Material Flow Analysis and Occupational Exposure Assessment in Additive Manufacturing End-of-Life Material Management.

Additive manufacturing (AM) offers a variety of material manufacturing techniques for a wide range of applications across many industries. Most efforts at process optimization and exposure assessment for AM are centered around the manufacturing process. However, identifying the material allocation and potentially harmful exposures in end-of-life (EoL) management is equally crucial to mitigating environmental releases and occupational health impacts within the AM supply chain. This research tracks the allocation and potential releases of AM EoL materials within the US through a material flow analysis. Of the generated AM EoL materials, 58% are incinerated, 33% are landfilled, and 9% are recycled by weight. The generated data set was then used to examine the theoretical occupational hazards during AM EoL material management practices through generic exposure scenario assessment, highlighting the importance of ventilation and personal protective equipment at all stages of AM material management. This research identifies pollution sources, offering policymakers and stakeholders insights to shape pollution prevention and worker safety strategies within the US AM EoL management pathways.

Review of generic scenario environmental release and occupational exposure models used in chemical risk assessment.

Under the Toxic Substances Control Act (TSCA), the United States Environmental Protection Agency (USEPA) is required to determine whether a new chemical substance poses an unreasonable risk to human health or the environment before the chemical is manufactured in or imported into the United States. This manuscript provides a review of the process used to evaluate the risk associated with a chemical based on the scenarios and models used in the evaluation. Specifically, the Generic Scenarios and Emission Scenario Documents developed by the USEPA were reviewed, along with background documentation prepared by USEPA to identify the core elements of the environmental release and occupational exposure scenarios used to assess the risk of the chemical being evaluated. Additionally, this contribution provides an overview of methods used to model occupational exposures and environmental releases as part of the chemical evaluation process used in other jurisdictions, along with work being performed to improve these models. Finally, the alternative methods to evaluate occupational exposures and environmental releases that may be used as part of the decision-making process regarding a chemical are identified. The contribution provides a path forward for reducing the time required and improving the chemical evaluation of the unreasonable risk determination regarding the manufacture or import of a chemical.

Cross-sector decision landscape in response to COVID-19: A qualitative network mapping analysis of North Carolina decision-makers.

Introduction: The COVID-19 pandemic response has demonstrated the interconnectedness of individuals, organizations, and other entities jointly contributing to the production of community health. This response has involved stakeholders from numerous sectors who have been faced with new decisions, objectives, and constraints. We examined the cross-sector organizational decision landscape that formed in response to the COVID-19 pandemic in North Carolina. Methods: We conducted virtual semi-structured interviews with 44 organizational decision-makers representing nine sectors in North Carolina between October 2020 and January 2021 to understand the decision-making landscape within the first year of the COVID-19 pandemic. In line with a complexity/systems thinking lens, we defined the decision landscape as including decision-maker roles, key decisions, and interrelationships involved in producing community health. We used network mapping and conventional content analysis to analyze transcribed interviews, identifying relationships between stakeholders and synthesizing key themes. Results: Decision-maker roles were characterized by underlying tensions between balancing organizational mission with employee/community health and navigating organizational vs. individual responsibility for reducing transmission. Decision-makers' roles informed their perspectives and goals, which influenced decision outcomes. Key decisions fell into several broad categories, including how to translate public health guidance into practice; when to institute, and subsequently loosen, public health restrictions; and how to address downstream social and economic impacts of public health restrictions. Lastly, given limited and changing information, as well as limited resources and expertise, the COVID-19 response required cross-sector collaboration, which was commonly coordinated by local health departments who had the most connections of all organization types in the resulting network map. Conclusions: By documenting the local, cross-sector decision landscape that formed in response to COVID-19, we illuminate the impacts different organizations may have on information/misinformation, prevention behaviors, and, ultimately, health. Public health researchers and practitioners must understand, and work within, this complex decision landscape when responding to COVID-19 and future community health challenges.

Use of Modeling to Inform Decision Making in North Carolina during the COVID-19 Pandemic: A Qualitative Study.

Background. The COVID-19 pandemic has popularized computer-based decision-support models, which are commonly used to inform decision making amidst complexity. Understanding what organizational decision makers prefer from these models is needed to inform model development during this and future crises. Methods. We recruited and interviewed decision makers from North Carolina across 9 sectors to understand organizational decision-making processes during the first year of the COVID-19 pandemic (N = 44). For this study, we identified and analyzed a subset of responses from interviewees (n = 19) who reported using modeling to inform decision making. We used conventional content analysis to analyze themes from this convenience sample with respect to the source of models and their applications, the value of modeling and recommended applications, and hesitancies toward the use of models. Results. Models were used to compare trends in disease spread across localities, estimate the effects of social distancing policies, and allocate scarce resources, with some interviewees depending on multiple models. Decision makers desired more granular models, capable of projecting disease spread within subpopulations and estimating where local outbreaks could occur, and incorporating a broad set of outcomes, such as social well-being. Hesitancies to the use of modeling included doubts that models could reflect nuances of human behavior, concerns about the quality of data used in models, and the limited amount of modeling specific to the local context. Conclusions. Decision makers perceived modeling as valuable for informing organizational decisions yet described varied ability and willingness to use models for this purpose. These data present an opportunity to educate organizational decision makers on the merits of decision-support modeling and to inform modeling teams on how to build more responsive models that address the needs of organizational decision makers. Highlights: Organizations from a diversity of sectors across North Carolina (including public health, education, business, government, religion, and public safety) have used decision-support modeling to inform decision making during COVID-19.Decision makers wish for models to project the spread of disease, especially at the local level (e.g., individual cities and counties), and to help estimate the outcomes of policies.Some organizational decision makers are hesitant to use modeling to inform their decisions, stemming from doubts that models could reflect nuances of human behavior, concerns about the accuracy and precision of data used in models, and the limited amount of modeling available at the local level.

Vaccinating children against COVID-19 is crucial to protect schools and communities.

To evaluate the joint impact of childhood vaccination rates and school masking policies on community transmission and severe outcomes due to COVID-19, we utilized a stochastic, agent-based simulation of North Carolina to test 24 health policy scenarios. In these scenarios, we varied the childhood (ages 5 to 19) vaccination rate relative to the adult's (ages 20 to 64) vaccination rate and the masking relaxation policies in schools. We measured the overall incidence of disease, COVID-19-related hospitalization, and mortality from 2021 July 1 to 2023 July 1. Our simulation estimates that removing all masks in schools in January 2022 could lead to a 31% to 45%, 23% to 35%, and 13% to 19% increase in cumulative infections for ages 5 to 9, 10 to 19, and the total population, respectively, depending on the childhood vaccination rate. Additionally, achieving a childhood vaccine uptake rate of 50% of adults could lead to a 31% to 39% reduction in peak hospitalizations overall masking scenarios compared with not vaccinating this group. Finally, our simulation estimates that increasing vaccination uptake for the entire eligible population can reduce peak hospitalizations in 2022 by an average of 83% and 87% across all masking scenarios compared to the scenarios where no children are vaccinated. Our simulation suggests that high vaccination uptake among both children and adults is necessary to mitigate the increase in infections from mask removal in schools and workplaces.

Recycling of Plastics in the United States: Plastic Material Flows and Polyethylene Terephthalate (PET) Recycling Processes.

As efforts are made toward establishing a circular economy that engages in activities that maintain resources at their highest values for as long as possible, an important aspect is understanding the systems which allow recycling to occur. In this article a common plastic, polyethylene terephthalate, i.e., PET or plastic #1, has been studied because it is recycled at relatively high rates in the U.S. as compared to other plastics. A material flow analysis is described for PET resin showing materials collected, reclaimed for flake, and converted into items with recycled content. Imports/exports, reclaimer residue, and disposal with mismanaged waste are all shown for U.S. flows of PET. Barriers to recycling PET exist in the collecting, sorting, reclaiming, and converting steps, and this article describes them, offers some solutions, and suggests some research that chemists and engineers could focus on to improve the systems. This effort also models sorting at material recovery facilities (MRF) and reclaimers, with detailed descriptions of the material streams involved, to characterize the resource use and emissions from these operations that are key processes in the recycling system. Example results include greenhouse gas intensities of 8.58 kg CO2 equiv per ton of MRF feed and 103.7 kg CO2 equiv per ton of reclaimer PET bale feed. The results can be used in system analyses for various scenarios and as inputs in economic input-output and life cycle assessments.

Association of Simulated COVID-19 Vaccination and Nonpharmaceutical Interventions With Infections, Hospitalizations, and Mortality.

Importance: Vaccination against SARS-CoV-2 has the potential to significantly reduce transmission and COVID-19 morbidity and mortality. The relative importance of vaccination strategies and nonpharmaceutical interventions (NPIs) is not well understood. Objective: To assess the association of simulated COVID-19 vaccine efficacy and coverage scenarios with and without NPIs with infections, hospitalizations, and deaths. Design, Setting, and Participants: An established agent-based decision analytical model was used to simulate COVID-19 transmission and progression from March 24, 2020, to September 23, 2021. The model simulated COVID-19 spread in North Carolina, a US state of 10.5 million people. A network of 1 017 720 agents was constructed from US Census data to represent the statewide population. Exposures: Scenarios of vaccine efficacy (50% and 90%), vaccine coverage (25%, 50%, and 75% at the end of a 6-month distribution period), and NPIs (reduced mobility, school closings, and use of face masks) maintained and removed during vaccine distribution. Main Outcomes and Measures: Risks of infection from the start of vaccine distribution and risk differences comparing scenarios. Outcome means and SDs were calculated across replications. Results: In the worst-case vaccination scenario (50% efficacy, 25% coverage), a mean (SD) of 2 231 134 (117 867) new infections occurred after vaccination began with NPIs removed, and a mean (SD) of 799 949 (60 279) new infections occurred with NPIs maintained during 11 months. In contrast, in the best-case scenario (90% efficacy, 75% coverage), a mean (SD) of 527 409 (40 637) new infections occurred with NPIs removed and a mean (SD) of 450 575 (32 716) new infections occurred with NPIs maintained. With NPIs removed, lower efficacy (50%) and higher coverage (75%) reduced infection risk by a greater magnitude than higher efficacy (90%) and lower coverage (25%) compared with the worst-case scenario (mean [SD] absolute risk reduction, 13% [1%] and 8% [1%], respectively). Conclusions and Relevance: Simulation outcomes suggest that removing NPIs while vaccines are distributed may result in substantial increases in infections, hospitalizations, and deaths. Furthermore, as NPIs are removed, higher vaccination coverage with less efficacious vaccines can contribute to a larger reduction in risk of SARS-CoV-2 infection compared with more efficacious vaccines at lower coverage. These findings highlight the need for well-resourced and coordinated efforts to achieve high vaccine coverage and continued adherence to NPIs before many prepandemic activities can be resumed.

The Joint Impact of COVID-19 Vaccination and Non-Pharmaceutical Interventions on Infections, Hospitalizations, and Mortality: An Agent-Based Simulation.

Background: Vaccination against SARS-CoV-2 has the potential to significantly reduce transmission and morbidity and mortality due to COVID-19. This modeling study simulated the comparative and joint impact of COVID-19 vaccine efficacy and coverage with and without non-pharmaceutical interventions (NPIs) on total infections, hospitalizations, and deaths. Methods: An agent-based simulation model was employed to estimate incident SARS-CoV-2 infections and COVID-19-associated hospitalizations and deaths over 18 months for the State of North Carolina, a population of roughly 10.5 million. Vaccine efficacy of 50% and 90% and vaccine coverage of 25%, 50%, and 75% (at the end of a 6-month distribution period) were evaluated. Six vaccination scenarios were simulated with NPIs (i.e., reduced mobility, school closings, face mask usage) maintained and removed during the period of vaccine distribution. Results: In the worst-case vaccination scenario (50% efficacy and 25% coverage), 2,231,134 new SARS-CoV-2 infections occurred with NPIs removed and 799,949 infections with NPIs maintained. In contrast, in the best-case scenario (90% efficacy and 75% coverage), there were 450,575 new infections with NPIs maintained and 527,409 with NPIs removed. When NPIs were removed, lower efficacy (50%) and higher coverage (75%) reduced infection risk by a greater magnitude than higher efficacy (90%) and lower coverage (25%) compared to the worst-case scenario (absolute risk reduction 13% and 8%, respectively). Conclusion: Simulation results suggest that premature lifting of NPIs while vaccines are distributed may result in substantial increases in infections, hospitalizations, and deaths. Furthermore, as NPIs are removed, higher vaccination coverage with less efficacious vaccines can contribute to a larger reduction in risk of SARS-CoV-2 infection compared to more efficacious vaccines at lower coverage. Our findings highlight the need for well-resourced and coordinated efforts to achieve high vaccine coverage and continued adherence to NPIs before many pre-pandemic activities can be resumed.

Data engineering for tracking chemicals and releases at industrial end-of-life activities.

Performing risk evaluation is necessary to determine whether a chemical substance presents an unreasonable risk of injury to human health or the environment across its life cycle stages. Data gathering, reconciliation, and management for supporting risk evaluation are time-consuming and challenging, especially for end-of-life (EoL) activities due to the need for proper reporting and traceability. A data engineering framework using publicly-available databases to track chemicals in waste streams generated by industrial activities and transferred to other facilities across different U.S. locations for waste management is implemented. The analysis tracks chemicals in waste streams generated at industrial processes and handling at off-site facilities and then estimates releases from EoL activities. The final product of this effort is a framework that identifies a set of chemical, activity, and industry sector categories as well as hazardous waste flows, emission factors, and uncertainty indicators to describe EoL activities. This framework helps to identify EoL exposure scenarios that would otherwise not be evaluated. As a case study, methylene chloride, one of the first ten chemicals to undergo risk evaluation under the amended U.S. Toxic Substances Control Act, was evaluated with results highlighting potential additional exposure scenarios.

Applying Environmental Release Inventories and Indicators to the Evaluation of Chemical Manufacturing Processes in Early Stage Development.

As manufacturing processes are developed through the early stages of technology readiness, various assessments can be used to evaluate their performance. Performance indicators describe processes by transforming attributes into scores that represent desirable objectives. One type of assessment is obtained by determining the life cycle inventories of inputs and outputs for processes. For a functional unit of product, the user finds the resources used and the releases to the environment, which can be compared to results for similar processes and/or combined with other processes in the life cycle. In this work, an expanded range of process inputs and releases is modeled, including forklift/loader, fugitive, storage, boiler, and cooling tower emissions. A generic scenario approach for the cooling tower releases provides a first approximation of emission and wastewater flows. These inventory values are used in performance indicators that can be placed on a scale between fixed best- and worst-case limits with the GREENSCOPE methodology, thus allowing comparisons across various technologies. The processes of interest are two conversion pathways for producing cellulosic ethanol from biomass via thermochemical and biochemical routes. The results can be used in risk assessments, decision making, evaluation of research, and in spurring future technology development.

Purpose-Driven Reconciliation of Approaches to Estimate Chemical Releases.

A framework is presented to address the toolbox of chemical release estimation methods available for manufacturing processes. Although scientists and engineers often strive for increased accuracy, the development of fit-for-purpose release estimates can speed results that could otherwise delay decisions important to protecting human health and the environment. A number of release estimation approaches are presented, with the newest using decision trees for regression and prediction. Each method is evaluated in a case study for cumene production to study the reconciliation of data quality concerns and requirements for time, resources, training, and knowledge. The evaluation of these decision support criteria and the lessons learned are used to develop a purpose-driven framework for estimating chemical releases.

A Process Systems Framework for Rapid Generation of Life Cycle Inventories for Pollution Control and Sustainability Evaluation.

Life Cycle Assessment (LCA) is a tool that aids in sustainable decision-making among product and process alternatives. When implementing LCA, the efficient and accurate modeling of chemical processes for Life Cycle Inventory (LCI) generation is still challenging. Challenges include a lack of systematic design and simulation tools and approaches to develop chemical process models for obtaining and analyzing more realistic LCI results. In this contribution, a novel process systems framework is proposed for estimating LCI results when implementing pollution control technologies. This framework involves the development and incorporation of pollution control unit (PCU) modules into process simulation and generation of LCI data associated with the PCUs for use in a sustainability evaluation. Different pollution control modules are designed for rapid LCI estimation and applied to obtain emissions, utility consumption, material, and land footprint results related to waste streams of a process simulation. Then, the LCI results are analyzed with the objectives of minimizing the environmental impact and utility consumption. The proposed framework is illustrated via a biomass/coal gasification process for syngas production with the end goal of acetic acid manufacturing. Results associated with this case study show that the developed framework can provide guidelines for sustainable decision-making based on generated LCI results.

Coupling Computer-Aided Process Simulation and Estimations of Emissions and Land Use for Rapid Life Cycle Inventory Modeling.

A methodology is described for developing a gate-to-gate life cycle inventory (LCI) of a chemical manufacturing process to support the application of life cycle assessment in the design and regulation of sustainable chemicals. The inventories were derived by first applying process design and simulation to develop a process flow diagram describing the energy and basic material flows of the system. Additional techniques developed by the United States Environmental Protection Agency for estimating uncontrolled emissions from chemical processing equipment were then applied to obtain a detailed emission profile for the process. Finally, land use for the process was estimated using a simple sizing model. The methodology was applied to a case study of acetic acid production based on the Cativa process. The results reveal improvements in the qualitative LCI for acetic acid production compared to commonly used databases and top-down methodologies. The modeling techniques improve the quantitative LCI results for inputs and uncontrolled emissions. With provisions for applying appropriate emission controls, the proposed method can provide an estimate of the LCI that can be used for subsequent life cycle assessments.

Toward Automated Inventory Modeling in Life Cycle Assessment: The Utility of Semantic Data Modeling to Predict Real-World Chemical Production.

A set of coupled semantic data models, i.e., ontologies, are presented to advance a methodology toward automated inventory modeling of chemical manufacturing in life cycle assessment. The cradle-to-gate life cycle inventory for chemical manufacturing is a detailed collection of the material and energy flows associated with a chemical's supply chain. Thus, there is a need to manage data describing both the lineage (or synthesis pathway) and processing conditions for a chemical. To this end, a Lineage ontology is proposed to reveal all the synthesis steps required to produce a chemical from raw materials, such as crude oil or biomaterials, while a Process ontology is developed to manage data describing the various unit processes associated with each synthesis step. The two ontologies are coupled such that process data, which is the basis for inventory modeling, is linked to lineage data through key concepts like the chemical reaction and reaction participants. To facilitate automated inventory modeling, a series of SPARQL queries, based on the concepts of ancestor and parent, are presented to generate a lineage for a chemical of interest from a set of reaction data. The proposed ontologies and SPARQL queries are evaluated and tested using a case study of nylon-6 production. Once a lineage is established, the process ontology can be used to guide inventory modeling based on both data mining (top-down) and simulation (bottom-up) approaches. The ability to generate a cradle-to-gate life cycle for a chemical represents a key achievement toward the ultimate goal of automated life cycle inventory modeling.

Mining Available Data from the United States Environmental Protection Agency to Support Rapid Life Cycle Inventory Modeling of Chemical Manufacturing.

Demands for quick and accurate life cycle assessments create a need for methods to rapidly generate reliable life cycle inventories (LCI). Data mining is a suitable tool for this purpose, especially given the large amount of available governmental data. These data are typically applied to LCIs on a case-by-case basis. As linked open data becomes more prevalent, it may be possible to automate LCI using data mining by establishing a reproducible approach for identifying, extracting, and processing the data. This work proposes a method for standardizing and eventually automating the discovery and use of publicly available data at the United States Environmental Protection Agency for chemical-manufacturing LCI. The method is developed using a case study of acetic acid. The data quality and gap analyses for the generated inventory found that the selected data sources can provide information with equal or better reliability and representativeness on air, water, hazardous waste, on-site energy usage, and production volumes but with key data gaps including material inputs, water usage, purchased electricity, and transportation requirements. A comparison of the generated LCI with existing data revealed that the data mining inventory is in reasonable agreement with existing data and may provide a more-comprehensive inventory of air emissions and water discharges. The case study highlighted challenges for current data management practices that must be overcome to successfully automate the method using semantic technology. Benefits of the method are that the openly available data can be compiled in a standardized and transparent approach that supports potential automation with flexibility to incorporate new data sources as needed.

Environmental indicators of biofuel sustainability: what about context?

Indicators of the environmental sustainability of biofuel production, distribution, and use should be selected, measured, and interpreted with respect to the context in which they are used. The context of a sustainability assessment includes the purpose, the particular biofuel production and distribution system, policy conditions, stakeholder values, location, temporal influences, spatial scale, baselines, and reference scenarios. We recommend that biofuel sustainability questions be formulated with respect to the context, that appropriate indicators of environmental sustainability be developed or selected from more generic suites, and that decision makers consider context in ascribing meaning to indicators. In addition, considerations such as technical objectives, varying values and perspectives of stakeholder groups, indicator cost, and availability and reliability of data need to be understood and considered. Sustainability indicators for biofuels are most useful if adequate historical data are available, information can be collected at appropriate spatial and temporal scales, organizations are committed to use indicator information in the decision-making process, and indicators can effectively guide behavior toward more sustainable practices.

Assessing resource intensity and renewability of cellulosic ethanol technologies using eco-LCA.

Recognizing the contributions of ecosystem services and the lack of their comprehensive accounting in life cycle assessment (LCA), an in-depth analysis of their contribution in the life cycle of cellulosic ethanol derived from five different feedstocks was conducted, with gasoline and corn ethanol as reference fuels. The relative use intensity of natural resources encompassing land and ecosystem goods and services by cellulosic ethanol was estimated using the Eco-LCA framework. Despite being resource intensive compared to gasoline, cellulosic ethanol offers the possibility of a reduction in crude oil consumption by as much as 96%. Soil erosion and land area requirements can be sources of concern for cellulosic ethanol derived directly from managed agriculture. The analysis of two broad types of thermodynamic metrics, namely: various types of physical return on investment and a renewability index, which indicate competitiveness and sustainability of cellulosic ethanol, respectively, show that only ethanol from waste resources combines a favorable thermodynamic return on investment with a higher renewability index. However, the production potential of ethanol from waste resources is limited. This finding conveys a possible dilemma of biofuels: combining high renewability, high thermodynamic return on investment, and large production capacity may remain elusive. A plot of renewability versus energy return on investment is suggested as one of the options for providing guidance on future biofuel selection.

Confidence limit variation for a single IMRT system following the TG119 protocol.

PURPOSE: To evaluate the robustness of TG119-based quality assurance metrics for an IMRT system. METHODS: Four planners constructed treatment plans for the five IMRT test cases described in TG119. All plans were delivered to a 30 cm x 30 cm x 15 cm solid water phantom in one treatment session in order to minimize session-dependent variation from phantom setup, film quality, machine performance, etc. Composite measurements utilized film and an ionization chamber. Per-field measurements were collected using a diode array device at an effective depth of 5 cm. All data collected were analyzed using the TG119 specifications to determine the confidence limit values for each planner separately and then compared. RESULTS: The mean variance of ion chamber measurements for each planner was within 1.7% of the planned dose. The resulting confidence limits were 3.13%, 1.98%, 3.65%, and 4.39%. Confidence limit values determined by composite film analysis were 8.06%, 13.4%, 9.30%, and 16.5%. Confidence limits from per-field measurements were 1.55%, 0.00%, 0.00%, and 2.89%. CONCLUSIONS: For a single IMRT system, the accuracy assessment provided by TG119-based quality assurance metrics showed significant variations in the confidence limits between planners across all composite and per-field evaluations. This observed variation is likely due to the different levels of modulation between each planner's set of plans. Performing the TG119 evaluation using plans produced by a single planner may not provide an adequate estimation of IMRT system accuracy.

Treatment of osteochondral lesions of the talus with cryopreserved talar allograft and ankle distraction with external fixation.

This article presents the results of a retrospective review of six osteochondral lesions on six patients (five men and one woman) treated with transplantation of cryopreserved talar allograft and ankle joint distraction. All patients complained of ankle pain existing for a long time secondary to a traumatic episode confirmed through MRI. Lesions ranged in measurement from 0.8 cm x 0.8 cm to 3.2 cm x 1.8 cm with an average size of 2.1 cm x 1.5 cm. Each patient underwent talar dome transplantation using fresh frozen talar allograft followed by ankle distraction. Distraction was obtained using a three-ring multiplanar external fixation device. All surgeries were performed between 2002 and 2004. All external fixators were removed at 8 weeks and patients remained partial-weight bearing in a removable cast boot for an additional 8 weeks. Serial postoperative radiographs showed complete consolidation of the allograft within 16 weeks. The average follow up time was 24 months, and all patients related a subjective decrease in symptoms and increase in activity levels. Patients were also evaluated utilizing the Maryland Foot Score both pre- and postoperatively. Preoperatively, four patients were graded as fair and two were graded as poor. Postoperatively, two patients related excellent results, three patients related good results, and one patient related fair results. Several patients experienced minor complications such as pin site irritation (five patients), painful talar wire (one patient), and periostitis (one patient). No patients experienced any major complications and none have required additional surgery. We feel that these initial results warrant further investigation of this treatment.