. The Begg and

Mazumdar rank correlation test (one-tailed

p1⁄40.02) and Egger’s test of the intercept (onetailed po0.01) were both statistically significant,

indicating the presence of publication bias.

However, the Orwin’s fail-safe N calculation

determined that 19 missing studies reporting a

null effect (OR1⁄41.0) would be needed to reduce

the fixed-effects summary OR from 1.41 to 1.10.

Prenatal exposure window. The weighted summary OR was 0.92 (95% CI1⁄40.78, 1.09) for the

four studies that used a prenatal exposure

window.47,49,51,53 There was no evidence of

heterogeneity (Q=0.96, df=3, p=0.81, I

2

=0.0%)

or publication bias. No statistically significant

differences were observed in the subgroup analysis for any of the study characteristics examined

(data not shown).

Discussion

This systematic review and meta-analysis examined the current body of evidence on residential

exposure to traffic and childhood leukemia and

found that the association is dependent on the

time period used to estimate traffic exposure.

According to seven published studies, there is a

positive and significant association between

childhood leukemia and high residential traffic

exposure during the postnatal period. No association was found among the four studies that

assessed traffic exposure during the prenatal

period. Although a statistical test was not conducted to compare the summary ORs because of

the dependency on effect estimates, the nonoverlapping 95% CIs indicate that the summary

ORs differ between the prenatal and postnatal

exposure windows. This finding is supported by

the three studies that analyzed more than one

Table 1.

exposure window within the same population.

Characteristics of included case-control studies examining the association between residential traffic exposure and childhood leukemia (continued)

Reference

Study

location and

time frame Cancer types

Number

of

leukemia

cases/

controls

Age of

subjects

(years)

Exposure

measure

type

Exposure

window

Traffic exposure

categories (high vs low)

Covariates addressed

in analysisa

Von

Behrenet al.,200851

U.S.

(NorthernCalifornia);1995–2002

ALL 310/396 0–14 Multiple

road(traffic

densitywithin500-ftradius)

Birth

address;diagnosisaddress;childhoodperiod

Z91,462 VMT/mi2 vs

0 VMT/mi2 (no major

road within 500 ft)

Age, gender, race, ethnicity, income

aPotential confounding by covariates can be addressed in the analysis through matching and appropriate use of conditional logistic regression, adjustment in a multivariate model, or indication by

authors that adjustment for these factors had little effect on their findings bClass 1, high-speed freeways and bypasses; Class 2, main roads connecting Class 1 roads; Class 3, secondary roads

cSES measured by the highest (maternal or paternal) professional/occupational category of parents at the time of the interviewdChildhood period includes addresses from birth through 12 months before diagnosiseSavitz et al. presented the unadjusted OR and reported qualitatively that adjustment for age, gender, year of diagnosis, birth location, paternal education, and per capita income had little to no effect on

the OR. Simultaneous adjustment for residence type, mother’s age, mother’s smoking, and wire configuration code at diagnosis slightly reduced the OR, but adjusted findings were only presented for the

dichotomous exposure variable

fStudy participants were asked about exposure to heavy traffic roads in the vicinity (

o50 m) of the children’s homes. It is not clear how the data were aggregated to represent the pregnancy and

childhood periods

ALL, acute lymphoblastic leukemia; ANLL, acute non-lymphoblastic leukemia; CNS, central nervous system; DWTD, distance-weighted traffic density; VMT, vehicle miles traveled; vpd, vehicles per day.

418 Boothe et al / Am J Prev Med 2014;46(4):413–422

www.ajpmonline.org
Specifically, these three studies reported a smaller effect

estimate when birth or pregnancy address was used

compared to their postnatal exposure effect estimate. In

two instances, the prenatal exposure OR was o1.0 and

the postnatal exposure OR was 41.0.47,51 These findings

confirm the importance of assessing exposure timing in

childhood cancer studies26,54,55 and suggest that the

critical exposure window for childhood leukemia associated with proximity to traffic might occur after birth.

The finding that residential exposure to traffic is

associated with childhood leukemia is supported by a

number of recent studies. Traffic emissions represent a

primary source of known carcinogens, including benzene.56 Recent mechanistic studies suggest that benzene

exposures can initiate both ALL and AML.57 Previous

studies have reported an association between childhood leukemia and modeled ambient concentrations

of benzene near the place of residence.58–60 Two of these

studies also examined the association between leukemia

subtypes and benzene exposure and found stronger

associations for AML than for ALL.59,60 Additionally,

Vinceti et al. reported independent but weaker associations between childhood cancer and exposure to trafficrelated particulate matter with a diameter r10 mm

(PM10).59

The findings of this meta-analysis reflect the current

literature, and the interpretation of results should

take into consideration the methodological limitations

of the included studies. First, inconsistent traffic exposure measures were used across individual studies.

Accordingly, residential traffic

exposure was examined by comparing “high” versus “low” traffic

exposure, which prohibited conclusions about a specific distance

or traffic density associated with

an increased odds of childhood

leukemia.

Second, residential mobility

could result in exposure misclassification, particularly when estimating exposure using a single

point-in-time birth or diagnosis

address. Studies have shown a

high degree of mobility among

U.S. children with leukemia, with

50%–66% having different birth

and diagnosis addresses.47,49,55

Two of eight included studies

conducted a subgroup analysis

by residential mobility, and both

reported stronger associations

between traffic exposure and

childhood leukemia among children with a stable residential history.43,50

Third, all but two of the original studies, and consequently this meta-analysis, aggregated childhood

leukemia subtypes as a single outcome. Von Behren

et al.53 included only cases of ALL in their analysis and

did not make comparisons with non-ALL subtypes.

Amigou et al.43 is the only included study that stratified their results by leukemia subtype, and they reported

no difference in the magnitude of association between

ALL and non-ALL subtypes. Use of an aggregate outcome measure could bias the results toward the null if

traffic exposure affects leukemia subtypes differentially.59,60 Thus, the results from this meta-analysis

represent the average association across all types of

leukemia.

Fourth, the assessment of potential confounders, such

as SES and exposure to electromagnetic fields, varied

across the individual studies. In the U.S., individuals with

a lower SES have greater exposure to traffic than those

with a higher SES;61–63 however, this association might

not hold in European cities.52 The literature is inconsistent regarding the association between SES and childhood leukemia, with results varying by study location

and time period, study design, and type of SES measure

used.64–66 Three of the included studies adjusted for SES

in multivariate analyses, but only Savitz et al. commented

on whether adjustment attenuated the association.43,50,53

Additionally, previously conducted meta-analyses and

pooled analyses have found an association between

Study name Statistics for each studyb Odds ratio and 95%CI

Raaschou-Nielsen, 2001 1.10 0.55 2.20 13.5

Steffen, 2004 1.10 0.76 1.60 25.3

Von Behren, 2008 1.24 0.74 2.08 19.1

Langholtz, 2002 1.90 0.90 4.01 12.2

Amigou, 2010 2.00 1.00 4.00 13.5

Crosignani, 2004 2.09 0.85 5.13 9.3

Savitz, 1989 4.70 1.62 13.65 7.1

1.53 1.12 2.10

0.1 0.2 0.5 1 2 5 10

Summary weighted OR

Odds

ratio

Lower

limit

Upper

limit

Relative

weight

Figure 1. Forest plot of case-control studies examining the association between

residential traffic exposure assessed during the postnatal perioda and childhood

leukemia, and the random effects weighted summary OR and 95% CI

Note: The weighted summary OR is derived from the random effects model. For each study, the center of

the box denotes the OR, the horizontal line denotes the 95% CI, and the size of the box is proportional to

the study’s weight in the calculation of the overall effect. The weighted summary OR is denoted by the

center of the diamond and the 95% CI is denoted by the points of the diamond.

a

Includes studies that assessed residential traffic exposure throughout the childhood period, at the time

of diagnosis, or at the address of longest duration between birth and diagnosis

b

Because of asymmetric 95% CIs due to rounding, either the lower (Amigou and Langholtz) or upper

(Raaschou-Nielsen and Steffen) confidence limit was selected to calculate the SE and the opposite

confidence limit Thus, the 95% CI values shown for these four studies are not identical to those of the

published results.

Boothe et al / Am J Prev Med 2014;46(4):413–422 419

April 2014
electromagnetic fields and childhood leukemia.67 Only

one included study assessed the impact of controlling for

electromagnetic fields and reported slightly reduced ORs

between traffic exposure and childhood leukemia.45

This meta-analysis was limited by the small number of

included studies, which prohibited extensive exploration of

potentially important sources of heterogeneity. Subgroup

analyses could not be conducted for all variables of interest,

and the small sample size limited the power to detect

statistically significant differences between subgroups.

This study has several strengths. To our knowledge, it is

the first systematic review and meta-analysis to examine

traffic exposure and childhood leukemia by exposure

window. A comprehensive search strategy minimized the

likelihood that relevant studies were missed. Rigorous

coding and data abstraction procedures ensured accurate

data collection. The results were robust to a number of

sensitivity analyses.

Conclusions

Nationwide, more than 10% of the U.S. population

resides near major roads,68 and in large urban areas the

estimate can be as high as 30%–45%.5

The finding that residential exposure to traffic after

birth is associated with

increased risk of childhood

leukemia can inform precautionary health messages

targeted to the general public and professionals responsible for community design.

Simple information on the

distance to roads or amount

of traffic on nearby roads

can be used when making

personal residential location

choices. Urban planners

and transportation engineers can use the results of

this review to inform future

land-use planning and

transportation systems. For

example, residential exposure to traffic could be

reduced by minimizing the

development of highdensity residential buildings

near busy roads. However,

public health and planning

professionals involved in the

design of communities will

need to balance the potentially competing public health

goals of promoting physical activity, preventing injuries, and

reducing traffic exposure.

Given the findings of this review and the biological

plausibility of an association between childhood leukemia and traffic-related air pollutants, further research is

warranted. Specifically, well-designed epidemiologic

studies of residential traffic exposure that estimate traffic

exposure using complete residential history, assess associations by leukemia subtypes, and examine known and

suspected confounding factors are needed to verify these

findings. In addition, further examination of the association between residential traffic exposure and other

health outcomes is needed to help guide public health

interventions and strategies to reduce population exposure to traffic-related air pollution.

We thank Christy Cechman, W. Dana Flanders, Julie Gilchrist,

Onnalee Gomez, Theresa Sipe, and Stephen Thacker for

contributing to the development and review of this study.

The findings and conclusions in this report are those of the

authors and do not necessarily represent the official position of

the CDC.

Table 2. Weighted summary ORs for case-control studies examining the association

between postnatal residential traffic exposure and childhood leukemia, stratified by study

characteristics

Variable

Subgroup

(no. of studies)

Subgroup summary

ORa

(95% CI) p-valueb

I

2

value

(%)c

Study location U.S. (3) 1.84 (1.08, 3.13) 0.42 60.1

Europe (4) 1.39 (0.91, 2.13) 15.3

Study time period Pre-1995 (4) 1.89 (1.18, 3.02) 0.24 42.0

1995 or later (3) 1.31 (0.89, 1.93) 9.7

Type of exposure

metric

Multiple road (2) 1.48 (0.79, 2.78) 0.81 0.0

Single road (5) 1.62 (1.06, 2.48) 54.5

Cancer typed Leukemia (4) 1.83 (1.22, 2.75) 0.59 42.0

Acute leukemia (2) 1.26 (0.91, 1.75) 54.8

Control for SES Yes (3) 1.87 (1.12, 3.11) 0.35 61.1

No (4) 1.36 (0.90, 2.07) 1.0

Quality scoree ZMedian score (4) 1.84 (1.22, 2.78) 0.18 42.2

oMedian score (3) 1.23 (0.81, 1.88) 0.0

a

Subgroup summary OR is derived from a random effects model using pooled estimates of tau-squared

b

p-value from Q-test based on ANOVA. po0.05 indicates that the summary effect OR is statistically

different between subgroups

c

Subgroup I

2 values show the proportion of variability in the subgroup summary estimate that can be

attributed to between-study heterogeneity. Subgroups with moderate I

2 values (40%–62%) include the

Savitz et al. study48

d

Leukemia outcomes were defined as “leukemia,” “acute leukemia,” or “acute lymphoblastic leukemia

(ALL).” Results from the one study that examined ALL (von Behren et al.51) are not included in this

subgroup analysis

e

Quality score: range1⁄423–35, median1⁄430

420 Boothe et al / Am J Prev Med 2014;46(4):413–422

www.ajpmonline.org
No financial disclosures were reported by the authors of

this paper.

References

1. Baldauf RW, Heist D, Isakov V, et al. Air quality variability near a

highway in a complex urban environment. Atmos Environ 2013;64:

169–78.

2. Karner AA, Eisinger DS, Niemeier DA. Near-roadway air quality:

synthesizing the findings from real-world data. Environ Sci Technol

2010;44(14):5334–44.

3. Zhu Y, Hinds WC, Kim S, Sioutas C. Concentration and size

distribution of ultrafine particles near a major highway. J Air Waste

Manage Assoc 2002;52(9):1032–42.

4. Zhou Y, Levy J. Factors influencing the spatial extent of mobile source

air pollution impacts: a meta-analysis. BMC Public Health 2007;

7(1):89.

5. HEI Panel on the Health Effects of Traffic-Related Air Pollution.

Traffic-related air pollution: a critical review of the literature on

emissions, exposure, and health effects. Boston MA: Health Effects

Institute, 2010. Report No.: HEI Special Report 17.

6. Rioux CL, Gute DM, Brugge D, Peterson S, Parmenter B. Characterizing

urban traffic exposures using transportation planning tools: an illustrated

methodology for health researchers. J Urban Health 2010;87(2):167–88.

7. Miranda ML, Edwards SE, Chang HH, Auten RL. Proximity to

roadways and pregnancy outcomes. J Expo Sci Environ Epidemiol

2013;23(1):32–8.

8. Balmes JR, Earnest G, Katz PP, et al. Exposure to traffic: lung function

and health status in adults with asthma. J Allergy Clin Immunol

2009;123(3):626–31.

9. Gauderman WJ, Vora H, McConnell R, et al. Effect of exposure to

traffic on lung development from 10 to 18 years of age: a cohort study

[see comment]. Lancet 2007;369(9561):571–7.

10. Kim JJ, Huen K, Adams S, et al. Residential traffic and children's

respiratory health. Environ Health Perspect 2008;116(9):1274–9.

11. McConnell R, Berhane K, Yao L, Lunnann FW, Avol E, Peters JM.

Predicting residential ozone deficits from nearby traffic. Sci Total

Environ 2006;363(13):166–74.

12. Morgenstern V, Zutavern A, Cyrys J, et al. Atopic diseases, allergic

sensitization, and exposure to traffic-related air pollution in children.

Am J Respir Crit Care Med 2008;177(12):1331–7.

13. Kan H, Heiss G, Rose KM, Whitsel EA, Lurmann F, London SJ.

Prospective analysis of traffic exposure as a risk factor for incident

coronary heart disease: the Atherosclerosis Risk in Communities

(ARIC) study. Environ Health Perspect 2008;116(11):1463–8.

14. Tonne C, Melly S, Mittleman M, Coull B, Goldberg R, Schwartz J. A

case-control analysis of exposure to traffic and acute myocardial

infarction. Environ Health Perspect 2007;115(1):53–7.

15. Van Hee VC, Adar SD, Szpiro AA, et al. Exposure to traffic and left

ventricular mass and function: the multi-ethnic study of atherosclerosis. Am J Respir Crit Care Med 2009;179(9):827–34.

16. Wilhelm M, Ritz B. Residential proximity to traffic and adverse birth

outcomes in Los Angeles county, California, 1994-1996. Environ

Health Perspect 2003;111(2):207–16.

17. Yorifuji T, Naruse H, Kashima S, et al. Residential proximity to major

roads and preterm births. Epidemiology 2011;22(1):74–80.

18. Gehring U, Heinrich J, Kramer U, et al. Long-term exposure to

ambient air pollution and cardiopulmonary mortality in women.

Epidemiology 2006;17(5):545–51.

19. Hoek G, Brunekreef B, Goldbohm S, Fischer P, van den Brandt PA.

Association between mortality and indicators of traffic-related air

pollution in the Netherlands: a cohort study [see comment]. Lancet

2002;360(9341):1203–9.

20. Jerrett M, Finkelstein MM, Brook JR, et al. A cohort study of trafficrelated air pollution and mortality in Toronto, Ontario, Canada.

Environ Health Perspect 2009;117(5):772–7.

21. American Cancer Society. Cancer Facts & Figures 2011. Atlanta GA:

American Cancer Society, 2011.

22. U.S. Cancer Statistics Working Group. U.S. cancer statistics: 1999–

2009 incidence and mortality web-based report. Atlanta GA:

USDHHS, CDC and National Cancer Institute, 2013.

23. Gurney JG, Severson RK, Davis S, Robison LL. Incidence of cancer in

children in the U.S. Sex-, race-, and 1-year age-specific rates by

histologic type. Cancer 1995;75(8):2186–95.

24. Howlader N, Noone A, Krapcho M, eds. SEER cancer statistics review,

1975-2009 (vintage 2009 populations). Bethesda MD: National Cancer

Institute, 2012.

25. Greaves MF, Alexander F. An infectious etiology for common acute

lymphoblastic leukemia in childhood? Leukemia 1993;7:349–60.

26. Buffler PA, Kwan ML, Reynolds P, Urayama KY. Environmental and

genetic risk factors for childhood leukemia: appraising the evidence.

Cancer Invest 2005;23(1):60–75.

27. Shu XO, Perentesis JP, Wen WQ, et al. Parental exposure to medications

and hydrocarbons and ras mutations in children with acute lymphoblastic leukemia: a report from the Children's Oncology Group. Cancer

Epidemiol Biomarkers Prev 2004;13(7):1230–5.

28. Castro-Jimenez MA, Orozco-Vargas LC. Parental exposure to carcinogens and risk for childhood acute lymphoblastic leukemia, Colombia, 2000-2005. Prev Chronic Dis 2011;8(5):A106.

29. Berman N, Parker R. Meta-analysis: neither quick nor easy. BMC Med

Res Methodol 2002;2:10.

30. The World Bank. Country and lending groups. 2012. http://data.world

bank.org/about/country-classifications/country-and-lending-groups.

31. Bullock SH, Jones BH, Gilchrist J, Marshall SW. Prevention of physical

training-related injuries recommendations for the military and other

active populations based on expedited systematic reviews. Am J Prev

Med 2010;38(1S):S156–S181.

32. Thacker SB, Stroup DF, Branche CM, Gilchrist J, Goodman RA,

Weitman EA. The prevention of ankle sprains in sports—a systematic

review of the literature. Am J Sports Med 1999;27(6):753–60.

33. Zaza S, Wright-De Aguero LK, Briss PA, et al. Data collection

instrument and procedure for systematic reviews in the Guide to

Community Preventive Services. Task Force on Community Preventive Services. Am J Prev Med 2000;18(1S):44–74.

34. Borenstein M, Hedges L, Higgins J, Rothstein H. Comprehensive metaanalysis version 2. In: Version 2.2.055 ed. Englewood NJ: Biostat Inc.,

2005.

35. Greenhouse J, Lyengar S. Sensitivity analysis and diagnostics. In:

Cooper HM, Hedges LV, eds. The handbook of research synthesis.

New York NY: Russell Sage Foundation Publications, 1994:383–98.

36. Cooper HM, Hedges LV. The handbook of research synthesis. New

York NY: Russell Sage Foundation Publications, 1994.

37. Higgins JPT, Thompson SG. Quantifying heterogeneity in a metaanalysis. Stat Med 2002;21(11):1539–58.

38. Higgins JPT, Thompson SG, Deeks JJ, Altman DG. Measuring

inconsistency in meta-analyses. BMJ 2003;327(7414):557–60.

39. Sutton AJ, Duval SJ, Tweedie RL, Abrams KR, Jones DR. Empirical

assessment of effect of publication bias on meta-analyses. BMJ

2000;320(7249):1574–7.

40. Begg CB, Mazumdar M. Operating characteristics of a rank correlation

test for publication bias. Biometrics 1994;50(4):1088–101.

41. Egger M, Smith GD, Schneider M, Minder C. Bias in meta-analysis

detected by a simple, graphical test. BMJ 1997;315(7109):629–34.

42. Orwin RG. A fail-safe N for effect size in meta-analysis. J Educ Stat

1983;8:157–9.

43. Amigou A, Sermage-Faure C, Orsi L, et al. Road traffic and childhood

leukemia: the ESCALE study (SFCE). Environ Health Perspect

2011;119(4):566–72.

Boothe et al / Am J Prev Med 2014;46(4):413–422 421

April 2014
44. Crosignani P, Tittarelli A, Borgini A, et al. Childhood leukemia and

road traffic: a population-based case-control study. Int J Cancer

2004;108(4):596–9.

45. Langholz B, Ebi KL, Thomas DC, Peters JM, London SJ. Traffic density

and the risk of childhood leukemia in a Los Angeles case-control study.

Ann Epidemiol 2002;12(7):482–7.

46. Pearson RL, Wachtel H, Ebi KL. Distance-weighted traffic density in

proximity to a home is a risk factor for leukemia and other childhood

cancers. J Air Waste Manage Assoc 2000;50(2):175–80.

47. Raaschou-Nielsen O, Hertel O, Thomsen BL, Olsen JH. Air pollution

from traffic at the residence of children with cancer. Am J Epidemiol

2001;153(5):433–43.

48. Reynolds P, Elkin E, Scalf R, et al. A case-control pilot study of traffic

exposures and early childhood leukemia using a geographic information system. Bioelectromagnetics 2001;22(S5):S58–S68.

49. Reynolds P, Von Behren J, Gunier RB, Goldberg DE, Hertz A.

Residential exposure to traffic in California and childhood cancer.

Epidemiology 2004;15(1):6–12.

50. Savitz DA, Feingold L. Association of childhood cancer with residential

traffic density. Scan J Work Environ Health 1989;15(5):360–3.

51. Steffen C, Auclerc MF, Auvrignon A, et al. Acute childhood leukaemia and

environmental exposure to potential sources of benzene and other hydrocarbons: a case-control study. Occup Environ Med 2004;61(9):773–8.

52. Visser O, van Wijnen JH, van Leeuwen FE. Residential traffic density

and cancer incidence in Amsterdam, 1989-1997. Cancer Causes

Control 2004;15(4):331–9.

53. Von Behren J, Reynolds P, Gunier RB, et al. Residential traffic density

and childhood leukemia risk. Cancer Epidemiol Biomarkers Prev

2008;17(9):2298–301.

54. Anderson LM, Diwan BA, Fear NT, Roman E. Critical windows of

exposure for children’s health: cancer in human epidemiological

studies and neoplasms in experimental animal models. Environ Health

Perspect 2000;108:573–94.

55. Urayama KY, Von Behren J, Reynolds P, Hertz A, Does M, Buffler PA.

Factors associated with residential mobility in children with leukemia:

implications for assigning exposures. Ann Epidemiol 2009;19(11):

834–40.

56. Duarte-Davidson R, Courage C, Rushton L, Levy L. Benzene in the

environment: an assessment of the potential risks to the health of the

population. Occup Environ Med 2001;58(1):2–13.

57. Smith MT. Advances in understanding benzene health effects and

susceptibility. Annu Rev Public Health 2010;31:133–48.

58. Reynolds P, Von Behren J, Gunier RB, Goldberg DE, Hertz A, Smith DF.

Childhood cancer incidence rates and hazardous air pollutants in California:

an exploratory analysis. Environ Health Perspect 2003;111(4):663–8.

59. Vinceti M, Rothman KJ, Crespi CM, et al. Leukemia risk in children

exposed to benzene and PM10 from vehicular traffic: a case-control

study in an Italian population. Eur J Epidemiol 2012;27(10):781–90.

60. Whitworth KW, Symanski E, Coker AL. Childhood lymphohematopoietic cancer incidence and hazardous air pollutants in southeast

Texas, 1995-2004. Environ Health Perspect 2008;116(11):1576–80.

61. Apelberg BJ, Buckley TJ, White RH. Socioeconomic and racial

disparities in cancer risk from air toxics in Maryland. Environ Health

Perspect 2005;113(6):693–9.

62. CDC. Residential proximity to major highways—U.S., 2010. Morbidity

and Mortality Weekly Report - CDC Health Disparities and Inequality

Report 2013 (in press).

63. Gunier RB, Hertz A, Von Behren J, Reynolds P. Traffic density in

California: socioeconomic and ethnic differences among potentially

exposed children. J Expo Anal Environ Epidemiol 2003;13(3):240–6.

64. Adam M, Rebholz CE, Egger M, Zwahlen M, Kuehni CE. Childhood

leukaemia and socioeconomic status: what is the evidence? Radiat Prot

Dosimetry 2008;132(2):246–54.

65. Pan IJ, Daniels J, Zhu K. Poverty and childhood cancer incidence in the

U.S. Cancer Causes Control 2010;21(7):1139–45.

66. Poole C, Greenland S, Luetters C, Kelsey JL, Mezei G. Socioeconomic

status and childhood leukaemia: a review. Int J Epidemiol 2006;35(2):

370–84.

67. Mezei G, Kheifets L. Selection bias and its implications for case-control

studies: a case study of magnetic field exposure and childhood

leukaemia. Int J Epidemiol 2006;35(2):397–406.

68. Brugge D, Durant JL, Rioux C. Near-highway pollutants in motor

vehicle exhaust: a review of epidemiologic evidence of cardiac and

pulmonary health risks. Environ Health 2007;6:23.

422 Boothe et al / Am J Prev Med 2014;46(4):413–422

www.ajpmonline.org