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In this paper, I ask how parents understand and make sense of their children's relationships with social media algorithms. Drawing upon 30 think-aloud interviews with parents raising children aged 0 to 18 in England, in this paper, I pay attention to parents' understandings of and consequent approaches to platform algorithms in relation to their children's lives. I locate this work within user-centric research on people's understandings of algorithms, and research about parents' perspectives on data and datafication in relation to sharenting. Through my data, I draw out four modes – misunderstandings, parked understandings, transactional understandings and pro-active understandings. I suggest that parents' often flawed understandings of their children's myriad interfaces with algorithms deserve scrutiny not through a lens of blame or individualised parental (ir) responsibility but within cross-cutting contexts of parenting cultures and families' diverse contextual resources and restraints. I conclude by highlighting attention to parents' approaches to algorithms in children's lives as critical to parents' data and algorithm literacies. Prior State of Knowledge: Parents in diverse contexts try to understand and support their children's digital lives, and also often share content about their children on a variety of platforms. Prior research has shed significant light on the datafication of childhood. Novel Contributions: This study investigates parents' diverse understandings of algorithms underlying social media platforms and the ways in which they approach algorithms in their children's lives. Practical Implications: Parents' knowledge about algorithms and datafication is uneven. Policymakers need to better support adult media literacies, including data and algorithm literacies. Schools' communication to families and carers could also become key vehicles to raise awareness about datafication. [ABSTRACT FROM AUTHOR]

In this paper, I present contextualizing factors, dimensions, and key markers of algorithm literacies, paying attention to the context of parenting and parenthood amidst datafication. Analyzing data from "think-aloud" interviews with 30 parents of children aged between 0 and 18, across England, I draw upon media and digital literacies scholarship to focus, first, in this paper, on the competencies, conversations, and events which contextualize parents' literacies with algorithmic interfaces. Next, I draw out four dimensions of parents' algorithm literacies including algorithm awareness, technical competencies, critical capacities, and championing their and their children's best interests, identifying practical markers for each dimension. I reflect on the broader implications of these for parenting and parenthood in datafied societies, and note that algorithm literacies are, forever, a work in progress, in fluidity and flux across the diverse courses of parenting journeys, deeply contextualized in the resources and restraints that parents encounter in their daily lives. [ABSTRACT FROM AUTHOR]

Copyright of Firat University Journal of Experimental & Computational Engineering (FUJECE) is the property of Firat University Journal of Experimental & Computational Engineering (FUJECE) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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.

Background: Trauma-focused cognitive behavioral therapy (Tf-CBT) and eye movement desensitization and reprocessing (EMDR) are two highly effective treatment options for posttraumatic stress disorder (PTSD). Yet, on an individual level, PTSD patients vary substantially in treatment response. The aim of the paper is to test the application of a treatment selection method based on a personalized advantage index (PAI)., Method: The study used clinical data for patients accessing treatment for PTSD in a primary care mental health service in the north of England. PTSD patients received either EMDR (N = 75) or Tf-CBT (N = 242). The Patient Health Questionnaire (PHQ-9) was used as an outcome measure for depressive symptoms associated with PTSD. Variables predicting differential treatment response were identified using an automated variable selection approach (genetic algorithm) and afterwards included in regression models, allowing the calculation of each patient's PAI., Results: Age, employment status, gender, and functional impairment were identified as relevant variables for Tf-CBT. For EMDR, baseline depressive symptoms as well as prescribed antidepressant medication were selected as predictor variables. Fifty-six percent of the patients (n = 125) had a PAI equal or higher than one standard deviation. From those patients, 62 (50%) did not receive their model-predicted treatment and could have benefited from a treatment assignment based on the PAI., Conclusions: Using a PAI-based algorithm has the potential to improve clinical decision making and to enhance individual patient outcomes, although further replication is necessary before such an approach can be implemented in prospective studies., (© 2018 Wiley Periodicals, Inc.)

Background: Childhood respiratory tract infections (RTIs) are common and can lead to unnecessary antibiotic use and antimicrobial resistance. The CHIldren with COugh (CHICO) intervention incorporates a clinician-focused algorithm (STARWAVe) to predict future hospitalisation risk, elicitation of carer concerns, and a carer-focused personalised leaflet recording treatment decisions and safety-netting information. Aim: To examine the implementation of the CHICO intervention by primary care clinicians. Design and setting: A qualitative study with primary care clinicians in England taking part in the CHICO randomised controlled trial. Method: Interviews explored the CHICO intervention's acceptability and use. Clinicians from a range of practices with high and low antibiotic dispensing rates were recruited. Normalisation process theory underpinned data collection and thematic analysis. Results: Most clinicians liked the intervention because it was quick and easy to use, it helped elicit carer concerns, and reassured clinicians and carers of the appropriateness of treatment decisions. However, clinicians used it as a supportive aid for treatment decisions rather than as a tool for behaviour change. The accompanying advice leaflet helped explain treatment decisions and support self-care. The intervention did not always align with clinicians' usual processes, which could affect use. Increased familiarisation with the algorithm led to reduced intervention use, which was further reduced during the COVID-19 pandemic as a result of changes to practice and remote consultations. Conclusion: Clinicians found the CHICO intervention useful to support decision making around antibiotic prescribing and it helped discussions with carers about concerns and treatment decisions. The intervention may need to be adapted to align more with clinicians' consultation flow and remote consultations. [ABSTRACT FROM AUTHOR]

Orthopaedic and musculoskeletal injuries are commonly identified in the emergency department (ED). Whilst much orthopaedic trauma literature focuses on fractures of the proximal femur, raising key issues such as length of stay and timely discharge, the start of the patients' journey is just as important in ensuring an appropriate assessment and a smooth transition through each stage of care. In the UK targets have been set for proximal hip fractured patients to attend theatre within 48 hours of admission, if fit. Appraising such patients expediently on initial point of contact in the ED has demonstrated that a number of factors can impinge and delay the patients' progress. This said a large number of other orthopaedic and musculoskeletal self presenting patients rely on the same appropriate transition to suitable medical assistance. The emergency department triage system has been used in the UK in its latest format since 2001, yet elderly patients with painful Colles fractures find they wait for specialist attention in a linear queue, possibly over extended lengths of time. This short paper explores how 'streaming' patients in one local ED has improved waiting/treatment times, and identified the fact that in some months (December 2012), 1 in 3 attendees present with a musculoskeletal problem. Using audit data collected over the last four years the benefits of 'streaming' patients is evident., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

The latest data on teacher recruitment and retention in England shows that teacher supply is in a critical state, representing a substantial risk to the quality of education. Last year's initial teacher training (ITT) recruitment was below target in almost every secondary subject, while this year's applications data suggests significant improvements for the current cycle are unlikely. Meanwhile, little progress has been made on reducing high teacher workload since the pandemic, which has a strong impact on retention. This affirms that actions that are ambitious and radical, yet costeffective are urgently needed. The aim of the National Foundation for Educational Research's (NFER) annual series of Teacher Labour Market reports is to monitor the progress the school system in England is making towards meeting the teacher supply challenge. The report summarises new research and key trends in teacher recruitment, retention and working conditions and points towards policy actions that are likely to have the greatest impact on addressing the challenges. [ABSTRACT FROM AUTHOR]

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]

Purpose - The purpose of this paper is to calculate the rate of suicide in different religious groups in people of South Asian origin in the UK. Design/methodology/approach - A cross-sectional, secondary analysis of a national data set. A name recognition algorithm was used to identify people of South Asian origin and their religion. Standardized mortality ratios (SMRs) were calculated using this data and data from the national census. Setting: a population study of all those who died by suicide in England and Wales in 2001. Participants: all cases of suicide and undetermined intent identified by the Office for National Statistics for England and Wales. Findings - There were 4,848 suicides in the UK in 2001 of which 125 (2.6 percent) were identified as people of South Asian origin by the algorithm. The suicide rate for all people of South Asian origin was 5.50/100,000 compared to 9.31/100,000 for the population of England and Wales. The age SMR for those whose names were of Hindu, Muslim or Sikh origin were 0.88, 0.47 and 0.85, respectively. Female South Asians have lower rates of suicide, than their South Asian male counterparts. Research limitations/implications - Religious classification by the computerized program does not guarantee religious affiliation. The data set were confined to one year because religion was not collected prior to the 2001 census. Originality/value - The rates of suicide for South Asian sub-populations in the UK differ by gender and religion. [ABSTRACT FROM AUTHOR]

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]

In 2012, The Royal College of Physicians (RCP) developed a National Early Warning Score (NEWS) as a standardised approach to assessment and response to critical illness. This paper reports the authors’ experiences whilst implementing NEWS across one large inner London NHS Trust. NEWS was introduced to all adult areas between November 2013 and January 2014. All healthcare staff completed the RCP’s online e-learning module and received scenario-based teaching. One of the challenges was the Trust’s geographical location over three sites. Comparisons across all sites will be made over time as data become available. Introducing NEWS has been a challenging but exciting initiative. [ABSTRACT FROM AUTHOR]

Despite the increasing ethnic diversity of England's school-age population, academic literature on ethnic school segregation remains small, dated and hindered by methodological challenges. This study seeks to address these issues by measuring ethnic school segregation between 2006–2019 using two methodological innovations. Firstly, it is the first national study in England to adopt a multi-group segregation index to measure segregation between five major groups in a single concise metric. Secondly, it uses a clustering algorithm to group local schools into 'pseudo-neighbourhoods', which allows for segregation to be measured within local educational markets. The article shows that between 2006 and 2019 the median level of ethnic school segregation within English pseudo-neighbourhoods fell by 25%, which suggests students from different ethnic groups have become more evenly spread across local schools. Additionally, areas in the North of England were typically found to have higher levels of segregation than those in the South. [ABSTRACT FROM AUTHOR]

Well parameterized epidemiological models including accurate representation of contacts are fundamental to controlling epidemics. However, age-stratified contacts are typically estimated from pre-pandemic/peace-time surveys, even though interventions and public response likely alter contacts. Here, we fit age-stratified models, including re-estimation of relative contact rates between age classes, to public data describing the 2020-2021 COVID-19 outbreak in England. This data includes age-stratified population size, cases, deaths, hospital admissions and results from the Coronavirus Infection Survey (almost 9000 observations in all). Fitting stochastic compartmental models to such detailed data is extremely challenging, especially considering the large number of model parameters being estimated (over 150). An efficient new inference algorithm ABC-MBP combining existing approximate Bayesian computation (ABC) methodology with model-based proposals (MBPs) is applied. Modified contact rates are inferred alongside time-varying reproduction numbers that quantify changes in overall transmission due to pandemic response, and age-stratified proportions of asymptomatic cases, hospitalization rates and deaths. These inferences are robust to a range of assumptions including the values of parameters that cannot be estimated from available data. ABC-MBP is shown to enable reliable joint analysis of complex epidemiological data yielding consistent parametrization of dynamic transmission models that can inform data-driven public health policy and interventions. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'. [ABSTRACT FROM AUTHOR]

Fairness in assessment has become increasingly topical and controversial in recent years. Assessment theoreticians are writing more about fairness and assessment practitioners have developed processes and good practice to minimise unfairness. There is also increased scrutiny by students, parents and the wider public – not only of the fairness of assessments themselves and their outcomes, but of their use, notably for selection for college or university. This is in a context of continued awareness of inequalities in society and their impact on education and assessment. And on top of all these questions has been the impact – and the continuing long shadow – of Covid. Can there be fair assessment in such an unfair world? We consider three types of challenge to fair assessment: •Theoretical challenges •Challenges from thinking about social justice •Challenges from the way that statistics were used to award assessment outcomes in 2020 (particularly in England) [ABSTRACT FROM AUTHOR]

Objectives: Current British HIV Association (BHIVA) guidelines recommend the use of FRAX for the routine assessment of bone fracture risk in people living with HIV over 50 years of age every 3 years. Bone mineral density measurement with dual‐energy X‐ray absorptiometry (DXA) scan is recommended for those with increased fracture risk (FRAX major > 10%). Our objectives were to estimate the prevalence of and risk factors for osteoporosis in a population of PLWH aged > 50 years and assess the utility of FRAX in predicting the presence of DXA‐proven osteoporosis in this cohort. Methods: This was a cross‐sectional study of a cohort of PLWH aged > 50 years attending the Chelsea and Westminster Hospital and who had a DXA scan between January 2009 and December 2018. FRAX scores were calculated using the Sheffield algorithm. Multiple regression models and Cohen's kappa values were used to assess risk factors for osteoporosis and agreement between FRAX and DXA scan results, respectively. Results: In all, 744 patients were included (92.9% male, mean age 56 ± 5 years). The prevalence rates of osteoporosis (at DXA scans) and osteopenia were 12.2% and 63.7%, respectively. FRAX major was > 10% in only two patients, while 90/91 (98.9%) patients with osteoporosis had a normal FRAX score. The presence of osteoporosis was significantly associated with low body mass index and estimated glomerular filtration rate (p < 0.05). Conclusion: Our results indicate that FRAX scores did not predict the presence of osteoporosis in our population of PLWH over 50 years of age and therefore FRAX scores may not be the appropriate tool to define eligibility to perform DXA scans in PLWH. [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: The literature contains limited information on the problems faced by dying patients with COVID-19 and the effectiveness of interventions to manage these. Aim: The aim of this audit was to assess the utility of our end-of-life care plan, and specifically the effectiveness of our standardised end-of-life care treatment algorithms, in dying patients with COVID-19. Design: The audit primarily involved data extraction from the end-of-life care plan, which includes four hourly nursing (ward nurses) assessments of specific problems: patients with problems were managed according to standardised treatment algorithms, and the intervention was deemed to be effective if the problem was not present at subsequent assessments. Setting/participants: This audit was undertaken at a general hospital in England, covered the 8 weeks from 16 March to 11 May 2020 and included all inpatients with COVID-19 who had an end-of-life care plan (and died). Results: Sixty-one patients met the audit criteria: the commonest problem was shortness of breath (57.5%), which was generally controlled with conservative doses of morphine (10–20 mg/24 h via a syringe pump). Cough and audible respiratory secretions were relatively uncommon. The second most common problem was agitation/delirium (55.5%), which was generally controlled with standard pharmacological interventions. The cumulative number of patients with shortness of breath, agitation and audible respiratory secretions increased over the last 72 h of life, but most patients were symptom controlled at the point of death. Conclusion: Patients dying of COVID-19 experience similar end-of-life problems to other groups of patients. Moreover, they generally respond to standard interventions for these end-of-life problems. [ABSTRACT FROM AUTHOR]

Background: Red blood cells are essential for modern medicine but managing their collection and supply to cope with fluctuating demands represents a major challenge. As deterministic models based on predicted population changes have been problematic, there remains a need for more precise and reliable prediction of use. Here, we develop three new time-series methods to predict red cell use 4 to 52 weeks ahead.Study Design and Methods: From daily aggregates of red blood cell (RBC) units issued from 2005 to 2011 from the NHS Blood and Transplant, we generated a new set of non-overlapping weekly data by summing the daily data over 7 days and derived the average blood use per week over 4-week and 52-week periods. We used three new methods for linear prediction of blood use by computing the coefficients using Minimum Mean Squared Error (MMSE) algorithm.Results: We optimized the time-window size, order of the prediction, and order of the polynomial fit for our data set. By exploiting the annual periodicity of the data, we achieved significant improvements in long-term predictions, as well as modest improvements in short-term predictions. The new methods predicted mean RBC use with a standard deviation of the percentage error of 2.5% for 4 weeks ahead and 3.4% for 52 weeks ahead.Conclusion: This paradigm allows short- and long-term prediction of RBC use and could provide reliable and precise prediction up to 52 weeks ahead to improve the efficiency of blood services and sufficiency of blood supply with reduced costs. [ABSTRACT FROM AUTHOR]

Copyright of Current Sociology is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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 Over the past decade, acute kidney injury (AKI) has become a global priority for improving patient safety and health outcomes. In the UK, a confidential inquiry into AKI led to the publication of clinical guidance and a range of policy initiatives. National patient safety directives have focused on the mandatory establishment of clinical decision support systems (CDSSs) within all acute National Health Service (NHS) trusts to improve the detection, alerting and response to AKI. We studied the organisational work of implementing AKI CDSSs within routine hospital care. Methods An ethnographic study comprising nonparticipant observation and interviews was conducted in two NHS hospitals, delivering AKI quality improvement programmes, located in one region of England. Three researchers conducted a total of 49 interviews and 150 hours of observation over an 18-month period. Analysis was conducted collaboratively and iteratively around emergent themes, relating to the organisational work of technology adoption. Results The two hospitals developed and implemented AKI CDSSs using very different approaches. Nevertheless, both resulted in adaptive work and trade-offs relating to the technology, the users, the organisation and the wider system of care. A common tension was associated with attempts to maximise benefit while minimise additional burden. In both hospitals, resource pressures exacerbated the tensions of translating AKI recommendations into routine practice. Conclusions Our analysis highlights a conflicted relationship between external context (policy and resources), and organisational structure and culture (eg, digital capability, attitudes to quality improvement). Greater consideration is required to the long-term effectiveness of the approaches taken, particularly in light of the ongoing need for adaptation to incorporate new practices into routine work. [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: Blood products are essential for modern medicine, but managing their collection and supply in the face of fluctuating demands represents a major challenge. As deterministic models based on predicted changes in population have been problematic, there remains a need for more precise and reliable prediction of demands. Here, we propose a paradigm incorporating four different time-series methods to predict red blood cell (RBC) issues 4 to 24 weeks ahead.Study Design and Methods: We used daily aggregates of RBC units issued from 2005 to 2011 from the National Health Service Blood and Transplant. We generated a new set of nonoverlapping weekly data by summing the daily data over 7 days and derived the average blood issues per week over 4-week periods. We used four methods for linear prediction of blood demand by computing the coefficients with the minimum mean squared error and weighted least squares error algorithms.Results: We optimized the time-window size, order of the prediction, and order of the polynomial fit for our data set. The four time-series methods, essentially using different weightings to data points, gave very similar results and predicted mean RBC issues with a standard deviation of the percentage error of 3.0% for 4 weeks ahead and 4.0% for 24 weeks ahead.Conclusion: This paradigm allows prediction of demand for RBCs and could be developed to provide reliable and precise prediction up to 24 weeks ahead to improve the efficiency of blood services and sufficiency of blood supply with reduced costs. [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 co‐occurrence of internalizing disorders is a common form of psychiatric comorbidity, raising questions about the boundaries between these diagnostic categories. We employ network psychometrics in order to: (a) determine whether internalizing symptoms cluster in a manner reflecting DSM diagnostic criteria, (b) gauge how distinct these diagnostic clusters are and (c) examine whether this network structure changes from childhood to early and then late adolescence. Method: Symptom‐level data were obtained for service users in publicly funded mental health services in England between 2011 and 2015 (N = 37,162). A symptom network (i.e. Gaussian graphical model) was estimated, and a community detection algorithm was used to explore the clustering of symptoms. Results: The estimated network was densely connected and characterized by a multitude of weak associations between symptoms. Six communities of symptoms were identified; however, they were weakly demarcated. Two of these communities corresponded to social phobia and panic disorder, and four did not clearly correspond with DSM diagnostic categories. The network structure was largely consistent by sex and across three age groups (8–11, 12–14 and 15–18 years). Symptom connectivity in the two older age groups was significantly greater compared to the youngest group and there were differences in centrality across the age groups, highlighting the age‐specific relevance of certain symptoms. Conclusions: These findings clearly demonstrate the interconnected nature of internalizing symptoms, challenging the view that such pathology takes the form of distinct disorders. [ABSTRACT FROM AUTHOR]

Objective: To determine the validity of assessing and recording the neurodevelopmental outcome of very preterm infants during routine clinical follow-up in England.Design: Children born <30 weeks gestation, attending routine clinical follow-up at post-term ages 20-28 months, were recruited. Data on neurodevelopmental outcomes were recorded by the reviewing clinician in a standardised format in the child's electronic patient record, based on a set of key questions designed to be used without formal training or developmental testing. Using a predefined algorithm, each participant was classified as having 'no', 'mild/moderate' or 'severe' impairment in cognitive, communication and motor domains. All participants also received a research assessment by a single assessor using the Bayley Scales of Infant Development, third edition (Bayley-III). The sensitivity and specificity of routine data in capturing impairment (any Bayley-III score <85) or severe impairment (any Bayley-III score <70) was calculated.Results: 190 children participated. The validity of routine assessments in identifying children with no impairment and no severe impairment was high across all domains (specificities 83.9%-100.0% and 96.6%-100.0%, respectively). However, identification of impairments, particularly in the cognitive (sensitivity 69.7% (55.1%-84.3%)) and communication (sensitivity (53.2% (42.0%-64.5%)) domains, was poor.Conclusions: Neurodevelopmental status determined during routine clinical assessment lacks adequate sensitivity in cognitive and communication domains. It is uncertain whether this reflects the assessment or/and the recording of findings. As early intervention may improve education and social outcomes, this is an important area for healthcare quality improvement research. [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]

Patient-reported outcome measures (PROMs) are now routinely collected in the English National Health Service and used to compare and reward hospital performance within a high-powered pay-for-performance scheme. However, PROMs are prone to missing data. For example, hospitals often fail to administer the pre-operative questionnaire at hospital admission, or patients may refuse to participate or fail to return their post-operative questionnaire. A key concern with missing PROMs is that the individuals with complete information tend to be an unrepresentative sample of patients within each provider and inferences based on the complete cases will be misleading. This study proposes a strategy for addressing missing data in the English PROM survey using multiple imputation techniques and investigates its impact on assessing provider performance. We find that inferences about relative provider performance are sensitive to the assumptions made about the reasons for the missing data. [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]

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 discusses a study on risk prediction equations to estimate future risk of blindness and lower limb amputation in patients with diabetes. Study highlights include the QResearch and Clinical Practice Research Datalink databases, the use of Cox proportional hazards models, and development of risk prediction equations to quantify absolute risk of blindness and amputation in men and women with diabetes.