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Deriving a preference-weighted measure for people with hypoglycaemia from the Hypo-RESOLVE-QoL
Get PDFObjective
Hypoglycaemia impacts the health-related quality of life (HRQoL) of people living with diabetes (PwD), and existing preference-weighted measures do not capture all important aspects. The study aimed to generate a preference-weighted measure capturing the HRQoL impact of hypoglycaemia in PwD.
Methods
Items for the health state classification system were selected from the hypoglycaemia-specific Hypo-RESOLVE QoL measure using: relevance in cognitive interviews, translatability, suitability for valuation, endorsement by patient advisors and experts, and psychometric performance in a large survey of PwD. Second, an online valuation survey using discrete choice experiment (DCE) with survival attribute was conducted with members of the UK public. DCE data was modelled using conditional logit analysis, and results scaled to produce preference weights for the classification system on a scale where 1 is equivalent to full health, 0 is equivalent to dead, and below zero is worse than dead.
Results
The health state classification system consists of eight items reflecting the factors of the Hypo-RESOLVE QoL (psychological, social and physical aspects). The valuation survey was completed by 1000 members of the UK public, representative for age and sex. Good understanding of DCE tasks was demonstrated. The item “do what I want to do in my life” had the largest preference weight, and “find it hard to stop thinking about my glucose levels” had the smallest.
Conclusions
This study generated Hypo-RESOLVE QoL-8D, a preference-weighted measure capturing the HRQoL impact of hypoglycaemia in PwD, with UK general public preference-weights. The measure can be generated from Hypo-RESOLVE QoL data
Carbon accumulation rate peaks at 1,000-m elevation in tropical planted and regrowth forests
Get PDFTropical planted and regrowth forests (TPRFs) are one of the most low-cost components for recovering biomass-stored carbon in the tropics. Nevertheless, challenges persist in pinpointing which elevational ranges exhibit the largest carbon accumulation rate (
) due to the highly inconsistent previous assessments. This prevents the selection of optimal locations for implementing large-scale reforestation in the tropics. Here, we proposed a refined approach that used a carbon accumulation threshold (<80% of the maximum value) to quantify
in TPRFs at various elevations. We find that
increases with elevations from 300 to 1,000 m and declines at elevations >1,000 m. TPRFs at elevations ∼1,000 m exhibit three times more
than lowland TPRFs. This optimal elevation, highly dependent on background temperatures, varies slightly but significantly across different mountains. These findings provide guidelines for policymakers to determine the optimal elevations from regional to continental scales when implementing reforestation initiatives in the tropics
Private Ordering, Generative AI and the ‘Platformisation Paradigm’: What Can We Learn From Comparative Analysis of Models Terms and Conditions?
Get PDFLarge or “foundation” models are now being widely used to generate not just text and images but also video, music and code from prompts. Although this “generative AI” revolution is clearly driving new opportunities for innovation and creativity, it is also enabling easy and rapid dissemination of harmful speech and potentially infringing existing laws. Much attention has been paid recently to how we can draft bespoke legislation to control these risks and harms; however, private ordering by generative AI providers, via user contracts, licenses and privacy policies, has so far attracted less attention. Drawing on the extensive history of study of the terms and conditions (T&C) and privacy policies of social media companies, this paper reports the results of pilot empirical work conducted in January–March 2023, in which T&C were mapped across a representative sample of generative AI. With the focus on copyright and data protection, our early findings indicate the emergence of a “platformisation paradigm,” in which providers of generative AI attempt to position themselves as neutral intermediaries. This study concludes that new laws targeting “big tech” must be carefully reconsidered to avoid repeating past power imbalances between users and platforms
Exponential Random Graph Models for Dynamic Signed Networks: An Application to International Relations
Get PDFSubstantive research in the Social Sciences regularly investigates signed networks, where edges between actors are positive or negative. One often-studied example within International Relations for this type of network consists of countries that can cooperate with or fight against each other. These analyses often build on structural balance theory, one of the earliest and most prominent network theories. While the theorization and description of signed networks have made significant progress, the inferential study of link formation within them remains limited in the absence of appropriate statistical models. We fill this gap by proposing the Signed Exponential Random Graph Model (SERGM), extending the well-known Exponential Random Graph Model (ERGM) to networks where ties are not binary but positive or negative if a tie exists. Since most networks are dynamically evolving systems, we specify the model for both cross-sectional and dynamic networks. Based on hypotheses derived from structural balance theory, we formulate interpretable signed network statistics, capturing dynamics such as “the enemy of my enemy is my friend”. In our empirical application, we use the SERGM to analyze cooperation and conflict between countries within the international state system. We find evidence for structural balance in International Relations
Assessing the impact of board sustainability committees on greenhouse gas performance: Evidence from industrialised European countries
Get PDFThis study examines the impact of executive compensation (EC) and board sustainability integration index (BSII) on both greenhouse gas emissions (GHGE) and greenhouse gas management processes (GGMP). Additionally, it investigates the relationship between GGMP and GHGE to assess the effectiveness of process-oriented measures in reducing actual emissions. Through the lens of legitimacy theory and incentive alignment theory, we harness an extensive dataset encompassing 15,876 firm-year observations across 22 industrialised European countries from 2002 to 2022. First, the findings show that although EC positively correlates with enhanced GGMP, it has an insignificant effect on GHGE reduction. Second, the results suggest that although BSII independently bolster sustainability initiatives, the moderating effect of BSII on EC (EC*BSII) may lead to a legitimacy gap. This gap emerges when the relationship of EC and BSII falls short of societal expectations regarding environmental performance, potentially eroding organisational legitimacy. Third, the findings indicate that firms that engage in GGMP also tend to have higher levels of GHGE, pointing to the use of GGMP by firms as a means of symbolic legitimation
Assessing methods for adjusting estimates of treatment effectiveness for patient nonadherence in the context of time-to-event outcomes and health technology assessment: a simulation study
No full textPurpose
We aim to assess the performance of methods for adjusting estimates of treatment effectiveness for patient nonadherence in the context of health technology assessment using simulation methods.
Methods
We simulated trial datasets with nonadherence, prognostic characteristics, and a time-to-event outcome. The simulated scenarios were based on a trial investigating immunosuppressive treatments for improving graft survival in patients who had had a kidney transplant. The primary estimand was the difference in restricted mean survival times in all patients had there been no nonadherence. We compared generalized methods (g-methods; marginal structural model with inverse probability of censoring weighting [IPCW], structural nested failure time model [SNFTM] with g-estimation) and simple methods (intention-to-treat [ITT] analysis, per-protocol [PP] analysis) in 90 scenarios each with 1,900 simulations. The methods’ performance was primarily assessed according to bias.
Results
In implementation nonadherence scenarios, the average percentage bias was 20% (ranging from 7% to 37%) for IPCW, 20% (8%–38%) for SNFTM, 20% (8%–38%) for PP, and 40% (20%–75%) for ITT. In persistence nonadherence scenarios, the average percentage bias was 26% (9%–36%) for IPCW, 26% (14%–39%) for SNFTM, 26% (14%–36%) for PP, and 47% (16%–72%) for ITT. In initiation nonadherence scenarios, the percentage bias ranged from −29% to 110% for IPCW, −34% to 108% for SNFTM, −32% to 102% for PP, and between −18% and 200% for ITT.
Conclusion
In this study, g-methods and PP produced more accurate estimates of the treatment effect adjusted for nonadherence than the ITT analysis did. However, considerable bias remained in some scenarios.
Highlights
Randomized controlled trials are usually analyzed using the intention-to-treat (ITT) principle, which produces a valid estimate of effectiveness relating to the underlying trial, but when patient adherence to medications in the real world is known to differ from that observed in the trial, such estimates are likely to result in a biased representation of real-world effectiveness and cost-effectiveness.
Our simulation study demonstrates that generalized methods (g-methods; IPCW, SNFTM) and per-protocol analysis provide more accurate estimates of the treatment effect than the ITT analysis does, when adjustment for nonadherence is required; however, even with these adjustment methods, considerable bias may remain in some scenarios.
When real-world adherence is expected to differ from adherence observed in a trial, adjustment methods should be used to provide estimates of real-world effectiveness
‘Flow’: a film about the disclosure of childhood sexual abuse
Get PDFTrauma-informed care is growing in importance in health and social care, with disclosure as a vital first step. Yet evidence suggests that individual interactions with professionals may not facilitate trauma disclosure. ‘Flow’ aimed to address this. The film represents a high-quality, behaviour change resource that can be utilised in a wide range of circumstances. It was co-produced by representatives from the University of Sheffield, NHS England, the Department of Health and people with lived experience of childhood sexual abuse. The film follows the story of Amy, who is preparing for her art exhibition when a comment reignites traumatic childhood memories. She wants to approach her General Practitioner for help but is hindered both by family loyalty and barriers to disclosure within the NHS. ‘Flow’ has been used both in statutory services and non-governmental organisations, as well as being selected for inclusion in two film festivals. It represents an innovative way to communicate research results and foster change
Why language matters: A qualitative inquiry into the implications of language used during provider-patient interactions on university students’ perceptions and understandings of their own mental health
Get PDFLanguage surrounding mental health has been utilised to mechanise and normalise stigma. Associated connotations of prejudice can deter individuals from accessing critical support. There are few studies investigating use of language within clinical contexts. This paper investigates implications of language used during provider-patient interactions in shaping patients’ understandings of their mental health. Semi-structured, online interviews were conducted with ten university students with previously obtained mental health diagnoses or treatment. Interpretative phenomenological analysis identified three core themes: impact of navigating complex language and services on diagnostic experiences; a lack of person centred care as dehumanising; existing prejudices of the individual and others impact experiences of ill mental health. Results suggested clinical language can be emotive, acting as either a barrier or enabler to sense making of mental health diagnoses. This research can translate to operational language use guidelines in clinical settings, ultimately contributing to patient well-being
The functionality of arbuscular mycorrhizal networks across scales of experimental complexity and ecological relevance
Get PDF1. One of the most prevalent symbioses on Earth is that formed between the majority of land plants and arbuscular mycorrhizal (AM) fungi. Through these intimate associations, AM fungi transfer soil nutrients to their plant hosts in exchange for photosynthetically fixed carbon resources.
2. It has been hypothesised that this nutritional mutualism is evolutionarily stable because both partners are in control of the exchange of resources and can discriminate between partners according to whichever offers the highest returns.
3. However, in nature, plant–AM symbioses are exposed to a wealth of additional biotic and abiotic interactions which can affect the regulation of carbon-for-nutrient exchange between symbionts. Moreover, the extraradical hyphae of AM fungi make up underground networks that may be interactive or physically connected, known as common mycorrhizal networks (CMNs). These can link neighbouring plants, potentially further influencing resource distribution across the network. How these layers of complexity interact to influence resource regulation and allocation between plants and AM fungi is not often considered by experimental designs.
4. Here, we review resource allocation in AM symbioses, scaling up from evidence from reductionist experimental systems using axenic root organ cultures to complex systems incorporating multiple neighbouring plants dealing with other, co-occurring symbionts.
5. As experimental designs increase in scale and ecologically relevant complexity, the carbon-for-nutrient exchange between plants and their AM symbionts is increasingly subject to disruption associated with the wider ecological context, such as the intricacies of the plant-fungal interactions in a CMN or the presence of co-occurring organisms
Quantum data centres: a simulation-based comparative noise analysis
Get PDFQuantum Data Centres (QDCs) could overcome the scalability challenges of modern quantum computers. Single-processor monolithic quantum computers are affected by increased cross talk and difficulty of implementing gates when the number of qubits is increased. In a QDC, multiple quantum processing units (QPUs) are linked together over short distances, allowing the total number of computational qubits to be increased without increasing the number of qubits on any one processor. In doing so, the error incurred by operations at each QPU can be kept small, however additional noise will be added to the system due to the latency cost and errors incurred during inter-QPU entanglement distribution. We investigate the relative impact of these different types of noise using a classically simulated QDC with two QPUs and compare the robustness to noise of the two main ways of implementing remote gates, cat-comm and TP-comm. We find that considering the quantity of gates or inter-QPU entangled links is often inadequate to predict the output fidelity from a quantum circuit and infer that an improved understanding of error propagation during distributed quantum circuits may represent a significant optimisation opportunity for compilation
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