Distance Traveled and Cross-State Commuting to Opioid Treatment Programs in the United States

Abstract

This study examined commuting patterns among 23,141 methadone patients enrolling in 84 opioid treatment programs (OTPs) in the United States. Patients completed an anonymous one-page survey. A linear mixed model analysis was used to predict distance traveled to the OTP. More than half (60%) the patients traveled < 10 miles and 6% travelled between 50 and 200 miles to attend an OTP; 8% travelled across a state border to attend an OTP. In the multivariate model (n = 17,792), factors significantly (P < .05) associated with distance were, residing in the Southeast or Midwest, low urbanicity, area of the patient's ZIP code, younger age, non-Hispanic white race/ethnicity, prescription opioid abuse, and no heroin use. A significant number of OTP patients travel considerable distances to access treatment. To reduce obstacles to OTP access, policy makers and treatment providers should be alert to patients' commuting patterns and to factors associated with them.

Full text

Hindawi Publishing Corporation
Journal of Environmental and Public Health
Volume 2011, Article ID 948789, 10 pages
doi:10.1155/2011/948789
Research Article
Distance Traveled and Cross-State Commuting to
Opioid Treatment Programs in the United States
Andrew Rosenblum,1Charles M. Cleland,1, 2 Chunki Fong,1Deborah J. Kayman,1
Barbara Tempalski,1and Mark Parrino3
1National Development and Research Institutes, Inc. (NDRI), 71 W 23 Street, 8th Floor, New York, NY 10010, USA
2College of Nursing, New York University (NYU), 726 Broadway, 10th floor, New York, NY 10003, USA
3American Association for the Treatment of Opioid Dependence (AATOD), 225 Varick Street, 4th Floor, New York, NY 10014, USA
Correspondence should be addressed to Andrew Rosenblum, rosenblum@ndri.org
Received 31 December 2010; Revised 7 April 2011; Accepted 21 April 2011
Academic Editor: Pam R. Factor-Litvak
Copyright © 2011 Andrew Rosenblum et al. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
cited.
This study examined commuting patterns among 23,141 methadone patients enrolling in 84 opioid treatment programs (OTPs) in
the United States. Patients completed an anonymous one-page survey. A linear mixed model analysis was used to predict distance
traveled to the OTP. More than half (60%) the patients traveled <10 miles and 6% travelled between 50 and 200 miles to attend an
OTP; 8% travelled across a state border to attend an OTP. In the multivariate model (n=17, 792), factors sig nificantly (P<.05)
associated with distance were, residing in the Southeast or Midwest, low urbanicity, area of the patient’s ZIP code, younger age,
non-Hispanic white race/ethnicity, prescription opioid abuse, and no heroin use. A significant number of OTP patients travel
considerable distances to access treatment. To reduce obstacles to OTP access, policy makers and treatment providers should be
alert to patients’ commuting patterns and to factors associated with them.
1. Introduction
It has been well documented that geographical access is
an important determinant of treatment utilization in the
general population [1]. Among substance abusers longer
travel distances are associated with shorter length of stay
and lower probability of completion and aftercare utilization
[2,3]. Ongoing utilization (i.e., treatment retention) is
especially important among methadone maintained patients
because of the importance of continued medication that
is often required to achieve and sustain treatment gains
[4,5]. Distance may be an especially relevant factor among
methadone patients who live in rural or other areas where
public transportation is limited. In addition to distance
another geographical relevant factor is whether methadone
patients cross state lines to attend an opioid treatment
program (OTP), typically the only setting where methadone,
for the treatment of opioid dependence, can be dispensed.
The primary objective of the current study was to
determine central tendency and variation in OTP enrollees’
travel distance between home and the treatment program.
A secondary objective was to identify program and patient
factors associated with travel distance. Since, to our knowl-
edge, this is the first study to describe commuting patterns
among OTP enrollees, it is primarily descriptive. We did
not have strong hypotheses regarding which patient or
program characteristics are likely to be associated with travel
distance. However, we expected that travel distance would be
negatively correlated with urbanicity (since there is likely to
be a higher concentration of OTPs within urban areas than
nonurban areas). Also, based on findings from our previous
study of OTP enrollees [6] we expected that factors associated
with low urbanicity such as prescription opioid abuse,
white race, employment, and first methadone treatment
episode would also be associated with longer travel distances.
The data collection protocol for this study was conceived
and executed under the auspices of the Researched Abuse,
Diversion and Addiction-Related Surveillance (RADARS)
System. The RADARS System, which was initiated through a
dialogue between the Food and Drug Administration (FDA)

2 Journal of Environmental and Public Health
and Purdue Pharma L.P. and which is now an independent
operation of Denver Health & Hospital Authority, is a
proactive surveillance program that monitors and charac-
terizes the abuse and diversion of opioid pain medication
[7].
2. Methods
2.1. Sample. Data were collected from January 2005 through
December 2009 from 23,141 enrollees in 84 OTPs located
in 34 states. The number of study participants across the
84 OTPs ranged from 2 to 1,947 (median 155; quartile
range: 71 to 372). Not all programs began study participation
in January of 2005; 68 (81%) OTPs participated for 2 or
more years; 12 (14%) participated for at least one year but
less than two years; and 4 (5%) for less than one year.
OTPs were selected primarily to represent regions in the
USA where prescription opioid (PO) abuse was believed
to be prevalent, for example, non-urban areas, especially
those in the Appalachian region. Some OTPs were located
in major metropolitan areas such as San Francisco and
New York City, where PO abuse among OTP patients is
believed to be relatively less prevalent. All programs were
federally approved opioid agonist treatment programs and
followed federal methadone treatment protocols that require
an opioid-dependence diagnosis and an addiction history of
at least 1 year [8]. The research protocol was approved by
the Institutional Review Board of the National Development
and Research Institutes, Inc. The respondents in this study
include treatment seeking persons who reported abusing
prescription opioids or heroin in the past 30 days and were
not in methadone treatment in the previous 30 days.
All patients enrolling in the study OTPs were targeted for
recruitment. Several study procedures were implemented to
maximize the number of OTP enrollees who participated.
Patients were given an information sheet explaining the
study’s rationale and procedures and that participation was
voluntary and anonymous. Patients who completed the one-
page survey instrument were compensated with a $5 food
coupon. The OTPs were instructed to have patients complete
the survey during the first week of admission and to fax
the surveys (along with a cover sheet that indicated the
number of surveys included in the fax transmission) via a
secure Internet-based fax service that forwarded the faxes
to NDRI as an email attachment. (The faxed surveys were
read by an automated form-driven data capture software
program, Teleform Version 8.2, Verity, Inc., Sunnyvale, CA).
In order to reinforce adherence to these procedures, each of
the participating programs signed a letter of agreement that
specified these study protocols and identified a liaison staff
person who served as the principal contact between the OTP
and the study’s project director at the American Association
for the Treatment of Opioid Dependence (AATOD; the
organization responsible for communicating with the OTPs).
The programs also received a stipend for their participation.
Program participation was further reinforced by sending
quarterly reports to each program of their data and an
aggregate report of all study data. Further details of OTP
participation and the subject recruitment protocol can be
found in a 2007 publication that described differences
between prescription opioid users and heroin users [6].
2.2. Variable Definitions. In order to assess travel distance
and correlates associated with it, we utilized relevant data
from the 1-page patient administered survey as well as
program location information. Survey items addressed the
following domains: demographics, treatment history, opioid
use, drug craving, and pain; the survey also asked patients for
their 5-digit ZIP code. Pain items were included because of
the high prevalence of pain complaints among OTP patients
[9,10]. These measures are described below.
2.2.1. Travel Distance. The distance in miles between the
center of the ZIP code of the patient’s residence and the
street location of the OTP was used to represent patient
travel distance. MapPoint (Microsoft Corp., Redmond, WA)
was used to determine the centroid point of the patient’s
residence ZIP code, the latitude/longitude of the OTP
address, and the distance between these two locations. For
statistical modeling, the natural logarithm of travel distance
(with a constant of 1 added to all distances) was used. In
order to avoid including subjects who may have written an
incorrect ZIP code we excluded 266 patients who reported
traveling more than two hundred miles to a treatment
program. An examination of dispersion of the travel distance
data showed that while distance was highly skewed to the
right, the steepness of the decline precipitously dropped and
remained at same level at approximately 200 miles. This
pattern suggests that distances up to 200 miles were valid
since they showed an expected pattern of fewer subjects with
greater distance.
2.2.2. United States Region. OTPs were classified as falling
into one of four USA regions. These regions, as designated
by the U.S. Census Bureau [11], are Northeast (Maine,
New Hampshire, Vermont, Massachusetts, Rhode Island,
Connecticut, New York, New Jersey, Pennsylvania); Midwest
(Ohio, Indiana, Illinois, Michigan, Wisconsin, Minnesota,
Iowa, Missouri, North Dakota, South Dakota, Nebraska,
Kansas); South (Delaware, Maryland, District of Columbia,
Virginia, West Virginia, North Carolina, South Carolina,
Georgia, Florida, Kentucky, Tennessee, Alabama, Mississippi,
Arkansas, Louisiana, Oklahoma, Texas); West (Montana,
Idaho, Wyoming, Colorado, New Mexico, Arizona, Utah,
Nevada, Washington, Oregon, California, Alaska, Hawaii).
2.2.3. Beale Urbanicity Code. OTPs were in counties coded
as high-density areas (population >1million);moderately
populated counties (≥250,000 and <1 million residents),
and low populated counties (<250,000 residents); these three
categories were determined by a modified version of the
Beale Urbanicity Code [12].TheBealecodehas9codeswith
6 codes representing counties with <250,000 residents. Our
previous research showed that a relatively small percent of
respondents resided in the low populated counties; therefore
we collapsed these 6 codes to reduce the skewness of the Beale
distribution.

Journal of Environmental and Public Health 3
2.2.4. Program and Patient Residence ZIP Code Areas. Both
sizes of the ZIP code in which the patient resided and in
which the program was located were indicated by area in
square miles. For statistical modeling, the natural logarithm
of square miles (with a constant of 1 added to all areas) was
used.
2.2.5. Bodily Pain a Reason for Enrolling. Patients were asked
“Other than drug withdrawal pain, is bodily pain a reason for
enrolling in methadone treatment? ” with responses of “Yes”
or “No.”
2.2.6. First Methadone Treatment. Patients were asked
“Before coming to this program, when were you last in a
methadone treatment program?” with a response of “Never”
indicating first methadone treatment (Yes/No).
2.2.7. Withdrawal Severity. Patients were asked “Which word
best describes your drug withdrawal pain in the past week? ”
with five responses ranging from “None” to “Very Severe.”
2.2.8. Urge to Use. Patients were asked “Which word best
describes your urge to use your primary drug in the past
week? ” with five responses ranging from “None” to “Very
Strong.”
2.2.9. Recent Heroin Use. Patients were asked whether they
had used heroin in the 30-day period before admission
(Yes/No).
2.2.10. Recent Prescription Opioid Use. Patients were asked
whether they had used a prescription opioid to get high in
the 30-day period before admission (Yes/No).
2.3. Data Analysis. A total of 5,349 respondents were not
included in a multivariable mixed model analysis due to
missing data on gender, race/ethnicity, source of income,
urge to use, withdrawal severity, past methadone treatment,
or pain as a reason for enrolling in treatment, resulting in a
sample for multivariable analysis of 17,792 and a total sample
of 23,141 OTP enrollees.
To account for clustering of the 17,792 patients in 84
OTPs, a linear mixed model analysis with a random intercept
[13] was used to predict distance traveled to the treatment
program by individual patients. The nlme package [14,15]
of the freely available, open-source Rprogram [16]was
used to fit linear mixed models. Program-level predictors
included population density and the natural logarithm of the
square miles of each OTP’s ZIP code. Patient-level predictors
included age, gender, race/ethnicity, employment, pain as a
reason for enrolling in treatment, first methadone treatment,
urge to use, withdrawal severity, use of prescription opioids
in the past 30 days, and use of heroin in the past 30 days.
Predictors were entered in conceptually related blocks
of variables. The first block included the program-level
variables of urbanicity. The second block included the
area of both the program ZIP code and the ZIP code
of the patient’s residence. The third block included the
following demographic variables: age; gender; race/ethnicity;
employment as the major source of income. The final
block included the following characteristics related to each
patient’s opioid use: pain as a reason for enrolling in treat-
ment; first methadone treatment; urge to use; withdrawal
severity; prescription opioid use in the past 30 days; heroin
use in the past 30 days. Each variable’s relation to travel
distance was estimated controlling for other variables on
the same block and for all variables entered in previous
blocks.
The use of a natural logarithm transformation effectively
reduced positive skew in travel distance. To convey the
effect of predictors in the mixed model analysis, fixed-
effects regression coefficients were exponentiated (i.e., the
Eulerian number, e, which is the base of the natural logarithm
and approximately equal to 2.718282, was raised to the
power of each regression coefficient). These exponentiated
regression coefficients indicate how a one-unit change in
the predictor multiplies travel distance. For example, a
regression coefficient of .70 is interpreted as roughly a
doubling (e.70 =2.014) of travel distance for a one-unit
change in the predictor (or for a coefficient representing the
contrast between a particular level of a categorical predictor
and the reference category of that categorical variable).
In a separate analysis we used a multilevel logistic
regression model to identify covariates among patients who
crossed a state border to attend an OTP.
2.4. Mapping OTPs. We prepared a map of study and non-
study OTPs to represent their location in the U.S. Location
data for OTPs was obtained from the USA Substance Abuse
and Mental Health Services Administration (SAMHSA) [17]
including an updated report that there are no OTPs in South
Dakota (Nick Reuter, SAMHSA, e-mail communication,
Oct. 13, 2010). A map can immediately display spatial
associations and patterns, otherwise impossible to identify
in a tabular format. Data were imported into MapPoint
(Microsoft Corp., Redmond, WA) to map the locations of
OTPs and patient 3DZ code level data throughout the USA
on a national map. Maps are traditionally a way of visualizing
tabular data.
3. Results
Tab l e 1 presents data on the characteristics of patients and
treatment programs. The area of the ZIP codes in which the
84 programs were located ranged from less than one square
mile to more than one thousand squares miles, with a median
of ten square miles. The number of patients responding to
the survey in each program ranged from two to more than
a thousand, with a median of 155 patients per program.
Morethanhalfofallprograms(54%)werelocatedindensely
populated urban areas, and at least fourteen programs were
sampled from each USA region.
Tab l e 1 presents characteristics of patients and programs
in the full sample (n=23,141). Patient age ranged
from 18 to 81 years with a median age of 32 years. Most
patients were male and non-Hispanic white. Thirty percent
of patients used both prescription opioids and heroin in
the month before treatment. The comparably higher rate of
prescription opioid use (73%) compared with heroin use

4 Journal of Environmental and Public Health
Tab l e 1: Opioid treatment program and patient characteristics.
% Mean SD Median Minimum Maximum
Program Variables (n=84)
USA Region
Northeast 25
Southeast 38
Midwest 17
West 2 0
Beale urbanicity
Metro Area >1million 53
≥250 K and <1million 29
<250 K 18
ZIP code square miles 40 134 10 <1 1,186
Number of patients sampled 275 347 155 2 1,947
Patient var iables (n=23, 141)
Miles traveled to OTP 15 23 7 <1 200
ZIP code square miles 38 89 13 <1 5,031
Age
18–29 43
30–43 33
44–81 24
Male 60
Race/ethnicity
African american 9
Hispanic 11
White 77
Other 3
Employed 44
First methadone treatment 50
Pain a reason for treatment 34
Strong urge to use 85
Severe withdrawal 71
Prescription opioid use past 30 days 73
Heroin use past 30 days 57
(57%) may be accounted for by the oversampling of OTPs
within regions were prescription opioid use is believed to be
prevalent.
3.1. Travel Distance. The average distance traveled from
patients’ residence to treatment programs was 15 miles, with
a median travel distance of 7 miles and an interquartile range
of 3 to 16 miles. More than half of all patients (60%) traveled
less than 10 miles, and most patients (94%) traveled less than
50 miles; 26% of the patients traveled more than 15 miles
and 2% traveled more than 100 miles to attend their OTP.
Figure 1 is a plot of travel distance (untransformed) which
shows the central tendency, variability, and shape of the
sample distribution in details (The mean and median travel
distances were the same for the full sample (n=23,141) and
for the sample included in the linear mixed model analysis
(n=17,792)).
3.2. Mixed-Effects Model for Travel Distance. Prior to control-
ling for program and patient level predictors we generated
zero-order correlations of these variables with the log
transformed value of distance. As can be seen in Tab l e 2,most
measures are significantly (P<.05) associated with distance.
The strongest correlates (r≥.20) include urbanicity,
ZIP code area of patient’s residence, race/ethnicity, recent
prescription opioid use, and no recent use heroin use.
In the first block of the linear mixed model analysis,
urbanicity was related to travel distance. Patients in moder-
ately and low populated counties traveled 1.359 and 1.683
times as many miles as patients in densely population urban
areas.
In the second block, the area of the ZIP code in which
the patient resided was related to travel distance. A one-unit
increase in the natural logarithm of square miles multiplied
the travel distance by 1.360 miles.

Journal of Environmental and Public Health 5
0 20406080100120140160180200
0
2
4
6
8
10
12
14
16
18
20
22
24
Miles traveled to opioid treatment program
Frequency/100
Figure 1: Distribution of travel distance (n=23,141).
In the third block, both age and race/ethnicity were
related to travel distance. Patients between 44 and 81 years
of age traveled 0.899 times as many miles as patients 18 to
29 years of age. Hispanics, African Americans, and patients
in other racial/ethnic groups traveled 0.709, 0.635, and 0.896
times as many miles as non-Hispanic white patients.
In the fourth and final block, travel distance was
positively related with prescription opioid use and negatively
related with heroin use. Patients using prescription opioids
in the month before enrollment traveled 1.085 times as many
miles as patients not using prescription opioids.
We checked for multicollinearity in the model with all
blocks entered, and all variance inflation factors were below
2.0, suggesting no problems.
3.3. Supplementary Analysis. Since frequency of racial/ethnic
groups was strongly associated with urbanicity (e.g., percent
non-Hispanic white in low, moderate, and high density
areas was respectively, 93%, 89%, and 70%) a stratified
analysis involved estimating the contrasts between patient
racial/ethnic groups only for patients (n=6, 902) enrolling
in programs located in moderately or less populated counties
(n=39). Consistent with our hypothesis, African American
patients traveled 0.679 and Hispanic patients traveled 0.852
times as many miles as non-Hispanic white patients (both
P<.01).
3.4. Cross-State Commuting. Eight percent of all patients
commuted to another state to attend an OTP, traveling
an average of 50 miles (median =36). More than 20%
of the patients in 10 OTPs traveled across a state border
to attend their program. Average (mean) distance traveled
among these interstate patients from these 10 OTPs was 49
miles (median =34). The average (mean) distance traveled
among those same 10 OTPs who did not cross a state
border was 25 miles (median =14). An additional 4 OTPs
had 5% to 19% of their patients commuting across state
lines to attend the program. Cross-state commuting was
more prevalent in the southeast (14% of patients) and
Midwest (24% of patients) compared to the West (<1%
of patients) and the Northeast (2% of patients). Among
patients attending programs located in counties of different
population densities, cross-state commuting was 8% in low,
15% in moderate, and 5% in high population density areas.
3.4.1. Correlates of Cross-State Commuting. Ta b le 3 shows the
results of the multilevel logistic regression analysis on cross-
state commuting. Patients were more likely to cross a state
border if their OTP was located in the Midwest (compared
with the Northeast) and less likely if their OTP was in the
West compared with the Northeast. Patients attending an
OTP in relatively low densely populated areas (250 K −1M)
compared with a metropolitan area (>1 M) as well as those
whose OTP was located in a large ZIP code area were more
likely to commute across a state border. The one significant
patient covariate was race/ethnicity: African-Americans and
other race/ethnicities (excluding Hispanics) were less likely
to cross a state border than whites. The adjusted odds ratios
for these same variables were also significant except for
Midwest OTP location.
3.5. Location of Study OTPs. Figure 2 displays a map repre-
senting the distribution of the participating OTPs (n=84) to
the distribution of nonparticipating OTPs (1,142) within the
4 US regions. Regionally, the respective distribution of study
OTPs and all OTPs is Southeast (38% and 35%); Northeast
(25% and 34%); Midwest (17% and 7%); West (20% and
24%). The respective Beale distribution of study OTPs and
all OTPs is densely populated counties (>1M: 54% and 62%);
moderately populated counties (250 K −1M; 29% and 31%),
and least densely populated counties (<250 K: 18% and 7%).
Among the 12% (n=2,878) of respondents who attended an
OTP in a county with a different Beale code, 91% (n=2,631)
went to an OTP located in a more densely populated county.
We also found that the more OTPs in a respondent’s ZIP code
the more likely they would travel within their ZIP code (OR =
14.9; CI =13.3 to 16.8), within their state (OR =7.5; 5.3 to
10.8) and a shorter distance (β=−.17, P<.001).
3.6. Comment on Missing Data. Approximately three quar-
ters (76%) of respondents had complete data. Missing data
on a self-administered survey is not surprising. With the
exception of Employment and First Methadone Treatment,
each item was answered by >95% of the respondents.
But since our multivariate approach was listwise deletion
only those cases with complete data were retained in the
multivariate analysis. Mean distance traveled for respon-
dents with complete data is greater than distance traveled
among respondents with missing data (15.4 versus 13.8
miles, P<.001). This does suggest some bias, although

6 Journal of Environmental and Public Health
Tab l e 2: Multilevel model predicting patient travel distance to OTP.
Predictor 95% Confidence interval
Zero-order
correlation
Regression
coefficient SE Lower Est. Upper
Block one
USA Region .21
Southeast versus Northeast 0.592∗∗ 0.145 1.354 1.808 2.413
Midwest versus Northeast 0.354∗0.170 1.015 1.424 2.000
West versus Northeast −0.215 0.169 0.576 0.807 1.130
Urbanicity −.18
250 K–1 M versus Metro >1M 0.307∗∗ 0.129 1.051 1.359 1.758
<250 K versus Metro >1M 0.521∗0.160 1.223 1.683 2.316
Block two
Patient ZIP code area .30∗∗ 0.307∗∗ 0.006 1.345 1.360 1.375
Program ZIP code area −.08 0.036 0.050 0.938 1.037 1.146
Block three
Age .18∗∗
30–43 versus 18–29 −0.021 0.013 0.954 0.979 1.004
43–81 versus 18–29 −0.107∗∗ 0.016 0.871 0.899 0.927
Female <.01 −0.019 0.012 0.958 0.981 1.005
Race/ethnicity .30∗∗
Hispanic versus non-Hispanic white −0.344∗∗ 0.022 0.680 0.709 0.740
Black versus non-Hispanic white −0.454∗∗ 0.025 0.605 0.635 0.666
Other versus non-Hispanic white −0.109∗∗ 0.036 0.836 0.896 0.961
Employed .13∗∗ 0.005 0.012 0.972 0.995 1.019
Block four
Pain a reason for treatment .01 −0.011 0.012 0.965 0.989 1.013
First methadone treatment .14∗∗ 0.004 0.012 0.980 1.004 1.028
Strong urge to use .03∗∗ <0.001 0.018 0.966 1.000 1.035
Severe withdrawal .02∗∗ 0.003 0.014 0.976 1.003 1.030
Prescription opioid use in past 30 days .26∗∗ 0.081∗∗ 0.016 1.051 1.085 1.119
Heroin use in past 30 days −.33∗∗ −0.031 0.017 0.939 0.970 1.002
Interval estimates for each predictor have been exponentiated and can be interpreted as how travel distance is multiplied given a one-unit change in the
predictor (or a contrast between one level of a categorical predictor and the reference category for that predictor). For predictors with multiple categories (i.e.,
USA Region, Urbanicity, Age, and Race/Ethnicity), the zero-order correlation is the multiple correlation when log distance is regressed on dummy variables.
∗P<.05; ∗∗ P<.01.
the magnitude of the distance between these two values is
modest. Consistent with this finding, that a shorter travel
distance is associated with missing data, we found that
patient characteristics associated with missing data on other
variables were also associated with most of the variables
that were correlated with shorter travel distance (older age,
nonwhite ethnicity, unemployment, previous methadone
treatment, no prescription opioid use, and heroin use).
4. Discussion and Summary
4.1. Summary of Findings. Although the median distance
traveled to an OTP was 7 miles, a small minority of
patients (6%) traveled more than 50 miles to their OTP.
Program variables related to patient travel distance included
USA region and urbanicity. Patients enrolling in programs
located in the Southeast and Midwest traveled greater
distances than patients enrolling in the Northeast and West.
Patients enrolling in programs located in moderately or low
populated counties traveled greater distances than patients
enrolling in densely populated urban areas.
Patient variables related to travel distance included
age, race/ethnicity, and the type of opioid used in the
month before enrollment. Younger patients traveled greater
distances than older patients. Non-Hispanic white patients
traveled greater distances than Hispanic, African American,
and patients in other racial/ethnic groups. Patients using
prescription opioids in the month before enrollment traveled
greater distances than patients using heroin in the month
before enrollment.

Journal of Environmental and Public Health 7
Tab l e 3: Multilevel logistic regression model predicting patient travel across state to OTP.
Predictor
95% Confidence interval of 95% Confidence interval of
the unadjusted odds ratio the adjusted odds ratio
Lower Odds ratio Upper Lower Odds ratio Upper
Block one
U.S. Reg ion
Southeast versus Northeast 0.314 2.516 20.140 0.126 1.051 8.737
Midwest versus Northeast 1.044 11.826 133.970 0.821 8.473 87.475
West versus Northeast 0.003 0.063 1.480 0.002 0.038 0.839
Urbanicity
250 K −1M versus Metro >1M 1.649 10.959 72.848 1.878 12.081 77.697
<250 K versus Metro >1M 0.105 1.384 18.160 0.230 3.148 42.997
Block two
Patient ZIP code area 0.921 0.983 1.049 0.918 0.980 1.046
Program ZIP code area 1.024 2.007 3.933 1.317 2.928 6.510
Block three
Age
30–43 versus 18–29 0.943 1.115 1.320 0.950 1.125 1.332
43–81 versus 18–29 0.891 1.126 1.423 0.900 1.142 1.448
Female 0.872 1.020 1.192 0.837 0.987 1.164
Race/Ethnicity
Hispanic versus white 0.262 0.559 1.192 0.266 0.570 1.220
Black versus white 0.198 0.416 0.875 0.191 0.402 0.847
Other versus white 0.196 0.403 0.830 0.195 0.403 0.829
Employed 0.778 0.911 1.068 0.767 0.906 1.072
Block four
Pain a reason for treatment 0.882 1.032 1.209 0.880 1.032 1.211
First methadone treatment 0.722 0.849 0.997 0.734 0.868 1.026
Strong urge to use 0.667 0.861 1.112 0.676 0.895 1.185
Severe withdrawal 0.778 0.934 1.123 0.801 0.978 1.194
Prescription opioid use in past 30 days 0.654 0.988 1.493 0.602 0.935 1.450
Heroin use in past 30 days 0.854 1.064 1.327 0.858 1.084 1.369
Patient ZIP code area and program ZIP code area are continuous measures; all other covariates are coded 0, 1.
We also found that 8% of all patients crossed a state
border to attend their OTP and that among a small number
(10) of OTPs more than 20% of patients commuted across
a state border. Minorities were less likely to cross a state
border as well as patients whose OTP was located in the West
(compared to the Northeast), in a metropolitan area or in
smaller ZIP code area.
4.2. Discussion of Findings. Patients in the Southeast and
Midwest traveled greater distances to treatment programs
than patients in the Northeast. These regional differences
cannot be entirely explained by regional differences in
urbanicity, because urbanicity was included on the same
block with USA region. Future research could determine
whether individuals residing in the Southeast and Midwest
regions travel greater distances because there may be pro-
portionally fewer programs per opioid-dependent patient in
those regions. We also found a lower likelihood of cross-
state travel to an OTP located in the West versus Northeast
(OR =.038). The small odds ratio suggests a large effect
but the wide confidence interval (.002 to .839) suggests not
much precision in estimating that effect. When these two
regions are compared, commuting may be more common
in the Northeast because smaller states are closer together
and less common in the West because states are larger. The
wide confidence interval (as well as the wide confidence
interval for Midwest versus Northeast) is likely due to the
small number of programs in the West and Midwest regions
(see map) and the relatively small percentage of clients who
commuteacrossstatestoanOTP.
Patients who enrolled in an OTP located in low or
moderately populated counties traveled greater distances
than patients in densely populated urban areas. (A similar
pattern was also observed for cross-state commuting.) These

8 Journal of Environmental and Public Health
US regions
Midwest
Northeast
South
West
Distribution of OTPs
Non-study OTP
Study OTP
0 200 400 600 800 1000 1200
(miles)
Figure 2: Distribution of opioid treatment programs (OTPs) within the continental United States: study OTPs =83; nonstudy OTPs =1130.
Since the map only represents the continental USA it does not include the study OTP in Alaska or nonstudy OTPs outside of the continental
USA.
differences may reflect the fact that travel distances for a
variety of activities are greater when population density is
lower. The youngest patients (18–29 years) traveled greater
distances than the oldest patients (43–81 years). For older
opioid-addicted individuals, other medical conditions may
present more barriers to travel.
African American, Hispanic, and opioid users in other
racial/ethnic groups traveled a shorter distance and (with
the exception of Hispanics) were less likely to cross a state
border when attending an OTP. Racial/ethnic minorities may
have less access to transportation to a treatment program
than non-Hispanic white patients. Also, it may be that more
treatment programs are located in neighborhoods that are
predominantly African American, Hispanic, or other non-
white racial/ethnic groups. Travel distance can suggest more
resources to travel a greater distance and possibly more
motivation to overcome distance-related barriers. In that
sense, travel distance may be both a sign of privilege and
at the same time a burden and risk factor for treatment
dropout. It should be noted that, though we know that non-
Hispanic white OTP patients are more likely than patients
from other racial/ethnic groups to reside in the Southeast
and low population areas (two factors associated with greater
commuting distance), these factors were controlled in the
analysis and therefore do not entirely explain race/ethnic
differences in distance traveled.
A possible reason why heroin use is associated with
shorter travel distance is that heroin is generally more
readily available in locations where OTPs are more densely
located (i.e., urban areas). In nonmetropolitan areas heroin
distribution markets are generally more informal and heroin
is comparatively more expensive [18]. The association of
prescription opioid abuse in less densely populated areas
may partially be attributed to the relatively recent emergence
of prescription opioids in rural areas [19] and the use
of prescription opioids to self-medicate pain complaints,

Journal of Environmental and Public Health 9
a phenomenon that has been observed in certain nonurban
areas with a high rate of prescription opioid abuse, for
example, Appalachia [20].
These data also have policy implications. In 2008 there
were 268,071 OTP patients in the United States [21]. Given
that 26% of patients traveled more than 15 miles to attend
their OTP, that 6% of patients reside more than 50 miles
from their OTP, and that among a subset of 10 OTPs
more than 20% of patients traveled across a state border to
attend their program, it is important for policy makers and
treatment providers to be alert regarding commuting factors.
Previous studies with other clinical populations report that
distance is negatively associated with health care utilization.
Although patients were only surveyed once (at enrollment) it
is reasonable to assume that disparities in travel distance are
also likely to be associated with treatment retention especially
because federal law requires newly enrolled patients to attend
the program at least 6 days a week to pick up their medication
[22]; opioid-dependent patients who live at a greater distance
from an OTP are likely to be at higher risk for dropping
out of treatment than patients who live closer to a program.
This may be especially evident in rural areas where OTPs
are more dispersed, access to drug treatment is less available
and underutilized [23], and where treatment continuity
is likely to decline compared to treatment continuity in
urban areas [24]. It is therefore important that programs
respond to these challenges by assuring treatment continuity,
such as establishing a more flexible take home policy,
mobile methadone maintenance services, and methadone
medical maintenance, that is, provision of methadone by an
office-based physician or pharmacy [25,26]. Efforts should
also be made to provide low threshold treatment which
may serve to attract and retain patients. Examples could
include expansion of buprenorphine within OTPs along with
establishing links with office-based physicians who prescribe
buprenorphine. Office-based buprenorphine treatment is
less stringently regulated than medication-assisted treatment
provided at an OTP. Another concern is disaster planning. If
local programs are wiped out by some natural disaster like
hurricane Katrina, plans should be in place to bring mobile
treatment to them, to set up programs in other facilities still
standing that may not have offered treatment before, and to
make sure that programs in neighboring states will promptly
take in patients who are evacuated or who choose later on to
relocate.
Although there are more than 1,200 OTPs throughout
the United States it is important to recognize that OTP
coverage varies across states [17]. For example, while some
states such as New York, California, and North Carolina are
relatively well saturated with OTPs (within certain regions),
3 states have no OTPs (North Dakota, South Dakota,
and Wyoming), and there are a number of other states
with only 1 to 3 operating OTPs, (e.g., Idaho, Mississippi,
Montana, and Nebraska). Moreover various states impose
moratoriums on the development of new OTPs from time
to time and various states are (as of 2010) proposing or
have recently passed extremely restrictive legislation with
regard to the zoning of OTPs [27]. Despite evidence-
based research demonstrating the effectiveness of methadone
maintenance treatment [28,29], expanding access to much-
needed services for chronic opioid addiction through the
OTPs has still not been realized. One means to address
this limitation would be a sustained and national public
education campaign about medication-assisted treatment.
4.3. Limitations. Travel distances were estimated based on
the center of the ZIP code in which the patient resided
and the exact location of the treatment program. If the
exact locations of the patients’ residence were known, travel
distances could be estimated with greater precision. The
straight line distance between the centroid of the patients’
ZIP code to the exact location of the OTP does not capture
other factors affecting travel time such as large variability
in ZIP code size, road infrastructure, traffic, and mode of
transportation. Also, travel cost and time may be offset if the
treatment program location is close to resources which allow
the patient to complete other life tasks such as going to work
and shopping [30].
BecauseOTPswereselectedtorepresentUSAregions
where prescription opioid use was expected to be prevalent,
the sample is not representative of the population of OTP
enrollees. Given the prevalence of prescription opioid use in
less densely populated USA regions, the central tendency for
travel distance observed in this sample may be higher than
the central tendency for the population of OTP enrollees.
The study did not formally determine the number of OTP
enrollees. Therefore it is possible that not all patients who
enrolled in an OTP during the study period completed
a survey. However, ongoing conversations between the
AATOD project director and the OTP liaison indicate that
more than 90% of patients completed the survey.
We recognize that there were several limitations to this
study. However, we feel that it is important to present these
results regarding travel distance to OTPs because it likely
has a significant impact on the quality of life (including
treatment retention) among patients. As far as we know this
is the first study to document OTP travel distance and cross-
state commuting across various locations throughout the
United States. Further studies such as qualitative studies on
how patients cope with long travel distances and longitudinal
studies to examine the impact of distance on treatment
retention should be conducted.
Acknowledgments
The study was funded through a subcontract with the Amer-
ican Association for the Treatment of Opioid Dependence
(AATOD) as part of the Researched Abuse, Diversion and
Addiction-Related Surveillance (RADARS) System. AATOD
subcontracted with NDRI to perform the study reported
here. The authors thank the patients and staffof the opioid
treatment programs for participating in the study.
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