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 (
It has been well documented that geographical access is an important determinant of treatment utilization in the general population [
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 knowledge, 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 [
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 [
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 [
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 [
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.
OTPs were classified as falling into one of four USA regions. These regions, as designated by the U.S. Census Bureau [
OTPs were in counties coded as high-density areas (population >1 million); 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 [
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.
Patients were asked “Other than drug withdrawal pain, is bodily pain a reason for enrolling in methadone treatment?” with responses of “Yes” or “No.”
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).
Patients were asked “Which word best describes your drug withdrawal pain in the past week?” with five responses ranging from “None” to “Very Severe.”
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.”
Patients were asked whether they had used heroin in the 30-day period before admission (Yes/No).
Patients were asked whether they had used a prescription opioid to get high in the 30-day period before admission (Yes/No).
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 [
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 treatment; 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,
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.
We prepared a map of study and nonstudy 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) [
Table
Opioid treatment program and patient characteristics.
% | Mean | SD | Median | Minimum | Maximum | |
---|---|---|---|---|---|---|
USA Region | ||||||
Northeast | 25 | |||||
Southeast | 38 | |||||
Midwest | 17 | |||||
West | 20 | |||||
Beale urbanicity | ||||||
Metro Area >1 million | 53 | |||||
≥250 K and <1 million | 29 | |||||
<250 K | 18 | |||||
ZIP code square miles | 40 | 134 | 10 | <1 | 1,186 | |
Number of patients sampled | 275 | 347 | 155 | 2 | 1,947 | |
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 |
Table
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
Distribution of travel distance (
Prior to controlling 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 Table
Multilevel model predicting patient travel distance to OTP.
Predictor | 95% Confidence interval | |||||
Zero-order correlation | Regression coefficient | SE | Lower | Est. | Upper | |
USA Region | ||||||
Southeast versus Northeast | 0.145 | 1.354 | 1.808 | 2.413 | ||
Midwest versus Northeast | 0.170 | 1.015 | 1.424 | 2.000 | ||
West versus Northeast | −0.215 | 0.169 | 0.576 | 0.807 | 1.130 | |
Urbanicity | ||||||
250 K–1 M versus Metro >1 M | 0.129 | 1.051 | 1.359 | 1.758 | ||
<250 K versus Metro >1 M | 0.160 | 1.223 | 1.683 | 2.316 | ||
Patient ZIP code area | 0.006 | 1.345 | 1.360 | 1.375 | ||
Program ZIP code area | 0.036 | 0.050 | 0.938 | 1.037 | 1.146 | |
Age | ||||||
30–43 versus 18–29 | −0.021 | 0.013 | 0.954 | 0.979 | 1.004 | |
43–81 versus 18–29 | 0.016 | 0.871 | 0.899 | 0.927 | ||
Female | <.01 | −0.019 | 0.012 | 0.958 | 0.981 | 1.005 |
Race/ethnicity | ||||||
Hispanic versus non-Hispanic white | 0.022 | 0.680 | 0.709 | 0.740 | ||
Black versus non-Hispanic white | 0.025 | 0.605 | 0.635 | 0.666 | ||
Other versus non-Hispanic white | 0.036 | 0.836 | 0.896 | 0.961 | ||
Employed | 0.005 | 0.012 | 0.972 | 0.995 | 1.019 | |
Pain a reason for treatment | .01 | −0.011 | 0.012 | 0.965 | 0.989 | 1.013 |
First methadone treatment | 0.004 | 0.012 | 0.980 | 1.004 | 1.028 | |
Strong urge to use | <0.001 | 0.018 | 0.966 | 1.000 | 1.035 | |
Severe withdrawal | 0.003 | 0.014 | 0.976 | 1.003 | 1.030 | |
Prescription opioid use in past 30 days | 0.016 | 1.051 | 1.085 | 1.119 | ||
Heroin use in past 30 days | −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.
*
In the first block of the linear mixed model analysis, urbanicity was related to travel distance. Patients in moderately 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.
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.
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 (
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.
Table
Multilevel logistic regression model predicting patient travel across state to OTP.
Predictor | 95% Confidence interval of the unadjusted odds ratio | 95% Confidence interval of the adjusted odds ratio | ||||
Lower | Odds ratio | Upper | Lower | Odds ratio | Upper | |
U.S. Region | ||||||
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 | 1.649 | 10.959 | 72.848 | 1.878 | 12.081 | 77.697 |
<250 K versus Metro >1 M | 0.105 | 1.384 | 18.160 | 0.230 | 3.148 | 42.997 |
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 |
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 |
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.
Figure
Distribution of opioid treatment programs (OTPs) within the continental United States: study
Approximately three quarters (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 respondents with complete data is greater than distance traveled among respondents with missing data (15.4 versus 13.8 miles,
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.
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.
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 proportionally 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 (
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 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 [
These data also have policy implications. In 2008 there were 268,071 OTP patients in the United States [
Although there are more than 1,200 OTPs throughout the United States it is important to recognize that OTP coverage varies across states [
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 [
Because OTPs were selected to represent USA regions 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.
The study was funded through a subcontract with the American 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 staff of the opioid treatment programs for participating in the study.