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<h3 class="c-article__sub-heading" id="Sec3">Sampling and Recruitment</h3>
<p>This research aimed to investigate transportation-related obstacles in accessing healthcare among groups that typically experience higher burdens, such as low-income individuals, seniors, and those with chronic illnesses. We specifically sampled individuals from these demographics, focusing on low-income persons, those aged 65 and above, and individuals who needed to access care multiple times over the previous year. Participants were recruited via data from the Carolina Data Warehouse for Health (CDW-H), a central repository containing clinical, research, and administrative information from the UNC Health system. UNC Health, a not-for-profit organization owned by North Carolina, operates multiple hospitals and medical practices statewide. From an initial pool of 34,387 individuals, we narrowed down to a sample of approximately 15,000 who met specific criteria: (1) having Medicaid or Medicare as their primary insurance; (2) being North Carolina residents; (3) being over 18; (4) possessing a valid email address; and (5) having six or more outpatient visits between April 2020 and April 2021.</p>
<p>The first criterion, having Medicaid or Medicare, led to a higher representation of individuals aged 65 and older in our sample. To achieve better representation of those aged 18–64, we oversampled from this demographic. We also ensured that the age groups of 65–79 and 80+ closely matched the state population, which comprises 15.9% aged 65–79 and 4.5% aged 80 and above, according to recent Census data. Ultimately, 14,723 individuals were included, with 6,945 aged 18–64, 6,201 aged 65–79, and 1,577 aged 80 or older (see Table 1, column 1).</p>
<h3 class="c-article__sub-heading" id="Sec4">Data Collection</h3>
<p>The research protocol and data collection tools received approval from the Institutional Review Board at the University of North Carolina at Chapel Hill. Data was gathered using REDCap, a secure web platform for managing online surveys and databases. We invited participants through email to complete a web-based survey, sending up to three reminders. The emails advertised a chance to win one of twenty $50 gift cards for participants. Respondents first filled an eligibility screener to verify they met the criteria, followed by a consent form and optional HIPAA authorization. Data collection took place from June 21 to July 23, 2021, with 728 individuals completing the eligibility screener, 433 completing the consent form, and 383 partially finishing the survey, leading to a 2.6% response rate.</p>
<h3 class="c-article__sub-heading" id="Sec5">Study Sample</h3>
<p>The study sample consisted of 323 eligible respondents who fully answered the questions analyzed in this study (see Table 1, column 2). Similar to the recruitment sample, a higher percentage of respondents were aged 65–79 (52.9%), with 38.7% aged 18–64 and 8.4% being 80 or older. Most respondents identified as female (57.9%). Additionally, a greater proportion identified as White or Caucasian (82.7%) compared to those identifying as Black or African American (13.0%), contrasting with the recruitment sample, which more accurately mirrored state demographics: 71.6% White and 21.9% Black for North Carolina residents aged 18 and older.</p>
<h3 class="c-article__sub-heading" id="Sec6">Data Analysis</h3>
<h4 class="c-article__sub-heading c-article__sub-heading--small" id="Sec7">Analytic Approaches</h4>
<p>We generated descriptive statistics to explore the prevalence of transportation "difficulties" or "problems," which we collectively termed "barriers." These barriers led to late arrivals, delays, or missed healthcare appointments. We quantified these issues and healthcare outcomes based on individual and household characteristics and reported the unadjusted associations using Fisher's exact test. Further, multivariate binomial logistic regressions were conducted to understand the adjusted correlations between individual, household, and geographic characteristics and these transportation barriers linked to negative healthcare outcomes.</p>
<h4 class="c-article__sub-heading c-article__sub-heading--small" id="Sec8">Independent Variables</h4>
<p>We gathered information on individual and household variables known to influence travel behavior and experiences with transportation and healthcare. Key to this study, we asked respondents how many times they attended medical appointments or treatments in the past year, as appointment frequency impacts the likelihood of late arrivals and has been associated with missed appointments in prior studies. Demographic information such as age, gender, race, and ethnicity was collected. Participants were categorized into age groups: 18–64 years and 65 years or older, and race was grouped into White or Non-White. Those identifying as "White," regardless of additional racial or ethnic identifications were classified in the White category. Additionally, respondents reported if they had a disability or chronic condition affecting daily activities; those affirming were classified as having a disability in our analyses. Information regarding insurance types was also gathered.</p>
<h4 class="c-article__sub-heading c-article__sub-heading--small" id="Sec9">Outcome Measures</h4>
<p>Four binary outcome measures were employed to assess how transportation barriers affected healthcare access. Respondents indicated if transportation problems resulted in any of the following in the past year: (1) delaying medical appointment or treatment scheduling; (2) missing a medical appointment or treatment; (3) arriving over 20 minutes late; or (4) experiencing any of these issues. Delayed care and missed appointments can lead to numerous adverse consequences for patients, including increased hospitalizations and poor long-term health outcomes, while late arrivals may disrupt clinic operations and diminish overall service quality for patients.</p>
<h4 class="c-article__sub-heading c-article__sub-heading--small" id="Sec10">Transportation Barriers</h4>
<p>By analyzing responses concerning "transportation problems" that led to late arrivals, delays, or missed care, we identified commonly reported barriers. We employed thematic analysis to further characterize these barriers through open-ended responses, utilizing Dedoose, a web-based application for qualitative research analysis. Such qualitative techniques can enrich findings related to subjective transportation experiences, adding depth to the understanding of travel behavior issues.</p>
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