A New Model of Global Reconstructive Surgery Training With Reduced Carbon Footprint of Outreach.

This economic evaluation compares carbon dioxide emissions from air transportation for surgical mission trips vs team training trips.

Modeling the Lifetime Impact of Reconstructive Plastic Surgery Training: Implications for Building Capacity in Global Surgery.

Background: Training local surgeons and building local surgical capacity is critical to closing the gap in unmet surgical burden in low- and middle-income country (LMIC) settings. We propose a conceptual framework to quantify the impact of a single surgeon's training across multiple generations of trainees. Methods: A literature review was conducted to identify existing models for quantifying the impact of training. A model to estimate the attributable impact of surgical training was devised, based on a surgeon's attributable impact on a trainee and the lifetime number of cases trainees would perform. A quantitative survey was sent to high-income country and LMIC-based surgeons to determine the model's inputs across eight index procedures in reconstructive plastic surgery. Results: We found no existing models for quantifying the multigenerational impact of training in surgery, medicine, or nonmedical fields. Twenty-eight US-based academic plastic surgeons and 19 LMIC-based surgeons representing 10 countries provided responses. The lifetime impact of multigenerational surgical training ranged from 4100 attributable cases (skin graft) to 51,900 attributable cases (cleft lip repair) in high-income countries and from 18,200 attributable cases (carpal tunnel release) to 134,300 attributable cases (cleft lip repair) in LMICs. Conclusions: There is a sizeable impact in the first generation of training, and this impact is even greater in the second generation of training, highlighting the importance of a "multiplier effect," particularly in LMIC settings. Given the paucity of surgeons, this multiplier effect is critical in closing the surgical gap, as efforts are underway to train new cohorts of reconstructive plastic surgeons.

Patient-specific Predictors of Surgical Delay in a Large Tertiary-care Hospital Operating Room.

PURPOSE: The purpose of this study was to describe patient-specific factors predictive of surgical delay in elective surgical cases. DESIGN: Retrospective cohort study. METHODS: Data were extracted retrospectively from the electronic health record of 32,818 patients who underwent surgery at a large academic hospital in Los Angeles between May 2012 and April 2017. Following bivariate analysis of patient-specific factors and surgical delay, statistically significant predictors were entered into a logistic regression model to determine the most significant predictors of surgical delay. FINDINGS: Predictors of delay included having monitored anesthesia care (odds ratio [OR], 1.28; 95% confidence intervals [CI], 1.20-1.36), American Society of Anesthesiologist class 3 or above (OR, 1.21; 95% CI, 1.15-1.28), African American race (OR, 1.25; 95% CI, 1.12-1.39), renal failure (OR, 1.20; 95% CI, 1.09-1.32), steroid medication (OR, 1.13; 95% CI, 1.04-1.23) and Medicaid (OR,1.18; 95%CI, 1.09-1.30) or medicare insurance (OR, 1.14; 95% CI, 1.07-1.21). Six surgical specialties also increased the odds of delay. Obesity and cardiovascular anesthesia decreased the odds of delay. CONCLUSIONS: Certain patient-specific factors including type of insurance, health status, and race were associated with surgical delay. Whereas monitored anesthesia care anesthesia was predictive of a delay, cardiovascular anesthesia reduced the odds of delay. Additionally, obese patients were less likely to experience a delay. While the electronic health record provided a large amount of detailed information, barriers existed to accessing meaningful data.

Economic Evaluation of a Global Reconstructive Surgery Visiting Educator Program.

Objective: The objective of this study was to quantify the cost-effectiveness and economic value of a reconstructive surgery visiting educator trip program in a resource-constrained setting. Background: Reconstructive surgical capacity remains inadequate in low- and middle-income countries, resulting in chronic disability and a significant economic toll. Education and training of the local surgical workforce to sustainably expand capacity have been increasingly encouraged, but economic analyses of these interventions are lacking. Methods: Data were analyzed from 12 visiting educator trips and independently-performed surgical procedures at 3 Vietnamese hospitals between 2014 and 2019. A cost-effectiveness analysis was performed using standardized methodology and thresholds to determine cost-effectiveness. Sensitivity analyses were performed with disability weights, discounting, and costs from different perspectives. Economic benefit was estimated using both the human capital method and the value of a statistical life method, and a benefit-cost ratio was computed. Results: In the base case analysis, the visiting educator program was very cost-effective at $581 per disability-adjusted life year (DALY) averted. Economic benefit was between $21·6 million and $29·3 million, corresponding to a 12- to 16-fold return on investment. Furthermore, when considering only costs to the organization, the cost decreased to $61 per DALY averted, with a 113- to 153-fold return on investment for the organization. Conclusions: Visiting educator programs, which build local reconstructive surgical capacity in limited-resource environments, can be very cost-effective with significant economic benefit and return on investment. These findings may help guide organizations, donors, and policymakers in resource allocation in global surgery.

Securing the Chain of Custody and Integrity of Data in a Global North-South Partnership to Monitor the Quality of Essential Medicines.

Substandard and falsified (SF) pharmaceuticals account for an estimated 10% of the pharmaceutical supply chain in low- and middle-income countries (LMICs), where a lack of regulatory and laboratory resources limits the ability to conduct effective post-market surveillance and allows SF products to penetrate the supply chain. The Distributed Pharmaceutical Analysis Laboratory (DPAL) was established in 2014 to expand testing of pharmaceutical dosage forms sourced from LMICs; DPAL is an alliance of academic institutions throughout the United States and abroad that provides high-quality, validated chemical analysis of pharmaceutical dosage forms sourced from partners in LMICs. Results from analysis are reported to relevant regulatory agencies and are used to inform purchasing decisions made by in-country stakeholders. As the DPAL program has expanded to testing more than 1,000 pharmaceutical dosage forms annually, challenges have surfaced regarding data management and sample tracking. Here, we describe a pilot project between DPAL and ARTiFACTs that applies the blockchain to organize and manage key data generated during the DPAL workflow, including a sample's progress through the workflow, its physical location, provenance of metadata, and lab reputability. Recording time and date stamps with these data will create a permanent and verifiable chain of custody for samples. This secure, distributed ledger will be linked to an easy-to-use dashboard, allowing stakeholders to view results and experimental details for each sample in real time and verify the integrity of DPAL analysis data. Introducing this blockchain-based system as a pilot will allow us to test the technology with real users analyzing real samples. Feedback from users will be recorded and necessary adjustments will be made to the system before the implementation of blockchain across all DPAL sites. Anticipated benefits of implementing the blockchain technology for managing DPAL data include efficient management for routing work, increasing throughput, creating a chain of custody for samples and their data in alignment with the distributed nature of DPAL, and using the analysis results to detect patterns of quality within and across brands of products and develop enhanced sampling techniques and best practices.

Combining natural language processing and metabarcoding to reveal pathogen-environment associations.

Cryptococcus neoformans is responsible for life-threatening infections that primarily affect immunocompromised individuals and has an estimated worldwide burden of 220,000 new cases each year-with 180,000 resulting deaths-mostly in sub-Saharan Africa. Surprisingly, little is known about the ecological niches occupied by C. neoformans in nature. To expand our understanding of the distribution and ecological associations of this pathogen we implement a Natural Language Processing approach to better describe the niche of C. neoformans. We use a Latent Dirichlet Allocation model to de novo topic model sets of metagenetic research articles written about varied subjects which either explicitly mention, inadvertently find, or fail to find C. neoformans. These articles are all linked to NCBI Sequence Read Archive datasets of 18S ribosomal RNA and/or Internal Transcribed Spacer gene-regions. The number of topics was determined based on the model coherence score, and articles were assigned to the created topics via a Machine Learning approach with a Random Forest algorithm. Our analysis provides support for a previously suggested linkage between C. neoformans and soils associated with decomposing wood. Our approach, using a search of single-locus metagenetic data, gathering papers connected to the datasets, de novo determination of topics, the number of topics, and assignment of articles to the topics, illustrates how such an analysis pipeline can harness large-scale datasets that are published/available but not necessarily fully analyzed, or whose metadata is not harmonized with other studies. Our approach can be applied to a variety of systems to assert potential evidence of environmental associations.

Preventing Surgical Delay and Cancellation with Patient-Centered Interventions.

Delay and cancellation can significantly impact cost and outcomes among surgical patients. While the causes of delay and cancellation are not fully enumerated, possible reasons include delivery-related causes such as facility, equipment, and provider availability as well as patient-related issues such as readiness and health status. Despite limited research explaining patient-related causes, there are many studies that evaluate patient-centered interventions to decrease delay and cancellation. This article highlights patient-centered interventions including preoperative clinics, preoperative screening, and focused education that have been shown to reduce delay and cancellation. This information provides perianesthesia nurses and advanced practice nurses ideas to maximize their roles in improving efficiency by prevention of delay and cancellation. This article should also stimulate additional research to help better understand the causes and the role of the nurse in the implementation of evidence-based practice projects that use patient-centered interventions.

Are we curing by cutting? A call for long-term follow up and outcomes research in global surgery interventions - perspective.

Success in global surgery interventions cannot be claimed until consistent long-term follow up is achieved and corresponding outcomes are studied. However, post-operative outcomes remain inconsistently collected and analyzed in the setting of global surgery, with current efforts largely focused on the delivery of surgical care. Barriers in low- and middle-income countries include patient cost and distance, low surgical workforce capacity, poor patient health literacy, lack of affordable technology availability, inconsistent documentation, and structural deficiencies. Here, we suggest that future work can be focused on (1) enhancing systems to facilitate long-term follow up and care, (2) expanding availability and adoption of electronic medical record systems, and (3) collaboration with local surgeons in the development of international cross-organizational registries and standardized quality measures. Long-term collaborations between local healthcare administrators and providers, policymakers, international bodies, nonprofit organizations, patients, and the private sector are necessary to build and sustain processes to achieve reliable long-term follow up and rigorous data collection, with the goal of ultimately ensuring better patient outcomes.

Fostering global data sharing: highlighting the recommendations of the Research Data Alliance COVID-19 working group.

The systemic challenges of the COVID-19 pandemic require cross-disciplinary collaboration in a global and timely fashion. Such collaboration needs open research practices and the sharing of research outputs, such as data and code, thereby facilitating research and research reproducibility and timely collaboration beyond borders. The Research Data Alliance COVID-19 Working Group recently published a set of recommendations and guidelines on data sharing and related best practices for COVID-19 research. These guidelines include recommendations for clinicians, researchers, policy- and decision-makers, funders, publishers, public health experts, disaster preparedness and response experts, infrastructure providers from the perspective of different domains (Clinical Medicine, Omics, Epidemiology, Social Sciences, Community Participation, Indigenous Peoples, Research Software, Legal and Ethical Considerations), and other potential users. These guidelines include recommendations for researchers, policymakers, funders, publishers and infrastructure providers from the perspective of different domains (Clinical Medicine, Omics, Epidemiology, Social Sciences, Community Participation, Indigenous Peoples, Research Software, Legal and Ethical Considerations). Several overarching themes have emerged from this document such as the need to balance the creation of data adherent to FAIR principles (findable, accessible, interoperable and reusable), with the need for quick data release; the use of trustworthy research data repositories; the use of well-annotated data with meaningful metadata; and practices of documenting methods and software. The resulting document marks an unprecedented cross-disciplinary, cross-sectoral, and cross-jurisdictional effort authored by over 160 experts from around the globe. This letter summarises key points of the Recommendations and Guidelines, highlights the relevant findings, shines a spotlight on the process, and suggests how these developments can be leveraged by the wider scientific community.