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Background: The Clinical Practice Research Datalink (CPRD) GOLD is an extremely influential U.K. primary care dataset for epidemiological research having a number of published papers based on its data much bigger than any other U.K. primary care dataset. The Office for National Statistics (ONS) death data for England can be linked to GOLD at the patient level and are considered the gold standard on mortality. GOLD, which also holds death data, has been recently assessed against ONS linked dataset and the accuracy of its dates of death has been deemed sufficient for the majority of observational studies. However, there is a lack of guidance on how to manage the challenges existing when ONS mortality and GOLD datasets are linked, including linkage coverage period, linkage correctness likelihood, linkage regional limitations and data discrepancy., Objectives: Provide reconciling guidelines on how to make maximum and at the same time trustworthy use of mortality information coming from both GOLD and ONS linked datasets with the aim of improving the quality, reproducibility, transparency and comparison of clinical research., Method and Results: We have developed recommendations on how to manage mortality data coming from both GOLD and linked ONS, taking into account linkage coverage period, linkage correctness likelihood, linkage regional limitations and data discrepancies between these two datasets. We have also implemented these guidelines in an SQL algorithm for researchers to use., Conclusion: We have provided detailed guidelines on the reconciliation of mortality data between GOLD and ONS linked death datasets, taking into account both their strengths and limitations. The consistent application of these guidelines made practical by an SQL algorithm, has the potential to improve clinical research quality, reproducibility, transparency and comparison., Competing Interests: Declaration of Competing Interest The authors have no competing interests to declare., (Copyright © 2019 Elsevier B.V. All rights reserved.)

This paper examines the association between given and family names and self-ascribed ethnicity as classified by the 2011 Census of Population for England and Wales. Using Census data in an innovative way under the new Office for National Statistics (ONS) Secure Research Service (SRS; previously the ONS Virtual Microdata Laboratory, VML), we investigate how bearers of a full range of given and family names assigned themselves to 2011 Census categories, using a names classification tool previously described in this journal. Based on these results, we develop a follow-up ethnicity estimation tool and describe how the tool may be used to observe changing relations between naming practices and ethnic identities as a facet of social integration and cosmopolitanism in an increasingly diverse society., Competing Interests: The authors have declared that no competing interests exist.

A large-scale multiple surveillance system for infectious disease outbreaks has been in operation in England and Wales since the early 1990s. Changes to the statistical algorithm at the heart of the system were proposed and the purpose of this paper is to compare two new algorithms with the original algorithm. Test data to evaluate performance are created from weekly counts of the number of cases of each of more than 2000 diseases over a twenty-year period. The time series of each disease is separated into one series giving the baseline (background) disease incidence and a second series giving disease outbreaks. One series is shifted forward by twelve months and the two are then recombined, giving a realistic series in which it is known where outbreaks have been added. The metrics used to evaluate performance include a scoring rule that appropriately balances sensitivity against specificity and is sensitive to variation in probabilities near 1. In the context of disease surveillance, a scoring rule can be adapted to reflect the size of outbreaks and this was done. Results indicate that the two new algorithms are comparable to each other and better than the algorithm they were designed to replace. [ABSTRACT FROM AUTHOR]

This paper presents the machine learning based machine translation system for Hindi to English, which learns the semantically correct corpus. The quantum neural based pattern recognizer is used to recognize and learn the pattern of corpus, using the information of part of speech of individual word in the corpus, like a human. The system performs the machine translation using its knowledge gained during the learning by inputting the pair of sentences of Devnagri-Hindi and English. To analyze the effectiveness of the proposed approach, 2600 sentences have been evaluated during simulation and evaluation. The accuracy achieved on BLEU score is 0.7502, on NIST score is 6.5773, on ROUGE-L score is 0.9233, and on METEOR score is 0.5456, which is significantly higher in comparison with Google Translation and Bing Translation for Hindi to English Machine Translation.

Background: Imperfect diagnostic testing reduces the power to detect significant predictors in classical cross-sectional studies. Assuming that the misclassification in diagnosis is random this can be dealt with by increasing the sample size of a study. However, the effects of imperfect tests in longitudinal data analyses are not as straightforward to anticipate, especially if the outcome of the test influences behaviour. The aim of this paper is to investigate the impact of imperfect test sensitivity on the determination of predictor variables in a longitudinal study., Methodology/principal Findings: To deal with imperfect test sensitivity affecting the response variable, we transformed the observed response variable into a set of possible temporal patterns of true disease status, whose prior probability was a function of the test sensitivity. We fitted a Bayesian discrete time survival model using an MCMC algorithm that treats the true response patterns as unknown parameters in the model. We applied our approach to epidemiological data of bovine tuberculosis outbreaks in England and investigated the effect of reduced test sensitivity in the determination of risk factors for the disease. We found that reduced test sensitivity led to changes to the collection of risk factors associated with the probability of an outbreak that were chosen in the 'best' model and to an increase in the uncertainty surrounding the parameter estimates for a model with a fixed set of risk factors that were associated with the response variable., Conclusions/significance: We propose a novel algorithm to fit discrete survival models for longitudinal data where values of the response variable are uncertain. When analysing longitudinal data, uncertainty surrounding the response variable will affect the significance of the predictors and should therefore be accounted for either at the design stage by increasing the sample size or at the post analysis stage by conducting appropriate sensitivity analyses.

It is uncommon for risk groups defined by statistical or artificial intelligence (AI) models to be chosen by jointly considering model performance and potential interventions available. We develop a framework to rapidly guide choice of risk groups in this manner, and apply it to guide breast cancer screening intervals using an AI model. Linear programming is used to define risk groups that minimize expected advanced cancer incidence subject to resource constraints. In the application risk stratification performance is estimated from a case–control study (2044 cases, 1:1 matching), and other parameters are taken from screening trials and the screening programme in England. Under the model, re-screening in 1 year for the highest 4% AI model risk, in 3 years for the middle 64%, and in 4 years for 32% of the population at lowest risk, was expected to reduce the number of advanced cancers diagnosed by approximately 18 advanced cancers per 1000 diagnosed with triennial screening, for the same average number of screens in the population as triennial screening for all. Sensitivity analyses found the choice of thresholds was robust to model parameters, but the estimated reduction in advanced cancers was not precise and requires further evaluation. Our framework helps define thresholds with the greatest chance of success for reducing the population health burden of cancer when used in risk-adapted screening, which should be further evaluated such as in health-economic modelling based on computer simulation models, and real-world evaluations. [ABSTRACT FROM AUTHOR]

This paper discusses the modelling of rainfall-flow (rainfall-run-off) and flow-routeing processes in river systems within the context of real-time flood forecasting. It is argued that deterministic, reductionist (or 'bottom-up') models are inappropriate for real-time forecasting because of the inherent uncertainty that characterizes river-catchment dynamics and the problems of model over-parametrization. The advantages of alternative, efficiently parametrized data-based mechanistic models, identified and estimated using statistical methods, are discussed. It is shown that such models are in an ideal form for incorporation in a real-time, adaptive forecasting system based on recursive state-space estimation (an adaptive version of the stochastic Kalman filter algorithm). An illustrative example, based on the analysis of a limited set of hourly rainfall-flow data from the River Hodder in northwest England, demonstrates the utility of this methodology in difficult circumstances and illustrates the advantages of incorporating real-time state and parameter adaption.

In this paper we present a novel Case-Based Reasoning (CBR) system called CABAROST (CAsed-BAsed ROSTering) which was developed for personnel scheduling problems. CBR is used to capture and store examples of personnel manager behaviour which are then used to solve future problems. Previous examples of constraint violations in schedules and the repairs that were used to solve the violations are stored as cases. The sequence in which violations are repaired can have a great impact on schedule quality. A novel memetic algorithm is proposed which evolves good quality sequences of repairs generated by CABAROST. The algorithm was tested on instances of the real-world nurse rostering problem at the Queens Medical Centre NHS Trust in Nottingham. [ABSTRACT FROM AUTHOR]

In this paper, an automatic near-real time (NRT) flood detection approach is presented, which combines histogram thresholding and segmentation based classification, specifically oriented to the analysis of single-polarized very high resolution Synthetic Aperture Radar (SAR) satellite data. The challenge of SAR-based flood detection is addressed in a completely unsupervised way, which assumes no training data and therefore no prior information about the class statistics to be available concerning the area of investigation. This is usually the case in NRT-disaster management, where the collection of ground truth information is not feasible due to time-constraints. A simple thresholding algorithm can be used in the most of the cases to distinguish between "flood" and "non-flood" pixels in a high resolution SAR image to detect the largest part of an inundation area. Due to the fact that local gray-level changes may not be distinguished by global thresholding techniques in large satellite scenes the thresholding algorithm is integrated into a split-based approach for the derivation of a global threshold by the analysis and combination of the split inherent information. The derived global threshold is then integrated into a multiscale segmentation step combining the advantages of small-, medium- and large-scale per parcel segmentation. Experimental investigations performed on a TerraSAR-X Stripmap scene from southwest England during large scale flooding in the summer 2007 show high classification accuracies of the proposed split-based approach in combination with image segmentation and optional integration of digital elevation models. [ABSTRACT FROM AUTHOR]

Background: Health surveys provide a rich array of information but on relatively small numbers of individuals and evidence suggests that they are becoming less representative as response levels fall. Routinely collected administrative data offer more extensive population coverage but typically comprise fewer health topics. We explore whether data combination and multiple imputation of health variables from survey data is a simple and robust way of generating these variables in the general population. Methods: We use the UK Integrated Household Survey and the English 2011 population census both of which included self-rated general health. Setting aside the census self-rated health data we multiply imputed self-rated health responses for the census using the survey data and compared these with the actual census results in 576 unique groups defined by age, sex, housing tenure and geographic region. Results: Compared with original census data across the groups, multiply imputed proportions of bad or very bad self-rated health were not a markedly better fit than those simply derived from the survey proportions. Conclusion: While multiple imputation may have the potential to augment population data with information from surveys, further testing and refinement is required. [ABSTRACT FROM AUTHOR]

Background: In elite football (soccer), periodic health examination (PHE) could provide prognostic factors to predict injury risk. Objective: To develop and internally validate a prognostic model to predict individualised indirect (non-contact) muscle injury (IMI) risk during a season in elite footballers, only using PHE-derived candidate prognostic factors. Methods: Routinely collected preseason PHE and injury data were used from 152 players over 5 seasons (1st July 2013 to 19th May 2018). Ten candidate prognostic factors (12 parameters) were included in model development. Multiple imputation was used to handle missing values. The outcome was any time-loss, index indirect muscle injury (I-IMI) affecting the lower extremity. A full logistic regression model was fitted, and a parsimonious model developed using backward-selection to remove factors that exceeded a threshold that was equivalent to Akaike's Information Criterion (alpha 0.157). Predictive performance was assessed through calibration, discrimination and decision-curve analysis, averaged across all imputed datasets. The model was internally validated using bootstrapping and adjusted for overfitting. Results: During 317 participant-seasons, 138 I-IMIs were recorded. The parsimonious model included only age and frequency of previous IMIs; apparent calibration was perfect, but discrimination was modest (C-index = 0.641, 95% confidence interval (CI) = 0.580 to 0.703), with clinical utility evident between risk thresholds of 37–71%. After validation and overfitting adjustment, performance deteriorated (C-index = 0.589 (95% CI = 0.528 to 0.651); calibration-in-the-large = − 0.009 (95% CI = − 0.239 to 0.239); calibration slope = 0.718 (95% CI = 0.275 to 1.161)). Conclusion: The selected PHE data were insufficient prognostic factors from which to develop a useful model for predicting IMI risk in elite footballers. Further research should prioritise identifying novel prognostic factors to improve future risk prediction models in this field. Trial registration: NCT03782389 [ABSTRACT FROM AUTHOR]

Background: We have developed an open-source ALgorithm for Generating Address Exposures (ALGAE) that cleans residential address records to construct address histories and assign spatially-determined exposures to cohort participants. The first application of this algorithm was to construct prenatal and early life air pollution exposure for individuals of the Avon Longitudinal Study of Parents and Children (ALSPAC) in the South West of England, using previously estimated particulate matter ≤10  µm (PM10) concentrations.Methods: ALSPAC recruited 14 541 pregnant women between 1991 and 1992. We assigned trimester-specific estimated PM10 exposures for 12 752 pregnancies, and first year of life exposures for 12 525 births, based on maternal residence and residential mobility.Results: Average PM10 exposure was 32.6  µg/m3 [standard deviation (S.D.) 3.0  µg/m3] during pregnancy and 31.4 µg/m3 (S.D. 2.6  µg/m3) during the first year of life; 6.7% of women changed address during pregnancy, and 18.0% moved during first year of life of their infant. Exposure differences ranged from -5.3  µg/m3 to 12.4  µg/m3 (up to 26% difference) during pregnancy and -7.22  µg/m3 to 7.64  µg/m3 (up to 27% difference) in the first year of life, when comparing estimated exposure using the address at birth and that assessed using the complete cleaned address history. For the majority of individuals exposure changed by <5%, but some relatively large changes were seen both in pregnancy and in infancy.Conclusions: ALGAE provides a generic and adaptable, open-source solution to clean addresses stored in a cohort contact database and assign life stage-specific exposure estimates with the potential to reduce exposure misclassification. [ABSTRACT FROM AUTHOR]

Background Area-based initiatives that include a focus on community empowerment are increasingly being seen as potentially an important way of improving health and reducing inequalities. However, there is little empirical evidence on the pathways between communities having more control and health outcomes. Purpose To identify pathways to health improvement in a community-led area-based community empowerment initiative. Methods Longitudinal data on mental health, community control, area belonging, satisfaction, social cohesion and safety were collected over two time points, 6 months apart from 48 participants engaged in the Big Local programme, England. Qualitative comparative analysis (QCA) was used to explore pathways to health improvement. Results There was no clear single pathway that led to mental health improvement but positive changes in 'neighbourhood belonging' featured in 4/5 health improvement configurations. Further, where respondents experienced no improvement in key social participation/control factors, they experienced no health improvement. Conclusion This study demonstrates a potential pathway between an improvement in 'neighbourhood belonging' and improved mental health outcomes in a community empowerment initiative. Increasing neighbourhood belonging could be a key target for mental health improvement interventions. [ABSTRACT FROM AUTHOR]

Background: The 2014 guidelines on cardiovascular risk assessment and lipid modification from the National Institute for Health and Care Excellence (NICE) recommend statin therapy for adults with prevalent cardiovascular disease (CVD), and for adults with a 10-year CVD risk of ≥10%, estimated using the QRISK2 algorithm.Aim: To determine risk factor levels required to exceed the risk threshold for statin therapy, and to estimate the number of adults in England who would require statin therapy under the guidelines.Design and Setting: Cross-sectional study using a sample representative of the English population aged 30-84 years.Method: To estimate 10-year CVD risk different combinations of risk factor levels were entered into the QRISK2 algorithm. The NICE guidelines were applied to the sample using data from the Health Survey for England 2011.Results: Even with optimal risk factor levels, males of different ethnicities would exceed the 10% risk threshold between the ages of 60 and 70 years, and females would exceed the threshold between 65 and 75 years. Under the NICE guidelines, 11.8 million males and females (37% of the adults aged 30-84 years) would require statin therapy, most of them (9.8 million) for primary prevention. When analysed by age, 95% of males and 66% of females without CVD in ages 60-74 years, including all males and females in ages 75-84 years, would require statin therapy.Conclusion: Under the 2014 NICE guidelines, 11.8 million (37%) adults in England aged 30-84 years, including almost all males >60 years in all females >75 years, require statin therapy. [ABSTRACT FROM AUTHOR]

Background: NHS England has mandated the use in hospital laboratories of an automated early warning algorithm to create a consistent method for the detection of acute kidney injury (AKI). It generates an 'alert' based on changes in serum creatinine level to notify attending clinicians of a possible incident case of the condition, and to provide an assessment of its severity. We aimed to explore the feasibility of secondary data analysis to reproduce the algorithm outside of the hospital laboratory, and to describe the epidemiology of AKI across primary and secondary care within a region.Methods: Using the Hampshire Health Record Analytical database, a patient-anonymised database linking primary care, secondary care and hospital laboratory data, we applied the algorithm to one year (1st January-31st December 2014) of retrospective longitudinal data. We developed database queries to modularise the collection of data from various sectors of the local health system, recreate the functions of the algorithm and undertake data cleaning.Results: Of a regional population of 642,337 patients, 176,113 (27.4%) had two or more serum creatinine test results available, with testing more common amongst older age groups. We identified 5361 (or 0.8%) with incident AKI indicated by the algorithm, generating a total of 13,845 individual AKI alerts. A cross-sectional assessment of each patient's first alert found that more than two-thirds of cases originated in the community, of which nearly half did not lead to a hospital admission.Conclusion: It is possible to reproduce the algorithm using linked primary care, secondary care and hospital laboratory data, although data completeness, data quality and technical issues must be overcome. Linked data is essential to follow the significant proportion of people with AKI who transition from primary to secondary care, and can be used to assess clinical outcomes and the impact of interventions across the health system. This study emphasises that the development of data systems bridging across different sectors of the health and social care system can provide benefits for researchers, clinicians, healthcare providers and commissioners. [ABSTRACT FROM AUTHOR]

Multiple drug intolerance to antihypertensive medications (MDI-HTN) is an overlooked cause of nonadherence. In this study, 55 patients with MDI-HTN were managed with a novel treatment algorithm utilizing sequentially initiated monotherapies or combinations of maximally tolerated doses of fractional tablet doses, liquid formulations, transdermal preparations, and off-label tablet medications. A total of 10% of referred patients had MDI-HTN, resulting in insufficient pharmacotherapy and baseline office blood pressure (OBP) of 178±24/94±15 mm Hg. At baseline, patients were intolerant to 7.6±3.6 antihypertensives; they were receiving 1.4±1.1 medications. After 6 months on the novel MDI-HTN treatment algorithm, both OBP and home blood pressure (HBP) were significantly reduced, with patients receiving 2.0±1.2 medications. At 12 months, OBP was reduced from baseline by 17±5/9±3 mm Hg (P<.01, P<.05) and HBP was reduced by 11±5/12±3 mm Hg (P<.01 for both) while patients were receiving 1.9±1.1 medications. Application of a stratified medicine approach allowed patients to tolerate increased numbers of medications and achieved significant long-term lowering of blood pressure. [ABSTRACT FROM AUTHOR]

Background: Patients at high risk of emergency hospitalisation are particularly likely to experience fragmentation in care. The virtual ward model attempts to integrate health and social care by offering multidisciplinary case management to people at high predicted risk of unplanned hospitalisation. Objective: To describe the care practice in three virtual ward sites in England and to explore how well each site had achieved meaningful integration. Method: Case studies conducted in Croydon, Devon and Wandsworth during 2011-2012, consisting of semi-structured interviews, workshops, and site visits. Results: Different versions of the virtual wards intervention had been implemented in each site. In Croydon, multidisciplinary care had reverted back to one-to-one case management. Conclusions: To integrate successfully, virtual ward projects should safeguard the multidisciplinary nature of the intervention, ensure the active involvement of General Practitioners, and establish feedback processes to monitor performance such as the number of professions represented at each team meeting. [ABSTRACT FROM AUTHOR]

Background: The United Kingdom NHS Breast Screening Programme was established in 1988, and women aged between 50 and 70 are routinely invited at three yearly intervals. Expected United Kingdom interval cancer rates have been calculated previously, but this is the first publication from an exercise to collate individual-based interval cancer data at a national level.Methods: Interval cancer case ascertainment is achieved by the regular exchange of data between Regional Breast Screening Quality Assurance Reference Centres and Cancer Registries. The present analysis includes interval cancers identified in women screened between 1st April 1997 and 31st March 2003, who were aged between 50 and 64 at the time of their last routine screen.Results: In the periods >0-<12 months, 12-<24 months and 24-<36 months after a negative screen, we found overall interval cancer rates and regional ranges of 0.55 (0.43-0.76), 1.13 (0.92-1.47) and 1.22 (0.93-1.57) per 1000 women screened, respectively. Rates in the period 33-<36 months showed a decline, possibly associated with early re-screening or delayed presentation.Conclusions: Interval cancer rates were higher than the expected rates in the 24-month period after a negative screen, but were similar to published results from other countries. Increases in background incidence may mean that the expected rates are underestimated. It is also possible that, as a result of incomplete case ascertainment, interval cancers rates were underestimated in some regions in which rates were less than the expected. [ABSTRACT FROM AUTHOR]

The Tower of London Test (TOL) used to assess executive functions was inspired in Artificial Intelligence tasks used to test problem-solving algorithms. In this study, we compare the performance of humans and of exploration algorithms. Instead of absolute execution times, we focus on how the execution time varies with the tasks and/or the number of moves. This approach used in Algorithmic Complexity provides a fair comparison between humans and computers, although humans are several orders of magnitude slower. On easy tasks (1 to 5 moves), healthy elderly persons performed like exploration algorithms using bounded memory resources, i.e., the execution time grew exponentially with the number of moves. This result was replicated with a group of healthy young participants. However, for difficult tasks (5 to 8 moves) the execution time of young participants did not increase significantly, whereas for exploration algorithms, the execution time keeps on increasing exponentially. A pre-and post-test control task showed a 25% improvement of visuo-motor skills but this was insufficient to explain this result. The findings suggest that naive participants used systematic exploration to solve the problem but under the effect of practice, they developed markedly more efficient strategies using the information acquired during the test. [ABSTRACT FROM AUTHOR]

Trainee teachers, new and non-specialist teachers of elementary mathematics have a tendency to avoid thought about reasons in mathematics. Instead, they tend to favour the development of computational skill through the rote application of procedures, routines and algorithms. Could elementary mathematics textbooks serve as models of practice and support the professional development of these teachers? Eighteen such textbooks for use by 7 to 11-year-old children in England were examined for their potential to help teachers attend to reasons. It was found that they were unlikely to point teachers towards reasons for patterns and procedures. Although some had the potential to help in introducing and structuring a lesson, they were unlikely to induce the teacher to address matters of understanding in their discourse. Their implicit message tended to be that mathematics education is about computational skill development through routines, algorithms and practice. [ABSTRACT FROM AUTHOR]

In 1986 The Office of Population Censuses and Surveys (OPCS) introduced new certificates for stillbirths and neonatal deaths. This allowed certifiers more flexibility in the completion of the certificate, and the number and ordering of the causes given. Tabulations have been published of the fetal and maternal causes of death mentioned on the certificates for every year from 1986 to 1991 in annual reference volumes. It has not been possible either to derive a single cause group for each death, however, or to compare the information available on neonatal deaths with that on postneonatal deaths, which are still derived from the standard death certificate. The aim of the work described here was to adapt previous classifications to derive a single cause grouping for stillbirths and infant deaths which would provide the maximum information about preventability and yet meet the national and international responsibilities of OPCS. The methods used and the tests carried out on the validity and consistency of the chosen classification are described. [ABSTRACT FROM AUTHOR]

Most countries operate procedures to safeguard children, including removal from parents in serious cases. In England, care applications and numbers have risen sharply, however, with wide variations not explained by levels of socio-economic deprivation alone. Drawing on extensive research, it is asserted that actuarial decision tools more accurately estimate risks to children and are needed to achieve consistency, transparency, and best outcomes for children. To date, however, child protection has not achieved gains made within comparable professions through statistical methods. The reasons are examined. To make progress requires understanding why statistical tools exert effect and how professionals use them in practice. Deep-rooted psychological factors operating within uncertainty can frustrate processes implemented to counter those forces. Crucially, tools constitute evidence; their use and interpretation should not fall to one practitioner or professional body and modifications must be open to scrutiny and adjudication. We explore the potential of novel big data technology to address the difficulties identified through tools that are accurate, simple, and universally applied within child protection. When embraced by all parties to proceedings, especially parents and their advisors, despite societal fears, big data may promote transparency of social work and judicial decisions. [ABSTRACT FROM AUTHOR]

The article describes the research work of Boris Motik, a research associate at the Oxford University Computing Laboratory in Oxford, England. Motik's research focuses on problems concerning the development of ontology languages and tools for ontology-based applications. During his PhD, Motik developed several reasoning algorithms, that allow the application of optimization techniques from deductive databases to Web Ontology Language (OWL) reasoning. Along with other collaborators, he also developed another OWL reasoning algorithm based on the theorem-proving calculus, hypertableau.