Average Travel Distance Research Articles

Hospital closure in urban and rural areas and patients’ welfare

The hospital sector is frequently subject to reconfigurations, with some departments closing and new ones opening. Using a conditional logit model based on observed patient choices, we quantify the effects of a hospital department closure on the welfare of elective hip replacement patients in England. We simulate eight separate closures of the provider with lowest quality, as measured by one of four quality measures: revisions, emergency readmissions, 30-day mortality, change in the Oxford Hip Score, in urban and rural areas. The average reduction in welfare for patients who attended the closed hospital is equivalent to having to travel between two and ten additional kilometres for treatment, compared to their average travel distance, pre-closure, of 17.4 kilometres. The reduction in patient welfare is generally more pronounced when closing a hospital in a rural area (about 50% higher when quality is measured by the Oxford Hip Score and emergency readmissions).

Mapping the Distance: An Analysis of Visitor Travel Distance to German National Parks and Biosphere Reserves

The ecologically valuable and unique natural and cultural landscapes of protected areas are popular nature tourism destinations. From a spatial perspective, travellers visit protected areas by moving from their place of origin to the destination for a temporary stay. This paper analyses the travel distances of day visitors and overnight guests to German national parks and biosphere reserves. National parks position themselves as both regional and national destinations. In contrast, biosphere reserve tourism is more regionally concentrated due to the proximity of biosphere reserves to urban centres, which are the primary places of origin for visitors. On average, visitors travel 278.6 km by car to visit a national park, taking 172.1 minutes (02:52 hours). Opting for public transport increases the average travel distance to 312.5 km, with a travel time of 284.5 minutes (04:43 hours). Visitors to German biosphere reserves travel 208.7 km by car, requiring 134.7 minutes (02:15 hours). When using public transport to visit biosphere reserves, the average distance increases to 234.0 km, with a travel time of 239.1 minutes (03:59 hours). The automated, GIS-supported distance analyses offer an innovative approach for assessing nature tourism catchment areas and the accessibility of protected areas, serving as a basis for effective spatial planning, development, and regional marketing.

Spatial Analysis of Middle-Mile Transport for Advanced Air Mobility: A Case Study of Rural North Dakota

Integrating advanced air mobility (AAM) into the logistics of high-value electronic commodities can enhance efficiency and promote sustainability. The objective of this study is to optimize the logistics network for high-value electronics by integrating AAM solutions, specifically using heavy-lift cargo drones for middle-mile transport and using the mostly rural and small urban U.S. state of North Dakota as a case study. The analysis utilized geographic information system (GIS) and spatial optimization models to strategically assign underutilized airports as multimodal freight hubs to facilitate the shift from long-haul trucks to middle-mile air transport. Key findings demonstrate that electronics, because of their high value-to-weight ratio, are ideally suited for air transport. Comparative analysis shows that transport by drones can reduce the average cost per ton by up to 60% compared to traditional trucking. Optimization results indicate that a small number of strategically placed logistical hubs can reduce average travel distances by more than 13% for last-mile deliveries. Cost analyses demonstrate the viability of drones for middle-mile transport, especially on lower-volume rural routes, highlighting their efficiency and flexibility. The study emphasizes the importance of utilizing existing infrastructure to optimize the logistics network. By replacing truck traffic with drones, AAM can mitigate road congestion, reduce emissions, and extend infrastructure lifespan. These insights have critical implications for supply chain managers, shippers, urban planners, and policymakers, providing a decision support system and a roadmap for integrating AAM into logistics strategies.

Risk and Reward: Nationwide Analysis of Cardiac Transplant Center Variation in Organ Travel Distance and the Effects on Outcomes.

The 2018 UNOS allocation policy change deprioritized geographic boundaries to organ distribution, and the effects of this change have been widespread. The aim of this investigation was to analyze changes in donor transplant center distance for organ travel and corresponding outcomes before and after the allocation policy change. The UNOS database was utilized to identify all adult patients waitlisted for heart transplants from 2016 to 2021. Transplant centers were grouped by average donor heart travel distance based on whether they received more or less than 50% of organs from >250 miles away. Descriptive statistics were provided for waitlisted and transplanted patients. Regression analyses modeled waitlist mortality, incidence of transplant, overall survival, and graft survival. Centers with a longer average travel distance had a higher mean annual transplant volume with a reduction in total days on a waitlist (86.6 vs. 149.2 days), an increased cold ischemic time (3.6 vs. 3.2 h), with no significant difference in post-transplant overall survival or graft survival. The benefits of reducing waitlist time while preserving post-transplant outcomes extend broadly. The trends observed in this investigation will be useful as we revise organ transplant policy in the era of new organ procurement and preservation techniques.

Nonlinear Relationship of Multi-Source Land Use Features with Temporal Travel Distances at Subway Station Level: Empirical Study from Xi’an City

The operation of the subway system necessitates a comprehensive understanding of passenger flow characteristics at station locations, as well as a keen awareness of the average travel distances at these stations. Moreover, the travel distances at the station level bear a direct relationship with the built environment composed of land use characteristics within the station’s catchment area. To this end, we selected the land use features within an 800 m radius of the station (land use area, distribution of points of interest, and the surrounding living environment) as the influencing factors, with the travel distances at peak hours on the subway network in Xi’an as the research subject. An improved SSA-XGBOOST-SHAP interpretable machine learning framework was established. The research findings demonstrate that the proposed enhanced model outperforms traditional machine learning or linear regression methods in terms of R-squared, MAE, and RMSE. Furthermore, the distance from the city center, road network density, the number of public transit routes, and the land use mix have a pronounced influence on travel distances, reflecting the significant impact that mature built environments can have on passenger attraction. Additionally, the analysis reveals a notable nonlinear relationship and threshold effect between the built environment variables comprising land use and the travel distances during peak hours. The research results provide data-driven support for operational strategy management and line capacity optimization, as well as theoretical underpinnings for enhancing the efficiency and sustainability of the entire subway system.

Development of an integrated urban modelling framework for examining the impacts of work from home on travel behavior

This paper develops an integrated urban modelling framework (IUMF) to predict how work from home (WFH) decision affects travel behavior. First, it conducts a questionnaire survey among working professionals in Halifax, Canada, to collect data on their socio-demographic characteristics, mode choice, vehicle ownership, and work-arrangement. Bayesian Belief network models are developed using the collected responses to calculate the cumulative probability tables (CPTs) of variables associated with the decision to WFH. Next, the ascertained CPTs are used as input to extend an integrated urban modelling framework (IUMF) that is further utilized to simulate individuals’ work from home choices and travel behavior up to 2025 for Halifax, Canada. Results indicate that around 57% of the workers would like to WFH and 7% wants to relocate closer to workplace. The model forecasts a significant preference for remote work among individuals with offices in the urban core. Results also show that auto mode share is increased to 79% in 2024, whereas transit, walking and biking trips decreased. Average travel distance is higher in the post-pandemic compared to the pre-pandemic, while travel distance of telecommuters is found to be higher than non-telecommuters. Statistically significant differences are observed between telecommuters and non-telecommuters for ‘number of activities’ and ‘distance travelled’ in a day. The outcomes of this study will offer policy makers a better understanding of long-term impacts of WFH on transport and land-use systems and help to develop effective travel demand management strategies.

Unveiling oncologists' geographical journey: Analyzing hematology/oncology fellowship graduates' relocation patterns and their impact on regional disparities in cancer care.

9006 Background: The geographical distribution of Hematology/Oncology fellowship graduates (HOFs) is essential for shaping healthcare delivery and illuminating regional health disparities in cancer care. This study explores the relocation patterns of HOFs during key transitions: from medical school to residency (T1), residency to fellowship (T2), and fellowship to the first attending position (T3). The aim is to gain insights into the geographical aspects of cancer care disparities. Methods: This study analyzes the transitions of 329 graduates from 61 ACGME-accredited HOF programs that offered public data about their alumni from 2019-2022. Geographical relocation was measured by the straight-line distance in miles, with the U.S. divided into four regions for retention assessment. A Welch two-sample t-test was conducted to compare average travel distance between each transition. Additionally, relocation patterns were compared between graduates from top 50 NIH-funded medical schools (T50MS) and others (nonT50MS). Results: During T1, HOFs covered a significant distance (661.7 miles), which decreased during T2 (436.5 miles) and T3 (344 miles) (p<.001). Retention analysis revealed a decrease in the percentage of trainees traveling more than 200 miles from T1 (57%) to T2 (46%) to T3 (35%). No significant distance difference was observed between T50MS and nonT50MS during T1 (616 vs 693 miles) and T2 (431 vs 439 miles). However, T3 showed T50MS graduates traveling less (262 miles) compared to their nonT50MS counterparts (401 miles, p<.05). Regional retention increased from T1 (57.5%), to T2 (70%) to T3 (75.4%), with the Western region demonstrating the highest retention across all transitions (T1: 78%, T2: 92%, T3: 83%). The Southern region displayed the lowest retention rates during T1: (44%) and T2: (48%), whereas the Northern region had the lowest during T3 (62%). Conclusions: The preference for relocation beyond 200 miles during T1 reflects a commitment to expanding clinical expertise and gaining varied perspectives critical in addressing complex cancer challenges. As trainees progress, a shift towards establishing roots near previous training sites suggests the allure of established professional and personal networks. T50MS graduates' inclination to stay within a smaller radius during T3 suggests expertise retention in resource-rich areas, potentially exacerbating regional disparities. Marked retention differences, particularly the Southern region’s low retention during training and the Northern region's during practice establishment, reveal a concerning workforce distribution trend. These findings emphasize the necessity for targeted policy interventions aimed at improving equitable distribution of the oncology workforce, thus ensuring uniform access to quality cancer care nationwide.

A multicenter evaluation of the time and travel burden on families with children treated for early-onset scoliosis.

Patients who undergo growth-friendly (GF) treatment for early-onset scoliosis (EOS) undergo multiple clinical and surgical encounters. We sought to quantify the associated temporal and travel burden and estimate subsequent cost. Four centers in an international study group combined data on EOS patients who underwent surgical GF treatment from 2006 to 2021. Data collected included demographics, scoliosis etiology, GF implant, encounter type, and driving distance. We applied 2022 IRS and BLS data or $0.625/mile and $208.2/day off work to calculate a relative financial burden. A total of 300 patients were analyzed (55% female). Etiologies were: congenital (33.3%), idiopathic (18.7%), neuromuscular (30.7%), and syndromic (17.3%). The average age at the index procedure was 5.5years. For the 300 patients, 5899 encounters were recorded (average 18 encounters/patient). Aggregate encounter types were 2521 clinical office encounters (43%), 2045 surgical lengthening encounters (35%), 1157 magnetic lengthening encounters (20%), 149 spinal fusions (3%), and 27 spinal fusion revisions (0.5%). When comparing patients by scoliosis etiology or by GF implant type, no significant differences were noted in the total number of encounters or average travel distance. Patients traveled a median round trip distance of 158 miles/encounter between their homes and treating institutions (range 2.4-5654 miles), with a cumulative median distance of 2651 miles for the entirety of their treatment (range 29-90,552 miles), at an estimated median cost of $1656.63. The mean number of days off work was 18 (range 3-75), with an associated loss of $3643.50 in income. Patients with EOS averaged 18 encounters for GF surgical treatment. These patients and their families traveled a median distance of 158 miles/encounter, with an estimated combined mileage and loss of income of $5300.

Navigating geographical disparities: access to obstetric hospitals in maternity care deserts and across the United States

BackgroundAccess to maternity care in the U.S. remains inequitable, impacting over two million women in maternity care “deserts." Living in these areas, exacerbated by hospital closures and workforce shortages, heightens the risks of pregnancy-related complications, particularly in rural regions. This study investigates travel distances and time to obstetric hospitals, emphasizing disparities faced by those in maternity care deserts and rural areas, while also exploring variances across races and ethnicities.MethodsThe research adopted a retrospective secondary data analysis, utilizing the American Hospital Association and Centers for Medicaid and Medicare Provider of Services Files to classify obstetric hospitals. The study population included census tract estimates of birthing individuals sourced from the U.S. Census Bureau's 2017-2021 American Community Survey. Using ArcGIS Pro Network Analyst, drive time and distance calculations to the nearest obstetric hospital were conducted. Furthermore, Hot Spot Analysis was employed to identify areas displaying significant spatial clusters of high and low travel distances.ResultsThe mean travel distance and time to the nearest obstetric facility was 8.3 miles and 14.1 minutes. The mean travel distance for maternity care deserts and rural counties was 28.1 and 17.3 miles, respectively. While birthing people living in rural maternity care deserts had the highest average travel distance overall (33.4 miles), those living in urban maternity care deserts also experienced inequities in travel distance (25.0 miles). States with hotspots indicating significantly higher travel distances included: Montana, North Dakota, South Dakota, and Nebraska. Census tracts where the predominant race is American Indian/Alaska Native (AIAN) had the highest travel distance and time compared to those of all other predominant races/ethnicities.ConclusionsOur study revealed significant disparities in obstetric hospital access, especially affecting birthing individuals in maternity care deserts, rural counties, and communities predominantly composed of AIAN individuals, resulting in extended travel distances and times. To rectify these inequities, sustained investment in the obstetric workforce and implementation of innovative programs are imperative, specifically targeting improved access in maternity care deserts as a priority area within healthcare policy and practice.

Studying Spatial Unevenness of Transport Demand in Cities Using Machine Learning Methods

The article discusses the issues of spatial unevenness of transport demand in the city by various transport modes. It describes the creation of models using an artificial neural network to estimate the travel time and share by private and public transport in a large city that does not have off-street transport. The city transport macromodel in PTV Visum (V.18) was used as a data source, from which data were obtained on 50 basic parameters taken into account in the specialized software during the development of the transport model. In total, 50 factors act as independent variables that do not have linear relationships with each other and with the dependent variable, which significantly complicates the use of other models. These models allow assessing the influence degree of the most important factors. Further, the article shows the uneven spatial distribution of the shares of trips by private and public transport across the areas of a city. Using machine learning methods, the transport areas of Tyumen were clustered into nine classes belonging to the central sector, where the share of public transport is significantly higher than at the city border. The dependence of the trip share by cars and shuttle buses on the average travel time and distance by private and public transport for each class of transport areas has been established. The research results can be used when creating new transport areas in the city macromodel and when adjusting transport planning documents. The methods used for analyzing big data on the operation of the transport complex can be implemented in the digital twin of the city and the Intelligent Transport System.

Assessment of the Impact of Intercity Bus Station Relocation on Passenger Flows in Urban Public Transport

Problem. The analysis of existing studies on transport hub operation in a city as well as on the methods for modelling the transport demand they generate showed that there are no specific recommendations for assessing the impact of the intercity bus station location on the trip patterns in urban transport system. This caused the need to search for a tool suitable to assess the influence of intercity bus station relocation on passenger service and passenger flows in the urban transport system. This paper shows the results of addressing this issue respective to urban public transport operation. Goal. The goal of the paper is to model the passenger flows to and from intercity bus stations considered transport anchors in urban public transport system as well as estimate the impact of station relocation on passenger flows and quality of service. Methodology. To achieve the stated goal, a survey of intercity passengers at the bus stations in the city of Rivne was made. This allowed for collecting the distances of the trips to and from existing bus stations and fitting the theoretical distribution to describe them. This distribution was used as the deterrence function during trip matrix calculation, which was required for the development of the public transport model. The model made it possible to quantify the potential impact of the Bus Station “Rivne” relocation on the urban transport system. Results. The considered bus station relocation can cause a decrease in the quality of service for passengers travelling to and from the station by public transport due to a probable significant increase in the average travel time and distance, the number of transfers, the passenger hours spent on travelling, and the passenger-kilometres covered to ensure the trips under study. Originality For the first time, the quantitative estimation of the impact of bus station relocation based on the regularities in the lengths of the trips to and from the stations was obtained. Practical value. The obtained trip length distribution can be used to calculate the origin-destination matrix, develop the public transport model, and quantify the probable consequences of the transport hub relocation.

Abstract 4814: Pediatric radiation treatment adherence in LMIC

Abstract Background: Radiation therapy plays a significant role in the treatment of pediatric cancer. However, there have been documented challenges and difficulties encountered by patients while accessing care for cancer and it has been postulated that travel distance contributes to treatment breaks and abandonment. Objectives: The objective of this study was to determine the prevalence of treatment breaks, assess the travel burden of patients receiving radiotherapy for pediatric cancers at a high-volume center in Nigeria and to determine the impact of travel burden on treatment breaks and treatment completion. Materials and Methods: A retrospective cross-sectional study design was utilized. Data was extracted from the electronic medical records of all pediatric patients referred to the center for radiation therapy between June 2019 and June 2023. Data was analyzed using Microsoft Excel and the results were presented in descriptive statistics. Association between data were analyzed using chi-square test with the level of significance (p) set at <0.05. Results: A total of 210 pediatric cancer patients were enrolled with an average age of 10.9 years. Children aged 0-14 years accounted for 69.05% while 30.95% were between the age of 15 and 19 years. Brain tumors were the most predominant diagnosis, representing 28.10% of cases. Treatment intent was predominantly radical or curative (81.69%). 61.43% (129) of the patient population came from the within the state with an average travel distance of 19.3 km. Of all the 210 patients, 44.29% of patients did not commence RT. The reasons for this were mostly financial constraints but were not explored in this study. Of those who did commence RT, 54.70% had breaks, and 45.30% did not have breaks. Also, 75.21% of patients completed their treatment; however, 24.79% were unable to complete their treatment. Travel distances varied, with the average distance to the treatment center being 165.3 km and the median at 28.5 km. The study categorized travel distance into quartiles: short (0-16 km), medium (>16 km to 28.5 km), long (>28.5 km to 302 km), and very long (>302 km to 1387 km). Chi-square test did not reveal a significant association between travel distance and treatment completion (p=0.57), nor between travel distance and treatment breaks (p=0.20). Conclusion: These findings highlight the complex interplay of demographic factors, disease profiles, and socioeconomic barriers affecting treatment continuity and completion among pediatric oncology patients in a resource-limited setting. These findings suggest that in this low - resource context, other variables such as socioeconomic status and financial barriers may play a more pivotal role than travel distance in treatment commencement, adherence and completion. As such, more detailed investigation of the obstacles through a comprehensive and targeted strategy could lead to more favorable outcomes in pediatric cancer radiation therapy adherence. Citation Format: Adedayo Joseph, Adeseye Akinsete, Ugonna Fakile, Chidiebere Agbakwuru, Lensa Keno, Omolola O. Adisa, Aishat Oladipo, Oluwafunmilayo Fagbemide, Muhammad Habeebu, Wilfred Ngwa. Pediatric radiation treatment adherence in LMIC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4814.

Geospatial methodology for determining the regional prevalence of hospital-reported childhood intussusception in patients from India

Both developed and developing countries carry a large burden of pediatric intussusception. Sentinel site surveillance-based studies have highlighted the difference in the regional incidence of intussusception. The objectives of this manuscript were to geospatially map the locations of hospital-confirmed childhood intussusception cases reported from sentinel hospitals, identify clustering and dispersion, and reveal the potential causes of the underlying pattern. Geospatial analysis revealed positive clustering patterns, i.e., a Moran’s I of 0.071 at a statistically significant (p value < 0.0010) Z score of 16.14 for the intussusception cases across India (cases mapped n = 2221), with 14 hotspots in two states (Kerala = 6 and Tamil Nadu = 8) at the 95% CI. Granular analysis indicated that 67% of the reported cases resided < 50 km from the sentinel hospitals, and the average travel distance to the sentinel hospital from the patient residence was calculated as 47 km (CI 95% min 1 km–max 378 km). Easy access and facility referral preferences were identified as the main causes of the existing clustering pattern of the disease. We recommend designing community-based surveillance studies to improve the understanding of the prevalence and regional epidemiological burden of the disease.

Quantifying the relationship between mobility patterns and socioeconomic status of dockless bike-sharing users

Cycling is among the healthiest, greenest, and most affordable means of transportation for a better future city, but mobility patterns of cyclists with different income have rarely been studied due to the limitation of data availability. Newly emergent dockless bike-sharing platforms that record detailed trip information provide us a unique opportunity. Attributing to its better usage flexibility and accessibility, dockless bike-sharing is booming over the past few years worldwide and reviving the cycling fashion in cities. In this work, by exploiting massive platform-collected trip records in four diversified Chinese cities, we reveal that individual mobility patterns, including radius of gyration and average travel distance, are similar among the users with different income, indicating that human beings all follow similar physical rules. However, collective mobility patterns, in terms of average range and diversity of visitation, and commuting direction, exhibit different behaviors and spatial patterns across income categories. Hotspot locations that attract a high volume of cycling activities are quite different over groups, and locations with either a lower or higher income level have a relatively low user ratio. Cyclists from lower income groups are inclined to visit less flourishing locations across all four cities, and have a higher fraction to commute toward the city center in larger cities and away from the city center in smaller cities. Middle income groups of cyclists generally have a higher visitation diversity except in Shanghai. Our findings would be helpful on designing better promotion strategies for dockless bike-sharing platforms and toward the transition to a more inclusive and sustainable transportation.

Telemedicine for Multidisciplinary Urologic Cancer Care: A Prospective Single Institution Study

BackgroundWe rapidly implemented a telemedicine Multidisciplinary Urologic Cancer Clinic (MDUCC) at the University of Washington/Seattle Cancer Care Alliance during the peak of the COVID-19 Public Health Emergency to maintain our ability to provide multidisciplinary cancer care. We report our experiences though assessment of patient-reported outcomes from our telemedicine MDUCC. MethodsVideo visits with a urologic oncologist, medical oncologist, and radiation oncologist were conducted in the same format as our in-person MDUCC. We prospectively collected patient demographic and clinical data. Patients were invited to complete a post-visit survey that assessed satisfaction, provider trust, travel time, and costs of the telemedicine visit. We estimated travel distances and times from each patient's home to our clinic. ResultsAmong invited patients, twenty-four patients completed a survey after their telemedicine MDUCC visit. Twenty patients (83%) were at home during the visit. Most (85%) were men, Caucasian (79%), and were being seen in our Bladder Cancer MDUCC (83%). All twenty-four patients responded that they would be willing to have future appointments via telemedicine; eighteen patients (75%) strongly agreed that the encounter was high quality; 19 patients strongly agreed that they were satisfied with their visit. Patients saved an estimated average one-way travel distance of 145 miles and one-way travel time of 179 minutes to convene a telemedicine visit. ConclusionsTelemedicine MDUCCs are feasible and effective in providing access to multidisciplinary urologic cancer care. Patient satisfaction was high, and many patients were spared a substantial travel burden. Telemedicine may continue to be leveraged to improve access to multidisciplinary urologic cancer care.

On Accessibility Fairness in Intermodal Autonomous Mobility-on-Demand Systems

Research on the operation of mobility systems so far has mostly focused on minimizing cost-centered metrics such as average travel time, distance driven, and operational costs. Whilst capturing economic indicators, such metrics do not account for transportation justice aspects. In this paper, we present an optimization model to plan the operation of Intermodal Autonomous Mobility-on-Demand (I-AMoD) systems, where self-driving vehicles provide on-demand mobility jointly with public transit and active modes, with the goal to minimize the accessibility unfairness experienced by the population. Specifically, we first leverage a previously developed network flow model to compute the I-AMoD system operation in a minimum-time manner. Second, we formally define accessibility unfairness, and use it to frame the minimum-accessibility-unfairness problem and cast it as a linear program. We showcase our framework for a real-world case-study in the city of Eindhoven, NL. Our results show that it is possible to reach an operation that is on average fully fair at the cost of a slight travel time increase compared to a minimum-travel-time solution. Thereby we observe that the accessibility fairness of individual paths is, on average, worse than the average values obtained from flows, setting the stage for a discussion on the definition of accessibility fairness itself.

A Study on the Vehicle Routing Problem Considering Infeasible Routing Based on the Improved Genetic Algorithm

The study aims to optimize the vehicle routing problem, considering infeasible routing, to minimize losses for the company. Firstly, a vehicle routing model with hard time windows and infeasible route constraints is established, considering both the minimization of total vehicle travel distance and the maximization of customer satisfaction. Subsequently, a Floyd-based improved genetic algorithm that incorporates local search is designed. Finally, the computational experiment demonstrates that compared with the classic genetic algorithm, the improved genetic algorithm reduced the average travel distance by 20.6% when focusing on travel distance and 18.4% when prioritizing customer satisfaction. In both scenarios, there was also a reduction of one in the average number of vehicles used. The proposed method effectively addresses the model introduced in this study, resulting in a reduction in total distance and an enhancement of customer satisfaction.

Potential demand for and access to medication abortion among North Carolina college students

Objective To estimate demand for medication abortion (MAB) among North Carolina (NC) college students and describe access to nearest clinics offering MAB to each campus. Methods We calculated demand using 2019-2020 campus demographics and NC abortion statistics. We used a mystery client technique to gather MAB cost and appointment wait times at the closest clinics and calculated travel distances and times. Results We estimated that 2,517 NC students seek MAB annually. Twenty-one clinics were closest to NC’s 111 colleges and universities, including five in neighboring states. Mean cost was $450, with an average wait time of six days to appointment. The average round-trip travel distance was 58 miles and time to the nearest clinic was 84 min by car. Conclusions Many NC college students likely obtain MAB every year and face high costs, long wait times and distances to care, which has likely worsened after the overturning of Roe v. Wade.

Telehealth experience during COVID-19 at an American burn Association (ABA) verified adult and pediatric burn center

IntroductionThe COVID-19 pandemic resulted in unprecedented changes to healthcare services. Non-emergent, out-patient care was either discontinued, greatly reduced, or switched to telehealth during the first wave of the pandemic. Here, we describe an American Burn Association (ABA) verified Burn Center’s experience with telehealth services during and after the COVID-19 emergency. Material and methodsIn this retrospective study, all patients who underwent out-patient care at a large academic hospital’s ABA Burn Center between March 2018 and March 2023 were identified from the electronic medical record system. Descriptive analysis was carried out to delineate trends in outpatient in-person and telehealth services during the COVID-19 pandemic. The travel distance saved from the introduction of telehealth was determined. ResultsDuring the study period, 3471 patients underwent a total of 7444 out-patient visits for burn care. There were no telehealth visits prior to the onset of the COVID-19 pandemic. In the first year of the COVID-19 pandemic, 14.9% of all out-patient visits were conducted with telehealth. This decreased to 8.3% and 6.8% of all out-patient care in the second and third years of the pandemic, respectively. The average round trip travel distance saved was 123 miles (2.8–––2312 miles). No complications were reported specific to receiving telehealth care. ConclusionsTelehealth is a feasible option for out-patient burn care in selected patients and reduces travel for patients. Further studies are needed to assess patient and clinician satisfaction, clinical outcomes, and the economic impact of telehealth utilization to help guide appropriateness of use.

Multiobjective Electric Vehicle Charging Network Planning Considering Chance-Constraint on the Travel Distance for Charging

Electric vehicle charging stations (EVCSs) are important infrastructures to support sustainable development of electric vehicles (EVs), by providing convenient, rapid charging services. Therefore, the planning of electric vehicle charging network (EVCN) has attracted wide interest from both industry and academia. In this paper, a multiobjective planning model for EVCN is developed, where a fixed number of EVCSs are planned in the traffic network (TN) to achieve two objectives, i.e., minimizing both average travel distance for charging (TDfC) of EVs and investment costs of EVCN. According to the random characteristics of EVs’ TDfC, its constraint is presented as a chance constraint in the developed EVCN planning model. The nondominated sorting genetic Algorithm II with the constraint domination principle (NSGA-II-CDP) is customized to solve the developed multiobjective EVCN planning model, by designing a special coding scheme, a crossover operator, and a mutation operator. Then, a maximum gradient principle of investment revenue is designed to select the optimal planning strategy from the Pareto-optimal solution set, when taking the investment return ratio as primary consideration. A 25-node TN is used to justify the effectiveness of the developed methodology.