Evidence map›Paper›PMID 40057737›Full record

ArticleBMC public health2025

Does travel time matter?: predictors of transportation vulnerability and access to HIV care among people living with HIV in South Carolina.

Sayward Elizabeth Harrison, Peiyin Hung, Katherine Green, Sarah J Miller, Mariajosé Paton, Divya Ahuja, Sharon Weissman, Caroline Rudisill, Tammeka Evans

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Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

9 authors.

Sayward Elizabeth Harrison *Department of Psychology, South Carolina SmartState Center for Healthcare Quality, University of South Carolina, Columbia, South Carolina, USA. harri764@mailbox.sc.edu.
Peiyin Hung *Department of Health Services Policy and Management, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA.
Katherine GreenDepartment of Internal Medicine, University of South Carolina School of Medicine, Prisma Health-Midlands, Columbia, SC, USA.
Sarah J MillerDepartment of Psychology, South Carolina SmartState Center for Healthcare Quality, University of South Carolina, Columbia, South Carolina, USA.
Mariajosé PatonDepartment of Psychology, South Carolina SmartState Center for Healthcare Quality, University of South Carolina, Columbia, South Carolina, USA.
Divya AhujaDepartment of Internal Medicine, University of South Carolina School of Medicine, Prisma Health-Midlands, Columbia, SC, USA.
Sharon WeissmanDepartment of Internal Medicine, University of South Carolina School of Medicine, Prisma Health-Midlands, Columbia, SC, USA.
Caroline RudisillDepartment of Health Promotion, Education and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA.
Tammeka EvansViiV Healthcare, Research Triangle Park, NC, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPeople living with HIV (PLHIV) in the southern United States (US) are at high risk for poor outcomes across the HIV care continuum leading to low rates of viral suppression. Understanding structural barriers to care-including transportation vulnerability-is critical to improve HIV outcomes. This study investigated relationships between travel time to HIV care, transportation vulnerability, and HIV care disruptions to inform future transportation interventions for PLHIV residing in South Carolina and other southern US states.

methodsA total of 160 PLHIV (N = 160) were recruited from a large immunology center in South Carolina. Participants reported on transportation experiences, transportation vulnerabilities, and residence. Differences in sociodemographic characteristics, transportation vulnerabilities, and HIV care disruptions were compared across travel time groups (< 15, 15-30, and > 30 min from residential location to the HIV clinic) using Mantel-Haenszel Chi-Square tests. Multivariable logistic regression tested our a priori hypothesis that travel time would predict HIV care disruptions.

resultsA majority of participants were aged 45-64 years old (54.4%), single (77.0%), male (63.8%), and Black (77.5%). Nearly 20% of participants lived < 15 min from their HIV clinic, 59.1% lived 15-30 min, and 21.4% lived > 30 min away. PLHIV who had to travel > 30 min to HIV care were more likely than those living < 15 min away to report transportation vulnerability (73.5% vs. 51.6%, p = 0.048), missed HIV care appointments (64.7% vs. 41.9%, p = 0.049), and transportation challenges that prevented them from seeing HIV care providers (67.7% vs. 39.4%; p = 0.014). Adjusted odds ratios (AOR) show that PLHIV who had to travel > 30 min were more likely to experience transportation-related disruptions to HIV care, including being late to appointments (AOR 5.25, 95% CI:1.06-25.92), missing appointments (AOR 3.85, 95% CI:1.04-15.89), and being unable to see HIV providers (AOR 7.06, 95% CI:0.59-14.89).

conclusionsIn South Carolina-a rural southern state with a disproportionate burden of HIV-long travel time (> 30 min) to HIV care is associated with care disruptions, including more missed visits. Transportation interventions, as well as other efforts to expand rural access to HIV care, are urgently needed to ensure that all PLHIV are able to engage in consistent HIV care in order to reach and maintain viral suppression.

Indexed as

Health Services AccessibilityHIV InfectionsTransportationTravelAdultFemaleHumansMaleMiddle AgedSouth CarolinaTime FactorsCare disruptionsHIV careHIV disparitiesTransportation accessTransportation vulnerability

Identifiers

PMID40057737
PMCPMC11889868

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LicenceCC BY-NC-ND
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Registered trials

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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.