Miles Of Travel Research Articles

Social and Environmental Benefits of Pediatric Infectious Disease Telemedicine.

We provided safe, effective pediatric infectious disease management through telemedicine over a 3.5-year period, saving patients over 31 000 vehicular travel miles. Telemedicine has potential to decrease indirect patient costs while sustainably increasing access to subspecialty care.

Access and adherence to cancer care through the Veteran Affairs Close to Me cancer care service.

111 Background: Diagnosing and treating more than 56,000 Veterans annually, the Veterans Affairs (VA) National Oncology Program (NOP) is one of the nation’s largest integrated providers of oncology services. Within VA, oncology care and parenteral therapies are primarily offered at VA Medical Centers (VAMCs) in urban areas, often inaccessible to rural patients. Issues with travel distance, parking, time, and caregiver support can prevent patients from adhering to treatment appointments as evidenced by historical annual No-Show rates for VAMC infusion clinics that range from 1.1-6.7%. To mitigate potential barriers to access, NOP launched the Close to Me (CTM) cancer care service. The service deploys Registered Nurses (RNs) to local VA Community Based Outpatient Clinics (CBOCs) to administer cancer treatments. VA has opened 23 CTM clinics across 5 geographical regions since October 2021. Methods: The CTM service was implemented at VAMCs with NOP providing direct project management, safety assessment and resources such as a comprehensive implementation toolkit, standard operating procedures, care coordination tools, a tiered risk drug matrix and a standardized Healthcare Failure Mode and Effect Analysis risk evaluation. RNs followed a standardized workflow to assess patient eligibility and verify treatment clearances. Access to care was reported by numbers of unique patients and treatment visits. Patient appointment adherence was monitored by no-show rates documented in the electronic health record. Infusion reactions and medical emergencies were reported by RNs. Travel miles saved was calculated by subtracting the difference in miles round trip from the patient’s home to main VAMC and from home to CBOC, then multiplied by number of treatment visits to CBOC. Results: From October 2021 through April 2024, 23 new CTM clinics opened across 5 regions. 515 unique patients were enrolled for a total of 1,916 patient visits. Treatment appointment no-show rate was 1% across all CTM clinics. The most commonly administered medication classes include gonadotropin-releasing hormone agonist (30%), immune globulin (14.5%), immune checkpoint inhibitors (9.3%) and bisphosphonates (8.3%). Zero infusion reactions or medical emergencies were recorded across 1,430 treatments administered. A total of 204,000 travel miles were saved by patients enrolled in this program. Conclusions: The CTM cancer care service safely optimizes current VA CBOC infrastructure to increase access points to oncology treatment for patients across various geographical regions. CTM reduced travel burden for patients and increased ability to adhere to treatment appointments as evidenced by the service’s low no-show rate. These consistent results across CTM services at VAMCs nationwide have cemented CTM as a safe and effective treatment delivery model to expand access. CTM will be scaled to an additional 30 regions by end of 2025.

Study of an Impedance Function for Mixed Traffic Flows Considering the Travel Time–Cost Characteristics of Long-Distance Electric Vehicle Trips

To quantify the travel time and cost characteristics of mixed traffic involving electric vehicles (EVs) and fuel-powered vehicles on roads, in this paper, we comprehensively consider three factors affecting road impedance: queue length, waiting time, and service rate. Initially, a time characteristic function and a cost characteristic function for mixed traffic impedance are constructed. From the perspective of travel time, we consider the impact of EV penetration on the actual road capacity and introduce a capacity coefficient to modify the BPR (Bureau of Public Roads) road impedance function. Given that different types of vehicles might need to wait at charging stations, we employ queuing theory to calculate the queuing time at these stations and construct an impedance model that considers travel time. From the perspective of travel costs, we account for the energy consumption costs and road usage fees for different types of vehicles. The energy consumption cost for travel mileage is obtained by multiplying the unit mileage energy consumption cost of mixed traffic by the travel mileage. For road usage fees, we adopt the conventional method of multiplying the per-kilometer rate for each vehicle type by the travel mileage, thus constructing an impedance model that incorporates travel costs. Finally, in the numerical analysis section, based on the vehicle travel mileage, we categorize travel into short-, medium-, and long-distance trips for analysis. With the constructed mixed traffic impedance model, we conduct a detailed analysis of the travel time and cost characteristics of mixed traffic over different travel distances. We explore the specific impacts of the electric vehicle penetration rate, traffic flow volume, and travel mileage on road impedance. The results indicate that as the penetration rate of electric vehicles increases, the total energy consumption of the transportation system significantly decreases. Moreover, at high electric vehicle penetration rates, although an increase in traffic flow leads to higher traffic impedance and longer travel times, the overall travel costs are reduced. This demonstrates that increasing the penetration rate of electric vehicles positively contributes to reducing the energy consumption and costs of transportation systems.

Prediction of bike-sharing station demand using explainable artificial intelligence

Bike-sharing systems have grown in popularity in metropolitan areas, providing a handy and environmentally friendly transportation choice for commuters and visitors alike. As demand for bike-sharing programs grows, efficient capacity planning becomes critical to ensuring good user experience and system sustainability in terms of demand. The random forest model was used in this study to predict bike-sharing station demand and is considered a strong ensemble learning approach that can successfully capture complicated nonlinear correlations and interactions between input variables. This study employed data from the Smart Location Database (SLD) to test the model accuracy in estimating station demand and used a form of explainable artificial intelligence (XAI) function to further understand machine learning (ML) prediction outcomes owing to the blackbox tendencies of ML models. Vehicle Miles of Travel (VMT) and Greenhouse Gas (GHG) emissions were the most important features in predicting docking station demand individually but not holistically based on the datasets. The percentage of zero-car households, gross residential density, road network density, aggregate frequency of transit service, and gross activity density were found to have a moderate influence on the prediction model. Further, there may be a better prediction model generating sensible results for every type of explanatory variable, but their contributions are minimum to the prediction outcome. By measuring each feature's contribution to demand prediction in feature engineering, bike-sharing operators can acquire a better understanding of the bike-sharing station capacity and forecast future demands during planning. At the same time, ML models will need further assessment before a holistic conclusion.

Pedestrian safety on the road to net zero: cross-sectional study of collisions with electric and hybrid-electric cars in Great Britain

BackgroundPlans to phase out fossil fuel-powered internal combustion engine (ICE) vehicles and to replace these with electric and hybrid-electric (E-HE) vehicles represent a historic step to reduce air pollution and...

Moderation Effects of Daily Behavior on Associations Between Symptoms and Social Participation Outcomes After Burn Injury: A 6-Month Digital Phenotyping Study

ObjectiveTo examine the moderation effects of daily behavior on the associations between symptoms and social participation outcomes after burn injury. DesignA 6-month prospective cohort study. SettingCommunity. ParticipantsTwenty-four adult burn survivors. InterventionsNot applicable. Main Outcome MeasuresSymptoms and social participation outcomes were assessed weekly using smartphone surveys, including symptoms of pain (Patient-Reported Outcomes Measurement Information System [PROMIS] Pain Intensity and Pain Interference), anxiety (PROMIS Anxiety), and depression (Patient Health Questionnaire), as well as outcomes of social interactions and social activities (Life Impact Burn Recovery Evaluation [LIBRE] Social Interactions and Social Activities). Daily behaviors were automatically recorded by a smartphone application and smartphone logs, including physical activity (steps, travel miles, and activity minutes), sleep (sleep hours), and social contact (number of phone calls and message contacts). ResultsMultilevel models controlling for demographic and burn injury variables examined the associations between symptoms and social participation outcomes and the moderation effects of daily behaviors. Lower (worse) LIBRE Social Interactions and LIBRE Social Activities scores were significantly associated with higher (worse) PROMIS Pain Intensity, PROMIS Pain Interference, PROMIS Anxiety, and Patient Health Questionnaire-8 scores (P<.05). Additionally, daily steps and activity minutes were associated with LIBRE Social Interactions and LIBRE Social Activities (P<.05), and significantly moderated the association between PROMIS Anxiety and LIBRE Social Activities (P<.001). ConclusionsSocial participation outcomes are associated with pain, anxiety, and depression symptoms after burn injury, and are buffered by daily physical activity. Future intervention studies should examine physical activity promotion to improve social recovery after burns.

Decision support framework for home health caregiver allocation using optimally tuned spectral clustering and genetic algorithm

Population aging is a global challenge, leading to increased demand for health care and social services for the elderly. Home Health Care (HHC) is a vital solution to serve this segment of the population. Given the increasing demand for HHC, it is essential to coordinate and regulate caregiver allocation efficiently. This is crucial for both budget-optimized planning and ensuring the delivery of high-quality care. This research addresses a fundamental question in home health agencies (HHAs): “How can caregiver allocation be optimized, especially when caregivers prefer flexibility in their visit sequences?”. While earlier studies proposed rigid visiting sequences, our study introduces a decision support framework that allocates caregivers through a hybrid method that considers the flexibility in visiting sequences and aims to reduce travel mileage, increase the number of visits per planning period, and maintain the continuity of care – a critical metric for patient satisfaction. Utilizing data from an HHA in Tennessee, United States, our approach led to an impressive reduction in average travel mileage (up to 42%, depending on discipline) without imposing restrictions on caregivers. Furthermore, the proposed framework is used for caregivers’ supply analysis to provide valuable insights into caregiver resource management.

Understanding the potential of taxi sharing: The case of Chengdu

The emergence of taxi sharing enhances urban transport efficiency and reduces carbon emissions. Using GPS tracking data from taxis in Chengdu, China, this study first outlines conditions for identifying shareable taxi orders based on their origins and destinations. We then develop a three-phase computational model to optimize matches among all potential shareable orders, calculating the shareable mileage and the proportion of original mileage that could be shared. Our comprehensive temporal and spatial analysis reveal a significant market for taxi sharing in Chengdu, with higher potential on workdays than non-workdays and four distinct demand peaks throughout the day. The morning peak on workdays and the night peak on non-workdays are particularly pronounced. Most shareable orders originate within major city districts. We find a positive correlation between the potential of taxi sharing and average traffic speed, and negative correlations with order volume, regional economic development, and population density. Functional zones related to Enterprises, Motorcycle Services, and Transportation Services exhibit significantly higher sharing potential. Compared to traditional taxi operations, taxi sharing significantly reduces total travel mileage. This quantitative analysis offers insights into the potential demand for taxi sharing among urban residents and may help government authorities optimize taxi resources for the sustainable development of urban transport.

A new highway cost allocation framework in the day of connected and autonomous vehicles

The objectives of the present study are to explore potential changes expected in highway infrastructure expenditures and revenues, and develop a robust framework that will facilitate highway cost allocation when connected and autonomous vehicles (CAVs) are in operation. The scope of this study covered both federal and state expenditures and revenue sources. The highway cost allocation elements covered in the paper include vehicle miles of travel, highway expenditures highway revenues, and equity analysis. In the day of CAVs, significant changes are expected in all these elements, and these changes are incorporated into the proposed highway cost allocation framework. Pavement-related expenditures would increase due to the addition of CAV-related infrastructure integrated into the pavement infrastructure to ensure it is an intelligent pavement. Bridge-related expenditures are expected to increase due to the infrastructure technologies on the bridges making them smart bridges, and the design loads of these bridges may have to be increased due to potential platooning on long span bridges. It is expected that many of the CAVs on the road will be electric vehicles. All these changes are incorporated into the proposed highway cost allocation framework.

Competitive advantage of car-sharing based on travel costs comparison model: A case study of Beijing, China

In recent years, car-sharing has emerged and developed in different cities in China, supplementing other urban transportation modes, but also triggering fierce competition with some of them, such as taxis. In this study, we use the car-sharing orders and GPS trajectory data of a company in Beijing, China, to discuss the competitive advantages, market, and development trends of car-sharing from the perspective of travel costs. By building a travel costs comparison model, we calculate the travel costs of car-sharing and taxis under the same travel scenario, comparing their cost differences in the dimensions of travel mileage, duration, utilization efficiency, etc., and locating the cost advantage space of car-sharing. Further, we discuss the impact of different elements of travel (such as time cost, waiting time, etc.) on the cost advantage of travel by car-sharing. The results reveal that, under the current pricing conditions, the cost advantage of car-sharing is particularly obvious when the rental duration is not too long (less than 14 h), the utilization efficiency is relatively high, and the stop time is shorter than 8 h. We also find that it makes great cost advantage of using car-sharing in periods or areas of urban taxi shortages. In the cases of short travel, car-sharing have bigger cost advantage for the middle and low-income groups. In addition, the popularity of bike-sharing can also increase the cost advantage of car-sharing, which indicates that the synergy of different travel models could promote the development of car-sharing. However, when people travel long, the cost advantage of car-sharing would be minimally affected by the elements, such as people's income and the development of bike-sharing. In other words, car-sharing would keep its cost advantage in the cases of long travel, especially when its utilization efficiency is high and the travel stop time is short.

Store-and-Forward Teledermatology Wound Checks Following Mohs Surgery: A Pilot Study.

Store-and-forward telemedicine is a form of electronic transmission in which patient images or clinical information are transmitted to clinicians for asynchronous clinical decision making. This study evaluated the use, feasibility, savings, and patient satisfaction of postoperative store-and-forward wound checks following Mohs surgery. Select patients were asked to participate in a virtual postoperative wound check after receiving Mohs surgery. Participants sent photos of their wound site and subsequently completed an anonymous survey. One hundred and ten patients were enrolled, mean age 68 (range 32-87). Patients saved an average of $14.16, 78.6 miles of travel, and 90 min of travel time. Ninety-eight percent of respondents felt their quality of care in teledermatology was "about the same" to "much better" than compared with traditional in-person care. Sixty-four of the respondents (90.1%) reported they would prefer their next Mohs wound check to be conducted through teledermatology. Store-and-forward telemedicine in patients undergoing Mohs micrographic surgery is a safe, effective, and efficient method for postoperative wound care.

Longitudinal impact of screening colonoscopy on greenhouse gas emissions.

Colonoscopy is the gold-standard screening test for colorectal cancer. However, it has come under scrutiny for its carbon footprint and contribution to greenhouse gas (GHG) emissions compared to other medical procedures. Notwithstanding, screening colonoscopies may have a positive effect on GHG emissions that is unknown. This study estimated the carbon emissions prevented by screening colonoscopies in the U.S. Using the reported number of screening colonoscopies performed annually in the U.S. and the absolute risk reduction (ARR) reported in the NorDICC trial, we calculated the expected minimum number of cancer treatment and surveillance visits prevented through screening based on the cancer stage. The average carbon emission averted per mile traveled was computed using the Environmental Protection Agency's (EPA) GHG equivalencies calculator. The final estimate of carbon emissions averted over a decade by screening colonoscopies performed in one year was determined. 6.3 million screening colonoscopies performed in one year prevent 1,134,000 colorectal cancers over a ten-year period. Of these, 38∙3% (434,254) are localized, 38∙8% (440,281) are regional, and 22∙9% (259,465) are metastatic disease. The minimum number of post-diagnosis visits prevented is 11 for stage I, ≥ 21 for stage II, ≥25 for stage III, and ≥ 20 for stage IV disease, comprised of diagnostic, surgical evaluation, chemotherapy, and surveillance visits. The total number of visits prevented by screening is 2,388,397 for stage I, 5,254,421 for stage II, 13,120,369 for stage III, and 9,210,972 for stage IV disease. Approximately 395 million miles of travel and 158,263 metric tons of CO2, equivalent to 177 million pounds of coal burned, 19 billion smartphones charged, or 18 million gallons of gasoline consumed, were saved over ten years through screening. Colorectal cancer screening decreases cancer-related GHG emissions and minimizes the environmental impact of cancer treatment.

I-24 MOTION: An instrument for freeway traffic science

The Interstate-24 MObility Technology Interstate Observation Network (I-24 MOTION) is a new instrument for traffic science located near Nashville, Tennessee. I-24 MOTION consists of 276 pole-mounted high-resolution traffic cameras that provide seamless coverage of approximately 4.2 miles I-24, a 4-5 lane (each direction) freeway with frequently observed congestion. The cameras are connected via fiber optic network to a compute facility where vehicle trajectories are extracted from the video imagery using computer vision techniques. Approximately 230 million vehicle miles of travel occur within I-24 MOTION annually. The main output of the instrument is vehicle trajectory datasets that contain the position of each vehicle on the freeway, as well as other supplementary information such as vehicle dimensions and class. This article describes the design and creation of the instrument, and provides the first publicly available datasets generated from the instrument. The datasets published with this article contains at least 4 hours of vehicle trajectory data for each of 10 days. As the system continues to mature, all trajectory data will be made publicly available at i24motion.org.

Energy management strategy and simulation analysis of a hybrid train based on a comprehensive efficiency optimization

Since trains usually have long travel mileage requirements, designing an optimal energy management strategy has great potential for reducing energy consumption. First, a hybrid train power component model was developed, and the train operating efficiency, as well as a map of the optimal integrated efficiency of the entire vehicle operation, was derived for each mode from simulation calculations by analyzing the power flows in different operating modes. Next, the overall optimal efficiency of the entire vehicle was used to determine the mode division rules, and proportional integral control was used to ensure that the engine and motor operated at the target speed under the target torque. Finally, with the optimization objectives of reducing the fuel consumption of the entire vehicle and maintaining a balanced battery state of charge, a forward simulation model for the overall efficiency optimization of the entire vehicle was built in the MATLAB/Simulink software. Then, the trajectories of the engine and motor operating points, the fuel consumption, and the battery SOC were obtained for certain operating conditions. The effectiveness of the energy management strategy based on integrated efficiency optimization was verified by comparing the simulation results for the hybrid train with those of a conventional internal combustion engine train.

Travel Burden as a Measure of Healthcare Access and the Impact of Telehealth within the Veterans Health Administration

BackgroundTravel is a major barrier to healthcare access for Veteran Affairs (VA) patients, and disproportionately affects rural Veterans (approximately one quarter of Veterans). The CHOICE/MISSION acts’ intent is to increase timeliness of care and decrease travel, although not clearly demonstrated. The impact on outcomes remains unclear. Increased community care increases VA costs and increases care fragmentation. Retaining Veterans within the VA is a high priority, and reduction of travel burdens will help achieve this goal. Sleep medicine is presented as a use case to quantify travel related barriers.ObjectiveThe Observed and Excess Travel Distances are proposed as two measures of healthcare access, allowing for quantification of healthcare delivery related to travel burden. A telehealth initiative that reduced travel burden is presented.DesignRetrospective, observational, utilizing administrative data.SubjectsVA patients with sleep related care between 2017 and 2021. In-person encounters: Office visits and polysomnograms; telehealth encounters: virtual visits and home sleep apnea tests (HSAT).Main MeasuresObserved distance: distance between Veteran’s home and treating VA facility. Excess distance: difference between where Veteran received care and nearest VA facility offering the service of interest. Avoided distance: distance between Veteran’s home and nearest VA facility offering in-person equivalent of telehealth service.Key ResultsIn-person encounters peaked between 2018 and 2019, and have down trended since, while telehealth encounters have increased. During the 5-year period, Veterans traveled an excess 14.1 million miles, while 10.9 million miles of travel were avoided due to telehealth encounters, and 48.4 million miles were avoided due to HSAT devices.ConclusionsVeterans often experience a substantial travel burden when seeking medical care. Observed and excess travel distances are valuable measures to quantify this major healthcare access barrier. These measures allow for assessment of novel healthcare approaches to improve Veteran healthcare access and identify specific regions that may benefit from additional resources.

116 Relationship between Smartphone-tracked Physical Activities and Psychosocial Outcomes for Burn Survivors Living in the Community

Abstract Introduction Following burn survivors in the community is critical to understand their problems and needs. Physical activities automatically recorded by smartphones have shown to be a convenient and burdenless method for post-discharge assessment. However, it is unclear if physical activities are associated with burn survivors’ physical and psychosocial outcomes. This study aims to examine the relationship between smartphone-tracked physical activities and scale-based outcomes. Methods Adult burn survivors living in the community were included in this study. A mobile application was downloaded to participants’ smartphones and automatically recorded daily number of steps, travel miles and activity minutes. Participants received weekly surveys assessing pain (Patient-Reported Outcomes Measurement Information System (PROMIS) Pain Intensity and Interference), depression (Patient Health Questionnaire (PHQ-8)), anxiety (PROMIS Anxiety) and social participation (Life Impact Burn Recovery Evaluation (LIBRE) Social Interactions and Social Activities). Correlation coefficients between physical activities and survey results were examined and interpreted as “very weak” (|r|≤0.19), “weak” (0.20≤|r|≤0.39), “moderate” (0.40≤|r|≤0.59), “strong” (0.60≤|r|≤0.79), and “very strong” (0.80≤|r|≤1.00). Results There were 24 burn survivors with an average of 24 years after injury included in this study. The 180-week data collected between November 2021 and August 2022 was analyzed. Daily number of steps, travel miles and activity minutes separately showed relationships with PROMIS Pain Intensity (r=-0.312, p&amp;lt; 0.001; r=-0.247, p=0.001; r=-0.239, p=0.001), PROMIS Pain Interference (r=-0.375, p&amp;lt; 0.001; r=-0.318, p&amp;lt; 0.001; r=-0.291, p&amp;lt; 0.001), PHQ-8 (r=-0.403, p&amp;lt; 0.001; r=-0.357, p&amp;lt; 0.001; r=-0.311, p&amp;lt; 0.001), PROMIS Anxiety (r=-0.310, p&amp;lt; 0.001; r=-0.209, p=0.007; r=-0.150, p=0.054), LIBRE Social Interactions (r=0.272, p&amp;lt; 0.001; r=0.291, p&amp;lt; 0.001; r=0.328, p&amp;lt; 0.001) and LIBRE Social Activities (r=0.381, p&amp;lt; 0.001; r=0.414, p&amp;lt; 0.001; r=0.404, p&amp;lt; 0.001). Conclusions Daily number of steps demonstrated a moderate correlation with depression while both daily travel miles and activity minutes showed moderate correlations with social participation. The results support that smartphone-tracked physical activities are related to burn survivors’ psychosocial outcomes. Applicability of Research to Practice This study may inform future use of smartphone-tracked physical activities to understand and track burn recovery and ultimately improve care.

Quantifying the impact of COVID-19 on travel behavior in different socio-economic segments

The COVID-19 pandemic has resulted in substantial negative impacts on social equity. To investigate transport inequities in communities with varying medical resources and COVID controlling measures during the COVID pandemic and to develop transport-related policies for the post-COVID-19 world, it is necessary to evaluate how the pandemic has affected travel behavior patterns in different socio-economic segments (SES). We first analyze the travel behavior change percentage due to COVID, e.g., increased working from home (WFH), decreased in-person shopping trips, decreased public transit trips, and canceled overnight trips of individuals with varying age, gender, education levels, and household income, based on the most recent US Household Pulse Survey census data during Aug 2020 ∼ Dec 2021. We then quantify the impact of COVID-19 on travel behavior of different socio-economic segments, using integrated mobile device location data in the USA over the period 1 Jan 2020–20 Apr 2021. Fixed-effect panel regression models are proposed to statistically estimate the impact of COVID monitoring measures and medical resources on travel behavior such as nonwork/work trips, travel miles, out-of-state trips, and the incidence of WFH for low SES and high SES. We find that as exposure to COVID increases, the number of trips, traveling miles, and overnight trips started to bounce back to pre-COVID levels, while the incidence of WFH remained relatively stable and did not tend to return to pre-COVID level. We find that the increase in new COVID cases has a significant impact on the number of work trips in the low SES but has little impact on the number of work trips in the high SES. We find that the fewer medical resources there are, the fewer mobility behavior changes that individuals in the low SES will undertake. The findings have implications for understanding the heterogeneous mobility response of individuals in different SES to various COVID waves and thus provide insights into the equitable transport governance and resiliency of the transport system in the “post-COVID” era.

Impact of Remote Cardiac Monitoring on Greenhouse Gas Emissions: Global Cardiovascular Carbon Footprint Project

BackgroundRemote monitoring (RM) of patients with cardiac implantable electronic devices (CIEDs) is efficient and requires fewer resources than conventional monitoring. However, the impact of RM on the carbon footprint (CF) is not known. ObjectivesThe authors sought to evaluate the reduction in cost and greenhouse gas (GHG) emissions with RM as compared to conventional monitoring of CIEDs and its relevance to CF. MethodsData were obtained from a third-party RM provider on 32,811 patients from 67 device clinics across the United States. Distance from home address to the device clinic for patients on RM was calculated. Savings in total distance traveled over 2 years was calculated using frequency of follow-up required for the device type. National fuel efficiency data and carbon emission data were obtained from the Bureau of Transportation Statistics and U.S. Environmental Protective Agency, respectively. The average gas price during the study period was obtained from U.S. Energy Information Administration. ResultsIn the study population, RM resulted in a total saving of 31.7 million travel miles at $3.45 million and reduction of 12,518 metric ton of GHG from gasoline. There was a reduction of 14.2-million-page printouts, $3 million in cost, and 78 tons of GHG. Improvement in workforce efficiency with RM resulted in savings of $3.7 million. There was a net saving of $10.15 million and 12,596 tons of GHG emissions. ConclusionsRM of patients with a CIED resulted in significant reductions in GHG emissions. Efforts to actively promoting RM can result in significant reduction in CF.

Escaping the heat: Climate change and visitation to the Lake Tahoe basin

The Lake Tahoe Basin of California and Nevada is a popular destination for visitors. As temperatures rise globally, visitation to this climatic refuge is likely to grow. Increased visitation contributes to increases in vehicle miles of travel (VMT), congestion, crashes, emissions, and deteriorating travel experiences at Lake Tahoe, while also contributing to friction between visitors and residents. Policymakers are exploring ways to mitigate these problems, but one key uncertainty to first address is exactly how many visitors are in the Tahoe Basin at a given time and how and where they travel while there. It is also unclear how visitation is influenced by heat in the nearby lower-elevation population centers during the summertime, which is when visitation is the highest. This study thus seeks to 1) quantify day and overnight visitors in the Tahoe Basin, 2) estimate VMT attributable to each group, and 3) model the influence of hot temperatures nearby on visitation and VMT within the Tahoe Basin. We use existing survey data, network GIS analysis, and publicly available weather data to model the relationship between particularly hot conditions and vehicle flow through two of the region’s primary entry points and estimate in-basin VMT attributable to travel through them. We evaluate several ways of defining “hot conditions” nearby and find that between 500 and 1,200 additional vehicles enter the basin at these two points alone, generating an additional in-basin daily VMT of about 12,000 to 33,000 when hot conditions exist. Quantifying the magnitude of the challenge enables managers to design and scale interventions to mitigate impacts.

Should I stay or should I go? Why nurses are leaving community nursing in the UK

Worsening working conditions for nursing workforce has seen a massive exodus of staff, particularly in community nursing in the UK. Aim: The study aim was to map working conditions as well as identify differentiating characteristics of community nurses that intend to leave their profession. Design: Eligibility criteria were community nurses working in all 4 UK countries. All data was collected by means of a cross-sectional survey via the largest closed, private community nursing online-forum. Logistic regression was carried out to ascertain the effects of the variables on the intention to leave. Results: The total number of respondents was 533. Findings showed that one in two of all community nurses (≈46%) are reporting job dissatisfaction. Length of unpaid overtime per shift (odds increase by 30% for each hour of overtime), manager support, proportion of permanent staff, team size, shift length, travel mileage, worsened conditions in the last year and overall self-rated working conditions were differentiating factors between those that intended to leave the job. The proportion of permanent staff on the team and perceived lack of support from management best predicted the likelihood of leave rates. Our findings imply that low nurse retention will fuel an even higher exodus because job dissatisfaction is highest on teams with lowest permanent staff ratios. Poor management that is inept at supporting frontline staff means that the fundamental retention issues are exacerbated and will not stop the unprecedented crisis that is predicted to lead to a collapse of care provision in community settings. Nurses play a central role and are ‘key’ to delivering the much- desired patient-centred care’ therefore their well-being and job satisfaction should become a priority for policymakers.