Number 45 n April 2, 2012

Linkage of the 1999–2008 National Health and
 

Nutrition Examination Surveys to Traffic Indicators
 

From the National Highway Planning Network
 by Jennifer D. Parker, Ph.D., Office of Analysis and Epidemiology, National Center for Health Statistics;
 

Nataliya Kravets, M.A., Office of Research and Methodology, National Center for Health Statistics; Keeve Nachman,
 

Ph.D., M.H.S., Johns Hopkins Center for a Livable Future, Johns Hopkins University; and Amir Sapkota, Ph.D.,
 

Maryland Institute for Applied Environmental Health, University of Maryland, College Park
 

Abstract

Objectives—Growing evidence has shown the harmful effects of trafficrelated pollution on human health, including adverse respiratory, cardiovascular,

and pregnancy outcomes. This report describes the linkage of data from the

1999–2008 National Health and Nutrition Examination Surveys (NHANES) and

traffic indicators from the 2005 National Highway Planning Network.

Methods—The residential addresses of NHANES participants were used to

assign the distance to the nearest road, the number of roads within concentric

buffers of specific radii, and the average annual daily traffic. Summaries of these

traffic indicators by participant characteristics, including urbanization of their

county of residence, race and ethnicity, poverty status, and health status, were

tabulated.

Results—Using the traffic indicators, these data show differences in traffic

exposure by several participant characteristics including poverty status. Further,

reporting of fair or poor health was more common among NHANES respondents

nearer to, compared with farther from, roads; this relationship was observed

overall and for subgroups defined by urban county of residence, poverty status,

and self-reported cigarette smoking.

Conclusions—These data may be a resource for understanding relationships

between traffic exposure and adverse health, and for identifying subgroups that

may be at increased risk. The NHANES-traffic data are restricted use and

available to data users in the Research Data Center at the Centers for Disease

Control and Prevention’s National Center for Health Statistics.

Keywords: air pollution

motorcycles, buses, and trucks were Introduction

registered in the United States in 2008

More than 250 million motor (1). Exhaust from these vehicles

vehicles including passenger cars, contains numerous air pollutants,

including volatile organic compounds,

polycyclic aromatic hydrocarbons,

particulate matter, and carbon monoxide

(2). Numerous studies have shown that

the concentration of traffic-related air

pollutants decreases sharply as the

distance from the curbside increases,

reaching background level within

300–500 meters (3–6). Therefore,

persons living in close proximity to

the roadways—characteristics of

inner-city urban environments—are

potentially exposed to high levels of

traffic-related air pollutants.

Exposure assessment methods used

to quantify traffic exposure can be

broadly classified into four groups: (a)

personal or area monitoring of specific

pollutants; (b) modeled concentration

(including land-use regression); (c)

distance from primary residence to the

roadways, referred to in this report as

distance-based traffic exposure; and (d)

traffic density [such as vehicle miles

traveled, average annual daily traffic

(AADT), and number of roadways]

within a specified radius of primary

residence, referred to in this report as

density-based traffic exposure. Using

these approaches, increasing numbers of

epidemiological studies in the United

States and elsewhere have examined the

U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES 

Centers for Disease Control and Prevention 

National Center for Health Statistics 
Page 2 National Health Statistics Reports n Number 45 n April 2, 2012

effects of traffic exposure with a host of

adverse health outcomes, including

pregnancy (7–9), respiratory (10–15),

and cardiovascular outcomes (16–21),

and cancer (22–24) and mortality

(21,25,26), although not all studies find

associations. Fewer studies of the

potential impact of traffic on other

diseases, such as diabetes (27) and

arthritis (28), have been reported. A

recent meta-analysis showed an

increased risk of wheezing and hospital

visits for treatment of asthma-related

symptoms associated with both distanceand density-based measures of traffic

exposure (10).

In 2009, a panel convened by the

Health Effects Institute (HEI)

systematically reviewed the scientific

literature from the United States and

other countries on many aspects of

traffic-related air pollution, including its

measurement and relationship with

health outcomes (2). The HEI panel

concluded that the evidence for causal

effects of traffic-related air pollution on

health outcomes was suggestive but not

sufficient for several outcomes,

including all-cause and cardiovascular

mortality, cardiovascular morbidity,

adult respiratory symptoms and

pulmonary function, childhood asthma

incidence and prevalence, and childhood

respiratory-related health care utilization.

The panel determined that existing

evidence supports a causal association

between traffic and asthma exacerbation

among children. This broad assessment

by HEI was based on several surrogates

of exposure, including direct and

modeled estimates of air pollutants (for

example, nitrogen dioxide or fine

particulate matter), distance-based

metrics between roads and residences,

and traffic density indicators based both

on the distance to roads and traffic

volume on the road.

This report describes the geographic

linkage of data from the 1999–2008

National Health and Nutrition

Examination Surveys (NHANES), a

large nationally representative health

survey, to traffic data from the National

Highway Planning Network (NHPN), to

provide information for examining the

relationship between proximity to traffic

and health indicators in the

United States. This report focuses on

traffic-derived measures of exposure

rather than measured levels of air

pollution. While directly monitoring air

quality can capture exposure to many

components of traffic emissions, direct

measures of traffic exposure based on

proximity and volume remain important

because of the complex mixture of

pollutants from vehicular emissions.

Traffic indicators are tabulated by

selected factors known to be related to

health in the United States. The

NHANES-traffic data are restricted use

and available to data users in the

Research Data Center (RDC) at the

Centers for Disease Control and

Prevention’s National Center for Health

Statistics (NCHS) (29).

Linkage of NHANES to

Traffic Data

Data files used for linkage

NHANES

NHANES is a large program of

studies designed to assess the health and

nutritional status of adults and children

in the United States (30). The survey is

unique in that it combines interviews

and physical examinations. The survey

examines a nationally representative

sample of about 5,000 participants each

year. The sample consists of 15

randomly selected locations—typically

counties (referred to as primary

sampling units or PSUs)—and the

participants are randomly selected from

within these PSUs. The NHANES

interview includes demographic,

socioeconomic, dietary, and healthrelated questions. The examination

component, conducted in the mobile

examination center (MEC), consists of

medical, dental, and physiological

measurements as well as laboratory tests

administered by highly trained medical

personnel. Respondent characteristics

considered in this report include

urbanization level of the county of

residence, race and ethnicity, age,

education (for adults aged 25 and over),

family poverty status, self-reported

smoking status (for adults aged 18 and

over), and self-reported health status.

Although not exhaustive, these factors

were chosen because of their possible

relationship with traffic exposure or

their potential effect on the relationship

between traffic exposure and health

outcomes.

Data from the 1999–2008 NHANES

were used, which include approximately

50,000 sampled persons from 75

locations. All interviewed and MECexamined NHANES respondents were

eligible to be included in the linkage.

Restricted-use files, available to users in

the NCHS RDC (29) have been

geocoded to participants’ residential

addresses and to census administrative

units such as residential block group

(31).

Traffic data

The primary traffic data used for

this linkage are from NHPN (32),

maintained by the Federal Highway

Administration as a component of the

Highway Performance Monitoring

System (HPMS) (33). These traffic data

(NHPN version 2005.08) are the most

comprehensive GIS-based network

database available, containing

information about the location and

features of the major roadways in the

United States. NHPN contains

information on more than 450,000 miles

of roadways in the 48 contiguous states

and the District of Columbia, Alaska,

Hawaii, and Puerto Rico, consisting of

rural principal arterials, urban principal

arterials, and all National Highway

System routes (see ‘‘Technical Notes’’

for definitions). Smaller roadways are

not in this system and could not be used

in the assessment of traffic exposure.

Linkage methods

Geocoded NHANES residential

addresses were linked with the traffic

information using ArcInfo (34).

Concentric traffic buffers of radius 100,

300, and 500 meters (m) were drawn

around each respondent’s residence to

create traffic exposure estimates based

on the roadways located within the

buffers and identify the nearest road to

each residence.
National Health Statistics Reports n Number 45 n April 2, 2012 Page 3

Exposure estimates

Traffic exposure variables were

assigned to each NHANES participant

using NHPN traffic data and geocoded

residential addresses. These variables

included (a) distance from respondent’s

residence to the nearest road; (b)

number of roads within concentric

traffic buffers of specific radii from

respondent’s residence; (c) aggregated

length of all roads within concentric

traffic buffers of specific radii from

respondent’s residence; and (d) sum of

AADT values for all roads within

concentric traffic buffers of specific

radii from respondent’s residence.

AADT values are the average number of

motor vehicles on the road each day for

each segment of the road; for roads with

multiple segments, each with a separate

AADT value within the traffic buffer,

the highest AADT was used to represent

the road for the traffic buffer.

Additional variables were created

for the aggregated length of all roads

and the sum of AADT values by

weighting the road-specific values by

the distance from the respondent’s

residence to each road within the traffic

buffer. These additional variables are

available but not described in this

report.

The functional classification

(FCLASS) system groups roads, streets,

and highways into different classes

based on the character of service they

provide. The underlying principle of this

classification is that individual roads and

streets do not serve as separate entities,

but rather as a network of roads through

which traffic moves. FCLASS

designations can differ among states;

consequently, although FCLASS

variables are available, they are not

described in this report.

Description of the

Linked Data Files

Methods

The number of NHANES

respondents geocoded to a residential

address was calculated. Of these, the

numbers and percentages of respondents

who could be linked to any road and

any road within three specified traffic

buffers (100 m, 300 m, and 500 m)

were calculated. In addition, among

those linked to one or more roads, the

percentages linked to only one road

within the same three traffic buffers

were calculated.

Medians and interquartile ranges

(IQR, 25th and 75th percentiles) were

calculated for the following traffic

measures: the distance from residence to

the nearest road overall and within three

specified buffers, the length of all roads

within three specified buffers, and the

AADT within three specified buffers.

All numbers and summary statistics

were tabulated overall and for subgroups

defined by selected respondent

characteristics.

The overall medians and IQRs for

each traffic variable were used to

identify quartiles and form categorical

traffic exposure variables. Associations

between selected respondent

characteristics and the categorical traffic

exposure variables were assessed using

chi-square statistics. Relationships

among reporting fair or poor health

status and quartiles of traffic exposure

measures were examined overall and for

potentially vulnerable subgroups,

including those living in the most urban

areas, non-Hispanic black and MexicanAmerican persons, adults with less than

a high school education, persons below

the poverty level, and adult current

smokers; tests for trend by quartile were

used to assess statistical significance. A

thorough examination of the

relationships among health outcomes

including reporting of fair or poor health

status and traffic exposure indicators

overall and for subgroups was not

performed. A lack of comment on any

characteristic or subgroup should not be

interpreted that a statistical test was

performed and the results found to be

not statistically significant. No

adjustments for multiple comparisons

were considered.

SUDAAN software (35), which

incorporates survey design information

including survey weights, strata

identifiers, and PSU identifiers, was

used to account for the complex

stratified cluster design. Sample size

numbers are unweighted. All

percentages and percentiles including

medians were weighted using NHANES

interview weights. Unstable estimates

are not shown; stability was determined

by assessment of the relative standard

error (RSE = 100 * standard error /

estimate) and the design degrees of

freedom; estimates for subgroups with

RSE less than 30% or with fewer than

12 degrees of freedom are not shown

(36).

Results

Table 1 describes the geocoding and

the linkage between the NHANES

respondents based on residential address

and the traffic buffers. Addresses of

about 90% of NHANES respondents

were geocoded. The percentage

geocoded differed significantly by all of

the characteristics shown in the table. Of

the respondents with address geocoded,

10% resided within 100 m of one or

more roads, almost 30% were within

300 m, and more than 40% were within

500 m of one or more roads in NHPN.

Living within a specific distance to one

or more roads differed by several

respondent characteristics; for example,

poverty status, urbanization, race/

ethnicity, self-reported smoking status

among adults, and health status were

associated with living within a specific

distance to one or more roads for each

of the three traffic buffers, 100 m,

300 m, and 500 m.

Table 2 shows the percentage living

near only one road within specified

traffic buffers. Of those respondents

who live within 100 m of the nearest

road, most (about 95%) lived within 100

m of only one road. Of those who lived

within 500 m from one or more roads,

only two-thirds (about 66%) lived

within 500 m of only one road. Among

those within 500 m from one or more

roads, living near only one road was

significantly related to urbanization,

race/ethnicity, poverty status, education

among adults aged 25 and over,

self-reported smoking status among

adults aged 18 and over, and selfreported health status.

Figure 1 shows the weighted

distribution of the distance from

residence to the nearest road for

NHANES participants within 5,000 m
Page 4 National Health Statistics Reports n Number 45 n April 2, 2012 Density 

.0015 

.001 

5.0e 

–04 

0 

0 1000 2000 3000 4000 5000 

Distance to nearest road in meters 

SOURCE: CDC/NCHS, National Health and Nutrition Examination Surveys, 1999–2008 linked with traffic data from 

the National Highway Planning Network, 2005. 

Figure 1. Weighted distribution of distance from residence to nearest road for NHANES

participants within 5,000 meters of nearest road

of the nearest road (over 98% of

participants). The distribution is skewed

to the left with nearly 6% living within

50 m of the nearest road; about

one-fourth live at least 1,000 m from the

nearest road.

Table 3 compares the percent

distribution among quartiles of distance

from residence to the nearest road by

selected respondent characteristics, with

quartiles defined by overall distance

distributions (see Table I for overall

values of the 25th, 50th, and 75th

percentiles used here and by subgroup).

For distance not restricted by traffic

buffer size, the distribution of the

distance from residence to the nearest

road varied by urbanization, race and

ethnicity, poverty status, education

among adults aged 25 and over,

self-reported smoking status among

adults, and self-reported health status,

but not by age category. These

relationships differed for respondents

within a specified distance to one or

more roads. For example, among those

within 100 m, the distribution of

distance to the nearest road varied by

urbanization and race/ethnicity, whereas

among those within 500 m, the

distribution was associated with poverty

status, education level among adults

aged 25 and over, smoking status among

adults aged 18 and over, and health

status, but not with urbanization, age, or

race/ethnicity.

Figure 2 shows the proportion

reporting fair or poor health status by

quartiles of the distance to the nearest

road and education among adults.

Overall and among those with a high

school education or more, reporting of

fair or poor health status was

significantly related to the quartile of

distance to the nearest road based on

tests for trend. Those in the highest

quartile of distance, that is, those living

farthest from the nearest road, were the

least likely to report fair or poor health

status. In addition, this relationship was

significant for other subgroups,

including those living in large central

metropolitan counties, non-Hispanic

black and non-Hispanic white persons,

those with family incomes below the

poverty threshold, and among adults

who were former and never smokers,

but not for current adult smokers or for

Mexican-American persons (not shown).

Table 4 shows the percent

distribution of the length of roads

among quartiles of the overall

distribution. See Table II for the overall

values of the 25th, 50th, and 75th

percentiles used here and by subgroup.

The percent distribution of the length of

the roads within 500 m differed

statistically among respondents

characterized by urbanization, race/

ethnicity, poverty status, education

(adults aged 25 and over), self-reported

smoking (adults aged 18 and over), and

self-reported health status, but not by

age; the length of roads within 100 m

was associated with race/ethnicity and

urbanization but not by other factors.

Percentage reporting fair or poor health status 1

p value < 0.05 based on test for trend. 

NOTE: GED is General Educational Development high school equivalency diploma. 

SOURCE: CDC/NCHS, National Health and Nutrition Examination Surveys, 1999–2008 linked with traffic data from the 

National Highway Planning Network, 2005. 

0 

5 

10 

15 

20 

25 

More than high school or GED1 

High school or GED1 

Less than high school or GED 

All1 

Distance to nearest road (quartiles) in meters 

Quartile 4 

(Greater than or 

equal to 1,085) 

Quartile 3 

(528 to less 

than 1,085) 

Quartile 2 

(237 to less than 528) 

Quartile 1 

(Less than 237) 

Figure 2. Percentage reporting fair or poor health status, by quartiles of distance to

nearest road and education among adults
National Health Statistics Reports n Number 45 n April 2, 2012 Page 5 Percentage reporting fair or poor health status 1

p value < 0.05 based on test for trend. 

NOTE: GED is General Educational Development high school equivalency diploma. 

SOURCE: CDC/NCHS, National Health and Nutrition Examination Surveys, 1999–2008 linked with traffic data from the 

National Highway Planning Network, 2005. 

0 

5 

10 

15 

20 

25 

More than high school or GED1 

High school or GED1 

Less than high school or GED 

All1 

Quartile 4 

(Greater than or 

equal to 1,227) 

Quartile 3 

(952 to less 

than 1,227) 

Combined length of roads within 500 meters (quartiles) in meters 

Quartile 2 

(737 to less than 952) 

Quartile 1 

(Less than 737) 

Figure 3. Percentage reporting fair or poor health among respondents, by quartiles of

length of roads within 500 meters, overall, and by educational attainment among adults

Figure 3 shows the percentage

reporting fair or poor health among

respondents by quartiles of the length of

roads within 500 m, overall, and by

educational attainment among adults.

The percentage reporting fair or poor

health generally increased with the

increasing length of roads; this

association was significant based on a

test for trend overall and among those

with a high school or General

Educational Development education.

The percentage reporting fair or poor

health was also significantly related to

the quartiles of length of road within

500 m for those below poverty level,

Mexican-American persons, and for

adults who were former and never

smokers (not shown).

Table 5 shows the percent

distribution among the overall quartiles

of AADT. See Table III for the overall

25th, 50th, and 75th percentiles used

here and by subgroup. These estimates

are based on a subset of NHANES

respondents who were linked to roads

containing AADT values. Of the 5,763

NHANES respondents within 100 m of

a road, only 1,519 (about one-fourth)

had AADT information; however, of the

24,826 within 500 m of a road, nearly

one-half had AADT information. AADT

within 500 m was significantly

associated with urbanization, education

(adults aged 25 and over), age category,

and race/ethnicity.

Discussion

The potential for exposure to

automobile exhaust is most pronounced

in urban locations where heavily

commuted roadways transect densely

populated communities. Human

exposure to these mobile source

emissions can be substantial due to

increasing traffic volume and

congestion; vehicle-miles driven; and

numbers of heavier, less efficient sport

utility vehicles (37). Epidemiological

studies have linked exposure to

automobile exhaust with elevated risks

of cancers, heart disease, asthma

exacerbation, preterm birth, low

birthweight, and mortality. Despite this

growing literature, there is a critical gap

in literature regarding how traffic-related

exposure and the associated health

outcomes may vary across different

geographical areas and population

groups.

This report describes the linkage

process and the availability of the linked

data to the wider scientific community.

This report does not provide a detailed

analysis of traffic exposure and specific

health outcomes; however, preliminary

examination of these linked data shows

possible associations between measures

of traffic exposure and reported health

status, albeit an imprecise measure.

Further, although the strength of

associations differed among metrics and

distances, those with incomes below

poverty lived closer to the nearest road

and closer to a larger number of roads.

These initial comparisons suggest that

analytic studies of specific morbidities,

considering other factors available from

NHANES, may improve our

understanding of the relationships

among poverty, health, and traffic

exposure.

Numerous analytic challenges exist

that should be considered when using

these data. Not all roads are included in

the NHPN network. Information about

smaller roads that are not part of the

national network is not available

through the linked NHANES-traffic file.

This lack of detail results in exposure

misclassification for those who live near

a small road. It is possible but unknown

whether living near smaller rather than

larger roads varies by respondent

characteristics, and in turn, indirectly or

directly, by health measures.

Furthermore, not all the roads in NHPN

have AADT values from the HPMS. In

addition to reduced sample size and

statistical power, other impacts of

missing AADT information on results

using that variable are unknown. As in

prior linkages of air monitoring data, the

impact and appropriate uses of survey

weights and other design information for

summary measures and variance

estimation are also unknown (38,39).

Further understanding missing values for

the AADT may provide insight into

analyses; for example, the fact that

traffic density is less likely to be

measured on less traveled roads may be

able to be incorporated into analyses.

Importantly, even with multiple

years of NHANES, the degrees of

freedom can be relatively small for

some analyses, leading to unstable

estimates. Decreased degrees of freedom

may be of particular concern for
Page 6	 National Health Statistics Reports n Number 45 n April 2, 2012

analyses restricted to participants living

within a specific distance to one or

more roads, for analyses restricted to

participants living close to a road with

AADT information, and for analyses

restricted to specific population

subgroups.

Additional information not fully

described in this report is available from

this linkage. The traffic measures

reported were calculated based on the

respondent’s latitude and longitude of

residence. Additional measures were

calculated using the latitude and

longitude of the respondent’s block

group of residence; these measures were

created to allow for comparison among

these linkage approaches because some

health data sources cannot be geocoded

to the exact residence. In addition, as

mentioned above, some roadways also

have an assigned FCLASS value.

The linkage of existing NHANES

respondents with traffic data provides

the scientific community with the

capability to investigate associations

between traffic exposure and health

using a large, nationally representative

sample of the U.S. population.

Individual-level biomarker data as well

as health outcome data are available that

will enable investigators to explore

traffic-related health outcomes,

controlling for individual-level

confounders. In addition, ambient air

quality indicators have been

geographically linked to NHANES data,

which can be used to further explore

these relationships.

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geographical study. Stroke

34(12):2776–80. 2003.
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23. Beelen R, Hoek G, van den Brandt

PA, Goldbohm RA, Fischer P,

Schouten LJ, et al. Long-term

exposure to traffic-related air

pollution and lung cancer risk.

Epidemiology 19(5):702–10. 2008.

24. Crosignani P, Tittarelli A, Borgini A,

Codazzi T, Rovelli A, Porro E, et al.

Childhood leukemia and road traffic: A population-based case-control

study. IntJ Cancer 108(4):596–9.

2004.

25. Von Behren J, Reynolds P, Gunier

RB, Rull RP, Hertz A, Urayama KY,

et al. Residential traffic density and

childhood leukemia risk. Cancer

Epidemiol Biomarkers Prev

17(9):2298–301. 2008.

26. 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. Lancet

360(9341):1203–9. 2002.

27. Puett RC, Hart JE, Schwartz J, Hu

FB, Liese AD, Laden F. Are

particulate matter exposures

associated with risk of type2

diabetes? Environ Health Perspect

119(3):384–9. 2011.

28. Hart JE, Laden F, Puett RC,

Costenbader KH, Karlson EW.

Exposure to traffic pollution and

increased risk of rheumatoid arthritis.

Environ Health Perspect

117(7):1065–9. 2009.

29. National Center for Health Statistics.

Research Data Center. Available

from: http://www.cdc.gov/rdc/

[Accessed 4/13/11].

30. National Center for Health Statistics.

National Health and Nutrition

Examination Survey. Available from:

http://www.cdc.gov/nchs/nhanes/

about_nhanes.htm [Accessed

2/17/11].

31. National Center for Health Statistics.

Research Data Center Geocodes.

Available from: http://www.cdc.gov/

rdc/B1DataType/Dt123Geocod.htm.

32. U.S. Department of Transportation.

Federal Highway Administration. The

National Highway Planning Network.

Available from: http://www.fhwa.

dot.gov/planning/nhpn/ [Accessed

2/17/11].

33. U.S. Department of Transportation.

Federal Highway Administration.

Highway Performance Monitoring

System. Available from: http://

www.fhwa.dot.gov/policyinformation/

hpms/hpmsprimer.cfm.

34. ESRI. ArcGIS desktop 9.3 help.

Available from: http://webhelp.

esri.com/arcgisdesktop/9.3/

index.cfm?TopicName=welcome

[Accessed 8/3/11].

35. SUDAAN Language Manual.

Release 10.0. Research Triangle

Park, NC: RTI International. 2008.

36. National Center for Health Statistics.

Continuous NHANES web tutorial.

Available from: http://www.cdc.gov/

nchs/tutorials/nhanes/SurveyDesign/

VarianceEstimation/intro.htm

[Accessed 3/28/11].

37. McAuley JW. Global sustainability

and key needs in future automotive

design. Environ Sci Technol

37(23):5414–6. 2003.

38. Parker JD, Kravets N, Woodruff TJ.

Linkage of the National Health

Interview Survey to air quality data.

National Center for Health Statistics.

Vital Health Stat 2(145). 2008.

39. Kravets N, Parker JD. Linkage of the

Third National Health and Nutrition

Examination Survey to air quality

data. National Center for Health

Statistics. Vital Health Stat 2(149).

2008.

40. U.S. Department of Transportation.

Federal Highway Administration.

FHWQ functional classification

guidelines. Available from: http://

www.fhwa.dot.gov/planning/

fcsec2_1.htm.
Page 8 National Health Statistics Reports n Number 45 n April 2, 2012

Table 1. Number and percentage of respondents with address of residence geocoded and linked to traffic data within buffers of specific

radii, by selected respondent characteristics

Linked to one or Linked to one or Linked to one or

more roads within more roads more roads within

Geocoded 100 meters within 300 meters 500 meters

Number of

respondents Number Percent Number Percent Number Percent Number Percent

Total ............................ 51,623 47,202 90 5,763 10 16,452 28 24,826 43

Urbanization

Large central metropolitan ............... 19,709 19,237 98 2,703 12 7,584 34 11,209 52

Other counties ...................... 31,914 27,965 86 3,060 9 8,868 26 13,617 39

Race and ethnicity

Mexican American .................... 13,692 12,518 90 1,834 13 4,963 34 7,551 53

Non-Hispanic white ................... 20,149 17,719 88 1,863 9 5,299 25 8,138 39

Non-Hispanic black ................... 12,493 11,968 96 1,332 11 4,142 33 6,221 49

Poverty status

Below poverty level ................... 12,591 11,303 87 1,651 12 4,634 35 6,690 51

100%–199% ....................... 12,668 11,391 86 1,585 12 4,336 31 6,274 45

200%–399% ....................... 12,200 11,264 91 1,313 10 3,589 27 5,527 40

400% or higher...................... 9,872 9,272 93 723 7 2,450 23 4,090 39

Age in years

Under 25 ......................... 28,286 26,065 90 3,176 10 9,265 29 13,911 44

25–64 ........................... 16,243 14,776 90 1,781 10 4,990 28 7,557 42

Over 65 .......................... 7,094 6,361 89 806 10 2,197 28 3,358 43

Education (over age 24)

Less than high school.................. 7,587 6,749 87 937 11 2,632 32 3,828 46

High school or GED1 .................. 5,588 4,995 88 677 11 1,712 28 2,587 42

More than high school ................. 10,548 9,757 91 1,026 9 2,985 26 4,697 41

Self-reported smoking status (adults)

Never smoker....................... 13,657 12,532 91 1,535 10 4,288 28 6,459 43

Past smoker ....................... 6,830 6,111 88 707 9 1,966 26 3,092 41

Current smoker...................... 5,709 5,102 89 717 12 1,914 31 2,795 45

Self-reported health status

Excellent. . . . . . . . . . . . . . . . . . . . . . . . . . 15,936 14,689 91 1,587 9 4,853 27 7,507 42

Very good ......................... 13,061 11,945 90 1,381 9 4,015 27 6,069 42

Good ............................ 14,655 13,420 89 1,746 10 4,815 29 7,172 44

Fair ............................. 6,439 5,832 89 855 12 2,250 32 3,307 46

Poor ............................ 1,505 1,296 82 189 11 512 31 760 46

1

GED is General Educational Development high school equivalency diploma.
 

SOURCE: CDC/NCHS, National Health and Nutrition Examination Surveys, 1999–2008 linked with traffic data from the National Highway Planning Network, 2005.
 
National Health Statistics Reports n Number 45 n April 2, 2012 Page 9

Table 2. Percentage of respondents with only one road within buffers of specific radii among those with one or more roads within

buffers of specific radii, by selected respondent characteristics

Within

100 meters

Within

300 meters

Within

500 meters

Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

Percent

79 66

Urbanization1,2,3

Large central metropolitan .........................................

Other counties.................................................

91

96

72

82

58

70

Race and ethnicity2,3

Mexican American . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Non-Hispanic white..............................................

Non-Hispanic black..............................................

93

96

93

74

82

74

60

69

61

Poverty status2,3

Below poverty level..............................................

100%–199% ..................................................

200%–399% ..................................................

400% or higher ................................................

93

94

96

96

74

76

81

83

59

64

68

71

Age in years

Under 25 ....................................................

25–64 ......................................................

Over 65. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

95

94

95

79

78

79

66

65

69

Education (over age 24)1,2,3

Less than high school ............................................

High school or GED4

.............................................

More than high school ............................................

91

96

96

73

79

81

60

66

68

Self-reported smoking status (adults)2,3

Never smoker .................................................

Past smoker ..................................................

Current smoker ................................................

94

95

95

78

82

77

65

69

63

Self-reported health status3

Excellent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Very good ...................................................

Good ......................................................

Fair .......................................................

Poor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

94

96

93

96

96

80

80

78

77

74

68

67

64

62

64

1

p value < 0.05 based on chi-square statistic for association between categories of respondent characteristic and number of roads (1 compared with 2 or more) for ‘‘within 100 meters.’’ 2

p value < 0.05 based on chi-square statistic for association between categories of respondent characteristic and number of roads (1 compared with 2 or more) for ‘‘within 300 meters.’’ 3

p value < 0.05 based on chi-square statistic for association between categories of respondent characteristic and number of roads (1 compared with 2 or more) for ‘‘within 500 meters.’’ 4

GED is General Educational Development high school equivalency diploma.

SOURCE: CDC/NCHS, National Health and Nutrition Examination Surveys, 1999–2008 linked with traffic data from the National Highway Planning Network, 2005.
Page 10 National Health Statistics Reports

n Number 45n April 2, 2012

ent distribution by quartiles of distance to the nearest road, overall, and within buffers of specific radii, by selected respondent

Total sample Within 100 meters Within 300 meters Within 500 meters

n 0–25 25–50 50–75 75–100 n 0–25 25–50 50–75 75–100 n 0–25 25–50 50–75 75–100 n 0–25 25–50 50–75 75–100

47,202 25 25 25 25 5,763 25 25 25 25 16,452 25 25 25 25 24,826 25 25 25 25

Percent distribution

19,237 28 28 27 17 2,703 18 19 29 34 7,584 22 27 26 25 11,209 26 25 25 24

27,965 24 24 24 29 3,060 29 28 23 20 8,868 27 24 24 25 13,617 25 25 25 25

12,518 31 29 24 16 1,834 21 19 32 28 4,963 26 26 25 23 7,551 26 25 24 25

17,719 23 24 25 28 1,863 27 27 24 22 5,299 26 24 25 26 8,138 25 25 26 25

11,968 27 26 27 20 1,332 21 27 23 29 4,142 23 26 26 25 6,221 25 26 25 24

11,303 32 28 21 19 1,651 23 32 21 25 4,634 25 24 25 27 6,690 26 26 26 22

11,391 30 24 24 22 1,585 25 26 27 23 4,336 29 27 23 21 6,274 29 26 23 22

11,264 24 22 27 26 1,313 27 24 23 25 3,589 26 26 24 23 5,527 27 26 23 24

9,272 19 25 26 29 723 25 21 27 27 2,450 21 24 27 27 4,090 20 23 27 30

26,065 26 26 25 24 3,176 25 26 24 26 9,265 24 25 26 26 13,911 24 25 25 25

14,776 25 25 25 26 1,781 25 24 26 25 4,990 25 25 25 25 7,557 25 25 25 25

6,361 25 25 25 25 806 26 27 26 21 2,197 28 25 24 23 3,358 26 24 24 25

21,324 26 26 24 24 2,630 25 24 25 27 7,774 24 25 25 26 11,585 25 26 26 24

6,448 27 24 24 25 869 27 26 23 25 2,271 28 25 25 22 3,398 28 25 22 25

11,227 23 25 26 26 1,235 25 26 26 23 3,490 25 25 25 25 5,440 24 24 26 26

12,532 24 25 26 25 1,535 24 24 26 27 4,288 24 25 25 26 6,459 25 24 26 25

6,111 23 25 26 26 707 27 25 28 20 1,966 27 24 24 26 3,092 25 23 25 27

5,102 29 24 23 24 717 27 29 21 23 1,914 28 26 25 21 2,795 29 27 23 22

14,689 23 25 26 26 1,587 24 25 26 25 4,853 24 24 26 26 7,507 23 25 25 27

11,945 23 25 25 26 1,381 23 26 24 26 4,015 24 25 25 25 6,069 25 24 26 25

13,420 26 25 25 24 1,746 27 25 24 24 4,815 26 25 24 25 7,172 25 25 25 25

5,832 30 24 25 21 855 25 23 25 26 2,250 27 26 24 23 3,307 28 27 24 21

1,296 29 30 21 21 189 26 26 28 20 512 28 22 22 27 760 27 23 26 24

ciation between categories of respondent characteristic and quartile of distance measure for ‘‘total sample.’’ciation between categories of respondent characteristic and quartile of distance measure for ‘‘within 100 meters.’’ciation between categories of respondent characteristic and quartile of distance measure for ‘‘within 300 meters.’’ciation between categories of respondent characteristic and quartile of distance measure for ‘‘within 500 meters.’’

ool equivalency diploma.

Quartiles of distance from residence to the nearest road within buffers of specific radii are defined by the median and interquarile ranges in Table I.

Examination Surveys, 1999–2008 linked with traffic data from the National Highway Planning Network, 2005.
National Health Statistics Reports

n Number 45n April 2, 2012 Page 11

tiles of length of roads within buffers of specific radii, by selected respondent characteristics

Radii

Within 100 meters Within 300 meters Within 500 meters

Percentiles Percentiles Percentiles

n 0–25 25–50 50–75 75–100 n 0–25 25–50 50–75 75–100 n 0–25 25–50 50–75 75–100

Percent distribution

...... 5,763 25 25 25 25 16,450 25 25 25 25 24,826 25 25 25 25

...... 2,703 31 31 17 21 7,584 24 24 24 27 11,209 24 24 20 31

...... 3,060 22 22 29 27 8,866 26 25 25 24 13,617 25 26 28 22

...... 1,834 28 31 17 24 4,962 22 24 26 27 7,551 24 24 22 30

...... 1,863 23 23 28 26 5,298 26 25 26 23 8,138 25 25 27 22

...... 1,332 28 25 23 24 4,142 25 25 22 28 6,221 24 26 21 29

...... 1,651 24 22 28 26 4,634 25 23 23 29 6,690 21 24 23 31

...... 1,585 23 27 25 24 4,334 22 23 28 28 6,274 23 22 27 28

...... 1,313 25 23 27 25 3,589 23 25 27 24 5,527 24 25 28 23

...... 723 28 26 22 24 2,450 28 29 23 20 4,090 30 27 23 20

...... 3,176 27 23 25 25 9,264 26 25 24 24 13,911 25 25 25 25

...... 1,781 25 26 25 24 4,990 25 25 25 26 7,557 25 25 25 26

. . . . . . 806 22 27 24 27 2,196 21 25 30 25 3,358 25 25 28 22

...... 2,630 26 25 23 26 7,774 26 24 23 27 11,585 24 25 24 27

...... 869 25 23 26 26 2,271 22 24 29 25 3,398 25 23 28 24

...... 1,235 24 26 27 23 3,489 25 26 25 24 5,440 26 25 24 24

2,3

...... 1,535 27 25 23 25 4,287 26 24 25 25 6,459 25 25 24 26

...... 707 20 28 26 26 1,966 26 25 27 22 3,092 27 25 26 21

...... 717 23 23 28 26 1,914 22 25 25 28 2,795 22 24 27 28

...... 1,587 25 26 25 24 4,851 26 25 24 25 7,507 27 26 25 22

...... 1,381 27 24 27 22 4,015 26 25 25 23 6,069 26 25 25 24

...... 1,746 23 24 25 28 4,815 24 25 25 25 7,172 24 25 24 26

...... 855 25 27 21 26 2,250 22 24 26 28 3,307 21 23 27 29

...... 189 22 28 24 27 512 27 20 26 26 760 25 23 24 28

ciation between categories of respondent characteristic and quartile of distance measure for ‘‘within 100 meters.’’ciation between categories of respondent characteristic and quartile of distance measure for ‘‘within 500 meters.’’ciation between categories of respondent characteristic and quartile of distance measure for ‘‘within 300 meters.’’

ool equivalency diploma.

Quartiles of distance from residence to the nearest road within buffers of specific radii are defined by the median and interquartile ranges shown in Table II.

Examination Surveys, 1999–2008 linked with traffic data from the National Highway Planning Network, 2005.
Page 12 National Health Statistics Reports

n Number 45n April 2, 2012

ent distribution by quartiles of average annual traffic density within buffers of specific radii, by selected respondent characteristics

Within 100 meters Within 300 meters Within 500 meters

n 0–25 25–50 50–75 75–100 n 0–25 25–50 50–75 75–100 n 0–25 25–50 50–75 75–100

1,519 25 25 25 25 6,418 25 25 25 25 11,279 25 25 25 25

† † † † † 3,038 6 20 29 45 5,483 8 20 28 44

821 36 28 20 †15 3,380 36 28 22 13 5,796 36 28 24 13

484 †17 13 †29 42 1,946 20 18 24 38 3,464 19 21 25 34

503 31 28 21 20 2,003 32 27 22 19 3,474 31 27 23 18

381 14a 30 31 25 1,672 16 23 32 29 3,036 15 25 30 31

454 23 20 31 26 1,840 27 25 23 25 3,096 25 26 22 27

435 23 34 20 23 1,743 29 26 24 22 2,936 28 25 25 22

312 34 23 20 23 1,366 27 23 25 25 2,481 29 22 25 23

178 20a 18a 32 30 903 20 27 28 26 1,707 21 27 28 23

853 25 24 26 25 3,675 25 25 25 26 6,382 24 25 25 26

459 28 23 25 25 1,948 25 24 25 26 3,420 25 24 25 25

207 19 36 22 22 795 28 30 25 17 1,477 29 27 26 18

683 26 24 23 26 2,997 23 25 25 27 5,302 25 25 25 25

238 23 31 25 21 915 31 27 21 21 1,542 31 28 19 22

315 28 21 27 25 1,330 24 23 28 25 2,388 23 23 29 25

412 23 24 28 25 1,674 23 25 26 27 2,941 24 23 27 26

167 25 22 21 32 721 26 24 27 23 1,376 25 25 27 23

200 30 29 24 17 735 29 28 20 23 1,240 28 28 23 22

414 28 20 26 26 1,877 23 25 25 27 3,395 24 23 27 26

370 21 27 26 26 1,581 25 24 27 24 2,722 25 25 25 26465 27 30 23 20 1,911 27 26 24 23 3,296 26 26 24 24

269 27 19 24 30 1,048 27 25 23 26 1,864 27 25 25 23

a dagger have a relative standard error (RSE) greater than 30%; estimates not shown have RSE greater than 50% or fewer than 12 degrees of freedom.

ciation between categories of respondent characteristic and quartile of distance measure for ‘‘within 300 meters.’’

association between categories of respondent characteristic and quartile of distance measure for ‘‘within 500 meters.’’

ciation between categories of respondent characteristic and quartile of distance measure for ‘‘within 100 meters.’’

ool equivalency diploma.

arest road within buffers of specific radii are defined by the median and interquartile ranges shown in Table III.

Examination Surveys, 1999–2008 linked with traffic data from the National Highway Planning Network, 2005.
National Health Statistics Reports n Number 45 n April 2, 2012 Page 13

Technical Notes

Selected definitions

Average annual daily traffic

(AADT)—The AADT value is the

average number of motor vehicles on

the road each day. Separate AADT

values can be estimated for different

segments of the road. For this linkage,

the highest AADT value was used for

roads with multiple segments within a

traffic buffer. The 2005.05 National

Highway Planning Network (NHPN)

includes AADT for selected roads from

the 2002 Highway Performance

Monitoring System (HPMS).

Functional classification

(FCLASS)—The FCLASS system

groups roads, streets, and highways into

different classes based on the character

of service they provide. The underlying

principle of this classification is that

individual roads and streets do not serve

as separate entities, but rather as a

network of roads through which traffic

moves. FCLASS designations can differ

among states, so an overall description

of this variable is not presented in this

report. The 2005.05 NHPN includes

FCLASS for selected roads from the

2002 HPMS.

National Highway Planning

Network (NHPN)—Maintained by the

Federal Highway Administration

(FHWA), NHPN is the most

comprehensive GIS-based network

database available, containing

information about the location and

features of the major roadways in the

United States. NHPN contains

information on over 450,000 miles of

roadways in the 48 contiguous states

and the District of Columbia, Alaska,

Hawaii, and Puerto Rico, consisting of

rural principal arterials, urban principal

arterials, and all National Highway

System routes. Smaller roadways are not

in this system. The NHPN’s primary

purpose is to help FHWA in highway

planning, policy analysis, network

modeling, and visualization of the

Highway Performance Monitoring

System database (HPMS).

National Highway System—

Approximately 160,000 miles (256,000

kilometers) of roadway important to the

U.S. economy, defense, and mobility.

Further information is available from

http://www.fhwa.dot.gov/planning/nhs/.

Highway Performance Monitoring

System (HPMS)—This database provides

data that reflect the extent, condition,

performance, use, and operating

characteristics of U.S. highways. It was

developed in 1978 as a national

highway transportation system database.

The database includes limited data on

all public roads, more detailed data for a

sample of the arterial and collector

functional systems, and certain statewide

summary information. Some highway

characteristics from the 2002 HPMS (for

example, AADT and FCLASS) are in

the 2005.08 NHPM database.

Rural principal arterials—The rural

principal arterial system consists of a

connected rural network of continuous

routes with the specific characteristics:

(a) serve corridor movements having

trip length and travel density

characteristics indicative of substantial

statewide or interstate travel; (b) serve

all or virtually all urban areas of 50,000

and over population and a large majority

of those with population of 25,000 and

over; and (c) provide an integrated

network without stub connections except

where unusual geographic or traffic flow

conditions dictate otherwise (for

example, international boundary

connections and connections to coastal

cities) (40).

Traffic buffer—For this linkage a

traffic buffer was defined as the area

included within a specified radius from

a respondent’s household address.

Traffic buffers of 100 m, 300 m, and

500 m were used in the calculations.

Urban principal arterials—Every

urban environment has a system of

streets and highways that can be

identified as unusually significant to the

area in which it lies in terms of the

nature and composition of travel it

serves. In smaller urban areas (under

population 50,000) these facilities may

be very limited in number and extent

and their importance may be primarily

derived from the service provided to

travel passing through the area. In larger

urban areas their importance also

derives from service to rural-oriented

traffic, but equally or even more

important, from service for major

movements within these urbanized areas.

This system of streets and highways is

the urban principal arterial system and

should serve the major centers of

activity of a metropolitan area, the

highest traffic volume corridors, and the

longest trip desired, and should carry a

high proportion of the total urban area

travel on a minimum of mileage. The

system should be integrated both

internally and between major rural

connections.
Page 14 National Health Statistics Reports n Number 45 n April 2, 2012

Table I. Median and interquartile range of distance from residence to nearest road within buffers of specific radii, by selected respondent

characteristics

Radii

Total Within 100 meters Within 300 meters Within 500 meters

Median (25th, 75th percentile)

Median p25 p75 Median p25 p75 Median p25 p75 Median p25 p75

Distance in meters

Total ............................... 528 237 1,085 36 15 70 145 66 223 227 111 349

Urbanization

Large central metropolitan ................. 454 207 869 51 19 78 148 75 222 224 107 346

Other counties......................... 565 254 1,229 28 12 63 144 60 224 230 112 350

Race and ethnicity

Mexican American ...................... 423 189 811 49 17 74 142 64 216 224 103 347

Non-Hispanic white...................... 578 258 1,226 32 13 64 147 62 224 231 112 349

Non-Hispanic black...................... 483 216 944 38 17 76 148 76 222 223 112 342

Poverty status

Below poverty level...................... 389 186 842 32 16 69 150 65 227 218 104 331

100%–199% .......................... 461 188 963 36 15 65 131 56 211 202 94 336

200%–399% .......................... 567 245 1,151 35 13 71 138 61 217 216 101 343

400% or higher ........................ 602 298 1,239 39 15 74 158 82 231 258 134 371

Age in years

Under 25 ............................ 514 234 1,049 36 15 73 150 73 225 230 113 350

25–64 .............................. 540 241 1,099 38 15 70 144 64 222 226 109 347

Over 65. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 524 234 1,083 33 14 65 138 56 219 224 105 351

Education (over age 24)

Less than high school .................... 498 225 1,045 38 15 73 149 73 227 226 111 341

High school or GED1

..................... 520 220 1,083 33 14 70 138 57 213 213 98 347

Greater than high school .................. 555 257 1,112 36 15 67 146 65 224 236 115 354

Self-reported smoking status (adults)

Never smoker ......................... 534 244 1,076 38 15 74 149 70 226 231 112 349

Past smoker .......................... 553 260 1,114 33 13 64 143 58 224 240 111 359

Current smoker ........................ 487 204 1,045 31 14 66 138 52 212 207 95 333

Self-reported health status

Excellent ............................ 551 254 1,119 38 15 71 150 73 224 235 118 358

Very good ........................... 554 254 1,139 36 16 73 147 68 224 233 111 349

Good .............................. 514 229 1,060 33 14 68 143 63 223 225 107 349

Fair ............................... 465 197 956 38 14 74 138 61 215 206 97 335

Poor ............................... 418 206 915 35 13 63 145 60 228 226 100 343

1

GED is General Educational Development high school equivalency diploma.
 

SOURCE: CDC/NCHS, National Health and Nutrition Examination Surveys, 1999–2008 linked with traffic data from the National Highway Planning Network, 2005.
 
National Health Statistics Reports n Number 45 n April 2, 2012 Page 15

Table II. Median and interquartile range of length of roads within buffers of specific radii, by selected respondent characteristics

Within 100 meters Within 300 meters Within 500 meters

Median (25th, 75th percentile)

Median p25 p75 Median p25 p75 Median p25 p75

Total .......................... 187 145 198 544 409 600 952 737 1,227

Urbanization

Large central metropolitan. . . . . . . . . . . . .

Other counties ....................

178

192

129

154

197

199

551

541

415

406

606

600

963

946

746

731

1,492

1,074

Race and ethnicity

Mexican American .................

Non-Hispanic white .................

Non-Hispanic black .................

177

190

184

137

151

132

198

199

198

555

540

545

427

405

411

600

599

602

964

948

955

747

734

752

1,393

1,091

1,425

Poverty status

Below poverty level .................

100%–199% .....................

200%–399% .....................

400% or higher ...................

190

186

189

184

150

152

145

135

199

198

199

198

554

559

550

516

405

441

420

386

602

601

600

597

982

971

956

915

795

766

751

676

1,429

1,336

1,141

1,025

Age in years

Under 25 .......................

25–64 .........................

Over 65 ........................

188

187

188

138

145

151

198

198

199

538

546

557

398

409

432

600

600

600

948

953

950

734

741

733

1,221

1,267

1,117

Education (over age 24)

Less than high school ...............

High school or GED1 ................

More than high school ...............

186

188

188

140

143

147

199

199

198

541

561

540

398

436

405

600

600

600

956

964

941

752

739

721

1,316

1,173

1,172

Self-reported smoking status (adults)

Never smoker ....................

Past smoker .....................

Current smoker ...................

185

190

192

140

156

152

198

199

199

546

542

559

403

408

431

600

599

601

950

932

976

734

705

774

1,275

1,072

1,349

Self-reported health status

Excellent .......................

Very good.......................

Good .........................

Fair. . . . . . . . . . . . . . . . . . . . . . . . . . .

Poor ..........................

187

186

190

185

188

145

136

150

140

156

198

198

199

199

199

539

540

546

559

556

403

400

412

423

397

600

599

600

601

600

932

949

952

981

963

716

731

746

779

737

1,111

1,173

1,283

1,406

1,341

1

GED is General Educational Development high school equivalency diploma.
 

SOURCE: CDC/NCHS, National Health and Nutrition Examination Surveys, 1999–2008 linked with traffic data from the National Highway Planning Network, 2005.
 
Page 16 National Health Statistics Reports n Number 45 n April 2, 2012

Table III. Median, 25th, and 75th percentiles of average annual traffic density within buffers of specific radii, by selected respondent

characteristics

Within 100 meters Within 300 meters Within 500 meters

Median (25th, 75th percentile)

n Median p25 p75 n Median p25 p75 n Median p25 p75

Total. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,519 21,586 13,844 43,451 6,418 31,953 17,508 76,531 11,279 38,049 19,667 101,416

Urbanization

Large central metropolitan ...................... † † † † 3,038 61,770 31,348 141,434 5,483 81,634 35,728 160,018

Other counties ............................. 821 18,020 9,701 27,625 3,380 23,317 13,856 45,333 5,796 27,302 15,501 51,811

Race and ethnicity

Mexican American ........................... 484 35,539 17,419 †83,359 1,946 42,242 21,851 129,891 3,464 51,731 24,710 141,276

Non-Hispanic white .......................... 503 19,648 11,651 31,096 2,003 26,352 15,530 55,740 3,474 29,928 16,399 71,980

Non-Hispanic black .......................... 381 25,295 15,973 44,386 1,672 38,744 22,902 90,817 3,036 48,858 25,948 120,228

Poverty status

Below poverty level .......................... 454 25,449 13,901 44,807 1,840 30,370 16,726 75,863 3,096 37,291 19,660 109,327

100%–199%............................... 435 19,729 14,310 40,065 1,743 29,335 16,149 63,074 2,936 36,652 18,100 81,119

200%–399%............................... 312 19,390 †9,280 41,198 1,366 31,914 16,430 73,008 2,481 35,871 17,388 93,974

400% or higher ............................. 178 27,139 15,271 44,557 903 36,077 19,993 79,357 1,707 38,948 21,544 98,375

Age in years

Under 25................................. 853 21,717 13,849 44,825 3,675 32,852 18,089 79,322 6,382 38,990 19,929 105,863

25–64................................... 459 21,617 12,652 44,384 1,948 34,402 17,693 79,774 3,420 38,970 19,724 105,123

Over 65 ................................. 207 20,937 17,657 37,550 795 27,834 16,348 55,489 1,477 33,127 18,562 71,987

Education (over age 24)

Less than high school ......................... 683 21,532 13,357 45,431 2,997 34,428 18,670 81,558 5,302 38,176 19,700 104,136

High school or GED1 ......................... 238 20,377 13,904 35,274 915 27,298 15,632 61,409 1,542 30,036 16,935 82,448

More than high school......................... 315 21,777 13,026 41,980 1,330 35,732 18,112 75,833 2,388 41,138 20,769 101,829

Self-reported smoking status (adults)

Never smoker.............................. 412 22,652 15,115 42,022 1,674 35,755 19,115 80,763 2,941 41,027 20,331 106,875

Past smoker............................... 167 22,430 13,895 50,215 721 32,160 16,940 68,878 1,376 37,460 19,650 87,595

Current smoker ............................. 200 19,665 12,488 37,383 735 27,809 16,402 63,108 1,240 32,201 17,565 90,375

Self-reported health status

Excellent ................................. 414 21,872 12,981 45,498 1,877 36,638 19,473 84,724 3,395 40,640 19,912 105,748

Very good ................................ 370 22,320 15,116 45,115 1,581 33,435 17,843 70,897 2,722 38,419 19,882 105,599

Good ................................... 465 19,606 12,903 39,642 1,911 30,050 16,733 71,374 3,296 36,676 19,661 96,751

Fair or poor ............................... 269 25,210 12,684 46,981 1,048 30,041 16,688 79,186 1,864 36,665 17,958 90,772

† Estimate may be unreliable; estimates preceded by a dagger have a relative standard error (RSE) greater than 30%; estimates not shown have RSE greater than 50% or fewer than 12 degrees
 

of freedom.
 1

GED is General Educational Development high school equivalency diploma.
 

SOURCE: CDC/NCHS, National Health and Nutrition Examination Surveys, 1999–2008 linked with traffic data from the National Highway Planning Network, 2005.
 
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Suggested citation

Parker JD, Kravets N, Nachman K, Sapkota A.

Linkage of the 1999–2008 National Health and

Nutrition Examination Surveys to traffic

indicators from the National Highway Planning

Network. National health statistics reports; no

45. Hyattsville, MD: National Center for Health

Statistics. 2012.

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DHHS Publication No. (PHS) 2012–1250 • CS229301