R&D and innovation after COVID-19 : what can we expect? A review of prior research and data trends after the great financial crisis

How did the great financial crisis (GFC) of 2008–2010 impact on R&D and innovation in the United Kingdom and internationally? What can we learn about the likely innovation effects of the COVID-19 crisis on small and medium enterprises (SME) innovation? Numerous international studies suggest the strong procyclicality of R&D and innovation investments in firms: investment rises in recovery and falls sharply in times of crisis. This procyclicality is driven in firms by both internal financial resources or slack and varying market incentives for innovation. Cash constraints, in particular, may impact most strongly on R&D and innovation investments by smaller firms. In the United Kingdom, the proportion of innovating firms fell by around a third during the GFC and took around four to six years to recover. Recovery was also uneven – notably weaker in some sectors and regions. The COVID-19 crisis seems likely to leave many firms financially weaker, with the most significant impacts on the willingness or ability of SMEs to sustain R&D and innovation. Where firms are able to sustain these investments, however, the evidence from the GFC suggests that they will lead to better survival chances, stronger growth and higher profitability. Some additional financial support for innovation has been announced by the UK government. Whether this will be sufficient to sustain SME levels of innovative activity, however, remains to be seen

Prioritising pre-hospital outcome measures with a multi-stakeholder group: a consensus methods study

Context: A consensus event to discuss and prioritise ambulance service care outcome measures was held with 43 participants from a range of professional backgrounds including Commissioners; Policy makers; clinicians; managers; academics and patient and public representatives. Problem: Ambulance services in England manage 8 million emergency calls per years and treat 6.5 million people. Services are currently unable to ascertain whether the care they provide is safe, effective and of good quality as they receive no information about patients once they have been discharged from their care. The lack of robust patient focussed outcome measures for ambulance care means there is no opportunity for identifying and sharing good practice, identifying problems and measuring the impact of service developments and innovations. Assessment of problem and analysis of its causes: Historically ambulance service performance has been measured using response time as a proxy measure for quality. Although the limitations of this measure are recognised there is a lack of consensus on which outcome measures are important and little opportunity to measure alternatives due to poor information on what happens to patients after their ambulance service contact. The PhOEBE NIHR research programme aims to develop a linked ambulance service and secondary care dataset and to assess quality of care in this patient group using outcome measures identified from the literature and in consultation with different stakeholder groups. This means that for the first time the ambulance service will be able to assess the quality of care they provide to patients, rather than just how quickly the ambulance arrived. Intervention: Potential outcome measures identified from 2 systematic reviews were categorised into 1 of 3 headings (Service/operational, patient management and patient outcomes) and participants were pre-allocated to a discussion group. All discussion groups contained participants representing a range of stakeholder view points. Participants took part in small group themed discussions relating to a number of pre-specified outcome measures. They were also able to add to the list of measures. Directly following the discussion participants voted on the importance of the outcome measures in relation to ambulance service care quality. This was done using Turning Point software. Participants rated each outcome measure as either ‘Essential’, ‘Desirable’ or ‘Irrelevant’ using individual key pads. The voting was done independently and anonymously. Real time results were displayed following each vote. Study design: We used an interactive voting system coupled with a modified nominal group technique for the prioritisation of potential ambulance service outcome measures. Strategy for change: Following on from this study the top ranking outcome measures will be further refined as part of a Delphi study, before using the outcome measures to assess ambulance service quality of care in our linked data sample. The methods for linking the ambulance service data to other health care information and the identified outcome measures will enable all UK ambulance services to assess the quality of care they provide to patients and the impact of any service changes on care quality and patient outcomes. Measurement of improvement: The results from the outcome prioritisation voting exercise were ranked based on the highest proportion of ‘Essential’ rated measures. Where over 50% of participants rated a measure as ‘Essential’ these were taken forward and considered in further consensus studies. Effects of changes: From undertaking the consensus event we have prioritised potential ambulance service outcome measures. Lessons learnt: We have established that it is possible to incorporate voting technology into consensus methodologies and provide real time results to participants. Message for others: This research prioritised ambulance service outcome measures. Out of the 40 number of measures considered, the top 5 measures were Accuracy of dispatch decisions; Completeness and accuracy of patient records; Accuracy of call taker identification of different conditions; pain measurement and symptom relief and Patient experience

Developing new ways of measuring the impact of ambulance service care

Background Pre-hospital care in England is provided by ambulance services who deliver a diverse range of services to over 9 million patients a year but there is limited evidence about the effectiveness of this care. Historically ambulance performance has been measured by response times rather than clinical need or effectiveness. Progress on developing more appropriate performance measures is constrained by a lack of information about what happens to patients and their outcome after the pre-hospital component of care. If ambulance service information about patients could be linked to process and outcome data further along the care pathway then relevant measurement tools could be developed that allow a better assessment of the impact of pre-hospital care. The Pre-hospital Outcomes for Evidence Based Evaluation (PhOEBE) project is a 5 year programme of research funded by the UK National Institute of Health Research. Aims & objectives The aim of the programme is to develop new ways of measuring the impact of care provided by the ambulance service to support quality improvement through monitoring, audit and service evaluation. The objectives are to: 1) Review and synthesise the research literature on pre-hospital care outcome measures and identify measures relevant to the NHS and patients for further development; 2) Create a dataset linking routinely collected pre-hospital data, hospital data and mortality data to provide outcome information; 3) Develop new ways of measuring process and outcome indicators including building risk adjustment models that predict the outcomes using the linked data; 4) Explore the practical use of the linked dataset and the risk adjustment models to measure the effectiveness and quality of ambulance service care. Research plans The programme has 4 linked stages; 1. Synthesis of evidence on outcome measures and identification of measures for further development - review and assessment of the evidence base on outcome measurement for pre-hospital care and a consensus studies to identify measures relevant to patients and NHS staff. 2: Linking pre-hospital data with other patient data sources – creating a single dataset that links ambulance service electronic care records with routinely collected Hospital Episode Statistics (HES) and national mortality data. 3. Development of risk adjustment models for outcomes in patients attended by the ambulance service – using the linked data to develop risk adjustment tools that will allow patient differences to be taken into account and differences between expected and actual outcomes to be detected. Particular emphasis will be made to include the broad EMS population and not specific conditions as has been the case in the past. 4. Testing the risk adjustment models to assess if they can be used to measure effectiveness and quality – exploring the practical application of the measures by using them to assess if different ways of providing ambulance service care result in different consequences for patients. Outputs, outcomes and impact The programme will: • Provide a summary of relevant evidence on pre-hospital care outcome measurement • Develop a method for linking healthcare information into a format that can be used to support quality improvement, is acceptable to patients and complies with information legislation • Develop population based models for measuring the impact of pre-hospital care that can be used to monitor quality and safety, evaluate new service innovations and support quality improvement • Provide added value by using routine information and NHS infrastructure to operationalise the process and outcome models so that they will be of use across the NHS Progress to date The programme commenced in June 2011 and ends in May 2016. Two systematic reviews of measures used to measure the impact of ambulance service care (one policy literature and one research literature based) have been completed as has a qualitative study of recent service users to identify aspects of service they value. Potential measures identified by these studies were presented at a consensus conference and then further refined in a Delphi study to prioritise and identify measures for further development. Linked data is currently being created and the next stage will be the development of risk adjusted predictive models for the final identified measures

Prehospital outcomes for ambulance service care: systematic review

Background: Ambulance service performance measurement has previously focused on response times and survival. We conducted a systematic review of the international literature on quality measures and outcomes relating to pre-hospital ambulance service care, aiming to identify a broad range of outcome measures to provide a more meaningful assessment of ambulance service care. Methods: We searched a number of electronic databases including CINAHL, the Cochrane Library, EMBASE, Medline, and Web of Science. For inclusion, studies had to report either research or evaluation conducted in a pre-hospital setting, published in the English language from 1982 to 2011, and reporting either outcome measures or specific outcome instruments. Results: Overall, 181 full-text articles were included: 83 (46%) studies from North America, 50 (28%) from Europe and 21 (12%) from the UK. A total of 176 articles were obtained after examining 257 full-text articles in detail from 5,088 abstracts screened. A further five papers were subsequently identified from references of the articles examined and studies known to the authors. There were 140 articles (77%) which contained at least one survival-related measure, 47 (34%) which included information about length of stay and 87 (48%) which identified at least one place of discharge as an outcome. Limitations: We encountered the problem of incomplete information, for instance studies not specifying which pain scales when these had been used or using survival without a specific time period. Conclusion and recommendations: In addition to measures relating to survival, length of stay and place of discharge, we identified 247 additional outcome measures. Few studies included patient reported or cost outcomes. By identifying a wide range of outcome measures this review will inform further research looking at the feasibility of using a wider range of outcome measures and developing new outcome measures in prehospital research and quality improvement

Nonparametric estimation of correlation functions in longitudinal and spatial data, with application to colon carcinogenesis experiments

In longitudinal and spatial studies, observations often demonstrate strong correlations that are stationary in time or distance lags, and the times or locations of these data being sampled may not be homogeneous. We propose a nonparametric estimator of the correlation function in such data, using kernel methods. We develop a pointwise asymptotic normal distribution for the proposed estimator, when the number of subjects is fixed and the number of vectors or functions within each subject goes to infinity. Based on the asymptotic theory, we propose a weighted block bootstrapping method for making inferences about the correlation function, where the weights account for the inhomogeneity of the distribution of the times or locations. The method is applied to a data set from a colon carcinogenesis study, in which colonic crypts were sampled from a piece of colon segment from each of the 12 rats in the experiment and the expression level of p27, an important cell cycle protein, was then measured for each cell within the sampled crypts. A simulation study is also provided to illustrate the numerical performance of the proposed method.Comment: Published in at http://dx.doi.org/10.1214/009053607000000082 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Two-Host, Two-Vector Basic Reproduction Ratio (R-0) for Bluetongue

Mathematical formulations for the basic reproduction ratio (R (0)) exist for several vector-borne diseases. Generally, these are based on models of one-host, one-vector systems or two-host, one-vector systems. For many vector borne diseases, however, two or more vector species often co-occur and, therefore, there is a need for more complex formulations. Here we derive a two-host, two-vector formulation for the R (0) of bluetongue, a vector-borne infection of ruminants that can have serious economic consequences; since 1998 for example, it has led to the deaths of well over 1 million sheep in Europe alone. We illustrate our results by considering the situation in South Africa, where there are two major hosts (sheep, cattle) and two vector species with differing ecologies and competencies as vectors, for which good data exist. We investigate the effects on R (0) of differences in vector abundance, vector competence and vector host preference between vector species. Our results indicate that R (0) can be underestimated if we assume that there is only one vector transmitting the infection (when there are in fact two or more) and/or vector host preferences are overlooked (unless the preferred host is less beneficial or more abundant). The two-host, one-vector formula provides a good approximation when the level of cross-infection between vector species is very small. As this approaches the level of intraspecies infection, a combination of the two-host, one-vector R (0) for each vector species becomes a better estimate. Otherwise, particularly when the level of cross-infection is high, the two-host, two-vector formula is required for accurate estimation of R (0). Our results are equally relevant to Europe, where at least two vector species, which co-occur in parts of the south, have been implicated in the recent epizootic of bluetongue

Modelling bluetongue virus transmission between farms using animal and vector movements.

Bluetongue is a notifiable disease of ruminants which, in 2007, occurred for the first time in England. We present the first model for bluetongue that explicitly incorporates farm to farm movements of the two main hosts, as well as vector dispersal. The model also includes a seasonal vector to host ratio and dynamic restriction zones that evolve as infection is detected. Batch movements of sheep were included by modelling degree of mixing at markets. We investigate the transmission of bluetongue virus between farms in eastern England (the focus of the outbreak). Results indicate that most parameters affecting outbreak size relate to vectors and that the infection generally cannot be maintained without between-herd vector transmission. Movement restrictions are effective at reducing outbreak size, and a targeted approach would be as effective as a total movement ban. The model framework is flexible and can be adapted to other vector-borne diseases of livestock

Does admission prevalence change after reconfiguration of inpatient services? An interrupted time series analysis of the impact of reconfiguration in five centres

Acknowledgements We are grateful to the following individuals who kindly provided admission data: Toby Tipper and Helen Rhodes (NHS Lothian), Graham Stewart and John Mullen (NHS Greater Glasgow and Clyde), Carole Angus and Donald Macgregor (NHS Tayside) and Rochelle Morgan (NHS Grampian). Availability of data and materials The dataset used and analysed during the current study is available from the corresponding author on reasonable request.Peer reviewedPublisher PD

Case studies in Bayesian microbial risk assessments

<p>Abstract</p> <p>Background</p> <p>The quantification of uncertainty and variability is a key component of quantitative risk analysis. Recent advances in Bayesian statistics make it ideal for integrating multiple sources of information, of different types and quality, and providing a realistic estimate of the combined uncertainty in the final risk estimates.</p> <p>Methods</p> <p>We present two case studies related to foodborne microbial risks. In the first, we combine models to describe the sequence of events resulting in illness from consumption of milk contaminated with VTEC O157. We used Monte Carlo simulation to propagate uncertainty in some of the inputs to computer models describing the farm and pasteurisation process. Resulting simulated contamination levels were then assigned to consumption events from a dietary survey. Finally we accounted for uncertainty in the dose-response relationship and uncertainty due to limited incidence data to derive uncertainty about yearly incidences of illness in young children. Options for altering the risk were considered by running the model with different hypothetical policy-driven exposure scenarios. In the second case study we illustrate an efficient Bayesian sensitivity analysis for identifying the most important parameters of a complex computer code that simulated VTEC O157 prevalence within a managed dairy herd. This was carried out in 2 stages, first to screen out the unimportant inputs, then to perform a more detailed analysis on the remaining inputs. The method works by building a Bayesian statistical approximation to the computer code using a number of known code input/output pairs (training runs).</p> <p>Results</p> <p>We estimated that the expected total number of children aged 1.5-4.5 who become ill due to VTEC O157 in milk is 8.6 per year, with 95% uncertainty interval (0,11.5). The most extreme policy we considered was banning on-farm pasteurisation of milk, which reduced the estimate to 6.4 with 95% interval (0,11). In the second case study the effective number of inputs was reduced from 30 to 7 in the screening stage, and just 2 inputs were found to explain 82.8% of the output variance. A combined total of 500 runs of the computer code were used.</p> <p>Conclusion</p> <p>These case studies illustrate the use of Bayesian statistics to perform detailed uncertainty and sensitivity analyses, integrating multiple information sources in a way that is both rigorous and efficient.</p

The use of cognitive behavioural therapy on two case reports of paraphilic infantilism, substance misuse and childhood abuse

Very limited research has looked at the use of cognitive behavioural therapy on paraphilic infantilism. Two case descriptions of paraphilic infantilism coexisting with substance use disorders and anxiety and/or a mood disorder are discussed. Both cases presented with a history of childhood physical and sexual abuse. One of the cases also reported engaging in transvestism during periods of stimulant abuse and paraphilic infantilism during substance misuse stabilisation or opiate intoxication. The application of cognitive behavioural techniques revealed that both cases’ paraphilic conduct was associated with their substance misuse and whilst they attended a drug maintenance programme, they did not wish to pursue any treatment intervention regarding their paraphilic behaviours. Both case descriptions of paraphilic behaviour are discussed in the context of substance misuse, mental health and the triggers associated with relapse