In this viewpoint, we present evidence of a marked increase in the use of assistive technology (AT) by older adults over the last 25 years. We also explain the way in which this use has expanded not only as an increase in terms of the total number of users but also by going beyond the typical scopes of use from its inception in 1999 to reach new categories of users. We outline our opinions on some of the key driving forces behind this expansion, such as population demographic changes, technological advances, and the promotion of AT as a means to enable older adults to achieve independent living. As well as our review of the evolution of AT over the past 25 years, we also discuss the future of AT research as a field and the need for harmonization of terminology in AT research. Finally, we outline how our experience in North Norfolk (notably the United Kingdom\'s most old age-dependent district) suggests that cocreation may be the key to not only successful research trials in the field of AT but also to the successful sustained adoption of AT beyond its original scope of use.

In 2021, 32% of adult meat consumers in Scotland exceeded the 70 g/d recommended limit of red and red processed meat (RPM) intake. We analyzed RPM consumption behaviors among adults in the Scottish Health Survey (2021) to better understand this consumer group. Consumers were categorized into low, medium, and high consumers, and overall, mean intake was 66 g/d. Males and individuals living in the most deprived areas were most likely to be high consumers (45% compared with 30% for males compared with females, respectively, and 44% compared with 32% for those in the most compared with least deprived areas, respectively). Dinners accounted for the majority of intake among high (55%) and medium (52%) consumers, whereas low consumers distributed intake between lunch (40%) and dinner (48%). Across all groups, consumption was highest on Sundays, and majority of RPM was purchased at supermarkets. Beef dishes and sandwiches were primary contributors among high and medium consumers. These insights can inform the design of effective strategies and policies aligned with meat reduction targets. For instance, strategies focusing on modifying traditional meat-centric dishes and sandwiches could be impactful.

BACKGROUND: Dietary changes are necessary to improve population health and meet environmental sustainability targets. Here we analyse the impact of promotional activities implemented in UK supermarkets on purchases of healthier and more sustainable foods.
METHODS: Three natural experiments examined the impact of promotional activities on sales of a) no-added-sugar (NAS) plant-based milk (in 199 stores), b) products promoted during \'Veganuary\' (in 96 stores), and c) seasonal fruit (in 100 non-randomised intervention and 100 matched control stores). Data were provided on store-level product sales, in units sold and monetary value (£), aggregated weekly. Predominant socioeconomic position (SEP) of the store population was provided by the retailer. Analyses used interrupted time series and multivariable hierarchical mixed-effects models.
RESULTS: Sales of both promoted and total NAS plant-based milks increased significantly during the promotional period (Promoted:+126 units, 95%CI: 105-148; Overall:+307 units, 95%CI: 264-349). The increase was greater in stores with predominately low SEP shoppers. During Veganuary, sales increased significantly for plant-based foods on promotion (+60 units, 95%CI: 37-84), but not for sales of plant-based foods overall (dairy alternatives: -1131 units, 95%CI: -5821-3559; meat alternatives: 1403 units, 95%CI: -749-3554). There was no evidence of a change in weekly sales of promoted seasonal fruit products (assessed via ratio change in units sold: 0.01, 95%CI: 0.00-0.02), and overall fruit category sales slightly decreased in intervention stores relative to control (ratio change in units sold: -0.01, 95%CI: -0.01-0.00).
CONCLUSIONS: During promotional campaigns there was evidence that sales of plant-based products increased, but not seasonal fruits. There was no evidence for any sustained change beyond the intervention period.

BACKGROUND: Early warning scores (EWS) are routinely used in hospitals to assess a patient\'s risk of deterioration. EWS are traditionally recorded on paper observation charts but are increasingly recorded digitally. In either case, evidence for the clinical effectiveness of such scores is mixed, and previous studies have not considered whether EWS leads to changes in how deteriorating patients are managed.
OBJECTIVE: This study aims to examine whether the introduction of a digital EWS system was associated with more frequent observation of patients with abnormal vital signs, a precursor to earlier clinical intervention.
METHODS: We conducted a 2-armed stepped-wedge study from February 2015 to December 2016, over 4 hospitals in 1 UK hospital trust. In the control arm, vital signs were recorded using paper observation charts. In the intervention arm, a digital EWS system was used. The primary outcome measure was time to next observation (TTNO), defined as the time between a patient\'s first elevated EWS (EWS ≥3) and subsequent observations set. Secondary outcomes were time to death in the hospital, length of stay, and time to unplanned intensive care unit admission. Differences between the 2 arms were analyzed using a mixed-effects Cox model. The usability of the system was assessed using the system usability score survey.
RESULTS: We included 12,802 admissions, 1084 in the paper (control) arm and 11,718 in the digital EWS (intervention) arm. The system usability score was 77.6, indicating good usability. The median TTNO in the control and intervention arms were 128 (IQR 73-218) minutes and 131 (IQR 73-223) minutes, respectively. The corresponding hazard ratio for TTNO was 0.99 (95% CI 0.91-1.07; P=.73).
CONCLUSIONS: We demonstrated strong clinical engagement with the system. We found no difference in any of the predefined patient outcomes, suggesting that the introduction of a highly usable electronic system can be achieved without impacting clinical care. Our findings contrast with previous claims that digital EWS systems are associated with improvement in clinical outcomes. Future research should investigate how digital EWS systems can be integrated with new clinical pathways adjusting staff behaviors to improve patient outcomes.

Pollution is a major cause of ill health globally. Low emission zones (LEZ) have been identified as effective in reducing pollution and are increasing in popularity but remain divisive. Understanding what factors help or hinder implementation is important. In the UK, Clean Air Zones (CAZ, a type of LEZ) are being implemented in several cities. We aimed to identify key barriers and enablers to the implementation of a CAZ in real time, as policy was being developed and implemented in a large Northern city in England, UK. Twenty-five semi-structured interviews were conducted with city stakeholders and implementors approximately 6 months before a CAZ charging non-compliant taxis, buses, heavy goods vehicles and vans was launched. Thematic analysis was used to analyse data. Implementers were required to operate within a tight policy framework. Key enablers included: freedom to adapt the framework to local context, financial support, and cross-sector working. A focus on health was felt to be useful in justifying the policy to the public. Key barriers included conflict and opposition from local industry, politicians, and communities. Implementation of air quality policy which involves traffic restrictions remains controversial. The voices which \'shout the loudest\' are often those with negative views, and these can create divisive discourse which shape public opinion and damage confidence of implementers. A systems perspective is needed to understand socio-political contexts which can influence implementation success. We provide recommendations to other areas considering implementing a LEZ.

Interstitial lung disease (ILD) is a significant complication of many systemic autoimmune rheumatic diseases (SARDs), although the clinical presentation, severity and outlook may vary widely between individuals. Despite the prevalence, there are no specific guidelines addressing the issue of screening, diagnosis and management of ILD across this diverse group. Guidelines from the ACR and EULAR are expected, but there is a need for UK-specific guidelines that consider the framework of the UK National Health Service, local licensing and funding strategies. This article outlines the intended scope for the British Society for Rheumatology guideline on the diagnosis and management of SARD-ILD developed by the guideline working group. It specifically identifies the SARDs for consideration, alongside the overarching principles for which systematic review will be conducted. Expert consensus will be produced based on the most up-to-date available evidence for inclusion within the final guideline. Key issues to be addressed include recommendations for screening of ILD, identifying the methodology and frequency of monitoring and pharmacological and non-pharmacological management. The guideline will be developed according to methods and processes outlined in Creating Clinical Guidelines: British Society for Rheumatology Protocol version 5.1.

The growing demand for electrified heating, electrified transportation, and power-intensive data centres challenge distribution networks. If electrification projects are carried out without considering electrical distribution infrastructure, there could be unexpected blackouts and financial losses. Datasets containing real-world distribution network information are required to address this. However, the existing dataset at NERC that covers the whole of Great Britain (GB) does not provide information about demand and capacity, which is insufficient for evaluating the connection feasibility. Although each distribution network operator (DNO) has detailed network information for their supply area, the information is scattered in separate files and different formats even within the same DNO, which limits usability. On the other hand, studying the coupling between energy systems and societal attributes such as household heating is important in promoting social welfare, which calls for more comprehensive datasets that integrate the social data and the energy network data. However, social datasets are usually provided on a regional basis, and the link to energy networks is not straightforward, which explains the lack of the comprehensive datasets. To fill these gaps, this paper introduces two datasets. The first is the main dataset for the GB distribution networks, collecting information on firm capacity, peak demands, locations, and parent transmission nodes (grid supply points, namely GSPs) for all primary substations (PSs). PSs are a crucial part of UK distribution networks and are at the lowest voltage level (11 kV) with publicly available data. Substation firm capacity and peak demand facilitate an understanding of the remaining room in the existing network. The parent GSP information helps link the released datasets to transmission networks. These datasets are collected, standardised, and merged from various files with different formats published by the six DNOs in GB, using a Python script and manual validation. The second dataset extends the main network dataset, linking each PS to the number of households that use different types of central heating recorded in census data (Census in year 2021 for England and Wales, and Census 2011 for Scotland as the up-to-date Census 2022 data is not fully released). The derivation of the second dataset is based on the locations of PSs collected in the main dataset with appropriate assumptions. The derivation process may be replicated to integrate other social datasets. The datasets have the following reuse potentials: 1) Given the PS demand, capacity, and locations in our datasets, users can estimate the connection feasibility and evaluate the optimal deployment locations for different energy technologies, including electric vehicles, heat pumps, and the growing data centres, under different scenarios and at a national scale. These evaluations are beneficial not only for academic research, but also for industrial planning and policy making. 2) Our extended dataset links household information to distribution networks. The integrated information facilitates cross-disciplinary research and analysis across social science, energy policy, and power systems. 3) The network demand and capacity information provided by the datasets can also help with realistic parameter settings to improve the accuracy of case studies in broader power system research.

This paper focuses on the relationships between people and farmed nonhuman animals, and between these animals and the farmed environments they encounter, in the enactment of interspecies endemic disease situations. It examines how the nonhuman embodied capacities, agency and subjectivities of cows and sheep on farms in the north of England make a difference to how the endemic conditions of lameness and bovine viral diarrhoea (BVD) are encountered and responded to by farmers and advisers. The paper draws on empirical research with farmers and their advisers, and explores three key, inter-related, themes: first, the importance of intersubjective relationships between people and animals on farms; second, the nonhuman components of the \'disease situations\' associated with endemic diseases, including animals\' embodied characteristics and behaviours and the relationships between bodies and environments on different farms; and finally the ways in which animal agency and resistance makes a difference to on-farm interventions aiming to prevent or treat lameness and BVD. The paper concludes by arguing that animals\' capacities, and nonhuman difference, should be taken further into account in future policy and practice interventions in endemic disease in farmed animals.

BACKGROUND: Migraine is a highly prevalent neurological disease with a substantial societal burden due to lost productivity. From a societal perspective, we assessed the cost-effectiveness of eptinezumab for the preventive treatment of migraine.
METHODS: An individual patient simulation of discrete competing events was developed to evaluate eptinezumab cost-effectiveness compared to best supportive care for adults in the United Kingdom with ≥ 4 migraine days per month and prior failure of ≥ 3 preventive migraine treatments. Individuals with sampled baseline characteristics were created to represent this population, which comprised dedicated episodic and chronic migraine subpopulations. Clinical efficacy, utility, and work productivity inputs were based on results from the DELIVER randomised controlled trial (NCT04418765). Timing of natural history events and treatment holidays-informed by the literature-were simulated to unmask any natural improvement of the disease unrelated to treatment. The primary outcomes were monthly migraine days, migraine-associated costs, quality-adjusted life years (QALYs), incremental cost-effectiveness ratio, and net monetary benefit, each evaluated over a 5-year time horizon from 2020. Secondary analyses explored a lifetime horizon and an alternative treatment stopping rule.
RESULTS: Treatment with eptinezumab resulted in an average of 0.231 QALYs gained at a saving of £4,894 over 5 years, making eptinezumab dominant over best supportive care (i.e., better health outcomes and less costly). This result was confirmed by the probabilistic analysis and all alternative assumption scenarios under the same societal perspective. Univariate testing of inputs showed net monetary benefit was most sensitive to the number of days of productivity loss, and monthly salary.
CONCLUSIONS: This economic evaluation shows that from a societal perspective, eptinezumab is a cost-effective treatment in patients with ≥ 4 migraine days per month and for whom ≥ 3 other preventive migraine treatments have failed.
BACKGROUND: N/A.

UNASSIGNED: In the past, two studies found ensembles of human judgement forecasts of COVID-19 to show predictive performance comparable to ensembles of computational models, at least when predicting case incidences. We present a follow-up to a study conducted in Germany and Poland and investigate a novel joint approach to combine human judgement and epidemiological modelling.
UNASSIGNED: From May 24th to August 16th 2021, we elicited weekly one to four week ahead forecasts of cases and deaths from COVID-19 in the UK from a crowd of human forecasters. A median ensemble of all forecasts was submitted to the European Forecast Hub. Participants could use two distinct interfaces: in one, forecasters submitted a predictive distribution directly, in the other forecasters instead submitted a forecast of the effective reproduction number R t. This was then used to forecast cases and deaths using simulation methods from the EpiNow2 R package. Forecasts were scored using the weighted interval score on the original forecasts, as well as after applying the natural logarithm to both forecasts and observations.
UNASSIGNED: The ensemble of human forecasters overall performed comparably to the official European Forecast Hub ensemble on both cases and deaths, although results were sensitive to changes in details of the evaluation. R t forecasts performed comparably to direct forecasts on cases, but worse on deaths. Self-identified \"experts\" tended to be better calibrated than \"non-experts\" for cases, but not for deaths.
UNASSIGNED: Human judgement forecasts and computational models can produce forecasts of similar quality for infectious disease such as COVID-19. The results of forecast evaluations can change depending on what metrics are chosen and judgement on what does or doesn\'t constitute a \"good\" forecast is dependent on the forecast consumer. Combinations of human and computational forecasts hold potential but present real-world challenges that need to be solved.