Evidence map›Paper›PMID 38376839›Full record

ArticleJAMA network open2024

Accessibility of Opioid Treatment Programs Based on Conventional vs Perceived Travel Time Measures.

Junghwan Kim, Jinhyung Lee, Thomas A Thornhill, Julia Dennett, Haidong Lu, Benjamin Howell, Lauretta E Grau, David A Fiellin, Robert Heimer, Gregg Gonsalves

Open access · goldAbstract read
In one paragraph

Article in JAMA network open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
6.2field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 16 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 3 institutions in 2 countries.

Junghwan KimDepartment of Geography, College of Natural Resources and Environment, Virginia Tech, Blacksburg.
Jinhyung LeeDepartment of Geography and Environment, Faculty of Social Science, Western University, London, Ontario, Canada.
Thomas A ThornhillDepartment of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut.
Julia DennettDepartment of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut.
Haidong LuDepartment of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut.
Benjamin HowellDepartments of Medicine and Emergency Medicine, Yale School of Medicine, New Haven, Connecticut.
Lauretta E GrauDepartment of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut.
David A FiellinDepartments of Medicine and Emergency Medicine, Yale School of Medicine, New Haven, Connecticut.
Robert HeimerDepartment of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut.
Gregg GonsalvesDepartment of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut.
Yale University · USVirginia Tech · USWestern University · CA

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Making Better Decisions: Policy Modeling for AIDS and Drug AbuseR37DA015612 · NIDA · STANFORD UNIVERSITY · PI DOUGLAS K OWENS · 2019 to 2026
$6.3M
Novel, Adaptive Approaches to Predicting and Responding to Outbreaks of Overdose, HIV and HCV Among People Who Use DrugsDP2DA049282 · NIDA · YALE UNIVERSITY · PI GONSALVES, GREGG STEVEN · 2019 to 2019
$2.5M
Using System Dynamics Modeling to Foster Real-time Connections to CareR61DA057675 · NIDA · YALE UNIVERSITY · PI HECKMANN, REBEKAH, S. SABOUNCHI, NASIM · 2022 to 2023
$1.4M
Evaluating and Optimizing Care for Opioid Use Disorder using a Structured Data-Science ApproachK99DA057487 · NIDA · YALE UNIVERSITY · PI LU, HAIDONG · 2023 to 2024
$327k
NCATS NIH HHS UL1 TR001863NIDA NIH HHS DP2 DA049282NIDA NIH HHS K99 DA057487NIDA NIH HHS R37 DA015612NIDA NIH HHS R61 DA057675
6 · The paper itself

Abstract

Importance: Transportation barriers have long been associated with poorer health outcomes; this burden is especially acute for individuals with opioid use disorder (OUD), a chronic disease often associated with low socioeconomic status. Conventional travel time analyses may not fully account for experiential components of travel, thereby understating the true travel burden and overstating treatment accessibility to opioid treatment programs (OTPs). Objective: To develop a metric of feels-like accessibility for those using public transit to access OTPs that accounts for the realistic travel burden on individuals with OUD. Design, Setting, and Participants: This cross-sectional study integrated high-resolution transit schedules and operating hours of OTPs to measure feels-like accessibility. Feels-like accessibility considers the differential outcomes of out-of-vehicle travel components and more realistically reflects individuals' transportation burden than conventional accessibility measures. Gini indices and spatial regression models were used to investigate inequities in accessibility. Geocoded data for residential addresses of 1018 overdose fatalities in Connecticut in 2019 were used as a proxy for the treatment needs of individuals with OUD. Data were analyzed between May and August 2023. Main Outcomes and Measures: Conventional and feels-like accessibility scores. Exposures: Fluctuations in public transit frequencies over the course of the day and the limited operating hours of the OTPs. Results: Of the 1018 individuals in the study, the mean (SD) age at death was 43.7 (12.6) years, 784 individuals (77%) were men, 111 (11%) were African American, and 889 (87%) were White, with other racial and ethnic categories including 18 individuals (2%). A total of 264 individuals in the sample (26%) could not access an OTP within 180 minutes. For those who could access these facilities, the average 1-way travel time was 45.6 minutes, with individuals spending approximately 70% of their trip duration on out-of-vehicle travel components. The conventional accessibility metric underestimates individuals' travel burden to OTPs as well as the inequity in accessibility compared with the feels-like accessibility metric. For example, the median (range) conventional accessibility score, defined as the number of OTPs within 120 minutes of transit travel time, was 5.0 (0.0-17.0); the median (range) feels-like accessibility score, defined as the number of OTPs within 120 minutes of transit travel time weighted to account for in- and out-of-vehicle segments, was 1.0 (0.0-10.0). There is a considerable temporal variation in travel time and accessibility depending on the departure times. Conclusions and Relevance: In this cross-sectional study of travel burdens, the calculated feels-like accessibility scores, which consider the differential outcomes of out-of-vehicle travel components (eg, walking and waiting), could better and more realistically reflect passengers' transportation burden. Policy recommendations derived from the conventional accessibility metric could be misleading, and decision-makers should use feels-like accessibility metrics that adequately capture individuals' travel burdens. In the context of access to OTPs, the findings from this study suggest that opening new OTP sites to address gaps in access due to distance to services or extending hours of operation at existing sites may ameliorate the travel burden for individuals.

Indexed as

Analgesics, OpioidOpioid-Related DisordersCross-Sectional StudiesFemaleHumansMaleTransportationTravelAnalgesics, Opioid

Identifiers

PMID38376839
PMCPMC10879949
OpenAlexW4391954347

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.