Environmental Impact of Adult Tonsillectomy: Life Cycle Assessment and Cost Comparison of Techniques.

OBJECTIVES: To quantify and compare the cost and environmental impact of different techniques for adult tonsillectomy surgery, and to identify target areas for impact reduction. METHODS: Fifteen consecutive adult tonsillectomy surgeries were prospectively randomized to one of three tonsillectomy techniques: cold, monopolar electrocautery, or low-temperature radiofrequency ablation (Coblation). Life cycle assessment was used to comprehensively evaluate the environmental impact of study surgeries. Outcomes assessed included multiple measures of environmental impact, including greenhouse gas (GHG) emissions, and cost. Environmental impact measures were analyzed to identify highest-yield areas for improvement, and outcomes were compared between surgical techniques using statistical analysis. RESULTS: GHG emissions for cold, monopolar electrocautery, and Coblation techniques were 157.6, 184.5, and 204.7 kilograms of carbon dioxide equivalents (kgCO2 -eq) per surgery, respectively, with costs totaling $472.51, $619.10, and $715.53 per surgery, respectively. Regardless of surgery technique, anesthesia medications and disposable equipment contributed most to environmental harm. Cold technique demonstrated reduced environmental impact related to disposable surgical equipment in the categories of greenhouse gas emissions, acidification of soil and water, eutrophication of air, ozone depletion, release of carcinogenic, and non-carcinogenic toxic substances, and respiratory pollutant production (p < 0.05 for all comparisons with other techniques). CONCLUSION: Within the boundaries of operating room processes, cold technique minimizes cost and environmental impact of adult tonsillectomy surgery, with statistical significance noted in the impact of disposable surgical equipment. Areas of highest potential for improvement identified include reducing use of disposable equipment and collaboration with the Anesthesiology care team to streamline medication use. LEVEL OF EVIDENCE: 2, randomized trial Laryngoscope, 134:622-628, 2024.

The environmental impact of surgical telemedicine: life cycle assessment of virtual vs. in-person preoperative evaluations for benign foregut disease.

BACKGROUND: Health care accounts for almost 10% of the United States' greenhouse gas emissions, accounting for a loss of 470,000 disability-adjusted life years based on the health effects of climate change. Telemedicine has the potential to decrease health care's carbon footprint by reducing patient travel and clinic-related emissions. At our institution, telemedicine visits for evaluation of benign foregut disease were implemented for patient care during the COVID-19 pandemic. We aimed to estimate the environmental impact of telemedicine usage for these clinic encounters. METHODS: We used life cycle assessment (LCA) to compare greenhouse gas (GHG) emissions for an in-person and a telemedicine visit. For in-person visits, travel distances to clinic were retrospectively assessed from 2020 visits as a representative sample, and prospective data were gathered on materials and processes related to in-person clinic visits. Prospective data on the length of telemedicine encounters were collected and environmental impact was calculated for equipment and internet usage. Upper and lower bounds scenarios for emissions were generated for each type of visit. RESULTS: For in-person visits, 145 patient travel distances were recorded with a median [IQR] distance travel distance of 29.5 [13.7, 85.1] miles resulting in 38.22-39.61 carbon dioxide equivalents (kgCO2-eq) emitted. For telemedicine visits, the mean (SD) visit time was 40.6 (17.1) min. Telemedicine GHG emissions ranged from 2.26 to 2.99 kgCO2-eq depending on the device used. An in-person visit resulted in 25 times more GHG emissions compared to a telemedicine visit (p < 0.001). CONCLUSION: Telemedicine has the potential to decrease health care's carbon footprint. Policy changes to facilitate telemedicine use are needed, as well as increased awareness of potential disparities of and barriers to telemedicine use. Moving toward telemedicine preoperative evaluations in appropriate surgical populations is a purposeful step toward actively addressing our role in health care's large carbon footprint.

Quantifying the impact of coefficient of thermal expansion of overlay concrete on unbonded concrete overlay performance.

With the advancement in pavement design and performance analysis procedures, the coefficient of thermal expansion (CTE) of concrete has emerged as a significant design input with a direct impact on concrete pavement performance parameters including transverse cracking, joint faulting, and pavement roughness. CTE is the measure of change in concrete volume with temperature change and the resulting curling of concrete pavement slab is directly proportional to CTE. Un-Bonded Concrete Overlay (UBCO) is a cost-effective and sustainable rehabilitation technique on Jointed Plain Concrete Pavements (JPCP) to improve the performance of deteriorated concrete pavements. This study examines the effects of variability of CTE on the performance of unbonded JPCP overlays for two different climatic regions. Simulations were conducted using AASHTO pavement ME design software with varying CTE values in the range of 6.8-10.8 micro-strain/°C and keeping all other design variables as constant. The performance predictions were evaluated for different values of CTE and the results indicated that with an increase in CTE value, the performance of UBCO is adversely affected by the increase in pavement distresses. Amongst all the performance parameters, transverse cracking is the most significantly affected parameter with the change in CTE. The impact of geometric properties of overlay pavement including transverse joint spacing and slab thickness on the pavement performance was also analyzed which indicated that these have a direct impact on the performance parameters. The overlay performance can be improved by increased overlay slab thickness or reduced joint spacing and with these modifications, the adverse effects of higher CTE can be compensated. Field performance data of UBCO extracted from the LTPP database showed that the pavement ME design software can accurately predict the performance of UBCO pavement systems.

Examining driver injury severity outcomes in rural non-interstate roadway crashes using a hierarchical ordered logit model.

Rural non-interstate crashes induce a significant amount of severe injuries and fatalities. Examination of such injury patterns and the associated contributing factors is of practical importance. Taking into account the ordinal nature of injury severity levels and the hierarchical feature of crash data, this study employs a hierarchical ordered logit model to examine the significant factors in predicting driver injury severities in rural non-interstate crashes based on two-year New Mexico crash records. Bayesian inference is utilized in model estimation procedure and 95% Bayesian Credible Interval (BCI) is applied to testing variable significance. An ordinary ordered logit model omitting the between-crash variance effect is evaluated as well for model performance comparison. Results indicate that the model employed in this study outperforms ordinary ordered logit model in model fit and parameter estimation. Variables regarding crash features, environment conditions, and driver and vehicle characteristics are found to have significant influence on the predictions of driver injury severities in rural non-interstate crashes. Factors such as road segments far from intersection, wet road surface condition, collision with animals, heavy vehicle drivers, male drivers and driver seatbelt used tend to induce less severe driver injury outcomes than the factors such as multiple-vehicle crashes, severe vehicle damage in a crash, motorcyclists, females, senior drivers, driver with alcohol or drug impairment, and other major collision types. Research limitations regarding crash data and model assumptions are also discussed. Overall, this research provides reasonable results and insight in developing effective road safety measures for crash injury severity reduction and prevention.

Heterogeneous impacts of gender-interpreted contributing factors on driver injury severities in single-vehicle rollover crashes.

In this study, a mixed logit model is developed to identify the heterogeneous impacts of gender-interpreted contributing factors on driver injury severities in single-vehicle rollover crashes. The random parameter of the variables in the mixed logit model, the heterogeneous mean, is elaborated by driver gender-based linear regression models. The model is estimated using crash data in New Mexico from 2010 to 2012. The percentage changes of factors' predicted probabilities are calculated in order to better understand the model specifications. Female drivers are found more likely to experience severe or fatal injuries in rollover crashes than male drivers. However, the probability of male drivers being severely injured is higher than female drivers when the road surface is unpaved. Two other factors with fixed parameters are also found to significantly increase driver injury severities, including Wet and Alcohol Influenced. This study provides a better understanding of contributing factors influencing driver injury severities in rollover crashes as well as their heterogeneous impacts in terms of driver gender. Those results are also helpful to develop appropriate countermeasures and policies to reduce driver injury severities in single-vehicle rollover crashes.

Analysis of driver injury severity in single-vehicle crashes on rural and urban roadways.

This study analyzes driver injury severities for single-vehicle crashes occurring in rural and urban areas using data collected in New Mexico from 2010 to 2011. Nested logit models and mixed logit models are developed in order to account for the correlation between severity categories (No injury, Possible injury, Visible injury, Incapacitating injury and fatality) and individual heterogeneity among drivers. Various factors, such as crash and environment characteristics, geometric features, and driver behavior are examined in this study. Nested logit model and mixed logit model reveal similar results in terms of identifying contributing factors for driver injury severities. In the analysis of urban crashes, only the nested logit model is presented since no random parameter is found in the mixed logit model. The results indicate that significant differences exist between factors contributing to driver injury severity in single-vehicle crashes in rural and urban areas. There are 5 variables found only significant in the rural model and six significant variables identified only in the urban crash model. These findings can help transportation agencies develop effective policies or appropriate strategies to reduce injury severity resulting from single-vehicle crashes.

Investigating driver injury severity patterns in rollover crashes using support vector machine models.

Rollover crash is one of the major types of traffic crashes that induce fatal injuries. It is important to investigate the factors that affect rollover crashes and their influence on driver injury severity outcomes. This study employs support vector machine (SVM) models to investigate driver injury severity patterns in rollover crashes based on two-year crash data gathered in New Mexico. The impacts of various explanatory variables are examined in terms of crash and environmental information, vehicle features, and driver demographics and behavior characteristics. A classification and regression tree (CART) model is utilized to identify significant variables and SVM models with polynomial and Gaussian radius basis function (RBF) kernels are used for model performance evaluation. It is shown that the SVM models produce reasonable prediction performance and the polynomial kernel outperforms the Gaussian RBF kernel. Variable impact analysis reveals that factors including comfortable driving environment conditions, driver alcohol or drug involvement, seatbelt use, number of travel lanes, driver demographic features, maximum vehicle damages in crashes, crash time, and crash location are significantly associated with driver incapacitating injuries and fatalities. These findings provide insights for better understanding rollover crash causes and the impacts of various explanatory factors on driver injury severity patterns.

Exploratory multinomial logit model-based driver injury severity analyses for teenage and adult drivers in intersection-related crashes.

OBJECTIVE: Teenage drivers are more likely to be involved in severely incapacitating and fatal crashes compared to adult drivers. Moreover, because two thirds of urban vehicle miles traveled are on signal-controlled roadways, significant research efforts are needed to investigate intersection-related teenage driver injury severities and their contributing factors in terms of driver behavior, vehicle-infrastructure interactions, environmental characteristics, roadway geometric features, and traffic compositions. Therefore, this study aims to explore the characteristic differences between teenage and adult drivers in intersection-related crashes, identify the significant contributing attributes, and analyze their impacts on driver injury severities. METHODS: Using crash data collected in New Mexico from 2010 to 2011, 2 multinomial logit regression models were developed to analyze injury severities for teenage and adult drivers, respectively. Elasticity analyses and transferability tests were conducted to better understand the quantitative impacts of these factors and the teenage driver injury severity model's generality. RESULTS: The results showed that although many of the same contributing factors were found to be significant in the both teenage and adult driver models, certain different attributes must be distinguished to specifically develop effective safety solutions for the 2 driver groups. CONCLUSIONS: The research findings are helpful to better understand teenage crash uniqueness and develop cost-effective solutions to reduce intersection-related teenage injury severities and facilitate driver injury mitigation research.

A multinomial logit model-Bayesian network hybrid approach for driver injury severity analyses in rear-end crashes.

Rear-end crash is one of the most common types of traffic crashes in the U.S. A good understanding of its characteristics and contributing factors is of practical importance. Previously, both multinomial Logit models and Bayesian network methods have been used in crash modeling and analysis, respectively, although each of them has its own application restrictions and limitations. In this study, a hybrid approach is developed to combine multinomial logit models and Bayesian network methods for comprehensively analyzing driver injury severities in rear-end crashes based on state-wide crash data collected in New Mexico from 2010 to 2011. A multinomial logit model is developed to investigate and identify significant contributing factors for rear-end crash driver injury severities classified into three categories: no injury, injury, and fatality. Then, the identified significant factors are utilized to establish a Bayesian network to explicitly formulate statistical associations between injury severity outcomes and explanatory attributes, including driver behavior, demographic features, vehicle factors, geometric and environmental characteristics, etc. The test results demonstrate that the proposed hybrid approach performs reasonably well. The Bayesian network reference analyses indicate that the factors including truck-involvement, inferior lighting conditions, windy weather conditions, the number of vehicles involved, etc. could significantly increase driver injury severities in rear-end crashes. The developed methodology and estimation results provide insights for developing effective countermeasures to reduce rear-end crash injury severities and improve traffic system safety performance.