Search

People who are disabled with multiple sclerosis (MS) may be at increased risk of osteoporosis. This review discusses issues relevant to bone health in MS and makes practical recommendations regarding prevention and screening for osteoporosis and fracture risk in MS. A search of the literature up until 5 April 2011 was performed using key search terms, and articles pertinent to bone health in MS were analysed. Bone mineral density (BMD) is reduced at the lumbar spine, hip and total body in MS, with the degree of reduction being greatest at the hip. A strong relationship exists between the disability level, measured by the Expanded Disability Status Score, and BMD at the lumbar spine and femoral neck, particularly the latter. The rate of loss of BMD also correlates with the level of disability. Pulsed corticosteroids for acute episodes of MS, even with a high cumulative steroid dose, do not significantly affect BMD, but an effect on fracture risk is yet to be elucidated. There appears to be no correlation between vitamin D levels and BMD, and the relationship between disability and vitamin D levels remains unclear. Falls and fractures are more common than in healthy controls, and the risk rises with increasing levels of disability. The principal factor resulting in low BMD and increased fracture risk in MS is immobility. Antiresorptive therapy with bisphosphonates and optimising vitamin D levels are likely to be effective interventions although there are no randomised studies of this therapy. [ABSTRACT FROM AUTHOR]

BackgroundThere are no risk models for the prediction of anxiety that may help in prevention. We aimed to develop a risk algorithm for the onset of generalized anxiety and panic syndromes.MethodFamily practice attendees were recruited between April 2003 and February 2005 and followed over 24 months in the UK, Spain, Portugal and Slovenia (Europe4 countries) and over 6 months in The Netherlands, Estonia and Chile. Our main outcome was generalized anxiety and panic syndromes as measured by the Patient Health Questionnaire. We entered 38 variables into a risk model using stepwise logistic regression in Europe4 data, corrected for over-fitting and tested it in The Netherlands, Estonia and Chile.ResultsThere were 4905 attendees in Europe4, 1094 in Estonia, 1221 in The Netherlands and 2825 in Chile. In the algorithm four variables were fixed characteristics (sex, age, lifetime depression screen, family history of psychological difficulties); three current status (Short Form 12 physical health subscale and mental health subscale scores, and unsupported difficulties in paid and/or unpaid work); one concerned country; and one time of follow-up. The overall C-index in Europe4 was 0.752 [95% confidence interval (CI) 0.724–0.780]. The effect size for difference in predicted log odds between developing and not developing anxiety was 0.972 (95% CI 0.837–1.107). The validation of predictA resulted in C-indices of 0.731 (95% CI 0.654–0.809) in Estonia, 0.811 (95% CI 0.736–0.886) in The Netherlands and 0.707 (95% CI 0.671–0.742) in Chile.ConclusionsPredictA accurately predicts the risk of anxiety syndromes. The algorithm is strikingly similar to the predictD algorithm for major depression, suggesting considerable overlap in the concepts of anxiety and depression. [ABSTRACT FROM PUBLISHER]

Summary: We examined the effect of glucocorticoid dose on FRAX® derived fracture probabilities in a UK setting. A relatively simple adjustment of conventional FRAX estimates of probabilities of hip fracture and a major osteoporotic fracture can be applied to modulate the risk assessment with knowledge of the dose of glucocorticoids. Introduction: The WHO fracture risk assessment (FRAX) tool estimates 10-year probability of fracture based upon multiple clinical risk factors and an optional femoral neck BMD measurement. Ever (past and current) use of systemic glucocorticoids is a dichotomous risk factor (yes/no) and does not therefore take account of the dose of glucocorticoids. The aim of this work was to estimate the adjustment for fracture probability based upon the dose of glucocorticoids. Methods: Dose responses for fracture risk during exposure to glucocorticoids were taken from the General Practice Research Database and used to adjust the relative risks for glucocorticoids in FRAX. In addition to fracture risk, a dose response for the death hazard was estimated and both variables were used to populate the FRAX model for the UK. Results: The exposure to glucocorticoids was found to significantly affect fracture probability. The following rule was formulated. For low-dose exposure (<2.5 mg daily of prednisolone or equivalent), the probability of a major fracture is decreased by about 20% depending on age. For medium doses (2.5-7.5 mg daily), the unadjusted FRAX value can be used. For high doses (>7.5 mg daily), probabilities can be upward revised by about 15%. Conversion factors were also determined for the adjustment of hip fracture probability. Conclusion: A relatively simple adjustment of conventional FRAX estimates of probabilities of hip fracture and a major osteoporotic fracture can be applied to modulate the risk assessment with knowledge of the dose of glucocorticoids. [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 central question in mechanism design is how to implement a given social choice function. One of the most studied concepts is that of truthful implementations in which truth-telling is always the best response of the players. The Revelation Principle says that one can focus on truthful implementations without loss of generality (if there is no truthful implementation then there is no implementation at all). Green and Laffont (Rev Econ Stud 53:447-456, 1986) showed that, in the scenario in which players' responses can be partially verified, the revelation principle holds only in some particular cases. When the Revelation Principle does not hold, non-truthful implementations become interesting since they might be the only way to implement a social choice function of interest. In this work we show that, although non-truthful implementations may exist, they are hard to find. Namely, it is NP-complete to decide if a given social choice function can be implemented in a non-truthful manner, or even if it can be implemented at all. This is in contrast to the fact that truthful implementability can be efficiently recognized, even when partial verification of the agents is allowed. Our results also show that there is no 'simple' characterization of those social choice functions for which it is worth looking for non-truthful implementations. [ABSTRACT FROM AUTHOR]

Background: We aimed to estimate and externally validate a new UK-specific prognostic model for predicting the long-term risk of a first recurrent event (local recurrence, metastatic recurrence, or second primary breast cancer) in women diagnosed with early breast cancer.Methods: Using data on the prognostic characteristics and outcomes of 1844 women treated for early breast cancer at the Churchill Hospital in Oxford, parametric regression-based survival analysis was used to estimate a prognostic model for recurrence-free survival. The model, which incorporated established prognostic factors, was externally validated using independent data. Its performance was compared with that of the Nottingham Prognostic Index (NPI) and Adjuvant! Online.Results: The number of positive axillary lymph nodes, tumour grade, tumour size and patient age were strong predictors of recurrence. Oestrogen receptor (ER) positivity was shown to afford a moderate protective effect. The model was able to separate patients into distinct prognostic groups, and predicted well at the patient level, mean Brier Accuracy Score=0.17 (s.e.=0.004) and overall C=0.745 (95% CI, 0.717-0.773). Its performance diminished only slightly when applied to a second independent data set. When compared with the NPI, the model was able to better discriminate between women with excellent and good prognoses, and it did not overestimate 10-year recurrence-free survival to the extent observed for Adjuvant! Online.Conclusion: The model estimated here predicts well at both the individual patient and group levels, and appears transportable to patients treated at other UK hospitals. Its parametric form permits long-term extrapolation giving it an advantage over other prognostic tools currently in use. A simple point scoring system and reference table allow 5-, 10-, and 15-year predictions from the model to be quickly and easily estimated. The model is also available to download as an interactive computer program. [ABSTRACT FROM AUTHOR]

Collections of suitably chosen borehole profiles can be used to infer large-scale trends in ground-surface temperature (GST) histories for the past few hundred years. These reconstructions are based on a large database of carefully selected borehole temperature measurements from around the globe. Since non-climatic thermal influences are difficult to identify, representative temperature histories are derived by averaging individual reconstructions to minimize the influence of these perturbing factors. This may lead to three potentially important drawbacks: the net signal of non-climatic factors may not be zero, meaning that the average does not reflect the best estimate of past climate; the averaging over large areas restricts the useful amount of more local climate change information available; and the inversion methods used to reconstruct the past temperatures at each site must be mathematically identical and are therefore not necessarily best suited to all data sets. In this work, we avoid these issues by using a Bayesian partition model (BPM), which is computed using a trans-dimensional form of a Markov chain Monte Carlo algorithm. This then allows the number and spatial distribution of different GST histories to be inferred from a given set of borehole data by partitioning the geographical area into discrete partitions. Profiles that are heavily influenced by non-climatic factors will be partitioned separately. Conversely, profiles with climatic information, which is consistent with neighbouring profiles, will then be inferred to lie in the same partition. The geographical extent of these partitions then leads to information on the regional extent of the climatic signal. In this study, three case studies are described using synthetic and real data. The first demonstrates that the Bayesian partition model method is able to correctly partition a suite of synthetic profiles according to the inferred GST history. In the second, more realistic case, a series of temperature profiles are calculated using surface air temperatures of a global climate model simulation. In the final case, 23 real boreholes from the United Kingdom, previously used for climatic reconstructions, are examined and the results compared with a local instrumental temperature series and the previous estimate derived from the same borehole data. The results indicate that the majority (17) of the 23 boreholes are unsuitable for climatic reconstruction purposes, at least without including other thermal processes in the forward model. [ABSTRACT FROM AUTHOR]

Background UK primary care records are computerised and these records are used for both research and quality improvement. However, there is disparity in the prevalence of diabetes found in epidemiological studies compared with that reported through the UK's national quality improvement scheme. Objective To investigate how non-diagnostic computer data could be used to identify, confirm or refute prevalent cases of people with diabetes. Method We carried out a literature review to identify the most accurate non-diagnostic markers. For each type of diabetes we focused on four broad areas; demographic details, biochemical markers, clinical features and therapeutic strategies. Sample markers were tested by calculating their positive predictive value (PPV) and sensitivity (Sn) and their ability to differentiate between types of diabetes. Results Biochemical markers were useful in identifying cases of diabetes but not in differentiating between types of diabetes as the same plasma glucose criterion is used to diagnose Type 1, Type 2, and 'other' types of diabetes; the lack of a 'fasting' qualifier blunts the use of this marker. Auto-immune markers were the most accurate in identifying Type 1 diabetes but are not recorded frequently in primary care. Clinical features of diabetes and therapeutic strategies were of some use - however, without time sequence data are difficult to interpret. Raised plasma glucose (PG), and glycated haemoglobin (HbA1c), had useful PPV but low Sn. When PG was more than 7.0 and less than 11.1 mmol/l, PPV equalled 77.8% and Sn 48%; and when PG was 11.1, PPV equalled 92% and Sn 17%. For an HbA1c of more than 6.5%, PPV was 89% and Sn 73.3%, and for anHbA1c of more than 8, PPV was 92% and Sn 26%. A person with a combination of aged under 30 years and prescribed insulin has an 84% PPV of Type 1 diabetes; if they also have a BMI <30 kg/m2 the PPV increases to 88%. A person age over 45 years and with a BMI >30 kg/m2 has a 5.3% PPV of Type 2 diabetes; if they are also hypertensive the PPV is 30%; Asian ethnicity increases PPV to 44%. Conclusion Non-diagnostic data has the potential to confirm or refute the diagnosis of diabetes and identify its type. [ABSTRACT FROM AUTHOR]

Aripiprazole has recently received approval for the treatment of moderate to severe manic episodes in bipolar I disorder and prevention of new manic episodes in aripiprazole-responsive patients. Aripiprazole differs from other antipsychotics in its pharmacology, and the need for prescribing guidance in the UK was recently identified. A UK multidisciplinary panel was convened in November 2007. This report describes the consensus agreed during the meeting on the optimal approach to prescribing aripiprazole: how best to approach initiation of, and switching to, treatment with aripiprazole and management strategies for side effects. A literature review of the randomised controlled clinical trials of aripiprazole in mania supports these recommendations. Aripiprazole should be initiated at 15 mg/day (range 5-20 mg/day). If necessary, adjunctive medication should be used in early treatment to manage side effects or assist in management of symptoms such as agitation. When switching to aripiprazole, the therapeutic dose of current treatment should be maintained while adding aripiprazole 15 (5-20) mg/day. Only once an effective dose of aripiprazole is reached should previous medication be reduced. Nausea, insomnia and agitation typically resolve within days. Some principles for dosing and switching are provided to assist with a successful treatment outcome with aripiprazole in mania. [ABSTRACT FROM AUTHOR]

We describe instrumental variable (IV) methods for the estimation of the 'dose'-response effects of psychological interventions in randomized controlled trials in which there is variability in the patients' adherence to the allocated therapy (that is, variability in the actual number of sessions of therapy attended) and also variability in the strength of the therapeutic alliance between patients and their therapists. The effect of the therapy on outcome is assumed to be a function of both the number of sessions attended and the strength of the therapeutic alliance, with no intervention effects in the absence of any sessions attended (an exclusion restriction) and the effect of the strength of the alliance being represented by a multiplicative term (interaction) in the treatment-effect model. The IV methods that are described allow for: (a) hidden confounding between sessions, alliance and outcome; (b) measurement errors in the alliance; and (c) that alliance is only measured in those receiving treatment. Three two-stage estimation procedures are illustrated, and their equivalence demonstrated, through Monte Carlo simulation and analysis of the results of an actual trial. [ABSTRACT FROM AUTHOR]

As part of the Vertical Offshore Reference Frames (VORF) project sponsored by the U. K. Hydrographic Office, a new model for Sea Surface Topography (SST) around the British Isles has been developed. For offshore areas (greater than 30 km from the coast), this model is largely derived from satellite altimetry. However, its accuracy and level of detail have been enhanced in coastal areas by the inclusion of not only the 60 PSMSL tide gauges with long-term records around the coasts of the United Kingdom and Ireland but also some 385 gauges established at different epochs and for different observation spans by the U. K. Admiralty. All tide gauge data were brought into a common reference frame by a combination of datum models and direct GPS observations, but a more significant challenge was to bring all short-term sea level observations to an unbiased value at a common epoch. This was achieved through developing a spatial-temporal correlation model for the variations in mean sea level around the British Isles, which in turn meant that gauges with long-term observation spans could be used as control points to improve the accuracy of Admiralty gauges. It is demonstrated that the latter can contribute point observations of mean sea level (MSL) with a precision of 0.078 m. A combination of least squares collocation and interpolation was developed to merge the coastal point and offshore gridded data sets, with particular algorithms having to be developed for different configurations of coastal topology. The resulting model of sea surface topography is shown to present a smooth transition from inshore coastal areas to offshore zones. Further benefits of the techniques developed include an enhanced methodology for detecting datum discontinuities at permanent tide gauges. [ABSTRACT FROM AUTHOR]

We present an approach to the construction of clusters of life course trajectories and use it to obtain ideal types of trajectories that can be interpreted and analysed meaningfully. We represent life courses as sequences on a monthly timescale and apply optimal matching analysis to compute dissimilarities between individuals. We introduce a new divisive clustering algorithm which has features that are in common with both Ward's agglomerative algorithm and classification and regression trees. We analyse British Household Panel Survey data on the employment and family trajectories of women. Our method produces clusters of sequences for which it is straightforward to determine who belongs to each cluster, making it easier to interpret the relative importance of life course factors in distinguishing subgroups of the population. Moreover our method gives guidance on selecting the number of clusters. [ABSTRACT FROM AUTHOR]

A consensus process has been developed to assist the UK National Confidential Enquiry into Patient Outcome and Death in identifying questions to be addressed in its studies. The process utilises the knowledge and experience of a panel of experts via a facilitated brainstorming exercise and employs a robust voting system to produce a list of candidate questions ordered in terms of the preferences expressed by individual panel members. The process which is described has been used successfully to assist the design of two national studies and it is anticipated that the process will be adopted for many studies in the future. [ABSTRACT FROM AUTHOR]

A layered approach for identifying communities in the Web is presented and explored by applying the flake exact community identification algorithm to the UK academic Web. Although community or topic identification is a common task in information retrieval, a new perspective is developed by: the application of alternative document models, shifting the focus from individual pages to aggregated collections based upon Web directories, domains and entire sites; the removal of internal site links; and the adaptation of a new fast algorithm to allow fully-automated community identification using all possible single starting points. The overall topology of the graphs in the three least-aggregated layers was first investigated and found to include a large number of isolated points but, surprisingly, with most of the remainder being in one huge connected component, exact proportions varying by layer. The community identification process then found that the number of communities far exceeded the number of topological components, indicating that community identification is a potentially useful technique, even with random starting points. Both the number and size of communities identified was dependent on the parameter of the algorithm, with very different results being obtained in each case. In conclusion, the UK academic Web is embedded with layers of non-trivial communities and, if it is not unique in this, then there is the promise of improved results for information retrieval algorithms that can exploit this additional structure, and the application of the technique directly to partially automate Web metrics tasks such as that of finding all pages related to a given subject hosted by a single country's universities. [ABSTRACT FROM AUTHOR]

Background Studies on ethnic variations in health have played an important role in aetiological and health services research. Most routine datasets, however, do not include information on ethnicity. South Asians, one of the largest minority ethnic groups in Britain, have distinctive names that also allow differentiation of the main sub‐groups with their important differences in health‐related exposures and disease risks [ABSTRACT FROM PUBLISHER]

A division test involving both context and bare problems was taken by year 5 pupils in 10 different schools at two points in the school year to identify changes in approach as the standard algorithm was introduced. Increase in the use of the standard algorithm led to many errors and only half of the attempts to use this strategy in the second test were successful. More successful were efficient informal strategies although difficulties in structuring a written record of such strategies were evident. Overall 52% of pupils gained a higher score in the second test while 48% remained the same or performed less well. In comparing context and bare problems, development of a successful strategy was most marked in problems that were given a context. In both tests girls performed better than boys with better improvements over the two tests. [ABSTRACT FROM AUTHOR]

Multiple projects within the rail industry across different regions have been initiated to address the issue of over-population. These expansion plans and upgrade of technologies increases the number of intersections, junctions, and level crossings. A level crossing is where a railway line is crossed by a road or right of way on the level without the use of a tunnel or bridge. Level crossings still pose a significant risk to the public, which often leads to serious accidents between rail, road, and footpath users and the risk is dependent on their unpredictable behavior. For Great Britain, there were three fatalities and 385 near misses at level crossings in 2015–2016. Furthermore, in its annual safety report, the Rail Safety and Standards Board (RSSB) highlighted the risk of incidents at level crossings during 2016/17 with a further six fatalities at level crossings including four pedestrians and two road vehicles. The relevant authorities have suggested an upgrade of the existing sensing system and the integration of new novel technology at level crossings. The present work addresses this key issue and discusses the current sensing systems along with the relevant algorithms used for post-processing the information. The given information is adequate for a manual operator to make a decision or start an automated operational cycle. Traditional sensors have certain limitations and are often installed as a "single sensor". The single sensor does not provide sufficient information; hence another sensor is required. The algorithms integrated with these sensing systems rely on the traditional approach, where background pixels are compared with new pixels. Such an approach is not effective in a dynamic and complex environment. The proposed model integrates deep learning technology with the current Vision system (e.g., CCTV to detect and localize an object at a level crossing). The proposed sensing system should be able to detect and localize particular objects (e.g., pedestrians, bicycles, and vehicles at level crossing areas.) The radar system is also discussed for a "two out of two" logic interlocking system in case of fail-mechanism. Different techniques to train a deep learning model are discussed along with their respective results. The model achieved an accuracy of about 88% from the MobileNet model for classification and a loss metric of 0.092 for object detection. Some related future work is also discussed. [ABSTRACT FROM AUTHOR]

The article examines the results from the numerical analysis literature and reports the results of tests undertaken on five large scale macroeconometric models which were available on the Economic and Social Research Council Macroeconomic Modeling Bureau User Service in 1984 in Great Britain. It presents algorithms that are simple to implement yet produce computational savings of up to 35 percent per solution. The study describes the convergence characteristics of iterative techniques for solving econometric models.

Detection of Identical-By-Descent (IBD) segments provides a fundamental measure of genetic relatedness and plays a key role in a wide range of analyses. We develop FastSMC, an IBD detection algorithm that combines a fast heuristic search with accurate coalescent-based likelihood calculations. FastSMC enables biobank-scale detection and dating of IBD segments within several thousands of years in the past. We apply FastSMC to 487,409 UK Biobank samples and detect ~214 billion IBD segments transmitted by shared ancestors within the past 1500 years, obtaining a fine-grained picture of genetic relatedness in the UK. Sharing of common ancestors strongly correlates with geographic distance, enabling the use of genomic data to localize a sample's birth coordinates with a median error of 45 km. We seek evidence of recent positive selection by identifying loci with unusually strong shared ancestry and detect 12 genome-wide significant signals. We devise an IBD-based test for association between phenotype and ultra-rare loss-of-function variation, identifying 29 association signals in 7 blood-related traits. Accurately measuring genetic relatedness by Identical-By-Descent (IBD) segments is challenging in biobank-level genome data. The authors present IBD method FastSMC, which when applied to the UK Biobank gives a detailed picture of genetic relatedness and evolutionary history in the UK over the past 2000 years. [ABSTRACT FROM AUTHOR]

Background: Femoral shaft stress fractures in athletes are not common but pose a great diagnostic challenge to clinicians. Because of few clinical signs, diagnosis and treatment are often delayed. Furthermore, if not treated correctly, these fractures are well known for complications and difficulties. Objective: To develop a well structured and reproducible treatment algorithm for athletes with femoral shaft stress fractures. Methods: The proposed algorithm is carried out in four phases, each lasting three weeks, and the move to the next phase is based on the result of the tests carried out at the end of the previous phase. Over nine years, we treated seven top level athletes, aged 17–21. In all athletes, diagnosis was based on physical examination, plain radiographs, and bone scan. Results: As a result of the treatment method, all the athletes were fully engaged in athletic activity 12–18 weeks after the beginning of treatment. After completion of the treatment, the athletes were followed up for 48–96 months. During the follow up, there was no recurrence of discomfort or pain, and all the athletes eventually returned to competition level. Conclusion: These results and data available from the literature suggest that the algorithm is the optimal treatment protocol for femoral shaft stress fractures in athletes, avoiding the common complications and difficulties. [ABSTRACT FROM AUTHOR]

Background: Increases in physical activity through active travel have the potential to have large beneficial effects on populations, through both better health outcomes and reduced motorized traffic. However accurately identifying travel mode in large datasets is problematic. Here we provide an open source tool to quantify time spent stationary and in four travel modes(walking, cycling, train, motorised vehicle) from accelerometer measured physical activity data, combined with GPS and GIS data. Methods: The Examining Neighbourhood Activities in Built Living Environments in London study evaluates the effect of the built environment on health behaviours, including physical activity. Participants wore accelerometers and GPS receivers on the hip for 7 days. We time-matched accelerometer and GPS, and then extracted data from the commutes of 326 adult participants, using stated commute times and modes, which were manually checked to confirm stated travel mode. This yielded examples of five travel modes: walking, cycling, motorised vehicle, train and stationary. We used this example data to train a gradient boosted tree, a form of supervised machine learning algorithm, on each data point (131,537 points), rather than on journeys. Accuracy during training was assessed using five-fold cross-validation. We also manually identified the travel behaviour of both 21 participants from ENABLE London (402,749 points), and 10 participants from a separate study (STAMP-2, 210,936 points), who were not included in the training data. We compared our predictions against this manual identification to further test accuracy and test generalisability. Results: Applying the algorithm, we correctly identified travel mode 97.3% of the time in cross-validation (mean sensitivity 96.3%, mean active travel sensitivity 94.6%). We showed 96.0% agreement between manual identification and prediction of 21 individuals' travel modes (mean sensitivity 92.3%, mean active travel sensitivity 84.9%) and 96.5% agreement between the STAMP-2 study and predictions (mean sensitivity 85.5%, mean active travel sensitivity 78.9%). Conclusion: We present a generalizable tool that identifies time spent stationary and time spent walking with very high precision, time spent in trains or vehicles with good precision, and time spent cycling with moderate precisionIn studies where both accelerometer and GPS data are available this tool complements analyses of physical activity, showing whether differences in PA may be explained by differences in travel mode. All code necessary to replicate, fit and predict to other datasets is provided to facilitate use by other researchers. [ABSTRACT FROM AUTHOR]

the evidence base for the management of severe or life-threathening exacerbations of asthma is not as strong as that for mild or moderate exacerbations. Despite this information deficit, intravenous (IV) salbutamol has become an accepted treatment option for children not responding to inhaled bronchodilator and corticosteroid therapy. Studies have shown that it is used in many centres in the UK replacing IV aminophylline, however, there is wide variation in dosing regimens and inconsistencies in practice. The authors audited their own prescribing practice and developed an algorithm-based approach to the use of IV salbutamol that includes patient selection and all aspects of prescribing, preparation, administration, monitoring and the requirements of the environment where care is delivered. It includes details of the IV salbutamol bolus and the infusion process, incorporating all available evidence. The algorithm provides guidance on IV salbutamol usage in children up to and including 18 years. The authors hope it will promote standardised practice, limit the opportunity for prescribing errors and highlight the need for a national paediatric study comparing IV salbutamol and IV aminophylline. It may also stimulate discussion on the IV salbutamol dosage used in adult practice. [ABSTRACT FROM AUTHOR]