Current Decision Research Articles

Promoting sustainable land management: An innovative approach to land-take decision-making

Land degradation presents significant global challenges, threatening natural resources, biodiversity, and food security. Addressing this issue requires more effective land-take decision-making processes, particularly in data-deficient cities where comprehensive land assessment methods are challenging to implement. This study introduces a streamlined land-take decision-making framework designed to promote sustainable land management practices. The framework consists of two key components: the Sustainable Development Index (SDI) for assessing current land-take decisions and the Decision-Making Rubric (DMR) for proposing mitigated solutions. Applied to a pilot case city in India, the framework demonstrated its practical utility by showing that land-take decisions between 2001 and 2021 resulted in a 69 % reduction of natural land cover. If these trends continue, the assessment of the 2031 master plan using SDI indicates that an additional 56 % of the remaining ecosystem-rich areas, which include regions with high biodiversity and ecological value, could be lost by 2031. However, the framework's application could potentially mitigate these impacts, reducing the projected 56 % loss to 14 %, thereby promoting more sustainable and equitable land management practices. The study's aim is to provide decision-makers with a practical tool to improve land identification methods and enhance the sustainability of land-take decisions. This research contributes to the existing body of knowledge by addressing the gap in practical, easily applicable tools for sustainable land management in data-deficient urban contexts.

Rural Development as a Basis for Food Security of The Union State: Methodology of an Integrated Approach

In this article, the author, based on a study of the current situation, systemic problems and regulatory decisions related to the situation of rural areas of the Russian Federation, proposes a number of conceptual points to strengthen the regulatory functions of the Union State institutions in relation to rural development, which could contribute to building up integration processes as part of a large association. The essential point of the article is that rural areas, as the organic basis of agro-industry, are the most important factor in food security both at the national level and at the level of the Union State. According to the author, the village today bears the main burden of protecting our Motherland, therefore it is unreasonable to save on the development of rural areas — and state regulation of rural development should be carried out as part of a program-oriented and goal-oriented approach, in the implementation of which a system of standards is used to determine the numerical values of target indicators.

Rethinking Sentience: Invertebrates as Worthy of Moral Consideration

The ethical debate on the moral consideration of non-human animals (hereafter animals) is currently centred on the evidence of sentience in these individuals. Legal protection for vertebrates and cephalopods (and decapods in the UK) has resulted from the recognition of sentience in these animals. Although one should celebrate the significant advances in the legal protection of animals in recent decades, current animal legislation is modulated by an instrumental viewpoint, remaining speciesist and anthropocentric. A sentient being is here understood as one who has the phenomenological experience of awareness, which is the most basic sense of phenomenal consciousness that implies the existence of a subject who is not indifferent to what happens to itself. This paper demonstrates, with reasonable assumptions, that this concept of sentience would apply to many invertebrate species, thus deeming them worthy of increased moral consideration and legal protection. In cases in which sentience cannot be demonstrated clearly, one should assume the precautionary principle and consider the intrinsic value of each animal to designate moral consideration. In considering sentience as the primary condition for moral consideration, science must expand who is recognized as sentient rather than being reductionist. Animal ethics must review to whom the moral consideration should be given. Animal legislation must include legislative innovations and invertebrates in its protective scope. Thereby, a significant improvement in the current political and legislative decisions would be rooted in animal ethics. Opening the ethical perception and broadening the debate are urgent, as moral consideration should be given to all animals.

Left Amyand’s Hernia in A 1-Year-Old Male Infant: Report of a Rare Case in Common Presentation

Abstract Amyand’s hernia is a rare type of hernia with the appendix and cecum trapped in a hernial sac. A one-year-old male infant was referred with irreducible left inguinal hernia for about 1 week. No signs of intestinal obstruction were presented, and pain was tolerated. A herniotomy was performed on the patient. Intraoperatively, the appendix and cecum were found in the left hernial sac, which this rare presentation named left Amyand’s hernia. Cecum and appendix were considered normal. No perforation and significant inflammation were found; hence, no appendectomy was performed. Left Amyand’s hernia is a very rare case, mostly in pediatric surgery, in which the current decision of diagnosis-making can only be made intraoperatively.

Seasonal forecasts have sufficient skill to inform some agricultural decisions

Abstract Seasonal forecasts, which look several months into the future, are currently underutilized in active decision-making, particularly for agricultural and natural resource management. This underutilization can be attributed to the absence of forecasts for decision-relevant variables at the required spatiotemporal resolution and at the time when the decisions are made and a perception of poor skill by decision-makers. Addressing these constraints, we quantified the skill of seasonal forecasts in informing two agricultural decisions with differing decision timeframes and influencer variables: (a) whether to apply fertilizer in fall or wait until spring based on expected winter temperatures, and (b) drought response, such as whether to lease water based on expectations of drought. We also looked into how early the forecast can be provided without significant degradation in skill. Currently, drought response decisions are typically formulated in April, utilizing drought forecasts issued in the same month, while fall fertilization decisions are generally made between August and September. There is growing interest among stakeholders in the availability of earlier forecasts to inform these critical choices. We utilized the North American Multi-Model Ensemble (NMME) hindcasts for the time period 1982 - 2020 over the Pacific Northwest US to obtain meteorological variables. Runoff was estimated via simulations of the coupled crop-hydrology VIC-CropSyst model. The skill assessment with the Heidke Skill Score (HSS) yielded promising outcomes in both decisions for the entire Pacific Northwest region. Notably, NMME’s positive skill (median HSS of 30%) in predicting warmer winters identifies years when fertilizer application should be avoided to prevent fertilizer loss through mineralization (and associated costs). Similarly, there is skill in forecasting drought conditions in most irrigated watersheds for up to two months in advance of April, the current decision time. In conclusion, our findings affirm that contrary to the perception of low skill and resulting underutilization, current seasonal forecasts hold the potential to inform at least some key agricultural decisions.

Abstract IA018: Liquid biopsy in head and neck cancers: A model for tissue-agnostic MRD detection

Abstract Head and neck cancer (HNC) is a molecularly heterogenous disease with a variable clinical course. Current treatment decisions rely primarily on pre-treatment prognostic factors without consideration of treatment response. This presentation will introduce liquid biopsy biomarkers that can be repeated in HNC patients for the purpose of dynamic risk stratification, treatment adaptation, and post-treatment surveillance. Novel approaches to measuring circulating tumor DNA (ctDNA) will be described, with particular attention to viral-associated and non-viral- associated subtypes of HNC. Detection of molecular residual disease (MRD) using ctDNA will be discussed. As HNC is often treated by non-surgical means, there is a pressing need for tissue- agnostic methods for ctDNA-based MRD detection. Analysis of tumor-specific genetic and epigenetic aberrations within ctDNA each present distinct opportunities and challenges for tissue-agnostic characterization of ctDNA. Viral ctDNA has emerged as a convenient target for MRD detection in viral-associated HNC, while ctDNA methylome profiling offers a versatile approach for tissue-agnostic MRD detection across HNC subtypes. Future applications of these methods may improve individualized management of HNC patients without requiring access to precious tumor tissue samples. Citation Format: Scott V Bratman. Liquid biopsy in head and neck cancers: A model for tissue-agnostic MRD detection [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr IA018.

Evade Unknown Pursuer via Pursuit Strategy Identification and Model Reference Policy Adaptation (MRPA) Algorithm

The game of pursuit–evasion has always been a popular research subject in the field of Unmanned Aerial Vehicles (UAVs). Current evasion decision making based on reinforcement learning is generally trained only for specific pursuers, and it has limited performance for evading unknown pursuers and exhibits poor generalizability. To enhance the ability of an evasion policy learned by reinforcement learning (RL) to evade unknown pursuers, this paper proposes a pursuit UAV attitude estimation and pursuit strategy identification method and a Model Reference Policy Adaptation (MRPA) algorithm. Firstly, this paper constructs a Markov decision model for the pursuit–evasion game of UAVs that includes the pursuer’s attitude and trains an evasion policy for a specific pursuit strategy using the Soft Actor–Critic (SAC) algorithm. Secondly, this paper establishes a novel relative motion model of UAVs in pursuit–evasion games under the assumption that proportional guidance is used as the pursuit strategy, based on which the pursuit UAV attitude estimation and pursuit strategy identification algorithm is proposed to provide adequate information for decision making and policy adaptation. Furthermore, a Model Reference Policy Adaptation (MRPA) algorithm is presented to improve the generalizability of the evasion policy trained by RL in certain environments. Finally, various numerical simulations imply the precision of pursuit UAV attitude estimation and the accuracy of pursuit strategy identification. Also, the ablation experiment verifies that the MRPA algorithm can effectively enhance the performance of the evasion policy to deal with unknown pursuers.

Measuring the U.S. monetary noise shocks

AbstractAgents' beliefs regarding future monetary policy changes influence their current decisions. However, these expectations may not always materialize in the future. This study shows that the monetary fundamental shocks (exogenous changes consistent with expectations) stabilize output and inflation, while the noise shocks (biased beliefs that fail to be realized in the future) increase economic fluctuations. Moreover, factors that amplify financial frictions—the spread between the capital return of entrepreneurs and the risk‐free interest rate of a central bank—can increase anticipation effects associated with these two types of monetary policy shocks.

A Machine Learning Approach to Adapt Local Land Use Planning to Climate Change

The impacts on living conditions and natural habitats deriving from planning decisions require complex analysis of cross-acting factors, which in turn require interdisciplinary data. At the municipal level, both data collection and the knowledge needed to interpret it are often lacking. Additionally, climate change and species extinction demand rapid and effective policies in order to preserve soil resources for future generations. Ex-ante evaluation of planning measures is insufficient owing to a lack of data and linear models capable of simulating the impacts of complex systemic relationships. Integrating machine learning (ML) into systemic planning increases awareness of impacts by providing decision-makers with predictive analysis and risk mitigation tools. ML can predict future scenarios beyond rigid linear models, identifying patterns, trends, and correlations within complex systems and depicting hidden relationships. This article focuses on a case study of single-family houses in Upper Austria, chosen for its transferability to other regions. It critically reflects on an ML approach, linking data on past and current planning regulations and decisions to the physical environment. We create an inventory of categories of areas with different features to inform nature-based solutions and backcasting planning decisions and build a training dataset for ML models. Our model predicts the effects of planning decisions on soil sealing. We discuss how ML can support local planning by providing area assessments in soil sealing within the case study. The article presents a working approach to planning and demonstrates that more data is needed to achieve well-founded planning statements.

Approximation Algorithms for Dynamic Inventory Management on Networks

We provide the first approximation algorithm for dynamic inventory management on a network with stochastic demand and backlogging. Specifically, under a mild cost condition, we prove the cost of a specially designed base-stock policy is less than 1.618 times the cost of an optimal policy. We develop a novel stochastic programming analysis to prove this result: We carefully calibrate two stochastic programs (providing upper and lower bounds on the optimal policy), and compare their objectives. The upper bound arises from a new class of base-stock policies we define to address the currently unresolved issue of how to assign and fulfill backlogs in a system with fulfillment flexibility. We show the optimal policy in this class takes a simple and intuitive form: backlogs are assigned to their lowest cost activities for replenishment, and the ordered resources for those activities are committed to the backlogs for fulfillment. Next, the lower bound stochastic program is derived through a novel cost accounting scheme that captures the tradeoff between current inventory decisions and future backlog decisions. We then exploit this tradeoff to bound the ratio of the two stochastic program objectives and prove our main result. We also demonstrate our policy’s practicality with numerical simulations that show it performs within 1% of optimal on average across a wide range of problem instances. Finally, we show that our techniques and results extend to more general settings, demonstrating their potential for broader applicability. Importantly, our approach extends to problems with lead times, where we prove an approximation guarantee for base-stock policies that is independent of both the lead time and network structure. Thus, our work provides the new managerial insight that properly designed base-stock policies can be effective in network settings. This paper was accepted by Victor Martínez-de-Albéniz, operations management. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.02965 .

Prognosis related to treatment plan in patients with biliary tract cancer: A nationwide database study

BackgroundBiliary tract cancer (BTC) is a malignancy characterized by a low 5-year survival rate (<20 %). Clinical aspects such as tumor resectability, Eastern Cooperative Oncology Group performance status score (ECOG-PS), and molecular profiling are used to determine the treatment for these patients. Diagnosis and treatment are typically established by a multidisciplinary team (MDT). However, standardized practices for BTC are lacking, and there is a need to assess the impact of current MDT treatment decisions on BTC outcomes. The purpose of this study was to investigate the role of the treatment plan proposed by the first MDT conference on survival in patients with BTC, and to identify risk factors for poor survival in this population. MethodThis nationwide, multicenter, retrospective cohort study examined data from the Danish Liver Cancer Group (2013–2020) with confirmed BTC diagnoses. Multiple imputation was used to handle missing data. Survival and variable-survival rate relationships were analyzed using the Kaplan-Meier estimator, and the Cox regression model, respectively. ResultsEligible BTC-confirmed cases: n=1923. The overall median survival was 7.7 months (95 % CI: 7.1–8.5), with a 5-year survival rate of 16.3 %. Patients over 70 years of age, with ECOG-PS 3 or 4, non-operable cases, and with unresectable tumors had lower survival rates. Surgery as the first therapeutic option showed the highest median survival (33.1 months, 95 % CI: 27.2–41.6; p < 0.0001). Multivariable analysis showed that poor ECOG-PS, palliative and neoadjuvant chemotherapy, stereotactic radiotherapy, and best supportive care significantly increased mortality risk in patients with BTC (p=0.05). ConclusionAmong the therapeutic options prescribed by the MDT for patients with BTC, surgery offered the best survival rates, likely due to patient-related prognostic factors. High ECOG-PS was linked to an increased mortality risk regardless of age, highlighting the importance of this criterion in treatment decisions.

External validation of an artificial intelligence multi-label deep learning model capable of ankle fracture classification

BackgroundAdvances in medical imaging have made it possible to classify ankle fractures using Artificial Intelligence (AI). Recent studies have demonstrated good internal validity for machine learning algorithms using the AO/OTA 2018 classification. This study aimed to externally validate one such model for ankle fracture classification and ways to improve external validity.MethodsIn this retrospective observation study, we trained a deep-learning neural network (7,500 ankle studies) to classify traumatic malleolar fractures according to the AO/OTA classification. Our internal validation dataset (IVD) contained 409 studies collected from Danderyd Hospital in Stockholm, Sweden, between 2002 and 2016. The external validation dataset (EVD) contained 399 studies collected from Flinders Medical Centre, Adelaide, Australia, between 2016 and 2020. Our primary outcome measures were the area under the receiver operating characteristic (AUC) and the area under the precision-recall curve (AUPR) for fracture classification of AO/OTA malleolar (44) fractures. Secondary outcomes were performance on other fractures visible on ankle radiographs and inter-observer reliability of reviewers.ResultsCompared to the weighted mean AUC (wAUC) 0.86 (95%CI 0.82–0.89) for fracture detection in the EVD, the network attained wAUC 0.95 (95%CI 0.94–0.97) for the IVD. The area under the precision-recall curve (AUPR) was 0.93 vs. 0.96. The wAUC for individual outcomes (type 44A-C, group 44A1-C3, and subgroup 44A1.1-C3.3) was 0.82 for the EVD and 0.93 for the IVD. The weighted mean AUPR (wAUPR) was 0.59 vs 0.63. Throughout, the performance was superior to that of a random classifier for the EVD.ConclusionAlthough the two datasets had considerable differences, the model transferred well to the EVD and the alternative clinical scenario it represents. The direct clinical implications of this study are that algorithms developed elsewhere need local validation and that discrepancies can be rectified using targeted training. In a wider sense, we believe this opens up possibilities for building advanced treatment recommendations based on exact fracture types that are more objective than current clinical decisions, often influenced by who is present during rounds.

Variable practice patterns in the surgical management of renal hyperparathyroidism

BackgroundHyperparathyroidism is common among patients with chronic kidney disease, end-stage kidney disease, and kidney transplant. The American Association for Endocrine Surgery published clinical practice guidelines that address the surgical treatment of secondary and tertiary hyperparathyroidism. The purpose of this study is to determine practice patterns for the surgical management of secondary and tertiary hyperparathyroidism prior to guideline publication. MethodsWith the approval of the American Association for Endocrine Surgery, a Qualtrics email survey was sent to the Association’s membership in 2022 about current clinical decision making for surgical treatment of secondary and tertiary hyperparathyroidism. Respondents were divided into groups based on surgical subspecialty (endocrine surgery versus non-endocrine surgery), yearly parathyroidectomy volume, and yearly parathyroidectomy volume for surgical treatment of secondary and tertiary hyperparathyroidism. Descriptive statistics were performed; the role of volume was evaluated. ResultsThere were 142 responses from 795 solicited email addresses (18% response rate); 114 (84%) identified as endocrine surgeons. The majority (62%) perform >50 parathyroidectomies yearly, but most perform <10 parathyroidectomies for surgical treatment of secondary and tertiary hyperparathyroidism per year (<10/y, 53.7%; 10–30/y, 41.9%; >30/y, 4.4%). Subtotal parathyroidectomy is most commonly performed for surgical treatment of secondary (83%) and tertiary (52%) hyperparathyroidism, but transcervical thymectomy variably performed for both. There was no consensus regarding starting calcitriol preoperatively (always 43%, never 25%, depends on vitamin D levels 24%) or stopping cinacalcet (2 weeks prior 28%, day of surgery 29%, postoperatively 20%). Surgeons who perform >10 parathyroidectomies per year for surgical treatment of secondary and tertiary hyperparathyroidism were less likely to consider the patient's preoperative vitamin D levels to inform their decision to start calcitriol before surgery (<10 cases/year, 34%; ≥10 cases/year 15%; P = .023), were more likely to have a postoperative hypocalcemia protocol managed by the surgical team (<10 cases/year, 49%; ≥10 cases/year, 58%; P = .029), and were more likely to use intraoperative parathyroid hormone monitoring for tertiary hyperparathyroidism (<10 cases/year, 70%; ≥10 cases/year, 87%; P = .046). ConclusionThe majority of respondents perform <10 parathyroidectomies yearly for surgical treatment of secondary and tertiary hyperparathyroidism. Subtotal parathyroidectomy was most commonly performed, but there was little other consensus regarding preoperative management, intraoperative decision-making, and postoperative care. Opportunity exists through guideline dissemination to improve heterogeneity of care provided to surgical treatment of secondary and tertiary hyperparathyroidism patients.

The thinking of several issues in the diagnosis and treatment of pancreatic cystic tumor

The incidence of pancreatic cystic neoplasms increased dramatically. The clinically optimal management guideline for pancreatic cysts has continued to improve in previous decades. Some issues in diagnosis and treatment require careful consideration and decision-making to achieve the best therapeutic effect and avoid overtreatment. This article analyzes the new progress in the differential diagnosis of pancreatic cystic tumors, surgical decision-making and scope of surgery, and long-term follow-up of patients who have not undergone surgery. In general, the current diagnosis and treatment decisions for pancreatic cystic tumors are more comprehensive and scientific as evidence becomes increasingly abundant, emphasizing the risks and benefits of precise diagnosis and treatment and balanced treatment.

The effect of professional social norms on corporate environmental compliance

While previous studies demonstrate the importance of social norms for explaining the pro-environmental behavior of individual consumers, very few studies examine the role of social norms in the context of businesses’ pro-environmental decisions. This study contributes to the rich social norm literature by exploring whether professional social norms influence the compliance decisions of regulated chemical manufacturing facilities. To this end, the empirical analysis uses data on major facilities regulated under the U.S. Clean Water Act to estimate the link from the compliance history of other major chemical manufacturing facilities operating in the same state to an individual facility’s current compliance decision. Using a fixed-effects model that includes a large set of confounding factors, we find a significant positive effect of other facilities’ compliance history: improvement in the average compliance history prompts the individual facility to increase its own performance. By controlling for other plausible channels that link facility’s compliance decisions, we interpret this relationship as reflecting descriptive professional norms. The relationship proves stronger for facilities that are either geographically or sectorally closer.

Optimized Tiny Machine Learning and Explainable AI for Trustable and Energy-Efficient Fog-Enabled Healthcare Decision Support System

The Internet of things (IoT)-based healthcare decision support system plays a crucial role in modern medicine, especially with the rise in chronic illnesses and an aging population necessitating continuous remote health monitoring. Current healthcare decision support systems struggle to deliver timely and accurate decisions with minimal latency due to limited real-time healthcare data and inefficient computational resources. There is a critical need for an optimized, energy-efficient machine learning model that reliably supports remote health monitoring within IoT and fog computing environments. Our study proposes an Optimized Tiny Machine Learning (TinyML) and Explainable AI (XAI) binary classification model for a trustable and energy-efficient healthcare decision support system, leveraging fog computing to optimize performance. The fog-based approach improves response times and enhances bandwidth usage, addressing critical needs such as reduced latency, higher bandwidth utilization, and decreased packet loss. To further improve efficiency, we incorporate the innovative mLZW data compression technique, significantly enhancing data communication efficiency and reducing response time to critical health alerts. However, limited real-time healthcare data records challenge machine learning classification performance. By implementing a TinyML algorithm, our system demonstrates superior performance to other machine learning models. The proposed optimized TinyML model achieves an impressive F1 score of 0.93 for health abnormalities detection, emphasizing its robustness and effectiveness. This paper highlights the potential of TinyML and XAI in delivering robust, trustworthy, and energy-aware healthcare solutions, making significant contributions toward effective remote health monitoring and decision support in fog-enabled IoT networks.Graphical abstract

Lnsights into Adjuvant Systemic Treatment Selection for Patients with Stage III Melanoma: Data from the Dutch Cancer Registry.

Patient demographics and shared decision making might influence the choice of adjuvant therapy for stage III melanoma. To identify factors for treatment selection of patients diagnosed with stage III melanoma to better understand current treatment decisions and improve further treatment counseling. Data from 2007 patients diagnosed with stage III melanoma, between December 2018 and 2021, sourced from the Dutch Cancer Registry, were analyzed. Among the cohort, 48.7% received no therapy, 45.8% received checkpoint inhibition, and 5.5% received targeted therapy (TT). Patients foregoing therapy were significantly older [67.0 years (range 53.0-77.0) vs. 62.0 year (range 52.0-72.0)], had poorer performance scores (PS), and higher Charlson Comorbidity Index scores compared to those receiving therapy (p<0.001). Patients undergoing therapy had significantly higher median Breslow thickness (3.3mm vs. 2.2mm) and higher prevalence of ulceration (49.9% vs. 38.1%). Those with connective tissue disease and/or congestive heart disease were more likely to receive TT [odds ration (OR) 8.1; 95% confidence interval (CI) 1.7-37.6 and OR 9.3; 95% CI 1.2-72.2, respectively]. Median treatment time among strata for disease recurrence was 4.26months (3.69-4.82) for immunotherapy and 3.1months (0.85-5.36) for TT (p=0.298). Patients who developed recurrent disease were equal across treatment types (p=0.656). The number of patients with grade 3 complications was different for each treatment type [immunotherapy: 17.8% vs. TT: 37.3% (p<0.001)]. Age, PS, and Breslow thickness seem to influence adjuvant treatment decisions. Clinicians' preference for immunotherapy might play a role in counseling BRAF-positive patients for adjuvant therapy, this however, cannot be confirmed in this dataset. Overall, only a small proportion of patients completed adjuvant treatment.

Adaptive multi-criteria decision making for electric vehicles: a hybrid approach based on RANCOM and ESP-SPOTIS

In today’s globalized technological area, aligning decisions with customer preferences is crucial yet challenging due to the complexities and uncertainties involved. Multi-Criteria Decision Analysis (MCDA) serves as a vital tool for constructing support systems that cater to customer-centric trends. While existing MCDA methods vary in their calculation concepts, some prioritize ideal solutions, while others accommodate personalized preferences within dynamic decision contexts. Moreover, determining the relevance of criteria based on expert knowledge adds another layer of personalization to the evaluation process, further individualizing decision-making. However, current decision models often fail to integrate these concepts, leaving a gap in how recommendations can be enhanced when both are combined. To address these challenges, this paper introduces an innovative approach integrating Ranking Comparison and Expected Solution Point Stable Preference Ordering Towards Ideal Solution methods. This hybrid model incorporates personalization into multi-criteria evaluation, catering to individual preferences. By representing customer preferences through two distinct measures, the proposed approach ensures personalized recommendations aligned with decision-makers’ needs. The efficacy of the hybrid model was validated through its application to the electric vehicle selection problem. The verification process highlighted potential disparities compared to other multi-criteria approaches, establishing a consumer preference-based Decision Support System approach for more precise and personalized selection recommendations.

Infection prevention and control in 2030: a first qualitative survey by the Crystal Ball Initiative

BackgroundHealthcare delivery is undergoing radical changes that influence effective infection prevention and control (IPC). Futures research (short: Futures), the science of deliberating on multiple potential future states, is increasingly employed in many core societal fields. Futures might also be helpful in IPC to facilitate current education and organisational decisions. Hence, we conducted an initial survey as part of the IPC Crystal Ball Initiative.MethodsIn 2019, international IPC experts were invited to answer a 10-item online questionnaire, including demographics, housekeeping, and open-ended core questions (Q) on the “status of IPC in 2030” (Q1), “people in charge of IPC” (Q2), “necessary skills in IPC” (Q3), and “burning research questions” (Q4). The four core questions were submitted to a three-step inductive and deductive qualitative content analysis. A subsequent cross-case matrix produced overarching leitmotifs. Q1 statements were additionally coded for sentiment analysis (positive, neutral, or negative).ResultsOverall, 18 of 44 (41%) invited experts responded (from 11 countries; 12 physicians, four nurses, one manager, one microbiologist; all of them in senior positions). The emerging leitmotifs were “System integration”, “Beyond the hospital”, “Behaviour change and implementation”, “Automation and digitalisation”, and “Anticipated scientific progress and innovation”. The statements reflected an optimistic outlook in 66% of all codes of Q1.ConclusionsThe first exercise of the IPC Crystal Ball Initiative reflected an optimistic outlook on IPC in 2030, and participants envisioned leveraging technological and medical progress to increase IPC effectiveness, freeing IPC personnel from administrative tasks to be more present at the point of care and increasing IPC integration and expansion through the application of a broad range of skills. Enhancing participant immersion in future Crystal Ball Initiative exercises through simulation would likely further increase the authenticity and comprehensiveness of the envisioned futures.

Canadians' experiences of alternative protein foods and their intentions to alter current dietary patterns

Despite established evidence about the environmental and health benefits of alternative protein foods (APF), considerable knowledge gaps and misconceptions remain toward APF. Drawing on a national survey (1800+ responses), the study explores Canadians' experiences of APF and intentions to alter current dietary patterns focusing on four APF – plant-based alternative proteins (PBAP) – plant-based (PB) milk, PB meat, lab-grown (LG) meat, and other alternative proteins (OAP) – and two animal-based foods (ANBF)– milk and meat. Data were collected based on a proportional stratified sampling method from all regions of Canada and analyzed using econometric models. While PB milk was the most consumed APF, followed by PB meat, LG meat was the least tried among participants. Perceived complexity of APF discouraged individuals from cutting back on ANBF, while increasing the consumption of APF. Perceived relative advantage was positively related to the adoption of APF. Perceived compatibility strongly influenced individuals’ adoption of PB meat and OAP. Likewise, perceived trialability significantly influenced the adoption of PB milk and PB meat. Despite the perception that APF may have high health related risks associated with processing, additives, calorie and sugar content, this did not prevent individuals from consuming PB milk and OAP. However, perceived risks did affect intentions to alter dietary patterns in the next 12 months. Additionally, meat attachment and sustainability orientation significantly predicted current consumption decisions of APF and intentions to alter dietary patterns. Although sustainability orientation motivates dietary changes, it did not always lead to extreme shifts. Finally, several demographic variables (age, gender, and education), dietary preferences (being omnivore), and residential area and region had influence on current consumption decisions and future intentions. In conclusion, by controlling several factors and through a comparative analysis of various protein sources, the study offers insights into the interplay of innovation-adoption characteristics, perceived risks, meat attachment and sustainability orientation in understanding dietary choices and provides some implications for industry stakeholders and policies promoting APF.